Estimating Summertime Evapotranspiration Across Indiana. Principal Investigators: Johnny Nykiel. Ryan Knutson. Jesse Steinweg-Woods

Transcription

1 Estimating Summertime Evapotranspiration Across Indiana Principal Investigators: Johnny Nykiel Undergraduate Department of Earth and Atmospheric Science Ryan Knutson Undergraduate Department of Earth and Atmospheric Science Jesse Steinweg-Woods Ph.D. student Texas A&M University Department of Atmospheric Science Ken Scheeringa Associate State Climatologist Dr. Dev Niyogi Associate Professor of Agronomy and Earth & Atmospheric Sciences and Indiana State Climatologist First Submission: July 2011 Second Submission: July

2 Abstract Evapotranspiration (ET) describes the sum of plant transpiration and evaporation into the atmosphere from soils. This is a critical component of the regional water cycle. Yet historical measurements of ET do not exist in Indiana. Therefore, models are needed to develop ET estimates. It is anticipated that using these models with verification from limited ET measurements, the development of an Indiana ET climatology can be completed. ET gages were installed at two Purdue Agricultural Centers (PAC) in the 2008 growing season and nine PAC locations in the 2009 and 2010 growing seasons over a grass reference vegetative surface. Comparisons were made between these reference ET (RefET) measurements and several RefET models. The model weather inputs included temperature, solar radiation, wind, and dew point temperature. Solar radiation serves as a proxy for net radiation which is not measured at the PAC. The comparisons were divided into two categories. The first compared RefET measurements with the simulation models when all measured weather inputs were taken at the PAC sites. The second category of comparisons focused on model weather inputs observed at nearby airports. These locations were determined by the proximity of the airport to the RefET gage location at the PAC. Six airport locations were used in this study. The airports and the paired RefET gage PAC include Valparaiso (PPAC), Evansville (SWPAC), Lafayette (ACRE and TPAC), Cincinnati (SEPAC), Fort Wayne (NEPAC), and Muncie (DPAC). A correlation analysis was undertaken comparing the measured and modeled RefET. In both study categories, 11 separate models were run to calculate RefET to compare with the RefET gage measurements. The results suggest the FAO 56 Penman-Monteith and Full ASCE Penman-Monteith model performed best compared to the measurements. Depending on crop type, the typical RefET rates for a growing season across Indiana are approximately 75 mm per month or about 45 mm of crop ET loss per month. Keywords: ET, Evapotranspiration, evaporation, transpiration, water cycle, RefET 2

3 1. Introduction This study expands the initial analysis shown in Niyogi et al. (2008): by increasing the data set to nine locations throughout Indiana in the 2009 and 2010 seasons. These locations are PPAC, NEPAC, TPAC, ACRE, DPAC, SEPAC, SIPAC, FPAC and SWPAC, 8 of which are shown in Fig. 1. ACRE, which is not considered a PAC but instead an Agronomy Center, is located 7 miles to the northwest of Purdue University s main campus in West Lafayette. By expanding the locations, the development of a true statewide ET climatology can be performed. The climatology contrasts two methods: (1) empirical measurements based on a new network of ET sensors installed at the regional Purdue Agricultural Research Centers (PACs) and the local Agricultural Center for Research and Education (ACRE) weather stations; and (2) estimates from a suite of simulation models (RefET) derived from other measured weather inputs. Figure 1. Location of Purdue Agricultural Centers (PAC) 3

