From: Campbell, Scott (MNH) To: Kevin Mooney (Kevin.Mooney@corporate.ge.com) Cc: Palmieri, Linda Subject: DCN: GE-112105-ACZB - Draft Sections 5.2 and 6.1 of EPA"s Validation Report for GE Review Date: Monday, November 21, 2005 4:10:31 PM Attachments: Validation Sections 5.2 and 6.1.zip

Hello Kevin,

As requested by Susan Svirsky, I have attached two draft sections of the validation report for GE review. Section 5.2 is the uncertainty section for HSPF and section 6.1 is the HSPF validation section.

Please do not hesitate to contact me with any question in regard to this submittal.

Scott

Scott Campbell Principal Project Scientist Weston Solutions, Inc. Suite 2 10 Lyman Street Pittsfield, MA 01201 (w) 413-442-4224 (mobile) 413-281-9574 (fax) 413-442-4447

mailto:/O=RFWESTON/OU=KEYSTONE/CN=RECIPIENTS/CN=CAMPBELSCmailto:Kevin.Mooney@corporate.ge.commailto:Linda.Palmieri@WestonSolutions.com

ValDr_6.1_.pdf

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6. VALIDATION 1

6.1 HOUSATONIC RIVER WATERSHED MODEL (HSPF) VALIDATION 2

6.1.1 Introduction 3

This section documents the validation of the EPA Hydrological Simulation Program–FORTRAN 4

(HSPF) (Bicknell et al., 2001) watershed model to the Housatonic River above Great Barrington, 5

MA, for hydrology, water temperature, and suspended solids. The HSPF model calibration was 6

documented in the Model Calibration Report, Appendix A (WESTON, 2004b). The Model 7

Calibration Report included presentation and discussion of: (1) the data available to support the 8

model application, (2) the model setup and application to the Housatonic River watershed, and 9

(3) the procedures and results of the model calibration. 10

Model validation is viewed in this modeling study as an extension of the calibration process. Its 11

purpose is to demonstrate and provide assurance that the calibrated model is working properly, in 12

the sense that the model appropriately utilizes the parameters and formulations that produce the 13

model results. Although there are several approaches to validating a model, perhaps the most 14

common procedure is to use only a portion of the available data record for calibration. Once the 15

final parameter values are determined through calibration, a simulation is performed for the 16

remaining period of data and goodness-of-fit between recorded and simulated values is 17

reassessed. This type of calibration/validation procedure was followed for the HSPF model 18

validation efforts described in the following sections. The model calibration was performed for 19

the time period 1990 through 2000, and model validation was performed for 1979 through 1989, 20

and from 1979 through 2004, which allowed for an additional comparison to be made for the 21

entire period of record (Table 6.1-1). 22

Table 6.1-1 23 24

Data Used for HSPF Model Calibration and Validation 25

Model Calibration Jan. 1990 – Dec. 2000 Model Initial Validation Jan. 1979 – Dec. 1989 Model Validation (period of record) Jan. 1979 – Dec. 2004

26

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6.1.2 Watershed Model Database Development 1

Database development for the 1990 through 2000 calibration period was documented in 2

Appendix A, Section A.2, of the Model Calibration Report (WESTON, 2004b) and included the 3

evaluation of data from 1979 through 2000. In preparing the database for the validation period 4

of 1979 through 2004, the same process used in the initial database development was applied for 5

the years 2001 through 2004. 6

Unfortunately, some of the meteorological stations that had provided air temperature and 7

precipitation data for the 1979 through 2000 simulations were discontinued in 2001, most 8

notably Great Barrington and West Otis. However, beginning in 2001, data were collected at a 9

new station located in Lenoxdale, in the south-central part of the watershed near Woods Pond 10

(see Figure 6.1-1); these data included daily precipitation totals and minimum and maximum air 11

temperature. The location of the station and data collected made it appropriate for use in the 12

extension of the Great Barrington and West Otis time series. 13

The water temperature data available to support the HSPF model validation consisted of 14

monthly/bi-monthly samples collected at the USGS gages located at Coltsville and Great 15

Barrington. The data allow synoptic comparisons to be made with model results for the years 16

1979 through 1993 at Coltsville, and 1979 through 1996 at Great Barrington. Additional high-17

frequency data were collected at numerous sites located throughout the watershed in 2000 and 18

2001 (data collected in 2000 were used in model calibration). 19

The USGS collected high-frequency total suspended solids (TSS) samples at Great Barrington 20

from April 1979 through September 1980. These data, along with data collected after the 21

calibration time period at the Pomeroy Avenue Bridge, were used to support model validation. 22

Table 6.1-2 briefly summarizes the data used to support model validation. 23

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1

Figure 6.1-1 Location of Meteorological Stations for Watershed Model Validation 2

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Table 6.1-2 1 2

Summary of Data Used for Validation 3 Constituent Location Start End Processing Notes

Calibration Data Coltsville 1/1/1979 12/31/2003 Daily Flow Great Barrington 1/1/1979 12/31/2003 Coltsville 10/1/1987 12/31/2003 Some missing hours Hourly Flow Great Barrington 10/1/1987 12/31/2003 Some missing hours Coltsville 1/1/1979 6/7/1993 Monthly/bi-monthly samples Great Barrington 1/1/1979 4/1/1996 Monthly/bi-monthly samples

Water Temperature

Storm Event Sites 3/27/2001 10/11/2001 High frequency data collected at Pomeroy, New Lenox, and Woods Pond

Great Barrington 4/1/1979 9/1/1980 High frequency sampling by USGS TSS Storm Event Sites 10/30/2001 10/30/2003 57 samples at Pomeroy; 37, New

Lenox; 36, Woods Pond; 31, Great Barrington.

Meteorological Data Hourly Precipitation Pittsfield AP 1/1/1979 12/31/2003 Missing values primarily in late 2003

(Oct- Dec.) - Filled using hourly stations Copake, Coltsville, and GE along with daily station Lenoxdale

Plainfield 1/1/1979 12/31/2003 Filled with Lenoxdale using ratio of means and disaggregated using WDMUtil and filled Pittsfield AP

Daily Precipitation

Lenoxdale (to extend Great Barrington and West Otis)

1/1/1979 12/31/2003 Filled with Plainfield using ratio of means and disaggregated using WDMUtil and filled Pittsfield AP

Daily Evaporation Avg. Albany and Hartford

1/1/1979 12/31/2003 No missing; disaggregated using WDMUtil to hourly

Daily Min. and Max. Temperatures

Lenoxdale 1/1/1979 12/31/2003 Filled using interpolation for short data gaps and Pittsfield AP daily min. and max. values for longer gaps

Hourly Temperature Pittsfield AP 1/1/1979 12/31/2003 Filled using interpolation for short data gaps and Lenoxdale for longer gaps

Hourly Dew Point Avg. Albany and Hartford

1/1/1979 12/31/2003 Filled Albany and Hartford with each other; remaining missing filled using the Pittsfield AP and a ratio of the means

15-min Wind Speed GE 1/1/1979 12/31/2003 Averaged to an hourly interval - interpolated some missing values and filled larger gaps with disaggregated average wind speed from Albany and Hartford

Hourly Cloud Cover Avg. Albany and Hartford

1/1/1979 3/25/2004 Filled Albany and Hartford with each other; remaining missing were interpolated

Hourly Solar Radiation

Avg. Albany and Hartford

1/1/1979 3/25/2004 No missing; disaggregated using WDMUtil to hourly

Point Sources Flow 1/1/1979 12/31/2004 Heat 1/1/1979 12/31/2004 TSS

Pittsfield WWTP

1/1/1979 12/31/2004

Extended using 1979-2000 monthly averages

Formatted: Not Highlight

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6.1.3 HSPF Hydrology Validation 1

Hydrologic validation was initially performed for the time period 1979 through 1989. This time 2

period was prior to the hydrologic calibration time period, 1990 through 2000; this type of 3

validation is referred to as a split-record validation. An additional validation was performed by 4

using the entire period of record for the majority of data, 1979 through 2004 (referred to as the 5

“period of record” validation). This additional validation made use of 26 years of data 6

encompassing a wide range of hydrologic conditions, including some of the largest storm events 7

recorded at the USGS Coltsville and Great Barrington gages. 8

The following comparisons of simulated and measured values were made for the Coltsville and 9

Great Barrington gage sites for the two validation periods: 10

Annual and monthly runoff volumes (inches). 11 Daily time series of flow (cfs). 12 Scatter plots of simulated vs. measured flow (cfs). 13 Flow frequency (flow duration) curves (cfs). 14

15

6.1.3.1 Annual Runoff – Coltsville and Great Barrington 16

The first step in validating the model was to review the annual water balance and assess how the 17

calibrated model performed during the validation time periods. Table 6.1-3 shows the resulting 18

agreement between simulated and measured mean annual inches of runoff at Coltsville and Great 19

Barrington from 1979 through 2004. At Coltsville, the annual percent errors range from -17% to 20

25% with an overall 4.7% error for the entire time period. Both of the extremes occur during the 21

1979 through 1989 validation time period, which had an overall percent error of 7.0%. The 22

larger errors during the 1979 through 1989 period appear to be due primarily to the fact that the 23

Pittsfield Airport and the GE meteorological stations used for the calibration were not available 24

during the 1979 through 1989 validation time period, and data from the less representative 25

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Table 6.1-3 1 2

Comparison of Simulated and Measured Annual Flow at Coltsville and Great 3 Barrington, 1979-2004 Validation Period 4

