Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and public health risks. This study develops a site-specific water quality index (WQI) for a gold mining area in Bam Province, Burkina Faso, with the objective of improving pollution monitoring and management in relation to tailing dams. Surface and groundwater samples were collected between 2021 and 2024. Physico-chemical and bacteriological analyses of groundwater sources including wells, piezometers and boreholes revealed that several parameters such as pH, turbidity, nitrates, sulphates, total iron, aluminium, arsenic, cadmium, cyanide and total and faecal coliforms exceeded international drinking water standards. Geospatial techniques were employed to identify the main contamination sources: domestic wastewater, industrial and artisanal mining and agricultural runoff. The evolution of these parameters in relation to the dynamics of soil occupation and the influence of geological structure has enabled the distinction of key parameters associated with discharges. Although individual contaminant levels were mostly moderate, their combined effects pose a significant long-term risk to ecosystems and human health. The tailored WQI is suitable for both surface water and groundwater. It provides an integrated tool for classifying and monitoring water quality in mining environments, supporting evidence-based decision making in the management of tailing dams, environmental protection and public health.

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.