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Dynamical and Statistical Downscaling of Climate Information and Its Hydrologic Implications over West-Central Florida

Permanent Link: http://ufdc.ufl.edu/UFE0043653/00001

Material Information

Title: Dynamical and Statistical Downscaling of Climate Information and Its Hydrologic Implications over West-Central Florida
Physical Description: 1 online resource (193 p.)
Language: english
Creator: Hwang, Syewoon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: climate-modeling -- downscaling -- dynamical-downscaling-model -- general-circulation-model -- hydrologic-implication -- integrated-hydrologic-model -- mm5-mesoscale-model -- statistical-downscaling-method -- west-central-florida
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: General circulation models (GCMs) have recently been developed for simulating current and future climate patterns. However, GCM results do not generally provide accurate prediction of climate variables on the scale needed to assess hydrologic impacts. In this study various existing dynamic and statistical GCM downscaling techniques were used to estimate precipitation fields over the state of Florida. These methods were quantitatively evaluated and found unable to reproduce fine-scale spatial characteristics of precipitation patterns. A new stochastic downscaling technique was developed which improves on previous methods and produces daily precipitation fields that accurately reproduces spatiotemporal variability of precipitation. The downscaled precipitation scenarios were used as input to an integrated hydrologic model to evaluate hydrologic implications of differences in the alternative downscaling methods. Results of this study should allow hydrologic modelers and water supply decision makers to more effectively use climate modeling results for water resource planning, management, and conservation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Syewoon Hwang.
Thesis: Thesis (Ph.D.)--University of Florida, 2011.
Local: Adviser: Graham, Wendy D.
Local: Co-adviser: Martinez, Christopher J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2013-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0043653:00001

Permanent Link: http://ufdc.ufl.edu/UFE0043653/00001

Material Information

Title: Dynamical and Statistical Downscaling of Climate Information and Its Hydrologic Implications over West-Central Florida
Physical Description: 1 online resource (193 p.)
Language: english
Creator: Hwang, Syewoon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: climate-modeling -- downscaling -- dynamical-downscaling-model -- general-circulation-model -- hydrologic-implication -- integrated-hydrologic-model -- mm5-mesoscale-model -- statistical-downscaling-method -- west-central-florida
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: General circulation models (GCMs) have recently been developed for simulating current and future climate patterns. However, GCM results do not generally provide accurate prediction of climate variables on the scale needed to assess hydrologic impacts. In this study various existing dynamic and statistical GCM downscaling techniques were used to estimate precipitation fields over the state of Florida. These methods were quantitatively evaluated and found unable to reproduce fine-scale spatial characteristics of precipitation patterns. A new stochastic downscaling technique was developed which improves on previous methods and produces daily precipitation fields that accurately reproduces spatiotemporal variability of precipitation. The downscaled precipitation scenarios were used as input to an integrated hydrologic model to evaluate hydrologic implications of differences in the alternative downscaling methods. Results of this study should allow hydrologic modelers and water supply decision makers to more effectively use climate modeling results for water resource planning, management, and conservation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Syewoon Hwang.
Thesis: Thesis (Ph.D.)--University of Florida, 2011.
Local: Adviser: Graham, Wendy D.
Local: Co-adviser: Martinez, Christopher J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2013-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0043653:00001


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1 DYNAMICAL AND STATISTICAL DOWNSCALING OF CLIMATE INFORMATION AND ITS HYDROLOGIC IMPLICATION S OVER WEST CENTRAL FLORIDA By SYEWOON HWANG 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 2011

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2 2011 Syewoon Hwang

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3 To my family

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4 ACKNOWLEDGMENTS First of all, I would like to express the deepest gratitude to my advisor, Dr. Wendy Graham I have been amazingly fortunate to have an advisor who gave me the freedom to explore on my own and at the same time the guidance to recover when my steps faltered. Sh e taught me how to question thoughts and express ideas. Her patience and support helped me overcome many crisis situations and finish this dissertation. I hope one day to become as good advisor to my students as s he has been to me. I am also grateful to Dr. Chris Martinez Dr. James Jones Dr. L o uis Motz and Dr. Alison Adams for their valuable time and interest in serving on my supervisory committee, as well as their comments, which helped improve the quality of this dissertation. I have been very fortuna te to communicate with thoughtful friend, Vibhava. My special acknowledgement goes to Mary Garvin Kathleen Mckee, and Lisette Staal in W ater I nstitute for their help, collaboration and valuable discussions. Finally, my utmost appreciation goes to my parents and sister for always believing in me. Their unceasing love and whole hearted support made finishing this work possible. Last but most, I thank my wife Miran for her love, support, patience, and late night prayers.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 7 LIST OF FIGURES ................................ ................................ ................................ ......................... 8 ABSTRACT ................................ ................................ ................................ ................................ ... 12 CHAPTER 1 INTRODUCT ION ................................ ................................ ................................ ..................... 13 2 QUANTITATIVE SPATIOTEMPORAL EVALUATION OF DYNAMICALLY DOWNSCALED MM5 PRECIPITATION PREDICTIONS OVER THE TAMPA BAY REGIO N, FLORIDA ................................ ................................ ................................ .............. 18 2.1 Background ................................ ................................ ................................ ....................... 18 2.2 Study Area and Data Collection ................................ ................................ ....................... 22 2.3 Methodology ................................ ................................ ................................ ..................... 23 2.3.1 MM5 Modeling ................................ ................................ ................................ ...... 23 2.3.2 Bias Correction ................................ ................................ ................................ ....... 24 2.3.3 Spatial Correlation Structure ................................ ................................ .................. 26 2.3.4 Error Statistics ................................ ................................ ................................ ........ 28 2.4 Results and Discussion ................................ ................................ ................................ ..... 29 2.4.1 MM5 Modeling and Bias correction Results ................................ ......................... 29 2.4.2 Precipitation Spatial Correlation Structure ................................ ............................. 33 2.4.3 Kriging Results ................................ ................................ ................................ ....... 34 2.5 Chapter Summary ................................ ................................ ................................ ............. 36 3 HYDROLOGIC IMPLICATIONS OF ERRORS IN DYNAMICALLY DOWNSCALED AND BIAS CORRECTED CLIMATE MODEL PREDICTIONS FOR WEST CENTRAL FLORIDA ................................ ................................ ................................ ............ 52 3.1 Background ................................ ................................ ................................ ....................... 52 3.2 Models and Data ................................ ................................ ................................ ............... 56 3.2.1 Regional Climate Modeling ................................ ................................ ................... 56 3.2.2 Hydrologic Modeling ................................ ................................ ............................. 58 3.2.3 INTB Model Domain and Calibration ................................ ................................ .... 59 3.2.4 Climate Data ................................ ................................ ................................ ........... 60 3.3 Method ology ................................ ................................ ................................ ..................... 62 3.3.1 Climate Prediction Adjustment ................................ ................................ .............. 62 3.3.2 Target Stations for INTB Model Evaluation ................................ .......................... 64 3.3.3 Error Statistics ................................ ................................ ................................ ........ 64 3.4 Results and Discussions ................................ ................................ ................................ .... 66

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6 3.4.1 MM5 Results ................................ ................................ ................................ .......... 66 3.4.2 Streamflow Simulation Results ................................ ................................ .............. 68 3.4.3 Groundwater Level Simulation Results ................................ ................................ .. 72 3.4.4 Springflow Simulation Results ................................ ................................ ............... 75 3.5 Chapter Summary ................................ ................................ ................................ ............. 75 4 DEVELOPMENT OF A STOCHASTIC DOWNSCALING METHOD TO REPRODUCE OBSERVED SPATIOTEMPORAL VARIABILITY OF DAILY PRECIPITATION ................................ ................................ ................................ ................. 103 4.1 Background ................................ ................................ ................................ ..................... 103 4.2 Data ................................ ................................ ................................ ................................ 107 4.3 Methods ................................ ................................ ................................ .......................... 108 4.3.1 Bias Correction and Spatial Downscaling (BCSD) Method ................................ 108 4.3.2 Spatial Downscaling and Bias Correction (SDBC) Method ................................ 109 4.3.3 Bias Correction and Stochastic Analog (BCSA) Method ................................ .... 110 4.3.4 Assessment of Downscaling Technique Skills ................................ ..................... 112 4.4 Applications and Discussion ................................ ................................ ........................... 114 4.4.1 Evaluation of Temporal Variability ................................ ................................ ...... 114 4.4.2 Evaluation of Spatial Variability ................................ ................................ .......... 118 4.5 Chapter Summary ................................ ................................ ................................ ........... 119 5 HYDROLOGIC IMPORTANCE OF SPATIAL VARIABILITY IN STATISTICALLY DOWNSCALED PRECIPITATION PREDICTIONS FROM GENERAL CIRCULATION MODELS FOR WEST CENTRAL FLORIDA ................................ ....... 135 5.1 Background ................................ ................................ ................................ ..................... 135 5.2 Study Domain and Data ................................ ................................ ................................ .. 138 5.2.1 Tampa Bay Region: the Integrated Northern Tampa Bay (INTB) Model Domain ................................ ................................ ................................ ....................... 138 5.2.2 GCM Archive ................................ ................................ ................................ ....... 139 5.2.3 Meteorological Data ................................ ................................ ............................. 140 5.2.4 Hydrologic Data ................................ ................................ ................................ ... 141 5.3 Methodology ................................ ................................ ................................ ................... 142 5.3.1 Bias Correction of Climate Data ................................ ................................ .......... 142 5.3.2 Statistical Downscaling ................................ ................................ ........................ 143 5.3.3 Hydrologic Modeling ................................ ................................ ........................... 14 6 5.4 Results and Discussion ................................ ................................ ................................ ... 148 5.4.1 Statistically Downscaled GCM Results ................................ ................................ 148 5.4.2 IHM Results ................................ ................................ ................................ .......... 153 5.5 Chapter Summary ................................ ................................ ................................ ........... 157 6 CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH ................... 178 LIST OF REFERENCES ................................ ................................ ................................ ............. 182 BIOGRAPH ICAL SKETCH ................................ ................................ ................................ ....... 193

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7 LIST OF TABLES Table page 2 1 Summary of Tampa Bay Region Rain Gage Data ................................ ............................. 39 2 2 Daily and monthly mean and standard deviation of long term observations and error statistics for daily and monthly total p recipitation for the MM5 results ............................ 40 2 3 Kriging error and normalized kriging error statistics for daily and monthly total point kr iged precipitation distributions ................................ ................................ .............. 41 3 1 Description of data collected for hydrologic modeling ................................ ..................... 78 3 2 Observed target stations for streamflow, Floridan and surficial aquifer, and springflow. ................................ ................................ ................................ ......................... 79 3 3 The mean monthly and annual averaged precipitation of sub basin and gridded observations and error statistics in MM5 precipitation predictions ................................ ... 80 3 4 Mean error, root mean square error, efficiency coefficient, and determination coefficient for daily and monthly streamflow simulations ................................ ................ 81 3 5 Mean error, root mean square error, efficiency coefficient, and determination coefficient for monthly groundwater level simulations ................................ ..................... 82 3 6 Mean error, root mean square error, efficiency coefficient, and determination coefficient for daily and monthl y springflow simulations ................................ ................. 83 4 1 4 GCMs used in this study ................................ ................................ ............................... 123 5 1 Averaged mean error of monthly average streamflow and temporal standard deviation of daily streamflow for wet season over the four GCMs. ................................ 161 5 2 Averaged mean error of monthly average groundwater level over the four GCMs for all stations. ................................ ................................ ................................ ....................... 161

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8 LIST OF FIGURES Figure page 2 1 Map of study area and rainfall stations. ................................ ................................ ............. 42 2 2 MM5 domain configuration ................................ ................................ ............................... 43 2 3 Example of cumulative distribution function (CDF) ................................ ......................... 43 2 4 Observed vs. raw simulated and bias corrected results of first order transition probabilities ................................ ................................ ................................ ........................ 44 2 5 Contributions of mean error, variance error, and correlation error to overall mean squared error for raw and bias corrected daily precipitation by month. ............................ 45 2 6 Comparison of mean monthly precipitation and standard deviation of monthly precipitation over the study period by month ................................ ................................ .... 45 2 7 The spatial distribution of averaged daily precipitation for dry, and wet seasons ........... 46 2 8 Annual total precipitation for raw MM5 results, bias correcte d MM5 results, and observations ................................ ................................ ................................ ....................... 47 2 9 Observed and bias corrected MM5 variograms of daily precipiation ............................... 48 2 10 Comparison of parameter A and parameter C for observed and simu lated variogram models by month ................................ ................................ ................................ ................ 50 2 11 The spatial distribution of precipitation volume for observed and kriged bias corrected MM5 results for three wet season precipitation events ................................ ...... 51 3 1 Map of hydrologic modeling domain, sub basins, and observation stations ..................... 84 3 2 Schematic representation of the study framework ................................ ............................. 85 3 3 Comparison of the mean monthly precipitation over the study period .............................. 86 3 4 Comparison of annual total precipitation time series ................................ ........................ 87 3 5 Comparison of seasonal total precipitation time series ................................ ...................... 88 3 6 The spatial distribution of mean monthly precipitation for dry, and wet seasons ............. 89 3 7 Comparison of mean monthly maximum and minimum temperature ............................... 90 3 8 Comp arison of the monthly streamflow hydrographs ................................ ....................... 91

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9 3 9 Comparison of daily and monthly observed and predicted precipitation and daily streamflow simulations for overestimated year 1998 and underestimated year 2003 ....... 92 3 10 Comparison of the total water budget over the domain ................................ ..................... 93 3 11 Comparison of simulated mean monthly streamflow for each target station .................... 94 3 12 The relationship between the mean RMSE of streamflow simulations and annual precipitation predictions and mean RMSE of daily precipitation predictions ................... 95 3 13 Comparison of CDF s for calibrated results and the simulatio ns with precipitation scenarios ................................ ................................ ................................ ............................. 96 3 14 Comparison of monthly averaged groundwater level predictions for each targ et station in surficial aquifer ................................ ................................ ................................ .. 97 3 15 Comparison of monthly averaged groundwater level predictions for 4 target stations in confined Floridan aquifer and 1 unconfined aquifer station ................................ .......... 98 3 16 Comparison of mean monthly groundwater level for 4 pairs of surficial/Floridan aquifer target station and 1 unconfined aquifer station ................................ ...................... 99 3 17 Comparison of CDFs of monthly groundwater level in surficial and Floridan aquifer for calibrated results and the simulations with precipitation scenarios .......................... 100 3 18 Comparison of monthly averaged springflow pr edictions for 2 target stations ............... 101 3 19 Comparison of mean monthly averaged springflow for 2 target stations ........................ 101 3 20 Comparison of CDFs of monthly springflow ................................ ................................ .. 102 4 1 The study domain and the center location of grids for 4 GCMs ................................ ...... 124 4 2 Comparison of spatially averaged annua l total precipitation time series ........................ 125 4 3 Comparison of the mean monthly precipitation over the study period ............................ 125 4 4 Spatial distribution of the mean of Gridded observation, BCSD, SDBC, and BC SA daily precipitation for wet and dry season ................................ ................................ ....... 126 4 5 Spatial distribution of the temporal standard deviation of Gobs., BCSD, SDBC, and BC SA daily precipitation for wet and dry season ................................ ............................ 127 4 6 Spatial distribution of the 90 th percentile daily precipitation ................................ ........... 128 4 7 Spatial distribution of the 50 th percentile daily precipitation ................................ ........... 129 4 8 First order wet to wet transition probability comparisons of observed vs. BCSD results, SDBC results, and BC SA results for 4 GCM products. ................................ ...... 130

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10 4 9 First order dry to wet transition probability comparisons ................................ ............... 131 4 10 Exceedence probability of the events for given wet (>0.1mm) and dry (<0.1mm) spell lengths ................................ ................................ ................................ ..................... 132 4 11 Comparison of observed and simulated mean dai ly spatial correlation indices .............. 132 4 12 Variogram comparison of BCSD, SDBC, and BC SA daily precipitation predictions for wet and dry season. ................................ ................................ ................................ .... 133 4 13 Comparison of the spatial distribution of 90 th percentile events ................................ ..... 134 5 1 Map of study area, s ub basin configuration, and hydrologic target stations ................... 162 5 2 The center location of grids for 4 GCMs ................................ ................................ ......... 163 5 3 Schematic representation of the methodology ................................ ................................ 163 5 4 Mean monthly Tmax and Tmin of basin based o bservation ................................ ........... 164 5 5 Standard deviation of monthly mean maximum temperature and minimum temperature over the study domain ................................ ................................ .................. 164 5 6 Averaged mean daily precipitation of basin based observation ................................ ...... 165 5 7 Averaged standard deviations of daily precipitation for basin based observation (Bobs.), BCSD, SDBC, and BC SA GCMs for each month. ................................ ............ 165 5 8 Scatter plots of temporal mean and standard deviation of 172 basin based daily precipitation data for sub basin observations vs. downscaled GCMs ............................ 166 5 9 Standard deviation of spatially averaged daily precipitation ................................ ........... 167 5 10 Comparison of transition probabilities of sub basin observations, BCSD, SDBC results, and BC SA results for wet and dry season ................................ ........................... 168 5 11 Comparison of number of rainy sub basins for the spatially averaged daily precipitation ................................ ................................ ................................ ..................... 169 5 12 Comparison of the relationship between spatial standard deviations of daily precipitation and the spatially averaged daily precipitation ................................ ............ 169 5 13 Comparison of variograms of sub basin based observation and the downscaled prediction using BCSD, SDBC, and BC SA technique ................................ .................... 170 5 14 Comparison of monthly average streamflow driven by downscaled climate scenarios using BCSD, SDBC, and BC SA to the calibrated results ................................ ................ 171

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11 5 15 Comparison of simulated mean annual evapotranspiration over the INTB model domain ................................ ................................ ................................ .............................. 172 5 16 Mean of errors of monthly average streamflow and temporal standard deviation of daily streamlfow over four GCM results for each target station. ................................ .... 173 5 17 Comparison of frequency of daily streamflow events per year ................................ ....... 174 5 18 Comparison of simulated monthly average groundwater levels at the surficial aquifer stations ................................ ................................ ................................ ............................. 175 5 19 Comparison of simulated monthly average groundwater levels at the Floridan aquifer stations ................................ ................................ ................................ ............................. 176 5 20 Compari s o n of the frequency of groundwat er level per year for surficial and Floridan target stations. ................................ ................................ ................................ .................. 177

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12 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 DYNAMICAL AND STATISTICAL DOWNSCALING OF CLIMATE INFORMATION AND ITS HYDROLOGIC IMPLICATION S OVER WEST CENTRAL FLORIDA By Syewoon Hwang December 2011 Chair: Wendy D. Graham Cochair: Chris J. Martinez Major: Agricultural and Biological Engineering General circulation models (GCMs) have recently been developed for simulating current and future climate patterns. However, GCM results do not generally provide accurate prediction of climate variables on the scale needed to assess hydrologic impacts. In this study various existing dynamic and statistical GCM downscaling techniques were used to estimate precipitation fields over the state of Florida. These methods were quantitatively evaluated and found unable to reproduce fine scale spatial characterist ics of precipitation patterns. A new stochastic downscaling technique was developed which improves on previous methods and produces daily precipitation fields that accurately reproduces spatiotemporal variability of precipitation. The downscaled precipita tion scenarios were used as input to an integrated hydrologic model to evaluate hydrologic implications of differences in the alternative downscaling methods. Results of this study should allow hydrologic modelers and water supply decision makers to more effectively use climate modeling results for water resource planning, management, and conservation.

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13 CHAPTER 1 INTRODUCTION As evidence for global climate change has mounted the need for weather forecasting, attention to climate modeling. Climate modeling systems that simulate present and f uture climate conditions have been continuously developed and improved since Norman Phillips (1956) first developed a mathematical model that could realistically represent monthly and seasonal patterns in the troposphere. Following this initial effort, a l arge number of climate research groups throughout the world have developed their own climate models and collaboratively evaluated the General circulation models (GCMs) are the primary tool to si mulate continental scale climate dynamics. Although r ecent improvements in climate models have led to enhanced ability to simulate many aspects of climate variability and climate extremes GCM results are generally insufficient to provide accurate predicti on of climate variables at the local to regional scale requir ed for hydrologic or agricultural impact assessments (McGregor, 1997 ; Christensen and Christensen, 2003; Wood et al., 2004). The coarse resolution of GCMs precludes the simulation of realistic ci rculation patterns and accurate representation of small scale spatial variability of climate variables (Giorgi et al., 2001; Jones et al., 2004; Lettenmaier, 1999 ). Moreover, the mismatch in spatial resolution between GCMs and regional hydrologic models ha s been one of the major challeng es to be resolved in order to conduct reasonable climate change impact assessments on hydrologic systems This has resulted in recent attention to improving downscaling techniques for regional applications and evaluations. GCM downscaling approaches are generally categorized into statistical downscaling and dynamic downscaling (Fowler et al., 2007; Mearns et al., 1999; Wilby et al., 2004). The former

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14 method uses empirical relations between features simulated by GCMs at coarse grid scales and surface observations at fine sub grid scales while the latter uses regional climate models (RCMs) which simulate physical links between climate patterns at the different scales D ynamical downscaling has been generally found to produ ce more accurate climate predictions that reflect temporal and spatial patterns of meteorological variables (Vasiliades et al., 2009). RCMs, however, require intensive computational time and thus the simulation period and number of ensemble members that ma y be used for impact stud ies are limited. Moreover, RCM predictions are subject to systematic biases, particularly for precipitation which is the dominant variable in most hydrological regimes (Mearns et al., 2003 ; Sato et al., 2007 ). On the other hand st atistical downscaling methods can provide unbiased climate data at high spatial resolution without intensive computer resources (Iizumi et al., 2011). However statistical downscaling often fail s to reproduce spatial cross correlation of multiple climate v ariables because the method is typically used to p redict one variable at one site and to reproduce realistic spatial variability. These differences among downscaling approaches may bring about substantial differences in climatological features of regional climate scenarios and resulting impact assessments Therefore, given the range of various techniques, in depth evaluat ion s of each downscaling approach is required before using them for application purpose s (e.g., water resource planning, management, and conservation ) Additionally t he methodologies by which downscaling skill is evaluated must be tuned to the particular application being considered rather than using standard assessment criteria (Fowler et al., 2007) Therefore it is important to e valuate downscaling methods for various climate variables over diverse regions and applications so that the strengths and weaknesses of downscaling can be better understood.

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15 Among climate variables, p recipitation and temperature are the most relevant for hydrological impact studies on climate change (Bronstert et al., 2007). In Florida regional variable precipitation patterns due to its extensive coastline and mid to low latitude peninsular location. P recipitation variability has a major influence on demand and availability of water and thus, predict ing and responding to present precipitation variability and potential future changes in precipitation patterns is essential for maintaining a reliable regional water supply in Florida. I t is important to provide clear measures of downscaling model capabilit ies and reliability specifically for Florida particularly with regard to the realistic simulation of daily pr ecipitation occurrence, persistence and volume This research investigate s the applicability of one existing dynamic and several statistical downscaling method s (Grell et al., 1994; Wood et al., 2004; Abatzoglou and Brown 2011) to generate precipitation fields o ver the Tampa Bay region of west central Florida. A new stochastic downscaling technique is developed which improve s over the existing statistical downscaling methods in reproduc ing observed spa t iotemporal variability of daily precipitation. The s kill s of each method are quantitatively evaluated in terms of both temporal and spatial precipitation patterns. T he downscaled precipitation scenarios a re used as input to an existing integrated hydrologic model (IHM, Geurink et al., 2006 a) to evaluate hydrologic implications of differences in climate scenarios over the study area This dissertation is comprised of four chapters, each of which meets requirements for independent publication in the peer reviewed literature including backgroun d, study area and data, methodology, results, and summary. Chapter 2 develops and evaluates an application of a dynamical downscaling model ( MM5 the Pennsylvania State University (PSU) National Center for Atmospheric Research

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16 (NCAR) Fifth Generation Mes oscale Model, Grell et al., 1994) for generating fine scale precipitation fields from coarse scale reanalysis data over west central Florida. This study evaluate s the ability of the model to reproduce observed spatiotemporal variability of precipitation in the region NCEP/NCAR reanalysis data are used as a surrogate for GCM predictions for specifying the initial and boundary conditions for MM5 This remove s the confounding factors of potential biases related to GCM process simulation and thus provide s a m ore objective measure of the skill of the MM5 downscaling accuracy. The goal of this effort is to assess the utility of using MM5 to downscale GCM forecast product s and/or climate change scenarios for improving water management decisions in the Tampa Bay r egion west central Florida In chapter 3 bias corrected MM5 results are used to drive t he IHM developed by Tampa Bay Water and the South w est Florida Water Management District. T he accuracy of hydrologic predictions ( e.g., streamflow, groundwater level, and springflow) produced using the MM5 outputs is assessed against hydrologic observations and hydrologic predictions generated from the IHM model that was calibrated using observed climate data. The ultimate goal of this study is to evaluate the applicabi lity of a regional climate model for generating local scale precipitation and temperature fields for use in a hydrologic application over west central Florida. In addition e xamination of the relationship between errors in the climate model and hydrologic s imulation results provide insights into the influence of climate model biases on hydrologic impact assessment. Chapter 4 introduces a new stochastic technique to downscale daily GCM precipitation predictions that reproduce both spatial and temporal precipi tation characteristics as well mean climatology of gridded observations The method is evaluated using precipitation projections

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17 from four GCMs ( selected based on availability and previous use in testing downscaling approaches ) over Florida and the skill of the method is compared to interpolation based statistical downscaling methods (Wood et al., 2004; Abatzoglou and Brown 2011). T he goal of this study is to develop and test an efficient technique for reproducing observed spat iotemporal precipitation features for future use in hydrologic models throughout the state of Florida. In chapter 5, the statistical downscaling methods evaluated in chapter 4 are used to downscale GCM precipitation predictions into the 172 sub basins tha t comprise the IHM model in west central Florida and t he skills of each method in reproducing temporal and spatial variability of daily precipitation are evaluated over the model domain The downscaled climate data are then used to drive the IHM model to examine hydrologic responses of streamflow and groundwate r levels to each climate input scenario. The hydrologic simulation results were evaluated against both observations and calibrated model predictions in terms of monthly mean hydrologic behavior and the frequency of hydrologic events. The goal of the study is to evaluate applicability of each statistical downscaling method to reproduce observed climatic features and hydrologic behavior and to determine whether the stochastic technique developed in Cha pter 4 improves over the previously developed interpolation based statistical downscaling methods In the final chapter the results of these studies are summarized and general conclusions are drawn. Additionally suggestions for future work which build s o n the research presented in this dissertation are provided.

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18 CHAPTER 2 QUANTITATIVE SPATIOT EMPORAL EVALUATION O F DYNAMICALLY DOWNSCALED MM5 PRECI PITATION PREDICTIONS OVER THE TAMPA BAY REGION, FLORIDA 2 .1 Background Florida has unique and highly variable precipitation patterns due its extensive coastline and mid to low latitude peninsular location (Sun and Furbish, 1997) In turn, precipitation variability has a major influence on demand and availability of water. Th us skilled weekly, seasonal, annual and multi decadal precipitation forecasts and/or predictions would be useful to help water resource managers provide a more reliable, environmentally sound supply of water. In the Tampa Bay region of Florida, understandi ng and responding to precipitation variability is particularly important to developing a reliable water supply (Schmidt et al., 2004). T ampa Bay Water provides water for more than two million residents in the area through a diverse regional water supply sy stem that includes a surface water treatment plant and three surface sources, six groundwater treatment plants, 13 regional wellfields, a seawater desalination plant, and almost 200 miles of pipeline. Groundwater provides the least expensive source of wat er for the region, but pumping is managed so that the 12 month running average does not exceed a permitted value intended to protect wetlands and lakes from environmental harm. Surface water sources are more expensive, and withdrawals are limited by permi t to times when the rivers are flowing above flow thresholds in order to protect the ecological integrity of the river systems. Desalinated water, the most expensive source to treat, is also constrained by regulatory, water quality operational considerati ons and economic considerations. Tampa Bay Water uses a suite of statistical and physically based hydrologic models to analyze hydrologic conditions and estimate water supply availability to ensure that water demand for the region can be met at the least cost and with minimal adverse environmental impacts.

