The establishment of the Sendai Framework for Disaster Risk Reduction 2015‒2030 prompted local governments to prepare flood risk assessments. However, within the rural contexts of low- and middle-income countries, such tools do not account for the details necessary for risk management. Limited data availability is the most frequently reported reason for such deficiencies. Moreover, flood exposure is particularly uncertain. This study aimed to overcome this obstacle through the use of fine-grained open-access information, local datasets, and ground truth data. Its novelty is the identification of flood-prone assets and the formulation of risk reduction measures. This approach was applied to a 58-km section of the Niger River upstream of Niamey. Notably, risk is considered the product of hazard and damage, expressed in monetary terms. On the basis of five hazard scenarios, hydraulic modelling was employed to determine the floodplain, which was then validated against Sentinel-2 satellite images. Buildings and crops were identified through visual interpretation of Google Earth satellite images and verified on the ground. Wells were located according to statistics from the Ministry of Hydraulics and Sanitation. The potential damage resulting from a flood with a 10-year return period was partitioned, with 57% attributed to crops and 43% to buildings. Horticulture alone accounted for 43% of the total damage. Half of the damage to buildings is concentrated in 13 settlements. Nine structural measures are recommended on the basis of the expected inundation depth.
