Estimates of Sediment Loading from Streambank Erosion Using Terrestrial LIDAR sediment in rivers using artificial neural networks: Implications for development of sediment budgets


TitleEstimates of Sediment Loading from Streambank Erosion Using Terrestrial LIDAR sediment in rivers using artificial neural networks: Implications for development of sediment budgets
Publication TypeConference Paper and Presentation
Year of Publication2013
AuthorsRizzo, DM, Hamshaw, SD, Anderson, H, Underwood, K, Dewoolkar, MM
Conference NameAmerican Geophysical Union, Fall Meeting
Date Published12/2013
Conference LocationSan Francisco, CA
Abstract

Channel and streambank erosion are common yet poorly quantified sources of sediment to streams and waterways worldwide. It is estimated that streambank erosion accounts for about one-third of the total sediment loading into Vermont lakes and waterways, contributing to water quality degradation, loss of agricultural lands, damaged infrastructure, sediment-bound nutrient loading and decreased habitat health. Therefore, an understanding of streambank stability and erosion is a prerequisite to developing accurate sediment budgets and reliably predicting geomorphic response of channels and resulting impacts on the health of streams and lakes. The motivation for this study came from rising concerns of eutrophication of Lake Champlain, Vermont. In the study presented here, periodic aerial photography and 3D scans of streambanks, using a handheld terrestrial LiDAR with sub-centimeter accuracy, were used to quantify erosion rates and sediment loadings at select streambank sites in the Mad River watershed (Winooski River basin) in Vermont over time periods that capture both single and multiple storm events. Estimates were compared to the predictions made using the USDA streambank stability model (BSTEM), parameterized via extensive fieldwork (i.e., borehole shear tests, jet erosion tests, and soil sampling and analysis for determining their index properties, soil matric suctions, and stream and bank water levels).

Status: 
Published
Attributale Grant: 
RACC
Grant Year: 
Year3