As an important part of global semi-arid grassland, adequately understanding the spatio-temporal variability of evapotranspiration (ET) over the temperate semi-arid grassland of China (TSGC) could advance our understanding of climate, hydrological and ecological processes over global semi-arid areas. Based on the largest number of in-situ ET measurements (13 flux towers) within the TSGC, we applied the support vector regression method to develop a high-quality ET dataset at 1 km spatial resolution and 8-day timescale for the TSGC from 1982 to 2015. The model performed well in validation against flux tower‐measured data and comparison with water-balance derived ET.

File naming and required software

Datasets are organized in netcdf files. There is one netcdf file per year, and they are stored as a 3D array with dimensions 2093 x 1123 x 46 (46 is the number of 8-day per year). Therefore, the first cell corresponds to the 1st 8-day of the corresponding year, and it is centred at latitude 35.5176 and longitude 103.4511. ‘DATA’ is 8-day ET [mm/8d]. Missing values in the files are masked with NaN.
See the file “Coverage_The temperate semi-arid grassland of China.tif” for study region.

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Cite as:

Lei, H. (2021). An evapotranspiration dataset based on upscaling eddy covariance observations over the temperate semi-arid grassland of China (1982-2015). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Hydro.tpdc.271660. CSTR: 18406.11.Hydro.tpdc.271660. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Pang, X., Lei, H., Cong, Z., Yang, H., Duan, L., & Yang, D. (2021). Long term variation of evapotranspiration and water balance based on upscaling eddy covariance observations over the temperate semi-arid grassland of China. Agricultural and Forest Meteorology, 308-309, 108566. doi:https://doi.org/10.1016/j.agrformet.2021.108566( 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

The National Natural Science Foundation (No:51979139)

The National Natural Science Foundation (No:51922063)

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