© 2014 by Pyrethroid Working Group. All rights reserved.

SWMM-AGRO: A Modeling System 

and Scenarios for Pesticide 

Aquatic Exposure Assessments in 

Urban Residential Environments

Michael Winchell1, Scott Jackson2, Lauren Padilla1

1. Stone Environmental, Inc.

2. BASF Corporation

EMPM Meeting, October 28th , 2014
Contents

 Background

 What is SWMM-AGRO?

 Model requirements

 Model Development

 SWMM

 AGRO-2014

 Model Application

 SWMM-AGRO Interface

 Summary and Conclusions

© 2014 by Pyrethroid Working Group. All rights reserved
Background:

What is SWMM-AGRO?

 SWMM-AGRO is a graphical user interface (GUI) that seamlessly 

integrates EPA’s Stormwater Management Model (SWMM) with 

the AGRO-2014 receiving water model (Padilla and Winchell, 2013).

 SWMM version 5.022, customized with CDPR’s pesticide washoff 

algorithm (Luo et al., 2014), is included with the modeling package.

© 2014 by Pyrethroid Working Group. All rights reserved

 SWMM-AGRO allows 

registrants, EPA scientists, 

and others to run the same 

residential pesticide exposure 

scenarios that were used in 

the PWG’s urban exposure 

assessments, as well as to 

simulate custom scenarios.
Background: Modeling Approach 

Requirements

 The urban pesticide 

modeling approach must:

 Capture the heterogeneity in 

use sites found in an urban 

environment.

 Account for the hydrologic 

processes in residential 

watersheds.

 Allow for flexibility in use 

assumptions.

 Permit evaluation of 

mitigation effectiveness.

 Model must be validated 

with monitoring data.

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development: SWMM-AGRO 

Model Selection

 Storm Water Management Model (SWMM): US EPA

 Watershed scale, urban/residential water quantity and quality model

 Strength in handling of sub-hourly runoff and flow routing 

 Able to model multiple surface types (lawn, driveway, etc.)

 AGRO: Canadian Center for Environmental Modeling (CEMC)

 Water Quality Model: Quantitative Water, Air, Sediment Interaction 

(QWASI) Fugacity model (Mackay, 2001).

 Accounts for chemical mass exchange between water column (dissolved 

and sorbed compartments), benthic layer (dissolved and sorbed

compartments), and air.

 Simulation of sediment dynamics, including handling of incoming 

sediment; important for high Koc pyrethroids.

 AGRO-2014 (Padilla and Winchell, 2013) includes improvement in 

parameterization based on comparison with cosm study data.

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development: Study Location

© 2014 by Pyrethroid Working Group. All rights reserved

 Aliso Viejo, Orange 

County, CA 

 Part of CA DPR / UC 

Riverside monitoring 

program (Oki and Haver, 2011).

 Drainage area: 67.2 acres

 307 homes

 Dwelling unit density: 4.6 

units/acre.
Model Development: National Housing 

Density Assessment

 Objective: Determine if, from a 

housing density perspective, Aliso 

Viejo represents a “high 

vulnerability” site.

 Approach: Use housing density as 

a surrogate for residential use site 

density.

 Datasets:

 US Census Block: Housing units per 

census block

 National Land Cover Dataset 2006 

(NLCD): Land cover classifications 

used to identify with single family 

housing blocks.

© 2014 by Pyrethroid Working Group. All rights reserved

Census Block Data
Model Development:

Density Assessment Results

 Targeted analysis of all residential census blocks within the lower 

48 states:

 “Developed, Low Intensity” and/or “Developed ,Medium Intensity” acreage is 

> 0 (to focus on single family home areas)

 “Developed, High Intensity” acreage is < 10% of the census block (to avoid 

apartment complexes and commercial/industrial areas)

 In California:

 Total number of census blocks assessed: 417,767

 Aliso Viejo housing unit density percentile in CA: 72nd %-ile

 In lower 48 states:

 Total number of census blocks assessed: 6,279,464

 Aliso Viejo housing unit density percentile in USA: 88th %-ile

 Aliso Viejo represents a high vulnerability site in terms of 

potential use site density of homes potentially receiving PCO/LCO 

treatments.

© 2014 by Pyrethroid Working Group. All rights reserved
Modeling Development:

Conceptual Model

 Aliso Viejo neighborhood was 

spatially delineated.

 Particular attention to 

impervious use sites.

 Lower driveway

 Upper driveway (within 5 ft)

 Garage door

 Impervious within 5-ft foundation 

perimeter

 Patios/walkways away from 

building

 Impervious areas near lawns 

(1.5 ft) receive irrigation.

 A fraction of impervious 

surfaces (other than driveway) 

flow into adjacent lawns.

