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Residential Irrigation Water Use in the Central Florida Ridge


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RESIDENTIAL IRRIGATION WATER USE IN THE CE NTRAL FLORIDA RIDGE By MELISSA C. BAUM 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 ENGINEERING UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Melissa C. Baum

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To my husband, Patrick E. Haley.

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ACKNOWLEDGMENTS I thank the following individuals for their help: Danny Burch, Clay Coarsey, Jeff Williams, Brent Addison, Justin Gregory, Kristen Femminella, and Mary Shedd. I would also like to thank my graduate committee members (Dr. Dorota Z. Haman and Dr. Grady L. Miller) for guidance and patience. Lastly, a most special thank you goes to Dr. Michael D. Dukes, for being a wonderful guru! This research was supported by the Florida Agricultural Experiment Station and a grant from St. Johns River Water Management District. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF TABLES ............................................................................................................vii LIST OF FIGURES .........................................................................................................viii LIST OF ABBREVIATIONS ............................................................................................ix ABSTRACT .........................................................................................................................x CHAPTER 1 INTRODUCTION........................................................................................................1 2 RESIDENTIAL IRRIGATION WATER USE..........................................................13 Materials and Methods ...............................................................................................14 Results and Discussion ...............................................................................................19 Summary and Conclusions .........................................................................................23 3 RESIDENTIAL IRRIGATION DISTRIBUTION UNIFORMITY...........................32 Materials and Methods ...............................................................................................34 Results and Discussion ...............................................................................................37 Residential Testing ..............................................................................................37 Control Testing ....................................................................................................39 Summary and Conclusions .........................................................................................40 4 COMPARISON OF UNIFORMITY MEASUREMENTS........................................46 Materials and Methods ...............................................................................................47 Results and Discussion ...............................................................................................50 Summary and Conclusions .........................................................................................51 5 CONCLUSIONS........................................................................................................55 APPENDIX A PHOTOGRAPHS.......................................................................................................60 v

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B STATISTICAL ANALYSIS......................................................................................71 LIST OF REFERENCES ...................................................................................................93 BIOGRAPHICAL SKETCH .............................................................................................97 vi

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LIST OF TABLES Table page 2-1. Monthly water use for Treatment 1 homes for all three locations combined. ...........25 2-2. Monthly water use for Treatment 2 homes for all three locations combined. ...........26 2-3. Monthly water use for Treatment 3 homes for all three locations combined. ...........27 2-4. Evapotranspiration, rainfall, and effective rainfall calculated per month. .................28 2-5. Seasonal water use, fraction of total water use, and turf quality rating with letter notations referring to the significant difference between treatments for each season. 29 2-6. Percentage if irrigated area which is turfgrass or landscaped bedding as well as the total irrigated area for each home. ............................................................................29 3-1. Mobile Irrigation Lab turf DUresults for five counties in Florida. lq .........................43 3-2. Recommended pressure and radii for tested spray and rotor heads under ideal conditions according to manufacturer guidelines. ....................................................43 3-3. Irrigation Association overall system quality ratings, related to distribution uniformity .................................................................................................................44 3-4. Residential system distribution uniformity catch-can test results .............................44 3-5. Control system distribution uniformity catch-can test results for these brands of rotor heads at recommended and low pressures. ......................................................45 3-6. Control system distribution uniformity catch-can test results for these brands of spray heads at recommended, low, and high pressures. ...........................................45 4-1. Uniformity values from both the catch-can tests and the TDR values. .....................53 4-2. Measurement results from both the catch-can and the TDR tests. ............................53 vii

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LIST OF FIGURES Figure page 2-1. Map of site locations. ...............................................................................................30 2-2. Effective rainfall plus applied irrigation for each treatment compared to reference evapotranspiration. ...................................................................................................31 4-1. Comparison of DU values calculated from both the TDR soil moisture and catch-can tests. lq ...................................................................................................................54 4-2. Comparison of soil moisture to can volume measurements taken during uniformity tests. ..........................................................................................................................54 A-1. Flow meter ................................................................................................................60 A-2. Weather station .........................................................................................................60 A-3. Control system spray head with pressure gage ........................................................61 A-4. Control system catch-can test ...................................................................................61 A-5. Residential system catch-can test .............................................................................62 A-6. Setup of catch-can grid formation ............................................................................62 A-7. Catch-can grid formation around bedded area .........................................................63 A-8. Measure catch-can volume with graduated cylinders ..............................................63 A-9. Turfgrass area with high turf quality rating .............................................................64 A-10. Turfgrass area with low turf quality rating ...............................................................64 A-11. Sample cooperator homes from each treatment in Marion County. A) T1. B) T2. C) T3. D) Another T3. .............................................................................................65 A-12. Sample cooperator homes from each treatment in Lake County. A) T1. B) T2. C) T3. D) Another T3. ..................................................................................................67 A-13. Sample cooperator homes from each treatment in Orange County. A) T1. B) T2. C) T3. D) Another T3..............................................................................................69 viii

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LIST OF ABBREVIATIONS ASAE American Society of Agricultural Engineers CU Coefficient of Uniformity DU lq Distribution Uniformity ET Evapotranspiration Rate ET o Reference Evapotranspiration GLM General Linear Model MIL Mobile Irrigation Lab NRCS Natural Resource Conservation Service NTEP National Turfgrass Evaluation Procedure SJRWMD St. Johns River Water Management District T1 Treatment One T2 Treatment Two T3 Treatment Three TDR Time Domain Reflectometry UF University of Florida USDA United States Department of Agriculture VWC Volumetric Water Content ix

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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 Engineering RESIDENTIAL IRRIGATION WATER USE IN THE CENTRAL FLORIDA RIDGE By Melissa C. Baum May 2005 Chair: Michael D. Dukes Major Department: Agricultural and Biological Engineering Automatic in-ground irrigation is almost a standard for residential homeowners desiring high-quality landscapes in Florida. The goal of this study was to document irrigation water use (T1) and system uniformity in the Central Florida Ridge region under typical irrigation practices, and to quantify distribution uniformity of residential sprinkler equipment under controlled conditions. The other major goal was to determine if scheduling irrigation by setting controllers based on historical evapo-transpiration (ET) (T2) and reducing the percentage of turf area combined with setting the controllers based on historical ET (T3) would lead to reductions in irrigation water use. The time frame of this study was 29 months beginning in 2002. Most of the homes in the study tended to over-irrigate. Irrigation system analysis for each home included irrigation water distribution uniformity tests, recorded water use, visual observation of the turf quality, and pressure testing across all zones in the system. Of the 27 houses in this study, average annual irrigation accounted for 62% of the residential water use volume. The T1 homes had an average monthly water use of 146 mm. Compared to the x

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T1 homes, T2 had a 21% reduction, and T3 had a 41% reduction in average monthly water use. Over-irrigation was a result of a lack of understanding of the run times based on equipment type and seasonal evapotranspiration rates. In many cases, homeowners did not decrease irrigation water use in the winter months. To test the distribution uniformity of the irrigation systems, a catch-can test was used. From these tests, the overall low quarter distribution uniformity (DU lq ) value was calculated as 0.45. Rotor sprinklers resulted in significantly higher DU lq compared to fixed pattern spray heads (0.49 compared to 0.41, respectively). The spray heads had higher uniformity (DU lq value) when fixed quarter-circle nozzles were used, as opposed to adjustable nozzles. Uniformity was higher in the tests where the manufacturer recommended pressure was maintained rather than tests performed at low pressure. For the control tests, the spacing was set according to manufacturer guidelines for head to head coverage. In contrast, the residential systems had less-than-ideal spacing, and thus had a decreased DU lq value. Residential irrigation system, uniformity can be improved by minimizing the occurrence of low pressure in the irrigation system and by ensuring that proper spacing is used in design and installation. The use of time domain reflectometry (TDR) probes is an effective nondestructive method of measuring soil moisture content. The study compared irrigation distribution uniformity evaluated by TDR in the upper 12 cm of the soil versus catch-can tests. The calculated DU lq determined from a TDR device tended to be 0.15 to 0.20 points higher than the DU lq value determined by the catch-can method. The TDR moisture content DU lq did not correlate with catch-can DU lq xi

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CHAPTER 1 INTRODUCTION Irrigation has become nearly a standard option for residential homeowners desiring high quality landscapes in Florida. Turfgrass is a key landscape component, and normally the most commonly used single type of plant in the residential landscape. Although Florida has a humid climate (the average precipitation rate is greater than the evapotranspiration rate), the spring and winter are normally dry. The average annual precipitation for the Central Florida ridge is approximately 1320 mm, with most of this in the summer months (June through August). The spring months (March through May) are typically the hottest and driest (USDA, 1981). This region is also characterized by sandy soils with a low water-holding capacity; therefore, storage of water is minimal. The dry spring weather and sporadic large rain events in the summer (coupled with the low water-holding capacity of the soil) make irrigation necessary for the high-quality landscapes desired by homeowners. Residential water use comprises 61% of public-supply water withdrawals (Fernald and Purdum, 1998). Public supply is responsible for most (43%) of the groundwater withdrawn in Florida. Between 1970 and 1995, public-supply water withdrawals increased 135%(Fernald and Purdum, 1998). Florida consumes more fresh water than any other state east of the Mississippi River (Solley al. (1998). Floridas current population of 16 million is projected to exceed 20 million by 2020 (USDC, 2001). With the average residential irrigation cycle consuming 2000 to 2500 gallons of water per cycle (Hayes, 2000), water conservation has become a state concern. 1

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2 In 1972 (in the Florida Water Resources Act, Chapter 373) the Florida Legislature created the five water management districts. In 1997, Chapter 97-160 of the Laws of Florida was ratified; this overruled Chapter 373 of the Florida Statutes, the previous water law. The revision included delegating responsibilities to the water management districts. Each district was assigned primary responsibility for conducting water resource development. This study focused on the Central Florida ridge in the St. Johns River Water Management District (SJRWMD). Due to drought conditions in the past few years, the SJRWMD has limited residential irrigation to 2 times per week. Residential irrigation is prohibited between 10 a.m. and 4 p.m., whether the water is from public supply, domestic self-supply (i.e., wells), or surface water (SJRWMD, 2002). Irrigation outside of these hours reduces evaporative and wind losses. Residential irrigation water is thought to be 50% of total irrigation water use, although, no literature confirmed this. Irrigation efficiency defines how well an irrigation system supplies water to a given crop or turf area. Efficiency is the ratio between water used beneficially and water applied, and is expressed as a percentage. There are three concepts of irrigation efficiency: water conveyance efficiency (E c ) (Eq. 1-1); water-application efficiency (E a ) (Eq. 1-2); and reservoir storage efficiency (E s ) (Eq. 1-3). idcWWE100 [1-1] dsaWWE100 [1-2] rspsWWE100 [1-3]

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3 where W d is the water delivered to the area being irrigated, W i is the water introduced into the distribution system, W s is the irrigated water stored in the root zone, W p is the water pumped from the reservoir, and W rs is the water stored in the reservoir (Smajstrla al. (1991). Water conveyance efficiency is calculated from the point of discharge (pump), while water application efficiency is calculated over an entire field (or lawn). Reservoir storage efficiency is the ratio of water pumped from the reservoir and water stored in the reservoir. Factors that lower efficiency are evaporation, wind drift, improper equipment adjustment, drainage below the root zone, and runoff. Reservoir storage efficiency is varies depending on site conditions. The lowest values can be attributed to surface reservoirs due to evapotranspiration (ET) and seepage. Since most residential irrigation water in Florida is derived from groundwater, reservoir storage efficiency is thought to be as high as technically possible. In pressurized sprinkler irrigation systems, water conveyance efficiency is nearly 100%, unless there is a leak in the pipeline or distribution equipment. Thus, application efficiency is the only component that may vary in residential irrigation systems. To achieve relatively high application efficiency, it is necessary to maintain even distribution of irrigated water over the target area. To determine if the water is used beneficially, it is necessary to determine the overall quality of the lawn. The assessment of turfgrass is a subjective process using the National Turfgrass Evaluation Procedures (NTEP) (Shearman and Morris, 1998). This evaluation is based on visual estimates such as color, stand density, leaf texture, uniformity, disease, pests, weeds, thatch accumulation, drought stress, traffic, and quality.

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4 Turfgrass quality is a measure of functional use and aesthetics (i.e., density, uniformity, texture, smoothness, growth habit, and color). Irrigation systems used by the households typically include stationary spray heads and gear driven rotor sprinklers for the turf and landscape. Water conservation oriented designs include microirrigation for the landscape bedding. Uniformity of water distribution measures the relative application depth, over a given area. This concept can be valuable in system design and selection, and can assign a numeric value to quantify how well a system is performing. The term uniformity refers to the measure of the spatial differences between applied or infiltrated waters over an irrigated area. Two methods have been developed to quantify uniformity: distribution uniformity (DU) and Christiansens coefficient of uniformity (CU). The low-quarter irrigation distribution uniformity (DU lq ) can be calculated with the following equation (Merriam and Keller, 1978): totlqlqDDDU [1-4] where lqD is the lower quarter of the average of a group of catch-can measurements, and totD is the total average of a group of catch-can measurements. Distribution uniformity is usually represented as a ratio, rather than a percent (Burt et al. (1997), to signify the difference between uniformity and efficiency. This method emphasizes the areas that receive the least irrigation by focusing on the lowest quarter. Burt et al. (1997) defined common irrigation performance measurements, standardized and clarified of irrigation definitions, and quantified irrigation measurements. Distribution uniformity is not considered efficiency. Although a system

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5 may have even distribution, over-irrigation can occur because of mismanagement. Low-quarter distribution uniformity uses a definable minimum range (lowest quarter) rather than the absolute minimum value (zero). The Irrigation Association (2003), recommended the following distribution of the lower half (DU lh ) for scheduling residential irrigation systems, totlhlqDDDU [1-5] lqlhDUDU614.386.0 [1-6] where lhD is the lower half of the average depth of the water irrigated, and totD is the total of the average depth of water irrigated in a given area. Determining distribution uniformity helps to reduce excess water used for irrigation purposes. DU lh is suggested over DU lq because the lower quarter overestimates the effect of non-uniformity for landscapes (IA, 2003). The coefficient of uniformity treats over-irrigation and under-irrigation equally as compared to the mean, and can be calculated by the Christiansen (1942) formula (Eq. 1-7), niiniiVVVCU111 [1-7] where equals the volume in a given catch-can, and iV V refers to the mean volume. In addition to the coefficient of uniformity and the distribution uniformity, there are other important factors in evaluation of a system. Application rates, system pressure

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6 variability, runoff, wind, amount of water applied, pump performance, and overall system management must be considered when evaluating total system performance. Several studies have used these concepts to determine efficiency and uniformity of irrigation systems used in urban and agricultural settings. In Utah, a model for estimating turf water requirements was created (Aurasteh, 1984). Urban irrigation was studied with the irrigation use measured weekly by 20 homeowners. The objectives of the study were to measure residential distribution uniformities, assess potential application efficiencies, and to compare water use to ET rate. The sprinkler uniformity tests were conducted using catch-cans. The ET rate was calculated, and an empirical model for determining urban irrigation needs was created. Residential solid set and movable systems were compared; analysis of the application efficiency these systems showed that the average water application was about 30% for hand-move and 37% for solid set systems (Aurasteh et al. (1984). It was also noted that these homeowners used approximately 61% of their total water supply for irrigation. Utah receives less average annual precipitation, 207 mm (8.2 in) (NRCS, 1990), compared to the 1320 mm (52 in) received in Florida. Due to the wide use of sprinkler irrigation as an irrigation method on sloping lands, the effects of surface slope on sprinkler uniformity were studied in Brazil. It was found that distribution uniformity has a direct correlation to nozzle and riser angle, increasing as the nozzle angle is varied from vertical to horizontal, perpendicular to the ground. However, the DU decreases with an increase in ground slope. The DU was improved with a triangular precipitation pattern for all ground slopes and nozzle angles (Soares al. (1991).

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7 A number of computer models have been created to aid in uniformity testing of sprinkler systems. In Brazil, a data acquisition system for sprinkler uniformity testing was created (Zanon et al., 2000). The system was designed to test a two radii precipitation pattern (head-to-head) for low to medium pressure sprinklers under no wind conditions. In Japan, a method was developed for evaluating water application rate and the coefficient of uniformity, CU, of sprinklers with head to head coverage. The tests were under realistic conditions, including monitoring the effect of wind drift (Fukui al. (1980). Numerous modeling studies have been conducted with regard to residential irrigation uniformity and efficiency. In Spain, the SIRIAS software was developed. This model for sprinkler irrigation uses the ballistic theory to predict the path of drops discharged, obtaining wind-distorted water distribution, and formulation for the air drag coefficient. To consider actual environmental conditions, the program has three options for evaporation and drift losses within the irrigation process (Carrion et al., 2000). The simplification and comparison of models has also been explored. At Oregon State University, a widely used model based on numerical solutions was modified for simplicity of use. Accurate analytical approximations for DU, CU, application efficiency, deficiently irrigated volume, and the average deficit over the deficiently irrigated area were developed. The approximations proved to be more accurate than earlier approximations and introduced negligible error when used for practical applications (Smesrud and Selker, 2001). At Colorado State University, the use of the normal distribution function in describing sprinkler irrigation uniformity was simplified for evaluation of irrigation system performance in terms of economic and environmental decisions (Walker, 1979). Colorado State University and Louisiana Technical University

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8 compared statistical models to approximate sprinkler patterns with various coefficients of uniformity, calculation of water volume needed, and irrigation efficiency. It was found that for uniformity coefficients the normal distribution was a better fit than the linear model. However, at uniformities below 0.65 the linear model fit best (Elliott al. (1980). In Colorado, granular matrix soil moisture sensors were used to control the irrigation for urban landscapes. The objective of the study was to evaluate the effectiveness and reliability of soil moisture sensors for irrigation control. The soil moisture systems proved to be very reliable and reduced the irrigation application below theoretical requirements. The calculated theoretical irrigation requirement was 726 mm, while the actual water applied, as allowed by the sensor system, was 533 mm (Qualls et al., 2001). According to the residential irrigation system audits conducted by the University of Georgia Water Resources Team (Thomas et al., 2003) the operating time was improperly set on many homes tested, therefore the systems were set to run too long applying more water than necessary. Of the systems audited, the spray heads distributed three to five times the water application rate per given area as compared to rotary sprinklers. To increase water conservation, a national sub-metering and allocation billing study found more multi-family dwellings are being converted to billing systems where the water and wastewater charges are paid separately, as opposed to including these charges as part of the total rent. Data suggested that sub-metering irrigation water use would further increase the outdoor water use efficiency and management. Sub-metering on multifamily apartment units and billing based on actual consumption resulted in water savings of 15% or 8,000 gallons per unit per year. Reduction of irrigation in the winter

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9 months resulted in a statistically significant impact on the overall water use (p<0.001). The percent of total property which was irrigated did not have a significant (p=0.150) affect on the total water use. However, water billing practices based on the allocation methods (ratio utility billing method) did not affect water savings (Mayer et al., 2004). The American Water Works Association (AWWA) Research Foundation funded a study on residential end uses of water (Mayer al. (1999). The study concluded the following homes with: in-ground irrigation systems used 35% more water than houses without these systems, automatic timer controls incorporated into the system led to 47% more water used, drip irrigation systems used 16% more water than homes which did not irrigate the area with in-ground irrigation, homes which only hand (hose) watered used 33% less water than those with in-ground systems, and homes which included a consistently maintained garden used 30% more outdoor water. The samples which were grouped into the low-water-use treatment applied an average of 20.3 gal/ft 2 per year for the irrigated area. The standard landscape treatment applied 22.8 gal/ft 2 per year. However, there was not a significant difference (at the 95 percent confidence interval) between these two treatments. One of the conclusions as to why there was an inconclusive finding was that the low-water-use landscaping required an initial establishment period of additional water. In Florida, Mobile Irrigation Labs (MILs) were established as a public service in 1992 as part of a water conservation program. Funding for this program comes from the United States Department of Agriculture (USDA) and the individual water management districts. The Florida MILs were modeled after those operating in California and Texas. They evaluate irrigation systems in both agricultural and urban areas by conducting a

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10 series of tests over a two-hour period, measuring pump flow rates, sprinkler pressures and flow rates, and application uniformities (Micker, 1996). While overall uniformity of irrigation systems has been measured in Florida in the past, most of the MILs no longer conduct actual system distribution uniformity tests; therefore, there is a lack of information regarding current residential irrigation system performance and water use. In some MILs distribution uniformity results that were judged to be low were discarded (anonymous MIL source). In field assessments of irrigation system performance in California, Pitts et al. (1996) found a mean DU lq of all systems tested as 0.64. The average DU lq for non-agricultural turfgrass sprinklers (large turfgrass areas) was 0.49. Greater than 40% of the tested systems had a DU lq of less than 0.40. This study concluded that the low DU lq values were based on the following reasons (listed in order of frequency): maintenance and faulty sprinkler heads, mixed zones (spray and rotor), excessive pressure variations, and poor head-to-head coverage. Many of the cooperators in this study were unaware of importance of scheduling based on potential evapotranspiration and uncertain about the application rates of their systems. It was found that scheduling was usually based on the appearance of the turfgrass. To the trained eye this would be acceptable, however typical homeowners do not know what signs are indicative of over-watering or drought related stress. Linaweaver et al. (1967) found that the amount of water used for residential lawns is effected by the total number of consumers, the economic level of the residential area, the area of turfgrass and bedding requiring irrigation, the evapotranspiration rate, and the quantity of effective rainfall. In Wyoming, from the summer 1975 through spring 1977,

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11 a study was conducted on actual lawn water application rates for residential households and evaporation rates of lawn turfgrass. The application rates found were between 122 and 156% above calculated seasonal evapotranspiration rates (Barnes, 1977). Evapotranspiration (ET) is the rate at which water may be removed from soil and plant surfaces to the atmosphere by a combination of evaporation and transpiration (Allen al. (1998). Evaporation (E) is the conversion of water into its vapor phase. The main factors influencing evaporation are the supply of energy by solar radiation and the transport of vapor away from the surface (e.g., by wind). Transpiration (T) refers to the water used by plants and is affected by plant physiology and environmental factors. The evapotranspiration process is climate controlled. Researchers at Texas A&M University (White et al., 2004) looked at using potential ET, a landscape coefficient (L c ), and the landscape size, to develop water budgets for residential landscapes. It was determined that potential ET irrigation budgeting with an L c of 1.0 would account for substantial irrigation water savings, especially in the summer months. A time domain refectometry (TDR) device can be used to measure soil water content by measuring the time needed for an electrical signal to travel along wave guides. As opposed to the measurement of irrigation application, soil moisture is measured as the volume of water within a volume of soil. A TDR device can be used to estimate the amount of water stored in a profile. It also can help to eliminate how much irrigation is required to reach a desired moisture content. The Northern Colorado Water Conservancy District compared catch-can tests and soil moisture sensor measurements in turfgrass irrigation auditing. When calculating DU lq it was found that the soil moisture uniformity was higher than the catch-can

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12 uniformity. From the tests in the study, the soil moisture DU lq was 0.15-0.20 (maximum value of 1.00) higher than the DU lq determined by the catch-can method (Mecham, 2001). Although the catch-can DU lq could help determine the overall system performance, these uniformity values did not properly express the distribution of the water through the thatch or as affected by the soil properties. Estimating irrigation run times based on the catch-can DU lq would lead to over-irrigation, due to the low nature of these DU lq values (Mecham, 2001). In Florida, a study compared microirrigation (drip) uniformity determined by both time domain reflectometry and the conventional volumetric method. The study concluded that the TDR can be a useful tool for quick determination of uniformity. Inversely in this study, for the drip systems the TDR DU lq was lower than the DU lq calculated by the conventional method. Differences were assumed to be a result of soil properties and point measurement locations (Dukes and Williams, 2002).

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CHAPTER 2 RESIDENTIAL IRRIGATION WATER USE Homeowners in Florida desire a year-round lush landscape; consequently, irrigation is required. Florida is reputed as the Sunshine State with lush foliage and beautiful weather. The population is steadily increasing and new housing developments are constantly being built. New Floridians expect a manicured and lush landscape around their homes. Unfortunately, this has resulted in excessive water used for irrigation purposes. Since the price of groundwater is not yet particularly high most homeowners would rather pay the price for a green lawn. As of 2000 Florida had a population of nearly 16 million and is projected to exceed 20 million people by 2020 (USDC, 2001), which has led to the consumption of more fresh water than any other state east of the Mississippi River (Solley al. (1998). Between 1970 and 1995 there was a 135% increase in groundwater withdrawals in Florida (Fernald and Purdum, 1998). Public supply is responsible for the largest portion, 43%, of groundwater withdrawn in Florida. Residential water use comprises 61% of the public supply category (Marella, 1999). Since irrigation is so widely used and the number of in-ground irrigation systems is increasing across the state, it is necessary to observe the residential irrigation water use trends. The objective of this project was to measure residential irrigation water use in the Central Florida Ridge across three landscape and irrigation scheduling treatments. 13

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14 Materials and Methods This study was conducted within the Central Florida ridge (Figure 2-1), which included eight homes in Marion County, nine homes in Lake County, and ten homes in Orange County. The homes were categorized into three treatments. Treatment one (T1) consisted of existing irrigation systems and typical landscape plantings, where the homeowner controlled the irrigation scheduling. Treatment two (T2) also consisted of existing irrigation systems and typical landscape plantings, but the irrigation scheduling was based on historical evapotranspiration (ET) rates from the Central Florida area over 30 years. Treatment three (T3) consisted of an irrigation system designed according to specifications for optimal efficiency including a landscape design that minimized turfgrass and maximized the use of native drought tolerant plants as classified by the SJRWMD. To further achieve water savings in T3, the landscape plants were irrigated by microirrigation (micro-spray heads, bubblers, and drip tubing) as opposed to standard spray and rotor heads. The T3 landscape designs and modifications to irrigation systems were installed as part of this project. The newly planted landscape in T3 required an establishment period of one to two months, with increased irrigation. This additional water use data has been omitted in water use analysis. Water use was included for analysis after the andscape material had been established for two months. Mayer et al. (1999) also found that new landscapes required an initial establishment period of additional water. The average annual precipitation in this area ranges from 1275 to 1400 mm, with the maximum rainfall in the summer months and the minimum rainfall from late fall through spring (USDA, 1981). The soils are excessively to moderately well drained sandy Quartzipsamments (USDA, 1981). The prevalent soil series in the Marion and

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15 Lake County sites is Astatula sand, which allows for rapid permeability, has a very low available water capacity, and little organic matter content (USDA, 1975). The dominant soil series in the Orange County site location is Urbanland-Tavares-Pomello, which is a moderately well drained soil that is sandy throughout (USDA, 1989). The Marion and Lake County sites included in this study are on the hills that were previously citrus farms, and have been built upon a layer of sand fill. The irrigation systems used by the households typically include stationary spray heads and gear driven rotor sprinklers for the turf and landscapes. The lawn areas of the yards all consisted of St. Augustine turfgrass, which is a warm season turfgrass and a common sod in new construction in Florida. The residences for this study where chosen if an in-ground automatic irrigation system was used and the irrigation system was supplied by potable city water (not well-drawn or reclaimed water). The homeowners were recruited at garden club or area community association meetings. All of the residences included in this study obtained water from local utilities. The utility water meter was used to determine the amount of water consumed by the household. For domestic water systems, positive displacement meters are used, which are relatively inexpensive and accurate (Munson al. (1998). To determine the volume of irrigation water used, a second flow meter was installed after the irrigation pipeline diverged from the main water line to the house, before distribution to the solenoid valves. The meters were installed with no obstruction within approximately ten diameters of the inlet and outlet of the meter. This was to ensure minimal turbulence in flow through the meter to maintain accuracy (Baum et al., 2003). Water use data was collected from January 2002 through May 2004. However, additional homes were