4 1.1. What is Reference Evapotranspiration? Evaporation is a form of vaporization in which a liquid is converted into a gas or vapor. On the other hand, transpiration is the process in which there is a loss of water vapor from the plant s stomata. Often these occur together in nature and are referred to as evapotranspiration (ET), that is, it includes both the evaporation and transpiration components. The rate of ET is highly regulated by plant and growth stage. To allow general application to many crop types, ET measurements are referenced to a standard crop, either grass or alfalfa. According to Niyogi et al (2008), international criteria have been set to define what a standard crop of grass and alfalfa are. The standard crop is an extensive surface of clipped grass or alfalfa that is well-watered and fully shades the ground. The clipped grass reference should be a cool-season variety such as perennial fescue or rye grass. Alfalfa that is greater than 30 cm in height and has full ground cover complies with the reference standard. In common usage the suggested height of the standard alfalfa crop is fixed at 50 cm. The term RefET (Reference ET) is defined as ET measurements for these standard crops. According to Niyogi et al (2008), in order to apply RefET results to all other non-standard crops, multiplier crop coefficients (K) have been developed to convert the reference data to each alternate crop and growth stage. Two sets of coefficients are available for each non-standard crop: one for conversion from a grass reference crop and the second set for an alfalfa reference crop. Only one of these sets of K is necessary depending on which standard crop exists in the immediate areas of the atmometer installation. 2. Materials and Methods An atmometer was used in this study to directly measure RefET. The gage used was the modified Bellani Plate type, manufactured by ET Gage Company of Loveland, Colorado. Figure 2 and 3 show the ETgage with its ceramic plate mounted on top of the distilled water reservoir. The reservoir capacity is 11.8 inches in depth. A green canvas (Gore-Tex) material covers the ceramic plate. This canvas fabric mimics the absorption of incoming solar radiation and outgoing water loss as if it were the crop canopy. 4

5 Fig ETgage installation at ACRE (Purdue Ag Center for Research and Education) The type of canvas cover can be changed to simulate the RefET rate of one of the two standard reference crops, either full-cover alfalfa (lucerne) or clipped cool-season grass. The canvas covering creates a diffusion barrier (resistance) that controls the evaporation rate, thus simulating the rate of evaporation from a healthy leaf in a well-watered plant. Water is drawn to the ceramic plate of the ETgage by suction through a plastic tube which runs inside the length of the reservoir. A rubber stopper connects the plastic tubing at the top of the reservoir to the ceramic plate, creating a vacuum allowing water to flow upward only. The upward vacuum pressure keeps the ceramic cup charged but prevents absorption of rainwater through the ceramic evaporation plate. There are manual and electronic versions of ETgage. In the manual model (Model A) the depth of water inside the ETgage is read from a graduated sight tube. The electronic model (Model E) also automatically generates a pulse signal every time 0.01 inch of water evaporates from the ETgage. A data logger can be used to record the pulse signals. The advantage of the electronic model is the elimination of potential human error when reading the site tube as well as to provide a detailed record as to when each 0.01 inch of evaporation occurs. The PACs were set up to use the Model E with the Onset Corporation Hobo pendant event datalogger as the recording device. In the latter Rice grant the Hobo datalogger at most PACs was replaced by the Campbell Scientific CR10x dataloggers already extant at these 5

6 automated weather stations. In 2007 a first local installation permitted us to become familiar with the operation of the ETgage and data collection by Onset Corporation Hobo Pendant data loggers. These loggers only store point data when an ET event occurs, that is, a pulse signal that another.01 inch of ET has evaporated. As these events are random points in time, the data were assigned to the corresponding 30 minute time slot during the day in which the event occurred. This was done for compatibility with the CR10x loggers which would replace most of the Hobo loggers in The ETgage was easy to install and required little maintenance. It was mounted on a wooden post 39 inches (one meter) above the ground, located over grass at a site representative of the immediate area. The PAC ETgages were installed 3 to 7 feet away from the automated weather station towers, enabling convenient connection to the data loggers. The ETgage ceramic plate should not be shaded, which could reduce the RefET rate. Nor should it be installed near tall trees, buildings, or tall crops that may prevent full exposure of the gauge to prevailing winds and other environmental factors affecting RefET. The installations at the PAC automated weather stations were in compliance with these requirements. The ETgage reservoir was filled with distilled water. This prevents accumulation of salts in the ceramic plate that could reduce its porosity and affect the evaporation rate. The ETgage cannot be exposed to freezing temperatures and the canvas cover should be kept as clean as possible. Bird spikes came with the ETgage to discourage birds from perching on the plate. Two more installations were added in 2008, one at NEPAC and a second at ACRE. These installations are co-located with official NWS cooperative weather stations. The ACRE site is also equipped with a Class A evaporation pan. Again the PACs were set up to use the ETgage Model E with the Hobo data logger as the recording device. ET gages were installed at nine Purdue Agricultural Centers (PAC) in the 2009 and 2010 growing seasons to extend the data record and expand the RefET network statewide.. At this time the Hobo data logger at most PACs were replaced by the Campbell Scientific CR10x data loggers at these automated weather stations. 6

