Work

A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980–2024

Year: 2026

Type: data-paper

Abstract: We present a global dataset of spatiotemporally clustered drought events for 1980-2024, derived from daily precipitation, potential evapotranspiration, soil moisture, and surface runoff data. Drought conditions were consistently defined using a 10th percentile threshold and clustered in space and time using a three-dimensional implementation of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The dataset represents droughts as coherent spatiotemporal events rather than isolated grid-cell anomalies. For each drought event, it provides detailed metadata on spatial extent, temporal duration, severity, and centroid position. By applying a consistent event-detection framework across atmospheric forcing, root-zone soil moisture, and runoff response, the dataset supports systematic analysis of global drought dynamics and compound extremes. The dataset is openly available at https://doi.org/10.5281/zenodo.18292641 , providing a reusable resource for climate, hydrology, and hazard research.

Source: Scientific Data

DOI: 10.1038/s41597-026-07750-x