1

BACKGROUND DOCUMENT IN SUPPORT OF THE

MEETING OF THE FIFRA

SCIENTIFIC ADVISORY PANEL

ON THE

DEVELOPMENT OF A SPATIAL AQUATIC MODEL (SAM)

FOR PESTICIDE ASSESSMENTS

SEPTEMBER 15-18, 2015

US EPA Office of Pesticide Programs

Environmental Fate and Effects Division
2

Development of a Spatial Aquatic Model (SAM) For Pesticide Assessments

Background Paper for the September 15-18, 2015, FIFRA SAP

Contents

Executive Summary......................................................................................................................... 8

A Note on Model Versions............................................................................................................ 11

Introduction .................................................................................................................................. 12

Objectives of OPP’s Pesticide Aquatic Exposure Assessments................................................. 12

Objectives of SAM ..................................................................................................................... 14

Preliminary Model Development.............................................................................................. 15

1. Conceptual model for SAM ................................................................................................... 20

1.1 Conceptual Model for Current Surface Water Assessments.............................................. 20

1.2 Adding a Spatial Context to Aquatic Exposure Assessments......................................... 22

1.2.1 SAM and the other OPP surface water models............................................................ 28

2 SAM Model Components: Core Modeling Components/Approach...................................... 29

2.1 Modeling Approach Used in SAM ....................................................................................... 30

2.1.1 SAM Components......................................................................................................... 31

2.1.2 Alpha Version of SAM (übertool Site) .......................................................................... 34

2.2 Proposed Updates and Additional Improvements under Consideration ........................... 35

2.2.1 Erosion and Sediment Routines ................................................................................... 35

2.2.2 Spray Drift..................................................................................................................... 36

2.2.3 Irrigation ....................................................................................................................... 37

2.2.4 Ponds and other small non-flowing water bodies ....................................................... 37

2.2.5 Other agricultural crops................................................................................................ 37

2.2.6 Non-agricultural uses.................................................................................................... 37

2.3 Uncertainty and Sensitivity Analyses.................................................................................. 37

2.3.1 Uncertainty Analysis..................................................................................................... 37

2.3.2 Sensitivity Analysis........................................................................................................ 39

2.4 Comparison of Model Outputs with Measured Data (Monitoring Data) ........................... 40

2.4.1 Atrazine Monitoring Datasets Used for Comparison ................................................... 41
3

2.4.2 Future Comparisons with Other Pesticides.................................................................. 42

2.4.3 Criteria for Model Evaluation ....................................................................................... 43

2.4.4 Initial Comparisons with Atrazine Monitoring Data..................................................... 43

3 SAM Data Inputs.................................................................................................................... 51

3.1 Data Inputs Used in SAM..................................................................................................... 52

3.1.1 Soil-Related Inputs from SSURGO ................................................................................ 53

3.1.2 Crop/Land Cover from the Cropland Data Layer (CDL)................................................ 54

3.1.3 Weather Data ............................................................................................................... 55

3.1.4 Crop Data Collection and Processing............................................................................ 57

3.1.5 National Hydrography Dataset (NHDPlus, Version 2.0) ............................................... 59

3.2 Creating Scenarios, Input Data, and Watershed Recipes ................................................... 60

3.2.1 Creating Scenarios........................................................................................................ 60

3.2.2 Generating Watershed Recipe Files............................................................................. 61

3.3 Soil Grouping Classes Based on USDA Soil Water Quality Index Groupings....................... 61

4 Accounting for Time of Travel and Downstream Accumulations.......................................... 65

4.1 “Instantaneous” Approach Used in the Initial Version of SAM .......................................... 65

4.2 Proposed Update to Account for Time of Travel Dispersion .............................................. 67

4.3 Initial Results and Comparisons with Monitoring Data ...................................................... 70

5 Defining the Likely Pesticide Application Window based on Crops and Weather................ 78

5.1 Context for Using Crop Milestones to Estimate Pesticide Application Windows............... 79

5.1.1 Defining Pesticide Application Windows...................................................................... 79

5.2 Using Empirical Data to Define Crop Milestones: USDA Crop Progress Reports................ 83

5.2.1 Availability of Empirical Data by Crop Groups.............................................................. 84

5.3 Crop Growth Models........................................................................................................... 86

5.4 Model Demonstration Using Corn ...................................................................................... 87

5.4.1 Model results................................................................................................................ 89

References .................................................................................................................................... 91
4

List of Appendices

These appendices, included as separate documents, provide supporting information for this 

briefing document. The first number for each appendix refers to the main section of this 

document for which it supports. Some of the appendices contain large amounts of data that are 

not easily configured into a text document. These are left as spreadsheets. This also allows the 

readers/reviewers to more easily explore the data on their own.

Appendix Description

2-A SAM Model Component Conceptualizations (pdf)

2-B Comparison of SAM Estimated Concentrations between Non-Uniform, MultiCompartment Approach and Uniform, Single Compartment Approach (pdf)

2-C Test Basins and Monitoring Data Used for Model Evaluation in SAM (spreadsheet, 

26 tabs)

2-D Comparisons of SAM Outputs to Monitoring Data for Selected Test Basins in

(1) Mark Twain Basin, MO (spreadsheet)

(2) Scioto River Basin, OH (spreadsheet)

(3) White River (East Fork) Basin, IN (spreadsheet)

3-A Description of Scenario Inputs Used in SAM (pdf)

3-B General Land Cover Classes Derived from the Cropland Data Layer (pdf and 

spreadsheet)

3-C Integrating Gridded Daily Meteorology Into Pesticide Aquatic Exposure Models 

(pdf)

3-D Crop Plant-Harvest Dates and Other Related Inputs (spreadsheet)

3-E Derivation of Curve Number for SAM (pdf)

3-F Evaluation of Soil WQI Grouping Classes as a Potential Replacement for Individual 

Soil Map Units in SAM Scenarios (pdf)

(1) Comparison of Outputs for Mark Twain Basin, MO (spreadsheet)

(2) Comparison of Outputs for Scioto River Basin, OH (spreadsheet)

(3) Comparison of Outputs for White River Basin, IN (spreadsheet)

5-A Comparison of Outputs Based on Different Pesticide Application Window 

Approaches

(1) Mark Twain Basin, MO (spreadsheet)

(2) Scioto River Basin, OH (spreadsheet)
5

Acronyms and Abbreviations

Abbreviation Description

AEEMP Atrazine Ecological Exposure Monitoring Program, conducted by Syngenta 

Crop Protection in the midwestern US

AMP Atrazine Monitoring Program, conducted by Syngenta Crop Protection

CDL Cropland Data Layer, provided by USDA National Agricultural Statistics 

Service

CN Curve Number, used in runoff calculations

CPR Crop Progress Reports, provided by USDA National Agricultural Statistics 

Service

CREM USEPA's Council for Regulatory Environmental Modeling

CSTR Completely Mixed Stirred Tank Reactor, used in SAM to calculate pesticide 

concentrations in water

EEC Estimated Environmental Concentration(s)

EFED USEPA OPP’s Environmental Fate and Effects Division

ESRL NOAA Earth System Research Laboratory

EXAMS Exposure Analysis Modeling System

FEMVTF FIFRA Environmental Modeling Validation Task Force

FFDCA Federal Food, Drug, and Cosmetic Act

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act

GDD Growing degree day

GUI Graphical user interface

HUC/HUC12 Hydrologic Unit Code

IRF Impulse response function, used to describe the response of a water body 

to a pulse input

MLRA Major Land Resource Area

MUKEY Map Unit Key, a unique identifier of soil map units used in SSURGO

MUSS/MUSLE Modified Universal Soil Loss Equation

NASS USDA National Agricultural Statistics Service

NAWQA USGS National Water Quality Assessment program

NCDC NOAA National Climatic Data Center

NCEP/NCAR National Centers for Environmental Prediction and Atmospheric Research

NHDPlus National Hydrography Dataset Plus, the geospatial hydrologic dataset 

developed by USEPA and USGS. Version 2 is used in the model.

NOAA National Oceanic and Atmospheric Administration 

NRCS USDA Natural Resources Conservation Service

NWIS National Water Information System, maintained by USGS

OPP USEPA Office of Pesticide Programs

ORD USEPA Office of Research and Development

PHZ Plant Hardiness Zone (USDA map)

PRZM / PRZM5 Pesticide Root Zone Model / version 5 (the current version)

SAM Spatial Aquatic Model
6

Abbreviation Description

SAMSON Solar and Meteorological Surface Observation Network

SSURGO Soil Survey Geographic database, the geospatial soil dataset developed by 

the USDA Natural Resources Conservation Service

SWCC Surface Water Concentration Calculator

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency

USGS United States Geological Survey

USLE Universal Soil Loss Equation 

VVWM Variable Volume Water Model

WARP Watershed Regression on Pesticides, USGS model
7

Spatial Aquatic Model (SAM) Development Team

USEPA, Office of Chemical Safety and Pollution Prevention, Office of Pesticide Programs:

Jim Carleton, Ph.D., Environmental Engineer

Meridith Fry, Ph.D., Environmental Engineer

James (Trip) Hook, GIS Specialist

Chris Koper, Environmental Fate Scientist

Paul Mastradone, Ph.D., Environmental Fate Scientist

Ron Parker, Ph.D., Environmental Engineer

Kurt Pluntke, Environmental Scientist

Gabe Rothman, Environmental Fate Scientist

Rick Shamblen, Environmental Fate Scientist

Michelle (Shelly) Thawley, GIS Specialist

Nelson Thurman, Senior Science Advisor; SAM Project Lead

Dirk Young, Ph.D., Environmental Engineer

USEPA, Office of Research and Development, Übertool Interface:

Jon Flaishans (ORISE)

Mike Galvin

Tom Purucker, Ph.D., Research Ecologist

Marcia Snyder (SSA)

Additional Peer Review Support:

Jonathan Becker, Biologist, OPP BEAD; Mark Corbin, Branch Chief, OPP EFED; Jim Cowles, 

Associate Division Director, OPP EFED; William Jordan, Associate Office Director, OPP; Steve 

Lennartz, GIS Specialist, OPP EFED; Anita Pease, Associate Division Director, OPP EFED.
8

Executive Summary

As a part of the requirements for pesticide registration and registration review, the US 

Environmental Protection Agency Office of Pesticide Programs (USEPA OPP) conducts aquatic 

exposure assessments to determine whether pesticides that are applied according to label 

directions can result in water concentrations that may adversely impact human health or 

aquatic organisms. To do this for the hundreds of registration and registration review actions 

each year, USEPA OPP uses a model that estimates pesticide concentrations in water that 

accounts for a combination of soil, weather, hydrology, and management/use conditions that 

are expected to maximize the potential for pesticides to move into water. If these resulting 

high-end exposures are less than the various toxicity endpoints of concern (for fish, 

invertebrates, plants, or humans consuming drinking water), then USEPA OPP can conclude that 

the pesticide is unlikely to pose adverse risks to humans or aquatic organisms based on its 

labeled uses. 

However, if the estimated pesticide concentrations from these high-exposure conditions 

exceed one or more of the toxicity endpoints, then USEPA OPP may need to determine how 

likely these estimated concentrations are to occur, how often they may exceed toxicity 

thresholds, where areas of greatest concern occur, and how confident we are in the exposure 

assessment. To do this, USEPA OPP has used a number of approaches, including modeling a 

wider range of conditions, including more typical use patterns, trying to identify the most 

vulnerable areas, and evaluating pesticide monitoring data. These approaches, however,

require a lot of time and resources and do not capture the full range of potential conditions 

affecting how much pesticide moves from the field to water bodies that occur throughout the 

country. While USEPA OPP uses monitoring data to get snapshots of actual pesticide 

concentrations in water, most monitoring studies do not sample water frequently enough to 

capture every peak that occurs in water (e.g., samples taken every two weeks are unlikely to 

capture the maximum daily concentration that may occur during that time); nor do the studies 

cover the full extent of the pesticide use area or the range in vulnerability. 

Because of an increasing need for more spatial specificity in our aquatic exposure assessments 

both for human health (from drinking water) and aquatic organisms for ecological assessments,

USEPA OPP needs a tool that adds both a spatial context for the diversity of conditions affecting

pesticide movement to water, and a time aspect for when and how frequently pesticide 

concentrations may exceed toxicity endpoints of concern. Given the number of risk 

assessments completed each year (on the order of hundreds), the tool needs to provide results 

in an efficient, consistent, and understandable way. 

To address this need, USEPA OPP has developed an alpha version of a Spatial Aquatic Model 

(SAM), focusing first on the Ohio River basin. The intent of this alpha model was to evaluate the 

feasibility of pulling together spatial data that captures the diversity of soil, land cover, 

weather, hydrology, and crop conditions/management practices as they occur across the 
9

country to generate daily estimates of pesticide concentrations over a number of years for a 

wide variety of streams, rivers, lakes, and reservoirs. This involved making some changes in the 

conceptual approach USEPA OPP uses to estimate aquatic exposures, as described in Section 1. 

USEPA OPP is asking this Scientific Advisory Panel (SAP) to comment on the clarity of this 

conceptual watershed modeling approach and its strengths and limitations for estimating 

pesticide concentrations over time across the country. 

One of the challenges in developing SAM was to make sure the model could process the large

datasets needed for a national scale assessment, simulate the key fate, transport, and 

hydrologic processes that drive pesticide movement to water, and generate outputs in a

concise and timely manner (on the order of minutes to hours rather than days to weeks). 

USEPA OPP has taken several steps to make this possible. First, the model processes were 

streamlined to reduce redundant calculations and focus on the main processes that drive 

pesticide fate and transport (these have been identified through a long history of pesticide 

modeling and analyses to determine the inputs that most influence pesticide movement to 

water). Second, those processes that are not impacted by pesticide applications – daily runoff 

and erosion – have been processed ahead of time. Finally, SAM will run as a web application to 

take advantage of cloud-based data storage and parallel processing across multiple computers.

The current “alpha” version of SAM (Section 2) focuses on the pesticide runoff routines in a 

dominantly corn-soybean-small grains agricultural setting. USEPA OPP has included initial 

comparisons with a set of monitoring data on atrazine to help identify areas for further model 

development and improvement. The atrazine studies provide some of the most intensive 

sampling (with daily to weekly frequency targeted to high use areas) available for pesticides. 

USEPA OPP is also looking for similarly intensive monitoring for other pesticides for further 

model testing. For this SAP consultation, we are asking for panel feedback on the model design, 

the streamlined pesticide transport calculations, and methods for calculating pesticide 

concentrations in water. 

This alpha version does not yet take into account pesticide loadings from sediment transport or 

spray drift; nor does it account for pesticide fate in the water body (degradation, sorption to 

sediment). USEPA OPP plans to add these processes to future versions of the model. In the 

meantime, we are asking the panel for recommendations on how best to account for sediment 

transport and pesticide fate in water bodies.

In Section 3, USEPA OPP describes what we believe are the best available national data sets for 

soil, land cover, weather, crop characteristics and management practices, and hydrology. We 

also describe how these diverse data sets are organized as soil-land cover-weather 

combinations (scenarios) for inputs to the model and to reflect the spatial diversity in 

conditions. USEPA OPP is asking the SAP to comment on the approach used to aggregate spatial 

inputs across varying scales. 
10

With an eye toward optimizing data storage and processing requirements for large national 

datasets, USEPA OPP evaluated an approach to reduce the total number of soil-land coverweather combinations (scenarios) by grouping individual soil map units based on similarities in 

properties that influence chemical transport to water (runoff potential, erosion potential, 

surface organic matter content, and slope). Section 3 includes a comparison of results from the 

grouping approach in support of a charge question on the implications of aggregating model 

input parameters using soil water quality classes. 

The alpha version of SAM calculates daily pesticide concentrations in water based on daily 

runoff and pesticide mass loads aggregated across the entire watershed area. While this may 

be a viable option for small watersheds, it does not account for differences in the time it takes 

pesticide loads from the upper end of a larger watershed to reach the outlet, compared to 

pesticide loads from fields near the outlet. The larger the watershed, the more spread out the 

pesticide loads in water are likely to be. This is likely to result in lower peak concentrations of 

pesticide at the outlet, but a longer duration of exposure. Section 4 describes an approach 

USEPA OPP is testing to account for differences in time of travel. Results have improved 

estimated concentrations in comparison to monitoring data across a range of watershed sizes 

and water body types. In the charge questions, USEPA OPP is asking the SAP to comment on the 

proposed approach, its adaptability across a range of watershed sizes, and the potential to 

adept this to reactive and/or sorbing chemicals.

In initial model comparisons to monitoring data, USEPA OPP noticed that, in some years, the 

time of occurrence of pesticides in water estimated using SAM did not always correspond well

with the timing of the pesticides in monitoring data for the same sites. The timing of pesticide 

applications is a key driver in determining the timing and amount of pesticides that reach 

water. However, detailed spatial information on the timing and amount of pesticide applied

across the country is not readily available. USEPA OPP is exploring some approaches to estimate 

the potential pesticide application window based on crop planting and crop growth information 

in Section 5. Preliminary tests using atrazine on corn showed that accounting for year-to-year 

variations in planting and emergence dates (using crop progress reports) resulted in better 

correspondence between estimated concentrations and monitoring data for both the timing 

and magnitude of concentrations in water. While this approach may not be applicable to all 

types of pesticides or crops, USEPA OPP is asking the SAP for feedback on these options for 

linking the timing and duration of pesticide applications to USDA crop progress reports and/or

crop growth models, or for recommendations on other methods for defining pesticide 

application windows in the absence of usage information.

USEPA OPP appreciates the opportunity to consult with this SAP on these issues as it moves 

forward in developing the Spatial Aquatic Model for pesticides.
11

A Note on Model Versions

USEPA OPP developed an alpha version of the Spatial Aquatic Model (SAM), using the Ohio 

River Basin, to develop and refine the conceptual model, evaluate key model components and 

data compilation methods, and test the feasibility of running the model on a national scale. This 

version of SAM focuses on hydrology and pesticide runoff routines in a dominantly cornsoybean-small grains setting. It does not yet take into account pesticide loadings from sediment 

transport or spray drift; nor does it account for pesticide fate in the water body (degradation, 

sorption to sediment). These will be included in future iterations of the model.

Because of the deployment cycle for SAM to the public, we are presenting results from two

versions of SAM exist. The first alpha version (SAM alpha 1.0), which is available on the übertool

site (http://qed.epa.gov/ubertool/sam), calculates concentrations for each HUC-12 unit based 

on volumes and flow rates for the immediate (local) HUC-12. Such estimates do not account for 

contributions from upstream HUC-12 units and the results could be misleading. Adjustments 

will be made to this model based on feedback from the SAP.

Since this initial development, USEPA OPP replaced the HUC-12 units with National 

Hydrography Dataset (NHDPlus) catchments for individual reaches (SAM alpha 2.0). The 

NHDPlus reaches, which are at a more detailed scale than HUC-12 units, provide a more 

efficient framework for modeling mass movement downstream and accounting for upstream 

contributions. Although results from SAM alpha 2.0 are presented to the SAP, this version is not 

publicly available. 

Based on model evaluations discussed in Section 2.4, USEPA OPP has proposed updates and 

refinements to the test version of SAM for SAP consideration: re-distributing daily pesticide 

loads from individual catchments based on their travel time to receiving water bodies in larger 

drainage networks (Section 4) and defining application windows (Section 5). 

Additional improvements to the model are under consideration to address several limitations of 

the test version. This section describes additions that USEPA OPP is evaluating for future 

versions of SAM. As part of the peer review process, USEPA OPP welcomes recommendations 

and feedback on these and other model improvements. Recommendations will be considered 

based on their scientific merit, ease of incorporation into the model, and the availability of 

intensive monitoring data for validation.

After this SAP consultation and consideration of panel recommendations, USEPA OPP will 

incorporate these components into a new test version of SAM on the übertool site. 
12

Introduction

The US Environmental Protection Agency (USEPA) Office of Pesticide Programs (OPP) has 

developed a Spatial Aquatic Model (SAM) for pesticides to address the goals and objectives of 

aquatic exposure assessments that are a part of the pesticide registration and registration 

review process. In developing SAM, USEPA OPP focused on improving the existing aquatic 

modeling approach in order to provide a higher-tiered assessment to use in regulatory decision 

making under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and the Federal 

Food, Drug, and Cosmetic Act (FFDCA). This background document describes the conceptual 

model for SAM (Section 1), the core model components (Section 2) and data used in the model

(Section 3), a method for taking into account time of travel for pesticide loadings in water as 

the watershed size and travel time increase (Section 4), and a proposed approach to define 

pesticide application windows on a watershed scale based on crop growth (Section 5). Specific 

charge questions for this FIFRA Scientific Advisory Panel (SAP) focus on the information 

provided in each section.

The model described here is an alpha version that focuses on key components for which USEPA 

OPP is soliciting comment from the SAP: 

- the watershed-based conceptual model; 

- a streamlined framework that captures main drivers for pesticide fate and transport to 

water; 

- runoff, mass transport, and water concentration estimations, particularly for flowing 

water bodies; 

- best available methods for aggregating spatial data of varying scales for model inputs; 

- methods to account for time of travel differences for pesticide loadings in water across 

larger watersheds; and

- an approach for varying pesticide application windows in response to crop growth and 

year-to-year variations in weather; 

The primary goal of this consultation is to get feedback on the scientific strengths and 

limitations of the model for predicting pesticide concentrations in surface water on a 

watershed basis across the U.S, and to seek recommendations on improving the model.

Objectives of OPP’s Pesticide Aquatic Exposure Assessments

As a part of its requirements for pesticide registration and registration review, USEPA OPP 

conducts aquatic exposure assessments to determine whether pesticides that are applied 

according to label directions can result in concentrations in water that may adversely impact 

human health or aquatic organisms. If estimated aquatic exposures indicate a potential for 

adverse effects, the assessment may need to characterize the likelihood of occurrence, 

including the range in magnitude of exposure, the frequency of exceeding toxicity thresholds, 
13

the likely locations of exposures that exceed the thresholds, and the degree of certainty in the 

risk assessment. 

Currently USEPA OPP estimates aquatic exposures using the Pesticide Root Zone Model (PRZM

version 5) and Variable Volume Water Model (VVWM1), which is based on the Exposure 

Analysis Modeling System (EXAMS). These models are linked with a user interface in the Surface 

Water Concentration Calculator (SWCC)2. As a part of its tiered screening approach, USEPA OPP

uses standard crop-specific scenarios to represent combinations of soil, crop, weather, and 

hydrological factors that are expected to contribute to high-end pesticide concentrations in 

water. The PRZM portion of the SWCC simulates pesticide sorption to soil, in-field decay, and 

runoff from an agricultural field/drainage area following pesticide application(s). The VVWM 

simulates resulting concentrations in an adjacent surface water body (a “standard” pond for 

aquatic organisms or an “index reservoir” for drinking water) receiving the pesticide loading. 

