Prediction of Annual Streambank Erosion for Sequoia National Forest, California
AdvisorSwanson, Sherman R
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The US EPA has consistently listed sediment as a leading cause of water quality impairment in rivers, streams, and lakes, costing approximately $16 billion annually. Yet prediction methods are not applicable to wildland systems. The Sequoia National Forest needs to understand mechanisms and rates of streambank erosion to evaluate with management issues , especially those associated with post-wildfire effects. This study uses Bank Erosion Hazard Index (BEHI) methods developed in Rosgen (2006) for predicting streambank erosion. Measurements of bank erosion over a year were evaluated using BEHI and estimates of Near Bank Stress (NBS). BEHI evaluates bank susceptibility to erosion based on bank angle, bank and bankfull height, rooting depth and density, surface protection, and stratification of material within the banks. NBS assesses energy distribution against the bank measured as a ratio of near-bank maximum depth to mean bankfull depth. BEHI and NBS were good to fair indicators of streambank erosion at or near bankfull conditions at riffle features. Individual BEHI variables and several other physical variables (e.g., elevation, drainage area, and vegetation) significantly correlated with streambank erosion but had low predictive power (i.e., r<super>2</super> 0.0007 to r2 0.18) indicating inconsistency in driving variables among locations. This indicates that a combination of several variables affects streambank erosion. A low <super>2</super>2 (0.23) from multiple regression analyses shows there may be variables other than those of BEHI that affect streambank erosion. Bank angle has the lowest predictive power for erosion while rooting depth had the highest.