Integrating Remote Sensing Data and MODFLOW Modeling for Sustainable Groundwater Resource Management and Monitoring in the Guinea Region, West Africa

Abstract

Groundwater is vital in ensuring water security, supporting agriculture, and sustaining ecosystems in the Guinea region of West Africa. However, the region faces increasing challenges related to groundwater availability and quality due to population growth and climate change. This research presents pixel-based spatial-temporal trends assessment of groundwater mappings using an integrated approach that combines remote sensing data with MODFLOW modelling. The study focuses on the Guinea region, characterized by diverse topography and hydrogeological conditions. Remote sensing data, including satellite imagery of shallow groundwater storage (GWS), relative humidity, temperature, NDVI, and rainfall, provided valuable insights into observing hydrological cycles and climate changes. The MODFLOW modelling framework was employed to simulate groundwater flow (Hydraulic Heads) and variability of groundwater levels. It enabled the investigation of groundwater recharge and discharge processes, as well as the impact of climatic variations on groundwater dynamics. Furthermore, the trend modelling approach facilitated and revealed that the direction of the western to the northern part of the study area suffered from a depletion rate of groundwater level. The integrated analysis of remote sensing data and MODFLOW modelling highlights the complex interactions between climate change and groundwater resources. This research underscores the importance of proactive and informed groundwater management strategies to ensure the sustainable use of this invaluable resource. By combining remote sensing technology and groundwater modelling, it offers a holistic approach to support evidence-based decision-making for groundwater resource management and monitoring in the Guinea region of the West Africa.