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.

Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality.

Despite widespread implementation of watershed nitrogen reduction programs across the globe, nitrogen levels in many surface waters remain high. Watershed legacy nitrogen storage, i.e., the long-term retention of nitrogen in soils and groundwater, is one of several explanations for this lack of progress. However scientists and water managers are ill-equipped to estimate how legacy nitrogen moderates in-stream nitrogen responses to land conservation practices, largely because modeling tools and associated long-term monitoring approaches to answering these questions remain inadequate. We demonstrate the need for improved watershed models to simulate legacy nitrogen processes and offer modeling solutions to support long-term nitrogen-based sustainable land management across the globe.

Representing soil health practice effects on soil properties and nutrient loss in a watershed-scale hydrologic model.

Watershed-scale hydrologic models are commonly used to assess the water quality effects of agricultural conservation practices that improve soil health (e.g., cover crops and no-till). However, models rarely account for how these practices (i.e., soil health practices) affect soil physical and functional properties such as water holding capacity and soil aggregate stability, which may, in turn, affect water quality. We introduce a method to represent changes in soil physical and functional properties caused by soil health practices in the Soil and Water Assessment Tool (SWAT) model. We used the SWAT model's default representation of winter cover crops and no-till and modified soil descriptive parameters to depict soil health practice effects on soil properties. We assumed that the soil health practices would increase soil organic carbon (SOC), a principal indicator of soil health, by 0.01 g C g-1 of soil and then estimated changes in other soil properties (e.g., water holding capacity) using SOC-based predictive equations and preceding literature. Results indicated that our soil property modifications had statistically significant effects on simulated hydrology and nutrient loss, though outputs were more substantially affected by the model's default representation of cover crops and no-till. Results also indicated that soil health practices can reduce nitrogen and total phosphorus loss but may increase dissolved reactive phosphorus loss. Our representation of soil health practices provides a more complete estimate of practice efficacy but underscores a need for additional observational data to verify results and guide further model improvements.

Wetland restoration yields dynamic nitrate responses across the Upper Mississippi river basin.

Wetland restoration is a primary management option for removing surplus nitrogen draining from agricultural landscapes. However, wetland capacity to mitigate nitrogen losses at large river-basin scales remains uncertain. This is largely due to a limited number of studies that address the cumulative and dynamic effects of restored wetlands across the landscape on downstream nutrient conditions. We analyzed wetland restoration impacts on modeled nitrate dynamics across 279 subbasins comprising the ∼0.5 million km2 Upper Mississippi River Basin (UMRB), USA, which covers eight states and houses ∼30 million people. Restoring ∼8,000 km2 of wetlands will reduce mean annual nitrate loads to the UMRB outlet by 12%, a substantial improvement over existing conditions but markedly less than widely cited estimates. Our lower wetland efficacy estimates are partly attributed to improved representation of processes not considered by preceding empirical studies - namely the potential for nitrate to bypass wetlands (i.e., via subsurface tile drainage) and be stored or transformed within the river network itself. Our novel findings reveal that wetlands mitigate surplus nitrogen basin-wide, yet they may not be as universally effective in tiled landscapes and because of river network processing.

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.

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.

Simulating internal watershed processes using multiple SWAT models.

The need for effective water quality models to help guide management and policy, and extend monitoring information, is at the forefront of recent discussions related to watershed management. These models are often calibrated and validated at the basin outlet, which ensures that models are capable of evaluating basin scale hydrology and water quality. However, there is a need to understand where these models succeed or fail with respect to internal process representation, as these watershed-scale models are used to inform management practices and mitigation strategies upstream. We evaluated an ensemble of models-each calibrated to in-stream observations at the basin outlet-against discharge and nutrient observations at the farm field scale to determine the extent to which these models capture field-scale dynamics. While all models performed well at the watershed outlet, upstream performance varied. Models tended to over-predict discharge through surface runoff and subsurface drainage, while under-predicting phosphorus loading through subsurface drainage and nitrogen loading through surface runoff. Our study suggests that while models may be applied to predict impacts of management at the basin scale, care should be taken in applying the models to evaluate field-scale management and processes in the absence of data that can be incorporated at that scale, even with the use of multiple models.

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.

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.

Mapping landscape-level hydrological connectivity of headwater wetlands to downstream waters: A catchment modeling approach - Part 2.

