Master of Science / Department of Biological & Agricultural Engineering / Trisha L. Moore / Excess sediment is a leading cause of stream impairment in the United States, resulting in poor water quality, sedimentation of downstream waterbodies, and damage to aquatic ecosystems. Numerous case studies have found that accelerated bank erosion can be the main contributor of sediment in impaired streams. An empirically-derived "Bank Assessment for Non-Point Source Consequences of Sediment" (BANCS) model can be developed for a specific hydrophysiographic region to rapidly estimate sediment yield from streambank erosion, based on both physical and observational measurements of a streambank. This study aims to address model criticisms by (1) evaluating the model’s repeatability and sensitivity and (2) examining the developmental process of a BANCS model by attempting to create an annual streambank erosion rate prediction curve for the Central Great Plains ecoregion. To conduct the repeatability and sensitivity analysis of the BANCS model, ten stream professionals with experience utilizing the model individually evaluated the same six streambanks twice in the summer of 2015. To determine the model’s repeatability, individual streambank evaluations, as well as groups of evaluations based on level of Rosgen course training, were compared utilizing Kendall’s coefficient of concordance and a linear model with a randomized complete block design. Additionally, a one-at-a-time design approach was implemented to test sensitivity of model inputs. Statistical analysis of individual streambank evaluations suggests that the implementation of the BANCS model may not be repeatable. This may be due to highly sensitive model inputs, such as streambank height and near-bank stress method selection, and/or highly uncertain model inputs, such as bank material. Furthermore, it was found that higher level of training may improve model implementation precision. In addition to the repeatability and sensitivity analysis, the BANCS model developmental process was examined through the creation of a provisional streambank erosion rate prediction curve for the Central Great Plains ecoregion. Streambank erosion data was collected sporadically from 2006 to 2016 from eighteen study banks within the sediment-impaired Little Arkansas River watershed of south-central Kansas. Model fit was observed to follow the same trends, but with greater dispersion, when compared to other created models throughout the United States and eastern India. This increase in variability could be due to (1) obtaining streambank erosion data sporadically over a 10-year period with variable streamflows, (2) BEHI/NBS ratings obtained only once in recent years, masking the spatiotemporal variability of streambank erosion, (3) lack of observations, and (4) use of both bank profiles and bank pin measurements to calculate average retreat rates. Based on the results of this study, a detailed model creation procedure was suggested that addresses several model limitations and criticisms. Recommendations provided in the methodology include (1) more accurate measurement of sensitive/uncertain BEHI/NBS parameters, (2) multiple assessments by trained professionals to obtain accurate and precise BEHI/NBS ratings, (3) the use of repeated bank profiles to calculate bank erosion rates, and (4) the development of flow-dependent curves based on annually assessed study banks. Subsequent studies should incorporate these findings to improve upon the suggested methodology and increase the predictive power of future BANCS models.

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on October 25, 2007) Vita. Includes bibliographical references.

Master of Science / Department of Biological and Agricultural Engineering / Stacy Hutchinson / Military maneuvers result in significant physical and environmental impacts to the landscape. These impacts generally result in a loss of vegetative cover and increased watershed runoff and rate depending on vehicle speed, turning radius, and soil moisture content. Unless adequately monitored or mitigated, this increased runoff can lead to excessive soil erosion and gully formation. Past studies have revealed that these gullies can impact water quality from excessive erosion and create concerns regarding soldier safety. In order to better understand how gullies form and evolve overtime on military installations, a study is being conducted at Fort Riley, KS. In 2010, approximately forty gullies were identified, assessed, and measured using common erosion monitoring and surveying techniques. These gully locations, and any newly formed gullies, were remeasured using these same methods in 2012 to determine the rate of growth for each site with respect to width, depth, and headcut. Of fifty-nine gullies total, twenty one were initially included in this study. Upon further analysis including the utilization of watershed characteristics and land management techniques, eleven of the 21 utilized gullies were deemed appropriate to include in predictive assessment, as these eleven systems exhibited singular headcut migration. Multiple Regression Analysis was utilized to produce predictive equations for Headcut Growth. This equation [Headcut Growth = 0.666 + 0.137(Watershed Slope) – 0.478(Training Intensity) + 0.757(log[Watershed Area]) – 0.278(Drainage Density) – 0.0138(Above Ground Biomass Change) + 0.187(Burning Frequency] resulted in a model relationship of approximately 90%, with Watershed Slope being the most significant variable when an output Headcut Growth was reached.

Thesis--Auburn University, 2010. / Abstract. Includes bibliographic references (p.94-113).

Soil erosion has been recognised as a global threat against the sustainability of natural ecosystem. The work in this thesis has been undertaken to assist in combating this threat, and addresses the soil erosion issues associated with urban construction activities. The Revised Universal Soil Loss Equation (RUSLE) was employed in this research work and the parameters associated with the model were calibrated. This model was chosen for calibration, as it has been proven to be an easy to use tool yet providing reasonable results. Large scale rainfall simulators developed at UWS were used for rainfall simulation at two sites with diverse soil types: dispersive clayey soils at Penrith and highly permeable sandy soil at Somersby (Both in New South Wales, Australia). It is concluded that RUSLE can be successfully used in single storms for erosion prediction. Calibrated values of RUSLE parameters are useful in predicting soil erosion from the construction sites in NSW. It is also identified that in rolled smooth land condition, clayey soils are more erodible than sandy soil. Specific support practices such as short grass strips, gravel bags and silt fences are identified as very effective erosion control measures in reducing soil erosion from 45% to 85%. These results will be very useful in soil erosion prediction planning and conservation management in NSW. / Master of Engineering (Hons)

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 84-88). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 84-88). Also available on the Internet.

Thesis (Ph. D.)--University of Idaho, 2005. / Also available online in PDF format. Abstract. "August 2005." Includes bibliographical references.

Thesis (M.Sc.)(Geography)--University of Pretoria, 2005. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Thesis (MS)--University of Montana, 2009. / Contents viewed on December 21, 2009. Title from author supplied metadata. Includes bibliographical references.