Coltsville Great Barrington

Precipitation Simulated

Flow Measured

Flow Percent Error Precipitation

Simulated Flow

Measured Flow

Percent Error

1979 62.6 34.9 33.1 5.6% 56.1 33.0 36.2 -9.1% 1980 40.3 17.5 16.2 8.1% 36.2 16.9 17.0 -0.5% 1981 46.0 17.8 15.1 18.3% 42.1 17.6 18.0 -2.3% 1982 45.6 26.4 22.5 17.4% 41.5 25.6 23.7 7.9% 1983 61.9 29.4 27.8 5.8% 54.4 28.8 28.4 1.4% 1984 57.8 39.2 31.4 24.6% 50.5 33.4 32.3 3.3% 1985 45.1 16.5 14.8 11.7% 39.9 16.0 15.7 1.7% 1986 52.7 27.1 24.3 11.7% 47.4 25.9 25.6 1.4% 1987 45.4 25.5 24.4 4.2% 42.8 24.7 23.8 3.6% 1988 43.0 19.5 23.4 -16.7% 42.1 21.7 22.6 -4.2% 1989 49.8 24.0 26.7 -10.2% 47.7 24.9 25.6 -2.8% 1990 60.6 34.8 36.5 -4.6% 58.9 35.4 35.6 -0.8% 1991 50.5 22.3 22.2 0.2% 46.2 22.7 22.8 -0.5% 1992 48.9 22.7 21.4 6.0% 46.2 23.6 20.0 17.6% 1993 49.9 27.5 27.7 -0.7% 48.2 29.2 26.1 12.2% 1994 49.5 25.3 24.7 2.1% 46.4 25.9 25.5 1.8% 1995 50.9 22.0 20.7 6.5% 43.1 20.3 21.0 -3.4% 1996 66.4 41.6 41.7 -0.3% 61.0 39.0 41.4 -5.8% 1997 46.7 21.7 22.0 -1.0% 42.3 21.1 23.2 -9.2% 1998 45.9 23.2 24.1 -3.9% 42.1 22.4 23.9 -6.6% 1999 50.2 20.1 21.3 -5.3% 50.9 24.7 24.8 -0.2% 2000 60.1 32.6 31.4 3.8% 56.1 33.0 30.8 7.0% 2001 45.3 25.0 22.9 9.3% 40.6 24.0 21.2 13.0% 2002 52.3 19.7 18.4 7.0% 47.8 20.2 17.9 12.9% 2003 66.9 43.3 35.7 21.5% 58.3 37.5 33.1 13.1% 2004 54.5 32.4 26.5 22.5% 51.0 31.5 26.5 19.1% Average (1979-1989) 50.0 25.3 23.6 7.0% 45.5 24.4 24.4 -0.2% Average (1979-2004) 51.9 26.6 25.3 5.4% 47.7 26.1 25.5 2.4%

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Lanesboro gage were used for most of the area above the Coltsville gage. The overall percent 1

errors at Great Barrington are 0.2% and 1.7% for 1979 through 1989 and for the period of record, 2

respectively. The largest errors for Great Barrington occur within the calibration time period, 3

which supports the conclusion that the model performs equally well for the validation time 4

periods as for the calibration period. 5

Based on the results at Coltsville and Great Barrington, the validation for both time periods is 6

well within the conventional HSPF error criteria of 10% for a “very good” calibration. At 7

Coltsville, the majority of the annual errors (all but 5 years) are within the 15% target for a 8

“good” calibration specified in the Modeling Study QAPP and final MFD, whereas only 7 of the 9

26 years have errors that exceed the 10% error criteria for a “very good” calibration. At Great 10

Barrington, the annual errors for all years except one are within the 15% target for a “good” 11

calibration and only 6 years have errors that exceed the target for a “very good” calibration. 12

Table 6.1-4 displays the annual flow summaries and error statistics calculated by HSPEXP. 13

HSPEXP is an expert system for aiding in hydrologic calibration and generating statistical 14

summaries, specifically designed for use with HSPF, developed under contract for the U.S. 15

Geological Survey (USGS) (Lumb et al., 1994). The summaries describe the average annual 16

distribution of high and low flows, actual evapotranspiration and potential evapotranspiration (PET), 17

and storm volumes and average peaks. The quantitative criteria listed are the default values of 18

acceptable error for model calibration/validation included with the expert system. All of the errors 19

are well within the ±15% hydrology validation target specified in Table 4-4 of the QAPP for the 20

validation over the period of record. The 1979 through 1989 validation period had only one 21

statistical error outside the criteria, the error at Coltsville for runoff volume resulting from the 10% 22

highest flows. This is primarily due to the input meteorological data available, as discussed above. 23

6.1.3.2 Daily and Monthly Flows – Coltsville and Great Barrington 24

After the annual water balance was reviewed, the daily and monthly time series were examined 25

concurrently to assess how the model was performing over shorter time scales. Table 6.1-5 26

presents statistics calculated for daily and average monthly flows at Coltsville and Great 27

Barrington for the 1979 through 1989 and period of record validations. In general, the mean 28

flows are in “very good” agreement, i.e., all of the percent mean errors are less than the 29

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Table 6.1-4 1 2

Annual Flow Summaries and Statistics from HSPEXP for Coltsville and Great 3 Barrington, 1979-1989 and 1979-2004 Validation Periods 4

Coltsville Great Barrington

Initial Validation (1979-1989)

Simulated Measured Simulated Measured Average runoff, in inches 25.3 23.6 24.4 24.5 Total of highest 10% flows, in inches 12.6 10.5 9.9 8.8 Total of lowest 50% flows, in inches 3.8 3.6 4.4 4.9 Evapotranspiration, in inches 22.0 23.7a 22.0 24.3a Total storm volume, in inchesb 4.2 3.4 3.2 2.8 Average of storm peaks, in cfsb 1,596.6 1,414.8 3,889.7 3,762.5

Calculated Criteria Calculated Criteria Error in total volume, % 7.0 10.0 -0.2 10.0 Error in 10% highest flows, % 20.5 15.0 13.1 15.0 Error in 50% lowest flows, % 5.1 10.0 -9.8 10.0 Error in storm peaks, %b 12.8 15.0 3.4 15.0

Period of Record Validation (1979-2004)

Simulated Measured Simulated Measured Average runoff, in inches 26.6 25.3 26.1 25.5 Total of highest 10% flows, in inches 12.1 10.7 9.7 8.9 Total of lowest 50% flows, in inches 4.3 4.1 4.9 5.1 Evapotranspiration, in inches 22.7 24.4a 22.6 25.0a Total storm volume, in inchesc 4.3 3.9 3.4 3.2 Average of storm peaks, in cfsc 961.1 976.7 2,762.3 2,727.3

Calculated Criteria Calculated Criteria Error in total volume, % 5.4 10.0 2.4 10.0 Error in 10% highest flows, % 13.5 15.0 9.6 15.0 Error in 50% lowest flows, % 5.0 10.0 -3.3 10.0 Error in storm peaks, % c -1.6 15.0 1.3 15.0 5 a PET (estimated by multiplying measured pan evaporation data by 0.73). 6 b Based on 12 storms occurring between 1979 and 1989. 7 c Based on 50 storms occurring between 1979 and 2004. 8

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Table 6.1-5 1 2

Daily and Monthly Average Flow Statistics at Coltsville and Great Barrington, 3 1979-1989 and 1979-2004 Validation Periods 4

Coltsville

Initial Validation Period (1979 – 1989) Period of Record Validation (1979 – 2004)

Daily Monthly Daily Monthly

Simulated Measured Simulated Measured Simulated Measured Simulated Measured

Count 4,018 4,018 132 132 9,497 9,497 312 312 Mean, cfs 106.3 99.3 106.6 99.5 112.0 106.3 112.2 106.4 Geometric Mean, cfs 59.5 58.7 70.7 70.0 66.7 65.2 80.0 77.9 Correlation Coefficient (r) 0.87 0.92 0.85 0.92 Coefficient of Determination (r2)

0.75 0.84 0.73 0.84

% Mean Error 7.0 7.1 5.4 5.5 % Mean Absolute Error 41.3 29.4 36.0 23.0 RMS Error, cfs 97.6 48.7 90.1 41.5 Model Fit Efficiency (1.0 is perfect)

0.63 0.72 0.65 0.79

Great Barrington

Initial Validation Period (1979 – 1989) Period of Record Validation (1979 – 2004)

Daily Monthly Daily Monthly

Simulated Measured Simulated Measured Simulated Measured Simulated Measured

Count 4,018 4,018 132 132 9,497 9,497 312 312 Mean, cfs 505.0 506.2 506.2 507.2 540.7 528.0 541.4 528.4 Geometric Mean, cfs 328.5 356.3 370.4 395.7 363.9 371.2 412.7 415.2 Correlation Coefficient (r) 0.90 0.93 0.90 0.94 Coefficient of Determination (r2)

0.81 0.86 0.8 0.88

% Mean Error -0.2 -0.2 2.4 2.46 % Mean Absolute Error 28.5 20.1 26.69 17.5 RMS Error, cfs 292.2 175.8 271.6 149.8 Model Fit Efficiency (1.0 is perfect)

0.75 0.79 0.76 0.85

5 conventional HSPF upper limit for a “very good” calibration of 10%, for both Coltsville and 6

Great Barrington under both validation time periods. The correlation coefficients at both 7

Coltsville and Great Barrington are ≥ 0.85 and ≥ 0.90 for daily and monthly flows, respectively, 8

for both time periods. The model fit efficiency (MFE) values for average monthly flows at 9

Coltsville and Great Barrington are approximately 0.8 and 0.85, respectively, for the period of 10

record. 11

Previous studies have defined an acceptable level of calibration as a correlation coefficient 12

greater than 0.85 and an MFE greater than 0.80 for monthly flows (WESTON, 2000). These 13

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requirements were met at both Coltsville and Great Barrington for the period of record and at 1

Great Barrington for the 1979 through 1989 validation time period. For the 1979 through 1989 2

validation time period at Coltsville, the MFE value is 0.72, which falls slightly below the 0.80 3

requirement for monthly flows. This again reflects on the input meteorological data that were 4

available for that time. 5

Table 6.1-6 displays the mean monthly measured and simulated runoff, average residual, and 6

percent error for the validation time periods. The monthly residuals for the 1979 through 1989 7

validation time period indicate that the model tends to overestimate the runoff at Coltsville and, 8

to a lesser extent, at Great Barrington, especially during the spring snowmelt in March and April. 9

