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Efficient use of natural water resources in agriculture is becoming an important issue in Florida because of the rapid depletion of freshwater resources due to the increasing trend of industrial development and population. Reliable and consistent estimates of evapotranspiration (ET) are a key element of managing water resources efficiently. Since the 1940s numerous grass- and alfalfa-reference evapotranspiration (ET[sub o] and ET[sub r], respectively) equations have been developed and used by researchers and decision makers, resulting in confusion as to which equation to select as the most accurate reference ET estimates. Twenty-one ET[sub o] and ET[sub r] methods were evaluated based on their daily performance in a humid climate. The Food and Agriculture Organization Penman-Monteith (FAO56-PM) equation was used as the basis for comparison for the other methods. Measured and carefully screened daily climate data during a 23-year period (1978–2000) were used for method performance analyses, in which the methods were ranked based on the standard error of estimate (SEE) on a daily basis. In addition, the performance of the four alfalfa-based ET (ET[sub r]) equations and the ratio of alfalfa ET to grass ET (K[sub r] values) were evaluated, which have not been studied before in Florida’s humid climatic conditions. The peak month ET[sub o] estimates by each method were also evaluated. All methods produced significantly different ET[sub o] estimates than the FAO56-PM method. The 1948 Penman method estimates were closest to the FAO56-PM method on a daily basis throughout the year, with the daily SEE averaging 0.11 mm·d-1; thus this method was ranked the second best overall. Although 1963 Penman (with the original wind function) slightly overestimated ET, especially at high ET[sub o] rates, it provided remarkably good estimates as well and ranked as the third best method, with a daily average SEE value of 0.14 mm·d-1. Both methods produced peak month ET[sub o] estimates closest to the FAO56-PM method among all methods evaluated, with daily peak month SEEs averaging 0.07 and 0.09 mm·d-1, respectively. Significant variations were observed in terms of the performance of the various forms of Penman’s equations. For example, the original Penman-Monteith method produced the poorest ET[sub o] estimates among the combination equations, with a daily SEE for all months and peak month averaging 0.50 and 0.35 mm·d-1, respectively and ranked 11th. An average value of 1.18 was used to convert ET[sub r] estimates to ET[sub o] values for alfalfa-reference methods. The K[sub r] value of 1.18 resulted in reasonable estimates of ET[sub o] throughout the year by the Kimberley forms of the Penman equations. Another ET[sub r]-based equation, Jensen-Haise, gave consistently poor estimates. The Stephens-Stewart radiation method was the highest-ranked (10th) noncombination method overall. The temperature-based McCloud method (ranked 19th) produced the poorest ET[sub o] estimates among all methods with a daily SEE for all months and for the peak month averaging 1.93 and 1.22 mm·d-1, respectively. In general, the results obtained from the temperature methods suggest that all of the temperature methods, with the possible exception of the Turc method, can only be applicable for these climatic conditions after they are calibrated or modified locally or regionally. The FAO and Christiansen pan evaporation methods (ranked 17th and 18th, respectively) produced poor ET[sub o] estimates and had the largest amount of point scatter in daily ET[sub o] estimates relative to the FAO56-PM ET[sub o]. Both methods resulted in the highest daily SEE of 1.18 and 1.19 mm·d-1 for all months, after the McCloud method (1.93 mm·d-1), and with the highest SEE of 1.30 and 1.24 mm·d-1 for the peak month of all methods evaluated. The FAO56-PM method uses solar radiation, wind speed, relative humidity, and minimum and maximum air temperature to estimate ET[sub o]. It has been recommended that the FAO56-PM be used for estimating ET[sub o] when all the necessary input parameters are available. However, all these input variables may not be available, or some of them may not be reliable for a given location if the FAO56-PM equation is used, and one may need to choose other temperature, radiation, or pan evaporation methods based on the availability of data for estimating ET[sub o]. The results of this study can be used as a reference tool to provide practical information on which method to select based on the availability of data for reliable and consistent estimates of daily ET[sub o] relative to the FAO56-PM method in a humid climate. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is the ratio of latent heat (LE in W m−2) to available energy at the land surface. It can aid in partitioning the Sun's energy available at the Earth's surface into surface energy fluxes to understand land–atmosphere interactions as well as monitoring Evapotranspiration and terrestrial drought conditions. In this study, the feasibility of estimating instantaneous, diurnal, and daily EF with minimal satellite and meteorological inputs based on the temperature difference-vegetation index framework is explored in South Florida, United States (US). The model performance is assessed against the ground observations collected at three flux stations between 2008 and 2011. It was found that the energy balance closure was improved by 9% at the long hydroperiod marsh site (i.e. US-Elm) and 6% at the short hydroperiod marsh site (i.e. US-Esm) after water heat storage (W) correction was applied on energy fluxes since W is more important than soil heat flux (G) for wetland ecosystems. The results further indicated that the model produced accurate EF estimates and performed the best on diurnal, then instantaneous, and lastly daily periods. Overall, the model can be used to produce spatially continuous EF and ET maps since the flux towers are no longer active. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays an important role in agricultural water management and crop modeling. The highest mean annual ET values (889–1016 mm) in the United States (US) occur in Florida where there is a combination of ample rainfall (R) and warm air temperatures. Therefore, it is crucial to know the synergistic influence of meteorological variables (MV) on ET variability. This study aims to evaluate how reference evapotranspiration (ET0) over Florida from 2008 to 2018 was influenced by MV using simultaneous changes in all variables. These changes were evaluated interannually, seasonally, monthly, and daily using trend analysis. We used weather information including R, relative humidity (RH), solar radiation (SR), wind, and temperature parameters as well as ET0 from 33 synoptic stations over Florida recorded by the Florida Automated Weather Network (FAWN). Results of this study showed that SR had the strongest positive annual correlation with ET0 for all climate regions over Florida. However, temporal analysis showed that during December and January, temperature was the dominant factor to control variations of ET0 which was highly consistent with anomalies of ET0 and temperature parameters in December. The correlation coefficients between ET0 and RH were negatively higher than −0.6 from May to September, compared to the entire year, where RH was negligible (between −0.1 and −0.2). The significant trend of air and soil temperature, SR, and RH might be considered as an early alarm system for climate variability over Florida. Finally, sensitivity analysis revealed that ET0 changed at least 1% for 16–18% variations of MV in 67% of the weather stations (22 stations); this range (16–18%) can be assigned as an average range to force ET0 to change at least 1% across Florida. The results of this study can be used as a guideline to assess the annual, seasonal, and monthly relationships between the most influential MV and ET0 across Florida, as a source to identify the most sensitive MV in the modeling of ET related studies, and as the base to develop climate-based management plans for agricultural water management. [ABSTRACT FROM AUTHOR]

