Spatiotemporal trajectories as a new approach for studying concentration-discharge relationships of hydrological events
Conference Year
January 2019
Abstract
Many river water quality constituents such as turbidity, suspended sediments, and nutrients are predominantly transported during storm events. The analysis of hydrological systems at event scales helps to characterize the dynamics and flux of such constituents. Hydrological events have commonly been analyzed through study of event concentration-discharge (C-Q) plots and identification of two-dimensional hysteresis loop patterns in the C-Q plots. While effective and informative to some extent, this approach has shortcomings in capturing the temporality of variables, as it ``collapses'' their values as projected on the C-Q plane. This study analyzes the categories of hydrological events using three-dimensional spatiotemporal trajectory plots. Specifically, computational clustering methods are used to categorize the trajectories of "moving points" that represent the measurements from two sensors -- in this study, river discharge and suspended sediment concentration. This in-progress research utilizes data from turbidity-based monitoring of suspended sediment from the Mad River watershed, located in the Lake Champlain Basin in the northeastern United States. The project aims toward building classes of spatiotemporal trajectories and comparison with the existing classes of hysteresis loops that are currently being used for categorizing storm events.
Primary Faculty Mentor Name
Byung S Lee
Secondary Mentor Name
Scott Hamshaw
Faculty/Staff Collaborators
Scott Hamshaw, Donna Rizzo
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Computer Science
Primary Research Category
Food & Environment Studies
Secondary Research Category
Engineering & Physical Sciences
Spatiotemporal trajectories as a new approach for studying concentration-discharge relationships of hydrological events
Many river water quality constituents such as turbidity, suspended sediments, and nutrients are predominantly transported during storm events. The analysis of hydrological systems at event scales helps to characterize the dynamics and flux of such constituents. Hydrological events have commonly been analyzed through study of event concentration-discharge (C-Q) plots and identification of two-dimensional hysteresis loop patterns in the C-Q plots. While effective and informative to some extent, this approach has shortcomings in capturing the temporality of variables, as it ``collapses'' their values as projected on the C-Q plane. This study analyzes the categories of hydrological events using three-dimensional spatiotemporal trajectory plots. Specifically, computational clustering methods are used to categorize the trajectories of "moving points" that represent the measurements from two sensors -- in this study, river discharge and suspended sediment concentration. This in-progress research utilizes data from turbidity-based monitoring of suspended sediment from the Mad River watershed, located in the Lake Champlain Basin in the northeastern United States. The project aims toward building classes of spatiotemporal trajectories and comparison with the existing classes of hysteresis loops that are currently being used for categorizing storm events.