Again, this is primarily the result of having to use the Lanesboro gage to supply the 10

meteorological conditions for much of the area above Coltsville during this time period, which 11

tends to overestimate the snowfall and snow pack and the subsequent spring melt. The monthly 12

percent errors for the period of record are, however, typically within the 10% criterion for a 13

“very good” calibration, with only the April percent error at Coltsville reaching/exceeding the 14

15% criterion for a “good” calibration. 15

The agreement between measured and simulated daily flows is generally quite good at both 16

Coltsville and Great Barrington, including comparisons for some of the largest storm events 17

recorded at the respective gages. The Coltsville and Great Barrington gages recorded data from 18

1936 to present and 1914 to present, respectively. Figures 6.1-2 and 6.1-3 provide examples of 19

the daily flow simulations for Coltsville and Great Barrington, for the first two years of the 1979 20

through 1989 period, and include some of the large flood events. The remaining years are 21

displayed in Figures C.1-__ through C.1-__ in Appendix C.1. 22

Figure 6.1-2 shows how the model results compare for flows recorded in 1979 at Coltsville and 23

Great Barrington. Because this is the first year of model simulation, it is expected that the model 24

results may initially deviate from the data due to the initial conditions (e.g., snow pack depth, 25

soil moisture storage). The March event resulted in the 8th largest flow recorded at Great 26

Barrington, and was the 23rd largest at Coltsville. At Great Barrington this event was modeled 27

quite well despite the potential for being influenced by specification of initial conditions. At 28

Coltsville, it appears that temperatures simulated by the model did not support the actual melt; 29

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Table 6.1-6 1 2

Average Measured Monthly Flow and Residuals at Coltsville and Great 3 Barrington, 1979-1989 and 1979-2004 Validation Periods 4

Initial Validation Period (1979-1989)

Coltsville Great Barrington

Month Average

Simulated Average

Measured

Average Residual

(Simulated - Measured)

Percent Error

Average Simulated

Average Measured

Average Residual

(Simulated - Measured)

Percent Error

JAN 1.12 1.42 -0.30 -21.3% 1.48 1.72 -0.24 -13.8% FEB 2.01 1.95 0.06 3.1% 2.13 2.16 -0.03 -1.2% MAR 4.13 3.82 0.31 8.2% 4.06 3.63 0.43 11.9% APR 6.29 4.84 1.46 30.1% 5.00 4.24 0.76 17.8% MAY 2.92 2.82 0.09 3.3% 2.51 2.80 -0.29 -10.2% JUN 1.82 1.54 0.28 18.2% 1.86 1.89 -0.03 -1.5% JUL 1.08 1.02 0.06 6.1% 1.09 1.13 -0.04 -3.7% AUG 0.75 0.68 0.07 10.2% 0.84 0.84 0.00 0.5% SEP 0.78 0.81 -0.03 -3.8% 0.80 0.88 -0.08 -9.3% OCT 1.23 1.35 -0.12 -8.6% 1.23 1.55 -0.32 -20.4% NOV 1.60 1.75 -0.15 -8.8% 1.63 1.80 -0.17 -9.3% DEC 1.52 1.60 -0.08 -5.3% 1.74 1.82 -0.07 -4.0% Totals 25.24 23.60 -0.08 7.0% 24.37 24.43 -0.07 -0.3%

Period of Record Validation (1979-2004)

Coltsville Great Barrington

Month Average

Simulated Average

Measured

Average Residual

(Simulated - Measured)

Percent Error

Average Simulated

Average Measured

Average Residual

(Simulated - Measured)

Percent Error

JAN 1.80 1.84 -0.02 -0.9% 2.14 2.14 0.00 -0.0% FEB 2.00 1.75 0.14 8.0% 2.11 1.92 0.19 9.8% MAR 3.97 3.84 0.20 5.1% 3.95 3.56 0.39 10.9% APR 5.56 4.84 0.74 15.2% 4.66 4.25 0.41 9.7% MAY 2.69 2.71 -0.04 -1.3% 2.46 2.73 -0.27 -9.8% JUN 1.92 1.72 0.18 10.5% 1.85 1.86 -0.01 -0.5% JUL 1.01 0.94 0.06 6.1% 1.07 1.08 -0.01 -0.7% AUG 1.00 0.97 0.03 3.6% 1.03 0.97 0.06 6.1% SEP 0.97 1.01 0.06 6.2% 1.04 1.01 0.03 2.6% OCT 1.51 1.59 -0.05 -3.3% 1.49 1.66 -0.17 -10.3% NOV 1.88 1.92 -0.02 -1.1% 1.89 1.98 -0.08 -4.2% DEC 2.13 2.12 0.07 3.5% 2.40 2.33 0.08 3.3% Totals 26.45 25.26 0.07 5.4% 26.10 25.49 0.08 2.4% 5

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1

Figure 6.1-2 Time Series of Simulated vs. Measured Daily Flow at Coltsville and 2 Great Barrington (1979) 3

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1

Figure 6.1-3 Time Series of Simulated vs. Measured Daily Flow at Coltsville and 2 Great Barrington (1980) 3

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therefore, some of the precipitation that fell as rain was simulated as snow, which under-1

simulated the March event and inflated the ensuing simulated melt in late March and April. 2

These types of discrepancies are typical when modeling snow processes. 3

Figure 6.1-3 shows how the model results compare for flows recorded in 1980. The March event 4

in 1980 was the 6th largest event recorded at Coltsville, and the 20th largest at Great Barrington, 5

and could be considered approximately a 10-year event at both gages. The model accurately 6

reproduces the data for these events. 7

Figure 6.1-4 shows scatter plots for daily and monthly flows at Coltsville and Great Barrington 8

for the period of record validations. Scatter plots for the 1979 through 1989 periods are 9

presented in Appendix C.1. The plots include a 1:1 line, equation of linear regression, and 10

coefficient of determination (r2). These plots show a good correlation for flows at Coltsville, and 11

a good-to-very good correlation for flows at Great Barrington. However, once again it is 12

apparent that the flows for the 1979 through 1989 validation time period tend to be slightly 13

overestimated, especially at Coltsville, whereas the plots for the period of record validation show 14

better agreement. 15

6.1.3.3 Flow Duration–Coltsville and Great Barrington 16

The daily flow duration curves are presented in Figure 6.1-5 for simulated and measured flows at 17

Coltsville and Great Barrington for both validation periods. The simulated flow duration curves 18

at Coltsville and Great Barrington are a good representation of the measured curves for the 19

period of record and a fair-to-good representation for the 1979 through 1989 validation. This 20

indicates that the model provides a reasonable representation of the rainfall/runoff processes 21

occurring in the watershed over a wide range of hydrologic conditions. The flow duration curves 22

are a direct reflection of the daily time series comparisons, with a tendency to overestimate storm 23

peaks during the 1979 through 1989 time period, but otherwise showing very close agreement 24

through most of the flow range, and generally better agreement for the period of record. 25

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1

Figure 6.1-4 Scatter Plots of Simulated vs. Measured Daily and Monthly Flows at 2 Coltsville and Great Barrington (1979-2004 Period of Record Validation) 3

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1

Figure 6.1-5 Simulated vs. Measured Daily Flow Duration Curves at Coltsville and 2 Great Barrington, 1979-1989 and 1979-2004 Validation Periods 3

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Figure 6.1-6 presents the hourly flow duration curves for simulated and measured flows at 1

Coltsville and Great Barrington for the period of record for which hourly data were available. The 2

hourly flow duration curves show that the model duplicates the short time behavior of the 3

watershed, even though the representation of specific individual hourly values and storm events 4

may be higher or lower than measured values. Approximately 3% of the hourly data at Great 5

Barrington were missing during the 1987 through 2004 time period, as indicated by the vertical 6

line in the figure. 7

The level of agreement and overall shapes of the curves from the model validation are consistent 8

with the results produced during the calibration phase of the watershed model development. 9

6.1.3.4 Storm Hydrograph Analysis 10

The next step in the hydrologic validation was to analyze storm event hydrographs for significant 11

storms occurring within the 1979 through 1989 validation time period or post 2000, i.e., events 12

not analyzed during the calibration (1990 through 2000). As discussed above, some of the 13

largest storms of record occurred during the 1979 through 1989 validation period, and provided a 14

good opportunity to assess the ability of the model to predict extreme events. Table 6.1-7 15

presents a summary of the major storm events analyzed and presented in the following figures. 16

Note that the USGS data in the table are reported as instantaneous peak flow, whereas only daily 17

average flows were available for the plots. Thus, the plots present daily average rates and may 18

not correspond directly with the peak flows presented in Table 6.1-7. 19

Figure 6.1-7 presents the May 1984 event, which approached a 100-year event at Great 20

Barrington while only being a 10-year event at Coltsville. The model reasonably simulates both 21

the peaks and volumes. The volume and peak are slightly overestimated at Coltsville; however, 22

these results still represent a good validation, considering the magnitude of the differences 23

between the results at Coltsville and Great Barrington and the spatial variability of the storm 24

event within the watershed. 25

DRAFT

11/21/2005 6-18

1

2

Figure 6.1-6 Simulated vs. Measured Hourly Flow Duration Curves at Coltsville 3 and Great Barrington, 1987-2004 4

Missing Data

Periods

DRAFT

11/21/2005 6-19

1

Figure 6.1-7 Simulated vs. Measured Daily Average Flow at Coltsville and Great 2 Barrington for May 1984 Storm Event 3

DRAFT

11/21/2005 6-20

Table 6.1-7 1 2

Summary Statistics for Major Storm Events, 1979-2004 Validation Period 3 (excluding storm events analyzed for the 1990-2000 HSPF Calibration Period) 4

USGS Event Rank Simulated Peak Hourly Flow

(cfs) USGS Return Period and

Peak Flow (cfs)c

Figure No.

