A global drought dataset of standardized moisture anomaly index incorporating snow dynamics (SZI snow ) and its application in identifying large-scale drought events

[摘要] Drought indices are hard to balance in terms of versatility (effectiveness for multiple types of drought), flexibility of timescales, andinclusivity (to what extent they include all physical processes). A lack of consistent source data increases the difficulty of quantifyingdrought. Here, we present a global monthly drought dataset with a spatial resolution of 0.25 ∘ from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly indexincorporating snow dynamics ( SZI snow ), driven by systematic fields from an advanced data assimilation system. The proposed SZI snow dataset includes different physical water–energy processes, especially snow processes. Our evaluation of the dataset demonstratesits ability to distinguish different types of drought across different timescales. Our assessment also indicates that the dataset adequatelycaptures droughts across different spatial scales. The consideration of snow processes improved the capability of SZI snow , and theimprovement is particularly evident over snow-covered high-latitude (e.g., Arctic region) and high-altitude areas (e.g., Tibetan Plateau). We foundthat 59.66 % of Earth's land area exhibited a drying trend between 1948 and 2010, and the remaining 40.34 % exhibited a wetting trend. Ourresults also indicate that the SZI snow dataset can be employed to capture the large-scale drought events that occurred across theworld. Our analysis shows there were 525 drought events with an area larger than 500 000  km 2 globally during the study period, of which68.38 % had a duration longer than 6 months. Therefore, this new drought dataset is well suited to monitoring, assessing, and characterizingdrought and can serve as a valuable resource for future drought studies. The database is available at http://doi.org/10.5281/zenodo.5627369 (Wu et al., 2021).

[学科分类] 眼科学