Waterborne Environmental, Inc.

EMPM

March 24, 2014 1

Waterborne Environmental, Inc.

EMPM

March 24, 2014 1

Comparing Groundwater models – Why 

are there differences?

Nathan Snyder

Waterborne Environmental, Inc.

Mark Cheplick (Waterborne)

Ishadeep Khanijo (Waterborne)

Daniel Perkins, Ph.D. (FMC)
Waterborne Environmental, Inc.

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March 24, 2014 2

Overview

 Look back of PRZM-GW rollout

 Evaluation of Tier 2 refinement strategies
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March 24, 2014 3

PRZM-GW “One year later”

 December 11, 2012 Memorandum from Don Brady “officially” 

made PRZM-GW the standard tool for calculating estimated 

drinking water concentrations (EDWC) from groundwater 

sources

 Announced one-year evaluation period (immediate inclusion in 

assessments)

 Tier 1: higher of SCI-GROW or PRZM-GW

 Tier 2: PRZM-GW with refinements

 Version Control

 Official: http://www.epa.gov/oppefed1/models/water/#przm_gw

 Interim: https://sites.google.com/site/przmsurfacewater/surfacewater-calculator-archive
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March 24, 2014 4

Conceptual Model and Scenarios

 Six vulnerable standard scenarios, conceptual model of 

application over potable well.

 Rapidly declining degradation in 1m soil profile
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March 24, 2014 5

Tiered Assessment Approach

 Tier 1

• Tier 2
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March 24, 2014 6

Comparing Groundwater Model Simulations – What are the 

differences? (Tier 2 Strategies – Impact on EDWCs)

 Refinements are to the Complete Risk Assessment

 EFED and Registrants working with EPA Risk Managers to evaluate 

Tier 2 refinements

 HED evaluates risk based on refined approach

 Incorporation of BEAD data in assessment noted in flowchart, but 

not in refinement strategy details

 USEPA Provides 5 Specific Refinement Strategies – 3 

Categories

 Modeling refinements – EDWC changes based on changes in 

environmental fate, scenarios, application representation

 Mitigation refinements – EDWC changes based on use guidance 

adjustments (well setbacks or reduced annual schedules)

 Analysis refinement – utilize daily time series or other relevant 

duration summary of results in refined HED assessment
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March 24, 2014 7

Modeling Refinement: “Refinement Strategy 2: Identify 

Pesticide Fate Parameters not Considered in the Tier 1 

Simulations” 

 Product Specific Consideration

 Primary inputs Sorption (Koc), Aerobic Soil Degradation, and 

Hydrolysis (subsurface aqueous phase degradation)

 Additional Considerations (some require outside of GUI inputs 

or post processing)

 Plant Processes (interception, wash off, foliar degradation, uptake)

 Anaerobic Degradation

 Modification to Total Toxic Residue (modeling parent and 

metabolites independently)

 Refined Sorption Processes (discussed earlier)

 Non Linear Sorption

 Time Dependent Sorption
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March 24, 2014 8

PRZM-GW Inputs
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March 24, 2014 9

Model and Parameter Sensitivity: Soil Degradation Rate
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March 24, 2014 10

Model and Parameter Sensitivity: Sorption and Hydrolysis
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March 24, 2014 11

Total Toxic Residue (Single Run or Addition of Components)

 Total Toxic Residue (TTR) assumptions are used when 

uncertainties in environmental fate and metabolite toxicity are 

present

 Methods to conservatively use available data provide more 

realistic EDWCs while maintaining TTR concepts

Efate

Soil 

DT50 

(days) Koc 

Hydrolysis 

DT50 (days)

Application 

Rate* 

(Kg/ha)

TTR 100 20 300 1

Parent 2 300 15 1

Metabolite 1 10 150 250 0.483

Metabolite 2 90 50 300 0.373

Metabolite 3 50 20 300 0.102

Metabolite 4 100 20 300 0.080

*Set based on maximum formation and Molecular weight (sum > TTR)
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March 24, 2014 12

Degradation Changes with Depth

 When anaerobic and aerobic degradation are similar, the 

assumption of decreasing degradation with depth does not 

seem appropriate

 If dissipation studies available, inverse modeling techniques 

can be used to validate (and verify conservatism)

Example: 

DT50=50, Koc=20, 

Hydrolysis=300
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March 24, 2014 13

Modeling Refinement: “Refinement Strategy 1: 

Development of Representative Scenario” 