For ecological assessments for aquatic species, USEPA OPP compares the 1-in-10 year return 

frequency concentration for either single-day (peak concentrations for estimating acute 

exposures) or time-averaged concentrations of different durations (for estimating chronic 

exposures) to relevant toxicity endpoints of concern. Drinking water assessments for human 

health risk assessments make use of the full distribution of simulated daily concentrations for 

acute toxicity concerns. This approach is intended to screen out pesticides (and/or specific 

uses) that are not likely to be of potential concern, but does not provide estimates of how 

pesticide concentrations vary across different watersheds in the pesticide use areas3. 

For pesticides that do not clear these screens, refinements to incorporate spatial and temporal 

details, when needed, require considerable time and resources, and have not yet been 

automated to consistently capture the extent of potential variability in exposures. Examples of 

pesticide risk assessments that have incorporated spatial refinements include drinking water 

assessments for organophosphate and N-methyl carbamate cumulative risk assessments, which 

focused on regional high-exposure areas4, and the proposed reassessment of ecological risks 

 1 The documentation for the 2004 FIFRA Scientific Advisory Panel on Refined (Level II) Terrestrial and Aquatic 

Models for Probabilistic and Ecological Assessment of Pesticides provides more information on the VVWM and the 

use of these models for aquatic exposures, at http://www.epa.gov/scipoly/sap/meetings/2004/033004_mtg.htm 2 Models and documentation are available through the USEPA OPP Water Models web site at 

http://www.epa.gov/oppefed1/models/water/. The models and approaches have been subject of a number of

FIFRA Scientific Advisory Panel consultations. A link to relevant SAP sessions related to aquatic modeling can be 

found at http://www.epa.gov/oppefed1/models/water/sap_sessions.htm. 3 A more detailed description of USEPA OPP’s ecological risk assessment process can be found in the 2004 

Overview of OPP’s ecological risk assessment process, available at 

http://www.epa.gov/oppfead1/endanger/consultation/ecorisk-overview.pdf. 4 Information on assessing pesticide cumulative risk, including links to the Organophosphates and N-Methyl 

Carbamates assessments, can be found at 

http://www.epa.gov/pesticides/cumulative/common_mech_groups.htm. 
14

for atrazine that will use a combination of spatially-linked modeling and monitoring data5. For 

example, assessments under the Endangered Species Act often would benefit from estimates of 

the spatial and temporal distributions of pesticide concentrations, and where these 

distributions may overlap with the ranges of listed species (NRC, 2013). 

USEPA OPP needs a tool to meet the increased demand for spatially-resolved human health 

(drinking water) and ecological aquatic exposure assessments. Such a tool should add both 

spatial and temporal contexts to aquatic exposures in an efficient, consistent way, ideally while 

minimizing the demand for extra work on the part of risk assessors.

Objectives of SAM

The goal of SAM is to improve on OPP’s existing aquatic exposure assessments by providing 

more systematic spatial- and temporal-contexts for aquatic exposure assessments for both 

human health (drinking water) and aquatic organisms. Such contexts are needed to address 

common risk management questions regarding the likelihood of exposures that may exceed 

toxicity thresholds of concern and, should such exposures occur, how often, how long, and 

where adverse impacts from pesticides in water overlap with populations (human and 

ecological) at potential risk. 

A spatial model is possible because key factors that drive pesticide concentrations in water 

occur in predictable patterns in time and space. These factors include pesticide use intensity, 

rainfall timing and intensity, soil factors related to runoff and/or erosion vulnerability, land 

cover type, and landscape/ hydrologic factors related to runoff. Evidence supporting this

concept include data collected through the US Geological Survey's (USGS) National Water 

Quality Assessment (NAWQA) program (Gilliom et al, 2006; see the Pesticide National Synthesis 

Project website at http://water.usgs.gov/nawqa/pnsp/ for further studies), the development of 

the Watershed Regression on Pesticides (WARP) model (Stone and Gilliom, 2009, 2012; Stone 

et al, 2013), and analyses presented at several FIFRA SAPs on atrazine monitoring studies 

(USEPA OPP, 2007, 2009a, 2010a, 2010b, 2011, 2012). While neither SAM nor other pesticide 

models include every possible variable that may impact pesticide concentrations in water, SAM 

simulates the major known drivers (described in Sections 2 and 3) to illuminate exposure

patterns in time and space, so that risk assessors can understand and account for them and 

better inform risk managers.

Though a number of runoff, erosion, and pesticide fate and transport algorithms used in SAM 

are similar to those used in USEPA OPP’s traditional field-based water models mentioned 

above, SAM brings a new spatial approach to modeling the fate and transport of pesticides and 

expansion to watershed-scale processes and outputs. Section 1 highlights differences in the 

 5 See the June 2012 FIFRA SAP on Problem Formulation for the Reassessment of Ecological Risks from the Use of 

Atrazine, available at http://www.epa.gov/scipoly/sap/meetings/2012/061212meeting.html. 
15

conceptual approaches moving from field- to watershed-scale models. As with USEPA OPP’s 

current aquatic modeling approach, SAM will generate daily concentrations and distributions 

for a variety of human health and ecological toxicity endpoints. However, instead of providing 

estimates for a single pond or index reservoir representative of expected high-exposure 

conditions, SAM will provide distributions for a variety of water body types across the country. 

Ideally, the model should be capable of providing relatively quick turnarounds on national-scale 

assessments and require little or no calibration. 

Once fully developed and evaluated, USEPA OPP intends to use SAM in regulatory assessments, 

primarily for pesticides that do not pass lower-tiered screening assessments. SAM will improve 

upon OPP’s current capacity by adding more spatial and temporal contexts to aquatic exposure 

assessments. No model can accurately predict patterns in contaminant concentrations at all 

spatial and temporal scales; SAM is intended to provide concentration estimates that 

realistically reflect ranges in magnitude that can occur while nonetheless minimizing Type II or 

“false negative” errors (i.e., systematically underestimating actual exposures).

Preliminary Model Development

In developing SAM, USEPA OPP started with key, well-known hydrology/runoff and pesticide 

fate/transport algorithms that represent the movement of pesticides from landscapes to 

receiving waters (Section 2). Section 3 describes the key inputs used by these algorithms in the 

model. One goal for increasing the level of complexity in a model is to reduce uncertainty and 

increase accuracy in model outputs, thereby moving estimates closer to actual or known values

(to the extent that such values can be reliably measured). 

Adding complexity to a model entails bringing in additional routines and/or inputs, which can 

increase the variability of model outputs. Where inputs aren’t readily available or have a high 

degree of inherent uncertainty and/or variability, the resulting increased uncertainty in model 

output may negate any potential increase in accuracy. Of course, not all potential model inputs 

exert equal influence over pesticide fate and transport. 

In model development, we ideally consider the uncertainty implications of all input sources 

(Figure 1). The single-field/drainage area approach used in USEPA OPP’s current screening 

assessments is low in complexity, but high in uncertainty with respect to capturing the full 

range of potential pesticide exposures. Nonetheless the approach is useful because if resulting

high-exposure estimates do not exceed toxicity thresholds of concern, then no further 

modeling is necessary. 

At the other end of the complexity spectrum would be to model pesticide applications to every 

field and simulate pesticide movement by every transport pathway into and through every 

drainage way from headwater reaches to the mouth of large rivers. Not only would such an

approach be data- and computationally-intensive, but the uncertainty in required modeling 
16

inputs and the adequacy of algorithms to represent processes across a range of scales would 

likely generate large uncertainty about model outputs. 

Figure 1: Conceptual Plot of Level of Model Complexity and Uncertainty in Model Outputs

Rather than simulating flow routing by field into drainage ways and streams, SAM accumulates 

pesticide mass and flows at pour points along the stream network (see Sections 2 and 3 for 

more detail). SAM accounts for spatial heterogeneity in soil, land cover, and hydrologic 

conditions within each contributing drainage area. In addition, SAM accounts for geographic 

differences in weather (e.g., precipitation), crop growth and timing, watershed area, and type

and flow characteristics of receiving waters.

USEPA OPP is following USEPA’s Council for Regulatory Environmental Modeling’s (CREM) 

guidance on model development and evaluation (USEPA, 2009) which was generated in 

response to an earlier report by the National Research Council on the use of environmental 

models in regulatory decision-making (NRC, 2007). Key components of the model development 

guidance are (1) scientific soundness of model routines, (2) characterization of model sensitivity 

and uncertainty analysis, and (3) comparison of model outputs against measured data, taking 

into account uncertainty in measured data. These are described in Section 2. In the case of 

SAM, the measured data consist of pesticide monitoring at sufficient sampling frequency to 

minimize inherent underestimation biases that often result from less-frequent sampling. 
17

USEPA OPP developed an alpha version of SAM (SAM alpha1.0), using the Ohio River Basin 

(Figure 2), to evaluate and refine the conceptual model for incorporating more spatial detail in 

surface water modeling (Section 1), develop and evaluate the adequacy of key modeling 

components for runoff and pesticide transport at watershed scales (Section 2), automate 

methods for compiling model inputs from spatial data (Section 3), and determine the data 

storage and computational requirements for supporting the model on a national scale. 

Figure 2: NHD Hydroregion 5, the Ohio River Basin, with land cover from the Cropland Data Layer.

The predominantly corn-soybean agriculture (yellow and dark green colors) occurs in the northern and 

western portions of the basin; pasture (pale yellowish-green) and forestry (pale green) dominate the 

rest of the basin. Major urban areas are in gray.

This alpha version has been the subject of workshops/webinars (October, 2014, and April, 

2015) with stakeholders and interested parties to provide an overview of the model, offer a 

chance to test an early version of SAM, and solicit feedback on key components in the model.

Because data storage and computer processing requirements exceed the capacities of most 
18

personal computers, OPP worked with USEPA’s Office of Research and Development (ORD) to 

set up a web application for SAM through USEPA’s test version of the übertool web site (web 

applications for ecological risk assessment)6. Data storage and processing are done in a cloud 

environment and outputs can be displayed or downloaded for further analysis.

This SAP is a continuation of the model development and evaluation process for SAM, building 

on feedback from the earlier public workshops. The SAP-test version of SAM described in 

Section 2 is similar to the alpha version except where noted below. The SAP-test version of SAM

focuses on hydrology and pesticide runoff routines in a dominantly corn-soybean-small grains 

setting. It does not yet take into account pesticide loadings from sediment transport or spray 

drift; nor does it account for pesticide fate in the water body (degradation, sorption to 

sediment). USEPA OPP plans to add these processes to the model in future iterations. For this 

SAP consultation, USEPA OPP is asking for panel feedback on the model design, the streamlined 

pesticide transport calculations, and methods for calculating pesticide concentrations in water. 

The alpha version of SAM on the übertool site generates outputs at the HUC-12 (USGS 

Hydrologic Unit Code7) level. This version only accounts for daily runoff and pesticide loadings

locally generated from soil-land cover-weather station scenarios within each HUC-12. This 

version did not account for contributions from upstream HUC-12s. The SAP-test version of SAM 

replaces the HUC-12 units with National Hydrography Dataset (NHDPlus) catchments for 

individual reaches. The NHDPlus reaches not only are at a more detailed scale than HUC-12 

units, but also provide a more efficient framework for modeling mass movement downstream 

and accounting for upstream contributions. 

The alpha version also does not include two potential model improvements described in this 

document:

(1) Incorporating upstream (i.e., full watershed) loadings, accounting for dispersion and 

other dilution effects generated by surface water transport and residence time 

differences, described in Section 4; and

(2) Using empirical data (such as US Department of Agriculture [USDA] Crop Progress 

Reports) and/or crop growth models to better define the timing and duration of likely 

pesticide application windows tied to crop stages. This approach is presented in Section 

5 of this paper. 

USEPA OPP is seeking feedback on these approaches from the SAP before incorporating the 

algorithms and supporting data into the model. 

 6 The alpha version of SAM is available through the übertool site at http://qed.epa.gov/ubertool/SAM. Model 

testers, including SAP panel members, can access by contacting the SAM development team. As noted above, this 

version generates outputs by HUC-12 unit and does not account for contributions from upstream HUC-12s. It also 

does not include to proposed improvements to the model described in Sections 4 and 5. 

7 Hydrologic Units are a hierarchical naming convention developed by USGS to subdivide watersheds into 2-, 4-, 6-, 

8-, 10-, and 12-digit codes (HUCs) for spatial coverages. More information is available through the USGS Hydrologic 

Unit Maps site at https://water.usgs.gov/GIS/huc.html. 
19

SAM currently simulates pesticide loadings from runoff for a corn-soybean-small grain 

agricultural area of the country (Section 2). Model components for pesticide loadings into water 

through sediment transport or by spray drift are not yet functional. SAM includes pesticide 

transport downstream for streams, rivers, reservoirs, lakes, and other impounded water bodies, 

but does not yet account for in-stream degradation or sorption to sediments. SAP feedback on 

approaches to incorporate these additional processes into the model also will be useful as 

USEPA OPP expands model development and testing to other agricultural settings across the 

country. 
20

1. Conceptual model for SAM

This section describes the conceptual watershed modeling approach for SAM, contrasting it 

with OPP’s current conceptual model (single soil watershed, fixed geometry, fixed water body), 

in support of the following charge question to the SAP:

Q1. The conceptual watershed model for SAM accounts for spatial and temporal variability in 

soil, land cover, weather, and crop/management inputs and integrates outputs at

watershed pour points by area-weighting. 

a. Please discuss the strengths and limitations of the conceptual watershed model for 

representing spatial and temporal variability in pesticide concentrations in water. 

b. Please comment on how clearly this conceptual approach is explained. What 

additional documentation, description, and/or characterization is necessary to ensure 

clarity and transparency?

Section 1.1 describes the current conceptual model USEPA OPP uses for surface water exposure 

assessments; Section 1.2 describes the watershed conceptual model used in SAM and contrasts 

it with the current approach.

1.1 Conceptual Model for Current Surface Water Assessments

In its current surface water exposure assessments, USEPA OPP uses models to estimate 

pesticide concentrations in water bodies resulting from labeled use of pesticides outdoors.

These models provide aquatic estimated environmental concentrations (EECs) that can occur at 

labeled application rates, using inputs derived from laboratory data that characterize how fast a

pesticide breaks down and how and where it moves in the environment. In these models, the 

amount of pesticide that moves from the field to a nearby water body depends not only on the 

pesticide application rate and pesticide properties, but also on the site conditions, management 

practices, and weather (Figure 3). 

The Pesticide Root Zone Model (PRZM) simulates pesticide fate and transport processes in the 

field, tracking the pesticide in the plant canopy, soil, and root zone. This field-scale model 

estimates daily edge-of-field runoff, sediment, and pesticide loads delivered to a receiving 

water body. Pesticide fate in the receiving water body is simulated with the Variable Volume 

Water Model (VVWM), which is based on the Exposure Analysis Modeling System (EXAMS).

PRZM simulates surface runoff using the USDA curve number technique and erosion using the 

modified Universal Soil Loss Equation (MUSLE). Water movement into and downward through 

the soil is simulated in a tipping-bucket manner using soil parameters for water retention and 

saturated water content. The chemical transport component simulates pesticide application on 

or into the soil or on plant foliage. Dissolved, sorbed, and vapor-phase concentrations of the 

pesticide in soil are estimated by simultaneously considering the processes of degradation, 
21

sorption, uptake by plants, surface runoff, soil erosion, leaching, volatilization, foliar 

degradation and washoff. 

Figure 3: Conceptual Model for Current USEPA OPP Pesticide Surface Water Exposure Assessments.

Modeled field scenarios in PRZM consist of crop management practices, site-specific soil, 

hydrology, and weather conditions, and pesticide-specific application and dissipation processes. 

For screening purposes, USEPA OPP uses a combination of site, crop, and pesticide practices 

that are expected to result in a high-end exposure in the aquatic environment. These conditions 

are assumed to be uniform across the modeled catchment area: a 10 ha (25 ac) field for 

ecological exposure and a 173 ha (427 ac) watershed for drinking water8. The ecological 

scenario assumes that the treated crop covers the entire field (drainage area). Because of the

larger catchment area in the drinking water scenario, a crop area adjustment is applied to 

account for the percentage of the watershed that is planted with a specific crop or set of 

crops9. 

 8 The watershed area and index reservoir for drinking water assessments are based on a small, vulnerable 

Midwestern community water system that had elevated levels of corn herbicides in the water supply. The 

development and use of the index reservoir approach is described in the USEPA OPP Water Models web site at 

http://www.epa.gov/oppefed1/models/water/index_reservoir_dwa.html. 9 Based on recommendations from earlier FIFRA SAPs, USEPA OPP developed percent crop area adjustments for 

major crops and crop combinations. A description of the approach for use in drinking water assessments is 

available on the USEPA OPP Water Models site at 

http://www.epa.gov/oppefed1/models/water/Development_and_Use_of_Community_Water_System.pdf. 
22

Current assessments use 30 years (1961-1990) of measured weather (rainfall, temperature) 

data to cover year-to-year variability in runoff. The model discharges daily edge-of-field 

loadings of pesticides dissolved in runoff and sorbed to eroded sediment into a modeled water 

body – a 20,000 m3 “standard pond” for ecological exposure or a 144,000 m3 “index reservoir”

for drinking water exposure.

VVWM uses the edge-of-field pesticide fluxes and spray drift loadings to estimate the 

concentration in the water body. The model accounts for pesticide degradation (hydrolysis, 

biodegradation, and photolysis), partitioning to benthic sediments, and volatilization losses. In 

most instances, the water bodies are modeled with a constant volume, although the model can 

vary volumes with weather. 

The outputs – 30 years of daily estimated pesticide concentrations – are used to evaluate the 

probability of exceeding toxicity thresholds of concern in any given year when the pesticide is 

used according to label directions under vulnerable field conditions over a range of weather 

conditions that are representative of the use area. These results are summarized to provide 1-

in-10-year maximum daily, 4-day, 21-day, 60-day, and annual average concentrations for 

comparison against ecotoxicological endpoints of concern. Drinking water exposure 

assessments use the entire simulated time series to assess acute effect endpoints of concern, 1-

in-10 year annual average concentrations for chronic endpoints of concern, and overall (30-

year) average concentrations for cancer-related endpoints. The full range of daily 

concentrations is also available for detailed temporal analyses, such as magnitude, duration, 

and frequency of exposures. 

While USEPA OPP describes and characterizes the use of pesticides within this framework for a 

limited number of crop-specific scenarios, these tiered approaches do not easily distinguish 

between areas where pesticide concentrations in water may possibly result in risks to human 

health and/or the environment and areas where potential risks are expected to be negligible. 

1.2Adding a Spatial Context to Aquatic Exposure Assessments

SAM adds more spatial detail and context to USEPA OPP’s scenario-based aquatic modeling 

approach to pesticide exposure estimates. Using mappable data and routines to automate 

scenario-building and exposure modeling, SAM allows risk assessors to more effectively and 

efficiently characterize the spatial extent of areas where pesticide concentrations in water may 

exceed toxicity thresholds.

OPP’s current field-scale aquatic modeling approach assumes uniform soil and landscape 

properties across an entire drainage area, with runoff, sediment, and pesticide loads routed 

into a standard, non-or minimally-flowing receiving water body (pond or reservoir). While soil, 

meteorological, hydrological, and crop-related inputs are representative of the crop geographic 
23

area that is expected to have high-end exposures, watershed and water body dimensions are 

generic and fixed. SAM incorporates spatial heterogeneity in soil properties, land use/cover, 

hydrology, meteorology, and crop growth and management practices within specific 

catchments and across the geographic range of the crop(s) under evaluation. 

One key difference in the conceptual model for SAM is that it accounts for a continuum of soilland cover-weather combinations within each watershed, rather than assuming a single, 

uniform set of soil properties and crop cover (Figure 4). 

Figure 4: Contrasting Current Single-Soil/Single-Crop Watershed Approach to SAM Approach of 

Accounting for Combinations of Soil and Land Cover Types.

Rather than a single soil-crop-weather station scenario for the entire drainage area, each 

combination of land cover type, soil map unit, and weather station grid (described in Section 3) 

is treated as a separate scenario in SAM, similar to pieces in a jigsaw puzzle (Figure 5a). These 

jigsaw pieces come together as the daily runoff, sediment, and pesticide mass fluxes generated 

from each soil-land cover-weather grid scenario are weighted by their contributing area within 

each catchment and summed as inputs to the pour point water body (Figure 5b).
24

Figure 5: SAM scenarios are constructed from soil, land cover, weather, and crop data (a). The unique 

combinations of scenarios in each watershed are combined with catchment hydrology in recipe files 

that are used to generate outputs at the pour points (b).
25

A second difference in the conceptual model is that SAM no longer assumes fixed water body 

volumes and catchment areas. Instead, SAM incorporates geographic differences in watershed 

areas, receiving water body volumes, and expected flow and water body residence times. In the 

alpha test version of the model, daily concentration estimates were generated at the pour 

points of each HUC-12 unit. Runoff volume and pesticide mass were assumed to enter the 

water body that was identified at the HUC-12 pour point. Whether stream, river, lake, or 

reservoir, concentrations in these receiving water bodies were estimated using the “mixing cell” 

or completely mixed stirred tank reactor (CSTR) approach described in Section 2. 

In this approach, only concentration estimates for headwater HUC-12 units, which have no 

farther-upstream contributing areas [Figure 6(a)], take into account all upstream pesticide and 

runoff loadings. To more adequately address pour points farther downstream [Figure 6(b) and 

6(c)], SAM should account for all upstream loadings. The most straightforward method for 

doing so is to aggregate runoff and pesticide mass for the entirety of an upstream drainage area 

in calculating concentrations at the pour point (this approach was taken in the model 

evaluation in Section 2.4). A more sophisticated method, under examination in Section 4, 

attempts to address “time of travel” effects (i.e., the influence longitudinal dispersive mixing 

and differences in travel time) on pesticide concentration dynamics at the pour point. 

Figure 6: Conceptual Illustration of Stream Network (blue) and HUC-12 Units (black). (a) All Headwater 

HUC-12s (no upstream outlet). (b) Identifies Upstream Drainage Area From Downstream Pour Point. 

(c) Further Aggregates Upstream Drainage Areas.

Another difference in the SAM conceptual model relates to the interplay between catchment 

area and pesticide application dates. The larger the drainage area, the less likely that pesticide

treatments will all occur on the same day across the entire area. To address this, SAM allows 

distribution of applications to occur over user-defined application periods. To better identify 
26

the timing, duration, and distribution of pesticide applications, USEPA OPP is exploring the use 

of a variety of USDA data on the timing of planting, harvesting, and different crop stages that 

can provide a framework for identifying the pesticide application period (see Section 5). 

Table 1 summarizes differences in the conceptual model approaches between SAM and the 

current aquatic screening models used by USEPA OPP.

Table 1 - Comparison of the Current Aquatic Modeling Approach and SAM.

