i



## Development of Community Water System Drinking Water Intake

Percent C ropp ed Area Adjustment Factors f or use in Drinking Water

Exposure Assessment s

Environmental Fate and Effects Division

Office of Chemical Safety and Pollution Prevention

United States Environmental Protection Agency

0 8 / 2 1 /1 3

Prepared by:

Rochelle F. H. Bohaty, Ph

. D

. J a m es N. Carleton, Ph

. D

. Tanja Crk

Marietta Echeverria

Mohammed Ruhman, Ph.D. Michelle Thawley

Nelson Thurman

Philip Villanueva

Katrina White, Ph

. D

.

1ii Forward 2

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

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

Community Water System Drinking Water Intake Percent Cropped Area Adjustment F actors for 5

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

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

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

provide comments on the document and respond to charge questions

. Consistent with USEPA’s 9

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

document was updated accordingly

. 11

12iii Summary 13

W atershed s large enough to support a drinking water facility are generally not comprised 14

of only one land cover type

, nor planted completely with a single crop . In order to account for 15

variability in land cover, the USEPA uses p ercent c ropp ed area (PCA) adjustment factors to 16

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

. 17

Modeled concentration s of pesticides in surface water s are multiplied by a PCA to account for 18

the areal fraction of a watershed that m ay be treated with a particular pesticide based on th e 19

pesticide uses and the land cover types ( i.e., crops) associated with those uses

. The PCA

20

adjusted concentrations are used as the estimated drinking water concentrations ( EDWC s ) in 21

human health risk assessment. Previously

, PCAs were generated for Hydrologic Unit Code 8 22

(HUC

- 8) 1 regions . In this update, PCAs have been generated for watersheds delineated based on 23

surface

- source drinking water intakes (DWI) of community water syst em (CWS) across the 24

United States. T he new PCAs are a n improvement over previously calculated PCAs in terms of 25

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

water sources

. A CWS

- DWI dataset, vers ion 1 .0

, was u sed as the source of intakes and 27

associated watersheds . Watersheds were delineated for CWS

- DWI s with available tools and 28

data

. F or some surface water

- sourced CWS either it did not make sense to delineate a watershed 29

for the DWIs, or watersheds were generated but subsequently failed to pass a q uality a ssurance 30

screen

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

delineation and passed a q uality a ssurance scre en, 74% ( 4,840 ) had unique, delineated 32

watersheds which also passed a q uality a ssurance screen . Of these, all but two (4,838) were 33

located in the continental United States

. For another 637 quality assured DWI locations in the 34

continental U.S. which lacke d quality assured watersheds, HUC

- 12 regions were taken as 35

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

- 12 36

surrogates in the continental U.S. ( listed by HUC

- 2 water resource region) to use in representing 37

various crops and two

- crop combinations are shown in Table 1 and Table 2 , respectively

. The 38

n ational maximum value ( s ) in each case are shaded in the tables

. When using PCAs to modify 39

surface water modeling results , all of the potential pesticid e use sit es ( i.e. , currently registered or 40

proposed for registration) must be considered together

, and used to select an appropriate PCA. 41

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 dig its. There are 2,264 8

- digit HUCs in the United

States.iv

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

Water Resource Region (HUC

- 2) . Listed PCA s are specifically recommended for use in 45

surface drinking water exposure modeling. 46

HUC

- 2 All

- Ag a Corn Cotton Orchard Soybean Vegetable Wheat Turf b

0 1 0.32 0.08 0.00 0.02 0.00 0.10 0.00 0.86

0 2 0.8 2 0.34 0.06 0.07 0.11 0.03 0.06 0.60

0 3 0. 52 0.09 0.08 0.12 0.20 0.08 0.10 0.6 4

0 4 0.9 2 0.35 0.00 0.05 0.44 0.04 0.11 0.85

0 5 0.90 0.51 0.0 2 0.03 0.5 9 0.02 0.10 0.49

0 6 0.45 0.10 0.04 0.02 0.10 0.01 0.03 0.38

0 7 1.00 0.68 0.00 0.00 0.58 0.01 0.27 0.59

0 8 0. 75 0.21 0.20 0.00 0.07 0.00 0.05 0.52

0 9 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 0.0 7 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 0.0 1 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 a ll

- a g riculture PCAs calculated both ways

. The highest All

- Ag value s from Table s 3

- 2

and 3

- 4 are list ed here.

b Represents residential turf including golf courses

, but does not include sod farms

. S od farm s are

included in the all

- agriculture land cover class PCA

. 47

48

49

50

51

52

53

54

55

56

57

58

59

60

61v 62

Table 2

. Maximum percent c ropp ed area (PCA) adjustment factors for various crop 63

combinations, by Water Resource Region (HUC

- 2) . Listed PCA s are specifically 64

recommended for use in surface drinking water exposure modeling

. 65

HUC

2 Corn

Wheat Soybean

Wheat Turf a

-

Corn Turf

a

-

Orchard Turf a

- Soybean Turf a

- Vegetable Turf a

-

Wheat Vegetable

Orchard Turf a ,b

-

All Ag

0 1 0.08 0.00 0.86 0.86 0.86 0.86 0.86 0.12 0.98

0 2 0.36 0.17 0.60 0.60 0.60 0.60 0.60 0.10 0. 82

0 3 0.18 0.25 0.6 4 0.6 4 0.6 4 0.6 4 0.6 4 0.13 0.65

0 4 0.42 0.52 1.00 0.85 0.85 0.85 0.85 0.06 1.00

0 5 0.52 0.59 0.58 0.49 0.64 0.49 0.49 0.04 0.96

0 6 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

0 7 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

0 8 0.22 0.12 0.53 0.52 0.52 0.52 0.52 0.00 0. 75

0 9 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

. S od farm s are included in the

all

- agriculture land cover class PCA.

b Because a ll

- a g riculture 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 subs tituted in this table for the

regional maximum Turf

- All

- Ag PCA listed in Table 3

- 7.

66

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

Office of Pesticide Programs (OPP)

, and the process used for calculating CWS

- DWI specific 68

PCA s

. Chapter 1 provides an overview of the history of PCAs

, and describes DWI PCAs

. 69

Chapter 2 d ocument s the development of the CWS

- DWI dataset and the use of HUC

- 12 70

surrogates for a subset of quality assured intakes that do not have quality ass ured watershed s

. 71

Chapter 3 describes the methods used to develop the PCAs

, and presents a summary of results. 72

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

observed concentrations reported in monitoring studies for a number of pesticides

. Chapter 5 74vi

discusses uncertainties and limitations of the DWI PCA dataset

, and present s sugges tions on 75

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

. 76

77vii



## Table of Contents 78

79

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

............. 10 80

1.1 Introduction

................................ ................................ ................................ .................... 10 81

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

......... 11 82

1.3 History of PCA Development

................................ ................................ ........................ 12 83

1.4 Applying PCAs to Estimated Drinking Water Concentrations (EDWCs)

..................... 16 84

1.5 Shifting from HUC

- 8 Subbasins to Community Water System Watersheds

................. 16 85

Chapter 2. Development of Drinking Water Intake Watershed Dataset

................................ .. 18 86

2.1 Background

................................ ................................ ................................ .................... 18 87

2.2 Database Development

................................ ................................ ................................ ... 18 88

Surface Water Intake Locations

................................ ................................ ............................ 18 89

DWI Watershed Delineation

................................ ................................ ................................ . 20 90

2.3 Da tabase Uncertainties

................................ ................................ ................................ ... 25 91

2.4 Conclusion

................................ ................................ ................................ ...................... 28 92

Chapter 3. Methods and Results

................................ ................................ ............................... 29 93

3. 1 Methods

................................ ................................ ................................ .......................... 29 94

Single Crops

................................ ................................ ................................ .......................... 30 95

All

- agriculture

................................ ................................ ................................ ....................... 30 96

Residential Turf

................................ ................................ ................................ .................... 30 97

3.2 Results

................................ ................................ ................................ ............................ 31 98

Maximum Water Resource

- Regional PCAs

................................ ................................ ......... 31 99

3.3 Validation of PCAs

................................ ................................ ................................ ........ 38 100

3.4 Data Limitations

................................ ................................ ................................ ............. 39 101

3.5 Future I mprovements

................................ ................................ ................................ ..... 40 102

Chapter 4. Monitoring Data Comparisons

................................ ................................ ................ 41 103

4.1 Methodology

................................ ................................ ................................ .................. 41 104

4.2 Results

................................ ................................ ................................ ............................ 43 105

4.3 Discussion

................................ ................................ ................................ ...................... 58 106viii

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

........... 60 107

5.1 National Screeni ng Level Assessments

................................ ................................ .......... 60 108

5.2 Regional Refinements

................................ ................................ ................................ .... 62 109

5.3 Summary

................................ ................................ ................................ ........................ 63 110

Chapter 6. Literature Cited

................................ ................................ ................................ ....... 65 111

112

113ix

Nomenclature and Acronyms 114

Abbreviation Definition

CDL Crop l and Data L ayer

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, NHD P lus 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 C ropp ed 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 Dataset10

Chapter 1. Purpose, History

, and Overview of Updates of PCA Adjustment Factors 115

1.1 Introduction 116

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

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

include s e stimat es of pesticide concentrations in drinking water as a part of its dietary exposure 119

assessment s supporting the pesticide registration process. Generation of estimate d drinking 120

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

and transport. USEPA’s Office of Pesticide Programs (OPP) uses t he Pesticide Root Zone 122

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

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

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

water body

, referred to as the “ index reservoir ”

, which has dimensions and characteristics based 126

on those of Shipman City Lake

— a small

, midwestern reservoir in an agricultural setting, 127

intended to represent vulnerable surface water source s of drinking water 2

. 128

Prior to 2000, the USEPA employed the conse rvative assumption s that the entire area of 129

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

, and thus the entire 130

watershed could potentially be treated with a given pesticide

. A fter a 1999 Federal Insecticide, 131

Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP) 3 consultation , the 132

USEPA began implement ing the use of PCA s in generating EDWCs

, to account for the fraction 133

of a watershed potentially planted with the same crop, recognizing that the entirety of wat ershed s 134

large enough to support drinking water facilit ies may not be used to grow single crop s . This step 135

was i ntend ed to improve the quality and accuracy of USEPA ’s EDWC s used for human 136

exposure

. In standard practice, to generate EDWCs, m odel

- estimated concentration s from 137

PRZM

- EXAMS are multiplied by the appropriate PCA 4

. 138

This document pr esents and describes the development of PCAs specific to commu nity 139

water system

- drinking water intakes (CWS

- DWI s ) that draw from surface water sources

. T he 140

CWS

- DWI watershed

- based PCA s are intended to update

, replace , and improve upon the 141

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 mo re 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

