# Spatial Aquatic Model: Pilot Project Update
March 24, 2014 Presentation to the EMPM Meridith Fry, Dirk Young, and Nelson Thurman

# Outline

• Overview of SAM

– Why SAM?

– Improving our current modeling approach



## • Overview of the Model

– Model structure and components

– Solution technique



## • Performance and validation

• Next steps

# Overview of SAM

# SAM: S patial [and Temporal] 

# A quatic M odel Intended to answer the risk management questions:

• Do exposures exceed toxicity levels of concern?

• If so, where do these exposures occur and how often/ how long do they occur?

4 Spatially Explicit Assessment

Stressor distribution Receptor range Area exceeding level of concern Pesticide

Where When How often Animals Plants Humans5 

# Why SAM?

Pass

Fail 

## When screening assessments exceed toxicity thresholds, we need a way to more effectively identify areas of potential concern

# Overarching goals of SAM

• Improve on what we currently do, advancing the science by keeping up with what’s available
• Keep things as simple as possible without losing vital information in oversimplification
• Focus on the drivers

– Add complexity where it has an impact

– Where it doesn’t make much difference, keep it simple


## • Be useful and understandable for both the modeler and the end users
• Provide confidence and reliability in our assessments

# Overview of the Model

# SAM Structure

# Spatial data inputs: constructing scenarios

9 

### Soils
- SSURGO Land Cover
- CDL



## Overlap soils with land cover to create basic scenarios (NRCS) (USDA)

# Additional spatial inputs

10 Meteorology Crop Growth/Management NCAR/NCEP Reanalysis Gridded Data 1948
– 2013 (daily) ↓ NOAA CPC: 0.25 ◦ x0.25 ◦ Daily US Precip Scenarios

# Constructing Watershed Recipes

11 

### List of unique scenarios and total area in a given basin NHD Plus (version 2) Mean flow data for each basin Watershed Recipes

# SAM Structure

# Pre - processed components

• Scenario Generator

– Calculates daily hydrologic values (irrigation, runoff, evaporation, vertical transport) and plant growth stages for each mapping unit

- crop combination (MUKEY

- CROP)

– Output : Daily runoff, daily soil moisture, plant growth stages for each MUKEY

- CROP


## • Super PRZM Hydro

– Sums daily runoff weighted by area of all MUKEY

- CROP combinations in each watershed

– Output : Total daily runoff from each watershed over simulated time period

# SAM Structure

# Chemical/site
- specific components

• Pesticide Transport and Water Body Concentration Calculator

– Links chemical transport and watershed hydrology to estimate pesticide concentrations in specific receiving water bodies

– Chemical and application details are read from graphical user interface (GUI)

– Output : Daily pesticide concentrations in receiving water body, daily flow, frequency of exceeding toxicity threshold, spatial maps

# Transport from application site based on PRZM

• As in PRZM, the Calculator simulates leaching, sorption, degradation, and transport via runoff in top 2 cm soil compartment

• At each successive daily time step, the transport equation is solved analytically

*Currently, transport equation does not include loss of pesticide mass sorbed to eroded soil Mass leached Mass stored Mass transported via runoff Mass applied

Mass degraded2 cm

# Mass transported via runoff concept from PRZM2 to PRZM3 to SAM

# Receiving water body volume and flow

• Receiving water body volume is first calculated:

• Mixing cell concept applied to all water bodies, given that volume of mixing cell is << mean flow rate

• Monthly mean base flows calculated using NHDPlus v2 seasonalized annual average flow and long

- term average daily watershed runoff

• Total daily flow in water body = Base flow + Daily Runoff Flowing water body (streams, rivers) Non - flowing water body (drinking water r eservoir , pond) Water body volume Cross

- sectional area (flow/velocity ) x Representative length (40 m) Hollister (2011) method used to estimate lake morphometry

# Solution technique for transport equation

Tridiagonal 2 diagonal Single Equation

# Receiving water body concentration

• Using estimated volume (V wb 

### ) and total flow (Q t 

### ), effective rate constant (k) for first

- order washout from water body is calculated:

• Initial water body concentration, C o,wb 

### :

• Water body concentration (C wb 

### ) at indefinite time later: Daily mean conc :

* Future work may handle reservoirs differently, as longer residence times permit mass to degrade or partition. For pilot study of atrazine , reservoir and flowing water body concentrations calculated identically. wb

t V

Q t k = ) (

kt wb o wb 

### e t C t C − 

### = ) ( ) (

, wb remaining runoff wb o V t M t M t C ) 1 ( ) ( ) (

, − +
=

) 1 ( ) ( ,

, k wb o wb avg e k C t C −

− =

# Performance and Validation

# Monitoring comparisons show SAM outputs are reasonable

• Confidence in results:

– Consistency in magnitude and seasonality between simulated flows & USGS stream gage measured flows

– Consistency in magnitude between simulated atrazine concentrations & AMP/AEEMP measured concentrations



## • Main finding
- SAM performs very well for a non
- calibrated model. While results do not match monitoring data perfectly, they are reasonable and representative.

# Consistency in magnitude of flows: SAM daily total flow vs. USGS stream gage 3353200

*Additional comparisons provided in Appendix A of Model Documentation

# Consistency in seasonality of flows: SAM monthly avg base & runoff flows vs. USGS stream gage 3353200

*Additional comparisons provided in Appendix B of Model Documentation J F M M A J J A S O N D

# Consistency in daily concentrations: SAM daily atrazine concentrations vs. AMP monitoring data

*Additional comparisons provided in Appendix C of Model Documentation Jan

- 03 Jan

- 04 Jan

- 05 Jan

- 06 Jan

- 07*Additional comparisons provided in Appendix D of Model Documentation 

# Consistency in magnitude of averaged concentrations: SAM single

- day, 14

- d avg, 30

- d avg max concs vs. AMP monitoring (with bias factors)

1

- d 14

- d 30

- d 2003 1

- d 14

- d 30

- d 2004 1

- d 14

- d 30

- d 2005 1

- d 14

- d 30

- d 2006

# Next Steps

# Next Steps

• Complete Pilot Region Case Study: In compilation

• Generate heat maps, temporal maps to illustrate SAM capabilities

• Provide Pilot Study documentation: Developing avenue for comments

• Build out national spatial datasets (data development, input processing)

• Flesh out erosion, spray drift, irrigation, other components

• Evaluate with other pesticide monitoring

• Expand to other regions in U.S.

• Build computer infrastructure: remote server, cloud computing • EFED assessors, OPP management, risk managers, HED, industry, other EPA and federal agencies

• Follow

- up discussions on model outputs and applications

• Beta testing

• “Virtual” workshop on SAM (industry, fed agencies, academia)

• Scientific Advisory Panel

• Incorporate feedback

• Finalize model ramp

- up process
• Full scale modeling • Roll

- out plan TBD

• Consistent with science, policy

• Continuing evaluation period Prepare Presentations for Target Audiences

Ramp Up National Model

Build Out SAM Model Implementation Feedback on SAM 1

3 4 2a 2b

# Thank you! Questions?
Contact: Nelson Thurman (Project Lead) Office of Pesticide Programs, US EPA thurman.nelson@epa.gov