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Scott Hamshaw

University of Vermont;Vermont EPSCoR

 Recent Activity

ABSTRACT:

When analyzing time series of streamflow and associated water quality sensor data such as turbidity, researchers and managers often are interested in isolating storm events since that is when behavior is dynamic and physical processes can be inferred. In this presentation, we will present preliminary development of a web-based data analysis tool that can be used for hydrological event detection and analysis (HEDA). Currently, a lack of options exist for detecting, delineating and analyzing hydrological events that don’t require utilizing a programming environment such as R or MATLAB. We will present a first look at a web-based tool capable of interfacing with time series stored on the CUAHSI HydroServer and USGS NWIS databases and then performing subsequent analysis. We will demonstrate the event-based analysis that can be obtained from the HEDA tool as well as encourage feedback from potential users of the tool.

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ABSTRACT:

Total phosphorus samples collected as part of the Vermont EPSCoR Research on Adaptation to Climate Change (RACC) project (EPS-1101317) and 15-min turbidity monitoring data for the Mad River. Collected at the location of the US Geological Survey stream gage (#04288000).

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ABSTRACT:

High-frequncy turbidity sensor data from six monitoring locations within the Mad River watershed in central Vermont collected between 2013 and 2015. Data set also includes tipping bucket rainfall data from seven rain gauges deployed in the Mad River Watershed and soil moisture data.
For methods and data collection details, see Hamshaw, S.D. (2018) Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatics and Machine Learning. Ph.D. Dissertation, University of Vermont, Burlington, VT, UA

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 Contact

Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Generic Generic

ABSTRACT:

High-frequncy turbidity sensor data from six monitoring locations within the Mad River watershed in central Vermont collected between 2013 and 2015. Data set also includes tipping bucket rainfall data from seven rain gauges deployed in the Mad River Watershed and soil moisture data.
For methods and data collection details, see Hamshaw, S.D. (2018) Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatics and Machine Learning. Ph.D. Dissertation, University of Vermont, Burlington, VT, UA

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Generic Generic
Mad River Total Phosphorus Samples 2012-2015
Created: March 17, 2018, 6:03 p.m.
Authors: Scott Hamshaw

ABSTRACT:

Total phosphorus samples collected as part of the Vermont EPSCoR Research on Adaptation to Climate Change (RACC) project (EPS-1101317) and 15-min turbidity monitoring data for the Mad River. Collected at the location of the US Geological Survey stream gage (#04288000).

Show More
Composite Resource Composite Resource

ABSTRACT:

When analyzing time series of streamflow and associated water quality sensor data such as turbidity, researchers and managers often are interested in isolating storm events since that is when behavior is dynamic and physical processes can be inferred. In this presentation, we will present preliminary development of a web-based data analysis tool that can be used for hydrological event detection and analysis (HEDA). Currently, a lack of options exist for detecting, delineating and analyzing hydrological events that don’t require utilizing a programming environment such as R or MATLAB. We will present a first look at a web-based tool capable of interfacing with time series stored on the CUAHSI HydroServer and USGS NWIS databases and then performing subsequent analysis. We will demonstrate the event-based analysis that can be obtained from the HEDA tool as well as encourage feedback from potential users of the tool.

Show More