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Object-Oriented Hydrologic and Water-Quality Model for High-Water-Table Environments

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PAGE 1

OBJECT-ORIENTED HYDROLOGIC AND WATER-QUALITY MODEL FOR HIGHWATER-TABLE ENVIRONMENTS By CHRISTOPHER JOHN MARTINEZ A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

PAGE 2

Copyright 2006 by Christopher John Martinez

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I would like to dedicate this dissertation and the time I have spent at the University of Florida furthering my education and prof essional and personal development to my mother. She would have liked to have seen this day.

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iv ACKNOWLEDGMENTS This work was made possible by a Nati onal Needs Fellowship provided by the United States Department of Agriculture. Without it, I may have never pursued a doctoral degree. I would like to thank Dr. Michael D. Annable and Dr. Kenneth L. Campbell for their help, time, and mentoring. I would also like to thank Dr. Wendy D. Graham, Dr. James W. Jawitz, Dr. Gregory A. Kike r, and Dr. William R. Wise for their willingness to participate in this endeavor.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...............................................................................................................x LIST OF FIGURES..........................................................................................................xii ABSTRACT...................................................................................................................xviii CHAPTER 1 INTRODUCTION........................................................................................................1 Objectives.....................................................................................................................2 Contribution of this Work: Additio ns Made to the ACRU2000 Model.......................3 Organization of this Dissertation..................................................................................5 2 FIELD-SCALE HYDROLOGY OF THE ACRU2000 MODEL................................9 Introduction...................................................................................................................9 Evapotranspiration........................................................................................................9 Interception.................................................................................................................11 Infiltration and Runoff................................................................................................11 Percolation and Soil Mois ture Redistribution.............................................................13 Baseflow.....................................................................................................................13 Application of the ACRU2000 Model to Shallow Water-Table Environments.........14 Summary.....................................................................................................................15 3 FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATERTABLE ENVIRONMENTS: DEVELOPMENT.......................................................18 Introduction.................................................................................................................18 Model Development...................................................................................................20 Background..........................................................................................................20 Structure..............................................................................................................21 Hydrologic Processes and Governing Equations................................................24 Evapotranspiration.......................................................................................25 Interception...................................................................................................28 Infiltration.....................................................................................................29

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vi Water-table depth and soil moisture distribution.........................................29 Groundwater flow........................................................................................34 Overland flow and depression storage.........................................................35 Summary.....................................................................................................................35 4 FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATERTABLE ENVIRONMENTS: VALIDATION............................................................40 Introduction.................................................................................................................40 Case Study 1: Paynes Pr airie State Preserve..............................................................42 Site Description and Experimental Design..........................................................42 Results and Discussion........................................................................................43 Case Study 2: W.F. Rucks Dairy................................................................................45 Site Description and Experimental Design..........................................................45 Results and Discussion........................................................................................46 Case Study 3: MacArthur Agro-Ecology Res earch Center at Buck Island Ranch.....48 Site Description and Experimental Design..........................................................48 Results and Discussion........................................................................................50 Sensitivity Analysis....................................................................................................53 Summary and Conclusions.........................................................................................55 5 FIELD-SCALE NITROGEN AND P HOSPHORUS MODULE OF THE ACRU2000 MODEL..................................................................................................71 Introduction.................................................................................................................71 Nutrient Models..........................................................................................................71 Phosphorus Model...............................................................................................72 Mineralization..............................................................................................73 Immobilization.............................................................................................74 Inorganic transformations............................................................................75 Plant uptake..................................................................................................77 Nitrogen Model...................................................................................................77 Mineralization..............................................................................................78 Immobilization.............................................................................................79 Atmospheric loss of nitrogen.......................................................................80 Inorganic nitrogen........................................................................................81 Plant uptake..................................................................................................81 Nutrient Transformation Response to Soil Moisture...........................................81 Nutrient Transformation Re sponse to Temperature............................................82 Extraction of Nutrients into Runoff.....................................................................83 Application of the Nitrogen and Phos phorus Module to Shallow Water-Table Environments..........................................................................................................84 Summary.....................................................................................................................86 6 MODIFICATION OF THE FIELD-SCA LE NITROGEN AND PHOSPHORUS MODULE OF ACRU2000 FOR SHALLOW WATER-TABLE ENVIRONMENTS.....................................................................................................90

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vii Introduction.................................................................................................................90 Nutrient Models..........................................................................................................90 Phosphorus Model...............................................................................................91 Nitrogen Model...................................................................................................93 Nutrient Transformation Response to Soil Moisture...........................................94 Extraction of Nutrients into Runoff.....................................................................97 Summary.....................................................................................................................99 7 FIELD-SCALE VALIDATION OF TH E NITROGEN AND PHOSPHORUS MODULE OF THE ACRU2000 MODEL FOR SHALLOW WATER-TABLE ENVIRONMENTS...................................................................................................103 Introduction...............................................................................................................103 Model Validation......................................................................................................105 Site Description.................................................................................................105 Experimental Design.........................................................................................106 Model Calibration..............................................................................................108 Results...............................................................................................................111 Sensitivity Analysis..................................................................................................114 Discussion.................................................................................................................117 Conclusions...............................................................................................................122 8 SUMMARY AND CONCLUSIONS.......................................................................161 APPENDIX A HYDROLOGIC PROCESS AND DATA OBJECTS AND DESCRIPTIONS.......165 Process Objects.........................................................................................................165 Data Objects..............................................................................................................165 Description of Process Objects.................................................................................166 Description of Data Objects......................................................................................168 B HYDROLOGIC PROCESS OBJECT UNI FIED MODELING LANGUAGE (UML) DIAGRAMS................................................................................................172 C HYDROLOGIC MODEL INPUT/OUTP UT VARIABLE REFERENCE..............191 Input Variable Reference..........................................................................................191 Output Variable Reference.......................................................................................193 D HYDROLOGIC MODEL TECHNICAL MANUAL..............................................195 Introduction...............................................................................................................195 Simulation of the Water-Table a nd Soil Moisture Distribution...............................195 Soil Water Characteristic Functions..................................................................196 Determining Upper-Limit Water Contents of Soil Layers................................200

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viii Developing the Relationship between the Water-Table Depth and Soil Air Volume...........................................................................................................202 Evapotranspiration and the Deviat ion from a Steady-State Profile...................202 New reference potential ev apotranspiration methods................................203 Actual evapotranspiration methods............................................................208 Upward flux of water in respons e to a depleted root zone.........................212 Infiltration and Redistribution of Infiltrated Water...........................................214 Deep Seepage....................................................................................................214 Determining a New Water-Table Dept h, Upper-Limit Water Contents, and Redistribution of Soil Water..........................................................................214 Other Phenomena that will change the Water Table Depth and Soil Moisture Distribution....................................................................................................215 Runoff from a Lumped Model..................................................................................215 E NUTRIENT PROCESS AND DATA OB JECTS AND DESCRIPTIONS.............217 Process Objects.........................................................................................................217 Data Objects..............................................................................................................217 Description of Process Objects.................................................................................217 Description of Data Objects......................................................................................218 F NUTRIENT PROCESS AND DATA OB JECT UNIFIED MODELING LANGUAGE (UML) DIAGRAMS.........................................................................220 G NUTRIENT MODEL TECHNICAL MANUAL.....................................................233 Introduction...............................................................................................................233 Nutrient Inputs..........................................................................................................233 Soil Moisture Effects on Nutrient Transformations.................................................233 Nutrient Transport in the Subsurface........................................................................241 Transport by Infiltration and Redistribution......................................................241 Transport by Evaporation..................................................................................243 Transport between Ponded and Soil Water..............................................................244 Surface Transport......................................................................................................246 H CONSERVATIVE SOLUTE TRANSPORT PROCESS, INTERFACE, AND DATA OBJECTS AND DESCRIPTIONS..............................................................247 Process Objects.........................................................................................................247 Interface Objects.......................................................................................................247 Data Objects..............................................................................................................247 Description of Process Objects.................................................................................247 Description of Interface Objects...............................................................................248 Description of Data Objects......................................................................................248 I CONSERVATIVE SOLUTE TRANSPORT PROCESS AND DATA OBJECT UNIFIED MODELING LANG UAGE (UML) DIAGRAMS..................................250

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ix J CONSERVATIVE SOLUTE TRANSPORT INPUT/OUTPUT VARIABLE REFERENCE...........................................................................................................256 Input Variable Reference..........................................................................................256 Output Variable Reference.......................................................................................256 K CONSERVATIVE SOLUTE TRAN SPORT TECHNICAL MANUAL.................257 Introduction...............................................................................................................257 Conservative Solute Inputs.......................................................................................257 Conservative Solute Transport in the Subsurface.....................................................258 Transport by Infiltration and Redistribution......................................................258 Transport by Evaporation..................................................................................259 Transport Between Ponded and Soil Water..............................................................260 Surface Transport......................................................................................................262 LIST OF REFERENCES.................................................................................................263 BIOGRAPHICAL SKETCH...........................................................................................276

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x LIST OF TABLES Table page 4-1 Wauberg sand soil characteristics............................................................................57 4-2 Paynes Prairie State Preserve e rror measures of daily outputs................................57 4-3 Myakka fine sand soil characteristics.......................................................................57 4-4 Crop coefficients for W.F. Rucks and MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch..................................................................58 4-5 W.F. Rucks error measures of daily outputs............................................................58 4-6 Pineda fine sand soil characteristics.........................................................................58 4-7 MacArthur Agro-Ecology Research Center at Buck Island Ranch error measures from the experimental pasture for daily outputs......................................................59 4-8 Hydrologic input parameters included in the sensitivity analysis............................60 4-9 Sensitivity of runoff, evapotranspiration, and groundwater flow to hydrologic parameters................................................................................................................61 7-1 Pineda fine sand so il physical properties...............................................................124 7-2 Felda fine sand soil physical properties.................................................................124 7-3 Pineda fine sand sa turated water content ( s), residual water content ( r), and n, and m parameters...............................................................................................124 7-4 Felda fine sand saturated water content ( s), residual water content ( r), and n, and m parameters...................................................................................................125 7-5 Pineda fine sand so il chemical properties..............................................................125 7-6 Felda fine sand soil chemical properties................................................................125 7-7 Annual observed and simulated runoff a nd N and P loads in runoff for winter pastures...................................................................................................................126

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xi 7-8 Annual observed and simulated runoff and N and P loads in runoff for summer pastures...................................................................................................................127 7-9 Observed and simulated average N and P concentrations in runoff for winter pastures...................................................................................................................128 7-10 Observed and simulated average N and P concentrations in runoff for summer pastures...................................................................................................................129 7-11 Mean absolute error (MAE), root mean square error (RMSE) and coefficient of efficiency (E) for annual runoff, N and P loads, and average N and P concentrations for all pastures................................................................................130 7-12 Mean absolute error (MAE), root mean square error (RMSE) and coefficient of efficiency (E) for annual N and P loads and average N and P concentrations for all pastures using or iginal, unmodified ACRU2000 N and P algorithms..............131 7-13 Hydrologic input parameters included in the sensitivity analysis..........................132 7-14 Nutrient input parameters included in the sensitivity analysis...............................133 7-15 Sensitivity of phosphorus and nitrogen loads to hydrologic parameters................134 7-16 Sensitivity of phosphorus and nitrogen to soil parameters and manure application rate.......................................................................................................135 7-17 Sensitivity of nitrogen load s to nitrogen parameters..............................................135 7-18 Sensitivity of phosphorus loads to phosphorus parameters...................................136

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xii LIST OF FIGURES Figure page 1-1 Flatwoods regions of Florida......................................................................................8 2-1 Transpiration reduction due to soil mois ture excess and deficiency as simulated in the ACRU2000 model..........................................................................................17 3-1 Sample UML diagram..............................................................................................38 3-2 Transpiration reduction factor as a function of soil pressure head..........................39 3-3 Root density distribution function g ( d )....................................................................39 4-1 Location of Paynes Pr airie State Preserve................................................................62 4-2 Measured and predicted water-table de pths at Paynes Prairie State Preserve.........62 4-3 Measured and predicted soil moisture c ontents within the top 25 cm of soil at Paynes Prairie State Preserve...................................................................................63 4-4 Measured and predicted evapotranspira tion at Paynes Prairie State Preserve.........63 4-5 Measured vs. predicted evapotranspira tion at Paynes Prairie State Preserve..........64 4-6 Location of W.F. Rucks Dairy.................................................................................64 4-7 Measured and predicted water-ta ble depths at W.F. Rucks Dairy...........................65 4-8 Measured and modified ACRU2000 predic ted daily runoff at W.F. Rucks Dairy..65 4-9 Measured and FHANTM predicted daily runoff at W.F. Rucks Dairy....................66 4-10 Measured and unmodifi ed ACRU2000 predicted daily runoff at W.F. Rucks Dairy.........................................................................................................................66 4-11 Measured and predicted cumulative annual runoff at W.F. Rucks Dairy................67 4-12 Location of MacArthur Agro-Ecology Re search Center at Buck Island Ranch......67 4-13 Groundwater level and adjacent canal stag e in the experimental pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch..........................68

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xiii 4-14 Measured and predicted water-table dept hs at the experiment al pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch..........................68 4-15 Measured and predicted daily runoff at the experimental pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch..........................69 4-16 Measured and predicted cumulative annua l runoff at the experimental pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch....................70 4-17 The parameters most sensitive on runoff volumes...................................................70 5-1 Nitrogen cycle of the ACRU2000 model.................................................................88 5-2 Phosphorus cycle of the ACRU2000 model............................................................88 5-3 Soil moisture response functions from the GLEAMS model..................................89 5-4 Soil temperature response func tions from the GLEAMS model.............................89 6-1 Conceptual model of the phosphorus cycle............................................................101 6-2 Conceptual model of the nitrogen cycle.................................................................101 6-3 Soil moisture response functions fo r ammonification (and P mineralization), nitrification, and denitrification..............................................................................102 7-1 Location of MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch...........................................................................................................137 7-2 Semi-improved winter pasture arra y at MacArthur Agro-Ecology Research Center at Buck Island Ranch..................................................................................137 7-3 Improved summer pasture array at MacA rthur Agro-Ecology Research Center at Buck Island Ranch.................................................................................................138 7-4 Groundwater levels at the 4-inch well (near the flume) and the 2-inch well (center of pasture) in winter pasture 6 (WP6)........................................................139 7-5 Groundwater levels at the 4-inch well (near the flume) and the 2-inch well (center of pasture) in summer pasture 1 (SP1).......................................................139 7-6 Groundwater levels from the three winter pastures compared to the canal stage as measured at the S70 spillway.............................................................................140 7-7 Groundwater levels from the three summe r pastures compared to the canal stage as measured at the S70 spillway.............................................................................140 7-8 Winter Pasture 6 (WP6) observed and simulated depth to water-table..................141

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xiv 7-9 Winter Pasture 7 (WP7) observed and simulated depth to water-table..................141 7-10 Winter Pasture 5 (WP5) observed and simulated depth to water-table..................142 7-11 Summer Pasture 1 (SP1) observed and simulated depth to water-table.................142 7-12 Summer Pasture 4 (SP4) observed and simulated depth to water-table.................143 7-13 Summer Pasture 3 (SP3) observed and simulated depth to water-table.................143 7-14 Winter Pasture 6 (WP6) obser ved and simulated daily runoff...............................144 7-15 Winter Pasture 7 (WP7) obser ved and simulated daily runoff...............................145 7-16 Winter Pasture 5 (WP5) obser ved and simulated daily runoff...............................146 7-17 Summer Pasture 1 (SP1) obser ved and simulated daily runoff..............................147 7-18 Summer Pasture 4 (SP4) obser ved and simulated daily runoff..............................148 7-19 Summer Pasture 3 (SP3) obser ved and simulated daily runoff..............................149 7-20 Winter Pasture 6 (WP6) cumulative annual runoff................................................150 7-21 Winter Pasture 7 (WP7) cumulative annual runoff................................................150 7-22 Winter Pasture 5 (WP5) cumulative annual runoff................................................151 7-23 Summer Pasture 1 (SP1 ) cumulative annual runoff...............................................151 7-24 Summer Pasture 4 (SP4 ) cumulative annual runoff...............................................152 7-25 Summer Pasture 3 (SP3 ) cumulative annual runoff...............................................152 7-26 Winter Pasture 7 (WP7) cumulative annual N load...............................................153 7-27 Winter Pasture 7 (WP7) cumulative annual P load................................................153 7-28 Winter Pasture 6 (WP6) cumulative annual N load...............................................154 7-29 Winter Pasture 6 (WP6) cumulative annual P load................................................154 7-30 Winter Pasture 5 (WP5) cumulative annual N load...............................................155 7-31 Winter Pasture 5 (WP5) cumulative annual P load................................................155 7-32 Summer Pasture 1 (SP1 ) cumulative annual N load..............................................156 7-33 Summer Pasture 1 (SP1 ) cumulative annual P load...............................................156

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xv 7-34 Summer Pasture 4 (SP4 ) cumulative annual N load..............................................157 7-35 Summer Pasture 4 (SP4 ) cumulative annual P load...............................................157 7-36 Summer Pasture 3 (SP3 ) cumulative annual N load..............................................158 7-37 Summer Pasture 3 (SP3 ) cumulative annual P load...............................................158 7-38 Hydrologic parameters showing the gr eatest sensitivity on N loads in runoff......159 7-39 Hydrologic parameters showing the gr eatest sensitivity on P loads in runoff.......159 7-40 Nutrient parameters showing the grea test sensitivity on N loads in runoff...........160 7-41 Nutrient parameters showing the grea test sensitivity on P loads in runoff............160 B-1 PAcruHWTRitchieEvapoTranspiration UML diagram.........................................172 B-2 PDeepSeepage UML diagram................................................................................173 B-3 PFAO56PenmanMonteithDailyEvap UML diagram.............................................174 B-4 PFindNewWaterTableDepth UML diagram..........................................................175 B-5 PHWTCropCoeffTrans UML diagram..................................................................176 B-6 PHWTPlantWaterStress UML diagram.................................................................177 B-7 PHWTRitchieSoilWaterEvap UML diagram.........................................................178 B-8 PHWTSimpleEvapoTranspiration UML diagram.................................................179 B-9 PInitialiseSoilUFOptionHWT UML diagram........................................................180 B-10 PMaximumUpwardFlux UML diagram.................................................................181 B-11 PPondedWaterEvaporation UML diagram............................................................182 B-12 PRootDistributionFunction UML diagram............................................................183 B-13 PSimpleRunoff UML diagram...............................................................................184 B-14 PSoilStorageAvailable UML diagram...................................................................185 B-15 PSoilWaterCharacteristic UML diagram...............................................................186 B-16 PStorageLimitedInfiltration UML diagram...........................................................187 B-17 PStorageLimitedRedistribution UML diagram......................................................188

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xvi B-18 PSuperSimpleEvapoTranspiration UML diagram.................................................189 B-19 PUpwardFlux UML diagram.................................................................................190 D-1 The three water characteristic func tions with the same input parameters..............199 D-2 Plant water stress as a functi on of soil water pressure head...................................211 D-3 Root distribution function......................................................................................212 F-1 PDetermineLayerPressureHeads UML diagram....................................................220 F-2 PHWTAmmonification UML diagram..................................................................221 F-3 PHWTDenitrification UML diagram.....................................................................222 F-4 PHWTImmobilization UML diagram....................................................................223 F-5 PHWTNitrification UML diagram.........................................................................224 F-6 PHWTNutrientInputs UML diagram.....................................................................225 F-7 PHWTPMineralization UML diagram...................................................................226 F-8 PHWTSubsurfaceTransport UML diagram...........................................................227 F-9 PHWTSurfaceTransport UML diagram.................................................................228 F-10 PMixingZoneExchangeModel UML diagram.......................................................229 F-11 PNutrientTransformationProcess UML diagram...................................................230 F-12 PNutrientTransfromTran sferProcess UML diagram..............................................230 F-13 PNutrientTransportProcess UML diagram.............................................................231 F-14 DNutrientFluxRecord UML diagram.....................................................................232 G-1 Soil moisture response function of GLEAMS.......................................................237 G-2 Soil moisture response functions used...................................................................239 G-3 Soil moisture response function to denitrification.................................................240 G-4 Comparison of the soil moisture re sponse functions of GLEAMS and those proposed.................................................................................................................241 I-1 PConservativeMixingZoneExchangeModel UML diagram..................................250 I-2 PConservativeSoluteEvapor ationTransport UML diagram...................................251

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xvii I-3 PConservativeSoluteInputs UML diagram............................................................252 I-4 PConservativeSoluteSubsurfaceTransport UML diagram.....................................253 I-5 PConservativeSoluteSurfaceTransport UML diagram...........................................254 I-6 PConservativeSoluteTransportProcess UML diagram...........................................254 I-7 DConservativeSoluteFluxRecord UML diagram...................................................255

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xviii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy OBJECT-ORIENTED HYDROLOGIC AND WATER-QUALITY MODEL FOR HIGH WATER-TABLE ENVIRONMENTS By Christopher John Martinez May 2006 Chair: Michael D. Annable Cochair: Kenneth L. Campbell Major Department: Environmental Engineering Sciences A hydrologic and water quality model was developed for high water-table environments such as the flatwoods of Flor ida. The model was developed within the object-oriented framework of the ACRU 2000 model. The model uses physical approximations suitable for highly conductive, poorly drained soils. The water quality component of the model uses nitrogen and phosphorus algorithms patterned after the GLEAMS model, with appropriate modifica tions for sandy, poorly drained, acid soils. The hydrologic model operates on a daily time-step and assumes a hydrostatic distribution of soil moisture. Reference pot ential evapotra nspiration can be estimated using the Penman-Monteith equation and the resulting atmospheric demand is applied in a top-down approach to intercepted water, ponded water on the ground surface, and to soil evaporation and plant transpiration. Ve rtical upward flow of soil moisture in response to evapotranspiration is approximate d using a steady-state solution of Darcys

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xix Law. Groundwater flow can occur to or from a deep aquifer or an adjacent water body. Runoff from the land surface is assumed to occur via saturation-excess only. The hydrologic component of the model wa s validated using observed data from three field sites. The validation establishe d the models ability to predict water-table depths, soil moisture contents, ev apotranspiration, and runoff volumes. The water quality component of the model employs modifications for poorly drained, flatwoods soils that in clude the specification of optim al ranges of water contents affecting the rate of nutrient transformations, the effect of soil moisture on transformation rates under saturated or near-saturated condi tions, the instantaneous, reversible sorption of phosphorus, and the extraction of nutrients into runoff water. The water quality component of the mode l was validated for six experimental pastures. Model validation, while providi ng improved predictions of runoff nutrient loads compared to the model without modifications for shallow water-table environments, indicated several shortcomings of the model. These include the need for explicit representation of plan t biomass and organic soil acc retion and the need for more siteor region-specific information on nitrog en contents and the factors that control N and P cycling and retention in these soils.

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1 CHAPTER 1 INTRODUCTION Humid, shallow water-table environments such as the flatwoods of the southeastern United States are characterize d by flat topography and modera tely to poorly drained soils with high infiltration capacities, where significant interaction between surface water and groundwater occurs. Flatwoods occur throughou t the southeastern coastal plain of the United States and cover approximately 50% of the land area of the state of Florida (Abrahamson and Harnett 1990) (Figure 1-1). The soils of the Florida flatwoods are composed primarily of Spodosols and to a lesser extent Alfisols. These sandy, acidic soils typically lack an abundance of the mineral components that are important to phosphorus (P) retention, Fe and Al oxides and aluminosilicate and metal-oxide clays (Mansell et al. 1995; Harris et al 1996), particularly in surfic ial soil horizons. The loss of P from soil has been shown to be an impor tant cause of eutrophica tion of surface water bodies. The low retention capacity of these so ils is further exacerbated by the application of organic fertilizers. Orga nic acids can adsorb to minera l surfaces, reducing the capacity for adsorption and stabilization of P (E ghball et al. 1996; Graetz et al. 1999). The mitigation of adverse effects from P lo sses from flatwoods soils to the aquatic environment requires Understanding the surface and subs urface hydrology of the flatwoods, understanding the fate of P in this environment, and disseminating knowledge in the form of best management practices. Extensive laboratory and field research to acco mplish these goals is expensive in terms of time and money, thus the interest in comput er simulation models. Successful modeling

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2 of the flatwoods requires accurate represen tation of the hydrology and P biogeochemical cycle. An accurate model of flatwoods sy stems should include an accurate representation of a shallow water-table, its contribution to evapotranspiration, a nd its effect on runoff generation. A successful model should also be ca pable of reflecting the factors that affect P retention and subsequent lo ss in runoff and groundwater. Many models suffer from a lack of specific knowledge of the most impor tant environmental conditions affecting P retention when applied to specific locations Thus models must be updated as our understanding of the environment grows; a nd models should be developed with such future expansions in mind. Such model de sign is enhanced by the concept of objectoriented programming whereby real world objects are presented more intuitively than they might be in procedural programmi ng (Liang 2001). Few object-oriented models exist in the fields of hydrology and agri cultural water quality. One object-oriented model, ACRU2000, is available for such model expansion. Objectives Using the object-oriented ACRU2000 mode l as the modeling platform to build upon, the main objectives of this work were twofold: 1. Develop and test a field-scale hydro logic module for shallow water-table environments, and 2. test the suitability of the nitrogen and phosphorus algorithms of the model with modifications for sandy, poorly drained flatwoods soils. It is proposed here that an approxima te hydrologic model, operating on a daily time-step and representing the major forcings affecting water-table depths and runoff generation, can effectively repr esent the hydrology of the flat woods. The major forcings are rainfall, evapotranspiration, and groundwater flow to or from adjacent water bodies or boundary conditions. The approximate methods include

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3 The assumption that hydrostatic soil moisture conditions prevail, the vertical upward movement of soil mo isture in response to evapotranspiration can be represented as a steady-state process, runoff occurs only by saturation-excess. The water-quality component of th e ACRU2000 model uses nitrogen and phosphorus cycling algorithms from the Gr oundwater Loading Effects of Agricultural Management Systems (GLEAMS) model as a result of a previous model expansion (Campbell et al. 2001). Following the second ma in objective of this work, the nitrogen and phosphorus algorithms of th e model are evaluated for ap plication to shallow watertable environments with modifications governing the effect of soil moisture on nutrient transformation rates, the extraction of nutrients into ponded/runoff water, the instantaneous, reversible sorption of P to soil particles. Contribution of this Work: Addi tions Made to the ACRU2000 Model The Java-based, object-oriented hydrol ogic model ACRU2000 was not developed for use in humid, shallow water-table envir onments. However, due to its flexible structure the ACRU2000 model was chosen as the platform to implement the approximate hydrologic model described a bove. Specific hydrologic, field-scale modifications to ACRU2000 include Addition of the standardized Penman-Mont eith equation (Allen et al. 1998) for estimating daily reference pot ential evapotranspiration, estimation of incoming solar radiation using the methods of Hargreaves and Samani (1982) and Samani (2000) wh en observations are unavailable, expansion of the number of soil layers represented by the model, explicit representation of ponde d water on the ground surface, evaporation from ponded water,

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4 changes in the transpiration response to excess and limited soil moisture conditions, addition of a closed-form root distribution function, representation of upward gradients and flow between the water-table and the plant root zone, representation of a depth-va riable specific yield using closed-form soil moisture characteristic equations to determine wate r-table depths and soil moisture contents, movement of groundwater to and from time-variable boundary conditions (both vertically and horizontally), and addition of a simple stage-discharge re lationship to route runoff from the land surface. Hydrologic processes retained from the or iginal, unmodified ACRU2000 model include The ability to estimate reference potentia l evapotranspiration using a variety of methods, the choice of applying evaporative demand to the soil as a lumped quantity or as separate soil evaporation and plant transpiration, the interception of rainfall, and evaporation of in tercepted water. As mentioned, the water-quality com ponent of ACRU2000 has been adapted, almost entirely, from the GLEAMS model. GLEAMS was developed to simulate edgeof-field and bottom-of-root-zone loadings of water, sediment, pesticides, and nutrients (Knisel et al. 1993). However, GLEAMS be ing a predominantly upland model may not respond appropriately to the moisture re gime and soil conditions seen in shallow water-table environments such as the Florid a flatwoods. For this reason the following modifications were made: maximum rates of nutrient transformati ons occur over specif ic ranges of soil moisture, mineralization and immobilization processe s may occur under sa turated or nearsaturated conditions, but at a depressed rate,

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5 nutrients within ponded water are represented explicitly, the movement of nutrients from soil to ponded water occurs via mixing and in response to concentration gradients, and phosphorus partitioning coefficients are pred icted based on factors that control P sorption in flatwoods soils. Organization of this Dissertation The representation of field-scale hy drologic processes by the (unmodified) ACRU2000 model are detailed in Chapter 2 of this document. Also in this chapter the validity of applying ACRU2000 to humid, sh allow water-table environments is discussed. In Chapter 3 of this document, the sha llow water-table hydrologic modifications are presented. The model is developed by inte grating a vadose zone component that uses an approximation of Richards equation, an ev apotranspiration component that represents plant response to soil moisture conditions and accounts for upward gradients in the vadose zone, a Variable-Source-Area (V SA) runoff generation component, and a horizontal groundwater flow component. In Chapter 4 the shallow water-table mode l proposed in Chapter 3 is validated for three experimental sites in the southeaste rn United States and its performance is compared to the Field Hydrologic And Nu trient Transport Model (FHANTM) and the original, unmodified ACRU2000 as described in Chapter 2. The first experimental field site was a wet prairie community within Payne s Prairie State Preser ve in north-central Florida, the second, a dairy past ure in south-central Florida, and the third a beef cattle pasture in south-central Flor ida. The model performance is evaluated by comparing observed water-table depths, soil moisture contents, evapotranspiration, and runoff volumes to field observations.

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6 In Chapter 5 of this document the N and P module of the ACRU2000 model are presented and its appropriateness for shallow wa ter-table environments is discussed. In Chapter 6 modifications are proposed to th e N and P module for shallow water-table environments and flatwoods soils. In Chapter 7 the N and P module developed in Chapter 6 is ev aluated using field observations from experimental pastures in south-central Florida. The model is also compared to the unmodified algorithms as described in Chapter 5. The model performance is evaluated by its ability to pr edict nitrogen and phosphorus loads in runoff. In Chapter 8 the main findings from this study are summarized and recommendations for future work are made. The appendices of this dissertation provi de documentation for future model users and developers. Appendix A gives a list a nd short description of the hydrologic process and data objects added to the ACRU2000 model in the course of this work. Appendix B shows the Unified Modeling Language (U ML) design diagrams for the hydrologic processes added to the model. Appendix C is an input and output va riable reference that describes the new hydrologic i nput and output variables for future model users. Appendix D is a technical manual that detail s the workings of the hydrologic model. This appendix replicates parts of Chapter 3; however it refers to parameters as they are referenced in the input/output reference (A ppendix C) and in the original, unmodified ACRU2000 model (Smithers and Schulze 1995) as well as providing some guidance for users in input parameter determination. A ppendices E, F, and G cover the nitrogen and phosphorus model in a similar manner as fo r the hydrologic model. Appendix E is a short list and description of the new process and data objects, Appendix F presents the

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7 UML diagrams of the objects, and Appendix G is a technical manual. Appendices H, I, J, and K detail a module for simulating the tr ansport of a conservative solute (not implemented in this work). Appendix H is a short list and descripti on of the new process, data, and interface objects; Appendix I s hows the UML diagrams of the objects, Appendix J is an input/output variable refere nce, and Appendix K is a technical manual.

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8 Figure 1-1. Flatwoods regions of Florida (adapted from United States Department of Agriculture Natural Resources Cons ervation Service [U SDA/NRCS] 2002)

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9 CHAPTER 2 FIELD-SCALE HYDROLOGY OF THE ACRU2000 MODEL Introduction The name ACRU began as an acronym for the Agricultural Catchment Research Unit of the Department of Agricultural Engi neering (now the School of Bioresources Engineering and Environmental Hydrology) at the University of KwaZulu-Natal in Pietermaritzburg, South Africa. The model, r ecently redesigned into an object-oriented framework (Clark et al. 2001; Kiker a nd Clark, 2001a) and adopting the ACRU2000 moniker, operates on a daily tim e-step, uses a two-layer soil (referred to as the A and B soil horizons) to repres ent the water budget of a field or catchment, and can be operated as either a lumped field-scale or a distributed basin-scale model. This chapter details the field-scale hydr ologic processes of the ACRU 2000 model and discusses its suitability for shallow water-table environments. A detailed description of the entire ACRU model can be found in Schulze (1995) and Smithers and Schulze (1995). The structure and design of the model are presented in the next chapter in the context of the developments made in this work. Evapotranspiration The calculation of refere nce potential evapotranspi ration in ACRU2000 can be determined by a variety of methods or input ted directly to the model (Schulze 1995). Reference potential evapotranspiration can al so be determined using daily or average monthly meteorologic parameters. Calcul ation methods include the Penman (1948)

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10 equation, the Hargreaves and Samani (1982; 1985) equations, the Blaney and Criddle (1950) equation, the Thornthwaite (1948) equation, and others (Schulze 1995). Evaporative demand is applied in a topdown approach in ACRU2000; it is applied first to previously intercepted water on the plant canopy with the remaining demand being applied to soil evaporat ion and plant transpiration us ing Ritchies (1972) method or as a lumped quantity. When part itioned, potential transpiration ( Tp) is estimated as a function of the leaf area index ( LAI ): 0 5 021 0 7 0 ET LAI Tp for LAI < 2.7 (2-1a) 095 0 ET Tp for LAI 2.7 (2-1b) with the remaining demand applied as potential soil evaporation, Ep to the A soil horizon. Potential soil evaporation Ep is adjusted for the percent su rface cover by mulch or litter, Cs: 100s p pC E E (2-2) According to Ritchies (1972) method, actual evaporation from the soil surface continues at a maximum rate equal to the potential rate (Stage 1 evaporation) until the accumulated soil water evaporation exceeds the stage 1 upper limit, U1 which is defined in units of mm: 42 0 13 sU (2-3) where s is a soil water transmission parameter th at is related to the texture of the soil (Ritchie 1972). After U1 is exceeded soil water evapor ation proceeds at a reduced (Stage 2) rate as a function of the square root of time:

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11 5 01 d d st t E (2-4) where td is the number of days since U1 has been exceeded. Transpiration (or lumped evapotranspirati on) occurs in proportion to the userspecified fraction of roots contained in the two soil horizons and is adjusted using a crop coefficient. The reduction of transpiration in response to water excess or deficiency is assumed to occur at water contents above fi eld capacity (water ex cess), taken as 100 cm of suction in the ACRU2000 m odel, or below a user-defined fraction of plant available water (water deficiency) where plant availa ble water is defined as the water stored between field capacity and the wilting point (Figure 2-1). Interception The interception of rainfall by the plant canopy is repres ented as either a userdefined maximum storage capacity (mm) or as a function of the leaf area index and gross daily rainfall using the mode l of Von Hoyingen-Huene (1983) as cited in Schulze (1995): 2007 0 013 0 13 0 27 0 30 0 LAI LAI P LAI P I (2-5) where I is in units of mm and P is gross daily rainfall (mm). Infiltration and Runoff Runoff and infiltration are determined using a modified SCS curve number method (USSCS 1972; Schulze 1995): S c P cS P Qn n 12 for Pn > cS (2-6a) 0 Q for Pn cS (2-6b) where Q is the depth of runoff (mm), Pn is net rainfall (mm) (rainfall less intercepted water), c is a coefficient of abstraction (considered constant at 0.2 in the original SCS equation), and S is the potential maximum retention (mm). The term cS is the initial

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12 abstraction, at Pn values below which no runoff will occur. The coefficient of abstraction, c is a user-supplied monthly value a nd may be as high as 0.4 immediately after plowing or under forested conditions or a low as 0.05 in regions of compacted soils according to Schulze (1995). As a variable pa rameter the coefficient of abstraction gives the user the ability to vary the runoff res ponse to reflect different vegetation, site conditions, and management practic es. The maximum retention, S is determined from a soil water deficit prior to a rainfall event down to a user-defined critical soil depth, Dc: c sD S (2-7) where s and are the water content at saturati on and the current water content, respectively. The critical soil depth varies with climatic, vegetative, and soil characteristics (Schulze 1995). A location with sparse vegetation, thin soils, and intense rainfall might have a relatively low value and a location of dense vegetation, deep soils, and low-intensity rainfall a high value (Figure 2-2). The ability to vary the critical soil depth, as well as the coefficient of abstrac tion, is an attempt in the ACRU2000 model to account for different runoff-produci ng mechanisms (Schulze 1995). The runoff generated using equation 2-6 is routed from the field by specifying a quickflow fraction that runs off on the same day it was generated. The remainder is retained to the following day; however it is not available for infiltration or evaporation. This delayed stormflow is intended to act as a surrogate for interflow according to Schulze (1995) and the specified quickflow fr action only affects the timing of runoff, not the amount generated.

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13 Percolation and Soil Moisture Redistribution Downward percolation from a soil horizon can occur when the horizon is above field capacity according to a user -specified fraction (mm/day): d K qfcA AB (2-8) where KAB is the fraction of water above field cap acity in the A horizon that will drain to the B horizon, fcA is the water content of the A horizon at field capacity, and d is the thickness of the A horizon. An identical relati onship is used for percolation out of the B horizon. Soil moisture may (as an option se lected by the user) move in response to gradients at moisture contents below field cap acity as a function of the gradient between the horizons downward (mm/day): fcB B fcA A Ad q 02 0 (2-9) and upward: fcA A fcB B Bd q 01 0 (2-10) where A and B are the moisture contents of the A and B horizons, fcB is the moisture contents of the B horizon at field capacity, and d is the thickness of the soil layer. Baseflow Water that percolates out of the B horiz on enters the groundwat er store. Water within the groundwater store can flow out to a nearby surface water body as baseflow. Baseflow is calculated as a f unction of the size of the groundw ater store by assuming that a fraction of the groundwater store is rele ased. The baseflow release fraction, Fbf, is a

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14 function of the size of the groundwater store, Sgw (mm), and a base release coefficient, Fbfi: bfi bfF F8 0 for Sgw < 0.015 (2-11a) bfi bfF F3 1 for Sgw > 0.100 (2-11b) bfi bfF F for 0.015 Sgw 0.100 (2-11c) Application of the ACRU2000 Model to Shallow Water-Table Environments The field-scale hydrology of the ACRU 2000 model, as described above, was developed principally for arid locations wher e runoff generation occurs primarily via an infiltration-excess mechanism and the inte raction of groundwater with nearby water bodies can be approximated as a one-way process. This is evidenced by The reduction of evapotranspiration at water contents above field capacity, the use of an SCS-type equa tion to estimate surface runoff, the assumption that groundwater flows onl y out from a catchment as baseflow. While the reduction of evapotranspiration a bove field capacity and the inability to represent the flow of groundwater into the field or catchment from a nearby waterbody can be considered shortcomings of the m odel, the modeling approach of ACRU2000 does provide some flexibility in applying it to shallow water-table environments. This flexibility is entirely due to the ability to vary the parameters that determine runoff and infiltration (the coefficient of abstraction, c, and the critical soil depth, Dc). In applying the ACRU2000 model to locations with highly permeable, shallow wate r-table soils there are a few recommendations that can be ma de for parameterization of the model: The total soil depth represented in the model should be as deep as the deepest water-table observation, the response fraction for percolation from the B horizon to the groundwater store should be set to zero in order to mimi c the poorly drained conditions caused by a shallow water-table,

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15 the response fraction for percolation from the A to B horizon should be set to a value of 1.0 in the case of highly permeable soils. This allows the A horizon to drain to field capacity if sufficient storag e is available in the B horizon where it can accumulate, mimicking the presence of a shallow water-table, plant roots must extend into the B horiz on in order for the moisture contained within it to be available for evapotrans piration (as water may only move up from the B horizon at moisture gradients below field capacity), the coefficient of initial ab straction should have a value of 1.0. This will cause runoff to only occur when the entire soil profile is saturated, the critical soil depth from which the soil moisture deficit is calculated should be the entire depth of the soil. As an alternative to allowing plant roots to extend into the B horizon and setting the response fraction from the A to B horizon to unity, the A horizon can be assumed to extend to the entire depth of the soil and c ontain all of the plant roots. Due to the reduction of evapotranspiration above field cap acity the splitting of the soil between two horizons is arbitrary and depending on thei r relative thickness will produce greatly varying results when assuming no drainage out of the B horizon. Summary This chapter details the field-scale hydr ology as simulated in the ACRU2000 model and its suitability for use in shallow water table environments. In the model, reference potential evapotranspiration can be determined by a variety of methods and is applied in a top-down approach to intercepted water and then to soil evaporation and plant transpiration. The model does not re present water ponded on the ground surface. Rainfall can be intercepted by the plant canopy, as represented by tw o different methods, and is partitioned between runoff and inf iltration using a modified SCS curve number method. Water may percolate out of a soil ho rizon at water contents above field capacity and may move between soil horizons at wate r contents below field capacity. Water

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16 percolating out of the B soil horizon is a dded to the groundwater store from which baseflow can occur according to a user-def ined baseflow fraction and the size of the groundwater store. Runoff that is genera ted is split between a quickflow fraction, occurring on the day generated, and a delayed stormflow fraction. The ACRU2000 model may not be appropria te for application to shallow watertable environments due to the reduction of evapotranspiration (or plant transpiration) at water contents above field capacity, the use of an infiltration-exce ss type procedure to determine runoff, and the inability of the m odel to represent groundwater flow into the model domain. However, the flexibility of the modified curve number procedure of the model offers some flexibility in represen ting the different runo ff producing mechanisms of infiltration-excess and saturation-excess. A field dominated by saturation-excess runoff may be sufficiently repr esented by using parameter valu es that are outside of the recommended ranges. Modifications to ACRU2000 that may be c onsidered to be more appropriate for shallow water-table environments are described in the next chapter. This modified model and the original model described here are evaluated against field data from shallow water-table environments in Chapter 4.

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17 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 wpsfc Figure 2-1. Transpiration reduction due to soil moisture excess and deficiency as simulated in the ACRU2000 mode l (adapted from Schulze 1995) 0.10 0.20 0.25 0.25 0.20 0.15 0.30 0.30 0.40 Arid Climate Humid Climate High-Intensity Rainfall Low-Intensity Rainfall Thin Soils Eutrophic Low organic Carbon High Organic Carbon Dystrophic Deep Soils Figure 2-2. Critical soil dept h as related to climatic, vegetative, and soil conditions (adapted from Schulze 1995)

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18 CHAPTER 3 FIELD-SCALE HYDROLOGIC MODEL FO R HUMID, SHALLOW WATER-TABLE ENVIRONMENTS: DEVELOPMENT Introduction Simulation of humid regions with highly pe rmeable, shallow soils has been shown to be inconsistent with the concept of inf iltration excess or Hort onian runoff generation (Dunne and Black 1970; Freeze 1972; Jayatilak a and Gillham 1996; Ogden and Watts 2000; Hernandez et al. 2003). In such envi ronments, runoff is typically generated by saturation excess whereby the water-table rise s to the ground surface, creating a Variable Source Area (VSA), a zone of saturation that expands and contracts seasonally as well as during individual storms. These VSAs often form where subsurface lateral flow converges, the ground slope changes, or the depth to a restrictive layer decreases (Frankenberger et al. 1999). Regions domin ated by VSA runoff include much of the southeastern coastal plain of the United Stat es, and the flatwoods regions of Florida, in particular. The flatwoods landscape is ch aracterized by very flat topography with moderately to poorly drained, highly permeab le, sandy soils that can often have standing water during wet weather. As a result, gr oundwater levels are heavily influenced by rainfall, evapotranspiration (ET), and nearby canal or stream stages (Yan and Smith 1994; Dukes and Evans 2003). Management of surface and groundwater quality has become an environmental priority, particularly in agricultural waters heds. In managing wate r-resource quantity and quality, modeling is the most cost-effective wa y to evaluate the impact of management

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19 alternatives. In the past, model develo pment efforts often emphasized land-surface processes or groundwater processes, but rare ly both. The land-surface models emphasize infiltration, ET, and surface-water movement while often ignoring or oversimplifying saturated groundwater, and the groundwater models emphasi ze prediction of groundwater levels in response to pumping, boundary c onditions, and recharge while oversimplifying ET and vadose zone processes. Both surface water and groundwater models work well on their own, when used in areas where th e interaction between surface and groundwater is weak or insignificant (Yan and Smith 1994). Several models have been developed a nd tested for use in flatwoods regions including CREAMS-WT (Heatwole et al. 1987; Heatwole et al. 1988) based on the CREAMS model (Knisel 1980); EAAMOD (Bo ttcher et al. 1998a); FHANTM (Tremwel and Campbell 1992; Fraisse and Campbe ll 1996) based on the DRAINMOD model (Skaggs 1980); and FLATWOODS (Sun et al 1998) based on the MODFLOW model (McDonald and Harbaugh 1988). All of these models were devel oped as field-scale models with the exception of FLATWOODS, whic h happens to be the only one of these models that (to date) does not contain a wate r quality component. The field-scale models mentioned have been incorporated into distributed models (H eatwole et al. 1986; Negahban et al. 1995; Bottcher et al. 1998b); howe ver, they are used to determine edge of field effects and, as such, are used in a loose coupling fram ework where individual fields (or grid cells) do not interact with one another. Sun et al. (1998) showed the need for distributed, fully-interactive modeling of flatwoods systems where groundwater gradients may be strongly affected by hete rogeneous distributions of vegetation type, seasonality effects, and ch anging management practices.

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20 To better simulate high wate r-table environments like th e coastal plain flatwoods, new model components have been develope d within the framework of the ACRU2000 model (Clark et al. 2001; Kiker and Clar k 2001a). The new components allow for distributed, physically based modeling of hi gh water-table environments. The objective of this work was to develop field-scale model components for the flatwoods landscape for use in the ACRU2000 distributed hydr ologic model; and to demonstrate the advantages of adding model components with in the object-oriented framework of the ACRU2000 model. Model Development Background The ACRU model (origina lly written in the FORTRAN programming language) has its origins in a distributed catchm ent evapotranspiration study in the Natal Drakensberg region of South Africa in the early 1970s (Schulze 1995) Since then the model has undergone many revisi ons and additions to meet th e water-related needs of the scientific-modeling community in South Africa and beyond. However, each consecutive improvement to the model has created a more difficult design and coding challenge for subsequent researchers. The many contri butions made to the model over the years resulted in a framework in which it was rela tively difficult to make new additions, and in some instances the model structure was unabl e to accommodate the desired additions at all. To better accommodate future mode l additions, the ACRU model was recently redesigned in an object-oriented framework (Clark et al. 2001; Kiker and Clark 2001a). As mentioned in the previous chapter, the ACRU2000 mode l can be used as either a lumped field-scale model or as a distribu ted basin-scale model. The model operates on a daily time-step, using a modified SCS curv e number procedure to generate daily runoff

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21 volumes (Schulze 1995). The model uses a tw o-layer soil to repr esent the water budget of the catchment, with any water above field capacity percolating out of a layer according to a user-defined fraction. Water draini ng out of the bottom soil layer enters the groundwater store, from which baseflow is gene rated as a function of the size of the store and a user-defined baseflow coefficient. Plant-canopy interception can be represented using two different methods and this intercep ted water can (in turn) be evaporated back into the atmosphere. Reference potential eva poration can be determined using a variety of methods and applied as a lumped quantit y, or it may be partitioned between soil evaporation and plant transpir ation, according to the met hod of Ritchie (1972). Plant water-stress, and a corresponding reduction in transpiration, is determined to occur at some water content between field capacity and wilting point (set by the user) for waterlimiting conditions, and is determined to occur at water contents above field capacity for water-excess conditions. Schulze (1995) and Smithers and Schulze (1995) give a more detailed description of the ACRU model. Structure The model was redesigned with the belief that the hydrologic system is complex, and thus the way we view it or model it shou ld be seen as a learning process that may require periodic reevaluation. The model was restructured usi ng an object-oriented methodology to produce a more flexible and ex tensible model structure. The new, object-oriented model is referred to as ACRU 2000. The model was designed using the Unified Modeling Language (UML) and the model was implemented in the Java programming language. Using UML allows gr aphic design of objects with diagrams for object-oriented programming, before writi ng computer code. The UML provides a standardized notation to sp ecify, design, visualize, and document object-oriented

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22 software (Jacobsen et al. 1998). The exte nsibility of the AC RU2000 model framework has been demonstrated previously by Ca mpbell et al. (2001), who added a module to simulate nitrogen and phosphorus transport a nd transformations, and by Kiker and Clark (2001b), who added a module to simulate s outhern African rangeland ecosystems. Object orientation uses the concept of object s, where an object consists of a small, well-written piece of computer code that contains its own attributes, methods, and behavior (Figure 3-1). Th e attributes describe the object, in terms of physical characteristics or other traits. The methods describe the objects internal functionality, and contain the equations the model uses to si mulate various events. The behavior of the object describes how it interacts with other obje cts. Object orientat ion thus encourages the creation of models that are modular in structure. Three main object types in ACRU2000 are of interest to the researcher simulati ng environmental events: Component, Data, and Process objects. Co mponent objects are physical components of the system, such as the climate, the soil, or a soil layer. Data objects are the descriptors, or attributes, of the Component such as the temperature, depth, or hydraulic conductivity. These data attributes of the Component objects are modeled as separate objects themselves, because as an object they can be re used or extended (rather than just being a simple variable). As an object, only a si ngle Data object representing a specific trait needs to be written in code. This single object can then be associated with different objects (with different values) representing the particular tr ait of each Component object. Process objects, the third type of object of in terest to the research er, represent the action or event that involves one or more Component objects, such as interception of rainfall by a vegetation canopy or the infiltration of water into the soil. Each Process object, when

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23 acting on one or more Component objects, uses the Data objects associated with that Component (Figure 3-1) (C lark et al. 2001; Kiker and Clark 2001a). Objects can interact with each other in three different ways: inheritance, aggregation, and association (Figure 3-1). Objects may i nherit properties from other objects. Inheritance indicates that one obj ect is a type of another object. This inheritance of functionality between objects al lows for the code that has already been written for the parent object to be used by the child object. Any difference between the two objects is made as needed in the ch ild object. In this manner the child object can be a more specialized version of th e parent object. Objects may also be aggregated, or be a part of other objects. In this role, one object may use the functionality of another. A nother way that objects may inte ract is by association (using information from other objects). This uses data from relationship allows an object to access data that is owned by another objec t. These three relationships among objects encourage the development of modular code, and result in a flexible and extensible programming structure that encourages code re-use. The structure of the object-oriented de sign of ACRU2000 allows new objects to be created and linked to the model without majo r revision to the existing code. To add a new module (a group of objects with a comm on overall purpose) the model developer needs to: Identify the Component, Process, and Data objects which will be used. Determine any new objects to be created. Define the relationships of all of the objects in the new module and to existing objects. Implement the design in Java code with in the framework set in Steps 1-3.

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24 Steps 1-3 are accomplished easily using UML. The developer only needs to write computer code at Step 4. Adding the new hydrology module to AC RU2000 focuses on adding new Process objects, and to a lesser extent on adding any n eeded Data objects. This is because the Component objects (the climate, soil, soil laye rs, etc.) already exist, along with the Data objects associated with them. Each new Pr ocess object has a UML diagram associated with it (Figure 3-1). The use of UML as th e standardized design tool provides a design that is easy to understand. The following section describes the field-sc ale hydrologic proce sses of the shallow water-table module of ACRU2000. The processes that have been retained from the original model are noted. A short description of the process and data objects added to the model can be seen in Appendix A, UML desi gn diagrams of the hydrologic processes can be seen in Appendix B, and Appendix C provi des and input/output variable reference for future users of the model. Hydrologic Processes and Governing Equations The ACRU2000 model was originally develo ped for use in upland watersheds that are characterized by infiltration-rate limited (Hortonian) runoff generation and topographic gradients that driv e overland and groundwater flow direction. To apply the model to humid, shallow water-table environm ents, several modifications were needed. These included the effects of a shallow water-table on evapotranspiration and runoff generation, a depth-variable specific yield, and the repr esentation of surface water and groundwater gradients that may reverse in res ponse to time-variable boundary conditions. In making these modifications the model was ex panded to simulate up to ten soil layers.

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25 Evapotranspiration In addition to the methods previously included in the model for calculating reference potential evapotranspiration (ET0), the standardized Pe nman-Monteith equation for grass reference potentia l evapotranspiration adopted by the Food and Agricultural Organization in the FAO Irrigation and Drai nage Paper No. 56 (Allen et al. 1998) has been added to the model: 2 2 034 0 1 273 900 408 0 u e e u T G R ETp a s mean p n (3-1) where ET0 is in units of mm day-1, Rn is the incoming net radiation (MJ m-2 day-1) and is the difference between net incoming shortwave radiation, Rns and the net outgoing longwave radiation, Rnl, G is the soil heat flux density (MJ m-2 day-1) and is assumed to be zero for daily calculations, Tmean is the mean daily air temperature at 2 m height [(Tmax+Tmin)/2,oC], u2 is the wind speed at 2 m height (m/s), es is the saturated vapor pressure (kPa), ea is the actual vapor pressure (kPa), is the slope of the vapor pressure curve (kPa/oC), and p is the psychrometric constant (kPa/oC). The net shortwave radiation, Rns is determined from the albedo, (assumed to be 0.23) and the incoming shortwave radiation, Rs: s nsR R 1 (3-2) Rs is supplied as daily input to the model or estimated fr om the Angstrom equation: a s s sR N n b a R (3-3) where n is the number of sunshine hours, N is the maximum possible number of sunshine hours, Ra is the extraterrestrial radiation (MJ m-2 day-1), as is a regression constant that

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26 expresses the fraction of extraterrestrial ra diation reaching the earth on overcast days (n = 0), and the quantity as + bs is the fraction of extraterrestri al radiation reaching the earth on clear days (n = N). In the absence of regional values for as and bs, Rs may be estimated using the equation of Hargr eaves and Samani (1982): 5 0TD R KT Ra s (3-4) where TD is the difference between maximu m and minimum air temperatures (oC) and KT is an empirical constant. Samani ( 2000) developed an equation to determine KT as a function of TD: 4023 0 0433 0 00185 02 TD TD KT (3-5) using 25 years of data for the continental U.S. Daily values for es, ea, p, N, Ra, and Rnl are calculated according to the equa tions given in Allen et al. (1998). Evaporative demand is applied in a t op-down approach in the model, with evaporation applied first to intercepted water, then to ponded water on the ground surface, then to soil evaporati on and plant transpiration. As in the original ACRU2000 model (Chapter 2), soil evapor ation and plant transpiration can be applied as a lumped quantity to soil layers contai ning plant roots or separated between soil evaporation and plant transpiration for conditi ons of incomplete cover using the methods of Ritchie (1972) where potential transpiration (Tp) is estimated as a functi on of the leaf area index (LAI): 0 5 021 0 7 0 ET LAI Tp for LAI < 2.7 (3-6a) 095 0ET Tp for LAI 2.7 (3-6b) Plant transpiration is further adjusted fo r different plant albedo, and stomatal and aerodynamic resistances at various stages of growth with a crop coefficient. Water

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27 extraction from each soil layer is determined as a function of the maximum transpiration rate Tp, a root distri bution function g(d), and soil moisture dependent reduction factor (h): pT d g h T (3-7) The soil moisture reduction factor as a function of pressure head, (h) used was proposed by Feddes et al. (1978) (Figure 3-2). At h < h1 (oxygen deficiency), and h > h4 (pressure greater than the wilting point) (h) is zero. Between h2 and h3 transpiration occurs at the potential transpiration rate, (h) is unity. Between h1 and h2 and h3 and h4 a linear reduction in transpiration is assumed. Th e ability to alter the point in which oxygen deficiency begins (h2) is advantageous since many pl ant species in the flatwoods ecosystem are quite tolerant to wet conditions and may transpire at their maximum rate even when the soil is saturate d. The linear root distributi on function used here is that proposed by Hoogland et al. (1981): 22 L L L d c d g -1 c 0, d L (3-8) where c is an empirical parameter expressing th e relative density of roots between the ground surface ( d = 0) and the maximum depth of roots ( d = L ) and is shown in Figure 3-3. Upon integration with respect to d Equation 3-8 defines the fraction of roots between the ground surface and a depth of d Potential soil evaporation is adjusted by a factor of 1.15 to account for the difference in albedo between bare soil and a vegetated surface as recommended by Allen et al. (1998). As in Chapter 2, potential soil evaporation Ep is further adjusted for the percent surface cover by mulch or litter, Cs:

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28 100s p pC E E (3-9) Soil water evaporation takes place to a user defined depth within th e soil profile with recommended values ranging from 0.1 to 0. 15 m depending on soil texture (Allen et al. 1998). According to Ritchies method actual evaporation from the soil surface continues at a maximum rate equal to the potential rate (Stage 1 evaporation) until the accumulated soil water evaporation exceeds the stage 1 upper limit, U1 which is defined in units of mm: 42 0 13 sU (3-10) where s is a soil water transmission parameter th at is related to the texture of the soil (Ritchie 1972). After U1 is exceeded soil water evapor ation proceeds at a reduced (Stage 2) rate as a function of the square root of time: 5 01 d d st t E (3-11) where td is the number of days since U1 has been exceeded. Interception The interception of rainfall by the plant canopy is represen ted as either a maximum storage capacity (mm) or as a function of the leaf area index and gross daily rainfall using the model of Von Hoyingen-Huene (1983) as cited in Schulze (1995) and used in the unmodified ACRU2000 model: 2007 0 013 0 13 0 27 0 30 0 LAI LAI P LAI P I (3-12) where I is in units of mm and P is gross daily rainfall (mm).