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19 There are three planning time scales important to Tampa Bay Water 1) a short term (weekly to monthly) operational time scale over which Tampa Bay Water must allocate supply among the various water sourc es to meet immediate demands, 2) a medium term (1 to 24 months) planning period over which Tampa Bay Water must allocate supply distributions and maintain reservoir storage to meet expected seasonal demands and to estimate annual costs in order to set wate r rates for the coming year, and 3) a long term (decades) planning period over which Tampa Bay Water must determine when new major water supplies must be brought on line to meet future demands. In these planning analyses recently observed weather patterns or historical climatology are currently used rather than quantitative climate forecast/predictions. The overarching hypothesis underlying this research is that reliability can be improved, and risk can be reduced, by incorporating climate model predictio ns into water resources planning process es in the Tampa Bay Region. Recent climate modeling improvements have resulted in an enhanced ability to simulate many aspects of climate variability and extremes. However, general circulation models (GCMs) have been found to have too coarse resolution to resolve small scale atmospheric circulation (McGregor, 1997) and they are characterized by systematic errors and limitations in accurately simulating regional climate conditions (Easterling et al., 2000). Hydrologic applications of climate model predictions often require data (precipitation, temperature, relative humidity) at a high spatial and temporal resolution (Hewitson and Crane, 1996; Enke and Spekat, 1997; Yu et al., 1999; Wilby et al., 2000; Leander et al., 20 08). This need has resulted in recent attention to improving downscaling techniques for regional applications and evaluations. Dynamical downscaling uses physically based regional climate models (RCMs) to translate the large scale predictions from a GCM i nto physically consistent, higher resolution

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20 predictions (Murphy, 1999; Schmidli et al., 2006). Recently it has been shown that, because it represents physical processes at a higher resolution, dynamical downscaling may have advantages over statistical do wnscaling in the simulation of extremes (Christensen and Christensen, 2003, 2004; Pal et al., 2004; Frei et al., 2006; Fowler et al., 2007a). Furthermore several studies have suggested that downscaling with physically based high resolution mesoscale RCMs m ore realistically predicts precipitation structures over regions with complex terrain and landuse (Washington Olympic mountains: Colle and Mass 1996; Colorado mountain region: Gaudet and Cotton 1998; Pacific northwest coastal region: Colle et al., 1999; Ph oenix metropolitan area: Zehnder, 2002; Great Lakes region: Zhong et al., 2005). Fowler et al. (2007b) pointed out that the methodologies by which downscaling skill is evaluated must be tuned to the particular catchment and application being considered rat her than using standard assessment criteria. Physically based dynamical downscaling methods must be examined for diverse regions with high climate variability (Hong, 2003) so that the strengths and weaknesses of dynamical downscaling can be better understo od (Wang et al., 2004). In this study the Pennsylvania State University (PSU) National Center for Atmospheric Research (NCAR) Fifth Generation Mesoscale Model (MM5; Grell et al., 1994) was set up for the Tampa Bay region to evaluate its ability to reproduce observed spatiotemporal variability in precipitation important for hydrologic modeling applications. The MM5 dynamical model has been widely applied in a variety of fi elds in historical and future climate studies. Various case studies have been conducted to examine MM5 parameter sensitivity analysis (Colle and Mass, 2000; Colle et al., 2000; Chen and Dudhia, 2001a; Kotroni and Lagouvardos, 2001; Yang and Tung, 2003), MM 5 performance assessment (Colle et al., 1999; Chen and Dudhia, 2001b; Westrick et al., 2002; Colle et al., 2003; Hong, 2003; Zhong and Fast, 2003; Boo et al., 2004;

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21 Zhong et al., 2005), and model modifications for performance improvement (Chen and Dudhia, 2001a; Zehnder, 2002). For example, Colle and Mass (2000), Colle et al. (1999) and Colle et al. (2000) verified precipitation forecasts from MM5 for cool seasons over the Pacific Northwest region at 36, 12, and 4 km horizontal resolution for the Feb. 1996 Dec. 1996 Apr. 1997, and Nov. 1997 Mar. 1999 time periods. They observed that incorporating high horizontal resolution (e.g. four km) in MM5 did not guarantee improvement of precipitation prediction skill for the Pacific Northwest, though noticeable im provement in bias occurred as the resolution was increased from 36 to 12 km. When GCM predictions are used as initial and boundary conditions for RCMs, biases contained in GCM fields are propagated to the RCM scale during the downscaling process. Applicat ions of dynamical downscaling in the literature have consistently shown that outputs from RCMs cannot be used in impact studies without first applying a bias correction to observations (Fowler et al., 2007b; Lim et al., 2007) because they are subject to sy stematic biases, particularly for precipitation (Varis et al., 2004), the dominant variable in most hydrological regimes. Therefore local to regional applications of dynamically downscaled climate predictions typically use bias corrected results (Wood et a l., 2004; Fowler and Kilsby, 2007c, and Fowler et al., 2007b). The purpose of this paper is to quantitatively evaluate the ability of MM5 to reproduce observed spatiotemporal variability of precipitation needed to drive hydrologic models in the Tampa Bay region over a 23 year period (1986 to 2008). This period was chosen because it encompasses the time period (1989 through 2006 ) for which Tampa Bay Water has calibrated and verified the integrated hydrologic model. The long term goal of this effort is to assess the utility of using MM5 to downscale GCM hindcasts, forecasts and/or climate change scenarios for

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22 improving water management decisions in the Tampa Bay region. For this study the National Center of Environmental Prediction and the National Center o f Atmospheric Research (NCEP/NCAR) reanalysis data (Kalnay et al 1996) was used as a surrogate for General Circulation Model (GCM) predictions for specifying the initial and boundary conditions for MM5. Use of the NCEP/NCAR reanalysis data is advantageou s due to the availability of long term daily precipitation and temperature data which is not always archived for GCMs predictions. Furthermore use of the NCEP/NCAR reanalysis data removes the confounding factors of potential biases related to GCM process simulation, and thus provides a more objective measure of the skill of the MM5 downscaling accuracy (Maurer and Hidalgo, 2008; Maurer et al ., 2010). The next section briefly describes the study area and data used for climate modeling and verification. Mod el configuration methodologies employed in the study are described in section 2. 3, and results are discussed in section 2. 4. Finally, the main conclusions are summarized and the future implications of this research are discussed in section 2. 5. 2 .2 Study A rea and D ata C ollection Tampa Bay is the second largest Gulf Coast estuary and the largest estuary in Florida. The bay covers about 1,031 km 2 receives freshwater from a 6,583 km 2 watershed, and encompasses most of Pinellas, Hillsborough, and Manatee Counties and portions of Pasco, Polk, and Sarasota Counties (Xian, 2007) Within the Tampa Bay watershed, f our major sources of surface water (the Hillsborough, Alafia, Little Manatee, and Manatee Ri vers) flow into the bay. This study focused on the Hillsborough (1850 km 2 ) and Alafia (1380 km 2 ) watersheds (Figure 2 1). Daily precipitation data from 53 observations from several different agency precipitation networks (41 Tampa Bay Water, 6 National Oceanic and Atmospheric Administration (NOAA), 4 Southwest Florida Water Management District (SWFWMD), and 2 United States Geological Survey (USGS)) were utilized to evaluate the MM5 precipitation simulations (Figure 2 1, Table

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23 2 1). The observation densi ty over the Hillsborough and Alafia river watersheds is approximately 1 station/100km 2 though the distribution of stations is irregular. The data were retrieved from the rainfall data manager web database ( http://gis.tampabaywater.org/rainfall/ ) maintained by Tampa Bay Water. NCEP/NCAR global reanalysis data from 1986 to 2008 ( Kalnay et al., 1996, Kistler et al., 2001) were utilized as the initial and boundary conditions for the MM5 model. The NCEP/NCA R global reanalysis data set is a joint product from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) that was created by assimilating observations and general circulation model predictions to provide a gridded data set representing the retrospective state of the Earth's atmosphere over time. The resolution of the global reanalysis data set is a 2.582.58 degree grid with 28 vertical sigma levels. 2 .3 Methodology 2 .3.1 MM5 M odeling MM5 was run to predict precipitation over a 1701km by 1620km domain at 2727 km 2 grid cell resolution and a nested 675km by 729km domain at 99 km 2 grid cell resolution (Figure 2 2). Predictions were output at a 6 hour temporal resolution continuously over the 23 year period from 1986 to 2008, using the University of Florida High Performance Computers (UFHPC) with massively parallel architecture and distributed memory codes. NCEP/NCAR reanalysis data were used as initial and boundary conditions for the outer domain, with boundary conditions updated on the outer grid every 6 hours. There was no additional nudging or adjustment in these runs. The physi cs configuration used in the present work was set based on sensitivity analyses by Hernandez and Jones (submitted, 201 1 ) and is defined as follows. The radiation scheme was

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24 set to use CCM2 (Kiehl et al., 1996). Here the annual variability of insolation at the top of the atmosphere depends on the solar constant, zenith angle and eccentricity. The CCM2 option evaluates long wave and shortwave fluxes, as well as heating rate in multi spectral bands, where clouds and aerosols absorb and/or scatter radiation. Fo r cumulus parameterization, the Grell scheme (Grell et al., 1994) was employed. In this simple scheme there is no mixing in the clouds except at the top and bottom due to downdraft and updraft atmospheric processes. The explicit moisture was set to the Sim treated as cloud ice and rain is treated as snow. Under these moisture physics, rain or snow vertical distribution and speed is controlled by aerosol size which is a function of accretion (conversion of cloud to rain or ice to snow). The planetary boundary layer (PBL) physics was set to a non local vertical diffusion scheme (Hong and Pan 1996) employed in the NCEP Medium Range Forecast Model which realistically represents large eddy fluxes and their evolution in the atmospheric well mixed layer. The surface 5 layer soil temperature model (Dudhia, 1996) was used for land surface processes. MM5 was configured to predict atmospheric conditions at 21 pressure levels between 100000 Pa to 20000 Pa and all simulations were performed in a two way nesting communication. The 25 category U.S. Geological Survey (USGS) 1999 landuse dataset with 1 km horizontal spacing was used for the entire 1986 2006 simulation period (Grell et al. 1994). 2 .3.2 Bias C o rrection A cumulative distribution function (CDF) mapping approach was used to bias correct the raw MM5 99 km 2 predictions using the following procedure (Wood et al., 2002; Ines and Hansen, 2006): 1) CDFs of observed daily precipitation were created ind ividually for each of the 53 observation stations for each month using available observed data. Thus 12 different monthly CDFs were used for each station for bias correction of the daily predictions; 2) CDFs of

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25 simulated daily precipitation were created f or the grid cell containing each station for each month; 3) daily grid cell predictions were bias corrected at each observation point using CDF mapping that preserves the probability of exceedence of the simulated precipitation over the grid cell, but corr ects the precipitation to the value that corresponds to the same probability of exceedence from the observed results at the observation point. Thus bias corrected rainfall on day i at station j was calculated as, Where and denote a CDF of daily precipitation x and its inverse, and subscripts sim and obs indicate downscaled simulation and observed daily rainfall, respectively. This bias correction process removes both bias in the precipitation predictions and the tendency of the model to under predict dry days and over predict the number of low volume rai nfall days, assuming direct correspondence between the grid cell and point prediction exceedence probabilities. The simulated and observed CDFs for September for station 36 (Plant city rainfall), and a schematic of the bias correction procedure are shown in Figure 2 3. This station was chosen as a representative example because of its comparatively long observation record. The predicted versus observed CDFs for the other 52 stations show similar behavior. The results of the bias corrected daily MM5 predict ions were evaluated separately for each month of each year independently using a cross validation procedure that sequentially excluded the observed data for that month and year in the computation of the observed CDFs used in the bias correction. Thus each month of each year was bias corrected using an observed data set that did not include the data from that month and year.

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26 2 .3.3 Spatial C orrelation S tructure Geostatistical methods were used to describe and model the spatial correlation structure of both the observed and simulated rainfall fields, and to interpolate the bias corrected precipitation predictions over the Alafia and Hillsborough River watersheds. Details of geostatistical methods used in this research are described in Goovaerts (1997) and Is aaks and Srivastava (1989). Variogram modeling : The variogram, defined as the expected value of the squared difference of the values of the random field separated by distance vector describes the degree of spatial variability and spatial correlation e xhibited by a spatial random field. In this research variogram estimation and modeling was conducted on both the observed and bias corrected simulated precipitation in order to evaluate how well MM5 reproduces the spatial correlation structure of the obse rved daily precipitation. The experimental variogram for the observed and simulated precipitation data was calculated using the following formula (Goovaerts, 1997). where denotes the number of pairs of observations separated by vector and is the spatial observation field (i.e. observed or simulated precipitation) at location The experimental variograms were then fit to the exponential isotropic variogram model: Where, h is the separation distance (km), is the nugget variance (mm 2 ), is the structural variance (mm 2 ), and is the effective range (km) which is the distance at which the variogram reaches approximately 95% of the asymptote.

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27 Point Kriging : In order to run spatially distributed hydrologic models, bias corrected precipitation predictions are typically needed over a model grid covering the domain of interest where long term data may not be available for bias correction. Therefore bias corrected daily precipitation values at the 53 rain gages were p oint kriged over a model grid covering the Alafia and Hillsborough watersheds by solving the following ordinary kriging equations: where is kriging weight associated with the bias corrected prediction at location for the kriged estimate of the point value of precipitation at location u ; is semivariogram between the bias corrected predictions at two locations and ; N is the number of bias corrected predictions used in the kriging process (53 in this case); is the Lagrange parameter that results from the imposition of the unbiasedness constraints on the kriged estimate at location u. Next the kriged estimate and kriging variance were calculated using the following equations: where, is the kriged estimate at location u, is the bias corrected prediction at location and is the kriging variance (or estimation error) associated with the kriged estimate, at location u

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28 The accuracy of the kriged estimates was assessed through cross validation by sequentially comparing the actual observed daily rainfall at each of the 53 rainfall stations to the kriged estimate obtained by ignoring the observation at that station and using only the bias corrected predicted values from the remaining 52 stations. 2 .3.4 Error S tatistics The raw simulated, bias corrected, and point kriged daily and monthly rainfall estimates were evaluated by comparison to the observed data. The accuracy of the daily and monthly precipitation estimates was quantified using the mean error (ME), the mean square error (MSE) and the root mean square error (RMSE), i.e. Where and are the estimate and the observation for day or month i and is the total number of estimates. Note that the mean square error (MSE) can be decomposed into three terms that quantify the accuracy of the results in terms of the bias of th e mean prediction, the variance of prediction, and the linear correlation between the prediction and observations (Murphy, 1988): where and are the means a nd standard deviations of the predictions and the observations respectively, and is the correlation coefficient between the predictions and

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29 observations. Each of the terms in the MSE was evaluated separately to determine its relative contribution t o the total mean squared error. In addition, the performance of kriging algorithm was evaluated by normalizing the actual kriging error by the predicted kriging standard deviation ( equation 2 7) i.e. Note that if the kriging estimator is performing well (i.e. producing unbiased estimates with errors well predicted by the kriging variance), the normalized kriging errors should have a mean error of approximately zero, and a root mean square error of approximately one. 2 .4 Results and D iscussion 2 .4.1 MM5 M odeling and B ias correction R esults Daily and monthly precipita tion totals predicted by the MM5 model at the 9 km grid spacing were evaluated at the 53 rain gage locations in the Tampa Bay region for the 23 year period from 1986 to 2008 using the cross validation procedure described in Section 2. 3. Table 2 2 presents the mean error (ME) and root mean square error (RMSE) for the daily and monthly raw MM5 results, as well as the bias corrected MM5 predictions, calculated over the 53 stations separately for each calendar month. The raw daily and monthly MM5 precipitatio n totals are generally positively biased, particularly during the dry season months (October through May), and the RMSEs of the raw predictions are larger than the standard deviation of long term observed daily rainfall for all months. Furthermore, the raw model predictions produced too many wet days with very low rainfall (i.e., below the minimum 0.25 mm recorded by the rain

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30 gages). CDF mapping effectively removes the bias in the daily and monthly predictions for all months (average reduction 95.9% and 95. 7% respectively), reduces the frequency of wet days with very low rainfall, and reduces the RMSEs of the daily and monthly predictions (av erage reduction 12.7% and 32.6 % respectively). In addition to the daily precipitation totals, day to day precipitati on patterns are important for most hydrologic applications. Hence, daily transitions between wet and dry states were calculated for both the observed data and cross validated bias corrected predictions using the First order Transition Probability (FTP; Haan, 1977). The observed FTPs, raw simulated FTPs, and bias corrected FTPs for each month are shown in Figure 2 4. As discussed previously the raw MM5 results overestimate the frequency of low rainfall events. However the bias corrected transition probab ilities match the observed dry wet and wet wet transition probabilities well (r 2 of 0.81 and 0.68, respectively) with higher probabilities of transitioning to wet states in the wet season from June to September, as expected. Figure 2 5 plots the decomposi tion of the mean squared error (MSE) of the raw and bias corrected daily precipitation totals for each month. This figure shows that bias correction significantly improves the prediction of the mean and variance of precipitation for each month, but does n ot significantly improve the correlation of actual daily predicted and observed precipitation. Figure 2 6 plots average monthly precipitation and the standard deviation of the monthly precipitation over the study period for the raw, bias corrected and obs erved monthly precipitation totals. This figure confirms that CDF mapping effectively eliminates the bias in the average monthly MM5 predictions, and to a lesser extent improves the standard deviation of the monthly precipitation totals over the study per iod.

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31 As noted above, the CDF mapping bias correction technique is designed to improve the fit of the MM5 predictions to historic climatology, but does nothing to improve the daily pattern of rainfall which is still driven by the climate model physics. Therefore it is possible that in cases where the raw results under predict mean climatology bias correction may increase the RMSE of the predictions by increasing rainfall amounts, and thus increasing errors on days when precipitation timing is off. In fa ct Table 2 2 shows that this occurs in August when the raw results under predict mean daily rainfall by 1.50 mm and the RMSE increases slightly from 19.72 mm to 20.31mm for the bias corrected results. Similarly, for August the raw MM5 results under predi ct mean monthly rainfall by 32.56 mm and the RMSE increases from 111.01 mm to 116.23 mm for the bias corrected results. Furthermore Figure 2 5 shows that although the mean and variance of August daily precipitation improve substantially, the correlation o f August daily rainfall degrades slightly. However for all other months correlation improves slightly, and to a larger extent in dry months (January through May) when the raw MM5 results significantly over predict rainfall. The spatial distribution of the mean daily precipitation for the dry (October through May) and wet (June through September) seasons are mapped in figure 2 7 for the raw MM5 results, the bias corrected MM5 results and the point observations. This figure shows that raw MM5 results produc e a mean daily precipitation field that is much higher in magnitude than the observations in the dry season (i.e. positively biased), and much smoother in space than the observations for both the dry and wet seasons. Bias correction improves both the magni tude and the spatial distribution of the average daily precipitation for both the wet and dry seasons. It should be noted that the bias correction technique used here maps the predicted CDF obtained from the 1986 2008 simulation to the observed CDF from th e entire time series of record for each station

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32 (which is variable, Table 2 1). This discrepancy in period of record results in the relatively minor differences between the observed and bias corrected spatial distribution of mean precipitation fields calc ulated over the 1986 2008 time period shown in Figure 2 7. Figure 2 8 plots the raw, bias corrected and observed total annual precipitation over the study period. This figure shows that the bias corrected MM5 results reproduce the long term mean annual pr ecipitation very well, follow the observed temporal pattern of total annual rainfall over the study period fairly well, but significantly over estimate the inter annual variability over the study period. Table 2 2 and Figures 2 4 through 2 8 indicate that while the bias corrected MM5 results successfully reproduce the historical climatology over the study area (i.e. long term mean and variance of daily and monthly precipitation totals and daily transition probabilities), the prediction of the actual time se ries of daily, monthly, and annual totals show significant errors, even after the results are bias corrected. Further improvement in day to day predictability will require improving the climate model physics, parameterization, and/or boundary condition us ed in MM5. The accuracy of the MM5 bias corrected historical simulation results reported here are similar to other results reported in the literature. Zhong et al (2005) ran MM5 for the Great Lakes region at 4km resolution for the 2002/03 winter and 2003 summer season and presented the bias score and threat score which indicate how well the model predicts the frequency of occurrences of a given precipitation class amount. These statistics, although helpful to assess model skill, offer little information ab out the actual precipitation prediction errors which are of interest when using the results to make temporally specific hydrologic predictions. However, 2 6 is appears that the MM5 predictions consistently overestimated

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33 rainfall a mounts for each month simulated, with errors ranging from approximately 20 mm for December 2002 to 100 mm for June 2003. Similarly, Schoof et al. (2009) presented results of dynamically downscaled seasonal precipitation hindcasts for the warm months (Mar. to Sep.) for 15 years from 1987 to 2001 over the Southeastern United States using the Nested Regional Spectral Model (NRSM) developed by Florida State University and the Center for Ocean Atmospheric Prediction Studies. According to their results, the avera ged RMSE for bias corrected monthly total precipitation for warm season from March to September over Florida was approximately 93.4 mm, similar to the average bias corrected monthly RMSE of 83.3 mm found in this study for the Tampa Bay region. 2 .4.2 Precip itation S patial C orrelation S tructure Figure 2 9 compares the variograms for bias corrected daily predictions and observed data calculated separately for each month assuming spatial stationarity and isotropy within each month and temporal stationarity o ver the study period. These figures show that the bias corrected MM5 results successfully reproduce the seasonal patterns of spatial rainfall variance in the Tampa Bay Region, with higher spatial variances in the wet season (June through October) when con vective storms dominate, and lower spatial variances in the dry season (November through May) when frontal systems dominate. However the figure also shows that MM5 consistently over predicts the spatial correlation of the rainfall fields, with the variogr ams for the simulated data approaching the variogram sill (or spatial rainfall variance) more slowly than the observed data. An exponential variogram model was fit to each observed and simulated precipitation variogram using least squares regression. Note that the variability at distances smaller than the typical sample spacing, including measurement error was observed to be negligible in most cases, so was assumed to be zero. The monthly variogram sills (or spatial rainfall variance),

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34 and the mont hly effective ranges (distance over which spatial correlation in precipitation drops by 95%), A are plotted in Figure 2 10 for both the observed and simulated precipitation variogram models. This figure reinforces the conclusion th at the bias corrected MM5 results successfully reproduce the seasonal patterns of spatial precipitation variance (i.e. the C parameters are quite close for the observed and simulated variograms). However effective ranges ( A parameters) for the simulated variograms are consistently higher than the effective ranges for the observed data, indicating that the bias corrected MM5 daily precipitation fields are spatially correlated over longer distances (i.e. smoother) than the observed data. 2 .4.3 Kriging R esul ts The bias corrected point precipitation predictions were kriged over the study area to produce the spatially distributed precipitation fields needed to run distributed hydrologic model. To preserve the observed spatial correlation, the monthly variog rams estimated from the observed data were used rather than the variograms estimated from the simulated data. Spatially distributed precipitation totals were created for each day over the 23 year period, and then summed to get monthly totals. Daily and monthly kriged values were cross validated at each rain gage using the procedure described in Section 2. 3. Note that the point kriged estimates of the precipitation distribution did not use the bias corrected prediction at the cross val idation point, but incorporated bias corrected predictions for all other rain gage locations. Table 2 3 compares the daily and monthly point kriging and normalized point kriging estimates. These data show that point kriging the bias corrected daily preci pitation fields over the watersheds reproduced the observed rainfall with a ME ranging from 0.22 mm in September to 0.65 mm in August and an average RMSE ranging from 9.02 mm in January to 17.72 mm in September over the 53 stations. The ME and RMSE for th e monthly total precipitation fields ranged from 5.89 mm in January to 18.46 mm in July and from 43.48 mm in January to 127.52

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35 mm in September, respectively. The average normalized point kriging ME was 0.01 for daily and 0.06 for monthly results indicatin g that the kriging procedure successfully produces unbiased results. The average normalized point kriging RMSE was 1.04 for daily and 1.27 for monthly results, indicating that the kriging procedure accurately predicted the uncertainty of its estimates. Interestingly, the error statistics in Table 2 3 indicate that the point kriging results that use bias corrected data from the other 52 rain stations but require no data at all at the location being estimated, improve the average RMSE for daily and monthl y precipitation predictions over direct bias correction using l ocal gage data by 38.1% and 8.9 %, respectively. The kriging methodology showed most significant reductions over the local bias correction methodology during the dry season (monthly RMSE reduced by 29 .5% in December, 32.4% in January, and 21.4 % in February) when the spatial variability of precipitation is comparatively low. This result supports the methodology used by Colle et al (1999 and 2000) who evaluated MM5 predictions in the Pacific North west by comparing point observations to corresponding simulations estimated by interpolating the four simulated grid results surrounding the observation. Additionally, the bias corrected point precipitation predictions were kriged to produce daily precipi tation totals at the centroids of the 172 surface catchments encompassed in the Tampa Bay Water Integrated Hydrologic Model (IHM). Figure 2 11 shows maps of precipitation volume for observed versus bias corrected MM5 estimates kriged over the centroids of the 172 surface catchments. Three wet season precipitation events are presented as representative examples of events in months with the largest daily RMSE (Table 2 2) from years that showed relatively good (2000), average (1990) and relatively poor (1995) annual total rainfall predictions (Figure 2 8). Figure 2 11 shows that while the range of precipitation volumes and degree of spatial variability over the domain is fairly well simulated for all these events, the precise spatial

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36 distribution of precipita tion is subject to error, as expected by the magnitude of the kriging error statistics shown in Table 2 3. As mentioned previously, further improvement in day to day predictability will require improving the climate model physics, parameterization, and/or boundary condition used in MM5. These results indicate that the methodology presented here most likely does not have sufficient accuracy to produce spatially distributed predictions of daily rainfall useful for weekly to seasonal water resource operations decisions. However the method should produce spatially distributed rainfall predictions with sufficient realism in the daily, seasonal and inter annual patterns to be useful for distributed hydrologic modeling applications for multi decadal planning deci sions in the Tampa Bay Region. The daily kriged precipitation fields could be used directly as a best estimate of the actual spatial distribution of the daily precipitation fields needed to drive the hydrologic models in a deterministic manner. Alternativ ely the methodology could be used in a conditional simulation algorithm (Deutsch and Journel, 1998) to generate an ensemble of possible daily precipitation fields that honor the bias corrected precipitation fields at observation points but represent equall y probably precipitation distributions that honor the spatial structure of the precipitation field (i.e. variogram) at model nodes that do not coincide with bias correction points. 2 .5 Chapter S ummary This study quantitatively evaluated the ability of the MM5 model to downscale NCAR NCEP reanalysis data to reproduce precipitation patterns over the Tampa Bay region for the 23 years period from 1986 to 2008. Data from 53 rainfall stations distributed over the study area were used to evaluate model predict ions. Raw MM5 model results were positively biased, significantly over estimating the mean of daily and monthly precipitation totals. Bias correction using a CDF mapping technique

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37 effectively removed the bias in the mean daily, monthly and annual precipit ation, and improved the root mean square error in the daily, monthly and annual rainfall totals over the study area. Decomposition of the mean square error of the raw and bias corrected daily predictions into bias, variance and correlation terms showed th at bias correction significantly improved the prediction of the temporal mean and variance of precipitation, but the correlation between daily precipitation predictions and daily precipitation observations was not improved after bias correction. These ran ges of MM5 daily and monthly precipitation prediction errors are consistent with previous studies, however they call into question the utility of using MM5 (at least as configured in this study) to downscale GCM climate predictions for weekly to seasonal w ater resource operation and planning decisions. After bias correction MM5 successfully reproduced seasonal patterns of spatial variability in precipitation, with higher spatial variance of daily precipitation over the study area in the wet season when conv ective storms dominate and lower spatial variance of daily precipitation in the dry season when frontal systems dominate. However the strength of the spatial correlation of the daily rainfall fields was significantly overestimated throughout the year, wit h the bias corrected MM5 precipitation fields showing more spatial regularity than the observed fields. Bias corrected daily precipitation fields were kriged over the study area using observed semi variograms to produce spatiotemporally distributed precipi tation fields over the dense grids needed to drive hydrologic models in the Tampa Bay Region. Cross validation at the 53 long term precipitation gages showed that kriging reproduced the observed rainfall with average RMSEs lower than the RMSEs of the indi vidually bias corrected point predictions. These results indicate that although significant error remains in actual daily predictions at point locations, kriging the bias corrected MM5 predictions over a distributed hydrologic model grid produces

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38 spatiotem porally distributed precipitation fields with sufficient realism in daily, seasonal and inter annual patterns to be useful for multi decadal water resource planning applications in the Tampa Bay Region. In the next phase of this work the kriged bias corre cted MM5 precipitation results for the period 1989 model to quantitatively evaluate the ability of the modeled precipitation fields to reproduce historic hydrologic behavior in the region. It should be noted that using these bias corrected MM5 predictions downscaled from the NCEP NCAR reanalysis data in hydrologic models for multi decadal water resource planning applications provides no real advantage over using the long term historical cl imate record since the error associated with the bias corrected MM5 daily rainfall predictions is on the order of the standard deviation of the long term daily observations. However the long term goal of this research is to evaluate the utility of MM5 for downscaling GCM historical simulations, forecasts and climate change scenarios for driving hydrologic models and improving water management decisions in the Tampa Bay region. Therefore future work will produce spatially distributed precipitation estimate s in the Alafia and Hillsborough river watersheds from downscaled MM5 predictions that use GCM hindcasts, forecasts and IPCC scenarios as boundary conditions using the methodology developed here. These precipitation fields will subsequently be used in hydr ologic models to predict changes in streamflow, permissible reservoir withdrawals from the streams, and subsequent measures of reservoir reliability, for an ensemble of historical hindcasts and future climate change scenarios.

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39 Table 2 1. Summary of Tampa Bay Region Rain Gage Data ID Num Starting date Ending Date Site Name Owner LAT LON 1 4/24/2003 9/2/2008 BUD 05 METEOROLOGIC TBW 1 27.941805 82.272999 2 2/18/2000 10/13/2004 BUD JMOORE_RG TBW 27.926218 82.286005 3 4/4/1981 9/2/2008 CBR GREGG'S RAIN TBW 28.353185 82.484684 4 4/4/1981 3/1/1999 CBR #4 JUMPUNG GULLY RAI TBW 28.385069 82.488800 5 2/5/1996 9/2/2008 CBR BIG FISH RAIN TBW 28.370791 82.407385 6 4/4/1981 9/2/2008 CBR CB 1 METEOROLOGIC TBW 28.354457 82.461395 7 4/4/1981 9/2/2008 CBR CB 13 RAIN TBW 28.406474 82.452661 8 10/21/1997 9/2/2008 CBR S 1S RAIN TBW 28.384044 82.484220 9 1/1/1901 12/31/2004 Clermont Rain Gauge NOAA 2 28.458333 81.746736 10 8/31/1999 9/2/2008 CNR CM 2 RAIN TBW 28.160020 82.070944 11 8/25/1999 9/2/2008 CNR CM 6 RAIN TBW 28.114049 82.100435 12 10/1/1988 9/2/2008 CNR T1 RAIN TBW 28.119710 82.130724 13 10/1/1988 9/2/2008 CNR T2 RAIN TBW 28.086024 82.076606 14 10/1/1988 9/1/2008 CNR -T3 RAIN TBW 28.147554 82.093290 15 12/7/1990 7/28/1999 CNR T 4 RAIN TBW 28.113208 82.105550 16 5/15/1992 8/31/1999 CNR T 5 RAIN TBW 28.162068 82.074481 17 6/4/2003 9/2/2008 COSME 18 RAIN TBW 28.100964 82.589020 18 10/4/1988 9/1/2008 CYB CY 7 RAIN TBW 28.176395 82.354110 19 2/21/1986 9/2/2008 CYB TOT RAIN TBW 28.223976 82.364857 20 9/1/1976 9/1/2008 CYC C 3 RAIN TBW 28.301071 82.380392 21 11/1/1985 9/2/2008 CYC N.GATE RAIN TBW 28.314009 82.370910 22 5/2/1976 9/2/2008 CYC PLANT RAIN TBW 28.286487 82.425381 23 2/25/1999 9/2/2008 ELW MTR PIT METEOROLOGIC TBW 28.172598 82.667422 24 8/1/1943 6/30/2007 Hills River St Pk SWFWMD 3 28.142780 82.226940 25 1/1/1901 12/31/2004 Inverness Rain Gauge NOAA 28.838594 82.326203 26 1/1/1900 9/1/2008 KPIE NOAA 27.910560 82.687500 27 7/1/1966 9/1/2008 KSPG NOAA 27.762803 82.631206 28 7/1/1977 3/5/2007 LARGO Rainfall SWFWMD 27.906131 82.782597 29 10/15/1993 9/2/2008 MB RN USGS USGS 4 28.097222 82.312500 30 10/2/1998 9/2/2008 MBR 3C RAIN TBW 28.114249 82.331275 31 11/1/1998 9/2/2008 MBR BOOSTER METEOROLOGIC TBW 28.119113 82.366665 32 10/1/1988 9/2/2008 NEB DAYS INN RAIN TBW 28.001998 82.302636 33 3/5/1990 9/2/2008 NEB TAMPA 22 RAIN TBW 28.030366 82.223097 34 4/9/1990 9/2/2008 NOP NPMW 1RAIN TBW 28.320817 82.559612 35 10/1/1983 9/2/2008 NWH NW 5 RAIN TBW 28.056449 82.542836 36 1/1/1901 11/30/2007 Plant City Rainfall NOAA/SWFWMD 28.020294 82.126747 37 5/24/1995 9/2/2008 Rainfall at Tampa Dam USGS 28.023611 82.427778 38 11/1/2001 9/2/2008 RES P S 50 METEOROLOGIC TBW 27.800348 82.201573 39 6/14/2001 9/2/2008 RES P S 71 RAIN TBW 27.817330 82.181622 40 11/8/2001 9/2/2008 RES P S 78 RAIN TBW 27.813110 82.163878 41 9/1/1984 9/2/2008 RN SOP METER PIT TBW 28.190598 82.515871 42 11/1/2001 8/31/2008 Ruskin NWS NOAA 27.700000 82.400000 43 10/1/1984 9/2/2008 S21 21 10 METEOROLOGIC TBW 28.114003 82.503159 44 2/19/1988 9/2/2008 SCHM 2 RAIN TBW 27.937052 82.161940 45 3/1/1985 9/2/2008 SCH SC 1 METEOROLOGIC TBW 27.862889 82.202306 46 1/13/1989 9/2/2008 SCH SC 17 RAIN TBW 27.868611 82.083519 47 10/1/1984 9/2/2008 SCH SC 4 RAIN TBW 27.862952 82.143718 48 1/1/1900 5/31/2008 St Leo Rainfall SWFWMD 28.336392 82.260361 49 10/4/1988 9/2/2008 STK 14 RAIN TBW 28.236639 82.614956 50 2/13/1986 9/2/2008 STK EAST RAIN TBW 28.249710 82.551250 51 7/1/1982 9/2/2008 STK WEST RAIN TBW 28.258541 82.652619 52 1/1/1900 4/30/2008 Tarpon Springs Rainfall NOAA 28.154733 82.753708 53 6/10/1999 9/2/2008 TBC STRUCTURE 162 TBW 27.982416 82.351778 indicates the stations located within the Hillsborough and Alafia watersheds. 1. TBW: Tampa Bay Water 2. NOAA: National Oceanic and Atmospheric Administration 3. SWFWMD: SouthWest Florida Water Management District 4. USGS: U.S. Geological Survey

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40 Table 2 2. Daily and monthly mean and standard deviation of long term observations and error statistics (ME, RMSE) for daily and monthly total precipitation for the MM5 results at 9 km resolution. Statistics are calculated monthly over all 53 s tations for the raw and bias corrected predictions. Observation (units: mm) Error statistics (units: mm) Raw results Bias corrected results Daily Monthly Daily Monthly Daily Monthly Mean Stdv. Mean Stdv. ME RMSE ME RMSE ME RMSE ME RMSE Jan. 5.78 12.88 71.59 56.96 3.68 17.80 56.00 83.25 0.64 18.05 5.00 44.15 Feb. 7.58 15.42 65.55 61.07 4.57 26.96 69.24 153.35 0.30 18.39 5.63 52.80 Mar. 8.34 14.93 79.75 74.69 5.65 25.02 92.94 147.16 0.41 17.36 3.21 43.85 Apr. 7.10 13.79 61.33 47.96 3.50 23.15 53.75 107.23 0.21 21.11 2.23 62.65 May. 6.26 12.12 57.09 50.56 5.98 27.68 80.25 171.62 1.63 20.09 1.93 78.61 Jun. 9.99 16.14 201.07 94.60 0.48 25.09 11.58 129.14 0.13 22.80 1.81 117.77 Jul. 8.88 14.91 202.48 87.70 0.05 22.94 2.58 123.59 0.11 20.94 2.62 116.53 Aug. 8.33 13.98 195.84 74.72 1.50 19.72 32.56 111.01 0.29 20.31 6.94 116.23 Sep. 8.99 19.31 169.62 109.15 0.05 24.55 0.50 123.33 0.25 24.00 4.97 126.46 Oct. 6.42 13.06 68.67 52.89 3.95 25.32 51.65 122.18 0.54 18.09 6.26 65.32 Nov. 6.05 14.26 49.41 48.22 3.22 17.95 40.20 82.53 0.21 17.60 1.93 61.41 Dec. 8.05 17.34 73.38 108.07 3.07 20.20 44.74 128.39 0.13 22.48 0.77 113.59 average 7.65 14.85 107.98 72.21 2.72 23.03 39.24 123.56 0.11 20.10 1.00 83.28

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41 Table 2 3. Kriging error and normalized kriging error statistics (ME, RMSE) for daily and monthly total point kriged precipitation distributions. Statistics are calculated for over all 53 stations. Daily (mm) Monthly (mm) Normalized daily ( ) Normalized monthly ( ) ME RMSE ME RMSE ME RMSE ME RMSE Jan. 0.20 9.02 5.89 43.48 0.01 0.69 0.08 0.63 Feb. 0.03 9.68 1.04 49.68 0.00 0.80 0.01 0.82 Mar. 0.12 10.08 4.69 54.64 0.01 0.78 0.05 0.77 Apr. 0.03 10.24 0.98 54.00 0.00 1.59 0.02 1.59 May. 0.39 10.32 9.18 90.79 0.03 0.79 0.14 1.31 Jun. 0.20 17.50 7.45 113.86 0.02 1.39 0.10 1.74 Jul. 0.64 16.98 18.46 108.10 0.05 1.37 0.28 1.64 Aug. 0.65 16.54 16.21 110.12 0.05 1.19 0.23 1.58 Sep. 0.22 17.72 4.87 127.52 0.01 1.42 0.06 1.89 Oct. 0.23 10.32 6.19 63.17 0.02 0.84 0.10 0.99 Nov. 0.07 10.09 2.39 69.55 0.01 0.77 0.03 0.97 Dec. 0.29 10.84 10.17 85.16 0.02 0.84 0.13 1.28 average 0.15 12.44 3.99 80.84 0.01 1.04 0.06 1.27

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42 Figure 2 1. Map of study area and rainfall stations used for bias correction and cross validation.

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43 Figure 2 2. MM5 domain configuration: domain1 (1701 km by 1620 km at 2727 km 2 resolution), domain2 (675 km by 729 km at 99 km 2 resolution) Figure 2 3. Example of cumulative distribution function (CDF) for simulated results (domain2, September) and observations at station 36, (Plant city rain gage, September), and CDF mapping methodology. Semi log plots with respect to precipitation amount are shown for c larity.

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44 Figure 2 4. Observed vs. (a) raw simulated and (b) bias corrected results of first order transition probabilities for dry to wet day (P_01, left column) and wet to wet day (P_11, right column) for each month. Dashed ellipses enclose the dry season months from October to May, and solid ellipses enclose the wet season months from June to September. R 2 and mean error (ME ) for each case are shown on each figure. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Sim. P_01 Obs. P_01 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. (a) Raw results 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Sim. P_11 Obs. P_11 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Sim. P_01 Obs. P_01 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Sim. P_11 Obs. P_11 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. ( b ) Bias corrected ( b ) ( a ) R 2 =0.09 ME=0.182 R 2 =0.66 ME=0.162 R 2 =0.81 ME=0.004 R 2 =0.68 ME=0.011

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4 5 Figure 2 5. Contributions of mean error (dash dot line), variance error (dash line), and correlation error (solid line) to overall mean squared error (MSE) for raw (bright line) and bias corrected (dark line) daily precipitation by month. Figure 2 6. Comparison of mean monthly precipitation and standard deviation of monthly precipitation over the study period by month for raw MM5 results, bias corrected MM5 results, and point observations.

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46 Figure 2 7. The spatial distribution of averaged daily precipitation for dry (top row; Jan., Feb., Mar., Apr., May, Oct., Nov., and Dec.), and wet seasons (bottom row; Jun., Jul., Aug., and Sep.) for raw MM5 results (first column), bias corrected MM5 results (secon d column), and observations (third column). Units of precipitation are mm.

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47 Figure 2 8. A nnual total precipitation for raw MM5 results, bias corrected MM5 results, and observations. Means and standard deviations of annual precipitation over the study per iod for each case are indicated in figure (mean, standard deviation in units of mm/year).

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48 Figure 2 9. Observed (first column) and bias corrected MM5 (second column) variograms of daily precipiation for each month. The empirical variograms are repres ented by the symbol and the dashed lines represent estimated exponential variogram models. Note the change in y axis scale between the June to September (wet) and October to May (dry) periods.

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49

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50 Figure 2 10. Comparison of (a) parameter A and (b) parameter C for observed and simulated variogram models by month. Dashed lines and values in (a) represent the annual averaged parameter A of observed and simulated results.

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51 Figure 2 11. The spatial distribution of precipitation volume (unit: mm/event) for observed (right column) and kriged bias corrected MM5 results (left column) for three wet season precipitation events (first row: 1990 Jul. 2~18, second row: 1995 Aug. 1~7, third row: 2000 S ep. 15~21).

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52 CHAPTER 3 HYDROLOGIC IMPLICATI ONS OF ERRORS IN DYN AMICALLY DOWNSCALED AND BIAS CORRECTED CLIMATE MO DEL PREDICTIONS FOR WEST CENTRAL FLORIDA 3 .1 Background The largest water supply agency in west central Florida, Tampa Bay Water (TBW) pro vides water for more than 2 million residents through a diverse regional water supply system. TBW uses a suite of statistical and physically based hydrologic models to analyze hydrologic conditions and estimate water supply availability to ensure that wate r demand for the region can be met at the least cost and with minimal adverse environmental impacts. TBW manages surface and groundwater water sources in compliance with permitted withdrawal limits in order to protect the ecological integrity of the rivers wetlands and lakes in the region. Precipitation is the main driver of variability in water availability over space and time in the Tampa Bay Region. Therefore understanding and responding to existing precipitation variability and potential future changes in precipitation patterns is particularly important for maintaining a reliable regional water supply (Schmidt et al., 2004). TBW currently uses observed weather patterns or historical climatology rather than quantitative climate forecasts/predictions in its water supply planning process. However, understanding the risks and benefits of using quantitative climate information for improved water supply management is of significant interest to the agency. The most common method of developing climate scenario s for quantitative impact assessments is to use results from general circulation model (GCM) experiments. General circulation modeling continues to be improved by incorporating more aspects of the complexities of the global system. GCMs are considered a r obust tool for understanding present and past continental scale climates (Karl and Trenberth, 2003). However, GCM results are generally insufficient to provide accurate prediction of climate variables on the local to regional scale

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53 needed to assess hydrol ogic impacts because of significant uncertainties in the modeling process (Allen and Ingram, 2002; Dibike and Coulibaly, 2005). The coarse resolution of existing GCMs (typically 200km by 200km) precludes the simulation of realistic circulation patterns and accurate representation of the small scale spatial variability of climate variables (Christensen and Christensen, 2003; Giorgi et al., 2001; Jones et al., 2004; Lettenmaier, 1999; Wood et al., 2002). Furthermore, mismatch of the spatial resolution between GCMs and typical hydrologic models (< 1 km2) generally precludes the direct use of GCM outputs to predict hydrologic impacts. To overcome this limitation, a number of downscaling methods have been developed. The two main downscaling approaches are statis tical downscaling methods using empirical relations between features simulated by GCMs at grid scales and surface observations at subgrid scales (Hay et al., 2002; Wilby and Wigley, 1997) and dynamic downscaling techniques using regional climate models (RC Ms) based on physical links between the climate at large and smaller scale (McGregor, 1997). These two approaches can produce substantial differences in regional climate scenarios since each utilizes different aspects of GCM output (Fowler et al., 2007; Mu rphy, 1998; Wilby et al., 2000). However both statistical and dynamic downscaled results provide better skill for hydrologic modeling (Andr asson et al., 2004; Graham et al., 2007; Wood et al., 2004) and agricultural crop modeling (Mearns et al., 1999, 2001) than using the coarse resolution GCM data directly. It is also important to note that skillful statistically downscaled results for the present climate do not necessarily guarantee reliable forecasts of future climate because statistic al downscaling is ultimately limited by the assumption of stationarity in the empirical relations between global scale predictions and historic observations (Fowler et al., 2007; Charles et al., 2004; Ramage, 1983; Wilby, 1997). In contrast, dynamic downsc aling techniques using RCMs are not limited by the statistical assumptions and have shown good skill

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54 in reproducing current and predicting future local climatology in climate research despite the comparatively high computational costs. It has been pointed out by several researchers that physically based dynamic downscaling methods must be evaluated for diverse regions with high climate variability so that the strengths and weaknesses of dynamic downscaling and its hydrologic implications can be better unde rstood (Fowler et al, 2007; Hong, 2003; Wang et al., 2004). While RCM results for mountainous regions with highly variable terrain have been shown to be reasonable (Seth and Giorgi, 1998; Washington Olympic mountains: Colle and Mass 1996; Colorado mountain region: Gaudet and Cotton 1998; Pacific northwest coastal region: Colle et al., 1999; Phoenix metropolitan area: Zehnder, 2002; Great Lakes region: Zhong et al., 2005), RCMs have not been tested for hydrologic applications in low relief precipitation domi nated coastal systems in Florida (Hwang et al., 2011; Sun and Furbish, 1997). Generally, in order to evaluate the credibility of RCM results, it must be shown that the model simulates local present day climate conditions accurately (Christensen et al., 19 97; Hay and Clark, 2003). This is typically achieved by running the RCM using reanalysis data as initial and boundary conditions for given historical periods (Mearns et al., 2003). Use of reanalysis data removes the confounding factors of potential biases related to GCM process simulation, and thus provides a more objective measure of the skill of the RCM downscaling accuracy (Maurer and Hidalgo, 2008; Maurer et al., 2010). Additionally, reanalysis data has been recently used for climate change impact resea rch instead of the direct GCM RCM combination approach to eliminate the influence of the coarse scale model error which may be strong and difficult to quantitatively estimate (e.g., Sato et al., 2007, Fujihara et al., 2008).

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55 Use of dynamically downscaled results for hydrologic modeling is not a straightforward process since meteorological variables from climate models are often subject to systematic errors (Frei et al., 2003; Graham et al., 2007). Even raw RCM results that are produced using reanalysis dat a as boundary conditions can produce considerable biases in precipitation and temperature (e.g., Fujihara et al., 2008; Hay et al., 2002; Maurer and Hidalgo, 2008; Widmann and Bretherton, 2000; Wilby et al. 2000) that will significantly affect hydrologic p redictions. As a result bias correction of RCM predictions using historic observations is typically necessary (Murphy, 1999; Wood et al., 2004). Therefore it is also worthwhile to evaluate the utility of various available observations (e.g., point, basin, and gridded data) for bias correction because the data availability and quality varies from location to location. Recently, a number of studies have been conducted to use bias corrected RCM results to conduct hydrologic assessments over various regions (e. g., Block et al., 2009; Fowler et al., 2007; Fujihara et al., 2008; Graham et al., 2007, Middelkoop et al., 2001; Teutschbein and Seibert, 2010; Wood et al., 2004). These studies have mainly focused on evaluating streamflow regimes because of their sensiti vity to changes in climate forcing. In Florida, groundwater is a major source of public water supply and it contributes significantly to springflow, streamflow and wetland hydroperiods due to strong surface groundwater interactions in the surficial system Therefore simultaneously evaluating ET, streamflow, groundwater and springflow predictions driven by climate modeling results is important for water resource management in Florida. The purpose of this research was to 1) comprehensively evaluate the abil ity of MM5 (the Pennsylvania State University (PSU) National Center for Atmospheric Research (NCAR) Fifth Generation Mesoscale Model; Grell et al., 1994) to dynamically downscale reanalysis data to reproduce observed sub basin scale precipitation and maxim um and minimum daily temperature

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56 in Tampa Bay region for the 1989 to 2006 time period; 2) investigate the utility of available observations at different scales (i.e., sub basin based and gridded observations) for bias correction of the climate modeling res ults; and 3) evaluate the accuracy of hydrologic predictions (streamflow, groundwater level, and springflow) produced using the bias corrected MM5 outputs to drive the TBW Integrated Hydrologic Model (IHM) calibrated for the Northern Tampa Bay Region (cal led the Integrated Northern Tampa Bay model or the INTB). The next section describes the climate and hydrologic models and data used in the study. The methodology for data analysis and evaluation are described in section 3. 3, and results are discussed in s ection 3. 4. Finally, the main conclusions are summarized in section 3. 5. 3 .2 Models and D ata 3 .2.1 Regional C limate M odeling The meso scale regional climate model, MM5 has been widely applied to a variety of regions for historical and future climate studies. A review of the MM5 model and its applications over the U.S is provided by Hwang et al. (2011). Hwang et al (2011) used MM5 to downscale NCEP/NCAR reanalysis data from a 2.58 degree by 2.58 degree grid to a 9 km by 9 km grid, then bias corrected using data from 53 long term precipitation gages irregularly distributed over the Tampa Bay region for the 23 year period from 1986 to 2008. Their study concluded that although significant error remains in predicting actual daily precipitation at rain ga ges, the precipitation fields showed sufficient realism in the daily, seasonal and inter annual patterns to be potentially useful for multi decadal water resource planning in the Tampa Bay Region. Hwang et al (2011) set up MM5 using the NCAR community mod el CCM2 radiation scheme (Kiehl et al., 1996), the Grell cumulus parameterization scheme (Grell et al., 1994), and the Simple ice scheme (Grell et al., 1994). The planetary boundary layer (PBL) physics was set

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57 to a non local vertical diffusion scheme (Hon g and Pan 1996) and the 5 layer soil temperature model (Dudhia, 1996) was used for land surface processes. MM5 was configured to predict atmospheric conditions at 21 pressure levels between 100000 Pa to 20000 Pa, and all simulations were performed in a two way nesting communication. The 25 category U.S. Geological Survey (USGS) 1999 landuse dataset with 1 km horizontal spacing was used for the entire 1986 2006 simulation period (Grell et al. 1994). Hwang et al (2011) used NCEP/NCAR global reanalysis data from 1986 to 2008 ( Kalnay et al., 1996) as the initial and boundary conditions for the MM5 model. The NCEP/NCAR global reanalysis data set is a joint product from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmosphe ric Research (NCAR) that was created by assimilating observations and reanalysis model predictions to provide a gridded data set representing the retrospective state of the earth's atmosphere over time. The resolution of the global reanalysis data set is a 2.582.58 degree grid with 28 vertical sigma levels. This study used the raw 99 km 2 grid MM5 precipitation, maximum temperature and minimum temperatures predictions from 1986 to 2008 that were produced by Hwang et al (2011). Bias correction of the ra w MM5 predictions was conducted using two sets of observations focused over the INTB model domain rather than the 53 regional long term stations used by Hwang et al (2011). The two sets of observations included 1) the precipitation and temperatures for e ach model sub basin centroid used for calibration and verification of the IHM model application (Geurink and Basso 2011) and 2) 1/8 degree (~1212 km 2 ) gridded observations of precipitation and temperature from Maurer et al. (2002). For the MM5 precipitat ion that was bias corrected using gridded observations, sub basin inputs for the model

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58 were prepared by interpolation onto the sub basin centroid using point kriging, as recommended by Hwang et al. (2011). 3 .2.2 Hydrologic M odeling In west central Flori da the fresh groundwater flow system generally consists of a thin surficial aquifer underlain by the thick, highly productive carbonate rocks of the Floridan aquifer system. Most of the Floridan aquifer is semi confined, recharged by means of leakage from the overlying surficial aquifer. However, in the northern extent of the region some portions of the Floridan aquifer are unconfined, receiving direct recharge from vadose zone infiltration. The significant temporally variable flux and storage connection be tween surface and groundwater systems is caused by the near surface water table condition that covers more than 50% of the region. In order to capture the dynamic interaction between surface and groundwater in this region an integrated hydrologic model is required. TBW and the local state regulatory agency for surface water and groundwater resources, the Southwest Florida Water Management District (SWFWMD), commissioned the development and application of an integrated surface water/groundwater model to gain an increased understanding of the surface and groundwater flow systems in the Tampa Bay Region (Geurink et al., 2006a). From this effort the Integrated Hydrologic Model (IHM) was developed which integrates the EPA Hydrologic Simulation Program Fortran (HS PF; Bicknell et al., 2001) for surface water modeling with the US Geological Survey MODFLOW96 (Harbaugh and McDonald, 1996) for groundwater modeling. IHM was designed to provide an advanced predictive capability of the complex interactions of surface wate r and groundwater features in shallow water table environments. The model can be characterized as deterministic, semi distributed parameter, semi implicit real time formulation, with variable time steps and spatial discretization (Ross et al., 2004). The m odel

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59 components explicitly account for all significant hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, baseflow, groundwater flow, and all the component storages of surface, vadose and saturated zones (Ross et al., 2005). Climate input data requirements include time series for precipitation and potential or reference evapotranspiration (Geurink et al., 2006b). 3 .2.3 INTB M odel D omain and C alibration Using the IHM simulation engine the Integra ted Northern Tampa Bay (INTB) model was developed and calibrated. The INTB model domain is bordered by the Gulf of Mexico and inland groundwater flow lines. The area is located in the west central Florida region and extends to the eastern boundary of SWFWM D as shown in Figure 3 1. Tampa Bay is located in the southwest part of the domain. The north and east boundaries follow Floridan aquifer flow lines (i.e. no flux boundaries) and the southern boundary is placed far enough from the area of interest for this study to minimize the influence of the general head boundary (Geurink et al., 2006a). In the study area, average annual rainfall for the model calibration (1989~1998) and verification periods (1999~2006) is 1295 mm and 1263 mm, respectively. In general, e vapotranspiration (ET) accounts for approximately 70% of annual precipitation. Land cover over the domain is diverse, including urban, grassland, forest, agricultural, mined land, water, and wetlands. Open water and wetlands cover 25% of the region. The s urface water component model domain is discretized into 172 sub basins based on surface drainage as shown in Figure 3 1. For each sub basin, hydrologic processes are simulated within hydrologic response units (land segments) based on five upland landuse c ategories and two water body categories (Ross et al., 2004). The groundwater component model domain is discretized into approximately 35,000 square and rectangular grid cells with cell dimension of one quarter mile over the area of interest and expanding t o one mile in outer regions.

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60 The INTB model was manually calibrated using hydrologic observations from 1989 to 1998 and verified using data from 1999 to 2006 (Geurink and Basso, 2011). Manual calibration efforts were significantly informed and enhanced by many applications of PEST (the automated Parameter Estimation software: Doherty, 2004) to the INTB model data. Hydrologic observations used as calibration targets included 38 streamflow monitoring stations, 200 locations each of surficial and Floridan aqui fer wells, 7 springs, and long term average annual target values of actual evapotranspiration assigned to landuse depth to water table combinations. Table 3 1 details the data sources used by Guerink and Basso (2011) to develop and calibrate the INTB. Tabl es 3 4 through 3 6 include calibration statistics for the period 1989 through 2005. 3 .2. 4 Climate D ata Temporal distribution and intensity of precipitation are important in deterministic, physically based hydrologic simulations. A short temporal resolution is required to adequately capture the effects of localized convective storms. Rokicki (2002) showed th at hydrologic modeling with time steps larger than 15 minutes results in significantly large error in runoff for west central Florida, and suggested discretizing precipitation into 15 minute intervals or smaller for accurate simulation of runoff. The INTB model uses 15 minute precipitation time series input for each of the 172 sub basins. For the calibrated INTB model, precipitation data over the model domain were obtained from 300 stations from three different sources including TBW, SWFWMD, and National O ceanic and Atmospheric Administration (NOAA). In order to estimate sub basin precipitation time series, available daily precipitation data within each basin were spatially distributed by Thiessen polygons and averaged over the sub basin (area weighted) to generate input for the hydrologic modeling. Daily precipitation values for each basin were temporally disaggregated using nearest NOAA 15 minute observations which are available within and around the modeling domain.

