Water table

From NAWQA:

 [atrazine]

 [DEA]

 [nitrate]

 

 

From SSURGO (for each soil horizon)

 

 

 Horizon depth and thickness

 Water content at 1,500 kPa 

 (~wilting point)

 Water content at 33 kPa

 (~field capacity)

 

 Saturated water content

 Specific gravity

 Percent sand

 Percent silt

 Percent clay

 Cation exchange capacity

 Other soil properties

 RZWQM

 (Simulates 

transport and

fate of water

 and solutes)

Precipitation [f (t)]

Temperature [f (t)]

] Bulk

density

Predicted daily values Flow-weighted mean 

 concentrations of 

 atrazine, DEA and nitrate

Weight by component percentage and combine 

values for all components to obtain final

concentrations for entire map unit.

N

N

N

N N

Cl

H H

(C H ; H) 2 5

Henry's law constant

Transformation rate

Soil-water partition

 coefficient (K )

Soil organic matter content (f )

d

Convert to soil organic 

 carbon content (f )

Koc

 SSURGO map unit

 (3 components) 

oc om

Component C

Component A Component B

[Component C]

 CLIGEN

(Stochastic weather simulator)

 Surface soil albedo

 Surface crust hydraulic

 conductivity

 Brooks-Corey hydraulic 

 parameters

 Total porosity

 Macroporosity

 Lateral saturated 

 hydraulic conductivity

 Diffusion coefficients

 Microbial population 

 sizes

 Nitrogen application

 rate

] Saturated 

 hydraulic

conductivity

Design and Testing of a Process-Based Groundwater 

Vulnerability Assessment (P-GWAVA) System for 

Predicting Concentrations of Agrichemicals in 

Groundwater Across the United States

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2014–5189

National Water-Quality Assessment Program
Cover: Diagram showing sources of data for several P-GWAVA-RZ (Phase 2) model 

input parameters related to soil properties, chemical properties, weather, and 

other site-specific variables. Soil (SSURGO) map-unit graphic provided by Leah 

Wasser, Pennsylvania State University Geospatial Technology Program, Land 

Analysis Laboratory (written commun., March 24, 2008). Used with permission.
Design and Testing of a Process-Based 

Groundwater Vulnerability Assessment 

(P-GWAVA) System for Predicting 

Concentrations of Agrichemicals in 

Groundwater Across the United States

By Jack E. Barbash and Frank D. Voss

National Water-Quality Assessment Program

Scientific Investigations Report 2014–5189

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director 

U.S. Geological Survey, Reston, Virginia: 2016

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Barbash, J.E., and Voss, F.D., 2016, Design and testing of a process-based groundwater vulnerability assessment 

(P-GWAVA) system for predicting concentrations of agrichemicals in groundwater across the United States: U.S. 

Geological Survey Scientific Investigations Report 2014–5189, 210 p., http://dx.doi.org/10.3133/sir20145189.

ISSN 2328-0328 (online)
iii

Contents

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

Introduction.....................................................................................................................................................3

Previous Assessments of Groundwater Vulnerability....................................................................3

Purpose and Scope .............................................................................................................................4

Study Design...................................................................................................................................................4

Design Objectives.................................................................................................................................4

Study Phases.........................................................................................................................................5

Methods...........................................................................................................................................................6

Study Site Selection and Characteristics.........................................................................................6

Locations of Study Sites Relative to National Water-Quality Assessment 

Groundwater Networks.................................................................................................6

Locations of Study Sites Relative to Spatial Patterns of Agrichemical Use and 

Corn Cultivation ..............................................................................................................6

Phase 1 .................................................................................................................................7

Phase 2 .................................................................................................................................7

Statistical Methods.............................................................................................................................11

Sample Collection and Chemical Analysis .....................................................................................11

P-GWAVA System ..............................................................................................................................12

Overall Simulation Approach............................................................................................................12

Initial Conditions and Simulation Periods..............................................................................12

Effects of Simulation Approaches on Model Predictions...................................................16

Vadose-Zone Models .........................................................................................................................16

Pesticide Root-Zone Model (Phase 1)....................................................................................16

Root-Zone Water-Quality Model (Phase 2)............................................................................17

Processes Simulated and Input Data..............................................................................................18

Physical and Chemical Properties of Soils............................................................................18

Phase 1 ...............................................................................................................................19

Phase 2 ...............................................................................................................................19

Characteristics and Use of SSURGO Data ..........................................................19

Reductions in Soil Permeability Caused by Surface Crusts .............................22

Soil Macroporosity ..................................................................................................24

Water Movement Through the Subsurface...........................................................................24

Phase 1 ..............................................................................................................................24

Phase 2 ..............................................................................................................................28

Weather.......................................................................................................................................31

Phase 1 ...............................................................................................................................31

Phase 2 ...............................................................................................................................33

Assessment Depths and Depths to Water.............................................................................33

Irrigation......................................................................................................................................35

Phase 1 ...............................................................................................................................35

Phase 2 ...............................................................................................................................37
iv

Contents

Methods

Study Site Selection and Characteristics

Runoff .........................................................................................................................................37

Phase 1 ...............................................................................................................................37

Phase 2 ...............................................................................................................................38

Evapotranspiration ....................................................................................................................38

Phase 1 ...............................................................................................................................38

Phase 2 ...............................................................................................................................38

Timing and Intensity of Agrichemical Applications .............................................................38

Atrazine...............................................................................................................................38

Nitrogen Fertilizers ...........................................................................................................41

Estimating Physical and Chemical Property Values for Atrazine and Deethylatrazine 41

Partitioning and Mass Transfer of Atrazine and Deethylatrazine......................................42

Henry’s Law Constant (Phase 2 only) ............................................................................42

Octanol-Water Partition Coefficient (Phase 2 only)....................................................42

Soil-Water Partition Coefficients ..................................................................................43

Wash-off Parameters for Crop Foliage and Residue (Phase 2 only)........................44

Mass Transfer Rates (Phase 2 only)..............................................................................45

Kinetic Controls on Sorption ..................................................................................45

Solute Exchange Between Micropores and Mesopores..................................46

Atrazine and Deethylatrazine Transformation Rates ...........................................................48

Atrazine and Deethylatrazine Transformation in the Hydrologic System................48

Atrazine and Deethylatrazine Disappearance Rates in Soil......................................51

Atrazine Disappearance Rates.......................................................................................51

Photochemical Transformations ...........................................................................51

Thermal Transformations in Soil............................................................................51

Initial Disappearance Rates and Temperature Variations................................52

Influence of Soil Moisture on Disappearance Rates (Phase 2 only) ..............55

Deethylatrazine Production from Atrazine Reaction in Soil (Phase 2 only)............56

Deethylatrazine Disappearance Rate in Soil (Phase 2 only).....................................56

Atrazine and Deethylatrazine Disappearance Rate Adjustments with Depth 

Below Land Surface ...........................................................................................59

Formation, Transport, and Fate of Nitrate 

(Phase 2 only)................................................................................................................59

Model Formulation and Output.........................................................................................................61

Soil Column Discretization .......................................................................................................61

Phase 1 ...............................................................................................................................61

Phase 2 ...............................................................................................................................61

Quantifying Solute Concentrations from Model Output ......................................................61

Accounting for the Potential Effects of Corn and-Soybean Rotations on Agrichemical 

Occurrence.............................................................................................................................62
v

Contents

P-GWAVA Simulation and Groundwater Sampling Results..................................................................63

Phase 1 (P-GWAVA-PR).....................................................................................................................64

Soil Column Discretization for Simulations ...........................................................................64

Effect of Spatial Variability of Atrazine Application Intensities on Simulation Results..66

Effect of Transformation Site (Dissolved versus Sorbed State) on Simulation Results.68

Approaches for Distributing Irrigation ...................................................................................70

Spatial Averaging of Soil Parameter Values .........................................................................73

Temporal Variability of Atrazine Concentrations and Water Flow.....................................78

Modeling Approaches and Simulation Results ....................................................................78

Modeling Approach for Final P-GWAVA-PR Simulations ..........................................78

Water Flow ........................................................................................................................81

Atrazine Concentrations and Detection Frequencies ................................................81

Geographic Distribution of Model Residuals ......................................................84

Statistical Correlations with Site Characteristics ..............................................84

Phase 2 (P-GWAVA-RZ) .....................................................................................................................88

Spatial and Temporal Patterns of Material Transport .........................................................88

Water Flow.........................................................................................................................88

Solute Transport................................................................................................................90

Solute Distributions Among Chemical Forms and Environmental Media.........................93

Atrazine and Deethylatrazine .........................................................................................93

Nitrogen..............................................................................................................................97

Model Residuals and Water-Table Depths............................................................................99

Atrazine and Deethylatrazine .........................................................................................99

Nitrate 102

Agreement Between Simulated and Observed Solute Concentrations .........................102

Atrazine.............................................................................................................................102

Simulated Atrazine Concentrations Compared with Concentrations 

Measured in Groundwater .....................................................................102

Simulated Frequencies of Atrazine Detection Compared with Measured 

Values.........................................................................................................105

Comparison of Predictions from the P-GWAVA-PR and P-GWAVA-RZ 

Simulations................................................................................................107

Deethylatrazine ...............................................................................................................111

Comparisons of Atrazine and Deethylatrazine Concentrations Predicted by 

P-GWAVA-RZ with those from Stackelberg Regression Model................115

Deethylatrazine Fractions .............................................................................................115

Nitrate 115

Influence of Corn-and-Soybean Rotations on Model Residuals .....................................121

Solute Detection Classifications ...........................................................................................124

Overall Frequencies of Simulated and Measured Solute Detection...............................127
vi

Contents—Continued

P-GWAVA Simulation and Groundwater Sampling Results

Phase 1 (P-GWAVA-PR)

Geographic Patterns of Agreement Between Simulated and Measured 

Frequencies of Detection..........................................................................................130

Statistical Correlations with Site Characteristics ..............................................................133

Suggestions for Future Work ...................................................................................................................135

Summary and Conclusions.......................................................................................................................135

Acknowledgments.....................................................................................................................................139

References Cited........................................................................................................................................140

Glossary.......................................................................................................................................................181

Appendix A. Previous Assessments of Groundwater Vulnerability.................................................187

Appendix B. Equations Used to Estimate Deethylatrazine Formation Percentage .......................202

Appendix C. Groundwater Sampling Networks Examined ................................................................206

Figures

1. Map showing locations of Phase 1 (P-GWAVA-PR) sites and the distribution of

atrazine application intensity across the conterminous United States during 1997 .........8

2. Map showing locations of Phase 2 (P-GWAVA-RZ) sites and the distribution of

atrazine application intensity across the 10 northernmost states in the Corn Belt of

the United States during 1997.....................................................................................................9

3. Map showing locations of Phase 2 (P-GWAVA-RZ) sites and the distribution of

nitrogen fertilizer application across the 10 northernmost states in the Corn Belt of

the United States during 2001...................................................................................................10

4. Graphs showing temporal variations in the routine reporting limits used for

atrazine, deethylatrazine, and nitrate at the 453 sites examined for Phase 2 of this

study and sampled in the 10 northernmost states in the Corn Belt of the United

States, 1992–2006........................................................................................................................13

5. Schematic diagram showing primary features of the P-GWAVA-PR (Phase 1)

system used to simulate the transport and fate of atrazine in the vadose zone 

using the Pesticide Root-Zone Model.....................................................................................14

6. Schematic diagram showing primary features of the P-GWAVA-RZ (Phase 2)

system used to simulate the transport and fate of atrazine, deethylatrazine, and

nitrate in the vadose zone using the Root-Zone Water-Quality Model .............................15

7. Diagram showing steps used with the P-GWAVA-PR (Phase 1) system to obtain

spatially weighted averages of individual soil parameters for each State Soil

Geographic database map unit, using soil organic matter as an example.......................20

8. Graph showing variations in soil organic carbon content among sites in the

groundwater networks examined in the conterminous United States for Phase 1 

of this study, 1992–98..................................................................................................................21

9. Diagram showing sources of data for several P-GWAVA-RZ (Phase 2) input

parameters related to soil properties, chemical properties, weather, and other

site-specific variables................................................................................................................23
vii

Figures—Continued

10. Flow chart showing procedure used to select the macroporosity value assigned 

to every soil horizon where soil texture, organic matter and depth data were 

available in the Soil Survey Geographic database for the sites examined in the 

10 northernmost states of the Corn Belt of the United States during Phase 2 of 

this study ......................................................................................................................................25

11. Generalized diagram showing variations in soil water content with depth in the

vadose zone.................................................................................................................................26

12. Graph showing comparison between estimates of field capacity (FC) at the

P-GWAVA-PR (Phase 1) study sites using the Rawls and Brakensiek (1983)

equation (R&B), estimates for the same sites from Rosetta, and the range of 

values typically measured in soils in the United States.......................................................27

13. Graph showing comparison between estimates of wilting point (WP) at the

P-GWAVA-PR (Phase 1) study sites using the Rawls and Brakensiek (1983)

equation (R&B), estimates for the same sites from Rosetta, and the range of values

typically measured in soils in the United States....................................................................28

14. Graph showing comparison between groundwater temperatures measured 

at each of the 1,224 P-GWAVA-PR (Phase 1) study sites examined across the 

conterminous United States and the average air temperature for May in the 

same locations from the National Oceanic and Atmospheric Administration (2010) .....32

15. Map showing average daily air temperature during May from 1971 to 2000 

and the P-GWAVA-RZ (Phase 2) study sites in the 10 northernmost states in the 

Corn Belt of the United States ..................................................................................................34

16. Boxplot showing average depths to water at the sites examined in the

conterminous United States (Phase 1) and in the 10 northernmost states in the 

Corn Belt (Phase 2) of the United States for this study, relative to the assessment 

depths used for the P-GWAVA simulations during each study phase...............................35

17. Map showing the prevalence of irrigated agricultural areas near each

P-GWAVA-PR (Phase 1) study site in the conterminous United States in 1992 ...............36

18. Boxplot showing variations in atrazine use intensity on agricultural land among

sites in the groundwater networks examined in the conterminous United States 

for Phase 1 of this study, 1992–98 ............................................................................................40

19. Graphs showing apparent diffusion coefficient for the exchange of atrazine

between micropores and mesopores compared with clay content (percent clay),

and cation exchange capacity normalized to clay content for 1,657 soil horizons 

at P-GWAVA-RZ (Phase 2) study sites in the 10 northernmost states of the 

Corn Belt.......................................................................................................................................49

20. Diagram showing the reactions producing the atrazine transformation products

that have been examined most extensively in the hydrologic system .............................50

21. Schematic diagram showing processes and storage compartments for nitrogen

cycling through water, soil, and biota in agricultural systems ...........................................60

22. Graphs showing effect of model grid discretization in the 10–100 centimeter depth

interval on the degree of agreement between atrazine concentrations simulated at

an assessment depth of 100 centimeters (1 meter) by the P-GWAVA-PR (Phase 1)

model and measured in shallow groundwater at 331 sites in unirrigated agricultural

areas in the conterminous United States with average depths to water greater than

1 meter..........................................................................................................................................65
viii

Figures—Continued

23. Graph showing effect of spatial variability of atrazine application intensities 

on model residuals for atrazine concentrations measured in shallow groundwater 

beneath unirrigated agricultural areas where the average depth to water was 

greater than 1 meter in the P-GWAVA-PR (Phase 1) study area in the conterminous 

United States, 1992–98...............................................................................................................67

24. Graphs showing effect of the location of simulated atrazine transformation on

model residuals for atrazine concentrations in shallow groundwater beneath

unirrigated agricultural areas where the average depth to water was greater than

1 meter in the P-GWAVA-PR (Phase 1) study area in the conterminous United

States, 1992–98............................................................................................................................69

25. Graph showing amounts of atrazine predicted in sorbed and dissolved states in 

soil at the end of a 5-year P-GWAVA-PR (Phase 1) simulation in an agricultural 

area (site 2086) in Grant County, Washington, 1991–95........................................................70

26. Graph showing effect of irrigation simulation approach on model residuals for

atrazine concentrations in shallow groundwater beneath agricultural areas 

where the average depth to water was greater than 1 meter in the P-GWAVA-PR 

(Phase 1) study area in the conterminous United States, 1992–98 ....................................71

27. Maps showing effect of irrigation simulation approach on P-GWAVA-PR model

residuals for frequencies of atrazine detection in groundwater networks with 10 

or more sites when irrigation was not simulated at any sites and irrigation was

simulated at all sites across the conterminous United States for Phase 1 of this

study, 1992–98..............................................................................................................................72

28. Graphs and map showing influence of spatial averaging of soil parameter values

on atrazine chemographs simulated at a depth of 153 centimeters in the vadose

zone by the P-GWAVA-PR (Phase 1) model using spatially averaged State Soil

Geographic database values and the original Soil Survey Geographic database

values for soil parameters at four agricultural sites in central Washington State,

1986–95 .........................................................................................................................................74

29. Graph showing influence of spatial averaging of soil parameter values on the

extent of agreement between atrazine concentrations predicted at a depth of 

153 centimeters in the vadose zone by a set of 10-year simulations using the

P-GWAVA-PR model (1986–1995) and atrazine concentrations measured in 

shallow groundwater at four irrigated agricultural sites in central Washington 

State, 1992–95..............................................................................................................................75

30. Maps showing locations where atrazine concentrations predicted by the

P-GWAVA-PR (Phase 1) simulations were compared with atrazine concentrations

measured in shallow groundwater at six unirrigated agricultural sites in an

agricultural area of Portage County, Wisconsin ...................................................................76

31. Graph showing influence of spatial averaging of soil parameter values on the

extent of agreement between atrazine concentrations predicted at a depth of 

153 centimeters in the vadose zone by a set of 10-year P-GWAVA-PR simulations

(1986–1995) and atrazine concentrations measured in shallow groundwater at six

unirrigated agricultural sites in Portage County, Wisconsin, 1992–95 ..............................77

32. Time series graphs showing temporal variability in measured precipitation, and in

the infiltration and atrazine concentrations simulated at an assessment depth of

153 centimeters using the P-GWAVA-PR (Phase 1) model at site 2233 in an

unirrigated agricultural area of Portage County, Wisconsin, during the 1986–95

simulation period ........................................................................................................................79
ix

Figures—Continued

33. Boxplots showing influence of different modeling approaches on the P-GWAVA-PR

model residuals for atrazine concentrations at all of the Phase 1 study sites in

agricultural areas of the conterminous United States, 1992–98.........................................80

34. Graph showing cumulative distributions of model residuals for atrazine

concentrations at Phase 1 study sites for the final set of P-GWAVA-PR 

simulations in agricultural areas of the conterminous United States, 1992–98 ...............81

35. Graph showing mean annual groundwater recharge rates simulated by the

P-GWAVA-PR (Phase 1) model with no irrigation, and by the base-flow index

method excluding irrigation, at all of the unirrigated agricultural sites in the

conterminous United States where the depth to water was known .................................82

36. Graph showing atrazine concentrations simulated in the vadose zone by the

P-GWAVA-PR system and concentrations measured in groundwater at Phase 1

sites in the conterminous United States, 1992–98.................................................................83

37. Graphs showing frequencies of atrazine detection simulated at a depth of 1 meter

in the vadose zone using the final set of P-GWAVA-PR (Phase 1) simulations and

the measured frequencies of atrazine detection in shallow groundwater networks

in agricultural areas of the conterminous United States, 1992–98.....................................85

38. Map showing distribution of P-GWAVA-PR (Phase 1) model residuals for atrazine

detection frequencies in 47 groundwater networks in agricultural areas of the

conterminous United States, 1992–98 .....................................................................................86

39. Graph showing cumulative distributions of P-GWAVA-PR model residuals for

atrazine concentrations at Phase 1 study sites in agricultural areas of the Corn 

Belt and western, southeastern, and northeastern regions of the United States...........87

40. Graphs showing temporal patterns of water fluxes estimated for precipitation and

irrigation, deep seepage and runoff, and crop transpiration and evaporation at a

site in south-central Wisconsin during the P-GWAVA-RZ (Phase 2) simulation

period, 1998–2004........................................................................................................................89

41. Table showing vertical variations of soil texture (from Soil Survey Geographic

database) and graphs showing vertical variations of simulated soil water content,

pressure head, total atrazine mass, total deethylatrazine mass, and nitrate

concentration at a site in south-central Wisconsin on the last day of the 7-year

P-GWAVA-RZ (Phase 2) simulation period (1998–2004) .......................................................91

42. Graph showing average annual estimated groundwater recharge rates simulated

at the 3-meter assessment depth by the P-GWAVA-RZ (Phase 2) model, and rates

estimated from the Base Flow Index method including irrigation withdrawals, in 

10 contiguous states in the Corn Belt of the United States .................................................92

43. Graphs showing variations in solute concentrations and water flow simulated

at the 3-meter assessment depth by the P-GWAVA-RZ (Phase 2) simulations for

atrazine concentration, deethylatrazine concentration, atrazine residue yield,

nitrate concentration, and deep seepage and runoffat Soil Survey Geographic

database site 21840 in south-central Wisconsin during the 7-year simulation

period, 1998–2004........................................................................................................................94

44. Pie charts showing distributions of atrazine and deethylatrazine, computed 

from the P-GWAVA-RZ (Phase 2) simulations, in water, soil, and plants after 

7 years of simulated annual atrazine applications at a rate of 2 kilograms of active 

ingredient per hectare per year to sites in Marquette County, Wisconsin , and 

Daviess County, Indiana ............................................................................................................96
x

Figures—Continued

45. Pie charts showing nitrogen distributions among primary environmental

compartments estimated by the P-GWAVA-RZ (Phase 2) simulations before and

after 7 years of nitrogen fertilizer applications, estimated inputs and losses of

nitrogen, and nitrate concentrations estimated in the vadose zone and measured

in shallow groundwater at study sites in Marquette County, Wisconsin and 

Daviess County, Indiana ............................................................................................................98

46. Graphs showing relations between P-GWAVA-RZ (Phase 2) model residuals for 

atrazine concentrations, deethylatrazine (DEA) concentrations, DEA fractions, and 

nitrate concentrations and the average depth of the water table at study sites in

the 10 northernmost states of the Corn Belt, 1992–2006 ....................................................100

47. Graphs showing cumulative distributions of P-GWAVA-RZ (Phase 2) model

residuals for atrazine concentrations at agricultural sites in the 10 northernmost

states of the Corn Belt, 1992–2006 .........................................................................................103

48. Graph showing atrazine concentrations simulated by the P-GWAVA-RZ 

(Phase 2) model at the 3-meter assessment depth in the vadose zone and 

measured concentrations in shallow groundwater at agricultural sites in the 

10 northernmost states of the Corn Belt, 1992–2006...........................................................104

49. Graphs showing frequencies of atrazine detection simulated at a depth of 3 meters

in the vadose zone using the P-GWAVA-RZ (Phase 2) model and the measured

frequencies of atrazine detection in shallow groundwater networks in agricultural

areas of the 10 northernmost states of the Corn Belt, 1992–2006 ....................................106

50. Graph showing cumulative distributions of model residuals for atrazine

concentrations simulated by P-GWAVA-PR (Phase 1, 1992–98) and P-GWAVA-RZ

(Phase 2, 1992–2006) at 136 study sites where simulations were done with both

models and the depth to water was greater than 3 meters in agricultural areas of

the 10 northernmost states in the Corn Belt.........................................................................108

51. Boxplots showing statistical distributions of model residuals for atrazine

concentrations from selected P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2)

scenarios at agricultural sites in the conterminous United States (Phase 1,

1992–1998) and the 10 northernmost states of the Corn Belt (Phase 2, 1992–2006)......110

52. Graph showing average annual precipitation at 338 of the agricultural sites

examined during one or both phases of this study in the 10 northernmost states 

of the Corn Belt, 1990–2007 .....................................................................................................111

53. Graphs showing cumulative distributions of P-GWAVA-RZ (Phase 2) model

residuals for deethylatrazine concentration at agricultural sites in the 10

northernmost states of the Corn Belt, 1992–2006................................................................112

54. Graphs showing frequencies of deethylatrazine (DEA) detection simulated at a

depth of 3 meters in the vadose zone by the P-GWAVA-RZ (Phase 2) model and

the measured frequencies of DEA detection in shallow groundwater networks in

agricultural areas of the 10 northernmost states of the Corn Belt, 1992–2006...............113

55. Graph showing deethylatrazine concentrations simulated by the P-GWAVA-RZ

(Phase 2) model at the 3-meter assessment depth in the vadose zone and

measured concentrations in shallow groundwater at agricultural sites in the 10

northernmost states of the Corn Belt, 1992–2006................................................................114

56. Graph showing cumulative distributions of P-GWAVA-RZ (Phase 2) model residual

for the deethylatrazine fraction at agricultural sites in the 10 northernmost states in

the Corn Belt, 1992–2006..........................................................................................................116
xi

Figures—Continued

57. Graph showing deethylatrazine fractions simulated by the P-GWAVA-RZ (Phase 2)

model at the 3-meter assessment depth in the vadose zone and values measured

in shallow groundwater at the same locations in agricultural areas of the 10

northernmost states in the Corn Belt, 1992–2006 ................................................................117

58. Graph showing cumulative distributions of P-GWAVA-RZ (Phase 2) model 

residuals for nitrate concentrations at agricultural sites in the 10 northernmost 

states in the Corn Belt, 1992–2006 .........................................................................................118

59. Graph showing nitrate concentrations simulated by the P-GWAVA-RZ (Phase 2) 

model at the 3-meter assessment depth in the vadose zone and measured 

concentrations in shallow groundwater at agricultural sites in the 10 northernmost 

states of the Corn Belt, 1992–2006 .........................................................................................119

60. Graphs showing frequencies of nitrate detection simulated by the P-GWAVA-RZ

(Phase 2) model at the 3-meter assessment depth in the vadose zone and

frequencies of nitrate detection measured in shallow groundwater networks in

agricultural areas of the 10 northernmost states of the Corn Belt, 1992–2006...............120

61. Graphs showing the P-GWAVA-RZ model residuals for atrazine concentrations,

deethylatrazine (DEA) concentrations, DEA fractions, and nitrate concentrations,

1992–2006, and average annual percentage of corn-and-soybean area in corn

cultivation in the surrounding county during 1995–2005 for all Phase 2 sites where

the depth to water was greater than the 3-meter assessment depth in the 10

northernmost states of the Corn Belt....................................................................................122

62. Graph showing relations between solute detection classifications and different

combinations of simulated and measured detections and non-detections for a

hypothetical solute ...................................................................................................................125

63. Graphs showing accuracy of solute detection classifications from the Phase 1

(P-GWAVA-PR) simulations for the conterminous United States, 1991–95 and the

Phase 2 (P-GWAVA-RZ) simulations for the 10 northernmost states in the Corn 

Belt, 2000–2004..........................................................................................................................126

64. Graphs showing overall frequencies of atrazine, deethylatrazine, and nitrate

detection predicted in the vadose zone by the final set of P-GWAVA-PR (Phase 1)

and P-GWAVA-RZ (Phase 2) simulations and measured in shallow groundwater in

agricultural areas of the conterminous United States (Phase 1) or the 10

northernmost states of the Corn Belt (Phase 2), using a fixed reporting limit for the

Phase 1 results and temporally varying reporting limits for the Phase 2 results...........128

65. Graphs showing overall frequencies of atrazine, deethylatrazine, and nitrate

detection predicted in the vadose zone by the final set of P-GWAVA-PR (Phase 1)

and P-GWAVA-RZ (Phase 2) simulations and measured in shallow groundwater in

agricultural areas of the conterminous United States (Phase 1) or the 10

northernmost states of the Corn Belt (Phase 2), using fixed reporting limits for the

results from both phases.........................................................................................................129

66. Map showing model residuals for the frequencies of atrazine detection calculated

from the P-GWAVA-RZ (Phase 2) model simulations and sampling results for the 

17 groundwater sampling networks with 10 or more sites in the 10 northernmost

states of the Corn Belt of the United States, 1992–2006.....................................................130

67. Map showing model residuals for the frequencies of deethylatrazine detection

calculated from the P-GWAVA-RZ (Phase 2) model simulations and sampling

results for the 17 groundwater sampling networks with 10 or more sites in the 10

northernmost states of the Corn Belt of the United States, 1992–2006 ...........................131
xii

Figures—Continued

68. Map showing model residuals for the frequencies of nitrate detection (calculated

from the P-GWAVA-RZ (Phase 2) model simulations and sampling results for the 

17 groundwater sampling networks with 10 or more sites in the 10 northernmost

states of the Corn Belt of the United States, 1992–2006.....................................................132

Tables

1. Distinguishing features of the two phases of the process-based groundwater

vulnerability assessment (P-GWAVA) system study of agrichemicals in

groundwater of the conterminous United States. .................................................................6

2. Principal characteristics of groundwater networks examined by the U.S.

Geological Survey National Water-Quality Assessment (NAWQA) program. ...................7

3. Ranges of Brooks-Corey parameter values reported for 11 U.S. Department of

Agriculture soil texture classes, and ranges estimated from van Genuchten

parameters using Rosetta .........................................................................................................30

4. Approaches selected to improve agreement between simulated and measured

solute concentrations for the final set of P-GWAVA-PR (Phase 1) simulations

across the conterminous United States, and for the final set of P-GWAVA-RZ

(Phase 2) simulations in the 10 northernmost states of the Corn Belt...............................31

5. Effects of model grid discretization in the 10–100 centimeter depth interval on the

level of agreement between atrazine concentrations simulated at the

100-centimeter (1 meter) assessment depth in the vadose zone using P-GWAVA-PR

(Phase 1) and those measured in shallow groundwater at 331 unirrigated

agricultural sites where the average depth to the water table was greater than 

1 meter in the conterminous United States, 1992–98. .........................................................66

6. Effects of 15 modeling approaches on the extent of agreement between atrazine

concentrations predicted at the 1-meter assessment depth in the vadose zone by

the P-GWAVA-PR (Phase 1) simulations and concentrations measured in shallow

groundwater in agricultural areas where the depth to water exceeded 1 meter in

the conterminous United States, 1992–98. ...........................................................................68

7. Correlations between atrazine concentrations simulated by the P-GWAVA-PR

(Phase 1) model at the 1-meter assessment depth in the vadose zone and

concentrations measured in shallow groundwater at the same locations in

agricultural areas of the conterminous United States, 1992–98. ......................................82

8. Correlations between the P-GWAVA-PR (Phase 1) model residuals for atrazine

detection frequencies in shallow groundwater in 47 groundwater networks in

agricultural areas of the conterminous United States, 1992–98 (1,149 sites), and

selected site characteristics. .................................................................................................88
xiii

Tables—Continued

9. Correlations between average depths to water and P-GWAVA model residuals for

atrazine concentrations, deethylatrazine (DEA) concentrations, DEA fractions, and

nitrate concentrations in agricultural areas of the United States....................................101

10. Agreement between atrazine, deethylatrazine (DEA), or nitrate concentrations (or

DEA fractions) simulated at the 3-meter assessment depth in the vadose zone by

the P-GWAVA-RZ (Phase 2) model and the concentrations (or DEA fractions)

measured in shallow groundwater at agricultural sites in the 10 northernmost

states of the Corn Belt, 1992–2006 .........................................................................................105

11. Correlations between simulated and measured frequencies of detection of

atrazine, deethylatrazine, and nitrate in shallow groundwater sampling networks

with 10 or more sites. .............................................................................................................107

12. Correlations between P-GWAVA-RZ model residuals for atrazine concentrations,

deethylatrazine (DEA) concentrations, DEA fractions, and nitrate concentrations,

1992–2006, and average annual percentages of corn-and-soybean area in corn

cultivation in the surrounding county during 1995–2005 for all Phase 2 sites where

the depth to water was greater than the 3-meter assessment depth in the 10

northernmost states of the Corn Belt. .................................................................................121

13. Correlations between model residuals for the detection frequencies of atrazine,

deethylatrazine, and nitrate in shallow groundwater simulated by the P-GWAVA-RZ

(Phase 2) model and selected site characteristics in the 10 northernmost states of

the Corn Belt, 1992–2006. .......................................................................................................134

14. Approaches used to obtain or estimate input parameter values for the simulations

conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling

systems.......................................................................................................................................157
xiv

Conversion Factors

[Inch/pound to International System of Units]

Multiply By To obtain

Area

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Flow rate

inch per hour (in/h) 0 .0254 meter per hour (m/h)

Pressure

bar 100 kilopascal (kPa) 

[International System of Units to inch/pound]

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2

)

square kilometer (km2

) 247.1 acre

square kilometer (km2

) 0.3861 square mile (mi2)

Flow rate

centimeter per day (cm/d) 0.3937 inch per day (in/d)

centimeter per year (cm/yr) 0.3937 inch per year (in/yr) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

Pressure

kilopascal (kPa) 0.01 bar

Hydraulic conductivity

centimeter per hour (cm/h) 0.7874 foot per day (ft/d) 

Density

gram per cubic centimeter (g/cm3

) 62.4220 pound per cubic foot (lb/ft3) 

Activation energy

kilojoule per mole (kJ/mol) 0.2390 kilocalorie per mole (kcal/mol)

Application rate

kilogram of active ingredient per 

hectare (kg a.i./ha)

0.8921 pound of active ingredient per acre 

(lb a.i./acre)
xv

Conversion Factors

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

°F=(1.8× °C) +3 2.

Temperature in degrees Celsius (°C) may be converted to absolute temperature, in Kelvin (K), as 

follows:

K=°C+ 273.15.

Supplemental Information

Concentrations of chemical constituents in water are given either in micrograms per liter (μg/L) 

or, for nitrate, in milligrams per liter as nitrogen (mg-N/L).

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Abbreviations

ACT agricultural chemicals team 

a.i. active ingredient

AWC available water capacity

BFI base-flow index

CEC cation-exchange capacity

CF crust factor

CFC chlorofluorocarbon (compound)

CI confidence interval

DEA deethylatrazine

DTW depth to water

EPA U.S. Environmental Protection Agency

eSW extended Shuttleworth-Wallace (method used to estimate evapotranspiration rate)

ET evapotranspiration

FC field capacity

FPS flow-path study (NAWQA groundwater study component)

FWM flow-weighted mean 

GIS geographic information system

GWAVA groundwater vulnerability assessment system
xvi

Abbreviations

LUS land-use study (NAWQA groundwater study component)

NATSGO National Soil Geographic database (NRCS)

NAWQA National Water-Quality Assessment (Program)

NCDC National Climatic Data Center (NOAA)

NLCD National Land-Cover Database

NOAA National Oceanic and Atmospheric Administration (U.S. Department of Commerce)

NRCS Natural Resources Conservation Service (USDA)

NRI National Resources Inventory (USDA)

OM organic matter

PAS principal aquifer study (NAWQA groundwater study component)

PCA principal components analysis

PET potential evapotranspiration

P-GWAVA process-based groundwater vulnerability assessment system

P-GWAVA-PR process-based groundwater vulnerability assessment system using PRZM to 

simulate the transport and fate of water and the solute(s) of interest

P-GWAVA-RZ process-based groundwater vulnerability assessment system using RZWQM to 

simulate the transport and fate of water and the solute(s) of interest

PM equation Penman-Monteith equation (used to estimate potential evapotranspiration rate)

PRISM Parameter-elevation Relationships on Independent Slopes Model

PRZM Pesticide Root-Zone Model

PTF pedotransfer function

QMRR quantitative medium-reactivity relation

QPPR quantitative property-property relation

QSPR quantitative structure-property relation

QSRR quantitative structure-reactivity relation

RCN runoff curve number

R2 Pearson coefficient of determination

RZWQM Root-Zone Water-Quality Model

SQL Structured Query Language

SSURGO Soil Survey Geographic database (NRCS)

STATSGO State Soil Geographic database (NRCS)

SUS study-unit survey (NAWQA groundwater study component)

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

v/v designation for a soil-related parameter expressed as volume of material per unit 

volume of soil

w/w designation for a soil-related parameter expressed as weight of material per unit 

weight of soil
Design and Testing of a Process-Based Groundwater 

Vulnerability Assessment (P-GWAVA) System for 

Predicting Concentrations of Agrichemicals in 

Groundwater Across the United States

By Jack E. Barbash and Frank D. Voss

Abstract

Efforts to assess the likelihood of groundwater 

contamination from surface-derived compounds have spanned 

more than three decades. Relatively few of these assessments, 

however, have involved the use of process-based simulations 

of contaminant transport and fate in the subsurface, or 

compared the predictions from such models with measured 

data—especially over regional to national scales. To address 

this need, a process-based groundwater vulnerability 

assessment (P-GWAVA) system was constructed to use 

transport-and-fate simulations to predict the concentration 

of any surface-derived compound at a specified depth in the 

vadose zone anywhere in the conterminous United States. 

The system was then used to simulate the concentrations of 

selected agrichemicals in the vadose zone beneath agricultural 

areas in multiple locations across the conterminous United 

States. The simulated concentrations were compared with 

measured concentrations of the compounds detected in 

shallow groundwater (that is, groundwater drawn from within 

a depth of 6.3 ± 0.5 meters [mean ± 95 percent confidence 

interval] below the water table) in more than 1,400 locations 

across the United States. The results from these comparisons 

were used to select the simulation approaches that led 

to the closest agreement between the simulated and the 

measured concentrations. 

The P-GWAVA system uses computer simulations that 

account for a broader range of the hydrologic, physical, 

biological and chemical phenomena known to control the 

transport and fate of solutes in the subsurface than has 

been accounted for by any other vulnerability assessment 

over regional to national scales. Such phenomena include 

preferential transport and the influences of temperature, 

soil properties, and depth on the partitioning, transport, and 

transformation of pesticides in the subsurface. Published 

methods and detailed soil property data are used to estimate a 

wide range of model input parameters for each site, including 

surface albedo, surface crust permeability, soil water content, 

Brooks-Corey parameters, saturated hydraulic conductivity, 

macroporosity and sizes of microbial populations, as well 

as solute partition coefficients, reaction rates, and mesomicropore diffusion rates. To ensure geographic consistency 

among the predictions, the only site-specific input data 

that are used are those that are available for all of the 

48 conterminous states. 

The investigation was carried out in two phases. For 

Phase 1, the Pesticide Root-Zone Model (PRZM) was used 

in the P-GWAVA system (designated as P-GWAVA-PR) to 

simulate the concentrations of atrazine at an assessment depth 

of 1 meter in the vadose zone beneath 1,224 agricultural 

sites across the United States. During Phase 2, the RootZone Water-Quality Model (RZWQM) was substituted for 

PRZM and used in the P-GWAVA system (designated as 

P-GWAVA-RZ) to simulate the concentrations of atrazine, 

the atrazine degradate deethylatrazine (DEA), and nitrate at 

an assessment depth of 3 meters in the vadose zone beneath 

453 agricultural sites across the 10 northernmost states 

of the corn-growing region of the midcontinent (the Corn 

Belt). Some aspects of the overall modeling approaches 

were modified to improve the fit between the simulated 

and measured concentrations. However, because the 

P-GWAVA system was designed to predict concentrations 

in millions of locations across the conterminous United 

States, the final simulations were completed without any 

site-specific calibration.

For both study phases, significant, positive Spearman 

rank correlations (P[ρ] less than or equal to [≤] 0.0001) were 

noted between the atrazine concentrations predicted by the 

final set of simulations and the concentrations measured in 

shallow groundwater. The model residuals for the atrazine 

concentrations (simulated minus measured values) among all 

sites exhibited 95th percentile values of 0.39 microgram per 

liter (μg/L) for the P-GWAVA-PR simulations and 0.87 μg/L 

for the P-GWAVA-RZ simulations—values that were an order 

of magnitude less than the Maximum Contaminant Level 

of 3 μg/L established by the U.S. Environmental Protection 

Agency for atrazine. Atrazine concentrations simulated by the 

P-GWAVA-RZ model generally were in closer agreement with 
2 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

those measured in groundwater than were the concentrations 

simulated by the P-GWAVA-PR model, perhaps in part 

because the P-GWAVA-RZ simulations were carried out 

over a substantially smaller geographic area and focused on 

shallower wells. However, the model residuals of atrazine 

concentrations for the two models showed a weak, negative 

correlation with one another among the 136 sites in the Corn 

Belt where simulations were carried out using both models 

and the depth to water was greater than the larger of the two 

assessment depths (Spearman ρ = -0.17; P[ρ] = 0.04). This 

indicated that different factors were likely responsible for the 

errors associated with the two models. 

As noted for atrazine, the DEA concentrations simulated 

by the P-GWAVA-RZ model were significantly and positively 

correlated with the measured values (P[ρ] ≤ 0.0001; Spearman 

rank correlation), with the model residuals exhibiting a 

95th percentile value of 0.003 μg/L among all 453 sites 

examined. Additionally, the sum of the concentrations of 

atrazine and DEA simulated in the vadose zone was positively 

correlated with the sum of the concentrations of the two 

compounds predicted in shallow groundwater by a 2012 

Tobit regression model at 329 of the Phase 2 sites in the 

Corn Belt (P[ρ] ≤ 0.0001; Spearman rank correlation). The 

nitrate concentrations simulated by the P-GWAVA-RZ model, 

however, were not significantly correlated with the measured 

values (P[ρ] greater than [>] 0.05; Spearman rank correlation). 

Statistically significant, positive correlations were noted 

between the simulated and the measured detection frequencies 

for atrazine (R2 = 0.24; P = 0.046) and DEA (R2 = 0.56; 

P = 0.0006) among the 17 Phase 2 groundwater sampling 

networks with 10 or more wells, but not for atrazine among 

the 47 such networks examined during Phase 1, nor for nitrate 

among the Phase 2 networks. The P-GWAVA-PR simulations 

generated correct predictions with regard to atrazine detections 

(either true detects or true non-detects) at 50 percent of the 

1,224 Phase 1 sites, whereas the P-GWAVA-RZ simulations 

produced correct predictions for atrazine at 62 percent of the 

453 Phase 2 sites. The solute with the highest rate of correct 

predictions during either of the two study phases was DEA, 

for which the P-GWAVA-RZ simulations predicted a correct 

result at 70 percent of the Phase 2 sites. The P-GWAVA-RZ 

simulations predicted overall frequencies of atrazine and DEA 

detection (39 percent and 23 percent, respectively) that were 

within the ranges spanned by the results reported for the two 

compounds by several previous large-scale studies of pesticide 

occurrence in shallow groundwater beneath agricultural areas 

of the Corn Belt.

The model residuals for atrazine, DEA, and nitrate 

concentrations at individual sites were significantly correlated 

with the depth of the water table (P[ρ] less than [<] 0.05). 

The positive relations with the depth of the water table noted 

during both study phases for atrazine (increasing degree of 

overprediction with increasing water-table depth) and the 

negative relation noted for DEA during Phase 2 (increasing 

degree of underprediction with increasing water-table depth) 

were consistent with previous research indicating that the 

extent of conversion of atrazine to DEA in the subsurface—

an aerobic process—increases with increasing vadose-zone 

thickness. Similarly, the negative relation noted for nitrate 

indicated that the formation of nitrate from the oxidation of 

the more reduced forms of nitrogen that are applied to the 

land—also an aerobic process—is likely to occur to a greater 

extent in locations with deep water tables than in areas where 

the water table is closer to the land surface. 

Model residuals for the concentrations of all three 

solutes examined during Phase 2 were significantly correlated 

(P[ρ] < 0.0004) with the average percentage of nearby cornand-soybean area that was in corn cultivation—relative to 

soybeans—at the individual study sites during the simulation 

period (2000–2004). A negative correlation for nitrate (greater 

underprediction with increasing percentage of corn) was 

consistent with expectation because the simulations assumed 

that corn was grown at every site during every year of both 

study phases, that nitrogen fertilizers were applied more 

heavily to corn than to soybeans, and that nitrate behaved as 

an essentially conservative solute as it migrated through the 

well-aerated soils where corn is commonly grown. Although 

atrazine is not applied to soybeans, a positive correlation 

(increasing overprediction with increasing percentage of 

corn) was noted for the herbicide, in contrast with the pattern 

noted for nitrate. This suggested that in areas where atrazine 

is applied in multiple years, microbial adaptation (a wellknown phenomenon in which the rate of biotransformation 

of some compounds increases with repeated applications) 

exerts greater influence over the amount of atrazine that 

reaches groundwater than the total amount applied. A negative 

correlation was observed between the model residuals for 

DEA concentrations and the percentage of nearby land in corn 

cultivation, consistent with the positive correlation noted for 

atrazine. 

Correlations were examined between the model residuals 

for the frequencies of detection of all three compounds and 

various network-averaged variables related to soil properties, 

climate and, for atrazine and DEA, the intensity of atrazine 

use. No statistically significant relations with any of the 

explanatory variables were observed for nitrate (P > 0.05; 

Pearson and Spearman [rank] correlations), but relations with 

several variables were significant for atrazine and DEA. The 

patterns of correlation with the soil-related variables indicated 

that the P-GWAVA-PR (Phase 1) simulations underemphasized 

the effect of hydraulic conductivity on atrazine transport to 

the water table, but exaggerated the effect of recharge. By 

contrast, the patterns of correlation during Phase 2 indicated 

that the P-GWAVA-RZ (Phase 2) simulations exaggerated 

the effects of hydraulic conductivity on the transport of 

atrazine and DEA to the water table. Although a statistically 

significant, positive correlation was evident between the 

mean annual air temperature and the model residuals for 

atrazine detection frequencies derived from the P-GWAVA-PR 

simulations, no significant correlations were noted between 

the average temperature in May and the model residuals for 

atrazine or DEA detection frequencies from the P-GWAVA-RZ 
Introduction 3

simulations. These observations suggest that the activation 

energy value used for the rate of atrazine disappearance 

during Phase 1 may have been too low, but that the higher 

value specified for atrazine and DEA for the P-GWAVA-RZ 

simulations adequately accounted for the effect of temperature 

on the rates of disappearance of both compounds. No 

significant correlations between the intensity of atrazine use 

and the model residuals for atrazine detection frequencies 

were noted during either study phase, and only a weak 

correlation was noted with the residuals for the frequencies 

of DEA detection. This indicated that the approaches used to 

quantify the intensities of atrazine use adequately accounted 

for the effects of atrazine use on the frequencies of detection 

of the two solutes during this study, and that factors other than 

the intensity of atrazine use are likely to be more important in 

controlling spatial patterns of atrazine and DEA occurrence in 

groundwater. 

Introduction

One of the most persuasive ways to demonstrate an 

understanding of a particular phenomenon is to simulate its 

occurrence and consequences with an acceptable degree of 

accuracy over a sufficiently wide range of circumstances. 

Thus, one of the main goals of contaminant hydrogeology is to 

produce accurate predictions of the concentrations of surfacederived solutes in groundwater. In pursuit of this goal, the 

present study involved developing and testing a process-based 

groundwater vulnerability assessment (P-GWAVA) system 

capable of predicting the concentrations of any surface-derived 

compound in the vadose zone anywhere in the conterminous 

United States. Groundwater vulnerability is defined herein 

as the likelihood of detecting a particular surface-derived 

contaminant at a concentration equal to or greater than a 

given value, such as its reporting limit or a water-quality 

criterion, in shallow groundwater. Shallow groundwater, in 

turn, is defined as water that is located at a relatively short 

distance below the water table; for this report, this consisted 

of water drawn from within a depth of 6.3 ± 0.5 m below the 

water table. (All confidence intervals [CIs] in this report are 

expressed as 95 percent CIs for the mean.) This study had four 

primary objectives: 

1. Develop a set of parameter estimation methods and 

models (the P-GWAVA system) that can predict 

the concentrations of agrichemicals and their 

transformation products at a specified depth in the 

vadose zone beneath any location in the conterminous 

United States by simulating the most important 

physical, hydrologic, biological, and chemical 

phenomena known to control the transport and fate 

of these compounds in the subsurface. (Throughout 

this report, the term subsurface is used to refer to all 

regions below the land surface, including the vadose 

and saturated zones.)

2. To the extent possible, use data with the finest spatial 

resolution available on a nationwide basis for the 

model input parameters. 

3. Assess the accuracy of the simulated concentrations 

by comparing them with the concentrations measured 

in shallow groundwater beneath agricultural areas 

of the United States, especially the 10 northernmost 

states of the corn-growing areas of the midcontinent 

(the Corn Belt). 

4. Use the results from these comparisons to 

select the simulation approaches that lead to the 

closest agreement between the simulated and 

measured concentrations.

Previous Assessments of Groundwater 

Vulnerability

Numerous groundwater vulnerability assessments 

(GWAVAs) have been carried out over the past three decades. 

An overview of the approaches that have been used to 

conduct these assessments is presented in appendix A. The 

assessment of groundwater vulnerability requires that the 

collective influence of three types of controlling variables be 

accounted for: (1) anthropogenic factors related to land and 

chemical management that influence the types and loads of 

specific contaminants that are released at the land surface 

(either intentionally or inadvertently) and the timing of those 

releases; (2) climatic, edaphic, chemical, biological and 

hydrogeologic factors that control the transport and fate of 

surface-derived solutes in the subsurface; and (3) chemical 

properties of the contaminants that influence their distribution, 

mobility and persistence in the hydrologic system. These three 

types of variables may exhibit considerable interdependence. 

For example, the likelihood of detecting a pesticide in 

groundwater may depend on the timing of its application 

relative to the timing of subsequent recharge, a linkage that 

is generally more evident for reactive compounds than for 

those that are more persistent (Barbash and Resek, 1996). 

(For this report, a pesticide is defined as a chemical that is 

used to kill or otherwise control unwanted plants, insects, or 

other organisms.)

Assessments of groundwater vulnerability have typically 

focused on the manner in which contaminant concentrations 

are distributed among different compounds, by location, 

or over time. Whereas some GWAVAs have focused on 

ranking chemicals according to their detection frequencies or 

concentrations, most have been directed toward predicting the 

spatial distributions of occurrence of individual compounds 

(appendix A). Although previous research has shown that 

the likelihood of detecting pesticides in groundwater may 

vary seasonally (Barbash and Resek, 1996; Silva and others, 

2012), most efforts to predict temporal variations in pesticide 

concentrations beneath the land surface have been confined to 
4 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

numerical simulations (for example, Sauer and others, 1990; 

Pollock and others, 2002; Bayless and others, 2008; Webb and 

others, 2008), rather than statistical models. 

Most of the physical, hydrologic, chemical, and 

biological processes that control the transport and fate of 

surface-derived contaminants in the subsurface have been well 

known for at least three decades (Barbash and Resek, 1996). 

Thus, the primary constraints on the development of GWAVA 

methods have not come from limitations in the conceptual 

understanding of the fundamental processes governing the 

likelihood of groundwater contamination, but from various 

challenges associated with the implementation and evaluation 

of these methods (appendix A). In particular, the development 

of large-scale GWAVA systems has been constrained primarily 

by limitations in (1) the availability, consistency, and spatial 

resolution of many of the physical, hydrologic, chemical, 

and land-use data that most GWAVA systems require; (2) the 

computing speeds needed to carry out the calculations for 

these assessments; and (3) the subsurface monitoring data 

needed to evaluate the accuracy of the predictions. 

Previous approaches for predicting the vulnerability of 

groundwater to contamination from surface-derived solutes 

over large geographic areas have not explicitly accounted for 

preferential transport in the subsurface (Barbash and Resek, 

1996)—a key process known to exert considerable influence 

over the rates of water and solute transport through the vadose 

zone in many hydrogeologic settings. However, as part of 

a study to assess the accuracy with which water flow and 

agrichemical concentrations predicted in the subsurface by 

seven vadose-zone models reproduced measurements beneath 

two agricultural sites, Nolan and others (2005) included 

several models that simulated preferential transport. 

Purpose and Scope 

This investigation was carried out to develop and 

test a process-based groundwater vulnerability assessment 

(P-GWAVA) system that uses transport-and-fate simulations 

in conjunction with national-scale input data to predict the 

concentrations of surface-derived compounds at a specified 

depth in the vadose zone anywhere in the conterminous United 

States. This report describes the approach and procedures 

used to develop the P-GWAVA system, as well as the results 

from a study during which the P-GWAVA system was used 

to simulate the concentrations of selected agrichemicals 

in the vadose zone beneath agricultural areas at more than 

1,400 locations across the conterminous United States. The 

accuracy of the simulated concentrations was evaluated 

through comparison with the concentrations of the compounds 

measured in shallow groundwater in the same locations 

during the period from 1992 to 2006. The results from these 

comparisons were used to select the simulation approaches 

that led to the closest agreement between the predicted and the 

measured concentrations. 

Study Design

Design Objectives

The design of the P-GWAVA system was guided by four 

main objectives:

1. Develop the capability to predict the concentration of 

any surface-derived contaminant for which the rates 

of release into the environment are known—or to 

predict the concentrations of any of its transformation 

products—in the subsurface anywhere in the 

conterminous United States, by simulating the transport 

and fate of the compounds within the vadose zone.

Each of two one-dimensional (or stream-tube [Jacques 

and others, 1997]) vadose-zone models were linked to 

a geographic information system (GIS) to perform the 

following tasks for any location in the conterminous United 

States: (1) retrieve all required site-specific input data for 

that location; (2) generate an appropriate input file for the 

model; (3) simulate water flow, as well as the transport and 

fate of the compounds of interest, within the vadose zone; and 

(4) compute simulated concentrations at a specified depth in 

the vadose zone for all compounds of interest. The models 

considered for use in the P-GWAVA system were restricted 

to those capable of simulating solute transport and fate in 

the vadose zone because of the critical importance of this 

region in affecting the likelihood of detecting surface-derived 

contaminants in shallow groundwater. As with the work of 

Nolan and others (2005), this study used cold simulations, 

that is, model simulations conducted without calibration. This 

approach made it feasible to assess the accuracy with which 

existing process knowledge, models, and input data may be 

combined to simulate the concentrations of surface-derived 

contaminants in shallow groundwater at hundreds of 

locations across large geographic areas. Along with the 

nitrate metamodeling work of Nolan and others (2012), this 

investigation represents one of the first instances where the 

simulation of solute transport and fate in the subsurface has 

been used to predict the concentrations of agrichemicals in the 

vadose zone, and the simulated concentrations compared with 

measured concentrations of the compounds in the underlying 

groundwater, over large spatial scales (regional to national).
Study Design 5

2. Maximize the extent to which the P-GWAVA predictions 

of solute concentrations account for the major physical, 

hydrologic, chemical, and biological phenomena known 

to control the transport and fate of surface-derived 

contaminants in the subsurface. 

Following the approach of Nolan and others (2005), models 

were considered for use in the P-GWAVA system only if 

they could simulate a relatively broad range of the physical, 

hydrologic, chemical, and biological phenomena known to 

influence the transport and fate of solutes in the subsurface. 

As a result, the two models used in the P-GWAVA system 

were among the seven examined by Nolan and others (2005). 

The use of models that could account for the effects of a wide 

range of controlling factors and phenomena was consistent 

with a recommendation made by an advisory group to the 

U.S. Environmental Protection Agency (EPA) that “...

the development of screening protocols should be more 

consistent, and where possible use common models between 

screening levels and more sophisticated levels of analysis” 

(ILSI Risk Science Institute, 1998, p. 7). One potential 

disadvantage of accounting for so many processes, however, 

is that uncertainties in the values of the parameters used to 

quantify these processes may result in non-unique solutions 

and decrease the accuracy of the resulting predictions.

3. Use existing models, databases, and methods wherever 

possible—especially those that have undergone the most 

extensive testing and verification.

Both of the models used to conduct the simulations for this 

study have a long history of extensive use. In addition, both 

codes have been employed, and their predictions tested 

against field or laboratory observations, by numerous studies 

published in the scientific literature (for example, Jones and 

others, 1986; Barbash and Resek, 1996; Watts and others, 

1999; Jones and Mangels, 2002; Malone and others, 2004a). 

4. Use only national-scale, nationally consistent sources of 

all site-specific input data (for example, soil properties, 

pesticide use and weather) compiled at the finest spatial 

scale available. 

To maximize the degree of consistency among the predictions 

made in different locations, all site-specific input data were 

obtained from sources that were compiled for the entire 

conterminous United States using nationally uniform methods. 

If multiple national-scale sources of data were available for a 

given parameter (for example, soil properties or pesticide use), 

the source that provided the data at the finest spatial scale was 

the one used, to ensure that the simulations of water and solute 

transport and fate were as site-specific as possible.

Study Phases

This study was carried out in two phases (table 1). 

During Phase 1, the Pesticide Root-Zone Model (PRZM) 

was used in the P-GWAVA system for the solute transportand-fate simulations, and the resulting version of the system 

designated as P-GWAVA-PR. During Phase 2, to account for 

the influence of several additional processes that were not 

simulated by PRZM but are known to affect the transport 

and fate of water and solutes in the subsurface (for example, 

upward flow, transport through macropores, and exchange 

between macropores and the soil matrix), PRZM was replaced 

with the Root-Zone Water-Quality Model (RZWQM), and the 

resulting version of the system designated as P-GWAVA-RZ. 

During both study phases, the model simulations were used 

to predict the subsurface concentrations of atrazine, one of 

the pesticides detected most frequently in groundwater in the 

United States (Kolpin and others, 1993; Barbash and Resek, 

1996; Gilliom and others, 2006). However, the Phase 2 

simulations also focused on nitrate, one of the most commonly 

detected groundwater contaminants in the Nation (Hallberg, 

1989), and deethylatrazine (DEA), the atrazine transformation 

product that, like its parent compound, has been among the 

contaminants detected most frequently in groundwater beneath 

agricultural areas of the United States (Kolpin and others, 

1993; Barbash and Resek, 1996; Gilliom and others, 2006). 

Atrazine and nitrate also were of interest during this 

study because the spatial patterns of application of these 

chemicals in agricultural areas of the United States are 

known in considerable detail (Ruddy and others, 2006; 

U.S. Geological Survey, 2014). To facilitate comparisons 

between the concentrations simulated in the vadose zone 

and the concentrations measured in groundwater, all of the 

simulations were done in locations where recently recharged 

shallow groundwater was sampled for these compounds by 

the National Water-Quality Assessment (NAWQA) program 

of the U.S. Geological Survey (Gilliom and others, 1995). 

(Among the wells examined for this study, the average 

depth of the top of the open interval below the water table 

was 6.3 ± 0.5 m.) Because Phase 2 began after Phase 1 was 

completed, the solute concentrations predicted by the Phase 2 

simulations were compared with concentrations measured over 

a longer time interval than was the case for Phase 1 (table 1). 

Additional research results and ancillary data became available 

during the course of this investigation; therefore, different 

values and, in some cases, different estimation procedures 

were used for several model input parameters during the two 

phases of the study. 
6 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Methods

Study Site Selection and Characteristics

Locations of Study Sites Relative to National 

Water-Quality Assessment Groundwater 

Networks

This study examined sites from the NAWQA 

groundwater sampling networks in agricultural areas (one 

well per site). The groundwater component of the NAWQA 

program involves the sampling of several types of well 

networks spanning a wide range of spatial scales (table 2). 

Listed in order of increasing spatial scale, these include 

agricultural chemicals team (ACT) studies, flow path studies 

(FPS), land-use studies (LUS), study-unit surveys (SUS) and 

principal aquifer studies (PAS). Phase 1 of this investigation 

encompassed all the agricultural LUS in the conterminous 

United States for which atrazine sampling results were 

available, as well as a single agricultural FPS in northwestern 

Washington State. Because Phase 2 focused on a smaller 

geographic region than Phase 1, it also included wells from 

the ACT studies and several FPS, to increase the number of 

sites examined. Additional information about the groundwater 

networks examined for this study is provided in appendix C. 

Locations of Study Sites Relative to Spatial 

Patterns of Agrichemical Use and Corn 

Cultivation

Both phases of this investigation focused on agricultural 

areas because, to date, these land-use settings have the most 

consistent and systematic data available on pesticide and 

fertilizer use across the country. Annual, state-level data 

from the U.S. Department of Agriculture (USDA) on the 

use of pesticides and fertilizers in agricultural areas across 

the United States have been available since the early 1990s, 

although reporting was suspended between 2008 and 2010 

(Engelhaupt, 2008; U.S. Government Accountability Office, 

2010; National Agricultural Statistics Service, 2011b). Use 

data have been apportioned for selected years at the county 

level for fertilizers (Ruddy and others, 2006), and at the subcounty level for pesticides (Thelin and Gianessi, 2000; U.S. 

Geological Survey, 2014). By contrast, comparatively little 

information has been reported about the use of pesticides for 

Table 1. Distinguishing features of the two phases of the process-based groundwater vulnerability assessment (P-GWAVA) system 

study of agrichemicals in groundwater of the conterminous United States. 

[Additional differences between the two study phases with respect to the vadose-zone models used, the modeling approaches, and the sources of input data are 

shown in table 14 (at back of report) or discussed in the text. Abbreviations: STATSGO, State Soil Geographic Database; SSURGO, Soil Survey Geographic 

Database; m, meter]

Feature Phase 1

(P-GWAVA-PR)

Phase 2

(P-GWAVA-RZ)

Vadose-zone simulation model used Pesticide Root-Zone Model (PRZM) Root-Zone Water-Quality Model (RZWQM).

Solutes examined Atrazine Atrazine, deethylatrazine (DEA), nitrate.

Study areas Selected agricultural areas in the 

conterminous United States (fig. 1)

Ten northernmost states of the Corn Belt in the 

midcontinental United States (fig. 2).

Time interval of sampling 1992–1998 1992–2006

Number of sites examined (all in agricultural 

settings)

1,224 453

Primary source of data on soil properties STATSGO SSURGO

Depth range for simulations Upper 1 m of soil column Upper 3 m of soil column.

Heterogeneity of simulated soil column Uniform soil properties (depth- and 

component-weighted averages)

Variations in soil properties retained among 

different horizons, often for two or more 

different soil columns (components) at each 

site.

Model stabilization period 01-01-86 to 12-31-90 1

01-01-98 to 12-31-99

Model simulation period 01-01-91 to 12-31-95 1

01-01-00 to 12-31-04

1

Because none of the input data for Phase 2 were keyed to specific dates, the simulation period was arbitrarily selected to encompass a majority of the most 

recently sampled sites.
Methods 7

Table 2. Principal characteristics of groundwater networks examined by the U.S. Geological Survey National Water-Quality 

Assessment (NAWQA) program. 

[From Gilliom and others, 1995; Squillace and others, 1996; and Lapham and others, 2005. Only sites from the ACT (agricultural chemicals team); FPS (flow 

path study) and LUS (land-use study) networks were examined for this study. Abbreviations: m, meter, km, kilometer; km2, square kilometer]

Network type

(abbreviation) Site characteristics Approximatespatial scale Examples

Agricultural chemicals 

team (ACT) study

New monitoring wells (and lysimeters) installed 

along inferred groundwater flow paths in specific 

agricultural settings.

102

–103 m long; 

101

–102 m wide

Steele and others, 2008

Flow path study (FPS) New monitoring wells installed along inferred 

groundwater flow paths in an area of a specific 

type of land use (for example, agriculture or 

urban).

100

–102 km long;

101

–102 m wide

Puckett, 2004; Tesoriero and 

others, 2007

Land-use study (LUS) New or existing wells sampling recently recharged 

shallow groundwater in an area of a specific type 

of land use.

100

–105 km2 Bruce and McMahon, 1996; 

Burow and others, 1998

Study-unit survey (SUS) Existing domestic and public-supply wells of widely 

varying depths, in addition to springs, across an 

extensive area of mixed land use.

102

–106 km2 Ferrari and Denis, 1999

Principal aquifer study (PAS) Existing domestic and public-supply wells screened 

in, and distributed throughout a single major 

aquifer (mixed land use).

104

–106 km2 Lapham and others, 2005

non-agricultural purposes in the United States. Exceptions to 

this include data reported for individual counties (for example, 

Schreder and Dickey, 2005; Thurston County [Washington] 

Pest and Vegetation Management Advisory Committee, 2011), 

multi-county areas (Voss and Embrey, 2000) and individual 

states (for example, Minnesota Department of Agriculture, 

2010; Moran, 2010; New Jersey Department of Environmental 

Protection, 2012). The only consistent data available regarding 

non-agricultural pesticide use on a national scale have been 

summarized for the Nation as a whole, rather than for specific 

locations (for example, Gianessi and Puffer, 1990; Grube and 

others, 2011). Data on non-agricultural use of fertilizers across 

the Nation are also sparse, available for approximately onehalf of the states, and only on a statewide basis (Ruddy and 

others, 2006). 

Phase 1

During the first phase of this study, the P-GWAVA-PR 

model was used to simulate the concentrations of atrazine 

in the vadose zone beneath all agricultural areas where the 

NAWQA program had measured the concentrations of the 

herbicide in shallow groundwater. Between 1992 and 1998, 

the NAWQA program measured the concentrations of atrazine 

(as well as many other pesticides) in shallow groundwater at 

1,224 agricultural sites across the country (table 1). Locations 

where atrazine concentrations were predicted using the 

P-GWAVA-PR simulations are shown in figure 1, relative to 

the distribution of atrazine use in 1997.

Phase 2

During the second phase of this investigation, the 

P-GWAVA-RZ model was used to simulate the concentrations 

of atrazine, DEA, and nitrate in the vadose zone beneath 

agricultural areas where the concentrations of these 

compounds had been measured in shallow groundwater by 

the NAWQA program. However, a smaller study area was 

examined for Phase 2 than for Phase 1, primarily because the 

input data related to soil properties were considerably more 

extensive for the P-GWAVA-RZ simulations than for the 

P-GWAVA-PR simulations, requiring substantially more time 

to develop the model at each site. The region of interest for 

Phase 2 encompassed the 10 northernmost states in the Corn 

Belt of the midcontinent, where corn is a predominant crop. 

This area was selected because it is the region where atrazine 

(fig. 2) and nitrogen fertilizer (fig. 3) have been applied 

most extensively, and where atrazine detection frequencies 

(Barbash and Resek, 1996; Gilliom and others, 2006) and 

nitrate concentrations (Nolan and Hitt, 2006) in groundwater 

are among the highest in the Nation. Additionally, the general 

weather patterns and agricultural management practices for 

corn are relatively homogeneous across this region. These 

considerations were important because of the current sparsity 

of weather stations and, despite some recent exceptions (for 

example, Baker, 2011), the limited availability of consistent 

information (in digital form) on the spatial distributions of 

different chemical-, water-, crop-, and land-management 

practices across the country.
8 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig01

120° 110° 100° 90° 80° 70°

45°

40°

35°

30°

25°

0 400 MILES

0 400 KILOMETERS

Phase 1 (P-GWAVA-PR) sites

0.0 to < 0.06

0.06 to < 0.22

0.22 to < 0.43

0.43 to < 1.14

1.14 to 2.18

1997 atrazine use intensity, in kilograms of 

active ingredient per hectare

EXPLANATION

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 1. Locations of Phase 1 (P-GWAVA-PR) sites and the distribution of atrazine application intensity across the 

conterminous United States during 1997. Atrazine use data from U.S. Geological Survey (2014).
Methods 9

tac12-0781_fig02

OHIO

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

MICHIGAN

IOWA

ILLINOIS

INDIANA

0 200 MILES

0 200 KILOMETERS

100° 90° 80°

45°

40°

0 to < 0.07

0.07 to < 0.23

0.23 to < 0.44

0.44 to 1.00

EXPLANATION

Phase 2 (P-GWAVA-RZ) sites

1997 atrazine use intensity, in kilograms of 

active ingredient per hectare

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 2. Locations of Phase 2 (P-GWAVA-RZ) sites and the distribution of atrazine application intensity across the 10 

northernmost states in the Corn Belt of the United States during 1997. Atrazine use data from U.S. Geological Survey (2014).
10 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Atrazine concentrations were measured in shallow 

groundwater by the NAWQA program at 7,639 sites in the 

United States between 1992 and 2006. Among these, 453 sites 

in the Corn Belt were selected for the Phase 2 simulations 

(table 1) after it was determined that they were (1) in counties 

in the Corn Belt where corn occupied 10 percent or more 

of the county area, (2) not included in NAWQA sampling 

networks that also encompassed non-Corn Belt sites (all such 

networks were in southern Nebraska), (3) in locations where 

the required soil property data had been digitized, (4) in 

predominantly agricultural areas where shallow groundwater 

was sampled, and (5) in areas where RZWQM simulations 

could be successfully completed. In most cases, the sites 

where the numerical model repeatedly failed to converge and 

complete the simulation were sites where low-permeability 

soil layers were underlain by relatively thick layers with 

substantially higher permeability. 

tac12-0781_fig03

0 200 MILES

0 200 KILOMETERS

100° 90° 80°

45°

40°

OHIO

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

MICHIGAN

IOWA

ILLINOIS

INDIANA

Nitrogen fertilizer applied in county in 2001, 

in kilograms per hectare

Phase 2 (P-GWAVA-RZ) sites

0 to < 12

12 to < 30

30 to < 52

52 to < 76

76 to 124

EXPLANATION

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 3. Locations of Phase 2 (P-GWAVA-RZ) sites and the distribution of nitrogen fertilizer application across the 

10 northernmost states in the Corn Belt of the United States during 2001. Nitrogen fertilizer use data from Ruddy and 

others (2006).
Methods 11

Statistical Methods

All statistical analyses used commercial software: 

Excel® (Microsoft, Inc.), DataDesk 6.1 (Data Description, 

Inc.), TIBCO Spotfire S+ 8.1 (TIBCO Software Inc.), or 

SYSTAT 13 (Systat Software). A Type-I error rate (α value) 

of 0.05 was used for all tests of statistical significance. 

Unless specified otherwise, all of the R2 values provided in 

this report refer to parametric relations, that is, Pearson R2

values. All probability values reported for Spearman rank 

(non-parametric) correlation coefficients (P[ρ]) were those 

associated with the R2 values for the correlations between 

the ranks of the two variables of interest. In most cases, these 

two variables represented simulated and measured values of 

analyte concentration or detection frequency. 

