@article{scavia_wang_obenour_2023, title={Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie}, volume={856}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2022.158959}, abstractNote={Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R2 = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets. Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Scavia, Donald and Wang, Yu-Chen and Obenour, Daniel R.}, year={2023}, month={Jan} }
 @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_obenour_2023, title={Bayesian hierarchical modeling characterizes spatio-temporal variability in phosphorus export across the contiguous United States}, url={https://doi.org/10.5194/egusphere-egu23-8609}, DOI={10.5194/egusphere-egu23-8609}, abstractNote={Phosphorus (P) inputs from anthropogenic activities are subject to riverine (hydrologic) P export, causing water quality problems in lakes and coastal systems. Nutrient budgets have been used as a quantitative means of assessing the amount of nutrients imported to and exported from a system. However, at large spatial scales, estimates of hydrologic P losses are usually not available or assumed as a fixed fraction of the budget terms. In addition, fluxes in nutrient budgets are generally not quantified at regular intervals. In this study, we estimate P losses across 150 US watersheds at an approximately 4-digit Hydrologic Unit Code (HUC 4) watershed scale from 1997-2017. To explain the spatio-temporal variability in these estimates, we develop a Bayesian model based on various anthropogenic P inputs (e.g., fertilizer, animal manure, point sources, and atmospheric deposition) and outputs (crop removal) from national inventories, climatic factors, background soil P content, and watershed characteristics. In addition, a hierarchical approach accounts for additional sources of variability across different regions. Model results help us identify hot spots of P loss, along with the primary factors contributing to these losses. Results indicate that the greatest P losses (per unit area) occur in the Mid-Atlantic and Great Lakes regions, mainly due to high anthropogenic inputs. Additionally, the Upper Colorado region is found to have the highest temporal variability in P loss, whereas the Lower Mississippi region has the lowest.}, author={Karimi, Kimia and Obenour, Daniel}, year={2023}, month={May} }
 @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={Nutrient pollution is a widespread environmental problem that degrades water quality worldwide. Addressing this issue calls for characterizing nutrient sources and retention rates, especially in seasons when water quality problems are most severe. Hybrid (statistical‐mechanistic) watershed models have been used to quantify nutrient loading from various source categories. However, these models are generally developed for long‐term average conditions, limiting their ability to assess temporal drivers of nutrient loading. They also have not been calibrated for season‐specific estimates of loading and retention rates. To address these issues, we developed a hybrid watershed model that incorporates interannual variability in land use and precipitation as temporal drivers of phosphorus loading and transport. We calibrate the hybrid watershed model within a Bayesian hierarchical framework on both an annual and summer basis over a multi‐decadal period (1982–2017). For our study area in the North Carolina Piedmont region (USA), we find that urban lands developed before 1980 are the largest contributor of phosphorus (per unit area), especially under dry conditions. Seasonally, summer phosphorus export rates are generally found to be lower than corresponding annual rates (kg/ha/mo), while in‐stream retention is found to be elevated in summer. In addition, we find that precipitation has a substantially larger influence on phosphorus export from agricultural lands than other source types, especially in summer, and that antecedent (May) precipitation significantly influences summer phosphorus export. Overall, our approach provides a data‐driven and probabilistic line of evidence to support watershed phosphorus management across different sources and seasons.}, 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={Significance Streams are under significant ecological stress in rapidly urbanizing watersheds around the world. However, estimates of the combined use and nonuse benefits of improving urban stream water quality, which are critical for guiding policy, are generally lacking. To address this gap, we develop an ecological production framework that is tailored to urban stream stressors, conditions, human uses, and preferences in the Piedmont ecoregion of the United States and that allows analysts to translate changes in measurable water quality indicators into monetary benefit estimates. An application of the framework to illustrative policy scenarios in an urban county of North Carolina indicates that these benefits can be substantial, and it provides a template for expanding the methods and findings geographically.}, 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{petre_salk_stapleton_ferguson_tait_obenour_knappe_genereux_2022, title={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}, volume={831}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2022.154763}, abstractNote={The Cape Fear River is an important source of drinking water in North Carolina, and many drinking water intakes in the watershed are affected by per- and polyfluoroalkyl substances (PFAS). We quantified PFAS concentrations and loads in river water upstream and downstream of a PFAS manufacturing plant that has been producing PFAS since 1980. River samples collected from September 2018 to February 2021 were analyzed for 13 PFAS at the upstream station and 43-57 PFAS downstream near Wilmington. Frequent PFAS sampling (daily to weekly) was conducted close to gauging stations (critical to load estimation), and near major drinking water intakes (relevant to human exposure). Perfluoroalkyl acids dominated upstream while fluoroethers associated with the plant made up about 47% on average of the detected PFAS downstream. Near Wilmington, Σ43PFAS concentration averaged 143 ng/L (range 40-377) and Σ43PFAS load averaged 3440 g/day (range 459-17,300), with 17-88% originating from the PFAS plant. LOADEST was a useful tool in quantifying individual and total quantified PFAS loads downstream, however, its use was limited at the upstream station where PFAS levels in the river were affected by variable inputs from a wastewater treatment plant. Long-term monitoring of PFAS concentrations is warranted, especially at the downstream station. Results suggest a slight downward trend in PFAS levels downstream, as indicated by a decrease in flow-weighted mean concentrations and the best-fitting LOADEST model. However, despite the cessation of PFAS process wastewater discharge from the plant in November 2017, and the phase-out of perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) in North America, both fluoroethers and legacy PFAS continue to reach the river in significant quantities, reflecting groundwater discharge to the river and other continuing inputs. Persistence of PFAS in surface water and drinking water supplies suggests that up to 1.5 million people in the Cape Fear watershed might be exposed.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Petre, M-A and Salk, K. R. and Stapleton, H. M. and Ferguson, P. L. and Tait, G. and Obenour, D. R. and Knappe, D. R. U. and Genereux, D. P.}, year={2022}, month={Jul} }
 @article{katin_del giudice_obenour_2022, title={Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling}, volume={26}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-26-1131-2022}, DOI={10.5194/hess-26-1131-2022}, abstractNote={Abstract. Low bottom water dissolved oxygen conditions (hypoxia) occur almost every summer in the northern Gulf of Mexico due to a combination of nutrient loadings and water column stratification. Several statistical and mechanistic models have been used to forecast the midsummer hypoxic area, based on spring nitrogen loading from major rivers. However, sub-seasonal forecasts are needed to fully characterize the dynamics of hypoxia over the summer season, which is important for informing fisheries and ecosystem management. Here, we present an approach to forecasting hypoxic conditions at a daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydrometeorological model inputs. We find that May precipitation over the Mississippi River basin is a key predictor of summer discharge and loading that substantially improves forecast performance. Accounting for spring wind conditions also improves
forecast performance, though to a lesser extent. The proposed approach generates forecasts for two different sections of the Louisiana–Texas shelf (east and west), and it explains about 50 % of the variability in the total hypoxic area when tested against historical observations (1985–2016). Results also show how forecast uncertainties build over the summer season, with longer lead times from the nominal forecast release date of 1 June, due to increasing stochasticity in riverine and meteorological inputs. Consequently, the portion of overall forecast variance associated with uncertainties in data inputs increases from 26 % to 41 % from June–July to August–September, respectively. Overall, the study demonstrates a unique approach to assessing and reducing uncertainties in temporally resolved hypoxia forecasting.