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16 incorporated into T1 and T2 until May 2003, and the last of the T3 homes was added in July 2003. The area of each yard was calculated from a scale drawing of the house, turf, and landscape beds. The irrigated area was necessary for calculating depth of irrigation applied from the volume data measured by the meters. Weather stations in Marion and Lake Counties were installed in late February 2002 and one was installed in Orange County in May 2002 to enable calculation of reference evapotranspiration (ET o ). The weather stations were located in flat-grassed areas so that the nearest obstruction was at least 61 m away from the station. Irrigated areas were chosen when possible; however, this resulted in one of the stations collecting irrigation water in the precipitation bucket. A separate rain bucket and data logger (Davis Instruments Corp., Hayward, CA and Onset Computer Corp., Bourne, MA) was installed in a non-irrigated area to separate precipitation events from irrigation events. The residential home sites were located within 1 km of the weather stations. Date, time, temperature, relative humidity and temperature (model HMP45C, Vaisala, Inc., Woburn, MA), soil heat flux (model HFT3, Radiation Energy Balance Systems, Bellevue, WA), solar radiation (model LI200X, Li-Cor, Inc., Lincoln, NE), wind speed and direction (model WAS425, Vaisala, Inc., Sunnyvale, CA) and, precipitation (model TE525WS, Texas Electronics, Inc., Dallas, TX), were recorded in 15 minute intervals via a CR10X data logger (Campbell Scientific, Inc., Logan UT). The Penman-Monteith equation is a widely used combination method for calculating ET o As outlined in FAO-56 this equation takes the following form (Allen al. (1998):

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17 ET o 2234.01273900408.0ueeuTGRasn [2-1] 23.2373.23727.17exp6108.04098TTT [2-2] R n = R ns R nl [2-3] R nl = 35.035.114.034.024min,4max,sosaKKRReTT [2-4] R ns = (1)R s [2-5] R so = (0.75 + z(2 x 10 -5 ))R a [2-6] R a = )cos()cos()sin()sin()sin()60(24ssrscdG [2-7] d r = 1 + 0.033 cos J3652 [2-8] = 0.409 sin 39.13652J [2-9] s = arcos [-tan()tan()][2-10] e s = 2)(Te )(Teminomaxo [2-11] e a = 2100RH)(Te 100RH)(Teminmaxomaxmino [2-12] e o (T) = 3.23727.17exp6108.0TT [2-13]

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18 where ET o = Potential evapotranspiration, mm/day slope of the vapor pressure curve, kPa o C -1 R n = net radiation of the turf surface, MJ m -2 day -1 R nl = net outgoing longwave radiation, MJ m -2 day -1 R ns = net solar or shortwave radiation, MJ m -2 day -1 R so = clear sky solar radiation, MJ m -2 day -1 R s = measured solar radiation W/m 2 x 0.0864, MJ m -2 day -1 R a = extraterrestrial radiation, MJ m -2 day -1 G = measured soil heat flux density, MJ m -2 day -1 G sc = solar constant, 0.0820 MJ m -2 min -1 T = measured air temperature at a 1.5 m height, o C u 2 = measured wind speed at a 2 m height, m s -1 e s = saturation vapor pressure, kPa e a = actual vapor pressure, kPa e o (T) = saturation vapour pressure at air temperature, kPa RH = relative humidity at 1.5 m height, % d r = inverse relative distance Earth-Sun s = sunset hour angle, rad = solar declination, rad = psychrometric constant, 0.067 kPa o C -1 = Stefan-Boltzmann constant, 4.903 x 10 -9 MJ K -4 m -2 J = Julian day = latitude, radians Effective rainfall is the portion of rainfall that is beneficial to the plants, and does not include that rainfall that produced runoff. Effective rainfall was estimated by the SCS method, presented by the following equation (Schwab al. (1993): ]10][93.225.1)[(000955.0824.0oETmePDfP [2-14] 37251032.210894.00116.053.0)(DDDDf [2-15] where P e = estimated effective rainfall for soil water deficit depth, mm P m = mean monthly rainfall, mm ET o = average monthly evapotranspiration, mm f(D) = adjustment factor for soil water deficits or net irrigation depths D = soil water deficit or net irrigation depth, mm (used 25 mm)

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19 To determine if the water is used beneficially, it is necessary to determine the overall quality of the lawn. The assessment of turfgrass is a subjective process following the National Turfgrass Evaluation Procedures (NTEP) (Shearman and Morris, 1998). This evaluation is based on visual estimates such as color, stand density, leaf texture, uniformity, disease, pests, weeds, thatch accumulation, drought stress, traffic, and quality. Turfgrass quality is a measure of functional use and aesthetics (i.e., density, uniformity, texture, smoothness, growth habit, and color). The statistical analysis of the collected data was analyzed using the general linear model (GLM) function of the SAS software for the anova tables. The means are reported as weighted means. All significance was at the 95% confidence interval, unless otherwise noted. Interactions, such as year or season with treatment were observed, and the three locations were nested for proper data analysis. Results and Discussion Overall, the average household used 63% of total water consumed for irrigation. Treatment 1 averaged 75% of the total water use for irrigation (Table 2-1), Treatment 2 used 66% (Table 2-2), and Treatment 3 used 49% (Table 2-3), which were statistically different (p<0.001). Many of the homeowners, particularly in Marion and Lake Counties, would leave town for extended periods of time in the summer months (June-August). Although the homeowner was not in town, irrigation of the landscape continued. Three of the T3 homes were vacant for part of the data collection period because the irrigation system was installed prior to the sale of the house. This lack of occupancy did not affect the irrigation water use for the homes because the homes were part of T3, where the controller settings were adjusted as part of the study. The lack of occupancy did

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20 however have and effect on the percentage of water used for irrigation by the household, so the months in which the percentage of water use was 100% were omitted. Treatment 1 (user controller setting with typical irrigation system) had the highest average monthly irrigation water use, 146 mm. Treatment 2 (60% historical ET replacement with typical irrigation system) consumed 116 mm for irrigation purposes. Treatment 3 (adjusted controller setting incorporating microirrigation) used the least water for irrigation, 86 mm. The average monthly irrigation depth was significantly different (p<0.001) across all treatments. The T2 homes consumed 21% less water than T1, and T3 consumed 41% less than T1. The evapotranspiration and rainfall data is reported in Table 2-4. The comparison of the effective rainfall plus the applied irrigation compared to ET o can be found in Figure 2-2. Across all three years, T1 had a higher water input than ET o The T2 water use was very similar to T1, especially in the summer months. There was a decrease in water input during the first winter; this is when the controller adjusting began for the T2 homes. The reasons the T2 water input did not decrease as much during the later part of 2003 and early 2004 was because: the homeowners would periodically re-adjust their controller; the controller settings was based on historical ET and during this time there was more rain than expected; and sometimes rain events occurred after scheduled irrigation. The T3 water input was much lower after the first year, this is probably because during the first year there was an initial establishment period for the landscapes. Although this period was removed, there were residual effects. Year two, 2003, was the only full year of data collection where the irrigation run times were seasonally adjusted. During this cycle of seasons, there were significant

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21 differences between treatment and season, there was not however an interaction between treatment and season. The T1 homes applied 141 mm of irrigation water, which was significantly more that T2 and T3, which applied 94 mm and 85 mm respectively during this year. Across the 29 months of data collection, all three treatments combined used significantly the least water in the winter months, 78mm. The summer months accounted for significantly the second lowest amount, 117 mm. There was not a significant difference between the fall and spring months, and during these the most water was used for irrigation purposes. Turf quality was rated seasonally (Table 2-5). In the winter months (December-February), when the turfgrass is typically dormant, T3 used the least water, 55 mm, primarily because the microirrigation zones result in a smaller effective irrigated area and turfgrass irrigation could be stopped or greatly reduced. In the spring months (March-May) T1 applied the most irrigation water, 179 mm, T2 used 132 mm, and T3 consumed the least, 94 mm. This is due to monthly adjustments of irrigation times and because the microirrigated areas in T3 homes required less water than if those areas were sprinkler irrigated. However, there was not a statistical difference between the treatments. During the spring months, ET o was the highest and the adjusted controller run time settings were similar to that of typical user set run times. In the fall months (September-November), T1 and T2 resulted in similar application amounts of 155 mm and 148 mm, and T3 significantly less at102 mm. The minimum turf quality rating for acceptable quality is 6. Lower ratings do not necessarily imply drought stress. The lawns in T1 and T2 maintained minimum or better

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22 quality during the project data collection period. The T2 turfgrass had no significant differences in quality from T1 under a decreased irrigation schedule. The T3 lawns did have lower quality ratings as compared to T1 and T2 in the winter (Table 2-5). The homes in T1 and T2 were irrigated solely by either rotary or spray irrigation heads. The homes in T3 incorporated a portion of the irrigated area covered by microirrigation. The landscape designs for T3 homes also included larger bedding and decreased turfgrass areas. The typical T1 or T2 landscape averaged 75-78% turfgrass (Table 2-6). The turfgrass portion of the T3 homes ranged from 66% to 5%, and averaged 35%. The remaining percentage of the landscaped area was considered bedding and irrigated with the microirrigation. In some sections of the T3 homes the bedded areas included the use of ground covers. The homes in Orange County had the highest average water use, 130 mm/month. This water use is directly correlated with the irrigation system design. The yards in Orange County had the smallest turfgrass area, which is typically irrigated by a greater percentage of spray zones versus rotary zones heads (a ratio of 5:1). The ratios of spray heads to rotor zones for Marion and Lake Counties were 4:1 and 4:3 respectively. Spray zones have a higher precipitation rate and the water output is more sensitive to the scheduled run time compared to rotor zones. For all treatments, the homes in Lake County used the greatest percentage of water for irrigation because the yards in this area were the largest, primarily composed of turfgrass (Table 2-6). The irrigation water use difference between the three counties was marginally significant (p-value of 0.06).

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23 Summary and Conclusions The average household in this study used, for irrigation, 63% of the total. Substantial over-irrigation occurred on all treatments when compared to ET o Over-irrigation resulted from poor uniformity and improper scheduling. Irrigation water use was greatest on the homes with typical irrigation systems where the homeowner set their own controller run times (T1). At the homes where the irrigation system still consisted of a typical design, but the controller run times were adjusted based on historical evapotranspiration rates (T2), the irrigation water consumption was decreased by 21% as compared to T1. The homes with both the adjusted controller run time settings and the incorporation of microirrigation in the bedding areas (T3) consumed the least amount of irrigation water, 41% water savings as compared to T1. From the figure comparing the water use by treatment including effective rainfall to ET o it was observed that T3 had the lowest water input, which was similar to the evapotranspiration. The water input for the T1 homes was always much higher than ET o Irrigation application with respect to ET o for T2 fluctuated, over-irrigation still occurred, the scheduling could be improved to maintain lower water input. In Florida, rainfall supplies a significant portion of the plant water requirements but since rain events are often intense and water holding capacity is low, high rainfall values will not supply crop water needs over time. Turfgrass quality did not vary significantly across treatments 1 and 2. The T3 lawns did have lower quality ratings as compared to T1 and T2 in the winter. The T3 ratings were below the NTEP acceptable rating of 6, but never lower than 5. In the fall and winter months there was a decrease in turf quality, because turfgrass went into partial

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24 dormancy. During dormancy, which is the normal state of turfgrass in the winter months, irrigation run times can be decreased because the plant has decreased water needs. When the turfgrass goes into dormancy, the turfgrass color changes to tan from green. The decreased turf quality was color related and not due to drought stress or winter injury. In the spring months, after green-up, when the grass comes out of dormancy, the T3 turf quality was better than T1.

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25 Table 2-1. Monthly water use for Treatment 1 homes for all three locations combined. Treatment 1 Month Water Use (mm) % of Total Water Use No. of Homes Mar-02 124 85 5 Apr-02 144 87 5 May-02 186 89 5 Jun-02 124 76 5 Jul-02 90 75 5 Aug-02 154 69 8 Sep-02 148 83 8 Oct-02 158 82 8 Nov-02 135 83 8 Dec-02 106 60 8 Jan-03 135 78 8 Feb-03 97 80 8 Mar-03 142 79 8 Apr-03 184 85 8 May-03 162 91 8 Jun-03 177 90 8 Jul-03 117 31 8 Aug-03 123 31 8 Sep-03 177 81 8 Oct-03 158 57 8 Nov-03 110 75 8 Dec-03 104 67 8 Jan-04 83 77 8 Feb-04 102 77 8 Mar-04 245 80 8 Apr-04 157 71 8 May-04 214 68 8 Average* 146 75 Median 142 78 Std. Dev. 39 15 Total 3856 Water use indicated as depth applied per month, the fraction of the total water consumed by the home which was used for irrigation purposes, and the number of homes included in the sample. *The average is a weighted average by the number of homes included in the treatment.

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26 Table 2-2. Monthly water use for Treatment 2 homes for all three locations combined. Treatment 2 Month Water Use (mm) % of Total Water Use No. of Homes 2-Mar 164 74 6 2-Apr 154 90 6 2-May 173 31 6 2-Jun 85 31 6 2-Jul 116 81 7 2-Aug 129 57 8 2-Sep 168 81 9 2-Oct 155 80 9 2-Nov 172 61 9 2-Dec 97 65 9 3-Jan 31 46 9 3-Feb 42 47 9 3-Mar 66 56 9 3-Apr 100 67 9 3-May 133 73 9 3-Jun 167 64 9 3-Jul 72 63 9 3-Aug 85 71 9 3-Sep 157 76 9 3-Oct 162 76 9 3-Nov 115 69 9 3-Dec 81 61 9 4-Jan 74 64 9 4-Feb 107 69 9 4-Mar 124 69 9 4-Apr 154 75 9 4-May 175 63 9 Average* 116 66 Median 124 67 Std. Dev. 43 14 Total 3258 Water use indicated as depth applied per month, the fraction of the total water consumed by the home which was used for irrigation purposes, and the number of homes included in the sample. *The average is a weighted average by the number of homes included in the treatment.

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27 Table 2-3. Monthly water use for Treatment 3 homes for all three locations combined. Treatment 3 Month Water Use (mm) % of Total Water Use No. of Homes 2-Mar 128 66 2 2-Apr 168 76 2 2-May 173 68 2 2-Jun 173 58 2 2-Jul 186 58 2 2-Aug 178 35 3 2-Sep 114 36 3 2-Oct 201 37 3 2-Nov 150 38 4 2-Dec 110 39 4 3-Jan 58 20 4 3-Feb 67 32 4 3-Mar 119 48 7 3-Apr 143 65 7 3-May 80 89 7 3-Jun 101 88 10 3-Jul 75 59 10 3-Aug 58 31 10 3-Sep 90 52 10 3-Oct 89 55 10 3-Nov 76 32 10 3-Dec 47 31 10 4-Jan 37 34 10 4-Feb 58 43 10 4-Mar 74 57 10 4-Apr 61 47 10 4-May 97 48 10 Average* 86 46 Median 97 48 Std. Dev. 48 18 Total 2911 Water use indicated as depth applied per month, the fraction of the total water consumed by the home which was used for irrigation purposes, and the number of homes included in the sample. *The average is a weighted average by the number of homes included in the treatment.

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28 Table 2-4. Evapotranspiration, rainfall, and effective rainfall calculated per month. Evapotranspiration Rainfall Effective Rainfall Month Year ET o (mm) Total Depth (mm) Events (#) Total Depth (mm) Mar 2002 123 98 7 56 Apr 2002 134 45 6 28 May 2002 156 184 10 102 Jun 2002 129 354 21 168 Jul 2002 139 389 23 186 Aug 2002 134 246 19 125 Sep 2002 124 111 13 62 Oct 2002 112 101 13 56 Nov 2002 91 50 15 29 Dec 2002 81 175 25 83 Jan 2003 86 16 11 9 Feb 2003 88 107 12 55 Mar 2003 109 129 23 68 Apr 2003 131 45 14 28 May 2003 151 112 19 66 Jun 2003 131 256 20 128 Jul 2003 139 84 11 50 Aug 2003 125 185 21 96 Sep 2003 107 103 14 56 Oct 2003 97 51 10 29 Nov 2003 75 52 15 29 Dec 2003 61 57 10 30 Jan 2004 59 64 10 33 Feb 2004 76 106 5 53 Mar 2004 112 50 6 30 Apr 2004 130 59 8 36 May 2004 155 78 5 49 Average* 113 122 14 64 Median 123 101 13 55 Std. Dev. 28 94 6 44 Total 3055 3307 366 1741 This data is the average from all three weather stations, one at each location. *The average is a weighted average by the number of homes included in the treatment.

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29 Table 2-5. Seasonal water use, fraction of total water use, and turf quality rating with letter notations referring to the significant difference between treatments for each season. Season Treatment Water Use (mm) Fraction of Total Water Use (%) Turf Quality Rating T1 103a 75 5.7a T2 73b 63 6.4a Winter T3 55b 37 5.4b T1 179a 77 5.9a T2 132b 74 6.6a Spring T3 94c 42 6.4a T1 139a 82 5.8a T2 110ab 66 5.6a Summer T3 96b 63 5.1a T1 155a 62 6.6ab T2 148a 61 6.9a Fall T3 102b 55 5.8b T1 142 75 6.0 T2 119 66 6.3 Average T3 87 46 5.7 Table 2-6. Percentage if irrigated area which is turfgrass or landscaped bedding as well as the total irrigated area for each home. Treatment 1 Treatment 2 Treatment 3 House Turfgrass (%) Bedding (%) Area (m 2 ) Turfgrass (%) Bedding (%) Area (m 2 ) Turfgrass (%) Bedding (%) Area (m 2 ) 1 66 33 2165 60 40 497 5 95 495 2 70 30 1709 66 33 2434 10 90 1636 3 74 26 495 74 26 495 15 85 1059 4 80 20 351 74 26 743 20 80 775 5 82 18 655 75 25 822 40 60 1050 6 85 15 3198 76 24 611 50 50 450 7 85 15 697 78 22 1059 50 50 400 8 88 12 1505 85 15 701 59 41 1737 9 . 85 15 1328 60 40 450 10 . . . 66 34 448 Average* 78 21 1347 74 25 966 35 65 850 The average is a weighted average based on area.

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30 Figure 2-1. Map of site locations. Grey counties encompassing the Central Florida Ridge and the dark counties encompassing the cooperator homes. Inset map shows the geographic location of the cities closest to the three residential locations, marked by stars.

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31 050100150200250300350Mar-02May-02Jul-02Sep-02Nov-02Jan-03Mar-03May-03Jul-03Sep-03Nov-03Jan-04Mar-04May-04Total Depth (mm ) ETo T1 T2 T3 Figure 2-2. Effective rainfall plus applied irrigation for each treatment compared to reference evapotranspiration.

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CHAPTER 3 RESIDENTIAL IRRIGATION DISTRIBUTION UNIFORMITY Irrigation efficiency defines how effectively an irrigation system supplies water for crop or turfgrass beneficial use. Application efficiency can be computed as the ratio between water used beneficially and water applied and is expressed as a percentage. Irrigation efficiency is difficult to quantify; therefore, distribution uniformity is often measured for sprinkler irrigated areas. Irrigation can be uniform and inefficient due to mismanagement; however, irrigation can not be non-uniform and efficient. As a result, irrigation uniformity can be a good indication of potential irrigation efficiency. Uniformity of water distribution measures the variability in application depth over a given area. Two methods have been developed to quantify uniformity: distribution uniformity (DU) and the coefficient of uniformity (CU). The low-quarter irrigation distribution uniformity (DU lq ) (Merriam and Keller, 1978) can be calculated with the following equation totlqlqDDDU [3-1] where lqD is the lower quarter of the average of a group of catch-can measurements, and totD is the total average of a group of catch-can measurements. Distribution uniformity is usually represented as a ratio, rather than a percent (Burt al. (1997), to signify the difference between uniformity and efficiency. This method emphasizes the areas that receive the least irrigation, by only focusing on the lowest quarter. Burt et al. (1997) defined common irrigation performance measurements, which 32

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33 discussed standardization and clarification of irrigation definitions and quantified irrigation measurements. Although an irrigation system may have even distribution, over-irrigation can occur due to mismanagement. The coefficient of uniformity treats over-irrigation and under-irrigation equally as compared to the mean, and can be calculated by the Christiansen formula as niiniiVVVCU111 [3-2] where, V i refers to the volume in a given catch-can and V refers to the mean volume (Christiansen, 1942). As part of a conservation program, in 1992 the Mobile Irrigation Labs (MILs) were established as a public service in Florida. The program is funded by the USDA and the individual water management districts. The Florida MILs were modeled after those operating in California and Texas. They evaluate irrigation systems conducting a series of tests over a two-hour period, measuring pump flow rates, sprinkler pressures and flow rates, and application uniformities (Micker, 1996). The MIL procedure requires 16 to 24 cans to be used, in selected irrigation zones, which is usually the largest turf area for residential tests. Table 3-1 shows the average DU lq ratios from residential irrigation systems of turf in various counties in Florida acquired from annual reports within the last decade. While uniformity of irrigation systems has been measured in Florida, many of the MILs no longer measure irrigation system uniformity by catch-can tests determining DU lq ; therefore, there is a lack of information regarding current residential irrigation system performance and water use in the state.

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34 The purpose of these tests was to evaluate residential irrigation system uniformity in the South Central Florida ridge, and determine typical residential equipment uniformity under controlled conditions. Materials and Methods The homes included in this study were located within the South Central Florida ridge. The study included 8 homes in Marion County, 9 homes in Lake County, and 10 homes in Orange County. The irrigation systems at the homes typically included stationary spray heads and gear driven rotary sprinklers for the turf and landscape areas. Spray heads and rotors were tested in this experiment since they are commonly used on turfgrass and designed to apply irrigation water as uniformly as possible. In most of the tested systems, the irrigation zones were not separated based on plant material. That is, an irrigation zone would commonly be installed to irrigate turfgrass and ornamental plants at the same time. Uniformity testing was only performed on turfgrass areas. An onsite weather station was in place to monitor wind speed, relative humidity, and temperature during testing. In residential testing, the catch-cans were distributed around the residential turf area in either a 1.5 or 3 m square grid depending on the irrigated area size (3 m grid for lawns with an area greater than 750 m 2 and 1.5 m grid otherwise). To minimize edge effects, the grid was positioned 0.8 m from property boundaries. This resulted in 100 to 500 cans used in each test. Pressure at the two furthest points in each zone was tested with a pitot tube and pressure gauge on rotors or with a in-line pressure gauge just beneath a spray head emitter. The control test site was located at the University of Florida (UF) Agricultural and Biological Engineering department in Gainesville, Florida. These test plots were set up

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35 to test the irrigation equipment from three different manufacturers. The tests were performed in a mowed turfgrass area without slope. The plot area for rotary sprinklers was 11.3 m x 11.3 m or 12.8 m x 12.8 m depending on equipment type and according to the manufacturer recommended spacing. The plot area for the spray heads was 4.6 m x 4.6 m according to manufacturer recommendations based on the equipment selected. Sprinklers were installed at each of the four corners of the plot area to insure spacing at 50% of manufacturers rated diameter at recommended pressure (Table 3-2). Pressure gages were installed before and after the pressure regulator entering the grid piping as well as at each nozzle. To quantify irrigation uniformity, the catch-can method of uniformity testing was used. The catch-can method of uniformity testing is described by both the ASAE and the NRCS (ASAE, 2000 and Micker, 1996). However, the procedure used in this project differs because it tests residential sprinkler irrigation systems rather than linear move, and center pivot sprinkler systems as in the ASAE Standard and is more detailed than that of the NRCS Mobile Irrigation Lab. For all test conditions (residential and control), 30 cm wire stem flags were used to mark the grid and were bent so as to level the catch-cans and prevent movement. The cans had an opening diameter of 15.5 cm and a depth of 20.0 cm. The irrigated area of each zone was recorded and the system was set to run for 25 min on spray zones and 45 min on rotor zones, to ensure that the average water application depth was at least 1.3 cm. At the residential test sites a sketch of the house and landscape beds was drawn to scale with the location of each can marked. Also, the type and location of each nozzle was recorded.

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36 According to the ASAE standards (ASAE, 2000) the wind speed was measured every 30 min during the test. The standard allows testing up to 5 m/s; however, if the wind speed was above 2.5 m/s or if the distribution was affected by the wind at lower speeds, the test was discontinued. If practical, the test was performed at night to minimize evaporative losses. If night time operation was impractical. (i.e., due to homeowner concerns or storms), the test was run during early morning hours when ET was lowest. Catch-can volumes were measured immediately following the test using a 500 or 1000 mL graduated cylinder depending on catch-can volume. These procedures were followed in both the residential testing and the control testing. Data analysis was performed using the Statistical Analysis System software (SAS Institute, Inc., 2003, version 8.02) using the GLM procedure to perform an analysis of variance. The GLM procedure enables the specification of any degree of interaction (i.e., crossed effects) and was designed for fixed effects models. The estimation of the fixed effects was based on ordinary least squares. Mean differences were determined using Duncans Multiple Range Test at the 95% confidence level. For the control tests at UF under ideal conditions, the cans were placed in either a 0.9 or 1.5 m square grid for spray or rotor heads, respectively and with a 0.3 m inset from the edge. The heads were all adjusted or fitted with appropriate nozzles to irrigate quarter circle arcs. The spray and rotary heads tested under ideal conditions were labeled as brand A, B, and C. For professionally installed irrigation systems in Central Florida, these three products comprise the most commonly used equipment. The spray heads with an adjustable arc (the coverage pattern is variable from part circle up to full circle) were denoted by adj. following the letter reference. All rotors had an adjustable arc by

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37 design. As shown in Table 3-2, the spray heads were tested at low pressure (69 kPa), high pressure (414 kPa), and manufacturer recommended pressure (207 kPa). The rotor heads were tested at low pressure (207 kPa) and manufacturer recommended pressure (345 kPa or 379 kPa). Each head test was replicated 5 times at each pressure. To maintain ideal testing pressure, gages were installed in the system piping immediately following an adjustable pressure regulator and at each irrigation head. Pressure varied less than 5% between the most distant two nozzles, indicating that pressure variations were not a source of non-uniformity. Results and Discussion Residential Testing The low-quarter distribution uniformities can be classified by the overall system quality ratings in Table 3-3 (IA, 2003). The uniformities of the residential systems tested in this study (Table 3-4) would be considered in the fair to fail range, with the exception of one good. When looking at the DU lq of the spray and rotor zones individually, it can be noted that the ratings of the spray zones were much lower, with half of the spray zone uniformities receiving a fail rating. The ratings of the rotor zones were normally distributed about the mean within the good to fail range. The mean DU lq (Table 3-4) of the rotor zones was 0.49 and the mean DU lq of the spray zones was 0.41, which was statistically different (p = 0.034). The overall low DU lq values for this study were lower than values reported by the MILs. The MIL DU lq values in Table 3-1 were significantly higher, averaging 0.53 (p = 0.02) than the overall DU lq values in Table 3-4 of 0.43. According to the overall system quality ratings in Table 3-2, two of the regions surveyed by the MIL result in an irrigation system quality rating of good or very good, one other as fair, one as poor and

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38 two others as fail. The DU lq value differences were in part due to testing procedure. As stated in the previous section, the catch-can tests performed for this study were a combination of the testing methods of both the ASAE standards and the NRCS MIL guidelines. The MIL catch-can test procedure requires only 16-24 cans to be distributed centrally within one of the largest zones. The procedures performed in this study used a grid with 100-500 cans distributed evenly across the entire irrigated turf area. Consequently, edge effects and challenging design areas, such as side lawns, were included in the tests of this study. Due to the greater number of catch-cans, a larger percentage of the under-irrigated areas were also included. Despite this difference in methodologies, it is thought that the procedures used in this study provided a more realistic determination of the variation in irrigation water application depth for the entire irrigation system. If the turfgrass edges of an irrigation zone in a residential setting begin to become stressed and turf quality declines, the homeowner will likely increase the irrigation volume applied to that area. As such, it is important to include the edge areas in uniformity testing. Table 3-4 compares DU lq determined with the catch-cans placed in the grid formation versus the DU lq determined by using only 16-24 can samples simulating the MIL procedure on the largest turfgrass area. The uniformity results are consistently significantly higher when following the MIL method. As previously mentioned, the MIL guidelines specify that the can placement should be in the largest area of the yard. Typically, rotar heads irrigate the largest area of the yard. Based on equipment alone, rotary heads tend to have greater uniformity (note Table 3-4). Therefore can location (i.e., center of zone vs. near edge of zone) will

