EVALUATION OF ACCURACY AND LONGEVITY OF EXPANDING-D ISK RA IN SENSORS

Transcription

1 EVALUATION OF ACCURACY AND LONGEVITY OF EXPANDING-D ISK RA IN SENSORS By LEAH MEEKS A THES IS 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 21 1

2 21 Leah Meeks 2

3 To my grandmothers, Geraldine Rosalee Meeks and Clara Lena Davis 3

4 ACKNOWLEDGMENTS I thank my mother, Sharon Lea Meeks, for her unconditional love and enco uragement, my fiancé and best friend, James Anthony Hernandez, for his support, and my aunt, the late Dr. Lynn Langer Meeks, for her inspiration. I would like to thank the members of my graduate committee, Dr. Kati White Migliaccio and Dr. Thomas Obreza, for their assistance on my research. A big thank you goes to my advisor Dr. Michael D. Dukes for his guidance and the chance to experience a new side of irrigation. I would also like thank Stacia Davis, Bernard Cardenas- Lailhacar, and Mary Shedd McCready for their help on this research project. 4

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS... 4 LIST OF TABLES... 7 LIST OF FIGURES... 9 LIST OF ABBREVIATIONS ABSTRACT CHAPTER 1 INTRODUCTION EXPANDING-DISK RAIN SENSOR ACCURACY Introduction Materials and Methods Treatments Monitoring Statistical Analysis Results and Discussion Climactic Conditions Number of Times in Open Switch Mode Accuracy of Rain Sensors... 4 Change in Accuracy of Rain Sensors over Time... 4 Summary and Conclusions EXPANDING-DISK RAIN SENSOR DRY-OUT AND POTENTIAL IRRIGATION SAVINGS Introduction Materials and Methods Treatments Monitoring Statistical Analysis Results and Discussion Climactic Conditions Time in Open Switch Mode (Dry-Out) Dry-out Tracki ng Potential Irrigation Savings Summary and Conclusions

6 4 RELATIONSHIP BETWEEN EXPANDING-DISK RAIN SENSOR DISK LENGTH AND PERFORMANCE Introduction Materials and Methods Treatments Monitoring Statistical Analysis Results and Discussion Length by Installation date and setting Disk Length and Traveling Distance Effect on Interruption Performance Summary and Conclusions CONCLUSIONS AND FUTURE WORK Conclusions Future Work LIST OF REFERENCES... 1 BIOGRAPHICAL SKETCH

7 LIST OF TABLES Table page 2-1 Rain sensor treatment description Summary of functionality problems for treatments and replicates Average depth of rainfall before rain sensors switched to Open Switch Mode Summary of changes in accuracy for change in rainfall required for Open Switch Mode Treatment description Monthly irrigation depth to replace historical evapotranspiration values based on Dukes and Haman (22a). Run times are based on an irrigation application rate of 38 mm/hr assuming system efficiency of 6% and considering effective rainfall. The Reduced UF IFAS irrigation schedule is 6% of the UF IFAS irrigation schedule Total potential water savings per treatment for all treatments compared with a 2 d/wk and a 1 d/wk irrigation schedule for the study period (Oct/Nov 26 to 1 Dec 29) Variation in total potential water savings per replicate for the WL and MC treatments compared with UF IFAS 2 d/wk irrigation recommendations Variation in total potential water savings per replicate for the Hunter, Irritrol, and Toro treatments compared with UF IFAS 2 d/wk irrigation recommendations Variation in total potential water savings per replicate for the WL and MC treatments compared with UF IFAS 1 d/ wk irrigation recommendations Variation in total potential water savings per replicate for the Hunter, Irritrol, and Toro treatments compared with UF IFAS 1 d/ wk irrigation recommendations Description of rain sensors details for each treatment Average disk length for each treatment at two intervals: initial and final (276 days of installation) Average disk length for the treatments installed 13 February 29 at three intervals: initial, 81 days of installation, and final (276 days of installation)

8 4-4 Disk length for replicates of treatments installed 25 March 25 at three intervals: initial (February), 81 days of installation (May), and final (November, 276 days of installation) Disk length for replicates of treatments installed 13 February 29 at three intervals: initial (February), 81 days of installation (May), and final (November, 276 days of installation) Comparison of average length change and travel distance from closed-switch mode to open-switch mode of treatments installed in 13 February 29. The February travel distance was measured on a rain sensor before installation Comparison of average length change of each treatment from 13 February 29 to 16 November 29 and the travel distance each treatment from closed-switch mode to open-switch mode. Travel distance was measured at the end of the study

9 LIST OF FIGURES Figure page 2-1 WL (model Wireless Rain-Clik, Hunter Industries, Inc., San Marcos, CA) rain sensor. A) Expanding disks inside ventilation window, B) quick-response expanding disks, C) Ventilation window adjustment knob, D) antenna MC (model Mini-Clik, Hunter Industries, Inc., San Marcos, CA) rain sensor. A) Rainfall threshold setting slots, B) expanding disks, C) dry-out adjustment ring and vents Irritrol (model RFS 1, Irritrol Systems, Inc., Riverside, CA.) rain sensor. A) Rainfall threshold setting slots, B) dry-out adjustment ring, C) antenna Toro (model TWRS, Toro Company, Inc., Riverside, CA) rain sensor. A) Rainfall threshold setting slots, B) dry-out vent, C) antenna Detail of expanding disk material and threshold adjustment of Mini-Clik (Hunter Industries, Inc.) rain sensor. A) Rainfall threshold setting slots, B) hygroscopic expanding disk material Research site located at the University of Florida Agricultural and Biological Engineering facilities. Shown: weather station on left, WL, 3MC, 3MC, and 13MC treatments installed on left board, and Hunter, Irritrol, and Toro on right board Installed Hunter Wireless Rain-Clik, three on left and one on right, and Mini- Clik rain sensors with four wireless receivers for WL and data logger Installed Hunter, Irritrol, and Toro rain sensors, left to right, with wireless receivers (Irritrol on left and Toro on right), and data logger Relationship between manual rain gauge and weather station tipping bucke t rain gauge with the calibration factor applied to the tipping bucket data with more than 15 mm of rainfall Relationship of rain events greater than 15mm between manual rain gauge and weather station tipping bucket rain gauge with the calibration factor applied to the tipping bucket data Relationship of rain events less than 15 mm between manual rain gauge and weather station tipping bucket rain gauge without the calibration factor applied to the tipping bucket data Comparison of monthly and cumulative rainfall during the study period for WL and MC treatments and average historical rainfall for north central Florida

10 2-13 Comparison of monthly and cumulative rainfall during the study period for Hunter, Irritrol, and Toro treatments and average historical rainfall for north central Florida Cumulative and daily rainfall during the WL and MC treatments study period with the rainfall setting and the respective theoretical number of times each should have gone into OSM Cumulative and daily rainfall during the Hunter, Irritrol, and Toro treatments study period with the rainfall setting and the respective theoretical number of times each should have gone into OSM Cumulative number of times into OSM for WL and MC treatments. Data from 28 January to 9 June 28 are not included due to all WL replicates not functioning. Erratic replicates within treatments are not included after their respective improper functioning dates (WL-B 21 September 27 and 13MC- C 8 July 28). Numbers with different letters indicate a statistical difference using Tuke y-kramer adjusted p-values of p< Cumulative number of times the WL replicates went into OSM. WL stopped functioning on 21 September Cumulative number of times the 3MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall Cumulative number of times the 6MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall Cumulative number of times the 13MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall Cumulative number of times into OSM for Hunter, Irritrol, and Toro treatments. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. Numbers with different letters indicate a statistical difference using Tuke y-kramer adjusted p-values of p< Cumulative number of times Hunter into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall Cumulative number of times Irritrol went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall

11 2-24 Cumulative number of times Toro went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall Accuracy of each treatment with a set point over the study period with an average (solid line) and 95% confidence bands (dashed lines) Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the WL treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 3MC treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 6MC treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 13MC treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the se nsors returns to closed-switch mode) for the Irritrol treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the se nsors returns to closed-switch mode) for the Toro treatment average Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average with the dry-out vents fully open (8 November 28 to 2 July 29) Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average with the dry-out vents fully closed (2 July 29 to 31 December 29) Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Irritrol 11

12 treatment average with the dry-out vents fully open (8 November 28 to 2 July 29) Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Irritrol treatment average with the dry-out vents fully closed (2 July 29 to 31 December 29) Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the one day water delay setting for four the Toro replicates Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the three day water delay setting for four the Toro replicates Dry-out tracking of average disk length for each treatment for the natural rain event on 1 July Dry-out tracking of average disk length for each treatment for the manual rain event on 18 September Dry-out tracking of average disk length for each treatment and temperature for the rain event on 1 July Dry-out tracking of average disk length for each treatment and solar radiation for the rain event on 1 July Dry-out tracking of average disk length for each treatment and relative humidity for the rain event on 1 July Mini-Clik (Hunter Industries, Inc.) rain sensor expanding disks installed in March 25 (left) and February 29 (right) set at 13 mm measured in August 29 (16 and 179 days of installation, respectively) Mini-Clik (Hunter Industries, Inc.) rain sensors expanding disks installed in 25 with settings (left to right) of 3 mm, 6 mm, and 13 mm and lengths 19.2, 19.8, and 2.1 mm, respectively, after 1,6 days of installation Cumulative and daily rainfall during the study period with number of rainfall events greater than the different rain sensor rainfall settings Average hygroscopic disk length for Mini-Clik rain sensors installed in 25 (MC) and 29 (R) over installation time. The 29 rain sensors were newly installed on day zero

13 4-5 Accuracy for each setting during the first 3 days of rain sensor installation compared to change in disk length. Accuracy data are the average amount of rainfall for open-switch mode for a given rainfall event for a treatment

14 LIST OF ABBREVIATIONS Avg CSM CV d/wk ET NOAA OSM RS SMS UF IFAS USCB Average Closed Switch Mode Coefficient of Variance Day per week or days per week Evapotranspiration National Oceanic and Atmospheric Administraion Open Switch Mode Rain se nsor Soil moisture sensor University of Flroida Institute of Food and Agriculutal Sciences United State Census Bureau 14

15 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 EVALUATION OF ACCURACY AND LONGEVITY OF EXPANDING-D ISK RA IN SENSORS By Leah Meeks August 21 Chair: Michael D. Dukes Major: Agricultural and Biological Engineering Rain sensors are devices that connect to automatic irrigation systems to interrupt scheduled irrigations with sufficient rainfall. The goal of this research was to evaluate the performance of expanding-disk rain sensors. The primary objectives of this study were to A) evaluate rain sensor accuracy with time with respect to the selected rainfall setting, B) evaluate the amount of time rain sensors remained in interruption mode (open-switch mode) after a rainfall event, C) quantify potential irrigation savings for different rainfall settings compared with a time-based schedule, and D) determine if the hygroscopic disks in the rain sensors change length with time. Ten treatments were established at the University of Florida Agricultural and Biological Engineering Department campus turfgrass plots, Gainesville, Florida. Mini- Clik rain se nsors with rainfall settings of 3, 6, and 13 mm (3MC, 6MC, and 13MC) and Wireless Rain-Clik (WL) rain sensors had four replicates for each treatment. Treatments Hunter, Irritrol, and Toro had rainfall settings of 6 mm with eight replicates each. Three other Mini-Clik rain sensor treatments (3R, 6R, and 13R had rainfall settings of 3, 6, and 13 mm, respectively) each had three replicates. 15

16 This experiment was carried out during a relatively dry period with rainfall on 28% of the days and 15% less rainfall than average. WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro required 3.2, 1.9, 1.6, 6.6, 3.8, 4.3, and 5.8 mm for open-switch mode, respectively. Accuracy ranged from 27% to 97%. The rain sensor accuracy had percentile point change from -36% to 59% with time, where a negative value indicated a decrease in accuracy. Dry-out is the amount of time a rain sensor stays in open-switch mode. Rain sensors dried-out within 24 hours 79% of the time. Changing the dry-out vent settings from fully open to fully had no effect on potential irrigation savings. Dry-out occurred with decreasing relative humidity and increasing temperature and solar radiation. The hygroscopic disks in expanding-disk rain sensors increased in length after co ntinuous rainfall exposure. Rain sensors with higher rainfall settings had the most increase. The disk length change did not influence accuracy. The potential water savings for a 2 d/wk and 1 d/wk irrigation schedule 13MC were 14% and 13% and the average for all other treatments was 24% and 21%, respectively. Potential irrigation savings should be considered in relation to the accuracy of rain sensors. Rainfall settings of 3, 6, and 13 mm are adequate for rain sensors in central Florida. If the rainfall setting needs to be changed after more than 3 months of use, a new rain sensor or new expanding disks be installed. For the best accuracy, Hunter Mini-Clik rain sensors should be replaced after 1 year while Irritrol RSF 1 and Toro TWRS rain sensors do not need to be replaced for at least 3 years. Rain sensors could increase water savings to homeowners and have environmental benefits but should not be used in applications requiring high accuracy. 16

