“

Using Modern Watershed and Cropping Data as a 

estimating percentage crop treatment” 

Nathan J. Snyder Aldos C. Barefoot Joshua J. Amos Peggy S. Lee 

Exposure Modelin
Presentation Overvi

• Review of USEPA Science Policy

• “Development and Use of Percent Cropped A

Factors in Drinking Water Exposure Assessm2010

• Percent Crop Area (PCA) use in assessments

• Options for Higher Tier Assessments

• Registration Defense• Monitoring Strategies• Stewardship
• Recent update to documentation

• Matches the practices seen in rece

registration assessments coming fr

• Provides a clear procedure for Tier 

assessments that registrants can foassess how new products will be re

Use of PCA in USEPA Tie
• National PCA Adjustment Factors

• 8-Digit HUCs intersected with County level Cr

USDA Census of Agriculture) all agriculture

• PCA Adjustment Factors for Major Crops

• Same as above, but cropping specific to targe• Available for Corn, Wheat, Soybeans and Cot• Method developed to combine EECs if use is 

crops with similar or different timing

• Regional PCA Adjustment Factors

• Same methodology but identified highest PCA 

regions

PCA Values
• National value of 87%

• Crop Specific (20 to 56%)• Regional values (7 to 87%)

Regional PCA Determi
Applying PCAs in Tier 2 Ass
• Minor (not corn, soybean, wheat, cotton)

• “For minor-use crops, the SAP found that the use o

factors derived from information on 8-digit HUCs...crop acreages might be too coarse of an estimate.”

• “SAP found using PCAs...potentially bias exposure 

use crops”

• “In the interim, OPP will use the default PCA adjust

major crops that do not yet have PCA adjustment fuse crops.”

• Crop Mixtures

• “The SAP provided only minimal guidance on apply

adjustment factor for multiple crop uses in a waters

• “They recommended that the PCA adjustment facto

be based on the single watershed that had the highthe combination of the crops being modeled, rather the maximum PCA adjustment factors for the individifferent watersheds.”

Limitations to Current M
• Refinements to address limitations 

• “The size of the 8-digit HUC may not p

reasonable estimates of actual PCAs fwatersheds.....may be better represenwatersheds defined for drinking water 

• “The conversion of the county level da

watershed-based percent cropped aredistribution of the crops within a county homogeneous throughout the county a

• Updating of crop data to current NASS 

Refinements to Tier 
• Re-registration process is identifying prod

have been used safely for years, that may current assessment process

• Monitoring data is not in agreement with 

predictions

• Registrants are being asked to make sign

modifications based on the modeling outpprocedures

• No Tier 3 procedure endorsed by USEPA• Refinements to PCA procedure as a Tier 

avenue to explore that will build on existinand answer limitations to current proceduby USEPA and SAP

Need for Tier 3
• A process to combine localized knowledge of drin

supplies and labeled crops should enable the devconservative yet refined PCA values

• Use actual watersheds used for drinking water rath

HUC-8 watersheds

• Use publically available source information• NHDPlus catchments can be used to hide specific LA

providing needed watersheds to produce refined PCA

• Use spatially distributed cropping data rather than “

county scale

• Additional scenarios/weather stations to address 

(not addressed in this talk) 

• Represent distribution and timing of applications w

use exists in watersheds (not addressed in this ta

Options for Tier 3
• SDWIS identifies Water Supplies and the 

Water

• USEPA Source Water Assessment Plans 

available for many water supplies

• State programs generally are providing a

information to identify source locations

• NHDPlus provides clear standard basis fo

watersheds

• Catchments (identified by “COMID”) can be us

watersheds while hiding specific locations of i

• EPAs Office of Water programs may be a 

HUC-8 Watershed vs. Actua

Water Watersheds
• DWMA seems to be the tool that co

standardize the process

• “ABSTRACT: The Drinking Water Mapping Ap

provides a secure application EPA staff can uand maps that help manage programs under tWater Act. The DWMA makes extensive use oNational Hydrography Dataset (the NHDPlus) water surface intake locations within the NHD define source water areas relative to the intak

Surface Water Intak
DWMA
• Until a Tier 3 is adopted, regional re

interest to registrants to design focumonitoring and explore label modifiaddress concerns