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39 increase the DU lq value. Since the testing in this study was more representative of actual conditions, the IA table may be unrealistic for the conditions of this study. Mathematical calculation methods also affected the uniformity values. The coefficient of uniformity (CU) method (Table 3-4) produced higher values than the DU lq method. This is because CU takes into account both over and under-irrigation, while DU lq only considers the lowest quarter on the under-irrigated area. Including both the overand under-irrigated areas resulted in an amount of mathematical equalizing. Pressure differences across residential irrigation zones did not vary more than 10%, which is considered acceptable (Pair, 1983). As a result it was concluded that pressure variations did not substantially impact uniformity. Control Testing Statistical analysis of the spray and rotor head uniformities tested under ideal circumstances was compared to results from the residential system tests. The difference in uniformity between residential and control tests was mostly due to design (i.e., spacing). There was a significant difference between uniformities (p = 0.001) based on testing condition. The overall mean DU lq of the tests performed under ideal circumstances was 0.53 compared to 0.45 on the residential systems. For the control tests, there was not a significant difference in uniformity between the rotor and spray heads. Although for the test under the ideal conditions, the rotors still performed better with a uniformity of 0.56 (Table 3-5), while the spray heads had a uniformity in 0.51 (Table 3-6) Spray head DU lq values were significantly lower at 69 kPa (low pressure) compared to the 207 kPa and 414 kPa tests. However, high pressure (407 kPa), above the pressure recommended by the manufacturers, did not result in significantly different DU lq

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40 compared to recommended pressure tests. There was an interaction between brand and pressure. From the spray head tests, brand C performed the best at recommended and high pressure with a mean DU lq of 0.68 at these two pressures. The next highest Duncan letter grouping for DU lq was measured under brands B at recommended (0.55) and high (0.54) pressures and A at the recommended (0.53) pressure. Low pressure significantly degraded spray head uniformity, across all brands. The poorest DU lq at high pressure was measured under brand B-adj. This brand consistently had the lowest DU lq averaging 0.37 across all pressures. The statistical analysis of the rotor head test showed significant differences in DU lq between brands (p = 0.004); while pressure resulted in a difference at the 90% confidence level (p = 0.090). The spray head test statistical analysis showed that both pressure (p = 0.001) and brand (p = 0.001) had significant influence on the DU lq values. The rotor heads showed moderate statistical differences across brand regardless of pressure with brand A producing the highest DU lq of 0.66 and C yielding the least uniform distribution of water with a DU lq of 0.46. Brand B was statistically similar to brands A and C at both pressure levels; however, differences were pronounced enough such that brands A and C were not similar. Summary and Conclusions The DU lq values reported in this study were lower than the Irrigation Association (2003) quality ratings and the historical average MIL findings. When examining the differences between the catch-can testing procedures employed in this study to the MIL guidelines, it can be inferred that one difference was in the testing methodologies. For the residential systems tested in this study, the low-quarter distribution uniformities classified by the overall system quality ratings would be considered in the

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41 fair to fail range, with the exception of one good. However, it should be noted that any degradation in turfgrass or plant quality on the edges of a residential site will likely result in the homeowner increasing irrigation volume to that area. Therefore, testing of the entire irrigated site including edges and irregular areas is important to define the variability in the overall irrigation system. When the uniformity of the spray and rotor zones were individually examined, the ratings of the spray zones were lower (0.41) than the ratings of the rotor zones (0.49). Overall, the control tests under ideal conditions still resulted in poor uniformity compared to the IA (2003) ratings. Rotary sprinklers DU lq averaged higher at 0.56 while spray heads averaged 0.51. The spray heads have closer spacing and a higher precipitation rate. Therefore, over-irrigation may be exacerbated in some areas, thus decreasing uniformity. The spray heads had the better uniformity when fixed quarter circle nozzles were used as opposed to adjustable arc nozzles. Distribution uniformity is a mathematical means for explaining how evenly a system is irrigating an area. According to the IA quality ratings, the DU lq values determined in this study were considered unacceptable or since the testing in this study was more representative of actual conditions, the IA table may be unrealistic for the conditions of this study. As determined from the results of this study, the DU values are subject to the testing procedure. Sprinkler brand and pressure also affected the uniformity values. For the rotor head control tests there was a significant difference between the brands, however there was not one based on pressure at the 95% confidence level. The pressure variation was only between high and the recommended setting. The equipment will still function

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42 properly under excessive pressure conditions, however the arc and through of the nozzle may not present the correct pattern. For the spray head control tests, there was an interaction between pressure and brand and the pressure. The results from these tests concurred with the assumption that the equipment can withstand higher pressure while still providing a comparable uniformity. Low pressure had an adverse affect on the equipment functionality regardless of brand. The trend which remained constant was that the rotary sprinkler heads create more uniform distributions than fixed spray heads. In addition, spacing the heads properly under controlled conditions resulted in higher uniformities compared to the actual residential sites. Therefore, irrigation system design is important to achieving higher irrigation uniformity distribution.

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43 Table 3-1. Mobile Irrigation Lab turf DU lq results for five counties in Florida. Distribution Uniformity (DU) County Average Minimum Maximum Sample Size Fort Myers (2002) 0.59 0.40 0.82 173 Hillsborough (1993) 0.48 0.11 0.71 68 Lake (2001) 0.38 0.12 0.74 64 St. Johns (2001) 0.39 0.12 0.74 64 South Dade (1993-94) 0.71 0.34 0.89 25 St. Lucie (2000) 0.64 0.38 0.8 75 St. Lucie (2001) 0.67 0.13 0.85 88 Average 0.55 0.23 0.79 80 CV 25 59 8 57 Table 3-2. Recommended pressure and radii for tested spray and rotor heads under ideal conditions according to manufacturer guidelines. Head Type Brand Recommended Pressure (kPa) Low Pressure (kPa) High Pressure* (kPa) Distance of Throw (m) A 345 207 12.8 B 379 207 11.3 Rotary C 345 207 11.3 A 207 69 414 4.6 A-adj. 207 69 414 4.6 B 207 69 414 4.6 B-adj. 207 69 414 4.6 Spray C 207 69 414 4.6 *High pressure tests were only performed on the spray heads

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44 Table 3-3. Irrigation Association overall system quality ratings, related to distribution uniformity Quality of Irrigation System Irrigation System Rating (ISR) Distribution Uniformity (DUlq) Exceptional 10 > 0.85 Excellent 9 0.75 0.85 Very Good 8 0.70 0.74 Good 7 0.60 0.69 Fair 5 0.50 0.59 Poor 3 0.40 0.49 Fail < 3 < 0.40 Table 3-4. Residential system distribution uniformity catch-can test results CU DU lq County Rep Overall System Overall System Spray Head Rotor Head MIL Style (16-24 cans) 1 0.60 0.44 0.54 2 0.59 0.39 0.12 0.45 0.51 3 0.72 0.60 0.57 0.63 0.70 4 0.60 0.46 0.58 5 0.65 0.47 0.51 0.49 0.54 6 0.55 0.35 0.35 0.64 7 0.54 0.50 0.50 0.47 0.60 Marion 8 0.55 0.39 0.39 0.45 1 0.57 0.39 0.15 0.45 0.64 2 0.68 0.58 0.67 0.55 0.63 3 0.61 0.50 0.49 0.48 0.50 4 0.60 0.42 0.16 0.49 0.42 5 0.55 0.40 0.41 0.50 6 0.64 0.50 0.66 0.47 0.64 7 0.71 0.54 0.52 0.59 0.65 8 0.52 0.33 0.41 0.32 0.82 Lake 9 0.60 0.54 0.45 0.64 0.70 1 0.60 0.48 0.42 0.49 0.64 2 0.57 0.38 0.33 0.50 0.51 3 0.50 0.32 0.31 0.34 0.48 4 0.57 0.44 0.47 0.50 0.49 5 0.54 0.36 0.32 0.39 0.42 6 0.50 0.34 0.23 0.44 0.65 7 0.62 0.56 0.43 0.63 0.68 Orange 8 0.63 0.47 0.47 0.67 Mean 0.59 0.45 0.41 0.49 0.58

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45 Table 3-5. Control system distribution uniformity catch-can test results for these brands of rotor heads at recommended and low pressures. Pressure [a] Rec. Low Brand of Rotor Head DU lq Sample Size DU lq Sample Size A 0.68 a [b] 5 0.6 a 5 B 0.57 a 5 0.5 b 5 C 0.51 a 5 0.4 c 5 Average 0.58 0.52 [a] High pressure tests only performed on spray heads. [b] Duncan letters show significant difference between brands at each pressure and are head type specific (i.e., spray or rotor). Table 3-6. Control system distribution uniformity catch-can test results for these brands of spray heads at recommended, low, and high pressures. Pressure [a] Rec. Low High Brand of Spray Head DU lq Sample Size DU lq Sample Size DU lq Sample Size A 0.48 b [b] 5 0.39 b 5 0.50 b 5 A-adj. 0.52 b 5 0.41 ab 5 0.52 b 5 B 0.55 b 5 0.44 ab 5 0.53 b 5 B-adj. 0.38 c 5 0.37 b 5 0.37 c 5 C 0.70 a 5 0.48 a 5 0.65 a 5 Average 0.53 0.42 0.52 [a] High pressure tests only performed on spray heads. [b] Duncan letters show significant difference between brands at each pressure and are head type specific (i.e., spray or rotor).

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CHAPTER 4 COMPARISON OF UNIFORMITY MEASUREMENTS As competition for limited water supplies increase, irrigation must become more efficient. Irrigation efficiency defines how effectively an irrigation system supplies water to a given crop or turf area. Application efficiency can be computed as the ratio between water used beneficially and water applied and is expressed as a percentage (Burt al. (1997). In an efficient residential irrigation system, the components that must be considered are: design, scheduling, and equipment. The design of a system (i.e., spacing) will affect the uniformity of the water distribution. It is important that irrigation systems are designed to apply water evenly across a target area such as turfgrass. Even with good design, scheduling will affect how much water is applied. Residential and commercial irrigation systems typically use stationary spray heads and gear driven rotor sprinklers for the turf and landscapes. Uniformity of water distribution measures the relative application depth over a given area. This concept can be valuable in system design and selection, and can quantify system performance. The term uniformity refers to the measure of the spatial differences between applied (or infiltrated) waters over an irrigated area. A common method which has been developed to quantify uniformity is distribution uniformity. A time domain refectometry (TDR) device can be used to measure soil volumetric water content (VWC), by relating the time needed for an electrical signal to travel along wave guides. As opposed to the measurement of irrigation application, soil water volume is measured as a function of the volume of the bulk soil. A TDR device can be used to 46

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47 measure the amount of stored water in a profile or how much irrigation is required to reach a desired amount of water. The use of TDR probes is an effective nondestructive method of measuring soil moisture content. The catch-can test requires a grid of cans to be placed across the desired testing location. When the system completed the irrigation cycle, the volume of water collected in the cans is measured and related to uniformity. The catch-can method, although not destructive, does necessitate recently mowed turfgrass and is subject to the slope of the area. This experiment compared irrigation distribution uniformity evaluated by the use of a TDR device to the catch-can test method. The uniformities of both residential irrigation systems and controlled equipment testing were evaluated. Materials and Methods The tests for this study included both residential lawns and a turfgrass area used for the control irrigation testing. The residential tests were conducted with the cooperation of homeowners within the Central Florida Ridge as discussed in Chapter 1. Only spray and rotor heads (as opposed to the micoirrigated areas) were tested in this part of the experiment since they are most commonly used on turfgrass and designed to apply irrigation water as uniformly as possible. In many of the tested systems, the irrigation zones were not separated based on plant material. That is, an irrigation zone would commonly be installed to irrigate turfgrass and ornamental plants. The control system test site was located at the University of Florida Agricultural and Biological Engineering department in Gainesville, Florida as part of a study to determine residential irrigation equipment performance parameters. These tests were performed in a mowed and maintained field without slope. The plot area for the rotor

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48 sprinklers was 11.3 m x 11.3 m or 12.8 m x 12.8 m depending on equipment type and according to the manufacturer recommended square spacing. The plot area for the spray heads was 4.6 m x 4.6 m. Nozzles were installed at each of the four corners of the plot area to insure spacing at 50% of manufacturers rated diameter at recommended pressure. To quantify the uniformity of the irrigation systems described previously, the low-quarter distribution uniformity (DU lq ) value was calculated for each system test. The catch-can method of uniformity testing used for this study is a modified combination of both the ASAE and the NRCS methods (ASAE, 2000 and Micker, 1996). The modifications from the ASAE method resulted from testing residential systems rather than agriculture systems, while it is more detailed than the procedures of the NRCS Mobile Irrigation Labs. The procedure used in this project differed because residential sprinkler irrigation systems were tested rather than linear move, and center pivot sprinkler systems as in the ASAE Standard and is more detailed than that of the NRCS Mobile Irrigation Lab. To test the irrigation systems, a grid was marked with 30 cm wire stem flags which were bent so as to level the catch-cans and prevent movement. The cans had an opening diameter of 15.5 cm and a depth of 20.0 cm. The systems were set to run for 25 min on spray zones and 45 min on rotor zones, this ensured the average water application depth was at least 1.3 cm within the catch-cans. In the residential tests, catch-cans were distributed around the turf area in either a 1.5 or 3 meter square grid depending on the irrigated area size (3 m grid for lawns with an area greater than 750 m 2 and 1.5 m grid otherwise). To account for edge effects the grid was positioned 0.8 meters from property boundaries. For the control system tests,

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49 the cans were placed in either a 0.9 or 1.5 m square grid for spray or rotor heads respectively, and with a 0.3 m inset from the edge. The heads were all adjusted or fitted with appropriate nozzles to irrigate quarter circle arcs. According to the ASAE standards (ASAE, 2000) the wind speed was measured every 30 min during the test. The standard allows testing up to 5 m/s; however, if the wind speed was above 2.5 m/s or if the distribution was affected by the wind at lower speeds, the test was discontinued. TDR measurements were performed at the time of catch-can tests. Catch-can volumes were measured immediately following the test using a 500 or 1000 mL graduated cylinder depending on catch-can volume. The TDR VWC percentage was taken within 0.5 m of each catch-can to ensure similarity in measurement point and grid location. TDR measurements were taken immediately after each irrigation run cycle. For this study the TDR device used was the Field Scout TDR 300 Soil Moisture Probe (Spectrum Technologies, Inc., Plainfield, Illinois) with 20 cm rods. The TDR device was used to determine irrigation distribution uniformity for turfgrass. The device was easy to operate and relatively nondestructive to the turfgrass area. Typically, irrigation uniformity is determined by the catch-can method, where DU lq is calculated based on the volume collected in the cans. When calculating the uniformity with the TDR, the DU lq was based on the soil moisture readings after irrigation. To determine the VWC percentage, the probes of the device must be inserted into the ground. The probes were checked when inserted into the ground each time because after multiple measurements the metal probes tended to splay outward if inserted too aggressively. This movement of the probes can give a false low soil moisture reading.

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50 Results and Discussion The soil moisture measurements collected by the TDR ranged from 0-45% VWC. The measurements collected by the catch-cans ranged from 0-1500 mL. The methods were compared by calculating dimensionless DU lq The uniformity calculated by the soil moisture method was higher than the uniformity calculated by the catch-can method (Table 4-1). Overall the, the DU lq calculated from the TDR measurements was 0.74, where the DU lq from the catch-can volumes was 0.51, with an average difference of 0.22. This concurs with the findings from a similar study in Colorado (Mecham, 2001). To compare the two methods, the coefficient of variation (CV) was calculated. The smaller the CV, the smaller the scatter of data about the mean, signifying smaller variability in the data. When considering all the tests in this study, the CV of the TDR DU lq was 11, where the CV of the catch-can volume DU lq was 25 (Table 4-2). The higher the DU lq in the catch-can tests, the smaller the difference was between the TDR and catch-can volume uniformities. This was because the TDR uniformity was higher on average (Table 4-1) with a smaller standard deviation (0.08). The smaller standard deviation would be expected due to the smaller range of values. There were significant differences between the uniformity values determined from the residential versus the control systems. The uniformity values for the residential locations determined from the catch-can tests averaged 0.45, and from the TDR measurements the uniformity was 0.68. For the control locations the uniformity values from the catch-can and TDR measurements were 0.54 and 0.78 respectively. The only apparent difference was with the control system equipped with rotor heads. The average rotor DU lq for the catch-can volumes and TDR measurements were 0.65 and 0.75, respectively.

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51 The TDR and catch-can volume uniformities were plotted against each other in Figure 4-1. The TDR measured moisture content DU compared to volume based DU is essentially horizontal, meaning that soil moisture content does not change predictably with a change in catch-can volume. If there were better correlation between the data, the points would surround the 1:1 line. The data however, was above this line due to the higher TDR VWC DU lq values. Table 4-2 lists the average catch-can volume and soil moisture measurements. Overall, the average volume collected per can was 271 mL, with a standard deviation of 180 mL. The average soil moisture reading was 24, with a standard deviation of only 6. The increase in variation between the measurements had an effect on the uniformity. Additionally, there were occurrences of volume measurement at or near 0 mL, where the soil moisture readings were not below 7%. It can be observed, in Figure 4-2, that there is not a strong correlation between increased soil moisture VWC measurements and catch-can volume measurements. The effects of the irrigation event were taken into consideration. The average soil moisture uniformity calculated prior to the irrigation event was 0.55, and after the irrigation event was 0.64. Summary and Conclusions This study compared irrigation distribution uniformity values determined by the catch-can test to those determined by soil moisture measurements. The TDR device would allow for a quick and easy method for calculating system uniformity, as there is no significant set up time as with the catch-can tests. One of the major differences between the uniformity results calculated by the two methods was from the scale of the measurements. The catch-can scale was larger than the

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52 TDR scale, which can account for a great deal of variation due to an increase in standard deviation. The methods were compared in the DU lq values to help diminish the range dissimilarity. It must be noted that in addition to the scales differing, only the catch-can volume measurements actually included the minimum (0 mL) and maximum (1500 mL) values. Although the soil moisture measurements could range from 0-45%, the actual measurement range was typically from 7-35%. When collecting the measurements, a large volume of water collected in a catch-can was typically correlated with a high TDR VWC reading. Ultimately, there was not enough correlation between the DU lq values determined by the TDR device and the catch-can method. Therefore the TDR device can not be used in place of the catch-cans to determine the uniformity of a system. However, perhaps the uniformity values determined from the catch-can tests are not the most important measure of uniformity, because the measurements ignore the effects of the soil properties, which do in turn affect the turfgrass. The TDR equipment may not be sensitive enough to detect the soil water changes. The soil properties affect the uniformity results. The redistribution of the soil water may lead to a higher DU from the soil water measurements compared to catch-can measurements.

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53 Table 4-1. Uniformity values from both the catch-can tests and the TDR values. Sample Method Average Standard Deviation Coefficient of Variation Point Difference Vol. DU lq 0.45 0.09 20 Residential VWC DU lq 0.68 0.08 12 0.20 Vol. DU lq 0.54 0.14 25 Control VWC DU lq 0.77 0.07 9 0.22 Vol. DU lq 0.51 0.13 25 Overall VWC DU lq 0.74 0.08 11 0.22 Table 4-2. Measurement results from both the catch-can and the TDR tests. Sample Method Average Standard Deviation Coefficient of Variation Volume (mL) 294 108 37 Residential VWC % 22 4 19 Volume (mL) 259 207 80 Control VWC % 25 6 25 Volume (mL) 271 180 66 Overall VWC % 24 6 24

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54 0.000.100.200.300.400.500.600.700.800.901.000.000.200.400.600.801.00Volume, DUlqTDR, DUlq 1:1 Figure 4-1. Comparison of DU lq values calculated from both the TDR soil moisture and catch-can tests. y = 4.8943Ln(x) 2.8285R2 = 0.245605101520253035404550025050075010001250Catch-Can Volume (mL)TDR Soil Moisture (%) Figure 4-2. Comparison of soil moisture to can volume measurements taken during uniformity tests.

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CHAPTER 5 CONCLUSIONS The goal of this project was to evaluate residential irrigation water use and uniformity. The research conducted for this study assessed residential irrigation water use and total water input, taking rainfall and evapotranspiration (ET) into account from January 2002 through May 2004. Both residential systems and individual equipment distribution uniformities were measured. These tests were used to compare catch-can volume to soil moisture uniformity testing methods. To determine water use for residential irrigation systems, irrigation water consumption was monitored on a monthly basis. The homes were separated into three treatments, each relating to the type of system (typical or designed) and the controller settings (homeowner controlled or adjusted based on historical evapotranspiration rates). T1 consisted of existing irrigation systems and typical landscape plantings, where the homeowner controlled the irrigation scheduling. T2 also consisted of existing irrigation systems and typical landscape plantings, but the irrigation scheduling was adjusted based on historical ET. T3 consisted of an irrigation system designed according to specifications for optimal efficiency and scheduled based on historical ET. T3 also included a landscape design that minimized turfgrass and maximized the use of native drought tolerant plants. To further achieve water savings in T3, the landscape plants were irrigated by microirrigation as opposed to the standard spray and rotor heads. 55

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56 The average residential irrigation system consumed 63% of the total water used in the home. The average monthly irrigation water depths for T1, T2, and T3 were 146 mm, 116 mm, and 86 mm respectively. Adjusting the controller run times and incorporating microirrigation into the bedding areas (T3) did result in less water use. In the summer months, all the treatments required similar water amounts. However, in the winter months, when the turfgrass went dormant, very little irrigation was necessary. In spring months, T1 consumed the most irrigation water 179 mm, and T3 consuming the least, 94 mm. This was due to the monthly adjustments of irrigation times and because the microirrigated areas on T3 homes used much less volume than if those areas were sprinkler irrigated. In the fall months, T1 and T2 consumed similar amounts, 155 mm and 148 mm, while T3 consumed significantly less (102 mm). Most of the homes still tended to over-irrigate. The over-irrigation resulted from poor uniformity and unnecessarily high irrigation run times. In this study, the amount of over irrigation was determined by comparing the amount of water applied (irrigation and effective rainfall) to the amount of water required (ET). The amount of over irrigation was especially high in the winter months. Irrigation alone was consistently higher than the crop water requirements. Water use could also be affected by the functionality and setting of rain sensors. Irrigation during periods of rainfall implies malfunctioned or improperly adjusted rain sensors connected to the irrigation controllers. However, rainfall could occur immediately after an irrigation event. In efforts to increase irrigation efficiency, the

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57 irrigation amounts should be adjusted seasonally, the system must be properly maintained, and should be designed to achieve acceptable distribution uniformity (DU lq ). The measured residential and control irrigation system uniformity values were lower than industry recommendations. The average overall system residential DU lq was 0.45. When the uniformity of the spray and rotor zones were individually examined, the ratings of the spray zones (0.41) were lower than the ratings of the rotor zones (0.49). Although the tests under controlled conditions yielded results better than the residential tests, the uniformities were still low. The rotary sprinklers DU lq averaged higher than spray heads with average DU lq of 0.56 and 0.51 respectively. The spray heads had better uniformity when fixed quarter circle nozzles were used as opposed to adjustable arc nozzles. Sprinkler brand and pressure also affected the uniformity values. Low pressure had an adverse affect on the equipment functionality regardless of brand. However, there was an interaction between brand and pressure in spray head controlled testing, but certain brands tended to perform better regardless of pressure. Both rotor and spray heads, performed similarly when tested at the recommended and high pressures. The controlled tests resulted in higher uniformity, regardless of pressure (0.51) versus the residential tests (0.45). Thus irrigation design and spacing of the heads, positively affects the uniformity. Distribution uniformity values are subject to the testing procedure. The methods for testing uniformity were compared by determining the DU lq from the catch-can volume measurements and the soil moisture at each measurement point. A Time Domain Reflectometry (TDR) device was used to determine the soil moisture. The TDR, which

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58 can be easily inserted into the ground at each measurement point, is is much quicker than catch can tests for determining uniformity. Overall, the uniformities calculated by the soil moisture measurements were higher (0.74) than those calculated by the catch-can volumes (0.51). The uniformity values determined from the catch-can tests may not be a proper representation of actual soil uniformity. The volume measurements ignore the effects of the soil properties, which have an impact on the turfgrass quality. The TDR equipment may not be sensitive enough to detect the soil water redistribution. The actual soil water movement may lead to a higher DU than the catch-can measurements predicts. In the future, residential irrigation system audits may not rely solely on catch-can tests to measure distribution uniformity. The surface distribution seems to differ from the actual soil moisture. The MIL procedure tended to yield higher uniformities, but the procedure ignores edge effects and uses less measurement samples. However, the grid formation outlined in the procedures of this study may be too stringent, and suggest DU values lower than the actual uniformity. Microirrigation increased irrigation water savings. Many contractors and homeowners are reluctant to install microirrigation components. The microirrigation required more maintenance and was more costly to install. However, the majority of the homeowners with the microirrigation incorporated into their systems (T3) were quite pleased with the results. Additionally, once the landscape plants became established the microirrigation equipment was almost unnoticeable. The observations and results found from this research will lead to a better understanding of residential irrigation uniformity and water use, which will aid in determining efficient residential irrigation. Upon interaction with the homeowners

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59 cooperating in this research, there were vast misconceptions about irrigation water use and scheduling. Changing the irrigation controller run times based on season was vaguely understood and the concept of significantly reducing the water in the winter time was initially met with some confusion. Some of the homeowners in the treatments 2 and 3 are now avid water conversationalists after becoming aware of the excessive over-irrigation that can be avoided.

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APPENDIX A PHOTOGRAPHS The following groups of photographs were taken during the period of data collection for this study, from January 2002 through May 2004. Figure A-1. Flow meter Figure A-2. Weather station 60

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61 Figure A-3. Control system spray head with pressure gage Figure A-4. Control system catch-can test

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62 Figure A-5. Residential system catch-can test Figure A-6. Setup of catch-can grid formation

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63 Figure A-7. Catch-can grid formation around bedded area Figure A-8. Measure catch-can volume with graduated cylinders

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64 Figure A-9. Turfgrass area with high turf quality rating Figure A-10. Turfgrass area with low turf quality rating

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65 A B Figure A-11. Sample cooperator homes from each treatment in Marion County. A) T1. B) T2. C) T3. D) Another T3.

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66 C D Figure A-11. Continued

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67 A B Figure A-12. Sample cooperator homes from each treatment in Lake County. A) T1. B) T2. C) T3. D) Another T3.

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68 C D Figure A-12. Continued

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69 A B Figure A-13. Sample cooperator homes from each treatment in Orange County. A) T1. B) T2. C) T3. D) Another T3.

