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United States Department of Agriculture

Agricultural Research Service

Title: Risk Analysis for Atrazine Nps Pollution Using Swat in the St. Joseph River Watershed in Ne Indiana

Authors
item Vazques-Amabile, G - PURDUE UNIVERSITY
item Engel, B - PURDUE UNIVERSITY
item Flanagan, Dennis

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 1, 2006
Publication Date: July 10, 2006
Repository URL: http://hdl.handle.net/10113/1367
Citation: Vazques-Amabile, G., Engel, B.A., Flanagan, D.C. 2006. Risk analysis for atrazine NPS pollution using SWAT in the St. Joseph River watershed in NE Indiana. Transactions of the American Society of Agricultural and Biological Engineers. 49(3):667-678.

Interpretive Summary: The quality of surface water that is used for drinking is becoming an increasing concern. Chemicals that farmers spray on their cropped fields to control weeds and insects can sometimes wash off the fields in runoff water and travel downstream, where they can end up in drinking water that is taken from rivers by municipal water companies. Treating this water to remove pollutants can be difficult and expensive. Measuring chemical losses at every field is impractical, so in order to estimate how much can be lost it is necessary to use computer simulation models. A model called SWAT (for Soil and Water Assessment Tool) was used to find out how much of the herbicide (weed killer) called atrazine could be lost from farmers’ fields, under different types of corn planting scenarios. First the model was tested and adjusted so that it matched measured runoff and atrazine losses from the St. Joseph River in northeastern Indiana. Then information from the SWAT model runs was used to find out the risk that atrazine levels in the water would be high, depending upon the type of corn planting season that occurred and the month of the growing season. We found that the risk of seeing higher atrazine concentrations was lower in the northern part of the St. Joseph River watershed, due largely to better drained soils there. This research impacts researchers, farmers and water consumers. By being better able estimate the possible loss of atrazine and the risk that this may happen, alternative cropping, herbicides, or herbicide application techniques or schedules may be possible. These could help to then reduce the amount of pollution that might occur and the impairment of the drinking water source.

Technical Abstract: The SWAT model (Soil and Water Assessment Tool) was calibrated and validated to predict atrazine loads in streams for the period 1996-2004 at eleven sampling sites in the St. Joseph River Watershed in northeast Indiana. This watershed encompasses 280,000 ha with 60% of the area in corn and soybean production. SWAT daily streamflow calibration and validation were completed before initiating pesticide calibration. During the validation period, Nash-Sutcliffe model efficiencies varied from 0.33 to 0.60 for daily streamflow and between 0.64 and 0.74 for monthly streamflow. The estimation of the timing of atrazine application was very important in the calibration- validation process, and proved to be a key input to predict the amount and timing of pesticide released to streams. Monthly atrazine concentrations were predicted with average R2 of 0.60 and 0.49 and average Nash-Sutcliffe model efficiencies of 0.38 and -0.91 for calibration and validation periods, respectively. The total mass of atrazine released by the whole basin between 2000 and 2003, for the period April-September, was closely predicted by the model. The observed average amount of atrazine released during the four seasons was 1002.1 kg/season and SWAT predicted 950.1 kg/season. The SWAT model is suitable to conduct NPS pollution risk analysis for atrazine at a basin scale to evaluate long term effects of management practices and environmental changes. Even though the model does not perform a risk analysis, it generates enough information to accomplish such an analysis outside the program. Risk analysis was performed by computation of exceedance probability curves and thematic and probability maps.

Last Modified: 4/23/2014
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