Model Component/

Process

Current Aquatic Modeling Approach Spatial Aquatic Model

Catchment area Drinking Water (DW): Small (173 ha/ 

427 ac) midwestern agricultural 

watershed. 

Ecological Assessment (Eco): 10 ha (25 

ac) agricultural field for eco exposure.

DW: Area varies depending on 

drainage area feeding the drinking 

water intake.

Eco: Builds from NHDPlus catchments, 

estimates concentrations by reach. 

Catchment soil 

conditions

Single, runoff-prone (Hydrologic Soil 

Group C or D) soil type for entire field 

or watershed. 

Runoff driven by curve numbers (crop 

and no crop) that represent the single 

soil and crop use being modeled.

Multiple soil types identified using 

USDA Soil Survey Geographic

(SSURGO) database map units. 

Multiple curve numbers based on 

Hydrologic Soil Group (A, B, C, or D) 

and land cover combinations.

Pesticide inputs into 

catchment

Application according to label rates, 

adjusted for crop area (assumes 100% 

of field for eco exposure; national or 

regional maximum percent crop area 

for drinking water). 

Application according to label rates, 

adjusted for crop area on a local 

(watershed-specific) basis, using USDA 

Cropland Data Layers (CDL).

Timing of pesticide 

applications

Single application date, based on best 

available USDA information on crop 

profiles, planting/harvesting dates, and 

likely application period.

Application window to simulate 

applications to multiple fields in the 

watershed; timing of application varies 

spatially and temporally based on 

USDA information on crop profiles, 

planting/ harvesting dates, crop 

progress data, and/or crop growth 

models, likely application period.
27

Model Component/

Process

Current Aquatic Modeling Approach Spatial Aquatic Model

Pesticide fate in 

catchment (and 

amount available for 

transport)

First-order degradation and linear 

equilibrium sorption in soil.

Multiple compartments with finite 

difference solution to advection/ 

dispersion equation.

Pesticide applied below runoff depth is 

unavailable (25% typical).

Exponentially-declining runoff flow 

profile in top 2 cm (PRZM5 

documentation).

26% of runoff flow interacts with soil.

Erosion from surface compartment 

based on MUSS (Carousel et al, 2005).

Leaching (tipping bucket) through soil 

profile.

Same.

Single mixing cell with first-order 

analytical solution.

Same. 

Uniform runoff flow profile in top 2 

cm.

Same.

Not yet implemented. 

Same.

Weather drivers 30 years of rainfall (1961-1990) from 

nearest meteorological (MET) station 

based on location, Major Land 

Resource Area (MLRA).

242 stations unevenly distributed 

across the US.

Rainfall data from 1948 to the present

for 0.25x0.25 degree US grids.

27,850 grid points evenly distributed 

across the US.

Water body DW: 5.3 ha x 2.7 m deep (13 ac x 9 ft) 

reservoir (144,320 m3).

Eco: 1 ha (2.5 ac) x 2 m deep (avg) pond 

(20,000 m3

). 

Flowing water bodies and 

reservoirs/lakes of varying size; water 

body volumes based on NHDPlus 

version 2 flow and velocity estimates 

for streams; lake/reservoir volume 

estimates based on method by 

Hollister and Milstead (2010), Hollister 

et al. (2011).

Pesticide inputs to 

water

Pesticide mass flux in runoff (dissolved) 

and sediment (sorbed) by rain events. 

Spray drift mass on application. 

Pesticide mass flux in runoff 

(dissolved) and sediment (sorbed) by 

rain events.

Spray drift methods in development. 

Pesticide fate in 

water

Aerobic aquatic half-life (metabolism, 

hydrolysis, photolysis); first-order mass 

transfer between water column and 

benthos; equilibrium partitioning to 

sediment; washout from Index 

Reservoir.

First-order washout from water body; 

degradation in water (aerobic aquatic 

metabolism half-life) and sediment 

layer not currently implemented in 

test version.

Water flow/ dilution Pesticide mass added to fixed water 

volume in Standard Pond or Index 

Reservoir.

Pesticide mass added to volume of 

reservoir or flowing water body at the 

watershed pour point.
28

Model Component/

Process

Current Aquatic Modeling Approach Spatial Aquatic Model

No flow in pond (static); steady flow 

based on long-term mean simulated 

runoff in Index Reservoir.

Daily-varying flow through water 

body, based on NHD Plus v2 monthly 

mean flow data and modeled eventdriven runoff. 

Although not typically considered, 

some endangered species assessments 

used downstream dilution from the 

edge of the use area based on a 

percent use area adjustment, reducing 

farm pond concentrations by the use 

area factor until concentrations are 

below toxicity thresholds.

Exposure estimates at each 

downstream pour point reflect the 

spatial heterogeneity of soil 

properties, crop areas. Relevant 

exposure estimates at each point 

compared to toxicity thresholds.

Convolution method added to 

simulate effects of time of travel. 

1.2.1 SAM and the other OPP surface water models

SAM algorithms consist largely of recompiled and streamlined hydrology, sediment, and 

pesticide transport components from PRZM5 (Young and Fry, 2014). SAM reduces model code 

redundancies and takes advantage of programming and hardware advances that have occurred 

since PRZM was first developed to speed up the modeling process. 

SAM includes a graphical user interface (GUI) that allows for user-defined inputs, including 

pesticide use (i.e., application rate, frequency, timing, method) and chemical fate parameters. 

SAM creates unique scenarios based on location-specific information, rather than using the 

SWCC scenarios, the current standard used for surface water simulations. SAM calculates daily 

runoff and pesticide yields for specific watersheds and daily pesticide concentrations in 

receiving water bodies (e.g., flowing, reservoir, pond) that serve as a drinking water supply or as

habitat for non-target aquatic organisms. 

Results generated using SAM are not directly comparable to those from SWCC because of 

differences in spatial inputs and underlying assumptions. One important distinction between 

the models is that, for each scenario, SWCC has a much coarser resolution (intended to cover 

not-well-defined cropping areas within a MLRA or state), while SAM accounts for full range in

soil-crop-weather heterogeneity within specific watersheds. 
29

2 SAM Model Components: Core Modeling Components/Approach

In order for SAM to process inputs, model algorithms, and outputs at a national level, USEPA 

OPP streamlined model routines to focus on the main processes that simulate pesticide 

movement to water. This SAP-version of SAM focuses on the pesticide runoff routines in a 

dominantly corn-soybean-small grains setting. This section describes the key model 

components used for SAM in support of the following questions for the SAP: 

Q2. Please comment on the model organization and improvements to model code.

a. SAM is organized into three components (scenario generator, hydrology, and calculator) 

to reduce redundant calculations, increase model efficiency, and make use of preprocessing for creating standalone scenarios and hydrology (Section 2.1). USEPA OPP

found this to be the best approach for handling the large quantity of the spatial and 

temporal inputs, while preserving the user’s ability to run unique simulations every 

time. Please comment on the separation of independent processes (e.g., hydrology, 

pesticide transport) to maximize computational efficiency and minimize user run-time.

b. USEPA OPP has improved the PRZM model code to eliminate inefficiencies and 

excessive calculations. For example, simplifying the soil surface into a single layer, as 

described in Section 2.1.1, increased computational time by 10-fold with negligible 

change in results. Please comment on this general approach to improve the model’s 

speed and efficiency without sacrificing accuracy and provide any additional 

recommendations for improving model efficiency. 

Q3. Sections 2.3 and 2.4 describe initial model evaluation steps for SAM to evaluate model 

uncertainty, sensitivity, and performance in comparison to measured (monitoring) data. 

Additional model evaluations occur in Sections 4.3 and 5.4. 

a. What additional sensitivity analyses would the SAP recommend for model evaluation?

b. Current model evaluation compared SAM estimates to available atrazine monitoring 

data collected at daily to weekly intervals. Given the importance of robust, frequentlysampled monitoring data for evaluation, what additional monitoring and other types of 

data are currently available to test and evaluate how well SAM meets EPA objectives 

of transparent processes and clear, consistent, and reasonable products for risk 

assessments and risk characterization?

Section 2.1 describes the modeling approach used in SAM, focusing on the model design, 

pesticide transport, and calculation of pesticide concentrations in water. Additional model 

components and details can be found in Appendix 2-A. Section 2.2 references proposed 

updates to the model that are explored in Sections 4 and 5 and discusses additional model 

improvements that are under consideration for addressing several limitations of the test 

version. Section 2.3 provides a brief description of the sensitivity and uncertainty analyses for 

SAM while Section 2.4 provides initial comparisons of SAM outputs with monitoring data. 
30

2.1 Modeling Approach Used in SAM

For SAM, USEPA OPP has followed the guidance on the development, evaluation, and 

application of environmental models established by the USEPA Council for Regulatory 

Environmental Modeling (CREM) (USEPA, 2009). USEPA OPP has applied the model 

development practices (i.e., problem definition, framework development, and 

parameterization) and used comprehensive model evaluation techniques (i.e., quality and 

performance evaluation). Transparent and open communication, including comprehensive 

documentation, has been provided to stakeholders and decision makers. USEPA OPP has held 

two public workshops (October 29, 2014 and April 29, 2015) to further engage stakeholders in 

the development process. Following the first workshop, a public SharePoint website was 

launched to aid communication with the public on key model approaches. An alpha version of 

the model was made available to stakeholders for testing and evaluation purposes. The model 

is also undergoing extensive peer review internally and externally through the SAP, in 

accordance with USEPA CREM best practices and recommendations. 

SAM was built to simulate the impact of pesticide use across heterogeneous land cover areas in 

US watersheds. SAM aggregates daily runoff and pesticide loads for defined drainage areas, and 

estimates daily concentrations in receiving water bodies. Under the SAM framework, scenario 

generation, hydrology, and chemical transport are handled as separate model components to 

reduce model code redundancies and increase computational speed. This is possible as the 

movement of pesticide does not influence the movement of water. Soil and crop properties, 

weather, and hydrology are pre-processed and packaged into unique combinations called 

“scenarios” (Scenario Generator) (see Section 3 and Appendix 3-A for specific inputs and

sources). Total runoff for each watershed (or drainage area) is calculated and stored based on 

the contributing scenarios and their corresponding areas (Super PRZM Hydro) (Figure 7). Finally, 

pesticide runoff loads and receiving water body concentrations are estimated using the 

Pesticide Transport and Water Body Concentration Calculator ("Calculator"). Here the user 

defines the chemical properties, crop(s), application method, and application dates in a GUI to 

estimate aquatic exposure concentrations in receiving water bodies.
31

Figure 7: Overview of the SAM structure and components: Scenario Generator, Super PRZM Hydro, 

Pesticide Transport and Water Body Concentration Calculator, and SAM Graphical User Interface 

(GUI).

2.1.1 SAM Components

Each SAM component has an important role in the overall framework and progression of 

simulations (Figure 7). The Scenario Generator organizes the spatial inputs (Section 3) and 

calculates key input parameters for each scenario. These parameters include the daily runoff, 

erosion, vertical velocity, and plant growth stages. Each scenario represents a unique soil, crop, 

and weather combination that exists within a simulated watershed or other drainage area.

Appendix 2-A provides further details about the Scenario Generator’s calculation methods.

Super PRZM Hydro uses recipes (Section 3.2; Figure 5) to define the scenarios (soil-cropweather) and associated areas (m2) that make up each watershed (or in the current SAP 

version, NHDPlus catchment) in a given year. Currently, recipes have been prepared for four

recent crop years (2000-2013) using USDA National Agricultural Statistics Service’s (NASS) 

Cropland Data Layer (CDL), but could be expanded as new data is released (Section 3.1.2). 

Super PRZM Hydro processes each NHDPlus catchment by summing the scenarios’ daily runoff,

weighted by area, to produce a daily time series of total runoff. 

The third component of the model, the Calculator, uses the pre-processed hydrology from 

Super PRZM Hydro and implements chemical fate and transport routines to estimate pesticide 
32

concentrations in the receiving water body associated with each catchment. Consistent with 

PRZM5, users specify an application rate and application timing, and the applied pesticide mass 

is divided between crop canopy and soil. In the soil, as in PRZM, only a fraction of the applied 

pesticide mass (75%) is available for transformation and transport in all simulations. The basis 

for this conceptualization comes from a limited number of documented PRZM evaluations 

(Carousel et al., 2005), which showed PRZM overestimated select monitoring locations prior to 

applying this percentage universally.

Given the available pesticide mass, the SAM Calculator simulates leaching, sorption, 

degradation, and runoff of pesticide from the field for a single soil compartment in the top 2 cm

(Figure 8). This single compartment is modeled as a zone of uniform mixing in the soil down to a 

specific depth (Ahuja, 1986; Ahuja and Lehman, 1983; Frere et al., 1980; Haith, 1980; Steenhuis 

and Walter, 1980). In PRZM2, a single soil compartment was similarly used, while PRZM3 (and 

PRZM5) assumed multiple sub-compartments with a declining amount of mass available with 

depth (Figure 9). SAM uses a single mixing cell in the surface layer, rather than the 10 subcompartments in the current PRZM5 because these two methods provide similar results, yet 

the latter is computationally 10 times slower. USEPA OPP has chosen computational speed (and 

simplicity) to take precedence over slower, more complex calculations. Appendix 2-A includes

additional information on the SAM transport routine conceptualization and solution technique. 

Figure 8: Pesticide mass transport in the Pesticide Transport and Water Body Concentration Calculator 

simulates leaching, sorption, degradation, and transport via runoff.

As in PRZM5, runoff only partially flows through the soil. It includes a fraction that interacts 

with the soil (26% of total runoff flow) and a fraction that does not (74%) (Figure 9). In SAM, the 

interacting portion of runoff has a uniform flow profile, whereas PRZM3 and PRZM5 use an 

exponentially declining runoff flow profile (Young and Fry, 2014). USEPA OPP found that

estimated concentrations were insensitive to the use of uniform (single compartment) mass 

and flow distributions compared to non-uniform, multi-compartment approaches (Appendix 2-

B). As a result, SAM implements the most efficient and least computationally intensive 

approach, using uniform, single compartment mass and flow distributions. 
33

Figure 9: Runoff (in purple) and pesticide (in orange) extraction conceptual models in SAM, with 

PRZM2 and PRZM3/PRZM5 shown for comparison.

A closer look at the validation work of Carousel et al. (2005) indicates that the primary 

reduction in pesticide runoff realized by PRZM3 over PRZM2 was due to reducing the fraction of 

mass available (0.75) and the fraction of interacting runoff flow (0.26). The product of these is 

0.19, which is the same as the reduction in observed concentrations. Thus, the use of multiple 

compartments and non-uniform extraction by Carousel et al. (2005) had, at most, a nonobservable impact.

To calculate the daily mean pesticide concentrations in receiving water bodies, the Calculator 

uses the daily mass transported by runoff, and receiving (pour point) water body volume and 

flow rate. For lotic water bodies (e.g., streams, rivers), water body volume is estimated as reach

cross-sectional area (derived from NHDPlus version 2) multiplied by a representative length. 

Using a mixing cell approach for the water body, a representative length of 40 meters is used,

based on typical longitudinal dispersivities measured in rivers (Fischer, 1979; Rutherford, 1994). 

For lentic water bodies (i.e., reservoirs, lakes, ponds) that are part of the drainage network, 

flows are also obtained from NHDPlus and volumes are preliminary estimates developed by 

USEPA’s Office of Research and Development (ORD) Atlantic Ecology Division based upon 

surrounding landscape topography. The methods used by USEPA ORD are similar to those

documented in Hollister and Milstead (2010) and Hollister et al. (2011). 
34

Flows are allowed to vary daily and are calculated as the sum of monthly-varying base flow and 

daily-varying runoff. For each tabulated reach, NHDPlus provides overall long-term and monthly 

mean flow estimates (Section 3.1.5). Monthly mean base flows are estimated as the difference 

between monthly mean flows (as reported by NHDPlus) and the long-term mean runoff 

(derived from the daily runoff generated by Super PRZM Hydro). Simulated daily runoff values 

are then added back to the monthly mean base flows, generating a daily hydrograph at each 

modeled location for the duration of the simulation. These hydrographs account for peaks and 

lulls in daily flow not captured in the monthly variability alone.

In the alpha version of SAM, receiving water body concentrations were calculated for each 

HUC-12 unit based on volumes and flow rates for the immediate (local) HUC-12. For the SAPtest version, similar concentration calculations were done for each NHDPlus catchment based 

on the volumes and flow rates for the immediate water body or reach. However, since these

estimates did not account for the contributions from upstream catchments, recipes files were 

modified to include the areas of all upstream NHDPlus catchments contributing to a single 

water body into one recipe. Concentrations were then recalculated using the updated recipes. 

Section 5 describes an additional approach to further refine calculated concentrations by 

temporally re-distributing daily pesticide loads from upstream NHDPlus catchments within a 

drainage area, based on their in-stream travel time to the water body. 

Within each receiving water body or reach, first-order dissipation is assumed for a single 

compartment due solely to washout, as described in Appendix 2-A. A benthic sediment layer is 

not currently included in the model, and sorbed pesticide transport by eroded sediment is 

currently under consideration, but depends on the availability of relevant monitoring datasets 

for validation (Section 2.4).

2.1.2 Alpha Version of SAM (übertool Site)

The alpha version of SAM on the übertool site calculates concentrations for each HUC-12 unit 

based on volumes and flow rates for the immediate (local) HUC-12. As noted above, such 

estimates do not account for contributions from upstream HUC-12 units and the results could 

be misleading. Adjustments will be made to this model based on feedback from the SAP.

To run the alpha version of the SAM, the übertool SAM GUI can be accessed at 

http://qed.epa.gov/ubertool/sam

The SAM GUI is hosted as part the USEPA übertool project (www.qed.epa.gov/ubertool). It

allows a user to run SAM simulations after specifying chemical and application details (Figure 

10). The Calculator runs each time a new simulation is launched, reading in the user-defined 

inputs and accessing all relevant pre-processed scenarios and hydrology for the particular 

region/crops being simulated.
35

Figure 10: SAM Graphical User Interface with four input tabs: Chemical, Application, Simulation, and 

Output.

In the GUI, the Chemical tab requires the chemical name, organic carbon partitioning 

coefficient (Koc), and soil metabolism half-life (in days). The Application tab asks the user to 

specify the crop(s), application rate, application method, and other application refinements. 

The Simulation tab allows for the user to select the region (Ohio River Basin in the test version), 

simulation type (Ecological in the test version), and simulation start and end dates. The Output 

tab allows for selection of output tables, plots, and geospatial maps.

2.2 Proposed Updates and Additional Improvements under Consideration 

Based on model evaluations discussed in Section 2.4, USEPA OPP has proposed updates and 

refinements to the test version of SAM for SAP consideration: re-distributing daily pesticide 

loads from individual catchments based on their travel time to receiving water bodies in larger 

drainage networks (Section 4) and defining application windows (Section 5). 

Additional improvements to the model are under consideration to address several limitations of 

the test version. This section describes additions that USEPA OPP is evaluating for future 

versions of SAM. As part of the peer review process, USEPA OPP welcomes recommendations 

and feedback on these and other model improvements. Recommendations will be considered 

based on their scientific merit, ease of incorporation into the model, and the availability of

intensive monitoring data for validation. 

2.2.1 Erosion and Sediment Routines

SAM currently uses a modified version of the Universal Soil Loss Equation (USLE), MUSS, to 

simulate soil erosion (Carousel et al, 2005). MUSS has been used in USEPA OPP’s Surface Water 

Concentration Calculator (SWCC) for calculating erosion at the field level, as well as in previous 
36

PRZM versions. The MUSS soil erosion routine, however, may not be scalable to a watershed 

model like SAM. At the watershed level, SAM can estimate erosion loss events for individual 

scenarios and then use an area-weighted sum of those losses for a given watershed. It may be 

difficult to track erosion and sediment yield between or across basins though, since those 

processes are not well understood and have not been modeled rigorously at the national scale 

in the past.

This area of improvement is important for determining sediment loadings that will affect 

soluble and sorbed pesticide concentration estimates in any modeled watershed. From the 

literature, the USLE usually performs well at simulating erosion loss due to rainfall at the field 

level (Wischmeier and Smith, 1978). However, less is known about modeling erosion and 

sediment yields at watershed scale. Recent literature also suggests that a more recent revised 

version of USLE (RUSLE) may overpredict erosion in certain instances (Brooks et.al. 2014). 

Some factors affecting sediment delivery at a watershed-scale include texture, distance from 

the main channel, channel density, field slope, length, land use/cover, and rainfall /runoff 

(Ouynang and Bartholic, 1997). Field measurements and observations would be necessary to 

validate watershed-level erosion and sorbed pesticide concentration estimates generated by 

SAM. 

USEPA OPP could consider chemicals with a higher affinity to sorb to soil or sediment and be 

transported to water by erosion. However, up to this point, the test version of the SAM has 

focused on evaluating the runoff transport algorithms using chemicals that are not highly 

sorbing, given the availability of monitoring data (Section 2.4). USEPA OPP is looking for 

similarly-robust water-column and sediment monitoring data for highly-sorbed chemicals to

evaluate how well the SAM erosion and sediment routines perform in estimating pesticide 

concentrations in water. 

2.2.2 Spray Drift 

The current test version of SAM does not account for the fraction of pesticides that may reach 

water bodies by spray drift. USEPA OPP’s current aquatic exposure assessment approach uses 

the AgDRIFT model to generate screening-level estimates of the fraction of pesticide 

applications that reach a water body from an adjacent treated field.10 The AgDRIFT model 

provides estimated deposition for pesticides applied by aerial, ground, and airblast equipment 

using both mechanistic algorithms and field data. 

USEPA OPP is considering methods to estimate spray drift at the watershed scale, taking into 

account that not all fields will contribute drift equally (both in terms of distance from the water 

body and prevailing wind direction). USEPA OPP is also open to considering other scientificallydefensible options for estimating spray drift at a watershed level. 

 10 More information on AgDRIFT can be found on USEPA OPP’s Water Models website at 

http://www.epa.gov/oppefed1/models/water/
37

2.2.3 Irrigation

Irrigation is built into the SAM Scenario Generator (Appendix 2-A), although the routine is not 

turned on in the alpha version. Additional inputs from the user are needed for the irrigation 

routine, including an irrigation depth and an available water fraction, below which irrigation is 

required. USEPA OPP plans to make these additional user inputs available in upcoming versions.

2.2.4 Ponds and other small non-flowing water bodies

While numerous ponds and other small non-flowing (lentic) water bodies are identified in the 

NHDPlus Waterbody spatial data set, many of these do not have well-defined catchment areas

or connections to flowlines. This makes it difficult for USEPA OPP to estimate concentrations in 

these disconnected water bodies. One option may be to use pesticide concentration estimates 

for the NHDPlus catchment as a surrogate for ponds and small water bodies within the 

catchment. However, monitoring data to evaluate estimates for small water bodies are, for the 

most part, lacking. 