}, number={4}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, publisher={Copernicus GmbH}, author={Katin, Alexey and Del Giudice, Dario and Obenour, Daniel R.}, year={2022}, month={Feb}, pages={1131–1143} }
 @article{miller_karimi_sankarasubramanian_obenour_2021, title={Assessing inter-annual variability in nitrogen sourcing and retention
through hybrid Bayesian watershed modeling}, volume={2}, url={http://dx.doi.org/10.5194/hess-2021-52}, DOI={10.5194/hess-2021-52}, abstractNote={Abstract. Excessive nutrient loading is a major cause of water quality problems worldwide, including in North Carolina (NC), where reservoirs and coastal systems are often subject to excessive algae and hypoxia. 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, statistical calibration of loading models does not always yield plausible results, given the noisiness and paucity of observational data common to many locations. To address these issues, we leverage data for two large NC Piedmont 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 (~ 7–13x) less nitrogen than built environments.}, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Miller, Jonathan W. and Karimi, Kimia and Sankarasubramanian, Arumugam and Obenour, Daniel R.}, year={2021}, month={Feb} }
 @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{katin_giudice_obenour_2021, title={Daily hypoxia forecasting and uncertainty assessment via Bayesian mechanistic model for the Northern Gulf of Mexico}, url={https://doi.org/10.5194/hess-2021-207}, DOI={10.5194/hess-2021-207}, abstractNote={Abstract. Low bottom water dissolved oxygen conditions (hypoxia) occur almost every summer in the northern Gulf of Mexico due to a combination of nutrient loadings and water column stratification. Several models have been used to forecast the midsummer hypoxic area based on spring nitrogen loading from major rivers. However, sub-seasonal forecasts are needed to fully characterize the dynamics of hypoxia over the summer season, which is important for informing fisheries and ecosystem management. Here, we present an approach to forecast hypoxic conditions at daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydro-meteorological model inputs. We find that May precipitation over the Mississippi River Basin is a key predictor of summer discharge and loading that substantially improves forecast performance. Accounting for spring wind conditions also improves forecast performance, though to a lesser extent. The proposed approach generates forecasts for two different sections of the Louisiana–Texas shelf (east and west), and it explains about 50 % of the variability in total hypoxic area when tested against historical observations (1985−2016). Results also show how forecast uncertainties build over the summer season, with longer lead times from the nominal forecast release date of 31 May, due to increasing stochasticity in riverine and meteorological inputs. Consequently, the portion of overall forecast variance associated with uncertainties in data inputs increases from 26 % to 41 % from June–July to August–September, respectively. Overall, the study demonstrates a unique approach to assessing and reducing uncertainties in dynamic hypoxia forecasting.}, author={Katin, Alexey and Giudice, Dario Del and Obenour, Daniel R.}, year={2021}, month={Apr} }
 @article{del giudice_fang_scavia_davis_evans_obenour_2021, title={Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling}, volume={755}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2020.142487}, abstractNote={The adverse impacts of harmful algal blooms (HABs) are increasing worldwide. Lake Erie is a North American Great Lake highly affected by cultural eutrophication and summer cyanobacterial HABs. While phosphorus loading is a known driver of bloom size, more nuanced yet crucial questions remain. For example, it is unclear what mechanisms are primarily responsible for initiating cyanobacterial dominance and subsequent biomass accumulation. To address these questions, we develop a mechanistic model describing June–October dynamics of chlorophyll a, nitrogen, and phosphorus near the Maumee River outlet, where blooms typically initiate and are most severe. We calibrate the model to a new, geostatistically-derived dataset of daily water quality spanning 2008–2017. A Bayesian framework enables us to embed prior knowledge on system characteristics and test alternative model formulations. Overall, the best model formulation explains 42% of the variability in chlorophyll a and 83% of nitrogen, and better captures bloom timing than previous models. Our results, supported by cross validation, show that onset of the major midsummer bloom is associated with about a month of water temperatures above 20 °C (occurring 19 July to 6 August), consistent with when cyanobacteria dominance is usually reported. Decreased phytoplankton loss rate is the main factor enabling biomass accumulation, consistent with reduced zooplankton grazing on cyanobacteria. The model also shows that phosphorus limitation is most severe in August, and nitrogen limitation tends to occur in early autumn. Our results highlight the role of temperature in regulating bloom initiation and subsequent loss rates, and suggest that a 2 °C increase could lead to blooms that start about 10 days earlier and grow 23% more intense.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Del Giudice, Dario and Fang, Shiqi and Scavia, Donald and Davis, Timothy W. and Evans, Mary Anne and Obenour, Daniel R.}, year={2021}, month={Feb} }
 @article{han_aziz_del giudice_hall_obenour_2021, title={Exploring nutrient and light limitation of algal production in a shallow turbid reservoir}, volume={269}, ISSN={["1873-6424"]}, DOI={10.1016/j.envpol.2020.116210}, abstractNote={Harmful algal blooms are increasingly recognized as a threat to the integrity of freshwater reservoirs, which serve as water supplies, wildlife habitats, and recreational attractions. While algal growth and accumulation is controlled by many environmental factors, the relative importance of these factors is unclear, particularly for turbid eutrophic systems. Here we develop and compare two models that test the relative importance of vertical mixing, light, and nutrients for explaining chlorophyll-a variability in shallow (2–3 m) embayments of a eutrophic reservoir, Jordan Lake, North Carolina. One is a multiple linear regression (statistical) model and the other is a process-based (mechanistic) model. Both models are calibrated using a 15-year data record of chlorophyll-a concentration (2003–2018) for the seasonal period of cyanobacteria dominance (June–October). The mechanistic model includes a novel representation of vertical mixing and is calibrated in a Bayesian framework, which allows for data-driven inference of important process rates. Both models show that chlorophyll-a concentration is much more responsive to nutrient variability than mixing, light, or temperature. While both models explain approximately 60% of the variability in chlorophyll-a, the mechanistic model is more robust in cross-validation and provides a more comprehensive assessment of algal drivers. Overall, these models indicate that nutrient reductions, rather than changes in mixing or background turbidity, are critical to controlling cyanobacteria in a shallow eutrophic freshwater system.}, journal={ENVIRONMENTAL POLLUTION}, author={Han, Yue and Aziz, Tarek N. and Del Giudice, Dario and Hall, Nathan S. and Obenour, Daniel R.}, year={2021}, month={Jan} }
 @article{scavia_wang_obenour_apostel_basile_kalcic_kirchhoff_miralha_muenich_steiner_2021, title={Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions}, volume={759}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2020.143487}, abstractNote={In response to increased harmful algal blooms (HABs), hypoxia, and nearshore algae growth in Lake Erie, the United States and Canada agreed to phosphorus load reduction targets. While the load targets were guided by an ensemble of models, none of them considered the effects of climate change. Some watershed models developed to guide load reduction strategies have simulated climate effects, but without extending the resulting loads or their uncertainties to HAB projections. In this study, we integrated an ensemble of four climate models, three watershed models, and four HAB models. Nutrient loads and HAB predictions were generated for historical (1985–1999), current (2002–2017), and mid-21st-century (2051–2065) periods. For the current and historical periods, modeled loads and HABs are comparable to observations but exhibit less interannual variability. Our results show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged. This reduction in load should help reduce the magnitude of future HABs, although increases in lake temperature could mitigate that decrease. Using Monte-Carlo analysis to attribute sources of uncertainty from this cascade of models, we show that the uncertainty associated with each model is significant, and that improvements in all three are needed to build confidence in future projections.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Scavia, Donald and Wang, Yu-Chen and Obenour, Daniel R. and Apostel, Anna and Basile, Samantha J. and Kalcic, Margaret M. and Kirchhoff, Christine J. and Miralha, Lorrayne and Muenich, Rebecca L. and Steiner, Allison L.}, year={2021}, month={Mar} }
