Section 3.0 AFO/CAFO Modeling Process

3-1

3.0 AFO/CAFO Modeling Process

3.1 Changes to NWPCAM Since the Proposed Rulemaking

Table 17 contains a summary of major differences in the versions of the NWPCAM

system used for the AFO/CAFO proposed rulemaking process and the final rulemaking process.

These changes are described in detail elsewhere (RTI, 2002). 

Table 17. Summary of Differences in NWPCAM Versions Used for the Proposed

Rulemaking and Final Rulemaking

Component

Proposed Rulemaking

(NWPCAM 1.5)

Final Rulemaking

(NWPCAM 1.6) Effect

Database Platform Microsoft Access Oracle • Automated model

runs

• Streamlined quality

control process

• Simplified analysis

of inputs or delivery

ratios

Reach Network • RF3 used to route

loadings

• RF3Lite used for

chlorophyll-a

modeling in lakes

• RF1 used for instream modeling 

• RF3 used to route

loadings

• RF3Lite used for

in-stream modeling

• Improved network

connectivity

• Better coverage of

open waters

Stream Flow • RF3 stream flows

based on average

annual runoff by

cataloging unit

• RF1 stream flows

based on RF1

characteristics data

set

• RF3 stream flows

calibrated using

USGS gaging

station data

• Stream flows in

western

hydroregions

adjusted for

intermittent stream

contribution 

• Improved RF3

stream-flow

estimates 

• Improved modeling

accuracy

(continued)
Table 17. (continued)

Section 3.0 AFO/CAFO Modeling Process

Component

Proposed Rulemaking

(NWPCAM 1.5)

Final Rulemaking

(NWPCAM 1.6) Effect

3-2

Slope by Cataloging

Unit

Used one-half of average

slope of first-order

streams in the cataloging

unit

Slope estimates based

on Digital Elevation

Model (DEM) 

• More accurate slope

estimates

• Higher channel

velocities and

delivery ratios from

land cells to RF3

Stream Velocity • Velocity estimates

based on Keup

(1985)

• Used RF1

characteristics

database for instream modeling

All velocity estimates

based on Jobson (1996)

Improved velocity

estimates

PS Inventory Used PS inventory from

NWPCAM 1.1

Used PS inventory

from NWPCAM 2.1

More comprehensive

account of PS loadings 

PS Delivery PS loads routed directly

to RF1

PS loads routed to

RF3Lite with decay

and transformation

Capitalizes on PS

location information 

Conventional NPS

Loads

Based on county-level

loadings apportioned to

reaches

• Based on landcover export

coefficients 

• Incorporated the 

RUSLE for TSS

loads on

agricultural cells

• Improved spatial

resolution

• Improved

consistency with

nutrient approach

• More accurate DO

modeling

Nutrient NPS Loads Export coefficients by

hydroregion/ecoregion/

land-cover type

calibrated to SPARROW

nutrient fluxes

Same loadings, but

speciated by land-cover

type 

Allows use of a water

quality index that

incorporates nutrient

measures

Non-AFO/CAFO

NPS Delivery

NPS loads routed to

RF3Lite and RF1 without

decay and transformation

NPS loads routed to

RF3 and RF3Lite with

decay and

transformation

Improved consistency

with PS load approach

AFO/CAFO Load

Distribution

• Loads assigned

randomly to

agricultural cells

within a county

• Limited loads on

land-use cells based

on Beaulac and

Reckhow (1982)

Loads assigned

randomly to

agricultural cells using

model farms areas and

without considering

nutrient load caps

See Section 5.1

(continued)
Table 17. (continued)

Section 3.0 AFO/CAFO Modeling Process

Component

Proposed Rulemaking

(NWPCAM 1.5)

Final Rulemaking

(NWPCAM 1.6) Effect

1

File name = Distribution11.xls; Date received = 8/12/02

2

Filename = StatePct(102502)corr.xls; Date Received = 11/15/02

3-3

In-Stream Modeling

of Nutrients

BATHTUB used to

model chlorophyll-a at

RF3Lite scale

NWPCAM 1.6

includes nutrient

modeling at RF3Lite

scale

• Permits use of a

water quality index

that includes nitrates

and phosphates 

• Oxygen demand

from nonAFO/CAFO

nutrients will be

modeled

In-Stream Modeling

of Conventional

Pollutants

Based on NWPCAM 1.1

kinetics with some peerreview comments

incorporated 

Based on NWPCAM

1.1 with all peerreview comments

incorporated

Changes in kinetics are

expected to have

minimal impact on

results

In-Stream Modeling

of Bacteria

Included in-stream

kinetics for FCB, but not

FS

Includes in-stream

kinetics for FCB and

FS

Although not used in

use support

determination or

benefits analysis,

provides in-stream

concentrations of FS

Benefits Metric • WQL

• Regionalized

chlorophyll-a water

quality ladder used to

estimate economic

benefits

Adds capability to

calculate six-parameter

WQI that includes

nitrates and phosphates

Integrated economics

benefits approach

3.2 AFO/CAFO Input Files

Four Microsoft Excel workbooks were supplied by EPA to characterize AFO/CAFO

loadings to surface water:

