Application of SM2RAIN algorithm and linear regression model for future estimation of irrigation water use in an arid region

Irrigated agricultural farms are crucial for ensuring food security, but they affect the global water cycle through freshwater withdrawals. In this work, the irrigation water requirement in Al-Ahsa region of Saudi Arabia is estimated by combining the satellite-based soil moisture active and passive (SMAP) product by the National Aeronautics and Space Administration (NASA), evapotranspiration products by the Global Land Evaporation Amsterdam Model (GLEAM) and the European Reanalysis-5 (ERA5), and the precipitation product by Saudi Rainfall (SaRa) with the SM2RAIN algorithm. The study underlines the restricted ability of coarse-resolution satellite products to capture small agricultural farms and stresses selecting the proper evapotranspiration dataset for irrigation water estimation in arid regions. For analyzing the future changes in IWR under climate change, the research also generated multiple linear regression (MLR) models utilizing climate model data from General Circulation Models under SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5 scenarios as predictor variables and SM2RAIN-estimated IWR as the target variable. The statistical evaluation of MLR models revealed that evapotranspiration was a significant predictive variable that allowed the models to account for 55% of the variation in future IWR. In summary, by estimating annual IWR and its change under various scenarios and baselines in Al-Ahsa Oasis, the results from this study offer comprehensive information and a point of reference for future research and highlight the factors that require further investigation for sustainable and dynamic water resource planning and agricultural management in arid environments under climate change scenarios.