Greener Lawns through Smart Irrigation

Environmental Analysis

Spring 2007

Marcie Hundis

Diana Speight

Table of Contents

Part 1- Evapotranspiration	Page 3
Part 2- Smart Irrigation At Evergreen	Page 11
Appendix	Page 23
Works Cited	Page 24

PART 1: Evapotranspiration

 

Abstract

            Evapotranspiration (ET) on The Evergreen State College was determined using a Bowen Ratio ET weather station. To determine ET accurately, stomatal resistance needed to be determined specifically for TESC. To make things easier in the future, all the variables were analyzed to determine if one could be used as a predictor for ET. MathCAD® software and Excel spreadsheets were utilized for much of the computing. Stomatal resistance was determined to be 57.2 s/m for Olympia’s lawns. However, it was determined that a 75 percent change in r_c had less than a one percent effect on ET. Net radiation was found to be highly correlated with ET, and so it may be possible in the future to use just a net radiometer to determine ET.

 

Acknowledgements

            Thanks to our professors who inspired us to work our hardest and comprehend as much as possible, Drs. Barlow, Kelly and Stroh. And thanks to Mark Kormondy, Greg Stewart, Rip Hemingway and Rich Davis for the professional assistance they have offered.

 

Introduction

            Evapotranspiration (ET) is a combination of evaporation, the phase change from liquid water to gaseous water, and transpiration, the evaporation of water specifically from plants. Evaporation takes into consideration the loss of water from the air, water and soil. Transpiration looks solely at plants and is affected by stomatal resistance. Stomatal resistance, r_c, takes into account how plants react to heat and humidity.

            ET is a complicated value to calculate. The Penman-Monteith equation:

gives a formula for determining actual evapotranspiration (Allen et al. 1998). In this formula, LE is latent energy in watts per square meter (W/m²), s is the slope of the vapor pressure curve in millibars, R_n is net radiation (W/m²), G is soil flux (W/m²), ρ is air density (g/m³), Cp_a is the heat capacity of air (J/g°C), e_s and e_a are the saturation vapor pressure and vapor pressure of air in millibars, r_h is aerodynamic resistance to heat flow in seconds per meter (s/m), λ is the psychrometric constant in millibars, and r_c is stomatal resistance (s/m). All of these variables, except for r_c, in this equation can be determined using a weather station, with a net radiometer, two dew hygrometers, at least two thermometers, and two soil heat flux capacitors.

Most calculations using the Penman-Monteith use a constant value for r_c. A good value for r_c is 100 s/m (Allen et al. 1998). This is the value for a crop height of .12 meters and assuming well-watered conditions. However, not all crops fit these conditions, and depending on the climate, stomatal resistance can change. Because the Pacific Northwest has such a unique climate, a unique value for r_c needs to be determined.

Once stomatal resistance has been determined, actual ET can be more accurately determined. By setting up a weather station for a three weeks and measuring all the factors from the Penman-Monteith equation actual ET can be measured for that period of time. Further analysis of the data can indicate what other factors, besides LE, can be used as predictors of actual ET.

 

Methods

The Bowen Ratio ET, shown in Figure 1.1, was set up next to field three on the TESC campus. Field three consists of short, well-watered grass, and the surrounding area is fairly level. The weather station was assembled on April 29^th, 2007. The equipment on the weather station consisted of two side-arms with air-intakes to measure the difference in dew points and thermocouples on the ends of the side-arms to measure the difference in temperature. Also attached to the main body of the weather station was an HMP35C probe to measure temperature and relative humidity, and an anemometer at the top to measure wind speed. There were two soil heat flux capacitors buried in the ground, at the base of the station, to a depth of eight centimeters, with two soil heat probes both above and below each capacitor. A net radiometer attached to a metal pipe was erected within two meters of the rest of the equipment, at a height of less than one meter above the ground. A rain gauge was attached to the same pipe as the net radiometer. Each of these various meters was connected to the main computer on the weather station, the Data Logger, via wires. The DataLogger uses a 12-volt battery for power. A grounding rod was also connected to the station, to prevent any electrical problems.  The weather station was dismantled on May 22^nd, 2007, providing 21 days worth of data.

