气候变化领域集成服务门户(CCPortal): 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

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DOI	10.5194/hess-24-615-2020
	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
	Mao Y.; Crow W.T.; Nijssen B.
发表日期	2020
ISSN	1027-5606
起始页码	615
结束页码	631
卷号	24 期号:2
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159503
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
推荐引用方式 GB/T 7714	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],2020,24(2).
APA	Mao Y.,Crow W.T.,&Nijssen B..(2020).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.Hydrology and Earth System Sciences,24(2).
MLA	Mao Y.,et al."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".Hydrology and Earth System Sciences 24.2(2020).