Hydrologic Unit Code level 8 ( HUC

- 8 ) polygon

- based PCAs used for calculating EDWC s

. 142

Previous PCA documentation 5 includes : 143

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

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

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

, 146

dated July 25, 2003 147

Development and Use of Percent Cropped Area and Percent T urf Area Adjustment 148

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

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

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

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

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

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

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

CWS have been quality

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

delineated and quality

- assured . All but 2 of these are located in the continental United States

, 157

and the 4,838 that are have been used as the basis for PCA calculat ion

. 158

1.2 Brief Overview of the Derivation of Percent C ropp ed Area Adjustment Factors 159

In order to deriv e PCA s

, US EPA incorporate d data on the acreage of crop s of interest 160

with in a geo spatial context. In keeping with methods first presented to the 1999 FIFRA SAP, 161

US EPA employed the most current update of the United States Department of Agriculture 162

( USDA ) National Agricul tural Statistics Service (NASS) Census of Agriculture (Ag Census)

, 163

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

every five years 6

, 7

. US EPA also used the most current ( 2006 ) version of the National Land 165

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 provi des 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 introdu ces uncertainty into PCA estimates.12

Cover Database ( NLCD ) geospatial data [ land cover categor ies 82 (cultivated crops 8 ) and/ or 81 166

(pasture/hay 9 ) ] to create a generic cropland distribution map within e a ch county. Crop acreage 167

with in each county was apportioned to the locations within the cou nty used for agriculture , as 168

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

maps

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

within each watershed area

. 171

The above

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

Censu s are uniformly distributed across all agricultural land within a county

, and that the 173

reported c ropping patterns are stable over time

. While it is known that these a ssumptions are not 174

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

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

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

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

PCAs may be used in drinking water assessments. 179

1.3 History of PCA Development 180

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

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

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

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

“ Proposed Methods for Determining Watershed

- derived Percent Crop Areas and Considerations 185

for Applying Crop Area Adjustments to Surface Water Screening Models” ( Effl a nd et al. , 1999 )

. 186

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

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

county and the United States Geological Survey ’s ( USGS ) HUC

- 8s as surrogates for CWS 189

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

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

( Figure 1

- 1 ) ( Seaber et al. , 1987 )

. In this system, hydrologic units are arranged within each 192

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

based on the level of class ification in the hydrologic unit system 10

. At the time of the original 194

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. Cr op 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 grass

legume 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 tot al vegetation.

10 http://water.usgs.gov/GIS/huc.html13

PCA analysis , the HUC

- 8 scale was the finest resolution available national ly

. Use of HUC

- 8 s 195

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

dataset. As a part of its submission to the SAP, USEPA compared PCA

- adjusted EDWCs to 197

relevant available monitoring data. 198

199

Figure 1

- 1

. Illustratio n of the Hierarchical Hydrologic Unit Code System Developed by 200

USGS to Categorize Hydrologic Units 201

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

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

1999 )

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

held for minor crops that covered less acreage

, and the degree to which HUC

- 8 s may not 205

represent sma ller CWS watersheds

, PCA s for other crops or crop combinations were not 206

adopted

. The SAP recommended moving from HUC

- 8 hydrologic units to CWS

- specific 207

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

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

adjustments do not result in underestimat ion of pesticide concentrations in drinking water. 21014

Subsequent SAPs on the Organophosphate Cumulative Risk Assessment 11 reiterated the 211

difficulties in deriving PCAs for minor crops due to non

- uniform cropping intensit ies and 212

concerns that HUC

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

drinking water supplies ( USEPA, 2002 )

. 214

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

years

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

four major crops endorsed by the 1999 SAP

, and regional default PCAs for the 18 HUC

- 2 217

“Water R esource R egions ” shown in Figure 1

- 2 ( Jones et al. , 2010 )

. USEPA also developed 218

additional guidance for selecting and applying combined or multi

- crop PCA s to PRZM

- EXAMS 219

m odel

- estimated concentration s for pesticides used on multiple crops

. 220

221

Figure 1

- 2

. The 18 M ajor Hydrologic Unit “Water Resource” Regions (HUC

- 2) in the 222

Continental United States, Overlain on State Boundaries 223

224

11 http://www.epa.gov/oppsrrd1/cumulative/2006

- op/op_cra_main.pdf15

I n 2012, PCAs were developed for additional crop groups including orchards/vineyards 12

, 225

turf

13 , vegetables 14 , rice, and combinations of crops or crop groups ( Echeverria et al. , 2012 )

. 226

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

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

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

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

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

HUC

- 8 s

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

CWS

- DWI were employed

. Table 1

- 1 summarizes the milestones and data sources used to 233

calculate PCAs utilized by USEPA prior to this pub lication

. 234

Table 1

- 1

. PCA Milestones and Data Sources 235

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 ( Effl a nd 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 c ombinations (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/vineyar ds, turf,

vegetables

- More combinations - 2011 HUC

- 8 subbasins

- 2006 NLCD land cover

- 2007 Census of Agriculture

- 2008 US EPA Turf Layer a

- County Boundary Data ( Echeverria et

al. , 2012 )

a 2001 NLCD developed land covers (21

- 2 4 ) 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 a ll 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 la nd cover data and the 1997 Agricultural Census data.16

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

For drinking water assessments, m odel

- estimated drinking water concentration s are 238

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

interest. T he use of PCA s to modify PRZM

- EXAMS m odel

- estimated concentration s for 240

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

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

is employ ed to estimate pesticide runoff and spray drift loadings. PRZM generates area

243

normalized output, and implicitly ignores factors such as landscape spatial non

- uniform ity, 244

distances between treated location s and water bodies , and differential r unoff from different land 245

cover s or soil types. PRZM is, in a spatial sense, essentially a one

- dimensional, “lumped 246

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

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

homogeneous within the simu lated runoff watershed. 249

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

the crop with in a ny known drinking water watershed

. If a pesticide is used on only one major 251

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

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

more or different crops than are captured in the existing PCA combinations, the default “ all

254

agriculture ” PCA is applied. Regional ( i.e ., HUC

- 2) maximum PCAs may be used for 255

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

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

Hawaii)

, a s data are not readily available to c alculate PCAs for th e se areas. Therefore, drinking 258

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

PCAs are not applicable to non

- agricultural uses aside from turf. 260

1.5 Shifting from HUC

- 8 Subbasins to Community Water System Watersheds 261

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

dataset defining watersheds specific to CWS DWI s was not available. The USEPA used HUC s 263

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

hydrologic delineation available at the time

. HUC

- 8 s consist of more than 2,200 contiguous, 265

non

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

sma ller areas are more likely to have homogeneous land cover ; therefore

, potentially higher 267

PCAs

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

than HUC

- 8 s, and th us may have higher fractions of cropped/ pesticide

- treated land

. 269

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

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

provides an overview of t he development of the “ CWS DWI versio n 1 .0 ” wat ershed polygon 27217

dataset that made the development of these DWI PCAs possible

. Chapter 3 describes the 273

methodology of PCA development , and presents a summary of the results

. Chapter 4 presents a 274

comparison between EDWCs generated using the DWI PCAs

, and me asured pesticide 275

concentrations in water

. Chapter 5 provides guidance on applying the DWI PCAs to calculat e 276

pesticide EDWCs. 277

278

27918

Chapter 2. Development of Drinking Water Intake Watershed Dataset 280

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

Drinking Water Intake (DWI) watershed dataset , version 1.0 (DWI_Basins

_ 08 2013 .mdb,)

. This 282

dataset provide s a geospatial representation of watersheds (and HUC

- 12 surrogates) associated 283

with the intakes of surface water

- source d CWS across the United States. The chapter includes a 284

summar y of strengths and weakness es of th is dataset for use in application s such as the 285

development of watershed

- specific PCAs. 286

2.1 Background 287

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

drinking water varies over time, and changes within SDWIS as reflected in qua r terly updates

. 289

Because a large portion of the population in the United States rel ies up on surface water as source 290

drinking water

, it is important to understand how the location s of intakes for CWS influence the 291

vulnerability of the water supply to contamination

. USEPA is particularly interested in the 292

geospatial relationship of agricultural land and turf cover to CWS intake location s

, because 293

e stimation of pesticide concentration s in surface water resulting from pesticide use is an aspect 294

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

the United States

. Delineation of watersheds associated with drinking water intake s p rovides a 296

mechanism for helping understand the vulnerab i l ity of different CWS to pesticide contamination. 297

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

location s of intakes for CWS in the United States

, as well as watersheds associated with these 299

intakes

. Although similar datasets have been developed previously (Bhaduri, 200 3 ), t his dataset 300

is referred to as the DWI watershed dataset , version 1.0 ( DWI_Basins_ 082013.mdb )

. 301

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

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

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

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

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

2.2 Database Development 307

Surface Water Intake Locations 308

The surface water intake s included in the DWI watershed dataset w ere derived using 309

locational data contained in the USGS ’s Public

- Supply Database (PSDB) of CWS intake 310

locations. PSDB was initially developed from : 1) data contained in earlier 16 versions of 311

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

16 1997 to 200419

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

SDWIS is a dynamic dataset of public water supplies that changes quarterly

. S tate s provide 314

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

function (active vs. inactive) and location]

. 316

In 2007, under an Interagency Agreement 17 between USEPA OPP and USGS

, PSDB was 317

updated by USGS with surface water source data contained in a then

- current download of CWS 318

intake location data from SDWIS ( 12/31/2006 )

. Updating PSDB employed the following 319

procedure (Horn, 2007): 320

1. R ecords in a then

- current version of PSDB were compared against those in the SDWIS 321

download

. Th is process revealed 267 DWI locations in SDWIS that were not in PSDB, 322

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

two datasets

. To resolve the discrepancy in the intake locations, an evaluat ion to 324

determine wh ich location was more accurate (SDWIS or PSDB) was conducted

. This 325

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

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

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

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

represented by the SDWIS location. 330

331

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

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

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

12/31/2004 SDWIS download ). T he Ohio sources were identified by a n April 2007 18 335

