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

Drought indices are hard to balance in terms of versatility (effectiveness for multiple types of drought), flexibility of timescales, and inclusivity (to what extent they include all physical processes). A lack of consistent source data increases the difficulty of quantifying drought. Here, we present a global monthly drought dataset with a spatial resolution of 0.25 degrees from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index incorporating snow dynamics (SZI(snow)), driven by systematic fields from an advanced data assimilation system. The proposed SZIsnow dataset includes different physical waterenergy processes, especially snow processes. Our evaluation of the dataset demonstrates its ability to distinguish different types of drought across different timescales. Our assessment also indicates that the dataset adequately captures droughts across different spatial scales. The consideration of snow processes improved the capability of SZI(snow), and the improvement is particularly evident over snow-covered high-latitude (e.g., Arctic region) and high-altitude areas (e.g., Tibetan Plateau). We found that 59.66% of Earth's land area exhibited a drying trend between 1948 and 2010, and the remaining 40.34% exhibited a wetting trend. Our results also indicate that the SZIsnow dataset can be employed to capture the large-scale drought events that occurred across the world. Our analysis shows there were 525 drought events with an area larger than 500 000 km2 globally during the study period, of which 68.38% had a duration longer than 6 months. Therefore, this new drought dataset is well suited to monitoring, assessing, and characterizing drought and can serve as a valuable resource for future drought studies.

Automated summary

This study presents a global monthly drought dataset based on a multitype and multiscalar drought index incorporating snow dynamics. The dataset demonstrates good performance in distinguishing different types of drought across various timescales and spatial scales. The consideration of snow processes improves the capability of the drought index, especially in snow-covered high-latitude and high-altitude areas. The dataset provides valuable information for monitoring, assessing, and characterizing drought events globally.