Figure 1.1. The Bowen Ratio ET, showing all the important components of the weather station.

            The Data Logger records information gathered from the instruments, saves the data, and when downloaded, provides records for every twenty minutes of each day. The variables it returns are year, Julian day, time (PST), temperature, dew point and vapor pressure of the lower arm and upper arm, change in temperature from the lower arm to the upper arm, net radiation, soil flux one and two, soil temperature, change in soil temperature, wind speed, temperature from the HMP35C probe and relative humidity (%).  This data was examined for spurious figures, such as -99999 for the lower arm temperature. Each day’s data was saved as an Excel text file, space delimited, so that it can be read by MathCAD® software. Once run through the MathCAD® program, the output file contains values for each day, for every twenty minutes, with the time, net radiation, soil heat flux, temperature, relative humidity, wind speed and six different values for latent energy. The program also returned several averaged or interpolated values for latent energy, the psychrometric constant and stomatal resistance, using various forms of the Penman-Monteith equation, the Penman equation and the Bowen ratio (Allen et al, 1998). Using the returned data, an average value for r_c was calculated.

Latent energy (LE) can be directly measured by the change in dew point from the lower arm’s dew hygrometer to the upper arm on the dew hygrometer. Using this value, the Penman-Monteith equation was solved for stomatal resistance. Using the reformulated equation, a unique value for r_c for the Olympia area, for the particular breed of grass grown on the fields of TESC, can be determined. To check the validity of the r_c value, LE was calculated using the Penman-Monteith equation, and checked against empirically determined values. One way to check the performance of these values is to determine ET from r_c and LE and compare the results. The formula used to determine daily ET (ETd) is:

with ΣLE equal to the sum of all 20 minute values for latent energy averages, Σλ equal to the sum of all 20 minute values for latent heat of vaporization and 86.4 being the conversion factor. This formula gives a value for ET in millimeters.

Finally, to try and determine if one variable in particular could be a reasonable predictor of actual ET, a correlation was performed. Looking at a table for all 72 records for each day with credible data, a correlation was run, to determine how much each factor in the ET equation affected the others.

 

Results

Figure 1.2. Average values of Stomatal Resistance for Each Day. This shows how much r_c varies over a period of three weeks.

 

Several days’ data had to be ignored, because of spurious values for the lower arm temperature. The days of May 11^th, 16^th, 17^th, 19^th and 20^th (Julian days 131,136, 137, 139 and 140) had several values of -99999 for the low arm temperature, during times when net radiation was positive. The MathCAD® program ignores all times of day when net radiation is negative, so it was only imperative to look at the data when the net radiation was positive. Once those dates were expunged from the data, stomatal resistance was determined for each remaining day, shown in Figure 1.2. The average value for r_c, over all days shown, was calculated to be 57.2 s/m.

Figure 1.3. Comparison of Daily ET Using Two Different Calculation Methods. This shows the similarity in ET values for different r_c values, and the difference in ET using MathCAD®.

           

Evapotranspiration was determined for each day, and these results are shown in Figure 1.3. The MathCAD® values for ET were determined using the value for xLEBR that was returned by the program. The other values were determined by calculating LE using the Penman-Monteith equation, including a value for r_c of 57.2 s/m and 100 s/m. The values calculated using xLEBR are consistently higher than the values calculated using Penman-Monteith and the two different r_c values (Allen et al, 1998). The values calculated for ET using an r_c of 100, compared to an r_c of 57.2 were nearly equal for each day.

 

 

 

 

Table 1.1. Correlation Values for Different Factors of the ET equation. This shows which variables influenced each other the most.

 

Table 1.1 shows correlation values for many of the factors in evapotranspiration. The factors with the highest correlation values were latent energy (xLEBR) and evapotranspiration for 20 minutes (Et-20), and temperature (T) and the psychrometric constant (λ). Other factors with a correlation value above .8 were xLEBR and net radiation (R_n), Et-20 and R_n, soil heat flux (G) and R_n, and relative humidity (RH) and T and λ. The other factors listed in the table are wind speed (u) and the evaporative fraction (EF).

