i

Development of Community Water System Drinking Water Intake 

Percent Cropped Area Adjustment Factors for use in Drinking Water 

Exposure Assessments

Environmental Fate and Effects Division

Office of Chemical Safety and Pollution Prevention

United States Environmental Protection Agency

08/21/13

Prepared by:

Rochelle F. H. Bohaty, Ph.D.

James N. Carleton, Ph.D.

Tanja Crk

Marietta Echeverria

Mohammed Ruhman, Ph.D.

Michelle Thawley

Nelson Thurman

Philip Villanueva

Katrina White, Ph.D.

1
ii

2 Forward

3 The United States Environmental Protection Agency (USEPA), Office of Pesticide Programs 

4 (OPP) initiated a peer review of an earlier draft of this document (titled Development of 

5 Community Water System Drinking Water Intake Percent Cropped Area Adjustment Factors for 

6 use in Drinking Water Exposure Assessments, dated 10/10/2012). Internal and external experts

7 who were familiar with the datasets used in the calculation of Percent Cropped Area (PCA) 

8 adjustment factors or with occurrence of pesticides in drinking or surface water were asked to 

9 provide comments on the document and respond to charge questions. Consistent with USEPA’s 

10 Peer Review guidelines, the feedback from reviewers was carefully considered and this

11 document was updated accordingly. 

12
iii

13 Summary

14 Watersheds large enough to support a drinking water facility are generally not comprised 

15 of only one land cover type, nor planted completely with a single crop. In order to account for 

16 variability in land cover, the USEPA uses percent cropped area (PCA) adjustment factors to

17 reflect the percentage of a watershed that is covered by a particular land cover type and/or crop. 

18 Modeled concentrations of pesticides in surface waters are multiplied by a PCA to account for 

19 the areal fraction of a watershed that may be treated with a particular pesticide based on the

20 pesticide uses and the land cover types (i.e., crops) associated with those uses. The PCA21 adjusted concentrations are used as the estimated drinking water concentrations (EDWCs) in 

22 human health risk assessment. Previously, PCAs were generated for Hydrologic Unit Code 8 

(HUC-8)123 regions. In this update, PCAs have been generated for watersheds delineated based on 

24 surface-source drinking water intakes (DWI) of community water system (CWS) across the 

25 United States. The new PCAs are an improvement over previously calculated PCAs in terms of 

26 relevance to human health risk assessment because the PCAs were derived for known drinking 

27 water sources. A CWS-DWI dataset, version 1.0, was used as the source of intakes and 

28 associated watersheds. Watersheds were delineated for CWS-DWIs with available tools and 

29 data. For some surface water-sourced CWS either it did not make sense to delineate a watershed 

30 for the DWIs, or watersheds were generated but subsequently failed to pass a quality assurance 

31 screen. Out of 6,550 DWI locations which both met the selection criteria for watershed 

32 delineation and passed a quality assurance screen, 74% (4,840) had unique, delineated 

33 watersheds which also passed a quality assurance screen. Of these, all but two (4,838) were 

34 located in the continental United States. For another 637 quality assured DWI locations in the 

35 continental U.S. which lacked quality assured watersheds, HUC-12 regions were taken as 

36 watershed surrogates. Maximum PCAs for the quality assured watersheds and HUC-12 

37 surrogates in the continental U.S. (listed by HUC-2 water resource region) to use in representing

38 various crops and two-crop combinations are shown in Table 1 and Table 2, respectively. The 

39 national maximum value(s) in each case are shaded in the tables. When using PCAs to modify 

40 surface water modeling results, all of the potential pesticide use sites (i.e., currently registered or 

41 proposed for registration) must be considered together, and used to select an appropriate PCA. 

42

43

 

1 Hydrologic units are part of a hierarchical system for classifying and mapping drainage areas in the United States 

(Seaber et al., 1987). The largest units (regions) are designated by two digits, and hence are often called 2-digit 

HUCs. Subdivisions of regions are designated with additional digits. There are 2,264 8-digit HUCs in the United 

States. 
iv

44 Table 1. Maximum percent cropped area (PCA) adjustment factors for various crops, by 

45 Water Resource Region (HUC-2). Listed PCAs are specifically recommended for use in 

46 surface drinking water exposure modeling.

HUC-2 All-Aga Corn Cotton Orchard Soybean Vegetable Wheat Turfb

01 0.32 0.08 0.00 0.02 0.00 0.10 0.00 0.86

02 0.82 0.34 0.06 0.07 0.11 0.03 0.06 0.60

03 0.52 0.09 0.08 0.12 0.20 0.08 0.10 0.64

04 0.92 0.35 0.00 0.05 0.44 0.04 0.11 0.85

05 0.90 0.51 0.02 0.03 0.59 0.02 0.10 0.49

06 0.45 0.10 0.04 0.02 0.10 0.01 0.03 0.38

07 1.00 0.68 0.00 0.00 0.58 0.01 0.27 0.59

08 0.75 0.21 0.20 0.00 0.07 0.00 0.05 0.52

09 0.95 0.16 0.00 0.00 0.21 0.02 0.41 0.06

10 1.00 0.36 0.00 0.00 0.37 0.02 0.27 0.51

11 0.79 0.20 0.07 0.05 0.34 0.01 0.39 0.70

12 0.77 0.11 0.21 0.03 0.01 0.04 0.18 0.25

13 0.52 0.01 0.01 0.01 0.00 0.01 0.00 0.11

14 0.37 0.02 0.00 0.00 0.00 0.01 0.06 0.08

15 0.41 0.01 0.04 0.03 0.00 0.19 0.08 0.49

16 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.04

17 0.72 0.01 0.00 0.02 0.00 0.01 0.38 0.35

18 0.74 0.15 0.05 0.33 0.00 0.16 0.07 0.59

*

Shaded cells indicate the highest value for each crop.

a Considers all-agriculture PCAs calculated both ways. The highest All-Ag values from Tables 3-2 

and 3-4 are listed here.

b Represents residential turf including golf courses, but does not include sod farms. Sod farms are 

included in the all-agriculture land cover class PCA.

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61
v

62

63 Table 2. Maximum percent cropped area (PCA) adjustment factors for various crop 

64 combinations, by Water Resource Region (HUC-2). Listed PCAs are specifically 

65 recommended for use in surface drinking water exposure modeling.

HUC2CornWheatSoybeanWheatTurfa-

Corn

Turfa-

Orchard

Turfa-

Soybean

Turfa-

Vegetable

Turfa-

Wheat

VegetableOrchardTurfa,b-

All Ag

01 0.08 0.00 0.86 0.86 0.86 0.86 0.86 0.12 0.98

02 0.36 0.17 0.60 0.60 0.60 0.60 0.60 0.10 0.82

03 0.18 0.25 0.64 0.64 0.64 0.64 0.64 0.13 0.65

04 0.42 0.52 1.00 0.85 0.85 0.85 0.85 0.06 1.00

05 0.52 0.59 0.58 0.49 0.64 0.49 0.49 0.04 0.96

06 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

07 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

08 0.22 0.12 0.53 0.52 0.52 0.52 0.52 0.00 0.75

09 0.42 0.44 0.20 0.06 0.25 0.07 0.46 0.02 0.99

10 0.38 0.47 0.51 0.51 0.51 0.51 0.51 0.02 1.00

11 0.41 0.55 0.70 0.70 0.70 0.70 0.70 0.05 0.81

12 0.20 0.18 0.29 0.25 0.25 0.25 0.26 0.07 0.82

13 0.01 0.00 0.11 0.11 0.11 0.11 0.11 0.02 0.57

14 0.06 0.06 0.09 0.08 0.08 0.08 0.10 0.01 0.41

15 0.08 0.08 0.49 0.49 0.49 0.50 0.50 0.23 0.57

16 0.00 0.00 0.04 0.04 0.04 0.04 0.04 0.00 0.07

17 0.38 0.38 0.35 0.35 0.35 0.35 0.40 0.03 0.74

18 0.18 0.07 0.59 0.59 0.59 0.59 0.59 0.34 0.83

*

Shaded cells indicate the highest value for each crop.

a Represents residential turf including golf courses, but does not include sod farms. Sod farms are included in the 

all-agriculture land cover class PCA.

b Because all-agriculture PCAs were calculated two different ways, some exceed the maximum Turf-All Ag PCAs. 

In such cases (i.e., for HUC-2 regions 2 and 8), the maximum all-agriculture PCA is substituted in this table for the 

regional maximum Turf-All-Ag PCA listed in Table 3-7.

66

67 This document presents a history of the development and use of PCAs as used by the 

68 Office of Pesticide Programs (OPP), and the process used for calculating CWS-DWI specific 

69 PCAs. Chapter 1 provides an overview of the history of PCAs, and describes DWI PCAs. 

70 Chapter 2 documents the development of the CWS-DWI dataset and the use of HUC-12 

71 surrogates for a subset of quality assured intakes that do not have quality assured watersheds. 

72 Chapter 3 describes the methods used to develop the PCAs, and presents a summary of results. 

73 Chapter 4 compares modeled drinking water concentrations, modified using DWI PCAs, to 

74 observed concentrations reported in monitoring studies for a number of pesticides. Chapter 5
vi

75 discusses uncertainties and limitations of the DWI PCA dataset, and presents suggestions on 

76 ways in which DWI PCAs may be used for risk assessment purposes.

77
vii

78 Table of Contents

79

80 Chapter 1. Purpose, History, and Overview of Updates of PCA Adjustment Factors............. 10

81 1.1 Introduction .................................................................................................................... 10

82 1.2 Brief Overview of the Derivation of Percent Cropped Area Adjustment Factors......... 11

83 1.3 History of PCA Development ........................................................................................ 12

84 1.4 Applying PCAs to Estimated Drinking Water Concentrations (EDWCs)..................... 16

85 1.5 Shifting from HUC-8 Subbasins to Community Water System Watersheds................. 16

86 Chapter 2. Development of Drinking Water Intake Watershed Dataset .................................. 18

87 2.1 Background .................................................................................................................... 18

88 2.2 Database Development................................................................................................... 18

89 Surface Water Intake Locations............................................................................................ 18

90 DWI Watershed Delineation................................................................................................. 20

91 2.3 Database Uncertainties................................................................................................... 25

92 2.4 Conclusion...................................................................................................................... 28

93 Chapter 3. Methods and Results............................................................................................... 29

94 3.1 Methods.......................................................................................................................... 29

95 Single Crops.......................................................................................................................... 30

96 All-agriculture....................................................................................................................... 30

97 Residential Turf .................................................................................................................... 30

98 3.2 Results............................................................................................................................ 31

99 Maximum Water Resource-Regional PCAs......................................................................... 31

100 3.3 Validation of PCAs ........................................................................................................ 38

101 3.4 Data Limitations............................................................................................................. 39

102 3.5 Future Improvements ..................................................................................................... 40

103 Chapter 4. Monitoring Data Comparisons................................................................................ 41

104 4.1 Methodology .................................................................................................................. 41

105 4.2 Results............................................................................................................................ 43

106 4.3 Discussion ...................................................................................................................... 58
viii

107 Chapter 5. Guidance on Applying PCAs to Estimate Drinking Water Concentrations........... 60

108 5.1 National Screening Level Assessments.......................................................................... 60

109 5.2 Regional Refinements.................................................................................................... 62

110 5.3 Summary ........................................................................................................................ 63

111 Chapter 6. Literature Cited ....................................................................................................... 65

112

113
ix

114 Nomenclature and Acronyms

Abbreviation Definition

CDL Cropland Data Layer

CWS-DWI Community Water System Drinking Water Intake

EDWC Estimated Drinking Water Concentration

EFED Environmental Fate and Effects Division in the USEPA 

Office of Pesticide Programs

EXAMs Exposure Analysis Modeling System

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act 

FQPA Food Quality Protection Act

GIS Geographic Information System

HUC Hydrologic Unit Code

NASS National Agricultural Statistics Service (USDA)

NHD, NHDPlus National Hydrography Dataset

NLCD National Land Cover Database

OCSPP Office of Chemical Safety and Pollution Prevention

(USEPA)

OGWDW Office of Ground Water and Drinking Water in the 

USEPA Office of Water

OPP Office of Pesticide Programs (USEPA)

OW Office of Water (USEPA)

PCA Percent Cropped Area 

PRZM Pesticide Root Zone Model

PSDB Public-Supply Database of Community Water 

System Intakes (developed by USGS)

SAP Scientific Advisory Panel

SDWIS OGWDW’s Safe Drinking Water Information 

System (SDWIS) database

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency

USGS United States Geological Survey

WBD Watershed Boundary Dataset
10

115 Chapter 1. Purpose, History, and Overview of Updates of PCA Adjustment Factors

116 1.1 Introduction

117 In response to requirements enacted by Congress under the Food Quality Protection Act 

118 of 1996 (FQPA), the United States Environmental Protection Agency (USEPA) routinely 

119 includes estimates of pesticide concentrations in drinking water as a part of its dietary exposure 

120 assessments supporting the pesticide registration process. Generation of estimated drinking 

121 water concentrations (EDWCs) typically employs computer modeling to simulate pesticide fate 

122 and transport. USEPA’s Office of Pesticide Programs (OPP) uses the Pesticide Root Zone 

123 Model (PRZM) to simulate chemical behavior on, and movement (via runoff) from, a treated 

124 field to a receiving water body that is simulated using the Exposure Analysis Modeling System 

125 (EXAMS) model. For drinking water exposure assessments, OPP simulates a standard receiving 

126 water body, referred to as the “index reservoir”, which has dimensions and characteristics based 

127 on those of Shipman City Lake — a small, midwestern reservoir in an agricultural setting, 

intended to represent vulnerable surface water sources of drinking water2128 . 

129 Prior to 2000, the USEPA employed the conservative assumptions that the entire area of 

130 a watershed was planted with the crop of interest (i.e., 100% crop coverage), and thus the entire 

131 watershed could potentially be treated with a given pesticide. After a 1999 Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP)3132 consultation, the 

133 USEPA began implementing the use of PCAs in generating EDWCs, to account for the fraction

134 of a watershed potentially planted with the same crop, recognizing that the entirety of watersheds

135 large enough to support drinking water facilities may not be used to grow single crops. This step 

136 was intended to improve the quality and accuracy of USEPA’s EDWCs used for human 

137 exposure. In standard practice, to generate EDWCs, model-estimated concentrations from 

PRZM-EXAMS are multiplied by the appropriate PCA4138 .

139 This document presents and describes the development of PCAs specific to community 

140 water system-drinking water intakes (CWS-DWIs) that draw from surface water sources. The 

141 CWS-DWI watershed-based PCAs are intended to update, replace, and improve upon the 

 

2

See “Development and Use of the Index Reservoir in Drinking Water Exposure Assessments” on the OPP Water 

Models web page (http://www.epa.gov/oppefed1/models/water/index_reservoir_dwa.html) for more information on 

the index reservoir approach.

3

http://www.epa.gov/scipoly/sap/index.htm

4

PRZM/EXAMs model inputs include values for certain physical-chemical properties of the pesticide, application 

practices, crop agronomic information, precipitation, and soil properties. In the standard scenarios used to estimate 

pesticide exposure, it is assumed that 100% of the field is treated. More information on these models is available at: 

http://www.epa.gov/oppefed1/models/water/.
11

142 Hydrologic Unit Code level 8 (HUC-8) polygon-based PCAs used for calculating EDWCs. 

Previous PCA documentation5143 includes:

144 Applying a Percent Crop Area Adjustment to Tier 2 Surface Water Model Estimates for 

145 Pesticide Drinking Water Exposure Assessments, dated December 7, 1999 

146 Use of Regional Percent Crop Area Factors in Refined Drinking Water Assessments, 

147 dated July 25, 2003

148 Development and Use of Percent Cropped Area and Percent Turf Area Adjustment 

149 Factors in Drinking Water Exposure Assessments: 2012 Update, dated March 16, 2012

150 The methods used to derive the DWI PCAs are essentially the same as those described in 

151 earlier documents, but improve on previous efforts by focusing on watersheds that are specific to 

152 CWS surface water sources. The USEPA Office of Water, Office of Groundwater and Drinking 

153 Water’s (OGWDW) Safe Drinking Water Information System (SDWIS) database contains a 

154 comprehensive list of CWS that report the use of surface water as a source for drinking water.

155 From this dataset, the locations of 6,550 surface water intakes that supply water to these reported 

156 CWS have been quality-assured. Watersheds for 4,840 of these intake locations have been 

157 delineated and quality-assured. All but 2 of these are located in the continental United States, 

158 and the 4,838 that are have been used as the basis for PCA calculation. 

159 1.2 Brief Overview of the Derivation of Percent Cropped Area Adjustment Factors

160 In order to derive PCAs, USEPA incorporated data on the acreage of crops of interest 

161 within a geospatial context. In keeping with methods first presented to the 1999 FIFRA SAP, 

162 USEPA employed the most current update of the United States Department of Agriculture 

163 (USDA) National Agricultural Statistics Service (NASS) Census of Agriculture (Ag Census), 

164 which reports total acres of various agricultural crops grown in each county in the United States

every five years6,7165 . USEPA also used the most current (2006) version of the National Land 

 

5 Current and archived documents relevant to PCA development are available at Environmental Fate and Effects 

Division Water Models website (http://www.epa.gov/oppefed1/models/water/).

6

Information on the Ag Census and data downloads are available from the USDA Census of Agriculture web site: 

http://www.agcensus.usda.gov/index.php. The most recently available Ag Census dataset for use in this analysis was 

for 2007.

7 The Census of Agriculture, taken every five years, provides the only comprehensive source of impartial crop 

acreage (acres harvested) for every county and farm in the United States. There is also an Agricultural Survey 

Program that provides crop acreage estimates (harvested and planted acres) for counties on a quarterly and annual 

basis based on a subset of responders from the Census of Agriculture. Information from the Census of Agriculture 

is considered a more reliable source for crop acreage as it does not involve any estimates based on a subset of 

responders. Therefore, the Census of Agriculture is used as the source of crop acreage information. It is noted that 

acres harvested will be lower than acres planted, and that this introduces uncertainty into PCA estimates.
12

Cover Database (NLCD) geospatial data [land cover categories 82 (cultivated crops8166 ) and/or 81 

(pasture/hay9167 )] to create a generic cropland distribution map within each county. Crop acreage 

168 within each county was apportioned to the locations within the county used for agriculture, as 

169 indicated by NLCD. DWI watersheds were then overlaid (conceptually) on the crop distribution 

170 maps, and PCAs were calculated for each watershed based on spatial overlap of the crop acreage 

171 within each watershed area. 