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

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

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

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

in 2007

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

used by the USGS to generate 6,550 quality

- assured CWS intake locations ( Figure 2

- 1 ) 341

( Horn

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

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

344

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

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

17 IAG # DW

- 14937871

- 01

- 0

18 Exact date not available20

flagged as inactive, or; 4) lacking location information

, so that the location could not be 347

determined. 348

349

Figure 2

- 1

. Geographic Distribu tion of 6,550 Quality

- assured Community Water Systems 350

Drinking Water Intakes Locations in Public

- Supply Database 351

DWI Watershed Delineation 352

In 2010

, USEPA co ntrac ted with INDUS Corporation 19 to index the 6,550 CWS DWI 353

locations to specific surface water features in USEPA ’s NHDPlus 20 dataset. To accomplish this 354

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

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

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

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

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

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

to i d entify candidate NHDPlus features that best matched the intake names and locations. 361

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

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

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

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

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

“ winners ” and candidate stream segments and water bodies

. 366

367

2. Next, OPP contracted with Horizon Systems 21 in 20 11

, to delineate watersheds for the 368

“winner” locations identified by INDUS

. Horizon Systems found that of the 6,550 DWI 369

locations

, only 5,557 we re indexed to NHDPlus features classified as streams, rivers, 370

lakes, or reservoirs 22 ; therefore, suitable for watershed delineation. The remaining DWI 371

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

galleries, canals, springs, the Great Lakes, aqueducts, off

- stream reservoirs, etc., for 373

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

delineated watersheds for the 5,557 NHDPlus

- indexed DWI locations using the NHDPlus 375

Basin Delineation Tool 23

. 376

377

3. In 20 11, US EPA requested that USGS assess the quality of the 5,557 contractor

378

delineated watersheds ( Wieczorek, 2011 )

. T he USGS conducted an accuracy assessment 379

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

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

1:24,000 topographic maps, and aerial photography

. The USGS evaluated watershed 382

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

assigned a code as follows: 384

385

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

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

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

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

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

site. 391

392

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

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

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

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

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

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

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

assigned a r ating value of “REASONABLE”. 400

401

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

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

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

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

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

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

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

watersheds. 409

During the analysis process

, USGS discovered 75 duplicate watersheds among the 5,557 410

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

watersheds for evaluation. Using the above

- detailed process, USGS determined that of the 5,482 412

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

or “ FAIL with PASS o ption” categories ( Figure 2

- 2

, Figure 2

- 3 ). These 4,840 watersheds were 414

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

( Wieczorek, 2011 )

. USGS provided US EPA with an ArcGIS geodata base (DWI_basins.mdb) 416

containing the 4,840 delineated watersheds. Table 2

- 1 details the results of this analysis by 417

HUC

- 2 water resource region

. 24 418

24 www.horizon

- systems.com/ NHD Plus/data/ NHD PLUS_UserGuide.pdf23 419

Figure 2

- 2

. Geographic Distribution of 4,8 38 Quality

- assured Watersheds for Surface 420

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

United States 422

423

424

425

426

427

428

429

430

431

43224

Table 2

- 1

. Results of USGS P ass/ F ail A nalysis on Drinking Water Intakes W atersheds, 433

L isted by W ater R esource R egion 434

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

43525 436

Figure 2

- 3

. Derivation Flowchart of the Quality

- Assured Drinking Water Intakes 437

Watershed Dataset 438

Unlike HUC

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

distribution of areas among the 4,8 38 quality

- assured watersheds in the continental United States 440

is highly skewed, with a 10 th percentile of 1.2 mi 2

, a median of 43.5 mi 2 , and a 90th percentile of 441

7

, 383 mi 2

. B y way of comparison , EPA’s “index reservoir” scenario, based upon Shipman City 442

Lake, Illinois , has a watershed of 0.7 mi 2 , which corresponds with about the 5 th percentile of the 443

area distribution

. U nlike HUCs at any given scale, which do not overlap, there is a fair degree of 444

spatial nesting among the watersheds

, such that smaller watershed s are contained inside larger 445

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

. 446

2.3 Database Uncertainties 447

The degree of conservatism inherent in employin g these 4

, 8 38 drainage basins to represent 448

drinking water exposures for all surface water

- sourced CWS in the United States cannot be 449

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

more vulnerable than C WS with intakes that lack quality

- assured watersheds, which 451

hypothetically could represent more vulnerable suppli es

. At this time EFED lacks the data and 452

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

international boundaries, or for sources such as canals, aqueducts, off

- stream reservoirs , and 454

large, incompletely

- mixed water bodies such as the Great Lakes. Nevertheless

, in order to 45526

investigate this issue, EFED examined the 1,710 DWIs possessing qualit y

- assured locations but 456

lacking quality

- assured watersheds ( Figure 2

- 4 ). These DWIs were categori zed as follows : 457

For 685 DW I s, delineated watersheds would either not be appropriate , or not 458

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

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

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

intakes are located outside of the continental U nited S tates inc luding Alaska, Hawaii, and 462

U

. S

. territories. 463

464

For 6 37 DW I s, delineated watersheds would be appropriate because the intakes draw 465

from source water types ( i.e.

, streams, rivers, lakes, ponds, reservoirs) for which surface 466

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

determine why validated watersheds do not exist for these intakes, OPP will us e HUC

468

12 hydrological units as surrogates in lieu of quality assured watersheds for these 469

DWI s

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

documentation for DWI watersheds 25

. Note: An additional 29 DWIs are also associated 471

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

calculation of HUC

- 12 based PCAs produces erroneous results. 473

474

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

appropriate because these intakes largely draw from aqueducts and canals

, and the 476

watershed s that ultimately provide the ir source water are not known

. In some cases, the 477

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

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

is in the process of collecting more information on this se t of DW I s to determine whether 480

watershed s can be appropriately identified to represent the ir contributing drainage areas 481

for PCA determination. 482

25 Initiated Octobe r 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, U nited 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 Pe sticide Management Regulatory Agency27

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

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

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

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

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

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

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

Analysis of the s e data indicate s the presence of about 700 DWIs that appear to be new intakes 490

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

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

493

494

Figure 2

- 4

. Locations of Quality

- A ssured DWIs in the Conterminous U.S. Lacking 495

Quality

- A ssured Watersheds 496

Unlike HUC

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

among the 4,8 38 DWI watersheds is broad and highly skewed

. The median area of HUC

- 8s is 498

about 1,300 mi 2 , which happens to fall at about the 79 th percentile of the watershed area 499

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

HUC

- 8s used in previous PCA efforts

, while about four fifths of DWI watersheds are smaller. 50128

Also, unlike HUCs, there is considerable spatial nesting among the 4, 838 DWI wate rsheds

. This 502

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

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

These and other unique features of this dataset

, including the lack of even spatial distribution 505

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

Canada or Mexico ( Figur e 2

- 2 )

, create novel challenges for the use of this DWI watershed 507

dataset in PCA development ha ve not be en issues in previous PCA efforts

. 508

2.4 Conclusion 509

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

and quality assured, for 6,550 quality

- assured intakes that provide source surface water for 511

community drinking w ater supplies throughout the country. Of the 1,710 quality

- assured intakes 512

without a quality

- assured delineated watershed, 685 are either outside the continental U.S. ( i.e.

, 513

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

HUC

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

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

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

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

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

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

PCA datasets

. 521

52229

Chapter 3. Methods and Results 523

3.1 Method s 524

PCA s were calculated for all

- agriculture , residential turf, and six individua l crops ( corn, 525

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

, 838 drinking 526

water watersheds and 637 HUC

- 12 surrogate watersheds described in Chapter 2 . The National 527

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

Agricultural Census data were comb ined to create a crop

- specific land cover map for each of the 529

specified crops

, including all

- agriculture . NASS provides total acres harvested by county for 530

each individual crop 26 and the NLCD provides information on the distribution of crop acreage at 531

the sub

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

and the corresponding NASS table numbers are listed in Table 3

- 1 . Corn acres for grain, silage 533

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

dataset , marked with a “D”, were ignored. Of the 4

, 838 delineated basins, 127 include upstream 535

areas in Canada

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

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

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

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

Table 3

- 1. 2007 NASS C rop D ata 540

Crop N ASS 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

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

be found in APPENDIX A

. 543

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

Single Crops 544

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

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

the NLCD class 82: cultivated crops. Using the Spatial Analyst toolbox for ArcGI S10.0, the 547

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

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

resulting in an acres

- per

- pixel value for each county. A c rop

- specific map was produced by 550

replacing all cultivated crop pixels (class 82) with the corresponding acres

- per

- pixel values, 551

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

watershed PCAs were calculated by su mming all area

- weighted crop pixels within each 553

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

command. 555

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

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

software limitations caused by the overlapping, or spatially

- nested, and the nature of the DWI 558

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

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

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

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

A ll

- agriculture 563

The NASS data set provide s data on acreage of “land in agriculture” that includes land in 564

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

crop” (class 82) and “pasture” (class 81) ; however

, miscla ssifications are known to occur 566

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

, class 567

81 )

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

effect of misclassifica tion on the all

- agriculture PCA results. To account for ambiguities in 569

acreage associated with th is issue

, all

- agriculture PCAs were calculated two different ways: 1) in 570

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

and 82 . The total number of pixels per county in the latter case

, thus

, includes a tally of all 572

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

NASS “land in agriculture” acreage values to determine the per

- county acre

- per

- pixel weighting 574

factors. 575

Residential Turf 576

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

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

the NLCD 2006 data product where e ach pixel represents an impervious surface area. N LCD

- 57931

based turf area was calculated as the non

- impervious area portion of all land cover pixels 580

classified as developed ( i.e., classes 21

– 24). Using this derived turf layer, turf PCAs were 581

calculated by summing the turf area for all pixels within each wa tershed. HUC

- 12 based PCAs, 582

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

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

introduces a degree of uncertaint y related to land cover changes during the interv ening period

