### 1 

## Appendix 3

- F: Evaluation of Soil WQI Grouping Classes as a Potential

Replacement for Individual Soil Map Units in SAM Scenarios

Because the potential number of individual soil map unit

- crop group

- weather station

combinations would run in the tens of millions, OPP explored the feasibility of grouping soil

map units into classes based on factors that have the greatest impact on pesticide loss due to

runoff and erosion, similar to approaches used by the European Union (Schneider et al, 2007). A

reduction in the number of scenarios that would need to be stored and processed would have

an impact on the amount of storage space that would be required for SAM on a national scale

and on the amount of computer processing time that would be needed for running nat ional

scale risk assessments on a routine basis for potentially hundreds of pesticide registration and

registration review assessments each year

.

This appendix describes the soil grouping approach based on USDA’s Water Quality Index

(Section 3

- F.1), initi al sensitivity analyses USEPA OPP conducted to determine the potential loss

of information by using the WQI group ings (Section 3

- F.2), and a comparison of model results

using scenarios based in individual soil map units or the soil WQI groupings (Section 3

- F.3 and

the accompanying 3

- F. A spreadsheet)

.

Th e s ensitivity analysis looked at the variance in outputs (estimated concentrations) within the

bounds of the proposed WQI ranges separately, using USEPA OPP’s Surface Water

Concentration Calculator (SWCC). O utputs used for comparisons

– runoff, mass of pesticide

lost, and pesticide concentrations

– were generated using SAM

.



## 3

- F.1 Soil Group Classes

USEPA OPP evaluated grouping individual soil map units into classes based on a USDA NRCS

Water Quality Index (W QI) for runoff from agricultural fields (Lal and McKinney, 2012). The WQI

used the following grouping classes to derive a WQI for each soil:

Runoff potential based on Hydrologic Soil Group : A, B, C, D

Slope gradient : <2%, 2

- 5%, 5

- 10%, 10

- 15%, >15%

Soil ero dibility potential based on K factor : < 0.10, 0.11

- 0.20, 0.21

- 0.32, 0.33

- 0.43, 0.44

- 0.64

Soil Organic Matter (SOM) c ontent (surface layer): <0.5%, 0.5

- 2%, 2

- 4%, 4

- 6%, 6

- 8%, >8%

T he model s use soil organic carbon (SOC) as an input. USEPA OPP c onverted SOM to SOC with

the general equation SOC = SOM/1.724.

The resulting SOC groups are: <0.29%, 0.29

- 1.16%, 1.16

- 2.32%, 2.32

- 3.48%, 3.48

- 4.64%, >4.64%

These grouping class combinations result in a pote ntial of 600 soil group classes:

4 HSG x 5 slope classes x 5 s oil erodibility classes x 6 SOM classes

### 2 This grouping will result in the following impacts :

- The curve number for the resulting soil group scenarios will not change because the

curve number and land cover classes remain unchanged.

- Variations in soil OC cont ent (impact on pesticide transport/sorption, as well as

erodibility potential, water retention properties in soil) are limited to 2% OM/1.16% OC

increments

.

- Variations in slope as a result of the slope gradient classes may be limited because soil

map unit s are split out in slope classes that are similar to the class breaks above; the

biggest change will be for steeper slopes (>15%). However, agriculture is generally

limited on such steeper slopes.

- Variations in sediment transport are limited by the range i n K factor values (Kwfactor in

SSURGO) for each class

- Other soil

- related inputs that may impact the estimated concentrations do not vary

independently of each other or of the properties listed above. Full sensitivity analyses

will need to take these corre lations into account.

o Bulk density, field capacity, and wilting point affect the water capacity of the soil.

These vary with SOM content , soil texture ( percent of sand, silt, and clay ), and

soil structure (aggregation of soil particles, forming larger pores that serve as

conduits for water ). In general, lower bulk densities and subsequent higher

water capacities are associated with higher SOM contents; higher bulk densities

and resulting lower water capacities are associated with lower SOM contents.

Sen sitivity analyses for bulk density and water capacity parameters should be

done within SOM classes, and needs to consider the correlation among the soil

properties.

o Soil erodibility (K factor) is also related to SOM content, soil texture, soil

structure, and permeability (which is related to SOM content, texture, and

structure). Sensitivity analyses for K factor classes should be done within SOM

classes and, possibly, within Hydrologic Soil Groups

.

o Slope gradient and slope length are used to derive the USL E LS (slope

- length)

factor used to estimate soil erosion and resultant sediment transport. Slope

length is estimated for soil map units from the average slope length

(SLOPELENUS in SSURGO) for the soil map unit. This average length and average

slope for th e map unit are used to estimate the USLELS factor used in the model.

Sensitivity analyses for USLELS values should be done within slope classes.



## 3

- F.2 Approaches to Evaluating Soil Groupings

USEPA OPP considered t hree approaches to evaluating the potential impacts of the aggregation

methods on SAM

. Section 3

- F.3 covers the sensitivity analysis; section 3

- F.4 covers the

comparison of model results. No work has been done on the PCA.

### 3 (1) Sensitivity analysis to determine the major driving inputs in the m odel. This would

determine which inputs have the greatest impact on estimated concentrations and at

what range(s). It would also determine which parameters should be constrained in

classes (such as those listed above) to reduce the variability in exposure estimates.

a. Many model parameters are related to each other so we need to account for

autocorrelations. We can’t vary values independently.

b. One option may be to use the actual combinations found in the scenarios to

bracket the high and low ends of the inpu t ranges.

c. Relative sensitivities to model inputs will likely vary with pesticide properties

(low vs high Koc, low vs high half

- life values). Recommend simulating pesticides

using a combination of pesticide properties.

i. Koc: 100, 500, 1000, 10,000

ii. Aerobic so il/aquatic half

- life, day: 7, 30, 90, 365

(2) Compar ison of model results from scenarios developed using all of the SSURGO Map

Units to those from scenarios using the soil groupings. These comparisons would

particularly focus on changes in spatial and temporal patterns in:

a. Runoff flux by watershed, which would indicate potential impacts from the

runoff routines

b. Pesticide mass flux by runoff by watershed, which would indicate potential

impacts from the pesticide fate/transport routines

c. Pesticide concentrations, which integrates the potential impacts

(3) Principle component analysis (PCA) to identify the major factors affecting estimated

concentrations.

a. Ideally, w e compare outputs to model inputs. However, with SAM, the model

inputs are compiled scenario

- by

- scenari o, while the outputs are compiled by

watershed and reflect the area

- weighting of individual scenarios as they occur in

the watersheds.

b. To do a comparison for sensitivity analysis, we would need an intermediary

output (for instance, edge

- of

- field flux value s by scenarios). An alternate

approach

– looking at area

- weighted input parameters at the watershed level

–

would also require extra computation but would likely average out individual

input differences.