172 The above-described process employs an assumption that crop acreages reported in the Ag

173 Census are uniformly distributed across all agricultural land within a county, and that the 

174 reported cropping patterns are stable over time. While it is known that these assumptions are not 

175 technically true (e.g., changes in crop acreage commonly occur from year to year and many 

176 crops are not evenly distributed across all agricultural land), these assumptions are necessary to 

177 calculate and use PCAs. As noted in the History of PCA Development section, these 

178 assumptions have implications regarding which crops are suitable for PCA development and how 

179 PCAs may be used in drinking water assessments.

180 1.3 History of PCA Development

181 The concept of using a factor to adjust modeled estimates of pesticide concentrations in 

182 surface drinking water to account for heterogeneous land uses was first proposed to the Federal 

183 Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP) in 

184 December, 1997 (Jones and Abel, 1997). In May 1999, USEPA presented to the SAP:

185 “Proposed Methods for Determining Watershed-derived Percent Crop Areas and Considerations 

186 for Applying Crop Area Adjustments to Surface Water Screening Models” (Effland et al., 1999). 

187 USEPA proposed PCAs for ten crops (corn, soybeans, wheat, cotton, apples, citrus, grapes, 

188 peanuts, potatoes, and strawberries), using the USDA Census of Agriculture for crop acreages by 

189 county and the United States Geological Survey’s (USGS) HUC-8s as surrogates for CWS 

190 watersheds. Though not usually true watersheds (Omernik, 2003), hydrologic units are a 

191 hierarchical system developed by USGS to classify contiguous drainage areas within the U.S.

192 (Figure 1-1) (Seaber et al., 1987). In this system, hydrologic units are arranged within each 

193 other, such that each unit is identified by a unique code (HUC) consisting of two or more digits 

based on the level of classification in the hydrologic unit system10 194 . At the time of the original 

 

8

2006 NLCD Land Cover Category 82 is labeled as cultivated crops. It is defined as, “areas used for the production 

of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as 

orchards and vineyards. Crop vegetation accounts for greater than 20% of the total vegetation. This class also 

includes all land being actively tilled” (USGS, 2011).

9

2006 NLCD Land Cover Category 81 is labeled Pasture/Hay. It is defined as, “areas of grasses, legumes, or grasslegume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle” 

(USGS, 2011). Pasture/hay vegetation accounts for greater than 20% of total vegetation.

10 http://water.usgs.gov/GIS/huc.html
13

195 PCA analysis, the HUC-8 scale was the finest resolution available nationally. Use of HUC-8s 

196 was proposed in lieu of CWS DWI watersheds, which were not available in a national spatial 

197 dataset. As a part of its submission to the SAP, USEPA compared PCA-adjusted EDWCs to 

198 relevant available monitoring data. 

199

200 Figure 1-1. Illustration of the Hierarchical Hydrologic Unit Code System Developed by 

201 USGS to Categorize Hydrologic Units

202 The SAP concluded that the PCA approach “appeared to perform reasonably well with 

203 major crops in the Midwest and can be comfortably applied under those conditions” (USEPA, 

204 1999). Based on SAP concerns about how well the assumption of uniform crop distributions 

205 held for minor crops that covered less acreage, and the degree to which HUC-8s may not 

206 represent smaller CWS watersheds, PCAs for other crops or crop combinations were not 

207 adopted. The SAP recommended moving from HUC-8 hydrologic units to CWS-specific 

208 drainage areas once such watershed delineations became available nationally. The SAP also 

209 recommended evaluating any future PCAs with pesticide monitoring data to ensure that the PCA 

210 adjustments do not result in underestimation of pesticide concentrations in drinking water.
14

Subsequent SAPs on the Organophosphate Cumulative Risk Assessment11 211 reiterated the 

212 difficulties in deriving PCAs for minor crops due to non-uniform cropping intensities and 

213 concerns that HUC-8s may not adequately represent smaller watersheds capable of supporting 

214 drinking water supplies (USEPA, 2002). 

215 In response to the need to refine drinking water exposure assessments over subsequent 

216 years, USEPA developed a national “default PCA” adjustment factor for crops other than the 

217 four major crops endorsed by the 1999 SAP, and regional default PCAs for the 18 HUC-2

218 “Water Resource Regions” shown in Figure 1-2 (Jones et al., 2010). USEPA also developed 

219 additional guidance for selecting and applying combined or multi-crop PCAs to PRZM-EXAMS

220 model-estimated concentrations for pesticides used on multiple crops. 

221

222 Figure 1-2. The 18 Major Hydrologic Unit “Water Resource” Regions (HUC-2) in the 

223 Continental United States, Overlain on State Boundaries

224

 

11 http://www.epa.gov/oppsrrd1/cumulative/2006-op/op_cra_main.pdf
15

In 2012, PCAs were developed for additional crop groups including orchards/vineyards12 225 , 

turf13, vegetables14 226 , rice, and combinations of crops or crop groups (Echeverria et al., 2012). 

227 While developing these PCAs the USEPA addressed concerns raised by the 1999 SAP regarding 

228 the uniformity of minor crop distributions within watersheds. This was done by taking an 

229 aggregate approach for generating PCAs. For example, a new “aggregated vegetables” crop 

230 group PCA was derived to represent individual as well as all vegetable crops. Agricultural PCAs 

231 were derived using the 2006 NLCD (Fry et al., 2011) and the 2007 Ag Census (USDA, 2009) for 

HUC-8s.

15 232 The only difference from this methodology in the current effort is that watersheds for

233 CWS-DWI were employed. Table 1-1 summarizes the milestones and data sources used to 

234 calculate PCAs utilized by USEPA prior to this publication. 

235 Table 1-1. PCA Milestones and Data Sources

Year Milestone Data Sources References

1997 - Water modeling SAP, 

proposed the use of PCAs

(Jones and Abel, 

1997)

1999 - PCA SAP proposed method 

for deriving watershed-based 

PCAs

1992 Census of Agriculture

HUC-8 subbasins

County Boundary data

(Effland et al., 

1999)

2000 - Corn, soybean, wheat, cotton 

PCA

- National all agricultural PCA

HUC-8 subbasins

1992 NLCD land cover

1992 Census of Agriculture

County Boundary data

(Water Quality 

Tech Team, 

1999)

2002 - Cumulative organophosphate 

SAP provided 

recommendations on use of 

PCAs for minor crops

- OP-crop specific and regional 

PCAs used in assessment

1997 Census of Agriculture

County boundary data

OP-specific use data

(USEPA, 2002)

2003 - HUC-2 Regional PCAs

- Crop combinations (corn, 

soybean, wheat, and cotton) 

- HUC-8 subbasins

- 2001 NLCD land cover

- 1997 Census of Agriculture

- County Boundary Data

(Water Quality 

Tech Team, 

2003)

2012 - Updated data used for 

established HUC-8 PCAs

- Orchards/vineyards, turf, 

vegetables

- More combinations

- 2011 HUC-8 subbasins

- 2006 NLCD land cover

- 2007 Census of Agriculture

- 2008 USEPA Turf Layera

- County Boundary Data

(Echeverria et 

al., 2012)

a

2001 NLCD developed land covers (21-24) with the impervious surface layer subtracted.

b

2006 NLCD developed land covers (21-24) with the impervious surface layer subtracted.

236

 

12 Orchards/vineyard PCAs are based on the “Land in Orchards” data in the Agricultural Census dataset.

13 The turf map was derived by using all developed land cover classes in NLCD (classes 21-24) and subtracting the 

impervious area.

14 Vegetables PCAs are based on the “vegetables harvested for sale” data in the Agricultural Census dataset. 

15 Previous PCAs were calculated using the 1992 NLCD land cover data and the 1997 Agricultural Census data.
16

237 1.4 Applying PCAs to Estimated Drinking Water Concentrations (EDWCs)

238 For drinking water assessments, model-estimated drinking water concentrations are 

239 multiplied by PCAs to reflect the fraction of the watershed covered by the crop or crops of 

240 interest. The use of PCAs to modify PRZM-EXAMS model-estimated concentrations for

241 surface water is based on the assumption that pesticide loadings to water bodies are directly 

242 proportional to treated area. This assumption is consistent with use of the PRZM model, which 

243 is employed to estimate pesticide runoff and spray drift loadings. PRZM generates area244 normalized output, and implicitly ignores factors such as landscape spatial non-uniformity, 

245 distances between treated locations and water bodies, and differential runoff from different land 

246 covers or soil types. PRZM is, in a spatial sense, essentially a one-dimensional, “lumped 

247 parameter” model. That is to say, soil and landscape properties and other governing variables 

248 are represented via numerous input parameters in PRZM, but are treated as if they were 

249 homogeneous within the simulated runoff watershed.

250 For national scale assessments, PCAs are selected which represent the highest fraction of 

251 the crop within any known drinking water watershed. If a pesticide is used on only one major 

252 crop, then the PCA for that particular crop is used. If the pesticide is used on a combination of 

253 crops, then the PCA for that particular combination of crops is used. Or, if pesticide uses include 

254 more or different crops than are captured in the existing PCA combinations, the default “all255 agriculture” PCA is applied. Regional (i.e., HUC-2) maximum PCAs may be used for 

256 refinement, but must reflect all potential pesticide use combinations for each region. 

257 PCAs are not applicable to areas outside the conterminous United States (e.g., Alaska, 

258 Hawaii), as data are not readily available to calculate PCAs for these areas. Therefore, drinking 

259 water exposures based on scenarios that represent these areas should not be adjusted by a PCA. 

260 PCAs are not applicable to non-agricultural uses aside from turf. 

261 1.5 Shifting from HUC-8 Subbasins to Community Water System Watersheds

262 When USEPA began developing PCAs for use in drinking water assessments, a national 

263 dataset defining watersheds specific to CWS DWIs was not available. The USEPA used HUCs 

264 as surrogates for CWS DWI watersheds because they were the only nationally consistent

265 hydrologic delineation available at the time. HUC-8s consist of more than 2,200 contiguous, 

266 non-overlapping polygons that average approximately 700 square miles in area. In general,

267 smaller areas are more likely to have homogeneous land cover; therefore, potentially higher 

268 PCAs. USEPA noted that many drinking water supplies are fed by watersheds that are smaller 

269 than HUC-8s, and thus may have higher fractions of cropped/pesticide-treated land. 

270 The 1999 SAP recommended that PCAs be developed specifically for drinking water 

271 intake watersheds. This report documents the development of such a dataset. Chapter 2

272 provides an overview of the development of the “CWS DWI version 1.0” watershed polygon 
17

273 dataset that made the development of these DWI PCAs possible. Chapter 3 describes the 

274 methodology of PCA development, and presents a summary of the results. Chapter 4 presents a 

275 comparison between EDWCs generated using the DWI PCAs, and measured pesticide 

276 concentrations in water. Chapter 5 provides guidance on applying the DWI PCAs to calculate

277 pesticide EDWCs.

278

279
18

280 Chapter 2. Development of Drinking Water Intake Watershed Dataset

281 This chapter documents the development of the Community Water System (CWS) 

282 Drinking Water Intake (DWI) watershed dataset, version 1.0 (DWI_Basins_082013.mdb,). This 

283 dataset provides a geospatial representation of watersheds (and HUC-12 surrogates) associated 

284 with the intakes of surface water-sourced CWS across the United States. The chapter includes a

285 summary of strengths and weaknesses of this dataset for use in applications such as the 

286 development of watershed-specific PCAs.

287 2.1 Background

288 The number of CWS in the United States which draw upon surface water as source

289 drinking water varies over time, and changes within SDWIS as reflected in quarterly updates. 

290 Because a large portion of the population in the United States relies upon surface water as source 

291 drinking water, it is important to understand how the locations of intakes for CWS influence the 

292 vulnerability of the water supply to contamination. USEPA is particularly interested in the 

293 geospatial relationship of agricultural land and turf cover to CWS intake locations, because 

294 estimation of pesticide concentrations in surface water resulting from pesticide use is an aspect 

295 of the exposure assessment process which underlies pesticide registration and reregistration in 

296 the United States. Delineation of watersheds associated with drinking water intakes provides a 

297 mechanism for helping understand the vulnerability of different CWS to pesticide contamination. 

298 For this reason USEPA sought to develop a comprehensive geospatial dataset containing the 

299 locations of intakes for CWS in the United States, as well as watersheds associated with these 

300 intakes. Although similar datasets have been developed previously (Bhaduri, 2003), this dataset 

301 is referred to as the DWI watershed dataset, version 1.0 (DWI_Basins_082013.mdb). 

302 Development of this dataset involved two key steps: identification of CWS surface water 

303 intake locations and delineation of catchments (watersheds) draining to these locations. The 

304 process of deriving this dataset was initiated by the Office of Pesticide Program’s Environmental 

305 Fate and Effects Division (EFED) in 2007, and included collaboration with USGS and contract 

306 support. The Database Development section below describes steps involved. 

307 2.2 Database Development

308 Surface Water Intake Locations

309 The surface water intakes included in the DWI watershed dataset were derived using 

310 locational data contained in the USGS’s Public-Supply Database (PSDB) of CWS intake 

locations. PSDB was initially developed from: 1) data contained in earlier16 311 versions of 

312 OGWDW’s Safe Drinking Water Information System (SDWIS) database; and 2) data received 

 

16 1997 to 2004
19

313 by USGS directly from states. The datasets only represent snapshots in time. For example, 

314 SDWIS is a dynamic dataset of public water supplies that changes quarterly. States provide 

315 updates that affect the number of water supplies in the dataset and the associated attributes [e.g., 

316 function (active vs. inactive) and location].

In 2007, under an Interagency Agreement17 317 between USEPA OPP and USGS, PSDB was 

318 updated by USGS with surface water source data contained in a then-current download of CWS 

319 intake location data from SDWIS (12/31/2006). Updating PSDB employed the following 

320 procedure (Horn, 2007):

321 1. Records in a then-current version of PSDB were compared against those in the SDWIS

322 download. This process revealed 267 DWI locations in SDWIS that were not in PSDB, 

323 and 954 intake locations that varied by more than 0.0017 decimal degrees between the 

324 two datasets. To resolve the discrepancy in the intake locations, an evaluation to 

325 determine which location was more accurate (SDWIS or PSDB) was conducted. This 

326 evaluation was conducted by mapping both locations for each intake, using DeLorme 

327 Street Atlas USA 8.0, Topozone, or Google Maps software to compare each location 

328 against other landscape features. Of the 954 intake locations that varied between the two 

329 datasets, 343 were judged to be best represented by the PSDB location and 611 to be best 

330 represented by the SDWIS location. 

331

332 2. Examination of SWDIS revealed there were no surface water sources included in the

333 download for Delaware or Ohio (which both had entries in a 12/31/2004 SDWIS 

334 download), and only one surface water source for Idaho (which had 54 entries in the 

12/31/2004 SDWIS download). The Ohio sources were identified by an April 200718 335

336 download of SDWIS and integrated into the PSDB update. No additional information 

337 was found for the Delaware sources or the 53 missing Idaho sources. As a result, existing 

338 data for Delaware and Idaho in PSDB originating from the 12/31/2004 SDWIS download

339 were retained under the assumption that the 2004 surface water sources were still active

340 in 2007. This updated dataset is referred to here as the “2007 PDSB”, and was the dataset 

341 used by the USGS to generate 6,550 quality-assured CWS intake locations (Figure 2-1)

342 (Horn, 2007). The final deliverable to USEPA was a spreadsheet 

343 (QA.SDWIS2006.locCWSSWsource.xls), dated June 29, 2007. 

344

345 Note: The final dataset did not include intakes that were 1) outside the 50 states; 2) not 

346 associated (judging based on name attribute) with streams, rivers, lakes or reservoirs; 3) 

 

17 IAG # DW-14937871-01-0

18 Exact date not available
20

347 flagged as inactive, or; 4) lacking location information, so that the location could not be 

348 determined.

349

350 Figure 2-1. Geographic Distribution of 6,550 Quality-assured Community Water Systems 

351 Drinking Water Intakes Locations in Public-Supply Database 

352 DWI Watershed Delineation

In 2010, USEPA contracted with INDUS Corporation19 353 to index the 6,550 CWS DWI 

locations to specific surface water features in USEPA’s NHDPlus20 354 dataset. To accomplish this 

355 task, INDUS used a process that included proximity analysis and name matching, as follows: 

356 1. First, INDUS identified the three nearest stream/river reach features and three nearest 

357 water body features in the NHDPlus data suite that best matched each DWI location. 

358 These features were considered potential watershed outlets (“pour points”) for the 

359 watersheds defined by the DWI locations. INDUS then used an algorithm that included 

360 distance from the intake location, name matching, and feature type such as lake or stream 

361 to identify candidate NHDPlus features that best matched the intake names and locations. 

362 For each DWI site, INDUS identified up to six candidate pour points on NHDPlus

363 features, assigned a numerical ranking based on a matching algorithm, and selected a 

 

19 INDUS is a non-governmental organization that specializes in providing federal information technology services.

20 NHDPlus is an integrated suite of application-ready geospatial datasets that incorporate many of the best features 

of the National Hydrography Dataset (NHD), the National Elevation Dataset (NED), the National Land Cover 

Dataset (NLCD), and the Watershed Boundary Dataset (WBD). http://www.horizon-systems.com/nhdplus/
21

364 “winner” that is the feature judged most likely to be the correct pour point. The final 

365 product was an ArcGIS geodatabase (pending_dwigs_20100924.gdb) that included all the 

366 “winners” and candidate stream segments and water bodies.

