Modeling Reference Evapotranspiration Using Evolutionary Neural Networks

The ability of evolutionary neural networks (ENN) to model reference evapotranspiration (ET0) was investigated in this study. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed of three stations in central California, Windsor, Oakville, and Santa Rosa, were used as inputs to the ENN models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. In the first part of the study, a comparison was made between the estimates provided by the ENN and those of the following empirical models: the California Irrigation Management System, Penman, Hargreaves, modified Hargreaves, and Ritchie methods. Root-mean-squared error, coefficient of efficiency, and correlation coefficient statistics were used as comparing criteria for the evaluation of the models’ accuracies. The ENN performed better than the empirical models. In the second part of the study, the ENN results were compared with those of the conventional artificial neural networks (ANN). The comparison results revealed that the ENN models were superior to ANN in modeling the ET0 process.     

Infilling Missing Daily Evapotranspiration Data Using Neural Networks

This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination (R2) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN and readily available weather data where the ET observations exist before and after the gap is a reliable and convenient method. It could be used to obtain continuous ET data for modeling and water management practices.     

Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation

The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ET0) is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM ET0 values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the ET0 process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation ET0 using nearby station data are investigated.     

Estimating Evapotranspiration using Artificial Neural Network

This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ETo) and compares the performance of ANNs with the conventional method (Penman–Monteith) used to estimate ETo. Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Three learning methods, namely, the standard back-propagation with learning rates of 0.2 and 0.8, and backpropagation with momentum were considered. The best ANN architecture for estimation of daily ETo was obtained for two different data sets (Sets 1 and 2) for Davis, Calif. Using data of Set 1, the networks were trained with daily climatic data (solar radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed) as input and the Penman–Monteith (PM) estimated ETo as output. The best ANN architecture was selected on the basis of weighted standard error of estimate (WSEE) and minimal ANN architecture. The ANN architecture of 6-7-1, (six, seven, and one neuron(s) in the input, hidden, and output layers, respectively) gave the minimum WSEE (less than 0.3 mm/day) for all learning methods. This value was lower than the WSEE (0.74 mm/day) between the PM method and lysimeter measured ETo as reported by Jensen et al. in 1990. Similarly, ANNs were trained, validated, and tested using the lysimeter measured ETo and corresponding climatic data (Set 2). Again, all learning methods gave less WSEE (less than 0.60 mm/day) as compared to the PM method (0.97 mm/day). Based on these results, it can be concluded that the ANN can predict ETo better than the conventional method (PM) for Davis.     

Evapotranspiration Modeling Using Linear Genetic Programming Technique

The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration (ET0) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville, and Santa Rosa, in central California, are used as inputs to the LGP to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. The accuracy of the LGP is compared with those of the support vector regression (SVR), artificial neural network (ANN), and those of the following empirical models: the California irrigation management system Penman, Hargreaves, Ritchie, and Turc methods. The root-mean-square errors, mean-absolute errors, and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the LGP is found to be superior alternative to the SVR and ANN techniques.     

Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotranspiration. The estimates of the ANN and ANFIS models were compared with the garlic crop evapotranspiration (ETc) values measured by lysimeter and those of the crop coefficient approach. Based on these comparisons, it can be concluded that the ANN and ANFIS techniques are suitable for simulation of ETc.     

Fuzzy Genetic Approach for Modeling Reference Evapotranspiration

This study investigates the ability of fuzzy genetic (FG) approach in modeling of reference evapotranspiration (ET0). The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville and Santa Rosa, in central California, are used as inputs to the FG models to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. A comparison is made between the estimates provided by the FG and those of the following empirical models: the California Irrigation Management System Penman, Hargreaves, Ritchie, and Turc methods. The FG results are also compared with the artificial neural networks. Root-mean-square errors (RMSE), mean-absolute errors (MAE), and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the FG models are superior to the ANN and empirical models in modeling ET0 process. For the Windsor, Oakville, and Santa Rosa stations, it was found that the FG models with RMSEW = 0.138, MAEW = 0.098, and RW = 0.999; RMSEO = 0.144, MAEO = 0.102, and RO = 0.999; and RMSES = 0.167, MAES = 0.115, and RS = 0.998 in test period is superior in modeling daily ET0 than the other models, respectively.     

