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Evaluation of the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) in Predicting Golf Course Irrigati...

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PAGE 1

EVALUATION OF THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS ) IN PREDICTING GOLF COURSE IRRIGATION REQUIREMENTS WI TH SITE-SPECIFIC DATA By MARK W. MITCHELL A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

PAGE 2

ACKNOWLEDGMENTS I would like to thank Dr. Grady Miller, chair of my committee, for his support and confidence in me to complete this work. I am also thankful for the help that my other committee member, Dr. Michael Dukes, provided me. Also I must thank Dr. Jennifer Jacobs for her technical support with the AFSIRS model. Special thanks go to Jan Weinbrecht and Nick Pressler, for their help and suggestions during this research. I would like to thank the St. Johns River Water Management District for funding this research. I am also grateful for the encouragement from my parents and my future wife, Sandra Buckley. ii

PAGE 3

TABLE OF CONTENTS page ACKNOWLEDGMENTS..................................................................................................ii LIST OF TABLES...............................................................................................................v LIST OF FIGURES...........................................................................................................vi ABSTRACT......................................................................................................................vii CHAPTER 1 INTRODUCTION........................................................................................................1 2 LITERATURE REVIEW.............................................................................................3 Golf Course Water Consumption.................................................................................3 Consumptive Use Permitting........................................................................................5 Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS)......................7 Modeling Crop Irrigation Requirements...............................................................7 Evapotranspiration and Weather Data Inputs........................................................8 Crop Coefficient Inputs.......................................................................................12 Irrigation Application Efficiency and Uniformity Inputs....................................13 Rooting Depth Inputs..........................................................................................14 Soil Type Inputs..................................................................................................15 Sensitivity Analysis....................................................................................................15 3 PREDICTING IRRIGATION REQUIREMENTS ON GOLF COURSES USING THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS)..........................................................................................17 Introduction.................................................................................................................17 Materials and Methods...............................................................................................18 Results and Discussion...............................................................................................22 Default Values.....................................................................................................22 Updated/Actual Values........................................................................................23 Default Versus Updated/Actual Irrigation Requirements...................................25 Conclusions.................................................................................................................29 iii

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4 SENSITIVITY ANALYSIS OF THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS) AND THE FAO 56 PENMAN-MONTEITH EQUATION........................................................................35 Introduction.................................................................................................................35 Materials and Methods...............................................................................................36 Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS).............36 FAO 56 Penman-Monteith Equation...................................................................37 Results and Discussion...............................................................................................38 Agricultural Field Scale Irrigation Requirement (AFSIRS) Sensitivity Analysis............................................................................................................38 FAO 56 Penman-Monteith Equation Sensitivity Analysis..................................39 Conclusions.................................................................................................................39 5 SUMMARY AND CONCLUSIONS.........................................................................43 APPENDIX SAS PROGRAMS.......................................................................................45 REFERENCES..................................................................................................................47 BIOGRAPHICAL SKETCH.............................................................................................51 iv

PAGE 5

LIST OF TABLES Table page 3-1 Soil types and average water contents for the five golf courses, average water content of all soil types in the model database, and average water content of a USGA green.............................................................................................................31 3-2 Mean squares for the analysis of variance on rooting depths as influenced by golf course, location within golf course, and month................................................31 3-3 Mean rainfall, reference ET, estimated mean irrigation requirement, irrigation requirement issued on consumptive use permit, mean irrigation requirement using updated/actual data, and applied irrigation.....................................................31 v

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LIST OF FIGURES Figure page 3-1 Yearly average reference ET (A) and rainfall (B) for the 20 year historical weather dataset (Orlando), and for the one year of on-site weather data from each of the five golf courses.....................................................................................32 3-2 AFSIRS predicted irrigation requirements (IRR) for five golf courses. Default: model run with all default data.................................................................................33 3-3 AFSIRS predicted irrigation requirements (IRR) for three golf greens built with USGA greens mix (sand and peat)...........................................................................34 4-1 Sensitivity analysis of the AFSIRS model output....................................................41 4-2 Sensitivity analysis of the FAO 56 Penman-Monteith equation..............................42 vi

PAGE 7

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EVALUATION OF THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS) IN PREDICTING GOLF COURSE IRRIGATION REQUIREMENTS WITH SITE-SPECIFIC DATA By Mark W. Mitchell December 2004 Chairman: Grady L. Miller Major Department: Horticultural Sciences Golf courses are often the focus when it comes to water use. Various water-use models are used by Floridas five Water Management Districts, including the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS), to estimate irrigation requirements on golf courses. The St. Johns River, South Florida, and North Florida Water Management Districts use the AFSIRS model with available default data to predict irrigation requirements for all golf courses in their jurisdiction. Default values are used because of the limited research on golf course irrigation requirements. In this study, data were collected at five golf courses in the Central Florida area to use in the AFSIRS model for comparisons to irrigation requirements made using default data. Irrigation system distribution uniformity (DU LQ ), rooting depths, and weather data were collected from each golf course. Updated crop coefficients (Kc) for turfgrass were used in place of the default values. Average soil water content for a green built to USGA specifications vii

PAGE 8

was used for golf courses with these types of greens, instead of the native soil average water content. A sensitivity analysis was used to determine which inputs had the greatest influence on the model outputs. When actual data were used, the predicted irrigation requirements increased between 15 and 46 cm (6 and 18 in) per year for the golf courses. Distribution uniformity had the greatest impact on predicted irrigation requirements. When only distribution uniformities were substituted for the default irrigation system efficiency, the irrigation requirement increased between 13 and 76 cm (5 and 30 in) per year. Because of a limited length of actual on-site weather data, and unusually high rainfall amounts during the year, it was difficult to use actual weather datasets in place of the 20-year historical datasets available to the model. Sensitivity analysis further indicated that DU LQ inputs have the greatest influence on the predicted irrigation requirements made by the AFSIRS model. Changing a DU LQ from 40 to 80% (a 100% increase) resulted in a 65% decrease in irrigation requirement. The sensitivity analysis also showed that daily maximum temperature and mean solar radiation had the largest impact on reference evapotranspiration rates (ET o ) calculated using the FAO 56 Penman-Monteith equation in the REF-ET program (computer program used to calculate ET o from meteorological data). A 25% increase in the starting point for maximum temperature resulted in a 45% increase in ET o and a 50% increase of the starting point for mean solar radiation results in a 33% change in ET o Using actual data in place of default values in the AFSIRS model can result in site-specific estimates of irrigation requirements on golf courses. According to the AFSIRS model, and further illustrated through sensitivity analysis, DU LQ and Kc values had the greatest impact on irrigation requirements. Therefore, these variables need to be measured with the most accuracy. viii

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CHAPTER 1 INTRODUCTION In recent years, golf has become popular in the United States. There were over 15,000 golf facilities in the country in 2000, and over 1,100 courses located in Florida (National Golf Foundation, 2004). Because of the number of golf courses in Florida, and their visibility, there is a public concern that the golf industry wastes water. Because of Floridas erratic distribution of rainfall and large extent of sandy soils, irrigation is necessary to maintain quality turfgrass. Water use in Florida is controlled by five Water Management Districts. Each District is responsible for issuing consumptive use permits to large water users (including golf courses) located in their jurisdiction. These permits allocate the maximum amount of water a golf course should use per year. A golf course is required to report these water use amounts yearly; and if the allotment is exceeded, a financial penalty may be issued by the District. The Water Management Districts use various mathematical models to determine crop irrigation requirements. The St. Johns River Water Management District (SJRWMD), South Florida Water Management District, and North Florida Water Management District use the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) to estimate crop water needs, including turfgrass. The AFSIRS model was developed at the University of Florida, and is a numerical simulation model that estimates irrigation requirements for Florida crops, soils, irrigation systems, climate conditions, and irrigation management practices (Smajstrla, 1990). Historical (default) datasets are 1

PAGE 10

2 available to the model for crop (turfgrass) coefficient values, rooting depths, irrigation system efficiencies, soil water capacities, rainfall, and reference evapotranspiration rates. Because the SJRWMD uses the default data when determining irrigation requirements for golf courses, the allotment amounts are not site-specific to each golf course in their jurisdiction. Because of limited research on golf course irrigation requirements, there were two objectives to this research. The first objective was to compare AFSIRS water requirement estimates made with default data to estimates made with actual data collected from golf courses. The second objective was to determine what model inputs have the greatest influence on outputs, in order to ascertain the variables that should be measured and monitored to the greatest extent.

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CHAPTER 2 LITERATURE REVIEW Golf Course Water Consumption Total water use by Florida golf courses in 2000 was estimated at 650 billion liters (172 billion gal) (Haydu and Hodges, 2002). According to this survey, nearly 321 billion liters (85 billion gal) came from recycled water, 185 billion liters (49 billion gal) from surface water, 132 billion liters (35 billion gal) from on-site wells, and 5.7 billion liters (1.5 billion gal) from municipal sources. It was estimated that the average water use per golf course was 503 million liters (133 million gal) per year. Golf courses receive water allotments from water management districts based on total irrigated acreage; but courses consist of areas managed with varying levels of inputs, and areas that often require different amounts of water. Greens (areas prepared for putting) usually receive the most maintenance, followed by the tees (areas prepared for playing the first shot of each hole); fairways (turfed areas between tees and greens); and rough (turfed area surrounding the greens, tees, and fairways) (Beard, 2002). The amount of water these areas require depends on the type of grass, rooting depth, soil type, maintenance level, amount of inputs, and the desired effect. A traditional golf course has 4 par 3-holes, 4 par 5-holes, and 10 par 4-holes, which occupy approximately 54 hectares (133 acres) (McCarty et al., 2001). Tees take up 0.16 to 1.2 hectares (0.4 to 3 acres) of a golf course (McCarty et al., 2001). Par 4 and par 5 tees typically occupy 9.3 to 18.6 m 2 (100 to 200 ft 2 ), and par 3 tees range from 18.6 to 33.2 m 2 (200 to 357 ft 2 ) per thousand rounds of golf annually (Beard, 1985). Greens 3

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4 typically range from 465 to 697 m 2 (5,000 to 7,500 ft 2 ) in size, and occupy 0.85 to 1.33 hectares (2.1 to 3.3 acres) on a typical 18-hole golf course (Beard, 2002). Fairways comprise 12 to 24 hectares (30 to 60 acres) on a golf course (Beard, 1985). The average size of a fairway (the turfed area between the tee and green) is approximately 1.2 hectares (3 acres), which is dependent on the playing length of the hole and the width of the fairway. The usual fairway width on a golf course is approximately 32 meters (35 yards) (Beard, 2002). Depending on the total acreage and design of the course, the rough can range from 26 to 49 hectares (65 to 120 acres) for a golf course (Beard, 2002). Approximately 26% of the total acreage of a golf course is considered fairways and 55% is rough. Therefore, most water use on a golf course occurs in irrigating fairways and rough, and increasing and decreasing the total area of these zones can have a great impact on the amount of water use on a golf course. In the past, golf courses were built using only the existing soil at the construction site. Greens were built by pushing up the soil, in order to promote runoff of water (Beard, 1982). Fairways and roughs are still generally built using available soil on site, but to construct some surface features, soil may be excavated. Therefore, fairways or rough may be established using subsoil, which usually has different soil characteristics than surface soil layers. Because the subsoil may have a different water-holding capacity than the surface soil, water requirements can differ in areas where subsoil was used. Although some golf courses still have push-up greens, most newly constructed or renovated greens have been built to United States Golf Association (USGA) green specifications. The profile of a USGA green consists of 30 to 36 cm (12 to 14 in) of

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5 rootzone medium (fine textured) above a 5 to 10 cm (2 to 4 in) coarse sand layer (choker layer) covering a 10 cm (4 in) layer of gravel (Higgins and McCarty, 2001). Drainage tile is installed underneath the gravel layer, in a herringbone design. This design allows for the entire rootzone to reach field capacity before water drains through the gravel layer and into the drainage tile. Field capacity is the percentage of water that remains in the soil after having been saturated and after free drainage has practically ceased (Brady and Weil, 1999). The field capacity of a USGA greens mix should be in the range of 0.16 to 0.33 m 3 m -3 (Beard, 2002). Consumptive Use Permitting Florida has five Water Management Districts that regulate water control and use. The Districts main responsibilities include management of water and related land resources; proper use of surface and groundwater resources; regulation of dams, impoundments, reservoirs, and other structures to alter surface water movement; combating damage from floods, soil erosion, and excessive drainage; developing water management plans; maintenance of navigable rivers and harbors, participation in flood control programs; and maintaining water management and use facilities (Olexa et al., 1998). Each District is run by a governing board consisting of nine members. The members serve 4-year terms, and are appointed by the governor and confirmed by the state. Generally, an executive director is responsible for the operation of the District, including the implementation of policies and rules (Olexa et al., 1998). Floridas Water Management Districts issue several types of water use permits, the most common being consumptive use permits (CUPs). A CUP authorizes how much water should be withdrawn from surface and ground water supplies for reasonable and beneficial uses such as public supply (drinking water), agricultural and landscape

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6 irrigation, industry, and power generation (SJRWMD, 2004). The water withdraw situations that require a CUP are: a well that measures 15.2 mm (6 in) or more in diameter, the annual average is more than 378,500 liters (100,000 gal) of water use per day, or there is the capacity to pump 3.78 million liters (1 million gal) or more of water per day (SJRWMD, 2004). The St. Johns River Water Management District (SJRWMD) began issuing consumptive use permits (CUPs) in 1983. The District covers parts of Central Florida and the Northeast portion of the state. Since 1991, all permitted users have been required to report their water use by using a meter or by an alternative method approved by the District. In the year 2000, 504 CUPs were issued by the District (SJRWMD, 2004). To receive a CUP, an applicant must submit an application form, along with a fee, a listing of adjacent property owners, and a water conservation plan which provides measures to reduce water use and preserve water resources for other beneficial uses. The District then reviews the application and determines the allotment duration and amount (SJRWMD, 2002). The permits are issued for approximately 20 years, and upon expiration, must be renewed. CUPs for golf courses are usually issued for a shorter period of time because of changes that are often made to courses such as adding golf holes, or changing water sources. The factors that cause the difference in water allotments from golf course to golf course are total irrigated acreage and soil type. The applicant reports the irrigated acreage on the CUP application, and the District uses existing soil maps to determine the soil type at the location where the golf course was built. Golf courses may use different sources of water such as: wells, lakes, and reclaimed water, but the total amount

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7 withdrawn from all sources must not exceed their CUP amount. During water shortages, the district may impose restrictions, and these restrictions supersede any conditions of the permit (SJRWMD, 2002). Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) Modeling Crop Irrigation Requirements When determining the amount of water to issue on a consumptive use permit, the Water Management District must predict the irrigation requirement for the crop. Irrigation requirement (IRR) for a crop is the amount of water, in addition to rainfall, that must be applied to meet a crops evapotranspiration needs without significant reduction in yield (Smajstrla and Zazueta, 1998). In terms of golf course turfgrass, quality must not be significantly reduced. Evapotranspiration (ET) includes water that is needed for both evaporation and transpiration. The amount of water issued on the CUP for a golf course is determined by predicting the IRR for the turfgrass on that course and any other area that may be irrigated on or around the course, e.g. home landscapes. Estimates of IRRs can be ascertained from historical observation, or by using numerical models (Smajstrla and Zazueta, 1998). If a long term record has been kept of irrigation water applied, this record could be used to estimate future uses. But few such long-term databases exist. Another problem with historical data is that its use may be limited to the location where it was collected (Smajstrla and Zazueta, 1998). The effects of differences in climate, soil, location, time of year during which the crop was grown, as well as other factors on irrigation requirements cannot be determined from the available data (Smajstrla and Zazueta, 1998). Numerical models may be based on statistical methods or on physical laws which govern crop water uptake and use (Smajstrla and Zazueta, 1998). A basic model that has

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8 been used is The Soil Conservation Service (SCS) procedure (SCS, 1970). This model is a statistical regression method that allows monthly crop irrigation requirements to be estimated based on three factors: monthly crop ET, monthly rainfall, and soil water-holding characteristics (SCS, 1970). Limitations of this model are: estimation of irrigation requirements for monthly or longer time periods only, that it is limited to sprinkler and surface irrigation systems which irrigate the entire soil surface, and soil types with deep water tables (SCS, 1970). The SJRWMD uses the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) model to predict IRR for a crop (SJRWMD, 2004). The AFSIRS model is a numerical simulation model which estimates IRR using inputs from Florida crops, soils, irrigation systems, growing seasons, climate conditions, and irrigation management practices (Smajstrla, 1990). This model is based on a water budget of the crop root zone. This water budget includes inputs to the crop root zone from rain and irrigation, and losses from the root zone by drainage and evapotranspiration. The water holding capacity in the crop root zone is the multiple of the water-holding capacity of the soil and the rooting depth of the crop being irrigated (Smajsrla, 1990). Evapotranspiration and Weather Data Inputs The AFSIRS model is based on the concept that actual crop ET is estimated from reference ET and crop water use coefficients. Reference evapotranspiration (ET o ) is the rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of uniform height, which is actively growing, completely shading the ground and not lacking water (Allen et al., 1998). A crop coefficient (Kc) is an adjustment factor which is determined by different crop characteristics, i.e. turfgrass type, quality, and height (Brown and Kopec, 2000). Daily ET o as well as rainfall, can be ascertained from

