Development and Evaluation of Bankfull Hydraulic Geometry Relationships for the Physiographic Regions of the United States1. (18th February 2015)

• Record Type: Journal Article
• Authors: Bieger, Katrin; Rathjens, Hendrik; Allen, Peter M.; Arnold, Jeffrey G.
• Is Part Of: Journal of the American Water Resources Association. Volume 51:Number 3(2015)
• Page Start: 842
• Page End: 858
• Publication Date: 2015-02-18
• Subjects: Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/jawr.12282
• Languages: English
• ISSNs: 1093-474X

Abstract

<abstract abstract-type="main" id="jawr12282-abs-0001"> <title>Abstract</title> <p>Bankfull hydraulic geometry relationships are used to estimate channel dimensions for streamflow simulation models, which require channel geometry data as input parameters. Often, one nationwide curve is used across the entire United States (U.S.) (e.g., in Soil and Water Assessment Tool), even though studies have shown that the use of regional curves can improve the reliability of predictions considerably. In this study, regional regression equations predicting bankfull width, depth, and cross‐sectional area as a function of drainage area are developed for the Physiographic Divisions and Provinces of the U.S. and compared to a nationwide equation. Results show that the regional curves at division level are more reliable than the nationwide curve. Reliability of the curves depends largely on the number of observations per region and how well the sample represents the population. Regional regression equations at province level yield even better results than the division‐level models, but because of small sample sizes, the development of meaningful regression models is not possible in some provinces. Results also show that drainage area is a less reliable predictor of bankfull channel dimensions than bankfull discharge. It is likely that the regional curves can be improved using multiple regression models to incorporate additional explanatory variables.</p> </abstract>