Constructing dataset of classified drainage areas based on surface water-supply patterns in High Mountain Asia

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Lu, Jieyu (2019): Constructing dataset of classified drainage areas based on surface water-supply patterns in High Mountain Asia. Version 1. 4TU.ResearchData. dataset.
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The High Mountain Asia (HMA) region is a geographical unit, holds the largest reservoir of glaciers and snow outside Earth poles, ranging from the Hindu Kush and Tien Shan in the west to the Eastern Himalaya, with altitude between 2000 to 8844m. In last decades, numerous glaciers and lake areas there have undergone tremendous changes, where the formation of surface runoff, glacier meltwater are the main sources of water inflow to lakes, modulating the distribution of water resources. Aim to better understand the pattern of distribution about water resources, and their dynamic changes at basin scale in HMA, a watershed classification dataset based on the water supply replenishment pattern was constructed using data of glacier and surface runoff from Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales(HydroSHEDS), the Second Glacier Inventory and the vector dataset of rivers and streams of HMA. Four datasets were thus obtained: Glacier- and Stream-fed Drainage Area (GSDA), Glacier-fed and stream-free Drainage Area(GDA), glacier-free and Stream-fed Drainage Area(SDA), and the Glacier- and Stream-free Drainage Area (NGSDA), with the numbers of 87, 107, 32, and 448 separately. The statistical results show GSDA has the largest surface area, accounting for 82.2% of the total basin area in HMA, mainly in the region of the outflow basin. Dominated by small basins, the GDA area accounts for the smallest surface area, only 3.86% of the total, the SDA accounts for 5.62%. For NGSDA, most of these are with small areas, accounting for 8.32%, and mainly distributes in the inflow basin of the Qiangtang Plateau. This dataset provides a fundamental classified data source for research on climate, ecology, environment and water resources in HMA.
  • 2019-12-20 first online, published, posted
4TU.Centre for Research Data
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  • Chinese Academy of Sciences, XDA19070201
  • International Cooperation Program of the Chinese Academy of Sciences, 131211KYSB20150035
  • Ministry of Science and Technology of the People's Republic of China, 2017YFE0111700
School of Resource & Environment and Safety Engineering, Hunan University of Science and Technology;
Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences;
AIR-FMI Joint Research Center for Arctic Observations, Aerospace Information Research Institute, Chinese Academy of Sciences;
Finnish Meteorological Institute, Sodankylä, Finland;
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing
  • Qiu, Y. (Yubao) orcid logo
  • Wang, X. (Xingxing)
  • Zhang, D. (Dongshui)
  • Liang, W. (Wenshan)
  • Shi, L. (Lijuan)
  • Xie, P. (Pengfei)
  • Menenti, M. (Massimo) orcid logo
  • Zhang, D. (Dongshui)


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