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61 For the calibrated INTB model, six ref erence ET (Hargreaves and Samani, 1985) time series were developed from minimum and maximum temperature data at six NOAA stations and then spatially assigned to the nearest neighbor basins over the model domain to define the upper limit of evaporative dema nd. Daily reference ET values were temporally disaggregated into hourly values for the INTB model input using an annual profile (i.e., ensemble results for each Julian day) of hourly reference ET values. Hourly values were generated with the FAO 56 PM meth od (Allen et al. 1998) using a data set with a shorter period of record but having a full suite of weather parameters. In addition to the observed climatic data developed to calibrate the INTB model, three additional sets of climate inputs were used to dr ive the INTB model: 1) spatially averaged observed precipitation over the model domain (i.e., daily spatial average of the 172 sub basin precipitation time series used for the calibrated model to evaluate the importance of accurately representing the sp atial variability of precipitation) and the spatially distributed maximum and minimum temperatures used for the calibrated model; 2) MM5 results (both precipitation and temperature) that were bias corrected using the 172 point (basin) precipitation and 6 point temperature time series described above for the calibrated model (MM5 results for the grid cells that contain the observed locations were assumed to be corresponding predictions); and 3) MM5 results (both precipitation and temperature) that were bias corrected with gridded precipitation and gridded temperature observations from Maurer et al. (2002). The bias corrected gridded precipitation estimates were then spatially interpolated onto the sub basin centroids using point kriging. For the third clim ate input scenario described above, daily gridded observations at 1/8 degree spatial resolution (about 12 km) were obtained over west central Florida from 1950 to

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62 1999 from Maurer et al. (2002). The Maurer et al. climate data (daily and monthly precipitati on, maximum, minimum, and average temperature, and wind speed) are archived in netCDF format at http://hydro.engr.scu.edu/files/gridded_obs/daily/ncfiles/. These products are available from 1950 through 1999 over the entire U.S. without missing data or un anticipated biases that sometimes occur in surface station measurements. Because these data are available throughout the US it is worthwhile to compare results obtained using these gridded observations for MM5 bias correction to results obtained using sub basin precipitation estimates used to calibrate the INTB model for bias correction. More details on the bias correction and kriging methodologies are given in section 3. 3 .1 below. 3 3 Methodology This research employed several methods to correct the bias es in MM5 results and estimate the climate data at the spatial resolution required for hydrologic modeling as described above and shown in Figure 3 2. 3.3.1 Climate P rediction A djustment A cumulative distribution function (CDF) mapping approach (Panofs ky and Brier, 1968; Wood et al., 2002; Ines and Hansen, 2006) was used to bias correct the raw MM5 99 km 2 predictions using the following procedure: 1) CDFs of observed daily precipitation were created individually for each of the observation locations fo r each month using available observed data (this was done separately for both the 172 point observations and the 12x12 km gridded observations). Thus, for each case, 12 different monthly CDFs were used for each location for bias correction of the daily pr edictions; 2) CDFs of simulated daily precipitation were created for the grid cell containing each observation location for each month; 3) daily grid cell predictions were bias corrected at each observation location using CDF mapping that preserves the pro bability of exceedence of the simulated precipitation over the grid cell containing the

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63 observation, but corrects the precipitation to the value that corresponds to the same probability of exceedence from the observed results at the observation location. Thus bias corrected rainfall on day i at location j was calculated as, where and denote a CDF of daily precipitation x and its inverse, and subscripts sim and obs indicate downscaled simulation and observed daily rainfall, respectively. Maximum and minimum daily temperature predictions were also bias corrected using same app roach. This bias correction process method was originally developed for adjusting general circulation model (GCM) results at the monthly time scale by Wood et al. (2002, 2004). It removes the bias in the precipitation and temperature predictions as well as the tendency of the model to under predict dry days and over predict the number of low volume rainfall days, assuming direct correspondence between the grid cell and observation location exceedence probabilities (Hwang et al., 2011). For the case of the 172 sub basin point observations the bias corrected results can be directly used as input to INTB because those results have been bias corrected directly onto the model sub basin centroids. For the case of the gridded observations point kriging with the semi variogram estimated from the gridded observation data was used to interpolate the bias corrected results from the 1212 km grid onto the 172 sub basin centroids. This approach was introduced by Hwang et al. (2011) who showed that kriging bias correcte d RCM predictions onto point locations reproduced observed rainfall with average RMSEs lower than the RMSEs of the RCM predictions bias corrected at individual points.

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64 3.3.2 Target S tations for INTB M odel E valuation Based on the importance to water su pply management and variability of flow characteristics over the study area four streamflow stations on the major rivers were chosen to evaluate hydrologic response forced by climate predictions (Figure 3 1 and Table 3 2). The Alafia and Hillsborough rive rs have a mean discharge of 9.6 m 3 /s and 6.9 m 3 /s, respectively with very few no flow days whereas Cypress Creek and Anclote River have a mean discharge of less than 2 m 3 /s. Furthermore Cypress Creek has a large percentage (approximately 25%) of no flow da ys. Investigating stations with large and small flow volumes is important to understand how different types of flow regimes are affected by changes in climate variables and/or errors in their estimates. Additionally, the rivers flows at these stations are important for water supply operations and management because they are either located near or downstream of wellfields or water is withdrawn from them to meet local water demand. Groundwater levels were evaluated for both unconfined and semi confined Flori dan aquifer conditions. Four pairs of surficial and Floridan aquifer monitoring wells were chosen in each of four TBW wellfields. In addition one unconfined Floridan aquifer well in the northern part of the model domain was evaluated (Figure 3 1). Two sp ringflow stations were also chosen for evaluation. These include Weeki Wachee spring located in the northern part of the domain and Lithia spring in the Alafia watershed (Figure 3 1). In general Weeki Wachee springflow comes from the unconfined Floridan aq uifer and Lithia springflow comes from the semi confined Floridan aquifer. 3.3.3 Error S tatistics The monthly and annual MM5 precipitation predictions and calibrated INTB simulation results were evaluated by comparison to observed data. The accuracy of model predictions was quantified using the mean error (ME) and the root mean square error (RMSE), i.e.

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65 Where and are the estimate and the observation for day or month i and is the total number of estimates. In addition, performance of the hydrologic model was evaluated by two indices (i.e., the coeffic ient of determination, R 2 and the coefficient of efficiency, E) given by Where and are mean of observations and predictions, respectively The coefficient of determination and describes the fraction of the total variance in the observed data that is explain ed by the model (Legates and McCabe Jr., 1999). The coefficient of efficiency (Nash and Sutcliffe, 1970) is one minus the ratio of the mean square error of predictions, to the standard deviation of the observations. The coefficient of efficiency range s from minus infinity to 1, with zero indicating that use of the model prediction is no better than using the mean of the observations as a predictor and one indicating perfect agreement between observations and model predictions. Note that, in this paper INTB simulation results using MM5 precipitation and temperature

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66 scenarios were evaluated by comparison to calibrated IHM results rather than hydrologic observations to focus on differences due to differences in climatic forcing. 3 4 Results and D iscussio ns 3.4.1 MM5 R esults Raw precipitation and temperature values predicted by MM5 at the 9 km grid spacing were bias corrected and evaluated in west central Florida for the 18 year period from 1989 to 2006. Figure 3 3 compares the mean monthly precipita tion over the study period for sub basin based observations (P obs.), gridded observations (G obs., Maurer et al., 2002), raw MM5 results, MM5 results that were bias corrected using P obs. (BC_P MM5), and MM5 results that were bias corrected using G obs. a nd then point kriged onto the basin centroids (BC_G MM5). The figure indicates that raw MM5 results significantly overestimate the monthly precipitation especially during the dry season. The bias corrected mean monthly predictions closely match the respect ive observations that were used for bias correction, with slightly higher monthly mean errors for the MM5 results that were bias corrected using gridded observations (BC_G MM5, Table 3 3). This is likely due to the fact that sub basin based observations are available for the entire study period, but gridded observations are currently available until 1999. Using observations that coincide exactly with the simulation period will remove mos t of the temporal mean bias. In this paper however, I used all available observations for bias corre ction because this approach is analogous to what would be appropriate when GCM predictions/forecasts are employed for simulation periods that do not necessa rily coincide with the period of record. When the period of record used for bias correction and the simulation period do not exactly coincide, small temporal biases can be expected due to differences in data over the averaging intervals. Raw MM5 annual tot al precipitation was also overestimated for the entire study period, and bias correction significantly improved the predictions as shown in Figure 3 4. Table 3 3 and

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67 Figure 3 4 show that mean total annual precipitation over the study period and the pattern s of inter annual fluctuations of total annual precipitation were fairly well reproduced by bias corrected MM5 results. However total annual precipitation was overestimated during the period from 1996 to 1998 (errors ranging from 137 mm in 1997 to 572 mm in 1998) and underestimated from 2002 to 2004 (errors from 306 mm in 2003 to 318 in 2004), resulting in an overestimate of the standard deviation of total annual precipitation over the study period. Figure 3 5 shows that MM5 over predictions for the dry season (October through May) dominate the biases in the raw results while the periodic over/underestimations in bias corrected results were driven mainly by the wet season performance (June to September). Bias correction using the daily precipitation CDF mapping approach was not able to completely remove these periodic annual errors, regardless of the magnitude of the bias. Overall Figures 3 3 to 3 5 and Table 3 3 indicate that bias correction using either point observations or gridded observations produc e very similar results. These results are similar to those found by Fujihara et al. (2008) who showed both significant bias of dynamically downscaled reanalysis data and improvement through the bias correction process over the Mediterranean region in Europ e. The spatial patterns of mean monthly precipitation for dry (October through January and February through May), and wet seasons (June through September) over the domain are mapped in Figure 3 6. Generally, the results indicate that bias correction of M M5 using point observations and gridded observations reproduced the observed mean spatial structure of P obs. and G obs. precipitation fields, respectively. However, the spatial pattern of the gridded data shows some differences from the spatial pattern of the point observation data especially for wet season months. This is likely due to the different period of record for the gridded observations

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68 (1950 to 1999) compared to model simulation (1986 to 2008) and the comparatively coarse resolution of the gridde d data (12 km 2 ). Figure 3 7 compares the maximum and minimum observed temperatures, raw MM5 results, and bias corrected results. The downscaled daily minimum temperature predicted observations accurately with mean bias less than 1 however the maximum temperature was considerably underestimated with mean bias about 7 Bias correction removed the overestimation reducing the mean biases to 0.5 The same bias corrected temperature data set was used with both of the MM5 precipitation scenarios (i.e., B C_P MM5 and BC_G MM5). 3.4.2 Streamflow S imulation R esults The calibrated INTB model was used to generate streamflow for each climate input scenario. Streamflow from each scenario was compared to INTB calibrated model results rather than observed stream flow to focus on differences in streamflow due to differences in climatic forcing. Monthly streamflow time series, the annual cycle of mean monthly discharge, and the cumulative distribution function (CDF) of daily streamflow were used to evaluate streamfl ow results. Figure 3 8 shows the monthly streamflow hydrographs from 1989 to 2005 for the selected target stations. The two bias corrected MM5 climate scenario simulations generally agree with calibrated predictions both in timing and magnitude of monthly streamflow with a few notable exceptions, i.e. overestimation for October through December 1998 and underestimation for December 2002 through September 2003 (marked boxes in Figure 3 8). Comparison of Figure 3 4 (total annual precipitation) with Figure 3 8 shows, as expected, that errors in streamflow simulation are strongly related to the accuracy of timing and intensity of precipitation inputs. Figure 3 9 compares more detailed monthly and daily precipitation predictions and daily

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69 streamflow simulations f or the two years that include the over/underestimated events marked in Figure 3 8. The significant precipitation overestimation in September and November 1998 and underestimation in May and June 2003 are evident in both bias corrected MM5 precipitation pre dictions and result in significant daily flow errors that drive the errors in the long term average monthly streamflow. These streamflow errors, caused by inaccuracies in precipitation timing, cannot be reduced by any additional statistical corrections, on ly by improving the climate model physics, parameterization, and boundary conditions for the RCM. Table 3 4 summarizes model error and performance scores for the daily and monthly streamflow simulations for the target stations. Figure 3 10 shows the tota l water budget over the domain by major category for each of the climate scenarios. The INTB model results using BC_G MM5 precipitation show larger underestimation of total daily and monthly streamflow volume (larger negative ME) but slightly smaller erro r in capturing temporal variability (smaller RMSE and larger E) at the target stations compared to the results using BC_P MM5. Figure 3 10 indicates that BC_G MM5 underestimates total streamflow leaving the domain and overestimates total ET over the domain Using spatially averaged precipitation that preserves the observed temporal precipitation patterns also underestimates average daily and monthly streamflow volumes at the target stations, and underestimates total streamflow leaving the domain and overest imates total ET over the domain. In both these cases the decrease in streamflow is likely due to lower spatial variability (Figure 3 6) and higher frequency of small precipitation events (greater than 0 but less than 10 mm/day) resulting in higher ET (Fig ure 3 10) and thus lower runoff. The simulations using BC_P MM5 predictions more accurately reproduced calibrated ET and total streamflow leaving the domain. However the temporal trend in streamflow response is better simulated with the spatially homogeneo us observed precipitation

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70 input compared to both MM5 scenarios (as measured by reduced RMSE and increased E). This indicates the timing of precipitation and antecedent moisture conditions are important factors in predicting the timing of streamflow. Figure 3 11 compares mean monthly streamflow volume predicted by the four climate data input scenarios at the four streamflow locations. At all locations mean monthly streamflow volume is similar for all scenarios during the January through May dry season, but b oth the MM5 scenarios and the spatially averaged rainfall scenario significantly underestimate the calibrated result during the early portion of the wet season (June August). The MM5 results peak higher than the calibration result in the wet season (Figur e 3 5 and Table 3 4) mainly due to the overestimation of precipitation during the wet seasons of 1996~1998 (Figure 3 5 and Figure 3 9). At locations with relatively low streamflow and significant surface flow attenuation due to conditionally connected wetlands (Cypress Creek and Anclote River, Figure 3 11) the annual timing of predicted mean peak monthly streamflow lags the calibrated mean peak monthly streamflow by one month altho ugh the magnitude of peak flow is similar. At locations with relatively high streamflow and low reliance on inflow from conditionally connected wetlands (Alafia and upper Hillsborough rivers) the annual timing and magnitude of peak monthly streamflow align s with the calibrated model. For the spatially averaged observed rainfall scenario, timing of peak streamflow is aligned with calibration results for all locations. Streamflow simulation errors tend to exponentially increase as the annual total precipitat ion increases and, increase approximately linearly with mean RMSE of daily precipitation (Figure 3 12). For Tampa Bay Water, extraction of surface water from the Hillsborough and Alafia Rivers is restricted to times when streamflow is above permitted thr esholds. For example,

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71 Hillsborough River streamflow withdrawals are permitted by regulation when flows are between 1 and 30 m 3 /s. When streamflows exceed 30 m 3 /s withdrawals are limited by infrastructure pumping and treatment capacities. Similarly for t he Alafia River at Lithia, streamflow withdrawals are permitted when flows range from 3 to 23 m 3 /s. Figure 3 13 shows CDFs for each climate input scenario over ranges of streamflow that significant for water resources decisions at target stations. For all stations deviations between the various climate scenario CDFs were found, with the average precipitation case over predicting streamflow exceedences in the ranges of interest for all locations. At both the Hillsborough River and the Alafia River locations the BC_P MM5 scenario over predicted the CDF value (i.e. under predicted exceedence probabilities) over the entire range of interest especially at the lower limit for streamflow withdrawal, implying more low flow events and less surface water availability for public supply than calibrated case would indicate. The BC_G MM5 matched the calibrated streamflow exceedence probabilities well throughout the flow range of interest at Hillsborough River, but slightly overestimated the CDF value (under estimated exce edence probabilities) for flows between 7 m 3 /s and 30 m 3 /s at the Alafia River. A similar frequency analysis of streamflow was derived from RCM results by Kleinn et al. (2005) for locations in central Europe. They also found significant errors for extreme streamflow conditions while mean monthly streamflow showed good agreement. Through the bias correction process previously described, the MM5 daily precipitation CDF was corrected to be identical to the daily CDF of observed precipitation, and as a result mean monthly precipitation over the 18 year period were virtually identical. However streamflow responses evaluated in this study indicate that reproducing more detailed precipitation characteristics (i.e actual annual totals, seasonal distribution, inter event duration,

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72 precipitation intensity, and spatial distribution) is import ant to accurately capture both the monthly mean streamflow and the daily streamflow CDF. In other words, reproducing the daily frequency of precipitation events by itself does not constrain all of these characteristics. Furthermore simulation accuracy of the temporal mean streamflow condition does not imply that the variability, change, and impacts of extreme events that are important for hydrologic applications have been adequately predicted (Frei et al., 2003; Seneviratne et al., 2002). Resolution of def iciencies in streamflow volume and temporal variability requires improvements in the climate model physics, parameterization, and/or boundary condition used in the RCM. 3.4.3 Groundwater L evel S imulation R esults The groundwater simulation results indica te two distinctly different responses to the climate input scenarios over the study domain. The two distinct regions are characterized by areas where surface runoff is prevalent or absent. Surface runoff is generally absent where the Floridan aquifer is un confined (in the northwest portion of model domain) causing climate variability to more directly influence the groundwater system. For the remaining 75 80% of the model domain, runoff processes are impacted more directly by the climate variability, especia lly where the water table is near land surface providing the opportunity for runoff to be generated by saturation excess conditions. Results of these analyses indicate that the impact of climate variability and errors in climate forcing data are less evid ent in the semi confined Floridan aquifer compared to the overlying surficial aquifer. Surficial/Floridan well pairs CYC TMR 5 SH/CYC TMR 5d, S21 J26As/S21 Jcksn26d, and STK Starkey 20s/STWF 10 DP are located in a region where the Floridan aquifer is semi confined and surface runoff is prevalent. Based on the statistics (Table 3 5), monthly hydrographs (Figures 3 14 and 3 15), mean monthly average plots (Figure 3 16), and CDFs of monthly groundwater level simulations (Figure 3 17), the various climate input scenarios show

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73 some influence on the groundwater system in the semi confined region, but this influence is not as pronounced compared to streamflow. The surficial/Floridan well pair CBR SERW s/ CBR SERW d is also located in the semi confined region, but i s close to the area where the Floridan becomes unconfined. Surface runoff occurs at this location but is of low magnitude because of the deep water table. Thus more climate variability is reflected in the groundwater system at this location than in other areas where the Floridan is semi confined. The groundwater level at the CBR SERW monitoring well is noticeably affected by over/underestimated precipitation estimates (Figure 3 14 and Figure 3 15). Groundwater level response to the various climate scenario inputs at the CBR SERW well pair is more similar to the unconfined Floridan response at the Masaryktown DP well than to the other well pairs located in the semi confined aquifer system region (Table 3 5 and Figures 3 14 to 3 17). Because surface runoff do es not attenuate the climate variability in this region, the groundwater system is much more influenced by errors and differences between the climate input scenarios. For instance the groundwater level errors reflect episodic errors in precipitation showin g the strong interaction between the surface and subsurface hydrologic systems as shown in the boxed areas of Figure 3 14 and 3 15. Errors in climate inputs persist in the groundwater system for much longer than for the surface water system due to the per sistent, diffusive properties of groundwater flow. For instance during the first half of the study period, the underestimated precipitation from 1989 to 1991 (Figure 3 4) led to lower groundwater levels than predicted by the calibrated model until 1996 (es pecially for the CBR SERW wells and unconfined Floridan well, Figure 3 14 and Figure 3 15). The over prediction of rainfall for the 1998 El Nino led to over prediction of groundwater levels which persisted until the end of 2002 when MM5 again underestimat ed precipitation. The

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74 persistent underestimations resulted in the low mean groundwater level (Figure 3 16) and higher frequency of lower groundwater levels (Figure 3 17) especially for the stations where the interaction between climate and groundwater syst em is significant (e.g., CBR SERW and Masaryktown DP station). The spatially averaged uniform precipitation case generally resulted in underestimation of the mean monthly groundwater levels (Figure 3 16) and overestimation of the frequency of lower grou ndwater level (Figure 3 17) at the target stations compared to simulation results with other scenarios especially for the CBR SERW and Masaryktown DP locations, most likely due to increased ET (Figure 3 10) and strong interaction between surface and subsur face hydrologic system that was discussed above. Groundwater level predictions with bias corrected MM5 climate inputs (i.e., BC_P MM5 and BC_G MM5) also underestimated groundwater levels for all target stations compared to calibrated results, although the results were better than for spatially averaged precipitation. This indicates that for hydrologic applications, preserving the spatial variability of climate inputs is at least as important as preserving the spatially averaged temporal patterns. The BC_P MM5 and BC_G MM5 climate scenarios showed no significant differences in terms of groundwater level simulation errors. Interestingly, BC_G MM5 climate inputs provided better groundwater level results for the region where temporal groundwater level fluctuati ons are substantial and simulation errors are comparatively large. This result may indicate that mean monthly groundwater levels could be better predicted using gridded observations for bias correction where there are significant surface groundwater intera ctions because the BC_G MM5 method predicts smaller spatial errors of climate inputs and thus smaller timing errors as well

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75 comparing to BC_P MM5. It should be noted however that Figure 3 10 indicates that the overall groundwater outflow from the domain is very similar for all climate scenarios. 3.4.4 Springflow S imulation R esults Figures 3 18 through 3 20 compare monthly springflow time series, the annual cycle of mean monthly discharge, and CDF of daily springflow, respectively. In the unconfined Flo ridan aquifer region, spring discharge from the Floridan aquifer near the coastline replaces surface runoff. Springflow at Weeki Wachee station, located in the unconfined Floridan aquifer domain, showed similar time series to water levels in the unconfined Floridan aquifer due to direct interaction between spring and aquifer system. Lithia spring in Alafia watershed (semi confined Floridan aquifer region) shows similar results to semi confined Floridan aquifer water levels. Model error and performance sco res for the daily and monthly springflow simulations are also summarized in Table 3 6. 3 5 C hapter S ummary The ultimate goal of this study was to evaluate the applicability of a regional climate model for generating local scale precipitation and temperatur Hydrologic model over west central Florida. In this research, the MM5 meso scale climate model was applied to investigate the utility of dynamically downscaled precipitation and temperature predictions for use in a hydrol ogic application. Examination of the relationship between errors in the climate model and hydrologic simulation results provided insights into the influence of climate model biases on hydrologic impact assessment. Climate modeling results indicated that ra w MM5 predictions tended to significantly overestimate the monthly precipitation especially for dry season and underestimate maximum temperature overall simulation period. Bias correction using the CDF mapping approach at daily scale successfully removed the biases in mean daily and monthly precipitation and temperature

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76 predictions and also improved annual precipitation trends. Spatial patterns of mean precipitation fields for both dry and wet seasons were well reproduced by bias corrected MM5 results. Tem poral fluctuations and the magnitudes of mean monthly and annual precipitation were fairly well reproduced by bias corrected MM5 results however several events were over/underestimated. While the bias corrected MM5 results successfully reproduced the his torical climatology over the study area (i.e. frequency distribution of daily precipitation and long term mean and variance of monthly and annual precipitation totals), the periodic errors in the simulated time series of daily and monthly total precipitati on produced significant errors and low model skill scores in hydrologic model predictions, i.e. periodic errors in MM5 predictions were directly reflected in stream discharge and even groundwater level and spring flow simulations. Simulated streamflow sho wed significant errors during specific periods when climate inputs deviated from those used in the calibrated model, with the RMSEs for mean monthly streamflow ranging from approximately 1.8 m 3 /s (or 28 % of calibrated mean flow) at the Hillsborough River to approximately 0.6 m 3 /s (or 50% of calibrated mean flow) at Cypress Creek. Both the timing and relative magnitude of the annual cycle of mean monthly streamflow was reproduced better for high streamflow stations likely because there is less flow contrib ution from conditionally connected wetlands. However, streamflow exceedence probability predictions showed significant errors in the flow ranges of water resource significance at the high streamflow stations. The frequency of extreme events is driven by pe rsistent weekly to seasonal variability in precipitation which were not well reproduced by climate models. The confined Floridan aquifer was not influenced by the temporal errors in climate input as much as streamflow. On the other hand, the unconfined Flo ridan aquifer appeared to be

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77 significantly affected by over/under estimated precipitation predictions due to strong interactions with precipitation and low runoff or the absence of runoff. Moreover, the under/over predicted groundwater levels produced by e rroneous climate input persisted much longer in the unconfined (surficial or Floridan) aquifer than for streamflow due to the persistent, diffusive properties of groundwater flow. This natural feature of the groundwater system leads to the significant unde r/over prediction of the annual cycle and frequency of mean monthly groundwater level. It is thus important to reduce the errors in the timing of precipitation events to improve long term mean groundwater level predictions. Hydrologic predictions produced by using MM5 outputs were more accurate than those produced by using spatially averaged observations. Thus accurately reproducing spatial variability of precipitation is important to predict long term mean hydrologic behavior in west central Florida. In conclusion, although the bias corrected climate estimates adequately preserved the daily frequency distribution, observed mean monthly precipitation, inter annual trends, and mean spatial trends of daily precipitation, the errors in reproducing actual dai ly precipitation patterns resulted in significant errors in the frequency of daily streamflow, the timing of peak monthly mean streamflow, as well as the daily frequency and long term monthly mean of groundwater level and spring flow estimations. These er rors could have significant implications for water resource planning since surface and ground water withdrawals are limited when streamflows and groundwater levels drop below permitted thresholds. In rainfall driven hydrologic systems, improvements in cli mate model physics, parameterization, and boundary conditions for the RCM will be required before climate model predictions can be used for water resources management.

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78 Table 3 1. Description of data collected for hydrologic modeling Data Source Descrip tion Streamflow discharge USGS Data for 38 streamflow stations were collected Groundwater level USGS SWFWMD TBW Data were obtained from 236 surficial and 192 Floridan aquifer monitoring wells over the model domain. Diversion SWFWMD TBW USGS Surface water diversions represent pumped surface water sources, non pumped surface water transfers, and augmented water bodies. Daily pumping data for facilities pumping more than 1 mgd were acquired. Monthly pumping data were acquired for all others. Ir rigation SWFWMD Agricultural irrigation flux rates were estimated from pumping records maintained by the SWFWMD and irrigated area from FLUCCS parcels classed as irrigated. Irrigation from potable or reclaimed water sources was not estimated and is not in cluded in the model. Spring discharge USGS, SWFWMD Daily data were obtained from 6 springs. Periodic discharge records were obtained for one springs and linearly interpolated to produce daily estimates. Well pumping TBW SWFWMD Daily rates obtained for all of TBW production wells. Monthly pumping rates for all other wells were obtained from SWFWMD. Land use data SWFWMD Coverage delineate areas of particular land use as classified by FLUCCS. Original 53 FLUCCS codes were reduced to 7 hydrologically unique classifications. USGS: U.S. Geological survey SWFWMD: South West Florida Water Management District TBW: Tampa Bay Water FLUCCS: The Florida Land Use and Cover forms Classification System

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79 Table 3 2. Observed target stations for streamflow, Floridan and surficial aquifer, and springflow. Name (data source) Watershed Lat. Lon. Drainage area, (km 2 ) Streamflow stations Alafia River at Lithia Alafia 27.8719 82.2114 867.3 Hillsborough River near Zephyrhills Hillsborough 28.1497 82.2325 569.6 Cypress Creek at Worthington Gardens Hillsborough 28.1856 82.4008 302.9 Anclote River near Elfers Anclote 28.2139 82.6667 187.7 Surficial aquifer monitoring wells CYC TMR 5 SH (SWFWMD) Hillsborough 28.2057 82.4680 S21 J26As (TBW) Anclote 28.0730 82.5800 STK Starkey 20s (TBW) Anclote 28.1956 82.6953 CBR SERW s (TBW) Springs coast 28.3151 82.5146 Floridan aquifer monitoring wells CYC TMR 5d (TBW) Hillsborough 28.2053 82.4680 S21 Jcksn26d (TBW) Anclote 28.0730 82.5800 STWF 10 DP (SWFWMD) Anclote 28.1963 82.6951 CBR SERW d (TBW) Springs coast 28.3151 82.5146 Masaryktown DP (SWFWMD)* Springs coast 28.3740 82.5262 Springflow stations Lithia spring Alafia 27.5159 82.1353 Weeki Wachee spring Springs coast 28.3048 82.4680 *: Unconfined Floridan aquifer monitoring well. Corresponding surficial aquifer, therefore, does not exist.

