Flood Hazard Detection in Data-Scarce Regions: A Case Study of a Semi-Arid River in Northeastern Morocco

Abstract

Forecasting flood hazard areas often presents substantial challenges in data-scarce regions. In our case study, we used HEC-RAS model to identify flood risk zones for various return periods along an ungauged river in a semi-arid region. Our focus was on the densely populated area surrounding the Larbaâ River in Taza City. The model inputs comprise peak flow estimates derived from the GRADEX method, physical parameters approximated using standardized tables (Manning coefficient), and other measurements taken directly in the field. During the calibration phase, critical adjustments were made to ensure the model’s stability and its ability to generate results within an acceptable range. Our findings indicated that the numerical model successfully identified vulnerable areas. The floodplain closely aligns with the extent of the 100-year flood, highlighting all regions susceptible to flooding. The results were also consistent with flood events from the past two decades, underscoring the model’s predictive accuracy regarding the river’s behavior. These insights will inform future urban planning initiatives, enabling local authorities to implement effective mitigation strategies. This study demonstrates that the model is a valuable tool for comprehensive flood risk assessment, particularly in areas lacking monitoring.