Sample Collection and Chemical Analysis

The NAWQA program uses nationally consistent 

procedures for selecting wells and well installation sites 

(Lapham and others, 1995), sampling water from wells 

(Koterba and others, 1995), measuring the concentrations 

of the contaminants of interest (for example, Fishman, 

1993; Zaugg and others, 1995), and managing the resulting 

data (Williamson, 2000). During the study period for this 

investigation (1992–2006), to maintain a relatively consistent 

level of effort from one year to the next, the NAWQA program 

concentrated most of its sampling into a 3-year, high-intensity 

phase in approximately one-third of the major hydrologic 

basins (or study units) examined across the Nation at any 

point in time. Longer-term variations in water quality were 

examined through the use of a rotating cycle in each study 

unit—3 years of high-intensity sampling activity followed 

by 6 years of relatively low-intensity sampling (Gilliom and 

others, 1995). 

Temperature, pH, dissolved oxygen concentrations, and 

other field parameters were measured in water drawn from 

wells during the NAWQA sampling operations, using the 

protocols described by Koterba and others (1995). All of the 

chemical analyses for the solutes of interest were carried out 

by the USGS National Water-Quality Laboratory in Denver, 

Colorado. Concentrations of atrazine and DEA were measured 

using solid-phase extraction followed by capillary-column 

gas chromatography/mass spectrometry (Zaugg and others, 

1995). Nitrate concentrations were determined using cadmium 

reduction colorimetry (Fishman, 1993). Although this report 

focuses only on the sampling results for atrazine, DEA, 

and nitrate in groundwater, the NAWQA program conducts 

analyses for a broad range of organic and inorganic analytes—

of either natural or human origin—in groundwater, surface 

water, aquatic biota, and stream sediments (Gilliom and 

others, 1995; Lapham and others, 1995). 

Reporting limits and analytical recoveries associated 

with a given chemical species commonly change over time. 

Such changes for a given analyte may be associated with 

periodic modifications in analytical procedures, temporary 

changes caused by the presence of interfering materials 

in individual samples, or other transient circumstances. 

Analytical recoveries for atrazine, computed from data 

reported by Martin and others (2009), were 100 ± 12 percent 

during the entire period of study (1992–2006); those for nitrate 

(computed from data provided by Tedmund Struzeski, U.S. 

Geological Survey, written commun., July 31, 2009) were 

98.9 ± 0.5 percent. Analytical recoveries for DEA during this 

same period (also computed from data reported by Martin 

and others, 2009) were 45 ± 24 percent, substantially lower 

and more highly variable than the mean recoveries for the 

other two solutes. As a result, the simulated concentrations of 

DEA and atrazine at each site examined during Phase 2 were 

corrected for temporal variations in the analytical recoveries of 

each compound. This procedure was carried out for atrazine as 

well as for DEA during Phase 2 because ratios computed from 

the concentrations of both solutes were examined for this part 

of the study. 

Corrections for varying analytical recoveries during 

Phase 2 were applied using the following formula:

C C

R

i p t c i p

i t

, , , ,

,

% = ∗



 



 100

(1)

where

Ci,p,t,c is the concentration of compound i, in 

micrograms per liter, simulated at the site 

of interest at time t, and corrected for the 

analytical recovery of the compound on the 

day the concentration was measured in the 

sample obtained from that location;

Ci,p is the concentration of compound i, in 

micrograms per liter, predicted at the site of 

interest by the P-GWAVA simulations; and

Ri,t is the analytical recovery of compound i

(percent) from groundwater spiked with a 

known amount of the compound on the date

(t) that the site was sampled.

These corrections were applied to the simulated 

concentrations, rather than to the measured values, to avoid 

the uncertainties associated with applying these adjustments to 

measured concentrations that were less than 

the reporting limit. 

To correct for temporal changes in analytical sensitivities 

for each of the three solutes (atrazine, DEA, and nitrate), 

the reporting limit in use at the time of analysis for each 

sample collected—referred to as the routine reporting 

limit—was applied to the value of Ci,p,t,c predicted by the 

P-GWAVA simulation for a particular location and time. 
12 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

This procedure was used to determine if the predicted 

concentration represented a simulated detection or a simulated 

non-detection. For any site where one or more of the analytes 

was not detected, but the reporting limit for the analyte in 

question was temporarily elevated at the time of analysis 

because of transient interferences, this raised reporting 

limit was used to determine whether or not the predicted 

concentration represented a simulated detection. However, 

because this approach has seldom been used to ascertain 

analyte detections during previous studies of water quality, 

overall frequencies of detection also were computed among 

the recovery-corrected simulated values (Ci,p,t,c) using the more 

common method of applying a single, uniform value for each 

solute. For the present investigation, the value used for this 

purpose was the highest reporting limit used for each analyte 

during the sampling period (1992–2006). Temporal variations 

in the routine reporting limits for atrazine, DEA, and nitrate 

during this time interval are shown in figure 4.

P-GWAVA System 

The P-GWAVA system consists of three main 

components: (1) a process-based model for simulating the 

transport and fate of water and solutes in the vadose zone, 

(2) a database for providing values for all the input parameters 

used by the vadose-zone model, and (3) translator programs 

for creating input files and processing model output. The main 

features of the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ 

(Phase 2) systems are shown in figures 5 and 6, respectively. 

Both P-GWAVA systems use a GIS to process and store 

spatial data pertaining to individual site characteristics, 

and a relational database to store and provide tabular data 

for non-spatial input parameters (for example, pesticide 

properties, crop properties, and so forth). Data management is 

accomplished using various Arc® tools in conjunction with a 

relational database and a series of Structured Query Language 

(SQL) scripts to generate input files for the transport-and-fate 

models, and to summarize the simulation results from the 

model output files.

The primary differences between the P-GWAVA-PR and 

P-GWAVA-RZ models—other than the vadose-zone models 

used—were related to some of the specific tools used for 

storing and processing geographic data (ArcInfo® compared 

with ArcGIS®, Arc Toolbox®, and ArcSDE®), for operating the 

relational database (SQL Server 2000 compared with 2005), 

for creating input files, and for summarizing the results (Visual 

Basic compared with C#.NET). The P-GWAVA approach 

(figs. 5 and 6) facilitates the process of re-running the 

simulations for some or all of the sites as changes are made to 

input parameter values, data sources, and other model features. 

As with other multi-media modeling systems (for example, 

Leavesley and others, 1996), this approach also provides a 

valuable framework for conducting simulations using other 

models or simulation techniques.

Overall Simulation Approach

Both PRZM and RZWQM offer the option of entering 

input parameter values for individual sites using a graphical 

user interface. However, because the P-GWAVA system is 

designed to carry out simulations at large numbers of locations 

(that is, thousands or more), rather than for one site at a time, 

input parameter values are provided to the models directly 

from a database (figs. 5 and 6). Because of the large number 

of sites examined, the site-specific manipulations of individual 

input parameters that are commonly implemented during 

small-scale modeling studies (for example, Lorber and Offutt, 

1986; Hanson and others, 1999; Nolan and others, 2010) were 

not used; instead, the P-GWAVA model runs were carried out 

as cold simulations, an approach that also has been used by 

other modeling studies (for example, Smith and others, 1991; 

Nolan and others, 2005).

Time constraints precluded a thorough quantitative 

analysis of the extent to which the variability in the model 

input parameters contributed to overall model error during 

this study. Previous work, however, has established that the 

pesticide concentrations simulated in the vadose zone by 

PRZM are particularly sensitive to variations in transformation 

rates, soil organic matter (OM) content, the organic carbonwater partition coefficient (Koc), bulk density, saturated 

hydraulic conductivity, and the water contents at field capacity 

and wilting point (Jones, 1986; Donigian and Carsel, 1987; 

Carsel and others, 1988a, 1988b; Shaffer and Penner, 1991; 

Smith and others, 1991; Fontaine and others, 1992; Russell 

and Jones, 2002; Wolt and others, 2002; Dubus and others, 

2003; Boesten, 2004). (Field capacity is commonly defined 

as the water content remaining in the soil after most gravity 

drainage has ceased; wilting point is the water content below 

which most plants are not able to extract water from the 

soil.) Agrichemical concentrations simulated in the vadose 

zone by RZWQM are also especially sensitive to variations 

in transformation rates, bulk density, hydraulic conductivity, 

and the water content at field capacity (Jaynes and Miller, 

1999; Malone and others, 2004b; Bayless and others, 2008; 

Nolan and others, 2010), as well as to variations in soil crust 

conductivity, average macropore radius, soil air-entry value, 

the Brooks-Corey unsaturated hydraulic conductivity constant 

(N2), and lateral saturated conductivity (Kumar and others, 

1998; Malone and others, 2004a, 2004b). The results from 

these previous assessments of the sensitivity of PRZM and 

RZWQM to variations in input parameter values were also 

presumed to apply to the predictions of the P-GWAVA-PR and 

P-GWAVA-RZ models.

Initial Conditions and Simulation Periods

At each site of interest, simulations were done for 

an initial stabilization period, followed by a simulation 

period when the predicted concentrations were computed. 
Methods 13

tac12-0781_fig04abc

C. Temporal variations in nitrate reporting limit, 1992–2006

0.0001 

0.001 

0.01 

0.1 

1 A. Temporal variations in atrazine reporting limit, 1992–2006

0.0001 

0.001 

0.01 

0.1 

1

Reporting limit, in micrograms per liter 

B. Temporal variations in DEA reporting limit, 1992–2006

0.01 

0.1 

1

08-19-92 05-16-95 02-09-98 11-05-00 08-02-03 04-28-06 

Sampling date 

Value assigned to non-detections to 

compute model residuals

Value assigned to non-detections to 

compute model residuals

Value assigned to non-detections to 

compute model residuals

Figure 4. Temporal variations in the routine reporting limits used for (A) atrazine, (B) 

deethylatrazine (DEA), and (C) nitrate at the 453 sites examined for Phase 2 of this study and 

sampled in the 10 northernmost states in the Corn Belt of the United States, 1992–2006. Values 

shown are only for those dates when the chemical analyses of groundwater samples from these 

sites were done. Locations of sites are shown in figures 2 and 3.
14 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig05

ArcInfo Relational Database

Site locationSoilsLand useCropsand so forth

Data Gathering Data Management Simulations

Input

File

Generator 

Input Files

PRZM

Output Files Output Processor

Pesticide propertiesCrop propertiesand so forth

Site characteristicsSoil propertiesManagement practicesSimulation summary

Related Tables

Spatial Data

Tabular Data

National Scaledatasets collectedSpatial data importedinto a geographical information system (ArcInfo)

Spatial data stored inArcInfo coveragesSpatial data processedusing Arc commands

Microsoft's SQL Server 2000selected for relational databaseSQL statements used to manage data and generate model input data

Combination of SQLstatements and Visual Basic programs used to create input files for model and to summarize simulation results

Pesticide RootZone Model (PRZM)used to simulate pesticide leachingthrough the vadosezone at each site

Rosetta

Tabular data importedinto tables in a relationaldatabase employing aStructured Query Language,or SQL (SQL Server)

Figure 5. Primary features of the P-GWAVA-PR (Phase 1) system used to simulate the transport and fate of atrazine in the vadose zone using the 

Pesticide Root-Zone Model (PRZM).
Methods 15

tac12-0781_fig06

ArcGIS

ArcSDE

Relational Database

Site locationSoilsLand useCropsand so forth

Data Gathering Data Management Simulations

Input

File

Generator

Input Files

RZWQM

Output Files Output Processor

Pesticide propertiesCrop propertiesand so forth

Site characteristicsSoil propertiesManagement practicesSimulation summary

Related Tables

Spatial Data

Tabular Data

National scaledatasets collectedSpatial data imported into a geographical information system (ArcInfo)Tabular data importedinto tables in a relationaldatabase employing aStructured Query Language,or SQL (SQL Server)

Spatial data stored inArcGIS geodatabasesSpatial data processedusing Tools in Arc ToolboxData transferred between ArcGIS and relational database using ArcSDE software

Microsoft's SQL Server 2005selected for relational databaseSQL statements used to manage data and generate model input data

Combination of SQL statements and C#.NET programs used to create input files for model and to summarizesimulation results

Root-Zone WaterQuality Model (RZWQM) used to simulate pesticide leachingthrough the vadosezone at each site

Figure 6. Primary features of the P-GWAVA-RZ (Phase 2) system used to simulate the transport and fate of atrazine, deethylatrazine(DEA), and nitrate in 

the vadose zone using the Root-Zone Water-Quality Model (RZWQM).
16 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The stabilization period was used to dissipate the transient 

response to the initial conditions specified at each site. The use 

of the stabilization period helped to establish distributions of 

microbial populations, soil OM, nitrogen, and other mutable 

system characteristics that were more representative at 

each location of interest than the initial conditions specified 

uniformly at all the sites for these parameters. Initial 

conditions that were designated as site-specific values for 

individual study locations at the beginning of the simulation 

periods included parameters related to weather and water 

content (see table 14 at back of report). The initial condition 

for water content in each soil layer was set to its value at field 

capacity. Although widely used, field capacity is recognized 

to be a highly imprecise parameter because some soils may 

continue to drain for extended periods (Twarakavi and 

others, 2009). However, the water content at field capacity 

provided a convenient starting point for water flow and 

atrazine transformation rates, because data for this parameter 

are widely available or could be estimated from other data 

provided by the soil property databases used for this study. 

The length of the stabilization period was of particular 

importance during Phase 2 for establishing the sizes of the 

five pools (types) of soil OM and the three populations of 

soil microorganisms that RZWQM uses to simulate various 

microbial and geochemical processes. These processes include 

the formation and decomposition of organic materials in the 

soil. The five pools of soil OM simulated by RZWQM include 

(1) slowly decaying crop residues, (2) rapidly decaying 

metabolic products from crop decomposition, (3) rapidly 

metabolized soil OM (fast OM), (4) soil OM metabolized at an 

intermediate rate (medium OM), and (5) slowly metabolized 

soil OM (slow OM). The three populations of microorganisms 

consist of (1) heterotrophic soil decomposers, (2) nitrifiers, 

and (3) facultative anaerobes (Ahuja and others, 2000). 

A 5-year interval was used for the stabilization period 

during Phase 1, but was shortened to 2 years during Phase 2 

(table 1) to accommodate the substantially longer execution 

times for the RZWQM model. (Because of the greater 

complexity of RZWQM relative to PRZM, simulations for 

Phase 2 took about 30 times longer to complete than for 

Phase 1.) Although a stabilization period longer than 2 years 

may have been preferable during Phase 2 to allow sufficient 

time for the sizes of the slow and medium OM pools to 

stabilize, the overall execution time of 7 years was consistent 

with the guidance of Hanson and others (1999), who 

recommended RZWQM simulation periods of 5 to 7 years to 

correct for potentially “poor initial estimates” of the sizes of 

the three microbial populations, the two crop residue pools, 

and the fast OM pool. 

A 5-year interval was used for the simulation period 

during both study phases. For Phase 1, this interval was 

from 1991 to 1995, to facilitate comparisons of the model 

simulation results with water-quality data from the first 

sampling cycle of NAWQA. For Phase 2, the simulation 

period was designated as the 5-year interval from 2000 to 

2004, to encompass a majority of the most recently sampled 

sites (table 1). During the 5-year simulation period for both 

study phases, recharge and flow-weighted mean solute 

concentrations were computed at a specific depth within 

the soil column, referred to herein as the assessment depth

(see section, “Quantifying Solute Concentrations from 

Model Output”). 

Effects of Simulation Approaches on Model 

Predictions

During Phase 1 and Phase 2, multiple simulations were 

carried out to examine the influence of different modeling 

approaches on the extent of agreement between the simulated 

and measured concentrations at individual sites. For Phase 1, 

these simulations were designed to determine the effects of 

(1) different grid discretizations; (2) the spatial variability of 

atrazine applications (areally averaged or spatially uniform); 

(3) the spatial distribution of simulated irrigation (at all sites, 

no sites, or sites selected at random in proportion to the 

amount of nearby land under irrigation); (4) the location(s) 

of atrazine transformation in the subsurface (in the sorbed 

state, in the dissolved state, in both, or in neither); and (5) the 

degree of spatial averaging of soil properties. For Phase 2, 

these simulation results were used to determine the effects of 

variations in (1) the spatial variability of atrazine applications 

(as for Phase 1), (2) the manner in which the initial rates of 

atrazine and DEA transformation were estimated, (3) the 

magnitude of the organic carbon-water partition coefficients 

(Koc) for atrazine and DEA, and (4) the relative sizes of the 

different soil OM pools.

Vadose-Zone Models

The vadose-zone model comparison study by Nolan 

and others (2005) examined two types of models that are 

distinguished by the manner in which they simulate water 

flow. Simple models use a simplified, tipping-bucket approach 

to transfer water downward from each soil layer to the layer 

below. In contrast, complex models simulate water flow 

through the soil using well-established equations derived 

from the fundamental principles of fluid flow through porous 

media. Among the models examined by Nolan and others 

(2005), one simple model (PRZM) and one complex model 

(RZWQM) were selected for use in the P-GWAVA system for 

this investigation. 

Pesticide Root-Zone Model (Phase 1)

PRZM is a one-dimensional, single-porosity, dynamic 

model that uses a finite-difference scheme to simulate the 

movement of water and the transport and fate of pesticides and 

nitrogen species, within and immediately below the plant root 

zone (Carsel and others, 1998). The version of the model used 
Methods 17

for this study includes modules for simulating the transport 

and fate of solutes in the root zone (PRZM) and the underlying 

vadose zone (VADOFT). The latter was not used because of 

persistent difficulties that could not be resolved in the time 

constraints of the project. However, when used alone (without 

VADOFT) PRZM does not impose any limitation on the 

maximum depth of simulations (Carsel and others, 1998).

One primary reason PRZM was selected for Phase 1 

of this investigation is that this model has been used 

extensively for small-scale field studies (Pennell and others, 

1990; Barbash and Resek, 1996; Jones and Mangels, 2002), 

thereby satisfying the design objective of using existing, 

widely tested tools and techniques as much as possible for 

this study. Although pesticide concentrations simulated in 

the subsurface by the model have recently been compared 

with those measured in groundwater by several previous 

monitoring studies (Baris and others, 2012), the use of PRZM 

for large-scale GWAVAs, as opposed to its use for smallscale field studies, appears to have been relatively limited to 

date (National Research Council, 1993; U.S. Environmental 

Protection Agency, 1993; Corwin and others, 1997; Miller, 

2010). PRZM also is among the process-based simulation 

models that have been used most extensively by pesticide 

manufacturers and regulatory agencies in the United States 

and Europe to estimate the concentrations of pesticides, or the 

likelihood of their detection, in the root zone (for example, 

Forum for Co-ordination of Pesticide Fate Models and their 

Use, 2000, 2004; Jones and Mangels, 2002; Morgan, 1999, 

2002; U.S. Environmental Protection Agency, 2001). 

Another reason PRZM was selected for this study 

was that it accounts for many of the primary physical, 

hydrological, chemical, and biological phenomena known 

to influence the transport and fate of surface-derived solutes 

in the subsurface. PRZM was also selected because of 

its inclusion in the vadose-zone model comparison study 

conducted by Nolan and others (2005). Additionally, Carsel 

and others (1998) determined that the concentration history 

predicted by the model for a hypothetical pesticide showed 

close agreement with an analytical solution for the advectiondispersion equation. 

Many versions of PRZM have been produced. Several 

variations of the model that were available at the beginning of 

this study were considered but not used (for example, FOCUS 

PRZM, Visual PRZM), either because they were still under 

development at the time, or because they appeared to lack 

the flexibility required for this investigation. The most recent 

summary of the primary features of PRZM available during 

Phase 1 was provided in the users’ manual for PRZM3.12 

by Carsel and others (1998); unless noted otherwise, all 

descriptions of the characteristics and data requirements of 

PRZM used for the present study were taken from Carsel 

and others (1998). Jones and Mangels (2002) provided a 

summary of results from many of the investigations that 

have compared the predictions from PRZM simulations with 

field observations, and a comprehensive summary of some 

of the limitations of the model that have been identified by 

other studies. The PRZM versions used by the investigations 

whose results were summarized by Jones and Mangels (2002) 

varied among the studies examined. The version used for the 

simulations during this study was PRZM3.12F (referred to 

herein as PRZM), obtained from Mark Cheplick, Waterborne 

Environmental, Inc. (written commun., July 2003). 

Root-Zone Water-Quality Model (Phase 2)

Created by the U.S. Department of Agriculture (USDA), 

RZWQM is a one-dimensional, finite-difference, dual-porosity 

model that simulates a wide variety of physical, hydrologic, 

chemical, and biological processes known to control the 

transport of water, heat, and dissolved solutes—and the fate 

of solutes and their transformation products—in the vadose 

zone (Ahuja and others, 2000). The model is also specifically 

designed to simulate the effects of different agronomic 

practices on crop growth, and the transport and fate of water 

and solutes above and beneath the land surface, over time 

intervals as long as 100 years. The RZWQM2 Science Model, 

version 2.2 (downloaded on February 9, 2012) was used for 

this study, with additional guidance provided by the users’ 

manual from Bartling and others (2011). For the sake of 

brevity, the abbreviation RZWQM will be used herein to refer 

to this version of the model. 

RZWQM was selected primarily because of results from 

the model comparison study by Nolan and others (2005). For 

that investigation, Nolan and others (2005) examined the main 

features of 20 published models that simulate the transport 

and fate of surface-derived solutes within the vadose zone. 

Using a set of screening criteria related to documentation 

quality, ease of use, availability, range of processes simulated 

and other characteristics, seven of the models were selected to 

carry out a set of cold simulations to simulate (1) water flow, 

the transport and fate of atrazine, and the production and fate 

of DEA and two other atrazine transformation products in the 

vadose zone at a field site in the White River Basin, Indiana; 

and (2) the transport of water and dissolved bromide in the 

vadose zone at a second field site in Merced, California. 

Nolan and others (2005) evaluated all seven models to 

determine the accuracy with which they predicted the field 

observations in the vadose zone at the two study locations. 

Among the atrazine chemographs (that is, plots showing 

variations in concentration in a given environmental 

medium over time) produced by each of the seven models 

for the Indiana site, the chemograph generated by RZWQM 

exhibited the closest agreement with the field data. Nolan 

and others (2005) attributed this observation to the fact that 

RZWQM was the only one of the seven models that simulates 

macropore flow, which had been known from previous 

research (Bayless, 2001) to occur at this site. The RZWQM 

simulations also provided predictions that substantially agreed 

with field observations regarding the evolution of matric 
18 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

potentials and DEA concentrations over time at the Indiana 

site, as well as the variations of moisture, matric potential, 

and bromide concentrations with depth in the vadose zone 

at the California site (Nolan and others, 2005). Additionally, 

Nachabe and Ahuja (1996) have shown that the partial piston 

displacement routine used by RZWQM to simulate dispersion 

produces chemographs that are in close agreement with an 

analytical solution to the convection-dispersion equation.

Many of the processes and factors that RZWQM 

has been designed to simulate, but that commonly are not 

accounted for by other vadose-zone models, are known to 

exert discernible effects on the transport and fate of water 

and solutes in the subsurface. These include a variety of 

agricultural management techniques, preferential transport 

through macropores, the exchange of water and solutes 

between regions of mobile and immobile water, the production 

and fate of agrichemical transformation products, the upward 

transport of solutes in response to evapotranspiration (ET), 

and the influence of soil depth (presumed to represent a proxy 

for microbial activity), temperature, and soil moisture on 

agrichemical transformation rates. Given the emphasis on 

accounting for as much current understanding as possible 

regarding the processes and factors that influence agrichemical 

transport and fate in the subsurface, these features were of 

considerable interest to the current study. Summaries of the 

features of RZWQM—as well as critiques of some of its 

limitations—have been provided in several publications (for 

example, Malone and others, 2001a, 2004a; Nolan and others, 

2005; Bayless and others, 2008). 

Processes Simulated and Input Data

Both of the vadose-zone models used for this study 

(PRZM and RZWQM) can account for the influence of many 

different processes and factors on the transport and fate of 

water and solutes in the root zone. As a result, many decisions 

were required in relation to the methods used to select or, in 

several cases, estimate values for model input parameters. 

Processes and factors that were simulated during each of the 

two phases of this investigation are listed in table 14, which 

also provides brief descriptions of the approaches used by 

the models to simulate many of the processes of interest. 

Sources of model input parameter values or, when such data 

were not readily available, brief descriptions of the methods 

used to estimate these values also are listed. Although most 

of the entries in table 14 are intended to be self-explanatory, 

additional clarification is provided below, where necessary.

Physical and Chemical Properties of Soils

Most of the quantitative information used to characterize 

the physical, hydraulic, and chemical properties of soils for 

this study was obtained from databases maintained by the 

USDA Natural Resources Conservation Service (NRCS; 

Natural Resources Conservation Service, 2011a). The NRCS 

provides these data for millions of irregularly shaped areas, or 

map units, for most of the United States. (The map units are 

irregularly shaped because their outlines are determined by the 

spatial distributions of the soil type[s] they contain, rather than 

by political boundaries.) The data are provided at three spatial 

scales: Major Land Resource Areas (for the National Soil 

Geographic database, or NATSGO), individual States (for the 

State Soil Geographic database, or STATSGO), and individual 

soil units from the NRCS county soil surveys (for the Soil 

Survey Geographic database, or SSURGO). The STATSGO 

data represent a spatial aggregation of the SSURGO data, and 

data in the NATSGO database are derived from the National 

Resources Inventory (NRI; U.S. Department of Agriculture, 

1991). By design, the spatial resolutions of the three databases 

differ considerably, exhibiting minimum sizes for their 

respective map units of several thousands of square miles for 

NATSGO (Natural Resources Conservation Service, 2006), 

approximately 2,500 acres for STATSGO, and 1–10 acres for 

SSURGO (Natural Resources Conservation Service, 2009). 

All STATSGO data for the conterminous United States 

were available in digital form at the beginning of Phase 1, 

whereas most SSURGO data were not. For this reason, the 

soils data for the P-GWAVA-PR simulations were obtained 

from the STATSGO database. By the beginning of Phase 2 of 

this study, however, the SSURGO data had been digitized to 

a sufficient extent across the country to encompass all of the 

Phase 2 study sites (Natural Resources Conservation Service, 

2014). Consequently, SSURGO data were used to characterize 

soil properties for the P-GWAVA-RZ simulations. 

In addition to the variations in the magnitudes of 

soil parameters among soil map units, the STATSGO and 

SSURGO databases account for at least three other types of 

variation for many of the parameters that they include (Natural 

Resources Conservation Service, 2013). First, the individual 

map units are comprised of one or more components—that 

is, individual soil sequences that may vary with respect to the 

properties, vertical locations, and number of soil horizons that 

they contain. The databases do not report the specific locations 

of individual components in a given map unit, but instead 

provide an estimate of the percentage of the total map-unit 

area that each component occupies (comppct). A second type 

of variation accounted for by the STATSGO and SSURGO 

data is the range of values spanned by many individual soil 

variables. For each of these parameters, three values are 

provided: a high value, a representative (intermediate) value, 

and a low value (for example, ksat_h, ksat_r, and ksat_l,

respectively, for saturated hydraulic conductivity). Third, for 

some individual soil horizons that are stratified or that contain 

more than one soil texture, STATSGO and SSURGO provide 

more than one texture designation, but with no indication of 

the relative prevalence of the different soil textures within the 

horizon of interest. 
Methods 19

Phase 1

PRZM simulates variations in soil properties with depth 

by representing the soil column as a series of layers with 

different physical, chemical, and biological characteristics. For 

computational purposes, each layer is subdivided into one or 

more separate grid cells. However, because of time constraints 

that precluded the acquisition and use of data on variations in 

soil properties with depth at each site, during Phase 1 the soil 

column at each location was represented as having uniform 

properties throughout its length. For each STATSGO map unit 

of interest, a single value for each soil parameter was obtained 

by computing spatially weighted averages in the vertical and 

horizontal dimensions. 

The process of calculating a spatially weighted average 

for a given soil parameter is shown in figure 7, using the 

example of computing an estimate of soil OM content for an 

idealized, hypothetical STATSGO map unit. The use of spatial 

averaging to obtain a single value for a given soil property for 

the entire soil column throughout an entire STATSGO map 

unit, although deemed necessary during Phase 1, resulted 

in the loss of a considerable amount of detail regarding the 

spatial variations of each parameter of interest. However, 

figure 8 indicates that the spatially averaged values of soil 

organic carbon content, for example, still showed considerable 

variability among groundwater networks and, in some cases, 

among sites in a given network. 

The STATSGO database provides information on soil 

properties to a depth of 152.4 cm, or to shallower depths 

where bedrock or other types of restrictive horizons are 

detected (Juracek and Wolock, 2002). However, because 

actual variations in soil properties with depth were not 

accounted for during Phase 1, the P-GWAVA-PR simulations 

were carried out only to an assessment depth of 1 m (table 1). 

This assessment depth was selected to correspond with 

the midrange among the rooting depths for corn (Carsel 

and others, 1998), to accommodate the specified model 

discretization (see section, “Soil Column Discretization for 

Simulations”), and because approximately 90 percent of corn 

roots typically are detected in the upper 1 m of the soil (North 

Dakota State University, 1997).

To examine the potential effects of the spatial averaging 

of soil parameters on the accuracy of the predictions of the 

P-GWAVA-PR modeling system, pairs of PRZM simulations 

were carried out at selected sites using soil parameter values 

derived from either STATSGO (with one parameter value for 

the entire soil column) or SSURGO (with different values for 

a given parameter among different horizons within the soil 

column). At each site, the atrazine concentrations simulated 

by the two approaches were compared with the concentration 

measured in shallow groundwater at that location, and with 

one another. These comparisons were carried out in two 

agricultural locations representing different environmental 

settings—four sites in an irrigated, arid region in eastern 

Washington (Grant County) and six sites in an unirrigated, 

humid area in eastern Wisconsin (Portage County)—to 

examine the extent to which any differences between the 

concentrations simulated using the two approaches might have 

been affected by the environmental setting.

Phase 2

By the time Phase 2 of this study began, nearly all 

the SSURGO data were available in digital form for the 

United States (Natural Resources Conservation Service, 

2014). As a result, soil properties could be characterized at 

the most detailed spatial scale possible in both the vertical 

and horizontal dimensions throughout the Phase 2 study 

area, eliminating the need for the spatial averaging used for 

Phase 1. Thus, by using the SSURGO data, the P-GWAVA-RZ 

simulations took full account of the known spatial variability 

in the soil properties of the Corn Belt. 

Characteristics and Use of SSURGO Data

The number, dimensions, and properties of individual soil 

horizons typically vary from one SSURGO component to the 

next, even in the same SSURGO map unit. Depending on the 

site in question, data on soil properties were available in the 

SSURGO database for between one and eight soil horizons for 

the SSURGO components examined during Phase 2. In every 

case where data for the low, representative (intermediate), 

and high values were all provided for a given parameter, the 

representative value was used. For soil horizons where the 

database provided more than one texture designation but no 

indication of the relative prevalence of the different textures, 

the soil texture was determined independently from the 

weight percentages of three grain-size fractions (percent sand, 

percent silt and percent clay) using a subroutine embedded 

in RZWQM that determines soil texture from a standard soil 

triangle (Brady, 1990). 

Although SSURGO provided data on soil OM content 

(expressed as a mass-based percentage) for the uppermost 

horizon in all of the SSURGO components examined during 

Phase 2, values for soil OM content were missing for one 

or more of the deeper horizons in about 20 components. In 

these cases, soil OM values for the deeper soil horizons were 

estimated by scaling the value for the uppermost horizon 

according to the average soil OM for the relative depth of the 

horizon of interest among all SSURGO components for which 

soil OM data were provided for that horizon. Because no 

components with missing data on the soil OM content had 

more than four horizons, this procedure was used for between 

one and three soil horizons beneath the uppermost horizon. 
20 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig07

Single STATSGO map unit 

with three soil components SEQNUM LAYER laydepl laydeph oml omh

1 1 0 10 0.5 2.0

1 2 10 30 0.1 0.2

1 3 30 60 0.1 0.2

2 1 0 30 5.0 10.0

2 2 30 60 0.5 2.0

3 1 0 5 0.5 2.0

3 2 5 15 0.5 1.0

3 3 15 30 0.1 0.2

3 4 30 60 0.1 0.2

LAYER table in STATSGO database (db)

SEQNUM = component soil in STATSGO map unit

LAYER = soil layer in soil component 

laydepl = depth from surface to top of layer

laydeph = depth from surface to bottom of layer 

oml = minimum percentage, by weight, of organic matter in layer 

omh = maximum percentage, by weight, of organic matter in layer

Component table in STATSGO db Calculated depth-weighted average OM

Step 5: Calculate area-weighted average of depth-weighted average OM

OM_awa = [(comppct1 * OM_dwa1) + (comppct2 * OM_dwa2) +... (comppctn * OM_dwan)] / 100

Result: Organic matter for STATSGO map unit = 1.3 percent by weight

SEQNUM comppct SEQNUM OM_dwa

1 50 1 0.333

2 25 2 4.375

3 25 3 0.342

Step 1: Calculate thickness of each layer

 lay_thick = laydeph - laydepl

Step 2: Calculate depth-weighted average (dwa) of OML and OMH for 

 each component

 oml_dwa = [(lay_thick1 * oml1) + (lay_thick2 * oml2) + .... (lay_thickn * omln)] / 

 (lay_thick1 + lay_thick2 + ... lay_thickn)

 omh_dwa = [(lay_thick1 * omh1) + (lay_thick2 * omh2) + .... (lay_thickn * omhn)] / 

 (lay_thick1 + lay_thick2 + ... lay_thickn)

Step 3: Calculate average amount of organic matter in each component

 om_avg_dwa = (oml_dwa + omh_dwa) / 2

Step 4: Link calculated depth-weighted average OM to component

 percentage (comppct) in STATSGO component table 

Figure 7. Steps used with the P-GWAVA-PR (Phase 1) system to obtain spatially weighted averages of individual 

soil parameters (in both the vertical and horizontal dimensions) for each State Soil Geographic database

(STATSGO) map unit, using soil organic matter (OM) as an example.
Methods 21

tac12-0781_fig08

acfblusag1

acfbluscr1

albelusag1

albelusag2

cazbluscr1

ccptlusag1

ccptlusag2

ccptlusor1

connlusag1

eiwaluscr1

hdsnlusag1

leriluscr1

linjluscr1

lirbluscr1

lirbluscr2

lsuslusag1

lsuslusag2

lsuslusag3

nvbrlusag1

ozrklusag1

ozrklusag1a

ozrklusag2

ozrklusag2a

potolusag1

potolusag2

pugtfpscr1

pugtluscr1

rednlusag1

rednlusag2

rioglusag1

riogluscr1

sacrluscr1

sanjluscr1

sanjluscr1a

sanjlusor1

sanjlusor1a

sanjlusor2

sanjlusor2a

santluscr1

sofllusag1

sofllusor1

spltluscr1

umisluscr1

usnkluscr1

usnkluscr2

usnkluscr3

usnkluscr4

utenluscr1

whitluscr1

whitluscr2

whitluscr3

whitluscr3a

willlusag1

willlusag2

wmiclusag1

wmiclusag1a

wmiclusag2

0.01 0.1 1 10 100 

Groundwater network

Soil organic carbon content, in percent by weight

Figure 8. Variations in soil organic carbon content among sites in the groundwater networks examined 

in the conterminous United States for Phase 1 of this study, 1992–98. Study site locations are shown in 

figure 1. Descriptions of groundwater networks are provided in appendix C. 
22 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

More specifically, the estimated soil OM content in the Nth 

horizon beneath the land surface for component i (%omi,N) was 

computed using the following equation:

% %

%

% , ,

,

,

om om

om

om

i N i

N avg

avg

= ∗













 1

1

(2)

where

%omi,1 is the soil OM content in the uppermost horizon 

of SSURGO component i (obtained from 

SSURGO);

%omN,avg is the average soil OM content in the Nth 

horizon beneath the land surface (N = 2, 3, or 

4) among all SSURGO components examined 

in the Phase 2 study area for which the values 

were reported to be less than 20 percent. 

(Soil OM contents equal to or greater than 20 

percent were considered anomalous for the 

agricultural areas examined, and were thus 

disregarded.) Values of %omN,avg were 1.1 

percent for N = 2, 0.53 percent for N = 3, and 

0. 375 percent for N = 4; and

%om1,avg is the average soil OM content in the uppermost 

horizon among all SSURGO components 

examined in the Phase 2 study area (3.12 

percent).

To account for the effects of variations in soil properties 

among different soil components in the map unit where a 

given site was located, a separate P-GWAVA-RZ simulation 

was done for every component that occupied 10 percent or 

more of the map unit area, and for which a minimum set of 

soil parameter data were provided in the SSURGO database. 

This minimum set of soil parameters consisted of percent sand 

and percent silt, which were required to estimate the BrooksCorey parameters (table 14). (The Brooks-Corey parameters

are used to construct simplified versions of the relations 

between matric suction and either hydraulic conductivity or 

water content for a given soil.) The simulated concentrations 

for the individual components were combined into a single 

value for each map unit, in proportion to the fractions of 

the map unit that the components occupied (see section, 

“Quantifying Solute Concentrations from Model Output”). 

Because many of the well sites of interest were in areas 

with relatively homogeneous environmental characteristics 

(table 2), many of the sites were co-located in individual map 

units, with some SSURGO map units containing as many as 

14 study sites within their boundaries. 

For several soil-related RZWQM input variables, 

parameter values were obtained directly from the SSURGO 

database (for example, depths and thicknesses of individual 

soil horizons, percent sand, percent silt, percent clay, soil 

OM content and wilting point). However, for several other 

parameters, one or more intermediate steps were required to 

obtain the necessary input values (fig. 9, table 14). Examples 

of these procedures included the use of SSURGO data to 

estimate partition coefficients, diffusion rates, transformation 

rates, nitrogen fertilizer application rates, and soil hydraulic 

properties (table 14).

In many areas, information on soil properties provided 

by the SSURGO database extended beyond the typical depth 

of 153 cm, reaching nearly 300 cm in some locations. For 

this reason, an assessment depth of 3 m was used for all 

P-GWAVA-RZ simulations. To avoid truncating any of the 

soils data, the soil columns for the Phase 2 simulations were 

constructed to reach a depth of 3 m at all sites of interest. 

For locations where the SSURGO data for an individual 

component did not reach this depth, the thickness of the 

bottom soil layer was increased so that the overall length of 

the simulated soil column for that component reached the 

assessment depth of 3 m. 

Reductions in Soil Permeability Caused by Surface Crusts

Rainfall has long been known to cause the formation 

of crusts on the surfaces of both undisturbed and 

anthropogenically modified soils, resulting in substantial 

decreases in soil permeability. Several decades of research 

have established that the formation and thickness of surface 

crusts are affected by a broad range of factors, including soil 

texture, aggregate stability, soil OM content and land-use 

practices (Rawls and others, 1990). RZWQM accounts for the 

influence of surface crusts on soil permeability by allowing the 

user to specify whether or not a surface crust is present and, if 

so, to specify its hydraulic conductivity (Kcr). Because tillage 

is known to promote their formation, surface crusts were 

assumed to be present at all sites where the P-GWAVA-RZ 

simulations were carried out. Values of Kcr were estimated 

using a set of pedotransfer functions devised for this purpose 

by Rawls and others (1990). Pedotransfer functions (PTFs) are 

statistical relations that estimate the value of a particular soil 

parameter from other properties of the soil.

Using measured rates of infiltration through recently 

tilled soils that either received or were protected from 

simulated rainfall in cropland and rangeland areas of 

24 states, Rawls and others (1990) produced a series 

of PTFs for predicting the extent to which the surface 

crusts formed by rainfall led to decreased infiltration rates. 
Methods 23 Figure 9. Sources of data for several P-GWAVA-RZ (Phase 2) input parameters related to soil properties, chemical properties, weather, and other site- specific variables. SSURGO map unit graphic provided by Leah Wasser, Pennsylvania State University Geospatial Technology Program, Land Analysis Laboratory (written commun., March 24, 2008).

tac12-0781_fig09

Water table

From NAWQA: [atrazine] [DEA] [nitrate] . .  .

From SSURGO (for each soil horizon)

 Horizon depth and thickness Water content at 1,500 kPa  (~wilting point) Water content at 33 kPa (~field capacity) Saturated water content Specific gravity Percent sand Percent silt Percent clay Cation exchange capacity Other soil properties

RZWQM

 (Simulates transport andfate of water and solutes)

Precipitation [f(t)]Temperature [f(t)]

] Bulk density

Predicted daily values Flow-weighted mean  concentrations (C ) of  atrazine, DEA and nitrate

 Weight by component percentage and combine values for all components to obtain final concentrations for entire map unit, using equation 49.

N

N

N

N N

Cl

H H

(C H ; H) 2 5

Henry's law constantTransformation rateSoil-water partition coefficient (K )

Soil organic matter content (f )

d

Convert to soil organic  carbon content (f )

Koc

 SSURGO map unit (3 components) 

oc

om

Component C

Component B Component A

[Component C]

FWM

CLIGEN

(Stochastic weather simulator)

 Surface soil albedo Surface crust hydraulic conductivity Brooks-Corey hydraulic  parameters Total porosity Macroporosity Lateral saturated  hydraulic conductivity Diffusion coefficients Microbial population  sizes Nitrogen application rate

] Saturated hydraulic conductivity
24 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

These reductions were quantified in terms of a crust factor

(CF), which represents the ratio between the hydraulic 

conductivity of soil subjected to rainfall (Kcr) and that of soil 

protected from rainfall (Ksat). Rawls and others (1990) used the 

following equation to estimate CF values: 

CF K

K

SC

L

cr

sat i

≡ =

+ 



 



 1 Ψ (3)

where

SC is the dimensionless reduction factor for 

subcrust conductivity (which corrects for 

partial saturation of the subcrust soil);

Ψi is the steady-state capillary potential at the 

crust-subcrust interface, in centimeters; 

and 

L is the wetted depth, in centimeters.

Although Rawls and others (1990) provided CF values 

that were calculated from measured data for 20 agricultural 

locations, their data encompassed only 9 of the 12 texture 

classes in the USDA soil triangle (Natural Resources 

Conservation Service, 2011b). However, Rawls and others 

(1990) did list values of Ψi and SC for 11 of the soil texture 

classes. These values were used in conjunction with equation 3 

to estimate a CF value for each of the 11 texture classes for the 

P-GWAVA-RZ simulations. The CF value for the remaining 

texture class (silt) was estimated using two additional PTFs, 

also provided by Rawls and others (1990), that expressed SC

as a function of sand content and Ψi as a function of SC. For 

these calculations, silt was presumed to contain 8 percent sand 

and 6 percent clay. The CF values obtained using equation 3 

were all computed using a wetted depth (L) of 5 cm, because 

this value provided the closest match between the predicted 

and measured CF values for each of the nine soil texture 

classes for which Rawls and others (1990) provided measured 

values. For the present study, the CF values produced using 

this approach (table 14) were multiplied by the value of Ksat

provided by SSURGO for the surface soil layer to obtain 

a value of Kcr for every soil component included in the 

P-GWAVA-RZ simulations. 

Soil Macroporosity

Several parameters are used by RZWQM to characterize 

the physical and hydraulic properties of macropores 

(table 14). However, because field measurements have been 

relatively sparse, values for most of these parameters have 

been estimated through model calibration (for example, 

Ahuja and others, 1995; Kumar and others, 1998; Malone 

and others, 2003, 2004a, 2004b). One exception has been 

macroporosity. As part of an effort to obtain values for several 

input parameters for solute transport-and-fate simulations 

using the MACRO model, Jarvis and others (2007b) devised 

a classification system that provides estimates of soil 

macroporosity as a function of soil texture, soil OM content, 

depth below land surface, and extent of tillage. This system 

was adapted into a flow chart (fig. 10) that was used to assign 

a macroporosity value (nm, expressed as a fraction of total 

soil volume) for each soil horizon in the Phase 2 study area 

for which SSURGO soil texture, soil OM and depth data 

were available.

Water Movement Through the Subsurface

One of the primary differences between PRZM and 

RZWQM is the way they simulate the flow of water through 

the vadose zone. The tipping-bucket algorithm used by 

PRZM conveys water downward through the soil profile by 

allowing water to collect in individual depth increments until 

a particular threshold is exceeded (the bucket “tips”) and the 

water is allowed to flow to the next layer below. By contrast, 

RZWQM uses established equations to simulate the flow of 

water in two primary flow domains: (1) the soil matrix (which 

contains both mesopores and micropores), and (2) the soil 

macropores. Micropores are voids within the soil matrix that 

are sufficiently small that any water they contain is considered 

stagnant, or immobile water, and solute movement occurs 

primarily through diffusion. Macropores are root channels, 

worm burrows, cracks or other conduits in the soil in which 

the aqueous phase is predominantly mobile water, and 

through which water and solutes move primarily by advection. 

Mesopores are voids within the soil that are intermediate 

in size between micropores and macropores, and through 

which water and solutes move at intermediate rates, by both 

advection and diffusion.

Phase 1 

Two primary water-retention variables are used by PRZM 

as reference points for characterizing water flow through the 

soil profile (fig. 11), field capacity and wilting point (Carsel 

and others, 1998). From an operational standpoint, field 

capacity is commonly defined as the water content at a matric 

suction of 33 kPa, designated herein as θ33. The wilting point 

is often operationally defined as the water content at a matric 

suction of 1,500 kPa, or θ1500 (Carsel and others, 1998; Saxton 

and Rawls, 2006).

The tipping-bucket algorithm used by PRZM to simulate 

water flow through the subsurface allows water to collect 

in a given grid cell until the water content exceeds the field 

capacity for the soil in which it is located. Once the water 

content in the grid cell exceeds field capacity, the water is 

allowed to drain to the underlying grid cell until the water 

content in the overlying grid cell drops to field capacity. Water 

uptake by crops, which only occurs within the active root 

zone, is allowed to take place in a given grid cell only if the 

water content is greater than the wilting point for that soil. 
Methods 25

tac12-0781_fig10

Organic Soil?

{ %oc > [12% + ( 0.1 * % clay ) ]

or %oc > 18% } [FAO, 2006]*

_

_

n m = 0.05 Yes

No

 Uppermost soil horizon?

Yes No

Perennial crop (that is, 

grassland, vineyard, 

orchard) or undisturbed?

Yes

n m = 0.05

No

 Intensively (secondary) tilled? Yes

n m = 0.05

 No

 (ploughed 

 only)

Clay,

silty clay, or

silty clay loam?

Yes

n m = 0.03

No

Sand or 

loamy sand?

Yes

n m = 0.05

No

n m = 0.04

Midpoint of horizon shallower 

than 50 cm depth?

Yes

Clay,

silty clay, or

silty clay loam?

Yes

n m = 0.016

No

Sand or 

loamy sand?

Yes

n m = 0.05

No

n m = 0.016

No

 Clay,

silty clay, or

 silty clay loam?

No

Sand or 

loamy sand?

No

n m = 0.008

Yes

n m = 0.008

Yes

n m = 0.05

Midpoint of horizon deeper than 100 cm?

(Suggested minimum depth for B or BC horizons [FAO, 2006*])

No

Yes

Clay, 

silty clay, or 

 silty clay loam? 

No

Sand or 

loamy sand?

No

n m = 0.004

Yes

n m = 0.002

Yes n = 0.035

 (Average of B

and BC values

 [FAO, 2006*])

m

*Food and Agriculture Organization of the United Nations (2006)

Figure 10. Procedure used to select the macroporosity value (nm, expressed as a fraction of total soil volume) assigned to every 

soil horizon where soil texture, organic matter and depth data were available in the Soil Survey Geographic database (SSURGO)

for the sites examined in the 10 northernmost states of the Corn Belt of the United States during Phase 2 of this study. Flow chart 

adapted from classification scheme of Jarvis and others (2007b).
26 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig11

Water table

Capillary

 fringe

Water content, in cubic centimeter per cubic centimeter

0 0.1 0.2 0.3

Field capacity 

 (-33 kPa)

 Wilting

 point

 (-1,500 kPa)

{

Saturated water content

 ( = porosity)

 Water content 

 (idealized profile)

Figure 11. Variations in soil water content with depth in the vadose zone. Approximate 

values shown for field capacity and wilting point are within ranges reported by Rawls and 

others (1982) for a loam soil. (kPa, kilopascal.)

These drainage rules are invoked to redistribute water through 

the soil column on a daily basis. The merits and drawbacks 

of this simplified approach to simulating the movement of 

water through a soil profile were discussed by Carsel and 

others (1998).

PRZM offers three internal methods for estimating 

field capacity and wilting point for each soil of interest, 

using data and procedures from the USDA (Rawls and 

Brakensiek, 1983): (1) computation from percentages of 

sand and clay, organic-matter content, and bulk density using 

a regression‐based PTF; (2) selection on the basis of the 

percentages of sand and clay; or (3) selection on the basis of 

soil texture class. Instead of using these procedures, however, 

field capacity and wilting point were estimated during Phase 1 

using the computer program Rosetta (Schaap and others, 

2001), which uses neural networks, rather than regression 

equations, to estimate soil hydraulic parameters from other 

soil properties. Like PRZM, Rosetta offers the flexibility of 

estimating water-retention property values using a hierarchy 

of sets of input parameters, depending on the available data. 

For Phase 1, field capacity and wilting point were estimated 

at each site using the PTF in Rosetta that uses percentages of 

sand, silt and clay, and bulk density, as input parameters. 

The use of Rosetta instead of the internal PRZM 

methods to estimate field capacity and wilting point at the 

Phase 1 sites was motivated by two primary considerations. 

First, whereas the methods internal to PRZM for estimating 

soil hydraulic parameters (Carsel and others, 1998) use 
Methods 27

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Field capacity–estimated from Rawls and Brakensiek (1983) equation,

in cubic centimeter per cubic centimeter

Field capacity–estimated from Rosetta, in cubic centimeter per cubic centimeter

Range of values for most soils (Dunne and Leopold, 1978) 

FC (R&B) = FC (Rosetta) 

Figure 12. Comparison between estimates of field capacity (FC) at the P-GWAVA-PR (Phase 1) study sites using 

the Rawls and Brakensiek (1983) equation (R&B), estimates for the same sites from Rosetta, and the range of values 

typically measured in soils in the United States.

data from 5,760 measurements on 640 samples (Rawls and 

Brakensiek, 1983), the water-retention parameter estimates 

provided by Rosetta are derived from a substantially more 

extensive database of at least 20,574 measurements on at 

least 2,134 samples (Schaap and others, 2001). Second, 

comparisons between the estimates obtained from Rosetta 

and those computed using the Rawls and Brakensiek (1983) 

equations for field capacity and wilting point for all sites of 

interest (figs. 12 and 13, respectively) indicated that a higher 

proportion of the values obtained using Rosetta were within 

the ranges measured in most soils for these two parameters 

(Dunne and Leopold, 1978) than was the case for the estimates 

obtained using the Rawls and Brakensiek (1983) equations. 

Values of parameters estimated by the Rawls and Brakensiek 

(1983) equations nearly always exceeded the corresponding 

values estimated by Rosetta (figs. 12 and 13)

Rosetta was not able to compute field capacity or wilting 

point for three of the Phase 1 sites with especially high 

amounts of OM, that is, soil with mass-based organic-carbon 

fractions (f

oc) exceeding 0.3 (30 percent by weight [w/w]). 

This may have been because the bulk densities of the soils 

in question were less than 0.5 g/cm3, the minimum value for 

which Rosetta is able to compute water-retention parameters. 

To address this problem, values of field capacity and wilting 

point were taken directly from moisture retention curves 

provided by Marcel Schaap (University of Arizona, written 

commun., September 2003) for a peat soil from Poland that 

exhibited a bulk density similar to the organic-rich soils 

of interest. Water retention data generally are rare for such 

organic-rich soils.
28 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig13

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.05 0.1 0.15 0.2

Wilting point–estimated from Rawls and Brakensiek (1983) equation,

in cubic centimeter per cubic centimeter

Wilting point–estimated using Rosetta, in cubic centimeter per cubic centimeter

WP (R&B) = WP (Rosetta) 

Range of values for most soils (Dunne and Leopold, 1978) 

Figure 13. Comparison between estimates of wilting point (WP) at the P-GWAVA-PR (Phase 1) study sites using 

the Rawls and Brakensiek (1983) equation (R&B), estimates for the same sites from Rosetta, and the range of values 

typically measured in soils in the United States.

Phase 2 

RZWQM simulates the movement of water and dissolved 

solutes within two distinct but interconnected flow domains 

in the vadose zone: the soil matrix and macropores. The void 

space in the soil matrix, in turn, is comprised of micropores 

and mesopores. The macropores, which are represented as 

either cylindrical channels (created by worms, plant roots, 

invertebrates, and other organisms) or planar cracks are also 

divided into two domains; vertical, continuous channels 

through which soil water migrates rapidly, and lateral, 

dead-end pores through which water and solutes may move 

from the continuous pores into the soil matrix. According to 

the default configuration of the model (which was used to 

characterize several macropore properties for this study), all 

the macropores in the uppermost horizon at each site consist of 

cylindrical channels that connect with planar cracks at greater 

depths beneath the land surface. The cylindrical channels are 

presumed to be open (that is, they have a nonzero radius) only 

in the uppermost soil horizon at each site, whereas the planar 

cracks are presumed to be open only in the horizons located at 

greater depths. Water and solutes moving through macropores, 

therefore, are presumed to enter through cylindrical channels 

at the land surface, and flow into the planar cracks to which 

the cylindrical channels are connected at greater depths. Flow 

through the vertical macropores occurs only if the rate of 

water deposition on the land surface (from either precipitation 

or irrigation) exceeds the rate of infiltration, causing the excess 

water to pond and flow downward into the macropores or off 

site as runoff (Ahuja and others, 2000). 

Once infiltration begins in response to a recharge event, 

RZWQM allows water to flow into both the micropores and 

the mesopores in the soil matrix, to the extent warranted by the 

infiltration rate. Soil water in the micropores is considered to 

be immobile, whereas the water in the mesopores, considered 

to represent mobile water, flows in response to the prevailing 

hydraulic gradient. Advection of soil water in the macropores 

occurs in the continuous vertical channels, but not in the 

dead-end macropores. Modes of preferential transport other 

than macropore flow, such as finger flow or funnel flow (for 

example, Kung, 1990; Ju and Kung, 1993; Jarvis and Dubus, 

2006; Chapwanya and Stockie, 2010; DiCarlo and others, 

2011; Perkins and others, 2011) are not simulated.
Methods 29

Although hydrodynamic dispersion generally is assumed 

to be caused by variations in pore-water velocities and 

migration path lengths in the subsurface (Freeze and Cherry, 

1979; Amoozegar-Fard and others, 1982; Gelhar and others, 

1992), RZWQM does not simulate it in this manner. Instead, 

the movement of water and solutes through the mesopores 

is simulated as a two-stage piston-flow process designed 

to mimic both miscible displacement and hydrodynamic 

dispersion. The exchange of dissolved solutes between 

micropores and mesopores takes place through molecular 

diffusion. Exchange of water and solutes between the 

macropores (either continuous or dead-end) and the soil matrix 

occurs through the macropore walls (Ahuja and others, 2000). 

RZWQM uses equations derived from fundamental 

hydraulic principles to simulate the flow of water through the 

subsurface (Ahuja and others, 2000; Malone and others, 2003, 

2004a). Infiltration from the land surface into the soil matrix, 

and the movement of water from macropores into the soil 

matrix, are described using different forms of the Green-Ampt 

equation. Flow in macropores, when it occurs, is simulated 

using Poiseuille’s law (Ahuja and others, 2000). Between 

infiltration events, the ongoing movement of water in the 

mesopores (a process sometimes referred to as redistribution) 

is simulated using the Richards equation—the unsaturated 

version of the Darcy equation that accounts for the fact 

that both water content (fig. 11) and hydraulic conductivity 

in the vadose zone vary with pressure head (Freeze and 

Cherry, 1979). 

RZWQM uses the Brooks-Corey model to quantify 

the characteristic curves for the soils of interest—that is, 

the functional relations between matric suction (τ, where 

τ = |h| and h is the soil water pressure head, in centimeters), 

water content (θ(τ), cm3/cm3) and hydraulic conductivity 

(K(τ), cm/hr) in the vadose zone (Ahuja and others, 2000; 

Malone and others, 2003). The Brooks-Corey model uses the 

following equations to describe the K(τ) function:

 ( )= for 0 K K sat bK τ ≤τ≤τ (4a)

2

2 ( )= * for > N K C bK τ τ ττ (4b)

where 

Ksat is the saturated hydraulic conductivity, in 

centimeters per hour;

τbK is the air-entry value, or bubbling suction, in 

centimeters, for the hydraulic conductivity 

relation (K(τ) curve); 

C2 is the coefficient for the τ > τbK segment of the 

K(τ) curve; and 

N2 is the unsaturated hydraulic conductivity 

constant (Malone and others, 2004b), 

calculated from the dimensionless poresize 

distribution index, λ (Schaap and others, 

2004), using the following relation (Malone 

and others, 2003; Ma and others, 2009; 

Agricultural Research Service, 2010):

N2 = 2 + (3 * λ) (5)

The coefficient associated with λ in equation 5 has been 

adjusted during other studies to improve the agreement 

between simulated and observed soil water contents (Robert 

Malone, U.S. Department of Agriculture, written commun., 

November 2011). For example, Malone and others (2004b) 

used the following version of this relation:

N2 = 2 + λ (6)

For the present study, however, equation 5 was used to 

compute N2 from λ because it is the relation that the RZWQM 

documentation recommends for this purpose (Agricultural 

Research Service, 2010). Rearranging equation 4b, and 

combining it with equation 4a at τ = τbK, the coefficient C2

 may 

be computed as follows:

2

2 = * ( ) N C Ksat bK

− τ (7)

As with the hydraulic conductivity form of the 

characteristic curve (equations 4a and 4b), the Brooks-Corey 

version of the water-content curve (θ(τ)) is also a function of 

λ. Additionally, the Brooks-Corey water-content curve is a 

function of the bubbling suction, τb, the soil water content at 

saturation, θs, and the residual water content, θr (Ahuja and 

others, 2000; Schaap and others, 2004). The residual water 

content is essentially a fitting parameter (McCuen and others, 

1981; Saxton and others, 1986), but its physical meaning is 

often inferred to be the “irreducible” water content of a soil at 

infinite suction, when no more water can be extracted (Marcel 

Schaap, University of Arizona, written commun., May 2007). 

The texture-class-average data reported by Rawls and others 

(1982) indicate that θ1500—a parameter that is often used as an 

estimate of the wilting point (table 14)—provides a reasonable 

approximation of θr in coarse soils (sand, loamy sand, and 

sandy loam). However, data reported by Rawls and others 

(1982) indicate that θ1500 may exceed θr by a factor of between 

2 and 9 in medium to fine soils. Consequently, for each soil 

horizon examined during the P-GWAVA-RZ simulations, the 

water-content characteristic curve was constructed using the 

texture-based θr values obtained by RZWQM from Rawls and 

others (1982), rather than the θ1500 values. 

Although SSURGO provides values of Ksat for nearly 

all of the soil horizons examined, in some cases the values 

given are identical across multiple soil textures—a pattern 

that is inconsistent with empirical observation (for example, 

see Rawls and others, 1982). As a result, values of Ksat were 

estimated from the SSURGO-derived weight percentages 
30 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

of sand and clay using the PTF that was devised for this 

purpose by Saxton and others (1986), and adapted by Nelson 

(2012) into an online program. This approach resulted in Ksat 

distributions that were considerably smoother among adjacent 

soil horizons than distributions obtained directly from the 

SSURGO Ksat data. 

When measured values of Ksat, τbK and λ are not available, 

RZWQM offers three estimation options (Bartling and others, 

2011). These options involve using either (1) soil texture alone 

(using the texture-based PTFs of Rawls and others [1982]); 

(2) soil texture, θ33 and θ1500 (Ma and others, 2009); or (3) soil 

texture and θ33 (Williams and Ahuja, 1992). Depending on the 

data available from SSURGO, the P-GWAVA-RZ simulations 

used one of these methods to estimate τbK and λ for each soil 

horizon. In most cases, the third option was used because 

values of θ33 were available from SSURGO for nearly all the 

soil horizons examined. 

As an alternative to using the texture class-averaged 

values reported by Rawls and others (1982), the BrooksCorey parameters also may be computed from variables in the 

van Genuchten equation that are estimated from soil texture 

data using Rosetta (Schaap and others, 2004). In table 3, the 

ranges of values for the Brooks-Corey parameters τb (or τbK),

N2 and C2 computed from the van Genuchten parameters 

for 11 of the USDA soil texture classes (using Rosetta) are 

compared with the values reported for the same texture classes 

by Rawls and others (1982). Although in some cases τbK = τb

Table 3. Ranges of Brooks-Corey parameter values reported for 11 U.S. Department of Agriculture soil texture classes, 

and ranges estimated from van Genuchten parameters using Rosetta.

[Although Schaap (2011) provided van Genuchten parameter values for all 12 texture classes in the U.S. Department of Agriculture (USDA) soil 

texture triangle (Natural Resources Conservation Service, 2011b), the texture classes included in this analysis were limited to the 11 classes that 

were examined by Rawls and others (1982)—that is, all of the USDA classes except silt. Abbreviations: cm, centimeter; cm/h, centimeter per 

hour; >, greater than] 

Brooks-Corey parameter

Range among texture classes 

reported by Rawls and

others (1982)

Range among texture classes 

estimated from van Genuchten 

parameters using Rosetta 

(Schaap, 2011)

Air-entry value1

 (τb or τbK) (cm) 7.26–37.30 28.38–197.70

Exponent for τ > τbK segment of K(τ) curve (N2

) 2.38–3.78 2.62–8.53

Coefficient2

 for τ > τbK segment of K(τ) curve (C2) 

(cm/h) 346–37,421 3,520–6.65×1013

1

Although in some cases τbK= τb (Ahuja and others, 2000), no distinctions were made by either Rawls and others (1982) or Schaap (2011) 

between τbK (value for hydraulic conductivity relation) and τb (value for water-content relation). In the absence of additional data, however, the 

same values were presumed to apply for both parameters.

2

Computed from τb and N2 using equation 7, that is 

C2=Ksat*(τbK

-N

2

)

where

Ksat is the saturated hydraulic conductivity of the soil (centimeters per hour);

τbK is the air-entry value (centimeters); and

N2 is the dimensionless unsaturated hydraulic conductivity constant (Malone and others, 2004b).

(Ahuja and others, 2000), neither Rawls and others (1982) 

nor Schaap (2011) made explicit distinctions between τbK

(the value for the hydraulic conductivity relation) and τb (the 

value for the water-content relation). Consequently, the same 

values of N2 and C2 were presumed to apply for either the 

hydraulic conductivity relation or the water-content relation 

for a given soil. For τb (or τbK) and N2, the ranges of values 

obtained using the two methods exhibit a considerable degree 

of overlap (table 3). However, in the case of C2 (computed 

from Ksat, τbK, and N2 using equation 7), the values generated 

using Rosetta were, in some cases, between one and nine 

orders of magnitude larger than those provided by Rawls and 

others (1982). 

Thus, whereas the estimates of soil water content at 

field capacity and wilting point obtained from Rosetta were 

suitable for the P-GWAVA-PR simulations during Phase 1 

(figs. 12 and 13, respectively), this was not the case for the 

Rosetta-derived values of the Brooks-Corey parameters during 

Phase 2. As a result, the Rosetta-derived values of the BrooksCorey parameters were not used for the P-GWAVA-RZ 

simulations. Instead, the Brooks-Corey parameter values 

reported for individual texture classes by Rawls and others 

(1982), which are generated internally by RZWQM and 

have been used extensively by other studies in the past, were 

used to produce the soil characteristic curves for the Phase 2 

simulations (table 4). 
Methods 31

Weather

The meteorological data used for the P-GWAVA 

simulations were obtained at different time scales for different 

purposes. Most of the temperature-dependent processes that 

are simulated internally by PRZM or RZWQM use daily 

values. However, for several temperature-sensitive parameters 

that were computed externally, values were calculated using 

the average air temperature for May (TMay), the month when 

atrazine and nitrate were assumed to have been applied 

every year at each site. This assumption was required 

because location-specific data on the timing of agrichemical 

applications and other agricultural management practices were 

not available on a nationwide basis at the time of this study. 

The temperature-sensitive parameters of interest included the 

initial rates of atrazine transformation (for both study phases), 

the rate of DEA transformation, and the Henry’s law constant 

(Phase 2 only). During Phase 1, daily values of precipitation 

were used. Hourly precipitation data were required during 

Table 4. Approaches selected to improve agreement between simulated and measured solute concentrations for the final set of 

P-GWAVA-PR (Phase 1) simulations across the conterminous United States, and for the final set of P-GWAVA-RZ (Phase 2) simulations 

in the 10 northernmost states of the Corn Belt.

[See text and table 14 for details. Other distinguishing features of the approaches used for the two study phases are listed in table 1. Abbreviations: cm, 

centimeter; DEA, deethylatrazine; Koc, organic carbon-water partition coefficient; kg a.i./ha, kilograms of active ingredient per hectare; °C, degrees Celsius]

Parameter, process, or feature Approach for each model

P-GWAVA-PR P-GWAVA-RZ

Height(s) of grid cells for simulations 0–9.9 cm interval: 0.1 cm

10–100 cm interval: 30 cm 

1 cm (entire soil column)

Spatial heterogeneity of atrazine

application

Areally averaged Spatially uniform (2 kg a.i./ha)

Irrigation Simulated at sites selected at random within 

each groundwater sampling network in 

proportion to percentage of nearby land in 

irrigation

Simulated at all sites

Method used to estimate or obtain values for 

selected soil hydraulic parameters

[parameters of interest shown in brackets]

Rosetta database (Schaap and others,

2001)

[Water contents at field capacity and wilting 

point]

Pedotransfer functions from Rawls and 

others (1982) 

[Brooks-Corey parameters]

Relative sizes of pools of soil organic matter 

(fast/medium/slow)

Not applicable (not used by model) Original values (percent): 2 / 18 / 80

Final values (percent): 10 / 20 / 70

Koc Literature value (atrazine) Reduced to 25 percent of their original 

literature values (atrazine and DEA)

Rate of atrazine disappearance in soil at 25°C Literature value Original approach: Estimated using updated 

Fenner-Borsuk relation (eq. B3)

Final approach: Literature value

Site(s) of atrazine transformation in soil Dissolved and sorbed states Not applicable (rate specified for whole 

soils only)

Phase 2, in order to simulate preferential flow. For all other 

meteorological parameters quantified using data acquired from 

other sources for this study, either daily or monthly values 

were used (table 14).

Phase 1

Daily values of soil temperature are used by PRZM for 

estimating the rates of volatilization and transformation of 

nitrogen species, as well as the rate of evapotranspiration 

(ET). However, because the transport and fate of nitrogen 

species were not examined during Phase 1 and an external 

method was used to estimate ET, no built-in temperaturedependent functions in PRZM were used during the Phase 1 

simulations. Nevertheless, soil temperature data were still 

required for the external calculations used to estimate potential 

evapotranspiration (PET) and to account for the effect of 

temperature on atrazine transformation rates.
32 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Nationwide coverages of precipitation, wind speed, 

and temperature data were obtained from the National 

Climatic Data Center (NCDC) of the National Oceanic 

and Atmospheric Administration (NOAA) during Phase 1 

(table 14). Daily values of minimum and maximum 

temperature (Tmin and Tmax, respectively), as well as 

precipitation, were acquired from 11,240 weather stations 

across the Nation. However, complete records for all three 

parameters during 1986–95 were available for only 4,953 of 

these stations. Of these, 331 stations were close to one or more 

of the 1,224 Phase 1 study sites of interest. After removing 

weather stations with errors that could not be corrected, 

327 stations were used to obtain daily values of Tmin, Tmax, 

and precipitation. Nationwide wind speed data for 1986–95, 

required for the ET computations, were available only as 

monthly averages, and were more sparsely distributed than the 

temperature and precipitation data. As a result, the irregularly 

shaped grid cells used in GIS to couple individual sites 

with their nearest weather station—referred to as Thiessen 

polygons—were much larger for the wind speed data than for 

the temperature or precipitation data.

The temperature of shallow groundwater was measured 

during the NAWQA sampling operations at all the sites 

examined for this study. However, in adherence to the design 

objective to use only sources of input data that provided 

parameter values on a nationwide basis, the results from the 

NAWQA temperature measurements were not used for any 

model simulations during either phase of this study. PRZM 

includes a subroutine for simulating temporal variations in 

soil temperature from heat-flux calculations, but it was not 

used because site-specific data for several of the parameters 

that it requires (for example, values for the heat capacity, 

thermal conductivity, and thermal diffusivity of the soil) were 

not available on a national scale. Consequently, daily values 

of air temperature from the National Climatic Data Center 

(2002b) were used during Phase 1 to estimate ET at each site. 

TMay was used to adjust the rate of atrazine transformation to 

account for spatial variations in temperature at the time of 

application. Annual average values of TMay, obtained from the 

Parameter-elevation Relationships on Independent Slopes 

Model (PRISM) Climate Group (2009), were moderately 

well correlated with the groundwater temperatures measured 

at the 1,224 study sites for Phase 1 (R2 = 0.39 [P ≤ 0.0001]; 

Spearman ρ = 0.62 [P(ρ) ≤ 0.0001]; N = 1,800 measurements) 

(fig. 14). As a result, the TMay data were considered to be 

adequate proxies for groundwater temperature for the 

P-GWAVA-PR simulations. 