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9 historical climate data available in the model. Records from nine Florida locations over approximately a 20 year period ending in the 1970s are part of the AFSIRS model database (Smajstrla, 1990). AFSIRS computes IRR for the mean year over those 20 years as well as IRR for different probabilities of occurrence. The SJRWMD permits are based on an 80% probability of occurrence, which means that the permittee, or golf course, should not exceed their allotment except for a 2 in 10 year drought (V. McDaniel, personal communication, 2004). There are many weather station networks that can be used by agricultural growers and turfgrass managers to determine ET o (Brown et al., 2001). The California Irrigation Information System (CIMIS) (Snyder, 1986) is an integrated network of over 120 computerized weather stations located throughout California. The Arizona Meteorological Network (AZMET) provides weather-based information in southern and central Arizona (Brown et al., 1988). These networks use the modified Penman equation to determine ET o The commonly used modified Penman and modified Penman-Monteith methods are two of the many mathematical models that compute ET o from measured weather data (Brown and Kopec, 2000). The Florida Automated Weather Network (FAWN) consists of 30 weather stations located throughout Florida. These stations collect data that can be use for determining ET o (FAWN, 2004). The common data collected daily by a weather station for computing ET o are: minimum and maximum temperature, minimum and maximum relative humidity, mean solar radiation, and mean wind speed. There are computer programs, such as REF-ET, that can calculate ET o from meteorological data using different mathematical models, including the commonly used

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10 modified Penman-Monteith method. REF-ET is a stand-alone computer program that calculates ET o from meteorological data made available by the user (Allen, 2002). The program provides standardized calculations of ET o for fifteen of the more common mathematical models that are currently in use in the United States and Europe (Allen, 2002). Daily ET o values in the AFSIRS database were calculated using the IFAS Penman equation (Smajstrla, 1990). The IFAS Penman was developed at the University of Floridas Institute of Food and Agricultural Sciences in 1984 to better represent regional climatic tendencies. The Penman formula is based on four major climatic factors: net radiation, air temperature, and wind speed and vapor pressure deficit (Jacobs and Satti, 2001). 42.042.108.056.014sosdsoRReTRET (2-1) daeeu20062.05.0263.0 where: ET o Reference evapotranspiration (mm day -1 ) Slope of saturated vapor pressure curve of air (mb/ o C) Psychometric constant (0.66 mb/ o C) Albedo or reflectivity of surface for R s R s Total incoming solar radiation (cal. cm -2 day -1 ) Stefan-Boltzmann constant (11.71 x 10 -8 cal.cm -2 day -1 K -1 ) T Average air temperature (K) e d vapor pressure at dewpoint temperature (mb) R so Total daily cloudless sky radiation (cal cm -2 day -1 ) u 2 wind speed at a height of 2 m (km day -1 ) e a vapor pressure of air (mb) Jacobs and Satti (2001) reported that the IFAS Penman equation is not as consistent as the FAO 56 Penman-Monteith (Allen et al.,1998) equation for Florida conditions. In their study, fourteen models that may be used to estimate ET o in consumptive use

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11 permitting were reviewed to identify the approaches that: best represented the physics of water losses from irrigated crops; easiest to use in terms of parameters needed; were able to consistently and accurately capture ET o losses in growing regions of Florida; and were considered acceptable to the general scientific community. Jacobs and Satti (2001) suggested that the ET o data available to the AFSIRS should be updated with newer weather data using the FAO 56 Penman-Monteith equation (Allen et al., 1998). 2234.01273900408.0ueeuTGRETasno (2-2) where: ET o Reference evapotranspiration (mm day -1 ) Slope of saturation vapor pressure temp. relationship (KPa o C -1 ) R n Net radiation (MJ m -2 day -1 ) G Soil heat flux (MJ m -2 day -1 generally assumed to be zero) Psychometric constant (KPa o C -1 ) T Average air temperature ( o C) u 2 wind speed at a height of 2 m (m s -1 ) e s saturation vapor pressure (KPa) e a actual vapor pressure (KPa) Turf ET rates can vary among genotypes as well as region to region. Carrow (1995) reported that ET rates for cool season grasses ranged from 1.99 6.05 mm d -1 and warm season grasses varied from 1.40 6.22 mm d -1 Tifway bermudagrass [Cynodon dactylon L. x C. transvaalensis Burt-Davy], the most common grass grown on Florida golf courses, had an average summer ET rate of 3.11 mm d -1 in Central Georgia (Carrow, 1995). Beard et al. (1992) reported summer averages of 5.10 mm d -1 for the same genotype in the arid west. Studies have indicated that turf ET increases with water availability. According to Kneebone and Pepper (1982), ET rates of bermudagrass [Cynodon dactylon (L.)] increased with increased irrigation application rates and increased water-holding

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12 capacities of soils. Also, turf ET rates increased with increased light levels, increased temperatures, lowered humidity, moderate to high wind speeds, and long days (Carrow, 1995). Cultural and fertilization practices influence turf ET rates. There have been a number of studies showing turf ET rates decrease as the cutting height was lowered (Kim and Beard, 1983; Parr et al., 1984; Unruh et al., 1999). High rates of nitrogen have produced an increase in shoot growth and a reduction in root growth (Beard, 1973; Goss and Law, 1967). The reduced root growth results in less available water to the turf and the increase in shoot growth requires more water to be taken up. Therefore, more frequent irrigation is needed to supply enough moisture for growth (Beard, 1973). Crop Coefficient Inputs Crop Coefficients (Kc) are available in the AFSIRS database for 60 different crops (Smajstrla, 1990). The Kc value in the database for golf course turf is one, and therefore the AFSIRS computes the actual evapotranspiration rate of golf course turf being equal to the reference evapotranspiration rate (Smajstrla, 1990). According to Jacobs and Satti (2001), the AFSIRS model should have additional Kc values for turfgrasses because the additional research conducted since the model was designed indicates differing turfgrass Kc values between grasses and months. In Georgia, Carrow (1995), using the FAO Penman, reported Kc values during the summer for Tifway bermudagrass varied from 0.53 to 0.97. Crop coefficient values for Tifway bermudagrass in Arizona, using the Penman Monteith, ranged from 0.78 to 0.85 and intermediate ryegrass [L. hybridum] ranged from 0.78 to 0.89 (Brown and Kopec, 2000). Because ryegrass is seeded in the winter time in some parts of Florida as the bermudagrass goes dormant, different Kc values within the year may need to be used for computing turf ET.

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13 Irrigation Application Efficiency and Uniformity Inputs There are eight types of irrigation systems in the AFSIRS database. Each system has a corresponding efficiency (Smajstrla, 1990). Irrigation application efficiency refers to the effectiveness of the irrigation system in applying water to the crop root zone where it can be utilized in production (Smajstrla, 1990). A multiple sprinkler system design, as used on golf courses, has a 75 percent efficiency value in the model (Smajstrla, 1990). Jacobs and Satti (2001) reported that there is a lack of irrigation efficiencies to choose from in the simulation. Previous research indicates that there is a high variability between irrigation efficiencies of multiple sprinkler systems on golf courses due to several factors, such as head spacing, nozzle type, pressure, maintenance, etc. (Miller et al., 2003). Improvements in the design and installation of these systems, such as head-to-head spacing, may enhance a coverage or efficiency. Because it is difficult to determine irrigation application efficiency in the field, irrigation distribution uniformity is often measured to determine the effectiveness of a system. Although the efficiency of a system can vary from its uniformity, coverage uniformity, is an indicator of the systems application efficiency. The more uniform a water application, the less operating time an irrigation system needs to make up for poor coverage (Wilson and Zoldoske, 1997). Precipitation rate can also be measured while determining uniformity. Precipitation rate is the amount of water applied over a specific area, in a specific amount of time (Bowman et al., 2001). If the precipitation rate varies significantly over the area being irrigated, then uniformity is poor (Huck, 1997; Meyer and Camenga, 1985; Pira, 1997). The method most commonly used to calculate distribution uniformity for turfgrass is called the Lower Quarter Distribution Uniformity or DU LQ (IA, 2003). The DU LQ is the

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14 average water applied in the twenty-five percent of the area receiving the least amount of water, divided by the average water applied over the entire area (IA, 2003). Pitts et al. (1996) evaluated 385 residential irrigation systems, and reported that the average DU LQ for agricultural sprinklers, micro-irrigation, furrow irrigation and turf irrigation were 65, 70, 70, and 49 percent, respectively. Of the 37 turf irrigation systems evaluated, 40% had DU LQ s less than 40%. Golf courses have historically had DU LQ s ranging from 55 to 85 percent (Thompson, 2002). The Irrigation Association (2003) suggests that a 70 percent uniformity is a good (expected) value when evaluated using their methodology. Rooting Depth Inputs Each of the 60 crops in the AFSIRS database has irrigated (average) and maximum rooting depth values. The model uses these values, along with soil properties, to compute how much water needs to be applied to reach field capacity. The AFSIRS model assumes that 70 percent of water uptake occurs in the irrigated root zone and the remaining 30 percent occurs below the irrigated root zone (Smajstrla, 1990). For golf course turfgrass, the model uses 15 and 61 cm (6 and 24 in), irrigated and maximum rooting depths (Smajstrla, 1990). Jacobs and Satti (2001) reported that the AFSIRS model needs additional rooting depths for turfgrasses because rooting depths can vary from golf course to golf course. Beard (1973) reported that the majority of a turfgrass root system mowed regularly at less than 5 cm, is located in the upper 7 cm of the soil. Reduced rooting depth is directly correlated with a decrease in cutting height. The very close cutting heights required to meet performance demands by todays golfers, may result in shallow rooting. There is very limited research currently available documenting effective rooting depths of golf course turfs.

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15 Soil Type Inputs There are 766 soil types and corresponding minimum (permanent wilting point) and maximum (field capacity) water holding capacities (volumetric) in the AFSIRS database (Smajstrla, 1990). Existing soil maps are used to determine the type of soil in which a crop is growing. The model can compute IRR using minimum, average, or maximum water holding capacities for the soil. Plant available water is a combination of rooting depth and the amount of water between minimum and maximum holding capacities for the soil. According to Jacobs and Satti (2001), the soil database needs to be improved for the most widely used soils. An alternative to the soil database is manual input of soil water characteristics measured or approximated for the soil type at the site. Using a soil map to determine soil type, and assuming that the characteristics of the soil at a site remain the same after construction of a golf course, can also be a concern. Earthmoving, the use of fill from excavation (lakes), and bringing in off-site materials can have a great impact on the soil characteristics of a site when construction is complete. Also, because of most golf courses being built with USGA greens mix, the available water on those greens is not the same as the native soil. According to the USGA (Hummel, 1993), the average soil water content of a green built to USGA specifications is 0.13 m 3 m -3 Sensitivity Analysis To determine how input parameters influence model outputs, a sensitivity analysis can be utilized. A sensitivity analysis requires varying selected parameters individually through an expected range of values and then comparing the range of output values from each input variable (James and Burges, 1982). A sensitivity analysis aims to ascertain how the model depends upon inputs, upon its structure, and upon the framing

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16 assumptions made to build it. As a whole, sensitivity analysis is used to increase the confidence in a model and its predictions, by providing an understanding of how the models response variables respond to changes in the inputs (A forum on sensitivity analysis, 2004). The input parameters with the greatest influence on the model output need to be measured with the highest accuracy. The South Florida Water Management District used sensitivity analysis to assess the impact of parameter errors on the uncertainty in output values for the South Florida Water Management Model and the Natural Systems Model (Loucks and Stedinger, 1994). Engineers in the District use these models to predict possible hydrologic impacts of alternative water management policies under a variety of hydrologic inputs. Once the key errors were identified, it was possible to determine the extent to which parameter uncertainty can be reduced through field investigations, development of better models, and other efforts (Loucks and Stedinger, 1994).

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CHAPTER 3 PREDICTING IRRIGATION REQUIREMENTS ON GOLF COURSES USING THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS) Introduction Floridas five Water Management Districts issue consumptive use permits to all golf courses within their areas of jurisdiction. These permits are based on the irrigation requirement (IRR) of the turfgrass on the golf course. Historical observations and computer models are tools that are used to predict IRR. The St. Johns River Water Management District (SJRWMD), South Florida Water Management District, and North Florida Water Management District use the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) model to predict IRR for golf courses. The AFSIRS model is a numerical simulation model which estimates IRR using inputs from Florida crops, soils, irrigation systems, growing seasons, climate conditions, and irrigation management practices (Smajstrla, 1990). This data is input into the model from datasets that were constructed using historical data and previous research. Due to the dependency on these datasets, there are some limitations to operational use of the model. The limitations specifically pertaining to golf courses were postulated during a study at the University of Florida by Jacobs and Satti (2001). They found that the major problem with the simulation is that it does not have the ability to input actual rainfall and climate data from the site of interest. They also found that the mathematical model used to calculate the reference ET rates for the datasets, the IFAS Penman Equation, is not as 17

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18 consistent as the FAO 56 Penman-Monteith equation (Allen et al., 1998) for Florida conditions. Jacobs and Satti (2001) reported that the crop coefficient values and rooting depths available to the model need to be updated. There has been more research conducted on crop coefficients since the model was designed, and rooting depths can vary from golf course to golf course. They also indicated that there is a lack of irrigation efficiency values available to the user. Previous research indicates that there is a high variability between distribution uniformities of multiple sprinkler systems on golf courses due to several factors such as head spacing, nozzle type, pressure, maintenance, etc. (Miller et al., 2003). The AFSIRS model has a soil database which is made up of 766 soil types found in Florida. Because most golf courses are constructed with the USGA greens mix, that has different soil water characteristics than native Florida soils, IRR for golf course greens can not be accurately predicted using the soil dataset. The objective of this study was to compare AFSIRS water requirement estimates made with default data to estimates made with actual data collected from golf courses. Materials and Methods Data were collected from five golf courses located in Central Florida. Irrigation system performance data measured included distribution uniformity and precipitation rates. Because it its extremely difficult to determine irrigation system efficiency in a field setting, distribution uniformity values were collected and used in the model to replace the default efficiency value. To determine irrigation distribution uniformities, irrigation audits were performed on three golf holes at each of the five courses in March through May 2002 (Pressler, 2003). The audits were conducted using the methods of ANSI/ASAE

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19 S436.1 MAR 01 Standards (ASAE, 2001), and using the evaluation methodology described in the Irrigation Association of Americas Certified Golf Course Irrigation Auditor training manual (IA, 2003). The catch-cans used in this study had an opening diameter of 7.6 cm (3.0 in) and a depth of 10.8 cm (4.25 in). For tee complexes (all tees) and greens, catch-cans were placed in a grid pattern on 3-m centers over the entire surface. For fairways, catch-cans were placed in a grid pattern on 9-m centers throughout the entire fairway and primary rough, if irrigated (Pressler, 2003). The number of sprinklers operating at one time was representative of the normal operating conditions of that particular system. Each location within individual courses received the same amount irrigation runtime. The runtime on fairway and tees ranged between 20 and 30 minutes per zone, and greens between 10 and 30 minutes per zone. Once it was determined that all the zones had run for a certain location, the collected water in each can was measured and recorded using a 500 mL graduated cylinder. Lower quarter distribution uniformity (DU LQ ) was determined by (Pressler, 2003): 100.xVLQAvgDUavgLQ (3-1) DU LQ = Lower Quarter Distribution Avg. LQ = Average volume of lowest 25% of observations V avg = Average catch can volume For modeling purposes, an average DU LQ for each of the three holes on the five courses was determined by weighting the DU LQ based on the total areas of tees, fairway, and green. Areas were determined from global positioning system maps, produced within 60 days of uniformity testing.

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20 Precipitation rates were also collected for each location on a golf course. The net precipitation rate (PR) is the rate that sprinklers apply water to a given area per unit time and can be calculated as follows: 60avgnetVPRTRCDA (3-2) PR net = Net Precipitation Rate, (cm h -1 ) V avg = Average catch can volume (mL) TR = Testing run time (min) CDA = Catch can opening (cm 2 ) The precipitation rates were used to determine how much water golf courses actually applied during the study. Those values were then compared to the IRR predicted by the model for each golf course. Maximum and average rooting depths were measured at three locations (tees, fairways, and greens) at each golf course. Measurements were taken in September and November 2002 and February and May 2003. Three random samples were taken at a 15 cm depth from each location using a Mascaro soil profiler (Turf-Tec International, Coral Springs, FL). The maximum rooting depth was determined by physically measuring the longest root in each of the three samples. Measurements were taken from the top of the thatch layer to the tip of the root. These values were then averaged and the mean maximum rooting depth of the three samples was recorded as the maximum rooting depth for that location. Average rooting depth was determined by visually assessing the samples where the majority of the roots were present. A ruler was then used to measure the distance to the top of the thatch layer. These measurements were then averaged and a mean rooting depth was calculated for each location (Pressler, 2003).

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21 Weather stations were installed to monitor environmental parameters from June 2002 to May 2003. The weather stations were located in flat-grassed areas so that the nearest obstruction was at least ten times its height away from the station. The stations were placed in irrigated areas on the golf course property. The stations recorded the date, time, temperature, soil heat flux, (HFT3, Radiation Energy Balance Systems, Bellevue, WA), solar radiation (LI-200SZ, Licor Inc. Lincoln, NE), wind speed and direction (WAS425, Vaisala, Inc., Sunnyvale, CA), relative humidity (HMP 45C, Vaisala, Inc., Woburn, MA), and precipitation (TE525 Tipping Bucket, Texas Electronics, Inc., Dallas TX) at 15 minute intervals via a CR10X datalogger (Campbell Scientific, Inc., Logan Utah). The data collected by the weather stations was used to develop five site specific weather datasets containing daily reference ET rates and rainfall. Reference ET (ET o ) rates were calculated using the computer program REF-ET. REF-ET was developed as a stand-alone computer program to calculate ET o from meteorological data made available by the user (Allen, 2002). Because the FAO 56 Penman-Monteith equation (Allen et al., 1998) was found to be the most consistent model for determining ET o for Florida conditions (Jacobs and Satti, 2001), this equation was used to calculate ET o for the site specific weather datasets. Because it is difficult to determine site-specific crop coefficients (Kc) in the field, updated Kc values were determined from a literature review to replace the default values in the model. Kc values for bermudagrass were determined from Carrows (1995) study in Georgia, and overseeded ryegrass Kc values were determined from Brown and Kopecs (2000) study in Arizona.

