An Optimization Method for Estimating Constituent Mean Concentrations in Base Flow‐Dominated Flow

Pollutant coefficients have been widely used to assess runoff nonpoint source pollution from individual land uses (e.g., agricultural, residential) of a watershed. Pollutant coefficients, known as event mean concentrations (EMCs), were developed by the U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) to serve as a national measure for characterizing pollutant loading in a receiving water body. The term “baseflow pollutant coefficient (BPC)” is used in this study as a surrogate for EMC to describe mean concentration of pollutants in base flow‐dominated flow. A method for characterizing base flow quantity and quality for different land uses was explored using inverse modeling with two optimization techniques (a least square method and a genetic algorithm [GA] optimization), land use information, and streamflow quantity and quality data. The inverse model was formulated as a constrained minimization problem and demonstrated with data for 15 watersheds in Indiana. Results showed that estimated pollutant coefficients are comparable to the published literature. This indicates that the proposed method has the potential to effectively estimate constituent mean concentrations for pollutant load determination in gauged and ungauged watersheds, albeit more analysis with larger and more robust datasets is desirable to further refine and validate the accuracy of the approach.     

Adaptive Targeting: Engaging Farmers to Improve Targeting and Adoption of Agricultural Conservation Practices

Targeting of agricultural conservation practices to cost‐effective locations has long been of interest to watershed managers, yet its implementation cannot succeed without meaningful engagement of agricultural producers who are decision makers on the lands they farm. In this study, we engaged 14 west‐central Indiana producers and landowners in an adaptive targeting experiment. Interviews carried out prior to targeting provided rich spatial information on existing conservation practices as well as producers' preferences for future conservation projects. We targeted six of the most accepted conservation practices using the Soil and Water Assessment Tool and spatial optimization using a genetic algorithm approach. Fairly optimal conservation scenarios were possible with even the most limiting constraints of farmer‐accepted practices. We presented in follow‐up interviews a total of 176 conservation practice recommendations on 103 farm fields to 10 farmers whose lands were targeted for conservation. Primary findings indicated producers were interested in the project, were open to hearing recommendations about their lands, and expressed a high likelihood of adopting 35% of targeted recommendations. Farmers generally viewed the interview process and presentation of results quite favorably, and the interviews were found to build trust and make the targeting process more acceptable to them.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

An In-depth Examination of Farmers’ Perceptions of Targeting Conservation Practices

Watershed managers have largely embraced targeting of agricultural conservation as a way to manage strategically non-point source pollution from agricultural lands. However, while targeting of particular watersheds is not uncommon, targeting farms and fields within a specific watershed has lagged. In this work, we employed a qualitative approach, using farmer interviews in west-central Indiana to better understand their views on targeting. Interviews focused on adoption of conservation practices on farmers’ lands and identified their views on targeting, disproportionality, and monetary incentives. Results show consistent support for the targeting approach, despite dramatic differences in farmers’ views of land stewardship, in their views about disproportionality of water quality impacts, and in their trust in conservation programming. While the theoretical concept of targeting was palatable to all participants, many raised concerns about its practical implementation, pointing to the need for flexibility when applying targeting solutions and revealing misgivings about the government agencies that perform targeting.     

Assessment of Bioenergy Cropping Scenarios for the Boone River Watershed in North Central Iowa,United States

Several biofuel cropping scenarios were evaluated with an improved version of Soil and Water Assessment Tool (SWAT) as part of the CenUSA Bioenergy consortium for the Boone River Watershed (BRW), which drains about 2,370 km² in north central Iowa. The adoption of corn stover removal, switchgrass, and/or Miscanthus biofuel cropping systems was simulated to assess the impact of cellulosic biofuel production on pollutant losses. The stover removal results indicate removal of 20 or 50% of corn stover in the BRW would have negligible effects on streamflow and relatively minor or negligible effects on sediment and nutrient losses, even on higher sloped cropland. Complete cropland conversion into switchgrass or Miscanthus, resulted in reductions of streamflow, sediment, nitrate, and other pollutants ranging between 23‐99%. The predicted nitrate reductions due to Miscanthus adoption were over two times greater compared to switchgrass, with the largest impacts occurring for tile‐drained cropland. Targeting of switchgrass or Miscanthus on cropland ≥2% slope or ≥7% slope revealed a disproportionate amount of sediment and sediment‐bound nutrient reductions could be obtained by protecting these relatively small areas of higher sloped cropland. Overall, the results indicate that all biofuel cropping systems could be effectively implemented in the BRW, with the most robust approach being corn stover removal adopted on tile‐drained cropland in combination with a perennial biofuel crop on higher sloped landscapes. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Influence of Bioenergy Crop Production and Climate Change on Ecosystem Services