7 Estimation of ET for specific crops ((ET c) is calculated by multiplying RefET by the appropriate crop coefficient K c : ET c = ET r x K c where ET r is the evapotranspiration (ET) of the reference crop (grass or alfalfa), expressed in units of water depth per unit time (inches per day, week, etc). 3. Measurements vs. Models 3.1. Measurements The Hobo pendant logger is designed to record an event and the timestamp at which the event occurred. An event in our application occurs when the ETgage sends an electronic pulse to the Hobo logger as each new 0.01 inch of ET evaporates from the canvas cover of the ETgage. In our case it is easier to analyze the data in uniform time intervals rather than as random events. With the expectation that in future years the Hobo logger would be replaced with a higher order data logger, the format change to uniform time intervals made sense. Our first data task then was to reformat the Hobo data into uniform hourly data intervals. Software was written to do this conversion by assigning each event to the corresponding hourly time bin. This was done for each ETgage location where the Hobo data logger was installed. The ETgage data were summed by calendar day. The hourly and daily ETgage values were then entered into an Excel spreadsheet. A column was added to the daily table to include evaporation pan measurements where available. Unfortunately, the data for the two stations SIPAC and FPAC were frequently missing (in some cases, over 50% of the time) due to reliability issues with the Hobo loggers that were used exclusively at these two sites. Because of this, no conclusions could be drawn from these two stations. 7

8 3.2. Models The software title Ref-ET (University of Idaho) offers many calculation models for estimating reference evapotranspiration using equations currently in practice throughout the United States and Europe. Figure 4 displays the full list of these models. Method Timesteps Type a) full ASCE Penman-Monteith with resistances by Allen et al, 1989 (M, D, or H)** ETo ETr b) full ASCE Penman-Monteith with user supplied surf. resistance (M, D, or H)* ETo ETr c) Standardized form of the ASCE Penman-Monteith by ASCE 2005 (M, D, or H)* ETo ETr d) 1982 Kimberly Penman (Wright, 1982; 1987; 1996) (M, D, or H)* ETo ETr e) FAO 56 Penman-Monteith (1998)1 with resistance for 0.12 m grass (M, D, or H)* ETo f) 1972 Kimberly Penman (fixed wind func.) (Wright & Jensen 1972) (M, D, or H)* ETr g) 1948 or 1963 Penman (Penman, 1948; 1963) (M, D, or H) ETo h) FAO-24 Corrected Penman (Doorenbos and Pruitt, 1975, 1977) (M or D) ETo i) FAO-PPP-17 Penman (Freres and Popov, 1979) (M or D) ETo j) CIMIS Penman (hourly only) with FAO-56 Rn and G=0 (H) ETo k) FAO-24 Radiation Method (Doorenbos and Pruitt, 1975, 1977) (M or D) ETo l) FAO-24 Blaney-Criddle (Doorenbos and Pruitt, 1975, 1977) (M or D) ETo m) FAO-24 Pan Evaporation Method (Doorenbos and Pruitt, 1977) (M or D) ETo n) 1985 Hargreaves Temperature Method (Hargreaves and Samani) (M or D) ETo o) Priestley-Taylor (1972) Radiation and Temperature Method (M or D)* ETo p) Makkink (1957) Radiation and Temperature Method (M or D)* ETo q) Turc (1961) Radiation and Temperature Method (M or D)* ETo Figure 4 Full list of Calculation Models In this study, these models were run and the results were compared to the empirical measurements. Weather inputs used in these models are the hourly data available from the Purdue automated weather stations. Since the data is run through the software as hourly data, the models return hourly products. The hourly ET model outputs were then summed into a daily RefET value for comparison to the actual ET measurements. Not all models were able to produce data. In some cases, required parameters and other criteria were not available. As a result, only 8

