## 1. Introduction

[2] The only spawning ground for the Kootenai River white sturgeon (*Acipenser transmontanus*) is a reach near Bonner's Ferry, Idaho (ID), USA [*Paragamian et al*., 2001]. This population of white sturgeon has been listed as endangered since 1994 due to ongoing failure of juvenile sturgeon to survive and become viable members of the population [*Duke et al*., 1999]. Failure of juvenile survival has been shown to be the result of changes to the sediment characteristics of the spawning reach after the Libby Dam became fully operational on the Kootenai River in 1974 [*Paragamian and Kruse*, 2001; *Paragamian et al*., 2001, 2002, 2009]. Once the dam was operational, regions of gravel and cobbles, which are conducive to sturgeon spawning [*Parsley and Beckman*, 1994], were covered by a layer of sandy sediment at least 1 m thick [*Barton et al*., 2004]. *Paragamian et al*. [2009] and *McDonald et al*. [2010] demonstrated that although the post-dam discharge and velocity magnitude varied from natural conditions, the patterns of velocity and maximum depth were consistent with pre-dam conditions, indicating that spawning locations were most likely constant over time. As a result, sturgeon continued to spawn in historical locations based on hydrodynamic conditions although sediment characteristics became unfavorable for juvenile survival.

[3] Beginning in the early 1990s, a recovery program was undertaken [*Duke et al*., 1999] with the aim of creating a habitat conducive to a sustainable sturgeon population. An important component of the recovery process includes restoration of morphologic and hydrodynamic conditions in the spawning ground habitat to improve juvenile survival. 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*, 2011].

[4] A range of numerical models have been applied in this critical-habitat reach of the Kootenai River. *Berenbrock* [2005] implemented the one-dimensional Hydraulic Engineering Centers River Analysis System (HEC-RAS) flow model with the objective of determining the backwater/free-flowing transition upstream of Lake Kootenay, British Columbia that was hypothesized to control the location of spawning. *Berenbrock and Bennett* [2005] also implemented a one-dimensional sediment transport model with the aim of relating changes in sedimentation rate to changes in white sturgeon spawning. *Barton et al*. [2005] 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* [2011] improved the *Berenbrock* [2005] flow model using higher resolution boundary conditions with the objective of using model results in river restoration decision making.

[5] Development of a reliable numerical model represents a significant investment of time, expertise, and finances for both observationalists and modelers. For example, boundary conditions for numerical models must be obtained by measuring bathymetry, water levels, and discharge, often at high resolution both spatially and temporally. The model must then be implemented, calibrated, and results validated against independent observations. Finally, the simulation results must be interpreted with respect to multiple restoration decisions. However, decision makers may not fully understand or control the simulations developed by the modelers.

[6] A significant challenge of river restoration is, therefore, integrating the understanding provided by these high-resolution model results into the decision making process [*Stewart-Koster et al*., 2010]. Bayesian networks are a tool that can be used to efficiently transfer the results of both conceptual and numerical models to nonmodelers [*Stewart-Koster et al*., 2010], while allowing nonmodelers to efficiently manipulate elements of the model. Bayesian networks make this possible by quantifying the probabilistic relationships between variables using a graphical format.

[7] As detailed by *Plant and Holland* [2011], this sort of Bayesian prediction approach has five main advantages. Primarily, this approach requires few computational resources once the network is trained (using detailed model results or observations) which streamlines end-user application over multiple queries. Also, computational complexity is minimized as predictions are focused on the locations and variables of most interest; so do not require solution over an entire model domain or under extensive boundary conditions. Additionally, the network is easily updated once it is formulated which allows straightforward modifications. The Bayesian approach also produces a forecast probability distribution resulting in estimation of the predictive uncertainty of the forecast. Lastly, predictions can be made when input data are uncertain or unavailable, and the approach can be applied in reverse to establish sensitivities to forcing conditions as simply as when it is applied as a forward model. In addition to the advantages described above, Bayesian networks can be trained to assimilate observations other than those explicitly included in physical model simulations, allowing development of a hybrid conceptual-physical model.

[8] We hypothesize that a network developed for the Kootenai River can not only reproduce the information with which it was trained, but can also be used to predict velocities at other times and locations that are dynamically consistent with the training set, making the network a useful simulation tool in itself. Our objective is to test this hypothesis by developing a Bayesian network for predicting depth-averaged velocity on the habitat-critical reach of Kootenai River based on numerical model results and then to use the network predictions to assess the spatial distribution of velocity related to sturgeon spawning habitat. We also aim to demonstrate the potential for broader applicability of this approach by making velocity predictions for the Apalachicola River, Florida (FL) which has similar hydrologic and hydrodynamic characteristics to the Kootenai River and is a spawning ground for a threatened population of Gulf sturgeon (*Acipenser oxyrhynchus desotoi*).

[9] In section 2, we describe the study site and the characteristics of sturgeon spawning locations in the Kootenai River. The physical properties upon which our network is based are described in section 3. Also in section 3, we review the Bayesian approach and following the methodology outlined by *Chen and Pollino* [2012], we outline our network design, training, and performance criteria for a section of the Kootenai River, ID. Results are presented in section 4, with respect to velocity forecast comparisons with observations on the Kootenai River at locations and times independent of where the network was trained. In section 5, we discuss the sensitivity of the forecast input variables and resolution and the transferability of our Bayesian network to the Apalachicola River, FL.