Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations

Abstract

Evapotranspiration estimation is of crucial importance in arid and hyper-arid regions, which suffer from water shortage, increasing dryness and heat. A modeling study is reported here to cross-station assessment between hyper-arid and humid conditions. The derived equations estimate ET0 values based on temperature-, radiation-, and mass transfer-based configurations. Using data from two meteorological stations in a hyper-arid region of Iran and two meteorological stations in a humid region of Spain, different local and cross-station approaches are applied for developing and validating the derived equations. The comparison of the gene expression programming (GEP)-based-derived equations with corresponding empirical-semi empirical ET0 estimation equations reveals the superiority of new formulas in comparison with the corresponding empirical equations. Therefore, the derived models can be successfully applied in these hyper-arid and humid regions as well as similar climatic contexts especially in data-lack situations. The results also show that when relying on proper input configurations, cross-station might be a promising alternative for locally trained models for the stations with data scarcity.