Modeling Hydrological Responses to Climate Change in Morocco’s Upper Tassaoute Basin

Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of PERSIANN-CDR satellite-based precipitation product, for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in-situ observations. Runoff is simulated using two hydrological approaches: the GR2M conceptual rainfall–runoff model and the Thornthwaite climatic water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation.Furthermore, climate change impacts are quantified using future climate projections from 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030-2060 and 2061-2090. Changes in key hydrological indicators, including precipitation, runoff, and water balance components, are analyzed for both observation-based and satellite-based simulations. Results consistently show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from -40% to -80%, despite notable inter-model variability. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, surface water availability is projected to decrease by -60 to -80% (GR2M) and -70 to -80% (Thornthwaite) when using observed data, and by -50 to -80% (GR2M) and -50 to -90% (Thornthwaite) when using PERSIANN-CDR forcing. The results highlight the strengths of satellite-based precipitation datasets for climate change impact studies and demonstrate their relevance as a complementary or alternative data source in regions with sparse observations.