A global drought dataset of standardized moisture anomaly index incorporating snow dynamics (SZI<sub>snow</sub>) and its application in identifying large-scale drought events
نویسندگان
چکیده
Abstract. Drought indices are hard to balance in terms of versatility (effectiveness for multiple types drought), flexibility timescales, and inclusivity (to what extent they include all physical processes). A lack consistent source data increases the difficulty quantifying drought. Here, we present a global monthly drought dataset with spatial resolution 0.25? from 1948 2010 based on multitype multiscalar index, standardized moisture anomaly index incorporating snow dynamics (SZIsnow), driven by systematic fields an advanced assimilation system. The proposed SZIsnow includes different water–energy processes, especially processes. Our evaluation demonstrates its ability distinguish across timescales. assessment also indicates that adequately captures droughts scales. consideration processes improved capability SZIsnow, improvement is particularly evident over snow-covered high-latitude (e.g., Arctic region) high-altitude areas Tibetan Plateau). We found 59.66 % Earth's land area exhibited drying trend between 2010, remaining 40.34 wetting trend. results indicate can be employed capture large-scale events occurred world. analysis shows there were 525 larger than 500 000 km2 globally during study period, which 68.38 had duration longer 6 months. Therefore, this new well suited monitoring, assessing, characterizing serve as valuable resource future studies. database available at http://doi.org/10.5281/zenodo.5627369 (Wu et al., 2021).
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ژورنال
عنوان ژورنال: Earth System Science Data
سال: 2022
ISSN: ['1866-3516', '1866-3508']
DOI: https://doi.org/10.5194/essd-14-2259-2022