17 CHAPTER 1 INTRODUCTION Introduction to Water in Florida Florida has an increasing need for water conservation measures. Between 195 and 2, the population of Florida increased 475% and total public supply withdrawals increased 1,33% (Marella, 24). In 2, Florida was the largest user of groundwater east of the Mississippi River (Hutson et al., 24). Water withdrawals for public supply in Florida in 2 totaled 9.2 million cubic meters per day, of which 9% was obtained from groundwater and 1% from surface water (Marella, 24). Florida, Texas, Nebraska, Arkansas, and California account for more than half of the fresh groundwater use nationwide (Hutson et al., 24). The five categories of factors affecting water demands are population, climate, socioeconomic conditions, water pricing, water conservation, and alternative supply sources (Marella, 1992). Florida receives an average of 135 mm of rainfall per year (National Oceanic and Atmospheric Administration, 23). Even with Florida s significant rainfall, the combination of relatively well drained soils and dry periods mean that irrigation is required to maintain landscape quality. Fifty-four percent of the freshwater withdrawn in 2 was between February and June (Marella, 24). Nonmunicipal irrigation withdrawals in 2 were greatest in February through June during drier conditions and the lowest in July through September when summer rain occurred (Marella, 24). These withdrawals coincide with an increase in public supply use (Marella, 1992). The biggest stresses on water supply from agricultural and municipal sectors occur during the same time of year making water conservation even more critical. 17

18 Residential Irrigation Practices in Florida Between 197 and 2, total freshwater withdrawals for public use increased by 176% (Marella, 24). Florida ranks fourth in overall state population with an estimated 29 population of 18.5 million (United States Census Bureau [USCB], 29). Residential irrigation has been reported to account for 64% of residential water use (Haley et al., 27). The volume of water required for residential irrigation continues to increase with the increasing Florida population and the years of less-than-average rainfall. From 2 to 25, Florida had a net population gain of approximately 1 people per day (USCB, 29). Extreme dry conditions occurred between February and June of 2 and the result was higher water demands from public supply primarily for lawn irrigation during these months. A study of water use in Pinellas County Florida found that the highest water use occurred in spring due to high evaporation and low precipitation (Dukes and Haley, 29). Lawn irrigation in central and south Florida occurs throughout the entire year (Marella, 24). Florida, which receives more rainfall than all states other than Louisiana, requires irrigation to meet the aesthetic demands of homeowners due to the wet /dry seasons and well drained soils. It is estimated that 7% of single family homes in southwest Florida have automatic irrigation systems (Tampa Bay Water, 25). In a national study, homes that only hand watered used 33% less water than those with in-ground systems (Mayer, et al., 1999). In-ground systems generally run off of automatic timers instead of homeowners turning on the irrigation system. Automatic timer controls on irrigation systems in Florida have been reported to lead to a 47% increase in water use (Mayer, et al., 1999). Automatic systems result in more irrigation than manually controlled 18

19 systems because people tend to set-and-forget and do not take climatic conditions into account. Research in Florida found that homeowners irrigate in the late fall and winter when turfgrass is dormant because it is inconvenient to change the settings of the timer or there is a misunderstanding of the actual amount of water that should be applied during the year (Haley et al., 27). Rain Sensors Rain sensors are devices designed to interrupt the cycle of an automatic irrigation system controller when a specific amount of rainfall has occurred (Dukes and Haman, 22b). The rain sensor or its receiver is wired into an automatic irrigation controller. When rain beyond a threshold has fallen, the rain se nsor will interrupt the irrigation controller circuit to potentially bypass an irrigation event depending on the irrigation schedule. Evaporation removes the water from the rain sensor so that irrigation will be allowed. The water-savings potential, simple design, reliability, low cost, and ease of installation have made them popular (Dewey, 23). Until the addition of soil moisture sensors in recent years, rain sensors were the only technology available commercially for residential irrigation reduction. Rain sensors have the potential to improve irrigation efficiency, reduce wear on the irrigation system, and reduce runoff and deep percolation (Dukes and Cardenas-Lailhacar, 27). States and municipalities throughout the country have mandated the use of rain sensors to conserve water. It has been estimated that half of single-family homes in Florida have in-ground irrigation systems with automatic timers, of which 25% report having rain sensor shutoff devices (Whitcomb, 25). 19

20 Types Several types of rain sensors are on the market. The sensors can be adjusted to interrupt at different depths of rainfall, generally between 3 and 25 mm. One type collects the rain water in a cup and interrupts the irrigation based on a preset weight of water. A disadvantage of the water weight devices is that debris can get into the collection cup and cause the system to interrupt irrigation without sufficient rainfall. Another type has a set of electrodes that detect the water level in a small collection dish (Dukes and Haman, 22b). Debris is also a problem with the electrical conductivity devices. The Rain Check (Rain Bird Corporation, Glendora, CA) is a rain sensor that measures the amount of rainfall with two electrodes in a collection cup. The stainless steel probes can be adjusted to interrupt irrigation between 3 and 13 mm of rainfall. The most commonly used rain sensors in Florida are expanding-disk rain sensors. Hygroscopic disks in the sensor expand proportionally to the amount of rainfall. The swelling and contracting of the disks opens and closes a switch. Expanding-disk sensors require less maintenance and are cheaper than other sensors. There are different models of rain sensors that can be used depending on the location characteristics. Sensors can operate as normally closed (normally allow irrigation) or normally open (normally does not allow irrigation). Most systems run on normally closed rain sensors. For more versatility, expanding-disk rain sensors are available in wired or wireless models. Some wireless models allow for the sensor to be placed up to 3 feet from the irrigation controller (Hunter Industries, 25). 2

21 Evaporation for Dry-out Rain sensors rely on evaporation to allow irrigation. In the case of normally closed rain sensors, the switch becomes closed after the dry-out period and the irrigation system circuit is complete. Dry-out settings can be adjusted for most sensors. A longer dry-out time has the potential to interrupt more scheduled irrigation cycles by the irrigation controller. The dry-out setting should be set so that it matches the drying rate of the site s soil (Dewey, 23). Dry-out is determined by weather conditions such as temperature, wind, solar radiation, and relative humidity. Installation Proper installation is critical to achieve water savings. In a study of si ngle-family homes in Florida, anecdotal evidence suggests that rain sensors are often improperly installed (Whitcomb, 25). The sensors need to be exposed to normal rainfall (Dewey, 23). Inappropriate installation locations include in the spray path of sprinklers, under a tree canopy, under leaky roof gutters, and in places easily vandalized. The effects of sun and shade on dry-out should be considered when choosing sensor location. Unlike many other irrigation sensors, once it is properly installed and set the rain se nsor settings do not have to be adjusted to achieve water savings (Dewey, 23). Water Savings Water and cost savings vary among rain sensor model and setting. Substantial savings can be obtained during a year of average rainfall in Florida (Dukes and Haman, 22b). In a study evaluating rain sensors at different rainfall settings when compared with a treatment irrigating 2 days/week without a rain sensor, a 3 mm set point reduced irrigation 3% while a 25 mm set point reduced irrigation only 3% (Cardenas-Lailhacar 21

22 and Dukes, 28). Other variables affecting savings include rain frequency, whether or not the controller is left on for automatic operation, and the amount of water applied by the system per cycle (Dukes and Haman, 22b). Marella (1992) suggests that rain sensors are part of long-term water conservation measures for reducing residential irrigation. Previous Studies Involving Rain Sensors and Smart Controllers While research on rain sensors alone is very limited, there have been residential irrigation studies involving rain sensors. Many of these studies have been conducted in the Southeastern United States due to the relatively high amount of rainfall compared to the rest of the country. Rain Sensor Accuracy Testing A study at the University of Florida Agricultural and Biological Engineering Department turfgrass plots in Gainesville, Florida by Cardenas-Lailhacar and Dukes assessed the accuracy of rain sensors at different set points. The data for the rain sensors (Hunter Industries, Inc., San Marcos, CA) were collected 25 March 25 through 31 December 25. The four treatments included three Mini-Clik rain sensors with different settings (3 mm, 13 mm, and 25 mm) and one Wireless Rain-Clik. These rain sensors were not connected to an irrigation system. The accuracies for the 3, 13, and 25 mm settings were 88%, 77%, and 98%, respectively. A longer study should also be conducted on rain sensors to evaluate the Hunter Mini-Clik s 5-year warranty (Cardenas-Lailhacar and Dukes, 28). 22

23 Potential Rain Sensor Irrigation Savings The rain sensor accuracy study also investigated potential irrigation savings with the addition of a rain sensor. The average depth of potential water savings for the wireless, 3 mm, 13 mm, and 25 mm sensors were 558, 337, 468, and 38 mm, respectively. This study indicated that irrigation water savings with rain sensors was dependent on the rain sensor settings (Cardenas-Lailhacar and Dukes, 28). This same conclusion was reached in a study in Citra, Florida with rain sensors set at 3 mm and 6 mm during a relatively dry period. The savings for the 3 mm and 6 mm set points were 25% and 17%, respectively, when compared with a time-based schedule with no water conservation devices (McCready et al., 29). Multiple studies involving soil moisture sensors (SMS) and evapotranspiration (ET) controllers conducted at the University of Florida have had a rain sensor included in a treatment for comparison. One study in Gainesville, Florida found that the addition of a rain sensor set to interrupt irrigation with 6 mm of rainfall to a time clock set to irrigation 2 days/week could reduce irrigation by 34% (Cardenas-Lailhacar et al., 28). In a study with ET controllers and rain sensors in southwestern Florida, an automatic irrigation system with a rain sensor conserved 21% of water compared with an irrigation controller operating on a time-based schedule (Davis et al., 29). Haley and Dukes (27) conducted a study that included rain sensors and educational material about irrigation controller scheduling. The addition of a rain sensor to a controller conserved 19%; the combination of a rain sensor and educational materials increased savings 58%. These studies give insight into the effects of including a rain sensor on an active automatic irrigation system. 23

24 State Statutes State governments are adding rain sensor devices to water conservation statute measures. The fact that more states are considering or including rain sensors in water conservation statutes indicates that more research into the effectiveness of rain sensors is necessary. Connecticut. The Connecticut statute regarding rain sensors applies to automatic lawn irrigation systems installed by state agencies or commercial enterprises. As of 1 October 23, all installations must be equipped with a rain sensor that interrupts the irrigation cycle after adequate rainfall occurs. The statute also allows municipalities to pass ordinances requiring rain sensors on irrigation systems installed after 1 October 23 within their respective jurisdictions. (Connecticut Statutes, Section ) Florida. Florida Statue required that all automatic irrigation systems installed after 1 May 1991 must have a maintained rain sensor or switch that overrides the irrigation cycle after adequate rainfall. Recently, Florida passed a new water conservation statute effective 1 July 29. The new bill requires all new automatic irrigation system installations be equipped with rain sensors and old installations, predating 1 May 1991, be retrofitted with rain sensors. Licensed contractors who install the irrigation systems must properly install or check for proper installation. A licensed contractor who does not comply can be fined $5 for a first offense, $1 for a second offense, and $25 for a third or subsequent offense. Funds from the penalties will be used for water-conservation programs by local government. (Florida Senate Bill 494) 24

25 Massachusetts. In January 29, a bill was introduced in Massachusetts requiring an interruption device on newly installed or renovated outdoor landscapes. The device should override irrigation during periods of sufficient moisture. All new irrigation systems must be inspected every 3 years by a certified irrigation contractor, a certified landscape irrigation auditor, or a certified irrigation designer. The bill would not apply to golf courses. It was not passed as of 3 June 29 and was pocket vetoed by Governor Patrick (Moriarty, 29). (Massachusetts Senate 186 th General Court) Minnesota. Minnesota s rain sensor statute effective 1 July 23 requires that all automatic irrigation systems have technology that interrupts operation in the event of sufficient rainfall. The device must be adjustable by the irrigation system user or installer. (Minnesota Statutes, Chapter 44-F No. 335) New Jersey. The New Jersey rain sensor statute requires that all automatic irrigation systems installed after 8 December 28 have a rain sensor. The device will override the irrigation cycle with adequate rainfall. (New Jersey Statutes, 52:24D ) Texas. The Texas ET controller statute applies to automatic irrigation systems owned by the state or political subdivisions of the state greater than.25 or.5 hectares (.1 or.2 acres) if using nonpotable water. New or existing irrigation systems must have an on-site ET controller as of 1 September 27. A remote ET controller can be used if the weather station is less than 5 miles away from the site and has a rain/freeze sensor. Both remote and on-site systems must have an independent rain/freeze sensor. The statute encourages the passage of local ordinances on ET controllers. (Texas House Bill 2299) 25