Generalization of ETo ANN Models through Data Supplanting

This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ETo prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ETo prediction, proposed by the FAO as the sole standard method for ETo estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider local data.     

Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (R), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data.     

Estimating Reference Evapotranspiration Using Limited Weather Data

The FAO-56 Penman-Monteith combination equation (FAO-56 PM) has been recommended by the Food and Agriculture Organization of the United Nations (FAO) as the standard equation for estimating reference evapotranspiration (ET0). The FAO-56 PM equation requires the numerous weather data that are not available in the most of the stations. This paper examines the potential of FAO-56 PM equation in estimating the ET0 under humid conditions from limited weather data. For this study, full weather data sets were collected from six humid weather stations from Serbia, South East Europe. FAO-56 reduced-set PM ET0 estimates were in closest agreement with FAO-56 full set PM ET0 estimates at the most of locations. The difference between FAO-56 full set PM ET0 estimates and FAO-56 PM reduced-set ET0 estimates generally increases by increasing the number of estimated weather parameters. Overall results indicate that FAO-56 reduced-set PM approaches mostly provided better results compared to Turc equation, adjusted Hargreaves equation and temperature-based RBF network. This fact strongly supports using the FAO-56 PM equation even in the absence of the complete weather data set. The minimum and maximum air temperature data and local default wind speed value are the minimum data requirements necessary to successfully use the FAO-56 PM equation under humid conditions.     

Evapotranspiration Adjustment Factors in Florida

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.     

History and Evaluation of Hargreaves Evapotranspiration Equation

A brief history of development of the 1985 Hargreaves equation and its comparison to evapotranspiration (ET) predicted by the Food and Agricultural Organization of the United Nations (FAO) Penman-Monteith method are described to provide background and information helpful in selecting an appropriate reference ET equation under various data situations. Early efforts in irrigation water requirement computations in California and other arid and semiarid regions required the development of simplified ET equations for use with limited weather data. Several initial efforts were directed towards improving the usefulness of pan evaporation for estimating irrigation water requirements. Similarity with climates of other countries allowed developments in California to be extended overseas. Criticism of empirical methods by H. L. Penman and others encouraged the search for a robust and practical method that was based on readily available climatic data for computing potential evapotranspiration or reference crop evapotranspiration (ETo). One of these efforts ultimately culminated in the 1985 Hargreaves ETo method. The 1985 Hargreaves ETo method requires only measured temperature data, is simple, and appears to be less impacted than Penman-type methods when data are collected from arid or semiarid, nonirrigated sites. For irrigated sites, the Hargreaves 1985 ETo method produces values for periods of five or more days that compare favorably with those of the FAO Penman-Monteith and California Irrigation Management Information Services (CIMIS) Penman methods. The Hargreaves ETo predicted 0.97 of lysimeter measured ETo at Kimberly, Idaho after adjustment of lysimeter data for differences in surface conductance from the FAO Penman-Monteith definition. Monthly ETo by the 1985 Hargreaves equation compares closely with ETo calculated using a simplified, “reduced-set” Penman-Monteith that requires air temperature data only.     

Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates

Reference crop evapotranspiration (ET0) is a key variable in procedures established for estimation of evapotranspiration rates of agricultural crops. In recent years, there is growing evidence to show that the more physically based FAO-56 Penman–Monteith (PM) combination method yields consistently more accurate ET0 estimates across a wide range of climates and is being proposed as the sole method for ET0 computations. However, other methods continue to remain popular among Indian practitioners either because of traditional usage or because of their simpler input data requirements. In this study, we evaluated the performances of several ET0 methods in the major climate regimes of India with a view to quantify differences in ET0 estimates as influenced by climatic conditions and also to identify methods that yield results closest to the FAO-56 PM method. Performances of seven ET0 methods, representing temperature-based, radiation-based, pan evaporation-based, and combination-type equations, were compared with the FAO-56 PM method using historical climate data from four stations located one each in arid (Jodhpur), semiarid (Hyderabad), subhumid (Bangalore), and humid (Pattambi) climates of India. For each location, ET0 estimates by all the methods for assumed hypothetical grass reference crop were statistically compared using daily climate records extending over periods of 3–4 years. Comparisons were performed for daily and monthly computational time steps. Overall results while providing information on variations in FAO-56 PM ET0 values across climates also indicated climate-specific differences in ET0 estimates obtained by the various methods. Among the ET0 methods evaluated, the FAO-56 Hargreaves (temperature-based) method yielded ET0 estimates closest to the FAO-56 PM method both for daily and monthly time steps, in all climates except the humid one where the Turc (radiation-based) was best. Considering daily comparisons, the associated minimum standard errors of estimate (SEE) were 1.35, 0.78, 0.67, and 0.31 mm/day, for the arid, semiarid, subhumid, and humid locations, respectively. For monthly comparisons, minimum SEE values were smaller at 0.95, 0.59, 0.38, and 0.20 mm/day for arid, semiarid, subhumid, and humid locations, respectively. These results indicate that the choice of an alternative simpler equation in a particular climate on the basis of SEE is dictated by the time step adopted and also it appears that the simpler equations yield much smaller errors when monthly computations are made. In order to provide simple ET0 estimation tools for practitioners, linear regression equations for preferred FAO-56 PM ET0 estimates in terms of ET0 estimates by the simpler methods were developed and validated for each climate. A novel attempt was made to investigate the reasons for the climate-dependent success of the simpler alternative ET0 equations using multivariate factor analysis techniques. For each climate, datasets comprising FAO-56 PM ET0 estimates and the climatic variables were subject to factor analysis and the resulting rotated factor loadings were used to interpret the relative importance of climatic variables in explaining the observed variabilities in ET0 estimates. Results of factor analysis more or less conformed the results of the statistical comparisons and provided a statistical justification for the ranking of alternative methods based on performance indices. Factor analysis also indicated that windspeed appears to be an important variable in the arid climate, whereas sunshine hours appear to be more dominant in subhumid and humid climates. Temperature related variables appear to be the most crucial inputs required to obtain ET0 estimates comparable to those from the FAO-56 PM method across all the climates considered.     

Use of Alternative Temperature Expressions with Blaney-Criddle

The widely used Penman-Monteith equation to estimate crop evapotranspiration (ET) has limited utility in many areas of the world because of its requirement for full meteorological data. Legal and engineering water agencies commonly use the original Blaney-Criddle method in their efforts to manage competing water demands in mountain basins, both for its longtime familiarity and minimal data requirements. The original Blaney-Criddle equation predicts crop ET based solely on readily available mean monthly air temperature, t, and percentage of daylight hours. However, in semiarid, high-elevation environments, Blaney-Criddle underestimates crop ET. Subsequent modifications have not fully corrected this underestimation. Low nighttime temperatures at high elevations incorrectly weight the estimate, resulting in significant variation between computed crop ET and lysimeter measurements. Our objective was to evaluate three modifications of the Blaney-Criddle temperature expression against the original equation with mean t, and another temperature method, Hargreaves, using lysimeter measurements from nine irrigated grass meadow sites in the upper Gunnison River basin of Colorado (1999–2003). Two of the modified temperature expressions resulted in improved correlation of Blaney-Criddle estimated crop ET with lysimeter ET. Similar improvements were observed when estimating with Hargreaves, which incorporates an additional term, Tdiff, the difference between maximum and minimum daily temperature. Correlations of solar radiation (Rs, the primary energy input to ET) with alternative temperature expressions and Tdiff were improved over correlations of Rs with mean t, supporting the improved prediction performance of alternative temperature expressions. These modifications to the original Blaney-Criddle can be applied successfully throughout Colorado mountain basins and may be globally applicable to high-elevation areas.     

