Dan Obenour

Assistant 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.


Application of packed bed reactor theory and Bayesian inference to upweller culture of juvenile oysters
Campbell, M. D., Hall, S. G., & Obenour, D. R. (2020), AQUACULTURAL ENGINEERING, 90. https://doi.org/10.1016/j.aquaeng.2020.102098
Assessing within-lake nutrient cycling through multi-decadal Bayesian mechanistic modeling
Obenour, D., Giudice, D. D., Aupperle, M., & Sankarasubramanian, A. (2020, March 23). , . https://doi.org/10.5194/egusphere-egu2020-4232
Fusion-Based Hypoxia Estimates: Combining Geostatistical and Mechanistic Models of Dissolved Oxygen Variability
Matli, V. R. R., Laurent, A., Fennel, K., Craig, K., Krause, J., & Obenour, D. R. (2020), Environmental Science & Technology, 10. https://doi.org/10.1021/acs.est.0c03655
Assessing Vertical Diffusion and Cyanobacteria Bloom Potential in a Shallow Eutrophic Reservoir
Han, Y., Smithheart, J. W., Smyth, R. L., Aziz, T. N., & Obenour, D. R. (2019), LAKE AND RESERVOIR MANAGEMENT. https://doi.org/10.1080/10402381.2019.1697402
Assessing potential anthropogenic drivers of ecological health in Piedmont streams through hierarchical modeling
Miller, J. W., Paul, M. J., & Obenour, D. R. (2019), FRESHWATER SCIENCE, 38(4), 771–789. https://doi.org/10.1086/705963
Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios
Del Giudice, D., Matli, V. R. R., & Obenour, D. R. (2019), ECOLOGICAL APPLICATIONS. https://doi.org/10.1002/eap.2032
Hypoxic volume is more responsive than hypoxic area to nutrient load reductions in the northern Gulf of Mexico-and it matters to fish and fisheries
Scavia, D., Justic, D., Obenour, D. R., Craig, J. K., & Wang, L. (2019), ENVIRONMENTAL RESEARCH LETTERS, 14(2). https://doi.org/10.1088/1748-9326/aaf938
Hierarchical modeling assessment of the influence of watershed stressors on fish and invertebrate species in Gulf of Mexico estuaries
Miller, J., Esselman, P. C., Alameddine, I., Blackhart, K., & Obenour, D. R. (2018), Ecological Indicators, 90, 142–153. https://doi.org/10.1016/J.ECOLIND.2018.02.040
Leveraging Spatial and Temporal Variability to Probabilistically Characterize Nutrient Sources and Export Rates in a Developing Watershed
Strickling, H. L., & Obenour, D. R. (2018), Water Resources Research, 54(7), 5143–5162. https://doi.org/10.1029/2017WR022220
Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling
Nelson, C. M., Li, K., Obenour, D. R., Miller, J., Misenheimer, J. C., Scheckel, K., … Bradham, K. D. (2018), Journal of Toxicology and Environmental Health, Part A, 81(6), 160–172. https://doi.org/10.1080/15287394.2018.1423798

View all publications via NC State Libraries


Assessing Controls on Nutrient Loading at the Watershed Scale through Data-Driven Modeling
NCSU Water Resources Research Institute(3/01/20 - 12/31/21)
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 - 8/31/21)
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)
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 - 5/31/21)