 @article{katin_del giudice_hall_paerl_obenour_2021, title={Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity}, volume={447}, ISSN={["1872-7026"]}, DOI={10.1016/j.ecolmodel.2021.109497}, abstractNote={The Neuse River Estuary (North Carolina, USA) is a valuable ecosystem that has been affected by the expansion of agricultural and urban watershed activities over the last several decades. Eutrophication, as a consequence of enhanced anthropogenic nutrient loadings, has promoted high phytoplankton biomass, hypoxia, and fish kills. This study compares and contrasts three models to better understand how nutrient loading and other environmental factors control phytoplankton biomass, as chl-a, over time. The first model is purely statistical, while the second model mechanistically simulates both chl-a and nitrogen dynamics, and the third additionally simulates phosphorus. The models are calibrated to a multi-decadal dataset (1997–2018) within a Bayesian framework, which systematically incorporates prior information and accounts for uncertainties. All three models explain over one third of log-transformed chl-a variability, with the mechanistic models additionally explaining the majority of the variability in bioavailable nutrients (R2 > 0.5). By disentangling the influences of riverine nutrient concentrations, flows, and loadings on estuary productivity we find that concentration reductions, rather than total loading reductions, are the key to controlling estuary chl-a levels. The third model indicates that the estuary, even in its upstream portion, is rarely phosphorus limited, and will continue to be mostly nitrogen limited even under a 30% phosphorus reduction scenario. This model also predicts that a 10% change in nitrogen loading (flow held constant) will produce an approximate 4.3% change in estuary chl-a concentration, while the statistical model suggests a larger (10%) effect. Overall, by including a more detailed representation of environmental factors controlling algal growth, the mechanistic models generate chl-a forecasts with less uncertainty across a range of nutrient loading scenarios. Methodologically, this study advances the use of Bayesian methods for modeling the eutrophication dynamics of an estuarine system over a multi-decadal period.}, journal={ECOLOGICAL MODELLING}, author={Katin, Alexey and Del Giudice, Dario and Hall, Nathan S. and Paerl, Hans W. and Obenour, Daniel R.}, year={2021}, month={May} }
 @article{miller_karimi_sankarasubramanian_obenour_2021, title={Supplementary material to "Assessing inter-annual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling"}, volume={2}, url={https://doi.org/10.5194/hess-2021-52-supplement}, DOI={10.5194/hess-2021-52-supplement}, publisher={Copernicus GmbH}, author={Miller, Jonathan W. and Karimi, Kimia and Sankarasubramanian, Arumugam and Obenour, Daniel R.}, year={2021}, month={Feb} }
 @article{katin_giudice_obenour_2021, title={Supplementary material to "Daily hypoxia forecasting and uncertainty assessment via Bayesian mechanistic model for the Northern Gulf of Mexico"}, url={https://doi.org/10.5194/hess-2021-207-supplement}, DOI={10.5194/hess-2021-207-supplement}, author={Katin, Alexey and Giudice, Dario Del and Obenour, Daniel R.}, year={2021}, month={Apr} }
 @article{campbell_hall_obenour_2020, title={Application of packed bed reactor theory and Bayesian inference to upweller culture of juvenile oysters}, volume={90}, ISSN={["1873-5614"]}, DOI={10.1016/j.aquaeng.2020.102098}, abstractNote={The use of upweller culture units in bivalve nurseries is widely practiced as a technique that enhances the ability to rear large quantities in a semi-controlled environment. However, guidance has varied for optimal flow rates, and thus there is a need to develop a more mechanistic assessment. The application of packed bed reactor theory, including axial diffusion models, would improve optimization of these culture methods. The following paper presents a series of controlled experiments to determine the hydrodynamic properties of a packed bed of oysters. The data gained from these experiments was used to develop mechanistic models calibrated through Bayesian inference. Specifically, the Ergun equation and the axial diffusion model were used to predict the experimental data. The Ergun equation was able to predict the hydrodynamic equivalent diameter distribution of oyster shells (μ = 3.18 mm, σ = 0.74 mm). This oyster shell diameter and void ratio distribution gained through the Ergun equation were used in the relationship of axial diffusion and superficial velocity. The mean axial diffusion coefficient in the oyster bed was estimated 1.65 × 104 m2/s at 0.01 m/s and 7.26 × 104 m2/s at 0.08 m/s. The use of Bayesian inference allows for greater understanding of the credibility of individual parameter distributions (i.e., rates and physical attributes) within these mechanistic formulations. This work establishes a baseline methodology to systematically evaluate and optimize bivalve upweller culture systems.}, journal={AQUACULTURAL ENGINEERING}, author={Campbell, Matthew D. and Hall, Steven G. and Obenour, Daniel R.}, year={2020}, month={Aug} }
 @article{han_smithheart_smyth_aziz_obenour_2020, title={Assessing Vertical Diffusion and Cyanobacteria Bloom Potential in a Shallow Eutrophic Reservoir}, volume={36}, ISSN={["2151-5530"]}, url={http://dx.doi.org/10.1080/10402381.2019.1697402}, DOI={10.1080/10402381.2019.1697402}, abstractNote={Abstract Han Y, Smithheart JW, Smyth RL, Aziz TN, Obenour CR. 2019. Assessing vertical diffusion and cyanobacteria bloom potential in a shallow eutrophic reservoir. Lake Reserv Manage. 36:169–185. Harmful blooms of cyanobacteria are an increasing threat to many lakes and reservoirs. While vertical mixing has been shown to be an important control on cyanobacteria dominance in some lakes, the relevance of mixing in relatively shallow turbid systems remains unclear. To explore mixing and its impact on cyanobacteria bloom potential, we leveraged data from a multiyear field campaign of a central North Carolina reservoir where artificial circulators were installed to (1) implement a parsimonious one-dimensional (1D) turbulent diffusion model with an artificial circulation term, (2) introduce a novel multi-objective calibration approach considering both water column temperature and stability, and (3) explore how mixing affects cyanobacteria bloom potential through changes in cyanobacteria light exposure relative to other algal taxa. Our multi-objective calibration approach is shown to realistically simulate both water temperature (R2 = 0.99) and water column stability (R2 = 0.62) throughout the year. Analysis of artificial mixing demonstrates the relative insignificance of the circulator deployment in our study area and suggests that at least eight times the implemented circulation rate would be required to substantially reduce the ability of buoyant cyanobacteria to outcompete other algal taxa for light. Overall, this study demonstrates an efficient and systematic approach for characterizing vertical mixing in lakes and reservoirs, which can be used to assess the viability of artificial circulation prior to deployment.}, number={2}, journal={LAKE AND RESERVOIR MANAGEMENT}, publisher={Informa UK Limited}, author={Han, Yue and Smithheart, Jeremy W. and Smyth, Robyn L. and Aziz, Tarek N. and Obenour, Daniel R.}, year={2020}, month={Apr}, pages={169–185} }
 @article{obenour_giudice_aupperle_sankarasubramanian_2020, title={Assessing within-lake nutrient cycling through multi-decadal Bayesian mechanistic modeling}, url={https://doi.org/10.5194/egusphere-egu2020-4232}, DOI={10.5194/egusphere-egu2020-4232}, abstractNote={
