In situ soil temperature and moisture observations at 7 stations and one region (Naqu) over the Qinghai-Xizang Plateau are used to validate two reanalysis products (i.e. ERA Interim and CFSR) and six land surface model products (i.e. ERA/land, MERRA/land, GLDAS-NOAH, GLDAS-CLM, GLDAS-MOSAIC and GLDAS-VIC). Four statistical quantities, i.e. mean bias (BIAS), standard deviation of differences (σd), correlation coefficient (R) and ratio of standard deviations (σr/σobs), are calculated at each site, and the Brunke ranking method is applied to quantify the relative performance of the eight datasets for each variable and statistical quantity. The results show that for daily soil temperature CFSR has the best overall performance, followed by MERRA/land and GLDAS-CLM, while ERA Interim and ERA/land perform the worst. GLDAS-CLM tends to overestimate daily soil temperatures, while other datasets tend to underestimate soil temperatures at most observation sites, with ERA Interim and ERA/land having large cold bias exceeding -20℃. For soil moisture during unfreezing period (May to October), GLDAS-CLM shows the best overall performance, followed by GLDAS-NOAH and ERA Interim. CFSR, ERA Interim, and ERA/land have wet biases, with most of the biases between 0.05 and 0.20 m3·m-3, while GLDAS-NOAH, GLDAS-CLM and GLDAS-MOSAIC tend to be drier than observations.
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