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Development and Testing of Method for 

Assessing and Mapping Agricultural Areas 

Susceptible to Atrazine Leaching in the 

State of Washington

By Frank D. Voss

Tacoma, Washington

2003

Water-Resources Investigations Report 03-4173

U.S. GEOLOGICAL SURVEY

Prepared in cooperation with the

WASHINGTON STATE DEPARTMENT OF AGRICULTURE and

WASHINGTON STATE DEPARTMENT OF ECOLOGY
U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does 

not imply endorsement by the U.S. Government.

For additional information write to:

Director, Washington Water Science Center

U.S. Geological Survey

1201 Pacific Avenue – Suite 600

Tacoma, Washington 98402

http://wa.water.usgs.gov

Copies of this report can be purchased from:

U.S. Geological Survey

Information Services

Building 810

Box 25286, Federal Center

Denver, CO 80225-0286

Suggested citation:

Voss, F.D., 2003, Development and testing of method for assessing and mapping agricultural areas 

susceptible to atrazine leaching in the State of Washington: U.S. Geological Survey WaterResources Investigations Report 03-4173, 13 p.
Contents iii

CONTENTS

Abstract ................................................................................................................................................................ 1

Introduction .......................................................................................................................................................... 1

Purpose and Scope ...................................................................................................................................... 2

Description of Study Area........................................................................................................................... 2

Acknowledgments....................................................................................................................................... 2

Methods of Study ................................................................................................................................................. 4

Model Selection and Description ................................................................................................................ 4

Construction of Simulated Atrazine Leaching Map.................................................................................... 4

Comparisons of Simulated Degree of Atrazine Leaching with Ground-Water Sampling Results ............. 7

Results .................................................................................................................................................................. 9

Discussion ............................................................................................................................................................ 10

Summary .............................................................................................................................................................. 12

References Cited .................................................................................................................................................. 13
iv Figures

FIGURES

Figure 1. Map showing location of the Columbia Basin Irrigation Project in Washington.............................. 3
Tables v

TABLES

Table 1. PRZM-2 processes selected for modeling atrazine leaching in agricultural areas in the 

Columbia Basin Irrigation Project, Washington................................................................................ 4

Table 2. Soil input parameters derived from the Soil Survey Geographic database (SSURGO) for the 

PRZM-2 model of the Columbia Basin Irrigation Project, Washington ........................................... 5

Table 3. Input parameters derived from the PRZM-2 user's manual for the PRZM-2 model of the 

Columbia Basin Irrigation Project, Washington................................................................................ 5

Table 4. Input parameters derived from published studies, local experts, or the Pesticide Assessment 

Tool for Rating Investigations of Transport (PATRIOT) for the PRZM-2 model of the 

Columbia Basin Irrigation Project, Washington................................................................................ 6

Table 5. Meteorological input parameters from a weather station in Yakima, Washington, for the 

PRZM-2 model of the Columbia Basin Irrigation Project, Washington ........................................... 6

Table 6. Results of statistical testing of reliability of the map of susceptibility to atrazine leaching 

of areas in the Columbia Basin Irrigation Project, Washington ........................................................ 9

Table 7. Atrazine application rates reported in published studies ................................................................... 11
vi Conversion Factors and Datums

CONVERSION FACTORS AND DATUMS

CONVERSION FACTORS

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=1.8 °C+32.

DATUMS

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 (NGVD29); horizontal 

coordinate information is referenced to the National American Datum of 1927 (NAD27).

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch

meter (m) 3.281 foot 

kilometer (km) 0.6214 mile 

Area

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

Volume

cubic centimeter (cm3) 0.06102 cubic inch 

cubic meter (m3) 35.31 cubic foot 

cubic meter (m3) 1.308 cubic yard 

Application Rate

kilogram per hectare (kg/ha) 2.205 pound per acre
Introduction 1

Development and Testing of Method for Assessing and 

Mapping Agricultural Areas Susceptible to Atrazine 

Leaching in the State of Washington

By Frank D. Voss

ABSTRACT

In a joint effort by the Washington State 

Department of Agriculture, the Washington State 

Department of Ecology, and the U.S. Geological 

Survey, the Environmental Protection Agency's 

Pesticide Root Zone Model and a Geographic 

Information System were used to develop and test 

a method for assessing and mapping the 

susceptibility of ground water in agricultural areas 

to pesticide contamination. The objective was to 

produce a map that would be used by the 

Washington State Department of Agriculture to 

allocate resources for monitoring pesticide 

concentrations in ground water. The method was 

tested by producing a map showing susceptibility 

to leaching of the pesticide atrazine for the 

Columbia Basin Irrigation Project, which 

encompasses an area of intensive agriculture in 

eastern Washington. The reliability of the atrazine 

map was assessed by using statistical procedures 

to determine whether the median of the percentage 

of atrazine simulated to leach below the root zone 

in wells where atrazine was detected was 

statistically greater than the median percentage at 

wells where atrazine was not detected (at or above 

0.001 microgram per liter) in 134 wells sampled 

by the U.S. Geological Survey. A statistical 

difference in medians was not found when all 134 

wells were compared. However, a statistical 

difference was found in medians for two subsets of 

the 134 wells that were used in land-use studies 

(studies examining the quality of ground water 

beneath specific crops). The statistical results from 

wells from the land-use studies indicate that the 

model potentially can be used to map the relative 

susceptibility of agricultural areas to atrazine 

leaching. However, the distinction between areas 

of high and low susceptibility may not yet be 

sufficient to use the method for allocating 

resources to monitor water quality. Several options 

are offered for improving the reliability of future 

simulations.

