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1 km Grid Precipitation Dataset in the Three-River Headwaters Region (2009-2013)


JIANG Yuhao1,2LI Baolin*1,2YUAN Yecheng1GAO Xizhang1ZHANG Tao1,2LIU Yan1,2LI Ying1,2LI Hong3LUO Zhiyong3MA Qiang3WANG Xiaomin3CIREN Duoji4
1Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China2University of Chinese Academy of Sciences,Beijing 100049,China3Meteorological Bureau of Henan Mongolian Autonomous County,Huangnan Tibetan Autonomous Prefecture,Qinghai Province,Huangnan 811599,China4Lhasa Meteorological Bureau,Lhasa 850000,China

DOI:10.3974/geodb.2019.05.17.V1

Published:Nov. 2019

Visitors:102       Data Files Downloaded:7      
Data Downloaded:2409.07 MB      Citations:

Key Words:

three-river headwaters,precipitation,downscale,Quadratic Parabolic Profile,Remote Sensing of Environment

Abstract:

1 km Grid Precipitation Dataset in the Three-River Headwaters Region (2009-2013) was developed by downscaling the TRMM 3B43 V7 precipitation data from 2009 to 2013. The spatial extent of the region ranges from 31.65° to 36.27°N and 89.40° to 102.38°E. And the downscaled TRMM data is based on the Quadratic Parabolic Profile (QPP) model of mountain precipitation proposed by Zhang et al. (2018) in Remote Sensing of Environment. The procedure of the dataset development includes: (1) estimating the QPP model parameters. Determining the elevation of the maximum precipitation based on the corresponding relationship with the NDVI peak altitude in the TRHR; and then estimating the precipitation in the maximum precipitation elevation and the quadratic term coefficient of the precipitation parabola equation; (2) Extrapolating model parameters. Firstly, spatial extrapolating the parameters in 0.25° scale based on the inverse distance weight (IDW) interpolation method; and then resampling these parameters from 0.25° to 1 km resolution based on the bilinear interpolation method to obtain the final parameters for each pixel. QPP model has better estimation accuracy and the average RMSE in the monthly and the cumulative precipitation of growing season (May to September) during 2009-2013 , compared with in situ data at national stations, was 14 mm, 18 mm, 19 mm, 13 mm, 16 mm, and 62 mm, respectively, and MAPE was 14%, 12%, 12%, 12%, 17%, and 11%, respectively. The dataset consists of four parts:(1) downscaled monthly and cumulative precipitation during the growing season (May to September) by the QPP model based on TRMM 3B43 V7 precipitation data from 2009 to 2013; (2) downscaled monthly and cumulative precipitation during the growing season (May to September) from 2009 to 2013 by the exponential regression, multiple linear regression and geographic weighted regression models; (3) digital elevation model in 1 km; (4) NDVI in the growing season from 2009 to 2013. The spatial resolution of this dataset is 0.0089285714°(1 km). The dataset is archived in .tif and ArcGIS Grid format, and consists of 846 data files with the data size of 438 MB (Compressed to 344 MB in one single file). The application of the dataset was published at the Remote Sensing of Environment, 2018, 215: 109-127.

Foundation Item:

Ministry of Science and Technology of P. R. China (2016YFC0500205, 2015CB954103)

Data Citation:

JIANG Yuhao,LI Baolin*,YUAN Yecheng,GAO Xizhang,ZHANG Tao,LIU Yan,LI Ying,LI Hong,LUO Zhiyong,MA Qiang,WANG Xiaomin,CIREN Duoji.1 km Grid Precipitation Dataset in the Three-River Headwaters Region (2009-2013)[DB/OL].Global Change Research Data Publishing & Repository,2019.DOI:10.3974/geodb.2019.05.17.V1.

Data Product:

ID Data Name Data Size Operation
1 PrecipThreeRiverHeadwaters_2009-2013.rar 352412.93KB DownLoad
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum