Comparison of Models for 

Estimating the Removal of Pesticides by Vegetative BuStrips

Michael Winchell1, Russell Jones

2, Tammara Estes1

1. Stone Environmental, Montpelier, Vermont

2. Bayer CropScience, Stilwell, Kansas

EMPM MeetingJuly 27, 2010
2

Contents

1 Background2 Models Evaluated3 Case Studies4 Conclusions
3

■ Vegetated filter strips (VFSs) have long 

been recommended as a Best Management Practice (BMP) to reduce sediment, nutrients, and pesticides entering water bodies.

■ VFSs have been mandated as 

requirements on labels for crop protection products in North America and the EU.

Background: Motivation

■ The use of physically-based models to estimate the effect

VFSs is a cost-effective supplement (or alternative) to field 
4

■ Objective 1: To test the ability of four models (APEX, PRZ

REMM, and VFSMOD) in simulating VFS effectiveness on common datasets in an uncalibrated simulation mode.

 20 runoff data points 20 sediment data points 33 pesticide data points

■ Objective 2: To understand the sensitivity of model predic

the uncertainty in key model input parameters.

■ Objective 3: To provide an analysis that will aide in the se

appropriate models for use as decision-making tools in es

VFS effectiveness at reducing pesticides in runoff and sed

Background: Study Objectiv
5

■ Developers: Texas Blackland Research and Extension Ce■ Model Scale: Farm, small watershed, field: Continuous, da■ Primary Use: Agricultural non-point source, crop growth, n

management

■ Model Structure:

 Agricultural fields and buffers are 

simulated as independent subareas

 Flow, sediment, nutrient, and 

pesticide are routed to downstream subareas via overland flow and channel flow

Agricultural Policy EnvironmExtender Model (APEX)
6

■ Developers: Waterborne Environmental■ Model Scale: Field plus buffer: Continuous, daily time step■ Primary Use: Pesticide exposure, agricultural non-point so■ Model Structure:

 Agricultural fields and buffers

are simulated as independentPRZM runs.

 Flow, sediment, and 

pesticide loads from field areaare added directly to buffer areas, simulated through additional PRZM runs.

Pesticide Root Zone Buffer M(PRZM-BUFF)
7

■ Developers: USDA / ARS■ Model Scale: Multi-zone buffer: Continuous, daily time ste■ Primary Use: Nutrient trapping and management of mana

unmanaged riparian buffers 

■ Model Structure:

 A three-zone buffer system is

simulated with REMM (grass filter, managed riparian, unmanaged riparian).

 Flow, sediment, and pesticide 

field loads are provided as input to the model and routed downstreamthrough the 3 zones.

Riparian Ecosystem ManageModel (REMM)
8

■ Developers: University of Florida■ Model Scale: Buffer: Sub-hourly, event-based■ Primary Use: Non-point source water quality, buffer desig

■ Model Structure:

 Flow and sediment field loads are 

provided as input to the model. 

 Infiltration and sediment 

deposition are simulated in a single buffer zone.

 Pesticide reduction is calculated 

as a function of infiltration, deposition chemical characteristics, and soil clay.

Vegetated Filter Strip Model: (VFSMOD)
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■ Location: Part of USDA experimental watershed near Tifto

■ Buffer Characteristics:

 Length x Width: 120 m x 8 m. Field to Buffer Area Ratio: 11.5 Slope: 2.5% Soil: Tifton loamy sand

■ Monitoring:

 3 years continuous (1992 – 1994)

■ Pesticides Simulated:

 Alachlor: Koc = 54 mL/g

Case Study: Gibbs Farm, Ge
10

Results: Gibbs Farm, Long-TPesticide Reductions

Total Alachlor Mass Reduction in Buffer, 1992 - 1994

0

102030405060708090

100

Full Year After Applications Between Applications

Pesticide

Percent Reduction (%) 

■ After applications represents app date through June 30th (or 25 cm rainfall)

■ Between applications represents June 30th (or 25 cm rainfall) thru following 
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■ Location: In the north Rhine-Westphalia region of German■ Buffer Characteristics:

 Length x Width: 1.5 m. x 3 m. Field to Buffer Area Ratio: 2.3 Slope: 10% Soil: Silty loam