 <p>Nutrient recycling from bottom sediments can provide substantial internal loading to eutrophic lakes and reservoirs, potentially exceeding external watershed loads. However, measurements of sediment nutrient fluxes are rare for most waterbodies in the United States, causing many modeling studies to parameterize these fluxes in simplistic ways or else make assumptions about complex sediment diagenetic rates. Here we propose an alternative approach to understanding internal cycling, using a mass-balance model combined with Bayesian inference to rigorously update prior information on nutrient flux parameters. The approach is applied to Jordan Lake, a major water supply reservoir in North Carolina (USA) that has been highly eutrophic since impoundment in the early 1980s, with chlorophyll a concentrations occasionally exceeding 100 &#181;g/L. We simulate monthly nitrogen and phosphorus dynamics in the water column and sediment layer of four longitudinal reservoir segments, forced by watershed flows, nutrient loads, and meteorology. The model is calibrated within the Bayesian framework and validated using a multi-decadal record of surface nutrient concentration data. We compare multiple versions of the model to assess the importance of prior knowledge from previous literature, the multi-decadal calibration period, and the mechanistic formulation for obtaining accurate and robust predictive performance. Overall, the model explains from 40-60% of the variability in observed nutrient concentrations. Model results indicate that a large fraction (>40%) of phosphorus is lost in the upstream reaches of the reservoir, likely due to rapid settling and burial of particulate material. Within the main body of the reservoir, phosphorus recycling rates were found to be higher than expected a priori, particularly in the summer season. Results show how nutrients stored in lacustrine sediment have been an important source of internal loading to the reservoir for multiple decades, and will dampen the effects of external watershed loading reductions, at least in the near term. To better understand potential time scales for reservoir recovery, we perform future simulations over a multi-decadal period and characterize forecast uncertainties.</p>
}, author={Obenour, Daniel and Giudice, Dario Del and Aupperle, Matthew and Sankarasubramanian, Arumugam}, year={2020}, month={Mar} }
 @article{del giudice_matli_obenour_2020, title={Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios}, volume={30}, ISSN={["1939-5582"]}, DOI={10.1002/eap.2032}, abstractNote={The hypoxic zone in the northern Gulf of Mexico is among the most dramatic examples of impairments to aquatic ecosystems. Despite having attracted substantial attention, management of this environmental crisis remains challenging, partially due to limited monitoring to support model development and long-term assessments. Here, we leverage new geostatistical estimates of hypoxia derived from nearly 150 monitoring cruises and a process-based model to improve characterization of controlling mechanisms, historic trends, and future responses of hypoxia while rigorously quantifying uncertainty in a Bayesian framework. We find that November-March nitrogen loads are important controls of sediment oxygen demand, which appears to be the major oxygen sink. In comparison, only ~23% of oxygen in the near-bottom region appears to be consumed by net water column respiration, which is driven by spring and summer loads. Hypoxia typically exceeds 15600 km2 in June, peaks in July, and declines below 10000 km2 in September. In contrast to some previous Gulf hindcasting studies, our simulations demonstrate that hypoxia was both severe and worsening prior to 1985, and has remained relatively stable since that time. Scenario analysis shows that halving nutrient loadings will reduce hypoxia by 37% w.r.t. 13900 km2 (1985-2016 median), while a +2°C change in water temperature will cause a 26% hypoxic area increase due to enhanced sediment respiration and reduced oxygen solubility. These new results highlight the challenges of achieving hypoxia reduction targets, particularly under warming conditions, and should be considered in ecosystem management.}, number={2}, journal={ECOLOGICAL APPLICATIONS}, author={Del Giudice, Dario and Matli, V. R. R. and Obenour, Daniel R.}, year={2020}, month={Mar} }
 @article{matli_laurent_fennel_craig_krause_obenour_2020, title={Fusion-Based Hypoxia Estimates: Combining Geostatistical and Mechanistic Models of Dissolved Oxygen Variability}, volume={54}, url={https://doi.org/10.1021/acs.est.0c03655}, DOI={10.1021/acs.est.0c03655}, abstractNote={The need to characterize and track coastal hypoxia has led to development of geostatistical models based on in situ observations of dissolved oxygen (DO) and mechanistic models based on a representation of biophysical processes. To integrate the benefits of these two distinct modeling approaches, we develop a space-time geostatistical framework for synthesizing DO observations with hydrodynamic-biogeochemical model simulations and meteorological time series (as covariates). This fusion-based approach is used to estimate hypoxia in the northern Gulf of Mexico across summers from 1985-2017. Deterministic trends with dynamic covariates explain over 35% of the variability in DO. Moreover, cross-validation results indicate that 58% of DO variability is explained when combining these trends with spatio-temporal interpolation; which is substantially better than mechanistic or conventional geostatistical hypoxia modeling alone. The fusion-based approach also reduces hypoxic area uncertainties by 11% on average, and up to 40% in months with sparse sampling. Moreover, our new estimates of mean summer hypoxic area changed by >10% in a majority of years, relative to previous geostatistical estimates. These fusion-based estimates can be a valuable resource when assessing the influence of hypoxia on the coastal ecosystem.}, number={20}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Matli, Venkata Rohith Reddy and Laurent, Arnaud and Fennel, Katja and Craig, Kevin and Krause, Jacob and Obenour, Daniel R.}, year={2020}, month={Oct}, pages={13016–13025} }
 @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} }
 @article{scavia_justic_obenour_craig_wang_2019, title={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}, volume={14}, ISSN={["1748-9326"]}, DOI={10.1088/1748-9326/aaf938}, abstractNote={While impacts of low oxygen on marine organisms have been reviewed from physiological and ecological perspectives, relating broad population- and ecosystem-level effects to the areal extent of hypoxia (dissolved oxygen concentration below 64 μM, or 2 mg l−1) has proven difficult. We suggest that hypoxic volume is a more appropriate metric compared to hypoxic area because volume better integrates the effects of hypoxia on ecological processes relevant to many marine taxa. In this paper, we compare the volume-based load responses from a simple biophysical model with results from an independent three-dimensional hydrodynamic-biogeochemical model, and discuss the implications with respect to potentially more ecologically-relevant hypoxia management goals. We also show that hypoxic volume appears more sensitive than hypoxic area to nutrient load reductions. Model simulations indicate that even under a modest 25% nitrogen load reduction, the thickness of the hypoxic layer in the northern Gulf of Mexico decreases markedly, and hypoxia remains localized to a relatively thin layer near the bottom that most fish and other mobile organisms can more effectively avoid. This finding should be considered when reviewing and potentially setting hypoxia management goals.}, number={2}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={Scavia, Donald and Justic, Dubravko and Obenour, Daniel R. and Craig, J. Kevin and Wang, Lixia}, year={2019}, month={Feb} }