## 3

- F.3 Sensitivity Analys i s

P reliminary scoping anal yses used the IL corn scenario and test chemicals with a half

- life of 30

days and Koc values of 100, 500, 1000, and 10,000. Scenarios were modified using the Surface

Water Concentration Calculator (SWCC) and run in batch mode for each test chemical. The se

scoping exercises are intended to determine the extent to which we need to consider further

sensitivity analyses. Outputs for the index reservoir were compared in this analysis.

### 4 3

- F.3.1 Soil Organic Matter Groupings

Soil organic matter (SOM) classes vary in 2% increments. For the revised soil grouping classes,

input values are calculated as the average of all of the values within each soil grouping class.

The preliminary analysis looked at outputs for SOM content at t he break points and at the

midpoint between breaks . F or the model runs, SOM was converted to soil organic carbon (SOC)

by the equation

SOC = SOM/1.724

SOM 0.5 1 2 3 4 5 6 8 14

SOC 0.29 0.58 1.16 1.74 2.32 2.90 3.48 4.64 8.0

For each test chemical, changes in 1

- in

- 10

- year concentrations (peak, 4

- , 21

- , 60

- , 90

- , and 365

-

day averages in water and peak and 21

- day averages in benthic pore water), as well as total

mass moving to the water body from runoff and erosion are compared between 1% increments

in SOM content. Peak values for each year we re compared; Figures 1

- 4 show the yearly

variations in concentrations across SOM contents of 0.5 to 14%.

T he estimated concentrations (maximum yearly peaks as well as various time

- averaged

maximums) in water generally decreased with increasing SOM content. Differences in

estimated concentrations in water between the lowest and highest simulated SOM content

averaged 3

- 4X (with a maximum of 20X) for the test chemical with a Koc of 100. Contras ts were

less pronounced as the Koc increased. Differences between the lowest and highest simulated

SOM content averaged 2X (with a maximum of 4X) for the test chemical with a Koc of 500 , and

1.5X (with maximum less than 3X) for Koc values of 1,000 and 10,0 00 (Figures 1

- 4)

.

The upper graph in Figures 1

- 4 show the range in estimated yearly peak concentrations (in

ug/L) across 30 years for the 4 test chemicals. The largest ratios between the highest and

lowest estimated peak concentrations occurred with the l owest estimated concentrations.

Most of the differences in estimated concentrations occurred in low SOM content soils. The

differences between the high and low end of the range within each SOM class averaged less

than 2X for the lowest Koc test chemical in the 0.5

- 2% SOM class, and were essentially the

samd for SOM classes of 2

- 4% or greater. As the Koc increased, the differences in estimated

yearly peaks decreased across all SOM classes (Figures 1

- 4).

The lower graph in Figures 1

- 4 plot the ratio between the estimated yearly peak concentration

at the upper end of the SOM range in each class to the estimated peak at the lower end of the

SOM range in that class. For the 0.5

- 2% SOM class, differences in estimated peak

concentrations across that range varied b y less than 6

- fold (for the test chemical with a Koc of

100 ) and averaged less than a factor of 2 greater. For the higher SOM classes (4

- 6% SOM and

greater), little difference yearly peak concentrations was seen across the SOM range. As the

test chemical Koc increased, differences across the SOM class ranges decreased.

### 5 The following trends are evident in a comparison of total pesticide mass lost by runoff and

erosion (Figure 5):

- The total mass of pesticide lost in runoff decreased as the SOM increased for test

chemicals with a Koc of 500 or greater. For the test chemical with a Koc of 100, the total

mass lost by runoff increased as SOM increased from 0.5 to 6% and then declined at

higher SOM contents. This different trend likely reflects the impact of sorpt ion on

competing leaching and runoff losses for low Koc chemicals.

- The total mass of pesticide lost in erosion/sediment increased as the SOM increased.

The total mass of pesticide lost by erosion also increased as the Koc increased. These

trends suggest th at more pesticide mass is held onto soil particles as both Koc and SOM

increase, with less mass lost to leaching and runoff.

- The total combined mass of pesticide lost by runoff and erosion increased as the SOM

and Koc increased. That increase is due to th e increase in pesticide mass on sediment.

This trend may in part reflect the greater retention of pesticide in the surface as

sorption increases. However, because the erosion/sediment inputs were not changed in

the scenarios as SOM increased, this simulati on does not reflect the impact of increasing

SOM on reducing the overall erodibility of the soil.

Based on these results, USEPA OPP concluded :

- V arying the SOM content within the 2% increments in the SOM classes does not have an

appreciable impact on estim ated peak or time

- averaged concentrations for SOM

contents of 2% or greater.

- For SOM contents less than 2%, the variability within the SOM class (0.5

- 2%) could be

reduced by breaking them down into 0.5% increments (<0.5, 0.5

- 1.0, 1.0

- 1.5, 1.5

- 2%).

This wo uld result in 3 additional SOM classes if we choose to go this route.

- Further sensitivity analyses, particularly for soil erodibility parameters, need to account

for the correlations between the various input parameters (particularly K factor and C

factor) and SOM. One approach may be to run sensitivity analyses on inputs within the

SOM groups.

### 6 Figure 1

- (a) Estimated yearly peak concentrations over 30 years for a range of soil organic matter

(SOM) content for IL corn scenario for a test chemical with Koc 100 and t1/2 30 days; (b) ratio in yearly

peaks between the low and high end of SOM increments.

### 7 Figure 2

- (a) Estimated yearly peak concentrations over 30 years for a range of soil organic ma tter

(SOM) content for IL corn scenario for a test chemical with Koc 5 00 and t1/2 30 days; (b) ratio in yearly

peaks between the low and high end of SOM increments.

### 8 Figure 3

- (a) Estimated yearly peak concentrations over 30 years for a range of soil organic matter

(SOM) content for IL corn scenario for a test chemical with Koc 1,0 00 and t1/2 30 days; (b) ratio in

yearly peaks between the low and high end of SOM increments.

### 9 Figure 4

- (a) Estimated yearly peak concentrations over 30 years for a range of soil organic matter

(SOM) content for IL corn scenario for a test chemical with Koc 1 0 ,000 and t1/2 30 days; (b) ratio in

yearly peaks between the low and high end of SOM increments.

### 10 Figure 5

- Cumulative Mass of Pesticide Lost by Runoff and Erosion from 30 Years of Simulations for

Test Chemicals with a Half

- life of 30 Days and Koc of (a) 100, (b) 500, (c) 1,000, and (d) 10,000.



