## 1. Introduction

River channel morphology and evolution are critical topics in the study of earth-surface processes [*Howard*, 1996; *Whipple and Tucker*, 2002]. In addition, predicting change in response to changing drivers is an important goal in fluvial research [*Shreve*, 1979; *Newson*, 2002; *Hardy*, 2006]. Progress on these topics facilitate holistic interpretations of channel changes in the context of past climate changes, shorter-term human impacts, and potential future climate change [*Dollar*, 2004]. Moreover, the need for progress is presently heightened because of worldwide efforts to restore, renaturalize, and reengineer rivers to meet changing social and ecologic objectives and expectations [*Wallick et al*., 2007; *Bernhardt et al*., 2005].

Bankfull channel width (*W _{bf}*) is a key measure of stream size that is used by hydraulic engineers, hydrologists, fluvial geomorphologists, and stream ecologists and biologists [

*Faustini et al*., 2009;

*McCandless and Everett*, 2002]. It is an attractive measure of channel size because it is relatively easy to measure and is available in many data sets and independent measurements of

*W*are more consistent—presumably due to

_{bf}*W*being insensitive to minor differences in estimates of the bankfull stage—than measurements of bankfull depth or cross-section area [

_{bf}*He and Wilkerson*, 2011;

*Leopold*, 1994;

*Wahl*, 1977]. However, using

*W*as a descriptor of stream size can be problematic because alluvial streams with identical flows might have different average widths due to differences in bed slope, sinuosity, bed or bank material composition, available sediment load, streambank vegetation, climate, human impacts, geology, large woody debris (LWD; usually defined as wood larger than 10 cm in diameter and 1 m in length) loading, or other factors. Furthermore, it is not universally agreed that

_{bf}*W*is the most important or consistent measure of channel size [e.g.,

_{bf}*Hey*, 2006;

*Roper et al*., 2010;

*Krstolic and Chaplin*, 2007]. Thus, in an assessment of “natural stable channel design” procedures,

*Hey*[2006] notes that cross-sectional area underpins all scaling procedures for stream restoration design, and

*Krstolic and Chaplin*[2007] conclude that bankfull cross-section area (computed as the product of bankfull width and mean depth) is more consistent than

*W*.

_{bf}Two commonly used models for predicting *W _{bf}* are

where *Q _{bf}* is the bankfull discharge,

*A*is the drainage area, and

_{da}*α*

_{0}and

*β*

_{1}are parameters. Given values for

*α*

_{0}and

*β*

_{1}typically apply to a region with a homogeneous hydrologic response. Common practice is to refer to equation (1) as a regional curve relationship and equation (2) as a (downstream) hydraulic geometry relationship. A range of

*α*

_{0}and

*β*

_{1}values have been reported, and reported values have been compiled by

*Anderson et al*. [2004],

*Knighton*[1998],

*Dunne and Leopold*[1978], and

*Soar and Thorne*[2001]. The variability associated with (2) is less than the variability associated with (1) [

*Soar and Thorne*, 2001]. Equation (1), however, has the advantage that values of

*A*are easier to obtain than values

_{da}*Q*. More specifically, values of

_{bf}*A*can be obtained from digital elevation models whereas substantial field campaigns are required to obtain reliable Q

_{da}_{bf}estimates. Recently,

*He and Wilkerson*[2011] proposed using

where *Q*_{2} is the 2 year return-period discharge, as an alternative to or alongside of equation (1) on the grounds that (a) *Q*_{2} reflects the geologic characteristics of a basin, (b) values of *Q*_{2} can be obtained almost as easily as values of *A _{da}* in the United States, and (c) estimates of

*W*derived using equation (3) are in some cases significantly better than

_{bf}*W*estimates obtained using equation (1). Related to regional curves are what

_{bf}*Faustini et al*. [2009] refer to as enhanced regional curves, which are models for predicting channel geometry that include drainage area and other landscape variables.

*Faustini et al*., for example, identify the following factors as important for predicting bankfull width: bed material, ecoregion, mean annual precipitation, elevation, mean reach slope, and human disturbance.