Dan Obenour
Associate Professor

- 919-515-7702
- drobenou@ncsu.edu
- Fitts-Woolard Hall 3205
- Visit My Website
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.
Education
Natural Resources/Environmental Engineering
University of Michigan
Environmental and Water Resources Engineering
The University of Texas at Austin
Civil Engineering
University of Akron
Research Description
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.
Publications
- Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie
- Scavia, D., Wang, Y.-C., & Obenour, D. R. (2023), SCIENCE OF THE TOTAL ENVIRONMENT, 856. https://doi.org/10.1016/j.scitotenv.2022.158959
- Bayesian hierarchical modeling characterizes spatio-temporal variability in phosphorus export across the contiguous United States
- Karimi, K., & Obenour, D. (2023, February 26). , . https://doi.org/10.5194/egusphere-egu23-8609
- Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model
- Karimi, K., Miller, J. W., Sankarasubramanian, A., & Obenour, D. R. (2023), Water Resources Research. https://doi.org/10.1029/2022WR033088
- Per- and polyfluoroalkyl substances (PFAS) in river discharge: Modeling loads upstream and downstream of a PFAS manufacturing plant in the Cape Fear watershed, North Carolina
- Petre, M.-A., Salk, K. R., Stapleton, H. M., Ferguson, P. L., Tait, G., Obenour, D. R., … Genereux, D. P. (2022), SCIENCE OF THE TOTAL ENVIRONMENT, 831. https://doi.org/10.1016/j.scitotenv.2022.154763
- Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling
- Katin, A., Del Giudice, D., & Obenour, D. R. (2022), HYDROLOGY AND EARTH SYSTEM SCIENCES, 26(4), 1131–1143. https://doi.org/10.5194/hess-26-1131-2022
- Assessing inter-annual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling
- Miller, J. W., Karimi, K., Sankarasubramanian, A., & Obenour, D. R. (2021), Hydrology and Earth System Sciences, 2. https://doi.org/10.5194/hess-2021-52
- Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling
- Miller, J. W., Karimi, K., Sankarasubramanian, A., & Obenour, D. R. (2021), HYDROLOGY AND EARTH SYSTEM SCIENCES, 25(5), 2789–2804. https://doi.org/10.5194/hess-25-2789-2021
- Daily hypoxia forecasting and uncertainty assessment via Bayesian mechanistic model for the Northern Gulf of Mexico
- Katin, A., Giudice, D. D., & Obenour, D. R. (2021, April 29). , . https://doi.org/10.5194/hess-2021-207
- Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling
- Del Giudice, D., Fang, S., Scavia, D., Davis, T. W., Evans, M. A., & Obenour, D. R. (2021), SCIENCE OF THE TOTAL ENVIRONMENT, 755. https://doi.org/10.1016/j.scitotenv.2020.142487
- Exploring nutrient and light limitation of algal production in a shallow turbid reservoir
- Han, Y., Aziz, T. N., Del Giudice, D., Hall, N. S., & Obenour, D. R. (2021), ENVIRONMENTAL POLLUTION, 269. https://doi.org/10.1016/j.envpol.2020.116210
Grants
- Fecal contamination source tracking and forecasting to support recreational and cultural development in the Black River watershed
- NCSU Water Resources Research Institute(9/01/21 - 8/31/23)
- Assessing Controls on Nutrient Loading at the Watershed Scale through Data-Driven Modeling
- NCSU Water Resources Research Institute(3/01/20 - 12/31/21)
- Science and Technologies for Phosphorus Sustainability (STEPS) Center
- National Science Foundation (NSF)(10/01/21 - 9/30/26)
- 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 - 8/31/22)
- 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 - 8/31/20)
- Predicting the Effectiveness of Artificial Mixing for Controlling Algal Blooms in Piedmont Reservoirs
- NCSU Water Resources Research Institute(3/01/16 - 6/30/19)
- 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)