Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024901 |
Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model | |
Ravindranath, Arun1; Devineni, Naresh2; Lall, Upmanu3; Cook, Edward R.4; Pederson, Greg5; Martin, Justin5; Woodhouse, Connie6 | |
2019-09-09 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:9页码:7694-7716 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree ring chronologies for paleo-streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries come together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. Our application to 18 streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted R-2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. The spatial network structure produced a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network aggregating information from upstream gauges and tree ring chronologies. Uncertainty was reduced by more than 50% at six gauges, between 6% and 50% at one gauge, and by less than 5% at the remaining 11 gauges when compared with the traditional principal component regression reconstruction model. |
英文关键词 | spatial Markov model paleo-reconstructions streamflow reconstructions Bayesian statistics water management stochastic hydrology |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000487408700001 |
WOS关键词 | RING-BASED RECONSTRUCTION ; TREE ; DROUGHT ; FLOW ; HISTORY ; ENSO |
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/186970 |
专题 | 资源环境科学 |
作者单位 | 1.CUNY City Coll, NOAA, Dept Civil Engn, Ctr Earth Syst Sci & Remote Sensing Technol,Ctr W, New York, NY 10031 USA; 2.CUNY City Coll, Dept Civil Engn, New York, NY 10031 USA; 3.Columbia Univ, Dept Earth & Environm Engn, Columbia Water Ctr, Earth Inst, New York, NY USA; 4.Columbia Univ, Lamont Doherty Earth Observ, Tree Ring Lab, Palisades, NY USA; 5.US Geol Survey, Northern Rocky Mt Sci Ctr, Bozeman, MT USA; 6.Univ Arizona, Dept Geosci, Sch Geog & Dev, Lab Tree Ring Res, Tucson, AZ 85721 USA |
推荐引用方式 GB/T 7714 | Ravindranath, Arun,Devineni, Naresh,Lall, Upmanu,et al. Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model[J]. WATER RESOURCES RESEARCH,2019,55(9):7694-7716. |
APA | Ravindranath, Arun.,Devineni, Naresh.,Lall, Upmanu.,Cook, Edward R..,Pederson, Greg.,...&Woodhouse, Connie.(2019).Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model.WATER RESOURCES RESEARCH,55(9),7694-7716. |
MLA | Ravindranath, Arun,et al."Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model".WATER RESOURCES RESEARCH 55.9(2019):7694-7716. |
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