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22 The AFSIRS model was run separately for each golf course using default and actual/updated (site-specific) data. The default runs mimicked the procedure that the SJRWMD uses to predict IRR for golf courses. Therefore, for the default runs, the only input parameter that differed between the golf courses was soil type. The soil type for each course was determined by using county soil survey maps created by the Soil Conservation Service, and assessing what soil type is most prevalent at that site. For golf courses with greens built to United States Golf Association (USGA) specifications (sand and peat mix), runs were made using the average soil water content (volumetric) for USGA greens mix instead of the average soil water content (volumetric) of each courses native soil type. The default model runs were used to compare with runs made with actual/updated data. Model runs were made in a stepwise manor where each input parameter was changed to actual/updated data while the remaining parameters used default values. Combinations were made where some or all inputs were changed to actual/updated data. Rooting depths influenced by month, golf course, and location within golf course were analyzed using analysis of variance procedures in SAS (SAS Institute, 1999). Results and Discussion Default Values The first AFSIRS model runs were made using default values for each golf course. The default values for each input were: 1.0 for crop coefficient (Kc), 75% for irrigation system efficiency, 15 and 61 cm (6 and 24 in) for avg. and max. rooting depths, and 20 year historical weather data for Orlando (nearest collection location for historical datasets to the five golf courses). The soil type was the only input that varied between

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23 golf courses. According to the SCS county soil survey maps, three of the five courses had the same soil type average water contents (Table 3-1). Updated/Actual Values Based on crop coefficient (Kc) research reported by Carrow (1995) and Brown and Kopec (2000) on bermudagrass and ryegrass monthly crop coefficients, updated Kc values were entered into the model for each corresponding month to replace the default value of 1.0. Crop coefficients inputted for bermudagrass were: 0.62 in May, 0.54 in June, 0.53 in July, 0.65 in August, 0.97 in September, and 0.73 in October. Crop coefficients inputted for ryegrass were: 0.83 in November, 0.80 in December, 0.78 in January, 0.79 in February, 0.86 in March, and 0.89 in April. Measured DU LQ for each golf course were used to replace the default (75%) irrigation system efficiency for a multiple sprinkler system. An average DU LQ for each of the three holes on the five courses was determined by weighting the DU LQ for each location (tee, fairway, green) based on their total areas. Because the fairway occupies the most area, the DU LQ for a hole was similar to the DU LQ of the fairway on that hole. The weighted average DU LQ s for the three holes on the five courses were calculated as follows: golf course A = 32, 37, and 59%, golf course B = 40, 42, and 49%, golf course C = 45, 55 and 62%, golf course D = 62, 67, and 67%, and golf course E = 41, 44, and 54%. Average and maximum rooting depths measured at each golf course were entered into the model in-place of the default values of 15 and 61 cm (6 and 24 in). Because two of the three holes on each golf course had reduced runtimes, in accordance with a concurrent irrigation conservation study (Pressler, 2003), only the rooting depths from the control holes (typical irrigation practices) were used for modeling. Analysis of

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24 variance indicated differences in rooting depths by month at a 95% probability level (Table 3-2). Rooting depths measured in August, 2002 were significantly less than depths measured in November, 2002, and February and May 2003. But because these rooting depth differences may have been caused by the extremely wet summer in 2002, rooting depths from each month and location were averaged together to get an average and maximum rooting depth for each golf course. The average (irrigated) and maximum rooting depths from the five golf courses, used for modeling, were (avg. and max.): golf course A = 4.3 and 6.6 cm (1.7 and 2.6 in), golf course B = 4.3 and 7.1 cm (1.7 and 2.8 in), golf course C = 4.6 and 7.4 cm (1.8 and 2.9 in), golf course D = 4.6 and 6.6 cm (1.8 and 2.6 in), and golf course E = 4.3 and 6.9 cm (1.7 and 2.7 in). The five weather datasets, created via on-site weather stations and the use of REF-ET, were used to replace the 20 year historical data from the Orlando area. Each golf course had a corresponding dataset with actual reference ET rates (ET o ) and rainfall for one year. During the study period, there was an unusually low ET o for three of the golf courses (Figure 3-1A), as well as a high amount of rainfall at all five courses (Figure 3-1B). The yearly average ET o (mm day -1 ) for golf courses A, C, and D were below the lower limit of the 95% probability of occurrence interval for the historical data. The yearly average rainfall (mm day -1 ) was higher than the upper limit of the interval for all five golf courses. The average amount of rainfall in the historical weather dataset for Orlando is 129 cm (50.7 in) per year; whereas, the average rainfall recorded at the five courses from June 2002 to May 2003 was 199 cm (78.4 in).

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25 Default Versus Updated/Actual Irrigation Requirements Irrigation requirements (IRR) for the five golf courses were calculated by the AFSIRS model using default and updated/actual values for comparison. The only parameter that differs by golf course in the default runs is soil type. Because golf courses A, B, and C have the same soil type, they have the same IRR for the default run (Figure 3-2). Golf course E has a soil type with a higher average water content (0.10 m 3 m -3 ) than golf courses A, B, and C (0.07, 0.07, and 0.07 m 3 m -3 respectively), and therefore requires less water (lower IRR) to reach field capacity. When using the historical weather data, IRR was estimated for the average year and for the 80% probability values (two in ten year drought) for the 20 years of data. Because drought years, requiring higher amounts of irrigation, are factored into the 80% probability IRR, those irrigation requirements are higher than estimates for the average year. Irrigation requirements estimated using actual weather data do not predict the 80% probability values because there was only one year worth of data in each dataset. Estimates were made using Kc values reported in the literature. Predicted IRR for all five courses dropped approximately 25 cm (10 in) per year from the default run (Figure 3-2). This was due to the updated monthly Kc values, which range from 0.53 to 0.97, being lower than the default Kc of 1.0 for turfgrass. Additional model runs were conducted with actual weighted DU LQ values for each course replacing the default irrigation system efficiency of 75%, for a multiple sprinkler system. Each course had three DU LQ values, one for each hole, and therefore had three estimates with the actual DU LQ model runs. To determine an average IRR for the golf course, the IRR values obtained from the model runs for each hole (separate DU LQ

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26 values) were averaged. With actual DU LQ values, the IRR for all five courses increased compared to the default run values (Figure 3-2). The IRR for courses B, C, and E went up 38 to 64 cm (15 to 25 in) per year when actual uniformities were used in the model. This is due to these courses having uniformities between 40% and 60%. The IRR for golf course A increased approximately 76 cm (30 in) per year from the default run values. The increase was a result of golf course A having two holes with low uniformities (32% and 37%) that reduced its average DU LQ to 43%. The IRR for golf course D increased by approximately 13 cm (5 in) per year. This golf course had uniformities from 62% to 67%, which were much closer to the default value. Actual root depths from each course were used for IRR estimations. All five IRR predictions increased about 18 cm (7 in) per year with actual rooting depths replacing the default root depths (Figure 3-2). The actual average and maximum rooting depths were 4.3 and 6.9 cm (1.7 and 2.7 in), and the default irrigated and maximum rooting depths for golf course turf are 15.2 and 61 cm (6 and 24 in), respectively. This difference in actual and default root depths, led to the increase in IRR for each golf course. The low mowing heights on the golf courses may have contributed to the shallow rooting depths when compared to the default depths. Actual weather datasets for each course were difficult to input with the programming structure of the AFSIRS model. It was apparent from comparing the historical weather data to the years weather data collected that the short-term weather data sets represented a much wetter year than average (Figure 3-1). The on-site weather data sets represented between 56 and 109 cm (22 and 43 in) more than the average rainfall compared to the 20 year historical weather dataset. Wet years are not factored

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27 into consumptive use permits as are drought years. Using the actual weather data from the golf courses decreased the irrigation water needed estimate by approximately 51 cm (20 in) per year (Figure 3-2). The model was also run for the five golf courses using all actual/updated data except for weather data, and all actual/updated data including weather data. Due to the wet year, it seemed appropriate to use the historical weather data rather than predict irrigation needs based on one years data. Since the actual weather datasets are not historically typical for the Orlando area, a better comparison to the default data set is to estimate water needs with actual data combined with the historically weather dataset. That comparison showed that actual/updated data with the historical weather data increased IRR between 15 and 46 cm (6 and 18 in) per year for golf courses A, B, C and E (Figure 3-2). This is primarily a direct result of these courses having low distribution uniformities. Golf course Es IRR increased approximately 15 cm (6 in) per year with an average DU LQ of 54%, and course As IRR increased approximately 46 cm (18 in) per year with an average DU LQ of 43%. Golf course D with an average DU LQ of 65% was predicted with no increase in IRR compared to running the model with default data. Irrigation requirements were estimated for the three golf greens built with sand and peat as per USGA specifications. Default IRR was included for comparison purposes. DU LQ of each green was used instead of the weighted DU LQ for each golf course except for the default. The model was run with the USGA green average soil water content (0.13 m 3 m -3 ) inputted in place of the average water content of the native soils. The IRR was decreased approximately 13 cm (5 in) per year for all three courses due to the increase in the average soil water content value (Figure 3-3).

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28 Predictions with all actual data, including USGA greens mix average water content, and historical weather data resulted in IRR approximately the same for all three courses when compared to the default IRR (Figure 3-3). This is because the average DU LQ on the three greens at each course (ranged from 50% to 69%) was higher than the weighted DU LQ of each golf course, and the higher average soil water content makes up for the DU LQ being lower than the default 75%. The model was run with all actual data including USGA greens mix average water content. Because actual weather datasets had high rainfall amounts, IRR was decreased about 51 cm (20 in) per year for all three golf courses. Golf courses A, B, C, and D each had a mean predicted IRR of about 86 cm (34 in) and golf course E had an IRR of 74 cm (29 in) (Figure 3-2). Based on rainfall and ET o the mean IRR was predicted to be about the same additional amount (depth) as the ET o values (Table 3-3). The IRR on the District issued consumptive use permit (CUP) for golf courses C, D, and E are slightly higher than the mean IRR because the permit accounts for a two in ten year drought. According to their respective permits, golf course A received 30% more water than the mean IRR because of a deep water table, and golf course B received 21% more than the 80% predicted value because a 70% irrigation efficiency value was used instead of 75%. Using the measured precipitation rates and run times, the depth of irrigation water applied to the fairway (largest irrigated area) of the control hole was determined. The golf courses managers irrigated from 12 to 67% less than what the model (default values) predicted was needed. Two golf courses irrigated approximately half of what the model (default values) predicted as the IRR. This was due to the rainfall of above normal, which was not accounted for by the model. Accounting

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29 for the actual rainfall received on each golf course, the turf managers irrigated from 42% less than they needed to 58% more than they needed. Conclusions Investigations of the AFSIRS model indicate that when actual data (crop coefficients, distribution uniformity, and rooting depth) was included in the model it predicted similar water needs compared to the default prediction for only one of the five golf courses evaluated. The use of actual data resulted in IRR increasing between 15 and 46 cm (6 and 18 in) per year for the golf courses. This was typically a direct result of those courses having low distribution uniformities. When only distribution uniformities were substituted for the default irrigation efficiency, IRR increased between 13 and 76 cm (5 and 30 in) per year. Although DU LQ provides an estimate of irrigation efficiency, the true efficiency of the system may be higher or lower than the measured DU LQ Weather has a significant potential to influence inputs and predicted values but since the AFSIRS is used primarily as a prediction equation, it is difficult to use current weather patterns to predict long-term future needs. Weather datasets consisting of more than one year of data would provide a better comparison to estimates calculated using long-term, historical weather data. Although a green built with USGA greens mix may require less water than the rest of the golf course, IRR issued on consumptive use permits do not account for this difference. But because of the stresses to a green (low mowing heights and high traffic), these areas may need the same, or more, water than the rest of the course on a per acre basis. Precipitation rates and run times can provide an estimate for how much water actually was applied to certain areas on golf courses. Because of the high rainfall

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30 amounts during the year, golf course managers used less water than the predicted irrigation requirements with default data. The high rainfall was not accounted for in the predicted IRR using default data. When comparing water use to irrigation requirements with updated/actual data, managers used less and more water than what was predicted. This is a result of courses receiving different amounts of rainfall, and having different DU LQ values.

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31 Table 3-1. Soil types and average water contents for the five golf courses, average water content of all soil types in the model database, and average water content of a USGA green Golf course Soil type Average water content (m 3 m -3 ) A Astatula sand 0.07 B Astatula sand 0.07 C Astatula sand 0.07 D Candler sand 0.06 E Blanton fine sand 0.10 Avg. of all soil types in -0.12 USGA green -0.13 Table 3-2. Mean squares for the analysis of variance on rooting depths as influenced by golf course, location within golf course, and month Source of variation df F value Golf course 4 0.57 ns Location (golf course) 10 1.40 ns Month 3 5.83 ** Error 42 *, **, *** significant at the 0.05, 0.01, 0.001 levels, respectively. ns, nonsignificant at the 0.05 level Table 3-3. Mean rainfall, reference ET, estimated mean irrigation requirement, irrigation requirement issued on consumptive use permit, mean irrigation requirement using updated/actual data, and applied irrigation Golf Course Rainfall ET o IRR CUP IRR IRR Irrigation ---------------------cm--------------------A 175 84 86 145 45 28 B 145 106 86 104 43 38 C 124 97 86 91 48 76 D 145 92 86 97 36 53 E 132 98 74 94 77 45 irrigation requirements predicted using AFSIRS with default values CUP = District issued consumptive use permit irrigation requirements predicted using updated/actual data

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32 ET Historical Actual Yearly Average mm day-1 3.03.23.43.63.84.0 Rainfall Historical Actual Yearly Average mm day-1 234567 ABCDABCDEEB.A. Figure 3-1. Yearly average reference ET (A) and rainfall (B) for the 20 year historical weather dataset (Orlando), and for the one year of on-site weather data from each of the five golf courses. Letters denote the five courses. Horizontal dashed lines indicate the upper and lower limits of the 95% probability of occurrence for the historical data.

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33 020406080100120140160180 Golf Course A Golf Course B Golf Course C Predicted Irrigation Requirement (cm yr-1) 020406080100120140160180 Golf Course D DefaultUpdated KcActual DUActual RDActual WDActuals with Historical WDAll Actuals 80% Probability Mean Golf Course E DefaultUpdated KcActual DUActual RDActual WDActuals with Historical WDAll Actuals 020406080100120140160180 Figure 3-2. AFSIRS predicted irrigation requirements (IRR) for five golf courses. Default: model run with all default data. All others were run with actual values collected on-site or in the case of crop coefficients, published values.

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34 Golf Course A 020406080100 Golf Course B DefaultUSGA Greens MixActuals with Historical WDAll Actuals 80% Probability Mean Golf Course C DefaultUSGA Greens MixActuals with Historical WDAll Actuals Predicted Irrigation Requirement (cm yr-1) 020406080100 Figure 3-3. AFSIRS predicted irrigation requirements (IRR) for three golf greens built with USGA greens mix (sand and peat). Default: model run with all default data. USGA greens mix: model run with average soil water content (0.13 m 3 m -3 by volume).

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CHAPTER 4 SENSITIVITY ANALYSIS OF THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION (AFSIRS) AND THE FAO 56 PENMAN-MONTEITH EQUATION Introduction To provide a better understanding for how a model works and how the input parameters influence the outputs, a sensitivity analysis can be utilized. A sensitivity analysis requires varying selected parameters individually through an expected range of values and then comparing the range of output values from each input variable (James and Burges, 1982). This analysis technique determines which parameters have the greatest impact on the output, therefore determining the level of measurement accuracy of each input variable. The South Florida Water Management District used a sensitivity analysis to assess the impact of parameter errors on the uncertainty in output values for the South Florida Water Management Model and the Natural Systems Model (Loucks and Stedinger, 1994). Once the key errors were identified, it was possible to determine the extent to which parameter uncertainty can be reduced through field investigations, development of better models, and other efforts (Loucks and Stedinger, 1994). The Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) is the model used by the St. Johns River Water Management District, South Florida Water Management District, and North Florida Water Management District to predict irrigation requirements (IRR) for golf courses. It is a numerical simulation model which estimates IRR for Florida crops, soil, irrigation systems, climate conditions, and irrigation management practices (Smajstrla, 1990). The FAO 56 Penman-Monteith equation (Allen 35

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36 et al., 1998) was found to be the most consistent model for determining reference evapotranspiration (ET o ) for Florida conditions (Jacobs and Satti, 2001). This equation can be used to calculate ET o in a computer program such as REF-ET (a stand-alone computer program that calculates reference ET from meteorological data made available by the user (Allen, 2002)). These calculated reference ET rates can be used to create site-specific datasets for use with the AFSIRS model. To establish how the output from the AFSIRS model and the FAO 56 Penman-Monteith equation (Allen et al., 1998) respond to changes in their inputs, a study was conducted using sensitivity analysis. The objective of this research was to determine what parameters have the greatest impact on the irrigation requirement predicted with the AFSIRS model and reference evapotranspiration (ET o ) using the FAO 56 Penman-Monteith equation (Allen et al., 1998). Materials and Methods Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) Crop Coefficient (Kc), distribution uniformity (DU LQ ) (in place of irrigation application efficiency), average and maximum rooting depth, and average soil water content were the parameters that were analyzed for sensitivity on the AFSIRS model. Individual model runs were made using a range of values for each parameter. The values were changed in increments from the starting point, in both directions, depending on the size of the range. Each of the four input parameters were analyzed separately, while the other parameter values remained constant. The constant value for the parameters not being analyzed was the average value observed for each parameter from a concurrent golf course water use study where site-specific data was collected from five golf courses for predicting IRR using the AFSIRS model (Pressler, 2003).