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25 30

Average May air temperature =

groundwater temperature 

R2 = 0.39 (P ≤ 0.0001)

ρ = 0.62 (P ≤ 0.0001)

N = 1,800 measurements 

Groundwater temperature, in degrees Celsius

Average May air temperature, in degrees Celsius

Figure 14. Comparison between groundwater temperatures measured at each of the 1,224 P-GWAVA-PR (Phase 1) study 

sites examined across the conterminous United States and the average air temperature for May in the same locations 

from the National Oceanic and Atmospheric Administration (2010). The number of measurements (1,800) exceeds the 

number of sites (1,224) because multiple measurements were made in some locations. Locations of study sites are shown 

in figure 1.
Methods 33

Phase 2

The simulation of weather by RZWQM involves the 

use of daily values for most of the meteorological parameters 

of interest (table 14). However, to generate macropore flow, 

hourly data from storms must be provided. Hourly data from 

storms were not available at a national scale after 1990 (Burns 

and others, 2007). Consequently, a program embedded in 

RZWQM known as Cligen (Agricultural Research Service, 

2009) was used to simulate weather at all of the study sites 

during Phase 2 (fig. 9). The specific version of the weather 

simulator used was Cligen90, denoted as “Cligen” herein. 

Cligen applies probabilistic methods, guided by the statistical 

properties of historical data gathered between 1949 and 

1996 (Agricultural Research Service, 2009), to generate 

simulated storms on an hourly basis. Similar methods are 

used to provide daily values for maximum and minimum 

temperature, wind speed, solar radiation, and relative humidity 

(table 14). Because Cligen generates these parameter values 

stochastically from historical data, rather than providing actual 

historical measurements, the specific years of the Phase 2 

simulations were essentially arbitrary. Consequently, all the 

P-GWAVA-RZ simulations (including those carried out for 

the initial stabilization period) were nominally carried out 

for the time interval from 1998 through the end of 2004, to 

accommodate most of the recent samples against which its 

predictions were to be compared (table 1). (No samples were 

collected at any sites in 2005 and samples were collected at 

only four sites in 2006.) Burns and others (2007) provide 

a valuable discussion of the relative merits and limitations 

of using Cligen and other weather simulators, rather than 

measured data, to simulate weather for solute transport-andfate simulations.

The data required for the Phase 2 weather simulations 

were assembled by reconstructing the 1-degree grid used by 

Cligen (that is, a grid for which each cell has a width in the 

east-west direction of 1 degree of longitude and a width in 

the north-south direction of 1 degree of latitude), selecting 

one Cligen weather station in each grid cell from a list of 

such stations provided by RZWQM, and using the weather 

data from that station for all of the Phase 2 sites in that cell. 

For 109 of the 453 Phase 2 sites, the nearest Cligen weather 

station that provided complete data for all of the parameters 

of interest was in the same cell. For these sites, the average 

distance to the weather station of interest was 25 mi. For most 

of the remaining sites, the climate data were obtained from 

the nearest Cligen station outside of the cell that provided 

complete data for all of the parameters of interest. Four of 

the Phase 2 sites were in Cligen cells without any weather 

stations. In these cases, the nearest station outside of the cell 

that provided complete data for all parameters of interest 

was used. As a result, no Phase 2 simulation site was located 

more than 85 mi from the weather station from which its 

meteorological data were obtained.

As with PRZM, RZWQM accounts for the influence of 

temperature on various physical, chemical, and biological 

processes. For RZWQM, these include heat flux through the 

soil, ET, crop development, solute transformation, ammonia 

volatilization, air-water partitioning of oxygen, and the 

growth and death of microorganisms. RZWQM assumes 

that the temperature at the soil surface is equal to the air 

temperature (Ahuja and others, 2000). The temperature used 

for calculating the initial values for the Henry’s law constants 

and transformation rates for atrazine and DEA at each site 

during Phase 2 was the average air temperature in May (TMay) 

because atrazine and nitrogen fertilizers were all assumed to 

have been applied at the end of May each year in each study 

location (table 14). At each site, TMay (fig. 15) was calculated 

as the arithmetic average of the daily minimum and daily 

maximum air temperatures for the month of May during 1971 

to 2000, using data from the PRISM climate mapping system 

(PRISM Climate Group, 2009). This parameter was used, 

rather than the temperature provided by Cligen on the assigned 

planting date of May 30, to avoid the variability introduced 

by the stochastic routines used by Cligen for estimating 

parameter values.

Assessment Depths and Depths to Water

Although efforts have been made to estimate the depth 

of the water table beneath the land surface across the United 

States (Fan and others, 2007, 2013), there are many areas 

around the Nation where the depth to groundwater has not 

been measured. Consequently, given the design objective 

of using only national-scale data as inputs to the P-GWAVA 

simulations, the selection of the assessment depths for the 

simulations was guided by (1) the depths to which the required 

soils data were available and (2) the maximum rooting depth 

of corn plants, rather than by the depth of the water table. 

The assessment depths used for this study were 1 and 3 m for 

the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) 

simulations, respectively (table 1). 

The distributions of depths to water for the sites 

examined during the two phases of this study are shown in 

figure 16. For each site, the depth to water (DTW) value used 

to construct figure 16 was the arithmetic average among all 

the measurements made at the site of interest. The numbers 

of water depth measurements varied widely among locations, 

ranging from 1 to 983 measurements per site, and spanning 

periods ranging from 5 days to 49 years (data not shown). 

Results from a statistical analysis of these data using the 

non-parametric Mann-Whitney test (Helsel and Hirsch, 

1992) indicated that the median DTW among the study sites 

examined for Phase 2 differed significantly from the median 

DTW for the sites examined for Phase 1 (P ≤ 0.0001). The 

data shown in figure 16 also indicate that at many of the study 

sites examined, the average depth of the water table beneath 

the land surface was shallower than the assessment depth. 

The analysis of the results from this study therefore included 

an examination of the extent to which the agreement between 

the recharge rates, solute concentrations, and detection 

frequencies predicted by the P-GWAVA simulations and their 

measured values may have been related to the depth of the 

water table.
34 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig15

OHIO

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

MICHIGAN

IOWA

ILLINOIS

INDIANA

0 200 MILES

0 200 KILOMETERS

100° 90° 80°

45°

40°

Phase 2 (P-GWAVA-RZ) sites

6.25 to 8

>8 to 10

>10 to 12

>12 to 14

>14 to 16

>16 to 18

>18 to 20

Average daily air temperature for 

May (1971-2000), in degrees Celsius

EXPLANATION

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 15. Average daily air temperature during May (TMay) from 1971 to 2000 and the P-GWAVA-RZ (Phase 2) study sites in the 

10 northernmost states in the Corn Belt of the United States. Temperature values were computed as the arithmetic average of the 

maximum and minimum air temperature in May at each site, using data from the PRISM Climate Group (2009).
Methods 35

Irrigation

Phase 1

PRZM includes an option (Carsel and others, 1998) 

to simulate the automatic application of irrigation water 

whenever the soil water content drops below a user-specified 

threshold, defined as the sum of the wilting point and a 

specified fraction of the available water capacity (AWC, 

calculated as the water content at field capacity minus the 

water content at the wilting point). When the water content 

within a given layer drops below this threshold, irrigation 

is applied at a specified rate until the water content exceeds 

the threshold value. For the P-GWAVA-PR simulations, a 

threshold equal to 40 percent of AWC was used (table 14). 

Initially, an irrigation rate of 1.0 in/hr was used for all sites, 

a value that was close to the midrange given by Carsel and 

others (1998) for sprinkler irrigation of a variety of soil types. 

However, because PRZM applies irrigation only in 1-day 

increments, use of this rate often led to the generation of 

excessive runoff, a circumstance not likely for most settings 

where irrigation is used. Consequently, the rate for all irrigated 

sites was decreased to 0.1 in/hr, resulting in substantially less 

runoff than when an irrigation rate of 1.0 in/hr was used. tac12-0781_fig16

 200

 150

 100

 50

0

Assessment

depth for

P-GWAVA-PR

simulations (1 m)

Assessment

depth for

P-GWAVA-RZ

simulations (3 m)

Average depth to water, in meters

Phase 1 wells

(P-GWAVA-PR 

simulations)

Phase 2 wells

(P-GWAVA-RZ

simulations)

3.15

4.38 ± 0.41

453

4.27

10.25 ± 0.99

1,171

Median, in meters (m):

Mean ± 95 percent Cl, in meters:

Number of wells:

EXPLANATION 

Outlier

Outlier

75

90th percentile

10th percentile

th percentile

50th percentile

(median)

25th percentile

Interquartile

range 

Figure 16. Average depths to water (DTW) at the sites examined in the conterminous United States (Phase 1) and in the 

10 northernmost states in the Corn Belt (Phase 2) of the United States for this study, relative to the assessment depths used 

for the P-GWAVA simulations during each study phase. The number of Phase 1 wells shown is less than the total of 1,224 

(table 1) because DTW data were not available for some sites. (CI, confidence interval.) 

Because irrigation is not applied uniformly across the 

Nation, information on the prevalence of nearby irrigation 

was collected at most of the study sites (Lapham and others, 

1995; Koterba, 1998) at the time of sampling (1992–2006). 

However, to adhere to the design objective of using only 

sources of input data that are available nationwide, these 

data were not used to determine which sites were irrigated 

during Phase 1. Instead, data from the NRI (Natural Resources 

Conservation Service, 2004) were combined with data from 

the EPA National Land-Cover Database, or NLCD (U.S. 

Environmental Protection Agency, 2007) to ascertain the 

prevalence of irrigation near each of the Phase 1 sites. For 

each location, data from the NRI (aggregated to the county 

level, rather than by NRI map unit) and NLCD were used 

to obtain an estimate of f

I

, the fraction of the surrounding 

agricultural land that was presumed to be irrigated (Naomi 

Nakagaki, U.S. Geological Survey, written commun., July 

2003). This parameter was then used to estimate the likelihood 

with which each site may have been irrigated at the time of 

sampling—and thus to determine whether irrigation would be 

applied during the P-GWAVA-PR simulation for that location.

For each study site, a value of f

1

 was computed using the 

following equation:
36 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

f f

f I

I

ag

= ,1992 (8)

where 

f

I,1992 is the fraction of the surrounding area irrigated in 

1992, based on NRI data (Natural Resources 

Conservation Service, 2004); and

f

ag is the fraction of agricultural land in the county 

(that is, farmland planted in row crops, 

orchards, vineyards, pasture, hay or small 

120° 110° 100° 90° 80° 70°

45°

40°

35°

30°

25°

0 400 MILES

0 400 KILOMETERS

Percentage of nearby land irrigated

0.0 (unirrigated)

≤ 10

> 10 to 20

> 20 to 50

> 50 to 100

EXPLANATION

National Albers Projection, North American

Datum of 1983 (NAD 83)

Figure 17. Prevalence of irrigated agricultural areas near each P-GWAVA-PR (Phase 1) study site in the conterminous United 

States in 1992. Computed from data from the Natural Resources Conservation Service (2004) and National Land Cover Dataset 

(U.S. Environmental Protection Agency, 2007) using equation 8.

grains, or left fallow), estimated from the 

NLCD data (U.S. Environmental Protection 

Agency, 2007). 

Because of uncertainties in the NRI and (or) NLCD data, the 

estimates of f

I

 obtained from equation 8 sometimes exceeded 

unity. In such cases, f

I

 was adjusted to unity, assuming that only 

agricultural irrigation was recorded by the NRI. Underestimates 

of f

I

, however, were less obvious, and therefore could be 

neither detected nor corrected. The resulting distribution of the 

prevalence of irrigation in agricultural areas near each of the 

Phase 1 study sites across the Nation is shown in figure 17. 
Methods 37

To account for the effects of irrigation in comparing the 

atrazine concentrations simulated by the P-GWAVA-PR model 

with the concentrations measured in shallow groundwater, 

two sets of simulations were carried out at each site—one set 

that simulated irrigation for all components of interest and 

one that did not simulate irrigation for any of the components. 

One of the two sets of simulations was selected for the site 

by generating a random number between 1 and 100 (using 

a Microsoft Excel® spreadsheet), dividing by 100, and 

comparing the result to the f

I

 value computed for that site with 

equation 8. If the resulting fraction was less than or equal to 

the f

I

 value for the site, the results from the simulations that 

included irrigation were used; otherwise, the results from the 

simulations without irrigation were used. 

Phase 2

RZWQM simulates irrigation using one of four 

application methods: sprinkler, furrow, flood, or drip. The 

model applies irrigation water either at fixed time intervals 

on specific dates or in response to specified degrees of soilwater depletion in the root zone (Ahuja and others, 2000; 

Agricultural Research Service, 2010; Bartling and others, 

2011). The P-GWAVA-RZ simulations used furrow irrigation, 

applied in response to particular soil-moisture thresholds 

for initiating and ending irrigation (rather than on a specific 

date or according to a fixed schedule) over the course of the 

100-day growing season. To avoid applying excessive amounts 

of water, a 3-day minimum time interval between successive 

irrigation events was specified. Additional details on the 

approach used to simulate irrigation with the P-GWAVA-RZ 

model are provided in table 14.

Runoff

Phase 1

PRZM simulates runoff flux (Qsw) using the curve number 

equation originally introduced by the Soil Conservation 

Service (SCS, now known as the NRCS), and modified to the 

following form by Carsel and others (1998):

Q

P SM S

P SM S sw = + −

+ +

( [ . * ]

( [ . * ])

0 2 )

0 8

2

(9)

where

Qsw is the runoff flux, in centimeters per day;

P is precipitation, in centimeters per day;

SM is snowmelt, in centimeters per day; and

S is the watershed retention parameter, in 

centimeters per day.

Snowmelt (SM) is estimated using the following equation:

SM = CM * T

where

CM is the degree-day snowmelt factor, in centimeters 

per degree Celsius per day; and

T is temperature, in degrees Celsius.

The watershed retention parameter (S) is estimated in the 

following manner:

S = (1,000/RCN) – 10

where 

RCN is the SCS (NRCS) runoff curve number, in 

days per centimeter. 

The degree-day snowmelt factor (CM) was approximated using 

the average value of this parameter provided by Carsel and 

others (1998) for open areas of the United States. The RCN 

value used by these equations is a function of the soil hydrologic 

group and the surface condition of the soil. The NRCS classifies 

soils into four soil hydrologic groups that vary in permeability 

from the well-drained sands and gravels of group A to the poorly 

drained, clay soils of group D. Group D soils also may exhibit 

high swelling potential and (or) be underlain by impervious 

layers (Natural Resources Conservation Service, 2007b). For the 

P-GWAVA-PR simulations, three surface conditions were used 

at different times of year: fallow prior to crop planting, cropping

during crop growth, and post-harvest (that is, with a residue 

cover). However, following the suggestion of Carsel and others 

(1998) for corn grown without conservation tillage—and in the 

absence of consistent, nationwide data on the spatial distribution 

of conservation tillage practices at the time—the RCN values 

for the cropping and post-harvest stages of corn cultivation were 

presumed to be identical.

The data associated with each STATSGO map unit 

provide percentages of each hydrologic group present, rather 

than a single hydrologic group. Consequently, a stochastic 

procedure was used to select a set of RCN values randomly 

at each site of interest. The percentage of each of the four 

soil hydrologic groups present within a 500-m radius of 

each site was determined (David Wolock, U.S. Geological 

Survey, written commun., March 2000) and a random-number 

generator was used to select a single hydrologic group for 

each site, the likelihood of selecting a given hydrologic 

group being proportional to its percentage within the 500 m 

radius surrounding the site. For example, a hydrologic group 

occupying 40 percent of the 500-m circle surrounding a given 

site had a 40 percent chance of being assigned to that site. Data 

from Carsel and others (1998) were then used to select a set of 

RCN values for the site (that is, one value for fallow conditions 

and a second value for cropping/post-harvest conditions) on the 

basis of its assigned hydrologic group. 
38 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Phase 2

During Phase 2, runoff was generated by RZWQM 

whenever the rate of water delivery from precipitation and (or) 

irrigation exceeded the rate of infiltration. RZWQM computes 

the rate of runoff as the difference between the rate of water 

delivery to the land surface (from precipitation and [or] 

irrigation) and the rate of infiltration (Ahuja and others, 2000).

Evapotranspiration

Phase 1

PRZM computes the rate of ET from estimates of 

PET which, in turn, are obtained either from measured pan 

evaporation data or, if such information is not available, 

from an empirical formula introduced by Hamon (1961). Pan 

evaporation data were not available on a nationwide basis 

for the Phase 1 simulation period (1986–95). However, the 

Penman-Monteith (PM) procedure (Allen and others, 1998) 

was selected to estimate PET, rather than the Hamon method 

embedded in PRZM, for several reasons. 

First, in keeping with the P-GWAVA design objectives, 

the PM equation was preferred because it accounts for a 

broader range of processes and factors known to affect 

PET than do most other methods that have been proposed 

for estimating this parameter. In particular, whereas the 

Hamon equation estimates PET from saturated water vapor 

density (that is, absolute humidity) and the duration of daily 

sunshine (Hamon, 1961), the PM method uses temperature, 

solar radiation, humidity, wind speed, surface albedo, leaf 

conductance, canopy radiation extinction, canopy height, 

and leaf area data to estimate PET (Vörösmarty and others, 

1998). Despite the relatively large number of variables for 

which data are required with the PM method, national-scale 

data sources or estimation procedures were available for all of 

its parameters. 

A second reason that PET rates predicted by the PM 

method were used for the P-GWAVA-PR simulations was that 

they have been found to be in close agreement with values 

measured in lysimeters beneath alfalfa and a variety of grasses 

in 11 locations across the United States, Denmark, Zaire, and 

Australia (Allen and others, 1989). Although Vörösmarty 

and others (1998) observed closer agreement with the results 

from global-scale water-balance calculations for the Hamon 

method than for the PM method, the agreement reported by 

Allen and others (1989) between PM-based estimates of ET 

and those obtained from direct, field-scale measurements was 

deemed to be more important for the purpose of simulating 

water movement in the vadose zone beneath specific locations 

for the P-GWAVA project. Additionally, the PM method 

has been recommended by the United Nations Food and 

Agriculture Organization (Allen and others, 1998) and the 

American Society of Civil Engineers (Ahuja and others, 2000) 

for estimating ET. 

Phase 2

The method used by RZWQM to estimate PET during 

Phase 2, the extended Shuttleworth-Wallace, or eSW model 

(table 14), represents a further refinement of the PM approach, 

broadening the scope of the method in at least three major 

ways. First, although the PM method simulates the crop 

canopy as a single, uniform layer (a “single big leaf”), the 

eSW model estimates changes in ET as the area of soil 

covered by the crop canopy increases over time during the 

growing season. Second, whereas the PM method designates 

the land surface as the sole source or sink for heat, the eSW 

model simulates heat fluxes separately for the crop canopy and 

the soil surface. Finally, the eSW model simulates the effect 

of crop residue on heat and water fluxes at the soil surface, 

a factor not accounted for by the PM approach (Ahuja and 

others, 2000).

Timing and Intensity of Agrichemical 

Applications

Information regarding the precise timing of agrichemical 

applications was not available on a nationwide basis at the 

time of this study. Consequently, atrazine (during both study 

phases) and nitrogen (during Phase 2) were assumed to have 

been applied annually at every site, following spring planting.

Atrazine

Results from previous studies have indicated that the 

likelihood of detecting pesticides in groundwater is positively 

related to the intensity of their use (Barbash and Resek, 1996; 

Kolpin and others, 1998, 2000; Barbash and others, 1999, 

2001; Stackelberg and others, 2006, 2012; Åkesson and 

others, 2013). Consequently, the spatial variability of atrazine 

concentrations in shallow groundwater may be affected by the 

spatial variability (if any) of atrazine applications at the land 

surface. The spatial variations in the intensities of atrazine 

application (figs. 1 and 2) reflect variations in the rate of 

atrazine application among different crops and counties, as 

well as variations in the percentage of each crop to which 

atrazine is applied in different counties. (Use intensity and 

application intensity refer to areally averaged rates of atrazine 

delivery to the land surface, to distinguish them from actual 

application rates, which are rarely known.) During some 

years, however, the actual rates of atrazine application to 

individual fields may be similar to that recommended on the 

product label, and thus more spatially uniform than the use 

intensities shown in figures 1 and 2. Groundwater flow in 

most hydrogeologic settings (other than areas with particularly 

large subsurface conduits, such as karst terranes, lava tubes, or 

highly fractured rock) tends to be more laminar than surfacewater flow, and therefore less well mixed. Consequently, 

small-scale spatial variations in pesticide concentrations 

caused by spatial variations in application intensity are likely 

to persist over longer distances along subsurface flow paths 

than in surface water. 
Methods 39

To investigate the potential effects of the spatial variability of atrazine applications on the 

geographic patterns of agreement between the simulated and measured concentrations of the 

herbicide in groundwater, two approaches for quantifying the rates of atrazine input at the land 

surface were examined during each phase of this study. One approach involved using areally 

averaged, site-specific estimates of atrazine use intensity for each site of interest (figs. 1 and 2); 

a second approach involved the use of a single, uniform application rate in all locations.

The areally averaged intensities of atrazine use were estimated in each location using a 

parameter that could be computed for any agricultural setting in the United States, namely, 

the ratio of the estimated amount of the herbicide that was applied for agricultural purposes in 

the county where the site was located to the estimated area of agricultural land in the county. 

These site-specific estimates of use intensity were calculated at each study site of interest 

using procedures introduced by Thelin and Gianessi (2000). For every county in which 

one or more of the sites were located, the intensity of atrazine use was estimated using the 

following formula:

 Uatr i

AppRate i j CropArea i j AcresTreated i j

,

( ) , * ( ) , *[(% ) , / ]

=

100

j

N

TotalAgriculturalLand i

=

∑

1

( )

 (10)

where

Uatr,i is the atrazine use intensity in county i, in kilograms of active 

ingredient per hectare;

N is the total number of major crops to which atrazine was applied 

in the state between 1995 and 1998 (Gianessi and Marcelli, 

2000), including (in descending order of use intensity) corn, 

sorghum, summer fallow land, sugar cane, sweet corn, sod, 

other hay, and seed crops, but excluding any greenhouse, 

ornamental plant, or post-harvest applications;

(AppRate)i,j is the average annual rate of atrazine application per unit area of 

crop j, in kilograms of active ingredient per hectare, between 

1995 and 1998 in the state where county i is located (Gianessi 

and Marcelli, 2000);

(CropArea)

i,j is the total area of crop j harvested in county i, in hectares, 

computed from the 1997 Census of Agriculture (U.S. 

Department of Agriculture, 1999);

(%AcresTreated)i,j is the average percentage of acres planted in crop j to which 

atrazine was applied between 1995 and 1998 in the state where 

county i is located (Gianessi and Marcelli, 2000); and

(TotalAgriculturalLand)

i is the total area of agricultural land mapped in county i, in 

hectares, derived from the 30-meter resolution 1992 National 

Land Cover Database (Vogelmann and others, 2001) enhanced 

by Nakagaki and others (2007), and including land classified as 

row crops, small grains, fallow land, pasture/hay, and orchards/

vineyards/other.
40 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The active ingredient (a.i.) is a chemical included in a 

commercial pesticide formulation to kill or otherwise 

control the target organism (for example, atrazine). Pesticide 

application rates are typically expressed in terms of the 

amount of active ingredient applied per unit area, rather 

than the amount of product applied per unit area, because 

commercial pesticide products typically contain other 

ingredients, known as adjuvants (often referred to as “inert 

ingredients”), that are added to improve the effectiveness 

of the active ingredient. Another reason for expressing 

application rates in this manner is that commercial pesticide 

products often contain more than one active ingredient. 

Equation 10 was used to compute the atrazine use 

intensities in agricultural settings for the Phase 1 and Phase 2 

study areas (figs. 1 and 2, respectively). Variations in atrazine 

use intensity were substantial among the locations examined 

across the country (fig. 18). Although data on atrazine use 

intensities have been compiled for circular areas of 500-m 

radius surrounding each of the sites examined for this study 

(for example, Stackelberg and others, 2012), the countybased values computed with equation 10 were used instead, 

in accord with the design objective of focusing solely on 

input data that were available for all locations throughout the 

conterminous United States.

acfblusag1 acfbluscr1 acfbluscr2 albelusag1

albelusag2cazbluscr1

ccptlusag1ccptlusag2

ccptlusor1

connlusag1

eiwaluscr1

hdsnlusag1

leriluscr1lerilusrc1

linjluscr1

lirbluscr1lirbluscr2

lsuslusag1lsuslusag2lsuslusag3

nvbrlusag1nvbrlusag2

ozrklusag1

ozrklusag1a

ozrklusag2

ozrklusag2a

potolusag1potolusag2

pugtfpscr1

pugtluscr1

rednlusag1rednlusag2

rioglusag1

riogluscr1

sacrluscr1

sanjluscr1

sanjluscr1a

sanjlusor1

sanjlusor1a

sanjlusor2

sanjlusor2a

santluscr1

sofllusag1

sofllusor1spltluscr1

umisluscr1usnkluscr1usnkluscr2usnkluscr3usnkluscr4

utenluscr1

whitluscr1whitluscr2whitluscr3

whitluscr3a

willlusag1willlusag2

wmiclusag1

wmiclusag1a

wmiclusag2

Groundwater network

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Atrazine use intensity, in kilograms of active ingredient per hectare

EXPLANATION 

Outlier

Outlier

75

90th percentile

10th percentile

th percentile

50th percentile

(median)

25th percentile

Interquartile

range 

Figure 18. Variations in atrazine use intensity on agricultural land among sites in the groundwater networks 

examined in the conterminous United States for Phase 1 of this study, 1992–98. Study site locations are shown in 

figure 1. Descriptions of groundwater networks are provided in appendix C.
Methods 41

The second approach for quantifying the rates of atrazine 

input at the land surface for this study involved using a 

spatially uniform value of 2 kilograms of active ingredient per 

hectare (kg a.i./ha) in all locations of interest. This application 

intensity represented the median among the atrazine 

application rates used by 18 field studies of atrazine behavior 

in the subsurface under corn cultivation (Foy and Hiranpradit, 

1989; Sophocleous and others, 1990; Steenhuis and others, 

1990; Adams and Thurman, 1991; Frank and others, 1991; 

Hall and others, 1991; Kalkhoff and others, 1992; Wauchope 

and others, 1993; Gish and others, 1994; Jayachandran and 

others, 1994; Sadeghi and Isensee, 1994; Kumar and others, 

1998; Baer and Calvet, 1999; Bayless, 2001; Capel and 

Larson, 2001; Hyer and others, 2001; Malone and others, 

2001b, 2004c). These values ranged from 0.8 kg a.i./ha (Frank 

and others, 1991) to 8.5 kg a.i./ha (Malone and others, 2004c). 

Most of these rates are consistent with the range of values (0.3 

to 3.0 kg a.i./ha) reported for agricultural areas of the United 

States by the USDA’s National Agricultural Statistics Service 

(Gail Thelin, U.S. Geological Survey, written commun., 

August 2008), the maximum allowable rate of 2.8 kg a.i./ha 

per year established by the EPA for corn and sorghum in 1992 

(U.S. Environmental Protection Agency, 2003), and the rate of 

2 kg a.i./ha used for a large-scale simulation study carried out 

for the State of Iowa by Eason and others (2004). 

During Phase 2, a preliminary set of P-GWAVA-RZ 

simulations using the areally averaged atrazine application 

intensities (computed using equation 10) resulted in predicted 

atrazine concentrations that were less than the reporting 

limits for the herbicide at all of the sites examined. Because 

atrazine was detected in shallow groundwater at many of 

these sites—and given the assumption that the concentrations 

of any surface-derived contaminant in the vadose zone are 

most likely to be greater than or similar to those in shallow 

groundwater beneath the same location—the RZWQM 

simulations may have underestimated the amounts of solute 

moving through the soil column. Were this the case, it would 

have been consistent with the observation by Nolan and others 

(2010) that in some settings, especially where macropore flow 

is significant, RZWQM appears to underestimate the rate 

of transport of nitrate and other conservative solutes (such 

as bromide) through the vadose zone. To obtain simulated 

atrazine concentrations that were more closely aligned 

with those measured in the underlying groundwater during 

Phase 2, higher intensities of simulated atrazine application 

were required. Consequently, the final set of P-GWAVA-RZ 

simulations carried out for Phase 2 used the uniform atrazine 

application intensity of 2 kg a.i./ha at all sites, a value that 

was higher than the areally averaged use intensities shown 

in figure 2 for the Phase 2 study area (0–1.0 kg a.i./ha). 

Scaling the areally averaged atrazine use intensities (fig. 2) 

to align more closely with allowed application rates was not 

implemented because it may have produced rates of atrazine 

use exceeding those that were actually applied to the land. 

This would have precluded the completion of one aspect of the 

Phase 2 analysis, in which the proportion of the applied atrazine 

that the P-GWAVA-RZ model predicted to have passed the 3-m 

assessment depth—as either unreacted atrazine or DEA—was 

computed for the 5-year simulation period. 

Nitrogen Fertilizers

Although the formation, transport, and fate of nitrate were 

not simulated during Phase 1, these processes were examined 

for the P-GWAVA-RZ simulations in Phase 2 of this study. 

Because of uncertainties regarding variations in its nitrogen 

content among different regions of the country, manure was not 

included as a source of applied nitrogen for these simulations. 

Instead, applications of nitrogen were presumed to have 

occurred solely through the use of synthetic nitrogen fertilizer. 

The amounts of synthetic nitrogen fertilizer applied at each site 

were computed using a method selected from among several 

state-specific algorithms described by Ahuja and others (2000) 

for estimating the most appropriate amount of nitrogen to use 

for growing corn. Of the five approaches described by Ahuja 

and others (2000), the method recommended by the University 

of Missouri Soil Testing Laboratory was selected for the 

initial Phase 2 simulations because it could be implemented 

without the use of locally derived input data. According to this 

method, the recommended nitrogen fertilizer application rate 

is computed as an empirical function of the number of seeds 

planted per acre, the intended crop yield (in bushels per acre), 

the mass-based fraction of OM in soil, and an adjustment factor 

to account for the effect of soil OM and cation exchange on the 

crop of interest. The values and sources of the input data used 

for these calculations are presented in table 14. 

Estimating Physical and Chemical Property Values 

for Atrazine and Deethylatrazine 

Values for many of the parameters used by PRZM and 

RZWQM to simulate the physical, chemical, and biological 

processes that affect the transport and fate of anthropogenic 

contaminants and their degradates in the hydrologic system 

have not been published for many of these compounds. In 

these situations, it is necessary to use one of various methods 

to estimate a value for the parameter and compound of interest 

(Barbash and others, 2009). For this study, the methods used 

to estimate these parameters for atrazine and DEA included 

quantitative structure-property relations (QSPRs), structurereactivity relations (QSRRs), property-property relations 

(QPPRs), and medium-reactivity relations (QMRRs)—

approaches that have been used for estimating the properties 

and reactivities of pesticides and other synthetic organic 

compounds for at least four decades (for example, Walker, 

1974; Hansch and Leo, 1979; Lyman and others, 1990; Roberts 

and others, 1993; Reinhard and Drefahl, 1999; Schüürmann and 

others, 2006; Capel and others, 2008). 
42 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Partitioning and Mass Transfer of Atrazine and Deethylatrazine

Both PRZM and RZWQM use Koc to characterize the partitioning of organic compounds 

between soil and water, and the Henry’s law constant (KH) to quantify partitioning between 

water and air. (However, air-water partitioning was not simulated during Phase 1.) Additionally, 

RZWQM uses the octanol-water partition coefficient (Kow) to quantify plant uptake of pesticide 

compounds (Ahuja and others, 2000). Because KH is more sensitive to temperature variations 

than either Kow or Koc, temperature corrections were applied to KH for atrazine and DEA during 

Phase 2, but not to their Kow or Koc values. RZWQM does not adjust any partitioning or masstransfer parameters for variations in temperature, however, so these corrections for KH were 

carried out externally. 

Henry’s Law Constant (Phase 2 only)

For atrazine, a KH value of 2.88 × 10-4 Pascal-cubic meter per mole (Pa-m3/mol) at 25°C, 

reported by Mackay and others (1997), was used. Because published KH values for DEA 

were not available, an estimate of 7.29 × 10-4 Pa-m3/mol was computed for this parameter by 

dividing the vapor pressure for DEA at 25 °C (0.0124 Pascals, reported by Neely and Blau, 

1985) by its water solubility (17 mol/m3

, measured at 22 °C by Mills and Thurman, 1994). 

The influence of temperature on KH for atrazine and DEA was accounted for during Phase 2 by 

using equation 11, adapted from Mackay and others (2000), to adjust the value of KH for each 

compound from 298.15 K (25°C, the temperature for which the KH value from Mackay and 

others [1997] was reported) to the average air temperature in May (TMay) at each site:

 K K

H

R K T H,T H s

May May

= ∗ 



 



∗ −





























,25C 298.15 

exp

∆ 1 1 

 (11)

where

 KH,TMay is the Henry’s law constant at TMay;

is the Henry’s law constant at 25 °C (298.15 K);

∆Hs is the enthalpy change for transfer from gaseous to solution phase, in kilojoules 

per mole (kJ/mol); and

R is the universal gas constant (0.008314 kJ/mol-K).

Because no published ∆Hs values were available specifically for either atrazine or DEA, a 

value of 47 kJ/mol, computed by Staudinger and Roberts (2001) from KH data for 197 organic 

compounds, was used for this parameter. Given the spatial variability of TMay within the 

Corn Belt (fig. 15), the use of equation 11 produced values of KH that ranged from 0.8 × 10-4 to 

4.1 × 10-4 Pa-m3/mol across the Phase 2 study area.

Octanol-Water Partition Coefficient (Phase 2 only)

The data compilation by Mackay and others (1997) included a recommended Kow value 

for atrazine (562), but none for DEA. (The latter compound was not included among those 

examined by Mackay and others [1997].) A Kow value for DEA was estimated for the present 

work by adjusting the atrazine value from Mackay and others (1997) to account for the 

structural differences between the two compounds. This was accomplished using Kow values 

computed by Finizio and others (1991) for the two compounds from their respective retention 

times in a reversed-phase, high-performance liquid chromatography (RP-HPLC) column (that 

is, the amount of time compounds require to migrate through the column under specified 

conditions). The data reported by Finizio and others (1991) appear to represent the only pair of 

Kow values for atrazine and DEA that have been measured to date with the same method during 

the same study. For Phase 2 of the present study, these two values were used with equation 12 

to estimate Kow for DEA by scaling the value recommended for atrazine by Mackay and 

others (1997):

KH ,25C
Methods 43

 K K

K

K ow ow

ow, meas

ow, meas

[ [ [ DEA] atr]

DEA]

, M 97

, F 91

, F 9

≈ ∗ +

+

+ 1[atr]

( ) . .

















= ∗



 



 562 = 32 4

407

44 6

 (12)

where

Kow [DEA] is the Kow value used for DEA for this study (dimensionless);

Kow,M+97[atr] is the Kow value recommended for atrazine by Mackay and others 

(1997), and used for the P-GWAVA-RZ simulations for this study;

Kow,meas, F+91[DEA] is the Kow value estimated for DEA by Finizio and others (1991) from 

measurements of its RP-HPLC retention time; and

Kow,meas, F+91[atr] is the Kow value estimated for atrazine by Finizio and others (1991) 

from measurements of its RP-HPLC retention time.

Adapted from the approach described by Barbash and others (2009), equation 12 represents a 

QSPR that is mathematically analogous to the structural fragment method described by Hansch 

and Leo (1979) and Lyman (1990) for estimating Kow values. As such, it provides an estimate 

of the quantitative effect on Kow of replacing the ethyl group in atrazine with a hydrogen atom. 

Finizio and others (1991) also estimated Kow values for atrazine and DEA using the fragment 

method of Hansch and Leo (1979). Application of equation 12 in conjunction with these values 

yielded an estimated Kow of 46.8 for DEA. The arithmetic average of the Kow values obtained 

for DEA using the RP-HPLC retention time data from Finizio and others (1991) and estimated 

using the Hansch and Leo (1979) fragment method (that is, 45.7) was the value used for the 

P-GWAVA-RZ simulations.

Soil-Water Partition Coefficients 

For both phases of this study, the partitioning of atrazine (and, during Phase 2, DEA) 

between soil and water was described using the well-known linear equilibrium sorption 

model, in which the soil-water partition coefficient (Kd), which is the ratio between the sorbed 

concentration (Cs) and the concentration in solution (Caq), is presumed to be independent of 

either concentration:

 K C

C d s

aq

= (13)

Individual values of Kd were computed, as milliliters per gram (mL/g) of soil, in each location 

of interest using a rearrangement of the equation first introduced by Hamaker and Thompson 

(1972) to define Koc, that is,

K f d o = c o K c * (14)

Values of f

oc, in turn, were computed from the mass fraction of OM in the soil (fom, calculated 

as %om/100 percent), under the assumption that soil OM, on average, is 58 percent carbon by 

weight (Chiou, 2002), and thus that:

f

oc = 0.58 * fom (15)

For Phase 1, equations 14 and 15 were used to compute a Kd value for each of the 1,224 sites 

examined, using the depth-averaged f

om value (fig. 7) in each location. For Phase 2, equations 

14 and 15 were used to calculate a separate Kd value for every soil horizon in each SSURGO 

component for which a simulation was carried out. 
44 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The P-GWAVA-PR simulations used a Koc value of 

130 mL/g of organic carbon for atrazine, as this represented 

the median among 73 measured values taken from 

25 published studies (Harris and Warren, 1964; Armstrong 

and others, 1967; Hance, 1967; Nearpass, 1967; Armstrong 

and Chesters, 1968; Grover and Hance, 1968; Hayes and 

others, 1968; Lavy, 1968; Obien and Green, 1969; Bouchard 

and Lavy, 1985; Gamerdinger and others, 1991; Kladivko 

and others, 1991; Pignatello and Huang, 1991; Sichani and 

others, 1991; Franklin and others, 1994; Laird and others, 

1994; Mills and Thurman, 1994; Novak and others, 1994; Roy 

and Krapac, 1994; Mersie and Seybold, 1996; Seybold and 

Mersie, 1996; Guo and others, 1997; Moreau and Mouvet, 

1997; Suzuki and others, 1998; Baer and Calvet, 1999). Use of 

this value—in conjunction with equations 14 and 15 and the 

STATSGO-derived f

om data for the individual sites—resulted 

in computed Kd values of 0.98 ± 0.07 mL/g for atrazine among 

the 1,224 locations examined during Phase 1. 

For the initial set of P-GWAVA-RZ simulations during 

Phase 2, the recommended Koc value of 100 mL/g from 

Mackay and others (1997) was used for atrazine. Using an 

approach analogous to that of equation 12, a Koc value for 

DEA was obtained by scaling the atrazine value with the 

following equation:

 

K K

K

K oc oc

oc, meas

oc, meas

[ [ [ DEA] atr]

DEA]

, M 97

, S M96

 , S

≈ ∗ +

+

+M96 atr]

 mL/g

 mL/g

 mL/g

 mL/g

[

( )

















= ∗



 



100  = 80

140

57

 (16)

where

Koc[DEA] is the initial Koc value used for 

simulating DEA partitioning 

during Phase 2;

Koc, M+97 [atr] is the Koc value recommended for 

atrazine by Mackay and others 

(1997);

Koc, meas, S+M96 [DEA] is the Koc value measured for 

DEA by Seybold and Mersie 

(1996) on a clay loam soil; and

Koc, meas, S+M96 [atr] is the Koc value measured for 

atrazine by Seybold and 

Mersie (1996) on a clay loam 

soil.

The Koc values measured and reported by Seybold and 

Mersie (1996) for atrazine and DEA were selected for use in 

equation 16 because compared with the other studies that have 

measured Koc values for both compounds, the value measured 

by Seybold and Mersie (1996) for atrazine was closest to the 

100 mL/g value recommended for the herbicide by Mackay 

and others (1997). As with equation 12, equation 16 represents 

a QSPR quantifying the apparent effect that replacing the ethyl 

group in atrazine with a hydrogen atom exerts on a chemical 

property of the molecule—in this case, its affinity for soil 

organic carbon. The observation that the estimated values of 

Kow (equation 12) and Koc (equation 16) for DEA were lower 

than their respective values for atrazine reflects the fact that 

an ethyl group is more hydrophobic than a hydrogen atom. 

This observation also is consistent with predictions from the 

fragment method of Hansch and Leo (1979), as well as with 

the observation that transport rates through soil are higher for 

DEA than for atrazine (Kruger and others, 1996b). 

During Phase 2, preliminary model simulations using 

P-GWAVA-RZ led to predicted concentrations of atrazine 

and DEA that were lower than their reporting limits in nearly 

all locations, even after the spatially uniform intensity of 

atrazine application was used. These initial model simulations 

also demonstrated that, consistent with the findings of 

several previous modeling studies (for example, Boesten and 

van der Linden, 1991; Dubus and Brown, 2002; Dubus and 

others, 2003), the simulated concentrations were sensitive to 

the value of Koc used. Consequently, the Koc values for atrazine 

and DEA were adjusted downward to bring the simulated 

concentrations of the two solutes closer to those measured in 

groundwater, while ensuring that the adjusted Koc values were 

consistent with the respective ranges reported by previous 

studies, and that the ratio between the values for the two 

solutes remained constant. As a result of this process, the Koc

values used for the initial set of simulations (100 mL/g for 

atrazine; 57 mL/g for DEA) were decreased by 75 percent 

(to 25 mL/g for atrazine and 14 mL/g for DEA) for the final 

set of P-GWAVA-RZ simulations (tables 4 and 14), leading 

to concomitant reductions in the Kd values computed using 

equation 14 for both compounds. (Measured Koc values 

reported by previous studies range from 13 mL/g [Armstrong 

and others, 1967] to 1,680 mL/g [Roy and Krapac, 1994] for 

atrazine, and from 16 mL/g [Thurman and Fallon, 1996] to 

2,700 mL/g [Bayless, 2001] for DEA.) 

Wash-off Parameters for Crop Foliage and Residue 

(Phase 2 only)

RZWQM uses the following empirical relations, 

originally developed by Willis and others (1980), to estimate 

the concentrations of an applied pesticide on crop foliage and 

residue (Cf and Cr, respectively) as a function of the initial 

concentration (Co(f) and Co(r), respectively) and the amount 

of rain that falls on these materials within a given interval 

of time: 

C C F e f o f wo f

P I t wo f R = ∗ ∗ − ∗ ∗

() () ( ) ∆ (17)
Methods 45

C C F e r o r wo r

P I t wo r R = ∗ ∗ − ∗ ∗

() () ( ) ∆ (18)

where

Fwo(f) is the wash-off coefficient for foliage, expressed 

as a fraction (Knisel and Davis, 1999);

Fwo(r) is the wash-off coefficient for residue, expressed 

as a fraction (Knisel and Davis, 1999);

Pwo(f) is the wash-off power for foliage, per millimeter 

(mm-1);

Pwo(r) is the wash-off power for residue, per millimeter;

I

R is the rainfall rate during time interval ∆t, in 

millimeters per hour; and 

∆t is the duration of rainfall, in hours.

Following the recommendation of Ahuja and others (2000), for 

each compound of interest, the values of Fwo were presumed 

to be identical for crop foliage and crop residue, as were the 

corresponding values for Pwo. Consequently, the subscripts 

“f” and “r” will not be used with these parameters for the 

remainder of this discussion. 

The values of Fwo and Pwo used for atrazine (0.8 and 

0.015 mm-1, respectively) were obtained for the herbicide by 

Malone and others (2001b) through RZWQM calibration. 

(Although Fwo appears as a fraction in equations 17 and 18 

[Knisel and Davis, 1999], it is expressed as a percentage when 

entered as an input parameter for RZWQM [Ahuja and others, 

2000].) Both Fwo and Pwo appear to be positively related to 

water solubility (Ahuja and others, 2000). Because published 

values for Fwo and Pwo do not seem to be available for DEA, 

they were estimated for this study on the basis of the contrast 

in water solubility (Sw) between atrazine and DEA. In one of 

the few studies that measured Sw for both compounds, Mills 

and Thurman (1994) reported Sw values of 33.8 mg L-1 for 

atrazine and 3,200 mg L-1 for DEA. Consequently, an Fwo

value of 0.9 was selected for DEA because it was intermediate 

between the value of 0.8 reported by Malone and others 

(2001b) for atrazine and the value of 1.0 recommended by 

Ahuja and others (2000) for pesticides with water solubilities 

less than or equal to 106

 mg L-1. A value of Pwo was estimated 

for DEA using the following quantitative property-property 

relation (QPPR):

 

P P P S

P S wo wo wo w

wo w

[ [ [

[

DEA] atr] 10 10 ]

10 100] ,M 01

3

A 00

A

≈ ∗ = −

= − + +

+

4

00

 mm

 mm

 mm

















= ∗













 = − −

− ( . ) .

. 0 015 . 0 033

0 005

0 09 1

1

1 9 1 mm−

(19) 

where

Pwo[DEA] is the wash-off power estimated 

for DEA, per millimeter;

Pwo,M+01[atr] is the wash-off power, per 

millimeter, reported for atrazine 

by Malone and others (2001b);

Pwo[Sw = 103 – 104]A+00 is the wash-off power, per 

millimeter, assigned by 

Ahuja and others (2000) 

for compounds with water 

solubilities between 103 and 104

milligrams per liter; and

Pwo[Sw = 10 – 100]A+00 is the wash-off power, per 

millimeter, assigned by 

Ahuja and others (2000) 

for compounds with water 

solubilities between 10 and 

100 milligrams per liter.

Although RZWQM simulates pesticide wash off from both 

crop foliage and crop residue, this process was expected to 

be relevant only for the crop residue because the application 

of atrazine was simulated to occur prior to crop emergence in 

the spring.

Mass Transfer Rates (Phase 2 only)

Kinetic Controls on Sorption

In addition to simulating the equilibrium partitioning of 

solutes between water and soil, RZWQM also accounts for the 

kinetics of adsorption and desorption through its use of two 

parameters, referred to as Ek2 and Rk2. The parameter Ek2 

represents the ratio between the rate constants for adsorption 

and desorption (Liwang Ma, U.S. Department of Agriculture, 

written commun., December 2007), and is designated here as 

the quantity kads/kdes for greater clarity:

Ek k

k

K F ads

des

d ads 2 ≡ = * (20)

where

kads is the rate constant for adsorption; 

kdes is the rate constant for desorption; and 

Fads is the fraction of sites on the soil surface that 

engage in slow, but reversible sorption (Ahuja 

and others, 2000). 

Measured values of most of the parameters in 

equation 20 are scarce in the published literature, but a study 

by Guo and others (1997) of atrazine transport in water 

during miscible displacement through a column containing an 

unamended silt-loam soil provided estimates of Kd and Fads. The 
46 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

experiments were conducted at two pore-water velocities: 

a “slow” rate (approximately 1 cm/h) and a “fast” rate 

(approximately 5 cm/h). Using equation 20, the data from 

Guo and others (1997) for the “slow” and “fast” experiments 

yielded the following estimates, expressed in cubic 

centimeters per gram (cm3/g), for the ratio of kads to kdes:

k

k

K F ads

des

d ads slow





 



 =

= ( ) ( ) =

slow

3 0.93 cm /g cm

*

* . .

,

0 73 0 68 3 /g

(21)

 k

k

K F ads

des

d ads fast





 



 = = ( ) ( )

=

fast

3

3

cm /g

cm

* . * .

.

, 0 93 0 67

0 62 /g

 (22) 

Taking an average of the two results yielded the selected 

value for kads/kdes (Ek2) of 0.65 cm3/g for atrazine. Given the 

similarity of the structures of atrazine and DEA, the same 

value for this parameter also was used for DEA during the 

P-GWAVA-RZ simulations.

Data from the study by Guo and others (1997) were 

also used to estimate the RZWQM parameter referred to as 

Rk2, which represents the kinetic rate constant for desorption 

(Malone and others, 2004b), that is, kdes. Using data from the 

two soil column experiments conducted at different pore-water 

velocities, Guo and others (1997) reported values for a 

parameter referred to as a “first-order kinetic rate constant” for 

sorption. Dimensional analysis suggests that this parameter is 

equivalent to kdes, rather than kads. From their experiments, Guo 

and others (1997) estimated values for this parameter of 0.17 

per hour (h-1) and 0.06 h-1 for the “fast” and “slow” velocities, 

respectively. Taking the average of the two values yielded the 

selected value of 0.12 h-1 used for kdes (Rk2) for atrazine during 

the P-GWAVA-RZ simulations. Given the similarity of the 

structures of atrazine and DEA, and the apparent absence of 

published data for the degradate, a kdes (Rk2) value of 0.12 h-1

was also used for DEA.

Solute Exchange Between Micropores and Mesopores

In addition to accounting for kinetic effects on sorption, 

RZWQM also simulates the exchange of pesticide compounds 

between micropores and mesopores during infiltration, using 

the following equation (Ahuja and others, 2000):

δ

δ

C

t

D C C meso

d

eff = − micro meso ( ) (23)

where

Cmeso is the solute concentration in the mesopores, in 

micrograms per gram (μg/g) 

of soil;

t is time, in hours;

 Dd

eff is the effective diffusion coefficient for 

micropore-macropore exchange, in square 

centimeters per hour (cm2/h); and

Cmicro is the solute concentration in the 

micropores (μg/g).

RZWQM does not simulate this process for nitrate because the 

model assumes instantaneous equilibrium between micropores 

and mesopores for this solute (Liwang Ma, U.S. Department 

of Agriculture, written commun., July 2011). The effective 

diffusion coefficient in equation 23 is calculated as follows 

(Ahuja and others, 2000):

D

D

K d

eff a

b sn d sn

sn

=

+ ∗ 1 ρ

θ

, ,

(24)

where

Da is the apparent diffusion coefficient in water 

(cm2/h);

ρb,sn is the bulk density of soil layer n, in grams per 

cubic centimeter (g/cm3);

Kd,sn is the distribution coefficient for soil layer n, in 

cubic centimeters of water per gram of soil 

(cm3/g); and

θsn is the volumetric water content of soil layer 

n, in cubic centimeters of water per cubic 

centimeter of soil (cm3/cm3).

Thus, equation 24 adjusts the value of Da to account for the 

influence of soil density, sorption and water content on the 

rate of solute exchange between micropores and macropores. 

The denominator in equation 24 is identical to what is 

commonly referred to as a retardation factor, or R (Freeze and 

Cherry, 1979).

The magnitude of Da for a given solute is determined 

by the rate at which the molecule diffuses through aqueous 

solution, and the sinuosity, or tortuosity of the path along 

which it must travel, in this instance, between mesopores and 

micropores. Methods for determining the rates of diffusion 

of individual solutes in homogeneous aqueous solution, by 

either direct measurement or calculation, are well established 

(Tucker and Nelkin, 1990). A relatively extensive amount of 

research has also been carried out to understand the factors 

that control the rates of solute exchange between mobile 

and immobile waters in the subsurface (for example, van 

Genuchten and others, 1977; Nielsen and others, 1986; 

Kookana and others, 1993; Gerke and van Genuchten, 1996; 

Shaw and others, 2000; Jarvis and others, 2007a). Among 

the few studies that have explicitly examined the effects of 

pore size and (or) geometry on this process, however, the 

investigation by Shaw and others (2000) appears to be the 

only one that has provided a PTF for predicting the rates of 

exchange of a solute (in their case, bromide ion) between 

mobile and immobile water from soil properties for which 

SSURGO provides data. 
Methods 47

As part of their study, Shaw and others (2000) examined the extent to which various soil 

properties were correlated (if at all) with several key parameters in the system of dual-domain 

equations introduced by van Genuchten and Wierenga (1976) to describe solute transport 

through porous media containing regions of mobile and immobile waters. One key parameter 

was the coefficient of mass transfer between mobile and immobile waters for bromide ion 

(αBr-). Among the various soil properties examined (which included sand content, clay content, 

cation exchange capacity, extractable iron content, and the sizes of soil structures larger than 

individual particles), those with which αBr- was significantly correlated were clay content and 

cation-exchange capacity normalized to clay content (CECg). As a result, both these properties 

were incorporated by Shaw and others (2000) into a PTF for predicting the mass-transfer 

coefficient for bromide. 

Because data for both CECg and clay content were provided by SSURGO for most soil 

horizons examined during Phase 2 of the present study, the following quantitative mediumproperty relation (QMPR) was used to estimate a value of Da

 for atrazine and DEA in each soil 

horizon for the P-GWAVA-RZ simulations:

D D a i sn w i

Br sn

Br

, , ,

,

,max

= ∗















−

−

α

α (25)

where

Da,i,sn is the apparent diffusion coefficient for the exchange of solute i between 

micropores and mesopores in soil layer n (cm2/h);

Dw,i is the coefficient of molecular diffusion in homogeneous aqueous solution for 

solute i (cm2/h);

αBr-,sn is the first-order rate constant for exchange of bromide ion between regions 

of mobile and immobile water in soil layer n, expressed in units of inverse 

minutes (min-1), and estimated from clay content and CECg using the PTF from 

Shaw and others (2000); and

αBr- ,max is the maximum value of the first-order rate constant for exchange of bromide 

ion between regions of mobile and immobile water (min-1), estimated using the 

PTF from Shaw and others (2000).

Published values for Dw were not available for atrazine or DEA. As a result, Dw values of 

0.0211 and 0.0237 cm2/h for atrazine and DEA, respectively, were estimated from the molar 

volume of each molecule and the viscosity of water at 25°C, using the equation of Hayduk and 

Laudie that was recommended for this purpose by Tucker and Nelkin (1990). Values of αBr-,sn

were computed using the following, exponential form of a corrected version of the PTF from 

Shaw and others (2000):

{ ( )] [ } 2

, exp 4.51 – 0.010 * %clay – 0.065*CECg α = Br sn − sn sn     (26)

where

%claysn is the weight percentage of clay in soil layer n (from SSURGO); and

CECgsn is the cation-exchange capacity for soil layer n, in milliequivalents per 100 grams 

of clay (meq/100 g clay), calculated from CEC7 (that is, the value of CEC at 

pH 7, also provided by SSURGO) and %clay.
48 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Equation 26 incorporates the correction of an error in the 

published version of this equation, in which the coefficients 

0.010 and 0.065 were inadvertently switched (Professor Joey 

Shaw, Auburn University, written commun., July 2011). 

For soil horizons for which no CEC data were provided by 

SSURGO, values of αBr-,sn were computed using the median 

CECg value among all of the soils used by Shaw and others 

(2000) to develop equation 26. Finally, αBr-,max was computed 

by setting %claysn and CECgsn in equation 26 to their 

minimum values among the soil horizons examined for this 

study (that is, %clay = 1.0%; CECg = 7.87 meq/100 g clay), 

yielding a αBr-,max value of 54.0 min-1.

Figure 19 illustrates the manner in which Da,sn for 

atrazine varied as a function of %clay and CECg among 

the 1,657 soil horizons at the Phase 2 study sites for which 

SSURGO provided data for both parameters. (Because the Dw

values computed for atrazine and DEA were so similar, the 

corresponding plots for DEA exhibited distributions that were 

identical to the distributions shown for atrazine, but the Da,sn

values were about 12 percent greater for DEA than the values 

for atrazine.) The data shown in figure 19 indicate that most of 

the Da,sn values computed for atrazine (and, by extension, for 

DEA) used values of %clay and CECg that were within the 

range of values used by Shaw and others (2000) to construct 

the PTF from which αBr-,sn (equation 26), and thus Da,i,sn

(equation 25), were estimated. 

Atrazine and Deethylatrazine 

Transformation Rates

The disappearance of atrazine was simulated during 

Phase 1 of this study, but not the formation, transport, or 

fate of any of its transformation products. During Phase 2, 

however, the production of DEA (from atrazine) and 

its disappearance over time, as well as the disappearance 

of atrazine, were simulated. For this discussion, the term 

disappearance is used to refer to the aggregate influence 

of all processes, either biological or abiotic, that convert 

a compound to one or more transformation products, 

or degradates.

An extensive body of research over the past four decades 

has elucidated the influence of various physical, chemical, and 

biological factors on the mechanisms and rates of pesticide 

transformation in the hydrologic system (Barbash, 2007). 

To account for several of these effects, different approaches 

were used for the two phases of this investigation (table 14). 

During Phase 1, adjustments were made to account for the 

effect of temperature on the rate of atrazine disappearance in 

the subsurface. The Phase 2 simulations adjusted the rates of 

disappearance of atrazine and DEA for temperature variations, 

accounted for the influence of soil moisture and depth within 

the soil column on these rates, and adjusted for the effects 

of temperature, soil OM, and sand content on the rate of 

conversion of atrazine to DEA.

Atrazine and Deethylatrazine Transformation in the 

Hydrologic System

Both atrazine and DEA are known to undergo 

transformation by several mechanisms in soils. As indicated 

in figure 20, the initial steps in these transformations for 

both compounds commonly involve either dealkylation or 

hydrolysis. In the absence of light, atrazine dealkylation (which 

results in the formation of either DEA or deisopropyl atrazine) 

occurs primarily through biotransformation, rather than through 

abiotic processes (Erickson and Lee, 1989). By contrast, 

atrazine hydrolysis is an abiotic process that does not require 

microbial assistance; however, the rate of this reaction has been 

shown to be substantially higher in non-sterile soils than in 

sterile soils (Erickson and Lee, 1989; Mandelbaum and others, 

1993; Rice and others, 2004; Krutz and others, 2008). The 

atrazine transformation products displayed in figure 20 are also 

known to undergo several additional reactions in situ that are 

not shown (Erickson and Lee, 1989; Fenner and others, 2003). 

Although several of the reactions shown in figure 20 may 

be either photochemically driven or microbially mediated, the 

product yields shown in the figure represent the percentages of 

reacted compound that have been known to form the designated 

product in non-sterile soils through thermal (that is, nonphotochemical) reactions. The product yields were computed 

from the results reported by between one and nine studies for 

each reaction, but did not control for the effects of variations 

in soil temperature, soil OM or pH. Additionally, the yields 

were not corrected for microbial adaptation, that is, increases 

in the rate of biotransformation that may occur in response to 

repeated applications of the compound, a phenomenon that 

has been reported for atrazine by several previous studies (for 

example, Zablotowicz and others, 2007; Krutz and others, 

2008, 2010a, 2010b). The product yields from individual 

compounds in the figure sum to less than 100 percent because 

several other products (for example, bound residues, more 

polar degradates, and so forth) were not accounted for. 

In the absence of light, the dealkylation reactions shown in 

figure 20 appear to require oxic conditions (Nair and Schnoor, 

1992; Papiernik and Spalding, 1998; Rügge and others, 1999), 

presumably because they involve the oxidation of alkyl carbons 

(to form carbon dioxide). By contrast, hydrolysis does not 

involve a change in oxidation state, and thus may take place 

under either oxic or anoxic conditions. Following the approach 

of McMahon and Chapelle (2007), the term oxic is used herein 

to denote an aqueous environment where dissolved oxygen is 

detected (at a concentration equal to or greater than 0.5 mg/L), 

and the term anoxic denotes conditions where the dissolved 

oxygen concentrations are low enough to allow denitrification, 

iron reduction, manganese reduction and (or) methanogenesis 

to take place (typically less than 0.5 mg/L). The terms 

aerobic and anaerobic, respectively, are also frequently used 

to describe these two conditions, but are used herein only to 

refer to the types and activities of microorganisms that are 

predominant in these environments, not the geochemical 

conditions themselves.
Methods 49

tac12-0781_fig19ab

0 

0.001 

0.002 

0.003 

0.004 

0.005 

0.006 

0.007 

0.008 

0 10 20 30 40 50 60 

Meso-micropore diffusion coefficient, in square centimeters per hour 

Percent clay 

A

Range of clay contents for soils used to compute

pedotransfer function employed to estimate Da

 

(Diffusion coefficient for atrazine in pure water = 0.0211 square centimeter per hour) 

0 

0.001 

0.002 

0.003 

0.004 

0.005 

0.006 

0.007 

0.008 

Meso-micropore diffusion coefficient, in square centimeters per hour 

(Diffusion coefficient for atrazine in pure water = 0.0211 square centimeter per hour) 

Range of CECg for soils used to

compute pedotransfer function

employed to estimate Da

 

CECg, in milliequivalents per 100 grams of clay

B

1 10 100 1,000 10,000 

Figure 19. Apparent diffusion coefficient (Da) for the exchange of atrazine between micropores 

and mesopores compared with (A) clay content (percent clay), and (B) cation exchange capacity 

normalized to clay content (CECg) for 1,657 soil horizons at P-GWAVA-RZ (Phase 2) study sites in 

the 10 northernmost states of the Corn Belt. Values are shown for all Phase 2 soil horizons with 

available SSURGO data for percent clay and CEC7. 
50 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig20

N

N

N

N N

Cl

H H

N

N

N

N N

Cl

H H

N

N

N

N N

Cl

H H

N

N

N

N N

Cl

H H

N

N

N

N N

H H

atrazine

deisopropyl atrazine deethyl atrazine

 (DEA)

hydroxyatrazine

didealkyl atrazine

H H

OH

3CO2 2CO2

Dealkylation

 Hydrolytic (1,4,5,7-9)

 or photo (2,3,10) 

 dechlorination

3CO 2CO 2 2

H H

H O2

HCl

N

N

N

N N

 OH

H H

 deisopropyl 

hydroxyatrazine

N

N

N

N N

H H

 deethyl 

hydroxyatrazine

(1–5) (1–6)

(1–4; 6,11,12)

3CO2 2CO2

H O2

HCl

N

N

N

N N

 OH

H H

didealkyl hydroxyatrazine

 (ammeline)

H H

H O2

HCl

3CO2

2CO2

H H

 OH

Dealkylation

Dealkylation

 (1–5) Dealkylation 

 Hydrolytic

dechlorination

 (5)

 Hydrolytic

dechlorination

 (11,12)

H O2

HCl

 (3,10)

 Dealkyl-

 ation

 (3,10)

Dealkylation

 Photo-

 dechlorination

 (2)

2.9 ± 0.8%

 {56}

12 ± 3% 

 {64}

13 ± 6% 

 {47}

1.9 ± 1.7%

 {10}

0.04 ± 0.02%

 {10}

0.84 ± 0.47%

 {8}

0.24 ± 0.11%

 {8} Other

compounds

Portion of each molecule undergoing reaction is shown in 

red in the corresponding transformation product. Product 

yields, in percent, given (where data were available) as 

95 percent confidence intervals. Numbers of observations 

are in braces. Numbers in parentheses refer to the original 

sources of information as follows: (1) Kaufman and 

Kearney, 1970; (2) Pelizzetti and others, 1990; (3) Torrents 

and others, 1997; (4) Beynon and others, 1972; (5) Kruger 

and others, 1993; (6) Rejto and others, 1983; (7) Armstrong 

and others, 1967; (8) Mandelbaum and others, 1993; 

(9) Krutz and others, 2008; (10) Hapeman-Somich, 1991; 

(11) Kruger and others, 1996a; (12) Kruger and others, 1997. 

Figure 20. Reactions producing the atrazine transformation products that have been examined most extensively in the hydrologic 

system.
Methods 51

Atrazine and Deethylatrazine Disappearance Rates in Soil

PRZM quantifies the rate of pesticide disappearance in 

terms of first-order rate constants (k), whereas RZWQM uses 

half-lives (t

1/2) for this purpose. Both approaches are derived 

from the assumption that the disappearance of the solute of 

interest displays first-order kinetics, and thus occurs at a rate 

(dC/dt) that at any given time (t) is directly proportional to the 

solute concentration at that moment (Ct), that is,

dC

dt

= k * Ct (27)

The half-life for the disappearance of a chemical 

compound represents the amount of time required for the 

initial concentration of the compound (Co) to diminish, 

through first-order kinetics, by 50 percent—that is, the time 

interval after which Ct /Co equals 0.5. The observed half-life 

((t

1/2)obs), expressed as a function of the observed rate constant 

(kobs) for the disappearance of the solute of interest, is obtained 

by separating variables in equation 27, integrating both sides 

and solving for t when Ct /Co equals 0.5:

t

obs k k obs obs 1 2

2 0 693

/

ln . ( ) = = (28)

For many pesticides following application (as well as many of 

their degradates), kobs commonly represents the sum of several 

individual rate constants, each of which reflects the combined 

influence of at least three factors, that is, the mechanism 

of transformation (for example, hydrolysis, dealkylation, 

dehydrohalogenation, and so forth), the source of the energy 

driving the process (thermal versus photochemical), and the 

presence or absence of biological assistance. Variations in the 

characteristics of environmental media, such as the presence 

or absence of solid surfaces, the composition and size of the 

microbial population, the mass fraction of organic carbon, 

and the concentrations of other reactive species, may also 

affect the rates and relative importance of different reaction 

mechanisms for a given compound (Barbash, 2007). 

Atrazine Disappearance Rates

Both PRZM and RZWQM offer the option of simulating 

pesticide disappearance on the surfaces of plant foliage, 

crop residues, and soil. PRZM also simulates disappearance 

in the soil solution and in the soil gas. Owing to a lack of 

evidence demonstrating its importance, however, atrazine 

disappearance in soil gas was presumed negligible for this 

investigation. RZWQM simulates pesticide disappearance 

through either biotransformation, phototransformation or other 

abiotic reactions.

Photochemical Transformations

Published estimates of the rates of atrazine 

transformation on plant surfaces are limited, but the rates of 

direct phototransformation of atrazine and DEA by sunlight 

are negligible in homogeneous aqueous solution at neutral 

pH (Pellizzetti and others, 1990; Lartiges and Garrigues, 

1995; Peñuela and Barceló, 2000; Lackhoff and Niessner, 

2002; Prosen and Zupančič-Kralj, 2005). Because atrazine is 

typically applied directly to the soil prior to crop emergence, 

the transformations of atrazine and DEA on crop foliage were 

neglected for this study. Following the guidance of Ahuja and 

others (2000), the rates of disappearance of both compounds 

on crop residue also were presumed negligible. Results from 

a limited number of previous investigations indicate that 

atrazine (Rice and others, 2004) and perhaps DEA (Prosen and 

Zupančič-Kralj, 2005) may undergo phototransformation at 

moderate rates in soils exposed to sunlight. However, owing 

to the absence of appropriate measurements from previous 

research, the P-GWAVA simulations did not account for the 

phototransformation of the two compounds on soil surfaces. 

Therefore, only thermal transformations of atrazine, DEA, or 

nitrogen species are discussed in the remainder of this report. 

Thermal Transformations in Soil

Atrazine reactions in the subsurface may occur in either 

the dissolved or the sorbed state. Previous research generally 

indicates that the biotransformation of pesticides and other 

anthropogenic compounds in soil occurs primarily, if not 

exclusively in the dissolved state, and that compounds in the 

sorbed state, including atrazine (Jacobsen and others, 2001), 

are mostly unavailable to microorganisms for these reactions 

(Alvarez-Cohen and others, 1993; Barbash and Resek, 1996; 

Zhang and others, 1998). Results from laboratory studies, 

however, suggest that atrazine reactions in the subsurface 

are likely to be most rapid in water that is in intimate contact 

with non-sterile soil (Erickson and Lee, 1989; Mandelbaum 

and others, 1993; Rice and others, 2004; Krutz and others, 

2008). Additionally, although abiotic atrazine hydrolysis 

may take place in the sorbed state or in aqueous solution, the 

reaction occurs more rapidly in the presence of soil than in 

aqueous solution alone (Armstrong and others, 1967; Skipper 

and others, 1967; Armstrong and Chesters, 1968; Rice and 

others, 2004). 

Simulations carried out during Phase 1 of this study 

provided opportunities to examine the relative importance 

of reactions in the sorbed and dissolved states in influencing 

the concentrations of atrazine in the vadose zone. Separate 

P-GWAVA-PR simulations were conducted at every Phase 1 

site using four different reaction scenarios. The four scenarios 

involved simulating reactions (1) solely in the dissolved state, 

(2) solely in the sorbed state, (3) in both dissolved and sorbed 
52 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

states, or (4) in neither state (that is, not at all). The atrazine 

concentrations simulated using the four reaction scenarios 

were then compared with those measured in groundwater 

at the same sites, to determine which approach resulted in 

the closest agreement between the simulated and measured 

atrazine concentrations. 

In contrast with the approach used by PRZM, RZWQM 

simulates pesticide transformation in the soil as a single 

medium, without distinguishing between reactions in the 

aqueous and sorbed states. RZWQM simulates pesticide 

disappearance in soil under either oxic conditions or anoxic 

conditions. The model simulates the onset of anoxic conditions 

after the soil moisture content exceeds specific depthdependent thresholds for longer than a specified period (Ahuja 

and others, 2000; Wauchope and others, 2004). In the soil 

column, RZWQM offers the option of specifying a lumped 

process to account for the simultaneous operation of multiple 

pathways of transformation. The model requires, however, 

that each transformation product of interest be generated 

from a single process. Consequently, the rates of atrazine 

and DEA disappearance under oxic conditions—and, by 

extension, the formation of DEA from atrazine dealkylation—

were quantified as lumped processes within the soil column, 

despite the fact that atrazine and DEA undergo transformation 

by both abiotic and biotic mechanisms (Winkelmann and 

Klaine, 1991a and 1991b; Kruger and others, 1997). For 

anoxic conditions, these reaction rates were specified using 

the RZWQM parameter referred to as the “soil subsurface 

anerobic [sic] biodegradation half-life.”

Initial Disappearance Rates and Temperature Variations

Among the environmental factors that are known 

to influence the rates of pesticide transformation in the 

hydrologic system, temperature is one of the most significant. 

