Surveying Cross Sections of the Kootenai River Between Libby Dam, Montana, and Kootenay Lake, British Columbia, Canada

Barton, Gary J.; Moran, Edward H.; Berenbrock, Charles

doi:10.3133/ofr20041045

Cited by 13 publications

(18 citation statements)

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“…Bathymetric data for this test case were collated from existing U.S. Geological Survey measurements (Barton et al 2004), as well as supplementary survey data collected in 2010, as described by Swick (2011). The same 2010 field program (Swick 2011) provided measurements of river stage and discharge on which we have based our tests.…”

Section: ) Test Case B: Kootenai River Idahomentioning

confidence: 99%

Ensemble-Based Data Assimilation for Estimation of River Depths

Wilson

Özkan-Haller

2012

Journal of Atmospheric and Oceanic Technology

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A method is presented for estimating bathymetry in a river, based on observations of depth-averaged velocity during steady flow. The estimator minimizes a cost function that combines known information in the form of a prior estimate and measured data (including measurement noise). State augmentation is used to relate the measured variable (velocity) to the unknown parameter (bathymetry). Specifically, the unknown consists of deviations in depth about a known along-channel mean. Verification of the method is performed using a simple 1D channel geometry as well as for two real-world reaches. In all cases, the verification is based on nominal river depths of 3-10 m, channel widths of 50-100 m, and Froude numbers much less than one. Further tests are performed to assess the usefulness of various observation types and sampling schemes for this type of estimation.

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Section: ) Test Case B: Kootenai River Idahomentioning

confidence: 99%

Ensemble-Based Data Assimilation for Estimation of River Depths

Wilson

Özkan-Haller

2012

Journal of Atmospheric and Oceanic Technology

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“…West of Bonners Ferry, the meandering reach (1.5 sinuosity) environment is characterized by large bends (1080-m average radius of curvature) with river widths between 120 and 190 m, a mean depth of 5.2 m, and pools as deep as 12 m (USACE 2012; Barton et al 2009). The water surface slope under summer low-flow conditions is documented to be around 2 3 10 25 m m 21 (Barton et al 2009).…”

Section: Study Sitementioning

confidence: 99%

“…The water surface slope under summer low-flow conditions is documented to be around 2 3 10 25 m m 21 (Barton et al 2009). For flows near 225 m 3 s 21 , Barton et al (2009) estimated the lateral eddy viscosity and quadratic drag to be approximately 0.015 m 2 s 21 and 0.005, respectively. The riverbed is relatively smooth and is primarily composed of sands with some clays and silts in the thalweg (Barton et al 2009).…”

Section: Study Sitementioning

confidence: 99%

“…For flows near 225 m 3 s 21 , Barton et al (2009) estimated the lateral eddy viscosity and quadratic drag to be approximately 0.015 m 2 s 21 and 0.005, respectively. The riverbed is relatively smooth and is primarily composed of sands with some clays and silts in the thalweg (Barton et al 2009). Two creeks, Deep Creek and Myrtle Creek, join the Kootenai in our study site.…”

Section: Study Sitementioning

confidence: 99%

“…East of Bonners Ferry, the braided reach is characterized by anastomosing channels with the main channel varying in width from 80 to 130 m with a mean depth of 2.3 m (Barton et al 2009). Note that the domain in the ''braided reach'' only captures the larger of the two flowing threads in this low-stage summer study period.…”

Section: Study Sitementioning

confidence: 99%

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Bathymetry Estimation Using Drifter-Based Velocity Measurements on the Kootenai River, Idaho

Landon

Wilson

Özkan-Haller

et al. 2014

Journal of Atmospheric and Oceanic Technology

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Velocity measurements from drifter GPS records are used in an ensemble-based data assimilation technique to extract the river bathymetry. The method is tested on a deep meandering reach and a shallow braided reach of the Kootenai River in Idaho. The Regional Ocean Modeling System (ROMS) is used to model numerous statistically varied bathymetries to create an ensemble of hydrodynamic states. These states, the drifter observations, and the uncertainty of each are combined to form a cost function that is minimized to produce an estimated velocity field and bathymetry. The goals of this study are to assess whether ROMS can accurately reproduce the Kootenai River flow to an extent that depth estimation is feasible, to investigate if drifter paths are sensitive enough to bottom topography to make depth estimation possible, and to establish practical limitations of the present methodology. At both test sites, the depth estimation method produced a bathymetry that was more accurate than the prior estimate.

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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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Surveying Cross Sections of the Kootenai River Between Libby Dam, Montana, and Kootenay Lake, British Columbia, Canada

Cited by 13 publications

References 1 publication

Ensemble-Based Data Assimilation for Estimation of River Depths

Ensemble-Based Data Assimilation for Estimation of River Depths

Bathymetry Estimation Using Drifter-Based Velocity Measurements on the Kootenai River, Idaho

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

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