Evaluation of reanalysis soil temperature and soil moisture products in permafrost regions on the Qinghai-Tibetan Plateau

• Published in: Geoderma 2020-11, Vol.377, p.114583, Article 114583
• Main Authors: Yang, Shuhua, Li, Ren, Wu, Tonghua, Hu, Guojie, Xiao, Yao, Du, Yizhen, Zhu, Xiaofan, Ni, Jie, Ma, Junjie, Zhang, Yuxin, Shi, Jianzong, Qiao, Yongping
• Format: Article
• Language: English
• Subjects: Permafrost; Qinghai-Tibetan Plateau; Reanalysis product; Soil moisture; Soil temperature
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2020.114583

•The GLDAS-Noah had the best performance for soil temperature.•The ERA-Interim had the best performance for soil moisture.•Soil texture and forcing data may be the main causes of simulation errors.•Parameterization schemes and mismatch of spatial scales may also bring uncertainties. Long-term and large-scale reanalysis products of soil temperature and soil moisture are very important for understanding the hydrothermal regime in permafrost regions. However, it is necessary to evaluate the reliability of these products before using them. In this study, five in situ observed sites with different land cover types were collected to evaluate the performance of soil temperature and soil moisture in four reanalysis products from 2013 to 2014 in permafrost regions on the Qinghai-Tibetan Plateau (QTP). The four reanalysis products included three widely used products derived from the Climate Forecast System Reanalysis version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), and the Noah model driven by the Global Land Data Assimilation System (GLDAS-Noah), as well as a latest reanalysis product from the fifth-generation reanalysis product by the ECMWF (ERA5). The results showed that all of these products could capture temporal dynamics of soil temperature (R > 0.8) and soil moisture (R > 0.4) well. However, soil temperature was underestimated, and soil moisture was overestimated during the thawing period. The investigated results showed, those errors may mainly be caused by soil properties and forcing data. In addition, the mismatch of spatial scales between the reanalysis products and in situ observed sites, and parameterization schemes in the land surface models, such as soil thermal and hydraulic conductivity schemes, may also contributed partly causes of simulation errors. Overall, the statistical results showed that GLDAS-Noah product ranked at the top of the four products in simulating soil temperature, especially in the alpine desert, alpine swamp and alpine grassy meadow. And ERA-Interim product was superior to the other products in simulating soil moisture in permafrost regions on the QTP, especially in the alpine desert and alpine meadow. Additionally, we found that ERA5 product was better than ERA-Interim product in simulating soil temperature, especially in topsoil, but it did not show superior performance in simulating soil moisture in permafrost regions of the QTP. This may be related to the dif