GSTDTAP  > 资源环境科学
DOI10.1029/2018WR022669
Estimating Basin-Scale Water Budgets With SMAP Soil Moisture Data
Koster, Randal D.1; Crow, Wade T.2; Reichle, Rolf H.1; Mahanama, Sarith P.1,3
2018-07-01
发表期刊WATER RESOURCES RESEARCH
ISSN0043-1397
EISSN1944-7973
出版年2018
卷号54期号:7页码:4228-4244
文章类型Article
语种英语
国家USA
英文摘要

Soil Moisture Active Passive (SMAP) Level-2 soil moisture retrievals collected during 2015-2017 are used in isolation to estimate 10-day warm season precipitation and streamflow totals within 145 medium-sized (2,000-10,000 km(2)) unregulated watersheds in the conterminous United States. The precipitation estimation algorithm, derived from a well-documented approach, includes a locally calibrated loss function component that significantly improves its performance. For the basin-scale water budget analysis, the precipitation and streamflow algorithms are calibrated with 2 years of SMAP retrievals in conjunction with observed precipitation and streamflow data and are then applied to SMAP retrievals alone during a third year. While estimation accuracy (as measured by the square of the correlation coefficient, r(2), between estimates and observations) varies by basin, the average r(2) for the basins is 0.53 for precipitation and 0.22 for streamflow. For the subset of 22 basins that calibrate particularly well, the r(2) increases to 0.63 for precipitation and to 0.51 for streamflow. The magnitudes of the estimated variables are also accurate, with sample pairs generally clustered about the 1:1 line. The chief limitation to the estimation involves large biases induced during periods of high rainfall; the accuracy of the estimates (in terms of r(2) and root-mean-square error) increases significantly when periods of higher rainfall are not considered. The potential for transferability is also demonstrated by calibrating the streamflow estimation equation in one basin and then applying the equation in another. Overall, the study demonstrates that SMAP retrievals contain, all by themselves, information that can be used to estimate large-scale water budgets.


领域资源环境
收录类别SCI-E
WOS记录号WOS:000442502100002
WOS关键词L-BAND ; PRECIPITATION ESTIMATION ; RAINFALL ; SURFACE ; ASSIMILATION ; VALIDATION ; PRODUCTS ; IMPROVE ; SENSORS ; DEPTH
WOS类目Environmental Sciences ; Limnology ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/19945
专题资源环境科学
作者单位1.NASA, Global Modeling & Assimilat Off, GSFC, Greenbelt, MD 20771 USA;
2.ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD USA;
3.Sci Syst & Appl Inc, Lanham, MD USA
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GB/T 7714
Koster, Randal D.,Crow, Wade T.,Reichle, Rolf H.,et al. Estimating Basin-Scale Water Budgets With SMAP Soil Moisture Data[J]. WATER RESOURCES RESEARCH,2018,54(7):4228-4244.
APA Koster, Randal D.,Crow, Wade T.,Reichle, Rolf H.,&Mahanama, Sarith P..(2018).Estimating Basin-Scale Water Budgets With SMAP Soil Moisture Data.WATER RESOURCES RESEARCH,54(7),4228-4244.
MLA Koster, Randal D.,et al."Estimating Basin-Scale Water Budgets With SMAP Soil Moisture Data".WATER RESOURCES RESEARCH 54.7(2018):4228-4244.
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