9 10 models were run for the case 1 study and 11 models for the case 2 study. Details on each case are described later. The following models did not provide results for either case: full ASCE Penman-Monteith with user supplied surface resistance FAO-24 Corrected Penman (Doorenbos and Pruitt) FAO-PPP-17 Penman (Freres and Popov) FAO-24 Radiation Method (Doorenbos and Pruitt) FAO-24 Blaney-Criddle (Doorenbos and Pruitt) FAO-24 Pan Evaporation Method (Doorenbos and Pruitt) 1985 Hargreaves Temperature Method In addition the following model did not provide results in case 1: full ASCE Penman-Monteith with user supplied surface resistance 3.3. Model Advantages Why is it important to understand and estimate ET through models? 1. ET plays a major role in the regional water cycle balance. During drought conditions, evapotranspiration continues to deplete the remaining water supply in lakes, streams, vegetation and soil. 2. ET measurements cannot be made on days with subfreezing temperatures; therefore, models become the only method for estimating RefET. Although the RefET values during such subfreezing days are quite low, cold season precipitation does play a major role in the recharge phase of the water cycle. 3. Models are useful for filling in missing data when measurements become unavailable. Given good correlation with measurements, models can estimate the results for these missing data points. 9

10 3.4. Model Disadvantages 1. RefET models require measured weather inputs; therefore it is necessary to perform quality assurance on this source data in order to trust model accuracy. 2. Some weather variables are not commonly measured and can be difficult to obtain, such as solar radiation. 3. Automated weather station maintenance is necessary so they continue working properly and the technology stays updated. Some common problems can include communication failures, dead backup batteries, sensor malfunction, and improper refilling of the ETgage. The installation of the ETgage does require time to install initially and refill in midseason. 4. Results Some questions can now be assessed to determine which model performs best in determining evapotranspiration under two scenarios or case studies. The first study compared hourly RefET measurements to simulation models when all weather inputs were present at a single PAC location. The second study compared measurements to models when model weather inputs except solar radiation were made at nearby airports Case 1: Hourly RefET measurements and models at the same location. Regression correlations can be run to compare the models to the observed ETgage data. In doing so, this can quantify the strength of the relationship between the models and the actual ETgage data. The correlation results for the years 2009 and 2010 for all locations are shown in fig

11 Figure 5 ACRE station correlation results Figure 6 NEPAC station correlation results 11

12 Figure 7 DPAC station correlation results Figure 8 TPAC station correlation results 12

13 Figure 9 SEPAC station correlation results Figure 10 SWPAC station correlation results 13

14 Figure 11 PPAC station correlation results Case 1 Result Summary: Most RefET models are in good agreement with the measurements. It is noted that the TPAC 2009, TPAC 2010, SEPAC 2010, SWPAC 2010, PPAC 2009 and PPAC 2010 data sets all have significant outliers which skew the R 2 values. This is a result of missing data and problems with the Hobo dataloggers and ETgage measurements. Overall, the FAO 56 Penman- Monteith and Full ASCE Penman-Monteith models correlated best with the measurements, while the Priestly-Taylor and the 1972 Kimberly-Penman were the worst performers. In nearly all comparisons the fitted regressions have slopes greater than one which implies that the models -in the Case 1 study- tend to overestimate the amount of evapotranspiration. 14