1. AFO facility counts by county, animal type, and operation size1

2. Percentage of AFOs considered CAFOs in each state under various regulatory

scenarios2
Section 3.0 AFO/CAFO Modeling Process

3

Nutrients: File name = Output (11132002) adjusted Opt5.xls; Date received = 11/13/02

4

File name = Speciation(1108).xls; Date received = 11/8/02

3-4

3. Nutrient, sediment, BOD, and pathogen loadings for unregulated AFOs and

regulated CAFOs under various technology options by U.S. region, animal type,

and operation size. Model farm areas were also included in this data set.3

4. Speciation factors to break TN and TP loadings into nutrient species under

various technology options by U.S. region, animal type, and operation size.4

Loadings were supplied for TN, TP, TSS, BOD, FCB, and FS. Loadings for each parameter

were separated into manure, commercial fertilizer, and feedlot load categories.

3.3 Methodology

All preprocessing steps (i.e., the parts of NWPCAM 1.6 unaffected by the AFO modeling

process) were outlined in Section 2.0 of this report. For each AFO/CAFO regulatory option

modeled by NWPCAM 1.6, several analytical and data management processes were conducted:

# Distribute AFO/CAFOs and associated edge-of-field loadings to agricultural landcover cells within each county.

# Route AFO/CAFO loadings from the land-cover cells to the nearest RF3 reach

using an overland transport, loss, and transformation routine.

# Route AFO/CAFO loadings from the RF3 network to the RF3Lite subset using an

in-stream transport, loss, and transformation routine.

# Simulate dilution, transport, and kinetics of the nutrients/pollutants in the RF3Lite

network.

# Relate the nutrient and pollutant concentrations in the RF3Lite reach to beneficial

use attainment criteria and goals.

# Calculate the overall WQI6.

# Compute economic benefits based on changes in water quality use-support.

# Compute economic benefits based on changes in WQI6.

See Section 2.1 for a flowchart of the NWPCAM 1.6 process employed for estimating the

benefits of AFO/CAFO regulations using the Vaughn WQL approach.
Section 3.0 AFO/CAFO Modeling Process

3-5

Figure 7. Mosaic Composite of Spatial Data at the Watershed (HUC) Level.

3.3.1 Method for Distributing AFO/CAFO Loadings

AFO/CAFO farm locations were supplied at the county level via county FIPS codes. In

order to associate farms with RF3 reaches, the loadings were randomly distributed onto

agricultural land-cover cells within each county. This process involved (1) identifying

agriculture land-cover cells within each county using the Anderson Land-Cover Class Code;

(2) assigning random identification numbers to each agricultural cell; and (3) writing a module to

integrate the AFO/CAFO data sets and distribute loadings to the agricultural land-cover cells. 

Figure 7 shows the RF3 network, land-use/land-cover data, and county overlay for one 8-

digit HUC. It is onto this mosaic that AFO/CAFO counts by county and associated edge-of-field

loadings are distributed. The load distribution module operated according to the following steps:

1. Selected a county using the county FIPS code

2. Generated a list of agricultural cells in that county, ordered by the random

identification number.

3. Selected the facility counts for the county. Each county was associated with

facility counts for 42 animal type and farm size combinations. 
Section 3.0 AFO/CAFO Modeling Process

3-6

( )

( )

Species Load reg reg load reg species fraction

unreg unreg load unreg species fraction

=× × +

× ×

% _ __

% _ __

(25)

( )

( )

Total Load regulated regulated load

unregulated unregulated load

=× +

×

% _

% _

(26)

4. Obtain the percentage of AFOs considered CAFOs for each animal type and farm

size combination under the regulatory scenario being simulated.

5. Obtained loadings data for regulated and unregulated facilities for each animal

type and farm size combination under the regulatory scenario being simulated.

6. Calculated total loadings according to the following equation:

7. Calculated nutrient species loads by the following equation:

8. Calculated the size of the farm in square kilometers using the model farm area.

This is equivalent to the number of agricultural land-cover cells onto which the

loads will be distributed.

9. Assigned loads to a land-cover cell with sufficient area available. If necessary,

distributed loads to additional land-cover cells until the farm area was depleted.

10. Looped through the 42 animal type and operation size combinations until all

AFO/CAFO loads in the county were distributed to agricultural land-cover cells.

11. Looped through the 3,045 counties in the conterminous 48 states.

Several rules were applied during the load distribution process to reflect regulatory

options or assumptions made during the load development process. These include the following:

# Feedlot loads were not distributed using model farm areas, because of the

modeling assumption that these loads have a direct hydrological connection to

surface water. Feedlot loads were uniformly distributed to land-cover cells that

were less than 100 ft from the nearest RF3 reach, and they did not undergo

overland loss or transformation.

