Optimizing Bankfull Discharge and Hydraulic Geometry Relations for Streams in New York State¹

Abstract

This study analyzes how various data stratification schemes can be used to optimize the accuracy and utility of regional hydraulic geometry (HG) models of bankfull discharge, width, depth, and cross-sectional area for streams in New York. Topographic surveys and discharge records from 281 cross sections at 82 gaging stations with drainage areas of 0.52-396 square miles were used to create log-log regressions of region-based relations between bankfull HG metrics and drainage area. The success with which regional models distinguished unique bankfull discharge and HG patterns was assessed by comparing each regional model to those for all other regions and a pooled statewide model. Gages were also stratified (grouped) by mean annual runoff (MAR), Rosgen stream type, and water-surface slope to test if these models were better predictors of HG to drainage area relations. Bankfull discharge models for Regions 4 and 7 were outside the 95% confidence interval bands of the statewide model, and bankfull width, depth, and cross-sectional area models for Region 3 differed significantly (p < 0.05) from those of other regions. This study found that statewide relations between drainage area and HG were strongest when data were stratified by hydrologic region, but that co-variable models could yield more accurate HG estimates in some local regional curve applications.

Development and Evaluation of Bankfull Hydraulic Geometry Relationships for the Physiographic Regions of the United States

“…Many authors have tried to reduce the variability of a population by regionalizing the available data according to a number of criteria to minimize the variability of influencing factors. Data have been stratified by ecoregions (Castro and Jackson, ; Faustini et al ., ; Splinter et al ., ), hydrologic regions (Mulvihill and Baldigo, ), water resources regions (Faustini et al ., ), and physiographic regions (Castro and Jackson, ; Johnson and Fecko, ). However, only Faustini et al .…”

“…Anderson et al . () and Mulvihill and Baldigo () argue that even highly regionalized curves are often subject to substantial variability and error. Also, finer stratification leads to a smaller number of representative data points per region, which results in less robust equations (Johnson and Fecko, ).…”

“…Nevertheless, the data presented by Dunne and Leopold () indicated major regional differences in channel dimensions, which justifies recent trends toward more detailed regional curves in order to more accurately assess HG in areas of varying physiography and climate. Recently, more localized HG curves have been developed for many areas across the country to address classification and natural channel design restoration needs (Mulvihill and Baldigo, ). An increasing use of bankfull HG relationships for the design of stream restoration projects has resulted in a number of studies carried out all over the U.S. (Harman et al ., ; Smith and Turrini‐Smith, ; Harman et al ., ; Castro and Jackson, ; White, ; McCandless and Everett, ; Cinotto, ; McCandless, , b; Sweet and Geratz, ; Emmert, ; Messinger and Wiley, ; Powell et al ., ; Babbit, ; Keaton et al ., ; Mohamoud and Parmar, ; Mulvihill et al ., ; Vesely et al ., ; Padmanabhan and Johnson, ).…”

“…Bankfull discharge is a deterministic discharge representing the channel‐forming discharge in a stream (Copeland et al ., ). It is defined as the highest flow a channel can convey before it starts to spill onto its floodplain (Leopold et al ., ) and can be identified in the field using physical indicators (Mulvihill and Baldigo, ).…”

Comparison of regression relations of bankfull discharge and channel geometry for the glaciated and nonglaciated settings of Pennsylvania and southern New York

“…In New York, regional curves were developed based on eight hydrologic regions for which only region 3 differed significantly (p-value<0.05) from the other regions (Lumia, 1991;Mulvihill and others, 2005Mulvihill and others, , 2007Mulvihill and others, , 2009Westergard and others, 2005;. Despite more accurate regional curves based on other covariable models (for example, mean annual runoff), data stratification by hydroregions that were derived with determinate, unbiased, and reproducible procedures has shown the most advantage to resource managers by providing better overall coefficients of determination (R 2 ) and standard errors of estimate (SEE) (Mulvihill and Baldigo, 2012). Regional curves for Pennsylvania were first published for the Piedmont Physiographic Province (White, 2001;Cinotto, 2003) region and later combined with the all physiographic provinces within the state to produce significant regression relations for the carbonate and noncarbonate settings (Chaplin, 2005).…”