Storm Event Date Coltsvillea

Great Barringtonb Coltsville

Great Barrington Coltsville

Great Barrington

6.1-7 May 1984 10 3 4,210 9,290 ~ 10 yr (3,340) ~ 100 yr (10,300)

6.1-8 April 1987 4 11 3,500 6,840 ~ 25 yr (5,000) ~ 10 yr (6,050)

6.1-9 March 1979 21 7 2,390 7,560 < 10 yr (2,250) 10-25 yr (6,850)

6.1-10 March 1980 5 19 3,610 9,110 10-25 yr (4,170) < 10 yr (5,110)

a Coltsville’s period of record is from 1936 to present. 5 b Great Barrington’s period of record is from 1914 to present. 6 c Rates from USGS peak flow database; return period based on published USGS flow frequency analysis. 7 8 Return Interval USGS Estimate at Coltsville USGS Estimate at Great Barrington 9 10 3,740 cfs 6,340 cfs 10 25 5,060 cfs 7,590 cfs 11 50 6,170 cfs 9,260 cfs 12 100 7,400 cfs 10,700 cfs 13 14 Figure 6.1-8 presents the model-to-data comparison for the 1987 event. Overall, the peaks are 15

simulated quite well, although the volume is somewhat high at Great Barrington for this 16

rain/snowmelt event. These results are considered “very good” due to the complexities involved 17

in simulating snow processes, especially when rain falls on the snow pack and increases the melt 18

rate and subsequent runoff. Figure 6.1-9 presents the 1979 event, which was relatively small and 19

underpredicted by the model at Coltsville. 20

At Great Barrington, where the storm flow was larger (approximately a 10- to 25-year event) the 21

results indicated a good simulation, with a slight underprediction of the peak flow rate and volume. 22

The March 1980 event is presented in Figure 6.1-10. This was the 6th largest event to be recorded at 23

Coltsville and is represented extremely well at both locations, with only a slight discrepancy in the 24

time of the peak at Great Barrington. This type of timing difference is expected when all but one of 25

the precipitation gages are daily recorders, and the intensities have to be estimated throughout the 26

watershed based on the pattern observed at the available hourly gages. 27 Formatted: Not Highlight

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11/21/2005 6-21

1

Figure 6.1-8 Simulated vs. Measured Daily Average Flow at Coltsville and Great 2 Barrington for April 1987 Storm Event 3

DRAFT

11/21/2005 6-22

1

Figure 6.1-9 Simulated vs. Measured Daily Average Flow at Coltsville and Great 2 Barrington for March 1979 Storm Event 3

DRAFT

11/21/2005 6-23

1 2

Figure 6.1-10 Simulated vs. Measured Daily Average Flow at Coltsville and Great 3 Barrington for March 1980 Storm Event 4

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11/21/2005 6-24

6.1.3.5 Water Balance Analysis 1

Table 6.1-8 shows the range of expected and simulated water balance components for the 2

calibration period and for both of the validation time periods. The water balance components are 3

relatively consistent for all time periods and within the ranges expected. The 1979 through 1989 4

time period is slightly drier than the other time periods and results in less runoff. 5

Table 6.1-8 6 7

Average Annual Expected and Simulated Water Balance for HSPF Calibration and 8 Validation Periods 9

Expected Ranges Calibration

Initial Validation (1979-1989)

Period of Record Validation (1979-2004)

Moisture Supply 43 - 53 49 45 47

Total Runoff 23 - 27 25 22 24

Total ET 20 - 23 23 22 22

Deep Recharge 1 - 4 1 1 1

10

Table 6.1-9 lists the average annual water balance components simulated for each land use 11

category in the model for the entire 1979 through 2004 validation period. The results reflect the 12

expected differences among land use categories. 13

6.1.3.6 Conclusions – Hydrology Validation 14

Table 6.1-10 presents the results of comparisons presented in this section and provides a 15

summary of the “weight-of-evidence” supporting validation of the hydrologic component of the 16

watershed model. Based on the model results presented in Table 6.1-10, the discussion above of 17

the hydrology validation, and Appendix A of the Calibration Report (WESTON, 2004b), the 18

following observations and conclusions are evident: 19

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11/21/2005 6-25

Table 6.1-9 1 2

Simulated Water Balance Components by Land Use for Period of Record 3 Validation (1979-2004) 4

Forest AgricultureUrban

Pervious Wetland Urban

Impervious

Moisture Supply 47.4 47.2 47.3 47.3 47.2 Total Runoff 22.8 25.7 25.8 20.8 42.0 Surface runoff 1.5 8.5 7.8 0.2 42.0 Interflow 9.3 8.5 8.2 4.1 0.0 Base flow 12.1 8.7 9.8 16.5 0.0 Total ET 23.4 20.9 20.9 23.5 5.1 Interception/retention ET 8.8 5.7 6.0 4.3 5.1 Upper zone ET 8.3 7.7 10.2 11.0 0.0 Lower zone ET 6.0 7.3 4.6 4.3 0.0 Active GW ET 0.0 0.0 0.0 3.0 0.0 Base flow ET 0.3 0.1 0.1 0.9 0.0 Deep Recharge 0.8 0.5 0.5 2.6 0.0

5 The overall errors for the validation period and the majority of the annual errors at 6

Coltsville and Great Barrington are less than the 10% HSPF tolerance for a “very 7 good” calibration specified in Table 4-3 of the Modeling Study QAPP (WESTON, 8 2000). Only one of the annual errors calculated by HSPEXP exceeds the ±15% 9 study-specific hydrology calibration target tolerance for the watershed model 10 specified in Table 4-4 of the Modeling Study QAPP (WESTON, 2000) and Appendix 11 I of the final MFD (WESTON, 2004a); the majority of the errors are less than ±10%. 12

The correlation coefficients for simulated vs. measured flows at both Coltsville and 13 Great Barrington are ≥ 0.85 and ≥ 0.90 for daily and monthly flows, respectively, for 14 both validation time periods. The MFE values for average monthly flows at 15 Coltsville and Great Barrington are approximately 0.8 and 0.85, respectively, for the 16 period of record. Previous studies have defined an acceptable level of calibration as a 17 correlation coefficient greater than 0.85 and an MFE greater than 0.80 for monthly 18 flows (WESTON, 2000). These criteria are satisfied for both Coltsville and Great 19 Barrington for the period of record and at Great Barrington for the 1979 through 1989 20 validation time period. The MFE at Coltsville for the 1979-1989 validation time 21 period falls slightly below the criterion, but is judged by the modeling team to be 22 sufficiently close to allow unrestricted use of the validated watershed model in the 23 modeling study. 24

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11/21/2005 6-26

Table 6.1-10 1 2

Weight-of-Evidence Summary for Watershed Model (HSPF) Hydrology Calibration 3 and Validation 4

Coltsville Great Barrington

Calibration (1990-2000)

Initial Validation

(1979-1989)

Period of Record

Validation (1979-2004)

Calibration (1990-2000)

Initial Validation

(1979-1989)

Period of Record

Validation (1979-2004)

Overall Model Performance*

Entire Period, % ME 0.6 7.0 5.4 1.6 -0.2 2.4 Very Good Annual Volume, % ME +6 / -5 +25 / -17 +25 / -17 +17 / -9 +8 / -9.1 +19 / -9 Fair / Very Good Monthly Volume, % ME +15 / -10 +30 / -21 +15 / -3 +22 / -15 +18 / -20 +11 / -10 Fair / Very Good Correlation Coefficient, r: - Daily r 0.87 0.87 0.86 0.90 0.90 0.90 Good / Very Good - Monthly r 0.95 0.92 0.92 0.95 0.93 0.94 Very Good Coefficient of Variation, r2: - Daily r2 0.76 0.75 0.73 0.81 0.81 0.80 Good / Very Good - Monthly r2 0.90 0.84 0.84 0.90 0.96 0.88 Very Good Model Fit Efficiency, MFE: - Daily MFE 0.74 0.63 0.65 0.80 0.75 0.76 Very Good - Monthly MFE 0.90 0.72 0.79 0.89 0.79 0.85 Very Good Flow-Duration Very Good Good Very Good Very Good Good Very Good Water Balance Very Good Very Good Very Good Very Good Very Good Very Good Storm Events: - Daily Storm Peak, % ∆ -7.2 12.8 -1.6 -3.2 3.4 1.3 Very Good

- Storm Volumes, % ∆ 1.3 24.9 9.5 -0.2 13.5 6.7 Very Good

- 10% High Flows, % ∆ 2.2 20.5 13.5 2.9 13.1 9.6 Good / Very Good

*See Table 4-3 of Modeling Study QAPP (WESTON, 2000) for definition of these terms. 5 6

The agreement between measured and simulated daily flows is generally very good at 7 both Coltsville and Great Barrington, including comparisons for some of the largest 8 storm events recorded at the respective gages. The arithmetic and geometric mean 9 flows are also in excellent agreement. 10

The simulated flow duration curves, both daily and hourly, at Coltsville and Great 11 Barrington are a good representation of the measured curves, indicating that the 12 model properly simulates the rainfall-runoff processes occurring in the watershed 13 over a wide range of hydrologic conditions. Some variation is apparent, however, 14 primarily during the 1979 through 1989 period for high flows at Coltsville and, to a 15 lesser extent, at Great Barrington, and for the lower tail of the flow duration curve at 16 Great Barrington. The curves for the period of record (1979-2004) are generally in 17 better agreement than those for 1979 through 1989. The hourly flow duration curves 18 show that the model is predicting the large events with similar magnitude and 19 frequency throughout the simulation time period. 20

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11/21/2005 6-27

Based on the entire weight-of-evidence of the full range of model results presented in 1 this section and in Appendix C.1, this hydrology calibration is judged to be validated 2 and to be capable of providing accurate long-term flow boundary conditions for the 3 EFDC hydrodynamic/sediment-contaminant transport model. 4

