Natalia Peranginangin 1 , Michael Winchell 2 , Solomon Folle 3
1 Syngenta Crop Protection, LLC, Greensboro, NC; 2 Stone Environmental, Montpelier, Vermont;
3 University of Minnesota
EMPM, March 19, 2013



### Evaluation of a Simplified SWAT Model Approach for Prediction of Insecticide Concentrations in a Small Watershed in Northwestern Oregon2 

### Outline

1 Background
2 Datasets and Model Development
3 Model Simulation Results
4 Conclusions

http://extension.unh.edu/fwt/HeadwaterStreams.htm3 

### 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.html4 

### Background: Simplified SWAT model in Midwest and Texas regions5 

### Background: Good Performance of Simplified SWAT Model (Herbicide in 27 Midwest and Texas Watersheds)
Monte Carlo simulations (100 simulations)
Median of 27 Watersheds NSE R 2 b br 2 MCE
Un - Calibrated 0.45 0.91 1.2 0.45 0.28 Calibrated 0.76 0.94 1.02 0.68 0.566 

### 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)7 

### 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 km 2 (15 mi 2 )

- A NAWQA site since 1992

- Additional sampling by State of Oregon8 

### 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 applications9 

### 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.10 

### 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 pixel11 

### 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.12 

### 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 Applica
tion 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”14 

### Model Development: Efate Parameters

● Three sets of efate parameters:

- EPA assessment (USEPA, 2009)

- ARS pesticide p roperties 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 66.3Parameter Set Solubility (mg/l) KOC (l/mg) Aquatic aerobic

t1/2 (days) Aquatic
anaer.

t1/2 (days)15 

### 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

- C urve 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.18 

### Model Simulation Results: Cumulative Distributions of Un
calibrated Simulation

● Single simulation ● Monte Carlo (200 simulations)

Average e

- fate parameters fit best.19 

### 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 interpretation20 

### 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 br 2 MCE Un-cal., average efate
Cal., average efate 0.78 11.6 0.92 0.93 0.75 Monthly Flow 5 th – 95 th percentile concentrations

0.7 10.6 0.86 0.9 0.8321 

### 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 M onte 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.22 

### Thank you