@article{li_blackhart_miller_obenour_2023, title={An estuary stress index based on nekton relationships with thematic watershed stressors}, volume={154}, ISSN={["1872-7034"]}, DOI={10.1016/j.ecolind.2023.110678}, journal={ECOLOGICAL INDICATORS}, author={Li, Kevin and Blackhart, Kristan and Miller, Jonathan and Obenour, Daniel}, year={2023}, month={Oct} }
 @article{karimi_miller_sankarasubramanian_obenour_2023, title={Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2022WR033088}, DOI={10.1029/2022WR033088}, abstractNote={Abstract}, number={1}, journal={WATER RESOURCES RESEARCH}, author={Karimi, K. and Miller, J. W. and Sankarasubramanian, A. and Obenour, D. R.}, year={2023}, month={Jan} }
 @article{haefen_van houtven_naumenko_obenour_miller_kenney_gerst_waters_2023, title={Estimating the benefits of stream water quality improvements in urbanizing watersheds: An ecological production function approach}, volume={120}, ISSN={["1091-6490"]}, url={https://doi.org/10.1073/pnas.2120252120}, DOI={10.1073/pnas.2120252120}, abstractNote={Streams in urbanizing watersheds are threatened by economic development that can lead to excessive sediment erosion and surface runoff. These anthropogenic stressors diminish valuable ecosystem services and result in pervasive degradation commonly referred to as “urban stream syndrome.” Understanding how the public perceives and values improvements in stream conditions is necessary to support efforts to quantify the economic benefits of water quality improvements. We develop an ecological production framework that translates measurable indicators of stream water quality into ecological endpoints. Our interdisciplinary approach integrates a predictive hierarchical water quality model that is well suited for sparse data environments, an expert elicitation that translates measurable water quality indicators into ecological endpoints that focus group participants identified as most relevant, and a stated preference survey that elicits the public’s willingness to pay for changes in these endpoints. To illustrate our methods, we develop an application to the Upper Neuse River Watershed located in the rapidly developing Triangle region of North Carolina (the United States). Our results suggest, for example, that residents are willing to pay roughly $127 per household and $54 million per year in aggregate (2021 US$) for water quality improvements resulting from a stylized intervention that increases stream bank canopy cover by 25% and decreases runoff from impervious surfaces, leading to improvements in water quality and ecological endpoints for local streams. Although the three components of our analysis are conducted with data from North Carolina, we discuss how our findings are generalizable to urban and urbanizing areas across the larger Piedmont ecoregion of the Eastern United States.}, number={18}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Haefen, Roger H. and Van Houtven, George and Naumenko, Alexandra and Obenour, Daniel R. and Miller, Jonathan W. and Kenney, Melissa A. and Gerst, Michael D. and Waters, Hillary}, year={2023}, month={Apr} }
 @article{miller_karimi_sankarasubramanian_obenour_2021, title={Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling}, volume={25}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-25-2789-2021}, DOI={10.5194/hess-25-2789-2021}, abstractNote={Abstract. Excessive nutrient loading is a major cause of water quality problems worldwide, often leading to harmful algal blooms and hypoxia in lakes and coastal systems. Efficient nutrient management requires that loading sources are accurately quantified. However, loading rates from various urban and rural non-point sources remain highly uncertain especially with respect to climatological variation. Furthermore, loading models calibrated using statistical techniques (i.e., hybrid models) often have limited capacity to differentiate export rates among various source types, given the noisiness and paucity of observational data common to many locations. To address these issues, we leverage data for two North Carolina Piedmont river basins collected over three decades (1982–2017) using a mechanistically parsimonious watershed loading and transport model
calibrated within a Bayesian hierarchical framework. We explore temporal
drivers of loading by incorporating annual changes in precipitation, land
use, livestock, and point sources within the model formulation. Also,
different representations of urban development are compared based on how
they constrain model uncertainties. Results show that urban lands built
before 1980 are the largest source of nutrients, exporting over twice as
much nitrogen per hectare than agricultural and post-1980 urban lands. In
addition, pre-1980 urban lands are the most hydrologically constant source
of nutrients, while agricultural lands show the most variation among high-
and low-flow years. Finally, undeveloped lands export an order of magnitude
(∼7–13×) less nitrogen than built environments.
                    }, number={5}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, author={Miller, Jonathan W. and Karimi, Kimia and Sankarasubramanian, Arumugam and Obenour, Daniel R.}, year={2021}, month={May}, pages={2789–2804} }
 @article{miller_paul_obenour_2019, title={Assessing potential anthropogenic drivers of ecological health in Piedmont streams through hierarchical modeling}, volume={38}, ISSN={["2161-9565"]}, DOI={10.1086/705963}, abstractNote={Urban streams consistently have poorer ecological condition than natural streams. Poor ecological condition is caused by a myriad of anthropogenic impacts that alter hydrology and increase pollutant concentrations. Urban streams are monitored frequently, but viable management options for improving stream condition are ill-defined. A more complete understanding of the factors that influence biological condition, as well the ability to identify sites that deviate from expected condition, would help managers develop more efficient stream restoration strategies. Here, we use a hierarchical (multilevel) framework to model >3000 macroinvertebrate samples from the North Carolina Piedmont region, identify important natural gradients and anthropogenic factors that relate to stream condition, and demonstrate how hierarchical modeling can help identify potential restoration sites. In addition, we explore spatial (e.g., watershed versus stream buffer) and temporal (e.g., age of construction) aspects of land cover development. We found that watershed impervious cover (IC) is the best predictor of biotic index (BI) values. Additional factors significantly related to BI include age of watershed IC, canopy loss in stream buffers, reservoirs, wastewater treatment plants, antecedent precipitation, and geologic soil types. Synthesizing these factors in a hierarchical multiple linear regression model explained 76% of the variability (R2) in the BI, relative to 65% with only watershed IC. Of the remaining variability in the observations (24%), most was accounted for by site-specific random effects (16%), which characterize the deviation between predicted and actual biological condition. The model also suggests that newer development (post-1980) degrades stream health 30% less than older development. Additionally, canopy removal in stream buffers had 2 to 9× the effect on BI relative to the addition of IC in upstream watersheds on a per hectare basis.}, number={4}, journal={FRESHWATER SCIENCE}, author={Miller, Jonathan W. and Paul, Michael J. and Obenour, Daniel R.}, year={2019}, month={Dec}, pages={771–789} }