Impact of climate classification on evapotranspiration variability in arid regions of Saudi Arabia

Water resource management in dry climates depends on an awareness of evapotranspiration variability. In this work, precipitation (Pr, mm/year) and air temperature (T °C) over Saudi Arabia (KSA) were extracted using long-term climate data (January 2000-December 2024) from the TerraClimate dataset. Three categories were used to classify both variables, therefore producing a new system of climate classification separating the Kingdom into nine different hydro-thermal groups. Using geo-spatial meteorological inputs, the Stand-Alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) model was employed to estimate reference evapotranspiration (ETo) for certain sites. Validation against the FAO-Penman-Monteith (FPM) model showed strong performance with correlation coefficients (r) ranging from 0.80 to 0.99 and Normalized Root Mean Square Error (NRMSE) values between 0.08 and 0.27 across the nine classes. Whereas relative humidity revealed a strong inverse association (r as low as -0.96), Tmax and solar radiation were found as main causes of ETo fluctuation (r up to 0.98 and 0.96, respectively). Especially in cooler, humid areas, wind speed showed secondary influence. These results underline the need of climate-specific evapotranspiration models for arid areas, where customized strategies are essential to maximize water consumption and guarantee sustainable resource management.