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Published version

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2020

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cc-by (c) Monjo, Robert et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/195334

Meteorological drought Lacunarity around the world and its classification

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https://doi.org/10.5194/essd-12-741-2020

Abstract

The measure of drought duration strongly depends on the definition considered. In meteorology, dryness is habitually measured by means of fixed thresholds (e.g. 0.1 or 1mm usually define dry spells) or climatic mean values (as is the case of the standardised precipitation index), but this also depends on the aggregation time interval considered. However, robust measurements of drought duration are required for analysing the statistical significance of possible changes. Herein we climatically classified the drought duration around the world according to its similarity to the voids of the Cantor set. Dryness time structure can be concisely measured by the index (from the regular or irregular alternation of dry or wet spells), which is closely related to the Gini n index and to a Cantor-based exponent. This enables the world's climates to be classified into six large types based on a new measure of drought duration. To conclude, outcomes provide the ability to determine when droughts start and finish. We performed the dry-spell analysis using the full global gridded daily Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. The MSWEP combines gauge-, satellite-, and reanalysis based data to provide reliable precipitation estimates. The study period comprises the years 1979-2016 (total of 45 165 d), and a spatial resolution of 0.5 , with a total of 259 197 grid points. The dataset is publicly available at https://doi.org/10.5281/zenodo.3247041 (Monjo et al., 2019).

Subject

Sequeres, Climatologia

Subject (English)

Droughts, Climatology

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Articles publicats en revistes (Geografia)

Citation

MONJO, Robert, ROYÉ, Dominic and MARTÍN VIDE, Javier. Meteorological drought Lacunarity around the world and its classification. Earth System Science Data. 2020. Vol. 12, num. 1, pags. 741-752. ISSN 1866-3508. [consulted: 11 of August of 2026]. Available at: https://hdl.handle.net/2445/195334

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