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80 Table 3 3. The mean monthly and annual averaged precipitation of sub basin and gridded observations and error statistics (ME) in mean monthly and annual MM5 precipitation predictions. Units of precipitation are mm. Units: mm P obs. G obs. raw MM5 BC_P MM5 BC_G MM5 Monthly obs. mean error Jan 65.68 63.29 77.58 1.16 3.22 Feb 69.91 81.22 98.76 1.65 0.26 Mar 71.69 93.05 148.07 3.06 1.53 Apr 61.74 63.71 97.68 1.76 5.02 May 53.02 89.35 86.28 0.73 10.66 Jun 202.75 173.40 79.02 6.57 13.86 Jul 192.90 185.86 91.28 2.39 5.81 Aug 182.60 184.79 11.72 2.33 6.80 Sep 172.26 170.18 2.64 8.59 13.94 Oct 69.10 79.91 39.19 3.37 8.35 Nov 41.13 51.87 52.92 6.65 8.12 Dec 71.18 57.91 43.81 2.39 4.35 avg. 104.50 107.88 68.64 0.24 0.51 Annual total precipitation Avg. 1272.82 1302.39 2038.50 1255.26 1304.56 Stdev. 213.82 204.95 550.05 323.85 247.46

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81 Table 3 4. Mean error (ME, in units of m 3 /s), root mean square error (RMSE, in units of m 3 /s), efficiency coefficient (E), and determination coefficient ( R 2 ) for daily and monthly streamflow simulations using sub basin precipitation observations from calibrated model, MM5 predictions ( i.e., BC_P MM5 and BC_G MM5), and spatially averaged precipitation observations from calibrated model. Daily streamflow rate evaluation Monthly streamflow rate evaluation Precipitation scenarios vs. Obs. vs. calibrated IHM results vs. Obs. vs. calibrated IHM results Calibrated BC_P MM5 BC_G MM5 Avg. Calibrated BC_P MM5 BC_G MM5 Avg. Alafia River at Lithia ME 0.36 0.94 1.29 1.03 0.36 0.92 1.27 1.03 RMSE 7.65 17.34 15.06 8.73 3.21 9.46 8.49 4.69 E 0.69 0.20 0.10 0.70 0.89 0.14 0.31 0.79 R 2 0.77 0.22 0.21 0.72 0.94 0.39 0.37 0.81 Hillsborough River near Zephyrhills ME 0.19 0.56 0.76 0.90 0.19 0.55 0.76 0.89 RMSE 2.84 11.48 9.91 3.86 1.44 6.38 6.21 2.47 E 0.94 0.04 0.29 0.89 0.97 0.48 0.51 0.92 R 2 0.94 0.33 0.34 0.90 0.98 0.54 0.52 0.93 Cypress Creek at Worthington Gardens ME 0.17 0.07 0.23 0.40 0.17 0.07 0.22 0.40 RMSE 1.63 2.59 2.45 1.02 1.18 2.24 2.15 0.93 E 0.78 0.13 0.01 0.83 0.85 0.02 0.06 0.82 R 2 0.83 0.21 0.18 0.90 0.90 0.23 0.20 0.92 Anclote River near Elfers ME 0.20 0.03 0.34 0.32 0.20 0.03 0.33 0.32 RMSE 2.10 3.64 3.05 1.42 1.15 2.20 1.94 0.90 E 0.78 0.44 0.01 0.78 0.87 0.03 0.24 0.84 R 2 0.83 0.16 0.14 0.80 0.95 0.32 0.29 0.87

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82 Table 3 5. Mean error (ME, in units of m), root mean square error (RMSE, in units of m), efficiency coefficient (E), and determination coefficient ( R 2 ) for monthly groundwater level simulations using sub basin precipitation observations from calibrated model, MM5 predictions (i.e., BC_P MM5 and BC_G MM5), and spatially averaged precipitation observations from calibrated model. Surficial aquifer Floridan aquifer Precipitation scenarios vs. Obs. vs. calibrated results vs. Obs. vs. calibrated results Calibrated BC_P MM5 BC_G MM5 Avg. Calibrated BC_P MM5 BC_G MM5 Avg. CYC TMR 5 SH CYC TMR 5d ME 0.30 0.16 0.03 0.05 0.14 0.22 0.01 0.17 RMSE 0.44 0.46 0.37 0.13 0.51 0.52 0.42 0.19 E 0.47 0.04 0.32 0.92 0.80 0.62 0.76 0.95 R 2 0.72 0.35 0.44 0.94 0.57 0.71 0.76 0.99 S21 J26As S21 Jcksn26d ME 0.73 0.30 0.38 0.34 0.32 0.29 0.38 0.36 RMSE 0.85 0.74 0.79 0.44 0.43 0.62 0.66 0.39 E 1.53 0.29 0.47 0.56 0.85 0.61 0.57 0.85 R 2 0.54 0.36 0.29 0.88 0.94 0.72 0.72 0.98 STK STARKEY 20s STWF 10 DP ME 0.15 0.15 0.06 0.13 0.16 0.14 0.05 0.12 RMSE 0.33 0.61 0.49 0.27 0.32 0.59 0.47 0.25 E 0.75 0.49 0.03 0.72 0.74 0.54 0.01 0.71 R 2 0.82 0.30 0.33 0.82 0.83 0.30 0.33 0.82 CBR SERW s CBR SERW d ME 0.12 0.77 0.56 1.10 0.15 0.72 0.59 1.15 RMSE 0.42 1.66 1.46 1.33 0.94 2.05 1.82 1.33 E 0.92 0.40 0.08 0.10 0.83 0.20 0.06 0.50 R 2 0.93 0.47 0.46 0.85 0.84 0.30 0.33 0.88 Masaryktown DP (unconfined) ME 0.20 0.52 0.49 0.93 RMSE 0.91 1.79 1.59 1.08 E 0.75 0.58 0.25 0.42 R 2 0.77 0.19 0.20 0.86

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83 Table 3 6. Mean error (ME, in units of m 3 /s), root mean square error (RMSE, in units of m 3 /s), efficiency coefficient (E), and determination coefficient ( R 2 ) for daily and monthly springflow simulations using sub basin precipitation observations from calibrated model, MM5 predictions (i.e., BC_P MM5 and BC_G MM5), and spatially averaged precipitation observations from calibrated model. Daily springflow rate evaluation Monthly springflow rate evaluation Precipitation scenarios vs. Obs. vs. calibrated results vs. Obs. vs. calibrated results Calibrated BC_P MM5 BC_G MM5 Avg. Calibrated BC_P MM5 BC_G MM5 Avg. Weeki Wachee spring ME 0.15 0.07 0.06 0.47 0.15 0.07 0.08 0.47 RMSE 0.45 1.27 1.19 0.61 0.43 1.26 1.11 0.60 E 0.80 0.60 0.40 0.64 0.81 0.58 0.23 0.64 R 2 0.83 0.12 0.12 0.87 0.84 0.11 0.13 0.77 Lithia spring ME 0.01 0.02 0.02 0.05 0.01 0.07 0.05 0.05 RMSE 0.24 0.19 0.17 0.09 0.23 0.21 0.19 0.09 E 0.45 0.68 0.72 0.93 0.49 0.58 0.68 0.92 R 2 0.50 0.66 0.68 0.94 0.55 0.44 0.42 0.55

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84 Figure 3 1. Map of hydrologic modeling domain, sub basins, and observation stations for streamflow, groundwater level, and springflow. SWFWMD area Florida Springflow station Unconfined Floridan aquifer well Surficial/Floridan aquifer well pair Streamflow stations Streams Watershed boundaries INTB sub basins Wellfields Spring coast Anclote watershed Hillsborough watershed Alafia watershed Kilometers

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85 Figure 3 2. Schematic representation of the study framework

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86 Figure 3 3. Comparison of the mean monthly precipitation over the study period for IHM sub basin observations (P obs.), gridded observations (G obs., available through 1999), raw MM5 results, bias corrected MM5 results using P obs. (BC_P MM5), and bias corrected MM5 using G obs. (BC_G MM5). The bright and dark gray zones represent total data range and 5 to 95 percentile of P obs., respectively, reflecti ng spatial variation of the mean monthly precipitation over the 172 sub basins. 0 1 2 3 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Averaged monthly precip. (x100mm) P obs. G obs. BC_P MM5 BC_G MM5 raw MM5

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87 Figure 3 4. Comparison of annual total precipitation time series for IHM sub basin observations (P obs.), gridded observations (G obs., available through 1999), raw MM5 resu lts, bias corrected MM5 results using P obs. (BC_P MM5), and bias corrected MM5 using G obs (BC_G MM5). The bright and dark gray zones represent total data range and 5 to 95 percentile of P obs. indicating the spatial variability of observed annual precipi tation over the 172 sub basins. 5 10 15 20 25 30 35 Annual precip. (x100mm) P obs. G obs. BC_P MM5 BC_G MM5 raw MM5

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88 Figure 3 5. Comparison of seasonal total precipitation time series for IHM sub basin observations (P obs.), gridded observations (G obs.), raw MM5 results, bias corrected MM5 results using P obs. (BC_P MM5), and bi as corrected MM5 using G obs. (BC_G MM5); top graph is for October to January, middle graph is for February to May, and bottom graph is for June to September (wet season). The bright and dark gray zones represent total data range and 5 to 95 percentile o f P obs. indicating the spatial variability of observed annual precipitation over the 172 sub basins. 0 5 10 15 20 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Seasonal precip. (ONDJ, x100mm) P obs. G obs. raw MM5 BC_P MM5 BC_G MM5 0 5 10 15 20 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Seasonal precip. (FMAM, x100mm) P obs. G obs. BC_P MM5 BC_G MM5 raw MM5 0 5 10 15 20 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Seasonal precip. (JJAS, x100mm) P obs. G obs. raw MM5 BC_P MM5 BC_G MM5

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89 Figure 3 6. The spatial distribution of mean monthly precipitation for dry (first row; Oct., Nov., Dec., and Jan., second row; Feb., Mar., Apr., and Ma y), and wet seasons (bottom row; Jun., Jul., Aug., and Sep.) for IHM sub basin observations (P obs. (1989~2006), first column), bias corrected MM5 results using P obs. (BC_P MM5 (1989~2006), second column), gridded observations (G obs. (1950~1999), third c olumn), and bias corrected MM5 using G obs (BC_G MM5 (1989~2006), forth column). Units of precipitation are mm.

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90 Figure 3 7. Comparison of mean monthly maximum and minimum temperature for observations, raw MM5 results, and bias corrected MM5 results. Error bars on the graphs represent the range of 6 point locations. 5 15 25 35 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Averaged monthly Tmax ( ) T_max obs T_max raw T_max BC 5 15 25 35 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Averaged monthly Tmin ( ) T_min obs T_min raw T_min BC

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91 Figure 3 8 Comparison of the monthly streamflow hydrographs of calibrated streamflow predictions and the streamflow predictions using MM5 precipitation bias corrected using P obs., MM5 preductions bias corrected using G obs (BC_G MM5) and spatially a veraged observed precipitation for 4 target stations. The vertical boxes indicate two periods for which streamflow significantly overestimated (first box) and underestimated (second box) for all stations. 0 25 50 75 100 Streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 25 50 75 100 Streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 10 20 30 Streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 10 20 30 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg Alafia River at Lithia Hillsborough River near Zephyrhills Cypress Creek at Worthington gardens Anclote River near Elfers 1998 Oct. ~ Dec. 2002 Dec. ~2003 Sep.

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92 Figure 3 9 Comparison of daily and monthly observed and predicted precipitation and daily streamflow simulations for overestimated year 1998 (first column) and underestimated year 2003 (second column). The boxes indicate the dominant months for the biases. 0 500 1000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly P (mm/month) Obs BC_P MM5 0 500 1000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Obs BC_P MM5 BC_G MM5 0 40 80 120 Daily P (mm/day) Obs 0 20 40 60 Obs 0 40 80 120 Daily P (mm/day) BC_P MM5 0 20 40 60 BC_G MM5 0 40 80 120 J-98 F-98 M-98 A-98 M-98 J-98 J-98 A-98 S-98 O-98 N-98 D-98 Daily P (mm/day) BC_G MM5 0 20 40 60 J-03 F-03 M-03 A-03 M-03 J-03 J-03 A-03 S-03 O-03 N-03 D-03 BC_P MM5 0 100 200 300 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Daily streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 0 100 200 300 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec cal BC_P MM5 BC_G MM5 0 100 200 300 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Daily streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 0 100 200 300 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec cal BC_P MM5 BC_G MM5 0 10 20 30 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Daily streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 0 10 20 30 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec cal BC_P MM5 BC_G MM5 0 20 40 60 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Daily streamflow (m3/s) cal BC_P MM5 BC_G MM5 0 20 40 60 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec cal BC_P MM5 BC_G MM5 Alafia River at Lithia Alafia River at Lithia Cypress Creek at Worthington Gardens Cypress Creek at Worthington Gardens Hillsborough River near Zephyrhills Hillsborough River near Zephyrhills Anclote River near Elfers Anclote River near Elfers

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93 Figure 3 10. Comparison of the total water budget over the domain by major category for calibrated results, spatially averaged, and MM5 climate scenarios. 0 400 800 1200 1600 Total Input Total ET Reach Discharge GW Discharge Other Discharge Total Storage Change inches/year calibrated averaged BC-P MM5 BC-G MM5

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94 Figure 3 1 1 Comparison of simulated mean monthly streamflow for each target station 0 5 10 15 20 25 Mean streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 5 10 15 20 25 Mean streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 2 4 6 Mean streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg Alafia River at Lithia Hillsbo rough River near Zephyrhills Cypress Creek at Worthington Gardens Anclote River near Elfers

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95 Figure 3 1 2 The relationship between the mean RMSE of streamflow simulations over the model domain and (a) annual precipitation predictions and (b) mean RMSE of daily precipitation predictions for each year. 0 3 6 9 12 15 500 1000 1500 2000 2500 Mean RMSE of streamflow sim. (m3/s) Annual total P (mm) BC_P MM5 BC_G MM5 Cal avg 0 3 6 9 12 15 5 10 15 20 Mean RMSE of streamflow sim. (m3/s) Mean RMSE of daily P (mm) BC_P MM5 BC_G MM5 Linear (BC_P MM5) Linear (BC_G MM5) (a) ( b )

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96 Figure 3 13. Comparison of cumulative distribution function (CDF) for calibrated results and the simulations with precipitation scenarios. The arrow ranges on the figures indicate the permitted thresholds range for streamflow withdrawals. 0 0.2 0.4 0.6 0.8 1 0.5 5 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 0.5 5 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 0.01 0.1 1 10 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 0.01 0.1 1 10 CDF Streamflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg Alafia River at Lithia Hillsborough River near Zephyrhills Cypress Creek at Worthington Gardens Anclote River near Elfers 1 m 3 /s 18 m 3 /s 0.1 m 3 /s 10 m 3 /s 3 m 3 /s 22 m 3 /s

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97 Figure 3 14. Comparison of monthly averaged groundwater level predictions for each target station in surficial aquifer. The boxes indicated the same period that is marked in Figure 3 8. 15 20 25 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 10 15 20 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 5 10 15 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 13 18 23 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg CYC TMR 5 SH STK STARKEY 20s CBR SERW s S21 J26As

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98 Figure 3 15. Comparison of monthly averaged groundwater level predictions for 4 target stations in confined Floridan aquifer and 1 unconfined aquifer station (Masaryktown DP station on the bottom). The boxes indicated the same period that is marked in Figu re 3 8. 10 15 20 25 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 5 10 15 20 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 0 5 10 15 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 5 10 15 20 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg 5 10 15 20 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Groundwater level (m) cal BC_P MM5 BC_G MM5 avg CYC TMR 5d STWF 10 DP CBR SERW d Masaryktown DP S21 Jcksn26d

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99 Figure 3 1 6 Comparison of mean monthly groundwater level for 4 pairs of surficial/Floridan aquifer target station and 1 unconfined aquifer station (on the bottom). 17 18 19 20 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 15 16 17 18 19 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 14 15 16 17 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 9 10 11 12 13 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 7 8 9 10 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 6 7 8 9 10 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 10 11 12 13 14 Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg 7 8 9 10 11 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avergaed groundwater level (m) cal BC_P MM5 BC_G MM5 avg CYC TMR 5 SH S21 J26As STK STARKEY 20s CBR SERW s CYC TMR 5d S21 Jcksn 26d STWF 10 DP CBR SERW d Masaryktown DP Unconfined Floridan aquifer Floridan aquifer Surficial aquifer

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100 Figure 3 1 7 Comparison of CDFs of monthly groundwater level in surficial and Floridan aquifer for calibrated results and the simulations with precipitation scenarios. 0 0.2 0.4 0.6 0.8 1 16 17 18 19 20 21 CDF cal BC_P MM5 BC_G MM5 0 0.2 0.4 0.6 0.8 1 10 11 12 13 14 15 16 17 18 19 20 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 13 14 15 16 17 18 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 5 6 7 8 9 10 11 12 13 14 15 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 5 6 7 8 9 10 CDF cal BC_P MM5 BC_G MM5 0 0.2 0.4 0.6 0.8 1 5 6 7 8 9 10 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 13 14 15 16 17 18 19 20 21 22 23 CDF Groundwater level (m) cal BC_P MM5 BC_G MM5 0 0.2 0.4 0.6 0.8 1 5 8 11 14 17 20 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 5 6 7 8 9 10 11 12 13 14 15 CDF Groundwater level (m) cal BC_P MM5 BC_G MM5 avg CYC TMR 5 SH S21 J26As STK STARKEY 20s CBR SERW s CYC TMR 5d S21 Jcksn 26d STWF 10 DP CBR SERW d Masaryktown DP Unconfined Floridan aquifer Floridan aquifer Surficial aquifer

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101 Figure 3 18. Comparison of monthly averaged springflow predictions for 2 target stations. The boxes indicated the same perio d that is marked in Figure 3 8. Figure 3 19. Comparison of mean monthly averaged springflow for 2 target stations 0 5 10 Spring flow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 1 2 3 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Spring flow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 2 3 4 5 6 Spring flow (m 3 /s) cal BC_P MM5 BC_G MM5 avg 0 0.5 1 1.5 2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Spring flow (m 3 /s) cal BC_P MM5 BC_G MM5 avg Lithia spring Weeki Wachee spring Lithia spring Weeki Wachee spring

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102 Figure 3 20 Comparison of CDFs of monthly springflow for calibrated results and the simulations with precipitation scenarios. 0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 CDF cal BC_P MM5 BC_G MM5 avg 0 0.2 0.4 0.6 0.8 1 0 1 2 3 CDF Springflow (m 3 /s) cal BC_P MM5 BC_G MM5 avg Lithia spring Weeki Wachee spring

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103 CHAPTER 4 DEVELOPMENT OF A STOCHASTIC DOWNSCALI NG METHOD TO REPRODU CE OBSERVED SPATIOTEMPO RAL VARIABILITY OF D AILY PRECIPITATION 4.1 Background General circulation models (GCMs) have been considered robust tools for simulating future changes in climate and for developing climate scenarios for quantitative impact assessments (Wilks, 1999; Karl and Trenberth, 2003; Fowler et al., 2007 ). General circulation modeling c ontinues to be improved by the incorporation of more aspects of the complexities of the global system. However, GCM results are generally insufficient to provide accurate prediction of climate variables on the local to regional scale needed to assess hydr ologic impacts because of significant uncertainties in the modeling process (Allen and Ingram, 2002; Didike and Coulibaly, 2004). The coarse resolution of existing GCMs (typically about 200km by 200km) precludes the simulation of realistic circulation patt erns and accurate representation of the small scale spatial variability of climate variables (Christensen and Christensen, 2003; Giorgi et al., 2001; Jones et al., 2004; Lettenmaier, 1999; Wood et al., 2002 ). Furthermore, mismatch of the spatial resolution between GCMs and hydrologic models generally precludes the direct use of GCM outputs to predict hydrologic impacts. To overcome this limitation of GCMs, a number of downscaling methods have been developed. It has been shown that fine scale downscaled res ults provide better skill for hydrologic modeling (Andr asson et al., 2004; Graham et al., 2007; Wood et al., 2004) and agricultural crop modeling (Mearns et al ., 1999, 2001) than using the coarse resolution GCM output directly. One of the commonly used d ownscaling techniques is the use of statistical methods employing empirical relations between features simulated by GCMs at large grid scales and surface observations at sub grid scales (Hay et al., 2002; Wilby and Wigley, 1997). The primary advantage of these techniques is that they are computationally inexpensive, and thus can

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104 be easily applied to multiple GCMs experiments. Although much progress on downscaling precipitation sequences has been made, current challenges include the need to represent reali stic levels of temporal variability (e.g., seasonal and inter annual variability) in the generated sequences, the generation of multisite sequences with realistic spatial dependence (i.e., spatial correlation), and the accurate representation of extreme be havior (Katz and Zheng, 1999). For many applications the biggest challenge is the representation of spatial dependence in precipitation field (Khalili et al., 2006; Zheng and Katz, 2008). Statistical downscaling typically provides site specific informat ion which can be useful for many climate change impact studies (Fowler et al., 2007; Murphy, 1998; Wilby et al., 2004) however it is often conducted for one variable at one site independently and thus ignores the spatial dependence between measurements at different station locations exhibited by the observations. Accurately representing the spatial variability and patterns of precipitation can be an important factor for predicting hydrologic response to climatic forcing, particularly in small, low relief w atersheds affected by convective storm systems as in Florida (Hwang et al., 2011a). Some techniques for multiple site generation of precipitation have been developed and investigated (Wilks, 1998; Wilks, 1999; Khalili, 2006; Baigorria and Jones, 2011) but these concepts have rarely been employed for downscaling GCM results. Statistical downscaling approaches are generally applied at a temporally aggregated scale (e.g., monthly or seasonally) rather than daily or sub daily time scales because of distortion of GCM daily results (Maurer and Hidalgo, 2008). When applied at a daily time scale, the direct use of GCM results makes them quite susceptible to model biases (Ines and Hansen, 2006). Means of addressing the problem include aggregating GCM predictions in to seasonal or sub seasonal means, downscaling to the target grid scale or station network, and then using a weather

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105 generator (Wilks, 2002; Wood et al., 2004; Feddersen and Andersen, 2005) or analog (re sampling the historic data, Salath et al., 2007; Ma urer et al., 2010) to disaggregate in time. Generally using a weather generator to generate daily climate sequences exhibits no skill at reproducing spatial correlation and is also limited by the assumption that current temporal daily patterns of precipita tion will be preserved in the future (Fowler et al., 2007). The use of analogs is also constrained by the requirement of a sufficiently long observation record so that reasonable analogs can be found (Zorita and Storch, 1998). Use of daily GCM outputs for climate change impact assessment has increased as GCM skills to exhibit daily variability of climate variables are improving (e.g., Maurer and Hidalgo, 2008; Maurer et al., 2010). Bias Corrected Spatial Downscaling (BCSD Wood et al., 2002; Maurer, 2007) is a simple and widely used technique to downscale GCM results and it has been extensively applied to assess hydrologic impacts of climate change in the U.S. (Christensen et al., 2004; Wood et al., 2004; Salath et al., 2007; Maurer and Hidalgo, 2008). BC SD generally preserves spatial relationships between large scale GCM results and local scale observed mean precipitation trends. Although this method was originally developed for downscaling monthly precipitation and temperature and determined to reproduc e the temporal mean statistics and spatial distribution of mean climatology accurately, Wood et al. (2002) pointed out that, in principle, daily GCM output can also be downscaled directly using this method. However realistic spatial variability of daily p recipitation events are not properly reproduced by this method because it focuses on preserving only the observed temporal mean and the spatial downscaling process is essentially a simple interpolation scheme. Recently Abatzoglou and Brown (2011) modifie d the BCSD method by changing the order of bias correction and spatial disaggregation procedures. That is, they interpolated GCM outputs

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106 onto the fine grid first and then the fields were bias corrected using the CDF mapping approach for each fine scale gr id cell (i.e. the target resolution of downscaling). This simple modification (hereafter referred to as SDBC) improves the downscaling skill in various ways to reproduce temporal features. However the SDBC method does nothing to improve skills in terms of reproducing spatial variability because the same approach (interpolation) as used in BCSD is employed for spatial disaggregation. The constructed analogue (CA; Hidalgo et al., 2008) method is another technique often used to downscale daily GCM products. CA is designed to use the simulated daily sequences from a GCM and downscales each simulated day. The CA technique uses a library of observed daily coarse resolution climate anomaly patterns of the variable to be downscaled by selecting an analogue with p atterns that closely match simulated anomalies. A linear combination of the selected observed daily coarse resolution climate anomalies patterns close to simulated anomaly is used to produce a coarse resolution analogue and the downscaled anomaly is gener ated by applying the same linear combination to the corresponding high resolution observed climate anomaly patterns. The CA approach retains daily sequencing of weather events from the GCM results and various possible climate variables (e.g., geopotential heights, sea level pressure) can be considered as predictors to construct the best analogue. However, a significant limitation of the CA approach is that the biases in the spatial and temporal variance exhibited by the GCM (resulting from imperfect model parameterization of physical processes or inadequate topographic description in the model) will be reconstructed in the downscaled fields (Maurer and Hidalgo, 2008; Maurer et al., 2010). The purpose of this study is to develop and test a new stochastic t echnique to downscale daily GCM precipitation prediction, which preserve observed spatial and temporal variability as

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107 well as mean climatology. The method is evaluated using precipitation projections from 4 GCMs over Florida and the skill of the method is compared to the downscaled results using the BCSD and SDBC techniques. The next section describes the climate data used in the study. The methods for downscaling and evaluation are described in section 4 3 and the results are discussed in section 4 4. Fina lly, the main conclusions are summarized in section 4. 5. 4.2 Data Daily gridded observations at 1/8 degree spatial resolution (about 12km) were obtained over Florida from 1950 to 1999 (Maurer et al., 2002). The climate data (daily and monthly precipitatio n, maximum, minimum, and average temperature, and wind speed) are archived in netCDF format at http://hydro.engr.scu.edu/files/gridded_obs/daily/ncfiles/. These products are available from 1950 through 1999 over the entire U.S. without missing data or una nticipated biases that sometimes occur in surface station measurements. This data was used to bias correct daily GCM results and to estimate observed spatial correlation structure. GCM predictions from 1960 to 1999 were obtained from the World Climate Re search Programme's (WCRP's) Coupled Model Inter comparison Project phase 3 (CMIP3) multi model dataset, which are referenced in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). For this study 4 GCMs were selected based on availability and previous use in testing downscaling approaches (Table 4 1). The GFDL, CGCM, and CCSM models have previously been used to drive a set of regional climate models (RCMs, dynamical downscaling models) over a domain covering the U.S. and mos t of Canada for the North American Regional Climate Change Assessment Program (NARCCAP). The grid resolutions for the GCMs range from 1.4 to 2.8. Figure 4 1 shows how each model grid configuration covers the study domain over Florida.

