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.
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 (ET o ) to generate gridded high-resolution daily ET o 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 ET o 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 ET o 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.
Precipitation is a key meteorological component that is directly related to climate change. Quantifying the changes in the precipitation bioclimate is crucial in planning climate-change adaptation and mitigation measures. Southeast Asia (SEA), home to the world’s greatest concentration of ecological variety, needs reliable monitoring of such changes. This study utilized the global-climate models from phase 6 of coupled model intercomparison project (CMIP6) to examine the variations in eight precipitation bioclimatic variables over SEA for two shared socioeconomic pathways (SSPs). All indicators were studied for the near (2020–2059) and far (2060–2099) futures to provide a better understanding of the temporal changes and their related uncertainty compared to a historical period (1975–2014). The results showed a high geographical variability of the changes in precipitation-bioclimatic indicators in SEA. The mainland of SEA would experience more changes in the bioclimate than the maritime region. The multimodel ensemble (MME) showed an increase in mean annual rainfall of 6.0–12.4% in most of SEA except the Philippines and southern SEA. The increase will be relatively less in the wettest month (15%) and more in the driest month (20.7%) in most of SEA; however, the precipitation in the wettest quarter would increase by 2.85%, while the driest quarter would decrease by 1.0%. The precipitation would be more seasonal. In addition, the precipitation would increase over a larger area in the wettest month than in the driest month, making precipitation vary more geographically.
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