Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Reference evapotranspiration (ETo) is an important and one of the most difficult components of the hydrologic cycle to quantify accurately. Estimation/measurement of ETo is not simple as there are number of climatic parameters that can affect the process. There exists copious conventional (direct and indirect) and non conventional/soft computing (artificial neural networks, ANNs) methods for estimating ETo. Direct methods have the limitations of measurement errors, expensive, impracticality of acquiring point measurements for spatially variable locations, whereas the indirect methods have the limitations of unavailability of all necessary climate data and lack of generalizability (needs local calibration). In contrast to conventional methods, soft computing models can estimate ETo accurately with minimum climate data which have advantages over limitations of conventional ETo methods. This chapter reviews the application of ANN methods in estimating ETo accurately for 15 locations in India using six climatic variables as input. The performance of ANN models were compared with the multiple linear regression (MLR) models in terms of root mean squared error, coefficient of determination and ratio of average output and target ETo values. The results suggested that the ANN models performed better as compared to MLR for all locations

Developing equations for estimating reference evapotranspiration in Australia

Quantifying reference evapotranspiration (ET0) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET0 using Honey-Bee Mating Optimization (HBMO) algorithm. The firs eq. estimates ET0 from extraterrestrial radiation (Ra), relative humidity (RH) and mean daily temperature (Tmean), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (Tmax). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET0 values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R2, of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET0 including Hargreaves-Samani, Thornthwaith, Hamon, McGuinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improve and the two newly proposed equations are suggested to be used in Australi

Modeling and analysis of actual evapotranspiration using data driven and wavelet techniques

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems’ functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape. Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori. The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series

Artificial neural network model of soil heat flux over multiple land covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America