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Updated: Jul 1, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980-2024
Vít Štovíček1,2, Martin Hanel3,4, Rohini Kumar5
1Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha, Suchdol, 165 00, Czech Republic. stovicekv@fzp.czu.cz.
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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