Contribution of Satellite Precipitation Data to Hydrological Modeling in Data-Limited Contexts: A Case Study in Morocco

Satellite precipitation products have become an important source of rainfall information for hydrological studies, particularly in regions where ground observations are sparse or incomplete. Evaluating their capability to reproduce watershed hydrological responses is therefore essential before they can be used for flood modelling and water resources applications. This study assesses the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) satellite precipitation product for rainfall–runoff simulation in the Oued Nfifikh watershed (Morocco) using the HEC-HMS rainfall–runoff model. A sensitivity analysis was first carried out to identify the most influential model parameters. The model was then calibrated manually and subsequently optimized through automatic calibration using six flood events, while three independent events were used for validation. To ensure an objective comparison, the same calibrated parameter set was applied to simulations driven by both observed rainfall and PERSIANN-CCS precipitation. Model performance was evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), correlation coefficient (R), and coefficient of determination (R²). The results showed that both rainfall datasets enabled satisfactory streamflow simulations, although their performance varied from one flood event to another. Event-based comparisons indicated that PERSIANN-CCS precipitation produced results comparable to those obtained with observed rainfall and even outperformed ground observations for several events, demonstrating its potential for rainfall–runoff modelling in semi-arid watersheds. These findings highlight the potential of satellite precipitation products as a reliable alternative or complementary source of rainfall information for hydrological modelling, particularly in data-scarce regions, and provide a sound basis for subsequent hydraulic modelling and flood hazard mapping.