Bridging Data Gaps for 1D-2D Flood Modeling in Northeast Central Morocco: Insights from Statistics of Extremes and Estimation Methods

Abstract

Abstract Floodplains are both a blessing and a curse. They offer fertile soil and water but pose a risk of flooding and habitat loss, particularly in semi-arid regions such as Northeast Central Morocco. Flood modeling is critical for mitigating flood impacts and improving disaster management strategies. However, data scarcity poses significant challenges in accurately simulating floods. This article discusses three knowledge gaps in flood risk management: (i) evaluating flood flow estimation methods, (ii) improving flood modeling accuracy, and (iii) updating plans to mitigate flood risks. This study addresses this challenge by using a two-step approach to fill hydrological data gaps and enhance flood modeling. The first step uses frequency analysis to predict extreme rainfall events. The second step compares the Gradex technique and empirical analysis of flood flows. These techniques consider rainfall-flow relationships, flood flow return time, and concentration time. By integrating 1D and 2D flood models and analyzing rainfall and topographic data, the study aimed to improve flood predictions and address challenges arising from limited data availability. It was revealed that the estimated flow for the 100-year return in the Nekor plain is 1,338.75 m3/s. This would result in extensive flooding, affecting an area of 2,017 Ha. The flooding would likely inundate farmlands, villages, and infrastructure, causing widespread damage and disruption. These findings have practical implications for decision-makers, planners, and researchers involved in hydraulic modeling and urban planning.