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We show that observations of water surface elevation, surface velocity, and bathymetry can be used to calibrate multiple reach-scale parameters such as the discharge, the overall bed friction, and a water surface datum bias in a hydraulic model (FaSTMECH) for the depth-averaged flow in the braided reach of the Kootenai River near Bonners Ferry, ID. Remotely sensed surface velocity data acquired during a 2010 field experiment are converted to depth-averaged velocity using a uniform ratio of 0.79 computed from concurrent in situ observation of vertical profiles of velocity. The in situ measured discharge of the Kootenai River was approximately 220 m3/s. For given discharge forcing and downstream boundary condition provided by the measured water surface elevation, the FaSTMECH model was used to predict the horizontal variation of the depth-averaged velocity and the water surface elevation over the braided reach. The calibration considered here consists of varying the discharge, the bed friction coefficient and the water surface datum offset to find the combination that minimizes error metrics based on the streamwise profiles of the measured water level and maximum depth-averaged velocity. Composite error metrics that combine water level and velocity measurements are found to be more sensitive with respect to variation of the datum bias and the bed friction. We find that a composite error based on the total head is minimized for a discharge of 220 m3/s, friction coefficient of 0.005, and a datum offset of 40 cm. [ABSTRACT FROM AUTHOR]

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 depth-averaged 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. [ABSTRACT FROM AUTHOR]