15 4.2. Case 2: Hourly RefET measurements at PAC and modeled with airport proxy data. Regression Correlations: In some regions automated hourly weather measurements are not available. Perhaps they are within a private network. This absence of weather inputs would prohibit model runs of RefET estimates. But an airport with public hourly ASOS weather measurements may be nearby. These inputs might serve as a proxy for model weather inputs when automated weather station sources are not available. Would such substitution greatly alter the case 1 correlations between RefET measurements and model outputs? To answer this question a second case study modeled RefET based on weather variables observed at nearby airports. RefET measurements at the PACs continued as the standard true values. In case 2 the same measurement intervals apply at each ET gage site as was defined in case 1. These airport correlation results are shown in Figures Acre LAF Model ID Model Description R 2 Regression Equation 1 Full ASCE Penman-Monteith with Allen resistances 0.85 y = x Standardized ASCE Penman-Monteith 0.84 y = 1.099x ASCE stpm 0.83 y = x FAO 56 Penman-Monteith 0.85 y = x Kimberly-Penman 0.78 y = x Kimberly-Penman 0.73 y = x Penman 0.81 y = x CIMIS Penman 0.81 y = x Priestly-Taylor 0.80 y = x Makkink 0.81 y = x Turc 0.82 y = x Acre LAF Model ID Model Description R 2 Regression Equation 1 Full ASCE Penman-Monteith with Allen resistances 0.79 y = x Standardized ASCE Penman-Monteith 0.78 y = x ASCE stpm 0.76 y = x FAO 56 Penman-Monteith 0.77 y = x Kimberly-Penman 0.72 y = x Kimberly-Penman 0.68 y = 1.058x Penman 0.73 y = x CIMIS Penman 0.72 y = x Priestly-Taylor 0.65 y = 1.113x Makkink 0.71 y = x Turc 0.74 y = x Figure 12 Case 2 correlation results ACRE station 15

16 Figure 13 Case 2 correlation results TPAC station Figure 14 Case 2 correlation results with SWPAC station 16

17 Figure 15 Case 2 correlation results with DPAC station Figure 16 Case 2 correlation results with NEPAC station 17

18 Figure 17 Case 2 correlation results with PPAC station Figure 18 Case 2 correlation results with SEPAC station 18

19 Daily ET by month: The climatology based upon the RefET gage measurements shows a large amount of variability in cumulative daily ET measurements throughout the month. As can be seen in figures 19-24, the daily ET measurement changes frequently. Since evapotranspiration amounts are affected by a number of weather variables, this high level of change in ET measurements is not surprising. ET levels tended to peak in the mid to late spring months, which correlates with when the state of Indiana receives most of its precipitation. Figure 19 Daily ET, May

20 Figure 20 Daily ET, June 2009 Figure 21 Daily ET, August

21 Figure 22 Daily ET, May 2010 Figure 23 Daily ET, July

22 Figure 24 Daily ET, September 2010 Typical hourly ET by month: This climatology shows the diurnal cycle of ET, which has some consistent patterns regardless of time of year. In figures 25-28, the diurnal cycle of ET is shown in a variety of months. Most stations began measuring ET sometime between 0900 and 1100 local time. Peak ET occurred between the hours of 1400 and 1800 which correlates fairly well with maximum sun exposure. After this peak time period, ET would level off and not resume again until around 0900 the next morning. As stated before, cumulative ET values decreased as the year progressed, with the most ET occurring in either May or June at most locations. 22

23 Cumulative ET 2009 ACRE Station ET (inches) May 20-Jun 20-Jul 20-Aug 20-Sep Hour Figure 25 Hourly ET 2009 ACRE station Cumulative ET 2009 PPAC Station ET (inches) May 20-Jun 20-Jul 20-Aug Hour Figure 26 Hourly ET 2009 PPAC station 23