367

2. Next, OPP contracted with Horizon Systems21 368 in 2011, to delineate watersheds for the 

369 “winner” locations identified by INDUS. Horizon Systems found that of the 6,550 DWI 

370 locations, only 5,557 were indexed to NHDPlus features classified as streams, rivers, 

lakes, or reservoirs22 371 ; therefore, suitable for watershed delineation. The remaining DWI 

372 locations were associated with other types of water sources including infiltration 

373 galleries, canals, springs, the Great Lakes, aqueducts, off-stream reservoirs, etc., for 

374 which watershed delineation is inappropriate or not readily feasible. Horizon Systems 

375 delineated watersheds for the 5,557 NHDPlus-indexed DWI locations using the NHDPlus 

Basin Delineation Tool23 376 . 

377

378 3. In 2011, USEPA requested that USGS assess the quality of the 5,557 contractor379 delineated watersheds (Wieczorek, 2011). The USGS conducted an accuracy assessment 

380 by overlaying the following: the DWI site locations, the pour point locations on 

381 NHDPlus, the candidate watershed polygons delineated by Horizon Systems, digital 

382 1:24,000 topographic maps, and aerial photography. The USGS evaluated watershed 

383 accuracy for each site through visual inspection of the geospatial data layers, and 

384 assigned a code as follows:

385

386 a. The INDUS “winner” DWI site location was compared to the PSDB intake 

387 location. If the pour point location of the “winner” was clearly incorrect, then 

388 the “winner” pour point was discarded and the accuracies of other candidate 

389 pour point locations identified by INDUS were evaluated. If none of the pour 

390 points appeared to be accurate, then a “FAIL” rating was assigned to the DWI 

391 site.

392

393 b. If the pour point location of the “winner” was judged to be reasonable, then 

394 the boundary of the corresponding watershed polygon was compared to the 

395 elevation contours of a topographic map. If visual inspection indicated that at 

396 least 95% of the watershed area was correctly delineated, then the INDUS 

 

21 Horizon Systems is a USEPA contractor that specializes in providing federal information technology services. 

http://www.horizonsystems.com/

22 NHDPlus Source Types 1, 2, 3, 6, 9, or 10

23 http://www.horizon-systems.com/NHDPlus/NHDPlusV1_tools.php#NHDPlus Basin Delineator Tool 

(BasinDelineator)
22

397 “winner” was assigned a “PASS” rating value and the other candidate pour

398 point locations were not evaluated. If visual inspection indicated that the 

399 watershed boundary was at least 85% accurate, then the INDUS “winner” was 

400 assigned a rating value of “REASONABLE”.

401

402 c. If the pour point location of the “winner” was obviously incorrect, but one of 

403 the other INDUS candidate pour point locations was reasonable based on 

404 visual inspection, then the boundary of the corresponding watershed polygon 

405 was compared to the elevation contours of the topographic map. If visual 

406 inspection of the alternative watershed indicated that the area was correctly 

407 delineated, then the INDUS candidate was given a “FAIL” grade with a 

408 “PASS” option, the option being one of the other alternative candidate 

409 watersheds. 

410 During the analysis process, USGS discovered 75 duplicate watersheds among the 5,557 

411 delineated by Horizon Systems. Removal of the duplicate watersheds left 5,482 unique 

412 watersheds for evaluation. Using the above-detailed process, USGS determined that of the 5,482 

413 unique watersheds, a total of 4,840 (or 88% of 5,482) fell into the “PASS”, “REASONABLE”, 

414 or “FAIL with PASS option” categories (Figure 2-2, Figure 2-3). These 4,840 watersheds were 

415 determined to be “reasonable and ready to use for subsequent analysis and cartographic purpose”

416 (Wieczorek, 2011). USGS provided USEPA with an ArcGIS geodatabase (DWI_basins.mdb) 

417 containing the 4,840 delineated watersheds. Table 2-1 details the results of this analysis by 

HUC-2 water resource region.

24 418 

 

24 www.horizon-systems.com/NHDPlus/data/NHDPLUS_UserGuide.pdf
23

419

420 Figure 2-2. Geographic Distribution of 4,838 Quality-assured Watersheds for Surface 

421 Water Source Drinking Water Intakes of Community Water Systems in the Continental

422 United States

423

424

425

426

427

428

429

430

431

432
24

433 Table 2-1. Results of USGS Pass/Fail Analysis on Drinking Water Intakes Watersheds, 

434 Listed by Water Resource Region

Water 

Resource 

Region

Assessed Pass Fail Reasonable

Fail 

with No 

Pass 

Option

Pass, 

Reasonable, 

or Fail with 

Pass Option

% Pass, 

Reasonable, 

or Fail with 

Pass Option

%Pass

1 594 382 188 24 108 486 81.82 64.31

2 806 600 183 23 112 694 86.10 74.44

3 492 387 96 9 24 468 95.12 78.66

4 206 120 83 3 54 152 73.79 58.25

5 707 554 150 3 81 626 88.54 78.36

6 199 163 36 0 24 175 87.94 81.91

7 166 115 40 11 16 150 90.36 69.28

8 72 52 20 0 16 56 77.78 72.22

9 21 8 8 5 5 16 76.19 38.10

10L 334 238 87 9 47 287 85.93 71.26

10U 105 74 26 5 12 93 88.57 70.48

11 452 361 81 10 35 417 92.26 79.87

12 280 251 26 3 13 267 95.36 89.64

13 37 16 15 6 2 35 94.59 43.24

14 145 102 36 7 18 127 87.59 70.34

15 34 14 7 13 3 31 91.18 41.18

16 54 41 10 3 7 47 87.04 75.93

17 363 276 64 23 35 328 90.36 76.03

18 487 300 150 37 104 383 78.64 61.60

19 0 0 0 0 0 0 0.00 0.00

20 3 1 1 1 1 2 66.67 33.33

Totals 5,557 4,055 1,307 195 717 4,840 87.10 63.26

435
25

436

437 Figure 2-3. Derivation Flowchart of the Quality-Assured Drinking Water Intakes 

438 Watershed Dataset

439 Unlike HUC-8s (upon which previous PCA development exercises have focused), the 

440 distribution of areas among the 4,838 quality-assured watersheds in the continental United States

is highly skewed, with a 10th percentile of 1.2 mi2, a median of 43.5 mi2441 , and a 90th percentile of 

7,383 mi2442 . By way of comparison, EPA’s “index reservoir” scenario, based upon Shipman City 

Lake, Illinois, has a watershed of 0.7 mi2, which corresponds with about the 5th 443 percentile of the 

444 area distribution. Unlike HUCs at any given scale, which do not overlap, there is a fair degree of 

445 spatial nesting among the watersheds, such that smaller watersheds are contained inside larger 

446 ones, contained in turn inside even larger ones, and so forth. 

447 2.3 Database Uncertainties

448 The degree of conservatism inherent in employing these 4,838 drainage basins to represent 

449 drinking water exposures for all surface water-sourced CWS in the United States cannot be 

450 definitively determined, in part because it cannot be determined that the existing entries are as or 

451 more vulnerable than CWS with intakes that lack quality-assured watersheds, which 

452 hypothetically could represent more vulnerable supplies. At this time EFED lacks the data and 

453 tools necessary to evaluate potential pesticide exposures for watersheds that extend beyond 

454 international boundaries, or for sources such as canals, aqueducts, off-stream reservoirs, and 

455 large, incompletely-mixed water bodies such as the Great Lakes. Nevertheless, in order to 

Development of DWI Watershed Dataset

6,550 

Intake locations in the 

U.S. quality-assured 

by USGS

4,838

Watersheds in the 

continental U.S. 

quality-assured by 

USGS: judged 

“reasonable and ready 

to use”

5,482 

Unique watersheds

delineated (with intakes 

on streams, rivers, lakes, 

reservoirs) 
26

456 investigate this issue, EFED examined the 1,710 DWIs possessing quality-assured locations but 

457 lacking quality-assured watersheds (Figure 2-4). These DWIs were categorized as follows:

458 For 685 DWIs, delineated watersheds would either not be appropriate, or not 

459 applicable to this project because (1) the intakes draw from source water types (springs 

460 or other ground water influences, sea water, Great Lakes) for which surface water 

461 watershed delineations are not applicable, (2) the intakes no longer exist, or (3) the 

462 intakes are located outside of the continental United States including Alaska, Hawaii, and 

463 U.S. territories.

464

465 For 637 DWIs, delineated watersheds would be appropriate because the intakes draw 

466 from source water types (i.e., streams, rivers, lakes, ponds, reservoirs) for which surface 

467 water watershed delineations are applicable. While more work could be done to 

468 determine why validated watersheds do not exist for these intakes, OPP will use HUC469 12 hydrological units as surrogates in lieu of quality assured watersheds for these

470 DWIs. This approach was largely supported by the recent targeted peer review on PCA 

documentation for DWI watersheds25 471 . Note: An additional 29 DWIs are also associated 

472 with the aforementioned source types, but are located in areas such as coasts where the 

473 calculation of HUC-12 based PCAs produces erroneous results.

474

475 For 359 DWI, more information is needed to determine if delineated watersheds are 

476 appropriate because these intakes largely draw from aqueducts and canals, and the

477 watersheds that ultimately provide their source water are not known. In some cases, the 

478 aqueducts and canals may be hydrologically isolated from the surrounding agricultural 

479 areas; in other cases, the feeder canals seem to come from within agricultural areas. OPP 

480 is in the process of collecting more information on this set of DWIs to determine whether 

481 watersheds can be appropriately identified to represent their contributing drainage areas 

482 for PCA determination.

 

25 Initiated October 1, 2012 by Donald Brady United States Environmental Fate and Effects Division Director. 

Reviewers: Jonathan Becker, United States Environmental Protection Agency, Office of Pesticide Programs, 

Biological and Economic Analysis Division; Roger Anzzolin, United States Environmental Protection Agency 

Office of Water Office of Groundwater Drinking Water; Tony Selle, United States Environmental Protection 

Agency Region 8; Anne Neale, Environmental Protection Agency, Office of Research and Development, National 

Exposure Research Laboratory; Jeff Bailey, United States Department of Agriculture, National Agricultural 

Statistics Service; Bob Gilliom, United States Geological Service (included comments from Mike Wieczorek); Terry 

Councell, United States Department of Agriculture, Pesticide Data Program; Jerry Johnson, United States

Department of the Interior; Ian Kennedy, Health Canada Pesticide Management Regulatory Agency
27

483 One important source of uncertainty has to do with the fact that the “universe” of DWIs is 

484 not fixed, but inherently dynamic, with existing intakes becoming inactive and new intakes being 

485 established continually over time. Because this project has taken years to complete, it is an 

486 unavoidable fact that the 2007 intake location information from which the quality assured DWI 

487 dataset was developed is several years old, and thus to some degree potentially incomplete 

488 and/or obsolete at the time of project completion in 2013. In order to investigate the potential 

489 scope of this issue, EPA obtained a more current download of SDWIS data in spring of 2013. 

490 Analysis of these data indicates the presence of about 700 DWIs that appear to be new intakes 

491 which are not part of our DWI dataset. This illustrates the importance of establishing a process 

492 for regularly updating DWI PCAs, for as long into the future as they continue to be used.

493

494

495 Figure 2-4. Locations of Quality-Assured DWIs in the Conterminous U.S. Lacking 

496 Quality-Assured Watersheds

497 Unlike HUC-8s, upon which previous PCA development focused, the area distribution 

498 among the 4,838 DWI watersheds is broad and highly skewed. The median area of HUC-8s is 

about 1,300 mi2, which happens to fall at about the 79th 499 percentile of the watershed area 

500 distribution. Thus only about one fifth of DWI watersheds are larger than the scale of typical 

501 HUC-8s used in previous PCA efforts, while about four fifths of DWI watersheds are smaller. 
28

502 Also, unlike HUCs, there is considerable spatial nesting among the 4,838 DWI watersheds. This

503 occurs for example when two or more intakes are located on the same stream or river, one 

504 upstream from another, such that smaller catchments are entirely contained within larger ones. 

505 These and other unique features of this dataset, including the lack of even spatial distribution 

506 across the country and the fact that some watersheds extend beyond the United States into 

507 Canada or Mexico (Figure 2-2), create novel challenges for the use of this DWI watershed 

508 dataset in PCA development have not been issues in previous PCA efforts.

509 2.4 Conclusion 

510 In summary, 4,438 DWI watersheds in the continental United States have been delineated 

511 and quality assured, for 6,550 quality-assured intakes that provide source surface water for 

512 community drinking water supplies throughout the country. Of the 1,710 quality-assured intakes 

513 without a quality-assured delineated watershed, 685 are either outside the continental U.S. (i.e., 

514 the “lower 48” states) or not appropriate for watershed delineation, 637 are represented with 

515 HUC-12 watersheds as surrogates, and 359 require additional information to determine whether 

516 or not delineation is appropriate. There are several sources of uncertainty to be considered in 

517 determining the appropriate use of the data in a regulatory context, perhaps most importantly the 

518 fact that the set of active DWIs is not fixed over time but dynamic in nature. This is evidenced 

519 by the observation that about 700 additional DWIs were apparently brought on line between 

520 2007 and 2013, and are therefore not represented in either the DWI watershed, or associated 

521 PCA datasets. 

522
29

523 Chapter 3. Methods and Results

524 3.1 Methods

525 PCAs were calculated for all-agriculture, residential turf, and six individual crops (corn, 

526 soybean, cotton, orchard, wheat and vegetables) or crop groups, for each of the 4,838 drinking 

527 water watersheds and 637 HUC-12 surrogate watersheds described in Chapter 2. The National 

528 Land Cover Dataset (NLCD) and the National Agricultural Statistics Service (NASS)

529 Agricultural Census data were combined to create a crop-specific land cover map for each of the 

530 specified crops, including all-agriculture. NASS provides total acres harvested by county for 

each individual crop26 531 and the NLCD provides information on the distribution of crop acreage at 

532 the sub-county level. Data on acres harvested were selected for each crop. The crops selected 

533 and the corresponding NASS table numbers are listed in Table 3-1. Corn acres for grain, silage 

534 and sweet corn were added to produce a total corn PCA. Unreported counties within the NASS

535 dataset, marked with a “D”, were ignored. Of the 4,838 delineated basins, 127 include upstream 

536 areas in Canada, and 14 include upstream areas in Mexico. Both NASS and NLCD cover only 

537 the continental United States. Basins that cross international borders were clipped at the border, 

538 such that PCAs for these watersheds were calculated based solely on the distribution of land uses 

539 within the portions of the watersheds within the United States. 

540 Table 3-1. 2007 NASS Crop Data

Crop NASS Table

Corn for grain 25

Corn for silage or 

greenchop 25

Sweet Corn 30

Soybean for beans 25

Wheat for grain 25

Cotton all 25

Vegetables 

harvested for sale 25

Land in Orchards 25

Total Cropland 1

541

542 A detailed description of the GIS methods used in validating the PCA calculations can 

543 be found in APPENDIX A.

 

26 http://www.nass.usda.gov/
30

544 Single Crops

545 PCAs for individual crops were calculated by conceptually “blending” the NLCD 

546 agriculture pixels with the NASS county harvested data. The six individual crops all fall under 

547 the NLCD class 82: cultivated crops. Using the Spatial Analyst toolbox for ArcGIS10.0, the 

548 number of cultivated crop pixels per county was calculated. The total number of crop pixels in 

549 each county was then divided by the total harvested acres in the county according to NASS

550 resulting in an acres-per-pixel value for each county. A crop-specific map was produced by 

551 replacing all cultivated crop pixels (class 82) with the corresponding acres-per-pixel values, 

552 removing all other land classes from the land cover map. Using the newly created crop map, 

553 watershed PCAs were calculated by summing all area-weighted crop pixels within each 

554 watershed, using the ArcGIS Spatial Analyst extension and the zonal statistics as table

555 command. 

556 The process of calculating 4,840 PCA values per crop was automated using Python 

557 scripting, which processed one catchment per shapefile per run. This was necessary due to GIS 

558 software limitations caused by the overlapping, or spatially-nested, and the nature of the DWI 

559 catchments. Nested drinking water watersheds may occur when two or more intakes are located 

560 on the same stream or river, one upstream from the other. The downstream watershed by 

561 definition includes all of the area in the upstream watershed, as well as additional area that drains 

562 to the stream or river below the location of the upstream intake. 

563 All-agriculture

564 The NASS dataset provides data on acreage of “land in agriculture” that includes land in 

565 pasture. The NLCD dataset classifies land uses into 16 categories including both “cultivated 

566 crop” (class 82) and “pasture” (class 81); however, misclassifications are known to occur 

567 wherein land that is actually cultivated cropland (e.g., alfalfa) is identified as pasture (i.e., class 

568 81). Until a detailed validation of the 2006 NLCD is published, it is difficult to quantify the 

569 effect of misclassification on the all-agriculture PCA results. To account for ambiguities in 

570 acreage associated with this issue, all-agriculture PCAs were calculated two different ways: 1) in 

571 the usual manner, based solely on class 82 pixels; and 2) based on the combination of classes 81 

572 and 82. The total number of pixels per county in the latter case, thus, includes a tally of all 

573 NLCD pixels classified as either 81 or 82. The total number of such pixels is then divided by the 

574 NASS “land in agriculture” acreage values to determine the per-county acre-per-pixel weighting 

575 factors. 

576 Residential Turf

577 A residential turf layer was derived from the 2006 NLCD based on an algorithm first 

578 proposed by Milesi et al. (2005). In summary, an impervious surface data layer is included in 

579 the NLCD 2006 data product where each pixel represents an impervious surface area. NLCD-
31

580 based turf area was calculated as the non-impervious area portion of all land cover pixels 

581 classified as developed (i.e., classes 21 – 24). Using this derived turf layer, turf PCAs were 

582 calculated by summing the turf area for all pixels within each watershed. HUC-12 based PCAs, 

583 used as surrogates for missing delineated watersheds, were based on 2001 NLCD turf estimates. 

584 The use of NLCD datasets from two different time points (2001 and 2006) to generate PCAs 

585 introduces a degree of uncertainty related to land cover changes during the intervening period. 

586 Specifically, developed land generally increased between 2001 and 2006, which has the effect of 

587 increasing turf acreage estimates.

