Jane Frankenberger

A GIS-based variable source area hydrology model

Effective control of nonpoint source pollution from contaminants transported by runoff requires information about the source areas of surface runoff. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runoff in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hydrology for watersheds with shallow sloping soils. The model combines elevation, soil, and land use data within the geographic information system GRASS, and predicts the spatial distribution of soil moisture, evapotranspiration, saturation-excess overland flow (i.e., surface runoff), and interflow throughout a watershed. The model was applied to a 170 hectare watershed in the Catskills region of New York State and observed stream flow hydrographs and soil moisture measurements were compared to model predictions. Stream flow prediction during non-winter periods generally agreed with measured flow resulting in an average r2 of 0·73, a standard error of 0·01 m3/s, and an average Nash-Sutcliffe efficiency R2 of 0·62. Soil moisture predictions showed trends similar to observations with errors on the order of the standard error of measurements. The model results were most accurate for non-winter conditions. The model is currently used for making management decisions for reducing non-point source pollution from manure spread fields in the Catskill watersheds which supply New York Citys drinking water. Copyright

MODELING LONG-TERM WATER QUALITY IMPACT OF STRUCTURAL BMPS

Structural best management practices (BMPs) that reduce soil erosion and nutrient losses have been recommended and installed on agricultural land for years. A structural BMP is expected to be fully functional only for a limited period after installation, after which degradation of the BMP is likely to lead to a reduction in the water quality improvement provided by the BMP. Assessing the impact of BMPs on water quality is of widespread interest, but no standard methods exist to determine the water quality impact of structural BMPs, particularly as the impact changes through time. The objective of this study was to determine the long-term (~20 year) impact of structural BMPs in two subwatersheds of Black Creek on sediment and phosphorus loads using the Soil and Water Assessment Tool (SWAT) model. The BMPs were represented by modifying SWAT parameters to reflect the impact the practice has on the processes simulated within SWAT, both when practices are fully functional and as their condition deteriorates. The current condition of the BMPs was determined using field evaluation results from a previously developed BMP condition evaluation tool. Based on simulations in the two subwatersheds, BMPs in good condition reduced the average annual sediment yield by 16% to 32% and the average annual phosphorus yield by 10% to 24%. BMPs in their current condition reduced sediment yield by only 7% to 10% and phosphorus yield by 7% to 17%.

Phosphorus transport in agricultural subsurface drainage: a review.

Phosphorus (P) loss from agricultural fields and watersheds has been an important water quality issue for decades because of the critical role P plays in eutrophication. Historically, most research has focused on P losses by surface runoff and erosion because subsurface P losses were often deemed to be negligible. Perceptions of subsurface P transport, however, have evolved, and considerable work has been conducted to better understand the magnitude and importance of subsurface P transport and to identify practices and treatments that decrease subsurface P loads to surface waters. The objectives of this paper were (i) to critically review research on P transport in subsurface drainage, (ii) to determine factors that control P losses, and (iii) to identify gaps in the current scientific understanding of the role of subsurface drainage in P transport. Factors that affect subsurface P transport are discussed within the framework of intensively drained agricultural settings. These factors include soil characteristics (e.g., preferential flow, P sorption capacity, and redox conditions), drainage design (e.g., tile spacing, tile depth, and the installation of surface inlets), prevailing conditions and management (e.g., soil-test P levels, tillage, cropping system, and the source, rate, placement, and timing of P application), and hydrologic and climatic variables (e.g., baseflow, event flow, and seasonal differences). Structural, treatment, and management approaches to mitigate subsurface P transport-such as practices that disconnect flow pathways between surface soils and tile drains, drainage water management, in-stream or end-of-tile treatments, and ditch design and management-are also discussed. The review concludes by identifying gaps in the current understanding of P transport in subsurface drains and suggesting areas where future research is needed.

