Agricultural Non-Point Source Pollution Model Research Articles

Impacts of input parameter spatial aggregation on an agricultural nonpoint source pollution model

The accuracy of agricultural nonpoint source pollution models depends in part on how well model input parameters describe the relevant characteristics of the watershed. The spatial extent of input parameter aggregation has previously been shown to have a substantial impact on model output. This study investigates this problem using the Soil and Water Assessment Tool (SWAT), a distributed-parameter agricultural nonpoint source pollution model. The primary question addressed here is: how does the size or number of subwatersheds used to partition the watershed affect model output, and what are the processes responsible for model behavior? SWAT was run on the Pheasant Branch watershed in Dane County, WI, using eight watershed delineations, each with a different number of subwatersheds. Model runs were conducted for the period 1990–1996. Streamflow and outlet sediment predictions were not seriously affected by changes in subwatershed size. The lack of change in outlet sediment is due to the transport-limited nature of the Pheasant Branch watershed and the stable transport capacity of the lower part of the channel network. This research identifies the importance of channel parameters in determining the behavior of SWAT's outlet sediment predictions. Sediment generation estimates do change substantially, dropping by 44% between the coarsest and the finest watershed delineations. This change is primarily due to the sensitivity of the runoff term in the Modified Universal Soil Loss Equation to the area of hydrologic response units (HRUs). This sensitivity likely occurs because SWAT was implemented in this study with a very detailed set of HRUs. In order to provide some insight on the scaling behavior of the model two indexes were derived using the mathematics of the model. The indexes predicted SWAT scaling behavior from the data inputs without a need for running the model. Such indexes could be useful for model users by providing a direct way to evaluate alternative models directly within a geographic information systems framework.

Calibration and validation of a non-point source pollution model

Surface runoff and sediment transport influence soil quality and the quality of receiving waters. Simulation models are useful tools to describe drainage behavior and sediment yield to develop management strategies for agricultural waters. The objective of this study was to investigate the performance of the Agricultural Non-Point Source Pollution Model (AGNPS) and modified versions comparing measured and predicted surface runoff and sediment yield at the drainage outlet. The study was carried out using 52 rainfall-runoff events from two small watersheds in Bavaria, 22 for calibration and 30 for validation. Evaluation of model outputs was based on graphical displays contrasting measured and predicted values for each rainfall-runoff event, and standard statistics such as coefficient of efficiency. A comparison between three different surface runoff methods Q 1 (uncalibrated curve number (CN) method), Q 2 (calibrated CN method), and Q 3 (Lutz method) showed that the uncalibrated CN method underestimated measured surface runoff considerable, while the Lutz method outperformed the calibrated CN method and was better than the uncalibrated CN method. The modifications made to sediment yield calculations encompassed: (i) replacement of the Universal Soil Loss Equation LS factor algorithm by one based on stream power theory (variant S 2), and (ii) linkage of channel erosion by individual categories of particle-size to runoff velocity (variant S 3). The sediment yield predictions by S 2 and S 3 outperformed the predictions of S 1 (USLE). Calculations based on S 3 gave the best agreement when compared to measured sediment yield values at the drainage outlet.

Using the modified agricultural non-point source pollution model in German watersheds

The modified agricultural non-point source pollution model (AGNPSm) was used in this study to predict runoff volume, peak flow rate, and sediment yield in three different watersheds in Germany. It is a distributed parameter soil erosion model which uses simple approaches to hydrological and sediment calculations. Simulations were carried out in the Glonn G1 (1.2 km 2), and Glonn G2 (1.6 km 2), and the Salzboede (81.7 km 2) watersheds in Germany. Runoff volume was predicted reliably in all three watersheds. Sediment yield predictions were excellent in the Glonn G1 and acceptable in the Glonn G2 watersheds. There were some uncertainties in the sediment yield calculations for the Salzboede watershed. This study shows that a less complex soil erosion model such as AGNPSm is able to produce reliable assessments of non-point source pollution for planning purposes.

