CSTR:11738.11.ncdc.isnow.db2520.2022
10.12072/ncdc.I-SNOW.db0018.2021
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This data set contains the daily data of snow water equivalent in the upper reaches of Heihe River Basin from January 2000 to December 2015, which is saved in TIFF format and fully open for sharing. Through the distributed hydrological model gbehm and remote sensing parameter calibration method, the data provides the calibration objective function and driving data for the model by using long-time series and high spatial resolution snow remote sensing data; Using the snow attribute data obtained from ground survey, more accurate parameter information is provided for the simulation system, and then the daily snow water equivalent data from 2000 to 2015 are estimated. File name: swe_ Yyyymm.tiff, Field Description: swe refers to snow water equivalent, yyyy is year, for example, 2010; Mm is the month, for example, March is 03
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The original data source includes meteorological station data, reanalysis data, remote sensing snow area data and hydrological runoff data. The data of meteorological stations are from the first subject of China snow survey project; Remote sensing snow area data comes from the second project of China snow survey project; The reanalysis data is independent simulation using WRF data; The hydrological runoff data are from the scientific data center of cold and arid areas.
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Through the distributed hydrological model gbehm and remote sensing parameter calibration method, the long-time series and high spatial resolution snow remote sensing data are used to provide the calibration objective function and driving data for the model; Using the snow attribute data obtained from ground survey, more accurate parameter information is provided for the simulation system, and then the daily snow water equivalent data from 2000 to 2015 are estimated.
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The data are simulated by gbehm hydrological model combined with meteorological element data of Heihe River Basin. The snow depth simulation data are compared with the measured data at the station scale, which meets the accuracy requirements. Compared with the simulated data of remote sensing observation, the Nash coefficient reaches 0.74, which meets the accuracy requirements.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
