Estimating Stream Channel Cross Sections from Watershed Characterisitcs
Location
Eccles Conference Center
Event Website
http://water.usu.edu/
Start Date
3-28-2006 10:40 AM
End Date
3-28-2006 11:00 AM
Description
Streamflow simulation models typically require a comprehensive set of stream channel geomorphologic characteristics as input parameters. These parameters often include channel length, slope, average width and depth. Some more advanced hydrologic models require more detailed channel cross section information including flood plain parameters. These data can be time consuming and cost prohibitive to acquire for all stream channels in a study area. In light of this it is convenient to estimate these parameters directly from GIS data. While channel length and slope can be readily estimated from digital elevation model (DEM) data using channel extraction algorithms, the parameters average width and depth are not as easily estimated. One commonly used approach is to define average width and depth parameters each as a function of watershed area recognizing that larger watersheds tend to result in wider channels and deeper average flow. However this approach does not account for variations between same-size watersheds with respect to such variables as topography, soils, land cover, and climatology. This presentation discusses efforts at Idaho State University to develop multivariate regression relationships which can be used to better predict average channel width and depth directly from a broad set of readily available GIS data layers.
Estimating Stream Channel Cross Sections from Watershed Characterisitcs
Eccles Conference Center
Streamflow simulation models typically require a comprehensive set of stream channel geomorphologic characteristics as input parameters. These parameters often include channel length, slope, average width and depth. Some more advanced hydrologic models require more detailed channel cross section information including flood plain parameters. These data can be time consuming and cost prohibitive to acquire for all stream channels in a study area. In light of this it is convenient to estimate these parameters directly from GIS data. While channel length and slope can be readily estimated from digital elevation model (DEM) data using channel extraction algorithms, the parameters average width and depth are not as easily estimated. One commonly used approach is to define average width and depth parameters each as a function of watershed area recognizing that larger watersheds tend to result in wider channels and deeper average flow. However this approach does not account for variations between same-size watersheds with respect to such variables as topography, soils, land cover, and climatology. This presentation discusses efforts at Idaho State University to develop multivariate regression relationships which can be used to better predict average channel width and depth directly from a broad set of readily available GIS data layers.
https://digitalcommons.usu.edu/runoff/2006/AllAbstracts/29