AN AGNPS-BASED RUNOFF AND SEDIMENT YIELD MODEL FOR TWO SMALLWATERSHEDS IN GERMANY

The event-based Agricultural Non-Point Source (AGNPS) pollution model is used extensively to simulatesurface runoff, sediment yield and nutrient transport in unmonitored watersheds. Investigation, that compare AGNPSpredictions to measured data are rare. The objective of the present study was to compare surface runoff and sedimentyield predictions from AGNPS water quality simulation model and modified versions to measured data. Shortcomings ofthe AGNPS model were examined. The study was carried out using 52 rainfall-runoff events, 22 for calibration and 30 forvalidation, from two small watersheds (G1 and G2) in Bavaria, Germany. Evaluation of model outputs was based onstatistical comparisons between measured and predicted values for each rainfall-runoff event. We compared threedifferent surface runoff prediction methods: uncalibrated curve number (Q1), calibrated curve number (Q2), and Lutz(Q3). The modifications made to sediment yield calculations encompassed: (i) replacement of the Universal Soil LossEquation LS factor algorithm (S1) by one based on stream power theory (S2), and (ii) linkage of channel erosion byindividual categories of particle size to runoff velocity (S3). Measured median for surface runoff was under-predicted by55.5% using Q1, overpredicted by 36.8% using Q2 and over-predicted by 13.1% using Q3 in G1. The largest coefficientof efficiency (E) was calculated for Q3 with 0.96 followed by 0.93 for Q2 and 0.25 for Q1 in G1. In G2, measured medianfor surface runoff was underpredicted by 80.0% using Q1, overpredicted by 45.0% using Q2, and overpredicted by 35.0%using Q3 in G2. Best performance in terms of E was calculated by Q3 (0.83) followed by 0.76 for Q2 and 0.24 for Q1 inG2. Median sediment yield measurement was underpredicted by 57.2% using S1, underpredicted by 47.6% using S2 andunderpredicted by 4.8% using S3 in G1. The largest E was calculated with 0.90 for S3 followed by 0.57 for S2 and 0.26for S1 in G1. Measured median for sediment yield was underpredicted by 53.9% using S1, underpredicted by 38.5% usingS2 and overpredicted by 3.3% using S3 in G2. E was largest with 0.72 (S3) followed by 0.60 (S2) and 0.57 (S1) in G2.Results of this study illustrated that a calibration of CN and Lutz method for surface runoff calculations and the use ofvariant S3 for sediment yield calculations with AGNPS model showed the highest merit to match measurements withpredictions at the drainage outlet.

Simulation of Dairy Best Management Practices Using the AGNPS Model

ABSTRACT Agricultural nonpoint sources of nutrients and sediment threaten the quality of the Cane Creek Reservoir, a Piedmont North Carolina water supply lake, in the absence of detailed monitoring, the Agricultural NonPoint Source Pollution Model (AGNPS) was used to predict water runoff and the delivery of dissolved and sediment-attached nitrogen and phosphorus to stream tributaries to the lake. The findings of the distributed parameter modeling study included four dairy watersheds (53 to 195 ha) and were part of a management plan developed to protect the impoundment from further degradation. Annual total nitrogen and total phosphorus export predictions for entire farms ranged from 1.0 to 3.7 and 0.7 to 1.8 kg · ha−1 for current conditions. Simulated exports of total nitrogen and total phosphorus were between 0.9 to 3.1 and between 0.4 to 1.6 kg · ha−1 due to best management practices (BMPs) installed. In addition, simulation studies of smaller areas such as individual pastures, cropland fields, and feedlots showed substantial nutrient export reductions due to refinements in dairy manure management, depending upon the site. For one feedlot needing structural improvements, simulated BMPs reduced annual total nitrogen and total phosphorus loads from 74 and 21 kg · ha−1 to 0.85 and 0.4 kg · ha−1. Also included are recommendations for additional BMPs and the cost for implementation.

