Assessing Drought Susceptibility and Risk using Dual Multi-Criteria Decision-Making Models

Abstract

Droughts, characterized by insufficient precipitation relative to evaporation, pose significant challenges for disaster preparedness and mitigation. This study investigates the integration of Remote Sensing (RS) and Geographical Information System (GIS) techniques to map drought-prone regions in Rehoboth, Namibia. It employs two Multi-Criteria Decision-Making (MCDM) methods, namely the Multi-Influencing Factor (MIF) and the Analytical Hierarchical Process (AHP), to assess factors influencing drought severity. Eight (8) key parameters, including rainfall, slope, drainage density, soil type, normalized difference water index, normalized difference vegetation index, land use land cover, and land surface temperature, serve as input variables for generating thematic maps representing drought-related factors. The study calculates the weightings of these factors using MIF and AHP, facilitating weighted overlay analysis and the creation of drought susceptibility maps. According to the AHP-based model, severe drought-prone areas in the study area cover 0.68%, moderately prone areas encompass 12.24%, and slight-prone areas occupy 87.09% of the region. In contrast, the MIF method indicates severe drought-prone areas at 0.12%, moderately prone areas at 12.8%, and slight-prone areas at 87.08%. A comparative analysis demonstrates the consistency and reliability of results produced by both methods. The findings provide valuable insights for stakeholders and policymakers, aiding the development of effective drought mitigation strategies and sustainable resource management.