Dataset Open Access

# Meteorological drought lacunarity around the world and its classification

Monjo, Robert; Royé, Dominic; Martin-Vide, Javier

### Dublin Core Export

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:creator>Monjo, Robert</dc:creator>
<dc:creator>Royé, Dominic</dc:creator>
<dc:creator>Martin-Vide, Javier</dc:creator>
<dc:date>2019-06-16</dc:date>
<dc:description>Drought duration strongly depends on the definition thereof. In meteorology, dryness is habitually measured by means of fixed thresholds (e.g. 0.1 or 1 mm 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 have climatically classified the drought duration around the world according to their similarity to the voids of the Cantor set. Dryness time structure can be concisely measured by the n-index (from the regular/irregular alternation of dry/wet spells), which is closely related to the Gini index and to a Cantor-based exponent. This enables the world’s climates to be classified into six large types based upon a new measure of drought duration. 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 45165 days), and a spatial resolution of 0.5º, with a total of 259,197 grid points.

FILES

1. "drought_class"  (geotiff)

2. "legend_drought_class" (csv): legend values for drought classification.

3. "rasterbrick_index_HurstCantorGini" (geotiff): raster with three layers (Hurst, Cantor and Gini Index applied to dry spells).

4. "rasterbrick_nindex_spells" (geotiff): raster with four layers (Dry Spell Spells n-index, maximum expected dry spellY1 , mean dry spell and mean wet spell).

Projection: "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs" (EPSG.4326)</dc:description>
<dc:description>We wish to acknowledge the support received from the Spanish projects CGL2017-83866-C3-2-R and Climatology Group 2017 SGR 1362.</dc:description>
<dc:identifier>https://zenodo.org/record/3247041</dc:identifier>
<dc:identifier>10.5281/zenodo.3247041</dc:identifier>
<dc:identifier>oai:zenodo.org:3247041</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>info:eu-repo/grantAgreement/EC/H2020/700174/</dc:relation>
<dc:relation>doi:10.5194/essd-2019-115</dc:relation>
<dc:relation>doi:10.5281/zenodo.3247040</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>drought</dc:subject>
<dc:subject>n-index</dc:subject>
<dc:subject>dry spell</dc:subject>
<dc:subject>world</dc:subject>
<dc:subject>classification</dc:subject>
<dc:title>Meteorological drought lacunarity around the world and its classification</dc:title>
<dc:type>info:eu-repo/semantics/other</dc:type>
<dc:type>dataset</dc:type>
</oai_dc:dc>

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