Vermont EPSCoR Publications and Products
] . High Frequency Turbidity Monitoring to Quantify Sediment Loading in the Mad River. 2014 NEAEB Conference. 2014 [cited 0BC].
. Hydrological Event Detection & Analysis (HEDA) tool for streamflow and water quality time series. CUAHSI Conference on Hydroinformatics [Internet]. 2019 . Available from: https://www.cuahsi.org/uploads/pages/img/2019_Conference_on_Hydroinformatics_Program.pdf
. A Monitoring Tool for Analyzing Sediment Dynamics Using Water Turbidity Sensor Data. 2020 AGU (American Geophysical Union) Fall Meeting [Internet]. 2020 . Available from: https://agu.confex.com/agu/fm20/webprogram/Paper748005.html
. Prediction of suspended sediment in rivers using artificial neural networks and future climate scenarios. NOAA 14th Annual Climate Prediction Applications Science Workshop [Internet]. 2016 . Available from: http://www.uvm.edu/~cpasw/agenda/CPASW_2016_Agenda.pdf
. Quantifying Nutrient Reductions Achieved by Erosion Remediation Projects on Vermont's Roads. 2020 VTrans Research and Innovation Symposium [Internet]. 2020 . Available from: https://vtrans.vermont.gov/planning/research/2020-symposium/env2
. Quantifying reach-scale erosion and deposition using unmanned aircraft system (UAS) photogrammetry and airborne lidar. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/410036
. Quantifying Sediment and Phosphorous Loading from Streambank Erosion using Terrestrial Laser Scanning to Support Sediment and Nutrient Budgets. 2014 AGU (American Geophysical Union) Fall Meeting. 2014 .
. Quantifying streambank erosion: a comparative study using an unmanned aerial system (UAS) and a terrestrial laser scanner. 2015 AGU (American Geophysical Union) Fall Meeting [Internet]. 2015 . Available from: https://agu.confex.com/agu/fm15/webprogram/Paper85568.html
. Sediment Loading and Sources in the Mad River: Implications for sediment-bound nutrient management. IAGLR 2015 [Internet]. 2015 . Available from: http://www.iaglr.org/conference/downloads/2015_program.pdf
. Spatiotemporal Trajectories as a New Approach for Studying Concentration-discharge Relationships of Hydrological Events. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/378480
. Suspended Sediment Prediction Using Artificial Neural Networks and Local Hydrometeorological Data. 2014 NEAEB Conference. 2014 .
. Using Distributed Continuous Turbidity Monitoring to Inform Sediment and Sediment-bound Nutrient Budgets in a Small Watershed. 2014 AGU (American Geophysical Union) Fall Meeting. 2014 .
. Watershed data science at the event scale: Machine learning for event concentration-discharge analysis. Virtual Summit: Incorporating Data Science and Open Science Techniques in Aquatic Research [Internet]. 2020 . Available from: https://freshwaterecology.wordpress.com/2020/07/08/conference-workshop-virtual-summit-incorporating-data-science-and-open-science-techniques-in-aquatic-research/
. Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatics and Machine Learning. Burlington, VT: University of Vermont; 2017.
. Suspended Sediment Prediction Using Artificial Neural Networks and Local Hydrometeorological Data (M.S. Thesis). Burlington VT: University of Vermont; 2014.