The main idea of water resources estimation is to establish a machine learning model using runoff coefficient and runoff impact factors (climate, topography, land use, soil), and then convert the estimated runoff coefficient to runoff depth, and then converted to water resources volumn. Based on global public open accessed data, establish the runoff coefficient topography, climate, soil, and land use, and the machine learning model for. Long-term annual runoff coefficient in the Belt and Road region was estimated and country level water resources was derived from precipitation of 2015 , The area of the country is estimated by the amount of water resources in the countries along the Belt and Road. A high-resolution runoff coefficient distribution map of the Belt and Road region was generated, which provided basic data support for water resources assessment and cross-border water distribution in the Belt and Road region.

File naming and required software

In excel format, can be opened in Microsoft EXCEL

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Cite as:

(2019). Dataset for country level water resources in 2015 in Belt and Road Region (2015). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Socioeco.tpdc.270475. CSTR: 18406.11.Socioeco.tpdc.270475. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yan, J.B., Jia, S.F., Lv, A.F., & Zhu, W.B. (2019). Water resources assessment of China's transboundary river basins using a machine learning approach. Water Resources Research, 55(1), 632-655.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)