. 585

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

increasing turf acreage estimates. 587

HUC12 Processing and Surrogate Selection 588

Surface water intakes which lacked quality

- assured, delineated watersheds were assigned 589

HUC

- 12

- based surrogate PCA values. B ased on the criteri a in Section 2.3

, 637 intakes received 590

HUC

- 12

- based surrogate PCA values based on the intake location. HUC

- 12 PCAs were 591

calculated for all

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

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

3.2 Results 594

Maximum Water Resource

- Regional PCAs 595

Each of the 4,8 38 watersheds w as assigned to a HUC

- 2 water res ource region based on 596

the intake location

. Maximum regional PCAs were then tabulated for each of the eighteen HUC

597

2 water resource regions in the conterminous United States, for each individual crop, all

598

agriculture and turf, and for various crop combinat ions. Results for specific crops and crop 599

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

, and 600

reproduced in this chapter in Tables 3

- 2 and 3

- 3 , respectively. Maximum HUC

- 12

- based PCAs

, 601

for the 63 7 quality

- assured DWIs lacking quality

- assured watersheds in each HUC

- 2, are 602

tabulated in Table 3

- 4 by crop

. Table 3

- 5 lists the maximum crop

- specific PCA for each HUC

603

2, either DWI

- based or HUC

- 12

- based, whichever is greater. Tables 3

- 6 and 3

- 7 display th e 604

analogous crop

- combination results for HUC

- 12

- based PCAs and overall maximum PCAs, 605

respectively. Values in Tables 3

- 5 and 3

- 7 that are HUC

- 12 based, and therefore greater than 606

the corresponding DWI watershed

- based values, are indicated in italics. As Ta bles 3

- 5 and 3

- 7 607

demonstrate, inclusion of HUC

- 12

- based PCAs for the 637 DWIs which hypothetically warrant 608

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

PCAs, for both individual crops and crop combinations. 610

611

612

61332

Table 3

- 2

. Maximum DWI

- PCA s for V arious C rops, by Water Resource Region (HUC

- 2) 614

HUC

2 All Ag a All Ag b Corn Cotton Orchard Soybean Vegetable Wheat Turf c

01 0.30 0.24 0.08 0.0 0 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 0.0 0 0.01 0.00 0.05

14 0.01 0.16 0.00 0.00 0.0 0 0.00 0. 00 0.00 0.07

15 0.00 0.35 0.00 0.00 0.0 0 0.00 0.0 0 0.00 0.10

16 0.01 0.04 0.00 0.00 0.00 0.00 0.0 0 0.00 0.04

17 0.61 0.72 0.01 0.00 0.02 0.0 0 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

. S od farm s 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

63433

Table 3

- 3

. Maximum DWI

- PCAs for V arious C rop C ombinations, by Water Resource 635

Region (HUC

- 2) 636

HUC

2 Corn

Wheat Soybean

Wheat Turf a

-

Corn Turf

a

-

Orchard Turf a

- Soybean Turf a

- Vegetable Turf a

-

Wheat Vegetable

Orchard Turf a

-

All Ag b

0 1 0.08 0. 00 0.45 0.45 0.45 0.45 0.45 0.12 0.46

0 2 0.36 0.17 0.58 0.58 0.58 0.58 0.58 0.06 0.79

0 3 0.18 0.25 0.64 0.64 0.64 0.64 0.64 0.13 0.65

0 4 0.42 0.52 0.47 0.32 0.51 0.32 0.32 0.06 0.98

0 5 0.52 0.59 0.58 0.49 0.65 0.49 0.49 0.04 0.96

0 6 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

0 7 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

0 8 0.22 0.12 0.28 0.08 0.12 0.08 0.09 0.00 0.58

0 9 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

. S od farm s 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

65434

Table 3

- 4

. Maximum C rop

- specific HUC

- 12

- based PCA s , L isted by Water Resource 655

Region (HUC

- 2), for the 63 7 Q uality

- A ssured DWIs L ack ing Q uality

- A ssured W atersheds 656

HUC

- 2 All Ag a Corn Cotton Orchard Soybean Vegetable Wheat Turf b

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

. S od farm s 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

67535

Table 3

- 5

. Maximum C rop

- specific PCA s, L isted by Water Resource Region (HUC

- 2), 676

I ncluding B oth 4,840 DWI PCAs for Q uality

- assured W atersheds, and the 63 7 HUC

- 12

- 677

based PCAs for Q uality

- A ssured DWIs that L ack ing Q uality

- A ssured W atersheds 678

HUC

- 2 All

- Ag a Corn Cotton Orchard Soybean Vegetable Wheat Turf b

0 1 0.32 0.08 0.00 0.02 0.00 0.10 0.00 0.86

0 2 0.8 2 0.34 0.06 0.07 0.11 0.03 0.06 0.60

0 3 0. 52 0.09 0.08 0.12 0.20 0.08 0.10 0.6 4

0 4 0.9 2 0.35 0.00 0.05 0.44 0.04 0.11 0.85

0 5 0.90 0.51 0.0 2 0.03 0.5 9 0.02 0.10 0.49

0 6 0.45 0.10 0.04 0.02 0.10 0.01 0.03 0.38

0 7 1.00 0.68 0.00 0.00 0.58 0.01 0.27 0.59

0 8 0. 75 0.21 0.20 0.00 0.07 0.00 0.05 0.52

0 9 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 0.0 7 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 0.0 1 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

- a g riculture PCAs calculated both ways

. The highest All

- Ag value s from Tables 3

- 2

and 3

- 4 are listed here.

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

. S od farm s are

included in the all

- agriculture land cover class PCA

. 679

680

681

682

683

684

685

686

687

688

689

690

691

69236

Table 3

- 6. Maximum HUC

- 12

- based PCAs for V arious C rop C ombinations, by Water 693

Resource Region (HUC

- 2) , for the 63 7 Q uality

- A ssured DWIs L ack ing Q uality

- A ssured 694

W atersheds 695

HUC

2 Corn

Wheat Soybean

Wheat Turf a

-

Corn Turf a

-

Orchard Turf a

- Soybean Turf a

- Vegetable Turf a

-

Wheat Vegetable

Orchard Turf a

-

All Ag b

0 1 0.05 0.00 0.86 0.86 0.86 0.86 0.86 0.02 0.98

0 2 0.29 0.11 0.60 0.60 0.60 0.60 0.60 0.10 0.72

0 3 0.00 0.00 0.41 0.41 0.41 0.41 0.41 0.03 0.41

0 4 0.40 0.44 1.00 0.85 0.85 0.85 0.85 0.05 1.00

0 5 0.52 0.37 0.55 0.38 0.41 0.38 0.38 0.00 0.95

0 6 0.04 0.03 0.22 0.22 0.22 0.22 0.22 0.00 0.31

0 7 0.33 0.23 0.51 0.45 0.47 0.45 0.45 0.00 0.64

0 8 0.01 0.00 0.53 0.52 0.52 0.52 0.52 0.00 0.55

0 9 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

. S od farm s are included in the all

agriculture 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

71037

Table 3

- 7. Maximum C rop

- combination PCA s, L isted by Water Resource Region (HUC

711

2), I ncluding both the 4,840 DWI PCAs for Q uality

- A ssured W atersheds, and the 637 712

HUC

- 12

- based PCAs for Q uality

- A ssured DWIs L ack ing Q uality

- A ssured W atersheds 713

HUC

2 Corn

Wheat Soybean

Wheat Turf a

-

Corn Turf

a

-

Orchard Turf a

- Soybean Turf a

- Vegetable Turf a

-

Wheat Vegetable

Orchard Turf a

- All Ag

0 1 0.08 0.00 0.86 0.86 0.86 0.86 0.86 0.12 0.98

0 2 0.36 0.17 0.60 0.60 0.60 0.60 0.60 0.10 0.79

0 3 0.18 0.25 0.6 4 0.6 4 0.6 4 0.6 4 0.6 4 0.13 0.65

0 4 0.42 0.52 1.00 0.85 0.85 0.85 0.85 0.06 1.00

0 5 0.52 0.59 0.58 0.49 0.64 0.49 0.49 0.04 0.96

0 6 0.13 0.13 0.38 0.38 0.38 0.38 0.38 0.03 0.58

0 7 0.73 0.85 0.72 0.59 0.65 0.59 0.59 0.01 1.00

0 8 0.22 0.12 0.53 0.52 0.52 0.52 0.52 0.00 0.58

0 9 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

. S od farm s are included in the

all

- agriculture land cover class PCA.

714

Appendix B comprises figures that display comparisons of national

- scale PCA cumulative 715

distribution functions (CDFs), between DWI

- watersheds, HUC

- 12 surrogates, and the two 716

combined. For a number of crops or crop combinations (orchard, vegetable, turf, turf

- corn, turf

717

orchard, turf

- soybean, turf

- vegetable, turf

- wheat, turf

- all

- agriculture, and vegetable

- orchard), the 718

distribution of PCAs for HUC

- 12 surrogates is shifted to the right as compared wi th PCAs for 719

DWI watersheds , meaning that the HUC

- 12 PCAs are generally more conservative. For soy and 720

soy

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

caused by including the HUC

- 12 based PCAs in the overall d istributions

. 722

Figure 3

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

inclusion of the HUC

- 12 based subset), against the existing HUC

- 8 based PCAs. Figure 3

- 2 724

similarly display s a comparison of maximum all

- agriculture PCAs per HUC

- 2 region, between 7253 8

the new DWI PCAs (calculated with and without pasture/hay) and the existing HUC

- 8 PCAs. In 726

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

H UC

- 8 based values. 728

3.3 Validation of PCA s 729

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

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

purpose of the validation was to identify any da ta processing errors or data anomalies (t he 732

accurac ies of the intake location s and basin delineation s were not evaluated by EFED , but were 733

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

watersheds for each of the crops

, and applied the alternate PCA calculation methodology. The 735

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

list of verified catchment PCA s is provided i n Table A

- 2

. 737

The validation process reveal ed some watersheds that were incorrectly identified as 738

having PCAs of zero. Further analysis revealed that the automated (Python script

- based) process 739

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

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

identified approximately 20

– 40 watersheds per crop

, for which the automated process failed to 742

correctly calculate a PCA. Once identified, PCAs for these watershed s were c alculated 743

manually. 744

74539

Figure 3

- 1. Comparison of existing HUC

- 8 based PCAs against DWI PCAs with and without 746

inclusion of the HUC

- 12 based subset. 747

748

Figure 3

- 2

. Comparison of maximum all

- agriculture PCAs by HUC

- 2 region. Shown are DWI 749

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

land plus pasture/hay), and current HUC

- 8 based PCAs. 751

3.4 Data Limitations 752

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

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

Rates of misclassification between NLCD classes 81 and 82

, and between 81, 82 and 755

other classes appear to be significant

, but have not yet been quantified. Such 756

misclassifications could either increase or decrease PCAs. 757

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

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

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

crop types at a sub

- county scale. 761

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

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

outside the U.S. are not available

. 76440

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

during the early 2000’s

, whereas the NASS data are from 2007. Any land use changes 766

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

due to the mismatch between datasets

. 768

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

the land was double

- cropped , thus resulting in double

- counting of total cropland in the 770

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

3.5 Future Improve ments 772

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

for future analysis. This would presumably allow for more accurate sub

- county 774

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

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

NASS data

. 77741

Chapter 4. Monitoring Data Comparison s 778

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

mo nitored pesticide concentrations and mo del

- estimat ed concentrations when DWI PCAs are 780

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

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

water , results in concen trations that are protective of vulnerable drinking water supplie s. This 783

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

4.1 Methodolog y 785

In the modeling part of the evaluation effort, for each of seven “ crop s ” (c orn, cotton, 786

soybeans, wheat, turf, vegetables, and orchards ) a s well as all

- agriculture

, for which DWI PCAs 787

were developed

, EFED identified up to three pesticides ( i.e.

