Testing of Climatologically-based Irrigation Controllers in Florida

Transcription

1 Proc. Fla. State Hort. Soc. 122: Testing of Climatologically-based Irrigation Controllers in Florida STACIA L. DAVIS 1* AND MICHAEL D. DUKES 1 1University of Florida, IFAS, Agricultural and Biological Engineering, Frazier Rogers Hall, P.O. Box , Gainesville, FL ADDITIONAL INDEX WORDS. evapotranspiration, irrigation scheduling, soil water balance, warm season grass Water resources are limited in Florida similar to other parts of the United States experiencing water shortages. It was found in recent research performed by the University of Florida that 64% of residential water use was used for irrigation in central Florida. Though automatic in-ground irrigation systems are the most commonly installed system, they have been shown to increase residential water use. The irrigation industry has developed so-called smart irrigation controllers that are designed to use water efficiently while maintaining landscape quality. Climatologically-based controllers are one type of smart irrigation controller that use an estimation of reference evapotranspiration (ET O ) for automatic irrigation scheduling. The objectives of this study were to evaluate the ability of three brands of climatologically-based controllers to schedule irrigation for a virtual landscape compared to a simulated soil water balance and determine the variability in irrigation scheduling by ET controllers of the same brand. The ET controllers were as follows: Smart Line Series controller (Weathermatic, Inc., Dallas, TX), Intelli-sense (Toro Company, Inc., Riverside, CA) utilizing the WeatherTRAK ET Everywhere service (Hydropoint Data Systems, Inc., Petaluma, CA), and Smart Controller 100 (ETwater Systems LCC, Corte Madera, CA). Results showed that there were no differences in weekly water application between controllers of different brands ranging from 14.7 mm/week to 16.5 mm/week. There were also no differences in weekly irrigation application within each brand. However, irrigation scheduling techniques were different between controllers of different brands. There was an inverse relationship between irrigation amount per event and number of events per week with extreme averages ranging from 3.0 mm/event and 5.2 events/week for the Weathermatic to 25.7 mm/event and 0.57 events/week for the soil water balance model. Some irrigation scheduling techniques did not account for reasonable irrigation system hydraulics. The ET controller s ability to handle rainfall is the key to the water conservation potential of the controller in Florida. Despite the possible drawbacks to the technology, the ET controllers were able to schedule irrigation so that weekly water application was similar to what was calculated as the water needs for the specified landscape. Water conservation has become critical as seen from shortages occurring in Florida. Recently, the City of Tampa banned all automatic outdoor irrigation as a temporary measure to conserve water during drought (City of Tampa, 2009). It was found in research performed by the University of Florida that 64% of residential water use was used for irrigation in central Florida (Haley et al., 2007). Ways to reduce outdoor water use are increasingly important as water continues to become scarce. Though automatic, in-ground irrigation systems are the most commonly installed system, they have been shown to increase residential irrigation. Climatologically-based controllers, also known as ET controllers, are one type of smart irrigation controller that use an estimation of reference evapotranspiration (ET O ) for automatic irrigation scheduling. Each controller works differently depending on manufacturer, but they are typically programmed with landscape-specific conditions intended to supply landscape water needs (Riley, 2005). ET controllers receive reference evapotranspiration (ET O ) information in three general ways, consequently dividing ET controllers into three main types: 1) stand-alone controllers, 2) signal-based controllers, and 3) historical-based controllers. Stand-alone controllers receive climatic data from on-site measurement sensors and use calculations to determine ET O ; whereas, signal-based controllers receive ET O calculated off-site from local weather *Corresponding author; stacia@ufl.edu; phone: (352) , ext. 263 stations through satellite or internet technology. Historical-based controllers rely on historical ET O information, but are not as efficient as other methods because actual changes in weather are not taken into account. Generally, a soil water balance approach