• Focus efforts to where Tier 2 analys

a regional problem

• Public information and tools can be 

most states

Regional Surface Water S
MN (Region 9)

PWSID PWSName EPA_Region Geography_type State PCounty Status_id PWSType GwSw PSource Owner SizeCat5_pt

MN5160330 Temperance River State Park/Upper Cpgrd. 05 State MN Cook 1 TNCWS sw SW State_Govt 1_Very_SmallMN5160404 Judge C.R. Magney State Park 05 State MN Cook 1 TNCWS sw SW State_Govt 1_Very_SmallMN5690953 Big Lake Wilderness Lodge 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5690244 Lodge of Whispering Pines 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5160137 Adventurous Christians Covenant 05 State MN Cook 1 TNCWS sw SW Public/Private 1_Very_SmallMN1460003 Fairmont 05 State MN Martin 1 CWS sw SW Local_Govt 4_LargeMN5690950 YMCA Camp Widjiwagan 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5690204 Burntside Lodge 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5690951 Tamarack Resort 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN1690014 Ely 05 State MN St. Louis 1 CWS sw SW Local_Govt 3_MediumMN1690004 Biwabik 05 State MN St. Louis 1 CWS sw SW Local_Govt 2_SmallMN5160328 Clearwater Canoe Outfitters 05 State MN Cook 1 TNCWS sw SW Private 1_Very_SmallMN1690028 Hoyt Lakes 05 State MN St. Louis 1 CWS sw SW Local_Govt 2_SmallMN1690033 McKinley 05 State MN St. Louis 1 CWS sw SW Local_Govt 1_Very_SmallMN5160108 Bearskin Lodge 05 State MN Cook 1 TNCWS sw SW Private 1_Very_SmallMN5380074 Timber Trail Lodge LLC 05 State MN Lake 1 TNCWS sw SW Private 1_Very_SmallMN5690988 Fenske Lake Cabins 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5160042 Golden Eagle Lodge 05 State MN Cook 1 TNCWS sw SW Public/Private 1_Very_SmallMN1690007 Chisholm 05 State MN St. Louis 1 CWS sw SW Local_Govt 3_MediumMN5160062 Gunflint Lodge 05 State MN Cook 1 TNCWS sw SW Private 1_Very_SmallMN5160107 Moosehorn Lodge 05 State MN Cook 1 TNCWS sw SW Public/Private 1_Very_SmallMN5160303 Gunflint Pines Resort 05 State MN Cook 1 TNCWS sw SW Private 1_Very_SmallMN5160318 Hungry Jack Outfitters 05 State MN Cook 1 TNCWS sw SW Public/Private 1_Very_SmallMN5690229 North Star Resort 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_SmallMN5690113 Deer Horn Resort 05 State MN St. Louis 1 TNCWS sw SW Private 1_Very_Small
ND (Region 9)

PWSID PWSName EPA_Regio Geography_type State P

ND0800080 BISMARCK CITY OF 8 State ND B

ND0900336 FARGO CITY OF 8 State ND C

ND0900336 FARGO CITY OF 8 State ND C

ND1000543 LANGDON CITY OF 8 State ND C

ND1000543 LANGDON CITY OF 8 State ND C

ND1800410 GRAND FORKS CITY OF 8 State ND G

ND2800389 GARRISON CITY OF 8 State ND M
FL (Region 3)
NHDPlus “COMID” Ide
• A list of COMIDs is passed to a custom “NHDPlus 

watershed delineation tool”, which can be called using ESRI’s ArcGIS-ArcMap. 

• The tool is built over “VAACOMObjectNavigator” that is 

distributed by the NHDPlus team along with the NHDPlus dataset. It uses a COMID as input, navigates upstream of the NHDPlus network and hydrologically identifies all related watersheds that are part of the network and goes all the way till the headwater watersheds. 

• The custom tool uses an automated process of going 

through the list of COMIDs, and creates upstream watersheds for each one of them separately using the NHDPlus data.