Satellite-Based Energy Balance to Assess Within-Population Variance of Crop Coefficient Curves

Quantifying evapotranspiration (ET) from agricultural fields is important for field water management, water resources planning, and water regulation. Traditionally, ET from agricultural fields has been estimated by multiplying the weather-based reference ET by crop coefficients (Kc) determined according to the crop type and the crop growth stage. Recent development of satellite remote sensing ET models has enabled us to estimate ET and Kc for large populations of fields. This study evaluated the distribution of Kc over space and time for a large number of individual fields by crop type using ET maps created by a satellite based energy balance (EB) model. Variation of Kc curves was found to be substantially larger than that for the normalized difference vegetation index because of the impacts of random wetting events on Kc, especially during initial and development growth stages. Two traditional Kc curves that are widely used in Idaho for crop management and water rights regulation were compared against the satellite-derived Kc curves. Simple adjustment of the traditional Kc curves by shifting dates for emergence, effective full cover, and termination enabled the traditional curves to better fit Kc curves as determined by the EB model. Applicability of the presented techniques in humid regions having higher chances of cloudy dates was discussed.     

Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model

Mapping evapotranspiration at high resolution with internalized calibration (METRIC) is a satellite-based image-processing model for calculating evapotranspiration (ET) as a residual of the surface energy balance. METRIC uses as its foundation the pioneering SEBAL energy balance process developed in The Netherlands by Bastiaanssen, where the near-surface temperature gradients are an indexed function of radiometric surface temperature, thereby eliminating the need for absolutely accurate surface temperature and the need for air-temperature measurements. The surface energy balance is internally calibrated using ground-based reference ET to reduce computational biases inherent to remote sensing-based energy balance and to provide congruency with traditional methods for ET. Slope and aspect functions and temperature lapsing are used in applications in mountainous terrain. METRIC algorithms are designed for relatively routine application by trained engineers and other technical professionals who possess a familiarity with energy balance and basic radiation physics. The primary inputs for the model are short-wave and long-wave (thermal) images from a satellite (e.g., Landsat and MODIS), a digital elevation model and ground-based weather data measured within or near the area of interest. ET “maps” (i.e., images) via METRIC provide the means to quantify ET on a field-by-field basis in terms of both the rate and spatial distribution. METRIC has some significant advantages over many traditional applications of satellite-based energy balance in that its calibration is made using reference ET, rather than the evaporative fraction. The use of reference ET for the extrapolation of instantaneous ET from periods of 24?h and longer compensates for regional advection effects by not tying the evaporative fraction to net radiation, since ET can exceed daily net radiation in many arid or semi-arid locations. METRIC has some significant advantages over conventional methods of estimating ET from crop coefficient curves in that neither the crop development stages, nor the specific crop type need to be known with METRIC. In addition, energy balance can detect reduced ET caused by water shortage.     

Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from 1H NMR data of human plasma

Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient "black-box' tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed. Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to those of sophisticated lineshape fitting (LF) analysis. The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks. The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap. The ANN analysis enables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a 1H NMR spectrum of a plasma sample in < 1 h. The ANN extension presented is believed to increase the value of the 1H NMR based lipoprotein quantification to the point that it could be the method of choice in some advanced research settings. Furthermore, the excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in biomedical NMR.     

Demand Forecasting for Irrigation Water Distribution Systems

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.     

Utility of Column Lysimeter for Design of Soil Aquifer Treatment System for Wastewater Renovation Using Artificial Neural Networks

An artificial neural network (ANN) model is developed to study the correlation of data with reference to various wastewater pollution parameters (biochemical oxygen demand, chemical oxygen demand, suspended solids, NH4, PO4) using two scales of experiments viz. column lysimeter and a pilot soil aquifer treatment (SAT) system for wastewater renovation in India. A unique feature of the study is that the primary treated wastewater was directly applied to SAT system for renovation in contrast to the secondary treated effluent used in most of the other studies that have been reported. The analysis of data using ANN as a tool indicates that the column lysimeter data are useful for design of SAT systems and it is possible to predict the effluent quality for SAT system based on the inputs from lysimeter experiments. The study highlights the utility of column lysimeter studies for evolving design parameters for a full-scale SAT system thereby obviating the need for pilot SAT studies which are site specific, time consuming, and expensive. Thus, the study suggests that the experimental data from lysimeter studies at a particular site can be used to predict performance of field-scale SAT systems without going in for actual experimentation. Further, the field data from one site could be utilized for design of SAT systems at other locations provided the climatic and hydrogeological conditions viz. soil matrix characteristics and wastewater characteristics, etc., are similar.