In the context of climate change and the subsequent pressure from geological structures, there is a pressing need to analyse hydroclimatic trend events on dam stability and establish a clear correlation between reservoir surface water and groundwater levels in the Bagré Dam. Hydrodynamic variables (evaporation, inflow, irrigation, spillage, water level, rainfall) collected in and around the Bagré dam were analysed, using the singular spectrum analysis algorithm to delineate trends and variability over the 1993–2022 period. Machine learning models were developed to predict the impact of surface water on groundwater level over groundwater data that spans 2002–2022. There is an observed increasing trend of spillage due to increasing inflow; evaporation and irrigation trends are also increasing, while rainfall has been decreasing since 2012. The prediction shows an increasing groundwater level due to an increasing available reservoir water level. Consistent inflow rise will affect dam stability, while a spill over might lead to flooding downstream. The increasing trend of irrigation benefits farmers, while consistent availability of reservoir surface water would enable the groundwater level to increase via recharge. Regular monitoring of hydrodynamic trends will enable dam stability, maintenance, and contribute to groundwater and surface water availability for agricultural activities amidst climate change.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions