HSPEI: A 1‐km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022

Abstract The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022.

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Publication Details

Journal
Geoscience Data Journal
Published
2024-10-04
DOI
https://doi.org/10.1002/gdj3.276
Citations
16
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
4.52

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article

HSPEI: A 1‐km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022

Yaochen Qin, Hongquan Song, Jia Yang, Wenzhe Jiao et al.
16 citations
Geoscience Data Journal
Climate variability and models
4.52
article

HSPEI: A 1‐km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022

Yaochen Qin, Hongquan Song, Jia Yang, Wenzhe Jiao, Xiaoyang Zhao, Haoming Xia, Wei Zhao, Yintao Sha
article en
16 citations

Abstract

Abstract The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022.

Geoscience Data Journal
Oklahoma State University (US), Henan University (CN), Institute of Mountain Hazards and Environment (CN), Indiana University – Purdue University Indianapolis (US)
National Natural Science Foundation of China, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 5%
Climate variability and models
4.52
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