Land use change can significantly affect the provision of ecosystem services and the effects could be exacerbated by projected climate change. We quantify ecosystem services of bioenergy‐based land use change and estimate the potential changes of ecosystem services due to climate change projections. We considered 17 bioenergy‐based scenarios with Miscanthus, switchgrass, and corn stover as candidate bioenergy feedstock. Soil and Water Assessment Tool simulations of biomass/grain yield, hydrology, and water quality were used to quantify ecosystem services freshwater provision (FWPI), food (FPI) and fuel provision, erosion regulation (ERI), and flood regulation (FRI). Nine climate projections from Coupled Model Intercomparison Project phase‐3 were used to quantify the potential climate change variability. Overall, ecosystem services of heavily row cropped Wildcat Creek watershed were lower than St. Joseph River watershed which had more forested and perennial pasture lands. The provision of ecosystem services for both study watersheds were improved with bioenergy production scenarios. Miscanthus in marginal lands of Wildcat Creek (9% of total area) increased FWPI by 27% and ERI by 14% and decreased FPI by 12% from the baseline. For St. Joseph watershed, Miscanthus in marginal lands (18% of total area) improved FWPI by 87% and ERI by 23% while decreasing FPI by 46%. The relative impacts of land use change were considerably larger than climate change impacts in this paper. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

SENSITIVITY ANALYSIS,CALIBRATION, AND VALIDATIONS FOR A MULTISITE AND MULTIVARIABLE SWAT MODEL1

The ability of a watershed model to mimic specified watershed processes is assessed through the calibration and validation process. The Soil and Water Assessment Tool (SWAT) watershed model was implemented in the Beaver Reservoir Watershed of Northwest Arkansas. The objectives were to: (1) provide detailed information on calibrating and applying a multisite and multivariable SWAT model; (2) conduct sensitivity analysis; and (3) perform calibration and validation at three different sites for flow, sediment, total phosphorus (TP), and nitrate‐nitrogen (NO₃‐N) plus nitrite‐nitrogen (NO₂‐N). Relative sensitivity analysis was conducted to identify parameters that most influenced predicted flow, sediment, and nutrient model outputs. A multi objective function was defined that consisted of optimizing three statistics: percent relative error (RE), Nash‐Sutcliffe Coefficient (R_NS²), and coefficient of determination (R²). This function was used to successfully calibrate and validate a SWAT model of Beaver Reservoir Watershed at multi‐sites while considering multivariables. Calibration and validation of the model is a key factor in reducing uncertainty and increasing user confidence in its predictive abilities, which makes the application of the model effective. Information on calibration and validation of multisite, multivariable SWAT models has been provided to assist watershed modelers in developing their models to achieve watershed management goals.     

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.     

QUANTIFYING MODEL OUTPUT UNCERTAINTY DUE TO SPATIAL VARIABILITY OF RAINFALL1

Traditionally in the application of hydrologic/water quality (H/WQ) models, rainfall is assumed to be spatially homogeneous and is considered not to contribute to output uncertainty. The objective of this study was to assess the uncertainty induced in model outputs solely due to rainfall spatial variability. The study was conducted using the AGNPS model and the rainfall pattern captured by a network of 17 rain gauges. For each rainfall event, the model was run using the rainfall captured by each rain gauge, one at a time, under the assumption of rainfall spatial homogeneity. A large uncertainty in the modeled outputs resulted from the rainfall spatial variability. The uncertainty in the modeled outputs exceeded the input rainfall uncertainty. Results of this study indicate that spatial variability of rainfall should be captured and used in H/WQ models in order to accurately assess the release and transport of pollutants. A large uncertainty in the model outputs can be expected if this rainfall property is not taken into account.