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29 Infiltration In humid, shallow water-table region s the dominant mechanism of runoff generation has been shown to be saturati on excess (Dunne and Black 1970; Freeze 1972; Hernandez et al. 2003). In these regions a sh allow water-table rises, saturating the entire soil profile and inundating the ground surface, creating a Variable Source Area (VSA). These VSAs vary with space and time, expanding in the wet season and contracting in the dry season. In contrast to infiltration-ra te limited or Hortonian runoff (Horton 1933), runoff from VSAs have shown little sensitivit y to temporal variability of rainfall or rainfall intensity (Hernandez et al. 2003). Due to the highly c onductive nature of the sandy soils of the Florida flatw oods the infiltration capacity of the soil is rare ly, if ever, limiting. Thus runoff generation in the model is assumed to be solely storage-limited, with infiltration proceeding until the entire soil profile becomes saturated. Water-table depth and soil moisture distribution The total volume of water c ontained within pore spaces between the water-table ( z = 0) and the ground surface ( z = z0) can be expressed as: 00 z wdz V (3-13) where Vw is the volume of water per unit area (cm3 cm-2) and is the moisture content (cm3 cm-3) at some height above the water-table z (cm). If the relationship between soil moisture and pressure head ( h ) within the soil profile is known and hydrostatic conditions can be adequately assumed, then closed form equations expressing as a function of z can be used in equation (3-13) to define explicitly the volume of water between the ground surface and the water-table. In th is hydrostatic approximation of Richards equation Vw is the basic state variable that is influenced by water movement into or out of

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30 the soil profile. This assumption of a hydrostatic condition has been shown to be adequate for regions with shallow water-ta bles (Skaggs 1980; Koi vusalo et al. 2000) and also specifically for shallow water-table re gions with highly conductive soils such as the flatwoods regions of Florida (Rogers 1985). In general, th e hydrostatic approximation is most accurate for higher conductivity soils and shallower water-tables. Under hydrostatic conditions the water content at any point can be described using the models of Brooks and Corey (1964), Hutson and Cass (1987), or va n Genuchten (1980). For a soil profile at hydrostatic equilibrium the wa ter content as a function of height above the water-table using the model of Brooks and Corey (1964) is z h zb r s r for z > hb (3-14a) sz for z hb (3-14b) where ( z ) is the moisture content (cm3 cm-3) as a function of height above the watertable z (cm), r is the residual moisture content (cm3 cm-3), s is the saturated moisture content (cm3 cm-3), hb is the bubbling pressure head, or ai r entry pressure head, of the soil (cm); and is the pore size distribution index (-). The model of Hutson and Cass (1987) which replaces the sh arp discontinuity at hb in the Brooks and Corey (1964) model with a parabolic segment can similarly be expressed as z h zb r s r for z > hi (3-15a) 2 2 21 1b s i s i r s rh z z for z hi (3-15b)

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31 where i is the water content (cm3 cm-3) at the inflection poin t between the exponential and parabolic portions of the water characte ristic curve at capillary pressure head hi (cm). i and hi are defined as 2 2 s i (3-16) 12 2 b ih h (3-17) Similarly the model of van Genuchten (1980) can be expressed as m n r s rz z 1 1 (3-18) where is in units of cm-1, and n, and m are empirical parameters with the constraint m = 1 1/n. The volume of water within a given so il layer can then be expressed as the integral of either one of these models: 2 1z z wdz z V (3-19) Upon integration Equation (3-14) becomes: 1 z z h z dz zb r s r for 1 and z > hb (3-20a) z h z dz zb r s rln for = 1 and z > hb (3-20b) z dz zs for z hb (3-20c) Similarly, upon integration Equation (3-15) becomes: 1z z h z dz zb r s r for 1 and z > hi (3-21a) z h z dz zb r s rln for = 1 and z > hi (3-21b)

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32 2 2 33 1b s i s i r s rh z z z dz z for z hi (3-21c) The model of van Genuchten (1980), Equation (3-18), cannot be inte grated analytically with the restriction that m = 1 1/n so it is integrated numerically in the model using a five-point Gauss-Legendre quadratu re (Chapra and Canale 1998). The removal of water from the plant root zone by evapotranspiration may cause a deviation from the hydrostatic profile. This devi ation creates a depleted root zone that is represented explicitly in the model for each soil layer. This depleted root zone implies that an upward gradient is induced within the soil profile. Wate r may move upwards in the soil profile in response to this gradient This upward movement of water defines the connectivity between a shallow water-table and evapotranspira tion. This upward movement of water is simulated by assuming that a steady state condition exists between the water-table and an evaporating surf ace. Assuming steady-state, the upward movement of water can be found from Darc ys Law, assuming the soil profile is homogeneous: 1 dz dh h K q (3-22) where q is the upward flux (m/day), K(h) is the hydraulic conductivity (m/day), h is the soil capillary pressure head, and z is the height of the ev aporating surface above the water-table (m). Integration of Equation (3-22) yields:

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33 hh K q dh z0/ 1 (3-23) Assuming a relationship between K and h allows Equation (3-23) to be solved with a lower boundary condition of h = 0 at z = 0 (at the water-table). The complexity of analytical or numerical so lutions to equation (3-23) depends on the choice of K ( h ) relationship. In developing analytical so lutions an upper limit of integration of h = is typically used for simplicity. Gardner (1958) showed that this upper limit is appropriate since upward flux quickly approaches a limiting va lue as soil suction increases. Anat et al. (1965) solved equation (3-23) using the Brooks and Corey (1964) hydraulic conductivity relationship and deri ved an approximate, algebraic solution that is explicit in q : b sh z K q 1 886 1 12 (3-24) where = 2+3. The actual amount of upward flux occurring during a time step is determined by using the first depleted so il layer above the water-table as the upper boundary. The maximum upward flux calculated to this bottom-most depleted layer is retained as the limiting maximum upward flux for the entire profile on a given day. Should this bottom-most depleted layer beco me fully replenished the calculation of upward flux proceeds to the next layer until either the limiting maximum is reached, or the maximum upward flux for a subsequent laye r is less than the amount to which that layer is depleted below its hydrostatic wate r content, or the entire root zone is replenished, whichever is smaller. Th e importance of such a time-varying upper boundary condition for upward flow from the water-table when simulating the

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34 fluctuations of the water-table, partic ularly during periods of drought, has been demonstrated by Rogers (1985) and Desmond et al. (1996). Groundwater flow Groundwater may flow into or out of the model domain both horizontally and vertically. Vertical moveme nt of groundwater can occur at a constant or time-varying specified rate or according to a constant or time-varying hydraulic head in a deep aquifer below a restrictive later according to Darcys Law: d wt rH H C q (3-25) where Hwt is the hydraulic head in the surficial aquifer (m), Hd is the hydraulic head in the deep aquifer (m), and Cr is the conductance of the rest rictive layer (1/day) and is defined as: r r rL K C (3-26) where Kr and Lr are the hydraulic conductivity (m /day) and thickness (m) of the restrictive layer, respectively. Horizontal flow in response to a cons tant or time-varying boundary condition is simulated using the Dupuit equation (Fetter, 1994): 2 2 '2b wt HH H L K q (3-27) where q is the flow per unit width (m2/day), KH is the horizontal hydraulic conductivity, L is the distance to the boundary (m), and Hb is the hydraulic head at the boundary (m).

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35 Overland flow and depression storage Overland flow is simulated with a simple stage-discharge relationship as used by Tremwel and Campbell (1992) and Kroes a nd van Dam (2003). The stage-discharge relationship is of the form: dep pondz h q 1 (3-28) where q is the runoff depth (mm), is the runoff resistance (days) and is typically calibrated to observed data, hpond is the depth of ponded water on the ground surface (mm), zdep is the depth of depression storage which must be filled before runoff can begin (mm), and is an exponent to be calibrated (-) but is usua lly given a value of 1.67 (assuming Mannings equation). Summary A field-scale hydrologic module for us e within the ACRU2000 distributed hydrologic model was developed to simulate the hydrology of humid, shallow water-table regions such as the flatwoods of the southeas tern United States. The module is intended to simulate the position of the water-table e xplicitly in order to accurately predict its contribution to evapotranspiration and the cr eation of Variable Source Area runoff. The standardized Penman-Monteith reference potential evapotranspiration equation recommended by the Food and Agricultural Or ganization (Allen et al. 1998) has been added to the model as well as the methods of Hargreaves and Samani (1982) and Samani (2000) to estimate incoming solar radiation. Evaporative demand is applied in a topdown approach to intercepted water, ponded wate r, and then applied as a lumped quantity to the soil or partitioned to soil evaporation and plant transpiration. Lumped evapotranspiration or partitione d transpiration is applied in proportion to the density of

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36 plant roots which is defined using the linear root distribution functi on of Hoogland et al. (1981). The response of plant transpiration or lumped eva potranspiration to water stress is represented as a function of soil water pr essure head using the functional relationship of Feddes et al. (1978). The model approxi mates soil moisture as having a hydrostatic distribution. Soil moisture contents may be represented by one of three soil moisture characteristic models. Soil moisture within the root-zone may dr op below the hydrostatic water content due to evapotranspiration. This reduction in root -zone water contents induces upward flow which is represented us ing an approximate, steady-state solution to Darcys Law. Saturated groundwater can flow in or out to a d eep aquifer and/or horizontally in response to a time-varying boundary condition. Runoff is assumed to occur via saturation-excess only and moves from the field according to a simple stagedischarge relationship. Application of the model, at the field-scal e, is limited to the scale at which model parameters can be appropriately considered to be homogeneous and to areas with highly permeable soils where runoff occurs primarily by saturation-excess. The object-oriented design of the ACRU2000 model made it an ideal candidate for adding such model components in a straight forward and consistent manner. Object design was made using UML to define ne w objects and their relationships. UML diagrams of the hydrologic module describe d here are shown in Appendix B and are accompanied by a short description of the objects in Appendix A, and input/output variable reference in Appendix C, and a tech nical manual in Appendix D. The resulting model design provides a modul ar and easily extensible model structure. The new

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37 hydrologic module is validated in Chapter 4 and its performance compared to the original, unmodified model described in Chapter 2.

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38 Figure 3-1. Sample UML diagram showing Component, Process, and Data objects and the inheritance, aggregat ion, and association rela tionships (adapted from Kiker and Clark 2001)

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39 0 0.2 0.4 0.6 0.8 1 h1h2h3h4 Figure 3-2. Transpiration reduction factor as a function of soil pressure head. 0 10 20 30 40 50 60 70 80 90 100 00.0050.010.0150.020.0250.03 Root Density (cm-1)Depth (cm)c = 0 c = -1 c = -0.5 Figure 3-3. Root dens ity distribution function g ( d ) of Hoogland et al. (1981). Maximum depth of roots ( L ) is 80 cm in this example.

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40 CHAPTER 4 FIELD-SCALE HYDROLOGIC MODEL FO R HUMID, SHALLOW WATER-TABLE ENVIRONMENTS: VALIDATION Introduction A physically based model for humid, sh allow water-table environments was developed in Chapter 3, this chapter validates the model, evaluates the sensitivity of the model to input parameters, and makes recommendations for model improvement. The model validation is conducted us ing observed data from three e xperimental sites. For the first experimental site validation is made by comparing model simulations to observed data and to a numerical, finite difference model that solves Richards equation. For the second experimental site validation is made by calibrating the model to a portion of the observed data and then using the remaining observations for verification of the model calibration. The models performance is al so compared to the original, unmodified ACRU2000 model (Chapter 2) and the fiel d-scale Field Hydrologic And Nutrient Transport Model (FHANTM) for the second experimental site. For the third experimental site validation is made by calib rating the model to a portion of the observed data and then using the remaining observations for verification of the model calibration. In addition to evaluating the models pe rformance by making visual comparisons between simulated results and field observat ions the model performance is evaluated using error measures of mean absolute error: n i i iO P n MAE11 (4-1) and root-mean square error:

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41 n O P RMSEn i i i 1 2 (4-2) by pair-wise comparison between model predicted ( Pi) and observed ( Oi) daily values. These measures are significant in that they pr ovide a quantification of the error in units of the variable in question. Using RMSE in conjunction with MAE is useful in that the degree to which RMSE exceeds MAE is an indicator of the extent that outliers or variance in the difference between simulated and obs erved values exist in the data since RMSE is more sensitive to extreme values due to the squaring of the differences between observations and predictions (Legates and McCabe 1999). Additionally, model performance is evaluated using a relative error measur e, the Nash-Sutcliffe coefficient of efficiency (Nash and Sutcliffe 1970): n i i n i i iO O O P E1 2 1 21 (4-3) where is the mean of the observations. E ranges from negative infinity to 1.0 with higher values indicating better agreement. Since E is a ratio of the mean square error to the variance of the observed data, subtracted from 1.0, E is equal to 0.0 if the squares of the differences of predicted and observed values is as large as the variability in observed data. This indicates that the observed mean is as good a predictor as the model. A value of E < 0.0 indicates that is a better pred ictor (Legates and McCabe, 1999).

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42 Case Study 1: Paynes Prairie State Preserve Site Description and Experimental Design Paynes Prairie State Preserve is a 5600 ha highland marsh system in north-central Florida (Figure 4-1). Approximately 4100 ha of the Preserve is wetland and is a surface expression of the surficial aquifer. Experime ntal data were collect ed by Jacobs et al. (2002) from a wet prairie commun ity within the Preserve (29o34'14"N, 82o16'46"W). Detailed site description and instrumentation information can be found in Jacobs et al. (2002) and Whitfield (2003), however a brief su mmary is included here. The wet prairie is a flat plain with emergent, herbaceous species such as maiden cane ( Panicum hemitomon Schultes), mild water-pepper ( Polygonum hydropiperoides Michx.), mock bishops weed ( Ptilimnium capillaceum Michx), and dog fennel ( Eupatorium capillifolium Lam.) (Jacobs et al. 2002). Observa tions by Jacobs et al. (2002) showed that the majority of plant roots were containe d in the top 10 cm of soil with 95% of roots within the top 25 cm. The marsh hydrogeology consists of a sandy su rficial aquifer that is separated from the Floridan aquifer by the Hawthorne Formation, a semi-confining clay unit that is approximately 1 meter be low ground surface. The predominant soil in the wet prairie was found to be Wauberg sand, a loamy, siliceous, hyperthermic Arenic Albaqualfs (Liu et al. 2005). Micrometeorological, soil moisture within the top 25 cm of soil, and water-table measurements were made during the experime ntal period. Water-table measurements were made between February 1 and June 30, 2001 and soil moisture measurements were made between April 10 and June 26, 2001. Measurements of actual evapotranspiration were made directly using an energy budget variation of the eddy correlation approach (Jacobs et al. 2002). These measurements were made between May 1 and July 20, 2001.

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43 Micrometeorological measurements of rainfa ll, net radiation, temperature, relative humidity, and wind speed were made during th e entire study period in order to calculate reference potential evapotranspiration. Results and Discussion Due to the extremely flat topography and the underlying semi-confining unit, vertical and horizontal flow into the m odel domain by overland or groundwater flow were assumed to be negligible. Using such assumptions the wet prairie can be represented as a single lumped element for modeling purposes where accumulation of water occurs via precipitation and losses vi a evapotranspiration onl y. For comparison purposes the wet prairie was also simulate d using the Soil-Water-Atmosphere-Plant (SWAP) model, a one-dimensional, finite-d ifference model that solves Richards equation (van Dam and Feddes 2000; Kroes a nd van Dam 2003). The SWAP model was chosen for comparison because it shares seve ral of the same algorithms with ACRU2000, specifically the determination of plant wa ter stress, the partitioning of potential evapotranspiration between soil evaporation a nd plant transpiration, and the transition from stage I to stage II soil evaporation. Following a prior modeling effort of this site the leaf area index was 2.7 and the plant root density distribution was represen ted as decreasing linearly from the ground surface to a depth of 0.325 meters below gr ound which satisfies the observation of 95% of the roots within th e top 0.25 meters of soil (Jacobs et al. 2002; Liu et al. 2005). Soil moisture characteristic parameter values ar e shown in Table 4-1 and are adapted from Liu et al. (2005). Reference potential evapotra nspiration was calculated using the PenmanMonteith equation as described by Jacobs et al. (2002) and wa s used along with measured precipitation as the climate forcings in the models. Reference potential

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44 evapotranspiration, as determined by Jacobs et al. (2002) was not adjusted with a crop coefficient. No additional model calibrati on, beyond that done by Liu et al. (2005) was made here. Observed and simulated water-table depths are shown in Figure 4-2. The simulated results agree with the measured data with the exception of days during and immediately following a large rain event, where the simulated rise of the water-table is not as intense. This observation is consistent with the res ponse caused by entrapped air and is supported by the rapid decline in the measured watertable soon after the ra in event (Fayer and Hillel 1986; Nachabe et al. 2004). Both mode ls deviated from observations when the observed water-table fell below approximately 1 meter in depth. During this period (roughly between 5/25 and 6/15), as the wil ting point of the top 25 cm of soil was approached (Figure 4-3), a re duction in the simulated evapot ranspiration can be seen as compared to observed values (Figure 4-4). This indicates that the soil, as represented in the model, was not capable of supplying adeq uate water vertically upwards during this period of dry-down. Observed and simulated soil moisture content in the top 0.25 m of soil is shown in Figure 4-3. ACRU2000 tended to under-predict the soil moisture content in the top soil during much of the simulation period, however this may be due to the nature of the model. Since the model does not compute water balance components simultaneously, but rather calculates them sequentially, the low soil moisture contents reported by the model at the end of the simulation day are likely due to the fact that ev apotranspiration removes water from the root zone after any upward fl ux (into the root zone) occurs within the soil profile.

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45 Simulated and observed evapotranspiration are shown in Figures 4-4 and 4-5. ACRU2000 often over-predicted on days with high observed ET and SWAP often underpredicted on days with low observed ET. The goodness of fit between measured and simulated daily values is shown in Table 4-2. Both models perfor med comparably according to the MAE RMSE and E with SWAP appearing to perf orm slightly better in predic ting water-table depths and actual evapotranspiration and ACRU2000 app earing to perform slightly better in predicting soil moisture contents. Valu es of the coefficient of efficiency, E compared well to those found by Liu et al (2005) using daily average potential evap otranspiration inputs. Liu et al. (1995) found values of 0.888, 0.902, and 0.605 for the water-table depth, soil moisture content, and evapotranspiration, respectively. Case Study 2: W.F. Rucks Dairy Site Description and Experimental Design W.F. Rucks Dairy is a low density improve d dairy pasture located in south-central Florida within the Kissimmee River Basin (27o27N 80o56W) (Figure 4-6). The pasture is approximately 3.9 ha with an average ground slope of 0.14%. The pasture contained primarily bahia grass ( Paspalum notatum ). The soil of this site was found to be a dominated by Myakka fine sand, a sandy, siliceous, hyperthermic, Aeric Haplaquods (Capece 1994). As part of a study to bette r understand the hydrologic and contaminant transport characteristics of the river basin the site was hydrologically isolated by the construction of a low earthen berm. Surface wa ter flows from the site were measured using a critical-depth trapezoidal flume insta lled in a breach in the berm. Field ditches within the sites were blocked to mimic undrained, natural conditions. This site has been the focus of prior modeling studies using the FHANTM model (Campbell et al. 1995;

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46 Zhang et al. 1995; Zhang and Gornak 1999) and was thus chosen as a good candidate for testing the ACRU2000 model. Groundwater level measurements were take n on an approximately weekly basis at 51 well stations throughout the site with each station composed of 2, 3, or 4 wells screened over various depths (Campbell et al. 1995). Rainfall, wind speed, solar radiation, temperature, and relative humidity we re measured at the site. The form of the Penman (Penman 1948) equation developed by J ones et al. (1984a) for Florida conditions was used to estimate daily reference poten tial evapotranspiration (Tremwel 1992). Micrometeorological, groundwater level and runoff measurements were made for 33 months from April 1, 1989 to December 31, 1991. Results and Discussion Because of the extremely flat topography and low groundwater gradients measured during the study period (Campbell et al. 1995) model simulation was conducted by treating the pasture as a single, lumped elem ent. Groundwater flow into or out of the model domain were assumed to be negligib le. Groundwater level measurements were spatially averaged for comparison to model outputs. Soil physical properties and water characteristics for the site are based on th e data of Tremwel ( 1992) and Sodek et al. (1990) and are shown in Table 43. Monthly crop co efficients were taken from the work of Tremwel (1992) and interpolated to daily values by Fourier anal ysis in the ACRU2000 model (Table 4-4). The plant root distribution was assumed to decrease linearly from the ground surface to a depth of 0.9 m for ba hia grass (Fraisse and Campbell 1997). Model calibration was conducted using the first 17 months of data (April 1, 1989 to August 31, 1990) leaving the final 16 months for model verification (September 1, 1990 to December 31, 1991). Model calibration c onsisted of adjusting the runoff resistance

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47 coefficient and the depression storage to match observed daily runoff timing and magnitude as well as minor changes to the water characteristic parameters of the soil layers within the range expected for the soil type and location as ascertained from the data of Sodek et al. (1990) in order to ma tch both water-table leve ls and the timing of runoff generation. Model calibration wa s made by graphical comparison between observed and simulated daily values. The m odel was (subjectively) considered to be sufficiently calibrated when successive parame ter adjustments appeared to provide little or no improvement in graphically matching observations. The observed and predicted water-table depths by ACRU2000 and by FHANTM, as reported by Tremwel (1992), ar e shown in Figure 4-7 for the entire 33 month period. Both models generally followed the observ ations. Both models deviated from the observations in early 1991. FHANTM deviat ed from the observations in late 1990 and 1991 as well. Figure 4-8 shows the simulated and obser ved daily runoff for the calibration and verification periods for the shallow water-ta ble version of ACRU2000. The timing of the runoff events correspond with periods wher e the water-table reach ed the ground surface causing saturation excess overland flow. Si mulated and observed daily runoff for the FHANTM and unmodified ACRU 2000 models is shown in Figures 4-9 and 4-10. As with the prediction of water-table depths (Figure 4-7) the FHANTM model performed similarly to the modified ACRU2000 model in predicting total runoff (Figure 4-11), however daily runoff events were not pred icted as well as by ACRU2000 (Figures 4-8 and 4-9). As seen in Figure 4-10, the prediction of daily runoff by the unmodified ACRU2000 model was poor. This poor predic tion is due, almost entirely, to the

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48 reduction of evapotranspiration by the model at soil water contents above field capacity (taken as 100 cm of suction) as detailed in Chapter 2. The original, unmodified ACRU2000 model could predict daily runoff much more accurately if the values of field capacity are set artificially high (near porosity) allowing evapotranspiration to continue at the potential rate under very wet conditions. This result is not shown here. The simulated and observed runoff is also show n in annual cumulative plots in Figure 4-11 for the three models and for the three years of the study. The statistical measures of the model pe rformance on daily predictions are shown in Table 4-5. Based on the absolute error meas ures and the coefficients of efficiency the model proposed in Chapter 3 appears to be a better predictor in capturing the water-table dynamics and the generation of saturation excess runoff of th e pasture as modeled as a single, lumped element compared to th e FHANTM and original, unmodified ACRU2000 models. Case Study 3: MacArthur Agro-Ecology Re search Center at Bu ck Island Ranch Site Description and Experimental Design MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch is a full-scale working cattle ranch owned by The John D. and Catherine T. MacArthur Foundation and leased to Archbol d Biological Station (Swain 1998). The site is located in south-central Florida (27o 9 N and 81o 11 W), approximately 21 km northwest of Lake Okeechobee (Figure 4-12). As part of a ma jor integrated research project to address the effects of best management practices on nutrient loads in runoff 16 experimental pastures were hydrologically isolated in order to quantif y runoff volumes leaving the pastures. For this case study a single 30.2-ha semi-native pasture is simulated as an example of the models performance at Buck Island Ranch. The pasture simulated is

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49 identified as Winter Pasture 7. The terrain of the site is extremely flat, with a slope of no greater than 0.02 % (Hendricks 2003) to the north towards the C41 (Harney Pond) canal, a major regional conveyance linking Lake Istokpoga to the north and Lake Okeechobee to the south. Shallow wetlands are interspe rsed throughout the site covering 4.3% of the land area of the pasture (MAERC 2004), most within 30 m of shallow drainage ditches. The stage of Harney Pond Canal is managed by the South Florida Water Management District (SFWMD) at the S70 spillway loca ted approximately 4 km downstream from the site. The pasture contains semi-native ve getation composed primarily of broomsedge ( Andropogon virginicus ), carpet grass ( Axonopus furcatus ), and bahia grass ( Paspalum notatum ), the wetlands are vegetated primarily wi th grasses such as carpet grass and maidencane ( Panicum hemitomon ) and with miscellaneous wetland species (MAERC, 2004). Soil surveys of the area were conducte d by the USDA-NRCS in June 1997, at a 0.5-ha resolution. Soils in the pasture ar e predominantly (95.7%) Pineda fine sand, a loamy, siliceous, hyperthermic Arenic Glossaqua lfs, with 90% coverage of a thin (2.5-15 cm) muck layer (MAERC 2004). The pasture is hydrologically isolated with a low earthen berm that forces all runoff from the pasture to exit through a trapezoidal flume located at the downstream end of the pasture. Existing ditches were interconnect ed to route runoff through the exit flume. Runoff was determined at the trapezoidal flum e from water level measurements made in stilling wells at both the upstream and dow nstream end of the flume in 20 minute intervals (Capece et al. 1999). Meteorological data were collected on an hourly basis at a weather station adjacent to the pasture as well as at 3 nearby stations. Rainfall, temperature, solar radiation, relative hu midity, wind speed, and wind direction were

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50 collected at each station. Groundwater levels were measured at 15-minute intervals in a 2-inch monitoring well installed in the cente r of the pasture. The well extended to a depth of 18 ft below ground surface with the screened portion beginning at 5 ft below ground surface. Runoff and climatic data collection began in May 1998 and groundwater level measurements began in September 2000. Data collection continued until the end of 2003. Results and Discussion Due to the extremely low groundwater gradie nts observed within the pastures (data not shown), and the extremely flat topogr aphy, model simulation was conducted by treating the pasture as a single, lumped elem ent. Figure 4-13 shows the canal stage as reported at the S70 spillway located four kilometers downstream as compared to the groundwater level recorded at the center of the experimental pasture. As can be seen, the gradient between the pasture and the canal re verses direction, w ith groundwater flowing towards the canal during wet periods (typically summer) and canal water flowing towards the pasture during dryer periods. The daily time se ries of canal stage serves as an input to the model. Groundwater flow between the pasture and the adjacent upland were assumed to be negligible. Soil physical properties a nd water characteristics fo r the site are based on the data of Sodek et al. ( 1990) and Gathumbi et al. (2005) and are shown in Table 4-6 (calibrated values). The plan t root density distribution wa s assumed to decline linearly from the ground surface to a depth of 0.8 meters for the combination of bahia and native grasses (Fraisse and Campbe ll 1997). Daily reference pot ential evapotranspiration was calculated using the standardized Penman-M onteith equation recommended by Allen et al. (1998). Crop coefficients used are shown if Table 4-4 and are based on the work of Smajstrla (1990) and Allen et al. (1998).

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51 Model calibration was conducted using th e observed runoff and groundwater level data from 1998 to 2001. The length of the calibration period was chosen in order to include adequate groundwater le vel data (data collection ha ving started in September, 2000). This period allows for 16 months of groundwater level data to be used in calibration. The remaining two years, 2002 and 2003, are used to verify the model calibration. Model calibration was perf ormed by changing the runoff resistance coefficient, increasing the crop coefficients reported by Smajstrla ( 1990) and Allen el al. (1998) for the winter months, reduci ng the saturated water content and n soil parameters slightly from the fitted values determined from the data of Sodek et al. (1990), and reducing the hydraulic conductivity by one order of magnitude of the A, E, and Bw soil layers from that reported by Sodek et al. (1990) (a djusted parameters shown in Table 4-6) in order to better replicate th e influence of the stage in the adjacent canal on groundwater levels within the pasture. Model calibrat ion was made by graphical comparison between observed and simulated daily values. The m odel was (subjectively) considered to be sufficiently calibrated when successive parame ter adjustments appeared to provide little or no improvement in graphically matching observations. Observed and simulated groundwater leve ls are shown in Figure 4-14. The discrepancy between observations and simulate d water-table depths, particularly during periods of deeper observed water-tables, may be due to the representation of the canals influence on groundwater levels within the pasture, the unc ertainty of soil hydraulic parameters due to the lack of field-collect ed data, and the assumption of no groundwater inflow or outflow from upland areas. No stag e measurements of the canal were made at the site, stage measurements at the S70 sp illway were assumed to represent the local

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52 canal stage by assuming level pool conditions over the 4 km distance. No groundwater measurements were made in areas upland from the pastures. As can be seen the model performed reasonably well in following the genera l trend in water-table fluctuations, with both periods of over and under prediction. As can also be seen in Figure 4-14, the model predicts the water-table r eaching the ground surface on numerous occasions (at which time runoff may occur by saturation excess) while this does not always appear in the observed data. This is due to the installati on of the flume in a shallow ditch which is contiguous with the other shallow ditches in the pasture (land elevation near the groundwater well was approximately 8.47 m and the flume bottom elevation was at 8.08 m). This experimental design causes runoff to occur from the field without the watertable being at the ground surface as reported at the monitoring well since the well is not located within one of these ditches. These shallow ditches are not explicitly represented in the model. Observed and simulated daily runoff can be seen in Figure 4-15 and reported as cumulative annual plots in Figure 4-16. During the study period there were a few instances of backflow from the canal into th e pasture as recorded at the flume (Figure 415). These few instances were ignored in gene rating the cumulative plots in Figure 4-16. As shown the calibration (1998-2001) followed the observations, with the exception of the very dry year of 2000 where runoff events were simulated that were not observed. Verification (2002-2003) matched the observed data less well, but general trends in runoff timing and magnitude were satisfactory. The goodness of fit between measured and simulated daily values is shown in Table 4-7. The coefficient of efficiency for both the water-table depth and runoff were

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53 lower for this site than for W.F. Rucks Dairy, however the values were greater than zero, indicating that the model is of value as a pr edictor of water-table depth, saturation excess runoff, and groundwater flow to a time-varyi ng canal stage when applied as a single, lumped element to a site of this size. Sensitivity Analysis Exact values for model input variables can be costly if not nearly impossible to obtain for even a field-scale model when a pplied to sites where spatial variation in properties is likely. The accura cy of model input parameter va lues is usually proportional to the time and resources invested in their de termination. Since model results will be more sensitive to certain inputs compared to ot hers it is important to perform a sensitivity analysis in order to establish priorities in collecting and determining model parameters. An analysis was performed to determine the sensitivity of model simulation of runoff, evapotranspiration, and groundwater fl ow to the hydrologic input parameters in Table 4-8. The sensitivity analysis was pe rformed using the six-year simulation of the experimental pasture at MAERC. Model sensitivity was determined for 25, 50, 75, and 100% of the base input value (shown in Tabl e 4-8). For cases where this range of variation was infeasible, or unr ealistic, the results were omitted. Input parameters that had a zero value in the simulation of the expe rimental pasture (depression storage and the transpiration reduction factor for oxygen defi ciency) were changed to the non-zero values shown in Table 4-8 for the sensitivity analys is. Model sensitivity is reported as the percent difference of model results as compared to the base simulation. The sensitivity of runoff, evapotrans piration, and groundwater flow to the hydrologic parameters in Table 4-8 is shown in Table 4-9. Input parameters showing relatively low sensitivity on all three outputs were root depth, root distribution parameter,

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54 the transpiration reduction factor due to water excess, vertical saturated hydraulic conductivity, upward flux exponent, and interception capacity. Pa rameters showing little sensitivity with the exception of very la rge positive or nega tive changes include depression storage, the transpiration reducti on factor due to oxyge n deficiency, runoff resistance, bubbling pressure head, and soil moisture shape parameter n. The runoff exponent showed little sensitiv ity except for at the -25% ch ange. This value was limited to values greater than one (and is a value of 1.67, the value used fo r the base simulation, for Mannings equation). The initial depth of the water-table was insensitive on runoff and evapotranspiration, but had a large eff ect on groundwater flow. This depth was not increased due to the relatively deep initial value of the base simulation. The remaining parameters, the crop coefficient, horizontal hydraulic conductiv ity, saturated water content, and soil moisture shape parameter caused the greatest changes in runoff, evapotranspiration, and groundwat er flow. The sensitivity of these parameters on runoff is presented in Figure 4-17. Considering the uncertainty associated with the parameters that showed the greatest sensitivity on runoff volumes, and the understand ing that model calibration rarely results in a single set of optimal parameters, a fe w conclusions can be made. Crop coefficients are usually reported in the literature, if availa ble at all, for single plant species and are often region-specific. Thei r application to locations other than where they were developed and where there ex ists a heterogeneous distri bution of vegetation species necessitates their use as calibra table parameters within defendable limits. Considering the sensitivity of the soil parameters on r unoff volumes, and the use of a set of soil parameters for each soil layer, the model proposed here may be over-parameterized.

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55 Even under circumstances where considerable effort is made to collect soil physical properties there exists a degree of uncertain ty in assigning effective model parameter values. For this reason it is postulated that future shallow water-table model development should attempt to reduce the number of para meters required by the model. These simplifications could include the representati on of soil moisture retention with a single curve using a single set of parameter values Horizontal hydraulic conductivity of the soil could be approximated using a single aver age value, or as decreasing linearly or exponentially with depth as used by Beven and Kirkby (1979). The model could also benefit from the representation of upward flux by a single set of parameters, rather than one for each soil layer representing the combined effects of the layers below it. Such a relationship could be approximated as an e xponential decrease with water-table depth and produce similar results to the analytic e xpressions developed for homogeneous soils (Gardner 1958; Anat et al. 1965; Cisler 1969; Warrick 1988). While approximate, these simplifications would allow for more strai ghtforward parameter adjustment and model calibration. Summary and Conclusions A field-scale module for use within th e ACRU2000 distribut ed hydrologic model was developed to simulate the hydrology of humid, shallow water-table environments such as the flatwoods of Florida. The fi eld-scale validation of the model for three experimental sites indicated the appropriate ness of the physical approximations made by the model, including the assumption of hydrosta tic conditions in the unsaturated zone and the use of a daily time-step when simula ting regions where saturation-excess is the dominant runoff producing mechanism. This is supported by the models ability to satisfactorily replicate field observations of evapotranspiration, soil moisture contents,

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56 water-table depths, and saturation-excess r unoff timing and magnitude as well as its ability to produce similar results compared to a numerical, one-dimensional, finitedifference model. The model was compared to the original unmodified ACRU2000 model as well as to the field-scale FHANTM model. The sha llow-water-table model performed similarly to the FHANTM model. The original, unmodified ACRU2000 model greatly overpredicted runoff due to the simulated reducti on of evapotranspiration below the potential rate at soil moisture cont ents above field capacity as detailed in Chapter 2. Model sensitivity to parame ter values on runoff volumes, evapotranspiration, and groundwater flow was shown to be greatest to crop coefficients and soil hydraulic parameters. Future field experimentation s hould focus on collecting these parameters to facilitate greater certainty in model simulation. In lieu of this, it is recommended that future model development should explore the simplification of the model by reducing the number of parameters required.

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57 Table 4-1. Wauberg sand soil characterist ics and van Genuchten (1980) soil moisture model parameters Layer Layer depth (cm) Ks (cm/hr) s (cm3/cm3) r (cm3/cm3) (cm-1) n (-) m (-) OA 0-5 246.0 0.58 0.10 0.0305 1.53 0.346 A 5-15 248.5 0.38 0.03 0.100 1.66 0.398 E 15-33 85.0 0.45 0.03 0.033 3.1 0.677 Btg 33-145 0.05 0.30 0.03 0.022 3.0 0.667 Table 4-2. Paynes Prairie State Preser ve error measures of daily outputs MAE RMSE E(-) Parameter ACRU2000 SWAP ACRU2000 SWAP ACRU2000 SWAP Water-table depth 0.083[a] 0.070[a] 0.110[a] 0.098[a] 0.927 0.940 Soil moisture content 0.022[b] 0.030[b] 0.031[b] 0.044[b] 0.863 0.718 Evapotranspiration 0.570[c] 0.499[c] 0.729[c] 0.632[c] 0.503 0.626 [a] Units of m. [b] Units of cm3 cm-3. [c] Units of mm/day. Table 4-3. Myakka fine sand soil characteristics and van Ge nuchten (1980) soil moisture model parameters Layer Layer depth (cm) Ks (cm/hr) s (cm3/cm3) r (cm3/cm3) (cm-1) n (-) m (-) A 0-10 23.0 0.38 0.05 0.0199 3.17 0.685 E 10-33 23.7 0.36 0.04 0.0221 3.42 0.708 Bh 33-46 12.4 0.32 0.16 0.0218 3.12 0.679 Bw 46-56 16.0 0.32 0.11 0.0231 3.15 0.683 Cg 56-203 17.8 0.38 0.07 0.0199 2.59 0.614

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58 Table 4-4. Crop coefficients for W.F. Rucks and MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch Month W.F. Rucks MAERC January 0.40 0.65 February 0.45 0.75 March 0.50 0.85 April 0.50 0.90 May 0.60 0.95 June 0.65 1.00 July 0.85 1.00 August 0.85 1.00 September 0.85 1.00 October 0.75 0.90 November 0.75 0.80 December 0.60 0.70 Table 4-5. W.F. Rucks error measures of daily outputs _____MAE_____ _____RMSE_____ _____E(-)_____ ACRU 2000 FHANTM Original ACRU 2000 ACRU 2000 FHANTM Original ACRU 2000 ACRU 2000 FHANTM Original ACRU 2000 Watertable depth[a] 0.095 0.108 0.132 0.157 0.866 0.812 Runoff[b] 3.32 8.55 8.33 6.44 13.78 12.86 0.683 -0.925 -1.49 [a] Units of m. [b] Units of mm. Table 4-6. Pineda fi ne sand soil characteristics and va n Genuchten (1980) soil moisture model parameters Layer Layer Depth (cm) Ks (cm/hr) s (cm3/cm3) r (cm3/cm3) (cm-1) n (-) m (-) A 0-10 4.32 0.42 0.10 0.0287 1.96 0.490 E 10-30 4.14 0.34 0.08 0.0224 2.57 0.611 Bw 30-80 2.70 0.32 0.07 0.0234 1.81 0.448 Btg 80-140 1.37 0.35 0.15 0.0177 1.67 0.401 Cg 140-160 2.95 0.30 0.06 0.0106 2.06 0.515

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59 Table 4-7. MacArthur Agro-Ecology Resear ch Center at Buck Island Ranch error measures from the experimental pasture for daily outputs Parameter MAE RMSE E (-) Water-table depth 0.253[a] 0.310[a] 0.631 Runoff 0.472[b] 1.53[b] 0.572 [a] Units of m. [b] Units of mm.

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60 Table 4-8. Hydrologic input parameters included in the sensitivity analysis Parameter Unit Base Value Description Kc 0.875 Crop coefficient L m 0.8 Maximum depth of roots c -1 Root distribution shape parameter h2 cm 20 Transpiration reduction due to O2 deficiency h3 cm 10000 Transpiration reduction due to water excess zDep mm 10.0 Depression storage I mm 1 Interception capacity 1/day 200 Runoff resistance 1.67 Runoff exponent dwt m 1.8 Initial depth to water-table s: Saturated water content cm3/cm3 0.42 E cm3/cm3 0.34 Bw cm3/cm3 0.32 Btg cm3/cm3 0.35 Cg cm3/cm3 0.30 : 1/cm 0.0287 Soil moisture shape parameter of van Genuchten (1980) E 1/cm 0.0224 Bw 1/cm 0.0234 Btg 1/cm 0.0177 Cg 1/cm 0.0106 n: 1.96 Soil moisture shape parameter of van Genuchten (1980) E 2.57 Bw 1.81 Btg 1.67 Cg 2.06 Ks,H: Horizontal saturated hydraulic conductivity cm/h 4.32 E cm/h 4.14 Bw cm/h 2.70 Btg cm/h 1.37 Cg cm/h 2.95 Ks,V: cm/h 13.57 E cm/h 11.7 Vertical saturated hydraulic conductivity used in the upward flux relationship of Anat et al. (1965) Bw cm/h 5.58 hb: cm 46.8 Bubbling pressure head used in the upward flux relationship of Anat et al. (1965) E cm 40.1 Bw cm 23.0 : 12.2 Exponent used in the upward flux relationship of Anat et al. (1965) E 8.80 Bw 4.98

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61Table 4-9. Sensitivity of runoff, evapotranspiration, and groundw ater flow to hydrologic parameters (reported as percent diffe rence of base simulation result). Values in left-hand column are percent change s in input parameters. % Change in Parameter Kc s Ks,H n zDep h2 hb dWT h3 L I c Ks,V Runoff +25% -29 -8 -7 3 7 -2 -2 2 -2 0 0 0 0 0 0 +50% -54 -15 -13 5 7 -3 -4 3 -3 1 -1 0 0 0 0 +75% -16 6 7 -3 -7 6 -4 1 0 0 0 0 +100% -19 8 7 -4 -9 8 -5 1 0 0 0 0 -25% 30 12 11 -4 -19 3 2 1 2 0 1 0 0 0 0 0 0 -50% 53 32 30 -11 12 3 -3 4 -1 1 1 0 1 0 0 0 -75% 56 -17 4 -5 6 -5 2 5 1 0 0 0 -100% -25 4 -5 2 0 0 ET +25% 35 3 2 -1 7 1 -2 -1 -1 0 0 0 0 0 0 +50% 73 5 3 -2 7 1 -4 -2 -3 0 1 0 0 0 0 +75% 4 -2 7 1 -6 -3 -4 0 0 0 -1 0 +100% 5 -2 7 1 -8 -6 -5 0 0 0 -1 0 -25% -31 -3 -3 1 -15 -1 1 0 2 0 0 0 0 0 0 0 0 -50% -59 -12 -9 2 -4 3 1 4 1 -1 -1 0 0 0 0 0 -75% -23 3 3 2 6 2 -3 -3 0 1 0 0 -100% 1 3 2 -3 1 0 Ground +25% -81 7 14 -7 -11 1 4 -1 3 -1 0 0 0 0 0 +50% -174 11 25 -11 -11 2 8 -3 5 -2 -2 0 0 0 0 +75% 32 -14 -11 2 12 -7 7 -2 0 1 0 0 +100% 38 -17 -11 3 16 -12 9 -2 0 1 -1 0 -25% 58 -15 -21 10 31 -2 -3 -1 -2 1 11 0 0 -1 0 0 0 -50% 97 -45 -55 25 -13 -5 3 -6 3 22 -1 0 -2 0 0 0 -75% -112 49 -6 5 -10 14 26 2 -2 0 0 0 -100% 100 -6 5 26 -1 0

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62 Figure 4-1. Location of Pa ynes Prairie State Preserve. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 4/1/015/1/016/1/017/1/01Depth to Water Table (m)0 20 40 60 80 100 120 140 160 180 200Rain (mm) Observed ACRU2000 SWAP Figure 4-2. Measured and pred icted water-table depths at Paynes Prairie State Preserve.

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63 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 4/1/015/1/016/1/017/1/01Volumetric Water Content (cm3 cm-3)0 10 20 30 40 50 60 70 80 90 100Rain (mm) Observed ACRU2000 SWAP Figure 4-3. Measured and predicted soil moistu re contents within the top 25 cm of soil at Paynes Prairie State Preserve. 0 1 2 3 4 5 6 7 5/1/016/1/017/1/01Evapotranspiration (mm/day) Observed ACRU2000 SWAP Figure 4-4. Measured and pr edicted evapotranspiration at Paynes Prairie State Preserve.

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64 0 1 2 3 4 5 6 7 01234567 Observed (mm/day)Simulated (mm/day) ACRU2000 SWAP Figure 4-5. Measured vs. predicted evapotrans piration at Paynes Prairie State Preserve. r2 = 0.83 and 0.74 for SWAP and ACRU2000, respectively. Figure 4-6. Location of W.F. Rucks Dairy.

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65 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.64/1/897/30/8911/27/893/27/907/25/9011/22/903/22/917/20/9111/17/91Depth to Water Table (m) Observed ACRU2000 FHANTM Figure 4-7. Measured and predicted water-table depths at W.F. Rucks Dairy. 0 10 20 30 40 50 60 4/1/896/1/898/1/8910/1/8912/1/892/1/904/1/906/1/908/1/90 0 50 100 150 200 0 10 20 30 40 50 60 9/1/9011/1/901/1/913/1/915/1/917/1/919/1/9111/1/911/1/92 0 50 100 150 200 Rain Observed ACRU2000Runoff (mm) Rain (mm) Figure 4-8. Measured and modified ACRU2000 predicted daily runoff at W.F. Rucks Dairy. For the calibration (top) and verification (bottom) periods.

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66 0 10 20 30 40 50 60 4/1/896/1/898/1/8910/1/8912/1/892/1/904/1/906/1/908/1/90 0 50 100 150 200 0 10 20 30 40 50 60 9/1/9011/1/901/1/913/1/915/1/917/1/919/1/9111/1/911/1/92 0 50 100 150 200 Rain Observed FHANTMRunoff (mm) Rain (mm) Figure 4-9. Measured and FH ANTM predicted daily runoff at W.F. Rucks Dairy. For the calibration (top) and veri fication (bottom) periods. 0 10 20 30 40 50 60 4/1/896/1/898/1/8910/1/8912/1/892/1/904/1/906/1/908/1/90 0 50 100 150 200 0 10 20 30 40 50 60 9/1/9011/1/901/1/913/1/915/1/917/1/919/1/9111/1/911/1/92 0 50 100 150 200 Rain Observed ACRU2000 (unmodified)Runoff (mm) Rain (mm) Figure 4-10. Measured and unmodified ACRU 2000 predicted daily runoff at W.F. Rucks Dairy. For the calibration (top) and verification (bottom) periods.

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67 0 200 400 600 800 1000 1200 1400 1600 1800 20004/1/898/1/8912/1/894/1/908/1/9012/1/904/1/918/1/9112/1/91Runoff (mm) Observed ACRU2000 FHANTM SCS ACRU2000 Figure 4-11. Measured and predicted cumu lative annual runoff at W.F. Rucks Dairy. Note: Observed runoff events during 1990 fell within the calibration period. Figure 4-12. Location of MacArthur Agro-E cology Research Center at Buck Island Ranch.

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68 6.8 7.3 7.8 8.3 8.8 1/1/001/1/011/1/021/1/031/1/04Groundwater Level (m) Groundwater Level Canal Stage Figure 4-13. Groundwater level and adjacent can al stage in the expe rimental pasture at the MacArthur Agro-Ecology Research Ce nter at Buck Island Ranch. Canal stage is the stage in the C41 canal as measured at the S-70 spillway located 4 km downstream and the groundwater level is from the 2-inch well (center of pasture). 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 4-14. Measured and predicted water-ta ble depths at the expe rimental pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch.

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69 -5 0 5 10 15 20 25 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 40 80 120 160 200 -5 0 5 10 15 20 251/1/033/1/035/1/037/1/039/1/0311/1/031/1/040 40 80 120 160 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 4-15. Measured and predicted daily runoff at the experimental pasture at the MacArthur Agro-Ecology Research Center at Buck Island Ranch. Calibration period: 1998-2001, verification period: 2002-2003.

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70 0 50 100 150 200 250 300 350 400 450 500 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 4-16. Measured and predicted cumu lative annual runoff at the experimental pasture at the MacArthu r Agro-Ecology Research Center at Buck Island Ranch. Calibration period: 1998-20 01, verification period: 2002-2003. -60 -40 -20 0 20 40 60 -100-50050100 % Change in Parameter% Difference in Runoff Kc qs Ks,H KcsKs,H Figure 4-17. The parameters most sensitive on runoff volumes.

