Daniel R. Obenour

Spatial and Temporal Trends in Lake Erie Hypoxia, 1987−2007

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.

Retrospective Analysis of Midsummer Hypoxic Area and Volume in the Northern Gulf of Mexico, 1985−2011

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.

Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts

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.

Quantifying the impacts of stratification and nutrient loading on hypoxia in the Northern Gulf of Mexico

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.

A Scenario and Forecast Model for Gulf of Mexico Hypoxic Area and Volume

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.

Independent Data Validation of an in Vitro Method for the Prediction of the Relative Bioavailability of Arsenic in Contaminated Soils

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.

Assessing biophysical controls on Gulf of Mexico hypoxia through probabilistic modeling

A mechanistic model was developed to predict midsummer bottom-water dissolved oxygen (BWDO) concentration and hypoxic area on the Louisiana shelf of the northern Gulf of Mexico, USA (1985-2011). Because of its parsimonious formulation, the model possesses many of the benefits of simpler, more empirical models, in that it is computationally efficient and can rigorously account for uncertainty through Bayesian inference. At the same time, the model incorporates important biophysical processes such that its parameterization can be informed by field-measured biological and physical rates. The model is used to explore how freshwater flow, nutrient load, benthic oxygen demand, and wind velocity affect hypoxia on the western and eastern sections of the shelf, delineated by the Atchafalaya River outfall. The model explains over 70% of the variability in BWDO on both shelf sections, and outperforms linear regression models developed from the same input variables. Model results suggest that physical factors (i.e., wind and flow) control a larger portion of the year-to-year variability in hypoxia than previously thought, especially on the western shelf, though seasonal nutrient loads remain an important driver of hypoxia, as well. Unlike several previous Gulf hypoxia modeling studies, results do not indicate a temporal shift in the systems propensity for hypoxia formation (i.e., no regime change). Results do indicate that benthic oxygen demand is a substantial BWDO sink, and a better understanding of the long-term dynamics of this sink is required to better predict how the size of the hypoxic zone will respond to proposed reductions in nutrient loading.

Ensemble modeling informs hypoxia management in the northern Gulf of Mexico

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.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

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.

Relationship Between Total and Bioaccessible Lead on Children’s Blood Lead Levels in Urban Residential Philadelphia Soils

Relationships between total soil or bioaccessible lead (Pb), measured using an in vitro bioaccessibility assay, and childrens 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 childrens BLLs. Total soil Pb concentration ranged from 58 to 2821 mg/kg; the bioaccessible Pb concentration ranged from 47 to 2567 mg/kg. Childrens 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 childrens 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. Childrens age did not contribute significantly to the prediction of BLLs.