The inclusion of channel variability in flow routing: an assessment of model performance for the Colorado River through Grand Canyon

An unsteady discharge wave routing model for the Colorado River through Grand Canyon was modified to include variability in channel geometric and hydraulic properties. First, a classification scheme was devised to facilitate division of the riparian corridor into eight subreaches of geomorphic s...

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Bibliographic Details
Main Author: Marlow, Jonathan Edward
Other Authors: Ince, Simon
Language:en_US
Published: The University of Arizona. 1995
Online Access:http://hdl.handle.net/10150/626816
http://arizona.openrepository.com/arizona/handle/10150/626816
Description
Summary:An unsteady discharge wave routing model for the Colorado River through Grand Canyon was modified to include variability in channel geometric and hydraulic properties. First, a classification scheme was devised to facilitate division of the riparian corridor into eight subreaches of geomorphic similarity. Channel variability was characterized by averaging geometric and hydraulic properties over the length of each subreach. The subreaches were then treated as separate modules through which three different discharge patterns were routed and model results compared with results from the unmodified model relative to United States Geological Survey stream gaging station records. The new model exhibited some increased accuracy in the timing and magnitude of discharge waves as well as wave shape. Model results were also found to be particularly sensitive to the friction co efficient used--a variable parameter designed to incorporate the effects of channel variabil ity on the flow. Results also show that a more detailed understanding and characterization of the friction coefficient used within the model would lead to greater overall improve ments in discharge predictions at particular points. The results of this study should serve as well to assist researchers pursuing similar studies in planning the types and amounts of field data to be collected, in order to optimize available research funding.