Natalia Peranginangin1, Michael Winchell2, Solomon Folle3

1Syngenta Crop Protection, LLC, Greensboro, NC; 2Stone Environmental, Montpelier, Vermont; 

3University of Minnesota

EMPM, March 19, 2013

Evaluation of a Simplified SWAT Model Approach 

for Prediction of Insecticide Concentrations in a 

Small Watershed in Northwestern Oregon
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Outline

1 Background

2 Datasets and Model 

Development

3 Model Simulation Results

4 Conclusions

http://extension.unh.edu/fwt/HeadwaterStreams.htm
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Background

● Can the Soil and Water Assessment Tool (SWAT) model be applied 

efficiently and effectively to predict pesticide exposure in flowing water 

for a large number of small watersheds?

http://water.usgs.gov/GIS/huc.html http://water.usgs.gov/GIS/huc.html
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Background: Simplified SWAT model in Midwest and Texas 

regions
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Background: Good Performance of Simplified SWAT Model 

(Herbicide in 27 Midwest and Texas Watersheds)

Monte Carlo simulations (100 simulations) 

Median of 27 Watersheds

NSE R2 b br2 MCE

Un-Calibrated 0.45 0.91 1.2 0.45 0.28

Calibrated 0.76 0.94 1.02 0.68 0.56
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Background: Research Objectives

● To evaluate whether a simplified 

SWAT modeling approach 

developed for an herbicide in the 

Midwest and Texas regions 

performs well with an insecticide in 

a different geographic area.

● Simple SWAT (ACS, 2011):

- Readily available inputs

- Careful choice of a parameters

- Minimal calibration (one

calibrated parameter)
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Background: Pesticide Information and Study Location 

● An insecticide was selected:

- Highly sorbed (Koc = 6,000-

10,000 mL/g)

- Aerobic soil half-lives of 30-80 

days

● Zollner Creek watershed in 

Marion County, Oregon

- Area: 39.1 km2 (15 mi2)

- A NAWQA site since 1992

- Additional sampling by State 

of Oregon
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Background: Study Location

● Major crops:

- Grass for sod/seed (33%)

- Hay/pasture (25%)

- Corn (5%), winter wheat (5%)

- Vegetables/ground fruit (6%)

● Percent treated area (PTA) of 

10%-40% for crops receiving 

applications
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Model Development: Topographic and Weather Data 

● 30-m NHDPlus DEM 

● Elevations: minimum 34m, 

median 66m, maximum 198m.

● Weather stations:

- Aurora airport (primary,1998-

2008). Elevation 60m, 

distance 11 miles. 

- Silverton (1993-2008). 

Elevation 124 m, distance 6 

miles.
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Model Development: Land Use Data

● Three years of NASS Cropland 

Data Layer (CDL, 2008-2010)

● Crop groups based on similarity in 

agronomic practices and use 

● The majority crop group over the 

3 year period was assigned to 

each pixel
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Model Development: Soils data

● Soil Survey Geographic 

(SSURGO) database 

● Total of 27 unique soil map 

units

● The majority component from 

each mapping unit was used 

to characterize the soil 

properties of each unit.
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Model Development: Pesticide Treated Area and Application 

Timing

● PTA of each crop group was based 

on a Crop Reporting District (CRD)-

level pesticide use survey of total 

acres and acres treated (1998-

2011).

● Temporal distribution of pesticide 

applications was based on:

- CRD-level pesticide 

application timing by crop 

growth stage data

- Product label
Model Development: Pesticide Application Scenarios

Crop, App Rate, 

PTA, Monthly 

App Timing 

Select Single HRU

for given crop

Generate Uniform 

Random Number

for Treatment

HRU

Treated?

Generate Uniform 

Random Number

for Timing

Within 

Timing Target?

Select All HRUs

for Given Crop

Within

PTA Target?

Assign Application to HRU

Crop

PTA Met?

Pest Apps for

Crop Completed

Yes Yes Yes

Yes

No

No No No

Choose HRU and 

assign application 

date randomly

Each crop group 

should be within 

+/- 2% of the 

target PTA

Follows the target 

monthly application 

timing distribution 

● “SWAT application scenario generator”
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Model Development: Efate Parameters

● Three sets of efate parameters:

- EPA assessment (USEPA, 2009)

- ARS pesticide properties database and other sources

- Average of both

Aerobic 

Soil 

t1/2

(days)

EPA 2.00 6070 76.9 153.8 81.5

ARS 1.18 9930 30.5 30.5 51.0

Average 1.59 8000 53.7 92.2 66.3

Parameter Set

Solubility 

(mg/l)

KOC 

(l/mg)

Aquatic 

aerobic 

 t1/2 

(days)

Aquatic 

anaer. 

t1/2 

(days)
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Model Development: Model Parameterization

● Same approach as developed for watersheds in the Midwest and Texas.

● Base flow:

- Base flow recession constant (ALPHA_BF): 0.01 

- Ground water delay (GW_DELAY): 31 days

● Curve number: 

- Adjusted to slope, plant ET method for daily adjustment 

- Curve number coefficient (CNCOEFF): 0.5 

● Subsurface drainage: poorly and very poorly drained soils have tiles.
Model Simulation Results: Un-calibrated Simulations

Flow well simulated.
Model Simulation Results: Un-calibrated Simulations (Single 

Run Simulation)

ARS and average efate parameters closest to observations.
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Model Simulation Results: Cumulative Distributions of Uncalibrated Simulation

● Single simulation ● Monte Carlo (200 simulations)

Average e-fate parameters fit best.
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Model Simulation Results: Minimally Calibrated Simulation

● Same approaches with the 

Midwest and Texas watersheds.

● Same single parameter adjusted 

for Midwest/Texas watersheds 

(CNCOEFF). 

● Calibrated CNCOEFF of 0.65

● Model performance evaluation:

- Flow simulation statistics

- Cumulative distribution 

statistics

- Visual interpretation
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Model Simulation Results: Minimally Calibrated Cumulative 

Distributions (Average Efate Parameters)

Calibrated model is slightly better than un-calibrated model.

Monte Carlo (200 simulations)

Simulation NSE

Bias 

(%) NSE br2 MCE

Un-cal., 

average 

efate

Cal., average

efate 0.78 11.6 0.92 0.93 0.75

Monthly Flow

5th – 95th percentile 

concentrations

0.7 10.6 0.86 0.9 0.83
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Conclusions

● The simplified SWAT modeling approach performed well for insecticide 

simulations in Oregon. 

- Un-calibrated simplified SWAT model simulated flow well. 

- Both un-calibrated and calibrated Monte Carlo based daily 

concentration distribution strongly agreed with the observed data.

● CRD-level insecticide use survey data proved to be adequate in 

representing use patterns in a mid-sized watershed.

● Concentration simulations were very sensitive to the e-fate parameters.
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Thank you