■ Monitoring:

 6 simulated rainfall events (1998 - 1999)

■ Pesticide Simulated:

 Pendimethalin: Koc = 12,495 mL/g

Case Study: Velbert-Neviges, Germany
12

Results: Germany, Event-BaPendimethalin Reductions

Total Pendimethalin Mass Reduction in Buffer

0

102030405060708090

100

5/28/98 6/5/98 6/8/98 5/27/99 6/1/99 6/1

Event

Percent Reduction (%) 
13

■ For several groups of simulations, model performanc

evaluated based upon the Mean Absolute Error (MAE

■ This is calculated as follows:■ Where: Robsi = Observed mass/volume reduction by 

event i (%)

Rsimi = Simulated mass/volume reduction by 

event i (%)

n = number of events in group

Results: Evaluation of Mode





 

n

i

Robsi Rsimi

n

MAE

1

1
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Results: Germany, Absolute Buffer Reduction Efficiency

Mean Absolute Error in Buffer Reductions Over 6 Eve

0

20406080

100120

APEX PRZM-BUFF REMM VFSM

Model

Absolute Error (%) 

Primary bar represents the mean of 6 Error bars represent min and max of 
15

■ Location: In western Sioux County Iowa■ Buffer Characteristics:

 Length x Width: 4.6 m. x 4.6 m; 0.46 m. x 4.6 m Field to Buffer Area Ratio: 15:1; 30:1 Slope: 5.25% Soil: Silty clay loam

■ Monitoring:

 4 simulated run-on events, 3 replicates

■ Pesticides Simulated:

 Chlorpyrifos: Koc = 9,930 mL/g Atrazine: Koc = 171 mL/g

Case Study: Western Sioux County, Iowa
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Results: Sioux County, EvenAtrazine Reduction

Total Atrazine Reduction in Buffer

12 Plots Simulated

R2 = 0.84

R2 = 0.84

R2 = 0.84

R2 = 0.82

0

102030405060708090

100

0 20 40 60 80 

Observed Reduction (%)

Simulated Reduction (%) 
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Results: Sioux County, EvenChlorpyrifos Reduction

Total Chlorpyrifos Reduction in Buffer

12 Plots Simulated

R2 = 0.

R2 = 0

R2 = 0

R2 = 0

0

102030405060708090

100

0 20 40 60 80 

Observed Reduction (%)

Simulated Reduction (%) 
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Results: Sioux County, AbsoError in Buffer Reduction Eff

Mean Absolute Error in Buffer Reductions Over 12 Plots

0

102030405060708090

APEX PRZM-BUFF REMM VFSM

Model

Absolute Error (%) 

Primary bar represents the mean of 1Error bars represent min and max of 
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■ Based on previous studies and experience, three sensitive 

with higher uncertainty were selected for each model.

■ Parameter mean values were set equal to the cold run va■ Saturated Conductivity (Ksat): lognormal distribution.■ Curve Number (CN): lognormal distribution.■ Manning’s N: triangular distribution.■ Initial soil moisture (theta): Uniform distribution.

Model Sensitivity and UncertApproach

Parameter Value 1st 10th 50th 90th Ksat (cm/hr) 0.8 2 5.7 16.7 CN 82 74 61 46 Manning's N 0.4 0.43 0.47 0.56 Theta (%) 0.01 0.1 0.5 0.9 

Parameter Percentile
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Results: Atrazine Reduction Sensitivity to Ksat/CN, Sheet 

Sensitivity of Atrazine Reduction to Ksat/Curve Numb

Block 1 Treatment 2 (Sheet Flow Conditions)

0

102030405060708090

100

0 20 40 60 80 1

Ksat/CN Percentile

Percent Reduction (%) 
21

Results: Atrazine Reduction Sensitivity to Ksat/CN, Conc

Sensitivity of Atrazine Reduction to Ksat/Curve Numb

Block 1 Treatment 4 (Concentrated Flow Conditions

0

102030405060708090

100

0 20 40 60 80 1

Ksat/CN Percentile

Percent Reduction (%) 
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Results: Chlorpyrifos ReducSensitivity to Manning’s N, S

