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|Title:||Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river||Authors:||Legleiter, Carl J.
McDonald, Richard R.
Nelson, Jonathan M.
|Keywords:||Spatialstochastic simulation strategy
|Issue Date:||Mar-2011||Publisher:||AGU Publications||Source:||Water Resources Research, 2011, Volume 47, Issue 3, pages 1-24||Abstract:||Many applications in river research and management rely upon two-dimensional (2D) numerical models to characterize flow fields, assess habitat conditions, and evaluate channel stability. Predictions from such models are potentially highly uncertain due to the uncertainty associated with the topographic data provided as input. This study used a spatial stochastic simulation strategy to examine the effects of topographic uncertainty on flow modeling. Many, equally likely bed elevation realizations for a simple meander bend were generated and propagated through a typical 2D model to produce distributions of water-surface elevation, depth, velocity, and boundary shear stress at each node of the model's computational grid. Ensemble summary statistics were used to characterize the uncertainty associated with these predictions and to examine the spatial structure of this uncertainty in relation to channel morphology. Simulations conditioned to different data configurations indicated that model predictions became increasingly uncertain as the spacing between surveyed cross sections increased. Model sensitivity to topographic uncertainty was greater for base flow conditions than for a higher, subbankfull flow (75% of bankfull discharge). The degree of sensitivity also varied spatially throughout the bend, with the greatest uncertainty occurring over the point bar where the flow field was influenced by topographic steering effects. Uncertain topography can therefore introduce significant uncertainty to analyses of habitat suitability and bed mobility based on flow model output. In the presence of such uncertainty, the results of these studies are most appropriately represented in probabilistic terms using distributions of model predictions derived from a series of topographic realizations.||URI:||http://ktisis.cut.ac.cy/jspui/handle/10488/8665||ISSN:||1944-7973 (Online)||DOI:||10.1029/2010WR009618||Rights:||Copyright by the American Geophysical Union.|
|Appears in Collections:||Άρθρα/Articles|
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