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70 C D Figure A-13. Continued

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APPENDIX B STATISTICAL ANALYSIS The following is the SAS output text files for the statistical analysis performed. OVERVIEW OF WATER USE STATISTICS The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 season 4 Fall Spring Summer Winter year 3 Y1 Y2 Y3 loc 3 HH OC SC Number of Observations Read 708 Number of Observations Used 581 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 23 1290736.388 56118.973 13.78 <.0001 Error 557 2269192.814 4073.955 Corrected Total 580 3559929.201 R-Square Coeff Var Root MSE mm Mean 0.362574 53.88991 63.82754 118.4406 Source DF Type III SS Mean Square F Value Pr > F tmt 2 140934.3261 70467.1631 17.30 <.0001 season 3 248629.7489 82876.5830 20.34 <.0001 year 2 121068.6074 60534.3037 14.86 <.0001 tmt*season 6 48040.4748 8006.7458 1.97 0.0687 tmt*year 4 80888.7765 20222.1941 4.96 0.0006 tmt(loc) 6 344599.2220 57433.2037 14.10 <.0001 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 557 Error Mean Square 4073.955 Harmonic Mean of Cell Sizes 189.8242 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 12.87 13.55 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 146.005 198 T1 B 116.866 224 T2 C 86.333 159 T3 71

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72 Alpha 0.05 Error Degrees of Freedom 557 Error Mean Square 4073.955 Harmonic Mean of Cell Sizes 142.5793 NOTE: Cell sizes are not equal. Number of Means 2 3 4 Critical Range 14.85 15.63 16.16 Means with the same letter are not significantly different. Duncan Grouping Mean N season A 138.071 140 Fall A 137.227 176 Spring B 116.967 120 Summer C 77.903 145 Winter Alpha 0.05 Error Degrees of Freedom 557 Error Mean Square 4073.955 Harmonic Mean of Cell Sizes 175.4245 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 13.39 14.09 Means with the same letter are not significantly different. Duncan Grouping Mean N year A 155.185 135 Y1 B 107.352 162 Y3 B 107.299 284 Y2

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73 WATER USE SORTED BY SEASON (Fall) The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 Number of Observations Read 162 Number of Observations Used 140 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 2 68119.0104 34059.5052 7.41 0.0009 Error 137 629962.2753 4598.2648 Corrected Total 139 698081.2857 R-Square Coeff Var Root MSE mm Mean 0.097580 49.11263 67.81051 138.0714 Source DF Type III SS Mean Square F Value Pr > F tmt 2 68119.01037 34059.50519 7.41 0.0009 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 137 Error Mean Square 4598.265 Harmonic Mean of Cell Sizes 45.6846 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 28.06 29.53 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 155.38 48 T1 A 147.85 54 T2 B 102.32 38 T3

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74 WATER USE SORTED BY SEASON (Spring) The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 Number of Observations Read 219 Number of Observations Used 176 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 2 197383.354 98691.677 15.47 <.0001 Error 173 1103675.555 6379.627 Corrected Total 175 1301058.909 R-Square Coeff Var Root MSE mm Mean 0.151710 58.20459 79.87257 137.2273 Source DF Type III SS Mean Square F Value Pr > F tmt 2 197383.3542 98691.6771 15.47 <.0001 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 173 Error Mean Square 6379.627 Harmonic Mean of Cell Sizes 57.83185 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 29.32 30.86 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 179.17 59 T1 B 132.30 67 T2 C 94.34 50 T3

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75 WATER USE SORTED BY SEASON (Summer) The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 Number of Observations Read 162 Number of Observations Used 120 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 2 35434.9238 17717.4619 3.08 0.0499 Error 117 673766.9429 5758.6918 Corrected Total 119 709201.8667 R-Square Coeff Var Root MSE mm Mean 0.049965 64.87835 75.88604 116.9667 Source DF Type III SS Mean Square F Value Pr > F tmt 2 35434.92381 17717.46190 3.08 0.0499 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 117 Error Mean Square 5758.692 Harmonic Mean of Cell Sizes 38.47328 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 34.27 36.06 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 139.02 42 T1 B A 110.75 48 T2 B 96.03 30 T3

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76 WATER USE SORTED BY SEASON (Winter) The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 Number of Observations Read 165 Number of Observations Used 145 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 2 54047.1816 27023.5908 8.66 0.0003 Error 142 442937.4666 3119.2779 Corrected Total 144 496984.6483 R-Square Coeff Var Root MSE mm Mean 0.108750 71.69194 55.85050 77.90345 Source DF Type III SS Mean Square F Value Pr > F tmt 2 54047.18164 27023.59082 8.66 0.0003 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 142 Error Mean Square 3119.278 Harmonic Mean of Cell Sizes 47.634 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 22.62 23.81 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 102.88 49 T1 B 72.98 55 T2 B B 54.66 41 T3

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77 WATER USE SORTED BY YEAR (Y2) The GLM Procedure Class Level Information Class Levels Values tmt 3 T1 T2 T3 season 4 Fall Spring Summer Winter Number of Observations Read 324 Number of Observations Used 284 Dependent Variable: mm Sum of Source DF Squares Mean Square F Value Pr > F Model 11 256997.215 23363.383 6.72 <.0001 Error 272 945824.345 3477.295 Corrected Total 283 1202821.560 R-Square Coeff Var Root MSE mm Mean 0.213662 54.95711 58.96860 107.2993 Source DF Type III SS Mean Square F Value Pr > F tmt 2 156270.7198 78135.3599 22.47 <.0001 season 3 54353.6781 18117.8927 5.21 0.0016 tmt*season 6 20698.8448 3449.8075 0.99 0.4309 Duncan's Multiple Range Test for mm NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 272 Error Mean Square 3477.295 Harmonic Mean of Cell Sizes 93.23741 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range 17.00 17.90 Means with the same letter are not significantly different. Duncan Grouping Mean N tmt A 140.573 96 T1 B 94.380 108 T2 B 84.813 80 T3 Alpha 0.05 Error Degrees of Freedom 272 Error Mean Square 3477.295 Harmonic Mean of Cell Sizes 70.22526 NOTE: Cell sizes are not equal. Number of Means 2 3 4 Critical Range 19.59 20.62 21.31 Means with the same letter are not significantly different. Duncan Grouping Mean N season A 124.303 66 Spring A 112.556 81 Fall A 107.453 75 Summer B 82.145 62 Winter

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78 WATER USE SAS CODE options nodate nonumber center formdlim="*"linesize=85; data mm; input tmt$ year$ month$ season$ loc$ mm @@; cards; /* Data is inputted here */ ; data mm; set mm; proc glm data=mm; title 'OVERVIEW OF WATER USE STATISTICS'; class tmt season year loc; model mm = tmt season year season*tmt year*tmt tmt(loc)/ss3; test h=loc e=tmt(loc); means tmt/duncan; means season/duncan; means year/duncan; means loc/duncan e=tmt(loc); run ; data mm3; set mm; proc sort data=mm3; by season; proc glm data=mm3; by season; title 'WATER USE SORTED BY SEASON'; class tmt; model mm = tmt/ss3; means tmt/duncan; run; data mm4; set mm (where=(year='Y2')); proc glm data=mm4; by year; title 'WATER USE SORTED BY YEAR'; class tmt season; model mm = tmt season season*tmt/ss3; means tmt/duncan; mea ns season/duncan; run;

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79 DIFFERENCE BETWEEN ZONES FOR BOTH RESIDENTIAL AND CONTROL TESTS AT REGULAR PRESSURE The GLM Procedure Class Level Information Class Levels Values study 2 control resident rep 6 1 2 3 4 5 6 zone 2 R S Number of Observations Read 92 Number of Observations Used 82 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 12 0.55746706 0.04645559 3.11 0.0014 Error 69 1.03058172 0.01493597 Corrected Total 81 1.58804878 R-Square Coeff Var Root MSE du Mean 0.351039 24.69554 0.122213 0.494878 Source DF Type III SS Mean Square F Value Pr > F study 1 0.28393850 0.28393850 19.01 <.0001 rep(study) 9 0.23677843 0.02630871 1.76 0.0917 zone 1 0.10334447 0.10334447 6.92 0.0105 study*zone 1 0.00373972 0.00373972 0.25 0.6184 Tests of Hypotheses Using the Type III MS for rep(study) as an Error Term Source DF Type III SS Mean Square F Value Pr > F zone 1 0.10334447 0.10334447 3.93 0.0788 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 9 Error Mean Square 0.026309 Harmonic Mean of Cell Sizes 40.97561 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range .08106 Means with the same letter are not significantly different. Duncan Grouping Mean N study A 0.54800 40 control B 0.44429 42 resident Alpha 0.05 Error Degrees of Freedom 69 Error Mean Square 0.014936 Harmonic Mean of Cell Sizes 40.12195 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range .05444 Means with the same letter are not significantly different. Duncan Grouping Mean N zone A 0.52857 35 R B 0.46979 47 S

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80 DIFFERENCE BETWEEM ZONES AND LOCATION FOR RESIDENTIAL STUDY AT REGULAR PRESSURE The GLM Procedure Class Level Information Class Levels Values zone 2 R S rep 6 1 2 3 4 5 6 loc 3 l m o Number of Observations Read 92 Number of Observations Used 42 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 19 0.39627950 0.02085682 1.54 0.1643 Error 22 0.29774907 0.01353405 Corrected Total 41 0.69402857 R-Square Coeff Var Root MSE du Mean 0.570984 26.18494 0.116336 0.444286 Source DF Type III SS Mean Square F Value Pr > F zone 1 0.07399772 0.07399772 5.47 0.0289 loc 2 0.02185570 0.01092785 0.81 0.4588 zone*loc 2 0.00376647 0.00188324 0.14 0.8709 rep(loc) 14 0.30645415 0.02188958 1.62 0.1516 Tests of Hypotheses Using the Type III MS for rep(loc) as an Error Term Source DF Type III SS Mean Square F Value Pr > F loc 2 0.02185570 0.01092785 0.50 0.6174 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 22 Error Mean Square 0.013534 Harmonic Mean of Cell Sizes 20.95238 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range .07454 Means with the same letter are not significantly different. Duncan Grouping Mean N zone A 0.48650 20 R B 0.40591 22 S Alpha 0.05 Error Degrees of Freedom 14 Error Mean Square 0.02189 Harmonic Mean of Cell Sizes 13.84615 NOTE: Cell sizes are not equal. Number of Means 2 3 Critical Range .1206 .1264 Means with the same letter are not significantly different. Duncan Grouping Mean N loc A 0.46750 12 m A 0.45200 15 l A 0.41800 15 o

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81 DIFFERENCE BETWEEN ZONES FOR CONTROL STUDY AT REGULAR PRESSURE The GLM Procedure Class Level Information Class Levels Values brand 8 H HA HS R RQ RV T TQ rep 5 1 2 3 4 5 zone 2 R S Number of Observations Read 92 Number of Observations Used 40 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 11 0.39931000 0.03630091 3.71 0.0025 Error 28 0.27433000 0.00979750 Corrected Total 39 0.67364000 R-Square Coeff Var Root MSE du Mean 0.592765 18.06247 0.098982 0.548000 Source DF Type III SS Mean Square F Value Pr > F zone 1 0.03226667 0.03226667 3.29 0.0803 brand(zone) 6 0.34385333 0.05730889 5.85 0.0005 rep 4 0.02319000 0.00579750 0.59 0.6714 Tests of Hypotheses Using the Type III MS for brand(zone) as an Error Term Source DF Type III SS Mean Square F Value Pr > F zone 1 0.03226667 0.03226667 0.56 0.4814 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 6 Error Mean Square 0.057309 Harmonic Mean of Cell Sizes 18.75 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range .1913 Means with the same letter are not significantly different. Duncan Grouping Mean N zone A 0.58467 15 R A 0.52600 25 S

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82 DIFFERENCE BETEWEEN BRANDS AND PRESSURE FOR CONTROL TESTS ROTOR ZONES The GLM Procedure Class Level Information Class Levels Values brand 3 H R T pressure 2 L R rep 5 1 2 3 4 5 Number of Observations Read 56 Number of Observations Used 30 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 9 0.28491000 0.03165667 3.63 0.0078 Error 20 0.17442667 0.00872133 Corrected Total 29 0.45933667 R-Square Coeff Var Root MSE du Mean 0.620264 16.84692 0.093388 0.554333 Source DF Type III SS Mean Square F Value Pr > F brand 2 0.20444667 0.10222333 11.72 0.0004 pressure 1 0.02760333 0.02760333 3.17 0.0904 brand*pressure 2 0.00580667 0.00290333 0.33 0.7207 rep 4 0.04705333 0.01176333 1.35 0.2868 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 20 Error Mean Square 0.008721 Number of Means 2 3 Critical Range .08712 .09145 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.65800 10 H B 0.54900 10 R C 0.45600 10 T Alpha 0.05 Error Degrees of Freedom 20 Error Mean Square 0.008721 Number of Means 2 Critical Range .07113 Means with the same letter are not significantly different. Duncan Grouping Mean N pressure A 0.58467 15 R A 0.52400 15 L

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83 Least Squares Means LSMEAN brand pressure du LSMEAN Number H L 0.63800000 1 H R 0.67800000 2 R L 0.52800000 3 R R 0.57000000 4 T L 0.40600000 5 T R 0.50600000 6 Least Squares Means for effect brand*pressure Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: du i/j 1 2 3 4 5 6 1 0.5060 0.0773 0.2632 0.0008 0.0370 2 0.5060 0.0195 0.0824 0.0002 0.0086 3 0.0773 0.0195 0.4852 0.0521 0.7135 4 0.2632 0.0824 0.4852 0.0116 0.2914 5 0.0008 0.0002 0.0521 0.0116 0.1060 6 0.0370 0.0086 0.7135 0.2914 0.1060 NOTE: To ensure overall protection level, only probabilities associated with pre-planned comparisons should be used.

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84 DIFFERENCE BETEWEEN BRANDS AND PRESSURE FOR CONTROL TESTS SPRAY ZONES The GLM Procedure Class Level Information Class Levels Values brand 5 HA HS RQ RV TQ pressure 3 H L R rep 5 1 2 3 4 5 Number of Observations Read 101 Number of Observations Used 75 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 18 0.69422667 0.03856815 9.56 <.0001 Error 56 0.22584000 0.00403286 Corrected Total 74 0.92006667 R-Square Coeff Var Root MSE du Mean 0.754540 13.08478 0.063505 0.485333 Source DF Type III SS Mean Square F Value Pr > F brand 4 0.44870667 0.11217667 27.82 <.0001 pressure 2 0.17817867 0.08908933 22.09 <.0001 brand*pressure 8 0.06434133 0.00804267 1.99 0.0639 rep 4 0.00300000 0.00075000 0.19 0.9448 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 0.004033 Number of Means 2 3 4 5 Critical Range .04645 .04886 .05045 .05161 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.61000 15 TQ B 0.50533 15 RQ B 0.48400 15 HA B 0.45667 15 HS C 0.37067 15 RV Alpha 0.05 Error Degrees of Freedom 56 Error Mean Square 0.004033 Number of Means 2 3 Critical Range .03598 .03785 Means with the same letter are not significantly different. Duncan Grouping Mean N pressure A 0.52600 25 R A 0.51320 25 H B 0.41680 25 L

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85 Least Squares Means LSMEAN brand pressure du LSMEAN Number HA H 0.52000000 1 HA L 0.40800000 2 HA R 0.52400000 3 HS H 0.49600000 4 HS L 0.39000000 5 HS R 0.48400000 6 RQ H 0.53400000 7 RQ L 0.43600000 8 RQ R 0.54600000 9 RV H 0.36600000 10 RV L 0.36800000 11 RV R 0.37800000 12 TQ H 0.65000000 13 TQ L 0.48200000 14 TQ R 0.69800000 15 Least Squares Means for effect brand*pressure Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: du i/j 1 2 3 4 5 6 7 8 1 0.0072 0.9210 0.5525 0.0020 0.3739 0.7287 0.0410 2 0.0072 0.0055 0.0326 0.6558 0.0636 0.0027 0.4886 3 0.9210 0.0055 0.4886 0.0015 0.3236 0.8043 0.0326 4 0.5525 0.0326 0.4886 0.0107 0.7662 0.3482 0.1408 5 0.0020 0.6558 0.0015 0.0107 0.0229 0.0007 0.2570 6 0.3739 0.0636 0.3236 0.7662 0.0229 0.2184 0.2371 7 0.7287 0.0027 0.8043 0.3482 0.0007 0.2184 0.0179 8 0.0410 0.4886 0.0326 0.1408 0.2570 0.2371 0.0179 9 0.5201 0.0011 0.5860 0.2184 0.0003 0.1283 0.7662 0.0083 10 0.0003 0.3002 0.0002 0.0020 0.5525 0.0048 0.0001 0.0868 11 0.0004 0.3236 0.0003 0.0024 0.5860 0.0055 0.0001 0.0960 12 0.0008 0.4582 0.0006 0.0048 0.7662 0.0107 0.0003 0.1543 13 0.0020 <.0001 0.0027 0.0003 <.0001 0.0001 0.0055 <.0001 14 0.3482 0.0707 0.3002 0.7287 0.0258 0.9605 0.2007 0.2570 15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0001 <.0001 i/j 9 10 11 12 13 14 15 1 0.5201 0.0003 0.0004 0.0008 0.0020 0.3482 <.0001 2 0.0011 0.3002 0.3236 0.4582 <.0001 0.0707 <.0001 3 0.5860 0.0002 0.0003 0.0006 0.0027 0.3002 <.0001 4 0.2184 0.0020 0.0024 0.0048 0.0003 0.7287 <.0001 5 0.0003 0.5525 0.5860 0.7662 <.0001 0.0258 <.0001 6 0.1283 0.0048 0.0055 0.0107 0.0001 0.9605 <.0001 i/j 9 10 11 12 13 14 15 7 0.7662 0.0001 0.0001 0.0003 0.0055 0.2007 0.0001 8 0.0083 0.0868 0.0960 0.1543 <.0001 0.2570 <.0001 9 <.0001 <.0001 0.0001 0.0122 0.1167 0.0004 10 <.0001 0.9605 0.7662 <.0001 0.0055 <.0001 11 <.0001 0.9605 0.8043 <.0001 0.0063 <.0001 12 0.0001 0.7662 0.8043 <.0001 0.0122 <.0001 13 0.0122 <.0001 <.0001 <.0001 0.0001 0.2371 14 0.1167 0.0055 0.0063 0.0122 0.0001 <.0001 15 0.0004 <.0001 <.0001 <.0001 0.2371 <.0001

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86 DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (High) The GLM Procedure Class Level Information Class Levels Values brand 5 HA HS RQ RV TQ rep 5 1 2 3 4 5 Number of Observations Read 25 Number of Observations Used 25 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 8 0.21620800 0.02702600 9.93 <.0001 Error 16 0.04353600 0.00272100 Corrected Total 24 0.25974400 R-Square Coeff Var Root MSE du Mean 0.832389 10.16430 0.052163 0.513200 Source DF Type III SS Mean Square F Value Pr > F brand 4 0.20578400 0.05144600 18.91 <.0001 rep 4 0.01042400 0.00260600 0.96 0.4571 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 16 Error Mean Square 0.002721 Number of Means 2 3 4 5 Critical Range .06994 .07334 .07547 .07692 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.65000 5 TQ B 0.53400 5 RQ B 0.52000 5 HA B 0.49600 5 HS C 0.36600 5 RV

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87 DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (Low) The GLM Procedure Class Level Information Class Levels Values brand 5 HA HS RQ RV TQ rep 5 1 2 3 4 5 Number of Observations Read 25 Number of Observations Used 25 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 8 0.04148800 0.00518600 1.31 0.3074 Error 16 0.06345600 0.00396600 Corrected Total 24 0.10494400 R-Square Coeff Var Root MSE du Mean 0.395335 15.10945 0.062976 0.416800 Source DF Type III SS Mean Square F Value Pr > F brand 4 0.03898400 0.00974600 2.46 0.0877 rep 4 0.00250400 0.00062600 0.16 0.9566 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 16 Error Mean Square 0.003966 Number of Means 2 3 4 5 Critical Range .08444 .08854 .09111 .09287 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.48200 5 TQ B A 0.43600 5 RQ B A 0.40800 5 HA B 0.39000 5 HS B 0.36800 5 RV

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88 DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (Recommended) The GLM Procedure Class Level Information Class Levels Values brand 5 HA HS RQ RV TQ rep 5 1 2 3 4 5 Number of Observations Read 51 Number of Observations Used 25 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 8 0.28036000 0.03504500 5.79 0.0014 Error 16 0.09684000 0.00605250 Corrected Total 24 0.37720000 R-Square Coeff Var Root MSE du Mean 0.743266 14.79046 0.077798 0.526000 Source DF Type III SS Mean Square F Value Pr > F brand 4 0.26828000 0.06707000 11.08 0.0002 rep 4 0.01208000 0.00302000 0.50 0.7369 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 16 Error Mean Square 0.006052 Number of Means 2 3 4 5 Critical Range .1043 .1094 .1126 .1147 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.69800 5 TQ B 0.54600 5 RQ B 0.52400 5 HA B 0.48400 5 HS C 0.37800 5 RV

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89 DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -ROTOR ZONES AT EACH PRESSURE (Low) The GLM Procedure Class Level Information Class Levels Values brand 3 H R T rep 5 1 2 3 4 5 Number of Observations Read 15 Number of Observations Used 15 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 6 0.14170667 0.02361778 7.31 0.0065 Error 8 0.02585333 0.00323167 Corrected Total 14 0.16756000 R-Square Coeff Var Root MSE du Mean 0.845707 10.84881 0.056848 0.524000 Source DF Type III SS Mean Square F Value Pr > F brand 2 0.13468000 0.06734000 20.84 0.0007 rep 4 0.00702667 0.00175667 0.54 0.7090 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 8 Error Mean Square 0.003232 Number of Means 2 3 Critical Range .08291 .08640 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.63800 5 H B 0.52800 5 R C 0.40600 5 T

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90 DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -ROTOR ZONES AT EACH PRESSURE (Recommended) The GLM Procedure Class Level Information Class Levels Values brand 3 H R T rep 5 1 2 3 4 5 Number of Observations Read 41 Number of Observations Used 15 Dependent Variable: du Sum of Source DF Squares Mean Square F Value Pr > F Model 6 0.14014667 0.02335778 1.51 0.2884 Error 8 0.12402667 0.01550333 Corrected Total 14 0.26417333 R-Square Coeff Var Root MSE du Mean 0.530510 21.29630 0.124512 0.584667 Source DF Type III SS Mean Square F Value Pr > F brand 2 0.07557333 0.03778667 2.44 0.1491 rep 4 0.06457333 0.01614333 1.04 0.4432 Duncan's Multiple Range Test for du NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 8 Error Mean Square 0.015503 Number of Means 2 3 Critical Range .1816 .1892 Means with the same letter are not significantly different. Duncan Grouping Mean N brand A 0.67800 5 H A 0.57000 5 R A 0.50600 5 T

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91 UNIFORMITY SAS CODE options nodate nonumber center formdlim="*"linesize=85; data du; input study$ loc$ rep zone$ brand$ pressure$ du; cards; /* Data is inputted here */ ; data du2; set u(w dhere=(pressure = 'R')); proc glm data=du2; title 'DIFFERENCE BETWEEN ZONES FOR BOTH RESIDENTIAL AND CONTROL TESTS AT REGULAR PRESSURE'; class study rep zone; model du = study rep(study) zone study*zone/ss3; test h=zone e=rep(study); means study/duncan e=rep(study); means zone/duncan; run ; data du3; set du2; if study=control then delete; proc glm data=du3; title 'DIFFERENCE BETWEEM ZONES AND LOCATION FOR RESIDENTIAL STUDY AT REGULAR PRESSURE'; class zone rep loc; model du = zone loc zone*loc rep(loc)/ss3; test h=loc e=rep(loc); means zone/duncan; mean s loc/duncan e=rep(loc); data du4; set du2; if study= residential then delete; proc glm data=du4; title 'DIFFERENCE BETWEEN ZONES FOR CONTROL STUDY AT REGULAR PRESSURE'; class brand rep zone; model du = zone brand(zone) rep/ss3; test h=zone e=brand(zone); means zone/duncan e=brand(zone); run; data du5; set du; if study=residential then delete; proc sort data=du5; by zone; proc glm data=du5; by zone; title 'DIFFERENCE BETEWEEN BRANDS AND PRESSURE FOR CONTROL TESTS SPRAY AND ROTOR ZONES'; class brand pressure rep; model du = brand pressure brand*pressure rep/ss3; means brand/duncan; means pressure/duncan; lsmeans brand*pressure/pdiff; run; data du6; set du5 (where=(zone = 'S')); proc sort data=du6; by pressure; proc glm data=du6; by pressure; title 'DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE'; class brand rep; model du = brand rep/ss3; mea ns brand/duncan; run; data du7; set du5 (where=(zone = 'R'));

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92 proc sort data=du7; by pressure; proc glm data=du7; by pressure; title 'DIFFERENCE BETEWEEN BRANDS FOR CONTROL TESTS -ROTOR ZONES AT EACH PRESSURE'; class brand rep; model du = brand rep/ss3; means brand/duncan; run;

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LIST OF REFERENCES Allen R.G., Pereira L.S., Raes D. and M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy. America Society of Agricultural Engineers. 2000. Testing Procedure for Determining Uniformity of Water Distribution of Center Pivot and Lateral Move Irrigation Machines Equipped with Spray or Sprinkler Nozzles. American Society of Agricultural Engineers Standards, 48th ed. St. Joseph, MI. Aurasteh, M.R. 1984. A Model for Estimating Lawn Grass Water Requirement, Considering Deficit Irrigation, Shading and Application. Ph.D. dissertation. Utah State University, Logan, UT. Aurasteh, M.R., M. Jafari, and L.S. Willardson. 1984. Residential Lawn Irrigation Management. Transactions ASAE 27(2): 470-472. Barnes, J.R. 1977. Analysis of Residential Lawn Water Use. Master thesis. Laramie: University of Wyoming, Laramie, WY. Baum, M.C.; M.D. Dukes, and D. Haman. 2003. Selection and Use of Water Meters for Irrigation Water Measurement. Florida Cooperative Extension Service, Institute of Food and Life Sciences, ABE 18. University of Florida, Gainesville, FL. Burney, L., T. Swihart, and J. Llewellyn. 1998. Water Supply Planning in Florida. Florida Water Resource Journal, October 1998, 27-28. Burt, C.M.; A.J. Clemmens, and K.H. Strelkoff. 1997. Irrigation Performance Measurements: Efficiency and Uniformity. Journal of Irrigation and Drainage Engineering 123(6): 423-442. Carrion, P.; J.M. Tarjuel, and J. Montero. 2000. SIRIAS: A Simulation Model for Sprinkler Irrigation. Irrigation Science 20(2), 73-84. Christiansen, J.E. 1942. Irrigation by sprinkling. California Agric. Exp. Stn. Bull. 670. University of California, Berkley, CA. Dukes, M.D. and J.L. Williams, 2002. Time Domain Reflectometry and Distribution Uniformity as Irrigation Performance Measures. ASAE paper no. FL03-100, American Society of Agricultural Engineers, St. Joseph, MI. 93

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94 Elliott, R.L. J.D. Nelson, J.C. Loftis, and W.E. Hart. 1980. Comparison of Sprinkler Uniformity Models. Journal of Irrigation and Drainage Engineering, ASCE, 106(4), 321-330. Fernald, E. and E. Purdum. 1998. Water Resource Atlas, Florida State University: Institute of Public Affairs. Tallahassee, FL. pgs. 114-119. Fukui, Y., K. Nakanishi, and S. Okamura. 1980. Computer Evaluation of Sprinkler Irrigation Uniformity. Irrigation Science 2(1), 23-32. Hayes, J. 2000. Saving Water Outdoors. Florida Yards and Neighbors Program Extension. University of Florida, Gainesville, FL. Irrigation Association. 2003. Landscape Irrigation Scheduling and Water Management. Irrigation Association Water Management Committee. Falls Church, VA. Linaweaver, F.P., Jr., J.C. Geyer, and J.B. Wolf. 1967. A Study of Residential Water Use. Federal Housing Administration Technical Studies Program, U.S. Government Printing Office, Washington, D.C. Marella, R.L. 1999. Water withdrawals, use, discharge, and trends in Florida, 1995. Water Resources Investigations Report 99-4002, U.S. Geological Survey, Denver, CO. Mayer, P.W., W.B. DeOreo, E.M. Opitz, J.C. Kiefer, W.Y. Davis, B. Dziegielewski and J.O. Nelson. 1999. Residential End Uses of Water. American Water Works Association Research Foundation. Denver, CO. Mayer, P.W., E. Towler, and W.B. DeOreo. 2004. National Multiple Family Submetering and Allocation Billing Program Study. Aquacraft, Inc. and the East Bay Municipal Utility District. Boulder, CO. Mecham, B.Q. 2001. Distribution Uniformity Results Comparing Catch-Can Tests and Soil Moisture Sensor Measurements in Turfgrass Irrigation. Irrigation Association 2001 Proceedings. pp. 133-139. Merriam, J.L., and J. Keller. 1978. Farm Irrigation System Evaluation: A Guide for Management. Department of Agricultural and Irrigation Engineering, Utah State University, Logan, Utah. Micker, J. 1996. Mobile Irrigation Laboratory Urban Irrigation Evaluation Training Manual. U.S. Department of Agriculture Natural Resources Conservation Service. Gainesville, FL. Munson, B., D. Young, and T. Okiishi. 1998. Fundamentals of Fluid Mechanics. John Wiley & Sons, Inc. New York, NY.