2.2.5 Other agricultural crops

Some agricultural crops such as vegetables and orchards may present challenges in processing 

the necessary land cover information because of a lack of training and evaluation data needed 

in the error analysis for USDA NASS’s Cropland Data Layer. USEPA plans to develop test 

approaches using a variety of land cover data sources in areas of the country that have a wider 

diversity of crops and land cover types.

2.2.6 Non-agricultural uses

The test version presently focuses on evaluating pesticide applications for agricultural 

purposes. Non-agricultural uses may be considered in the future, but may be limited by the 

availability of monitoring datasets for validation.

2.3 Uncertainty and Sensitivity Analyses

Key elements of environmental model evaluation are sensitivity and uncertainty analyses and 

comparisons of model outputs to measured data (USEPA, 2009). Sections 2.3.1 and 2.3.2 

provide a brief summary of uncertainty and sensitivity analyses that have informed the 

development of SAM. Section 2.4 provides initial comparisons between SAM outputs 

(estimated concentrations) and measured data (pesticide monitoring). 

2.3.1 Uncertainty Analysis

Uncertainty analysis considers the effects of lack of knowledge or potential errors of model 

inputs (USEPA, 2009). The following is a quick summary of the major inputs that contribute to 

uncertainty in model outputs and how USEPA OPP is addressing those uncertainties. 
38

• Pesticide environmental fate properties: OPP relies on registrant-submitted studies to 

reflect the rates and modes by which a pesticide degrades, and the degree to which a 

pesticide sorbs to soil or sediment. The accuracy of model inputs will depend on the 

quality of these studies, which will be pesticide-specific. Guidance for model inputs 

focuses on central tendencies of measured pesticide properties, and confidence limits 

as appropriate to ensure a degree of conservatism (USEPA OPP, 2009b). 

• Pesticide application: This is one of the important drivers in pesticide exposure. All 

other conditions being equal, the mass of pesticide reaching water bodies is 

proportional to the mass of pesticide applied, which is itself a function of area treated. 

By assuming maximum label application rates and 100% crop treated, USEPA OPP can 

set an upper bound on potential exposure. In refinements, proprietary or USDA NASS 

survey data, if available, can be used to estimate less-than-maximum application rates, 

from “typical” or median rates to upper percentiles of reported rates. This introduces 

additional uncertainties related to the sample size of the survey and to the geographic 

scope represented by the sampling. 

• Weather: The amount, intensity, timing and frequency of rainfall have a major impact 

on pesticide transport to water. Because precipitation varies over time (as well as by 

location), OPP runs multiple years of historical weather data to reflect this variability. 

OPP’s currently-used regulatory models rely on a few hundred weather stations to 

reflect variations across the country, and run simulations driven by 30 years of data 

(from the years 1961-1990). SAM will employ many more weather data points at 0.25-

degree grids across the country to obtain finer spatial resolution, with the flexibility to 

cover time periods from 1948 to the present. Further evaluations of the weather data 

can be found in Appendix 3-C.

• Soil properties: Most soil properties vary within a range that has been defined for each 

soil map unit. Depending on the property, the map unit may provide a non-overlapping 

bound with adjacent map units (for instance, soil map units are often separated by 

distinct slope classes). The range in other properties, such as bulk density and water 

capacity, may overlap among soil map units. The soil property inputs to SAM use a 

representative value that is generally a central tendency (mean or median) from the 

reported range in properties for the particular soil type. While a single representative 

quantity doesn’t represent the range in properties, it is expected to generally be valueneutral in terms of impact on concentration estimates. Appendix 3-A includes a 

characterization of the relative impact of each input parameter on model outputs. 

• Land cover: Land cover is used as a surrogate for pesticide use by defining the extent of 

the land cover type being treated. The USDA Cropland Data Layer (CDL) provides the 

most detailed spatial resolution for various crops and crop groups. Key areas of 

uncertainty include (a) the relative accuracy of the CDL in identifying actual crops, which 
39

is covered in detail in their accuracy assessments11, (b) generic land cover classes (for 

example, orchards and vineyards, vegetables and ground fruit) used for crops that, 

individually, have poor accuracy in CDL, and (c) year-to-year variation in crop cover. The 

first two points are described elsewhere (Appendix 3-B). To address year-to-year 

variation in land cover, USEPA OPP currently plans to use the most recent 5 years of 

national CDL coverage, with model simulations covering the variations in land cover 

patterns over the 5 year period for each watershed (or other drainage area). However, 

the number of years of coverage can be expanded, provided that the national coverage 

continues to be collected in the future. 

• Crop and Management Practices: Crop planting, growth, and harvesting times are 

important parameters in terms of their impact on the timing of pesticide applications. 

These vary across the country based on general climate differences, and vary from year 

to year with annual differences in weather. The test version of SAM uses USDA Usual 

Planting and Harvesting Dates publications (USDA NASS, 2006, 2007, 2010) to define the 

planting and harvesting windows in a given location. Pesticide applications are currently 

spread across the most active window. Other approaches to better define application 

windows are considered in Section 5. 

2.3.2 Sensitivity Analysis

Sensitivity analysis evaluates the effect of changes in input values or assumptions (including 

boundaries) on model outputs (USEPA, 2009). The general factors that affect the transport of 

pesticides from the field(s) of application to water bodies in SAM are the amount of pesticide 

applied, persistence and mobility of the pesticide, timing and intensity of rainfall (particularly in 

relation to the time of pesticide application), and vulnerability of the application area to 

pesticide loss via surface runoff (determined in the model primarily by the curve number). 

In the late 1990’s the FIFRA Environmental Modeling Validation Task Force (FEMVTF) compared 

edge-of-field outputs from PRZM against measured data and conducted a sensitivity analysis of 

input parameters (Jones and Russell, 2001; Jones and Mangels, 2002). The results helped 

identify inputs and ranges for which the PRZM model was most sensitive. 

FEMVTF found that the following input parameters were important for runoff outputs:

- Rainfall during the key period when the pesticide is on the field/crop

- Runoff curve number (CN)

- Pesticide half-life

- Pesticide sorption coefficient, Koc

- Soil organic carbon fraction

 11 The description of NASS’s accuracy assessment is available at 

http://www.nass.usda.gov/research/Cropland/Method/cropland.pdf. Metadata that include error analysis are 

available for download at http://www.nass.usda.gov/research/Cropland/metadata/meta.htm.
40

- Available water in the soil surface horizon, derived from field capacity, bulk density

- Bulk density of soil in the surface horizon

- Total pesticide applied

For fixed pesticide properties, the sensitivity of runoff loading to model inputs is as follows:

Weather (rainfall) >>> Curve Number >> all other inputs

While OPP expects to see similar results for SAM because similar runoff and pesticide 

fate/transport algorithms are used, some inputs that are dominant at a field scale, such as 

those based on soil properties, may be less so at watershed scales that integrate the 

contributions of multiple soils and crops. Other factors, such as the relative proportions of 

various components in a watershed (captured in the recipe files), are expected to affect 

pesticide concentrations. Watershed-scale properties that have been shown to affect pesticide 

concentrations in water include the acreage in crop(s) upon which the pesticide can be used, 

pesticide use intensity within the watershed, proportion of runoff-prone soils (reflected by 

properties such as hydrologic soil groups (HSG) C and/or D, soils with a drainage-restrictive 

layer, surface runoff potential), amount of rainfall during or shortly after the pesticide 

application period, broad management conditions such as area in artificial drainage, and 

hydrologic indicators of runoff (USEPA OPP, 2009a; Stone and Gilliom, 2012; Stone et al, 2013).

2.4 Comparison of Model Outputs with Measured Data (Monitoring Data)

Evaluation of models should consider the uncertainty in both model outputs and the measured 

data used for comparison (USEPA, 2009). To be useful in evaluating how accurately SAM

estimates pesticide concentrations in water, monitoring data need to be collected sufficiently 

frequently to capture day-to-day, seasonal, and yearly variations in pesticide concentrations in 

water (USEPA OPP, 2011). The extent to which monitoring data adequately reflect short-term 

variability in pesticide concentrations in water depends on how frequently samples are 

collected, and whether the sampling was targeted to pesticide use areas and times of the year 

in which pesticides have been applied. As the interval between sampling events increases, the 

likelihood of capturing short-duration or single-day peaks in pesticide concentrations decreases, 

particularly in fast flowing waters. Even weekly sampling will often provide a biased 

(underestimating) perspective on pesticide concentrations in water as variations in pesticide 

concentrations over time may be defined on daily or sub-daily time scales. Sampling bias has 

been discussed in recent FIFRA Scientific Advisory Panel meetings on atrazine (USEPA OPP, 

2010a, 2010b, 2011, 2012). To evaluate the potential uncertainty in sampling frequency for 

predicting actual concentrations, USEPA OPP (2011) simulated 4-, 7-, 14-, and 28-day sampling 

intervals on monitoring datasets that had daily- to near-daily sampling intervals. Median annual 

peak estimates ranged from 75-78% of the true annual peaks for a 4-day interval, 36-70% for a 

7-day interval, and 25-54% for a 14-day interval (USEPA OPP, 2011). 
41

2.4.1 Atrazine Monitoring Datasets Used for Comparison

For the initial evaluation of SAM here and in Sections 4 and 5, USEPA OPP used two monitoring 

studies for atrazine conducted in the corn-growing regions of the US between 2003 and 2014. 

Syngenta Crop Protection’s Atrazine Ecological Exposure Monitoring (AEEMP) of headwater 

streams in areas expected to be vulnerable to atrazine runoff is one of the few pesticide 

monitoring studies that include daily sampling for pesticide concentrations in water at some 

sites (USEPA OPP, 2009a). A second set of atrazine monitoring data – Syngenta’s Atrazine 

Monitoring Program for Drinking Water (AMP) – included weekly sampling for atrazine during 

the likely use season (USEPA OPP, 2011, 2012). These two data sets, which covered a range of 

watershed sizes and water body types (headwater streams to larger streams and rivers, 

reservoirs and other lentic waters), focused on areas where atrazine is widely used. Details on 

these studies, as well as the data themselves, can be obtained through USEPA OPP’s Atrazine 

Information page (http://www.epa.gov/oppsrrd1/reregistration/atrazine/).

USEPA OPP focused on three test basin areas – the Scioto River (OH) basin and the White 

River/East Fork (IN) basin within the greater Ohio River basin and the Mark Twain basin in 

northeastern Missouri (Figure 11). These three basins were selected because they contain 

multiple monitoring sites, predominantly for atrazine, with multiple years of sampling and 

sampling frequencies that range from weekly to daily. Appendix 2-C contains more information, 

including monitoring data for each test basin.

Figure 11: Location of Test Basins for Further Model Development and Evaluation.
42

2.4.2 Future Comparisons with Other Pesticides

Ultimately, SAM needs to reliably estimate concentrations for pesticides that have a wide range 

of properties, including those with a higher potential to sorb to soil and sediments and those 

with shorter or longer half-lives than atrazine. Initial model evaluations used atrazine because 

of the ready availability of robust monitoring data. However, USEPA OPP plans to expand the 

model evaluation to include additional pesticides and other areas of the country. For 

monitoring data with less frequent sampling intervals, USEPA OPP will use methods considered 

in USEPA OPP (2011 and 2012) to characterize the uncertainty in concentration estimates based 

on less frequent sampling. 

In an effort to obtain monitoring data with more frequent sampling intervals for a wide range of 

pesticides, USEPA’s OPP and Office of Water (OW) are collaborating with USGS’s NAWQA

Program to sample and analyze daily surface water samples from selected regions of the United 

States for pesticides. The multi-year effort is an outgrowth of initial collaboration that 

leveraged USEPA OW’s National Aquatic Resource Survey (NARS) National Rivers and Streams 

Assessment (NRSA) program to assess stream quality. In 2013 NRSA evaluated the condition of 

the Nation’s rivers and streams with an emphasis on ecological condition at over 1000 sites 

selected using statistical survey methods. As part of the overall effort, USGS NAWQA and 

USEPA OPP collaborated on a focused sampling effort at a subset of 100 sites across a region 

embedded within the national NRSA effort. The initial region selected was the Midwest “corn 

belt” region with the goals to characterize water-quality stressors, such as pesticide 

contaminants, nutrients, and suspended sediment, and to determine the effects of these 

stressors on ecological communities in the streams. Each of the 100 USGS sites included weekly 

surface water samples across a four month window and were analyzed for 230+ pesticides and 

degradates. 

Additionally, USGS and USEPA OPP selected 7 of the 100 sites for daily analysis. USEPA’s 

analytical laboratory at Ft Meade, MD, analyzed the daily samples. After quality assurance 

checks are completed, the expanded set of pesticide will be used not only for evaluating SAM 

but also for a more robust analysis of sampling bias across chemical types (e.g.,

organophosphate pesticides) and modes of action (e.g., insecticides, fungicides).

USEPA OPP and USGS NAWQA have since expanded this monitoring effort into other regions of 

the country including the Southeastern US and Pacific Northwest. Both of these data sets have 

been collected and are undergoing final QA of the analytical results. Planning is underway for 

additional sampling in the Northeastern US in 2016 and within irrigated portions of California in 

2017. Once sampling is completed, USGS and USEPA OPP will have a robust data set spanning a 

range of geographies, land use types, climatic and hydrologic conditions that will allow for a 

robust analysis of this high quality data.
43

2.4.3 Criteria for Model Evaluation

SAM is an uncalibrated model and USEPA OPP does not expect that SAM estimates of pesticide 

concentrations to exactly match the patterns of pesticide concentrations in measured data. 

Instead, evaluations focus on how well the magnitude and ranges in estimated concentrations 

compare to measured concentrations within years and across the span of sampling periods. The 

following criteria were used to compare estimated annual maximum concentrations to 

reported monitoring data, taking into account potential bias errors from sampling frequency:

- Minimal model underestimates compared to monitoring (Type II errors), with no 

estimated annual maximum concentrations that are more than an order of magnitude 

(10X) less than measured data;

- Most SAM estimates of peak concentrations should fall within the range of 1 to 5X 

greater than corresponding measured maximum concentrations;

- Minimal overestimates by factors of 10 or greater; model estimates that are within 5-

10X greater than measured concentrations should be considered in the context of 

potential underestimations in measured maximums due to sampling frequency.

Where model estimates do not correspond well with measured data, USEPA OPP will be looking 

for systematic trends that may help explain the differences and lead to model upgrades that 

result in improved accuracies in estimated concentrations.

2.4.4 Initial Comparisons with Atrazine Monitoring Data

Available monitoring data from these programs covered 2003 through 2014. For an initial test 

comparison, USEPA OPP simulated daily atrazine concentrations from 2000 through 2014, using 

the following conditions:

Chemical Atrazine

Half-life in soil 123 days

Sorption coefficient (Koc) 100

Years Simulated 2000-2014

Application Rate 1.69 kg/ha (MO), 1.32 kg/ha (OH), 1.47 kg/ha (IN), based 

on average application rates over 2004-09 in the AEEMP 

study sites reported by Syngenta (USEPA OPP, 2009a)

Application Window Determination Begin 14 days before emergence (from USDA Typical 

Planting/Harvest Dates; consistent across years); pesticide 

application divided across 50-day window using uniform 

distribution (based on average length of planting period)

CDL Crop Years Used for Recipes 2010-2013 (4-year rotation repeated across the simulation 

period)
44

Test Basins, MO (Mark Twain Basin, 

northeastern MO)

AEEMP sites MO-01 (comid 4989415)a, MO-01c (4988183), 

MO-01d (4988241), MO-02 (5042358), MO-4a (4989385), 

MO-04b (4989739), MO-05 (5042400), MO-05b (5039952), 

MO-08 (2508563)

AMP sites MO-16 (4867529), MO-17-river (5641174), MO17-reservoir (5641630), MO-19 (4869843), MO-21

(4867727)

Test Basins, IN (East Fork of White River 

Basin, central IN)

AEEMP sites IN-10 (18459870)

AMP sites IN-04 (18454511), IN-06 (18450057), IN-07

(18445592), IN-08 (18445664), IN-13 (18451035)

Test Basins, OH (Scioto River Basin, 

central OH)

AEMP sites OH-04 (3483775)

AMP sites OH-10 (5214753), OH-11 (5213585)

Heidelberg Univ. NCWQR Scioto River station (3486865)

Pesticide, Runoff Processing Daily flux outputs accumulated at the pour point for each 

test basin; no adjustments for time of travel.

a For modeling purposes, each test basin is also identified by the NHDplus comid that is associated with 

the monitoring site/pour point location.

Appendix 2-D includes the daily concentrations, pesticide mass losses, runoff fluxes, and flows 

generated by SAM, along with yearly summaries and comparisons to monitoring data for each 

test basin. Table 2 summarizes the comparison of estimated yearly maximum concentrations 

for the individual sampling stations within each test basin. Sampling bias errors in the measured 

(monitored) data are not considered in this evaluation. Particularly for daily concentrations, 

those sites with sampling intervals of 4-7 days or more are more likely to underestimate 

maximum concentrations. 

Table 2: Summary of Comparisons of SAM Estimated Annual Maximum Concentrations with 

Monitoring Data for Test Basins in MO, OH, and IN.

Monitoring Site

Range in Annual 

Maximum

Concentrations (ug/L)

Number and % of site years 

underestimating measured 

annual maximums by a factor of

Number and % of site years 

overestimating measured 

annual maximums by a factor of

Measured Modeled >10X 5-10X 1-5X 1-5X 5-10X >10X

MO-01 

AEEMP

14-183 (11 

yr)

64-183 0 0 3 7 0 1

MO-02 

AEEMP

9-155 (11 yr) 64-183 0 0 1 8 1 1

MO-04a 

AEEMP

41-145 (6 yr) 28-159 0 0 4 2 0 0

MO-04b 

AEEMP

24-43 (3 yr) 22-96 0 0 2 1 0 0
45

Monitoring Site

Range in Annual 

Maximum

Concentrations (ug/L)

Number and % of site years 

underestimating measured 

annual maximums by a factor of

Number and % of site years 

overestimating measured 

annual maximums by a factor of

Measured Modeled >10X 5-10X 1-5X 1-5X 5-10X >10X

MO-05 

AEEMP

14-84 (6 yr) 61-193 0 0 0 4 1 1

MO-05b 

AEEMP

22-112 (5 yr) 49-139 0 0 1 4 1 0

MO-08 

AEEMP

41-70 (5 yr) 53-179 0 0 2 3 0 0

All MO 

AEEMP

0 (0%) 0 (0%) 14 (22%) 31 (48%) 17 (26%) 3 (5%)

MO-16 res. 

AMP

0.5-5 (6 yr) 49-183 0 0 0 0 0 6

MO-17 

river AMP

8-13 (6 yr) 17-54 0 0 0 6 0 0

MO-17 res. 

AMP

2-7 (6 yr) 16-94 0 0 0 1 1 4

MO-19 res. 

AMP

1-11 (6 yr) 46-136 0 0 0 1 1 4

MO-21 res. 

AMP

2-7 (4 yr) 36-147 0 0 0 0 1 3

All MO 

AMP

0 (0%) 0 (0%) 0 (0%) 8 (29%) 3 (11%) 17 (61%)

OH-04 

AEEMP

12-16 (2 yr) 33-67 0 0 0 1 1 0

OH-10 river 

AMP

6-30 (9 yr) 27-63 0 0 0 7 2 0

OH-11 res 

AMP

0.5-8 (5 yr) 20-45 0 0 0 2 2 1

NCWQR 

Scioto R

3-15 (5 yr) 23-55 0 0 0 3 1 1

All OH 0 (0%) 0 (0%) 0 (0%) 13 (62%) 6 (29%) 2 (9%)

IN-10 

AEEMP

12-16 (2 yr) 44-159 0 0 0 1 1 0

IN-04 river 

AMP

1-3 (9 yr) 41-154 0 0 0 0 0 9

IN-06 river 

AMP

3-26 (5 yr) 49-161 0 0 0 1 3 1

IN-07 river 

AMP

5-28 (9 yr) 39-91 0 0 0 4 4 1

IN-08 river 

AMP

6-21 (6 yr) 39-91 0 0 0 3 3 0

IN-13 river 2-20 (7 yr) 21-114 0 0 0 5 1 1

All IN 0 (0%) 0 (0%) 0 (0%) 14 (37%) 12 (32%) 12 (32%)

The model best met the evaluation criteria (Section 2.4.3) with the MO AEEMP monitoring 

sites. For these sites, the concentration patterns produced by SAM generally tracked the 
46

patterns in measured concentrations (Figures 12 and 13). While annual maximum SAM 

estimates didn’t always match maximum annual measured concentrations within a given year,

the correspondence was generally reasonable, and the overall range in estimated annualmaximum concentrations overlapped the corresponding range in measured concentrations.

Figure 12: Comparison of Estimated Concentrations of Atrazine from SAM with Measured 

Concentrations from the AEEMP MO-01 Site (NHD comid 4989415).
47

Figure 13: Comparison of Estimated Concentrations of Atrazine from SAM with Measured 

Concentrations from the AEEMP MO-02 Site (NHD comid 5042380).

SAM estimates consistently exceeded monitored concentrations at the 5 AMP sites in MO, 

often by more than 10X (61% of site years). Estimated concentrations for the reservoir sites in 

Table 2 reflect the effective influent concentrations into the reservoirs rather than 

concentrations in the reservoir itself. SAM is designed to generate estimates for both lotic 

(flowing waters such as streams and rivers) and lentic (lakes, reservoirs, other impounded 

waters) waters. However, given the need to account for time of travel differences described 

below, USEPA OPP shifted the water volume processing for lentic waters to follow the 

convolution process described in Section 4. 

While SAM results also exceeded monitored concentrations at the IN and OH sites, the 

estimated annual maxima were within an order of magnitude for the majority of site years 

(Table 2). As discussed below, further examination of the model-monitoring comparisons and 

the monitoring sites and associated water body types and drainage areas (Figure 14) provides

insights regarding the estimation errors.
48

Figure 14: Locations of the AEEMP and AMP Monitoring Sites within the Mark Twain Basin in 

Northeastern MO. The Underlying Land Cover Map Shows Corn (yellow) and Soybeans (dark green).

These initial comparisons identified areas where model adjustments may improve 

concentration estimates:

- Results for larger watersheds, such as MO-17 river (Figure 15), overestimate 

concentrations to a greater degree (Appendix 2-D), suggesting potential for 

improvement by accounting for travel time related dispersive effects in larger 

watersheds. 

- In some years, the timing of estimated concentrations from SAM does not coincide with 

that seen in monitoring data (Figure 16). The timing of pesticide runoff loading to water 

reflects timing of both applications and rain events. In cases when plantings (and related 

pre-emergent pesticide applications) begin earlier than usual due to an early warm 

spring, or later than usual due to a cold spring, the altered timing of pesticide 

applications would lead to different temporal concentration profiles in the water body.
49

Figure 15: Comparison of Estimated Concentrations of Atrazine from SAM with Measured 

Concentrations from the AMP MO-17 Site (NHD comid 5641174).