24 Cumulative ET 2009 SEPAC Station ET (inches) May 20-Jun 20-Jul 20-Aug Hour Figure 27 Hourly ET 2009 SEPAC station Cumulative ET 2009 SWPAC Station ET (inches) Apr 20-May 20-Jun 20-Jul 20-Aug Hour Figure 28 Hourly ET 2009 SWPAC station 24

25 Sunny day vs. Cloudy day ET: For this comparison, a few case days were chosen when the weather was sunny and ones where there was strong cloud cover. As can be expected, cumulative ET values were significantly less during cloudy days than sunny days (see Fig ). During cloudy days, incoming solar radiation is reduced, resulting in less evaporation of water from surface temperature increases and usually higher relative humidity values which can inhibit evaporation. Figure 29 Sunny day case, August

26 Figure 30 Sunny day case, July 2009 Figure 31 Cloudy day case, May

27 Figure 32 Cloudy day case, September 2010 ET Gage Correlations to Specific Weather Variables: The models utilized for modeling ET use a variety of input variables. Among these are incoming solar radiation, air temperature, winds, and relative humidity. It was then investigated which of these variables had the strongest correlation to the final measured ET value. As can be seen in figures 33-36, different variables had different correlations to the observed ET data. The strongest correlation was a negative correlation between relative humidity and ET. As relative humidity increased, ET tended to decrease and vice versa. In order to identify a possible correlation, the weather variable data was compared with the measured ET data for an entire month. Of the weather variables tested, relative humidity had the strongest correlation with a R 2 value of The weakest correlation was air temperature with the R 2 value at 0.340, although this correlation was positive unlike relative humidity. 27

28 Observing the pattern of relative humidity over the course of the diurnal cycle, it became clear that ET started to begin about the same time relative humidity began to decrease. The greater the rate at which relative humidity decreased, the greater the rate ET increased (see Fig. 37). Consequently, when relative humidity stopped decreasing, the rate of ET slowed. Thus, changes in relative humidity can be used as a rough proxy for predicting changes in ET over the diurnal cycle. May 2009 ET vs Relative Humidity ACRE Station ET (inches) y = x R 2 = Relative Humidity (%) Figure 33 Relative Humidity correlation with ET 28

29 May 2009 ET vs Solar Radiation ACRE Station ET (inches) y = x R 2 = Solar Radiation (MJ/m^2) Figure 34 Solar Radiation correlation with ET May 2009 ET vs Air Temperature ACRE Station ET (inches) y = x R 2 = Temp (F) Figure 35 Air Temperature correlation with ET 29

30 May 2009 ET vs Bare Soil Temperature ACRE Station ET (inches) y = x R 2 = Bare Soil Temp (F) Figure 36 Bare Soil Temperature correlation with ET Relative Humidity and Cumulative ET May ACRE Station Inches*100/% ET Relative Humidity Hours Figure 37 Comparison of Relative Humidity and Cumulative ET 30

31 ETgage vs. pan evaporation: This study also examined RefET gage measurements vs. pan evaporation. A sampling of regressions shows that there is little correlation between these variables. This is due in part to the fact that pan evaporation only measures free water evaporation into the atmosphere. The RefET gage simulates both atmospheric evaporation and water loss through plant transpiration. The ability of a leaf to open and close its stomata regulates the amount of water allowed to leave the plant. The evaporation pan has no such restriction and simulates an unlimited supply of water available for evaporation. Therefore the ETgage and pan evaporation do not correlate well as shown in the above graphs (Fig ). Figure 38 PPAC 2009 Pan and ET correlation 31

32 Figure 39 ACRE 2009 Pan and ET correlation Figure 40 PPAC 2009 Pan and ET correlation 32

33 Figure 41 ACRE 2010 Pan and ET correlation Figure 42 DPAC 2009 Pan and ET correlation 33

34 Figure 43 DPAC 2010 Pan and ET correlation Figure 44 SIPAC 2010 Pan and ET correlation 34

35 ET after a rainfall event: Plots were made of rainfall events versus the temporary suspension of RefET among all PAC locations. It may be possible to assess the lapse in time between the end of a rain event and the resumption of RefET (Fig ). This relationship may be helpful in determining the duration of leaf wetness after various intensities of rainfall. This is an important factor in the potential development of leaf diseases. Figure 45 NEPAC 2009 rain event 35

