Get access

Improved Bankfull Channel Geometry Prediction Using Two-Year Return-Period Discharge

Authors

  • Laien He,

    1. Respectively, Graduate Research Assistant (He) and Assistant Professor (Wilkerson), Department of Civil and Environmental Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Dr. (MC 6603), Carbondale, Illinois 62901
    Search for more papers by this author
  • Gregory V. Wilkerson

    1. Respectively, Graduate Research Assistant (He) and Assistant Professor (Wilkerson), Department of Civil and Environmental Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Dr. (MC 6603), Carbondale, Illinois 62901
    Search for more papers by this author

  • Paper No. JAWRA-10-0142-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.

(E-Mail/Wilkerson: gwilkers@siu.edu).

Abstract

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 (Qbf) and bankfull channel geometry (i.e., width, Wbf; mean depth, Dbf; and cross-section area, Abf) 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 Qbf, Wbf, Dbf, and Abf are predicted as a function of drainage area, Ada). This paper explores the potential for the two-year return-period discharge (Q2) to improve predictions of Wbf, Dbf, and Abf. Improved predictions are expected because Q2 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 Q2 by comparing statistical measures of regression model performance (e.g., coefficient of determination and Akaike’s information criterion). Compared to using Ada, Q2 is shown to be a “clearly superior” predictor of Wbf, Dbf, and Abf, respectively, for 21, 13, and 25% of the datasets. By contrast, Ada yielded a clearly superior model for predicting Wbf, Dbf, and Abf, respectively, for 0, 0, and 14% of the datasets. Our conclusion is that it alongside with developing conventional regional curves using Ada it is prudent to develop regional curves that use Q2 as an independent variable because in some cases the resulting model will be superior.

Ancillary