INTRODUCTION

The U.S. Environmental Protection Agency 

(USEPA) has mandated that all States and Tribes 

develop a pesticide management plan (PMP) for the 

herbicides alachlor, atrazine, metolachlor, and simazine 

because these herbicides have been detected in ground 

water at concentrations exceeding health-based 

standards in agricultural areas in the United States 

(Barbash and others, 1999). All PMPs, including those 

for Washington, must ensure that none of the four 

herbicides will be detected in ground water at 

concentrations exceeding health-based criteria in 

agricultural areas; otherwise, the USEPA will restrict 

their sale and use (U.S. Environmental Protection 

Agency, 1996). The Washington State Department of 

Agriculture (WSDA) was designated as the agency 

responsible for developing the PMP for Washington. 
2 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

As a part of Washington's PMP, the WSDA 

decided to incorporate a method for mapping the 

susceptibility of ground water to pesticide leaching in 

agricultural areas. This capability would allow the 

WSDA to efficiently allocate management resources to 

locations where pesticide leaching was most likely to 

occur. The WSDA joined in a cooperative study with 

the Washington Department of Ecology (Ecology) and 

the U.S. Geological Survey (USGS) to develop, test, 

and evaluate a method that could be used to create 

maps characterizing the susceptibility of ground water 

to pesticide contamination in agricultural areas. The 

approach was to use a deterministic numerical model to 

simulate atrazine leaching and a Geographic 

Information System (GIS) to map susceptible areas, 

and then test the method in the Columbia Basin 

Irrigation Project (CBIP; fig. 1), an agricultural area in 

eastern Washington. Atrazine leaching was simulated 

for the years 1974-83, and the reliability of the 

resulting atrazine map was tested by comparing it with 

atrazine-concentration data from 134 wells in the CBIP, 

collected between 1993 and 1995 for another study. 

The study included options for improving the reliability 

of future simulations.

Purpose and Scope

The purpose of this report is to present the 

development and testing of a method for mapping areas 

susceptible to pesticide contamination in ground water 

and to provide options for improving this method for 

future studies. The report (1) describes the construction 

of the numerical model, simulation of atrazine 

leaching, and creation of an atrazine-susceptibility map 

for the CBIP; (2) compares the atrazine-susceptibility 

map with atrazine-concentration data collected in the 

study area; and (3) presents options for improving the 

method. 

The method was developed using a geographic 

information system (GIS) and version 2.0 of the 

Pesticide Root Zone Model (PRZM-2) developed by 

the USEPA (Carsel and others, 1984). PRZM-2 is a 

site-specific model that simulates most of the physical, 

chemical, and biological processes that affect the fate 

of a pesticide in the subsurface following its 

application. For this study, PRZM-2 had to be adapted 

to simulate over a regional scale.

Description of Study Area

The Columbia Basin Irrigation Project (CBIP) 

includes about 6,500 km2 (square kilometers) of Grant, 

Adams, and Franklin Counties in eastern Washington, 

one of the most productive agricultural areas in the 

country (fig. 1). The climate in the area is arid, with 

less than 23 cm (centimeters) of rainfall a year, an 

amount that is insufficient to support perennial streams 

(Williamson and others, 1998). Because of the dry 

summers, most of the agricultural land is irrigated. To 

help irrigate these agricultural lands, the CBIP was 

created in the early 1930s (Williamson and others, 

1998). The project currently diverts more than 3 billion 

m3 (cubic meters) of water per year from the Columbia 

River for irrigation. This extensive irrigation has 

greatly increased the rate of recharge (the movement of 

water downward from the land surface to ground 

water). The average rate of recharge in the CBIP is 

30 cm per year, substantially higher than the 5 cm per 

year observed in adjacent, nonirrigated areas. Over the 

years, increased recharge from irrigation water has 

substantially raised the water table, creating or 

increasing base flow to many of the streams and 

wasteways. The result is an overall increase in annual 

streamflow controlled mostly by irrigation, with base 

flow dominating in the winter and high flows occurring 

mostly in the summer (Williamson and others, 1998). 

The combination of intensive agriculture, high rates of 

recharge, and a rising water table make the CBIP area 

potentially susceptible to ground-water contamination 

from pesticides and other agricultural chemicals.

Acknowledgments

Laurie Morgan, Washington State Department of 

Ecology, is acknowledged for her innovative work in 

unsaturated-zone modeling and for her generosity with 

time and information. Cindy Moore and Rod Baker, 

Washington State Department of Agriculture, are 

acknowledged for their cooperation and guidance on 

this project. Jack Barbash and Randall Bayless, U.S. 

Geological Survey, are acknowledged for their 

technical support.
Introduction 3

Othello

Quincy

Ephrata

Pasco

Moses

Lake

I 90

S

2

EXPLANATION

CANALS AND STREAMS

ROADS

COUNTY BOUNDARY

COLUMBIA BASIN IRRIGATION 

PROJECT BOUNDARY

0

0

10

10 20 MILES

20 KILOMETERS

WASHINGTON Columbia Basin

Irrigation Project area

Base from U.S. Geological Survey digital data, 1:100 000, 2003 

Universal Transverse Mercator projection, 

Zone 11

COLUMBIA

RIVER

GRANT

COUNTY ADAMS

COUNTY

FRANKLIN

COUNTY

120 ̊

47 ̊

119 ̊

46 ̊

30'

Figure 1. Location of the Columbia Basin Irrigation Project in Washington.
4 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

METHODS OF STUDY

Model Selection and Description

PRZM-2 was selected for simulating pesticide 

leaching because this model has been used and tested 

extensively in many studies since its development by 

the USEPA in the early 1980s. PRZM-2 simulates most 

of the physical, chemical, and biological processes that 

affect the fate of a pesticide in the subsurface after its 

application. The model user specifies which processes 

to simulate by setting flags in the PRZM-2 input files. 