 @article{miller_esselman_alameddine_blackhart_obenour_2018, title={Hierarchical modeling assessment of the influence of watershed stressors on fish and invertebrate species in Gulf of Mexico estuaries}, volume={90}, ISSN={1470-160X}, url={http://dx.doi.org/10.1016/J.ECOLIND.2018.02.040}, DOI={10.1016/J.ECOLIND.2018.02.040}, abstractNote={The northern Gulf of Mexico (GoM) spans five U.S. states and encompasses estuaries that vary greatly in size, shape, upstream river input, eutrophication status, and biotic communities. Given the variability among these estuaries, assessing their biological condition relative to anthropogenic stressors is challenging, but important to regional fisheries management and habitat conservation initiatives. Here, a hierarchical generalized linear modeling approach was developed to predict species presence in bottom trawl samples, using data from 33 estuaries over a nineteen-year study period. This is the first GoM estuary assessment to leverage Gulf-wide trawl data to develop species-level indicators and a quantitative index of estuary disturbance. After controlling for sources of variability at the sampling event, estuary, state, and sampling program levels, our approach screened for statistically significant relationships between watershed-level anthropogenic stressors and fish and invertebrate species presence. Modeling results indicate species level indicators with sensitivities to landscape stressor gradients. The most influential stressors include total anthropogenic land use, crop land use, and the number of toxic release sites in upstream watersheds, as well as agriculture in the shoreline buffer, each of which was significantly related to between 21% and 39% of the 57 species studied. Averaging the effects of these influential stressors across species, we develop a quantitative estuary stress index that can be compared against benchmark conditions. In general, disturbance levels were greatest in estuaries west of the Mississippi delta and in highly developed estuaries in southwest Florida. Estuaries from the Florida panhandle to the eastern Mississippi delta had less anthropogenic stress.}, journal={Ecological Indicators}, publisher={Elsevier BV}, author={Miller, Jonathan and Esselman, Peter C. and Alameddine, Ibrahim and Blackhart, Kristan and Obenour, Daniel R.}, year={2018}, month={Jul}, pages={142–153} }
 @article{strickling_obenour_2018, title={Leveraging Spatial and Temporal Variability to Probabilistically Characterize Nutrient Sources and Export Rates in a Developing Watershed}, url={https://doi.org/10.1029/2017WR022220}, DOI={10.1029/2017WR022220}, abstractNote={Hybrid watershed models based on nonlinear regression are useful tools for estimating the magnitude of loading rates (i.e., export coefficients) for various pollutant sources within large‐scale river basins. Few such models, however, have incorporated temporal variability in either source distributions or climate, despite evidence that precipitation is the primary driver in interannual variability in loading rates. The model developed here includes changes in precipitation, land use, point source discharge, and livestock operations to capture temporal variability in nitrogen loads. Precipitation is incorporated directly in the formulation of export rates using coefficients that vary by source type. Instream and reservoir retention of nitrogen is included to account for nitrogen sinks within the watershed. A Bayesian hierarchical approach is employed to integrate uncertainty in loading estimates, include prior knowledge of parameters, address intrawatershed correlation, and estimate export coefficients probabilistically. We apply this method to three North Carolina river basins that have experienced substantial growth in urban development and livestock operations in the past few decades, and where eutrophication‐related water quality problems are common. Accounting for temporal variability constrains uncertainties in nonpoint source export coefficients by nearly 50%, relative to a spatial‐only model. Results indicate that livestock operations are a significant contributor of nitrogen throughout much of the study area. Precipitation is shown to have a larger influence on export rates for agricultural than for developed lands, creating a system dominated by agricultural total nitrogen during high precipitation years and by developed (urban) regions during low precipitation years.}, journal={Water Resources Research}, author={Strickling, H. L. and Obenour, D. R.}, year={2018}, month={Jul} }
 @article{nelson_li_obenour_miller_misenheimer_scheckel_betts_juhasz_thomas_bradham_2018, title={Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling}, volume={81}, ISSN={1528-7394 1087-2620}, url={http://dx.doi.org/10.1080/15287394.2018.1423798}, DOI={10.1080/15287394.2018.1423798}, abstractNote={ABSTRACT Interest in improved understanding of relationships among soil properties and arsenic (As) bioaccessibility has motivated the use of regression models for As bioaccessibility prediction. However, limits in the numbers and types of soils included in previous studies restrict the usefulness of these models beyond the range of soil conditions evaluated, as evidenced by reduced predictive performance when applied to new data. In response, hierarchical models that consider variability in relationships among soil properties and As bioaccessibility across geographic locations and contaminant sources were developed to predict As bioaccessibility in 139 soils on both a mass fraction (mg/kg) and % basis. The hierarchical approach improved the estimation of As bioaccessibility in studied soils. In addition, the number of soil elements identified as statistically significant explanatory variables increased when compared to previous investigations. Specifically, total soil Fe, P, Ca, Co, and V were significant explanatory variables in both models, while total As, Cd, Cu, Ni, and Zn were also significant in the mass fraction model and Mg was significant in the % model. This developed hierarchical approach provides a novel tool to (1) explore relationships between soil properties and As bioaccessibility across a broad range of soil types and As contaminant sources encountered in the environment and (2) identify areas of future mechanistic research to better understand the complexity of interactions between soil properties and As bioaccessibility.}, number={6}, journal={Journal of Toxicology and Environmental Health, Part A}, publisher={Informa UK Limited}, author={Nelson, Clay M. and Li, Kevin and Obenour, Daniel R. and Miller, Jonathan and Misenheimer, John C. and Scheckel, Kirk and Betts, Aaron and Juhasz, Albert and Thomas, David J. and Bradham, Karen D.}, year={2018}, month={Jan}, pages={160–172} }
 @article{matli_fang_guinness_rabalais_craig_obenour_2018, title={Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico}, volume={52}, ISSN={["1520-5851"]}, url={https://doi.org/10.1021/acs.est.8b03474}, DOI={10.1021/acs.est.8b03474}, abstractNote={Nearly every summer, a large hypoxic zone forms in the northern Gulf of Mexico. Research on the causes and consequences of hypoxia requires reliable estimates of hypoxic extent, which can vary at submonthly time scales due to hydro-meteorological variability. Here, we use an innovative space-time geostatistical model and data collected by multiple research organizations to estimate bottom-water dissolved oxygen (BWDO) concentrations and hypoxic area across summers from 1985 to 2016. We find that 27% of variability in BWDO is explained by deterministic trends with location, depth, and date, while correlated stochasticity accounts for 62% of observational variance within a range of 185 km and 28 days. Space-time modeling reduces uncertainty in estimated hypoxic area by 30% when compared to a spatial-only model, and results provide new insights into the temporal variability of hypoxia. For years with shelf-wide cruises in multiple months, hypoxia is most severe in July in 59% of years, 29% in August, and 12% in June. Also, midsummer cruise estimates of hypoxic area are only modestly correlated with summer-wide (June-August) average estimates ( r2 = 0.5), suggesting midsummer cruises are not necessarily reflective of seasonal hypoxic severity. Furthermore, summer-wide estimates are more strongly correlated with nutrient loading than midsummer estimates.}, number={21}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Matli, V. Rohith Reddy and Fang, Shiqi and Guinness, Joseph and Rabalais, Nancy. N. and Craig, J. Kevin and Obenour, Daniel R.}, year={2018}, month={Nov}, pages={12484–12493} }
