Dan Obenour is an Associate Professor in the Department of Civil, Construction, and Environmental Engineering at North Carolina State University. He is also a Faculty Fellow at the University’s Center for Geospatial Analytics. He received his PhD from the University of Michigan in 2013, and he completed a postdoc based at the NOAA Great Lakes Environmental Research Laboratory in 2014. He also has several years of experience in environmental and water resources consulting.
Dan’s research focuses on the development and application of probabilistic models for predicting water-quality outcomes in aquatic systems under varying management and climate scenarios. His models have been used in annual forecasts of harmful algal blooms and hypoxia in Lake Erie, the northern Gulf of Mexico, and the Neuse River Estuary. His research also encompasses data-driven pollutant fate and transport modeling in regional watersheds.
SHORT DESCRIPTION OF INTERESTS:
water quality and water resources management
- Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie , SCIENCE OF THE TOTAL ENVIRONMENT (2023)
- An estuary stress index based on nekton relationships with thematic watershed stressors , ECOLOGICAL INDICATORS (2023)
- Bayesian hierarchical modeling characterizes spatio-temporal variability in phosphorus export across the contiguous United States , (2023)
- Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model , WATER RESOURCES RESEARCH (2023)
- Estimating the benefits of stream water quality improvements in urbanizing watersheds: An ecological production function approach , PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2023)
- 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 , SCIENCE OF THE TOTAL ENVIRONMENT (2022)
- Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling , HYDROLOGY AND EARTH SYSTEM SCIENCES (2022)
- Assessing inter-annual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling , Hydrology and Earth System Sciences (2021)
- Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling , HYDROLOGY AND EARTH SYSTEM SCIENCES (2021)
- Daily hypoxia forecasting and uncertainty assessment via Bayesian mechanistic model for the Northern Gulf of Mexico , (2021)
The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.
Water quality modeling is a critical tool for evaluating municipal and industrial discharges to North Carolinaâ€™s surface waters. This project will provide guidance on modeling approaches to assess discharger impacts on dissolved oxygen conditions. Recommendations will include suggested model types, model configurations, data input requirements, and model performance standards.
The Great Coharie River (AKA Great Coharie Creek) is a culturally and environmentally significant water body in Eastern North Carolina. The river has exhibited elevated levels of nutrients and microbial contamination, even after extreme flooding events, and community groups, particularly the Coharie Tribe, are eager to develop a more nuanced understanding of the temporal and spatial dynamics of contamination in the river to ensure human safety during cultural and recreational activities on the river. We propose to conduct high temporal resolution sampling at 4 sites along the river during different seasons and rainfall conditions. Water samples will be analyzed for nutrients, E. coli (fecal indicator bacteria) and source-specific molecular markers of fecal contamination (e.g., human, swine, and poultry). Forecast models will be develop to predict contamination with environmental covariates (e.g., temperature, rainfall, discharge). Working with Coharie Tribe leaders and other community members and applying insights from this research, we will support the development of long-term monitoring plans and decision-making tools for protecting and using the river.
Over the past three decades, an enormous amount of data has been collected in the Northern Gulf of Mexico to study hypoxia and its impacts on coastal ecosystems and associated fisheries. These data have been collected by federal and academic institutions during monitoring cruises conducted at various spatial scales with frequencies ranging from bi-weekly to annually. While the individual data products from these cruises have been made available through scientific publications and online data repositories, there has been limited progress in synthesizing these data within a common analysis framework. The proposed study will systematically integrating existing datasets using probabilistic, data-centric modeling approaches to more fully evaluate the spatiotemporal dynamics of hypoxia and to understand and forecast ecosystem impacts. Components of this work focused on hypoxia dynamics include (1) geostatistical modeling of all available dissolved oxygen data, (2) parsimonious biophysical modeling of hypoxia dynamics, (3) and fusion of geostatistical and mechanistic modeling results to develop optimal estimates of hypoxia through time and over multiple sections of the Louisiana-Texas Shelf. Leveraging these new hypoxia estimates, additional study components will focus predicting fisheries and ecosystem dynamics as a function of hypoxia and other natural and anthropogenic stressors. The improved hypoxia and ecosystem prediction capabilities will be leveraged to develop enhanced fisheries forecasts that explicitly consider recent and future (forecasted) hypoxic conditions on the Shelf. This study will provide a data-centric approach to understanding hypoxia and its consequences that will both compliment and contrast with more mechanistically complex hydrodynamic and ecosystem models. The proposed approach outlined here will focus on data-driven inference of factors driving hypoxia and fisheries dynamics, rigorous uncertainty quantification, and parsimonious forecasting methodologies that can be readily operationalized in the Gulf and other coastal areas.
