Mapping geothermal power potential using GIS aided multi-criteria decision analysis, a case study of Zimbabwe.

Abstract

Abstract Threatened by climate change and the associated negative impacts of climate variability, there are growing efforts to explore alternative energy sources world over. This study assesses the geothermal energy potential across Zimbabwe using Geographic Information Systems (GIS) and remote sensing aided multi-criteria decision analysis (MCDA). Key factors influencing geothermal potential were considered, including heat flow, geology, seismic activity, soil moisture, soil depth to bedrock, soil bulk density, and elevation. Data were collected from various sources, including satellite imagery, the Global Heat Flow Database, and local geoportals, and pre-processed to ensure consistency in spatial alignment and resolution. The Analytical Hierarchy Process (AHP) was used to assign weights to each factor based on its relative importance. These weighted factors were then combined using a Weighted Overlay Analysis in ArcGIS to generate a geothermal potential map. The results show that Matabeleland North and Manicaland provinces exhibit the highest geothermal potential, while Masvingo has the lowest. The study validates its findings using known locations of thermal hot springs, demonstrating a strong correlation between high geothermal potential and the presence of hot springs. This study thus provides a systematic and spatially sound assessment of geothermal energy potential, offering a foundation for future geothermal energy exploration and development in Zimbabwe.