588 HUC12 Processing and Surrogate Selection

589 Surface water intakes which lacked quality-assured, delineated watersheds were assigned 

590 HUC-12-based surrogate PCA values. Based on the criteria in Section 2.3, 637 intakes received

591 HUC-12-based surrogate PCA values based on the intake location. HUC-12 PCAs were 

592 calculated for all-agriculture and each of the individual crops. The Watershed Boundary Dataset 

593 (Version May 16, 2011) was used for HUC12 boundary delineations. 

594 3.2 Results

595 Maximum Water Resource-Regional PCAs

596 Each of the 4,838 watersheds was assigned to a HUC-2 water resource region based on 

597 the intake location. Maximum regional PCAs were then tabulated for each of the eighteen HUC598 2 water resource regions in the conterminous United States, for each individual crop, all599 agriculture and turf, and for various crop combinations. Results for specific crops and crop 

600 combinations are tabulated in Tables 1 and 2 (in the Summary section of this document), and 

601 reproduced in this chapter in Tables 3-2 and 3-3, respectively. Maximum HUC-12-based PCAs, 

602 for the 637 quality-assured DWIs lacking quality-assured watersheds in each HUC-2, are 

603 tabulated in Table 3-4 by crop. Table 3-5 lists the maximum crop-specific PCA for each HUC604 2, either DWI-based or HUC-12-based, whichever is greater. Tables 3-6 and 3-7 display the 

605 analogous crop-combination results for HUC-12-based PCAs and overall maximum PCAs, 

606 respectively. Values in Tables 3-5 and 3-7 that are HUC-12 based, and therefore greater than 

607 the corresponding DWI watershed-based values, are indicated in italics. As Tables 3-5 and 3-7

608 demonstrate, inclusion of HUC-12-based PCAs for the 637 DWIs which hypothetically warrant 

609 (currently lacking) watershed delineations, results in a number of increased regional maximum 

610 PCAs, for both individual crops and crop combinations.

611

612

613
32

614 Table 3-2. Maximum DWI-PCAs for Various Crops, by Water Resource Region (HUC-2)

HUC2

All Aga All Agb Corn Cotton Orchard Soybean Vegetable Wheat Turfc

01 0.30 0.24 0.08 0.00 0.02 0.00 0.10 0.00 0.45

02 0.82 0.72 0.34 0.06 0.05 0.11 0.03 0.06 0.58

03 0.52 0.44 0.09 0.08 0.12 0.20 0.08 0.10 0.64

04 0.92 0.91 0.35 0.00 0.05 0.44 0.04 0.11 0.32

05 0.88 0.89 0.51 0.20 0.03 0.59 0.02 0.10 0.49

06 0.20 0.45 0.10 0.04 0.02 0.10 0.01 0.03 0.38

07 0.95 1.00 0.68 0.00 0.00 0.58 0.01 0.27 0.59

08 0.75 0.51 0.21 0.20 0.00 0.07 0.00 0.05 0.08

09 0.88 0.95 0.16 0.00 0.00 0.21 0.02 0.41 0.06

10 0.80 0.85 0.36 0.00 0.00 0.37 0.01 0.20 0.51

11 0.69 0.79 0.20 0.07 0.05 0.34 0.01 0.39 0.70

12 0.67 0.77 0.11 0.21 0.02 0.01 0.01 0.18 0.25

13 0.36 0.52 0.01 0.01 0.01 0.00 0.01 0.00 0.05

14 0.01 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.07

15 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.10

16 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.04

17 0.61 0.72 0.01 0.00 0.02 0.00 0.01 0.38 0.28

18 0.28 0.27 0.03 0.02 0.21 0.00 0.07 0.01 0.59

*

Shaded cells indicate the highest value for each crop.

a All Ag PCAs based on NLCD land use 82 and Ag Census data.

b All Ag PCAs based on NLCD land uses 81 and 82, and Ag Census data.

c Represents residential turf including golf courses, but does not include sod farms. Sod farms are 

included in the all-agriculture land cover class. 

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634
33

635 Table 3-3. Maximum DWI-PCAs for Various Crop Combinations, by Water Resource 

636 Region (HUC-2)

HUC2CornWheatSoybeanWheatTurfa-

Corn

Turfa-

Orchard

Turfa-

Soybean

Turfa-

Vegetable

Turfa-

Wheat

VegetableOrchardTurfa-

All Agb

01 0.08 0.00 0.45 0.45 0.45 0.45 0.45 0.12 0.46

02 0.36 0.17 0.58 0.58 0.58 0.58 0.58 0.06 0.79

03 0.18 0.25 0.64 0.64 0.64 0.64 0.64 0.13 0.65

04 0.42 0.52 0.47 0.32 0.51 0.32 0.32 0.06 0.98

05 0.52 0.59 0.58 0.49 0.65 0.49 0.49 0.04 0.96

06 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

07 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

08 0.22 0.12 0.28 0.08 0.12 0.08 0.09 0.00 0.58

09 0.42 0.44 0.20 0.06 0.25 0.07 0.46 0.02 0.99

10 0.38 0.47 0.51 0.51 0.51 0.51 0.51 0.01 0.88

11 0.41 0.55 0.70 0.70 0.70 0.70 0.70 0.05 0.81

12 0.20 0.18 0.29 0.25 0.25 0.25 0.26 0.02 0.82

13 0.01 0.00 0.06 0.06 0.05 0.07 0.05 0.02 0.57

14 0.00 0.00 0.07 0.07 0.07 0.07 0.07 0.00 0.18

15 0.00 0.00 0.10 0.10 0.10 0.10 0.10 0.00 0.39

16 0.00 0.00 0.04 0.04 0.04 0.04 0.04 0.00 0.07

17 0.38 0.38 0.28 0.28 0.28 0.28 0.40 0.02 0.74

18 0.03 0.01 0.59 0.59 0.59 0.59 0.59 0.21 0.59

*

Shaded cells indicate the highest value for each crop.

a Represents residential turf including golf courses, but does not include sod farms. Sod farms are included in the 

all-agriculture land cover class. 

b All Ag PCAs based on NLCD land uses 81 and 82, and Ag Census data.

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654
34

655 Table 3-4. Maximum Crop-specific HUC-12-based PCAs, Listed by Water Resource 

656 Region (HUC-2), for the 637 Quality-Assured DWIs Lacking Quality-Assured Watersheds

HUC-2 All Aga Corn Cotton Orchard Soybean Vegetable Wheat Turfb

01 0.32 0.05 0.00 0.01 0.00 0.02 0.00 0.86

02 0.65 0.25 0.05 0.07 0.08 0.03 0.04 0.60

03 0.15 0.00 0.00 0.02 0.00 0.02 0.00 0.41

04 0.82 0.31 0.00 0.05 0.34 0.03 0.10 0.85

05 0.90 0.50 0.00 0.00 0.35 0.00 0.06 0.38

06 0.20 0.03 0.01 0.00 0.03 0.00 0.00 0.22

07 0.59 0.32 0.00 0.00 0.22 0.00 0.03 0.45

08 0.24 0.01 0.00 0.00 0.00 0.00 0.00 0.52

09 N/A N/A N/A N/A N/A N/A N/A N/A

10 1.00 0.23 0.00 0.00 0.26 0.02 0.27 0.47

11 0.63 0.04 0.00 0.01 0.01 0.01 0.17 0.39

12 0.66 0.04 0.07 0.03 0.00 0.04 0.02 0.19

13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11

14 0.37 0.02 0.00 0.00 0.00 0.01 0.06 0.08

15 0.41 0.01 0.04 0.03 0.00 0.19 0.08 0.49

16 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.03

17 0.40 0.01 0.00 0.02 0.00 0.01 0.01 0.35

18 0.74 0.15 0.05 0.33 0.00 0.16 0.07 0.47

*

Shaded cells indicate the highest value for each crop.

a All Ag PCAs based on NLCD land use 82 and Ag Census data.

b Represents residential turf including golf courses, but does not include sod farms. Sod farms are included 

in the all-agriculture land cover class. HUC12 turf results are derived from the 2001 NLCD.

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675
35

676 Table 3-5. Maximum Crop-specific PCAs, Listed by Water Resource Region (HUC-2),

677 Including Both 4,840 DWI PCAs for Quality-assured Watersheds, and the 637 HUC-12-

678 based PCAs for Quality-Assured DWIs that Lacking Quality-Assured Watersheds

HUC-2 All-Aga Corn Cotton Orchard Soybean Vegetable Wheat Turfb

01 0.32 0.08 0.00 0.02 0.00 0.10 0.00 0.86

02 0.82 0.34 0.06 0.07 0.11 0.03 0.06 0.60

03 0.52 0.09 0.08 0.12 0.20 0.08 0.10 0.64

04 0.92 0.35 0.00 0.05 0.44 0.04 0.11 0.85

05 0.90 0.51 0.02 0.03 0.59 0.02 0.10 0.49

06 0.45 0.10 0.04 0.02 0.10 0.01 0.03 0.38

07 1.00 0.68 0.00 0.00 0.58 0.01 0.27 0.59

08 0.75 0.21 0.20 0.00 0.07 0.00 0.05 0.52

09 0.95 0.16 0.00 0.00 0.21 0.02 0.41 0.06

10 1.00 0.36 0.00 0.00 0.37 0.02 0.27 0.51

11 0.79 0.20 0.07 0.05 0.34 0.01 0.39 0.70

12 0.77 0.11 0.21 0.03 0.01 0.04 0.18 0.25

13 0.52 0.01 0.01 0.01 0.00 0.01 0.00 0.11

14 0.37 0.02 0.00 0.00 0.00 0.01 0.06 0.08

15 0.41 0.01 0.04 0.03 0.00 0.19 0.08 0.49

16 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.04

17 0.72 0.01 0.00 0.02 0.00 0.01 0.38 0.35

18 0.74 0.15 0.05 0.33 0.00 0.16 0.07 0.59

*

Shaded cells indicate the highest value for each crop.

a Considers all-agriculture PCAs calculated both ways. The highest All-Ag values from Tables 3-2 

and 3-4 are listed here.

b Represents residential turf including golf courses, but does not include sod farms. Sod farms are 

included in the all-agriculture land cover class PCA.

679

680

681

682

683

684

685

686

687

688

689

690

691

692
36

693 Table 3-6. Maximum HUC-12-based PCAs for Various Crop Combinations, by Water 

694 Resource Region (HUC-2), for the 637 Quality-Assured DWIs Lacking Quality-Assured 

695 Watersheds

HUC2CornWheatSoybeanWheatTurfa-

Corn

Turfa-

Orchard

Turfa-

Soybean

Turfa-

Vegetable

Turfa-

Wheat

VegetableOrchardTurfa-

All Agb

01 0.05 0.00 0.86 0.86 0.86 0.86 0.86 0.02 0.98

02 0.29 0.11 0.60 0.60 0.60 0.60 0.60 0.10 0.72

03 0.00 0.00 0.41 0.41 0.41 0.41 0.41 0.03 0.41

04 0.40 0.44 1.00 0.85 0.85 0.85 0.85 0.05 1.00

05 0.52 0.37 0.55 0.38 0.41 0.38 0.38 0.00 0.95

06 0.04 0.03 0.22 0.22 0.22 0.22 0.22 0.00 0.31

07 0.33 0.23 0.51 0.45 0.47 0.45 0.45 0.00 0.64

08 0.01 0.00 0.53 0.52 0.52 0.52 0.52 0.00 0.55

09 N/A N/A N/A N/A N/A N/A N/A N/A N/A

10 0.27 0.28 0.47 0.47 0.47 0.47 0.47 0.02 1.00

11 0.17 0.17 0.39 0.39 0.39 0.39 0.41 0.01 0.67

12 0.05 0.02 0.22 0.23 0.19 0.23 0.19 0.07 0.78

13 0.00 0.00 0.11 0.11 0.11 0.11 0.11 0.00 0.11

14 0.06 0.06 0.09 0.08 0.08 0.08 0.10 0.01 0.41

15 0.08 0.08 0.49 0.49 0.49 0.50 0.50 0.23 0.57

16 0.00 0.00 0.03 0.03 0.03 0.03 0.03 0.00 0.04

17 0.01 0.01 0.35 0.35 0.35 0.35 0.35 0.03 0.49

18 0.18 0.07 0.47 0.47 0.47 0.47 0.47 0.34 0.83

*

Shaded cells indicate the highest value for each crop.

a Represents residential turf including golf courses, but does not include sod farms. Sod farms are included in the allagriculture land cover class. HUC12 turf values are derived from the 2001 NLCD.

b All Ag PCAs based on NLCD land use 82, and Ag Census data.

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710
37

711 Table 3-7. Maximum Crop-combination PCAs, Listed by Water Resource Region (HUC712 2), Including both the 4,840 DWI PCAs for Quality-Assured Watersheds, and the 637 

713 HUC-12-based PCAs for Quality-Assured DWIs Lacking Quality-Assured Watersheds

HUC2CornWheatSoybeanWheatTurfa-

Corn

Turfa-

Orchard

Turfa-

Soybean

Turfa-

Vegetable

Turfa-

Wheat

VegetableOrchardTurfa-

All Ag

01 0.08 0.00 0.86 0.86 0.86 0.86 0.86 0.12 0.98

02 0.36 0.17 0.60 0.60 0.60 0.60 0.60 0.10 0.79

03 0.18 0.25 0.64 0.64 0.64 0.64 0.64 0.13 0.65

04 0.42 0.52 1.00 0.85 0.85 0.85 0.85 0.06 1.00

05 0.52 0.59 0.58 0.49 0.64 0.49 0.49 0.04 0.96

06 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

07 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

08 0.22 0.12 0.53 0.52 0.52 0.52 0.52 0.00 0.58

09 0.42 0.44 0.20 0.06 0.25 0.07 0.46 0.02 0.99

10 0.38 0.47 0.51 0.51 0.51 0.51 0.51 0.02 1.00

11 0.41 0.55 0.70 0.70 0.70 0.70 0.70 0.05 0.81

12 0.20 0.18 0.29 0.25 0.25 0.25 0.26 0.07 0.82

13 0.01 0.00 0.11 0.11 0.11 0.11 0.11 0.02 0.57

14 0.06 0.06 0.09 0.08 0.08 0.08 0.10 0.01 0.41

15 0.08 0.08 0.49 0.49 0.49 0.50 0.50 0.23 0.57

16 0.00 0.00 0.04 0.04 0.04 0.04 0.04 0.00 0.07

17 0.38 0.38 0.35 0.35 0.35 0.35 0.40 0.03 0.74

18 0.18 0.07 0.59 0.59 0.59 0.59 0.59 0.34 0.83

*

Shaded cells indicate the highest value for each crop.

a Represents residential turf including golf courses, but does not include sod farms. Sod farms are included in the 

all-agriculture land cover class PCA.

714

715 Appendix B comprises figures that display comparisons of national-scale PCA cumulative 

716 distribution functions (CDFs), between DWI-watersheds, HUC-12 surrogates, and the two 

717 combined. For a number of crops or crop combinations (orchard, vegetable, turf, turf-corn, turf718 orchard, turf-soybean, turf-vegetable, turf-wheat, turf-all-agriculture, and vegetable-orchard), the 

719 distribution of PCAs for HUC-12 surrogates is shifted to the right as compared with PCAs for 

720 DWI watersheds, meaning that the HUC-12 PCAs are generally more conservative. For soy and 

721 soy-wheat the opposite is true. However in most cases, the figures show that little change is 

722 caused by including the HUC-12 based PCAs in the overall distributions. 

723 Figure 3-1 shows a comparison of national maximum DWI PCAs (with and without 

724 inclusion of the HUC-12 based subset), against the existing HUC-8 based PCAs. Figure 3-2

725 similarly displays a comparison of maximum all-agriculture PCAs per HUC-2 region, between 
38

726 the new DWI PCAs (calculated with and without pasture/hay) and the existing HUC-8 PCAs. In 

727 most cases, both figures show that the new maximum DWI PCAs are slightly greater than the 

728 HUC-8 based values.

729 3.3 Validation of PCAs

730 An independent validation of the PCAs was conducted using an alternate set of GIS 

731 procedures to derive PCAs for a random selection of DWI watersheds (APPENDIX A). The 

732 purpose of the validation was to identify any data processing errors or data anomalies (the 

733 accuracies of the intake locations and basin delineations were not evaluated by EFED, but were 

734 evaluated by USGS as described in Chapter 2). A team of EFED reviewers selected a variety of 

735 watersheds for each of the crops, and applied the alternate PCA calculation methodology. The 

736 resulting PCAs for these watersheds were compared with the original PCAs for verification. The 

737 list of verified catchment PCAs is provided in Table A-2. 

738 The validation process revealed some watersheds that were incorrectly identified as 

739 having PCAs of zero. Further analysis revealed that the automated (Python script-based) process 

740 occasionally skipped watersheds, for reasons that have not yet been identified. The watersheds 

741 that were skipped in this manner varied from one crop to another. An independent analysis 

742 identified approximately 20 – 40 watersheds per crop, for which the automated process failed to 

743 correctly calculate a PCA. Once identified, PCAs for these watersheds were calculated 

744 manually.

745

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent Cropped Area

Results: National Maximum PCAs for Various Crops

QA'd watersheds QA'd watersheds & HUC-12s Current (HUC-8 based)
39

746 Figure 3-1. Comparison of existing HUC-8 based PCAs against DWI PCAs with and without 

747 inclusion of the HUC-12 based subset.

748

749 Figure 3-2. Comparison of maximum all-agriculture PCAs by HUC-2 region. Shown are DWI 

750 PCAs based on NLCD land use 82 (cultivated crop land) and based on land uses 81 and 82 (crop 

751 land plus pasture/hay), and current HUC-8 based PCAs.

752 3.4 Data Limitations

753 There are several potential sources of error in the spatial data and methodology used. 

754 The magnitudes of these errors and limitations are not known. Limitations include:

755 Rates of misclassification between NLCD classes 81 and 82, and between 81, 82 and 

756 other classes appear to be significant, but have not yet been quantified. Such 

757 misclassifications could either increase or decrease PCAs.

758 A necessary assumption under the current PCA calculation methodology is that the 

759 NASS acres for a given crop are evenly distributed across all agricultural land within 

760 each county. This is unlikely to be true, and may mask significant regional clustering of 

761 crop types at a sub-county scale.

