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Assessment of reanalysis soil moisture products in the permafrost regions of the central of the Qinghai–Tibet Plateau

Qin, Yanhui, Wu, Tonghua, Wu, Xiaodong, Li, Ren, Xie, Changwei, Qiao, Yongping, Hu, Guojie, Zhu, Xiaofan, Wang, Weihua, Shang, Wen
Hydrological processes 2017 v.31 no.26 pp. 4647-4659
Earth system science, alpine meadows, climate, data collection, ecosystems, models, permafrost, soil texture, soil water, steppes, swamps, thawing, water resources, water security, weather forecasting, China
The long‐term and large‐scale soil moisture (SM) record is important for understanding land atmosphere interactions and their impacts on the weather, climate, and regional ecosystem. SM products are one of the parameters used in some Earth system models, but these records require evaluation before use. The water resources on the Qinghai–Tibet Plateau (QTP) are important to the water security of billions of people in Asia. Therefore, it is necessary to know the SM conditions on the QTP. In this study, the evaluation metrics of multilayer (0–10, 10–40, and 40–100 cm) SM in different reanalysis datasets of the European Centre for Medium‐Range Weather Forecasts interim reanalysis (ERA‐Interim [ERA]), National Centers for Environmental Prediction Climate Forecast System and the Climate Forecast System version 2 (CFSv2), and China Meteorological Administration Land Data Assimilation System (CLDAS) are compared with in situ observations at 5 observation sites, which represent alpine meadow, alpine swamp meadow, alpine grassy meadow, alpine desert steppe, and alpine steppe environments during the thawing season from January 1, 2011, to December 31, 2013, on the QTP. The ERA SM remains constant at approximately 0.2 m³⋅m⁻³ at all observation sites during the entire thawing season. The CLDAS and CFSv2 SM products show similar patterns with those of the in situ SM observations during the thawing season. The CLDAS SM product performs better than the CFSv2 and ERA for all vegetation types except the alpine swamp meadow. The results indicate that the soil texture and land cover types play a more important role than the precipitation to increase the biases of the CLDAS SM product on the QTP.