Flood Risk Assessment of the Lower Sirba River Basin, Burkina Faso–Niger
Description
The Sirba River is the largest right-bank tributary of the Middle Niger River. Its basin covers 39,000 km², mainly in the Sahel and East regions of Burkina Faso and, in its lower reaches, in the Tillabéri region of Niger. This dataset covers the Lower Sirba River Basin (LSRB), which is remote, sparsely populated, and poorly gauged. The LSRB includes the Sirba River (329 km) and its main tributaries, the Faga (145 km) and the Yali (82 km). The upper basin has numerous dams that manage floods and enable agricultural irrigation. This dataset covers flood-prone areas, exposed assets, risk levels, risk-reduction measures, residual risk, and the cost-benefit analysis of risk treatment in the LSRB. This dataset is innovative in its participatory production process, the high resolution used to identify assets, and the inclusion of boreholes and flood recovery costs in the risk assessment and cost-benefit analysis. The dataset is particularly relevant to riverside communities and, downstream of the confluence with the River Niger, the large city of Niamey. The flood risk assessment (FRA) of the LSRB contributes to the impact-based flood early warning system that the SLAPIS-Sahel project, funded by the Italian Agency for Development Cooperation, is implementing in partnership with the national meteorological agencies and the Hydrology Directorates of Burkina Faso and Niger. The dataset comprises 14 tables and 8 shapefiles covering: 1. Geographical coordinates of the LSRB target points within the catchment 2. Catchment parameters 3. LSRB discharge by return period at targeted and ungauged sites 4. LSRB discharge at gauged sites by return period 5. Hydraulic model parameters for the LSRB 6. Rain-fed and irrigated crop yields and average prices in the LSRB 7. Assets at risk in the LSRB by municipality and main settlements 8. Housing metrics in selected settlements of the LSRB 9. Flood depth–damage function in the LSRB 10. Expected flood damage to buildings and agriculture by return period and municipality 11. Flood risk in the LSRB by return period and municipality 12. Flood risk reduction measures for flood-prone communities in the LSRB 13. Residual risk by return period and municipality in the LSRB 14. Cost-benefit analysis of risk management measures in the LSRB by return period and municipality The shapefiles relate to: 15. Flood-prone areas by 5-, 10- and 100-year return period (RP) 16. Flood-prone areas with 3 classes of inundation depths by 5-, 10- and 100-year RP 17. Buildings exposed to a 100-year RP flood 18. Fields exposed to a 100-year RP flood The dataset identifies 2,176 residential units, 138 hectares of irrigated and rain-fed crops, and 10,880 people exposed to 100-year floods. These figures fall to 1,128 buildings and 5,640 inhabitants exposed to floods with a 5-year RP. 82% of the buildings exposed to a 100-year flood are in Burkina Faso, while 58% of the crops are in Niger. Risk reduction is economically worth with frequent flooding.
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Steps to reproduce
We identified flood-prone areas using BASEMENT 2D hydraulic modelling. A 4 m resolution digital surface model (DSM) was derived from PlanetScope imagery for Burkina Faso. A 10 m resolution digital terrein model (DTM) produced by Harris Geospatial, anchored to 120 ground control points surveyed using differential GPS was used for Niger. We calculated discharge rates for return periods (RP) of 5-, 10-, and 100-years using a regional flood model based on basin physiographic parameters extracted in QGIS. We assessed accuracy by comparing the modelled flood with Sentinel observations on the day of the 2020 flood peak (approximately a 100-year RP). The sensitivity analysis compared the flood-modelled area using the 4 m DSM with the flood-modelled area using a 1 m resolution DEM on a section of the Yali River near Sebba. We identified assets by visually interpreting satellite imagery with a resolution of < 1 m, acquired between December 2024 and March 2025 and available on Google Earth. The Directorate-General for Water Resources of Burkina Faso provided the locations of boreholes. We calculated the risk level by multiplying the value of the exposed assets by the probability of flooding. We determined the building types and average number of inhabitants through site visits in 7 settlements, covering 35 sample buildings. The value of the buildings was determined in consultation with the 5 municipalities and the 7 most exposed settlements, during a meeting held at the National Meteorological Agency, on 21–22 July 2026, and, for the agricultural component, using yields provided by the national statistical institutes of Burkina Faso and Niger, and crops prices by the agricultural market information systems of Burkina Faso (SONAGE) and Niger (SIMA). The damage estimate utilises the flood depth–damage function. Risk treatment was identified by the most exposed communities using a SWOT analysis. The residual risk assessed the effect of risk treatment on the risk level. We assumed that buildings exposed to flood depths of < 1 m can be protected, whilst those exposed to greater depths must be relocated. The large rice-growing site of Namaro, at the confluence of the Sirba and Niger rivers, can be protected by raising the levees. All other crops remain exposed, determining the level of residual risk. The cost-benefit analysis (CBA) considered as costs the protection of buildings exposed to flood depths of less than 1 m, raising latrines, signposting the flood-prone area, local communication of flood warnings, the resettlement of residents in flood-prone areas where water depths exceed 1 m, and raising the embankment at the Sirba–Niger confluence to protect the Namaro AHA. The benefits include the value of protected buildings, latrines, and the AHA; shelter and relief kits for affected populations; and avoided costs of anti-malaria and anti-cholera treatments. The tables allow you to update the values of exposed assets and, in turn, refresh the risk level and CBA.
Institutions
- Politecnico di TorinoPiedmont, Turin