, active i ngredients ) used primarily 788

on that crop

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

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

then collected physical

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

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

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

drinking water or endangere d species assessment. Using the s e data, EFED ran the PRZM and 794

EXAMS models with the PE 5 shell (PRZM

- EXAMS Model Shell) 27

, using standard scenarios 795

for each crop and the index reservoir

, to estimate 1

- in

- 10 year retur n frequency daily 796

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

develop EDWCs. 798

The ideal monitoring site for evaluating PCA

- adjusted modeling data would reflect an 799

area of high pesticide use on a specific, indivi dual crop or crop group

, in an area considered 800

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

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

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

or allow for calculation of reasonable statistical bounds on measured concentrations 28

. 804

Unfortunately, monitoring data meeting all these criteria particularly runoff

- vulner ability and 805

crop

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

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

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

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

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 chara cterizing the uncertainties in those estimates for use in risk assessments (USEPA,

2010a

, 2010b , 2011, and 2012 ).42

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

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

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

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

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

2002

– 05

. 29 T he USGS CWS dataset was selected because this study focused on a number of 815

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

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

Program (PDP) as well, however

, these concentrations were lower than the maximum detections 818

from the USGS study ; therefore

, not used in present evaluation . EFED supplemented the CWS 819

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

used as source drinking water by CWS, from registrant

- conducted monitoring studies submitted 821

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

large river systems ( Table 4

- 1 ) with large drain age areas

, thus, are inherently heterogeneous and 823

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

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

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

in sample collection coinciding with peak pesticide concentrations

. The registrant

- sponsored 827

sampling for atrazine and iprodione was conducted approximately weekly. 828

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

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

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

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

information to suggest that any of the monitored water bodies ( Table 4

- 2 ) in this case are used as 833

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

studies

. 835

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

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

modify the PRZM

- EXAMS results: the national maximum crop

- specific PCA, and a regional 838

maximum

, crop

- sp ecific PCA, for the water resource region corresponding with the location 839

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

compared the national and regional PCA

- adjusted EDWCs with measured drinking water supply 841

( Tabl e 4

- 1 and Figure 4

- 1 ) and miscellaneous surface water ( Table 4

- 2 and Figure 4

- 3 ) 842

concentrations

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

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

PCAs in drinking water assessment s

. For comparison against the NAWQA dataset, EFED also 844

developed EDWCs using regional PCAs that reflect all label ed pesticide uses ( Table 4

- 3 and 845

Figure 4

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

Monitoring in m any surface water bodies

, particularly lotic systems or others with short 847

residence times

, is unlikely to capture true maximum concentrations because hydrologic 848

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

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

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

absence of continuous monitoring, true maximum concentrations are unknown. While PCA

852

adju sted modeled pesticide concentrations that exceed measu r ed pesticide concentrations provide 853

som e assura nce that EFED’s assessment process using PCAs has not underestimate d pesticide 854

concentrations in drinking water supplies

, a ctual peak pesticide concentrations in surface water 855

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

2010b, 2011, and 2012)

. To evaluate the possible degree of under

- representing true peak 857

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

exploring the use of concentration

- adjustment “bias factors”. 30 Based upon an analysis of daily 859

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

are 5.46 and 44.48, respectively

. 31 These values were empirically derived from a limited 861

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

concentrations include water body hydrology, pesticide fate properties, and pesticide applicatio n 863

patterns and practices

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

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

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

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

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

monitoring data. Figur e 4

- 2 shows the same modeling

- monitoring comparison as in Figure 4

- 1

, 869

but with the measured concentrations adjusted using sampling interval

- appropriate bias factors. 870

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

durations at a given location

, thus

, it was not possible to develop bias factors to use in this 872

analysis

. 873

4.2 Results 874

Tables 4

- 1 and 4

- 2 provide comparison s between measured and modeled estimated 875

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

30 U.S. EPA 2012 . FIFRA Science Advisory Panel

– Problem For mulation 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 Pestici de

Programs, Environmental Fate and Effects Division.44

watershed (plus HUC

- 12 surrogate) PCAs. Tables 4

- 1 and 4

- 2 include DWI PCA

- adjusted 1

- in

877

10 year peak EDWCs

, along with the crop

- specific maximum measured concentration of each 878

chemical in CWS

- supply surface water, and miscellaneous surface water respectively

. A ll of the 879

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

above method detection limits, and not bias factor

- adjusted concentrations . National

- maximum 881

crop

- sp ecific PCAs (for DWI watersheds plus HUC

- 12 surrogates) , and region

- specific PCAs for 882

the HUC

- 2 regions corresponding to the sampling locations are tabulated. Figure 4

- 1 displays 883

scatterplots of the PCA

- adjusted EDWCs against the maximum detected concentr ations of each 884

pesticide in surface drinking water supplies. Figure 4

- 2 displays the same PCA

- adjusted 885

EDWCs plotted against the estimated maximum bias factor

- adjusted concentrations in the same 886

water bodies. Figure 4

- 3 displays scatterplots of the PCA

- adjusted EDWCs against maximum 887

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

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

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

calculation of chronic or longer

- term concentrations does not exist for the majority of pesticides. 891

Figure 4

- 4 displays a scatterplot of EDWCs adjusted using region al specific, all

- use

- inclusive 892

PCAs against the same NAWQA monitoring data. The all

- use

- inclusive PCA values are 893

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

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

in Chapter 5 ). The results of th e s e comparison s are as follows: 896

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

- 12 897

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

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

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

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

monitored concentra tions for all chemicals except atrazine. 902

903

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

surface waters; DWI PCA

- adjusted EDWCs are greater than these concentrations for all 905

but four chemicals when either national o r regional maximum PCAs are used

. 906

907

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

pesticides in miscellaneous ( i.e.

, NAWQA) surface waters when national maximum DWI 909

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

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

pesticides. Using regional maximum all

- use

- inclusive DWI PCAs, adjusted EDWCs 912

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

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

miscellaneous (NAWQA) surface waters. 91545 91646

Table 4

- 1. Comparison B etween M aximum M onitored P esticide C oncentrations in D rinking (surface) W ater S ources (USGS 917

study), and A cute EDWC s A djusted using DWI PCAs 918

Crop Pesticide DW

Measured

Conc.

( μg/L) State

where

measured Water body National

Max PCA Regional

Max

PCA a PE5

C onc.

(μg/L) National

EDWC

(μg/L) Regional

EDWC

(μg/L) Ratio

National

EDWC

/ Meas.

DW

Conc. Ratio

Regional

EDWC /

Meas.

DW

Conc.

All Ag d iazinon 0.0855 GA Chattahoochee

River 1.00 0.52 150 150 78 1754 912

All Ag chlor o thalonil

(TTR) b 0.71 c IN White River 1.00 0. 89 363 363 3 23 511 45 5

All Ag chlor o thalonil

(parent) b 0.71 c IN White River 1.00 0.8 9 83.9 79 7 5 118 10 5

All Ag carbofuran 0.0141 IN White River 1.00 0.8 9 27.9 2 8 25 1860 17 6 1

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

Corn atrazine 227 OH d Sterling 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 0.5 9 0.5 9 19.3 11 11 7 1 7 1

Soybean acifluorfen 0.011 IN White River 0.5 9 0.5 9 86.8 5 1 5 1 4 656 4 656

Soybean bentazon 0.11 IN White River 0.5 9 0.5 9 20.5 12 12 1 10 1 10

Turf picloram 0.17 IN White River 0.86 0. 4 9 19 16 9 96 55

Turf triclopyr 0.45 IN White River 0.86 0. 4 9 1143 983 560 2184 1254

Turf iprodione 0.564 NJ 2 Rahway River 0.86 0.60 361 310 217 550 384

Vegetable dcpa 0.0049 OR Clackamas River 0.1 9 0.01 1427 143 14 29 122 2 912

Wheat bromoxynil 0.0046 IN White River 0.41 0. 10 5.16 2.1 0.5 46 0 11247

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

94348

Table 4

- 2 . Comparison B etween M aximum M onitored P esticide C oncentrations in S urface W ater (USGS NAWQA), and 944

A cute EDWC s A djusted U sing DWI PCAs 945

Crop Pesticide Max.

NAWQA

conc.

(μg/L) State

where

measured Water body National

Max

PCA Regional

Max

PCA a 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 chlor o thalonil

(TTR) b,c 0.71 b NJ Great Egg Harbor

River 1.00 0. 82 363 363 2 61 511 410

All Ag chlor o thalonil

(parent) b,c 0.71 b NJ Great Egg Harbor

River 1.00 0. 8 2 83.9 84 6 9 118 97

All Ag carbofuran 32.2 OR Zollner Creek 1.00 0.72 27.9 27.9 20 0.8 7 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 a trazine 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 0.5 9 0.20 19.3 11 4 1.9 0.7

Soybean acifluorfen 2.2 IN Kessinger Ditch 0.5 9 0.5 9 86.8 5 1 5 1 23 23

Soybean bentazon 8.6 TX White's Bayou 0.5 9 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 0.1 9 0.02 1427 271 29 2.7 0.2949

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

96950

Table 4

- 3 . Comparison B etween M aximum M onitored P esticide C oncentrations in D rinking ( S urface) W ater S ources (USGS 970

study), and A cute EDWC s A djusted U sing C rop and R egionally

- A ppropriate DWI PCAs 971

Pesticide Uses Max.