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71 CHAPTER 5 FIELD-SCALE NITROGEN AND PHOSPH ORUS MODULE OF THE ACRU2000 MODEL Introduction The nitrogen (N) and phosphorus (P) components of the Groundwater Loading Effects of Agricultural Management System s (GLEAMS) model were incorporated into the ACRU2000 model in a previous model e xpansion (Campbell et al. 2001). GLEAMS, a commonly used field-scale hydrology and wate r quality model, represents the major N and P components and transformations (Knise l et al. 1993). The P algorithms used in GLEAMS are largely incorporated from the EPIC (ErosionProductivity Impact Calculator) model (Jones et al. 1984b). This chapter details the N and P algorithms of the ACRU2000 model, as added by Campbell et al. (2001), and discusses th eir suitability for shallow water-table environments. Nutrient Models The algorithms of the model describe the mobilization and transport of dissolved forms of N and P. Sediment -bound nutrients are not currently represented explicitly in the model since sediment yield is not, to date, separated by particle-size class. In the case of N the soluble component is split be tween nitrate-N and ammonium-N, with mineralization of organic forms represented as a two-stage process. Soluble organic N is not simulated in the model. P is represente d as a single soluble form, labile P. Both ammonium-N and labile P undergo fast, reversible sorption to soil partic les. Transport of soluble nutrients occurs be tween completely mixed soil layers. The exchange of

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72 nutrients with runoff water is assumed to o ccur within the top one centimeter of soil. Most nutrient transformations are mediated by soil moisture and soil temperature. Phosphorus Model The P model is represented by six main pools (Figure 5-1) a labile pool ( Pl), an active inorganic pool ( Pa), a slowly changing stable inorganic pool ( Ps), a fresh organic pool ( Pf) representing plant residue, a stable organic (organic humus) pool ( Ph), and a plant pool ( Pp). Of the six pools, only Pl is mobile. The mass balance of the pools can be written as: perc gw ro uP hlP flP alP awP fert rain lP P P R R R R R P P dt dP 75 0 (5-1) alP saP aR R dt dP (5-2) saP sR dt dP (5-3) fhP flP pfP fR R R dt dP (5-4) hlP fhP awP hR R R dt dP 25 0 (5-5) pfP uP pR R dt dP (5-6) where Prain is the quantity of P in rainfall (kg/ha/day), Pfert is the rate of application of P in inorganic fertilizer (kg/ha/day), RawP is the rate of decay of P in animal waste on the ground surface (kg/ha/day), RalP is the rate of transformation from Pa to Pl (kg/ha/day), RflP is the rate of transformation from Pf to Pl (kg/ha/day), RhlP is the rate of transformation from Ph to Pl (kg/ha/day), RuP is the rate of plant uptake (kg/ha/day), RsaP is the rate of transformation from Ps to Pa (kg/ha/day), RpfP is the rate of transformation of Pp to Pf (kg/ha/day), RfhP is the rate of transformation from Pf to Ph (kg/ha/day), and Pro,

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73 Pgw, and Pperc are the quantities of P lost to r unoff, groundwater flow, and percolation (kg/ha/day), respectively. Mineralization Mineralization of organic forms of P is re presented as first-order processes. The fast-cycling fresh organic pool ( Pf) consists of surface cr op residues resulting from harvest and sub-surface root resi dues (C:P ratios generally greater than 200). The slowcycling organic humus pool ( Ph) consists of more recalcitrant organic forms (C:P ratios between 125 and 200). The mine ralization of animal waste ( RawP), fresh organic P ( RflP), and organic humus P ( RhlP), are defined as, respectiv ely (Knisel et al. 1993): min minT aw d NP awPf f P k C R (5-7) min minT f d NP flPf f P k C R (5-8) min minT h hf actN hlPf f P k f R (5-9) where Paw is the P content in animal waste (kg/ha), factN is the fraction of total N that is active N which is used to infer the fraction of Ph that is mineralizable, khf is the rate of organic humus P decomposition under optim um conditions (assumed to be 0.0001 day-1), CNP is a factor that varies from 0 to 1 that is a function of C:N and C:P ratios of the organic material (J ones et al. 1984b): 0 1 200 / 200 : 693 0 exp 25 / 25 : 693 0 exp min P C N C CNP (5-10) where the C:N and C:P ratios are determined as the ratios of fresh residue and animal waste mass to the mass of organic and inorga nic N and P present in fresh residue and animal waste, kd is an organic matter composition fact or and represents the age of the

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74 decomposing material by assuming the first 20% is carbohydrate-like material, the final 10% is lignin, and the intermediate is cellulose (Jones et al. 1984b): 8 0 dk for fdec > 0.8 (5-11a) 05 0 dk for 0.8 fdec > 0.1 (5-11b) 0095 0 dk for fdec 0.1 (5-11c) where fdec is the fraction of the initial material remaining, fmin and fTmin are soil moisture and soil temperature response factors that va ry from 0 to 1 and are described below. Mineralization of animal waste P, Paw, and fresh organic P, Pf, are assumed to be partitioned to Pl (75%) and Ph (25%) (Knisel et al. 1993). Immobilization The high C:P ratios of fresh crop residue (gen erally greater than 200) results in the immobilization of labile P by soil microbes during the decomposition process. The rate of uptake of labile P by decomposing ma terial is dependent upon the stage of decomposition, the C:N and C:P ratios of the residue, the concentrations of P in fresh organic matter, and labile P (Knisel et al. 1993): fP IP T f d NP lfPC f f f P k C R 16 0min min (5-12) where the value 0.16 results from assuming th at carbon is 40% of fresh organic matter and that 40% of the carbon can be assimila ted by soil microbes (Jones et al. 1984b), CfP is the concentration of fr esh organic P (kg/kg) and fIP is a phosphorus immobilization factor: lP IPC f 001 0 01 0 for ClP 10.0 (5-13a) 02 0 IPf for ClP > 10.0 (5-13b)

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75 and ClP is the concentrations of labile P (mg/kg). The rate of immobilization can be limited by either N or P if the amount of N or P immobilized is less than that available (Knisel et al. 1993). Inorganic transformations As P is added to soil solution the equilibrium between mobile and immobile forms is disturbed. Following P additions, net P m ovement occurs into immobile forms. The initial rate of these adsorption/fixation reacti ons is rapid, leaving newly formed immobile forms relatively unstable and readily return ed to solution (McGech an and Lewis 2002). The movement between the labile P and act ive P pool is considered to be a rapid equilibrium (several days to weeks) (Jones et al. 1984b). P moves between the pools as a function of the relative size of the pools, moisture content, and temperature (Jones et al. 1984b): al a l Tal al alPP P f f k R min (5-14) where kal is the rate constant at optimal conditions (assumed to be 0.1 day-1), and fmin and fTal are soil moisture and soil temperature response factors (defined below), the equilibrium constant of proporti onality between the two pools, al is estimated from a P sorption parameter, PSP (Jones et al. 1984b): PSP PSPal 1 (5-15) The P sorption parameter, also referred to as the P availability index, is defined as the fraction of P added to a soil sample that re mains labile after a long incubation period: added ile InitialLab e FinalLabilP P P PSP (5-16) Following Sharpley et al. (1984), Sh arpley and Williams (1990) defined PSP by dividing

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76 soils into three groups based on soil taxonomy and weathering. The three groups defined were calcareous, slightly weat hered, and highly weathered so ils. For slightly weathered soils such as Spodosols and Alfisols (e xcept Ultic subgroups) PSP was defined as a function of base saturation, BSAT (%) and pH : 73 0 116 0 0054 0 pH B PSPSAT (5-17) and is constrained between valu es of 0.05 and 0.75. This results in values between 0.05 and 3 for al, defining Pl as 0.05 to 3 times as large as Pa at equilibrium. The active P pool is considered to be in a slow equilibrium with the stable P pool. The differentiation between active and stable inorganic P pools is made in order to account for the initial rapid decrease in labi le P typically seen after P application followed by a much slower decrease in observe d labile P over long periods (Jones et al. 1984b). This representation is a simplific ation of a continuum of time-dependent adsorption or fixation reactions (McGechan a nd Lewis 2002). The transfer of P between active P and stable P pools is a function of the relative size of the pools (Jones et al. 1984b): s a sa sa saPP P k R (5-18) where sa is an equilibrium constant of proportionality and is a ssumed to be a value of 4 (Jones et al. 1984b), and ksa is a rate constant that is de fined in the GLEAMS model as a function of PSP for non-calcareous soils (Jones et al. 1984b): 05 7 77 1 exp PSP ksa (5-19) In addition to the rapid adsorption/fixation reactions between the labile and active P pools, the labile P pool undergoes instantane ous, reversible sorpti on governed by a linear

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77 isotherm in order to determine the portion of labile P that is in solution and available for transport into runoff, groundwat er, and percolating water: C K Sd (5-20) where S is the portion of labile P adsorbed (mg/kg), C is the portion in solution (mg/L) and Kd is the partitioning coefficient (L/kg). Th e partitioning coefficient is assumed to be a function of the clay content of the soil, CL (%) (Knisel et al. 1993): CL Kd5 2 100 (5-21) where Kd is in units of L/kg. Plant uptake Plant uptake of P is assumed to occur from each soil layer from which transpiration occurs, with the total uptake limited to a calculated plant demand. Plant demand is determined from the plant growth rate and pl ant nutrient characteristics (Knisel et al. 1993). Nitrogen Model The nitrogen model is represente d by six main pools, nitrate-N ( NNO3), ammoniumN ( NNH4), active organic N ( Na), stable organic N ( Ns), fresh organic N ( Nf), and plant N ( Np) (Figure 5-2). The mass balance of the pools can be written as: 3 3 3 3 3 3 gwNO roNO uNO immNO denit nit fertNO rain NON N R R R R N N dt dN (5-22) 3percNON 4 4 48 0 8 0immNH vol nit ammfN ammaN awN fertNH NHR R R R R R N dt dN (5-23) 4 4 4 4percNH gwNH roNH uNHN N N R ammaN saN ammfN awN aR R R R dt dN 2 0 2 0 (5-24) saN sR dt dN (5-25)

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78 ammfN immNH immNO pfN fR R R R dt dN 4 3 (5-26) pfN uNH uNO pR R R dt dN 4 3 (5-27) where Nrain is the quantity of N in rainfall (kg/ha/day), and is assumed to occur completely as nitrate-N, NfertNO3 and NfertNH4 are nitrate-N and ammonium-N quantities in inorganic fertilizer (kg/ha/day), RawN is the rate of animal waste N decay (kg/ha/day), Rnit is the rate of nitrification of am monium-N to nitrate-N (kg/ha/day), Rdenit is the rate of loss of nitrate-N to the atmosphe re by dentrification (kg/ha/day), RammaN and RammfN are the rates of ammonification of the active and fresh or ganic N pools (kg/ha/day), respectively, Rvol is the rate of loss of ammonium -N in manure to the atmosphere by ammonia volatilization (kg/ha/day), RsaN is the rate of transformation of stable N to active N (kg/ha/day), RimmNO3 and RimmNH4 are, respectively, the rates of immobilization of nitrate-N and ammonium-N by fresh organic matter (kg/ha/day), RuNO3 and RuNH4 are the rates of plant uptake of nitrate-N and ammonium-N (kg/ha/day), RpfN is the rate of transformation of plant N to fresh organic N (kg/ha/day), NroNO3 and NroNH4 are the quantities of nitrate-N and ammonium -N lost in runoff (kg/ha/day), NgwNO3 and NgwNH4 are the amount of nitrate-N and ammonium-N lost in groundwater fl ow (kg/ha/day), and NpercNO3 and NpercNH4 are the quantities if nitrate-N a nd ammonium-N in percolating water (kg/ha/day). Mineralization Mineralization of organic N forms is repr esented as a first-order process in a similar manner as for P. Mineralization of fresh organic N and animal waste N is partitioned to ammonium-N (80%) and active organic-N (20%). Mineralization of active organic N is assumed to be completely converted to ammonium-N. The rates of

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79 mineralization of animal waste ( RawN), fresh organic N ( RammfN), and active organic N ( RammaN) are defined as (Knisel et al. 1993): min minT aw d NP awPf f N k C R (5-28) min minT f d NP ammfNf f N k C R (5-29) min minT a a ammaNf f N k R (5-30) where Naw is the amount of N present in animal waste (kg/ha), ka is the ammonification rate constant for active organic N an d is assumed to have a value of 1x10-4 day-1, the remaining parameters are defined previously for P. The second stage of mineralization, nitrification, is simulated as a ze ro-order process (Knisel et al. 1993): soil nit T nit nitM k f f Rmin (5-31) where knit is the maximum rate of nitrification, 1.43x10-5 kg/kg/day, Msoil is the soil mass (kg/ha), and fnit is a soil moisture respons e factor (defined below). The exchange between the active N (C:N ratio less than 25) and stable N pools occurs according to the relative size of the pools (Knisel et al. 1993): s actN a asN asNN f N k R1 (5-32) where kasN is a rate constant (1x10-5 day-1). Immobilization The immobilization of nitrate-N and amm onium-N is represented in a similar manner as that for labile P: fN T f d NP immC f f N k C R 016 0min min (5-33) where CfN is the concentration of N in fresh re sidue (kg/kg) and the constant 0.016 comes from assuming that carbon is 40% of fresh or ganic matter and that 40% of the carbon can

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80 be assimilated by soil microbes and that the microbial biomass and its products have a C:N ratio of 10 (Knisel et al. 1993). Nitr ate-N and ammonium-N are immobilized in proportion to their availability. Atmospheric loss of nitrogen Both ammonium-N and nitrate-N may be lost to the atmosphere via the volatilization of ammonia gas a nd the denitrificiation of nitr ate. Ammonia volatilization is assumed to occur from the portion of NNH4 contained in animal waste and only for a period of one week following application. The rate of volatilization is given by (Knisel et al. 1993): t k N Rv NH vol exp4 (5-34) where t is time in days and kv is volatilization rate constant (day-1): 2008 1 409 0T vk (5-35) The rate of denitrification is defined as (Knise l et al. 1993): denit T denit NO denitf f k N Rmin 3exp 1 (5-36) where fdenit is the soil moisture response factor to denitrification (defined below), kdenit is the rate coefficient for dentrification which is a function of active soil carbon, SC (mg/kg) (Knisel et al. 1993): 202 0 106 0 SC kdenit (5-37) The active soil carbon is estimated from the active N pool (Knisel et al. 1993): soil aM N SC018 0 (5-38)

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81 Inorganic nitrogen Nitrate-N is assumed to be completely conservative in solution. Ammonium-N is assumed to undergo instantaneous, reversib le sorption following a linear isotherm (Equation 5-15) where the partitioning coefficien t is assumed to be a function of the clay content of the soil (Knisel et al. 1993): CL Kd083 0 34 1 (5-39) Plant uptake Plant uptake of N occurs in the same manne r as for labile P, from each soil layer that transpiration occurs. The total uptake is limited to a calculated plant demand in the same manner as for P. Uptake of nitrateN and ammonium-N is assumed to occur in proportion to their availabil ity (Knisel et al. 1993). Nutrient Transformation Response to Soil Moisture In the model, there are three soil mo isture functions employed for various processes. For ammonification, P mineralization, and mineral N and P immobilization the soil moisture response function is of th e form (shown in Figure 5-3) (Knisel et al. 1993): wp fc wpf min for fc (5-40a) 0minf for > fc (5-40b) where wp, and fc are the moisture content of a soil layer (cm3 cm-3), the moisture content at the w ilting point (cm3 cm-3), and the moisture content at field capacity (cm3 cm-3), respectively. As can be seen in figure 53, the response to soil moisture rises from zero at the wilting point to an optimum value at field capacity. Immediately above field

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82 capacity the response reduces to zero, implyi ng a complete cessation of microbial activity under wet conditions. For nitrification the soil moisture response function is at an optimum value at field capacity and decreases linearly to zero at saturation and at th e wilting point (Figure 5-3) (Knisel et al. 1993): wp fc wp nitf for fc (5-41a) fc s fc nitf 1 for fc < < s (5-41b) where s is the water content at saturation (cm3 cm-3). For denitrification the soil moisture re sponse function begins when the water content is 10% above field cap acity and increases linearly to saturation (Figure 5-3) (Knisel et al. 1993): fc s fc s fc s fc denitf 1 0 1 0 for fc + 0.1(s fc) (5-42a) 0denitf for < fc + 0.1(s fc) (5-42b) Nutrient Transformation Response to Temperature The model uses two temperature respons e factors (Figure 5-4) to adjust transformation rates. For P mineralization, P immobilization, N ammonification, N immobilization, and denitrification the relationship is (Knisel et al. 1993): T T T fT312 0 93 9 expmin (5-43) where T is the temperature (oC). For the transformation between Pl and Pa the soil temperature factor is (Jones et al. 1984b): 88 2 115 0 exp T fTal (5-44)

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83 Extraction of Nutrients into Runoff The approach to the extraction of nutri ents into runoff by the model assumes a constant rate of rainfall (R) falling on an area of saturate d soil with negligible surface ponding. If the mixing of rainfall with the so il water can be considered complete and instantaneous, the mass balance of an adsorbi ng nutrient can be writ ten as (Steenhuis and Walter 1980): Rt dt Cd K Cd db d s (5-45) where d is the depth of soil considered to be completely mixed with runoff water (sometimes referred to as the effective depth of interaction, EDI), C is the concentration of the nutrient in solution, b is the bulk density of the soil, and t is time. Integration of Equation 5-45 results in: d K d Rt C Cb d s exp0 (5-46) where Co is the initial concentration in solution. In the model the effective depth of interaction is assumed constant at 1 cm, ho wever mixing is assumed to be incomplete. This is accomplished by defining the mass of nutrient within the mass of soil, Y, as the product of the concentration in soil water as defined in Equation 5-46 and the soil mass per unit volume of overland flow, (Leonard et al. 1987): C Y (5-47) as the nutrient equilibrates between soil and runoff (Leonard et al. 1987): S V C Yro (5-48) where Cro is the concentration in runoff, V is the volume of wate r unit volume of runoff interface, and S is the amount of nutrien t in the sorbed phase. By disregarding the soil

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84 mass compared to the volume of water (making the V = 1), substituting Equation 5-20 for S (S = KdCro), and equating Equations 5-47 and 5-48 (Leonard et al. 1987): d roK C C 1 (5-49) where in the case of a conservative substance, Cro = C, and for very large values of Kd, Cro approaches 0. As the parameter is unmeasureable in practice, it is referred to as the extraction coefficient (Knisel et al. 1993). The extraction coe fficient is assumed to vary between 0.1 and 0.5 as a function of the part itioning coefficient. For ammonium N and labile P, is defined as (Knisel et al. 1993): 5 0 for Kd 1.0 (5-50a) dK179 0 exp 598 0 for 1.0 < Kd < 10.0 (5-50b) 1 0 for Kd 10.0 (5-50c) Application of the Nitrogen and Phos phorus Module to Shallow Water-Table Environments From the above description of the N and P module of the ACRU2000 model, several potential shortcomings of the model can be noted. These shortcomings apply to simulating shallow water-table environments in particular, and vari ed field, soil, and management conditions in general. The GLEAMS model, from which the N and P models have been adapted, has been eval uated under different field and management conditions with varied results (e.g. Stone et al. 1998; Bakhsh et al. 2000; Dukes and Ritter 2000; Chinkuyu and Kanwar 2001). Genera l shortcomings of the model include: the use of constant, hard-coded maxi mum reaction rates and proportionality coefficients for various tran sformations and nutrient pools, the assumption that linear partitioning coe fficients can be sufficiently described by soil clay content only, and

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85 the assumption that no plant litter (residue) is produced until harvest operations are simulated. It is evident from these shortcomings th at the GLEAMS model was developed using a one size fits all approach. It should be noted however that fi eld-specific data of maximum rates of reaction, equilibrium st ates between nutrient pools, and sorption characteristics are rarely, if ever, availabl e and the approach used by GLEAMS is an attempt to simplify the use of the model. Additionally, the development of the GLEAMS model envisioned its use primarily for cr opping systems, for which the approximation that crop residue is only produced at harvest is likely acceptable. From the description of the model some specific potential shortcomings concerning its application to shallow-water tabl e environments can also be noted: the response of nutrient transformations to soil moisture conditions, and the functional approach used by the model to represent the extraction of nutrients into runoff. The assumption that the nutrient transf ormation rates of mineralization and immobilization cease completely at soil moistu re contents above field capacity is known to be untrue (Brady and Weil 1996). The use of such a relationship by a model like GLEAMS is indicative of the field conditions for which it wa s developed, upland sites. For an upland site, where extreme soil wetne ss is a transient occurrence, the assumption that microbially mediated reactions ceas e under wet conditions may be an acceptable approximation of the expected depressed micr obial activity. Additionally, it has been made quite clear in the literature that many transformations occur at maximal rates over a range of soil water contents and not at a si ngular value of field cap acity (Rodrigo et al. 1997).

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86 The form of the equation used to determin e the extraction of nutrients into runoff (5-46) has been shown to be inappropriate under saturated soil c onditions (Ahuja 1982). Under such conditions the extr action of solutes into runoff does not follow an exponential curve and the assumption that only the top one centimeter of soil interacts with runoff has been shown to be inaccurate (Ahuja 1982). Additionally, neglecting the presence of ponded water and nutrient concentrations in ponded water (and concentration gradients between ponded water and the soil water) may be problematic when applied to a shallow water-table environment where runoff occurs by saturation-excess and where low ground slopes retain ponded water for large times. Over larger time periods concentration gradients between ponded/runoff water and soil water may affect the relative movement of solutes. The concentration gradients w ill also be of importance should the model be applied in a distributed framework wher e runoff from one land segment becomes runon to another. In such a situation it might be expected that less transfer of nutrients would take place into the surf ace water due to a low concentr ation gradient between the soil and run-on water. Add itionally, the meaning of the extraction coefficient and its relationship to partitioning co efficients does not appear to be well supported for N and P in the literature (K nisel et al. 1993). Summary This chapter describes the field-scale N and P transformations and transport as simulated by the ACRU2000 model (Campbell et al. 2001) using algo rithms from the GLEAMS model (Knisel et al 1993). The model represents N and P using six main pools. Phosphorus consists of a labile, an im mobile active inorganic, an immobile stable inorganic, and immobile fresh, stable humus, and plant orga nic pools. Nitrogen consists of two mobile forms, ammonium and nitrate, and organic pools repres enting fresh, active,

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87 stable and plant pools. The mobile fo rms, labile P and ammonium N, undergo instantaneous, reversible sorption to soil part icles. Most of the nutrient transformations are affected by soil moisture and temperature. The model may not be appropriate for sha llow water-table environments due to the representation of nutrient tran sformations in response to soil moisture conditions and due to the relationship defining th e extraction of nutrients into runoff. The model assumes many transformation rates to cease at water contents above field capacity and uses a relationship that has been shown to be in appropriate in estimating the extraction of solutes into runoff/ponded water under conditions of saturation-excess. Additionally, the model assumes that plant residue is only produc ed in response to harvest operations and that the instantaneous, reversible sorption of P can be defined from the clay content of the soil only. Modifications to ACRU2000 that may be more appropriate for shallow-water table environments and sandy flatwoods soils are descri bed in the next chapter. Only some of the potential issues of the model detailed here are addressed in the following chapter. The remaining are addressed by current research not included in this dissertation. The modified model proposed in the next chapter is compared to the model as described here in Chapter 7.

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88 Labile PActive PStable P Runoff Percolation Groundwater Rain, Fertilizer Animal Waste 75% 25% Organic Humus P Plant P Fresh Organic P Figure 5-1. Nitrogen cycle of the ACRU2000 m odel (adapted from Knisel et al. 1993) AmmoniumActive NStable N Runoff, and Percolation Groundwater Fertilizer Animal Waste 80%20% Nitrate Runoff, and Percolation Rain DenitrificationVolatilization Plant N Fresh Organic N 80%20% Figure 5-2. Phosphorus cycle of the ACRU2000 model (adapted from Knisel et al. 1993)

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89 0 1f Mineralization Nitrification Denitrificationfcwps Figure 5-3. Soil moisture response functions from the GLEAMS model 0 0.5 1 1.5 2 051015202530oCfT Mineralization Active to Labile P Figure 5-4. Soil temperature respons e functions from the GLEAMS model

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90 CHAPTER 6 MODIFICATION OF THE FIELD-SCALE NITROGEN AND PHOSPHORUS MODULE OF ACRU2000 FOR SHALLO W WATER-TABLE ENVIRONMENTS Introduction The nitrogen (N) and phosphorus (P) module of the ACRU2000 model may be most appropriate for applica tion to upland environments as detailed in the previous chapter. Potential shortcomings of the mode l in general, and for application to shallow water-table environments in particular, were de tailed. In this chapter modifications of the model for shallow water-table environments and for Florida flatwoods soils are made. These modifications address only a few of the potential shortcomings noted (Chapter 5). Specifically, modifications pr oposed include the effect of soil moisture contents on transformation rates, the extraction of nutrien ts into ponded/runoff water, and the factors that control the instantaneous, reversible sorp tion of P. Simplifica tions are made to the model in describing the mineralization of plant nutrients and the immobilization of nutrients by decomposing plant matter. A mo re rigorous description of plant growth, nutrient uptake, senescence, and residue depos ition is currently under development but is not a part of this dissertation. Nutrient Models The nutrient models describe the mobilizat ion and transport of dissolved forms of nitrogen and phosphorus. Sediment-bound nutrients are not represented in the model. In the case of N the soluble component is spli t between nitrate-N a nd ammonium-N, with mineralization of organic forms represented as a two-stage process. Soluble organic

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91 nitrogen is not simulated in the model. P is represented as a single soluble form. Both ammonium-N and labile P undergo fast, reversible sorption to soil partic les. Transport of soluble nutrients occurs between completely mixed soil layers a nd between the soil and adjacent water bodies. The exchange of nutri ents with ponded/runoff water is assumed to occur within the top few centimeters of soil. The processes of litter fall, litter accumulation, and litter decay ar e assumed to be in equilibrium with plant uptake and residue immobilization. This assumption ignores the seasonal variation of these processes, but is considered to be a reasona ble simplification for situations where harvest operations are largely absent. The following sections descri be the modifications made to the N and P module of the ACRU2000 model. A short description of the process and data objects added to the model can be found in Appendix E. UML diag rams of the process objects are presented in Appendix F. Appendix G is a tech nical manual for the N and P module. Phosphorus Model The P model is represented by three ma in pools (Figure 6-1), a labile pool (Pl), and two immobile forms, an active pool (Pa) and a slowly changing stable pool (Ps). This representation is much simpler compared to those proposed (e.g. Jones et al. 1984b; Knisel et al. 1993; Groene ndijk and Kroes 1999) where organic forms are further described by additional pools which transform at various rates. In this model it is assumed that the transfer between the two immobile pools accounts fo r the net effects of slow adsorption/desorption of inorganic forms and mineralization/immobilization between organic and inorganic forms. The ma ss balance of the three pools can be written as:

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92 perc gw ro alP awP fert rain lP P P R R P P dt dP 75 0 (6-1) alP saP aR R dt dP (6-2) awP saP sR R dt dP25 0 (6-3) where Prain is the quantity of P in rainfall (kg/ha/day), Pfert is the rate of application of P in inorganic fertilizer (kg/ha/day), RawP is the rate of decay of P in animal waste on the ground surface (kg/ha/day), RalP is the rate of transformation from Pa to Pl (kg/ha/day), RsaP is the rate of transformation from Ps to Pa (kg/ha/day), and Pro, Pgw, and Pperc are the quantities of P lost to runoff, groundwa ter flow, and percolation (kg/ha/day), respectively. The equations that govern the transformation rates are described in Chapter 5. The reversible adsorption of P in sandy so ils has been found to be related to soil properties such as double-acid-extractable magnesium, oxalate-extractable aluminum, oxalate-extractable iron, and organic carbon (Nair et al. 1998; Schoumans and Groenendijk 2000; Vadas and Sims 2002). Based on prior research in the Lake Okeechobee basin by Fraisse and Campbell (1997), the partitioning coefficients for A, E, Bh and Bw soil horizons are assumed to be a function of double-acid-extractable magnesium (mg/kg), MgDA, oxalate-extractable aluminum (mg/kg), AlOX, and organic carbon (%), OC. The linear partitioning co efficient for A horizons is defined as: 2390 2e Kd for MgDA 103.2 (6-4a) 2233 0 e Kd for MgDA < 103.2 and OC 1.865 (6-4b) 4420 1e Kd for MgDA < 103.2 and OC < 1.865 (6-4c) for E horizons: 2410 4e Kd for AlOX 496.45 (6-5a)

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93 0480 1e Kd for AlOX < 30.1 (6-5b) 4440 1e Kd for 496.45 > AlOX 30.1 and MgDA 4.95 (6-5c) 3500 3e Kd for 496.45 > AlOX 57.25 and MgDA < 4.95 (6-5d) 7670 1e Kd for 57.25 > AlOX 30.1 and MgDA < 4.95 (6-5e) for Bh horizons: 751 3e Kd for AlOX 1327.5 (6-6a) 195 2e Kd for AlOX < 1327.5 (6-6b) for Bw horizons: 212 3e Kd for AlOX 570.8 (6-7a) 604 1e Kd for AlOX < 570.8 (6-7b) For Bt horizons the partitioning coefficient is assumed to be a function of the clay content of the soil as in Chapter 5, CL (%) (Knisel et al. 1993): CL Kd5 2 100 (6-8) where Kd is in units of L/kg. Nitrogen Model The nitrogen model is represented by four main pools, nitrate-N (NNO3), ammonium-N (NNH4), active organic N (Na), and stable organic N (Ns) (Figure 6-2). As with the P model, the N model represented here is much simpler compared to others that have been proposed (Johnsson et al. 1987; Hansen et al. 1991; Knisel et al. 1993; Groenendijk and Kroes 1999). The mass bala nce of the four pools can be written as: 3 3 3 3 3percNO gwNO roNO denit nit fertNO rain NON N N R R N N dt dN (6-9) 4 4 4 4 48 0percNH gwNH roNH vol nit ammaN awN fertNH NHN N N R R R R N dt dN (6-10) ammaN saN awN aR R R dt dN 2 0 (6-11)

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94 saN sR dt dN (6-12) where Nrain is the quantity of nitrogen in rainfall (kg/ha/day), and is assumed to occur completely as nitrate, NfertNO3 and NfertNH4 are nitrate and ammonium quantities in inorganic fertilizer (kg/ha/day), RawN is the rate of animal waste nitrogen decay (kg/ha/day), Rnit is the rate of nitrification of ammonium to nitrate (kg/ha/day), Rdenit is the rate of loss of nitrate to the at mosphere by dentrification (kg/ha/day), RammaN is the rate of ammonification of the active n itrogen pool to amm onium (kg/ha/day), Rvol is the rate of loss of ammonium in manure to the atmosphere by ammonia volatilization (kg/ha/day), RsaN is the rate of transformation of stable N to active N (kg/ha/day), NroNO3 and NroNH4 are the quantities of nitrate and am monium lost in runoff (kg/ha/day), NgwNO3 and NgwNH4 are the amount of nitrate and a mmonium lost in groundwater flow (kg/ha/day), and NpercNO3 and NpercNH4 are the quantities if nitrate and ammonium in percolating water (kg/ha/day). The equati ons describing the transformation rates are detailed in Chapter 5. Nutrient Transformation Response to Soil Moisture The use of soil moisture response factor s in simulation models is inherently approximate as the response of microbial activ ity to soil moisture c onditions is a function of a number of factors. The relationship be tween soil moisture and microbial activity has been shown to vary between soils, depending on the shape of the soil moisture curve, the abundance of organic matter, pH, and depth (Goncalves and Carlyle 1994; Rodrigo et al. 1997; Leiros et al. 1999). There are several mechanisms that cause a decrease in microbial activity in dry soil. These include reduced mobility of both soluble substrate and microbes, and a direct effect

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95 of dryness on microbial growth and survival. Under low soil moisture conditions a reduced rate of decomposition is caused by two factors: first, as the pores within the soil dry and the water film coating the particle surfaces becomes thinner, diffusion path lengths become more tortuous and the rate of both substrate and microbe diffusion declines; second, low water contents corre spond to low water potentials that lower intracellular water potentials which in tu rn reduce hydration and enzymatic activity (Porporato et al. 2003). Under wet conditions a decr ease in aerobic microbial activity is caused by a reduction of oxygen diffusion (Grant and Ro chette 1994). During periods of high soil moisture anoxic conditions prevent bacteria from aerobically oxidizi ng organic matter. Anaerobic respiration has been shown to be approximately one-third of aerobic respiration, regardless of substr ate quality (DeBusk and Reddy 1998). Rodrigo et al. (1997) note th at defining functions in term s of water pressure allows for comparison between soils of different textures, and that using soil water contents may be more useful in describing processes that can limit microbial activity in soils such as solute and oxygen diffusion, while expressi ng functions in terms of water filled pore space, or relative saturation, appears to be the best indicator of aerobic/anaerobic microbial activity. The soil moisture conditions that yiel d optimal decomposition and mineralization rates have been reported to occur at soil water pressure heads between 100 and 500 cm (Rodrigo et al. 1997). Kladivko and Keeney (1987) have show n that mineralization rates could be well represented as a linear function of water cont ent or a logarithmic function of soil water pressure head. Soil moisture response functions fo r ammonification and P

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96 mineralization are described as logarithmic f unctions of soil water pr essure head (Figure 6-3) as is done in the models of Hansen et al. (1991), Rijetma and Kroes (1991), and Vanclooster et al. (1996). Expressed in units of pF (log10 of negative pressure head in units of cm): 7 2min wp wppF pF pF f for pF > 2.7 (6-13a) s spF pF pF f 2 4 0 6 0min for pF < 2 (6-13b) 1minf for 2 pF 2.7 (6-13c) where pFwp is the pF value at the wilting point (15000 cm), pFs is the pF near saturation (taken as 1 cm for mathematical reasons). The response to soil moisture rises from zero at the wilting point to an optimum between pF of 2 and 2.7 (100 cm and 500 cm of soil water pressure head, respectiv ely), followed by a decrease to a minimum of 0.6 under saturated conditions. Similarly, nitrification is represented as a logarithmic f unction of soil water pressure head (Hansen et al. 1991; Rijetma and Kroes 1991; Vancloos ter et al. 1996) but reduces to zero as saturate d conditions are approached (Linn and Doran 1984; Skopp et al. 1990) (Figure 6-3): 7 2 wp wp nitpF pF pF f for pF > 2.7 (6-14a) s s nitpF pF pF f 2 for pF < 2 (6-14b) 1nitf for 2 pF 2.7 (6-14c) The soil moisture response of denitrificat ion is simulated as a function of relative saturation as proposed by John sson et al. (1987) as used by Vanclooster et al. (1996) and similar to that developed by Rols ton et al. (1984) (Figure 6-3):

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97 d d s d df (6-15) where d is a threshold water content which defines the water content above which denitrification occurs and is assumed to corr espond to an effective saturation of 0.8, and d is an empirical exponent assumed to be a value of 2 (Vanclooster et al. 1996). Extraction of Nutrients into Runoff During runoff events water entrains some of the soil porewater, this extraction of porewater solutes has been shown to occur pr incipally near the soil surface and rapidly diminishes with depth (Ahuja et al. 1981). The exchange of solutes between the soil and ponded or runoff water has been simulated as a convective mass tran sfer across a thin boundary layer at the soil-water in terface (Wallach et al. 1988; Havis et al. 1992) or as an enhanced diffusion process with in the soil (Parr et al. 1987 ; Ahuja 1990). The rate of mass transfer has been related to the depth of overland flow, solute diffusion coefficients, soil permeability, runoff shear ve locities, and the energy of ra in impact (Parr et al. 1987; Richardson and Parr 1988; Wallach et al. 1989; Gao et al. 2004). However these models, both numerical and analytical, have to date on ly been applied to individual, controlled laboratory events and have yet to be integrat ed into a continuous field-scale model. In lieu of such a complex approach severa l approximate models have been adopted. Early modeling efforts assumed that soil water within a thin zone of surface soil mixes completely and instantaneously with runoff (Crawford and Donigian 1973; Steenhuis and Walter 1980). The depth of this surface z one was often a calibrated parameter and has been shown to vary between values of 0.3 cm to over 5 cm in laboratory experiments (Ahuja and Lehman 1983; Snyder and Woolhi ser 1985) with values increasing as free drainage is reduced during the runoff event. Some observations showed that solute

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98 concentrations were usually much lower in runoff water compared to those in soil solution even at low infiltration rates (Snyde r and Woolhiser 1985). In response, some modeling efforts considered a surface soil zone that mixes incompletely with runoff with the degree of mixing determined by an empirical extraction coefficient (Frere et al. 1980; Leonard et al. 1987). Ahuja a nd Lehman (1983) suggested that this degree of soil water mixing should be an exponential function of depth, however the maximum depth of interaction and the rate of d ecrease in the extraction coefficient with depth being, in practice, calibrated parameters. The extraction of solutes into runoff in the ACRU2000 model is assumed to occur from the top 1 cm of soil. The runoff and soil water are assumed to be incompletely mixed with the degree of mixing defi ned by an extraction coefficient, which ranges from 0.1 to 0.5 as a function of the partitioning coefficient (Equation 5-50). This extraction of solutes into runoff assumes the runoff (or ponded) water to have an initial solute concentration of zero (Ahuja and Lehman 1983). Additionally, runoff and the solutes within are assumed to leave the field on the same day they were generated. To better reflect the slow runoff response typically seen in flatwoods sites (often lasting several days), the exchange of solutes be tween water on the ground surface and water in the top soil is considered to be a functi on of the concentration differences between ponded and soil water. Thus the concentrati on of solutes in runoff or ponded water is assumed to be the equilibrium concentrati on between water on the ground surface and an incompletely mixed top soil layer, or effective depth of interaction (EDI): H EDI H C EDI C Cs w s s eq (6-16) where Ceq, Cs, and Cw are the solution concentrations (mg/L) at equilibrium, in the top

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99 soil layer, and in ponded water, respectively, s is the saturated water content of the top soil layer (cm3/cm3), EDI is the thickness of the top soil layer (also referred to as the effective depth of interaction) (mm), H is the depth of ponded water on the ground surface (mm), and is the extraction coefficient which is assumed to be a constant value of 0.5. Mathematically can be interpreted as the fraction of the effective depth of interaction that is completely mixed with r unoff water. Due to the experimental evidence showing that the depth of soil that interact s with runoff increases as infiltration rates decrease (Ahuja and Lehman 1983) the effective depth of interaction (EDI) is allowed to vary and is a calibrated parameter. This is considered to be appropriate for regions where runoff is generated primarily by saturation-exce ss such as the flatwoods compared to the infiltration-limited runoff generation mech anism as simulated by the ACRU2000 model (Chapter 2). Summary Modifications to the field-scale nitrog en and phosphorus module of the ACRU2000 model were proposed for shallow water-table environments and sandy flatwoods soils. The response of nutrient transformations to so il moisture conditions was changed in order to define an optimal range of water conten ts and to better reflect the effect of wet conditions on mineralization pr ocesses. The extraction of nutrients into ponded/runoff water was changed in order to reflect con centration gradients between soil water and ponded/runoff water and to define the effective depth of soil that interacts with surface water as a calibratable parameter. Additionally the factors that affect the instantaneous, reversible sorption of P were changed to bett er reflect the sandy, low-clay soil horizons

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100 of flatwoods soils. Finally, the model wa s simplified by assuming that plant uptake of nutrients is in equilibrium with the mineralization of plant residue. The modified N and P module is evaluated for use in shallow wa ter-table flatwoods sites in the next chapter. The model proposed here is also compared to the unmodified model described in Chapter 5 in order to gauge improvements.

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101 Labile PActive PStable P Runoff Percolation Groundwater Rain, FertilizerAnimal Waste 75%25% Figure 6-1. Conceptual m odel of the phosphorus cycle AmmoniumActive NStable N Runoff, and Percolation Groundwater Fertilizer Animal Waste 80% 20% Nitrate Runoff, and Percolation Rain DenitrificationVolatilization Figure 6-2. Conceptual m odel of the nitrogen cycle

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102 0 1 pF f Ammonification Nitrification Denitrification 2 pFs2.7 pFwp Figure 6-3. Soil moisture response functions for ammonification (a nd P mineralization), nitrification, and denitrification

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103 CHAPTER 7 FIELD-SCALE VALIDATION OF THE NI TROGEN AND PHOSPHORUS MODULE OF THE ACRU2000 MODEL FOR SHALLO W WATER-TABLE ENVIRONMENTS Introduction The eutrophication of Lake Okeechobee in southern Florida has been attributed primarily to phosphorus (P) loads in agricu ltural runoff (Anders on and Flaig 1995) and has been shown to impact the ecological cond ition of the lake (Steinman et al. 1999). A large proportion of the P loading has been iden tified as coming from drainage areas north of the lake (Federico et al. 1981; SFWM D 1997; USEPA and SFWMD 1999). Beef cowcalf operations make up 51% of the Lake Okeechobee Basin, making them a large contributor of P to the lake (H iscock et al. 2003). Several remediation projects have been initiated in order to restore the lake by improving water quality and lake ecosystem functions. In order to meet the target in -lake P level set for Lake Okeechobee (40 ppb) these projects are aimed at reducing both the internal and external P loads to the lake (USEPA and SFWMD 1999). In order to determine which management practices will prove most effective in reducing external P loading to the lake, longterm, continuous models are often used to evaluate Best Management Prac tices (BMPs) prior to implementation. Due to the unique hydrology of the flatwoods watersheds (flat topography and poorly drained, sandy soils with shallow water-tables) that make up a la rge portion of the lakes drainage basin the traditional models used in other locations in the U.S. often perform poorly (Heatwole et al. 1987). In addition, many models have had difficulty in predicting P loads in

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104 flatwoods regions due to the limited P retenti on capacity of the regi ons soils (Graetz and Nair 1995). The ACRU2000 model is a dist ributed, daily time-step, ob ject-oriented model that has recently been expanded for use in sha llow water-table environments (Chapter 3). Hydrologic modifications made to the mode l include the simulation of a fluctuating water-table by assuming soil moisture to be in hydrostatic equilibrium with the watertable as has been done in similar models (Skaggs 1980; Koivusal o et al. 2000). The contribution of a shallow water-table to an upward gradient induced by evapotranspiration is simulated using the approximate, algebr aic relationship of Anat et al. (1965). Reference potential evapotra nspiration can be simulated using the standardized Penman-Montei th equation adopted by the Food and Agricultural Organization (Allen et al. 1998). Plant root distributions are simulated using the relationship proposed by Hoogl and et al. (1981). Plant ev apotranspiration response to soil moisture is simulated as a function of soil water pressure head within the root zone using the relationship of Fedde s et al. (1978). Runoff is assumed to occur by saturation excess only and is routed from the land su rface using a simple power law relationship (Kroes and van Dam 2003). Gr oundwater inflow/outflow is simulated in response to a time-varying boundary condition using the Dupuit equation (Fetter 1994) or to or from a deep aquifer below a restric tive layer using Darcys Law. The ACRU2000 model represents th e major N and P components and transformations using algorithms from the GL EAMS model (Chapter 5; Knisel et al. 1993). Modifications to these algorithms that may be considered more appropriate for shallow water-table environments and Florid a flatwoods soils were proposed in Chapter

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105 6. The objective of this work is to test the suitability of the ACRU2000 model, with the proposed modifications, to predict N and P load s from field sites in the flatwoods of the Lake Okeechobee Basin. The model is valida ted using observed N and P runoff load data from six experimental sites. Model evaluation is made by its ability to predict annual N and P loads in runoff. The model, as described in Chapter 6 is also compared to the unchanged algorithms in Chapter 5 in order to judge any improvement in the models predictive ability. Model Validation Site Description The sites chosen for validation of the mode l are experimental pastures within the MacArthur Agro-Ecology Research Center (M AERC) at Buck Island Ranch in Highlands County, Florida (Figure 7-1). The site is situated at 27o 7.9 N and 81o 12.3 W, approximately 21 km northwest of Lake Ok eechobee in the C41 basin. The Center consists of an array of 16 pastures divi ded between two typical land uses, improved and semi-improved pasture (Figures 7-2 and 73). The improved pastures are vegetated primarily with bahia grass (Paspalum notatum) and the semi-improved pastures are composed of a mixture of bahia grass and na tive herbaceous vegeta tion including carpet grass (Axonopus furcatus), broomsedge (Andropogon virginicus), bluestem (Andropogon glomeratus), and field paspalum (Paspalum laeve) (MAERC 2004). Historically the improved pastures were fertilized with reco mmended amounts of N, P, and K from the 1970s to 1987. After 1987 the improved pastures were fertilized each year in the spring with N only (56 kg N/ha) (MAERC 2004). The improved pastures had also been periodically limed every 3-5 years. The semi -improved pastures were believed to have never been fertilized (MAERC 2004). Improve d pastures are approximately 20.2 ha and

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106 semi-improved pastures are approximately 32.4 ha. The improved pastures are grazed primarily in the summer wet season (May October) and the semi-improved in the winter dry season (November April) (hereafter referred to as summer and winter pastures). The terrain of the pastures is nearly flat (<0.1% slope) and is dr ained by a series of shallow ditches with the summer pastures more intensely drained compared to the winter pastures. The elevation of the pastures ra nge from 7.9 to 8.5 m above mean sea level with the summer pastures generally 10-15 cm lower than the winter pastures (MAERC 2004). The pastures slope gently toward s Harney Pond Canal, a major regional conveyance linking Lake Istokpoga to the north and Lake Okeechobee to the south. Soil surveys of the area were conducted by the USDA-NRCS in June 1997, at a 0.5-ha resolution. Soils in the summer past ures are predominantly Felda fine sand, a loamy, siliceous, hyperthermic Arenic Endoaqualfs and are predominantly Pineda fine sand, a loamy, siliceous, hyperthermic Arenic Glossaqualfs, with 60% coverage of a thin (2.5-15 cm) muck layer in the winter pastures (MAERC 2004). Experimental Design The pastures are divided by an earthen be rm (4 m wide, 0.5 m above grade). The original drainage ditches were connected to two existing ditches and to new collection ditches allowing runoff from each pasture to flow through an exit flume. Flume elevations were set at 7.99 and 8.08 m above mean sea level in the summer and winter pastures, respectively. Runoff from each pasture was determined at the trapezoidal flumes from water level measurements made in stilling wells at both the upstream and downstream end of the flume in 20 minute inte rvals (Capece et al. 1999). Water samples were taken using ISCO automatic water samp lers at intervals based on flow volume and

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107 hydrograph shape (Tremwel et al. 1996). Sa mples were analyzed for total phosphorus, nitrate/nitrite, ammonium, and to tal Kjeldahl nitrogen. Mete orologic data were collected on an hourly basis at weather st ations adjacent to the pastur es. Rainfall, temperature, solar radiation, relative humi dity, wind speed, and wind direc tion were collected at each station. Groundwater levels were measured at 15-minute intervals in monitoring wells in the pastures. Three pastures in both the wi nter and summer pastures were instrumented with two monitoring wells, one 4-inch diam eter well at the downstream end of the pasture near the flume and one 2-inch diamet er well located in the center of the pasture (Figures 7-2 and 7-3). The remaining te n pastures were instrumented with one monitoring well, a 2-inch diameter well located in the center of the pasture (Figures 7-2 and 7-3). Wells extended to a depth of 18 ft below ground surface with the screened portion beginning at 5 ft below ground surface. Limited groundwater quality measurements were made. Runoff and clim atic data collection began in May 1998 and groundwater level measurements began in Se ptember 2000. Data collection continued until the end of 2003. Each pasture type had two replicates of f our different cattle stocking rate treatments during the study period: 0, 15, 20, and 35 cowcalf pairs. The stocking rate of each pasture is shown in Figures 72 and 7-3. Cattle were stoc ked in the summer and winter pastures for a total of 1025 and 677 days duri ng the study period, respectively. During the study period nitrogen fertilizer was applie d at 56 kg N/ha in May 2000, April 2001, and March 2003 to the summer pastures. No fer tilizer was applied to the winter pastures. Prescribed burning was conducted in the winter pastures in November 1998 and February 2002 and in the summer pastures in February 1999 and April 2002. For validation of the

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108 model six pastures were select ed for simulation. These incl ude three pastures from each land use, one control pasture (no stocking), one lo w stocked pasture ( 15 pairs), and one high stocked pasture ( 35 pairs). The control pastures are winter pasture 7 (WP7) and summer pasture 1 (SP1), the low stocked pa stures are winter pasture 6 (WP6) and summer pasture 4 (SP4), and the high stocke d pastures are winter pasture 5 (WP5) and summer pasture 3 (SP3) (Figures 7-2 and 7-3). These pastures were selected in order to first evaluate the models ability to predict nutrient loads resulting fr om nutrients already present in the soil and then to evaluate the models ability to predict the contribution from grazing cattle. Model Calibration Model hydrologic calibration for each past ure was conducted using the observed runoff and groundwater level data from 1998 to 2001. The length of the calibration period was chosen in order to include ade quate groundwater level data (data collection started 9/2000). This period allowed for 16 m onths of groundwater level data to be used in the calibration. The remaining two year s, 2002 and 2003, were used to verify the model calibration. Hydrologic calibration was conducted (as desc ribed in Chapter 4) first for WP6 where values of sa turated water contents (s) and van Genuchten (1980) n soil parameters (Table 7-3) were reduced sli ghtly from curve fits of published moisture contents for the Pineda fine sand in nei ghboring Glades county (Sodek et al. 1990). These changes were made in order to better reflect the magnitude of observed water-table fluctuations and were consider ed to be appropriate as no fi eld data were collected. The changes made fell within the observed ranges fo r Pineda fine sand and closely associated Felda, Malabar, Myakka, and other soils re ported by Sodek et al. (1990). Hydraulic

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109 conductivities of the A, E, and Bw soil layers (Table 7-1) were reduced one order of magnitude from that reported by Sodek et al. (1 990) in order to more accurately represent the effect of the stage in the adjacent canal. The remaini ng hydrologic calibration consisted of changing the runoff resistance in order to better matc h runoff event peaks and duration. Model calibration was made by graphical comparison between observed and simulated daily values. WP7 and WP5 were then calibrated with only slight changes to the runoff resistance only. Hydrologic ca libration of SP1, SP4, and SP3 consisted of manipulating the runoff resistance. No cha nges were made to hydraulic conductivities or the soil moisture relationships of the Felda fine sand (Table 7-2 and 7-4) fitted from the data of Sodek et al. (1990) for the summer pastures. Observed groundwater level data from the two wells in WP6 and SP1 are shown in Figures 7-4 and 7-5. Observations were qui te similar at both locations, near the flume and at the center of the pasture. As can be seen there are periods where there is a notable gradient between the two wells, indicating that groundwater level is affected by the neighboring canal stage. Figur es 7-6 and 7-7 show the canal stage reported at the S70 spillway located four kilometers downstream compared to the groundwater level recorded at the 4-inch well located near the flume for WP6 and SP1 and the 2-inch well located mid-pasture for WP7, WP5, SP4, and SP3. As observed, the gradient between the groundwater level and the canal reverses direction, with gr oundwater flowing towards the canal during wet periods (typi cally summer) and canal water flowing towards the pasture during dryer periods. The daily time series of can al stage serves as an input to the model. Soil physical and chemical properties are show n in Tables 7-1, 7-2, 7-5, and 7-6 for the Pineda fine sand found in the winter pa stures and the Felda fine sand found in the

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110 summer pastures. Soil phosphorus values were reported by Capece et al. (2003) for the A horizon of each pasture and were reported for the subhorizons by Hill (2003). Phosphorus pools were initialized according to the procedures of Fraisse and Campbell (1997) and Knisel et al. (1993). Initial pool sizes of labile P compared well to the values determined by Hill (2003) and compared well to double-acid extractable P values as reported by MAERC (2004). Values of double-acid-extractable Mg and oxalateextractable Al were assumed to be the sa me for horizons in bot h pastures and were assumed to have similar values in A, E, and Bw horizons as Spodosols in the region. Values reported by Nair et al (1998) were used since no fiel d data were collected from the pastures. No soil N data were collected from the pastures. Ratios of total N to total P in the pastures were assumed to be the same as those reported by Na ir et al. (1995) for pasture landuse in the Lake Okeechobee basi n and a single value was assumed for the winter and summer pastures. N and P contents in rainfall we re determined to be 0.20 and 0.03 ppm, respectively based on data fr om NADP (2005) and USEPA and SFWMD (1999). Nutrient calibration of the model was firs t conducted for the control pastures WP7 and SP1. The depth of soil interacting with runoff water was assumed to be 1 cm, as in the original ACRU2000 model (Cha pter 5), for WP7. For SP1 this depth was increased to 3 cm in order to better match field data. This increase may be justified due to the more intensive drainage network of the summer pastures (MAE RC 2004), allowing for a more direct route for shallow subsurface water into th e ditches and out of the pasture as runoff. These values were used for simulating the rema ining pastures without further calibration. Application of the original, unmodified N a nd P module (Chapter 5) was made using the

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111 same approximations as that proposed in the modified model (Chapter 6), that the uptake of nutrients by the plant was in equilibrium with the mine ralization of fresh organic material. This was done in order to provi de results that would be more directly comparable. Results Observed and simulated groundwater levels are shown in Figures 7-8 to 7-13. The discrepancy between observations and simulate d water-table depths, particularly during periods of deeper observed water-tables, may be due to the representation of the canals influence on groundwater levels within the pasture, the unc ertainty of soil hydraulic parameters due to the lack of field-collect ed data, and the assumption of no groundwater inflow or outflow from upland areas. No stag e measurements of the canal were made at the site, stage measurements at the S70 sp illway were assumed to represent the local canal stage by assuming level pool conditions over the 4 km distance. No groundwater measurements were made in areas upland from the pastures. As can be seen the model followed the general trend in water-table fl uctuations, with both periods of over and under prediction. Observed and simulated daily runoff can be seen in Figures 7-14 to 719 and reported as cumulative annual plots in Figures 7-20 to 7-25. As shown the calibration (1998-2001) matched observations, with the exception of the very dry year of 2000. Verification (2002-2003) matched the observed data less well compared to the calibration period, but general trends in runoff timing and amounts were followed with larger variability observed in the winter pastures. Annual runoff totals from each pasture are shown in Tables 7-7 and 7-8. Mean absolute error (MAE), root-mean square error (RMSE), and Nash-Sutcliffe coefficients of efficiency (E) (Nash and Sutcliffe 1970) usi ng these annual values are reported in Table

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112 7-11. MAE and RMSE values ranged fr om 28.2 to 81.2 mm and 33.4 to 103.1 mm, respectively. Nash-Sutcliffe efficiency values indicate that the model was a valuable tool for estimating annual runoff depths for al l pastures (range between 0.722 and 0.987). Observed and simulated N and P loads from WP7 (control) are shown in Figures 726 and 7-27. N loads generally matched fi eld observations, while P loads were underpredicted in 2001 and 2002. Observed and simulated N and P loads from WP6 (low stocking) are shown in Figures 7-28 and 729. N loads followed observations, while P was under-predicted in 2001. N and P load s from WP5 (high stoc king) are shown in Figures 7-30 and 7-31. N loads again ge nerally followed observations, while P was under-predicted in 2001, 2002, and 2003. It is also of interest to note the apparent influence on cattle stocking in this pastur e during the winter of 2002-2003, where larger magnitude loads are simulated during the stocking period. Observed and simulated N and P loads from SP1 (control) are shown in Figures 732 and 7-33. The simulated N load agreed with observed data with the exception of 2001, however there was a large under-predic tion of P in 2001 and 2002. In Figures 7-34 and 7-35, the effects of stocking can be seen on SP4. Over-predictions can be seen of N and P in 1999 and 2003. P was also under-pre dicted in 2001 and 2002. The effects of stocking on simulated loads is further illust rated for SP3 in Figur es 7-36 and 7-37 where N is over-predicted in all years and P is over-predicted in 1999 and 2003, but (interestingly) matches observe d values well in 2001 and 2002. Annual totals of N and P load s are shown in Tables 7-7 and 7-8. Annual average N and P concentrations (determi ned as the annual load divi ded by the annual runoff depth and converted to units of mg/L) are shown in Tables 7-9 and 7-10. Mean absolute error

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113 (MAE), root-mean square error (RMSE), and Na sh-Sutcliffe coefficients of efficiency using these annual values are reported in Table 7-11. MAE and RMSE values for N and P loads were greater for the summer pastures compared to the winter. Coefficients of efficiency values indicate the model was a good predictor of N and P loads for all winter pastures and a poor predictor of both N and P for all summer pastures with the exception of P in SP4. E values also show that the model was a poor predictor of average N and P concentrations in all pastures. For comparison, the annual simulated loads using the unmodified N and P algorithms of the ACRU2000 model, as described in Chapter 5, are also shown in Tables 7-7 and 7-8. The simulated annual average N and P concentrations in runoff are shown in Tables 7-9 and 7-10. N and P loads a nd concentrations predicted by the unmodified ACRU2000 were consistently lower than observa tions and that simulated by the modified model. Mean absolute error (MAE), root-m ean square error (RMSE), and Nash-Sutcliffe coefficients of efficiency us ing these annual values are repor ted in Table 7-12. MAE and RMSE values of N and P loads were higher compared to the modi fied model (Table 711) with the exception of N and P loads in SP1 and SP3 and N load in SP4. MAE and RMSE were higher for N loads in the winter pastures and higher for P loads in the summer pastures. Coefficients of efficiency show the model to be a poor predictor for N and P loads and N and P concentrations for a ll pastures with the exception of N load in SP4. Coefficients of effici ency were higher than that fo r the modified model (Table 711), indicating that the unmodifi ed model was a poorer predicto r, with the exception of N loads in SP4 and SP3 and N and P concentrations in SP3.

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114 Sensitivity Analysis Obtaining accurate values for model input va riables can be costly or impractical for field-scale models, particularly for large si tes where spatial variation in properties is likely. The accuracy of model input parameter values is us ually proportional to the time and resources invested in determination. Si nce model results will be more sensitive to certain inputs compared to others it is useful to perform a sensitivity analysis in order to establish priorities in collecting a nd determining model parameters. An analysis was performed to determine the sensitivity of model simulation output of N and P loads to the hydrologic input pa rameters in Table 7-13 and to the nutrient input parameters in Table 7-14. The sensitiv ity analysis was performed by using the sixyear simulation of SP4. Model sensitivity was determined for 25%, 50%, 75%, and 100% of the base input value (shown in Tabl es 7-13 and 7-14). For cases where this range of variation was unfeasible, or unr ealistic, the results were omitted. Model sensitivity is reported as the percent differen ce of model results as compared to the base simulation. The sensitivity of N and P loads to the hydrologic para meters in Table 7-13 is shown in Table 7-15. Input parameters showing very low sensitivity on N and P loads were root depth, root dist ribution parameter, vertical saturated hydraulic conductivity, upward flux exponent, the transpir ation factor for water deficiency, interception capacity, and the initial depth of the water-table (this value was not increased due to the relatively deep initial value used). Parameters showi ng relatively low sensitivity with the exception of very large positive or negative changes include the soil moisture shape parameter n, bubbling pressure head, the tr anspiration reduction factor due to oxygen deficiency, and runoff resistance. The remaining paramete rs, in approximate order of increasing

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115 sensitivity were the horizont al hydraulic conductivity, th e runoff exponent (limited to values greater than 1), soil shape parameter saturated water content, and the crop coefficient. The sensitivity of N and P loads to soil parameters (not including soil N and soil P parameters) was greatest to the application rate of manure and the effective depth of interaction, and was large for P loads for large increases in pH due to the effect pH has on the P availability index (PSP) as shown in Equation 5-17 (Table 7-16) Base saturation, clay content, and organic matter content show ed no sensitivity over the range of values tested. The sensitivity of N and P loads to N and P parameters is shown in Table 7-17 and 7-18. N loads showed greatest sensitivity to nitrogen concentrations in rainfall, and to total N and active N contents. N loads were slightly sensitive to the nitrate extraction coefficient and ammonium partitioning coeffici ent. P loads were not affected by the N parameters. P loads were most sensitive to total phosphorus, stable phosphorus, and the phosphorus availability index (T able 7-18). With the exceptio n of the concentration of P in the nearby stream, P loads were slightly sensitive to all other P parameters with the largest sensitivity occurring at the extrem e high or low values. N loads showed no sensitivity to the P parameters over the range of values tested. To summarize, the hydrologic parameters with the largest sensitivity on N and P loads in runoff were the crop coeffi cient, saturated water contents, soil water shape parameters, runoff exponent, depression st orage, and horizontal saturated hydraulic conductivities (Figures 7-38 and 7-39). In addition to these parameters, N loads were most sensitive to manure application rates, th e effective depth of in teraction, nitrogen

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116 concentrations in rainfall, and total and active nitrogen contents (Figure 7-40). In addition to the hydrologic parameters, P loads were most sensitive to high values of pH, manure application rates, the effective depth of interaction, total and stable phosphorus contents, and the P availability index (Figure 7-41). The sensitivity of N and P loads to the hydrologic para meters in Table 7-13 is largely due to the sensitivity of runoff vol umes to these parameters. Decreasing crop coefficients causes a decrease in evapotranspira tion, resulting in greater runoff. Increases in s and increases the volume of empty soil po res at a given water table depth, decreasing the amount of runoff produced and resulting nutrient loads. An increase in zdep also increases the volume that must be fi lled prior to runoff occurring, decreasing the total volume of runoff and loads in runoff. As would be expected, N and P loads both increased with increased manure application rates. Interestingly, P loads increased with increasing EDI, while N loads decreased. This indicates that N became mo re diluted when the effective depth of soil that interacts with ponded wa ter increased. The sensitiv ity of N loads on rainfall concentrations indicates its relatively large c ontribution to the nitrogen balance in these pastures. N loads also decreased wi th increasing values of TN and Na. This is likely due the use of the ratio of Na to TN in determining the transfer of N from Na to Ns (Equation 5-32). P loads increased with increasing TP and Ps, showing the effect of P already present in the soil and the cont ribution of stable forms to produce labile P. The large sensitivity of P loads to PSP indicates the importance of the equilibrium between labile P and active P forms.

