This study focus on maps soil loss in the Zou catchment in central Benin to help protect farmland. Using satellite images, rainfall records and soil data, we identified areas where soil lost is most at risk. Most of the basin shows low soil loss risk, while small zones on steep slopes are more exposed. The results show the important for better land care and future work with higher-resolution satellite data.

This study aims to improve river flow prediction in a region where hydrological data are limited, which is essential for water management and flood preparedness. We combined a traditional rainfall–runoff model with data-driven learning methods to correct systematic simulation errors. Results show that the combined approach predicts river flow more accurately than the traditional model alone. These findings highlight a practical way to improve water resource planning in data-limited regions.

Communities along the Gambia River have low awareness of water rules and limited participation in management, leading to conflicts between farmers and herders. Supporting local organizations improves cooperation and fair water sharing. Strengthening community involvement and communication is key to sustainable water management and reducing disputes.

This study examined the quality of water delivered by commercial tanker suppliers in Tamale Metropolis. Water samples were collected from tankers and tested in the laboratory, alongside interviews with operators. Results showed that although the source water was treated, water quality often declined during transport, with high cloudiness and signs of contamination. These findings highlight health risks and the need for better cleaning, monitoring, and regulation of tanker water services.