# Under RTI Scenarios 1 and 2, a 100 ft. setback was used for land application of

manure. Under the setback, regulated manure loadings were not distributed to

land-cover cells that were less than 100 ft from the nearest RF3 reach.

Unregulated manure loadings were not affected by the 100 ft. setback. 

# Commercial fertilizer load distribution was not affected by the 100 ft. setback.
Section 3.0 AFO/CAFO Modeling Process

3-7

3.3.2 Routing AFO/CAFO Loads to RF3 Reaches

In order to be hydrologically routed through the stream network, the manure and

commercial fertilizer loads are routed from the agriculture land-cover cells to the nearest RF3

reach using a routine that simulates overland transport, loss, and transformation. The feedlot

loads are routed to the nearest RF3 reach without any loss or transformation of loads. Overland

travel times are based on flow in a natural channel such as may be found on agricultural lands

(see Section 2.2.4). A unit runoff (ft3/sec/km2) was derived for each HUC based on data from

USGS stream gages in the HCDN network. Travel distances were calculated from the center of

the agricultural cell to the nearest RF3 reach.

3.3.3 Routing AFO/CAFO Loads to RF3Lite Reaches

AFO/CAFO loads were routed to the RF3Lite network using an in-stream transport,

decay, and transformation module. 

3.3.4 In-Stream Modeling in the RF3Lite network

The final stage of in-stream modeling is conducted in the RF3Lite network. At this stage,

a module routes through the RF3Lite network according to the hydrologic sequence order. For

each reach, NPS, PS, and AFO/CAFO loads at the RF3Lite scale are combined. The loads are

decayed and transformed to the middle of the reach to produce an estimate of average in-stream

concentration that is inserted into the results table. The loads are then decayed and transformed

to the end of the reach to continue routing down the RF3Lite network.

3.3.5 Water Quality Assessment Ladder

NWPCAM 1.6 uses the water quality ladder described in Table 18 to translate in-stream

concentration estimates for BOD5, TSS, DO, and FCB into corresponding use-support categories

using an approach developed by Vaughn for Resources for the Future (Mitchell and Carson,

1986). This approach assigns maximum pollutant levels for BOD, TSS, and FCB that

correspond to boatable, fishable, and swimmable waters. Minimum threshold values are also

established for DO. A water resource that fails to meet the boating criteria is classified as a

“nonsupport” resource. Vaughn’s original water quality ladder included BOD5, turbidity, DO,

pH, and FC. In NWPCAM, TSS is used as a surrogate for turbidity. 

Table 18. Water Quality Ladder Threshold Concentrations

Beneficial Use

Biological

Oxygen Demand

(mg/L)

Total Suspended

Solids (mg/L)

Dissolved

Oxygen 

(% saturated)

Fecal Coliforms

(MPN/100mL)

Swimmable 1.5 10 0.83 200

Fishable 3 50 0.64 1000

Boatable 4 100 0.45 2000
Section 3.0 AFO/CAFO Modeling Process

3-8

The Vaughn WQL model categorizes reaches as boatable, fishable, swimmable according

to the worst pollutant. Benefits are assigned only for reaches that are move across the designated

use categories. Thus, for a reach that is classified as “boatable” prior to regulation, even if there

is significant improvement in one or more water quality parameters (e.g., TSS and DO)

following regulation, no monetized benefit is assigned to that reach if any other parameter

remains in the boatable category.

3.3.6 Economic Benefits Calculations Using the WQL

Each RF3Lite reach is categorized using the WQL for each AFO/CAFO regulatory

scenario. The difference in the miles for each use category between baseline conditions and a

given rulemaking scenario is a measure of the improvement in water quality attributable to the

scenario. These differences in miles are converted into economic benefits (dollars) based on the

population and their willingness to pay (WTP) for improvement in water quality.

Benefits are calculated state-by-state and are broken down into local and nonlocal

benefits. Local benefits correspond to the amount a population is willing to pay for water quality

improvements within their own state. Nonlocal benefits correspond to the amount a population

is willing to pay for water quality improvements outside of their own state. Local benefits are

calculated as follows:

BOAT_WTP = (BOAT_SCN - BOAT_BASE) / seg_length × pop / 2.62 × 206 × C

FISH_WTP = (FISH_SCN - FISH_BASE) / seg_length × pop / 2.62 × 155 × C (27)

 SWIM_WTP = (SWIM_SCN - SWIM_BASE) / seg_length × pop / 2.62 × 173 × C

where

BOAT_SCN = miles of the state’s boatable waters for rulemaking scenario

BOAT_BASE = miles of the state’s boatable waters for baseline conditions

FISH_SCN = miles of the state’s fishable waters for rulemaking scenario

FISH_BASE = miles of the state’s fishable waters for baseline conditions

SWIM_SCN = miles of the state’s swimmable waters for rulemaking scenario

SWIM_BASE = miles of the state’s swimmable waters for baseline conditions 

seg_length = total miles of RF3Lite reaches in the state

pop = population of the state (from 2000)

2.62 = average number of people per household in the United States

206 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from nonboatable to boatable levels

155 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from boatable to fishable levels

173 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from fishable to swimmable levels

C = fraction of WTP applied to local benefits = 2/3. 