• Each COMID may have one or more watersheds, 

depending on its location on the NHDPlus network. An intake location close to the headwater stream may have one or two upstream watersheds, where as an intake location on a river will have several upstream watersheds. If the COMID has several watersheds, they are all spatially dissolved to create a single large watershed

Watersheds - NHDP
Watersheds
Intake Locations
• USEPA Assessment utilized county 

Census of Agriculture from 1997 (omissing)

• Used “Cropped Area” for default an

“Soybean”, “Cotton”, or “Wheat” for 

• Distributed county acreage to HUC

Watersheds

• Did not utilize minor crop data • Cropland Data Layer (CDL) now se

Continental US

Landuse
NASS CDL Data
Watershed Land-use Des
PCA Calculations

• Summarize CDL for each 

Watershed

• Accuracy assessment of 

Counties

• Conservative CDL 

groupings based on County Census of Agriculture
• CDL offers a complete 

spatial view at a 56m resolution

• CDL has 130 classes, 

including some from the NLCD

• Census of Agriculture is 

summarized at a county resolution

• Includes only agricultural 

areas

• Includes >200 crop 

categories

• A comparison of the 2 

NASS sources is needed to identify groupings needed for detailed PCA calculations

CDL versus Census 

of Agriculture
CDL Crop Groupin

NLCD ‐ Barren Barley NLCD ‐ Barren 

NLCD ‐ Deciduous Forest Blueberry NLCD ‐ Deciduous Forest 

NLCD ‐ Developed/High Intensity Camelina NLCD ‐ Developed/High 

NLCD ‐ Developed/Low Intensity Canola NLCD ‐ Developed/Low NLCD ‐ Developed/Medium Intensit Celery NLCD ‐ Developed/Medium 

NLCD ‐ Developed/Open Space Citrus NLCD ‐ Developed/Open Spac

NLCD ‐ Evergreen Forest Dry Beans NLCD ‐ Evergreen Forest 

NLCD ‐ Grassland Herbaceous Flaxseed NLCD ‐ Herbaceous Wetlands 

NLCD ‐ Herbaceous Wetlands Herbs NLCD ‐ Mixed Forest NLCD ‐ Mixed Forest Lentils NLCD ‐ Open Water 

NLCD ‐ Open Water Millet NLCD ‐ Shrubland 

NLCD ‐ Shrubland Misc. Vegs.

& Fruits NLCD ‐ Woody Wetlands 

NLCD ‐ Woody Wetlands Oats Wetlands 

Wetlands Oranges Woodland 

Woodland Other Crops Corn 

Corn Other Small Grains Cotton 

Cotton Other Tree Fruits Durum Wheat 

Durum Wheat Peanuts Soybeans Soybeans Peas Spring Wheat 

Spring Wheat Potatoes Sweet Corn 

Sweet Corn Rice Winter Wheat 

Winter Wheat Rye 

Seed/Sod Grass 

Sorghum Strawberries Sugarbeets Sugarcane Sunflowers Watermelon Alfalfa Clover/Wildflowers Fallow/Idle Cropland 

NLCD ‐ Pasture/Hay Other Hays Pasture/Grass 

Major Crops Non‐Ag

Florida CDL Groupings Regio

Pasture/Hay Minor Crop

Non‐Ag Major Crops
• County review of CDL versus Census of A

• Neither dataset is perfect (omissions, bord

incorrect categorizations, under/over repodamage)

CDL versus Census of Ag

Category Census of Ag CDL CDL % of CensusNon‐Agriculture NA 9,506,731.12Total Cropland 3,796,669.01 3,673,464.21 96.8%

   ‐Major Crops 4,733.14 4,995.15 105.5%

   ‐Forage and Pasture 2,453,258.04 2,434,969.93 99.3%   ‐Minor Crops 1,338,677.83 1,233,499.13 92.1%

Florida (Counties with Drinking Water Intakes)

Category Census of Ag 

Non‐Agriculture NA 

Total Cropland 24,912,617.02 

   ‐Major Crops 13,061,637.04 

   ‐Minor and Forage/Pasture 11,850,979.98         ‐Forage and Pasture 4,626,551.06         ‐Minor Crops 7,224,428.92 