Anthropogenic nutrient loading is a critical driver of water quality throughout North Carolina and much of the world. Nutrient loading has increased over the last century due to fertilization of crops and green spaces, as well as waste from humans, pets, and livestock. The most salient outcome of nutrient loading is increased eutrophication (organic matter accumulation in surface waters), often leading to harmful algal blooms and hypoxia, which jeopardize water supplies and public recreation. As such, developing nutrient criteria and management strategies is a timely objective for state water resources managers. While sources of nutrients have been identified and many nutrient control measures have been proposed, there remains a need to quantitatively assess these sources and controls, particularly at the watershed scale. In this study, we propose a modern, data-driven approach to update our knowledge of the magnitudes of various sources and the effectiveness of various nutrient control strategies. The approach leverages large databases of water quality, hydro-meteorology, and watershed attributes, which have been developed by federal, state, and local governments over the last few decades. The approach will also leverage a sophisticated ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œhybridÃƒÂ¢Ã¢â€šÂ¬Ã‚Â watershed model that combines a mechanistic representation of nutrient fate and transport within a probabilistic (Bayesian) framework where prior knowledge of loading and transport rates is updated through data-driven inference, and where uncertainty is rigorously quantified. Our project will focus on the Falls and Jordan Lake watersheds of North Carolina, for which preliminary models and data are already available. Key objectives include (1) development of an integrated geospatial database on watershed development, (2) adaptation of the hybrid watershed model to assess watershed development practices, and (3) application of the model to assess future management scenarios. Expected outcomes include quantitative guidance for developing nutrient reduction goals and watershed management strategies.
The main objective of this proposed research project is to develop, apply, and demonstrate methods for valuing the benefits of wadeable stream water quality improvements in urbanizing watersheds in the Southeastern U.S. To conduct this research, we take an ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œecological production functionÃƒÂ¢Ã¢â€šÂ¬Ã‚Â approach that (1) translates changes in pollution loadings generated by a policy intervention into predicted changes in objective, biophysical measures of water quality and biodiversity; (2) transforms these changes in water quality into changes in ecological endpoints the general public values; and (3) monetizes householdsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ willingness to pay for changes in these endpoints using stated preference choice experiments. We envision presenting respondents with hypothetical choice alternatives for wadeable streams in urbanizing watersheds where the attributes include the ecological endpoints identified in focus groups and include appropriate metrics for the geographic scope and scale of water quality improvements (e.g., miles of improved streams, proximity to the respondentÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s home, and the additional cost to households for achieving the water quality improvements). The final component of our work will be to demonstrate the policy relevance by combining results from the individual components of the research to estimate the urban stream quality benefits associated with actual nutrient management strategies proposed for the Upper Neuse (i.e., Falls Lake) watershed.