Nonlocal benefits can also be calculated using Equation 27, with the following variable

definitions:
Section 3.0 AFO/CAFO Modeling Process

3-9

BOAT_SCN = miles of the nation’s boatable waters for rulemaking scenario

BOAT_BASE = miles of the nation’s boatable waters for baseline conditions

FISH_SCN = miles of the nation’s fishable waters for rulemaking scenario

FISH_BASE = miles of the nation’s fishable waters for baseline conditions

SWIM_SCN = miles of the nation’s swimmable waters for rulemaking scenario

SWIM_BASE = miles of the nation’s swimmable waters for baseline conditions 

seg_length = total miles of RF3Lite reaches outside of the state

pop = population of the state (from 2000)

2.62 = average number of people per household in the United States

206 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from nonboatable to boatable levels

155 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from boatable to fishable levels

173 = average annual household WTP (in 2000 dollars) to increase U.S.

waters from fishable to swimmable levels

C = fraction of WTP applied to local benefits = 1/3. 

3.3.7 Water Quality Index

McClelland (1974) suggested using a Water Quality Index (WQI) that falls on a

continuous scale from 0 to 100, rather than four discrete beneficial-use attainment indicators

used in the WQL. The original WQI included nine water quality characteristics: BOD5, DO,

FCB, TSS, NO3, PO4, temperature, turbidity, and pH. NWPCAM 1.6 uses a six-parameter Water

Quality Index (WQI6) that incorporates BOD5, DO, FCB, TSS, NO3, and PO4. The remaining

three parameters are not modeled in NWPCAM 1.6 and are factored out of the WQI. 

The WQI is derived by converting concentrations of each water quality characteristic into

a corresponding score (qi

) between 0 and 100. As documented in McClelland (1974), this was

accomplished by averaging the judgments from 142 water quality experts regarding the

appropriate functional relationship between conventional measures and a 0–100 scale. 

Appendix B includes charts of the six functional relationships used by NWPCAM 1.6. Weights

for each of the scores (wi) were derived, again based on the summary judgments of the expert

panel. These weights were designed to sum to 1 for the nine water quality characteristics. The qi

and wi values were combined into a composite multiplicative index of the following form:

Jn

i=1 qi

wi (28)

The i subscript refers to the i-th parameter, and n is the number of parameters (in this

case, n = 9). By design, WQI varies between and is bounded by 0 and 100.

To apply McClelland’s index to output from NWPCAM 1.6, it must be modified to

account for the three characteristics (i.e., temperature, turbidity, and pH) that are not modeled.

To accomplish this, new weights are calculated for the remaining six parameters so that the

ratios of the six weights are retained and the weights sum to 1. Table presents the original and

revised parameter weights for the nine pollutants. Under the revised index, n = 6, and the wi

’s are

specified using the revised weights in Table 19. 
Section 3.0 AFO/CAFO Modeling Process

3-10

Table 19. Original and Revised Weights for WQI Parameters

Parameters Original Weights Revised Weights

BOD5 0.11 0.15

DO 0.17 0.24

FCB 0.16 0.23

TSS 0.07 0.1

NO3 0.1 0.14

PO4 0.1 0.14

Temperature 0.1 --

Turbidity 0.08 --

pH 0.11 --

Total 1 1

3.3.8 Economic Benefits Analysis Using the WQI6

The following WTP function is used to derive economic benefits using the WQI6

approach. This equation was estimated and reported by Mitchell and Carson using WTP

responses from their survey sample.

TOTWTP = exp [0.413 + 0.819 × ln(WQI/10) + 0.959 × ln(Y/1000) + 0.207 × W + 0.46 × A] (29)

where

TOTWTP = each household’s total annual WTP (in 1983 dollars) for increasing

water quality up to each of the three WQI values

 

Y = annual household income (sample average = $24,220 in 1983 dollars)

W = dummy variable indicating whether the household engaged in waterbased recreation in the previous year (sample average = 0.59)

A = dummy variable indicating whether the respondent regarded the nation

goal of protecting nature and controlling pollution as very important

(sample average = 0.65).

In solving this equation, Mitchell and Carson used Vaughn’s WQL to map each

beneficial-use category to a corresponding WQI value (boatable = 25, fishable = 50, and

swimmable = 70). These values were applied in the right-hand side of Equation 28. 