26 City and Area Ordinances There is a growing trend for local governments to require rain sensors. Many of these rain sensor requirements are included in irrigation restriction ordinances. Compared with state rain sensor statutes, the city and area ordinances are more likely to have fines for noncompliance. Metropolitan North Georgia Water Planning District, Georgia. The Metropolitan North Georgia Water Planning District, Georgia, regulation applies all systems receiving water from the public water system, not including golf courses. After 1 January 25, all automatic irrigation systems must be equipped with a rain se nsor. Perso ns in violation by installing a system without a rain sensor can be fined up to $1 for each violation. (Georgia Metropolitan North Georgia Water Planning District, Water conservation action no. 4) Water Authority of Great Neck North, New York. The Water Authority of Great Neck North, New York ordinance effective 15 April 1994 includes irrigation times, rain sensors, and soil moisture sensors. The ordinance applies to persons usi ng water that is directly from the district or beneath the district if the source is underground. Users must irrigate no more than 3 days/week depending on the address and all irrigations must take place between 4: p.m. and 1: a.m. from 15 April to 1 November. The rain sensor used must be able to detect a minimum of 1/8-inch (3 mm) of rain. Rain sensors must be set to interrupt irrigation with a ¼-inch (6 mm) or less of rainfall. (Water Authority of Great Neck North New York) Derby, Kansas. The ordinance for Derby, Kansas applies to all persons owning property with an automatic irrigation system, whether or not the irrigation water is 26

27 supplied by the public water system. All automatic irrigation systems installed or substantially replaced after 24 May 28 must have rain sensors. After 1 July 29, all automatic irrigation systems must be either installed with rain sensors or retrofitted with rain sensors. Rain sensors must be set to interrupt irrigation with at least ½-inch (13 mm) of rainfall. City code enforcement officers can inspect systems with notice if there is doubt of compliance. Fines to property owners can range from $25 to $5. Cessation of public water supply service may be an additional penalty if a property owner fails or refuses to install and maintain a rain sensor. (Derby, Kansas Ordinance No. 1932) Cary, North Carolina. The Cary, North Carolina rain sensor ordinance defines an irrigation system and rain sensors for residents. This ordinance applies to all systems that receive water from the town of Cary. Effective 14 August 1997, new automatic irrigation systems must be equipped with rain sensors. Existing systems must be retrofitted with a rain sensor on or before 1 May Rain sensors must be set to interrupt the irrigation cycle after ¼-inch (6 mm) of rainfall and be located in an area of full exposure. A rain sensor set to bypass irrigation at a setting greater than ¼-inch (6 mm) is considered in non-compliance. On the second notice of non-compliance, the property owner will be fined $1 each subsequent day not in compliance (i.e. - $1 first day, $2 second day, etc). Termination of service can be a consequence of continued noncompliance. (Cary, North Carolina, Ordinance section 36-84) Harrisburg, South Dakota. The Harrisburg, South Dakota ordinance requires rain sensors on all automatic irrigation systems installed after the effective day of 4 April 26 that receive water from the city public supply. Rain sensors must be set to 27

28 interrupt the irrigation cycle after ¼-inch (6 mm) of rainfall. A rain sensor set to bypass irrigation at a setting greater than ¼-inch (6 mm) is considered in noncompliance. (Harrisburg, South Dakota Ordinances, Ordinance 26-4, Chapter 8.1) Arlington, Texas. The Arlington, Texas Lawn and Landscape Irrigation Conservation ordinance calls for irrigation time restrictions and rain/freeze sensor. Automatic irrigation systems cannot operate between 1: a.m. and 6: p.m. from 1 June to 3 September unless during periods of grass establishment, dust control, maintenance, repair, or testing. The rain/freeze sensor requirement does not apply to a single family residential or duplex property or an individually metered townhome or condominium unity. The city council created a list of approved rain/freeze sensors to be used. All new automatic irrigation systems installed after 4 March 25 within the city limits must be equipped with a rain/freeze sensor. As of 4 March 27, all existing systems must be retrofitted with a rain/freeze sensor. Those in noncompliance will by guilty of a misdemeanor with a possible fine of up to $5 for each violation. (Arlington, Texas, Lawn and landscape irrigation conservation ordinance section 4.27) Colleyville, Texas. The Water Conservation Ordinance of Colleyville, Texas was created to conserve water by preventing automatic irrigation systems from running during wet periods. All new automatic irrigation systems installed after 31 August 26 must be equipped with a rain sensor. As of 31 August 28, all existing systems must be retrofitted with a rain sensor. Termination of service can be a consequence of continued noncompliance. (Colleyville, Texas, Water conservation ordinance ) 28

29 Dallas, Texas. The purpose of the Dallas, Texas landscape irrigation ordinance is to promote irrigation practices that prevent waste, conserve water resources for the most beneficial and vital use, and protect the public health. Automatic irrigation systems cannot operate between 1: a.m. and 6: p.m. from 1 April to 31 October. Irrigation restrictions also include not significantly irrigating on impervious surfaces, not irrigating with a broken or missing sprinkler head, and not properly maintaining the system. Effective 1 January 22, new automatic irrigation systems must be equipped with rain/freeze sensors. Existing systems must be retrofitted with a rain/freeze sensor on or before 1 January 25. Violators are those who own, lease, or manage property with a system not equipped with a rain/freeze sensor or operate and/or permit operation of an irrigation system not in compliance with the sensor or irrigation time restrictions. Variances or exceptions can be made in cases of extreme hardship or when approved by the city attorney. All exceptions to the ordinance must not adversely affect the health, safety, or welfare of other people and must not cause an immediate negative impact on the city s water supply. Violators can be fined up to $25 for the first offense, doubled for the second offense, and continued for each subsequent offense within a 12- month period. The total fine for a 12-month period cannot exceed $2,. (Dallas, Texas Ordinances, Section ) Lucas, Texas. The Lucas, Texas Code of Ordinances includes requirements for rain/freeze sensors. This ordinance applies to automatic irrigation systems in the city. The party responsible is the owner, leasee, occupier, or manager of the property on which the irrigation system is located. All new automatic irrigation systems installed after 1 January 26 must be equipped with a rain/freeze sensor. As of 1 July 27, all 29

30 existing systems must be retrofitted with a rain/freeze sensor. Fines to property owners can be up to $5. (Lucas, Texas Ordinances, Article 8, Irrigation system regulations, Section 3-22) San Antonio, Texas. San Antonio, Texas has a permanent year-round water conservation ordinance to reduce per capita use of water. The ordinance defines many terms that play into conservation such as automatic irrigation controller, impervious surface, rain sensor, recycled water, and water flow restrictor. Among the many regulations such as ice machines and xeriscapes on new home developments is a rain sensor regulation. Effective 1 January 26, all automatic irrigation controllers must have rain se nsors installed and maintained (San Antonio Water Systems, Ordinance ). Study Objectives The goal of this research was to determine the performance of three brands of expanding-disk rain sensors. The first objective was to evaluate the number of times in open-switch mode and the accuracy over time with respect to the selected set point by comparing when the rain sensors interrupted irrigation with rainfall recorded from an onsite weather station (Chapter 2). The second objective was to evaluate the dry-out time (amount of time in open-switch mode) and potential irrigation savings by comparing rain sensor irrigation interruptions to a University of Florida Institute of Food and Agriculture Science (UF IFAS) recommended irrigation schedules (Chapter 3). The third objective was to determine if the length of the hygroscopic disks in expanding-disk rain sensors change size based on the amount of time installed and the rainfall setting (Chapter 4). 3

31 CHAPTER 2 EXPANDING-DISK RAIN SENSOR ACCURACY Introduction Although Florida ranks second in annual state precipitation, irrigation is required to meet the aesthetic landscape requirements. Between 195 and 2, the population of Florida increased five times and total public supply withdrawals increased 13 times (Marella, 24). In 25, Florida ranked first in single family home construction with 29,162 homes built (United States Census Bureau [USCB], 27) and it has been estimated that 7% of single family homes have automatic irrigation systems (Tampa Bay Water, 25). Water conservation measures are needed to reduce water volumes applied. Haley et al. (27) reported that residential irrigation accounted for 64% of total residential water volumes in central Florida. One water conservation measure is adding a rain se nsor (RS) to an automatic irrigation system. It is thought that automatic systems irrigate more than manual irrigation because of the set-and-forget mentality of an irrigation timer with no consideration of climatic conditions. RSs are devices designed to interrupt the cycle of an automatic irrigation system controller when a specific amount of rainfall has occurred (Dukes and Haman, 22b). Unlike many other irrigation sensors, once it is properly installed and set, the RS settings do not have to be adjusted to achieve water savings (Dewey, 23). Water and cost savings vary among rain sensor models and settings. Variables affecting savings include rain frequency, whether or not the controller is left on for automatic operation, and the amount of water applied by the system per cycle (Dukes and Haman, 22b). 31

32 States and municipalities throughout the country have mandated the use of rain sensors to conserve water. States that have had bills introduced requiring RSs on certain landscape irrigation applications include Connecticut, Florida, Massachusetts, Minnesota, New Jersey, and Texas (see Chapter 1). Florida, Minnesota, and New Jersey require homeowners to install and maintain a RS on all automatic irrigation systems. Several types of RSs are on the market. All of the sensors can be adjusted to interrupt at different depths of rainfall, generally between 3 and 25 mm. Research by Cardenas-Lailhacar and Dukes (28) determined that the 25 mm setting is too high in Florida to net practical savings. One type of RS collects the rain water in a cup and interrupts the irrigation based on a preset weight of water. Another type has a set of electrodes that detect the water level in a small collection dish that measures the amount of rainfall with two electrodes in a collection cup (Dukes and Haman, 22b). A disadvantage of both of these water weight devices is that debris can get into the collection cup and cause the system to interrupt irrigation without sufficient rainfall. The most commonly used RSs are expanding-disk rain sensors (Figures 2-1 through 2-4). Hygroscopic disks in the sensor expand proportionally to the amount of rainfall (Figure 2-5). The swelling of the disks typically causes a switch to interrupt the signal to open an irrigation valve. Expanding-disk sensors require less maintenance and are less expensive than other sensors. There are different models of RSs that can be used depending on the application. Sensors can operate as normally closed (normally allow irrigation) or normally open (normally does not allow irrigation). Most systems run on normally closed rain sensors. 32

33 For a normally closed RS, the RS is in closed-switch mode until sufficient rainfall changes it to open-switch mode. Open-switch mode means that the irrigation circuit is incomplete such that a scheduled irrigation event will be interrupted. Research by Cardenas-Lailhacar and Dukes (28) investigated the performance of expanding disk rain sensors in a 25 study at the University of Florida. The four treatments were three Hunter Mini-Clik (Hunter Industries, Inc, San Marcos, CA) rain sensors with different settings (3 mm, 13 mm, and 25 mm) and one Hunter Wireless Rain-Clik. As would be expected, the lower set points corresponded to a greater number of times in open-switch mode occurrences than a higher setting. All treatments had replicate variability in the number of times in open-switch mode and depth of rainfall required for open-switch mode. The average depth of rainfall triggering the Wireless 3 mm, 13 mm, and 25 mm settings was 1.4, 3.4, 1., and 24.5 mm, with resulting accuracy of 88%, 77%, and 98% for 3 mm, 13 mm, and 25 mm, respectively. This research provides a base-line for rain sensor research. A longer study over a variety of precipitation conditions would offer more insights into rain sensor performance. The average potential water savings in depth of irrigation for the Wireless Rain Clik and Mini-Cliks with 3 mm, 13 mm, and 25 mm settings was 588, 337, 468, and 38 mm, respectively. For the Mini-Clik treatments, the lower setting showed more water saving potential. This study concluded that a setting of 25 mm was too high and not applicable in north central Florida. The objective of this study was to evaluate the accuracy with time of three brands of expanding-disk rain sensors with respect to the selected set point by comparing when 33

34 the rain sensors interrupted irrigation with rainfall recorded from an on-site weather station. Materials and Methods This study was conducted at the University of Florida Agricultural and Biological Engineering Department campus turfgrass plots, Gainesville, Florida. There were a total of 4 rain sensors installed at a height of 2 m (Figure 2-6). Treatments Seven treatments composed of different rain sensor brands and set points were established (Table 2-1). The Wireless Rain-Clik (WL) and Mini-Clik (MC) rain sensors were from Hunter Industries, Inc., San Marcos, CA. The WL did not have a rainfall setting and was designed to interrupt irrigation immediately after rain begins. The three MC treatments had rainfall settings of 3 mm, 6 mm, and 13 mm (3MC, 6MC, and 13MC) (Figure 2-7). Data collection for treatments WL, 3MC, 6MC, and 13MC was started 2 October 26 and was completed 31 December 29 (1,186 days). These four treatments were installed on 25 March 25 with four replications each. The 6MC treatment was originally set to 25 mm and was changed to 6 mm on 2 October 26 since previous work indicated that minimal savings occurred at a 25 mm setting (Cardenas-Lailhacar and Dukes, 28). The three remaining treatments were installed at a later date with 6 mm settings for three brands. The brands and respective treatment codes were Hunter Industries, Inc., model Mini-Clik (Hunter), Irritrol Systems, Inc., model RFS 1, Riverside, CA (Irritrol), and Toro Company, Inc., model TWRS, Riverside, CA, (Toro) (Figure 2-8) with eight replicates for each treatment. Data collection for Hunter, Irritrol, and Toro treatments was started on 8 November 26 and was completed 31 December 29 (1,15 days). Problems with rain sensor function are 34