36 Figure 46 SWPAC 2009 rain event Figure 47 ACRE 2009 rain event 36

37 In general we find that evapotranspiration resumes 7 to 9 hours after the conclusion of a rainfall event. This is shown in the graphs above. 5. Conclusion Two case studies were presented. The first study compared RefET measurements to models when all weather inputs were observed at the same location. The second case study considered impacts on the results when weather inputs for the models were moved from the RefET measurement location to a nearby airport. In each of the cases 15 ET models were run to estimate RefET for comparison to the RefET gage measurements. The RefET measurements were considered the ground truth values while the model data were treated as the estimates. The second case study shows comparable results to the first study, with only slightly reduced R 2 values. This is to be expected, as the airport data is technically off-site. However, the results show that the decrease in model performance is very minor, which suggests using proximity data for modeling ET would be adequate. Figures 5-11 summarize the results of the first study, while figures summarize the results of the second study. As can be seen from the first study results, the performance of the models decreased significantly in 2010 compared to 2009 at most locations was a year with greater levels of precipitation than average, while 2010 was a very dry year. It is possible the models do not perform correctly for unusually dry situations, but further investigation would be necessary to find out why the model performance decreased. Investigating the performance variance of the best model at each station, the R 2 value ranges from a low of for SEPAC 2010 to a high of for SEPAC The SEPAC 2010 shows the limitations of only having modeled values. These models tended to overestimate ET, depending on the slope of the linear regression line. This slope varied from a minimum of for SWPAC 2010 to a maximum of for NEPAC 2009 among the strongest performing models. 37

38 If modeled values alone were used, locations such as NEPAC would have a significant overestimation of ET based upon the data in this report. Because of this, adding ET gages would be recommended to reduce this bias. It is possible the bias could be reduced through MOS (Model Output Statistics) techniques, but this would have to be applied on an individual station basis as accuracy varies widely from station to station. Even in the case of SEPAC, were MOS to be applied, it may not work properly due to the wide swings from year to year. Although these swings were primarily due to missing data, having an ET gage would correct this problem. At sites where the correlation coefficient varied little from year to year (such as DPAC), it may be possible to get away with not having an ET gage and simply rely on MOS bias adjustments alone. If funding is not an issue, however, this study recommends ET gages at all sites. An accurate ET measurement would make these gages especially necessary at stations like SEPAC where there are wide swings in accuracy of the model from year to year. 6. Additional Products In addition to this report, the authors are also developing a website that can be utilized for current ET information. Users will be able to input their location by county, along with the desired planting date, crop type, and optional local precipitation levels. Based upon this information, the website will be able to tell the user the overall water balance left for their crop to date. This would be a very useful tool for farmers who need to keep track of how much water their crops have. The website utilizes RefET information calculated from the model outputs or local ET sensor depending on location. Once the RefET has been defined, a coefficient is utilized depending on the crop type and age. This overall ET is then balanced with the precipitation to date in order to give a sense of overall water balance. The website will be available at the Indiana State Climate Office s site (iclimate.org). A second product will be a CD-ROM copy of this report and all of the associated figures and tables produced by this project. This copy is intended for users who wish to go into more detailed analysis of the results included in this report. The actual measurements from the ET gages and other associated instrumentation will be included here. 38

39 References Niyogi, Dev, Scheeringa, Ken, 2009: Estimating Evapotranspiration Across Purdue Agricultural Research Centers. Colorado State University Cooperative Extension, AGRONOMY NEWS, Vol. 19, JUNE "REF-ET Reference Evapotranspiration Software." University of Idaho Kimberly R&E Center. Web. < 39