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<p>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 <a href="https://doi.org/10.5281/zenodo.18292641">https://doi.org/10.5281/zenodo.18292641</a>, providing a reusable resource for climate, hydrology, and hazard research.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Vít Štovíček,
  • Martin Hanel,
  • Rohini Kumar,
  • Vojtĕch Moravec,
  • Yannis Markonis,
  • Carmelo Cammalleri,
  • Jan Řehoř,
  • Miroslav Trnka,
  • Oldrich Rakovec

摘要

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.