Integrated assessment modeling reveals near-channel management as cost-effective to improve water quality in agricultural watersheds.

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Simulation of fluvial sediment dynamics through strategic assessment of stream gaging data: A targeted watershed sediment loading analysis.

Near-channel sediment loading (NCSL) is localized and episodic, making it difficult to accurately quantify its cumulative contribution to watershed sediment loading, let alone predict the effects from changes in river discharge due to climate change or land management practices. We developed a methodological framework, using commonly available stream gaging data, for estimating watershed-scale NCSL, a feature generally absent in most watershed models. The method utilizes a network of paired gages that bracket the incised river corridors of 15 tributaries to the Minnesota River, in which near-channel sources are often the dominant contributors of sediment loading. For each set of paired gages, we calculate NCSL as the difference between the upstream and downstream sediment loading minus the field contribution between the gages. NCSL generally increases with river discharge when it exceeds the observed threshold benchmark in the tributaries of Minnesota River Basin; accordingly, we developed a predictive model for quantifying NCSL using river discharge as the independent variable. This approach provides a predictive basis for evaluating the impacts on near-channel sediment supply from increases in runoff and river discharge. Application of this approach includes evaluation of watershed-scale conservation trade-offs, where benefits of landscape management practices, such as wetlands and reservoirs are measured in terms of reduction in downstream near-channel sediment loading in the incised river corridors.

Implementing landscape connectivity with topographic filtering model: A simulation of suspended sediment delivery in an agricultural watershed.

The widespread availability of high-fidelity topography combined with advances in geospatial analysis offer the opportunity to reimagine approaches to the difficult problem of predicting sediment delivery from watersheds. Here we present a model that uses high-resolution topography to filter sediment sources to quantify sediment delivery to the watershed outlet. It is a reduced-complexity, top-down model that defines transfer functions-topographic filters-between spatially distributed sediment sources and spatially integrated sediment delivery. The goal of the model is to forecast changes in watershed suspended sediment delivery in response to spatially distributed changes in sediment source magnitude or delivery, whether a result of watershed drivers or intentional management actions. Such an application requires the context of a watershed model that accounts for all sediment sources, enforces sediment mass balance throughout the spatial domain, and accommodates sediment storage and delivery over time. The model is developed for a HUC-8 watershed with a flat upland dominated by corn-soybean agriculture and deeply incised valleys near the watershed outlet with large sediment contributions from near-channel sources. Topofilter computes delivery and storage of field-derived sediment according to its spatial and structural connectivity to the stream channel network; subsequently, delivery of both field- and near-channel-derived sediment along with floodplain storage are computed in the stream channel network to the watershed outlet. The model outputs provide a spatially rich representation of sediment delivery and storage on field and along the stream that is consistent with available independent information on sediment accumulations and fluxes. Rather than a single best-calibrated solution, Topofilter uses the Generalized Likelihood Uncertainty Estimate (GLUE) approach to develop many possible solutions with sediment delivery rates expressed as probability distributions across the watershed. The ensemble of simulation outputs provides a useful basis for estimating uncertainty in sediment delivery and the effectiveness of different landscape management allocation across a watershed.