The rates of thermal reactions are known to increase with 

temperature to a maximum value determined either by the 

thermal stability of the reactants or by the viability of any 

organisms that may be responsible for facilitating the reactions 

of interest. Although temperature effects are often observed 

for some indirect photolytic reactions, the rates of direct 

photolysis are independent of temperature (Mill and Mabey, 

1985). Initial values for the rates of solute transformation 

at each location of interest during both study phases were 

computed for the average air temperature in May (TMay), 

when the annual agrichemical applications were assumed to 

have occurred (see section, “Weather”). During the Phase 2 

simulations, the rates of these reactions were also adjusted in 

response to spatial and temporal variations in temperature, 

and additional adjustments made to account for the influence 

of variations in moisture content and depth below the 

land surface. 

During Phase 1 of this study, the initial rate of atrazine 

disappearance was computed for TMay (in K) in each location 

using the Arrhenius equation (Atkins, 1982):

k A E

RT atr o a

May

=  −













 *exp (29)

where

katr is the first-order rate constant for atrazine 

disappearance in non-sterile soil, in inverse 

days (d-1);

Ao is the Arrhenius pre-exponential factor (d-1);

Ea is the activation energy (kJ/mol); and

R is the universal gas constant (0.00831 kJ/K-mol).

Values of Ao and Ea for Phase 1 were computed from data 

reported by Walker and Zimdahl (1981) for the rates of 

atrazine disappearance at four temperatures (5, 15, 25, 

and 35 °C) in loam soils from three locations in the United 

States. Given the similarity in the reported Ea values for 

the three soils examined (45.4, 50.6, and 51.1 kJ/mol for a 

Mississippi silt loam, a Colorado clay loam, and a New York 

sandy loam, respectively), the data from all three sites were 

combined to compute more representative estimates of the 

Arrhenius parameters of interest for the Phase 1 simulations 

(Ao = 2,920,024 d-1; Ea = 46.2 kJ/mol). 

The second phase of this study involved the use 

of various methods to account for the effects of several 

environmental factors, in addition to temperature, on the 

rates of atrazine disappearance in soil. Previous research has 

established that the rates and mechanisms of transformation of 

atrazine and other pesticide compounds in the soil—through 

abiotic and (or) microbially mediated reactions—may be 

influenced by a wide variety of physical, hydrologic, chemical, 

and biological factors (Barbash, 2007). Some of this research 

has led to the development of QMRRs that can be used to 

predict the rates of atrazine disappearance in non-sterile, 

oxic soils from various system characteristics, including the 

temperature, moisture content, organic carbon content, sand 

content, and pH of the soil, as well as the depth below the land 

surface and the depth interval over which the transformation 

rate is to be estimated (Walker, 1978; Walker and Zimdahl, 

1981; Dinelli and others, 2000; Fenner and others, 2007). 

Among the QMRRs devised by these previous studies, 

the Fenner-Borsuk relation, a linear, multivariate correlation 

equation introduced by Fenner and others (2007), accounts for 

the influence of the widest range of variables determined to be 

significantly correlated with the rates of atrazine disappearance 

from thermal reactions in oxic, non-sterile soils. Thus, 

although RZWQM provides options for adjusting pesticide 

transformation rates to account for variations in temperature, 
Methods 53

soil moisture, and depth below the land surface, the use of an updated version of the FennerBorsuk relation (that is, a version in which the original coefficients were recalculated using a 

larger number of laboratory data, as described in appendix B) was examined for the Phase 2 

simulations, to account for the potential influence of a wider range of explanatory factors on the 

initial rates of atrazine disappearance.

Preliminary model simulations using the updated version of the Fenner-Borsuk relation 

to estimate initial atrazine disappearance rates in soil led to predicted atrazine concentrations 

that were lower than the atrazine reporting limits in nearly all locations in the Phase 2 study 

area, resulting in simulated concentrations and frequencies of detection that were substantially 

less than those measured in groundwater at the same sites. This indicated that the updated 

version of the Fenner-Borsuk relation may have systematically overestimated the actual 

atrazine disappearance rates. As an alternative approach, atrazine reaction rates obtained 

directly from data reported by previous laboratory studies, rather than values predicted by 

the updated version of the Fenner-Borsuk relation, were used to obtain initial estimates of the 

half-life for the disappearance of atrazine at the soil surface (table 4). However, the updated 

Fenner-Borsuk relation was still used to estimate the fraction of reacting atrazine that formed 

DEA (appendix B). 

For each SSURGO component of interest, the half-life for the disappearance of atrazine 

in oxic soil was obtained during Phase 2 by adjusting a value selected from the published 

literature to TMay for that location. To select this value, 259 rates of atrazine disappearance in 

non-sterile, oxic soils in the absence of light at circumneutral pH (that is, pH values between 6 

and 8) were retrieved or computed from the results reported by 20 previous laboratory studies. 

From these data, the median half-life was selected from among the 125 values measured by 

7 studies at 25 °C, the temperature at which the largest number of measurements were made 

(Walker, 1978; Walker and Zimdahl, 1981; Kruger and others, 1993; Topp and others, 1994; 

Rocha and Walker, 1995; Issa and Wood, 1999; Dinelli and others, 2000). An initial value of 

the half-life for atrazine disappearance was then calculated for each SSURGO component by 

adjusting this median value (109 days, measured by Issa and Wood [1999]) to the reference 

temperature of TMay for the location of interest using the following form of the Arrhenuis 

equation:

 t t

E

R T atr ox T atr ox

a

May May 1 2 1 2 25

1 1

29 / , , / , , ( ) = ( ) *exp * 



 



 − °C 8 15

109 50 9

0 00831441

.

*exp .

.

K

kJ/mol

kJ/K

































= ( ) − d

mol K





 



 − 































= ( )

* .

*exp , .

1 1

298 15

109 6 121

T

d

May

9 1 1

298 15

K

K ( ) − 





























 * TMa . y

 (30)

where 

 t atr ox TMay 1 2/ , , ( ) is the half-life, in days (d), for the disappearance of atrazine in nonsterile, oxic soil in the absence of light at TMay; and

t

1 2/ atr o, , x 25 ( ) °C is the half-life (d) for the disappearance of atrazine in non-sterile, oxic

soil in the absence of light at 25°C.

The activation energy (Ea) value of 50.9 kJ/mol used in equation 30 represented the 

median among 31 independent values of this parameter that were either retrieved or computed 

from the results reported by 6 previous studies of the effects of temperature, moisture, 

application history, and other factors on the rates of atrazine disappearance in oxic, non-sterile 
54 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

soils at circumneutral pH in the absence of light (Roeth and others, 1969; Walker, 1978; Walker 

and Zimdahl, 1981; Rocha and Walker, 1995; Dinelli and others, 2000; and Krutz and others, 

2008). Each of the 31 Ea values was obtained from a separate experiment involving a different soil 

and (or) combination of system conditions. In several instances where individual rate constants or 

half-lives were provided in the original publications (Walker, 1978; Walker and Zimdahl, 1981; 

Rocha and Walker, 1995; Dinelli and others, 2000), the Ea value calculated for this study from the 

published data differed from the value provided by the original authors. In these instances, the Ea

value calculated from the original kinetic data was included among those from which the median 

Ea value was selected, rather than the published Ea value. The Ea value of 50.9 kJ/mol was also 

used to adjust for the effects of temperature variations on the rates of DEA disappearance in soil 

under oxic conditions, and on the rates of disappearance of atrazine and DEA in soil under anoxic 

conditions, given that published data from which Ea values for these other reactions could be 

retrieved or computed did not appear to be available. 

Use of equation 30 for the P-GWAVA-RZ simulations resulted in predicted atrazine 

concentrations and detection frequencies that were in substantially closer agreement with those 

measured in shallow groundwater than was the case using the updated Fenner-Borsuk relation. 

The marked change in the level of agreement between simulated and measured concentrations in 

response to this change in approach was consistent with the common observation that pesticide 

concentrations predicted in the subsurface by computer simulations are sensitive to variations 

in the transformation rate specified for the solute of interest (for example, Wagenet and Hutson, 

1986; Boesten and van der Linden, 1991; Soutter and Musy, 1999; Tiktak, 1999; Dubus and 

Brown, 2002; Dubus and others, 2003; Dann and others, 2006).

Equation 30 was used to estimate the rates of atrazine disappearance in soil under oxic 

conditions for the RZWQM simulations. To estimate the rates of atrazine disappearance under 

anoxic conditions, the results from experiments conducted under anoxic conditions by Accinelli 

and others (2001) with a non-sterile soil collected from a depth interval from 80 to 100 cm (the 

only soil for which the pH was between 6 and 8) were used. This was accomplished for each 

SSURGO component of interest at the reference temperature of TMay using the following QMRR:

k k

k

k atr anox T atr ox T

atr anox

atr ox A May May , , , ,

, , ,

, ,

≈ * +

+

A C 01 15

0

°

1 15

1

1

0 00170

, C 0 00580 , , * .

. °















 = 













 =

−

− katr ox TMay

d

d

katr ox TMay , , * . 0 293 (31)

where

 katr,anox,TMay is the first-order rate constant for atrazine disappearance in anoxic soil at

temperature TMay (d-1);

 katr,ox,TMay is the first-order rate constant for atrazine disappearance in oxic soil at

temperature TMay (d-1);

katr,anox,A+01,15°C is the first-order rate constant for atrazine disappearance measured in

anoxic soil by Accinelli and others (2001) at circumneutral pH and 

15 °C (d-1); and

 katr,ox,A+01,15°C is the first-order rate constant for atrazine disappearance measured in oxic

soil by Accinelli and others (2001) at circumneutral pH and 

15 °C (d-1).

The data used in equation 31 from the study by Accinelli and others (2001) are consistent with 

the observation, noted by several previous investigations, that atrazine is more persistent under 

anoxic conditions than under oxic conditions (for example, Kaufman and Kearney, 1970; Nair 

and Schnoor, 1992; Papiernik and Spalding, 1998; Rügge and others, 1999; and Larsen and 

others, 2000). 

Expressing the reaction rates in equation 31 in terms of half-lives (using equation 28), 

substituting the right side of equation 30 for t

atr ox TMay 1 2/ , , ( ) and collecting terms yields

the expression used for computing the half-life for atrazine disappearance in non-sterile soils at 

the reference temperature of TMay whenever anoxic conditions were simulated by RZWQM during 

Phase 2:
Methods 55

t

d K

T atr anox T

May May 1 2

109

0 293

6 121 9 1

/ , , . ( ) = , . 



 



 *exp ( ) ∗ − 1

372 6 121 9 1 1

298.15K

 d *exp 29

































= ( ) ( ) , . K ∗ − TMay 8.15K

































 (32)

Use of equations 31 and 32 to estimate the rate of atrazine disappearance under anoxic 

conditions at temperatures other than 15°C was predicated on the assumption that the activation 

energies for the disappearance of atrazine under oxic and anoxic conditions are identical, 

and thus that the ratio katr,anox/katr,ox (in equation 31) is independent of temperature, within the 

temperature range of interest to the Phase 2 simulations (10–20°C [fig. 15]). This assumption 

was necessary because of the apparent lack of published data on the temperature dependence of 

atrazine disappearance rates in anoxic soil.

As the P-GWAVA-RZ simulation in each SSURGO component progressed, the halflife for atrazine disappearance estimated at the reference temperature of TMay using either 

equation 30 (for oxic conditions) or equation 32 (for anoxic conditions) was adjusted by an 

internal routine in RZWQM to account for temporal variations in temperature in each soil layer. 

RZWQM makes these adjustments using the same form of the Arrhenius relation used for 

equations 30 and 32: 

 ( ) ( )

( )

( ) ( )

1/2 , , 1/2

1/2 ,

1/2 ,

1 1 *exp

50.9 kJ/mol 1 1 *exp 0.00831441 kJ/K • mol

1 1 *exp 6,121.9K

sn May

May

May

a

atr T atr T

sn May

atr T

sn May

atr T

sn May

E

t t

R TT

t

T T

t

T T

      =   ∗ −          

      =   ∗ −          

    =   ∗ −  

   

 (33)

where 

 t atr Tsn 1 2/ , ( ) is the half-life for the disappearance of atrazine in soil layer n, in days; and

 Tsn is the temperature, in K, of soil layer n.

Equation 33 incorporates a correction to the original version of this equation presented by 

Ahuja and others (2000), in which the last two temperature terms were inadvertently reversed 

(Liwang Ma, U.S. Department of Agriculture, written commun., July 2007). 

Influence of Soil Moisture on Disappearance Rates (Phase 2 only)

More than three decades of research have established that under unsaturated conditions, 

the rates at which many pesticides disappear in non-sterile soil generally increase with 

soil moisture content (for example, Walker, 1974; Gottesbüren, 1991). This effect was first 

quantified by Walker (1974) using the following QMRR:

t

1/2 (θ) = a * θ-b (34)

where 

	 θ is the percent moisture content of interest, expressed on a weight basis 

(%, w/w); 

t

1/2(θ) is the half-life, in days, for the disappearance of the compound of interest at 

moisture content θ; and 

a and b are empirical constants. 
56 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

(Unless specified otherwise, all water contents mentioned in 

this report are expressed on a weight basis [w/w].) According 

to equation 34 (also referred to as the Walker equation), at a 

water content equal to the field capacity of the soil (θFC), the 

half-life for the disappearance of a given compound in soil 

(t

1/2(θFC)) may be estimated as follows:

t a FC FC

b

1 2/ ( ) θ θ = ∗ − (35)

Dividing each side of equation 34 by its corresponding side in 

equation 35 and isolating t

1/2(θ) yields the form of the Walker 

equation used in RZWQM to adjust pesticide transformation 

rates for variations in soil moisture: 

t t FC

FC

b

12 12 / / θ θ θ

θ ( ) = ( ) ∗





 





−

(36)

Thus, normalizing the expression for t

1/2(θ) in equation 34 

with respect to t

1/2(θFC) from equation 35 yields an expression 

(equation 36) that requires only one empirical parameter, the 

Walker exponent b, to compute t

1/2(θ).

At least 13 previous studies have examined the influence 

of soil water content on the rates of atrazine disappearance 

in non-sterile soils in the absence of light. Among these 

investigations, six either reported values for b, or provided the 

experimental data from which b values could be computed. 

The results from these studies were used to retrieve or 

calculate 14 independent values of b (that is, values derived 

from experiments using different soils or other variations in 

system conditions) for the disappearance of atrazine in nonsterile soils under oxic conditions at circumneutral pH in the 

dark at 25 °C (Walker and Zimdahl, 1981; Rocha and Walker, 

1995; Dinelli and others, 2000), at 20°C (Walker, 1978; Krutz 

and others, 2008), or as an average of values measured at 15 

and 28°C (Baer and Calvet, 1999). For cases where individual 

studies did not provide the values of θFC needed to compute b

from equation 36, data provided by Saxton and Rawls (2006) 

were used to infer θFC from soil texture. Where necessary, 

bulk density (ρb

) was used to convert soil water contents from 

a volumetric basis (θ(v/v)) to a weight basis (θ(w/w)) using the 

following equation derived from dimensional analysis:

θ

θ

ρ ( / )

( / )

w w

v v

b

= (37)

The median among the 14 independent b values (0.43) was 

used for atrazine in the P-GWAVA-RZ simulations (table 14). 

No b data were found in the published literature for DEA, so 

the atrazine value (0.43) was used for this compound, as well. 

Owing to a lack of b data in the published literature for anoxic 

conditions, the quantitative dependence of atrazine and DEA 

disappearance rates on water content was presumed to be the 

same in anoxic soils as in oxic ones. 

Deethylatrazine Production from Atrazine Reaction in Soil 

(Phase 2 only)

RZWQM quantifies the production of a pesticide 

degradate as a fixed proportion of the total amount of the 

parent compound that reacts over time, a parameter referred to 

as the daughter product formation percentage. For this study, 

the DEA formation percentage (Fatr>DEA) was computed using 

the following equation:

F k

k atr DEA atr DEA

atr

> > = ∗



 



100%  (38)

where 

katr>DEA is the first-order rate constant for the production 

of DEA from atrazine in non-sterile, oxic 

soils in the dark (d-1); and

katr is the first-order rate constant for the 

disappearance of atrazine in non-sterile, oxic 

soils in the dark (d-1). 

An analysis of data reported by previous laboratory studies 

(appendix B) indicated that the magnitude of katr>DEA is 

significantly correlated with the same explanatory parameters 

whose influences on atrazine disappearance rates (katr) are 

accounted for by the Fenner-Borsuk relation. Consequently, 

rather than using a single Fatr>DEA value for all of the Phase 2 

sites, modified versions of the Fenner-Borsuk relation were 

used to estimate katr>DEA and katr, and thus Fatr>DEA, for each 

SSURGO component for the P-GWAVA-RZ simulations. 

These equations, and the approach used to develop them, 

are presented in appendix B. Use of equation 38 to estimate 

Fatr>DEA for all of the SSURGO components examined during 

Phase 2 yielded predicted values of this parameter that 

ranged from 1.0 to 25.4 percent. These values were well 

within the range of 0.4 to 51.1 percent spanned by the 58 

Fatr>DEA values computed from the eight laboratory studies 

(fig. B1) from which equation B4, used to predict katr>DEA, was 

derived (Winkelmann and Klaine, 1991a and 1991b; Kruger 

and others, 1993; Assaf and Turco, 1994; Topp and others, 

1994; Kruger and others, 1997; Rodriguez and Harkin, 1997; 

Zablotowicz and others, 2006). 

Deethylatrazine Disappearance Rate in Soil (Phase 2 only)

For the P-GWAVA-RZ simulations, the rates of DEA 

disappearance in oxic soils were estimated by scaling the 

atrazine values using a QSRR, rather than taking the DEA 

reaction rates directly from the published literature. This 

approach was used because of the similarities between DEA 

and atrazine in relation to their structure and their reactivities 

(fig. 20), and because the number of reported measurements 

of atrazine disappearance rates in non-sterile, oxic soils at 

circumneutral pH is substantially larger than the number of 
Methods 57

reported measurements for DEA. (Only 4 such measurements at circumneutral pH have been 

reported to date for DEA, whereas at least 259 measurements are available for atrazine.)

The rate of DEA disappearance at TMay was estimated by adjusting the atrazine 

disappearance rate at TMay with the following QSRR:

k k

k

k DEA,ox,T atr,ox, T

DEA,ox

atr,ox, May May ≈ ∗

+ °

+

, K 97,24 C

 K 97,24 C

d

° d

−

−















 = ∗













 k k atr, ox, T = May

0 0038

0 0034

1

1

.

. atr, ox, TMay ∗1 1. (39)

where

kDEA,ox,TMay is the first-order rate constant (d-1) for the disappearance of 

DEA in oxic, non-sterile soil at temperature TMay;

katr,ox, TMay is the first-order rate constant(d-1) for the disappearance of 

atrazine in oxic, non-sterile soil at temperature TMay, 

estimated using equations 28 and 30;

kDEA,ox, K 97,24 C + ° is the first-order rate constant for the disappearance of DEA in 

oxic, non-sterile soil at circumneutral pH, measured at 24°C 

by Kruger and others (1997); and

katr,ox, K 97,24 C + ° is the first-order rate constant for the disappearance of atrazine 

in oxic, non-sterile soil at circumneutral pH, measured at 

24 °C by Kruger and others (1997).

Equation 39 quantifies the inferred influence of replacing the ethyl group in atrazine with 

a hydrogen atom (fig. 20) on the rate of disappearance of the molecule in oxic, non-sterile soil 

at circumneutral pH. The investigation by Kruger and others (1997) was used as the source of 

the kinetic data in equation 39 because it reports disappearance rates for DEA and atrazine in 

the same soil at the same temperature under oxic, non-sterile conditions at circumneutral pH 

in the dark. The data used by equation 39 from Kruger and others (1997) were measured using 

a soil sample collected from a depth interval from 90 to 120 cm, the only soil examined with 

a pH between 6 and 8. Expressing the reaction rates in equation 39 in terms of disappearance 

half-lives (using equation 28), substituting the right side of equation 30 for ( ) 1/2 t atr,ox, TMay

and collecting terms yields the expression used to estimate the half-life for DEA disappearance

in oxic soils at TMay for each SSURGO component of interest:

 t

t d

DEA,ox,T

atr,ox, T

May

May

1 2

109

1 1 / . ( ) = = 



 



∗ ( )

1.1 exp 

1/2

6 121 9 1 1

99 6 121

, .

,

K

298.15K

exp 

( ) ∗ −

































= ( ) ∗

T

d

May

.9 1 1 K

298.15K ( ) ∗ −































TMay 

 (40)

The use of equations 39 and 40 for estimating the rate of DEA disappearance at temperatures 

other than 24°C (equation 39) was predicated on the assumption that the activation energy 

for the disappearance of DEA in soil is identical to that for atrazine, and thus that the ratio

kDEA,ox/katr,ox is independent of temperature, within the temperature range of interest for the

Phase 2 simulations (10–20°C [fig. 15]). Support for this assumption is provided by the 

similarity in structure between the two compounds, as well as the similarities in the reactions 

that transform them in soil (fig. 20).

Kruger and others (1997) also measured the rates of atrazine and DEA disappearance 

in soil under anoxic conditions. Consequently, data from that study were used to estimate 

the rates of DEA disappearance under anoxic conditions. The DEA disappearance rates were 

estimated with the following QSRR, which scaled the rate of atrazine disappearance under 
58 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

anoxic conditions ( katr,anox,TMay , from equation 31) using the ratio of rate constants for DEA and 

atrazine disappearance measured under anoxic conditions by Kruger and others (1997):

 k k

k

k DEA,anox,T atr,anox,T

DEA, anox,

atr, an May May ≈ ∗ K 97,24 C + °ox

atr,anox, T k d

d May

,K 97,24 C + °

−

−

















= ∗





0 0086

0 013

1

1

.

. 







 = ∗ katr,anox,TMay 0 66.

 (41)

where

 kDEA,anox,TMay is the first-order rate constant for DEA disappearance in anoxic soil 

 at temperature TMay (d-1);

 kDEA,anox,K+97,24 °C is the first-order rate constant for DEA disappearance measured in 

 anoxic soil by Kruger and others (1997) at circumneutral pH and

24°C; and

 katr,anox,K+97,24°C is the first-order rate constant for atrazine disappearance measured

 in anoxic soil by Kruger and others (1997) at circumneutral pH 

and 24 °C.

Expressing the reaction rates in equation 41 in terms of half-lives (using equation 28),

substituting the right side of equation 32 for t atr anox TMay 1 2/ , , ( ) , and collecting terms yields

the expression used to compute the half-life for DEA disappearance in soil for the reference 

temperature of TMay whenever anoxic conditions were simulated by RZWQM during Phase 2:

 t

d

T DEA anox T

May May 1 2

372

0 66

6 121 9 1 1

/ , , . ( ) = , . 



 



 *exp K ( ) ∗ − 298.15K

 *exp K 298

































= ( ) 562 6 121 9 ( ) ∗ − 1 1 d

TMay

, . .15K

































 (42)

The use of equation 42 to estimate the rate of DEA disappearance under anoxic conditions 

at temperatures other than the temperature for which equation 41 was derived (24°C) was 

predicated on the assumption that the activation energies for the disappearance of DEA and 

atrazine under anoxic conditions are identical, and thus that the ratio kDEA,anox/katr,anox is

independent of temperature, within the temperature range of interest to the Phase 2 simulations 

(10–20 °C [fig. 15]). This assumption was necessary given the apparent absence of published 

data on the temperature dependence of atrazine and DEA disappearance rates in anoxic soil. 

As for atrazine, the half-life for the disappearance of DEA estimated at TMay using either 

equation 40 (for oxic conditions) or 42 (for anoxic conditions) was adjusted by RZWQM for 

temporal variations in temperature in each soil layer during the course of the simulation for 

each SSURGO component of interest, using the corresponding version of equation 33, that is:

 t t

T T DEA T DEA T sn May sn May 1 2 1 2 6 121 9 1 1

/ , / , ( ) = ( ) *exp ( ) , . ∗ −











K























 (43)

where 

 t Tsn 1 2/ , ( )DEA is the half-life for the disappearance of DEA in soil layer n, in days, at the

 temperature of the soil layer (Tsn); and

 t TMay 1 2/ , ( )DEA is the half-life for the disappearance of DEA, in days, at the site of interest 

 at a temperature of TMay. 
Methods 59

Atrazine and Deethylatrazine Disappearance Rate 

Adjustments with Depth Below Land Surface

Numerous studies have established that, all other 

factors being equal, the rates of pesticide transformation 

in the subsurface generally decrease with increasing depth 

below the land surface (Barbash and Resek, 1996). This 

reduction in reaction rates with depth may be attributable to 

concomitant decreases with depth in one or more of several 

factors, including the populations of the microorganisms 

that are responsible for these reactions, the concentrations of 

the soil OM and nutrients upon which the microorganisms 

feed, and temperature (Jury and others, 1987; Erickson and 

Lee, 1989; Alvey and Crowley, 1996; Issa and Wood, 1999; 

Zablotowicz and others, 2006). For pesticide compounds 

that exhibit this pattern, RZWQM can simulate variations 

in transformation rates with depth in the vadose zone. In 

RZWQM, this involves using a transformation rate that is 

uniform from the land surface to a depth of 25 cm, decreases 

in a linear fashion from 25 to 100 cm depth, and is uniform at 

depths greater than 100 cm (Ahuja and others, 2000). Results 

from previous studies of relations between persistence and 

depth within the vadose zone are consistent with this pattern 

for atrazine (for example, Roeth and others, 1969; Kruger and 

others, 1993; Stolpe and Shea, 1995; Kruger and others, 1997; 

Issa and Wood, 1999; Larsen and others, 2000; Accinelli and 

others, 2001; Jacobsen and others, 2001) and DEA (Kruger 

and others, 1997). The decrease in reaction rate within the 

vadose zone over the depth interval between 25 and 100 cm 

beneath land surface is simulated by RZWQM using the 

following QMRR:

t t V z 12 12 z z 0 mhf 1 1 25

75 ( ) / / = ( ) ∗ + ( ) − ∗ −



 



 



 















 = (44)

where

(t

1/2)z is the half-life for compound disappearance 

in oxic soil at a depth of z beneath the land 

surface (25 cm ≤ z ≤ 100 cm);

(t

1/2) z=0 is the half-life for compound disappearance in 

oxic soil at the land surface 

(z = 0); 

and

V

t

t mhf z

z

= ( )

( )=

=

1 2 100

1 2 0

/

/

(45)

where 

(t

1/2)z =100 is the half-life for compound disappearance in 

oxic soil at a depth of 100 cm beneath the 

land surface. 

Equation 44 incorporates a correction to the original form 

of this relation presented by Ahuja and others (2000) and 

Wauchope and others (2004), wherein the parameter Vmhf

appeared alone, rather than as (Vmhf –1).

Given the relation presented earlier between the rate 

constant and half-life for first-order reactions (equation 28), the 

following equation was used to estimate the most appropriate 

value of Vmhf from published data on the rates of atrazine 

and DEA transformation as functions of depth below the 

land surface:

V

t

t

k

k

k

k mhf z

z

z

z

z ≡ ( )

( ) = = =

=

=

=

1 2 100 =

1 2 0

100

0

0 2

2

/

/

[(ln ) / ]

[(ln ) / ] z

z

z

k

k =

= 



 



 ≈ 



 





100

0

100~

(46)

where 

kz=0, kz=100, 

 and kz ~100 represent the first-order rate constants for the 

disappearance of the compound of interest 

at the soil surface, at a depth of 100 cm, 

and at a depth of approximately 100 cm, 

respectively. 

Because the data reported by previous investigations did not 

always include a measurement at a depth of precisely 100 cm, 

the parameter kz~100 represents the transformation rate measured 

at the depth closest to 100 cm for that location. 

At least 12 previous studies have examined the extent 

to which atrazine persistence in non-sterile, oxic, unsaturated 

soils in the absence of light varies with depth below the land 

surface. Among the investigations that measured reaction rates 

at a uniform temperature in soil samples collected from the 

land surface and from a depth of (or near) 100 cm, the largest 

number of observations was reported by the three studies that 

measured atrazine disappearance rates at 25°C (Kruger and 

others, 1993; Stolpe and Shea, 1995; Issa and Wood, 1999). 

Calculations using the results from these three studies yielded a 

median of 6.58 among 11 independent values of (kz=0 /kz ~100). 

Consequently, this was the value of Vmhf used for atrazine 

during the P-GWAVA-RZ simulations (table 14). For DEA, 

results from measurements carried out at 25°C by Winkelmann 

and Klaine (1991b) and at 24°C by Kruger and others (1997) 

appear to be the only published data available for estimating 

(kz=0 /kz~100). Calculations using the results from these two 

studies yielded the Vmhf value of 7.95 used for DEA for the 

P-GWAVA-RZ simulations (table 14).

Formation, Transport, and Fate of Nitrate 

(Phase 2 only)

Figure 21 depicts the compartments and processes 

associated with the cycling of nitrogen through water, 

soils, and biota in agricultural systems, highlighting those 

that are accounted for by RZWQM. (For this report, the 

term compartment refers to an environmental medium, 

such as soil organic matter, plant tissues, crop residues, 

groundwater, or surface water.) Values for most RZWQM 

input parameters associated with the formation, transport, 

and fate of nitrate (table 14) were those recommended by 

the model documentation (Ahuja and others, 2000; Agricultural 

Research Service, 2010; Bartling and others, 2011). 
60 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig21

Atmospheric

nitrogen

Industrial

fixation

Fertilizer

Volatilization

Crop

removal

Biological

fixation

Legumes

Manure

Inorganic

N pool

Mineralization

Ammonification

Nitrification

Organic

matter

Assimilation

Denitrification

Groundwater

Soil

surface

Runoff and 

lateral flow Surface

water

Nitrification

NH3

N2, N2O

N2

, N2O

NH4

+

NH3

NO3

-

NO3

-

NH4

+

NO3

-

NO3

-

N2

N2

Denitrification

NO3

-

Water

table

Irrigation 

 water Precipi-

 tation

Leaching through vadose zone and tile drains

Dead roots

 Crop

residue

 Crop

uptake

EXPLANATION

Organic

N pool

NO3

-

Color coding for processes 

and compartments 

simulated by RZWQM

 Nitrogen (N) source

 N sink

 N storage reservoir

Figure 21. Processes and storage compartments for nitrogen cycling through water, soil, and biota in agricultural systems. 

Adapted from Makuch and Ward (1986). Highlighted processes and compartments are those that Root-Zone Water-Quality 

Model (RZWQM) accounts for as either nitrogen sources, sinks, or storage compartments.
Methods 61

One of the exceptions was related to the relative sizes of the 

three pools of soil OM that show fast, intermediate, and slow 

rates of oxidation (Ahuja and others, 2000). For the initial 

model simulations, the RZWQM default values were used 

for the relative proportions among these fractions (that is, 2, 

18, and 80 percent, respectively). However, to achieve closer 

agreement between the nitrate concentrations predicted by 

the model and those measured in groundwater, these relative 

proportions were subsequently adjusted for all sites to 10, 20, 

and 70 percent, respectively (tables 4 and 14). 

Model Formulation and Output

Both PRZM and RZWQM simulate the movement of 

water within the vadose zone in response to precipitation, 

irrigation, and ET. Both models also simulate the transport and 

fate of agrichemicals and their transformation products in the 

vadose zone in response to (1) the rates, timing, and method 

of their application to the land (for pesticides and fertilizers); 

(2) their formation in situ (for nitrogen species and pesticide 

degradates formed by microbial or abiotic processes); (3) their 

uptake and release by plants; (4) their transformations; 

and (5) the effects of biogeochemical conditions (such 

as temperature, pH, oxygen concentrations, soil organic 

matter, and microbial activities) on these processes. Table 14

provides a summary of the processes simulated, initial and 

boundary conditions, and input parameter values. In addition 

to the selection of input parameter values and approaches 

for simulating various physical, chemical, biological, and 

hydrologic processes, efforts also were required to determine 

the most appropriate sizes of the grid cells to be used for the 

simulations carried out by the two models.

Soil Column Discretization

Phase 1

Previous studies have demonstrated that the solute 

concentrations predicted by PRZM in the subsurface are 

highly sensitive to the sizes of the computational grid cells 

used for the simulations (for example, Jones and Mangels, 

2002; Nolan and others, 2005). Thus, after initial simulations 

using the P-GWAVA-PR model predicted negligible 

concentrations of atrazine at the 1-m assessment depth in 

many study locations where the herbicide had been detected 

in shallow groundwater, the effect of adjusting the grid 

discretization on the simulated concentrations was examined. 

These adjustments, however, were applied only to the deeper 

parts of the soil column; following a suggestion by Carsel and 

others (1998), a grid-cell thickness of 0.1 cm was used for the 

top 10 cm of the soil for all model simulations. To select the 

most appropriate discretization for the depth interval between 

10 and 100 cm beneath the land surface, model simulations 

were done using three grid-cell thicknesses: 1 cm, 10 cm and 

the maximum value of 30 cm recommended by Carsel and 

others (1998). To ensure that unsaturated conditions were 

present from the land surface down to the Phase 1 assessment 

depth of 1 m in all the locations examined, the analysis was 

restricted to sites where the average DTW exceeded 1 m.

To avoid the potential influence of uncertainties regarding 

the prevalence, timing, and magnitude of irrigation near any of 

the sites of interest, this exercise was done without simulating 

irrigation, and only in those locations where the NRI data 

indicated that irrigation was not used (fig. 17). Additionally, 

the analysis was carried out using the areally averaged 

intensities of atrazine application, because preliminary model 

simulations indicated that the atrazine concentrations predicted 

in the vadose zone by the P-GWAVA-PR simulations were 

in closer agreement with those measured in groundwater 

when the areally averaged intensities of atrazine application 

were used than when the use intensities were presumed to be 

spatially uniform (see section, “Effect of Spatial Variability of 

Atrazine Application Intensities on Simulation Results”).

Phase 2

RZWQM uses three discretization schemes for the 

soil column to (1) capture the variations in hydraulic and 

other properties among individual soil horizons at the site of 

interest, (2) simulate the redistribution of water and solutes 

within the soil column, and (3) simulate infiltration and heat 

flow (Ahuja and others, 2000). The first scheme uses sitespecific data to delineate the boundaries between the horizons 

within the soil column. At each site for the P-GWAVA-RZ 

simulations, these boundaries were set to coincide with those 

between adjacent soil horizons, as indicated by the SSURGO 

data. Where necessary, and following the guidance of Ahuja 

and others (2000), slight adjustments were made to the 

layer thicknesses to ensure that grid-cell boundaries always 

coincided with horizon boundaries. To simulate the processes 

of water and solute redistribution, infiltration and heat flow, 

these layers were subdivided further into 1-cm grid cells 

throughout the entire length of the simulated soil column (that 

is, from the land surface to the 3-m assessment depth).

Quantifying Solute Concentrations from Model 

Output

At any location within the simulated soil column, 

PRZM quantifies solute amounts in terms of concentrations 

(mass [M] per unit volume [length cubed], or ML-3), whereas 

RZWQM does so in terms of mass fluxes (mass per unit area 

[length squared] per unit time, or ML-2T-1). Because PRZM 

and RZWQM use different approaches to quantify model 

outputs, slightly different methods were used to compute the 

concentrations simulated by the two models at their respective 
62 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

assessment depths. At each site, predictions of solute mass 

and water flow were aggregated over time and combined to 

obtain simulated values of the flow‐weighted mean (FWM) 

concentration (CFWM) at the assessment depth of interest. 

Each FWM concentration represented the ratio between the 

simulated flux of the solute and the simulated flow of water 

at the assessment depth of interest over the entire 5-year 

simulation period. For the P-GWAVA-PR simulations, the 

following equation was used to compute CFWM:

C

C Qw

Qw

FWM

j j

j

t

j

j

t

sim

sim

=

( ) ∗

=

( ) ∗

=

( ) ∗

∑

∑

1

365

1

365 (47)

where

t

sim is the simulation period over which CFWM was 

computed, in years (5 years for both study 

phases);

j is the daily time-step number;

Cj is the simulated concentration of the solute of 

interest (ML-3) at the assessment depth, at 

time step j; and

Qwj is the simulated water flow (volume per unit 

time, or L3T-1) at the assessment depth, at 

time step j.

For the P-GWAVA-RZ simulations, the following equation 

was used for each SSURGO component examined:

C

Qs

Qw

FWM

j

j

t

j

j

t

sim

sim

= =

( ) ∗

=

( ) ∗

∑

∑

1

365

1

365 (48)

where 

Qsj is the solute flux (ML-2T-1) past the assessment 

depth at time step j. 

Because the P-GWAVA-RZ simulations were done 

for more than one SSURGO component in many of the 

map units of interest, it was necessary to combine the CFWM

values from multiple components in these locations. This 

was accomplished for each solute by using equation 48 to 

compute a CFWM value for each component, and then using 

the following equation to combine the CFWM values from the 

individual components, in proportion to the percentages of the 

map unit that they occupied:

C

C P

P

FWM

FWM i i

i

N

i

i

N

c

c

=

( ) ∗

=

=

∑

∑

,

1

1

(49)

where

Nc is the number of soil components for which 

P-GWAVA-RZ simulations were carried out 

in the SSURGO map unit of interest;

i is the soil component number;

CFWM,i is the flow-weighted mean concentration of the 

solute of interest for component i, computed 

using equation 48; and

Pi is the percentage of the map unit area occupied 

by component i.

The denominator in equation 49 was less than 100 percent for 

those map units of interest where one or more components 

were disregarded for one or both of the reasons mentioned 

earlier—that is, because of an absence of data for one or more 

of the required soil parameters, or because the component 

represented less than 10 percent of the map-unit area (see 

section, “Physical and Chemical Properties of Soils”). An 

analogous version of equation 49 was used to compute a 

simulated flow-weighted mean recharge value (Qw,FWM) for 

each SSURGO map unit from the recharge values simulated 

for its individual components (Qw,FWM,i).

Accounting for the Potential Effects of Corn andSoybean Rotations on Agrichemical Occurrence

The use of corn-and-soybean crop rotations (rather 

than planting corn each year, often referred to as continuous 

corn) is widespread in the Corn Belt, and thus has occurred 

at many of the Phase 2 sites. (Locations where corn-andsoybean rotations are used are referred to herein as cornand-soybean areas.) Because atrazine is not applied during 

soybean cultivation (U.S. Environmental Protection Agency, 

2003), the use of corn-and-soybean rotations likely results 

in spatial and temporal patterns of variation in atrazine and 

DEA occurrence in the subsurface that differ from those 

associated with continuous corn cultivation. Similarly, because 

the annual amounts of nitrogen fertilizer applied to soybeans 

in the United States are between 11 and 24 percent of the 

amounts applied to corn (Economic Research Service, 2011), 

the patterns of nitrate occurrence in the subsurface during 

corn-and-soybean rotations also are likely to differ from 

those during continuous corn cultivation. This disparity for 

nitrate may be diminished to some extent because as legumes, 

soybeans add nitrogen to the soil through fixation. 
P-GWAVA Simulation and Groundwater Sampling Results 63

Detailed information on crop rotation schedules across 

the Nation was not available during Phase 2 of this study. 

Consequently, all the P-GWAVA simulations assumed that 

corn was grown every year (table 14). As such, the simulations 

did not account for the spatial and temporal fluctuations in 

atrazine or nitrogen fertilizer input that would have been 

related to variations in the prevalence of corn cultivation—

relative to soybeans—among individual locations. The 

analysis of the Phase 2 results, therefore, included an 

examination of the potential influence of corn-and-soybean 

rotations on the extent of agreement between the agrichemical 

concentrations simulated in the vadose zone by the 

P-GWAVA-RZ model and those measured in the underlying 

shallow groundwater.

To determine whether or not the assumption of 

continuous corn during the Phase 2 simulations may have 

affected the accuracy of the P-GWAVA-RZ predictions, 

statistical correlations were examined between the average 

percentage of the land area where corn was grown between 

1995 and 2005 and the model residuals for the concentrations 

of the three solutes of interest. For the purposes of this study, 

a model residual for a given constituent concentration or 

detection frequency is defined as the value simulated by 

the P-GWAVA model at a specific depth in the vadose zone 

(usually the assessment depth), minus the value measured in 

shallow groundwater at the same site. Thus, positive residuals 

denote overpredictions by the model and negative residuals 

denote underpredictions. For each county where one or more 

Phase 2 sites were located, data obtained from the USDA 

National Agricultural Statistics Service (2010b) were used to 

compute the average annual percentage of land area where 

corn was grown during the simulation period (pc,avg) using the 

following equation:

p

A

A A

c avg

c t

t c t s t

,

,

, ,

% *

= +





























 =  ∑ 100

11

1995

2005

yr

(50)

where

Ac,t is the number of acres from which corn was 

harvested for either grain or silage in the 

county of interest in year t; and

As,t is the number of acres from which soybeans 

were harvested in the county of interest in 

year t. 

P-GWAVA Simulation and Groundwater 

Sampling Results

Several approaches were used to compare the results 

from the P-GWAVA model simulations with the results from 

groundwater sampling or with data obtained from other sources. 

Although measured rates of groundwater recharge were not 

available for most sites examined for this study, nationwide 

results from a separate approach for estimating groundwater 

recharge rates, known as the base-flow index (BFI) method, 

provided a set of independent estimates of recharge against 

which to compare the rates of groundwater recharge predicted 

by the P-GWAVA simulations. The BFI approach uses an 

automated hydrograph separation technique to estimate annual 

rates of groundwater recharge in a given watershed from the 

amount of flow in unregulated streams that is inferred to be 

supplied by groundwater discharge. Wolock (2003) computed 

the BFI-derived estimates of recharge for a 1-km2

 grid for 

the conterminous United States by calculating point values 

using streamflow data from 1951 to 1980 at 8,249 individual 

streamgages across the country and interpolating between the 

streamgage sites using an inverse-distance weighting method.

For each solute of interest to this study, three levels of 

spatial aggregation were used to compare the flow-weighted 

mean concentrations predicted in the vadose zone by the 

P-GWAVA simulations with the concentrations measured 

in groundwater at the same locations: (1) concentrations 

at individual sites, (2) detection frequencies in individual 

groundwater sampling networks, and (3) overall detection 

frequencies among all of the sites in each study area. Some 

of the key characteristics of the groundwater sampling 

networks are listed in table 2 and appendix C. For the first 

two levels of aggregation, the extent of agreement between 

simulation and measurement was assessed by examining the 

statistical distributions of model residuals, as well as through 

correlation analyses. In several instances, the data are presented 

graphically through the use of scatter plots (to compare the 

absolute magnitudes of simulated and measured values at 

individual sites) as well as cumulative distribution plots of 

the model residuals (to display the statistical distributions of 

model over- and underpredictions among all sites examined). 

All the detection frequencies that were computed from the 

simulated solute concentrations or measured concentrations in 

groundwater were obtained from locations representing subsets 

of the Phase 1 or Phase 2 sites (figs. 1 and 2, respectively). 

In selected locations, the simulated distributions of atrazine 

and DEA between the dissolved and sorbed states were also 

examined, as were the distributions of nitrogen among various 

chemical forms and environmental compartments.
64 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

At sites where simulated and measured aqueous 

concentrations of a given analyte were compared graphically 

or through the use of model residuals, but one or both values 

were less than the analytical reporting limit, a concentration 

of 0.0001 μg/L was assigned for a non-detection of atrazine 

or DEA, and a value of 0.01 mg/L as N was assigned for a 

non-detection of nitrate. All of these values were substantially 

less (by at least 80 percent) than the minimum reporting 

limits used for their respective compounds (fig. 4). Because 

simulations were carried out for different periods during 

the two phases of this study (table 1), in some locations the 

measured concentrations against which the simulated values 

were compared may have been measured during different 

years. Consequently, at some of the sites examined for both 

Phase 1 and Phase 2, the measured atrazine concentration used 

for comparison with the Phase 1 prediction was different from 

the value used for comparison with the Phase 2 prediction. 

Examination of DEA and atrazine during Phase 2 

made it possible to compare the P-GWAVA-RZ predictions 

in the vadose zone with measured values of the DEA 

fraction (f

DEA) in groundwater. This parameter is the molar 

proportion represented by DEA among all the atrazine-derived 

compounds detected in a given sample (that is, atrazine and 

DEA for this study), and was computed as follows:

f

DEA

atrazine DEA DEA ≡ [ ]

( ) [ ] +[ ] (51)

where

[DEA] is the DEA concentration, in moles per liter; 

and

[atrazine] is the atrazine concentration, in moles per 

liter.

DEA fractions thus span a range from 0 (when only atrazine 

is detected) to 1 (when only DEA is detected). Because of 

the manner in which they are calculated, f

DEA values were 

not computed for sites where neither solute was detected in 

groundwater, or where the simulated concentrations for both 

were less than their respective reporting limits.

In several locations examined, the average depth to the 

water table was shallower than the assessment depth used 

for the simulations (fig. 16). As a result, contrasts between 

the simulated solute concentrations and the concentrations 

measured in groundwater—or between the simulated recharge 

values and the BFI-derived estimates of recharge—might 

have been affected by the possible occurrence of saturated 

conditions at depths shallower than the assessment depth 

during part or all of the simulation period in these locations. 

To avoid this potentially confounding influence, during the 

analyses of the effects of different simulation approaches on 

model residuals, all sites were excluded where the average 

water-table depth (fig. 16) was less than the assessment 

depth (1 m for Phase 1, 3 m for Phase 2). However, because 

the P-GWAVA system can simulate recharge and solute 

concentrations anywhere in the conterminous United 

States—regardless of whether or not the DTW is known—

the assessments of agreement between the simulated and 

measured concentrations (or between the simulated and the 

BFI-derived recharge values) were done using (1) all of the 

sites, and (2) only those sites where the average DTW was 

greater than the assessment depth. This approach made it 

possible to assess the extent to which the accuracy of the 

P-GWAVA predictions may have been influenced (if at all) by 

the fact that the assessment depth was below the water table 

during some part of the simulation period in some of the study 

locations. 

Phase 1 (P-GWAVA-PR)

Preliminary simulations were carried out with the 

P-GWAVA-PR model during Phase 1 to determine the most 

appropriate simulation approach with respect to various model 

features, process formulation, and inputs. These preliminary 

simulations examined the effects of grid discretization, 

atrazine application intensities, the sites of atrazine 

transformation (dissolved versus sorbed state), the presence 

or absence of irrigation, and the degree of spatial averaging 

of soil properties (STATSGO versus SSURGO) on the level 

of agreement between the atrazine concentrations or detection 

frequencies predicted in the vadose zone by the simulations 

and those measured in groundwater. 

Soil Column Discretization for Simulations

The extent of agreement between the atrazine 

concentrations predicted in the vadose zone at the 1-m 

assessment depth by the P-GWAVA-PR simulations using 

the three discretization schemes and those measured in 

shallow groundwater is shown in figure 22. Results are 

shown for the three discretization schemes, which used 

0.1-cm grid cells between the land surface and a depth of 

9.9 cm for all simulations, and grid cells with thicknesses 

of 1, 10, or 30 cm for the interval from 10 to 100 cm. These 

results indicate that the atrazine concentrations predicted 

by the P-GWAVA-PR simulations increased with increasing 

grid-cell size in the depth interval between 10 and 100 cm. 

(The reasons the atrazine concentrations predicted by the 

P-GWAVA-PR simulations increased with increasing gridcell size, however—rather than decreasing, as is typically 

observed for finite-difference models—remain unclear.) 
P-GWAVA Simulation and Groundwater Sampling Results 65

tac12-0781_fig22

0.0001 

0.001 

0.01 

0.1 

1 

10 

0.0001 

0.001 

0.01 

0.1 

1 

10 

0.0001 

0.001 

0.01 

0.1 

1 

10 

0.0001 0.001 0.01 0.1 1 10

Simulated atrazine concentration, in micrograms per liter

Measured atrazine concentration, in micrograms per liter

Simulated = measured

Simulated = measured

Simulated = measured

B. 10-cm grid cells

A. 1-cm grid cells

C. 30-cm grid cells

Figure 22. Effect of model grid 

discretization in the 10–100 centimeter 

depth interval on the degree of agreement 

between atrazine concentrations simulated 

at an assessment depth of 100 centimeters 

(1 meter) by the P-GWAVA-PR (Phase 1) 

model and measured in shallow groundwater 

at 331 sites in unirrigated agricultural 

areas in the conterminous United States 

with average depths to water greater than 

1 meter. Simulations were carried out with 

areally averaged atrazine use intensities, and 

irrigation was not simulated. A grid-cell size 

of 0.1 centimeter was used for the upper 10 

centimeters for all simulations. All atrazine 

concentrations (simulated or measured) 

less than the 0.001 microgram per liter (μg/L) 

reporting limit were assigned a value of 

0.0001 μg/L for the purpose of display.
66 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Additionally, the data summarized in table 5 indicate that the 

use of a grid-cell size of 30 cm within the 10–100 cm depth 

interval resulted in simulated atrazine concentrations that were 

more strongly correlated with those measured in groundwater 

than the concentrations simulated using either the 10-cm or the 

1-cm cell sizes. Consequently, the 30-cm grid cells were used 

for the 10-100 cm depth interval for the Phase 1 simulations 

(figure 22C, table 4). The only simulations for which the 

30-cm discretization between 10 and 100 cm depth was not 

used were simulations carried out to compare the results 

obtained using the SSURGO-derived soil property values with 

those derived from the STATSGO data. This exception was 

required because the 30-cm discretization did not always align 

with the soil layering specified by the SSURGO data.

The 30-cm grid-cell size used for the 10–100 cm depth 

interval is the maximum value recommended by Carsel and 

others (1998), and larger than most values that have been 

used for previous studies. However, grid-cell sizes of this 

magnitude are not without precedent for PRZM simulations. 

The EPA Office of Pesticide Programs has sometimes used 

grid-cell sizes of as much as 20 cm for PRZM simulations 

of pesticide transport (Dirk Young, U.S. Environmental 

Protection Agency Office of Pesticide Programs, oral 

commun., August 21, 2009). Modeling investigations seldom 

provide information on the discretizations used, but grid-cell 

sizes of 5 cm (Sauer and others, 1990; Parrish and others, 

1992) and as much as 10 cm (Garratt and others, 2002) have 

been reported by earlier studies using PRZM. 

Effect of Spatial Variability of Atrazine 

Application Intensities on Simulation Results

To examine the degree to which the spatial variability 

of simulated atrazine applications may have influenced the 

extent of agreement between the concentrations of atrazine 

predicted in the vadose zone with the P-GWAVA-PR model 

and those measured in shallow groundwater, separate sets of 

simulations were done using the spatially uniform application 

intensity of 2 kg a.i./ha and the areally averaged application 

intensities computed with equation 10 (and shown in fig. 1). 

Table 5. Effects of model grid discretization in the 10–100 centimeter depth interval on the 

level of agreement between atrazine concentrations simulated at the 100-centimeter (1 meter) 

assessment depth in the vadose zone using P-GWAVA-PR (Phase 1) and those measured 

in shallow groundwater at 331 unirrigated agricultural sites where the average depth to the 

water table was greater than 1 meter in the conterminous United States, 1992–98. 

[All P-GWAVA-PR simulations used grid cells that were 0.1 centimeter (cm) in thickness for the depth interval 

from 0–9.9 cm below land surface, and the areally averaged intensities of atrazine use. Irrigation not simulated. 

For the purposes of these calculations, all atrazine concentrations—either simulated or measured—below the 

Phase 1 reporting limit of 0.001 microgram per liter (μg/L) were assigned a value of 0.0001 μg/L. Selected 

statistical characteristics: Model residuals calculated as the simulated concentration minus the measured 

concentration at each site. Simulated concentrations are flow-weighted mean concentrations in the vadose zone 

at a depth of 1 meter, derived from P-GWAVA-PR simulations and calculated using equation 47. Measured 

concentrations are those observed in groundwater during the NAWQA program. Spearman ρ: Values that 

were statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. 

P (ρ): Probability associated with the Pearson correlation coefficient (R2) for the relation between the ranks of 

the simulated and measured atrazine concentrations. 95th percentile: for each case, 95 percent of the model 

residuals were smaller than the value shown. ≤, less than or equal to]

Thickness of grid 

cell used between 

10 and 100 cm depth

(cm)

Relations between atrazine concentration simulated by 

P-GWAVA-PR in the vadose zone and concentration

measured in groundwater at each site

Nonparametric rank

correlations

Selected statistical

characteristics of

model residuals

(μg/L)

Spearman ρ P (ρ) Median 95th

percentile

30 0.24 ≤0.0001 0.0000 0.068

10 0.15 0.005 0.0000 0.0012

1 0.07 0.22 0.0000 0.0000
P-GWAVA Simulation and Groundwater Sampling Results 67

The simulations presumed that atrazine transformation 

occurred in the sorbed and dissolved states. As with the 

analysis of the effects of grid discretization on the model 

predictions, the potential influence of irrigation was avoided 

by conducting the simulations without irrigation, and 

restricting the analysis to unirrigated areas.

Cumulative distribution plots (fig. 23) were used to 

compare the model residuals for the atrazine concentrations 

predicted by the P-GWAVA-PR simulations using the areally 

averaged atrazine application intensities with those predicted 

by the simulations using the spatially uniform application 

intensity. Substantially closer agreement was noted between 

the simulated and measured atrazine concentrations when the 

areally averaged application intensities were used than when 

the spatially uniform intensity was used. The fact that the 

atrazine concentrations simulated using the spatially uniform 

application intensity were almost always greater than those 

simulated using the areally averaged intensities is consistent 

with the fact that the spatially uniform application intensity 

(2 kg a.i./ha) was greater than the areally averaged intensities 

for most of the Phase 1 sites (fig. 1).

The effects of 15 modeling approaches on the extent 

of agreement between the atrazine concentrations predicted 

by the P-GWAVA-PR simulations at the 1-m assessment 

depth in the vadose zone and the concentrations measured 

in shallow groundwater at all 1,125 Phase 1 sites where the 

average DTW exceeded 1 m are shown in table 6. The table 

provides a summary of the results from statistical analyses 

of correlations (if any) between the simulated and measured 

atrazine concentrations for each modeling approach. When 

irrigation was simulated at all Phase 1 sites and atrazine 

transformations were simulated in the sorbed and dissolved 

states, the use of the areally averaged application intensities 

led to a correlation (table 6) between the simulated and 

measured atrazine concentrations that was statistically 

significant (P[ρ] ≤ 0.0001), whereas the use of the spatially 

uniform application rate did not (P[ρ] > 0.05). Consequently, 

the final set of predicted atrazine concentrations for Phase 1 

were derived from the P-GWAVA-PR simulations that used 

areally averaged atrazine application intensities, instead of the 

spatially uniform application rate (table 4). 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-3 -2 -1 0 1 2 3 4 5 6 7 

Sites with model residuals greater than x-axis value, in percent

Model residual (simulated minus measured concentration), in micrograms per liter

Spatially uniform application intensity of 2 kilograms of 

active ingredient per hectare (335 sites)

Areally averaged application intensities (337 sites) 

Pesticide Root-Zone Model (PRZM)

underpredicts concentrations 

PRZM overpredicts

concentrations 

EXPLANATION

Figure 23. Effect of spatial variability of atrazine application intensities on model residuals for 

atrazine concentrations measured in shallow groundwater beneath unirrigated agricultural areas 

where the average depth to water was greater than 1 meter in the P-GWAVA-PR (Phase 1) study 

area in the conterminous United States, 1992–98. Transformations of atrazine were simulated in the 

dissolved and the sorbed states; irrigation was not simulated. All simulated or measured atrazine 

concentrations less than the 0.001 microgram per liter (μg/L) reporting limit were assigned a value 

of 0.0001 μg/L for the purposes of the calculations.
68 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Effect of Transformation Site (Dissolved versus 

Sorbed State) on Simulation Results

To examine the extent to which the agreement between 

predicted and measured atrazine concentrations was 

influenced by the location(s) in the subsurface where atrazine 

transformation was simulated by PRZM, the results from four 

P-GWAVA-PR simulation scenarios were compared. These 

scenarios involved the simulation of atrazine transformation 

in (1) the dissolved state alone, (2) the sorbed state alone, 

(3) both states, and (4) neither state (that is, no reaction). 

The simulations were carried out at all unirrigated Phase 1 

sites where the DTW was 1 m or more; irrigation was not 

simulated. All four scenarios used the areally averaged 

intensities of atrazine application at all the sites of interest. 

The results from this analysis (fig. 24), indicate that the 

atrazine concentrations measured in groundwater matched the 

concentrations predicted in the vadose zone most closely when 

atrazine transformation was simulated in both the dissolved and 

the sorbed states. A similar conclusion was reached from the 

statistical analyses of the model residuals obtained when irrigation 

was simulated at all sites and irrigated sites were included among 

those examined (table 6). As a result, atrazine transformation was 

presumed to occur in both the sorbed and the dissolved states for 

the final set of P-GWAVA-PR model simulations (table 4)—an 

approach that is consistent with the fact, noted earlier, that the rate 

of atrazine transformation in water is known to increase in the 

presence of soil (Armstrong and others, 1967; Skipper and others, 

1967; Armstrong and Chesters, 1968; Erickson and Lee, 1989; 

Mandelbaum and others, 1993; Rice and others, 2004; Krutz and 

others, 2008). 

Table 6. Effects of 15 modeling approaches on the extent of agreement between atrazine concentrations predicted at the 1-meter 

assessment depth in the vadose zone by the P-GWAVA-PR (Phase 1) simulations and concentrations measured in shallow groundwater 

in agricultural areas where the depth to water exceeded 1 meter in the conterminous United States, 1992–98. 

[Simulated concentrations are flow-weighted mean concentrations in the vadose zone, derived from the P-GWAVA-PR simulations and calculated using 

equation 47. For the purposes of these calculations, all concentrations of atrazine below the Phase 1 reporting limit of 0.001 microgram per liter (μg/L)—either 

measured or simulated—were assigned a value of 0.0001 μg/L. Spatial variability of atrazine application: Uniform indicates a spatially homogeneous 

application intensity of 2 kilograms of active ingredient per hectare (kg a.i./ha). Sites irrigated: Indicates whether or not irrigation was simulated by 

P-GWAVA-PR for all sites. Stochastic denotes simulations for which sites were selected at random for irrigation, in the proportion at which irrigation was 

implemented in the surrounding area, estimated using equation 8. Spearman ρ: Values that were statistically significant (P <0.05) and their associated 

probabilities (P (ρ)) are displayed in boldface. P (ρ): The probability associated with the Pearson correlation coefficient (R2) for the correlation between 

the ranks of the simulated and measured values. Model residuals: Calculated for each site as the atrazine concentration simulated by P-GWAVA-PR at the 

assessment depth (1 meter) in the vadose zone minus the concentration measured in shallow groundwater in the same location. 95th percentile: For each case, 

95 percent of the model residuals were less than the value shown. <, less than; ≤, less than or equal to]

Spatial variability 

of atrazine 

application

Sites

irrigated

Reaction site(s)

where atrazine 

transformation

was simulated

(all occurrences)

Correlation (if any) between 

simulated and measured

atrazine concentrations

Model residuals

Number

of sites

Spearman ρ P (ρ) Median 

(μg/L)

95th 

percentile

(μg/L)

Areally averaged None Sorbed and dissolved 0.17 ≤0.0001 0.000 0.25 1,125

Stochastic 0.13 ≤0.0001 0.005 0.35 1,125

All 0.15 ≤0.0001 0.049 0.93 1,125

Areally averaged None Sorbed 0.16 ≤0.0001 0.000 0.55 1,125

Stochastic 0.13 ≤0.0001 0.011 0.74 1,125

All 0.14 ≤0.0001 0.078 2.0 1,125

Areally averaged None Dissolved 0.19 ≤0.0001 0.37 9.1 1,125

Stochastic 0.18 ≤0.0001 0.88 9.4 1,125

All 0.21 ≤0.0001 2.3 12 1,125

Areally averaged None None 0.14 ≤0.0001 2.4 120 1,125

Stochastic 0.12 ≤0.0001 5.9 122 1,125

All 0.14 ≤0.0001 18 155 1,125

Uniform None Sorbed and dissolved 0.06 0.034 0.064 2.4 1,123

Stochastic -0.009 0.76 0.47 25 1,123

All -0.05 0.095 2.4 28 1,123
P-GWAVA Simulation and Groundwater Sampling Results 69

tac12-0781_fig24

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-100 -50 0 50 100 150 200 250 300 350 400 

Model residual (simulated minus measured concentration), in micrograms per liter 

Pesticide Root-Zone Model (PRZM)

 underpredicts concentrations 

PRZM overpredicts

concentrations 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-1 0 1 2 3 4 5 

Model residual (simulated minus measured concentration), in micrograms per liter 

Dissolved and sorbed states

Sorbed state only

Dissolved state only

Transformation not simulated 

EXPLANATION

Sites with model residuals greater than x-axis value, in percent

Sites with model residuals greater than x-axis value, in percent

Figure 24. Effect of the location of simulated atrazine transformation (that is, in the sorbed and [or] 

dissolved state) on model residuals for atrazine concentrations in shallow groundwater beneath unirrigated 

agricultural areas where the average depth to water was greater than 1 meter (337 sites) in the P-GWAVA-PR 

(Phase 1) study area in the conterminous United States, 1992–98. Simulated concentrations were obtained 

assuming areally averaged atrazine application intensities, and without simulating irrigation. All simulated or 

measured atrazine concentrations less than the 0.001 microgram per liter (μg/L) reporting limit were assigned 

a value of 0.0001 μg/L for the purposes of the calculations.
70 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The results shown in figure 24 and table 6 indicate 

that during the P-GWAVA-PR simulations, transformation 

in the sorbed state exerted a more pronounced effect on the 

predicted atrazine concentrations than transformation in the 

dissolved state. This is likely a consequence of the differences 

between the atrazine concentrations predicted in the sorbed 

and dissolved states. At a site in central Washington, for 

example, 93 percent of the atrazine remaining in the soil 

column at the end of the 5-year simulation period was present 

in the sorbed state, rather than in the dissolved state (fig. 25). 

As indicated by equation 27, for any compound that reacts 

by first-order kinetics (as is presumed to be the case for the 

thermal transformations of atrazine in soil or water), the rate 

of disappearance at any moment is directly proportional to 

the concentration of the compound at that time. The higher 

concentrations of atrazine in the sorbed state relative to the 

dissolved state might have been expected to lead to higher 

predicted rates of atrazine transformation in the sorbed state, 

relative to its rates of transformation in solution. This, in turn, 

would have contributed to the contrast in the relative influence 

of the two reaction locations indicated by the results shown in 

figure 24 and table 6.

Approaches for Distributing Irrigation

The approach for selecting the sites where irrigation was 

to be simulated during Phase 1 was anticipated to influence the 

level of agreement between the simulated and measured atrazine 

concentrations. To assess the potential importance of irrigation, 

the results from three modeling scenarios were examined. 

These scenarios involved simulating irrigation (1) at all the 

sites of interest; (2) at sites selected randomly in proportion to 

the percentage of irrigated land nearby (equation 8; fig. 17); 

and (3) at none of the sites. Despite its inclusion in this part 

of the analysis, the approach of assigning irrigation randomly 

in individual sampling networks was devised primarily for 

predicting frequencies of atrazine detection, rather than atrazine 

concentrations in individual locations. The simulations for all 

three scenarios involved assuming that atrazine was applied 

with areally averaged use intensities, allowing transformation of 

the herbicide to take place in both the dissolved and the sorbed 

states, and focusing solely on those locations where the average 

DTW exceeded 1 m. For both phases of this investigation, 

irrigation was simulated at each location by applying water 

only when soil moisture was below specified thresholds 

(table 14), rather than by applying water in fixed amounts on 

specified schedules. 

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 

1

11

21

31

41

51

61

71

81

91

101

111

121

131

141

151

161

171

181

191

201

211

221

231

241

Simulated atrazine mass, in milligrams per kilogram of soil

Depth below ground surface, in centimeters 

Sorbed

Dissolved 

EXPLANATION

Figure 25. Amounts of atrazine predicted in sorbed and dissolved states in soil at the end of a 5-year 

P-GWAVA-PR (Phase 1) simulation in an agricultural area (site 2086) in Grant County, Washington, 1991–95.
P-GWAVA Simulation and Groundwater Sampling Results 71

Statistical analyses of the results from the three irrigation 

scenarios indicated that all three scenarios led to simulated 

atrazine concentrations that were significantly correlated with 

the measured values (see the first three entries in table 6). 

Simulating irrigation at all of the sites resulted in a greater 

degree of overprediction of atrazine concentrations—but a 

lower degree of underprediction—than for the non-irrigated 

and stochastically distributed irrigation scenarios (fig. 26). 

This is consistent with the general pattern reported by previous 

field studies, which have commonly observed increases in 

pesticide concentrations in subsurface waters in response to 

recent recharge (Barbash and Resek, 1996). 

The spatial distributions of the P-GWAVA-PR model 

residuals for the frequencies of atrazine detection at 

agricultural sites with depths to water greater than 1 m (in 

groundwater networks with 10 or more such sites) are shown 

in figure 27 for two of the irrigation scenarios. These results 

indicate that when irrigation was not simulated at any of the 

sites (fig. 27A), the geographic distribution of model residuals 

for the atrazine detection frequencies reflected the distribution 

of precipitation in the conterminous United States (Daly, 

2010), with most areas of underprediction corresponding 

to more arid regions, and areas of overprediction generally 

corresponding to more humid regions. This relation was 

considerably less evident when irrigation was simulated at 

all of the sites (fig. 27B). Consistent with these observations, 

the model residuals for the atrazine detection frequencies 

in the 46 groundwater networks were positively correlated 

with mean annual precipitation (from 1961 to 1990 [PRISM 

Climate Group, 2009]) when irrigation was not simulated (R2

= 0.55; P ≤ 0.0001) or stochastically distributed (R2 = 0.31; P ≤ 

0.0001), but not when irrigation was simulated at all sites (P = 

0.22). These results demonstrate the importance of accounting 

for irrigation during the P-GWAVA-PR simulations, to 

counteract the spatial bias caused by the uneven distribution of 

precipitation across the Nation. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-2.0 -1.0 0 1.0 2.0 

Model residual (simulated minus measured concentration), in micrograms per liter

All sites irrigated

Irrigation distributed stochastically

among sites

No Irrigation 

Pesticide Root-Zone Model (PRZM)

underpredicts concentrations 

PRZM overpredicts

concentrations

EXPLANATION

Sites with model residuals greater than x-axis value, in percent

Figure 26. Effect of irrigation simulation approach on model residuals for atrazine concentrations 

in shallow groundwater beneath agricultural areas where the average depth to water was 

greater than 1 meter (1,124 sites) in the P-GWAVA-PR (Phase 1) study area in the conterminous 

United States, 1992–98. Simulated concentrations were obtained using areally averaged atrazine 

application intensities and assuming that atrazine transformation occurred in both the sorbed and 

dissolved states. All simulated or measured atrazine concentrations less than the 0.001 microgram 

per liter (μg/L) reporting limit were assigned a value of 0.0001 μg/L for the purposes of the 

calculations.
72 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig27

120° 110° 100° 90° 80° 70°

45°

40°

35°

30°

25°

0 400 MILES

0 400 KILOMETERS

120° 110° 100° 90° 80° 70°

45°

40°

35°

30°

25°

0 400 MILES

0 400 KILOMETERS

A. Irrigation not simulated

B. Irrigation simulated at all sites

Simulated (P-GWAVA-PR) minus 

measured (NAWQA) frequencies 

of atrazine detection, in percent

-36.7 to -0.1

0 (simulated = measured)

0.1 to 49.9

50.0 to 100

EXPLANATION

Simulated (P-GWAVA-PR) minus 

measured (NAWQA) frequencies 

of atrazine detection, in percent

-85.7 to -50.0

-49.9 to -0.1

0 (simulated = measured)

0.1 to 49.9

50.0 to 100

EXPLANATION

P-GWAVA-PR

underpredicts detection frequency

P-GWAVA-PR 

overpredicts detection frequency

}

}

P-GWAVA-PR

underpredicts detection frequency

P-GWAVA-PR 

overpredicts detection frequency }

National Albers Projection, North American 

Datum of 1983 (NAD 83)

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 27. Effect of irrigation simulation approach on P-GWAVA-PR model residuals for frequencies of atrazine 

detection in groundwater networks with 10 or more sites when (A) irrigation was not simulated at any sites and (B)

irrigation was simulated at all sites across the conterminous United States for Phase 1 of this study, 1992–98. All 

sites included in the analysis had average depths to water greater than 1 meter (46 groundwater networks; 1,051 

sites). Model simulations used an assessment depth of 1 m and areally averaged intensities of atrazine use, and 

assumed that atrazine transformation occurred in both the sorbed and dissolved states.
P-GWAVA Simulation and Groundwater Sampling Results 73

Spatial Averaging of Soil Parameter Values

Soil parameter values from the STATSGO data—rather 

than from the more detailed SSURGO data—were used as 

input for the Phase 1 model simulations, after being averaged 

both areally and with depth (fig. 7). To examine the potential 

effects of this spatial averaging on the atrazine concentrations 

predicted by the P-GWAVA-PR model, the results from a 

series of 10-year simulations using soil parameter values 

derived from STATSGO (spatially averaged) were compared 

with results from an analogous set of simulations using soil 

property data from SSURGO (as is). These comparisons were 

carried out at multiple sites in two agricultural locations: four 

sites in an arid, irrigated area with relatively low atrazine 

use in central Washington State, and six sites in a humid, 

unirrigated area with higher atrazine use in Wisconsin. 

All the simulations in both locations used the areally 

averaged intensities of atrazine application and assumed that 

atrazine transformation occurred in both the sorbed and the 

dissolved states.

