Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-2259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 May 2022
Data description paper |
| 12 May 2022
| 12 May 2022
A global drought dataset of standardized moisture anomaly index incorporating snow dynamics (SZIsnow) and its application in identifying large-scale drought events
Lei Tian
Institute of Green Development for the Yellow River Drainage Basin, Lanzhou University, Lanzhou, 730000, China
Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
Shiyang River Basin Scientific Observing Station of Gansu Province, Lanzhou, 730000, China
Baoqing Zhang
CORRESPONDING AUTHOR
Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
Pute Wu
Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, China
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Short summary
We propose a global monthly drought dataset with a resolution of 0.25° 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 its capability, and the improvement is prominent 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.
We propose a global monthly drought dataset with a resolution of 0.25° from 1948 to 2010 based...
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