A new approach to describing the associated topography at different scales in computational fluid dynamic applications to gravel bed rivers was developed. Surveyed topographic data were interpolated, using geostatistical methods, into different spatial discretizations, and grain-size data were used with fractal methods to reconstruct the microtopography at scales finer than the measurement (subgrid) scale. The combination of both scales of topography was then used to construct the spatial discretization of a three-dimensional finite volume Computational Fluid Dynamics (CFD) scheme where the topography was included using a mass flux scaling approach. The method was applied and tested on a 15 m stretch of Solfatara Creek, Wyoming, United States, using spatially distributed elevation and grain-size data. Model runs were undertaken for each topography using a steady state solution. This paper evaluates the impact of the model spatial discretization and additional reconstructed-variability upon the spatial structure of predicted three-dimensional flow. The paper shows how microtopography modifies the spatial structure of predicted flow at scales finer than measurement scale in terms of variability whereas the characteristic scale of predicted flow is determined by the CFD scale. Changes in microtopography modify the predicted mean velocity value by 3.6% for a mesh resolution of 5 cm whereas a change in the computational scale modifies model results by 60%. The paper also points out how the spatial variability of predicted velocities is determined by the topographic complexity at different scales of the input topographic model
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