would be implemented when scheduling irrigation using ET O. This approach consists of creating a mass balance of inputs (i.e., rainfall, irrigation) and outputs (i.e., ET O, deep percolation, runoff) to a root zone. It represents a simplistic model of the soil and water interactions over a certain time step. Most ET controllers input ET O, adjusted for specific landscapes, in a daily soil water balance to schedule irrigation. The objectives of this study were to evaluate the ability of three brands of climatologically-based controllers to schedule irrigation for a virtual landscape compared to a simulated soil water balance model and determine the variability in irrigation scheduling by ET controllers of the same brand. Materials and Methods TEST SETUP. The study occurred at the University of Florida Agricultural and Biological Engineering turfgrass plots in Gainesville, FL. Three brands of ET controllers were selected for study; each brand was replicated three times totaling nine controllers. The ET controllers were as follows: Weathermatic SL1600 (Dallas, TX), Toro Intelli-sense (Riverside, CA) utilizing the WeatherTRAK ET Everywhere service (Hydropoint Data 365

2 Systems, Inc., Petaluma, CA), and ETwater Smart Controller 100 (Corte Madera, CA). Each Weathermatic controller performed as a stand-alone ET controller and was accompanied by an SLW10 weather monitor (Weathermatic, Inc., Dallas, TX) used to gather temperature and rainfall information. Each weather monitor has a hydroscopic disk rain sensor used as a rain shut-off device; each sensor was set to bypass irrigation when rainfall reached a 6-mm threshold. The Toro and ETwater controllers were both signal-based and required ET service subscriptions. Mini-clik rain sensors (Hunter Industries, Inc., San Marco, CA), also expanding disk rain sensors set at a 6-mm threshold, were added to the Toro and ETwater controllers that did not have on-site sensors. One rain sensor was installed for each of the Toro and ETwater controllers totaling six sensor additions. All nine controllers were installed and functional and testing began by 9 Sept Testing ended 18 Apr. 2009, a total of 588 d. Each controller was programmed with soil and landscape characteristics similar to zone two of the SWAT testing protocol (Irrigation Association, 2008) with sand chosen as the soil type instead of silty clay to compare to typical Florida landscapes (Table 1). This irrigation zone simulates a warm season turfgrass area irrigated by spray head sprinklers. The study was set up as a virtual test and the controllers were not connected to irrigation systems. Instead, one zone for each controller was wired to a CR-10X datalogger (Campbell Scientific, Logan, UT) via a set of relays to record time and date at the beginning and end of each irrigation event for the active zones. Runtimes were calculated from the recorded timestamp to determine depth of irrigation application. A weather station managed by UF-IFAS research personnel was located on-site within 25 m of the controllers. Data collection from this station included temperature, relative humidity, solar radiation, wind speed, and rainfall at 15-min intervals. This data was used to calculate ET O using the ASCE-EWRI standardized reference evapotranspiration equation (ASCE method; ASCE- EWRI, 2005). The ET O and rainfall for the study period was compared to 6 years ( ) of calculated ET O and measured rainfall using data collected from a National Oceanic and Atmospheric Administration (NOAA) weather station (NOAA, 2007). This weather station was located at the Gainesville Regional Airport approximately 18 km from the location of the study. Even though only 6 years of data were used to determine average ET O and rainfall, these specific years varied significantly with weather conditions of very rainy ( ) and droughty ( ). Irrigation application and number of events was summed into weekly totals for statistical comparisons between treatments. SAS statistical software (SAS Institute, Inc., Cary, NC) was used for all statistical analysis, utilizing the General Linear Model (GLM) procedure with a 95% confidence level. Means separation was conducted using Duncan s multiple range test. SOIL WATER BALANCE CALCULATIONS. The soil water balance simulates the soil water dynamics of a landscape plant system. A daily soil water balance was used to evaluate the ET controllers and is calculated by the following equation: MB i = MB i 1 ET C + R E + I E [Eq. 1] where MB is the soil water content (mm) on day i or i 1, ET C is the plant specific ET (mm) on day i, R E is effective rainfall (mm) on day i, and I E