 

Discussion

Further examination of the data gives an average value for stomatal resistance of 57.2 m/s, with a relative standard deviation of 40%. Looking at Figure 1.2, r_c shows a lot of variance over the three weeks that data was collected. Closer scrutiny of Figure 1.3 shows that an increase in r_c of 75% made very little difference in the end value for ET. There was less than one percent difference in ET, with a change in r_c of 75%. There was a difference between the MathCAD® calculated ET and the r_c’s calculated ET, which ranged from one to 23%, with an average difference of ten percent. If the MathCAD® value was considered to be the most accurate, then the Penman-Monteith r_c calculated ET consistently underestimated ET.

Looking at Table 1.1 indicates which of the variables were most highly correlated with each other. The closer the absolute value of the correlation fraction is to one, the more the variables can be used as predictors of each other. The correlation value for xLEBR and ET-20 is equal to one, which would indicate that it was a good predictor of ET. However, because xLEBR was used to calculate ET-20, this was a misleading predictor. The same was true for the correlation value of T and λ: the formula for calculating λ uses T as its only variable. A more interesting and unexplained correlation existed between R_n and xLEBR.

Determining an equation that could be used to predict xLEBR using R_n, would greatly simplify the process of determining ET. Conceivably, ET could be determined using just a net radiometer, as opposed to the entire weather station set-up.

PART 2: Smart Irrigation at Evergreen

 

 Abstract

The benefits of “smart” irrigation at The Evergreen State College were explored. The four sites on campus with the highest level of irrigation were chosen as the focus of the study. The sites included the Red Square plaza, tennis courts, and fields one, three, and four. TESC’s irrigation water usage for water years 2002 through 2006 was examined and compared to campus water deficits created using weather data collected from the Olympia Airport and the Penman-Monteith equation for actual evapotranspiration. The comparison shows that TESC is over watering its lawns providing the potential of water savings from “smart” irrigation. The possible water and cost savings from using “smart” irrigation were investigated and show that with a “smart” system with potential water savings of a high of 59%, TESC could save 432,527 ft³ and 10,348 dollars per year.

Introduction

Traditional irrigation systems are based upon water scheduling that typically over-waters the crops, leading to less healthy crops and water waste. Irrigation is designed to counteract the effects of evapotranspiration. Irrigation scheduled by using evapotranspiration is frequently referred to as ET-controlled or “smart irrigation”. ET-controlled irrigation requires less water to maintain lawns since it is based on replenishing the actual amount of water loss due to evapotranspiration and not on the specific watering schedule typically used in irrigation.

Green lawns are the reason for irrigation at The Evergreen State College (TESC). The most heavily irrigated lawns at TESC are located in the plaza, the tennis courts area, and the sports fields. These are the areas that the TESC administration requires to be maintained at optimal levels in order to enhance the college’s appearance and benefit the campus community. The plaza is located at the main entrance to the campus and its lawns are the focal point of many campus activities. The tennis courts area and fields one, three, and four create a vast expanse of green used by many students and community members. These sports areas need to be irrigated because poorly watered sport fields can be a hazard to athletes and other recreational users. Dry soil can compact and become hard and over watering creates swampy conditions that can lead to damaged turf.

Water conservation is the primary goal of ET-controlled irrigation. With an increasing population in the Puget Sound, water conservation has become an important environmental issue. As the most heavily irrigated lands on campus, the plaza and the sports fields are the areas where the largest impact can be made in conserving water on campus.

Water balances are a useful tool designed to demonstrate how water interacts with the environment. A water balance, or water budget, is an accounting of the amount of water that enters or departs from the soil every month throughout a given year (Pielou, 1998). A water year runs from October 1^st to September 30^th and is named for the year in which the water year ends; for example October 1^st, 1999, to September 30^th, 2000, is the 2000 water year. Precipitation is the major source of water entering a system that is accounted for by the water balance, while the major output is evapotranspiration. A key component of a water balance is the soil moisture storage, which is the amount of water held in the soil at any given time. Soil moisture storage depends on factors such as the soil’s texture and organic matter present. The maximum amount of water that the soil can hold is referred to as the field capacity. Fine-grained soils have larger capacities than coarse-grained soils. The soil moisture storage is at zero when the soil has dried out (Ritter, 2006).