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108 4.3 Methods The p rocess of downscaling GCM results for producing climate scenarios at local scales consists of two operations: 1) bias correction to remove the biases in raw GCM results compared to climatological observations and 2) spatial downscaling to disaggregate coar se climate information to the individual station or smaller scale grid level. This research compared three processes to correct the biases in GCM results and produce the proper climate data at the spatial resolution required for hydrologic modeling. 4.3.1 Bias C orrection and S patial D ownscaling (BCSD) M ethod The BCSD method is an empirical statistical technique that was developed by Wood et al. (2002). BCSD consists of two separate steps for bias correction and spatial downscaling. In the first step, raw GC M predictions are bias corrected at the large GCM grid scale using the CDF mapping approach (Panofsky and Brier, 1968, described in detail in the next paragraph) In the second step anomalies (i.e., the ratio of simulated precipitation field to observed temporal mean precipitation field) of the bias corrected GCM output are spatially interpolated to the downscaled resolution using an inverse distance weighting technique. Finally these fine scale anomalies are re scaled with the mean precipitation field a t the fine grid scale resolution. The bias correction procedure used in this study is similar to that used by Wood et al. (2002); Ines and Hansen (2006); Salath et al. (2007); and Maurer and Hidalgo (2008) and is described as follows: 1) CDFs of observe d daily precipitation at the coarse GCM scale were created individually for each month using the spatial average of available observed data from Maurer et al. (2002) within each GCM grid. Thus 12 observed monthly CDFs were created for each GCM grid cell; 2) CDFs of simulated daily precipitation were created for each grid cell for each month; 3) daily grid cell predictions were bias corrected at the large scale GCM prediction resolution using CDF mapping that preserves the probability of exceedence of the s imulated

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109 precipitation over the grid cell, but corrects the precipitation to the value that corresponds to the same probability of exceedence from the spatially averaged observation over the GCM grid. Thus bias corrected rainfall on day t at grid i was calculated as, where and denote the CDF of daily precipitation x and its inverse, and subscripts sim and obs indicate GCM simulation and observed daily rainfall, respectively. This bias correction process removes both bias in the precipitation predictions and the tendency of the model to under predict dry days and over predict the number of low volume rainfall days (Hwang et al., 2011a). b) Spatial downscaling and bias correction (SDBC) method The SDBC method developed by Abatzoglou and Brown (2011) was the second methodology evaluated in this study. As described abov e the SDBC method is a modified version of the BCSD method in which the order of bias correction and spatial disaggregation is reversed. That is, GCM outputs are interpolated to the fine grid scale using inverse distance weighting first and then the inter polated precipitation fields are bias corrected using the CDF mapping approach described above for each fine scale grid cell. As will be shown, this modification improves the downscaling skill in reproducing temporal features. However the SDBC method does nothing to improve skills in terms of reproducing spatial variability because the same interpolation approach as used in BCSD is employed for spatial disaggregation. 4.3.2 Spatial D ownscaling and B ias C orrection (SDBC) M ethod The SDBC method developed by Abatzoglou and Brown (2011) was the second methodology evaluated in this study. As described above the SDBC method is a modified version of the BCSD method in which the order of bias correction and spatial disaggregation is reversed. That is, GCM outputs are interpolated to the fine grid scale using inverse distance

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110 weighting first and then the interpolated precipitation fields are bias corrected using the CDF mapping approach described above for each fine scale grid cell. As will be shown, this modificat ion improves the downscaling skill in reproducing temporal features. However the SDBC method does nothing to improve skills in terms of reproducing spatial variability because the same interpolation approach as used in BCSD is employed for spatial disaggre gation. 4.3. 3 Bias C orrection and Stochastic Analog (BC SA ) M ethod A new spatial downscaling technique was developed to generate spatially correlated downscaled precipitation predictions which preserve both the temporal statistical characteristics as well a s the small scale spatial correlation structure of observed precipitation fields. The technique will be referred to as the BC SA method hereafter. Because the spatiotemporal features (e.g., frequency, spatial patterns, and correlation) of precipitation even ts may change monthly or seasonally, the BC SA process was performed using temporal and spatial statistics calculated separately for each month. The procedure for the BC SA method performed for each month is described as follows: i. where is the normal score for (i.e., observed daily precipitation on day t at grid i ), is the inverse transform function of the standard Gaussian CDF and denotes the CDF of daily gridded observation for grid i ii.

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111 where is the number of grid cells. iii. Taussky and Todd., 2006 iv. v.

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112 where denotes the CDF of generated normal scores for grid i and is the precipitation estimation for day t and grid i This procedure was repeated for every grid cell to get that preserve the mean, variance, and spatial correlation structure if the observed field. vi. vii. The process i through vii was conducted for each GCM grid cell independently for each month. 4.3. 4 Assessment of D ownscaling T echnique S kills The temporal mean, 50% percentile, 90% percentile, and standard deviation of the precipitation time series for observed and downscaled predictions were calculated for each grid cell and mapped over the state of Florida to evaluate the spatial distribution of these temporal statistics. Daily transitions between wet and dry states were calculated for both the observed data and predictions using the first order transit ion probability (Haan, 1977) and averaged exceedence probability of the events with specific wet/dry spell durations were estimated over the study area to investigate day to day precipitation occurrence patterns.

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113 In terms of spatial features, observations and predictions were evaluated using several indexes indicating spatial correlation and variability (Hubert et al., 1981). I (Moran, 1950; Thomas and Huggett, 1981) index, a commonly used statistical test for identifying spatial dependence, was ca lculated using the following formula. where and refer to the precipitation in station i and j on day t respectively. is the overall spatial mean precipitation on day t is an adjacency weight based on inverse distance weighting. The I values are between 1 and 1. Like the correlation coefficient, I is positive if both and lie on the same side of the mea n (above or below), while it is negative if one is of precipitation among the grid cells, was calculated as follows C values are between 0 and 2. The spatial autocorrelation is positive if C is lower than 1, negative if C is between 1 and 2, and zero if C is equal to 1. represent measures of spatial autocorrelation for each spatial field at day t however the relationship between the geographical distance and correlation are not measured by these statistics. I used the variog ram, defined as the expected value of the squared difference of the values of the random field separated by distance vector to describe the degree of spatial variability exhibited by each spatial random field. The experimental variogram

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114 for the observed and simulated precipitation data was calculated using the following formula (Goovaerts, 1997). where denotes the number of pairs of observations sep arated by distance and and are the observed or simulated precipitation at locations and respectively. In this research averaged I and C indices were calculated over the prediction period from 1960 t o 1999 and the variogram was estimated for both daily observations and the downscaled daily GCM precipitations for each month in order to evaluate how well each downscaling technique reproduces the spatial correlation structure of the observed daily precip itation. 4. 4 Applications and D iscussion The BCSD, SDBC, and BC SA methods described above were evaluated over the state of Florida. Generally the effectiveness of downscaling approaches is evaluated based on their ability to accurately reproduce properties of observed precipitation sequences. In this study various indices of downscaled precipitation predictions were compared to those of gridded observations. 4.4.1 Evaluation of T emporal V ariability Gridded annual total precipitation observation s, spatially averaged over the state of Florida, ranged from 1048mm to 1657mm with a mean of 1343mm over the 1950 1999 period. The standard deviation of the spatially averaged annual total observation time series was 152mm over the study period. Figure 4 2 shows the spatially averaged annual total precipitation time series of gridded observation (Gobs) and statistically downscaled GCM results using BCSD, SDBC, and BC SA This figure indicates that the mean annual precipitation over the study period

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115 was ac curately reproduced by all three downscaling methods. The temporal variance was slightly underestimated by BCSD results, slightly overestimated by BC SA results and significantly overestimated by the SDBC results. The SDBC method overestimates the tempora l variance of spatially averaged precipitation because the large scale daily GCM precipitation predictions are spatially disaggregated by interpolation and then bias correction at the downscaled grid resolution. Thus each fine scale grid cell preserves th e precipitation percentile event predicted by the large scale GCM, exaggerating high and low percentile events. Note that predicted annual time series from GCMs are not expected to reproduce the actual annual time series for the study period since they do not use actual observed initial conditions or boundary conditions in the simulations. Figure 4 3 compares the mean monthly precipitation over the study period and again shows that the three statistical downscaling methods all successfully reproduced the o bserved mean monthly precipitation cycle. Similarly, the spatial distribution of mean precipitation for the study period was accurately reproduced over the state of Florida by all the methods (Figure 4 4). These results are expected since the CDF mapping bias correction technique employed in BCSD, SDBC, and BC SA is designed to fit the predictions to historic mean climatology. The spatial distribution of the temporal variance of precipitation however, showed significant differences between BCSD and the ot her downscaling methods. Figure 4 5 compares the spatial distribution of the temporal standard deviation of the daily precipitation time series over the state of Florida for the study period. While the SDBC and BC SA results accurately reproduce the standar d deviation for both the wet (June through September) and dry (October through May) seasons, the BCSD results significantly underestimate the standard deviation for both seasons. Figure 4 6 and Figure 4 7 show the spatial distributions of 90 th percentile

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116 (5mm~20mm) and 50 th percentile (<3mm) daily precipitation for wet and dry seasons, respectively. Note that on average, the 40th percentile of daily precipitation of gridded observations is less than 0.01 mm. The BCSD method underestimated the observed 90 th percentile daily precipitation amount, particularly for the wet season and overestimated the 50 th percentile of daily precipitation for both seasons. On the other hand the SDBC method accurately reproduced the temporal variance of daily precipitation at each grid cell because the bias correction in the last step of the procedure forces the CDFs of downscaled results to reproduce those of gridded observations. The BC SA method also accurately reproduced the 90 th percentile and 50 th percentile daily preci pitation for both seasons similarly because the BC SA results, are forced to reproduce the CDF of Gobs., through the normal score transformation process which uses the observed CDF of the gridded observations. The inaccuracies in the tempo ral variability produced by the BCSD method are caused by the interpolation scheme that is used to disaggregate the bias corrected GCM predictions which produces smooth downscaled results. Note that the temporal variance at the downscaled location corresp onding to the center point of GCM grid produces slightly higher temporal variability (Figure 4 5) because the interpolation procedure produces less smoothing at this location. This weakness of the BCSD method is improved by exchanging the order of the bias correction and interpolation procedures (i.e., spatially downscale then bias correct, SDBC) as shown in Figure 4 5 through Figure 4 7. When the interpolated GCM results are bias corrected using fine scale gridded observations at the last step of the down scaling process, the final results reproduce the full observed CDF and thus both the observed temporal mean and temporal standard deviation. Although SDBC has been recently introduced for downscaling daily GCM products (Abatzoglou

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117 and Brown, 2011), explic it insight into these distinctions between the BCSD and SDBC downscaling frameworks has not been provided by the previous studies. In addition to the daily precipitation volume and spatial variability, day to day precipitation patterns are also important for most hydrologic applications. The differences in precipitation occurrence between local and coarse grid scale precipitation series are quite large because precipitation at the coarse grid scale is considered to have occurred when precipitation occurs a t any single station or local grid cell. Due to the spatial averaging process, the probability of precipitation occurrence for area averaged time series is necessarily larger than the corresponding probabilities at any of the constituent stations. Daily t ransitions between wet and dry states estimated for the observed gridded data and the downscaled GCM predictions obtained using the BCSD, SDBC, and BC SA methods are shown in Figure 4 8 and Figure 4 9. The BCSD results produced more low rainfall events and thus wet to wet transition probabilities were overestimated and dry to wet probabilities were underestimated for this method. The SDBC approach shows improvement over the BCSD results because SDBC reproduces the observed CDF of daily precipitation at the small scale. The BC SA transition probabilities also match the wet to wet and dry to wet transition probabilities at the gridded observation scale quite accurately. Further improvement in day to day predictability will require improving the climate model physics, parameterization, and/or boundary condition used in a climate model (Hwang et al., 2011a). The frequency and duration of consecutive wet and dry days reflect dynamic properties of precipitation that has important implications for producing extreme hydrologic behavior (i.e., flood and drought events). For evaluation purposes a wet spell was defin ed as the length of a period of consecutive wet days (P>0.1 mm) that are preceded and followed by a dry day, and a

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118 dry spell was defined as the length of a period of consecutive dry days (P<0.1 mm) that are preceded and followed by a wet day. The averaged exceedence probabilities of wet and dry spell events over the study area for gridded observation and projections are shown in Figure 4 10. The results indicate that BCSD results significantly overestimated the wet spell length and underestimated the dry spell length for all GCMs. In contrast, the SDBC and BC SA methods reproduced wet and dry spell lengths much more accurately for all GCMs. 4.4.2 Evaluation of S patial V ariability The spatial variability of daily Gobs and downscaled GCMs were quantified by calculating 4 11). In general the BCSD and SDBC results produced precipitation fields with overestimated spatial correlation (high Moran I, i.e ~0.4 and 0.3, respectively, compared to ~0.2 for obs ervations) and low spatial variance (low ~0.4~0.5 compared to 0.6~0.8 for observations). On the other hand the BC SA variance of precipitation ( July and August and lower in December and January. No significant seasonality if spatial Figure 4 12 compares wet season and dry season variograms ca lculated for the BCSD, SDBC, and BC SA results to the gridded observations. These figures indicate that the spatial variances of the BCSD results were significantly underestimated at all separation distances for both wet (June through September) and dry (Oc tober through May) seasons, and the SDBC method better reproduced the spatial variance but still underestimated variograms as similarly shown in Figure 4 11 and Figure 4 12. Finally the BC SA results reproduced the observed variograms accurately for both se asons.

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119 To further illustrate the relative skill in representing spatial variability of precipitation events, the 90 th percentile total daily precipitation amounts over the state of Florida are mapped for the gridded observations and three statistically do wnscaled results for each of the 4 GCMs (i.e. the spatial distribution of rainfall on the day corresponding to the 90 th percentile total precipitation over the state of Florida, Figure 4 13). The precipitation total over the state for the observed 90 th pe rcentile event was 4.01 mm. The SDBC results reproduced comparatively high precipitation totals (5.15 mm~7.53 mm) over the state for the 90 percentile events while the BCSD and BC SA GCM 90 th percentile events over the state ranged from 3.98 mm to 5.02 mm. also shown on the maps in Figure 4 0.34, respectively for the 90 th percentile observed event. The BCSD method generally produced 90 th percentile events that are more spatially persistent than the gridded observations, with higher 0.90) and lower Geary C indices (0.16 0.26). The SDBC method showed better spatial variability with I in dices ranging from 0.28 to 0.89 and C indices ranging from 0.10 to 0.34. These results indicate that, in terms of spatial variability of precipitation event, the SDBC method show s improvement over the BCSD results but still fail s to accurately reproduce o bserved spatial variability. In contrast the BC SA method produces more spatially variable 90 th C indices ranging from 0.44 to 0.80. 4. 5 Chapter S ummary The goal of this study was to demonstrate a new technique to downscale bias corrected daily GCM precipitation predictions to reproduce observed temporal and spatial variability. Four bias corrected GCM results were used to examine the skill of the new downsca ling technique in reproducing local temporal mean, standard deviation, 90 th (5~20 mm) and 50 th (<3

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120 mm) percentile daily precipitation, wet wet and wet dry transition probabilities and the length of wet and dry spell compared to the gridded observations, th e commonly used BCSD downscaling technique, and the SDBC method. Downscaled GCM results using the BCSD, SDBC, and BC SA methods accurately reproduced the temporal mean of the daily precipitation as well as the annual cycle of monthly mean precipitation. H owever the temporal standard deviation and the magnitude of 90 th percentile daily precipitation were significantly underestimated by the BCSD method especially for the wet season. Furthermore low precipitation frequency was overestimated by BCSD, wet to w et transition probabilities and wet spell length were overestimated by BCSD, and dry to wet transition probabilities and dry spell length were underestimated by BCSD. These results indicate that interpolation based spatial disaggregation such as BCSD will underestimate the frequency of extreme precipitation events (e.g., flood and drought). These inaccuracies of the BCSD results in reproducing temporal variability of daily precipitation at the fine grid scale were improved by the SDBC method. However th e SDBC results tended to overestimate the temporal variance of spatially averaged precipitation because of the bias correction of spatially disaggregated fields at the fine grid scale. The BC SA reproduced the observed temporal standard deviation, magnitud es of both high (90 th percentile) and low (50 th percentile) rainfall amounts and wet wet transition probabilities more accurately than the BCSD method. More significantly, the interpolation based downscaling methods (both BCSD and SDBC) were unable to re produce the observed spatial variability of daily precipitation, which may have important implications for predicting hydrologic behavior (Hwang et al., 2011b). The proposed BC SA technique generates daily precipitation fields that accurately reproduce obse rved spatial

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121 BCSD, SDBC, and BC SA results showed quantitatively that BC SA accurately reproduced the spatial variance and correlation of observed daily precipitatio n whereas BCSD and SDBC significantly underestimated spatial variance and overestimated spatial correlation. The BC SA method is designed to produce precipitation estimates that reproduce both the temporal and spatial variability of observed precipitation fields. One of important merit of this technique is that it can be applied to downscale coarse resolution climate data into any temporal (e.g., monthly, sub daily) and spatial scale (e.g., irregularly distributed points) wherever observations are available to estimate the cumulative distribution functions and spatial correlation structure of precipitation. Additionally the uncertainty in downscaling process could be examined and also attenuated by employing an ensemble approach using a collection of equall y probably downscaled climate fields. The procedure also could be applied to temperature and other surface weather variables. One drawback of using the BC SA technique is that spatial disaggregation of coarse scale precipitation scenario is independently conducted on a daily basis, not taking into account day to day, week to week or seasonal temporal relationships. Thus the temporal trends and persistence of downscaled precipitation results depend only on the bias transit ion trends and temporal correlation of precipitation patterns. Nonetheless I found that the observed transition probabilities and wet and dry spell lengths were reasonably reproduced by the BC SA method. This result indicates that the GCMs have acceptable skill in representing plausible temporal precipitation patterns. The long term goal of this research is to develop a framework to produce reasonable future climate scenarios from GCM products and subsequently drive hydrologic models for assessing

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122 climat e change impacts on regional hydrology (e.g., streamflow and groundwater levels) as well as water resource management (e.g. reservoir operation and reliability). Future work will investigate hydrologic implications of downscaled retrospective and future G CMs predictions for the Tampa Bay region of Florida.

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123 Table 4 1. 4 GCMs used in this study Modeling Group, Country WCRP CMIP3 I.D. Primary Reference Bjerknes Centre for Climate Research, Norway BCCR BCM2.0 Furevik et al., 2003 US Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory, USA GFDL CM2.0 Delworth et al., 2006 Canadian Centre for Climate Modeling & Analysis, Canada CGCM3.1 Flato and Boer, 2001 National Center for Atmospheric Research, USA CCSM Collins et al., 2006 WCRP CMIP3: World Climate Research Programme's Coupled Model Inter comparison Project phase 3

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124 Figure 4 1. The study domain and the center location of grids for 4 GCMs

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125 Figure 4 2. Comparison of spatially averaged annual total precipitation time series for gridded observations (Gobs) and downscaled GCM results using BCSD method (BCSD gcm), SDBC method (SDBC gcm), and BC SA method ( BC SA gcm) over the state of Florida. The bright and da rk gray zones represent total data range and 5 th to 95 th percentile of Gobs at the 12 km grid scale indicating the spatial variability of observed annual total precipitation over Florida. Figure 4 3. Comparison of the mean monthly precipitation over th e study period for gridded observations (Gobs) and downscaled GCM results using BCSD, SDBC, and BC SA technique. The bright and dark gray zones represent total data range and 5 th to 95 th percentile of Gobs, respectively, reflecting spatial variation of the mean monthly precipitation over Florida. 5 10 15 20 25 30 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Annual precipitation (x100mm) BCSD bccr (1359, 147) SDBC bccr (1356, 233) BCSA bccr (1356, 178) BCSD gfdl (1359, 162) SDBC gfdl (1357, 247) BCSA gfdl (1357, 187) BCSD cgcm (1362, 132) SDBC cgcm (1361, 223) BCSA cgcm (1360, 167) BCSD ccsm (1363, 114) SDBC ccsm (1361, 153) BCSA ccsm (1360, 125) Gobs (1343, 152) 0 1 2 3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly preciptiation (X100mm) BCSD bccr SDBC bccr BCSA bccr BCSD gfdl SDBC gfdl BCSA gfdl BCSD cgcm SDBC cgcm BCSA cgcm BCSD ccsm SDBC ccsm BCSA ccsm Gobs

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126 Figure 4 4. Spatial distribution of the mean of (a) Gridded observation (Gobs.), (b) BCSD, (c) SDBC, and (d) BC SA daily precipitation for wet (June through September) and dry (October through May) season units in mm (a) wet (a) dry (b) wet (b) dry (b) wet (b) dry (b) wet (b) dry (b) wet (b) dry (a) wet (a) dry ( c ) wet ( c ) dry ( c ) wet ( c ) dry ( c ) wet ( c ) dry ( c ) wet ( c ) dry (a) wet (a) dry ( d ) wet ( d ) dry ( d ) wet ( d ) dry ( d ) wet ( d ) dry ( d ) wet ( d ) dry

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127 Figure 4 5. Spatial distribution of the temporal standard deviation of (a) Gobs., (b) BCSD, (c) SDBC, and (d) BC SA daily precipitation for wet (June through September) and dry (October through May) season units in mm (a) wet (a) dry (a) wet (a) dry (b) wet (b) dry (c) wet (c) dry (a) wet (a) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry

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128 Figure 4 6. Spatial distribution of the 90 th percentile daily precipitation of (a) Gobs, (b) BCSD, (c) SDBC and (d) BC SA GCMs for each grid cell for wet (June through September) and dry (October through May) season units in mm (a) wet (a) dry (a) wet (a) dry (b) wet (b) dry (c) wet (c) dry (a) wet (a) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry

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129 Figure 4 7. Spatial distribution of the 50 th percentile daily precipitation of (a) Gobs, (b) BCSD, (c) SDBC, and (c) BC SA GCMs for each grid cell for wet (June through September) and dry (October through May) season uni ts in mm (a) wet (a) dry (a) wet (a) dry (b) wet (b) dry (c) wet (c) dry (a) wet (a) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry (b) wet (b) dry (c) wet (c) dry (d) wet (d) dry

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130 Figure 4 8. First order wet to wet transition probability (P_11) comparisons of observed vs. (a) BCSD results, (b) SDBC results, and (c) BC SA results for each month and 4 GCM products. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSD bccr P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSD gfdl P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSD cgcm P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSD ccsm P11 Obs. P11 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 SDBC bccr P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 SDBC gfdl P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 SDBC cgcm P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 SDBC ccsm P11 Obs. P11 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSA bccr P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSA gfdl P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSA cgcm P11 Obs. P11 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 BCSA ccsm P11 Obs. P11 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (a) BCSD ( c ) BC SA (a) (a) (a) ( c ) ( c ) ( c ) (b) SDBC (b) (b) (b)

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131 Figure 4 9. First order dry to wet transition probability (P_01) comparisons of observed vs. (a) BCSD results, (b) SDBC results, and (c) BC SA results for each month and 4 GCM products. 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSD bccr P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSD gfdl P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSD cgcm P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSD ccsm P01 Obs. P01 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 SDBC bccr P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 SDBC gfdl P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 SDBC cgcm P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 SDBC ccsm P01 Obs. P01 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSA bccr P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSA gfdl P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSA cgcm P01 Obs. P01 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 BCSA ccsm P01 Obs. P01 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (a) BCSD ( c ) BC SA (a) (a) (a) ( c ) ( c ) ( c ) (b) SDBC (b) (b) (b)

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132 Figure 4 10. Exceedence probability of the events for given (a) wet (> 0.1mm) and (b) dry (<0.1mm) spell lengths for the Gobs. and statistically downscaled GCM results using BCSD, SDBC, and BC SA Figure 4 11. Comparison of observed and simulated mean daily spatial correlation indices (a) I and (b) C for each month. 0 0.2 0.4 0.6 0.8 1 1 4 7 10 13 16 19 22 25 28 Exceedence Probability wet spell length (days) Gobs BCSD GCMs SDBC GCMs BCSA GCMs 0 0.2 0.4 0.6 0.8 1 1 4 7 10 13 16 19 22 25 28 Exceedence Probability dry spell length (days) Gobs BCSD GCMs SDBC GCMs BCSA GCMs 0 0.2 0.4 0.6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Moran's I index Gobs BCSD GCMs SDBC GCMs BCSA GCMs 0 0.2 0.4 0.6 0.8 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Geary's C index Gobs BCSD GCMs SDBC GCMs BCSA GCMs (a) (b) (a) (b)

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133 Figure 4 12. Variogram comparison of BCSD (first raw), SDBC (second raw), and BC SA (third raw) daily precipitation predictions for wet (first column, June through September) and dry season (second column, October through May). 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_wet bccr_wet gfdl_wet cgcm_wet ccsm_wet 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_dry bccr_dry gfdl_dry cgcm_dry ccsm_dry 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_wet bccr_wet gfdl_wet cgcm_wet ccsm_wet 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_dry bccr_dry gfdl_dry cgcm_dry ccsm_dry 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_wet bccr_wet gfdl_wet cgcm_wet ccsm_wet 0 0.05 0.1 0.15 0.2 0 100 200 300 400 500 variogram (mm 2 ) distance (km) obs_dry bccr_dry gfdl_dry cgcm_dry ccsm_dry (a) BCSD_dry (c) BC SA _wet (a) BCSD_wet (c) BC SA _dry (b) SDBC_dry (b) SDBC_wet

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134 Figure 4 13. Comparison of the spatial distribution of 90 th percentile events of statewide total daily precipitation for (a) BCSD, (b) SDBC, and (c) BC SA GCMs. Gobs P a vg =4.01 I =0.55 C=0.34 P avg =4.14 I =0.55 C=0.55 P avg = 5 14 I =0.7 8 C=0. 10 P avg =4.65 I =0.46 C=0.44 P avg = 7 18 I =0. 49 C=0. 34 P avg =4.95 I =0.23 C=0.80 P avg =5. 49 I =0. 28 C=0.2 9 P avg =4.27 I =0.68 C=0.44 P avg = 7 53 I =0. 89 C=0.1 1 P avg =3.98 I =0.79 C=0.19 P avg =4.50 I =0.62 C=0.17 P avg =5.02 I =0.67 C=0.26 P avg =4.24 I =0.90 C=0.16 (a) BCSD (a) (a) (a) (b) SDBC (b) (b) (b) (c) BCSA (c) (c) (c)

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135 CHAPTER 5 HYDROLOGIC IMPORTANC E OF SPATIAL VARIABI LITY IN STATISTICALL Y DOWNSCALED PRECIPITA TION PREDICTIONS FRO M GENERAL CIRCULATION MODELS FOR WE ST CENTRAL FLORIDA 5 .1 Background General circulation models (GCMs) have been one of the most effective and influential tools for understanding present and past climate ( Fowler et al., 2007 ) and have been continually improved as understanding of the global system has advance d (Karl and Trenberth, 2003 ). However, the current generation of GCMs is not suitable for providing a long term simulation of atmospheric processes for precipitation and temperature at small spatial scales because of intensive computational costs. It has been well documented that the resolution of the current generation of GCMs (>100km) ha s not been effective for direct application to hydrologic and agricultural impact assessments (e.g. Christensen and Christensen, 2003; Wood e t al., 2004). The effective assessment of water resource impacts and adaptation strategies to climate change requires point scale (gauge based) or sub basin based climate data to run hydrologic models (Andr asson et al., 2004; Enke and Spekat, 1997; Graham et al., 2007; Leander et al., 2008). This incongruity between the spatial resolution of GCMs and that needed by regional hydrologic models has been one of the major issues in developing reliable assessments of climate change impacts on water resources. T his has led to a demand for improved downscaling techniques for better regional applications and evaluations (Feddersen and Andersen, 2005; Can et al., 2011 ). There are two categories of GCM downscaling: statistical downscaling methods which use empiri cal relationships between features simulated by GCMs at grid scales and surface observations at subgrid scales (Hay et al., 2002; Wilby and Wigley, 1997) and dynamical downscaling techniques using regional climate models (RCMs) based on physical links betw een the climate at large and small scale s (McGregor, 1997; Murphy, 1999). There have been