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117 Discussion While the prediction of runoff volumes by the model were in general agreement with observations, and that of water-table dept hs adequate considering the lack of fieldcollected soil hydraulic data and the approximation of the pastures as single lumped elements in the model, the validation of the model elucidates three major shortcomings in simulating N and P loads. First, the model s inability to capture the large loads of P observed in most pastures duri ng 2001. Second, the prediction of elevated loads of N and P in response to the increased stocking rates compared to obser vations. Third, the overall poor performance of the model and the rela tively poorer performance of the model in simulating the summer pastures as compared to the winter. The model did however produce annual N and P load and concentration predictions that were in better agreement with observations compared to the original N and P module of AC RU2000 as described in Chapter 5. The inability of the model to match elev ated observed loads in 2001 may be due to the lack of a complete representation of organic nutrient cycling in the model. A complete representation would include the partitioning of biomass between above ground and below ground components. These components would respond to environmental conditions such as water-excess or deficient stress, temperature as it affects the plant growth stage, and nutrient availability. In re sponse to environmental stresses, portions of the above ground and below ground biomass shoul d experience die-back and senescence, contributing to decomposable biomass and nutrient pools (where it may mineralize or immobilize nutrients). It is also important to note the pastures contain wetlands which may accrete and store biomass and nutrients during periods of anaerobiosis. During dryer periods this accreted organic matter would d ecompose at an accelerated rate by aerobic

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118 microbes. As 2000 was a relatively dry year it is postulated that organic soils that had been accreting within the we tlands and drainage ditches within the pasture were mineralized at an enhanced rate. The biomass built in 1998 and 1999 (and prior to the beginning of the model simulation) may ha ve largely decomposed, releasing large quantities of nutrients in the process that we re subsequently flushed from the pasture in 2001. This may be supported by the fact that as can be seen in Tables 7-9 and 7-10, 2000 and 2001 generally had the highest averag e observed N and P c oncentrations for most pastures. After 2000, the process of or ganic soil accumulation ma y have reinitiated, binding nutrients in the proce ss and reducing the simulated lo ads in later years. As simulated here the process of mineralization of nutrien ts from decomposing plant material was assumed to be in equilibri um with plant uptake and immobilization by organic material. In ACRU2000, as based on the GLEAMS algorithms, fresh organic matter is currently only available for deco mposition at the beginning of the simulation period and after harvest operations (Chapter 5) rather than being represented as a continuum of growth, senescence, litter fa ll, organic soil accretion and decomposition. The prediction of elevated lo ads in later years in the stocked pastures may have several possible causes. For both N and P it may be due to a lack of organic matter building as described above. There may al so be specific reasons for both N and P. For the case of nitrogen, th e elevated loads in later years may be due to underprediction of ammonia volatili zation and denitrification of nitrate. The nitrogen model allows for ammonia volatiliza tion to occur only from ammonium from animal waste, and only for a period of one week (Knisel et al. 1993). However, it is well known that volatilization can be a large component of nitrogen loss from wetland environments

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119 (Brady and Weil 1996). Denitrification, wh ich occurs under reducing conditions, may also be enhanced in wetland environments The accurate prediction of these two processes is further complicated by the in ability of the nitrogen algorithms, as incorporated from the GLEAMS model, to adequately predict the nitrification of ammonium to nitrate (data not shown). Th e model largely over-pre dicted nitrate in runoff loads, indicating a large over-predic tion in nitrification. The process of nitrification can only occur in the presence of oxygen, indicating the importance of representing reducing conditions. Due to the lack of field colle cted N data and/or siteor region-specific observed process rates or responses to abiotic factors such as temperature or soil moisture it is not possible to determine the source of the error. In the case of P the increased simulated loads in higher stocked pastures may be due to the rate at which labile P is transfor med into more immobile forms. The apparent sorption of P is usually concep tualized as a combination of a fast (almost instantaneous) reversible true sorption ont o particle surfaces and various slower time-dependent processes where P diffuses into and is de posited below surfaces that may not be completely reversible (McGech an and Lewis 2002). It has been shown that the fast, reversible sorption of P is highly non-linear and should be represented mathematically with non-linear relationships such as the Freundlich or Langmuir isotherm s (Graetz and Nair 1995; Mansell et al. 1995; McGechan and Lewis 2002). The slower time-dependent processes are sometimes divi ded into a relatively faster component (the exchange between the labile and active pools) and a very slow com ponent (exchange between the active and stable pools). The ra te at which this slow adsorp tion or desorption occurs is represented as a function of PSP in the G LEAMS algorithms, with the same equation

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120 used for both adsorption and desorption. However, Barrow (1979) and others have shown that the desorption process does not retrace the path of the sorption process, particularly after long incuba tion periods. This indicates that the rate constants kal and ksa in Equations 5-14 and 5-18 should have differe nt values depending on the direction of the reaction. This is a method employed by the Soil and Water Assessment Tool (SWAT) model, which also uses P algorithms almost entirely from GLEAMS where the transfer from active P to labile P and from stable P to active P is assumed to be one-tenth that of the reverse reaction (Neitsch et al. 2002). Additionally, PSP is determined as a function of the base saturation and pH of the soil for slightly weathered soils (such as the Spodosols and Alfisols found in the flatwoods). Vadas (2001) recen tly showed that for sandy soils in the Delaware coastal plain this P availability index is better represented as a function of oxalate-extractable aluminum a nd labile P concentrations. It is likely a similar relationship could be develo ped for Florida flatwoods soils. The model showed poorer predictions from the summer pastures compared to the winter, and was overall a poor predictor for all pastures. The poorer predictions in the summer pastures is partially due to the incr eased effects of stocking on these pastures since the cattle density is larg er (due to the smaller pasture size compared to the winter pastures) and the cattle are present for larger periods of each year. The poor predictions of the model are likely also due to the one -size-fits-all approach of the algorithms as adopted from GLEAMS. These algorithms use constant, hard-coded maximum rate constants and proportionality coefficients th at are based on limited data. Due to the relative uncertainty in these parameters it ma y be more appropriate for these parameters to be inputted, calibratable va lues. As inputted values the model can more easily take

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121 advantage of the best available data and the sensitivity of the model to these parameters can be evaluated. The algorithms of the original, unmodified N and P module (Chapter 5) predicted consistently lower N and P loads and concentrat ions for all pastures and for all years of simulation (Tables 7-7 to 7-10). For the case of P this is partly due to the relatively large partitioning coefficients predicted by the model from the soil clay content (Equation 521). These partitioning coefficients have b een noted to overpredict P adsorption in flatwoods soils in Florida (Fraisse and Cam pbell 1997). The relatively large partitioning coefficient results in a relatively low concen tration of P in runoff water as determined by Equation 5-49 due to the application of the partitioning coefficient in Equation 5-46 and 5-50. For the case of both N and P the low pr edicted loads and concentrations are due to the cessation of transformations that c ould supply labile forms. The rate of transformation from active P (Equation 5-14), active N (Equation 5-30), animal waste P (Equation 5-7), and animal waste N (5-28) are all affected by a soil moisture response factor (Equation 5-40) th at reduces the transformations to zero at water contents above field capacity. Thus upon satura tion of the soil, and the rem oval of labile nutrients by a runoff event, no new labile forms are produced in the surface soil layer beyond that entering via rainfall, upward fl ow, and groundwater flow until soil moisture contents are reduced below field capacity. The shortcomings in the prediction of N and P loads would likely be overcome by a more accurate representation of organic form s and the use of site-specific or regionspecific relationships. The relationships us ed by the GLEAMS m odel were determined using a limited number of soils from thr oughout the continental U.S. and may not be

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122 representative of conditions of the Florida flatwoods. The need for such specific relationships limits the predictive ability of the model for application to ungauged sites. For most practical applications, where site-s pecific soil data are unavailable, a simplified process model (as applied here and described in Chapter 6) that is more flexible in terms of rate constants and coefficients of proporti onality may be more suitable. Such rate constants and coefficients coul d be inputs to the model that could be based on the best available data and/or calibratable parameters. Conclusions The ACRU2000 model, using nitrogen a nd phosphorus cycling algorithms that are patterned after those used in the GLEAMS mo del, was modified to better represent the hydrology and water quality of shallow wate r-table flatwoods regions of the Lake Okeechobee basin. Modifications were made to the response of N and P transformations to soil moisture, the estimation of linear partitioning coefficients that govern the adsorption of labile phosphorus, and to the of the extraction of N and P into runoff. Validation results, while generally in agr eement with observations in terms of the prediction of runoff, indicate several shortc omings in the representation of N and P processes by the model. The model prove d incapable of reproducing large observed loads following a drought year, possibly due to the lack of an exp licit representation of biomass production, senescence, and organic soil accretion and mineralization. The model produced increased loads from increase d cattle stocking rates that were not observed. This may be due to the model s representation of atmospheric losses of nitrogen and the rate that P transforms into more stable, recalcitrant forms. The model also produced generally worse simulations on on e set of experimental pastures compared to the other. This was likely due to the mo re intensive simulated stocking on the pastures

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123 as well as to the assumed reaction rates and assumed proportional ity between nutrient pools by the model. Compared to the original, unmodified algorithms of the ACRU2000 N and P module (Chapter 5), the model si mulated loads and concentrati ons that were generally in better agreement with observations. These we re in better agreement due to a more appropriate prediction of sorption of P in flatwoods soils, affect of soil moisture on transformation rates, and extraction of solu tes into ponded/runoff water as proposed in Chapter 6. Model sensitivity of N and P loads to hydrologic parameters indicated the importance of correct hydrologic prediction in or der to accurately predict nutrient loads. The hydrologic parameters having the largest effects on loads were largely the same as those having the largest effect on runoff prediction (Chapter 4). Model sensitivity of N and P loads to nutrient parameters showed the model to be most sensitive to manure application rates, the depth of soil that inte racts with runoff, concentrations of N in rainfall, total and active soil N contents, to tal and stable P contents, and the phosphorus availability index. It is recommended that the model incorpor ate a more detailed plant/organic carbon representation in order to bette r capture the effects of plan t residue production, organic soil formation and mineralization and associated accumulation/release of nutrients. It is also recommended that maximum reaction rates and constants that define the equilibrium between nutrient pools should be user-variable parameters where the most current knowledge can be used.

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124 Table 7-1. Pineda fine sand soil physical properties Layer[a] Layer Depth[a] (cm) Bulk Density (g/cm3) Hydraulic Conductivity (cm/hr) Organic Matter (%) Silt (%) Clay (%) A 0-10 1.22, 1.20, 1.01[b] 4.32 14.6, 16.4, 18.8[b] 0.4 0.7 E 10-30 1.63 4.14 0.24 0.8 0.1 Bw 30-80 1.69 2.70 0.07 0.7 0.1 Btg 80-140 1.72 1.37 0.67 2.6 18.6 Cg 140-160 1.66 2.95 0.11 3.2 3.1 [a] From Gathumbi et al. (2005). [b] From Capece et al. (2003). Values are for winter pastures 5, 6 and 7, respectively. All other values adapted from Sodek et al. (1990). Table 7-2. Felda fine sa nd soil physical properties Layer[a] Layer Depth[a] (cm) Bulk Density (g/cm3) Hydraulic Conductivity (cm/hr) Organic Matter (%) Silt (%) Clay (%) A 0-18 1.13, 1.27, 1.25[b] 25.35 14.6, 13.5, 15.8[b] 1.3 1.2 E 18-60 1.59 8.76 0.12 2.4 2.0 Btg 60-100 1.52 0.76 0.08 5.9 15.5 Cg 100-170 1.66 4.31 0.05 3.6 4.4 [a] From Gathumbi et al. (2005). [b] From Capece et al. (2003). Values are for su mmer pastures 1, 3, and 4, respectively. All other values adapted from Sodek et al. (1990). Table 7-3. Pineda fine sa nd saturated water content (s), residual water content (r), and van Genuchten (1980) n, and m parameters Layer Layer Depth (cm) s (cm3/cm3) r (cm3/cm3) (cm-1) n (-) m (-) A 0-10 0.42 0.10 0.0287 1.96 0.490 E 10-30 0.34 0.08 0.0224 2.57 0.611 Bw 30-80 0.32 0.07 0.0234 1.81 0.448 Btg 80-140 0.35 0.15 0.0177 1.67 0.401 Cg 140-160 0.30 0.06 0.0106 2.06 0.515 Parameter values adapted from th e data of Sodek et al. (1990).

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125 Table 7-4. Felda fine sa nd saturated water content (s), residual water content (r), and van Genuchten (1980) n, and m parameters Layer Layer Depth (cm) s (cm3/cm3) r (cm3/cm3) (cm-1) n (-) m (-) A 0-18 0.45 0.14 0.0158 3.52 0.716 E 18-60 0.35 0.11 0.0150 3.13 0.681 Btg 60-100 0.42 0.18 0.0055 1.26 0.206 Cg 100-170 0.36 0.11 0.0114 3.38 0.704 Parameter values adapted from th e data of Sodek et al. (1990). Table 7-5. Pineda fine sand soil chemical properties Layer Layer Depth (cm) pH[a] Base Saturation[b] (%) MgDA (mg/kg) AlOX (mg/kg) Total Nitrogen[c] (mg/kg) Total Phosphorus[a] (mg/kg) A 0-10 4.3, 4.3, 4.1[d] 41 99 201 500 125, 104, 119[d] E 10-30 4.5 39 16 158 100 5.6 Bw 30-80 5.0 22 17 1472 100 1.5 Btg 80-140 5.9 51 100 2.0 Cg 140-160 6.0 99 16 158 100 1.8 [a] Data for A Horizon from Capece et al (2003), other horizons from Hill (2003). [b] From Sodek et al. (1990). [c] From Nair et al. (1995). [d] Values are for winter pastures 5, 6 and 7, respectively. Table 7-6. Felda fine sa nd soil chemical properties Layer Layer Depth (cm) pH[a] Base Saturation[b] (%) MgDA (mg/kg) AlOX (mg/kg) Total Nitrogen[c] (mg/kg) Total Phosphorus[a] (mg/kg) A 0-18 4.7, 4.9, 4.5[d] 14 99 201 900 212, 130, 199[d] E 18-60 4.9 45 16 158 100 5.6 Btg 60-100 5.7 86 100 2.0 Cg 100-170 7.7 93 16 158 100 1.8 [a] Data for A Horizon from Capece et al (2003), other horizons from Hill (2003). [b] From Sodek et al. (1990). [c] From Nair et al. (1995). [d] Values are for summer pastures 1, 3 and 4, respectively.

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126 Table 7-7. Annual observed and simulated ru noff and N and P loads in runoff for winter pastures Pasture Runoff (mm) N Load (kg/ha) P Load (kg/ha) and Year Obs. Sim. Obs. Sim.[a] Sim.[b] Obs. Sim.[a] Sim.[b] WP7 1998 244.6 198.1 0.570 0.434 0.031 0.144 0.268 0.037 1999 149.3 148.8 0.335 0.349 0.001 0.181 0.185 0.009 2000 9.8 81.4 0.065 0.184 0.006 0.040 0.086 0.004 2001 367.1 373.7 0.929 0.797 0.001 0.902 0.271 0.016 2002 380.3 484.8 0.751 1.036 0.002 0.545 0.353 0.019 2003 459.2 371.6 0.709 0.849 0.010 0.212 0.308 0.015 Total: 1610.3 1658.4 3.359 3.649 0.072 2.024 1.471 0.100 WP6 1998 214.1 145.8 0.346 0.322 0.027 0.149 0.209 0.030 1999 154.2 92.8 0.304 0.205 0.001 0.117 0.126 0.006 2000 3.3 49.3 0.081 0.103 0.013 0.076 0.054 0.003 2001 303.8 302.9 0.847 0.624 0.001 0.708 0.240 0.012 2002 265.8 372.0 0.544 0.806 0.045 0.420 0.318 0.014 2003 468.4 263.7 0.581 0.628 0.034 0.404 0.302 0.010 Total: 1409.6 1226.5 2.703 2.688 0.120 1.874 1.249 0.074 WP5 1998 250.6 155.2 0.381 0.400 0.064 0.173 0.275 0.026 1999 135.8 108.1 0.364 0.253 0.001 0.116 0.180 0.007 2000 17.7 53.5 0.319 0.115 0.015 0.058 0.079 0.003 2001 399.9 324.1 0.978 0.724 0.021 0.728 0.375 0.015 2002 370.3 412.4 0.809 1.060 0.146 0.754 0.558 0.017 2003 473.1 307.2 0.835 0.869 0.269 0.801 0.552 0.014 Total: 1647.4 1360.5 3.686 3.421 0.516 2.630 2.019 0.082 [a] Simulated with the modifications made for shallow water-table and flatwoods conditions as described in Chapter 6. [b] Simulated using original, unmodified equati ons as adapted from GLEAMS and described in Chapter 5.

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127 Table 7-8. Annual observed and simulate d runoff and N and P loads in runoff for summer pastures Pasture Runoff (mm) N Load (kg/ha) P Load (kg/ha) and Year Obs. Sim. Obs. Sim.[a] Sim.[b] Obs. Sim.[a] Sim.[b] SP1 1998 94.3 109.3 0.114 0.170 0.056 0.603 0.740 0.017 1999 92.9 126.5 0.355 0.195 0.002 0.552 0.768 0.012 2000 0.03 62.6 0.0 0.103 0.018 0.0 0.329 0.005 2001 286.0 223.6 3.256 0.358 0.011 4.443 1.080 0.018 2002 319.4 208.2 0.692 0.332 0.003 2.775 0.770 0.016 2003 154.1 159.8 0.262 0.261 0.015 1.304 0.803 0.013 Total: 946.7 890.0 4.683 1.419 0.105 9.675 4.490 0.083 SP4 1998 105.9 124.4 0.197 0.251 0.031 0.720 0.935 0.015 1999 157.4 149.5 0.401 0.618 0.326 0.891 1.653 0.013 2000 5.5 63.0 0.035 0.206 0.062 0.008 0.418 0.005 2001 272.7 247.5 0.958 1.016 0.462 4.438 2.173 0.019 2002 293.1 246.5 0.704 0.940 0.431 3.191 1.777 0.018 2003 201.4 215.1 0.204 1.020 0.483 1.166 1.970 0.017 Total: 1036.0 1046.0 2.499 4.051 1.795 10.419 8.926 0.087 SP3 1998 145.8 124.2 0.089 0.315 0.061 0.600 0.790 0.011 1999 96.4 149.7 0.465 1.098 0.749 0.473 2.214 0.010 2000 0.24 62.9 0.0 0.354 0.125 0.0 0.428 0.003 2001 331.9 247.4 1.179 1.734 1.049 2.722 2.891 0.012 2002 365.3 246.2 0.688 1.652 0.982 2.126 2.401 0.012 2003 146.1 214.8 0.138 1.727 1.039 0.427 2.738 0.010 Total: 1085.7 1045.2 2.559 6.880 4.004 6.348 11.462 0.060 [a] Simulated with the modifications made for shallow water-table and flatwoods conditions as described in Chapter 6. [b] Simulated using original, unmodified equati ons as adapted from GLEAMS and described in Chapter 5.

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128 Table 7-9. Observed and simulated average N and P concentrations in runoff for winter pastures Pasture N Concentration (mg/L) P Concentration (mg/L) and Year Obs. Sim.[a] Sim.[b] Obs. Sim.[a] Sim.[b] WP7 1998 0.23 0.22 0.017 0.06 0.14 0.020 1999 0.22 0.23 0.0003 0.12 0.12 0.005 2000 0.66 0.23 0.006 0.41 0.11 0.005 2001 0.25 0.21 0.0003 0.25 0.07 0.004 2002 0.20 0.21 0.005 0.14 0.07 0.004 2003 0.15 0.23 0.003 0.05 0.08 0.004 Mean: 0.29 0.22 0.005 0.17 0.10 0.007 WP6 1998 0.16 0.22 0.019 0.07 0.14 0.021 1999 0.20 0.22 0.0005 0.07 0.14 0.004 2000 2.43 0.21 0.019 2.28 0.11 0.004 2001 0.28 0.21 0.0004 0.23 0.08 0.004 2002 0.20 0.22 0.012 0.16 0.09 0.004 2003 0.12 0.24 0.012 0.09 0.11 0.003 Mean: 0.56 0.22 0.011 0.48 0.11 0.007 WP5 1998 0.15 0.26 0.042 0.07 0.18 0.017 1999 0.27 0.23 0.004 0.09 0.17 0.007 2000 1.8 0.21 0.028 0.33 0.15 0.005 2001 0.24 0.22 0.007 0.18 0.12 0.005 2002 0.22 0.26 0.036 0.20 0.14 0.004 2003 0.18 0.28 0.087 0.17 0.18 0.004 Mean: 0.48 0.25 0.033 0.17 0.15 0.007 [a] Simulated with the modifications made for shallow water-table and flatwoods conditions as described in Chapter 6. [b] Simulated using original, unmodified equati ons as adapted from GLEAMS and described in Chapter 5.

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129 Table 7-10. Observed and simulated aver age N and P concentrations in runoff for summer pastures Pasture N Concentration (mg/L) P Concentration (mg/L) and Year Obs. Sim.[a] Sim.[b] Obs. Sim.[a] Sim.[b] SP1 1998 0.12 0.16 0.068 0.64 0.68 0.021 1999 0.38 0.15 0.001 0.59 0.61 0.008 2000 0.0 0.16 0.074 0.0 0.53 0.023 2001 1.14 0.16 0.003 1.55 0.48 0.006 2002 0.22 0.16 0.0007 0.87 0.37 0.004 2003 0.17 0.16 0.004 0.85 0.50 0.004 Mean: 0.34 0.16 0.025 0.75 0.53 0.011 SP4 1998 0.19 0.20 0.023 0.68 0.75 0.011 1999 0.25 0.41 0.14 0.57 1.11 0.006 2000 0.64 0.33 0.13 0.15 0.66 0.010 2001 0.35 0.41 0.12 1.63 0.88 0.005 2002 0.24 0.38 0.081 1.09 0.72 0.003 2003 0.10 0.47 0.11 0.58 0.92 0.004 Mean: 0.30 0.37 0.10 0.78 0.84 0.007 SP3 1998 0.06 0.25 0.049 0.41 0.64 0.009 1999 0.48 0.73 0.50 0.49 1.48 0.006 2000 0.0 0.56 0.20 0.0 0.68 0.005 2001 0.36 0.70 0.42 0.82 1.17 0.005 2002 0.19 0.67 0.40 0.58 0.98 0.005 2003 0.09 0.80 0.48 0.29 1.27 0.005 Mean: 0.20 0.62 0.34 0.43 1.04 0.006 [a] Simulated with the modifications made for shallow water-table and flatwoods conditions as described in Chapter 6. [b] Simulated using original, unmodified equati ons as adapted from GLEAMS and described in Chapter 5.

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130 Table 7-11. Mean absolute error (MAE), root mean square error (RMSE) and coefficient of efficiency (E) for annual runoff, N and P loads, and average N and P concentrations for all pastures Pasture and Parameter MAE RMSE E (-) WP7 Runoff (mm) 52.9 65.7 0.966 N Load (kg/ha) 0.138 0.159 0.905 P Load (kg/ha) 0.182 0.277 0.235 N Conc. (mg/L) 0.099 0.183 -0.288 P Conc. (mg/L) 0.111 0.149 -1.054 WP6 Runoff (mm) 81.2 103.1 0.722 N Load (kg/ha) 0.113 0.148 0.860 P Load (kg/ha) 0.127 0.202 0.326 N Conc. (mg/L) 0.416 0.906 -0.404 P Conc. (mg/L) 0.426 0.888 -0.490 WP5 Runoff (mm) 73.8 87.7 0.905 N Load (kg/ha) 0.146 0.175 0.813 P Load (kg/ha) 0.164 0.200 0.857 N Conc. (mg/L) 0.316 0.651 -0.745 P Conc. (mg/L) 0.086 0.100 -0.899 SP1 Runoff (mm) 48.4 59.9 0.919 N Load (kg/ha) 0.596 1.195 -0.258 P Load (kg/ha) 1.092 1.620 -0.231 N Conc. (mg/L) 0.245 0.416 -0.510 P Conc. (mg/L) 0.415 0.546 -0.995 SP4 Runoff (mm) 28.2 33.4 0.987 N Load (kg/ha) 0.259 0.367 -0.710 P Load (kg/ha) 0.978 1.195 0.649 N Conc. (mg/L) 0.176 0.218 -1.65 P Conc. (mg/L) 0.431 0.479 -0.098 SP3 Runoff (mm) 68.3 74.5 0.887 N Load (kg/ha) 0.720 0.850 -17.3 P Load (kg/ha) 0.852 1.204 -1.12 N Conc. (mg/L) 0.424 0.461 -52.3 P Conc. (mg/L) 0.603 0.675 -49.8

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131 Table 7-12. Mean absolute error (MAE), root mean square error (RMSE) and coefficient of efficiency (E) for annual N and P lo ads and average N and P concentrations for all pastures using original, unm odified ACRU2000 N and P algorithms Pasture and Parameter MAE RMSE E (-) WP7 N Load (kg/ha) 0.472 0.534 -18.0 P Load (kg/ha) 0.208 0.268 -5.07 N Conc. (mg/L) 0.288 0.344 -10.4 P Conc. (mg/L) 0.148 0.200 -4.28 WP6 N Load (kg/ha) 0.347 0.387 -19.9 P Load (kg/ha) 0.221 0.266 -9.39 N Conc. (mg/L) 0.608 1.08 -1.09 P Conc. (mg/L) 0.525 1.02 -0.868 WP5 N Load (kg/ha) 0.443 0.466 -15.8 P Load (kg/ha) 0.367 0.490 -4.05 N Conc. (mg/L) 0.485 0.809 -1.55 P Conc. (mg/L) 0.164 0.190 -16.28 SP1 N Load (kg/ha) 0.273 0.364 -4.56 P Load (kg/ha) 1.04 1.41 -3.65 N Conc. (mg/L) 0.118 0.213 -7.47 P Conc. (mg/L) 0.587 0.661 -18.6 SP4 N Load (kg/ha) 0.164 0.193 0.499 P Load (kg/ha) 1.18 1.59 -3.92 N Conc. (mg/L) 0.190 0.254 -2.54 P Conc. (mg/L) 0.605 0.677 -24.7 SP3 N Load (kg/ha) 0.326 0.446 -7.80 P Load (kg/ha) 0.717 1.02 -2.84 N Conc. (mg/L) 0.166 0.217 -1.67 P Conc. (mg/L) 0.351 0.403 -15.0

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132 Table 7-13. Hydrologic input parameters included in the sensitivity analysis Parameter Unit Base Value Description Kc 0.875 Crop coefficient L m 0.8 Maximum depth of roots C -1 Root distribution shape parameter h2 cm 20 Transpiration reduction due to O2 deficiency h3 cm 10000 Transpiration reduction due to water deficiency zDep mm 10 Depression storage I mm 1 Interception capacity 1/day 150 Runoff resistance 1.67 Runoff exponent dwt m 1.9 Initial depth to water-table s: Saturated water content cm3/cm3 0.45 E cm3/cm3 0.35 Btg cm3/cm3 0.42 Cg cm3/cm3 0.36 : 1/cm 0.0158 Soil moisture shape parameter of van Genuchten (1980) E 1/cm 0.0150 Btg 1/cm 0.0055 Cg 1/cm 0.0114 n: 3.52 Soil moisture shape parameter of van Genuchten (1980) E 3.13 Btg 1.26 Cg 3.38 Ks,H: Horizontal saturated hydraulic conductivity cm/h 25.35 E cm/h 8.76 Btg cm/h 0.76 Cg cm/h 4.31 Ks,V: cm/h 13.57 E cm/h 11.7 Vertical saturated hydraulic conductivity used in the upward flux relationship of Anat et al. (1965) Btg cm/h 5.58 hb: cm 46.8 Bubbling pressure head used in the upward flux relationship of Anat et al. (1965) E cm 40.1 Btg cm 23.0 : 12.2 Exponent used in the upward flux relationship of Anat et al. (1965) E 8.80 Btg 4.98

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133 Table 7-14. Nutrient input parameters included in the sensitivity analysis Parameter Unit Base Value Description BSAT % 14.0 Base saturation CL % 1.2 Clay content OM % 15.8 Organic matter content pH 4.52 pH App kg/ha/day 1.81 Manure application rate EDI cm 4.0 Soil depth of interaction with runoff TN kg/ha 875.0 Total nitrogen Ns kg/ha 736.3 Stable organic nitrogen Na kg/ha 131.3 Active organic nitrogen NNH4 kg/ha 2.5 Initial ammonium content NNO3 kg/ha 12.5 Initial nitrate content Nrain mg/L 0.2 Concentration of N in rain Nstream mg/L 0.4 Concentration of N in stream NH4 0.5 Ammonium extraction coefficient NO3 0.5 Nitrate extraction coefficient d,NH4L/kg Ammonium partitioning coefficient A 1.44 E 1.51 Btg 2.63 Cg 1.71 TP kg/ha 248.1 Total phosphorus Ps kg/ha 89.0 Stable phosphorus Pa kg/ha 17.4 Active phosphorus Pl kg/ha 17.3 Initial labile phosphorus Prain mg/L 0.03 Concentration of P in rain Pstream mg/L 0.1 Concentration of P in stream P 0.5 Phosphorus extraction coefficient PSP 0.05 Phosphorus availability index Kd,P: L/kg 0.80 Labile phosphorus partitioning coefficient E 4.24 Btg 138.8 Cg 4.24

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134Table 7-15. Sensitivity of phosphorus and nitrogen loads to hydr ologic parameters (reported as percent difference of base simu lation result). Values in left-hand column are percent changes in in put parameter values. % Change in Parameter Kc s zDep Ks,H h2 hb n dWT I Ks,V L c h3 P load +25% -20 -4 -7 -13 -3 -2 2 2 0 -2 1 -1 0 0 0 +50% -50 -6 -15 -13 -4 -3 3 2 1 -1 0 0 0 0 0 +75% -8 -13 -6 -3 7 3 0 0 0 0 0 0 +100% -11 -13 -8 -5 8 4 0 -1 0 1 0 0 -25% 20 6 10 22 2 2 -2 -1 0 2 0 1 0 0 0 0 0 -50% 39 18 28 7 1 -7 -1 -1 6 0 0 0 0 0 0 0 -75% 40 10 -1 -11 -2 -3 1 0 0 0 0 0 -100% 10 -6 -4 3 0 0 N load +25% -23 -5 -13 -8 -8 2 -6 3 0 0 -1 0 0 0 0 +50% -49 -8 -23 -8 -9 2 -3 4 -4 1 0 0 0 0 0 +75% -12 -8 -13 2 -1 5 0 2 -1 -1 0 0 +100% -13 -8 -15 4 -2 7 0 1 0 -1 0 0 -25% 28 8 20 10 5 -4 -2 -2 0 1 0 0 0 0 0 0 0 -50% 115 35 51 6 -11 -3 -4 -2 6 0 0 0 0 0 0 0 -75% 139 18 -15 -7 -5 -5 4 0 1 -1 0 0 -100% 21 -17 -5 6 0 0

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135 Table 7-16. Sensitivity of phosphorus and ni trogen to soil parameters and manure application rate (reported as percent difference of base simulation result) % Change in Parameter App EDI pH BSAT CL OM P load +25% 11 8 0 0 0 0 +50% 23 16 56 0 0 0 +75% 34 24 96 0 0 0 +100% 45 32 0 0 0 -25% -11 -10 0 0 0 0 -50% -23 -18 0 0 0 0 -75% -34 -28 0 0 0 0 -100% -45 0 0 0 N load +25% 17 -6 0 0 0 0 +50% 36 -10 0 0 0 0 +75% 50 -12 0 0 0 0 +100% 65 -15 0 0 0 -25% -15 9 0 0 0 0 -50% -30 18 0 0 0 0 -75% -44 34 0 0 0 0 -100% -64 0 0 0 Table 7-17. Sensitivity of nitrogen loads to nitrogen parameters (reported as percent difference of base simulation result) % Change in Parameter Nrain TN Na NO3 Kd,NH4 Ns NNH4 NNO3 streamNH4 N load +25% 8 -2 -2 1 0 0 0 0 0 0 +50% 16 -4 -4 1 0 0 0 0 0 0 +75% 24 -6 -6 2 -1 0 0 0 0 0 +100% 32 -8 -8 2 -2 0 0 0 0 0 -25% -8 3 3 -1 0 0 0 0 0 0 -50% -16 6 6 -3 0 0 0 0 0 0 -75% -24 10 10 -6 1 0 0 0 0 0 -100% -32 15 15 2 0 0 0 0

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136 Table 7-18. Sensitivity of phosphorus loads to phosphorus parameters (reported as percent difference of base simulation result) % Change in Parameter TP PSP Ps Pa Pl Prain d,P PPstream P load +25% 11 11 7 2 2 2 -1 1 0 +50% 22 21 14 4 5 3 -3 2 0 +75% 33 30 21 6 7 5 -5 3 0 +100% 44 38 28 9 9 7 -6 4 0 -25% -11 -12 -7 -2 -2 -2 3 -2 0 -50% -22 -25 -14 -4 -5 -3 6 -6 0 -75% -33 -40 -21 -7 -7 -5 9 -16 0 -100% -39 -29 -9 -9 -7 7 0

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137 Figure 7-1. Location of M acArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch Figure 7-2. Semi-improved wi nter pasture array at Mac Arthur Agro-Ecology Research Center at Buck Island Ranch. S howing location of weather stations, groundwater wells, and flumes. Also s hown are pasture ID and cow-calf pairs stocked in each pasture.

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138 Figure 7-3. Improved summer pasture arra y at MacArthur Agro -Ecology Research Center at Buck Island Ranch. S howing location of weather stations, groundwater wells, and flumes. Also s hown are pasture ID and cow-calf pairs stocked in each pasture

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139 6.8 7.3 7.8 8.3 8.8 1/1/001/1/011/1/021/1/031/1/04Groundwater Level (m) 4-inch Well 2-inch Well Figure 7-4. Groundwater levels at the 4-inch well (near the flume) and the 2-inch well (center of pasture) in winter pastur e 6 (WP6). Ground surface elevations at the 4-inch and 2-inch wells are 8. 69 and 8.66 meters above sea level, respectively. 6.4 6.8 7.2 7.6 8 8.4 1/1/001/1/011/1/021/1/031/1/04Groundwater Level (m) 4-inch Well 2-inch Well Figure 7-5. Groundwater levels at the 4-inch well (near the flume) and the 2-inch well (center of pasture) in summer pasture 1 (SP1). Ground surface elevations at the 4-inch and 2-inch wells are 8. 14 and 8.42 meters above sea level, respectively.

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140 6.6 6.8 7 7.2 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 1/1/001/1/011/1/021/1/031/1/04Groundwater Level (m) Canal Stage Winter 5 Winter 6 Winter 7 Figure 7-6. Groundwater levels from the three winter past ures compared to the canal stage as measured at the S70 spillway Groundwater levels are from the 4inch well in Winter Pasture 6 (WP6) a nd the 2-inch well in Winter Pasture 5 (WP5) and Winter Pasture 7 (WP7). 6.6 6.8 7 7.2 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 1/1/001/1/011/1/021/1/031/1/04Groundwater Level (m) Canal Stage Summer 1 Summer 3 Summer 4 Figure 7-7. Groundwater levels from the th ree summer pastures compared to the canal stage as measured at the S70 spillway Groundwater levels are from the 4inch well in Summer Pasture 1 (SP1) a nd the 2-inch well in Summer Pasture 3 (SP3) and Summer Pasture 4 (SP4).

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141 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-8. Winter Pasture 6 (WP6) obser ved and simulated depth to water-table 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-9. Winter Pasture 7 (WP7) obser ved and simulated depth to water-table

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142 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-10. Winter Pastur e 5 (WP5) observed and simulated depth to water-table 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-11. Summer Pastur e 1 (SP1) observed and simula ted depth to water-table

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143 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-12. Summer Pastur e 4 (SP4) observed and simula ted depth to water-table 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1/1/001/1/011/1/021/1/031/1/04Depth to Water Table (m) Observed Simulated Figure 7-13. Summer Pastur e 3 (SP3) observed and simula ted depth to water-table

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144 -5 0 5 10 15 20 25 30 35 40 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/033/1/035/1/037/1/039/1/0311/1/031/1/04 0 50 100 150 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-14. Winter Pasture 6 (WP6 ) observed and simulated daily runoff

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145 -5 0 5 10 15 20 25 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 40 80 120 160 200 -5 0 5 10 15 20 25 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 40 80 120 160 200 -5 0 5 10 15 20 251/1/033/1/035/1/037/1/039/1/0311/1/031/1/040 40 80 120 160 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-15. Winter Pasture 7 (WP7 ) observed and simulated daily runoff

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146 -5 0 5 10 15 20 25 30 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 50 100 150 200 -5 0 5 10 15 20 25 30 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 50 100 150 200 -5 0 5 10 15 20 25 30 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 50 100 150 200 -5 0 5 10 15 20 25 30 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 50 100 150 200 -5 0 5 10 15 20 25 30 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 50 100 150 200 -5 0 5 10 15 20 25 30 1/1/033/1/035/1/037/1/039/1/0311/1/031/1/04 0 50 100 150 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-16. Winter Pasture 5 (WP5 ) observed and simulated daily runoff

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147 -5 0 5 10 15 20 25 30 35 40 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 40 1/1/033/1/035/1/037/1/039/1/0311/1/031/1/04 0 50 100 150 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-17. Summer Pa sture 1 (SP1) observed a nd simulated daily runoff

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148 -5 0 5 10 15 20 25 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 50 100 150 200 -5 0 5 10 15 20 25 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 50 100 150 200 -5 0 5 10 15 20 25 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 50 100 150 200 -5 0 5 10 15 20 25 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 50 100 150 200 -5 0 5 10 15 20 25 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 50 100 150 200 -5 0 5 10 15 20 25 1/1/033/1/035/1/037/1/039/1/0311/1/031/1/04 0 50 100 150 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-18. Summer Pa sture 4 (SP4) observed a nd simulated daily runoff

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149 -5 0 5 10 15 20 25 30 35 1/1/983/1/985/1/987/1/989/1/9811/1/981/1/99 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 1/1/993/1/995/1/997/1/999/1/9911/1/991/1/00 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 1/1/003/1/005/1/007/1/009/1/0011/1/001/1/01 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 1/1/013/1/015/1/017/1/019/1/0111/1/011/1/02 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 1/1/023/1/025/1/027/1/029/1/0211/1/021/1/03 0 50 100 150 200 -5 0 5 10 15 20 25 30 35 1/1/033/1/035/1/037/1/039/1/0311/1/031/1/04 0 50 100 150 200 Rain Observed SimulatedRunoff (mm) Rain (mm) Figure 7-19. Summer Pa sture 3 (SP3) observed a nd simulated daily runoff

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150 0 50 100 150 200 250 300 350 400 450 500 1/1/981/1/991/1/001/1/011/1/021/1/031/1/04Runoff (mm) Observed Simulated Figure 7-20. Winter Pasture 6 (WP6) cumulative annual runoff 0 50 100 150 200 250 300 350 400 450 500 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 7-21. Winter Pasture 7 (WP7) cumulative annual runoff

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151 0 50 100 150 200 250 300 350 400 450 500 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 7-22. Winter Pasture 5 (WP5) cumulative annual runoff 0 50 100 150 200 250 300 350 400 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 7-23. Summer Pasture 1 (SP1) cumulative annual runoff

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152 0 50 100 150 200 250 300 350 400 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 7-24. Summer Pasture 4 (SP4) cumulative annual runoff 0 50 100 150 200 250 300 350 400 1/1/981/1/991/1/001/1/011/1/021/1/03Runoff (mm) Observed Simulated Figure 7-25. Summer Pasture 3 (SP3) cumulative annual runoff

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153 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-26. Winter Pasture 7 (WP7) cumulative annual N load 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-27. Winter Pasture 7 (WP7) cumulative annual P load

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154 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-28. Winter Pasture 6 (WP6) cumulative annual N load 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-29. Winter Pasture 6 (WP6) cumulative annual P load

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155 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-30. Winter Pasture 5 (WP5) cumulative annual N load 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-31. Winter Pasture 5 (WP5) cumulative annual P load

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156 0 0.5 1 1.5 2 2.5 3 3.5 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-32. Summer Pasture 1 (SP1) cumulative annual N load 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-33. Summer Pasture 1 (SP1) cumulative annual P load

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157 0 0.2 0.4 0.6 0.8 1 1.2 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-34. Summer Pasture 4 (SP4) cumulative annual N load 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-35. Summer Pasture 4 (SP4) cumulative annual P load

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158 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04N (kg/ha) Observed N Simulated N Figure 7-36. Summer Pasture 3 (SP3) cumulative annual N load 0 0.5 1 1.5 2 2.5 3 01/01/9801/01/9901/01/0001/01/0101/01/0201/01/0301/01/04P (kg/ha) Observed P Simulated P Figure 7-37. Summer Pasture 3 (SP3) cumulative annual P load

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159 -100 -50 0 50 100 150 -100-50050100 % Change in Parameter% Difference in N Load Kc qs b zdep Ks,H Kcs zdepKs,H Figure 7-38. Hydrologic parameters showing the greatest sensitivity on N loads in runoff -50 -25 0 25 50 -100-50050100 % Change in Parameter% Difference in P Load Kc qs b zdep Ks,H Kcs zdepKs,H Figure 7-39. Hydrologic parameters showing the greatest sensitivity on P loads in runoff

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160 -80 -60 -40 -20 0 20 40 60 80 -100-50050100 % Change in Parameter% Difference in N Load App EDI Nrain TN Figure 7-40. Nutrient parameters showing th e greatest sensitivity on N loads in runoff -50 -25 0 25 50 75 100 -100-50050100 % Change in Parameter% Difference in P Load pH App EDI TP Ps PSP Figure 7-41. Nutrient parameters showing th e greatest sensitivity on P loads in runoff

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161 CHAPTER 8 SUMMARY AND CONCLUSIONS In this study an object-or iented hydrologic and water quality module within the framework of the ACRU2000 mode l was proposed, developed, and tested for high watertable regions such as the flat woods of Florida. The hydrologic model uses approximate methods appropriate for sandy, yet poorly dr ained soils. The water quality component of the model, a prior model addition of the nitrogen and phosphorus algorithms of the Groundwater Loading Effects of Agricultura l Management Systems model (Campbell et al. 2001), was adapted for use in poorly drained conditions. The proposed approximate, daily time-ste p hydrologic module of the model was designed by assuming a hydrostatic moisture distributi on within the soil pr ofile using three different soil moisture retention models, approximating the upward flow of water in response to evapotranspiration as a steady-state process, adding the standardized Penman-Moneith equation of the Food and Agricultural Organization (Allen et al. 1998) to estimate reference potential evapotranspiration, estimating incoming solar radiation usi ng extraterrestrial radiation and the difference between maximum and minimum air temperat ures when observations are absent, representing plant water stress as a function of soil water pressure head, including a closed-form r oot distribution function, including the interaction of groundwater with adjacent water bodies or boundary conditions, and assuming that runoff occurs via saturation-excess only.

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162 The accuracy of the proposed approximate hydrologic model was demonstrated by applying the model to three se ts of experimental observati ons. The model was shown to adequately predict water-table depths, soil moisture contents, evapotranspiration, and runoff volumes with minimal field-collected da ta and when applied at a relatively coarse resolution. The model compared well to a more detailed, one-dimensional, finitedifference model in predicting water table depths, soil moisture contents, and daily evapotranspiration from a wet prairie in north-central Flor ida. The model was also compared to the Field Hydrologic And Nutrient Transport Model (FHANTM) and provided similar results. The original, unm odified field-scale hydr ologic module of the ACRU2000 model was shown to greatly overpredict runoff unless parameterized with unrealistic values. Given the approximations made by the model, the model is only suitable for use in shallow water-table regions with highly permeable soils where runoff can be assumed to occur primarily by saturation-excess. The nitrogen and phosphorus module of the model was modified for shallow watertable environments w ith flatwoods soils by Relating the prediction of phosphorus part itioning coefficients to aluminum, magnesium, organic carbon, and clay contents in the soil, defining the maximum rate of reaction of mineralization, immobilization, and nitrification processes as aff ected by soil moisture to occur over a range of moisture contents, allowing mineralization and immobilization processes to occur under relatively wet conditions, but at a depressed rate, and representing the movement of mobile soil nutrients with runoff water as a partial mixing process, accounting for concentration gradients, within a defined depth of soil. The ability of the model to predict N and P loads from flatwoods fields was evaluated by applying the mode l to six experimental cattle pastures, two control (no

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163 cattle), two low-stocked pastur es, and two high-stocked pastur es. One set of differentlystocked pastures was from a location with no fertilization history and the other set from a location with a history of fertilization which is reflected by relatively higher soil P contents. Applying the mode l with minimal field-specific data and with minimal calibration indicated that the model was overa ll a relatively poor pr edictor of N and P loads from these sites and provided insight on potential improvements to the models performance. However the model was in be tter agreement with observations compared to the original, unmodified model. Future research needs and recommendations. The evaluation of the model proposed here indicated the need for site-specific data in or der to accurately predict both hydrologic and water quality outputs from ungauged sites. Evaluation of the model also showed possible inadequacies in the repres entation of nutrient cycling processes and potential inflexibilites in the model algorithms. Soil hydraulic parameters were shown to ha ve a large effect on the prediction of runoff and nutrient loads in the sensitivity an alyses performed. These results indicated that, while valuable, sources of soil hydrauli c information such as Sodek et al. (1990) may only provide a first approximation of the soil properties found at a particular site. In future model development for the Florida flatwoods it is recommended that greater resources be devoted to colle cting these parameters. In lieu of detailed site-specific soil da ta, and recognizing the difficulty in defining effective parameters at the scale of mode l application compared to the scale of measurement methods, it is recommended th at the number of soil hydraulic parameters required by the model be reduced. In such an approximate, daily time-step model, soils

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164 could likely be just as well represented by a single set of soil moisture parameters. Horizontal hydraulic conductiviti es could likely be just as we ll represented using a single average value or as linearly or exponentia lly decreasing with depth. Upward flux relationships could be simplified to a single empirical, ca libratable exponential relationship. These simplifications would be commensurate with the level of approximations made in the model, particularly when these parameters are to be calibrated. Predictions of nitrogen and phosphorus load s in runoff indicate the need for more detailed, site-specific soil information. The site-specific needs include data of soil nitrogen, and the properties cons idered to have the greatest effect on P retention, namely soil clay, Al, and Fe contents. The models performance also indicated the need for a better representation of organi c nutrient forms that should include organic soil accretion and mineralization, and associated nutrient retention and release, in response to hydrologic and environmental conditions. The model used hard-coded, constant optimal rates of reaction and coefficients of proportiona lity that determine the equilibrium states between nutrient pools. Such constants should be user-defin ed values where the most up to date and/or siteor region-sp ecific data can be used. In the absence of such data these values should be calibratable in order to provide a best-fit to observations.

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165 APPENDIX A HYDROLOGIC PROCESS AND DATA OBJECTS AND DESCRIPTIONS Process Objects PAcruHWTRitchieEvapoTranspiration PDeepSeepage PFAO56PenmanMonteithDailyEvap PFindNewWaterTableDepth PHWTCropCoefTrans PHWTPlantWaterStress PHWTRitchieSoilWaterEvap PHWTSimpleEvapoTranspiration PInitialiseSoilUFOptionHWT PMaximumUpwardFlux PPondedWaterEvaporation PRootDistributionFunction PSimpleRunoff PSoilStorageAvailable PSoilWaterCharacteristic PStorageLimitedInfiltration PStorageLimitedRedistribution PSuperSimpleEvapoTranspiration PUpwardFlux Data Objects DActualUpwardFlux DActualVaporPressureOption DAirVolume DDeepSeepage DDeepSeepageHeadBoundary DDeepSeepageOption DDewPointTemperature DDrainedtoEquilMoistureContent DInstrumentPsychrometricCoefficient DMaximumUpwardFlux DMaxRelativeHumidity DMinRelativeHumidity DNetRainfall

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166 DNumberOfSoilHorizons DOldAirVolume DPoreSizeDistributionIndex DPressureHead1 DPressureHead2 DPressureHead3 DPressureHead4 DRDFRootDepth DRDFCoefficient DResidualMoistureContent DRestrictiveLayerHydraulicConductivity DRestrictiveLayerThickness DRootZoneDeficit DSoilBubblingPressureHead DSoilLayerLocation DSoilStorageAvailable DSoilWaterCharacteristicOption DSoilWaterEvapDepth DSWAPRunoffExponent DSWAPRunoffResistance DUpFluxBubblingPressureHead DUpFluxExponent DUpFluxSaturatedHydraulicConductivity DUFHighWaterTableOption DUpwardFluxDistance DWaterTableDepth DWaterTableDepthvsAirVolume DWetBulbTemperature Description of Process Objects PAcruHWTRitchieEvapoTranspiration. This process calls the PHWTRitchieSoilWaterEvap and PHWTCropCoefTrans which determine soil water evaporation and transpiration, re spectively using the method of Ritchie (1972). PDeepSeepage. This process determines and transfers deep seepage between the soil profile and the groundwater store underneath a restri ctive layer. PFAO56PenmanMonteithDailyEvap. This process calculates reference potential transpiration according to the FAO Penm an-Monteith equation described in Irrigation and Drainage paper # 56. Add itional features include multiple methods of determining actual vapor pressure and a new method for estimating incoming solar radiation as outlined by Hargreaves and Samani (1982) and Samani (2000).

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167 PFindNewWaterTableDepth. This process determines a new water table depth and new drained to equilibrium water c ontents for the new water table depth. PHWTCropCoefTrans. This process determines transpiration when Ritchies method is chosen for simulating ET. PHWTPlantWaterStress. This process determines a plant water stress factor that causes a decrease in transpiration or ev apotranspiration below the maximum rate (EVTR = 1 or 2) using the reduction relati onship of Feddes et al (1978). This is not used when EVTR = 3. PHWTRitchieSoilWaterEvap. This process determines soil water evaporation when Ritchies method is used. PHWTSimpleEvapoTranspiration. This process determines evapotranspiration together, and accounts for plant water stress. PInitialiseSoilUFOptionHWT. This process initializes several properties of the soil. A data object is set for each soil la yer that contains the soil layers distance from the ground surface, called DSoilLayerLo cation, so it does not have to be calculated each time it is required. This process also initializes the relationship between water table depth and air volume (or void volume) of the soil, assuming hydrostatic conditions. This re lationship, in the form of an array data object called DWaterTablevsAirVolume, is used later to calculate changes in the water table depth. This process also sets the initial wa ter content of all of the soil layers for a given water table depth assu ming hydrostatic conditions. PMaximumUpwardFlux. This process determines the maximum possible steadystate upward flux from the water tabl e for a given water table depth. PPondedWaterEvaporation. This process determines evaporation from water ponded on the land surface. PRootDistributionFunction. This process determines the fraction of plant roots in each soil layer using the root density f unction proposed by Hoogland et al. (1981). This process is used when UFHWT is on and EVTR = 1 or 2. When this process is used the fraction of plant roots in each soil horizon does not have to be entered manually. PSimpleRunoff. This process determines and tran sfers runoff from a landsegment using a simple equation as is done in the SWAP and FHANTM models. This process is only used when the mode l is run in lumped mode (only one landsegment). PSoilStorageAvailable. This process determines the storage available, or air volume, for the entire soil profile. This process determines the actual air volume,

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168 which includes any depleted root zone, not the idealized air volume assumed when imposing hydrostatic conditions. PSoilWaterCharacteristic. This process calculates th e soil moisture of a soil layer for a given distance to the water table. This process determines the water content or drained to equilibrium moisture content. This process does not set the value of storage to the soil layer, it just determines what the upper limit is. PStorageLimitedInfiltration. This process determines infiltration for a storageonly limited soil profile. Infiltration is considered to never be rate-limited. PStorageLimitedRedistribution. This process redistributes water vertically to account for the changes in water contents a nd water table depths that have occurred on a given day. PSuperSimpleEvapoTranspiration. This process determines evapotranspiration in a very simple manner. ET proceeds at a rate equal to the maximum until the wiling point is reached. Water is removed from the ro ot zone soil layers from top down. No plant water stress is accounted for. PUpwardFlux. This process determines and tran sfers the amount of upward flux from the water table to depleted soil layers. Description of Data Objects DActualUpwardFlux. This DDailyData data object holds the value of the actual upward flux that occurred for a given day. DActualVaporPressureOption. This DInteger holds the users option for determining the actual vapor pressure in the FAO Penman-Monteith equation in PFAO56PenmanMonteithDailyEvap. DAirVolume. This DDouble holds the current value of the air volume, or void volume, of the soil. Every process that will raise or drop th e water table depth adjusts this value accordingly. At the e nd of the day this value determines the new water table depth. DDeepSeepage. This DDailyData data object hold s the amount of deep seepage that has occurred on a given day. DDeepSeepageHeadBoundary. This DDailyData data obj ect holds the head of the groundwater below the restrictive layer. DDeepSeepageOption. This DInteger data object determines whether deep seepage is simulated. If it is not then the restrictive layer is considered to be completely impermeable.

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169 DDewPointTemperature. This DDailyData data object holds the dew point temperature for a given day. DDrainedtoEquilMoistureContent. This DDouble data object holds the upper limit water content in a given soil layer. This value is a function of only the water table depth since hydrostatic conditions are assumed. DInstrumentPsychrometricCoefficient. This DDouble data object holds the psychometric constant of the instrument (psychronometer) that records the wet bulb temperature. DMaximumUpwardFlux. This data object holds the maximum upward flux that is calculated for a gi ven water table depth. DMaxRelativeHumidity. This DDailyData data object holds the maximum relative humidity for a given day. DMinRelativeHumidity. This DDailyData data object holds the minimum relative humidity for a given day. DNetRainfall. This DDailyData object holds the amount of rainfall minus loss to interception and belongs to the climate. DNumberOfSoilHorizons. This DInteger data object holds the number of soil horizons that are to be simulated. DOldAirVolume. This DDouble data object holds th e air volume of the previous day. DPoreSizeDistributionIndex. This DDouble data object holds the pore size distribution parameter, or its equivalent, that is used in defining the soil water characteristic. DPressureHead1. This DDouble data object holds th e pressure head (cm) below which transpiration (EVTR = 2) or lump ed evapotranspiration (EVTR = 1) is reduced to zero due to water excess. DPressureHead2. This DDouble data object holds th e pressure head (cm) below which transpiration (EVTR = 2) or lumped evapotranspiration (EVTR = 1) begins to be reduced below the maximu m rate due to water excess. DPressureHead3. This DDouble data object holds th e pressure head (cm) above which transpiration (EVTR = 2) or lumped evapotranspiration (EVTR = 1) begins to be reduced below the maximum rate due to water limitation. DPressureHead4. This DDouble data object holds th e pressure head (cm) above which transpiration (EVTR = 2) or lump ed evapotranspiration (EVTR = 1) is

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170 reduced to zero due to water limitation. This is the pressure head at the wilting point. DRDFRootDepth. This DDailyData object holds the root depth that is used in the root distribution function propos ed by Hoogland et al. (1981). DRDFCoefficient. This DDailyData object holds the shape parameter that is used in the root distribution function proposed by Hoogland et al. (1981). DResidualMoistureContent. This DDouble data objec t holds the residual moisture content that is used in de fining the soil water characteristic. DRestrictiveLayerHydra ulicConductivity. This DDouble data object holds the vertical saturated hyd raulic conductivity of the rest rictive layer below the soil. DRestrictiveLayerThickness. This DDouble data object holds the thickness of the restrictive layer below the soil. DRootZoneDeficit. This DDouble data object holds th e amount that the root zone of the soil profile is depleted below drained to equilibrium. DSoilBubblingPressureHead. This DDouble data object holds the bubbling pressure head parameter, or its equivalent that is used in defining the soil water characteristic. DSoilLayerLocation. This DDouble data object holds the soil layer location, the distance from the ground surface to the bottom of the soil layer. DSoilStorageAvailable. This DDouble data object ho lds the value of the total available storage available within the soil pr ofile, including the root zone deficit. DSoilWaterCharacteristicOption. This DInteger data object holds the users choice of soil water characteristic to be used. DSoilWaterEvapDepth. This DDouble data object hol ds the maximum depth to which soil water evaporation can oc cur when using Ritchies method. DSWAPRunoffResistance. This DDouble data object ho lds the coefficient used in the SWAP runoff equation used in PSimpleRunoff process. DSWAPRunoffExponent. This DDouble data object holds the exponent used in the SWAP runoff equation used in PSimpleRunoff process. DUFBubblingPressureHead. This DDouble data object holds the bubbling pressure head used to calculate upward flux.

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171 DUFExponent. This DDouble data object holds th e exponent used to calculate upward flux. DUFSaturatedHydraulicConductivity. This DDouble data object holds the vertical saturated hydrau lic conductivity used to calculate upward flux. DUFHighWaterTableOption. This DInteger data object determines whether the UF High Water Table Option is used. DUpwardFluxDistance. This DDouble data object holds the distance between the water table depth and the bottom of the so il layer for which upward flux is to be calculated. DWaterTableDepth. This DDouble data object holds the depth of the water table from the ground surface. DWaterTableDepthvsAirVolume. This DDoubleArray hold s the relationship between the water table depth and air vol ume (void volume) of the soil profile assuming hydrostatic conditions. DWetBulbTemperature. This DDailyData data object holds the wet bulb temperature.

