Vermont EPSCoR Publications and Products
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Submitted Publications and Products
Time-series fluxes of meltwater-derived nutrients to the Gulf of Alaska. Geophysical Research Letters. Submitted Publications and Products ..
A Framework for Estimating Nutrient and Sediment Loads that Leverages the Temporal Variability Embedded in Water Monitoring Data. Journal of Great Lakes Research. Under Review ..
Climate-driven changes in energy and mass inputs systematically alter nutrient concentration and stoichiometry in deep and shallow regions of Lake Champlain. Biogeochemistry [Internet]. 2017 . Available from: http://link.springer.com/10.1007/s10533-017-0327-8.
Combined Temperature and Precipitation Variability May Increase the Frequency of Harmful Algal Blooms in Lake Champlain, 1992-2100. In: 2017 AGU (American Geophysical Union) Chapman Conference. 2017 AGU (American Geophysical Union) Chapman Conference. San Juan, PR: American Geophysical Union (AGU); 2017. Available from: https://agu.confex.com/agu/17chapman1/webprogram/Paper201893.html.
Coupling of reactive riverine phosphorus and iron species during hot transport moments: impacts of land cover and seasonality. Biogeochemistry [Internet]. 2017 :1-20. Available from: http://link.springer.com/article/10.1007/s10533-016-0290-9/fulltext.html.
Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps. Water Resources Research [Internet]. 2017 ;53. Available from: http://onlinelibrary.wiley.com/doi/10.1002/2017WR021353/full.
High-frequency dissolved organic carbon and nitrate measurements reveal differences in storm hysteresis and loading in relation to land cover and seasonality. Water Resources Research [Internet]. 2017 . Available from: http://onlinelibrary.wiley.com/wol1/doi/10.1002/2017WR020491/fullpdf
Iron speciation and provenance during high flow events from catchments to receiving. In: 2017 AGU (American Geophysical Union) Chapman Conference. 2017 AGU (American Geophysical Union) Chapman Conference. San Juan, PR; 2017. Available from: https://agu.confex.com/agu/17chapman1/webprogram/Paper202417.html.
Modeling the drivers of interannual variability in cyanobacterial bloom severity using self-organizing maps and high-frequency data. Inland Waters [Internet]. 2017 . Available from: http://www.tandfonline.com/doi/full/10.1080/20442041.2017.1318640.
Winter weather and lake-watershed physical configuration drive phospnorus, iron and manganese dynamics in water and sediment of ice-covered lakes. Limnology and Oceanography [Internet]. 2017 ;62(4):1620-1635. Available from: http://onlinelibrary.wiley.com/doi/10.1002/lno.10521/full.
Coupled impacts of climate and land use change across a river-lake continuum: insights from an integrated assessment model of Lake Champlain’s Missisquoi Basin, 2000-2040. Environmental Research Letters [Internet]. 2016 ;11(11):114026. Available from: http://stacks.iop.org/1748-9326/11/i=11/a=114026?key=crossref.46f2304a408b74e1eb473acc5fa39830
Drivers Of Inter-Annual And Spatial Variability In The Behavior Of Iron, Manganese And Phosphorus In The Sediment And Water Column Of Frozen Lakes. In: ASLO 2016. ASLO 2016. Santa Fe, NM: Association for the Sciences of Limnology and Oceanography; 2016. Available from: http://www.sgmeet.com/aslo/santafe2016/viewabstract.asp?AbstractID=28045.
Ecosystem Metabolism in Three Streams with Contrasting Land Use. In: Society for Freshwater Science Annual Meeting 2016. Society for Freshwater Science Annual Meeting 2016. Sacramento, CA: Society for Freshwater Science; 2016..
The Lake Champlain Basin as a Complex Adaptive System: Insights from the Research on Adaptation to Climate Change (RACC) Project. Vermont Journal of Environmental Law [Internet]. 2016 ;17(4):533-563. Available from: http://www-assets.vermontlaw.edu/Assets/news/VJEL_VOL_17_ISSUE_4_LAKE_CHAMPLAIN.pdf.
Lake productivity through a lens of ice: is lake trophic status a seasonally-dependent concept?. In: ASLO 2016. ASLO 2016. Santa Fe, NM: Association for the Sciences of Limnology and Oceanography; 2016. Available from: http://www.sgmeet.com/aslo/santafe2016/viewabstract.asp?AbstractID=28018.
Phos Cycle and Lake Champlain Relationship – The Real Story. In: Vermont Feed Dealers Conference. Vermont Feed Dealers Conference. Stowe, VT; 2016..
Applying spectral analysis to quantify the drivers of cyanobacterial blooms. In: IAGLR's 58th Annual Conference on Great Lakes Research. IAGLR's 58th Annual Conference on Great Lakes Research. Burlington, VT; 2015..
An autoregressive bayesian network to assess climate & nutrient variability impacts on water quality. In: IAGLR 58th Annual Conference on Great Lake Research. IAGLR 58th Annual Conference on Great Lake Research. ; 2015..
Characterization of Organic Phosphorus Form and Bioavailability in Lake Sediments using P Nuclear Magnetic Resonance and Enzymatic Hydrolysis. Journal of Environment Quality [Internet]. 2015 . Available from: https://dl.sciencesocieties.org/publications/jeq/abstracts/0/0/jeq2014.06.0273.
Climate-driven changes in energy and mass inputs alter N:P stoichiometry differently in deep and shallow sites in a large lake. GLEON 17 [Internet]. 2015 . Available from: http://gleon.org/sites/default/files/uploaded/PosterAbstract.pdf.
Developing a 21st Century framework for lake-specific eutrophication assessment using quantile regression. Limnology and Oceanography: Methods [Internet]. 2015 ;13(5):237 - 249. Available from: http://doi.wiley.com/10.1002/lom3.10021.
Dynamic Coupling of Iron, Manganese, and Phosphorus Behavior in Water and Sediment of Shallow Ice-Covered Eutrophic Lakes. Environmental Science & Technology [Internet]. 2015 ;49(16):9758 - 9767. Available from: http://pubs.acs.org/doi/pdf/10.1021/acs.est.5b02057.
Dynamic internal drivers of a historically severe cyanobacteria bloom in Lake Champlain revealed through comprehensive monitoring. J Great Lakes Research [Internet]. 2015 ;41(3):818-829. Available from: http://www.sciencedirect.com/science/article/pii/S0380133015001343.
“Hot moments” of carbon and nitrogen in streams: Key insights from in-situ, high-frequency optical sensors from the North East Water Resources Network (NEWRnet). In: 2015 AGU Fall Meeting. 2015 AGU Fall Meeting. San Francisco, CA; 2015.
Iron isotope systematics in Arctic rivers. Comptes Rendus Geoscience [Internet]. 2015 ;347(7-8):377 - 385. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1631071315000504.