PRZM-2 originally was intended for simulating 

pesticide leaching at a specific site in an agricultural 

field using values derived from measurements made at 

the site for many of the input parameters. Because the 

objective of the study was to map pesticide leaching 

over county-sized areas of agricultural land, a major 

step in the process for Ecology and the USGS was to 

adapt the site-specific PRZM-2 model to simulate 

pesticide leaching regionally.

Construction of Simulated Atrazine Leaching Map

The simulated pesticide leaching map was based 

on county soil survey maps from Franklin and Grant 

Counties (fig. 1). Atrazine leaching was simulated for 

each soil type represented as individual polygons in the 

soil survey maps. A GIS was used to extract data from 

the soil-survey databases for PRZM-2 input and to 

assign simulation results for individual soil types to 

their corresponding polygons on the soil survey maps.

The creation of a map showing susceptibility of 

ground water to atrazine contamination involved three 

principal tasks:

1. Formulating a simulation scenario for PRZM-2 

consisting of a set of simplifying assumptions that 

described the conditions under which pesticide 

leaching was to be modeled;

2. Selecting model-input parameter values; and

3. Running PRZM-2 simulations and mapping 

variations in ground-water susceptibility to 

atrazine contamination using a GIS.

Ecology chose the PRZM-2 processes to be used 

to simulate the transport and fate of atrazine in the root 

zone in the CBIP (table 1; fig. 1) and developed a 

hypothetical scenario to serve as a basis for the 

PRZM-2 simulation runs. The scenario was based on a 

set of assumptions regarding the crop characteristics, 

the rates of pesticide application and transformation, 

and the source of water in the system. All agricultural 

areas in the region were assumed to consist of 

cornfields, and a maximum root depth for corn was set 

at 40 cm. The emergence date for the corn crop was 

designated as May 30th, the date of corn maturation 

was assumed to be September 1st, and the date of the 

corn harvest was assumed to be September 16th. For 

the model, atrazine was assumed to be applied once a 

year, on June 4th, at a uniform rate of 1.12 kg/ha 

(kilograms per hectare) and a depth of incorporation of 

2 cm. Atrazine was assumed to undergo transformation 

at a constant rate. All irrigation water was presumed to 

have been applied at a rate of 2.54 cm per hour through 

over-canopy sprinklers. Irrigation was applied when 

the average soil moisture at the maximum simulation 

depth (40 cm) was estimated to be halfway between the 

wilting point (the lowest amount of water that remains 

in the soil because a plant is unable to extract it) and 

the field capacity of the soil (the amount of water in a 

wetted soil after it has drained). Irrigation drainage tiles 

were not considered in the model because information 

on their location in agricultural areas is often difficult 

to obtain. All water was provided to the system by 

precipitation and irrigation and was lost as 

evapotranspiration, runoff, or recharge to ground water. 

Table 1. PRZM-2 processes selected for modeling atrazine leaching in 

agricultural areas in the Columbia Basin Irrigation Project, Washington

Simulated process in PRZM-2

Used to 

create the 

atrazine 

vulnerability 

map

Processes for pesticide in dissolved phase

Loss due to dispersion and diffusion No

Loss due to advection Yes

Loss due to degradation Yes

Loss due to plant uptake No

Loss due to runoff No

Gain due to deposition on the soil surface No

Gain due to wash off from plants No

Gain or loss due to parent-daughter transformations No

Processes for pesticide in sorbed phase

Loss due to degradation Yes

Loss due to removal on eroded sediment No

Processes for pesticide in vapor phase

Loss due to dispersion and diffusion No

Loss due to degradation No

Loss through volatilization No
Methods of Study 5

Approximately 256 variables (the exact number 

depended on the type of soil being modeled) were set 

in the PRZM-2 input file for each simulation. Values 

for the parameters used to characterize the soil were 

changed for each polygon in the CBIP for which 

simulations were run; all other values for parameters in 

the input file were the same for all polygons simulated 

(for example, the crop root depth was the same for all 

polygons in the CBIP). 

Values for the soil parameters used in the 

PRZM-2 simulations were obtained from the Soil 

Survey Geographic database (SSURGO) developed by 

the Natural Resources Conservation Service (table 2). 

The SSURGO database was designed primarily to aid 

land owners and local agencies in natural-resources 

planning and management (U.S. Department of 

Agriculture, 1995). Soil maps in SSURGO range in 

scale from 1:12,000 to 1:63,360 and are similar in 

appearance to maps found in county soil surveys. The 

SSURGO database is composed of a set of attribute 

tables that are linked by a key called a map unit 

identifier. 

The values of parameters that were the same for 

all polygons were obtained from the user's manual that 

is included with the PRZM-2 software system (table 3) 

and from published information from previous studies, 

local experts such as county agents, and default values 

set by the Pesticide Assessment Tool for Rating 

Investigations of Transport (PATRIOT) (Imhoff and 

others, 1994), a program that can be used to generate 

PRZM-2 input files (table 4). 

Three files are needed to run a PRZM-2 

simulation: (1) a file with daily weather data, 

(2) a non-weather parameter file containing values for 

parameters used for calculations and model processing, 

and (3) a file for an uncertainty preprocessor that is part 

of the PRZM-2 software package. The USGS used the 

Visual Basic programming language to create 

programs to automate the generation of the three types 

of files for all polygons to be modeled. 

