Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment

Abstract

Existing models and methods report crop coefficient (Kc) as a function of time but do not consider the variations due to surface conditions, wetting methods, meteorological conditions, and other biophysical factors. These limitations result in erroneous crop evapotranspiration (ETc) estimates, especially for non-standard conditions (e.g. plastic mulch). We present Support Vector Machine (SVM), a data-driven model based on statistical learning theory, for predicting generic Kc and ETc using a uniquely large dataset (10 seasons) from lysimeters for multiple crop-seasons combination under the plastic mulch conditions. The data used in this study were obtained from six years of lysimeter-based measurements (Shukla et al., 2006, 2012, 2014a, 2014b; Shukla and Knowles, 2011) for two distinctly different crop types (vine and erect) under two contrasting irrigation methods, drip and sub-irrigation. The SVM-based models predicted bell pepper (erect crop; r2=0.71) and watermelon (vine crop; r2=0.82) Kc as a function of time, water table depth, and number of rainfall events. The time since transplant represents the plant growth and, therefore, transpiration. The water table depth and rainfall events capture the effect of surface soil moisture on evaporation. The crop type-specific model is robust since it works for two different irrigation methods and growing seasons (spring and fall). The SVM model was superior to the Artificial Neural Network and Relevance Vector Machine models, two data-driven models used in hydrology. The errors in predicting ETc from the SVM model were only 2.6% and 11.2% for watermelon and bell pepper, respectively, highlighting the model accuracy. For both crops, the SVM predicted Kc values were not statistically different from the actual Kc values. In contrast, the FAO-56 values were significantly lower than the actual Kc values for both bell pepper (p=0.016) and watermelon (p=0.025). When evaluated in the context of watershed-scale budgets, the SVM model improved the accuracy in ETc estimates by 49.3mm over the FAO-56 method, and this improvement represents 70% (70.7mm) of the observed surface flow. Improved accuracy of the SVM model makes it useful in deriving local Kc using readily available hydro-climatic data for applications ranging from field-scale water management to watershed-scale modeling. The proposed model can be used to develop region-specific Kc to improve ETc estimates. Future efforts should be made to explore the development of similar models for open-field crops.