### 3

- F.3.2 Soil Bulk Density and Water C apacity Variability within Organic Matter Groupings

Bulk density, field capacity, and wilting point affect the water capacity of the soil. These vary

with SOM content, soil texture (percent of sand, silt, and clay), and soil structure (aggregation

of soil particles, forming larger pores that serve as conduits for water). In general, lower bulk

densities and subsequent higher water capacities are associated with higher SOM contents;

higher bulk densities and resulting lower water capacities are associated wi th lower SOM

contents. For the preliminary analyses , we selected highest and lowest bulk density (BD) values

in each SOM group class, and water holding properties, characterized by lowest and highest

values for field capacity (FC) and accompanying wilting point (WP) that resulted in the lowest

and highest water holding capacity, within each SOM classes

. For this initial scoping analysis , we

selected bulk density, field capacity, and wilting point values for low (0.5%), medium (3%), and

high (6%) SOM soils ( see Table 1 below). We used the test chemical with Koc of 500 and t1/2 of

30 days.

### 11 Table 1

- Inputs for SOM, Bulk Density (BD), Field Capacity (FC), and Wilting Point (WP) for Surface and

Subsurface Layers for Preliminary Analyses

Soil Scenario OM OC BD1 FC1 WP1 BD2 FC2 WP2

Lo w S OM, Hi gh B ulk D ensity , Lo w

W ater C apacity 0.50 0.29 1.95 0.03 0.02 1.89 0.02 0.01

Low SOM, Low Bulk Density, High

Water Capacity 0.50 0.29 1.15 0.43 0.23 1.15 0.35 0.18

Medium SOM, High Bulk Density, Low

Water Capacity 3.00 1.74 1.80 0.02 0.01 1.90 0.02 0.01

Medium SOM, Low Bulk Density, High

Water Capacity 3.00 1.74 1.10 0.38 0.11 1.10 0.39 0.15

High SOM, High Bulk Density, Low

Water Capacity 6.00 3.48 1.60 0.05 0.04 1.58 0.02 0.01

High SOM, Low Bulk Density, High

Water Capacity 6.00 3.48 1.00 0.38 0.19 1.00 0.32 0.20



### While the evaluation focused on a limited scope (only 1 test chemical and 3 SOM contents),

results of the comparisons suggest that differences in yearly estimated peak and time

- averaged

exposures for the three representative SOM soils indicate that differe nces resulting from

variations in bulk density and water capacity input values are likely to have minimal effect on

estimated concentrations (Figure 6 ).

Based on these results, USEPA OPP concluded:

- Model runs using the highest and lowest soil bulk density values and corresponding

lowest and highest water capacity (represented by FC and WP) showed minimal impacts

of these inputs on yearly maximum (peak) estimated concentrations for soils with

varying SOM content.

- No additional adjustments are needed for soil grouping classes to account for variability

in bulk density, field capacity, or wilting point within the SOM groups.

### 12 Figure 6

- Comparisons Of Estimated Yearly Concentrations Over 30 Years For Varying Bulk Density

And Water Capacity Inputs For Soils With A SOM Content Of 0.5% (Top), 3% (Middle), And 6%

(Bottom).

### 13 3

- F.3.3 Soil Erodibility (K Factor) Groupings

The K factor is an indicator of the inherent erodibility of the soil. While the alpha test version of

SAM is not accounting for pesticide transport on eroded sediment, this factor will be important

once that routine is included. Although it is a unitless fraction, the K factor actually consists of

11 distinct classes ( 0.02

, 0.05

, 0.10

, 0.17

, 0.24

, 0.28

, 0.32

, 0.43

, 0.49

, 0.55

, 0.64 ) . The K factor is

derived from SOM content, soil texture (particularly, percent of sand and silt) , soil structure,

and permeability (which is related to SOM content, texture, and structure). While s ensitivity

analyses for K factor classes should be done within SOM classes and, possibly, Hydrologic Soil

Groups , this preliminary analysis compared estimated concentrations for each K factor value

us ing the IL Corn standard scenario

.

Comparisons using two text chemicals

– one with a Koc of 500 and the other with a Koc of

10,000

– looked at yearly peak concentrations in water and benthic compartments (Figures 7

and 8 show peak water concentrations; Fig ures 9 and 10 show peak benthic concentrations ).

The comparisons looked at changes in estimated peak concentrations as the K factor value

changed and differences in concentrations at the high and low end of the K factor class breaks.

Estimated yearly peak concentrations in water and in benthic (pore water) increased as the K

factor value increased, largely due to increases in pesticide mass carried in erosion/sediment

loss from the field to the water body.

For the test chemical with a Koc of 500 (considered to be mobile, with transport primarily

dissolved in runoff water), the estimated concentrations in water increased by roughly 4

- 7%

between the low and high end of the K factor breaks. Estimated concentrations in benthic

sediment (pore water) increased by roughly 10

- 25% within the K factor breaks.

For the less mobile test chemical with a Koc of 10,000, estimated peak concentrations were

lower. Differences in estimated concentrations between the low and high end of the K factor

breaks were greater for the h igh Koc (10,000) test chemical than for the low Koc (500) chemical.

However, the estimated concentrations were within a factor of 3 of each other for the lowest K

factor class ( < 0.10) and within a factor of 1.5 for the remaining K

- factor classes.

### 14 Figure 7

- (a) Estimated yearly peak water concentrations over 30 years for a range of K factor values

for IL corn scenario for a test chemical with Koc 5 00 and t1/2 30 days; (b) ratio in yearly peaks

between the hig h and low end of K fa ctor increments

.

### 15 Figure 8

- (a) Estimated yearly peak water concentrations over 30 years for a range of K factor values

for IL corn scenario for a test chemical with Koc 10,0 00 and t1/2 30 days; (b) ratio in yearly peaks

between the high and low end of K factor increments.

### 16 Figure 9

- (a) Estimated yearly peak benthic concentrations over 30 years for a range of K factor values

for IL corn scenario for a test chemical with Koc 500 and t1/2 30 days; (b) rat io in yearly peaks

between the high and low end of K factor increments.

### 17 Figure 10

- (a) Estimated yearly peak benthic concentrations over 30 years for a range of K factor

values for IL corn scenario for a test chemical with Koc 1 0,0 00 and t1/2 30 days; (b) ratio in yearly

peaks between the high and low end of K factor increments.

### 18 

## 3

- F.3 Comparison of Outputs Using Individual Soil Map Units and Soils Grouped by

WQI Classes

USEPA OPP generated scenarios based on individual soil map units (individual MU scenarios)

and on the resulting aggregation of soils into the water quality criteria (aggregated soil

scenarios) for the test basins used for this evaluation (see Appendix 2.B). Test evaluations

covered 15 years of weather data (20 00

- 2014).

