DOI: 10.57760/sciencedb.ecodb.00090
title: High-resolution SPEI Dataset for Drought Monitoring and Impact Analysis in Mainland China from 2001 to 2020
title: 2001-2020年中国1km分辨率多时间尺度SPEI数据集

description: The Standardized Precipitation Evapotranspiration Index (SPEI) is one of the commonly used drought indices internationally. At present, the SPEI index has been widely applied in monitoring and forecasting research of meteorological drought, hydrological drought, agricultural drought, and socio-economic drought. However, existing SPEI datasets have issues such as small coverage, spatial discontinuity, or low spatial resolution. Therefore, based on the SPEI calculated by meteorological stations, combined with GPM (Global Precision Measurement) precipitation, MODIS (Mode Resolution Imaging Spectroradiometer) surface temperature (LST), ERA5 Land short wave radiation, SRTM (Shuttle Radar Topography Mission) digital elevation model (DEM) data and Random forest regression model, this study developed a high-precision machine learning algorithm to calculate grid SPEI index on a large regional scale, A set of SPEI_RF grid data set with multiple time scales (1 month, 3 months, 6 months, 12 months, 24 months) and high spatial resolution (1km) covering the whole Chinese Mainland from 2001 to 2020 has been produced. We found that the SPEI developed in this study_ The RF dataset and the currently widely recognized SPEIbase v.2.6 (developed by Vicente Serrano) dataset have highly consistent spatiotemporal distribution characteristics. Compared to the SPEIbase v.2.6 dataset, SPEI_ The RF dataset has the advantages of higher spatial resolution and more accurate recognition of local small-scale droughts. Overall, the SPEI developed in this study_ RF data set can accurately simulate the spatial-temporal evolution characteristics of drought in Chinese Mainland during 2001-2020, which will play an important role in further deepening drought research in the future. For the production algorithm of this dataset, please refer to the national invention patents: Xia Haoming, Zhao Xiaoyang, Zhao Wei High resolution SPEI dataset development method based on Random forest regression model, 2023.05.30, China, CN 02211123216.8.
description: The Standardized Precipitation Evapotranspiration Index (SPEI) is one of the commonly used drought indices internationally. At present, the SPEI index has been widely applied in monitoring and forecasting research of meteorological drought, hydrological drought, agricultural drought, and socio-economic drought. However, existing SPEI datasets have issues such as small coverage, spatial discontinuity, or low spatial resolution. Therefore, based on the SPEI calculated by meteorological stations, combined with GPM (Global Precision Measurement) precipitation, MODIS (Mode Resolution Imaging Spectroradiometer) surface temperature (LST), ERA5 Land short wave radiation, SRTM (Shuttle Radar Topography Mission) digital elevation model (DEM) data and Random forest regression model, this study developed a high-precision machine learning algorithm to calculate grid SPEI index on a large regional scale, A set of SPEI_RF grid data set with multiple time scales (1 month, 3 months, 6 months, 12 months, 24 months) and high spatial resolution (1km) covering the whole Chinese Mainland from 2001 to 2020 has been produced. We found that the SPEI developed in this study_ The RF dataset and the currently widely recognized SPEIbase v.2.6 (developed by Vicente Serrano) dataset have highly consistent spatiotemporal distribution characteristics. Compared to the SPEIbase v.2.6 dataset, SPEI_ The RF dataset has the advantages of higher spatial resolution and more accurate recognition of local small-scale droughts. Overall, the SPEI developed in this study_ RF data set can accurately simulate the spatial-temporal evolution characteristics of drought in Chinese Mainland during 2001-2020, which will play an important role in further deepening drought research in the future. For the production algorithm of this dataset, please refer to the national invention patents: Xia Haoming, Zhao Xiaoyang, Zhao Wei High resolution SPEI dataset development method based on Random forest regression model, 2023.05.30, China, CN 02211123216.8.