is effective irrigation (mm) on day i. MB represents the water storage level in the soil profile on any given day. This level fluctuates from field capacity (FC) to permanent wilting point (PWP) where maximum allowable depletion (MAD) is 50% of the difference between FC and PWP. The FC and PWP values were chosen to be 20% and 3%, respectively, based on zone two of the SWAT protocol (Irrigation Association, 2008). Readily available water (RAW) is the water between FC and MAD; the plant material will no longer be considered wellwatered when RAW is depleted. Plant-specific evapotranspiration (ET C ) is calculated for any given plant material by applying a crop coefficient (K C ) using the following equation (Allen et al., 1998): ETC = K C ET O [Eq. 2] K C values were chosen for a warm season turfgrass in full sun as specified in the SWAT protocol (Irrigation Association, 2008). Effective rainfall is calculated from total daily rainfall and is the depth that causes the MB to reach FC after ET C has been lost for the day. Excess rainfall (that which exceeds soil storage capacity) is lost due to surface runoff or deep percolation. ET controllers schedule irrigation by calculating the depth of irrigation required to reach FC as net irrigation (I NET ). These Table 1. The program settings used by the controllers for the study. Description Model Weathermatic Toro ETwater Soil Sand Sand Sand Sand Slope (%) z Exposure Full sun NA y Sunny all day Sunny all day Readily available water x (mm) 14.0 NA NA NA Maximum allowable depletion (%) 40 NA Vegetation Bermuda Custom Warm season grass Warm season grass Root depth (inches) 8.1 NA 6 6 Landscape coefficient Varies monthly w Varies monthly Varies monthly Unknown Precipitation rate (mm/hour) Application efficiency (%) 60 NA Adjustments v (%) NA 165% 0 0 zthe slope for the Weathermatic is programmed in degrees and not as a percentage. yna refers to program settings that are not available for selection during controller setup. xrzwws represents the root zone working water storage or the depth of water readily available to the plant material. wturfgrass crop coefficients varied monthly based on Bermuda in full sun (IA 2008). vadjustments are percentage changes made to the runtime after it has been calculated by the controller, resulting in gross irrigation to account for irrigation inefficiency. 366

3 controllers apply more irrigation than is scheduled to take into account system application inefficiencies (Table 1). The depth actually applied by the controllers, including efficiency adjustments, is considered gross irrigation. Gross irrigation was used for data analysis. Fig 1. Cumulative reference evapotranspiration (ET O ) is compared to reference evapotranspiration determined from six years of data collected from a National Oceanic and Atmospheric Administration (NOAA) weather station located at the Gainesville Regional Airport approximately 18 km from the study site (NOAA 2007). ET O was calculated using the ASCE-EWRI standardized ET O equation (ASCE-EWRI, 2005). Results and Discussion Cumulatively over the 84-week study period, ET O was 20% less than the average (2470 mm) totaling 1982 mm (Fig. 1). Monthly rainfall approximated the 6-year average until Sept when only 13% of rainfall occurred (Fig. 2). During the rest of the study period, rainfall depths alternated between approximately half the average to approximately the same as the average until Apr. 2009, where the total rainfall was over twice the average. Due to the unpredictable nature of rainfall, irrigation was necessary to satisfy theoretical turfgrass demands. CONTROLLER RESULTS. Weekly irrigation by the Toro controllers averaged 15.6 mm/week and ranged from no irrigation per week, during rainy periods, to 53.5 mm/week during periods of high ET and little rainfall (Table 2). Irrigation application per event ranged from 7.5 to 13.5 mm, averaging 9.4 mm, and there were an average of 1.7 events per week with as many as six events per week. There were no differences between controller replications for weekly gross irrigation (P = ), weekly irrigation events (P = 1.000), and irrigation per event (P = ). Only slight variations would be expected from a signalbased controller since they each receive the same ET O value and rainfall information to schedule irrigation. The small variation was introduced from the expanding disk rain sensors to each replication which has Fig 2. Monthly and cumulative rainfall totals for the study period compared to the average monthly and cumulative rainfall determined from six years of data collected from a National Oceanic and Atmospheric Administration (NOAA) weather station located at the Gainesville Regional Airport approximately 18 km from the study site (NOAA 2007). 367

