Evaluating the Visual Classification of Suspended Sediment – Discharge Hysteresis via Crowd-sourcing and In-stream Monitoring
Conference Year
January 2019
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.
Primary Faculty Mentor Name
Scott Hamshaw
Secondary Mentor Name
Donna Rizzo
Graduate Student Mentors
Douglas Denu (Research Mentor/Masters Student)
Faculty/Staff Collaborators
Nicole Dávila Torres (Undergraduate Research Partner), Scott Hamshaw (Research Mentor), Donna Rizzo (Research Mentor/Adviser), Mandar Dewoolkar (Research Mentor)
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
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.