Table 2. Soil input parameters derived from the Soil Survey Geographic 

database (SSURGO) for the PRZM-2 model of the Columbia Basin Irrigation 

Project, Washington

PRZM-2 parameter Parameter description

CN numbers Runoff curve numbers

Depth of soil core Depth of soil core

Number of soil horizons Number of horizons in soil core

Thickness of soil horizon Thickness of soil horizon

Bulk density of soil 

horizon

Soil bulk density

Initial soil-water content in 

soil

Initial soil-water content at 

beginning of simulation 

period

Field capacity in soil 

horizon

Moisture content of a soil after it 

has been saturated and 

allowed to drain for 2 days

Wilting point in soil 

horizon

Moisture content of a soil where 

a plant can no longer extract 

moisture from soil

Organic carbon in soil 

horizon

Percentage of organic carbon in 

soil

Table 3. Input parameters derived from the PRZM-2 user's manual for 

the PRZM-2 model of the Columbia Basin Irrigation Project, Washington

PRZM-2 parameter Parameter description

Pan factor Dimensionless number used to 

convert daily pan evaporation to 

daily potential evaporation

Snowmelt factor Used to calculate snow melt rates 

in relation to air temperature

Minimum depth of which 

evaporation is extracted

Depth of soil evaporation loss 

during a fallow period

Maximum interception 

storage of the crop

Used to estimate the amount of 

rainfall that is intercepted by a 

fully developed crop canopy and 

retained on the plant surface

Maximum rooting depth of 

crop

Maximum active root depth of crop

Maximum areal coverage of 

crop

Area of ground a crop canopy will 

cover

Surface condition after crop 

harvest

Condition of field after harvests

Maximum canopy height at 

maturation date

Maximum height the plant will 

grow
6 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

PRZM-2 requires daily values for several 

meteorological parameters during the period being 

simulated (table 5). These values are in a separate file 

that is read into PRZM-2 as it runs through a 

simulation period. The values for this study came from 

the National Oceanic and Atmospheric Administration 

(NOAA) weather station that was located closest to the 

CBIP area, in Yakima, Wash., approximately 80 km 

(kilometers) southwest. The file has one record for 

each day from January 1, 1974, to December 31, 1983. 

The precipitation value is the total accumulation for the 

day. Daily average values are used for pan evaporation, 

temperature, wind speed, and solar radiation.

Additional monthly precipitation averages from 

20 weather stations within the CBIP were used to 

adjust the precipitation data from Yakima to reflect 

local conditions in the CBIP (Morgan, 2002). To make 

these adjustments, the CBIP was divided into 

20 distinct weather zones by using the Thiessen 

method (Linsley and others, 1975) to create boundaries 

around each of the 20 weather stations in the area. The 

USGS developed a computer program to sum the 

precipitation for each month of the 10 years of daily 

precipitation data in the Yakima weather file (January 

1, 1974-December 31, 1983). The monthly Yakima 

values were then compared with the monthly values at 

each of the 20 weather stations and the ratio of the 

average monthly precipitation for each weather station 

to that for the Yakima weather station was calculated 

for each month. Each daily precipitation value in the 

Yakima file was then multiplied by this ratio to create 

an adjusted weather file to be used for daily 

simulations in each weather zone. 

For each soil type in each weather zone, a nonweather parameter file was created using a custom 

program written by the USGS in the Visual Basic 

programming language. The program read soils data 

from a database (developed by Ecology from processed 

SSURGO data) and inserted the values for soils into 

the PRZM-2 input files.

PRZM-2 was originally designed to simulate 

leaching at a site where the values for the input 

parameters (such as the percentage of organic matter in 

the soil and the pesticide transformation rate) were 

measured. Parameter values obtained from regionalscale data typically are averaged over large areas, and 

the averages may contain significant uncertainties that 

can introduce considerable variation in simulation 

results. Therefore, it is useful for the model user to 

quantify the effect of uncertainty in input values on the 

variability of simulation results. Uncertainty was 

modeled for field capacity of a soil and for the organic 

carbon content in the soil. Input fields for the 

uncertainty processor were created for each soil type in 

a given weather zone. A Monte Carlo routine was used 

in the uncertainty processor to randomly select 

numbers between user-specified minimum and 

maximum values for each of the parameters. The 

Monte Carlo routine in PRZM-2 runs for a userselected number of times to generate a distribution of 

simulation output values. 

Table 4. Input parameters derived from published studies, local experts, 

or the Pesticide Assessment Tool for Rating Investigations of Transport 

(PATRIOT) for the PRZM-2 model of the Columbia Basin Irrigation Project, 

Washington

PRZM-2 parameter Parameter description

Date of crop emergence Date crop emerges from soil

Date of crop maturation Date crop matures

Date of crop harvest Date crop is harvested

Depth of pesticide 

incorporation into soil

Depth pesticide is plowed into soil

Pesticide application rate Rate pesticide is applied

Leaching factor Used to specify amount of 

irrigation water used to leach 

salts from soil

Irrigation factor Specifies type of irrigation used

Pesticide solubility Pesticide water solubility

Dissolved-phase pesticide 

decay rate

Rate pesticide disappears in 

solution phase

Adsorbed-phase pesticide 

decay rate

Rate pesticide disappears in 

adsorbed phase

Table 5. Meteorological input parameters from a weather station in 

Yakima, Washington, for the PRZM-2 model of the Columbia Basin 

Irrigation Project, Washington

PRZM-2 parameter Parameter description

Precipitation Daily amount of rain or snow

Pan evaporation Rate of water evaporating from a 

pan

Air temperature Average daily air temperature

Wind speed Average daily wind speed

Solar radiation Average daily amount of solar 

radiation
Methods of Study 7

Ecology conducted this procedure, plotted the 

results on a graph, and then selected the pesticideleaching value from the graph (in kilograms per 

hectare) that was exceeded by 90 percent of the 

simulations (Morgan, 2002). Ecology used this value to 

create the map showing the likelihood of atrazine 

contamination of ground water by writing the output 

values from the simulations to a table and relating them 

to a map of modeled polygons in a GIS. The output 

values then were color-coded into five levels using a 

Jenk's natural-break classification routine (Karnes, 

1998) in the ArcView GIS software program.

