ArticleScientific data2026
A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980-2024.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980-2024.Scientific data · 2026Article
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9 authors.
Funding
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.
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