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136 numerous studies on the use of downscaling and bias correction methods to correct climate model outputs to produce realistic simulations of hydrological responses of the current climate (Fowler et al., 2007; Diaz Nieto and Wilby, 2005; Mearns et al., 1999; Widmann et al., 2003; Wilby et al., 2004; Zorita and von Storch, 1999). In general, dynamical downscaling has been shown to produce more accurate climate regimes th at reflect temporal and spatial patterns of meteorological variables since regional climate models provide physically coherent spatio temporal variations of climate variables (Vasiliades et al., 2009). However the use of RCMs has significant computational cost and thus their routine application for the generation of ensembles of climate predictions using multiple GCMs and multiple scenarios is limited Furthermore, RCMs have their own bias in addition to the GCM bias propagated from boundary conditions (Hwa ng et al., 2011a; Sato et al., 2007; Mearns et al., 2003). Statistical downscaling methods can reduce the bias in climate data at high spatial resolution without the intensive computer resources required by dynamical downscaling (Iizumi et al., 2011), and are thus more amenable to climate impact research. For this reason, several studies have relied on statistical downscaling methods (e.g. Busuioc et al., 2001; Charles et al., 2004; Diaz Nieto and Wilby, 2005). While some statistical downscaling methods u se various large scale atmospheric variables as predictors (i.e., explanatory variables) to estimate local climatic elements (Wilby et al., 1998; Wilby and Wigley, 2000), other studies have been devoted to developing and evaluating methods that directly us e GCM daily precipitation as a predictor (e.g., Widmann et al., 2003; Wood et al., 2004). However, these statistical downscaling techniques often fail to reproduce the spatial cross correlation of multiple climate variables of interest since the methods w ere developed to predict a single variable at a single site. In addition,

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137 simple statistical downscaling methods often fail to reproduce adequate spatial variability (Maurer et al., 2010) which is considered an important factor for predicting hydrologic re sponse to climatic forcing. Although some techniques for simultaneously generating precipitation data for multiple sites have been developed (Harpham and Wilby, 2005; Khalili et al., 2006; Wilks, 1999), few have been employed to downscale GCM results. Rece ntly, Hwang and Graham (2011c) developed a stochastic technique, the bias correction and stochastic analog method (BCSA) using spatially correlated normal score transform to generate precipitation fields that honor the spatial autocorrelation structure of observed daily precipitation fields as well as temporal characteristics of the observed precipitation sequence and mean climatology. They applied the BC SA technique to downscale daily precipitation projections from 4 GCMs to a 12 km grid scale over the st ate of Florida and evaluated their method compared to an interpolation based simple statistical downscaling method (i.e., Bias Correction and Spatial Disaggregation method, hereafter BCSD, Wood et al., 2002) and a modified version of BCSD which reverses th e orders of the procedure (i.e., spatially downscaled followed by bias correction, SDBC, Abatzoglou and Brown, 2011). They showed that the BC SA accurately reproduced both the spatial and temporal variability of observed gridded daily precipitation whereas the other methods significantly underestimated spatial variability. In this study, three statistical downscaling methods: 1) the BCSD method; 2) the SDBC method; 3) the BC SA method were applied to downscale 4 GCM historical simulations of precipitation fr om 1961 to 2000 over a 10,370 km 2 region in west central Florida. The skill of each method in reproducing temporal and spatial variability of daily precipitation was then evaluated using various indices (e.g., spatial and temporal statistics, transition pr obabilities,

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138 wet/dry spell lengths, spatial correlation index, and variograms).The temperature simulations from each GCM were also downscaled using a simple statistical method that assumes direct correspondence between the GCM grid cell and observation loc ation exceedence probabilities. The use of a simpler bias correction procedure for temperature is warranted because small scale variability of temperature is not as significant as for precipitation in this region. The downscaled precipitation and temperatu re datasets were then used in an integrated surface subsurface hydrologic model (IHM) to examine hydrologic responses of streamflow and groundwater levels for each different climate input over the study area. IHM results were evaluated in terms of the mon thly mean hydrologic behavior and frequency of streamflow and groundwater level events. The goal of this study is to evaluate the applicability of the statistical downscaling methods introduced above for west central Florida and to investigate hydrologic i mplications of downscaled daily GCM precipitation outputs. The primary hypothesis of this research is that the spatiotemporal variability of precipitation plays an important role in hydrologic responses in west central Florida and the statistical downscal ing method (BC SA ) developed by Hwang and Graham (2011c) will most reliably reproduce the observed variability of precipitation and hydrologic behavior. The next section describes the study area and data used in the study. The methodologies for bias correct ion and downscaling are described in section 5. 3 and the results are discussed in section 5. 4. Finally the main conclusions are summarized in section 5. 5. 5 .2 Study Domain and Data 5.2.1 Tampa Bay Region: the Integrated Northern Tampa Bay (INTB) Model Domain T ampa Bay Water, the largest water supply agency in west central Florida, operates a diverse regional water supply system in the Tampa Bay region. Tampa Bay Water manages surface and groundwater water sources in compliance with permitted withdrawa l limits in order to protect the ecological integrity of rivers, wetlands and lakes in the region. Their mission is to

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139 ensure that potable water demand for is met at least cost and with minimal adverse environmental impact Additionall y, to ensure the reliability of future water supplies, they are challenged to assess the hydrologic behavior and water supply risks under various current and pot ential future climate conditions To assist their water supply planning and operations, Tampa Bay W ater has developed the Integrated Northern Tampa Bay (INTB) model using the Integrated Hydrologic Model (IHM, Geurink et al., 2006a section 5. 3.3) simulation engine calibrated to simulate hydrology in west central Florida. The INTB model domain (Figu re 5 1) is bordered by the Gulf of Mexico and inland groundwater flow lines. Tampa Bay is located in the southwest part of the domain. The north and east boundaries follow Floridan aquifer flow lines (i.e. no flux boundaries) and the southern boundary is p laced far enough from the area of interest for this study to minimize the influence of the general head boundary (Geurink et al., 2006a). The surface water component of the model domain is discretized into 172 sub basins based on surface drainage for hydro logic modeling as shown in Figure 5 1. For each sub basin, hydrologic processes are simulated within hydrologic response units (land segments) based on five upland landuse categories and two water body categories (Ross et al., 2004). Land cover over the d omain is diverse, including urban, grassland, forest, agricultural, mined land, water, and wetlands. Open water and wetlands cover about 25% of the region. The groundwater component model domain is discretized into approximately 35,000 square and rectangu lar grid cells with cell dimension of one quarter mile over the area of interest and expanding to one mile in outer regions. 5. 2 2 GCM Archive For this study, 4 GCM historical projections: BCCR BCM 2.0, CCSM, CGCM 3.1, and GFDL CM 2.0 outputs from 1961 to 2000 were obtained from the World Climate Research

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140 comparison Project phase 3 (CMIP3) multi model data set. These GCM results were previously downscaled to a 1212 km 2 grid and tested over the state of Florida em ploying the statistical methods used in this study (Hwang and Graham, 2011 c ). The grid resolutions for the GCMs range from 1.4 to 2.8 and thus whole study area (INTB model domain, approximately 10,370 km2) is covered by a few GCM grids. Figure 5 2 shows how each model grid configuration covers the study domain. 5. 2 3 Meteorological Data Observed p recipitation data over the INTB model domain were obtained from 300 stations from three different sources including Tampa Bay Water, Southwest Florida Water Man agement District ( SWFWMD), and National Oceanic and Atmospheric Administration (NOAA). In order to estimate sub basin precipitation time series as input for the hydrologic modeling available daily precipitation data within each basin were spatially distri buted by Thiessen polygons area weighted over the 172 sub basins. Data from 1989 to 2006 were used for hydrologic model calibration, verification (Geurink and Basso, 2011). These data were also used in bias correction and downscaling of GCM precipitation results. The temporal distribution and intensity of precipitation are important factors in deterministic, physically based hydrologic simulations A short temporal resolution is required to adequately capture the effects of localized convective storms, a dominant type of precipitation events during the wet season in Florida (Rokicki, 2002). The INTB model uses 15 minute precipitation time series input for each of the 172 sub basins. Daily precipitation values for each basin were temporally disaggregated us ing the nearest NOAA 15 minute observations which are available within and around the modeling domain. The same disaggregation approach was applied for statistically downscaled daily precipitation results to generate appropriate climate input data for IHM.

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141 Minimum and maximum daily temperature data were obtained at six NOAA stations in the region. The reference evapotranspiration (reference ET, Hargreaves and Samani, 1985) time series were estimated using these observations and then spatially assigned to the nearest neighbor basins over the model domain. Daily reference ET values were temporally disaggregated into hourly values for the INTB model input using an annual profile (i.e., ensemble results for each Julian day) of hourly reference ET values. Hourl y values were generated using a data set with a shorter period of record but with a full suite of weather parameters required for the FAO 56 PM method ( Allen et al. 1998 ). 5. 2 4 Hydrologic D ata Hydrologic observations from 1989 to 1998 and from 1999 to 200 6 were used for the INTB model calibration and verification, respectively (Geurink and Basso, 2011). Hydrologic observations used for calibration and verification included 38 streamflow monitoring stations and 200 locations each of surficial and Floridan aquifer wells. Additional hydrologic data for diversions, irrigation, pumping, and land use were collected from U.S. Geological survey (USGS), the SWFWMD, and Tampa Bay Water and used to develop and calibrate the INTB model ( Guerink and Basso 2011). Four streamflow stations on the major rivers (i.e., Alafia River, Hillsborough River, Cypress Creek, and Anclote River, Figure 5 1) were chosen to evaluate hydrologic response to the alternative downscaled climate predictions based on the importance to water s upply management and the variability of flow characteristics over the study area. The rivers flows at these stations are important for water supply operations and management because they are either located near or downstream of wellfields or water is withd rawn from them to meet local water demand.

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142 The available data for target stations were collected and used to evaluate the differences in hydrologic responses of alternative climate inputs. The Alafia and Hillsborough rivers have a mean discharge of 9.6 m 3 / s and 6.9 m 3 /s, respectively with very few no flow days whereas Cypress Creek and the Anclote River have a mean discharge of less than 2 m 3 /s. Furthermore Cypress Creek has a large percentage (approximately 25%) of no flow days. Investigating stations with large and small flow volumes is important to understand how different types of flow regimes are affected by changes in climate variables. Additionally in this study groundwater level response s to the alternative downscaled climate predictions were evalu ated. Four pairs of surficial and Floridan aquifer monitoring wells (CYC TMR 5SH/CYC TMR 5D, S21 J26As/S21 Jcksn 26d, STK Starkey 20s/STWF 10DP, and CBR SERW s/CBR SERW d) were chosen in each of four wellfields important to Tampa Bay Water operati on. 5 3 Methodology 5 3 .1 Bias C orrection of Climate Data The statistical downscaling methods examined in this study are composed of two processes: bias correction and spatial disaggregation. While the procedure for bias correction is the same in each cas e the spatial disaggregation methods and order of the processes are different among the methods A cumulative distribution function (CDF) mapping approach (Panofsky and Brier, 1968; Wood et al., 2002; Ines and Hansen, 2006) was used to bias correct the r aw GCM outputs or downscaled raw GCM outputs for precipitation and maximum and minimum daily temperature. CDF mapping is the most common method for bias correction of climate model outputs (Woods et al., 2004). It removes the bias in the temporal mean and variance of precipitation and

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143 temperature simulations by adjusting the simulated CDF to fit the observed CDF (Hwang et al., 2011a). The procedure for bias correction used in this study is described as follows: 1) CDFs of observed daily precipitation (at the appropriate target resolution for bias correction, such as GCM grid cell or sub basin) were created individually for each calendar month. These twelve monthly CDFs were used for bias correction of the daily outputs; 2) CDFs of simulated daily precipit ation at the same spatial resolution as the observations were created for each month; 3) daily outputs were bias corrected at the simulation resolution using CDF mapping that preserves the probability of exceedence of the simulated precipitation, but corre cts the precipitation to the value that corresponds to the same probability of exceedence from the observed CDFs. Thus bias corrected rainfall on day t at grid or sub basin i was calculated as, where and denote a CDF of daily precipitation x and its inverse, and subscripts sim and obs indicate downscaled simulation and observed daily rainfall, respectively. The bias corrected results can be directly used as input to a hydrologic model when those results are bias corrected directly onto the model sub basin grid (as was done for temperat ure downscaling in this study). 5 3 .2 Statistical D ownscaling In this study the BCSD and SDBC methods ( interpolation based downscaling methods ) and the BC SA method ( stochastic downscaling method ) were used to produce daily precipitation result s. The th ree downscaled GCM results were quantitatively evaluated in various ways (section 5. 4.1). Figure 5 3 schematically describes the methodology of the three statistical downscaling techniques applied in the study.

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144 5 3 2.1 BCSD Method The BCSD method is an empirical statistical technique developed by Wood et al. (2002) to downscale GCM products. While there is disagreement as to whether the simple interpolation method should be categorized as a statistical downscaling method (Schmidli et al., 2006), the BCSD method was classified as a statistical downscaling method for this study because it bridges the coarse resolution GCM outputs and climate inputs required for impact assessment models (Wood et al., 2004; Iizumi et al., 2011). The B CSD method consists of two separate steps: bias correction followed by spatial downscaling (Figure 5 3). First, raw daily GCM simulations are bias corrected using the CDF mapping approach at the coarse GCM resolution scale to match the statistical moments of GCM outputs and observations aggregated to the GCM resolution. Then coarse scale anomalies (i.e., simulated precipitation field/observed temporal mean precipitation field) of bias corrected GCM output are spatially interpolated to the downscaled resolu tion and rescaled by the temporal mean precipitation fields at the local scale. Whereas the BCSD method has conventionally been used to downscale climate data at monthly scales (Wood et al., 2004; Maurer et al., 2010), in this study the method was extende d to downscale daily GCM results. 5.3.2.2 SDBC Method Recently Abatzoglou and Brown (2011) modified the BCSD method by changing the order of the bias correction and spatial disaggregation procedures to improve the BCSD method results at the daily timescale That is, coarse scale GCM outputs were interpolated to the target downscal e resolution (i.e., fine grid, point, or sub basin scale) first and then the fields were bias corrected using the CDF mapping approach at the fine scale resolution using available observations. They estimated the CDF at each downscaled grid cell using a 15 day moving

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145 window centered on each calendar day for CDF mapping bias correction instead of using a distribution based on a single calendar day or month of the year (365 CDFs are created, one for each calendar day). This simple modification improves the downscaling skill for reproduc ing temporal features such as temporal variance and transition probability. However the SDBC method does not improve skills in terms of reproducing spa tial variability because the simple interpolation scheme employed for spatial disaggregation produces very smooth downscaled precipitation fields (Hwang and Graham, 2011c). The SDBC method applied in this study generally follows the procedure developed by Abatzoglou and Brown (2011), except the CDFs of daily precipitation were aggregated for each month instead of using the moving window concept for CDF construction. T herefore only 12 district CDFs were used in this study (rather than 365) for each sub basi n 5.3.2.3 BC SA Method The BC SA method was developed by Hwang and Graham (2011c) to downscale daily GCM results to produce spatially correlated daily precipitation outputs which preserve both the temporal statistical characteristics as well as the small sc ale spatial correlation structure of observed precipitation fields. This method was designed to downscale daily GCM precipitation outputs to any spatial configuration or resolution (e.g., point, grid, or sub basin) where the observed spatial correlation c an be estimated. This method uses the temporal distribution characteristics and spatial correlations among the observations to generate precipitation fields. The BC SA procedure is described as follows: 1) transform local scale observations into standard n ormal variables using the normal score transformation approach (Goovaerts, 1997); 2) estimate correlation coefficients between the normal score transform variables for all pairs of observations; 3) generate an ensemble of synthetic spatially correlated ran dom fields honoring

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146 the estimated correlations; 4) back transform normal score transform field into the original observed distribution s ; 5) for each day, select one realization from the ensemble for which the spatial mean of the generated precipitation fie lds equals the coarse scale GCM simulation result. The entire process is conducted separately for each calendar month. That is, the BC SA process was performed using different temporal and spatial statistics for each month because the spatiotemporal feature s (e.g., frequency, spatial patterns, and correlation) of precipitation events vary over the year For a more detailed description of the method, see Hwang and Graham (2011c). 5.3. 3 Hydrologic Modeling In west central Florida the fresh groundwater flow system generally consists of a thin surficial aquifer underlain by the thick, highly productive carbonate rocks of the Floridan aquifer system. Most of the Floridan aquifer is semi confined, recharged by m eans of leakage from the overlying surficial aquifer. However, in the northern extent of the region some portions of the Floridan aquifer are unconfined, receiving direct recharge from vadose zone infiltration. The significant temporally variable flux and storage connection between surface water and groundwater systems are caused by the near surface water table condition that covers more than 50% of the region. In order to capture the dynamic interaction between surface water and groundwater in this region an integrated hydrologic model has been developed (Geurink et al., 2006a). Due to the unique geophysical features in west central Florida, the local state regulatory agencies for surface water and groundwater resources in west central Florida, Tampa Bay W ater and the Southwest Florida Water Management District (SWFWMD) commissioned the development and application of an integrated surface water/groundwater for the Tampa Bay Region (Geurink et al., 2006a). The Integrated Hydrologic Model (IHM) was developed which

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147 integrates the EPA Hydrologic Simulation Program Fortran (HSPF; Bicknell et al., 2001) for surface water modeling with the US Geological Survey MODFLOW96 (Harbaugh and McDonald, 1996) for groundwater modeling. The IHM was designed to provide advanc ed simulation capability of the complex interactions of surface water and groundwater features in shallow water table environments. The model can be characterized as deterministic, semi distributed parameter, semi implicit real time formulation, with varia ble time steps and spatial discretization (Ross et al., 2004). The model components explicitly account for all significant hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, baseflow, groundwater f low, and all the component storages of surface, vadose and saturated zones (Ross et al., 2005). Climate input data requirements include time series for precipitation and potential or reference evapotranspiration (Geurink et al., 2006b). The IHM model was manually calibrated from 1989 to 1998 and verified from 1999 to 2006 (18 year total period) using observed data over the study area to produce the INTB application. In the application presented here the observed precipitation and temperature data used in the calibrated INTB model was replaced by the downscaled GCM daily climate input datasets developed in this study. Though the downscaled climate inputs were available from 1961 to 2000, only the latest 36 years data from 1965 to 2000 were used to force the calibrated hydrologic model. F or each downscaled GCM the INTB mode l simulation was run for two 18 year periods, 1965 to 1982 and 1983 to 2000, assuming that the same 18 year sequence of irrigation and public water supply pumping and surface water extract ions that actually occurred during 1989 ~ 2006 also occurred during 1965 ~ 1982 and 1983 ~ 2000. All other parameters and

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148 boundary conditions for hydrologic simulation were identical to those used in the calibrated model. 5 4 Result s and D iscussion 5 4 .1 Stat istically D ownscaled GCM R esults 5.4.1.1 Temperature Figure 5 4 shows the monthly mean of observed minimum and maximum daily temperatures from 1987 to 2006. The monthly mean maximum temperature ranged from 22.0C (in January) to 32.8C (in August) and the monthly mean minimum temperature ranged from 10.4C (in Janua ry) to 23.1C (in August). The spatial variability over the six mean maximum and minimum temperature observations for each month is represented by error bars on the figure. This plot indicates that the spatial variance of maximum temperature was higher (i .e., 1.6C ~2.2C) during the dry season from December to April than the wet season (i.e., 0.9C ~1.5C) and minimum temperature was in general more spatially variable than maximum temperature over the annual cycle. Downscaled temperature data always fol low the mean temporal cycle of the observed minimum and maximum temperature shown in Figure 5 4 exactly since the temperature data are bias corrected at each observed station. The bias correction using CDF mapping removes not only bias in the mean but also corrects errors in higher moments. Figure 5 5 compares the temporal standard deviation of observed monthly mean maximum and minimum temperature over the study domain to that of the four downscaled GCM temperature datasets. The graphs show the inter annua l variability of averaged maximum and minimum temperature for each month. The observed long term variability of mean monthly maximum temperature is higher (0.6C ~ 2.3C) during the dry season (October to May) than the wet season (0.4C ~ 0.7C, June to S eptember) T here was no significant difference between the

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149 cycles of minimum and maximum temperature. Overall, the results indicate that the temporal variance of monthly temperature was well reproduced by the bias correction technique. 5 4 1.2 Precipitatio n Figure 5 6 shows the observed mean daily precipitation over the study period from 1989 to 2006 and the spatial variation over the 172 sub basins. Bias c orrection using the CDF mapping approach at each sub basin forces the reproduction of the temporal m ean of precipitation observations as shown by Hwang and Graham (2011c), thus the downscaling methods match th e observed mean daily precipitation cycle exactly. Figure 5 7 compares average standard deviations of the downscaled daily GCM precipitation to ob served temporal standard deviations of daily precipitation for each month. The graph shows that while SDBC and BC SA successfully reproduce the observed temporal standard deviation of daily precipitation, the BCSD method underestimates the temporal variance of daily precipitation by 12% ~ 39% with more significant underestimation during the wet season from June to September This result is due to the temporally smoothed time series created by the interpolation of bias correct ed precipitation from the coarse GCM resolution to the sub basin scale. Iizumi et al. (2011) found similar results and concluded that simple statistical downscaling methods may be inaccurate for reproducing temporal variation patterns and less plausible in a physical sense because of oversimplification of underlying physical processes. Note that for daily precipitation statistics, differences among the four GCM result s are negligible (< 0.05mm). Figure 5 8 compares temporal means and standard deviations of the daily precipitation results using the three downscaling methods over the entire data period for each of the 172 sub basins. The observed mean daily precipitation ranged from 3.1mm to 3.9mm and the observed standard deviation of daily precipitation ranged from 7.6mm to 11.4mm, respectively over the sub basins. These results reinforce that while SDBC successfully reproduce the ob served

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150 temporal mean and standard deviation of daily precipitation for each sub basin, the BCSD method significantly underestimates the temporal variance of daily precipitation by 8% ~ 32% over the sub basins similar to the result shown in Figure 5 7. Alth ough Figure 5 7 and 5 8 show that the BC SA method reproduces the first and second moment statistics of daily precipitation in general, the errors are larger than for the SDBC methods. This is a result of differences between the number of replicates in the synthetic daily precipitation ensemble and the actual number of days in the GCM data. The BC SA ensemble is generated randomly from the observed CDF; thus differences between the mean generated from the ensemble and from the data are due to sampling from a limited number of replicates (3000 in this study). This could possibly be improved by increasing the size of ensemble or improving the accuracy of the random field generator. Figure 5 9 compares the standard deviation of spatially averaged precipitation o ver the study area. The graph indicates that BCSD and BC SA accurately reproduced the temporal variability of average precipitation over the study area. Although the SDBC showed acceptable skills in reproducing temporal variability of daily precipitation fo r each basin (Figure 5 6 and Figure 5 7), Figure 5 9 shows that this method results in significant overestimation of the temporal variance of spatially averaged precipitation. This is due to the fact that the daily GCM precipitation prediction s are spatial ly disaggregated by interpolation and then bias correction at downscaled grid resolution. Thus the probability of exceedence of the daily precipitation generated at the coarse GCM grid scale is reproduced at each local grid cell, exaggerating the spatial extent effects of extreme events, particular in the summer wet season which is dominated by small scale convective thunderstorms.

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151 In addition to the temporal mean and variance of daily precipitation, day to day precipitation patterns are important for most hydrologic applications. For instance the occurrence of consecutive wet and dry days reflects dynamic properties of precipitation that have important implications for producing extreme hydrologic behavior (i.e., flood and drought events). Hence, daily tra nsitions between wet and dry states were calculated for both the observed data and the downscaled datasets using the first order transition probability (Haan, 1977) and are shown in Figure 5 10. These results indicate that the dry to wet (P01) and wet to w et (P11) transition probabilities calculated using SDBC and BC SA results matched observed transition probabilities well for both seasons, with higher transition probabilities (both P01 and P11) in the wet season. The BCSD results, however, underestimated d ry to wet transition probabilities for the wet season and significantly overestimated wet to wet probabilities. This is because interpolation of bias corrected coarse scale GCM results creates more rainy days with small precipitation events than exhibited by the observations at sub basin scale. Note that the observed spatial variation of wet to wet transition probability over 172 sub basins is large in the wet season and this observed spatial variance is accurately reproduced by both the SDBC and BC SA metho ds. Spatial variability of daily precipitation was also examined by estimating the number of rainy sub basins (Figure 5 11) and the spatial standard deviation of daily precipitation (Figure 5 12) across sub basins as a function of the magnitude of spatial ly averaged daily precipitation for both the wet and dry seasons. The interpolation based spatial downscaling methods ( i.e., BCSD and SDBC ) tend to overestimate spatial correlation and thus simulate a larger number of rainy sub basin s for a given magnitud e of precipitation event Figure 5 11 indicates that the numbers of sub basins simulated to be rainy (>0.1mm) are significantly different among the results from the three statistical methods. Generally the number of rainy basins increases as the spatially

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152 averaged precipitation volume increases in both the dry and wet seasons. The BCSD results showed the most rapid increase of rainy area with more than 50% of study domain estimated to be rainy in the wet season even for small precipitation events (<0.1mm). The SDBC method showed improvement over the BCSD method but still simulated too many rainy sub basins for both seasons. On the other hand, the BC SA method successfully reproduced the number of rainy sub basins for both seasons. Similarly, standard deviati ons of the daily precipitation field that were estimated as a function of spatially averaged precipitation volume indicate that the BCSD and SDBC results underestimated the spatial variability and BC SA method accurately reproduced the observed spatial vari ability (Figure 5 12). The average standard deviation s of BCSD and SDBC results over 172 sub basins were lower than the minimum of observed standard deviations for any precipitation volume for both seasons indicating a significant underestimation of spatia l variability. Finally variogram s were estimated for daily observations and the downscaled daily GCM precipitation results for both the wet and dry seasons in order to evaluate how well each downscaling technique reproduces the spatial correlation structu re of the observed daily precipitation. Figure 5 13 compares the estimated variograms with respect to separation distances up to 110 km. These figures indicate that the spatial variance of the BCSD and SDBC results w as significantly underestimated at all separation distances and BC SA reproduced the observed variograms accurately for both wet (June through September) and dry (October through May) seasons. Note that differences among the GCM result s were not as significan t as differences among the downscaling methods. These results were consistent with those from Hwang and Graham (2011c) who compared these statistical methods using gridded precipitation over the entire state of Florida.

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153 5.4.2 IHM Results 5.4.2. 1 Streamflo w Simulation Results. The calibrated INTB model was run to simulate streamflows and groundwater levels for each statistically downscaled climate dataset. The annual cycle of monthly averaged streamflow is commonly used to evaluate hydrologic implications of climate predictions (e.g., Wood et al., 2004; Dibike et al., 2005). Figure 5 14 compares monthly averaged streamflow predicted using the climate input data (i.e., precipitation and temperature) from the various downscaling methods to INTB calibrated mo del results and observations for the four target stations. Additionally the averaged mean errors (i.e., simulated calibrated) of monthly average streamflow and temporal standard deviation of daily streamflow for the wet season over the four GCMs were estim ated for all target stations and compared in Table 5 1. Note that it is more meaningful to compare the simulated results to the model calibration results rather than observed streamflow to focus on differences due to variations in climatic forcing. Observe d monthly averaged streamflow was plotted primarily to illustrate the performance of the calibrated model. Results indicate that at all locations, monthly averaged streamflow volumes were similar for all scenarios during the dry season from October throu gh May, but underestimated the streamflow during the early portion of the wet season from June through August. Furthermore the BCSD method tends to underestimate streamflow for the wet season more than the SDBC and BCSA methods at all stations (Table 5 1) This is due to the high spatial correlation of the BCSD daily precipitation fields (Figure 5 12 and Figure 5 13) and higher frequency of low precipitation events (Figure 5 11) resulting in higher evapotranspiration (ET). Figure 5 15 compares the total a nnual ET estimated over the domain for each climate input and the calibrated model. These results indicate that the BCSD results overestimated the total ET by 3.1% ~ 5.3% over the model domain.