A water deficit occurs when the precipitation entering the system is less than the potential evapotranspiration causing the actual evapotranspiration to necessarily fall below its potential. The deficit is equal to the difference between the actual and potential evapotranspirations (Pielou, 1998). A water surplus happens when the level of precipitation entering the system is more than the potential evapotranspiration; when this takes place, the actual evapotranspiration is equal to the potential. The excess water is able to infiltrate into the aquifer below or becomes surface overflow (Pielou, 1998).

An important equation used to determine a water balance is the Penman equation. In 1948, Penman combined energy balance with mass transfer method and developed an equation to compute evaporation from an open water surface using standard climatological records such as temperature and wind speed. Later forms of the equation included resistance factors, such as the Penman-Monteith equation which includes stomatal resistance for calculating actual evapotranspiration (Allen, 1998).

Two models of the water balance were created for TESC campus and they were both contingent upon an exponential soil moisture model. The differences in the models were based on the different soil moisture storage values used. The plaza, tennis courts, and fields one and three were considered to have a retention capacity of 0.25 inches water per inch soil based on twelve inches of soil of mixed Norma Series and Skipopa Series, this produced a soil moisture storage of 76.2 mm. Field four is an elevated field constructed of sandy soils. The retention capacity was considered to be 0.1 inches water per inch soil and twelve inches of soil, giving a soil storage capacity of approximately 50 mm. The values derived from the two models were combined to give an accurate depiction of the water balance at TESC. These exponential models show the rapid then declining level of the soil moisture entering or leaving the system after each precipitation event and are a more accurate simulation of the actual processes than an instantaneous model would give. Using the Penman-Monteith equation and the models of the soil moisture, the water balance was determined for the TESC campus to explore how much the current campus irrigation system was over watering compared to the actual water deficits.

Method

In order to determine the level of water conservation and cost savings possible using “smart” or ET-controlled irrigation, it was necessary to determine many factors. These factors included the water balance of TESC, the water deficits for the chosen irrigation sites, the actual levels of irrigation present at chosen irrigation sites, the difference between the actual irrigation and the amount of water required to replenish the evapotranspiration, and the costs associated with the irrigation.

The computation of the water balance required many steps including the determination of the net radiation, the evapotranspiration, and the compilation of the data in to the actual water balance. Six years of weather data  (years 2001 through 2006) from the Olympia Airport weather station (collected by the National Oceanic and Atmospheric Administration, NOAA) were compiled for the determination. The climate data from the Olympia Airport was assumed to be the same as that which would have been recorded at the TESC.

Next, the net radiation in W/m² (R_n), which is the difference between inward-bound and outward-bound radiation of both short and long wavelengths, was calculated using the airport data. It is the proportion of the amount of energy absorbed, reflected and emitted by the surface of the earth (Allen, 1998).

Potential evapotranspiration (mm) was calculated by inputting the previous data and the net calculations into the Penman-Monteith equation. The Penman-Monteith equation enables the calculation of evaporation without the use of the surface temperature of the water.

The daily water balances (mm) for all six years were calculated using the precipitation (mm), the potential evapotranspiration (Penman values), the soil moisture storage (mm) (the field capacity for TESC were considered to be 76.2 mm and 50 mm, depending on the site), and the change in the soil moisture (mm), the “actual” evapotranspiration (mm) (an accounting of the precipitation, potential evapotranspiration, and change in soil moisture), the water deficits (mm), and the water surpluses (mm). The data from the six years were then trimmed and compiled into five water years (water years 2002 through 2006). These water balances were then examined for the water deficits occurring in those water years.

The water deficits of each of the five water years were the reason for calculating the water balances. The water deficits at TESC for both the 76.2 mm and 50 mm soil storage capacities were determined using the water balances created as outlined above. The total water deficits in millimeters for the two different soil moisture capacities for each water year were then converted into feet of water. The water deficits in feet for both of the different soil moisture storages were then multiplied by the areas (in square feet) of sites on the TESC campus to get the cubic feet of the water deficits. The water deficits determined by the 76.2 mm soil storage were multiplied by the area (ft²) of the plaza, tennis courts, and fields one and three; while the water deficits computed using the 50 mm soil storage were multiplied by the area (ft²) of field four. These values (in ft³) were then combined to create the total water deficits for the TESC campus. In addition to determining the water deficits, it was necessary to calculate the level of actual irrigation that occurred during the five water years.