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37 Starting values and ranges were chosen for the four parameters based on the data collected in the concurrent water use study. The starting value for each parameter was the average value calculated for each parameter. The Kc value starting point for this study was 0.7. This value was changed in increments of 0.1 in both directions, and ranged from 0.5 to 1.0. The DU LQ starting point value was 50%. The values were changed in increments of 10% in both directions, and ranged from 20% to 100%. Rooting depths had two starting points. The average depth starting point was 12.7 cm and maximum depth starting point was 50.8 cm (5 and 20 in, respectively). These depths were changed in increments of 2.54 and 10.2 cm (1 and 4 in) in both directions, and ranged from 2.54 and 10.2 to 20.3 and 81.3 cm (1 and 4 to 8 and 32 in), respectively. Average soil water content starting point was 0.09 m 3 m -3 was changed in increments of 0.02 m 3 m -3 in both directions, and ranged from 0.03 to 0.17 m 3 m -3 (by volume). FAO 56 Penman-Monteith Equation Maximum and minimum temperature, maximum and minimum relative humidity, mean solar radiation, mean soil heat flux, and mean wind speed were parameters that were analyzed for sensitivity on the FAO 56 Penman-Monteith equation (Allen et al., 1998) in the REF-ET program. Each of the eight input parameters were analyzed separately, while the other parameter values remained constant. The starting values for each parameter were the average values observed from collected weather data during the concurrent water use study. The values were changed in increments from the starting point, in both directions, depending on the size of the range. Because the temperature data collected by the weather station and analyzed in REF-ET was in Fahrenheit, the values used and reported in the sensitivity analysis are also in Fahrenheit. The min. temperature starting point was 60 o F. The values were changed in

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38 increments of 10 o F in both directions, and ranged from 20 to 80 o F. The max. temperature starting point value was 80 o F. The values were changed in increments of 10 o F in both directions, and ranged from 40 to 100 o F. The min. relative humidity starting point was 50%. The values were changed in increments of 10% in both directions, and ranged from 10 to 90%. The max. relative humidity starting point was 95%. The values were changed in increments of 10% in both directions, and ranged from 60 to 100%. The mean solar radiation starting point value was 160 W/m 2 The values were changed in increments of 40 W/m 2 in both directions, and ranged from 0 to 320 W/m 2 The mean soil heat flux starting point was 0 W/m 2 The values were changed in increments of 5 W/m 2 in both directions, and ranged from -25 to 15 W/m 2 The mean wind speed starting point value was 8 km/hr. The values were changed in increments of 1.61 km/hr in the decreasing direction and 4.8 km/hr in the increasing direction, and ranged from 3.2 to 37 km/hr. Results and Discussion Agricultural Field Scale Irrigation Requirement (AFSIRS) Sensitivity Analysis Changes in DU LQ resulted in the largest changes in IRR (Figure 4-1). Changing a DU LQ from 40% to 80%, a 100% increase, resulted in a 65% decrease in irrigation requirement. The Irrigation Association suggests that a 70% DU LQ is expected and an 80% DU LQ is achievable for a golf course. Modifying Kc values had the second largest impact on IRR. IRR increased linearly as Kc values decreased. A 15% increase or decrease of the starting point resulted in a 20% change in IRR in the respective direction. Changes in rooting depth and average soil water content had very little impact on IRR. As rooting depth and average soil water content increase, IRR decreases. Increasing and decreasing both inputs by 60% of the starting points resulted in less than 16% increases and decreases in IRR.

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39 FAO 56 Penman-Monteith Equation Sensitivity Analysis A change in max. temperature resulted in the largest change in ET o (Figure 4-2). A 25% increase of the starting point resulted in a 45% increase in ET o Modifying mean solar radiation had the second greatest impact on ET o A 50% increase of the starting point resulted in a 33% change in ET o Changes in min. relative humidity and mean wind speed had the third and fourth largest impact on ET o respectively. Increasing both inputs by 60% of the starting points resulted in a 25% decrease in ET o for min. relative humidity, and a 15% increase in ET o for mean wind speed. Changes in min. temperature, mean soil heat flux, and max. relative humidity had very little impact on ET o A 25% decrease of the starting points for each of the three parameters resulted in a less than 8% increase in ET o Conclusions Sensitivity analysis further indicated that there can be significant differences between default values and true values when using the AFSIRS model. This was most apparent with DU LQ (in place of efficiency) and crop coefficients. Because these parameters have the most impact on IRR, the data used in these inputs needs to have the highest accuracy. By collecting site-specific data, a true picture can be illustrated for irrigation requirements, and as a result, more water may be conserved. According to the AFSIRS, by attaining a relatively high DU LQ (60 to 80%), golf course managers can greatly reduce wasteful water use, and as a result, meet or stay within their water allotments. Because it is difficult to determine site-specific crop coefficients in the field, published data is the best alternative. More research needs to be conducted to determine Kc values for different types of turfgrasses grown under Florida conditions.

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40 When determining reference ET rates using REF-ET and the FAO 56 Penman-Monteith equation (Allen et al.,1998), daily maximum temperature and mean solar radiation have the greatest effect on the output, followed by minimum relative humidity and mean wind speed. The data collected for these parameters with the most influence on ET o should be measured with the highest accuracy to reduce variability. If the reference ET rates calculated by the FAO 56 Penman-Monteith equation (Allen et al., 1998) are used in the AFSIRS model, DU LQ and Kc values as well as daily maximum temperature and mean solar radiation have a great impact on predicted irrigation requirement. But because three of those parameters are out of a golf course managers control, the focus should be on improving DU LQ to conserve water.

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41 -75-50-250255075100125150175 Kc DULQ % Change in Initial Parameter Input -100-80-60-40-20020406080100120 % Change in Irrigation Requirement -75-50-250255075100125150175 Rooting Depth Soil (AWC) Figure 4-1. Sensitivity analysis of the AFSIRS model output. Values across x-axis represent a typical value as the starting point (0) and a range of values in the % change of the initial value.

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42 -40-20020406080 Max. Temp. Mean Solar Rad. Mean Wind Speed Min. Rel. Humidity % Change in Climatic Parameter -100-50050100150 % Change in Reference Evapotranspiration Rate -40-20020406080 Min. Temp. Mean Heat Flux -100-50050100150200 Max. Rel. Humidity Figure 4-2. Sensitivity analysis of the FAO 56 Penman-Monteith equation (Allen et al., 1998). Values were changed across a range of typical values. Values across x-axis represent a typical value as the starting point (0) and a range of values in the % change of the initial value.

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CHAPTER 5 SUMMARY AND CONCLUSIONS Using site-specific data instead of default values in the AFSIRS model can result in site-specific estimates of irrigation requirements on golf courses. For prediction purposes it is better to use long-term (historical) weather data rather than short-term data. It is important for golf course managers to be familiar with how their Water Management District determines golf course irrigation requirements. Knowing what data goes into the AFSIRS model, can allow a manager to use their allotted water more effectively, e.g. improving distribution uniformity. Evaluation of the AFSIRS model and its effectiveness in predicting irrigation requirements using default and actual (on-site) data from five golf courses was discussed in Chapter 3. The following results were obtained: Updated crop coefficients resulted in IRR predictions to drop approximately 25.4 cm (10 in) from the default estimates. Actual rooting depths caused IRR predictions to increase approximately 17.8 cm (7 in). Replacing distribution uniformities with the default irrigation efficiency, caused the greatest change in IRR predictions (increase between 12.7 to 76.2 cm or 5 to 30 in depending on the golf course). The one year of weather data collected at each golf course represented 56 and 109 cm (22 and 43 in) more than the average rainfall compared to the 20 year historical weather dataset. As a result, the estimates using the actual weather data decreased IRR predictions by approximately 51 cm (20 in). Due to the wet year, the model was run with all actual data and with the historical weather dataset. This combination of actual and historical data resulted in an increase in IRR predictions of 15 to 46 cm (0 to 18 in). 43

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44 Predictions with all actual data, including USGA green soil substrate average water content, and historical weather data resulted in little to no change in IRR for the three courses with USGA specification greens. Sensitivity analysis allowed for a better understanding of how the AFSIRS model and FAO 56 Penman-Monteith equation (Allen et al., 1998) work. It indicated what inputs had the greatest impact on the outputs. The inputs with the most influence on outputs of interest needs to be measured with the highest accuracy to reduce variability. Sensitivity analysis on the AFSIRS model and FAO 56 Penman-Monteith equation (Allen et al., 1998) was discussed in Chapter 4. The following results were obtained: Sensitivity analysis on the AFSIRS model further indicated that distribution uniformity (irrigation efficiency input) had the greatest influence on the IRR prediction value, followed by: Kc, rooting depth, and average soil water content. Changing a DU LQ from 40% to 80%, a 100% increase, resulted in a 65% decrease in irrigation requirement. Sensitivity analysis on the FAO 56 Penman-Monteith (Allen et al., 1998) using the REF-ET program to predict ET o illustrated that changes in daily max. temperature resulted in the largest changes in ET o followed by: mean solar radiation, min. relative humidity, mean wind speed, min. temperature, max. relative humidity, and mean soil heat flux. A 25% increase of the starting point for max. temperature resulted in a 45% increase in ET o and a 50% increase of the starting point for mean solar radiation resulted in a 33% change in ET o

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APPENDIX SAS PROGRAMS Program to organize weather station data into columns for REF-ET. data ds1; infile 'd:\glm test 91803.prn' firstobs=2; input year day min Temp RHb solar soil soil2 windsp @@; /*drop bar;*/ drop min; soilmean=(soil+soil2)/2; drop soil soil2; /*proc print; run;*/ cards; proc sort; by year day; proc means noprint; by year day; var temp RHb; output out=data1 min=mn_t mn_RH max=mx_t mx_RH; proc means data=ds1 noprint; by year day; var solar soilmean windsp; output out=data2 mean=m_sol m_soil m_wind ; proc sort data=data1; by year day; proc sort data=data2; by year day; data all; merge data1 data2; by year day; /*proc print;*/ proc print data=all; var year day mn_t mx_t mn_RH mx_RH m_sol m_soil m_wind; run; 45

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46 Rooting depths influenced by month, golf course, and location within golf course using analysis of variance procedure. Title 'Mean Rooting Depth'; Data; Input GC $ trt $ loc $month $ RD; Cards; proc sort; by trt; proc glm;by trt; class loc gc month; model RD= gc loc(gc) month; test h=gc e=loc(gc); means loc(gc)/lsd lines; means month/lsd lines; run;

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REFERENCES A forum on sensitivity analysis. 2004. Institute for Systems, Informatics, and Safety. Joint Research Centre. The European Commission. [Online]. Available at http://sensitivity-analysis.jrc.cec.eu.int (Verified 16 June 2004). Allen, R.A. 2002. REF-ET reference evapotranspiration software. [Online] http://www.kimberly.uidaho.edu/REF-ET/ (Verified 3 Feb. 2004). Allen, R.G., L.S. Pereira, D. Raes and M. Smith. 1998. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrig. and Drain. Paper56. American Society of Agricultural Engineers (ASAE). 2001. Test procedure for determining the uniformity of water distribution of center pivot and lateral move irrigation machines equipped with spra y or sprinkler nozzles. Irrig. Assoc. and the ASAE Sprinkler Irrigation Comm. ANSI/ASAE Standard S436.1. ASAE St.Joseph, MI. Beard, J.B. 1973. Turfgrass science and culture. Prentice-Hall. Englewood Cliffs, NJ. Beard, J.B. 1982. Turfgrass management for golf courses. Macmillan Publishing Company. New York, NY. Beard, J.B. 1985. An assessment of water use by turfgrasses. In V.A. Gibeault and S.T. Cockerham (ed.) Turfgrass water conservation. Cooper. Ext Publ. 21405, Univ. of California, Oakland, CA. Beard, J.B. 2002. Turfgrass management for golf courses 2 nd ed. Ann Arbor Press. Chelsea MI. Beard, J.B., R.L. Green, and S.I. Sifers. 1992. Evapotranspiration and leaf extension rates of 24 well-watered, turf-type Cynodon genotypes. HortScience 27:986-988. Bowman, D.C., L.B. McCarty, and J. Camberato. 2001. Water management in turf. p. 261-280. In L.B McCarty (ed.) Best golf course management practices. PrenticeHall, Inc. Upper Saddle River, NJ. Brady, N.C., and R.R. Weil. 1999. Elements of the nature and properties of soils 12 th ed. Prentice-Hall, Inc. Upper Saddle River, NJ. 47

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48 Brown, P.W., and D.M. Kopec. 2000. Converting reference evapotranspiration into turf water use. Turf irrigation management series: II. Publ. AZ1195. Available online at http://ag.arizona.edu/pubs/water/az1195.pdf (Verified 4 February 2004). Brown, P.W., D.M. Kopec, and C.F. Mancino. 1988. Estimating turfgrass water use with AZMET. Turfgrass & Ornamentals Research Summary. Arizona Agric. Exp. Stn. P-75:25-33. Brown, P.W., C. F. Mancino. M.I. Young, T. L. Thompson, P.J. Wierenga, and D.M. Kopec. 2001. Penman monteith crop coefficients for use with desert turf systems. Crop Sci. 41:1197-1206. Carrow, R. N. 1995. Drought resistant aspects of turfgrasses in the southeast: evapotranspiration and crop coefficients. Crop Sci. 35:1685-1690. Florida Automated Weather Network (FAWN). 2004. [Online]. Available at http://fawn.ifas.ufl.edu (Verified 16 June 2004). Goss., R.L., and A.G. Law. 1967. Performance of bluegrass varieties at two cutting heights and two nitrogen levels. Agron. J. 59:516-518. Haydu, J.J., and A.W. Hodges. 2002. Economic impacts of the Florida golf industry [Online]. Available at http://economicimpact.ifas.ufl.edu/publications/EIR02-4r.pdf (Verified 16 June 2004). University of Florida Economic Information Report. Higgins, J., and L.B. McCarty. 2001. Golf course construction and renovation. p. 119-148. In L.B. McCarty (ed.) Best golf course management practices. Upper Saddle River, NJ. Huck, M. 1997. Irrigation design, rocket science, and the space program. USGA Green Section Record. 35(1):1-7. Hummel, N.W., Jr. 1993. Rationale for the revisions of the USGA green construction specifications. USGA Green Section Record. 31(2):7-21. Irrigation Association (IA). 2003. Certified golf irrigation auditor training manual. Irrigation Assoc. America. Falls Church, VA. Jacobs, J., and S. Satti. 2001. Evaluation of reference evapotranspiration methodologies and AFSIRS crop water use simulation model. Publ. SJ2001-SP8. Available online at http:// www.sjrwmd.com/programs/outreach/pubs/techpubs/sj2001sp8.pdf (Verified 16 June 2004). James, L. D., and S.J. Burges. 1982. Selection, calibration, and testing of hydrologic models. p. 437-470. In Hydrologic modeling of small watersheds. Amer. Soc. Agric. Eng., St. Joseph, MI.

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49 Kim, K.S., and J.B. Beard. 1983. Comparitive ET rates of eleven major warm-season turfgrasses grown under both uniform and optimal cultural regimes. p. 127. In Agronomy abstracts. ASA, Madison, WI. Kneebone, W.R., and I.L. Pepper. 1982. Consumptive water use by sub-irrigated turfgrasses under desert conditions. Agron. J. 74:419-423. Loucks D.P., and J.R. Stedinger. 1994. Sensitivity and uncertainty analysis in hydrologic simulation modeling of the South Florida Water Management District. School of Civil and Environmental Engineering, Cornell Univ. Ithica, NY. McCarty, L.B., G. Landry, Jr., and A.R. Mazur. 2001.Turfgrass. p. 3-44. In L.B.McCarty (ed.) Best Golf Course Management Practices. Upper Saddle River, NJ. Meyer, J.L., and B.C. Camenga. 1985. Irrigation systems for water conservation. p. 105-112. In V.A. Gibeault and S.T. Cockerham (ed.) Turfgrass water conservation. Cooper. Ext. Publ. 21405, Univ. of California, Oakland, CA. Miller, G.L., N.D. Pressler, and M. Dukes. 2003. How uniform is coverage of your irrigation system? Golf Course Manage. 71(8):100-102. National Golf Foundation. 2004. Frequently asked questions about the game andbusiness of golf in the United States [Online]. Available at http://www.ngf.org/cgi/who faq.asp#2 (Verified 16 June 2004). Olexa, M.T., L. Minton, D. Miller, and S. Corbett. 1998. Handbook of Florida water regulation: water management districts. FL Extension Bull. 039, FL Coop. Ext. Serv., Gainesville, FL. Par, T.W., R. Cox, and R.A. Plant. 1984. The effects of cutting height on root distribution and water use of ryegrass turf. J. Sports Turf Res. Inst. 60:45-53. Pira, E.S. 1997. A guide to golf course irrigation system design and drainage. Ann Arbor Press, Inc. Chelsea, MI. Pitts, D., K. Peterson, and R. Fastenau.1996. Field assessment of irrigation system performance. Appl. Eng. Agric. 12:307-313. Pressler, N. 2003. Evaluation of golf course irrigation system performance and the effects of irrigation amounts on turf quality and rooting depths of golf course turfgrasses. M.S. thesis. Univ. of Florida, Gainesville. St. Johns River Water Management District (SJRWMD). 2002. Applicants handbook: consumptive uses of water, chapter 40C-2, F.A.C. [Online]. Available at http://www.sjrwmd.org/programs/regulation/index.html (Verified 16 June 2004).

