Shiambuku Stellar and Mayer 2016

1. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E299 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 Outdoor water use in the United States remains the largest end use of water among single-family residential customers (DeOreo et al. 2014, Haley et al. 2007, Mayer et al. 1999), and some results indicate that outdoor use may be increasing in newer develop- ments (e.g., DeOreo 2011). At the same time, there is ongoing debate about the most efficient and effective methods and tech- nologies for irrigating landscapes. Automatic inground irrigation systems have been promoted by the landscape industry as a con- venient and efficient option for watering a landscape (e.g., IA 2015). However, recent work by Friedman et al. (2013) shows that inground systems have led to increased irrigation application rates. Analysis of survey respondents by Fair and Safely (2013) showed that respondents with an automated irrigation system used more water on average than those without an automated system. Efficient or not, automatic sprinkler systems are increasingly popular in residential America, and it is increasingly important that demand management efforts focus on outdoor water use. Water utilities recognize that customers who manually irrigate with no assistance from an automated underground system (i.e., “hose draggers”) tend to use substantially less water outdoors on average (Mayer et al. 1999). DeOreo (2011) showed that in Salt Lake City, Utah, in households built post-2001 equipped with automatic inground irrigation, average outdoor use was significantly higher than in houses built pre-2001, even though they tended to be irrigating smaller areas. One intuitive explana- tion for the disparity in watering habits between automatic and manual irrigators is that most manual irrigators are unmotivated to water until visual cues from their landscape suggest that it needs watering, while those who use an automatic system do not frequently adjust their system, allowing it to water whether the landscape needs it or not (Vickers 2001). This analysis presents the findings from a water conservation– focused residential sprinkler inspection and education program in Colorado that seeks to address the problem of excess irrigation. The program, Slow the Flow Colorado (STF), is the largest outdoor irrigation auditing program in the state of Colorado, serving 1,500–2,500 homeowners annually. Trained technicians go to residential households, evaluate and test a household’s irrigation system, measure the landscape, and provide each homeowner with a customized report on ways to help improve the efficiency of the irrigation system and save water. The program is owned and operated by the Center for ReSource Conservation (CRC), a nonprofit organization based in Boulder, Colo., with approximately 25 participating water utilities across the state. Six key questions were addressed in this research: •• What was the impact of the sprinkler audits on outdoor water use? •• Were there measureable water savings from the STF audits? •• If present, how long did the water savings persist? •• What factors contributed to outdoor water use? •• What factors were related to application adequacy? •• What factors helped to predict water savings from the STF audits? To answer these questions, the research team obtained historical residential consumption data (up to seven years) from a sample of approximately 2,100 residential participants in the STF program, spread across five implementation years and nine Colorado water utilities. Residential sprinkler audit programs are commonly used as a tool for municipalities to achieve their water conservation goals, but little evaluative work has been done to measure the water savings from these efforts. Results include a statistical analysis of the impact of 2,000 residential sprinkler audits in Colorado and guidance for future conservation program design. Analysis of water billing records from at least two years pre- and two years post-irrigation audit from five seasons (2007–2012) were used to answer key questions about irrigation audit impact on water use as well as to understand factors that contribute to water savings from irrigation programs.While the average water savings one year post-audit were 5,000 gal/participant and median savings were 2,000 gal/ participant, high variability among savings beyond one year suggests that audits may not produce as robust long-term benefits. Impact Evaluation of Residential Irrigation Audits on Water Conservation in Colorado MORGAN SHIMABUKU,1 DAN STELLAR,1 AND PETER MAYER2 1Center for ReSource Management, Boulder, Colo. 2Water Demand Management LLC, Boulder, Colo. Keywords: conservation program, irrigation, residential water use, water audit

2. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E300 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 THE STF PROGRAM The irrigation inspection. STF is offered as a free sprinkler inspection program, available primarily to residential customers of participating Colorado water providers. The goal of the audit, simply stated, is to help the customer get the right amount of water, in the right location, at the right time. The irrigation inspections, also known as irrigation audits, are performed by professional water auditors, trained according to standards and methods that are based on the Irrigation Association audit (IA 2015), but tailored for residential irrigation systems. These are the steps of an audit: 1. Homeowner meeting 2. Visual inspection 3. Catch-cup tests 4. Pressure measurements 5. Soil and root depth assessment 6. Irrigated landscape size measurements 7. Determination of a customized watering schedule 8. Sharing of results and recommendations with the homeowner IMPACT ANALYSIS An important aspect of the STF program has been a thorough investigation of the program impacts to ensure effectiveness and to identify changes in the program for future improvement. To measure the impact of the program, seven years of single-family household water use data pre- and post-audit were used to compare irrigation adequacy on an annual basis. Single-family household irrigation water use. The STF program operated at the single-family household level. Irrigated area was measured at every house (in square feet) during the audit and was differentiated by either turf or non-turf areas. The researchers requested water use data for each household from the appropriate water utilities for a minimum of two years before and two years following the audit. For example, if the audit was performed at a household in 2007, water use data for 2005 and 2006 were used for the pre-audit data, and water use data from 2008 and 2009 (and 2010 and 2011, if available) were used for the post-audit data. If less than two years of water use data pre-audit and/or less than one year of water use data post-audit were not available, that household was not included in the analysis. Using water account number and address information, the CRC matched the water use data to the landscape size data. Households that were audited during one summer from 2007 to 2011 were included in this analysis; therefore, water records were analyzed from the years 2005 to 2012. Incomplete water records were removed, including those that did not have the same homeowner for at least two full years (January through December) before the audit and a full year (January through December) after the audit. This eliminated the possibility of measuring water use of a homeowner who had not participated in the audit but had simply moved into the home in which an audit had been performed, as well as comparing the water use of a participant in the program to the water use of the same household but with different owners in the year following the audit. All water use data provided in gallons were converted to thousands of gallons. Annual outdoor water use was calculated using the minimum- month method, which can be