NAWQA

C onc.

(μg/L) Name of

water

body

where

measured State

where

measured Region

where

measured Use

appropriate

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 waterc ress 3.8 Orestimba

Creek CA 18 0. 74 All Ag 150 111 29

chlor o thalonil

(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 0.7 1 b Great Egg

Harbor

River NJ 2 0.82 All Ag 363 2 61 368

chlor o thalonil

(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 0.7 1 b Great Egg

Harbor

River NJ 2 0.82 All Ag 83.9 60 8551

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 O R 17 0.7 4 All Ag 27.9 20 0.62

acetochlor corn and "rotational crops" 215 T ributary

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 AL 3 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 0.6 4 Turf

orchard 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. 7 5 All Ag 11.1 10.5 167.4

imazaquin soybeans, turf, ornamentals 5.9 Three Mile

Creek AL 3 0.6 4 Turf

soybeans 19.3 12 2.152

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

- of

way, 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 2.1653 972

Figure 4

- 1

. Comparison of Modeled 1

- in

- 10 Year Maximum EDWCs Against Maximum 973

Single Sample Concentrations of the Same Chemicals Measured in Studies of Rivers and 974

One Creek that Serve as Source Water for Community Water Supplies, Using: a) National 975

Maximum DWI PCAs, and b) Regional Maximum DWI PCAs, with Region DWI PCA 976

Selected to Match Lo cations of Monitored Concentrations 977

97854 979

Figure 4

- 2

. Comparison of Modeled 1

- in

- 10 Year Maximum EDWCs Against Bias Factor

980

adjusted Maximum Single Sample Concentrations of the Same Chemicals Measured in 981

Studies of Rivers and One Creek that Serve as Source Wat er for Community Water 982

Supplies, using: a) National Maximum DWI PCAs, and b) Regional Maximum DWI PCAs, 983

with Region Selected to Match Locations of Monitored Concentrations 984

98555 986

Figure 4

- 3

. Comparison of Modeled 1

- in

- 10 Year Maximum EDWCs Against Maximum 987

Sing le Sample Concentrations of the Same Chemicals Measured in Surface Waters by the 988

USGS NAWQA Program, Using: a) National Maximum DWI PCAs, and b) Regional 989

Maximum DWI PCAs, with Region Selected to Match Locations of Monitored 990

Concentrations

. [ Note that PCA s in this comparison largely reflect only single crops, and may 991

not correspond with PCAs that would be used in a reg ional drinking water assessment ] 99256 993

Figure 4

- 4

. Comparison of Modeled 1

- in

- 10 Year Maximum EDWCs Against Maximum 994

Single Sample Concentrations of the Same Chemicals Measured in Surface Waters by the 995

USGS NAWQA Program, Using PCAs reflective of all labeled uses of the pesticide, as 996

indicated in Table 4

- 3 997

998

999

1000

1001

1002

1003

1004

100557 1006

Figure 4

- 5. Ratio of Regional PCA Adjusted Modeled Pesticide Concentrations (EDWCs ) 1007

to Maximum Measured (NAWQA) Pesticide Concentrations, v s. Regional Crop

- specific 1008

PCA Employed in Calculating the Modeled Concentrations 1009

1010

Figure 4

- 6

. Comparison of M odeled 1

- in

- 10 Y ear M aximum EDWCs (calculated using no 1011

PCA adjustment) A gainst M aximum S ingle S ample C oncentrations of the S ame C hemicals 1012

M easured in S urface W aters by the USGS NAWQA P rogram 1013 fipronil acetochlor58

4.3 Discussion 1014

For all but one pesticide, atrazine

, results of aforementioned analyses demonstrate that 1015

PRZM

- EXAMS modeling coupled with the use of both national and regional

- maximum DWI 1016

PCAs adjustments, generates concentration estimates that exceed maximum observed pesticide 1017

concentrations in lotic surface waters used for CWS supply

. The fact that the atrazine data, 1018

which w ere collected at a higher frequency than most of the other data used in this analysis, are 1019

the lone exception to this observation is noteworthy. In addition, atrazine uses include use on 1020

rights

- of

- ways and ornamentals; thus, a PCA of 1.0 would be used in drinking water exposure 1021

assessment. When standard methodologies are used, the modeled concentration exceeds the 1022

measured value. In flowing water bodies, less often than daily sampling is unlikely to capture 1023

peak pesticide concentrations generated by sto rmflow runoff events, which may transport 1024

pesticides into water bodies over much shorter time scales. The use of bias factors, as in Figure 1025

4

- 2 , is an attempt to adjust monitoring data to account for limitations in sampling frequency. 1026

Many of the CWS sup ply sampling locations from which data used in this analysis were 1027

collected are unlikely to represent the kind of highly vulnerable sites that USEPA’s PRZM

1028

EXAMS modeling is intended to represent, thus, modeled estimated concentrations are expected 1029

to gene rally exceed maximum concentrations observed in actual waters. Figure 4

- 2 supports this 1030

assumption. 1031

Results of the second phase of this analysis, in which modeled concentrations are 1032

compared against national maximum pesticide concentrations measured in su rface water by the 1033

USGS NAWQA sampling program, demonstrate that PCA

- adjusted PRZM

- EXAMS 1034

concentrations can underestimate real pesticide concentrations, at least sometimes in some water 1035

bodies. The implications of this for pesticides in drinking water sup plies are not clear. The 1036

degree to which conditions in these water bodies resemble conditions in vulnerable CWS 1037

supplies is not known. Figure 4

- 5 demonstrates an apparent (though weak) relationship between 1038

the regional PCAs used in the NAWQA data compari son of Figure 4

- 3

, and the degree to which 1039

modeled concentrations are higher or lower than observed concentrations. Such a relationship 1040

suggests that the degree of under or overestimation for any pesticide may be dominated by the 1041

PCA rather than by the PR ZM

- EXAMS results, which suggests that underestimates may be a 1042

consequence of use of a “wrong” PCA, i.e., one that underestimates real crop coverage or 1043

pesticide use. Additionally, Figure 4

- 5 shows that when low PCAs are used (those less than 1044

0.4), PCA adj usted EDWC are commonly lower than monitored concentrations. While it is 1045

unknown whether the NAWQA sampling sites occurred in water bodies near DWIs, it is possible 1046

that some of the sampling sites are near DWIs or are representative sites where DWI may be 1047

found. 1048

Figure 4

- 6 displays a comparison between the same NAWQA data, and unadjusted ( i.e., 1049

no PCA) PRZM

- EXAMS concentration results. As the figure illustrates, in this case modeled 1050

concentrations exceed measured concentrations for all but four pesticid es. Modeled 105159

concentrations underestimate measured concentrations by more than 20% for only two 1052

pesticides: fipronil and acetochlor. The largest degree of underestimation, which is by less than 1053

an order of magnitude, is seen for fipronil. The fipronil detection in qu estion occurred in a 1054

Louisiana bayou, and may be influenced by use of the chemical on rice cultivation in the area. A 1055

PCA for rice is not reported in this document. The results displayed in Figure 4

- 6 illustrate that 1056

modeled pesticide concentrations even without PCA adjustment do not always exceed real world 1057

concentrations, and serve to emphasize the apparent importance of selecting PCAs appropriately. 1058

USEPA will continue to evaluate PCA

- adjusted EDWCs against ava ilable monitoring 1059

data to ensure that estimated exposures do not underestimate measured concentrations in 1060

drinking water sources. Additionally, USEPA will continue to work on developing reliable 1061

methods of quantifying confidence bounds on measured concent rations to account for 1062

uncertainties related to sampling frequency, and providing spatial context to monitoring data. 106360

Chapter 5. Guidance on Applying PCAs to Estimate Drinking Water Concentrations 1064

5.1 National Screening Level Assessments 1065

OPP uses the coupled PRZM and EXA MS model s 32 to generate EDWC s for pesticides 1066

applied to a crop at an expected high

- end exposure scenario for that crop use

. The resulting 1067

EDWCs are multiplied by the PCA ( expressed as a fraction ) to adjust for the maximum potential 1068

percent of the watershed that is covered by the crop of interest ; thus

, could potentially be treated 1069

by the pesticide of interest. 1070

For an initial, national screening level assessment of a pesticide used on a single crop, the 1071

PCA used represents the highest value for the given cro p in any CWS

- DWI watershed (plus 1072

HUC

- 12 surrogates) nationally ( i.e., the highlighted crop

- specific value in Table 1 ). If the 1073

pesticide is only used on one of the crop combinations listed in Table 2 , then the national

1074

maximum PCA for that crop combination ( i.e., the highlighted value in Table 2 ) is used. If the 1075

pesticide is used on other crops or crop combinations than those listed in Tables 1 and 2 , then 1076

the default “ all

- agriculture ” PCA should be applied. PCAs can be applied to the 1

- in

- 10

- year 1077

peak, a nnual, or other time

- weighted average duration, or to the full time

- series generated by 1078

PRZM

- EXAMS in a Tier 2 drinking water assessment. 1079

Figure 5

- 1 provides a decision tree for selecting PCAs for national

- level drinking water 1080

assessments. Estimates may be developed for the following situations: (A) a single crop with a 1081

crop specific PCA; (B) multiple crops that all have crop

- specific PCAs; or (C) a single crop 1082

without a crop specific PCA, or multiple crops besides those with specific crop combination 1083

PC As. A discussion of these three situations follows. 1084

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

Figure 5

- 1

. Decision T ree for N ational PCA A djustment F actor D etermination

. 108762 A) If a pesticide is only registered for use on a s ingle crop for which a crop

- specific 1088

PCA exists (co rn , soybeans, wheat, cotton, vegetable, orchard, or turf), multiply 1089

the unadjusted EDWC by the appropriate national PCA for that crop ( Table 1 ) to 1090

obtain the final EDWC. For Tier 2 screening modeling, apply the maximum 1091

national PCA regardless of the modeled scenario locat ion

. If an individual crop is 1092

in an established crop group (such as tomatoes, which would be included in the 1093

vegetable group), or if all of the uses fall within the same crop group, ap ply the 1094