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50 St. Johns River Water Management District (SJRWMD). 2004. Water resource permitting program overview: consumptive use permitting. [Online]. Available at http://sjr.state.fl.us/programs/regulation/cup/overview.html (Verified 16 June2004). SAS Institute. 1999. SAS users guide: Statistics. 8 th ed. SAS Institute, Inc. Cary, NC. Smajstrla, A.G., 1990. Technical Manual: Agricultural field scale irrigation requirements simulation (AFSIRS) model, Version 5.5, Agricultural Engineering Department, Univ. Florida, FL. Smajstrla, A.G and F.S. Zazueta. 1998. Estimating crop irrigation requirements for irrigation system design and consumptive use permitting. Publ. AE254. Univ. of Florida. Gainesville, FL. Snyder, R.L. 1986. Evapotranspiration-based irrigation scheduling-the CIMIS experience. p.245-263. In Irrig. Assoc. Tech. Conf. Proc. San Antonio, TX. Soil Conservation Service (SCS). 1970. Irrigation water requirements. Technical Release No. 21. USDA Soil Conservation Service. Washington, D.C. Thompson, K. 2002. Whats uniformity worth? Improving distribution uniformity cansave golf courses money [Online]. Available at http://irrigation.org/ibt/ibt1002/p38.htm (Verified 16 June 2004). Unruh, J.B., J.L. Cisar, and G.L. Miller. 1999. Mowing. p.103-108. In J.B Unruh and M.L. Elliot (eds.) Best management practices for Florida golf courses, 2nd ed. Institute of Food and Agricultural Sciences, Univ. Florida, FL. Wilson, T.P., and D.F. Zoldoske. 1997. Evaluating sprinkler irrigation uniformity [Online]. Available at http://www.wateright.org/site/publications/970703.html (Verified 16 June 2004).

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BIOGRAPHICAL SKETCH Mark Mitchell was born in Palm Beach Gardens, Florida, June 4, 1980. Mark graduated in 2002 with a Bachelor of Science in Turfgrass Science. Mark enrolled in the horticultural science graduate program in the Fall of 2002. After graduation in 2004, he plans to work as an assistant superintendent in Jacksonville, FL. 51


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Title: Evaluation of the Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) in Predicting Golf Course Irrigation Requirements with Site-Specific Data
Physical Description: Mixed Material
Copyright Date: 2008

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EVALUATION OF THE AGRICULTURAL FIELD SCALE IRRIGATION
REQUIREMENT SIMULATION (AFSIRS) IN PREDICTING GOLF COURSE
IRRIGATION REQUIREMENTS WITH SITE-SPECIFIC DATA













By

MARK W. MITCHELL


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004















ACKNOWLEDGMENTS

I would like to thank Dr. Grady Miller, chair of my committee, for his support and

confidence in me to complete this work. I am also thankful for the help that my other

committee member, Dr. Michael Dukes, provided me.

Also I must thank Dr. Jennifer Jacobs for her technical support with the AFSIRS

model. Special thanks go to Jan Weinbrecht and Nick Pressler, for their help and

suggestions during this research.

I would like to thank the St. Johns River Water Management District for funding

this research. I am also grateful for the encouragement from my parents and my future

wife, Sandra Buckley.
















TABLE OF CONTENTS

page

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

LIST O F TA B LE S ............... .. ......................... .......... .... ............... v

LIST O F FIG U R E S .... .............................. ....................... ........ .. ............... vi

ABSTRACT ........ .............. ............. .. ...... .......... .......... vii

CHAPTER

1 INTRODUCTION ............... ..................................................... 1

2 LITER A TU R E REV IEW ............................................................. ....................... 3

G olf Course W ater Consum ption ........................................ ........................... 3
C onsum ptive U se P erm hitting ................................................................. ............... ....5
Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) ..................7
M odeling Crop Irrigation R equirem ents ........................................ ....................7
Evapotranspiration and Weather Data Inputs..................................................8
C rop C oefficient Inputs ................................................................. ................ ... 12
Irrigation Application Efficiency and Uniformity Inputs.................................13
R ooting D epth Inputs .................................... ................... ......... 14
Soil T ype Inputs .................................................................... 15
Sensitivity A analysis .......................................... ... .... ........ .... ... ... 15

3 PREDICTING IRRIGATION REQUIREMENTS ON GOLF COURSES USING
THE AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT
SIM U L A T IO N (A F SIR S) ............................................................... ..................... 17

In tro d u ctio n ...................................... ................................................ 17
M materials and M methods ....................................................................... .................. 18
R results and D iscu ssion .............................. ........................ .. ...... .... ...... ...... 22
Default Values ............................................ .................22
U pdated/A ctual V alues............... .... ............. ........... ................... 23
Default Versus Updated/Actual Irrigation Requirements .................................25
C o n clu sio n s..................................................... ................ 2 9









4 SENSITIVITY ANALYSIS OF THE AGRICULTURAL FIELD SCALE
IRRIGATION REQUIREMENT SIMULATION (AFSIRS) AND THE FAO 56
PENMAN-MONTEITH EQUATION..............................................................35

Intro du action ...................................... ................................................ 3 5
M materials and M methods ............ .......................... ... .................................................. 36
Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) ............36
FAO 56 Penman-M onteith Equation........................................ ............... 37
R results and D discussion ..... ........ ..... .......... ... .............. ........ .. ....... ......... .. 38
Agricultural Field Scale Irrigation Requirement (AFSIRS) Sensitivity
A n a ly sis ................................ .. .... .... ... ........................................... 3 8
FAO 56 Penman-Monteith Equation Sensitivity Analysis..............................39
C o n c lu sio n s..................................................... ................ 3 9

5 SUMMARY AND CONCLUSIONS....................................... ...............43

APPEND IX SA S PROGRAM S ............................................... ............................ 45

REFERENCES ....................... ....... .. ...... ... ..................47

BIOGRAPHICAL SKETCH .............. ............................... ...............51
















LIST OF TABLES


Table p

3-1 Soil types and average water contents for the five golf courses, average water
content of all soil types in the model database, and average water content of a
U SGA green ...................................... ................................ ..........31

3-2 Mean squares for the analysis of variance on rooting depths as influenced by
golf course, location within golf course, and month..............................................31

3-3 Mean rainfall, reference ET, estimated mean irrigation requirement, irrigation
requirement issued on consumptive use permit, mean irrigation requirement
using updated/actual data, and applied irrigation..................................................31















LIST OF FIGURES


Figure page

3-1 Yearly average reference ET (A) and rainfall (B) for the 20 year historical
weather dataset (Orlando), and for the one year of on-site weather data from
each of the five golf courses........................................................................... ..32

3-2 AFSIRS predicted irrigation requirements (IRR) for five golf courses. Default:
model run with all default data ................................................... 33

3-3 AFSIRS predicted irrigation requirements (IRR) for three golf greens built with
USGA greens mix (sand and peat). ........................................ ....................... 34

4-1 Sensitivity analysis of the AFSIRS model output......................... ................. 41

4-2 Sensitivity analysis of the FAO 56 Penman-Monteith equation...........................42















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

EVALUATION OF THE AGRICULTURAL FIELD SCALE IRRIGATION
REQUIREMENT SIMULATION (AFSIRS) IN PREDICTING GOLF COURSE
IRRIGATION REQUIREMENTS WITH SITE-SPECIFIC DATA

By

Mark W. Mitchell

December 2004

Chairman: Grady L. Miller
Major Department: Horticultural Sciences

Golf courses are often the focus when it comes to water use. Various water-use

models are used by Florida's five Water Management Districts, including the

Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS), to estimate

irrigation requirements on golf courses. The St. Johns River, South Florida, and North

Florida Water Management Districts use the AFSIRS model with available default data to

predict irrigation requirements for all golf courses in their jurisdiction. Default values are

used because of the limited research on golf course irrigation requirements. In this study,

data were collected at five golf courses in the Central Florida area to use in the AFSIRS

model for comparisons to irrigation requirements made using default data. Irrigation

system distribution uniformity (DULQ), rooting depths, and weather data were collected

from each golf course. Updated crop coefficients (Kc) for turfgrass were used in place of

the default values. Average soil water content for a green built to USGA specifications









was used for golf courses with these types of greens, instead of the native soil average

water content. A sensitivity analysis was used to determine which inputs had the greatest

influence on the model outputs. When actual data were used, the predicted irrigation

requirements increased between 15 and 46 cm (6 and 18 in) per year for the golf courses.

Distribution uniformity had the greatest impact on predicted irrigation requirements.

When only distribution uniformities were substituted for the default irrigation system

efficiency, the irrigation requirement increased between 13 and 76 cm (5 and 30 in) per

year. Because of a limited length of actual on-site weather data, and unusually high

rainfall amounts during the year, it was difficult to use actual weather datasets in place of

the 20-year historical datasets available to the model. Sensitivity analysis further

indicated that DULQ inputs have the greatest influence on the predicted irrigation

requirements made by the AFSIRS model. Changing a DULQ from 40 to 80% (a 100%

increase) resulted in a 65% decrease in irrigation requirement. The sensitivity analysis

also showed that daily maximum temperature and mean solar radiation had the largest

impact on reference evapotranspiration rates (ETo) calculated using the FAO 56 Penman-

Monteith equation in the REF-ET program (computer program used to calculate ETo

from meteorological data). A 25% increase in the starting point for maximum

temperature resulted in a 45% increase in ETo, and a 50% increase of the starting point

for mean solar radiation results in a 33% change in ETo. Using actual data in place of

default values in the AFSIRS model can result in site-specific estimates of irrigation

requirements on golf courses. According to the AFSIRS model, and further illustrated

through sensitivity analysis, DULQ and Kc values had the greatest impact on irrigation

requirements. Therefore, these variables need to be measured with the most accuracy.














CHAPTER 1
INTRODUCTION

In recent years, golf has become popular in the United States. There were over

15,000 golf facilities in the country in 2000, and over 1,100 courses located in Florida

(National Golf Foundation, 2004). Because of the number of golf courses in Florida, and

their visibility, there is a public concern that the golf industry wastes water. Because of

Florida's erratic distribution of rainfall and large extent of sandy soils, irrigation is

necessary to maintain quality turfgrass.

Water use in Florida is controlled by five Water Management Districts. Each

District is responsible for issuing consumptive use permits to large water users (including

golf courses) located in their jurisdiction. These permits allocate the maximum amount of

water a golf course should use per year. A golf course is required to report these water

use amounts yearly; and if the allotment is exceeded, a financial penalty may be issued by

the District.

The Water Management Districts use various mathematical models to determine

crop irrigation requirements. The St. Johns River Water Management District

(SJRWMD), South Florida Water Management District, and North Florida Water

Management District use the Agricultural Field Scale Irrigation Requirement Simulation

(AFSIRS) to estimate crop water needs, including turfgrass. The AFSIRS model was

developed at the University of Florida, and is a numerical simulation model that estimates

irrigation requirements for Florida crops, soils, irrigation systems, climate conditions, and

irrigation management practices (Smajstrla, 1990). Historical (default) datasets are









available to the model for crop (turfgrass) coefficient values, rooting depths, irrigation

system efficiencies, soil water capacities, rainfall, and reference evapotranspiration rates.

Because the SJRWMD uses the default data when determining irrigation requirements for

golf courses, the allotment amounts are not site-specific to each golf course in their

jurisdiction.

Because of limited research on golf course irrigation requirements, there were two

objectives to this research. The first objective was to compare AFSIRS water requirement

estimates made with default data to estimates made with actual data collected from golf

courses. The second objective was to determine what model inputs have the greatest

influence on outputs, in order to ascertain the variables that should be measured and

monitored to the greatest extent.














CHAPTER 2
LITERATURE REVIEW

Golf Course Water Consumption

Total water use by Florida golf courses in 2000 was estimated at 650 billion liters

(172 billion gal) (Haydu and Hodges, 2002). According to this survey, nearly 321 billion

liters (85 billion gal) came from recycled water, 185 billion liters (49 billion gal) from

surface water, 132 billion liters (35 billion gal) from on-site wells, and 5.7 billion liters

(1.5 billion gal) from municipal sources. It was estimated that the average water use per

golf course was 503 million liters (133 million gal) per year.

Golf courses receive water allotments from water management districts based on

total irrigated acreage; but courses consist of areas managed with varying levels of inputs,

and areas that often require different amounts of water. Greens (areas prepared for

putting) usually receive the most maintenance, followed by the tees (areas prepared for

playing the first shot of each hole); fairways (turfed areas between tees and greens); and

rough (turfed area surrounding the greens, tees, and fairways) (Beard, 2002). The amount

of water these areas require depends on the type of grass, rooting depth, soil type,

maintenance level, amount of inputs, and the desired effect.

A traditional golf course has 4 par 3-holes, 4 par 5-holes, and 10 par 4-holes, which

occupy approximately 54 hectares (133 acres) (McCarty et al., 2001). Tees take up 0.16

to 1.2 hectares (0.4 to 3 acres) of a golf course (McCarty et al., 2001). Par 4 and par 5

tees typically occupy 9.3 to 18.6 m2 (100 to 200 ft 2), and par 3 tees range from 18.6 to

33.2 m2 (200 to 357 ft 2) per thousand rounds of golf annually (Beard, 1985). Greens









typically range from 465 to 697 m2 (5,000 to 7,500 ft 2) in size, and occupy 0.85 to 1.33

hectares (2.1 to 3.3 acres) on a typical 18-hole golf course (Beard, 2002).

Fairways comprise 12 to 24 hectares (30 to 60 acres) on a golf course (Beard,

1985). The average size of a fairway (the turfed area between the tee and green) is

approximately 1.2 hectares (3 acres), which is dependent on the playing length of the hole

and the width of the fairway. The usual fairway width on a golf course is approximately

32 meters (35 yards) (Beard, 2002). Depending on the total acreage and design of the

course, the rough can range from 26 to 49 hectares (65 to 120 acres) for a golf course

(Beard, 2002).

Approximately 26% of the total acreage of a golf course is considered fairways and

55% is rough. Therefore, most water use on a golf course occurs in irrigating fairways

and rough, and increasing and decreasing the total area of these zones can have a great

impact on the amount of water use on a golf course.

In the past, golf courses were built using only the existing soil at the construction

site. Greens were built by pushing up the soil, in order to promote runoff of water (Beard,

1982). Fairways and roughs are still generally built using available soil on site, but to

construct some surface features, soil may be excavated. Therefore, fairways or rough may

be established using subsoil, which usually has different soil characteristics than surface

soil layers. Because the subsoil may have a different water-holding capacity than the

surface soil, water requirements can differ in areas where subsoil was used.

Although some golf courses still have push-up greens, most newly constructed or

renovated greens have been built to United States Golf Association (USGA) green

specifications. The profile of a USGA green consists of 30 to 36 cm (12 to 14 in) of









rootzone medium (fine textured) above a 5 to 10 cm (2 to 4 in) coarse sand layer (choker

layer) covering a 10 cm (4 in) layer of gravel (Higgins and McCarty, 2001). Drainage

tile is installed underneath the gravel layer, in a herringbone design. This design allows

for the entire rootzone to reach field capacity before water drains through the gravel layer

and into the drainage tile. Field capacity is the percentage of water that remains in the soil

after having been saturated and after free drainage has practically ceased (Brady and

Weil, 1999). The field capacity of a USGA greens mix should be in the range of 0.16 to

0.33 m3 m-3 (Beard, 2002).

Consumptive Use Permitting

Florida has five Water Management Districts that regulate water control and use.

The Districts' main responsibilities include management of water and related land

resources; proper use of surface and groundwater resources; regulation of dams,

impoundments, reservoirs, and other structures to alter surface water movement;

combating damage from floods, soil erosion, and excessive drainage; developing water

management plans; maintenance of navigable rivers and harbors, participation in flood

control programs; and maintaining water management and use facilities (Olexa et al.,

1998). Each District is run by a governing board consisting of nine members. The

members serve 4-year terms, and are appointed by the governor and confirmed by the

state. Generally, an executive director is responsible for the operation of the District,

including the implementation of policies and rules (Olexa et al., 1998).

Florida's Water Management Districts issue several types of water use permits, the

most common being consumptive use permits (CUPs). A CUP authorizes how much

water should be withdrawn from surface and ground water supplies for reasonable and

beneficial uses such as public supply (drinking water), agricultural and landscape









irrigation, industry, and power generation (SJRWMD, 2004). The water withdraw

situations that require a CUP are: a well that measures 15.2 mm (6 in) or more in

diameter, the annual average is more than 378,500 liters (100,000 gal) of water use per

day, or there is the capacity to pump 3.78 million liters (1 million gal) or more of water

per day (SJRWMD, 2004).