Table 2 lists the test basins used for this analysis.

Table 2 : Test basin monitoring sites used for evaluating results based on individual soil map unit

scenarios or aggregated soil group scenarios.



### Monitoring

Site NHD+

C atchment

ID Monitoring

Site NHD+

Catchment

ID Monitoring

Site NHD+

Catchment

ID

MO

- 01 4989415 OH

- 04 3483775 IN

- 10 eco 18459870

MO

- 04a 4989385 OH

- 10 river 5214753 IN

- 04 river 18454511

MO

- 04b 4989739 OH

- 11

reservoir 5213585 IN

- 06 river 18450057

MO

- 02 504238 0 IN

- 08 river 18445664

MO

- 05 5042400 IN

- 13 river 18451035

MO

- 05b 5039952

MO

- 08 2508563

Detailed distributions and comparisons can be found in Appendices 3

- F(1), 3

- F(2), and 3

- F(3).

3

- F.3.1 Comparisons

USEPA OPP generated scenarios based on individual soil map units (individual MU scenarios)

and on the resulting aggregation of soils into the water quality criteria (aggregated soil

scenarios) for the test basins used for this evaluation. Test evaluations covered 15 years of

weather da ta (2000

- 2014).

OPP EFED examined the annual total runoff volume, mass runoff loss, and maximum annual

concentrations over the 15 years of simulations in each test basin (Tables 3

, 4 , and 5 ).

For all seven test basins in MO, the total annual runoff was within < 1% and the total annual

pesticide mass lost was within < 2%. Estimated annual maximum concentrations were within 5%

of each other in 4 of the 7 basins and within 10% in 6 of the 7 test bas ins (Table 3 ). The MO

- 02

site showed the greatest difference in estimated maximum concentrations, with estimates

### 19 generated using scenarios based on aggregated soil groupings more than 10% greater than

estimates from individual map units in 3 of 15 model ye ars.

Table 3 : Comparison of Annual Total Runoff, Total Mass Loss, and Maximum Concentrations for

Scenarios based on Individual Soil Map Units vs Scenarios based on Soils Grouped by WQI Categories

for MO Test Basins.