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172 APPENDIX B HYDROLOGIC PROCESS OBJECT UNIFIED MODELING LANGUAGE (UML) DIAGRAMS +evaporateWater() : void +flowWater() : void +runProcess() : void PEvapoTranpiration +initialise() : double +evaporateWater() : void -setRequiredData() : void PAcruHWTRitchieEvapoTranspiration Data Objects Required: CClimate: DPotentialEvaporation CSoil DPotEvapoTranspiration CSoil +initialise() : void +evaporateWater() : void -setRequiredData() : void PHWTRitchieSoilWaterEvap CClimate +transpireWater() : void +adjustTranspiration() : double -setRequiredData() : void PHWTCropCoefTrans Figure B-1. PAcruHWTRitchieE vapoTranspiration UML diagram

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173 +flowWater() : void +runProcess() : void PSubSurfaceFlow +initialise() : void +flowWater() : void -setRequiredData() : void PDeepSeepage Data Objects Required: CSoil: DAirVolume DWaterTableDepth CSoilLayer: DWaterFluxRecord CGroundwaterStore: DDeepSeepage DDeepSeepageHeadBoundary DRestrictiveLayer HydraulicConductivity DRestrictiveLayerThickness DWaterFluxRecord CGroundwaterStore CSoilLayer CSoil Figure B-2. PDeepSeepage UML diagram

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174 +getAirDensity() : double +getAirPressure() : double +getDayLengthFraction() : double +getDayLegthFraction() : double +getDegreesLattitude() : double +getDelta() : double +getEnergyBudgetWeightingFactor() : double +getEstimatedExtraterrestrialRadiation() : double +getEstimatedExtraterrestrialRadiation() : double +getGamma() : double +getJulianDay() : double +getJulianDay() : double +getLatentHeatOfVapourisation() : double +getMaxSunshineDuration() : double +getMaxSunshineDuration() : double +getRadiansLattitude() : double +getSaturatedVapourPressure() : double +getSunriseHourAngle() : double +getSunriseHourAngle() : double +getSunsDeclination() : double +getSunsDeclination() : double +getSunsRadiusVector() : double +getSunsRadiusVector() : double PEstimatedDailyAPanEvap +calculateDailyAPanEvap() : void #getActualVaporPressure() : double #getAirPressureFAO56() : double #getDeltaFAO56() : double #getEstimatedExtraterrestrialRadiationFAO56() : double #getEstimatedExtraterrestrialRadiationFAO56() : double #getGammaFAO56() : double #getMaxSunshineDurationFAO56() : double #getMaxSunshineDurationFAO56() : double #getNetLongWaveRadiationFAO56() : double #getNetShortWaveRadiationFAO56() : double #getRelDistanceEarthSunFAO56() : double #getRelDistanceEarthSunFAO56() : double #getSunsDeclinationFAO56() : double #getSunsDeclinationFAO56() : double #getSunsetHourAngleFAO56() : double #getSunsetHourAngleFAO56() : double -setRequiredData() : void PFAO56PenmanMonteithDailyEvap Data Objects Required: CCimate: DActualVapourPressureOption DARadiationConstant DBaseReferencePotentialEvap DBRadiationConstant DDewPointTemperature DInstrumentPsychrometricCoefficient DMaximumTemperature DMaxRelativeHumidity DMeanTemperature DMinimumTemperature DMinRelativeHumidity DRadiationFluxDensity DRelativeHumidity DSunshineDuration DSWRadiationFluxDensityOption DWetBulbTemperature DWindrun CSpatialUnit: DElevation CSpatialUnit CClimate Figure B-3. PFAO56PenmanMont eithDailyEvap UML diagram

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175 +flowWater() : void +runProcess() : void PSubSurfaceFlow +calcDrainedtoEquilMoistureContent() : void +calcWaterTableDepth() : void +runProcess() : void -setRequiredData() : void PFindNewWaterTableDepth Data Objects Required: CSoil: DAirVolume DOldAirVolume DWaterTableDepth DWaterTableDepthvsAirVolume CSoilLayer: DDepth DDrainedtoEquilMoistureContent DPorosity DSoilLayerLocation DWaterFluxRecord CSoilLayer +calcSMDeficit() : double +runProcess() : void -setRequiredData() : void PSoilStorageAvailable CSoil +calcSM() : double +brooksandCorey() : double +hutsonandCass() : double +vanGenuchten() : double +runProcess() : void -setRequiredData() : void PSoilWaterCharacteristic Figure B-4. PFindNewWate rTableDepth UML diagram

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176 +transpireWater() : void +flowWater() : void +runProcess() : void PTranpiration +transpireWater() : void #adjustTranspiration() : double -setRequiredData() : void PHWTCropCoefTrans Data Objects Required: CClimate: DPotentialEvaporation DGrossEvapoT ranspiration CEvaporationStore: DWaterFluxRecord CSoil DAirVolume DEvapoTranspiration DPotEvapoTranspiration DPotTranspiration DTranspiredWater DRootZoneDeficit DWaterTableDepth CSoilLayer DDepth DEvapoTranspiration DPorosity DPotTranspiration DRootFrac DSoilLayerLocation DTranspiredWater DWaterFluxRecord DWiltingPoint CVegetation DCropCoefficient DPlantStressIndicator DSoilStressFraction CLeafCanopy DLeafAreaIndex DLeafAreaIndexAvailability CEvaporationStore CSoil CLeafCanopy CClimate CSoilLayer +getPlantWaterStress() : double +runProcess() : void -setRequiredData() : void PHWTPlantWaterStress CVegetation Figure B-5. PH WTCropCoeffTrans UML diagram

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177 +initialise() : void +runProcess() : void PProcess +getPlantWaterStressFactor() : double +runProcess() : void -setRequiredData() : void PHWTPlantWaterStress Data Objects Required: CSoil: DSoilWaterCharacteristicOption DPressureHead1 DPressureHead2 DPressureHead3 DPressureHead4 CSoilLayer: DDepth DPoreSizeDistributionIndex DPorosity DResidualMoistureContent DSoilBubblingPressureHead DWaterFluxRecord CVegetation: DSoilStressFraction CSoilLayer CVegetation CSoil Figure B-6. PHWTPlantW aterStress UML diagram

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178 PSoilWaterEvaporation +initialise() : double +evaporateWater() : void -setRequiredData() : void PHWTRitchieSoilWaterEvap Data Objects Required: CClimate: DPotentialEvaporation DGrossEvapoTranspiration DReferencePotentialEvapMethod CEvaporationStore DWaterFluxRecord CLandSegment DPercentSurfaceCover DSurfaceInfiltration CLeafCanopy DLeafAreaIndex DLeafAreaIndexAvailability CSoil DAlphaSoil DEvapoTranspiration DPotEvapoTranspiration DPotSoilWaterEvap DSoilTexture DSoilWaterEvapDepth DSoilWaterEvaporation CSoilLayer DActualUpwardFlux DDepth DEvapoTranspiration DPotSoilWaterEvap DSoilWaterEvaporation DWaterFluxRecord DWiltingPoint CEvaporationStore CSoil CClimate CSoilLayer +evaporateWater() : void +flowWater() : void +runProcess() : void PEvaporation CLeafCanopy CLandSegment Figure B-7. PHWTRitchieSoilWaterEvap UML diagram

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179 +evaporateWater() : void +flowWater() : void +runProcess() : void PEvapoTranpiration +evaporateWater() : void #adjustEvapoTranspi ration() : double -setRequiredData() : void PHWTSimpleEvapoTranspiration Data Objects Required: CClimate: DPotentialEvaporation DGrossEvapoT ranspiration CEvaporationStore: DWaterFluxRecord CSoil DAirVolume DEvapoTranspiration DPotEvapoTranspiration DRootZoneDeficit DSoilWaterEvaporation DTranspiredWater DWaterTableDepth CSoilLayer DDepth DEvapoTranspiration DPorosity DPotEvapoTranspiration DRootFrac DSoilLayerLocation DSoilWaterEvaporation DTranspiredWater DWaterFluxRecord DWiltingPoint CVegetation DCropCoefficient DPlantStressIndicator DSoilStressFraction CEvaporationStore CSoil CVegetation CClimate CSoilLayer +getPlantWaterStress() : double +runProcess() : void -setRequiredData() : void PHWTPlantWaterStress Figure B-8. PHWTSimpleEva poTranspiration UML diagram

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180 +initialise() : void +runProcess() : void PProcess +initialise() : void +initialiseAirVolumevsWaterTableRelation() : void +initialiseSoilLay erLocation() : void +initialiseSoilMoistureContent() : void +runProcess() : void -setRequiredData() : void PInitialiseSoilUFOptionHWT Data Objects Required: CSoil: DAirVolume DWaterTableDepth DWaterTableDepthvsAirVolume CSoilLayer: DDepth DDrainedtoEquilMoistureContent DPorosity DSoilLayerLocation DWaterFluxRecord CSoil CSoilLayer +calcSMDeficit() : double +runProcess() : void -setRequiredData() : void PSoilStorageAvailable +calcSM() : double +brooksandCorey() : double +hutsonandCass() : double +vanGenuchten() : double +runProcess() : double -setRequiredData() : void PSoilWaterCharacteristic Figure B-9. PInitialiseSo ilUFOptionHWT UML diagram

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181 +flowWater() : void +runProcess() : void PSubSurfaceFlow +calcMaxUpwardFlux() : double +flowWater() : void -setRequiredData() : void PMaximumUpwardFlux Data Objects Required: CSoil: DMaximumUpwardFlux DUpwardFluxDistance DWaterTableDepth CSoilLayer: DUpFluxExponent DUpFluxBubblingPressureHead DUpFluxSaturatedHydraulicConductivity CSoil CSoilLayer Figure B-10. PMaximumUpwardFlux UML diagram

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182 +evaporateWater() : void +flowWater() : void +runProcess() : void PEvaporation +evaporateWater() : void -setRequiredData() : void PPondedWaterEvaporation Data Objects Required: CClimate: DPotentialEvaporation DGrossEvapoTranspiration CEvaporationStore: DWaterFluxRecord CLandSegment DWaterFluxRecord CClimate CEvaporationStore CLandSegment Figure B-11. PPondedWaterEvaporation UML diagram

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183 +initialise() : void +runProcess() : void PProcess +runProcess() : void +rootDistributionFunction() : void -setRequiredData() : void PRootDistributionFunction Data Objects Required: CSoil: DRDFRootDepth DRDFCoefficient CSoilLayer: DDepth DSoilLayerLocation DRootFrac CSoil CSoilLayer Figure B-12. PRootDistri butionFunction UML diagram

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184 +initialise() : void +runProcess() : void PProcess +calculateRunoff() : void +runProcess() : void -setRequiredData() : void PSimpleRunoff Data Objects Required: CLandSegment DArea DMaximumSurfaceDepressionStorage DQuickflowDepth DSWAPRunoffCoefficient DSWAPRunoffExponent DWaterFluxRecord CLandSegment Figure B-13. PSimpleRunoff UML diagram

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185 +initialise() : void +runProcess() : void PProcess +calcSMDeficit() : double +runProcess() : void -setRequiredData() : void PSoilStorageAvailable Data Objects Required: CSoil: DSoilStorageAvailable CSoilLayer: DDepth DPorosity DWaterFluxRecord CSoil CSoilLayer Figure B-14. PSoilStorageAvailable UML diagram

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186 +initialis e() : void +runProcess() : void PProcess +calcSM() : double +brooksandCorey() : double +hutsonandCass() : double +vanGenuchten() : double +runProcess() : void -setRequiredData() : void PSoilWaterCharacteristic Data Objects Required: CSoil: DSoilWaterCharacteristicOption CSoilLayer: DPoreSizeDistributionIndex DPorosity DSoilBubblingPressureHead DResidualMoistureContent CSoil CSoilLayer Figure B-15. PSoilWaterCharacteristic UML diagram

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187 +infiltrateWater() : void +flowWater() : void +runProcess() : void PInfiltration +infiltrateWater() : void +runProcess() : void -setRequiredData() : void PStorageLimtedInfiltration Data Objects Required: CLandSegment: DEffectiveRainfall DSurfaceInfiltration DWaterFluxRecord CSoil DAirVolume DRootZoneDeficit CSoilLayer DWaterFluxRecord CSoil CLandSegment CSoilLayer +calcSMDeficit() : double +runProcess() : void -setRequiredData() : void PSoilStorageAvailable Figure B-16. PStorageLimitedInfiltration UML diagram

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188 +flowWater() : void +runProcess() : void PSubSurfaceFlow +flowWater() : void +runProcess() : void -setRequiredData() : void PStorageLimtedRedistribution Data Objects Required: CSoilLayer DDepth DDrainedtoEquilMoistureContent DWaterFluxRecord CLandSegment DEffectiveRainfall DSurfaceInfiltration CSoilLayer CLandSegment Figure B-17. PStorageLimitedRedistribution UML diagram

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189 +evaporateWater() : void +flowWater() : void +runProcess() : void PEvapoTranpiration +evaporateWater() : void -setRequiredData() : void PSuperSimpleEvapoTranspiration Data Objects Required: CClimate: DPotentialEvaporation DGrossEvapoT ranspiration CEvaporationStore: DWaterFluxRecord CSoil DAirVolume DEvapoTranspiration DPotEvapoTranspiration DRootZoneDeficit DSoilWaterEvaporation DTranspiredWater DWaterTableDepth CSoilLayer DDepth DEvapoTranspiration DPotEvapoTranspiration DRootFrac DSoilWaterEvaporation DTranspiredWater DWaterFluxRecord DWiltingPoint CVegetation DCropCoefficient CEvaporationStore CSoil CVegetation CClimate CSoilLayer Figure B-18. PSuperSimpleEva poTranspiration UML diagram

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190 +flowWater() : void +runProcess() : void PSubSurfaceFlow +flowWater() : void -setRequiredData() : void PUpwardFlux Data Objects Required: CSoil: DActualUpwardFlux DAirVolume DMaximumUpwardFlux DRootZoneDeficit DUpwardFluxDistance DWaterTableDepth CSoilLayer: DActualUpwardFlux DDepth DDrainedtoEquilMoistureContent DSoilLayerLocation DWaterFluxRecord CSoil CSoilLayer +calcMaxUpwardFlux() : double +flowWater() : void -setRequiredData() : void PMaximumUpwardFlux Figure B-19. PUpwardFlux UML diagram

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191 APPENDIX C HYDROLOGIC MODEL INPUT/OUTPUT VARIABLE REFERENCE Input Variable Reference BCHB1: Shape parameter used in the soil water characteristic models. This is the bubbling pressure head, hb (cm), when the Brooks a nd Corey (1964) or Hutson and Cass (1987) models are used. This is 1/ when the van Genuchten (1980) model is used ( = 1/hb). Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. BCL1: Shape parameter used in the soil water characteristic models. This is the pore size distribution index, when the Brooks and Corey (1964) or Hutson and Cass (1987) models are used. This is n1 when the van Genuchten (1980) model is used (n = +1). Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. DEEPH: Deep seepage head boundary (m). Th is is the hydraulic head of the groundwater below the restrictive layer and is measured downwards from the ground surface. This is only used when deep seepage is simulated. This is input as a daily time-series. DEEPKV: Vertical saturated hydrau lic conductivity (m/s) for the restrictive layer. This is only used when deep seepage is simulated. DEEPTHK: Thickness (m) of the restrictive late r. This is only used when deep seepage is simulated. EQPET: Variable entered to specify which method of calculating reference potential evapotranspiration is used. This is not a new parameter, but new options have been added. EQPET = 116 FAO Pe nman-Monteith Equation daily input EVDEP: Soil evaporation depth used in R itchies method for soil evaporation (EVTR = 2). This is the depth from whic h evaporation from soil occurs (units of m). Recommended values range from 0.1 to 0.15 m (Allen et al. 1998). EVTR: Option to designate the choice of actual evapotranspiration simulation. This is not a new parameter, but a new option has been added. EVTR = 3 Super Simple Evapotranspiration option. HEAD1: Pressure head (cm) below which tran spiration (or evapotranspiration) is reduced to zero under soil water exce ss conditions (anaerobiosis point).

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192 HEAD2: Pressure head (cm) below which tr anspiration (or evapotranspiration) begins to be reduced below the potential ra te due to soil wa ter excess conditions. HEAD3: Pressure head (cm) above which tr anspiration (or evapotranspiration) begins to be reduced below the potential ra te due to soil water limited conditions. HEAD4: Pressure head (cm) above which tr anspiration (or evapotranspiration) begins to be reduced to zero due to so il water limited conditions. This is the pressure head at the wilting point. IAVP: Option to calculate actual vapor pre ssure by different methods. This is currently used only with the FAO Penman-Monteith equation. IAVP = 0 Mean Relative Humidity is used; IAVP = 1 Maximum Relative Humidity is used; IAVP = 2 Maximum and Minimum Re lative Humidity are used; IAVP = 3 Dew Point Temperature is used, IAVP = 4 Psychrometric data is used (Web Bulb Temperature). IDEEP: Option that specifies whether deep seepage is simulated. IDEEP = 0 no deep seepage; IDEEP = 1 deep seepage is simulated. ISWAVE: Variable entered to specify whether incoming solar radiation is available as an input, and if not which met hod will be used to estimate it. This is not a new parameter, but ne w options have been added. ISWAVE = 2 incoming solar radiation is estimated using the e quation of Hargreaves and Samani (1982). KT coefficient is estimated usi ng the equation of Samani (2000). MAXRH: Maximum relative humidity (%). MINRH: Minimum relative humidity (%). NUMHOR: Variable specifies the number of soil horizons for which parameters are entered. PSYCOEF: Psychrometric coefficient of the instrument. Values recommended by the FAO in Irrigation and Drainage Paper # 56. PSYCOEF = 0.000662 for ventilated (Asmann type) ps ychometers, with air movement of 5 m/s. PSYCOEF = 0.000800 for naturally ventilated pyschrometers (about 1 m/s). PSYCOEF: 0.001200 for non-ventilated psychr ometers installed indoors. RDFRD: Root density function root depth (m). This is the root depth used when EVTR = 1 or 2 and the UFHWT option is used This is input as a daily time-series. RDFC: Root density function coefficient in the RDF proposed by Hooland et al. (1981). This is used when EVTR = 1 or 2 and the UFHWT option is used. This is input as a daily time-series.

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193 RSM1: Residual soil moisture (cm3/cm3) used in the soil water characteristic models. Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. SWAPRES: Resistance coefficient in the runo ff routing coefficient in the SWAP model. This is only used when a single land segment is simulated (lumped model). SWAPEXP: Exponent in the SWAP runoff equati on. This is only used when a single land segment is simulated (lumped model). SWCHAR: Variable specifies the users choice of soil water characteristic model that will be used. SWCHAR = 1 Brooks and Corey (1964); = 2 Hutson and Cass (1987); = 3 van Genuchten (1980). TDEW: Dew point temperature (oC). UFEXP1: Exponent used to calculate upward fl ux. Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. UFHB1: Bubbling pressure head (cm) used to calculate upward flux. Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. UFKSAT1: Saturated hydraulic conductivity (m/s ) used to calculate upward flux. Values should be entered for as many soil horizons as are specified and for the surface layer (SS) when NUTRI = 1. UFHWT: Option to specify mode of model simulation. When turned on the UF High Water Table processes are used. UFHWT = NO (0) Original ACRU; = YES (1) High water table processes are used WETBULB: Wet bulb temperature (oC). WTDEP: Initial water table dept h (meters). As measured from ground surface. Output Variable Reference AUF: Actual upward flux (m) calculated on a given day. DEEP: Amount of deep seepage (m) that occurs on a given day. Deep seepage is positive downwards (leaving the soil profile) and negative upwards (entering the soil profile). DPOND: Depth of ponded water (mm) on the land surface.

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194 DTOE1: Drained to equilibrium water content for a soil layer. This is the water content that the soil layer would c ontain by assuming a hydrostatic profile, no hysteresis, and no depleti on by evapotranspiration. WTDEP: Water table depth (meters). As measured from ground surface.

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195 APPENDIX D HYDROLOGIC MODEL TECHNICAL MANUAL Introduction This manual describes the technical details of the High Water Table simulation component of the ACRU model developed at th e University of Florida. The model uses physically based approximations which are suitable for simulating a dynamic water table on a daily time-step. Appropriate approxi mations are made specifically for highly conductive soils with a shallow water table. In order to accurately simulate a dynamic water table the importance of upward gradients within the unsaturated portion of the soil and the contribution of a high water table to evapotranspiration is accounted for. Parameters which are input or output variable s are noted in italicized capital letters in parenthesis. In order to use the High Water table simulation the switch variable (UFHWT) must be on. Simulation of the Water-Table and Soil Moisture Distribution The water table and soil moisture distri bution is simulated by assuming a steadystate condition within the soil pr ofile. In addition, hysteresis is ignored. Thus, the soil profile is assumed to be hydrostatic, and th e soil moisture distri bution within the soil profile at a drained to equilibrium condi tion with the water table. Under these conditions the volume of water, or conversely the air volume, within the soil profile is a unique function of the water table dept h and can be easily defined by a water characteristic function for each soil layer.

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196 To make the model more realistic, the soil moisture distribution is allowed to deviate from steady-state due to the removal of water from the profile by evapotranspiration. This devi ation from steady-state creates a depleted root zone. The amount to which the root zone is depleted is refe rred to here as the root zone deficit. This deviation from steady-state implies that an upw ard gradient is induced between the water table and the depleted root zone. Water ma y move upwards in the soil profile by upward flux in response to this gradient. Th is upward movement of water defines the connectivity between a shallow water table and evapotranspiration. Soil Water Characteristic Functions Three different water characteristic functions have been included in the model: Brooks and Corey (1964), Hutson and Cass (1987), and van Genuchten (1980). The Brooks and Corey (1964) water char acteristic curve is defined as: h h hb r s r for h > hb (D-1a) sh for h hb (D-1b) where (h) is the moisture content (cm3/cm3) as a function of capillary pressure head h (cm) (taken as positive in the unsaturated portion of the soil above the water table); r (RSM1) is the residual moisture content (cm3/cm3), which is taken as the moisture content at infinite capillary pressure head; s (PO1) is the saturated moisture content (cm3/cm3) and is taken to be equal to the porosity of the soil; hb (BCHB1) is the bubbling pressure head, or air entry pressure head, of the soil (cm); and (BCL1) is the pore size distribution index (-).

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197 For a soil profile that is assumed to be in hydrostatic equilibrium with a water table the capillary pressure head is equivalent to the distance above the water table and thus the water content can be expressed as a function of distance above the water table. Equation (D-1) then becomes: z h zb r s r for z > hb (D-2a) sz for z hb (D-2b) where z is the distance above the water table (cm) An example of the Brooks and Corey water characteristic function can be seen in Figure D-1. Hutson and Cass (1987) modified the wate r characteristic curve of Brooks and Corey (1964) by replacing the sharp disconti nuity at the bubbling pressure with a parabolic equation without requ iring any additional paramete rs to be specified. The Hutson and Cass (1987) water charac teristic curve is defined as: h h hb r s r for h > hi (D-3a) 2 2 21 1b s i s i r s rh h h for h hi (D-3b) where i is the water content (cm3/cm3) at the inflection point in the water characteristic curve at capillary pressure head hi (cm). i and hi are defined as: 2 2 s i (D-4)

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198 12 2 b ih h (D-5) For a soil profile that is assumed to be in hydrostatic equilibrium with a water table the capillary pressure head is equal to the dist ance from the water table. Thus equation (D-3) can be expressed as: z h zb r s r for z > hi (D-6a) 2 2 21 1b s i s i r s rh z z for z hi (D-6b) where z is the distance above the water table (c m). An example of the Hutson and Cass water characteristic function can be seen in Figure D-1. The van Genuchten (1980) water char acteristic curve is defined as: m n r s rh h 1 1 (D-7) where n, and m are empirical parameters. It is assumed that m = 1 1/n. For a soil profile that is assumed to be in hydrostatic equilibrium with a water table the capillary pressure head is equal to the dist ance from the water table. Thus equation (D-7) can be expressed as: m n r s rz z 1 1 (D-8) where z is the distance above the water tabl e (cm). An example of the van Genuchten water characteristic function can be seen in Figure D-1.

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199 0 10 20 30 40 50 60 70 80 90 100 00.050.10.150.20.250.30.350.4 (cm3/cm3)h (cm) Brooks and Corey (1964) van Genuchten (1980) Hutson and Cass (1987) Figure D-1. The three water characteristic functions with the same input parameters: hb = 30 cm and = 1 (van Genuchten parameters are = 0.0333 cm-1, n = 2, and m = 0.5). Note that the curves are similar but not the same. Specifically, the areas under the curves are not the same. When entering parameters for the particul ar soil water characteristic function chosen the user enters the parameters as hb (BCHB1) and (BCL1) regardless of the soil water characteristic function used. When these parameters are entered for the van Genuchten (1980) equation the follo wing equalities are assumed: = hb -1, and n = + 1. This equivalency between model paramete rs was noted by van Genuchten (1980) but does not necessarily imply that the resulting so il water characterist ics are equivalent.

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200 This can be seen in Figure D1. It should be noted that wh ile the same values for the soil water characteristic parameters can be used with different soil water characteristic functions this does not mean that these soil water characteri stics are the same! The equivalence between parameters in the Brooks and Corey (1964) and van Genuchten (1980) soil water char acteristic functions have been explored by Lenhard et al. (1989) in terms of preserving the shape of the water characteristic function and by Morel-Seytoux et al. (1 996) in terms of preserving the effective capillary drive using the hydraulic conductivity functions of Bu rdine (1953) and Maulem (1976). This effective capillary drive is defi ned as (Morel-Sey toux et al. 1996): 0dh k Hrw cM (D-9) These two methods of determining equivalent parameters do not produce the same results in terms of both the shape of the water ch aracteristic curve and th e effective capillary drive. The desired result will dictate the method used. Determining Upper-Limit Wat er Contents of Soil Layers Assuming hydrostatic equilibrium, the average water contents for discrete soil layers (referred to here as either the upper limit or drained to equilibrium (DTOE1) water content), given the wate r table depth and soil layer parameters, can be found to be: 2 11 21z zdz z z z (D-10) where z2 and z1 are the distances from the water tabl e to the top and bottom of the soil layer, respectively. The Brooks and Corey (1964) water charac teristic function may be integrated analytically. The integral of equation (D-2) is:

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201 1z z h z dz zb r s r for 1 and z > hb (D-11a) z h z dz zb r s rln for = 1 and z > hb (D-11b) z dz zs for z hb (D-11c) The Hutson and Cass (1987) water characteri stic function may also be integrated analytically. The integral of equation (D-6) is: 1z z h z dz zb r s r for 1 and z > hi (D-12a) z h z dz zb r s rln for = 1 and z > hi (D-12b) 2 2 33 1b s i s i r s rh z z z dz z for z hi (D-12c) The van Genuchten (1980) wa ter characteristic functi on can not be integrated analytically, so the integral of the function must be evaluated nume rically. In order to retain computational efficiency, the water ch aracteristic equation is integrated by GaussLegendre quadrature. Compared to basic numer ical techniques such as the trapezoidal rule, Gauss-Legendre quadrature is not constr ained to evaluating th e functions at fixed locations and can achieve higher accuracy for a given number of integration points (Press 1986). Gauss-Legendre quadratur e may only be used for a continuous function and not for tabulated data. Gauss-Legendre quadratu re chooses the points, according to the method of undetermined coefficients, to evalua te the function to be integrated in such a

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202 manner that positive and negative errors are balanced (Chapra and Canale 1998). Specifically, a five-point Gau ss-Legendre integration is perf ormed for a single soil layer in order to integrate equation (D-8). Developing the Relationship between the Water-Table Depth and Soil Air Volume The unique relationship between the water ta ble and air volume in the soil profile, assuming steady-state and negl ecting, for now, any depletio n of the root zone is developed by determining the sum of the air volume of each discrete soil layer for a given depth of the water table. Th is is determined as the diffe rence between the porosity and the drained to equilibrium water content of the soil layer multiplied by the thickness of the soil layer. This relationship is applied to determ ine a new depth of the water table given changes within the soil profile (i.e. upward flux or infiltration in excess of the root zone deficit, etc). Since this rela tionship is defined for the entire soil profile, as defined by the user, the water table can not be allowed to drop below the bottom of the soil profile. It should be noted that the relationship between the water table depth and soil air volume does not account for the root zone depletion. If it di d, the relation ship would no longer be unique. Evapotranspiration and the Deviat ion from a Steady-State Profile Since a steady-state profile is unrealistic in a soil profile that is actively evapotranspiring, the root zone (where evapor ation or transpiration from the soil is taking place) is allowed to deviate from steadystate by being reduced below the hydrostatic water content. The degree to which the root zone is below the drained to equilibrium water content is called the root zone deficit. As will be discussed below, the creation of a depleted root zone creates an upward gradient between the root zone and the soil below.

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203 This gradient will induce an upward flux of wa ter within the soil. This depleted root zone also must first be replenished before infiltrating water can cause a rise in the water table. New reference potential evapotranspiration methods The standardized Penman-Monteith gra ss reference potential evapotranspiration adopted by the Food and Agricultural Organi zation in the FAO Irrigation and Drainage Paper No. 56 (Allen et al. 1998) has been a dded to the model. The reference surface assumed is a hypothetical grass reference crop with an assumed crop height of 0.12 m, a fixed surface resistance of 70 s/m and an al bedo of 0.23. This standardized PenmanMonteith equation is referred to here as FAO56PM (mm/day): 2 2 034 0 1 273 900 408 0u e e u T G R ETa s mean n (D-13) where Rn is the incoming net radiation (MJ m-2 day-1), G is the soil heat flux density (MJ m-2 day-1) as is assumed to be zero for daily calculations, Tmean (TMEAN) is the mean daily air temperatur e at 2 m height (oC), u2 (WIND and is input to the model as km/day) is the wind speed at 2 m height (m/s), es is the saturated vapor pressure (kPa), ea is the actual vapor pressure (kPa), the quantity es-ea is the vapor pressure deficit (kPa), is the slope of the vapor pr essure curve (kPa/oC), and is the psychrometric constant (kPa/oC). The psychrometric constant, is given by: P x P cp310 665 0 (D-14) where cp is the specific heat of moisture at constant pressure (MJ kg-1 oC-1) (for average atmospheric conditions it is assumed constant at 1.013x10-3), is the latent heat of

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204 vaporization and is assumed to be constant at 2.45 MJ/kg, is the ratio of the molecular weight of water vapor to dry air (0.622), and P is the atmospheric pressure (kPa) and is estimated as: 26 5293 0065 0 293 3 101 z P (D-15) where z (ELEV) is the elevation above sea level (m). The saturated vapor pressure, es is estimated from the mean of the saturated vapor pressure at the daily maximu m and minimum temperatures (oC) (TMAX and TMIN): 2min maxT e T e eo o s (D-16) where: 3 237 27 17 exp 6108 0T T T eo (D-17) The actual vapor pressure, ea can be determined (in order of desirability) from either the dew point temperature (TDEW), pyschrometric data, maximum and minimum relative humidity (MAXRH and MINRH), just maximum relative humidity, or from average relative humidity (RH). Using the dewpoint temperature (when IAVP = 3): dew o aT e e (D-18) Using psychrometric data (IAVP = 4): wet dry psy wet o aT T T e e (D-19) where Twet is the wet bulb temperature oC (WETBULB), Tdry is the dry bulb temperature (assumed to be equal to the mean daily temperature), and psy is the psychrometric constant of the instrument:

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205 P apsy psy (D-20) where apsy is the psychrometric coefficient of the instrument (oC-1) (PSYCOEF) and P is the atmospheric pressure. Using ma ximum and minimum relative humidity ea can be estimated as (IAVP = 2): 2 100 100min max max minRH T e RH T e eo o a (D-21) Using just maximum relative humidity ea can be estimated as ( IAVP = 1): 100max maxRH T e eo a (D-22) When only mean relative humidity is available ea can be estimated from ( IAVP = 0): 2 100min maxT e T e RH eo o mean a (D-23) The slope of the saturati on vapor pressure curve, is given by: 23 237 4098 mean mean oT T e (D-24) The incoming net radiation Rn is the difference between the net incoming shortwave radiation, Rns and the net incoming longwave radiation, Rnl: nl ns nR R R (D-25) The net shortwave radiation, Rns is determined from the albedo, (assumed to be 0.23) and the incoming shortwave radiation Rs (RADMET): s nsR R 1 (D-26) Rs can be supplied as daily input to the model (ISWAVE = 0) or estimated from the Angstrom equation (ISWAVE = 1):

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206 a s s sR N n b a R (D-27) where n is the number of sunshine hours, N is the maximum possible number of sunshine hours and can be determined from the sunset hour angle, s (defined below): sN 24 (D-28) Ra is the extraterrest rial radiation (MJ m-2 day-1), as (ACONS) is a regression constant that expresses the fraction of extraterrestrial ra diation reaching the earth on overcast days (n = 0), and the quantity as + bs (BCONS) is the fraction of extrater restrial radiation reaching the earth on clear days (n = N). Rs may also be estimated using the equation of Hargreaves and Samani (1982) (ISWAVE = 2): 5 0TD R KT Ra s (D-29) where TD is the difference between maximu m and minimum air temperatures (oC) (TMAX and TMEAN) and KT is an empirical constant. Samani (2000) developed an equation to determine KT as a function of TD: 4023 0 0433 0 00185 02 TD TD KT (D-30) using 25 years of data for the continental U.S. This method of estimating KT is included in the model. Extraterrestrial radiation, Ra can be estimated from: s s r sc ad G R sin cos cos sin sin 60 24 (D-31) where Gsc is the solar constant (the amount of radiation striki ng a surface perpendicular to the suns rays at the top of th e earths atmosphere), 0.0820 MJ m-2 min-1, dr is the inverse relative distance between the earth and sun (corrects for ecce ntricity of the earths orbit):

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207 J dr365 2 cos 033 0 1 (D-32) where J number of the day of year. is the solar declination and is given by: 39 1 365 2 sin 409 0J (D-33) is latitude of the location, expressed in radians, and s is the sunset hour angle and is given by: tan tan arccos s (D-34) The net longwave radiation, Rnl can be estimated as (ISWAVE = 0): 035 35 1 14 0 034 24 min, 4 max,so s a K K nlR R e T T R (D-35) where the first term represents the theoreti cal maximum longwave radiation to leave the earths surface, the second term is a humidity correction, and the third term is a correction for cloudiness. is the Stefan-Boltzmann constant (4.903x10-9 MJ K-4 m-2 day-1), Tmax,K and Tmin,K are the maximum and minimum te mperatures in Kelvin, and Rso is the clear sky radiation (MJ m-2 day-1) and can be estimated as (ISWAVE = 0 or 1): a s s soR b a R (D-36) or when values of as and bs are not available (ISWAVE = 2): a soR z x R510 2 75 0 (D-37) The use of the FAO56PM for calculating re ference potential eva potranspiration is not restricted to use with only the High Wate r Table simulation components discussed in this manual, it may also be used with the original version of the ACRU model.

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208 It should be noted that th e FAO56PM calculates referen ce evapotranspiration for a reference crop while the remaining methods of calculating evapotra nspiration in ACRU use an open water surface as the reference. For further information about the FAO56PM the reader is referred to Allen et al. (1998). Actual evapotranspiration methods The two existing actual evapotranspira tion calculation methods of ACRU have been adapted for use when using the High Wate r Table simulation opti on. In addition, a third simple actual evapotranspiration method has also been added. For all methods evapotranspiration is calculated in a top-down approach. Fi rst the evaporation demand is applied to intercepted water, next to ponde d water on the ground surface, and then to the soil as evaporation and transpiration. The ACRU model calculates act ual evapotranspiration as either a lumped quantity (EVTR = 1) or by determining soil evapor ation and transpiration separately (EVTR = 2) using Ritchies method (Ritchie 1972). In or der to use the methods when simulating a high water table the removal of water from be low the water table (when the water table is high) must be accounted for when determini ng an updated depth of the water table. Evaporation or transpiration from a soil laye r containing or below the water table will change the air volume and hence water table de pth directly. Additi onally, the removal of water from above the water table (which creates or contributes to the r oot zone deficit) is accounted for. When simulating evapotranspiration as a lumped quantity (EVTR = 1) the reference potential evapotranspiration (input or calcula ted by the model) is multiplied by the crop coefficient (V1CAY). An approximate method is used to divide actual evapotranspiration

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209 between soil water evaporation and plant transp iration for use for pl ant uptake of solutes and nutrients: 8 0 2 0 1 95 0CAY V T T for V1CAY > 0.2 (D-38a) 0 T for V1CAY =< 0.2 (D-38b) When using Ritchies method to determ ine evapotranspiration the potential soil water evaporation and plant transpiration are se parated as a function of the leaf area index (V1LAI): o pE LAI V T 21 0 1 7 05 0 for V1LAI < 2.7 (D-39a) o pE T95 0 for V1LAI >= 2.7 (D-39b) where Tp is the potential transpiration (m m) with the remaining potential evapotranspiration being apportioned to soil evap oration. The potenti al transpiration is further adjusted by being multiplied by a crop coefficient. Potential evaporation is multiplied by a f actor of 1.15 to account for the difference in albedo between bare soil and a vegetate d surface as recommended by Allen et al. (1998). Potential soil evaporation Ep is further adjusted for the percent surface cover by mulch, litter, Cs (PSUCO): 100s p pC E E (D-40) Soil water evaporation takes place down to a user defined depth of the soil (EVDEP) with recommended values ranging from 0. 1 to 0.15 m (Allen et al. 1998). According to Ritchies method actual evapor ation from the soil surface continues at a maximum rate equal to the potential rate (S tage 1 evaporation) until the accumulated soil water evaporation exceeds the stage 1 upper limit, U1 which is defined as (mm):

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210 42 0 13 sU (D-41) where as is a soil water transmission paramete r that is related to the texture of the soil (ITEXT). After U1 is exceeded soil water evaporation proceeds at a reduced (Stage 2) rate: 5 0 5 01 d d st t E (D-42) where td is the number of days since U1 has been exceeded. The contribution of rainfall and upward flux from the water table to the root zone for a given day reduces the accumulated soil wa ter evaporation as it is accounted for to determine the transition between St age 1 and 2 evaporation. The new actual evapotranspiration method that has been added to the model (EVTR = 3) is a simplification to the lumped evapotranspiration (EVTR = 1) method described above. The simplifications in clude neglecting water stress (actual evapotranspiration occurs at its maximum rate until the wilting point is reached) and the apportionment of evapotranspiration to different layers. The original model applies evapotranspiration (or transpiration) to individual soil layers accord ing to the fraction of plant roots within that layer. The new method described here applies evapotranspiration in a top-down approach. In this manner evapotranspiration oc curs from the top-most soil layer until the wilting point is reached and then applies the remainder to the layer below until the bottom of the root zone (the last la yer to contain roots) is reached. In the High Water Table option the response of transpiration (when using Ritchies method) or lumped evapotranspiration to wate r-stress is different than in the original ACRU2000 model. Here the reduction below the potential rate is made using the method proposed by Feddes et al. (1978) as show n in Figure D-2 (and used only when EVTR = 1

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211 or 2). Transpiration (or eva potranspiration) is reduced to zero when soil pressure heads are below h1 (HEAD1 representing oxygen de pletion) and above h4 (HEAD4 above the wilting point). Between h2 (HEAD2) and h3 (HEAD3) transpiration proceeds at its maximum rate. Between h1 and h2, and h3 and h4 a linear reduction of transpiration with pressure head is assumed. 0 0.2 0.4 0.6 0.8 1 h1h2h3h4 Figure D-2. Plant water stress as a function of soil water pressure head. Additionally, the root distribution functi on proposed by Hoogland et al. (1981) has been added to the model: 22L L L d c d g (D-43) where c (RDFC) is a shape parameter (varies between -1, a linear decrease in density to 0, constant density with de pth as seen in Figure D-3), d is the depth from the ground surface, and L (RDFRD) is the depth of plant roots.

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212 0 10 20 30 40 50 60 70 80 90 100 00.0050.010.0150.020.0250.03 Root Density (cm-1)Depth (cm)c = 0 c = -1 c = -0.5 Figure D-3. Root di stribution function of Hoogland et al. (1980). Upward flux of water in response to a depleted root zone In response to the deviation from a steadystate profile created by a depleted root zone an upward gradient exists within the soil profile. For simulating the upward flux of water between the water table and the depleted root zone a steady-state approximation is made. The steady-state upward flux is given by Darcys Law: 1dz dh h K q (D-44) where q is the upward flux (m/s), K(h) is the hydraulic c onductivity (m/s), and z is the height above the water table (m). Rearra nging and integrating e quation (D-12) yields: hh K q dh z0/ 1 (D-45)

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213 Assuming a relationship between K and h allows eqn. (13) to be solved with a lower boundary condition of h = 0 at z = 0 (at the water table). The complexity of analytical or numerical solutions to eqn. (13) depends on the choice of K(h) relationship. Once integrated values of q can then be determined at a given height above the water table and soil suction. An upper limit of integration of h = is typically used for simplicity. Gardner (1958) showed that this upper limit is appropr iate since upward flux quickly approaches a limiting value as soil su ction increases. Thus solving equation (D13) with this upper boundary condition gives the maximum steady-state upward flux. Currently an approximate method develope d by Anat et al. (1965) is used to calculate the maximum steady state upward flux: b sh d K q1 886 1 12 (D-46) where Ks is the saturated hydraulic co nductivity of the soil (m/s) (UFKSAT1), hb is the bubbling pressure head (UFHB1), d is the distance between the water table and the depleted root zone (m), and (UFEXP1) is related to the pore size distribution index of the Brooks and Corey (1964) model by: 3 2 (D-47) This relationship for upward flux assumes th at the soil profile is homogeneous. For each soil layer a set of parameters are entered (UFHB1, UFEXP1, and UFKSAT1). These parameters represent all of the soil layers be low it and can be best obtained by fitting the above equation to a steady-state solution of Ri chards equation for all water table depths below the layer in question.

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214 The amount of upward flux that actually o ccurs on a given day is the minimum of the maximum value calculated and the amount to which the root zone is depleted (and is now fully replenished by upward flux). The amount of upward flux that has occurred increases the air volume of the soil profile even though th ere has been no net loss of water. The water has contributed to replenishing the root zone deficit. Infiltration and Redistri bution of Infiltrated Water Water on the ground surface is allowed to in filtrate until the soil profile becomes completely saturated. Thus only satura ted-excess ponding and runoff is simulated. Water that infiltrates into the soil profile will cause a rise in the water table after any depleted root zone is replenished. This de pleted root zone is cr eated by the removal of water from the soil profile by evapotranspira tion. The amount of infiltrating water in excess of that required to re plenish the root zone is th e exact amount by which the air volume of the soil will be decreased. By knowing this change of air volume the new water table depth can be found easily from th e water table vs. air volume relationship previously developed. Deep Seepage Deep seepage occurs through the restrictive layer locat ed below the soil profile according to Darcys Law. The restrictive la yer is defined by its thickness in meters (DEEPTHK) and saturated hydraulic conductivity in m/s (DEEPKV). Currently the hydraulic head below the restrictive la yer is entered as a time-series (DEEPH). Determining a New Water-Table Depth, Upper-Limit Water Contents, and Redistribution of Soil Water Each process which adds or removes water from the soil profile changes the value of the air volume in the soil or the root zone deficit or both. As e xplained earlier, the root

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215 zone deficit is created by eva potranspiration from soil layers w ithin the root zone. It is replenished by upward flux, which simultaneous ly increases the air volume (causing a drop in the water table) or it is replenished by infiltration. Infiltration in excess of the root zone deficit will decr ease the air volume (causing a ri se in the water table). Evapotranspiration from a soil layer containing or below the water table increases the air volume directly. Given the value of the air volume, after all of these processes have added or removed water, a new water table is found from the unique relationship discussed earlier. Once the new water table depth is found the new upper limit water contents can be defined for each soil layer. Once these upper limits are defined water is then redistributed (upwards or downwards) accordingly. Other Phenomena that will change th e Water Table Depth and Soil Moisture Distribution Other processes that will cause a change in the water table depth must account for changes in the air volume in the soil prof ile. In addition care must be taken in determining when a portion of water must repl enish a depleted root zone (and hence not affect the air volume). The value of the root zone deficit represents the degree to which the entire root zone (not just a single soil layer) is depleted In order to determine which soil layers contain this root zone deficit the current water content and the drained to equilibrium water content must be known. Runoff from a Lumped Model When the model is used in lumped mode (only a single land segment) a simple, empirical equation is used to route runoff fr om the land segment. The equation used is

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216 identical to that used in the SWAP model (Kroes and van Dam 2003) and is similar to that used in the FHANTM model (Tremwel 1992): dep pondz h q 1 (D-48) where q is the runoff depth (mm/day), is the runoff resistance coefficient (day) to be calibrated, hpond is the depth of ponded water, zdep is the maximum depressional storage which must be filled prior to the occurrence of runoff, and is an exponent to be calibrated but is usually taken as 1.67 for turbulent flow in the FHANTM model.

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217 APPENDIX E NUTRIENT PROCESS AND DATA OBJECTS AND DESCRIPTIONS Process Objects PDetermineLayerPressureHeads PHWTAmmonification PHWTDenitrification PHWTImmobilization PHWTNitrification PHWTNutrientInputs PHWTPMineralization PHWTSubsurfaceTransport PHWTSurfaceTransport PMixingZoneExchangeModel PNutrientTransformationProcess PNutrientTransportProcess Data Objects DDenitrificationThreshold DLayerPressureHead DNutrientFluxRecord DPondedAmmoniumNConc DPondedLabilePConc DPondedNitrateNConc Description of Process Objects PDetermineLayerPressureHeads. This process determines the average pressure head in each soil layer on each day for us e in soil moisture factors in nutrient transformations. This process also sets the denitrification th reshold of each soil layer. PHWTAmmonification. This process determines the first step of the mineralization of organic N to ammonium. Th is process is identical to the original PAmmonification process with the excep tion of the different soil moisture reduction factors used.

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218 PHWTDenitrification. This process determines the denitrification of nitrate to the atmosphere. This process is the same as PDentrification with the only difference being the soil moisture response factor. PHWTImmobilization. This process determines th e immobilization of N and P by soil microbial biomass in the case that the decomposing organic matter is nutrient poor. This process is the same as PImmobilizati on with the only difference being the soil moisture response factor. PHWTNitrification. This process determines the nitrification of ammonium N to nitrate N. This process is the same as PNitrification with the only difference being the soil moisture response factor. PHWTNutrientInputs. This process determines the input of N and P to the land segment and to the soil by rainfall, fertilization, and infiltration. PHWTPMineralization. This process determines the mineralization of organic P to labile P. This process is the same as PPMineralization with the only difference being the soil moisture response factor. PHWTSubsurfaceTransport. This process transports labile nutrient forms between soil layers both upwards (by upward flux) and downwards (by percolation). PHWTSurfaceTransport. This process transports labile nutrient forms in runoff. PMixingZoneExchangeModel. This process determines and transports nutrients between soil and ponded water. PNutrientTransformationProcess. This process is an abstract process that is extended by every process th at transforms nutrients. PNutrientTransformTransferProcess. This process is an abstract process that extends PNutrientTransformationProcess and it is extended by every process that transforms and transfers nutrients simultaneously. PNutrientTransportProcess. This process is an abstract process that is extended by every process that transports nutrients. Description of Data Objects DDenitrificationThreshold. This DDouble data object holds the value of the dentrification threshold (water content a bove which denitrifi cation can occur) for each soil layer. DLayerPressureHead. This DDouble data object holds the value of the pressure head (cm) in each soil layer.

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219 DNutrientFluxRecord. This DDoubleFluxRecord data object holds the flux record which includes the current storage and all of the methods to transport or transform nutrients. DPondedAmmoniumNConc. This DDailyData data object holds the concentration of ammonium N (mg/L) in ponded water. DPondedLabilePConc. This DDailyData data object holds the concentration of labile P (mg/L) in ponded water. DPondedNitrateNConc. This DDailyData data object holds the concentration of nitrate N (mg/L) in ponded water.

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220 APPENDIX F NUTRIENT PROCESS AND DATA OBJECT UNIFIED MODELING LANGUAGE (UML) DIAGRAMS +initialise() : void +runProcess() : void PProcess +runProcess() : void +calcPressureHeads() : void -setRequiredData() : void PDetermineLayerPressureHeads Data Objects Required: CSoilLayer: DDenitrificationThreshold DDepth DLayerPressureHead DPorosity DPoreSizeDistributionIndex DSoilBubblingPressureHead DResidualMoistureContent DWaterFluxRecord CSoilLayer Figure F-1. PDetermineLaye rPressureHeads UML diagram

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221 +cycleNutrient() : void +runProcess() : void PNutrientTransfo rmationProcess +calcAmmonification() : void +cycleNutrient() : void -setRequiredData() : void +calcAmmonificationByLayer() : double +calcSoilNAmmonifica tionByLayer() : void PHWTAmmonification Data Objects Required: CPlantResidueLayer: DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalWasteDecayRate DAnimalDefecationOMInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DAnimalDefecationDecayRate DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CSoilSurfaceLayer: DActiveNFrac DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalDefecationOMInitial DAnimalWasteDecayRate DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DAnimalDefecationDecayRate DWaterFluxRecord DStableSoilNFluxRecord DActiveSoilNFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CHorizon: DActiveNFrac DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalDefecationOMInitial DAnimalWasteDecayRate DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DAnimalDefecationDecayRate DWaterFluxRecord DStableSoilNFluxRecord DActiveSoilNFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CPlantResidueLayer CSoilSurfaceLayer CHorizon Figure F-2. PHWTAmm onification UML diagram

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222 +cycleNutrient() : void +runProcess() : void PNutrientTransformTransferProcess +calcDenitrification() : void +cycleNutrient() : void -setRequiredData() : void +calcDenitrificationByLayer() : void PHWTDenitrification Data Objects Required: CClimate: DAtmosphericNFluxRecord CSoilSurfaceLayer: DPlantResidue DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DPorosity DDenitrificationThreshold DMeanSoilTemp DSoilBulkDensity DDepth DNDenitrification DWaterFluxRecord DNitrateNFluxRecord DActiveSoilNFluxRecord CHorizon: DPlantResidue DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DPorosity DDenitrificationThreshold DMeanSoilTemp DSoilBulkDensity DDepth DNDenitrification DWaterFluxRecord DNitrateNFluxRecord DActiveSoilNFluxRecord CClimate CSoilSurfaceLayer CHorizon Figure F-3. PHWTDenitr ification UML diagram

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223 +cycleNutrient() : void +runProcess() : void PNutrientTransformTransferProcess +calcImmobilization() : void +cycleNutrient() : void -setRequiredData() : void +calcImmobilizationB yLayer() : double PHWTImmobilization CPlantResidueLayer CSoilSurfaceLayer CHorizon CClimate Data Objects Required: CClimate: DMeanTemperature CPlantResidueLayer: DPlantResidue DPlantResidueInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueDecayRate DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CSoilSurfaceLayer: DSoilBulkDensity DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DPlantResidue DPlantResidueInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueDecayRate DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CHorizon: DSoilBulkDensity DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DPlantResidue DPlantResidueInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueDecayRate DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord Figure F-4. PHWTImm obilization UML diagram

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224 +cycleNutrient() : void +runProcess() : void PNutrientTransformationProcess +calcNitrification() : void +cycleNutrient() : void -setRequiredData() : void +calcNitrificationByLayer() : void PHWTNitrification CPlantResidueLayer CSoilSurfaceLayer CHorizon CClimate Data Objects Required: CClimate: DMeanTemperature CPlantResidueLayer: DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteAm moniumFluxRecord DAnimalDefecationAmmoniumFluxRecord CSoilSurfaceLayer: DResidualMoistureContent DLayerPressureHead DPorosity DMeanSoilTemp DSoilBulkDensity DDepth DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord CHorizon: DResidualMoistureContent DLayerPressureHead DPorosity DMeanSoilTemp DSoilBulkDensity DDepth DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord Figure F-5. PHWTNitr ification UML diagram

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225 +flowNutrient() : void +runProcess() : void PNutrientTransportProcess +calcNutrientInputs() : void +flowNutrient() : void -setRequiredData() : void PHWTNutrientInputs Data Objects Required: CClimate: DRainfall DRainfallNConc DRainfallPConc DRainfallN DRainfallP DNetRainfall CLandSegment: DApplicationMethod DAnimalWasteComposition DAnimalWasteType DAnimalWasteApplRate DAnimalWasteOrganicNApplied DAnimalWasteAmmoniumNApplied DAnimalWasteNitrateNApplied DAnimalWasteOrganicPApplied DAnimalWasteLabilePApplied DAnimalWasteOMApplied DFertilizerAmmoniumN DFertilizerNitrateN DFertilizerLabileP DFertilizerIncorporationDepth DSurfaceInfiltration DPondedWaterEvaporation DWaterFluxRecord DQuickflowDepth CPlantResidueLayer: DAnimalWateOrganicMatter DAnimalWasteOMInitial DAnimalWasteOrganicNFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalWasteAmmoniumNFluxRecord DAmmoniumNFluxRecord DNitrateNFluxRecord DLabilePFluxRecord CSoilLayer: DDepth DAmmoniumNFluxRecord DNitrateNFluxRecord DLabilePFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalWasteOrganicMatter DAnimalWasteOMInitial CClimate CLandSegment CPlantResidueLayer CSoilLayer Figure F-6. PHWTNutrie ntInputs UML diagram

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226 +cycleNutrient() : void +runProcess() : void PNutrientTransformationProcess +calcNitrification() : void +cycleNutrient() : void -setRequiredData() : void +calcPMineralizationByLayer() : void +calcSoilPMineralizationByLayer() : void PHWTPMineralization CPlantResidueLayer CSoilSurfaceLayer CHorizon CClimate CSoil Data Objects Required: CClimate: DMeanTemperature CPlantResidueLayer: DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalDefecationOMInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CSoilSurfaceLayer: DActiveNFrac DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DSoilCaCarbonateConc DSoilBaseSaturation DSoilpH DSoilClayContent DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalDefecationOMInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DWaterFluxRecord DOrganicHumusPFluxRecord DStableInorganicPFluxRecord DActiveInorganicPFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord CHorizon: DActiveNFrac DDepth DResidualMoistureContent DPorosity DLayerPressureHead DMeanSoilTemp DSoilCaCarbonateConc DSoilBaseSaturation DSoilpH DSoilClayContent DPlantResidue DPlantResidueInitial DAnimalWasteOMInitial DAnimalDefecationOMInitial DAnimalWasteOrganicMatter DAnimalDefecationOrganicMatter DWaterFluxRecord DOrganicHumusPFluxRecord DStableInorganicPFluxRecord DActiveInorganicPFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DAnimalWasteOrganicNFluxRecord DAnimalDefecationOrganicNFluxRecord DLabilePFluxRecord DPlantResidueNFluxRecord DPlantResiduePFluxRecord DAnimalWasteOrganicPFluxRecord DAnimalDefecationOrganicPFluxRecord Figure F-7. PHWTPMiner alization UML diagram

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227 +flowNutrient() : void +runProcess() : void PNutrientTransportProcess +calcSubSurfaceTransport() : void +flowNutrient() : void -setRequiredData() : void PHWTSubsurfaceTransport Data Objects Required: CSoilLayer: DSoilBulkDensity DDepth DLeachateNitrateConc DLeachateNitrateLoad DLeachateAmmoniumConc DLeachateAmmoniumLoad DLeachateLabilePConc DLeachateLabilePLoad DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DLabilePFluxRecord CGroundwater: DNitrateNFluxRecord DAmmoniumNFluxRecord DLabilePFluxRecord CSoilLayer CGroundwater +calcAmmPartitionCoeff() : double +runProcess() : void -setRequiredData() : void PAmmoniumPartitioningCoefficient +calcLabPPartitionCoeff() : double +runProcess() : void -setRequiredData() : void PLabilePPartitioningCoefficient Figure F-8. PHWTSubsur faceTransport UML diagram

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228 +flowNutrient() : void +runProcess() : void PNutrientTransportProcess +calcSurfaceTransport() : void +flowNutrient() : void -setRequiredData() : void PHWTSurfaceTransport Data Objects Required: CLandSegment: DQuickflowDepth DRunoffAmmoniumLoad DRunoffNitrateLoad DRunoffLabilePLoad DRunoffAmmoniumConc DRunoffNitrateConc DRunoffLabilePConc DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DLabilePFluxRecord DPondedAmmoniumConc DPondedNitrateConc DPondedLabilePConc CLandSegment Figure F-9. PHWTSurfa ceTransport UML diagram

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229 +flowNutrient() : void +runProcess() : void PNutrientTransportProcess +calcExchange() : void +flowNutrient() : void -setRequiredData() : void PMixingZoneExchangeModel Data Objects Required: CSoilLayer: DSoilBulkDensity DDepth DPorosity DWaterFluxRecord DNitrateNFluxRecord DAmmoniumNFluxRecord DLabilePFluxRecord CLandSegment: DNitrateNFluxRecord DAmmoniumNFluxRecord DLabilePFluxRecord DWaterFluxRecord DPondedAmmoniumNConc DPondedNitrateNConc DPondedLabilePConc CPlantResidueLayer: DAmmoniumNFluxRecord DNitrateNFluxRecord DLabilePFluxRecord CSoilLayer CLandSegment +calcAmmPartitionCoeff() : double +runProcess() : void -setRequiredData() : void PAmmoniumPartitioningCoefficient +calcLabPPartitionCoeff() : double +runProcess() : void -setRequiredData() : void PLabilePPartitioningCoefficient CPlantResidueLayer Figure F-10. PMixingZoneExchangeModel UML diagram

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230 +initialise() : void +runProcess() : void PProcess +cycleNutrient() : void +runProcess() : void PNutrientTransformationProcess +cycleNutrient() : void INutrientCycle Figure F-11. PNutrientTransformationProcess UML diagram +initialise() : void +runProcess() : void PProcess +cycleNutrient() : void +runProcess() : void PNutrientTransformationProcess +cycleNutrient() : void INutrientCycle +cycleNutrient() : void +runProcess() : void PNutrientTransformTransferProcess Figure F-12. PNutrientTransfr omTransferProcess UML diagram

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231 +initialis e() : void +runProcess() : void PProcess +flowNutrient() : void +runProcess() : void PNutrientTransportProcess +flowNutrient() : void INutrientFlow Figure F-13. PNutrientTransportProcess UML diagram

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232 +addInFluxComponent() : void +addInFluxDataSource() : void +addInFluxDataSource() : void +addOutFluxComponent() : void +addOutFluxDataStore() : void +addOutFluxDataStore() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +getAllocatedStorage() : double +getAmountOwnedIn() : double +getCurrentStorage() : double +getInFluxDataSources() : double +getInFluxRecordString() : string +getInFluxValue() : double +getInFluxValue() : double +getInFluxValue() : double +getLowerStorageLimit() : double +getOutFluxDataStores() : double +getOutfluxRecordString() : string +getOutFluxValue() : double +getOutFluxValue() : double +getOutFluxValue() : double +getPrecisionTolerance() : double +getPreviousStorage() : double +getResourceAllocationString() : string +getResourceOwnershipString() : string +getTotalAllocatedStorage() : double +getTotalAmountOwned() : double +getTotalInFluxes() : double +getTotalOutFluxes() : double +getUnallocatedStorage() : double +getUpperStorageLimit() : double +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +resetInOutFluxes() : void +setLowerStorageLimit() : void +setPrecisionTolerance() : void +setUpperStorageLimit() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +toString() : string #addInFluxValue() : void #addInFluxValue() : void #addInFluxValue() : void #addOutFluxValue() : void #addOutFluxValue() : void #addOutFluxValue() : void #addToAllocatedStorage() : void #addToAmountOwnedIn() : void #addToCurrentStorage() : void #addToUnallocatedStorage() : void #checkValue() : void #setAllocatedStorage() : void #setAmountOwnedIn() : void #setCurrentStorage() : void #setInFluxValue() : void #setInFluxValue() #setInFluxValue() : void #setInFluxValue() : void #setOutFluxValue() : void #setOutFluxValue() : void #setOutFluxValue() : void #setUnallocatedStorage() : void #subtractFromAllocatedStorage() : void #subtractFromAmountOwnedIn() : void #subtractFromCurrentStorage() : void #subtractFromUnallocatedStorage() : void DDoubleFluxRecord +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transferNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void +transformTransferNutrient() : void DNutrientFluxRecord Figure F-14. DNutrient FluxRecord UML diagram

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233 APPENDIX G NUTRIENT MODEL TECHNICAL MANUAL Introduction This manual describes the technical detail s of the nitrogen and phosphorus cycling algorithms used with the High Water Table si mulation of the ACRU model developed at the University of Florida. The algorithms used are an adaption of those used in the GLEAMS model (Knisel et al. 1993). Only the appropriate changes are mentioned here. The model employs a simple accounting procedur e to route nutrients between soil layers with the soil water. Tran sport occurs both upwards and downwards in response to soil evaporation, upward flux, inf iltration, and percolation. The exchange of nutrients between soil and runoff/ponded water occurs by assuming a constant depth to which water is completely mixed. The effects of soil wetness on nutrient transformations is also outlined. Parameters which are input or output variables are noted in italicized capital letters in parenthesis. In order to use the nutrient simulation option the switch variables (NUTRI) and (UFHWT) must be on. Nutrient Inputs Nutrients may enter the system in rainfall, fertilizer, and animal wastes. Details of the various methods are outlined in Knisel et al. (1993). Soil Moisture Effects on Nutrient Transformations The nutrient transformations in the GLEAMS model are generally simulated as first order (or zero order in the case of nitrification) pr ocesses. The main components governing the rate of transf ormations are a maximum rate (sometimes constant and

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234 sometimes a function of the rate of plant residue decomposition and C:N and C:P ratios, etc.) and factors that define the effect of temperature and soil moisture (these factors usually vary from 0 to 1). In general, the transformation rate coefficients take the form: f f k kT Max (G-1) and in the case of nitrification: f f k kT Max (G-2) where k is the transformation rate coefficient (day-1), kMax is the maximum rate of transformation under optimal temperatur e and soil moisture conditions (day-1), and fT and f are the temperature a nd soil moisture response factors, respectively. The temperature and soil mo isture response factors, fT and f, are often determined empirically and can differ for each transforma tion. The use of soil moisture response factors in simulation models are inherently approximate as the response of microbial activity to soil moisture conditi ons is a function of a number of factors. The relationship between soil moisture and microbial activity has been shown to vary between soils, depending on the shape of the soil moisture curve, the abundance of organic matter, pH, and depth (Goncalves and Carlyle 1994; Rodrig o et al. 1997; Leiros et al. 1999). For the inclusion of the GLEAMS nutri ent transformations in the ACRU high water table model it was determined that the response to soil moisture as simulated in GLEAMS was inadequate as GLEAMS was developed as an upland model where processes such as decomposition of organic matter is assumed to cease completely when the soil moisture is just above the field capacity of the soil. There are several mechanisms that cause a decrease in microbial activity in dry soil. These include reduced mobility of both soluble substrate and microbes, and a direct effect

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235 of dryness on microbial growth and survival. Under low soil moisture conditions a reduced rate of decomposition is caused by two factors; first, as the pores within the soil dry and water film coating the sediment surf aces becomes thinner, diffusion path lengths become more tortuous and the rate of bot h substrate and microbe diffusion declines, second, low water contents correspond to low water potentials that lower intracellular water potentials which in turn reduce hydration and enzymatic activity (Porporato et al. 2003). Under wet conditions a decr ease in aerobic microbial activity is caused by a reduction of oxygen diffusion (Grant and Ro chette 1994). During periods of high soil moisture anoxic conditions prevent bacteria from aerobically oxidi zing organic matter (decomposition). In the GLEAMS model, th ere are three soil moisture functions employed for various processes. For ammonification (deco mposition of organic nitrogen), phosphorus mineralization, and mineral nitrogen and phos phorus immobilization (the uptake of N and P by soil microbes when substrate is nutrient poor, generally a C:N ratio greater than 25 or a C:P ratio greater than 200) the soil mois ture response function is of the form (shown in figure G-1): wp fc wpf 1 for fc (G-3a) 01f for > fc (G-3b) where wp, and fc are the moisture content of a soil layer, the moisture content at the wilting point, and the moisture content at field capacity, respectively.

