Factors Influencing Watershed Average Erosion Rates Calculated from Reservoir Sedimentation in Eastern USA

Abstract:

The Reservoir Sedimentation Database (ResSed), a catalogue of reservoirs and depositional data that has recently become publically available, allows for rapid calculation of sedimentation rates and rates of capacity loss over short (annual to decadal) timescales. This study is a statistical investigation of factors controlling watershed average erosion rates (E) in eastern United States watersheds. We develop an ArcGIS-based model that delineates watersheds upstream of ResSed dams and calculate drainage areas to determine E for 191 eastern US watersheds. Geomorphic, geologic, regional, climatic, and land use variables are quantified within study watersheds using GIS. Erosion rates exhibit a large amount of scatter, ranging from 0.001 to 1.25 mm/yr. A weak inverse power law relationship between drainage area (A) and E (R² = 0.09) is evident, similar to other studies (e.g. Milliman and Syvitski, 1992; Koppes and Montgomery, 2009). Linear regressions reveal no relationship between mean watershed slope (S) and E, possibly due to the relatively low relief of the region (mean S for all watersheds is 6°). Analysis of Variance shows that watersheds in formerly glaciated regions exhibit a statistically significant lower mean E (0.06 mm/year) than watersheds in unglaciated regions (0.12 mm/year), but that watersheds with different dam purposes show no significant differences in mean E. Linear regressions reveal no relationships between E and land use parameters like percent agricultural land and percent impervious surfaces (I), but classification and regression trees indicate that watersheds in highly developed regions (I > 34%) exhibit mean E (0.36 mm/year) that is four times higher than watersheds in less developed (I < 34%) regions (0.09 mm/year). Further, interactions between land use variables emerge in formerly glaciated regions, where increased agricultural land results in higher rates of annual capacity loss in reservoirs (R² = 0.56). Plots of E versus timescale of measurement (e.g., Sadler and Jerolmack, 2014) show that nearly the full range of observed E, including the highest values, are seen over short survey intervals (< 20 years), suggesting that whether or not large sedimentation events (such as floods) occur between two surveys may explain the high degree of variability in measured rates.