TESC’s irrigation for the water years 2002 through 2006 were calculated using the campus water meter readings (TESC, 2007). The original campus water meter readings had been compiled for the years 2000 through March 2007. The data for the four chosen sites of the plaza, tennis courts, and fields one, three, and four were then selected from the original records, and these readings were trimmed and compiled into the water years 2002 through 2006. Then the readings for these water years were used to produce the actual amounts of water (in ft³) that were used to irrigate the chosen sites. The water deficits (ft³) were subtracted from the actual irrigation values (ft³) to show whether the current campus irrigation system is just compensating/ or is overcompensating for the levels of evapotranspiration that are occurring on campus.

An additional benefit of TESC switching to “smart irrigation” would be the potential monetary savings of such a system were determined. TESC pays two different rates for water; from November through June the college pays $1.46 per ccf (one hundred cubic feet) and from July through October it pays $2.55 per ccf. Theses two different rates necessitated dividing each of the five water year’s deficits (ft³) into the two rate brackets (Nov-Jun and July-Oct). Using these two rates, the total amount of water usage and costs were calculated. The actual irrigation costs and the hypothetical costs associated with a perfect “smart” irrigation system were then compared to show the potential cost savings of “smart” irrigation.

To investigate the potential savings of an actual “smart” irrigation system, the values of TESC’s actual irrigation water usage and expenditures for water years 2002 through 2006 were evaluated at a range of potential savings. The TESC values were compared to an average, minimum, and maximum percentage savings.

Results

Table (2.1). The calculated total water deficits (ft³) and total irrigation water usage (ft³) per water year. This shows that there was a positive difference between the two values and means that there is room for water conservation measures.

Water Year	Total water deficits (ft^3)	Total Irrigation water usage (ft^3)	Difference (ft^3)
WY 2002	315464	607163	291699
WY 2003	765509	905820	140311
WY 2004	306844	752660	445816
WY 2005	268339	505510	237171
WY 2006	595052	894330	299278
Total	2251208	3665483	1414275
Average Per Year	450242	733097	282855

 

The TESC water balances showed that the chosen campus sites of the plaza, tennis courts, and fields one, three, and four experienced water deficits for each of the five water years. These sites experienced a water deficit on average of 450,000 cubic feet per water year (Table 2.1). TESC used 61% more water, on average, to irrigate each year than was necessary based upon the average irrigation water usage (ft³) and the average water deficits (ft³) (Table 2.1).

The campus water meter readings were used to calculate the actual levels of irrigation present at chosen irrigation sites. In the past five years, TESC has used an average of 733,000 cubic feet per year just to irrigate these four sections (Table2.1).

A comparison of the actual irrigation usage and the water deficits for the five water years shows a positive difference between the amount of water used and the amount of water required to replenish the evapotranspiration. The total difference between the water used for irrigation and the water deficits for the five years averaged out to roughly 283,000 cubic feet of water per water year (Table 2.1).

 

 

Table (2.2). The actual TESC irrigation expenditures (dollar) for the water years 2002 through 2006 and the expenditures required with a “smart” irrigation system. This indicates the potential cost savings of switching to a “smart” irrigation system.

Water Year	Actual Irrigation Expenditures ($)	Costs Required w/ Smart Irrigation ($)	Difference ($)
WY 2002	15338	7957	7381
WY 2003	22655	18472	4182
WY 2004	17512	7765	9747
WY 2005	12823	6843	5981
WY 2006	19370	15153	4218
Total ($)	87699	56189	31510
Average Spent Per Year ($)	17540	11238	6302

 

The costs of both the current irrigation system at TESC and a perfect “smart” irrigation system were calculated. Over the past five years, TESC has spent an average of 17,500 dollars per water year irrigating the chosen sites (Table 2.2). It was determined that a “smart” irrigation system that used just enough water to replace the water which was lost to evapotranspiration would have required an average of 11,200 dollars per year (Table 2.2). The difference between the two systems for the five years was calculated to be an average of 6,300 dollars per water year (Table 2.2).

