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Channel Evolution Models as Predictors of Sediment Yield
Author(s) -
Keane Tim D.,
Sass Christopher K.
Publication year - 2017
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12598
Subject(s) - tributary , bank erosion , channelized , erosion , channel (broadcasting) , hydrology (agriculture) , sedimentation , sediment , structural basin , environmental science , drainage basin , geology , geomorphology , geography , geotechnical engineering , telecommunications , cartography , engineering , computer science , electrical engineering
This paper recounts our predictions of channel evolution of the Black Vermillion River ( BVR ) and sediment yields associated with the evolutionary sequence. Channel design parameters allowed for the prediction of stable channel form and coincident sediment yields. Measured erosion rates and basin‐specific bank erosion curves aided in prediction of the stream channel succession time frame. This understanding is critical in determining how and when to mitigate a myriad of instability consequences. The BVR drains approximately 1,062 km 2 in the glaciated region of Northeast Kansas. Once tallgrass prairie, the basin has been modified extensively for agricultural production. As such, channelization has shortened the river by nearly 26 km from pre‐European dimensions; shortening combined with the construction of numerous flow‐through structures have produced dramatic impacts on discharge and sediment dynamics. Nine stream reaches were established within three main tributaries of the BVR in 2007. Reaches averaged 490 m in length, were surveyed, and assessed for channel stability, while resurveys were conducted annually through 2010 to monitor change. This work illustrates the association of current stream state, in‐channel sediment contributions, and prediction of future erosion rates based on stream evolution informed by multiple models. Our findings suggest greater and more rapid sedimentation of a federal reservoir than has been predicted using standard sediment prediction methods.