Mapping Spatial Distributions of Stream Power and Channel Change along a Gravel-Bed River in Northern Yellowstone
Thursday, 18 December 2014
Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing. This study used remotely sensed data and field measurements to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8 km reach. Aerial photographs from 1994-2012 and cross-section surveys were used to assess lateral channel mobility and develop a morphologic sediment budget for quantifying net sediment flux for a series of budget cells. A drainage area-to-discharge relationship and digital elevation model (DEM) developed from LiDAR data were used to obtain the discharge and slope values, respectively, needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of channel mobility and sediment transfer. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volume of sediment eroded or deposited during each time increment. Our results indicated a lack of strong correlation between stream power gradients and sediment flux, which we attributed to the geomorphic complexity of the Soda Butte Creek watershed and the inability of our relatively simple statistical approach to link sediment dynamics expressed at a sub-budget cell scale to larger-scale driving forces such as stream power gradients. Future studies should compare the moderate spatial resolution techniques used in this study to very-high resolution data acquired from new fluvial remote sensing technologies to better understand the amount of error associated with stream power, sediment transport, and channel change calculated from historical datasets.