Dan Obenour

Associate Professor

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


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.


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

View all publications via NC State Libraries


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)