For a preliminary set of simulations at the four 

Washington sites, the 0.1-cm discretization was used for the 

upper 10 cm of the soil column as recommended by Carsel 

and others (1998), and 1-cm grid cells used from 10 cm to 

an assessment depth of 153 cm. The 153-cm assessment 

depth was used for these analyses to take maximal advantage 

of all the available data on soil properties—data that are 

provided to at least this depth in all STATSGO and SSURGO 

map units. The 1-cm grid cells were used for depths below 

10 cm to obtain closer alignment with the SSURGO soil 

layer boundaries for the model simulations. The average 

depths to the water table at the four Washington sites ranged 

from 3.8 to 37.8 m, substantially greater than the 1.53-m 

assessment depth.

As anticipated from the results shown in figure 22A, the 

atrazine fluxes predicted by these initial simulations were 

negligible when the 1-cm discretization was used below 10 

cm (data not shown). Given the positive relation between 

grid-cell sizes and the concentrations predicted at a depth 

of 100 cm (fig. 22), an additional set of P-GWAVA-PR 

simulations was done using the same discretization scheme 

for the upper 93 cm, but with 10-cm grid cells (rather than 

1-cm cells) occupying the depth interval from 93 to 153 cm. 

The results (fig. 28) showed clear differences between the 

atrazine chemographs created using the STATSGO and 

SSURGO data at some sites, especially at the times of the 

annual peak concentrations. However, the differences between 

the chemographs for the two approaches were not systematic 

in time or space; all four of the sites exhibited reversals in the 

relative magnitudes of the atrazine concentrations simulated 

by the two approaches over time. Furthermore, although 

the atrazine concentrations predicted using the spatially 

averaged soil parameter values from STATSGO exceeded 

those predicted using the data from SSURGO throughout 

most of the 10-year simulation at one of the sites (site 2098), 

the opposite pattern was observed in most years at the other 

three sites. 

Similar results were observed when the simulations 

were repeated using grid-cell thicknesses that were equal 

to those of the SSURGO layers for both cases. The atrazine 

concentrations predicted by the P-GWAVA-PR simulations 

using this approach in conjunction with the SSURGO and 

STATSGO soil property data at the four Washington sites 

are compared with the concentrations measured in shallow 

groundwater at the same sites in figure 29. As anticipated from 

the results shown in figure 28, the atrazine concentrations 

simulated using the two sets of soils data were in relatively 

close agreement at all four sites. All of the simulated 

concentrations, however, were substantially higher than the 

atrazine concentrations measured in groundwater at the four 

sites, only one of which was greater than the reporting limit. 

Analogous comparisons were carried out at six sites 

with atrazine detections in groundwater beneath a humid, 

unirrigated agricultural area in Portage County, Wisconsin 

(fig. 30), where atrazine use is considerably higher than at 

the Washington sites. As with the four locations examined 

in Washington, the average depths to the water table at all 

six Wisconsin sites were greater than the 1.53-m assessment 

depth, ranging from 6.1 to 22.4 m beneath the land surface. 

Using the same approach as at the Washington sites, atrazine 

concentrations measured in groundwater at the Wisconsin 

sites were compared with concentrations predicted by 

P-GWAVA-PR simulations using either (1) the original 

SSURGO data with grid-cell thicknesses equal to those of 

the SSURGO layers, or (2) the spatially averaged STATSGO 

data with grid-cell thicknesses equal to those of the SSURGO 

layers. At five of the six sites, the concentrations simulated 

using the SSURGO data were in closer agreement with 

those measured in groundwater than was the case for those 

simulated using the STATSGO data—none of which were 

greater than the reporting limit at any of the six sites (fig. 31). 

Although the reasons for the differences between the atrazine 

concentrations simulated using the two sets of soil property 

data are unclear, these results provided support for the use of 

the SSURGO data to characterize soil properties—instead of 

the spatially averaged STATSGO values—during Phase 2 of 

this project. 
74 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig28

N

WASHINGTON

Site 2086 (SSURGO map unit 55) Site 2098 (SSURGO map unit 98) 0.35

0.30

0.25

0.20

0.15

0.10

0.05

0

0.6

0.5

0.4

0.3

0.2

0.1

0

Site 2100 (SSURGO map unit 98) Site 2111 (SSURGO map unit 121) 0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0

Simulated atrazine concentration,

in micrograms per liter

Simulated atrazine concentration,

in micrograms per liter

Year

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

Year

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

Year

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

Year

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

STATSGO

SSURGO

Sample site

0 10 20 KILOMETERS

0 10 20 MILES

STATSGO map-unit boundary

SSURGO map unit

EXPLANATION

55

98

121

Figure 28. Influence of spatial averaging of soil parameter values on atrazine chemographs simulated at a depth of 

153 centimeters in the vadose zone by the P-GWAVA-PR (Phase 1) model using spatially averaged State Soil Geographic 

database (STATSGO) values and the original Soil Survey Geographic database (SSURGO) values for soil parameters at four 

agricultural sites in central Washington State, 1986–95. All model simulations used areally averaged intensities of atrazine 

application and assumed that atrazine transformation occurred in both the sorbed and dissolved states.
P-GWAVA Simulation and Groundwater Sampling Results 75

tac12-0781_fig29

0

0.02 

0.04 

0.06 

0.08 

0.1 

0.12 

0.14 

0.16 

0.18 

Measured in groundwater

Simulated with SSURGO soils data

Simulated with STATSGO soils data

Not detected at or above 

reporting limit 

2086 2098 2100 2111

← Atrazine reporting limit

(0.001 micrograms per liter)

(ND)

(ND)

(ND) (ND)

Atrazine concentration, in micrograms per liter

Site number

EXPLANATION

Figure 29. Influence of spatial averaging of soil parameter values on the extent of agreement between atrazine 

concentrations predicted at a depth of 153 centimeters in the vadose zone by a set of 10-year simulations 

using the P-GWAVA-PR model (1986–1995) and atrazine concentrations measured in shallow groundwater at 

four irrigated agricultural sites in central Washington State, 1992–95. Model simulations used areally averaged 

intensities of atrazine application and assumed that atrazine transformation occurred in both the sorbed and 

dissolved states. The simulations used grid-cell thicknesses equal to the soil-layer thicknesses specified by the 

Soil Survey Geographic database (SSURGO) and either the spatially averaged State Soil Geographic database 

(STATSGO) values or the original SSURGO values for soil parameters. See figure 28 for site locations.
76 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig30

WISCONSIN

MICHIGAN

IOWA

ILLINOIS

MINNESOTA

94° 92° 90° 88° 86°

46°

44°

0 20 40 60 80 100 KILOMETERS

0 20 40 60 80 100 MILES

PORTAGE COUNTY

PORTAGE

Lake

Michigan

2229

2230

2232

2231

2233

2234

39

10

51

54

54

Stevens 66

Point

Plover

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 30. Locations where atrazine concentrations predicted by the P-GWAVA-PR (Phase 1) 

simulations were compared with atrazine concentrations measured in shallow groundwater at six 

unirrigated agricultural sites in an agricultural area of Portage County, Wisconsin.
P-GWAVA Simulation and Groundwater Sampling Results 77

tac12-0781_fig31

(ND)

(ND) 

0 

0.1 

0.4 

0.5 

0.6 

2229 2230 2231 2232 2233 2234

← Atrazine reporting limit

(0.001 micrograms per liter) 

Atrazine concentration, in micrograms per liter 

Site number 

Measured in groundwater

Simulated with SSURGO soils data

Simulated concentration less than the reporting limit 

EXPLANATION

0.2 

0.3 

Figure 31. Influence of spatial averaging of soil parameter values on the extent of agreement between 

atrazine concentrations predicted at a depth of 153 centimeters in the vadose zone by a set of 10-year 

P-GWAVA-PR simulations (1986–1995) and atrazine concentrations measured in shallow groundwater at 

six unirrigated agricultural sites in Portage County, Wisconsin, 1992–95. Model simulations used areally 

averaged intensities of atrazine application, and assumed that atrazine transformation occurred in both 

the sorbed and dissolved states. Irrigation not simulated. The simulations used grid-cell thicknesses equal 

to the soil-layer thicknesses specified by the Soil Survey Geographic database (SSURGO) and either 

the spatially averaged State Soil Geographic database (STATSGO) values or the original (unaggregated) 

SSURGO values for soil parameters. None of the simulations using the STATSGO values for soil parameters 

resulted in predicted atrazine concentrations above the reporting limit. See figure 30 for site locations.
78 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Temporal Variability of Atrazine Concentrations 

and Water Flow

The temporal variations in atrazine concentrations 

simulated by the P-GWAVA-PR model were consistent with 

variations commonly seen in relation to the timing of recharge 

and pesticide applications during field studies of pesticide 

transport and fate in the subsurface (for example, Bottcher 

and others, 1981; McKenna and others, 1988; Barbash and 

Resek, 1996). As indicated by the atrazine chemographs 

shown in figure 32 for one of the sites in Portage County, 

Wisconsin, these patterns included familiar features such as 

the abrupt increases in atrazine concentration in response to 

major recharge events (rather than immediately following 

the annual applications of the herbicide in the spring of each 

year) and the gradual tailing of each atrazine peak (caused by 

hydrodynamic dispersion) after the maximum concentration 

passed. The results from a simulation using the (unaggregated) 

SSURGO soils data (fig. 32B) show the same general features 

as the corresponding atrazine chemograph derived from the 

(aggregated) STATSGO data for the same site (fig. 32A), but 

at concentrations that were substantially closer to the value 

measured in shallow groundwater at this site—as anticipated 

from the results shown in figure 31. 

Modeling Approaches and Simulation Results

Preliminary simulations were carried out during Phase 1 

to examine the effects of (1) the spatial variability of atrazine 

use intensity (areally averaged or uniform), (2) the approach 

for selecting sites where irrigation was simulated (all sites, no 

sites, or selected stochastically in proportion to the amount 

of irrigated land nearby), and (3) the location(s) of atrazine 

transformation in the subsurface (in the sorbed state, the 

dissolved state, both, or neither). The influences of these 

modeling approaches on the extent of agreement between the 

simulated and the measured atrazine concentrations at the 

Phase 1 sites are shown in figure 33. The residuals displayed 

in the figure were compiled for all Phase 1 study sites, 

regardless of whether or not the DTW was greater than the 

1-m assessment depth. The box plots in the figure display the 

distributions of model residuals for the atrazine concentrations 

predicted by each of the 15 simulation approaches explored 

with the P-GWAVA-PR model. 

Modeling Approach for Final P-GWAVA-PR Simulations

A nonparametric multiple comparisons test—the 

Dwass-Steel-Chritchlow-Fligner (DSCF) test (Chritchlow 

and Fligner, 1991)—was applied to the 15 sets of model 

residuals (fig. 33) to determine which sets of residuals were 

significantly different from one another (Dr. Robert Black, 

U.S. Geological Survey, written commun., January 2011). The 

results from this analysis are shown in figure 33. Scenarios 

with statistical distributions of ranks not significantly different 

from one another (P > 0.05) are labeled with identical letters; 

those with significantly different distributions of ranks 

are assigned different letters. These results indicate that 

all four P-GWAVA-PR modeling scenarios that simulated 

atrazine concentrations that were in closest agreement with 

concentrations measured in groundwater used the areally 

averaged atrazine use intensities. Three of the four scenarios 

involved simulating atrazine transformation in both the sorbed 

and the dissolved state, and the fourth scenario involved 

transformation in the sorbed state only. These observations 

represent the combined influence of the factors examined 

separately in figures 23, 24, and 26. 

The preceding discussion appears to support two 

approaches for selecting the sites where irrigation was 

to be simulated (if at all) during Phase 1. For atrazine 

concentrations, the distributions of model residuals indicate 

that the most appropriate approach would have been to not 

simulate irrigation (fig. 26; table 6). However, when the 

model residuals were expressed in terms of atrazine detection 

frequencies, the statistically significant geographic bias 

introduced by precipitation (see section, “Approaches for 

Distributing Irrigation”) was eliminated only when irrigation 

was simulated at all the sites (fig. 27). As a compromise 

between the two approaches (that is, simulating irrigation at all 

sites or at none of the sites), the stochastic method of assigning 

irrigation to the sites (using equation 8) was used for the final 

set of P-GWAVA-PR simulations, along with the previously 

mentioned approaches of using areally averaged intensities 

of atrazine application and simulating atrazine transformation 

in the sorbed and the dissolved states (table 4). This approach 

resulted in a 95th percentile for the model residuals with 

respect to atrazine concentrations among all of the 1,224 

Phase 1 sites (0.39 μg/L) that was similar to the value 

observed for the non-irrigated scenario (0.27 μg/L). These 

model residuals are an order of magnitude smaller than the 

Maximum Contaminant Level (MCL) of 3 μg/L established 

by the EPA for atrazine in water (U.S. Environmental 

Protection Agency, 2003), indicating a low probability of the 

P-GWAVA-PR model either under- or overpredicting atrazine 

concentrations by margins greater than the MCL.

The cumulative distribution plots displayed in figure 34

for the model residuals for atrazine concentrations from 

the final set of P-GWAVA-PR simulations indicate that the 

inclusion of all sites in the analysis—regardless of the average 

DTW—exerted a negligible effect on the extent of agreement 

between the simulated and measured atrazine concentrations 

at the Phase 1 sites, relative to the level of agreement when 

sites with depths to water equal to or shallower than the 

1-m assessment depth were excluded. One reason for this 

observation may have been that the 77 sites with depths to 

water of 1 m or less represented a relatively small percentage 

of the full set of 1,224 Phase 1 sites. The results displayed 

in figure 34 also indicate that at the sites with depths to 

water equal to or less than the 1-m assessment depth, the 

P-GWAVA-PR model was more likely to overpredict the 

atrazine concentrations than to underpredict them. 
P-GWAVA Simulation and Groundwater Sampling Results 79

tac12-0781_fig32ab

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0

0.00005 

0.00010 

0.00015 

0.00020 

0.00025 

0.00030 

0.00035 

0.00040 

0.00045 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 

Precipitation (measured) or infiltration (simulated), in centimeters per day 

Simulated atrazine concentration, in micrograms per liter

Year

Precipitation - measured, in 

centimeters per day

Infiltration - simulated, in 

centimeters per day

Simulated atrazine applications

(0.11 kilogram of active 

ingredient per hectare 

per year) 

Atrazine concentration - simulated,

in micrograms per liter

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 

Precipitation (measured) or infiltration (simulated), in centimeters per day

Simulated atrazine concentration, in micrograms per liter

Year

Simulated atrazine applications

(0.11 kilogram of active 

ingredient per hectare 

per year) 

A

B

EXPLANATION

Simulated infiltration: 6.1 centimeters per year

Simulated atrazine concentration: 0.56 microgram per liter

Simulated infiltration: 4.9 centimeters per year

Simulated atrazine concentration: 0.000098 microgram per liter

Figure 32. Temporal variability in measured precipitation, and in the infiltration and atrazine concentrations simulated at an assessment 

depth of 153 centimeters using the P-GWAVA-PR (Phase 1) model at site 2233 in an unirrigated agricultural area of Portage County, Wisconsin, 

during the 1986–95 simulation period. Model simulations used areally averaged intensities of atrazine application, and assumed that atrazine 

transformation occurred in both the sorbed and dissolved states. Irrigation not simulated. Soil property values used for the simulations were 

either (A) spatially averaged using data from the State Soil Geographic database (STATSGO), or (B) the original (unaggregated) data from the 

Soil Survey Geographic database (SSURGO). Atrazine concentration measured in shallow groundwater at this site was 0.37 microgram per 

liter; estimated recharge (from the base-flow index) was 35.6 centimeters per year. See figure 30 for site location.
80 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig33

Atrazine concentration residuals for all P-GWAVA-PR simulations

(N = 1,224 sites for spatially variable atrazine applications; N = 1,220 sites for uniform applications)

Results from Dwass-Steel-

 Chritchlow-Fligner test

Atrazine applications:

Irrigation simulation:

Reaction in sorbed state:

Reaction in dissolved state: Atrazine concentration residual (value simulated in vadose zone minus value measured in groundwater), in micrograms per liter

None

Yes

Yes

None

Yes

No

None

No

Yes

None

No

No

None

Yes

Yes

Stochastic

Areally averaged Spatially uniform

All sites

Yes

Yes

Stochastic

Yes

No

Stochastic

No

Yes

Stochastic

No

No

Stochastic

Yes

Yes

Yes

Yes

All sites

Yes

No

All sites

No

Yes

All sites

No

No

All sites

Yes

Yes

420

390

360

330

300

270

240

210

180

150

120

90

60

30

0

a a a a b c d e f f f g c d f

EXPLANATION 

Outlier

Outlier

75

90th percentile

10th percentile

th percentile

50th percentile

(median)

25th percentile

Interquartile

range 

Figure 33. Influence of different modeling approaches on the P-GWAVA-PR model residuals for atrazine concentrations at all of the 

Phase 1 study sites (regardless of water-table depth) in agricultural areas of the conterminous United States, 1992–98. Each boxplot 

displays the statistical distribution of model residuals for a different permutation of simulation approaches related to (1) the spatial 

variability of atrazine applications, (2) the approach used for selecting sites where irrigation was simulated (if at all), and (3) whether 

atrazine transformation was simulated in the sorbed state and (or) the dissolved state (if at all). All non-detections of atrazine, either 

simulated or measured, were assigned a concentration of 0.0001 microgram per liter for the purposes of the calculations. Letters 

beneath individual boxplots display the results from a nonparametric multiple comparison among the sets of residuals for all 15 modeling 

approaches, using the Dwass-Steel-Chritchlow-Fligner (DSCF) test (Chritchlow and Fligner, 1991). The distributions of model residuals 

from simulation runs with different letters were significantly different from one another (P less than or equal to 0.05). 
P-GWAVA Simulation and Groundwater Sampling Results 81

tac12-0781_fig34

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-4.0 -3.0 -2.0 -1.0 0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 

Model residual (simulated minus measured concentration), in micrograms per liter

Sites with average depths to water less than or 

equal to 1 meter (N = 77)

Sites with average depths to water greater than 

1 meter (N = 1,125)

All sites (N = 1,224) 

EXPLANATION

Pesticide Root-Zone Model (PRZM)

underpredicts concentrations 

PRZM overpredicts

concentrations 

Sites with model residuals greater than x-axis value, in percent

Figure 34. Cumulative distributions of model residuals for atrazine concentrations at Phase 1 study 

sites for the final set of P-GWAVA-PR simulations (table 4) in agricultural areas of the conterminous 

United States, 1992–98. All atrazine concentrations (simulated or measured) less than the 

0.001 microgram per liter (μg/L) reporting limit were assigned a value of 0.0001 μg/L for the purposes 

of the calculations. Descriptive statistics for the model residuals are presented in table 7.

Water Flow 

The mean annual rates of groundwater recharge predicted 

at the Phase 1 sites by the P-GWAVA-PR simulations are 

compared with the computed base-flow index (BFI) values in 

figure 35. To avoid the potential influence of irrigation on this 

analysis, the BFI values excluded water flows contributed by 

irrigation (Wolock, 2003), the P-GWAVA-PR simulations were 

carried out without irrigation, and the analysis was restricted 

to sites that were unirrigated (fig. 17). The correlation 

between the estimates of annual recharge provided by the 

P-GWAVA-PR simulations and the BFI at all of the unirrigated 

sites where the DTW was greater than 1 m was moderate but 

statistically significant (Spearman ρ = 0.30; P[ρ] ≤ 0.0001; 

676 sites). 

Atrazine Concentrations and Detection Frequencies

Summary statistics describing the extent of correlation 

between the atrazine concentrations predicted at the 1-m 

assessment depth in the vadose zone by the final set of 

P-GWAVA-PR simulations and the concentrations measured 

at the same sites in shallow groundwater are presented in 

table 7. These final simulations assumed that the atrazine 

use intensities were areally averaged, that atrazine 

transformation took place in the sorbed and dissolved 

state, and that irrigation was distributed stochastically 

among sites in each sampling network (table 4). Moderate,

but statistically significant correlations were observed 

(P[ρ] ≤ 0.0001; Spearman rank correlations) when the 

analysis included all the Phase 1 sites or just those where the 

water table was deeper than 1 m (table 7). 
82 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig35

0 

5 

10 

15 

20 

25 

0 10 20 30 40 50 60 70 

Mean annual recharge simulated by P-GWAVA-PR, in centimeters per year 

Base-flow index (with no irrigation), in centimeters per year

Average depth to water greater than 

1 meter (676 sites)

Average depth to water less than or 

equal to 1 meter (53 sites) 

1:1 line (P-GWAVA-PR estimate = BFI estimate) 

EXPLANATION

Figure 35. Mean annual groundwater recharge rates simulated by the P-GWAVA-PR (Phase 1) model with no 

irrigation, and by the base-flow index (BFI) method excluding irrigation, at all of the unirrigated agricultural sites in the 

conterminous United States where the depth to water was known. BFI data from Wolock (2003).

Table 7. Correlations between atrazine concentrations simulated by the P-GWAVA-PR (Phase 1) model at the 

1-meter assessment depth in the vadose zone and concentrations measured in shallow groundwater at the same 

locations in agricultural areas of the conterminous United States, 1992–98. 

[Concentrations of atrazine simulated by the P-GWAVA-PR system were obtained by using areally averaged intensities of atrazine 

use, and by simulating atrazine transformation in both the dissolved and sorbed states. Irrigation was simulated at locations selected 

at random using equation 8. For the purposes of these calculations, non-detections of atrazine—either simulated or measured—were 

assigned a concentration of 0.0001 microgram per liter (μg/L). Range of depths to water: Average annual values for each site. 

Spearman ρ: Values that were statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. 

P (ρ): The probability associated with the Pearson correlation coefficient (R2) for the correlation between the ranks of the simulated 

and measured values. Model residuals: The model residual for a given parameter is equal to the value predicted in the vadose zone by 

the P-GWAVA simulation minus the value measured in groundwater at the same site. 95th percentile: For each case, 95 percent of the 

model residuals were less than the value shown. Abbreviations: m, meter; <, less than; ≤, less than or equal to]

Range of depths

to water

(m)

Correlation (if any) 

between simulated and

measured concentrations

Model residuals

(simulated minus 

measured values)

(μg/L)

Number of

sites

Spearman ρ P (ρ) Median 95th

percentile

<1 0.021 0.86 0.001 1.5 77

1.01–178.27 0.13 ≤0.0001 0.005 0.35 1,125

All sites1 0.14 ≤0.0001 0.002 0.39 1

1,224

1

Depth-to-water measurements were not available for 22 of the sites.
P-GWAVA Simulation and Groundwater Sampling Results 83

No significant correlation was observed, however, between 

the simulated and measured atrazine concentrations (P[ρ] 

> 0.05) when the analysis was restricted to sites where the 

water table was shallower than the 1-m assessment depth, 

perhaps in part because of the relatively small sample size 

(77 sites) and narrower depth range examined (0–1 m). 

However, the transport and persistence of atrazine under the 

unsaturated conditions simulated by the model are also likely 

to have been substantially different from what occurred under 

the intermittently saturated conditions at depths shallower 

than 1 m during part or all of the simulation period at 

these locations. 

The atrazine concentrations predicted by the final 

set of P-GWAVA-PR simulations in the vadose zone at all 

of the Phase 1 sites are compared with those measured in 

groundwater in the same locations in figure 36. Consistent 

with the results shown in figures 33 and 34, figure 36

indicates that despite a considerable degree of scatter, the 

model residuals for the final set of P-GWAVA-PR simulations 

were relatively evenly distributed between over- and 

underprediction of the measured atrazine concentrations over 

more than three orders of magnitude. 

0.0001 

0.001 

0.01 

0.1 

1

10

0.0001 0.001 0.01 0.1 1 10 

Simulated atrazine concentration, in micrograms per liter 

Measured atrazine concentration, in micrograms per liter

Average depth to water greater than 

1 meter (1,125 sites)

Average depth to water less than or 

equal to 1 meter (77 sites) 

Average depth to water unknown 

(22 sites) 

Simulated concentration = measured concentration 

EXPLANATION

Figure 36. Atrazine concentrations simulated in the vadose zone by the P-GWAVA-PR system and concentrations measured in 

groundwater at Phase 1 sites in the conterminous United States, 1992–98. The P-GWAVA-PR simulations used areally averaged 

intensities of atrazine application, atrazine transformation in both the sorbed and dissolved states, and a stochastic distribution of 

irrigation (table 4). All atrazine concentrations (simulated or measured) less than the 0.001 microgram per liter (μg/L) reporting limit 

were assigned a value of 0.0001 μg/L for the purposes of display.
84 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The frequencies of atrazine detection (at concentrations 

equal to or greater than a reporting limit of 0.001 μg/L) 

predicted by the final set of P-GWAVA-PR simulations in 

the 47 groundwater networks in the Phase 1 study area are 

compared with those measured in shallow groundwater 

in figure 37. The results for 2 subsets of sites are shown: 

all sites in networks with 10 or more wells (fig. 37A), and 

sites where the depths to water were greater than the 1-m 

assessment depth in all networks with 10 or more such wells 

(fig. 37B). No significant correlations were observed between 

the simulated and the measured frequencies of atrazine 

detection, regardless of the range of water-table depths 

examined (P[ρ] > 0.05; Spearman rank correlations). 

Geographic Distribution of Model Residuals

The geographic distribution of P-GWAVA-PR model 

residuals for the frequencies of atrazine detection at or above 

a reporting limit of 0.001 μg/L in all 47 of the groundwater 

networks with 10 or more wells in agricultural areas of the 

conterminous United States is shown in figure 38. These 

results are derived from the final set of P-GWAVA-PR 

simulations, which used stochastically distributed irrigation, 

areally averaged atrazine application intensities, and atrazine 

transformation in the sorbed and dissolved states (table 4). 

The data are shown for all Phase 1 sites, regardless of 

water-table depth. As was the case when no irrigation was 

simulated (fig. 27A), most locations where the atrazine 

detection frequencies were underpredicted were in the arid 

West, in the Northeast, and in the highly permeable soils of 

central Wisconsin. In contrast, the simulations overpredicted 

atrazine detection frequencies in areas with low-permeability 

soils, such as central Illinois, southwestern Indiana, and the 

Southeast. These patterns may have been a consequence of 

the spatial averaging of the SSURGO data used to create the 

STATSGO database—a procedure that would have tended 

to contract the range of values spanned by each parameter 

of interest. For the present example, such averaging may 

have influenced the spatial distributions of model residuals 

shown in figure 38 by reducing the highest permeabilities 

(for example, in Wisconsin) and increasing the lowest 

permeabilities (for example, in Illinois, Indiana, and Georgia), 

relative to the range of values provided by the original 

SSURGO data. Along the Atlantic coast, the distribution 

of model residuals shown in figure 38 reflects the influence 

of precipitation, with overpredictions predominating in the 

more humid Southeast, and underpredictions confined to 

the Northeast, where precipitation is somewhat lower (Daly, 

2010). Overpredictions of atrazine detection frequencies in 

Florida, Georgia, and New Jersey also may have been abetted 

by irrigation, which is otherwise uncommon across most 

of the eastern United States (fig. 17). Some of the regional 

patterns indicated by figure 38 are also evident in figure 39, 

which displays the cumulative distribution functions for the 

model residuals with respect to atrazine concentrations for the 

Phase 1 sites in the West, the Corn Belt, the Northeast, and the 

Southeast. Consistent with the patterns evident in figure 38, 

these results indicate that many of the largest overpredictions 

(and smallest underpredictions) of atrazine concentrations 

occurred in the humid Southeast, with some of the more 

substantial underpredictions occurring in the Northeast. The 

sites examined in the West and Corn Belt, however, showed 

similar distributions of model residuals.

Statistical Correlations with Site Characteristics

To examine the extent to which the level of agreement 

between the simulated and measured frequencies of atrazine 

detection may have been related to site characteristics, the 

degrees of correlation (if any) between the P-GWAVA-PR 

model residuals for the atrazine detection frequencies and 

various site characteristics related to chemical use, soil 

properties, and climate (table 8) were evaluated. All values 

used to quantify the soil parameters were computed as depthand component-weighted averages from the STATSGO data 

(Wolock, 1997; David Wolock, U.S. Geological Survey, 

written commun., April 2009), using the approach shown 

in figure 7. The analysis encompassed all of the Phase 1 

groundwater networks that included 10 or more wells (fig. 38), 

and included all of the sites in each network, regardless of 

the DTW. 

The results summarized in table 8 indicate that the 

model residuals for atrazine detection frequencies were 

not significantly correlated with spatial variations in the 

intensity of atrazine use among the Phase 1 sites. Use of the 

areally averaged intensities of atrazine application for the 

P-GWAVA-PR simulations thus seemed to account adequately 

for the effects of spatial variations in atrazine use on the 

predicted frequencies of atrazine detection (and, by extension, 

the predicted atrazine concentrations). These results are 

consistent with the observations that other factors may exert 

greater control over the spatial variability in atrazine detection 

frequencies (Kolpin and others, 2002; Stackelberg and others, 

2006) and concentrations in groundwater (Stackelberg and 

others, 2012) than atrazine use intensity. For example, the 

results shown in table 8 indicate that the extent to which 

the P-GWAVA-PR simulations overpredicted the atrazine 

detection frequencies in shallow groundwater was positively 

correlated with soil clay content, runoff, precipitation, and 

temperature, and negatively correlated with soil permeability 

(P < 0.05; Pearson and Spearman [rank] correlations). The 

positive correlation with clay content and inverse correlation 

with vertical permeability were consistent with the geographic 

pattern of agreement shown in figure 38, and indicate that 

the P-GWAVA-PR system may underestimate the effect of 

hydraulic conductivity on the downward movement of atrazine 

through the soil. 
P-GWAVA Simulation and Groundwater Sampling Results 85

tac12-0781_fig37ab

Simulated = measured

Simulated = measured

A. Networks with 10 or more sites (all sites; 47 networks; 1,149 sites)

B. Networks with 10 or more sites with depths to water greater than the 

1-meter assessment depth (sites with depths to water greater 

than 1 meter only; 46 networks; 1,051 sites)

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of atrazine detection, in percent 

Measured frequency of atrazine detection, in percent 

Network with depths to water greater than 1 meter 

for all sites

Network with depths to water greater than 1 meter 

for 10 or more sites 

Network with depths to water greater than 1 meter 

for fewer than 10 sites 

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of atrazine detection, in percent 

Measured frequency of atrazine detection, in percent 

Network with depths to water greater than 1 meter 

for all wells in original network 

Network with depths to water less than or equal to 

1 meter for some wells in original network 

EXPLANATION

Figure 37. Frequencies of atrazine detection 

simulated at a depth of 1 meter in the vadose 

zone using the final set of P-GWAVA-PR 

(Phase 1) simulations and the measured 

frequencies of atrazine detection in shallow 

groundwater networks in agricultural areas of 

the conterminous United States, 1992–98. Areally 

averaged intensities of atrazine application, 

atrazine transformation in the sorbed and 

dissolved states, and a stochastic distribution of 

irrigation (table 4) were used for the simulations. 

All frequencies of atrazine detection (both 

simulated and measured) were calculated using a 

reporting limit of 0.001 microgram per liter.
86 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig38

120° 110° 100° 90° 80° 70°

45°

40°

35°

30°

25°

0 400 MILES

0 400 KILOMETERS

Simulated (P-GWAVA-PR) minus 

measured frequencies of 

atrazine detection, in percent

-64.3 to -50.1

-50.0 to -0.1

0 (simulated = measured)

0.1 to 50.0

50.1 to 100

EXPLANATION

P-GWAVA-PR 

underpredicts detection frequency

P-GWAVA-PR 

overpredicts detection frequency

}

}

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 38. Distribution of P-GWAVA-PR (Phase 1) model residuals for atrazine detection frequencies in 47 groundwater networks 

in agricultural areas of the conterminous United States, 1992–98. Predicted detection frequencies were derived from the final set 

of P-GWAVA-PR simulations, which assumed areally averaged atrazine applications, atrazine transformation in the sorbed and 

dissolved states, and a stochastic distribution of irrigation (table 4). Data for all sites in networks containing 10 or more wells are 

shown, regardless of depth to water. All frequencies of atrazine detection (simulated and measured) were calculated using a 

reporting limit of 0.001 microgram per liter.
P-GWAVA Simulation and Groundwater Sampling Results 87

tac12-0781_fig39

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-4.0 -3.0 -2.0 -1.0 0 1.0 2.0 3.0 4.0 

Sites with model residuals greater than x-axis value, in percent

Model residual (simulated minus measured concentration), in micrograms per liter

Corn Belt sites (N = 334)

Western sites (N = 259) 

Southeastern sites (N = 103) 

Northeastern sites (N = 222) 

EXPLANATION

Pesticide Root-Zone Model (PRZM)

underpredicts concentrations 

PRZM overpredicts

concentrations 

Figure 39. Cumulative distributions of P-GWAVA-PR model residuals for atrazine concentrations 

at Phase 1 study sites in agricultural areas of the Corn Belt and western, southeastern, and 

northeastern regions of the United States. Simulations were done assuming areally averaged 

atrazine applications, atrazine transformation in the sorbed and dissolved states, and a stochastic 

distribution of irrigation (table 4). All atrazine concentrations (simulated or measured) less than the 

0.001 microgram per liter (μg/L) reporting limit were assigned a value of 0.0001 μg/L for the purposes 

of the calculations.
88 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Table 8. Correlations between the P-GWAVA-PR (Phase 1) model residuals for atrazine detection frequencies in 

shallow groundwater in 47 groundwater networks in agricultural areas of the conterminous United States, 1992–98 

(1,149 sites), and selected site characteristics. 

[The geographic distribution of the networks is shown in figure 38. Detection frequencies were computed using simulated and measured 

atrazine concentrations at all sites in networks containing 10 or more wells for which the required ancillary data were available, regardless 

of water-table depth. The P-GWAVA-PR simulations used areally averaged intensities of atrazine use, atrazine transformation in both 

the sorbed and dissolved states, and irrigation at sites selected at random in each network using equation 8. Detections—either simulated 

or measured—were determined using a uniform reporting limit of 0.001 microgram per liter (μg/L). Areally averaged atrazine use 

intensities were computed using equation 10. Network-based values for soil-related site characteristics were computed as depth- and 

component-weighted averages from STATSGO data for the entire area encompassed by each network (Wolock, 1997; David Wolock, 

U.S. Geological Survey, written commun., April 2009). Mean annual precipitation and temperature data were computed for 1980–97; 

mean annual runoff data were for 1951–80. R2: Pearson correlation coefficient. P (R2): Probability associated with the Pearson correlation 

coefficient. Spearman ρ: Spearman rank correlation coefficient. P (ρ): Probability associated with the Pearson correlation coefficient for 

the correlation between the ranks of the simulated and measured values. Results from statistically significant correlations (P <0.05) are 

shown in boldface. Abbreviations: cm, centimeter; in., inch; in/hr, inch per hour; kg a.i./ha, kilograms of active ingredient per hectare; 

°C, degrees Celsius; <, less than; ≤, less than or equal to]

Site feature 

Correlations between model residuals and site feature

R2 P (R2

) Spearman ρ P (ρ)

Areally averaged atrazine use intensity (kg a.i./ha) 0.041 0.17 0.26 0.079

Clay content (weight percent) 0.13 0.012 0.39 0.0074

Silt content (weight percent) 0.007 0.58 -0.080 0.59

Sand content (weight percent) 0.012 0.46 -0.057 0.70

Soil organic matter content (weight percent) 0.033 0.22 -0.13 0.38

Available water capacity (volume percent) 0 0.995 -0.052 0.73

Vertical permeability (in/hr) 0.089 0.041 -0.30 0.044

Mean annual runoff (in.) 0.089 0.042 0.27 0.07

Mean annual precipitation (cm) 0.17 0.004 0.49 0.0006

Mean annual air temperature (°C) 0.23 0.0006 0.57 ≤0.0001

The positive correlations between the model residuals 

for the atrazine detection frequencies and both runoff and 

precipitation (table 8) indicate that the degree to which 

the P-GWAVA-PR simulations overpredicted atrazine 

concentrations in groundwater tended to increase with 

increasing recharge. This is in agreement with the statistical 

patterns noted earlier for the simulated concentrations in 

response to irrigation (fig. 26) or increased precipitation, as 

well as the geographic pattern of model residuals for detection 

frequencies shown in figure 38. Finally, because the degree 

of overprediction of the detection frequencies was positively 

correlated with temperature, the model likely underpredicted 

the extent to which atrazine transformation rates increase with 

temperature. Thus, the activation energy value used during 

Phase 1 (46.2 kJ/mol [table 14]) may have been too low. 

Phase 2 (P-GWAVA-RZ)

The P-GWAVA-RZ (Phase 2) simulations were carried 

out assuming a spatially uniform intensity of atrazine use 

(2 kg a.i./ha-yr) and applying irrigation on demand, in 

response to soil moisture deficits, at all sites (tables 4 and 14). 

As with Phase 1, comparisons between the predictions 

from the P-GWAVA-RZ simulations and measurements in 

groundwater focused on multiple subsets of sites, depending 

on whether the average depth of the water table in each 

location was greater or less than the Phase 2 assessment 

depth (3 m). Although many of the time series examined here 

spanned the full 7 years of the simulations (that is, the 2-year 

stabilization period followed by the 5-year period of the actual 

simulations), all of the water fluxes and solute concentrations 

calculated from the final set of P-GWAVA-RZ simulations 

were computed from the last 5 years of the simulation period, 

2000–2004 (table 1). 

Spatial and Temporal Patterns of Material 

Transport

Water Flow

The temporal and spatial patterns of water distribution 

and flow predicted by the P-GWAVA-RZ simulations were 

consistent with those that are commonly observed in the 

subsurface. Using the results from a site in south-central 

Wisconsin (Marquette County) as an example, figure 40
P-GWAVA Simulation and Groundwater Sampling Results 89

illustrates the temporal variations in water inputs (precipitation 

and irrigation) and losses (deep seepage, runoff, crop 

transpiration, and evaporation) predicted during the course 

of the 7-year P-GWAVA-RZ simulation at that location. 

As expected, irrigation and transpiration occurred only during 

the growing season (May through September) each year, 

and deep seepage tended to occur more readily when natural 

precipitation was augmented by irrigation. (Transpiration 

was confined to the growing season because it was simulated 

only in the corn plants.) Additionally, evaporation fluxes were 

higher during warmer times of the year, but lower during 

periods of high transpiration in late summer.

0 

2 

4 

6 

8 

10 

12 

14 

Centimeters per day 

Precipitation

Irrigation

EXPLANATION

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.0 

Deep seepage

Runoff

EXPLANATION

Centimeters per day 

0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

Centimeters per day 

Crop transpiration

Evaporation

EXPLANATION

3.0

Year

1998 1999 2000 2001 2002 2003 2004

A

B

C

Figure 40. Temporal patterns of water fluxes estimated for (A) precipitation and irrigation, (B) deep seepage and 

runoff, and (C) crop transpiration and evaporation at a site in south-central Wisconsin (Soil Survey Geographic database 

[SSURGO] component 21840; Marquette County) during the P-GWAVA-RZ (Phase 2) simulation period, 1998–2004.
90 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Model simulation results at the Wisconsin site are shown 

from a spatial perspective in figure 41, displaying the vertical 

variations in water content, pressure head, and solute mass 

predicted by P-GWAVA-RZ in the soil on the last day of 

the 7-year simulation period. Data indicating the variations 

in soil texture in the soil column (from SSURGO) are also 

shown. The model predictions displayed in the figure indicate 

a pattern of water distribution in the shallow vadose zone 

that is commonly observed following several days without a 

major recharge event (figs. 40A and 40B). The discontinuities 

in the soil-water-content profile for this site (fig. 41A) reflect 

the influence of variations in water-holding capacity among 

the layers, with the highest values simulated in the sandy 

loam (layer 3) and the lowest gradient simulated in the sand 

(layer 4). The effects of an extended period of soil drainage 

without any major recent recharge events are indicated by the 

simulated increases in water content (fig. 41A) and pressure 

head (fig. 41B) with depth in each layer. 

The average annual groundwater recharge rates predicted 

by the P-GWAVA-RZ simulations are compared with the 

values estimated at the same sites using the BFI method in 

figure 42. The recharge rates predicted by the P-GWAVA-RZ 

simulations were computed for the 5-year simulation period 

from 2000 to 2004 (table 1), as was done for all final solute 

concentrations computed from the P-GWAVA-RZ simulation 

results. Because irrigation was simulated (when needed) at 

all of the Phase 2 sites, the BFI values used for this analysis 

included estimates of groundwater withdrawals for irrigation 

in each location (David Wolock, U.S. Geological Survey, 

written commun., February 2011), and are referred to herein 

as irrigated BFI (or iBFI) values, to distinguish them from the 

BFI values examined during Phase 1 (fig. 35), which did not 

include estimates of irrigation. 

As with the BFI values for the Phase 1 model simulation 

results, the recharge rates predicted by the P-GWAVA-RZ 

simulations were significantly correlated with those 

estimated using the iBFI method. Statistically significant 

correlations between the P-GWAVA-RZ predictions and 

the iBFI values were observed whether the comparisons 

encompassed all of the sites for which the iBFI estimates 

were available (R2 = 0.15 [P ≤ 0.0001]; Spearman ρ = 0.30 

[P(ρ) < 0.001]; 447 sites) or only sites where the average 

DTW was greater than the 3-m assessment depth (R2 = 0.29 

[P ≤ 0.0001]; Spearman ρ = 0.39 [P(ρ) < 0.001]; 232 sites). 

These results indicate that the iBFI accounted for a higher 

percentage of the overall variability in the recharge values 

predicted by the P-GWAVA-RZ simulations when the only 

sites examined were those where the average DTW was 

greater than the 3-m assessment depth than when all of 

the sites were included in the analysis. The recharge rates 

predicted by the P-GWAVA-RZ simulations were lower 

than the iBFI values at 60 percent of all of the sites, and 

at 61 percent of the sites with depths to water exceeding 

3 m. The recharge rates simulated for all of the 453 Phase 2 

sites spanned a range from 0.5 to 33.8 cm/yr, exhibiting a 

considerable degree of overlap with the range of groundwater 

recharge values estimated by Fisher and Healy (2008) from the 

rates of water table fluctuation in four agricultural areas of the 

Nation (11.9 to 47.5 cm/yr). For reasons that remain unclear, 

however, no significant correlation was observed between the 

mean annual recharge values predicted by the P-GWAVA-RZ 

and P-GWAVA-PR simulations (with both models simulating 

irrigation in response to soil moisture demand at all sites) for 

the 146 sites where both models were run and where the DTW 

was greater than 3 m (P > 0.05). This indicates that different 

combinations of factors may be responsible for influencing the 

estimates of water flow predicted by PRZM and RZWQM.

Solute Transport

As was the case for water flow, the P-GWAVA-RZ 

simulations predicted temporal patterns of solute 

concentrations in the subsurface that also were consistent with 

those commonly observed in situ. Patterns for the simulated 

concentrations of atrazine, DEA, and nitrate in water at the 

3-m assessment depth over the course of the 7-year simulation 

period for SSURGO component 21840 in south-central 

Wisconsin (Marquette County) are shown in figure 43. The 

time evolution of the atrazine residue yield (that is, the mole 

percentage of applied atrazine represented by the cumulative 

amount of atrazine and [or] DEA predicted to have passed the 

assessment depth in the deep seepage since the beginning of 

a P-GWAVA-RZ simulation) is also displayed (fig. 43C). For 

ease of analysis, the hydrographs for deep seepage and runoff, 

reproduced from figure 40B, are also provided (fig. 43E). 

The chemographs in figure 43 indicate that more than 

1 year was required for the initial peak concentrations of 

the applied compounds predicted by the P-GWAVA-RZ 

simulations to appear in deep seepage at the 3-m assessment 

depth at the Wisconsin site. These predictions are consistent 

with results from other studies that have reported similar 

delays in the detection of acetochlor (Barbash and others, 

1999, 2001) and other pesticides (Barbash and Resek, 1996) 

in shallow groundwater following their initial application. 

The chemographs in figure 43 also provide support for the use 

of the 2-year stabilization period prior to the simulation period 

when the flow-weighted mean solute concentrations were 

computed (table 1). The first nitrate peak appeared earlier than 

the peaks for atrazine or DEA, consistent with the fact that 

under the oxic conditions that predominate in the vadose zone, 

nitrate migrates in water as a conservative solute, whereas 

the movement of the more hydrophobic atrazine and DEA is 

slowed by sorption interactions with the soil. The atrazine and 

DEA peaks appeared at similar times during the simulation, 

but those for DEA were observed slightly earlier than those 

for atrazine, as expected from the fact that the Koc value for the 

degradate is lower than that of its parent compound (table 14).
P-GWAVA Simulation and Groundwater Sampling Results 91

tac12-0781_fig41

0 

50 

100 

150 

200 

250 

300 

0 0.1 0.2 0.3 0.4 0.5 

Soil depth, in centimeters Soil depth, in centimeters

Soil water content, in cubic centimeters 

per cubic centimeter 

-1,000 -500 0 -1,500 -2,000 -2,500 -3,000 

Pressure head, in centimeters of water

0 

50 

100 

150 

200 

250 

300 

0.0001 0.001 0.01 0.1 1 0.00001 0.0001 0.001 0.01 0 2 4 6 8 10 

Total atrazine mass, 

in kilograms per hectare

Total DEA mass,

in kilograms per hectare

Nitrate concentration in soil, 

in milligrams per gram

A. Soil water content B. Pressure head

C. Total atrazine mass D. Total DEA mass E. Nitrate concentration

Soil texture

[Last day of simulation period: December 31, 2004]

Layer Maximum depth 

(centimeters)

 Sand

(percent)

 Silt

(percent)

 Clay

(percent)

1 23 80 17 3

2 51 80 17 3

3 66 77 16 7

4 300 97 1 2

Soil

texture

Loamy sand

Loamy sand

Sandy loam

Sand

Figure 41. Vertical variations of soil texture (from Soil Survey Geographic database [SSURGO]) and vertical variations 

of simulated (A) soil water content, (B) pressure head, (C) total atrazine mass, (D) total deethylatrazine (DEA) mass, and 

(E) nitrate concentration at a site in south-central Wisconsin (SSURGO component 21840; Marquette County) on the last 

day of the 7-year P-GWAVA-RZ (Phase 2) simulation period (1998–2004).
92 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig42

0 

5 

10 

15 

20 

25 

30 

35 

40 

0 5 10 15 20 25 30 

Mean annual recharge simulated by P-GWAVA-RZ, in centimeters per year 

Mean annual recharge estimated from iBFI, in centimeters per year

Average depth to water 3 meters or 

less (215 sites) 

Average depth to water greater than 

3 meters (232 sites)

P-GWAVA-RZ value = iBFI value 

EXPLANATION

Figure 42. Average annual estimated groundwater recharge rates simulated at the 3-meter assessment depth by the 

P-GWAVA-RZ (Phase 2) model, and rates estimated from the Base Flow Index method including irrigation withdrawals

(iBFI), in 10 contiguous states in the Corn Belt of the United States. Estimates of iBFI are from David Wolock, U.S. 

Geological Survey (written commun., February 2011).
P-GWAVA Simulation and Groundwater Sampling Results 93

Comparisons with the hydrograph for deep seepage 

(fig. 43E) indicate that a major recharge event during summer 

1999 may have been largely responsible for the simulated 

appearance of the initial atrazine and DEA peaks at the 

assessment depth soon thereafter. This conclusion is consistent 

with the general observation that major recharge events can 

lead to the transport of substantial amounts of surface-applied 

solutes through soil—including hydrophobic compounds—

especially if the events occur relatively soon after the solutes 

are applied (Barbash and Resek, 1996; McGrath and others, 

2010). Additionally, the temporal changes in the atrazine 

residue yield that were simulated in the deep seepage over 

the course of the 7-year simulation period (fig. 43C) indicate 

that multiple years may be required before this parameter 

approaches a semi-stable value, if ever.

Comparisons between the simulated chemographs for 

atrazine, DEA and nitrate at the Wisconsin site (figs. 43A, B, 

and D, respectively), and the corresponding profiles for each 

solute in the soil column at the end of the P-GWAVA-RZ 

simulation (figs. 41C, D, and E, respectively) illustrate the 

contrast in the rates at which some solutes may migrate 

through soil, relative to their rates of movement in water. 

At least five of the seven concentration peaks expected for 

atrazine, DEA and nitrate (on the basis of the atrazine and 

nitrogen application histories) were simulated in deep seepage 

at the 3-m assessment depth during the 7-year simulation 

period (figs. 43A, B, and D, respectively), indicating 

roughly similar rates of movement of all three, relatively 

water-soluble solutes in the dissolved state. However, whereas 

three of the concentration peaks for atrazine and DEA still 

remained in the soil profile at the end of the simulation 

(figs. 41C and D, respectively), only one of the peaks for 

nitrate (which does not sorb to most soil surfaces) was still 

present in the soil at the end of the simulation (fig. 41E). 

As in figure 43, the soil profiles in figure 41C and D also 

display the effect of the lower Koc value for DEA, relative to 

atrazine, with the DEA peaks reaching slightly greater depths 

in the soil than those for atrazine by the end of the simulation. 

These predictions reflect the influence of sorption interactions 

on the rates of movement of even moderately hydrophobic 

solutes through the subsurface. (Such effects are analogous to 

the influence of hydrophobicity on the relative rates of analyte 

migration within the stationary phase of a liquid- or gaschromatographic column [Grob and Barry, 2004].) Finally, the 

soil profiles for atrazine and DEA (figs. 41C and D) exhibit 

a small but abrupt drop in concentration at the bottom of 

layer 3, reflecting a slightly higher retention of both solutes 

by the finer-grained sandy loam in that layer, relative to the 

underlying sand.

Solute Distributions Among Chemical Forms and 

Environmental Media

Data from the P-GWAVA-RZ simulations at two of the 

Phase 2 sites were used to examine the predicted distributions 

of the applied atrazine and nitrogen among different chemical 

forms and environmental media at the end of the simulation 

period. Because the simulated annual applications of atrazine 

to corn were presumed to be the sole source of the herbicide 

and DEA in the subsurface, a mass-balance approach was used 

to calculate the percentages of the applied herbicide that were 

simulated to be present as either the parent compound or DEA 

in water and soil. By contrast, synthetic fertilizer, applied in 

irrigation water for the P-GWAVA-RZ simulations (a practice 

referred to as fertigation), represents only one of several 

potential sources of nitrate in the soil (Komor and Anderson, 

1993; Burns and others, 2009; Bartling and others, 2011; 

fig. 21). Consequently, for the nitrogen species, the model 

outputs were used solely to examine nitrogen fluxes and shifts 

(if any) in the distribution of nitrogen among various chemical 

forms and media between the beginning and the end of the 

simulations, rather than to account for the fate of all of the 

applied nitrogen.

Atrazine and Deethylatrazine

The simulated distributions of atrazine and DEA 

between the sorbed and dissolved states in the subsurface at 

two Phase 2 sites are shown in figure 44, expressed as mole 

percentages of the total amount of atrazine applied over the 

7 years of simulated annual applications. The amounts of 

atrazine and DEA in water represent the total amounts that 

flowed past the 3-m assessment depth (per hectare) over the 

course of the 7-year simulation period (that is, the numerator 

in equation 48), whereas those associated with the soil are the 

amounts present on the last day of the simulation. No specific 

values are shown for the simulated concentrations of either 

compound in plant materials because these quantities are not 

reported by RZWQM. 

Consistent with the results from the Phase 1 simulations 

(fig. 25), substantial proportions of the remaining, unreacted 

atrazine were predicted to be sorbed to the soil at both sites 

examined in figure 44. Considerable fractions of the total 

amount of the DEA formed during the simulations were 

also associated with the soil, as might have been expected 

from the similarities of its chemical structure and properties 

to those of atrazine (fig. 20; table 14). Substantially higher 

amounts of atrazine and DEA were simulated and measured 

in the water at the Wisconsin site than at the Indiana site. 
94 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig43abc

Year

Year

1998 1999 2000 2001 2002 2003 2004

1998 1999 2000 2001 2002 2003 2004

1998 1999 2000 2001 2002 2003 2004

Year

A. Atrazine concentration in deep seepage

B. DEA concentration in deep seepage

C. Atrazine residue yield in deep seepage

0.00 

1.00 

2.00 

3.00 

4.00 

5.00 

Atrazine concentration,

in micrograms per liter 

0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

DEA concentration,

in micrograms per liter 

0 

2 

4 

6 

8 

10 

12 

14 

16 

Cumulative yield, in mole 

percent of applied atrazine 

EXPLANATION

EXPLANATION

EXPLANATION

Atrazine concentration in deep seepage

(Simulated: 1.4 microgram per liter [μg/L]; measured in 

groundwater: 1.2 μg/L)

Atrazine applications (2 kilograms of active ingredient per 

hectare [kg a.i./ha])

DEA concentration in deep seepage

(Simulated: 0.01 μg/L; measured in groundwater: 0.3 μg/L)

Atrazine

DEA

Atrazine plus DEA

Figure 43. Variations in solute concentrations and water flow simulated at the 3-meter assessment depth by the 

P-GWAVA-RZ (Phase 2) simulations for (A) atrazine concentration, (B) deethylatrazine (DEA) concentration, (C) atrazine 

residue yield, (D) nitrate concentration, and (E) deep seepage and runoff (copied from fig. 40B) at Soil Survey Geographic 

database (SSURGO) site 21840 in south-central Wisconsin (Marquette County) during the 7-year simulation period, 

1998–2004. Where applicable, concentrations simulated as flow-weighted means at the assessment depth, or measured in 

the underlying groundwater, are provided in the figure explanations. 
P-GWAVA Simulation and Groundwater Sampling Results 95

tac12-0781_fig43de

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.0 

Deep seepage

Runoff

EXPLANATION

Flow, in centimeters per day 

3.0

E. Water flow

Year

Year

D. Nitrate concentration in deep seepage

0 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

0.35 

0.40 

0.45 

Nitrate concentration, mg-N/L; 

N applied with irrigation,

 in kilograms per hectare 

per day times 0.01

EXPLANATION

Nitrate concentration in deep seepage 

[Simulated: 0.06 milligrams per liter as N (mg-N/L); 

measured in groundwater: 11 mg-N/L]

Nitrogen applications in irrigation water (kilogram per hectare per day x 10-2)

1998 1999 2000 2001 2002 2003 2004

1998 1999 2000 2001 2002 2003 2004

Figure 43.—Continued
96 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig44

Atrazine in soil—9.0%

(5.8 mol/ha [1.3 kg/ha])

Atrazine in water—

10.3 percent (%)

(6.7 mol/ha [1.5 kg/ha])

[Concentration simulated in 

vadose zone: 1.4 μg/L

Concentration measured in 

groundwater: 1.2 μg/L]

DEA in water—0.3%

(0.2 mol/ha [40 g/ha])

[Concentration simulated in

vadose zone: 0.012 μg/L

Concentration measured in

groundwater: 0.3 μg/L]

DEA in soil—0.1% 

(0.08 mol/ha [14 g/ha])

Atrazine in plants and other atrazine degradates 

in water, soil, and plants—80.2% (52 mol/ha)

A. South-central Wisconsin (Marquette County; SSURGO component #21840)

B. Southwestern Indiana (Daviess County; SSURGO component #20001)

Atrazine in plants and atrazine degradates 

in water, soil, and plants—91.9% (59.7 mol/ha)

Atrazine in soil—7.7% 

(5.0 mol/ha [1.1 kg/ha])

DEA in water—0.0005% 

(0.3 mmol/ha [0.1 g/ha])

[Concentration simulated in 

vadose zone: < 0.002 μg/L

Concentration measured in

groundwater: < 0.002 μg/L]

DEA in soil—0.3% 

(0.19 mol/ha [36 g/ha])

Atrazine in water—0.0015% 

(1.0 mmol/ha [0.2 g/ha])

[Concentration simulated in 

vadose zone: < 0.001 μg/L

Concentration measured in

groundwater: < 0.001 μg/L]

Figure 44. Distributions of atrazine and deethylatrazine (DEA), computed from the P-GWAVA-RZ (Phase 2) simulations, in water, 

soil, and plants (in mole percentages of the total amount of atrazine applied) after 7 years of simulated annual atrazine applications 

at a rate of 2 kilograms of active ingredient per hectare per year (kg a.i./ha-yr) to sites in (A) Marquette County, Wisconsin (Soil 

Survey Geographic database [SSURGO] component number 21840), and (B) Daviess County, Indiana (SSURGO component number 

20001). (mol/ha, mole per hectare; kg/ha, kilogram per hectare, μg/L, microgram per liter; mmol/ha, millimole per hectare; g/ha, gram 

per hectare.)
P-GWAVA Simulation and Groundwater Sampling Results 97

This may have been partly because the permeability of the 

soil is between 3 and 30 times higher at the Wisconsin site 

than at the Indiana site (Natural Resources Conservation 

Service, 2011a). The simulated concentrations of atrazine and 

DEA were negligible in the runoff at both locations, so all the 

remaining mass of applied atrazine not accounted for in soil 

or water was presumed to have been either present in plant 

materials, or converted to degradates other than DEA (fig. 20). 

However, because DEA was the only atrazine degradate for 

which transport and fate were simulated, the amounts of the 

other atrazine degradates that may have been present in the 

dissolved or sorbed states were not estimated individually.

Most field studies that have measured the percentages of 

applied atrazine in the subsurface following application have 

detected less than 1 percent of the mass of applied herbicide 

in vadose-zone water (Barbash and Resek, 1996; Flury, 

1996; Ma and Selim, 1996; Bergström and Stenström, 1998; 

Bayless, 2001; Kördel and others, 2008). The results shown 

in figure 44 for DEA at the Wisconsin site, and for atrazine 

and DEA at the Indiana site were consistent with this general 

pattern, but the amount of applied atrazine predicted to have 

been leached past the 3-m assessment depth at the Wisconsin 

site (10.3 percent of the atrazine applied) was considerably 

higher than those reported by most of these earlier studies. 

The latter observation may have been partly related to the high 

permeability of the soil at this site. However, it also may have 

been a consequence of the fact that the amounts of atrazine 

and DEA that were predicted to have leached past the 3-m 

assessment depth at both sites were computed over a time 

interval that was substantially longer than the time intervals 

used by most previous studies. 

Whereas the percentages of applied atrazine that were 

simulated to be present as either atrazine or DEA in the 

dissolved state (fig. 44) were computed over a 7-year interval, 

nearly all other investigations that have measured pesticide 

recoveries in water as a percentage of the amount applied have 

been carried out over periods of less than 2 years (Barbash 

and Resek, 1996; Flury, 1996). The chemograph displayed 

in figure 43C, however, indicates that if the P-GWAVA-RZ 

simulations are accurate, several years may be required 

for the overall percent recovery of an annually applied, 

surface-derived contaminant (including its degradates, as well 

as the unreacted parent compound) to reach a quasi-stable 

value in deep seepage and shallow groundwater—even for 

only moderately hydrophobic compounds such as atrazine or 

DEA at a site with highly permeable soils. The amount of time 

required for this to occur (about 4 years for atrazine and DEA 

at the Wisconsin site) is presumed to be the time required for 

the total mass of the retained compounds to reach a quasistable value in the soil column above the assessment depth. 

Support for this hypothesis is provided by the results from an 

investigation of atrazine in the vadose zone and groundwater 

beneath an area of continuous corn cultivation in Nebraska, 

which led the study authors to estimate that it would take 

about 30 years for atrazine concentrations in the groundwater 

at that site to reach a stable value (Wehtje and others, 1981).

In contrast with the results for the dissolved state, the 

amounts of atrazine that were predicted to be in the sorbed 

state at the Wisconsin and Indiana sites (fig. 44) are consistent 

with results from several previous investigations that have 

measured the concentrations of the herbicide in soils as 

percentages of the amounts applied. The results from 11 of 

these earlier studies, tabulated by Barbash and Resek (1996), 

indicated that for the 10 measurements made 90 or more days 

after application, an average of 11 ± 3 percent of the applied 

atrazine was present in the soil. (As might have been expected 

for a compound as reactive as atrazine, the percentages of the 

applied herbicide detected in soils by previous studies were 

generally lower for measurements made after several months 

had passed following application; the amounts of atrazine 

reported to have been recovered within the first 24 hours after 

application ranged from 46 to 130 percent.) 

Because sorption to soil OM typically slows the 

migration of neutral organic solutes through soils, these 

observations from previous studies—along with the data 

displayed in figures 41 and 44—indicate that large proportions 

of atrazine and its hydrophobic degradates may be retained 

within the vadose zone for substantially longer periods than 

the residence times inferred for most of the water. This 

hypothesis is supported by the results from a combined field 

and modeling investigation by Bayless (2001) in the White 

River Basin, Indiana, where atrazine and its degradates were 

inferred to have been retained in the vadose zone long enough 

to undergo transformation to other, unknown compounds 

before reaching the water table. Bayless (2001) suggested 

that one of the processes that may have contributed to this 

was ET, which causes upward flow of water toward the land 

surface, and thus a slowing of the downward migration of 

any of the solutes that it may contain. McMahon and others 

(2006) suggested that a similar mechanism has contributed to 

the accumulation of substantial quantities of atrazine, DEA, 

nitrate and other agriculturally derived chemicals in the thick 

vadose zones underlying irrigated corn-growing areas in 

the High Plains.

Nitrogen

Figures 45A and B illustrate the shifts that were 

predicted to have occurred in the distributions of nitrogen 

among the various forms tracked by RZWQM during the 

7-year simulation period at the Wisconsin and Indiana sites, 

respectively. At both sites over the course of the simulation 

period, the P-GWAVA-RZ model predicted decreases in 

the percentages of nitrogen in the fast OM pool, small or 

negligible reductions in the percentages in the medium 

OM pool, and small increases in the percentages in the 

slow OM pool. Increases were also observed in the nitrogen 

content of the microbial populations and the residue pools. 
98 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig45ab

N in slow OM pool (65%)

N in medium OM

pool (20%)

N in slow OM pool (66%)

N in medium OM

pool (20%)

N in slow OM pool (66%)

N in medium OM

pool (21%)

N in slow OM pool (72%)

N in mediumOM

pool (17%)

 t = 0 years

 ∑ N = 8,059 kg/ha

Nitrogen (N) in microbes (aerobic and anaerobic 

heterotrophs and autotrophs) (1.3 percent [%])

 t = 7 years

 ∑ N = 8,245 kg/ha

 (2.3% increase)

N in fast OM

pool (13%)

N in fast OM

 pool (8.6%)

 N in slow and fast 

residue pools (0.04%)

N in microbes (aerobic and anaerobic 

 heterotrophs and autotrophs) [3.7%]

 N in slow and fast 

residue pools (1.9%)

 LOSS

 (kg/ha-yr)

N uptake by crop (120)

Denitrification and volatilization (0.022)

Mineralization (67)

N in deep seepage (7.3)

(Concentration simulated in deep seepage: 

 0.06 milligram per liter as nitrogen 

 [mg-N/L]

 Concentration measured in groundwater: 

 11 mg-N/L)

 

INPUT

(kilograms per hectare per year [kg/ha-yr])

N applied in fertilizer (81)

N from dead plant roots (30)

N from incorporated residue (51)

Beginning of simulation End of simulation

 t = 0 years

 ∑ N = 8,472 kg/ha

N in microbes (aerobic and anaerobic 

 heterotrophs and autotrophs) [1.1%]

 t = 7 years

 ∑ N = 8,281 kg/ha

 (2.3% decrease)

N in fast OM

 pool (13%)

N in fast OM

 pool (5.3%)

 N in slow and fast 

residue pools (0.03%)

N in microbes (aerobic and anaerobic 

 heterotrophs and autotrophs) [4.9%]

 N in slow and fast 

residue pools (0.86%)

 LOSS

 (kg/ha-yr)

N uptake by crop (179)

Denitrification and volatilization (0.003)

Mineralization (139)

N in deep seepage (6.8)

(Concentration simulated in deep 

 seepage: 0.06 mg-N/L

 Concentration measured in

 groundwater: < 0.05 mg-N/L)

 

 INPUT 

 (kg/ha-yr)

N applied in fertilizer (112)

N from dead plant roots (25)

N from incorporated residue (76)

Beginning of simulation End of simulation

A.

B.

Figure 45. Nitrogen (N) distributions among primary environmental compartments estimated by the P-GWAVA-RZ (Phase 2) 

simulations before and after 7 years of nitrogen fertilizer applications (fig. 43D), estimated inputs and losses of nitrogen, and 

nitrate concentrations estimated in the vadose zone and measured in shallow groundwater at study sites in (A) Marquette 

County, Wisconsin (Soil Survey Geographic database [SSURGO] component 21840) and (B) Daviess County, Indiana (SSURGO 

component 20001). (OM, organic matter.)
P-GWAVA Simulation and Groundwater Sampling Results 99

These changes are consistent with an overall transfer of 

nitrogen from more labile chemical forms to living biomass or 

more refractory forms, as might be expected to occur during 

the biological processing of nitrogen in the soil. According 

to the guidance provided by Hanson and others (1999), the 

7-year simulation period used during Phase 2 was likely to 

have been sufficient for the pools of the more labile forms of 

nitrogen to stabilize at the sites of interest, but may not have 

been long enough for the slow OM pools to stabilize. 

The rates of nitrate input from fertilizer applications 

shown in figure 45, as well as the estimates of the total 

nitrogen inventories stored in the soil column, were 

within the ranges of these parameters estimated from 

a series of RZWQM simulations done by Nolan and 

others (2010) for areas of corn cultivation in California, 

Maryland, and Nebraska. The nitrogen fluxes of 7.3 and 

6.8 kg N/ha/yr predicted by the P-GWAVA-RZ simulations 

in deep seepage at the Wisconsin and Indiana sites, 

respectively, were consistent with the interquartile range of 

nitrate-derived nitrogen fluxes through the vadose zone (0–30 

kg NO3-N/ha/yr) reported at a site in central Indiana by Green 

and others (2008), but an order of magnitude lower than the 

nitrogen fluxes of 56–102 kg N/ha-yr simulated by Nolan 

and others (2010) for their three study areas. Results from 

a preliminary set of P-GWAVA-RZ model simulations done 

for the this study indicated that among the 349 SSURGO 

components for which such predictions were examined, 

changes in the total amount of nitrogen in the upper 3 m of the 

soil over the 7-year simulation period ranged from a reduction 

of 4.0 percent to an increase of 4.3 percent. 

Model Residuals and Water-Table Depths

Because the accuracy of the P-GWAVA predictions at 

individual sites was often evaluated by comparing the solute 

concentrations simulated at a fixed depth in the vadose zone 

(the assessment depth) with those measured in shallow 

groundwater, it was necessary to examine the possibility that 

the extent of agreement between the simulated and measured 

concentrations may have been influenced by variations in the 

depth of the water table among different sites. Consequently, a 

series of statistical analyses were done to examine the degrees 

of correlation (if any) between the average DTW and the 

P-GWAVA model residuals for the concentrations of atrazine, 

DEA, and nitrate, as well as those for the DEA fraction, at 

the study sites of interest. The results from these analyses are 

summarized in table 9. The distributions of the P-GWAVA-RZ 

model residuals for the concentrations of the three solutes and 

the DEA fraction at the Phase 2 sites, relative to the DTW, are 

shown in figure 46. 

Significant, positive correlations with the DTW were 

observed for the model residuals for atrazine concentrations 

for both study phases (table 9). This pattern was noted 

regardless of whether or not sites with water tables shallower 

than the assessment depth were included in the analysis. 

No significant correlations were observed, however, when 

the analysis was restricted to sites with water-table depths 

shallower than the assessment depth. In contrast with the 

atrazine results, significant negative correlations with the 

DTW were observed for the model residuals for the DEA and 

nitrate concentrations. The strength of these correlations varied 

among the response variables and among the depth ranges 

examined, but the general trends are consistent with current 

understanding regarding the influence of redox conditions and 

vadose-zone residence time on the environmental chemistry of 

the three solutes. 

Atrazine and Deethylatrazine

The results shown in figure 46 and table 9 indicate 

that the deeper the water table, the greater the extent to 

which the P-GWAVA-RZ simulations overpredicted the 

atrazine concentrations—and underpredicted the DEA 

concentrations—measured in groundwater. Although no 

statistically significant correlations were observed between 

DTW and the model residuals for the DEA fraction, the signs 

of the correlation coefficients were consistent with those 

observed for the model residuals for the DEA and atrazine 

concentrations. These patterns are in agreement with the work 

of Tesoriero and others (2007), who observed more extensive 

conversion of atrazine to DEA in the subsurface beneath 

agricultural areas where the thickness of the vadose zone is 

greater and, by inference, the residence time of the parent 

compound in the vadose zone is longer.

Microorganisms are ubiquitous in near-surface 

environments (Ghiorse, 1997; Flynn and others, 2008). 

