A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
The SZI snow 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 SZI snow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the d...
| Main Authors: | , , |
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| Format: | Dataset |
| Language: | English |
| Published: |
Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.5627369 https://zenodo.org/record/5627369 |
| Summary: | The SZI snow 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 SZI snow 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 SZI snow , 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 SZI snow dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies. |
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