Equation 28 can also be used as a benefit-transfer function, to assess the value of

increasing water quality along the continuous WQI scale. In other words, assuming that W and A
Section 3.0 AFO/CAFO Modeling Process

3-11

are representative of the current population, the incremental value associated with increasing

WQI from WQI0 to WQI1 can be calculated as

 aTOTWTP = exp[0.8341 + 0.819 × log(WQI1/10) + 0.959 × log(Y)]

 - exp[0.8341 + 0.819 × log(WQI0/10) + 0.959 × log(Y)] (30)

Y, in this case, would be selected to correspond to average (or median) household income

in the year of the water quality change (expressed in 1983 dollars). The resulting value estimates

can be inflated to 2000 dollars using the growth rate in the consumer price index (CPI) of 1.72

since 1983.

Benefits are calculated state-by-state and are broken down into local and nonlocal

benefits. Local benefits correspond to the amount a population is willing to pay for water quality

improvements within their own state. Nonlocal benefits correspond to the amount a population

is willing to pay for water quality improvements outside of their own state.
Section 4.0 Results of AFO/CAFO Analyses

4-1

4.0 Results of AFO/CAFO Analyses

This section summarizes the results of the NWPCAM 1.6 analyses for the AFO/CAFO

rulemaking scenarios.

4.1 AFO/CAFO Loadings

 Total national AFO/CAFO loadings are key inputs estimated by EPA and used to drive

the NWPCAM 1.6 model simulations. The AFO/CAFO nutrient and pollutant loadings to

agricultural cells and their production area loads input directly into RF3 reaches for baseline

conditions and rulemaking scenarios are summarized in Table 20. These represent the total

national AFO/CAFO loadings actually distributed to agricultural cells and production area loads

input directly into RF3 reaches. According to EPA estimates, nutrients and sediments decline

moderately under both scenarios. FCB loads decline slightly under all scenarios. FS loads

decline slightly under both scenarios. For further discussion of the AFO/CAFO loading

calculations, please see the document entitled “Pollutant Loading Reductions for the Revised

Effluent Limitation Guidelines for Concentrated Animal Feeding Operations” in the rulemaking

record.

Table 20. National AFO/CAFO Loadings on Agricultural Cells*

Rulemaking

Scenario TN (g/s) TP (g/s)

TSS

 (g/s)

Biochemical

Oxygen

Demand (g/s) FCB (cfu/s) FS (cfu/s)

Baseline 3316 5076 958,380 1012 3.29 × 1014 4.30 × 1015

RTI Scenario 1 2977 4332 931,008 779 2.91 × 1014 3.50 × 1015

RTI Scenario 2 3166 4682 936,444 927 3.22 × 1014 4.16 × 1015

* Note: To calculate a loading rate per unit area, the values in this table should be divided by the agricultural area in

 the country.

The AFO/CAFO nutrient and pollutant loadings in the RF3 network for baseline

conditions and rulemaking scenarios are summarized in Table 21. These represent the total

national loadings delivered to the RF3 reaches after overland transport from the agricultural cells

to the nearest reach occurs (manure and commercial fertilizer loads only). Table 22 lists the

delivery ratios to RF3 for baseline and scenarios. Between 75 and 90 percent of the total

national loads are delivered to the RF3 reaches for TN, TP, TSS, BOD, FCB and FS. 
Section 4.0 Results of AFO/CAFO Analyses

4-2

Table 21. AFO/CAFO Nutrient/Pollutant Loadings to RF3 Rivers/Streams

Rulemaking

Scenario

TN

 (g/s) 

TP

 (g/s)

TSS

(g/s)

BOD

(g/s)

FCB

(cfu/s)

FS

 (cfu/s)

Baseline 2674 3832 748,148 907 2.52 × 1014 3.75 × 1015

RTI Scenario 1 2414 3296 733,841 686 2.20 × 1014 3.02 × 1015

RTI Scenario 2 2571 3563 738,741 826 2.48 × 1014 3.62 × 1015

Table 22. AFO/CAFO Delivery Ratios to the RF3 Network

Rulemaking

Scenario

TN

(g/s) 

TP 

(g/s)

TSS

 (g/s)

BOD

(g/s)

FCB

(cfu/s)

FS

 (cfu/s)

Baseline 0.81 0.75 0.78 0.90 0.77 0.87

RTI Scenario 1 0.81 0.76 0.79 0.88 0.76 0.86

RTI Scenario 2 0.81 0.76 0.79 0.89 0.77 0.87

AFO/CAFO loadings to the RF3Lite subset of RF3 reaches for baseline conditions and

rulemaking scenarios are summarized in Table 23. These represent the total national AFO/

CAFO loadings delivered to the RF3Lite subset of RF3 reaches after transport down the RF3

network to the first RF3Lite reach segment encountered. Table 24 lists the delivery ratios to

RF3Lite for baseline and the scenarios. Between 69 and 86 percent of the total national loading

are delivered to the RF3Lite reaches for TN, TP, TSS, BOD, and FS. FCB have a high die-off

rate, which translates into a smaller delivery ratio at around 62 percent.

