Integrating modelling and community engagement for flood risk management in data scarce contexts: insights from Metuge district in northern Mozambique.

Quantitative flood risk assessment is essential for local disaster risk reduction and management strategies. However, data scarcity which typically characterizes the Global South, poses significant challenges to the application of conventional risk assessment methodologies developed in data-rich contexts. This study addresses these challenges by providing an exportable and comprehensive flood risk framework designed for the Metuge district, a flood-prone region in northern Mozambique that is crossed by the Muaguide River. This framework integrates hydrological, hydrodynamic, and damage modelling with a multi-level participatory process that involves stakeholders from governmental to community levels. To overcome data deficiency, the modelling leverages global data sources, field survey data, and open-access tools. Feedback gathered through participatory activities has allowed to refine modelling assumptions, enhancing the reliability of the outcome. Specifically, the participatory activities were designed to reach multiple objectives: increasing the building capacity of local authorities, empowering the resilience of the local population, and validating the results. In fact, the absence of observed data for the study area has made the comparison of the results with community experience of past flood events the sole viable option for their validation. Results from this case study indicate an average of 2,000 individuals at risk annually and an Annual Average Damage (AAD) of approximately 300,000 USD/year to roads and buildings. The ratio between the AAD and the population of the study area corresponds to 0.5% of Mozambique’s GDP per capita. Moreover, the district population's access to the hospital during flooded periods has been assessed by analyzing the practicability of roads. These findings provide critical insights for local authorities for flood risk management and serve as a foundation for the design and implementation of mitigation measures.