6.1.4 HSPF Total Suspended Solids Validation 5

Stormwater monitoring data were collected from 1999 through 2000 for 11 storm events at 9 6

mainstem and tributary locations. The data collected included flow rates and measurements of 7

TSS in the water column. These data, along with historical data from these locations, were used 8

to develop estimates of TSS mass flux entering and passing through various points in the PSA 9

for the time period of 1988 through 2001 (TSS flux for the year 2000 [part of the HSPF 10

calibration period] was reported in the Model Calibration Report [WESTON, 2004b], 11

Attachment B.3). The flux analysis was performed at four of the nine sampling locations: the 12

two upstream boundaries of the PSA on the East and West Branches, New Lenox Road near the 13

middle of the PSA, and Woods Pond Outlet at the downstream boundary of the PSA. The 1990 14

through 2000 loading estimates aided in the watershed model suspended solids calibration (see 15

Model Calibration Report, Appendix A). The calculated 1988-1989 and 2001 loading rates 16

allowed comparisons to be made at the same sites for the validation time periods. Additional 17

comparisons were also made with data from synoptic sampling conducted on the East Branch at 18

Pomeroy Avenue from October 2001 through October 2004. 19

The USGS gage at Great Barrington was operated as a continuous-record TSS station from April 20

1979 through September 1980. This sampling included high-frequency sampling of TSS and 21

concurrent flow measurements. These data allow additional annual TSS flux to be estimated and 22

comparisons made between the data and results of the model simulation at Great Barrington. 23

Even with the additional channel surveys conducted for the downstream model calibration (see 24

Section 6.5), the data on channel morphology between Woods Pond and Great Barrington remain 25

limited in comparison to the PSA, which complicates comparisons between TSS concentrations 26

and model results. Consequently, during the watershed model calibration, only annual TSS loads 27

were compared to assess general concurrence with model results. A similar approach was 28

adopted for the model validation. 29

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11/21/2005 6-28

6.1.4.1 Total Suspended Solids Validation Results 1

Figure 6.1-11 presents the annual TSS loads simulated by HSPF versus the flux analysis 2

estimates for the years 1988 through 1989 and 2001, respectively, at each of the locations for 3

which data are available. Table 6.1-11 presents the values displayed in the figure and the overall 4

average annual loading rate for the 1988 through 2001 period, along with the percent difference 5

calculations. The overall average differences for both the 1988 through 1989 and the 2001 time 6

periods are less than or equal to the target tolerance for TSS loading of ±30% specified in Table 7

4-4 of the Modeling Study QAPP (WESTON, 2000). The differences for the average annual 8

load for the 1988 though 2001 time period are also within the ±30% target tolerance, with all 9

sites having errors less than or equal to ±25%. 10

In general, the model tends to underpredict TSS loads at all locations. However, these types of 11

differences are expected due to uncertainties within the model, the data, and the loading 12

estimates extrapolated for flow rates beyond the limits of those used in developing the TSS 13

rating curves for the flux analysis. Overall, the agreement between the model and the estimates 14

based on data appear to be reasonable and consistent with what was seen in the calibration, and 15

are within the ±30% target tolerance specified in the QAPP. 16

The loads simulated by the model (10,412 MT/yr) versus TSS loading calculations generated by 17

the USGS (9,723 MT/yr) at Great Barrington, based on the data collected during April 1979 18

through September 1980, indicate good agreement between the model simulation and the data. 19

The small difference between model results and data (7%) strongly supports model validation 20

and is well within the ± 30% target tolerance specified in the QAPP. 21

22

DRAFT

11/21/2005 6-29

1

Figure 6.1-11 Simulated vs. Calculated Annual TSS Loads at Primary Sampling 2 Locations (MT/year) for 1988-1999 and 2001 3

1988 1989 2001 Ave0

2000

4000

6000

8000

10000Pomeroy

TSS

Loa

d (M

T/yr

)

CalculatedSimulated

1988 1989 2001 Ave0

2000

4000

6000

8000

10000West Branch

TSS

Loa

d (M

T/yr

)

CalculatedSimulated

1988 1989 2001 Ave0

2000

4000

6000

8000

10000New Lenox Rd

TSS

Loa

d (M

T/yr

)

CalculatedSimulated

1988 1989 2001 Ave0

2000

4000

6000

8000

10000Woods Pond Outlet

TSS

Loa

d (M

T/yr

)

Year

CalculatedSimulated

DRAFT

11/21/2005 6-30

Table 6.1-11 1 2

Summary Statistics for Simulated and Calculated Annual TSS Loads at Primary 3 Sampling Locations (MT/yr) for 1988-1989 and 2001 4

Pomeroy Avenue West Branch

Time Period Calculated Simulated % Diff Calculated Simulated % Diff

1988 4,078 2,529 -38% 1,320 887 -33%

1989 5,221 3,776 -28% 1,938 1,392 -28%

2001 5,555 4,699 -15% 1,262 2,241 78%

Average 4,951 3,668 -26% 1,507 1,507 0%

Annual Avg. 1988-2001 4,444 3,609 -19% 2,020 1,733 -14%

New Lenox Rd Woods Pond Outlet

Time Period Calculated Simulated % Diff Calculated Simulated % Diff

1988 4,239 2,397 -43% 1,479 646 -56%

1989 4,939 3,731 -24% 1,938 1,027 -47%

2001 5,710 4,308 -25% 1,555 2,671 72%

Average 4,963 3,479 -30% 1,658 1,448 -13%

Annual Avg. 1988-2001 4,775 3,768 -21% 1,639 1,260 -23%

5 Table 6.1-12 presents the TSS budget for each channel module (RCHRES) simulated within the 6

model. PSA reaches are highlighted in light green. The table presents tabulations of the average 7

annual TSS erosion (nonpoint) loads, point loads, upstream and total inflow loads, total outflow 8

loads, and both cumulative and reach-specific trapping efficiencies; the values in the table are 9

averages over the 26-year validation period (i.e., 1979-2004). This information was compared 10

with results from the flux analysis, historical information, field observations, and professional 11

judgment to ensure the model was predicting reasonable behavior for each RCHRES and was 12

consistent with the results of the simulation during the calibration phase. 13

Table 6.1-13 presents a comparison of trapping efficiencies for the calibration period and the 14

period of record validation period; the results indicate consistent behavior for the reaches for 15

both time periods. There is a larger variation apparent for Woods Pond between the two time 16

periods. This is primarily a result of large storm events that occurred within the 1979 through 17

1989 time period, which allowed less deposition to occur within the pond than during the 18

calibration period and thus, lowered the cumulative trapping efficiency. 19

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11/21/2005 6-31

Table 6.1-12 1 2

TSS Budget by Reach for Period of Record Validation (1979-2004) 3 Average Annual Totals (tons) Time Span: 26 yr from 01/01/79 through 12/31/04 Reaches in PSA are shaded green

SOSED/SOSLD

Reach Segment Nonpoint

(tons)

Point Source (tons)

Upstream Inflow (tons)

Total Inflow (tons)

Outflow (tons)

Deposit(+)Scour(-)

(tons)

CumulativePoint/NonPt

(tons)

CumulativeTrapping Efficiency

(%)