762 PCA values for basins that cross into Canada and Mexico have a particularly high degree 

763 of uncertainty associated with them, as data on land uses and pesticide applications 

764 outside the U.S. are not available. 

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 1 0 11 1 2 1 3 1 4 1 5 1 6 1 7 1 8 Percent Cropped Area

HUC-2 Region

Results: Regional Maximum All-Ag PCAs

Cultivated Crop Land Cultivated Crop Land + Pasture/Hay Current (HUC-8 based)
40

765 The remote sensing data used to generate the 2006 NLCD land cover map were collected 

766 during the early 2000’s, whereas the NASS data are from 2007. Any land use changes 

767 during the interim, e.g., conversion of marginal land to agriculture, would result in errors

768 due to the mismatch between datasets.

769 It is possible that two separate crops were reported for the same acreage in places where

770 the land was double-cropped, thus resulting in double-counting of total cropland in the 

771 agricultural census for such areas. This could result in PCAs greater than 100%.

772 3.5 Future Improvements

773 Use of the USDA cropland data layer (CDL) in place of NLCD data will be considered 

774 for future analysis. This would presumably allow for more accurate sub-county 

775 designations of the locations of specific crops. Since a new CDL product is now 

776 released annually, it should be easier to select data from a time frame that matches 

777 NASS data.
41

778 Chapter 4. Monitoring Data Comparisons

779 An aspect of the evaluation process for the DWI PCAs was a comparison between

780 monitored pesticide concentrations and model-estimated concentrations when DWI PCAs are

781 applied. The objective of the evaluation was to ensure that the use of DWI PCAs, in conjunction 

782 with USEPA’s screening modeling approach for estimating pesticide concentrations in surface 

783 water, results in concentrations that are protective of vulnerable drinking water supplies. This 

784 section describes the methodology and the results of the evaluation.

785 4.1 Methodology

786 In the modeling part of the evaluation effort, for each of seven “crops” (corn, cotton, 

787 soybeans, wheat, turf, vegetables, and orchards) as well as all-agriculture, for which DWI PCAs 

788 were developed, EFED identified up to three pesticides (i.e., active ingredients) used primarily

789 on that crop. EFED utilized data supplied by OPP’s Biological and Economic Analysis Division 

790 (BEAD) to define the extent to which each pesticide was used on each particular crop. EFED 

791 then collected physical-chemical properties, fate data, and application information (e.g.,

792 application method and maximum single rates, minimum retreatment interval, date of first 

793 application, and maximum number of applications/year) for each pesticide from its most recent 

794 drinking water or endangered species assessment. Using these data, EFED ran the PRZM and 

EXAMS models with the PE5 shell (PRZM-EXAMS Model Shell)27 795 , using standard scenarios 

796 for each crop and the index reservoir, to estimate 1-in-10 year return frequency daily

797 concentrations. EFED then multiplied these concentrations by the appropriate DWI PCAs to 

798 develop EDWCs.

799 The ideal monitoring site for evaluating PCA-adjusted modeling data would reflect an 

800 area of high pesticide use on a specific, individual crop or crop group, in an area considered 

801 particularly vulnerable to runoff due to rainfall, hydrology, soil conditions, etc., and from water 

802 actually used as a source water supply for a CWS. Ideally, such a site would also be sampled 

803 frequently enough during the pesticide use season to either capture peak pesticide concentrations, 

or allow for calculation of reasonable statistical bounds on measured concentrations28 804 . 

805 Unfortunately, monitoring data meeting all these criteria particularly runoff-vulnerability and 

806 crop-specificity are not usually available. All of the CWS data employed in this analysis were 

807 collected at monthly sampling intervals, with the exception of atrazine and iprodione, which 

808 were sampled at weekly intervals. For each of the three identified chemicals used on each crop, 

809 EFED sought to compare EDWCs against monitored concentrations in two fundamental 

 

27 The PE5 shell is a graphical user interface for running the PRZM and EXAMS models.

28 In recent FIFRA Scientific Advisory Panel consultations on the pesticide atrazine, the USEPA reviewed methods 

for designing monitoring studies to capture exposures of concern and approaches for analyzing and interpreting 

existing monitoring data and characterizing the uncertainties in those estimates for use in risk assessments (USEPA, 

2010a, 2010b, 2011, and 2012).
42

810 categories: (1) maximum detections in surface water used specifically as source drinking water 

811 by CWSs, and (2) maximum detections in surface water of any kind. 

812 For the former category, EFED employed data primarily from a USGS study on CWS 

813 source water: Concentration Data for Anthropogenic Organic Compounds in Ground Water, 

814 Surface Water, and Finished Water of Selected Community Water Systems in the United States, 

2002–05.

29 815 The USGS CWS dataset was selected because this study focused on a number of 

816 rivers across the country that are used as source drinking water, with monitoring consisting of 

817 several years of monthly sampling. EFED considered CWS data from the Pesticide Data 

818 Program (PDP) as well, however, these concentrations were lower than the maximum detections 

819 from the USGS study; therefore, not used in present evaluation. EFED supplemented the CWS 

820 data with data on two specific chemicals (atrazine and iprodione) also measured in surface water 

821 used as source drinking water by CWS, from registrant-conducted monitoring studies submitted 

822 to USEPA. Although all these data represent real drinking water supplies, most are on fairly 

823 large river systems (Table 4-1) with large drainage areas, thus, are inherently heterogeneous and

824 unlikely to be representative of water supplies most vulnerable to pesticide contamination. 

825 Additionally, given that the sampled systems were all lotic (flowing) rather than lentic (i.e.,

826 lakes, reservoirs), the monthly sampling interval employed by USGS is unlikely to have resulted 

827 in sample collection coinciding with peak pesticide concentrations. The registrant-sponsored 

828 sampling for atrazine and iprodione was conducted approximately weekly.

829 To address some of these weaknesses, for the second category of comparisons against 

830 modeling results, EFED obtained the maximum measured concentration of each chemical in 

831 surface water of any kind (i.e., not limited to community water supplies) throughout the United 

832 States measured by the USGS as part of the NAWQA program. Though EFED has no 

833 information to suggest that any of the monitored water bodies (Table 4-2) in this case are used as 

834 drinking water supplies, the concentrations generally exceed concentrations reported in the CWS 

835 studies. 

836 For each crop and pesticide combination and each type of measured concentration (i.e.,

837 CWS supply and miscellaneous surface water), EFED used two different kinds of DWI PCAs to 

838 modify the PRZM-EXAMS results: the national maximum crop-specific PCA, and a regional 

839 maximum, crop-specific PCA, for the water resource region corresponding with the location 

840 where the reported concentration was measured. Using each of these sets of PCAs, EFED 

841 compared the national and regional PCA-adjusted EDWCs with measured drinking water supply 

842 (Table 4-1 and Figure 4-1) and miscellaneous surface water (Table 4-2 and Figure 4-3) 

843 concentrations. This was done to evaluate the level of protectiveness implicit in utilizing these 

 

29 http://pubs.usgs.gov/ds/2007/268/
43

844 PCAs in drinking water assessments. For comparison against the NAWQA dataset, EFED also 

845 developed EDWCs using regional PCAs that reflect all labeled pesticide uses (Table 4-3 and 

846 Figure 4-4); similar to what might be done as part of a drinking water assessment.

847 Monitoring in many surface water bodies, particularly lotic systems or others with short

848 residence times, is unlikely to capture true maximum concentrations because hydrologic 

849 conditions and the concentrations these conditions influence typically exhibit fluctuations on 

850 shorter time scales than the intervals between samples. In general, as sampling frequency 

851 increases, maximum concentrations measured in such samples increase (SAP 2010). In the 

852 absence of continuous monitoring, true maximum concentrations are unknown. While PCA853 adjusted modeled pesticide concentrations that exceed measured pesticide concentrations provide

854 some assurance that EFED’s assessment process using PCAs has not underestimated pesticide 

855 concentrations in drinking water supplies, actual peak pesticide concentrations in surface water 

856 may exceed maximum measured concentrations by an order of magnitude or more (USEPA, 

857 2010b, 2011, and 2012). To evaluate the possible degree of under-representing true peak 

858 concentrations inherent in sampling water bodies at less often than daily frequencies, EFED is 

exploring the use of concentration-adjustment “bias factors”.30 859 Based upon an analysis of daily 

860 atrazine monitoring data, estimated mean bias factors for weekly and monthly sampling intervals 

are 5.46 and 44.48, respectively.

31 861 These values were empirically derived from a limited 

862 monitoring dataset. In addition to sampling frequency, factors that may influence measured 

863 concentrations include water body hydrology, pesticide fate properties, and pesticide application 

864 patterns and practices. Bias factors based upon atrazine data may thus not be appropriate for use 

865 with chemicals that have very different fate properties or application patterns, for example. The 

866 use in this exercise is therefore included for illustrative purposes only. To use the bias factor, the 

867 maximum monitored concentration from a weekly interval sampling methodology is multiplied 

868 by the bias factor of 5.46 to estimate an upper bound concentration to account for the limited 

869 monitoring data. Figure 4-2 shows the same modeling-monitoring comparison as in Figure 4-1, 

870 but with the measured concentrations adjusted using sampling interval-appropriate bias factors. 

871 Unlike the CWS data, the NAWQA data were not collected at regular intervals over extended 

872 durations at a given location, thus, it was not possible to develop bias factors to use in this 

873 analysis.

874 4.2 Results

875 Tables 4-1 and 4-2 provide comparisons between measured and modeled estimated 

876 pesticide concentrations that have been adjusted using national and regional maximum DWI 

 

30 U.S. EPA 2012. FIFRA Science Advisory Panel– Problem Formulation for the Reassessment of Ecological Risks 

from the Use of Atrizine. Available at http://www.epa.gov/scipoly/sap/meetings/2012/061212meeting.html

31 Personal Communication: Jim Hetrick, United States Environmental Protection Agency, Office of Pesticide 

Programs, Environmental Fate and Effects Division.
44

877 watershed (plus HUC-12 surrogate) PCAs. Tables 4-1 and 4-2 include DWI PCA-adjusted 1-in878 10 year peak EDWCs, along with the crop-specific maximum measured concentration of each 

879 chemical in CWS-supply surface water, and miscellaneous surface water respectively. All of the 

880 sample concentrations listed in both tables are actual detections, i.e., concentrations measured 

881 above method detection limits, and not bias factor-adjusted concentrations. National-maximum 

882 crop-specific PCAs (for DWI watersheds plus HUC-12 surrogates), and region-specific PCAs for 

883 the HUC-2 regions corresponding to the sampling locations are tabulated. Figure 4-1 displays 

884 scatterplots of the PCA-adjusted EDWCs against the maximum detected concentrations of each 

885 pesticide in surface drinking water supplies. Figure 4-2 displays the same PCA-adjusted 

886 EDWCs plotted against the estimated maximum bias factor-adjusted concentrations in the same 

887 water bodies. Figure 4-3 displays scatterplots of the PCA-adjusted EDWCs against maximum 

888 detected concentrations of each chemical in miscellaneous surface water bodies (i.e., NAWQA 

889 monitoring). Modeled concentrations in each case are compared only against the peak measured 

890 concentrations because monitoring data of sufficient frequency, duration and regularity to permit 

891 calculation of chronic or longer-term concentrations does not exist for the majority of pesticides. 

892 Figure 4-4 displays a scatterplot of EDWCs adjusted using regional specific, all-use-inclusive 

893 PCAs against the same NAWQA monitoring data. The all-use-inclusive PCA values are 

894 consistent with the EDWCs that would be presented in a drinking water exposure assessment for 

895 use in dietary risk assessment. This is consistent with EFED standard methodologies (described 

896 in Chapter 5). The results of these comparisons are as follows: 

897 1. Using national maximum DWI PCAs (based on DWI watersheds plus HUC-12 

898 surrogates), adjusted EDWCs are greater than maximum monitored concentrations in 

899 CWS source water, for all chemicals except atrazine. In the case of atrazine, a single 

900 monitored sample had a concentration of 227 μg/L, which is 56% higher than the EDWC 

901 of 145.6 μg/L. Using regional maximum DWI PCAs, EDWCs are also greater than 

902 monitored concentrations for all chemicals except atrazine.

903 

904 2. When bias factors are used to estimate maximum pesticide concentrations in CWS source 

905 surface waters; DWI PCA-adjusted EDWCs are greater than these concentrations for all 

906 but four chemicals when either national or regional maximum PCAs are used.

907

908 3. Adjusted EDWCs are greater than maximum measured concentrations for 13 out of 20 

909 pesticides in miscellaneous (i.e., NAWQA) surface waters when national maximum DWI 

910 PCAs are used. Using regional maximum DWI PCAs, adjusted EDWCs are greater than 

911 maximum measured concentrations for less than half (9 out of 20) of the evaluated 

912 pesticides. Using regional maximum all-use-inclusive DWI PCAs, adjusted EDWCs

913 (i.e., EDWC that would be used in EFED drinking water exposure assessments) are 

914 greater than maximum measured concentrations for 14 out of 20 pesticides in 

915 miscellaneous (NAWQA) surface waters.
45

916
46

917 Table 4-1. Comparison Between Maximum Monitored Pesticide Concentrations in Drinking (surface) Water Sources (USGS 

918 study), and Acute EDWCs Adjusted using DWI PCAs

Crop Pesticide

DW 

Measured 

Conc. 

(μg/L)

State 

where 

measured

Water body National 

Max PCA

Regional 

Max 

PCAa

PE5 

Conc. 

(μg/L)

National

EDWC 

(μg/L)

Regional 

EDWC 

(μg/L)

Ratio 

National 

EDWC 

/Meas. 

DW

Conc.

Ratio

Regional

EDWC /

Meas. 

DW

Conc.

All Ag diazinon 0.0855 GA Chattahoochee 

River 1.00 0.52 150 150 78 1754 912

All Ag chlorothalonil 

(TTR)b 0.71c

IN White River 1.00 0.89 363 363 323 511 455

All Ag chlorothalonil 

(parent)b 0.71c

IN White River 1.00 0.89 83.9 79 75 118 105

All Ag carbofuran 0.0141 IN White River 1.00 0.89 27.9 28 25 1860 1761

Corn acetochlor 4.77 IN White River 0.68 0.51 82.3 56 42 12 8.8

Corn atrazine 227 OHdSterling Run Creek 0.68 0.51 214 146 109 0.64 0.48

Corn fipronil 0.0375 NC Neuse River 0.68 0.09 0.813 0.55 0.07 15 2.0

Cotton fluometuron 0.0065 NC Neuse River 0.21 0.08 89.6 19 7.2 2895 1103

Orchard bromacil 0.0927 GA Chattahoochee 

River 0.33 0.12 268 88 32 954 347

Orchard oryzalin 0.065 MD Potomac River 0.33 0.05 261 86 13 1325 201

Orchard phosmet 0.0074 MD Potomac River 0.33 0.05 11.1 3.7 0.56 495 75

Soybean imazaquin 0.16 IN White River 0.59 0.59 19.3 11 11 71 71

Soybean acifluorfen 0.011 IN White River 0.59 0.59 86.8 51 51 4656 4656

Soybean bentazon 0.11 IN White River 0.59 0.59 20.5 12 12 110 110

Turf picloram 0.17 IN White River 0.86 0.49 19 16 9 96 55

Turf triclopyr 0.45 IN White River 0.86 0.49 1143 983 560 2184 1254

Turf iprodione 0.564 NJ2 Rahway River 0.86 0.60 361 310 217 550 384

Vegetable dcpa 0.0049 OR Clackamas River 0.19 0.01 1427 143 14 29122 2912

Wheat bromoxynil 0.0046 IN White River 0.41 0.10 5.16 2.1 0.5 460 112
47

Wheat mcpa 0.47 GA Chattahoochee 

River 0.41 0.10 40.2 16 4.0 35 8.6

Concentration (Conc.)

Measured Drinking Water Concentration (Meas. DW Conc.)

a Region based on location where listed concentration was measured. 

b Chlorothalonil was run twice, once using the fate data for all of the residues of concern, then again using just the fate data for the parent.

c Monitoring value obtained from turf drinking water study (MRID 47881501).

d Monitoring value obtained from Syngenta’s atrazine monitoring program (AMP) dataset collected between 2004 and 2010.

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

939

940

941

942

943
48

944 Table 4-2. Comparison Between Maximum Monitored Pesticide Concentrations in Surface Water (USGS NAWQA), and 

945 Acute EDWCs Adjusted Using DWI PCAs

Crop Pesticide

Max. 

NAWQA 

conc. 

(μg/L)

State 

where 

measured

Water body 

National 

Max 

PCA

Regional 

Max 

PCAa

PE5 conc. 

(μg/L)

National

EDWC 

(μg/L)

Regional 

EDWC 

(μg/L)

Ratio 

National 

EDWC 

/Meas. 

DW

Conc.

Ratio

Regional

EDWC /

Meas. DW

Conc.

All Ag diazinon 3.8 CA Orestimba Creek 1.00 0.74 150 150 111 39 29

All Ag chlorothalonil 

(TTR)b,c 0.71b NJ Great Egg Harbor River 1.00 0.82 363 363 261 511 410

All Ag chlorothalonil 

(parent) b,c 0.71b NJ Great Egg Harbor River 1.00 0.82 83.9 84 69 118 97

All Ag carbofuran 32.2 OR Zollner Creek 1.00 0.72 27.9 27.9 20 0.87 0.62

Corn acetochlor 215 NE tributary to South 

Fork Dry Creek 0.68 0.36 82.3 56 30 0.26 0.14

Corn atrazine 201 AL Bogue Chitto Creek 0.68 0.09 214 146 19 0.72 0.10

Corn fipronil 6.41 LA Bayou Blue 0.68 0.21 0.813 0.6 0.2 0.09 0.03

Cotton fluometuron 37.8 LA Bayou Macon 0.21 0.20 89.6 19 17.9 0.50 0.47

Orchard bromacil 5 GA Peachtree Creek 0.33 0.12 268 88 32 18 6.4

Orchard oryzalin 1.9 VA Accotink Creek 0.33 0.12 261 86 31 45 16.5

Orchard phosmet 0.063 MS Yazoo River 0.33 0.00 11.1 3.7 0.0 58 0.0

Soybean imazaquin 5.9 AL Three Mile Creek 0.59 0.20 19.3 11 4 1.9 0.7

Soybean acifluorfen 2.2 IN Kessinger Ditch 0.59 0.59 86.8 51 51 23 23

Soybean bentazon 8.6 TX White's Bayou 0.59 0.01 20.5 12 0 1.4 0.0

Turf picloram 2.7 TX East Fork Trinity 

River 0.86 0.25 19 16 5 6.1 1.8

Turf triclopyr 16 TX White's Bayou 0.86 0.25 1143 983 286 61 18

Turf iprodione 141 CA culvert discharging 

to Mustang Creek 0.86 0.59 361 310 213 2.2 1.5

Vegetable dcpa 100 CO Lonetree Creek 0.19 0.02 1427 271 29 2.7 0.29
49

Wheat bromoxynil 6.1 CO Lonetree Creek 0.41 0.27 5.16 2.1 1.4 0.35 0.23

Wheat mcpa 18.6 MI Clinton River 0.41 0.11 40.2 16 4.4 0.89 0.24

Concentration (Conc.)

