River Basin Simulations Reveal Wide-Ranging Wetland-Mediated Nitrate Reductions.

River basin-scale wetland restoration and creation is a primary management option for mitigating nitrogen-based water quality challenges. However, the magnitude of nitrogen reduction that will result from adding wetlands across large river basins is uncertain, partly because the areal extent, location, and physical and functional characteristics of the wetlands are unknown. We simulated over 3600 wetland restoration scenarios across the ∼450,000 km2 Upper Mississippi River Basin (UMRB) depicting varied assumptions for wetland areal extent, physical and functional characteristics, and placement strategy. These simulations indicated that restoring wetlands will reduce local nitrate yields and nitrate loads at the UMRB outlet. However, the projected magnitude of nitrate reduction varied widely across disparate scenario assumptions─e.g., restoring 4500 km2 of wetlands (i.e., 1% of UMRB area) decreased mean annual nitrate loads at the UMRB outlet between 3 and 42%. Higher magnitude nitrate reductions correlated with best-case assumptions, particularly for characteristics controlling nitrate loading rates to the wetlands. These results show that simplified claims about basin-scale wetland-mediated water quality improvements discount the breadth of possible wetland impacts across disparate wetland physical and functional conditions and highlight a need for greater clarity regarding the likelihood of these conditions at river basin scales.

Assessing the Accuracy of Farmers' Nutrient Loss Risk Perceptions.

Use of nutrient management practices to reduce nutrient loss from agriculture and its associated water quality consequences, including hypoxia and eutrophication, is widely encouraged. However, little is known about which factors influence farmers' risk perceptions associated with nutrient loss, and thus possibly influence their decisions to adopt such practices. To determine which factors were associated with relative "accuracy" of nutrient loss-associated risk perceptions, specific farm field management information was used as inputs to a Soil and Water Assessment Tool model of the study watershed to produce water quality outputs for each modeled farm field. This information was paired with farmers' risk perceptions associated with nutrient loss on their farm to assess relative "accuracy" of each farmer's perceptions compared to the rest of the farmers in the study. We then investigated characteristics of the farm and farmer that are associated with comparative "overprediction" and "underprediction" of risk, and found that characteristics of the individual (conservation identity, prior conservation practice adoption, efficacy beliefs, and perceived seriousness of the consequences of nutrient loss) are more important in determining whether farmers are likely to "overpredict" or "underpredict" risk than is the objective (modeled) vulnerability of their land to nutrient loss.

Source contribution to phosphorus loads from the Maumee River watershed to Lake Erie.

Coastal eutrophication is a leading cause of degraded water quality around the world. Identifying the sources and their relative contributions to impaired downstream water quality is an important step in developing management plans to address water quality concerns. Recent mass-balance studies of Total Phosphorus (TP) loads of the Maumee River watershed highlight the considerable phosphorus contributions of non-point sources, including agricultural sources, degrading regional downstream water quality. This analysis builds upon these mass-balance studies by using the Soil and Water Assessment Tool to simulate the movement of phosphorus from manure, inorganic fertilizer, point sources, and soil sources, and respective loads of TP and Dissolved Reactive Phosphorus (DRP). This yields a more explicit estimation of source contribution from the watershed. Model simulations indicate that inorganic fertilizers contribute a greater proportion of TP (45% compared to 8%) and DRP (58% compared to 12%) discharged from the watershed than manure sources in the March-July period, the season driving harmful algal blooms. Although inorganic fertilizers contributed a greater mass of TP and DRP than manure sources, the two sources had similar average delivery fractions of TP (2.7% for inorganic fertilizers vs. 3.0% for manure sources) as well as DRP (0.7% for inorganic fertilizers vs. 1.2% for manure sources). Point sources contributed similar proportions of TP (5%) and DRP (12%) discharged in March-July as manure sources. Soil sources of phosphorus contributed over 40% of the March-July TP load and 20% of the March-July DRP load from the watershed to Lake Erie. Reductions of manures and inorganic fertilizers corresponded to a greater proportion of phosphorus delivered from soil sources of phosphorus, indicating that legacy phosphorus in soils may need to be a focus of management efforts to reach nutrient load reduction goals. In agricultural watersheds aground the world, including the Maumee River watershed, upstream nutrient management should not focus solely on an individual nutrient source; rather a comprehensive approach involving numerous sources should be undertaken.

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.

Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions.

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.

Climate Change and Nutrient Loading in the Western Lake Erie Basin: Warming Can Counteract a Wetter Future.

In the past 20 years, Lake Erie has experienced a resurgence of harmful algal blooms and hypoxia driven by increased nutrient loading from its agriculturally dominated watersheds. The increase in phosphorus loading, specifically the dissolved reactive portion, has been attributed to a combination of changing climate and agricultural management. While many management practices and strategies have been identified to reduce phosphorus loads, the impacts of future climate remain uncertain. This is particularly the case for the Great Lakes region because many global climate models do not accurately represent the land-lake interactions that govern regional climate. For this study, we used midcentury (2046-2065) climate projections from one global model and four regional dynamically downscaled models as drivers for the Soil and Water Assessment Tool configured for the Maumee River watershed, the source of almost 50% of Lake Erie's Western Basin phosphorus load. Our findings suggest that future warming may lead to less nutrient runoff due to increased evapotranspiration and decreased snowfall, despite projected moderate increases in intensity and overall amount of precipitation. Results highlight the benefits of considering multiple environmental drivers in determining the fate of nutrients in the environment and demonstrate a need to improve approaches for climate change assessment using watershed models.