Considering evapotranspiration takes a basic role in the hydrologic cycle, water resources management, and irrigation water requirements. Evapotranspiration estimation is not an easy case because of the number of direct and indirect effects. The ability of the M5 model tree (M5T); adaptive neuro-fuzzy inference system (ANFIS); support vector machines (SVM); Hargreaves-Samani, Ritchie, Turc, and Penman FAO 56 empirical equations; and multilinear regression (MLR) for modeling daily reference evapotranspiration is investigated. Daily climatic data, air temperature (T), relative humidity (RH), wind speed (U), and solar radiation (SR) from De Soto County, Florida, USA, station are used as inputs for the training of the models and calculation of equations. Mean square error (MSE), mean absolute error (MAE), and correlation coefficient statistics are computed to evaluate the performances of the created models. A total comparison is done between all results to underline how effective is soft computing techniques. Also, the impact of each meteorological parameter on evapotranspiration is investigated by using ANFIS, MLR, and SVM methods as a part of the parameter effect study. According to the error calculations and correlation coefficient, Turc empirical formula found better than other empirical equations. All data-driven techniques gave better results than empirical equations. The highest correlation coefficient is calculated for ANFIS, and the minimum errors are calculated for radial basis function SVM. [ABSTRACT FROM AUTHOR]

In the past ten years, smart irrigation controllers have been developed by a number of manufacturers and have been promoted by water purveyors in an attempt to reduce excessive irrigation. Legislation has been introduced in California and Texas and passed in Florida mandating or incentivizing the use of these controllers. As a result of the interest in smart controllers, their use is increasing in new installations and retrofits of residential and light commercial irrigation systems. A number of controlled research studies using formal experimental design and statistical analyses indicate substantial water savings of anywhere from 40% to more than 70% when using these devices; however, real-world savings in larger pilot-scale projects indicate savings of typically less than 10%. Reasons for the divergence between the apparent potential savings and the realized savings in pilot projects are related to the lack of: targeting of high irrigation users (on either a relative or absolute scale), education for contractors and end users, and timely follow-up to assess water savings. In addition, much of the scientific research on smart controllers has been conducted in humid regions where higher potential savings are likely due to irrigation needed only to supplement rainfall. Future pilot projects should include comprehensive educational components aimed at irrigation sites with potential irrigation savings based on estimated landscape irrigation demand from climatic variables (i.e., high irrigation users). [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (ET) is commonly estimated at daily time intervals as the product of a crop coefficient and a reference-crop evapotranspiration (ET0) that is calculated by using a daily time step. When subdaily time steps are used, crop coefficients must be multiplied by adjustment factors to account for the discrepancy between ET0 calculated by using daily and subdaily time steps. These adjustment factors depend on the method used to calculate ET0. By using the ASCE and FAO-56 Penman-Monteith methods with data from several meteorological stations in Florida, the ASCE equation is shown to be preferable for all locations and seasons because it requires the least adjustment to the crop coefficient when 15-min and 1-h time steps are used. The required adjustment factors depend on location and season, are greatest in the summer, and are approximately the same for 15-min and 1-h time steps. A comparative evaluation between daily ET0 and values of potential evapotranspiration (PET) provided by three public databases shows that PET estimates should generally not be used as substitutes for ET0, because the relationship between PET and ET0 varies significantly with location and season. For all locations and seasons considered in this study, daily ET0 agrees most closely with the PET given by the Florida Automated Weather Network. [ABSTRACT FROM AUTHOR]

This study investigates the validity of fundamental assumptions regarding the partitioning of energy that are implicit in the Penman-Monteith (PM) and Priestly-Taylor (PT) models of potential evapotranspiration (PET). Both these models require energy conservation, but differ in the partitioning of sensible and latent heat fluxes as measured by the Bowen ratio. Application of these models at four research sites in Florida collectively show that these models can be calibrated to provide adequate PET prediction at all sites. The prediction of Bowen ratios using the calibrated PET models showed that the PT model is preferable for lakes and is acceptable for marsh and forest; the crop-coefficient application of the PM model is preferable for grass and is acceptable for marsh; and the standard PM model is acceptable for marsh. For the marsh and forest covers, the margins of preference between the three models are not very strong. This investigation was limited to climatic conditions in Florida and caution should be exercised in extending these results to other sites and climates. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration estimation methods that require minimal data are necessary when climatic data sets are incomplete, inaccurate, or unavailable. This study was conducted to evaluate temperature-based reference evapotranspiration methods in Florida. Using reference evapotranspiration estimates using satellite-derived radiation as the standard for comparison, the “reduced-set” Penman-Monteith, Hargreaves, and Turc equations were evaluated using monthly temperature data from 72 weather stations in Florida. The reduced-set Penman-Monteith equation requires maximum and minimum temperature only and uses recommended methods to estimate radiation, humidity, and wind speed. The reduced-set Penman-Monteith and Hargreaves equations were found to overestimate reference evapotranspiration while the Turc equation neither overestimated nor underestimated. The reduced-set Penman-Monteith equation showed greatest error in coastal stations while the Hargreaves equation showed greatest error at inland and island locations. In the absence of regionally calibrated methods the Turc equation is recommended for estimating reference evapotranspiration using measured maximum and minimum temperature and estimated radiation in Florida. [ABSTRACT FROM AUTHOR]

A discussion is presented to the article “Case Study on the Accuracy and Cost/Effectiveness in Simulating Reference Evapotranspiration in West-Central Florida” by Michael Grant Exner-Kittridge and Mark Cable Rains from the September 2010 issue. The author reviews the various meteorological mathematical equations used for the estimation of reference evapotranspiration.

The objective of the research reported in this paper was to determine the potential water-savings effectiveness of Toro Intelli-Sense evapotranspiration (ET)-based irrigation controllers in single-family homes. Of the 36 cooperators selected, 21 cooperators were outfitted with Toro Intelli-Sense TIS-612 (the ET group) and the remaining 15 were used as comparisons. The ET group reduced irrigation compared to the gross irrigation requirement by 23-41% and the 9-year historical average by 23-34%. Results varied based on the relationship between historical irrigation and the gross irrigation requirement, with water savings of 24%, no difference, and an increase of 54% when the historical value exceeded, approximated, and was less than the gross irrigation requirement, respectively. Turfgrass quality was maintained above an acceptable threshold for both treatments. In southwest Florida, a properly programmed Intelli-Sense controller is recommended when irrigation exceeds the gross irrigation requirement and at least 696 mm of irrigation is applied annually. Other smart-irrigation technologies would be more appropriate in locations for which the irrigation rate is less than the gross irrigation requirement. [ABSTRACT FROM AUTHOR]