Site Year Runo ff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

MO

- 01

4989415 2000 2.46E+06 2.44E+06 0.5% 63.4 63.3 0.1% 106.0 104.0 1.9%

2001 7.28E+06 7.24E+06 0.5% 127.8 127.4 0.3% 82.8 82.7 0.1%

2002 4.56E+06 4.54E+06 0.3% 65.6 65.9

- 0.4% 75.7 76.8

- 1.5%

2003 6.97E+06 6.94E+06 0.4% 71.6 70.5 1.6% 115.0 118.0

- 2.6%

2004 5.31E+06 5.29E+06 0.3% 66.4 66.2 0.3% 100.0 101.0

- 1.0%

2005 2.77E+06 2.77E+06 0.0% 77.1 77.2

- 0.2% 151.0 155.0

- 2.6%

2006 3.40E+06 3.39E+06 0.2% 54.2 54.4

- 0.3% 86.1 86.4

- 0.3%

2007 3.75E+06 3.74E+06 0.2% 45.0 44.9 0.4% 102.0 103.0

- 1.0%

2008 1.11E+07 1.11E+07 0.3% 156.1 155.0 0.7% 82.3 83.3

- 1.2%

2009 8.45E+06 8.44E+06 0.1% 89.2 89.3

- 0.1% 93.1 93.9

- 0.9%

2010 9.76E+06 9.74E+06 0.3% 75.2 74.6 0.9% 45.0 45.4

- 0.9%

2011 5.56E+06 5.55E+06 0.3% 97.5 97.3 0.2% 72.0 72.8

- 1.1%

2012 3.77E+06 3.77E+06 0.1% 62.3 61.4 1.5% 180.0 180.0 0.0%

2013 7.26E+06 7.24E+06 0.3% 94.6 94.4 0.2% 99.3 101.0

- 1.7%

2014 7.40E+06 7.34E+06 0.8% 78.5 78.0 0.6% 47.2 47.7

- 1.1%

MO

- 04a

4989385 2000 1.66E+06 1.67E+06

- 0.1% 38.9 39.0

- 0.2% 95.4 93.8 1.7%

2001 4.91E+06 4.93E+06

- 0.3% 75.3 75.2 0.0% 75.3 68.2 9.4%

2002 2.80E+06 2.81E+06

- 0.3% 27.6 27.6 0.1% 66.3 71.8

- 8.3%

2003 4.75E+06 4.76E+06

- 0.1% 71.8 70.4 2.0% 145.0 146.0

- 0.7%

2004 3.96E+06 3.98E+06

- 0.4% 44.1 44.0 0.1% 96.0 98.5

- 2.6%

2005 1.94E+06 1.94E+06

- 0.2% 48.3 48.3

- 0.1% 97.0 94.4 2.7%

2006 2.61E+06 2.62E+06

- 0.5% 29.2 29.2 0.1% 48.6 50.1

- 3.1%

2007 2.28E+06 2.29E+06

- 0.4% 34.7 34.4 0.7% 157.0 159.0

- 1.3%

2008 7.75E+06 7.80E+06

- 0.6% 100.9 100.4 0.4% 70.1 70.6

- 0.7%

2009 6.03E+06 6.08E+06

- 0.8% 56.5 56.7

- 0.4% 73.3 73.9

- 0.8%

2010 6.65E+06 6.69E+06

- 0.6% 33.6 33.6

- 0.1% 29.3 29.1 0.7%

2011 3.90E+06 3.93E+06

- 0.6% 102.9 103.4

- 0.5% 84.0 85.9

- 2.3%

2012 2.57E+06 2.55E+06 0.7% 39.9 39.1 2.0% 134.0 137.0

- 2.2%

2013 5.20E+06 5.23E+06

- 0.6% 55.2 55.6

- 0.7% 83.5 82.7 1.0%

2014 5.00E+06 4.97E+06 0.6% 33.8 33.7 0.5% 28.3 28.1 0.7%

MO

- 04b

4989739 2000 6.03E+07 5.97E+07 1.0% 931.1 929.5 0.2% 46.5 46.2 0.6%

2001 1.98E+08 1.96E+08 1.0% 1613.1 1604.9 0.5% 25.4 25.7

- 1.2%

2002 1.40E+08 1.39E+08 0.6% 966.1 967.9

- 0.2% 45.3 45.3 0.0%

2003 1.72E+08 1.70E+08 0.7% 907.1 894.4 1.4% 49.9 50.3

- 0.8%

### 20 Site Year Runo ff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

2004 1.40E+08 1.39E+08 0.6% 845.2 841.7 0.4% 44.3 45.1

- 1.8%

2005 7.93E+07 7.88E+07 0.7% 1031.4 1027.3 0.4% 47.2 47.7

- 1.1%

2006 8.20E+07 8.14E+07 0.7% 720.7 716.7 0.6% 39.7 39.4 0.8%

2007 1.34E+08 1.33E+08 0.7% 698.1 696.5 0.2% 37.7 38.0

- 0.8%

2008 3.56E+08 3.53E+08 0.7% 1952.6 1938.0 0.7% 32.2 32.8

- 1.9%

2009 2.41E+08 2.40E+08 0.4% 1184.9 1186.0

- 0.1% 35.9 36.1

- 0.6%

2010 2.97E+08 2.94E+08 0.8% 1100.8 1097.6 0.3% 22.0 22.4

- 1.8%

2011 1.41E+08 1.40E+08 0.9% 1181.7 1181.4 0.0% 27.0 27.2

- 0.7%

2012 9.96E+07 9.93E+07 0.3% 862.1 852.8 1.1% 96.8 95.6 1.2%

2013 2.04E+08 2.03E+08 0.7% 1269.9 1270.4 0.0% 36.8 39.0

- 6.0%

2014 2.54E+08 2.52E+08 1.0% 1059.2 1052.1 0.7% 21.7 21.8

- 0.5%

MO

- 02

5042358 2000 1.30E+07 1.30E+07 0.3% 164.4 167.0

- 1.6% 112.0 113.0

- 0.9%

2001 1.46E+07 1.46E+07 0.1% 128.1 126.9 0.9% 64.1 94.3

- 47%

2002 1.34E+07 1.35E+07

- 0.4% 194.5 186.1 4.3% 118.0 120.0

- 1.7%

2003 1.42E+07 1.42E+07

- 0.3% 106.9 107.3

- 0.4% 129.0 127.0 1.6%

2004 1.87E+07 1.87E+07

- 0.2% 89.6 89.9

- 0.4% 81.6 84.2

- 3.2%

2005 9.27E+06 9.29E+06

- 0.2% 118.7 117.1 1.4% 66.7 65.9 1.2%

2006 1.35E+07 1.35E+07 0.3% 256.6 251.4 2.0% 141.0 144.0

- 2.1%

2007 7.48E+06 7.48E+06 0.0% 113.6 116.9

- 2.9% 110.0 108.0 1.8%

2008 3.36E+07 3.37E+07

- 0.4% 130.4 136.4

- 4.7% 62.7 69.4

- 1 1 %

2009 2.07E+07 2.08E+07

- 0.2% 128.0 122.1 4.6% 65.9 65.9 0.0%

2010 2.40E+07 2.40E+07 0.0% 154.5 153.4 0.7% 68.2 72.0