35 summarized in Table 2-2. The dry out vents for Hunter and Irritrol were fully open from installation until 2 July 29 and were then changed to fully closed. The Toro rain sensor receivers were set to. day dry out; the water delay feature was set to 1. day for four replicates and 3. days for the remaining four replicates. Monitoring Each time a rain sensor changed mode between open switch mode (OSM) and closed switch mode (CSM), the date and time were recorded at a one-second sampling interval using AM16/32 multiplexers (Campbell Scientific, Inc., Logan, UT) attached to a CR1X model data logger (Campbell Scientific, Inc., Logan, UT). An onsite automated weather station (Campbell Scientific, Logan, UT) located within 15 m of the experimental site recorded weather conditions using a CR1X model data logger (Campbell Scientific, Logan, UT). Data such as relative humidity, temperature (model HMP45C, Vaisala, Inc., Woburn MA), solar radiation (model LI2X, Li-Cor, Inc., Lincoln, NE), and wind speed and direction (model WAS425, Vaisala, Inc., Sunnyvale, CA) were recorded at 15 minute intervals. Precipitation was measured by a tipping bucket rain gauge (model TE525WS, Texas Electronics, Inc., Dallas, TX) with a 1-second sampling interval time stamp for each.25 mm of rain. A manual rain gauge located within 5 m of the rain sensors was used to verify the accuracy of the tipping bucket rain gauge measurements (Figure 2-9). The weather station tipping bucket rain gauge was calibrated using the Texas Electronics Calibration Kit (Texas Electronics, Inc., Dallas, TX). Calibration testing was conducted during November 29. The first test indicated that the tipping bucket was out of calibration; it recorded 23.4 mm for every 25.4 mm that actually fell. After two calibration adjustments, the tipping bucket rain gauge was recording 24.9 mm for every 25.4 mm of rain that fell 35

36 which is within the Texas Electronics acceptable range of 2% error (Texas Electronics, Inc. (a)). Measured rainfall was multiplied by and adjustment factor of 1.7 so that measured rainfall would be accurate and equal to actual rainfall. Since the tipping bucket had not been calibrated since 23, a calculation adjustment was applied to recorded rain events during the duration of the study. Based on a linear regression of the data with and without the calibration, the calibration was applied to rainfall events greater than 15 mm and not applied to rainfall events less than 15 mm (Figures 2-1 and 2-11). Rainfall event depths were collected and analyzed from the study period. Monthly rainfall from a 3-year historical period from 197 to 2 from the National Oceanic and Atmospheric Administration (NOAA) was used as a comparison to data collected during the study. Establishment of current and historical weather patterns will affect setting recommendations. Statistical Analysis SAS statistical software (SAS Institute, Inc., Cary, NC) was used for all statistical analysis. A general mixed model with an auto regressive error structure was used to model the continuous responses (PROC MIXED). Tukey-Kramer adjusted p-values (p<.5) were used for pairwise comparisons of mean. Results and Discussion Climactic Conditions During the 1,186 days of the WL and MC study period and the 1,15 days of the Hunter, Irritrol, and Toro study period, 28% of the days received rain. For the WL and MC experiment, the cumulative rainfall was 3,551 mm, 14% less than the historical average of 4,121 mm (Figure 2-12). For the Hunter, Irritrol, and Toro experiment, the 36

37 cumulative rainfall was 3,41 mm, 16% less than the historical average of 4,55 mm (Figure 2-13). If the amount of rainfall during study would have been closer to historical values, the rain se nsors would have gone into OSM more times. Number of Times in Open Switch Mode Figures 2-14 and 2-15 show the daily and cumulative rainfall during each period and the theoretical count for the number of OSM occurrences for each treatment. The theoretical number of OSM occurrences for the 3MC, 6MC, and 13MC treatments should was 192, 139, and 82, respectively. The percentage of rain events greater than 3, 6, and 13 mm during the WL and MC study period was 57%, 42%, and 25%. The theoretical number of OSM occurrences for the Hunter Irritrol, and Toro treatments were 136 times and 41% of rain events being greater than 6 mm. Figure 2-16 shows the average cumulative number of OSM events for the WL and MC treatments. The average number of OSM events for WL, 3MC, 6MC, and 13MC were 146, 154, 16, and 19, respectively. The 13MC treatment had fewer OSM events than the other three treatments. The 6MC treatment went into OSM more times than expected since it was not statistically different from 3MC (p<.5). This result could be due to the change of these RSs from a setting of 25 mm to 6 mm in October 26. The possible effects of disk size change with time are discussed in Chapter 4. All treatments showed replicate variability and not all replicates were functioning during the entire experiment period. One WL replicate stopped functioning on 21 September 27, after 91 days of operation. This replicate was not considered in any analysis for means comparisons. The remaining three WL replicates were not functioning 28 January to 9 June 28 due to an electrical problem such as data logger or batteries in the receivers. One of the four 13-MC replicates displayed some erratic 37

38 behavior such as going into OSM without rainfall and not going into OSM with sufficient rainfall starting 8 July 28, after 121 days of continuous operation but remained functioning throughout the study. The cumulative numbers of OSM events for the functioning WL replicates were 131, 145, and 162 (Figure 2-17). For 3MC with a theoretical OSM value of 192, the replicates went into OSM 149, 162, 173, and 182 times (Figure 2-18). The 6MC replicates went into OSM 162, 173, 173, and 188 times, which were all more than the theoretical value of 139 (Figure 2-19). The theoretical OSM value for 13MC was 82 while the replicates went into OSM 59, 111, 116, and 131 times (Figure 2-2). The coefficient of variance (CV) for WL, 3MC, 6MC, and 13MC were 11%, 8%, 6%, and 3%, respectively. One 13MC replicate increased the variability from 8% to 3% because it displayed somewhat erratic behavior but did continue functioning. The CV values for the WL, 3MC, 13MC, and 25MC after 282 days of installation were 3%, 28%, 24%, and 8%, respectively (Cardenas-Lailhacar and Dukes, 28). The variability of the 25MC rain sensors was 8% and after changing the setting to 6MC the variability was 6% indicating that changing the setting of these replicates did not influence the variability. The weighted average CV for WL, 3MC, 25/6MC, and 13MC for both studies was 8%, 12%, 7%, and 25%. The WL and 25/6MC rain sensors had the least amount of variability with each treatment. From the initial study to this study, the WL treatment became more variable, 13MC variability remained the same, and 3MC became more stable. Tipping bucket rain gauges have a range of accuracies with.5% to 4% variability (Omega (1995), Sutron Corporation, Spectrum Technologies, Inc, and Texas Electronics (b)). The variability for rain sensors is 38

39 relatively high when compared to tipping buckets, which are both for a measurement instrument. Figure 2-21 shows the cumulative number of OSM events for the Hunter, Irritrol, and Toro during their study period and the theoretical value based on rainfall. The average number of OSM events for Hunter, Irritrol, and Toro were 144, 19, and 114 with a theoretical value of 136. Not all replicates were functioning during the entire experiment period. A Hunter replicate started showing erratic behavior by not responding to high amounts of rainfall and not reacting to manual triggering the same as the other replicates; this replicate was not considered in evaluation analysis after 21 December 27, corresponding to 48 days of operation. This replicate was not considered in any analysis for means comparisons. Toro had some issues with the right wireless receiver receiving OSM and CSM signals from the right rain sensor. Six of the eight replicates received the same information from one rain sensor from 2 April 29 to 22 September 29. Figures 2-22 to 2-24 show the theoretical number OSM occurrences for each treatment and variability within treatments. The number OSM occurrences for the seven functioning Hunter replicates were 16, 135, 143, 144, 144, 144, and 151 times (Figure 2-22). The number of OSM occurrences Irritrol replicates were 175, 182, 182, 19, 191, 196, 196, and 24 (Figure 2-23); they all went into OSM more than the theoretical value due to the number of OSM occurrences without rain. The number of OSM occurrences for the Toro treatment were 83, 87, 87 92, 96, 96, 14, and 114 with data from 2 April 29 to 22 September 29 excluded due to receiver problems (Figure 2-24). The CV values for Hunter, Irritrol, and Toro were 11%, 6%, and 1%, respectively. 39

40 Accuracy of Rain Sensors The accuracy of the instrument is its ability to indicate an exact true value (Figliola and Beasley, 2). Accuracy is related to the difference between the true value and the indicated value of a measurement called the absolute error (ε). The percent accuracy (A) is calculated by: ε A = 1 *1 _ true value Table 2-3 shows the average depth of rainfall before the RSs went into OSM. (3-1) Because of acceptable error in the tipping bucket rain gauge, there was a plus or minus 2% in the ca lculated rain sensor accuracy. The WL does not have a set point, so accuracy cannot be determined. The WL had an average rainfall depth of 3.2 mm required to go into OSM. The 3MC, 6MC, and 13MC went into OSM after 1.9, 1.6, and 6.6 mm with accuracies of 64%, 27%, and 51%, respectively. The low accuracy from 6MC could be attributed to the setting change on 6 October 26 on the treatment from 25 mm to 6 mm because of disk size changes discussed in Chapter 4. The Hunter, Irritrol, and Toro went into OSM after 3.8, 4.3, and 5.8 mm with accuracies of 64%, 71%, and 97%, respectively. The 3MC, 6MC, 13MC, Hunter, and Irritrol treatments required a different depth of water for OSM than their respective rainfall setting. The CV values for depth of rainfall required for OSM for WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro were 67%, 51%, 51%, 51%, 37%, 36%, and 33%, respectively. Change in Accuracy of Rain Sensors over Time The accuracy of the treatments varied over the study period as summarized in Table 2-4. The 13MC treatment became less accurate with time while 6MC, Hunter, and Irritrol showed an increase in accuracy, and 3MC and Toro had no change in 4

41 accuracy. The amount of rainfall required before OSM for WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro at the beginning and end of the study was 2.6 and 4. mm, 1.9 and 1.9 mm, 1.3 and 2.1 mm, 8.3 and 5.3 mm, 3.3 and 4.5 mm, 3.6 and 5.1, and 5.7 and 6.1 mm, respectively. Figure 2-25 shows the progression in the change of rainfall required for OSM for all treatments with set points over the study period. The percentile point change in accuracy during the study period for 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro was -1%, 59%, -36%, 36%, 42%, 7%, respectively, where a negative value indicated a decrease in accuracy. Other than 3MC, there was a trending relationship between rainfall depth for OSM CV value and accuracy. A treatment with low accuracy also had a high CV value. The initial 282-day study by Cardenas-Lailhacar and Dukes (28) found that the WL required 1.4 mm of rainfall for OSM. WL rainfall requirement increased 2.5 times after an additional 94 days of installation. The 3MC and 13MC went into OSM after 3.4 and 1. mm with accuracies of 88% and 77%, respectively. The 3MC and 13MC were more accurate during their first 282 days of installation. When newly installed, the rain sensors had an average error of 15% (Cardenas-Lailhacar and Dukes, 28). The weighted average error of the sensors at the end of this study was 46%, a tripling in the error of the sensors over time. Summary and Conclusions This experiment occurred during a relatively dry period with rainfall on 28% of the days. The percentages of rain events greater than 3, 6, and 13 mm during the WL and MC study period were 57%, 42%, and 25% The Hunter, Irritrol, and Toro treatments theoretically had 136 opportunities for OSM with 41% of rain events being greater than 6 mm. 41

42 Most treatments showed variability and erratic behavior of some replicates during the study. The coefficient of variance for depth of rainfall required for open-switch mode varied between 33% and 67%. Some replicates showed erratic behavior such as not detecting rainfall events much higher than their setting, going into closed-switch mode in the middle of a rain event and returning to open-switch mode a few minutes later with.5 mm of rainfall, or going into open-switch mode with little or no rainfall. Two of the replicates completely stopped functioning during the study period for unknown reasons. The accuracy of the rain sensors changed with time. The percentile point change in accuracy during the study period ranged from an increase of 59% to a decrease of 36%. The 13MC treatment accuracy decreased during the study while other treatments had small change in or improved accuracy. Cardenas-Lailhacar and Dukes (28) showed an average weighted accuracy of 85% in the first 282 days of installation while this study had an average weighted accuracy of 47% with the same sensors and 6% overall. There was no single trend for all rain sensors or all rainfall settings with respect to accuracy with time. For the best accuracy, there is evidence based on historical studies and results from this study that Hunter Mini-Clik rain sensors should be replaced after 1 year of installation. The accuracy of a Hunter Mini-Clik set to 3 mm stabilized after 2 years of installation. The higher rainfall setting corresponded to lower accuracy with the same brand of rain sensor. Irritrol RSF 1 rain sensor accuracy increased as the study progressed. Toro TWRS rain sensors retained their relatively good accuracy during the 3 years of this study. Further research is needed to verify these results. Changing the setting of a rain sensor after it has been installed more than 3 months is 42