Figure 16: Estimated Concentrations of Atrazine from SAM Occur Later than Measured Concentrations 

from the AEEMP MO-01 Site (comid 4989415) in 2006.
50

Section 4 presents an approach USEPA OPP is exploring to address the influence of upstream 

lentic waters, and travel time related dispersive effects; section 5 presents approaches USEPA is 

exploring to address year-to-year variations in the timing of pesticide applications.
51

3 SAM Data Inputs

This section describes the national data sets for soil, land cover, weather, crop characteristics 

and management practices, and hydrology, and how these diverse data sets are organized as 

soil-land cover-weather combinations (scenarios) for inputs to the model and to reflect the 

spatial diversity in conditions. USEPA OPP is requesting the SAP to respond to the following 

questions related to data inputs: 

Q4. To substantially lessen the number of scenarios and improve computational speed of the 

model, USEPA OPP evaluated the option of grouping soil map units into classes based on 

factors that have the greatest impact on pesticide loss due to runoff and erosion (USDA 

water quality index, described in Section 3.3). A comparison of runoff volume, pesticide 

mass, and pesticide concentration outputs showed little difference between the two 

approaches. Please comment on any implications for using soil grouping classes for 

watershed-scale modeling.

Q6. In order to generate the soil-land cover-weather station scenarios needed for modeling, 

USEPA OPP took spatial data at different scales and re-gridded them to the same scale for 

aggregation (Section 3.2). Based on comparative testing of the model (Sections 2.4, 4.3, and 

5.4), this appears to be a feasible approach. Please comment on the implications for 

aggregating spatial inputs across varying scales.

The SAM model described in Section 2 uses scenarios that are created by intersecting soil map 

units from USDA’s Soil Survey Geographic database (Section 3.1.1), general land cover/crop 

groups of the Cropland Data Layer (Section 3.1.2), and weather station grids generated from 

NOAA data (Section 3.1.3). Each of the spatial datasets described in Section 3.1 represents what 

USEPA OPP has determined to be the best nationally-available data for SAM. These data (and 

corresponding model frameworks) were chosen so that, as newer and/or improved datasets 

become available, they can be used in place of existing datasets. 

As described in Section 3.2, USEPA OPP conceptually “stacks” the spatial data layers on top of 

each other, and then creates scenarios identified by the unique combinations of soil 

identification, weather station grid identification, and crop group value. An input table 

generated from this intersection of datasets provides model inputs for each scenario. These 

inputs are used by the Scenario Generator and Super PRZM Hydro components of SAM (Section 

2.1). 

To process model outputs for NHDPlus catchments or other drainage areas (such as HUC-12s), 

SAM employs “recipe” files that contain lists of scenarios and their corresponding areas within 

each catchment or drainage area being modeled (described in Section 3.2). SAM takes daily 

runoff and pesticide load values and aggregates them at the catchment pour point, based on 

the area represented by each scenario in a recipe. 
52

Detailed documentation on input data sources and compilation are available in Appendices 3-A 

through 3-F. Section 3.3 contrasts the differences in model inputs and outputs between 

scenarios developed using individual soil map units and soil groupings based on USDA water 

quality index criteria (Lal and McKinney, 2012).

3.1 Data Inputs Used in SAM

USEPA OPP compiled spatial data for a twelve-state region (Figure 17) that encompasses the 

Ohio River Basin used in the initial model development and the three test basins (Figure 11), 

which were used for further model development and testing. Ultimately, USEPA OPP intends to

expand the data compilation to a national scale. The scenarios used for SAM are derived from 

the spatial intersection of soil map units (Section 3.1.1), land cover classes (Section 3.1.2), and 

weather grids (Section 3.1.3). Other data inputs are spatially associated with these scenario 

components. The National Hydrography Database Plus version 2 (NHD+) provides catchment 

areas (both on NHD+ reach and HUC12/watershed boundary scales) and flow data used in the 

water body calculator, as described in Section 2.1. 

Figure 17: NHD Hydroregion 5, the Ohio River Basin, and Monitoring Site Locations.
53

3.1.1 Soil-Related Inputs from SSURGO 

Soil-related inputs are available through the USDA’s Natural Resources Conservation Service 

(NRCS) Soil Survey Geographic (SSURGO) database. In the standard aquatic exposure modeling 

approach currently used for regulatory decision making, USEPA OPP employs data for a single, 

runoff-prone (typically hydrologic group C or D) soil associated with a particular crop, 

corresponding to a soil-crop combination that occurs in the major crop-growing area. By 

contrast, in SAM OPP is using the entire soil coverage, with data including each soil map unit on

which crops are grown.

USEPA obtained the gridded version of SSURGO (gSSURGO) for soil map unit boundaries and 

associated parameters from the USDA Geospatial Data Gateway12. The pixel resolution for the 

gridded product is ten (10 x 10, or 100 m2) meters.

SSURGO spatial data layers depict the extent of the soil map unit on the landscape. The spatial 

data include a map unit key (MUKEY) that links map units to soil data attributes. 

Modifications to the gSSURGO tabular data extracts included:

1. converting units to those used in the SAM model, 

2. calculating Universal Soil Loss Equation (USLE) slope/length (LS) values

3. processing soil horizon data to match SAM horizons, including generation of depthweighted averages

4. replacing missing values, if needed, as described below

5. aggregating soil properties as described in Section 3.3

SSURGO map units can have one to three major components and a number of minor 

components. The SSURGO data provide a range and representative percentage of each 

component in each map unit. The presence of multiple components in a map unit reflects both 

the degree to which differing soils can be distinguished in the field and the scale of the map. 

For SAM, USEPA OPP generally selected the major component consisting of the greatest 

percentage of the map unit. However, if the major component did not include all of the soilderived data required for model inputs, then USEPA OPP selected the highest-percentage 

component that did include a complete set of these values. In instances when one or more 

major components made up an equal percentage of the map unit, USEPA OPP selected 

components according to hydrologic soil group in order from more to less prone to runoff: D > 

C > B > A.

 12 Available at https://gdg.sc.egov.usda.gov/. The 2015 Gridded Soil Survey Geographic (gSSURGO) database, 

derived from a December 1, 2014, snapshot of the Soil Data Mart database, was released on February 23, 2015 

(FY2015 Soil Survey Geographic Database or SSURGO source).
54

The SSURGO database includes non-soil map units such as quarries, pits, mines, etc., that do 

not contain data. For this version of SAM, such non-soil map units were not included in scenario 

input datasets or in watershed recipe files. USEPA OPP plans to develop systematic rules for 

distinguishing non-soil units that might contribute runoff to surface waters (such as 

unidentified urban/ developed map units) from units that are less likely to contribute runoff 

(such as quarries and pits). 

3.1.2 Crop/Land Cover from the Cropland Data Layer (CDL) 

USDA NASS’s Cropland Data Layer (CDL) provides detailed spatial distributions of numerous 

crops using satellite imagery13. The CDL offers cropland coverage for the 48 contiguous states 

from 2008 to the present. This provides USEPA OPP with the opportunity to use multiple years 

of CDL data to determine cropping patterns and to capture year-to-year variations in crop 

practices. Because 2008-09 CDL data are provided at 56-m pixel size while subsequent data are 

available at 30-m (30x30, or 900 m2) pixel resolution, USEPA OPP began with the 2010 CDL 

data; this SAP version of SAM uses CDL data for four years, through 2013. Additional years of 

land cover can be added as they become available. To generate scenario combinations for SAM 

(see Section 3.2), USEPA OPP intersected SSURGO soil map units with each year of CDL 

coverage separately. This resulted in four sets of watershed recipes, one for each year of CDL. 

While the CDL identifies 111 agricultural land classes and 133 total land classes, not all of these 

land classes are equally reliable. Accuracy assessments documented by USDA NASS on a stateby-state basis show that the CDL is relatively accurate (90% or greater) for major commodity 

crops, such as corn, soybeans, wheat, and cotton, which are grown over extensive contiguous 

areas, and for which the USDA has sufficient independent data for training and quality 

assurance analysis14. However, a high frequency of error for a number of crops suggests that 

the CDL may not be suitable for representing minor crops such as individual vegetables or 

orchards.

As an alternative, USEPA OPP is exploring ways to group some of these minor crops together. 

Viable crop groupings should account for agricultural similarities, likely rotations, and the ability 

of the CDL to differentiate among crop signals from remotely sensed imagery. Ideally, the crop 

groups would be sufficient to distinguish major commodity crops (such as corn, soybeans, 

wheat, and cotton), as well as reasonably distinct crop groupings (such as vegetables, orchards, 

vineyards, grain crops, row crops, and total cultivated crops). They should also be sufficient to 

identify the general agricultural and non-agricultural land classes used for deriving curve 

numbers based on USDA NRCS (2008b) guidance: fallow, row crop, small grain, close-seeded 

 13 National CDL coverage for 2008-2014 is available for download from the USDA NASS site at 

http://www.nass.usda.gov/research/Cropland/SARS1a.htm. 14 The description of NASS’s accuracy assessment is available at 

http://www.nass.usda.gov/research/Cropland/Method/cropland.pdf. Metadata that include error analysis are 

available for download at http://www.nass.usda.gov/research/Cropland/metadata/meta.htm.
55

legume or rotation meadow, pasture/grassland/range, orchard [woods-grass], woods, 

brush/weed/grass, meadow, various developed/ residential intensities. 

The current land class groupings are described in Appendix 3-B. The general land cover class 

groupings that are relevant to the Ohio River Basin/test basins are described below. 

Corn: Includes corn and those double-cropped classes (corn with wheat, soybeans, or other 

grains) with corn in the rotation. 

Soybeans: Includes soybeans and those double-cropped classes (soybeans with corn, cotton, 

wheat, or other grains) with soybeans in the rotation.

Wheat: Includes durum, spring, and winter wheat classes, along with those double-cropped 

classes (wheat with corn, cotton, soybeans, other grains, or vegetables) with wheat in the 

rotation. 

Vegetable and ground fruit: This pulls together individual all of the vegetable crops, which have 

low accuracy rates in CDL. It also includes ground fruit which, while using different cultivation 

patterns, aren’t well distinguished from surrounding vegetable (or other) land classes. 

Orchards and vineyards: This includes all nut and fruit trees, citrus crops, and grape vineyards. 

Crops in this group have a relatively high error rate. A number of orchard areas are often 

misidentified as pasture, grassland or pasture (presumably the grass lanes between tree rows 

contribute more to the optical signal than do the trees), shrubland, or forest. 

Other grains: This includes all small grain crops other than wheat.

Pasture/hay/forage: This includes specific hay crops, such as alfalfa, clover, and vetch, and 

general pasture, hay, and forage classes. It also includes the grassland class, which may include 

pasture land in some parts of the country. 

Forest: This merges deciduous, evergreen, and mixed forest classes, along with more generic 

forest classes. 

Grassland and Shrubland classes remain separate because they fall into different land cover 

types for curve number determination. 

Developed categories (open; low, medium, and high intensity) were kept separate because the 

intensity differences can be used to estimate turf area and relative impervious surface area and 

curve number determination also depends on intensity of development.

Wetlands: Herbaceous and wetlands were kept as separate groups because of differences in 

curve number determinations.

3.1.3 Weather Data 

Currently, USEPA OPP’s aquatic exposure models use SAMSON (Solar and Meteorological 

Surface Observation Network) weather station data, publically available from NOAA’s National 

Climatic Data Center (NCDC). SAMSON provides daily precipitation, pan evaporation, solar 
56

radiation, temperature, and wind speed required for aquatic exposure models, for 237 National 

Weather Service (NWS) locations, spanning the years 1961 to 1990. The SAMSON dataset was 

originally developed by NOAA’s NCDC and USEPA’s National Exposure Research Laboratory 

(NREL), but is no longer maintained and will not be updated in the future. 

SAM uses publically available gridded meteorological datasets that provide a source of updated 

daily weather data at uniform spatial resolution across the country, for a wide variety of 

meteorological variables. The attributes of gridded datasets, characterized by continuous and 

uniform spatial densities, are thought to be optimal for the spatial computational approach to 

pesticide fate and transport modeling in SAM. The gridded meteorological dataset used to 

evaluate SAM’s performance was developed from the following sources maintained by NOAA: 

Unified Gauge-Based Analysis of Daily Precipitation from the Climate Prediction Center (CPC) 

(NOAA ESRL, 2014) and Reanalysis Data from the National Centers for Environmental Prediction 

(NCEP). Both datasets provide national gridded weather analyses available between 1948 and 

the present day (Kalnay et al., 1996; NCEP/NCAR, 2014). Together, these two sources provide 

all the weather inputs needed for SAM.

The NCEP/NCAR and CPC analyses are actively maintained by NOAA, and continue to be 

extended to the present day, as of this writing. As a result, the compiled dataset can be easily 

expanded as needed with the most recent NCEP/NCAR and CPC daily values as they become 

available. 

SAM requires precipitation, temperature, wind speed, solar radiation, and potential 

evapotranspiration. Table 3 lists the variables within each dataset from NOAA used in deriving 

the SAM computational weather gridded dataset. 

Table 3: Weather variables from NOAA NCEP/NCAR Reanalysis and NOAA CPC Unified Rain Gauge 

Analysis.

NOAA NCEP/NCAR Reanalysis (global) NOAA CPC Unified Rain Gauge Analysis (US)

Air temperature at 2ma

Maximum air temperature at 2ma

Minimum air temperature at 2ma

Zonal wind speed at 10 ma

Meridional wind speed at 10 ma

Downward solar radiation flux at surfacea

Downward solar radiation flux at nominal top 

of atmosphere (NTAT)b

Daily Total Precipitationc

a Available: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html

NCEP/NCAR Reanalysis 1 data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA

b Available: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.other_flux.html

NCEP/NCAR Reanalysis 1 data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA

cAvailable: http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.html (1984 – 2006); 

http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.rt.html (2007 – 2014)

CPC US Unified Precipitation data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA 
57

The NCEP Reanalysis data are available at 2.5 x 2.5 degree grid resolution; the CPC data are at 

0.25 x 0.25 degree US grid resolution. To combine the CPC precipitation data with the NCEP 

Reanalysis meteorological fields for use in SAM, the SAM computational weather gridded 

dataset was produced by re-gridding the coarser-scale NCEP data to the finer grid resolution of 

the CPC precipitation (0.25° x 0.25°). Daily potential evapotranspiration (not offered explicitly

by the NOAA gridded datasets) was derived using NOAA NCEP/NCAR Reanalysis daily mean 

temperature, daily minimum and maximum temperatures, and downward solar radiation at the 

nominal top of the atmosphere. A more detailed methodology for the development of the 

processing of the NOAA meteorological parameters and the development of the final 

computation weather grid for SAM is provided in Appendix 3-C. This compiled dataset was used 

as input to the pre-processed scenarios for the example simulations discussed in this document.

An evaluation and comparison of this fine-scale meteorological computation gridded dataset 

for SAM to the existing SAMSON dataset shows similar magnitudes and meteorological trends. 

This evaluation is provided in Appendix 3-C. 

3.1.4 Crop Data Collection and Processing 

As noted in Section 2.3 and Appendix 3-A, key crop-related inputs needed for SAM are planting 

and harvesting dates (both to provide a reference point for pesticide applications and to 

simulate canopy cover), maximum canopy cover and crop intercept (for rainfall and pesticide 

applications), depth of the active rooting zone (to define the zone of water extraction from 

soil), and crop-specific USLE inputs. 

USEPA OPP made use of available USDA publications to obtain key agricultural crop stage dates 

– active planting and harvesting dates for field crops (USDA NASS, 2010) and vegetables (USDA 

NASS, 2007) and blooming and harvesting dates for fruits and tree nuts (USDA NASS, 2006). 

These date ranges represent variations in time over individual states. USEPA OPP explored 

using the USDA Plant Hardiness Zone (PHZ) Map15 to split up the range in planting, harvesting, 

and blooming dates in geographic subdivisions within each state. 

Figure 18 illustrates the approach used to divide the beginning of the corn planting range for a 

single state (in this case, MO) by the plant hardiness zones (PHZ) within that state. The 

beginning of the range in planting dates was assigned to the warmest PHZ; the initial planting 

date was then adjusted to progressively later dates through increasingly colder PHZs. A similar 

proportioning was done for the end dates in the plant range, as well as for harvest dates. 

Appendix 3-D summarizes the date ranges for the dominant agricultural crops found in the pilot 

project study region. 

The PHZ map is simply used to proportion the ranges in crop milestone dates within states, as 

reported in the USDA publications. Since these publications do not provide year-to-year data, 

 15 The 2012 USDA Plant Hardiness Zone (PHZ) Map is available at http://planthardiness.ars.usda.gov/PHZMWeb/
58

USEPA OPP is exploring alternative methods for capturing both spatial and temporal variations 

in crop planting and harvesting dates, and other stages (see Section 5).

Figure 18: Illustration of the Method Used to Proportion the Beginning of the Corn Planting Date for 

MO Across Plant Hardiness Zones.

Additional crop-related inputs related to canopy cover, rainfall interception, active rooting 

depth, and USLE factors (Appendix 3-A) have been collected from a variety of sources, including 

the PRZM Manual (Carousel et al, 2005), existing USEPA OPP standard crop scenarios16, and 

USDA Crop Profiles17. Appendix 3-D also includes data compiled for the major crops in the pilot 

project, published planting and harvesting dates, and crop class relations between CDL and 

NASS crop date publications. Such data will be updated as additional data sources are found. 

 16 The individual crop scenarios are imbedded in the SWCC currently used by USEPA OPP. Data and documentation 

for the scenarios have been archived in http://www.epa.gov/oppefed1/models/water/water_models_archive.htm

and can be downloaded from links under the “PRZM/EXAMS SHELL – Superseded” section. 17 Crop profiles, with descriptions of crop production and pesticide management practices, can be found in links 

provided by the National Integrated Pest Management (IPM) Centers: http://www.ipmcenters.org/cropprofiles/
59

3.1.5 National Hydrography Dataset (NHDPlus, Version 2.0) 

The National Hydrography Dataset is a national spatial data set widely used as a framework for 

mapping and analyzing hydrologic information. The NHDPlus18 is a USEPA data product that 

provides value-added information to a “snapshot” (taken April 2010) of the NHD, including 

stream catchment delineations and stream flow-related information. As noted in Section 2.1, 

USEPA OPP used the NHDplus for river/stream networks, water bodies, watershed boundaries 

(HUC12 and reach catchments), and the hydrology data associated with these features. The 

NHDplus provides the best available data, at a national scale, on stream networks, flow data 

and flow direction, and associated drainage areas for use in modeling. 

Pesticide concentration calculations for rapidly-flowing (lotic) water bodies require estimates of 

cross-sectional area orthogonal to the direction of flow, to enable calculation of the volume of 

the pour point mixing cell (Section 2). NHDPlus Version 2 provides monthly mean streamflow 

and velocity estimates for each reach that can be used to generate such values with the 

following equation:

AAAAAAAAxx = FFFFFFFF

VVVV VVVVVVVV = mean cross-sectional area for the reach

For use in SAM, USEPA OPP employed flow and velocity estimates generated by the Enhanced 

Unit Runoff Method (EROM, see McKay et al, 2012, for information):

Mean flow = Q0001E, which represents the best estimate of streamflow, reported in ft3/s (cfs)

Mean velocity = V001E, which represents the best estimate of velocity, reported in ft/s

The mean cross-sectional area, converting from ft2 to m2, is calculated as: 

AAxx = �

QQ0001 , ffff3/ss

VV0001 , ff/ss � xx � mm210.7639 ffff2

� = mean cross-sectional area (m2) for the reach

The relevant flow and velocity data were collected by identifying the most-downstream 

(highest hydrologic sequence) NHDplus reach to intersect the boundary of the watershed or 

HUC-12 of interest. Reach mixing cell volume was calculated as the product of reach crosssectional area and a representative mixing length. 

For lentic receiving waters (e.g., lakes, reservoirs), USEPA OPP used preliminary volume 

estimates developed by USEPA ORD’s Atlantic Ecology Division. Although the dataset, which 

provides estimates of lake depth and volume has not been finalized or publically released, the 

methods used to calculate the estimates are similar to those documented in Hollister and 

Milstead (2010), and Hollister et al. (2011). 

 18 Version 2 of NHDplus is available for download at http://www.horizonsystems.com/nhdplus/NHDPlusV2_home.php.
60

3.2 Creating Scenarios, Input Data, and Watershed Recipes

Scenarios used for modeling were created by intersecting soil map units, general land 

cover/crop groups of the CDL, and weather station grids (Figure 6). The resulting scenarios are 

identified by unique combinations of soil identification, weather station grid identification, and 

crop group value. 

3.2.1 Creating Scenarios

Scenarios were created from the intersection of SSURGO soil map units or groupings, weather 

station grids, and general crop groups of the CDL, based on a reclassification of the CDL classes 

into general land cover classes. 

The data sources that define the spatial extent of a scenario are published in varying spatial 

resolutions. The weather data has a pixel size of 0.25 X 0.25 degree (or approximately 15 km2), 

the CDL has a pixel resolution of 30 m (30x30, or 900 m2), and the gridded SSURGO is at 10 m 

resolution (10x10, or 100 m2). The variation in pixel resolution creates the potential for 

generating unrealistic scenarios, for example a corn pixel that crosses over a shoreline to 

overlap with open water. The extent of such unrealistic scenarios is expected to be relatively 

small, likely < %1 of watershed area. Where identified, these were omitted from recipes. 

Differing spatial resolutions will likely impact modeling results, although quantifying the impact 

of this effect may be difficult, and has not yet been attempted.

For the current effort, all spatial data layers were resampled to 10 meter resolution raster 

images for ease of processing. USEPA OPP used ESRI’s Spatial Analyst toolbox to generate a list 

of individual scenarios from unique combinations of the three component raster layers. ESRI’s

“Combine” tool was used to generate sets of unique combinations of soil, weather and crop, 

with a unique key field created to identify each such combination. A new raster layer was thus 

created and classified to represent the combined (scenario) data. All of the remaining SAM 

inputs for running each scenario are obtained using information linked to one or more of the 

three underlying identifiers.

Scenario input data, described in Appendix 3-A, were gathered into a matrix using a 

spreadsheet template (listing all of the required model inputs in the correct order) using Python 

scripts. The scenario IDs, along with soil, weather grid, and general land cover class identifiers, 

were added to the template for reference.

All of the soil data (in Appendix 3-A) are linked to the scenarios through the MUKEY (soil map 

unit identifier). For the soil grouping classes (see Section 3.3), the input values represent the 

mean for all of the individual soil map units that fall into each soil class. 