The St. Johns River Water Management District (SJRWMD) began issuing

consumptive use permits (CUPs) in 1983. The District covers parts of Central Florida and

the Northeast portion of the state. Since 1991, all permitted users have been required to

report their water use by using a meter or by an alternative method approved by the

District. In the year 2000, 504 CUPs were issued by the District (SJRWMD, 2004).

To receive a CUP, an applicant must submit an application form, along with a fee,

a listing of adjacent property owners, and a water conservation plan which provides

measures to reduce water use and preserve water resources for other beneficial uses. The

District then reviews the application and determines the allotment duration and amount

(SJRWMD, 2002). The permits are issued for approximately 20 years, and upon

expiration, must be renewed. CUPs for golf courses are usually issued for a shorter period

of time because of changes that are often made to courses such as adding golf holes, or

changing water sources.

The factors that cause the difference in water allotments from golf course to golf

course are total irrigated acreage and soil type. The applicant reports the irrigated

acreage on the CUP application, and the District uses existing soil maps to determine the

soil type at the location where the golf course was built. Golf courses may use different

sources of water such as: wells, lakes, and reclaimed water, but the total amount









withdrawn from all sources must not exceed their CUP amount. During water shortages,

the district may impose restrictions, and these restrictions supersede any conditions of the

permit (SJRWMD, 2002).

Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS)

Modeling Crop Irrigation Requirements

When determining the amount of water to issue on a consumptive use permit, the

Water Management District must predict the irrigation requirement for the crop.

Irrigation requirement (IRR) for a crop is the amount of water, in addition to rainfall, that

must be applied to meet a crop's evapotranspiration needs without significant reduction

in yield (Smaj strla and Zazueta, 1998). In terms of golf course turfgrass, quality must not

be significantly reduced. Evapotranspiration (ET) includes water that is needed for both

evaporation and transpiration. The amount of water issued on the CUP for a golf course is

determined by predicting the IRR for the turfgrass on that course and any other area that

may be irrigated on or around the course, e.g. home landscapes.

Estimates of IRRs can be ascertained from historical observation, or by using

numerical models (Smajstrla and Zazueta, 1998). If a long term record has been kept of

irrigation water applied, this record could be used to estimate future uses. But few such

long-term databases exist. Another problem with historical data is that its use may be

limited to the location where it was collected (Smaj strla and Zazueta, 1998). The effects

of differences in climate, soil, location, time of year during which the crop was grown, as

well as other factors on irrigation requirements cannot be determined from the available

data (Smaj strla and Zazueta, 1998).

Numerical models may be based on statistical methods or on physical laws which

govern crop water uptake and use (Smaj strla and Zazueta, 1998). A basic model that has









been used is The Soil Conservation Service (SCS) procedure (SCS, 1970). This model is

a statistical regression method that allows monthly crop irrigation requirements to be

estimated based on three factors: monthly crop ET, monthly rainfall, and soil water-

holding characteristics (SCS, 1970). Limitations of this model are: estimation of

irrigation requirements for monthly or longer time periods only, that it is limited to

sprinkler and surface irrigation systems which irrigate the entire soil surface, and soil

types with deep water tables (SCS, 1970).

The SJRWMD uses the Agricultural Field Scale Irrigation Requirement Simulation

(AFSIRS) model to predict IRR for a crop (SJRWMD, 2004). The AFSIRS model is a

numerical simulation model which estimates IRR using inputs from Florida crops, soils,

irrigation systems, growing seasons, climate conditions, and irrigation management

practices (Smajstrla, 1990). This model is based on a water budget of the crop root zone.

This water budget includes inputs to the crop root zone from rain and irrigation, and

losses from the root zone by drainage and evapotranspiration. The water holding capacity

in the crop root zone is the multiple of the water-holding capacity of the soil and the

rooting depth of the crop being irrigated (Smajsrla, 1990).

Evapotranspiration and Weather Data Inputs

The AFSIRS model is based on the concept that actual crop ET is estimated from

reference ET and crop water use coefficients. Reference evapotranspiration (ETo) is the

rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover

of uniform height, which is actively growing, completely shading the ground and not

lacking water (Allen et al., 1998). A crop coefficient (Kc) is an adjustment factor which

is determined by different crop characteristics, i.e. turfgrass type, quality, and height

(Brown and Kopec, 2000). Daily ETo, as well as rainfall, can be ascertained from









historical climate data available in the model. Records from nine Florida locations over

approximately a 20 year period ending in the 1970s are part of the AFSIRS model

database (Smaj strla, 1990). AFSIRS computes IRR for the mean year over those 20 years

as well as IRR for different probabilities of occurrence. The SJRWMD permits are based

on an 80% probability of occurrence, which means that the permitted, or golf course,

should not exceed their allotment except for a 2 in 10 year drought (V. McDaniel,

personal communication, 2004).

There are many weather station networks that can be used by agricultural growers

and turfgrass managers to determine ETo (Brown et al., 2001). The California Irrigation

Information System (CIMIS) (Snyder, 1986) is an integrated network of over 120

computerized weather stations located throughout California. The Arizona

Meteorological Network (AZMET) provides weather-based information in southern and

central Arizona (Brown et al., 1988). These networks use the modified Penman equation

to determine ETo. The commonly used modified Penman and modified Penman-Monteith

methods are two of the many mathematical models that compute ETo from measured

weather data (Brown and Kopec, 2000). The Florida Automated Weather Network

(FAWN) consists of 30 weather stations located throughout Florida. These stations

collect data that can be use for determining ETo (FAWN, 2004). The common data

collected daily by a weather station for computing ETo are: minimum and maximum

temperature, minimum and maximum relative humidity, mean solar radiation, and mean

wind speed.

There are computer programs, such as REF-ET, that can calculate ETo from

meteorological data using different mathematical models, including the commonly used









modified Penman-Monteith method. REF-ET is a stand-alone computer program that

calculates ETo from meteorological data made available by the user (Allen, 2002). The

program provides standardized calculations of ETo for fifteen of the more common

mathematical models that are currently in use in the United States and Europe (Allen,

2002). Daily ETo values in the AFSIRS database were calculated using the IFAS Penman

equation (Smajstrla, 1990). The IFAS Penman was developed at the University of

Florida's Institute of Food and Agricultural Sciences in 1984 to better represent regional

climatic tendencies. The Penman formula is based on four major climatic factors: net

radiation, air temperature, and wind speed and vapor pressure deficit (Jacobs and Satti,

2001).


A +(-)R,- o4 0.56 -0.08e7 1.42 R 0.42
ETO = (2-1)


+ [0.263(0.5 + 0.0062u2)(e e)]
A+7

where: ETo Reference evapotranspiration (mm day1)
A Slope of saturated vapor pressure curve of air (mb/C)
y Psychometric constant (0.66 mb/C)
a Albedo or reflectivity of surface for Rs
Rs Total incoming solar radiation (cal. cm-2 day 1)
C Stefan-Boltzmann constant (11.71 x 10-8 cal.cm-2 day- K-1)
T Average air temperature (K)
ed vapor pressure at dewpoint temperature (mb)
Rso Total daily cloudless sky radiation (cal cm-2 day1)
u2 wind speed at a height of 2 m (km day1)
ea vapor pressure of air (mb)

Jacobs and Satti (2001) reported that the IFAS Penman equation is not as consistent

as the FAO 56 Penman-Monteith (Allen et al.,1998) equation for Florida conditions. In

their study, fourteen models that may be used to estimate ETo in consumptive use









permitting were reviewed to identify the approaches that: best represented the physics of

water losses from irrigated crops; easiest to use in terms of parameters needed; were able

to consistently and accurately capture ETo losses in growing regions of Florida; and were

considered acceptable to the general scientific community. Jacobs and Satti (2001)

suggested that the ETo data available to the AFSIRS should be updated with newer

weather data using the FAO 56 Penman-Monteith equation (Allen et al., 1998).


0.408A(R G)+ 900 u(e, e)
ET, = 2 (2-2)
A + (1 + 0.34u2)

where: ETo Reference evapotranspiration (mm day1)
A Slope of saturation vapor pressure temp. relationship (KPa C-1)
Rn Net radiation (MJ m-2 day-1)
G Soil heat flux (MJ m-2 day-', generally assumed to be zero)
y Psychometric constant (KPa oC1)
T Average air temperature (C)
U2 wind speed at a height of 2 m (m s1)
es saturation vapor pressure (KPa)
ea actual vapor pressure (KPa)

Turf ET rates can vary among genotypes as well as region to region. Carrow (1995)

reported that ET rates for cool season grasses ranged from 1.99 6.05 mm d-1 and warm

season grasses varied from 1.40 6.22 mm d-1. 'Tifway' bermudagrass [Cynodon

dactylon L. x C. transvaalensis Burt-Davy], the most common grass grown on Florida

golf courses, had an average summer ET rate of 3.11 mm d-1 in Central Georgia (Carrow,

1995). Beard et al. (1992) reported summer averages of 5.10 mm d-1 for the same

genotype in the arid west.

Studies have indicated that turf ET increases with water availability. According to

Kneebone and Pepper (1982), ET rates of bermudagrass [Cynodon dactylon (L.)]

increased with increased irrigation application rates and increased water-holding









capacities of soils. Also, turf ET rates increased with increased light levels, increased

temperatures, lowered humidity, moderate to high wind speeds, and long days (Carrow,

1995).

Cultural and fertilization practices influence turf ET rates. There have been a

number of studies showing turf ET rates decrease as the cutting height was lowered (Kim

and Beard, 1983; Parr et al., 1984; Unruh et al., 1999). High rates of nitrogen have

produced an increase in shoot growth and a reduction in root growth (Beard, 1973; Goss

and Law, 1967). The reduced root growth results in less available water to the turf and

the increase in shoot growth requires more water to be taken up. Therefore, more

frequent irrigation is needed to supply enough moisture for growth (Beard, 1973).

Crop Coefficient Inputs

Crop Coefficients (Kc) are available in the AFSIRS database for 60 different crops

(Smaj strla, 1990). The Kc value in the database for golf course turf is one, and therefore

the AFSIRS computes the actual evapotranspiration rate of golf course turf being equal to

the reference evapotranspiration rate (Smajstrla, 1990). According to Jacobs and Satti

(2001), the AFSIRS model should have additional Kc values for turfgrasses because the

additional research conducted since the model was designed indicates differing turfgrass

Kc values between grasses and months. In Georgia, Carrow (1995), using the FAO

Penman, reported Kc values during the summer for Tifway bermudagrass varied from

0.53 to 0.97. Crop coefficient values for Tifway bermudagrass in Arizona, using the

Penman Monteith, ranged from 0.78 to 0.85 and intermediate ryegrass [L. hybridum]

ranged from 0.78 to 0.89 (Brown and Kopec, 2000). Because ryegrass is seeded in the

winter time in some parts of Florida as the bermudagrass goes dormant, different Kc

values within the year may need to be used for computing turf ET.









Irrigation Application Efficiency and Uniformity Inputs

There are eight types of irrigation systems in the AFSIRS database. Each system

has a corresponding efficiency (Smajstrla, 1990). Irrigation application efficiency refers

to the effectiveness of the irrigation system in applying water to the crop root zone where

it can be utilized in production (Smaj strla, 1990). A multiple sprinkler system design, as

used on golf courses, has a 75 percent efficiency value in the model (Smaj strla, 1990).

Jacobs and Satti (2001) reported that there is a lack of irrigation efficiencies to choose

from in the simulation. Previous research indicates that there is a high variability between

irrigation efficiencies of multiple sprinkler systems on golf courses due to several factors,

such as head spacing, nozzle type, pressure, maintenance, etc. (Miller et al., 2003).

Improvements in the design and installation of these systems, such as head-to-head

spacing, may enhance a coverage or efficiency.

Because it is difficult to determine irrigation application efficiency in the field,

irrigation distribution uniformity is often measured to determine the effectiveness of a

system. Although the efficiency of a system can vary from its uniformity, coverage

uniformity, is an indicator of the systems application efficiency. The more uniform a

water application, the less operating time an irrigation system needs to make up for poor

coverage (Wilson and Zoldoske, 1997). Precipitation rate can also be measured while

determining uniformity. Precipitation rate is the amount of water applied over a specific

area, in a specific amount of time (Bowman et al., 2001). If the precipitation rate varies

significantly over the area being irrigated, then uniformity is poor (Huck, 1997; Meyer

and Camenga, 1985; Pira, 1997).

The method most commonly used to calculate distribution uniformity for turfgrass

is called the Lower Quarter Distribution Uniformity or DULQ (IA, 2003). The DULQ is the









average water applied in the twenty-five percent of the area receiving the least amount of

water, divided by the average water applied over the entire area (IA, 2003). Pitts et al.

(1996) evaluated 385 residential irrigation systems, and reported that the average DULQ

for agricultural sprinklers, micro-irrigation, furrow irrigation and turf irrigation were 65,

70, 70, and 49 percent, respectively. Of the 37 turf irrigation systems evaluated, 40% had

DULQ'S less than 40%. Golf courses have historically had DULQ'S ranging from 55 to 85

percent (Thompson, 2002). The Irrigation Association (2003) suggests that a 70 percent

uniformity is a good (expected) value when evaluated using their methodology.

Rooting Depth Inputs

Each of the 60 crops in the AFSIRS database has irrigated (average) and maximum

rooting depth values. The model uses these values, along with soil properties, to compute

how much water needs to be applied to reach field capacity. The AFSIRS model assumes

that 70 percent of water uptake occurs in the irrigated root zone and the remaining 30

percent occurs below the irrigated root zone (Smaj strla, 1990). For golf course turfgrass,

the model uses 15 and 61 cm (6 and 24 in), irrigated and maximum rooting depths

(Smajstrla, 1990). Jacobs and Satti (2001) reported that the AFSIRS model needs

additional rooting depths for turfgrasses because rooting depths can vary from golf course

to golf course.

Beard (1973) reported that the majority of a turfgrass root system mowed regularly

at less than 5 cm, is located in the upper 7 cm of the soil. Reduced rooting depth is

directly correlated with a decrease in cutting height. The very close cutting heights

required to meet performance demands by today's golfers, may result in shallow rooting.

There is very limited research currently available documenting effective rooting depths of

golf course turfs.









Soil Type Inputs

There are 766 soil types and corresponding minimum (permanent wilting point)

and maximum (field capacity) water holding capacities volumetricc) in the AFSIRS

database (Smajstrla, 1990). Existing soil maps are used to determine the type of soil in

which a crop is growing. The model can compute IRR using minimum, average, or

maximum water holding capacities for the soil. Plant available water is a combination of

rooting depth and the amount of water between minimum and maximum holding

capacities for the soil. According to Jacobs and Satti (2001), the soil database needs to be

improved for the most widely used soils. An alternative to the soil database is manual

input of soil water characteristics measured or approximated for the soil type at the site.

Using a soil map to determine soil type, and assuming that the characteristics of the

soil at a site remain the same after construction of a golf course, can also be a concern.

Earthmoving, the use of fill from excavation (lakes), and bringing in off-site materials

can have a great impact on the soil characteristics of a site when construction is complete.

Also, because of most golf courses being built with USGA greens mix, the available

water on those greens is not the same as the native soil. According to the USGA

(Hummel, 1993), the average soil water content of a green built to USGA specifications

is 0.13 m3 m3.

Sensitivity Analysis

To determine how input parameters influence model outputs, a sensitivity analysis

can be utilized. A sensitivity analysis requires varying selected parameters individually

through an expected range of values and then comparing the range of output values from

each input variable (James and Burges, 1982). A sensitivity analysis aims to ascertain

how the model depends upon inputs, upon its structure, and upon the framing









assumptions made to build it. As a whole, sensitivity analysis is used to increase the

confidence in a model and its predictions, by providing an understanding of how the

model's response variables respond to changes in the inputs (A forum on sensitivity

analysis, 2004).

The input parameters with the greatest influence on the model output need to be

measured with the highest accuracy. The South Florida Water Management District used

sensitivity analysis to assess the impact of parameter errors on the uncertainty in output

values for the South Florida Water Management Model and the Natural Systems Model

(Loucks and Stedinger, 1994). Engineers in the District use these models to predict

possible hydrologic impacts of alternative water management policies under a variety of

hydrologic inputs. Once the key errors were identified, it was possible to determine the

extent to which parameter uncertainty can be reduced through field investigations,

development of better models, and other efforts (Loucks and Stedinger, 1994).














CHAPTER 3
PREDICTING IRRIGATION REQUIREMENTS ON GOLF COURSES USING THE
AGRICULTURAL FIELD SCALE IRRIGATION REQUIREMENT SIMULATION
(AFSIRS)

Introduction

Florida's five Water Management Districts issue consumptive use permits to all

golf courses within their areas of jurisdiction. These permits are based on the irrigation

requirement (IRR) of the turfgrass on the golf course. Historical observations and

computer models are tools that are used to predict IRR. The St. Johns River Water

Management District (SJRWMD), South Florida Water Management District, and North

Florida Water Management District use the Agricultural Field Scale Irrigation

Requirement Simulation (AFSIRS) model to predict IRR for golf courses.

The AFSIRS model is a numerical simulation model which estimates IRR using

inputs from Florida crops, soils, irrigation systems, growing seasons, climate conditions,

and irrigation management practices (Smajstrla, 1990). This data is input into the model

from datasets that were constructed using historical data and previous research. Due to

the dependency on these datasets, there are some limitations to operational use of the

model.