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BIOGRAPHICAL SKETCH My name is Melissa C. Baum and I am attending the University of Florida. I am studying in the department of Agricultural and Biological Engineering, focusing on land and water engineering, researching residential irrigation water use. In 2002, I obtained my B.S. from the department of Agricultural and Biological Engineering, at the University of Florida (UF). During my undergraduate study, I had many research experiences. I worked with the Engineering Research Center and Dupont Mining, on a water quality project. I also spent 4 months at the Universite de Technologie in Compiegne, France, working on a hydrophobicity project. As a student I was very active in clubs and organizations. I was president of the UF student chapter of the American Society of Agricultural Engineers and vice-president of the UF Alpha Epsilon Agricultural Engineering Honor Society. I also conducted water-lab demonstrations for undergraduate classes and touring high-school students. I enjoyed my teaching and research experience during my time at the University. 97


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Permanent Link: http://ufdc.ufl.edu/UFE0009540/00001

Material Information

Title: Residential Irrigation Water Use in the Central Florida Ridge
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0009540:00001

Permanent Link: http://ufdc.ufl.edu/UFE0009540/00001

Material Information

Title: Residential Irrigation Water Use in the Central Florida Ridge
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0009540:00001


This item has the following downloads:


Full Text












RESIDENTIAL IRRIGATION WATER USE IN THE CENTRAL FLORIDA RIDGE


By

MELISSA C. BAUM

















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

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

Melissa C. Baum

































To my husband, Patrick E. Haley.















ACKNOWLEDGMENTS

I thank the following individuals for their help: Danny Burch, Clay Coarsey, Jeff

Williams, Brent Addison, Justin Gregory, Kristen Femminella, and Mary Shedd. I would

also like to thank my graduate committee members (Dr. Dorota Z. Haman and Dr. Grady

L. Miller) for guidance and patience. Lastly, a most special "thank you" goes to Dr.

Michael D. Dukes, for being a wonderful guru! This research was supported by the

Florida Agricultural Experiment Station and a grant from St. Johns River Water

Management District.
















TABLE OF CONTENTS

page

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

LIST OF TABLES ............... ............................... ... .................. vii

LIST OF FIGURES ............... ....................................... .. ................. viii

LIST OF ABBREVIATIONS............................................................ .............. ix

A B STR A C T ................................................. ..................................... .. x

CHAPTER

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

2 RESIDENTIAL IRRIGATION WATER USE .......................................................13

M materials an d M eth o d s .................................................................... ..................... 14
R results and D discussion ...................... .................. ................... .. ...... 19
Sum m ary and C onclu sions .............................................................. .....................23

3 RESIDENTIAL IRRIGATION DISTRIBUTION UNIFORMITY...........................32

M materials an d M eth od s .................................................................... .....................34
R results and D discussion ...................... .................. ................... .. ...... 37
R residential Testing ........................................... .. ....... .............. ... 37
C control T testing .................................................................................. 39
Sum m ary and C onclu sions .............................................................. .....................40

4 COMPARISON OF UNIFORMITY MEASUREMENTS .....................................46

M materials and M methods ..................................................................... ....................47
R results and D discussion ........................ ................ ................... .. ...... 50
Sum m ary and C conclusions ............................................................... ............... 51

5 C O N C L U SIO N S ..................... .... .......................... ........ ........ ...... ........... 55

APPENDIX

A P H O T O G R A P H S ............................................................................. .................... 60









B STA TISTIC A L A N A L Y SIS ............................................................. ....................71

L IST O F R E F E R E N C E S ......... ................. ........................................ .......................... 93

B IO G R A PH IC A L SK E TCH ...................................................................... ..................97
















LIST OF TABLES


Table page

2-1. Monthly water use for Treatment 1 homes for all three locations combined............25

2-2. Monthly water use for Treatment 2 homes for all three locations combined............26

2-3. Monthly water use for Treatment 3 homes for all three locations combined............27

2-4. Evapotranspiration, rainfall, and effective rainfall calculated per month. ................28

2-5. Seasonal water use, fraction of total water use, and turf quality rating with letter
notations referring to the significant difference between treatments for each season.29

2-6. Percentage if irrigated area which is turfgrass or landscaped bedding as well as the
total irrigated area for each hom e ................................................. ....... ........ 29

3-1. Mobile Irrigation Lab turf DUiq results for five counties in Florida..........................43

3-2. Recommended pressure and radii for tested spray and rotor heads under ideal
conditions according to manufacturer guidelines.........................................43

3-3. Irrigation Association overall system quality ratings, related to distribution
uniform ity ..................................... ................................. ........... 44

3-4. Residential system distribution uniformity catch-can test results ...........................44

3-5. Control system distribution uniformity catch-can test results for these brands of
rotor heads at recommended and low pressures.....................................................45

3-6. Control system distribution uniformity catch-can test results for these brands of
spray heads at recommended, low, and high pressures........................................45

4-1. Uniformity values from both the catch-can tests and the TDR values....................53

4-2. Measurement results from both the catch-can and the TDR tests...........................53
















LIST OF FIGURES


Figure p

2-1. M ap of site locations. ...................................................................... ................... 30

2-2. Effective rainfall plus applied irrigation for each treatment compared to reference
evapotranspiration. ................................. .. .. .. ...... .. ............31

4-1. Comparison of DUlq values calculated from both the TDR soil moisture and catch-
can te sts. .......................................................... ................ 5 4

4-2. Comparison of soil moisture to can volume measurements taken during uniformity
te sts ........................................................................... 5 4

A -1. F low m eter .................................................. .................. ...................6 0

A -2 W weather station ........... ...... ............................................ ................ .......... ....... 60

A-3. Control system spray head with pressure gage ................................ ............... 61

A -4. Control system catch-can test........................................................ ............... 61

A -5. R residential system catch-can test........... ........................................ ................62

A-6. Setup of catch-can grid formation.... ............................ ..62

A-7. Catch-can grid formation around bedded area.............................................. 63

A-8. Measure catch-can volume with graduated cylinders ...........................................63

A-9. Turfgrass area with high turf quality rating .................................. ............... 64

A-10. Turfgrass area with low turf quality rating......................................................64

A-11. Sample cooperator homes from each treatment in Marion County. A) T1. B) T2.
C) T3. D ) A another T3 ............................................ .. ......... ........ .... 65

A-12. Sample cooperator homes from each treatment in Lake County. A) T1. B) T2. C)
T3. D) Another T3...................................................... 67

A-13. Sample cooperator homes from each treatment in Orange County. A) T1. B) T2.
C) T3. D ) A another T3 ............................................ .. ......... ........ .... 69
















LIST OF ABBREVIATIONS


ASAE

CU

DUlq

ET

ETo

GLM

MIL

NRCS

NTEP

SJRWMD

Tl

T2

T3

TDR

UF

USDA

VWC


American Society of Agricultural Engineers

Coefficient of Uniformity

Distribution Uniformity

Evapotranspiration Rate

Reference Evapotranspiration

General Linear Model

Mobile Irrigation Lab

Natural Resource Conservation Service

National Turfgrass Evaluation Procedure

St. Johns River Water Management District

Treatment One

Treatment Two

Treatment Three

Time Domain Reflectometry

University of Florida

United States Department of Agriculture

Volumetric Water Content















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 Engineering

RESIDENTIAL IRRIGATION WATER USE IN THE CENTRAL FLORIDA RIDGE

By

Melissa C. Baum

May 2005

Chair: Michael D. Dukes
Major Department: Agricultural and Biological Engineering

Automatic in-ground irrigation is almost a standard for residential homeowners

desiring high-quality landscapes in Florida. The goal of this study was to document

irrigation water use (T1) and system uniformity in the Central Florida Ridge region under

typical irrigation practices, and to quantify distribution uniformity of residential sprinkler

equipment under controlled conditions. The other major goal was to determine if

scheduling irrigation by setting controllers based on historical evapo-transpiration (ET)

(T2) and reducing the percentage of turf area combined with setting the controllers based

on historical ET (T3) would lead to reductions in irrigation water use.

The time frame of this study was 29 months beginning in 2002. Most of the homes

in the study tended to over-irrigate. Irrigation system analysis for each home included

irrigation water distribution uniformity tests, recorded water use, visual observation of

the turf quality, and pressure testing across all zones in the system. Of the 27 houses in

this study, average annual irrigation accounted for 62% of the residential water use

volume. The T1 homes had an average monthly water use of 146 mm. Compared to the









Tl homes, T2 had a 21% reduction, and T3 had a 41% reduction in average monthly

water use. Over-irrigation was a result of a lack of understanding of the run times based

on equipment type and seasonal evapotranspiration rates. In many cases, homeowners

did not decrease irrigation water use in the winter months.

To test the distribution uniformity of the irrigation systems, a catch-can test was

used. From these tests, the overall low quarter distribution uniformity (DUiq) value was

calculated as 0.45. Rotor sprinklers resulted in significantly higher DUiq compared to

fixed pattern spray heads (0.49 compared to 0.41, respectively). The spray heads had

higher uniformity (DUlq value) when fixed quarter-circle nozzles were used, as opposed

to adjustable nozzles. Uniformity was higher in the tests where the manufacturer

recommended pressure was maintained rather than tests performed at low pressure. For

the control tests, the spacing was set according to manufacturer guidelines for head to

head coverage. In contrast, the residential systems had less-than-ideal spacing, and thus

had a decreased DUlq value. Residential irrigation system, uniformity can be improved by

minimizing the occurrence of low pressure in the irrigation system and by ensuring that

proper spacing is used in design and installation.

The use of time domain reflectometry (TDR) probes is an effective nondestructive

method of measuring soil moisture content. The study compared irrigation distribution

uniformity evaluated by TDR in the upper 12 cm of the soil versus catch-can tests. The

calculated DUlq determined from a TDR device tended to be 0.15 to 0.20 points higher

than the DUlq value determined by the catch-can method. The TDR moisture content

DUlq did not correlate with catch-can DUiq.














CHAPTER 1
INTRODUCTION

Irrigation has become nearly a standard option for residential homeowners desiring

high quality landscapes in Florida. Turfgrass is a key landscape component, and

normally the most commonly used single type of plant in the residential landscape.

Although Florida has a humid climate (the average precipitation rate is greater than the

evapotranspiration rate), the spring and winter are normally dry. The average annual

precipitation for the Central Florida ridge is approximately 1320 mm, with most of this in

the summer months (June through August). The spring months (March through May) are

typically the hottest and driest (USDA, 1981). This region is also characterized by sandy

soils with a low water-holding capacity; therefore, storage of water is minimal. The dry

spring weather and sporadic large rain events in the summer (coupled with the low water-

holding capacity of the soil) make irrigation necessary for the high-quality landscapes

desired by homeowners.

Residential water use comprises 61% of public-supply water withdrawals (Fernald

and Purdum, 1998). Public supply is responsible for most (43%) of the groundwater

withdrawn in Florida. Between 1970 and 1995, public-supply water withdrawals

increased 135%(Femald and Purdum, 1998). Florida consumes more fresh water than

any other state east of the Mississippi River (Solley al. (1998).

Florida's current population of 16 million is projected to exceed 20 million by 2020

(USDC, 2001). With the average residential irrigation cycle consuming 2000 to 2500

gallons of water per cycle (Hayes, 2000), water conservation has become a state concern.









In 1972 (in the Florida Water Resources Act, Chapter 373) the Florida Legislature

created the five water management districts. In 1997, Chapter 97-160 of the Laws of

Florida was ratified; this overruled Chapter 373 of the Florida Statutes, the previous

water law. The revision included delegating responsibilities to the water management

districts. Each district was assigned primary responsibility for conducting water resource

development.

This study focused on the Central Florida ridge in the St. Johns River Water

Management District (SJRWMD). Due to drought conditions in the past few years, the

SJRWMD has limited residential irrigation to 2 times per week. Residential irrigation is

prohibited between 10 a.m. and 4 p.m., whether the water is from public supply, domestic

self-supply (i.e., wells), or surface water (SJRWMD, 2002). Irrigation outside of these

hours reduces evaporative and wind losses. Residential irrigation water is thought to be

50% of total irrigation water use, although, no literature confirmed this.

Irrigation efficiency defines how well an irrigation system supplies water to a given

crop or turf area. Efficiency is the ratio between water used beneficially and water

applied, and is expressed as a percentage. There are three concepts of irrigation

efficiency: water conveyance efficiency (Ec) (Eq. 1-1); water-application efficiency (Ea)

(Eq. 1-2); and reservoir storage efficiency (Es) (Eq. 1-3).


E, = 100. [1-1]
W,


E, = 100 W, [1-2]
W,


E =100 P [1-3]
Wr









where Wd is the water delivered to the area being irrigated, Wi is the water introduced

into the distribution system, Ws is the irrigated water stored in the root zone, Wp is the

water pumped from the reservoir, and Wrs is the water stored in the reservoir (Smaj strla

al. (1991).

Water conveyance efficiency is calculated from the point of discharge (pump),

while water application efficiency is calculated over an entire field (or lawn). Reservoir

storage efficiency is the ratio of water pumped from the reservoir and water stored in the

reservoir. Factors that lower efficiency are evaporation, wind drift, improper equipment

adjustment, drainage below the root zone, and runoff. Reservoir storage efficiency is

varies depending on site conditions. The lowest values can be attributed to surface

reservoirs due to evapotranspiration (ET) and seepage. Since most residential irrigation

water in Florida is derived from groundwater, reservoir storage efficiency is thought to be

as high as technically possible. In pressurized sprinkler irrigation systems, water

conveyance efficiency is nearly 100%, unless there is a leak in the pipeline or distribution

equipment. Thus, application efficiency is the only component that may vary in

residential irrigation systems. To achieve relatively high application efficiency, it is

necessary to maintain even distribution of irrigated water over the target area.

To determine if the water is used beneficially, it is necessary to determine the

overall quality of the lawn. The assessment of turfgrass is a subjective process using the

National Turfgrass Evaluation Procedures (NTEP) (Shearman and Morris, 1998). This

evaluation is based on visual estimates such as color, stand density, leaf texture,

uniformity, disease, pests, weeds, thatch accumulation, drought stress, traffic, and quality.









Turfgrass quality is a measure of functional use and aesthetics (i.e., density, uniformity,

texture, smoothness, growth habit, and color).

Irrigation systems used by the households typically include stationary spray heads

and gear driven rotor sprinklers for the turf and landscape. Water conservation oriented

designs include microirrigation for the landscape bedding.

Uniformity of water distribution measures the relative application depth, over a

given area. This concept can be valuable in system design and selection, and can assign a

numeric value to quantify how well a system is performing. The term uniformity refers to

the measure of the spatial differences between applied or infiltrated waters over an

irrigated area. Two methods have been developed to quantify uniformity: distribution

uniformity (DU) and Christiansen's coefficient of uniformity (CU).

The low-quarter irrigation distribution uniformity (DUiq) can be calculated with the

following equation (Merriam and Keller, 1978):


DU1q [1-4]


where Diq is the lower quarter of the average of a group of catch-can measurements, and

Dtot is the total average of a group of catch-can measurements.

Distribution uniformity is usually represented as a ratio, rather than a percent (Burt

et al. (1997), to signify the difference between uniformity and efficiency. This method

emphasizes the areas that receive the least irrigation by focusing on the lowest quarter.

Burt et al. (1997) defined common irrigation performance measurements,

standardized and clarified of irrigation definitions, and quantified irrigation

measurements. Distribution uniformity is not considered efficiency. Although a system









may have even distribution, over-irrigation can occur because of mismanagement. Low-

quarter distribution uniformity uses a definable minimum range (lowest quarter) rather

than the absolute minimum value (zero). The Irrigation Association (2003),

recommended the following distribution of the lower half (DUlh) for scheduling

residential irrigation systems,

D
DU Dh [1-5]
Dtot

DUlh =0.386+.614xDUlq [1-6]

where DIh is the lower half of the average depth of the water irrigated, and Dtot is the total

of the average depth of water irrigated in a given area. Determining distribution

uniformity helps to reduce excess water used for irrigation purposes. DUlh is suggested

over DUlq because the lower quarter overestimates the effect of non-uniformity for

landscapes (IA, 2003).

The coefficient of uniformity treats over-irrigation and under-irrigation equally as

compared to the mean, and can be calculated by the Christiansen (1942) formula (Eq. 1-

7),


-
CU =1- '1 [1-7]

1=1

where V, equals the volume in a given catch-can, and V refers to the mean volume. In

addition to the coefficient of uniformity and the distribution uniformity, there are other

important factors in evaluation of a system. Application rates, system pressure









variability, runoff, wind, amount of water applied, pump performance, and overall system

management must be considered when evaluating total system performance.

Several studies have used these concepts to determine efficiency and uniformity of

irrigation systems used in urban and agricultural settings. In Utah, a model for estimating

turf water requirements was created (Aurasteh, 1984). Urban irrigation was studied with

the irrigation use measured weekly by 20 homeowners. The objectives of the study were

to measure residential distribution uniformities, assess potential application efficiencies,

and to compare water use to ET rate. The sprinkler uniformity tests were conducted

using catch-cans. The ET rate was calculated, and an empirical model for determining

urban irrigation needs was created. Residential solid set and movable systems were

compared; analysis of the application efficiency these systems showed that the average

water application was about 30% for hand-move and 37% for solid set systems (Aurasteh

et al. (1984). It was also noted that these homeowners used approximately 61% of their

total water supply for irrigation. Utah receives less average annual precipitation, 207 mm

(8.2 in) (NRCS, 1990), compared to the 1320 mm (52 in) received in Florida.

Due to the wide use of sprinkler irrigation as an irrigation method on sloping lands,

the effects of surface slope on sprinkler uniformity were studied in Brazil. It was found

that distribution uniformity has a direct correlation to nozzle and riser angle, increasing as

the nozzle angle is varied from vertical to horizontal, perpendicular to the ground.

However, the DU decreases with an increase in ground slope. The DU was improved

with a triangular precipitation pattern for all ground slopes and nozzle angles (Soares al.

(1991).









A number of computer models have been created to aid in uniformity testing of

sprinkler systems. In Brazil, a data acquisition system for sprinkler uniformity testing was

created (Zanon et al., 2000). The system was designed to test a two radii precipitation

pattern (head-to-head) for low to medium pressure sprinklers under no wind conditions.

In Japan, a method was developed for evaluating water application rate and the

coefficient of uniformity, CU, of sprinklers with head to head coverage. The tests were

under realistic conditions, including monitoring the effect of wind drift (Fukui al. (1980).

Numerous modeling studies have been conducted with regard to residential

irrigation uniformity and efficiency. In Spain, the SIRIAS software was developed. This

model for sprinkler irrigation uses the ballistic theory to predict the path of drops

discharged, obtaining wind-distorted water distribution, and formulation for the air drag

coefficient. To consider actual environmental conditions, the program has three options

for evaporation and drift losses within the irrigation process (Carrion et al., 2000). The

simplification and comparison of models has also been explored. At Oregon State

University, a widely used model based on numerical solutions was modified for

simplicity of use. Accurate analytical approximations for DU, CU, application

efficiency, deficiently irrigated volume, and the average deficit over the deficiently

irrigated area were developed. The approximations proved to be more accurate than

earlier approximations and introduced negligible error when used for practical

applications (Smesrud and Selker, 2001). At Colorado State University, the use of the

normal distribution function in describing sprinkler irrigation uniformity was simplified

for evaluation of irrigation system performance in terms of economic and environmental

decisions (Walker, 1979). Colorado State University and Louisiana Technical University









compared statistical models to approximate sprinkler patterns with various coefficients of

uniformity, calculation of water volume needed, and irrigation efficiency. It was found

that for uniformity coefficients the normal distribution was a better fit than the linear

model. However, at uniformities below 0.65 the linear model fit best (Elliott al. (1980).

In Colorado, granular matrix soil moisture sensors were used to control the

irrigation for urban landscapes. The objective of the study was to evaluate the

effectiveness and reliability of soil moisture sensors for irrigation control. The soil

moisture systems proved to be very reliable and reduced the irrigation application below

theoretical requirements. The calculated theoretical irrigation requirement was 726 mm,

while the actual water applied, as allowed by the sensor system, was 533 mm (Qualls et

al., 2001).

According to the residential irrigation system audits conducted by the University of

Georgia Water Resources Team (Thomas et al., 2003) the operating time was improperly

set on many homes tested, therefore the systems were set to run too long applying more

water than necessary. Of the systems audited, the spray heads distributed three to five

times the water application rate per given area as compared to rotary sprinklers.

To increase water conservation, a national sub-metering and allocation billing study

found, more multi-family dwellings are being converted to billing systems where the

water and wastewater charges are paid separately, as opposed to including these charges

as part of the total rent. Data suggested that sub-metering irrigation water use would

further increase the outdoor water use efficiency and management. Sub-metering on

multifamily apartment units and billing based on actual consumption resulted in water

savings of 15% or 8,000 gallons per unit per year. Reduction of irrigation in the winter









months resulted in a statistically significant impact on the overall water use (p<0.001).

The percent of total property which was irrigated did not have a significant (p=0.150)

affect on the total water use. However, water billing practices based on the allocation

methods (ratio utility billing method) did not affect water savings (Mayer et al., 2004).

The American Water Works Association (AWWA) Research Foundation funded a

study on residential end uses of water (Mayer al. (1999). The study concluded the

following homes with: in-ground irrigation systems used 35% more water than houses

without these systems, automatic timer controls incorporated into the system led to 47%

more water used, drip irrigation systems used 16% more water than homes which did not

irrigate the area with in-ground irrigation, homes which only hand (hose) watered used

33% less water than those with in-ground systems, and homes which included a

consistently maintained garden used 30% more outdoor water. The samples which were

grouped into the low-water-use treatment applied an average of 20.3 gal/ft2 per year for

the irrigated area. The standard landscape treatment applied 22.8 gal/ft2per year.

However, there was not a significant difference (at the 95 percent confidence interval)

between these two treatments. One of the conclusions as to why there was an

inconclusive finding was that the low-water-use landscaping required an initial

establishment period of additional water.

In Florida, Mobile Irrigation Labs (MILs) were established as a public service in

1992 as part of a water conservation program. Funding for this program comes from the

United States Department of Agriculture (USDA) and the individual water management

districts. The Florida MILs were modeled after those operating in California and Texas.

They evaluate irrigation systems in both agricultural and urban areas by conducting a









series of tests over a two-hour period, measuring pump flow rates, sprinkler pressures and

flow rates, and application uniformities (Micker, 1996).

While overall uniformity of irrigation systems has been measured in Florida in the

past, most of the MILs no longer conduct actual system distribution uniformity tests;

therefore, there is a lack of information regarding current residential irrigation system

performance and water use. In some MILs distribution uniformity results that were

judged to be low were discarded (anonymous MIL source).

In field assessments of irrigation system performance in California, Pitts et al.

(1996) found a mean DUiq of all systems tested as 0.64. The average DUlq for non-

agricultural turfgrass sprinklers (large turfgrass areas) was 0.49. Greater than 40% of the

tested systems had a DUiq of less than 0.40. This study concluded that the low DUlq

values were based on the following reasons (listed in order of frequency): maintenance

and faulty sprinkler heads, mixed zones (spray and rotor), excessive pressure variations,

and poor head-to-head coverage. Many of the cooperators in this study were unaware of

importance of scheduling based on potential evapotranspiration and uncertain about the

application rates of their systems. It was found that scheduling was usually based on the

appearance of the turfgrass. To the "trained" eye this would be acceptable, however

typical homeowners do not know what signs are indicative of over-watering or drought

related stress.

Linaweaver et al. (1967) found that the amount of water used for residential lawns

is effected by the total number of consumers, the economic level of the residential area,

the area of turfgrass and bedding requiring irrigation, the evapotranspiration rate, and the

quantity of effective rainfall. In Wyoming, from the summer 1975 through spring 1977,









a study was conducted on actual lawn water application rates for residential households

and evaporation rates of lawn turfgrass. The application rates found were between 122

and 156% above calculated seasonal evapotranspiration rates (Barnes, 1977).

Evapotranspiration (ET) is the rate at which water may be removed from soil and

plant surfaces to the atmosphere by a combination of evaporation and transpiration (Allen

al. (1998). Evaporation (E) is the conversion of water into its vapor phase. The main

factors influencing evaporation are the supply of energy by solar radiation and the

transport of vapor away from the surface (e.g., by wind). Transpiration (T) refers to the

water used by plants and is affected by plant physiology and environmental factors. The

evapotranspiration process is climate controlled. Researchers at Texas A&M University

(White et al., 2004) looked at using potential ET, a landscape coefficient (L,), and the

landscape size, to develop water budgets for residential landscapes. It was determined

that potential ET irrigation budgeting with an L, of 1.0 would account for substantial

irrigation water savings, especially in the summer months.

A time domain refectometry (TDR) device can be used to measure soil water

content by measuring the time needed for an electrical signal to travel along wave guides.

As opposed to the measurement of irrigation application, soil moisture is measured as the

volume of water within a volume of soil. A TDR device can be used to estimate the

amount of water stored in a profile. It also can help to eliminate how much irrigation is

required to reach a desired moisture content.

The Northern Colorado Water Conservancy District compared catch-can tests and

soil moisture sensor measurements in turfgrass irrigation auditing. When calculating

DUlq, it was found that the soil moisture uniformity was higher than the catch-can









uniformity. From the tests in the study, the soil moisture DUlq was 0.15-0.20 (maximum

value of 1.00) higher than the DUiq determined by the catch-can method (Mecham,

2001). Although the catch-can DUiq could help determine the overall system

performance, these uniformity values did not properly express the distribution of the

water through the thatch or as affected by the soil properties. Estimating irrigation run

times based on the catch-can DUiq would lead to over-irrigation, due to the low nature of

these DUiq values (Mecham, 2001).

In Florida, a study compared microirrigation (drip) uniformity determined by both

time domain reflectometry and the conventional volumetric method. The study

concluded that the TDR can be a useful tool for quick determination of uniformity.

Inversely in this study, for the drip systems the TDR DUlq was lower than the DUlq

calculated by the conventional method. Differences were assumed to be a result of soil

properties and point measurement locations (Dukes and Williams, 2002).














CHAPTER 2
RESIDENTIAL IRRIGATION WATER USE

Homeowners in Florida desire a year-round lush landscape; consequently,

irrigation is required. Florida is reputed as the "Sunshine State" with lush foliage and

beautiful weather. The population is steadily increasing and new housing developments

are constantly being built. New Floridians expect a manicured and lush landscape around

their homes. Unfortunately, this has resulted in excessive water used for irrigation

purposes. Since the price of groundwater is not yet particularly high most homeowners

would rather pay the price for a green lawn.

As of 2000 Florida had a population of nearly 16 million and is projected to exceed

20 million people by 2020 (USDC, 2001), which has led to the consumption of more

fresh water than any other state east of the Mississippi River (Solley al. (1998). Between

1970 and 1995 there was a 135% increase in groundwater withdrawals in Florida

(Femald and Purdum, 1998). Public supply is responsible for the largest portion, 43%, of

groundwater withdrawn in Florida. Residential water use comprises 61% of the public

supply category (Marella, 1999). Since irrigation is so widely used and the number of in-

ground irrigation systems is increasing across the state, it is necessary to observe the

residential irrigation water use trends. The objective of this project was to measure

residential irrigation water use in the Central Florida Ridge across three landscape and

irrigation scheduling treatments.









Materials and Methods

This study was conducted within the Central Florida ridge (Figure 2-1), which

included eight homes in Marion County, nine homes in Lake County, and ten homes in

Orange County. The homes were categorized into three treatments. Treatment one (T1)

consisted of existing irrigation systems and typical landscape plantings, where the

homeowner controlled the irrigation scheduling. Treatment two (T2) also consisted of

existing irrigation systems and typical landscape plantings, but the irrigation scheduling

was based on historical evapotranspiration (ET) rates from the Central Florida area over

30 years. Treatment three (T3) consisted of an irrigation system designed according to

specifications for optimal efficiency including a landscape design that minimized

turfgrass and maximized the use of native drought tolerant plants as classified by the

SJRWMD. To further achieve water savings in T3, the landscape plants were irrigated

by microirrigation (micro-spray heads, bubblers, and drip tubing) as opposed to standard

spray and rotor heads. The T3 landscape designs and modifications to irrigation systems

were installed as part of this project. The newly planted landscape in T3 required an

establishment period of one to two months, with increased irrigation. This additional

water use data has been omitted in water use analysis. Water use was included for

analysis after the landscape material had been established for two months. Mayer et al.

(1999) also found that new landscapes required an initial establishment period of

additional water.

