Assessing Salinity Impacts and Modeling Rice Irrigation Demands Under Climate Scenarios in Morocco’s Gharb Irrigated Region

Salinity and climate change represent critical threats to sustainable rice cultivation in Morocco’s Gharb irrigated region. In this study, we integrated long-term remote sensing (1990–2024) with climate-driven irrigation water requirement (IWR) modeling (2022–2100) to assess their combined impacts on rice production in the regions of Kenitra and Sidi Kacem. We analyzed Landsat imagery using Google Earth Engine (GEE) to monitor vegetation vigor and soil salinity stress during the rice-growing season (June–September), applying NDVI and NDSI indices. We integrated climate projections under RCP 4.5 and RCP 8.5 using Python to simulate future changes in climatology-driven IWR. Additionally, we trained a Random Forest machine learning model on historical data (1990–2021) to forecast future rice cultivation area and irrigation efficiency through 2100. Our findings show that salinity-affected zones consistently exhibit reduced vegetation vigor. IWR projections differ by approximately 69.3% by the year 2100 between the RCP 4.5 and RCP 8.5 climate scenarios. Specifically, under RCP 4.5, increased evapotranspiration leads to higher irrigation demand, whereas under RCP 8.5, accelerated crop phenology reduces seasonal IWR, with implications for potential yield reductions. These insights underscore the urgency of adaptive water management strategies that combine climate modeling, salinity forecasting, machine learning, and precision irrigation to sustain rice production under future environmental stressors.