Furthermore, the potential for the microbial transformation 

of atrazine in the subsurface has been demonstrated 

above and below the water table to depths of at least 25 m 

below the land surface (for example, Konopka and Turco, 

1991; Issa and Wood, 1999). Because they are oxidation 

reactions, the dealkylation of atrazine to form either DEA, 

deisopropylatrazine or didealkyl atrazine (fig. 20) should 

occur more readily in the presence of higher concentrations 

of dissolved oxygen. This would explain why atrazine is 

generally more reactive under oxic conditions than under 

anoxic conditions (for example, Kaufman and Kearney, 

1970; Nair and Schnoor, 1992; Papiernik and Spalding, 

1998; Rügge and others, 1999; and Larsen and others, 2000). 
100 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig46abcd

0 

10 

20 

30 

40 

50 

60 

-3.0 -2.0 -1.0 0 1.0 2.0 3.0 

Average depth of water table below land surface, in meters 

Simulated minus measured concentration, in micrograms per liter

A. Atrazine (N = 453 sites) 

Maximum depth 

of simulation 

Model overpredicts

concentration 

Model underpredicts

concentration 

[atrazine] sim = [atrazine] meas

-3.0 -2.0 -1.0 0 1.0 2.0 3.0 

Simulated minus measured concentration, in micrograms per liter

B. DEA (N = 453 sites) 

Maximum depth

of simulation 

Model overpredicts

concentration 

Model underpredicts

concentration 

[DEA] sim = [DEA] meas

0 

10 

20 

30 

40 

50 

60 

-1.0 -0.5 0 0.5 1.0 

Average depth of water table below land surface, in meters 

Simulated minus measured value, dimensionless

C. DEA fraction (N = 96 sites; excludes locations where detections 

of atrazine and DEA were not simulated or not observed)

Maximum depth

of simulation 

Model overpredicts

fraction value 

Model underpredicts

fraction value 

ƒDEA sim = ƒDEA meas

-100 -50 0 50 100 

Simulated minus measured concentration, in milligrams per liter as N

D. Nitrate (N = 453 sites) 

Maximum depth

of simulation 

Model overpredicts

concentration 

Model underpredicts

concentration 

[NO3] sim = [NO3] meas

Figure 46. Relations between P-GWAVA-RZ (Phase 2) model residuals for (A) atrazine concentrations, (B) deethylatrazine 

(DEA) concentrations, (C) DEA fractions, and (D) nitrate concentrations and the average depth of the water table at study 

sites in the 10 northernmost states of the Corn Belt, 1992–2006.
P-GWAVA Simulation and Groundwater Sampling Results 101

Table 9. Correlations between average depths to water and P-GWAVA model residuals for atrazine concentrations, deethylatrazine 

(DEA) concentrations, DEA fractions, and nitrate concentrations in agricultural areas of the United States.

[P-GWAVA-PR simulations used areally averaged intensities of atrazine application, stochastically distributed irrigation, and atrazine transformation in both 

the sorbed and dissolved states; P-GWAVA-RZ simulations used a uniform intensity of atrazine application (2 kilograms of active ingredient per hectare) and 

simulated irrigation (table 4). Other differences between the approaches used for the P-GWAVA-PR and P-GWAVA-RZ simulations are summarized in tables 1, 

4, and 14. Spearman ρ: Values that were statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. P (ρ): Probability 

associated with the Pearson correlation coefficient (R2) for the correlation between the ranks of the model residuals (each of which represents the value predicted 

by the P-GWAVA simulations minus the value measured in groundwater) and the average water-table depths. Abbreviations: m, meter; <, less than; ≤, less than 

or equal to]

Response variable for

model residuals Model

Average water-table 

depth range

(m)

Correlations between model 

residuals and average

water-table depth

Number

of sites

Spearman ρ P (ρ)

Atrazine concentration P-GWAVA-PR

(Assessment depth: 1 m)

0.25–178.27

0.25–0.99

1.01–178.27

0.07

0.11

0.08

0.012

0.35

0.008

1,202

77

1,125

P-GWAVA-RZ

(Assessment depth: 3 m)

0.00–51.33

0.00–2.99

3.02–51.33

0.13

0.028

0.21

0.005

0.69

0.001

443

208

236

DEA concentration P-GWAVA-RZ

(Assessment depth: 3 m)

0.00–51.33

0.00–2.99

3.02–51.33

-0.096

-0.019

-0.074

0.04

0.79

0.26

443

208

236

DEA fraction1 P-GWAVA-RZ

(Assessment depth: 3 m)

0.41–32.94

0.41–2.99

3.02–32.94

-0.092

-0.081

-0.040

0.37

0.68

0.74

96

28

68

Nitrate concentration P-GWAVA-RZ

(Assessment depth: 3 m)

0.00–51.33

0.00–2.99

3.02–51.33

-0.27

-0.25

-0.22

≤ 0.0001

0.0003

0.0006

443

208

236

1

Examined only at sites where detections of atrazine and (or) DEA were simulated or measured. Sites where neither compound was detected—or where the 

detection of neither compound was simulated—were excluded from the calculations.

Because diffusion rates are considerably higher in air than in 

water, the dissolved oxygen consumed by the conversion of 

atrazine to DEA is likely to be replenished more rapidly in the 

vadose zone than in the saturated zone. In areas with greater 

depths to water, the longer transit times from the land surface 

to the water table might therefore be expected to result in more 

extensive formation of DEA from atrazine than in areas with 

thinner vadose zones. This, in turn, would lead to the positive 

correlation observed between DTW and the model residuals 

for atrazine concentrations and the negative correlation 

observed between DTW and the DEA concentration residuals 

during this study (table 9), as well as the direct relation 

noted by Tesoriero and others (2007) between vadose-zone 

thickness and the extent of conversion of atrazine to DEA in 

the subsurface.
102 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Nitrate

Significant, negative correlations were noted between 

DTW and the P-GWAVA-RZ model residuals for nitrate 

concentrations, regardless of the depth range examined 

(table 9). As with the pattern noted for atrazine and DEA, 

the negative correlations between the model residuals for 

nitrate concentrations and DTW also indicate a direct relation 

between residence time in the vadose zone and the extent of 

a biochemical reaction that requires oxic conditions. In this 

case, the reaction involves the formation of nitrate from the 

oxidation of the more reduced forms in which nitrogen is 

often applied (fig. 21). As part of a comprehensive analysis 

of groundwater chemistry in 15 major aquifers across the 

United States, McMahon and Chapelle (2007) determined 

that shallow groundwater is more likely to exhibit reducing 

conditions in areas with relatively thin vadose zones than in 

areas with greater depths to the water table. A similar pattern 

was reported on a more localized scale by Landon and others 

(2011) for groundwater in the San Joaquin Valley, California. 

Because nitrate is more stable under oxic conditions than 

when the supply of dissolved oxygen is limited, the results 

from these earlier studies indicate that, if all other factors are 

equal, nitrate concentrations in groundwater may be higher in 

locations with a relatively thick vadose zone than in areas with 

a relatively thin vadose zone. This expectation is consistent 

with the significant negative correlations observed between the 

model residuals for nitrate concentrations and the DTW in the 

Phase 2 study area (table 9).

Agreement Between Simulated and Observed 

Solute Concentrations

Much of the analysis of the Phase 1 results involved 

comparing the atrazine concentrations predicted by the 

P-GWAVA-PR simulations in the vadose zone with those 

measured in shallow groundwater at the same sites. During 

Phase 2, concentrations simulated in the vadose zone using 

the P-GWAVA-RZ model were compared with concentrations 

measured in groundwater for DEA and nitrate, as well as 

for atrazine. The inclusion of DEA in the P-GWAVA-RZ 

simulations also made it possible to compare predicted DEA 

fractions with DEA fractions measured in groundwater. In 

addition, the fact that many of the sites examined in the Corn 

Belt during Phase 2 were also among those examined during 

Phase 1 provided opportunities to compare the P-GWAVA-PR 

and P-GWAVA-RZ models with respect to the level of 

agreement between their simulated atrazine concentrations and 

the atrazine concentrations measured in groundwater. 

Atrazine

Simulated Atrazine Concentrations Compared with 

Concentrations Measured in Groundwater

As with the P-GWAVA-PR simulations (for example, 

figs. 26 and 33; table 7), the final set of P-GWAVA-RZ 

simulations were more likely to overpredict the atrazine 

concentrations measured in groundwater than to underpredict 

them. The cumulative distributions of the model residual for 

the atrazine concentration predicted by the P-GWAVA-RZ 

simulations (that is, the value simulated at the assessment 

depth of 3 m in the vadose zone minus the value measured in 

shallow groundwater in the same location) among all study 

sites in the Corn Belt for Phase 2 are shown in figure 47. To 

facilitate an examination of the potential influence of watertable depth on the model residuals, the results are displayed 

for three subsets of sites: (1) sites where the DTW was less 

than or equal to 3 m, (2) sites where the DTW was greater 

than 3 m, and (3) all sites. The extent of agreement between 

the simulated and measured atrazine concentrations at sites 

where the water table was deeper than 3 m, as well as at 

sites where it was shallower than or equal to this depth, is 

shown in figure 48. Summary statistics describing the degrees 

of correlation between simulated and measured atrazine 

concentrations for the two site categories, as well as when all 

sites are considered, are presented in table 10. 

Atrazine concentrations predicted by the P-GWAVA-RZ 

simulations in the vadose zone showed patterns of correlation 

with the concentrations measured in groundwater (table 10) 

that were similar to those observed for the P-GWAVA-PR 

simulations (table 7). When the analysis was restricted to 

sites where the average DTW was shallower than the Phase 2 

assessment depth (< 3 m), the atrazine concentrations 

predicted by the P-GWAVA-RZ simulations were not 

significantly correlated with those measured in groundwater 

(table 10). However, the simulated atrazine concentrations 

were positively correlated with those measured in groundwater 

when all of the Phase 2 sites were examined, or just those 

where the DTW was greater than the assessment depth (P[ρ] 

≤ 0.0001; Spearman rank correlations). Additionally, the 

degree of correlation was stronger when the analysis of the 

P-GWAVA-RZ predictions was restricted to sites with DTW 

greater than the assessment depth than when all of the sites 

were examined. As with the Phase 1 results, the lack of a 

significant correlation between the simulated and measured 

atrazine concentrations for the sites where DTW was 

shallower than the assessment depth during Phase 2 may have 

been partly caused by the narrow depth range examined.
P-GWAVA Simulation and Groundwater Sampling Results 103

tac12-0781_fig47

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 

Model residual (simulated minus measured atrazine concentration), in micrograms per liter 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-2 -1 0 1 2 

Sites with model residuals greater than x-axis value, in percent 

Root-Zone Water-Quality Model (RZWQM)

underpredicts concentrations 

RZWQM overpredicts concentrations 

Average depth to water 

less than or equal to 

3 meters (217 sites)

Average depth to water 

greater than 3 meters 

(236 sites)

All sites (N = 453) 

EXPLANATION

Sites with model residuals greater than x-axis value, in percent 

Figure 47. Cumulative distributions of P-GWAVA-RZ (Phase 2) model residuals for atrazine concentrations (that is, the concentration 

simulated at the 3-meter assessment depth in the vadose zone at each site minus the concentration measured in shallow groundwater 

in the same location) at agricultural sites in the 10 northernmost states of the Corn Belt, 1992–2006. Data shown depend on the range of 

depths to water for each subset of sites. Simulated and measured detections were determined using reporting limits that varied over 

time (fig. 4A). Atrazine concentrations (simulated or measured) less than the reporting limit were assigned a value of 0.0001 microgram 

per liter for the purposes of the calculations. Summary statistics are presented for correlations between the model residuals and 

water-table depths in table 9; medians and 95th percentiles of the model residuals are provided in table 10.
104 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig48

0.0001 

0.001 

0.01 

0.1 

1

10

0.0001 0.001 0.01 0.1 1 10 

Simulated atrazine concentration, in micrograms per liter

Measured atrazine concentration, in micrograms per liter

Average depth to water less than or 

equal to 3 meters (217 sites)

Average depth to water greater than 

3 meters (236 sites) 

1:1 line (simulated = measured concentration) 

EXPLANATION

Figure 48. Atrazine concentrations simulated by the P-GWAVA-RZ (Phase 2) model at the 3-meter 

assessment depth in the vadose zone and measured concentrations in shallow groundwater 

at agricultural sites in the 10 northernmost states of the Corn Belt, 1992–2006. Simulated and 

measured detections were determined using reporting limits that varied over time (fig. 4A). Atrazine 

concentrations (simulated or measured) less than the reporting limit were assigned a value of 

0.0001 microgram per liter for the purposes of display. Summary statistics for correlations between 

simulated and measured atrazine concentrations are presented in table 10.
P-GWAVA Simulation and Groundwater Sampling Results 105

Table 10. Agreement between atrazine, deethylatrazine (DEA), or nitrate concentrations (or DEA fractions) simulated 

at the 3-meter assessment depth in the vadose zone by the P-GWAVA-RZ (Phase 2) model and the concentrations (or 

DEA fractions) measured in shallow groundwater at agricultural sites in the 10 northernmost states of the Corn Belt, 

1992–2006.

[Concentrations predicted for atrazine and DEA were obtained with P-GWAVA-RZ simulations using the uniform atrazine application 

intensity (2 kilograms of active ingredient per hectare); predicted nitrate concentrations were obtained with simulations using fertilizer 

application intensities that varied spatially with soil organic-carbon content (table 14). Irrigation was simulated at all sites. For the 

purposes of these calculations, non-detections of atrazine and DEA—either measured or simulated—were assigned a concentration of 

0.0001 microgram per liter (μg/L); for nitrate, non-detections were assigned a concentration of 0.01 milligram per liter as N (mg-N/L). 

Spearman ρ: Values that were statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. P (ρ):

The probability associated with the Pearson correlation coefficient (R2) for the correlation between the ranks of the simulated and measured 

values. Model residuals: The model residual for a given parameter is equal to the value predicted in the vadose zone by the P-GWAVA-RZ 

simulation minus the value measured in groundwater at the same site. 95th percentile: For each case, 95 percent of the model residuals were 

smaller than the value shown. Abbreviations: m, meter]

Parameter

Range of 

depths to 

water at

the sites

(m)

Correlation (if any)

between simulated

and measured values

Model residuals

(simulated minus 

measured values) Number

of sites

Spearman ρ P (ρ) Median 95th 

percentile

Atrazine concentration <3 0.024 0.73 0.000 μg/L 0.55 μg/L 217

3.02–51.33 0.42 ≤0.0001 0.000 μg/L 0.90 μg/L 236

All sites 0.25 ≤0.0001 0.000 μg/L 0.87 μg/L 453

DEA concentration <3 0.26 0.0001 0.000 μg/L 0.002 μg/L 217

3.02–51.33 0.49 ≤0.0001 0.000 μg/L 0.004 μg/L 236

All sites 0.41 ≤0.0001 0.000 μg/L 0.003 μg/L 453

DEA fraction1 <3 0.072 0.71 -0.64 -0.12 28

3.02–32.94 -0.17 0.17 -0.61 -0.073 68

All sites -0.085 0.41 -0.62 -0.078 96

Nitrate concentration <3 -0.004 0.96 -0.063 mg-N/L 2.2 mg-N/L 217

3.02–51.33 0.027 0.68 -2.2 mg-N/L 0.72 mg-N/L 236

All sites -0.018 0.71 -0.84 mg-N/L 1.6 mg-N/L 453

1

Examined only at sites where detections of atrazine and DEA were simulated or measured. Sites where neither compound was detected—

or where the detection of neither compound was simulated—were excluded from the calculations.

Simulated Frequencies of Atrazine Detection Compared with 

Measured Values

The frequencies of atrazine detection predicted in 

the vadose zone by the P-GWAVA-RZ simulations for the 

NAWQA groundwater sampling networks with 10 or more 

sites are compared with the measured frequencies of detection 

in shallow groundwater for these networks in figure 49. 

As with the Phase 1 results (fig. 37), these comparisons are 

shown for all the networks with 10 or more sites (all sites; 

fig. 49A), and for the subset of networks where the depths to 

water exceeded the 3-m assessment depth at 10 or more sites 

(sites with DTW greater than 3 m only; fig. 49B). In contrast 

with the results from the analysis for Phase 1, statistically 

significant correlations were observed between the frequencies 

of atrazine detection predicted by the P-GWAVA-RZ 

simulations and the measured values (table 11). Consistent 

with the results from the analysis of the atrazine concentration 

data (table 10), table 11 indicates that the correlation between 

the simulated and measured frequencies of atrazine detection 

for Phase 2 was stronger when sites with depths to water equal 

to or shallower than the assessment depth were excluded from 

the analysis than when all of the sites were examined.
106 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig49ab

Simulated = measured

Simulated = measured

A. Networks with 10 or more sites (all sites; 17 networks; 406 sites)

B. Networks with 10 or more sites with average depths to water greater than the 

3-meter assessment depth (sites with depth to water greater than 3 meters only; 

12 networks; 189 sites)

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of atrazine detection in vadose zone, in percent

Measured frequency of atrazine detection in groundwater, in percent

Network with depths to water greater than 

3 meters for 10 or more sites

Network with depths to water greater than 

3 meters for fewer than 10 sites

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of atrazine detection in vadose zone, in percent

Measured frequency of atrazine detection in groundwater, in percent

Figure 49. Frequencies of atrazine detection simulated at a depth of 3 meters in the vadose zone using the P-GWAVA-RZ 

(Phase 2) model and the measured frequencies of atrazine detection in shallow groundwater networks in agricultural areas of the 

10 northernmost states of the Corn Belt, 1992–2006. Data are shown for networks consisting of (A) all sites in groundwater networks 

with 10 or more sites, and (B) sites with depths to water greater than the 3-meter assessment depth in networks with 10 or more such 

sites. Simulated and measured detections of atrazine were determined using reporting limits that varied over time (fig. 4A). Summary 

statistics for correlations between simulated and measured detection frequencies are presented in table 11.
P-GWAVA Simulation and Groundwater Sampling Results 107

Table 11. Correlations between simulated and measured frequencies of detection of atrazine, deethylatrazine, and nitrate in 

shallow groundwater sampling networks with 10 or more sites. 

[Results are shown for Phase 1 (P-GWAVA-PR simulations; conterminous United States, 1992–98; atrazine only) and Phase 2 (P-GWAVA-RZ simulations; 

10 northernmost states of the Corn Belt of the United States, 1992–2006; atrazine, deethylatrazine [DEA], and nitrate). The geographic locations of the 

Phase 1 networks are shown in figure 38; those of the Phase 2 networks are shown in figures 66–68. P-GWAVA-PR simulations used areally averaged 

intensities of atrazine application, stochastically distributed irrigation, and atrazine transformation in both the sorbed and dissolved states; P-GWAVA-RZ 

simulations used a uniform intensity of atrazine application (2 kilograms of active ingredient per hectare) and irrigation at all sites (table 4). Detections—

either simulated or measured—were determined using a reporting limit of 0.001 microgram per liter (μg/L) for atrazine during Phase 1, and reporting 

limits that varied over time for atrazine, DEA, and nitrate (fig. 4) during Phase 2. Results from statistically significant (P <0.05) correlations are shown 

in boldface. R2: Pearson correlation coefficient. P (R2): Probability associated with the Pearson correlation coefficient. Spearman ρ: Values that were 

statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. P (ρ): The probability associated with the Pearson 

correlation coefficient (R2) for the correlation between the ranks of the simulated and measured values. Number of networks: Number of networks 

included in each analysis. Number of sites: Number of sites included in each analysis. Abbreviation: m, meter]

Compound

(model)

Depths to water at

the sites examined

(m)

Correlations between simulated and

measured frequencies of detection Number of 

networks 

Number of

sites

R2 P (R2

) Spearman ρ P (ρ)

Atrazine 

(P-GWAVA-PR)

 1.01–178.27 0.025 0.29 0.13 0.38 46 1,051

All sites 0.036 0.20 0.18 0.24 47 1,149

Atrazine 

(P-GWAVA-RZ)

3.02–32.94 0.69 0.0009 0.66 0.020 12 189

All sites 0.24 0.046 0.29 0.26 17 406

DEA 3.02–32.94 0.66 0.0013 0.78 0.0026 12 189

All sites 0.56 0.0006 0.57 0.018 17 406

Nitrate 3.02–32.94 0.060 0.44 -0.18 0.58 12 189

All sites 0.061 0.34 -0.076 0.77 17 406

Comparison of Predictions from the P-GWAVA-PR and 

P-GWAVA-RZ Simulations

For the statistical analyses that included the sites where 

the DTW exceeded the assessment depth for each model of 

interest, the atrazine concentrations predicted by the final sets 

of P-GWAVA simulations were significantly correlated with 

those measured in shallow groundwater at the same locations 

for both study phases (tables 7 and 10). However, although 

significant correlations were noted between the simulated and 

observed frequencies of atrazine detection for the Phase 2 

results (P < 0.05), the corresponding relations for the Phase 1 

results were not statistically significant (table 11). These 

disparities in the relative strengths of the correlations between 

the simulated and observed patterns of atrazine occurrence 

for the two models may have been partly because the Phase 1 

study sites (fig. 1) encompassed a much wider variety of 

agricultural, hydrogeologic, and climatic settings than the 

Phase 2 sites (fig. 2). 

Because simulations were conducted using both 

P-GWAVA-PR and P-GWAVA-RZ at many of the study sites 

in the Corn Belt, opportunities were available to make direct 

comparisons between the two models with respect to (1) their 

simulated atrazine concentrations, and (2) the accuracy with 

which each model predicted these concentrations in shallow 

groundwater. To account for the potential effects of irrigation, 

the comparisons were made using the predictions from the 

set of P-GWAVA-PR model simulations for which irrigation 

had been simulated at all sites (as was the case for all the 

P-GWAVA-RZ simulations), rather than those for which the 

irrigated sites had been randomly distributed among sites 

in individual groundwater networks. Except for the manner 

in which irrigation was assigned to different sites for the 

P-GWAVA-PR simulations, however, these comparisons 

were made using the final simulation approaches described in 

table 4. Three methods were used to compare the predictions 

from the two models: (1) graphical comparisons of the 

cumulative distributions of the model residuals, (2) statistical 

analysis of the correlation between the atrazine concentrations 

simulated by the two models at individual sites, and 

(3) statistical analysis of the correlation between the model 

residuals for atrazine concentrations from the two models at 

individual sites.
108 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

The cumulative distributions of the model residuals for 

atrazine concentrations for the two models at the 136 sites in 

the Corn Belt where simulations were done using both models 

and the depths to water exceeded 3 m (the greater of the 

assessment depths for the two models) are shown in figure 50. 

Virtually identical distributions were observed for each 

model in the corresponding plots (not shown) that included 

the data from all 268 of the sites in the Corn Belt where both 

models were run, regardless of water-table depth. The atrazine 

concentrations predicted by the P-GWAVA-RZ simulations 

were generally in closer agreement with the concentrations 

measured in groundwater than were the concentrations 

predicted by the P-GWAVA-PR simulations (fig. 50). On 

the basis of the results shown in figure 31, this pattern may 

have been partly because the P-GWAVA-PR simulations 

used soil property data drawn from STATSGO, whereas 

the P-GWAVA-RZ simulations used soils data obtained 

from SSURGO. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 

Model residual (simulated minus measured atrazine concentration), in micrograms per liter

Model underpredicts

concentrations 

Model overpredicts

concentrations 

P-GWAVA-PR residuals (spatially variable atrazine applications) 

P-GWAVA-RZ residuals (spatially uniform atrazine applications) 

EXPLANATION

Sites with model residuals greater than x-axis value, in percent 

Figure 50. Cumulative distributions of model residuals for atrazine concentrations simulated by P-GWAVA-PR 

(Phase 1, 1992–98) and P-GWAVA-RZ (Phase 2, 1992–2006) at 136 study sites where simulations were done with 

both models and the depth to water was greater than 3 meters in agricultural areas of the 10 northernmost 

states in the Corn Belt. Simulations were carried out as described in table 4, with the exception that irrigation 

was simulated at all sites with both models. Concentrations were not corrected for temporal variations in 

analytical recoveries. A uniform reporting limit of 0.001 microgram per liter (μg/L) was applied to all simulated 

concentrations, and all sites with non-detections were assigned a concentration of 0.0001 μg/L for the purposes 

of the calculations. 
P-GWAVA Simulation and Groundwater Sampling Results 109

Additionally, the atrazine concentrations in shallow 

groundwater may have been overestimated by the 

P-GWAVA-PR simulations to a greater extent than by the 

P-GWAVA-RZ simulations because RZWQM simulates the 

upward transport of solutes in response to the upward flow of 

water during ET, whereas PRZM does not. Previous studies 

(Bayless, 2001; McMahon and others, 2006) have inferred that 

this process may be responsible for the retention of surfacederived solutes within the root zone for extended periods, long 

after the water that carried them has moved to greater depths 

(or transferred to the atmosphere through ET). For compounds 

like atrazine that react at higher rates within the root zone than 

at greater depths beneath the land surface, this process could 

result in lower fluxes of the herbicide to the water table than 

would occur otherwise, causing the P-GWAVA-PR model to 

overpredict atrazine concentrations to a greater extent than the 

P-GWAVA-RZ model (fig. 50).

At the 136 Corn Belt sites where the DTW was greater 

than 3 m and simulations using both models were carried 

out (with irrigation simulated in all locations), the atrazine 

concentrations predicted by the two models were negatively 

correlated (Spearman ρ = -0.43; P[ρ] ≤ 0.0001). Similarly, 

the residuals for atrazine concentrations derived from the 

two models at these sites were also negatively correlated 

(Spearman ρ = -0.17; P[ρ] = 0.04). Given the considerable 

differences between the two models, and between the 

simulation approaches used for the two phases of this study 

(table 4), the precise reasons for these disparities between 

the atrazine concentrations simulated by the two models are 

not clear. However, these observations indicate that different 

factors likely were responsible for the errors associated with 

the two models.

Despite these disparities, the data shown in tables 7

and 10, and figures 34 and 47, indicate that the atrazine 

concentrations simulated by the two models were in 

relatively close agreement with the values measured in 

shallow groundwater. In figure 51, the distributions of the 

model residuals for the atrazine concentrations derived 

from the P-GWAVA-RZ simulations are compared with the 

distributions of model residuals from the three P-GWAVA-PR 

scenarios that showed the closest agreement between the 

simulated and measured atrazine concentrations among all 

the Phase 1 sites (namely, the simulations for which the 

areally averaged intensities of atrazine application were used 

and where atrazine transformation was simulated in both the 

sorbed and the dissolved states). These results, presented for 

two subsets of sites for each model scenario (that is, all sites, 

and sites where DTW was greater than the assessment depth 

for each study phase), indicate that the model residuals for 

the atrazine concentrations derived from the P-GWAVA-PR 

simulations spanned a wider range (-3.6 to 7.4 μg/L) than 

the residuals from the P-GWAVA-RZ simulations (-2.2 to 

1.5 μg/L). This may have been caused, in part, by the fact that 

the P-GWAVA-PR simulations were carried out over a much 

larger geographic area. 

As with the data shown in tables 7 and 10, figure 51

indicates that for more than 95 percent of the sites of interest, 

the atrazine concentrations simulated by the two models were 

in agreement with the measured concentrations by margins 

that were an order of magnitude less than the MCL of 3 μg/L 

established by the U.S. Environmental Protection Agency 

(2003) for atrazine. Additionally, in most locations the atrazine 

concentrations simulated in the vadose zone by both models 

were greater than or equal to those measured in the saturated 

zone (figs. 34, 47, 51; tables 7, 10). This is consistent with the 

general expectation that the concentrations of any surfacederived solute would tend to be higher in the vadose zone than 

in the underlying groundwater, owing to the effects of dilution, 

hydrodynamic dispersion, sorption, and transformation during 

solute transport toward, and below, the water table.

Results from several previous investigations indicate 

that pesticide concentrations measured in groundwater tend 

to be higher for locations or periods with higher recharge 

rates (Barbash and Resek, 1996). Therefore, differences in 

precipitation between the Phase 1 and Phase 2 simulation 

periods may have been one of the factors contributing to the 

differences in the levels of agreement between the simulated 

and measured atrazine concentrations during the two study 

phases (figs. 50 and 51). The P-GWAVA-PR simulations 

were conducted from 1991–95 (table 1). The P-GWAVA-RZ 

simulation period was a 5-year window from 2000 through 

2004, but used weather data that were generated stochastically 

by Cligen from historical data gathered between 1949 and 

1996. Consequently, actual meteorological data could not be 

used to make direct comparisons between the precipitation 

inputs used for the two study phases. However, historical 

data from 1991 to 2007 for 338 of the agricultural sites in 

the Corn Belt where simulations were carried out with one 

or both models provide little evidence of significant shifts in 

precipitation during this period (fig. 52). Therefore, temporal 

variations in precipitation alone are not likely responsible for 

systematic changes in the atrazine concentrations measured in 

groundwater in the Corn Belt between 1991 and 2004 and, by 

extension, the contrast in the levels of agreement between the 

simulated and measured atrazine concentrations or detection 

frequencies for the two phases of this study.
110 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig51 Atrazine concentration residual (value simulated in vadose zone minus value measured in groundwater), in micrograms per liter

 Atrazine applications: Areally averaged Atrazine applications: Spatially uniform

 Irrigation simulation: None Stochastic All sites None Stochastic All sites Irrigation simulation: All sites All sites

 Depth to water at site, in meters: All sites All sites All sites > 1 > 1 > 1 Depth to water at site, in meters: All sites > 3 

 Number of sites: 1,224 1,224 1,224 1,125 1,125 1,125 Number of sites: 453 236 

P-GWAVA-RZ

(Sites located in the Corn Belt)

 Models overpredict by more

 than the MCL for atrazine (3 μg/L) 

 Models underpredict by more

 than the MCL for atrazine (3 μg/L) 

P-GWAVA-PR

 (Sites located across the conterminous United States) 

EXPLANATION 

Outlier

Outlier

75

90th percentile

10th percentile

th percentile

50th percentile

(median)

25th percentile

Interquartile

range 

8

7

6

5

4

3

2

1

0

-1

-2

-3

-4

Figure 51. Statistical distributions of model residuals for atrazine concentrations from selected P-GWAVA-PR (Phase 1) and P-GWAVA-RZ 

(Phase 2) scenarios at agricultural sites in the conterminous United States (Phase 1, 1992–1998) and the 10 northernmost states of the Corn 

Belt (Phase 2, 1992–2006). Results shown for Phase 1 are from the P-GWAVA-PR simulations where atrazine transformation was assumed 

to occur in both the sorbed and the dissolved states. For each modeling scenario (for both study phases), simulation results are shown 

for all sites, as well as for sites with depths to water greater than the assessment depth. A uniform reporting limit of 0.001 microgram per 

liter (μg/L) was applied to all concentrations (simulated or measured), and no concentrations were corrected for temporal variations in 

analytical recoveries. All non-detections of atrazine (either simulated or measured) were assigned a concentration of 0.0001 μg/L for the 

purposes of the calculations. (MCL, maximum contaminant level.)
P-GWAVA Simulation and Groundwater Sampling Results 111

tac12-0781_fig52

0 

20 

40 

60 

80 

100 

120 

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 

Average annual precipitation, in centimeters per year

Year 

P-GWAVA-PR 

simulation period

P-GWAVA-RZ 

simulation period

EXPLANATION

Upper 95 percent confidence limit

Mean

Lower 95 percent confidence limit

Figure 52. Average annual precipitation at 338 of the agricultural sites examined during one or both phases of this study 

in the 10 northernmost states of the Corn Belt, 1990–2007. Precipitation data from PRISM Climate Group (2009) and David 

Wolock, U.S. Geological Survey (written commun., June 2011).

Deethylatrazine

The DEA concentrations predicted by the P-GWAVA-RZ 

simulations were in relatively close agreement and 

significantly correlated with the concentrations measured in 

groundwater, regardless of whether or not sites with depths 

to water equal to or shallower than the 3-m assessment 

depth were included in the analysis (table 10; fig. 53). The 

strongest correlation between the simulated and measured 

DEA concentrations was detected when the sites with depths 

to water equal to or shallower than the assessment depth 

were excluded (also observed for atrazine), and the weakest 

correlation observed when the analysis was restricted to such 

sites. Similarly, the frequencies of DEA detection predicted by 

the P-GWAVA-RZ simulations also were correlated with those 

measured in groundwater (table 11; fig. 54), with the stronger 

relation being noted when the sites with depths to water equal 

to or shallower than the 3-m assessment depth were excluded 

from the analysis. 

The P-GWAVA-RZ model simulated a much narrower 

range of DEA concentrations (0.002–0.023 μg/L) than were 

measured in the groundwater (0.001–2.6 μg/L) (fig. 55). This 

was in marked contrast with the atrazine results, in which 

both the simulated and measured concentrations spanned 

approximately the same range (fig. 48). The results shown in 

figures 53, 54, and 55 also indicate that most of the disparities 

between the simulated DEA concentrations (or detection 

frequencies) and those measured in groundwater were biased 

toward underprediction. This pattern is consistent with the 

assumptions that (1) DEA concentrations are likely to increase 

with residence time in the vadose zone, and (2) atrazine or 

DEA detected in the saturated zone are likely to have a longer 

subsurface residence time, and thus reflect a more extensive 

conversion of the parent compound to the degradate, than 

the same solutes detected in the overlying vadose zone in the 

same location.
112 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig53

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-2 -1 0 1 2 

Root-Zone Water-Quality Model (RZWQM)

 underpredicts concentrations 

RZWQM

overpredicts concentrations 

Average depth to water less 

than or equal to 3 meters 

(217 sites) 

Average depth to water 

greater than 3 meters

(236 sites)

All sites (N = 453) 

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Model residual (simulated minus measured DEA concentration), in micrograms per liter

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 

Sites with model residuals greater than x-axis value, in percent Sites with model residuals greater than x-axis value, in percent 

Figure 53. Cumulative distributions of P-GWAVA-RZ (Phase 2) model residuals for deethylatrazine (DEA) concentration (value 

simulated at the 3-meter assessment depth in the vadose zone minus value measured in shallow groundwater at each site) at 

agricultural sites in the 10 northernmost states of the Corn Belt, 1992–2006. Data shown depend on the range of depths to water for 

each subset of sites. Simulated and measured detections were determined using reporting limits that varied over time (fig. 4B). DEA 

concentrations (simulated or measured) less than the reporting limit were assigned a value of 0.0001 microgram per liter for the 

purposes of the calculations. Summary statistics for the model residuals are presented in table 10.
P-GWAVA Simulation and Groundwater Sampling Results 113

tac12-0781_fig54ab

Simulated = measured

Simulated = measured

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of DEA detection in vadose zone, in percent

Measured frequency of DEA detection in groundwater, in percent

Network with depths to water greater than 

3 meters for 10 or more sites

Network with depths to water greater than 

3 meters for fewer than 10 sites

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of DEA detection in vadose zone, in percent

Measured frequency of DEA detection in groundwater, in percent

A. Networks with 10 or more sites (all sites; 17 networks; 406 sites)

B. Networks with 10 or more sites with average depths to water greater than the 

3-meter assessment depth (sites with depths to water greater than 3 meters only; 

12 networks; 189 sites)

Figure 54. Frequencies of deethylatrazine (DEA) detection simulated at a depth of 3 meters in the vadose zone by the P-GWAVA-RZ 

(Phase 2) model and the measured frequencies of DEA detection in shallow groundwater networks in agricultural areas of the 

10 northernmost states of the Corn Belt, 1992–2006. Data are shown for networks consisting of (A) all sites in groundwater networks 

with 10 or more sites, and (B) sites with depths to water greater than the 3-meter assessment depth in networks with 10 or more such 

sites. Simulated and measured detections were determined using reporting limits that varied over time (fig. 4B). Summary statistics for 

correlations between simulated and measured detection frequencies are presented in table 11.
114 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig55

1:1 line (simulated = measured concentration) 

0.0001 

0.001 

0.01 

0.1 

1 

10 

0.0001 0.001 0.01 0.1 1 10 

Simulated DEA concentration, in micrograms per liter

Measured DEA concentration, in micrograms per liter

Average depth to water less than or equal to 

3 meters (217 sites)

Average depth to water greater than 

3 meters (236 sites) 

EXPLANATION

Figure 55. Deethylatrazine (DEA) concentrations simulated by the P-GWAVA-RZ (Phase 2) model at the 3-meter 

assessment depth in the vadose zone and measured concentrations in shallow groundwater at agricultural sites 

in the 10 northernmost states of the Corn Belt, 1992–2006. Simulated and measured detections were determined 

using reporting limits that varied over time (fig. 4B). DEA concentrations (simulated or measured) less than the 

reporting limit were assigned a value of 0.0001 microgram per liter for the purposes of display. Summary statistics 

for correlations between simulated and measured DEA concentrations are presented in table 10.
P-GWAVA Simulation and Groundwater Sampling Results 115

Comparisons of Atrazine and Deethylatrazine 

Concentrations Predicted by P-GWAVA-RZ with those 

from Stackelberg Regression Model

Stackelberg and others (2012) used the NAWQA 

data to develop two linear Tobit regression models for 

predicting the sum of the concentrations of atrazine and 

DEA (computed on a mass basis, rather than a molar basis) 

in shallow groundwater in the conterminous United States. 

One model was developed using only national-scale data 

for the explanatory variables, whereas the other model used 

more site-specific values for some of the parameters. The 

sum of the (mass based) atrazine and DEA concentrations 

simulated by the P-GWAVA-RZ model was compared with 

the sum of the atrazine and DEA concentrations predicted by 

the national-scale model of Stackelberg and others (2012) 

at all 329 Phase 2 sites that were included in both studies. 

This comparison only could be carried out using the Phase 2 

results because the concentrations of DEA were not simulated 

during Phase 1. Because of the exclusive focus of this study 

on national-scale sources of input data, the comparison was 

made with the model of Stackelberg and others (2012) that 

used only national-scale data. To be consistent with the data 

from Stackelberg and others (2012), the solute concentrations 

predicted by the P-GWAVA-RZ simulations were censored 

using the Stackelberg and others (2012) reporting limits of 

0.004 μg/L for atrazine and 0.007 μg/L for DEA. Similarly, the 

sum of the atrazine and DEA concentrations predicted by the 

regression model was censored at the sum of these reporting 

limits for the two compounds (0.011 μg/L). A value of 

0.001 μg/L was assigned to all the regression-predicted values 

less than this threshold for the purposes of the calculations. 

The sum of the atrazine and DEA concentrations simulated 

by the P-GWAVA-RZ model exhibited a significant, positive 

correlation with the concentrations predicted by the nationalscale regression model of Stackelberg and others (2012) at 

the 329 Corn Belt sites (R2 = 0.21 [P ≤ 0.0001]; Spearman 

ρ = 0.37 [P(ρ) ≤ 0.0001]).

Deethylatrazine Fractions

The DEA fractions predicted by the P-GWAVA-RZ 

simulations (and calculated using equation 51) exhibited no 

significant correlations with the values computed from the 

concentrations of DEA and atrazine measured in groundwater 

at the same locations (figs. 56 and 57; table 10). This was 

observed regardless of whether or not sites with DTW less 

than or equal to the 3-m assessment depth were included in 

the analysis. Thus, although the P-GWAVA-RZ simulations 

predicted concentrations of atrazine and DEA that were 

significantly correlated with the corresponding values 

measured in groundwater (table 10), they did not provide 

reliable predictions of the mole fraction of the applied 

atrazine that was converted to DEA in the subsurface. For 

nearly every site where the DEA fraction could be predicted, 

the model simulation results underpredicted the values 

measured in groundwater, consistent with the fact that most 

of the disparities between the simulated and measured DEA 

concentrations also were underpredictions (figs. 53, 54, 

and 55). This observation, in turn, is consistent with the 

expectation that the conversion of atrazine to DEA is likely to 

have proceeded to a greater extent in the saturated zone than in 

the overlying vadose zone in the same locations.

Nitrate

At most of the study sites, the nitrate concentrations 

simulated by the P-GWAVA-RZ model at the 3-m assessment 

depth in the vadose zone were less than the concentrations 

measured in shallow groundwater (figs. 58 and 59). 

Additionally, the simulated nitrate concentrations were not 

significantly correlated with the measured values, regardless 

of whether or not sites with depths to water less than or equal 

to 3 m were included in the analysis (table 10). The fact 

that the simulated nitrate concentrations were less than the 

concentrations measured in groundwater at most of the study 

sites is consistent with the observation by Nolan and others 

(2010) that in some settings, especially those where macropore 

flow is significant, RZWQM tends to underpredict the rate 

of transport of nitrate and other conservative solutes (such as 

bromide) in soil macropores. Another possible reason for the 

underestimation of the nitrate concentrations in groundwater 

by the P-GWAVA-RZ simulations was that nitrogen inputs 

were derived from crop requirements (table 14), rather than 

from known rates of application of fertilizer and manure; 

therefore, P-GWAVA-RZ may have underestimated the actual 

nitrogen inputs in many locations. Consistent with the results 

for the nitrate concentrations, the frequencies of nitrate 

detection simulated by the P-GWAVA-RZ model in the vadose 

zone showed no significant correlations with those measured 

in shallow groundwater, regardless of whether or not sites with 

depths to water less than or equal to the 3-m assessment depth 

were included in the analysis (table 11, fig. 60). 
116 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig56

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-1 0 1 

Model residual (simulated minus measured DEA fraction), dimensionless 

Average depth to water less than or 

equal to 3 meters (28 sites)

Average depth to water greater than 

3 meters (68 sites) 

Root-Zone Water-Quality Model (RZWQM)

underpredicts fraction values 

RZWQM

overpredicts fraction values 

EXPLANATION

Sites with model residuals greater than x-axis value, in percent 

Figure 56. Cumulative distributions of P-GWAVA-RZ (Phase 2) model residual for the deethylatrazine

(DEA) fraction (value simulated at the 3-meter assessment depth in the vadose zone minus value 

measured in shallow groundwater at each site) at agricultural sites in the 10 northernmost states 

in the Corn Belt, 1992–2006. Data shown depend on the range of depths to water for each subset of 

sites. Data not shown for sites where simulated concentrations of DEA and atrazine were below their 

reporting limits, or sites where detections of neither DEA nor atrazine were observed. Simulated and 

measured detections of DEA and atrazine were determined using reporting limits that varied over 

time (figs. 4A, 4B). Atrazine or DEA concentrations (simulated or measured) less than their reporting 

limits were assigned a value of 0 microgram per liter for the purposes of the calculations. Summary 

statistics for the model residuals are presented in table 10. 
P-GWAVA Simulation and Groundwater Sampling Results 117

tac12-0781_fig57

1:1 line (simulated = measured value) 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0.09 

0.1 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

Simulated DEA fraction, dimensionless 

Measured DEA fraction, dimensionless

Average depth to water less than or equal to 

3 meters (28 sites)

Average depth to water greater than 

3 meters (68 sites) 

EXPLANATION

Figure 57. Deethylatrazine (DEA) fractions simulated by the P-GWAVA-RZ (Phase 2) model at the 3-meter 

assessment depth in the vadose zone and values measured in shallow groundwater at the same locations in 

agricultural areas of the 10 northernmost states in the Corn Belt, 1992–2006. Data not shown for sites where 

simulated concentrations of DEA and atrazine were below their reporting limits, or sites where detections 

of neither DEA nor atrazine were observed. Simulated and measured detections of DEA and atrazine 

were determined using reporting limits that varied over time (figs. 4A, 4B). Atrazine or DEA concentrations 

(simulated or measured) less than their reporting limits were assigned a value of 0 microgram per liter for 

the purposes of display. Summary statistics for correlations between simulated and measured DEA fractions 

are presented in table 10.
118 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig58

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

Measured residual (simulated minus measured nitrate concentration), in milligrams per liter as N 

Average depth to water less than or 

equal to 3 meters (217 sites)

Average depth to water greater than 

3 meters (236 sites)

All sites (453)

Root-Zone Water-Quality Model (RZWQM)

underpredicts concentrations 

RZWQM

overpredicts concentrations 

EXPLANATION

Sites with model residuals greater than x-axis value, in percent 

Figure 58. Cumulative distributions of P-GWAVA-RZ (Phase 2) model residuals for nitrate 

concentrations (value simulated at the 3-meter assessment depth in the vadose zone minus value 

measured in shallow groundwater) at agricultural sites in the 10 northernmost states in the Corn 

Belt, 1992–2006. Data shown depend on the range of depths to water for each subset of sites. 

Simulated and measured detections were determined using reporting limits that varied over time 

(fig. 4C). Nitrate concentrations (simulated or measured) less than the reporting limit were assigned 

a value of 0.01 milligram per liter as nitrogen for the purposes of the calculations. Summary 

statistics for the model residuals are presented in table 10.
P-GWAVA Simulation and Groundwater Sampling Results 119

tac12-0781_fig59

1:1 line (simulated concentration = measured concentration) 

0.01 

0.1 

1 

10 

100 

0.01 0.1 1 10 100 

Simulated nitrate concentration, in miligrams per liter as N 

Measured nitrate concentration, in milligrams per liter as N 

Average depth to water less than or 

equal to 3 meters (217 sites)

Average depth to water greater than 

3 meters (236 sites) 

EXPLANATION

Figure 59. Nitrate concentrations simulated by the P-GWAVA-RZ (Phase 2) model at the 3-meter 

assessment depth in the vadose zone and measured concentrations in shallow groundwater at agricultural 

sites in the 10 northernmost states of the Corn Belt, 1992–2006. Simulated and measured detections were 

determined using reporting limits that varied over time (fig. 4C). Nitrate concentrations (simulated or 

measured) less than the reporting limit were assigned a value of 0.01 milligram per liter as nitrogen for 

the purposes of display. Summary statistics for correlations between simulated and measured nitrate 

concentrations are presented in table 10.
120 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig60ab

Simulated = measured 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated = measured 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 10 20 30 40 50 60 70 80 90 100 

Simulated frequency of nitrate detection in vadose zone, in percent

Measured frequency of nitrate detection in groundwater, in percent

Simulated frequency of nitrate detection in vadose zone, in percent

Measured frequency of nitrate detection in groundwater, in percent

A. Networks with 10 or more sites (all sites; 17 networks; 406 sites)

B. Networks with 10 or more sites with average depths to water greater than the 

3-meter assessment depth (sites with depths to water greater than 3 meters only; 

12 networks; 189 sites)

Network with depths to water greater than 

3 meters for 10 or more sites

Network with depths to water greater than 

3 meters for fewer than 10 sites

EXPLANATION

Figure 60. Frequencies of nitrate detection simulated by the P-GWAVA-RZ (Phase 2) model at the 3-meter assessment depth 

in the vadose zone and frequencies of nitrate detection measured in shallow groundwater networks in agricultural areas of the 

10 northernmost states of the Corn Belt, 1992–2006. Data are shown for networks consisting of (A) all sites in groundwater networks 

with 10 or more sites, and (B) sites with depths to water greater than the 3-meter assessment depth in networks with 10 or more such 

sites. Simulated and measured detections were determined using reporting limits that varied over time (fig. 4C). Summary statistics for 

correlations between simulated and measured detection frequencies are presented in table 11. 
P-GWAVA Simulation and Groundwater Sampling Results 121

Table 12. Correlations between P-GWAVA-RZ model residuals for atrazine concentrations, deethylatrazine 

(DEA) concentrations, DEA fractions, and nitrate concentrations, 1992–2006, and average annual percentages 

of corn-and-soybean area in corn cultivation in the surrounding county during 1995–2005 for all Phase 2 sites 

where the depth to water was greater than the 3-meter assessment depth in the 10 northernmost states of the 

Corn Belt. 

[The average annual percentage of nearby corn-and-soybean area in corn cultivation was estimated using equation 50. R2:

Pearson correlation coefficient. P (R2): Probability associated with the Pearson correlation coefficient. Spearman ρ: Values that 

were statistically significant (P <0.05) and their associated probabilities (P (ρ)) are displayed in boldface. P (ρ): The probability 

associated with the Pearson correlation coefficient (R2) for the correlation between the ranks of the simulated and measured values]

Response variable

Correlations between P-GWAVA-RZ

model residuals and average annual percentage of 

nearby corn-and-soybean area in corn cultivation

Number

of sites

R2 P (R2

) Spearman ρ P (ρ)

Atrazine concentration 0.25 ≤0.0001 0.30 ≤0.0001 234

DEA concentration 0.055 0.0003 -0.29 ≤0.0001 234

DEA fraction 0.036 0.12 -0.16 0.19 67

Nitrate concentration 0.023 0.019 -0.23 0.0004 234

Influence of Corn-and-Soybean Rotations on 

Model Residuals

All of the P-GWAVA simulations assumed that corn was 

grown every year at every study site (table 14), despite the 

fact that corn is commonly grown in rotation with soybeans, 

especially in the Corn Belt. Therefore, it was anticipated that 

the extent of agreement between the solute concentrations 

(or DEA fraction) simulated in the vadose zone by the 

P-GWAVA-RZ model and those measured in groundwater may 

have been influenced by the percentage of corn-and-soybean 

area in corn cultivation. Significant correlations were observed 

between the average annual percentage of corn-and-soybean 

area on which corn was grown each year, relative to soybeans, 

between 1995 and 2005 (computed using equation 50) and 

the P-GWAVA-RZ model residuals for the concentrations 

of atrazine, DEA and nitrate at the Phase 2 sites where the 

DTW was greater than the 3-m assessment depth (fig. 61; 

table 12). No significant correlation was observed, however, 

with the model residuals for the DEA fraction. Among the 

three solutes, the strongest relation was a positive correlation 

between the percentage of corn-and-soybean area in corn 

cultivation and the model residuals for atrazine concentrations, 

with weaker, negative correlations noted for DEA and nitrate.

Because atrazine is not typically applied to soybeans, the 

total amounts of the herbicide applied to the land are expected to 

be higher in areas where corn is grown more frequently, relative 

to soybeans. Although the rates of atrazine transformation by 

soil microorganisms might be expected to be higher in areas 

with more frequent applications of the herbicide because of 

microbial adaptation (Zablotowicz and others, 2007; Krutz and 

others, 2008, 2010a, 2010b), this phenomenon was not accounted 

for in estimating the atrazine reaction rates for the P-GWAVA 

simulations. The results shown in table 12 and figure 61A

indicate that significantly higher degrees of overprediction of the 

atrazine concentrations occurred in areas with more extensive 

corn cultivation. This suggests that the atrazine concentrations 

in the groundwater in the Phase 2 study area were more strongly 

influenced by the adaptation of the soil microorganisms to 

repeated atrazine applications than by the total amounts of the 

herbicide that were applied during the simulation period. The 

significant, negative correlation between the model residuals for 

the DEA concentrations and the percentage of corn-and-soybean 

area under corn cultivation, however, would be consistent with 

either an increase in atrazine inputs or an increase in atrazine 

biotransformation rates in response to repeated applications of the 

herbicide, because both phenomena would be expected to cause 

increases in DEA production.
122 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig61ab

-3.0 

-2.0 

-1.0 

0

1.0 

2.0 

3.0 

0 10 20 30 40 50 60 70 80 90 100 

Simulated minus measured atrazine concentration, in micrograms per liter 

Average annual percentage of corn-and-soybean area in corn cultivation during 1995–2005

Root-Zone

Water-Quality

Model

(RZWQM)

overpredicts

concentrations 

RZWQM

underpredicts

concentrations 

RZWQM

overpredicts

concentrations 

RZWQM

underpredicts

concentrations 

-3.0 

-2.0 

-1.0 

0.0 

1.0 

2.0 

3.0 

0 10 20 30 40 50 60 70 80 90 100 

Simulated minus measured DEA concentration, in micrograms per liter

Average annual percentage of corn-and-soybean area in corn cultivation during 1995–2005

A. Atrazine

B. DEA

Figure 61. P-GWAVA-RZ model residuals (simulated values minus measured values) for (A) atrazine 

concentrations, (B) deethylatrazine (DEA) concentrations, (C) DEA fractions, and (D) nitrate concentrations, 

1992–2006, and average annual percentage of corn-and-soybean area in corn cultivation in the surrounding county 

during 1995–2005 (calculated using equation 50), for all Phase 2 sites where the depth to water was greater than the 

3-meter assessment depth in the 10 northernmost states of the Corn Belt.
P-GWAVA Simulation and Groundwater Sampling Results 123

tac12-0781_fig61cd

Average annual percentage of corn-and-soybean area in corn cultivation during 1995–2005

0 10 20 30 40 50 60 70 80 90 100 

Average annual percentage of corn-and-soybean area in corn cultivation during 1995–2005

C. DEA fractions

D. Nitrate

-1.0 

-0.8 

-0.6 

-0.4 

-0.2 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

0 10 20 30 40 50 60 70 80 90 100 

Simulated minus measured DEA fraction, dimensionless

-80 

-70 

-60 

-50 

-40 

-30 

-20 

-10 

0 

10 

20 

Simulated minus measured nitrate concentration, in milligrams per liter as N 

RZWQM

overpredicts

concentrations 

RZWQM

underpredicts

concentrations 

RZWQM

overpredicts

concentrations 

RZWQM

underpredicts

concentrations 

Figure 61.—Continued
124 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

In locations where oxic conditions predominate within 

the vadose zone (as is commonly the case in the permeable 

soils that are preferred for corn cultivation), most of the 

nitrate applied to the land, fixed by plants, or formed from the 

oxidation of more reduced forms of applied nitrogen (fig. 21) 

and not taken up by plants is likely to pass through to the 

water table with relatively little additional transformation. 

The annual amounts of nitrogen fertilizer applied to soybeans 

in the United States are between 11 and 24 percent of the 

amounts applied to corn (Economic Research Service, 2011). 

The significant, negative correlation between the model 

residuals for the nitrate concentrations and the amount of 

nearby corn-and-soybean area in corn cultivation (table 12; 

fig. 61D) indicates that, as anticipated, nitrate behaves 

conservatively in most of the areas examined in the Corn Belt, 

with higher amounts reaching the water table in areas where 

more nitrogen is applied. Although nitrogen fixation by the 

soybean plants represents another potential source of nitrate 

in areas where soybeans are grown (in addition to fertilizer 

applications), this negative correlation also indicates that the 

amounts of nitrate passing below the root zone under soybean 

cultivation are still substantially lower than the amounts of 

nitrate passing below the root zone under continuous corn 

cultivation. The observation of a significant correlation for 

nitrate that was opposite to that for atrazine provides further 

support for the hypothesis that microbial adaptation to 

repeated atrazine applications exerted a stronger effect on the 

amounts of atrazine reaching the water table than the total 

quantities of the herbicide being applied to the land.

Solute Detection Classifications

The graphical comparisons between the simulated and 

measured solute concentrations (or DEA fraction) displayed 

in figures 36, 48, 55, and 59 may be reformulated to convey 

the accuracy of the final sets of P-GWAVA model predictions 

in more a generalized, categorical manner by calculating the 

frequencies with which actual detections or non-detections 

of a given solute coincided with simulated detections or 

non-detections of that solute. The concentration relations 

represented by the four resulting categories of classification 

are shown in figure 62. False positives occur at sites where 

the detection of the solute was simulated by the model in 

the vadose zone but the compound was not detected in 

shallow groundwater; false negatives represent the opposite 

circumstance. True positives indicate that the detection of the 

solute was both simulated and measured, and true negatives

indicate that a non-detection was accurately simulated. 

For each solute examined, the distributions of 

true positives, true negatives, false positives, and false 

negatives resulting from the P-GWAVA simulations are 

shown in figure 63 for all of the Phase 1 and Phase 2 sites, 

regardless of water-table depth. To provide a more direct 

comparison between the predictions of the P-GWAVA-PR 

and P-GWAVA-RZ models, a separate set of results is also 

displayed for the 267 sites in the Corn Belt where both 

models were run—again, regardless of water-table depth 

(fig. 63B). The P-GWAVA-PR simulations generated more 

detections (positives) than non-detections (negatives) for 

atrazine (fig. 63A). By contrast, for the P-GWAVA-RZ 

simulations, non-detections of atrazine (negatives) were 

predicted more often than detections (positives) (fig. 63C), 

with the same pattern being observed for DEA (fig. 63D). The 

direct comparison between the atrazine results for the 267 

sites in the Corn Belt where both models were run (fig. 63B) 

reflects patterns similar to those observed separately for the 

two models, with more simulated detections of atrazine than 

non-detections observed for the P-GWAVA-PR model, and 

the opposite pattern noted for the P-GWAVA-RZ model. 

Although figure 58 indicated that the P-GWAVA-RZ model 

underpredicted nitrate concentrations more often than it 

overpredicted them, the distribution of detection classifications 

shown in figure 63E for nitrate was similar to that observed for 

atrazine during Phase 1, with simulated detections being more 

common than simulated non-detections.

Figure 63 also presents more generalized summaries 

of the accuracy of the model predictions for each solute 

by grouping the true positives and true negatives together 

as correct predictions and grouping the false positives and 

false negatives together as incorrect predictions. Viewed 

from this perspective, correct and incorrect predictions of 

atrazine detections by the P-GWAVA-PR model occurred 

with equal frequencies (50 percent) when all sites were 

considered (fig. 63A), but with a higher percentage of incorrect 

predictions (65 percent) when the analysis was restricted to the 

267 sites in the Corn Belt where simulations were carried out 

with both models (fig. 63B). By contrast, the P-GWAVA-RZ 

simulations produced correct predictions more frequently than 

incorrect predictions for all three solutes, with the highest 

rate of correct predictions for DEA (70 percent, fig. 63D), 

the lowest rate for nitrate (56 percent, fig. 63E), and a rate of 

62 percent for atrazine (fig. 63C).
P-GWAVA Simulation and Groundwater Sampling Results 125

tac12-0781_fig62

True positives

False negatives

False positives

True negatives

0.0001

0.001

0.01

0.1

1

10

0.0001 0.001 0.01 0.1 1 10

(Non-detections assigned a concentration of 0.0001 micrograms per liter) 

Simulated solute concentration, in micrograms per liter

Measured solute concentration, in micrograms per liter

Figure 62. Relations between solute detection classifications and different combinations of simulated and measured 

detections and non-detections for a hypothetical solute. For the purposes of display, simulated or measured concentrations 

below the reporting limit were assigned a value of 0.0001 microgram per liter.
126 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig63

Phase 1

Phase 2

EXPLANATION

0 

10 

20 

30 

40 

50 

60 

True

positives 

True

negatives 

False

positives 

False

negatives 

True

positives 

True

negatives 

False

positives 

False

negatives 

A. Atrazine—P-GWAVA-PR (1,224 sites, conterminous United States)

0 

10 

20 

30 

40 

50 

60 

Correct

predictions 

Incorrect

predictions 

0 

20 

40 

60 

80 

B. Atrazine—P-GWAVA-PR versus P-GWAVA-RZ (267 sites, Corn Belt)

0 

20 

40 

60 

80 

0 

10 

20 

30 

40 

50 

60 

70 

True

positives 

True

negatives 

False

positives 

False

negatives 

C. Atrazine—P-GWAVA-RZ (453 sites, Corn Belt)

0 

10 

20 

30 

40 

50 

60 

70 

Correct

predictions 

Incorrect

predictions 

Correct

predictions 

Incorrect

predictions 

0 

10 

20 

30 

40 

50 

60 

70 

80 

True

positives 

True

negatives 

False

positives 

False

negatives 

D. DEA—P-GWAVA-RZ (453 sites, Corn Belt)

0 

10 

20 

30 

40 

50 

60 

70 

80 

Correct

predictions 

Incorrect

predictions 

0 

10 

20 

30 

40 

50 

60 

True

positives 

True

negatives 

False

positives 

False

negatives 

E. Nitrate—P-GWAVA-RZ (453 sites, Corn Belt)

0 

10 

20 

30 

40 

50 

60 

Correct

predictions 

Incorrect

predictions 

Percentage of sitesPercentage of sites

Figure 63. Accuracy of solute detection classifications from the Phase 1 (P-GWAVA-PR) simulations for the conterminous United 

States, 1991–95 and the Phase 2 (P-GWAVA-RZ) simulations for the 10 northernmost states in the Corn Belt, 2000–2004. Results 

are shown, regardless of depth to water, for (A) atrazine at sites in the conterminous United States; (B) atrazine at sites in the 10 

northernmost states of the Corn Belt where simulations were done with both models; (C) atrazine at all sites in the Corn Belt where 

simulations were done with the P-GWAVA-RZ model; (D) deethylatrazine (DEA) at all sites in the Corn Belt where simulations were done 

with the P-GWAVA-RZ model; and (E) nitrate at all sites in the Corn Belt where simulations were done with the P-GWAVA-RZ model. 

Solute detections (both simulated and measured) determined using a fixed reporting limit of 0.001 microgram per liter for the Phase 1 

results, and temporally varying reporting limits (fig. 4) for the Phase 2 results.
P-GWAVA Simulation and Groundwater Sampling Results 127

Overall Frequencies of Simulated and Measured 

Solute Detection

For each of the three solutes of interest, figures 64 and 65

compare the overall detection frequencies predicted in the 

vadose zone by the final sets of P-GWAVA simulations with 

the overall frequencies of detection in shallow groundwater 

among the study sites in the conterminous United States 

(Phase 1) or in the Corn Belt (Phase 2). Whereas the atrazine 

detection frequencies computed for Phase 1 (both simulated 

and measured) were obtained using a fixed reporting limit 

(0.001 μg/L), the frequencies of detection of the three solutes 

of interest during Phase 2 (simulated and measured) are 

displayed in these figures using either temporally varying 

reporting limits (fig. 64) or uniform reporting limits (fig. 65). 

In both figures, the pattern of agreement between the 

simulated and measured detection frequencies when all the 

sites were examined (fig. 64A and fig. 65A) was similar to 

the pattern noted when only sites with DTW greater than 

the model assessment depths were included in the analyses 

(fig. 64B and fig. 65B). The detection frequencies calculated 

from the P-GWAVA-RZ simulations for atrazine (39 percent) 

and DEA (23 percent) using the temporally varying reporting 

limits (fig. 64) were consistent with the results from several 

other large-scale sampling studies in the United States that, 

like the NAWQA program (Barbash and others, 1999; Kolpin 

and others, 2000; Gilliom and others, 2006), also observed the 

frequency of DEA detection to be similar to that of atrazine in 

shallow groundwater beneath agricultural areas (Kolpin and 

others, 1993; Barbash and Resek, 1996).

The reporting limit used to compute the simulated and 

measured detection frequencies shown in figure 65 for each 

solute examined during Phase 2 was the highest value used 

for each compound (fig. 4). As a result, all the detection 

frequencies computed using these reporting limits were less 

than the corresponding values obtained with temporally 

variable reporting limits (fig. 64). The most pronounced 

illustration of this effect was for DEA, for which the simulated 

frequency of detection using the fixed reporting limit was 

zero, even when all Phase 2 sites were included in the 

calculation. Using the temporally varying reporting limits, 

the overall frequencies of DEA detection simulated by the 

P-GWAVA-RZ model were 23 percent when all sites were 

examined, and 30 percent when the sites with DTW less 

than or equal to 3 m were excluded. For nitrate, however, 

the simulated and measured overall frequencies of detection 

were in close agreement when the fixed reporting limit 

was used (fig. 65), as well as when the temporally varied 

reporting limits were used (fig. 64). The fact that the level 

of agreement between simulated and measured detection 

frequencies was substantially less affected by the approach 

used for quantifying detections for nitrate than for atrazine 

or DEA is likely because the simulated and measured nitrate 

concentrations were generally much larger, relative to their 

reporting limit, than for atrazine or DEA. This is consistent 

with the general observation that detections of environmental 

contaminants tend to be considerably more intermittent 

when their concentrations are close to their reporting limits 

than at higher concentrations (Barbash and Resek, 1996). 

Temporally varying reporting limits were used to determine 

solute detections for Phase 2 (figs. 49, 54, 60 and 64) because 

this approach retains more information than does the use of 

uniform reporting limits. 

When calculated using the temporally varying reporting 

limits (fig. 64), the overall frequencies of atrazine detection 

predicted by the P-GWAVA-RZ simulations were in closer 

agreement with those measured in shallow groundwater than 

were the concentrations predicted by the P-GWAVA-PR 

simulations. Although the substantially larger geographic 

area covered during Phase 1 may have contributed to this 

disparity, other factors may have been involved. The results 

presented in figures 50 and 63 indicate that even when the 

predictions from the two models were compared directly with 

one another in the same locations, the atrazine concentrations 

predicted by the P-GWAVA-RZ simulations were still in closer 

agreement with those measured in groundwater than were the 

concentrations predicted by the P-GWAVA-PR simulations. 

Among the three solutes examined during Phase 2, 

the overall frequencies of detection simulated by the 

P-GWAVA-RZ model for the entire study area were in 

substantially closer agreement with those measured in 

shallow groundwater for nitrate and atrazine than for DEA 

(fig. 64). The fact that the simulations overpredicted the 

overall detection frequency for atrazine, but underpredicted 

the overall frequency for DEA, was consistent with the 

patterns of agreement for the concentrations of the two 

solutes (figs. 47 and 53, respectively). The close agreement 

displayed in figure 64 between the simulated and measured 

overall frequencies of detection for nitrate in the Phase 2 

study area, however, is in marked contrast with the lack of any 

significant correlations between the simulated and measured 

concentrations (fig. 58, table 10) or network-based detection 

frequencies (fig. 60, table 11) for this solute. Applied in the 

manner used for this study, the P-GWAVA-RZ model thus 

provided a relatively accurate simulation of the presence 

or absence of nitrate in shallow groundwater, but only at 

the coarsest scale examined, that is, the entire study area. 

Significant correlations between simulated and measured 

concentrations (table 10) or detection frequencies (table 11) 

in groundwater at finer spatial scales during Phase 2 were 

observed only for atrazine and DEA.
128 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig64

A. All sites

B. Sites with depths to water greater than assessment depths

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Phase 2

(Corn belt - 453 sites)

Phase 1

(Nationwide - 1,224 sites)

Phase 2

(Corn belt - 236 sites)

Phase 1

(Nationwide - 1,125 sites)

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Sites with concentrations equal to or greater than reporting limit, in percent

Atrazine

(RL = 0.001

microgram per liter)

Atrazine DEA Nitrate

Simulated

(P-GWAVA-PR)

Simulated

(P-GWAVA-RZ)

Measured Measured Measured Simulated Measured

(P-GWAVA-RZ)

Simulated

(P-GWAVA-RZ)

Temporally varying reporting limits (fig. 4)

Figure 64. Overall frequencies of atrazine, deethylatrazine (DEA), and nitrate detection predicted in the vadose 

zone by the final set of P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) simulations and measured in shallow 

groundwater in agricultural areas of the conterminous United States (Phase 1, 1992–1998) or the 10 northernmost 

states of the Corn Belt (Phase 2, 1992–2006), using a fixed reporting limit for the Phase 1 results and temporally 

varying reporting limits for the Phase 2 results. Results are shown for (A) all sites and (B) sites where the depth 

to water was greater than the assessment depth for each model. Detections (simulated and measured) were 

determined using a fixed reporting limit (RL) of 0.001 microgram per liter for the Phase 1 results, and temporally 

varying reporting limits (fig. 4) for the Phase 2 results. Assessment depths were 1 meter for the P-GWAVA-PR 

simulations and 3 meters for the P-GWAVA-RZ simulations. Simulation approaches used for each of the two models 

are summarized in tables 1, 4, and 14.
P-GWAVA Simulation and Groundwater Sampling Results 129

tac12-0781_fig65

A. All sites

B. Sites with depths to water greater than assessment depths

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Sites with concentrations equal to or greater than reporting limit, in percent

Phase 1

(Nationwide - 1,125 sites)

Phase 2

(Corn belt - 236 sites)

Phase 1

(Nationwide - 1,224 sites)

Phase 2

(Corn belt - 453 sites)

Atrazine

(RL = 0.001 

microgram per liter)

Atrazine

(RL = 0.074 

microgram per liter)

DEA

(RL = 0.087 

microgram per liter)

Nitrate

(RL = 0.06 

milligram per liter as N)

Simulated

(P-GWAVA-PR)

Measured Measured Simulated Measured Measured

(P-GWAVA-RZ)

Simulated

(P-GWAVA-RZ)

Simulated

(P-GWAVA-RZ)

Figure 65. Overall frequencies of atrazine, deethylatrazine (DEA), and nitrate detection predicted in the vadose 

zone by the final set of P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) simulations and measured in shallow 

groundwater in agricultural areas of the conterminous United States (Phase 1, 1992–1998) or the 10 northernmost 

states of the Corn Belt (Phase 2, 1992–2006), using fixed reporting limits for the results from both phases. Results are 

shown for (A) all sites and (B) sites where the depth to water was greater than the assessment depth for each model. 

Detections (simulated and measured) were determined using fixed reporting limits (RLs) in all cases. Assessment 

depths were 1 meter for the P-GWAVA-PR simulations and 3 meters for the P-GWAVA-RZ simulations. Simulation 

approaches used for each of the two models are summarized in tables 1, 4, and 14.
130 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Geographic Patterns of Agreement Between 

Simulated and Measured Frequencies of 

Detection

The geographic distributions of P-GWAVA-RZ model 

residuals (simulated minus measured values) for the 

frequencies of detection of atrazine, DEA, and nitrate for the 

17 sampling networks with 10 or more wells in the Phase 2 

study area are shown in figures 66, 67, and 68, respectively. To 

maximize the geographic coverage of the data, and to examine 

the accuracy of the model predictions when information on 

water-table depth is not available (as is likely to be the case 

in most study locations), the results shown in these figures 

included the data from all Phase 2 sites, regardless of DTW. 

All detections, predicted and observed, were determined using 

the temporally varying reporting limits (fig. 4).

0 200 MILES

0 200 KILOMETERS

100° 90°

45°

40°

Simulated (vadose zone) minus 

measured (groundwater) frequencies 

of atrazine detection, in percent

P-GWAVA-RZ underpredicts 

 detection frequency

P-GWAVA-RZ overpredicts } detection frequency

EXPLANATION

 - 41.7 to - 0.1

0 (simulated = measured)

0.1 to 50

50.1 to 77.3

National Albers Projection, North American 

Datum of 1983 (NAD 83)

KANSAS

OHIO

MISSOURI

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

KENTUCKY

MICHIGAN

IOWA

ILLINOIS

INDIANA

Figure 66. Model residuals for the frequencies of atrazine detection (simulated minus measured values) 

calculated from the P-GWAVA-RZ (Phase 2) model simulations and sampling results for the 17 groundwater 

sampling networks with 10 or more sites in the 10 northernmost states of the Corn Belt of the United States, 

1992–2006. All sites were included regardless of water-table depth. All simulated detections accounted for 

temporal variations in reporting limits (fig. 4A).
P-GWAVA Simulation and Groundwater Sampling Results 131

Atrazine (fig. 66) and DEA (fig. 67) detection 

frequencies exhibited similar geographic patterns of overand underprediction across the Phase 2 study area, with 

most of the underpredicted frequencies of detection of either 

solute occurring in the southern part of the study area and 

central Wisconsin. The similarity in the spatial distributions 

of occurrence for atrazine and DEA is consistent with the 

fact that they share a common source. For nitrate, the most 

pronounced underpredictions of detection frequencies also 

were in central Wisconsin, with other areas of underprediction 

scattered across the southern part of the study area (fig. 68). 