Table 23. AFO/CAFO Nutrient/Pollutant Loadings to RF3Lite Network

Rulemaking

Scenario

TN

 (g/s) 

TP

 (g/s)

TSS

 (g/s)

BOD

(g/s)

FCB

(cfu/s)

FS

 (cfu/s)

Baseline 2383 3502 683,817 875 2.05 × 1014 3.53 × 1015

RTI Scenario 1 2149 3007 670,391 663 1.80 × 1014 2.84 × 1015

RTI Scenario 2 2290 3252 674,921 798 2.02 × 1014 3.40 × 1015

Table 24. AFO/CAFO Delivery Ratios to the RF3Lite Network

Rulemaking

Scenario

TN

 (g/s) 

TP

 (g/s)

TSS

 (g/s)

BOD

(g/s)

FCB

(cfu/s)

FS

 (cfu/s)

Baseline 0.72 0.69 0.71 0.86 0.62 0.82

RTI Scenario 1 0.72 0.69 0.72 0.85 0.62 0.81

RTI Scenario 2 0.72 0.69 0.72 0.86 0.63 0.82
Section 4.0 Results of AFO/CAFO Analyses

4-3

4.2 Economic Benefits

Table 25 provides a summary of the annual economic benefits for each scenario using the

WQL. This summary was computed by summing the local and nonlocal benefits for each state.

Scenario 1 exhibited a higher benefit because of its layer reduction of all constituents.

Table 25. Annual Economic Benefits Using the WQL (2001 dollars, thousands)

Rulemaking

Scenario

Boatable

Waters*

Fishable 

Waters*

 Swimmable

Waters*

Total

Benefit

RTI Scenario 1 114,051 38,811 13,322 166,184

RTI Scenario 2 73,065 23,202 6,122 102,389

* Boatable benefits include only those benefits attributable to improvements from non-boatable to boatable. 

Benefits from improvements to other beneficial use categories appear in the other columns. For a reach that

improved from nonbotable to fishable, for example, a portion of the benefits appear in the boatable column, and

the remainder appears in the fishable column. Similarly, fishable and swimmable benefits include only those

benefits attributable to improvements from boatable to fishable and from fishable to swimmable, respectively. 

Benefits from improvements to other use categories appear in the other columns as described above.

Table 26 provides a summary of the annual economic benefits for each scenario using the

WQI. This summary was computed by summing the local and nonlocal benefits for each state. 

Scenario 1 exhibited a higher benefit because of its layer reduction of all constituents.

Using the WQL, the bulk of monetary benefits occur in the boatable waters category. 

Using the WQI, the majority of the benefits occur in the middle (i.e., 26<WQI<70) category. 

The total estimated benefit using the WQI is substantially larger than the benefit using the WQL. 

Both of these conditions are a result of the process used to categorize improvements in water

quality in each of the models. In the WQL model, reaches are categorized into designated uses

according to the worst pollutant, and benefits are assigned only for reaches that move between

designated use categories. In the WQI model, reaches are assigned WQI values between 0 and

100 using a weighted average function to determine overall water quality. A WTP function was

developed that interpolates what people are willing to pay along the continuous 0–100 scale. 

Therefore, any improvement in water quality is included in the benefit using the WQI model.

Table 26. Annual Economic Benefits Using the WQI (2001 dollars, thousands)

Rulemaking

Scenario WQI < 26 26 < WQI < 70* WQI > 70** Total Benefit

RTI Scenario 1 10,088 24,154 46,950 298,552

RTI Scenario 2 7,187 135,266 40,105 182,558

 * This category includes only the benefits attributable to improvements between 26 and 70. For example, for a

reach that improved from 24 to 30, the portion of benefits attributable to the increase from 24 to 26 appears in

the WQI<26 category; the remainder appears in the 26<WQI<70 category.

** This category includes only the benefits attributable to improvements to a WQI >70. For a reach that

improved from 24 to 80, for example, a portion of the benefits is allocated to each of the WQI<26, the

26<WQI<70, and the WQI>70 categories.
Section 4.0 Results of AFO/CAFO Analyses

4-4

4.3 Discussion of Benefit Results

Both estimation methods rely on WTP values derived by Carson and Mitchell (1993). 

The WQL captures the benefits of discrete changes in the type of uses or amenities provided by

waterbodies and, in doing so, reflects the principles of water quality standards where

determinants of beneficial use attainment are based on water quality criteria. Carson and

Mitchell (1993) indicate that amenities such as boatable, fishable, and swimmable water quality

are “concepts that are widely understood.” 

However, the pollutant criteria for making use determinations in the discrete ladder

include criteria for which federal guidance has not been developed. Criteria for TSS and BOD

are not typically adopted for the boatable, fishable, and swimmable amenities, and inclusion of

criteria for these pollutants implies lower probability of beneficial use attainment under the

ladder than might be indicated by other methods for determining use attainment in the nation’s

waters. 

