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Title: Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

Abstract

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 R2 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 themore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [5]
  1. City Univ. of New York (CUNY), NY (United States)
  2. Columbia Univ., New York, NY (United States)
  3. Columbia Univ., Palisades, NY (United States)
  4. Northern Rocky Mountain Science Center, US. Geological Survey, Bozeman, MT (United States)
  5. Univ. of Arizona, Tucson, AZ (United States)
Publication Date:
Research Org.:
City Univ. of New York (CUNY), NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1612849
Alternate Identifier(s):
OSTI ID: 1560770
Grant/Contract Number:  
SC0018124; 1401698; 1404188; 1360446
Resource Type:
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 55; Journal Issue: 9; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; Environmental Sciences & Ecology; Marine & Freshwater Biology; Water Resources; spatial Markov model; paleo‐reconstructions; streamflow reconstructions; Bayesian statistics; water management; stochastic hydrology

Citation Formats

Ravindranath, Arun, Devineni, Naresh, Lall, Upmanu, Cook, Edward R., Pederson, Greg, Martin, Justin, and Woodhouse, Connie. Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model. United States: N. p., 2019. Web. https://doi.org/10.1029/2019wr024901.
Ravindranath, Arun, Devineni, Naresh, Lall, Upmanu, Cook, Edward R., Pederson, Greg, Martin, Justin, & Woodhouse, Connie. Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model. United States. https://doi.org/10.1029/2019wr024901
Ravindranath, Arun, Devineni, Naresh, Lall, Upmanu, Cook, Edward R., Pederson, Greg, Martin, Justin, and Woodhouse, Connie. Thu . "Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model". United States. https://doi.org/10.1029/2019wr024901. https://www.osti.gov/servlets/purl/1612849.
@article{osti_1612849,
title = {Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model},
author = {Ravindranath, Arun and Devineni, Naresh and Lall, Upmanu and Cook, Edward R. and Pederson, Greg and Martin, Justin and Woodhouse, Connie},
abstractNote = {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 R2 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.},
doi = {10.1029/2019wr024901},
journal = {Water Resources Research},
number = 9,
volume = 55,
place = {United States},
year = {2019},
month = {8}
}

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Figures / Tables:

Figure 1 Figure 1: Conceptual sketch of the network Bayesian model. Six streamflow gauges (A–F) and 13 tree ring chronology sites (T1–T13) are shown in the sketch in order to illustrate the concept of the graphical network model. Physically informed modeling structure using regional tree ring chronologies and feeder streamflow gauges ismore » explored using factorization into lower dimensional conditional probability distributions as shown in the directed graph. The conditional distributions generated at each stage of the graph serve as statistical interpretations of the modeling structure and lay the groundwork for converting the graphical model into a set of equations for estimating the parameters of the streamflow network's likelihood function for all gauges (nodes) in the network simultaneously using a Bayesian estimation scheme. Although we allude to the use of regional tree ring chronologies in this figure, in our analysis for the Upper Missouri River Basin, we used the leading principal components of the basin‐wide tree predictors.« less

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