2001–2020年中国广西及东盟区域1 km分辨率降尺度月度降水数据集  

A dataset of downscaled monthly precipitation at 1 km resolution in Guangxi,China and ASEAN regions from 2001 to 2020

作　　者：赵宁 邱玉宝 贾国强 孙希延[1] 傅文学 ZHAO Ning;QIU Yubao;JIA Guoqiang;SUN Xiyan;FU Wenxue(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,P.R.China;China-ASEAN Regional Innovation Center for Big Earth Data,Nanning 530022,P.R.China;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,P.R.China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,P.R.China)

机构地区：[1]桂林电子科技大学,信息与通信学院,广西桂林541004 [2]中国-东盟地球大数据区域创新中心,南宁530022 [3]可持续发展大数据国际研究中心,北京100094 [4]中国科学院空天信息创新研究院,中国科学院数字地球重点实验室,北京100094

出　　处：《中国科学数据（中英文网络版）》2024年第2期34-49,共16页China Scientific Data

基　　金：广西创新驱动发展专项资金项目(桂科AA20302022);中国科学院战略性先导科技专项(XDA19090130)。

摘　　要：中国广西及东盟区域位于亚洲东南部,同属亚热带季风气候,降水充沛,洪涝灾害频发,直接影响社会经济活动,高分辨率、高精度降水资料可有效支撑区域水资源、农业、灾害及生态等管理和研究。本研究以2001–2020年全球降水观测计划降水数据(GPM IMERG)为因变量,结合中分辨率成像光谱仪(MODIS)增强植被指数(EVI)、地表蒸散发(ET)、地表温度(LST)、先进星载热发射和反射辐射仪(ASTER)海拔(ELV)等解释变量,引入考虑变量随地理环境影响变化的地理加权回归(GWR)模型,构建空间分辨率为10 km的年尺度模型,经过测试选取验证精度良好的指数核函数。通过回代,基于空间分辨率为1 km的解释变量,构建了2001–2020年中国广西及东盟区域1 km年度降水数据集,并进一步采用比例指数法获得研究区2001–2020年1 km月度降水数据集。采用2001–2020年2679个地面观测站点数据对降尺度数据集进行验证,相关系数、均方根误差和偏差分别为0.792、74.610mm、-0.122%。本数据集能够有效反映1 km分辨率下的降水时空分布及其差异性,可广泛应用于水资源、农业、生态环境、灾害模拟等研究领域。Located in the southeastern Asia,Guangxi,China and the ASEAN regions have a subtropical monsoon climate with abundant rainfall and frequent floods,which has a great impact on social and economic activities.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,we used the Global Precipitation Measurement Mission precipitation data(GPM IMERG)from 2001 to 2020 as the dependent variable in combination with the enhanced vegetation index(EVI),surface evapotranspiration(ET),land surface temperature(LST)from MODIS data and elevation(ELV)from ASTER data as explanatory variables.Then we introduced a geographically weighted regression(GWR)model to construct an annual scale model,depicting the spatial variation of 10-km satellite precipitation with the influence of geographical environmental conditions.Five types of kernel functions were adopted in our GWR model,including the gaussian,exponential,bisquare,tricube,and boxcar kernel function.The optimal kernel function was selected based on the correlation coefficient,the root means square error and bias.Thus,we established the dataset of downscaled monthly precipitation in Guangxi,China and ASEAN regions from 2001 to 2020 by extrapolating the GWR model with input variables at 1 km resolution.The dataset of 1-km monthly precipitation from 2001 to 2020 was also generated by the proportional coefficient method.Moreover,ground observation data from 2,679 stations during 2001–2020 were used for verification.The correlation coefficient,root mean square error and deviation were 0.792,74.610mm and-0.122%,respectively.The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations at 1-km resolution in detail,and could be potentially promising for ecological environment,hydrological management,flood predictio

关 键 词：降水 广西及东盟区域 全球降水观测计划(GPM) 地理加权回归模型(GWR) 核函数 

分 类 号：P412.13[天文地球—大气科学及气象学]