In contrast, the WQI approach adopted for this final rule characterizes changes in water

quality using an aggregate index derived from six individual pollutant concentrations. Carson

and Mitchell (1993) state that the use of this type of index greatly facilitates the task of

communicating the several quality levels (i.e., amenities) to the (survey) respondents. This

observation accentuates the fact that different respondents are likely to rely on different

measures of water quality to make value judgments. The minimum index values (25 for

boatable, 50 for fishable, and 70 for swimmable) adopted by Carson and Mitchell help explain

why the magnitude and distribution of benefits differ between the discrete ladder and the

continuous WQI approaches. 

Differences in magnitude are due in part to the likelihood that the distribution of

predicted changes in some parameters is not sufficiently large to meet criteria necessary for an

amenity change, including the boatable category. As a consequence, changes in beneficial use

are unlikely to occur, and corresponding benefits are lower under the discrete ladder. Under the

continuous WQI, benefit estimates are not constrained by “limiting parameter” distributions, and

the benefits from all changes in water quality are captured, regardless of changes in amenity

support. The relative difference in magnitude of benefits is a function of the baseline

distribution of water quality parameters; in some special cases, the benefits under the ladder

could approximate or even exceed those under the continuous index (when baseline measures of

central tendency (e.g., median) are approximately equal to the threshold criteria for supporting

amenities).

Apparent inconsistencies in the distribution of benefits between the two methods arise

because many waterbodies fail to meet beneficial use criteria in the ladder, yet most of these

same waterbodies have WQI values that exceed the minimum index thresholds specified in the

ladder. For example, most of the benefits realized under the ladder occur when waters improve

from nonboatable to boatable because, as noted above, a majority of waterbodies are not capable

of meeting the criteria for higher uses. However, in the case of the continuous index, a majority

of benefits are due to changes in water quality within an index range of 26 to 70; this range

reflects a boatable and/or fishable attainment, based on index thresholds in the ladder (boatable =

25, fishable =50, swimmable = 70). The discrepancy occurs because many nonboatable reaches
Section 4.0 Results of AFO/CAFO Analyses

4-5

under the ladder actually have index values that are far higher than the minimum threshold for

boating. Approximately 80 percent of reach segments designated as nonboatable under the

ladder under baseline conditions have WQI values that range from 29 to 79 based on NWPCAM

output for a four-parameter index, implying that many waterbodies deemed nonboatable under

the ladder would be considered boatable, fishable, or even swimmable under the continuous

index. It is felt that many people would be willing to boat or fish in waters that are deemed

unboatable under the ladder. As a final note regarding the distribution of benefits, it is also

possible that a particular regulation, such as the final CAFO rule, may affect specific geographic

areas where nonboatable waters predominate, thus implying that a majority of benefits are

attributable to improvements from nonboatable to boatable.

A comparison of the two valuation methods is most easily understood within the context

of the original Carson and Mitchell survey. Recall that the Carson and Mitchell survey presents

(1) explicit relationships between beneficial use categories and numeric values of the WQI, and

(2) baseline water quality conditions for the nation that are similar in some respect to the results

in the NWQI (2000). The results from the survey are used to estimate (1) mean WTP values for

water quality levels supporting different amenity categories, and (2) a valuation function that

predicts WTP as a function of water quality index values. The ladder approach to estimating

benefits maintains consistency with the explicit correlation between WTP and beneficial use

categories specified by Carson and Mitchell (e.g., a change in water quality and WTP can be

related to changes in amenities), but is not consistent with baseline water quality conditions. The

WQI approach maintains consistency with baseline water quality conditions but is less capable

of maintaining consistent relationships between WTP and changes in beneficial use categories. 

Other advantages of the continuous index approach include (1) use of a decreasing marginal

benefits curve with respect to the WQI (consistent with economic theory), (2) the ability to

capture benefits of marginal changes in individual water quality parameters without triggering

changes in amenities, and (3) the ability to capture benefits associated with changes in other

parameters (i.e., nitrate and phosphate) that are not included in the ladder.
Section 5.0 Quality Assurance

5-1

5.0 Quality Assurance

Potential sources of error and uncertainty in the analysis include model inputs (e.g.,

hydrologic inputs from RF3), data processing, model parameters (e.g., decay rates), and benefits

monetization methods. This section describes measures taken to reduce these errors and

uncertainties for the AFO/CAFO analysis, including (1) reviewing hydrologic inputs for

reasonableness, (2) evaluating the robustness of model predictions to changes in model

parameters, (3) performing quality assurance on all data processing steps, including the

computational modules, (4) evaluating modeling results for reasonableness, and (5) evaluating

the sensitivity of estimated benefits to the monetization method selected. 