- 5.6%

2011 9.86E+06 9.89E+06

- 0.4% 113.4 116.0

- 2.3% 78.3 78.3 0.0%

2012 1.17E+07 1.17E+07

- 0.1% 164.0 160.9 1.9% 177.0 182.0

- 2.8%

2013 1.45E+07 1.46E+07

- 0.4% 110.5 109.1 1.2% 64.0 75.7

- 18%

2014 1.56E+07 1.56E+07 0.3% 73.7 70.8 3.9% 69.8 68.5 1.9%

MO

- 05

5042400 2000 1.38E+07 1.37E+07 0.2% 242.6 239.4 1.3% 87.8 87.1 0.8%

2001 1.54E+07 1.54E+07 0.1% 210.1 208.0 1.0% 60.8 60.8 0.0%

2002 1.39E+07 1.40E+07

- 0.6% 298.5 297.1 0.5% 125.0 127.0

- 1.6%

2003 1.35E+07 1.37E+07

- 0.9% 145.2 144.6 0.4% 89.8 91.3

- 1.7%

2004 1.93E+07 1.94E+07

- 0.3% 125.2 124.3 0.7% 85.2 87.2

- 2.3%

2005 1.05E+07 1.05E+07

- 0.4% 188.7 185.3 1.8% 103.0 102.0 1.0%

2006 1.36E+07 1.36E+07 0.2% 391.4 385.5 1.5% 104.0 105.0

- 1.0%

2007 7.66E+06 7.67E+06

- 0.2% 154.4 149.9 2.9% 120.0 116.0 3.3%

2008 3.31E+07 3.34E+07

- 0.7% 185.3 185.8

- 0.3% 71.4 71.7

- 0.4%

2009 2.21E+07 2.22E+07

- 0.4% 213.0 212.8 0.1% 85.6 84.9 0.8%

2010 2.44E+07 2.45E+07

- 0.3% 234.5 232.4 0.9% 84.1 84.4

- 0.4%

2011 1.03E+07 1.04E+07

- 0.7% 156.2 155.8 0.3% 84.8 82.0 3.3%

2012 1.27E+07 1.27E+07 0.0% 252.9 249.5 1.3% 190.0 193.0

- 1.6%

2013 1.54E+07 1.55E+07

- 0.8% 181.4 181.3 0.1% 64.5 64.5 0.0%

2014 1.54E+07 1.54E+07 0.0% 114.6 113.1 1.3% 84.8 84.6 0.2%

### 21 Site Year Runo ff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

MO

- 05b

5039952 2000 8.18E+07 8.15E+07 0.3% 1254.8 1238.6 1.3% 74.6 73.9 0.9%

2001 9.15E+07 9.15E+07 0.1% 1023.4 1012.7 1.1% 49.6 49.3 0.6%

2002 8.56E+07 8.59E+07

- 0.4% 1410.8 1408.4 0.2% 73.5 73.3 0.3%

2003 9.55E+07 9.59E+07

- 0.5% 762.9 758.0 0.6% 67.6 67.2 0.6%

2004 1.19E+08 1.19E+08

- 0.2% 700.8 699.8 0.1% 60.0 59.9 0.2%

2005 5.67E+07 5.68E+07

- 0.2% 934.0 916.4 1.9% 80.4 79.6 1.0%

2006 8.82E+07 8.80E+07 0.2% 1878.4 1847.0 1.7% 68.4 68.3 0.1%

2007 4.69E+07 4.69E+07 0.0% 813.8 790.1 2.9% 76.8 75.5 1.7%

2008 2.19E+08 2.20E+08

- 0.5% 1056.5 1058.9

- 0.2% 62.5 62.4 0.2%

2009 1.36E+08 1.36E+08

- 0.3% 1116.1 1114.6 0.1% 70.7 69.9 1.1%

2010 1.54E+08 1.54E+08

- 0.1% 1144.2 1135.4 0.8% 64.2 64.1 0.2%

2011 6.26E+07 6.29E+07

- 0.6% 793.2 790.5 0.3% 55.4 53.5 3.4%

2012 6.98E+07 6.96E+07 0.2% 1212.2 1196.6 1.3% 139.0 139.0 0.0%

2013 9.41E+07 9.47E+07

- 0.6% 926.2 926.6 0.0% 53.7 53.8

- 0.2%

2014 1.02E+08 1.01E+08 0.2% 499.9 494.4 1.1% 55.0 54.3 1.3%

MO

- 08

2508563 2000 1.86E+07 1.86E+07

- 0.2% 239.0 241.5

- 1.1% 82.4 81.2 1.5%

2001 2.18E+07 2.18E+07

- 0.1% 327.2 324.7 0.8% 63.9 63.2 1.1%

2002 1.42E+07 1.43E+07

- 0.3% 263.4 264.5

- 0.4% 96.4 94.4 2.1%

2003 1.49E+07 1.49E+07

- 0.2% 176.1 175.4 0.4% 83.5 82.6 1.1%

2004 2.27E+07 2.28E+07

- 0.4% 158.6 158.4 0.1% 52.9 52.6 0.6%

2005 9.71E+06 9.72E+06

- 0.1% 100.3 97.4 2.9% 107.0 105.0 1.9%

2006 1.69E+07 1.68E+07 0.3% 409.7 405.6 1.0% 75.2 75.0 0.3%

2007 7.52E+06 7.53E+06

- 0.1% 153.9 151.6 1.5% 76.2 75.6 0.8%

2008 5.02E+07 5.04E+07

- 0.4% 284.4 287.0

- 0.9% 64.0 65.0

- 1.6%

2009 3.44E+07 3.45E+07

- 0.3% 327.1 328.8

- 0.5% 62.8 62.8 0.0%

2010 3.54E+07 3.55E+07

- 0.2% 244.9 243.0 0.8% 72.4 70.5 2.6%

2011 1.85E+07 1.86E+07

- 0.2% 237.5 237.9

- 0.1% 67.5 66.6 1.3%

2012 1.48E+07 1.47E+07 0.6% 285.8 281.8 1.4% 183.0 179.0 2.2%

2013 2.62E+07 2.63E+07

- 0.5% 302.0 304.3

- 0.7% 80.3 82.4

- 2.6%

2014 2.10E+07 2.10E+07

- 0.1% 147.2 148.0

- 0.6% 56.8 55.4 2.5%



### For the five test basins in IN, the total annual runoff was within < 1% and the total annual

pesticide mass lost was within < 2%. Estimated annual maximum concentrations were within 5%

of each other in 4 of the 5 basins and within 10% in all 5 test basins (Table 4 ).

### 22 Table 4 : Comparison of Annual Total Runoff, Total Mass Loss, and Maximum Concentrations f or

Scenarios based on Individual Soil Map Units vs Scenarios based on Soils Grouped by WQI Categories

for IN Test Basins.