Measured Drinking Water Concentration (Meas. DW Conc.)

a Region based on location where listed concentration was measured. 

b Chlorothalonil was run twice, once using the fate data for all of the residues of concern, then again using just the fate data for the parent.

c Monitoring value obtained from turf drinking water study (MRID 47881501).

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969
50

970 Table 4-3. Comparison Between Maximum Monitored Pesticide Concentrations in Drinking (Surface) Water Sources (USGS 

971 study), and Acute EDWCs Adjusted Using Crop and Regionally-Appropriate DWI PCAs

Pesticide Uses

Max. 

NAWQA 

Conc. 

(μg/L)

Name of 

water 

body 

where 

measured

State 

where 

measured

Region 

where 

measured

Useappropriate 

PCA

PCA 

type

PE5 

conc. 

(μg/L)

Regional 

EDWC 

(μg/L)

Regional 

Ratio 

EDWC 

/max.

Diazinon

almonds, apples, apricots, bananas, 

beets (red, table), blackberries, 

blueberries, cabbage, carrots, 

cauliflower, celery, cherries, collards, 

sweet corn, cranberries, cucumbers, 

endive (escarole), figs, filberts, ginseng, 

grapes, hops, kale, lettuce, loganberries, 

melons, mushrooms, nectarines, onions, 

parsley, parsnips, peaches, pears, peas, 

peppers, pineapples, plums, Irish

potatoes, prunes, radishes, radishes 

(Chinese), raspberries, rutabagas, 

squash (winter and summer), spinach, 

strawberries, sugar beets, sweet 

potatoes, Swiss chard, tomatoes, turnips 

(roots and tops), vegetables (Brassica 

leafy group), walnuts, and watercress

3.8 Orestimba 

Creek CA 18 0.74 All Ag 150 111 29

chlorothalonil 

(TTR)a

apricot, berries, cabbage, cantaloupes, 

carrots, celery, cherries, cotton, 

cucumber, melons, nectarines, onions, 

peanuts, potatoes, tomatoes, 

watermelons, container-grown nursery 

crops, flowers, greens, golf courses, sod 

farms, lawns, greenhouse crops 

0.71

b

Great Egg 

Harbor 

River

NJ 2 0.82 All Ag 363 261 368

chlorothalonil 

(parent)s

apricot, berries, cabbage, cantaloupes, 

carrots, celery, cherries, cotton, 

cucumber, melons, nectarines, onions, 

peanuts, potatoes, tomatoes, 

watermelons, container-grown nursery 

crops, flowers, greens, golf courses, sod 

farms, lawns, greenhouse crops 

0.71

b

Great Egg 

Harbor 

River

NJ 2 0.82 All Ag 83.9 60 85
51

carbofuran

alfalfa, artichoke, banana, barley, 

coffee, corn (field, pop, and sweet), 

cotton, cucurbits

(cucumber, melons, and squash), 

grapes, oats, pepper, plantain, potato, 

sorghum, soybean, sugar beet, 

sugarcane, sunflower, wheat, 

agricultural fallow land, cotton, 

ornamental and/or shade trees, 

ornamental herbaceous plants, 

ornamental non-flowering plants, 

ornamental woody shrubs and vines, 

pine, spinach grown for seed, tobacco

32.2 Zollner 

Creek

OR 17 0.74 All Ag 27.9 20 0.62

acetochlor corn and "rotational crops" 215

Tributary 

to South 

Fork Dry 

Creek

NE 10 0.38 Corn

-

wheat 82.3 31 0.15

atrazine

corn (field & sweet), sorghum, 

suragcane, turf, range grasses, wheat 

stubble on fallow land

201

Bogue 

Chitto 

Creek

AL3 0.65 Turf-All 

Ag 214 139 0.69

fipronil rice, turf, other residential uses (fire ant 

mounds), potatoes, turnips, rutubagas 6.41 Bayou Blue LA

8 0.58 Turf-All 

Ag 0.813 0.5 0.07

fluometuron cotton 37.8 Bayou 

Macon LA

8 0.20 Cotton 89.6 17.9 0.47

bromacil grapefruit, lemons, oranges, limes, 

tangerines, rights-of-way

5

Peachtree 

Creek GA

3 0.64

Turforchard 268 169 33.8

oryzalin

orchards, berries, and vine crops, 

Christmas tree plantations, field-grown 

roses, nursery stock, ornamentals, golf 

courses, non-crop areas, and parks and

greenhouse drainage areas, turf

1.9 Accotink 

Creek VA

2 0.79 Turf-All 

Ag 261 206 108.5

phosmet

corn (sweet), citrus; homeowner uses 

(trees, shrubs, ornamental plants, 

gardens, fire ant mounds); fruit & nut 

trees, grapes, kiwi, blueberries, 

cranberries, cotton, peas, potatoes, 

sweet potatoes

0.063 Yazoo 

River MS

8 0.75 All Ag 11.1 10.5 167.4

imazaquin soybeans, turf, ornamentals 5.9 Three Mile 

Creek AL

3 0.64

Turfsoybeans 19.3 12 2.1
52

acifluorfen

soybeans, rice, peanuts, mulch, 

ornamental shrubs, lawns, herbaceous 

plants and shade trees, private roads, 

sidewalks, patios, paths

2.2 Kessinger 

Ditch IN

5 0.96 Turf-All 

Ag 86.8 83 38

bentazon soybeans, alfalfa, corn, sorghum, turf 8.6 White's 

Bayou TX 12 0.82 Turf-All Ag 20.5 17 2.0

picloram pasture, wheat, barley, oats, rangeland, 

forest trees, roadsides 2.7

East Fork 

Trinity 

River

TX 12 0.82 Turf-All 

Ag 19 16 5.8

triclopyr rice, pasture and rangeland, rights

-ofway, forestry, turf 16 White's 

Bayou TX 12 0.82 Turf-All Ag 1143 286 18

iprodione

almonds, apricots, cherries, nectarines, 

peaches, pecans, plums, prunes, beans 

(dried, lima, and snap), blackberries, 

blueberries, broccoli, bushberries, 

caneberries, carrots, garlic, grapes, 

ginseng, gooseberries, huckleberries, 

lettuce (head and leaf), loganberries, 

mustard, cabbage, Chinese cabbage, dry 

bulb onions, peanuts, potatoes, 

raspberries, strawberries, ornamentals, 

turf, ornamentals

141

culvert 

discharging 

to Mustang 

Creek

CA 18 0.83 Turf-All 

Ag 361 300 2.1

dcpa

beans, beets, broccoli, Brussels sprouts, 

cabbage, cauliflower, cole crops, 

collards, cress, cucumber, eggplant, 

garlic, kale, melons, mustards, onion, 

peas (southern), pepper, radish, squash, 

strawberry, sweet potato, tomato, 

turnip, yam

100 Lonetree 

Creek CO 10 1.00 All

-Ag 1427 1427 14.27

bromoxynil

wheat, garlic/onions, mint, flax, 

spearmint, peppermint, barley, oats, 

rye, triticale, sorghum, cotton, corn 

(field, sweet, and pop), fodder/hay, 

grass, millet (proso), alfalfa, 

sudangrass, fallow land, turf, 

ornamentals

6.1 Lonetree 

Creek CO 10 1.00 Turf-All Ag 5.16 5.16 0.85

mcpa

wheat, alfalfa, barley, clover, flax, 

lespedeza, oats, pasture and rangeland 

grass, peas, rice, rye, sorghum, trefoil, 

triticale, turf, rights-of-way, forestry

18.6 Clinton 

River MI

4 1.00 Turf-All 

Ag 40.2 40.2 2.16
53

972

973 Figure 4-1. Comparison of Modeled 1-in-10 Year Maximum EDWCs Against Maximum 

974 Single Sample Concentrations of the Same Chemicals Measured in Studies of Rivers and 

975 One Creek that Serve as Source Water for Community Water Supplies, Using: a) National 

976 Maximum DWI PCAs, and b) Regional Maximum DWI PCAs, with Region DWI PCA 

977 Selected to Match Locations of Monitored Concentrations

978

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled acute conc. (ppb)

max measured CWS source conc. (ppb)

national maximum PCAs a )

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled acute conc. (ppb)

max measured CWS source conc. (ppb)

regional maximum PCAs b )
54

979

980 Figure 4-2. Comparison of Modeled 1-in-10 Year Maximum EDWCs Against Bias Factor981 adjusted Maximum Single Sample Concentrations of the Same Chemicals Measured in 

982 Studies of Rivers and One Creek that Serve as Source Water for Community Water 

983 Supplies, using: a) National Maximum DWI PCAs, and b) Regional Maximum DWI PCAs, 

984 with Region Selected to Match Locations of Monitored Concentrations

985

0.001

0.01

0.1

1

10

100

1000

10000

0.00 0.01 0.10 1.00 10.00 100.00 1000.00 10000.00

modeled acute conc. (ppb)

estimated max CWS source conc. (ppb)

national maximum PCAs a )

0.001

0.01

0.1

1

10

100

1000

10000

0.00 0.01 0.10 1.00 10.00 100.00 1000.00 10000.00

modeled acute conc. (ppb)

estimated max CWS source conc. (ppb)

regional maximum PCAs b )
55

986

987 Figure 4-3. Comparison of Modeled 1-in-10 Year Maximum EDWCs Against Maximum 

988 Single Sample Concentrations of the Same Chemicals Measured in Surface Waters by the 

989 USGS NAWQA Program, Using: a) National Maximum DWI PCAs, and b) Regional 

990 Maximum DWI PCAs, with Region Selected to Match Locations of Monitored 

991 Concentrations. [Note that PCAs in this comparison largely reflect only single crops, and may 

992 not correspond with PCAs that would be used in a regional drinking water assessment]

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled acute conc. (ppb)

max NAWQA surface water conc.(ppb)

national maximum PCAs

a )

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled acute conc. (ppb)

max NAWQA surface water conc. (ppb)

regional maximum PCAs b )
56

993

994 Figure 4-4. Comparison of Modeled 1-in-10 Year Maximum EDWCs Against Maximum 

995 Single Sample Concentrations of the Same Chemicals Measured in Surface Waters by the 

996 USGS NAWQA Program, Using PCAs reflective of all labeled uses of the pesticide, as 

997 indicated in Table 4-3

998

999

1000

1001

1002

1003

1004

1005

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled conc. (ppb)

max NAWQA surface water conc. (ppb)
57

1006

1007 Figure 4-5. Ratio of Regional PCA Adjusted Modeled Pesticide Concentrations (EDWCs) 

1008 to Maximum Measured (NAWQA) Pesticide Concentrations, vs. Regional Crop-specific 

1009 PCA Employed in Calculating the Modeled Concentrations

1010

1011 Figure 4-6. Comparison of Modeled 1-in-10 Year Maximum EDWCs (calculated using no

1012 PCA adjustment) Against Maximum Single Sample Concentrations of the Same Chemicals 

1013 Measured in Surface Waters by the USGS NAWQA Program

0.01

0.10

1.00

10.00

100.00

1000.00

0.00 0.20 0.40 0.60 0.80 1.00

EDWCR/max. conc.

Regional Max. PCA

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

modeled acute conc. (ppb)

max NAWQA surface water conc. (ppb)

PE5 results (no PCA used)

fipronil

acetochlor
58

1014 4.3 Discussion

1015 For all but one pesticide, atrazine, results of aforementioned analyses demonstrate that 

1016 PRZM-EXAMS modeling coupled with the use of both national and regional-maximum DWI 

1017 PCAs adjustments, generates concentration estimates that exceed maximum observed pesticide 

1018 concentrations in lotic surface waters used for CWS supply. The fact that the atrazine data, 

1019 which were collected at a higher frequency than most of the other data used in this analysis, are 

1020 the lone exception to this observation is noteworthy. In addition, atrazine uses include use on 

1021 rights-of-ways and ornamentals; thus, a PCA of 1.0 would be used in drinking water exposure 

1022 assessment. When standard methodologies are used, the modeled concentration exceeds the 

1023 measured value. In flowing water bodies, less often than daily sampling is unlikely to capture 

1024 peak pesticide concentrations generated by stormflow runoff events, which may transport 

1025 pesticides into water bodies over much shorter time scales. The use of bias factors, as in Figure 

1026 4-2, is an attempt to adjust monitoring data to account for limitations in sampling frequency. 

1027 Many of the CWS supply sampling locations from which data used in this analysis were 

1028 collected are unlikely to represent the kind of highly vulnerable sites that USEPA’s PRZM1029 EXAMS modeling is intended to represent, thus, modeled estimated concentrations are expected 

1030 to generally exceed maximum concentrations observed in actual waters. Figure 4-2 supports this 

1031 assumption. 

1032 Results of the second phase of this analysis, in which modeled concentrations are 

1033 compared against national maximum pesticide concentrations measured in surface water by the 

1034 USGS NAWQA sampling program, demonstrate that PCA-adjusted PRZM-EXAMS 

1035 concentrations can underestimate real pesticide concentrations, at least sometimes in some water 

1036 bodies. The implications of this for pesticides in drinking water supplies are not clear. The 

1037 degree to which conditions in these water bodies resemble conditions in vulnerable CWS 

1038 supplies is not known. Figure 4-5 demonstrates an apparent (though weak) relationship between 

1039 the regional PCAs used in the NAWQA data comparison of Figure 4-3, and the degree to which 

1040 modeled concentrations are higher or lower than observed concentrations. Such a relationship 

1041 suggests that the degree of under or overestimation for any pesticide may be dominated by the 

1042 PCA rather than by the PRZM-EXAMS results, which suggests that underestimates may be a 

1043 consequence of use of a “wrong” PCA, i.e., one that underestimates real crop coverage or 

1044 pesticide use. Additionally, Figure 4-5 shows that when low PCAs are used (those less than 

1045 0.4), PCA adjusted EDWC are commonly lower than monitored concentrations. While it is 

1046 unknown whether the NAWQA sampling sites occurred in water bodies near DWIs, it is possible 

1047 that some of the sampling sites are near DWIs or are representative sites where DWI may be

1048 found. 

1049 Figure 4-6 displays a comparison between the same NAWQA data, and unadjusted (i.e.,

1050 no PCA) PRZM-EXAMS concentration results. As the figure illustrates, in this case modeled 

1051 concentrations exceed measured concentrations for all but four pesticides. Modeled 
59

1052 concentrations underestimate measured concentrations by more than 20% for only two 

1053 pesticides: fipronil and acetochlor. The largest degree of underestimation, which is by less than 

1054 an order of magnitude, is seen for fipronil. The fipronil detection in question occurred in a 

1055 Louisiana bayou, and may be influenced by use of the chemical on rice cultivation in the area. A 

1056 PCA for rice is not reported in this document. The results displayed in Figure 4-6 illustrate that 

1057 modeled pesticide concentrations even without PCA adjustment do not always exceed real world 

1058 concentrations, and serve to emphasize the apparent importance of selecting PCAs appropriately.

1059 USEPA will continue to evaluate PCA-adjusted EDWCs against available monitoring 

1060 data to ensure that estimated exposures do not underestimate measured concentrations in 

1061 drinking water sources. Additionally, USEPA will continue to work on developing reliable 

1062 methods of quantifying confidence bounds on measured concentrations to account for 

1063 uncertainties related to sampling frequency, and providing spatial context to monitoring data.
60

1064 Chapter 5. Guidance on Applying PCAs to Estimate Drinking Water Concentrations

1065 5.1 National Screening Level Assessments

OPP uses the coupled PRZM and EXAMS models

32 1066 to generate EDWCs for pesticides 

1067 applied to a crop at an expected high-end exposure scenario for that crop use. The resulting 

1068 EDWCs are multiplied by the PCA (expressed as a fraction) to adjust for the maximum potential 

1069 percent of the watershed that is covered by the crop of interest; thus, could potentially be treated 

1070 by the pesticide of interest.

1071 For an initial, national screening level assessment of a pesticide used on a single crop, the 

1072 PCA used represents the highest value for the given crop in any CWS-DWI watershed (plus 

1073 HUC-12 surrogates) nationally (i.e., the highlighted crop-specific value in Table 1). If the 

1074 pesticide is only used on one of the crop combinations listed in Table 2, then the national1075 maximum PCA for that crop combination (i.e., the highlighted value in Table 2) is used. If the 

1076 pesticide is used on other crops or crop combinations than those listed in Tables 1 and 2, then 

1077 the default “all-agriculture” PCA should be applied. PCAs can be applied to the 1-in-10-year 

1078 peak, annual, or other time-weighted average duration, or to the full time-series generated by 

1079 PRZM-EXAMS in a Tier 2 drinking water assessment. 

1080 Figure 5-1 provides a decision tree for selecting PCAs for national-level drinking water 

1081 assessments. Estimates may be developed for the following situations: (A) a single crop with a 

1082 crop specific PCA; (B) multiple crops that all have crop-specific PCAs; or (C) a single crop 

1083 without a crop specific PCA, or multiple crops besides those with specific crop combination 

1084 PCAs. A discussion of these three situations follows.

1085

 

32 See http://www.epa.gov/oppefed1/models/water/ to learn more about how these models generate EDWCs based 

on the physical-chemical properties of a pesticide, application parameters, agronomic information on a crop, 

precipitation, and other soil, hydrologic, and landscape properties. 
61

1086

1087 Figure 5-1. Decision Tree for National PCA Adjustment Factor Determination.

Is a.i. applied to a

crop other than corn,

wheat, soybeans, cotton,

orchards, vegetables, or 

residential turf?

