The dataset includes precipitation and temperature raster (interpolated) data with a spatial resolution of 0.1 ° from 1971 to 2020 in the Three River Source National Park area, as well as simulated snow cover, snow water equivalent, glacier meltwater, and snow meltwater data, and future time series (2020-2100) of precipitation, temperature, glacier meltwater, and snow meltwater estimated by IPCC CMIP6 and the Three River Source Large Scale Ice and Snow Water Resource Estimation Model under SSP2-4.5 climate scenarios.
The temperature and precipitation station data in this dataset are sourced from the China Meteorological Administration, while glacier data is obtained from the first and second glacier inventory data. The estimated temperature and precipitation data are calculated using GCMs climate models and downscaled. Other data are obtained through the large-scale ice and snow water resource estimation model of Sanjiangyuan National Park.
1) Using meteorological data observed at stations and WorldClim's annual monthly average data as covariates, a temperature and precipitation raster dataset for the Three Rivers Source National Park area was created using TPS interpolation method;
2) Using historical temperature, precipitation and other meteorological grid data to drive the large-scale ice and snow water resource estimation model of Sanjiangyuan National Park for water element calculation;
3) Use MODIS snow data and glacier area change data between two glacier inventories to calibrate the model parameters;
4) Downscaling the temperature, precipitation, and other factors estimated by the selected IPCC CMIP6 GCMs;
5) Using downscaled temperature, precipitation and other data to drive the large-scale ice and snow water resource estimation model of Sanjiangyuan National Park to obtain future water balance factor data;
6) The detailed production process and methods can be found in the Data Product Production Instructions. docx.
The data quality is good.
This work is licensed under a
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CSTR:11738.11.NCDC.NIEER.DB6545.2024
10.12072/ncdc.nieer.db6545.2024