Presented by : 

## Application of a Probabilistic Aquatic Exposure Modeling Approach for Use in Endangered Species Assessments

Michael Winchell 1 , Lauren Padilla 1 , Charlie Hofmann 1 , Katie Budreski 1 , Scott Teed 2 , Paul Whatling 3 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).


##  N ational 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.

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### 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.
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### Topics for Discussion


##  Description of case study



##  Modeling approach



##  Modeling results



##  Implications for National Endangered Species Assessments



##  Summary and conclusions

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### 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. 7

### 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. 8

### 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.

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### Modeling Approach: Water Body Size and Depth


##  Water body surface area and depth were represented as probability distributions. These factors influenced:

− V olume

− 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 DEM based 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. 16

### 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.

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### Modeling Results: Comparison of Screening Level EECs with Refined EEC Distribution


##  S creening 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.

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### 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