Site Year Runoff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

IN

- 10

eco

18459870 2000 1.08E+07 1.08E+07 0.2% 161.6 161.5 0.1% 59.4 60.5

- 1.9%

2001 1.13E+07 1.13E+07 0.8% 128.3 126.2 1.7% 73.8 74.6

- 1.1%

2002 1.12E+07 1.11E+07 0.4% 130.1 131.7

- 1.3% 70.0 71.3

- 1.9%

2003 1.68E+07 1.68E+07 0.4% 298.4 298.7

- 0.1% 66.8 67.7

- 1.3%

2004 9.94E+06 9.91E+06 0.3% 201.2 202.9

- 0.8% 61.4 62.1

- 1.1%

2005 1.56E+07 1.55E+07 0.5% 189.7 188.2 0.8% 94.6 97.7

- 3.3%

2006 1.55E+07 1.54E+07 0.2% 113.7 113.3 0.3% 58.9 59.7

- 1.4%

2007 1.18E+07 1.17E+07 0.7% 101.4 100.0 1.4% 93.2 94.7

- 1.6%

2008 1.78E+07 1.77E+07 0.5% 232.9 233.6

- 0.3% 55.3 56.8

- 2.7%

2009 1.13E+07 1.13E+07 0.3% 152.7 152.7 0.1% 57.6 58.7

- 1.9%

2010 1.09E+07 1.09E+07 0.5% 168.6 167.7 0.5% 42.7 43.5

- 1.9%

2011 1.97E+07 1.96E+07 0.7% 123.3 122.6 0.6% 74.5 76.0

- 2.0%

2012 8.41E+06 8.39E+06 0.2% 83.8 83.2 0.8% 155.0 159.0

- 2.6%

2013 1.29E+07 1.28E+07 0.6% 66.5 65.2 1.9% 92.9 93.7

- 0.9%

2014 7.09E+06 7.03E+06 0.8% 56.4 56.5

- 0.2% 49.6 51.0

- 2.8%

IN

- 04

river

18454511 2000 5.20E+07 5.18E+07 0.5% 678.1 679.9

- 0.3% 71.4 70.5 1.3%

2001 5.60E+07 5.57E+07 0.6% 558.4 562.8

- 0.8% 60.1 59.7 0.7%

2002 4.98E+07 4.94E+07 0.8% 454.0 453.3 0.2% 46.6 46.5 0.2%

2003 4.24E+07 4.20E+07 0.8% 376.0 376.8

- 0.2% 64.4 64.2 0.3%

2004 4.76E+07 4.73E+07 0.7% 518.1 518.5

- 0.1% 59.2 58.5 1.2%

2005 4.81E+07 4.78E+07 0.5% 434.1 440.2

- 1.4% 76.0 75.9 0.1%

2006 6.33E+07 6.30E+07 0.5% 427.7 429.0

- 0.3% 63.8 63.0 1.3%

2007 3.91E+07 3.89E+07 0.6% 480.4 492.0

- 2.4% 58.3 59.8

- 2.6%

2008 8.06E+07 8.00E+07 0.8% 824.8 829.8

- 0.6% 59.6 59.0 1.0%

2009 5.71E+07 5.68E+07 0.5% 623.4 623.5 0.0% 67.2 66.1 1.6%

2010 4.36E+07 4.32E+07 0.7% 537.3 537.5 0.0% 54.1 53.5 1.1%

2011 7.49E+07 7.42E+07 0.9% 585.4 586.6

- 0.2% 72.0 71.2 1.1%

2012 3.40E+07 3.38E+07 0.4% 295.6 302.0

- 2.1% 161.0 154.0 4.3%

2013 3.75E+07 3.72E+07 0.7% 217.9 218.9

- 0.5% 72.5 71.3 1.7%

2014 2.35E+07 2.33E+07 0.9% 123.3 124.7

- 1.1% 41.1 40.9 0.5%

IN

- 06

river

18450057 2000 6.47E+07 6.43E+07 0.6% 606.0 610.0

- 0.6% 70.4 67.2 4.5%

2001 6.16E+07 6.13E+07 0.5% 328.0 333.1

- 1.6% 61.3 62.0

- 1.1%

2002 6.17E+07 6.14E+07 0.4% 502.6 502.4 0.1% 49.4 48.5 1.8%

2003 4.63E+07 4.60E+07 0.5% 345.1 354.3

- 2.7% 68.3 67.1 1.8%

2004 5.60E+07 5.57E+07 0.5% 543.8 550.8

- 1.3% 62.5 60.2 3.7%

2005 5.14E+07 5.11E+07 0.6% 334.7 336.8

- 0.6% 80.9 81.6

- 0.9%

2006 7.04E+07 7.01E+07 0.5% 301.6 303.4

- 0.6% 70.9 69.8 1.6%

2007 3.62E+07 3.61E+07 0.3% 288.4 299.4

- 3.8% 60.9 65.4

- 7.4%

### 23 Site Year Runoff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

2008 9.04E+07 8.99E+07 0.6% 712.7 723.5

- 1.5% 64.5 63.4 1.7%

2009 6.21E+07 6.18E+07 0.5% 523.6 527.9

- 0.8% 74.2 72.8 1.9%

2010 3.69E+07 3.68E+07 0.4% 380.5 389.1

- 2.3% 61.7 60.0 2.8%

2011 7.91E+07 7.87E+07 0.5% 579.7 582.9

- 0.5% 75.4 74.4 1.3%

2012 3.51E+07 3.49E+07 0.5% 232.0 235.4

- 1.5% 165.0 161.0 2.4%

2013 4.27E+07 4.24E+07 0.5% 257.9 261.8

- 1.5% 82.9 80.8 2.5%

2014 2.39E+07 2.37E+07 0.7% 123.7 125.4

- 1.4% 48.5 48.5 0.0%

IN

- 08

river

18445664 2000 1.84E+09 1.82E+09 1.0% 19020.7 19024.5 0.0% 64.6 64.9

- 0.5%

2001 1.71E+09 1.69E+09 1.1% 14202.8 14179.6 0.2% 52.4 52.5

- 0.2%

2002 1.79E+09 1.78E+09 1.0% 14047.5 14049.3 0.0% 53.9 49.3 8.5%

2003 1.82E+09 1.80E+09 1.1% 17803.6 17779.8 0.1% 56.7 56.0 1.2%

2004 1.87E+09 1.85E+09 1.1% 21633.3 21646.3

- 0.1% 45.9 46.0

- 0.2%

2005 2.06E+09 2.04E+09 1.0% 20515.4 20577.1

- 0.3% 60.8 61.7

- 1.5%

2006 2.46E+09 2.44E+09 0.9% 14783.7 14778.2 0.0% 44.8 45.0

- 0.4%

2007 1.36E+09 1.35E+09 1.0% 8757.2 8823.7

- 0.8% 57.8 58.9

- 1.9%

2008 2.95E+09 2.92E+09 0.9% 28350.0 28347.4 0.0% 45.1 45.4

- 0.7%

2009 2.26E+09 2.24E+09 0.9% 21455.4 21451.7 0.0% 49.7 49.3 0.8%

2010 1.54E+09 1.53E+09 0.9% 19940.3 19919.6 0.1% 49.7 50.1

- 0.8%

2011 2.93E+09 2.90E+09 1.1% 19986.1 20004.2

- 0.1% 63.2 63.7

- 0.8%

2012 1.28E+09 1.26E+09 1.0% 10946.1 10995.3

- 0.4% 93.6 90.7 3.1%

2013 1.70E+09 1.69E+09 1.0% 8919.5 8966.5

- 0.5% 56.8 56.9

- 0.2%

2014 9.96E+08 9.84E+08 1.2% 5752.9 5764.8

- 0.2% 39.3 39.4

- 0.3%

IN

- 13

river

18451035 2000 1.90E+08 1.90E+08 0.0% 966.5 974.2

- 0.8% 48.7 47.5 2.5%

2001 1.38E+08 1.38E+08 0.3% 511.4 515.3

- 0.8% 49.2 48.6 1.2%

2002 1.87E+08 1.87E+08 0.2% 709.9 708.3 0.2% 27.0 26.9 0.4%

2003 1.23E+08 1.23E+08 0.2% 528.6 533.3

- 0.9% 41.7 41.3 1.0%

2004 2.05E+08 2.04E+08 0.2% 1018.1 1022.9

- 0.5% 37.1 36.8 0.8%

2005 1.48E+08 1.48E+08 0.2% 695.8 698.8

- 0.4% 55.2 55.0 0.4%

2006 1.98E+08 1.98E+08 0.2% 442.5 442.1 0.1% 35.2 34.6 1.7%

2007 1.11E+08 1.11E+08 0.0% 292.1 297.6

- 1.9% 37.0 38.0

- 2.7%

2008 2.22E+08 2.22E+08 0.2% 1038.9 1040.4

- 0.1% 28.0 28.8

- 2.9%

2009 1.87E+08 1.86E+08 0.2% 861.2 863.6

- 0.3% 49.3 48.5 1.6%

2010 1.04E+08 1.03E+08 0.3% 565.1 568.3

- 0.6% 36.1 36.2

- 0.3%

2011 2.45E+08 2.44E+08 0.2% 1003.8 1004.7

- 0.1% 62.2 61.5 1.1%

2012 1.11E+08 1.10E+08 0.2% 685.0 686.1

- 0.1% 117.0 114.0 2.6%

2013 1.39E+08 1.39E+08 0.1% 526.6 532.6

- 1.1% 56.9 55.8 1.9%

2014 8.16E+07 8.15E+07 0.1% 182.4 184.8

- 1.3% 20.2 20.6

- 2.0%

### 24 For the three test basins in OH, the total annual runoff was within < 1%, total annual pesticide

mass lost was within < 2%, and estimated annual maximum concentrations were within 2% of

each other (Table 5 ).

Table 5 : Comparison of Annual Total Runoff, Total Mass Loss, and Maximum Concentrations for

Scenarios based on Individual Soil Map Units vs Sc enarios based on Soils Grouped by WQI Categories

for OH Test Basins.