UNCERTAINTY IN TMDL MODELS

Although the U.S. Congress established the Total Maximum Daily Load (TMDL) program in the original Clean Water Act of 1972, Section 303(d), it did not receive attention until the 1990s. Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the TMDL process on improving the quality of impaired water bodies: field monitoring and mathematical/computer modeling. Field monitoring may be the most appropriate method, but its use is limited due to high costs and extreme spatial and temporal ecosystem variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. Uncertainty is defined as the estimated amount by which an observed or calculated value may depart from the true value, and it has important policy, regulatory, and management implications. The source and magnitude of uncertainty and its impact on TMDL assessment has not been studied in depth. This article describes the collective experience of scientists and engineers in the assessment of uncertainty associated with TMDL models. It reviews sources of uncertainty (e.g., input variability, model algorithms, model calibration data, and scale), methods of uncertainty evaluation (e.g., first-order approximation, mean value first-order reliability method, Monte Carlo, Latin hypercube sampling with constrained Monte Carlo, and generalized likelihood uncertainty estimation), and strategies for communicating uncertainty in TMDL models to users. Four case studies are presented to highlight uncertainty quantification in TMDL models. Results indicate that uncertainty in TMDL models is a real issue and should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. First-order error (FOE) analysis and Monte Carlo simulation (MCS) or any modified versions of these two basic methods may be used to assess uncertainty. This collective study concludes that a more scientific method to account for uncertainty would be to develop uncertainty probability distribution functions and transfer such uncertainties to TMDL load allocation through the margin of safety component, which is selected arbitrarily at the present time. It is proposed that explicit quantification of uncertainty be made an integral part of the TMDL process. This will benefit private industry, the scientific community, regulatory agencies, and action agencies involved with TMDL development and implementation.

Impacts of drainage water management on subsurface drain flow, nitrate concentration, and nitrate loads in Indiana

Drainage water management is a conservation practice that has the potential to reduce drainage outflow and nitrate (NO3) loss from agricultural fields while maintaining or improving crop yields. The goal of this study was to quantify the impact of drainage water management on drain flow, NO3 concentration, and NO3 load from subsurface drainage on two farms in Indiana. Paired field studies were conducted following the paired watershed statistical approach modified to accommodate autocorrelation. Annual NO3 load reductions ranged from 15% to 31%, with an overall reduction of 18% to 23% over the 2-year period, resulting from reductions in both flow and NO3 concentration. Although the study revealed weaknesses in using the paired statistical approach for a dynamic practice like drainage water management, the results of this study support the use of drainage water management as a conservation practice and provide information for decision-makers about the level of benefits that can be anticipated.

Use of CERES-Maize to study effect of spatial precipitation variability on yield

Abstract The objective of this study was to determine the usefulness of on-farm precipitation measurement, through determining spatial and temporal precipitation variability and its effect on corn yield. CERES-Maize (DSSAT version 3.5) was used with three precipitation data sources, for an Indiana farm—an on-farm National Weather Service (NWS) station, the nearest non-urban NWS station with electronic reporting (27 km from the farm), and a weighted mean of the three nearest such stations (27–35 km away)—to simulate 31 years of crop yield on 1-ha grid cells. Described as a percentage of the mean, spatial precipitation variability among the three data sources by corn phenological phase was 21–104%, while temporal (year-to-year) variability was 20–49%. The difference in simulated yield based on spatial precipitation variability was 15.8%, while year-to-year yield variability was 21.5%. The apparent yield difference based on spatial precipitation variability was of the same order as year-to-year variability, which suggests having on-farm precipitation data may be necessary for accurate yield modeling.

