DOI: 10.5194/hess-22-2575-2018
Scopus记录号: 2-s2.0-85046123764
论文题名: Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model
作者: Pinnington E ; , Quaife T ; , Black E
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 4 起始页码: 2575
结束页码: 2588
语种: 英语
Scopus关键词: Data reduction
; Rain
; Remote sensing
; Soil surveys
; Surface measurement
; Wetting
; Data assimilation
; Data assimilation systems
; Data assimilation techniques
; Land surface modeling
; Remotely sensed soil moisture
; Root mean squared
; Soil moisture predictions
; Sub-saharan africa
; Soil moisture
英文摘要: We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18% reduction in unbiased root-mean-squared differences in the north of Ghana and a 21% reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6% of this reduction in deviation for northern Ghana and 10% for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying-down. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa. © 2018 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79318
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Meteorology, University of Reading, Reading, United Kingdom; National Centre for Earth Observation, University of Reading, Reading, United Kingdom; National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
Recommended Citation:
Pinnington E,, Quaife T,, Black E. Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model[J]. Hydrology and Earth System Sciences,2018-01-01,22(4)