DETERMINING THE DECISION-MAKING RISK FROM AGNPS SIMULATIONS

This research presents a methodology and an application for predicting the risk from decisions made with probabilistic Agricultural Nonpoint Source Pollution Model (AGNPS) outputs. Assuming a worst-case scenario for data availability, uniform distributions for all appropriate AGNPS inputs can be assumed. Output distributions can be produced using a Monte Carlo simulation method at increments of input variation, ranging from 0 to 50% variation at 5% increments. Decision risk calculations are based on the non-parametric Bootstrap resampling technique. This methodology was applied to the Hazelton Drain subwatershed (400 ha) of the Sycamore Creek watershed (27 410 ha) in southcentral Michigan. Eight AGNPS input file scenarios were created with the Hydrologic Unit/Water Quality Tool. For each AGNPS scenario, the Monte Carlo method produced 1000 AGNPS simulations for each input variation level. This created 1000 AGNPS output files, from which were extracted the values for sediment yield, sediment nitrogen, sediment phosphorus, soluble nitrogen, soluble phosphorus, soluble COD, and runoff volume at the subwatershed outlet. Output distributions were created for each AGNPS scenario, at each input variation and for each output variable. Analysis of the output distributions and decision risk values revealed several findings. Decisions made using water-based outputs evidence much less risk than those decisions based on sediment-based outputs, because water-based outputs are calculated using fewer equations than sediment-based outputs. Decision risk will usually increase when the input variation increases and when the two output distributions being compared are closer or have more overlap. A precise value for input variation is not needed for most decisions based on these AGNPS outputs.

USING GEOSTATISTICS TO SELECT GRID-CELL LAYOUTS FOR THE AGNPS MODEL

Currently, little guidance is available for the design of accurate grid-cell layouts for spatially distributedmodels like the Agricultural Nonpoint Source Pollution (AGNPS) model. Inaccuracies in the grid-cell input could negatethe advantages provided by the spatially distributed approach to watershed (field) representation. The objective of thisstudy was to develop procedures for designing grid-cell layouts using geostatistical methods. Geostatistical spatialanalysis tools were used to select a base cell size and locate cell sub-divisions for use in AGNPS. The overall spatialcorrelation structure of SCS Curve Number data was used to select the base cell size. The specific criterion used to selectthe base cell size was the average squared residual of kriged estimates for randomly selected locations within the studyarea. The cell size with the minimum average squared residual was selected as the base cell size. Next, cells thatwarranted sub-divisions were identified using the squared residual of kriged estimates for each cell in the base cell layout.The grid-cell layout resulting from the application of the geostatistical methods and the squared residual criteria had anaverage squared residual less than the grid-cell representation without sub-divisions. Furthermore, the cells that weresub-divided fell within the most heterogeneous areas of the watershed. The geostatistical methods provided quantitativeinformation about the error associated with various grid-cell designs. This information was then used to design accurategrid-cell layouts that preserved the spatial heterogeneity of the original data. Further development and application of thegeostatistical methods used to design grid-cell layouts will lead to greater improvements in the simulation accuracy ofspatially distributed models including AGNPS.

Multiobjective Zone TP Reduction Analyses for an Off-Stream Reservoir

Multiobjective linear programming (MOLP) models are developed to evaluate total phosphorus (TP) load reduction strategies for an off-stream reservoir. Cost, water quality, and equity are the three major objectives considered. Watershed-based, subwatershed-based, and equal-transport-path-length-based strategies are the three zone control strategies analyzed and compared. The agricultural nonpoint-source pollution (AGNSP) model is used to simulate the non–point-source pollution loads on the reservoir. A two step modeling procedure is applied to verify sediment mass balance and water quality of storm runoff. MOLP models are established based on the simulation results for the three control strategies and are solved using the ADBASE package to generate the noninferior solution sets of the models. The subwatershed-based control strategy is observed to be the most cost-effective one based on results obtained for TP concentration between 13 and 20 ppb. With respect to equity, however, the watershed-based control strategy is superior to the others. The proposed multiobjective analyses are intended to improve the comparison and evaluation of nonpoint source pollution control strategies.