Table 2.3. The original calculated values for TESC’s irrigation water usage. The average, minimum, and maximum values show what the potential water savings are from a “smart” irrigation system.

Water Year	Original Value	Average (33%)	Minimum (17%)	Maximum (59%)
WY 2002	607163	200364	103218	358226
WY 2003	905820	298921	153989	534434
WY 2004	752660	248378	127952	444069
WY 2005	505510	166818	85937	298251
WY 2006	894330	295129	152036	527655
Total	3665483	1209609	623132	2162635
Average Per Year	733097	241922	124626	432527

TESC’s irrigation water usage from water years 2002 through 2006 were investigated. The average amount of irrigation water used for the five years was approximately 733,000 cubic feet per year (Table 2.3). The range of potential savings, per year, from a perfect “smart” irrigation system was roughly 125,000 ft³ to 433,000 ft³, with an average of about 242,000 ft³ (Table 2.3).

 

Table 2.4. The original calculated values for TESC’s irrigation water expenditures ($). The average, minimum, and maximum values show what the potential cost savings are from a “smart” irrigation system.

Water Year	Original Value ($)	Average (33%) ($)	Minimum (17%) ($)	Maximum (59%) ($)
WY 2002	15338	5062	2607	9049
WY 2003	22655	7476	3851	13366
WY 2004	17512	5779	2977	10332
WY 2005	12823	4232	2180	7566
WY 2006	19370	6392	3293	11429
Total	87699	28941	14909	51742
Average Per Year	17540	5788	2982	10348

 

The money that TESC has spent for irrigation water of the chosen sites was examined. For the past five years, TESC has spent an average of 17,500 dollars (Table 2.4). The possible savings from a perfect “smart” irrigation system produced a range of approximately 3,000 dollars to 10,300 dollars per year, or an average of 5,800 dollars (Table 2.4).

 

 

 

 

Table 2.5. The potential maximum savings from an actual “smart” irrigation system compared to a perfect “smart” system. This shows that the values from an actual system are still considerably large even when compared to the perfect system.

	Original Value	Maximum (59%)	Perfect System	Difference between Max and Perfect System
Total Irrigation (ft^3)	3665483	2162635	2251208	-88573
Avg. Irrigation (ft^3)	733097	432527	450242	-17715
Total Irrigation Cost ($)	87699	51742	56189	-4447
Avg. Cost ($)	17540	10348	11238	-889

 

The differences between the potential maximum saving from an actual “smart” irrigation system and a hypothetical perfect system were contrasted. The average irrigation per year presented a maximum savings of 433,000 ft³ compared with the perfect system’s 450,000 ft³, for a difference of -17,700 ft³ (the negative difference shows that the maximum savings is less than the perfect system) (Table 2.5). The Average costs per year showed a maximum savings of 10,300 dollars contrasted with the perfect system’s 11,200 dollars, for a difference of -889 dollars (once again the negative means that the maximum is less than the perfect system) (Table 2.5).

 

Discussion

Four sites were chosen at TESC to investigate the benefits of bringing “smart” irrigation to the campus. The four sites, which were the plaza, tennis courts, fields one, three, and four were selected because these were the sites where the majority of the campus irrigation occurs and would be mostly likely to bring the largest savings from a “smart” irrigation system. Excess irrigation water is being used at TESC. The examination of the water deficits experienced at the four chosen locations with the actual level of irrigation used shows that TESC is using more water than is necessary to replenish the water deficits (Table 2.1). Over the past five years, TESC used a total of 61% more water than was needed to water the chosen sites (Table 2.1). Currently the TESC irrigation system is mainly based upon set watering schedule with some manual interventions; this could be improved with the introduction of a “smart” irrigation system

Since TESC is using more water than is required for the replacement of water lost through evapotranspiration, it is also spending more money than is needed for irrigation water. The costs required for the actual irrigation usage over the past five years compared with a “smart” irrigation system shows that there are cost savings to be had (Table 2.4). . TESC could save an average of 5,800 dollars per year on irrigation water with a “smart” system (Table 2.4).