For all three solutes, detection frequencies were overpredicted 

in North Dakota and Minnesota, and underpredicted in 

Nebraska and Illinois. 

0 200 MILES

0 200 KILOMETERS

100° 90°

45°

40°

KANSAS

OHIO

MISSOURI

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

KENTUCKY

MICHIGAN

IOWA

ILLINOIS

INDIANA

Simulated (vadose zone) minus 

measured (groundwater) frequencies 

of DEA detection, in percent

- 65.2 to - 50 

- 49.9 to - 0.1

0 (simulated = measured)

0.1 to 31.8

EXPLANATION

P-GWAVA-RZ underpredicts 

 detection frequency

P-GWAVA-RZ overpredicts 

 detection frequency

}

National Albers Projection, North American 

Datum of 1983 (NAD 83)

Figure 67. Model residuals for the frequencies of deethylatrazine (DEA) detection (simulated minus 

measured values) calculated from the P-GWAVA-RZ (Phase 2) model simulations and sampling results 

for the 17 groundwater sampling networks with 10 or more sites in the 10 northernmost states of the Corn 

Belt of the United States, 1992–2006. All sites were included regardless of water-table depth. All simulated 

detections accounted for temporal variations in reporting limits (fig. 4B).
132 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_fig68

0 200 MILES

0 200 KILOMETERS

100° 90°

45°

40°

National Albers Projection, North American 

Datum of 1983 (NAD 83)

KANSAS

OHIO

MISSOURI

MINNESOTA

NEBRASKA

SOUTH DAKOTA

WISCONSIN

NORTH DAKOTA

MICHIGAN

KENTUCKY

MICHIGAN

IOWA

ILLINOIS

INDIANA

Simulated (vadose zone) minus 

measured (groundwater) frequencies 

of nitrate detection, in percent

- 100 to - 50

- 49.9 to - 0.1

0 (simulated = measured)

0 to 50

50.1 to 54.5

EXPLANATION

P-GWAVA-RZ underpredicts 

 detection frequency

P-GWAVA-RZ overpredicts 

 detection frequency

}

}

Figure 68. Model residuals for the frequencies of nitrate detection (simulated minus measured values) 

calculated from the P-GWAVA-RZ (Phase 2) model simulations and sampling results for the 17 groundwater 

sampling networks with 10 or more sites in the 10 northernmost states of the Corn Belt of the United States, 

1992–2006. All sites were included regardless of water-table depth. All simulated detections accounted for 

temporal variations in reporting limits (fig. 4C).
P-GWAVA Simulation and Groundwater Sampling Results 133

Statistical Correlations with Site Characteristics

To identify factors that may have contributed to the 

disparities between the simulated and the measured detection 

frequencies for atrazine, DEA, and nitrate during Phase 2, 

a series of statistical comparisons was made between the 

P-GWAVA-RZ model residuals for the solute detection 

frequencies and network-averaged values of a variety of 

explanatory parameters (table 13). This analysis was similar 

to the one conducted for Phase 1 (table 8), but used different 

sources of data for the explanatory variables. Most of the 

comparisons involving soil-related parameters used depthand component-weighted averages (fig. 7) computed for 

two depth intervals: the entire 3-m assessment depth and 

the uppermost 50 cm. However, for all of the parameters 

examined in this manner, the statistically significant relations 

observed using the data computed for the uppermost 50 cm 

of the soil column always exhibited higher Pearson R2

 values 

than those observed when data for all 3 m were used (data not 

shown). This is consistent with the expectation that processes 

and site characteristics close to the land surface tend to exert 

a predominant level of control over the transport and fate of 

surface-derived solutes in the vadose zone. Consequently, 

for the comparisons involving these variables, this analysis 

focused only on the results obtained using the parameter 

values computed for the uppermost 50 cm of the soil column. 

The results shown in table 13 reveal statistically 

significant relations with several of the explanatory variables 

for atrazine and DEA (P < 0.05; Pearson and Spearman 

[rank] correlations), but none for nitrate. Consistent with 

the similarity between the geographic distributions of their 

model residuals (figs. 66 and 67), the patterns of correlation 

for atrazine and DEA were also similar. However, when 

significant correlations with the same explanatory parameter 

were noted for both solutes, the strength of the relation was 

usually greater for atrazine than for DEA. The two exceptions 

to this pattern were the correlations with the areally averaged 

intensities of atrazine use and the percentage of sites receiving 

annual precipitation of less than 60 cm (“dry sites”). 

Model residuals computed for the frequencies of 

atrazine detection from the P-GWAVA-RZ simulations were 

not significantly correlated with the intensity of atrazine 

use (table 13). As with the results from the P-GWAVA-PR 

simulations (table 8), this observation indicates that the 

intensity of atrazine use was less important than other 

site factors in controlling the concentrations (and thus the 

frequencies of detection) of atrazine in the subsurface. 

However, the significant, negative correlation between 

the model residuals for the frequencies of DEA detection 

and atrazine use intensity (as well as the negative, albeit 

non-significant Spearman ρ value for atrazine) may have 

been caused by the use of the spatially uniform intensity of 

atrazine application for the Phase 2 simulations. All other 

factors remaining constant, if the simulations at all of the 

sites assumed a spatially uniform intensity of use, areas 

where the intensity of atrazine use was, in fact, higher than in 

other locations might have higher frequencies of detection of 

DEA and atrazine in groundwater, resulting in smaller model 

residuals for both compounds. However, because the atrazine 

use intensity was not correlated with the model residuals for 

atrazine detection frequencies during either study phase, and 

only weakly correlated with those for DEA during Phase 2, the 

spatial patterns of atrazine use appeared to have been largely 

accounted for by the model simulations.

The statistical results summarized in table 13 indicate that 

factors controlling the rates of water movement through the 

subsurface tended to exert a greater influence over the extent 

to which the model overpredicted the observed frequencies 

of atrazine or DEA detection in shallow groundwater than 

factors related to climate. For both atrazine and DEA, the 

degree of overprediction was negatively correlated with 

available water capacity (AWC), clay content, and silt content, 

and positively correlated with sand content (expressed either 

as the weight percent of sand present, or as the percentage 

of sites containing more than 95 percent sand in one or 

more layers) and vertical permeability. Thus, whereas the 

P-GWAVA-PR simulations underpredicted the effects of 

hydraulic conductivity on atrazine movement through the soil 

(table 8), the P-GWAVA-RZ simulations tended to exaggerate 

the effects of soil properties (AWC, texture, and permeability) 

on the rates of water flow. No significant correlations with 

either bulk density or soil OM were noted for atrazine or DEA, 

which may reflect the less direct connection between these soil 

properties and hydraulic conductivity, relative to the other soil 

parameters examined. 

No significant correlation was noted between the 

percentage of sites receiving 60 cm or less of precipitation 

per year and the model residuals for atrazine detection 

frequencies, and only a moderate positive correlation 

was noted between these variables for DEA (table 13). 

Additionally, no significant correlations were observed 

between the average air temperature in May and the model 

residuals for either the atrazine or DEA detection frequencies. 

As with the results for the parameters related to soil 

permeability, these Phase 2 results for atrazine contrast with 

the pattern of correlation observed for the P-GWAVA-PR 

simulations (table 8). The P-GWAVA-RZ simulations appear 

to have accounted for the effects of spatial variations in 

precipitation and temperature on the likelihood of detecting 

atrazine in shallow groundwater more accurately than did 

the P-GWAVA-PR simulations. The contrast in the relations 

with temperature for the two study phases is consistent with 

the hypothesis that the activation energy used to estimate 

atrazine transformation rates for Phase 1 was too low, and that 

the higher value used for Phase 2 (table 14) may have been 

more appropriate. 
134 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Table 13. Correlations between model residuals for the detection frequencies of atrazine, deethylatrazine, and nitrate in shallow 

groundwater simulated by the P-GWAVA-RZ (Phase 2) model and selected site characteristics in the 10 northernmost states of the Corn 

Belt, 1992–2006. 

[Geographic locations of the groundwater networks are shown in figures 66–68. Data were computed for all sites in groundwater networks with 10 or more 

wells for which the required ancillary data were available, regardless of water-table depth. The P-GWAVA-RZ simulations used irrigation and a spatially 

uniform intensity of atrazine application (2 kg a.i./ha) at all sites (table 4). Detections—either simulated or measured—were determined using reporting limits 

that varied over time for atrazine, deethylatrazine (DEA), and nitrate (fig. 4). Areally averaged atrazine use intensities were computed using equation 10. All 

data related to soil properties were derived from the SSURGO database (Natural Resources Conservation Service, 2011a); precipitation and air temperature 

data were obtained from Cligen (Agricultural Research Service, 2009). Results from statistically significant correlations (P <0.05) are shown in boldface. R2:

Pearson correlation coefficient. P (R2): Probability associated with the Pearson correlation coefficient. Spearman ρ: Values that were statistically significant (P 

<0.05) and their associated probabilities (P (ρ)) are displayed in boldface. P (ρ): The probability associated with the Pearson correlation coefficient (R2) for the 

correlation between the ranks of the simulated and measured values. Number of networks/sites: Some of the ancillary data were not available for some sites. 

Abbreviations: cm, centimeter; °C, degrees Celsius; dwcw, computed to the specified depth for each site as a depth- and component-weighted average value 

for the SSURGO map unit in which it was located (fig. 7) and obtained as a network-averaged value among all of the sites for which the ancillary data were 

available in each network; g/cm3, gram per cubic centimeter; in/hr, inch per hour; kg a.i./ha, kilograms of active ingredient per hectare]

Solute Site feature 

Correlations between

model residuals and site feature Number of 

networks

Number of 

sites

R2 P (R2

) Spearman ρ P (ρ)

Atrazine Atrazine use intensity, 1997, areally averaged (kg a.i./ha) 0.098 0.22 -0.44 0.078 17 402

Available water capacity–uppermost 50 cm (dwcw), 

volume percent

0.52 0.0037 -0.69 0.0070 14 321

Bulk density–uppermost 50 cm (dwcw), g/cm3 0 0.95 0.040 0.84 14 321

Soil organic matter content–uppermost 50 cm (dwcw), 

weight percent

0 0.95 0.19 0.53 14 321

Clay content–uppermost 50 cm (dwcw), weight percent 0.41 0.014 -0.58 0.029 14 321

Silt content–uppermost 50 cm (dwcw), weight percent 0.55 0.0023 -0.78 0.0010 14 321

Sand content–uppermost 50 cm (dwcw), weight percent 0.53 0.0032 0.67 0.0094 14 321

Sandy sites1

 (percent) 0.35 0.012 0.58 0.015 17 406

Dry sites2

 (percent) 0.20 0.076 0.48 0.053 17 406

Vertical permeability–uppermost 50 cm (dwcw), in/hr 0.51 0.0041 0.61 0.020 14 321

Average air temperature in May (TMay), °C 0.071 0.36 -0.32 0.26 14 314

DEA Atrazine use intensity, 1997, areally averaged (kg a.i./ha) 0.26 0.039 -0.48 0.051 17 402

Available water capacity–uppermost 50 cm (dwcw), 

volume percent

0.42 0.013 -0.68 0.0090 14 321

Bulk density–uppermost 50 cm (dwcw), g/cm3 0.008 0.76 0.21 0.47 14 321

Soil organic matter content–uppermost 50 cm (dwcw), 

weight percent

0.009 0.75 0.022 0.97 14 321

Clay content–uppermost 50 cm (dwcw), weight percent 0.28 0.050 -0.55 0.041 14 321

Silt content–uppermost 50 cm (dwcw), weight percent 0.41 0.014 -0.64 0.019 14 321

Sand content–uppermost 50 cm (dwcw), weight percent 0.38 0.018 0.60 0.027 14 321

Sandy sites1

 (percent) 0.34 0.015 0.50 0.041 17 406

Dry sites2

 (percent) 0.28 0.030 0.56 0.020 17 406

Vertical permeability–uppermost 50 cm (dwcw), in/hr 0.39 0.018 0.43 0.12 14 321

Average air temperature in May (TMay), °C 0.24 0.075 -0.47 0.088 14 314

Nitrate Available water capacity–uppermost 50 cm (dwcw), 

volume percent

0.002 0.88 -0.16 0.56 14 321

Bulk density–uppermost 50 cm (dwcw), g/cm3 0.06 0.40 -0.19 0.51 14 321

Soil organic matter content–uppermost 50 cm (dwcw), 

weight percent

0 0.99 -0.13 0.71 14 321

Clay content–uppermost 50 cm (dwcw), weight percent 0.005 0.81 -0.32 0.25 14 321

Silt content–uppermost 50 cm (dwcw), weight percent 0.008 0.77 -0.13 0.60 14 321

Sand content–uppermost 50 cm (dwcw), weight percent 0.007 0.78 0.19 0.48 14 321

Sandy sites1

 (percent) 0.017 0.62 0.11 0.67 17 406

Dry sites2

 (percent) 0.056 0.36 0.32 0.21 17 406

Vertical permeability–uppermost 50 cm (dwcw), in/hr 0.017 0.66 0.22 0.43 14 321

Average air temperature in May (TMay), °C 0.009 0.75 0.11 0.71 14 314

1

Percentage of sites in each network with at least one horizon containing more than 95 percent sand.

2

Percentage of sites in each network receiving less than 60 centimeters of precipitation per year.
Summary and Conclusions 135

Suggestions for Future Work

Because the RZWQM model is sufficiently 

comprehensive and flexible to simulate most of the 

physical, hydrologic, chemical, and biological processes 

and agricultural management practices known to affect the 

transport and fate of water and solutes in the subsurface 

(especially preferential transport), future work with the 

P-GWAVA system will focus on P-GWAVA-RZ, rather than 

P-GWAVA-PR. The findings from this investigation, however, 

have revealed several ways that future efforts might improve 

and expand on the current version of P-GWAVA-RZ. These 

include the following:

1. Using simulated weather data from the Cligen station 

closest to each site, even if the station is in an adjacent 

Cligen grid cell;

2. Using measured, rather than simulated historical data 

for all weather parameters (if available on a nationwide 

basis), especially those related to the intensities and 

timing of storms;

3. Incorporating nationally available data on management 

practices and cropping patterns, to account for the 

influence of geographic variations in tillage (Baker, 

2011), irrigation (National Agricultural Statistics 

Service, 2010a), artificial drainage (Jaynes and James, 

2007), crop rotations (Kipka and others, 2013) and other 

agricultural activities on the transport and fate of water 

and agrichemicals in the subsurface;

4. Automating the procedure for adjusting the RZWQM 

grid-cell thicknesses to align their boundaries with the 

boundaries between the soil horizons specified by the 

SSURGO data;

5. Modifying RZWQM to simulate agrichemical transport 

and fate under inundated agriculture, especially for 

growing rice;

6. Calibrating the crop simulation parameters in RZWQM 

using county-scale crop yields across the United States 

(National Agricultural Statistics Service, 2011a);

7. Explicitly accounting for the applications of both 

manure and fertilizer as potential sources of nitrogen in 

agricultural areas across the Nation;

8. Using a degree-day approach (Jackson, 2003) to account 

for the effects of diurnal temperature variations on solute 

transformation rates, water-air partitioning and other 

temperature-sensitive processes;

9. Incorporating an option in RZWQM for simulating 

water and solute movement through water films along 

macropore walls (Nimmo, 2012), rather than requiring 

that macropores be filled before movement can occur;

10. Activating the non-linear sorption module in RZWQM 

(Ahuja and others, 2000);

11. Modifying the Rosetta database (Schaap and others, 

2001) to provide Brooks-Corey parameter values that are 

consistent with their known ranges for all soil textures;

12. Developing a series of compound-specific variants of the 

modified Fenner-Borsuk relation to predict the reaction 

rates of other contaminants and transformation products 

from soil properties and other site-specific parameters, 

using data from previous laboratory studies;

13. Using P-GWAVA-RZ to simulate the transport and fate 

of water and solutes to the water table—as demonstrated 

with RZWQM2 on a local scale by Nolan and others 

(2010)—throughout the conterminous United States 

using nationwide depth-to-water estimates such as those 

provided by Fan and others (2007, 2013); and

14. Developing a module for estimating the values of 

selected input parameters, subject to the constraints 

imposed by the range of values in the published 

literature, using inverse modeling and other techniques 

used by Nolan and others (2010).

Future applications of the P-GWAVA-RZ model might 

include the development of metamodels to generate largescale maps of simulated concentrations of selected compounds 

with the methods used by Nolan and others (2012) for nitrate 

in the Corn Belt. Other work might involve manipulation of 

the appropriate P-GWAVA-RZ input parameters to simulate 

the concentrations of surface-derived contaminants in the 

vadose zone beneath non-agricultural land-use settings 

across the United States, including lawns, gardens, golf 

courses, roads, railroads, utility rights-of-way, septic-system 

drainfields, herbicide-treated forestlands, and areas irrigated 

with chlorinated water.

Summary and Conclusions

Numerous studies over the past three decades have used 

a variety of approaches to predict the likelihood of detecting 

surface-derived contaminants in groundwater. However, in 

many cases, these predictions have not been compared with 

measured contaminant concentrations in groundwater in 

the locations of interest—especially at regional to national 

scales. Additionally, most methods introduced by these 

earlier studies have been predominantly empirical (that is, 

based on regressions, neural networks, or other statistical 

relations) or dependent upon the use of semi-arbitrary 

index or scoring systems. Relatively few studies have used 

established, quantitative representations of the physical, 

hydrologic, chemical, and biological processes known to 

control chemical transport and fate in the subsurface to 
136 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

simulate the concentrations of surface-derived contaminants 

in groundwater. As a result, most of the existing approaches 

are either limited to the specific areas, conditions, and 

contaminants for which they were derived (for the methods 

that are based on statistical correlations and [or] the measured 

concentrations of indicator solutes), or lack an up-todate underpinning of quantitative process understanding 

(for the methods that depend on simple overlay, index, or 

scoring methods).

To address these limitations, a process-based 

groundwater vulnerability assessment (P-GWAVA) system 

was constructed and tested that uses numerical simulations of 

a broad range of well-known physical, hydrologic, chemical, 

and biological processes and agricultural management 

practices to predict the concentrations of surface-derived 

agrichemicals in the vadose zone. To ensure consistency 

among the predictions for locations across the conterminous 

United States, the P-GWAVA simulations drew exclusively 

on national-scale sources of model input data at the finest 

spatial resolution available for each parameter. The study 

also made extensive use of published techniques to estimate 

a variety of model input parameters from basic soil property 

data at each site. These input parameters include surface 

albedo, surface crust permeability, soil water retention 

parameters, Brooks-Corey parameters, saturated hydraulic 

conductivity, macroporosity and the sizes of microbial 

populations, as well as solute partition coefficients, reaction 

rates, and meso-micropore diffusion rates. To assess the 

accuracy of the model predictions, the simulations were done 

in agricultural areas across the United States where shallow 

groundwater was sampled for the target compounds by the 

U.S. Geological Survey, and the simulated concentrations or 

detection frequencies compared with the values measured 

in groundwater. 

This study was done in two phases. During Phase 1, a 

single-porosity, tipping-bucket model, the Pesticide Root-Zone 

Model (PRZM), was used in the P-GWAVA system (thereby 

designated as P-GWAVA-PR) to simulate concentrations 

of atrazine at an assessment depth of 1 meter in the vadose 

zone beneath 1,224 agricultural sites across the United 

States. For Phase 2, PRZM was replaced with the Root-Zone 

Water-Quality Model (RZWQM), a dual-porosity model that 

simulates the transport and fate of solutes in macropores, as 

well as in the soil matrix, within the vadose zone. Through 

its use of RZWQM during Phase 2, the P-GWAVA system 

(designated as P-GWAVA-RZ) simulated the preferential 

transport of water and solutes in the subsurface, a phenomenon 

that has been known for more than three decades to exert 

substantial effects on solute transport through porous media in 

many hydrogeologic settings. The P-GWAVA-RZ model was 

used to simulate the concentrations of atrazine, deethylatrazine 

(DEA, an atrazine transformation product), and nitrate at 

an assessment depth of 3 meters in the vadose zone beneath 

453 agricultural sites in the 10 northernmost states of the 

United States Corn Belt.

This study built directly upon the results from a 

previous investigation that assessed the accuracy with which 

simulations done without calibration using seven existing 

solute transport-and-fate models—including PRZM and 

RZWQM—predicted moisture contents, matric potentials, 

and the concentrations of surface-derived contaminants in 

the subsurface beneath two agricultural sites. To facilitate 

their execution at large numbers of sites, the P-GWAVA-PR 

and P-GWAVA-RZ simulations for the present study were 

also conducted without calibration. However, several global 

adjustments to the modeling approach were implemented to 

improve the fit between the solute concentrations simulated in 

the vadose zone and the concentrations measured in shallow 

groundwater. These adjustments consisted of (1) increasing the 

size of the P-GWAVA-PR model grid cells to 30 centimeters 

for the depth interval between 10 and 100 centimeters; 

(2) reducing the Koc values for atrazine and DEA during 

Phase 2 while remaining consistent with the ranges of the 

published values; (3) varying the amounts of organic matter 

that are metabolized by soil microorganisms at rapid and 

intermediate rates during Phase 2; and (4) selecting specific 

approaches from among multiple options for simulating 

material inputs and processes during both study phases. 

The last of these adjustments involved comparing simulated 

and measured contaminant concentrations (or frequencies 

of detection) to determine the most appropriate approaches 

for specifying (1) the spatial heterogeneity of irrigation and 

atrazine applications (for both study phases); (2) whether 

atrazine transformation in the soil took place in the dissolved 

state, the sorbed state, both or neither (Phase 1); and (3) the 

methods used to estimate values for selected soil hydraulic 

parameters (both study phases) and solute reaction rates 

(Phase 2). No adjustments were made to any input parameters 

at any of the individual sites, however, in obeisance to the 

study objective of evaluating the utility of the P-GWAVA-PR 

and P-GWAVA-RZ models for regional- to national-scale 

groundwater vulnerability assessments, for which such sitebased adjustments would be impractical.

The P-GWAVA-PR and the P-GWAVA-RZ simulations 

produced chemographs (that is, time series of solute 

concentrations) that were qualitatively similar to those 

reported by previous field and laboratory studies. This 

indicates that solute dispersion in the subsurface can be 

realistically mimicked using mathematical representations 

of partial piston displacement, molecular diffusion, sorption, 

and the exchange of water and solutes between macropores 

and the soil matrix (the methods used by RZWQM), or by 

invoking the more empirical approach of using numerical 

dispersion to simulate hydrodynamic dispersion (the method 

used with PRZM). 

For many of the study sites, the average depth to water 

(DTW) was shallower than the model assessment depths, 

which were the depths in the vadose zone where the model 

simulations estimated water fluxes and solute concentrations 

(1 meter for Phase 1; 3 meters for Phase 2). In most cases 

where statistically significant correlations were observed 
Summary and Conclusions 137

(P less than [<] 0.05) between simulated and measured 

solute concentrations (or detection frequencies), or the 

level of agreement was significantly correlated with another 

explanatory variable, the strength of the correlation was 

greater among the sites with DTW greater than the assessment 

depth than for the sites where the water table was shallower 

than the assessment depth. In many such cases, however, 

the strength of the correlation observed when all sites were 

included in the analysis was similar to that observed when 

sites with DTW shallower than the assessment depth were 

excluded. This suggests that the value of the P-GWAVA model 

simulation results in most locations may not be substantially 

compromised by a lack of knowledge of water-table depth. 

The levels of correlation between the P-GWAVA 

simulation results and the field measurements, or between 

the model residuals (that is, simulated minus measured 

values) and other site-based explanatory variables, differed 

between the two study phases, and among different output 

parameters (recharge rates, solute concentrations, and 

solute detection frequencies). Significant correlations were 

noted between the simulated recharge rates and the baseflow index (an estimate of groundwater recharge that is 

calculated from streamflow during base-flow periods), both 

for the P-GWAVA-PR simulations (Spearman ρ = 0.30; 

(P[ρ] less than or equal to [≤] 0.0001); 676 sites) and the 

P-GWAVA-RZ simulations (Spearman ρ = 0.30; P[ρ] < 0.001; 

447 sites). Significant correlations were also noted between 

the simulated and measured concentrations of atrazine for 

both the P-GWAVA-PR (Spearman ρ = 0.14; P[ρ] ≤ 0.0001; 

1,224 sites) and the P-GWAVA-RZ simulations (Spearman 

ρ = 0.25; P[ρ] ≤ 0.0001; 453 sites). Somewhat stronger 

correlations were observed between the DEA concentrations 

predicted by the P-GWAVA-RZ simulations and the measured 

values (Spearman ρ = 0.41; P[ρ] ≤ 0.0001; 453 sites). No 

statistically significant correlations were observed, however, 

between the P-GWAVA-RZ model simulations and the 

measured values for the nitrate concentrations or the DEA 

fractions (P[ρ] > 0.05; Spearman rank correlations). (The 

DEA fraction represents the molar percentage of atrazinederived compounds, including atrazine itself, which is present 

in the form of DEA.) Agreement between the simulated and 

measured atrazine concentrations was relatively close for both 

models, with 95th percentile values for the model residuals 

of 0.39 microgram per liter (μg/L) for the P-GWAVA-PR 

simulations (1,224 sites) and 0.87 μg/L for the P-GWAVA-RZ 

simulations (453 sites). These values were an order of 

magnitude less than the Maximum Contaminant Level of 

3 μg/L established by the U.S. Environmental Protection 

Agency for atrazine. The DEA concentrations simulated by the 

P-GWAVA-RZ model in the Corn Belt also were in relatively 

close agreement with the measured values, exhibiting model 

residuals with a 95th percentile value of 0.003 μg/L among the 

453 sites examined. For reasons that remain unclear, however, 

the atrazine concentration residuals for the two models were 

negatively correlated with one another among the 136 sites in 

the Corn Belt where simulations were done during both study 

phases and where the DTW was greater than the larger of the 

two assessment depths (Spearman ρ = -0.17; P[ρ] = 0.04). This 

indicates that different factors may have been responsible for 

the errors associated with the two models. 

The concentrations of atrazine and DEA predicted by the 

P-GWAVA-RZ simulations were combined to examine their 

level of agreement with the predictions of a Tobit regression 

model. The Tobit regression model was created by an earlier 

study to predict the sum of atrazine and DEA concentrations 

in shallow groundwater beneath agricultural areas of the 

conterminous United States. The sum of the atrazine and DEA 

concentrations predicted by the P-GWAVA-RZ simulations at 

329 Phase 2 sites was significantly correlated with the values 

predicted at the same sites by the Tobit regression model 

(Spearman ρ = 0.37; P[ρ] ≤ 0.0001).

Statistically significant correlations between simulated 

and measured frequencies of detection (for the 12 sampling 

networks with 10 or more wells exhibiting average DTW 

greater than the assessment depths) were noted only 

during Phase 2 of this study, and only for atrazine (Spearman 

ρ = 0.66; P[ρ] = 0.020) and DEA (Spearman ρ = 0.78; 

P[ρ] = 0.0026). No significant correlations were noted 

for such networks for atrazine during Phase 1 or nitrate 

during Phase 2. As noted for the solute concentrations, the 

correlations involving the detection frequencies for Phase 2 

were stronger when the sites with DTW equal to or shallower 

than 3 meters were excluded from the analysis. 

At the broadest spatial scale, the overall frequency of 

atrazine detection derived from the P-GWAVA-RZ simulations 

for all of the sites in the Phase 2 study area (computed using 

the same temporally varying reporting limits that were applied 

to the measured concentration values) matched the measured 

detection frequency more closely than the atrazine detection 

frequency computed from the P-GWAVA-PR simulations 

matched the measured value for the conterminous United 

States during Phase 1 (using a fixed reporting limit). This 

was the case regardless of whether or not sites with average 

DTW shallower than the assessment depths were included 

in the analysis. The P-GWAVA-PR simulations generated 

correct predictions with regard to atrazine detections (true 

positives plus true negatives) at 50 percent of the Phase 1 sites 

examined throughout the conterminous United States, and the 

P-GWAVA-RZ simulations produced correct predictions for 

atrazine at 62 percent of the Phase 2 sites. These disparities 

between the two study phases with respect to the levels 

of agreement between simulated and measured values for 

the atrazine results may have been a consequence, in part, 

of the fact that the P-GWAVA-PR simulations were used 

to predict the concentrations of the herbicide over a much 

larger geographic area—and thus a wider range of climatic, 

hydrogeologic, and land-use settings—than was the case for 

the P-GWAVA-RZ simulations. However, direct comparisons 

between the predictions at the 136 sites in the Corn Belt 

where both models were run, and the DTW was greater 

than 3 meters, indicated that the atrazine concentrations 

simulated by the P-GWAVA-RZ model agreed more closely 
138 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

with those measured in groundwater than was the case for the 

P-GWAVA-PR simulations in the same locations. Among all of 

the sites examined during Phase 2, the simulated and measured 

overall frequencies of detection showed substantially closer 

agreement with one another for atrazine and nitrate than 

for DEA. The solute exhibiting the highest rate of correct 

predictions during the two study phases, however, was DEA, 

for which the P-GWAVA-RZ simulations predicted a correct 

result (either a true positive or a true negative) at 70 percent of 

the Phase 2 sites. 

During Phase 2 of this study, the simulated solute 

concentrations were adjusted to account for temporal 

variations in analytical recoveries, and the temporally varying 

reporting limits used for the chemical analyses were also used 

to designate simulated concentrations as either detections 

or non-detections. Because this approach has not been 

commonly implemented by previous investigations, the levels 

of agreement between the simulated and measured detection 

frequencies for atrazine, DEA, and nitrate during Phase 2 

were also evaluated using the more conventional approach of 

applying uniform reporting limits to identify detections (both 

simulated and measured). To include all sites in the analysis, 

the highest reporting limit for all of the samples analyzed for 

Phase 2 was used for each solute. As expected, the simulated 

and measured frequencies of detection for all three solutes 

were lower using the fixed reporting limits than when the 

temporally varying reporting limits were used. Use of the 

fixed reporting limits, however, also led to poorer agreement 

between the simulated and measured overall frequencies 

of detection of atrazine and DEA than did the use of the 

temporally varying reporting limits. For nitrate, however, 

the simulated and measured overall frequencies of detection 

were in close agreement when using both the fixed and the 

temporally varying reporting limits. The similarity of the 

results for the temporally varying and fixed reporting limits for 

nitrate likely was because the simulated and measured nitrate 

concentrations generally were much larger, relative to their 

reporting limit, than was the case for either atrazine or DEA.

In addition to assessing the levels of agreement between 

the simulated and measured patterns of solute detection, the 

extent to which the observed disparities between the simulated 

and measured concentrations or detection frequencies were 

related to various system characteristics and assessment 

approaches was also examined. Comparisons among the 

results for six sites in Wisconsin indicated that when the 

most detailed soil property data available (obtained from the 

Soil Survey Geographic database, or SSURGO) were used, 

the atrazine concentrations simulated by the P-GWAVA-PR 

model were in substantially closer agreement with the 

atrazine concentrations measured in groundwater than 

when the soil property data from the State Soil Geographic 

database (STATSGO, which represent spatial aggregations 

of the SSURGO data) were used. This lent support to the use 

of the soil property data from SSURGO, rather than those 

from STATSGO, for the Phase 2 simulations. These results 

also indicated that the use of the soils data from SSURGO, 

rather than the STATSGO data, may have been one reason 

the atrazine concentrations simulated by the P-GWAVA-RZ 

model were in closer agreement with those observed in 

shallow groundwater than the concentrations simulated by the 

P-GWAVA-PR model.

Statistically significant correlations noted between the 

DTW and model residuals for atrazine, DEA, and nitrate 

concentrations during Phase 2 were consistent with anticipated 

relations between the thickness of the vadose zone and the 

progress of aerobic reactions in the subsurface. A positive 

relation with DTW for atrazine and a negative relation for 

DEA were in agreement with observations from previous 

research indicating that the extent of conversion of atrazine 

to DEA in the subsurface—an aerobic process—tends to be 

higher in areas with thicker vadose zones. Similarly, a negative 

relation for nitrate indicated that the formation of nitrate from 

the oxidation of the more reduced forms of nitrogen that are 

commonly applied as fertilizer—also an aerobic process—is 

likely to occur to a greater extent in locations with a deep 

water table than in locations with a shallower water table.

Given the absence of detailed, national-scale data on 

cropping patterns at the time of this study, all of the model 

simulations assumed that corn was the only crop grown, and 

that atrazine and nitrogen fertilizer were applied, during every 

year of each simulation. Model residuals for the concentrations 

of all three solutes examined during Phase 2, however, 

were significantly correlated with the average percentage of 

nearby corn-and-soybean area in corn cultivation (relative to 

soybeans, the crop grown most commonly in rotation with 

corn in the Corn Belt) during the simulation period. Because 

atrazine is not applied to soybeans, the positive correlation 

with the percentage of corn observed for atrazine and the 

negative correlation for DEA provide evidence (consistent 

with previous studies) suggesting that the rates at which soil 

microorganisms convert atrazine to DEA may increase with 

repeated applications of the herbicide—a well-documented 

process known as microbial adaptation. These results indicate 

that in areas where atrazine is applied repeatedly over 

several years (as is common where corn is grown), microbial 

adaptation may exert more substantial control over the atrazine 

concentrations in the subsurface than the total amounts of the 

herbicide applied over time. 

A significant negative correlation between the model 

residuals for nitrate concentrations and the percentage of 

corn grown nearby was consistent with expectation, given 

that nitrogen fertilizers are applied more heavily to corn 

than to soybeans, and that nitrate behaves as an essentially 

conservative solute as it migrates through the well-aerated 

soils in which corn is commonly grown. This correlation 

also indicated that the amounts of nitrate added to the soil by 

nitrogen fixation in soybean plants are substantially smaller 

than the additional quantities of nitrogen fertilizer that are 

typically used to grow corn. The contrast in the directions 

of correlation for nitrate and atrazine also provided further 
Acknowledgments 139

support for the hypothesis that microbial adaptation to 

repeated atrazine applications may be more important than the 

total amounts of atrazine applied in determining the subsurface 

concentrations of the herbicide beneath areas of intensive 

corn cultivation. 

The model residuals for the solute detection frequencies 

exhibited geographic variations that provided clues regarding 

the influence of soil and climatic factors on the levels of 

agreement between the simulation results and the observed 

occurrence of the solutes in shallow groundwater. To test 

hypotheses regarding the potential reasons for these variations, 

correlations were examined between the model residuals for 

the frequencies of detection of the three solutes of interest 

and various parameters associated with soil properties, 

climate and, for atrazine and DEA, the intensity of atrazine 

use. Although no statistically significant relations with 

any of the explanatory variables were observed for nitrate, 

several relations examined for atrazine and DEA were 

significant. The nature of these correlations indicated that 

the P-GWAVA-PR simulations tended to underemphasize 

the effect of hydraulic conductivity on atrazine transport to 

the water table (as reflected in relations to clay content and 

vertical permeability), but exaggerate the effect of recharge (as 

reflected in relations to runoff and annual precipitation). By 

contrast, the P-GWAVA-RZ simulations tended to exaggerate 

the effects of soil properties (available water content and 

vertical permeability, as well as clay, silt, and sand content) on 

the transport of atrazine and DEA to the water table. Whereas 

there was a statistically significant, positive correlation of 

the mean annual air temperature with the model residuals 

for atrazine detection frequencies from the P-GWAVA-PR 

simulations, no significant correlations were noted between 

the average air temperature in May and the model residuals for 

atrazine or DEA detection frequencies from the P-GWAVA-RZ 

simulations. These results suggest that the activation energy 

value used for atrazine transformation during Phase 1 may 

have been inappropriately low, but that the higher value used 

for atrazine and DEA during Phase 2 adequately accounted 

for the effect of temperature on the rates of disappearance 

of both solutes. Together, these disparities between the two 

models regarding the manner in which the accuracy of their 

predictions was correlated with different soil- and climaterelated parameters may help explain why the predictions from 

the two models were in such poor agreement. 

Correlations also were examined between the areally 

averaged intensities of atrazine use and the model residuals 

for the frequencies of atrazine and DEA detection. The model 

residuals were not significantly correlated with the intensity 

of atrazine use for atrazine during either study phase, and 

only weakly correlated for DEA. These patterns of correlation 

indicate that the intensity of atrazine use may be less important 

in controlling the concentrations of atrazine (and by extension, 

DEA) in the subsurface than other site factors, especially 

factors that control the rates of water flow. 

This study has demonstrated the feasibility of 

constructing and testing a modeling system that uses 

current understanding of processes and factors controlling 

the transport and fate of surface-derived contaminants in 

the subsurface, in conjunction with nationwide sources 

of ancillary data, to simulate the concentrations of these 

compounds in the vadose zone anywhere in the conterminous 

United States. This system was shown to simulate 

concentrations and frequencies of detection of atrazine and 

DEA in the vadose zone that are significantly correlated with 

those measured in shallow groundwater over large geographic 

areas. Several large-scale sampling studies have reported 

frequencies of atrazine and DEA detection in groundwater in 

the United States that are similar to one another over regional 

to national scales. The results from this study represent the 

first time that process-based simulations have reproduced this 

pattern on a regional scale. 

Acknowledgments

The authors wish to thank the many colleagues who 

provided invaluable assistance during this study. This report 

represents the culmination of a long series of discussions 

about groundwater vulnerability with Bernard T. Nolan 

(U.S. Geological Survey [USGS], Reston, Virginia) that 

began more than a decade ago—an ongoing collaboration for 

which the authors are most grateful. Robert Gilliom (USGS, 

Sacramento, California) provided invaluable guidance and 

support throughout the course of the project. David Wolock 

(USGS, Lawrence, Kansas) and Michael Wieczorek (USGS, 

Baltimore, Maryland) provided many of the ancillary 

data associated with soil properties, weather, and other 

environmental variables that were used to quantify several 

of the model input parameters. Naomi Nakagaki (USGS, 

Sacramento, California) kindly provided the data on the 

geographic distributions of irrigation, atrazine use, and study 

sites across the Nation. Marcel Schaap (University of Arizona) 

provided extensive help with the Rosetta system. Assistance 

in working with the Natural Resources Conservation Service 

soils data was provided by Walter Russell (U.S. Department 

of Agriculture [USDA] Soil Salinity Laboratory, Riverside, 

California), Rien van Genuchten (Federal University of Rio 

de Janeiro, Brazil), Paul Finnell and Jim Fortner (USDA, 

Lincoln, Nebraska). Tharacad Ramanarayanan (Bayer Crop 

Science, Research Triangle Park, North Carolina) and Keith 

Saxton (Saxton Engineering, Pullman, Washington) provided 

assistance and additional references on the estimation of soilwater retention properties. Liwang Ma (USDA, Ft. Collins, 

Colorado) and Randall Bayless (USGS, Indianapolis, Indiana) 

provided extensive, ongoing assistance with the RZWQM 

model for which the authors are grateful, with additional help 

provided by Patricia Bartling, Laj Ahuja, James Ascough and 
140 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Ken Rojas (USDA, Ft. Collins, Colorado). Nicholas Jarvis 

(Swedish University of Agricultural Sciences, Uppsala, 

Sweden) provided information and valuable assistance related 

to his system for estimating macroporosity parameter values 

from basic soils data. The willingness of Kathrin Fenner 

(Swiss Federal Institute of Aquatic Science and Technology, 

Dübendorf, Switzerland) and Mark Borsuk (Dartmouth 

College, Hanover, New Hampshire) to share and discuss 

their data on the effects of temperature and other ancillary 

parameters on atrazine transformation rates was greatly 

appreciated. Jeffrey Martin (USGS, Indianapolis, Indiana) 

kindly provided an extensive body of data on analytical 

reporting limits and recoveries for atrazine and DEA, and 

Tedmund Struzeski (USGS, Lakewood, Colorado) provided 

analogous assistance for the nitrate data. Robert Black 

(USGS, Tacoma, Washington) provided extensive and helpful 

assistance with some of the nonparametric statistical analyses. 

Leah Wasser (Pennsylvania State University Geospatial 

Technology Program, Land Analysis Laboratory) provided 

the SSURGO map unit graphic for figure 9, and Anthony J. 

Tesoriero (USGS, Portland, Oregon) and Larry Puckett 

(USGS, retired) provided the original version of the diagram 

shown in figure 21. Joey Shaw (Auburn University) provided 

extensive help in explaining some of the details of his work 

on solute exchange between mobile and immobile waters, and 

Hein ten Berge (Wageningen University and Research Centre, 

The Netherlands) kindly provided photocopied portions 

of his PhD thesis. Valuable and extensive modifications 

of the text were carried out in response to very thoughtful 

and informed reviews by Bernard T. Nolan, Randall 

Bayless, Robert Gilliom, and Devin Galloway (USGS, 

Sacramento, California).

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Table 14 157

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.[Abbreviations: Cligen, climate simulator provided by RZWQM; DEA, deethylatrazine; EPA, Environmental Protection Agency; ET, evapotranspiration; FAO, Food and Agriculture Organization of the United 

Nations; foc, weight fraction of organic carbon in soil; fom, weight fraction of organic matter in soil; FWM, flow-weighted mean; HPLC, high-performance liquid chromatography; LUS, National Water-Quality 

Assessment (NAWQA) land-use study; NRCS, Natural Resources Conservation Service (USDA); NRI, Natural Resources Inventory (NRCS); P-GWAVA, process-based groundwater vulnerability assessment system; P-GWAVA-PR, P-GWAVA system using PRZM to simulate solute and water behavior in the vadose zone (fig. 5); P-GWAVA-RZ, P-GWAVA system using RZWQM to simulate solute and water behavior in the vadose zone (fig. 6); PET, potential evapotranspiration; PRZM, Pesticide Root-Zone Model (EPA); PTFs, pedotransfer functions (statistical relations used to predict soil hydraulic parameter values from other soil properties); QMPR, quantitative medium-property relation; QMRR, quantitative medium-reactivity relation; QPPR, quantitative property-property relation; QSPR, quantitative structureproperty relation; QSRR, quantitative structure-reactivity relation; RZWQM, Root-Zone Water-Quality Model (USDA); SSURGO, Soil Survey Geographic Database (NRCS); STATSGO, State Soil Geographic 

database (NRCS); TMay, average air temperature in May; TPAW, total plant available water (Bartling and others, 2011); USDA, U.S. Department of Agriculture; °C, degrees Celsius; μg C/g, microgram of 

carbon per gram of soil; cm, centimeter; cm-1, per centimeter; cm/d, centimeter per day; cm/h, centimeter per hour; cm3/g, cubic centimeter per gram of soil; d-1, per day; h-1, per hour; ha, hectare; in/hr, inch per 

hour; kg a.i./ha, kilograms of active ingredient per hectare; m, meter; kJ/mol, kilojoules per mole; mL/g, milliliter per gram; mm, millimeter; mm-1, per millimeter; %om, percentage of soil organic matter; 

≥, greater than or equal to; >, greater than; <, less than; –, process not simulated or information not needed]

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Land surface configuration

Elevation, aspect and slope of map unit surface

Obtained from STATSGO • STATSGO data obtained from 

Natural Resources Conservation Service (2011a)

• Data processed by David 

Wolock, U.S. Geological Survey, (written commun., March 23, 2000)

• Obtained from SSURGO or digital 

elevation model

• Aspect assumed to be zero (true 

North) if data missing

Natural ResourceConservation Service (2011a)

Weather

Maximum and minimum airtemperature (Tmax and Tmin,

respectively) (°C)

• Daily values• 327 stations located near study 

sites (4,953 stations nationwide)

• Values from station nearest to

each site assigned using Thiessen polygons

• Data source: National Climatic 

Data Center (2002b)

• Missing values filled in using 

method of Eischeid and others (2000)

• Processed data provided by 

David Wolock, U.S. Geological Survey (written commun., December 5, 2002)

• Daily values

• Data obtained from 87

meteorological stations

• Values for each site obtained from 

weather station located within the same Cligen grid cell as the site or, if no weather stations were available in the same cell, from the nearest station outside of the cell (4 sites)

Cligen (AgriculturalResearch Service, 2009)

Average air temperature in May (TMay) (°C)

Computed for each site as thearithmetic average of monthlyaverages of Tmax and Tmin for 

May from 1971 to 2000, using the PRISM data

PRISM Climate Group (2009) Computed for each site as the

arithmetic average of monthly averagesof T

max

 and Tmin for May from 1971 to

2000, using the PRISM data

PRISM Climate Group (2009)
158 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Weather—Continued Solar radiation Estimated from latitude using the Penman-Monteith equation for calculation of PET Allen and others (1998) Daily values of short-wave radiation obtained from the same locations as those for 

T

max

and 

Tmin

Cligen (Agricultural ResearchService, 2009)

Sunshine fraction – – • Defined as the average annual 

fraction of daytime hours without clouds (Food and Agriculture Organization of the United Nations, 2011a; Liwang Ma, U.S. Department of Agriculture, Agricultural Research Service, written commun., November 20, 2011)

• Value set at 0.6 for all sites, estimated 

for the northern Corn Belt from worldwide map provided by the Food and Agriculture Organizationof the United Nations (2011b)

–

Wind speed Monthly values • Data source: National Climatic 

Data Center (1998)

• Processed data provided by 

David Wolock, U.S. Geological Survey (written commun., December 5, 2002)

• Daily values, obtained from the same 

locations as those for 

T

max

 and 

Tmin

• Measured from a height of 10 m

• Cligen (Agricultural 

Research Service, 2009)

• Measurement height 

provided by Burns and others (2007)

Pan evaporation (cm/d) Estimated using the Penman

Monteith equation

Allen and others (1998) Not used –

Relative humidity (percent) – – Daily values, obtained from the same

locations as those for 

T

max

 and 

Tmin

Cligen (AgriculturalResearch Service, 2009)

Precipitation Daily values • Data source: National Climatic 

Data Center (2002a)

• Processed data provided by

David Wolock, U.S. Geological Survey (written commun., December 11, 2002)

• Hourly data on storms required in 

order to generate macropore flow 

• Storm data generated stochastically, 

using information from the same locations as those for 

T

max

 and 

Tmin

Cligen (AgriculturalResearch Service, 2009)
Table 14 159

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Water movement at or below land surface

Irrigation • Sites selected at random for 

irrigation in each LUS, based on NRI percentages (fig. 17; eq. 8)

• Carried out only during the 

growing season (May 1– September 30)

• Triggered when fraction of 

available water capacity (AWC ≡ θFC - θWP) dropped 

below 0.4

• Irrigation rates:

– Initial value: 1.0 in/hr (midrange 

of rates given by Carsel and others [1998] for sprinkler irrigation)

– Reduced to 0.1 in/hr to avoid 

generating excessive runoff

– • Simulated as furrow irrigation

• Carried out only during growing 

season (May 30–September 7)

• Triggered when fraction of Total Plant 

Available Water in the active rooting zone (TPAW ≡ θ33 - θ1500) dropped 

below the following thresholds (TPAWmin):

– First 15 days after planting: 

TPAWmin = 0.5

– 73 days or more after planting: 

TPAWmin = 0.3

• Each irrigation event continued until 

a specified maximum value of TPAW (TPAWmax) was reached in the active 

rooting zone:

– First 15 days after planting: 

TPAW

max

 = 0.75

– 73 days after planting: 

TPAW

max

 = 0.5

• Minimum number of days between 

successive irrigation events: 3

• Furrow irrigation commonly 

used on corn in the United States (Christensen, 2002)

• Corn sensitivity to water 

stress increases at silking stage, which typically occurs approximately 63–68 days after emergence (McWilliams and others, 1999)

Runoff Curve number method, utilizing

two soil properties:• Predominant soil hydrologic group• Surface condition of soil (fallow, 

crop, or residue)

• Soil hydrologic groups from 

STATSGO (Natural Resources Conservation Service, 2011a) 

• Curve numbers from Carsel and 

others (1998)

• Generated only when rate of water 

input exceeded infiltration

• Rate estimated as the difference 

between rate of water delivery to land surface (from precipitation and [or] irrigation) and the rate of infiltration (Ahuja and others, 2000)

–

Vertical infiltration into the soil Computed as difference between

rate of precipitation (rainfall plus snowmelt) and the sum ofrunoff and evaporation

Carsel and others (1998) Computed from Ksat and other

parameters using the Green-Amptequation (Ahuja and others, 2000)

–

Lateral infiltration from macropores into the soil matrix

Not simulated – Computed from macropore radius,

wetted radius, Ksat and radial infiltration

rate reduction factor, using the GreenAmpt equation (Ahuja and others, 2000)

–
160 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Water movement at or below land surface—Continued Soil drainage • Free drainage for all soils • Redistribution simulated to occur within 1 day

– • Water flow through soil matrix 

(redistribution) simulated using Richards’ equation

• Water flow through macropores 

simulated using Poiseuille’s law

Ahuja and others (2000);Malone and others (2003)

Rate of evapotranspiration (ET) • Computed daily, from potential 

evapotranspiration (PET), wilting point and soil water content

• PET estimated as function of 

Tmin, Tmax, wind speed, solar 

radiation and saturation water vapor pressure, using the PenmanMonteith method, recommended by FAO (Allen and others, 1996)

• No distinction made between 

evaporation and transpiration (Carsel and others, 1998)

• See entries for water content at 

wilting point, 

Tmin, Tmax, wind 

speed and solar radiation.

• Saturation water vapor pressure 

estimated from 

Tmin and 

T

max

• Computed daily as function of 

Tmin, 

T

max, wind speed, solar radiation, 

saturation water vapor pressure and the surface albedo of dry soil, using the extended Shuttleworth-Wallace model (Ahuja and others, 2000)

• Saturation water vapor pressure 

estimated from 

Tmin and 

T

max

See entries for 

Tmin, Tmax, 

wind speed, solar radiation, 

and

surface albedo of dry soil.

Maximum depth of evaporation • Values taken from map in Carsel 

and others (1998) 

• Fallow, dormant conditions 

assumed

– Not needed Ahuja and others (2000)

Surface boundary condition (z

=0) Not applicable (tipping-bucket

algorithm)

– • Evaporative flux specified when 

h [z

=0] >-20 bar (≈ θWP)

• Constant head specified 

(h [z

=0] ≡ -20 bar) when evaporative 

flux can no longer be sustained

Ahuja and others (2000)

Bottom boundary condition Not applicable (tipping-bucket

algorithm)

– Unit gradient boundary –

Hydraulic gradient duringinfiltration

Not applicable (tipping-bucketalgorithm)

– Unit gradient –

Depth, size and spacing of tiledrains

Tile drainage not simulated Insufficient information available

on prevalence of tile drains acrossthe United States

Tile drainage not simulated Insufficient information

available on prevalence of tiledrains across the United States
Table 14 161

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Physical, chemical, and hydraulic properties of soils

Soil erosion Neglected – Neglected -

Soil texture (weight percentages)• Percent sand• Percent silt• Percent clay• Soil organic matter content 

(%om)

Values for all soil componentscombined as spatial averages,weighted vertically (by horizonthicknesses) and horizontally (bypercentage of map unit area occupied by each component), asillustrated in figure 7 for soil organic matter

• Original data from STATSGO• Averages computed by David

Wolock, U.S. Geological Survey (written commun., March 23, 2000)

• Fractions for individual soil 

constituents for each horizon obtained from SSURGO, as a weight percent 

of the <2 mm size fraction (Natural Resources Conservation Service, 2007a)

• For soil horizons for which SSURGO 

did not provide percent organic matter (%om) data, values were scaled from the value in the uppermost horizon (for which SSURGO data were always available) according to the average %om values for deeper horizons, using equation 2

• Sites with soil horizons for which 

%om ≥ 20 percent were considered anomalous (for an agricultural area) and eliminated from consideration

SSURGO (Natural ResourcesConservation Service, 2011a)

Number, depth, thickness, andtexture class for individual soilhorizons

One uniform layer only (table 1) – • Data assembled from SSURGO for 

all soil horizons in each component satisfying minimum data criteria (see section entitled “Characteristics and Use of SSURGO Data”)

• Texture class determined by 

RZWQM from percent sand, silt and clay

• Texture for soil horizons with 

multiple texture designations determined from percent sand, silt and clay, using soil texture triangle program embedded within RZWQM (Brady, 1990) or available online (Gerakis and Baer, 2011)

SSURGO (Natural ResourcesConservation Service, 2011a)

Organic carbon content (foc), as

mass fraction

Computed from mass fraction of soilorganic matter (fom = %om/100%) 

using equation 15

See entry for soil texture (whichincludes %om)

Same as for Phase 1 SSURGO (Natural Resources

Conservation Service, 2011a)
162 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Physical, chemical, and hydraulic properties of soils—Continued Surface albedo of soil, as fraction of incident light • Specified on a monthly basis • Value depends upon soil texture, wetness, surface cover, and angle of solar incidence Carsel and others (1998, table 5-21) • Defined as the fraction of incident short-wave solar radiation reflected by the <2 mm fraction of air-dried surface soil (Natural Resources Conservation Service, 2007a) • Values for dry soil (Ad) obtained from SSURGO for uppermost horizon of each component of interest • Sites with missing values of Ad assigned average value among all SSURGO values from surficial horizons in the same texture class within the Phase 2 study area, that is: Soil texture class Ad Clay 10.26 Clay loam 0.19 Loam 0.25 Loamy sand 0.21 Sand 0.28 Sandy clay 20.24 Sandy clay loam 0.23 Sandy loam 0.24 Silt 0.08 Silty clay 0.11 Silty clay loam 0.10 Silty loam 0.26 

1Average among subsurface values only.2No data available among Phase 2 sites. 

 Intermediate value selected between those for texture classes on either side of sandy clay on the soil texture triangle (Gerakis and Baer, 2011), that is, clay and sandy clay loam.

• Values for wet soils (Aw) estimated 

from Ad (from SSURGO) using the 

following relation from Berge (1986): 

Aw = 0.5 Ad

SSURGO (Natural ResourcesConservation Service, 2011a)
Table 14 163

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Physical, chemical, and hydraulic properties of soils—Continued

Dry volume heat capacity(J – mm-3 K-1) 

– – Value of 0.002120 J – mm-3 K-1

used for all soil horizons

Suggested RZWQM defaultvalue (Pat Bartling, U.S.Department of Agriculture,written commun.,

December 17, 2007)

Particle density (specific gravityof solids, ρs), in mass per unit

volume

– – Obtained from SSURGO SSURGO (Natural Resources

Conservation Service, 2011a)

Bulk density (ρb), in mass per 

unit volume

Averaged vertically and horizontally,as illustrated in figure 7 for soilorganic matter

STATSGO (Natural ResourcesConservation Service, 2011a)

Obtained from SSURGO or, wherenecessary, estimated from specificgravity of solids (ρs ) and porosity

(n) using the following equation,rearranged from Freeze and Cherry(1979):

ρb = ρs (1- n)

SSURGO (Natural ResourcesConservation Service, 2011a)

Porosity (n) – – Set equal to saturated water content 

(fig. 11)

–

Saturated water content (θs), in

percent by volume

– – Defined as the volumetric water content 

of soil at or near zero bar tension (Natural Resources Conservation Service, 2007a)

SSURGO (Natural Resources Conservation Service, 2011a)

Saturation fraction (actual saturated water content, as afraction of θ

s)

– – • Defined as the maximum degree of 

saturation that can be achieved above the water table. Accounts primarily for the influence of entrapped air (Liwang Ma, U.S. Department of Agriculture, written commun., November 20, 2011).

• Value of 0.9 used for all sites (that is, 

90 percent of θs )

Previous work suggests thatthis fraction typically ranges between 0.8 and 0.9(Agricultural Research Service, 2010)
164 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Physical, chemical, and hydraulic properties of soils—Continued Soil water content at field capacity (θFC), as a fraction by volume • Presumed to be equal to the water content at 33 kPa tension (θ33) (Carsel and others, 1998) • Estimated from soil texture using Rosetta Schaap and others, 2001 (Rosetta) • Presumed to be equal to the water content at 33 kPa tension (θ33) • θ33 obtained directly from SSURGO or, if missing, approximated using values from adjacent horizons • To avoid computational difficulties, value set to 6 percent for soil horizons for which θ33 was lower than 6 percent • Used as the initial condition of water content for all soil horizons SSURGO (Natural Resources Conservation Service, 2011a) Soil water content at wilting point (θWP), as a fraction by volume • Presumed to be equal to the water content at 1,500 kPa tension (θ1500) (Carsel and others, 1998) • Estimated from soil texture using Rosetta Schaap and others, 2001 (Rosetta) • Presumed to be equal to the water content at 1,500 kPa tension (θ1500) • θ1500 obtained directly from SSURGO SSURGO (Natural Resources Conservation Service, 2011a) Saturated hydraulic conductivity

(Ksat), in cm/h

Not applicable (tipping-bucketalgorithm)

– Estimated for each soil horizon from

weight percentages of sand and clayusing an online program (Nelson, 2012)employing the PTF from Saxton andothers (1986)

Weight percentagesof sand and clay obtainedfrom SSURGO (NaturalResources ConservationService, 2011a)

Lateral saturated hydraulicconductivity (

Klat), in cm/h

Not applicable (tipping-bucketalgorithm)

– Klat = 1.5 (

K

sat) See entry for 

K

sat

Hydraulic conductivity of surfacesoil crust (

K

cr), in cm/h

Not characterized – Estimated as follows:

K

cr

 = 

K

sat CF

where CF is crust factor, that is, the 

ratio of hydraulic conductivity of the surface soil crust to that of the bulk soil (

K

cr/Ksat)

• Surface crusts, commonly 

formed from raindrop impacts on both tilled and undisturbed soils (for example, Rawls and others, 1990), presumed to be present at all sites

• See entries for 

K

sat

 and CF
Table 14 165

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Physical, chemical, and hydraulic properties of soils—Continued

Crust factor (CF), that is, the ratioof hydraulic conductivity ofsurface soil crust to that of thebulk soil (Kcr/Ksat)

Not characterized – CF values for each of 12 soil texture

classes, computed using data andmethods provided by Rawls and others(1990), as described in text:

Soil texture class CF

Clay 0.27Clay loam 0.32Loam 0.34Loamy sand 0.56Sand 0.65Sandy clay 0.36Sandy clay loam 0.43Sandy loam 0.39Silt 0.25Silty clay 0.23Silty clay loam 0.25Silty loam 0.27 

–

Brooks-Corey parameters (usedto describe dependence ofhydraulic conductivity and soilwater content on matric suction inthe vadose zone)

Not applicable (tipping-bucketalgorithm)

– Estimated by RZWQM from soil

texture, using the PTFs from Rawlsand others (1982) (see section entitled“Water Movement Through the 

Subsurface”)

• Ahuja and others (2000)• Bartling and others (2011)

Microporosity Not characterized – • 1 percent for uppermost horizon at 

each site

• 10 percent for all deeper horizons

• Value for uppermost horizon 

represented minimum value in range recommended by RZWQM (Agricultural Research Service, 2010), consistent with results reported by Ahuja and others (1995) for wet initial conditions

• Value for deeper horizons 

obtained from example provided by RZWQM (Agricultural Research Service, 2010)
166 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Physical, chemical, and hydraulic properties of soils—Continued Radial infiltration rate reduction factor (accounts for the influence of compaction and [or] soil organic matter on lateral water flow into macropores) Not characterized – 0.1 (used for all sites) Value used by Ahuja and others (1995), Malone and others (2003), and Fox and others (2004) Wetting thickness for lateral infiltration Not characterized – Values used at all sites: • For radial holes: 0.6 cm • For cracks and columns: 0.6 cm • Value for radial holes suggested by Malone and others (2003) • Same value presumed to be appropriate for cracks and columns Total macroporosity Not characterized – Estimated for each soil horizon using flow chart (fig. 10) derived from approach of Jarvis and others (2007b) See figure 10. Average radius of cylindrical macropores Not characterized – Values used at all sites: • Top horizon: 0.1 cm • All other horizons: 0 cm (assumed by RZWQM to be closed [Agricultural Research Service, 2010]) Top horizon value: Jaynes and Miller (1999) Width of rectangular cracks Not characterized – Values used at all sites: • Top horizon: 0 cm (assumed by RZWQM to be closed [Agricultural Research Service, 2010]) • All other horizons: 0.05 cm Value for lower horizons: Jaynes and Miller (1999) Length of rectangular cracks Not characterized – Values used at all sites: • Top horizon: 0 cm (assumed by RZWQM to be closed [Agricultural Research Service, 2010]) • All other horizons: 10 cm Value for lower horizons obtained from example provided by RZWQM (Agricultural Research Service, 2010) Average length of soil aggregates Not characterized – Values used at all sites: • Top horizon: 0 cm (aggregates assumed to be absent from the soil surface) • All other horizons: 10 cm Values obtained from example provided by RZWQM (Agricultural Research Service, 2010)
Table 14 167

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Physical, chemical, and hydraulic properties of soils—Continued

Number of macropores per unit area

Not characterized – Computed by RZWQM from total

macroporosity and macropore radii

–

Length of cracks per unit area Not characterized – Computed by RZWQM from total

macroporosity, crack lengths, and crack widths

–

Fraction of pore volume in dead-end pores

Not characterized – Values used at all sites:

• Top horizon: 0.001• All other horizons: 0.5

Default values, obtainedfrom example provided byRZWQM (AgriculturalResearch Service, 2010)

Percentage of crop residueincorporated into soil each year through bioturbation

Not characterized – 0.6 percent • Caused primarily by the 

actions of earthworms and other soil organisms

• Value provided by Bartling 

and others (2011)

Soil management

Tillage method Conventional Common practice Conventional Common practice

Land surface management Conditions used for all sites:

• Initial condition: Fallow• During crop growth: Cropping• After harvest: Residue

Common practice Conditions used for all sites:

• 3 metric tons of 100-day-old corn 

residue (as dry mass) per hectare on soil surface

• Average depth of residue layer: 3.0 cm• C:N ratio of predominant (corn) 

residue material: 50:1

• All residue presumed to lie flat

RZWQM default values(Agricultural Research Service, 2010)

Crop characteristics and management

Crop type Corn • Corn represented 84 percent of 

atrazine use nationwide in 1997 (U.S. Geological Survey, 2014)

• Continuous corn, rather than 

corn-and-soybean rotations, used for the sake of model simplicity

• Corn, simulated using the “generic 

plant model” in RZWQM

• Potential effect of continuous corn 

on model predictions, relative to corn-and-soybean rotations, also investigated, using equation 50

Same reasoning as for Phase 1

Cropping frequency One cropping period per year

(“continuous corn”)

Common practice One cropping period per year

(“continuous corn”)

Common practice
168 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Crop characteristics and management—Continued Timing of events Uniform dates for all sites: • Planting: May 1 • Emergence: May 15 • Maturation: September 15 • Harvest: September 30 Midrange values from U.S. Department of Agriculture/ National Agricultural Statistics Service data for 17 states Uniform dates for all sites: • Planted: May 30 • Harvest: September 7 • Planting date selected to minimize encounters with killing frosts • Harvest date set at 100 days after planting (corn assumed to be fully developed by then) • Initial choice of harvest date (September 17) moved 10 days earlier to bring amounts of applied irrigation water in closer agreement with those required to grow corn Planting population (Ppop) – – • Required for computing nitrogen fertilizer inputs • Ppop = 85,000 seeds / ha • Source: Hoskins (2008) • Recommended value Rooting depth Increases over time to 90 cm Midpoint of range given for corn by Carsel and others (1998, table 5-9) Increases over time to 200 cm Value specified by Generic Crop Growth model in RZWQM for corn (Bartling and others, 2011) Maximum plant height 200 cm Midpoint of range given for corn by Carsel and others (1998, table 5-16) 170 cm Default value specified by RZWQM for corn (Agricultural Research Service, 2010) Maximum coverage Increases over time to 100 percent Carsel and others (1998) Computed as exponential function of Leaf Area Index Default parameter values specified by RZWQM for corn (Bartling and others, 2011) Albedo of mature crop and fresh residue Values obtained from Carsel and others (1998) Carsel and others (1998, table 5-21) Values used for all sites: • Corn: 0.22 • Fresh crop residue: 0.22 Pat Bartling, U.S. Department of Agriculture, Agricultural Research Service (written commun., September 2, 2011)
Table 14 169

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Crop characteristics and management—Continued

Percentage of crop residueincorporated naturally into soil each year by bioturbation.

Not characterized – 0.6 percent • Caused by the activities of 

earthworms and other soil organisms

• Bartling and others (2011)

Rainfall caught and stored in canopy

Maximum value used (0.3 cm) Default value for corn given

by Carsel and others (1998,

table 5-4)

Not simulated –

Rates of nitrogen and water uptake and biomass formation

– – Default values from Generic Crop

Growth Model

Bartling and others (2011)

Harvest parameters – – – Default values provided

by RZWQM for corncultivation (AgriculturalResearch Service, 2010)

Target crop yield (Ygoal) – – • Required for computing nitrogen 

fertilizer inputs

• Ygoal = 494 bushels / ha

• Source: Hoskins (2008)• Upper limit of range

Concentrations, conversion, composition, and reactivity of organic materials

Characteristics of crop residue – – • All residue assumed flat (none 

standing)

• Dry mass of residue on surface: 

3 metric tons/ha

• Age of residue at start of simulation: 

100 days

• Average height of residue: 3 cm

Agricultural Research Service (2010)

Carbon-to-nitrogen ratios of cropresidue and soil organic matter.

– – • Dominant residue material: 50

• Slow residue pool: 19• Fast residue pool: 80• Fast soil humus pool: 8• Intermediate soil humus pool: 10• Slow soil humus pool: 11• Carbon sink pool: 0• Aerobic heterotroph pool: 8• Autotroph pool: 8• Anaerobic heterotroph pool: 8

Bartling and others (2011)
170 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Concentrations, conversion, composition and reactivity of organic materials—Continued Initial amounts of organic carbon in pools of organic residue (slow, fast) and humus (slow, medium, fast)

– – • Residue pools – Default values

– Slow residue pool: 1 μg C/g soil– Fast residue pool: 1 μg C/g soil

• Initial distribution of soil organic 

matter between fast, medium and slow pools (2, 18 and 70 percent, respectively) adjusted to 10, 20 and 70 percent, respectively, to improve fit between simulated and measured nitrate concentrations

–

Fractions of conversion amongthe five pools of soil organic matter

– – RZWQM default values used for all

parameters

Bartling and others (2011)

Efficiencies of conversion amongdifferent forms of soil organicmatter, including biomass creation

– – RZWQM default values used for all

parameters

Bartling and others (2011)

Initial sizes of aerobic, autotrophicand anaerobic microbialpopulations

– – Computed by RZWQM from soil 

organic matter content (fom) for each 

soil horizon

Bartling and others (2011)

Arrhenius parameters for soilorganic matter decay

– – RZWQM default values used for all

parameters

Bartling and others (2011)

Arrhenius parameters for rates of microbial death

– – RZWQM default values used for all

parameters

Bartling and others (2011)

Atrazine application

Land-use settings Agricultural settings only Data on spatial distributions of 

non-agricultural use are notavailable on a national scale 

Agricultural settings only Same reasoning as for Phase 1

Application frequency Once per year Common practice Once per year Common practice

Application date May 2 One day after planting May 31 One day after planting

Application method Surface applied (not incorporated) Common practice Surface applied (not incorporated) Common practice
Table 14 171

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Atrazine application—Continued

Application intensity (mass applied per unit area)

• Option 1: Uniform intensity

(2 kg a.i./ha)

• Option 2: Areally averaged 

intensities (eq. 10; fig. 1)

• Option 1: Within range for 

small-scale studies

• Option 2: U.S. Geological 

Survey (2014)

Uniform intensity (2 kg ai/ha) Preliminary simulations using

areally averaged atrazineapplication intensities resultedin predicted atrazine andDEA concentrations that were less than the reporting limits at all sites.

Application efficiency 95 percent (surface applied) Estimated Not quantified –

Partitioning and transport of atrazine (Phases 1 and 2) and DEA (Phase 2 only)

Initial concentrations of atrazineand DEA on plant and residuesurfaces, and in soil.

Set to zero All atrazine presumed to have 

come only from atrazine applied during simulations

Set to zero All atrazine and DEA was

presumed to have come only from atrazine appliedduring simulations.