Reach Trapping Efficiency

(%) RCHRES 9 - Windsor 909.9 0.0 0.0 908.1 748.7 159.4 909.9 17.7 17.6 RCHRES 10 - Windsor Res. 83.6 0.0 0.0 83.5 17.6 68.2 83.6 78.9 78.9 RCHRES 11 - Wahconah 459.0 0.0 17.6 475.7 410.2 65.6 542.6 24.4 13.8 RCHRES 12 - Cleveland Res. 44.8 0.0 748.7 793.4 521.6 284.2 954.7 45.4 34.3 RCHRES 13 - Cleveland Bk 72.9 0.0 521.6 547.6 598.9 -51.3 1,027.6 41.7 -9.4 RCHRES 20 - Ashmere Lk 311.8 0.0 0.0 311.1 44.6 272.9 311.8 85.7 85.7 RCHRES 21 - Bennett Bk 422.9 0.0 44.6 466.7 502.3 -35.5 734.7 31.6 -7.6 RCHRES 30 - East Branch 593.9 0.0 0.0 592.8 628.1 -35.3 593.9 -5.8 -6.0 RCHRES 40 - East Branch 594.7 0.0 1,130.4 1,724.0 1,954.1 -230.0 1,923.3 -1.6 -13.3 RCHRES 100 - East Branch 287.5 0.0 2,963.2 3,250.2 3,177.8 72.4 3,781.0 16.0 2.2 RCHRES 110 - Coltsville 99.1 0.0 3,177.8 3,276.7 3,421.6 -144.9 3,880.2 11.8 -4.4 RCHRES 115 - Unkamet Bk 205.3 0.0 0.0 204.9 259.0 -54.1 205.3 -26.2 -26.4 RCHRES 120 - East Branch 134.9 0.0 3,680.7 3,815.3 3,934.1 -118.8 4,220.4 6.8 -3.1 RCHRES 201 - Brattle Bk 218.9 0.0 0.0 218.4 253.3 -34.9 218.9 -15.8 -16.0 RCHRES 200 - East Branch 140.7 0.0 4,187.5 4,327.9 4,502.0 -174.0 4,580.0 1.7 -4.0 RCHRES 300 - East Branch 107.2 0.0 4,502.0 4,609.0 4,668.2 -59.2 4,687.2 0.4 -1.3 RCHRES 400 - Pomeroy Brdg (4A) 156.1 0.0 4,668.2 4,824.1 5,457.0 -632.9 4,843.3 -12.7 -13.1 RCHRES 410 - East Branch (4A) 2.5 0.0 5,457.0 5,459.5 5,146.4 313.1 4,845.8 -6.2 5.7 RCHRES 50 - Town Brook 945.5 0.0 0.0 943.7 887.5 56.2 945.5 6.1 6.0 RCHRES 60 - Secum Bk 304.6 0.0 0.0 304.0 335.2 -31.2 304.6 -10.1 -10.3 RCHRES 70 - Pontoosuc Res. 161.3 0.0 1,222.7 1,383.7 403.3 990.8 1,411.4 71.4 70.9 RCHRES 71 - West Branch 115.4 0.0 403.3 518.4 686.2 -167.8 1,526.7 55.1 -32.4 RCHRES 80 - Onota Res. 490.6 0.0 0.0 489.6 47.5 453.2 490.6 90.3 90.3 RCHRES 81 - Daniels Bk 104.9 0.0 47.5 152.2 162.8 -10.6 595.5 72.7 -7.0 RCHRES 90 - West Branch 149.0 0.0 849.1 997.7 954.0 43.7 2,271.2 58.0 4.4 RCHRES 810 - Richmond Pond 512.0 0.0 0.0 511.0 219.9 293.8 512.0 57.1 57.0 RCHRES 811 - Southwest Branch 1,155.3 0.0 219.9 1,372.9 1,256.4 116.6 1,667.3 24.6 8.5 RCHRES 820 - West Branch 14.3 0.0 2,210.5 2,224.7 2,210.0 14.8 3,952.7 44.1 0.7 RCHRES 500 - Housatonic (5A) 213.1 0.0 7,356.4 7,569.1 7,442.1 127.2 9,011.6 17.4 1.7 RCHRES 510 - Housatonic (5A) 68.5 0.0 7,442.1 7,510.4 6,257.9 1,252.6 9,080.1 31.1 16.7 RCHRES 830 - Sackett Bk 305.6 0.0 0.0 305.0 244.5 60.5 305.6 20.0 19.8 RCHRES 520 - Housatonic (5A) 142.8 0.0 6,502.5 6,645.0 6,014.4 630.7 9,528.5 36.9 9.5 RCHRES 530 - Housatonic (5A) 70.4 0.0 6,014.4 6,084.7 5,394.7 690.2 9,598.9 43.8 11.3 RCHRES 540 - New Lenox Bridge 187.0 134.3 5,394.7 5,710.9 5,140.0 571.1 9,920.3 48.2 10.0 RCHRES 550 - Housatonic (5B) 60.3 0.0 5,140.0 5,200.2 4,550.9 649.3 9,980.6 54.4 12.5 RCHRES 840 - Roaring Bk 103.7 0.0 0.0 103.5 142.8 -39.3 103.7 -37.7 -38.0 RCHRES 560 - Housatonic (5C) 5.5 0.0 4,693.8 4,699.2 4,198.9 500.5 10,089.8 58.4 10.6 RCHRES 850 - Yokun Bk 250.8 0.0 0.0 250.3 246.8 3.6 250.8 1.6 1.4 RCHRES 570 - Housatonic (5C) 218.6 0.0 4,445.7 4,663.8 3,730.2 933.8 10,559.2 64.7 20.0 RCHRES 580 - Woods Pd Hdw (5C) 67.9 0.0 3,730.2 3,798.0 2,964.6 835.6 10,627.1 72.1 21.9 RCHRES 600 - Woods Pond (6) 96.0 0.0 2,964.6 3,060.4 2,158.0 905.0 10,723.1 79.9 29.5 RCHRES 700 - Housatonic 206.8 0.0 2,158.0 2,364.3 2,472.8 -108.4 10,929.9 77.4 -4.6 RCHRES 860 - October Mtn. Res. 35.0 0.0 0.0 34.9 5.0 32.8 35.0 85.7 85.7 RCHRES 861 - Washington Mtn. 247.0 0.0 5.0 251.5 255.8 -4.2 282.0 9.3 -1.7 RCHRES 710 - Columbia Mill 245.0 0.0 2,728.6 2,973.1 2,383.7 589.6 11,456.8 79.2 19.8 RCHRES 721 - Laurel Lk 269.2 0.0 0.0 268.7 239.2 31.3 269.2 11.2 11.0 RCHRES 722 - Laurel Bk 90.1 0.0 239.2 329.1 324.8 4.3 359.3 9.6 1.3 RCHRES 720 - Housatonic 276.5 0.0 2,708.5 2,984.5 3,285.0 -300.4 12,092.6 72.8 -10.1 RCHRES 870 - Greenwater Bk 289.5 0.0 0.0 288.9 287.4 1.5 289.5 0.7 0.5 RCHRES 871 - Goose Pond 94.0 0.0 0.0 93.8 348.3 -250.6 94.0 -270.7 -271.4 RCHRES 872 - Goose Pond Bk 68.1 0.0 348.3 416.3 421.7 -5.5 162.1 -160.2 -1.3 RCHRES 730 - Housatonic 448.6 0.0 3,994.1 4,441.8 4,189.3 252.7 12,992.8 67.8 5.7 RCHRES 880 - Hop Bk 1,237.7 0.0 0.0 1,235.3 1,129.2 106.3 1,237.7 8.8 8.6 RCHRES 740 - Housatonic 138.0 0.0 5,318.5 5,456.2 5,406.3 50.0 14,368.5 62.4 0.9 RCHRES 890 - West Bk 212.9 0.0 0.0 212.5 191.4 21.1 212.9 10.1 9.9 RCHRES 750 - Willow Mill Dam 151.7 0.0 5,597.8 5,749.1 4,868.4 880.9 14,733.1 67.0 15.3 RCHRES 760 - Housatonic 265.4 0.0 4,868.4 5,133.3 5,275.9 -142.5 14,998.5 64.8 -2.8 RCHRES 910 - Konkapot Bk 579.4 0.0 0.0 578.2 528.7 49.7 579.4 8.7 8.6 RCHRES 770 - Housatonic 154.0 0.0 5,804.6 5,958.3 5,813.3 145.1 15,731.9 63.0 2.4 RCHRES 920 - Stockbridge Bowl 710.4 0.0 0.0 709.0 21.8 711.1 710.4 96.9 96.9 RCHRES 921 - Larrywaug Bk 256.2 0.0 21.8 277.5 251.9 25.7 966.5 73.9 9.2 RCHRES 780 - Glendale Dam 93.0 0.0 6,065.2 6,158.0 5,496.9 661.4 16,791.4 67.3 10.7 RCHRES 790 - Housatonic 120.0 0.0 5,496.9 5,616.7 5,843.3 -226.5 16,911.4 65.4 -4.0 RCHRES 800 - Rising Pond 55.4 0.0 5,843.3 5,898.6 5,224.8 674.6 16,966.9 69.2 11.4 RCHRES 900 - Housatonic (GB) 58.4 0.0 5,224.8 5,283.1 5,348.7 -65.5 17,025.3 68.6 -1.2

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11/21/2005 6-32

Table 6.1-13 1 2

PSA Reach Trapping Efficiencies 3

Reach Trapping Efficiencies (%)

PSA Reach 1990-2000 1979-2004

RCHRES 500 - Housatonic (5A) 1.3 1.7 RCHRES 510 - Housatonic (5A) 17.0 16.7 RCHRES 520 - Housatonic (5A) 10.1 9.5 RCHRES 530 - Housatonic (5A) 12.1 11.3 RCHRES 540 - New Lenox Road Bridge 10.0 10.0 RCHRES 550 - Housatonic (5B) 12.9 12.5 RCHRES 560 - Housatonic (5C) 10.2 10.6 RCHRES 570 - Housatonic (5C) 20.2 20.0 RCHRES 580 - Woods Pd Hdw (5C) 26.7 21.9 RCHRES 600 - Woods Pond (6) 42.7 29.5

4

Figures 6.1-12 and 6.1-13 show daily average TSS concentrations simulated by HSPF versus 5

data collected at New Lenox Road and Woods Pond, respectively. Figures showing TSS 6

concentrations for Pomeroy Avenue and Great Barrington are presented in Appendix C.1. The 7

simulated concentrations appear to be reasonable and are in the range of the data for Pomeroy 8

Avenue and New Lenox Road. The model tends to underpredict the base flow concentrations at 9

Woods Pond and Great Barrington, due to detailed channel deposition/scour processes not fully 10

captured by HSPF. Figure 6.1-14 presents comparisons with data collected from 1979 through 11

1989. The model results are consistent with the range of the data, and the time-varying behavior 12

is consistent with the calibration results. As noted above, detailed comparisons of model 13

simulations downstream of the PSA can only be interpreted as consistency checks because HSPF 14

does not provide the level of process detail, like that in EFDC, necessary to produce simulated 15

results that would be expected to closely match the measured concentrations. 16

6.1.4.2 Conclusions – Suspended Solids Validation 17

The agreement between the watershed model TSS loadings and the estimates based on data is 18

reasonable and consistent with the calibration results, and is within the ±30% target tolerance 19

specified in the Modeling Study QAPP (WESTON, 2000). The limited TSS concentration data 20

collected during the validation period of record introduce greater uncertainty in assessing the 21

Deleted: t

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Concentrations at Woods Pond Outlet (2001-2003) 6

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Figure 6.1-14 Time Series of Simulated vs. Measured Daily Average TSS 2 Concentrations at Great Barrington (1979-1989) 3

model validation results; however, the consistency of model predictions over the full range of 4

measured TSS loadings and concentrations supports the conclusion that HSPF is validated for 5

providing TSS loads from tributaries and direct runoff to EFDC (note that TSS loads from the 6

upstream boundaries will be provided using simulated flow and TSS relationships established 7

from the flux analysis). 8

6.1.5 HSPF Water Temperature Validation 9

To model instream water temperature needed as input for the bioaccumulation model (FCM), 10