applied to Colorado water records analysis because December, January, and February are months when irrigation systems are turned off as a result of consistent temperatures below 32ºF. Irrigation application adequacy evaluation. Irrigation application adequacy can be evaluated in different ways, including compo- nents such as application adequacy, application efficiency, and landscape quality. Application adequacy refers to a supply of water to the landscape to meet plants’ requirements, while efficiency refers to reducing water waste (Grabow et al. 2013). Similar to Knight et al. (2015), the research team assumed application efficiency to be 100% and thus focused on evaluating application adequacy. This may have introduced error into the analysis as audit measurements of pre-audit application efficiency revealed significant deviation from 100%. However, post-audit application efficiency was not measured and therefore, to remain consistent in the irrigation application adequacy evaluation, 100% was assumed, as is typical for irrigation studies (e.g., Knight et al. 2015, Mayer & DeOreo 2010). Irrigation application adequacy is based on the theoretical irrigation requirement (TIR) and irrigated area (square feet of turf or non-turf). The TIR is the estimated depth of water, in inches, that a landscape needs, per square foot, in order to remain healthy on an annual basis within the study area. The TIR was calculated on an annual basis such that a single TIR value was used for each year of the analysis. For this study, local meteorological data were gathered, including daily reference evapotranspiration (ETo, in inches) for bluegrass and daily measured precipitation (P, in inches) from Northern Colorado Water Conservancy District (Northern Water 2013) and the Town of Castle Rock weather stations (Schultz 2013). Between four and seven meteorological stations from the Northern Water network that measure parameters used to calculate ETo from bluegrass sites were selected to include in the study. The Town of Castle Rock has operated and maintained four weather stations with full ETo and P measurements back to 2008. Both Northern Water and Castle Rock weather stations follow the 2005 American Society of Civil Engineers (ASCE) standardized reference evapotranspiration equation for all ETo calculations (ASCE-EWRI 2005). ETo and P data from 2009 and forward from these stations were included in this study. Only a few dates between Jan. 1, 2005, and Oct. 31, 2012, had missing ETo and P data. To fill missing spaces, the research team used the average value for that day of the year from all nonmissing years for that station. The growing season (April–October) TIR (in inches) was calculated on an annual basis using the sum of daily ETo and P for each year in the study period. TIR is calculated following Eq 1: TIR = (ETo × Kc) – Peff (1) where ETo is the sum of the growing season ETo; Peff, effective P, is the sum of growing season P multiplied by 0.5 to account for runoff (Brouwer Heibloem 1986); and Kc is the landscape coefficient. A landscape coefficient of 0.8 was selected as a rea- sonable approximation for the adjustment of reference ET to the

3. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E301 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 ET for cool-season turf (e.g., ANSI/ASABE 2015); this has been used in similar analyses of urban mixed-turf shrub landscapes (Mayer DeOreo 2010). Figure 1 displays growing season TIRs from 2005 to 2012. To compare outdoor water use relative to the TIR and to measure the impact of the STF audit on each household, a rate of water application (RWA) was calculated for each participant for at least two years pre- and two years post-audit. The RWA is similar to the application ratio used by Mayer DeOreo (2010) and the irrigation application ratio used by Knight et al. (2015). RWA is a measurement of irrigation adequacy. Before this rate was calculated, the water need (N), in gallons, for each individual landscape and year was calculated using Eq 2: N  (  TIR  12 × T × 7.48   TIR  12 × S × 7.48 × 0.7 )/1,000(2) where 12 is a conversion factor to change TIR from inches to feet, T and S are turf and shrub landscape size in square feet, respectively, 7.48 is a conversion factor to convert from cubic feet to gallons, 0.7 is the shrub ET-adjustment factor to convert TIR for bluegrass to a shrub landscape, and 1,000 converts the final value from gallons to thousands of gallons. The shrub coefficient of 0.7 effectively acts to reduce the Kc value used in Eq 1 to 0.56, just for irrigated areas measured as “shrub,” which is close to the recommended plant factor from the pending ANSI/ASABE (2015) Standard for Woody Plants and Herbaceous Perennials in a Dry Climate, defined as 26 in. of precipitation annually. Eq 2 was modified only if either measured T or S did not exist, and in those cases, that half of the equation was removed. Next, the difference between N and the amount of annual outdoor water used (Uo) (both in thousands of gallons) was calculated to quantify the amount that the participant over- or under-watered for that year (UD): UD = Uo – N (3) Furthermore, Uo can also be compared with N directly to get a measure of watering efficiency that is based on the amount of water applied to the landscape (in thousands of gallons) relative to the amount of water that the landscape needed (in thousands of gallons), or the participant’s RWA: RWA = (Uo/N) – 1 (4) The ratio of Uo:N is a value that represents the rate at which each participant over- or under-watered on an annual basis, and over- and under-watering are defined relative to the need determined for their landscape on an annual basis (Eq 2). A ratio of 1.0 indicated perfect watering. By subtracting 1 from this ratio and converting the new value to a percentage, the RWA is estab- lished. The RWA is the measure for irrigation adequacy that was used for this study. Negative RWAs indicated watering below N (inadequate application), positive values indicated watering above N (excess application), and 0% indicated perfect watering (ade- quate application). Measuring program water savings. In addition to calculating each participant’s RWA for each year that water data were available, the research team attempted to measure program water savings, in gallons. As stated earlier, the overarching goal of the STF program was to improve each participant’s irrigation adequacy (i.e., RWA) so that residents would be using the appropriate amount of water, in the appropriate location, at the appropriate time. Following the assumption that the majority of participants were overwatering before their audit (an assumption that was proved correct by the data), it was expected that the majority of participants would improve their irrigation adequacy (i.e., reduce the RWA), therefore saving water, by participating in this program. The average pre-audit RWA was used to calculate the projected water use (Up) (thousands of gallons) of each participant for all years following the audit: Up = (RWApre + 1) × N (5) A final calculation to quantify the amount of water saved (WS) (thousands of gallons) was done by finding the difference between Up and Uo for all years following the audit: WS = Up – Uo(6) If participants watered at a rate below the pre-audit RWA, their WS was positive and water was considered to have been saved. If participants watered at a rate above their pre-audit RWA, their WS was negative and water was not considered to have been saved. For the longitudinal (i.e., long-term) water savings estimation, the research team looked for sustained patterns of irrigation 0.0 5.0 10.0 15.0 20.0 25.0 30.0 2005 2006 2007 2008 2009 2010 2011 2012 TIR—in./year FIGURE 1 TIR determined for irrigation audits conducted 2007–2011 for STFa Year STF—Slow the Flow,TIR—theoretical irrigation requirement aColorado-based irrigation audit program Standard deviation is shown with error bars. Water records were analyzed for 2005–2012.

4. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E302 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 adequacy change. If participants improved their irrigation adequacy in the first year post-audit but then had a decrease (relative to their initial irrigation adequacy) two years post-audit, those records were removed for all future years of analysis. The ratio- nale was that even if these residents increased their irrigation adequacy again at some other year post-audit, that increase would no longer be attributable to the audit. The research team performed this analysis by applying a set of criteria to the RWA for each year post-audit, as this value best represents each participant’s irrigation adequacy. First, participants’ increase or decrease in their RWA in the first year post-audit was recorded relative to their average pre-audit RWA. This binary designation (increase or decrease) was then used to evaluate all of the following RWA values for each participant. For those whose RWA decreased in their first year post-audit, their savings were kept in the analysis as long as their RWA did not go above their average pre-audit RWA. For those whose RWA increased in their first year post- audit, their losses (i.e., negative savings) were kept as long as their annual RWA did not go below their average pre-audit RWA. For example, if participants had an average pre-audit RWA of 50%, and in their first year post-audit their RWA was lower than 50% (i.e., their application adequacy improved), then in all following years they had to have an RWA less than 50% in order for their calculated savings to be kept in the analysis. The same applied to someone who increased use; for example, if participants had an average pre-audit RWA of 10% and in the first year post-audit their RWA was greater than 10% (i.e., their application adequacy worsened), then in all following years they had to have an RWA greater than 10% to be kept in the analysis. Once participants’ savings were removed on the basis of these criteria, their data were no longer used in the analysis of future years. This process eliminated the chance of counting water savings (positive or negative) when someone’s watering habits changed opposite of how their watering habits changed in the year after the audit. The example in Table 1 is provided for clarification. Determining sample size for statistical significance. The researchers calculated the sample size necessary for detecting a statistically significant change in water use following Eq 7 (Devore 1991): n  (z1–  z1–)2  2  2 (7) where z is the z score of the desired confidence interval and the z score of the statistical power of the inference, z1-a, is the z score where a quantifies the chance of incorrectly designating a statistically significant difference when no difference truly exists (i.e., Type I error), z1–b is the z score where b quantifies the chance of not noting a statistically significant difference when one does truly exist (i.e., Type II error), s∆ is the standard deviation of the observed differences, and ∆ is the size of the change that you desire to detect. In order to have a 95% confidence level, the a and b levels were set to 0.05, and z was set to 1.64. The sample size (N) was derived using the data set from a pilot study that included 1,775 STF participant records from 2007 to 2010. Using these data, it was found that in order to detect a statistically significant change in RWA at the 95% confidence level, a minimum of 809 samples would be needed. For detecting a statistically significant change in outdoor use at the 95% confidence level, a minimum of 78 samples would be needed. The study sample size of 2,055 was therefore acceptable for this analysis. However, as mentioned earlier, because of a lack of participant records in the fourth and fifth years post-audit, the results from these years do not have the same level of statistical power as do results from the other years. Sources of error. While the research team did all it could to ensure the accuracy and statistical validity of the results pre- sented in this analysis, there are several sources of error that were not controllable or quantifiable. Sources are error from data sets received from outside parties (due to misread water meters or from challenges of accurately measuring ET and P), error within the STF audit data set (due to poor measurements in the field or error introduced during transfer from field notes into the computer database), error from possible landscape modifications post-audit at individual homes, and error introduced by the calculation methods (e.g., inability to identify outdoor use for irrigation versus outdoor use for other purposes such as washing cars or filling pools). To ensure the highest accuracy of the data, cleaning was done to remove all data that were known to be incorrect. To remain unbiased, however, data were not removed simply because the information seemed unrea- sonable or unlikely, except via standard outlier identification methods (Tukey 1977). The criterion used to identify outliers was based on the calculated RWA values. Any participant with an RWA below the first quartile plus 1.5 times the inter-quartile range (IQR), or above the third quartile plus 1.5 times the IQR for all RWA values in all years for which there were data, was considered an outlier and was removed. This decision to remove the most extreme outliers was also made with the knowledge that some of the highest RWA values were caused by inaccurate landscape area measurements, which were discovered with TABLE 1 Example of binary process for evaluating irrigation adequacy Identification Average Pre-audit RWA % OneYear Post-Audit RWA % Increase or Decrease TwoYears Post-Audit RWA % Keep Calculated Savings or Remove? ThreeYears Post-Audit RWA % Keep Calculated Savings or Remove? Participant 1 150 140 Decrease 144 Keep savings 152 Remove Participant 2 110 130 Increase 145 Keep losses 115 Keep losses RWA—rate of water application

5. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E303 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 checks of both a selection of non-extreme outlier and extreme outlier properties using Google Earth software. Following this outlier identification method, 26 records were removed. The researchers emphasize that calculated values should be viewed as best approximations. This is mainly because the tech- nique used to determine outdoor water use cannot provide exact outdoor use but rather a best estimate of outdoor water use. Also, while the calculated savings (or lack thereof) were attributed to the STF program, many other factors likely affected participant outdoor water use in the years following the audit. For example, watering restrictions, both mandatory and recommended, have been imposed in many partnering municipalities during the analysis years, possibly affecting participant outdoor water use. Other sources of error in the calculations included the assumption that no outdoor water use occurs between December and February, that no changes are made to the landscape after the audit, and that the evaporative needs of turf and shrub areas, respectively, are uniform within each participant household. Error from the weather data from Northern Water and the Town of Castle Rock is possible from missing dates or inaccuracy of the equipment measuring the data or from techniques for calculating the ETo. Further details on the specifications of the equipment, the techniques, and the accuracy of each, are found in Northern Water (2013) and Schultz (2013). RESULTS Effect of audits on outdoor use. Although the program was self- selecting, 85% of the sample group was overwatering (beyond the ET requirement of the landscape, RWA 0%) in the two years before the audit, suggesting that most participants had potential to improve application adequacy. Those who were overwatering were doing so at an average rate of 112% (standard deviation [s] = 102%) above the need of their landscapes (Eq 2) in the year before the audit. Those who were underwatering the year before the audit were watering at an average rate of 27% (s = 21%) below their landscape’s need. After the audit, the average rate of overwatering was significantly (p = 0.012) lower than pre-audit, at 104% (s = 97%) above the need of the landscapes, an eight-percentile point decrease in RWA. The rate of underwatering, however, did not change significantly (p = 0.723). This suggests that the audit helped improve the application adequacy of the participants who were overwatering and did not appear to change the application adequacy of those who were underwatering. Measureable water savings. Water saved was calculated as the difference between the projected outdoor use and the actual outdoor use (Eq 6) in the post-audit years. Figure 2 shows that in the first year post-audit, the average projected outdoor use was 81,000 gal (s = 50,000 gal), while the average actual outdoor use was 76,000 gal (s = 49,000 gal). The average water saved from one year post-audit was therefore 5,000 gal (s = 32,000 gal) per participant, but ranged from –223,000 gal to 221,000 gal. Negative savings, more directly called losses, simply indicate that the projected use was smaller than the actual use in the first year post-audit. While 5,000 gal represents 6% of the average outdoor use—76,000 gal—from one year post-audit, the actual average percentage of savings in the first year post-audit was 28% (s = 151%) per participant. Median savings per participant in the first year post-audit were slightly lower than the mean at 2,000 gal. This positive value for the median indicates that a majority of participants saved water in their first year post-audit; however, that majority was small. When removing those participants whose calculated savings or losses were less than the detection limit of an absolute value of 1,000 gal, 1,064 (52%) were found to have savings, 892 (43%) were found to have losses, and 99 (5%) had savings or losses below the detection limit. Water savings per participant varied by year (Table 2).The range in mean water savings was between –3,000 gal (s = 24,000 gal) in 2011 and 13,000 gal (s = 40,000 gal) in 2008.The range in median water savings was smaller, but always in the same direction (positive or negative) as the means. The two years with negative mean and median savings and losses were 2010 and 2011, which were both years with relatively high TIR (Figure 1), following 2009, which had relatively low TIR. Besides variation in TIR (i.e., weather), the variation in savings and the large standard deviations indicate that the STF program did not lead to all participants reducing their outdoor watering by reducing their irrigation adequacy. Another factor that revealed the variability in water savings was the utility provider associated with each participant. Participants from nine communities were included in the analysis, and participants from six of the nine utilities included in this analysis showed net positive average water savings, while three showed a net negative or zero water savings. Water provider–specific data cannot be displayed in this analysis because of privacy requests; however, the lowest average annual savings by water provider was –13,000 gpy and the highest was 19,000 gpy. Water savings appear to last for several years after the audit. The water savings derived from many water conservation programs, 81 76 0 10 20 30 40 50 60 70 80 90 Projected Use One Year Post-Audit Actual Use One Year Post-Audit OutdoorWaterUse—1,000gal FIGURE 2 Average projected outdoor use compared with average actual outdoor use for one year post-audit The difference between the two bars equals the average water savings from the audits—i.e., 5,000 gal.

6. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E304 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 such as indoor home audits, are considered permanent, allowing the utility offering the program and the customer participating in the program continued reduction in water use relative to pre- program participation. Irrigation audits are different from indoor conservation programs that deal most directly with replacing and upgrading fixtures to high-efficiency models. The final “product” offered to the participant in the CRC’s irrigation audits is a customized watering schedule along with a report detailing other sprinkler system issues and the associated zone, noted during the audit (e.g., sunken heads, overspray). If allowed, the auditor set the irrigation control clock with the recommended watering schedule to ensure that this improvement was implemented. At a minimum, irrigation auditors communicated the findings and recommendations to homeowners, providing them with knowledge of how to change their control clock and other relevant information from the audit. While these “products” could not guarantee water savings, the analysis showed that at least in the first year post-audit, savings were present at the home of the average program participant. Further analysis suggests that these savings persisted beyond one year post-audit as well (Figure 3). Figure 3 shows both the average water savings, by number of years post-audit, as well as the percent of STF participants who were estimated to still be affected by the audit. This percentage was calculated as the number of participants whose water use either consistently increased or consistently decreased in the years post-audit relative to the total number of participants whose water use was included in the analysis. Over time, fewer participants appeared to be affected by the program; however, of those whose RWA appeared to be continu- ing the shift initiated by the audit, the average water savings continued to grow (Table 3). In the first year post-audit, the average amount saved was 5,000 gal (s = 32,000 gal), and this average savings increased to 20,000 gal (s = 57,000 gal) through five years post-audit. The percentage of participants affected by STF was reduced over the same period, from 100% to only 37% five years post-audit. These findings show that while the average participant who received an audit maintained and even improved upon the gains found in the first year post-audit, the proportion of participants that maintained changes to their watering habits decreased. This finding also suggests that an education-based program, while highly effective for certain individuals, may not produce long-term, measurable water savings. However, the large standard deviation of the water savings value and decreasing sample size reduce the certainty of these conclusions considerably. Predicting efficient and inefficient outdoor water use. An analysis of some of the factors that may have contributed to outdoor water use, irrigation adequacy, and water savings from the STF TABLE 2 Summary statistics for water savings per participant in the first year post-irrigation audit and for overall water savings Savings and Audit Count Year Audits Were Performed Statistics for All AuditYearsa2007 2008 2009 2010 2011 Mean savings/participant—thousands of gallons 5 13 7 –1 –3 5 Standard deviation of savings 37 40 29 27 24 32 Median savings/participant—thousands of gallons 0 8 5 –1 –1 2 Sum of all savings for all participants—thousands of gallons 1,164 5,576 3,860 –325 –528 9,748 Count of audits analyzed 219 443 568 624 201 2,055 aAll first year post-audit data combined 5 8 14 16 20 100% 70% 56% 46% 37% 0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 1 2 3 4 5 ParticipantsAffectedbySTF—% AverageWaterSavings—1,000gal/participant Average savings/participant Participants affected by STF—% FIGURE 3 Average water savings per STFa participant underlying percent of participants affected Number of Years Post-Audit STF—Slow the Flow aColorado-based irrigation audit program TABLE 3 Average water savings per participant by number of years post-audit Years Post-Audit Sample Size (N) Mean Savings Per Participant thousands of gallons Standard Deviation thousands of gallons 1 2,055 5 32 2 1,298 8 35 3 690 14 43 4 310 16 52 5 81 20 57

7. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E305 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 program was conducted. The analysis included a sample of 2,055 participants and incorporated descriptive and parametric statistical tests for significant differences in water use, irrigation adequacy, and water savings on the basis of a variety of landscape and irrigation system factors. (Descriptive statistics are used to assess the range, central tendency—i.e., mean, median—and other general attributes of the data. Parametric tests are used for data that come from a normal probability distribution. Significance is reported as a p value. The p value is the probability that the outcome being tested has occurred by random chance. A p value of 0.05 or less was required for the outcome to be considered significant; this ensures a 95% or greater probability that the outcome did not occur by random chance.) Factors included in the analysis were sprinkler system age, presence of drip systems, amount of xeriscape landscape, severity of irrigation system problems (e.g., broken or tilted heads, overspray, poor spacing), distribution uniformity (DU), precipitation rate, and other factors that may have contributed to overuse of water. Three main questions were answered with this analysis: •• What factors contributed to outdoor water use? •• What factors are related to irrigation adequacy? •• What factors helped predict water savings from the STF program? These questions were answered using single-factor analysis of variance (ANOVA) and linear regression. Both of these tests were used to evaluate the significance of a single factor (the independent variable, X) on the outcome of another factor (the dependent variable, Y). With the ANOVA test, the conclusion that could be drawn was whether there was a significant difference in the dependent variable’s mean based on the categories designated by the independent variable. Linear regression provided the proportion of total variability explained by the model (adjusted R2), as well as the intercept and slope of the model, with associated significance levels. To answer the first question, the researchers evaluated participant outdoor water use pre- and post-audit with landscape type and size, the weather (represented by TIR), the number of days that the irrigation system ran per week, and number of cycles that the irrigation system ran per day. Table 4 presents the detailed results of each test. From the tests it was found that outdoor use pre-audit was not significantly different on the basis of the presence of xeriscape in the yard (none versus some); however, post- audit, outdoor use was significantly different on the basis of the presence of xeriscape. Participants without xeriscape had a significantly lower mean outdoor water use of 77,300 gpy than participants with xeriscape, who had a mean of 83,700 gpy. This difference may be caused by the greater reductions in outdoor use experienced by participants with larger proportions of turf, as the audit and recommendations were focused on applying appropriate water to turf landscapes. The amount of turf, non-turf, and total landscape area (in square feet) were found to all weakly but significantly and positively relate to outdoor water use.Turf and total landscape area seemed to have a stronger effect on outdoor water use, both having adjusted R2 values around 0.25, relative to non- turf area, which had an adjusted R2 value less than 0.1. However, the main finding from these analyses was that landscape size in TABLE 4 Results of statistical tests to evaluate which variables contribute to the dependent variable, participant-level outdoor water use pre- and post-audit Independent Variable (X) Dependent Variable (Y) Test Used Regression Statistic Significance (p value) Regression Coefficients Conclusion Xeriscape (none, some) Outdoor use pre-audit ANOVA NA 0.276 NA Outdoor water use pre-audit is not significantly different on the basis of presence of xeriscape (none or some). Xeriscape (none, some) Outdoor use post-audit ANOVA NA 0.007 NA Outdoor use post-audit is significantly different on the basis of presence of xeriscape (none or some). When there is no xeriscape, participants had significantly less outdoor water use post-audit than those who had some xeriscape.This may be because the audit is mostly focused on turf irrigation rather than on xeriscape. Turf area (ft2) Outdoor use pre-audit Regression Adj. R2 = 0.25 0.422 0.0001 Intercept = 113 Slope = 35 Outdoor use pre-audit is significantly and positively related to turf area.As turf area increases, outdoor use increases. Non-turf area (ft2) Outdoor use pre-audit Regression Adj. R2 = 0.09 0.000111 0.0001 Intercept = 294 Slope = 10 Outdoor use pre-audit is significantly and positively related to non-turf area.As non-turf area increases, outdoor use increases. Total landscape area (ft2) Outdoor use pre-audit Regression Adj. R2 = 0.28 0.000381 0.0001 Intercept = 536 Slope = 43 Outdoor use pre-audit is significantly and positively related to total landscape area.As total landscape area increases, outdoor use increases. TIR Outdoor use pre-audit Regression Adj. R2 = 0.66 0.03 0.01 Intercept = –19.4 Slope = 4.5 Outdoor use pre-audit is significantly and positively related to TIR.As TIR increases, outdoor use increases. Number of cycles Outdoor use pre- and post-audit ANOVA NA 0.75 NA Outdoor use is not significantly different on the basis of the number of cycles. Watering days per week Outdoor use pre-and post- audit ANOVA NA 0.98 NA Outdoor use is not significantly different on the basis of the number of watering days per week. Adj.—adjusted, ANOVA—analysis of variance, NA—not applicable, TIR—theoretical irrigation requirement Significant results are in bold.

8. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E306 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 all three forms explains, at most, a quarter of the variation in outdoor use for this sample. The weather, represented by the TIR (in inches), was highly positively correlated to outdoor use (Figure 4). This finding was not surprising and highlighted the importance of the weather in influencing outdoor watering habits of Colorado’s Front Range residents. Neither the number of cycles nor the number of watering days per week were found to have significantly affected outdoor water use. For this analysis, each participant’s average pre-audit RWA was used. The pre-audit RWA was chosen for evaluation because there was no information as to whether homeowners made changes to the various sprinkler system parameters after their audit. Table 5 contains the results of comparing RWA pre-audit to 20 factors that were assumed to be directly related to irrigation system efficiency and health. Surprisingly, of these 20 factors, only one—presence of a drip system (yes/no)—was found to have a significant relationship to RWA. The results from this test indicated that those participants with drip systems had lower RWA pre-audit than those who did not have a drip system, which would be expected because drip irrigation is considered to be a highly efficient system for delivering water to plants (Vickers 2001). DU and precipitation rates also had p values close to 0.1, suggesting that these were also likely contributing factors to pre-audit RWA. The most likely reason that virtually none of the factors were significantly related to RWA is because it is dependent on multiple factors; therefore, the ANOVA and linear regression tests that evaluate only the influence of a single factor at a time do not provide accurate results. Variability in the data set may also be masking potential significant factors related to irrigation adequacy. The authors also thought that this result could be an indica- tion of another major driving factor of RWA and water savings from the audits. While no data were collected as to whether participants used the watering schedule recommended in their STF report, these findings suggest that efficiency does not depend on the health of the sprinkler system. Therefore, savings from the audits were possibly attributable to the education provided to each participant, orally and in report form, around a customized watering schedule for their landscape and system as is. Future work needs to incorporate knowledge of post-audit action taken by residents and multiple regression analysis that takes into account multiple factors at once. Another job for future investigation is to address the data sets with unequal variances, which made them unsuitable for the single-factor ANOVA test. In Figures 5 and 6, RWA is plotted as a function of irrigation system age and dynamic operating pressure. The predicted Y values show that there was little relationship between the independent variables and the dependent variable (RWA [Y]). Predicting water savings. The final set of tests evaluated the factors that contributed to water savings in the first year post-audit. This set of tests essentially measured how much each independent variable helped “predict” water savings. Outdoor water use pre- audit and pre-audit RWA were both found to have significant p values; however, the associated R2 value for both variables showed that neither explained more than 11% of the variability of the water savings. Efficiency of a participant’s watering schedule, based on the auditor’s assessment of the control clock schedule, did not have a significant relationship to water savings. Figures 7 and 8 contain the plotted water savings values against outdoor water use and pre-audit RWA. Both plots demonstrate a slightly positive relationship between water savings and the two independent variables, but also show the high variability of the data set. SUMMARY The STF program provided measureable water savings to the average participant from a sample of 2,055 participants from five years of program operation. While the average amount of water saved one year post-audit was 5,000 gal, only a small majority (52%) showed detectible savings. Annual variation suggested that audit years followed by high TIR may not find positive average savings. On the basis of this finding, operational changes have been made to STF, including eliminating recommendations to change the watering schedule if participants are found to be underwatering yet their landscape appears to be healthy and they are satisfied with their landscape health. Water savings were evaluated up to five years post-audit, and while the only statistically significant savings were measured through two years post-audit, the analysis found that for those participants who maintained their initial post-audit habits, the savings increased to an average of 20,000 gal/participant in the fifth year post-audit. At the same time, the percentage of participants that appeared to maintain their initial post-audit habits decreased to only 37% by five years post-audit, showing that 63% of participants did not maintain their post-audit habits, whether that had been to save water or not save. Further work is needed before there can be a certainty that the irrigation audits continue to produce water savings over time, and to better understand what causes the majority of participants to lose their initial post-audit habits over time. 2005 2006 2007 2008 2009 2010 2011 2012 y = 4.5213x – 19.82 R² = 0.7091 40 50 60 70 80 90 100 15.0 17.0 19.0 21.0 23.0 25.0 27.0 TIR—in./year FIGURE 4 TIR determined for irrigation audits conducted 2007–2011 for STFa versus pre-audit average outdoor use AverageOutdoorUse— 1,000gal/participant STF—Slow the Flow,TIR—theoretical irrigation requirement aColorado-based irrigation audit program

9. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E307 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 Factors contributing to the volume of average outdoor water use before the audits were turf area, non-turf area, and total landscape area as well as TIR, which have all been cited in previous work as contributing to outdoor use at single-family homes (e.g., DeOreo 2011). Post-audit presence of xeriscape showed that there was a significant difference in outdoor water use, with those houses without xeriscape having significantly lower outdoor use, suggesting the audits more greatly affected participants with turf. A more accurate measure of the effectiveness of irrigation audits requires taking weather (i.e., TIR), landscape size, and turf versus non-turf area into account. The RWA uses these factors and therefore indicates change in homeowners’ application adequacy in the years following the audit. Exam- ination of pre-audit RWA at all 2,055 participant households showed, surprisingly, that it was not significantly correlated with what are considered standard measurements of sprinkler system health (e.g., DU, sprinkler system age, overspray). Only drip-system presence correlated with lower RWA pre-audit. These results, combined with the finding that significant water savings were generated by the program, suggest that education of homeowners around setting the sprinkler system’s control clock to appropriate run times was the reason the audits were effective. Finally, no factors were found to help predict water savings from the audits, including pre-audit outdoor use, TABLE 5 Results of statistical tests to evaluate which factors contributed to participant-level average RWA pre-audit Independent Variable (X) Test Used Statistic Significance (p value) Regression Coefficients Conclusion Sprinkler system age Regression Adj. R2 = 0.0 NA NA RWA is not significantly related to sprinkler system age. Backflow preventer Failed F-test for equal variances NA Unequal variances; cannot compare with ANOVA. Drip system presence (yes/no) ANOVA NA 0.01 NA RWA is significantly lower pre-audit for those participants who have a drip system in at least part of their yard. Rotators ANOVA NA 0.39 NA No detectable significant difference exists in RWA on the basis of presence of rotators (some versus none). Check valves Failed F-test for equal variances NA Unequal variances; cannot compare with ANOVA. ET/soil moisture sensor ANOVA NA 0.45 NA No detectable significant difference exists in RWA on the basis of presence of ET or soil moisture sensors. Rain sensor ANOVA NA 0.88 NA No detectable significant difference exists in RWA on the basis of presence of a rain sensor. psi zone A Regression Adj. R2 = 0.0 1.02 × 10–39 0.19 Intercept = 0.88 Slope = 0.0 RWA is not significantly related to psi in zone A or zone B. psi zone B Regression Adj. R2 = 0.0 0.99 0 Intercept = 0.0 Slope = 0.14 DU zone A (poor, acceptable, good, excellent) ANOVA NA 0.14 NA No detectable significant difference exists in RWA on the basis of the DU in zone A. DU zone B (poor, acceptable, good, excellent) ANOVA NA 0.98 NA No detectable significant difference exists in RWA on the basis of the DU in zone B. Broken heads Failed F-test for equal variances NA Unequal variances; cannot compare with ANOVA. Low heads ANOVA NA 0.44 NA RWA is not significantly different pre-audit for those participants with some, few, or no low sprinkler heads. Clogged heads ANOVA NA 0.17 NA RWA is not significantly different pre-audit for those participants with none, few, or some clogged sprinkler heads. Overspray Failed F-test for equal variances NA Unequal variances; cannot compare with parametric tests. Unmatched precipitation rates ANOVA NA 0.12 NA RWA is not significantly different pre-audit for those participants with none versus few unmatched precipitation rates. Poor head spacing ANOVA NA 0.45 NA RWA is not significantly different pre-audit for those participants with few versus some versus many poorly spaced sprinkler heads. Broken/leaking valve Failed F-test for equal variances NA Unequal variances; cannot compare with ANOVA. Inefficient watering schedule ANOVA NA 0.28 NA RWA is not significantly different pre-audit for those participants with an efficient versus moderately inefficient watering schedule. Improper pressure ANOVA NA 0.97 NA RWA is not significantly different pre-audit for those participants with a small amount of improper pressure versus a moderate amount of improper pressure. Adj.—adjusted, ANOVA—analysis of variance, DU—distribution uniformity, ET—evapotranspiration, NA—not applicable, RWA—rate of water application Significant results are in bold.

10. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E308 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 0 200 400 600 800 1,000 1,200 1,400 0 10 20 30 40 50 60 Irrigation System Age—years Actual Y value Predicted Y value FIGURE 5 Irrigation system age at households receiving an STFa audit versus average pre-audit RWA with actual and predicted value AveragePre-auditRWA—%/participant RWA—rate of water application, STF—Slow the Flow aColorado-based irrigation audit program 0 500 1,000 1,500 2,000 2,500 0 20 40 60 80 100 120 140 160 FIGURE 6 Dynamic operating pressure of tested irrigation system zonea versus average pre-audit RWA with actual and predicted values AveragePre-auditRWA—%/participant Actual Y value Predicted Y value RWA—rate of water application aMeasured at a single sprinkler head within the zone Dynamic Operating Pressure—psi –300 –200 –100 0 100 200 300 400 0 100 200 300 400 500 600 700 FIGURE 7 Mean outdoor water use pre-audit versus water savings with actual and predicted values Actual Y value Predicted Y value WaterSavingsOneYearPost-Audit— 1,000gal/participant Average Annual Outdoor Water Use Pre-audit— 1,000 gal/year –300 –200 –100 0 100 200 300 400 0 500 1,000 1,500 2,000 2,500 RWA Pre-audit—% FIGURE 8 Mean RWA per participant, pre-audit versus water savings with actual and predicted values Actual Y value Predicted Y value WaterSavings—1,000gal/participant RWA—rate of water application

11. Shimabuku et al. | http://dx.doi.org/10.5942/jawwa.2016.108.0076 Peer-Reviewed E309 2016 © American Water Works AssociationJOURNAL AWWA MAY 2016 | 108:5 pre-audit RWA, or the efficiency of a participant’s watering schedule pre-audit. This analysis indicates that the STF residential irrigation auditing program, which incorporates homeowner education around setting the control clock, provided an effective water conservation tool in the water districts where the program was offered. High variability in savings and lack of many strong relationships between predictive factors, RWA, and savings overall indicate that more work is needed to fully understand what causes conservation effectiveness of irrigation auditing programs in the first place. ACKNOWLEGDMENT This research was funded by the Colorado Water Conservation Board through Water Conservation Grant number OE PDA 13000000102 as well as by the City of Boulder, Colo. Thanks to the 11 water providers in the Denver-metro area that participate in STF that provided CRC with the historical water use data for their respective residential customers. ABOUT THE AUTHORS Morgan Shimabuku (to whom correspondence may be addressed) is the senior manager of sustainability programs at the Center for ReSource Conservation (CRC), 2639 Spruce St., Boulder, CO 80302 USA; mshimabuku@conservationcenter.org. She started at CRC as an intern in 2012 and at that time began and eventually led the work on the impact analysis of the STF program. Since her graduation from the University of Colorado Boulder in 2013 with a masters degree in geography, Shimabuku has been a full- time employee of CRC. She also holds a bachelor’s degree from Whitman College (Walla Walla, Wash.) in environmental studies and geology and has experience as a staff scientist from the water resource consulting firm S.S. Papadopoulos Associates. Dan Stellar serves as CRC’s senior director of sustainability programs, overseeing the organization’s water, energy, and materials initiatives. Peter Mayer is principal and founder of Water Demand Management in Boulder, Colo. PEER REVIEW Date of submission: 09/25/2015 Date of acceptance: 01/13/2016 REFERENCES ANSI/ASABE (American National Standards Institute/American Society of Agricultural and Biological Engineers), 2015. S623: Determining Landscape Plant Water Requirements. Draft, ASABE, St. Joseph, Mich. ASCE-EWRI (American Society of Civil Engineers-Environmental and Water Resource Institute), 2005. The ASCE Standardized Reference Evapotranspiration Equation. ASCE-EWRI Task Committee, Reston, Va. Brouwer, C. Heibloem, M., 1986. Part I: Principles of Irrigation Water Needs. Irrigation Water Management: Training Manual No. 3. Food and Agriculture Organization of the United Nations. Rome, Italy. www.fao.org/docrep/s2022e/ s2022e00.htm#Contents (accessed Nov. 2, 2015). DeOreo, W., 2011. Analysis of Water Use in New, Single-Family Homes. Report by Aquacraft Water Engineering Management, submitted to Salt Lake City Corp. US Environmental Protection Agency. DeOreo, W.; Mayer, P.; Caldwell, E.; Gauley, B.; Kiefer, J.; Dziegielewski, B., 2014. Some Key Results From REUWS2, Single Family Residential End Uses of Water Study Update. Presentation, WaterSmart Innovations, Las Vegas. Devore, J., 1991. Probability and Statistics for Engineering and the Sciences. Duxbury Press, Belmont, Calif. Fair, B. Safely, C., 2013. Residential Landscape Water Use in 13 North Carolina Communities. Journal AWWA, 105:10:E568. http://dx.doi.org/10.5942/ jawwa.2013.105.0120. Friedman, K.; Heaney, J.; Morales, M.; Palenchar, J., 2013. Predicting and Managing Residential Potable Irrigation Using Parcel-Level Databases. Journal AWWA, 105:7:E372. http://dx.doi.org/10.5942/jawwa.2013.105.0087. Grabow, G.; Ghali, I.; Huffman, R.; Miller, G.; Bowman, D.; Vasanth, A., 2013. Water Application Efficiency and Adequacy of ET-Based and Soil Moisture-Based Irrigation Controllers for Turfgrass Irrigation. Journal of Irrigation and Drainage Engineers, 139:2:113. http://dx.doi.org/10.1061/ (ASCE)IR.1943-4774.0000528. Haley, M.; Dukes, M.; Miller, G., 2007. Residential Irrigation Water Use in Central Florida. Journal of Irrigation and Drainage Engineering. 133:5:427. http://dx.doi.org/10.1061/(ASCE)0733-9437(2007)133:5(427). IA (Irrigation Association), 2015. Technical Resources: Irrigation Audit Guidelines. www.irrigation.org/Resources/Audit_Guidelines.aspx (accessed Aug. 21, 2015). Knight, S.L.; Heaney, J.P.; Morales, M.A., 2015. Flat-Rate Reclaimed Use and Savings in Single-Family Homes. Journal AWWA, 107:5:E263. http://dx.doi. org/10.5942/jawwa.2015.107.0054. Mayer, P. DeOreo, W., 2010. Improving Urban Irrigation Efficiency by Capitalizing on the Conservation Potential of Weather-Based “Smart” Controllers. Journal AWWA, 102:2:86. Mayer, P.; DeOreo, W.; Opitz, E.; Keifer, J.; Davis, W.; Dziegielewski, B.; Nelson, J., 1999. Residential End Uses of Water. AWWA Research Foundation, Denver. Northern Water, 2013. Weather Evapotranspiration Data. www.northernwater. org/WaterConservation/WeatherandETData.aspx. Northern Water, Berthoud, Colo. (accessed Jan. 20, 2013). Schultz, R., 2013. Water conservation specialist, Town of Castle Rock, Colo. Personal communication, Nov. 21. Tukey, J., 1977. Exploratory Data Analysis. Addison-Wesley, Reading, Pa. Vickers, A., 2001. Handbook of Water Use and Conservation. Water Plow Press, Amherst, Mass.

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