globalchange  > 气候变化与战略
DOI: 10.5194/hess-24-615-2020
论文题名:
Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: Evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data
作者: Mao Y.; Crow W.T.; Nijssen B.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期:2
起始页码: 615
结束页码: 631
语种: 英语
Scopus关键词: Rain ; Random errors ; Satellites ; Soil moisture ; Stream flow ; Systematic errors ; Data assimilation techniques ; Global precipitation measurement missions ; Hydrologic systems ; Land surface modeling ; Rainfall estimates ; Satellite products ; Soil moisture active passive (SMAP) ; Streamflow simulations ; Soil surveys ; data assimilation ; detection method ; land surface ; rainfall ; satellite data ; simulation ; soil moisture ; streamflow ; Arkansas ; Red River [United States] ; United States
英文摘要: Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and withinstorm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas-Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas-Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems. © Author(s) 2020.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162793
Appears in Collections:气候变化与战略

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作者单位: Mao, Y., Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States; Crow, W.T., Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, MD, United States; Nijssen, B., Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States

Recommended Citation:
Mao Y.,Crow W.T.,Nijssen B.. Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: Evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data[J]. Hydrology and Earth System Sciences,2020-01-01,24(2)
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