HSPF calculates the heat loadings to a reach from all sources, including the runoff components, 11

and then performs a balance of the heat fluxes across the reach boundaries to arrive at the reach 12

water temperature for each modeling time step. The Watershed Model Calibration (Appendix A 13

of the Model Calibration Report [WESTON, 2004b]), presents an overview of the model 14

computations and calibration procedures for water temperature, along with the calibration 15

results. The remainder of this section presents water temperature results for the validation time 16

periods and presents comparisons with data to support the model validation. 17

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6.1.5.1 Water Temperature Validation Results 1

Data are available at numerous locations for model-data comparisons of water temperature 2

simulations. The data used during the calibration phase included high-frequency data collected 3

by USGS at Great Barrington from April 1994 through April 1996, high-frequency data collected 4

by GE at numerous locations from May 2000 through September 2000 (R2 Resource 5

Consultants, Inc., 2002), and monthly measurements collected by USGS at Coltsville from 6

January 1990 through May 1993. The GE sampling locations included the East Branch above 7

the confluence, the West Branch above the confluence, the mainstem at Holmes Road, and 8

Woods Pond Footbridge. 9

For the validation periods, data are also available for these same locations. Monthly/bi-monthly 10

measurements were collected at the USGS Coltsville gage for 1979 through 1993, and at the 11

Great Barrington gage for 1979 through 1996. High-frequency temperature data were collected 12

at the GE locations from March through October 2001. 13

Figure 6.1-15 presents the model-data comparisons at the USGS gages. This figure shows that 14

the model reproduces the seasonal water temperature pattern over multiple years and flow 15

regimes. Some of the measured temperatures appear to be anomalous; e.g., temperatures above 16

30 °C (86 ° F) are likely not representative of the average temperature within the reach, and it is 17

not expected that the model would reproduce these data points. 18

Figures 6.1-16 and 6.1-17 present comparisons of the high-frequency data collected by GE in 19

2001 and the model results at both an hourly and daily average time interval. These figures 20

demonstrate that the model reasonably simulates both the hourly and daily average water 21

temperature throughout the spring and beginning of fall for reaches above and within the PSA. 22

These results are consistent with the results obtained during the model calibration. 23

6.1.5.2 Conclusions – Water Temperature Validation 24

Table 6.1-14 presents daily average water temperature statistics for the high-frequency data 25

collected by GE versus model estimates. The overall percent mean errors are all less than the 26

27

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1 Figure 6.1-15 Simulated vs. Measured Water Temperature at Coltsville and Great 2

Barrington, 1979-1989 Validation Period 3

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1

Figure 6.1-16 Simulated vs. Measured Daily Average Water Temperatures at PSA 2 Locations for 2001 3

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1

Figure 6.1-17 Simulated vs. Measured Hourly Average Water Temperatures at 2 PSA Locations for 2001 3

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Table 6.1-14 1 2

Statistical Summary of Daily Average Results for Watershed Model (HSPF) Water 3 Temperature Validation 4

East Branch West Branch Holmes Rd Woods Pond

Simulated Measured Simulated Measured Simulated Measured Simulated Measured Count 199 199 199 199 199 199 198 198 Mean (deg C) 14.5 15.4 15.8 16.1 15.0 15.6 17.5 17.8 Geometric Mean (deg C) 12.7 13.3 14.0 14.3 13.4 13.7 15.1 15.5 Std Deviation (deg C) 5.5 5.9 5.7 5.8 5.5 5.8 6.6 6.7 Avg Ratio of Sim:Obs 1.00 1.00 1.00 1.00 r 0.97 0.98 0.98 0.98 r2 0.94 0.97 0.95 0.96 Mean Error (deg C) -0.88 -0.33 -0.60 -0.31 Mean Abs. Error (deg C) 1.46 0.89 1.15 1.07 % Mean Error -6% -2% -4% -2% 5

Table 6.1-15 6 7

Statistical Summary of Monthly Average Results for Watershed Model (HSPF) 8 Water Temperature Validation 9

East Branch West Branch

Simulated Measured % error Simulated Measured % error March 2001a 1.1 1.7 -36% 1.7 2.2 -22% April 2001 6.3 5.2 22% 7.2 6.7 7% May 2001 12.5 14.8 -16% 14.5 15.3 -6% June 2001 17.7 18.4 -4% 19.9 19.5 2% July 2001 18.7 19.5 -4% 20.3 20.4 -1% August 2001 20.3 21.1 -4% 21.1 21.3 -1% September 2001 15.3 16.7 -8% 15.7 16.9 -7% October 2001b 9.8 11.8 -16% 10.5 11.9 -11% Averagec 12.7 13.6 -7% 13.9 14.3 -3%

Holmes Road Lower Woods Pond

Simulated Measured % error Simulated Measured % error March 2001a 1.7 1.9 -13% 0.8 1.6 -52% April 2001 6.7 5.8 15% 7.1 6.3 13% May 2001 13.4 15.0 -11% 14.6 16.5 -11% June 2001 18.7 18.6 1% 20.4 20.3 0% July 2001 19.3 19.9 -3% 22.7 22.7 0% August 2001 20.5 21.1 -3% 24.6 24.7 -1% September 2001 15.4 16.7 -8% 19.0 19.3 -1% October 2001b 10.0 11.8 -15% 12.6 13.6 -8% Averagec 13.2 13.8 -5% 15.2 15.6 -3%

a 3 days sampled in March. 10 b 11 days sampled in October. 11 c Each month weighted equally. 12

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target tolerance of ±10% listed in the Modeling Study QAPP (WESTON, 2000) and the final 1

MFD (WESTON, 2004a), with all locations having mean errors less than ±6%. Table 6.1-15 2

presents the monthly average water temperature and associated percent errors for the high-3

frequency data collected by GE versus the model estimates. 4

The monthly errors range from -52% to 22%, whereas the overall errors are less than the ±10% 5

QAPP target tolerance. The larger errors occur in March and April and are associated with times 6

of snowmelt and ice cover conditions, which can be difficult to represent in the model. In 7

March, only 3 days were sampled and the temperatures were quite low, which allows for a 8

relatively large percent error to be calculated from a relatively small absolute difference. 9

The consistently high correlation coefficients, low percent mean errors, and time series plots for 10

each of the stations show that the HSPF water temperature simulation is very good both spatially 11

and temporally and well within the target tolerances presented in the Modeling Study QAPP and 12

final MFD. Based on the statistics and time series plots, the watershed model is judged to be 13

validated for water temperature. 14

6.1.6 References 15

Bicknell, B.R., J.C. Imhoff, J.L. Kittle, Jr., T.H. Jobes, and A.S. Donigian, Jr. 2001. 16 Hydrological Simulation Program - Fortran (HSPF). User’s Manual for Release 12. U.S. 17 Environmental Protection Agency, National Exposure Research Laboratory, Athens, GA, in 18 cooperation with U.S. Geological Survey, Water Resources Division, Reston, VA. 19

Lumb, A.M., R.B. McCammon, and J.L. Kittle. 1994. User’s Manual for an Expert System 20 (HSPEXP) for Calibration of the Hydrologic Simulation Program–FORTRAN. Water 21 Resources Investigation Report 94-4068. USGS, 1994. 22

R2 Resource Consultants, Inc. 2002. Evaluation of Largemouth Bass Habitat, Population 23 Structure, and Reproduction in the Upper Housatonic River, Massachusetts. Prepared for: 24 General Electric Company, Pittsfield, MA. July 23, 2002. 25

WESTON (Weston Solutions, Inc.). 2000. Quality Assurance Project Plan: Modeling Study of 26 PCB Contamination in the Housatonic River. Prepared for U.S. Army Corps of Engineers 27 and U.S. Environmental Protection Agency. DCN GE-100500-AADY. 28

WESTON (Weston Solutions, Inc.). 2004a. Modeling Framework Design: Modeling Study of 29 PCB Contamination in the Housatonic River. Prepared for U.S. Army Corps of Engineers 30 and U.S. Environmental Protection Agency. DCN GE-042104-ACDP. 31

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WESTON (Weston Solutions, Inc.). 2004b. Model Calibration: Modeling Study of PCB 1 Contamination in the Housatonic River. Prepared for U.S. Army Corps of Engineers and 2 U.S. Environmental Protection Agency. DCN GE-122304-ACMG. 3

4

ValDr_5_2.pdf

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5.2 UNCERTAINTY 1

5.2.1 Approach 2

5.2.1.1 Watershed Model (HSPF) 3

This section presents an overview of the approach to the Housatonic River Watershed Model 4

(HSPF) uncertainty analyses, including a discussion of the development of model parameter 5

distributions, the model output (variables and locations) analyzed, and the operational 6

procedures. 7

5.2.1.1.1 Overview 8

The uncertainty analysis for HSPF was conducted using a Monte Carlo approach. Multiple runs 9

of HSPF were performed using the 11-year Phase 2 Calibration period (water years 1990-2000), 10

with values for selected model parameters randomly chosen from assigned probability 11

distributions. The Monte Carlo analysis was implemented using the Sandia National 12

Laboratories (SNL) LHS software (Wyss and Jorgensen, 1998). The time period for the 13

uncertainty analysis was selected to be consistent with the time period of the EFDC and FCM 14

uncertainty analyses to facilitate investigation of the propagation of uncertainty through the 15

linked models (see Section 5.2.2). 16

To analyze and quantify the expected uncertainty in the model predictions, the Monte Carlo 17

results were processed for the same output variables and locations as used in the sensitivity 18

analysis (Section 5.1.1). Model parameters generated for the Monte Carlo runs were plotted and 19

checked for adherence to their assigned distributions and bounds, and model results were 20

checked in comparison to the full range of calibration/validation results. These quality assurance 21