Abstract: Harmful algal blooms (HABs) have become endemic to the western basin of Lake Erie. When HABs occur they impact multiple ecosystem services, especially in the western Lake Erie coastal communities. The goals of this work are to characterize the spatio-temporal variability in harmful algal blooms, to investigate the biophysical factors leading to bloom and surface scum formation, and to probabilistically predict the location, intensity, and duration of blooms under different future scenarios. Bloom forecasts will be driven by various future climate, watershed nutrient loading, and invasive mussel scenarios. Results will be provided in a format suitable for assessing ecosystem services impacts, and will include the probability of the bloom reaching critical levels within various nearshore and offshore areas of western Lake Erie, under various scenarios. Scope of Work: Dr. Daniel Obenour will be responsible for the geostatistical and predictive modeling of harmful algal blooms (HABs) in this study, as well as for the mentoring of two graduate students who will assist him in this work. The geostatistical component of the work will include the compilation of remote sensing imagery and in situ field data, the development of geosatistical models specifically suited to these data, and the production of enhanced cyanobacteria concentration and surface-scum estimates. The results of the geostatistical modeling will be used to calibrate new HABs forecasting models and to quantify the probability of HABs occurring within critical areas associated with ecosystem services (as a function of overall bloom size). The predictive modeling component of this work will include the creation and advancement of two HAB forecasting models. An existing seasonal bloom-forecasting model will be enhanced through the inclusion of an expanded suite of predictor variables, in order to assess the impacts of climate variability and invasive mussels (in addition to nutrient loading) on long-term bloom variability. In addition, a new, process-based bloom forecasting model will be developed to study the short-term temporal dynamics of bloom formation, operating on an approximately weekly timescale. This model will incorporate various biophysical processes related to bloom initiation, growth, and, dispersal, utilizing input data such as wind speed, river outflow, temperature, and solar radiation. By integrating the geostatistical and predictive modeling results, we will characterize the probability of the bloom reaching critical levels (impacting ecosystem services) across space and time, under various future scenarios. This work will be performed in coordination with research staff at the University of Michigan Water Center.
Severe hypoxia and elevated chlorophyll levels (algal blooms) are common problems in the Neuse Estuary, North Carolina. These problems stem largely from anthropogenic nutrient enrichment, a process often referred to as ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œcultural eutrophicationÃƒÂ¢Ã¢â€šÂ¬Ã‚Â. Because the estuary serves as an important fisheries habitat for North Carolina, mitigating hypoxia and improving water quality is an important management goal. The objectives of this study include the development of a predictive model capable of: (1) making seasonal forecasts relevant to fisheries management, (2) making long-term, scenario-based forecasts relevant to watershed management, and (3) testing hypotheses about biophysical factors driving the temporal variability in dissolved oxygen and chlorophyll concentrations. The project will build on past Neuse Estuary modeling efforts (circa 2000), and this research will test whether the assumptions and processes used in past studies are consistent with the variability observed in the estuary in recent years. This is an important question given that the Neuse Estuary has experienced severe droughts and changes in nitrogen load speciation over the last decade. In addition, the model will be used to test the ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œmemoryÃƒÂ¢Ã¢â€šÂ¬Ã‚Â of the estuary, as it relates to the long-term build-up of organic matter in bottom sediments. This will be accomplished by modeling the sediment layer to understand the time scales at which organic matter (e.g., nutrients and oxygen demand) accumulate and affect estuary water quality. All modeling will be performed within a statistical (Bayesian) framework to provide rigorous uncertainty quantification and hypothesis testing.
The use of artificial mixing has been proposed as a means of suppressing the formation of algal (phytoplankton) blooms in freshwater and coastal waterbodies. However, there are conflicting reports on the performance of such systems, with sparse data relating to how artificial mixing affects bloom formation in North Carolina (NC) reservoirs. An understanding of the linkages between blooms, artificial mixing, climate variability, and other water quality constituents is critical to effectively managing water supplies and developing useful geo-engineering solutions. In this proposed research we aim to (1) conduct field campaigns in multiple Piedmont reservoirs to measure vertical diffusivity, water quality, and phytoplankton assemblages in natural and artificially mixed conditions, (2) perform statistical (hierarchical) modeling of vertical diffusivity and phytoplankton concentrations to help identify and quantify key biophysical relationships, (3) perform mechanistic water-column modeling to generalize the results obtained in (2), and (4) develop a decision-support tool from the data and analysis performed in objectives (1) ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ (3) to predict algal type and abundance under different artificial mixing and background physical and chemical scenarios. Findings from this research will provide new insights into the impacts of both natural and artificial mixing in Piedmont reservoirs, and aid engineers and managers in developing strategies to protect the beneficial uses of these reservoirs.
Forecasting models for Gulf of Mexico hypoxia play a critical role in regional watershed and water quality management. In this study, we will adapt an existing Bayesian mechanistic hypoxia model to provide annual hypoxia forecasts, and to address relevant management scenarios. We will also provide the model, along with appropriate guidance and documentation, to environmental managers, in order to facilitate transition to an operational framework. This project will include documentation of a geostatistical approach to estimating hypoxic area and volume.