 @article{scavia_bertani_obenour_turner_forrest_katin_2017, title={Ensemble modeling informs hypoxia management in the northern Gulf of Mexico}, volume={114}, ISSN={["0027-8424"]}, DOI={10.1073/pnas.1705293114}, abstractNote={Significance The number of coastal hypoxia areas is spreading worldwide, with severe environmental and societal impacts. The second-largest hypoxic zone occurs in the northern Gulf of Mexico, where anthropogenic nutrient load is a key driving factor, as in many coastal waters. We address policy-relevant questions raised by Gulf stakeholders and decision-makers using an ensemble approach that integrates results from multiple models. Through development of a rigorous framework to propagate intramodel and intermodel uncertainty into the ensemble, we provide policymakers with the response of hypoxic area to a range of different nitrogen load reduction scenarios, with corresponding probabilistic statements that allow for quantitative risk assessment of alternative policy strategies. A large region of low-dissolved-oxygen bottom waters (hypoxia) forms nearly every summer in the northern Gulf of Mexico because of nutrient inputs from the Mississippi River Basin and water column stratification. Policymakers developed goals to reduce the area of hypoxic extent because of its ecological, economic, and commercial fisheries impacts. However, the goals remain elusive after 30 y of research and monitoring and 15 y of goal-setting and assessment because there has been little change in river nitrogen concentrations. An intergovernmental Task Force recently extended to 2035 the deadline for achieving the goal of a 5,000-km2 5-y average hypoxic zone and set an interim load target of a 20% reduction of the spring nitrogen loading from the Mississippi River by 2025 as part of their adaptive management process. The Task Force has asked modelers to reassess the loading reduction required to achieve the 2035 goal and to determine the effect of the 20% interim load reduction. Here, we address both questions using a probabilistic ensemble of four substantially different hypoxia models. Our results indicate that, under typical weather conditions, a 59% reduction in Mississippi River nitrogen load is required to reduce hypoxic area to 5,000 km2. The interim goal of a 20% load reduction is expected to produce an 18% reduction in hypoxic area over the long term. However, due to substantial interannual variability, a 25% load reduction is required before there is 95% certainty of observing any hypoxic area reduction between consecutive 5-y assessment periods.}, number={33}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, publisher={National Acad Sciences}, author={Scavia, Donald and Bertani, Isabella and Obenour, Daniel R. and Turner, R. Eugene and Forrest, David R. and Katin, Alexey}, year={2017}, month={Aug}, pages={8823–8828} }
 @article{bradham_nelson_kelly_pomales_scruton_dignam_misenheimer_li_obenour_thomas_2017, title={Relationship Between Total and Bioaccessible Lead on Children's Blood Lead Levels in Urban Residential Philadelphia Soils}, volume={51}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.7b02058}, abstractNote={Relationships between total soil or bioaccessible lead (Pb), measured using an in vitro bioaccessibility assay, and children's blood lead levels (BLL) were investigated in an urban neighborhood in Philadelphia, PA, with a history of soil Pb contamination. Soil samples from 38 homes were analyzed to determine whether accounting for the bioaccessible Pb fraction improves statistical relationships with children's BLLs. Total soil Pb concentration ranged from 58 to 2821 mg/kg; the bioaccessible Pb concentration ranged from 47 to 2567 mg/kg. Children's BLLs ranged from 0.3 to 9.8 μg/dL. Hierarchical models were used to compare relationships between total or bioaccessible Pb in soil and children's BLLs. Total soil Pb concentration as the predictor accounted for 23% of the variability in child BLL; bioaccessible soil Pb concentration as the predictor accounted for 26% of BLL variability. A bootstrapping analysis confirmed a significant increase in R2 for the model using bioaccessible soil Pb concentration as the predictor with 99.0% of bootstraps showing a positive increase. Estimated increases of 1.3 μg/dL and 1.5 μg/dL in BLL per 1000 mg/kg Pb in soil were observed for this study area using total and bioaccessible Pb concentrations, respectively. Children's age did not contribute significantly to the prediction of BLLs.}, number={17}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={ACS Publications}, author={Bradham, Karen D. and Nelson, Clay M. and Kelly, Jack and Pomales, Ana and Scruton, Karen and Dignam, Tim and Misenheimer, John C. and Li, Kevin and Obenour, Daniel R. and Thomas, David J.}, year={2017}, month={Sep}, pages={10005–10011} }
 @article{bertani_steger_obenour_fahnenstiel_bridgeman_johengen_sayers_shuchman_scavia_2017, title={Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?}, volume={575}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2016.10.023}, abstractNote={Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier}, author={Bertani, Isabella and Steger, Cara E. and Obenour, Daniel R. and Fahnenstiel, Gary L. and Bridgeman, Thomas B. and Johengen, Thomas H. and Sayers, Michael J. and Shuchman, Robert A. and Scavia, Donald}, year={2017}, month={Jan}, pages={294–308} }
 @inproceedings{ayub_obenour_messier_serre_mahinthakumar_2016, title={Non-point source evaluation of groundwater contamination from agriculture under geologic and hydrologic uncertainty}, DOI={10.1061/9780784479865.035}, abstractNote={The long-term effect of non-point source pollution on groundwater from agricultural practices is a major concern globally. Non-point source pollutants such as nitrate that occur through fertilizers and animal waste eventually make their way into the aquifer by infiltrating soil. The goal of this study is to characterize the probability distributions of non-point source locations and time release history of nitrate contamination into groundwater resources. A Bayesian framework using a Markov Chain Monte Carlo approach (MCMC) is developed to estimate posterior distributions of non-point sources by incorporating groundwater nitrate concentration data as well as geologic and hydrologic uncertainties. Hypothetical scenarios are used to test the approach and then apply it to a basin in North Carolina.The likelihood function computation involves a mechanistic model that simulates nitrate transport in groundwater from non-point agricultural sources and predicts nitrate concentrations at observation wells. Effectiveness of the proposed approach is tested through a convergence analysis of the MCMC algorithm. The Bayesian inference analysis methodology developed in this research will help decision makers and water managers identify potential source containment areas and to decide if further sampling is required.}, booktitle={World Environmental and Water Resources Congress 2016: Environmental, Sustainability, Groundwater, Hydraulic Fracturing, and Water Distribution Systems analysis}, author={Ayub, R. and Obenour, D. R. and Messier, K. P. and Serre, M. L. and Mahinthakumar, K.}, year={2016}, pages={329–336} }
 @article{bertani_obenour_steger_stow_gronewold_scavia_2016, title={Probabilistically assessing the role of nutrient loading in harmful algal bloom formation in western Lake Erie}, volume={42}, ISSN={["0380-1330"]}, DOI={10.1016/j.jglr.2016.04.002}, abstractNote={Harmful algal blooms (HABs) have increased in frequency and magnitude in western Lake Erie and spring phosphorus (P) load was shown to be a key driver of bloom intensity. A recently developed Bayesian hierarchical model that predicts peak bloom size as a function of Maumee River phosphorus load suggested an apparent increased susceptibility of the lake to HABs. We applied that model to develop load–response curves to inform revision of Lake Erie phosphorus load targets under the 2012 Great Lakes Water Quality Agreement. In this application, the model was modified to estimate the fraction of the particulate P (PP) load that becomes bioavailable, and it was recalibrated with additional bloom observations. Although the uncertainty surrounding the estimate of the bioavailable PP fraction is large, inclusion in the model improves prediction of bloom variability compared to dissolved reactive P (DRP) alone. The ability to characterize model and measurement uncertainty through hierarchical modeling allowed us to show that inconsistencies in bloom measurement represent a considerable portion of the overall uncertainty associated with load–response curves. The updated calibration also lends support to the system's apparent enhanced susceptibility to blooms. The temporal trend estimated by the model results in an upward shift of the load–response curve over time such that a larger load reduction is required to achieve a target bloom size today compared to earlier years. More research is needed to further test the hypothesis of a shift in the lake's response to stressors over time and, if confirmed, to explore underlying mechanisms.}, number={6}, journal={JOURNAL OF GREAT LAKES RESEARCH}, publisher={Elsevier}, author={Bertani, Isabella and Obenour, Daniel R. and Steger, Cara E. and Stow, Craig A. and Gronewold, Andrew D. and Scavia, Donald}, year={2016}, month={Dec}, pages={1184–1192} }
