Drought susceptibility modeling with geospatial techniques and AHP model: a case of Bilate River Watershed, Central Rift Valley of Ethiopia

Abstract

Drought continues to be the worst natural hazard in the world affecting ecosystems, economies and overall human welfare with severe consequences in developing countries. While drought is an unavoidable climatic phenomenon, actions can be made to improve preparedness and mitigate the impacts upon proper forecast on susceptibility. Drought susceptibility modeling plays a key role in determining how best to mitigate and adapt to drought occurrences. This research explores the application of geospatial techniques and Analytic Hierarchy Process (AHP) in drought susceptibility modeling for the drought-prone Bilate River watershed, located in the central Rift Valley drylands of Ethiopia. A total of 15 parameters were used including rainfall, temperature, evapotranspiration, soil moisture, normalized difference vegetation index, land surface temperature, soil texture, land use-land cover, topographic wetness index, modified normalized difference water index, drainage density, slope, elevation, population density and aspect to model drought susceptibility. Findings showed that nearly 70.2% of the area falls under the moderate drought category, followed by the severe (23.2%), mild drought (6.6%) and extreme (0.02%) drought categories in the watershed. Based on zonal administration, Wolayta Zone has a high spatial coverage of severe drought with 54.1% (659.3 km2) while Hadiya has a high spatial coverage of moderate drought with 24.6% (908.9 km2). The drought susceptibility model (DSM) receiver operating characteristic (ROC) curve was then created, and the area under curve (AUC) was calculated. The results of the analysis showed that the AUC is 0.701 (70.1%) indicating that the model is reasonably good model. Hence, geospatial approaches in conjunction with the AHP model improved the drought susceptibility modeling’s reliability, which has significant implications for drought adaptive management and preparedness planning.