Application of Rosgen’s BANCS Model for NE Kansas and the Development of Predictive Streambank Erosion Curves1

Sass, Christopher K. and Tim D. Keane, 2012. Application of Rosgen’s BANCS Model for NE Kansas and the Development of Predictive Streambank Erosion Curves. Journal of the American Water Resources Association (JAWRA) 48(4): 774‐787. DOI: 10.1111/j.1752‐1688.2012.00644.x Abstract: Sedimentation of waterways and reservoirs directly related to streambank erosion threatens freshwater supply. This study sought to provide a tool that accurately predicts annual streambank erosion rates in NE Kansas. Rosgen (2001, 2006) methods were employed and 18 study banks were measured and monitored from 2007 through 2010 (May‐June). Bank profiles were overlaid to calculate toe pin area change due to erosional processes. Streambanks experienced varied erosion rates from similar Bank Erosion Hazard Index (BEHI)‐Near Bank Stress (NBS) combinations producing R² values of 0.77 High‐Very High BEHI rating and 0.75 Moderate BEHI rating regarding predictive erosion curves for NE Kansas. Moderate ratings demonstrated higher erosion rates than High‐Very High ratings and BEHI trend lines intersected at lower NBS ratings, suggesting a discrepancy in the fit of the model to conditions in the NE Kansas region. BEHI model factors were evaluated and assessed for additional influences exerted in the region. Woody vegetation adjacent to the stream seemed to provide the most variation in erosion rates. This study’s findings allowed us to calibrate and modify the existing BEHI model according to woody vegetation occurrence levels along streambanks with high clay content. Modifications regarding vegetation occurrence of the BEHI model was completed and the results of these modifications generated new curves resulting in R² values of 0.84 High‐Very High BEHI and 0.88 Moderate BEHI ratings.     

EVALUATION OF ROSGEN'S STREAMBANK EROSION POTENTIAL ASSESSMENT IN NORTHEAST OKLAHOMA1

ABSTRACT: In this study three components of the Rosgen Level III Stream Reach Condition Assessment were tested for their ability to predict short-term erosion rates. Rosgen's bank erosion potential (BEP) ratings and near bank stress (NBS) estimates and the Pfankuch channel stability ratings were evaluated. Thirty-six banks with a range of BEP ratings and NBS estimates were selected on the 101 km Upper Illinois River in northeast Oklahoma. The Upper Illinois River is a meandering, gravel-dominated, riffle/pool channel. Cumulative erosion data measured with bank pins after four 2.0 to 2.5-year return period flows from September 1996 to July 1997 were used in the analyses. When integrated as indicated in Rosgen (1996), the BEP indices and NBS estimates were poor predictors of bank erosion. Individually, the grouped BEP ratings and Pfankuch ratings performed relatively well compared to grouped NBS estimates in predicting erosion; however, the variability of erosion was large within each rating group. Linear regression between erosion and BEP numerical indices and Pfankuch scores was significant (a = 0.05), but variability was high (illustrated by low r² values). Regression between erosion and NBS estimates was not significant.     

Repeatability,Sensitivity, and Uncertainty Analyses of the BANCS Model Developed to Predict Annual Streambank Erosion Rates

Accelerated streambank erosion caused by channel instability can be the leading cause of sediment impairment of streams. Obtaining accurate streambank erosion rates for sediment budgeting and prioritizing mitigation efforts can be difficult and costly. One approach to quantifying streambank erosion rates is through the development and implementation of an empirically derived “Bank Assessment for Non‐point Source Consequences of Sediment” (BANCS) model. This study aims to improve the BANCS model application by evaluating repeatability between users and identifying sensitive and/or uncertain model inputs. Statistical analysis of streambank evaluations conducted by 10 different individuals suggests the implementation of the BANCS model may not be repeatable. This finding may be due to sensitive model inputs, such as streambank height and near‐bank stress level prediction method selection, and/or uncertain model inputs, such as bank material identification and the associated adjustment of erosion potential. Furthermore, it was found assessing streambanks as a group by obtaining a measure of central tendency from individual evaluations, as well as obtaining a higher level of training, may improve model implementation precision. Application of these suggestions may result in improved prediction of streambank erosion rates utilizing the BANCS model methodology.     

Prediction of Annual Streambank Erosion for Sequoia National Forest,California

Many bank erosion models have limitations that restrict their use in wildland settings. Scientists and land managers at the Sequoia National Forest would like to understand the mechanisms and rates of streambank erosion to evaluate management issues and post‐wildfire effects. This study uses bank erosion hazard index (BEHI) and near‐bank stress (NBS) methods developed in Rosgen (2006 Watershed Assessment of River Stability and Sediment Supply [WARSSS]) for predicting streambank erosion in a geographic area that is dominated by colluvium and in which streambank erosion modeling has not been previously evaluated. BEHI evaluates bank susceptibility to erosion based on bank angle, bank and bankfull height, rooting depth and density, surface protection, and stratification of material within the banks. NBS assesses energy distribution against the bank measured as a ratio of bankfull near‐bank maximum depth to mean bankfull depth. We compared BEHI classes and NBS to actual bank erosion measured from 2008 to 2012. This index predicted streambank erosion with clear separation among BEHI ratings with R² values of 0.76 for extreme, 0.37 for high/very high, 0.49 for moderate, and 0.70 for low BEHI. The relationships between measured erosion and BEHI extend the application of BEHI/NBS to a new region where they can inform management priorities, afforestation, stream/riparian restoration projects, and potentially burned area rehabilitation.     