43 not recommended (see Chapter 4 for more details). The change of the 6MC treatment from a 25 mm to a 6 mm setting reduced the accuracy of the same sensors from 98% to 27%. During the same time, the 3MC and 13MC treatments average weighted accuracy declined from 84% to 59% Overall, the rain se nsors showed that they have high variability with time. However, due to low cost and low maintenance requirements, rain sensors can be a useful device for potential water savings. The variability, erratic behavior, and low accuracy of some replicates showed that rain sensors should not be used in applications requiring high accuracy and precision. Table 2-1. Rain sensor treatment description. Treatment Model Replicates Set Point WL 3MC 6MC 13MC Hunter Irritrol Toro Wireless Rain-Clik X Mini-Clik X Mini-Clik X Mini-Clik X Mini-Clik X Irritrol RFS 1 Y Toro TWRS Z 4 a b mm 6 mm 13 mm 6 mm 6 mm 6 mm Installation Date 25 Mar Mar Mar 25* 25 Mar 25 2 Oct 26 2 Oct 26 2 Oct 26 a 3 replicates were included in means separation analysis due to one failed replicate b 7 replicates were included in means separation analysis due to one failed replicate X Hunter Industries, San Marcos, CA Y Irritrol Systems Inc., Riverside, CA Z Toro Company, Inc., Riverside CA *changed setting from 25-mm to 6-mm on 2 October 26 Table 2-2. Summary of functionality problems for treatments and replicates. Treatment Model Problems with Rain Sensors WL Wireless Rain-Clik 3MC 6MC 13MC Hunter Irritrol Toro Mini-Clik Mini-Clik Mini-Clik Mini-Clik Irritrol RFS 1 Toro TWRS a These replicates were not included in means separation analysis Study Start Date 2 Oct 26 2 Oct 26 2 Oct 26 2 Oct 26 8 Nov 26 8 Nov 26 8 Nov 26 Not operational from 28 Jan 28 to 9 June 29 WL-B stopped functioning 21 Sept 27 a None None 13-C showing somewhat erratic behavior 8 July 28 H-D showed erratic behavior after 21 Dec 27 a None Six wireless receivers were incorrectly connected to one rain sensor from 2 April 29 to 22 Sept

44 Table 2-3. Average depth of rainfall before rain sensors switched to Open Switch Mode. Treatment Model Set point Rainfall for OSM Accuracy (mm) Depth (mm) Standard CV (%) (%) a Deviation (mm) Wireless Rain-Clik Mini-Clik Mini-Clik Mini-Clik Mini-Clik Irritrol RFS 1 Toro TWRS a Accuracy is +/- 2% due acceptable error in the tipping bucket rain gauge Table 2-4. Summary of changes in accuracy for change in rainfall required for Open Switch Mode. Treatment Model Set point Days in study Rainfall depth for OSM (mm) Change in accuracy (percentile points) (mm) Beginning End Wireless Rain-Clik Mini-Clik Mini-Clik Mini-Clik Mini-Clik Irritrol RFS 1 Toro TWRS ,186 1,186 1,186 1,186 1,5 1,5 1, a a 5.3 a 4.5 a 5.1 a 6.1 a The depth of rainfall required for open switch mode changed over the study period

45 C B A Figure 2-1. WL (model Wireless Rain-Clik, Hunter Industries, Inc., San Marcos, CA) rain sensor. A) Expanding disks inside ventilation window, B) quick-response expanding disks, C) Ventilation window adjustment knob, D) antenna. D B A C Figure 2-2. MC (model Mini-Clik, Hunter Industries, Inc., San Marcos, CA) rain sensor. A) Rainfall threshold setting slots, B) expanding disks, C) dry-out adjustment ring and vents. 45

46 A B C Figure 2-3. Irritrol (model RFS 1, Irritrol Systems, Inc., Riverside, CA.) rain se nsor. A) Rainfall threshold setting slots, B) dry-out adjustment ring, C) antenna. B A Figure 2-4. Toro (model TWRS, Toro Company, Inc., Riverside, CA) rain sensor. A) Rainfall threshold setting slots, B) dry-out vent, C) antenna. C 46

47 A B Figure 2-5. Detail of expanding disk material and threshold adjustment of Mini-Clik (Hunter Industries, Inc.) rain sensor. A) Rainfall threshold setting slots, B) hygroscopic expanding disk material. Figure 2-6. Research site located at the University of Florida Agricultural and Biological Engineering facilities. Shown: weather station on left, WL, 3MC, 3MC, and 13MC treatments installed on left board, and Hunter, Irritrol, and Toro on right board. 47

48 Figure 2-7. Installed Hunter Wireless Rain-Clik, three on left and one on right, and Mini- Clik rain sensors with four wireless receivers for WL and data logger. Figure 2-8. Installed Hunter, Irritrol, and Toro rain sensors, left to right, with wireless receivers (Irritrol on left and Toro on right), and data logger. 48

49 Tipping Bucket Data (mm) y = 1.453x R² = Rain Gauge Data (mm) Figure 2-9. Relationship between manual rain gauge and weather station tipping bucket rain gauge with the calibration factor applied to the tipping bucket data with more than 15 mm of rainfall. Tipping Bucket Data (mm) y =.9589x R² = Rain Gauge Data (mm) Figure 2-1. Relationship of rain events greater than 15mm between manual rain gauge and weather station tipping bucket rain gauge with the calibration factor applied to the tipping bucket data. 49

50 Tipping Bucket Data (mm) y =.9938x R² = Rain Gauge Data (mm) Figure Relationship of rain events less than 15 mm between manual rain gauge and weather station tipping bucket rain gauge without the calibration factor applied to the tipping bucket data. Monthly rainfall (mm) Cumulative rainfall (mm) Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Date, Study Period Historical Study Cumulative Historical Cumulative Figure Comparison of monthly and cumulative rainfall during the study period for WL and MC treatments and average historical rainfall for north central Florida. 5

51 Monthly rainfall (mm) Cumulative rainfall (mm) Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Date, Study Period Historical Study Cumulative Historical Cumulative Figure Comparison of monthly and cumulative rainfall during the study period for Hunter, Irritrol, and Toro treatments and average historical rainfall for north central Florida. 51

52 25 43 events with more than 25 mm of rain ranging from 26 to 113 mm Daily rainfall (mm) mm set point 82 events with rainfall > 13 mm 6 mm set point 139 events with rainfall > 6 mm 3 mm set point 192 events with rainfall > 3 mm Cumulalative rainfall (mm) Date, Figure Cumulative and daily rainfall during the WL and MC treatments study period with the rainfall setting and the respective theoretical number of times each should have gone into OSM. 52

53 25 42 events with more than 25mm of rain ranging from 26 to 77 mm Daily rainfall (mm) mm set point 136 events with rainfall > 6 mm Cumulative rainfall (mm) 5 Date, Figure Cumulative and daily rainfall during the Hunter, Irritrol, and Toro treatments study period with the rainfall setting and the respective theoretical number of times each should have gone into OSM. 53

54 Cummulative times in OSM Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, WL 3MC 6MC 13MC 16a 154a 146a 19b Figure Cumulative number of times into OSM for WL and MC treatments. Data from 28 January to 9 June 28 are not included due to all WL replicates not functioning. Erratic replicates within treatments are not included after their respective improper functioning dates (WL-B 21 September 27 and 13MC- C 8 July 28). Numbers with different letters indicate a statistical difference using Tuke y-kramer adjusted p-values of p<.5. Cummulative times in OSM Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, WL-A WL-B WL-C WL-D Figure Cumulative number of times the WL replicates went into OSM. WL stopped functioning on 21 September

55 Cummulative times in OSM Theoretical = Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, MC-A 3MC-B 3MC-C 3MC-D Theoretical Figure Cumulative number of times the 3MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. Cummulative times in OSM Theoretical = 139 Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, MC-A 6MC-B 6MC-C 6MC-D Theoretical Figure Cumulative number of times the 6MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. 55

56 Cummulative times in OSM Theoretical = Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, MC-A 13MC-B 13MC-C 13MC-D Theoretical Figure 2-2. Cumulative number of times the 13MC replicates went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. Cumulative events in OSM Theoretical = Nov 6Jan 7 Apr 7 Jul 7 Sep 7Dec 7Mar 8May 8Aug 8Oct 8 Jan 9 Apr 9Jun 9Sep 9Dec 9 Date, Hunter Irritrol Toro 6 mm 19a 144b 114c Figure Cumulative number of times into OSM for Hunter, Irritrol, and Toro treatments. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. Numbers with different letters indicate a statistical difference usi ng Tuke y-kramer adjusted p-values of p<.5. 56

57 Cumulative events in OSM Nov 6 Jan 7 Apr 7 Jul 7 Sep 7 Dec 7 Mar 8 May 8 Aug 8 Oct 8 Jan 9 Apr 9 Jun 9 Sep 9 Dec 9 Date, Theoretical = A B C D E F G H Theoretical Figure Cumulative number of times Hunter into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. Cumulative events in OSM Theoretical = Nov 6 Jan 7 Apr 7 Jul 7 Sep 7 Dec 7 Mar 8 May 8 Aug 8 Oct 8 Jan 9 Apr 9 Jun 9 Sep 9 Dec 9 Date, A B C D E F G H Theoretical Figure Cumulative number of times Irritrol went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. 57

58 Cumulative events in OSM Nov 6 Jan 7 Apr 7 Jul 7 Sep 7Dec 7Mar 8May 8Aug 8 Oct 8 Jan 9 Apr 9 Jun 9 Sep 9Dec 9 Date, Theoretical = 11 A B C D E F G H Theoretical Figure Cumulative number of times Toro went into OSM. The Theoretical value is the number of times the replicates should have gone into OSM based on rainfall. 1 Amount of rainfall for OSM (mm) Oct 6 Jan 7 Apr 7 Jul 7 Oct 7 Dec 7 Mar 8 Jun 8 Sep 8 Dec 8 Mar 9 Jun 9 Sep 9 Dec 9 Date, MC 6MC 13MC Hunter Irritrol Toro Figure Accuracy of each treatment with a set point over the study period with an average (solid line) and 95% confidence bands (dashed lines). 58

59 CHAPTER 3 EXPANDING-DISK RAIN SENSOR DRY-OUT AND POTENTIAL IRRIGATION SAVINGS Introduction Water conservation measures are becoming more critical in Florida due to increased resource demand. Florida receives an average of 1,35 mm of rainfall a year (NOAA, 23). From 2 to 25, Florida had a net population gain of approximately 1, people per day and ranks fourth in population (USCB, 29). Seventy percent of single family homes have automatic irrigation systems (Tampa Bay Water, 25). Florida is second wettest state in the nation but irrigation is required to meet the aesthetic landscape requirements. Residential irrigation has been reported to account for 64% of residential water use in one area of the state (Haley et al., 27). Rain sensors are a conservation device used with automatic irrigation systems to reduce applied irrigation. Rain sensors have adjustment settings allowing use rs to choose the amount of rainfall required for the irrigation cycle to be interrupted, generally between 3 and 25 mm. States such as Florida, Minnesota, and New Jersey and many municipalities throughout the co untry have mandated the use of rain sensors to conserve water. Variables affecting water and cost savings with rain sensors include rain frequency, whether or not the controller is left on for automatic operation, and the amount of water applied by the system per cycle (Dukes and Haman, 22b). The most common rain sensors used in Florida are expanding-disk rain sensors (see Chapter 2, Figures 2-1 to 2-4). The swelling of the hygroscopic disks in the sensor expand proportionally to the amount of rainfall (see Chapter 2, Figure 2-5). The expanding-disk rain sensor is in closed-switch mode until sufficient rainfall changes it to open-switch mode. The sensors rely on evaporation to go return to open-switch mode 59

