image: Spatial variation of (a) precipitation, (b) evaporation, (c) near-surface soil moisture, (d) soil porosity in Mainland China. The values in (a-c) are averaged over July of 2002-2011, and Figure (d) is from Shangguan et al. (2013). The grids covering large lakes and near-coastal areas are removed. Low soil moisture content in part of the southwestern region (oval-covered part), reflecting the region's predominance of coarse-grained purple soils with low soil porosity (d) view more
Credit: ©Science China Press
As one of the so-called essential climate variable (ECV), soil moisture plays an important role in the water-energy cycle and land-atmosphere interactions. While quite some microwave-based satellite missions have made soil moisture retrieval on top of their other objectives, it is still tough work in obtaining high-quality soil moisture products at regional scales mainly due to the impacts of vegetation and surface roughness. Land data assimilation that constrains model predictions with passive microwave signals, on the other hand, can be an effective way to reduce uncertainties in soil moisture estimation, but it also faces challenges from such as biased meteorological forcing, uncertain model parameters, and inadequate in-situ soil moisture observations for validations.
Considering the above, over the past decade, researchers from Tsinghua University have established a dual-pass data assimilation system (LDAS) that auto-calibrates key model parameters, developed a China Meteorological Forcing Dataset with high spatiotemporal resolution to drive the LDAS, established four soil moisture observation networks consisting more than 100 stations with other teams to provide ground truth, and eventually produced a gridded soil moisture dataset by assimilating signals of a passive microwave satellite. This new product consists of 2002-2011 daily soil moisture content in the surface layer, root layer and deep layers, and holds a spatial resolution of 0.25° over China. Evaluation against aforementioned soil moisture networks suggests higher consistency of this dataset as compared to remote sensing retrievals and land surface simulations. The product is now officially publicized in National Tibetan Plateau Data Center, and has already contributed to studies involving land-atmosphere interaction, permafrost degradation, and alpine ecology, etc.
Based on the newly released product, the authors further reveal that: (1) spatial pattern of soil moisture is basically consistent with that of precipitation and evaporation, i.e., wetter in southeastern China and drier in northwestern China (Figure 1a-c); (2) parts of the southwestern China have relative low soil moisture content, indicating unique feature of this specific region, which is dominated by coarse-grained purple soils and characterized by low soil porosity (Figure 1c-d); (3) soil moisture exhibits stronger seasonal variability in the climatic transition zone of China than in humid regions and arid regions, and larger interannual variability in the relatively drier north than in the relatively wetter south with more pronounced in spring and autumn. These findings shall contribute to the understanding of climate, hydrology and ecology-related spatial patterns and interannual variabilities in China.
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This study was published in "Science China Earth Sciences" entitled "Development of a daily soil moisture product for the period of 2002-2011 in Mainland China". The authors of the paper are from Tsinghua University, Institute of Tibetan Plateau Research of Chinese Academy of Sciences, and Southwest University.
This research was funded by the National Key R&D Program (No. 2018YFA0605400).
See the article:
Yang K., Che, Y.Y., He J., Zhao L., Lu H., Qin J., Zheng D.H., Li X. (2020). Development of a daily soil moisture product for the period of 2002-2011 in Mainland China. Science China Earth Sciences, 63, doi: 10.1007/s11430-019-9588-5. https://link.springer.com/epdf/10.1007/s11430-019-9588-5
See the data:
http://data.tpdc.ac.cn/zh-hans/data/add77205-ad37-4b93-bfe8-25618529229a
Journal
Science China Earth Sciences