One of the major drivers in SAM (Section 2.3.2) is the curve number, values of which are based 

on major hydrologic soil group and land cover class (see Appendix 3-E). Curve numbers for both 

cropped and fallow conditions were assigned to each scenario using a script written in Python 

based on the tables in Appendix 3-E.
61

Crop-related inputs (rainfall intercept, maximum canopy cover, USLE C factors, and maximum

active rooting depth) were joined to the scenarios by general land cover class.

Plant, bloom, and harvest dates in the crop input database were linked to the scenarios by a 

State-Plant Hardiness Zone-CDL group field. To create the link in the scenario matrix, OPP 

assigned a state and USDA Plant Hardiness Zone (PHZ) to each of the weather station grids 

using a spatial join in ArcGIS, linked this to each scenario using the weather station ID, and then 

created an equivalent state-PHZ-CDL group field based on the general crop cover for the 

scenario. Plant, bloom, and harvest dates were then able to be joined by the common StatePlant Hardiness Zone-CDL group field.

3.2.2 Generating Watershed Recipe Files

Watershed recipe files consist of a list of scenarios that fall within each watershed or drainage 

area, along with the area associated with each scenario. The summed area of the individual 

scenarios is thus equal to the watershed area. Recipe files have been generated for HUC-12s

(used in the alpha model on the übertool site), NHDPlus catchments, and monitoring site 

watersheds. USEPA OPP plans to use recipes for each individual NHDPlus catchment to 

estimate runoff volume and pesticide mass loadings contributed by each catchment across the 

contiguous “lower 48” states. NHDPlus catchments are the finest-resolution surface drainage 

area dataset available nationally, and represent the drainage area for a single stream reach 

segment or waterbody at the resolution of the NHD dataset. The contributions from each 

catchment are aggregated to represent whole watersheds, and may be manipulated 

mathematically using a process described in Section 4 to allow for a fuller accounting of 

watershed hydrology and the influences of constituent travel time (from field edge to pour 

point) heterogeneity on temporal concentration dynamics. 

The recipe files for each HUC12, NHD+ catchment, and select monitoring basins were generated 

using a python script and the combination soil-land cover-weather grid raster layer described 

earlier. Using the raster layer’s attribute table, unique combinations were translated into 

scenario IDs based on soil, weather and CDL identification keys. Area estimates were derived 

for pixel counts included in the combination layer. Each drainage area or watershed has 

separate recipe files that reflect each of the years of CDL coverage.

3.3 Soil Grouping Classes Based on USDA Soil Water Quality Index Groupings

Because the potential number of individual soil map unit-crop group-weather station 

combinations would run in the tens of millions, USEPA OPP explored the feasibility of grouping

soil map units into classes based on factors that have the greatest impact on pesticide loss due 

to runoff and erosion, similar to approaches used by the European Union (Schneider et al, 

2007). A reduction in the number of scenarios that would need to be stored and processed 

would have an impact on the amount of storage space that would be required for SAM on a 
62

national scale and on the amount of computer processing time that would be needed for 

running national-scale risk assessments on a routine basis. 

USEPA OPP evaluated grouping individual soil map units into classes based on a USDA NRCS 

Water Quality Index (WQI) for runoff from agricultural fields (Lal and McKinney, 2012). The WQI 

used the following grouping classes to derive a WQI for each soil:

• Runoff potential based on Hydrologic Soil Group (HSG): A, B, C, D

• Slope gradient: <2%, 2-5%, 5-10%, 10-15%, >15%

• Soil erodibility potential based on K factor: <0.10, 0.11-0.20, 0.21-0.32, 0.33-0.43, 0.44-

0.64

• Soil Organic Matter (SOM) content (surface layer): <0.5%, 0.5-2%, 2-4%, 4-6%, 6-8%, 

>8%

These grouping class combinations result in a potential of 600 soil group classes:

4 HSG x 5 slope classes x 5 soil erodibility classes x 6 SOM classes

For a given pesticide, the amount that may move from a field to off-field surface water is 

affected by the vulnerability of the soil to runoff (represented by the curve number in the 

model) and/or erosion (reflected by the K factor, slope, and associated USLE parameters) and 

how much pesticide is retained by the soil (reflected largely by the SOM content of the surface 

layer). Because the grouping classes preserve the hydrologic soil group classification, the curve 

number for the resulting soil group scenarios will not change. Variations in SOM (represented 

as soil organic carbon content in the model inputs) are limited to 2% OM (or 1.16% OC) 

increments. 

Variations in slope as a function of slope gradient classes may be limited because soil map units 

are split out in slope classes that are similar to the class breaks above; the biggest change will 

be for steeper slopes (>15%). However, agriculture is generally limited on such steeper slopes. 

Variations in sediment transport are limited by the range in K factor values for each class. Other 

USLE input parameters, such as the slope/length, or LS, factor, and associated crop and 

management practice factors, would vary within the limits of the slope-K factor classes. 

The soil map units in the pilot project area (Ohio River basin) were classified into HSG-slopeerodibility-SOM classes. Appendix 3-F provides a cross-reference between the individual soil 

map units (MUKEY) and the new soil classes derived from the WQI groupings (SoilClass1).

These soil group classes were combined with the CDL land cover classes and weather station 

grids to create unique soil-land cover-weather station scenarios for the pilot project area. 

USEPA OPP generated scenarios based on individual soil map units (individual MU scenarios) 

and on the resulting aggregation of soils into the water quality criteria (aggregated soil 

scenarios) for the test basins used for this evaluation. Test evaluations covered 15 years of 

weather data (2000-2014).
63

USEPA OPP compared the annual total runoff volume, pesticide mass runoff loss, and maximum 

annual concentrations over the 15 years of simulations in each test basin (Appendix 3-F). 

Total annual runoff in all of the test basins was within 1% of that generated based on nongrouped soil units. This suggests that the differences between scenarios based on individual soil 

map units and those based on soil groupings had negligible impact on the curve number that 

drives runoff. This was expected since soil aggregation preserved the Hydrologic Soil Group 

designation, which is one of the key components in curve number determination.

The total annual pesticide mass lost was within 2% of that generated based on non-grouped soil 

units for all of the test basins. This suggests that soil grouping had minimal impact on pesticide 

mass lost over the duration of the application period. However, some variations may occur with 

individual events, which would be reflected in pesticide concentrations. Estimated annual 

maximum concentrations were within 5% of each other in 11 of the 15 basins and within 10% in 

11 of the 12 basins. The MO-02 site showed the greatest difference in estimated maximum 

concentrations, with estimates generated using scenarios based on aggregated soil groupings 

more than 10% greater than estimates from individual map units in 3 of 15 model years. 

Visual comparison of plots of estimated concentration distributions over time (Figure 19) for 

the two scenario approaches (individual map units and aggregated soil groups) shows nearly 

identical patterns, for most test basin sites and years. Figure 20 shows the difference between 

the results using the two methods, for the years with greatest variability. Even for these sites, 

the majority of estimated peak concentrations are in close agreement. 

Figure 19: Comparison of Estimated Atrazine Concentrations Using Individual Soil Map Unit (blue) or 

Aggregated Soil Group (yellow dash) Scenarios for MO-01 (NHD comid 4989415).
64

Figure 20: Comparison of Estimated Atrazine Concentrations Using Individual Soil Map Unit (blue) or 

Aggregated Soil Group (yellow dash) Scenarios for MO-02.
65

4 Accounting for Time of Travel and Downstream Accumulations

In initial SAM evaluations (Section 2.4), USEPA OPP noted that SAM outputs overestimated 

pesticide concentrations in comparison to measured (monitoring) data for the sampling sites 

that draw from larger watersheds, indicating the need to account for differences in time of 

travel for pesticide mass across larger watersheds. This section provides a description of USEPA 

OPP’s proposed approach for addressing time of travel differences using convolution, in 

support of the following charge questions to the SAP:

Q6. As described in Sections 4.1 and 4.2, USEPA OPP has evaluated an approach for 

representing concentrations at the pour points of drainage networks that involves 

aggregating upstream drainage areas into integer-day stream travel-time zones, and the use 

of mathematical convolution to represent in-stream dispersive spreading of both influent 

runoff volumes and pesticide masses. Please comment on the use of this approach, and on 

any modifications or alternative approaches that USEPA OPP might consider for 

accomplishing the same ends.

Q7. USEPA OPP has not yet investigated possible adaptations of the approach referred to in 

Question 6 to simulate reactive and/or sorbing chemicals. Please comment on the potential 

for modifying this approach to simulate such chemicals. 

a. Given the risk assessment purpose of SAM, please comment on the applicability of the 

described approach in dual-compartment (aqueous and benthos) systems for 

representing chemical decay and sorption during transport in surface waters across a 

range of spatial scales.

b. Please recommend any watershed-scale monitoring datasets that may be suitable for 

use in evaluating estimated concentrations of pesticides that sorb non-negligibly to 

sediment, and any possible sources of data for representing the benthic sediment layer 

in surface waters throughout the country.

Section 4.1 provides more depth to the approach for accumulating runoff volumes and 

pesticide mass loadings by watershed using a simple watershed recipes described in Section 

2.1. It also notes where the model greatly overestimates pesticide concentrations in

comparison to measured concentrations. Section 4.2 describes the proposed approach to 

address this issue. Section 4.3 provides initial comparisons between the proposed model 

improvement, the current approach, and monitoring data. 

4.1 “Instantaneous” Approach Used in the Initial Version of SAM 

As described in Section 2.1, SAM generates daily time series of area-normalized runoff (water) 

volume and runoff-entrained pesticide mass transported from the land to the water for each 

“scenario” (i.e., soil/land cover class/weather combination). Recipe files specify the land areas 
66

of the scenarios that together comprise each hydrologic unit or drainage area (e.g., HUC-12, 

NHDPlus catchment, or monitoring basin). SAM uses these recipe files to weight the simulated 

hydrologic and pesticide mass time series by each scenario’s contributing area, then sums the 

results to produce net time series representing total runoff water and pesticide loading 

contributed by the drainage area. 

At a particular pour point, pesticide load is assumed to enter the receiving water body 

instantaneously at the beginning of the time step. Receiving water bodies are of two basic 

types: lotic (streams, rivers) and lentic (lakes, reservoirs). Because these are the simplest 

possible representations, both types are assumed to be completely mixed and in steady flow 

during the time step. First-order wash-out thus occurs, and mean concentration CC̅during the 

time step (in the absence of decay or sorption) is given by:

or

where M is influent pesticide mass, V is water volume of the water body, Q is the volume of 

water that passes through the water body during one time step (i.e., daily flow volume), and τ is 

hydraulic mean residence time. It can be shown that when τ<<1 day, CC̅≈ MM

QQ, and that when 

τ>>1 day, CC̅≈ MM

VV.

Although lentic water body volumes were not used in the model comparisons in Section 2.4,

SAM can estimate pesticide concentrations in lakes, reservoirs, and other lentic waters using

water body volume estimates derived by USEPA ORD’s Atlantic Ecology Division. The methods 

are similar to those documented in Hollister and Milstead (2010) and Hollister et al. (2011).

SAM derives lotic water body pour point volumes from tabulated NHDPlus reach monthly mean 

flows and velocities, and daily runoff volumes generated by PRZM Hydro. Daily flow is divided 

by velocity to produce daily-varying reach cross-sectional area, which together with 

representative mixing cell length specifies a daily-varying reach water volume (V). 

Representative mixing cell length is assumed to be 40 m, which is based on typical longitudinal 

dispersivities (the rationale being that this is a length below which it would not be 

hypothetically possible to simulate advection-dispersion using a finite-difference approach, and 

thus in effect the ultimate spatial limit of resolution for representing Fickian longitudinal 

dispersion) measured in rivers (Fischer, 1979; Rutherford, 1994). 

The alpha version of SAM processes watershed-water body pairs one at a time, as if they 

existed in isolation from each other. In doing so, certain mixing processes are inevitably ignored 

that may be relevant to the accuracy of concentration calculation, including longitudinal 

CC̅= MM

QQ �1 − −QQ⁄VV�

CC̅= MM

QQ �1 − −1/ττ �
67

dispersive effects (peak spreading and flattening) and differential arrival times at the pour 

point, of solute pulses that originate from different upstream travel distances/times (peak desynchronization). 

In its initial “alpha” implementation, SAM treats each HUC-12/water body pair as if they 

constituted essentially a headwater system: hydrologic and pollutant loads originating from 

upstream of the HUC12 are simply ignored. A more thorough approach considers hydrologic 

and pesticide loadings originating from throughout the entirety of a pour point location’s 

catchment. This approach is expected in general to generate higher-accuracy estimates and,

indeed as documented in Section 2.4, appears to reasonably represent peak atrazine 

concentrations in example comparisons against monitoring data. However even this more 

thorough approach entails conceptual weaknesses that may tend to systematically distort 

results at some kinds of locations. In addition to previously-mentioned dispersive and peak desynchronization effects, errors are likely to be introduced by the fact that SAM only accounts 

for the hydraulic properties of the water body at the pour point. Consider for example the 

calculation of concentrations in a stream at a point a short distance downstream from where 

the stream receives effluent from a reservoir possessing a large hydraulic residence time. In 

reality, mixing within the reservoir will have a profound influence on the temporal 

concentration dynamics in the reservoir’s effluent (dampening, or decreasing, but lengthening 

the duration of influent peaks), and this effect will continue to be evident for some distance 

downstream. However, even in a more-thorough implementation such as described above, and 

as documented in Section 2.4, SAM does not account for such effects. The proposed update 

described below represents an initial effort to enhance the sophistication of SAM in order to 

remedy such deficiencies.

4.2 Proposed Update to Account for Time of Travel Dispersion

USEPA OPP developed a conceptual modification of SAM – a convolution approach – to address 

limitations in the model to account for time of travel dispersion. A preliminary evaluation of 

results was made using a subset of the test basins described in Section 2.4. 

In a mathematical context, a convolution is a function derived by integrating two separate

functions such that one function changes the shape of the other. In the case of the SAM timeof-travel convolution routine, the chemograph (pesticide mass in runoff over time) and 

hydrograph (runoff over time) generated as output are convolved with a gamma distribution, a 

two-parameter probability distribution that closely fits the movement of water and solutes 

through drainage networks. The parameters of the gamma distribution are altered based on 

the time of travel between the source reach and the watershed pour point to generate an 

impulse response function (IRF) that is used in the convolution. Longer travel times result in the 

lengthening and flattening of peaks in the hydrograph and chemograph. Figure 21 displays IRFs 
68

for 1, 2, and 3 day travel times, compared with a null IDF in which the gamma distribution is not 

applied.

Figure 21: Graph Illustrating Impulse Response Functions (IRF) for Different Travel Times.

For a hydrologic transport system with assumed linear, statistically stationary properties, 

convolution can be used to calculate the water body’s effluent concentration Co at a given time 

point t, as a weighted sum of its influent concentrations (Ci) at all previous time points. The 

influent concentration weightings are given by the water body’s time-invariant IRF, G(t), which 

describes the effluent response to a unit pulse input:

CCoo(tt) = ∫ GG(θθ) ∞

0 CCii(tt − θθ)dd

For a non-reactive constituent, the IRF is the same as the water body’s residence time 

distribution. The essence of this proposed update is to employ mathematical convolution using

gamma-distributed IRFs to redistribute, over time, daily SAM-generated pulses of both 

pesticide mass and runoff volume, prior to the ultimate calculation of pour point 

concentrations.

Convolution is intended to ultimately approximate the net influences of upstream hydrology on 

downstream concentration. The approach is similar to one proposed by Carleton (2015), with a 

few differences. As in the alpha version of SAM, water bodies (or pour points) are processed 

one at a time, and the model can thus hypothetically be applied at even the highest currently 

available level of spatial resolution: the NHDPlus reach/catchment. 
69

The methodology is as follows: SAM processes recipes representing all upstream drainage 

areas, generating loading time series for each scenario. Any scenarios whose contributions pass 

through a lentic system on the way to the pour point first have their pesticide load time series’

convolved with an IRF consisting of a gamma probability density function that has a “shape” 

factor equal to 1 (representing a completely-mixed tank) and a “scale” factor equal to the water 

body’s residence time in days. If more than one lentic system lies along a given flow path, 

convolutions are performed sequentially for each. Next, scenarios located in contributing 

NHDPlus catchments are grouped into nominal integer-day total travel times (based upon 

tabulated reach length and mean flow velocity attributes) to the pour point (lentic systems are 

assumed to contribute no travel time), and summed into total load time series (the summation 

includes any already-convolved time series for catchments with lentic waters in their flow 

path). For travel times <1 day, these time series are delivered to the pour point water body 

unmodified. For travel times >1 day, the load time series are convolved against gammadistributed IRFs that essentially represent a tanks-in-series style of mixing, with one “tank” per 

nominal day. This is essentially equivalent to assuming that inert tracer would wash out of a 

“dayshed” (see Carleton, 2015) in an exponential fashion. The shape factor thus corresponds 

with the travel time in integer days, and the scale factor is always 1. These convolutions are 

applied both to pesticide masses and to runoff water volumes, and in this manner both 

hydrographs and chemographs are ultimately simulated. 

With this approach, estimated water body volume V in essentially all lotic water bodies 

(reaches) is expected to be <<Q, thus τ<<1, and for all intents and purposes the approximation 

CC̅= MM

QQ can be employed to calculate daily pour point concentration. At first glance analogous 

logic would seem to imply that concentrations in lentic water bodies with large residence times 

could be adequately approximated using CC̅= MM

VV. However upon further consideration it 

becomes clear that a more-proper expression for concentration in lentic water bodies is CC̅= MM

QQ, 

where Q is effluent flow rate. The reason for this has to do with the fact that, for purposes of 

calculation, convolutions that represent the influence of lentic water bodies are applied to 

influent mass time series rather than influent concentration time series. Effluent 

concentration, which for a completely-mixed water body is identical to concentration in the 

water body, is thus essentially convolved mass divided by effluent reach daily flow volume, 

rather than by water body volume. The dilution effect created by a relatively large lentic 

volume is accounted for by the water body’s IRF in the convolution itself. In contrast with the 

process described previously for lotic pour point water bodies, concentration calculations for 

lentic water bodies employs overall mean daily flow values (for “artificial” NHD reaches that 

represent lake/reservoir outlets) as “Q”, rather than daily-varying (baseflow plus runoff) flow 

values. This process is expected to generate more reasonable concentration estimates 

particularly for lentic water bodies with large residence times, in which concentrations 

necessarily change relatively slowly.
70

Note that IRFs in this conceptual modification are not required to correspond to gammadistributed or tanks-in-series mixing as described above. The approach employing convolution 

is general, and sufficiently flexible to permit essentially any form of IRF, including theoretically 

or empirically-based functions (indeed other forms of IRFs may be required for simulating 

chemical sorption and/or decay – see Carleton, 2015). The approach described above was 

selected for the purpose of initial exploration, and not necessarily because it generates the 

most-accurate possible representation of dispersion and other mixing/dilution effects. During 

in-stream transport, atrazine was simulated in the examples described below as if it were an 

inert tracer, i.e., neither chemical sorption nor decay were considered.

4.3 Initial Results and Comparisons with Monitoring Data

Simulations of atrazine transport in test watersheds in two states (Mark Twain Lake in Missouri 

and White River in Indiana, described in Section 2.4), have been generated using the “SAMconvolved” test model described above. The maps of the watersheds in Figures 22 and 26 show 

pour point locations for which extensive monitoring data sets exist, along with their 

corresponding NHDPlus “comid” attribute, and delineated catchments that drain to these 

locations. Comparisons between “SAM-convolved” results, original (SAP version) SAM results 

for the basins that do not account for travel distances, and monitored water column 

concentration data are shown for some of these locations in subsequent figures. Note that the 

“SAM” results shown in these figures correspond with the “more-thorough” implementation 

described above, accounting for loadings from the entire drainage areas to pour points.

Table 4 provides a cross-reference between the monitoring study identifications and the

NHDPlus comid attribute used in the figures and graphs that follow. More information on the 

monitoring sites, along with monitoring data, can be found in Appendix 2-C.

Table 4: Cross-reference between Monitoring Study Site IDs and SAM Catchment IDs

Test Basin Site ID (Study) NHD comid Drainage 

Area (km2)

Monitoring 

Sampling 

Interval

Mark Twain 

Basin (MO)

MO-01 (AEEMP) 4989415 30.1 1-4 da

MO-01C (AEEMP) 4988183 11.5 1-4 da

MO-01D (AEEMP) 4988241 23.0 1-4 da

MO-04A (AEEMP) 4989385 21.8 1-4 da

MO-04B (AEEMP) 4989739 1004.4 1-4 da

MO-02 (AEEMP) 5042380 72.9 1-4 da

MO-05 (AEEMP) 5042400 65.5 1-4 da

MO-05b (AEEMP) 5039952 472.2 1-4 da

MO-17 river (AMP) 5641174 1192.5 7 da

MO-17 (reservoir) (AMP) 5641630 6.0 7 da

MO-21 (reservoir) (AMP) 4867727 6093.1 7 da
71

Test Basin Site ID (Study) NHD comid Drainage 

Area (km2)

Monitoring 

Sampling 

Interval

White River 

(East Fork) 

Basin (IN)

IN-10 (AEEMP) 18459870 52.3 4 da

IN-04 (river) (AMP) 18454511 252.2 7 da

IN-06 (river) (AMP) 18450057 282.9 7 da

IN-07 (river) (AMP) 18445592 10467.0 7 da

IN-08 (river) (AMP) 18445664 10013.7 7 da

IN-13 (river) (AMP) 18451035 924.0 7 da

Figure 22: East branch of White River watershed, Indiana, Showing Test Basins and Monitoring Site 

Locations.

In the White River (East Branch) watershed examples in Figures 23-25, the “SAM-convolved” 

model usually appears to do a somewhat better job than SAM alone at matching observed 

atrazine concentrations, in terms of both maximum values and temporal dynamics. SAM-
72

convolved estimates of maximum concentrations drop by roughly 30-50% as compared with 

SAM estimates for the IN-08 (comid 18445646) site, near the lower end of the White River 

basin (Figure 23), bringing them generally within 2-5X of monitored concentrations. SAMconvolved estimates for IN-13 (18451035) are generally within 1-2X of monitored 

concentrations (Figure 24). On the other hand, while SAM-convolved estimates of 

concentration peaks at IN-04 (18454511) are less than those generated by SAM without 

convolution, they are nonetheless more than 5X to 10X greater than maximum monitored

concentrations (Figure 25). These monitoring samples were collected weekly and are therefore 

unlikely to reflect true maximum daily concentrations in these water bodies (USEPA OPP, 

2010a, 2010b, 2011, 2012). If sampling bias factors (see Section 2.4) were applied to adjust the 

monitoring data for these comparisons (USEPA OPP, 2011), the SAM model estimates

presumably would be more similar to the results. 