THE SENSITIVITY OF TWO DISTRIBUTED NON-POINT SOURCE POLLUTION MODELS TO THE SPATIAL ARRANGEMENT OF THE LANDSCAPE

Distributed hydrological models are becoming increasingly complex with respect to spatial phenomena, and with the widespread availability of spatial data from GIS, this trend is likely to increase. In all such models the spatial arrangement of phenomena, such as soil properties and land-use categories is fundamental, and so the arrangement should have an influence on the model output. Testing for this influence we term spatial sensitivity analysis. Here, we report on the spatial sensitivity of two widely used models, AgNPS (agricultural non-point source pollution model) and ANSWERS (areal nonpoint source watershed environment response simulation). The input spatial data were subjected to spatially random mixing to varying degrees, such that the organized landscape became disorganized. The chemical discharge from AgNPS, and the sediment and water discharge from ANSWERS, are examined. In both cases most outputs exhibited little or no sensitivity to the spatial distribution of most input data. Only infiltration-related inputs produced large variations, but these changes were not in the sense that might have been predicted. Although the analytical methods used require further refinement, there must now be some doubt as to the validity of the models, and whether they repay their computational complexity. Furthermore, it is felt that spatial sensitivity analysis should become a fundamental part of the verification of all such models. © 1997 by John Wiley & Sons, Ltd.

AGNPS: A Software Program for Assessing Nonpoint Source Pesticide Pollution

Nonpoint-source pesticide pollution from horticultural and other agricultural activities is a primary factor determining the quality of surface water. A menu-driven, interactive pesticide transport submodel has been developed for the agricultural nonpoint-source (AGNPS) pollution model. AGNPS simulates the surface transport of pesticides, sediment, and water from the headwaters to the outlet in a stepwise manner so that an assessment can be made at any point within a watershed boundary. The model can be used by farmers, agricultural extension agents, agrichemical industry workers, or researchers to develop agricultural activities that minimize the surface transport of pesticides. This user-friendly pesticide transport model is available at no cost to users through the Internet.

Managing Agricultural Pollution Using a Linked Geographical Information System and Non-Point Source Pollution Model

This study documents the development of a link between a geographical information system (GIS) and a non-point source pollution model. The GIS ARC/INFO was linked to the agricultural non-point source pollution model and ORACLE data sources. Application of the system is demonstrated using the Bedford-Ouse catchment as a suitable case study. Water quality impacts are predicted from source data describing topography, soils, land use and river network. The model results were in agreement with observed nitrate concentrations at the catchment outlet, and more appropriate data sources are considered to be the main priority for improving model predictive ability. Management scenarios were established to assess the impact of changing agricultural management practices on predicted water quality. The approach has significant potential for the management of agricultural pollution in the UK.

Integrating nonpoint source pollution modeling with a geographic information system

The major impediments in using computer models for simulating the consequences of nonpoint source pollution are the intensive data requirements and time involved in compiling the model input file. These problems can be overcome by integrating nonpoint source pollution models with geographic information systems (CIS). This paper presents a computerised system developed by integrating CIS and a dynamic, event-based, nonpoint source pollution model. The CIS software used for the integration was pc-ARC/INFO (Environmental Systems Research Institute, Redlands, CA). Using the pc-ARC/INFO Simple Macro language (SML), Pascal, and batch programming, a menu-driven system was developed thai integrates the Agricultural Nonpoint Source pollution model (AGNPS) willi pc-ARC/INFO. Running on a DOS platform with 640 KB of memory, the integration prototype converts a set of pc-ARC/INFO coverages into an AGNPS input data file. Tlie output from the ACNPS model is converted back into pc-ARC/INFO coverage and can he displayed as a map using tlie user-selected map format.

