Preprints
https://doi.org/10.5194/essd-2021-399
https://doi.org/10.5194/essd-2021-399

  26 Nov 2021

26 Nov 2021

Review status: this preprint is currently under review for the journal ESSD.

A global dataset of standardized moisture anomaly index incorporating snow dynamics from 1948 to 2010

Lei Tian1,2, Baoqing Zhang2, and Pute Wu3 Lei Tian et al.
  • 1Institute of Green Development for the Yellow River Drainage Basin, Lanzhou University, Lanzhou, 730000, China
  • 2Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
  • 3Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, China

Abstract. 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 from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index incorporating snow dynamics (SZIsnow), driven by systematic fields from an advanced data assimilation system. The proposed SZIsnow dataset includes different physical water‒energy 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 SZIsnow, 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 show that the SZIsnow dataset successfully captured the large-scale drought events that occurred across the world; there were 525 drought events with an area larger than 500,000 square kilometers globally during the study period, of which nearly 70 % 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.

Lei Tian et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-399', Anonymous Referee #1, 10 Dec 2021
    • AC1: 'Reply on RC1', Baoqing Zhang, 19 Dec 2021
  • RC2: 'Comment on essd-2021-399', Anonymous Referee #2, 11 Dec 2021
    • AC2: 'Reply on RC2', Baoqing Zhang, 02 Jan 2022
  • RC3: 'Comment on essd-2021-399', Anonymous Referee #3, 20 Dec 2021
    • AC4: 'Reply on RC3', Baoqing Zhang, 16 Jan 2022
  • RC4: 'Comment on essd-2021-399', Anonymous Referee #4, 20 Dec 2021
    • AC3: 'Reply on RC4', Baoqing Zhang, 12 Jan 2022

Lei Tian et al.

Data sets

A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010 Pute Wu; Lei Tian; Baoqing Zhang https://doi.org/10.5281/zenodo.5627369

A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010 Pute Wu; Lei Tian; Baoqing Zhang http://data.tpdc.ac.cn/en/disallow/b039fde6-face-4d24-af45-d238a6af18b7/

Lei Tian et al.

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Short summary
We propose a global monthly drought dataset from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index adding snow processes (SZIsnow). The consideration of snow processes improved the capability of SZIsnow, and the improvement is particularly evident over snow-covered high-latitude and high-altitude areas. This new dataset is well suited to monitoring, assessing, and characterizing drought, and is a valuable resource for drought studies.