Site Year Runoff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

OH

- 04

eco

3483775 2000 8.03E+06 8.03E+06 0.1% 69.0 68.3 1.0% 59.0 58.3 1.2%

2001 7.01E+06 6.98E+06 0.4% 116.0 114.4 1.4% 41.1 40.8 0.7%

2002 7.94E+06 7.90E+06 0.5% 110.8 109.3 1.4% 61.8 61.6 0.3%

2003 4.87E+06 4.84E+06 0.6% 40.8 40.5 0.8% 33.3 32.8 1.5%

2004 1.10E+07 1.10E+07 0.5% 131.0 129.4 1.2% 44.0 43.7 0.7%

2005 9.60E+06 9.57E+06 0.3% 64.8 65.0

- 0.3% 63.2 63.0 0.3%

2006 1.05E+07 1.04E+07 0.5% 98.5 97.8 0.7% 56.1 55.7 0.7%

2007 1.11E+07 1.10E+07 0.3% 31.1 31.5

- 1.2% 40.6 39.5 2.7%

2008 1.09E+07 1.08E+07 0.5% 151.0 148.8 1.5% 47.0 46.5 1.1%

2009 7.23E+06 7.20E+06 0.3% 91.0 90.7 0.4% 61.2 60.5 1.1%

2010 7.72E+06 7.69E+06 0.4% 107.4 106.1 1.3% 47.9 47.7 0.4%

2011 1.07E+07 1.06E+07 0.7% 50.7 50.5 0.5% 40.8 40.3 1.2%

2012 5.77E+06 5.73E+06 0.7% 78.3 77.9 0.5% 67.9 67.2 1.0%

2013 4.94E+06 4.92E+06 0.5% 41.1 41.2

- 0.2% 64.4 63.6 1.2%

2014 2.84E+06 2.83E+06 0.3% 27.2 27.2 0.2% 41.2 41.5

- 0.7%

OH

- 10

river

5214753 2000 1.75E+08 1.75E+08 0.0% 1803.8 1761.9 2.3% 39.9 39.5 1.0%

2001 1.13E+08 1.14E+08

- 0.4% 850.7 846.2 0.5% 37.0 36.9 0.3%

2002 6.31E+07 6.32E+07

- 0.2% 619.5 601.0 3.0% 44.1 43.6 1.1%

2003 1.43E+08 1.44E+08

- 0.3% 861.7 862.0 0.0% 38.7 39.0

- 0.8%

2004 1.55E+08 1.55E+08

- 0.3% 1993.5 2011.6

- 0.9% 32.7 32.9

- 0.6%

2005 1.59E+08 1.59E+08

- 0.2% 757.4 746.3 1.5% 38.8 38.9

- 0.3%

2006 1.80E+08 1.79E+08 0.3% 1050.0 1043.8 0.6% 32.9 32.5 1.2%

2007 2.11E+08 2.11E+08 0.4% 936.4 907.2 3.1% 34.5 34.0 1.4%

2008 1.91E+08 1.91E+08

- 0.1% 1934.7 1916.8 0.9% 51.4 51.4 0.0%

2009 1.20E+08 1.19E+08 0.7% 1103.2 1079.6 2.1% 42.1 42.8

- 1.7%

2010 1.20E+08 1.19E+08 0.7% 1439.2 1435.6 0.2% 45.9 45.7 0.4%

2011 2.78E+08 2.79E+08

- 0.3% 1470.1 1454.6 1.1% 38.3 38.4

- 0.3%

2012 1.33E+08 1.33E+08 0.7% 1356.7 1322.4 2.5% 64.3 62.9 2.2%

2013 1.94E+08 1.93E+08 0.2% 1463.1 1461.7 0.1% 50.6 50.5 0.2%

2014 6.77E+07 6.65E+07 1.8% 757.2 737.3 2.6% 28.1 27.4 2.5%

OH

- 11

reservoir

5213585 2000 6.84E+07 6.78E+07 0.8% 475.2 473.9 0.3% 30.3 30.8

- 1.7%

2001 3.70E+07 3.67E+07 0.8% 237.8 236.1 0.7% 24.5 25.2

- 2.9%

2002 3.04E+07 3.02E+07 0.6% 341.1 342.3

- 0.3% 33.9 34.3

- 1.2%

### 25 Site Year Runoff (m) Mass Runoff Loss (kg) Max. Annual Conc (ug/L)

Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff Indiv. MU

Scenarios Aggreg

Scenarios Pct

Diff

2003 5.92E+07 5.88E+07 0.6% 241.8 238.7 1.3% 20.1 20.3

- 1.0%

2004 5.46E+07 5.43E+07 0.5% 521.0 522.4

- 0.3% 29.0 29.4

- 1.4%

2005 6.57E+07 6.54E+07 0.4% 349.4 345.6 1.1% 38.9 39.2

- 0.8%

2006 4.60E+07 4.58E+07 0.4% 293.5 288.2 1.8% 33.0 33.9

- 2.7%

2007 5.26E+07 5.23E+07 0.5% 209.1 206.3 1.4% 30.8 32.0

- 3.9%

2008 6.42E+07 6.36E+07 0.8% 627.0 623.2 0.6% 43.5 45.1

- 3.7%

2009 4.26E+07 4.24E+07 0.5% 358.0 351.4 1.8% 32.3 32.7

- 1.2%

2010 3.52E+07 3.50E+07 0.6% 357.2 357.1 0.0% 37.8 39.2

- 3.7%

2011 8.40E+07 8.34E+07 0.7% 374.9 373.4 0.4% 31.5 32.3

- 2.5%

2012 3.59E+07 3.56E+07 0.8% 310.4 306.9 1.1% 42.5 43.4

- 2.1%

2013 4.92E+07 4.88E+07 0.9% 325.8 319.0 2.1% 38.3 38.9

- 1.6%

2014 3.34E+07 3.31E+07 0.8% 291.3 290.8 0.2% 23.7 23.9

- 0.8%



### A visual comparison of plots of the estimated concentration distributions over time (Figure 1 1)

for the two scenario (individual map units or aggregated soil group) approaches showed nearly

identical overlaps for most of the test basin sites and years. Figure 1 2 shows the gap between

the two methods for the years with greatest variability. Even w ith these sites, the majority of

estimated peaks are in close agreement.

Figure 11 : Comparison of Estimated Atrazine Concentrations Using Individual Soil Map Unit (blue) or

Aggregated Soil Group (yellow dash) Scenarios for MO

- 01.

### 26 Figure 12 : Comparison of Estimated Atrazine Concentrations Using Individual Soil Map Unit (blue) or

Aggregated Soil Group (yellow dash) Scenarios for MO

- 02.



### Comparisons for all of the test basins can be found in the accompan ying Appendix 3

- F.B

spreadsheet.