Comparisons of Simulated Degree of Atrazine 

Leaching with Ground-Water Sampling Results

The USGS compared results from PRZM-2 

simulations with data from ground-water sampling at 

134 wells sampled for atrazine to determine whether 

simulated leaching occurred at higher levels in wells 

where atrazine has been detected than in wells where 

atrazine has not been detected. The 134 wells used in 

the statistical comparison were sampled between 1993 

and 1995 as part of ground-water studies conducted by 

the USGS Central Columbia Plateau (CCPT) study unit 

(which included the CBIP) for the National Water 

Quality Assessment (NAWQA) Program. Three studies 

were used: a study unit survey, a land-use study for 

orchards, and a land-use study for row crops.

The study-unit survey was a large-scale 

assessment of ground-water quality throughout the 

entire CCPT NAWQA study area. Of the 121 public 

wells across the study-unit, 43 were sampled once in 

1994 for major ions, nutrients, 85 pesticides, 60 

volatile organic compounds, dissolved organic carbon, 

and radon, and 78 wells were sampled once in 1994 for 

47 pesticides only (Jones and Roberts, 1998). In the 

study unit survey, 51 of the 121 wells were located in 

the CBIP and were used for comparison with the 

PRZM simulations. 

The land-use studies for row crops and orchards 

were conducted exclusively in the CBIP and were 

designed to investigate relations between land use and 

shallow ground-water quality within specific, 

consistently defined land-use settings. The studies were 

conducted in areas where agricultural chemicals were 

applied to crops, rather than in locations where these 

chemicals may have been spilled. The focus of the 

studies was on shallow ground water because it 

provides the earliest indication of contamination or 

other water-quality changes. It also minimizes the 

influence of factors other than land use on water 

quality prior to sampling (Jones and Roberts, 1998).

The land-use study for row crops investigated 

pesticide occurrence in ground water beneath areas 

where row crops (such as potatoes and corn) were 

predominant (the term "predominant crop" means the 

crop covered more area with in a 500-meter (m) radius 

of the sampled well than any other crop within that 

radius). Two types of wells were sampled:

1. 30 (average depth 42 m) domestic wells that were 

selected because they were easy to sample and 

provided water consumed by residence; and 

2. 19 shallow (average depth 13 m) monitoring wells 

installed specifically for the NAWQA study at 

locations and depths that would provide the 

earliest indication of potential water-quality 

concerns. 

The wells were generally within 30 m of row 

crop fields and were sampled once between 1993 and 

1995 during the irrigation season for major ions, 

nutrients, 85 pesticides, 60 volatile organic 

compounds, dissolved organic carbon, and radon. 

The land-use study for orchards specifically 

investigated pesticide occurrence in ground water 

beneath areas where orchards were the predominant 

crop. Samples included 18 domestic wells and 22 

shallow monitoring wells. The wells were generally 

within 30 m of the orchard and were sampled once in 

1994-95 for the same set of chemicals as the wells in 

the land-use study for row crops (Jones and Roberts, 

1998). 

Samples were collected using established 

NAWQA protocols, which required the use of 

noncontaminating sampling equipment, well purging, 

and quality-assurance sampling. Methods for well-site 

selection, sample collection, and chemical analysis for 

the land-use studies are reported in detail in Jones and 

Roberts (1998).
8 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

The USGS used a GIS to link the sampling data 

at the well sites to the simulated amount of atrazine 

leaching below the root zone at the 134 well sites. First, 

a digital map of the 134 locations was created using the 

latitude and longitude coordinates of the wells. The 

map of the well locations was then combined with the 

map of simulated atrazine leaching using an overlay 

operation that produced a table containing the well 

identification number, the atrazine concentration found 

in the sampled ground water, and the kilograms of 

atrazine simulated to leach below the root zone for 

each well. The data in the table were then exported 

from the GIS into a database (Microsoft Access).

Two operations were performed in the database. 

The first operation was to calculate the percentage of 

the total amount of atrazine applied over the 10-year 

simulation period that leached below the root zone at 

each of the 134 well sites. This was done by dividing 

the kilograms of atrazine predicted to leach below the 

root zone by the total kilograms of atrazine applied 

(11.2 kg/ha) at the sites. The second operation was to 

add a new field to the database to store values that 

indicated whether atrazine was detected in ground 

water at the well at levels equal to or greater than the 

detection limit of 0.001 μg/L (micrograms per liter). 

The entry for the field was set to "detect" if atrazine 

was found in the ground water and to "no-detect" if it 

was not. 

Finally, four fields were exported from the 

database into the statistical software program 

(S-PLUS®) in order to assess the reliability of the 

atrazine susceptibility maps: (1) well identification 

number, (2) ground-water study identification code, 

(3) flag indicating whether atrazine was detected at 

well site, and (4) percentage of total atrazine predicted 

to leach below the root zone at the well site.

