Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models.

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Uncertainty in critical source area predictions from watershed-scale hydrologic models.

Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or 'targeted' for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%-46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.

The hydrologic model as a source of nutrient loading uncertainty in a future climate.

Hydrologic models are applied increasingly with climate projections to provide insights into future hydrologic conditions. However, both hydrologic models and climate models can produce a wide range of predictions based on model inputs, assumptions, and structure. To characterize a range of future predictions, it is common to use multiple climate models to drive hydrologic models, yet it is less common to also use a suite of hydrologic models. It is also common for hydrologic models to report riverine discharge and assume that nutrient loading will follow similar patterns, but this may not be the case. In this study, we characterized uncertainty from both climate models and hydrologic models in predicting riverine discharge and nutrient loading. Six climate models drawn from the Coupled Model Intercomparison Project Phase 5 ensemble were used to drive five independently developed and calibrated Soil and Water Assessment Tool models to assess hydrology and nutrient loadings for mid-century (2046-2065) in the Maumee River Watershed,the largest watershedsdraining to the Laurentian Great Lakes. Under those conditions, there was no clear agreement on the direction of change in future nutrient loadings or discharge. Analysis of variance demonstrated that variation among climate models was the dominant source of uncertainty in predicting future total discharge, tile discharge (i.e. subsurface drainage), evapotranspiration, and total nitrogen loading, while hydrologic models were the main source of uncertainty in predicted surface runoff and phosphorus loadings. This innovative study quantifies the importance of hydrologic model in the prediction of riverine nutrient loadings under a future climate.

Leveraging a Landscape-Level Monitoring and Assessment Program for Developing Resilient Shorelines throughout the Laurentian Great Lakes.

Traditionally, ecosystem monitoring, conservation, and restoration have been conducted in a piecemeal manner at the local scale without regional landscape context. However, scientifically driven conservation and restoration decisions benefit greatly when they are based on regionally determined benchmarks and goals. Unfortunately, required data sets rarely exist for regionally important ecosystems. Because of early recognition of the extreme ecological importance of Laurentian Great Lakes coastal wetlands, and the extensive degradation that had already occurred, significant investments in coastal wetland research, protection, and restoration have been made in recent decades and continue today. Continued and refined assessment of wetland condition and trends, and the evaluation of restoration practices are all essential to ensuring the success of these investments. To provide wetland managers and decision makers throughout the Laurentian Great Lakes basin with the optimal tools and data needed to make scientifically-based decisions, our regional team of Great Lakes wetland scientists developed standardized methods and indicators used for assessing wetland condition. From a landscape perspective, at the Laurentian Great Lakes ecosystem scale, we established a stratified random-site-selection process to monitor birds, anurans, fish, macroinvertebrates, vegetation, and physicochemical conditions of coastal wetlands in the US and Canada. Monitoring of approximately 200 wetlands per year began in 2011 as the Great Lakes Coastal Wetland Monitoring Program. In this paper, we describe the development, delivery, and expected results of this ongoing international, multi-disciplinary, multi-stakeholder, landscape-scale monitoring program as a case example of successful application of landscape conservation design.

Using models of farmer behavior to inform eutrophication policy in the Great Lakes.

To address the management of eutrophication in aquatic systems, the behavioral mechanisms that drive change at the individual level must be considered when designing policy interventions. This analysis identifies the beliefs that are critical to behavioral change, and explores the likelihood that farmers will adopt two management practices believed to be critical to reducing nutrient loading to recommended levels in Lake Erie. We find that there is potential for farmers to adopt key infield practices needed to reduce nutrient inputs. And further, that increased adoption of such practices is possible by increasing the perceived efficacy of the majority of farmers who are motivated to take action. Integrating these findings with physical models of nutrient movement indicates that adoption of these practices in combination with edge of field practices can attain phosphorus reduction targets for the lake. Future research should focus on measuring the effectiveness of education and outreach programs aimed at engaging farmers and promoting adoption of recommended practices. Such programs may only be effective if they are successfully building farmer confidence in their ability to implement the practices (i.e., perceived self efficacy) and increasing farmer's belief in the effectiveness of the practices at reducing nutrient loss and improving local water quality (i.e., perceived response efficacy).

Environmental impact of ongoing sources of metal contamination on remediated sediments.

A challenge to all remedial approaches for contaminated sediments is the continued influx of contaminants from uncontrolled sources following remediation. We investigated the effects of ongoing contamination in mesocosms employing sediments remediated by different types of active and passive caps and in-situ treatment. Our hypothesis was that the sequestering agents used in active caps and in situ treatment will bind elements (arsenic, chromium, cadmium, cobalt, copper, nickel, lead, selenium, and zinc) from ongoing sources thereby reducing their bioavailability and protecting underlying remediated sediments from recontamination. Most element concentrations in surface water remained significantly lower in mesocosms with apatite and mixed amendment caps than in mesocosms with passive caps (sand), uncapped sediment, and spike solution throughout the 2520h experiment. Element concentrations were significantly higher in Lumbriculus variegatus from untreated sediment than in Lumbriculus from most active caps. Pearson correlations between element concentrations in Lumbriculus and metal concentrations in the top 2.5cm of sediment or cap measured by diffusive gradient in thin films (DGT) sediment probes were generally strong (as high as 0.98) and significant (p<0.05) for almost all tested elements. Metal concentrations in both Lumbriculus and sediment/cap were lowest in apatite, mixed amendment, and activated carbon treatments. These findings show that some active caps can protect remediated sediments by reducing the bioavailable pool of metals/metalloids in ongoing sources of contamination.