 PRZM-GW may be expanded to capture the variability in 

anticipated leaching to groundwater based on differences in 

soil and weather (National Implementation)

 Used to place standard scenarios into context and link to 

monitoring locations

 May be used to focus analysis to anticipated use area with 

adjustments to allow for crop/product specific analysis
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March 24, 2014 14

National Implementation of PRZM-GW

 PRZM-GW methodology applied to national soils database 

(STATSGO) and distributed weather stations

 STATSGO Soils with layer thickness and bulk density used directly and 

FC and WP calculated from sand, clay, organic carbon using Rawls and 

Brakensiek method

 Weather from 237 SAMSON Stations assigned from10,498 STASGO 

MUIDs based on largest area of the MUID polygon created by 

intersecting MUID centroid with MLRA SAMSON weather region. 

 Cropping assumed as row crops with regional planting dates and 

cropping parameters based on USEPA standard scenarios and the 

PRZM manual (can be modified based on label)

 CN for Row Crops based on Soil Hydrologic Soil Group

 HSG A:74/70/74 B:83/80/83 C: 89/87/89 D: 92/90/92

 Groundwater table depth for all scenarios assumed as 9 m with 1 

m screen length.

 Post Processing results include maximum daily concentration, 

simulation average, post breakthrough average and breakthrough 

time.
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March 24, 2014 15

National PRZM-GW
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March 24, 2014 16

National PRZM-GW: Placing Standard Scenarios into 

Context

 Example DT50=50 days, Koc=20, Hydrolysis=0 days, CN=10
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March 24, 2014 17

National PRZM-GW: Impact of CN

 The CN in PRZM influences runoff processes

 Setting CN artificially low, highlights areas of runoff 

vulnerability as having leaching vulnerability

 Example DT50=50 days, Koc=20, Hydrolysis=0 days
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March 24, 2014 18

National PRZM-GW: Environmental Fate Sensitivity

Example: DT50=300, 

Koc=20, Hydrolysis=300
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March 24, 2014 19

Spatial Representation

 Presentation of results mapped by soil polygon (STATSGO 

MUID)

 Options to scale up to political boundaries (counties) or down to 

specific SSURGO layers based on soil properties and location

 Filter Results based on cropping patterns
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March 24, 2014 20

Mapping Options

 Maximum in a Soil Polygon versus Area Weighted Average
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March 24, 2014 21

Spatial Comparison of Inputs

 Evaluate differences in Environmental Fate

 Hydrolysis=300, Koc=20, Variable Soil Degradation

Soil Aerobic Degradation Rate

50 Days 300 days 
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March 24, 2014 22

Review Areas Exceeding Level of Concern
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March 24, 2014 23

Mitigation Refinement: “Refinement Strategy 3: 

Consideration of Annual Application Retreatment” 

 Resistance management plans control use frequency, while 

registration focused on the product label

 BEAD data can help Risk Manager and HED bridge the label 

and actual use
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March 24, 2014 24

Mitigation Refinement: “Refinement Strategy 4: 

Considerations of Well Setbacks”

 C/C0 = exp((-L/v)k)

 where:

 C = concentration at well [mass/volume]

 C0= concentration at point of application 

[mass/vol]

 L = well setback distance [length]

 v = lateral groundwater velocity 

[length/time] (e.g., 0.15 m/day)

 k = degradation rate in aquifer [time-1]

 Hydrolysis as surrogate for degradation 

rate

 Other Study Designs – non-guideline and 

generally not available
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March 24, 2014 25

Analysis Refinement: “Refinement Strategy 5: Exploring 

Exposure Durations That Are Representative of the 

Exposure Duration of Concern”

 Compound specific determination

 Environmental fate and use pattern must result in a significant 

annual fluctuation

 Limited utility to most compounds identified as having a 

groundwater concern
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March 24, 2014 26

Discussion

 Change in estimation approach (SCI-GROW to PRZM-GW) has 

resulted in a significant increase in the perception of risk

 Actual exposure has not changed....just our way of viewing it

 New modeling tools are causing registration delays across industry 

with the impact felt immediately (during evaluation period)

 PRZM-GW continues to evolve as bugs are fixed and capabilities 

added

 Pragmatic approaches to shifting perception of risk and use of 

refinements are needed to support registrations 

 Using best available data and reasonable surrogates for 

incomplete data should be included in discussion

 Interaction between EFED, Registrant, HED, and Risk Manager 

on refinement and analysis approaches and management of 

risk is a critical element of the process