 @article{le_lehrter_hu_obenour_2016, title={Satellite‐based empirical models linking river plume dynamics with hypoxic area and volume}, volume={43}, ISSN={0094-8276 1944-8007}, url={http://dx.doi.org/10.1002/2015GL067521}, DOI={10.1002/2015GL067521}, abstractNote={Satellite‐based empirical models explaining hypoxic area and volume variation were developed for the seasonally hypoxic (O2 < 2 mg L−1) northern Gulf of Mexico adjacent to the Mississippi River. Annual variations in midsummer hypoxic area and volume were related to Moderate Resolution Imaging Spectroradiometer‐derived monthly estimates of river plume area (km2) and average, inner shelf chlorophyll a concentration (Chl a, mg m−3). River plume area in June was negatively related with midsummer hypoxic area (km2) and volume (km3), while July inner shelf Chl a was positively related to hypoxic area and volume. Multiple regression models using river plume area and Chl a as independent variables accounted for most of the variability in hypoxic area (R2 = 0.92) or volume (R2 = 0.89). These models explain more variation in hypoxic area than models using Mississippi River nutrient loads as independent variables. The results here also support a hypothesis that confinement of the river plume to the inner shelf is an important mechanism controlling hypoxia area and volume in this region.}, number={6}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Le, Chengfeng and Lehrter, John C. and Hu, Chuanmin and Obenour, Daniel R.}, year={2016}, month={Mar}, pages={2693–2699} }
 @article{obenour_michalak_scavia_2015, title={Assessing biophysical controls on Gulf of Mexico hypoxia through probabilistic modeling}, volume={25}, number={2}, journal={Ecological Applications}, publisher={Wiley Online Library}, author={Obenour, Daniel R and Michalak, Anna M and Scavia, Donald}, year={2015}, pages={492–505} }
 @article{bradham_nelson_juhasz_smith_scheckel_obenour_miller_thomas_2015, title={Independent Data Validation of an in Vitro Method for the Prediction of the Relative Bioavailability of Arsenic in Contaminated Soils}, volume={49}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.5b00905}, abstractNote={In vitro bioaccessibility (IVBA) assays estimate arsenic (As) relative bioavailability (RBA) in contaminated soils to improve accuracy in human exposure assessments. Previous studies correlating soil As IVBA with RBA have been limited by the use of few soil types and sources of As, and the predictive value of As IVBA has not been validated using an independent set of As-contaminated soils. In this study, a robust linear model was developed to predict As RBA in mice using IVBA, and the predictive capability of the model was independently validated using a unique set of As-contaminated soils. Forty As-contaminated soils varying in soil type and contaminant source were included in this study, with 31 soils used for initial model development and nine soils used for independent model validation. The initial model reliably predicted As RBA values in the independent data set, with a mean As RBA prediction error of 5.4%. Following validation, 40 soils were used for final model development, resulting in a linear model with the equation RBA = 0.65 × IVBA + 7.8 and an R(2) of 0.81. The in vivo-in vitro correlation and independent data validation presented provide critical verification necessary for regulatory acceptance in human health risk assessment.}, number={10}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={ACS Publications}, author={Bradham, Karen D. and Nelson, Clay and Juhasz, Albert L. and Smith, Euan and Scheckel, Kirk and Obenour, Daniel R. and Miller, Bradley W. and Thomas, David J.}, year={2015}, month={May}, pages={6312–6318} }
 @article{rowe_obenour_nalepa_vanderploeg_yousef_kerfoot_2015, title={Mapping the spatial distribution of the biomass and filter-feeding effect of invasive dreissenid mussels on the winter-spring phytoplankton bloom in Lake Michigan}, volume={60}, ISSN={["1365-2427"]}, DOI={10.1111/fwb.12653}, abstractNote={The effects of the invasive bivalves Dreissena polymorpha (zebra mussel) and Dreissena rostriformis bugensis (quagga mussel) on aquatic ecosystems, including Lake Michigan, are a topic of current interest to scientists and resource managers. We hypothesised that the winter–spring phytoplankton bloom in Lake Michigan is reduced at locations where the fraction of the water column cleared per day by Dreissena filter feeding approached the net growth rate of phytoplankton, when the water column was not stratified. To test this hypothesis, we compared the spatial distribution of Dreissena filter‐feeding intensity (determined from geostatistical modelling) to the spatial distribution of chlorophyll (determined from satellite remote sensing). To map the spatial distribution of Dreissena biomass and filter‐feeding intensity, we developed a geostatistical model based on point observations of mussel biomass measured in Lake Michigan in 1994/1995, 2000, 2005 and 2010. The model provided fine‐scale estimates of the spatial distribution of biomass for the survey years and provided estimates, with their uncertainty, of total biomass lakewide and within subregions. The approach outlined could be applied more generally to map the distribution of benthic biota in lakes from point observations. Total biomass of Dreissena in Lake Michigan, estimated from the geostatistical model, increased significantly over each five‐year period. The total biomass in units of 10⁶ kg ash‐free dry mass (AFDM) (with 90% confidence interval) was 6 (4–8) in 1994/1995, 18 (14–23) in 2000, 408 (338–485) in 2005 and 610 (547–680) in 2010. From 1994/1995 to 2005, increases were observed in all regions of the lake (northern, central and southern) and in all depth zones ( 90). However, from 2005 to 2010, for depths of 50 m. The filter‐feeding intensity of Dreissena exceeded the benchmark spring phytoplankton growth rate of 0.06 day⁻¹ in 2005 for depths <50 m (lakewide). In 2010, the filter‐feeding impact exceeded 0.06 day⁻¹ within depths <90 m (lakewide), which greatly increased the spatial area affected relative to 2005. A regression analysis indicated a significant relationship between the reduction in satellite‐derived chlorophyll concentration (pre‐D. r. bugensis period to post‐D. r. bugensis period) and spatially co‐located filter‐feeding intensity (fraction of water column cleared per day) during periods when the water column was not stratified (December to April).}, number={11}, journal={FRESHWATER BIOLOGY}, publisher={Wiley Online Library}, author={Rowe, Mark D. and Obenour, Daniel R. and Nalepa, Thomas F. and Vanderploeg, Henry A. and Yousef, Foad and Kerfoot, W. Charles}, year={2015}, month={Nov}, pages={2270–2285} }
 @article{obenour_gronewold_stow_scavia_2014, title={Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts}, volume={50}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2014WR015616}, DOI={10.1002/2014WR015616}, abstractNote={The last decade has seen a dramatic increase in the size of western Lake Erie cyanobacteria blooms, renewing concerns over phosphorus loading, a common driver of freshwater productivity. However, there is considerable uncertainty in the phosphorus load‐bloom relationship, because of other biophysical factors that influence bloom size, and because the observed bloom size is not necessarily the true bloom size, owing to measurement error. In this study, we address these uncertainties by relating late‐summer bloom observations to spring phosphorus load within a Bayesian modeling framework. This flexible framework allows us to evaluate three different forms of the load‐bloom relationship, each with a particular combination of statistical error distribution and response transformation. We find that a novel implementation of a gamma error distribution, along with an untransformed response, results in a model with relatively high predictive skill and realistic uncertainty characterization, when compared to models based on more common statistical formulations. Our results also underscore the benefits of a hierarchical approach that enables assimilation of multiple sets of bloom observations within the calibration processes, allowing for more thorough uncertainty quantification and explicit differentiation between measurement and model error. Finally, in addition to phosphorus loading, the model includes a temporal trend component indicating that Lake Erie has become increasingly susceptible to large cyanobacteria blooms over the study period (2002–2013). Results suggest that current phosphorus loading targets will be insufficient for reducing the intensity of cyanobacteria blooms to desired levels, so long as the lake remains in a heightened state of bloom susceptibility.}, number={10}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Obenour, Daniel R. and Gronewold, Andrew D. and Stow, Craig A. and Scavia, Donald}, year={2014}, month={Oct}, pages={7847–7860} }