5.1 Reviewing Hydrologic Inputs

RTI has performed extensive quality assurance on the flow and velocity estimates

included in NWPCAM 1.6. Comparisons were made between NWPCAM 1.6 and observational

values of flow and velocity obtained from the USGS HCDN network. The results of this work,

including the methodology used to develop the NWPCAM 1.6 estimates, are contained

elsewhere (RTI, 2001).

5.2 Model Robustness

A full calibration exercise on NWPCAM 1.6 has not been conducted. However, a

sensitivity analysis was performed in Hydroregion 5 to evaluate changes in predicted water

quality due to changes in modeling inputs (RTI, 2002). Ten parameters (i.e., flow, velocity,

depth, PS loads, non-AFO NPS loads, BOD oxidation rate, TSS settling rate, FCB die-off rate,

sediment oxygen demand, and CBODU:BOD5 ratio) were varied by a factor of 1.5 to 2. 

Because NWPCAM is a screening-level model, the sensitivity analysis was aimed at evaluating

whether changes in water quality from baseline to scenario were robust, as opposed to absolute

water quality. Four model runs were conducted for each of the 10 parameters: (1) low parameter

value, baseline AFO/CAFO loads; (2) low parameter value, scenario AFO/CAFO loads; (3) high

parameter value, baseline AFO/CAFO loads; and (4) high parameter value, scenario AFO/CAFO

loads. For these analyses, the baseline AFO/CAFO loadings were taken from the dummy

loadings files supplied by EPA on March 27, 2002. Scenario loadings were taken from Option 2

in the same dummy loadings file. 

Flow, velocity, and non-AFO NPS loads had the greatest impact on absolute water

quality as assessed by the WQI6. However, changes in water quality were robust, with average

WQI improvements of approximately 1.3 for all runs. This indicates that uncertainty in model

coefficients and inputs may not have a significant impact on predicted water quality changes

under regulatory scenarios. However, absolute model results (e.g., DO concentrations by reach)

will be affected significantly by uncertainty in model coefficients and inputs.
Section 5.0 Quality Assurance

5-2

5.3 Data Processing

The compatibility between AFO/CAFO loads distributed onto agriculture cells and

AFO/CAFO input files was checked using a hand calculation. Table 27 shows the AFO/CAFO

loads distributed in Vermont using the two methods.

Table 27. Verification of Loads Distribution Module

Method

TN Load

(g/s) TP Load (g/s) 

TSS Load

(g/s)

FCB Load

(MPN/s)

FS Load

(MPN/s) 

Manual 10.99 16.14 1.388 1.21 × 1010 6.62 × 1010

Module 11.02 16.17 1.391 1.21 × 1010 6.66 × 1010

A shapefile was created in ArcView 3.2 under baseline regulations to confirm that high

loadings occur in rural areas (see Figure 8). The shapefile was also used to identify counties with

zero AFO/CAFO loadings. Five of these counties were selected to confirm they did not have

associated animal farms in the input files.

Other quality assurance steps include the following:

# A check on the data import from Excel to Oracle by summing across columns and

comparing column totals.

# A qualitative comparison of loads reductions was conducted between scenarios to

ensure reasonableness based on technology options and/or percent of regulated

facilities. It confirmed that higher loads in NPDES scenarios agreed with smaller

state percentages in medium categories.

# A check on the loading distribution algorithm by summing the number of landuse/land-cover cells receiving loads. To compare loads distribution between

baseline and scenario, confirmed that fewer Layer A land-use/land-cover cells

(i.e. < 100 ft from the nearest RF3 reach) received loads for the scenario (due to

the 100 ft setback ).

# A check on the delivery ratios calculated for each run. Delivery ratios from landcover cells to the RF3 network were compared to literature values and found to be

within acceptable ranges (SCS, 1983). 

5.4 Modeling Results

Checks have been made on the distributions of predicted water quality for FC and TSS. 

FC water quality standards are typically expressed in terms of geometric means. For example,

EPA water quality criteria suggest a geometric mean of 200 MPN/100mL as a guideline for

swimmable waters (U.S. EPA, 1986). Mean values of 100 to 200 MPN/100 mL were calculated

in Hydroregion 1 after a log transform was applied to the FC concentrations.
Section 5.0 Quality Assurance

5-3

Figure 8. Total nitrogen loadings (g/s) by county FIPS code for baseline.

General comparisons of baseline results to National Stream Quality Accounting Network

(NASQAN) data for TSS, TN, and TP developed by EPA staff show similar ranges of values and

patterns of high and low values.

Other quality assurance steps include the following:

1. An examination of individual use-support for each run to assess reasonableness of

overall use-support and differences between scenarios (i.e., examined that

changes in individual use-support agreed with changes in loadings).

2. An examination of estimated benefits per mile of improvement. For RTI

Scenario 7, a geographic analysis was conducted to justify the large estimated

economic benefit.

3. A hand calculation of miles affected under Scenarios 6 and 7 using the Vaughan

WQL. 
Section 6.0 References

6-1

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