The USGS used statistical procedures to test 

whether the median of the percentage of atrazine 

simulated to leach below the root zone in wells where 

atrazine was detected was significantly greater 

statistically than the median percentage simulated in 

wells where atrazine was not detected. The following 

procedures were used for all 134 wells and for two 

subsets of the wells: the 46 wells in the land-use study 

where row crops were predominant and the 37 wells in 

the land-use study where orchards were predominant.

First, the wells were separated into two sets: one 

consisting of the wells where atrazine had been 

detected and the other consisting of the wells where 

atrazine had not been detected. The following null 

hypothesis (Ho1) and alternative hypothesis (Ha1) were 

constructed:

where

The significance level for the test was set at 

95 percent.

Second, the Kolmogorov-Smirnov Test of 

Composite Normality (K-S Test) (Millard and 

Neerchal, 2001) was used to determine whether the 

values in the data sets were normally distributed. If the 

values were normally distributed, a parametric 

statistical method would be applied for testing the null 

hypothesis; if the values were not normally distributed, 

a nonparametric method would be used. The K-S Test 

was used to test the following null hypothesis (Ho2) 

and alternative hypothesis (Ha2):

Ho1: Ud < Und

Hal: Ud > Und

Ud is the median percentage of atrazine simulated to 

leach below the root zone at wells where atrazine 

was detected in the ground water; and

Und is the median percentage of atrazine simulated to 

leach below the root zone at wells where atrazine 

was not detected in the ground water.

Ho2: Values in data set are normally distributed; 

and

Ha2: Values in data set are not normally 

distributed.
Results 9

Finally, the nonparametric Wilcoxon rank sum 

test (Millard and Neerchal, 2001) was used to test the 

null hypothesis (Ho1) because results from the K-S Test 

showed the values in the data sets were not normally 

distributed. The nonparametric Wilcoxon rank sum test 

statistic is based on ranks of the observations instead of 

the observations themselves.

An examination of the PRZM-2 output files 

revealed that irrigation was misapplied during the 

simulations. Simulated irrigation in PRZM-2 is applied 

when the average soil-moisture content in the root zone 

reaches a level that is set by the model user, a value 

(cubic centimeters of water/cubic centimeters of soil) 

between the wilting point and field capacity of the soil 

in the maximum root zone. However, an inspection of 

the PRZM-2 output files revealed that irrigation in the 

simulations was not begun until 6 weeks after the plant 

started to grow. 

The USGS conducted additional PRZM-2 

simulations to explore the effect of underapplying 

irrigation on atrazine leaching. The irrigation process 

was set to off, and instead, irrigation was simulated by 

adding water as precipitation in the weather files. The 

median percentage of atrazine leaching increased from 

47.9 percent to 74.5 percent for the 22 wells where 

atrazine was detected and from 38 percent to 

66 percent for wells where atrazine was not detected. 

The same statistical methods that were used for wells 

in the study-unit survey and land-use studies were also 

applied to the wells in the land-use study for row crops 

where simulations were rerun to increase amounts of 

irrigation.

RESULTS

The purpose of the statistical comparison was to 

determine whether the median of the percentage of 

total atrazine to leach below the root zone was greater 

for wells where atrazine was detected compared to 

wells where atrazine was not detected.

For the 134 wells in the combined three 

NAWQA studies, the p value was 0.067 for the 

Wilcoxon rank sum test statistic (table 6). Therefore, 

the null hypothesis was accepted at a 95-percent 

significance level and no statistical difference was 

found between the medians of the two groups. For 

wells in the land-use study for orchards, the p value 

was 0.044, resulting in rejection of the null hypothesis 

at a 95-percent significance level. This indicates that 

the median percentage predicted by the model for 

atrazine leaching was statistically greater in the wells 

where atrazine had been detected than in the median 

for wells where atrazine had not been detected. For 

wells in the land-use study for row crops, the p value 

was 0.020, again resulting in rejection of the null 

hypothesis and indicating that the median percentage 

of atrazine predicted to leach below the root zone was 

statistically greater for wells where atrazine had been 

detected than in wells where atrazine had not been 

detected. Finally, for wells in the land-use study for 

row crops that were rerun with greater amounts of 

applied irrigation, the p value was 0.057. Therefore, the 

null hypothesis was accepted at a 95-percent 

significance level, indicating no statistical difference in 

medians of the two groups.

Table 6. Results of statistical testing of reliability of the map of 

susceptibility to atrazine leaching of areas in the Columbia Basin Irrigation 

Project, Washington

[Results of hypothesis testing are at 95-percent significance; Ho, null 

hypothesis]

Well group tested p-value Ho

All 134 wells 0.067 Accepted

Wells in orchard land-use study 

(37 wells)

0.044 Rejected

Wells in row-crop land-use 

study (46 wells)

0.020 Rejected

Wells in row-crop land-use 

study (with adjusted 

irrigation)

0.057 Accepted
10 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

DISCUSSION

When all 134 wells from the three studies were 

compared, no statistically significant difference was 

found between medians of the percentage of total 

applied atrazine to leach below the root zone for wells 

where atrazine had been detected and for wells where 

atrazine was not detected. However, all of the wells in 

the study unit survey were domestic wells, which were 

deeper than the wells in the land-use studies. Water 

sampled from the deeper wells is not as likely to 

represent land use close to the wells as is water from 

the shallower wells in the land-use studies. This is 

because source water in the deep wells is more likely to 

have traveled from an upgradient area, and more 

mixing probably occurred. Therefore, the ground water 

in the shallow wells of the land-use studies is more 

representative of the land use at the well sites.

A statistically significant difference 

(p = 0.020) was found between medians for the two 

groups of wells in the land-use study for row crops. In 

the CBIP, atrazine is applied mainly to corn (Anderson 

and Gianessi, 1995). Although corn was not the 

predominant row crop studied, (the focus of the 

NAWQA study was on potatoes), a land-use survey 

(http://sd.water.usgs.gov/usgs/nawqa_gis/lulcfs/

accessed August 13, 2003), conducted by the USGS as 

part of the land use study for row crops found 13 of the 

43 wells having corn in rotation with potatoes, 

indicating corn might have been planted near the wells 

during the crop rotation cycle (3 to 5 years). Therefore, 

the wells from the land-use study for row crops 

probably were the most representative of the scenarios 

used for the simulations.

A statistically significant difference (p = 0.044) 

between the medians also was observed for the two 

groups of wells in the land-use study for orchards. 

Although atrazine is not listed as being applied to 

orchards (Anderson and Gianessis, 1995), many of the 

orchards are small and surrounded by row crops (Jones 

and Roberts, 1998). A USGS land-use survey 

(http://sd.water.usgs.gov/usgs/nawqa_gis/lulcfs/

accessed August 13, 2003) found that row crops may 

have corn as part of the crop rotation, and in the CBIP, 

atrazine is applied mainly to corn (Anderson and 

Gianessi, 1995). Therefore, the atrazine may be 

originating from nearby locations, and the probability 

of atrazine being applied nearby is assumed to be the 

same for all wells in the land-use study.

No statistically significant difference (p = 0.057) 

was found between the medians for the two groups of 

land-use wells for row crops, when simulations were 

rerun with adjusted irrigation. The reason for a lack of 

significant difference could be that the increased 

amounts of irrigation transport the atrazine through the 

root zone before the mass can be reduced by 

transformation.

The statistical results from wells from the landuse studies for orchards and row crops indicate that the 

model potentially can be used to map the relative 

susceptibility of agricultural areas to atrazine leaching. 

However, the distinction between areas of high and low 

susceptibility may not yet be sufficient to use the 

method for allocating resources for monitoring water 

quality. There are some options that may improve 

results for future simulations.

One option for improving simulation results is to 

limit the number of values taken from published 

studies. For regional-scale simulations, many input 

values for PRZM-2 must be estimated on the basis of 

values found in published studies because large 

databases of site-specific information are not available. 

In many instances, it may be necessary to use a single 

value for the entire study area for a parameter that can 

be highly variable (such as a pesticide transformation 

rate). Using parameters from published studies can 

introduce substantial uncertainty into the simulation 

results. For example, Mulla and others (1989) found 

that the half-lives of atrazine measured at study sites in 

central Washington were about three times larger than 

the values in published studies. As a result, when 

published values (rather than measured values) were 

used to quantify the rate of transformation, the relative 

mass of atrazine predicted to be remaining in the soil 

column was approximately one-fifth of what was 

actually observed. The uncertainty processor that is 

part of the PRZM-2 package can be used to estimate 

how the uncertainty of an input parameter value can 

affect simulation results. However, although 

information about the distribution of the input 

parameter values is needed if the uncertainty processor 

is to be used correctly, this information is usually 

difficult to obtain. The PRZM-2 model user must 

determine whether using input values from published 

studies will introduce unacceptable uncertainty into the 

simulation results. Where available, local information 

about pesticide application rates is likely to improve 

simulation results. County agents and pesticide 
Discussion 11

manufacturers are valuable sources for local estimates 

of pesticide application rates. Pesticide application 

rates for atrazine from published studies in the United 

States ranged from 1.5 to 3.0 kg/ha (table 7). 

The agreement between prediction and 

measurement might also be improved by representing 

irrigation schedules more accurately in the PRZM-2 

runs. This is not an easy task because, as a USDA 

report states, "Application amounts need to be 

determined by a trained technician for each soil type, 

crop, and irrigation system" (U.S. Department of 

Agriculture, 1989). As noted earlier, PRZM-2 adds 

irrigation water to the soil when the average soil 

moisture in the root zone reaches a user-defined 

threshold, set between the soil's wilting point and the 

field capacity. As a result, the user can control the flux 

of irrigation water by adjusting this threshold. 

However, care must be taken to ensure that irrigation 

water is being applied properly during a simulation. 

Also, flow to drainage tiles was not simulated in the 

CBIP because information on the location of irrigation 

drainage tiles was difficult to obtain. Because of the 

significant effect that drainage tiles can have on 

pesticide leaching, they should be included in 

simulations when possible.

Simulating water movement through macropores 

(small cracks in the soil caused by weathering or holes 

created by earthworms and insects) may also improve 

the agreement between prediction and observation for 

certain soils. Macropores can allow water to flow 

rapidly through the root zone, minimizing interactions 

with most of the soil. Furthermore, a study has found 

that the movement and fate of pesticides may be 

profoundly affected by macropores (Mallawatantri and 

others, 1994). However, PRZM-2 does not simulate 

water movement through macropores in soil. If 

pesticide transport through macopores in soils in the 

study area may be significant, then models that 

simulate transport through macropores, such as the 

Root Zone Water Quality Model (RZWQM; Ahuja and 

others, 2000), should be considered.