4 been found to be variable compared to their replicates (Cardenas and Dukes, 2008). The ETwater controllers averaged 16.5 mm/week of irrigation with a maximum of 42.0 mm/week (Table 3). These controllers averaged 2.6 irrigation events per week, but ranged from no irrigation per week to 7 events/week. The ETwater controllers averaged 6.5 mm/event of irrigation with a range from 4.7 mm/event to 7.5 mm/event. These results correspond to the methodology used by the controller where the same depth of irrigation should be applied for every event. During periods of high water needs, the controller irrigates the same depth more often than during periods of low water need. Similar to the Toro replications, there were no differences between these signal-based controllers for weekly irrigation (P = ), events per week (P = ), and irrigation application per event (P = ). However, the ETwater controllers irrigated smaller depths more often compared to the Toro controllers (Table 4). The Weathermatic controllers applied an average of 15.7 mm/ week of irrigation with a range of zero to 47.4 mm/week (Table 5). The Weathermatic replications were not different for weekly irrigation application (P = ), weekly irrigation events (P = ), and irrigation application per event (P = ). The weather monitor used by replication A failed in late July 2008 and was replaced in mid-sept Data for this replication were removed for statistical analyses during this time period. Any differences in irrigation scheduling were due to independent rainfall detection and ET O calculations using rainfall and temperature collected from individual weather monitors. The weather monitors use expanding disks for their rain sensor that are similar to the Mini-Clik rain sensors added to the signal-based controllers and are subject to the same variability. The ET O values used for irrigation scheduling are calculated by the controller, also increasing the variability compared to the signal-based controllers. Compared to the other controllers, the Weathermatic controllers applied irrigation using smaller irrigation events, averaging 3.1 mm/event, but applied irrigation more often, averaging 5.2 events/week (Table 4). These controllers sometimes irrigated every day (7 events/week) and could irrigate as little as 1.4 mm/ event of gross irrigation (Table 5). When using the efficiency listed in Table 1, the net irrigation would be calculated as 0.84 mm/event. Assuming a zone with an average application rate of 30 mm/h, the zone would run for 1.7 min. With irrigation events with small run times, inefficiencies can increase dramatically. It could take 30 s for the irrigation pipes to fill with water before applying water to the landscape. This would result in 30% less water than expected for every event this short. It is possible that the hydraulics of a residential irrigation system would make this type of irrigation event inefficient and unnecessary. COMPARISON TO THE MODEL. The soil water balance model results in a perfect irrigation schedule based on the theory described earlier. The model applied averages of the following: 14.7 mm/ week of irrigation, 0.57 events/week, and 25.7 mm/event (Table 4). The model applied more irrigation per event (P < ) and irrigated less often (P < ) compared to all brands of the tested ET controllers. However, the model applied similar depths of weekly irrigation as the other treatments (P = ). It is possible that the ET controllers are purposely designed to irrigate smaller amounts more often to ensure residential irrigators do not see a decline in landscape quality. Despite the variable methods for scheduling irrigation between brands of controllers, cumulatively the controllers applied similar irrigation to each other, ranging from 1308 mm by the Table 2. Irrigation statistics z for the Toro replications for the length of the study period. Replication A 15.5 a 1.7 a 9.4 a Replication B 15.6 a 1.7 a 9.4 a Replication C 15.6 a 1.7 a 9.4 a Average Maximum Minimum Table 3. Irrigation statistics z for the ETwater replications for the length of the study period. Replication A 16.4 a 2.6 a 6.5 a Replication B 16.6 a 2.6 a 6.5 a Replication C 16.6 a 2.6 a 6.5 a Average Maximum Minimum Table 4. Average irrigation statistics z for each ET controller compared to the model developed using a soil water balance. Toro Weathermatic ETwater Model Weekly gross irrigation (mm/wk) 15.6 a 15.7 a 16.5 a 14.7 a Weekly irrigation events (events/wk) 1.7 c 5.2 a 2.6 b 0.57 d Irrigation per event (mm/event) 9.4 b 3.0 d 6.5 c 25.7 a Table 5. Irrigation statistics z for the Weathermatic replications for the length of the study period. Replication A 15.0 a 5.0 a 3.0 a Replication B 16.4 a 5.3 a 3.1 a Replication C 15.6 a 5.2 a 3.0 a Average Maximum Minimum Toro controller to 1389 mm by the ETwater controller, as well as the model, totaling 1236 mm (Fig. 3). Irrigation application by the ET controllers followed the model closely until week 51 corresponding to mid-aug The controllers were not able to account for rainfall as well as prior to Sept This was 368

