Comparing Channel Classification Frameworks to Better Inform Watershed Management
Location
Ellen Eccles Conference Center
Event Website
https://forestry.usu.edu/htm/video/conferences/restoring-the-west-conference-2014/
Abstract
1. Department of Watershed Sciences, Utah State University, Logan, Utah, 84322-5210, USA
2. Ecology Center, Utah State University, Logan, Utah, 84322-5210, USA
3. Watershed Program, Fish Ecology Division, Northwest Fisheries Science Center, NOAA Fisheries, Seattle, Washington, 98112, USA
4. School of Environment, University of Auckland, Auckland, New Zealand
Channel classification frameworks provide a means for understanding the distribution of channel types across a watershed and drawing linkages between the geomorphic form of a river and the processes acting to shape the channel. Moreover, classification is frequently employed by watershed managers to distinguish natural and altered streams and set restoration priorities for the latter. In classifying channels, watershed managers have a large number of potential frameworks to choose from; yet there is little information about which frameworks perform best at various scales, and perhaps more importantly, how consistent the results of classification are between frameworks. Here we apply four classification methods across a physiographically-diverse basin in the interior Columbia River Watershed. We compare the results of Natural Channel Design classification, the River Styles Framework, a semi-automated machine learning classification termed Natural Channel Classification, and a statistical clustering classification. We find moderate agreement between Natural Channel Classification and Natural Channel Design, likely in part due to their use of reach-scale channel planform as a driving variable in classification. There is less agreement between these two metrics and the River Styles Framework, likely an effect of River Styles lending more weight to broader, valley-scale confinement as a driving classification variable. We also examine the relative effort and level of expertise necessary to complete each classification and note that where classification results differ, inferences about channel departure from natural conditions can be drawn. The results of this work will allow watershed managers to better choose and employ channel classification, along with providing an improved understanding of classification results in prioritizing channel restoration.
Comparing Channel Classification Frameworks to Better Inform Watershed Management
Ellen Eccles Conference Center
1. Department of Watershed Sciences, Utah State University, Logan, Utah, 84322-5210, USA
2. Ecology Center, Utah State University, Logan, Utah, 84322-5210, USA
3. Watershed Program, Fish Ecology Division, Northwest Fisheries Science Center, NOAA Fisheries, Seattle, Washington, 98112, USA
4. School of Environment, University of Auckland, Auckland, New Zealand
Channel classification frameworks provide a means for understanding the distribution of channel types across a watershed and drawing linkages between the geomorphic form of a river and the processes acting to shape the channel. Moreover, classification is frequently employed by watershed managers to distinguish natural and altered streams and set restoration priorities for the latter. In classifying channels, watershed managers have a large number of potential frameworks to choose from; yet there is little information about which frameworks perform best at various scales, and perhaps more importantly, how consistent the results of classification are between frameworks. Here we apply four classification methods across a physiographically-diverse basin in the interior Columbia River Watershed. We compare the results of Natural Channel Design classification, the River Styles Framework, a semi-automated machine learning classification termed Natural Channel Classification, and a statistical clustering classification. We find moderate agreement between Natural Channel Classification and Natural Channel Design, likely in part due to their use of reach-scale channel planform as a driving variable in classification. There is less agreement between these two metrics and the River Styles Framework, likely an effect of River Styles lending more weight to broader, valley-scale confinement as a driving classification variable. We also examine the relative effort and level of expertise necessary to complete each classification and note that where classification results differ, inferences about channel departure from natural conditions can be drawn. The results of this work will allow watershed managers to better choose and employ channel classification, along with providing an improved understanding of classification results in prioritizing channel restoration.
https://digitalcommons.usu.edu/rtw/2014/Posters/12