Integrating high-resolution geoinformation with local knowledge enhances flood risk assessment: A case study from rural Niger

Most flood risk assessments in rural areas of Low- and Middle-Income Countries rely on outdated, coarse-resolution geoinformation. Participation from exposed communities remains rare, and risk reduction measures are seldom identified. Our purpose is to address these shortcomings in a densely populated, frequently flooded rural area along the Niger River. The novelty of the study lies in the use of fine-grained spatial information and its integration with local knowledge, which led to the selection of appropriate risk-reduction measures. Risk was quantified monetarily as the product of hazard and expected damage. Riverine flooding and flash floods from tributaries are identified as the primary threats. Flood-prone areas for three return periods were identified using a 4-m resolution surface elevation model and BASEMENT’s two-dimensional hydraulic modelling. Assets were first identified using Google Earth imagery and subsequently verified on-site. Risk reduction measures were initially selected through a participatory SWOT analysis and then prioritised using eight criteria. The study found that the areas under cultivation have remained stable over the last 10 years, but settlements significantly expanded in flood-prone areas. A centennial flood during the dry season could cause €5.4 million in damage, with the municipality of Kourteye accounting for €3.4 million. Risk was primarily determined by damage to irrigated crops rather than buildings. Priority measures included extending and reinforcing levees in rice-growing areas, providing an Early Warning System, and improving sanitation and hygiene. However, the benefits of reducing the risk outweigh the costs only in the municipalities of Karma and Namaro.