MN, ND, SD (Region 9 Coutn
• Tabular summary of All Ag, Major C

Crops – Conservative view of using identified watersheds

• Highlights many watersheds that ar

agricultural

• Major groupings could be divided fu

county and CDL comparison in smaspecific counties support 

• Specific crop composition can be u

appropriate standard scenarios and weighting

Final PCA Summa
Florida Specific PC

COMID Total Area Non‐Ag Major Crop Pasture/hay Minor Crop % Non‐Ag % 

10247733 558,771 322,044 0 133,304 103,423 58% 11003339 1,434 692 0 742 0 48% 11003367 7,976 7,261 0 705 9 91% 16715740 39,713 14,125 0 25,301 287 36% 16767080 6,446 6,345 0 5 95 98% 16809709 1,159,388 625,497 0 334,236 199,656 54% 16814857 218,996 91,520 0 90,915 36,561 42% 16838208 59,139 47,063 0 11,601 475 80% 16839124 68,210 38,213 0 27,853 2,144 56% 16878340 81,033 51,074 0 22,240 7,718 63% 16896620 235,138 191,343 0 32,423 11,372 81% 16907115 395,483 298,289 0 84,294 12,900 75% 16916882 9,256 8,278 0 961 17 89% 16921582 10,043 9,062 0 964 17 90% 

2179191 1,571 1,558 1 9 4 99% 

67182 11,575 11,538 0 15 22 100% 
Region 9 (MN, ND, SD) Spe

COMID Total Area Non‐Ag Major Crop Other Crops % Non‐Ag % Major C

14144901 614,187 116,116 365,672 132,399 19% 14415209 20,928 2,315 8,682 9,931 11% 14415927 30,888 3,282 13,515 14,091 11% 14428870 430,519 68,455 181,822 180,242 16% 14828149 3,219,902 625,848 1,269,441 1,324,614 19% 

6658333 731,514 463,029 77,423 191,063 63% 6667833 7,149,129 1,812,604 3,285,680 2,050,846 25% 6667867 7,126,189 1,805,159 3,273,340 2,047,690 25% 7038041 2,077,005 1,619,844 199,025 258,135 78% 7039505 15,701,965 5,149,688 7,005,220 3,547,057 33% 7065884 17,307,241 5,429,108 7,790,269 4,087,863 31% 7077518 18,765,799 5,702,757 8,447,763 4,615,279 30% 7094291 16,397 16,391 0 5 100% 7094301 15,452 15,447 0 5 100% 7094305 22,201 22,191 0 9 100% 7094455 811 811 0 0 100% 7094459 49,856 49,852 0 4 100% 7105265 7,246 7,239 0 6 100% 7111417 10,936 10,919 0 18 100% 7111443 1,204 1,203 0 1 100% 7111483 935 934 0 1 100% 7111509 12,689 12,689 0 1 100% 7111709 10,479 10,467 0 12 100% 7111777 7,483 7,478 0 5 100% 7115443 4,078 4,075 0 2 100% 7145072 33,484 33,389 1 94 100% 7145080 259,320 257,642 0 1,678 99% 7145088 27,265 27,189 1 75 100% 7152000 11,794 11,699 0 95 99% 7152014 211,022 209,283 0 1,740 99% 7158314 1,274 1,270 0 4 100% 7158454 14,852 14,813 0 39 100% 7158468 2,896 2,890 0 5 100% 7158574 4,650 4,609 0 40 99% 7163988 2,805,256 2,796,356 4 8,896 100% 7748114 5,066 5,050 3 13 100% 7748122 6,775,728 6,601,387 52,523 121,819 97% 9398232 15,877,315 5,182,141 7,104,099 3,591,076 33% 
• Refined PCAs are feasible as a Tier 3 assessmen

• Limitations and accuracy concerns are real, but n

• Identify vulnerable monitoring locations

• Analysis identifies range of land-use patterns which 

design monitoring programs focused on highest po

• Identify areas requiring possible stewardship activ

use patterns

• Analysis may result in areas still not resulting in a s

requiring focused use pattern adjustments

• Protect important use areas that fall outside of po

drinking water supplies 

• Focus analysis may result in important markets bei

unnecessary use pattern changes as they may fall drinking water supplies

Conclusions
Questions?