Figure 23: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 18445646 (Site IN-08 river)
73

Figure 24: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 18451035 (Site IN-13)

Figure 25: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 18454511 (Site IN-04)
74

For the monitoring sites around Mark Twain Lake (figure 26), comparisons shown in Figures 27-

31, SAM-convolved estimates again appear to better match observed atrazine concentrations

than do SAM estimates, in terms of maximum values and temporal dynamics. 

Figure 26: Mark Twain Lake Watershed, Missouri, Showing Test Basins and Monitoring Site Locations.

Figure 27 shows SAM-convolved and SAM results compared with monitored concentrations in a 

large residence time (estimated at 126 days) lentic water body, the Mark Twain Lake reservoir. 

Maximum SAM-convolved estimates exceed measured concentrations, though not by nearly as 

much (i.e., <10X) as do unmodified SAM estimates. Convolution dampens and broadens 

influent pesticide pulses to more accurately reflect temporal trends within the lake, lessening 

maximum concentrations but also increasing concentrations during later times of the year, 

when significant exposure might not otherwise be expected to occur.

Figure 28 shows monitored concentrations from a site located on the Salt River within the Mark 

Twain basin (compare with Figure 15 in section 2.4.4). The SAM-convolved peak concentrations 

are less than those generated by SAM alone, but are nonetheless within the range of 1 to 5X 

greater than monitored peak concentrations for all six years of monitoring. 
75

Figure 27: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 4867727 (Site 21 reservoir)

Figure 28: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 5651174 (Site MO-17 river)
76

Figures 29 to 31 illustrate model-data comparisons for a series of nested monitoring sites 

located just north of the Mark Twain Lake basin. MO-01D (4988241) is located upstream from 

MO-01 (4989415), which in turn is located upstream from MO-04B (4989739). This series 

provides a good test area to evaluate the effect of the convolution approach as we simulate 

pesticide mass moving from upper headwaters to increasingly downstream locations. 

Figure 29: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 4988241 (Site MO-01D)
77

Figure 30: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 4989415 (Site MO-01)

Figure 31: Comparison of SAM Outputs with Convolution Approach (Blue) with Original SAM Output 

(Orange) and Monitoring Data for Basin 4989739 (Site MO-04B)
78

5 Defining the Likely Pesticide Application Window based on Crops and 

Weather

Not only is the pesticide application date a sensitive parameter in USEPA OPP’s aquatic models 

(Jones and Russell, 2001; Jones and Mangels, 2002), the timing of the application, particularly in 

relation to rainfall events, impacts both timing and magnitude of the resulting pesticide 

concentrations in water (USEPA OPP, 2009a, 2011, 2012). For some types of pesticides 

(particularly herbicides), the timing of applications are often linked to crop milestones such as 

planting, crop growth stages, and harvest. The timing of these milestones vary spatially and 

temporally with weather. 

In its current aquatic exposure models, USEPA OPP compares pesticide label recommendations 

with available resources such as USDA crop profiles, published planting and harvest dates, and 

likely timing of pest pressures to identify a likely application period for the pesticide being 

assessed. For SAM, the user will still have the option of using these resources. The alpha version 

of SAM used USDA reports on usual planting and harvest dates (USDA NASS, 2006, 2007, and 

2010) and the Plant Hardiness Zone (PHZ) map to stratify planting and harvesting dates within 

states (Section 3.1.4). This provided a context for varying crop dates spatially and can be used 

to similarly vary pesticide application windows. However, this approach does not account for 

yearly variations as a result of differences in weather. 

For those pesticides for which the likely application window can be linked to specific crop 

growth stages or milestones, USEPA OPP is exploring methods to better vary planting, harvest, 

and specific crop- growth stages spatially as well as temporally. Promising options include using 

empirical data such as USDA Crop Progress reports, where available, or crop growth 

(phenology) models where data are incomplete. The preferred approach is to use empirical 

data when possible. However, due to the complexities of a national scale model, empirical data 

is not always available across the conterminous US for a single crop, much less for many minor 

crops that may be of interest. A preliminary evaluations of these approaches using corn is 

presented in support of the following charge questions to the SAP:

Q8. Pesticide applications often depend on planting dates, crop growth, and harvest dates, 

which vary with weather. To improve upon the initial approach of stratifying planting and 

harvesting dates within states by using a Plant Hardiness Zone map (Section 3.1.4), USEPA 

OPP tested the potential for using empirical data (USDA weekly crop progress reports) and, 

where such data are incomplete, crop growth models (Sections 5.2 and 5.3). 

a. Please comment on the use of crop planting dates and growth stages to provide 

reference points for pesticide application windows. How applicable is this approach for 

predicting the application window for all types of conventional pesticides (e.g., 

herbicides, growth regulators, fungicides, insecticides, etc.). For pesticide or pest types 

for which this approach may not work, what alternative methods are available? 
79

b. As noted in Section 5.2, empirical crop progress data are not available for all crops, all 

areas, or all years. Please recommend any additional data sources that could provide 

useful information on spatial and temporal (year-to-year) variability in crop planting, 

growth, and harvesting dates for use in modeling.

c. Where empirical data are missing, USEPA OPP explored the possibility of using crop 

growth/phenology models such as growing degree days (GDD) to fill in missing data. 

Please comment on the number of crops with available GDD models and availability of 

alternative models/data for other crops or crop groupings.

Q9. The test version of SAM provides the user with options for defining the extent of the 

pesticide application window and the distribution of pesticide applications across that 

window (e.g., uniform distribution, triangular distribution). Crop progress reports or, in their 

absence, crop growth models, offer an option for defining the application window and 

shape of the distribution (Section 5.1). Please comment on the use of empirical data or 

models to define the distribution of pesticide applications within an application window.

Section 5.1 provides a context for using crop growth data as a framework for determining likely 

windows for applying pesticides. As noted, this approach may be more viable for some types of 

pesticides, such as herbicides) than for others (such as insecticides and fungicides). Section 5.2 

explores the potential for using empirical data such as USDA weekly crop progress reports, to 

provide the spatial and temporal context to crop growth stages, while Section 5.3 looks at a 

possible crop growth model to fill in where empirical data are missing. Section 5.4 shows results 

of preliminary model comparisons with available monitoring data for atrazine. 

5.1 Context for Using Crop Milestones to Estimate Pesticide Application Windows

Pursuant to the Federal Insecticide, Fungicide, Rodenticide Act (FIFRA), pesticide product labels 

must provide information about the target pest (e.g., weeds, insects, fungi, etc.), active 

ingredient, application rates, application timing, and other production application information.

USEPA OPP uses this information, in combination with toxicity data, to assess ecological and 

drinking water exposure risks from these pesticides. Depending on the type of pesticide (e.g., 

herbicide, insecticide, fungicide) and the target pest, the pesticide application may coincide 

with a specific crop milestone (planting, harvest) or phenological (growth) stage (e.g., 

emergence, bloom, maturity) (Loux et al., 2015). Where such coincidence occurs, USEPA OPP 

may be able to link application periods to crop stages and vary both spatially and temporally 

using empirical or model data.

5.1.1 Defining Pesticide Application Windows

In USEPA OPP’s current aquatic exposure assessments, which use national or regional scenarios 

for various crops, the risk assessor currently selects a single pesticide application date to

represent the label’s application timing. While a single date may be reasonable for representing 
80

small watersheds such as those associated with the farm pond used in ecological assessments 

and the index reservoir used for drinking water, a range in application dates is more likely to 

reflect actual practice in larger watershed areas, with more farms within the drainage area.

For SAM, the user has the option to simulate pesticide applications over a range of dates or to 

input multiple dates. The alpha version of SAM includes three options the model user can select 

for distributing pesticide applications (Figure 32). These application windows are defined 

relative to crop emergence, maturity or harvest so that actual dates will vary spatially with 

plant milestones (Sections 1.1 and 3.1.4). In the alpha version of the model, the linkage for 

application dates relative to crop milestones is used solely to provide a framework for varying 

application dates spatially (e.g., ensuring that a pesticide application on a post-emergent pest 

does not occur on the same date in Tennessee as it would in northern Indiana).

Figure 32: Illustration of the three methods to distribution pesticide application timing in SAM.

These distributions are mathematical options for proportioning a pesticide application over a

user-defined time period. The user determines the timing, duration and shape of the 

distribution of the application. However, where the pesticide application is linked to one or 

more crop planting or growth stages, information on the timing and duration of crop 

milestones may be useful in providing spatial and temporal contexts for defining the timing, 

duration, and potential distribution of the pesticide application. To illustrate, consider the 

weekly Crop Progress Report (CPR) data from USDA NASS for corn in Illinois and Ohio (Figure 

33)19. 

 19 CPR weekly survey data are available at state and sub-state levels via the USDA NASS Quick Stats web-site: 

http://quickstats.nass.usda.gov/
81

Figure 33: Comparison of CPR milestones for corn among Ohio (top) and Illinois (bottom).

The solid lines in each graph in Figure 33 represent the cumulative percent progress for corn 

development for 2011. These crop stages are represented: planted, emerged, silking, doughing, 

dented, mature and harvest. Temporal (year-to-year) variability is evident in the dashed lines

that represent previous year and previous 5-year averages for the progress of each stage of 

corn production. 

Spatial variability is evident among these states not only in the timing of the crop stages but 

also the shape of cumulative progress. For example, while the planting window was broad for 

both states (i.e. April to early June), corn planting intensified earlier in the planting window in 

Indiana than in Ohio. In Ohio, most of the corn planting occurred nearly two months later than 

the previous five-year average, likely due to heavy rainfall in Ohio during that time. 

For the preliminary evaluation discussed in this section USEPA OPP focused on using the timing 

of the crop stages, as reported by CPR data, to vary the timing of the pesticide application. 

However, the CPR data could also be used to define the distribution of the application across 

the likely window. For instance, the timing and shape of the distribution for a pesticide that is 

applied around pre-plant or pre-emergence would likely be similar to the planted lines in Figure 

33. Applications associated with later crop stages would likely follow those patterns. This is the 

subject of charge question 9.

Figure 34 illustrates year-to-year variability in percent corn planted in central Ohio, using the 

NASS reported weekly corn planted data (interpolated to derived daily percent corn planted). In 

2002, a warm dry spring resulted in early planting of corn, while in 1999, a cooler wet spring 

shifted most planting later in the season. When such data are available, USDA NASS crop 
82

progress data could be used to better define the timing and shape of the distribution for preplant or pre-emergence pesticide applications. 

Figure 34: Daily percent corn planted in Central Ohio for 2002 (a) and 1999 (b), with the planting 

window (dashed lines).

USEPA OPP proposes the following steps to derive appropriate spatially and temporally 

distributed pesticide application windows, where appropriate:
83

1) Determine pesticide types, modes of action, and crops for which pesticide applications 

are most likely to correlate to crop milestones/growth stages;

2) Define links between pesticide product labeling and target pest(s) and crop 

milestones/phenological (growth) stages for each crop type, where appropriate;

3) Determine the availability and spatial/temporal scale of empirical data for crop growth 

stages;

4) Where necessary, identify and evaluate available crop growth models that could be 

used to fill in where empirical data are missing;

5) Evaluate resulting model estimations; and, 

6) Update models as needed.

In the charge questions, USEPA OPP is asking the panel for comments on the extent to which 

such an approach might be applicable and for recommendations on improving the approach. 

For preliminary explorations, USEPA OPP evaluated empirical data (Section 5.2) and a simple 

crop growth model (Section 5.3) for corn. Section 5.4 summarizes results of a comparison of the 

approaches for atrazine using monitoring test basins for evaluation.

5.2 Using Empirical Data to Define Crop Milestones: USDA Crop Progress Reports

USEPA OPP first searched USDA NASS products for empirical data to better characterize the 

spatial and temporal variability in crop milestones (such as planting and harvest) and growth 

stages. USDA NASS publishes census and survey agricultural statistics at national, state, county 

and other geographical levels for major commodity crops that characterize where the crops are 

grown, and when production activities occur (USDA NASS, 2010). 

In the Crop Progress Report (CPR) survey data, USDA NASS collects and tabulates state or cropreporting district level survey data of agricultural crop development, beginning in the spring of 

each year. CPR data are surveyed weekly on broad array of crops, with information presented 

for four groups: Field Crops, Vegetables, Fruit and Tree Nuts, and Horticulture, as previously 

discussed. 

CPR data are used to develop Usual Planting & Harvesting Dates for Field Crops (USDA NASS, 

2010); Fruits and Tree Nuts: Blooming, Harvesting, Marketing Dates (USDA NASS, 2006); and 

Usual Planting and Harvesting Dates for Fresh Market and Processing Vegetables (USDA NASS, 

2007). These data were previously used by USEPA OPP to select the average planting and 

harvest dates entered into the appropriate pesticide exposure models. The CPR weekly survey 

data are available through the USDA NASS Quick Stats site (http://quickstats.nass.usda.gov/). 

This dataset provides weekly progress on planting, crop phenological stages, and harvest, and 

can be used to identify and characterize the spatial and temporal distribution of crop stages 

during the growing season. 
84

The NASS CPR data accounts for year-to-year variations in crop development dates due to 

weather and other factors, and has been used by others (Sadler et al, 2014; Baffaut et al, 2015). 

However, CPR data has some limitations:

1) Resolution varies (i.e., crop reporting districts to state-level);

2) Data are not available for all crops, especially vegetable crops;

3) Data are not available for all crops in all states; and

4) Some crops have limited historical data sets.

5.2.1 Availability of Empirical Data by Crop Groups

To determine the extent to which empirical data may be available for use in SAM, USEPA OPP 

inventoried the availability of USDA NASS data (CPR and CDL) for field crops (Table 5). The USDA 

National Agricultural Statistics Service (NASS) has also organized agricultural commodities into 

crop groups. These groups have been used for organizing agricultural census and survey data, 

and include:

1) Field crops;

2) Fruit and Tree Nuts; 

3) Vegetables; and

4) Horticultural products.

Crop groups listed on the pesticide labels are based on commodity end use for the purpose of 

establishing crop residue tolerances. These are described the Federal Register under 40 CFR 

180.4120. The groups are organized by representative crops and related crop subgroups, and 

some commodities are separated into groups by product end-use. Although these crop groups 

have been used to establish residue tolerances, registrants often submit pesticide labels linked 

to all, or a portion of, crops within a crop group. 

Because crop groups identified on the label (based on 40 CFR 180.41) may not directly coincide 

with USDA NASS commodity field crop groups, USEPA OPP generated a crosswalk table for field 

crops (Table 5). The crosswalk identifies how crops are organized among the various agencies’ 

data sets, and reveals potential data gaps. 

A crop group crosswalk for Fruit and Tree Nuts, Vegetables and Horticultural crops will be 

generated in subsequent phases of the SAM development project.

 20 Section 40 of the Code of Federal Regulations (Protection of Environment) is available digitally at 

http://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title40/40tab_02.tpl. The crop group tables are available at 

http://www.ecfr.gov/cgi-bin/textidx?SID=e725d2005679847d163190d5196aaaad&mc=true&node=se40.24.180_141&rgn=div8. 
85

Table 5: Crosswalk between 40 CFR and USDA NASS commodity field crops.

Commodity

(Representative)

NASS Usual 

Planting and 

Harvest 

Dates (2010)

NASS Crop 

Progress 

Reports 

(Annual)

NASS 

Cropland 

Data Layer 

(Annual)

40 CFR 180.41: Crop Groups

Barley X X X 15 (cereal grains), 16 (cereal 

forage)

Beans, Dry Edible X 6 (legumes)

Buckwheat X 15 (cereal grains), 16 (cereal 

forage)

Camelina X X

Canola X X 20 (oilseed)

Corn, Grain X X X 15 (cereal grains), 16 (cereal 

forage)

Corn, Popcorn X 15 (cereal grains), 16 (cereal 

forage)

Cotton X X X

Flaxseed X X 20 (oilseed)

Hay, Alfalfa X X X 18 (non-grass feed)

Hay, Other X X 17 (grass for forage)

Hops X

Lentils X X 6 (legumes)

Millet X X 15 (cereal grains), 16 (cereal 

forage)

Mint X

Mustard X X

Oats X X X 15 (cereal grains), 16 (cereal 

forage)

Pasture Land X

Peanuts X X X

Peas (dry edible) X X

Rapeseed X 20 (oilseed)

Rice X X X 15 (cereal grains), 16 (cereal 

forage)

Rice, Wild 15 (cereal grains), 16 (cereal 

forage)

Rye X X 15 (cereal grains), 16 (cereal 

forage)

Safflower X X

Sorghum X X X 15 (cereal grains), 16 (cereal 

forage)

Soybeans X X X 6 (legumes)
86

Commodity

(Representative)

NASS Usual 

Planting and 

Harvest 

Dates (2010)

NASS Crop 

Progress 

Reports 

(Annual)

NASS 

Cropland 

Data Layer 

(Annual)

40 CFR 180.41: Crop Groups

Sugarbeets X X X 1 (root and tuber)

Sugarcane X X

Sunflowers X X 20 (oilseed)

Taro X

Teosinte 15 (cereal grains), 16 (cereal 

forage)

Tobacco X X X

Triticale 15 (cereal grains), 16 (cereal 

forage)

Wheat, Durum X X X 15 (cereal grains), 16 (cereal 

forage)

Wheat, Spring X X X 15 (cereal grains), 16 (cereal 

forage)

Wheat, Winter X X X 15 (cereal grains), 16 (cereal 

forage)

5.3 Crop Growth Models

Although the USDA CPR data provide crop phenological stages, these data are not available for 

all crops or for all crop stages that may be included on pesticide labels. USEPA OPP has 

considered the potential for using crop growth models to fill in where empirical NASS crop data 

are unavailable. This is only a preliminary exploration; before proceeding, USEPA OPP is asking 

the SAP to comment on the feasibility of using such models, the extent to which the models 

would be applicable, and the kinds of crop growth models to consider (Question 8). 

Crop growth (phenology) models of varying complexities have been used for crop management

(Miller et al, 2001; Sakamoto et al, 2013; Torrion et al, 2012) and water needs (Ines et al, 2001; 

McMaster et al, 2011), evaluating the impacts of different climate scenarios on crops (Murthy, 

2003), and addressing pest management needs (Murray, 2008; Pruess, 1983)21. This is not 

meant to be an exhaustive search. A number of crop phenology models have been developed 

by USDA’s Agricultural Research Service (ARS) and by various research institutions; some have 

been incorporated into broader models such as the Environmental Policy Integrated (EPIC) and 

Agricultural Policy/Environmental Extender (APEX) models22. 

 21 Additional phenology and growing degree-day models for use in pest management are available through the 

USDA IPM Pest and Plant Disease Models and Forecasting page at http://uspest.org/wea/ 22 These models are available through Texas A&M University at http://epicapex.tamu.edu/
87

For this scoping exercise, USEPA OPP used a simple growing degree day (GDD) model for corn. 

Additional models are available for other crops and may include additional environmental 

factors to model crop progress. The GDD model uses simple weather data that are available for 

SAM (daily maximum and minimum air temperature). 

The GDD index used for this example is defined by the following equation:

Where Tmax and Tmin are daily maximum and minimum air temperatures at a given weather grid 

location, and

Tbase is the established threshold temperature at which a given crop becomes active and begins 

to accumulate GDD units. 

For corn, Tbase = 50 F. Daily temperatures below this do not accumulate GDD units. Although not 

reflected in the equation, the upper temperature threshold is 86 F, above which GDD units to 

not accumulate. Since Tbase and the upper threshold temperature vary for each crop, it is 

possible to determine the GDD value at any given day of the year and, hence, infer the stage for 

that particular crop.

This approach for determining crop stage does not take precipitation or available moisture into 

account; nor does it predict planting date. USEPA OPP compared the arrival of threshold air 

temperatures (Tbase) by weather grid to the published most active planting dates described in 

CPR data. The comparison showed that the dates in which threshold air temperatures dates 

occurred preceded published planting dates by one to two weeks. This may reflect soil 

temperature taking longer to reach the ideal Tbase target. It is possible that the GDD model can 

stand by itself and use CPR data to seed planting dates for a given area in a hybrid approach. 

However this technique would bring with it the limitations of temporal, spatial and crop 

categories coverage inherent in the published reports. Another limitation of the GDD approach 

is widely different measures of time for a given crop to reach a given state of maturity. There 

are regional differences reflected by a states’ cooperative extension bulletin on how many 

GDDs are needed for a crop to reach a given target stage.

5.4 Model Demonstration Using Corn

To evaluate the impacts of using crop growth data as a reference for pesticide applications, 

USEPA OPP configured SAM to use corn growth stages defined by CPR and a GDD model for 

Missouri (MO) and Ohio (OH). The model was also updated to allow users to specify pesticide 

application dates relative to a particular growth stage (planting, emergence, maturity, harvest). 

Table 6 compares the test version of the model (baseline) to the two new approaches (CPR, 
88

GDD+CPR). The actual CPR dates used for the years 2006-2013 are summarized in Tables 7 and 

8 for MO and OH, respectively.

Table 6: Alpha version, CPR, and GDD+CPR approaches: Definition of planting, emergence, maturity, 

harvest, and crop growth curve.

Approach Planting Emergence Maturity Harvest

Alpha version 

(baseline)

USDA NASS, 2006, 

2007, and 2010; 

PHZ (Sec. 3.1.4)

7 days after 

planting date

Midpoint of 

planting and 

harvest dates

USDA NASS, 2006, 

2007, and 2010; 

PHZ (Sec. 3.1.4)

Crop Progress 

Report (CPR) 

Approach

CPR planting 

window for 2006-

2013 

At beginning of 

CPR emergence 

window for 2006-

2013

At beginning of 

CPR maturity 

window for 2006-

2013

At beginning of 

CPR harvest 

window for 2006-

2013

Growing Degree 

Day (GDD) + CPR 

Approach

CPR planting 

window for 2006-

2013

Cumulative GDD = 

100, relative to 

planting date

Cumulative GDD = 

1360, relative to 

planting date

At beginning of 

CPR harvest 

window for 2006-

2013

Table 7: Beginning date of corn stages from Missouri CPR for 2006-2013 in Julian days (range from 1 to 

365).

MO Crop Stage 2006 2007 2008 2009 2010 2011 2012 2013

Planting 98 97 102 101 93 92 90 103

Emergence 112 118 123 122 114 113 111 131

Maturity 229 235 242 241 233 230 228 243

Harvest 238 244 263 255 254 239 230 250

Table 8: Beginning date of corn stages from Ohio CPR for 2006-2013 in Julian days (range from 1 to 

365).

OH Crop Stage 2006 2007 2008 2009 2010 2011 2012 2013

Planting 99 97 111 102 100 113 90 103

Emergence 118 119 124 122 114 120 111 125

Maturity 232 230 239 241 235 241 225 244

Harvest 260 231 263 269 254 242 226 245

In the Scenario Generator, the CPR and GDD+CPR approaches were applied in the years 2006-

2013, and in the year 2014, the 2013 dates were used. The crop growth dates from the test 

version (baseline) were used for all other years simulated (e.g., 2000-2005). 
89

The GDD+CPR approach used the CPR corn planting dates (2006-2013), since the GDD model 

requires a beginning planting date to start accumulating GDDs. The CPR corn harvest dates 

were also used as the GDD+CPR harvest dates.