In Part 1 of this two-part manuscript series, we presented an effective assessment method for mapping inundation of geographically isolated wetlands (GIWs) and quantifying their cumulative landscape-scale hydrological connectivity with downstream waters using time series remotely sensed data (Yeo et al., 2018). This study suggested strong hydrological coupling between GIWs and downstream waters at the seasonal timescale via groundwater. This follow-on paper investigates the hydrological connectivity of GIWs with downstream waters and cumulative watershed-scale hydrological impacts over multiple time scales. Modifications were made to the representation of wetland processes within the Soil and Water Assessment Tool (SWAT). A version of SWAT with improved wetland function, SWAT-WET, was applied to Greensboro Watershed, which is located in the Mid-Atlantic Region of USA, to simulate hydrological processes over 1985-2015 under two contrasting land use scenarios (i.e., presence and absence of GIWs). Comparative analysis of simulation outputs elucidated how GIWs could influence partitioning of precipitation between evapotranspiration (ET) and terrestrial water storage, and affect water transport mechanisms and routing processes that generate streamflow. Model results showed that GIWs influenced the watershed water budget and stream flow generation processes over the long-term (30 year), inter-annual, and monthly time scales. GIWs in the study watershed increased terrestrial water storage during the wet season, and buffered the dynamics of shallow groundwater during the dry season. The inter-annual modeling analysis illustrated that densely distributed GIWs can exert strong hydrological influence on downstream waters by regulating surface water runoff, while maintaining groundwater recharge and ET under changing (wetter) climate conditions. The study findings highlight the hydrological connectivity of GIWs with downstream waters and the cumulative hydrological influence of GIWs as hydrologic sources to downstream ecosystems through different runoff processes over multiple time scales.

Modeling Connectivity of Non-floodplain Wetlands: Insights, Approaches, and Recommendations.

Representing hydrologic connectivity of non-floodplain wetlands (NFWs) to downstream waters in process-based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process-based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended "best modeling practices" to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process-based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.

Estimating restorable wetland water storage at landscape scales.

Globally, hydrologic modifications such as ditching and subsurface drainage have significantly reduced wetland water storage capacity (i.e., volume of surface water a wetland can retain) and consequent wetland functions. While wetland area has been well documented across many landscapes and used to guide restoration efforts, few studies have directly quantified the associated wetland storage capacity. Here, we present a novel raster-based approach to quantify both contemporary and potential (i.e., restorable) storage capacities of individual depressional basins across landscapes. We demonstrate the utility of this method by applying it to the Delmarva Peninsula, a region punctuated by both depressional wetlands and drainage ditches. Across the entire peninsula, we estimated that restoration (i.e., plugging ditches) could increase storage capacity by 80%. Focusing on an individual watershed, we found that over 59% of restorable storage capacity occurs within 20 m of the drainage network, and that 93% occurs within 1 m elevation of the drainage network. Our demonstration highlights widespread ditching in this landscape, spatial patterns of both contemporary and potential storage capacities, and clear opportunities for hydrologic restoration. In Delmarva and more broadly, our novel approach can inform targeted landscape-scale conservation and restoration efforts to optimize hydrologically mediated wetland functions.

Depressional wetlands affect watershed hydrological, biogeochemical, and ecological functions.

Depressional wetlands of the extensive U.S. and Canadian Prairie Pothole Region afford numerous ecosystem processes that maintain healthy watershed functioning. However, these wetlands have been lost at a prodigious rate over past decades due to drainage for development, climate effects, and other causes. Options for management entities to protect the existing wetlands, and their functions, may focus on conserving wetlands based on spatial location vis-à-vis a floodplain or on size limitations (e.g., permitting smaller wetlands to be destroyed but not larger wetlands). Yet the effects of such management practices and the concomitant loss of depressional wetlands on watershed-scale hydrological, biogeochemical, and ecological functions are largely unknown. Using a hydrological model, we analyzed how different loss scenarios by wetland size and proximal location to the stream network affected watershed storage (i.e., inundation patterns and residence times), connectivity (i.e., streamflow contributing areas), and export (i.e., streamflow) in a large watershed in the Prairie Pothole Region of North Dakota, USA. Depressional wetlands store consequential amounts of precipitation and snowmelt. The loss of smaller depressional wetlands (<3.0 ha) substantially decreased landscape-scale inundation heterogeneity, total inundated area, and hydrological residence times. Larger wetlands act as hydrologic "gatekeepers," preventing surface runoff from reaching the stream network, and their modeled loss had a greater effect on streamflow due to changes in watershed connectivity and storage characteristics of larger wetlands. The wetland management scenario based on stream proximity (i.e., protecting wetlands 30 m and ~450 m from the stream) alone resulted in considerable landscape heterogeneity loss and decreased inundated area and residence times. With more snowmelt and precipitation available for runoff with wetland losses, contributing area increased across all loss scenarios. We additionally found that depressional wetlands attenuated peak flows; the probability of increased downstream flooding from wetland loss was also consistent across all loss scenarios. It is evident from this study that optimizing wetland management for one end goal (e.g., protection of large depressional wetlands for flood attenuation) over another (e.g., protecting of small depressional wetlands for biodiversity) may come at a cost for overall watershed hydrological, biogeochemical, and ecological resilience, functioning, and integrity.

Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters.

A hydrologic model, calibrated using only streamflow data, can produce acceptable streamflow simulation at the watershed outlet yet unrealistic representations of water balance across the landscape. Recent studies have demonstrated the potential of multi-objective calibration using remotely sensed evapotranspiration (ET) and gaged streamflow data to spatially improve the water balance. However, methodological clarity on how to "best" integrate ET data and model parameters in multi-objective model calibration to improve simulations is lacking. To address these limitations, we assessed how a spatially explicit, distributed calibration approach that uses (1) remotely sensed ET data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) frequently overlooked biophysical parameters can improve the overall predictability of two key components of the water balance: streamflow and ET at different locations throughout the watershed. We used the Soil and Water Assessment Tool (SWAT), previously modified to represent hydrologic transport and filling-spilling of landscape depressions, in a large watershed of the Prairie Pothole Region, United States. We employed a novel stepwise series of calibration experiments to isolate the effects (on streamflow and simulated ET) of integrating biophysical parameters and spatially explicit remotely sensed ET data into model calibration. Results suggest that the inclusion of biophysical parameters involving vegetation dynamics and energy utilization mechanisms tend to increase model accuracy. Furthermore, we found that using a lumped, versus a spatially explicit, approach for integrating ET into model calibration produces a sub-optimal model state with no potential improvement in model performance across large spatial scales. However, when we utilized the same MODIS ET datasets but calibrated each sub-basin in the spatially explicit approach, water yield prediction uncertainty decreased, including a distinct improvement in the temporal and spatial accuracy of simulated ET and streamflow. This further resulted in a more realistic simulation of vegetation growth when compared to MODIS Leaf-Area Index data. These findings afford critical insights into the efficient integration of remotely sensed "big data" into hydrologic modeling and associated watershed management decisions. Our approach can be generalized and potentially replicated using other hydrologic models and remotely sensed data resources - and in different geophysical settings of the globe.

A watershed-scale model for depressional wetland-rich landscapes.

Wetlands are often dominant features in low relief, depressional landscapes and provide an array of hydrologically driven ecosystem services. However, contemporary models do not adequately represent the role of spatially distributed wetlands in watershed-scale water storage and flows. Such tools are critical to better understand wetland hydrological, biogeochemical, and biological functions and predict management and policy outcomes at varying spatial scales. To develop a new approach for simulating depressional landscapes, we modified the Soil and Water Assessment Tool (SWAT) model to incorporate improved representations of depressional wetland structure and hydrological processes. Specifically, we refined the model to incorporate: (1) water storage capacity and surface flowpaths of individual wetlands and (2) local wetland surface and subsurface exchange. We utilized this model, termed SWAT-DSF (DSF for Depressional Storage and Flows), to simulate the ~289 km2 Greensboro watershed within the Delmarva Peninsula of the US Coastal Plain. Model calibration and verification used both daily streamflow observations and remotely sensed surface water extent data (ca. 2-week temporal resolution), allowing us to assess model performance with respect to both streamflow and watershed inundation patterns. Our findings demonstrate that SWAT-DSF can successfully replicate distributed wetland processes and resultant watershed-scale hydrology. SWAT-DSF provides improved temporal and spatial characterization of watershed-scale water storage and flows in depressional landscapes, providing a new tool to quantify wetland functions at broad spatial scales.

Integrating geographically isolated wetlands into land management decisions.

Wetlands across the globe provide extensive ecosystem services. However, many wetlands - especially those surrounded by uplands, often referred to as geographically isolated wetlands (GIWs) - remain poorly protected. Protection and restoration of wetlands frequently requires information on their hydrologic connectivity to other surface waters, and their cumulative watershed-scale effects. The integration of measurements and models can supply this information. However, the types of measurements and models that should be integrated are dependent on management questions and information compatibility. We summarize the importance of GIWs in watersheds and discuss what wetland connectivity means in both science and management contexts. We then describe the latest tools available to quantify GIW connectivity and explore crucial next steps to enhancing and integrating such tools. These advancements will ensure that appropriate tools are used in GIW decision making and maintaining the important ecosystem services that these wetlands support.