Sensitivity of Chlorpyrifos Reduction to Manning's 

Block 1 Treatment 2 (Sheet Flow Conditions)

0

102030405060708090

100

0 20 40 60 80 1

Manning's N Percentile

Percent Reduction (%) 
23

Results: Chlorpyrifos ReducSensitivity to Manning’s N, C

Sensitivity of Chlorpyrifos Reduction to Manning's 

Block 1 Treatment 4 (Concentrated Flow Condition

0

102030405060708090

100

0 20 40 60 80 

Manning's N Percentile

Percent Reduction (%) 
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Results: Atrazine Reduction Sensitivity to Initial Soil Mois

Sensitivity of Atrazine Reduction to Initial Soil Mositu

Block 1 Treatment 2 (Sheet Flow Conditions)

0

102030405060708090

100

0 20 40 60 80 

Initial Soil Mositure Percentile

Percent Reduction (%) 
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Results: Chlorpyrifos ReducSensitivity to Initial Soil Mois

Sensitivity of Chlorpyrifos Reduction to Initial Soil Mos

Block 1 Treatment 2 (Sheet Flow Conditions)

0

102030405060708090

100

0 20 40 60 80 

Initial Soil Mositure Percentile

Percent Reduction (%) 
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Results: Chlorpyrifos ReducDistribution, Sheet Flow

Cumulative Distribution of Simulated Chlorpyrifos Reduction

30:1 Field-to-Buffer Area Ratio, Sheet Flow Conditions

0

102030405060708090

100

0 20 40 60 80 100

Reduction (%)

Percentile

ObsAPEXPRZMREMMVFSM

■ 1 observation■ 20 simulations, Ksat, N, and theta varied concurre
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Results: Chlorpyrifos ReducDistribution, Concentrated F

Cumulative Distribution of Simulated Chlorpyrifos Reduction

30:1 Field-to-Buffer Area Ratio, Concentrated Flow Conditions

0

102030405060708090

100

0 20 40 60 80 100

Reduction (%)

Percentile

ObsAPEXPRZMREMMVFSM

■ 1 observation■ 20 simulations, Ksat, N, and theta varied concurre
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■ Criteria: Absolute error in runoff, sediment, and pe

reduction

■ Data Points: 73 Total

 Gibbs Farm: 2 runoff, 2 sediment, 3 pesticide Velbert-Neviges, Germany: 6 runoff, 6 sediment, 6 p Sioux County, Iowa: 12 runoff, 12 sediment, 24 pest

■ Mean of absolute error calculated for runoff, sedim

pesticide simulations independently

Results: Model Performance Evaluation
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■ Mean absolute error in runoff, sediment, and pesticid

shown in (%)

■ Number in parentheses is the standard deviation of t

absolute error in (%)

Results: Model Performance Summary

Sediment 19.4 (17) 31.0 (31) 30.3 (24) 1Runoff 30.4 (20) 36.9 (28) 34.5 (32) Pesticide 15.6 (10) 16.3 (14) 31.2 (31) 

REMM V

PRZM 

BUFF

Model APEX
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■ Encouraging results from this study support the use of the

based models to estimate buffer effectiveness.

■ The models, as parameterized here, were generally conse

predictions, especially for events with a high observed rem

■ All four models were much more sensitive to Ksat/curve n

compared to Manning’s N and initial soil moisture. The se

Manning’s N and initial soil moisture was less than expect

■ Following standard parameter estimation procedures, the 

% reduction of runoff, sediment, and pesticide for all 4 mobetween 9% and 37%.

Summary, Conclusions, and Recommendations
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■ Funding for this research was provided by the European C

Association, Bayer CropScience, Syngenta Crop ProtectioMonsanto Company.

■ PRZM-BUFF modeling was conducted by Amy Ritter and 

of Waterborne Environmental.

■ VFSMOD modeling was conducted by George Sabbagh o

CropScience.

■ Addition information on the study datasets was provided b

 Gibbs Farm: Richard Lowrance and Randy Williams, USDA/

 Velbert-Neviges: Christine Klein, SCC Scientific Consulting Sioux County: Nick Poletika, Dow AgroSciences

Acknowledgements