The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.

File naming and required software

The dataset file name is SZIsnow_1948_2010_01-48.7z that is a compressed file. The software used for compression is a free and open source software named 7-Zip (download address: https://www.7-zip.org/). The extracted file contains 48 files in the format of NetCDF (netcdfnetcommon data form), that is, SZIsnow_1948_2010_XX.nc. In detail, 1948 and 2010 are the start and end dates of the dataset respectively, and "XX" represents the time scale of SZIsnow (1 to 48 months). Data access: NetCDF is widely used in many fields such as atmospheric science and hydrology. Data can be read in many ways (R language, MATLAB, NCL, etc.).

Data Citations Data citation guideline What's data citation?

Cite as:

Wu, P., Tian, L., Zhang, B. (2021). A global dataset of standardized moisture anomaly index incorporating snow dynamics (SZIsnow) from 1948 to 2010. A Big Earth Data Platform for Three Poles, DOI: 10.5281/zenodo.5627369. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Tian, L., Zhang, B., and Wu, P. (2022). A global drought dataset of standardized moisture anomaly index incorporating snow dynamics (SZIsnow) and its application in identifying large-scale drought events. Earth Syst. Sci. Data, 14, 2259–2278, https://doi.org/10.5194/essd-14-2259-2022.( View Details | Bibtex)

2. Zhang, B.Q, Xia, Y.L., Huning, L.S., Wei, J.H., Wang, G.Q., and AghaKouchak, A. (2019). A framework for global multicategory and multiscalar drought characterization accounting for snow processes. Water Resources Research, 55, 9258-9278. https://doi.org/10.1029/2019WR025529.( View Details | Bibtex)

3. Zhang, B.Q., Zhao, X.N., Jin, J.M., and Wu, P.T. (2015). Development and evaluation of a physically based multiscalar drought index: The Standardized Moisture Anomaly Index. Journal of Geophysical Research: Atmospheres, 120, 11,575-511,588. https://doi.org/10.1002/2015JD023772.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

the National Key Research and Development Program of China (No:2020YFA0608403)

Natural Science Foundation of China (NSFC) (No:42022001)

Natural Science Foundation of China (NSFC) (No:42001029)

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