Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1038/s41597-026-07750-x
Licence creative commons licence
Title (Primary) A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980–2024
Author Štovíček, V.; Hanel, M.; Kumar, R. ORCID logo ; Moravec, V.; Markonis, Y.; Cammalleri, C.; Řehoř, J.; Trnka, M.; Rakovec, O.
Source Titel Scientific Data
Year 2026
Department CHS
Language englisch
Topic T5 Future Landscapes
Supplements Supplement 1
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.
Štovíček, V., Hanel, M., Kumar, R., Moravec, V., Markonis, Y., Cammalleri, C., Řehoř, J., Trnka, M., Rakovec, O. (2026):
A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980–2024
Sci. Data
10.1038/s41597-026-07750-x