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Dr. Dan Obenour is interested in the development of probabilistic models that improve our ability to understand and manage complex environmental systems. His primary focus is on water quality dynamics in streams, lakes, and coastal areas. He uses mechanistic and empirical modeling approaches for assessing the severity and causes of environmental impairments, particularly those related to surface water quality.
Dr. Obenour has an extensive background in water quality and watershed modeling. At the University of Texas, Dan developed GIS approaches for creating, managing, and visualizing hydrologic and hydraulic modeling information. As a consulting engineer, he developed watershed and water quality models to address environmental impairments in streams and reservoirs. As a PhD student,Dr. Obenour developed probabilistic modeling approaches for assessing how natural and anthropogenic stressors affect water quality in lakes and coastal areas. Prior to joining the NC State faculty, he was a lecturer and post-doctoral fellow, conducting research at the University of Michigan Water Center and the NOAA Great Lakes Environmental Research Laboratory. This ongoing work aims to improve our ability to forecast harmful algal blooms in Lake Erie, in response to nutrient loading and climate variability. He looks forward to expanding his research to address environmental issues in North Carolina in the coming years.
Natural Resources/Environmental Engineering
University of Michigan
Environmental and Water Resources Engineering
The University of Texas at Austin
University of Akron
A common theme of Dr. Obenour's research is to provide rigorous uncertainty quantification, so that policy makers and the public can be presented with the ranges of likely outcomes associated with different future scenarios, allowing for more informed decision-making. Uncertainty quantification is also useful to the scientific community, as it provides an honest assessment of our level of system understanding, and it often suggests where additional research or data collection would be most beneficial. Dr. Obenour's research also aims to reduce model uncertainty by more effectively leveraging available information, such as field monitoring data, satellite imagery, and the results of previous experiments and related biophysical modeling studies. This auxiliary information is incorporated through various methods, such as the geostatistical fusion of multiple spatial data layers, and the specification of prior probabilities and multiple calibration endpoints using Bayesian statistics.
- Ensemble modeling informs hypoxia management in the northern Gulf of Mexico
- Scavia, D. and Bertani, I. and Obenour, D. R. and Turner, R. E. and Forrest, D. R. and Katin, A. (2017), Proceedings of the National Academy of Sciences of the United States of America, 114(33), 8823-8828.
- Relationship between total and bioaccessible lead on children's blood lead levels in urban residential Philadelphia soils
- Bradham, K. D. and Nelson, C. M. and Kelly, J. and Pomales, A. and Scruton, K. and Dignam, T. and Misenheimer, J. C. and Li, K. and Obenour, D. R. and Thomas, D. J. (2017), Environmental Science & Technology, 51(17), 10005-10011.
- Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?
- Bertani, I. and Steger, C. E. and Obenour, D. R. and Fahnenstiel, G. L. and Bridgeman, T. B. and Johengen, T. H. and Sayers, M. J. and Shuchman, R. A. and Scavia, D. (2017), Science of the Total Environment, 575(), 294-308.
- Non-point source evaluation of groundwater contamination from agriculture under geologic and hydrologic uncertainty
- Ayub, R. and Obenour, D. R. and Messier, K. P. and Serre, M. L. and Mahinthakumar, K. (2016), World Environmental and Water Resources Congress 2016: Environmental, Sustainability, Groundwater, Hydraulic Fracturing, and Water Distribution Systems analysis, (), 329-336.
- Probabilistically assessing the role of nutrient loading in harmful algal bloom formation in western lake erie
- Bertani, I. and Obenour, D. R. and Steger, C. E. and Stow, C. A. and Gronewold, A. D. and Scavia, D. (2016), Journal of Great Lakes Research, 42(6), 1184-1192.
- Independent data validation of an in vitro method for the prediction of the relative bioavailability of arsenic in contaminated soils
- Bradham, K. D. and Nelson, C. and Juhasz, A. L. and Smith, E. and Scheckel, K. and Obenour, D. R. and Miller, B. W. and Thomas, D. J. (2015), Environmental Science & Technology, 49(10), 6312-6318.
- Mapping the spatial distribution of the biomass and filter-feeding effect of invasive dreissenid mussels on the winter-spring phytoplankton bloom in Lake Michigan
- Rowe, M. D. and Obenour, D. R. and Nalepa, T. F. and Vanderploeg, H. A. and Yousef, F. and Kerfoot, W. C. (2015), Freshwater Biology, 60(11), 2270-2285.
- NGOMEX 2016: Synthesis and Integrated Modeling of Long-term Data Sets to Support Fisheries and Hypoxia Management in the Northern Gulf of Mexico
- US Dept. of Commerce (DOC)(9/01/16 - 8/31/19)
- Coastal SEES: Enhancing Sustainability in Coastal Communities Threatened by Harmful Algal Blooms by Advancing and Integrating Environmental and Socio-Economic Modeling
- National Science Foundation (NSF)(9/01/16 - 8/31/19)
- Predicting the Effectiveness of Artificial Mixing for Controlling Algal Blooms in Piedmont Reservoirs
- NCSU Water Resources Research Institute(3/01/16 - 12/01/18)
- Transitioning to Operations NOAA-Supported Statistical Hypoxia Models and Forecasts in the Gulf of Mexico and Chesapeake Bay
- National Oceanic & Atmospheric Administration (NOAA)(7/01/15 - 8/31/18)
- Hypoxia and Algal Bloom Forecasting for the Neuse River Estuary
- NCSU Sea Grant Program(2/01/16 - 7/31/19)
- Gulf of Mexico and Pacific Coast Estuarine and Marine Fish Habitat Assessment: A Submission to the National Sea Grant College Program 2014 Special Project "F" Competition
- US Dept. of Commerce (DOC)(8/01/15 - 9/30/17)
- Effects of Enhanced Circulation on Vertical Mixing and Algal Blooms In Freshwater Reservoirs
- National Science Foundation (NSF)(6/01/15 - 5/31/18)
- Demonstration of a Bayesian Mechanistic Model for Falls Lake
- NCSU Faculty Research & Professional Development Fund(7/01/15 - 6/30/16)
- Estimating the Benefits of Stream Water Quality Improvements in Urbanizing Watersheds: An Ecological Production Function Approach
- US Environmental Protection Agency (EPA)(6/01/16 - 5/31/19)
- Gulf of Mexico Estuarine and Marine Fish Habitat Assessment: A submission to the National Sea Grant College Program 2014 Special Project â€œFâ€ competition
- US Dept. of Commerce (DOC)(10/01/14 - 9/30/15)