Atmospheric input Assumed negligible – Assumed negligible –Washoff from crop foliage and crop residues

Assumed negligible – • Proportion of each compound in 

washoff from plant residue assumed to be identical to that from plant surfaces (Ahuja and others, 2000)

• Quantified using two parameters from 

equation of Willis and others (1980): washoff fraction (Fwo) and washoff 

power (Pwo) (eqs. 17 and 18)

• Values of F

wo

 and P

wo

 used for 

atrazine (obtained by Malone and others [2001] through calibration of RZWQM):F

wo = 0.8 (80 percent)

P

wo

 = 0.015 mm-1

• Values of F

wo

 and P

wo

 used for 

DEA (obtained by scaling values for atrazine with a QPPR [eq. 19] according to the water solubilities of the two compounds):F

wo = 0.9 (90 percent)

P

wo

 = 0.099 mm-1

Washoff from crop foliage,although accounted for by theP-GWAVA-RZ simulations,was expected to be negligiblebecause atrazine was appliedprior to crop emergence.
172 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Partitioning and transport of atrazine (Phases 1 and 2) and DEA (Phase 2 only)—Continued Mixing in soil prior to runoff Not simulated – • Average degree of mixing between rainfall and soil solution (Mavg) expressed by RZWQM as an exponential function of depth in soil (z, in cm) as follows:

M

avg

 = Ae-Bz

where A is typically equal to unity 

(Agricultural Research Service, 2010) and B is an empirical parameter

• For all sites:

B = 4.4 cm-1

• Values of A and B selected 

to be consistent with those derived from a laboratory column study (Ahuja and others, 1995), and a field study employing a tilled soil (Malone and others, 2003)

• Values of A and B also 

consistent with RZWQM default values (Agricultural Research Service, 2010)

Partitioning between water andair (

KH)

Not simulated – • Atrazine: Henry’s law constant

 (

KH) value at 25 °C (2.88 × 10-4

Pa–m3/mol) obtained from Mackay 

and others (1997)

• DEA: KH value for 25 °C (7.29 × 10-4

Pa– m3/mol) computed by dividing 

vapor pressure at 25 °C (Neely and Blau, 1985) by water solubility at 

22 °C (Mills and Thurman, 1994)

KH value for DEA presumedto represent value at 25 °Crather than 22 °C—becausevapor pressure is substantiallymore sensitive to temperaturethan water solubility

Correcting air-water partitioning(KH) for variations in temperature

– – KH values for atrazine and DEA adjusted

to 

TMay at each site using equation 11 

and a value for enthalpy change fromvapor to dissolved state (∆

Hs) of 

47 kJ/mol (from Staudinger and Roberts, 2001)

–

Volatilization from soil Assumed negligible – • Rate estimated by RZWQM as a 

nonlinear function of 

KH, Koc, and the 

molecular weight of the solute (Ahuja and others, 2000)

• Rate not explicitly corrected for 

variations in temperature or moisture content (Ahuja and others, 2000), except through temperature-correction of 

KH

–
Table 14 173

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Partitioning and transport of atrazine (Phases 1 and 2) and DEA (Phase 2 only)—Continued

First acid-base dissociationconstant (pKa)

– – • Atrazine: Value (1.68) obtained as 

median among those compiled from literature by Mackay and others (1997); consistent with range of 1.65–1.8 reported from literature by Schmitt and others (1996)

• DEA: Value (1.65) obtained from 

Schmitt and others (1996)

–

Second acid-base dissociationconstant (pKb)

– – • Atrazine: Value (12.32) obtained from 

Mackay and others (1997)

• DEA: No value available; presumed 

equal to that for atrazine (12.32)

–

Octanol-water partition coefficient (Kow)

– – • Atrazine: Value (562) obtained from 

Mackay and others (1997)

• DEA Value (45.7) computed as 

average of values obtained by employing a QSPR (eq. 12) to scale atrazine value using Kow estimates 

computed by Finizio and others (1991) from HPLC retention times (44.6) and from the Hansch and Leo (1979) fragment method (46.8)

–

Organic carbon-water partition coefficient (Koc)

K

oc = 130 mL/g (atrazine) Median value among 73 values 

reported by 25 published studies

• Initial values used:

Atrazine: 100 mL/g (Mackay and others, 1997)DEA: 57 mL/g, estimated by scaling from atrazine value selected by Mackay and others (1997) using a QSPR (eq. 16)

• K

oc

 values for atrazine and DEA 

reduced by 75 percent to 25 and 

14 mL/g, respectively, to bring simulated concentrations closer to those measured in groundwater

Adjustment of Koc constrained

by two criteria:

– Ratio between the values for the two compounds (atrazine and DEA) maintained – Selected values remained consistent with the ranges of values reported by previous studies for the two compounds
174 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Partitioning and transport of atrazine (Phases 1 and 2) and DEA (Phase 2 only)—Continued Soil-water partition coefficient (

Kd

)

• Linear, reversible • 

Kd = 

foc

K

oc (eq. 14)

• Different 

Kd value for each site

See entries for 

foc and 

K

oc • Linear, reversible

• 

Kd = 

foc

K

oc (eq. 14)

• Different 

Kd value computed by 

RZWQM for each soil horizon

• Freundlich (non-linear) 

sorption simulation not yet operative in RZWQM (Bartling and others, 2011)

• See entries for 

foc and 

K

oc

Ratio of kinetic constants foradsorption to and desorption from soil surfaces (kads/kdes) [Ek2]

Not simulated – • Value for atrazine, estimated using 

equations 20–22 and data reported from column experiments by Guo and others (1997):

0.65 cm3/g

• Value for DEA presumed to be equal 

to that for atrazine

–

Kinetic constant for desorptionfrom soil surfaces (kdes) [Rk2]

Not simulated – • Value for atrazine, estimated from 

data reported from column experiments by Guo and others (1997) (see section, “Kinetic Controls on Sorption”): 

0.12 h-1

• Value for DEA presumed to be equal 

to that for atrazine

–

Hydrodynamic dispersion Mimicked through numerical

dispersion by setting dispersioncoefficient (

D) to zero

Approach recommended by Carsel and others (1998) in absence of field-derived values of 

D

Simulated through the combined effects of partial piston displacement,molecular diffusion of solutes betweenzones of mobile and immobile water,and sorption

Ahuja and others (2000)

Effective diffusion coefficient for solute movement betweensoil mesopores and micropores(Ddeff)

Not simulated – Computed by RZWQM for each soil

horizon from soil bulk density (ρb), soil

partition coefficient (

Kd) volumetric

water content (θ) and apparent diffusioncoefficient (

Da) using equation 24 

Ahuja and others (2000)
Table 14 175

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Partitioning and transport of atrazine (Phases 1 and 2) and DEA (Phase 2 only)—Continued

Apparent diffusion coefficient(Da)

– – Computed using a QMPR (eq. 25)

to scale the molecular diffusioncoefficient in water (Dw) using the

van Genuchten mass-transfer coefficient(α), estimated from clay content andcation exchange capacity (CEC) using acorrected version of a PTF provided byShaw and others (2000) for bromide(eq. 26)

–

Molecular diffusion coefficient in water (Dw)

– – Estimated using the method of Hayduk

and Laudie, recommended by Tuckerand Nelkin (1990)

–

Plant uptake Not simulated – Calculated by RZWQM as the product

of plant water uptake rate, soluteconcentration in the soil water, andtranspiration stream concentration factor (a non-linear function of Kow)

Ahuja and others (2000)

Chemical reactions involving atrazine and DEA

Reference conditions from whichtransformation rates were adjusted

Tref = average air temperature in 

May (TMay)

– • θref ≡ θFC ≡ θ33 (Ahuja and others, 

2000), used to adjust rates of atrazine and DEA disappearance 

for variations in soil moisture (eq. 36)

• Tref = average air temperature in 

May (TMay)

• pHref = 7 (used for computing 

fraction of atrazine that reacts to form DEA [Fatr>DEA])

–

Rates of atrazine and DEAdisappearance on plant foliage and crop residue

Neglected (see section entitled “PhotochemicalTransformations”)

– Neglected (see section 

entitled “PhotochemicalTransformations”)

–

Rates of atrazine and DEAdisappearance from photochemical transformationon soil surface (kɸ,soil)

Neglected (see section entitled “Photochemical

Transformations”)

– Neglected (see section 

entitled “PhotochemicalTransformations”)

–
176 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Chemical reactions involving atrazine and DEA—Continued Initial half-life for atrazine disappearance in oxic soil at 

TMay

• Reaction rates presumed to be 

identical in the dissolved and sorbed states

• Initial rate at each site estimated 

for 

TMay using data reported for 

three loam soils by Walker and Zimdahl (1981) and equation 29

– • Reaction rates presumed to be 

identical in dissolved and sorbed states

• Median half-life at 25 °C: 109 days 

(from Issa and Wood [1999]), selected from among 125 values reported by 7 laboratory studies

• Value adjusted to 

TMay using 

equation 30

Ea value used for theseadjustments (50.9 kJ/ mol)represented the medianamong 31 values that wereretrieved or computed from6 previous studies of theeffects of temperature andother factors on atrazinedisappearance rates in soil 

Half-life for atrazine disappearance in anoxic soil at 

TMay

Not simulated – • Reaction rates presumed to be 

identical in dissolved and sorbed states

• Median half-life at 25 °C (372 days) 

estimated by adjusting value for oxic conditions (109 days) to anoxic conditions using data from Accinelli and others (2001) and a QMRR using equation 31

• Value adjusted to 

TMay using 

equation 32

In the apparent absence of amore appropriate publishedvalue, the Ea value used

to adjust reaction rates toTMay(50.9 kJ/mol) was

identical to the value usedto adjust rates of atrazinedisappearance in oxic soil toTMay

Percentage of reacted atrazine that forms DEA (DEA “formation percentage”), orF

atr>DEA at 

TMay

Not simulated – • Computed for each SSURGO 

component as a function of %om and percent sand in the uppermost soil horizon and 

TMay, using QMRRs 

(eqs. 38, B3, and B4)

• In absence of published data to the 

contrary, same value presumed to apply for both oxic and anoxic conditions

–
Table 14 177

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Chemical reactions involving atrazine and DEA—Continued

Initial half-life for DEAdisappearance in oxic soil at TMay

Not simulated – • Reaction rates presumed to be 

identical in dissolved and sorbed states

• Value estimated by employing a 

QSRR to scale the reaction rate for atrazine disappearance at TMay

(obtained from eq. 30) using data reported by Kruger and others (1997) for DEA and atrazine reactions in oxic soil (eqs. 39 and 40)

In the apparent absence of amore appropriate publishedvalue, the Ea value used to

adjust reaction rates toTMay (50.9 kJ/ mol) was

identical to the value usedto adjust rates of atrazinedisappearance in oxic soil toTMay

Half-life for DEA disappearance in anoxic soil at TMay

Not simulated – • Reaction rates presumed to be 

identical in dissolved and sorbed states

• Value estimated by employing a 

QSRR to scale the reaction rate for atrazine disappearance under anoxic conditions at TMay (obtained from eq. 

32) using data reported by Kruger and others (1997) for DEA and atrazine 

reactions in anoxic soil (eqs. 41 and 42)

In the apparent absence of a more appropriate published value, the Ea

value used to adjust reactionrates to TMay (50.9 kJ/mol) was

identical to the value used toadjust rates of atrazinedisappearance in oxic soil toTMay

Adjustment of rates ofdisappearance of atrazine andDEA in soil for variations intemperature

• Rates of atrazine disappearance 

in soil corrected for TMay at each 

site using equation 29

• Arrhenius pre-exponential factor, 

A

o (2,920,024 d-1) and activation 

energy, Ea (46.2 kJ/mol) 

computed from data reported by Walker and Zimdahl (1981) 

– • Initial half-lives for the disappearance 

of atrazine and DEA at TMay—under 

either oxic or anoxic conditions—adjusted by RZWQM for subsequent temperature variations using the Arrhenius equation (eq. 33 for atrazine, eq. 43 for DEA)

• Activation energy (Ea) value 

used (50.9 kJ/mol) represented median among 31 values that were retrieved or computed from data reported by 6 laboratory studies 

In the apparent absence of amore appropriate publishedvalue, the Ea value used to

adjust reaction rates to TMay

for DEA (50.9 kJ/ mol) wasidentical to the value usedto adjust rates of atrazinedisappearance in oxic soil toTMay
178 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Chemical reactions involving atrazine and DEA—Continued Adjusting transformation rates for variations in soil moisture Not corrected. (PRZM provides moisture corrections of transformation rates for nutrients, but not for pesticides)

– • Accounted for by RZWQM using 

a variation of the Walker equation 

(eq. 36)

• Median among 14 values of the 

Walker exponent (b) derived from6 previous studies of atrazine reaction in oxic soils (0.43) used for both atrazine and DEA under both oxic and anoxic conditions

Data from previous studies ofthe effect of soil moisturecontent on disappearance ratesappear to be available foratrazine, but not DEA, andonly in oxic soils

Adjusting disappearance halflives for variations in depth below land surface

Not corrected – • Disappearance half-lives presumed 

by RZWQM to exhibit a uniform value ([t1/2] z=0) from the land surface 

to a depth of 25 cm, increase linearly from 25–100 cm depth, and exhibit a uniform value ([t1/2] z=100) below 

100- cm depth (Ahuja and others, 2000)

• RZWQM uses 

Vmhf, the ratio of the 

two values, ([t1/2] z=100 / [t1/2] z=0), to 

adjust disappearance rates with depth (eqs. 45 and 46)

• 

Vmhf values used:

Atrazine: 6.58DEA: 7.95

Variations in transformationrates with depth are presumedto be a reflection ofconcomitant decreases inmicrobial populations, soilorganic matter, nutrients andtemperature with depth

Nitrogen application, transport, and fate

Initial concentrations of nitrate,ammonia and urea in soil

– – Set to zero All nitrogen species in the 

soil presumed to have comeonly from fertilizer appliedduring simulations

Form of nitrogen applied – – N fertilizer only (as nitrate) Nationwide data on manurederived N applied to corn

apparently not available at the time of study

Application date – – First application on June 1 (2 days after 

planting)

–
Table 14 179

Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling 

systems.—Continued

Process, factor, or parameter

Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ)

Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s)

Nitrogen application, transport, and fate—Continued

Application rate – – Overall recommended N application

rate calculated for corn as a functionof desired yield (Ygoal), planting

population (Ppop), and foc using the

Missouri N algorithm (Ahuja and others, 2000)

See entries for Ygoal, Ppop, 

and foc

Application method – – • Applied in irrigation water 

(“fertigation”)

• No nitrification inhibitors used

–

Nitrogen-related crop parameters(N content; efficiency of N uptakeand use)

– – RZWQM default values (for the Generic

Crop Growth Model for corn) used forall parameters

Bartling and others (2011)

Arrhenius parameters forconversions among different forms of nitrogen

– – RZWQM default values used for all

parameters

Bartling and others (2011)

Fate – – • Lag time for nitrification to resume 

following application of anhydrous ammonia: 14 days

• Additional time required for 

nitrification to fully recover after it resumes following application of anhydrous ammonia: 14 days

• RZWQM default values used for 

other parameters

• Rates of formation and transformation 

of nitrate dependent upon microbial population sizes which, in turn, are dependent on relative sizes of organic-matter pools showing fast, medium and slow oxidation rates

• Bartling and others (2011)• Ahuja and others (2000)
180 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table 14. Approaches used to obtain or estimate input parameter values for the simulations conducted with the P-GWAVA-PR (Phase 1) and P-GWAVA-RZ (Phase 2) modeling systems.—Continued Process, factor, or parameter Phase 1 (P-GWAVA-PR) Phase 2 (P-GWAVA-RZ) Approach Rationales / remarks / data source(s) Approach Rationales / remarks / data source(s) Output and calibration Water flow Total flow past the 1-m assessment depth 

– Total flow past the 3-m assessment

depth 

Maximum depth in soil forwhich SSURGO data areavailable

Atrazine and DEA concentrations FWM concentration of atrazine

computed at the 1-m assessment depth using equation 47

– FWM concentrations of atrazine and

DEA computed at the 3-m assessment depth using equation 48

–

Nitrogen species concentration Not simulated – • RZWQM predicts concentrations of 

nitrate plus ammonium, as well as the total amounts of N in different environmental compartments

• Ammonium concentrations presumed 

negligible

–

Input parameters or model features modified to improve fit between simulated and measured solute concentrations (table 4)

Grid cell size for 10–100 cm depthinterval (final value: 30 cm)

– • Spatially uniform atrazine application 

rate used, rather than the areally averaged rates, because atrazine concentrations simulated with the latter approach were below reporting limits at all sites

• Approach for estimating atrazine 

disappearance rates changed from use of updated Fenner-Borsuk relation (eq. B3) to use of median among literature values reported 

for 25 °C

• 

K

oc

 values for atrazine and DEA 

reduced to 25 percent of their median values from previous studies, while maintaining their relative magnitudes and remaining consistent with the range of values obtained from the published literature

• Initial distribution of soil organic 

matter between fast, medium, and slow pools (2, 18, and 80 percent, respectively) adjusted to 10, 20, and 70 percent, respectively, to improve fit between simulated and measured nitrate concentrations

–
Glossary

(italicized terms in text) 

Active ingredient Chemical 

compound in a commercial 

pesticide formulation (for example, 

atrazine) that is included to kill or 

otherwise control the target pest.

Adjuvants Chemical compounds 

in a commercial pesticide 

formulation that are included to 

enhance the effectiveness of the 

active ingredient (often referred to 

as “inert ingredients”).

Aerobic Characterized by, 

or occurring in the presence of 

detectable concentrations of 

dissolved oxygen (that is, equal or 

greater than 0.5 mg/L).

Anaerobic Characterized by, or 

occurring under conditions where 

the concentrations of dissolved 

oxygen are low enough (typically 

less than 0.5 mg/L) to allow 

denitrification, iron reduction, 

manganese reduction, and (or) 

methanogenesis, to occur.

Anoxic Environmental condition 

(for example, within the subsurface 

or in a surface water body) where 

the concentrations of dissolved 

oxygen are low enough (typically 

less than 0.5 mg/L) to allow 

denitrification, iron reduction, 

manganese reduction, and (or) 

methanogenesis to occur.

Application intensity Areally 

averaged rate of delivery of a given 

substance (for example, a pesticide) 

to the land surface.

Assessment depth Depth 

within the soil column where 

flow-weighted estimates 

of solute concentration or 

recharge were computed from 

computer simulations.

Atrazine residue yield The mole 

percentage of applied atrazine 

represented by the cumulative 

amount of atrazine and its 

transformation products that is 

either simulated or observed to have 

passed a particular depth in the 

subsurface over a specified period 

following atrazine application.

Available water capacity Amount 

of water in soil that is presumed to 

be available for uptake by plants, 

calculated as the water content 

at field capacity minus the water 

content at the wilting point.

Brooks-Corey parameters

Variables used to describe simplified 

versions of the quantitative relations 

between soil matric suction and 

either hydraulic conductivity or 

water content.

Characteristic curve Functional 

relation describing the manner 

in which either water content or 

hydraulic conductivity vary with 

matric suction in the vadose zone.

Chemical ranking method

A groundwater vulnerability 

assessment approach that 

differentiates among different 

potential contaminants on the 

basis of their propensity to 

reach groundwater in detectable 

concentrations following their 

application (either intentional or 

unintentional) to the land surface.

Chemograph Plot showing 

variations in concentration 

of a given solute in a given 

environmental medium (for 

example, water or soil) over time.
182 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Dead-end pores Channels 

within a porous medium that are 

connected at one end to macropores, 

cracks or other continuous pores, 

and through which water flow is 

relatively restricted.

Degradate Transformation product.

Disappearance The aggregate 

influence of all processes, either 

biological or abiotic, that convert 

a compound to one or more 

transformation products.

Drainage rules Algorithms used 

to simulate the movement of water 

within a soil profile

Dry volume heat capacity The 

amount of energy required to raise 

the temperature of a dry material 

(for this study, dry soil) by one 

degree (Fahrenheit or Kelvin), per 

unit volume. 

Fallow Period in the annual cycle of 

agricultural activity, between harvest 

and planting, during which no crops 

are grown.

False negative A predicted nondetection of a chemical substance at 

a site where the substance was, in 

fact, detected.

False positive A predicted detection 

of a chemical substance at a site 

where the substance was not, in 

fact, detected.

Fast organic matter Soil organic 

matter that is metabolized relatively 

rapidly by soil microorganisms.

Fertigation The practice of applying 

fertilizer in water used for irrigation.

Field capacity The water content 

remaining in the soil after most 

gravity drainage has ceased.

Groundwater vulnerability

Likelihood of detecting a particular 

surface-derived contaminant at a 

concentration equal to or greater than 

a given value (such as its reporting 

limit or a water-quality criterion) in 

shallow groundwater.

Cligen A program embedded in 

RZWQM to simulate weather.

Cold simulation Computer 

simulation conducted 

without calibration.

Compartment Environmental 

medium (for example, soil organic 

matter, plant tissues, crop residues, 

groundwater or surface water).

Complex model Computer model 

that simulates water movement 

using well-established equations 

derived from the fundamental 

principles of fluid flow.

Component Sequence of soil 

horizons described within a 

SSURGO or STATSGO map unit.

Continuous corn The practice 

of planting corn in the same field 

every year.

Corn-and-soybean area An 

agricultural location where corn and 

soybeans are grown in rotation. 

Corn Belt Corn-growing areas 

of the northern midcontinental 

United States.

Correct prediction A modelsimulated concentration that 

represents either a true positive or a 

true negative.

Cropping Period in the annual 

cycle of agricultural activity during 

which crops are grown.

Crust factor Ratio between the 

hydraulic conductivity of soil after 

it has been subjected to rainfall and 

the hydraulic conductivity of the 

same soil when it has been protected 

from rainfall.

Daughter product formation 

percentage Proportion of the mass 

of a compound that is transformed 

to another compound through either 

abiotic or biochemical processes.

DEA fraction Percentage of 

atrazine that is transformed to 

DEA through either abiotic or 

biochemical processes.
Glossary 183

Immobile water Water contained 

in dead-end pores and other 

locations within the soil where 

water flow is restricted or stagnant, 

and solute movement occurs 

predominantly through diffusion, 

rather than advection.

Incorrect prediction A modelsimulated concentration that 

represents either a false positive or a 

false negative.

Index method A groundwater 

vulnerability assessment approach 

that uses linear combinations 

of semi-arbitrary numerical 

values that have been assigned to 

specific ranges or categories of 

explanatory variables in order to 

identify spatial variations in the 

likelihood with which a specific 

chemical will reach groundwater 

in detectable concentrations 

following its application (either 

intentional or unintentional) to the 

land surface. Also referred to as a 

scoring method.

Lumped process An overall 

process that is the result of several 

individual processes occurring 

simultaneously (for example, the 

disappearance of a compound 

that results from several different 

transformation processes taking 

place at the same time).

Macropores Root channels, worm 

burrows, cracks or other conduits 

in the soil in which the aqueous 

phase is predominantly mobile 

water, and through which water 

and solutes move relatively rapidly 

by advection.

Map unit Irregularly shaped area 

containing a particular soil type 

(or set of soil types), as specified 

by the SSURGO and STATSGO 

soil databases of U.S. Department 

of Agriculture Natural Resources 

Conservation Service. Map units 

are irregularly shaped because 

their boundaries are determined by 

the spatial distributions of the soil 

type(s) they contain.

Medium organic matter Soil 

organic matter that is metabolized 

at an intermediate rate by 

soil microorganisms.

Mesopores Voids within the soil 

that are intermediate in size between 

micropores and macropores, 

and through which water and 

solutes move by both advection 

and diffusion.

Microbial adaptation The 

observed increase in the rate of 

microbial transformation of a 

pesticide or other applied compound 

that occurs in the soil with repeated 

applications of the compound.

Micropores Voids within the 

soil matrix that are sufficiently 

small that any water they contain 

is considered to be immobile 

water, and solute movement occurs 

primarily through diffusion. 

Mobile water Water within 

macropores, cracks, mesopores 

and other regions within the soil 

where the rates of water flow are 

comparatively high, and solute 

movement occurs primarily by 

advection, as well as by diffusive 

exchange with regions of 

immobile water.

Model residual A measure of 

the agreement between the value 

of a given parameter (such as a 

solute concentration or detection 

frequency, or recharge rate) 

predicted by a computer simulation 

and its corresponding measured 

value, quantified for this report 

as the simulated value minus the 

measured value.

Nonleacher A chemical that 

purportedly will not reach 

groundwater in detectable 

concentrations following its release 

(either intentional or unintentional) 

to the land surface.
184 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Overlay method A groundwater 

vulnerability assessment approach 

that uses a superposition of multiple 

data layers related to climate, soil 

properties, and other site-specific 

parameters to identify spatial 

variations in the likelihood with 

which a specific chemical will 

reach groundwater in detectable 

concentrations following its release 

(either intentional or unintentional) 

to the land surface.

Oxic An environmental condition 

(for example, within the subsurface 

or in a surface water body) 

where detectable concentrations 

of dissolved oxygen are present 

(that is, equal to or greater than 

0.5 mg/L).

Pedotransfer function (PTF) A 

statistical relation that is used to 

estimate a particular soil parameter 

(for example, water content at field 

capacity) from other properties of 

the soil (for example, sand content).

Pesticide A chemical that is 

used to kill or otherwise control 

unwanted plants, insects or 

other organisms.

Pool A specific form of soil 

organic matter, the formation and 

decay of which are simulated 

by RZWQM.

Post-harvest Period in the annual 

cycle of agricultural activity 

between harvest and planting.

Raised reporting limit A reporting 

limit that is temporarily elevated 

relative to the one that is usually 

used for the analyte of interest 

(the routine reporting limit) 

because of matrix interferences, 

instrument issues, or other 

transient circumstances.

Redistribution Gradual movement 

of water between micropores, 

mesopores and macropores within 

soil that occurs between significant 

recharge events.

Residual water content Fitting 

parameter in the Brooks-Corey 

water-content curve that is often 

inferred to be the water content 

of a soil at infinite suction, when 

water can no longer be extracted 

under suction.

Retention time Amount of time 

required for a chemical to migrate 

through a chromatographic column 

under specified conditions.

Routine reporting limit Reporting 

limit for a given analyte that is most 

commonly used for a particular 

analytical method. 

Scoring method A groundwater 

vulnerability assessment approach 

that uses linear combinations of 

semi-arbitrary numerical values 

that have been assigned to specific 

ranges or categories of explanatory 

variables in order to identify spatial 

variations in the likelihood with 

which a specific chemical will 

reach groundwater in detectable 

concentrations following its 

application (either intentional or 

unintentional) to the land surface. 

Also referred to as an index method.

Shallow groundwater

Groundwater located a relatively 

short distance below the water table 

(for this report, within a depth of 

6.3 ± 0.5 m below the water table).

Simple model Computer model 

that simulates water movement 

using a simplified, tippingbucket approach.

Simulation period Time interval 

during which a computer model 

simulates a given process or 

set of processes (for example, 

the transport and fate of solutes 

within soil).

Slow organic matter Soil organic 

matter that is metabolized relatively 

slowly by soil microorganisms.
Glossary 185

Soil hydrologic group A class 

of soil that is characterized by a 

particular range of permeability, 

runoff potential, swelling potential 

and other hydrologic properties.

Stabilization period Time interval 

at the beginning of a computer 

simulation that is used to dissipate 

the transient response to the initial 

conditions specified.

Stream-tube model A numerical 

model that simulates the 

movement of water and solutes 

in one dimension (as if through a 

narrow tube).

Study unit Major hydrologic basin 

in which water quality has been 

examined by the NAWQA program.

Subsurface All regions below the 

land surface, including the vadose 

and saturated zones.

Thermal reactions

Transformations of a chemical that 

are driven by the kinetic energy 

of the reactants, rather than by the 

input of photochemical energy. 

Thiessen polygon Geographical 

region surrounding a particular 

location, or sample point, at which 

the value of a particular parameter 

(for example, a meteorological 

variable such as air temperature) 

is presumed to be known. Each 

Thiessen polygon defines an area of 

influence around its sample point 

such that any location inside the 

polygon is closer to that point than 

to any of the other sample points.

Tipping bucket A simplified 

approach for simulating downward 

water movement through a soil 

profile, in which water collects in 

each individual depth increment 

until a particular water-content 

threshold is exceeded (the bucket 

“tips”) and the water is allowed to 

flow to the next layer below. 

Tortuosity Degree to which a path 

between two points deviates from a 

straight line.

True negative A predicted nondetection of a chemical substance at 

a site where the substance was, in 

fact, not detected.

True positive A predicted 

detection of a chemical substance at 

a site where the substance was, in 

fact, detected.

Use intensity See application 

intensity.

Wilting point Soil water content 

below which most plants are not 

able to extract water from the soil.
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Appendix A 187

Appendix A. Previous Assessments of Groundwater 

Vulnerability

Previous groundwater vulnerability assessments 

(GWAVAs) have spanned more than three decades. Several 

earlier reviews provided summaries and classifications of the 

various approaches that have been used by GWAVAs in the 

past (for example, National Research Council, 1993; U.S. 

Environmental Protection Agency, 1993; Barbash and Resek, 

1996; Corwin and others, 1997; Nolan, 1998; Focazio and 

others, 2002; Pavlis and others, 2010). Table A1 lists key 

features of several GWAVAs that were either not included 

in these reviews or mentioned only briefly by them. For the 

sake of brevity, the table excludes publications that provided 

updates of GWAVAs already described in one or more of 

the earlier reviews (for example, Druliner and others, 1996; 

Kellogg and others, 2000; Kookana and others, 2005). 

The GWAVAs listed in the table involved characterizing 

groundwater vulnerability either with respect to different 

compounds (chemical ranking methods) or among geographic 

locations; none of the investigations examined changes 

in groundwater vulnerability over time. The studies are 

presented in general groups that represent an expansion of 

the framework originally provided by the National Research 

Council (1993). Several of the investigations, however, 

could have been placed in more than one category, such as 

those using regression methods to assess vulnerability using 

chemical indicators (Lopes, 2006), multiple data layers (for 

example, Nolan and Hitt, 2006; Stackelberg and others, 2006, 

2012), or the results from process-based simulation modeling 

(for example, Gurdak and Qi, 2006). 

Chemical Ranking Methods

Many of the chemical ranking methods that have been 

proposed to date incorporate site-specific characteristics such 

as soil properties, estimated recharge rates, or agronomic 

practices (for example, Jury and others, 1987; Rao and others, 

1985; Goss, 1992; Vickery, 2000; Reus and others, 2002). 

The chemical ranking approaches that do not incorporate such 

information (for example, Laskowski and others, 1982; Britt 

and others, 1992)—including the widely cited Groundwater 

Ubiquity Score (Gustafson, 1989; Suzuki and others, 1998; 

Fava and others, 2000; Barra Caracciolo and others, 2005; 

Kördel and others, 2008; Milfont and others, 2008; Silva 

and others, 2012)—have the drawback of neglecting the 

potential influence of local environmental and land-use factors 

on contaminant mobility and persistence in the subsurface. 

Perhaps the greatest concern associated with the chemical 

ranking methods, however, is that several pesticides that these 

techniques have predicted would not reach groundwater—

compounds often referred to as nonleachers (Creeger, 

1986; Gustafson, 1989; Cohen, 1990; Johnson, 1991; Barra 

Caracciolo and others, 2005; Silva and others, 2012)—have, in 

fact, been detected in groundwater (Barbash and Resek, 1996; 

U.S. Geological Survey, 2012). 

Overlay Methods

Most of the fundamental mathematical relations among 

the various physical, hydrologic, chemical, and biological 

parameters that control the transport and fate of contaminants 

in the subsurface have been known for more than three 

decades (for example, van Genuchten and others, 1977; Freeze 

and Cherry, 1979). However, before high-speed computers 

became widely available, it was largely infeasible to use these 

equations to conduct GWAVAs over large spatial scales. As 

a result, many methods have been devised to infer spatial 

variations in the likelihood of detecting surface-derived 

contaminants in groundwater on the basis of one or more 

environmental and (or) anthropogenic factors known to 

affect contaminant transport and fate. The simplest of these 

approaches have been overlay methods, in which differences 

in vulnerability among locations are identified on the basis of 

geographic variations in the values of one or more explanatory 

parameters, all of which are weighted equally. 

Index and Scoring Methods

By contrast with overlay methods, scoring or index 

methods use linear combinations of semi-arbitrary numerical 

values that are assigned to specific ranges or categories of 

selected explanatory variables to infer spatial variations in 

groundwater vulnerability to contamination by surface-derived 

chemicals. Among these methods, the most well-known and 

widely used has been the DRASTIC system (Aller and others, 

1987; Banton and Villeneuve, 1989). Such systems provide 

some indication of the relative vulnerability of groundwater 

to contamination among land-use settings, but neither the 

magnitudes of the scores (or indexes) that have been assigned 

to different parameter ranges or system characteristics (for 

example, well depths, types of geologic materials, or land-use 

settings), nor the relative weights assigned to these factors, 
188 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

have been shown to be correlated with the magnitudes of 

specific, measurable parameters. These and other difficulties 

with scoring and indexing systems have been discussed in 

detail (see, for example, U.S. Environmental Protection 

Agency, 1993; Barbash and Resek, 1996). Despite these 

shortcomings, table A1 indicates that GWAVA systems using 

scoring and weighting schemes continue to be proposed (for 

example, Zhang and others, 1996; Hamerlinck and Arneson, 

1998a, 1998b; Vickery, 2000; Rupert, 2001; Reus and others, 

2002; Cook and Baker, 2006), although their use has been 

discouraged (Alley, 2000).

Hydrogeologic or Chemical Indicator 

Methods

Several investigations have used indirect methods to 

infer the likelihood that surface-derived contaminants may 

be detected in groundwater. At least two such studies have 

focused solely on hydrogeologic phenomena, on the basis of 

responses of water-table depth to variations in atmospheric 

pressure (Landmeyer, 1996) or measured rates of recharge 

(Nolan and others, 2003). All the other studies listed for this 

category in table A1 (as well as all the studies listed in other 

categories where one or more indicator solutes are listed in the 

table) used either the concentrations or simply the detection 

of other solutes as indirect indicators of the potential for 

surface-derived contamination. Many of these solutes are 

clearly anthropogenic (for example, synthetic pesticides, 

chlorofluorocarbon compounds [CFCs] and other volatile 

organic compounds [VOCs]). Others represent naturally 

occurring indicators of hydrologic connection with the land 

surface, the magnitudes of which may also may have been 

influenced by human activities (for example, 2H/1H, 16O/18O, 

or the concentrations of 3H [tritium], 14C, SF6, nitrate, iron, 

or other redox-active solutes). One limitation that all these 

indicator methods share, however, is that they require the 

availability of locally derived data, and therefore cannot 

be used to infer groundwater vulnerability in areas where 

the concentrations (or ratios) of these indicators have not 

been measured.

Statistical Models

Early awareness of the problem of groundwater 

contamination by surface-derived anthropogenic compounds 

in the 1970s led to the initiation of efforts to collect data on 

the occurrence of pesticides (Cohen and others, 1984), VOCs 

(Barbash and Roberts, 1986), and other contaminants (Pye 

and Patrick, 1983) in groundwater over large spatial scales. 

The availability of results from such large-scale sampling 

investigations, coupled with technological improvements 

in computational speed and software, made it possible to 

examine statistical relations between quantitative measures 

of the occurrence of contaminants in groundwater across 

large geographic areas and the various explanatory variables 

that govern their likelihood of detection in the subsurface. 

As indicated by table A1, such statistical models—which use 

logistic regression, linear regression, neural networks and other 

multivariate techniques—are among the most commonly used 

approaches for predicting spatial patterns in the occurrence of 

surface-derived contaminants in groundwater. These efforts 

have involved the construction and application of statistical 

GWAVAs across a broad spectrum of spatial scales, ranging 

from individual counties (Burow and others, 1998), to major 

hydrologic basins (Rupert, 1997; Tesoriero and Voss, 1997; 

Frans, 2000), individual states (Ryker and Williamson, 1996), 

large multi-state regions (Nolan, 1999; Gurdak and Qi, 2006), 

and the entire Nation (Squillace and others, 1999a, 1999b; 

Kolpin and others, 2002; Nolan and Hitt, 2006; Stackelberg and 

others, 2006, 2012). 

A related use of statistical methods to identify the controls 

on groundwater chemistry involves the application of principal 

components analysis (PCA) to water-quality data. Nolan 

(1999) applied PCA to the concentrations of a broad range of 

chemical constituents to examine the possible geochemical 

reasons that nitrate has been detected in groundwater in the 

southeastern United States much less frequently than might 

have been expected from the high rates of fertilizer and 

manure application in that region. Troiano and others (1998, 

1999, 2000) devised an approach that uses cluster analysis, 

PCA, and other related methods to distinguish locations in 

agricultural areas of California where pesticides are likely to 

be detected in groundwater from those where pesticides are 

not likely to be detected. Burow and others (1998) used PCA 

over a more limited geographic area in California to investigate 

correlations among pesticide detections, soil characteristics, 

and concentrations of several major ions and redox-sensitive 

solutes in shallow groundwaters beneath agricultural areas of 

the eastern San Joaquin Valley. PCA was also used by Ator 

(2008), in conjunction with logistic regression, to examine 

the extent to which similar predictive variables explained the 

occurrence of pesticides, nitrate, VOCs, and major ions in 

shallow groundwater beneath the Northern Atlantic Coastal 

Plain. Stackelberg and others (2012) used PCA to develop a 

site-based parameter referred to as a “residence time indicator” 

that explained 25 percent of the variability in the summed 

concentrations of atrazine and deethylatrazine (DEA) predicted 

in groundwater across the United States by a Tobit regression 

model—half of the total variability explained by the overall 

model (pseudo-R2

 = 0.50). Silva and others (2012) used joint 

correspondence analysis—a technique similar to PCA—to 

examine some of the factors related to pesticide occurrence in 

shallow groundwater in central Portugal.
Appendix A 189

Process-Based Simulation Methods

The equations introduced for most of the statistical 

GWAVA methods that have been described to date are 

empirical relations among a specific set of explanatory and 

response parameters for a particular compound, time period, 

and spatial domain. As a result, such equations typically 

exhibit substantial differences from one study to the next 

with respect to the sets of parameters that they include 

(table A1), the magnitudes of the coefficients assigned to 

a given parameter and, in some cases, the mathematical 

transformations to which individual parameters are subjected 

(for example, compare Rupert, 2003 with Stackelberg and 

others, 2006). By contrast, the equations that have been 

elucidated by mechanistic studies of solute transport and fate 

in the hydrologic system are conceptually designed to be 

independent of the specific location, time, or compound in 

question, although calibration for specific applications may 

result in variations in the values of individual coefficients 

among different studies. 

Thus, one of the primary advantages of a simulationbased approach for assessing the vulnerability of groundwater 

to surface-derived contamination, relative to the other 

methods listed in table A1, is that it can, in principle, predict 

contaminant occurrence among different compounds, locations 

or times with little or no additional modification. Another 

advantage of using process-based simulations for GWAVAs 

is that the equations used for such simulations can, with the 

selection of suitable values for their input parameters, be used 

to predict the effects of a variety of different land-management 

techniques, chemical use patterns or climate change on the 

transport and fate of the solutes of interest without further 

modification. To be useful, such an approach requires that 

these equations provide relatively accurate descriptions of the 

physical, hydrologic, chemical and biological processes that 

control the transport and fate of surface-derived solutes in the 

subsurface (Focazio and others, 2002). Model calibration may 

be used, however, to improve the accuracy of the resulting 

predictions by adjusting the values of individual parameters 

that cannot be measured easily (for example, parameters 

associated with hydrodynamic dispersion or pore dimensions) 

or, for parameters with multiple independent measurements 

reported by earlier studies, by adjusting the parameter values 

within the ranges established by previous research. 

Most early applications of numerical models in the 

field of contaminant hydrogeology involved using computer 

simulations to predict the transport and fate of individual 

solutes in soils contained in laboratory columns or beneath 

individual fields (Anderson, 1979). Indeed, systematic 

discrepancies between the simulated and observed behavior of 

solutes in these experimental systems led to the discovery of 

some of the complex phenomena that are now known to affect 

solute transport and fate in porous media, such as preferential 

transport (for example, van Genuchten and others, 1977; 

Bilkert and Rao, 1985) and kinetically controlled sorption 

(for example, van Genuchten and others, 1974; James and 

Rubin, 1979). Numerous studies to date have used solute 

transport-and-fate simulations to predict the concentrations 

of contaminants in the subsurface at selected sites across the 

Nation (for example, Jones and others, 1986; Shaffer and 

Penner, 1991; Flury, 1996; Cowdery, 1997; Mullaney and 

Grady, 1997; Saad and Thorstenson, 1998; Burow and others, 

1999; Tesoriero and others, 2000, 2001; Bayless, 2001; Malone 

and others, 2004b; Bayless and others, 2008; McMahon and 

others, 2008; Webb and others, 2008; Nolan and others, 2010; 

Webb and others, 2011; Liao and others, 2012). 

Dramatic improvements in the availability of large-scale 

electronic repositories for data on soil properties, weather, 

land use, and other environmental information (for example, 

Carsel and Jones, 1990; U.S. Environmental Protection Agency, 

2007; Kipka and others, 2013; Natural Resources Conservation 

Service, 2014; U.S. Geological Survey, 2014)—as well as 

advances in computational speed and in the sophistication of 

numerical techniques—have facilitated the practice of linking 

solute transport-and-fate models with geographic information 

systems to predict the concentrations of surface-derived solutes 

in the subsurface over large spatial scales (Corwin and others, 

1997; Eason and others, 2004; Sood and Bhagat, 2005). Many 

of the process-based assessments to date, however, did not 

compare their predictions with actual monitoring data on the 

occurrence of the target solutes in groundwater (table A1). 

Furthermore, most of the comparisons that have been carried 

out between simulated and measured concentrations in 

groundwater have been applied across spatial scales ranging 

from individual fields to counties (for example, Khakural 

and Robert, 1993; Wu and others, 1996). Relatively few such 

studies to date have focused on regional to national scales (for 

example, Holman and others, 2004; Mouvet and others, 2004; 

Tiktak and others, 2005; Nolan and others, 2012). 
190 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Table A1. Groundwater vulnerability assessment systems (GWAVAs) that were not included, or mentioned only briefly, in earlier 

reviews. 

[Previous reviews were published by National Research Council (1993), U.S. Environmental Protection Agency (1993), Barbash and Resek (1996), Corwin 

and others (1997), Nolan (1998), Focazio and others (2002), and Pavlis and others (2010). Contaminant use or release: Includes direct measures of use, such 

as pesticide, fertilizer or manure application rates, as well as indirect measures of use, that is, land use (LU), population density (PD) and proximity to areas of 

likely use (PALU) such as residential, agricultural, or livestock-confinement areas. Hydrogeology: Includes parameters related to water-table depth, hydraulic 

conductivity, recharge rates, subsurface geology, subsurface residence times, proximity to surface waters and, for the Environmental Performance Indicator for 

Pesticides (Reus and others, 2002), the (undefined) “vulnerability of aquifer.” Hydrogeology also includes parameters related to well characteristics, that is, 

depth of well screen below water table (DWS), well age (WA), well depth (WD) and well type (WT). Predictions compared with independent monitoring 

data: Indicates whether or not predictions from the method of interest were compared with actual observations of contaminant occurrence in groundwater or 

soil, either in the cited reference or in other publications cited in the reference. Because the statistical models are derived from monitoring data, a “yes” in this 

column for these models implies that the cited study included a comparison between model simulations and a set of data different from those used to develop the 

model. Abbreviations: 3H, tritium; 3He, helium-3; 13C, carbon-13; 14C, carbon-14; CaCO3(s), calcium carbonate (solid); CFCs, chlorofluorocarbon compounds; 

EH, oxidation-reduction potential, referenced to hydrogen; Fe, iron; H, hydrogen; Koc, organic carbon-water partition coefficient; Mn, manganese; O, oxygen; O2, 

dissolved oxygen; NH3, ammonia; NO3, nitrate; NO2, nitrite; SF6, sulfur hexafluoride; SO4, sulfate; VOCs, volatile organic compounds]

Reference or

system name

(reference)

Types of parameters used to assess vulnerability Predictions 

compared with 

independent 

monitoring data

Contaminant 

properties

Contaminant 

use or release

Soil 

properties

HydrogeologyWeather 

data

Agronomic 

practices

Indicator chemical 

concentrations

Chemical ranking methods

Johnson, 1991  Yes

Kookana and Aylmore, 1994    Yes

McLaughlin and others, 1993, 1994     No

Newman, 1995   Pesticides No

Hippe and Hall, 1996   Yes

Shukla and others, 1996    Yes

Kolpin and others, 1998   Yes

IATP, 20001     No

Worrall and others, 2000  Yes

Environmental Performance Indicator 

for Pesticides1

 (Reus and others, 

2002)

    No

Environmental Potential Risk Indicator 

for Pesticides1,2 (Reus and others, 

2002)

    No

Environmental Yardstick for Pesticides2

(Reus and others, 2002)

      No

Hasse Diagram (Reus and others, 2002)   Yes

Pesticide Environmental Impact 

Indicator1

 (Reus and others, 2002)

    No

Pesticide Environmental Risk Indicator1

(Reus and others, 2002)

    No

SYNOPS_2 (Reus and others,2002)  No

System for Predicting the 

Environmental Impact of Pesticides1,2

(Reus and others, 2002)

      No

Overlay methods

Carter, 1989  No

Lemme and others, 1990   No

Loague and others, 1990     No

Hollis, 1991  LU    No

Hoyer and Hallberg, 1991  No

McKenna and Keefer, 1991  No

Rupert and others, 1991    No

Leidy and Taylor, 19922   No

Lampman, 1995  Yes

Richards and others, 1996 LU  WA, WD, 

WT

Yes

Ryker and Williamson, 1996 LU WD NO3 No

Nolan and others, 1997 ; LU  Yes
Appendix A 191

Table A1. Groundwater vulnerability assessment systems (GWAVAs) that were not included, or mentioned only briefly, in earlier 

reviews.—Continued

Reference or

system name

(reference)

Types of parameters used to assess vulnerability Predictions 

compared with 

independent 

monitoring data

Contaminant 

properties

Contaminant 

use or release

Soil 

properties

HydrogeologyWeather 

data

Agronomic 

practices

Indicator chemical 

concentrations

Overlay methods—Continued

Desimone and Ostiguy, 1999 LU   NO3 Yes

Lin and others, 1999   Yes

Bernknopf and others, 2001    No

Lowe and Sanderson, 2003     No

Pfaff and Glennon, 2004 LU No

Miller and others, 2006   ; WD  Yes

Index and scoring methods

Corte-Real, 1986  ; LU    No

Schmidt, 1987   No

Porcher, 1989   No

Trojan and Perry, 1989    No

Chilton and others,1990  LU  Yes

DeLuca and others, 1990; National Park 

Service, 2009

     No

Ray and O’dell, 1993  No

Seelig, 1994    CaCO3

(s) No

Engel and others, 1996     Yes

Vowinkel, 1996 LU  ; WD Yes

Zhang and others, 1996   No

Rupert, 1997 ; LU  Yes

Hamerlinck and Arneson, 1998a,b     No

Hanson, 1998   Yes

Arizona Department of Environmental 

Quality, 2000

    No

Cook and Baker, 2006     Yes

Hydrogeologic or chemical indicator methods

Landmeyer, 1996  No

Thomas, 2000 Pesticides, NO3 No

Nelms and others, 2003 ; WT CFCs, SF6

, 3H, 3He, 14C, 13CNo

Moran and others, 2002 H and O isotopes, 

VOCs

Yes

Trojan and others, 2002 O2

, EH, Fe Yes

Nolan and others, 2003   Yes

Worrall and Kolpin, 2003 Pesticides No

Tesoriero and others, 2004 Major ions, redoxactive solutes

Yes

Manning and others, 2005 3

H, 3He Yes

Lopes, 2006   CFCs, NO3

, H and 

O isotopes, VOCs, 

pesticides

Yes

Rupert and Plummer, 2009    CFCs, NO3

, H and 

O isotopes, VOCs, 

pesticides

No

Statistical models

Anderson and others, 1985   WD   No

Steichen and others,19881 LU; PALU WA No

Tesoriero and Voss, 1997 LU ; WD  Yes

Troiano and others, 1998, 1999     Pesticides Yes

Rupert, 1998 LU  WD   Yes

Tesoriero and others, 1998 LU ; WD  Yes
192 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Table A1. Groundwater vulnerability assessment systems (GWAVAs) that were not included, or mentioned only briefly, in earlier 

reviews.—Continued

Reference or

system name

(reference)

Types of parameters used to assess vulnerability Predictions 

compared with 

independent 

monitoring data

Contaminant 

properties

Contaminant 

use or release

Soil 

properties

HydrogeologyWeather 

data

Agronomic 

practices

Indicator chemical 

concentrations

Statistical models—Continued

Lin and others, 1999     Nutrients, 

major ions

No

Squillace and others, 1999b ; LU; PD DWS No

Squillace and others, 1999a PD Yes

Frans, 2000   WD No

Kolpin and others, 2000   No

Nolan, 2001 ; LU; PD   No

Rupert, 20011 LU   Yes

SCIGROW (U.S. Environmental 

Protection Agency, 2001)

  No

Kolpin and others, 2002    Total dissolved N 

(NO2

 + NO3

+ NH3

)3

No

Nolan and others, 2002    Yes

Ramasamy and others, 2003 PALU Season Fe Yes

Rupert, 2003   ; WT  Yes

Worrall and Kolpin, 2004    No

Greene and others, 2005 LU    Yes

Gurdak and Qi, 20062    No

Nolan and Hitt, 2006    Yes

Sahoo and others, 2006 ; WD, 

DWS

Yes

Stackelberg and others, 2006    No

Fram and Belitz, 20111 LU; PALU ; WD, 

DWS

 NO3

, pesticides, 

solvents, fuel 

compounds, 

trihalomethanes, 

other redox-active 

solutes

No

Hynds and others, 2012 LU; PALU  ; WT, 

DWS

 Yes

Stackelberg and others, 2012      NO3

, other redoxactive solutes, 

major ions, pH

Yes

Process-based simulation methods

Petach and others, 1991; Hutson, 1993     No

Goss, 1992; Goss and others, 1998      No

Hoag and Hornsby, 1992  No

Foussereau and others, 1993     No

Khakural and Robert, 1993    Yes

Franklin and others, 1994   No

Bleecker and others, 1995     No

Görres and Gold, 1996    No

Soutter and Pannatier, 1996     No

Tiktak and others, 1996      No

Wilson and others, 1996     No

Wu and others, 1996     Yes

Zhang and others, 1996   No

Williamson, 1997       No

Holtschlag and Luukkonen, 1998 4    CFCs Yes

Hoogeweg and Hornsby, 1998      No

Snyder and others, 1998  Yes

Morgan, 1999, 2002      No
Appendix A 193

Table A1. Groundwater vulnerability assessment systems (GWAVAs) that were not included, or mentioned only briefly, in earlier 

reviews.—Continued

Reference or

system name

(reference)

Types of parameters used to assess vulnerability Predictions 

compared with 

independent 

monitoring data

Contaminant 

properties

Contaminant 

use or release

Soil 

properties

HydrogeologyWeather 

data

Agronomic 

practices

Indicator chemical 

concentrations

Process-based simulation methods—Continued

van Wesenbeeck and Havens, 1999     No

Macur and others, 2000      Yes

Ulery, 2000; Ulery and Rogers, 2005    Yes

Schlosser and McCray, 2002    No

Schlosser and others, 2002    Yes

Stewart and Loague, 2003     No

Eason and others, 2004   No

Holman and others, 2004     Yes

Jørgensen and others, 2004     No

Mouvet and others, 2004      Yes

Tiktak and others, 2005       Yes

Clark and others, 2006   Yes

FOOTPRINT project (Dubus, 2006)       No

Jackson and others, 2006      No

Almasri and Kaluarachchi, 2007      Yes

Kauffman and Chapelle, 2010     pH, O2

, NO3, Fe, 

Mn, SO4

Yes

Miller, 2010      No

Kourakos and others, 2012   No

Nolan and others, 2012     Yes

1

Method also involves the use of numerical scores or indices to quantify one or more of its explanatory factors. 

2

Method also incorporates results from simulations of solute transport.

3

Explanatory parameters considered in constructing the statistical model (though not included in its final form) also included pH and the concentrations of 

dissolved oxygen, iron, phosphorus, and chloride measured in groundwater at the time the concentrations of the target contaminants (atrazine and metolachlor) 

were measured.

4

Study involved adjusting Koc and transformation half-life for atrazine within the ranges established by variations in literature values.

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202 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Appendix B. Equations Used to Estimate 

Deethylatrazine Formation Percentage

Equation 38 was used to estimate the DEA formation percentage in each SSURGO component 

for which P-GWAVA-RZ simulations were carried out during Phase 2 of this study. Values of katr>DEA

and katr for equation 38 were estimated using modified versions of the Fenner-Borsuk equation, a 

quantitative medium-reactivity relation (QMRR) introduced by Fenner and others (2007) to predict 

the rates of atrazine disappearance in oxic, non-sterile soil in the dark. The original version of the 

Fenner-Borsuk equation, rearranged to present its two temperature-related terms in the same form as 

the others, may be expressed as follows:

ln ( pH) (0.43*[pH] sand]) 2 katr ) . = −780 5 5 + − ( .31* ) − ( . 1 21*ln[% −

− − −

(0.53*ln[

%

d

oc oc

diff ])

( . 0 60 0 *ln[% ]) ( . * 36 {ln[ ]} ) (0 3. *9 2 ln[ ]) . . d

T T min +

 ×













 −

 ×















4 52 10 6 66 10 5 7

2

 (B1)

where

pH is the soil pH;

% sand is the percentage of sand content of the soil, by weight;

ddiff is the depth interval from which the soil was obtained (cm);

%oc is the percentage of organic carbon content of the soil, by weight;

dmin is the minimum depth from which the soil was obtained (cm); and

T is the temperature at which the transformation rate was measured (K).

According to Mark Borsuk, Dartmouth College (written commun., October 2007), the coefficient 

in the term expressed as “147 T-1” in the original version of the equation presented by Fenner 

and others (2007) was incomplete, and should have read “147.41”. Equation B1 incorporates this 

correction. Equation B1 explained 71 percent of the overall variance (R2 = 0.71; P ≤ 0.0001) among 

the 111 laboratory-derived values of ln(katr) from which it was developed by Fenner and others 

(original data provided on July 1, 2007, by Kathrin Fenner, Swiss Federal Institute of Aquatic 

Science and Technology). 

A separate search of the literature for this study yielded 300 measured values of katr from 18 

laboratory investigations that provided data for all of the explanatory parameters in equation B1 

(Roeth and others, 1969; Walker, 1978; Winkelmann and Klaine, 1991a and 1991b; Kruger and 

others, 1993; Assaf and Turco, 1994; Topp and others, 1994; Rocha and Walker, 1995; Stolpe and 

Shea, 1995; Kruger and others, 1997; Rodriguez and Harkin, 1997; Baer and Calvet, 1999; Issa 

and Wood, 1999; Dinelli and others, 2000; Accinelli and others, 2001; Jacobsen and others, 2001; 

Zablotowicz and others, 2006 and 2007). Because of differences between this study and Fenner 

and others (2007) in the criteria used to select data for inclusion in the calculations (and limitations 

in the availability of the original publications used by Fenner and others [2007]), only 4 of the 

18 laboratory studies were among the 10 from which Fenner and others (2007) derived equation B1. 

In instances where the studies of interest reported the humic material content of the soil in terms of 

percent soil OM (%om), rather than %oc, %om values were converted to %oc using the assumption 

that soil OM contains an average of 58 percent organic carbon (Chiou, 2002), that is:

% . oc om = 0 58*% (B2)
Appendix B 203

The data from the 18 laboratory studies of interest were used to recalculate the coefficients 

in equation B1, yielding the following, modified version of the Fenner-Borsuk relation for 

predicting the rate of atrazine disappearance in oxic, non-sterile soil in the absence of light:

 ln ( pH) (0.145612*[pH]2 katr ) . = −84 0141 1 92561 − + ( . * ) − ( . 0 194685

0 263303 0 0

*ln[%

]) ( . *ln[% ]) ( .

sand])

− + (0.478998*ln[d o diff c + 0586421

0 211478 57586 5 92

2 *{ln[% ]} )

( . *ln[ ]) .

oc

d

T

− + min





 



 − 97840

T 2





 





 (B3)

Equation B3 explained 68 percent of the overall variation among the 300 laboratory-derived 

values of ln(katr) from which it was derived (R2 = 0.68; P ≤ 0.0001), similar to the percentage of 

the variation in measured atrazine disappearance rates explained by the original Fenner-Borsuk 

relation (equation B1). However, because it was derived from nearly three times as many 

laboratory observations as were used for equation B1, equation B3 was the QMRR used to 

estimate the values of katr required to compute Fatr>DEA with equation 38. 

Because of the incomplete coverage of data for soil pH across the Nation, the use of 

equation B3 to compute katr for the P-GWAVA-RZ simulations assumed that the soil pH was 

neutral (pH = 7) at all of the sites of interest. Additionally, because the transformation rates 

computed with equation B3 pertained to reactions that initially occurred close to the soil 

surface, the %oc and %sand values used in the equation for each SSURGO soil component 

were from the uppermost soil horizon, and ddiff and dmin were assumed to be 1 cm for all sites. 

Given that the agrichemicals of interest were presumed to have been applied at the end of 

May, the temperature used to compute katr for each SSURGO component was the average air 

temperature in May (TMay) at that location. 

Using data retrieved or calculated from eight laboratory studies of the rates of DEA 

production from atrazine dealkylation in non-sterile, oxic soils in the dark (Winkelmann and 

Klaine, 1991a and 1991b; Kruger and others, 1993; Assaf and Turco, 1994; Topp and others, 

1994; Kruger and others, 1997; Rodriguez and Harkin, 1997; Zablotowicz and others, 2006), as 

well as the same explanatory variables and transformations used in the Fenner-Borsuk relation, 

an approach similar to that used to develop equation B3 was used to obtain the following 

QMRR for predicting katr>DEA for each SSURGO component of interest:

ln ( pH) (0.44668*[pH]2 katr>DEA ) . = −118 437 6 54703 + − ( . * ) + ( . 0 921228

0 133383 0

*ln[%

]) ( . *ln[% ]) (

sand])

− − (1.52988*ln[d o diff c − . *{ln[% ]} )

( . *ln[ ]) .

292146

0 203394 41294 1 43

2

oc

d

T

− + min





 



 − 34410

T 2





 





(B4)

Each katr>DEA value used to develop equation B4 was computed from published data by 

multiplying the katr value for the experiment in question by the fraction of reacted atrazine 

that appeared as DEA. Equation B4 accounted for 62 percent of the variability among the 58 

values of ln(katr>DEA) computed from the results reported by the eight laboratory investigations 

(R2 = 0.62; P ≤ 0.0001). Data from the eight studies were then used in conjunction with 

equations B3, B4, and 38 to compute a predicted value of Fatr>DEA corresponding to each of 

the 58 measurements derived from the laboratory studies. The computed values explained 

64 percent of the variability in the measured values (R2 = 0.64; P ≤ 0.0001; N = 58). The extent 

of agreement between the computed and measured Fatr>DEA values is shown in figure B1.
204 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

tac12-0781_figB1

0 

5 

10 

15 

20 

25 

30 

35 

40 

0 10 20 30 40 50 60 

Simulated DEA formation, in percent

Measured DEA formation, in percent

Simulated = measured 

References Cited

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Figure B1. Deethylatrazine (DEA) formation percentages (Fatr>DEA) in non-sterile, oxic soils in the absence of light computed 

with equations 38, B3, and B4 using explanatory data associated with 58 measurements from eight laboratory studies, 

compared with the values computed from the measurements reported by the same studies.
Appendix B 205

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206 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Appendix C. Groundwater Sampling Networks Examined
Appendix C 207

Table C1. Groundwater sampling networks examined for this study.[The names, locations, land-use settings and numbers of sites in the National Water-Quality Assessment (NAWQA) groundwater sampling networks examined during the two phases of this study are listed. For locations of, and summaries of major findings from the NAWQA studies conducted in each area, see U.S. Geological Survey (2013). NAWQA network type: See table 2 for general descriptions of the NAWQA networks. ACT, Agricultural Chemicals Team; LUS, NAWQA land-use study; FPS, flow path study. Abbreviations: DTW, average depth to water; NE, Northeast; SE, Southeast; USGS, U.S. Geological Survey; m, meter; >, greater than; –, not applicable]

Groundwater 

network 

code

Hydrologic basin Region

NAWQA 

network

type

Land-use setting

Phase 1 networks Phase 2 networks

Total 

number

of sites

Number of sites 

with DTW greater 

than the Phase 1 

assessment depth 

(> 1 m)

Total 

number

of sites

Number of sites 

with DTW greater 

than the Phase 2 

assessment depth 

(> 3 m)

acfblusag1 Apalachicola-Chattahoochee-Flint River Basin SE LUS cropland and pasture 17 17 – –

acfbluscr1 Apalachicola-Chattahoochee-Flint River Basin SE LUS cropland and pasture 10 10 – –albelusag1 Albemarle-Pamlico Drainage Basin SE LUS cropland and pasture 17 11 – –

albelusag2 Albemarle-Pamlico Drainage Basin SE LUS cropland and pasture 6 3 – –

cazbluscr1 Central Arizona Basins West LUS cropland and pasture 4 4 – –

ccptlusag1 Central Columbia Plateau West LUS cropland and pasture 28 28 – –

ccptlusag2 Central Columbia Plateau West LUS cropland and pasture 49 49 – –

ccptlusor1 Central Columbia Plateau West LUS orchards and vineyards 40 40 – –

cnbract Central Nebraska Basins Corn Belt ACT study cropland and pasture – – 2 0

cnbrluscr1 Central Nebraska Basins Corn Belt LUS cropland and pasture – – 27 21

connlusag1 Connecticut, Housatonic and Thames River Basins NE LUS cropland and pasture 40 40 – –

eiwaluscr1 Eastern Iowa Basins Corn Belt LUS cropland and pasture 31 28 24 2hdsnlusag1 Hudson River Basin NE LUS cropland and pasture 14 11 – –

hpgwlusag3 High Plains Regional Groundwater Study West LUS cropland and pasture – – 1 1

leriluscr1 Lake Erie-Lake Saint Clair Drainages Corn Belt LUS cropland and pasture 30 27 28 12linjluscr1 Long Island-New Jersey Coastal Drainages NE LUS cropland and pasture 15 15 – –

lirbluscr1 Lower Illinois River Basin Corn Belt LUS cropland and pasture 27 26 25 14lirbluscr2 Lower Illinois River Basin Corn Belt LUS cropland and pasture 29 29 28 16lsuslusag1 Lower Susquehanna River Basin NE LUS cropland and pasture 30 30 – –

lsuslusag2 Lower Susquehanna River Basin NE LUS cropland and pasture 30 29 – –lsuslusag3 Lower Susquehanna River Basin NE LUS cropland and pasture 30 30 – –miamluscr1 Great and Little Miami River Basins Corn Belt LUS cropland and pasture – – 24 16

miamluscr1a Great and Little Miami River Basins Corn Belt LUS cropland and pasture – – 8 6

nvbrlusag1 Las Vegas Valley Area and the Carson and Truckee River Basins West LUS cropland and pasture 20 12 – –

ozrklusag1 Ozark Plateaus Corn Belt LUS cropland and pasture 17 8 – –

ozrklusag1a Ozark Plateaus Corn Belt LUS cropland and pasture 2 1 – –

ozrklusag2 Ozark Plateaus Corn Belt LUS cropland and pasture 13 12 – –

ozrklusag2a Ozark Plateaus Corn Belt LUS cropland and pasture 7 7 – –

potolusag1 Potomac River Basin NE LUS cropland and pasture 29 29 – –

potolusag2 Potomac River Basin NE LUS cropland and pasture 25 21 – –

pugtfpscr1 Puget Sound Basin West FPS cropland and pasture 6 5 – –

pugtluscr1 Puget Sound Basin West LUS cropland and pasture 8 8 – –
208 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States Table C1. Groundwater sampling networks examined for this study.—Continued [The names, locations, land-use settings and numbers of sites in the National Water-Quality Assessment (NAWQA) groundwater sampling networks examined during the two phases of this study are listed. For locations of, and summaries of major findings from the NAWQA studies conducted in each area, see U.S. Geological Survey (2013). NAWQA network type: See table 2 for general descriptions of the NAWQA networks. ACT, Agricultural Chemicals Team; LUS, NAWQA land-use study; FPS, flow path study. Abbreviations: DTW, average depth to water; NE, Northeast; SE, Southeast; USGS, U.S. Geological Survey; m, meter; >, greater than; –, not applicable] Groundwater network code Hydrologic basin Region NAWQA network type Land-use setting Phase 1 networks Phase 2 networks Total number of sites Number of sites with DTW greater than the Phase 1 assessment depth (> 1 m) Total number of sites Number of sites with DTW greater than the Phase 2 assessment depth (> 3 m) rednfpsag1 Red River of the North Basin Corn Belt FPS cropland and pasture – – 2 1 rednfpsag2 Red River of the North Basin Corn Belt FPS cropland and pasture – – 6 2 rednlusag1 Red River of the North Basin Corn Belt LUS cropland and pasture 29 28 25 18 rednlusag2 Red River of the North Basin Corn Belt LUS cropland and pasture 29 24 22 4 rioglusag1 Rio Grande Valley West LUS cropland and pasture 23 23 – – riogluscr1 Rio Grande Valley West LUS cropland and pasture 35 35 – – sacrluscr1 Sacramento River Basin West LUS cropland and pasture 28 15 – – sanjluscr1 San Joaquin-Tulare Basins West LUS cropland and pasture 9 9 – – sanjluscr1a San Joaquin-Tulare Basins West LUS cropland and pasture 11 11 – – sanjlusor1 San Joaquin-Tulare Basins West LUS orchards and vineyards 8 8 – – sanjlusor1a San Joaquin-Tulare Basins West LUS orchards and vineyards 12 12 – – sanjlusor2 San Joaquin-Tulare Basins West LUS orchards and vineyards 8 8 – – sanjlusor2a San Joaquin-Tulare Basins West LUS orchards and vineyards 12 12 – – santluscr1 Santee River Basin and Coastal Drainages SE LUS cropland and pasture 31 31 – – sofllusag1 Southern Florida SE LUS cropland and pasture 7 4 – – sofllusor1 Southern Florida SE LUS orchards and vineyards 31 19 – – spltluscr1 South Platte River Basin West LUS cropland and pasture 28 28 – – uirbluscr1 Upper Illinois River Basin Corn Belt LUS cropland and pasture – – 29 7 umisluscr1 Upper Mississippi River Basin Corn Belt LUS cropland and pasture 29 29 21 18 usnkluscr1 Upper Snake River Basin West LUS cropland and pasture 29 28 – – usnkluscr2 Upper Snake River Basin West LUS cropland and pasture 31 30 – – usnkluscr3 Upper Snake River Basin West LUS cropland and pasture 30 30 – – usnkluscr4 Upper Snake River Basin West LUS cropland and pasture 14 14 – – utenluscr1 Upper Tennessee River Basin NE LUS cropland and pasture 30 18 – – whitfpsag1 White River Basin Corn Belt FPS cropland and pasture – – 13 6 whitfpsag2 White River Basin Corn Belt FPS cropland and pasture – – 2 0 whitluscr1 White River Basin Corn Belt LUS cropland and pasture 22 22 26 14 whitluscr2 White River Basin Corn Belt LUS cropland and pasture 21 20 22 9 whitluscr3 White River Basin Corn Belt LUS cropland and pasture 7 7 – – whitluscr3a White River Basin Corn Belt LUS cropland and pasture 17 17 23 11 whitluscr3b White River Basin Corn Belt LUS cropland and pasture – – 6 3
Appendix C 209

Table C1. Groundwater sampling networks examined for this study.—Continued[The names, locations, land-use settings and numbers of sites in the National Water-Quality Assessment (NAWQA) groundwater sampling networks examined during the two phases of this study are listed. For locations of, and summaries of major findings from the NAWQA studies conducted in each area, see U.S. Geological Survey (2013). NAWQA network type: See table 2 for general descriptions of the NAWQA networks. ACT, Agricultural Chemicals Team; LUS, NAWQA land-use study; FPS, flow path study. Abbreviations: DTW, average depth to water; NE, Northeast; SE, Southeast; USGS, U.S. Geological Survey; m, meter; >, greater than; –, not applicable]

Groundwater 

network 

code

Hydrologic basin Region

NAWQA 

network

type

Land-use setting

Phase 1 networks Phase 2 networks

Total 

number

of sites

Number of sites 

with DTW greater 

than the Phase 1 

assessment depth 

(> 1 m)

Total 

number

of sites

Number of sites 

with DTW greater 

than the Phase 2 

assessment depth 

(> 3 m)

whmiact White-Miami River Basins Corn Belt ACT study cropland and pasture – – 7 1

willlusag1 Willamette Basin West LUS cropland and pasture 15 15 – –

willlusag2 Willamette Basin West LUS cropland and pasture 28 27 – –

wmicfpsag1a Western Lake Michigan Drainages Corn Belt FPS cropland and pasture – – 6 5

wmicfpsag1b Western Lake Michigan Drainages Corn Belt FPS cropland and pasture – – 3 0

wmicfpsag2a Western Lake Michigan Drainages Corn Belt FPS cropland and pasture – – 17 11

wmicfpsag2b Western Lake Michigan Drainages Corn Belt FPS cropland and pasture – – 2 0

wmiclusag1 Western Lake Michigan Drainages Corn Belt LUS cropland and pasture 3 2 – –

wmiclusag1a Western Lake Michigan Drainages Corn Belt LUS cropland and pasture 23 19 24 14wmiclusag1b Western Lake Michigan Drainages Corn Belt LUS cropland and pasture – – 2 0wmiclusag2 Western Lake Michigan Drainages Corn Belt LUS cropland and pasture 30 30 28 24
210 Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater in the United States

Reference Cited

U.S. Geological Survey, 2013, Complete listing of NAWQA’s 

study units and summary reports: U.S. Geological Survey 

data base, accessed September 28, 2013, at http://water.

usgs.gov/nawqa/studies/study_units_listing.html.
Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

 Director, Washington Water Science Center

U.S. Geological Survey

934 Broadway, Suite 300

Tacoma, Washington 98402 

http://wa.water.usgs.gov
Barbash and Voss—Process-Based Groundwater Vulnerability Assessment System for Agrichemicals in Groundwater Across the United States—Scientific Investigations Report 2014–5189

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20145189