PCA for that crop group

. 1095

1096

B) If a pesticide is registered for use only on multiple crops for which specific crop

1097

combination PCA have been developed ( Table 2)

, then multiply the unadjusted 1098

EDWC by the PCA for the crop combination to obtain the final E DWC. The 1099

PCA that should be used represents the maximum potential percentage of the 1100

watershed that could be planted with these crops. 1101

1102

C) If a pesticide is applied to a single crop that lacks a crop

- specific PCA, multiply 1103

the unadjusted estimated drinking w ater concentration (EDWC) for the crop by 1104

the national default PCA ( 1.0 for all

- agriculture ) to obtain the f ina l EDWC. If the 1105

pesticide is applied to multiple crops not limited to co rn , soybeans, wheat, cotton, 1106

vegetable, orchard, and turf, multiply EDWCs by the national default PCA to 1107

obtain the final EDWCs. If the pesticide is used for non

- agricultural purposes 1108

other than residential turf ( e.g., ornamental plant s , non

- cropland areas, pasture 1109

and rangeland, forestry, or rights

- of

- way ), multiply EDWCs by the national 1110

default PCA to obtain the final EDWCs

. In such cases, if any of the crops 1111

(including turf) or crop combinations to which the pesticide is applied include 1112

crops for which PCAs have been developed, the reviewer should note this 1113

circumstance, as it has potential relevance for regional refinements that may be 1114

possible. 1115

5.2 Regional Refinements 1116

If dietary risks are exceeded in a national screening level assessment, the risk manager 1117

may ask EFED to refine the pesticide exposure assessment. The risk assessor can either refine 1118

the assessment qualitatively by providing additional characterization of potential exposure, or 1119

refine it quantitatively by simulating additional sites for each crop and/or by using regional PCAs 1120

in place of national PCAs

. 1121

Regional PCAs can be used to determine whether potential screening level EDWCs 1122

exceed dietary risks nationwide or only in certain regions of the country. The most appropriate 1123

use of regional PCAs can be best determined in discussions between risk assessors and risk 1124

managers. Possible options that may be considered include the following: 112563

Use of regional default PCAs to refine EDWCs for uses with limited regional extent, 1126

such as Section 18 emergency exemptions, Section 24(c) Special Local Need requests, or 1127

crops with limited, well

- defined growing regions ( e.g. , citrus). 1128

Use of regional PCAs to better disti nguish which crop

- pesticide scenarios in a pesticide 1129

use area may result in the highest surface water EDWCs. For instance, a crop with a 1130

small percentage of total usage of the pesticide grown in the South Atlantic

- Gulf region 1131

may have a maximum use rate m uch higher than the predominant usage on corn in the 1132

Midwest. However, after application of the regional PCA to the minor use scenario, the 1133

EDWC from the Midwest corn scenario might be higher and, therefore, the driver of the 1134

drinking water exposure asses sment. 1135

Use of regional PCAs in association with a pesticide usage map to distinguish between 1136

magnitudes of possible exposure for the same crop in different regions of the country, 1137

and to focus spatial modeling refinements. 1138

Use of regional PCAs to populate a matrix of regional peak or chronic EDWCs for each 1139

labeled use of a pesticide that is expected to result in dietary risk exceedances, in order to 1140

refine a drinking water exposure assessment. This begins with tabulating states by water 1141

resource (HUC

- 2) r egion where labeled crops are grown and allowed for use. Then 1142

PRZM

- EXAMS scenarios or surrogates for each relevant HUC

- 2 are derived. These 1143

scenarios incorporate the maximum labeled use pattern (which may be geographically

1144

specific) for each use. Region al PCA

- adjusted EDWCs are then tabulated for 1145

comparative purposes. The risk assessor may request from HED a back

- calculated 1146

drinking water level of concern for reference ( e.g. , highlight on the table regions with 1147

EDWCs that exceed the level of concern). 1148

A s an additional refinement, for uses with regional PCA

- adjusted EDWCs greater than 1149

the level of concern, the risk assessor in consultation with the risk manager may request reported 1150

use information from areas of the United States with similar geography and /or crop management 1151

practices from the BEAD. This information can be used to model and characterize “reported” 1152

use patterns, based on the average number of applications and high

- ends of the application rate 1153

distributions reported. Characterization of the se reported use patterns may help risk managers to 1154

determine whether use pattern reductions to levels that appear to remain efficacious, may 1155

constitute worthwhile label mitigation. 1156

5.3 Summary 1157

T he DWI, DWI watershed, and DWI watershed PCA dataset s provide USE PA with new 1158

tools that open the possibility of exploring geographically specific drinking water assessments 1159

beyond standard regional refinements

, in ways that have not previously been available using 1160

existing tools. The geospatial nature of the se dataset s means that l ocations with greater risk ( i.e.

, 1161

hot

- spots) and more vulnerable communities can be identified . That information can be coupled 116264

with geographically specific soil, hydrology, weather, and landscape propert y data

, so that 1163

targeted mitigation me asures can potentially be developed. Methodologies and procedures for 1164

conducting such analyses need further development , and policy and resource implications for 1165

USEPA to adopt such approaches into risk assessment practice need further investigation

. 1166

However, under the assumption that these datasets will prove to be as useful as anticipated, it is 1167

USEPA’s current plan to update the DWI, DWI watershed, and DWI watershed PCA datasets on 1168

an ongoing five year basi s for the foreseeable future. 1169

1170

117165

Chapter 6. Literature Cited 1172

1173

Bhaduri B, 2003. Development of a GIS

- Based Watershed Characterization and Pesticide 1174

Usage Assessment Tool . Oak Ridge National Laboratory. Project report submitted to 1175

USEPA Office of Pesticide Programs. 1176

Echeverria M, Jones RD, P eck C, Ruhman M, Shelby A

, Thawley S

, 2012

. Development and 1177

Use of Percent Cropped Area and Percent Turf Area Adjustment Factors in Drinking 1178

Water Exposure Assessments: 2012 Update . March 16, 2012. Environmental Fate and 1179

Effects Division. Office of Pesti cide Programs. United States Environmental Protection 1180

Agency. Available at 1181

http://www.epa.gov/oppefed1/models/water/pca_adjustment_dwa.html (Accessed April 1182

25, 2012). 1183

Effl a nd W R, Thurman N

, Kennedy I

, 1999. Proposed Methods for Determining Watershed

1184

Derived Percent Cropped Areas and Considerations for Applying Crop Area Adjustments 1185

to Surface Water Screening Models . May 27, 1999. Environmental Fate and Effects 1186

Division. Office of Pesticide Programs. United States Environmental Protection Agency. 1187

Available at http://www.epa.gov/scipoly/sap/meetings/1999/052599_mtg.htm (Accessed 1188

June 8, 2012). 1189

Fry J, Xian G, Jin S, Dewitz J, Homer C

, Yang L

, 2011. Completion of the 2006 National Land 1190

Cover Database for the Conterminous United States. Photogrammetric Engineering & 1191

Remote Sensing 77 (9), 858

- 864. 1192

Horn M

, 2007. Memo from Marilee Horn to Shelly Thawley: IAG # D W

- 14937871

- 01

- 0 SDWIS 1193

Source

- Data Location Quality Assurance

. June 28, 2007. 1194

Jones RD

, Abel S

, 1997. Presentation to the Scientific Advisory Panel. Use of a Crop Area 1195

Factor in Estimating Surface

- Water

- Source Drinking Water Exposure. December 1997. 1196

Enviro nmental Fate and Effects Division. Office of Pesticide Programs. United States 1197

Environmental Protection Agency. 1198

Jones RD, Costello K, Hetrick J, Lin J, Parker R

, Thurman N

, 2010. Development and Use of 1199

Percent Cropped Area Adjustment Factors in Drinking Water Exposure Assessment

. 1200

September 9, 2010. Environmental Fate and Effects Division. Office of Pesticide 1201

Programs. United States Environmental Protection Agency. 1202

Milesi C, Running SW, Elvidge CD, Dietz JB, Tuttle BT

, Nemani RR, 2005. Mapping and 1203

mod eling the biogeochemical cycling of turf grasses in the United States. E nvironmental 1204

Management 36(3):426

- 438. 120566

Omernik JM

, 2003. The misuse of hydrologic unit maps for extrapolation, reporting, and 1206

ecosystem management. Journal of the American Water Resources Association 39(3): 1207

563

- 573. 1208

Seaber PR, Kapinos FP

, Knapp GL

, 1987. Hydrologic Unit Maps. W. S. P. United States 1209

Geological Survey. March 2007. Available at http://pubs.usgs.gov/wsp/wsp2294/ 1210

(Accesse d June 11, 2012). 1211

USDA

, 2009. 2007 Census of Agriculture. United States. Summary and State Data . AC

- 07

- A

1212

51. Geographic and Area Series Part 51. National Agricultural Statistics Service. United 1213

States Department of Agriculture. Available at 1214

http://www.agcensus.usda.gov/Publications/2007/Full_Report/usv1.pdf (Accessed June 1215

11, 2011). 1216

USEPA

, 1999. Sets of Scientific Issues Being Considered by the Environmental Prote ction 1217

Agency Regarding: Session 1

- Office of Pesticide Programs Policy for the use of the 1218

FQPA 10x Safety Factor; Section II

- Statistical Methods for Use of Composite Data in 1219

Acute Dietary Exposure Assessment; Session III

- Use of Watershet

- derived Perce nt Crop 1220

Areas as a Refinement Tool in FQPA Drinking Water Exposure Assessments for 1221

Tolerance Reassessment . S. R. N. 99

- 03. May 25, 1999. Office of Pesticide Programs. 1222

United States Environmental Protection Agency. Available at 1223

http://www.epa.gov/scipoly/sap/meetings/1999/052599_mtg.htm (Accessed June 11, 1224

2012). 1225

USEPA

, 2002. Organophosphate Pesticides: Revised Cumulative Risk Assessment . June 10, 1226

2002. Office of Pesticide Programs. United States Environmental Protection Agency. 1227

Available at http://www.epa.gov/pesticides/cumulative/rra

- op/ (Accessed June 11, 2012). 1228

USEPA, 2010a. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered 1229

by the Environmental Protection Agency Regarding: Re

- Evaluation of Human Health 1230

Effects of Atrazine: Review of Experimental Animal and In Vitro Studies and Drinking 1231