Critical Evaluation of How the Rosgen Classification and Associated “Natural Channel Design” Methods Fail to Integrate and Quantify Fluvial Processes and Channel Response1

Abstract: Over the past 10 years the Rosgen classification system and its associated methods of “natural channel design” have become synonymous to some with the term “stream restoration” and the science of fluvial geomorphology. Since the mid 1990s, this classification approach has become widely adopted by governmental agencies, particularly those funding restoration projects. The purposes of this article are to present a critical review, highlight inconsistencies and identify technical problems of Rosgen’s “natural channel design” approach to stream restoration. This paper’s primary thesis is that alluvial streams are open systems that adjust to altered inputs of energy and materials, and that a form‐based system largely ignores this critical component. Problems with the use of the classification are encountered with identifying bankfull dimensions, particularly in incising channels and with the mixing of bed and bank sediment into a single population. Its use for engineering design and restoration may be flawed by ignoring some processes governed by force and resistance, and the imbalance between sediment supply and transporting power in unstable systems. An example of how C5 channels composed of different bank sediments adjust differently and to different equilibrium morphologies in response to an identical disturbance is shown. This contradicts the fundamental underpinning of “natural channel design” and the “reference‐reach approach.” The Rosgen classification is probably best applied as a communication tool to describe channel form but, in combination with “natural channel design” techniques, are not diagnostic of how to mitigate channel instability or predict equilibrium morphologies. For this, physically based, mechanistic approaches that rely on quantifying the driving and resisting forces that control active processes and ultimate channel morphology are better suited as the physics of erosion, transport, and deposition are the same regardless of the hydro‐physiographic province or stream type because of the uniformity of physical laws.     

WATERSHED CLASSIFICATION USING CANONICAL CORRESPONDENCE ANALYSIS AND CLUSTERING TECHNIQUES: A CAUTIONARY NOTE1

ABSTRACT: Watershed classification using multivariate techniques requires the incorporation of continuous datasets representing controlling environmental variables. Often, out of convenience and availability rather than importance to the structure of the system being modeled, the environmental data used originate from a variety of sources and scales. To demonstrate the importance of appropriate environmental data selection, classifications of six‐digit hydrologic units (1:24,000) across selected geographic areas within the Interior Columbia River Basin were produced. Canonical correspondence analysis was used to select and test environmental variables important in predicting Rosgen stream types and valley bottom classes. Then, hierarchical agglomerative clustering was used to group (classify) watersheds based on these variables. Statistically significant results were derived from the use of organized classification data with presumed predictive relationships to watershed properties, and a random distribution of environmental variables from the same datasets provided similar results. The results contained herein demonstrate that these analysis techniques do not necessarily select meaningful variables from a broad spectrum of data and that significant results are easily generated from randomly associated data. It is suggested that classifications produced using these multivariate techniques, especially when using multi‐scale data or data of unknown significance, are subject to invalid inferences and should be used with caution.     

Evaluating the BANCS Streambank Erosion Framework on the Northern Gulf of Mexico Coastal Plain

The Bank Assessment of Nonpoint source Consequences of Sediment (BANCS) framework allows river scientists to predict annual sediment yield from eroding streambanks within a hydrophysiographic region. BANCS involves field data collection and the calibration of an empirical model incorporating a bank erodibility hazard index (BEHI) and near‐bank shear stress (NBS) estimate. Here we evaluate the applicability of BANCS to the northern Gulf of Mexico coastal plain, a region that has not been previously studied in this context. Erosion rates averaged over two years expressed the highest variability of any existing BANCS study. As a result, four standard BANCS models did not yield statistically significant correlations to measured erosion rates. Modifications to two widely used NBS estimates improved their correlations (r² = 0.31 and r² = 0.33), but further grouping of the data by BEHI weakened these correlations. The high variability in measured erosion rates is partly due to the regional hydrologic and climatic characteristics of the Gulf coastal plains, which include large, infrequent precipitation events. Other sources of variability include variations in bank vegetation and the complex hydro‐ and morphodynamics of meandering, sand bed channels. We discuss directions for future research in developing a streambank erosion model for this and similar regions.     

The Role of Observer Variation in Determining Rosgen Stream Types in Northeastern Oregon Mountain Streams1

Abstract: Consistency in determining Rosgen stream types was evaluated in 12 streams within the John Day Basin, northeastern Oregon. The Rosgen classification system is commonly used in the western United States and is based on the measurement of five stream attributes: entrenchment ratio, width‐to‐depth ratio, sinuosity, slope, and substrate size. Streams were classified from measurements made by three monitoring groups, with each group fielding multiple crews that conducted two to three independent surveys of each stream. In only four streams (33%) did measurements from all crews in all monitoring groups yield the same stream type. Most differences found among field crews and monitoring groups could be attributed to differences in estimates of the entrenchment ratio. Differences in entrenchment ratio were likely due to small discrepancies in determination of maximum bankfull depth, leading to potentially large differences in determination of Rosgen’s flood‐prone width and consequent values of entrenchment. The result was considerable measurement variability among crews within a monitoring group, and because entrenchment ratio is the first discriminator in the Rosgen classification, differences in the assessment of this value often resulted in different determination of primary stream types. In contrast, we found that consistently evaluated attributes, such as channel slope, rarely resulted in any differences in classification. We also found that the Rosgen method can yield nonunique solutions (multiple channel types), with no clear guidance for resolving these situations, and we found that some assigned stream types did not match the appearance of the evaluated stream. Based on these observations we caution the use of Rosgen stream classes for communicating conditions of a single stream or as strata when analyzing many streams due to the reliance of the Rosgen approach on bankfull estimates which are inherently uncertain.