Previous studies of “smart” irrigations show that there is a range of potential savings possible. Some of the calculations conducted on the water usage and costs of the “smart” irrigation system were based upon a perfect system (Table 2.2). “Smart” irrigation systems are unfortunately not perfect, and according to sources (see Appendix), the possible savings of a “smart” irrigation system range from 17% to 59%, with an average of 33% (Tables 2.3, 2.4). The potential savings of a “smart” irrigation system are based upon the type of “smart” system chosen, the soil and terrain types, and the amount of previous over watering at the location (IA, 2006).

The differences between the hypothetical perfect “smart” irrigation system and a system that provides 59% savings were explored (Table 2.5). Possible reasons for actual “smart” irrigation systems to generate less water conservation than the hypothetical system include: mixed soil types, the location of the weather station used, and tampering. Mixed soil types can make the evapotranspiration calculations more difficult due to varying soil moisture storages and the possibility of calculating more than one value for evapotranspiration which would make calibrating the irrigation system based more on conjecture. Additionally, the location of the weather station chosen to calculate the evapotranspiration is important, the station needs to be located in the same microclimate as the site of the irrigation or the evapotranspiration information provided to the system may be inaccurate- effecting the amount of water conserved (CDWR, 2007). Also previously studied “smart” systems were often subjected to tampering which could drastically reduce the possible water savings (Bamezai, 2004). An actual “smart” irrigation system would yield less water and cost savings than the perfect system but the savings possible still make switching to a “smart” irrigation system beneficial.

Bringing “smart” irrigation to TESC would provide many benefits. Currently, TESC’s irrigation system is over compensating for the campus water deficits and spending more money than necessary. By switching to a “smart” system, TESC would conserve water and save money.

 

 

 

 

 

Appendix: Survey of Water Savings Using ET-Controlled Irrigation

 

 

Stanford University Energy and Water Conservation

http://facilities.stanford.edu/conservation/waterconservation.htm

 

The Ground Services website for the weather station located at Stanford listed a water savings of 27%

<http://grounds.stanford.edu/topics/weather.html>

 

Rain Bird Landscape Irrigation

<http://www.rainbird.com/landscape/products/central/maxicom.htm>

 

The Maxicom² brochure lists a potential water savings of 25 to 45%

<http://www.rainbird.com/pdf/turf/bro_maxicom2.pdf>

 

Irrigation Association

<http://www.irrigation.org/>

 

The Smart Water Application Technologies is located in the Irrigation Association. The website has a FAQ sheet that lists water savings as 20 to 40%

http://www.irrigation.org/smartwater/businesses/faq.html

 

Bamezai A. 2004. LADWP Weather-Based Irrigation Controller Pilot Study. Western

Policy Research.

This is a study written for the Los Angeles Department of Water and Power about weather-based irrigation. Two different companies were involved in the study, using two different controller systems. It shows the amount of water savings was 17% for one company and 28% for the other. This study combined large multi-family residential and small commercial sites in its examination. The possibility of water savings may be reduced in this setting due to increased risk of tampering with the system as mentioned in the report.

 

Shedd M, Dukes M D, Miller G L. 2007. Evaluation of Evapotranspiration and Soil

Moisture-based Irrigation Control on Turfgrass. ASCE EWRI Environmental & Water Resources Congress.

 

A study designed to evaluate the effectiveness of different systems of irrigation water application in St. Augustine, FL. The potential water savings from ET-controllers in the study was 36 to 59%.

 

 

 

Works Cited

Allen R G, Pereira L S, Raes D, Smith M. 1998. Crop evapotranspiration:

Guidelines for Computing Crop Water Requirements. FAO Irrigation and

Drainage, Paper 56. FAO – Food and Agriculture Organization of the United Nations. RomeAvailable at <http://www.fao.org/docrep/X0490E/X0490E00.htm>. Retrieved 2007 Mar 05.

 

Bamezai A. 2004. LADWP Weather-Based Irrigation Controller Pilot Study. Western

Policy Research.

 

California Department of Water Resources. 2007. California Irrigation Management

Information System- Overview. Retrieved 04 Apr 2007 from <http://wwwcimis.water.ca.gov/cimis/infoGenCimisOverview.jsp>

 

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Questions. Retrieved 2007Jun 05 from

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