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154 Figure 5 16 compares the mean errors of the monthly mean an d standard deviation of daily streamflow rate over the four GCM results for each downscaling method. This figure and Table 5 1 indicate that the BCSD method underestimates the monthly average streamflow for all stations during the wet season compared to th e other methods. Overall the SDBC and BCSA methods accurately reproduce the timing and magnitude of peak streamflows compared to the calibration results, though some underestimation during the wet season was found as illustrated in Figure 5 14. The SDBC re sults significantly overestimate the temporal variance of daily streamflow especially during the wet season from June to September, while the BCSD and BCSA methods provide better results although slightly underestimated (Table 5 1). This is caused by over estimation of the spatially averaged precipitation volume in the SDBC results (Figure 5 9) which leads to overestimation of extreme streamflow during the wet season. Additionally BCSD reproduced lower temporal variability then the SDBC results because of u nderestimated temporal variability of daily precipitation for each sub basin (Figure 5 7 and Figure 5 8), particularly for low flow stations (i.e., Cypress Creek and Anclote River). Note that based on Figure 5 14 and Figure 5 16, the difference in downscal ing methods is more significant than the differences between GCMs. In addition to monthly average streamflow, the frequency distribution of flow events is critical for water management. For example, when streamflow rate is within a range of permitted thr esholds, Tampa Bay Water is allowed to withdraw surface water from designated places for public water supply. Figure 5 17 compares the frequency of streamflow as a function of the daily streamflow rate. The results indicate that while the frequency of ord inary streamflow was accurately reproduced by all methods, the simulation extreme daily streamflow events exceeded the observed extreme daily streamflow events for all downscaling methods. The

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155 BCSD and BCSA climate simulations generally agree with the cali brated estimation better than the SDBC method which overestimated streamflow events especially for high flow stations (i.e., Alafia River and Hillsborough River station). As discussed previously, the SDBC method tends to overestimate the temporal variance of spatially averaged precipitation because the large scale daily GCM precipitation simulations are bias corrected at the downscaled grid resolution at the last step in the procedure. Thus each downscaled sub basin data preserves the precipitation percent ile event predicted by the large scale GCM, exaggerating high and low percentile events. Note that the frequency of daily streamflow was not reproduced by SDBC as closely as for the BCSA results although the monthly average streamflow predicted by the SD BC results are reasonably close to calibrated results and similar to the BCSA results (Figure 5 14 and Figure 5 16). Thus it is evident that the under/overestimations of extreme events is canceled out when evaluating only the monthly mean streamflow. It sh ould also be noted that, through the bias correction process, daily frequency of precipitation was corrected to be identical to that of observed precipitation, and as a result mean monthly precipitation over the study period were virtually identical for al l downscaling methods. However the results of this streamflow analyses indicate that reproducing more detailed precipitation characteristics (i.e., inter event duration, frequency of spatially averaged precipitation, spatial distribution) is required to a ccurately capture the both the monthly mean streamflow and the daily streamflow frequency distribution. 5.4.2.2 Groundwater Level Simu lation Results. In the study area, groundwater is a major source of public water supply and it contributes significantly to springflow, streamflow and wetland hydro periods due to strong surface groundwater interactions in the surficial system. Therefore simultaneously evaluating streamflow and groundwater simulations driven by different climate inputs is important for wate r resource management in west central Florida.

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156 Figure 5 18 and Figure 5 19 show monthly average groundwater level simulated using downscaled climate results for the Surficial and Floridan aquifer stations, respectively. Table 5 1 compares the averaged mean errors (i.e., simulated calibrated) of monthly average groundwater level over the four GCMs for all target stations. Figure 5 20 compares the frequency of simulated groundwater levels on an annual basis as a function of groundwater level. Surficial/Florid an well pairs CYC TMR 5 SH/CYC TMR 5d, S21 J26As/S21 Jcksn26d, and STK Starkey 20s/STWF 10 DP are located in a region where the Floridan aquifer is semi confined. The CBR SERW s/ CBR SERW d well pair is located in the northern portion of model domain where the Floridan aquifer becomes unconfined. Based on Figure 5 18 and Figure 5 19, the various climate inputs show some influence on the groundwater system in the semi confined region. The statistically downscaled GCM results tended to underestimate monthly average groundwater levels and the errors of monthly average groundwater level are consistent over the month for all stations compared to the calibrated results. The influence of different downscaling methods on simulated groundwater levels is not as stron g as the influence on simulated streamflows. The reason is most likely because runoff processes are generally impacted more directly by the climate variability than subsurface hydrologic processes. The most significant underestimation was found at the CBR SERW s/ CBR SERW d stations indicating that the impacts of spatial variability in climate forcing data are attenuated in the semi confined Floridan aquifer compared to the overlying surficial aquifer. Previously the CBR SERW stations were found to show s imilar hydrologic behavior to the unconfined Floridan of west central Florida by Hwang et al. (2011b). Regarding the differences among the GCMs, the BCCR and CCSM results showed better performance for the CBR SERW stations compared to calibrated results w hile all GCMs performed similarly for the rest of the groundwater target stations. Among the downscaling

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157 methods, the BCSD resulted in considerable underestimation of mean monthly groundwater levels (Figure 5 18, Figure 5 19, and Table 5 2) especially for the CBR SERW s/CBR SERW d stations. For the other stations BCSD showed similar skills to the other methods in reproducing the annual cycle of groundwater level. This result is most likely due to increased ET (Figure 5 15) and strong interaction between th e surface and subsurface hydrologic systems as previously discussed. This result also indicates that spatial variability of precipitation is more readily reflected in the groundwater system at this unconfined location than in other areas where the Floridan aquifer is semi confined. The SDBC and BCSA results showed no significant differences in terms of estimating monthly average groundwater level (Table 5 2). Mean errors for temporal variability of groundwater level (analogous to Figure 5 16 for streamflow) are not presented here because the temporal variability of groundwater level is not significant compared to streamflow. No substantial difference of skill in reproducing the frequency of groundwater level was found among the various downscaling techniques (Figure 5 20). The SDBC showed some overestimation of the frequency of lower and higher groundwater level at the CBR SE RW and S21 stations compared to the calibrated results. This may be due to the same mechanism that resulted in overestimating the frequency of extreme streamflow events and peak flow in the wet season by the SDBC. That is, the SDBC may produce unrealistic precipitation fields for low and high precipitation events over the model domain and thus lead to simulation of more extreme cases in groundwater system. 5 5 Chapter S ummary The ultimate goal of this study was to investigate the applicability of a stocha stic downscaling technique (i.e., BC SA ) developed by Hwang and Graham (2011c) to reproduce local precipitation characteristics at the sub basin scale for evaluating hydrologic implications of

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158 climate variability in west central Florida. The downscaled pre cipitation results using the BC SA technique were evaluated compared to those of interpolation based statistical downscaling methods (i.e., BCSD and SDBC). The sensitivity of the hydrologic responses to differences in climate inputs was assessed through IHM modeling. Four GCMs were chosen to examine the skills of statistical downscaling methods in reproducing temporal and spatial features exhibited by sub basin based observations. While the precipitation outputs were downscaled using a variety of statistic al downscaling methods, the temperature simulations from GCMs were downscaled using CDF mapping bias correction method assuming direct correspondence between the exceedence probabilities of GCM grid cell and observation location for all cases The results indicate that temperature commonly has a strong seasonal cycle and less spatial variability compared to precipitation, and that the temporal mean and variance of maximum and minimum temperature were well reproduced by bias correction based downscaling. F or the precipitation results, while all downscaled precipitation results accurately reproduced the temporal mean of daily and monthly precipitation for all sub basins, the performance for reproducing the realistic temporal and spatial variability and corre lation structure varied among the methods. The BCSD results significantly underestimated temporal variability of daily rainfall. Moreover, BCSD overestimated the wet to wet transition probabilities and underestimated dry to wet transition probabilities es pecially for wet season. The SDBC method improved over the BCSD results in reproducing temporal variability and daily transition probabilities by bias correcting at the downscaled grid resolution rather than at the coarse GCM resolution. However the SDBC results tended to overestimate the temporal variance of spatially averaged

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159 precipitation because of the bias correction of smooth spatially interpolated GCM results at the sub basin scale. In contrast, the BC SA reproduced the observed temporal standard deviation and wet wet transition probabilities more accurately than the BCSD and SDBC methods. In terms of spatial variability of precipitation, the limitations of using interpolation based downscaling methods v ersus the new BC SA method were clearly demonstrated. Overall, BCSD failed to reproduce the observed spatial variability and produced significantly over correlated precipitation field s. SDBC showed better skill in reproducing spatial variability compared t o BCSD but was still found to overestimate spatial correlation. The BC SA results successfully reproduced the spatial variability exhibited by observations. The limitations of interpolation based statistical downscaling methods (i.e., BCSD and SDBC) showe d some influence in hydrologic simulation results. BCSD tended to underestimate monthly average streamflow for both seasons at all target stations, likely due to less spatiotemporal variability of precipitation fields and thus increased ET loss. The SDBC s howed comparable mean streamflow estimations to the BC SA results however, the SDBC predicted too many extreme (high) streamflow events and thus overestimate d temporal variance of daily streamflow. While the SDBC and BC SA results reproduced the average str eamflow more accurately than BCSD, both methods underestimated August streamflow for all stations. These consistent errors, regardless of the climate models and downscaling methods, are caused by inaccuracies of raw climate model outputs in precipitation t iming and variability. These inaccuracies cannot be reduced by any statistical corrections, but only by improving the climate model physics and parameterization. In terms of groundwater behavior, the climate variability did not affect the subsurface hydro logic system as much as the surface system. However, in the area where the Floridan

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160 aquifer is unconfined and interaction s between the surface and subsurface are strong, the BCSD method underestimated the monthly average groundwater level. The SDBC method performed similarly to the BC SA method in reproducing the average annual cycle of groundwater fluctuation, but overestimated the frequency of extreme events as it did for streamflow. In conclusion, accurately simulating climate variability is important wh en assessing hydrologic implications of climate scenarios. Therefore accurately reproducing the spatiotemporal variability is required for practical use of the climate scenarios. Reproducing the daily frequency and temporal mean of precipitation alone is n ot sufficient for accurately assessing hydrologic impacts in west central Florida Furthermore, the accuracy of temporal mean precipitation does not necessarily reproduce the mean and frequency of hydrologic behaviors that are important for long term water management. This study showed that simple interpolation based statistical downscaling methods have significant weaknesses, and the BC SA method improves on these methods in terms of reconstructing the spatiotemporal variability of precipitation.

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161 Table 5 1. Averaged mean error (ME, simulated calibrated) of monthly average streamflow and temporal standard deviation of daily streamflow for wet season over the four GCMs. unit: m 3 /s Monthly average streamflow T emporal standard deviation of daily streamflow Downscaling methods BCSD SDBC BC SA BCSD SDBC BC SA Streamflow Hillsborough 4.201 1.499 2.078 2.528 3.703 1.386 Alafia 4.595 1.513 2.226 3 .401 4.089 2.222 Cypress Creek 1.181 0.445 0.513 1.141 0.196 0.607 Anclote 0.843 0.169 0.381 0.669 1.330 0.337 Table 5 2. Averaged mean error ( ME simulated calibrated ) of monthly average groundwater level over the four GCMs for all stations. unit: m AME of m onthly average groundwater level Downscaling methods BCSD SDBC BC SA Surficial aquifer stations CYC TMR 5 SH 0.077 0.106 0.067 S21 J26As Ext 0.345 0.280 0.263 STK STARKEY 20s 0.105 0.054 0.037 CBR SERW s 0.760 0.384 0.448 Floridan aquifer stations CYC TMR 5 D 0.288 0.254 0.196 S21 Jcksn26d 0.139 0.117 0.092 STWF 10 DP 0.070 0.038 0.024 CBR SERW D 0.759 0.386 0.438

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162 Figure 5 1. Map of study area (INTB model domain), sub basin configuration, and target stations for streamflow and groundwater level evaluation. Cypress Creek

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163 Figure 5 2. Th e center location of grids for 4 GCMs Figure 5 3 Schematic representation of the methodology for BCSD SDBC, and BC SA downscaling technique s.

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164 Figure 5 4 Mean monthly Tmax and Tmin of basin based observation. Mean monthly temperatures for the bias corrected GCMs are identical to observed. Error bars on the graphs represent the range of Tmax and Tmin average over 6 point location s Figure 5 5 Standard deviation ( Std.) of monthly mean (a) maximum temperature and (b) minimum temperature over the study domain for observation and each bias corrected GCM. The graphs indicate the inter annual variability of max. and min. monthly temperature for each month. The gray zone represents data range of monthly mean observations over 6 point locations, reflecting spatial variation of long term variability over the study domain. 5 10 15 20 25 30 35 40 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean monthly T ('C) Obs. Tmax Obs. Tmin 0 1 2 3 4 Jan Feb Mar Apr Jun Jul Aug Sep Oct Nov Dec Std of averaged monthly Tmax ('C) Obs. bccr ccsm cgcm gfdl 0 1 2 3 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Std of averaged monthly Tmin ('C) Obs. bccr ccsm cgcm gfdl (b) ( a )

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165 Figure 5 6. Averaged mean daily precipitation of basin based observation (Bobs., 1988~2006). The bright and dark gray zones represent total data range and 5 to 95 percentile of Bobs., respectively, reflecting spatial variation of the mean daily precipitation over the 172 sub basins. All downscaling methods match the observed average daily precipitati on cycle exactly. Figure 5 7. Averaged standard deviations (std.) of daily precipitation for basin based observation (Bobs.), BCSD, SDBC, and BC SA GCMs for each month. The bright and dark gray zones represent total data range and 5 to 95 percentile of Bobs., respectively, reflecting spatial variation of the std. over the 172 sub basins. 0 2 4 6 8 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Averaged daily preciptiation (mm) Bobs 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Std. of daily precipitation (mm) Bobs BCSD GCMs SDBC GCMs BCSA GCMs

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166 Figure 5 8. Scatter plots of temporal mean and standard deviation of 172 basin based daily precipitation data for sub basin observations vs. downscaled GCMs. 2.5 3 3.5 4 4.5 2.5 3 3.5 4 4.5 Temporal mean of sim. P Temporal mean of obs. P BCSD_bccr BCSD_ccsm BCSD_cgcm BCSD_gfdl 6 8 10 12 14 6 8 10 12 14 Temporal stdev. of sim. P Temporal stdev. of obs. P BCSD_bccr BCSD_ccsm BCSD_cgcm BCSD_gfdl 2.5 3 3.5 4 4.5 2.5 3 3.5 4 4.5 Temporal mean of sim. P Temporal mean of obs. P SDBC_bccr SDBC_ccsm SDBC_cgcm SDBC_gfdl 6 8 10 12 14 6 8 10 12 14 Temporal stdev. of sim. P Temporal stdev. of obs. P SDBC_bccr SDBC_ccsm SDBC_cgcm SDBC_gfdl 2.5 3 3.5 4 4.5 2.5 3 3.5 4 4.5 Temporal mean of sim. P Temporal mean of obs. P BCSA_bccr BCSA_ccsm BCSA_cgcm BCSA_gfdl 6 8 10 12 14 6 8 10 12 14 Temporal stdev. of sim. P Temporal stdev. of obs. P BCSA_bccr BCSA_ccsm BCSA_cgcm BCSA_gfdl (a) Mean BCSD (a) Stdev. BCSD (a) Mean SDBC (a) Stdev. SDBC (a) Mean BC SA (a) Stdev. BC SA

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167 Figure 5 9. Standard deviation of spatially averaged daily precipitation for basin based observation (Bobs.), BCSD, SDBC, and BC SA GCMs for each month. 0 5 10 15 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Std. of daily precipitation (mm) Bobs BCSD_GCMs SDBC_GCMs BCSA_GCMs

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168 Figure 5 10. Comparison of transition probabilities (i.e., dry to wet day (P_01, left column) and wet to wet day (P_11, right column) ) of sub basin observations (Bobs.), BCSD, SDBC results, and BC SA results for wet (top) and dry season (bottom). Box plots represent the minimum, 10 th percentile, 90 th percentile, and maximum transition probability over 172 sub basins indicating spatial variation of probability. The dashed lines indicate the mean of Bob s. 0 0.2 0.4 0.6 0.8 1 Bobs. bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl BCSD SDBC BCSA P01_wet season 0 0.2 0.4 0.6 0.8 1 Bobs. bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl BCSD SDBC BCSA P11_wet season 0 0.2 0.4 0.6 0.8 1 Bobs. bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl BCSD SDBC BCSA P01_dry season 0 0.2 0.4 0.6 0.8 1 Bobs. bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl BCSD SDBC BCSA P11_dry season

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169 Figure 5 11. Comparison of number of rainy sub basins for the spatially averaged daily precipitation for (a) wet and (b) dry season. Figure 5 12. Comparison of the relationship between spatial standard deviations of daily precipitation and the spatially averaged daily precipitation for (a) wet and (b) dry season. Gray lines indicate the range of observed spatial standard deviation with respect to the amount of daily precipitation. 0 40 80 120 160 0.01 0.1 1 10 100 Number of sub basins spatially averaged precipitation (mm) Bobs BCSD GCMs SDBC_GCMs BCSA GCMs 0 40 80 120 160 0.01 0.1 1 10 100 Number of sub basins spatially averaged precipitation (mm) Bobs BCSD GCMs SDBC_GCMs BCSA GCMs 0.001 0.01 0.1 1 10 100 0.1 1 10 100 Std. of daily precipitation (mm) Spatially averaged precipitation (mm) Bobs BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.001 0.01 0.1 1 10 100 0.1 1 10 100 Std. of daily precipitation (mm) Spatially averaged precipitation (mm) Bobs BCSD_GCMs SDBC_GCMs BCSA_GCMs (b) dry (a) wet (b) dry (a) wet

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170 Figure 5 13. Comparison of variograms of sub basin based observation and the downscaled prediction using BCSD, SDBC, and BC SA technique for (a) wet and (b) dry season. 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Variogram (mm 2 ) Separation distance (km) Bobs BCSD_GCMs SDBC_GCMs BCSA_GCMs 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Variogram (mm 2 ) Separation distance (km) Bobs BCSD_GCMs SDBC_GCMs BCSA_GCMs (b) dry (a) we t

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171 Figure 5 1 4 Comparison of monthly average streamflow driven by downscaled climate scenarios using BCSD (first column), SDBC (second column), and BC SA (third column) to the calibrated results and observations. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. SDBC_GCMs 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSA_GCMs 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. SDBC_GCMs 0 5 10 15 20 25 Jan Feb Apr Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSA_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. SDBC_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSA_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. SDBC_GCMs 0 2 4 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Streamflow (m 3 /s) Obs. Cal. BCSA_GCMs Alafia River Hillsborough River Cypress Creek Anclote River

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172 Figure 5 1 5 Comparison of simulated mean annual evapotranspiration (ET) over the INTB model domain 850 900 950 1000 bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl bccr ccsm cgcm gfdl cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs Evapotranspiration

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173 Figure 5 16. Mean of errors (simulated calibrated) of monthly average streamflow (first column) and temporal standard deviation of daily streamlfow (second column) over four GCM results for each target station. -8 -4 0 4 8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -20 -10 0 10 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -8 -4 0 4 8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -20 -10 0 10 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -4 -2 0 2 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -4 -2 0 2 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error (m 3 /s) BCSD SDBC BCSA -4 -2 0 2 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error ( m 3 /s ) BCSD SDBC BCSA -4 -2 0 2 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mean error ( m 3 /s ) BCSD SDBC BCSA Alafia River Hillsborough River Cypress Creek Anclote River Mean errors of monthly average streamflow Mean errors of temporal std. of daily streamflow

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174 Figure 5 1 7 Comparison of frequency of daily streamflow events per year for each target station. The solid and dashed lines indicate the maximum daily streamflow of calibrated results and observations, respectively and dotted circles indicate the notable extreme events simulated by the SDBC results. 0.01 0.1 1 10 100 1000 0 100 200 300 400 500 600 # of events per year Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 0 100 200 300 400 500 600 # of events per year Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 0 10 20 30 40 50 60 70 80 90 # of events per year Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 0 10 20 30 40 50 60 70 80 90 # of events per year Streamflow (m 3 /s) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs Alafia River Hillsborough River Cypress Creek Anclote River

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175 Figure 5 18. Comparison of simulated monthly average groundwater levels at the surficial aquifer stations for the downscaled climate scenarios using BCSD (first column), SDBC (second column), and BC SA (third column) to the calibrated results and observations. 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 14 15 16 17 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 14 15 16 17 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 14 15 16 17 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 17 18 19 20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs CYC TMR 5 SH S21 J26As STK STARKEY 20s CBR SERW s

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176 Figure 5 19. Comparison of simulated monthly average groundwater levels at the Floridan aquifer stations for the downscaled climate scenarios using BCSD (first column), SDBC (second column), and BC SA (third column) to the calibrated results and observations. 15 16 17 18 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 15 16 17 18 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 15 16 17 18 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 10 11 12 13 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 10 11 12 13 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 10 11 12 13 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 7 8 9 10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs 16 17 18 19 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSD_GCMs 16 17 18 19 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. SDBC_GCMs 16 17 18 19 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Groundwater level (m) Obs. Cal. BCSA_GCMs CYC TMR 5 D S21 Jcksn 26d STWF 10 DP CBR SERW D

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177 Figure 5 20 Compari s o n of the frequency of groundwater level per year for surficial (first column) and Floridan (second column) target stations. 0.01 0.1 1 10 100 1000 14 16 18 20 22 24 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 10 15 20 25 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.1 1 10 100 1000 11 13 15 17 19 21 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 5 10 15 20 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 4 6 8 10 12 14 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 2 5 8 11 14 17 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.1 1 10 100 1000 12 15 18 21 24 27 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs 0.01 0.1 1 10 100 1000 10 13 16 19 22 25 # of events per year Groundwater level (m) Obs. Cal. BCSD_GCMs SDBC_GCMs BCSA_GCMs CYC TMR 5 SH CYC TMR 5 D S21 J26As S21 Jcksn 26d STK STARKEY 20s STWF 10 DP CBR SERW s CBR SERW D

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178 CHAPTER 6 CONCLUSION S AND RECOMMENDATIONS FOR FUTURE RESEARCH This study investigated the applicability of various existing dynamic and statistical downscaling techniques to generate precipitation fields from general circulation model outputs over the state of Florida. These methods were quantitatively evaluated and found unable to reproduce fine scale spatial characteristics of observed precipitation patterns. A new stochastic downscaling technique was developed to improve on the previous ly developed statistical downscaling methods The new technique produce d daily precipitation fields that accurately reproduce spatiotemporal variability of precipitation. In order to evaluate hydrologic implications of differences in the alternative downscal ing methods t he downscaled precipitation scenarios were used as input to an integrated hydrologic model and the results were evaluated Based on the research framework described above, the contents of this dissertation consist of four sections entitled: 1) Q uantitative spatiotemporal evaluation of dynamically downscaled MM5 precipitation predictions over the Tampa Bay region, Florida; 2) H ydrologic implication s of errors in dynamically downscaled and bias corrected climate model predictions for west cent ral Florida; 3) D evelopment of a stochastic downscaling method to reproduce observed spatiotemporal variability of daily precipitation; 4) H ydrologic importance of spatial variability in statistically downscaled precipitation predictions from general clim ate models for west central Florida. The main conclusions of this dissertation are summarized by section, are as follow s: 1) Evaluation of MM5 results a) Raw MM5 model results were positively biased, significantly overestimating the mean of daily and monthly p recipitation totals and underestimating maximum temperature over west central Florida. b) Bias correction using a CDF mapping technique effectively removed the bias in the temporal mean and variance of precipitation The b ias corrected MM5 results successfu lly reproduced seasonal patterns of spatial variability in precipitation, with higher spatial variance of daily precipitation over the study area in the wet season when

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179 convective storms dominate and lower spatial variance of daily precipitation in the dry season when frontal systems dominate. c) The strength of the spatial correlation of the daily bias corrected MM5 rainfall fields was significantly overestimated throughout the year, with the bias corrected MM5 precipitation fields showing more spatial regu larity than the observed fields. 2) H ydrologic implication s of MM5 results a) While the bias corrected MM5 results adequately reproduced the historical climatology over the study area (i.e. frequency distribution of daily precipitation and long term mean and v ariance of monthly and annual precipitation totals), the periodic errors in the simulated time series of daily and monthly total precipitation produced significant errors and low model skill scores in hydrologic model predictions. b) Usi ng the bias corrected MM5 predictions downscaled from the NCEP NCAR reanalysis data to force hydrologic models for multi decadal water resource planning applications provides no real advantage over using the long term historical climate record since the error associated with t he bias corrected MM5 daily rainfall predictions is on the order of the standard deviation of the long term daily observations. 3) D evelopment of a new stochastic downscaling method a) Statistically downscaled GCM results using the BCSD, SDBC, and BC SA methods accurately reproduced the temporal mean of the daily precipitation as well as the annual cycle of monthly mean precipitation compared to gridded observations over the state of Florida b) L ow precipitation frequency, wet to wet transition probabilit ies and wet spell length were overestimated by BCSD T he temporal variance, dry to wet transition probabilities dry spell length and the magnitude of 90 th percentile daily precipitation were underestimated especially for the wet season. c) The SDBC method resulted in improve ments in reproducing temporal variability of daily precipitation at the fine grid scale compared to the BCSD method but tended to overestimate the temporal variance of spatially averaged precipitation. d) The interpolation based downsca ling methods (both BCSD and SDBC) were unable to reproduce the observed spatial variability of daily precipitation i.e., significantly underestimated spatial variance and overestimated spatial correlation. e) The BC SA method developed in this study reproduce d the observed temporal variance, frequency of daily rainfall amounts and transition probabilities accurately compared to the other methods. This technique also generate d daily precipitation fields that accurately reproduce d observed spatial correlation of daily rainfall.

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180 4) H ydrologic implication s of statistically downscaled results a) The drawbacks in the interpolation based statistical downscaling methods (i.e., BCSD and SDBC) were amplified into hydrologic modeling The BCSD method tended to underestimate monthly average streamflow for both seasons at all target stations likely due to smaller spatiotemporal variability of precipitation fields and thus increased ET loss. b) Whereas the SDBC method showed comparable results for average s treamflow prediction s to the BC SA results, high extreme daily streamflow events were over predicted by the SDBC forcing input. c) Errors in climate inputs did not affect the subsurface hydrologic system as much as the surface system. However, in the area where Floridan aquifer becomes unconfined and interaction between surface and subsurface is strong er the BCSD underestimated the monthly average groundwater level. d) While the BC SA results pr oduce the best groundwater l evel results, the SDBC method performs similarly to the BC SA method in reproducing the average annual cycle of groundwater fluctuation The SDBC method also overestimate d the frequency of extreme groundwater levels, as it did for streamflow. In summary, t his study evaluated the abilities of both dynamical and statistical downscaling methods to generate fine resolution precipitation fields needed for hydrologic applications in west central Florida, and revealed limitation s of each of the methods that may ha ve significant implications for water resource planning Th e study focused on evaluating downscaled climate modeling results for retrospective simulations to investigate how to provide plausible local climate data from GCM outputs with coarse resolution Whereas the BCSA technique improves skills in reproducing spatiotemporal variability of precipitation compared to the existing interpolation based statistical downscaling methods as shown in the study, various assumptions are also required for the future a pplications. The BCSA technique ensures that the downscaled results for the retrospective application have same temporal statistical moments as the historical observations. However, for future applications, it is important to consider the change in future climate rather than reproducing historical climatology. A possible solution would be to use the differences between the CDFs of GCM retrospective

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181 simulations and future projections. The differences of precipitation amounts for each exceedence probabilities between the CDFs of current and future GCM simulations would be added to the corresponding historical precipitation for given exceedence probabilities Then the adjusted observation CDFs (i.e., hypothesized future climate CDFs) would be used to bias corre ct GCM future projection scenarios. This process assumes that GCM biases have the same structure during current and future simulations because the changes in biases of CDFs of GCM retrospective simulation and future projections are not considered. Addition ally, BCSA generates precipitation fields that honor the spatial correlation estimated from the historical observations. For future application, this observed spatial correlation would also be used for downscaling unless changes in small scale spatial corr elation structure can be inferred from other climate information. This process assumes spatial correlation would remain consistent in the future. The long term goal of this research program is to assess potential future climate change impacts on the hydrologic system and support sustainable water management in west central Florida. Future work should apply the BC SA downscaling technique developed in this dissertation to downscale alternative future climate scenarios over the state of Florida, investigate potential hydrologic impacts of future climate change, and investigate methods to mitigate future water management risks in the s tate.

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193 BIOGRAPHICAL SKETCH Syewoon Hwang was born in Jinju si Kyungsangnam do, South Korea. He received his B.S and M.S. degrees in agricultural and biological engineering from Seoul National University, South Korea in 2004 and 2006 respectively. While seeking his B.S., he also served in the Korean military for two and half years (1999 2001). After graduation, he worked as a research assistant in Research Institute of Agricultural and Life Sciences, Seoul National University, South Korea for one year. Since 2007, he has been working toward his Ph.D. degree in agricultural and biological engineering at the University of Florida, Gaines ville. H is academic advisor was Dr. Wendy Graham and h is research interests include dynamical/statistical downscaling of climate information, hydrologic impact of climate change hydrologic modeling, and water resources management/risk assessment.