5 Fig 3. Cumulative average irrigation by each treatment over the study period occurring from 9 Sept.2007 to 18 Apr (84 weeks). mostly due to irrigation occurring early in the day and rainfall occurring in the afternoon or late in the day. Irrigation calculated by the model occurred at the end of the day after rainfall was taken into account. When rainfall occurs before irrigation is scheduled, less irrigation is necessary due to more effective rainfall. However, the ET controllers were given early morning water windows causing the controllers to sometimes irrigate when rainfall occurred later in the day. The signal-based controllers incorporated rainfall in irrigation scheduling using two sources: the rainfall determined from the weather data source used by the controller and from the on-site rain sensor. Rain sensors have been shown to dry out most often within 48 h of the end of a rainfall event (Cardenas and Dukes, 2008). The rain sensor may dry out within 2 d for large rainfall events, but the signal-based controllers will continue to pause irrigation for a length of time based on proprietary algorithms. However, with small, localized rainfall events commonly seen in Florida, it is sometimes difficult for the signal-based ET controller to pause irrigation for longer than 2 d if the rainfall event was not detected by the controller s weather data source. The controller s ability to schedule irrigation around rainfall can greatly affect the water conservation potential of the ET controller technology. Conclusion Results showed that there were no differences in weekly water application between controllers of different brands ranging from 14.7 mm/week to 16.5 mm/week. There were also no differences in weekly irrigation application within each brand. However, irrigation scheduling techniques were different between controllers of different brands. There was an inverse relationship between irrigation amount per event and number of events per week with extreme averages ranging from 3.0 mm/event and 5.2 events/week for the Weathermatic to 25.7 mm/event and 0.57 events/week for the soil water balance model. Some techniques did not account for reasonable irrigation system hydraulics with net irrigation as low as 0.84 mm/event. The ET controller s ability to handle rainfall is important to the water conservation potential of the controller in Florida. Because Florida experiences small, localized rainfall events, it is important for the controllers to schedule irrigation so that maximum amount of rainfall can be considered effective. Despite the possible drawbacks to the technology, the ET controllers were able to schedule irrigation so that weekly water application was similar to what was calculated as the water needs for the specified landscape. Literature Cited Allen, R.G., L.S. Pereira, D. Raes, and M. Smith Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy. ASCE-EWRI The ASCE standardized reference evapotranspiration equation. Tech. Committee Rpt. to the Environ. and Water Resources Inst. of the Amer. Soc. of Civil Engineers from the Task Committee on Standardization of Reference Evapotranspiration. ASCE-EWRI, Reston, VA. Cardenas-Lailhacar, B. and M.D. Dukes Expanding disk rain sensor performance and potential water savings. J. Irr. Drainage Eng. 134(1): City of Tampa Emergency Ordinance Located at: < Accessed on 23 Apr Haley, M.B., M.D. Dukes, and G.L. Miller Residential irrigation water use in central Florida. J. Irr. Drainage Eng. 133(5): Irrigation Association Smart water application technology (SWAT) Turf and landscape irrigation equipment testing protocol for climatologically based controllers: 8th Draft. Irr. Assn., Falls Church, VA. Available at: < Accessed 15 Mar National Oceanic and Atmospheric Administration (NOAA) Gainesville Rgnl. Airport weather station data. Available at: < www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwdi~stnsrch~stnid~ >. Accessed 30 Nov Riley, M The cutting edge of residential smart irrigation technology. California Landscaping. July/Aug. p