Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR021626 |
BAM: Bayesian AMHG-Manning Inference of Discharge Using Remotely Sensed Stream Width, Slope, and Height | |
Hagemann, M. W.1; Gleason, C. J.1; Durand, M. T.2,3 | |
2017-11-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:11 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | The forthcoming Surface Water and Ocean Topography (SWOT) NASA satellite mission will measure water surface width, height, and slope of major rivers worldwide. The resulting data could provide an unprecedented account of river discharge at continental scales, but reliable methods need to be identified prior to launch. Here we present a novel algorithm for discharge estimation from only remotely sensed stream width, slope, and height at multiple locations along a mass-conserved river segment. The algorithm, termed the Bayesian AMHG-Manning (BAM) algorithm, implements a Bayesian formulation of streamflow uncertainty using a combination of Manning's equation and at-many-stations hydraulic geometry (AMHG). Bayesian methods provide a statistically defensible approach to generating discharge estimates in a physically underconstrained system but rely on prior distributions that quantify the a priori uncertainty of unknown quantities including discharge and hydraulic equation parameters. These were obtained from literature-reported values and from a USGS data set of acoustic Doppler current profiler (ADCP) measurements at USGS stream gauges. A data set of simulated widths, slopes, and heights from 19 rivers was used to evaluate the algorithms using a set of performance metrics. Results across the 19 rivers indicate an improvement in performance of BAM over previously tested methods and highlight a path forward in solving discharge estimation using solely satellite remote sensing. |
英文关键词 | remote sensing Bayesian inference SWOT mission discharge estimation |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000418736700057 |
WOS关键词 | STATIONS HYDRAULIC GEOMETRY ; SATELLITE IMAGERY ; WATER-SURFACE ; RIVERS ; ASSIMILATION ; ELEVATION ; ALTIMETRY ; BASINS |
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/20089 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Massachusetts, Dept Civil & Environm Engn, Amherst, MA 01003 USA; 2.Ohio State Univ, Sch Earth Sci, Columbus, OH 43210 USA; 3.Ohio State Univ, Byrd Polar & Climate Res Ctr, Columbus, OH 43210 USA |
推荐引用方式 GB/T 7714 | Hagemann, M. W.,Gleason, C. J.,Durand, M. T.. BAM: Bayesian AMHG-Manning Inference of Discharge Using Remotely Sensed Stream Width, Slope, and Height[J]. WATER RESOURCES RESEARCH,2017,53(11). |
APA | Hagemann, M. W.,Gleason, C. J.,&Durand, M. T..(2017).BAM: Bayesian AMHG-Manning Inference of Discharge Using Remotely Sensed Stream Width, Slope, and Height.WATER RESOURCES RESEARCH,53(11). |
MLA | Hagemann, M. W.,et al."BAM: Bayesian AMHG-Manning Inference of Discharge Using Remotely Sensed Stream Width, Slope, and Height".WATER RESOURCES RESEARCH 53.11(2017). |
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