60 (OSM). Dry-out settings can vary between 2 hours and 3 days (Hunter Industries, Inc., 25). Dry-out time is the amount of time the sensors stay in OSM. A longer dry-out time has the potential to interrupt more scheduled irrigation cycles. Dry-out time is meant to represent the time it would take for the rainfall to leave the root profile via evaporation, transpiration, and other pathways. Dry-out settings can be adjusted for most sensors such that it matches the drying rate of the site s soil (Dewey, 23). Cardenas-Lailhacar and Dukes (28) investigated the performance of expanding disk rain sensors in a 25 study at the University of Florida campus in Gainesville, Florida. The performance of Hunter Mini-Clik rain sensors with three different settings (3 mm, 13 mm, and 25 mm) and one Hunter Wireless Rain-Clik were monitored and compared with rainfall depth. The frequency of disk dry-out within 24 hours for the Wireless, 3 mm, and 13 mm treatments was 8%, 51%, and 47%, respectively. The average percentage of potential water savings for the WL, 3MC, 13MC, and 25MC was 44%, 3%, 17%, and 3%, respectively, based on a 2 d/wk irrigation schedule. Researchers at the University of Florida have conducted irrigation studies with smart controllers that included expanding-disk rain sensors set on a 2 d/wk irrigation schedule to represent homeowner irrigation under watering restrictions. In a study with ET controllers and rain sensors in southwestern Florida, the addition of a rain sensor set at 6 mm reduced irrigation 21% compared with a time-based schedule (Davis et al., 29). A study in central Florida compared applied irrigation among a controller only, a controller with a rain sensor set at 3 mm, and a controller with a rain sensor set at 6 mm. The savings for the 3 mm and 6 mm set points were 25% and 17%, respectively (McCready et al., 29). 6

61 The objective of this study was to evaluate the dry-out time of three brands of expanding-disk rain sensors and potential irrigation savings by comparing rain sensor irrigation interruptions to University of Florida Institute of Food and Agriculture Science (UF IFAS) recommended irrigation schedules irrigating 2 d/wk and 1 d/wk to represent a homeowner sc hedule. Materials and Methods This study was conducted at the University of Florida Agricultural and Biological Engineering Department campus turfgrass plots, Gainesville, Florida. There were a total of 4 rain sensors installed at a height of 2 m (see Chapter 2, Figure 2-6). Treatments Seven treatments were established at the site (Table 3-1). The Wireless Rain-Clik (WL) and Mini-Clik (MC) rain sensors were from Hunter Industries, Inc., San Marcos, CA. The WL did not have a rainfall setting. The three MC treatments had rainfall settings of 3 mm, 6 mm, and 13 mm (3MC, 6MC, and 13MC) (see Chapter 2, Figure 2-7). Analysis for treatments WL, 3MC, 6MC, and 13MC included data collected between 2 October 26 and 31 December 29 (1,186 days). These treatments were installed on 25 March 25 with four replications each. The 6MC treatment was originally set to 25 mm and was changed to 6 mm on 2 October 26 to have a setting better fit for north central Florida. The remaining treatments were installed at a later date with 6 mm settings for three brands. The brands and respective treatment codes were Hunter Industries, Inc. (Hunter), Irritrol Systems, Inc., Riverside, CA (Irritrol), and Toro Company, Inc., Riverside, CA, (Toro) (see Chapter 2, Figure 2-8). Analysis for treatments Hunter, Irritrol, and Toro included data collected between 8 November 26 and 31 December 29 (1,15 days). 61

62 Each rain se nsor brand had adjustments for the dry-out time. WL dry-out vents were set at half open. The dry-out vents for 3MC, 6MC, and 13MC were fully open during the study. The dry out vents for Hunter and Irritrol were fully open from installation until 2 July 29 and were then changed to fully closed. The Toro rain sensor receivers were set to. day dry-out; the water delay feature was set to 1. day for four replicates and 3. days for the remaining four replicates. The dry-out setting was changed from. to 4. days to validate that electrical connections were correctly established for the Toro rain sensors and wireless receivers. The average amount of time required for dry-out for. day setting and 4. days setting was 15 hours and 99 hours (4 days), respectively. The difference between dry-out times for different settings confirmed that the Toro installation was done correctly. To estimate the potential water savings, theoretical irrigation schedules were compared with each treatment. The two schedules used were a 1 d/wk schedule (Tuesdays) and a 2 d/wk schedule (Tuesdays and Saturdays) set to irrigate at 6 a.m. A scheduled irrigation was considered interrupted if the rain sensors were in open-switch mode due to rainfall. Potential savings was the number of irrigations interrupted by each rain sensor multiplied by the depth of scheduled irrigation. Weekly irrigation depths were calculated to satisfy historical net irrigation required to replace water lost to evaoptranspiration based on Dukes and Haman (22a) recommendations (Table 3-2). Monitoring Each time a rain sensor changed mode between open-switch mode (OSM) and closed-switch mode (CSM), the date and time was recorded at a 1-second sampling interval using AM16/32 multiplexers (Campbell Scientific, Inc., Logan, UT) attached to a CR1X model data logger (Campbell Scientific, Inc., Logan, UT). 62

63 An onsite automated weather station (Campbell Scientific, Logan, UT) located within 15 meters of the experimental site recorded weather conditions using a CR1X model data logger (Campbell Scientific, Logan, UT). Data such as relative humidity, temperature (model HMP45C, Vaisala, Inc., Woburn MA), solar radiation (model LI2X, Li-Cor, Inc., Lincoln, NE), and wind speed and direction (model WAS425, Vaisala, Inc., Sunnyvale, CA) were recorded at 15 minute intervals. Precipitation was measured by a tipping bucket rain gauge (model TE525WS, Texas Electronics, Inc., Dallas, TX) with a 1-second sampling interval time stamp for each.25 mm of rain. A manual rain gauge located within 5 meters of the rain sensors was used to verify the accuracy of the tipping bucket rain gauge measurements (See Chapter 2 Figure 2-9). The weather station tipping bucket rain gauge was calibrated using the Texas Electronics Calibration Kit (Texas Electronics, Inc., Dallas, TX). Calibration testing was conducted November 29 on the tipping bucket rain gauge and is explained in detail in Chapter 2. Rainfall event depths from the study period were collected and analyzed. A rain event was considered started when the tipping bucket made the first tip. A 5-hour or longer period between tips of the tipping bucket rain gauge was defined as a new rainfall event. Establishment of current and historical weather patterns affected rainfall setting recommendations. Monthly rainfall from a 3-year 197 to 2 from the National Oceanic and Atmospheric Administration (NOAA) was used to compare study weather data with historical normals. Hourly disk length measurements with a dial caliper were conducted twice to better understand how the hygroscopic disks dry-out after a rain event. The disk length 63

64 measurements during dry-out tracking were compared with temperature, solar radiation, and relative humidity to relate the physical parameters to rain sensor function. One tracking period was conducted on 11 July 29 after a 6-mm rain event on 1 July 29. The disks were measured every hour for 2 hours after the rain stopped. To ensure that the disks had time to expand, there were 3 hours between when the rain stopped and measurements started. The rain sensors were inspected for interruption mode visually before disk measurements were taken. The second tracking period on 18 September 29 was performed by manually watering the disks. The rain sensors were drenched and were given 2 hours to expand before measuring. Measurements were not taken throughout the night beca use the first dry-out tracking showed relatively small disk length changes during the night. Statistical Analysis SAS statistical software (SAS Institute, Inc., Cary, NC) was used for all statistical analysis. A general mixed model with an auto regressive error structure was used to model the continuous responses (PROC MIXED). Tukey-Kramer adjusted p-values (p<.5) were used for pairwise comparisons of mean. Results and Discussion Climactic Conditions During the 1,186 days of the WL and MC study period and the 1,15 days of the Hunter, Irritrol, and Toro study period, 28% of the days received rain. For WL and MC, the cumulative rainfall was 3,551 mm which is 14% less than the historical average of 4,121 mm (see Chapter 2, Figure 2-12). For Hunter, Irritrol, and Toro, the cumulative rainfall was 3,41 mm which is 16% less than the historical average of 4,55 mm (see Chapter 2, Figure 2-13). In Chapter 2, Figures 2-14 and 2-15 show the daily and 64

65 cumulative rainfalls during each period and the theoretical count for the number of OSM events by treatment. Time in Open Switch Mode (Dry-Out) Dry-out is the amount of time the rain sensors are in OSM. After the dry-out period, irrigation would be allowed to occur. Figures 3-1 to 3-4 show frequency distributions of time in 6-hour intervals that the RSs stayed in OSM for all treatments. The WL dried-out within 24 hours 84% of the time with 1% requiring 49 to 53 hours (Figure 3-1). The 3MC dried-out within 24 hours 83% of the time and with 1% requiring 5 and 66 hours (Figure 3-2). The 6MC dried-out within 24 hours 64% of the time and with 4% requiring 49 and 69 hours (Figure 3-3). The 13MC dried-out within 24 hours 8% of the time and with 1% requiring 5 hours (Figure 3-4). Hunter dried-out within 24 hours 71% of the time and with 3% requiring 48 and 77 hours (Figure 3-5). Of the 3% of events with more than 48 hours of dry-out. Irritrol dried-out within 24 hours 84% of the time and all dried-out out within 48 hours (Figure 3-16). Toro dried-out within 24 hours 83% of the time and all dried-out within 48 hours (Figure 3-7). The frequency of dry-out within 24 hours for 6MC was 64% while the average for all other treatments was of 81%. This reduced percentage was a remnant of the rainfall setting change from 25 mm to 6 mm. As discussed in detail in Chapter 4, the hygroscopic disks change length with time in use and rainfall setting. The initial 25 mm setting caused the hygroscopic disks to change length differently than what the disks would have done with a 6 mm setting, which affected the dry-out of the disks for 6MC. Table 3-3 summarizes the percentage of time each treatment dried-out in 24 hours and how many hours each treatment required for 95% dry-out. The number of hours the WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro required for dry-out 95% of the time 65

66 were 36, 36, 42, 37, 44, 36, and 33 hours, respectively. These values are relatively close for all sensors and settings and indicated that a majority of the rain sensors driedout within 24 hours and 95% of the time they dried-out in less than 2 days. Rainfall needs to occur within 24 hours of a scheduled irrigation for most rain sensors to still be in OSM to interrupt the irrigation. Previous research (Cardenas-Lailhacar and Dukes, 28) investigated dry-out on the WL, 3MC, and 13MC for the first year of installation. Their results indicated that WL dried-out within 24 hours 8% of the time with 8% requiring 54 and 78 hours, which is consistent with this study. The 3MC dried-out within 24 hours 51% of the time with 12% requiring 48 and 78 hours and 7% more than 78 hours. The 13MC dried-out within 24 hours 57% of the time with 6% requiring 48 and 72 hours. The 3MC and 13MC treatments had a shorter dry-out during this study than the Cardenas-Lailhacar and Dukes (28) study, but treatment dry-out times did not change within this study period. The amount of time in dry-out did not change during the study period for any of the treatments. On 2 July 29, the Hunter and Irritrol dry-out vents were changed from fully open to fully closed. Hunter (Figures 3-8 and 3-9) and Irritrol (Figures 3-1 and 3-11) had average dry-out increase times of 14% or 3 hours and 32% of 6 hours, respectively, after closing the dry-out vents. Hunter dried-out within 24 hours 73% and 65 % of the time, with fully open and fully closed vents, respectively. Irritrol dried-out within 24 hours 82% and 77% of the time, with fully open and fully closed vents, respectively. The increase in dry-out time did not increase potential water savings. All of the Toro replicates were set to. day dry-out. There was no difference in dry-out time for the 66

67 four Toro replicates set at 1. day water delay and 3. day water delay (Figures 3-12 and 3-13). Dry-out Tracking Figures 3-14 to 3-15 show the dry-out of the disks over time for the two dry-out tracking events. A 6 mm rainfall event did not trigger all rainfall sensors to OSM: all 3MC, three 6MC, two 13MC, and three Hunter remained in CSM. The second tracking period was formed by manually watering the disks. This event did not rigger all rainfall sensors to OSM: two 3MC, one 6MC, two 13MC, and two Hunter remained in CSM. The same sensors did not go into OSM during both dry-out tracking events likely because of wear of the hygroscopic disks. Though these disks did not go into OSM, the disks did expand with the rain event and contract over the dry-out period. The dry-out patterns in the September tracking event were similar to July. Figures 3-16 to 3-18 show the relationship between the disk length and temperature, solar radiation, and relative humidity for the dry-out tracking on 11 July 29 since it has more detail. Decreases in relative humidity and increases in temperature and solar radiation were followed about 2 to 3 hours later with significant disk length decreases (Figures 3-14 to 3-16). Disk length reduction was caused by evaporation of rainfall from the disks. The lag time in response to the changes in the physical parameters was due to the time and energy required to vaporize the rain water in the disks for evaporation. The changes in climate preceding the significant disk contraction were a temperature increase from 24 to 31 C, an increase in solar radiation from 159 to 15 W/m 2, and a decrease in relative humidity from 93 to 54%. Each of the three parameters influenced dry-out time. 67

