Presented by:

Application of a Probabilistic 

Aquatic Exposure Modeling 

Approach for Use in Endangered 

Species Assessments

Michael Winchell1, Lauren Padilla1, Charlie Hofmann1, Katie 

Budreski1, Scott Teed2, Paul Whatling3

1. Stone Environmental, 2. Intrinsik Environmental, 3. Cheminova

EMPM, April 28th, 2015
Background

 Assessing pesticide exposure risk for endangered species will 

likely require that higher tier, probabilistic modeling approaches 

be applied in making likely to adversely affect (LAA)/not likely to 

adversely affect (NLAA) determinations (NAS, 2013).

 National geospatial datasets allow for exposure modeling that 

accounts for a broad range of environmental conditions which 

influence exposure potential.

 These environmental conditions can be quantified for specific 

species habitat ranges of interest, resulting in customized 

exposure predictions pertinent to a given species, group of 

species, or taxa.

2
Motivation: Why Might we Need to Refine 

Exposure Estimates?

 Existing SWCC crop scenarios vary in terms of a regional or 

national vulnerability.

 A single scenario does not account for the variability and 

frequency of important environmental factors.

 The use of a single 

scenario per crop for 

an entire HUC2 may 

result in inappropriate 

model assumptions 

for some species.

Piping Plover

Niangua Darter

3
Objectives

 Develop an approach for refining aquatic pesticide exposure 

estimates used in endangered species assessments that:

1. Accounts for the variability and uncertainty in environmental 

conditions relevant to species habitat.

2. Applies probabilistic approaches to account for this 

variability/uncertainty when parameterizing aquatic 

exposure models.

3. Produces a robust EEC probability distribution representing 

the likelihood of exceeding pesticide exposure 

concentrations that can be used directly in a refined risk 

assessment.

4
Topics for Discussion

 Description of case study

 Modeling approach

 Modeling results

 Implications for National Endangered Species Assessments

 Summary and conclusions

5
Description of Case Study

 Species: California Tiger 

Salamander (CTS)

 Species habitat location: 3 

distinct population segments 

(DPSs):

− Central California

− Santa Barbara County

− Sonoma County

6
Modeling Approach: Overview

 Identify aquatic habitats (water bodies) specific to the CTS.

 Develop probability distributions of critical environmental and 

agronomic factors to provide a more comprehensive estimate of 

exposure likelihood:

− Pesticide application date

− Weather (spatial/temporal)

− Soils and slope

− Percent cropped area (PCA)

− Use site proximity to water bodies (drift fraction)

− Water body (habitat) characteristics (drainage area, surface area, volume)

 Generate many model simulations by sampling from these input 

distributions using a robust sampling method that ensures each 

distribution is sufficiently sampled 

 Construct an exposure distribution reflective of environmental 

conditions relevant to species habitat.

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Modeling Approach: 

Identify Aquatic Habitats of Interest

 Vernal pools are aquatic 

habitat for the CTS.

 High resolution (1:24,000 

scale) National Hydrography 

Dataset (NHD) identified 

many of these water bodies.

 Additional vernal pools were 

hand delineated based on 

review of aerial imagery over 

time.

 In total, 4,297 

vernal pools in 

CTS habitat 

were assessed.

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Modeling Approach: 

Application Date

 Screening level EECs are often based on a single simulation 

and the assumption of a single application date.

 Application dates for some pesticide uses can span a broad 

window, and are often driven by pest pressure.

 Probability distributions of application dates can be developed 

from local or regional agronomic datasets and/or pesticide 

label restrictions.

 The CTS example assumed a uniform probability distribution 

of application dates based on the timing allowed on the 

pesticide label.

9
Modeling Approach: 

Water Body Size and Depth 

 Water body surface area and depth were represented as 

probability distributions. These factors influenced:

− Volume

− Integrated drift fraction

 A discrete distribution of surface area was developed from the 

spatial layer.

 A depth range of 0.15 

- 1.0 m., represented 

by a uniform 

distribution, was used 

based on preferred CTS

habitat characteristics. 

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Modeling Approach: 

Weather Station Distribution

 The CTS habitat extends 

from Santa Barbara County 

in the South to Sonoma 

County in the North.