No Yes Apply national default

PCA adjustment

factor1 to all EDWCs

Exceed dietary risks? No Report EDWCs

Yes

Consider exploring refinements

(e.g., regional PCA adjustment

factors, characterization, etc.)

Is a.i. applied

to more than

one crop?

Apply national

combination PCA to all individual

crop EDWCs

Apply national cropspecific PCA adjustment

factor to EDWCs

No

Yes

1 . The national default PCA adjustment factor is 1.0. 
62

1088 A) If a pesticide is only registered for use on a single crop for which a crop-specific 

1089 PCA exists (corn, soybeans, wheat, cotton, vegetable, orchard, or turf), multiply 

1090 the unadjusted EDWC by the appropriate national PCA for that crop (Table 1) to 

1091 obtain the final EDWC. For Tier 2 screening modeling, apply the maximum 

1092 national PCA regardless of the modeled scenario location. If an individual crop is 

1093 in an established crop group (such as tomatoes, which would be included in the 

1094 vegetable group), or if all of the uses fall within the same crop group, apply the 

1095 PCA for that crop group.

1096

1097 B) If a pesticide is registered for use only on multiple crops for which specific crop1098 combination PCA have been developed (Table 2), then multiply the unadjusted 

1099 EDWC by the PCA for the crop combination to obtain the final EDWC. The 

1100 PCA that should be used represents the maximum potential percentage of the 

1101 watershed that could be planted with these crops. 

1102

1103 C) If a pesticide is applied to a single crop that lacks a crop-specific PCA, multiply 

1104 the unadjusted estimated drinking water concentration (EDWC) for the crop by 

1105 the national default PCA (1.0 for all-agriculture) to obtain the final EDWC. If the 

1106 pesticide is applied to multiple crops not limited to corn, soybeans, wheat, cotton, 

1107 vegetable, orchard, and turf, multiply EDWCs by the national default PCA to 

1108 obtain the final EDWCs. If the pesticide is used for non-agricultural purposes 

1109 other than residential turf (e.g., ornamental plants, non-cropland areas, pasture 

1110 and rangeland, forestry, or rights-of-way), multiply EDWCs by the national 

1111 default PCA to obtain the final EDWCs. In such cases, if any of the crops 

1112 (including turf) or crop combinations to which the pesticide is applied include 

1113 crops for which PCAs have been developed, the reviewer should note this 

1114 circumstance, as it has potential relevance for regional refinements that may be 

1115 possible.

1116 5.2 Regional Refinements

1117 If dietary risks are exceeded in a national screening level assessment, the risk manager 

1118 may ask EFED to refine the pesticide exposure assessment. The risk assessor can either refine 

1119 the assessment qualitatively by providing additional characterization of potential exposure, or 

1120 refine it quantitatively by simulating additional sites for each crop and/or by using regional PCAs 

1121 in place of national PCAs. 

1122 Regional PCAs can be used to determine whether potential screening level EDWCs 

1123 exceed dietary risks nationwide or only in certain regions of the country. The most appropriate 

1124 use of regional PCAs can be best determined in discussions between risk assessors and risk 

1125 managers. Possible options that may be considered include the following:
63

1126 Use of regional default PCAs to refine EDWCs for uses with limited regional extent, 

1127 such as Section 18 emergency exemptions, Section 24(c) Special Local Need requests, or 

1128 crops with limited, well-defined growing regions (e.g., citrus).

1129 Use of regional PCAs to better distinguish which crop-pesticide scenarios in a pesticide 

1130 use area may result in the highest surface water EDWCs. For instance, a crop with a 

1131 small percentage of total usage of the pesticide grown in the South Atlantic-Gulf region 

1132 may have a maximum use rate much higher than the predominant usage on corn in the 

1133 Midwest. However, after application of the regional PCA to the minor use scenario, the 

1134 EDWC from the Midwest corn scenario might be higher and, therefore, the driver of the 

1135 drinking water exposure assessment.

1136 Use of regional PCAs in association with a pesticide usage map to distinguish between 

1137 magnitudes of possible exposure for the same crop in different regions of the country, 

1138 and to focus spatial modeling refinements. 

1139 Use of regional PCAs to populate a matrix of regional peak or chronic EDWCs for each 

1140 labeled use of a pesticide that is expected to result in dietary risk exceedances, in order to 

1141 refine a drinking water exposure assessment. This begins with tabulating states by water 

1142 resource (HUC-2) region where labeled crops are grown and allowed for use. Then 

1143 PRZM-EXAMS scenarios or surrogates for each relevant HUC-2 are derived. These 

1144 scenarios incorporate the maximum labeled use pattern (which may be geographically1145 specific) for each use. Regional PCA-adjusted EDWCs are then tabulated for 

1146 comparative purposes. The risk assessor may request from HED a back-calculated 

1147 drinking water level of concern for reference (e.g., highlight on the table regions with 

1148 EDWCs that exceed the level of concern).

1149 As an additional refinement, for uses with regional PCA-adjusted EDWCs greater than 

1150 the level of concern, the risk assessor in consultation with the risk manager may request reported 

1151 use information from areas of the United States with similar geography and/or crop management 

1152 practices from the BEAD. This information can be used to model and characterize “reported” 

1153 use patterns, based on the average number of applications and high-ends of the application rate 

1154 distributions reported. Characterization of these reported use patterns may help risk managers to 

1155 determine whether use pattern reductions to levels that appear to remain efficacious, may 

1156 constitute worthwhile label mitigation.

1157 5.3 Summary

1158 The DWI, DWI watershed, and DWI watershed PCA datasets provide USEPA with new 

1159 tools that open the possibility of exploring geographically specific drinking water assessments 

1160 beyond standard regional refinements, in ways that have not previously been available using 

1161 existing tools. The geospatial nature of these datasets means that locations with greater risk (i.e., 

1162 hot-spots) and more vulnerable communities can be identified. That information can be coupled 
64

1163 with geographically specific soil, hydrology, weather, and landscape property data, so that

1164 targeted mitigation measures can potentially be developed. Methodologies and procedures for 

1165 conducting such analyses need further development, and policy and resource implications for 

1166 USEPA to adopt such approaches into risk assessment practice need further investigation. 

1167 However, under the assumption that these datasets will prove to be as useful as anticipated, it is 

1168 USEPA’s current plan to update the DWI, DWI watershed, and DWI watershed PCA datasets on 

1169 an ongoing five year basis for the foreseeable future.

1170

1171
65

1172 Chapter 6. Literature Cited

1173

1174 Bhaduri B, 2003. Development of a GIS-Based Watershed Characterization and Pesticide 

1175 Usage Assessment Tool. Oak Ridge National Laboratory. Project report submitted to 

1176 USEPA Office of Pesticide Programs.

1177 Echeverria M, Jones RD, Peck C, Ruhman M, Shelby A, Thawley S, 2012. Development and 

1178 Use of Percent Cropped Area and Percent Turf Area Adjustment Factors in Drinking 

1179 Water Exposure Assessments: 2012 Update. March 16, 2012. Environmental Fate and 

1180 Effects Division. Office of Pesticide Programs. United States Environmental Protection 

1181 Agency. Available at 

1182 http://www.epa.gov/oppefed1/models/water/pca_adjustment_dwa.html (Accessed April 

1183 25, 2012).

1184 Effland WR, Thurman N, Kennedy I, 1999. Proposed Methods for Determining Watershed1185 Derived Percent Cropped Areas and Considerations for Applying Crop Area Adjustments 

1186 to Surface Water Screening Models. May 27, 1999. Environmental Fate and Effects 

1187 Division. Office of Pesticide Programs. United States Environmental Protection Agency. 

1188 Available at http://www.epa.gov/scipoly/sap/meetings/1999/052599_mtg.htm (Accessed 

1189 June 8, 2012).

1190 Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, 2011. Completion of the 2006 National Land 

1191 Cover Database for the Conterminous United States. Photogrammetric Engineering & 

1192 Remote Sensing 77(9), 858-864.

1193 Horn M, 2007. Memo from Marilee Horn to Shelly Thawley: IAG # DW-14937871-01-0 SDWIS 

1194 Source-Data Location Quality Assurance. June 28, 2007.

1195 Jones RD, Abel S, 1997. Presentation to the Scientific Advisory Panel. Use of a Crop Area 

1196 Factor in Estimating Surface-Water-Source Drinking Water Exposure. December 1997. 

1197 Environmental Fate and Effects Division. Office of Pesticide Programs. United States 

1198 Environmental Protection Agency. 

1199 Jones RD, Costello K, Hetrick J, Lin J, Parker R, Thurman N, 2010. Development and Use of 

1200 Percent Cropped Area Adjustment Factors in Drinking Water Exposure Assessment. 

1201 September 9, 2010. Environmental Fate and Effects Division. Office of Pesticide 

1202 Programs. United States Environmental Protection Agency. 

1203 Milesi C, Running SW, Elvidge CD, Dietz JB, Tuttle BT, Nemani RR, 2005. Mapping and 

1204 modeling the biogeochemical cycling of turf grasses in the United States. Environmental 

1205 Management 36(3):426-438. 
66

1206 Omernik JM, 2003. The misuse of hydrologic unit maps for extrapolation, reporting, and 

1207 ecosystem management. Journal of the American Water Resources Association 39(3): 

1208 563-573.

1209 Seaber PR, Kapinos FP, Knapp GL, 1987. Hydrologic Unit Maps. W. S. P. United States 

1210 Geological Survey. March 2007. Available at http://pubs.usgs.gov/wsp/wsp2294/

1211 (Accessed June 11, 2012).

1212 USDA, 2009. 2007 Census of Agriculture. United States. Summary and State Data. AC-07-A1213 51. Geographic and Area Series Part 51. National Agricultural Statistics Service. United 

1214 States Department of Agriculture. Available at 

1215 http://www.agcensus.usda.gov/Publications/2007/Full_Report/usv1.pdf (Accessed June 

1216 11, 2011).

1217 USEPA, 1999. Sets of Scientific Issues Being Considered by the Environmental Protection 

1218 Agency Regarding: Session 1 - Office of Pesticide Programs Policy for the use of the 

1219 FQPA 10x Safety Factor; Section II - Statistical Methods for Use of Composite Data in 

1220 Acute Dietary Exposure Assessment; Session III - Use of Watershet-derived Percent Crop 

1221 Areas as a Refinement Tool in FQPA Drinking Water Exposure Assessments for 

1222 Tolerance Reassessment. S. R. N. 99-03. May 25, 1999. Office of Pesticide Programs. 

1223 United States Environmental Protection Agency. Available at 

1224 http://www.epa.gov/scipoly/sap/meetings/1999/052599_mtg.htm (Accessed June 11, 

1225 2012).

1226 USEPA, 2002. Organophosphate Pesticides: Revised Cumulative Risk Assessment. June 10, 

1227 2002. Office of Pesticide Programs. United States Environmental Protection Agency. 

1228 Available at http://www.epa.gov/pesticides/cumulative/rra-op/ (Accessed June 11, 2012).

1229 USEPA, 2010a. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered 

1230 by the Environmental Protection Agency Regarding: Re- Evaluation of Human Health 

1231 Effects of Atrazine: Review of Experimental Animal and In Vitro Studies and Drinking 

1232 Water Monitoring Frequency. April 26-29, 2010. Documentnumber EPA-HQ-OPP-2010-

1233 0125

1234 USEPA, 2010b. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered 

1235 by the Environmental Protection Agency Regarding: Re- Evaluation of Human Health 

1236 Effects of Atrazine: Review of Non-cancer Effects and Drinking Water Monitoring 

1237 Frequency. September 14-17, 2010. Document number EPA-HQ-OPP-2010-0481

1238 USEPA, 2011. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered by 

1239 the Environmental Protection Agency Regarding: Re- Evaluation of Human Health 

1240 Effects of Atrazine: Review of Cancer Epidemiology, Non-cancer Experimental Animal 
67

1241 and In vitro Studies and Drinking Water Monitoring Frequency. July 26-29, 2011 FIFRA 

1242 Scientific Advisory Panel. Document number EPA-HQ-OPP-2011-0399

1243 USEPA, 2012. FIFRA Scientific Advisory Panel: Problem Formulation for the Environmental

1244 Fate and Ecological Risk Assessment for Atrazine. June 12-15, 2012 FIFRA Scientific 

1245 Advisory Panel. Document available at EPA-HQ-OPP-2012-0230. 

1246 USGS, 2011. National Land Cover Database 2006. Product Legend. October 27, 2011. Multi1247 Resolution Land Characteristics Consortium (MRLC). National Land Cover Database 

1248 (NLCD). Available at http://www.mrlc.gov/nlcd06_leg.php (Accessed June 11, 2012).

1249 Water Quality Tech Team, 1999. Applying a Percent Crop Area to Tier 2 Surface Water Model 

1250 Estimates for Pesticide Drinking Water Exposure Assessments. Environmental Fate and 

1251 Effects Division. Office of Pesticide Programs. United States Environmental Protection 

1252 Agency. 

1253 Water Quality Tech Team, 2003. Use of Regional Percent Crop Area Factors in Refined 

1254 Drinking Water Assessments. July 25, 2003. Environmental Fate and Effects Division. 

1255 Office of Pesticide Programs. United States Environmental Protection Agency. 

1256 Wieczorek M, 2011. USGS Evaluation of Drinking-Water Intake Watersheds. September 8, 

1257 2011. U.S. Geological Survey. 

1258

1259

1260

1261

1262
68

1263 Appendix A

1264 QA/QC of Drinking Water PCAs for Various Crops and Crop Groups

1265

1266 (1) Objectives

1267

1268 A process was devised to verify the validity of the drinking water PCA values obtained for corn, 

1269 cotton, orchards, rice, soybean, total cropland, vegetables and wheat. The process consisted of 

1270 two main components, namely: 

1271 (a) Verify that county NASS agricultural statistics data were correctly retrieved from the 

1272 source, and

1273 (b) Verify that GIS processing of the source data is correct and reproducible.

1274

1275 (2) Methodology

1276

1277 A. Validation of NASS Data

1278

1279 The following steps were executed to check that NASS data were correctly retrieved from the 

1280 source:

1281 (a) “County NASS Agricultural Statistics data” used in deriving PCA values were entered into 

1282 the attached verification spreadsheet (Table A-1). For each crop or crop group, data 

1283 included the total county acreage in counties associated with the HUC-8, plus additional 

1284 counties selected at random. For example, the total number of counties checked was 6 each 

1285 for soybean/ orchards/vegetables, 7 each for corn/rice/wheat, and 8 each for cotton/total 

1286 cropland. In addition, the total US acreage for these crops/crop groups was recorded; and

1287

1288 (b) Data used in deriving PCAs (Table A-1, highlighted in yellow) were compared with that 

1289 obtained by the above stated QA/QC step (Table A-1, highlighted in green).

1290

1291

1292

1293

1294
69

1295 Table A-1 NASS Validation Sheet

Associated With:

Thawley (Data Used to derive PCAs) Ruhman (QA/AC)

Total From Original NASS Table # Total QA/QC NASS Table #

Corn for grain PCA 593.0684 217,106 25 593.0684 217,106 1

Corn for grain PCA 567.9952 215,502 25 567.9952 215,502 1

Corn for grain PCA 592.4867 233,657 25 592.4867 233,657 1

Corn for grain PCA 570.4783 219,435 25 570.4783 219,435 1

Corn for grain: Random Selection 565.9654 203,297 25 565.9654 203,297 1

Corn for grain: Random Selection 471.9035 124,421 25 471.9035 124,421 1

Corn for grain: Random Selection 566.6673 161,542 25 566.6673 161,542 1

Total for USA 86,248,542 25 86,248,542 1

Corn for silage PCA 593.0684 1,172 25 593.0684 1,172 1

Corn for silage PCA 567.9952 561 25 567.9952 561 1

Corn for silage PCA 592.4867 1,358 25 592.4867 1,358 1

Corn for silage PCA 570.4783 1,033 25 570.4783 1,033 1

Corn for silage: Random Selection 565.9654 539 25 565.9654 539 1

Corn for silage: Random Selection 471.9035 4,639 25 471.9035 4,639 1

Corn for silage: Random Selection 566.6673 2,031 25 566.6673 2,031 1

Total for USA 5,979,661 25 5,979,661 1

Corn, sweet PCA 593.0684 0 30 593.0684 0 30

Corn, sweet PCA 567.9952 0 30 567.9952 0 30

Corn, sweet PCA 592.4867 0 30 592.4867 0 30

Corn, sweet PCA 570.4783 0 30 570.4783 0 30

Corn, sweet: Random Selection 565.9654 0 30 565.9654 0 30

Corn, sweet: Random Selection 471.9035 0 30 471.9035 0 30

Corn, sweet: Random Selection 566.6673 4 30 566.6673 4 30
70

Associated With:

Thawley (Data Used to derive PCAs) Ruhman (QA/AC)