The average annual precipitation in this area ranges from 1275 to 1400 mm, with

the maximum rainfall in the summer months and the minimum rainfall from late fall

through spring (USDA, 1981). The soils are excessively to moderately well drained

sandy Quartzipsamments (USDA, 1981). The prevalent soil series in the Marion and









Lake County sites is Astatula sand, which allows for rapid permeability, has a very low

available water capacity, and little organic matter content (USDA, 1975). The dominant

soil series in the Orange County site location is Urbanland-Tavares-Pomello, which is a

moderately well drained soil that is sandy throughout (USDA, 1989). The Marion and

Lake County sites included in this study are on the hills that were previously citrus farms,

and have been built upon a layer of sand fill. The irrigation systems used by the

households typically include stationary spray heads and gear driven rotor sprinklers for

the turf and landscapes. The lawn areas of the yards all consisted of St. Augustine

turfgrass, which is a warm season turfgrass and a common sod in new construction in

Florida.

The residences for this study where chosen if an in-ground automatic irrigation

system was used and the irrigation system was supplied by potable city water (not well-

drawn or reclaimed water). The homeowners were recruited at garden club or area

community association meetings. All of the residences included in this study obtained

water from local utilities. The utility water meter was used to determine the amount of

water consumed by the household. For domestic water systems, positive displacement

meters are used, which are relatively inexpensive and accurate (Munson al. (1998). To

determine the volume of irrigation water used, a second flow meter was installed after the

irrigation pipeline diverged from the main water line to the house, before distribution to

the solenoid valves. The meters were installed with no obstruction within approximately

ten diameters of the inlet and outlet of the meter. This was to ensure minimal turbulence

in flow through the meter to maintain accuracy (Baum et al., 2003). Water use data was

collected from January 2002 through May 2004. However, additional homes were









incorporated into T1 and T2 until May 2003, and the last of the T3 homes was added in

July 2003.

The area of each yard was calculated from a scale drawing of the house, turf, and

landscape beds. The irrigated area was necessary for calculating depth of irrigation

applied from the volume data measured by the meters.

Weather stations in Marion and Lake Counties were installed in late February 2002

and one was installed in Orange County in May 2002 to enable calculation of reference

evapotranspiration (ETo). The weather stations were located in flat-grassed areas so that

the nearest obstruction was at least 61 m away from the station. Irrigated areas were

chosen when possible; however, this resulted in one of the stations collecting irrigation

water in the precipitation bucket. A separate rain bucket and data logger (Davis

Instruments Corp., Hayward, CA and Onset Computer Corp., Bourne, MA) was installed

in a non-irrigated area to separate precipitation events from irrigation events. The

residential home sites were located within 1 km of the weather stations. Date, time,

temperature, relative humidity and temperature (model HMP45C, Vaisala, Inc., Woburn,

MA), soil heat flux (model HFT3, Radiation Energy Balance Systems, Bellevue, WA),

solar radiation (model LI200X, Li-Cor, Inc., Lincoln, NE), wind speed and direction

(model WAS425, Vaisala, Inc., Sunnyvale, CA) and, precipitation (model TE525WS,

Texas Electronics, Inc., Dallas, TX), were recorded in 15 minute intervals via a CR10X

data logger (Campbell Scientific, Inc., Logan UT).

The Penman-Monteith equation is a widely used combination method for

calculating ETo. As outlined in FAO-56 this equation takes the following form (Allen al.

(1998):










900
0.408A(R -G)y+ Y u2(e e)
ET T + 273 [2-1]
A + (1 +0.34u2)


4098 0.6108expC 17.27T
A = [2-2]
(T+ 237.3)2

Rn R=ns Rnl [2-3]


Rn1= crTmaxK + TmmK (0.34-0.14e7 1.35R -0.35 [2-4]
2 I Rso

Rns = (1- a)Rs [2-5]

Rso = (0.75 + z(2 x 10-5))Ra [2-6]

Ra 24(60) Gd, [,s sin(o) sin(3) + sin(cm) cos(o) cos(3)] [2-7]


dr = 1 + 0.033 cos 2J [2-8]
365


6 = 0.409 sin 2 J -1.39' [2-9]
365

cos = arcos [-tan(ip)tan(6)] [2-10]

eo (Tmax) + e (Tm,)
es = [2-11]
2

RH RH
eo (Tmm ) max eo(Tmx m
100 100
ea 10 = [2-12]
2

e(T)= 0.6108exp 17.27T [2-13]
[T+237.3









where ETo = Potential evapotranspiration, mm/day
A = slope of the vapor pressure curve, kPa C-1
Rn= net radiation of the turf surface, MJ m-2 day-1
Rn1= net outgoing longwave radiation, MJ m-2 day-'
Rns = net solar or shortwave radiation, MJ m-2 day-1
Rso = clear sky solar radiation, MJ m-2 day-1
Rs = measured solar radiation W/m2 x 0.0864, MJ m-2 day-'
Ra = extraterrestrial radiation, MJ m-2 day-1
G = measured soil heat flux density, MJ m-2 day-'
Gs, = solar constant, 0.0820 MJ m-2 min-'
T = measured air temperature at a 1.5 m height, C
U2 = measured wind speed at a 2 m height, m s-1
es = saturation vapor pressure, kPa
ea = actual vapor pressure, kPa
e(T) = saturation vapour pressure at air temperature, kPa
RH = relative humidity at 1.5 m height, %
dr = inverse relative distance Earth-Sun
Cos = sunset hour angle, rad
6 = solar declination, rad
y = psychrometric constant, 0.067 kPa C-1
o = Stefan-Boltzmann constant, 4.903 x 10-9 MJ K-4 m-2
J = Julian day



Effective rainfall is the portion of rainfall that is beneficial to the plants, and does

not include that rainfall that produced runoff Effective rainfall was estimated by the

SCS method, presented by the following equation (Schwab al. (1993):

Pe = f(D)[1.25P, 824 2.93][100 000955ET ] [2-14]

f(D) = 0.53 + 0.0116D 0.894 x 10 5D2 + 2.32 x 10 7D3 [2-15]

where Pe = estimated effective rainfall for soil water deficit depth, mm

Pm = mean monthly rainfall, mm

ETo = average monthly evapotranspiration, mm

f(D) = adjustment factor for soil water deficits or net irrigation depths


D = soil water deficit or net irrigation depth, mm (used 25 mm)









To determine if the water is used beneficially, it is necessary to determine the

overall quality of the lawn. The assessment of turfgrass is a subjective process following

the National Turfgrass Evaluation Procedures (NTEP) (Shearman and Morris, 1998).

This evaluation is based on visual estimates such as color, stand density, leaf texture,

uniformity, disease, pests, weeds, thatch accumulation, drought stress, traffic, and quality.

Turfgrass quality is a measure of functional use and aesthetics (i.e., density, uniformity,

texture, smoothness, growth habit, and color).

The statistical analysis of the collected data was analyzed using the general linear

model (GLM) function of the SAS software for the anova tables. The means are reported

as weighted means. All significance was at the 95% confidence interval, unless

otherwise noted. Interactions, such as year or season with treatment were observed, and

the three locations were nested for proper data analysis.

Results and Discussion

Overall, the average household used 63% of total water consumed for irrigation.

Treatment 1 averaged 75% of the total water use for irrigation (Table 2-1), Treatment 2

used 66% (Table 2-2), and Treatment 3 used 49% (Table 2-3), which were statistically

different (p<0.001).

Many of the homeowners, particularly in Marion and Lake Counties, would leave

town for extended periods of time in the summer months (June-August). Although the

homeowner was not in town, irrigation of the landscape continued. Three of the T3

homes were vacant for part of the data collection period because the irrigation system

was installed prior to the sale of the house. This lack of occupancy did not affect the

irrigation water use for the homes because the homes were part of T3, where the

controller settings were adjusted as part of the study. The lack of occupancy did









however have and effect on the percentage of water used for irrigation by the household,

so the months in which the percentage of water use was 100% were omitted.

Treatment 1 (user controller setting with typical irrigation system) had the highest

average monthly irrigation water use, 146 mm. Treatment 2 (60% historical ET

replacement with typical irrigation system) consumed 116 mm for irrigation purposes.

Treatment 3 (adjusted controller setting incorporating microirrigation) used the least

water for irrigation, 86 mm. The average monthly irrigation depth was significantly

different (p<0.001) across all treatments. The T2 homes consumed 21% less water than

Tl, and T3 consumed 41% less than T1.

The evapotranspiration and rainfall data is reported in Table 2-4. The comparison

of the effective rainfall plus the applied irrigation compared to ETo can be found in

Figure 2-2. Across all three years, T1 had a higher water input than ETo. The T2 water

use was very similar to T1, especially in the summer months. There was a decrease in

water input during the first winter; this is when the controller adjusting began for the T2

homes. The reasons the T2 water input did not decrease as much during the later part of

2003 and early 2004 was because: the homeowners would periodically re-adjust their

controller; the controller settings was based on historical ET and during this time there

was more rain than expected; and sometimes rain events occurred after scheduled

irrigation. The T3 water input was much lower after the first year, this is probably

because during the first year there was an initial establishment period for the landscapes.

Although this period was removed, there were residual effects.

Year two, 2003, was the only full year of data collection where the irrigation run

times were seasonally adjusted. During this cycle of seasons, there were significant









differences between treatment and season, there was not however an interaction between

treatment and season. The T1 homes applied 141 mm of irrigation water, which was

significantly more that T2 and T3, which applied 94 mm and 85 mm respectively during

this year.

Across the 29 months of data collection, all three treatments combined used

significantly the least water in the winter months, 78mm. The summer months accounted

for significantly the second lowest amount, 117 mm. There was not a significant

difference between the fall and spring months, and during these the most water was used

for irrigation purposes.

Turf quality was rated seasonally (Table 2-5). In the winter months (December-

February), when the turfgrass is typically dormant, T3 used the least water, 55 mm,

primarily because the microirrigation zones result in a smaller effective irrigated area and

turfgrass irrigation could be stopped or greatly reduced. In the spring months (March-

May) T1 applied the most irrigation water, 179 mm, T2 used 132 mm, and T3 consumed

the least, 94 mm. This is due to monthly adjustments of irrigation times and because the

microirrigated areas in T3 homes required less water than if those areas were sprinkler

irrigated. However, there was not a statistical difference between the treatments. During

the spring months, ETo was the highest and the adjusted controller run time settings were

similar to that of typical user set run times. In the fall months (September-November),

Tl and T2 resulted in similar application amounts of 155 mm and 148 mm, and T3

significantly less atl02 mm.

The minimum turf quality rating for acceptable quality is 6. Lower ratings do not

necessarily imply drought stress. The lawns in T1 and T2 maintained minimum or better









quality during the project data collection period. The T2 turfgrass had no significant

differences in quality from T1 under a decreased irrigation schedule. The T3 lawns did

have lower quality ratings as compared to T1 and T2 in the winter (Table 2-5).

The homes in T1 and T2 were irrigated solely by either rotary or spray irrigation

heads. The homes in T3 incorporated a portion of the irrigated area covered by

microirrigation. The landscape designs for T3 homes also included larger bedding and

decreased turfgrass areas. The typical T1 or T2 landscape averaged 75-78% turfgrass

(Table 2-6). The turfgrass portion of the T3 homes ranged from 66% to 5%, and

averaged 35%. The remaining percentage of the landscaped area was considered bedding

and irrigated with the microirrigation. In some sections of the T3 homes the bedded areas

included the use of ground covers.

The homes in Orange County had the highest average water use, 130 mm/month.

This water use is directly correlated with the irrigation system design. The yards in

Orange County had the smallest turfgrass area, which is typically irrigated by a greater

percentage of spray zones versus rotary zones heads (a ratio of 5:1). The ratios of spray

heads to rotor zones for Marion and Lake Counties were 4:1 and 4:3 respectively. Spray

zones have a higher precipitation rate and the water output is more sensitive to the

scheduled run time compared to rotor zones. For all treatments, the homes in Lake

County used the greatest percentage of water for irrigation because the yards in this area

were the largest, primarily composed of turfgrass (Table 2-6). The irrigation water use

difference between the three counties was marginally significant (p-value of 0.06).









Summary and Conclusions

The average household in this study used, for irrigation, 63% of the total.

Substantial over-irrigation occurred on all treatments when compared to ETo. Over-

irrigation resulted from poor uniformity and improper scheduling.

Irrigation water use was greatest on the homes with typical irrigation systems

where the homeowner set their own controller run times (T1). At the homes where the

irrigation system still consisted of a typical design, but the controller run times were

adjusted based on historical evapotranspiration rates (T2), the irrigation water

consumption was decreased by 21% as compared to T1. The homes with both the

adjusted controller run time settings and the incorporation of microirrigation in the

bedding areas (T3) consumed the least amount of irrigation water, 41% water savings as

compared to T1.

From the figure comparing the water use by treatment including effective rainfall to

ETo, it was observed that T3 had the lowest water input, which was similar to the

evapotranspiration. The water input for the T1 homes was always much higher than ETo.

Irrigation application with respect to ETo for T2 fluctuated, over-irrigation still occurred,

the scheduling could be improved to maintain lower water input.

In Florida, rainfall supplies a significant portion of the plant water requirements but

since rain events are often intense and water holding capacity is low, high rainfall values

will not supply crop water needs over time.

Turfgrass quality did not vary significantly across treatments 1 and 2. The T3 lawns

did have lower quality ratings as compared to T1 and T2 in the winter. The T3 ratings

were below the NTEP acceptable rating of 6, but never lower than 5. In the fall and

winter months there was a decrease in turf quality, because turfgrass went into partial






24


dormancy. During dormancy, which is the normal state of turfgrass in the winter months,

irrigation run times can be decreased because the plant has decreased water needs. When

the turfgrass goes into dormancy, the turfgrass color changes to tan from green. The

decreased turf quality was color related and not due to drought stress or winter injury. In

the spring months, after "green-up", when the grass comes out of dormancy, the T3 turf

quality was better than T1.










Table 2-1. Monthly water use for Treatment 1 homes for all three locations combined.
Treatment 1
Water Use % of Total No. of


Month
Mar-02
Apr-02
May-02
Jun-02
Jul-02
Aug-02
Sep-02
Oct-02
Nov-02
Dec-02
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Average*
Median
Std. Dev.
Total


(mm)
124
144
186
124
90
154
148
158
135
106
135
97
142
184
162
177
117
123
177
158
110
104
83
102
245
157
214
146
142
39
3856


Water Use
85
87
89
76
75
69
83
82
83
60
78
80
79
85
91
90
31
31
81
57
75
67
77
77
80
71
68
75
78
15


Homes


Water use indicated as depth applied per month, the fraction of the total water consumed
by the home which was used for irrigation purposes, and the number of homes included
in the sample.
*The average is a weighted average by the number of homes included in the treatment.










Table 2-2. Monthly water use for Treatment 2 homes for all three locations combined.
Treatment 2
Water Use % of Total No. of
Month (mm) Water Use Homes
2-Mar 164 74 6
2-Apr 154 90 6
2-May 173 31 6
2-Jun 85 31 6
2-Jul 116 81 7
2-Aug 129 57 8
2-Sep 168 81 9
2-Oct 155 80 9
2-Nov 172 61 9
2-Dec 97 65 9
3-Jan 31 46 9
3-Feb 42 47 9
3-Mar 66 56 9
3-Apr 100 67 9
3-May 133 73 9
3-Jun 167 64 9
3-Jul 72 63 9
3-Aug 85 71 9
3-Sep 157 76 9
3-Oct 162 76 9
3-Nov 115 69 9
3-Dec 81 61 9
4-Jan 74 64 9
4-Feb 107 69 9
4-Mar 124 69 9
4-Apr 154 75 9
4-May 175 63 9
Average* 116 66
Median 124 67
Std. Dev. 43 14
Total 3258
Water use indicated as depth applied per month, the fraction of the total water consumed
by the home which was used for irrigation purposes, and the number of homes included
in the sample.
*The average is a weighted average by the number of homes included in the treatment.










Table 2-3. Monthly water use for Treatment 3 homes for all three locations combined.
Treatment 3
Water Use % of Total No. of
Month (mm) Water Use Homes
2-Mar 128 66 2
2-Apr 168 76 2
2-May 173 68 2
2-Jun 173 58 2
2-Jul 186 58 2
2-Aug 178 35 3
2-Sep 114 36 3
2-Oct 201 37 3
2-Nov 150 38 4
2-Dec 110 39 4
3-Jan 58 20 4
3-Feb 67 32 4
3-Mar 119 48 7
3-Apr 143 65 7
3-May 80 89 7
3-Jun 101 88 10
3-Jul 75 59 10
3-Aug 58 31 10
3-Sep 90 52 10
3-Oct 89 55 10
3-Nov 76 32 10
3-Dec 47 31 10
4-Jan 37 34 10
4-Feb 58 43 10
4-Mar 74 57 10
4-Apr 61 47 10
4-May 97 48 10
Average* 86 46
Median 97 48
Std. Dev. 48 18
Total 2911
Water use indicated as depth applied per month, the fraction of the total water consumed
by the home which was used for irrigation purposes, and the number of homes included
in the sample.
*The average is a weighted average by the number of homes included in the treatment.










Table 2-4. Evapotranspiration, rainfall, and effective rainfall calculated per month.
Effective
Evapotranspiration Rainfall Rainfall
Total Total
ETo Depth Events Depth
Month Year (mm) (mm) (#) (mm)
Mar 2002 123 98 7 56
Apr 2002 134 45 6 28
May 2002 156 184 10 102
Jun 2002 129 354 21 168
Jul 2002 139 389 23 186
Aug 2002 134 246 19 125
Sep 2002 124 111 13 62
Oct 2002 112 101 13 56
Nov 2002 91 50 15 29
Dec 2002 81 175 25 83
Jan 2003 86 16 11 9
Feb 2003 88 107 12 55
Mar 2003 109 129 23 68
Apr 2003 131 45 14 28
May 2003 151 112 19 66
Jun 2003 131 256 20 128
Jul 2003 139 84 11 50
Aug 2003 125 185 21 96
Sep 2003 107 103 14 56
Oct 2003 97 51 10 29
Nov 2003 75 52 15 29
Dec 2003 61 57 10 30
Jan 2004 59 64 10 33
Feb 2004 76 106 5 53
Mar 2004 112 50 6 30
Apr 2004 130 59 8 36
May 2004 155 78 5 49
Average* 113 122 14 64
Median 123 101 13 55
Std. Dev. 28 94 6 44
Total 3055 3307 366 1741
This data is the average from all three weather stations, one at each location.
*The average is a weighted average by the number of homes included in the treatment.










Table 2-5. Seasonal water use, fraction of total water use, and turf quality rating with
letter notations referring to the significant difference between treatments for
each season.


Season Treatment
T1
T2
Winter T3
T1
T2
Spring T3
T1
T2
Summer T3
T1
T2
Fall T3
T1
T2
Average T3


Water Use
(mm)
103a
73b
55b
179a
132b
94c
139a
110ab
96b
155a
148a
102b
142
119
87


Fraction of Total
Water Use (%)
75
63
37
77
74
42
82
66
63
62
61
55
75
66
46


Turf Quality
Rating
5.7a
6.4a
5.4b
5.9a
6.6a
6.4a
5.8a
5.6a
5.1a
6.6ab
6.9a
5.8b
6.0
6.3
5.7


Table 2-6. Percentage if irrigated area which is turfgrass or landscaped bedding as well as


the total irrigated area for each home.
Treatment 1 Treatment 2


Turfgrass Bedding


House
1
2
3
4
5
6
7
8


10
Average*


Area Turfgrass Bedding Area
(m2) (%) (%) (m2)
2165 60 40 497
1709 66 33 2434
495 74 26 495
351 74 26 743
655 75 25 822
3198 76 24 611
697 78 22 1059
1505 85 15 701
85 15 1328


21 1347 74


* The average is a weighted average based on area.


25 966


Turfgrass Bedding
(%) (%)
5 95
10 90
15 85
20 80
40 60
50 50
50 50
59 41
60 40
66 34
35 65


Treatment 3


Area
(m2)
495
1636
1059
775
1050
450
400
1737
450
448
850









































Figure 2-1. Map of site locations.

Grey counties encompassing the Central Florida Ridge and the dark counties encompassing the
cooperator homes. Inset map shows the geographic location of the cities closest to the three
residential locations, marked by stars.












350

300

250-

200 - ETo
0 ------- T I
150 "
-T2
100 T3

50
0







Figure 2-2. Effective rainfall plus applied irrigation for each treatment compared to
reference evapotranspiration.














CHAPTER 3
RESIDENTIAL IRRIGATION DISTRIBUTION UNIFORMITY

Irrigation efficiency defines how effectively an irrigation system supplies water for

crop or turfgrass beneficial use. Application efficiency can be computed as the ratio

between water used beneficially and water applied and is expressed as a percentage.

Irrigation efficiency is difficult to quantify; therefore, distribution uniformity is often

measured for sprinkler irrigated areas. Irrigation can be uniform and inefficient due to

mismanagement; however, irrigation can not be non-uniform and efficient. As a result,

irrigation uniformity can be a good indication of potential irrigation efficiency.

Uniformity of water distribution measures the variability in application depth over a

given area. Two methods have been developed to quantify uniformity: distribution

uniformity (DU) and the coefficient of uniformity (CU).

The low-quarter irrigation distribution uniformity (DUiq) (Merriam and Keller,

1978) can be calculated with the following equation


DU1q =- [3-1]


where Diq is the lower quarter of the average of a group of catch-can measurements, and

Dtot is the total average of a group of catch-can measurements.

Distribution uniformity is usually represented as a ratio, rather than a percent (Burt

al. (1997), to signify the difference between uniformity and efficiency. This method

emphasizes the areas that receive the least irrigation, by only focusing on the lowest

quarter. Burt et al. (1997) defined common irrigation performance measurements, which









discussed standardization and clarification of irrigation definitions and quantified

irrigation measurements. Although an irrigation system may have even distribution,

over-irrigation can occur due to mismanagement.

The coefficient of uniformity treats over-irrigation and under-irrigation equally as

compared to the mean, and can be calculated by the Christiansen formula as


ZV -
CU= 1- [3-2]

1=1

where, Vi refers to the volume in a given catch-can and V refers to the mean volume

(Christiansen, 1942).

As part of a conservation program, in 1992 the Mobile Irrigation Labs (MILs) were

established as a public service in Florida. The program is funded by the USDA and the

individual water management districts. The Florida MILs were modeled after those

operating in California and Texas. They evaluate irrigation systems conducting a series

of tests over a two-hour period, measuring pump flow rates, sprinkler pressures and flow

rates, and application uniformities (Micker, 1996). The MIL procedure requires 16 to 24

cans to be used, in selected irrigation zones, which is usually the largest turf area for

residential tests. Table 3-1 shows the average DUiq ratios from residential irrigation

systems of turf in various counties in Florida acquired from annual reports within the last

decade. While uniformity of irrigation systems has been measured in Florida, many of

the MILs no longer measure irrigation system uniformity by catch-can tests determining

DUlq; therefore, there is a lack of information regarding current residential irrigation

system performance and water use in the state.









The purpose of these tests was to evaluate residential irrigation system uniformity

in the South Central Florida ridge, and determine typical residential equipment

uniformity under controlled conditions.

Materials and Methods

The homes included in this study were located within the South Central Florida

ridge. The study included 8 homes in Marion County, 9 homes in Lake County, and 10

homes in Orange County. The irrigation systems at the homes typically included

stationary spray heads and gear driven rotary sprinklers for the turf and landscape areas.

Spray heads and rotors were tested in this experiment since they are commonly used on

turfgrass and designed to apply irrigation water as uniformly as possible. In most of the

tested systems, the irrigation zones were not separated based on plant material. That is,

an irrigation zone would commonly be installed to irrigate turfgrass and ornamental

plants at the same time. Uniformity testing was only performed on turfgrass areas. An

onsite weather station was in place to monitor wind speed, relative humidity, and

temperature during testing.

In residential testing, the catch-cans were distributed around the residential turf area

in either a 1.5 or 3 m square grid depending on the irrigated area size (3 m grid for lawns

with an area greater than 750 m2 and 1.5 m grid otherwise). To minimize edge effects,

the grid was positioned 0.8 m from property boundaries. This resulted in 100 to 500 cans

used in each test. Pressure at the two furthest points in each zone was tested with a pitot

tube and pressure gauge on rotors or with a in-line pressure gauge just beneath a spray

head emitter.

The control test site was located at the University of Florida (UF) Agricultural and

Biological Engineering department in Gainesville, Florida. These test plots were set up









to test the irrigation equipment from three different manufacturers. The tests were

performed in a mowed turfgrass area without slope. The plot area for rotary sprinklers

was 11.3 m x 11.3 m or 12.8 m x 12.8 m depending on equipment type and according to

the manufacturer recommended spacing. The plot area for the spray heads was 4.6 m x

4.6 m according to manufacturer recommendations based on the equipment selected.

Sprinklers were installed at each of the four corners of the plot area to insure spacing at

50% of manufacturers rated diameter at recommended pressure (Table 3-2). Pressure

gages were installed before and after the pressure regulator entering the grid piping as

well as at each nozzle.

To quantify irrigation uniformity, the catch-can method of uniformity testing was

used. The catch-can method of uniformity testing is described by both the ASAE and the

NRCS (ASAE, 2000 and Micker, 1996). However, the procedure used in this project

differs because it tests residential sprinkler irrigation systems rather than linear move, and

center pivot sprinkler systems as in the ASAE Standard and is more detailed than that of

the NRCS Mobile Irrigation Lab.

For all test conditions (residential and control), 30 cm wire stem flags were used to

mark the grid and were bent so as to level the catch-cans and prevent movement. The

cans had an opening diameter of 15.5 cm and a depth of 20.0 cm. The irrigated area of

each zone was recorded and the system was set to run for 25 min on spray zones and 45

min on rotor zones, to ensure that the average water application depth was at least 1.3 cm.

At the residential test sites a sketch of the house and landscape beds was drawn to scale

with the location of each can marked. Also, the type and location of each nozzle was

recorded.









According to the ASAE standards (ASAE, 2000) the wind speed was measured

every 30 min during the test. The standard allows testing up to 5 m/s; however, if the

wind speed was above 2.5 m/s or if the distribution was affected by the wind at lower

speeds, the test was discontinued. If practical, the test was performed at night to

minimize evaporative losses. If night time operation was impractical. (i.e., due to

homeowner concerns or storms), the test was run during early morning hours when ET

was lowest. Catch-can volumes were measured immediately following the test using a

500 or 1000 mL graduated cylinder depending on catch-can volume. These procedures

were followed in both the residential testing and the control testing.

Data analysis was performed using the Statistical Analysis System software (SAS

Institute, Inc., 2003, version 8.02) using the GLM procedure to perform an analysis of

variance. The GLM procedure enables the specification of any degree of interaction (i.e.,

crossed effects) and was designed for fixed effects models. The estimation of the fixed

effects was based on ordinary least squares. Mean differences were determined using

Duncan's Multiple Range Test at the 95% confidence level.

For the control tests at UF under ideal conditions, the cans were placed in either a

0.9 or 1.5 m square grid for spray or rotor heads, respectively and with a 0.3 m inset from

the edge. The heads were all adjusted or fitted with appropriate nozzles to irrigate quarter

circle arcs. The spray and rotary heads tested under ideal conditions were labeled as

brand A, B, and C. For professionally installed irrigation systems in Central Florida,

these three products comprise the most commonly used equipment. The spray heads with

an adjustable arc (the coverage pattern is variable from part circle up to full circle) were

denoted by "adj." following the letter reference. All rotors had an adjustable arc by









design. As shown in Table 3-2, the spray heads were tested at low pressure (69 kPa),

high pressure (414 kPa), and manufacturer recommended pressure (207 kPa). The rotor

heads were tested at low pressure (207 kPa) and manufacturer recommended pressure

(345 kPa or 379 kPa). Each head test was replicated 5 times at each pressure. To

maintain ideal testing pressure, gages were installed in the system piping immediately

following an adjustable pressure regulator and at each irrigation head. Pressure varied

less than 5% between the most distant two nozzles, indicating that pressure variations

were not a source of non-uniformity.

Results and Discussion

Residential Testing

The low-quarter distribution uniformities can be classified by the overall system

quality ratings in Table 3-3 (IA, 2003). The uniformities of the residential systems tested

in this study (Table 3-4) would be considered in the "fair" to "fail" range, with the

exception of one "good". When looking at the DUiq of the spray and rotor zones

individually, it can be noted that the ratings of the spray zones were much lower, with

half of the spray zone uniformities receiving a "fail" rating. The ratings of the rotor

zones were normally distributed about the mean within the "good" to "fail" range. The

mean DUlq (Table 3-4) of the rotor zones was 0.49 and the mean DUiq of the spray zones

was 0.41, which was statistically different (p = 0.034).