The limitations specifically pertaining to golf courses were postulated during a

study at the University of Florida by Jacobs and Satti (2001). They found that the major

problem with the simulation is that it does not have the ability to input actual rainfall and

climate data from the site of interest. They also found that the mathematical model used

to calculate the reference ET rates for the datasets, the IFAS Penman Equation, is not as









consistent as the FAO 56 Penman-Monteith equation (Allen et al., 1998) for Florida

conditions.

Jacobs and Satti (2001) reported that the crop coefficient values and rooting depths

available to the model need to be updated. There has been more research conducted on

crop coefficients since the model was designed, and rooting depths can vary from golf

course to golf course. They also indicated that there is a lack of irrigation efficiency

values available to the user. Previous research indicates that there is a high variability

between distribution uniformities of multiple sprinkler systems on golf courses due to

several factors such as head spacing, nozzle type, pressure, maintenance, etc. (Miller et

al., 2003).

The AFSIRS model has a soil database which is made up of 766 soil types found in

Florida. Because most golf courses are constructed with the USGA greens mix, that has

different soil water characteristics than native Florida soils, IRR for golf course greens

can not be accurately predicted using the soil dataset. The objective of this study was to

compare AFSIRS water requirement estimates made with default data to estimates made

with actual data collected from golf courses.

Materials and Methods

Data were collected from five golf courses located in Central Florida. Irrigation

system performance data measured included distribution uniformity and precipitation

rates. Because it its extremely difficult to determine irrigation system efficiency in a field

setting, distribution uniformity values were collected and used in the model to replace the

default efficiency value. To determine irrigation distribution uniformities, irrigation

audits were performed on three golf holes at each of the five courses in March through

May 2002 (Pressler, 2003). The audits were conducted using the methods of ANSI/ASAE









S436.1 MAR 01 Standards (ASAE, 2001), and using the evaluation methodology

described in the Irrigation Association of America's Certified Golf Course Irrigation

Auditor training manual (IA, 2003).

The catch-cans used in this study had an opening diameter of 7.6 cm (3.0 in) and a

depth of 10.8 cm (4.25 in). For tee complexes (all tees) and greens, catch-cans were

placed in a grid pattern on 3-m centers over the entire surface. For fairways, catch-cans

were placed in a grid pattern on 9-m centers throughout the entire fairway and primary

rough, if irrigated (Pressler, 2003).

The number of sprinklers operating at one time was representative of the normal

operating conditions of that particular system. Each location within individual courses

received the same amount irrigation runtime. The runtime on fairway and tees ranged

between 20 and 30 minutes per zone, and greens between 10 and 30 minutes per zone.

Once it was determined that all the zones had run for a certain location, the collected

water in each can was measured and recorded using a 500 mL graduated cylinder. Lower

quarter distribution uniformity (DULQ) was determined by (Pressler, 2003):


DULQ Avg.LQ x100 (3-1)
avg

DULQ = Lower Quarter Distribution
Avg. LQ = Average volume of lowest 25% of observations
Vavg = Average catch can volume

For modeling purposes, an average DULQ for each of the three holes on the five

courses was determined by weighting the DULQ based on the total areas of tees, fairway,

and green. Areas were determined from global positioning system maps, produced within

60 days of uniformity testing.









Precipitation rates were also collected for each location on a golf course. The net

precipitation rate (PR) is the rate that sprinklers apply water to a given area per unit time

and can be calculated as follows:

VaV x 60
PR ,t (3-2)
TR x CDA

PRnet = Net Precipitation Rate, (cm h-1)
Vavg = Average catch can volume (mL)
TR = Testing run time (min)
CDA = Catch can opening (cm2)

The precipitation rates were used to determine how much water golf courses

actually applied during the study. Those values were then compared to the IRR predicted

by the model for each golf course.

Maximum and average rooting depths were measured at three locations (tees,

fairways, and greens) at each golf course. Measurements were taken in September and

November 2002 and February and May 2003. Three random samples were taken at a 15

cm depth from each location using a Mascaro soil profiler (Turf-Tec International, Coral

Springs, FL). The maximum rooting depth was determined by physically measuring the

longest root in each of the three samples. Measurements were taken from the top of the

thatch layer to the tip of the root. These values were then averaged and the mean

maximum rooting depth of the three samples was recorded as the maximum rooting depth

for that location. Average rooting depth was determined by visually assessing the

samples where the majority of the roots were present. A ruler was then used to measure

the distance to the top of the thatch layer. These measurements were then averaged and a

mean rooting depth was calculated for each location (Pressler, 2003).









Weather stations were installed to monitor environmental parameters from June

2002 to May 2003. The weather stations were located in flat-grassed areas so that the

nearest obstruction was at least ten times its height away from the station. The stations

were placed in irrigated areas on the golf course property. The stations recorded the date,

time, temperature, soil heat flux, (HFT3, Radiation Energy Balance Systems, Bellevue,

WA), solar radiation (LI-200SZ, Licor Inc. Lincoln, NE), wind speed and direction

(WAS425, Vaisala, Inc., Sunnyvale, CA), relative humidity (HMP 45C, Vaisala, Inc.,

Woburn, MA), and precipitation (TE525 Tipping Bucket, Texas Electronics, Inc., Dallas

TX) at 15 minute intervals via a CR10X datalogger (Campbell Scientific, Inc., Logan

Utah).

The data collected by the weather stations was used to develop five site specific

weather datasets containing daily reference ET rates and rainfall. Reference ET (ETo)

rates were calculated using the computer program REF-ET. REF-ET was developed as a

stand-alone computer program to calculate ETo from meteorological data made available

by the user (Allen, 2002). Because the FAO 56 Penman-Monteith equation (Allen et al.,

1998) was found to be the most consistent model for determining ETo for Florida

conditions (Jacobs and Satti, 2001), this equation was used to calculate ETo for the site

specific weather datasets.

Because it is difficult to determine site-specific crop coefficients (Kc) in the field,

updated Kc values were determined from a literature review to replace the default values

in the model. Kc values for bermudagrass were determined from Carrow's (1995) study

in Georgia, and overseeded ryegrass Kc values were determined from Brown and

Kopec's (2000) study in Arizona.









The AFSIRS model was run separately for each golf course using default and

actual/updated (site-specific) data. The default runs mimicked the procedure that the

SJRWMD uses to predict IRR for golf courses. Therefore, for the default runs, the only

input parameter that differed between the golf courses was soil type. The soil type for

each course was determined by using county soil survey maps created by the Soil

Conservation Service, and assessing what soil type is most prevalent at that site. For golf

courses with greens built to United States Golf Association (USGA) specifications (sand

and peat mix), runs were made using the average soil water content volumetricc) for

USGA greens mix instead of the average soil water content volumetricc) of each courses

native soil type.

The default model runs were used to compare with runs made with actual/updated

data. Model runs were made in a stepwise manor where each input parameter was

changed to actual/updated data while the remaining parameters used default values.

Combinations were made where some or all inputs were changed to actual/updated data.

Rooting depths influenced by month, golf course, and location within golf course were

analyzed using analysis of variance procedures in SAS (SAS Institute, 1999).

Results and Discussion

Default Values

The first AFSIRS model runs were made using default values for each golf

course. The default values for each input were: 1.0 for crop coefficient (Kc), 75% for

irrigation system efficiency, 15 and 61 cm (6 and 24 in) for avg. and max. rooting depths,

and 20 year historical weather data for Orlando (nearest collection location for historical

datasets to the five golf courses). The soil type was the only input that varied between









golf courses. According to the SCS county soil survey maps, three of the five courses had

the same soil type average water contents (Table 3-1).


Updated/Actual Values

Based on crop coefficient (Kc) research reported by Carrow (1995) and Brown and

Kopec (2000) on bermudagrass and ryegrass monthly crop coefficients, updated Kc

values were entered into the model for each corresponding month to replace the default

value of 1.0. Crop coefficients inputted for bermudagrass were: 0.62 in May, 0.54 in

June, 0.53 in July, 0.65 in August, 0.97 in September, and 0.73 in October. Crop

coefficients inputted for ryegrass were: 0.83 in November, 0.80 in December, 0.78 in

January, 0.79 in February, 0.86 in March, and 0.89 in April.

Measured DULQ for each golf course were used to replace the default (75%)

irrigation system efficiency for a multiple sprinkler system. An average DULQ for each of

the three holes on the five courses was determined by weighting the DULQ for each

location (tee, fairway, green) based on their total areas. Because the fairway occupies the

most area, the DULQ for a hole was similar to the DULQ of the fairway on that hole. The

weighted average DULQ' for the three holes on the five courses were calculated as

follows: golf course A = 32, 37, and 59%, golf course B = 40, 42, and 49%, golf course C

= 45, 55 and 62%, golf course D = 62, 67, and 67%, and golf course E = 41, 44, and 54%.

Average and maximum rooting depths measured at each golf course were entered

into the model in-place of the default values of 15 and 61 cm (6 and 24 in). Because two

of the three holes on each golf course had reduced runtimes, in accordance with a

concurrent irrigation conservation study (Pressler, 2003), only the rooting depths from

the control holes (typical irrigation practices) were used for modeling. Analysis of









variance indicated differences in rooting depths by month at a 95% probability level

(Table 3-2).

Rooting depths measured in August, 2002 were significantly less than depths

measured in November, 2002, and February and May 2003. But because these rooting

depth differences may have been caused by the extremely wet summer in 2002, rooting

depths from each month and location were averaged together to get an average and

maximum rooting depth for each golf course. The average (irrigated) and maximum

rooting depths from the five golf courses, used for modeling, were (avg. and max.): golf

course A = 4.3 and 6.6 cm (1.7 and 2.6 in), golf course B = 4.3 and 7.1 cm (1.7 and 2.8

in), golf course C = 4.6 and 7.4 cm (1.8 and 2.9 in), golf course D = 4.6 and 6.6 cm (1.8

and 2.6 in), and golf course E = 4.3 and 6.9 cm (1.7 and 2.7 in).

The five weather datasets, created via on-site weather stations and the use of REF-

ET, were used to replace the 20 year historical data from the Orlando area. Each golf

course had a corresponding dataset with actual reference ET rates (ETo) and rainfall for

one year. During the study period, there was an unusually low ETo for three of the golf

courses (Figure 3-1A), as well as a high amount of rainfall at all five courses (Figure

3-1B). The yearly average ETo (mm day-) for golf courses A, C, and D were below the

lower limit of the 95% probability of occurrence interval for the historical data. The

yearly average rainfall (mm day-) was higher than the upper limit of the interval for all

five golf courses. The average amount of rainfall in the historical weather dataset for

Orlando is 129 cm (50.7 in) per year; whereas, the average rainfall recorded at the five

courses from June 2002 to May 2003 was 199 cm (78.4 in).









Default Versus Updated/Actual Irrigation Requirements

Irrigation requirements (IRR) for the five golf courses were calculated by the

AFSIRS model using default and updated/actual values for comparison. The only

parameter that differs by golf course in the default runs is soil type. Because golf courses

A, B, and C have the same soil type, they have the same IRR for the default run (Figure

3-2). Golf course E has a soil type with a higher average water content (0.10 m3 m-3) than

golf courses A, B, and C (0.07, 0.07, and 0.07 m3 m-3, respectively), and therefore

requires less water (lower IRR) to reach field capacity.

When using the historical weather data, IRR was estimated for the average year and

for the 80% probability values (two in ten year drought) for the 20 years of data. Because

drought years, requiring higher amounts of irrigation, are factored into the 80%

probability IRR, those irrigation requirements are higher than estimates for the average

year. Irrigation requirements estimated using actual weather data do not predict the 80%

probability values because there was only one year worth of data in each dataset.

Estimates were made using Kc values reported in the literature. Predicted IRR for

all five courses dropped approximately 25 cm (10 in) per year from the default run

(Figure 3-2). This was due to the updated monthly Kc values, which range from 0.53 to

0.97, being lower than the default Kc of 1.0 for turfgrass.

Additional model runs were conducted with actual weighted DULQ values for each

course replacing the default irrigation system efficiency of 75%, for a multiple sprinkler

system. Each course had three DULQ values, one for each hole, and therefore had three

estimates with the actual DULQ model runs. To determine an average IRR for the golf

course, the IRR values obtained from the model runs for each hole (separate DULQ









values) were averaged. With actual DULQ values, the IRR for all five courses increased

compared to the default run values (Figure 3-2). The IRR for courses B, C, and E went up

38 to 64 cm (15 to 25 in) per year when actual uniformities were used in the model. This

is due to these courses having uniformities between 40% and 60%. The IRR for golf

course A increased approximately 76 cm (30 in) per year from the default run values. The

increase was a result of golf course A having two holes with low uniformities (32% and

37%) that reduced its average DULQ to 43%. The IRR for golf course D increased by

approximately 13 cm (5 in) per year. This golf course had uniformities from 62% to 67%,

which were much closer to the default value.

Actual root depths from each course were used for IRR estimations. All five IRR

predictions increased about 18 cm (7 in) per year with actual rooting depths replacing the

default root depths (Figure 3-2). The actual average and maximum rooting depths were

4.3 and 6.9 cm (1.7 and 2.7 in), and the default irrigated and maximum rooting depths for

golf course turf are 15.2 and 61 cm (6 and 24 in), respectively. This difference in actual

and default root depths, led to the increase in IRR for each golf course. The low mowing

heights on the golf courses may have contributed to the shallow rooting depths when

compared to the default depths.

Actual weather datasets for each course were difficult to input with the

programming structure of the AFSIRS model. It was apparent from comparing the

historical weather data to the year's weather data collected that the short-term weather

data sets represented a much wetter year than average (Figure 3-1). The on-site weather

data sets represented between 56 and 109 cm (22 and 43 in) more than the average

rainfall compared to the 20 year historical weather dataset. Wet years are not factored









into consumptive use permits as are drought years. Using the actual weather data from

the golf courses decreased the irrigation water needed estimate by approximately 51 cm

(20 in) per year (Figure 3-2).

The model was also run for the five golf courses using all actual/updated data

except for weather data, and all actual/updated data including weather data. Due to the

wet year, it seemed appropriate to use the historical weather data rather than predict

irrigation needs based on one year's data. Since the actual weather datasets are not

historically typical for the Orlando area, a better comparison to the default data set is to

estimate water needs with actual data combined with the historically weather dataset.

That comparison showed that actual/updated data with the historical weather data

increased IRR between 15 and 46 cm (6 and 18 in) per year for golf courses A, B, C and

E (Figure 3-2). This is primarily a direct result of these courses having low distribution

uniformities. Golf course E's IRR increased approximately 15 cm (6 in) per year with an

average DULQ of 54%, and course A's IRR increased approximately 46 cm (18 in) per

year with an average DULQ of 43%. Golf course D with an average DULQ of 65% was

predicted with no increase in IRR compared to running the model with default data.

Irrigation requirements were estimated for the three golf greens built with sand and

peat as per USGA specifications. Default IRR was included for comparison purposes.

DULQ of each green was used instead of the weighted DULQ for each golf course except

for the default. The model was run with the USGA green average soil water content (0.13

m3 m-3) inputted in place of the average water content of the native soils. The IRR was

decreased approximately 13 cm (5 in) per year for all three courses due to the increase in

the average soil water content value (Figure 3-3).









Predictions with all actual data, including USGA greens mix average water content,

and historical weather data resulted in IRR approximately the same for all three courses

when compared to the default IRR (Figure 3-3). This is because the average DULQ on the

three greens at each course (ranged from 50% to 69%) was higher than the weighted

DULQ of each golf course, and the higher average soil water content makes up for the

DULQ being lower than the default 75%. The model was run with all actual data including

USGA greens mix average water content. Because actual weather datasets had high

rainfall amounts, IRR was decreased about 51 cm (20 in) per year for all three golf

courses.

Golf courses A, B, C, and D each had a mean predicted IRR of about 86 cm (34 in)

and golf course E had an IRR of 74 cm (29 in) (Figure 3-2). Based on rainfall and ETo,

the mean IRR was predicted to be about the same additional amount (depth) as the ETo

values (Table 3-3). The IRR on the District issued consumptive use permit (CUP) for golf

courses C, D, and E are slightly higher than the mean IRR because the permit accounts

for a two in ten year drought. According to their respective permits, golf course A

received 30% more water than the mean IRR because of a deep water table, and golf

course B received 21% more than the 80% predicted value because a 70% irrigation

efficiency value was used instead of 75%. Using the measured precipitation rates and run

times, the depth of irrigation water applied to the fairway (largest irrigated area) of the

control hole was determined. The golf courses managers irrigated from 12 to 67% less

than what the model (default values) predicted was needed. Two golf courses irrigated

approximately half of what the model (default values) predicted as the IRR. This was due

to the rainfall of above normal, which was not accounted for by the model. Accounting









for the actual rainfall received on each golf course, the turf managers irrigated from 42%

less than they needed to 58% more than they needed.

Conclusions

Investigations of the AFSIRS model indicate that when actual data (crop

coefficients, distribution uniformity, and rooting depth) was included in the model it

predicted similar water needs compared to the default prediction for only one of the five

golf courses evaluated. The use of actual data resulted in IRR increasing between 15 and

46 cm (6 and 18 in) per year for the golf courses. This was typically a direct result of

those courses having low distribution uniformities. When only distribution uniformities

were substituted for the default irrigation efficiency, IRR increased between 13 and 76

cm (5 and 30 in) per year. Although DULQ provides an estimate of irrigation efficiency,

the true efficiency of the system may be higher or lower than the measured DULQ.

Weather has a significant potential to influence inputs and predicted values but

since the AFSIRS is used primarily as a prediction equation, it is difficult to use current

weather patterns to predict long-term future needs. Weather datasets consisting of more

than one year of data would provide a better comparison to estimates calculated using

long-term, historical weather data.

