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Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge 1
Author(s) -
He Laien,
Wilkerson Gregory V.
Publication year - 2011
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/j.1752-1688.2011.00567.x
Subject(s) - akaike information criterion , return period , hydrology (agriculture) , structural basin , regression , channel (broadcasting) , drainage , drainage basin , environmental science , statistics , mathematics , geometry , geology , geography , computer science , cartography , geomorphology , geotechnical engineering , archaeology , telecommunications , ecology , biology , flood myth
He, Laien and Gregory V. Wilkerson, 2011. Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge. Journal of the American Water Resources Association (JAWRA) 47(6):1298–1316. DOI: 10.1111/j.1752‐1688.2011.00567.x Abstract: Bankfull discharge ( Q bf ) and bankfull channel geometry (i.e., width, W bf ; mean depth, D bf ; and cross‐section area, A bf ) are important design parameters in stream restoration, habitat creation, mined land reclamation, and related projects. The selection of values for these parameters is facilitated by regional curves (regression models in which Q bf , W bf , D bf , and A bf are predicted as a function of drainage area, A da ). This paper explores the potential for the two‐year return‐period discharge ( Q 2 ) to improve predictions of W bf , D bf , and A bf . Improved predictions are expected because Q 2 estimates integrate the effects of basin drainage area, climate, and geology. For conducting this study, 29 datasets (each representing one hydrologic region) spanning 14 states in the United States were analyzed. We assessed the utility of using Q 2 by comparing statistical measures of regression model performance (e.g., coefficient of determination and Akaike’s information criterion). Compared to using A da , Q 2 is shown to be a “clearly superior” predictor of W bf , D bf , and A bf , respectively, for 21, 13, and 25% of the datasets. By contrast, A da yielded a clearly superior model for predicting W bf , D bf , and A bf , respectively, for 0, 0, and 14% of the datasets. Our conclusion is that it alongside with developing conventional regional curves using A da it is prudent to develop regional curves that use Q 2 as an independent variable because in some cases the resulting model will be superior.