Presentation Title

A New Tool for Analyzing Event Sediment Dynamics Using High-Frequency Turbidity Sensor Data

Presenter's Name(s)

LInh NguyenFollow

Project Collaborators

Ali Javed (Graduate Student Mentor), Donna Rizzo (Advisor), Scott D. Hamshaw (Advisor)

Abstract

A common water resource issue that needs addressing to ensure clean drinking water is the presence and excessive amount of turbidity in a waterbody. Recent years have seen a rise in popularity of turbidity sensor deploy at water monitoring stations, which allow for continuous suspended sediment monitoring. Such monitoring network collects massive volumes of high-frequency, real-time streamflow (Q) and turbidity (C) data, offering opportunities for developing new data-driven analysis methods. Our research presents an event-based analysis tool in MATLAB to 1) process the high-frequency C, Q data, 2) automate detection and delineation of storm events using C, Q data simultaneously, 3) visualize C-Q hysteresis, and 4) relate storm events across multiple stations. We demonstrate the application of the tool using sensor data collected at several USGS water stations in the Upper Esopus Creek watershed in New York, which experiences significant erosion issues resulting in high turbidity. The results will assist watershed managers in designing and assessing the effectiveness of erosion mitigation strategies. This tool can be adapted to studies that investigate other water quality constituents such as solutes and nutrients at the event scale.

Primary Faculty Mentor Name

Scott D. Hamshaw

Secondary Mentor NetID

drizzo

Secondary Mentor Name

Donna M. Rizzo

Graduate Student Mentors

Ali Javed

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Civil Engineering

Primary Research Category

Engineering & Physical Sciences

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A New Tool for Analyzing Event Sediment Dynamics Using High-Frequency Turbidity Sensor Data

A common water resource issue that needs addressing to ensure clean drinking water is the presence and excessive amount of turbidity in a waterbody. Recent years have seen a rise in popularity of turbidity sensor deploy at water monitoring stations, which allow for continuous suspended sediment monitoring. Such monitoring network collects massive volumes of high-frequency, real-time streamflow (Q) and turbidity (C) data, offering opportunities for developing new data-driven analysis methods. Our research presents an event-based analysis tool in MATLAB to 1) process the high-frequency C, Q data, 2) automate detection and delineation of storm events using C, Q data simultaneously, 3) visualize C-Q hysteresis, and 4) relate storm events across multiple stations. We demonstrate the application of the tool using sensor data collected at several USGS water stations in the Upper Esopus Creek watershed in New York, which experiences significant erosion issues resulting in high turbidity. The results will assist watershed managers in designing and assessing the effectiveness of erosion mitigation strategies. This tool can be adapted to studies that investigate other water quality constituents such as solutes and nutrients at the event scale.