Premium
Continental‐scale relationship between bankfull width and drainage area for single‐thread alluvial channels
Author(s) -
Wilkerson Gregory V.,
Kandel Dinesh R.,
Perg Lesley A.,
Dietrich William E.,
Wilcock Peter R.,
Whiles Matt R.
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2013wr013916
Subject(s) - analysis of covariance , linear regression , statistics , covariance , hydrology (agriculture) , geology , mathematics , geotechnical engineering
We explore the bankfull width ( W bf ) versus drainage area ( A da ) relationship across a range of climatic and geologic environments and ask (1) is the relationship between ln( W bf ) and ln( A da ) best described by a linear function and (2) can a reliable relationship be developed for predicting W bf with A da as the only independent variable. The principal data set for this study was compiled from regional curve studies and other reports that represent 1018 sites (1 m ≤ W bf ≤ 110 m and 0.50 km 2 ≤ A da ≤ 22,000 km 2 ) in the continental United States. Two additional data sets were used for validation. After dividing the data into small, medium, and large‐size basins which, respectfully, correspond to A da < 4.95 km 2 , 4.95 km 2 ≤ A da < 337 km 2 , and A da ≥ 337 km 2 , regression lines from each data set were compared using one‐way analysis of covariance (ANCOVA). A second ANCOVA was performed to determine if mean annual precipitation ( P ) is an extraneous factor in the W bf versus A da relationship. The ANCOVA results reveal that using A da alone does not yield a reliable W bf versus A da relationship that is applicable across a wide range of environments and that P is a significant extraneous factor in the relationship. Considering data for very small basins ( A da ≤ 0.49 km 2 ) and very large basins ( A da ≥ 1.0 × 10 5 km 2 ) we conclude that a two‐segment linear model is the most probable form of the ln( W bf ) versus ln( A da ) relationship. This study provides useful information for building complex multivariate models for predicting W bf .