Drought risks are projected to increase in the future in central and southern regions of the Middle East

Abstract

Drought prediction is vital for sustaining water security in regions highly exposed to climate change. Here we present a machine learning-based method that integrates climate model outputs to improve drought monitoring in the Middle East. We introduce a spatially adaptive index called the Geographically Weighted Temperature Vegetation Dryness Index, developed using local regression techniques and trend analysis. This index integrates temperature and vegetation signals while accounting for variations across space and time. It substantially improves prediction accuracy compared to previous methods. We used recent climate projections under three socioeconomic scenarios to estimate future drought patterns. Results show spatial shifts and intensification of drought conditions in parts of the region by the end of the century under high-emission conditions. Our method also detects localized drought hotspots that broader indices may miss, offering valuable insights for targeted and adaptive water resource planning. Drought conditions could intensify by 25–35% in the future in the Middle East under a high-emissions scenario, with the most affected regions concentrated in central and southern areas, according to a drought index based on ensemble machine learning methods and climate model simulations.

Topics & Concepts