Abstract
Developing accurate and efficient modeling techniques for streamflow at tens-kilometer spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate- and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient three-dimensional (3D) hydrodynamic modeling of natural rivers at 30-kilometer and 5-year scales is feasible using the following three techniques within OpenFOAM, an open source computational fluid dynamics platform: 1) generating a distributed hydraulic roughness field for the streambed by integrating water stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; 2) prescribing the boundary condition for the inflow and outflow by integrating pre-computed results of a one-dimensional (1D) hydraulic model with the 3D model; and 3) reducing computational time using multiple parallel runs constrained by 1D inflow and outflow boundary conditions. Streamflow modeling for a 30-kilometer-long reach in the Columbia River (CR) over 58 months can be achieved in less than six days using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from −16 cm to 9 cm (equivalent to ca. ±7 % relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75 % of the survey cross-sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impact of climate- and human-induced discharge variations on river hydrodynamics at tens kilometer and decade scales.
Highlights
As a major element of the water cycle, streamflow varies with upstream discharge, interacts with ambient physical and biological environments, and creates a variety of social, economic, and environmental functions (Wampler, 2012; Wohl et al, 2015; Harvey, 2016; Biddanda, 2017; Hiemstra et al, 2020)
The flood control function is largely determined by 25 accurate prediction of the water depth and flow speed that are further controlled by upstream discharge variations and the hydraulic roughness generated by flow-streambed interactions (USACE, 1994; Ferguson, 2019)
The water quality management and biodiversity protection functions are strongly affected by the hydrological exchange flows (Harvey, 2016) that are driven by hydrostatic pressure and flow-sediment induced dynamic pressure (Tonina and Buffington, 2007; Cardenas and Wilson, 2007)
Summary
As a major element of the water cycle, streamflow varies with upstream discharge, interacts with ambient physical and biological environments, and creates a variety of social, economic, and environmental functions (Wampler, 2012; Wohl et al, 2015; Harvey, 2016; Biddanda, 2017; Hiemstra et al, 2020). As the 1D models provide only cross sectional averaged velocity and water depth, these models are usually problematic if flow manifests large variations in either the vertical or the cross-sectional direction (Lane and Ferguson, 2005). Due to these reasons, the two-dimensional (2D) numerical models, which solve the depth averaged Navier-Stokes equations, have been developed to better capture the cross-sectional variations in flow (Miller, 1994; Bates et al, 1995; Lane and Richards, 1998; Thompson et al, 1998; Cao et al, 2003) and resulted influences on sediment transport Though quasi-3D models, e.g., Princeton Ocean Mode (Blumberg and Mellor, 1983), 50 Environmental Fluid Dynamics Code–3D (Hamrick, 1992), Delft3D (Deltares, 2021), and CH3D (Johnson et al, 1993), have been commonly used for ocean, coastal, and river applications, they are not adequate to model the dynamic pressure
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