SENSITIVITY ANALYSES OF THE NITROGEN SIMULATION MODEL, DRAINMOD-N II

A two-step global sensitivity analysis was conducted for the nitrogen simulation model DRAINMOD-N II to assess the sensitivity of model predictions of NO3-N losses with drainage water to various model inputs. Factors screening using the LH-OAT (Latin hypercube sampling - one at a time) sensitivity analysis method was performed as a first step considering 48 model parameters; then a variance-based sensitivity analysis was conducted for 20 model parameters, which were the parameters ranked 1 to 14 by the LH-OAT method, five organic carbon (OC) decomposition parameters, and the empirical shape factor of the temperature response function for the nitrification process. DRAINMOD-N II simulated a continuous corn production on a subsurface drained sandy loam soil using a 40-year (1951-1990) eastern North Carolina climatological record. Results from the first 20-year period of the simulations were used to initialize the soil organic matter pools, and results from the last 20-year period of the simulations were considered for the sensitivity analyses. Both yearly and 20-year average model predictions of NO3-N losses through drainage flow were used in the analyses. Both sensitivity analysis methods indicated that DRAINMOD-N II is most sensitive to denitrification parameters, especially those controlling temperature effect on process rate. Results also indicated that the model is mildly sensitive to the parameters controlling OC decomposition and associated N mineralization/immobilization. The use of different sensitivity analysis methods with dissimilar theoretical foundations increases the confidence in key parameters identification. More efforts should be focused on quantifying key parameters for more accurate model predictions.

Spatial Optimization of Six Conservation Practices Using Swat in Tile‐Drained Agricultural Watersheds

Targeting of agricultural conservation practices to the most effective locations in a watershed can promote wise use of conservation funds to protect surface waters from agricultural nonpoint source pollution. A spatial optimization procedure using the Soil and Water Assessment Tool was used to target six widely used conservation practices, namely no-tillage, cereal rye cover crops (CC), filter strips (FS), grassed waterways (GW), created wetlands, and restored prairie habitats, in two west-central Indiana watersheds. These watersheds were small, fairly flat, extensively agricultural, and heavily subsurface tile-drained. The targeting approach was also used to evaluate the models representation of conservation practices in cost and water quality improvement, defined as export of total nitrogen, total phosphorus, and sediment from cropped fields. FS, GW, and habitats were the most effective at improving water quality, while CC and wetlands made the greatest water quality improvement in lands with multiple existing conservation practices. Spatial optimization resulted in similar cost-environmental benefit tradeoff curves for each watershed, with the greatest possible water quality improvement being a reduction in total pollutant loads by approximately 60%, with nitrogen reduced by 20-30%, phosphorus by 70%, and sediment by 80-90%.

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.

Effect of conservation practices implemented by USDA programs at field and watershed scales

The objective of this study was to evaluate effects of conservation practices actually implemented in reducing sediment and nutrient loads at field and watershed scales. To contribute to the USDA Agency Priority Goal for Water Pilot Projects, we obtained information on conservation practices implemented in the St. Joseph River watershed. Considering expected water quality impacts and simulation ability of the Soil and Water Assessment Tool (SWAT) model, 5,583 of them were selected and incorporated into modeling at a hydrologic response unit (HRU) level by adjusting associated parameters. A calibrated SWAT model was used to estimate load reduction effectiveness of the selected practices. Model results indicated that many of the practices reduced pollutant loads between 10% and 50% at the field scale, with high variability among the practices. Most conservation practices reduced less than 1% of the loads when calculated for the entire watershed, but the load reduction was still large and thus their cumulative long-term effects were expected to be significant. Conservation crop rotation and no-till, which were the most widely applied conservation practices in the study watershed, provided the greatest sediment load reduction, while conservation crop rotation and cover crop reduced the greatest amount of nutrients. Conservation crop rotation, cover crop, no-till, and mulch-till sometimes increased loads of soluble nutrients, resulting in the overall decrease in their effectiveness. Comparison of the spatial distributions of the selected conservation practices and simulated pollutant loads showed existing conservation practices were not targeted for areas producing relatively greater loads. The findings of this study demonstrated different effectiveness of conservation practices at the different spatial scales, suggesting application area, field-scale effectiveness, and placement of the practices are equally critical in achieving watershed-scale water quality improvement.