A Global Drought Dataset from Clustering-Based Event Identification with Integrated Population, and GDP Exposure and Socioeconomic Impacts

Abstract. Drought events pose significant challenges to both ecosystems and human societies, requiring precise methodologies for their detection and impact assessment. A key challenge is linking physical drought indicators to socioeconomic consequences, such as the number of people affected or economic losses. This study introduces a robust two-step framework that integrates drought detection with impact analysis. In the first step, a clustering algorithm is used to identify coherent drought events and extract key characteristics such as severity and spatial extent. These events are tracked as spatially and temporally evolving objects. In the second step, the drought events are linked to population and GDP exposure, as well as to impact data from global disaster databases.

To characterize droughts, the study employs two widely used drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation and temperature data from the ERA5 reanalysis are used to compute these indices at four different timescales (1, 3, 6, and 12 months). Drought events are identified for different severity levels (-1, -1.5, and -2). The study also incorporates high resolution gridded datasets of global population and economic activity, alongside disaster impact data on affected populations and economic losses. The resulting drought dataset provides valuable information on the association between drought characteristics, exposure, and recorded impacts.

The analysis shows that a relatively large buffer distance is needed to match the identified drought events to impacts from disaster databases, and that more severe drought thresholds isolate fewer but higher-impact events. Population exposure is found to be highest in Asia, while GDP exposure is largest in North America. This integrated framework (https://doi.org/10.5281/zenodo.17251815; Samantaray & Messori, 2025) bridges the gap between physical drought characteristics, exposure, and documented impacts, supporting vulnerability analyses, improved climate adaptation planning and disaster risk management.