High-resolution Global Multi-Index Drought (GMID) dataset, 1980-2024

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Dataset information

Country of origin
Updated
Created
Available languages
English
Keywords
standardized drought indices, Standardized Precipitation Index (SPI), Evaporative Drought Demand Index (EDDI), Standardized Precipitation Evapotranspiration Inde, flash drought, Standardized Evapotranspiration Deficit Index (SED, multiscalar, Standardized Vapor Pressure Deficit Index (SVPDI), Atmospheric Conditions
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Dataset description

Here, we present the Global Multi-Index Drought (GMID) dataset, which includes some of the most used and widely recommended standardised drought indices: the Standardized Precipitation Index (SPI), the Evaporative Drought Demand Index (EDDI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Evapotranspiration Deficit Index (SEDI), and the Standardized Vapor Pressure Deficit Index (SVPDI). This dataset is available on a global scale covering 1-12, 18, 24, 48-month time scales for the period 1980 to 2024, with high spatial (0.1 degrees) and temporal (weekly and monthly) resolution. GMID was generated using different meteorological input datasets, including: Multi-Source Weighted-Ensemble Precipitation (MSWEP) v2.8 (https://www.gloh2o.org/mswep/), Global Land Evaporation Amsterdam Model (GLEAM) v4.2a (https://www.gleam.eu/), and Multi-Source Weather (MSWX) vMSWX-Past (https://www.gloh2o.org/mswx/). The GMID dataset is provided weekly and monthly, including two versions of the five drought indices: one generated using the "reference" approach (i.e., based on the probability distribution typically recommended in the literature for calculating these drought indices), and another using the "best-fit" approach (i.e., based on the probability distributions that provide better normalised resulting series for each case). GMID represents a comprehensive and robust global long-term dataset for drought (and flash drought) assessment, which will be further updated annually as input data become available. Full details about this dataset can be found at https://doi.org/10.5285/f5ce92b0-03e8-4719-82ff-c62e6ebe927b
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2019
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