Flood forecasting in data-scarce regions poses significant challenges due to irregular rainfall patterns and limited hydrological monitoring networks, particularly in semi-arid regions in Africa, South America, and Asia. However, despite significant efforts and advancements, there remains a substantial gap in the accurate prediction of flood events necessary for effective risk management and mitigation, evidenced by the recurrence of devastating floods in middle to low-income countries in recent years. Here, we address this problem by testing advanced modeling techniques in a local African case, using a combination of statistical methods for extreme event prediction, hydrodynamic modeling, and remote sensing data, to recommend the most adapted and accurate approach under a variety of settings. Our case study is an emerging urban area in Northern Morocco, situated in a triangular plain interposed between adverse geomorphological and precipitation settings, and unregulated expansion flow, creating an exceptionally overwhelming context for disastrous floods. In the absence of previous studies, we integrate frequency distribution analysis to predict extreme rainfall events and flood flow modeling to simulate floodplain inundation. Data sources included high-resolution remote sensing, local hydrological measurements, fine topographical data, and interviews with stakeholders. We found the Pearson Type 3 distribution to be the most suitable for modeling extreme precipitation in coastal areas, whereas the Generalized Extreme Value (GEV) distribution better fits inland areas. For flood flow assessment, the Gradex method proved to be the most accurate, while other empirical methods outlined critical limitations. Findings reveal that advanced hydrodynamic models significantly enhance flood hazard assessments, even in regions with limited data, showing outstanding correlations with previous flood records and stakeholder feedback. The outcomes carry critical implications for highlighting the importance of selecting appropriate models based on geographical and climatic conditions to inform more resilient urban planning and disaster management practices. We anticipate that these insights will support local decision-makers and urban planners in developing strategies that enhance community resilience and reduce the adverse impacts of flooding. Our work contributes to the broader field of flood risk management, providing a foundation for future developments and practical applications in similar regions worldwide.
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