The GDD approach relates the growth of a crop to the accumulation of heat over time (Barker 

et al., 2005). In the GDD calculations, boundary conditions are first applied to the daily 

minimum and maximum temperatures:

1) If the daily minimum temperature (Tmin) is less than the base temperature for corn 

growth (Tbase = 10°C), then Tmin set equal to Tbase. 

2) If the daily maximum temperature (Tmax) is greater than the threshold temperature 

for corn growth (Tthreshold = 30°C), then Tmax is set equal to Tthreshold.

The average daily temperature (Tavg) is calculated from daily maximum and minimum 

temperatures, after taking into account the above boundary conditions: 

Tavg = (Tmax + Tmin)/2

Then, the daily GDDs are calculated as an accumulation of the difference between Tavg and Tbase, 

beginning from the CPR planting date.

GDD(d) = Tavg – Tbase + GDD(d-1) 

The emergence and maturity dates for each year are finally identified as the first days to reach 

100 and 1360 GDDs, respectively. 

5.4.1 Model results

SAM use the CPR and GDD+CPR approaches for several test basins in MO and OH. Pesticide 

applications (Section 2.4) were assumed to be uniform over a 50-day window, beginning 14 

days prior to the first day of emergence (i.e., one-fiftieth, or 2%, of the application was applied 

each day during the application window). 

Results from the preliminary comparison showed that varying the start of the planting season 

(and subsequent crop stages) to coincide with dates reported in CPR improved pesticide 

concentration estimates (Appendix 5-A; Figure 35). These examples suggest that using CPR 

and/or crop growth models show promise for improving concentration estimates in SAM. 

Appendix 5-A provides results of the comparisons for the test basins across all years. The results 

show that the CPR and GDD+CPR approaches shift the timing of simulated exposure 

concentrations closer to the occurrence of measured concentrations, given the updated crop 

growth stages based on CPR and GDDs. The simulated magnitudes for the CPR and GDD+CPR 

approaches also show closer agreement with measured concentrations.

In Figure 35 (and Appendix 5-A), the results for individual years are shown, including simulated 

concentrations from the three approaches, measured concentrations, and the CPR percentage 

planted and emerged. The CPR and GDD+CPR concentrations first appear ~14 days prior to the 
90

first day of emergence (or 0% emerged), and correspond well with the measured 

concentrations in terms of magnitude and timing.

Figure 35: Estimated concentrations for MO-01 (comid 4989415) from the test version, CPR, and 

GDD+CPR approaches compared to measured concentrations, with the CPR percentage planted and 

emerged also shown on right axis, for a) 2007 and b) 2009.
91

References

Ahuja, L.R. 1986. Characterization and modeling of chemical transfer to runoff. Advances in Soil 

Science 4:149-188.

Ahuja, L.R. and Lehman, O.R. 1983. The nature and extent of rainfall-soil interaction in the 

release of soluble chemicals to runoff. Journal of Environmental Quality. 12(1), 34-40.

Banks R.B. 1974. A mixing cell model for longitudinal dispersion in open channels. Water 

Resources Research 10(2):357-358.

Bauffaut, C, E.J. Sadler, F. Ghidey, and S.H. Anderson. 2015. Long-Term Agroecosystem 

Research in the Central Mississippi River Basin: SWAT Simulation of Flow and Water Quality in 

the Goodwater Creek Experimental Watershed. J. Environ. Qual. 44:84–96. 

doi:10.2134/jeq2014.02.0068

Brooks, A., J. Spencer, D. Borombovits, T. Pietsch, and J. Olley. 2014. Measured hillslope erosion 

rates in the wet-dry tropics of Cape York, northern Australia: Part 2, RUSLE-based modeling 

significantly over-predicts hillslope sediment production. Catena 122:1-17. 

Carleton, J. N. 2015. A linear systems approach to watershed transport simulation, Hydrol. 

Process., DOI: 10.1002/hyp.10242.

Carousel, R.F., J.C. Imhoff, P.R. Hummel, J.M. Cheplick, A.S. Donigian, Jr., and L.A. Suárez. 2005. 

PRZM-3, A Model for Predicting Pesticide and Nitrogen Fate in the Crop Root and Unsaturated 

Soil Zones: Users Manual for Release 3.12.2. Center for Exposure Assessment Modeling (CEAM), 

National Exposure Research Laboratory - Ecosystems Research Division, Office of Research and 

Development (ORD), U.S. Environmental Protection Agency (U.S. EPA)

Carsel, R.F., Smith, C.N., Mulkey, L.A., Dean, J.D. and Jowise, P. 1984. Users manual for pesticide 

root zone model (PRZM): Release 1, Rep. EPA-600/3-84-109, 219 pp. Environmental Research 

Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 

Athens, GA.

Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak. 2008. 

Assessing objective techniques for gauge-based analyses of global daily precipitation, J. 

Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.

Chen, M., P. Xie, and Co-authors, 2008b. CPC Unified Gauge-based Analysis of Global Daily 

Precipitation, Western Pacific Geophysics Meeting, Cairns, Australia, 29 July - 1 August, 2008.

Dooge, J. C. I. 1959. A General Theory of the Unit Hydrograph. Journal of Geophysical

Research, 64(2): 241-256.

Farnsworth, R.K., and E.L. Peck. 1982. Evaporation Atlas for the Contiguous 48 United States, 

NOAA Technical Report NWS 33, U.S. Department of Commerce, 1982. Available on-line: 

http://www.nws.noaa.gov/oh/hdsc/Technical_reports/TR33.pdf
92

Fischer, H.B. 1979. Mixing in Inland and Coastal Waters. Academic Press, London.

Frere, M.H., J.D. Ross, L.J. Lane. 1980. The nutrient submodel. In CREAMS: A field scale model 

for chemicals, runoff, and erosion from agricultural management systems. 65-87. W.G Kniesel, 

ed. USDA Conservation Research Report 26. Washington, DC.

Gilliom, R.J., J.E. Barbash, C.G. Crawford, P.A. Hamilton, J.D. Martin, N. Nakagaki, L.H. Nowell, 

J.C. Scott, P.E. Stackelberg, G.P. Thelin, and D.M. Wolock. 2006. Pesticides in the Nation’s

Streams and Ground Water, 1992–2001. USgS Circular 1291. Revised February 15, 2007. 

Available at http://pubs.usgs.gov/circ/2005/1291/. 

Hargreaves, G.H. and Z.A. Samani, 1985. Reference crop evapotranspiration from temperature, 

Applied Engrg. in Agric., 1(2):96-99.

Hay, L.E. and M.P. Clark, 2003. Use of statistically and dynamically downscaled atmospheric 

model output for hydrologica simulations in three mountainous basins in the western United 

States, J. Hydrol., 282,1-4, pp.56-75, doi:10.1016/S0022-1694(03)00252-X.

Higgins, R. W., J. E. Janowiak and Y. Yao, 1996: A gridded hourly precipitation data base for the 

United States (1963-1993). NCEP/Climate Prediction Center ATLAS No. 1., 47 pp.

Higgins, R. W., W. Shi, E. Yarosh and R. Joyce , 2000: Improved United States Precipitation 

Quality Control System and Analysis. NCEP/Climate Prediction Center Atlas No. 7, NOAA, U. S. 

Department of Commerce.

Haith, D.A. 1980. A mathematical model for estimating pesticide losses in runoff. J Environ Qual 

9(3):428-433.

Hollister, J. W., and W.B. Milstead. 2010. Using GIS to Estimate Lake Volume from Limited Data. 

Lake and Reservoir Management. 26(3)194-199. 

Hollister, J. W., W.B. Milstead, and M.A. Urrutia. 2011. Predicting Maximum Lake Depth from 

Surrounding Topography. PLoS ONE 6(9): Link: 

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0025764

Ines, A.V.M., A.D. Gupta, and R. Loof. 2002. Application of GIS and crop growth models in 

estimating water productivity. Agric. Water Management 54:205-225.

Jobson, H.E. 1996. Prediction of traveltime and longitudinal dispersion in rivers and streams: 

U.S. Geological Survey Water-Resources Investigations Report 96–4013, 69 p. Available: 

http://pubs.usgs.gov/of/1996/4013.

Jones, R.L., and M.H. Russell (ed.). 2001. FIFRA Model Validation Task Force Final Report. The 

FIFRA Environmental Model Validation Task Force. 

Jones, R.L., and G. Mangels. 2002. Review of the validation of models used in federal 

insecticide, fungicide, and rodenticide act environmental exposure assessments. Environ. Tox. 

Chem. 21:1535-1544.
93

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. 

White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K.C. Mo, C. 

Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph. 1996. The NCEP/NCAR 

40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-470.

Kinnell, P.I.A., 2004. Letter to the Editor on “The Mathematical Integrity of Some Universal Soil 

Lass Equation Variants”, Soil Science Society of America Journal, 68(1), 336-337.

Kistler, R., E. Kalnay, W. Collins, S. Saha, G.White, J.Woollen, M. Chelliah, W. Ebisuzaki, M. 

Kanamitsu, V. Kousky, H. van den Dool, R. Jenne, and M. Fiorino, 1999: The NCEP-NCAR 50-Year 

Reanalysis: Monthly Means CD-ROM and Documentation. Bull. Amer. Meteor. Soc., 82, 247-

267.

Lal, H., and S. McKinney, 2012. Documentation for Water Quality Index for Runoff from 

Agricultural Fields (WQIag). Available at 

ftp://ftp.wcc.nrcs.usda.gov/wntsc/WQI/RunoffWaterQualityIndex.pdf. 

Loux, M., D. Doohan, A.F. Dobbels, W.G. Johnson, B.G. Young, T.R. Legleiter, and A. Hager. 

2015. A. Weed Control Guide for Ohio, Indiana and Illinois. Bulletin 789. The Ohio State 

University Extension, Purdue University Extension, and University of Illinois. Columbus, OH.

McKay, L., T. Bondelid, A. Rea, C. Johnston, R. Moore, and T. Dewald. 2012. NHDPlus Version 2:

User Guide. Available at ftp://ftp.horizonsystems.com/NHDPlus/NHDPlusV21/Documentation/NHDPlusV2_User_Guide.pdf

McMaster, G.S., D.A. Edmunds, W.W. Wilhelm, D.C. Nielsen, P.V.V. Prasad, and J.C. Ascough II. 

2011. PhenologyMMS: A program to simulate crop phenological responses to water stress. 

Computers and Electronics in Agriculture 77:118-125. Publications from USDA-ARS / UNL 

Faculty. Paper 865. http://digitalcommons.unl.edu/usdaarsfacpub/865

Miller, P., W. Lanier, and S. Brandt. 2001. Using Growing Degree Days to Predict Plant Stages. 

MT200103 AG 7/2001. Montana State University Extension Service. Bozeman, MT.

Murray, M.S. 2008. Using Degree Days to Time Treatments for Insect Pests. Utah State 

University Extension and Utah Plant Pest Diagnostic Laboratory. IPM-05-08. Logan, UT.

Murthy, V.R.K. 2003. Crop Growth Modeling and its Applications in Agricultural Meteorology. p. 

235-261 in M.V.K. Sivakumar, P.S. Roy, K. Harmsen, and S.K. Saha (Ed.). Satellite Remote 

Sensing and GIS Applications in Agricultural Meteorology. Proceedings of a Training Workshop 

held 7-11 July 2003 in Dehra Dun, India. Available at 

http://www.wmo.int/pages/prog/wcp/agm/publications/AGM8_en.php

Nash, J. E. 1957. The Form of the Instantaneous Unit Hydrograph. International Association of 

Scientific Hydrology Publication, 45(3), 114-121.

Nash, J. E. 1958. Determining Runoff from Rainfall. Proceedings of the Institution of

Civil Engineers, 10, 163-184.
94

National Center for Atmospheric Research (NCAR). 2014. NCAR Command Language (NCL and 

Regridding Analysis. Available on-line: https://www.ncl.ucar.edu/Applications/regrid.shtml

National Center for Environmental Prediction/National Center for Atmospheric Research 

(NCEP/NCAR). 2014. NCEP/NCAR Reanalysis 1 Data provided by NOAA/OAR/ESRL PSD, Boulder, 

Colorado, USA, at 

http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html

National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center, 2014. 

Standard Quality Control of Unified Rain Gauge Dataset. Available on-line: 

http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/7/atlas_7.html

National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA 

ESRL). 2014. CPC US Unified Precipitation data provided by NOAA/OAR/ESRL PSD, Boulder, 

Colorado, USA, at http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.html 

(1984 – 2006) and http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.rt.html 

(2007 – 2014) 

National Research Council (NRC). 2007. Models in Environmental Decision Making, Committee 

on Models in the Regulatory Decision Process, Board on Environmental Studies and Toxicology, 

Division on Earth and Life Studies. National Academic Press, Washington D.C.

National Research Council (NRC). 2013. Assessing Risks to Endangered and Threatened Species 

from Pesticides. National Academy of Sciences, National Research Council. National Academic 

Press, Washington, DC. Available at http://www.nap.edu/catalog.php?record_id=18344

Ouyang, Da, and Jon Bartholic. 1997. Predicting Sediment Delivery Ratio In Saginaw Bay 

Watershed. The 22nd National Association of Environmental Professionals Conference 

Proceedings. May 19-23, 1997, Orlando, FL. pp 659-671. Available online: 

http://www.iwr.msu.edu/rusle/sdr/sag-sdr.htm

Parker, R., J.G. Arnold, M. Barrett, L. Burns, L. Carrubba, S.L. Neitsch, N.J. Snyder, and R. 

Srinivasan. 2007. Evaluation of Three Watershed-Scale Pesticide Environmental Transport and 

Fate Models. J. Amer. Water Resources Assoc. 43(5):1-20.

Pruess, Kenneth P. 1983. Day-Degree Methods for Pest Management. Environmental 

Entomology. 12(3): 613-619.

Rutherford, J.C. 1994. River Mixing. John Wiley and Sons, Chichester.

Sadler, E.J., K.A. Sudduth, R.N. Lerch, C. Baffaut, and N.R. Kitchen. 2014. A simple index explains 

annual atrazine transport from surface runoff-prone watersheds in the north-central USA. 

Hydrol. Processes 28:210–217. doi:10.1002/hyp.9544

Sakamoto, T., A.A. Gitelson, and T.J. Arkebauer. 2013. MODIS-based corn grain yield estimation 

model incorporating crop phenology information. Remote Sensing of Environ. 131:215-231.
95

Schneider, M.K., F. Brunner, J.M. Hollis, and C. Stamm. Towards a hydrological classification of

European soils: preliminary test of its predictive power for the base flow index using river 

discharge data. Hydrol. Earth Syst. Sci. Discuss., 4, 831–861, 2007. www.hydrol-earth-syst-scidiscuss.net/4/831/2007/

Steenhuis, T.S. and M.F. Walter. 1980. Closed form solution for pesticide loss in runoff water. 

Transactions of the ASAE 23:615-628.

Stefan HG, Demetracopoulos AC. 1981. Cells-in-series simulation of riverine transport. Journal 

of the Hydraulics Division, American Society of Civil Engineers 105(8):999-1016.

Stone, W.W., and R.J. Gilliom. 2009. Update of Watershed Regressions for Pesticides (WARP) 

for predicting atrazine concentration in streams. U.S. Geological Survey Open-File Report 2009–

1122. http://pubs.usgs.gov/of/2009/1122/.

Stone, W.W., and Gilliom, R.J., 2012. Watershed Regressions for Pesticides (WARP) models for 

predicting atrazine concentrations in Corn Belt streams. Journal of the American Water 

Resources Association (JAWRA) 48(2):970–986. DOI:10.1111/ j.1752-1688.2012.00661.x, 

available at http://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2012.00661.x/full.

Stone, W.W., C.G. Crawford, and R.J. Gilliom. 2013. Watershed Regressions for Pesticides 

(WARP) Models for Predicting Stream Concentrations of Multiple Pesticides. J. Environ. Qual. 

42(6):1838-1851. doi:10.2134/jeq2013.05.0179

Torrion, J., T.D. Setiyono, K. Cassman, and J. Specht. 2012. Soybean Phenology Simulation in the 

North-Central United States. Agron. J. 103:1661-1667.

U.S. Department of Agriculture (USDA), 1991. Soil-Plant-Water Relationships, in National 

Engineering Handbook, Section 15 Irrigation. NEH 15-1, 2nd Edition. United States Dept. of 

Agriculture.

USDA National Agricultural Statistics Service (USDA NASS). 2006. Fruit and Tree Nuts: Blooming,

Harvesting, and Marketing Dates. Agricultural Handbook Number 729. December 2006.

USDA National Agricultural Statistics Service (USDA NASS). 2007. Vegetables: Usual Planting and 

Harvesting Dates. Agricultural Handbook Number 507. May 2007.

USDA National Agricultural Statistics Service (USDA NASS). 2010. Field Crops: Usual Planting and 

Harvesting Dates. Agricultural Handbook Number 628. October 2010.

USDA Natural Resource Conservation Service (USDA NRCS). 1986. Urban Hydrology for Small 

Watersheds, TR-55, Natural Resources Conservation Service, United States Department of 

Agriculture, Washington DC.

USDA Natural Resource Conservation Service (USDA NRCS). 2003. National Engineering 

Handbook Section 4: Hydrology. Natural Resources Conservation Service, United States 

Department of Agriculture, Washington DC.
96

USDA Natural Resource Conservation Service (USDA NRCS), 2008a. Part 630 Hydrology, 

National Engineering Handbook, U.S. Department of Agriculture, Washington, DC.

U.S. Department of Agriculture Natural Resources Conservation Service (USDA NRCS). 2008b. 

2C-5 NRCS TR-55 Methodology. Available: 

http://www.ctre.iastate.edu/pubs/stormwater/documents/2C-5NRCSTR-55Methodology.pdf

US Environmental Protection Agency (USEPA). 2009. Guidance on the development, evaluation, 

and application of environmental models. Office of the Science Advisor, Council for Regulatory 

Environmental Modeling. EPA/100/K-09/003. March 2009. Available:

http://www.epa.gov/crem/library/cred_guidance_0309.pdf

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2007. 

Preliminary Interpretation of the Ecological Significance of Atrazine Stream-Water 

Concentrations Using a Statistically-Designed Monitoring Program. Prepared for the December 

4 - 7, 2007 FIFRA Scientific Advisory Panel. Document available at EPA-HQ-OPP-2007-0934.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2009a. FIFRA 

Scientific Advisory Panel: A Set of Scientific Issues Being Considered by the Environmental 

Protection Agency Regarding: The Ecological Significance of Atrazine Effects on Primary 

Producers in Surface Water Streams in the Corn and Sorghum Growing Region of the United 

States (Part II). May 12 – 15, 2009 FIFRA Scientific Advisory Panel. Document available at EPAHQ-OPP-2009-0104.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP EFED). 2009b. 

Guidance for Selecting Input Parameters in Modeling the Environmental Fate and Transport of 

Pesticides. Version 2.1. October 22, 2009. Available:

http://www.epa.gov/oppefed1/models/water/input_parameter_guidance.htm

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2010a. FIFRA 

Scientific Advisory Panel: A Set of Scientific Issues Being Considered by the Environmental 

Protection Agency Regarding: Re- Evaluation of Human Health Effects of Atrazine: Review of 

Experimental Animal and In Vitro Studies and Drinking Water Monitoring Frequency. April 26-

29, 2010. Document available at EPA-HQ-OPP-2010-0125.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2010b. FIFRA 

Scientific Advisory Panel: A Set of Scientific Issues Being Considered by the Environmental 

Protection Agency Regarding: Re- Evaluation of Human Health Effects of Atrazine: Review of 

Non-cancer Effects and Drinking Water Monitoring Frequency. September 14-17, 2010. 

Document available at EPA-HQ-OPP-2010-0481.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2011. FIFRA 

Scientific Advisory Panel: A Set of Scientific Issues Being Considered by the Environmental 

Protection Agency Regarding: Re- Evaluation of Human Health Effects of Atrazine: Review of 

Cancer Epidemiology, Non-cancer Experimental Animal and In vitro Studies and Drinking Water 
97

Monitoring Frequency. July 26-29, 2011 FIFRA Scientific Advisory Panel. Document available at 

EPA-HQ-OPP-2011-0399.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP). 2012. FIFRA 

Scientific Advisory Panel: Problem Formulation for the Reassessment of Ecological Risks from 

the Use of Atrazine. June 12-14, 2012 FIFRA Scientific Advisory Panel. Document available at 

EPA-HQ-OPP-2012-0230.

U.S. Environmental Protection Agency Office of Pesticide Programs (USEPA OPP EFED). 2007. 

Parameterization of Pesticide Root Zone Model (PRZM) Scenarios for Use in Surface Water 

Exposure Assessments. USEPA OPP Environmental Fate and Effects Division, Water Quality Tech 

Team (WQTT) Advisory Note, dated May 9, 2007. 

Weiland, F. C. S., C. Tisseuil, H. H. Dürr, M. Vrac, and L. P. H. van Beek, 2012. Selecting the 

optimal method to calculate daily global reference potential evaporation from CFSR reanalysis 

data for application in a hydrological model study, Hydrol. Earth Syst. Sci., 16, 983-1000, 

doi:10.5194/hess-16-983-2012.

Willmott, Cort J., and Matsuura Kenji (2001): Terrestrial Water Budget Data Archive: Monthly 

Time Series (1950-1999). Available: 

http://climate.geog.udel.edu/~climate/html_pages/README.wb_ts2.html.

Wischmeier, W. H., and Smith, D.D. 1978. Predicting rainfall erosion losses—a guide to 

conservation planning. U.S. Department of Agriculture, Agriculture Handbook No. 537. 

Available for download at: http://naldc.nal.usda.gov/download/CAT79706928/PDF

Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang (2007), A gaugebased analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607. 626.

Young, D.F. and Fry, M.M. 2014. A Model for Predicting Pesticide in Runoff, Erosion, and 

Leachate: User Manual, U.S. Environmental Protection Agency, Washington, DC. The Surface 

Water Concentration Calculator (SWCC) is available for download from the USEPA OPP Water 

Models web site at http://www.epa.gov/oppefed1/models/water/#swcc

Zomer RJ, Bossio DA, Trabucco A, Yuanjie L, Gupta DC & Singh VP, 2007. Trees and Water: 

Smallholder Agroforestry on Irrigated Lands in Northern India. Colombo, Sri Lanka: International 

Water Management Institute. pp 45. (IWMI Research Report 122).

Zomer RJ, Trabucco A, Bossio DA, van Straaten O, Verchot LV, 2008. Climate Change Mitigation: 

A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation 

and Reforestation. Agric. Ecosystems and Envir. 126: 67-80.