The overall low DUlq values for this study were lower than values reported by the

MILs. The MIL DUlq values in Table 3-1 were significantly higher, averaging 0.53 (p =

0.02) than the overall DUlq values in Table 3-4 of 0.43. According to the overall system

quality ratings in Table 3-2, two of the regions surveyed by the MIL result in an irrigation

system quality rating of "good" or "very good", one other as "fair", one as "poor" and









two others as "fail". The DUlq value differences were in part due to testing procedure.

As stated in the previous section, the catch-can tests performed for this study were a

combination of the testing methods of both the ASAE standards and the NRCS MIL

guidelines. The MIL catch-can test procedure requires only 16-24 cans to be distributed

centrally within one of the largest zones. The procedures performed in this study used a

grid with 100-500 cans distributed evenly across the entire irrigated turf area.

Consequently, edge effects and challenging design areas, such as side lawns, were

included in the tests of this study. Due to the greater number of catch-cans, a larger

percentage of the under-irrigated areas were also included. Despite this difference in

methodologies, it is thought that the procedures used in this study provided a more

realistic determination of the variation in irrigation water application depth for the entire

irrigation system. If the turfgrass edges of an irrigation zone in a residential setting begin

to become stressed and turf quality declines, the homeowner will likely increase the

irrigation volume applied to that area. As such, it is important to include the edge areas

in uniformity testing. Table 3-4 compares DUlq determined with the catch-cans placed in

the grid formation versus the DUlq determined by using only 16-24 can samples

simulating the MIL procedure on the largest turfgrass area. The uniformity results are

consistently significantly higher when following the MIL method.

As previously mentioned, the MIL guidelines specify that the can placement should

be in the largest area of the yard. Typically, rotar heads irrigate the largest area of the

yard. Based on equipment alone, rotary heads tend to have greater uniformity (note

Table 3-4). Therefore can location (i.e., center of zone vs. near edge of zone) will









increase the DUlq value. Since the testing in this study was more representative of actual

conditions, the IA table may be unrealistic for the conditions of this study.

Mathematical calculation methods also affected the uniformity values. The

coefficient of uniformity (CU) method (Table 3-4) produced higher values than the DUlq

method. This is because CU takes into account both over and under-irrigation, while

DUiq only considers the lowest quarter on the under-irrigated area. Including both the

over- and under-irrigated areas resulted in an amount of mathematical equalizing.

Pressure differences across residential irrigation zones did not vary more than 10%,

which is considered acceptable (Pair, 1983). As a result it was concluded that pressure

variations did not substantially impact uniformity.

Control Testing

Statistical analysis of the spray and rotor head uniformities tested under ideal

circumstances was compared to results from the residential system tests. The difference

in uniformity between residential and control tests was mostly due to design (i.e.,

spacing). There was a significant difference between uniformities (p = 0.001) based on

testing condition. The overall mean DUiq of the tests performed under ideal

circumstances was 0.53 compared to 0.45 on the residential systems. For the control

tests, there was not a significant difference in uniformity between the rotor and spray

heads. Although for the test under the ideal conditions, the rotors still performed better

with a uniformity of 0.56 (Table 3-5), while the spray heads had a uniformity in 0.51

(Table 3-6)

Spray head DUlq values were significantly lower at 69 kPa (low pressure) compared

to the 207 kPa and 414 kPa tests. However, high pressure (407 kPa), above the pressure

recommended by the manufacturers, did not result in significantly different DUlq









compared to recommended pressure tests. There was an interaction between brand and

pressure. From the spray head tests, brand C performed the best at recommended and

high pressure with a mean DUiq of 0.68 at these two pressures. The next highest Duncan

letter grouping for DUiq was measured under brands B at recommended (0.55) and high

(0.54) pressures and A at the recommended (0.53) pressure. Low pressure significantly

degraded spray head uniformity, across all brands. The poorest DUlq at high pressure

was measured under brand B-adj. This brand consistently had the lowest DUlq, averaging

0.37 across all pressures.

The statistical analysis of the rotor head test showed significant differences in DUlq

between brands (p = 0.004); while pressure resulted in a difference at the 90% confidence

level (p = 0.090). The spray head test statistical analysis showed that both pressure (p =

0.001) and brand (p = 0.001) had significant influence on the DUlq values. The rotor

heads showed moderate statistical differences across brand regardless of pressure with

brand A producing the highest DUiq of 0.66 and C yielding the least uniform distribution

of water with a DUiq of 0.46. Brand B was statistically similar to brands A and C at both

pressure levels; however, differences were pronounced enough such that brands A and C

were not similar.

Summary and Conclusions

The DUlq values reported in this study were lower than the Irrigation Association

(2003) quality ratings and the historical average MIL findings. When examining the

differences between the catch-can testing procedures employed in this study to the MIL

guidelines, it can be inferred that one difference was in the testing methodologies.

For the residential systems tested in this study, the low-quarter distribution

uniformities classified by the overall system quality ratings would be considered in the









fair to fail range, with the exception of one good. However, it should be noted that any

degradation in turfgrass or plant quality on the edges of a residential site will likely result

in the homeowner increasing irrigation volume to that area. Therefore, testing of the

entire irrigated site including edges and irregular areas is important to define the

variability in the overall irrigation system. When the uniformity of the spray and rotor

zones were individually examined, the ratings of the spray zones were lower (0.41) than

the ratings of the rotor zones (0.49).

Overall, the control tests under ideal conditions still resulted in poor uniformity

compared to the IA (2003) ratings. Rotary sprinklers DUlq averaged higher at 0.56 while

spray heads averaged 0.51. The spray heads have closer spacing and a higher

precipitation rate. Therefore, over-irrigation may be exacerbated in some areas, thus

decreasing uniformity. The spray heads had the better uniformity when fixed quarter

circle nozzles were used as opposed to adjustable arc nozzles.

Distribution uniformity is a mathematical means for explaining how evenly a

system is irrigating an area. According to the IA quality ratings, the DUlq values

determined in this study were considered unacceptable or since the testing in this study

was more representative of actual conditions, the IA table may be unrealistic for the

conditions of this study. As determined from the results of this study, the DU values are

subject to the testing procedure.

Sprinkler brand and pressure also affected the uniformity values. For the rotor

head control tests there was a significant difference between the brands, however there

was not one based on pressure at the 95% confidence level. The pressure variation was

only between high and the recommended setting. The equipment will still function









properly under excessive pressure conditions, however the arc and through of the nozzle

may not present the correct pattern. For the spray head control tests, there was an

interaction between pressure and brand and the pressure. The results from these tests

concurred with the assumption that the equipment can withstand higher pressure while

still providing a comparable uniformity. Low pressure had an adverse affect on the

equipment functionality regardless of brand.

The trend which remained constant was that the rotary sprinkler heads create more

uniform distributions than fixed spray heads. In addition, spacing the heads properly

under controlled conditions resulted in higher uniformities compared to the actual

residential sites. Therefore, irrigation system design is important to achieving higher

irrigation uniformity distribution.










Table 3-1. Mobile Irrigation Lab turfDUiq results for five counties in Florida.
Distribution Uniformity (DU) Sample
County Average Minimum Maximum Size
Fort Myers (2002) 0.59 0.40 0.82 173
Hillsborough (1993) 0.48 0.11 0.71 68
Lake (2001) 0.38 0.12 0.74 64
St. Johns (2001) 0.39 0.12 0.74 64
South Dade (1993-94) 0.71 0.34 0.89 25
St. Lucie (2000) 0.64 0.38 0.8 75
St. Lucie (2001) 0.67 0.13 0.85 88
Average 0.55 0.23 0.79 80
CV 25 59 8 57

Table 3-2. Recommended pressure and radii for tested spray and rotor heads under ideal
conditions according to manufacturer guidelines.
High Distance
Recommended Low Pressure Pressure* of Throw
Head Type Brand Pressure (kPa) (kPa) (kPa) (m)
A 345 207 12.8
B 379 207 11.3
Rotary C 345 207 11.3
A 207 69 414 4.6
A-adj. 207 69 414 4.6
B 207 69 414 4.6
B-adj. 207 69 414 4.6
Spray C 207 69 414 4.6
*High pressure tests were only performed on the spray heads










Table 3-3. Irrigation
uniformity


Association overall system quality ratings, related to distribution


Quality of
irrigation
System


Irrigation System
Rating (ISR)


Distribution
Uniformity
(DUlq)


Exceptional 10 > 0.85
Excellent 9 0.75 0.85
Very Good 8 0.70 0.74
Good 7 0.60 0.69
Fair 5 0.50 0.59
Poor 3 0.40 0.49
Fail < 3 < 0.40


Table 3-4. Residential system distribution uniformity catch-can test results


County Rep
1
2
3
4
5
6
7
Marion 8
1
2
3
4
5
6
7
8
Lake 9
1
2
3
4
5
6
7
Orange 8
Mean


CU
Overall
System
0.60
0.59
0.72
0.60
0.65
0.55
0.54
0.55
0.57
0.68
0.61
0.60
0.55
0.64
0.71
0.52
0.60
0.60
0.57
0.50
0.57
0.54
0.50
0.62
0.63
0.59


DUlq
Overall
System
0.44
0.39
0.60
0.46
0.47
0.35
0.50
0.39
0.39
0.58
0.50
0.42
0.40
0.50
0.54
0.33
0.54
0.48
0.38
0.32
0.44
0.36
0.34
0.56
0.47
0.45


Spray Rotor MIL Style
Head Head (16-24 cans)
0.54
0.12 0.45 0.51
0.57 0.63 0.70
0.58
0.51 0.49 0.54
0.35 0.64
0.50 0.47 0.60
0.39 0.45
0.15 0.45 0.64
0.67 0.55 0.63
0.49 0.48 0.50
0.16 0.49 0.42
0.41 0.50
0.66 0.47 0.64
0.52 0.59 0.65
0.41 0.32 0.82
0.45 0.64 0.70
0.42 0.49 0.64
0.33 0.50 0.51
0.31 0.34 0.48
0.47 0.50 0.49
0.32 0.39 0.42
0.23 0.44 0.65
0.43 0.63 0.68
0.47 0.67
0.41 0.49 0.58











Table 3-5. Control system distribution uniformity catch-can test results for these brands
of rotor heads at recommended and low pressures.
Pressure[a]
Rec. Low
Brand of Sample Sample
Rotor Head DUi, Size DUg, Size
A 0.68 a[b] 5 0.6 a 5
B 0.57 a 5 0.5 b 5
C 0.51 a 5 0.4 c 5
Average 0.58 0.52
[a] High pressure tests only performed on spray heads.
[b] Duncan letters show significant difference between brands at each pressure and are
head type specific (i.e., spray or rotor).

Table 3-6. Control system distribution uniformity catch-can test results for these brands
of spray heads at recommended, low, and high pressures.
Pressure[a]
Brand of Rec. Low High
Spray Sample Sample Sample
Head DUi, Size DUI, Size DUg, Size
A 0.48 b[b] 5 0.39 b 5 0.50 b 5
A-adj. 0.52 b 5 0.41 ab 5 0.52 b 5
B 0.55 b 5 0.44 ab 5 0.53 b 5
B-adj. 0.38 c 5 0.37 b 5 0.37 c 5
C 0.70 a 5 0.48 a 5 0.65 a 5
Average 0.53 0.42 0.52
[a] High pressure tests only performed on spray heads.
[b] Duncan letters show significant difference between brands at each pressure and are
head type specific (i.e., spray or rotor).














CHAPTER 4
COMPARISON OF UNIFORMITY MEASUREMENTS

As competition for limited water supplies increase, irrigation must become more

efficient. Irrigation efficiency defines how effectively an irrigation system supplies water

to a given crop or turf area. Application efficiency can be computed as the ratio between

water used beneficially and water applied and is expressed as a percentage (Burt al.

(1997). In an efficient residential irrigation system, the components that must be

considered are: design, scheduling, and equipment. The design of a system (i.e.,

spacing) will affect the uniformity of the water distribution. It is important that irrigation

systems are designed to apply water evenly across a target area such as turfgrass. Even

with good design, scheduling will affect how much water is applied. Residential and

commercial irrigation systems typically use stationary spray heads and gear driven rotor

sprinklers for the turf and landscapes.

Uniformity of water distribution measures the relative application depth over a

given area. This concept can be valuable in system design and selection, and can

quantify system performance. The term uniformity refers to the measure of the spatial

differences between applied (or infiltrated) waters over an irrigated area. A common

method which has been developed to quantify uniformity is distribution uniformity.

A time domain refectometry (TDR) device can be used to measure soil volumetric

water content (VWC), by relating the time needed for an electrical signal to travel along

wave guides. As opposed to the measurement of irrigation application, soil water volume

is measured as a function of the volume of the bulk soil. A TDR device can be used to









measure the amount of stored water in a profile or how much irrigation is required to

reach a desired amount of water. The use of TDR probes is an effective nondestructive

method of measuring soil moisture content.

The catch-can test requires a grid of cans to be placed across the desired testing

location. When the system completed the irrigation cycle, the volume of water collected

in the cans is measured and related to uniformity. The catch-can method, although not

destructive, does necessitate recently mowed turfgrass and is subject to the slope of the

area.

This experiment compared irrigation distribution uniformity evaluated by the use of

a TDR device to the catch-can test method. The uniformities of both residential irrigation

systems and controlled equipment testing were evaluated.

Materials and Methods

The tests for this study included both residential lawns and a turfgrass area used for

the control irrigation testing. The residential tests were conducted with the cooperation

of homeowners within the Central Florida Ridge as discussed in Chapter 1. Only spray

and rotor heads (as opposed to the micoirrigated areas) were tested in this part of the

experiment since they are most commonly used on turfgrass and designed to apply

irrigation water as uniformly as possible. In many of the tested systems, the irrigation

zones were not separated based on plant material. That is, an irrigation zone would

commonly be installed to irrigate turfgrass and ornamental plants.

The control system test site was located at the University of Florida Agricultural

and Biological Engineering department in Gainesville, Florida as part of a study to

determine residential irrigation equipment performance parameters. These tests were

performed in a mowed and maintained field without slope. The plot area for the rotor









sprinklers was 11.3 m x 11.3 m or 12.8 m x 12.8 m depending on equipment type and

according to the manufacturer recommended square spacing. The plot area for the spray

heads was 4.6 m x 4.6 m. Nozzles were installed at each of the four covers of the plot

area to insure spacing at 50% of manufacturers rated diameter at recommended pressure.

To quantify the uniformity of the irrigation systems described previously, the low-

quarter distribution uniformity (DUiq) value was calculated for each system test. The

catch-can method of uniformity testing used for this study is a modified combination of

both the ASAE and the NRCS methods (ASAE, 2000 and Micker, 1996). The

modifications from the ASAE method resulted from testing residential systems rather

than agriculture systems, while it is more detailed than the procedures of the NRCS

Mobile Irrigation Labs. The procedure used in this project differed because residential

sprinkler irrigation systems were tested rather than linear move, and center pivot

sprinkler systems as in the ASAE Standard and is more detailed than that of the NRCS

Mobile Irrigation Lab.

To test the irrigation systems, a grid was marked with 30 cm wire stem flags which

were bent so as to level the catch-cans and prevent movement. The cans had an opening

diameter of 15.5 cm and a depth of 20.0 cm. The systems were set to run for 25 min on

spray zones and 45 min on rotor zones, this ensured the average water application depth

was at least 1.3 cm within the catch-cans.

In the residential tests, catch-cans were distributed around the turf area in either a

1.5 or 3 meter square grid depending on the irrigated area size (3 m grid for lawns with

an area greater than 750 m2 and 1.5 m grid otherwise). To account for edge effects the

grid was positioned 0.8 meters from property boundaries. For the control system tests,









the cans were placed in either a 0.9 or 1.5 m square grid for spray or rotor heads

respectively, and with a 0.3 m inset from the edge. The heads were all adjusted or fitted

with appropriate nozzles to irrigate quarter circle arcs.

According to the ASAE standards (ASAE, 2000) the wind speed was measured

every 30 min during the test. The standard allows testing up to 5 m/s; however, if the

wind speed was above 2.5 m/s or if the distribution was affected by the wind at lower

speeds, the test was discontinued.

TDR measurements were performed at the time of catch-can tests. Catch-can

volumes were measured immediately following the test using a 500 or 1000 mL

graduated cylinder depending on catch-can volume. The TDR VWC percentage was

taken within 0.5 m of each catch-can to ensure similarity in measurement point and grid

location. TDR measurements were taken immediately after each irrigation run cycle.

For this study the TDR device used was the Field Scout TDR 300 Soil Moisture

Probe (Spectrum Technologies, Inc., Plainfield, Illinois) with 20 cm rods. The TDR

device was used to determine irrigation distribution uniformity for turfgrass. The device

was easy to operate and relatively nondestructive to the turfgrass area. Typically,

irrigation uniformity is determined by the catch-can method, where DUiq is calculated

based on the volume collected in the cans. When calculating the uniformity with the

TDR, the DUiq was based on the soil moisture readings after irrigation.

To determine the VWC percentage, the probes of the device must be inserted into

the ground. The probes were checked when inserted into the ground each time because

after multiple measurements the metal probes tended to splay outward if inserted too

aggressively. This movement of the probes can give a false low soil moisture reading.









Results and Discussion

The soil moisture measurements collected by the TDR ranged from 0-45% VWC.

The measurements collected by the catch-cans ranged from 0-1500 mL. The methods

were compared by calculating dimensionless DUlq. The uniformity calculated by the soil

moisture method was higher than the uniformity calculated by the catch-can method

(Table 4-1). Overall the, the DUlq calculated from the TDR measurements was 0.74,

where the DUiq from the catch-can volumes was 0.51, with an average difference of 0.22.

This concurs with the findings from a similar study in Colorado (Mecham, 2001). To

compare the two methods, the coefficient of variation (CV) was calculated. The smaller

the CV, the smaller the scatter of data about the mean, signifying smaller variability in

the data. When considering all the tests in this study, the CV of the TDR DUiq was 11,

where the CV of the catch-can volume DUiq was 25 (Table 4-2).

The higher the DUlq in the catch-can tests, the smaller the difference was between

the TDR and catch-can volume uniformities. This was because the TDR uniformity was

higher on average (Table 4-1) with a smaller standard deviation (0.08). The smaller

standard deviation would be expected due to the smaller range of values.

There were significant differences between the uniformity values determined from

the residential versus the control systems. The uniformity values for the residential

locations determined from the catch-can tests averaged 0.45, and from the TDR

measurements the uniformity was 0.68. For the control locations the uniformity values

from the catch-can and TDR measurements were 0.54 and 0.78 respectively. The only

apparent difference was with the control system equipped with rotor heads. The average

rotor DUiq for the catch-can volumes and TDR measurements were 0.65 and 0.75,

respectively.









The TDR and catch-can volume uniformities were plotted against each other in

Figure 4-1. The TDR measured moisture content DU compared to volume based DU is

essentially horizontal, meaning that soil moisture content does not change predictably

with a change in catch-can volume. If there were better correlation between the data, the

points would surround the 1:1 line. The data however, was above this line due to the

higher TDR VWC DUlq values.

Table 4-2 lists the average catch-can volume and soil moisture measurements.

Overall, the average volume collected per can was 271 mL, with a standard deviation of

180 mL. The average soil moisture reading was 24, with a standard deviation of only 6.

The increase in variation between the measurements had an effect on the uniformity.

Additionally, there were occurrences of volume measurement at or near 0 mL, where the

soil moisture readings were not below 7%. It can be observed, in Figure 4-2, that there is

not a strong correlation between increased soil moisture VWC measurements and catch-

can volume measurements.

The effects of the irrigation event were taken into consideration. The average soil

moisture uniformity calculated prior to the irrigation event was 0.55, and after the

irrigation event was 0.64.

Summary and Conclusions

This study compared irrigation distribution uniformity values determined by the

catch-can test to those determined by soil moisture measurements. The TDR device

would allow for a quick and easy method for calculating system uniformity, as there is no

significant set up time as with the catch-can tests.

One of the major differences between the uniformity results calculated by the two

methods was from the scale of the measurements. The catch-can scale was larger than the









TDR scale, which can account for a great deal of variation due to an increase in standard

deviation. The methods were compared in the DUlq values to help diminish the range

dissimilarity. It must be noted that in addition to the scales differing, only the catch-can

volume measurements actually included the minimum (0 mL) and maximum (1500 mL)

values. Although the soil moisture measurements could range from 0-45%, the actual

measurement range was typically from 7-35%. When collecting the measurements, a

large volume of water collected in a catch-can was typically correlated with a high TDR

VWC reading.

Ultimately, there was not enough correlation between the DUlq values determined

by the TDR device and the catch-can method. Therefore the TDR device can not be used

in place of the catch-cans to determine the uniformity of a system. However, perhaps the

uniformity values determined from the catch-can tests are not the most important measure

of uniformity, because the measurements ignore the effects of the soil properties, which

do in turn affect the turfgrass. The TDR equipment may not be sensitive enough to detect

the soil water changes. The soil properties affect the uniformity results. The

redistribution of the soil water may lead to a higher DU from the soil water

measurements compared to catch-can measurements.










Table 4-1. Uniformity values from both the catch-can tests and the TDR values.
Standard Coefficient Point
Sample Method Average Deviation of Variation Difference
Vol. DUIq 0.45 0.09 20
Residential VWC DUi, 0.68 0.08 12 0.20
Vol. DUiq 0.54 0.14 25
Control VWC DUI, 0.77 0.07 9 0.22
Vol. DUiq 0.51 0.13 25
Overall VWC DUI, 0.74 0.08 11 0.22

Table 4-2. Measurement results from both the catch-can and the TDR tests.


Sample Method
Volume (mL)
Residential VWC %
Volume (mL)
Control VWC %
Volume (mL)
Overall VWC %


Standard
Average Deviation
294 108


Coefficient
of Variation
37
19
80
25
66
24













1.00

0.90

0.80

0.70

0.60
a /
0.50
a /
0.40

0.30

0.20

0.10

0.00
0.00 0.20 0.40 0.60
Volume, DLq


1:1

















0.80 1.00


Figure 4-1. Comparison of DUlq values calculated from both the TDR soil moisture and
catch-can tests.



50

45


y = 4.8943Ln(x) 2.8285
R2 = 0.2456


500 750

Catch-Can Volume (mL)


1000


1250


Figure 4-2. Comparison of soil moisture to can volume measurements taken during
uniformity tests.














CHAPTER 5
CONCLUSIONS

The goal of this project was to evaluate residential irrigation water use and

uniformity. The research conducted for this study assessed residential irrigation water

use and total water input, taking rainfall and evapotranspiration (ET) into account from

January 2002 through May 2004. Both residential systems and individual equipment

distribution uniformities were measured. These tests were used to compare catch-can

volume to soil moisture uniformity testing methods.

To determine water use for residential irrigation systems, irrigation water

consumption was monitored on a monthly basis. The homes were separated into three

treatments, each relating to the type of system (typical or designed) and the controller

settings (homeowner controlled or adjusted based on historical evapotranspiration rates).

Tl consisted of existing irrigation systems and typical landscape plantings, where the

homeowner controlled the irrigation scheduling. T2 also consisted of existing irrigation

systems and typical landscape plantings, but the irrigation scheduling was adjusted based

on historical ET. T3 consisted of an irrigation system designed according to

specifications for optimal efficiency and scheduled based on historical ET. T3 also

included a landscape design that minimized turfgrass and maximized the use of native

drought tolerant plants. To further achieve water savings in T3, the landscape plants

were irrigated by microirrigation as opposed to the standard spray and rotor heads.









The average residential irrigation system consumed 63% of the total water used in

the home. The average monthly irrigation water depths for T1, T2, and T3 were 146 mm,

116 mm, and 86 mm respectively.

Adjusting the controller run times and incorporating microirrigation into the

bedding areas (T3) did result in less water use. In the summer months, all the treatments

required similar water amounts. However, in the winter months, when the turfgrass went

dormant, very little irrigation was necessary. In spring months, T1 consumed the most

irrigation water 179 mm, and T3 consuming the least, 94 mm. This was due to the

monthly adjustments of irrigation times and because the microirrigated areas on T3

homes used much less volume than if those areas were sprinkler irrigated. In the fall

months, T1 and T2 consumed similar amounts, 155 mm and 148 mm, while T3

consumed significantly less (102 mm).

Most of the homes still tended to over-irrigate. The over-irrigation resulted from

poor uniformity and unnecessarily high irrigation run times. In this study, the amount of

over irrigation was determined by comparing the amount of water applied (irrigation and

effective rainfall) to the amount of water required (ET). The amount of over irrigation

was especially high in the winter months. Irrigation alone was consistently higher than

the crop water requirements.

Water use could also be affected by the functionality and setting of rain sensors.

Irrigation during periods of rainfall implies malfunctioned or improperly adjusted rain

sensors connected to the irrigation controllers. However, rainfall could occur

immediately after an irrigation event. In efforts to increase irrigation efficiency, the









irrigation amounts should be adjusted seasonally, the system must be properly

maintained, and should be designed to achieve acceptable distribution uniformity (DUlq).

The measured residential and control irrigation system uniformity values were

lower than industry recommendations. The average overall system residential DUiq was

0.45. When the uniformity of the spray and rotor zones were individually examined, the

ratings of the spray zones (0.41) were lower than the ratings of the rotor zones (0.49).

Although the tests under controlled conditions yielded results better than the residential

tests, the uniformities were still low. The rotary sprinklers DUiq averaged higher than

spray heads with average DUiq of 0.56 and 0.51 respectively. The spray heads had better

uniformity when fixed quarter circle nozzles were used as opposed to adjustable arc

nozzles.

Sprinkler brand and pressure also affected the uniformity values. Low pressure had

an adverse affect on the equipment functionality regardless of brand. However, there

was an interaction between brand and pressure in spray head controlled testing, but

certain brands tended to perform better regardless of pressure. Both rotor and spray

heads, performed similarly when tested at the recommended and high pressures. The

controlled tests resulted in higher uniformity, regardless of pressure (0.51) versus the

residential tests (0.45). Thus irrigation design and spacing of the heads, positively affects

the uniformity.

Distribution uniformity values are subject to the testing procedure. The methods

for testing uniformity were compared by determining the DUiq from the catch-can

volume measurements and the soil moisture at each measurement point. A Time Domain

Reflectometry (TDR) device was used to determine the soil moisture. The TDR, which









can be easily inserted into the ground at each measurement point, is is much quicker than

catch can tests for determining uniformity. Overall, the uniformities calculated by the

soil moisture measurements were higher (0.74) than those calculated by the catch-can

volumes (0.51). The uniformity values determined from the catch-can tests may not be a

proper representation of actual soil uniformity. The volume measurements ignore the

effects of the soil properties, which have an impact on the turfgrass quality. The TDR

equipment may not be sensitive enough to detect the soil water redistribution. The actual

soil water movement may lead to a higher DU than the catch-can measurements predicts.

In the future, residential irrigation system audits may not rely solely on catch-can

tests to measure distribution uniformity. The surface distribution seems to differ from the

actual soil moisture. The MIL procedure tended to yield higher uniformities, but the

procedure ignores edge effects and uses less measurement samples. However, the grid

formation outlined in the procedures of this study may be too stringent, and suggest DU

values lower than the actual uniformity.

Microirrigation increased irrigation water savings. Many contractors and

homeowners are reluctant to install microirrigation components. The microirrigation

required more maintenance and was more costly to install. However, the majority of the

homeowners with the microirrigation incorporated into their systems (T3) were quite

pleased with the results. Additionally, once the landscape plants became established the

microirrigation equipment was almost unnoticeable.

The observations and results found from this research will lead to a better

understanding of residential irrigation uniformity and water use, which will aid in

determining efficient residential irrigation. Upon interaction with the homeowners






59


cooperating in this research, there were vast misconceptions about irrigation water use

and scheduling. Changing the irrigation controller run times based on season was

vaguely understood and the concept of significantly reducing the water in the winter time

was initially met with some confusion. Some of the homeowners in the treatments 2 and

3 are now avid water conversationalists after becoming aware of the excessive over-

irrigation that can be avoided.
















APPENDIX A
PHOTOGRAPHS

The following groups of photographs were taken during the period of data

collection for this study, from January 2002 through May 2004.



