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development: Incorporation of 

CDPR Washoff Method

 The standard SWMM model provides three washoff options 

(exponential equation, rating curve, event mean 

concentration), which all are a function of flow rate.

 Recently research from CDPR by Luo et al. (2014) described a 

new, theoretically-based washoff algorithm specifically 

designed for predicting pesticide washoff from hard surfaces. 

 The Luo washoff method was incorporated into the SWMM 

model code base, then tested and calibrated using Pathway ID 

study data.

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development:

SWMM Model Calibration Approach

 The monitoring study led by Oki and Haver (2011) provided the 

following datasets for calibration of the Aliso Viejo scenario:

 Daily and hourly flow from the stormwater outfall 

 30 bifenthrin concentration analyses from both wet and dry periods 

 Estimated weekly and annual pyrethroid mass load

 Flow calibrated through adjustment of:

 Irrigation practices

 Hydrologic connectivity of impervious surfaces

 Subsurface flow contributions

 Routing parameterization

 Bifenthrin mass and concentrations calibrated by adjusting:

 Irrigation practices

 Hydrologic connectivity of impervious surfaces

 Washoff parameters

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development: Neighborhood 

Pyrethroid Use Assumptions

 A best estimate of the extent and frequency of actual pyrethroid use 

in California was required for scenario calibration.

 Bifenthrin was chosen based on its relatively extensive use and 

frequency of detections in the monitoring data. 

 Key assumptions include:

 75.9% of households use outdoor insecticides (Winchell, 2013)

 Some households are treated every 6 weeks, and some every 12 weeks

 Fraction of use sites treated with bifenthrin (of households using insecticides) at 

these intervals was estimated from survey data and were set as follows:

© 2014 by Pyrethroid Working Group. All rights reserved

Use Site

Estimated 

Total Percent 

Treated (%)

Estimated 

Percent 

Treated Every 

6 Weeks (%)

Estimated Percent 

Treated Every 12 

Weeks (%)

Foundation Perimeter 25.7 13.1 12.6

Patios/Walkways 24.9 12.7 12.2

Driveways 24.1 10.6 13.5

Lawns 24.4 5.4 18.9
Model Development:

SWMM Hydrology Calibration

 Mean daily flows and hourly flows are well simulated:

© 2014 by Pyrethroid Working Group. All rights reserved

 Flow Bias:

-2%

 Daily NSE (Nash 

Sutcliffe 

Efficiency):

0.83 (Nash and 

Sutcliffe, 1970)

 Hourly NSE:

0.57

 Surface runoff

rarely occurs 

from lawns.
Model Development:

SWMM Bifenthrin Load Calibration

 The simulated cumulative bifenthrin mass load over 1 year was 

compared to the observed mass load.

 Over the 1

year period, 

the model

predicted 10%

more mass load

than observed.

 Given high bias 

in simulated 

mass, 

calibration is

conservative.

© 2014 by Pyrethroid Working Group. All rights reserved
Model Development: AGRO2014,Cosm Data Calibration (Drift)

© 2014 by Pyrethroid Working Group. All rights reserved

 AGRO-2014 best simulates 

the observed data (given 

the observed range in 

TSS) 

 AGRO-2014 is more 

conservative than EXAMS 

and VVWM

 Observed data from 

Leistra et al., 2003
Model Development: AGRO2014,Cosm Data Validation (Drift)

© 2014 by Pyrethroid Working Group. All rights reserved

 AGRO-2014 is generally in good agreement with 

observed, slightly more conservative than 

EXAMS and VVWM.

 Uncertainty in pond volume and TSS, and 

therefore concentrations.

 Default 30 mg/L TSS assumed in all models.

 Observed data from Springer et al., 1996
Model Development: AGRO2014,Cosm Data Validation (Erosion)

© 2014 by Pyrethroid Working Group. All rights reserved

 AGRO-2014 is generally in good agreement with 

observed, and more conservative than EXAMS 

and VVWM.

 Observed erosion event has faster response rate 

than drift event. 

 Observed data from Springer et al., 1996
SWMM-AGRO-2014 Application:

Simulation of Regional Aquatic EECs

 Regional parameterizations were developed for the Southeast, South 

Central, Northwest, North Central, Northeast, and Mid-Atlantic.

 Weather (30-yr):

 Hourly Precip.

 Daily Temp.

 Monthly Evap.

 Irrigation: Based

on regional 

climate.

 Pesticide Use: 

From regional 

Kline survey data

(Winchell, 2013).

© 2014 by Pyrethroid Working Group. All rights reserved
SWMM-AGRO Interface: Capabilities

 Run SWMM-AGRO simulations for ALL of the scenarios reported in 

PWG’s urban exposure assessment report (Winchell et al., 2014).

 Seven different geographic regions: California, Northwest, North Central, 

Northeast, Mid-Atlantic, Southeast, South Central

 Both historical and current pyrethroid labels for California

 Allow creation of custom use scenarios based on the regional 

scenarios provided.

