The Need for More Realistic Aquatic Exposure Predictions:

Opportunities for Improved Modeling Approaches

CLA-Rise, April 11, 2014Michael Winchell, Stone Environmental, Inc.

Nathan Snyder, Waterborne Environmental, Inc.

Sponsored by Crop Life America

OutlineBackground and ObjectivesDesirable Characteristics in Drinking Water /

Ecological Watershed ModelingCurrent and Potential Modeling ApproachesModel Comparison MatrixExample Model ApplicationsSummary and ConclusionsNext Steps and Discussion

BackgroundCurrent modeling approaches to assess drinking water

and ecological risk from pesticides undergoing new registrations or re-registrations are conservative and designed to provide point estimates of risk.

There are models currently available that:Provide flexibility to model actual drinking water watershed

dynamics and incorporate spatial and agronomic variability.Are fast, efficient, and may be used across a range of

chemicals and geography.

New tools being developed at US EPA (Surface Water Concentration Calculator and Spatial Aquatic Model) may address some of the shortcomings of currently available modeling options.

Background (continued)Based on a recent publication in JAFC (Winchell and Snyder, 2014):

In 50% of the modeling/monitoring comparisons, model predictions were more than 229 times greater than the observations

In 25% of the comparisons, model predictions were more than 4,500 times greater than the observations.

ObjectivesCritically review:

The current modeling framework utilized by US EPAReview and compare the capabilities of tools for use in

regulatory modeling

Make recommendations regarding:Modeling approach(es) or capabilities to provide realistic,

yet still protective, predictions of concentrations of pesticides in surface drinking water sources

Required Watershed Characteristics and Model ProcessesWatershed and receiving water characteristics

Drainage area to normal capacity ratio Storage capacity to surface water area Water body types (static, flowing)

Accurate representation of pesticide use (rates, timing, spatial distribution)

Watershed Heterogeneity: Land-use (mixture of labeled, unlabeled crop, and non-agricultural land) Soil and weather Agronomic practices BMPs (buffers, contour cropping, terraces, grass waterways)

Watershed scale drift assumptions, proximity to water bodies in larger systems

Environmental fate representation that may vary by soil

FIRST and Index ReservoirEPA Tier I drinking water exposure model

Uses basic chemical parameters (e.g., half-life in soil) and pesticide label application information.

Estimates peak values (acute) and long-term average concentrations (chronic) of pesticides in drinking water.

Assumes up to 8% Runoff

Utilizes the same drift, PCA, and scenario assumptions as Tier II

No variability for soil, weather

Index ReservoirBased on Shipman, IL172.8 ha Watershed, draining to a 5.3ha reservoir that is 2.7m

deep

PRZM/EXAMS and Index ReservoirEPA Tier II drinking water exposure model

PRZM model of runoff/erosion processes and EXAMS model of water body processes, coupled using user friendly shells (PE5 or EXPRESS)

Standard scenarios representing high vulnerability crop/soil/weather combinations

Environmental fate including soil/aquatic degradation, sorption, volatilization processes

Single soil, weather, cropping for watershed

Percent crop area (PCA) assumptions used to scale results based on assumed area receiving applications

WARP and WARP-CBThe United States Geological Survey Watershed Regressions for

Pesticides (WARP) model.

Designed to predict percentiles of annual maximum atrazine concentration in flowing water bodies.

Originally developed based on a statistical analysis of atrazine monitoring data and has since been adopted for use with other pesticides through incorporation of a surface water mobility index.

Built on robust monitoring datasets; however, because it is not physically based, it is unable to provide important functions such as the simulation of alternative Best Management Practices.

Limited testing on pesticides for target crops with a smaller geographic extent than corn.

SWAT (Soil and Water Assessment Tool)Watershed‐scale, continuous, physically‐based, semi‐

distributed model used in a broad range of hydrologic and water quality applications

Strength in simulating the water quality impact of alternative management practices including tillage practices, buffers and grassed waterways, and pesticide application practices.

Pesticide transport modeling with SWAT has included assessments of pesticides in both static and flowing water bodies.

Can be implemented using readily available data in place of extensive site‐specific calibration for use in aquatic pesticide concentration predictions in complex watersheds.

PRZM-HybridThe PRZM‐Hybrid approach utilizes spatially explicit

high‐resolution NEXRAD radar rainfall data, additional meteorology data, field‐scale soil properties from the US national SSURGO database, and spatially explicit land use data as input data to model daily watershed runoff concentrations.

Growing Degree Day (GDD) and soil workability routines developed to estimate application timing.

Synthetic hydrograph determination using time of concentration and estimated travel time.

Methodology developed as a tool to fill in the gaps between monitoring data but broader uses are appropriate

Comparison MatrixThe five modeling approaches were evaluated based on 9

categories of criteria, seven of which are summarized here: Environmental FateSystem HydrologySoilCroppingWeatherApplication and DriftTransport Processes

Environmental Fate

Parameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Soil Processes Good Good Fair Good Good

Plant Processes

N/A Good N/A Fair Good

Aquatic Processes

Fair Good N/A Good N/A

System Hydrology

Parameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Watershed Fair Fair Fair Good Good

Receiving body Fair Fair Poor Good Poor

Soil/CroppingParameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Soil Poor Fair Fair Good Good

Cropping Poor Fair N/A Good Good

Weather/Drift

Parameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Weather Fair Good Fair Good Good

Drift Fair Fair N/A Fair N/A

Pesticide ApplicationsParameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Methods Good Good Poor Fair Good

Timing Fair Fair Poor Good Good

Distribution Poor Poor Poor Good Good

Transport ProcessesParameter or Process

FIRST PRZM-EXAMS

WARP and WARP-CB

SWAT PRZM-Hybrid

Runoff Poor Good Poor Good Good

Leaching N/A Fair N/A N/A Good

Sub-Surface N/A N/A N/A Good Fair

Summary of Modeling Approach ComparisonRanking FIRST PRZM-

EXAMSWARP and WARP-CB

SWAT PRZM-Hybrid

Good 2 6 0 11 11

Fair 6 7 4 3 1

Poor or N/A 7 2 11 1 3

SWAT and PRZM-Hybrid best meet the criteria that were established for watershed scale exposure modeling.

Example, PRZM-HybridMore accurate rainfall, soils, crop, and agronomic

practice inputs result in good agreement between observed and simulated pesticide concentrations.

Example, SWATModeling drinking water or

ecological exposure in larger, complex watersheds (e.g., California Delta) requires approaches that:Represent the landscape

heterogeneityAccount for hydrologic

routing of water and pesticide

Landuse Soils

Example, SWATFor flowing water body

segments within and surrounding the species critical habitat, observed maximum concentrations were compared with modeled 90th percentile concentrations.

Modeled concentrations were within the same order of magnitude as monitoring data for a variety of water body types, from major rivers to small sloughs.

Summary and ConclusionsSeveral currently available models (SWAT or PRZM-Hybrid)

have the ability to represent more complex and realistic hydrology, soil, weather, and application technologies than is possible with the models currently used by EPA.

US EPA scientists and pesticide registrants have evaluated the use of watershed modeling approaches in the past, and because many of the tools have matured, a similar effort should be explored again.

Regardless of the model platform, achieving more realistic model predictions will require incorporating accurate assumptions for inputs, particularly pesticide use intensity.

There is opportunity to include more spatial and temporal sophistication in exposure modeling with minimal additional dedication of time and resource to completing assessments.