 @article{scavia_evans_obenour_2013, title={A Scenario and Forecast Model for Gulf of Mexico Hypoxic Area and Volume}, volume={47}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/ES4025035}, DOI={10.1021/ES4025035}, abstractNote={For almost three decades, the relative size of the hypoxic region on the Louisiana-Texas continental shelf has drawn scientific and policy attention. During that time, both simple and complex models have been used to explore hypoxia dynamics and to provide management guidance relating the size of the hypoxic zone to key drivers. Throughout much of that development, analyses had to accommodate an apparent change in hypoxic sensitivity to loads and often cull observations due to anomalous meteorological conditions. Here, we describe an adaptation of our earlier, simple biophysical model, calibrated to revised hypoxic area estimates and new hypoxic volume estimates through Bayesian estimation. This application eliminates the need to cull observations and provides revised hypoxic extent estimates with uncertainties corresponding to different nutrient loading reduction scenarios. We compare guidance from this model application, suggesting an approximately 62% nutrient loading reduction is required to reduce Gulf hypoxia to the Action Plan goal of 5000 km(2), to that of previous applications. In addition, we describe for the first time, the corresponding response of hypoxic volume. We also analyze model results to test for increasing system sensitivity to hypoxia formation, but find no strong evidence of such change.}, number={18}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Scavia, Donald and Evans, Mary Anne and Obenour, Daniel R.}, year={2013}, month={Sep}, pages={10423–10428} }
 @article{zhou_obenour_scavia_johengen_michalak_2013, title={Correction to Spatial and Temporal Trends in Lake Erie Hypoxia, 1987–2007}, volume={47}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/ES401561C}, DOI={10.1021/ES401561C}, abstractNote={1987−2007 Yuntao Zhou,*,†,‡ Daniel R. Obenour,‡,§ Donald Scavia,‡,§,∥ Thomas H. Johengen, and Anna M. Michalak† †Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, United States ‡Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States School of Natural Resources and Environment, University of Michigan, Ann Arbor, Michigan 48109, United States Graham Sustainability Institute, University of Michigan, Ann Arbor, Michigan 48103, United States Cooperative Institute for Limnology and Ecosystems Research, School of Natural Resources and Environment, University of Michigan, Ann Arbor, Michigan 48109, United States}, number={9}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Zhou, Yuntao and Obenour, Daniel R. and Scavia, Donald and Johengen, Thomas H. and Michalak, Anna M.}, year={2013}, month={Apr}, pages={4958–4958} }
 @article{obenour_scavia_rabalais_turner_michalak_2013, title={Retrospective Analysis of Midsummer Hypoxic Area and Volume in the Northern Gulf of Mexico, 1985–2011}, volume={47}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/ES400983G}, DOI={10.1021/ES400983G}, abstractNote={Robust estimates of hypoxic extent (both area and volume) are important for assessing the impacts of low dissolved oxygen on aquatic ecosystems at large spatial scales. Such estimates are also important for calibrating models linking hypoxia to causal factors, such as nutrient loading and stratification, and for informing management decisions. In this study, we develop a rigorous geostatistical modeling framework to estimate the hypoxic extent in the northern Gulf of Mexico from data collected during midsummer, quasi-synoptic monitoring cruises (1985–2011). Instead of a traditional interpolation-based approach, we use a simulation-based approach that yields more robust extent estimates and quantified uncertainty. The modeling framework also makes use of covariate information (i.e., trend variables such as depth and spatial position), to reduce estimation uncertainty. Furthermore, adjustments are made to account for observational bias resulting from the use of different sampling instruments in different years. Our results suggest an increasing trend in hypoxic layer thickness (p = 0.05) from 1985 to 2011, but less than significant increases in volume (p = 0.12) and area (p = 0.42). The uncertainties in the extent estimates vary with sampling network coverage and instrument type, and generally decrease over the study period.}, number={17}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Obenour, Daniel R. and Scavia, Donald and Rabalais, Nancy N. and Turner, R. Eugene and Michalak, Anna M.}, year={2013}, month={Aug}, pages={9808–9815} }
 @article{zhou_obenour_scavia_johengen_michalak_2013, title={Spatial and Temporal Trends in Lake Erie Hypoxia, 1987–2007}, volume={47}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es303401b}, DOI={10.1021/es303401b}, abstractNote={Hypoxic conditions, defined as dissolved oxygen (DO) concentrations below 2 mg/L, are a regular summertime occurrence in Lake Erie, but the spatial extent has been poorly understood due to sparse sampling. We use geostatistical kriging and conditional realizations to provide quantitative estimates of the extent of hypoxia in the central basin of Lake Erie for August and September of 1987 to 2007, along with their associated uncertainties. The applied geostatistical approach combines the limited in situ DO measurements with auxiliary data selected using the Bayesian Information Criterion. Bathymetry and longitude are found to be highly significant in explaining the spatial distribution of DO, while satellite observations of sea surface temperature and satellite chlorophyll are not. The hypoxic extent was generally lowest in the mid-1990s, with the late 1980s (1987, 1988) and the 2000s (2003, 2005) experiencing the largest hypoxic zones. A simple exponential relationship based on the squared average measured bottom DO explains 97% of the estimated variability in the hypoxic extent. The change in the observed maximum extent between August and September is found to be sensitive to the corresponding variability in the hypolimnion thickness.}, number={2}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Zhou, Yuntao and Obenour, Daniel R. and Scavia, Donald and Johengen, Thomas H. and Michalak, Anna M.}, year={2013}, month={Jan}, pages={899–905} }
 @article{obenour_michalak_zhou_scavia_2012, title={Quantifying the Impacts of Stratification and Nutrient Loading on Hypoxia in the Northern Gulf of Mexico}, volume={46}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es204481a}, DOI={10.1021/es204481a}, abstractNote={Stratification and nutrient loading are two primary factors leading to hypoxia in coastal systems. However, where these factors are temporally correlated, it can be difficult to isolate and quantify their individual impacts. This study provides a novel solution to this problem by determining the effect of stratification based on its spatial relationship with bottom-water dissolved oxygen (BWDO) concentration using a geostatistical regression. Ten years (1998–2007) of midsummer Gulf of Mexico BWDO measurements are modeled using stratification metrics along with trends based on spatial coordinates and bathymetry, which together explain 27–61% of the spatial variability in BWDO for individual years. Because stratification effects explain only a portion of the year-to-year variability in mean BWDO; the remaining variability is explained by other factors, with May nitrate plus nitrite river concentration the most important. Overall, 82% of the year-to-year variability in mean BWDO is explained. The results suggest that while both stratification and nutrients play important roles in determining the annual extent of midsummer hypoxia, reducing nutrient inputs alone will substantially reduce the average extent.}, number={10}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Obenour, Daniel R. and Michalak, Anna M. and Zhou, Yuntao and Scavia, Donald}, year={2012}, month={May}, pages={5489–5496} }
 @article{whiteaker_robayo_maidment_obenour_2006, title={From a NEXRAD rainfall map to a flood inundation map}, volume={11}, number={1}, journal={Journal of Hydrologic Engineering}, publisher={American Society of Civil Engineers}, author={Whiteaker, Timothy L and Robayo, Oscar and Maidment, David R and Obenour, Dan}, year={2006}, pages={37–45} }
 @inproceedings{obenour_maidment_evans_yates_2004, title={An Interface Data Model for HEC-HMS}, booktitle={Proc. AWRA 2004 Annual Conference}, author={Obenour, Daniel and Maidment, David and Evans, Thomas and Yates, Daniel}, year={2004} }