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

Abstract

Accurate 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 (ETo) to generate gridded high-resolution daily ETo 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), as well as Dynamic Evolving Neural Fuzzy Interference System (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ETo using FAO56 Penman-Monteith equation as a reference estimate. The multi-parameter 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 replication FAO56-PM followed by temperature-and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.75) followed by Caprio (median KGE 0.65), 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. The generated 0.10°×0.10° daily estimates of ETo enabled the detection of a significant increase of 0.12-0.16 mm/decade in the agricultural-dependent Nile Delta using the modified Mann Kendall test and Sen's slope estimator.

Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions

“…This study suggests best practices for estimating ET 0 using empirical models, which can aid water resources management decision-making in regions with limited meteorological data, as long-term data are limited in most developing countries. Sobh et al [37] investigated the performance of 31 empirical equations and 20 models developed using five artificial intelligence algorithms to estimate reference evapotranspiration (ET 0 ) in arid regions (Egypt). They proved that empirical equations based on radiation, temperature, and mass transfer perform better in replicating FAO56-PM.…”

A novel approach for evaluation of CMIP6 GCMs in simulating temperature and precipitation extremes of Pakistan

“…The KGE metric, which combines the normalized variance, the spatial variability ratio, and Pearson's correlation (represented as “ r ”), was used to quantify the skills of GCMs. The KGE metric is chosen due to its potential to capture extremes and ranges from 1 to ∞ (Hamed et al, 2023; Sobh et al, 2022; Salehie, Hamed et al, 2022). A value of 1 KGE score shows the best match.…”