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236 As can be seen in figure G-1, the respons e to soil moisture rises from zero at the wilting point to an optimum value at field cap acity. Immediately above field capacity the response reduces to zero, implying a comple te cessation of microbial activity in the decomposition process. For nitrification the soil moisture response function in the GLEAMS model is at an optimum value at field capacity and decrease s linearly to zero at saturation and at the wilting point (figure G-1): wp fc wp nf for fc (G-4a) fc s fc nf 1 for fc < < s (G-4b) where s is the water content at saturation. For denitrification the soil moisture re sponse function in GLEAMS begins when the water content is 10 % above field capacity and increases linearly to saturation (figure G-1): fc s fc s fc s fc df 1 0 1 0 for fc + 0.1(s fc) (G-5a) 0df for < fc + 0.1(s fc) (G-5b) In a comparison of nine nitrogen simu lation models Rodri go et al. (1997) has shown significant differences in their respons e to both soil moisture and temperature. However Rodrigo et al. (1997) note that defining functions in terms of water pressure allows for comparison between soils of differe nt textures, using soil water contents may be more useful in describing processes that can limit microbial activity in soils such as solute and oxygen diffusion, while expressi ng functions in terms of water filled pore

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237 space, or relative saturation, appears to be the best indicator of aerobic/anaerobic microbial activity. 0 1 f Ammonification Nitrification Denitrification fcwps Figure G-1. Soil moisture response f unction of GLEAMS. The response of P mineralization, exchange of labile and stable P, and N and P immobilization to soil moisture follow the same curve as ammonification. Previous studies have reported the highest rates of mineralization occurring near the field capacity of the soil, decreasing as the soil dries. The optimal soil moisture conditions that yield optimal decomposition and mineralization rates has been reported to occur at soil water pressure heads between 100 and 500 cm (Rodrigo et al. 1997). Contradictory results have been measured between the range of field capacity and saturation and there is no c onsensus on the moisture cont ent or pressure at which microbial activity stops (Rodri go et al. 1997). However the general form of the response functions to both temperature and soil moistu re are often broadly similar in many models but the soil moisture functions often differ at the point and rate at which they decline from the optimum value (Wu and McGechan 1998). Differences in soil moisture

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238 response functions between models can be the one of the main causes for differences in prediction (Rodrigo et al. 1997). In order to better represent the respon se of transformation processes on soil moisture a range of soil moisture contents under which maximum transformation rates occur is desired. In order to do this soil moisture response functions for ammonification (and the other processes that use the same re sponse) and nitrification are described as logarithmic functions of soil water pressure head as is done in several other models (Hansen et al. 1991; Rijetma and Kroes 1991; Vanclooster et al. 1996) Sommers et al. (1980) and Kladivko and Keeney (1987) have sh own that mineralization rates of nitrogen could be well represented as a linear function of water cont ent or a logarithmic function of soil water pressure head. The soil moisture response of denitrifi cation is simulated as a function of relative saturation as proposed by John sson et al. (1987) and used by Vanclooster et al. (1996). The soil moisture response function of ammonification, phosphorus mineralization, and mineral nitrogen and phosphorus immobilizat ion used here is expressed in units of pF (log10 of negative pressure head in units of cm) and is illustrated in figure G-2: 7 21 wp wppF pF pF f for pF > 2.7 (G-6a) s spF pF pF f 2 4 0 6 01 for pF < 2 (G-6b) 11f for 2 pF 2.7 (G-6c) where pFwp is the pF value at the wilting point (15000 cm), pFs is the pF near saturation (taken as 1 cm for mathematical reasons), and the optimal soil water response occurs

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239 between a pF of 2 and 2.7 which correspond to 100 cm and 500 cm of soil water pressure head, respectively. 0 1 pF f Ammonification Nitrification 2 pFs2.7 pFwp Figure G-2. Soil moisture response functions used. The response of P mineralization, exchange of labile and stable P, and N and P immobilization to soil moisture follow the same curve as ammonification. The optimum soil moisture conditions of nitrification are similar to that of ammonification (decomposition of organic matte r) with the exception that nitrification tends to zero under saturated conditions (L inn and Doran 1984; Skopp et al. 1990). For nitrification the soil moisture response function is of the form (figure G-2): 7 2 wp wp npF pF pF f for pF > 2.7 (G-7a) s s npF pF pF f 2 for pF < 2 (G-7b) 1nf for 2 pF 2.7 (G-7c)

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240 The response of denitrification to soil we tness is defined using the form proposed by Johnsson et al. (1987) and similar to that developed by Rolston et al. (1984) and is shown in figure G-3: d d s d df (G-8) where and s are the water content and satura ted water content, repectively, d is a threshold water content which defines the water content above which denitrification occurs and is assumed to correspond to an effective saturation of 0.8, and d is an empirical exponent assumed to be e qual to 2 (Vanclooster et al. 1996). 0 1 f Denitrification dswp Figure G-3. Soil moisture response function to denitrification. A comparison of the soil moisture re sponse functions of GLEAMS and those proposed here can be seen in fi gure G-4 by applying them to a fict itious soil. It should be noted that there is consider able uncertainty in the soil moisture response functions proposed here, as there is with all soil moisture response functions. In particular, the

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241 limits of the optimal ranges for ammonification and nitrification as well as the point at which denitrification begins to occur should be reevaluated for speci fic applications of the model. 0 1 pF f Ammonification GLEAMS Ammonification Nitrification GLEAMS Nitrification Denitrification GLEAMS Denitrification 2 pFs2.7 pFwp Figure G-4. Comparison of the soil moisture response functions of GLEAMS and those proposed. Using a soil where s = 0.38, r = 0.05, = 0.02 cm-1, and n = 3 using the soil moisture model of va n Genuchten (1980). Field capacity and wilting point are assumed to occur at 15,000 and 300 cm, respectively. Nutrient Transport in the Subsurface Transport by Infiltration and Redistribution In order to determine the mass of nutrients that infiltrate on a given day, the volume of water that had been present at the ground surface is determined as: RO I E H HPond End Max (G-9) where HMax is the maximum amount of water that has been on the ground surface during the day (mm), HEnd (DPOND) is the depth of ponded water at the end of the day (mm)

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242 after all water movements have occurred, Epond is the amount of evaporation that has occurred from ponded water (mm), I is the depth of water infiltrated (mm), and RO (mm) is the depth of runoff or quickflow (QUICKF). If ponded water is present at the end of the day (Hend > 0) then the mass of nutrients that infiltrate MInfil (kg/ha) is determined according to the ratio of the maximum depth of water at the ground surface and the depth of infiltrated water: GS Max InfilM H I M (G-10) where MGS is the mass of nutrient present at the ground surface (kg/ha). If no ponded water was present at the end of the day and rainfall occurred then the entire mass of nutrient at the ground surface is assumed to infiltrate. Downward movement of the nutrient in the soil profile occurs according to the net percolation, qnet (cm): UpwardFlux Perc netq q q (G-11) where qperc (cm) is the amount of water that ha s moved downward out of the soil layer and qUpwardFlux is the amount of water that has moved upward into the layer from below. The concentration in percolating water (CPerc) is determined for nitrate: Out UpwardFlux net Layer Percq q d M C 10 (G-12) where MLayer is the mass of nutrient contai ned in a soil layer (kg/ha), is the average water content of the layer (cm3 cm-3), d is the thickness of the layer (cm), qUpwardFluxOut is the amount of water that has moved upward out of the soil layer, and the value 10 is a unit conversion (mg ha cm kg-1 L-1). For ammonium and labile phosphorus the concentrations are determined from:

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243 Out UpwardFlux net soil d Layer Percq q d M K M C 10 (G-13) where Kd is the partitioning coefficient and Msoil is the soil mass (Mg/ha). The partitioning coefficient for ammonium is re lated to the clay content of the soil (CL in units of %): CL Kd 083 0 34 1 (G-14) and the partitioning coefficient of labile phosphorus can be related to parameters such as the oxalate extractable aluminum and double ac id extractable magnesi um content of the soil. In the GLEAMS model it is assumed to be a function solely of the clay content: CL Kd 5 2 100 (G-15) The mass of nutrient that move s with the net percolation (MPerc) is calculated as: Perc net PercC q M 1 0 (G-16) where MPerc is in units of kg/ha and the valu e 0.1 is a unit conversion (kg L mg-1 cm-1 ha1). The mass of nutrient that m oves upward out of the layer is: Perc Out UpwardFlux UpwardFluxC q M 1 0 (G-17) Transport by Evaporation Upward migration caused by so il evaporation (not transp iration) is approximated by allowing the nutrients to move up a single layer. The mass that moves is determined from: perc Layer EvapC E M 01 0 (G-18) where MEvap is in units of kg/ha, ELayer is the soil evaporation o ccurring from the layer and the value 0.01 is a uni t conversion (kg L mg-1 mm-1 ha-1).

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244 Transport between Ponded and Soil Water During runoff events water entrains some of the soil porewater this entrainment was referred to as a process of accelerated diffusion by Ahuja and Lehman (1983) when inifiltration is absent. The extraction of porewater solutes has been shown to occur principally near the soil surface and rapidly diminishing with depth (Ahuja et al. 1981). The exchange of solutes between the soil a nd ponded or runoff water has been simulated as a convective mass transfer or enhanced di ffusion process as done by Parr et al. (1987), Wallach et al. (1989), Ahuja (1990), and Havis et al. (1992) However these models, both numerical and analytical, have to date only been applied on single controlled laboratory events and have yet to be integrated into a continuous model. In works such as that of Wallach et al. (1989), Kesseler ( 1999), Boudreau (1997), and Boudreau and Jorgensen (2001) the mass tran sfer coefficient can be related to the diffusion coefficient and the thickness of a thin boundary layer, or diffusive sublayer. It is assumed that there is a thin film of wa ter above the soil surface that is stagnant, through which chemical transport occurs by di ffusion only. When r unoff is flowing over this film the transport is enhanced. This thin film is assumed to contain all of the resistance to mass transfer (Kessler 1999). In lieu of such a complex approach severa l approximate models have been adopted. Early modeling efforts assumed that soil water within a thin zone of surface soil mixes completely and instantaneously with runoff (Crawford and Donigian 1973; Steenhuis and Walter 1980). Some other modeling efforts considered a surface soil zone that mixes incompletely with runoff with the degr ee of mixing determined by an empirical extraction coefficient (Frere et al. 1980; Leonard et al. 1987). Ahuja and Lehman (1983)

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245 suggested that the degree of soil water mixing should be an exponential function of depth, however with empirical parame ters that must be known a-priori. Since the depth of the soil that interact s with runoff has been shown to increase with reduced infiltration during the runoff ev ent the thickness of th e soil surface layer is allowed to vary and is a calibratable parameter. Extraction coefficients as determined in the GLEAMS model are retained. Exchange of nutrients betw een the soil and ponded or runoff water is simulated by assuming a depth within the soil in which su rface and soil water are completely mixed. The depth of ponded water that in teracts with so il water is: RO H HEnd Max (G-19) The top 1 cm of soil is assumed to mix w ith the ponded water. This portion of the soil is assumed to be incompletely mixed. The degree of mixing is defined by an extraction coefficient () as is done in the GLEAMS m odel, and ranges between 0.1 and 0.5 as a function of the partition coefficient of a particular nutrient (Knisel et al. 1993). Operationally, this extraction coefficient can be taken as being equivalent to the fraction of the top 1 cm layer that is completely mixed with the ponded water. The amount of water within this completely mixed soil zone, dmixed, is: ss mixedd d (G-20) where is the porosity of the top, or soil surface, layer, and dss is the depth of the soil surface layer (1 cm). Under ponded conditions, soluble nutrient s within the plant residue layer are assumed to mix completely with the ponded water. Upon complete mixing between the

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246 ponded water and the portion of the soil an eq uilibrium concentration of solute can be defined as (mg/L): Max mixed Max w mixed ss eqH d H C d C C (G-21) where Css and Cw are the concentration of solute in the surface soil layer and ponded water (mg/L), respectively, and HMax is expressed in units of cm. Mass of solute is transported in the appropr iate direction (upward out of the soil, or downward into the soil) in order to a ttain this equilibrium concentration. Surface Transport The transport of nutrients in runoff water is determined to be proportional to the fraction of ponded water that runs off on a given day: w RunoffC RO M 01 0 (G-22) where MRunoff is the mass of nutrient carried in runoff water, RO is the depth of runoff (mm), and the value 0.01 converts units.

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247 APPENDIX H CONSERVATIVE SOLUTE TRANSPORT PROCESS, INTERFACE, AND DATA OBJECTS AND DESCRIPTIONS Process Objects PConservativeMixingZoneExchangeModel PConservativeSoluteEvaporationTransport PConservativeSoluteInputs PConservativeSoluteSubsurfaceTransport PConservativeSoluteSurfaceTransport PConservativeSoluteTransportProcess Interface Objects IConservativeSoluteFlow Data Objects DConservativeSoluteFluxRecord DConservativeSoluteOption DLeachateConservativeSoluteConc DLeachateConservativeSoluteLoad DPondedConservativeSoluteConc DPondedWaterEvaporation DRainfallSoluteConc DRainfallSolute DRunoffConservativeSoluteConc DRunoffConservativeSoluteLoad DSoluteApplicationMethod DSoluteAmountApplied Description of Process Objects PConservativeMixingZoneExchangeModel. This process determines and transports solute between soil and ponded water. PConservativeSoluteEvaporationTransport. This process transports solute upwards in response to evaporation from the soil.

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248 PConservativeSoluteInputs. This process determines the input of solute to the landsegment and to the soil by rainfall, la nd surface application, and infiltration. PConservativeSoluteSubsurfaceTransport. This process transports solute between soil layers both upwards (by upward flux) and downwards (by percolation). PConservativeSoluteSurfaceTransport. This process transports solute forms in runoff. PConservativeSoluteTransportProcess. This process is an abstract process that is extended by every process that transports solute. Description of Interface Objects IConservativeSoluteFlow. This interface is implemented by PConservativeSoluteTransportProcess. Description of Data Objects DConservativeSoluteFluxRecord. This DDoubleFluxRecord data object holds the flux record which includes the curren t storage and all of the methods to transport solute. DConservativeSoluteOption. This DInteger data object determines whether conservative solute transport is simulated. DLeachateConservativeSoluteConc. This DDailyData data object holds the concentration of leaching solute from a soil layer. DLeachateConservativeSoluteLoad. This DDailyData data object holds the load of leaching solute from a soil layer. DPondedWaterEvaporation. This DDailyData data object holds the amount of water that has been evaporated from the surface. DRainfallSoluteConc. This DDailyData data object holds the concentration of solute in rain. DRainfallSolute. This DDailyData data object holds the mass of solute in rain. DRunoffConservativeSoluteConc. This DDailyData data object holds the concentration of solute in runoff. DLeachateConservativeSoluteLoad. This DDailyData data object holds the load of solute in runoff.

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249 DSoluteAmountApplied. This DDailyData data object holds the mass of solute applied to the land surface. DSoluteApplicationMethod. This DDailyInteger data object holds the method of application of applied solute. Curr ently only one method is supported, the application to the ground surface.

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250 APPENDIX I CONSERVATIVE SOLUTE TRANSPORT PROCESS AND DATA OBJECT UNIFIED MODELING LANGUAGE (UML) DIAGRAMS +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +calcExchange() : void +flowConservativeSolute() : void -setRequiredData() : void PConservativeMixingZoneExchangeModel Data Objects Required: CLandSegment: DConservativeSoluteFluxRecord DPondedConservativeSoluteConc DWaterFluxRecord CSoilLayer: DDepth DPorosity DConservativeSoluteFluxRecord CSoilLayer CLandSegment Figure I-1. PConservativeMixi ngZoneExchangeModel UML diagram

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251 +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +calcEvaporationTransport() : void +flowConservativeSolute() : void -setRequiredData() : void PConservativeSoluteEvaporationTransport Data Objects Required: CSoilLayer: DSoilWaterEvaporation DLeachateConservativeSoluteConc DConservativeSoluteFluxRecord CSoilLayer Figure I-2. PConservativeSolute EvaporationTransport UML diagram

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252 +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +calcConservativeSoluteInputs() : void +flowConservativeSolute() : void -setRequiredData() : void PConservativeSoluteInputs Data Objects Required: CClimate: DNetRainfall DRainfallSolute DRainfallSoluteConc CLandSegment: DConservativeSoluteFluxRecord DPondedWaterEvaporation DQuickflowDepth DSoluteAmountApplied DSoluteApplicationMethod DSurfaceInfiltration DWaterFluxRecord CSoilLayer: DDepth DConservativeSoluteFluxRecord CSoilLayer CClimate CLandSegment Figure I-3. PConservative SoluteInputs UML diagram

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253 +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +calcSubSurfaceTransport() : void +flowConservativeSolute() : void -setRequiredData() : void PConservativeSoluteSubsurfaceTransport Data Objects Required: CSoilLayer: DDepth DLeachateConservativeSoluteConc DLeachateConservativeSoluteLoad DWaterFluxRecord DConservativeSoluteFluxRecord CGroundwater: DConservativeSoluteFluxRecord CSoilLayer CGroundwater Figure I-4. PConservativeSolute SubsurfaceTransport UML diagram

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254 +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +calcConservativeSoluteSurfaceTransport() : void +flowConservativeSolute() : void -setRequiredData() : void PConservativeSoluteSurfaceTransport Data Objects Required: CLandSegment: DConservativeSoluteFluxRecord DQuickflowDepth DPondedConservativeSoluteConc DRunoffConservativeSoluteConc DRunoffConservativeSoluteLoad CLandSegment Figure I-5. PConservativeSolu teSurfaceTransport UML diagram +initialise() : void +runProcess() : void PProcess +flowConservativeSolute() : void +runProcess() : void PConservativeSoluteTransportProcess +flowConservativeSolute() : void IConservativeSoluteFlow Figure I-6. PConservativeSolu teTransportProcess UML diagram

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255 +addInFluxComponent() : void +addInFluxDataSource() : void +addInFluxDataSource() : void +addOutFluxComponent() : void +addOutFluxDataStore() : void +addOutFluxDataStore() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +addToCurrentStorage() : void +getAllocatedStorage() : double +getAmountOwnedIn() : double +getCurrentStorage() : double +getInFluxDataSources() : double +getInFluxRecordString() : string +getInFluxValue() : double +getInFluxValue() : double +getInFluxValue() : double +getLowerStorageLimit() : double +getOutFluxDataStores() : double +getOutfluxRecordString() : string +getOutFluxValue() : double +getOutFluxValue() : double +getOutFluxValue() : double +getPrecisionTolerance() : double +getPreviousStorage() : double +getResourceAllocationString() : string +getResourceOwnershipString() : string +getTotalAllocatedStorage() : double +getTotalAmountOwned() : double +getTotalInFluxes() : double +getTotalOutFluxes() : double +getUnallocatedStorage() : double +getUpperStorageLimit() : double +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +reallocateStorage() : void +resetInOutFluxes() : void +setLowerStorageLimit() : void +setPrecisionTolerance() : void +setUpperStorageLimit() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +subtractFromCurrentStorage() : void +toString() : string #addInFluxValue() : void #addInFluxValue() : void #addInFluxValue() : void #addOutFluxValue() : void #addOutFluxValue() : void #addOutFluxValue() : void #addToAllocatedStorage() : void #addToAmountOwnedIn() : void #addToCurrentStorage() : void #addToUnallocatedStorage() : void #checkValue() : void #setAllocatedStorage() : void #setAmountOwnedIn() : void #setCurrentStorage() : void #setInFluxValue() : void #setInFluxValue() #setInFluxValue() : void #setInFluxValue() : void #setOutFluxValue() : void #setOutFluxValue() : void #setOutFluxValue() : void #setUnallocatedStorage() : void #subtractFromAllocatedStorage() : void #subtractFromAmountOwnedIn() : void #subtractFromCurrentStorage() : void #subtractFromUnallocatedStorage() : void DDoubleFluxRecord +transferSolute() : void +transferSolute() : void +transferSolute() : void +transferSolute() : void +transferSolute() : void +transferSolute() : void +transferSolute() : void +transferSolute() : void DConservativeSoluteFluxRecord Figure I-7. DConservativeSo luteFluxRecord UML diagram

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256 APPENDIX J CONSERVATIVE SOLUTE TRANSPO RT INPUT/OUTPUT VARIABLE REFERENCE Input Variable Reference CONSERV: Option to specify the simulation of a conservative solute. CONSERV = NO(0) No solute simulated; = YES(1) Solute simulated RAINSOL: Mass of solute applied in rainfall (kg/ha). RSOLCONC: Concentration of solute in rainfall (mg/L). SOLAMT: Mass of solute applied on the land surface (kg/ha). SOLMTD: Solute application method. SOLMTD: No solute applied SOLMTD = 1 Solute applied to land surface. Output Variable Reference

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257 APPENDIX K CONSERVATIVE SOLUTE TRANSPORT TECHNICAL MANUAL Introduction This manual describes the technical details of the conservative solute transport used with the High Water Table simulation of the ACRU model developed at the University of Florida. The model employs a simple accoun ting procedure to route solute between soil layers with the soil water. Transport o ccurs both upward and downward in response to soil evaporation, upward flux, infiltration, and percolation. The exchange of solute between soil and runoff/ponded water occurs by assuming a constant depth within the soil to which water is completely mixed. Parameters which are input or output variables are noted in italicized capital letters in parent hesis. In order to use this conservative solute option the switch variable (CONSERV) must be on. Conservative Solute Inputs Solute may enter the system in rainfall or by surface application. In rainfall the concentration, CRain is entered in units of mg/L (RSOLCONC). The total mass of solute input as rain on a given day is reported in units of kg/ha (RAINSOL). The mass of solute input by rain MRain (RAINSOL) is determined from: Rain RainC R M 01 0 (K-1) where R is rainfall (mm) and the value 0.01 is a unit conversion (kg L mg-1 ha-1 mm-1). The solute application method (SOLMTD) and mass of solute applied (SOLAMT), in units of kg/ha, are input as daily time series. Currently there is only a single application method available, the applicati on to the ground surface. The mass applied is

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258 added to the ground surface of the land segm ent and may dissolve into water if ponded water is present or rainfall has occurred. Otherwise the solute will remain on the ground surface until a rainfall event occurs at which time it will infiltrate or runoff. Conservative Solute Transport in the Subsurface Transport by Infiltration and Redistribution In order to determine the mass of solute that infiltrates on a given day, the volume of water that had been present at the ground surface is determined as: RO I E H HPond End Max (K-2) where HMax is the maximum amount of water that has been on the ground surface during the day (mm), HEnd (DPOND) is the depth of ponded water at the end of the day (mm) after all water movements have occurred, Epond is the amount of evaporation that has occurred from ponded water (mm), I is the depth of water infiltrated (mm), and RO (mm) is the depth of runoff or quickflow (QUICKF). If ponded water is present at the end of the day (Hend > 0) then the mass of solute that infiltrates MInfil (kg/ha) is determined according to the ratio of the maximum depth of water at the ground surface and the depth of infiltrated water: GS Max InfilM H I M (K-3) where MGS is the mass of solute present at th e ground surface (kg/ha). If no ponded water was present at the end of the day and rainfall occurred then the entire mass of solute at the ground surface is assumed to infiltrate. Downward movement of the solute in the soil profile occurs according to the net percolation, qnet (cm): UpwardFlux Perc netq q q (K-4)

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259 where qperc (cm) is the amount of water that ha s moved downward out of the soil layer and qUpwardFlux is the amount of water that has moved upward into the layer from below. The concentration in percolating water (CPerc) is determined from: Out UpwardFlux net Layer Percq q d M C 10 (K-5) where MLayer is the mass of solute contai ned in a soil layer (kg/ha), is the average water content of the layer (cm3 cm-3), d is the thickness of the layer (cm), qUpwardFluxOut is the amount of water that has move d upward out of the soil layer, and the value 10 is a unit conversion (mg ha cm kg-1 L-1). The mass of solute that moves with the net percolation (MPerc) is calculated as: Perc net PercC q M 1 0 (K-6) where MPerc is in units of kg/ha and the valu e 0.1 is a unit conversion (kg L mg-1 cm-1 ha1). The mass of solute that moves upward out of the layer is: Perc Out UpwardFlux UpwardFluxC q M 1 0 (K-7) Transport by Evaporation Upward migration caused by so il evaporation (not transp iration) is approximated by allowing the solute to move up a single layer. The mass that moves is determined from: perc Layer EvapC E M 01 0 (K-8) where MEvap is in units of kg/ha, ELayer is the soil evaporation o ccurring from the layer and the value 0.01 is a uni t conversion (kg L mg-1 mm-1 ha-1).

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260 Transport Between Ponded and Soil Water During runoff events water entrains some of the soil porewater. This entrainment was referred to as a process of accelerated diffusion by Ahuja and Lehman (1983) when inifiltration is absent. The extraction of porewater solutes has been shown to occur principally near the soil surface and rapidly diminishing with depth (Ahuja et al. 1981). The exchange of solutes between the soil a nd ponded or runoff water has been simulated as a convective mass transfer or enhanced di ffusion process as done by Parr et al. (1987), Wallach et al. (1989), Ahuja (1990), and Havis et al. (1992) However these models, both numerical and analytical, have to date only been applied on single controlled laboratory events and have yet to be integrated into a continuous model. In works such as that of Wallach et al. (1989), Kesseler ( 1999), Boudreau (1997), and Boudreau and Jorgensen (2001) the mass tran sfer coefficient can be related to the diffusion coefficient and the thickness of a thin boundary layer, or diffusive sublayer. It is assumed that there is a thin film of wa ter above the soil surface that is stagnant, through which chemical transport occurs by di ffusion only. When r unoff is flowing over this film the transport is enhanced. This thin film is assumed to contain all of the resistance to mass transfer (Kessler 1999). In lieu of such a complex approach severa l approximate models have been adopted. Early modeling efforts assumed that soil water within a thin zone of surface soil mixes completely and instantaneously with runoff (Crawford and Donigian 1973; Steenhuis and Walter 1980). Some other modeling efforts considered a surface soil zone that mixes incompletely with runoff with the degr ee of mixing determined by an empirical extraction coefficient (Frere et al. 1980; Leonard et al. 1987). Ahuja and Lehman (1983)

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261 suggested that the degree of soil water mixing should be an exponential function of depth, however with empirical parame ters that must be known a-priori. Since the depth of the soil that interact s with runoff has been shown to increase with reduced infiltration during the runoff ev ent the thickness of th e soil surface layer is allowed to vary and is a calibratable para meter. The extraction coefficient for a conservative solute is assumed to be 0.5 as is done with nitrate in the GLEAMS model. Exchange of solute between the soil a nd ponded or runoff water is simulated by assuming a depth within the soil in which su rface and soil water are completely mixed. The depth of ponded water that in teracts with so il water is: RO H HEnd Max (K-9) The top 1 cm of soil is assumed to mix w ith the ponded water. This portion of the soil is assumed to be incompletely mixed. The degree of mixing is defined by an extraction coefficient () as is done in the GLEAMS model. Operationally, this extraction coefficient can be ta ken as being equivalent to the fraction of the top 1 cm layer that is completely mixed with the ponde d water. The amount of water within this completely mixed soil zone, dmixed, is: ss mixedd d (K-10) where is the porosity of the top, or soil surface, layer, and dss is the depth of the soil surface layer (1 cm). Under ponded conditions, any solute within the plant residue la yer is assumed to mix completely with the ponded water. Upon complete mixing between the ponded water and the portion of the soil an equilibrium concentration of solute can be defined as (mg/L):

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262 Max mixed Max w mixed ss eqH d H C d C C (K-11) where Css and Cw are the concentration of solute in the surface soil layer and ponded water (mg/L), respectively, and HMax is expressed in units of cm. Mass of solute is transported in the appropr iate direction (upward out of the soil, or downward into the soil) in order to a ttain this equilibrium concentration. Surface Transport The transport of solute in runoff water is determined to be proportional to the fraction of ponded water that runs off on a given day: w RunoffC RO M 01 0 (K-12) where MRunoff is the mass of solute carried in runoff water, RO is the depth of runoff (mm), and the value 0.01 converts units.

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263 LIST OF REFERENCES Abrahamson, W.G. and D.C. Harnett. 1990. Pi ne flatwoods and dry praries. In R.L. Myers and J.J. Ewel (eds) Ecosystems of Florida. University of Central Florida Press, Orlando, FL. Ahuja, L.R. 1982. Release of soluble chemical from soil to runoff. Transactions of the ASAE 25: 948-953, 960. Ahuja, L.R. 1990. Modeling soluble chemical tran sfer to runoff with rainfall impact as a diffusion process. Soil Science Society of America Journal 54: 312-321. Ahuja, L.R. and O.R. Lehman. 1983. The extent and nature of rainfall-soil interaction in the release of soluble chemicals into r unoff. Journal of Environmental Quality 12(1): 34-40. Ahuja, L.R., Sharpley, A.N., Yamamato, M., and R.G. Menzel. 1981. The depth of rainfall-runoff-soil interactions determined by 32P. Water Resources Research 17(4): 969-974. Allen, R.G., Pereira, L.S., Raes, D., a nd M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop water requi rements. FAO Irrigation and Drainage Paper Number 56, Food and Agricultura l Organization of the United Nations, Rome. 300 pp. Anat, A., Duke, H.R., and A.T. Corey. 1965. Steady upward flow from water tables. Hydrology Paper No. 7, Colorado State University, Fort Collins, CO. Anderson, D.L. and E.G. Flaig. 1995. Agri cultural best management practices and surface water improvement and manage ment. Water Science and Technology 31(8): 109-121. Bakhsh, A., Kanwar, R.S., Jaynes, D.B., Colv in, T.S., and L.R. Ahuja. 2000. Prediction of NO3-N losses with subsurface drainage wa ter from manured and UAN-fertilized plots using GLEAMS. Transactions of the ASAE 43(1):69-77. Barrow, N.J. 1979. The description of desorp tion of phosphate from soil. Journal of Soil Science 30: 259-270. Blaney, H.F. and W.D. Criddle. 1950. Determin ing water requirements in irrigated areas from climatological data. USDA-SCS, Wash ington, D.C. Technical Publication 96.

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264 Bottcher, A.B., Hiscock, J., Pickering, N. B., and R.T. Hilburn. 1998b. WAM-Watershed Assessment Model. Proceedings of the 1998 Watershed Management Conference: Moving from Theory to Implementation. Water Environment Federation, Alexandria, VA. Bottcher, A.B., Pickering, N.B., Cooper A.B., and B.M. Jacobson. 1998a. EAAMODFIELD: A flow and phosphorus model for high water tables. Proceedings of the 7th International Drainage Symposium, Or lando, FL, March 8-10, 1998. American Society of Agricultural Engineers, St. Joseph, MI. Boudreau, B.P. 1997. Diagenetic Models and their Implementation. Springer-Verlag, Berlin. 414 pp. Boudreau, B.P. and B.B. Jorgensen. Eds. 2001. The Benthic Boundary Layer. Oxford University Press. 404 pp. Brady, N.C. and R.R. Weil. 1996. The nature and properties of soils. 11th Edition. Prentice Hall, Upper Saddle River, NJ. 960 pp. Brooks, R.H. and A.T. Corey. 1964. Hydrau lic properties of porous media. Hydrology Paper No. 3, Colorado State University, Fort Collins, CO. Burdine, N.T. 1953. Relative permeability calc ulations from pore-si ze distribution data. Transactions AIME 198: 71-77. Campbell, K.L., Capece, J.C., and T.K. Tremwel. 1995. Surface/subsurface hydrology and phosphorus transport in the Kissimm ee River Basin, Florida. Ecological Engineering 5: 301-330. Campbell, K.L., Kiker, G.A., and D.J. Clar k. 2001. Development a nd testing of nitrogen and phosphorus process model for southern African water quality issues. ASAE Paper No. 01-2085. Presented at the 2001 American Society of Agricultural Engineers Annual International Meeting, Sacramento, CA, July 30 August 3, 2001. Capece, J.C. 1994. Hydrology and Contaminan t Transport on Flatwoods Watersheds. PhD Dissertation. University of Florida, Gainesville, FL. Capece, J.C., Campbell, K.L., Graetz, D.A. Portier, K.M. and P.J. Bohlen. 1999. Optimization of best management practices for beef cattle ranching in the Lake Okeechobee Basin. Final Report to the Florida Department of Environmental Protection. University of Florida, Gainesville, FL. Capece, J.C., Campbell, K.L., Graetz, D.A., Portier, K.M., Bohlen, P.J., Siddo, M., Fidler, M., and G.S. Hendricks. 2003. Op timization of best management practices for beef cattle ranching in the Lake Okeechobe e Basin Part 2. Final Report to the Florida Department of Environmental Pr otection. University of Florida, Gainesville, FL.

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276 BIOGRAPHICAL SKETCH Christopher John Martinez was born in Thousand Oaks, Calif ornia, in 1973. He graduated from the Richard Stockton College of New Jersey in 1996, with a B.S. from the Department of Environmental Studies. Shortly thereafter he moved to Florida and began graduate studies in the Department of Environmental Engineering Sciences at the University of Florida in 1998. There he studied the hydrology and hydraulics of constructed treatment wetlands for wastewater treatment and received his M.E. degree in 2001. He continued his education at the Department of Environmental Engineering Sciences at the University of Florid a as a U.S.D.A. National Needs Fellow.


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Title: Object-Oriented Hydrologic and Water-Quality Model for High-Water-Table Environments
Physical Description: Mixed Material
Copyright Date: 2008

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Source Institution: University of Florida
Holding Location: University of Florida
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OBJECT-ORIENTED HYDROLOGIC AND WATER-QUALITY MODEL FOR HIGH-
WATER-TABLE ENVIRONMENTS















By

CHRISTOPHER JOHN MARTINEZ


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006





























Copyright 2006

by

Christopher John Martinez

































I would like to dedicate this dissertation and the time I have spent at the University of
Florida furthering my education and professional and personal development to my
mother. She would have liked to have seen this day.















ACKNOWLEDGMENTS

This work was made possible by a National Needs Fellowship provided by the

United States Department of Agriculture. Without it, I may have never pursued a

doctoral degree.

I would like to thank Dr. Michael D. Annable and Dr. Kenneth L. Campbell for

their help, time, and mentoring. I would also like to thank Dr. Wendy D. Graham,

Dr. James W. Jawitz, Dr. Gregory A. Kiker, and Dr. William R. Wise for their

willingness to participate in this endeavor.
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ................................................... .................. .... .... x

LIST OF FIGURE S ......... ..................................... ........... xii

A B STR A C T ........................................................................................ ....... .................. xviii

CHAPTER

1 IN T R O D U C T IO N .................................................................................. 1

O bj ectives ........ .............. .. ..... .... ............. ........................ .......... ... ........... 2
Contribution of this Work: Additions Made to the ACRU2000 Model ...................3
Organization of this Dissertation .......................... ........... .. .................. 5

2 FIELD-SCALE HYDROLOGY OF THE ACRU2000 MODEL ..............................9

In tro d u ctio n ............................................................................ 9
Evapotranspiration .................. .............. ................. ........ ............. .... .9
In tercep tio n .................................11.............................
Infiltration and R unoff......................................... .................................. ............ 11
Percolation and Soil Moisture Redistribution............... .....................13
B aseflow ............... ............... ........... ... ............. ...... ............ .13
Application of the ACRU2000 Model to Shallow Water-Table Environments......... 14
Su m m ary ............................................................... ...............................15

3 FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATER-
TABLE ENVIRONMENTS: DEVELOPMENT ................................................. 18

Introduction .......................................................................................................18
M odel D evelopm ent .............................. .......................... .. ........ .... ..... ...... 20
B background ................................................................... ............ 20
Structure ............................ ...... .. .....................21
Hydrologic Processes and Governing Equations .............................................24
Evapotranspiration ....................................... .......................25
Interception ............... ........ ......... ........28
Infiltration ................................................................................................... 29









Water-table depth and soil moisture distribution ......................................29
G round ater flow ............................................... ............................. 34
Overland flow and depression storage ............................... ................35
Sum m ary ...................................... ................. ................. ......... 35

4 FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATER-
TABLE ENVIRONMENTS: VALIDATION.........................................................40

Introduction .................. ................................................................ .... 40
Case Study 1: Paynes Prairie State Preserve ................................... .................42
Site Description and Experim ental Design............................... ............... 42
R results and D discussion ......................................................... ............... 43
C ase Study 2: W .F R ucks D airy .................................................................... ..... 45
Site Description and Experim ental Design............................... ............... 45
R results and D iscu ssion ................................................................................... 46
Case Study 3: MacArthur Agro-Ecology Research Center at Buck Island Ranch.....48
Site Description and Experim ental Design............................... ............... 48
R results and D discussion ........... ............................................ ............... 50
Sensitivity A analysis .......................................... .. .. .... ........ ......... 53
Sum m ary and C onclu sions .............................................................. .....................55

5 FIELD-SCALE NITROGEN AND PHOSPHORUS MODULE OF THE
A C R U 2000 M O D E L .......................... ................................................ ..................7 1

Introduction .............. ..... .. .......... ......... .. ......... .... ..... ........ 71
N utrient M models ........................................................................................... ........7 1
P hosphorus M odel .............................. ........................ .. ...... .... ...... ...... 72
M in eralization ...........................................................73
Im m ob ilization .............................. ........................ .. ........ .... ............74
Inorganic transform nations ........................................ ........................ 75
Plant uptake ...... ......... ......... .......... ........77
N nitrogen M odel ......................... ............................... ... ...... .... ..... ...... 77
M in eralization ...........................................................7 8
Im m ob ilization .............................. ........................ .. ........ .... ............7 9
Atm ospheric loss of nitrogen .......................................... ............... 80
Inorganic nitrogen ..................... .... .. ...... ......... ...... 1
Plant uptake ......... ......... .. ..... ....... ..... ............ .. 81
Nutrient Transformation Response to Soil M oisture ...........................................81
Nutrient Transformation Response to Temperature ............................................82
Extraction of Nutrients into Runoff.................................... .................. 83
Application of the Nitrogen and Phosphorus Module to Shallow Water-Table
E nv iron m ents ................................................................84
S u m m a ry ............. .................. ....................................................................8 6

6 MODIFICATION OF THE FIELD-SCALE NITROGEN AND PHOSPHORUS
MODULE OF ACRU2000 FOR SHALLOW WATER-TABLE
E N V IR O N M E N T S ......... .. ............. .....................................................................90









In tro d u ctio n .................................................................................. 9 0
N utrient M models ....................................................... .......... ...... ........ .. 90
P hosphorus M odel .............................. ........................ .. ...... .... ............9 1
N nitrogen M odel ............... ................. .. ................................ .. .......... 93
Nutrient Transformation Response to Soil Moisture................ ..................94
Extraction of Nutrients into Runoff........................ ............... ..............97
Summary .............. ............ .... ....... ....... .......................... 99

7 FIELD-SCALE VALIDATION OF THE NITROGEN AND PHOSPHORUS
MODULE OF THE ACRU2000 MODEL FOR SHALLOW WATER-TABLE
E N V IR O N M E N T S ......... .. ............. ................................................................... 103

Introduction .............. ...... .............. ........ ............................ 103
M odel V alidation ........ .... .... .................... ........ ..... ............................ 105
Site Description ...... .................. .......... .........105
Experimental Design ........... .............. ................. ............... 106
M odel C alibration .......... ..... ........................................................ .... .... .... 108
Results ............. .......... ........ ...............111
Sensitivity A analysis ..................................... ......... ........ .. ................ 114
Discussion ........................ ..........................117
C o n c lu sio n s......................................................................................................... 12 2

8 SUMMARY AND CONCLUSIONS.................. ......... ...............161

APPENDIX

A HYDROLOGIC PROCESS AND DATA OBJECTS AND DESCRIPTIONS....... 165

P ro cess O objects .................................................................................. 16 5
D ata O objects ................................................................................................ .... 16 5
D description of Process O bjects..................................................................... ...... 166
D description of D ata O bjects......................................................................... ... ... 168

B HYDROLOGIC PROCESS OBJECT UNIFIED MODELING LANGUAGE
(U M L ) D IA G R A M S ...................................................................... ....................172

C HYDROLOGIC MODEL INPUT/OUTPUT VARIABLE REFERENCE............191

Input V ariab le R eferen ce .......................................................................................... 19 1
O utput V ariable R reference ............................................... ............................ 193

D HYDROLOGIC MODEL TECHNICAL MANUAL ...........................................195

In tro d u ctio n ....................... .. ...... .... ....... ...................................... 19 5
Simulation of the Water-Table and Soil Moisture Distribution .............................195
Soil W ater Characteristic Functions............................................................... 196
Determining Upper-Limit Water Contents of Soil Layers..............................200









Developing the Relationship between the Water-Table Depth and Soil Air
V o lu m e ................................................................ ...............2 0 2
Evapotranspiration and the Deviation from a Steady-State Profile.................202
New reference potential evapotranspiration methods .............................203
A ctual evapotranspiration m ethods ...................................... .................208
Upward flux of water in response to a depleted root zone......................212
Infiltration and Redistribution of Infiltrated Water ....................................214
D eep S eep ag e ............................................ .... ............ ..... .................... .. 2 14
Determining a New Water-Table Depth, Upper-Limit Water Contents, and
Redistribution of Soil W ater ..................................................................... 214
Other Phenomena that will change the Water Table Depth and Soil Moisture
D istrib u tio n .................................................................. 2 15
Runoff from a Lum ped M odel............................ .......................... ................215

E NUTRIENT PROCESS AND DATA OBJECTS AND DESCRIPTIONS .............217

P ro cess O objects ............................... ......... ...... ............... ................ 2 17
D ata Objects .................. ......... ................ ....... ..........217
Description of Process Objects ................................................ ...................... 217
Description of Data Objects ............. .. ......... ........................ 218

F NUTRIENT PROCESS AND DATA OBJECT UNIFIED MODELING
LANGUAGE (UML) DIAGRAMS ............................. .................... 220

G NUTRIENT MODEL TECHNICAL MANUAL................................................233

In tro d u ctio n ......................................................................................................... 2 3 3
N utrient Inputs ................................... ............................233
Soil Moisture Effects on Nutrient Transformations ...........................................233
Nutrient Transport in the Subsurface ............................ ..................................... 241
Transport by Infiltration and Redistribution.................... .................241
Transport by E vaporation ............................................................. ............... 243
Transport between Ponded and Soil W ater ................................... .................244
Surface Transport .................. .......................................... .............. 246

H CONSERVATIVE SOLUTE TRANSPORT PROCESS, INTERFACE, AND
DATA OBJECTS AND DESCRIPTIONS ................................... .................247

P process O objects ................................................................ ........... ..... 24 7
Interface O objects ... .......... .................................................... ............. ... .. .. 2 4 7
Data Objects ............ ................... ................. 247
D description of Process Objects ............................................................ ..........247
D description of Interface O bjects............................ ............................. ............ 248
D description of D ata O bjects......... ............................................................ ... ... 248

I CONSERVATIVE SOLUTE TRANSPORT PROCESS AND DATA OBJECT
UNIFIED MODELING LANGUAGE (UML) DIAGRAMS ..................................250









J CONSERVATIVE SOLUTE TRANSPORT INPUT/OUTPUT VARIABLE
R E F E R E N C E ................................................................................ ................. .. 2 5 6

Input V variable R reference ......... ................. ..........................................................256
O utput V ariable R reference ............................................... ............................ 256

K CONSERVATIVE SOLUTE TRANSPORT TECHNICAL MANUAL ................257

In tro du ctio n ............. .. ................. ............................................................... 2 5 7
C conservative Solute Inputs ......... ........ ...... ..................................................257
Conservative Solute Transport in the Subsurface...............................................258
Transport by Infiltration and Redistribution.................... .................258
Transport by E vaporation ............................................................. ............... 259
Transport Betw een Ponded and Soil W ater.............................................................260
Surface Transport .............. .. ....... ... ......... ................................... .. ...... 262

LIST O F R EFEREN CE S ......... .................................................. ...... ............... 263

BIOGRAPH ICAL SKETCH ...................................................... 276
















LIST OF TABLES


Table page

4-1 W auberg sand soil characteristics ........................................ ........................ 57

4-2 Paynes Prairie State Preserve error measures of daily outputs .............................57

4-3 Myakka fine sand soil characteristics.................... ..... ........................ 57

4-4 Crop coefficients for W.F. Rucks and MacArthur Agro-Ecology Research
Center (MAERC) at Buck Island Ranch .................. ................... ..........58

4-5 W .F. Rucks error measures of daily outputs.................................... .................58

4-6 Pineda fine sand soil characteristics................... ...... ......................... 58

4-7 MacArthur Agro-Ecology Research Center at Buck Island Ranch error measures
from the experimental pasture for daily outputs ............................................... 59

4-8 Hydrologic input parameters included in the sensitivity analysis..........................60

4-9 Sensitivity of runoff, evapotranspiration, and groundwater flow to hydrologic
p aram eters ........................................................................... 6 1

7-1 Pineda fine sand soil physical properties .................................... ............... 124

7-2 Felda fine sand soil physical properties ...................................... ............... 124

7-3 Pineda fine sand saturated water content (0s), residual water content (Or), and a,
n, and m param eters ....................... .. .... ................ ............................ 124

7-4 Felda fine sand saturated water content (0s), residual water content (Or), and c, n,
and m param eters ........................... .... ........................ ...... ...... .... .......... 125

7-5 Pineda fine sand soil chem ical properties ................................... .................125

7-6 Felda fine sand soil chem ical properties ..................................... ............... ..125

7-7 Annual observed and simulated runoff and N and P loads in runoff for winter
pastures .............. ............ .... ............. ........................ 126









7-8 Annual observed and simulated runoff and N and P loads in runoff for summer
pastures ................ .......... .......................... ...........................127

7-9 Observed and simulated average N and P concentrations in runoff for winter
pastures ................ .......... .......................... ...........................128

7-10 Observed and simulated average N and P concentrations in runoff for summer
pastures ................ .......... .......................... ...........................129

7-11 Mean absolute error (MAE), root mean square error (RMSE) and coefficient of
efficiency (E) for annual runoff, N and P loads, and average N and P
concentrations for all pastures ..................................................... ...... ......... 130

7-12 Mean absolute error (MAE), root mean square error (RMSE) and coefficient of
efficiency (E) for annual N and P loads and average N and P concentrations for
all pastures using original, unmodified ACRU2000 N and P algorithms ............131

7-13 Hydrologic input parameters included in the sensitivity analysis........................132

7-14 Nutrient input parameters included in the sensitivity analysis.............................133

7-15 Sensitivity of phosphorus and nitrogen loads to hydrologic parameters................134

7-16 Sensitivity of phosphorus and nitrogen to soil parameters and manure
application rate .................................................................... .........135

7-17 Sensitivity of nitrogen loads to nitrogen parameters.............................................135

7-18 Sensitivity of phosphorus loads to phosphorus parameters .................................136
















LIST OF FIGURES


Figure page

1-1 Flatw oods regions of Florida............................................................................... .....8

2-1 Transpiration reduction due to soil moisture excess and deficiency as simulated
in the A CRU 2000 m odel ......... ................. ................. ................................. 17

3-1 Sam ple U M L diagram ..........................................................................38

3-2 Transpiration reduction factor as a function of soil pressure head ........................39

3-3 Root density distribution function g(d) ........................................ ............... 39

4-1 Location of Paynes Prairie State Preserve.......................................................62

4-2 Measured and predicted water-table depths at Paynes Prairie State Preserve. ........62

4-3 Measured and predicted soil moisture contents within the top 25 cm of soil at
Paynes Prairie State Preserve. ...........................................................................63

4-4 Measured and predicted evapotranspiration at Paynes Prairie State Preserve.........63

4-5 Measured vs. predicted evapotranspiration at Paynes Prairie State Preserve ..........64

4-6 Location ofW .F. Rucks Dairy. ........................................ .......................... 64

4-7 Measured and predicted water-table depths at W.F. Rucks Dairy.........................65

4-8 Measured and modified ACRU2000 predicted daily runoff at W.F. Rucks Dairy..65

4-9 Measured and FHANTM predicted daily runoff at W.F. Rucks Dairy....................66

4-10 Measured and unmodified ACRU2000 predicted daily runoff at W.F. Rucks
D airy ...................................... .................................................... 6 6

4-11 Measured and predicted cumulative annual runoff at W.F. Rucks Dairy ...............67

4-12 Location of MacArthur Agro-Ecology Research Center at Buck Island Ranch.....67

4-13 Groundwater level and adjacent canal stage in the experimental pasture at the
MacArthur Agro-Ecology Research Center at Buck Island Ranch..........................68