Pesticide transformation rates vary with different 

soil conditions and temperatures. For example, during 

their study conducted in central Washington, Mulla and 

others (1989) showed that as temperatures decreased 

from 23 to 5°C, the half-life of atrazine increased from 

150 days to 500 days. The longer the half-life of a 

pesticide, the more likely it is to be detected in ground 

water. Varying the pesticide transformation rate (or 

half-life) over the course of a simulation may therefore 

improve the agreement between prediction and 

observation. Using one transformation rate for a 

pesticide in all soils in the study area and keeping it 

constant for the entire simulation period (often 

presumed to be necessary for regional-scale 

simulations) can therefore introduce error into 

simulation results. Transformation rates can be 

changed in PRZM-2 on user-specified dates throughout 

a simulation period, therefore the PRZM-2 model user 

must determine whether the error introduced into the 

simulation results by using one transformation rate is 

acceptable for the purpose for which the simulation 

results are to be used.

Calibrating the model may improve the match 

between prediction and observation (Corwin and 

others, 1999). PRZM-2 was originally designed by the 

USEPA for simulating pesticide leaching below the 

root zone at a specific location. Simulations at a site 

can be calibrated by comparing simulated and 

measured pesticide concentrations in root-zone soil 

water and adjusting the values of selected input 

parameters until closer agreement is observed. 

Although regional databases of measured pesticide 

concentrations in soil water do not exist, data on 

measured pesticide concentrations in ground water can 

be used as proxies for concentrations in the unsaturated 

zone. However, if this is to be done, it is important to 

account for the effects of physical and chemical 

processes on the concentration of a pesticide as it 

travels from the bottom of the root zone to the saturated 

zone. A model (VADOFT) can be linked to PRZM-2 to 

simulate the effects of these processes on the 

concentration of a pesticide during its transport through 

the unsaturated zone. Criteria for the calibration of 

PRZM-2 simulation results should be established early 

in the modeling process.

Table 7. Atrazine application rates reported in published studies

Application rate

(kg/ha) Cited report

1.5 Panshin and others, 2000

2.0 Capel and Zhang, 2000

2.2 Donigian and others, 1990

3.0 Banton and Villeneuve, 1989
12 Method for Assessing and Mapping Agricultural Areas Susceptible to Atrazine Leaching in the State of Washington

Sensitivity analysis could be used to determine 

variables to be input into the Monte Carlo routine in the 

uncertainty processor. Variables that are not measured 

at the sites for which simulations are conducted should 

be considered for inputting into the uncertainty 

processor. However, as the number of variables entered 

into the uncertainty processor increases, so does the 

time required to run the model. Potentially large 

amounts of computer processing time can be required 

if uncertainty is to be simulated for many variables for 

a large number of areas. By checking the sensitivity of 

model inputs, a few key variables can be input into the 

Monte Carlo routine and can reduce computerprocessing time.

SUMMARY

The U.S. Geological Survey (USGS), the 

Washington State Department of Ecology, and the 

Washington State Department of Agriculture 

cooperatively developed a method for assessing and 

mapping susceptibility of ground water to pesticide 

leaching in agricultural areas of Washington. The 

method employed the deterministic Pesticide Root 

Zone Model (PRZM-2) and a Geographic Information 

System (GIS). The method was tested by creating a 

map of ground-water susceptibility to atrazine leaching 

for the Columbia Basin Irrigation Project (CBIP) and 

comparing the percentages of applied atrazine 

simulated to leach below the root zone with groundwater monitoring data from 134 sites within the 

mapped area. 

Three principal steps were involved in creating 

the map of susceptibility to atrazine leaching for the 

CBIP. First, a simulation scenario for PRZM-2 was 

formulated by making simplifying assumptions about 

atrazine applications, application site characteristics, 

and transport processes. Second, values for the model 

input parameters were selected from previous pesticide 

leaching studies, PRZM-2 documentation, the 

recommendations of local experts, a PRZM-2 

preprocessor software program (PATRIOT), and a 

U.S. Department of Agriculture (USDA) soils database 

(SSURGO). Finally, simulations were run and areas of 

ground-water susceptibility to atrazine leaching were 

mapped using a GIS.

The USGS used statistical procedures to test 

whether the median of the percentage of atrazine 

simulated to leach below the root zone in wells where 

atrazine was detected was statistically greater than the 

median percentage at wells where atrazine was not 

detected. For the first statistical comparison, 134 wells 

from the CBIP were separated into two sets: (1) wells 

where atrazine had been detected (at or above 

0.001 micrograms per liter) and (2) wells where 

atrazine had not been detected. The comparison of the 

two sets of wells with the non-parametric Wilcoxon 

rank-sum test indicated that the percentages of applied 

atrazine simulated to have leached below the root zone 

for wells with detections was not significantly different 

(at the 95-percent level) from those for wells where 

atrazine had not been detected (p = 0.067). The same 

statistical comparisons were used for wells in the landuse study for orchards where a significant difference (at 

the 95-percent level) in medians was found (p = 0.044), 

and also for the wells in the land-use study for row 

crops where a significant difference (at the 95-percent 

level) in medians was found (p = 0.019). The statistical 

results from wells from the land-use studies indicate 

that the model potentially can be used to map the 

relative susceptibility of agricultural areas to atrazine 

leaching. However, the distinction between areas of 

high and low susceptibility may not yet be sufficient to 

use the method as a tool for allocating resources for 

monitoring water quality.

Adapting the PRZM-2 model to perform 

regional-scale simulations is challenging. Most largescale datasets available for model input have large 

uncertainties associated with parameter values, and 

these uncertainties cause errors in simulation output 

values. Options for reducing the uncertainty in input 

data and improving future simulations include (1) 

limiting input values used from published studies and 

using more measured values, (2) more accurately 

representing irrigation schedules, (3) simulating water 

movement through macropores, (4) varying pesticide 

transformation rates over the course of the simulation, 

and (5) calibrating the simulation model.
References Cited 13

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