Total From Original NASS Table # Total QA/QC NASS Table #

Total for USA 662,946 30 662,946 30

Total All corn USA 92,891,149 92,891,149

Cotton: Random Selection 887.9326 23,240 25 887.9326 23,240 1

Cotton PCA 913.2301 171,616 25 913.2301 171,616 1

Cotton: Random Selection 895.2175 58,223 25 895.2175 58,223 1

Cotton PCA 904.0704 44,952 25 904.0704 44,952 1

Cotton PCA 980.0022 164,297 25 980.0022 164,297 1

Cotton PCA 920.3648 218,069 25 920.3648 218,069 1

Cotton: Random Selection 987.5633 121,319 25 987.5633 121,319 1

Cotton PCA 908.9309 242,656 25 908.9309 242,656 1

Total for USA 10,493,238 25 10,493,238 1

Rice: Random Selection 1,663.6851 97,845 25 1,663.6851 97,845 1

Rice PCA 1,164.6440 147,817 25 1,164.6440 147,817 1

Rice PCA 1,317.6992 93,817 25 1,317.6992 93,817 1

Rice: Random Selection 1,497.0533 9,313 25 1,497.0533 9,313 1

Rice: Random Selection 964.4799 0 25 964.4799 0 1

Rice PCA 607.9575 99,284 25 607.9575 99,284 1

Rice PCA 1,029.1834 29,675 25 1,029.1834 29,675 1

Total for USA 2,758,792 25 2,758,792 1

Orchards PCA 5,939.5449 4,008 25 5,939.5449 4,008 1

Orchards: Random Selection 8,186.6423 836 25 8,186.6423 836 1

Orchards PCA 1,381.1756 434 25 1,381.1756 434 1

Orchards: Random Selection 3,138.0985 1 25 3,138.0985 1 1

Orchards PCA 1,383.6187 218 25 1,383.6187 218 1
71

Associated With:

Thawley (Data Used to derive PCAs) Ruhman (QA/AC)

Total From Original NASS Table # Total QA/QC NASS Table #

Orchards: Random Selection 4,754.1505 3,671 25 4,754.1505 3,671 1

Total for USA 115,935 25 115,935 1

Soybean for beans: Random 

Selection 701.8973 101,713 25 701.8973 101,713 1

Soybean for beans: Random 

Selection 589.6797 65,993 25 589.6797 65,993 1

Soybean for beans: PCA 445.2008 143,739 25 445.2008 143,739 1

Soybean for beans: PCA 696.9727 144,817 25 696.9727 144,817 1

Soybean for beans: PCA 429.9780 89,173 25 429.9780 89,173 1

Soybean for beans: Random 

Selection 555.6067 90,448 25 555.6067 90,448 1

Total for USA 63,915,821 25 63,915,821 1

Vegetables harvested for sale: 

Random Selection 5,939.5449 195,401 25 5,939.5449 195,401 1

Vegetables harvested for sale: 

Random Selection 1,954.2251 59,533 25 1,954.2251 59,533 1

Vegetables harvested for sale: PCA 3,323.2549 253,704 25 3,323.2549 253,704 1

Vegetables harvested for sale: PCA 1,383.6187 26,878 25 1,383.6187 26,878 1

Vegetables harvested for sale: 

Random Selection 443.3458 1,808 25 443.3458 1,808 1

Vegetables harvested for sale: PCA ? 0? 25 ? 0? 1

Total for USA 4,682,588 25 4,682,588 1

Wheat for grain PCA 535.1650 114,175 25 535.1650 114,175 1

Wheat for grain : Random Selection 901.0108 212,115 25 901.0108 212,115 1

Wheat for grain: Random Selection 747.8055 135,502 25 747.8055 135,502 1
72

Associated With:

Thawley (Data Used to derive PCAs) Ruhman (QA/AC)

Total From Original NASS Table # Total QA/QC NASS Table #

Wheat for grain PCA 1,280.3259 232,736 25 1,280.3259 232,736 1

Wheat for grain PCA 725.5505 161,148 25 725.5505 161,148 1

Wheat for grain PCA 1,017.5079 159,192 25 1,017.5079 159,192 1

Wheat for grain PCA 793.6035 155,734 25 793.6035 155,734 1

Wheat for grain: Random Selection 1,188.1477 182,524 25 1,188.1477 182,524 1

Total for USA 50,932,969 25 50,932,969 1

1296

1297 B. Validation of GIS Data Processing

1298

1299 Using GIS data generated in the process of calculating PCAs, a stepwise verification procedure 

1300 was devised to check the accuracy of the calculations (Attachment 1). Results obtained are 

1301 included in Table A-2 for total cropland and Table A-3 for all other crops/crop groups. GIS1302 Processed PCAs (columns highlighted in yellow) were compared with those obtained by the 

1303 QA/QC step (column highlighted in green).

1304

1305 Table A-2 Total Cropland (See Abbreviations below)

Basin ID A B C D E Checked by

2864984-4 21,015,232 15,690 14,121,000 0.672 0.672 Nelson

2465311-4 6,093,064,572 2,758,944 2,483,049,600 0.408 0.408 Nelson

5558420-4 14,805,585 7,272 6,544,800 0.443 0.442 Nelson

2465225-1 1,192,544,133 393,351 354,015,900 0.297 0.297 Nelson

2465228-4 6,047,787 3,152 2,836,800 0.469 0.469 Nelson

5307907-1 444,046,508,554 244,400,740 219,960,666,000 0.495 0.495 Nelson

2475894-1 1,340,158,995,240 366,799,272 330,119,344,800 0.246 0.246 Nelson
73

Basin ID A B C D E Checked by

2865475-1 1,824,376,444,050 625,065,457 562,558,911,300 0.308 0.308 Nelson

2110236-4 3,201,355,326,230 945,584,945 851,026,450,500 0.266 0.266 Nelson

2518031-4 92,573,313 285 256,500 0.003 0.003 Carleton

1803354-4 171,491,820 1,216 1,094,400 0.006 0.006 Carleton

1824462-1 5,340,517,534 739,312 665,380,800 0.125 0.125 Carleton

5165295-1 2,466,515,941 25 22,500 0.000 0.000 Bohaty

3546663-1 6,066,516,584 16,436 14,792,400 0.002 0.002 Bohaty

5619627-2 21,442,384,816 1,770,705 1,593,634,500 0.074 0.074 Bohaty

4456096-1 13,756,943,650 1,055,683 950,114,700 0.069 0.069 Bohaty

1313266-4 3,917,970 0 0.000 white

4789703-1 327,978,347 36,286 32,657,400 0.099 0.100 Crk

2699520-1 551,272,695 42,220 37,998,000 0.069 0.069 Crk

2656247-4 45,207,026 929 836,100 0.018 0.018 Crk

8994820-1 123,348,488 0 0 0.000 0.000 Ruhman

5097721-1 52,695,985,932 6,329,296 5,696,366,400 0.108 0.108 Ruhman

2162560-3 7,538,527,565 123,585 111,226,500 0.015 0.015 Ruhman

2170726-4 410,098,449 161,701 145,530,900 0.355 0.355 Ruhman

2171471-4 60,492,273,360 21,970,846 19,773,761,400 0.327 0.327 Ruhman

2172683-4 397,390,615 20,038 18,034,200 0.045 0.045 Ruhman

2175072-1 10,877,194,565 43,876 39,488,400 0.004 0.004 Ruhman

2183398-1 524,227,547,489 2,641,165 2,377,048,500 0.005 0.005 Ruhman

2185119-4 94,791,486,928 11,848,042 10,663,237,800 0.112 0.112 Ruhman

2238412-4 615,434,824 13,718 12,346,200 0.020 0.020 Ruhman

2675257-4 816,014,418 374 336,600 0.000 0.000 Ruhman

4947011-4 165,126,368 0 0 0.000 0.000 Ruhman
74

Basin ID A B C D E Checked by

4964080-2 36,345,999,013 9,188,639 8,269,775,100 0.228 0.228 Ruhman

5093574-2 6,094,228,382 11,681 10,512,900 0.002 0.002 Ruhman

5102087-1 308,631,027 0 0 0.000 0.000 Ruhman

2464830-4 3,391,030 744 669,600 0.197 0.197 white

2307903-1 9771300 0 0 0.000 0.000 white

2441299-4 2799415.415 0 0 0.000 0.000 white

5557005-4 13285940.07 3543 3,188,700 0.241 0.240 white

2354497-2 2700 0 0 0.000 0.000 white

4828820-4 1106177.317 0 0 0.000 0.000 white

5320907-4 2352489.05 1382 1,243,800 0.529 0.529 white

4830079-4 1527636.091 16 14,400 0.009 0.009 white

5556951-1 42561000 3090 2,781,000 0.065 0.065 white

5163403-1 330109284.3 0 0 0.000 0.000 white

3820202-1 1993500 0 0 0.000 0.000 white

2717511-1 339300 0 0 0.000 0.000 white

Abbreviations:

Title Description Source

A Basin Area (m2) From map

B Total Cropland (Pixels) QA/QC process

C Total Cropland (m2

) Conversion of acres to m2(C= B* 900) (Single pixel area= 900 m

2

)

D PCA To be checked Data obtained by the procedure used for calculating PCAs.

E PCA calculated by the QA/QC process E= C/A

1306

1307
75

1308 Table A-3: Other Crops/Crop Group (See Abbreviations below)

Basin ID Crop A B C D E Checked by

2864984-4 Corn 21 1,357 5 0.2615 0.2614 Nelson

2465311-4 Corn 6,093 197,930 801 0.1315 0.1315 Nelson

5558420-4 Corn 15 952 4 0.258 0.2603 Nelson

2460190-1 Corn 2,291 78,903 319 0.1393 0.1394 Nelson

2465225-1 Corn 1,193 23,782 96 0 0.0807 Nelson

5307907-1 Corn 444,047 31,094,211 125,838 0.2835 0.2834 Nelson

9999969-4 Cotton 3,945 202,039 817.65 0.2074 0.2073 Ruhman

2156379-4 Cotton 7,857 378,714 1,532.66 0.1952 0.1951 Ruhman

8994588-1 Cotton 38,201 1,225,212 4,958.43 0.1298 0.1298 Ruhman

2465311-4 Orchard 6,093 185 1 0.0001 0.0001 Nelson

5558420-4 Orchard 15 0 0.0003 0 Nelson

5307907-1 Orchard 444,047 12,218 49 0.0001 0.0001 Nelson

2518031-4 Orchard 93 0 0 7.07E-06 7.07E-06 Carleton

2210000-4 Rice 780 81,604 330.25 0.4237 0.4235 Ruhman

2229033-4 Rice 5 316 1.28 0.2568 0.2564 Ruhman

2673430-4 Rice 4,804 128,089 518.38 0.1079 0.1079 Ruhman

2864984-4 Soybeans 21 1,647 7 0.3173 0.3172 Nelson

2465311-4 Soybeans 6,093 328,012 1,327 0.2179 0.2179 Nelson

5558420-4 Soybeans 15 0 0.2626 0 Nelson

2460190-1 Soybeans 2,291 74,728 302 0.1319 0.132 Nelson

2465225-1 Soybeans 1,193 50,648 205 0 0.1719 Nelson

5307907-1 Soybeans 444,047 17,321,311 70,099 0.1579 0.1579 Nelson

2465311-4 Vegetables 6,093 37 0 0 0 Nelson

5307907-1 Vegetables 444,047 432,485 1,750 0.0039 0.0039 Nelson
76

Basin ID Crop A B C D E Checked by

2170726-4 Vegetables 410 1,395 5.65 0.0138 0.0138 Ruhman

2171471-4 Vegetables 60,492 12,005 48.59 0.0008 0.0008 Ruhman

2172683-4 Vegetables 397 100 0.41 0.0001 0.001 Ruhman

2183398-1 Vegetables 301,672 44,525 180.19 0.0006 0.0006 Ruhman

2185119-4 Vegetables 94,791 8,722 35.3 0.0004 0.0004 Ruhman

2675257-4 Vegetables 816 60 0.24 0.0003 0.0003 Ruhman

5097721-1 Vegetables 52,696 50,454 204.19 0.0039 0.0039 Ruhman

5102087-1 Vegetables 309 0 0 0 0 Ruhman

2864984-4 Wheat 21 355 1 0.0683 0.0683 Nelson

2465311-4 Wheat 6,093 51,874 210 0.0345 0.0345 Nelson

5558420-4 Wheat 15 0 0.0551 0 Nelson

2465225-1 Wheat 1,193 5,073 21 0 0.0172 Nelson

5307907-1 Wheat 444,047 851,872 3,448 0.0078 0.0078 Nelson

2423671-1 Wheat 10,612 433,995 1,756 0.166 0.166 Carleton

Title Description Source

A Basin Area (km

2

) From map

B Total Crop Acres QA/QC process

C Total Crop (km

2

) Conversion of acres to km

2

(C= B* 0.004047)

D PCA To be checked Data obtained by the procedure used for calculating PCAs

E PCA calculated by the QA/QC process E= C/A

1309

1310 For this process a total of 47 drinking water watersheds were examined for the total-agriculture

1311 PCAs, and 38 for other crops/crop groups. All watersheds were randomly chosen and were 

1312 distributed as follows: Corn (6), Cotton (3), Orchards (4), Rice (3), Soybeans (6), Vegetables 

1313 (10), and Wheat (6). A map of the locations of the QA/QC drinking water intake watersheds is 

1314 included in Figure A-1.
77

1315

1316 Figure A-1. Locations of the DWI watersheds examined in the QA/QC process (note some of 

1317 the watersheds are too small to see in this map, and some are masked by larger watersheds; the 

1318 borders for the five large watersheds (>1,000 km2) are shown in light pink).
78

1319 (3) Results

1320

1321 NASS data used in calculating PCAs were found to be accurate for all crops/crop groups. Data 

1322 were found to be accurate even when different NASS Tables were used (Table A-1). 

1323 Additionally, the PCA verification process produced the same results (within two decimal 

1324 places) as the original processed PCAs. The exceptions are included in the Table, below:

1325

Basin ID Crop A B C D E Checked by

2465225-1 Corn 1,193 23,782 96 0 0.0807 Nelson

5558420-4 Soybeans 15 0 0.2626 0 Nelson

2465225-1 Soybeans 1,193 50,648 205 0 0.1719 Nelson

2465225-1 Wheat 1,193 5,073 21 0 0.0172 Nelson

1326

1327 For reference, an example containing all processing data is included in the following directory: 

1328 P:\CWS_PCA_Data\QA-QC Example. This main folder contains data in the following folders:

1329

1330 Acre pixels Data (APD)

1331 Chosen DWI watersheds (CDWIW)

1332 Exported DWI watersheds (EDWIW)

1333 Extraction Results (ER)

1334 PCA Maps-Verification Spreadsheet (PCAM-VS)

1335
79

1336 Attachment 1: Step by Step Process for PCA Verification

1337

1338 Step 1: Selection of the drinking water intakes watershed for cotton: ID 9999969-4

1339 (Select by attribute: "USGSWINNER" = '9999969-4')

1340

1341

1342

1343
80

1344 Step 2: Export data for the selected watershed into the folder: Exported DWI watersheds 

1345 (EDWIW)

1346 Export Data into the folder specified above: P:\CWS_PCA_Data\QA-QC Example\Exported 

1347 DWI watersheds (EDWIW)\Export_Output.sh

1348
81

1349 Answer NO

1350
82

1351 Step 3: Add the exported watershed data into a new map and zoom to the selected watershed 

1352 (show only the outline of the selected watershed).

1353
83

1354
84

1355 Step 4: Intersect the exported shapefile with the county shapefile using the intersect tool from 

1356 the toolbox (Analysis tools/Overlay/Intersect). Note the input features chosen (export-output and 

1357 counties).

1358
85

1359 Hereunder, the resultant map (the watershed areas in various counties will appear):

1360
86

1361 Step 5: Perform Extraction by Mask (Spatial Analyst Tools/Extraction/Extract by Mask)

1362 Enter: Input raster= acrepix “cott_acrepix”; Feature mask data= exported watershed data 

1363 “Export_Output; and Output raster= folder where you want the extraction output of the analysis 

1364 to reside, in this case “P:\CWS_PCA_Data\QA-QC Example\Extraction Results 

1365 (ER)\Extract_cott3”

1366

1367

1368
87

1369 Then select “OK” and wait for completion of the extraction procedure, which may take several 

1370 hours depending on the speed of the computer (please note that a moving message will be 

1371 displayed stating the progress in %, e.g., “Extract by Mask 60%”. Even after the value reaches 

1372 100%, the extraction results may not appear instantly; it is necessary to wait until the extraction 

1373 results appear, as shown in the following screen shot:

1374

1375
88

Step 6: Open the attribute Table/ Open the Table options (1st 1376 tab)

1377

1378
89

1379 Add field (Crop-area/Double)/OK

1380

1381

1382 After you press OK, the results will be as shown, below. Then right click on the newly added 

1383 field to the field menu and select from it “Field Calculator:

1384
90

1385 Enter the following in the field calculator by choosing “double click” the fields/calculation 

1386 function from the lists as follows: [VALUE] * [COUNT]/20000; the 20,000 value is the scale 

1387 factor for cotton.

1388

1389

1390
91

1391 Important Note:

1392 The scale factor is different for different crop/crop group as shown in the Table below:

Crop Scale Factor

Corn 4,000

Cotton 20,000

Orchards 7,000

Rice 60,000

Soybeans 10,000

Vegetables 2,400
92

Crop Scale Factor

Wheat 5,000

1393 After clicking OK you get the area calculated; then get the statistics for the calculated field as 

1394 shown below:

1395

1396

1397
93

From displayed statistics, the total Crop-area= 202038.96125 acres (=818 km21398 ). Enter this value 

1399 in the verification spreadsheet as shown below to arrive at PCA= 818/3,945= 0.207

Basin ID Crop A B C D E Checked by

9999969-4 Cotton 3,945 202,039 817.65 0.2074 0.2073 Ruhman

1400

Title Description Source

A Basin Area (km

2

) From map

B Total Crop Acres QA/QC process

C Total Crop (km

2

) Conversion of acres to km

2

(C= B* 0.004047)

D PCA To be checked Data obtained by the procedure used for calculating PCAs

E PCA calculated by the QA/QC process E= C/A

1401

1402 Step 7: Save the map as “Cotton PCA- 4- 9999969-4” with the final saved map in the PCA 

1403 Maps-Verification Spreadsheet (PCAM-VS) folder. The map will look like the image below:
94

1404

1405

1406

1407

1408

1409

1410

1411

1412
95

1413 Appendix B

1414 Cumulative Distributions of National-Scale DWI PCAs

1415
96

1416

1417
97

1418
98

1419

1420
99

1421

1422
100

1423

1424
10

1

1425

1426
102

1427
103

1428

1429
104

1430
105

1431

1432
106

1433

1434
107

1435

1436
108

1437

1438
109

1439

1440
110

1441

1442
111

1443

1444