:1




































Figure A-3. Control system spray head with pressure gage


figure A-4. control system caten-can test




























Figure A-5. Residential system catch-can test


Figure A-6. Setup of catch-can grid formation










i -aIll'.

31.1


Figure A-7. Catch-can gnd formation around bedded area


L


Figure A-8. Measure catch-can volume with graduated cylinders









































Figure A-9. Turtgrass area with high turt quality rating


Figure A-10. Turfgrass area with low turf quality rating


n,.
~r~il-::;v
1;;
*-, ...-

























































Figure A-11. Sample cooperator homes from each treatment in Marion County. A) T1.
B) T2. C) T3. D) Another T3.




























































Figure A-11. Continued






67




















A



















B











Figure A-12. Sample cooperator homes from each treatment in Lake County. A) T1. B)
T2. C) T3. D) Another T3.




























C


Figure A-12. Continued






69























A



















B






Figure A-13. Sample cooperator homes from each treatment in Orange County. A) T1.
B) T2. C) T3. D) Another T3.



























































Figure A-13. Continued




















APPENDIX B
STATISTICAL ANALYSIS





The following is the SAS output text files for the statistical analysis performed.


Dependent Variabl

Source
Model
Error
Corrected Total



Source
tmt
season
year
tmt*season
tmt*year
tmt(loc)


Class
tmt
season
year
loc



e: mm


OVERVIEW OF WATER USE STATISTICS
The GLM Procedure
Class Level Information
Levels Values
3 T1 T2 T3
4 Fall Spring Summer W:
3 Y1 Y2 Y3
3 HH OC SC
Number of Observations Read 70!
Number of Observations Used 58


inter


Sum of
DF Squares Mean Square
23 1290736.388 56118.973
557 2269192.814 4073.955
580 3559929.201
R-Square Coeff Var Root MSE mm Mean
0.362574 53.88991 63.82754 118.4406
DF Type III SS Mean Square
2 140934.3261 70467.1631
3 248629.7489 82876.5830
2 121068.6074 60534.3037
6 48040.4748 8006.7458
4 80888.7765 20222.1941
6 344599.2220 57433.2037


F Value Pr > F
13.78 <.0001


F Value
17.30
20.34
14.86
1.97
4.96
14.10


Pr > F
<.0001
<.0001
<.0001
0.0687
0.0006
<.0001


Duncan's Multiple Range Test for mm
This test controls the Type I comparisonwise error rate, nc
experimentwise error rate.
Alpha 0.05
Error Degrees of Freedom 557
Error Mean Square 4073.955
Harmonic Mean of Cell Sizes 189.8242
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 12.87 13.55
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 146.005 198 T1
B 116.866 224 T2
C 86.333 159 T3


NOTE:


t the












Alpha 0.05
Error Degrees of Freedom 557
Error Mean Square 4073.955
Harmonic Mean of Cell Sizes 142.5793
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 14.85 15.63
Means with the same letter are not significantly
Duncan Grouping Mean N season
A 138.071 140 Fall
A 137.227 176 Spring
B 116.967 120 Summer
C 77.903 145 Winter


4
16.16
different.


Alpha 0.05
Error Degrees of Freedom 557
Error Mean Square 4073.955
Harmonic Mean of Cell Sizes 175.4245
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 13.39 14.09
Means with the same letter are not significantly different.
Duncan Grouping Mean N year
A 155.185 135 Y1
B 107.352 162 Y3
B 107.299 284 Y2





















Dependent Variabl

Source
Model
Error
Corrected Total



Source
tmt


73



WATER USE SORTED BY SEASON (Fall)
The GLM Procedure
Class Level Information
Class Levels Values
tmt 3 T1 T2 T3
Number of Observations Read 162
Number of Observations Used 140
e: mm
Sum of
DF Squares Mean Square
2 68119.0104 34059.5052
137 629962.2753 4598.2648
139 698081.2857
R-Square Coeff Var Root MSE mm Mean
0.097580 49.11263 67.81051 138.0714
DF Type III SS Mean Square
2 68119.01037 34059.50519


F Value Pr > F
7.41 0.0009





F Value Pr > F
7.41 0.0009


NOTE: This test


Duncan's Multiple Range Test for mm
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 137
Error Mean Square 4598.265
Harmonic Mean of Cell Sizes 45.6846
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 28.06 29.53
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 155.38 48 T1
A 147.85 54 T2
B 102.32 38 T3





















Dependent Variabl

Source
Model
Error
Corrected Total



Source
tmt


74



WATER USE SORTED BY SEASON (Spring)
The GLM Procedure
Class Level Information
Class Levels Values
tmt 3 T1 T2 T3
Number of Observations Read 219
Number of Observations Used 176
e: mm
Sum of
DF Squares Mean Square
2 197383.354 98691.677
173 1103675.555 6379.627
175 1301058.909
R-Square Coeff Var Root MSE mm Mean
0.151710 58.20459 79.87257 137.2273
DF Type III SS Mean Square
2 197383.3542 98691.6771


F Value Pr > F
15.47 <.0001





F Value Pr > F
15.47 <.0001


NOTE: This test


Duncan's Multiple Range Test for mm
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 173
Error Mean Square 6379.627
Harmonic Mean of Cell Sizes 57.83185
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 29.32 30.86
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 179.17 59 T1
B 132.30 67 T2
C 94.34 50 T3






















Dependent Variabl

Source
Model
Error
Corrected Total



Source
tmt


WATER USE SORTED BY SEASON (Summer)
The GLM Procedure
Class Level Information
Class Levels Values
tmt 3 T1 T2 T3
Number of Observations Read 162
Number of Observations Used 120
e: mm
Sum of
DF Squares Mean Square
2 35434.9238 17717.4619
117 673766.9429 5758.6918
119 709201.8667
R-Square Coeff Var Root MSE mm Mean
0.049965 64.87835 75.88604 116.9667
DF Type III SS Mean Square
2 35434.92381 17717.46190


F Value Pr > F
3.08 0.0499





F Value Pr > F
3.08 0.0499


NOTE: This test


Duncan's Multiple Range Test for mm
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 117
Error Mean Square 5758.692
Harmonic Mean of Cell Sizes 38.47328
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 34.27 36.06
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 139.02 42 T1
B A 110.75 48 T2
B 96.03 30 T3





















Dependent Variabl

Source
Model
Error
Corrected Total



Source
tmt


76



WATER USE SORTED BY SEASON (Winter)
The GLM Procedure
Class Level Information
Class Levels Values
tmt 3 T1 T2 T3
Number of Observations Read 165
Number of Observations Used 145
e: mm
Sum of
DF Squares Mean Square
2 54047.1816 27023.5908
142 442937.4666 3119.2779
144 496984.6483
R-Square Coeff Var Root MSE mm Mean
0.108750 71.69194 55.85050 77.90345
DF Type III SS Mean Square
2 54047.18164 27023.59082


F Value Pr > F
8.66 0.0003





F Value Pr > F
8.66 0.0003


NOTE: This test


Duncan's Multiple Range Test for mm
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 142
Error Mean Square 3119.278
Harmonic Mean of Cell Sizes 47.634
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 22.62 23.81
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 102.88 49 T1
B 72.98 55 T2


B 54.66 41 T3
















Class
tmt
season


WATER USE SORTED BY YEAR (Y2)
The GLM Procedure
Class Level Information
Levels Values
3 T1 T2 T3
4 Fall Spring Summer
Number of Observations Read
Number of Observations Used


Winter
324
284


Dependent Variable: mm


Source
Model
Error
Corrected Total



Source
tmt
season
tmt*season


Sum of
DF Squares Mean Square
11 256997.215 23363.383
272 945824.345 3477.295
283 1202821.560
R-Square Coeff Var Root MSE mm Mean
0.213662 54.95711 58.96860 107.2993
DF Type III SS Mean Square
2 156270.7198 78135.3599
3 54353.6781 18117.8927
6 20698.8448 3449.8075


F Value Pr > F
6.72 <.0001


F Value
22.47
5.21
0.99


Pr > F
<.0001
0.0016
0.4309


NOTE: This test


Duncan's Multiple Range Test for mm
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 272
Error Mean Square 3477.295
Harmonic Mean of Cell Sizes 93.23741
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range 17.00 17.90
Means with the same letter are not significantly different.
Duncan Grouping Mean N tmt
A 140.573 96 T1
B 94.380 108 T2
B 84.813 80 T3


Alpha
Error Degrees of Freedom
Error Mean Square
Harmonic Mean of Cell Sizes
NOTE: Cell sizes are not
Number of Means 2
Critical Range 19.59 2
Means with the same letter are not sig
Duncan Grouping Mean N


A 124.303 6(
A 112.556 8
A 107.453 7!
B 82.145 6;


0.05
272
3477.295
70.22526
equal.
3 4
0.62 21.31
nificantly different.
season
Spring
Fall
Summer
Winter








78


WATER USE SAS CODE
options nodate nonumber center formdlim="*"linesize=85;
data mm;
input tmt$ year$ month$ season$ loc$ mm @@;
cards;
/* Data is inputted here */

data mm; set mm;
proc glm data=mm;
title 'OVERVIEW OF WATER USE STATISTICS';
class tmt season year loc;
model mm = tmt season year season*tmt year*tmt tmt(loc)/ss3;
test h=loc e=tmt(loc);
means tmt/duncan;
means season/duncan;
means year/duncan;
means loc/duncan e=tmt(loc);
run;
data mm3; set mm;
proc sort data=mm3; by season;
proc glm data=mm3; by season;
title 'WATER USE SORTED BY SEASON';
class tmt;
model mm = tmt/ss3;
means tmt/duncan;
run;
data mm4; set mm (where=(year='Y2'));
proc glm data=mm4; by year;
title 'WATER USE SORTED BY YEAR';
class tmt season;
model mm = tmt season season*tmt/ss3;
means tmt/duncan;
means season/duncan;
run;












DIFFERENCE BETWEEN ZONES FOR



Class
study
rep
zone
Number
Number


BOTH RESIDENTIAL AND CONTROL TESTS AT REGULAR PRESSURE
The GLM Procedure
Class Level Information
Levels Values
2 control resident
6 1 2 3 4 5 6
2 RS
of Observations Read 92
of Observations Used 82


Dependent Variabl

Source
Model
Error
Corrected Total



Source
study
rep(study)
zone
study*zone
Tests of Hy
Source
zone


e: du
Sum of
DF Squares Mean Square
12 0.55746706 0.04645559
69 1.03058172 0.01493597
81 1.58804878
R-Square Coeff Var Root MSE du Mean
0.351039 24.69554 0.122213 0.494878
DF Type III SS Mean Square
1 0.28393850 0.28393850
9 0.23677843 0.02630871
1 0.10334447 0.10334447
1 0.00373972 0.00373972
'potheses Using the Type III MS for rep(study) as ai
DF Type III SS Mean Square
1 0.10334447 0.10334447


F Value Pr > F
3.11 0.0014


F Value
19.01
1.76
6.92
0.25
n Error
F Value
3.93


Pr > F
<.0001
0.0917
0.0105
0.6184
Term
Pr > F
0.0788


Dur
NOTE: This test contr


Alpt
Err
Err
Har




Means with the
Duncan Grouping


ncan's Multiple Range Test for du
rols the Type I comparisonwise error rate, not the
experimentwise error rate.


ha
or Degrees of Freedomi
or Mean Square
nonic Mean of Cell Si
NOTE: Cell sizes are
Number of Means
Critical Range
same letter are not
Mean N
0.54800 40
0.44429 42


0.05
9
0.026309
zes 40.97561
not equal.
2
.08106
significantly different.
study
control
resident


Alpha 0.05
Error Degrees of Freedom 69
Error Mean Square 0.014936
Harmonic Mean of Cell Sizes 40.12195
NOTE: Cell sizes are not equal.
Number of Means 2
Critical Range .05444
Means with the same letter are not significantly different.
Duncan Grouping Mean N zone
A 0.52857 35 R
B 0.46979 47 S








80



DIFFERENCE BETWEEN ZONES AND LOCATION FOR RESIDENTIAL STUDY AT REGULAR PRESSURE
The GLM Procedure
Class Level Information
Class Levels Values
zone 2 RS
rep 6 1 2 3 4 5 6
loc 3 m o
Number of Observations Read 92
Number of Observations Used 42


Dependent Variabl

Source
Model
Error
Corrected Total



Source
zone
loc
zone*loc
rep(loc)
Tests of H
Source
loc


e: du
Sum of
DF Squares Mean Square F
19 0.39627950 0.02085682
22 0.29774907 0.01353405
41 0.69402857
R-Square Coeff Var Root MSE du Mean
0.570984 26.18494 0.116336 0.444286
DF Type III SS Mean Square F
1 0.07399772 0.07399772
2 0.02185570 0.01092785
2 0.00376647 0.00188324
14 0.30645415 0.02188958
ypotheses Using the Type III MS for rep(loc) as an
DF Type III SS Mean Square F
2 0.02185570 0.01092785


Value Pr > F
1.54 0.1643


Value
5.47
0.81
0.14
1.62
Error Term
Value
0.50


Pr > F
0.0289
0.4588
0.8709
0.1516

Pr > F
0.6174


NOTE: This test


Duncan's Multiple Range Test for du
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 22
Error Mean Square 0.013534
Harmonic Mean of Cell Sizes 20.95238
NOTE: Cell sizes are not equal.
Number of Means 2
Critical Range .07454
Means with the same letter are not significantly different.
Duncan Grouping Mean N zone
A 0.48650 20 R
B 0.40591 22 S

Alpha 0.05
Error Degrees of Freedom 14
Error Mean Square 0.02189
Harmonic Mean of Cell Sizes 13.84615
NOTE: Cell sizes are not equal.
Number of Means 2 3
Critical Range .1206 .1264
Means with the same letter are not significantly different.
Duncan Grouping Mean N loc
A 0.46750 12 m
A 0.45200 15 1
A 0.41800 15 o












DII











Dependent Var

Source
Model
Error
Corrected Tot



Source
zone
brand(zone)
rep
Tests of
Source
zone


FFERENCE BETWEEN



Class
brand
rep
zone
Number
Number
able: du


ZONES FOR CONTROL STUDY AT REGULAR
The GLM Procedure
Class Level Information
Levels Values
8 H HA HS R RQ RV T TQ
5 1 2 3 4 5
2 RS
of Observations Read 92
of Observations Used 40


Sum of
DF Squares Mean Square
11 0.39931000 0.03630091
28 0.27433000 0.00979750
tal 39 0.67364000
R-Square Coeff Var Root MSE du Mean
0.592765 18.06247 0.098982 0.548000
DF Type III SS Mean Square
1 0.03226667 0.03226667
6 0.34385333 0.05730889
4 0.02319000 0.00579750
Hypotheses Using the Type III MS for brand(zone) as ai
DF Type III SS Mean Square
1 0.03226667 0.03226667


PRESSURE


F Value Pr > F
3.71 0.0025


F Value
3.29
5.85
0.59
n Error
F Value
0.56


Pr > F
0.0803
0.0005
0.6714
Term
Pr > F
0.4814


NOTE: This test


Duncan's Multiple Range Test for du
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha 0.05
Error Degrees of Freedom 6
Error Mean Square 0.057309
Harmonic Mean of Cell Sizes 18.75
NOTE: Cell sizes are not equal.
Number of Means 2
Critical Range .1913
Means with the same letter are not significantly different.
Duncan Grouping Mean N zone
A 0.58467 15 R
A 0.52600 25 S








82



DIFFERENCE BETWEEN BRANDS AND PRESSURE FOR CONTROL TESTS ROTOR ZONES
The GLM Procedure
Class Level Information
Class Levels Values
brand 3 H R T
pressure 2 L R
rep 5 1 2 3 4 5
Number of Observations Read 56
Number of Observations Used 30


Dependent Variabl

Source
Model
Error
Corrected Total



Source
brand
pressure
brand*pressure
rep


e: du
Sum of
DF Squares Mean Square
9 0.28491000 0.03165667
20 0.17442667 0.00872133
29 0.45933667
R-Square Coeff Var Root MSE du Mean
0.620264 16.84692 0.093388 0.554333
DF Type III SS Mean Square
2 0.20444667 0.10222333
1 0.02760333 0.02760333
2 0.00580667 0.00290333
4 0.04705333 0.01176333


F Value Pr > F
3.63 0.0078


F Value
11.72
3.17
0.33
1.35


Pr > F
0.0004
0.0904
0.7207
0.2868


NOTE: This test


Duncan's Multiple Range Test for du
controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha
Error Degrees of Freedom
Error Mean Square
Number of Means 2
Critical Range .08712
Means with the same letter are not sic
Duncan Grouping Mean N
A 0.65800 10
B 0.54900 10
C 0.45600 10


Alpha
Error Degrees of Freed
Error Mean Square
Number of Means
Critical Range
Means with the same letter are not
Duncan Grouping Mean N
A 0.58467 15
A 0.52400 15


0.05
20
0.008721
3
.09145
nnificantly different.
brand
H
R
T


0.05
lom 20
0.008721
2
.07113
significantly different.
pressure
R
L












Least Squares Means


brand pressure
H L
H R


du LSMEAN
0.63800000
0.67800000
0.52800000
0.57000000
0.40600000
0.50600000


LSMEAN
Number
1
2
3
4
5
6


Least Squares Means for effect brand*pressure
Pr > |t| for HO: LSMean(i)=LSMean(j)
Dependent Variable: du


2
0.5060


3
0.0773


0.5060 0.0195
0.0773 0.0195
0.2632 0.0824 0.4852
0.0008 0.0002 0.0521
0.0370 0.0086 0.7135
To ensure overall protection level, only
pre-planned comparisons should be used.


4
0.2632
0.0824
0.4852


5
0.0008
0.0002
0.0521
0.0116


0.0116
0.2914 0.1060
probabilities associated with


2
3
4
5
6
NOTE:


6
0.0370
0.0086
0.7135
0.2914
0.1060












DIFFERENCE BETE











Dependent Variabl

Source
Model
Error
Corrected Total



Source
brand
pressure
brand*pressure
rep


.WEEN











e: du


BRANDS AND PRESSURE FOR CONTROL TESTS -
The GLM Procedure
Class Level Information
Class Levels Values
brand 5 HA HS RQ RV TQ
pressure 3 H L R
rep 5 1 2 3 4 5
Number of Observations Read 10
Number of Observations Used 7\


SPRAY ZONES


Sum of
DF Squares Mean Square
18 0.69422667 0.03856815
56 0.22584000 0.00403286
74 0.92006667
R-Square Coeff Var Root MSE du Mean
0.754540 13.08478 0.063505 0.485333
DF Type III SS Mean Square
4 0.44870667 0.11217667
2 0.17817867 0.08908933
8 0.06434133 0.00804267
4 0.00300000 0.00075000


F Value Pr > F
9.56 <.0001


F Value
27.82
22.09
1.99
0.19


Pr > F
<.0001
<.0001
0.0639
0.9448


Duncan's


NOTE: This test cor






Number of Mean
Critical Range
Means with th
Duncan Group:


ntrols t
expe


SMultiple Range Test for du
:he Type I comparisonwise error rate, not the
rrimentwise error rate.


Alpha
Error Degrees of Freedom
Error Mean Square
S2 3
.04645 .04886
he same letter are not sic
ing Mean N
A 0.61000 15
B 0.50533 15
B 0.48400 15
B 0.45667 15
C 0.37067 15


Alpha
Error Degrees of Freedom
Error Mean Square
Number of Means 2
Critical Range .03598
Means with the same letter are not sic
Duncan Grouping Mean N
A 0.52600 25
A 0.51320 25
B 0.41680 25


0.05
56
0.004033
4 5
.05045 .05161
nnificantly different.
brand
TQ
RQ
HA
HS
RV


0.05
56
0.004033
3
.03785
nnificantly different.
pressure
R
H
L












Least Squares Means


brand
HA
HA
HA
HS
HS
HS
RQ
RQ
RQ
RV
RV
RV
TQ
TQ
TQ


pressure


du LSMEAN
0.52000000
0.40800000
0.52400000
0.49600000
0.39000000
0.48400000
0.53400000
0.43600000
0.54600000
0.36600000
0.36800000
0.37800000
0.65000000
0.48200000
0.69800000


LSMEAN
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15


Least Squares Means for effect brand*pressure
Pr > |t| for HO: LSMean(i)=LSMean(j)
Dependent Variable: du


2 3
0.0072 0.9210
0.0055


0.0055
0.0326
0.6558
0.0636
0.0027
0.4886
0.0011
0.3002
0.3236
0.4582
<.0001
0.0707
<.0001

10
0.0003
0.3002
0.0002
0.0020
0.5525
0.0048

10
0.0001
0.0868
<.0001

0.9605
0.7662
<.0001
0.0055
<.0001


0.4886
0.0015
0.3236
0.8043
0.0326
0.5860
0.0002
0.0003
0.0006
0.0027
0.3002
<.0001


11
0.0004
0.3236
0.0003
0.0024
0.5860
0.0055

11
0.0001
0.0960
<.0001
0.9605

0.8043
<.0001
0.0063
<.0001


4
0.5525
0.0326
0.4886

0.0107
0.7662
0.3482
0.1408
0.2184
0.0020
0.0024
0.0048
0.0003
0.7287
<.0001


0.002(
0.655!
0.001
0.0107

0.022!
0.0007
0.257(
0.000;
0.552;
0.586(
0.7662
<.0001
0.025!
<.0001


0.0072
0.9210
0.5525
0.0020
0.3739
0.7287
0.0410
0.5201
0.0003
0.0004
0.0008
0.0020
0.3482
<.0001

9
0.5201
0.0011
0.5860
0.2184
0.0003
0.1283

9
0.7662
0.0083

<.0001
<.0001
0.0001
0.0122
0.1167
0.0004


5 6
0 0.3739
3 0.0636
5 0.3236
7 0.7662
0.0229
9
7 0.2184
0 0.2371
3 0.1283
5 0.0048
0 0.0055
2 0.0107
1 0.0001
3 0.9605
1 <.0001

13
0.0020
<.0001
0.0027
0.0003
<.0001
0.0001

13
0.0055
<.0001
0.0122
<.0001
<.0001
<.0001

0.0001
0.2371


7
0.7287
0.0027
0.8043
0.3482
0.0007
0.2184

0.0179
0.7662
0.0001
0.0001
0.0003
0.0055
0.2007
0.0001

14
0.3482
0.0707
0.3002
0.7287
0.0258
0.9605

14
0.2007
0.2570
0.1167
0.0055
0.0063
0.0122
0.0001


8
0.0410
0.4886
0.0326
0.1408
0.2570
0.2371
0.0179

0.0083
0.0868
0.0960
0.1543
<.0001
0.2570
<.0001

15
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001

15
0.0001
<.0001
0.0004
<.0001
<.0001
<.0001
0.2371
<.0001


i/j
1
2
3
4
5
6


i/j
7
8
9
10
11
12
13
14
15


12
0.0008
0.4582
0.0006
0.0048
0.7662
0.0107

12
0.0003
0.1543
0.0001
0.7662
0.8043

<.0001
0.0122
<.0001


<.0001








86



DIFFERENCE BETWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (High)
The GLM Procedure
Class Level Information
Class Levels Values
brand 5 HA HS RQ RV TQ
rep 5 1 2 3 4 5
Number of Observations Read 25
Number of Observations Used 25
Dependent Variable: du


Source
Model
Error
Corrected Total



Source
brand
rep


Sum of
DF Squares Mean Square
8 0.21620800 0.02702600
16 0.04353600 0.00272100
24 0.25974400
R-Square Coeff Var Root MSE du Mean
0.832389 10.16430 0.052163 0.513200
DF Type III SS Mean Square
4 0.20578400 0.05144600
4 0.01042400 0.00260600


F Value Pr > F
9.93 <.0001


F Value
18.91
0.96


Pr > F
<.0001
0.4571


Duncan's Multiple Range Test for du
NOTE: This test controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Number of Mean
Critical Range
Means with t
Duncan Group:


Alpha
Error Degrees of Freedom
Error Mean Square
S2 3
.06994 .07334
he same letter are not sic
ing Mean N
A 0.65000 5
B 0.53400 5
B 0.52000 5
B 0.49600 5
C 0.36600 5


0.05
16
0.002721
4 5
.07547 .07692
gnificantly different.
brand
TQ
RQ
HA
HS
RV








87



DIFFERENCE BETWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (Low)
The GLM Procedure
Class Level Information
Class Levels Values
brand 5 HA HS RQ RV TQ
rep 5 1 2 3 4 5
Number of Observations Read 25
Number of Observations Used 25
Dependent Variable: du


Source
Model
Error
Corrected Total



Source
brand
rep


Sum of
DF Squares Mean Square
8 0.04148800 0.00518600
16 0.06345600 0.00396600
24 0.10494400
R-Square Coeff Var Root MSE du Mean
0.395335 15.10945 0.062976 0.416800
DF Type III SS Mean Square
4 0.03898400 0.00974600
4 0.00250400 0.00062600


F Value Pr > F
1.31 0.3074


F Value
2.46
0.16


Pr > F
0.0877
0.9566


Duncan's Multiple Range Test for du
NOTE: This test controls the Type I comparisonwise error rate, not the
experimentwise error rate.
Alpha 0.05
Error Degrees of Freedom 16
Error Mean Square 0.003966
Number of Means 2 3 4 5
Critical Range .08444 .08854 .09111 .09287
Means with the same letter are not significantly different.
Duncan Grouping Mean N brand
A 0.48200 5 TQ
B A 0.43600 5 RQ
B A 0.40800 5 HA
B 0.39000 5 HS
B 0.36800 5 RV








88



DIFFERENCE BETWEEN BRANDS FOR CONTROL TESTS -SPRAY ZONES AT EACH PRESSURE (Recommended)
The GLM Procedure
Class Level Information
Class Levels Values
brand 5 HA HS RQ RV TQ
rep 5 1 2 3 4 5
Number of Observations Read 51
Number of Observations Used 25
Dependent Variable: du


Source
Model
Error
Corrected Total



Source
brand
rep


Sum of
DF Squares Mean Square
8 0.28036000 0.03504500
16 0.09684000 0.00605250
24 0.37720000
R-Square Coeff Var Root MSE du Mean
0.743266 14.79046 0.077798 0.526000
DF Type III SS Mean Square
4 0.26828000 0.06707000
4 0.01208000 0.00302000


F Value Pr > F
5.79 0.0014


F Value
11.08
0.50


Pr > F
0.0002
0.7369


Duncan's Multiple Range Test for du
NOTE: This test controls the Type I comparisonwise error
experimentwise error rate.


Alpha
Error Degrees of Freedom
Error Mean Square
Number of Means 2 3
Critical Range .1043 .1094
Means with the same letter are not sic
Duncan Grouping Mean N
A 0.69800 5
B 0.54600 5
B 0.52400 5
B 0.48400 5
C 0.37800 5


rate, not the


0.05
16
0.006052
4 5
.1126 .1147
jnificantly different.
brand
TQ
RQ
HA
HS
RV












DIFFERENCE BETWEEN BRANDS FOR CONTROL TESTS -ROTOR ZONES AT EACH PRESSURE (Low)
The GLM Procedure
Class Level Information
Class Levels Values
brand 3 H R T
rep 5 1 2 3 4 5
Number of Observations Read 15
Number of Observations Used 15
Dependent Variable: du


Source
Model
Error
Corrected Total



Source
brand
rep


Sum of
DF Squares Mean Square
6 0.14170667 0.02361778
8 0.02585333 0.00323167
14 0.16756000
R-Square Coeff Var Root MSE du Mean
0.845707 10.84881 0.056848 0.524000
DF Type III SS Mean Square
2 0.13468000 0.06734000
4 0.00702667 0.00175667


F Value Pr > F
7.31 0.0065


F Value
20.84
0.54


Pr > F
0.0007
0.7090


Duncan's Multiple Range Test for du
NOTE: This test controls the Type I comparisonwise error rate, not the
experimentwise error rate.


Alpha
Error Degrees of Freedom
Error Mean Square
Number of Means 2
Critical Range .08291
Means with the same letter are not sic
Duncan Grouping Mean N
A 0.63800 5
B 0.52800 5
C 0.40600 5


0.05
8
0.003232
3
.08640
nnificantly different.
brand
H
R
T