A new precipitation dataset is provided since 2014 by the Global Precipitation Measurement (GPM) satellite constellation measurements combined in the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm. This recent GPM-IMERG dataset provides potentially useful precipitation data for regions with a low density of rain gauges. The main objective of this study is to evaluate the accuracy of the near real-time product (IMERG-E) compared to observed rainfall and its suitability for hydrological modeling over a mountainous watershed in Morocco, the Ghdat located upstream the city of Marrakech. Several statistical indices have been computed and a hydrological model has been driven with IMERG-E rainfall to estimate its suitability to simulate floods during the period from 2011 to 2018. The following results were obtained: (1) Compared to the rain gauge data, satellite precipitation data overestimates rainfall amounts with a relative bias of +35.61% (2) In terms of the precipitation detection capability, the IMERG-E performs better at reproducing the different precipitation statistics at the catchment scale, rather than at the pixel scale (3) The flood events can be simulated with the hydrological model using both the observed and the IMERG-E satellite precipitation data with a Nash–Sutcliffe efficiency coefficient of 0.58 and 0.71, respectively. The results of this study indicate that the GPM-IMERG-E precipitation estimates can be used for flood modeling in semi-arid regions such as Morocco and provide a valuable alternative to ground-based precipitation measurements.

Several satellite precipitation estimates are becoming available globally, offering new possibilities for modeling water resources, especially in regions where data are scarce. This work provides the first validation of four satellite precipitation products, CHIRPS v2, Tamsat, Persiann CDR and TerraClimate data, in a semi-arid region of Essaouira city (Morocco). The precipitation data from different satellites are first compared with the ground observations from 4 rain gauges measurement stations using the different comparison methods, namely: Pearson correlation coefficient (r), Bias, mean square error (RMSE), Nash-Sutcliffe efficiency coefficient and mean absolute error (MAE). Secondly, a rainfall-runoff modeling for a basin of the study area (Ksob Basin S = 1483 km²) was carried out based on artificial neural networks type MLP (Multi Layers Perceptron). This model was -then used to evaluate the best satellite products for estimating the discharge. The results indicate that TerraClimate is the most appropriate product for estimating precipitation (R² = 0.77 and 0.62 for the training and validation phase, respectively). By using this product in combination with hydrological modeling based on ANN (Artificial Neural Network) approach, the simulations of the monthly flow in the watershed were not very satisfactory. However, a clear improvement of the flow estimations occurred when the ESA-CCI (European Space Agency’s (ESA) Climate Change Initiative (CCI)) soil moisture was added (training phase: R² = 0.88, validation phase: R² = 0.69 and Nash ≥ 92%). The results offer interesting prospects for modeling the water resources of the coastal zone watersheds with this data.

An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult in complex terrain. The main objective of this work was to investigate the potential of a Two-Source-Energy-Balance model (TSEB) driven by the Landsat and MODIS data for estimating ET over a complex mountain region. The complexity is associated with the type of the vegetation canopy as well as the changes in topography. For validating purposes, a large-aperture scintillometer (LAS) was set up over a heterogeneous transect of about 1.4 km to measure sensible (H) and latent heat (LE) fluxes. Additionally, two towers of eddy covariance (EC) systems were installed along the LAS transect. First, the model was tested at the local scale against the EC measurements using multi-scale remote sensing (MODIS and Landsat) inputs at the satellite overpasses. The obtained averaged values of the root mean square error (RMSE) and correlation coefficient (R) were about 72.4 Wm⁻² and 0.79 and 82.0 Wm⁻² and 0.52 for Landsat and MODIS data, respectively. Secondly, the potential of the TSEB model for evaluating the latent heat fluxes at large scale was investigated by aggregating the derived parameters from both satellites based on the LAS footprint. As for the local scale, the comparison of the latent heat fluxes simulated by TSEB driven by Landsat data performed well against those measured by the LAS (R = 0.69, RMSE = 68.0 Wm⁻²), while slightly more scattering was observed when MODIS products were used (R = 0.38, RMSE = 99.8 Wm⁻²). Based on the obtained results, it can be concluded that (1) the TSEB model can be fairly used to estimate the evapotranspiration over the mountain regions; and (2) medium- to high-resolution inputs are a better option than coarse-resolution products for describing this kind of complex terrain.

Floods are natural disasters that often impact communities living in low-lying areas in the northern and central parts of Morocco. In this study, our aim was to create a flood susceptibility map using three methods; the hierarchy process (AHP) frequency ratio model (FR) and the weights of evidence (WoE) model. We extensively examined the area identified by these approaches using a hydraulic analysis software called HEC-RAS (version 6.3.1). Our analysis focused on the Essaouira watersheds in Morocco, where we identified around 197 flood locations. Out of these, we randomly selected 70% for modeling purposes while the remaining 30% were used for validation. Ten factors that influence floods were considered, such as slope, elevation, proximity to rivers, drainage density, stream order, land use patterns, rainfall data, lithology (permeability level) index (TWI), and curvature. We obtained these factors from data sources. Finally, we generated a flood susceptibility map and evaluated its accuracy by calculating the area under the curve (AUC). The validation results confirmed that all three models were robust and effective with an AUC of 90. Moreover, the research uncovered a trend of vulnerability with the most susceptible area being in close proximity to the city of Essaouira along the Oued Ksob. A detailed analysis using HEC-RAS was conducted at this identified location, pinpointing the village of Diabat as highly exposed. These findings hold significance for flood management, empowering decision makers, scholars, and urban planners to make informed choices and implement strategies that can minimize the impact of floods in susceptible regions while minimizing potential damages.

Understanding how climate change may alter river discharge in semi-arid regions is essential for sustainable water-resource management. This study assesses future streamflow in the Zat River Basin (High Atlas Mountains, Morocco) using a hybrid framework that combines machine-learning rainfall–runoff modeling, CMIP6 multi-model climate forcing, monthly quantile-mapping post-processing of simulated discharge, and an exploratory temperature-sensitivity assessment. Monthly hydroclimatic observations of precipitation, air temperature, reference evapotranspiration, and discharge were compiled from February 1962 to August 2024. The period 1962–2005 was used for model development, the 2006–2014 window for chronological validation, and the more recent observations for supplementary evaluation of climate-driven simulations. Four algorithms were compared: Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Performance was assessed using NSE, KGE, RMSE, MAE, and R². GBR provided the best validation performance (NSE = 0.71, KGE = 0.80, and R² = 0.72). The selected model was then forced with CMIP6 projections under SSP2-4.5 and SSP5-8.5 to simulate streamflow to 2100. Quantile mapping was applied to the simulated discharge, rather than separately to precipitation, temperature, and reference evapotranspiration. The multi-model ensemble indicates a persistent drying tendency: relative to the historical baseline and without an additional temperature-sensitivity adjustment, mean annual discharge is projected to decline by approximately 12.1% under SSP2-4.5 and 27.3% under SSP5-8.5 by 2081–2100. Under an exploratory sensitivity case using a runoff-temperature-sensitivity coefficient of 0.04 °C⁻¹, the projected declines increase to approximately 23.3% and 44.1%, respectively. Episodic high-flow events nevertheless remain possible, suggesting a shift toward lower mean flows combined with persistent hydrological extremes.