4-14 Measured and predicted water-table depths at the experimental pasture at the
MacArthur Agro-Ecology Research Center at Buck Island Ranch........................68

4-15 Measured and predicted daily runoff at the experimental pasture at the
MacArthur Agro-Ecology Research Center at Buck Island Ranch..........................69

4-16 Measured and predicted cumulative annual runoff at the experimental pasture at
the MacArthur Agro-Ecology Research Center at Buck Island Ranch ..................70

4-17 The parameters most sensitive on runoff volumes............................................70

5-1 Nitrogen cycle of the ACRU2000 model..... ....................... ...........88

5-2 Phosphorus cycle of the ACRU2000 model ................................. ...... ............ ...88

5-3 Soil moisture response functions from the GLEAMS model ..................................89

5-4 Soil temperature response functions from the GLEAMS model ...........................89

6-1 Conceptual m odel of the phosphorus cycle ............................... .....................101

6-2 Conceptual model of the nitrogen cycle............................................... 101

6-3 Soil moisture response functions for ammonification (and P mineralization),
nitrification, and denitrification........................................ .......................... 102

7-1 Location of MacArthur Agro-Ecology Research Center (MAERC) at Buck
Islan d R an ch ...................................... ............................. ................ 13 7

7-2 Semi-improved winter pasture array at MacArthur Agro-Ecology Research
Center at Buck Island Ranch ............. .. ............ ............... 137

7-3 Improved summer pasture array at MacArthur Agro-Ecology Research Center at
B uck Island R anch ....................... .. ...................... ... .... .... ............... 138

7-4 Groundwater levels at the 4-inch well (near the flume) and the 2-inch well
(center of pasture) in winter pasture 6 (W P6)....................................................... 139

7-5 Groundwater levels at the 4-inch well (near the flume) and the 2-inch well
(center of pasture) in sum m er pasture 1 (SP1)...................................................... 139

7-6 Groundwater levels from the three winter pastures compared to the canal stage
as m measured at the S70 spillway................ .......................... ... ............ 140

7-7 Groundwater levels from the three summer pastures compared to the canal stage
as m measured at the S70 spillway................ .......................... ... ............ 140

7-8 Winter Pasture 6 (WP6) observed and simulated depth to water-table................141









7-9 Winter Pasture 7 (WP7) observed and simulated depth to water-table................141

7-10 Winter Pasture 5 (WP5) observed and simulated depth to water-table................42

7-11 Summer Pasture 1 (SP1) observed and simulated depth to water-table...............142

7-12 Summer Pasture 4 (SP4) observed and simulated depth to water-table...............143

7-13 Summer Pasture 3 (SP3) observed and simulated depth to water-table...............143

7-14 Winter Pasture 6 (WP6) observed and simulated daily runoff ............................144

7-15 Winter Pasture 7 (WP7) observed and simulated daily runoff ............................145

7-16 Winter Pasture 5 (WP5) observed and simulated daily runoff ............................146

7-17 Summer Pasture 1 (SP1) observed and simulated daily runoff..........................147

7-18 Summer Pasture 4 (SP4) observed and simulated daily runoff.............................148

7-19 Summer Pasture 3 (SP3) observed and simulated daily runoff.............................149

7-20 Winter Pasture 6 (WP6) cumulative annual runoff ............... ...... ............150

7-21 Winter Pasture 7 (WP7) cumulative annual runoff .........................................150

7-22 Winter Pasture 5 (WP5) cumulative annual runoff......................... ...............151

7-23 Summer Pasture 1 (SP1) cumulative annual runoff..................... ...............151

7-24 Summer Pasture 4 (SP4) cumulative annual runoff..................... ...............152

7-25 Summer Pasture 3 (SP3) cumulative annual runoff..................... ...............152

7-26 Winter Pasture 7 (WP7) cumulative annual N load .........................................153

7-27 Winter Pasture 7 (WP7) cumulative annual P load......................................153

7-28 Winter Pasture 6 (WP6) cumulative annual N load .........................................154

7-29 W inter Pasture 6 (W P6) cumulative annual P load........................... ...............154

7-30 Winter Pasture 5 (WP5) cumulative annual N load .......................................... 155

7-31 Winter Pasture 5 (WP5) cumulative annual P load..................................155

7-32 Summer Pasture 1 (SP1) cumulative annual N load ........................................156

7-33 Summer Pasture 1 (SP1) cumulative annual P load.........................................156









7-34 Summer Pasture 4 (SP4) cumulative annual N load ...........................................157

7-35 Summer Pasture 4 (SP4) cumulative annual P load..............................................157

7-36 Summer Pasture 3 (SP3) cumulative annual N load ...........................................158

7-37 Summer Pasture 3 (SP3) cumulative annual P load..............................................158

7-38 Hydrologic parameters showing the greatest sensitivity on N loads in runoff ......159

7-39 Hydrologic parameters showing the greatest sensitivity on P loads in runoff .......159

7-40 Nutrient parameters showing the greatest sensitivity on N loads in runoff ...........160

7-41 Nutrient parameters showing the greatest sensitivity on P loads in runoff............160

B-l PAcruHWTRitchieEvapoTranspiration UML diagram ......................................172

B -2 PD eepSeepage U M L diagram ............... ........... ..................... ..................... 173

B-3 PFAO56PenmanMonteithDailyEvap UML diagram........................ ..................174

B-4 PFindNewWaterTableDepth UML diagram ................... ......................... 175

B-5 PHW TCropCoeffTrans UML diagram ....................................... ............... 176

B-6 PHW TPlantW aterStress UM L diagram ......................... ................................177

B-7 PHWTRitchieSoilWaterEvap UML diagram ............................................... 178

B-8 PHWTSimpleEvapoTranspiration UML diagram ............... .. ....................1.79

B-9 PInitialiseSoilUFOptionHWT UML diagram................................ ..................1.80

B-10 PMaximumUpwardFlux UML diagram..........................................................181

B-11 PPondedW aterEvaporation UML diagram ....................................... ............... 182

B-12 PRootDistributionFunction UML diagram ........................... ........................183

B-13 PSimpleRunoff UML diagram .................................... ............... 184

B-14 PSoilStorageAvailable UM L diagram ....................................... ............... 185

B-15 PSoilWaterCharacteristic UML diagram ................ .............. ..............186

B-16 PStorageLimitedInfiltration UM L diagram .......................................................187

B-17 PStorageLimitedRedistribution UM L diagram .................................................. 188









B-18 PSuperSimpleEvapoTranspiration UML diagram ............................ ..................189

B-19 PUpwardFlux UM L diagram ...........................................................................190

D-1 The three water characteristic functions with the same input parameters............199

D-2 Plant water stress as a function of soil water pressure head................................211

D -3 R oot distribution function ............................................. ............................. 212

F-l PDetermineLayerPressureHeads UML diagram............................220

F-2 PHWTAmmonification UML diagram .............................. ............... 221

F-3 PHWTDenitrification UML diagram .......... ................................ .............222

F-4 PHW TImmobilization UM L diagram .......................... ............... ............... 223

F-5 PHW TNitrification UM L diagram ...................................................................... 224

F-6 PHW TNutrientInputs UM L diagram ........................................ ............... 225

F-7 PHWTPMineralization UML diagram ............ .............................................226

F-8 PHW TSubsurfaceTransport UM L diagram ..........................................................227

F-9 PHW TSurfaceTransport UM L diagram .............................................................. 228

F-10 PMixingZoneExchangeModel UML diagram ............................. ................229

F-11 PNutrientTransformationProcess UML diagram ................................................230

F-12 PNutrientTransfromTransferProcess UML diagram.......................................230

F-13 PNutrientTransportProcess UML diagram....................... ..... ...............231

F-14 DNutrientFluxRecord UML diagram.......................... ......... ...............232

G-1 Soil moisture response function of GLEAM S .............................................237

G-2 Soil moisture response functions used ...................................... ............... 239

G-3 Soil moisture response function to denitrification. ..............................................240

G-4 Comparison of the soil moisture response functions of GLEAMS and those
p rop o sed ...........................................................................2 4 1

I-1 PConservativeMixingZoneExchangeModel UML diagram ...............................250

I-2 PConservativeSoluteEvaporationTransport UML diagram ...................................251









1-3 PConservativeSoluteInputs UM L diagram ................................. ............... 252

I-4 PConservativeSoluteSubsurfaceTransport UML diagram.............................. 253

I-5 PConservativeSoluteSurfaceTransport UML diagram.......................................254

1-6 PConservativeSoluteTransportProcess UML diagram......................................254

1-7 DConservativeSoluteFluxRecord UML diagram....................... ..................255















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

OBJECT-ORIENTED HYDROLOGIC AND WATER-QUALITY MODEL FOR HIGH
WATER-TABLE ENVIRONMENTS

By

Christopher John Martinez

May 2006

Chair: Michael D. Annable
Cochair: Kenneth L. Campbell
Major Department: Environmental Engineering Sciences

A hydrologic and water quality model was developed for high water-table

environments such as the flatwoods of Florida. The model was developed within the

object-oriented framework of the ACRU2000 model. The model uses physical

approximations suitable for highly conductive, poorly drained soils. The water quality

component of the model uses nitrogen and phosphorus algorithms patterned after the

GLEAMS model, with appropriate modifications for sandy, poorly drained, acid soils.

The hydrologic model operates on a daily time-step and assumes a hydrostatic

distribution of soil moisture. Reference potential evapotranspiration can be estimated

using the Penman-Monteith equation and the resulting atmospheric demand is applied in

a top-down approach to intercepted water, ponded water on the ground surface, and to

soil evaporation and plant transpiration. Vertical upward flow of soil moisture in

response to evapotranspiration is approximated using a steady-state solution of Darcy's


xviii









Law. Groundwater flow can occur to or from a deep aquifer or an adjacent water body.

Runoff from the land surface is assumed to occur via saturation-excess only.

The hydrologic component of the model was validated using observed data from

three field sites. The validation established the model's ability to predict water-table

depths, soil moisture contents, evapotranspiration, and runoff volumes.

The water quality component of the model employs modifications for poorly

drained, flatwoods soils that include the specification of optimal ranges of water contents

affecting the rate of nutrient transformations, the effect of soil moisture on transformation

rates under saturated or near-saturated conditions, the instantaneous, reversible sorption

of phosphorus, and the extraction of nutrients into runoff water.

The water quality component of the model was validated for six experimental

pastures. Model validation, while providing improved predictions of runoff nutrient

loads compared to the model without modifications for shallow water-table

environments, indicated several shortcomings of the model. These include the need for

explicit representation of plant biomass and organic soil accretion and the need for more

site- or region-specific information on nitrogen contents and the factors that control N

and P cycling and retention in these soils.














CHAPTER 1
INTRODUCTION

Humid, shallow water-table environments such as the flatwoods of the southeastern

United States are characterized by flat topography and moderately to poorly drained soils

with high infiltration capacities, where significant interaction between surface water and

groundwater occurs. Flatwoods occur throughout the southeastern coastal plain of the

United States and cover approximately 50% of the land area of the state of Florida

(Abrahamson and Harnett 1990) (Figure 1-1). The soils of the Florida flatwoods are

composed primarily of Spodosols and to a lesser extent Alfisols. These sandy, acidic

soils typically lack an abundance of the mineral components that are important to

phosphorus (P) retention, Fe and Al oxides and aluminosilicate and metal-oxide clays

(Mansell et al. 1995; Harris et al. 1996), particularly in surficial soil horizons. The loss

of P from soil has been shown to be an important cause of eutrophication of surface water

bodies. The low retention capacity of these soils is further exacerbated by the application

of organic fertilizers. Organic acids can adsorb to mineral surfaces, reducing the capacity

for adsorption and stabilization of P (Eghball et al. 1996; Graetz et al. 1999).

The mitigation of adverse effects from P losses from flatwoods soils to the aquatic

environment requires

* Understanding the surface and subsurface hydrology of the flatwoods,
* understanding the fate of P in this environment, and
* disseminating knowledge in the form of best management practices.

Extensive laboratory and field research to accomplish these goals is expensive in terms of

time and money, thus the interest in computer simulation models. Successful modeling









of the flatwoods requires accurate representation of the hydrology and P biogeochemical

cycle. An accurate model of flatwoods systems should include an accurate representation

of a shallow water-table, its contribution to evapotranspiration, and its effect on runoff

generation. A successful model should also be capable of reflecting the factors that affect

P retention and subsequent loss in runoff and groundwater. Many models suffer from a

lack of specific knowledge of the most important environmental conditions affecting P

retention when applied to specific locations. Thus models must be updated as our

understanding of the environment grows; and models should be developed with such

future expansions in mind. Such model design is enhanced by the concept of object-

oriented programming whereby real world objects are presented more intuitively than

they might be in procedural programming (Liang 2001). Few object-oriented models

exist in the fields of hydrology and agricultural water quality. One object-oriented

model, ACRU2000, is available for such model expansion.

Objectives

Using the object-oriented ACRU2000 model as the modeling platform to build

upon, the main objectives of this work were twofold:

1. Develop and test a field-scale hydrologic module for shallow water-table
environments, and

2. test the suitability of the nitrogen and phosphorus algorithms of the model with
modifications for sandy, poorly drained flatwoods soils.

It is proposed here that an approximate hydrologic model, operating on a daily

time-step and representing the major forcing affecting water-table depths and runoff

generation, can effectively represent the hydrology of the flatwoods. The major forcing

are rainfall, evapotranspiration, and groundwater flow to or from adjacent water bodies or

boundary conditions. The approximate methods include









* The assumption that hydrostatic soil moisture conditions prevail,

* the vertical upward movement of soil moisture in response to evapotranspiration
can be represented as a steady-state process,

* runoff occurs only by saturation-excess.

The water-quality component of the ACRU2000 model uses nitrogen and

phosphorus cycling algorithms from the Groundwater Loading Effects of Agricultural

Management Systems (GLEAMS) model as a result of a previous model expansion

(Campbell et al. 2001). Following the second main objective of this work, the nitrogen

and phosphorus algorithms of the model are evaluated for application to shallow water-

table environments with modifications governing

* the effect of soil moisture on nutrient transformation rates,
* the extraction of nutrients into ponded/runoff water,
* the instantaneous, reversible sorption of P to soil particles.

Contribution of this Work: Additions Made to the ACRU2000 Model

The Java-based, object-oriented hydrologic model ACRU2000 was not developed

for use in humid, shallow water-table environments. However, due to its flexible

structure the ACRU2000 model was chosen as the platform to implement the

approximate hydrologic model described above. Specific hydrologic, field-scale

modifications to ACRU2000 include

* Addition of the standardized Penman-Monteith equation (Allen et al. 1998) for
estimating daily reference potential evapotranspiration,

* estimation of incoming solar radiation using the methods of Hargreaves and
Samani (1982) and Samani (2000) when observations are unavailable,

* expansion of the number of soil layers represented by the model,

* explicit representation of ponded water on the ground surface,

* evaporation from ponded water,









* changes in the transpiration response to excess and limited soil moisture conditions,

* addition of a closed-form root distribution function,

* representation of upward gradients and flow between the water-table and the plant
root zone,

* representation of a depth-variable specific yield using closed-form soil moisture
characteristic equations to determine water-table depths and soil moisture contents,

* movement of groundwater to and from time-variable boundary conditions (both
vertically and horizontally), and

* addition of a simple stage-discharge relationship to route runoff from the land
surface.

Hydrologic processes retained from the original, unmodified ACRU2000 model include

* The ability to estimate reference potential evapotranspiration using a variety of
methods,

* the choice of applying evaporative demand to the soil as a lumped quantity or as
separate soil evaporation and plant transpiration,

* the interception of rainfall, and

* evaporation of intercepted water.

As mentioned, the water-quality component of ACRU2000 has been adapted,

almost entirely, from the GLEAMS model. GLEAMS was developed to simulate edge-

of-field and bottom-of-root-zone loadings of water, sediment, pesticides, and nutrients

(Knisel et al. 1993). However, GLEAMS being a predominantly "upland" model may

not respond appropriately to the moisture regime and soil conditions seen in shallow

water-table environments such as the Florida flatwoods. For this reason the following

modifications were made:

* maximum rates of nutrient transformations occur over specific ranges of soil
moisture,

* mineralization and immobilization processes may occur under saturated or near-
saturated conditions, but at a depressed rate,









* nutrients within ponded water are represented explicitly,

* the movement of nutrients from soil to ponded water occurs via mixing and in
response to concentration gradients, and

* phosphorus partitioning coefficients are predicted based on factors that control P
sorption in flatwoods soils.

Organization of this Dissertation

The representation of field-scale hydrologic processes by the (unmodified)

ACRU2000 model are detailed in Chapter 2 of this document. Also in this chapter the

validity of applying ACRU2000 to humid, shallow water-table environments is

discussed.

In Chapter 3 of this document, the shallow water-table hydrologic modifications

are presented. The model is developed by integrating a vadose zone component that uses

an approximation of Richards' equation, an evapotranspiration component that represents

plant response to soil moisture conditions and accounts for upward gradients in the

vadose zone, a Variable-Source-Area (VSA) runoff generation component, and a

horizontal groundwater flow component.

In Chapter 4 the shallow water-table model proposed in Chapter 3 is validated for

three experimental sites in the southeastern United States and its performance is

compared to the Field Hydrologic And Nutrient Transport Model (FHANTM) and the

original, unmodified ACRU2000 as described in Chapter 2. The first experimental field

site was a wet prairie community within Paynes Prairie State Preserve in north-central

Florida, the second, a dairy pasture in south-central Florida, and the third a beef cattle

pasture in south-central Florida. The model performance is evaluated by comparing

observed water-table depths, soil moisture contents, evapotranspiration, and runoff

volumes to field observations.









In Chapter 5 of this document the N and P module of the ACRU2000 model are

presented and its appropriateness for shallow water-table environments is discussed. In

Chapter 6 modifications are proposed to the N and P module for shallow water-table

environments and flatwoods soils.

In Chapter 7 the N and P module developed in Chapter 6 is evaluated using field

observations from experimental pastures in south-central Florida. The model is also

compared to the unmodified algorithms as described in Chapter 5. The model

performance is evaluated by its ability to predict nitrogen and phosphorus loads in runoff.

In Chapter 8 the main findings from this study are summarized and

recommendations for future work are made.

The appendices of this dissertation provide documentation for future model users

and developers. Appendix A gives a list and short description of the hydrologic process

and data objects added to the ACRU2000 model in the course of this work. Appendix B

shows the Unified Modeling Language (UML) design diagrams for the hydrologic

processes added to the model. Appendix C is an input and output variable reference that

describes the new hydrologic input and output variables for future model users.

Appendix D is a technical manual that details the workings of the hydrologic model.

This appendix replicates parts of Chapter 3; however it refers to parameters as they are

referenced in the input/output reference (Appendix C) and in the original, unmodified

ACRU2000 model (Smithers and Schulze 1995) as well as providing some guidance for

users in input parameter determination. Appendices E, F, and G cover the nitrogen and

phosphorus model in a similar manner as for the hydrologic model. Appendix E is a

short list and description of the new process and data objects, Appendix F presents the






7


UML diagrams of the objects, and Appendix G is a technical manual. Appendices H, I, J,

and K detail a module for simulating the transport of a conservative solute (not

implemented in this work). Appendix H is a short list and description of the new process,

data, and interface objects; Appendix I shows the UML diagrams of the objects,

Appendix J is an input/output variable reference, and Appendix K is a technical manual.
























ATLANTIC COAST FLATWOODS
M EASTERN GULF COAST FLATWOODS
M SOUTHERN FLORIDA FLATWOODS


, ,a


Figure 1-1. Flatwoods regions of Florida (adapted from United States Department of
Agriculture Natural Resources Conservation Service [USDA/NRCS] 2002)


i A,














CHAPTER 2
FIELD-SCALE HYDROLOGY OF THE ACRU2000 MODEL

Introduction

The name ACRU began as an acronym for the Agricultural Catchment Research

Unit of the Department of Agricultural Engineering (now the School of Bioresources

Engineering and Environmental Hydrology) at the University of KwaZulu-Natal in

Pietermaritzburg, South Africa. The model, recently redesigned into an object-oriented

framework (Clark et al. 2001; Kiker and Clark, 2001a) and adopting the ACRU2000

moniker, operates on a daily time-step, uses a two-layer soil (referred to as the "A" and

"B" soil horizons) to represent the water budget of a field or catchment, and can be

operated as either a lumped field-scale or a distributed basin-scale model. This chapter

details the field-scale hydrologic processes of the ACRU2000 model and discusses its

suitability for shallow water-table environments. A detailed description of the entire

ACRU model can be found in Schulze (1995) and Smithers and Schulze (1995). The

structure and design of the model are presented in the next chapter in the context of the

developments made in this work.

Evapotranspiration

The calculation of reference potential evapotranspiration in ACRU2000 can be

determined by a variety of methods or inputted directly to the model (Schulze 1995).

Reference potential evapotranspiration can also be determined using daily or average

monthly meteorologic parameters. Calculation methods include the Penman (1948)









equation, the Hargreaves and Samani (1982; 1985) equations, the Blaney and Criddle

(1950) equation, the Thornthwaite (1948) equation, and others (Schulze 1995).

Evaporative demand is applied in a top-down approach in ACRU2000; it is applied

first to previously intercepted water on the plant canopy with the remaining demand

being applied to soil evaporation and plant transpiration using Ritchie's (1972) method or

as a lumped quantity. When partitioned, potential transpiration (Tp) is estimated as a

function of the leaf area index (LAI):

T, =(o.7LAI05 -0.21)ET for LAI < 2.7 (2-la)
Tp = 0.95ETo for LAI > 2.7 (2-1b)

with the remaining demand applied as potential soil evaporation, Ep to the A soil horizon.

Potential soil evaporation Ep is adjusted for the percent surface cover by mulch or litter,

Cs:


E = E (2-2)
100

According to Ritchie's (1972) method, actual evaporation from the soil surface continues

at a maximum rate equal to the potential rate (Stage 1 evaporation) until the accumulated

soil water evaporation exceeds the stage 1 upper limit, U1 which is defined in units of

mm:

U1 = ( 3)042 (2-3)

where a, is a soil water transmission parameter that is related to the texture of the soil

(Ritchie 1972). After U1 is exceeded soil water evaporation proceeds at a reduced

(Stage 2) rate as a function of the square root of time:









E = a (td -1)05 (2-4)

where td is the number of days since U1 has been exceeded.

Transpiration (or lumped evapotranspiration) occurs in proportion to the user-

specified fraction of roots contained in the two soil horizons and is adjusted using a crop

coefficient. The reduction of transpiration in response to water excess or deficiency is

assumed to occur at water contents above field capacity (water excess), taken as 100 cm

of suction in the ACRU2000 model, or below a user-defined fraction of plant available

water (water deficiency) where plant available water is defined as the water stored

between field capacity and the wilting point (Figure 2-1).

Interception

The interception of rainfall by the plant canopy is represented as either a user-

defined maximum storage capacity (mm) or as a function of the leaf area index and gross

daily rainfall using the model of Von Hoyingen-Huene (1983) as cited in Schulze (1995):

I = 0.30 + 0.27P + 0.13LAI 0.0 13P LAI 0.007LAI2 (2-5)

where I is in units of mm and P is gross daily rainfall (mm).

Infiltration and Runoff

Runoff and infiltration are determined using a modified SCS curve number method

(USSCS 1972; Schulze 1995):


Q = )2 for P, > cS (2-6a)
P + ( c)
Q = 0 for P, < cS (2-6b)

where Q is the depth of runoff (mm), P, is net rainfall (mm) (rainfall less intercepted

water), c is a coefficient of abstraction (considered constant at 0.2 in the original SCS

equation), and S is the potential maximum retention (mm). The term cS is the initial









abstraction, at Pn values below which no runoff will occur. The coefficient of

abstraction, c, is a user-supplied monthly value and may be as high as 0.4 immediately

after plowing or under forested conditions or a low as 0.05 in regions of compacted soils

according to Schulze (1995). As a variable parameter the coefficient of abstraction gives

the user the ability to vary the runoff response to reflect different vegetation, site

conditions, and management practices. The maximum retention, S, is determined from a

soil water deficit prior to a rainfall event down to a user-defined critical soil depth, Dc:

S =-(, ,-)Dc (2-7)

where 0, and 0 are the water content at saturation and the current water content,

respectively. The critical soil depth varies with climatic, vegetative, and soil

characteristics (Schulze 1995). A location with sparse vegetation, thin soils, and intense

rainfall might have a relatively low value and a location of dense vegetation, deep soils,

and low-intensity rainfall a high value (Figure 2-2). The ability to vary the critical soil

depth, as well as the coefficient of abstraction, is an attempt in the ACRU2000 model to

account for different runoff-producing mechanisms (Schulze 1995).

The runoff generated using equation 2-6 is routed from the field by specifying a

"quickflow" fraction that runs off on the same day it was generated. The remainder is

retained to the following day; however it is not available for infiltration or evaporation.

This "delayed stormflow" is intended to act as a surrogate for interflow according to

Schulze (1995) and the specified quickflow fraction only affects the timing of runoff, not

the amount generated.









Percolation and Soil Moisture Redistribution

Downward percolation from a soil horizon can occur when the horizon is above

field capacity according to a user-specified fraction (mm/day):

q = K AB Of (2-8)

where KB is the fraction of water above field capacity in the A horizon that will drain to

the B horizon, OfcA is the water content of the A horizon at field capacity, and dis the

thickness of the A horizon. An identical relationship is used for percolation out of the B

horizon. Soil moisture may (as an option selected by the user) move in response to

gradients at moisture contents below field capacity as a function of the gradient between

the horizons downward (mm/day):


q = 0.020A d (A _OB (2-9)
SfcA fcB

and upward:


q = 0.010, .d(B A (2-10)
OfcB OfcA

where OA and OB are the moisture contents of the A and B horizons, OfcB is the moisture

contents of the B horizon at field capacity, and d is the thickness of the soil layer.

Baseflow

Water that percolates out of the B horizon enters the groundwater store. Water

within the groundwater store can flow out to a nearby surface water body as baseflow.

Baseflow is calculated as a function of the size of the groundwater store by assuming that

a fraction of the groundwater store is released. The baseflow release fraction, Fbf, is a









function of the size of the groundwater store, Sgw (mm), and a base release coefficient,

Fbfi:

Fbf = 0.8Fbf for Sgw < 0.015 (2-11a)
Fbf = 1.3Fbf for Sg > 0.100 (2-1 lb)
Fb = Fbfi for 0.015 < Sgw < 0.100 (2-11c)

Application of the ACRU2000 Model to Shallow Water-Table Environments

The field-scale hydrology of the ACRU2000 model, as described above, was

developed principally for arid locations where runoff generation occurs primarily via an

infiltration-excess mechanism and the interaction of groundwater with nearby water

bodies can be approximated as a one-way process. This is evidenced by

* The reduction of evapotranspiration at water contents above field capacity,
* the use of an SCS-type equation to estimate surface runoff,
* the assumption that groundwater flows only out from a catchment as baseflow.

While the reduction of evapotranspiration above field capacity and the inability to

represent the flow of groundwater into the field or catchment from a nearby waterbody

can be considered shortcomings of the model, the modeling approach of ACRU2000 does

provide some flexibility in applying it to shallow water-table environments. This

flexibility is entirely due to the ability to vary the parameters that determine runoff and

infiltration (the coefficient of abstraction, c, and the critical soil depth, Dc). In applying

the ACRU2000 model to locations with highly permeable, shallow water-table soils there

are a few recommendations that can be made for parameterization of the model:

* The total soil depth represented in the model should be as deep as the deepest
water-table observation,

* the response fraction for percolation from the B horizon to the groundwater store
should be set to zero in order to mimic the poorly drained conditions caused by a
shallow water-table,









* the response fraction for percolation from the A to B horizon should be set to a
value of 1.0 in the case of highly permeable soils. This allows the A horizon to
drain to field capacity if sufficient storage is available in the B horizon where it can
accumulate, mimicking the presence of a shallow water-table,

* plant roots must extend into the B horizon in order for the moisture contained
within it to be available for evapotranspiration (as water may only move up from
the B horizon at moisture gradients below field capacity),

* the coefficient of initial abstraction should have a value of 1.0. This will cause
runoff to only occur when the entire soil profile is saturated,

* the critical soil depth from which the soil moisture deficit is calculated should be
the entire depth of the soil.

As an alternative to allowing plant roots to extend into the B horizon and setting the

response fraction from the A to B horizon to unity, the A horizon can be assumed to

extend to the entire depth of the soil and contain all of the plant roots. Due to the

reduction of evapotranspiration above field capacity the splitting of the soil between two

horizons is arbitrary and depending on their relative thickness will produce greatly

varying results when assuming no drainage out of the B horizon.

Summary

This chapter details the field-scale hydrology as simulated in the ACRU2000 model

and its suitability for use in shallow water table environments. In the model, reference

potential evapotranspiration can be determined by a variety of methods and is applied in a

top-down approach to intercepted water and then to soil evaporation and plant

transpiration. The model does not represent water ponded on the ground surface.

Rainfall can be intercepted by the plant canopy, as represented by two different methods,

and is partitioned between runoff and infiltration using a modified SCS curve number

method. Water may percolate out of a soil horizon at water contents above field capacity

and may move between soil horizons at water contents below field capacity. Water









percolating out of the B soil horizon is added to the groundwater store from which

baseflow can occur according to a user-defined baseflow fraction and the size of the

groundwater store. Runoff that is generated is split between a quickflow fraction,

occurring on the day generated, and a delayed stormflow fraction.

The ACRU2000 model may not be appropriate for application to shallow water-

table environments due to the reduction of evapotranspiration (or plant transpiration) at

water contents above field capacity, the use of an infiltration-excess type procedure to

determine runoff, and the inability of the model to represent groundwater flow into the

model domain. However, the flexibility of the modified curve number procedure of the

model offers some flexibility in representing the different runoff producing mechanisms

of infiltration-excess and saturation-excess. A field dominated by saturation-excess

runoff may be sufficiently represented by using parameter values that are outside of the

recommended ranges.

Modifications to ACRU2000 that may be considered to be more appropriate for

shallow water-table environments are described in the next chapter. This modified model

and the original model described here are evaluated against field data from shallow

water-table environments in Chapter 4.











1
0.9
0.8
0.7 -
0.6 -
a
0.5
0.4
0.3
0.2
0.1
0
Os Ofc 6wp

Figure 2-1. Transpiration reduction due to soil moisture excess and deficiency as
simulated in the ACRU2000 model (adapted from Schulze 1995)


Thin Soils
Eutrophic
Low organic Carbon


I
High Organic Carbon
Dystrophic
Deep Soils


Humid
Climate


High-Intensity Low-Intensity
Rainfall Rainfall


Figure 2-2. Critical soil depth as related to climatic, vegetative, and soil conditions
(adapted from Schulze 1995)


0.10 0.15 0.20



0.20 0.25 0.30


0.25 0.30 0.40


Arid
Climate


4---














CHAPTER 3
FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATER-TABLE
ENVIRONMENTS: DEVELOPMENT

Introduction

Simulation of humid regions with highly permeable, shallow soils has been shown

to be inconsistent with the concept of infiltration excess or "Hortonian" runoff generation

(Dunne and Black 1970; Freeze 1972; Jayatilaka and Gillham 1996; Ogden and Watts

2000; Hernandez et al. 2003). In such environments, runoff is typically generated by

saturation excess whereby the water-table rises to the ground surface, creating a Variable

Source Area (VSA), a zone of saturation that expands and contracts seasonally as well as

during individual storms. These VSAs often form where subsurface lateral flow

converges, the ground slope changes, or the depth to a restrictive layer decreases

(Frankenberger et al. 1999). Regions dominated by VSA runoff include much of the

southeastern coastal plain of the United States, and the flatwoods regions of Florida, in

particular. The flatwoods landscape is characterized by very flat topography with

moderately to poorly drained, highly permeable, sandy soils that can often have standing

water during wet weather. As a result, groundwater levels are heavily influenced by

rainfall, evapotranspiration (ET), and nearby canal or stream stages (Yan and Smith

1994; Dukes and Evans 2003).

Management of surface and groundwater quality has become an environmental

priority, particularly in agricultural watersheds. In managing water-resource quantity and

quality, modeling is the most cost-effective way to evaluate the impact of management









alternatives. In the past, model development efforts often emphasized land-surface

processes or groundwater processes, but rarely both. The land-surface models emphasize

infiltration, ET, and surface-water movement while often ignoring or oversimplifying

saturated groundwater, and the groundwater models emphasize prediction of groundwater

levels in response to pumping, boundary conditions, and recharge while oversimplifying

ET and vadose zone processes. Both surface water and groundwater models work well

on their own, when used in areas where the interaction between surface and groundwater

is weak or insignificant (Yan and Smith 1994).

Several models have been developed and tested for use in flatwoods regions

including CREAMS-WT (Heatwole et al. 1987; Heatwole et al. 1988) based on the

CREAMS model (Knisel 1980); EAAMOD (Bottcher et al. 1998a); FHANTM (Tremwel

and Campbell 1992; Fraisse and Campbell 1996) based on the DRAINMOD model

(Skaggs 1980); and FLATWOODS (Sun et al. 1998) based on the MODFLOW model

(McDonald and Harbaugh 1988). All of these models were developed as field-scale

models with the exception of FLATWOODS, which happens to be the only one of these

models that (to date) does not contain a water quality component. The field-scale models

mentioned have been incorporated into distributed models (Heatwole et al. 1986;

Negahban et al. 1995; Bottcher et al. 1998b); however, they are used to determine "edge

of field" effects and, as such, are used in a "loose coupling" framework where individual

fields (or grid cells) do not interact with one another. Sun et al. (1998) showed the need

for distributed, fully-interactive modeling of flatwoods systems where groundwater

gradients may be strongly affected by heterogeneous distributions of vegetation type,

seasonality effects, and changing management practices.









To better simulate high water-table environments like the coastal plain flatwoods,

new model components have been developed within the framework of the ACRU2000

model (Clark et al. 2001; Kiker and Clark 2001a). The new components allow for

distributed, physically based modeling of high water-table environments. The objective

of this work was to develop field-scale model components for the flatwoods landscape

for use in the ACRU2000 distributed hydrologic model; and to demonstrate the

advantages of adding model components within the object-oriented framework of the

ACRU2000 model.

Model Development

Background

The ACRU model (originally written in the FORTRAN programming language)

has its origins in a distributed catchment evapotranspiration study in the Natal

Drakensberg region of South Africa in the early 1970s (Schulze 1995). Since then the

model has undergone many revisions and additions to meet the water-related needs of the

scientific-modeling community in South Africa and beyond. However, each consecutive

improvement to the model has created a more difficult design and coding challenge for

subsequent researchers. The many contributions made to the model over the years

resulted in a framework in which it was relatively difficult to make new additions, and in

some instances the model structure was unable to accommodate the desired additions at

all. To better accommodate future model additions, the ACRU model was recently

redesigned in an object-oriented framework (Clark et al. 2001; Kiker and Clark 2001a).

As mentioned in the previous chapter, the ACRU2000 model can be used as either

a lumped field-scale model or as a distributed basin-scale model. The model operates on

a daily time-step, using a modified SCS curve number procedure to generate daily runoff









volumes (Schulze 1995). The model uses a two-layer soil to represent the water budget

of the catchment, with any water above field capacity percolating out of a layer according

to a user-defined fraction. Water draining out of the bottom soil layer enters the

groundwater store, from which baseflow is generated as a function of the size of the store

and a user-defined baseflow coefficient. Plant-canopy interception can be represented

using two different methods and this intercepted water can (in turn) be evaporated back

into the atmosphere. Reference potential evaporation can be determined using a variety

of methods and applied as a lumped quantity, or it may be partitioned between soil

evaporation and plant transpiration, according to the method of Ritchie (1972). Plant

water-stress, and a corresponding reduction in transpiration, is determined to occur at

some water content between field capacity and wilting point (set by the user) for water-

limiting conditions, and is determined to occur at water contents above field capacity for

water-excess conditions. Schulze (1995) and Smithers and Schulze (1995) give a more

detailed description of the ACRU model.

Structure

The model was redesigned with the belief that the hydrologic system is complex,

and thus the way we view it or model it should be seen as a learning process that may

require periodic reevaluation. The model was restructured using an object-oriented

methodology to produce a more flexible and extensible model structure. The new,

object-oriented model is referred to as ACRU2000. The model was designed using the

Unified Modeling Language (UML) and the model was implemented in the Java

programming language. Using UML allows graphic design of objects with diagrams for

object-oriented programming, before writing computer code. The UML provides a

standardized notation to specify, design, visualize, and document object-oriented









software (Jacobsen et al. 1998). The extensibility of the ACRU2000 model framework

has been demonstrated previously by Campbell et al. (2001), who added a module to

simulate nitrogen and phosphorus transport and transformations, and by Kiker and Clark

(2001b), who added a module to simulate southern African rangeland ecosystems.

Object orientation uses the concept of objects, where an object consists of a small,

well-written piece of computer code that contains its own attributes, methods, and

behavior (Figure 3-1). The attributes describe the object, in terms of physical

characteristics or other traits. The methods describe the object's internal functionality,

and contain the equations the model uses to simulate various events. The behavior of the

object describes how it interacts with other objects. Object orientation thus encourages

the creation of models that are modular in structure. Three main object types in

ACRU2000 are of interest to the researcher simulating environmental events:

Component, Data, and Process objects. Component objects are physical components of

the system, such as the climate, the soil, or a soil layer. Data objects are the descriptors,

or attributes, of the Component such as the temperature, depth, or hydraulic conductivity.

These data attributes of the Component objects are modeled as separate objects

themselves, because as an object they can be reused or extended (rather than just being a

simple variable). As an object, only a single Data object representing a specific trait

needs to be written in code. This single object can then be associated with different

objects (with different values) representing the particular trait of each Component object.

Process objects, the third type of object of interest to the researcher, represent the action

or event that involves one or more Component objects, such as interception of rainfall by

a vegetation canopy or the infiltration of water into the soil. Each Process object, when









acting on one or more Component objects, uses the Data objects associated with that

Component (Figure 3-1) (Clark et al. 2001; Kiker and Clark 2001a).

Objects can interact with each other in three different ways: inheritance,

aggregation, and association (Figure 3-1). Objects may inherit properties from other

objects. Inheritance indicates that one object is a "type of' another object. This

inheritance of functionality between objects allows for the code that has already been

written for the "parent" object to be used by the "child" object. Any difference between

the two objects is made as needed in the "child" object. In this manner the "child" object

can be a more specialized version of the "parent" object. Objects may also be

aggregated, or be a "part of' other objects. In this role, one object may use the

functionality of another. Another way that objects may interact is by association (using

information from other objects). This "uses data from" relationship allows an object to

access data that is "owned" by another object. These three relationships among objects

encourage the development of modular code, and result in a flexible and extensible

programming structure that encourages code re-use.

The structure of the object-oriented design of ACRU2000 allows new objects to be

created and linked to the model without major revision to the existing code. To add a

new module (a group of objects with a common overall purpose) the model developer

needs to:

* Identify the Component, Process, and Data objects which will be used.

* Determine any new objects to be created.

* Define the relationships of all of the objects in the new module and to existing
objects.

* Implement the design in Java code within the framework set in Steps 1-3.









Steps 1-3 are accomplished easily using UML. The developer only needs to write

computer code at Step 4.

Adding the new hydrology module to ACRU2000 focuses on adding new Process

objects, and to a lesser extent on adding any needed Data objects. This is because the

Component objects (the climate, soil, soil layers, etc.) already exist, along with the Data

objects associated with them. Each new Process object has a UML diagram associated

with it (Figure 3-1). The use of UML as the standardized design tool provides a design

that is easy to understand.

The following section describes the field-scale hydrologic processes of the shallow

water-table module of ACRU2000. The processes that have been retained from the

original model are noted. A short description of the process and data objects added to the

model can be seen in Appendix A, UML design diagrams of the hydrologic processes can

be seen in Appendix B, and Appendix C provides and input/output variable reference for

future users of the model.

Hydrologic Processes and Governing Equations

The ACRU2000 model was originally developed for use in upland watersheds that

are characterized by infiltration-rate limited (Hortonian) runoff generation and

topographic gradients that drive overland and groundwater flow direction. To apply the

model to humid, shallow water-table environments, several modifications were needed.

These included the effects of a shallow water-table on evapotranspiration and runoff

generation, a depth-variable specific yield, and the representation of surface water and

groundwater gradients that may reverse in response to time-variable boundary conditions.

In making these modifications the model was expanded to simulate up to ten soil layers.









Evapotranspiration

In addition to the methods previously included in the model for calculating

reference potential evapotranspiration (ETo), the standardized Penman-Monteith equation

for grass reference potential evapotranspiration adopted by the Food and Agricultural

Organization in the FAO Irrigation and Drainage Paper No. 56 (Allen et al. 1998) has

been added to the model:

900
0.408A(R -G))+ 900 u2(e es )
0.408A(Rn -G)+ --yu Tean + 273
ETo = +2 (3-1)
A + p (1+0.34u2)

where ETo is in units of mm day-', Rn is the incoming net radiation (MJ m-2 day-) and is

the difference between net incoming shortwave radiation, Rns and the net outgoing

longwave radiation, Rjn, G is the soil heat flux density (MJ m-2 day-) and is assumed to

be zero for daily calculations, Tmean is the mean daily air temperature at 2 m height

[(Tax+Tm,)/2,oC], u2 is the wind speed at 2 m height (m/s), e, is the saturated vapor

pressure (kPa), e, is the actual vapor pressure (kPa), A is the slope of the vapor pressure

curve (kPa/0C), and yp is the psychrometric constant (kPa/0C). The net shortwave

radiation, R,, is determined from the albedo, a (assumed to be 0.23) and the incoming

shortwave radiation, R,:

R. = (1- a)R, (3-2)

Rs is supplied as daily input to the model or estimated from the Angstrom equation:


R = as +b n R (3-3)


where n is the number of sunshine hours, Nis the maximum possible number of sunshine

hours, Ra is the extraterrestrial radiation (MJ m-2 day-1), as is a regression constant that









expresses the fraction of extraterrestrial radiation reaching the earth on overcast days (n =

0), and the quantity a, + b, is the fraction of extraterrestrial radiation reaching the earth on

clear days (n = N). In the absence of regional values for a, and bs, R, may be estimated

using the equation of Hargreaves and Samani (1982):

R, =(KT)(R, XTD)o0 (3-4)

where TD is the difference between maximum and minimum air temperatures (C) and

KTis an empirical constant. Samani (2000) developed an equation to determine KT as a

function of TD:

KT = 0.00185(TD)2 0.0433TD + 0.4023 (3-5)

using 25 years of data for the continental U.S. Daily values for A, es, ea, yp, N, Ra, and Rnl

are calculated according to the equations given in Allen et al. (1998).

Evaporative demand is applied in a top-down approach in the model, with

evaporation applied first to intercepted water, then to ponded water on the ground

surface, then to soil evaporation and plant transpiration. As in the original ACRU2000

model (Chapter 2), soil evaporation and plant transpiration can be applied as a lumped

quantity to soil layers containing plant roots or separated between soil evaporation and

plant transpiration for conditions of incomplete cover using the methods of Ritchie

(1972) where potential transpiration (Tp) is estimated as a function of the leaf area index

(LAI):

T, = (.7LAI05 0.21)ET for LAI < 2.7 (3-6a)
T, = 0.95ETo for LAI >2.7 (3-6b)

Plant transpiration is further adjusted for different plant albedo, and stomatal and

aerodynamic resistances at various stages of growth with a crop coefficient. Water









extraction from each soil layer is determined as a function of the maximum transpiration

rate Tp, a root distribution function g(d), and soil moisture dependent reduction factor

a(h):

T = a(h)g(d)T, (3-7)

The soil moisture reduction factor as a function of pressure head, a(h) used was proposed

by Feddes et al. (1978) (Figure 3-2). At h < hi (oxygen deficiency), and h > h4 (pressure

greater than the wilting point) a(h) is zero. Between h2 and h3 transpiration occurs at the

potential transpiration rate, a(h) is unity. Between hi and h2 and h3 and h4 a linear

reduction in transpiration is assumed. The ability to alter the point in which oxygen

deficiency begins (h2) is advantageous since many plant species in the flatwoods

ecosystem are quite tolerant to wet conditions and may transpire at their maximum rate

even when the soil is saturated. The linear root distribution function used here is that

proposed by Hoogland et al. (1981):

c(2d L)+ L
g(d) 2 -< c <0, d

where c is an empirical parameter expressing the relative density of roots between the

ground surface (d = 0) and the maximum depth of roots (d = L) and is shown in Figure

3-3. Upon integration with respect to d, Equation 3-8 defines the fraction of roots

between the ground surface and a depth ofd.

Potential soil evaporation is adjusted by a factor of 1.15 to account for the

difference in albedo between bare soil and a vegetated surface as recommended by Allen

et al. (1998). As in Chapter 2, potential soil evaporation Ep is further adjusted for the

percent surface cover by mulch or litter, Cs:










E = E (3-9)
P 100

Soil water evaporation takes place to a user defined depth within the soil profile with

recommended values ranging from 0.1 to 0.15 m depending on soil texture (Allen et al.

1998). According to Ritchie's method actual evaporation from the soil surface continues

at a maximum rate equal to the potential rate (Stage 1 evaporation) until the accumulated

soil water evaporation exceeds the stage 1 upper limit, U1 which is defined in units of

mm:

U, = 3)042 (3-10)

where a, is a soil water transmission parameter that is related to the texture of the soil

(Ritchie 1972). After U1 is exceeded soil water evaporation proceeds at a reduced

(Stage 2) rate as a function of the square root of time:

E = atd d 1)05 (3-11)

where td is the number of days since U1 has been exceeded.

Interception

The interception of rainfall by the plant canopy is represented as either a maximum

storage capacity (mm) or as a function of the leaf area index and gross daily rainfall using

the model of Von Hoyingen-Huene (1983) as cited in Schulze (1995) and used in the

unmodified ACRU2000 model:

I= 0.30 + 0.27P + 0.13LAI 0.013PLAI 0.007LAI2 (3-12)

where I is in units of mm and P is gross daily rainfall (mm).









Infiltration

In humid, shallow water-table regions the dominant mechanism of runoff

generation has been shown to be saturation excess (Dunne and Black 1970; Freeze 1972;

Hernandez et al. 2003). In these regions a shallow water-table rises, saturating the entire

soil profile and inundating the ground surface, creating a Variable Source Area (VSA).

These VSAs vary with space and time, expanding in the wet season and contracting in the

dry season. In contrast to infiltration-rate limited or Hortonian runoff (Horton 1933),

runoff from VSAs have shown little sensitivity to temporal variability of rainfall or

rainfall intensity (Hernandez et al. 2003). Due to the highly conductive nature of the

sandy soils of the Florida flatwoods the infiltration capacity of the soil is rarely, if ever,

limiting. Thus runoff generation in the model is assumed to be solely storage-limited,

with infiltration proceeding until the entire soil profile becomes saturated.

Water-table depth and soil moisture distribution

The total volume of water contained within pore spaces between the water-table (z

= 0) and the ground surface (z = zo) can be expressed as:


V= ]Odz (3-13)
0

where V, is the volume of water per unit area (cm3 cm-2) and 6 is the moisture content

(cm3 cm-3) at some height above the water-table z (cm). If the relationship between soil

moisture and pressure head (h) within the soil profile is known and hydrostatic conditions

can be adequately assumed, then closed form equations expressing 0 as a function of z

can be used in equation (3-13) to define explicitly the volume of water between the

ground surface and the water-table. In this hydrostatic approximation of Richards'

equation Vw is the basic state variable that is influenced by water movement into or out of








the soil profile. This assumption of a hydrostatic condition has been shown to be

adequate for regions with shallow water-tables (Skaggs 1980; Koivusalo et al. 2000) and

also specifically for shallow water-table regions with highly conductive soils such as the

flatwoods regions of Florida (Rogers 1985). In general, the hydrostatic approximation is

most accurate for higher conductivity soils and shallower water-tables. Under hydrostatic

conditions the water content at any point can be described using the models of Brooks

and Corey (1964), Hutson and Cass (1987), or van Genuchten (1980). For a soil profile

at hydrostatic equilibrium the water content as a function of height above the water-table

using the model of Brooks and Corey (1964) is


0(z)=O +(,0 -0'hb for z > hb (3-14a)
0(z)= 0, for z < hb (3-14b)

where 6(z) is the moisture content (cm3 cm-3) as a function of height above the water-

table z (cm), Or is the residual moisture content (cm3 cm-3), 0s is the saturated moisture

content (cm3 cm-3), hb is the bubbling pressure head, or air entry pressure head, of the soil

(cm); and A is the pore size distribution index (-). The model of Hutson and Cass (1987)

which replaces the sharp discontinuity at hb in the Brooks and Corey (1964) model with a

parabolic segment can similarly be expressed as


0(z)= +(0-, -0) h for z > h, (3-15a)



O(z)= 0 +(08 -0 ) 02 !1si for z hb









where 0, is the water content (cm3 cm-3) at the inflection point between the exponential

and parabolic portions of the water characteristic curve at capillary pressure head h, (cm).

O, and h, are defined as

27/
O,-= (3-16)
A+2

h = hb kA2 (3-17)

Similarly the model of van Genuchten (1980) can be expressed as


0(z)= 0 +(O O +(z) (3-18)
1 + (az)

where a is in units of cm-1, and n, and m are empirical parameters with the constraint m =

1 1/n. The volume of water within a given soil layer can then be expressed as the

integral of either one of these models:

-2
V,= fO(z)dz (3-19)
zi

Upon integration Equation (3-14) becomes:


(0, h 0 kZ
JO(z)j = ,z + 1-A- zfor AI 1 and z > hb (3-20a)

0O(zdlz = 6,z +(0, -0,)hb In(z) for A= 1 and z > hb (3-20b)
f0(z)dz = z for z < hb (3-20c)
Similarly, upon integration Equation (3-15) becomes:


(0, Or/hb Z
0O(z)dz = ,z+-+ z for A 1 and z >h, (3-21a)
(zz = ,+ ( -,h ) r = and>h, (3-21b)
J0(z)z=zOrz+(0 -Or)hbln(z) for A= 1 and z> h (3-21b)











0 (z)dz= Oz+(O -0,) z- 0 for z J+ 3h?



The model of van Genuchten (1980), Equation (3-18), cannot be integrated analytically

with the restriction that m = 1 1/n so it is integrated numerically in the model using a

five-point Gauss-Legendre quadrature (Chapra and Canale 1998).

The removal of water from the plant root zone by evapotranspiration may cause a

deviation from the hydrostatic profile. This deviation creates a depleted root zone that is

represented explicitly in the model for each soil layer. This depleted root zone implies

that an upward gradient is induced within the soil profile. Water may move upwards in

the soil profile in response to this gradient. This upward movement of water defines the

connectivity between a shallow water-table and evapotranspiration. This upward

movement of water is simulated by assuming that a steady state condition exists between

the water-table and an evaporating surface. Assuming steady-state, the upward

movement of water can be found from Darcy's Law, assuming the soil profile is

homogeneous:


q= K(h 1 (3-22)


where q is the upward flux (m/day), K(h) is the hydraulic conductivity (m/day), h is the

soil capillary pressure head, and z is the height of the evaporating surface above the

water-table (m). Integration of Equation (3-22) yields:









h dh
z = + h (3-23)
SI+ q / K(h)

Assuming a relationship between K and h allows Equation (3-23) to be solved with a

lower boundary condition of h= 0 at z = 0 (at the water-table). The complexity of

analytical or numerical solutions to equation (3-23) depends on the choice of K(h)

relationship. In developing analytical solutions an upper limit of integration of h = oo is

typically used for simplicity. Gardner (1958) showed that this upper limit is appropriate

since upward flux quickly approaches a limiting value as soil suction increases. Anat et

al. (1965) solved equation (3-23) using the Brooks and Corey (1964) hydraulic

conductivity relationship and derived an approximate, algebraic solution that is explicit in

q:


q =K, 1+ +1.886] (3-24)


where = 2+3 A. The actual amount of upward flux occurring during a time step is

determined by using the first depleted soil layer above the water-table as the upper

boundary. The maximum upward flux calculated to this bottom-most depleted layer is

retained as the limiting maximum upward flux for the entire profile on a given day.

Should this bottom-most depleted layer become fully replenished the calculation of

upward flux proceeds to the next layer until either the limiting maximum is reached, or

the maximum upward flux for a subsequent layer is less than the amount to which that

layer is depleted below its hydrostatic water content, or the entire root zone is

replenished, whichever is smaller. The importance of such a time-varying upper

boundary condition for upward flow from the water-table when simulating the









fluctuations of the water-table, particularly during periods of drought, has been

demonstrated by Rogers (1985) and Desmond et al. (1996).

Groundwater flow

Groundwater may flow into or out of the model domain both horizontally and

vertically. Vertical movement of groundwater can occur at a constant or time-varying

specified rate or according to a constant or time-varying hydraulic head in a deep aquifer

below a restrictive later according to Darcy's Law:

q = -C(H,- Hd) (3-25)

where Hw, is the hydraulic head in the surficial aquifer (m), Hd is the hydraulic head in

the deep aquifer (m), and Cr is the conductance of the restrictive layer (1/day) and is

defined as:


Cr = (3-26)
L

where Kr and Lr are the hydraulic conductivity (m/day) and thickness (m) of the

restrictive layer, respectively.

Horizontal flow in response to a constant or time-varying boundary condition is

simulated using the Dupuit equation (Fetter, 1994):

SK(H2 H) (3-27)
2L

where q' is the flow per unit width (m2/day), KH is the horizontal hydraulic conductivity,

L is the distance to the boundary (m), and Hb is the hydraulic head at the boundary (m).









Overland flow and depression storage

Overland flow is simulated with a simple stage-discharge relationship as used by

Tremwel and Campbell (1992) and Kroes and van Dam (2003). The stage-discharge

relationship is of the form:


q= (hpond Zdep (3-28)


where q is the runoff depth (mm), yis the runoff resistance (days) and is typically

calibrated to observed data, hpond is the depth of ponded water on the ground surface

(mm), Zdep is the depth of depression storage which must be filled before runoff can begin

(mm), and fis an exponent to be calibrated (-) but is usually given a value of 1.67

(assuming Manning's equation).

Summary

A field-scale hydrologic module for use within the ACRU2000 distributed

hydrologic model was developed to simulate the hydrology of humid, shallow water-table

regions such as the flatwoods of the southeastern United States. The module is intended

to simulate the position of the water-table explicitly in order to accurately predict its

contribution to evapotranspiration and the creation of Variable Source Area runoff. The

standardized Penman-Monteith reference potential evapotranspiration equation

recommended by the Food and Agricultural Organization (Allen et al. 1998) has been

added to the model as well as the methods of Hargreaves and Samani (1982) and Samani

(2000) to estimate incoming solar radiation. Evaporative demand is applied in a top-

down approach to intercepted water, ponded water, and then applied as a lumped quantity

to the soil or partitioned to soil evaporation and plant transpiration. Lumped

evapotranspiration or partitioned transpiration is applied in proportion to the density of









plant roots which is defined using the linear root distribution function of Hoogland et al.

(1981). The response of plant transpiration or lumped evapotranspiration to water stress

is represented as a function of soil water pressure head using the functional relationship

ofFeddes et al. (1978). The model approximates soil moisture as having a hydrostatic

distribution. Soil moisture contents may be represented by one of three soil moisture

characteristic models. Soil moisture within the root-zone may drop below the hydrostatic

water content due to evapotranspiration. This reduction in root-zone water contents

induces upward flow which is represented using an approximate, steady-state solution to

Darcy's Law. Saturated groundwater can flow in or out to a deep aquifer and/or

horizontally in response to a time-varying boundary condition. Runoff is assumed to

occur via saturation-excess only and moves from the field according to a simple stage-

discharge relationship.

Application of the model, at the field-scale, is limited to the scale at which model

parameters can be appropriately considered to be homogeneous and to areas with highly

permeable soils where runoff occurs primarily by saturation-excess.

The object-oriented design of the ACRU2000 model made it an ideal candidate for

adding such model components in a straightforward and consistent manner. Object

design was made using UML to define new objects and their relationships. UML

diagrams of the hydrologic module described here are shown in Appendix B and are

accompanied by a short description of the objects in Appendix A, and input/output

variable reference in Appendix C, and a technical manual in Appendix D. The resulting

model design provides a modular and easily extensible model structure. The new






37


hydrologic module is validated in Chapter 4 and its performance compared to the

original, unmodified model described in Chapter 2.
