Water Monitoring Frequency. April 26

- 29, 2010. Document number EPA

- HQ

- OPP

- 2010

- 1232

0125 1233

USEPA

, 2010b. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered 1234

by the Environmental Protection Agency Regarding: Re

- Evaluation of Human Health 1235

Effect s of Atrazine: Review of Non

- cancer Effects and Drinking Water Monitoring 1236

Frequency. September 14

- 17, 2010. Document number EPA

- HQ

- OPP

- 2010

- 0481 1237

USEPA

, 2011. FIFRA Scientific Advisory Panel: A Set of Scientific Issues Being Considered by 1238

the Environmental Protection Agency Regarding: Re

- Evaluation of Human Health 1239

Effects of Atrazine: Review of Cancer Epidemiology, Non

- cancer Experimental Animal 124067

and In vitro Studies and Drinking Water Monitoring Frequency. July 26

- 29, 2011 FIFRA 1241

Scientific Advisory Panel. D ocument number EPA

- HQ

- OPP

- 2011

- 0399 1242

USEPA

, 2012. FIFRA Scientific Advisory Panel: P roblem F ormulation for the E nvironmental 1243

F ate and E cological R isk A ssessment for A trazine. June 12

- 15, 2012 FIFRA Scientific 1244

Advisory Panel. Document available at EPA

- HQ

- OPP

- 2012

- 0230

. 1245

USGS

, 2011. National Land Cover Database 2006. Product Legend . October 27, 2011. Multi

1246

Resolution Land Characteristics Consortium (MRLC). National Land Cover Database 1247

(NLCD). Available at htt p://www.mrlc.gov/nlcd06_leg.php (Accessed June 11, 2012). 1248

Water Quality Tech Team

, 1999. Applying a Percent Crop Area to Tier 2 Surface Water Model 1249

Estimates for Pesticide Drinking Water Exposure Assessments . Environmental Fate and 1250

Effects Division. Offi ce of Pesticide Programs. United States Environmental Protection 1251

Agency. 1252

Water Quality Tech Team

, 2003. Use of Regional Percent Crop Area Factors in Refined 1253

Drinking Water Assessments . July 25, 2003. Environmental Fate and Effects Division. 1254

Office of P esticide Programs. United States Environmental Protection Agency. 1255

Wieczorek M

, 2011. USGS Evaluation of Drinking

- Water Intake Watersheds . September 8, 1256

2011. U.S. Geological Survey. 1257

1258

1259

1260

1261

126268 Appendix A 1263

QA/QC of Drinking Water PCAs for Various Crops and Crop Groups 1264

1265

(1) Objectives 1266

1267

A process was devised to verify the validity of the drinking water PCA values obtained for corn, 1268

cotton, orchards, rice, soybean, total cropland, vegetables and wheat. The process consist ed of 1269

two main components, namely : 1270

(a) Verify that c ounty NASS agricultural s tatistics data were correctly retrieved from the 1271

source, and 1272

(b) Verify that GIS processing of the source data is correct and reproducible

. 1273

1274

(2) Methodology 1275

1276

A. Validation of NASS Data 1277

1278

The following steps were executed to check that NASS data were correctly retrieved from the 1279

source : 1280

(a) “ County NASS Agricultural Statistics data ” used in deriving PCA values were entered into 1281

the attached verification spreadsheet ( Table A

- 1 ). For each crop or crop group, data 1282

included the total county acr eage in counties associated with the HUC

- 8, plus additional 1283

counties selected at random. For example, the total number of counties checked was 6 each 1284

for soybean/ orchards/vegetables, 7 each for corn/rice/wheat, and 8 each for cotton/total 1285

cropland. In ad dition, the total US acreage for these crops/crop groups was recorded; and 1286

1287

(b) Data used in deriving PCA s ( Table A

- 1 , highlighted in yellow) were compared with that 1288

obtained by the above stated QA/QC step ( Table A

- 1 , highlighted in green). 1289

1290

1291

1292

1293

129469

Table A

- 1 NASS Validation Sheet 1295

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 3070 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 218 171 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 135,502 172 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

B. Validation of GIS Data Processing 1297

1298

Using GIS data generated in the process of calculating PCAs, a stepwise verification procedure 1299

was devised to check the accuracy of the calculations ( Attachment 1 ). Results obtained are 1300

included in Table A

- 2 for total cropland and Table A

- 3 for all other crops/crop groups

. GIS

1301

Processed PCAs ( c olumns highlighted in yellow) w ere compared with th ose obtained by the 1302

QA/QC s tep ( c olumn highlighted in green). 1303

1304

Table A

- 2 Total Cropland (See Abbreviations below) 1305

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 Nelson73

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 Ruhman74

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 (m 2 ) Conversion of acres to m 2 (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

130775

Table A

- 3: Other Crops/ Crop Group (See Abbreviations below) 1308

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 Nelson76

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 (k m 2 ) From map

B Total Crop Acres QA/QC process

C Total Crop (k

m 2 ) Conversion of acres to k m 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

For this process a total of 47 drinking water watersheds were examined for the total

- agriculture 1310

PCAs

, and 38 for other crops/crop groups. All watersheds were randomly chosen and were 1311

distributed as follows: Corn (6), Cotton (3), Orchards (4), Rice (3), Soybeans (6), Vegetables 1312

(10), and Wheat (6). A map of the locations of the QA/QC drinking water intake watersheds is 1313

included in Figure A

- 1

. 131477 1315

Figure A

- 1

. Locations of the DWI watersheds examined in the QA/QC process ( n ote some of 1316

the watersheds are too small to see in this map

, and some are masked by larger watersheds; the 1317

borders for the five large watersheds (> 1,000 km2) are shown in light pink)

. 1318 DWI Watersheds

Chosen for QA/QC78

(3) Results 1319

1320

NASS data used in calculating PCAs were found to be accurate for all crops/crop groups. Data 1321

were found to be accurate even when different NASS Tables were used ( Table A

- 1 )

. 1322

Additionally, the PCA verification process produced the same results (within two decimal 1323

places) as the original processed PCAs. The exceptions are included in the Table, below: 1324

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

For reference, an example containing all processing data is included in the following directory: 1327

P: \ CWS_PCA_Data \ QA

- QC Example

. This main folder contains data in the following folders: 1328

1329

Acre pixels Data (APD) 1330

Chosen DWI watersheds (CDWIW) 1331

Exported DWI watersheds (EDWIW) 1332

Extraction Results (ER) 1333

PCA Maps

- Verification Spreadsheet (PCAM

- VS) 1334

133579 Attachment 1: Step by Step Process for PCA Verification 1336

1337

Step 1: Selection of the drinking water intakes watershed for cotton: ID 9999969

- 4 1338

(Select by attribute: "USGSWINNER" = '9999969

- 4' ) 1339

1340

1341

1342

134380

Step 2: Export data for the selected watershed into the folder: Exported DWI watersheds 1344

(EDWIW) 1345

Export Data into the folder specified above: P: \ CWS_PCA_Data \ QA

- QC Example \ Exported 1346

DWI watersheds (EDWIW) \ Export_Output.sh 1347

134881



### Answer NO 1349

135082

Step 3: Add the exported watershed data into a new map and zoom to the selected watershed 1351

( s how only the outline of the selected watershed)

. 1352

135383 135484

Step 4: Intersect the exported shapefile with the county shapefile using the intersect tool fro m 1355

the toolbox (Analysis tools/Overlay/Intersect). Note the input features chosen (export

- output and 1356

counties)

. 1357

135885

Hereunder, the resultant map (the watershed areas in various counties will appear) : 1359

136086

Step 5: Perform Extraction by Mask (Spatial Analyst Too ls/Extraction/Extract by Mask) 1361

Enter: Input raster= acrepix “cott_acrepix”; Feature mask data= exported watershed data 1362

“Export_Output; and Output raster= folder where you want the extraction output of the analysis 1363

to reside, in this case “ P: \ CWS_PCA_Data \ Q A

- QC Example \ Extraction Results 1364

(ER) \ Extract_cott3 ” 1365

1366

1367

136887

Then select “ OK ” and wait for complet ion of the extraction procedure

, which may take several 1369

hours depending on the speed of the computer (please note that a moving message will be 1370

displayed stating the progress in %

, e.g., “ Extract by Mask 60%”. Even after the value reaches 1371

100%, the extraction results may not appear instantly ; it is necessary to wait until the extraction 1372

results appear

, as shown in the following screen shot : 1373

1374

137588

Step 6: Open the attribute Table/ Open the Table options (1 st tab) 1376

1377

137889

Add field (Crop

- area/Double)/OK 1379

1380

1381

After you press OK, the results will be as shown, below. Then right click on the newly added 1382

field to the field menu and select from it “Field Calculator: 1383

138490

Enter the following in the field calculator by choosing “double click” the fields/calculation 1385

function from the lists as follows: [VALUE] * [COUNT]/20000 ; the 20,000 value is the scale 1386

factor for co tton. 1387

1388

1389

139091



### Important Note: 1391

The scale factor is different for different crop/crop group as shown in the Table below: 1392

Crop Scale Factor

Corn 4,000

Cotton 20,000

Orchards 7,000

Rice 60,000

Soybeans 10,000

Vegetables 2,40092 Crop Scale Factor

Wheat 5,000

After clicking OK you get the area calculated; then get the statistics for the calculated field as 1393

shown below: 1394

1395

1396

139793

From displayed statistics, the total Crop

- area= 202038.96125 acres (=818 km 2 ). Enter this value 1398

in the verification spreadsheet as shown below to arrive at PCA= 818/3,945= 0.207 1399

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 (k m 2 ) From map

B Total Crop Acres QA/QC process

C Total Crop (k

m 2 ) Conversion of acres to k m 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

Step 7 : Save the map as “Cotton PCA

- 4

- 9999969

- 4” with the final saved map in the PCA 1402

Maps

- Verification Spreadsheet (PCAM

- VS) folder. The map will look like the image below: 140394 1404

1405

1406

1407

1408

1409

1410

1411

141295 Appendix B 1413

Cumulative Distributions of National

- Scale DWI PCAs 1414

141596 1416

141797 141898 1419

142099 1421

1422100 1423

142410 1 1425

1426102 1427103 1428

1429104 1430105 1431

1432106 1433

1434107 1435

1436108 1437

1438109 1439

1440110 1441

1442111 1443

1444