Updated one-dimensional hydraulic model of the Kootenai River, Idaho: A supplement to Scientific Investigations Report 2005-5110

Czuba, Christiana R.; Barton, Gary J.

doi:10.3133/sir20115128

Cited by 3 publications

(3 citation statements)

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“…In order to achieve our objective of estimating depth‐averaged velocity, we designed our Bayesian network based on simulations from the depth‐averaged equations describing the flow of water in a one‐dimensional channel often referred to as the Saint Venant equations,

\frac{\partial (h)}{\partial t} + \frac{\partial (u h)}{\partial x} = 0

and

\frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} + g \frac{\partial h}{\partial x} + \frac{n^{2} u | u |}{h^{4 / 3}} = 0

where h is water head elevation from the reference level, t is time, x is the distance along the channel, u is the flow velocity in the x direction, g is the gravitational constant, and n is the Manning coefficient describing bed roughness [ Stelling et al ., ]. Several previous studies [ Berenbrock and Bennett , ; Czuba and Barton , ] demonstrate that one‐dimensional approximations of flow produce errors less than 20% of depth‐averaged and cross‐section averaged velocity when compared with observations on the Kootenai River.…”

Section: Methodsmentioning

confidence: 99%

“…In order to design and implement a recovery program, information about past, present, and future hydrologic conditions must be known. Since data are not available for all river restoration scenarios, hydrodynamic model simulations of actual and plausible river conditions must be implemented [ Czuba and Barton , ].…”

Section: Introductionmentioning

confidence: 99%

“…[] extended this approach by simulating flow and sediment transport using the quasi three‐dimensional FastMech flow model to enhance the understanding of biological data in the context of river hydrodynamics. Most recently, Czuba and Barton [] improved the Berenbrock [] flow model using higher resolution boundary conditions with the objective of using model results in river restoration decision making.…”

Section: Introductionmentioning

confidence: 99%

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Velocity estimation using a Bayesian network in a critical-habitat reach of the Kootenai River, Idaho

2013

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[1] Numerous numerical modeling studies have been completed in support of an extensive recovery program for the endangered white sturgeon (Acipenser transmontanus) on the Kootenai River near Bonner's Ferry, ID. A technical hurdle in the interpretation of these model results is the transfer of information from the specialist to nonspecialist such that practical decisions utilizing the numerical simulations can be made. To address this, we designed and trained a Bayesian network to provide probabilistic prediction of depthaveraged velocity. Prediction of this critical parameter governing suitable spawning habitat was obtained by exploiting the dynamic relationships between variables derived from model simulations with associated parameter uncertainties. Postdesign assessment indicates that the most influential environmental variables in order of importance are river discharge, depth, and width, and water surface slope. We demonstrate that the probabilistic network not only reproduces the training data with accuracy similar to the accuracy of a numerical model (root-mean-squared error of 0.10 m/s), but that it makes reliable predictions on the same river at times and locations other than where the network was trained (root mean squared error of 0.09 m/s). Additionally, the network showed similar skill (root mean square error of 0.04 m/s) when predicting velocity on the Apalachicola River, FL, a river of similar shape and size to the Kootenai River where a related sturgeon population is also threatened.

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\frac{\partial (h)}{\partial t} + \frac{\partial (u h)}{\partial x} = 0

and

\frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} + g \frac{\partial h}{\partial x} + \frac{n^{2} u | u |}{h^{4 / 3}} = 0

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Velocity estimation using a Bayesian network in a critical-habitat reach of the Kootenai River, Idaho

2013

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Limitations in Extraction of Survey Data from Real-Time Kinematic GPS ADCP Systems

Muirhead

Annable

2014

J. Hydraul. Eng.

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Patterns of Fish Assemblage Structure and Habitat Use among Main‐ and Side‐Channel Environments in the Lower Kootenai River, Idaho

et al. 2015

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The lower Kootenai River, Idaho, was sampled during the summers of 2012 and 2013 to evaluate its fish assemblage structure at seven sites within main-and side-channel habitats where large-scale habitat rehabilitation was undertaken. Understanding the current patterns of fish assemblage structure and their relationships with habitat is important for evaluating the effects of past and future rehabilitation projects on the river. Species-specific habitat associations were modeled, and the variables that best explained the occurrence and relative abundance of fish were identified in order to guide future habitat rehabilitation so that it benefits native species. The results indicated that the side-channel habitats supported higher species richness than the main-channel habitats and that nonnative fishes were closely associated with newly rehabilitated habitats. This research provides valuable insight on the current fish assemblages in the Kootenai River and the assemblage-level responses that may occur as a result of future rehabilitation activities.The inherent nature of running waters has long made them a focus of human settlement and exploitation, resulting in various land-and water-use alterations (Behnke 1990;Bayley 1995). Large rivers throughout the world have been modified by development activities that serve human needs (e.g., transportation, irrigation, and power generation), which has caused the degradation and loss of fish habitat (Dynesius and Nilsson 1994;Nilsson et al. 2005). In particular, the presence of dams

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Updated one-dimensional hydraulic model of the Kootenai River, Idaho: A supplement to Scientific Investigations Report 2005-5110

Abstract: Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88). Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Cited by 3 publications

References 5 publications

Velocity estimation using a Bayesian network in a critical-habitat reach of the Kootenai River, Idaho

Velocity estimation using a Bayesian network in a critical-habitat reach of the Kootenai River, Idaho

Limitations in Extraction of Survey Data from Real-Time Kinematic GPS ADCP Systems

Patterns of Fish Assemblage Structure and Habitat Use among Main‐ and Side‐Channel Environments in the Lower Kootenai River, Idaho

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