DOI: 10.57760/sciencedb.j00001.00384
title: Downscaled monthly precipitation dataset at 1 km resolution over Guangxi, China and ASEAN regions from 2001 to 2020
title: 2001~2020年中国广西及东盟区域1 km分辨率降尺度降水数据集

description: Guangxi, China and the ASEAN regions, located in the southeastern Asia, have a subtropical monsoon climate with abundant rainfall and frequent floods, which have a great impact on daily life and social production. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 were selected as the dependent variable. Combined with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and topographic elevation (ELV) from ASTER data as explanatory variables, a geographically weighted regression (GWR) model was constructed by using annual input, depicting the change of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel function are adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function is selected based on the correlation coefficient, the root mean square error and bias. Thus, we established the 1-km annual precipitation data in 2001-2020 for Guangxi, China and the ASEAN regions. The 1-km monthly precipitation dataset from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2679 stations in 2001-2020 were used for verification, and the overall pearson correlation and correlation coefficient were 0.890 and 0.792, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations under 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.
description: Guangxi, China and the ASEAN regions, located in the southeastern Asia, have a subtropical monsoon climate with abundant rainfall and frequent floods, which have a great impact on daily life and social production. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 were selected as the dependent variable. Combined with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and topographic elevation (ELV) from ASTER data as explanatory variables, a geographically weighted regression (GWR) model was constructed by using annual input, depicting the change of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel function are adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function is selected based on the correlation coefficient, the root mean square error and bias. Thus, we established the 1-km annual precipitation data in 2001-2020 for Guangxi, China and the ASEAN regions. The 1-km monthly precipitation dataset from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2679 stations in 2001-2020 were used for verification, and the overall pearson correlation and correlation coefficient were 0.890 and 0.792, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations under 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.