Primary Faculty Mentor Name

Scott Hamshaw

Project Collaborators

Nicole Dávila Torres (Undergraduate Research Partner), Scott Hamshaw (Research Mentor), Donna Rizzo (Research Mentor/Adviser), Mandar Dewoolkar (Research Mentor)

Secondary Mentor NetID

drizzo

Secondary Mentor Name

Donna Rizzo

Graduate Student Mentors

Douglas Denu (Research Mentor/Masters Student)

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Environmental Engineering

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Vermont Studies

Tertiary Research Category

Food & Environment Studies

Presentation Title

Evaluating the Visual Classification of Suspended Sediment – Discharge Hysteresis via Crowd-sourcing and In-stream Monitoring

Time

1:00 PM

Location

Silver Maple Ballroom - Engineering & Physical Sciences

Abstract

Understanding what controls storm event responses within a watershed (e.g., antecedent conditions, geomorphic condition of river channels) can help determine the sources of fine sediments and sediment-bound nutrients. Addressing the sources of erosion is of paramount importance in addressing eutrophication and harmful algal blooms in receiving waters, where excessive fine sediment and nutrient loading is problematic, such as the case with Lake Champlain in northeastern United States. Studying the hysteresis in the suspended sediment–discharge relationship enables storm events to be categorized based on observed hysteresis patterns, and therefore helps characterize dominant sediment dynamics within a river system. This study seeks to (1) determine the reliability of visually interpreting hysteresis patterns and (2) assess the consistency of observed hysteresis patterns along river segments.

Storm events in the Mad River watershed, located in the Lake Champlain Basin, were classified into 14 hysteresis patterns or types. A survey containing a subset of 100 hysteresis patterns from the total number of storm events monitored was presented to survey respondents comprised of six domain experts and 22 non-experts. Analysis identified significant variability in classification performance across both the expert and non-expert groups. These results allow for refining the 14 hysteresis type categories, quantifying the variability in visual classifications, and developing a benchmark to compare human classification with machine-learning algorithm performance.

To help validate using relatively low-cost, in-stream sensors and the consistency of characterizing suspended sediment dynamics using hysteresis patterns along short river segments, turbidity sensors were installed at multiple locations along a 3 km segment of the main stem in the Lewis Creek watershed, also located in the Lake Champlain Basin. Together, this research helps improve characterization of storm event sediment dynamics and nutrient loading to rivers.

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Evaluating the Visual Classification of Suspended Sediment – Discharge Hysteresis via Crowd-sourcing and In-stream Monitoring

Understanding what controls storm event responses within a watershed (e.g., antecedent conditions, geomorphic condition of river channels) can help determine the sources of fine sediments and sediment-bound nutrients. Addressing the sources of erosion is of paramount importance in addressing eutrophication and harmful algal blooms in receiving waters, where excessive fine sediment and nutrient loading is problematic, such as the case with Lake Champlain in northeastern United States. Studying the hysteresis in the suspended sediment–discharge relationship enables storm events to be categorized based on observed hysteresis patterns, and therefore helps characterize dominant sediment dynamics within a river system. This study seeks to (1) determine the reliability of visually interpreting hysteresis patterns and (2) assess the consistency of observed hysteresis patterns along river segments.

Storm events in the Mad River watershed, located in the Lake Champlain Basin, were classified into 14 hysteresis patterns or types. A survey containing a subset of 100 hysteresis patterns from the total number of storm events monitored was presented to survey respondents comprised of six domain experts and 22 non-experts. Analysis identified significant variability in classification performance across both the expert and non-expert groups. These results allow for refining the 14 hysteresis type categories, quantifying the variability in visual classifications, and developing a benchmark to compare human classification with machine-learning algorithm performance.

To help validate using relatively low-cost, in-stream sensors and the consistency of characterizing suspended sediment dynamics using hysteresis patterns along short river segments, turbidity sensors were installed at multiple locations along a 3 km segment of the main stem in the Lewis Creek watershed, also located in the Lake Champlain Basin. Together, this research helps improve characterization of storm event sediment dynamics and nutrient loading to rivers.