68 Potential Irrigation Savings The total potential water savings for each treatment under the different irrigation schedules are in Table 3-4. The values of potential irrigation savings need to be considered with reference to the respective accuracy of the rain sensors.tables 3-5 to 3-8 show the variation of total potential water savings for each replicate. The average percent water savings for the 2 d/wk schedule for the WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro treatments were 26%, 26%, 28%, 14%, 23%, 21%, and 21%, respectively. The average percentage water savings for the 1 d/wk schedule for the WL, 3MC, 6MC, 13MC, Hunter, Irritrol, and Toro treatments were 25%, 23%, 25%, 13%, 2%, 19%, and 14%, respectively. Cardenas-Lailhacar et al. (28) found that a rain sensor set at 6 mm on a timer with the same UF IFAS schedule used in this study could reduce applied irrigation by 34% in Gainesville, Florida. A study in southwestern Florida found 21% irrigation savings with a rain sensor set at 6 mm with a 2 d/wk irrigation schedule (Davis et al., 29). The 2 d/wk irrigation schedule potential savings in this study was 28% with the 6MC indicating that the 6MC treatment acted within the range of previous studies. McCready et al. (29) found that the 3 mm and 6 mm settings saved 25% and 17%, respectively, under a 2 d/wk irrigation schedule. The McCready et al. (29) findings for the 3 mm setting match this study s savings of 26%. McCready et al. (29) and Davis et al. (29) had similar savings with a 6 mm rainfall setting while Cardenas-Lailhacar et al. (28) had higher savings. The increased savings of Cardenas-Lailhacar et al. (28) was due to the higher rainfall in 25 compared with the later studies in 26 and 27. The 28% savings in this study for the 6 mm setting fall in between savings of 68

69 the other studies. The average potential irrigation savings for rain sensors set at 6 mm was 24% also in the range of 17% to 34% from previous research. The amount of savings should have been less for the 6MC in this study. The 6CM treatment savings should be between 26% and 14% which are the savings for 3MC and 13MC. The lack of difference in the 3MC and 6MC savings is due to the hygroscopic disk properties of the 6MC after being changed from a 25 mm setting to 6 mm setting a year after original installation (see Chapter 4 for hygroscopic disk details). From Chapter 2, the 3MC, 6MC, and 13MC sensors were in OSM after 2., 1.7, and 7. mm of rainfall with accuracies of 64%, 27%, and 51%, respectively. The 6MC treatment had a very low accuracy, and it should have had a depth required for OSM between the depths required by 3MC and 13MC. The Hunter treatment, which was the Mini-Clik sensor set to 6 mm from its time of installation, went into OSM after 4.1 mm as expected based on the performance of 3MC and 13MC. The 6MC treatment did not perform as it would have without the setting adjustment. Summary and Conclusions This experiment was carried out during a relatively dry period with rainfall on 28% of the days. Rain sensor dry-out times did not change throughout the study period. The 13MC had greater variability of dry-out times than any other treatment. Averaged across the treatments, the rain sensors dried-out within 24 hours 79% of the time and in 38 hours 95% of the time. Changing the vent settings from fully open to fully closed on some treatments increased the dry-out time an average of 23%, but the potential irrigation savings was unchanged. The Toro water delay feature does not have an effect on the number of OSM occurrences or potential irrigation savings. 69

70 During the dry out process with the expanding disks, the most significant disk contraction occurred 2 or 3 hours after changes in climatic conditions. The disks reacted to decreasing relative humidity (from 93 to 54%) and increasing temperature (from 24 to 31 C) and solar radiation (from 159 to 15 W/m 2 ). Apparently, 2 to 3 hours was needed for a sufficient amount of water to vaporize from the hygroscopic disks to result in a significant size reduction. Potential water savings were determined by comparing the number of times the sensors went into OSM and dry-out time to a theoretical UF IFAS irrigation schedule. Potential irrigation savings should be considered with the accuracy of rain sensors. All treatments, except Toro, had accuracies of less than 65%. The potential irrigation savings presented in this research were higher than they would be with more accurate rain sensors since most treatments went into open-switch mode with less rainfall than their respective rainfall setting. For a 2 d/wk and 1 d/wk irrigation schedule, the percentage water savings for the 13MC was 14% and 13% and the average for all other treatments was 24% and 21%, respectively. As expected, the rain sensors with lower rainfall setting had higher potential water savings. The average irrigation savings for previous research with a 2 d/wk irrigation schedule with rainfall settings of 3 and 6 mm was 24% and 21%, respectively. This virtual study had similar potential water savings as previous research. There was no difference in potential irrigation savings between the rain sensors with 3 and 6 mm rainfall settings because of the combination of the inherent low accuracy of rain sensors and relative closeness of the settings (3 and 6 mm versus 6 and 13 mm). 7

71 Rainfall settings of 3, 6, and 13 mm are adequate for rain sensors in central Florida because all settings conserved water. Rain sensors should be set to 3 or 6 mm, which conserve more than a setting of 13 mm, until the user determines that landscape quality or climatic conditions require the higher setting. The rainfall settings for a rain sensor should not be changed after a particular setting has been established for more than 3 months (see Chapter 4 for more details). If the rainfall setting needs to be changed after 3 months of use, it is recommended that a new rain sensor be installed. Table 3-1. Treatment description. Treatment Model Replicates Set Point WL 3MC 6MC 13MC Hunter Irritrol Toro Wireless Rain-Clik Mini-Clik Mini-Clik Mini-Clik Mini-Clik Irritrol RFS 1 Toro TWRS a 8 a 8-3 mm 6 mm 13 mm 6 mm 6 mm 6 mm a Dry-out vents changed from fully open to fully closed on 2 July 29. Dry-out Vent Setting half open fully open fully open fully open fully open and fully closed e fully open and fully closed e day dry-out Table 3-2. Monthly irrigation depth to replace historical evapotranspiration values based on Dukes and Haman (22a). Run times are based on an irrigation application rate of 38 mm/hr assuming system efficiency of 6% and considering effective rainfall. The Reduced UF IFAS irrigation schedule is 6% of the UF IFAS irrigation schedule. Month Irrigation depth (mm) January February March April 81 May 16 June 143 July 131 August 12 September 154 October 13 November 56 December Total

72 Table 3-3. Summary of dry-out time for all treatments. Treatment Model Dry-out Vent Setting Frequency of dry-out within 24 hours WL 3MC 6MC 13MC Hunter Irritrol Toro Wireless Rain-Clik x Mini-Clik x Mini-Clik x Mini-Clik x Mini-Clik x Irritrol RFS 1 y Toro TWRS z half open fully open fully open fully open fully open and fully closed a fully open and fully closed a day dry-out a Dry-out vents changed from fully open to fully closed on 2 July % 83% 64% 8% 71% 84% 83% Hours dried-out 95% of the time Table 3-4. Total potential water savings per treatment for all treatments compared with a 2 d/wk and a 1 d/wk irrigation schedule for the study period (Oct/Nov 26 to 1 Dec 29). Treatment 2 d/wk Irrigation Schedule 1 d/wk Irrigation Schedule Irrigation Water Savings Irrigation Water Savings depth (mm) (%) Depth (mm) (%) WL 3MC 6MC 13MC Hunter Irritrol Toro (mm) 3,5 3,5 3,5 3,5 2,92 2,92 2, (mm) 2,968 2,968 2,968 2,968 2,869 2,869 2, Table 3-5. Variation in total potential water savings per replicate for the WL and MC treatments compared with UF IFAS 2 d/wk irrigation recommendations. Water saved by replicates (mm) Treatment A B C D Average CV (%) WL 3MC 6MC 13MC z a 775 a 841 a 415 b 8 z Numbers with different letters indicate a statistical difference at the 95% confindence level using Duncan s Multiple Range Test. z Average and CV do not include WL-B 72

73 Table 3-6. Variation in total potential water savings per replicate for the Hunter, Irritrol, and Toro treatments compared with UF IFAS 2 d/wk irrigation recommendations. Water saved by replicates (mm) Treatment Hunter Irritrol Toro A B C D E F G H Average CV (%) z a 2 z a a Numbers with different letters indicate a statistical difference at the 95% confindence level using Duncan s Multiple Range Test. z Average and CV do not include Hunter-D Table 3-7. Variation in total potential water savings per replicate for the WL and MC treatments compared with UF IFAS 1 d/ wk irrigation recommendations. Water saved by replicates (mm) Treatment A B C D Average CV (%) WL 3MC 6MC 13MC z a 677 a 745 a 41 b 5 z Numbers with different letters indicate a statistical difference using Tukey-Kramer adjusted p-values of p<.5. z Average and CV do not include WL-B Table 3-8. Variation in total potential water savings per replicate for the Hunter, Irritrol, and Toro treatments compared with UF IFAS 1 d/ wk irrigation recommendations. Water saved by replicates (mm) Treatment Hunter Irritrol Toro A B C D E F G H Average CV (%) z a 24 z a a Numbers with different letters indicate a statistical difference using Tukey-Kramer adjusted p-values of p<.5. z Average and CV do not include Hunter-D 73

74 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-1. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the WL treatment average. Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-2. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 3MC treatment average. 74

75 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-3. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 6MC treatment average. Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-4. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the 13MC treatment average. 75

76 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-5. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-6. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Irritrol treatment average. 76

77 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-7. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Toro treatment average Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-8. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average with the dry-out vents fully open (8 November 28 to 2 July 29). 77

78 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-9. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Hunter treatment average with the dry-out vents fully closed (2 July 29 to 31 December 29) Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure 3-1. Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Irritrol treatment average with the dry-out vents fully open (8 November 28 to 2 July 29). 78

79 6 12 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the Irritrol treatment average with the dry-out vents fully closed (2 July 29 to 31 December 29). Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the one day water delay setting for four the Toro replicates. 79

80 Frequency of occurances (%) Cumulative frequency of occurances (%) Interval of hours for dry-out period Figure Histogram and cumulative frequency of dry-out time (time from the end of the rain event until the sensors returns to closed-switch mode) for the three day water delay setting for four the Toro replicates Disk length (mm) : 3: 6: 9: 12: 15: 18: 21: : Time in dry-out (hour) 3MC 6MC 13MC Hunter Irritrol Toro Figure Dry-out tracking of average disk length for each treatment for the natural rain event on 1 July 29. 8

81 25 24 Disk length (mm) : 3: 6: 9: 12: 15: 18: 21: : Timer in dry-out (hours) 3MC 6MC 13MC Hunter Irritrol Toro Figure Dry-out tracking of average disk length for each treatment for the manual rain event on 18 September Disk length (mm) Temperature (deg C) 19 2 : 3: 6: 9: 12: 15: 18: 21: : Time in dry-out (hour) 3MC 6MC 13MC Hunter Irritrol Toro Temperature Figure Dry-out tracking of average disk length for each treatment and temperature for the rain event on 1 July

82 Disk length (mm) Solar Radiation (W/m 2 ) 19-2 : 3: 6: 9: 12: 15: 18: 21: : Time in dry-out (hour) 3MC 6MC 13MC Hunter Irritrol Toro Solar Radiation Figure Dry-out tracking of average disk length for each treatment and solar radiation for the rain event on 1 July 29. Disk length (mm) : 3: 6: 9: 12: 15: 18: 21: : Time in dry-out (hour) Relative Humidity (%) 3MC 6MC 13MC Hunter Irritrol Toro Relative Humidity Figure Dry-out tracking of average disk length for each treatment and relative humidity for the rain event on 1 July

83 CHAPTER 4 RELATIONSHIP BETWEEN EXPANDING-D ISK RAIN SENSOR DISK LENGTH AND PERFORMANCE Introduction In 2, Florida was the largest user of groundwater east of the Mississippi River (Hutson et al., 24). Florida s growing population continues to put a stress on the water supply. Florida ranked first in single family home construction in 25 with 29,162 homes built (United States Census Bureau [USCB], 27). Seventy percent of single family homes have automatic irrigation systems (Tampa Bay Water, 25). Automatic timer controls on irrigation systems in Florida have been reported to lead to a 47% increase in water use (Mayer, et al., 1999). Water conservation measures are needed to reduce water use. Rain sensors interrupt the cycle of an automatic irrigation system controller when a specific amount of rainfall has occurred (Dukes and Haman, 22b). Rain sensors remain in closed-switch mode that allows irrigation until sufficient rainfall changes it to open-switch mode. Several types of rain sensors are available to homeowners. Most rain sensor models can be adjusted to interrupt at different depths of rainfall, generally between 3 and 25 mm. One type of rain sensor collects the rain water in a cup and interrupts the irrigation based on a preset weight of water. Another type has a set of electrodes that detect the water level in a small collection dish that measures the amount of rainfall with two electrodes in a collection cup (Dukes and Haman, 22b). A disadvantage to both of these devices is that debris can get into the collection cup and cause the system to interrupt irrigation without sufficient rainfall. The most common type of rain sensor used in Florida is the expanding-disk rain sensor. Compared with other types of rain sensors, it requires less maintenance and is 83