 Simulation of potential application mitigations.

 Evaluation of a range of pesticides (not only pyrethroids).

© 2014 by Pyrethroid Working Group. All rights reserved
SWMM-AGRO Interface: 

Customizing Scenarios

 Custom scenarios created 

from standard scenarios.

 Application extents 

(fraction treated at 

neighborhood scale) are 

defined independently for 

each use site.

 Specific controls for 

defining the foundation 

perimeter treatment 

extent.

 Irrigation options (none, 

overspray, no overspray).

© 2014 by Pyrethroid Working Group. All rights reserved
SWMM-AGRO Interface: 

Pesticide Application Time Series

 Custom application 

inputs created from 

standard scenarios.

 Application time series 

are defined 

independently for each 

use site.

 Application rate and 

number of apps per year, 

and specific dates for 

each application are 

customizable for each 

use site.

© 2014 by Pyrethroid Working Group. All rights reserved
SWMM-AGRO Interface: 

Pesticide E-Fate Properties

 Pyrethroid e-fate 

properties are stored in 

the SWMM-AGRO 

database.

 New pesticides may be 

added to the SWMMAGRO database.

 Pesticide properties 

required by SWMM-AGRO 

are defined through the 

interface form.

© 2014 by Pyrethroid Working Group. All rights reserved
SWMM-AGRO Interface: 

Results

 Results are provided in the AGRO-2014 spreadsheet output tabs, 

including annual maximum and 90th percentile concentrations of:

 Total water column

 Dissolved water column

 Benthic pore water

 Benthic sediment

© 2014 by Pyrethroid Working Group. All rights reserved
Summary and Conclusions

 A SWMM-AGRO model and associated pesticide aquatic exposure 

scenarios have been developed for use in assessments of 

residential pesticide uses.

 The SWMM portion of the model was designed to represent a high 

vulnerability residential watershed and was calibrated with 

hydrologic and water quality monitoring data.

 AGRO-2014 simulations were compared with three COSM 

experiments where it was shown to accurately predict the 

observations, and was more conservative than EXAMS or VVWM. 

 The SWMM and AGRO-2014 models were linked in a single 

“SWMM-AGRO” interface that is capable of running the standard 

residential scenarios as well as custom scenarios. 

 The SWMM-AGRO model is available for public download at:

http://www.stone-env.com/agchem/agres.php

© 2014 by Pyrethroid Working Group. All rights reserved
References

 Leistra, M., Zweers, A., Warinton, J., Crum, S., Hand, L., Beltman, W., et al. 2003. Fate of the 

insecticide lambda-cyhalothrin in ditch enclosures differing in vegetation density. Pest 

Management Science, 60, 75-84.

 Luo, Y., B. C. Jorgenson, D.Q. Thuyet, T.M. Young, F. Spurlock, and K. S. Goh. 2014. Insecticide 

washoff from concrete surfaces: characterization and prediction. Environmental Science & 

Technology 48: 234-243.

 Mackay, D. 2001. Multimedia Environmental Models: The Fugacity Approach - Second Edition. 

Lewis Publishers, Boca Raton, pp.1-261.

 Nash, J. E. and Sutcliffe, J. V.. 1970. River flow forecasting through conceptual models, Part I - A 

discussion of principles. J. Hydrol. 10: 282–290.

 Oki, L, and D. Haver. 2011. Evaluating Best Management Practices (BMPs) Effectiveness to 

Reduce Volumes of Runoff and Improve the Quality of Runoff from Urban Environments.

 Padilla, L. E., & Winchell, M. F. 2013. Development And Testing Of An Improved Agro Model 

(AGRO-2014) For Use In Predicting Aquatic And Benthic Pesticide Concentrations In Ponds. PWG 

Report - PWG-ERA-03b. Stone Environmental Inc.

 Springer et al. 1996. An evaluation of the impact of fenpropathrin insecticide exposure on aquatic 

mesocosm ecosystems. Unpublished study prepared by Wildlife International Ltd. 2169 p. EPA 

MRID No. 43958801.

 Winchell, M.F. 2013. Pyrethroid Use Characteristics in Geographically Diverse Regions of the 

United States: Parameterization of Estimated Pyrethroid Treatment Extent and Frequency for 

Urban Exposure Modeling. PWG-ERA-02b. Stone Environmental Inc.

 Winchell, M.F., L. Padilla, and S. Jackson. 2014. Development and Application of a Higher Tier 

Urban Modeling Approach for Estimating Pyrethroid Concentrations in Static Water Bodies Using 

SWMM and AGRO-2014. PWG-ERA-11. Stone Environmental Inc.

© 2014 by Pyrethroid Working Group. All rights reserved