 Weather station polygons 

were intersected with the 

water bodies to determine 

weather station probability.

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Modeling Approach: 

Watershed Delineation

 Watersheds boundaries were 

estimated from a DEMbased delineation using 

10-meter National Elevation

Dataset (NED) data.

 Watersheds ranged in size

from 0.001 ha to 688 ha 

(mean of ~ 7.6 ha)

 These watersheds were then

used to identify relevant soils

and percent cropped areas.

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Modeling Approach:

Crop Footprint Development

 For a broad use pesticide, a 

conservative “All Ag” crop 

footprint was developed from 2 

years of NLCD (2006 and 

2011).

 The crop footprint was

intersected with watersheds to 

identify the extent of potential 

use sites within the drainage 

areas of CTS aquatic habitat.

 A CDL-based All Ag footprint 

would be very similar, and 

crop group-based refinements 

could be applied as needed. 

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Modeling Approach: 

Soils Analysis

 The soils co-occurring with crops within the habitat watersheds 

were identified and grouped into “bins”.

 The soil binning approach was adopted from the Spatial Aquatic 

Model’s method which groups soils by:

− Hydrologic group

− USLE K-factor

− Organic carbon in surface layer

− Slope

 96 soil bins were 

represented in CTS habitat

Crop Footprint

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Modeling Approach: 

PCA Calculation

 Percent Cropped Areas (PCAs) are calculated based on both 

watersheds and a 792-meter buffer “drift-shed” (AgDRIFT limit).

 The maximum of the watershed and “drift-shed” PCA is used in 

developing the PCA 

distribution based on 

4,297 water bodies.

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Modeling Approach: 

Spray Drift Fraction

 The actual proximity of an edge of field to a water body in CTS 

habitat is highly variable.

 A probability distribution of the closest distance from an edge 

of field to

CTS aquatic 

habitat can be 

readily 

constructed.

 AgDRIFT was

used to 

calculate 

resulting drift.

 Existing label

buffers were

accounted for. 

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Modeling Approach : 

Latin Hypercube Sampling of Distributions

 Latin Hypercube Sampling (LHS) is an alternative to Monte 

Carlo (random) sampling.

 In LHS, each input distribution is divided into the same 

number of equally probable intervals.

 The approach allows a multi-dimensional parameter space to 

be more fully sampled with a smaller number of points.

 For the CTS exposure modeling, 8 input distributions were 

sampled 5,000 times to generate input data sets for SWCC 

simulations.

17
Modeling Results: Comparison of Screening 

Level EECs with Refined EEC Distribution

 Screening EECs: 1 30-year CA strawberry scenario simulation.

 Refined EECs: 5,000 30-year CA strawberry scenario 

simulations, based on sampling 8 probability distributions of 

habitat specific inputs.

90th Percentile

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Implications for National Endangered 

Species Assessments

 The current proposed aquatic exposure modeling approach for 

early in Step 2 will consider 1 crop scenario per crop group, 

per HUC2, applied to appropriate habitat bins.

 While this is a an appropriate screening level approach, 

refinements may be needed if there is a desire to forego 

progression to Step 3 and consultation with the Services.

 The datasets and processing techniques required to 

characterize the variability of key environmental factors that 

drive aquatic exposure, as demonstrated in this example, are 

currently available.

 Methods can be developed to efficiently characterize aquatic 

exposure model inputs for taxa, species, or region-specific 

habitat areas for use in national assessments.

19
Summary and Conclusions

 A probabilistic approach for refining aquatic EECs for use in 

Endangered Species Assessments was developed for use in 

assessing the California Tiger Salamander.

 Probability distributions of 8 input characteristics specific to CTS 

aquatic habitat, 6 based on spatial data, were developed for 

parameterizing the SWCC model.

 A Latin Hypercube sampling method was use to generate the 

inputs to 5,000 SWCC scenarios and simulations.

 The refined EEC distribution, specifically representative of CTS 

habitat conditions, showed a wider range of annual peak EECs, 

and a 90th percentile that was a factor of 7.6 times lower than the 

screening level.

 Many aspects of this approach can be adopted for refined 

exposure modeling at Step 2 of the national ESA process, and 

can be used to identify conditions to target for mitigation. 20