Components:
Datal
Data2
Data3


Componenl2-
Datal
Data2


S Inheritance (type of)
0 Aggregation (part of)
Association (uses data from)


Figure 3-1. Sample UML diagram showing Component, Process, and Data objects and
the inheritance, aggregation, and association relationships (adapted from
Kiker and Clark 2001)

























0.4


0.2


0
hi h2 h3 h4

Figure 3-2. Transpiration reduction factor as a function of soil pressure head.



Root Density (cm"1)


0 0.005 0.01


0.015 0.02 0.025 0.03


70

80

90

100


-
.'
A '
.


c =-0.5..


,
/
,. /
4 '


I.
I.


c=0


Figure 3-3. Root density distribution function g(d) of Hoogland et al. (1981). Maximum
depth of roots (L) is 80 cm in this example.














CHAPTER 4
FIELD-SCALE HYDROLOGIC MODEL FOR HUMID, SHALLOW WATER-TABLE
ENVIRONMENTS: VALIDATION

Introduction

A physically based model for humid, shallow water-table environments was

developed in Chapter 3, this chapter validates the model, evaluates the sensitivity of the

model to input parameters, and makes recommendations for model improvement. The

model validation is conducted using observed data from three experimental sites. For the

first experimental site validation is made by comparing model simulations to observed

data and to a numerical, finite difference model that solves Richards' equation. For the

second experimental site validation is made by calibrating the model to a portion of the

observed data and then using the remaining observations for verification of the model

calibration. The model's performance is also compared to the original, unmodified

ACRU2000 model (Chapter 2) and the field-scale Field Hydrologic And Nutrient

Transport Model (FHANTM) for the second experimental site. For the third

experimental site validation is made by calibrating the model to a portion of the observed

data and then using the remaining observations for verification of the model calibration.

In addition to evaluating the model's performance by making visual comparisons

between simulated results and field observations the model performance is evaluated

using error measures of mean absolute error:

1 in
MAE= j -0, (4-1)
and root-mean square error:
and root-mean square error:











RMSE = 1 n (4-2)


by pair-wise comparison between model predicted (P,) and observed (O,) daily values.

These measures are significant in that they provide a quantification of the error in units of

the variable in question. Using RMSE in conjunction with MAE is useful in that the

degree to which RMSE exceeds MAE is an indicator of the extent that outliers or variance

in the difference between simulated and observed values exist in the data since RMSE is

more sensitive to extreme values due to the squaring of the differences between

observations and predictions (Legates and McCabe 1999). Additionally, model

performance is evaluated using a relative error measure, the Nash-Sutcliffe coefficient of

efficiency (Nash and Sutcliffe 1970):



E= 1- 1 (4-3)
Yo, -U)2
,=1

where 0 is the mean of the observations. E ranges from negative infinity to 1.0 with

higher values indicating better agreement. Since E is a ratio of the mean square error to

the variance of the observed data, subtracted from 1.0, E is equal to 0.0 if the squares of

the differences of predicted and observed values is as large as the variability in observed

data. This indicates that the observed mean 0 is as good a predictor as the model. A

value of E < 0.0 indicates that 0 is a better predictor (Legates and McCabe, 1999).









Case Study 1: Paynes Prairie State Preserve

Site Description and Experimental Design

Paynes Prairie State Preserve is a 5600 ha highland marsh system in north-central

Florida (Figure 4-1). Approximately 4100 ha of the Preserve is wetland and is a surface

expression of the surficial aquifer. Experimental data were collected by Jacobs et al.

(2002) from a wet prairie community within the Preserve (29034'14"N, 8216'46"W).

Detailed site description and instrumentation information can be found in Jacobs et al.

(2002) and Whitfield (2003), however a brief summary is included here. The wet prairie

is a flat plain with emergent, herbaceous species such as maiden cane (Panicum

hemitomon Schultes), mild water-pepper (Polygonum hydropiperoides Michx.), mock

bishop's weed (Ptilimnium capillaceum Michx), and dog fennel (Eupatorium

capillifolium Lam.) (Jacobs et al. 2002). Observations by Jacobs et al. (2002) showed

that the majority of plant roots were contained in the top 10 cm of soil with 95% of roots

within the top 25 cm. The marsh hydrogeology consists of a sandy surficial aquifer that

is separated from the Floridan aquifer by the Hawthorne Formation, a semi-confining

clay unit that is approximately 1 meter below ground surface. The predominant soil in

the wet prairie was found to be Wauberg sand, a loamy, siliceous, hyperthermic Arenic

Albaqualfs (Liu et al. 2005).

Micrometeorological, soil moisture within the top 25 cm of soil, and water-table

measurements were made during the experimental period. Water-table measurements

were made between February 1 and June 30, 2001 and soil moisture measurements were

made between April 10 and June 26, 2001. Measurements of actual evapotranspiration

were made directly using an energy budget variation of the eddy correlation approach

(Jacobs et al. 2002). These measurements were made between May 1 and July 20, 2001.









Micrometeorological measurements of rainfall, net radiation, temperature, relative

humidity, and wind speed were made during the entire study period in order to calculate

reference potential evapotranspiration.

Results and Discussion

Due to the extremely flat topography and the underlying semi-confining unit,

vertical and horizontal flow into the model domain by overland or groundwater flow

were assumed to be negligible. Using such assumptions the wet prairie can be

represented as a single lumped element for modeling purposes where accumulation of

water occurs via precipitation and losses via evapotranspiration only. For comparison

purposes the wet prairie was also simulated using the Soil-Water-Atmosphere-Plant

(SWAP) model, a one-dimensional, finite-difference model that solves Richards'

equation (van Dam and Feddes 2000; Kroes and van Dam 2003). The SWAP model was

chosen for comparison because it shares several of the same algorithms with ACRU2000,

specifically the determination of plant water stress, the partitioning of potential

evapotranspiration between soil evaporation and plant transpiration, and the transition

from stage I to stage II soil evaporation.

Following a prior modeling effort of this site the leaf area index was 2.7 and the

plant root density distribution was represented as decreasing linearly from the ground

surface to a depth of 0.325 meters below ground which satisfies the observation of 95%

of the roots within the top 0.25 meters of soil (Jacobs et al. 2002; Liu et al. 2005). Soil

moisture characteristic parameter values are shown in Table 4-1 and are adapted from Liu

et al. (2005). Reference potential evapotranspiration was calculated using the Penman-

Monteith equation as described by Jacobs et al. (2002) and was used along with measured

precipitation as the climate forcing in the models. Reference potential









evapotranspiration, as determined by Jacobs et al. (2002) was not adjusted with a crop

coefficient. No additional model calibration, beyond that done by Liu et al. (2005) was

made here.

Observed and simulated water-table depths are shown in Figure 4-2. The simulated

results agree with the measured data with the exception of days during and immediately

following a large rain event, where the simulated rise of the water-table is not as intense.

This observation is consistent with the response caused by entrapped air and is supported

by the rapid decline in the measured water-table soon after the rain event (Fayer and

Hillel 1986; Nachabe et al. 2004). Both models deviated from observations when the

observed water-table fell below approximately 1 meter in depth. During this period

(roughly between 5/25 and 6/15), as the wilting point of the top 25 cm of soil was

approached (Figure 4-3), a reduction in the simulated evapotranspiration can be seen as

compared to observed values (Figure 4-4). This indicates that the soil, as represented in

the model, was not capable of supplying adequate water vertically upwards during this

period of dry-down.

Observed and simulated soil moisture content in the top 0.25 m of soil is shown in

Figure 4-3. ACRU2000 tended to under-predict the soil moisture content in the top soil

during much of the simulation period, however this may be due to the nature of the

model. Since the model does not compute water balance components simultaneously, but

rather calculates them sequentially, the low soil moisture contents reported by the model

at the end of the simulation day are likely due to the fact that evapotranspiration removes

water from the root zone after any upward flux (into the root zone) occurs within the soil

profile.









Simulated and observed evapotranspiration are shown in Figures 4-4 and 4-5.

ACRU2000 often over-predicted on days with high observed ET and SWAP often under-

predicted on days with low observed ET.

The goodness of fit between measured and simulated daily values is shown in

Table 4-2. Both models performed comparably according to the MAE, RMSE, and E,

with SWAP appearing to perform slightly better in predicting water-table depths and

actual evapotranspiration and ACRU2000 appearing to perform slightly better in

predicting soil moisture contents. Values of the coefficient of efficiency, E, compared

well to those found by Liu et al. (2005) using daily average potential evapotranspiration

inputs. Liu et al. (1995) found values of 0.888, 0.902, and 0.605 for the water-table

depth, soil moisture content, and evapotranspiration, respectively.

Case Study 2: W.F. Rucks Dairy

Site Description and Experimental Design

W.F. Rucks Dairy is a low density improved dairy pasture located in south-central

Florida within the Kissimmee River Basin (2727'N 8056'W) (Figure 4-6). The pasture

is approximately 3.9 ha with an average ground slope of 0.14%. The pasture contained

primarily bahia grass (Paspalum notatum). The soil of this site was found to be a

dominated by Myakka fine sand, a sandy, siliceous, hyperthermic, Aeric Haplaquods

(Capece 1994). As part of a study to better understand the hydrologic and contaminant

transport characteristics of the river basin the site was hydrologically isolated by the

construction of a low earthen berm. Surface water flows from the site were measured

using a critical-depth trapezoidal flume installed in a breach in the berm. Field ditches

within the sites were blocked to mimic undrained, natural conditions. This site has been

the focus of prior modeling studies using the FHANTM model (Campbell et al. 1995;









Zhang et al. 1995; Zhang and Gornak 1999) and was thus chosen as a good candidate for

testing the ACRU2000 model.

Groundwater level measurements were taken on an approximately weekly basis at

51 well stations throughout the site with each station composed of 2, 3, or 4 wells

screened over various depths (Campbell et al. 1995). Rainfall, wind speed, solar

radiation, temperature, and relative humidity were measured at the site. The form of the

Penman (Penman 1948) equation developed by Jones et al. (1984a) for Florida conditions

was used to estimate daily reference potential evapotranspiration (Tremwel 1992).

Micrometeorological, groundwater level and runoff measurements were made for 33

months from April 1, 1989 to December 31, 1991.

Results and Discussion

Because of the extremely flat topography and low groundwater gradients measured

during the study period (Campbell et al. 1995) model simulation was conducted by

treating the pasture as a single, lumped element. Groundwater flow into or out of the

model domain were assumed to be negligible. Groundwater level measurements were

spatially averaged for comparison to model outputs. Soil physical properties and water

characteristics for the site are based on the data of Tremwel (1992) and Sodek et al.

(1990) and are shown in Table 4-3. Monthly crop coefficients were taken from the work

of Tremwel (1992) and interpolated to daily values by Fourier analysis in the ACRU2000

model (Table 4-4). The plant root distribution was assumed to decrease linearly from the

ground surface to a depth of 0.9 m for bahia grass (Fraisse and Campbell 1997).

Model calibration was conducted using the first 17 months of data (April 1, 1989 to

August 31, 1990) leaving the final 16 months for model verification (September 1, 1990

to December 31, 1991). Model calibration consisted of adjusting the runoff resistance









coefficient and the depression storage to match observed daily runoff timing and

magnitude as well as minor changes to the water characteristic parameters of the soil

layers within the range expected for the soil type and location as ascertained from the

data of Sodek et al. (1990) in order to match both water-table levels and the timing of

runoff generation. Model calibration was made by graphical comparison between

observed and simulated daily values. The model was (subjectively) considered to be

sufficiently calibrated when successive parameter adjustments appeared to provide little

or no improvement in graphically matching observations.

The observed and predicted water-table depths by ACRU2000 and by FHANTM,

as reported by Tremwel (1992), are shown in Figure 4-7 for the entire 33 month period.

Both models generally followed the observations. Both models deviated from the

observations in early 1991. FHANTM deviated from the observations in late 1990 and

1991 as well.

Figure 4-8 shows the simulated and observed daily runoff for the calibration and

verification periods for the shallow water-table version of ACRU2000. The timing of the

runoff events correspond with periods where the water-table reached the ground surface

causing saturation excess overland flow. Simulated and observed daily runoff for the

FHANTM and unmodified ACRU2000 models is shown in Figures 4-9 and 4-10. As

with the prediction of water-table depths (Figure 4-7) the FHANTM model performed

similarly to the modified ACRU2000 model in predicting total runoff (Figure 4-11),

however daily runoff events were not predicted as well as by ACRU2000 (Figures 4-8

and 4-9). As seen in Figure 4-10, the prediction of daily runoff by the unmodified

ACRU2000 model was poor. This poor prediction is due, almost entirely, to the









reduction of evapotranspiration by the model at soil water contents above field capacity

(taken as 100 cm of suction) as detailed in Chapter 2. The original, unmodified

ACRU2000 model could predict daily runoff much more accurately if the values of field

capacity are set artificially high (near porosity) allowing evapotranspiration to continue at

the potential rate under very wet conditions. This result is not shown here. The

simulated and observed runoff is also shown in annual cumulative plots in Figure 4-11

for the three models and for the three years of the study.

The statistical measures of the model performance on daily predictions are shown

in Table 4-5. Based on the absolute error measures and the coefficients of efficiency the

model proposed in Chapter 3 appears to be a better predictor in capturing the water-table

dynamics and the generation of saturation excess runoff of the pasture as modeled as a

single, lumped element compared to the FHANTM and original, unmodified ACRU2000

models.

Case Study 3: MacArthur Agro-Ecology Research Center at Buck Island Ranch

Site Description and Experimental Design

MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch is a

full-scale working cattle ranch owned by The John D. and Catherine T. MacArthur

Foundation and leased to Archbold Biological Station (Swain 1998). The site is located

in south-central Florida (27 9' N and 81 11' W), approximately 21 km northwest of

Lake Okeechobee (Figure 4-12). As part of a major integrated research project to address

the effects of best management practices on nutrient loads in runoff 16 experimental

pastures were hydrologically isolated in order to quantify runoff volumes leaving the

pastures. For this case study a single 30.2-ha semi-native pasture is simulated as an

example of the model's performance at Buck Island Ranch. The pasture simulated is









identified as Winter Pasture 7. The terrain of the site is extremely flat, with a slope of no

greater than 0.02 % (Hendricks 2003) to the north towards the C41 (Harney Pond) canal,

a major regional conveyance linking Lake Istokpoga to the north and Lake Okeechobee

to the south. Shallow wetlands are interspersed throughout the site covering 4.3% of the

land area of the pasture (MAERC 2004), most within 30 m of shallow drainage ditches.

The stage of Harney Pond Canal is managed by the South Florida Water Management

District (SFWMD) at the S70 spillway located approximately 4 km downstream from the

site. The pasture contains semi-native vegetation composed primarily of broomsedge

(Andropogon virginicus), carpet grass (Axonopusfurcatus), and bahia grass (Paspalum

notatum), the wetlands are vegetated primarily with grasses such as carpet grass and

maidencane (Panicum hemitomon) and with miscellaneous wetland species (MAERC,

2004). Soil surveys of the area were conducted by the USDA-NRCS in June 1997, at a

0.5-ha resolution. Soils in the pasture are predominantly (95.7%) Pineda fine sand, a

loamy, siliceous, hyperthermic Arenic Glossaqualfs, with 90% coverage of a thin (2.5-15

cm) muck layer (MAERC 2004).

The pasture is hydrologically isolated with a low earthen berm that forces all runoff

from the pasture to exit through a trapezoidal flume located at the downstream end of the

pasture. Existing ditches were interconnected to route runoff through the exit flume.

Runoff was determined at the trapezoidal flume from water level measurements made in

stilling wells at both the upstream and downstream end of the flume in 20 minute

intervals (Capece et al. 1999). Meteorological data were collected on an hourly basis at a

weather station adjacent to the pasture as well as at 3 nearby stations. Rainfall,

temperature, solar radiation, relative humidity, wind speed, and wind direction were









collected at each station. Groundwater levels were measured at 15-minute intervals in a

2-inch monitoring well installed in the center of the pasture. The well extended to a

depth of 18 ft below ground surface with the screened portion beginning at 5 ft below

ground surface. Runoff and climatic data collection began in May 1998 and groundwater

level measurements began in September 2000. Data collection continued until the end of

2003.

Results and Discussion

Due to the extremely low groundwater gradients observed within the pastures (data

not shown), and the extremely flat topography, model simulation was conducted by

treating the pasture as a single, lumped element. Figure 4-13 shows the canal stage as

reported at the S70 spillway located four kilometers downstream as compared to the

groundwater level recorded at the center of the experimental pasture. As can be seen, the

gradient between the pasture and the canal reverses direction, with groundwater flowing

towards the canal during wet periods (typically summer) and canal water flowing towards

the pasture during dryer periods. The daily time series of canal stage serves as an input to

the model. Groundwater flow between the pasture and the adjacent upland were assumed

to be negligible. Soil physical properties and water characteristics for the site are based

on the data of Sodek et al. (1990) and Gathumbi et al. (2005) and are shown in Table 4-6

(calibrated values). The plant root density distribution was assumed to decline linearly

from the ground surface to a depth of 0.8 meters for the combination of bahia and native

grasses (Fraisse and Campbell 1997). Daily reference potential evapotranspiration was

calculated using the standardized Penman-Monteith equation recommended by Allen et

al. (1998). Crop coefficients used are shown if Table 4-4 and are based on the work of

Smajstrla (1990) and Allen et al. (1998).









Model calibration was conducted using the observed runoff and groundwater level

data from 1998 to 2001. The length of the calibration period was chosen in order to

include adequate groundwater level data (data collection having started in September,

2000). This period allows for 16 months of groundwater level data to be used in

calibration. The remaining two years, 2002 and 2003, are used to verify the model

calibration. Model calibration was performed by changing the runoff resistance

coefficient, increasing the crop coefficients reported by Smaj strla (1990) and Allen el al.

(1998) for the winter months, reducing the saturated water content and n soil parameters

slightly from the fitted values determined from the data of Sodek et al. (1990), and

reducing the hydraulic conductivity by one order of magnitude of the A, E, and Bw soil

layers from that reported by Sodek et al. (1990) (adjusted parameters shown in Table 4-6)

in order to better replicate the influence of the stage in the adjacent canal on groundwater

levels within the pasture. Model calibration was made by graphical comparison between

observed and simulated daily values. The model was (subjectively) considered to be

sufficiently calibrated when successive parameter adjustments appeared to provide little

or no improvement in graphically matching observations.

Observed and simulated groundwater levels are shown in Figure 4-14. The

discrepancy between observations and simulated water-table depths, particularly during

periods of deeper observed water-tables, may be due to the representation of the canal's

influence on groundwater levels within the pasture, the uncertainty of soil hydraulic

parameters due to the lack of field-collected data, and the assumption of no groundwater

inflow or outflow from upland areas. No stage measurements of the canal were made at

the site, stage measurements at the S70 spillway were assumed to represent the local









canal stage by assuming level pool conditions over the 4 km distance. No groundwater

measurements were made in areas upland from the pastures. As can be seen the model

performed reasonably well in following the general trend in water-table fluctuations, with

both periods of over and under prediction. As can also be seen in Figure 4-14, the model

predicts the water-table reaching the ground surface on numerous occasions (at which

time runoff may occur by saturation excess) while this does not always appear in the

observed data. This is due to the installation of the flume in a shallow ditch which is

contiguous with the other shallow ditches in the pasture (land elevation near the

groundwater well was approximately 8.47 m and the flume bottom elevation was at 8.08

m). This experimental design causes runoff to occur from the field without the water-

table being at the ground surface as reported at the monitoring well since the well is not

located within one of these ditches. These shallow ditches are not explicitly represented

in the model.

Observed and simulated daily runoff can be seen in Figure 4-15 and reported as

cumulative annual plots in Figure 4-16. During the study period there were a few

instances ofbackflow from the canal into the pasture as recorded at the flume (Figure 4-

15). These few instances were ignored in generating the cumulative plots in Figure 4-16.

As shown the calibration (1998-2001) followed the observations, with the exception of

the very dry year of 2000 where runoff events were simulated that were not observed.

Verification (2002-2003) matched the observed data less well, but general trends in

runoff timing and magnitude were satisfactory.

The goodness of fit between measured and simulated daily values is shown in

Table 4-7. The coefficient of efficiency for both the water-table depth and runoff were









lower for this site than for W.F. Rucks Dairy, however the values were greater than zero,

indicating that the model is of value as a predictor of water-table depth, saturation excess

runoff, and groundwater flow to a time-varying canal stage when applied as a single,

lumped element to a site of this size.

Sensitivity Analysis

Exact values for model input variables can be costly if not nearly impossible to

obtain for even a field-scale model when applied to sites where spatial variation in

properties is likely. The accuracy of model input parameter values is usually proportional

to the time and resources invested in their determination. Since model results will be

more sensitive to certain inputs compared to others it is important to perform a sensitivity

analysis in order to establish priorities in collecting and determining model parameters.

An analysis was performed to determine the sensitivity of model simulation of

runoff, evapotranspiration, and groundwater flow to the hydrologic input parameters in

Table 4-8. The sensitivity analysis was performed using the six-year simulation of the

experimental pasture at MAERC. Model sensitivity was determined for + 25, 50, 75, and

100% of the base input value (shown in Table 4-8). For cases where this range of

variation was infeasible, or unrealistic, the results were omitted. Input parameters that

had a zero value in the simulation of the experimental pasture (depression storage and the

transpiration reduction factor for oxygen deficiency) were changed to the non-zero values

shown in Table 4-8 for the sensitivity analysis. Model sensitivity is reported as the

percent difference of model results as compared to the base simulation.

The sensitivity of runoff, evapotranspiration, and groundwater flow to the

hydrologic parameters in Table 4-8 is shown in Table 4-9. Input parameters showing

relatively low sensitivity on all three outputs were root depth, root distribution parameter,









the transpiration reduction factor due to water excess, vertical saturated hydraulic

conductivity, upward flux exponent, and interception capacity. Parameters showing little

sensitivity with the exception of very large positive or negative changes include

depression storage, the transpiration reduction factor due to oxygen deficiency, runoff

resistance, bubbling pressure head, and soil moisture shape parameter n. The runoff

exponent showed little sensitivity except for at the -25% change. This value was limited

to values greater than one (and is a value of 1.67, the value used for the base simulation,

for Manning's equation). The initial depth of the water-table was insensitive on runoff

and evapotranspiration, but had a large effect on groundwater flow. This depth was not

increased due to the relatively deep initial value of the base simulation. The remaining

parameters, the crop coefficient, horizontal hydraulic conductivity, saturated water

content, and soil moisture shape parameter ca, caused the greatest changes in runoff,

evapotranspiration, and groundwater flow. The sensitivity of these parameters on runoff

is presented in Figure 4-17.

Considering the uncertainty associated with the parameters that showed the greatest

sensitivity on runoff volumes, and the understanding that model calibration rarely results

in a single set of "optimal" parameters, a few conclusions can be made. Crop coefficients

are usually reported in the literature, if available at all, for single plant species and are

often region-specific. Their application to locations other than where they were

developed and where there exists a heterogeneous distribution of vegetation species

necessitates their use as calibratable parameters within defendable limits. Considering

the sensitivity of the soil parameters on runoff volumes, and the use of a set of soil

parameters for each soil layer, the model proposed here may be over-parameterized.









Even under circumstances where considerable effort is made to collect soil physical

properties there exists a degree of uncertainty in assigning effective model parameter

values. For this reason it is postulated that future shallow water-table model development

should attempt to reduce the number of parameters required by the model. These

simplifications could include the representation of soil moisture retention with a single

curve using a single set of parameter values. Horizontal hydraulic conductivity of the

soil could be approximated using a single average value, or as decreasing linearly or

exponentially with depth as used by Beven and Kirkby (1979). The model could also

benefit from the representation of upward flux by a single set of parameters, rather than

one for each soil layer representing the combined effects of the layers below it. Such a

relationship could be approximated as an exponential decrease with water-table depth and

produce similar results to the analytic expressions developed for homogeneous soils

(Gardner 1958; Anat et al. 1965; Cisler 1969; Warrick 1988). While approximate, these

simplifications would allow for more straightforward parameter adjustment and model

calibration.

Summary and Conclusions

A field-scale module for use within the ACRU2000 distributed hydrologic model

was developed to simulate the hydrology of humid, shallow water-table environments

such as the flatwoods of Florida. The field-scale validation of the model for three

experimental sites indicated the appropriateness of the physical approximations made by

the model, including the assumption of hydrostatic conditions in the unsaturated zone and

the use of a daily time-step when simulating regions where saturation-excess is the

dominant runoff producing mechanism. This is supported by the model's ability to

satisfactorily replicate field observations of evapotranspiration, soil moisture contents,









water-table depths, and saturation-excess runoff timing and magnitude as well as its

ability to produce similar results compared to a numerical, one-dimensional, finite-

difference model.

The model was compared to the original, unmodified ACRU2000 model as well as

to the field-scale FHANTM model. The shallow-water-table model performed similarly

to the FHANTM model. The original, unmodified ACRU2000 model greatly over-

predicted runoff due to the simulated reduction of evapotranspiration below the potential

rate at soil moisture contents above field capacity as detailed in Chapter 2.

Model sensitivity to parameter values on runoff volumes, evapotranspiration, and

groundwater flow was shown to be greatest to crop coefficients and soil hydraulic

parameters. Future field experimentation should focus on collecting these parameters to

facilitate greater certainty in model simulation. In lieu of this, it is recommended that

future model development should explore the simplification of the model by reducing the

number of parameters required.










Table 4-1. Wauberg sand soil characteristics and van Genuchten (1980) soil moisture
model parameters


Layer
OA
A
E
Btg


Layer
depth
(cm)
0-5
5-15
15-33
33-145


Ks
(cm/hr)
246.0
248.5
85.0
0.05


Os
(cm3/cm3)
0.58
0.38
0.45
0.30


Or
(cm3/cm3)
0.10
0.03
0.03
0.03


a
(cm-1
(cml)
0.0305
0.100
0.033
0.022


n
(-)
1.53
1.66
3.1
3.0


m
(-)
0.346
0.398
0.677
0.667


Table 4-2. Paynes Prairie State Preserve error measures of daily outputs
MAE RMSE -
Parameter ACRU2000 SWAP ACRU2000 SWAP ACRU2000 SWAP
Water-table depth 0.083[a] 0.070[a] 0.110[a] 0.098[a] 0.927 0.940
Soil moisture 0.022[b] 0.030[b] 0.031[b] 0.044[b] 0.863 0.718
content
Evapotranspiration 0.570[c] 0.499[c] 0.729[c] 0.632[c] 0.503 0.626
[a] Units of m. [b] Units of cmcm -. [c] Units of mm/day.

Table 4-3. Myakka fine sand soil characteristics and van Genuchten (1980) soil moisture
model parameters


Layer
A
E
Bh
Bw
Cg


Layer
depth
(cm)
0-10
10-33
33-46
46-56
56-203


Ks
(cm/hr)
23.0
23.7
12.4
16.0
17.8


Os
(cm3/cm3)
0.38
0.36
0.32
0.32
0.38


Or
(cm3/cm3)
0.05
0.04
0.16
0.11
0.07


a
(cm-1
(cml)
0.0199
0.0221
0.0218
0.0231
0.0199


n
(-)
3.17
3.42
3.12
3.15
2.59


m
(-)
0.685
0.708
0.679
0.683
0.614










Table 4-4. Crop coefficients for W.F. Rucks and MacArthur Agro-Ecology Research
Center (MAERC) at Buck Island Ranch
Month W.F. Rucks MAERC


January
February
March
April
May
June
July
August
September
October
November
December


0.40
0.45
0.50
0.50
0.60
0.65
0.85
0.85
0.85
0.75
0.75
0.60


0.65
0.75
0.85
0.90
0.95
1.00
1.00
1.00
1.00
0.90
0.80
0.70


Table 4-5. W.F. Rucks error measures of daily outputs
MAE RMSE E(-)
ACRU FHANTM Original ACRU FHANTM Original ACRU FHANTM Original
2000 ACRU 2000 ACRU 2000 ACRU
2000 2000 2000
Water- 0.095 0.108 0.132 0.157 0.866 0.812
table
depth[]a
Runoff 3.32 8.55 8.33 6.44 13.78 12.86 0.683 -0.925 -1.49
[b]
[a] Units of m. [b] Units of mm.

Table 4-6. Pineda fine sand soil characteristics and van Genuchten (1980) soil moisture
model parameters
Layer
Depth Ks Os Or a n m
Layer (cm) (cm/hr) (cm3/cm3) (cm3/cm3) (cm-1) () (-)
A 0-10 4.32 0.42 0.10 0.0287 1.96 0.490
E 10-30 4.14 0.34 0.08 0.0224 2.57 0.611
Bw 30-80 2.70 0.32 0.07 0.0234 1.81 0.448
Btg 80-140 1.37 0.35 0.15 0.0177 1.67 0.401
Cg 140-160 2.95 0.30 0.06 0.0106 2.06 0.515






59


Table 4-7. MacArthur Agro-Ecology Research Center at Buck Island Ranch error
measures from the experimental pasture for daily outputs
Parameter MAE RMSE E (-)
Water-table 0.253[a] 0.310 [a 0.631
depth
Runoff 0.472[b] 1.53[b 0.572
[a] Units of m. [b] Units of mm.










Table 4-8. Hydrologic input parameters included in the sensitivity analysis
Parameter Unit Base Value Description
K, 0.875 Crop coefficient
L m 0.8 Maximum depth of roots
c -1 Root distribution shape parameter
hz cm 20 Transpiration reduction due to 02 deficiency
h3 cm 10000 Transpiration reduction due to water excess
ZDep mm 10.0 Depression storage
I mm 1 Interception capacity
Y I/day 200 Runoff resistance
P 1.67 Runoff exponent
dwt m 1.8 Initial depth to water-table
0s: Saturated water content
A cm3/cm3 0.42
E cm3/cm3 0.34
Bw cm3/cm3 0.32
Btg cm3/cm3 0.35
Cg cm3/cm3 0.30
a: Soil moisture shape parameter of van
A 1/cm 0.0287 Genuchten (1980)
E 1/cm 0.0224
Bw 1/cm 0.0234
Btg 1/cm 0.0177
Cg 1/cm 0.0106
n: Soil moisture shape parameter of van
A 1.96 Genuchten (1980)
E 2.57
Bw 1.81
Btg 1.67
Cg 2.06
Ks,H: Horizontal saturated hydraulic conductivity
A cm/h 4.32
E cm/h 4.14
Bw cm/h 2.70
Btg cm/h 1.37
Cg cm/h 2.95
Ks,v: Vertical saturated hydraulic conductivity used
A cm/h 13.57 in the upward flux relationship of Anat et al.
E cm/h 11.7 (1965)
Bw cm/h 5.58
hb: Bubbling pressure head used in the upward
A cm 46.8 flux relationship ofAnat et al. (1965)
E cm 40.1
Bw cm 23.0
iT: Exponent used in the upward flux relationship
A -12.2 of Anat et al. (1965)
E 8.80
Bw 4.98














Table 4-9. Sensitivity of runoff, evapotranspiration, and groundwater flow to hydrologic parameters (reported as percent difference of
base simulation result). Values in left-hand column are percent changes in input parameters.
% Change in K, 0, a Ks,H P n ZDep h2 y hb dWT h3 L r I c Ks,
Parameter
Runoff
+25% -29 -8 -7 3 7 -2 -2 2 -2 0 0 0 0 0 0
+50% -54 -15 -13 5 7 -3 -4 3 -3 1 -1 0 0 0 0
+75% -16 6 7 -3 -7 6 -4 1 0 0 0 0
+100% -19 8 7 -4 -9 8 -5 1 0 0 0 0
-25% 30 12 11 -4 -19 3 2 1 2 0 1 0 0 0 0 0 0
-50% 53 32 30 -11 12 3 -3 4 -1 1 1 0 1 0 0 0
-75% 56 -17 4 -5 6 -5 2 5 1 0 0 0
-100% -25 4 -5 2 0 0
ET
+25% 35 3 2 -1 7 1 -2 -1 -1 0 0 0 0 0 0
+50% 73 5 3 -2 7 1 -4 -2 -3 0 1 0 0 0 0
+75% 4 -2 7 1 -6 -3 -4 0 0 0 -1 0
+100% 5 -2 7 1 -8 -6 -5 0 0 0 -1 0
-25% -31 -3 -3 1 -15 -1 1 0 2 0 0 0 0 0 0 0 0
-50% -59 -12 -9 2 -4 3 1 4 1 -1 -1 0 0 0 0 0
-75% -23 3 3 2 6 2 -3 -3 0 1 0 0
-100% 1 3 2 -3 1 0
Ground
+25% -81 7 14 -7 -11 1 4 -1 3 -1 0 0 0 0 0
+50% -174 11 25 -11 -11 2 8 -3 5 -2 -2 0 0 0 0
+75% 32 -14 -11 2 12 -7 7 -2 0 1 0 0
+100% 38 -17 -11 3 16 -12 9 -2 0 1 -1 0
-25% 58 -15 -21 10 31 -2 -3 -1 -2 1 11 0 0 -1 0 0 0
-50% 97 -45 -55 25 -13 -5 3 -6 3 22 -1 0 -2 0 0 0
-75% -112 49 -6 5 -10 14 26 2 -2 0 0 0
-100% 100 -6 5 26 -1 0









Florida



Alachua County


Figure 4-1. Location of Paynes Prairie State Preserve.

Observed ACRU2000 ------ SWAP


5/1/01 6/1/01 7/1/01


Figure 4-2. Measured and predicted water-table depths at Paynes Prairie State Preserve.


Alachua County




Paynes Prairie

4f


200
180
160
140
120
100
80
60
40
20
0


1.4 `-
4/1/01







63



Observed ACRU2000 -------SWAP


0.45 -
-
E 0.4


- 0.3
4-
0 0.25
o
. 0.2

0.15
a 0.1
E
0.05

0
4/1/01


0
10
20
30
40
50
60
70
80
90
100


5/1/01 6/1/01 7/1/01


Figure 4-3. Measured and predicted soil moisture contents within the top 25 cm of soil at
Paynes Prairie State Preserve.


Observed -- ACRU2000 --SWAP


5/1/01


6/1/01 7/1/01


Figure 4-4. Measured and predicted evapotranspiration at Paynes Prairie State Preserve.







64




7


6 0'
A A0 n I


E4



2 3 /3 5
A A





1 >'*" AACRU2000
3 A SWAP
0







Observe d (mmday)

Figure 4-5. Measured vs. predicted evapotranspiration at Paynes Prairie State Preserve.
r2 = 0.83 and 0.74 for SWAP and ACRU2000, respectively.


Florida Okeechobee County
0 0
















W.F. Ruckls Dairy ,
Okeechobee County
I k I A-- ACRU2Cut

r 0rr
F ah C


Figure 4-6. Location ofW.F. Rucks Dairy.











4/1/89 7/30/89 11/27/89 3/27/90 7/25/90 11/22/90 3/22/91 7/20/91 11/17/91
0 iTip ,i- i ur.i


0.2

E0.4

o 0.6
S1

0.8




1.2


* Observed ACRU2000 ------- FHANTM


Figure 4-7. Measured and predicted water-table depths at W.F. Rucks Dairy.


60 1 I
50
40
30
20
10
E L L .
E 4/1/89 6/1/89 8/1/89 10/1/89 12/1/89 2/1/90 4/1/90 6/1/90 8/1/90


60
50
40
30
20
10
0 -9
9/1/90


0

50

100

150

200
E
E


0 *

50

100

150


S-- 200
11/1/90 1/1/91 3/1/91 5/1/91 7/1/91 9/1/91 11/1/91 1/1/92


Rain Observed ACRU200


Figure 4-8. Measured and modified ACRU2000 predicted daily runoff at W.F. Rucks
Dairy. For the calibration (top) and verification (bottom) periods.











60
50I
40
30 I
20
10
0
4/1/89 6/1/89 8/1/89 10/1/89 12/1/89 2/1/90 4/1/90 6/1/90 8/1/90


60
50
40
30
20
10
0 9/
9/1/90


0

50

100

150

200 E
E
c
0 a

50

100

150


11 A 'iTI 200
11/1/90 1/1/91 3/1/91 5/1/91 7/1/91 9/1/91 11/1/91 1/1/92
11/1/90 1/1191 3/1191 5/1191 7/1/91 9/1191 11/1/91 111192


a Rain Observed- FHANTM


Figure 4-9. Measured and FHANTM predicted daily runoff at W.F. Rucks Dairy. For
the calibration (top) and verification (bottom) periods.


60 i
50
40
30
20
10


0
4/

60
50
40
30
20
10
0


' 1 Ill r "Ir


"1 -I1


1I n- i f1011-11


IJ


1/89 6/1/89 8/1/89 10/1/89 12/1/89 2/1/90 4/1/90 6/1/90 8/1/90



T fi i


9/1/90 11/1/90 1/1/91 3/1/91


5/1/91 7/1/91 9/1/91 11/1/91 1/1/92


Rain Observed ACRU2000 (unmodified)


Figure 4-10. Measured and unmodified ACRU2000 predicted daily runoff at W.F. Rucks
Dairy. For the calibration (top) and verification (bottom) periods.


0

50

100

150

200 E
E
c
0 a

50

100

150


200


i i'


Nil -


. I .












Observed ------ ACRU2000 FHANTM

2000

1800

1600

1400
E 1200
E
4 1000
o
S800

600

400

200 E .--
---------------


SCS ACRU2000


0 '-,---- ----1I
4/1/89 8/1/89 12/1/89 4/1/90 8/1/90 12/1/90 4/1/91 8/1/91 12/1/91


Figure 4-11. Measured and predicted cumulative annual runoff at W.F. Rucks Dairy.
Note: Observed runoff events during 1990 fell within the calibration period.


Figure 4-12. Location of MacArthur Agro-Ecology Research Center at Buck Island
Ranch.


*1











1/1/00
8.8 -


E 8.3



a 7.8

-I
2 7.3


1/1/01


1/1/02


1/1/03


1/1/04


------- Groundwater Level -- Canal Stage


Figure 4-13. Groundwater level and adjacent canal stage in the experimental pasture at
the MacArthur Agro-Ecology Research Center at Buck Island Ranch. Canal
stage is the stage in the C41 canal as measured at the S-70 spillway located 4
km downstream and the groundwater level is from the 2-inch well (center of
pasture).


1/1/00
0

0.2
0.4


1/1/01


1/1/02


1/1/03


1/1/04


* Observed -Simulated


Figure 4-14. Measured and predicted water-table depths at the experimental pasture at
the MacArthur Agro-Ecology Research Center at Buck Island Ranch.
























1/1/98 3/1/98 5/1/98 7/1/98 9/1/98 11/1/98


21
15 I


1/1/99 3/1/99 5/1/99 7/1/99 9/1/99 11/1/99 1/1/00

25 0

20 40
15
80
10
120
5-
0- 160

-5- 200


S 1/1/00

0 25
l 20


3/1/00


5/1/00


7/1/00


9/1/00


11/1/00


1/1











1/1


1/1/01 3/1/01 5/1/01 7/1/01 9/1/01 11/1/01


11/1/02


25
20
15
10
5
0 -
-5
1/1/03


1/1












1/'


3/1/03 5/1/03 7/1/03 9/1/03 11/1/03

im Rain Observed -------Simulated


/01 E
E
S0 .E

40

80

120

160

S200
/02

0

40

80

120

160

200
/03

O

40

80

120

S160

200
1/04


Figure 4-15. Measured and predicted daily runoff at the experimental pasture at the

MacArthur Agro-Ecology Research Center at Buck Island Ranch. Calibration

period: 1998-2001, verification period: 2002-2003.


40

80

120

160

200
1/1/99


* I"' '1' 1 "'' l I. r"" 1 ["
i IT


0 -


Nii~x~


C







70


- Observed -------Simulated


500
450
400
350
300
250
200
150
100
50
0
1/1/98


Figure 4-16. Measured and predicted cumulative annual runoff at the experimental
pasture at the MacArthur Agro-Ecology Research Center at Buck Island
Ranch. Calibration period: 1998-2001, verification period: 2002-2003.


A- Ks, H


-50 0 50
% Change in Parameter


Figure 4-17. The parameters most sensitive on runoff volumes.


1/1/99 1/1/00 1/1/01 1/1/02 1/1/03


-60
-100














CHAPTER 5
FIELD-SCALE NITROGEN AND PHOSPHORUS MODULE OF THE ACRU2000
MODEL

Introduction

The nitrogen (N) and phosphorus (P) components of the Groundwater Loading

Effects of Agricultural Management Systems (GLEAMS) model were incorporated into

the ACRU2000 model in a previous model expansion (Campbell et al. 2001). GLEAMS,

a commonly used field-scale hydrology and water quality model, represents the major N

and P components and transformations (Knisel et al. 1993). The P algorithms used in

GLEAMS are largely incorporated from the EPIC (Erosion-Productivity Impact

Calculator) model (Jones et al. 1984b). This chapter details the N and P algorithms of the

ACRU2000 model, as added by Campbell et al. (2001), and discusses their suitability for

shallow water-table environments.

Nutrient Models

The algorithms of the model describe the mobilization and transport of dissolved

forms ofN and P. Sediment-bound nutrients are not currently represented explicitly in

the model since sediment yield is not, to date, separated by particle-size class. In the case

of N the soluble component is split between nitrate-N and ammonium-N, with

mineralization of organic forms represented as a two-stage process. Soluble organic N is

not simulated in the model. P is represented as a single soluble form, labile P. Both

ammonium-N and labile P undergo fast, reversible sorption to soil particles. Transport of

soluble nutrients occurs between completely mixed soil layers. The exchange of









nutrients with runoff water is assumed to occur within the top one centimeter of soil.

Most nutrient transformations are mediated by soil moisture and soil temperature.

Phosphorus Model

The P model is represented by six main pools (Figure 5-1), a labile pool (Pi), an

"active" inorganic pool (Pa), a slowly changing "stable" inorganic pool (Ps), a fresh

organic pool (Pf) representing plant residue, a stable organic (organic humus) pool (Ph),

and a plant pool (Pp). Of the six pools, only P1 is mobile. The mass balance of the pools

can be written as:

dP
= P,,,n + Pe,, + 0.75Rap + RlP + R Pfl + RhP RuP P, P P (5-1)
dP
S= R p Ral (5-2)
dt
dP
= -RsaP (5-3)
dt
dPf
S= Rpf Rf Rp (5-4)

dP
= 0.25R + R,, RhP (5-5)
dt
dP
P = RUp Rpfp (5-6)

where Pr,,, is the quantity of P in rainfall (kg/ha/day), Pfer is the rate of application of P

in inorganic fertilizer (kg/ha/day), R,,p is the rate of decay of P in animal waste on the

ground surface (kg/ha/day), RalP is the rate of transformation from Pa to Pi (kg/ha/day),

Rflp is the rate of transformation from Pf to P (kg/ha/day), Rhlp is the rate of

transformation from Ph to P1 (kg/ha/day), Rup is the rate of plant uptake (kg/ha/day), Rsa,

is the rate of transformation from P, to Pa (kg/ha/day), Rpfp is the rate of transformation of

P, to Pf (kg/ha/day), Rjhp is the rate of transformation from Pf to Ph (kg/ha/day), and Pro,









Pgw, and Pperc are the quantities of P lost to runoff, groundwater flow, and percolation

(kg/ha/day), respectively.

Mineralization

Mineralization of organic forms of P is represented as first-order processes. The

fast-cycling fresh organic pool (Pf) consists of surface crop residues resulting from

harvest and sub-surface root residues (C:P ratios generally greater than 200). The slow-

cycling organic humus pool (Ph) consists of more recalcitrant organic forms (C:P ratios

between 125 and 200). The mineralization of animal waste (Rawp), fresh organic P (Rflp),

and organic humus P (Rhlp), are defined as, respectively (Knisel et al. 1993):

Raw = CNPkdPaw. f minfTminn (5-7)
Rfl = CNPkdPf f0minfTmn (5-8)
RhlP = fotNkhfPh f0mmf Tmmn (5-9)

where Paw is the P content in animal waste (kg/ha), ftN is the fraction of total N that is

active N which is used to infer the fraction of Ph that is mineralizable, khfis the rate of

organic humus P decomposition under optimum conditions (assumed to be 0.0001 day-l),

CNP is a factor that varies from 0 to 1 that is a function of C:N and C:P ratios of the

organic material (Jones et al. 1984b):

Sexp[- 0.693(C: N- 25)/25]
CNP = minexp[-0.693(C:P-200)/200] (5-10)
[ 1.0

where the C:N and C:P ratios are determined as the ratios of fresh residue and animal

waste mass to the mass of organic and inorganic N and P present in fresh residue and

animal waste, kd is an organic matter composition factor and represents the age of the









decomposing material by assuming the first 20% is carbohydrate-like material, the final

10% is lignin, and the intermediate is cellulose (Jones et al. 1984b):

kd = 0.8 for fdec > 0.8 (5-1 la)
kd =0.05 for 0.8 >fdc > 0.1 (5-1 b)
kd = 0.0095 for fde < 0.1 (5-1 c)

wherefdec is the fraction of the initial material remaining, fmon andfTrm are soil moisture

and soil temperature response factors that vary from 0 to 1 and are described below.

Mineralization of animal waste P, Paw, and fresh organic P, Pf, are assumed to be

partitioned to P1 (75%) and Ph (25%) (Knisel et al. 1993).

Immobilization

The high C:P ratios of fresh crop residue (generally greater than 200) results in the

immobilization of labile P by soil microbes during the decomposition process. The rate

of uptake of labile P by decomposing material is dependent upon the stage of

decomposition, the C:N and C:P ratios of the residue, the concentrations of P in fresh

organic matter, and labile P (Knisel et al. 1993):

R~P = CkPfmm n J (.16f P C) (5-12)

where the value 0.16 results from assuming that carbon is 40% of fresh organic matter

and that 40% of the carbon can be assimilated by soil microbes (Jones et al. 1984b), Cfp

is the concentration of fresh organic P (kg/kg) andfip is a phosphorus immobilization

factor:

f, = 0.01 +0.001Cp for Cip < 10.0 (5-13a)
f1p = 0.02 for CIP > 10.0 (5-13b)









and Cip is the concentrations of labile P (mg/kg). The rate of immobilization can be

limited by either N or P if the amount of N or P immobilized is less than that available

(Knisel et al. 1993).

Inorganic transformations

As P is added to soil solution the equilibrium between mobile and immobile forms

is disturbed. Following P additions, net P movement occurs into immobile forms. The

initial rate of these adsorption/fixation reactions is rapid, leaving newly formed immobile

forms relatively unstable and readily returned to solution (McGechan and Lewis 2002).

The movement between the labile P and active P pool is considered to be a rapid

equilibrium (several days to weeks) (Jones et al. 1984b). P moves between the pools as a

function of the relative size of the pools, moisture content, and temperature (Jones et al.

1984b):

RalP = kalmln f a [P Pa ] (5-14)

where kal is the rate constant at optimal conditions (assumed to be 0.1 day-1), andfom,, and

fral are soil moisture and soil temperature response factors (defined below), the

equilibrium constant of proportionality between the two pools, al1 is estimated from a P

sorption parameter, PSP (Jones et al. 1984b):

PSP
oaz= (5-15)
1 -PSP

The P sorption parameter, also referred to as the P availability index, is defined as the

fraction of P added to a soil sample that remains labile after a long incubation period:

PSP = PFinalLabile PnmhalLabile (5-16)
Padded

Following Sharpley et al. (1984), Sharpley and Williams (1990) defined PSP by dividing









soils into three groups based on soil taxonomy and weathering. The three groups defined

were calcareous, slightly weathered, and highly weathered soils. For slightly weathered

soils such as Spodosols and Alfisols (except Ultic subgroups) PSP was defined as a

function of base saturation, BSAT (%) and pH:

PSP = 0.0054B, + 0. 116pH 0.73 (5-17)

and is constrained between values of 0.05 and 0.75. This results in values between 0.05

and 3 for o-al, defining P1 as 0.05 to 3 times as large as Pa at equilibrium.

The active P pool is considered to be in a slow equilibrium with the stable P pool.

The differentiation between "active" and "stable" inorganic P pools is made in order to

account for the initial rapid decrease in labile P typically seen after P application

followed by a much slower decrease in observed labile P over long periods (Jones et al.

1984b). This representation is a simplification of a continuum of time-dependent

adsorption or fixation reactions (McGechan and Lewis 2002). The transfer ofP between

active P and stable P pools is a function of the relative size of the pools (Jones et al.

1984b):

Rsp = k o [o-, -P,] (5-18)

where os is an equilibrium constant of proportionality and is assumed to be a value of 4

(Jones et al. 1984b), and ks, is a rate constant that is defined in the GLEAMS model as a

function of PSP for non-calcareous soils (Jones et al. 1984b):

k, = exp(-1.77PSP- 7.05) (5-19)

In addition to the rapid adsorption/fixation reactions between the labile and active P

pools, the labile P pool undergoes instantaneous, reversible sorption governed by a linear









isotherm in order to determine the portion of labile P that is in solution and available for

transport into runoff, groundwater, and percolating water:

S = KdC (5-20)

where S is the portion of labile P adsorbed (mg/kg), C is the portion in solution (mg/L)

and Kd is the partitioning coefficient (L/kg). The partitioning coefficient is assumed to be

a function of the clay content of the soil, CL (%) (Knisel et al. 1993):

Kd =100 + 2.5CL (5-21)

where Kd is in units of L/kg.

Plant uptake

Plant uptake of P is assumed to occur from each soil layer from which transpiration

occurs, with the total uptake limited to a calculated plant demand. Plant demand is

determined from the plant growth rate and plant nutrient characteristics (Knisel et al.

1993).

Nitrogen Model

The nitrogen model is represented by six main pools, nitrate-N (NNO3), ammonium-

N (NNH4), active organic N (Na), stable organic N (N,), fresh organic N (Nf), and plant N

(Np) (Figure 5-2). The mass balance of the pools can be written as:

dN
N03 Nrain +NfertNO3 + R,,- Rdenit mmNO3 PuNO3 -NroNO3 -NgwNO3 (5-22)
dt
NpercNO3
d-- NfertNH4 +0.8Raw + RmmN + 0.8RammN Rt Rol RmmNH 4 (5-23)
dt H4 aw amma ammfi
RuNH 4 NroNH4 NgwNH4 NpercNH4

dN- = 0. 2R +0.2R + R Rmm (5-24)
dt
dN
S= -RaN (5-25)
dt









dN
S= RpN + R mmNO3 + RmmNH4 Ramm (5-26)

dN
t= RuN3 +RuNH4 Rp (5-27)

where Nran, is the quantity of N in rainfall (kg/ha/day), and is assumed to occur

completely as nitrate-N, NfertNO3 and NfertNH4 are nitrate-N and ammonium-N quantities in

inorganic fertilizer (kg/ha/day), RawN is the rate of animal waste N decay (kg/ha/day), Rnit

is the rate of nitrification of ammonium-N to nitrate-N (kg/ha/day), Rdenit is the rate of

loss of nitrate-N to the atmosphere by dentrification (kg/ha/day), RammaN and RammN, are

the rates of ammonification of the active and fresh organic N pools (kg/ha/day),

respectively, R0vo is the rate of loss of ammonium-N in manure to the atmosphere by

ammonia volatilization (kg/ha/day), RsaN is the rate of transformation of stable N to active

N (kg/ha/day), RimmNO3 and RmmNH4 are, respectively, the rates of immobilization of

nitrate-N and ammonium-N by fresh organic matter (kg/ha/day), RuNO3 and RuNH4 are the

rates of plant uptake of nitrate-N and ammonium-N (kg/ha/day), RpfN is the rate of

transformation of plant N to fresh organic N (kg/ha/day), NroNO3 and NroNH4 are the

quantities of nitrate-N and ammonium-N lost in runoff (kg/ha/day), NgwN03 and NgwNH4

are the amount of nitrate-N and ammonium-N lost in groundwater flow (kg/ha/day), and

NpercN03 and NpercNH4 are the quantities if nitrate-N and ammonium-N in percolating water

(kg/ha/day).

Mineralization

Mineralization of organic N forms is represented as a first-order process in a

similar manner as for P. Mineralization of fresh organic N and animal waste N is

partitioned to ammonium-N (80%) and active organic-N (20%). Mineralization of active

organic N is assumed to be completely converted to ammonium-N. The rates of









mineralization of animal waste (RawN), fresh organic N (RammN), and active organic N

(RammaN) are defined as (Knisel et al. 1993):

RawP = CNPkd w N min min (5-28)
RammfN = CNkd f f mi min (5-29)
Ramma = ka N f mmf/mm (5-30)

where Naw is the amount of N present in animal waste (kg/ha), ka is the ammonification

rate constant for active organic N and is assumed to have a value of lx10-4 day-, the

remaining parameters are defined previously for P. The second stage of mineralization,

nitrification, is simulated as a zero-order process (Knisel et al. 1993):

Rt = fenhtfrmin kMtAsol1 (5-31)

where knt is the maximum rate of nitrification, 1.43x10-5 kg/kg/day, Mso,, is the soil mass

(kg/ha), andfo/,t is a soil moisture response factor (defined below).

The exchange between the active N (C:N ratio less than 25) and stable N pools

occurs according to the relative size of the pools (Knisel et al. 1993):


RLN = ks ~ Na t- N (5-32)


where kasN is a rate constant (1x105 day-').

Immobilization

The immobilization of nitrate-N and ammonium-N is represented in a similar

manner as that for labile P:

R mm =CNPkd f min Tmin (0.016 C (5-33)

where CA is the concentration of N in fresh residue (kg/kg) and the constant 0.016 comes

from assuming that carbon is 40% of fresh organic matter and that 40% of the carbon can









be assimilated by soil microbes and that the microbial biomass and its products have a

C:N ratio of 10 (Knisel et al. 1993). Nitrate-N and ammonium-N are immobilized in

proportion to their availability.

Atmospheric loss of nitrogen

Both ammonium-N and nitrate-N may be lost to the atmosphere via the

volatilization of ammonia gas and the denitrificiation of nitrate. Ammonia volatilization

is assumed to occur from the portion of NH4 contained in animal waste and only for a

period of one week following application. The rate of volatilization is given by (Knisel et

al. 1993):

Rvo = NH,4 exp(- kt) (5-34)

where t is time in days and k, is volatilization rate constant (day-l):

k, =0.409(1.08)' 20 (5-35)

The rate of denitrification is defined as (Knisel et al. 1993):

Rden,, = NN3 [1- exp(- kdetfT, mnfOdem,)] (5-36)

where fdenit is the soil moisture response factor to denitrification (defined below), kdenit is

the rate coefficient for dentrification which is a function of active soil carbon, SC (mg/kg)

(Knisel et al. 1993):

kdent =0.106SC + 0.202 (5-37)

The active soil carbon is estimated from the active N pool (Knisel et al. 1993):

sc = 0 (5-38)
AMsoIl









Inorganic nitrogen

Nitrate-N is assumed to be completely conservative in solution. Ammonium-N is

assumed to undergo instantaneous, reversible sorption following a linear isotherm

(Equation 5-15) where the partitioning coefficient is assumed to be a function of the clay

content of the soil (Knisel et al. 1993):

Kd =1.34+0.083CL (5-39)

Plant uptake

Plant uptake of N occurs in the same manner as for labile P, from each soil layer

that transpiration occurs. The total uptake is limited to a calculated plant demand in the

same manner as for P. Uptake of nitrate-N and ammonium-N is assumed to occur in

proportion to their availability (Knisel et al. 1993).

Nutrient Transformation Response to Soil Moisture

In the model, there are three soil moisture functions employed for various

processes. For ammonification, P mineralization, and mineral N and P immobilization

the soil moisture response function is of the form (shown in Figure 5-3) (Knisel et al.

1993):

0-0wp
fOmin =- for 0< 0f, (5-40a)
Ofc OWP
fOmn = 0 for 0> 0f (5-40b)

where 0, Owp, and Ofc are the moisture content of a soil layer (cm3 cm-3), the moisture

content at the wilting point (cm3 cm-3), and the moisture content at field capacity (cm3

cm-3), respectively. As can be seen in figure 5-3, the response to soil moisture rises from

zero at the wilting point to an optimum value at field capacity. Immediately above field