84 less expensive. The swelling of the hygroscopic disks in the sensor expand proportionally to the amount of rainfall. When the disks swell to the selected rainfall setting, the system goes into open-switch mode. The sensors rely on evaporation to allow irrigation after a being in open-switch mode (dry-out). A longer dry-out time has the potential to interrupt more scheduled irrigations by the irrigation controller. Dry-out settings can be adjusted for most sensors. Settings should be chosen to match soil conditions such that the disks dry-out at a rate similar to water leaving the root zone soil profile. The hygroscopic disk dry-out time is influenced by weather co nditions such as temperature, wind, solar radiation, and relative humidity. After investigating expanding-disk rain sensors since 25, researchers noticed that the hygroscopic disks appear to have different sizes with time (Figures 4-1 and 4-2). The disks may lose elasticity with time due to the repeated shrinking and swelling from rain events resulting in a longer total length because they appear to not shrink to their original length. A longer length of disks inside the sensor may cause the RS to interrupt the irrigation cycle with less rain. Cardenas-Lailhacar et al. (29) determined that the se nsitivity of the RS changed during 282 days of installation. One possible cause of the change in sensitivity is a change in size of the hygroscopic disks themselves. The objectives of this study were to determine if the length of the hygroscopic disks in expanding-disk rain sensors change size based on the amount of time installed and the set point, and if so determine the effect of disk change on switching accuracy. Materials and Methods This study was conducted at the University of Florida Agricultural and Biological Engineering Department campus turfgrass plots, Gainesville, Florida. The rain sensors 84

85 were installed on a 2-m high board next to each other. Figure 2-2 (see Chapter 2) shows the details of the expanding-disk rain sensor used for this study. Treatments Five treatments with varying rain sensor set points were established (Table 4-1). The rain sensors used were Mini-Clik (MC) rain sensors from Hunter Industries, Inc., (San Marcos, CA). Treatments 3MC and 13MC had rainfall settings of 3 mm and 13 mm, respectively, and were installed on 25 March 25. The 3MC and 13MC treatments had four replicates each. Treatments 3R, 6R, and 13R with rainfall settings of 3 mm, 6 mm, and 13 mm, respectively, were installed on 13 February 29. The 3R, 6R, and 13R treatments were replicated three times. Monitoring The total disk length was measured about once per month with a Mitutoyo Series 55 dial caliper (Mitutoyo Corporation, Aurora, IL) starting February 29. Figure 2-5 (see Chapter 2) shows the hygroscopic disks inside the rain sensor. Disks were measured when fully contracted and all rainfall had evaporated. Treatments 3R, 6R, and 13R were measured before installation to give the length of a new device. To determine the accuracy of the 3MC and 13MC treatments, each time a rain sensor changed mode between open switch mode (OSM) and closed switch mode (CSM), the date and time were recorded at a 1-second sampling interval using AM16/32 multiplexers (Campbell Scientific, Inc., Logan, UT) attached to a CR1X model data logger (Campbell Scientific, Inc., Logan, UT). An onsite automated weather station (Campbell Scientific, Logan, UT) located within 15 m of the experimental site recorded climatic conditions using a CR1X model data logger (Campbell Scientific, Logan, UT). Precipitation was measured by a tipping bucket rain gauge with a one-second sampling 85

86 interval time stamp for each.25 mm of rain. A manual rain gauge located within 5 meters of the rain sensors was used to verify the accuracy of the tipping bucket rain gauge measurements (See Chapter 2, Figure 2-9). The travel distance from CSM to OSM of each sensor was measured to determine if possible disk length changes were significant. The travel distance was the required increase in disk length due to rainfall for the rain sensor to go into OSM. Travel distance was determined with dry rain sensors by measuring the post on the top of the rain sensor in a stationary position (CSM) and in the position of full disk expansion (OSM). Total travel distance included the difference in the rain sensor post as described above plus the distance required to compress the trigger device underneath the hygroscopic disks. Statistical Analysis SAS statistical software (SAS Institute, Inc., Cary, NC) was used for all statistical analysis. A general mixed model with an auto regressive error structure was used to model the continuous responses (PROC MIXED). A paired T-Test was used for means separation with significant F values (p<.5). Results and Discussion Disk measurements were performed to track size changes in the hygroscopic disk length. Figure 4-3 shows the daily and cumulative rainfall during the study period and the number of rainfall events greater than the three rain sensor rainfall settings investigated (3, 6, and 13 mm). Length by Installation date and setting The difference between initial and final disk length for 3MC, 13MC, 3R, 6R, and 13R was -.1,.4, 1.2, 1.8, and 2.3 mm, respectively. Table 4-2 shows the initial and 86

87 final disk measurements for each treatment. The disk length of 3MC and 13MC did not change significantly during the measurement period. After 81 days of installation, the disk lengths of 3R, 6R, and 13R were significantly expanded from the initial length, by 1.2, 1.8, and 2.3 mm, respectively. The length of disks after 81 days of installation and at the end of the study (276 days) for 3R, 6R, and 13R were 18.1 and 18.7 mm, 18.7 and 19.2 mm, and 19. and 19.6 mm. The disk lengths of 3R and 13R approached the lengths of the 3MC and 13MC, respectively. The treatments installed in 13 February 29 started with the same average length of 17.4 mm (Table 4-3). After 81 days of installation, the disk lengths for 3R, 6R, and 13R were all significantly different (p-value <.5) from the initial measurements with lengths of 18.1, 18.7, and 19. mm, respectively. The disk lengths by treatment remained statistically different during the rest of the study. This result was consistent with the older sensors, 3MC and 13MC, in which the disks had different lengths, 19. and 2.2 mm respectively. Figure 4-4 shows the change in length during the study period for each treatment. There was little variation of disk length within treatments (Tables 4-4 and 4-5). The average Coefficient of Variance (CV) values for February, May, and November measurements for the 3MC, 13MC, 3R, 6R, and 13R were 1.%, 1.2%,.8%, 1.6, and 1.%, respectively. The average CV for disk length was 1.1% while the average CV for the depth of rainfall required for OSM was 47% (see Chapter 2 for details). Disk Length and Traveling Distance The length that the disks travel when switching between CSM and OSM was analyzed to determine if the disk length changes were substantial. Travel distance was dependent on rainfall setting. For the rain sensors installed in February, the average 87

88 length increased 1.7 mm and the average travel distance decreased.9 mm (Table 4-6). The changes in disk length were substantial with respect to the travel distance. Table 4-7 shows the values of final disk length, length change, and travel distance at the end of the study. Disk length change and the total travel distance were larger in rain sensors with higher rainfall setting because there was more space inside the rain sensor. Treatments 3MC and 13MC had a shorter travel distance than 3R and 13R, respectively, because the 3MC and 13MC disks were longer. Effect on Interruption Performance The accuracy of the 3MC and 13MC units for the first 3 days of installation was analyzed to evaluate the effect of disk length change on accuracy. Analysis is based on the assumption that the disks installed in 25 and 29 had the same properties. Figure 4-5 compares accuracy of 3MC and 13MC to the disk size change in the 3R and 13R. Treatments 3MC and 13MC had no correlation of accuracy and disk length change. Summary and Conclusions New ( to 276 days old) and old (1,421 to 1,697 days old) rain sensors were analyzed to determine the effect of disk length change on rain sensor accuracy. The hygroscopic disks in expanding-disk rain se nsors change length after rainfall exposure. Disk length was analyzed based on time installed in the field and rainfall setting. The new rain sensors had an average disk length increase of 1.8 mm and the old rain sensors changed.1 mm in size during the same time. The average length change for the new rain sensors was more than the travel distance inside the sensor when switching from closed-switch mode to open-switch mode (1.7 mm versus.9 mm). This 88

89 finding indicated that the amount of disk length increase was significant enough to possibly influence rain sensor performance. The lengths of the RS disks after 81 days of installation and 178 mm of rainfall were significantly greater than the initial measurements. Higher rainfall settings had more disk length increase due to the larger space within the rain sensor device at higher rainfall settings and the material losing elasticity while expanding and contracting. The amount of time the rain sensors remained in open-switch mode was not related to the setting; the disk length difference between 3 mm and 13 mm settings did not influence the dry-out (see Chapter 3 for details). Increased disk length change was not related to decreased rain sensor accuracy. The accuracies presented in Chapter 2 showed that both 3MC and 13MC were more accurate in the first 282 days of installation than the accuracy of days 56-1,742 of installation. It was assumed that the properties of the disks installed in 25 were the same as those installed in 29. All of the disk length change occurred during the initial Cardenas-Lailhacar and Dukes (28) study in which the average accuracy was 83%. In this study, the accuracy of the same sensors was 62% in which no significant disk length change occurred. The decreased accuracy cannot be attributed to disk length change because the time in which the disk lengths changed did not correspond to the time of decreased accuracy. There was no relationship between the disk length and accuracy. Decreasing accuracy of the rain sensors was due to the aging of the entire unit. The outer casing of the rain sensor became more brittle with sun and rain exposure. The triggering mechanism required less movement on the older rain sensors than the new rain sensors when manually going into open-switch mode. 89

90 The reason that the disks could have a significant length change without affecting accuracy was because of the disks properties. The rain sensors with higher rainfall settings had more increase in disk length because they have more space inside the rain sensor in which to lengthen. The increase in length also meant that there was more pore space inside each disk. While the pore space shortened the travel distance for the sensor, the amount of rainfall required to expand the full travel distance for open-switch mode did not change because those pores then needed to be filled with water. Thus, an increase in disk length did not correspond to the rain sensor requiring less rainfall for open-switch mode. Table 4-1. Description of rain sensors details for each treatment. Treatment Model Replicates Set Point Installation Date 3MC 13MC 3R 6R 13R Mini-Clik x Mini-Clik x Mini-Clik x Mini-Clik x Mini-Clik x mm 13 mm 3 mm 6 mm 13 mm 25 Mar Mar Feb Feb Feb 29 x Hunter Industries, San Marcos, CA Table 4-2. Average disk length for each treatment at two intervals: initial and final (276 days of installation). Treatment Installation Set point Disk Length Measurement (mm) Date (mm) February November Difference 3MC 13MC 3R 6R 13R CV (%) 25 Mar Mar Feb Feb Feb NS = no statistical difference between February and November measurements * = statistical difference at.5 p-value level between February and November measurements *** = statistical difference at.1 p-value level between February and November measurements NS NS * * *** - 9

91 Table 4-3. Average disk length for the treatments installed 13 February 29 at three intervals: initial, 81 days of installation, and final (276 days of installation). Treatment Set point Disk Length Measurement (mm) February May November 3R 6R 13R a 17.4 a 17.3 a 18.1 a 18.7 b 19. c 18.7 a 19.2 b 19.6 c Numbers with different letters indicate a statistical difference using at-test for means separation with significant F values (p<.5) Table 4-4. Disk length for replicates of treatments installed 25 March 25 at three intervals: initial (February), 81 days of installation (May), and final (November, 276 days of installation). Treatment Replicate 3MC-A 3MC-B 3MC-C 3MC-D Average CV (%) Disk Length Measurement (mm) Treatment Replicate Disk Length Measurement (mm) Feb May Nov Feb May Nov MC-A MC-B MC-C MC-D Average CV (%) Table 4-5. Disk length for replicates of treatments installed 13 February 29 at three intervals: initial (February), 81 days of installation (May), and final (November, 276 days of installation). Treatment 3R 6R 13R Replicate Disk Length Measurement (mm) Disk Length Measurement (mm) Disk Length Measurement (mm) A B C Avg CV (%) Feb May Nov Feb May Nov Feb May Nov

92 Table 4-6. Comparison of average length change and travel distance from closed-switch mode to open-switch mode of treatments installed in 13 February 29. The February travel distance was measured on a rain sensor before installation. Treatment February November Change 3R 6R 13RC Avg Disk Length (mm) Travel Distance (mm) Disk Length (mm) Travel Distance (mm) Disk Length (mm) Travel Distance (mm) Table 4-7.Comparison of average length change of each treatment from 13 February 29 to 16 November 29 and the travel distance each treatment from closed-switch mode to open-switch mode. Travel distance was measured at the end of the study. Treatment Set Point (mm) Final Disk Length (mm) Length Change (mm) Travel Distance (mm) 3MC 13MC 3R 6R 13R Avg Figure 4-1.Mini-Clik (Hunter Industries, Inc.) rain sensor expanding disks installed in March 25 (left) and February 29 (right) set at 13 mm measured in August 29 (16 and 179 days of installation, respectively). 92

93 Figure 4-2.Mini-Clik (Hunter Industries, Inc.) rain sensors expanding disks installed in 25 with settings (left to right) of 3 mm, 6 mm, and 13 mm and lengths 19.2, 19.8, and 2.1 mm, respectively, after 1,6 days of installation. 93