Although a green built with USGA greens mix may require less water than the rest

of the golf course, IRR issued on consumptive use permits do not account for this

difference. But because of the stresses to a green (low mowing heights and high traffic),

these areas may need the same, or more, water than the rest of the course on a per acre

basis.

Precipitation rates and run times can provide an estimate for how much water

actually was applied to certain areas on golf courses. Because of the high rainfall






30


amounts during the year, golf course managers used less water than the predicted

irrigation requirements with default data. The high rainfall was not accounted for in the

predicted IRR using default data. When comparing water use to irrigation requirements

with updated/actual data, managers used less and more water than what was predicted.

This is a result of courses receiving different amounts of rainfall, and having different

DULQ values.









Table 3-1. Soil types and average water contents for the five golf courses, average water
content of all soil types in the model database, and average water content of a
USGA green


Soil type
Astatula sand
Astatula sand
Astatula sand
Candler sand
Blanton fine sand


Avg. of all soil types in
USGA green


Average water content (m' m' )
0.07
0.07
0.07
0.06
0.10
0.12
0.13


Table 3-2. Mean squares for the analysis of variance on rooting depths as influenced by
golf course, location within golf course, and month
Source of variation df F value t
Golf course 4 0.57 ns
Location (golf course) 10 1.40 ns
Month 3 5.83 **
Error 42
t *, **, *** significant at the 0.05, 0.01, 0.001 levels, respectively.
ns, nonsignificant at the 0.05 level

Table 3-3. Mean rainfall, reference ET, estimated mean irrigation requirement, irrigation
requirement issued on consumptive use permit, mean irrigation requirement
using updated/actual data, and applied irrigation
CUP
Golf Course Rainfall ETo IRRt IRRR IRR Irrigation
---------------------cm------------
A 175 84 86 145 45 28
B 145 106 86 104 43 38
C 124 97 86 91 48 76
D 145 92 86 97 36 53
E 132 98 74 94 77 45
t irrigation requirements predicted using AFSIRS with default values
CUP = District issued consumptive use permit
irrigation requirements predicted using updated/actual data


Golf course













A ET







EA



C
0!


4.0 -



-o
3.8 -

-

S3.6







3.2



3.0
-


0


Figure 3-1. Yearly average reference ET (A) and rainfall (B) for the 20 year historical
weather dataset (Orlando), and for the one year of on-site weather data from
each of the five golf courses. Letters denote the five courses. Horizontal
dashed lines indicate the upper and lower limits of the 95% probability of
occurrence for the historical data.


B. Rainfall
AA

6-

DA
BA
5 C
E


4 L


Nj


N


cj
P
















180 O- tCoII Lourse A -j- Cl course 15

160

140

120

100 -

80

60 -

20 -



0
Sso18 Golf Course C Golf Course D

160

S140

120-

S100-

80 -

60 -


S20-
10


S0 -






120
o 3-
100

80



4Mean

20

0 ,F ,















Figure 3-2. AFSIRS predicted irrigation requirements (IRR) for five golf courses.

Default: model run with all default data. All others were run with actual

values collected on-site or in the case of crop coefficients, published values.













1oo olt Course A -oll Course I


80 -


7 60 -


'- 40


0






80 ,M
60 -




40-

20 80%Probability
S I I Meant













Figure 3-3. AFSIRS predicted irrigation requirements (IRR) for three golf greens built
with USGA greens mix (sand and peat). Default: model run with all default
data. USGA greens mix: model run with average soil water content (0.13 m3
m-3 by volume).














CHAPTER 4
SENSITIVITY ANALYSIS OF THE AGRICULTURAL FIELD SCALE IRRIGATION
REQUIREMENT SIMULATION (AFSIRS) AND THE FAO 56 PENMAN-
MONTEITH EQUATION

Introduction

To provide a better understanding for how a model works and how the input

parameters influence the outputs, a sensitivity analysis can be utilized. A sensitivity

analysis requires varying selected parameters individually through an expected range of

values and then comparing the range of output values from each input variable (James

and Burges, 1982). This analysis technique determines which parameters have the

greatest impact on the output, therefore determining the level of measurement accuracy

of each input variable. The South Florida Water Management District used a sensitivity

analysis to assess the impact of parameter errors on the uncertainty in output values for

the South Florida Water Management Model and the Natural Systems Model (Loucks

and Stedinger, 1994). Once the key errors were identified, it was possible to determine

the extent to which parameter uncertainty can be reduced through field investigations,

development of better models, and other efforts (Loucks and Stedinger, 1994).

The Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS) is the

model used by the St. Johns River Water Management District, South Florida Water

Management District, and North Florida Water Management District to predict irrigation

requirements (IRR) for golf courses. It is a numerical simulation model which estimates

IRR for Florida crops, soil, irrigation systems, climate conditions, and irrigation

management practices (Smajstrla, 1990). The FAO 56 Penman-Monteith equation (Allen









et al., 1998) was found to be the most consistent model for determining reference

evapotranspiration (ETo) for Florida conditions (Jacobs and Satti, 2001). This equation

can be used to calculate ETo in a computer program such as REF-ET (a stand-alone

computer program that calculates reference ET from meteorological data made available

by the user (Allen, 2002)). These calculated reference ET rates can be used to create site-

specific datasets for use with the AFSIRS model.

To establish how the output from the AFSIRS model and the FAO 56 Penman-

Monteith equation (Allen et al., 1998) respond to changes in their inputs, a study was

conducted using sensitivity analysis. The objective of this research was to determine what

parameters have the greatest impact on the irrigation requirement predicted with the

AFSIRS model and reference evapotranspiration (ETo) using the FAO 56 Penman-

Monteith equation (Allen et al., 1998).

Materials and Methods

Agricultural Field Scale Irrigation Requirement Simulation (AFSIRS)

Crop Coefficient (Kc), distribution uniformity (DULQ) (in place of irrigation

application efficiency), average and maximum rooting depth, and average soil water

content were the parameters that were analyzed for sensitivity on the AFSIRS model.

Individual model runs were made using a range of values for each parameter. The values

were changed in increments from the starting point, in both directions, depending on the

size of the range. Each of the four input parameters were analyzed separately, while the

other parameter values remained constant. The constant value for the parameters not

being analyzed was the average value observed for each parameter from a concurrent golf

course water use study where site-specific data was collected from five golf courses for

predicting IRR using the AFSIRS model (Pressler, 2003).









Starting values and ranges were chosen for the four parameters based on the data

collected in the concurrent water use study. The starting value for each parameter was the

average value calculated for each parameter. The Kc value starting point for this study

was 0.7. This value was changed in increments of 0.1 in both directions, and ranged from

0.5 to 1.0. The DULQ starting point value was 50%. The values were changed in

increments of 10% in both directions, and ranged from 20% to 100%. Rooting depths had

two starting points. The average depth starting point was 12.7 cm and maximum depth

starting point was 50.8 cm (5 and 20 in, respectively). These depths were changed in

increments of 2.54 and 10.2 cm (1 and 4 in) in both directions, and ranged from 2.54 and

10.2 to 20.3 and 81.3 cm (1 and 4 to 8 and 32 in), respectively. Average soil water

content starting point was 0.09 m3 m-3, was changed in increments of 0.02 m3 m-3 in both

directions, and ranged from 0.03 to 0.17 m3 m-3 (by volume).

FAO 56 Penman-Monteith Equation

Maximum and minimum temperature, maximum and minimum relative humidity,

mean solar radiation, mean soil heat flux, and mean wind speed were parameters that

were analyzed for sensitivity on the FAO 56 Penman-Monteith equation (Allen et al.,

1998) in the REF-ET program. Each of the eight input parameters were analyzed

separately, while the other parameter values remained constant. The starting values for

each parameter were the average values observed from collected weather data during the

concurrent water use study. The values were changed in increments from the starting

point, in both directions, depending on the size of the range.

Because the temperature data collected by the weather station and analyzed in REF-

ET was in Fahrenheit, the values used and reported in the sensitivity analysis are also in

Fahrenheit. The min. temperature starting point was 60F. The values were changed in









increments of 10F in both directions, and ranged from 20 to 80F. The max. temperature

starting point value was 80F. The values were changed in increments of 10F in both

directions, and ranged from 40 to 100F. The min. relative humidity starting point was

50%. The values were changed in increments of 10% in both directions, and ranged from

10 to 90%. The max. relative humidity starting point was 95%. The values were changed

in increments of 10% in both directions, and ranged from 60 to 100%. The mean solar

radiation starting point value was 160 W/m2. The values were changed in increments of

40 W/m2 in both directions, and ranged from 0 to 320 W/m2. The mean soil heat flux

starting point was 0 W/m2. The values were changed in increments of 5 W/m2 in both

directions, and ranged from -25 to 15 W/m2. The mean wind speed starting point value

was 8 km/hr. The values were changed in increments of 1.61 km/hr in the decreasing

direction and 4.8 km/hr in the increasing direction, and ranged from 3.2 to 37 km/hr.

Results and Discussion

Agricultural Field Scale Irrigation Requirement (AFSIRS) Sensitivity Analysis

Changes in DULQ resulted in the largest changes in IRR (Figure 4-1). Changing a

DULQ from 40% to 80%, a 100% increase, resulted in a 65% decrease in irrigation

requirement. The Irrigation Association suggests that a 70% DULQ is expected and an

80% DULQ is achievable for a golf course. Modifying Kc values had the second largest

impact on IRR. IRR increased linearly as Kc values decreased. A 15% increase or

decrease of the starting point resulted in a 20% change in IRR in the respective direction.

Changes in rooting depth and average soil water content had very little impact on

IRR. As rooting depth and average soil water content increase, IRR decreases. Increasing

and decreasing both inputs by 60% of the starting points resulted in less than 16%

increases and decreases in IRR.









FAO 56 Penman-Monteith Equation Sensitivity Analysis

A change in max. temperature resulted in the largest change in ETo (Figure 4-2).

A 25% increase of the starting point resulted in a 45% increase in ETo. Modifying mean

solar radiation had the second greatest impact on ETo. A 50% increase of the starting

point resulted in a 33% change in ETo.

Changes in min. relative humidity and mean wind speed had the third and fourth

largest impact on ETo, respectively. Increasing both inputs by 60% of the starting points

resulted in a 25% decrease in ETo for min. relative humidity, and a 15% increase in ETo

for mean wind speed. Changes in min. temperature, mean soil heat flux, and max. relative

humidity had very little impact on ETo. A 25% decrease of the starting points for each of

the three parameters resulted in a less than 8% increase in ETo.

Conclusions

Sensitivity analysis further indicated that there can be significant differences

between default values and true values when using the AFSIRS model. This was most

apparent with DULQ (in place of efficiency) and crop coefficients. Because these

parameters have the most impact on IRR, the data used in these inputs needs to have the

highest accuracy. By collecting site-specific data, a true picture can be illustrated for

irrigation requirements, and as a result, more water may be conserved. According to the

AFSIRS, by attaining a relatively high DULQ (60 to 80%), golf course managers can

greatly reduce wasteful water use, and as a result, meet or stay within their water

allotments.

Because it is difficult to determine site-specific crop coefficients in the field,

published data is the best alternative. More research needs to be conducted to determine

Kc values for different types of turfgrasses grown under Florida conditions.









When determining reference ET rates using REF-ET and the FAO 56 Penman-

Monteith equation (Allen et al.,1998), daily maximum temperature and mean solar

radiation have the greatest effect on the output, followed by minimum relative humidity

and mean wind speed. The data collected for these parameters with the most influence on

ETo, should be measured with the highest accuracy to reduce variability.

If the reference ET rates calculated by the FAO 56 Penman-Monteith equation

(Allen et al., 1998) are used in the AFSIRS model, DULQ and Kc values as well as daily

maximum temperature and mean solar radiation have a great impact on predicted

irrigation requirement. But because three of those parameters are out of a golf course

manager's control, the focus should be on improving DULQ to conserve water.








41




175
-U-- Kc
150 --- DULQ

125

100

75



2 25
S0

S -25

W -50
-75 -

S 175 Rooting Depth
S 150 -- Soil (AWC)

125

100 -

75

50

25



-25

-50

-75

-100 -80 -60 -40 -20 0 20 40 60 80 100 120


% Change in Initial Parameter Input


Figure 4-1. Sensitivity analysis of the AFSIRS model output. Values across x-axis
represent a typical value as the starting point (0) and a range of values in the
% change of the initial value.























S-- Max. Temp.
-*- Mean Solar Rad.


-*- Min. Temp.
Mean Heat Flux



__r\^y____


-100 -50 0 50 100 150 -100 -50


--- Mean Wind Speed
- Min. Rel. Humidity


Max. Rel. Humidity







__lL


0 50 100 150 200


% Change in Climatic Parameter


Figure 4-2. Sensitivity analysis of the FAO 56 Penman-Monteith equation (Allen et al.,
1998). Values were changed across a range of typical values. Values across x-
axis represent a typical value as the starting point (0) and a range of values in
the % change of the initial value.


0














CHAPTER 5
SUMMARY AND CONCLUSIONS

Using site-specific data instead of default values in the AFSIRS model can result in

site-specific estimates of irrigation requirements on golf courses. For prediction purposes

it is better to use long-term (historical) weather data rather than short-term data.

It is important for golf course managers to be familiar with how their Water

Management District determines golf course irrigation requirements. Knowing what data

goes into the AFSIRS model, can allow a manager to use their allotted water more

effectively, e.g. improving distribution uniformity. Evaluation of the AFSIRS model and

its effectiveness in predicting irrigation requirements using default and actual (on-site)

data from five golf courses was discussed in Chapter 3. The following results were

obtained:

* Updated crop coefficients resulted in IRR predictions to drop approximately 25.4
cm (10 in) from the default estimates. Actual rooting depths caused IRR predictions
to increase approximately 17.8 cm (7 in).

* Replacing distribution uniformities with the default irrigation efficiency, caused the
greatest change in IRR predictions (increase between 12.7 to 76.2 cm or 5 to 30 in
depending on the golf course).

* The one year of weather data collected at each golf course represented 56 and 109
cm (22 and 43 in) more than the average rainfall compared to the 20 year historical
weather dataset. As a result, the estimates using the actual weather data decreased
IRR predictions by approximately 51 cm (20 in).

* Due to the wet year, the model was run with all actual data and with the historical
weather dataset. This combination of actual and historical data resulted in an
increase in IRR predictions of 15 to 46 cm (0 to 18 in).









* Predictions with all actual data, including USGA green soil substrate average water
content, and historical weather data resulted in little to no change in IRR for the
three courses with USGA specification greens.

Sensitivity analysis allowed for a better understanding of how the AFSIRS model

and FAO 56 Penman-Monteith equation (Allen et al., 1998) work. It indicated what

inputs had the greatest impact on the outputs. The inputs with the most influence on

outputs of interest needs to be measured with the highest accuracy to reduce variability.

Sensitivity analysis on the AFSIRS model and FAO 56 Penman-Monteith equation

(Allen et al., 1998) was discussed in Chapter 4. The following results were obtained:

* Sensitivity analysis on the AFSIRS model further indicated that distribution
uniformity (irrigation efficiency input) had the greatest influence on the IRR
prediction value, followed by: Kc, rooting depth, and average soil water content.
Changing a DULQ from 40% to 80%, a 100% increase, resulted in a 65% decrease
in irrigation requirement.

* Sensitivity analysis on the FAO 56 Penman-Monteith (Allen et al., 1998) using the
REF-ET program to predict ETo illustrated that changes in daily max. temperature
resulted in the largest changes in ETo, followed by: mean solar radiation, min.
relative humidity, mean wind speed, min. temperature, max. relative humidity, and
mean soil heat flux. A 25% increase of the starting point for max. temperature
resulted in a 45% increase in ETo, and a 50% increase of the starting point for mean
solar radiation resulted in a 33% change in ETo.















APPENDIX
SAS PROGRAMS

Program to organize weather station data into columns for REF-ET.

data dsl;
infile 'd:\glm test 91803.prn' firstobs=2;
input year day min Temp RHb solar soil soil2 windsp @@;
/*drop bar;*/ drop min;
soilmean=(soil+soil2)/2;
drop soil soil2;


/*proc print; run;*/
cards;
proc sort;
by year day;
proc means noprint;
by year day;
var temp RHb;
output out=datal min=mn t mn
proc means data=dsl noprint;
by year day;
var solar soilmean windsp;
output out=data2 mean=m_sol m
proc sort data=datal;
by year day;
proc sort data=data2;
by year day;
data all;
merge datal data2;
by year day;
/*proc print;*/
proc print data=all;
var year day mnt mxt mnRH
run;


RH max=mxt mxRH;



soil m wind;


mx_RH msol msoil mwind;









Rooting depths influenced by month, golf course, and location within golf course
using analysis of variance procedure.

Title 'Mean Rooting Depth';
Data;
Input GC $ trt $ loc $month $ RD;
Cards;

proc sort; by trt;
proc glm;by trt;
class loc gc month;
model RD= gc loc(gc) month;
test h=gc e=loc(gc);
means loc(gc)/lsd lines;
means month/lsd lines;
run;















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BIOGRAPHICAL SKETCH

Mark Mitchell was born in Palm Beach Gardens, Florida, June 4, 1980. Mark

graduated in 2002 with a Bachelor of Science in Turfgrass Science. Mark enrolled in the

horticultural science graduate program in the Fall of 2002. After graduation in 2004, he

plans to work as an assistant superintendent in Jacksonville, FL.