Nonpoint‐Pollution Model Sensitivity to Grid‐Cell Size

Nonpoint‐pollution models estimate loadings of chemicals, sediment, and nutrients that degrade water quality. Before controls can be implemented, location and severity of pollution must be identified in the watershed basin. Geographic information systems (GISs) are computer‐automated, data management systems simplifying the input, organization, analysis, and mapping of spatial information. Because nonpoint‐pollution models simulate distributed watershed basin processes, a heterogeneous and complex land surface must be divided into computational elements such as grid cells. Model parameters can be derived from each grid cell directly from maps using GIS. Cell size selection, if arbitrarily determined though, yields ambiguous if not erroneous results. This paper investigates the effects of cell size selection through a sensitivity analysis of input parameters for the nonpoint‐pollution model, Agricultural Nonpoint Source Pollution Model (AGNPS), using a GIS for a small research watershed. Model grid‐cell si...

INCORPORATING PESTICIDE TRANSPORT INTO THE AGNPS (AGRICULTURAL NONPOINT SOURCE) POLLUTION MODEL

A pesticide transport submodel has been incorporated into a distributed parameter simulation model. AGNPS (AGricultural NonPoint Source Pollution Model) can evaluate the effect of agricultural pollution sources on surface runoff. Six pesticide classes are used: herbicides, insecticides, fungicides, nematicides, plant growth regulators, and desiccants/defoliants. User inputs for the model include the time of pesticide application (preplant, preemergence, or postemergence), application rate, application efficiency, percent canopy cover, soil and foliar pesticide residues, soil and foliar pesticide decay, water solubility, foliar washoff threshold and fraction, incorporation depth and efficiency, and sorption coefficient. Areas of pesticide losses and accumulations are indicated in tabular and graphical outputs. Alternative management practices can be simulated, and therefore assist in the optimization of practices to reduce pesticide runoff.

Economic feasibility of agricultural management practices for reducing sedimentation in a water supply lake

The economic feasibility of using alternative agricultural management practices to reduce sedimentation and enhance water storage capacity is evaluated for a water supply lake located in northern Missouri (USA). Sedimentation of lakes is a major concern in this region because many of them serve as a municipal water supply. Net sediment delivery to the lake is estimated for 18 agricultural management practices and eight storm events using the Agricultural Nonpoint Source Pollution (AGNPS) model. Event-based sediment delivery is aggregated to an annual basis using daily rainfall and a sediment yield curve estimated with the AGNPS model. Sediment reduction benefits are estimated by the savings in groundwater pumping costs. Many of the management practices that reduce sediment delivery to the lake are not likely to be adopted by farmers because they reduce net returns to land and management relative to practices employed in 1987 and 1988. Decreases in net returns are largest when clover replaces maize in the rotation. Net returns are somewhat lower with no tillage than with minimum tillage and highest with a maize/soybean rotation, maize/soybean/wheat rotation or continuous soybeans. Sediment reduction benefits are relatively small and insufficient to offset the decreases in net returns to farmers. Five practices increase net returns and have positive social value, namely those involving the maize/wheat, maize/soybean/wheat or continuous soybean rotation and practices that maximize total net returns in the watershed.

A comparison of erosion and water pollution control strategies for an agricultural watershed

The effectiveness and efficiency of two erosion control strategies and one water pollution control (riparian) strategy are compared for Idaho's Tom Beall watershed. Erosion control strategies maximize annualized net returns per hectare on each field and restrict field erosion rates to no more than 11.2 or 16.8 tons per hectare. The riparian strategy uses good vegetative cover on all fields adjacent to the creek and in noncropland areas and the resource management system that maximizes annualized net returns per hectare on remaining fields. The Agricultural Nonpoint Source Pollution model is used to simulate the levels and concentrations of sediment, nitrogen, phosphorus, and chemical oxygen demand at the outlet of the watershed. Erosion control strategies generate less total erosion and water pollution but are less efficient than the riparian strategy. The riparian strategy is less equitable for farmers than the erosion control strategies.