Predicting Global Change impacts on streamflow dynamics using distributed hydrological modeling in a data-scarce nested tropical catchment in West Africa.

Currently, the global water cycle is experiencing radical shifts and the associated global water crisis requires rapid action by stakeholders to mitigate adverse impacts on both human populations and ecosystems. This urgency in action is driven by the combined effect of Climate Change and Land-use land cover change (LULCC) and the associated challenges in securing clean water sources. The Global Change from climate change is making water scarcity worse in places that are water-stressed, causing more competition and even conflicts over water resources. Addressing the global water crisis is especially challenging in the data-scarce region of the Global South where the status of hydrological processes and water availability is poorly constrained. Here, progress in hydrological predictions through robust hydrological models remains on top of the research agenda. General for the Global South, and particularly for West Africa, is the limited hydrological process understanding of tropical catchments with accelerating land cover change. The focus of the research study seeks to address the following research questions:
•    How does climate change alter hydrological processes in tropical catchments and does this alter streamflow regimes across nested catchments? 
•    How does and what are the contribution of LULCC in spatial-temporal changes of streamflow in a nested catchment in addition to the alterations driven by climate change within a given West African region?
To address the questions above, we will rely on data from the Pra River Basin in West Africa. In the present study, we employed Google Earth Engine (GEE) and Random Forest Classifier (RFC) to assess a time-series spatio-temporal land-use/cover change and change detection of the Pra River Basin for the period 2007 to 2023. Focusing on five (5) LULCC classifications has become crucial to the region's unregulated large and small-scale mining activities. The use of the Normalised Difference Water Index (NDWI), and Modified NDWI (MNDWI), was effective in extracting water surface areas for the change detection and pressure on the Pra River Basin and dealing with the overestimation phenomenon. We next integrate the processed LULCC into an eco-hydrological model that is validated against observed and reanalysed streamflow at different stations, soil moisture, and groundwater data. Future work will consist of estimating the impact analysis of Global Change on streamflow using an ecohydrological model that will be driven with the downscaled climate scenarios from CMIP6 and time-series land use change scenarios. This multifaceted approach is novel to the scientific understanding of water resource dynamics in the face of Global Change in tropical systems.