High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

Abstract

AbstractAccurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ET0) to generate gridded high‐resolution daily ET0 estimates over Egypt. The AI algorithms include support vector machine‐radial basis function (SVM‐RBF), random forest (RF), group method of data handling neural network (GMDH‐NN), multivariate adaptive regression splines (MARS), and dynamic evolving neural fuzzy interference system (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ET0 using FAO56 Penman–Monteith equation as a reference estimate. The multiparameter Kling‐Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replicating FAO56‐PM followed by temperature‐ and mass‐transfer‐based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.76) followed by Caprio (median KGE 0.64), and Penman (median KGE 0.52) equations based on station‐wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. Overall, the RF model performed the best among all the AI models and empirical equations. The generated 0.10° × 0.10° daily estimates of ET0 enabled the detection of a significant increase of 0.12–0.16 mm·decade−1 in the agricultural‐dependent Nile Delta using the modified Mann–Kendall test and Sen's slope estimator.