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

¹ Faculty of Sciences Dhar El Mahrez, Sidi Mohamed Ben Abdellah University, Fes, Morocco
² Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, Fes, Morocco
³ Higher School of Education and Training, Mohamed First University, Oujda, Morocco
⁴ Faculty of art and human sciences, Sidi Mohamed Ben Abdellah University, Fes, Morocco
⁵ Higher Institute of Nursing Professions and Health Techniques of Fez (Annex Taza), Taza, Morocco

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

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.