Daniel Obenour

Assoc Professor

  • Fitts-Woolard Hall 3205

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.


Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling
Karimi, K., & Obenour, D. R. (2024), Environmental Science & Technology. https://doi.org/10.1021/acs.est.3c07479
Trends and drivers of hypoxic thickness and volume in the Northern Gulf of Mexico: 1985-2018
Matli, V. R. R., & Obenour, D. (2024, April 15). , . https://doi.org/10.1101/2024.04.12.589301
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
An estuary stress index based on nekton relationships with thematic watershed stressors
Li, K., Blackhart, K., Miller, J., & Obenour, D. (2023), ECOLOGICAL INDICATORS, 154. https://doi.org/10.1016/j.ecolind.2023.110678
Bayesian hierarchical modeling characterizes spatio-temporal variability in phosphorus export across the contiguous United States
Karimi, K., & Obenour, D. (2023, May 15). , . 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, 59(1). https://doi.org/10.1029/2022WR033088
Estimating the benefits of stream water quality improvements in urbanizing watersheds: An ecological production function approach
Haefen, R. H., Van Houtven, G., Naumenko, A., Obenour, D. R., Miller, J. W., Kenney, M. A., … Waters, H. (2023), PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 120(18). https://doi.org/10.1073/pnas.2120252120
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

View all publications via NC State Libraries


STC: Science and Technologies for Phosphorus Sustainability (STEPS) Center
National Science Foundation (NSF)(10/01/21 - 9/30/26)
Designing Model Protocols to Assess Impacts to Receiving Waters that have Low Dissolved Oxygen
NC Department of Environmental Quality (DEQ)(8/01/23 - 7/31/25)
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 - 2/29/24)
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)
Assessing Controls on Nutrient Loading at the Watershed Scale through Data-Driven Modeling
NCSU Water Resources Research Institute(3/01/20 - 12/31/21)
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)
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)
Hypoxia and Algal Bloom Forecasting for the Neuse River Estuary
NCSU Sea Grant Program(2/01/16 - 7/31/19)
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)