Bio

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.

Area(s) of Expertise

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.