(QA) checks were performed to ensure both the plausibility of parameter distributions and model 22

results, and the stability of the Monte Carlo procedures. In this context, stability refers to the 23

sensitivity of the outcome of the simulation to the sample size (i.e., number of runs), and was 24

checked by running the model multiple times and analyzing the degree of convergence of the 25

results. 26

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Following verification that the Monte Carlo methodology and procedures were operating as 1

expected, uncertainty in the model predictions was expressed by calculating the 5th and 95th 2

percentiles of the ranked output, representing the range for 90% of the model results. The 3

differences between the mean value and the 5th and 95th percentiles values were calculated, and 4

expressed as percentage of the mean, and averaged to express uncertainty as the percent 5

deviation from the mean. Normalizing to the mean allowed for uncertainty comparisons to be 6

made among the output variables (i.e., flow, TSS, temperature) and within specific percentiles of 7

output variables, e.g., uncertainty in the 10% highest flows, 25% lowest flows, and throughout 8

the flow duration curve. 9

5.2.1.1.2 Parameter Distributions 10

For the sensitivity analysis presented in the Model Calibration Report (Table A.6-1; WESTON, 11

2004b), model runs were performed for 25 years with perturbations applied to 27 model 12

parameters and 3 input meteorological time series. 13

For the uncertainty analysis, an 11-year time period was simulated for water years 1990-2000 to 14

be consistent with the EFDC uncertainty analysis, allowing evaluation of the propagation of 15

uncertainty. For each run, values were randomly selected from probability distributions for 28 16

parameters; the uncertainty analysis included 26 of the 27 parameters evaluated for sensitivity 17

and two new parameters to represent the separate settling velocities for silt and clay used in the 18

current model. (The previous sensitivity analysis was based on a single settling velocity for 19

cohesive particles.) The initial soil moisture conditions for each land use category at the start of 20

each run (i.e., for October 1989) were assigned as a fraction of the corresponding nominal 21

storage values (LZSN, UZSN). The fractions assigned were the mean fractions for October 22

conditions (the beginning of the water year) from the full validation period of 1979 through 23

2004. 24

Table 5.2-1 shows the 28 parameters, their units and definitions, and the assigned distributions. 25

The parameter distributions were assigned as bounded normal (NO) or bounded lognormal (LN). 26

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Table 5.2-1 1 2

HSPF Model Parameter Values Used to Calculate MFACT Distribution Parameters 3

CategoryModel

Input/Parameter Parameter DefinitionCalibrated

Minimum ValueCalibrated

Maximum Value Parameter TypeDistribution

Typea Mean b Low High Range c Standard Deviation d

Error Factor e

Lower Bound

Upper Bound

Hydrology LZSN, in Lower zone nominal soil moisture storage 3.3 5.2 Soil/Climate LN 4.33 3 6 3 1.413 2.5 9INFILT, in/hr Index to infiltration capacity of the soil 0.025 0.25 Soil LN 0.10 0.025 0.25 0.225 3.151 0.005 0.5CEPSC, in Interception storage capacity 0.01 0.22 Veg NO 0.12 0.03 0.2 0.17 0.052 0.005 0.35LZETP Lower zone evapotranspiration parameter 0.2 0.65 Veg NO 0.43 0.2 0.7 0.5 0.152 0.1 0.85INTFW Interflow inflow parameter 1.5 4.5 Soil LN 2.74 1.5 4.5 3 1.729 0.5 6DEEPFR Fraction of groundwater inflow to deep losses 0.03 0.12 Soil/GW LN 0.07 0.01 0.2 0.19 4.452 0.001 0.3UZSN, in Upper zone nominal soil moisture storage 0.2 2.1 Soil LN 0.99 0.5 1.7 1.2 1.840 0.1 3

Snow TSNOW, degrees F Threshold temperature for snow conditions 32 32 Climate NO 32.00 31 33 2 0.606 30 34CCFACT Adjustment factor for condensation/convection 1.5 1.5 Site LN 1.64 1 2.5 1.5 1.579 0.5 5SNOWCF Gage catch efficiency factor snow 1.1 1.2 Site/Climate LN 1.29 1.1 1.5 0.4 1.167 1 2.5SHADE Fraction of land shaded from solar radation 0.15 0.6 Site/Veg NO 0.38 0.15 0.6 0.45 0.136 0.1 0.8

Soil/Sediment KSER Coefficient in sediment washoff equation 0.05 0.3 Soil/Veg LN 0.26 0.05 0.7 0.65 3.727 0.01 1.5KRER Coefficient in soil detachment equation 0.18 0.3 Soil/Veg LN 0.26 0.15 0.4 0.25 1.631 0.05 0.7COVER Fraction of land protected from raindrop splash 0.5 0.97 Veg NO 0.74 0.3 0.93 0.63 0.191 0.2 1TAUCD, lb/ft2 Critical bed shear stress for deposition 0.0220 1.5 Soil/Sediment LN 0.20 0.05 0.5 0.45 3.151 0.01 1TAUCS, lb/ft2 Critical bed shear stress for scout 0.0320 7.5 Soil/Sediment LN 0.54 0.1 1.5 1.4 3.857 0.05 2M, lb/ft2.d Bed/bank erodibility factor 0.01 0.50 Soil/Sediment LN 0.14 0.01 0.5 0.49 7.029 0.005 1W: Silt, in/sec Particle settling velocity for silt 0.0035 0.0070 Soil/Sediment LN 0.00502 0.002 0.01 0.008 2.231 0.001 0.03W: Clay, in/sec Particle settling velocity for clay 0.0004 0.0015 Soil/Sediment LN 0.00080 0.0002 0.002 0.0018 3.151 0.0001 0.005KSAND Coefficient in sandload equation 0.05 8.00 Soil/Sediment LN 2.05 0.05 8 7.95 12.552 0.01 10EXPSND Exponent in sandload equation 1.00 5.50 Soil/Sediment LN 2.67 1 5.5 4.5 2.339 0.5 7

Water CFSAEX Correction factor for solar radiation on water surface 0.3 0.9 Veg/Riparian NO 0.60 0.2 0.9 0.7 0.212 0.1 0.95Temperature BSLT Slope of surface layer temperature regression 0.3 0.38 Soil/Veg LN 0.32 0.25 0.4 0.15 1.264 0.15 0.6

ULTP2 Slope of the upper zone soil temperature regression 0.29 0.38 Soil/Veg LN 0.27 0.2 0.35 0.15 1.322 0.1 0.55LGTP1, degrees F Lower layer/groundwater soil temperature 35 62 Site/Climate NO 48.50 35 62 27 8.182 34 70KCOND Conduction-convection heat transport coefficient 6.12 6.12 Site/Climate NO 6.12 4 8 4 1.212 3 10KATRAD Longwave radiation coefficient 9 9 Site/Climate NO 9.00 7 12 5 1.515 5 15KEVAP Evaporation coefficient 2.5 2.5 Site/Climate NO 2.50 1.5 4 2.5 0.758 1 64

5

a Distribution to Parameter Mapping: Soil → LN 6 Climate → NO 7 Veg → NO 8 GW → LN 9 Sediment → LN 10 Site → NO 11 b Mean for NO distributions set equal to calibrated mean; mean for LN distributions calculated as e^(ux + sigmax2 / 2); where, 12 ux = Ln (low) + 1.645 * sigmax and sigmax = Ln (range) / 3.3. ux and sigmax are the mean and standard deviation of underlying NO distribution, respectively. 13 c Range represents the interval over which 90% of the values are expected (high - low). 14 d Standard deviation for NO distributions calculated as range/3.3. 15 eee Error factor for LN distributions calculated as e^(1.645*sigmax). 16 17

18

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Bounded distributions were used to constrain parameter values within physically realistic limits 1

for the Housatonic River watershed, and within computational limits imposed by HSPF. For 2

example, the SHADE and COVER parameters can vary only between 0 and 1 because they 3

represent a fraction of the land surface. The bounds on the other distributions were similarly 4

imposed to ensure adherence to the parameter limits. 5

Table 5.2-1 also shows the minimum and maximum calibrated values across the watershed, the 6

range that includes 90% of expected values for the watershed, and the lower and upper bounds 7

on the parameter values used in the uncertainty analysis. The model calibration resulted in 8

parameter values that varied across the watershed as a function of soil type, land use, and local 9

climate variations; these variations are the source of the minimum and maximum calibrated 10

values. The 90% range of parameter values and the lower and upper bounds are based on both 11

the Housatonic River watershed calibration and historical experience with HSPF. These values 12

were then used to calculate a standard deviation for the NO distributions and an “error factor” 13

(see below) for the LN distributions. The standard deviations and error factors define the 14

dispersion of the distribution about the mean for the NO and LN distributions, respectively. 15

The lower and upper bounds in Table 5.2-1 establish the allowable limits for each parameter, and 16

the parameter random generation procedures were constrained to select values within these 17

ranges. Because the values for HSPF parameters vary spatially across the watershed, and some 18

are variable over time (monthly), the parameter changes were implemented as multipliers 19

(factors) of the calibrated values, referred to as MFACTs. The ranges specified in Table 5.2-1 20

account for the spatial and temporal variations in the parameter values expected within the 21

watershed. The MFACTs were generated by scaling each of the distributions to the means listed 22

in Table 5.2-1, i.e., each distribution was divided by its mean to create the MFACT, which was 23

then multiplied times the calibrated values contained in the HSPF input for the Housatonic River 24

watershed model. 25

For example, as shown in Table 5.2-1, the parameter SHADE (fraction of land shaded from solar 26

radiation) in the calibrated watershed model varied throughout the watershed in space and time 27

from a minimum value of 0.15 to a maximum value of 0.60, with a mean of 0.38 and a standard 28

deviation of 0.136. In the uncertainty analysis, the lower and upper bounds for SHADE were 29

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established as 0.10 and 0.80, respectively. The bounded final distribution of SHADE was 1

divided by its mean (0.38) to create an MFACT distribution for SHADE with a mean of 1.0, a 2

lower bound of 0.26 (0.1/0.38), and an upper bound of 2.11 (0.8/0.38). For each model run, a 3

value was randomly selected from this distribution of MFACT, and the selected value was 4

applied as a multiplier for every value of SHADE in the watershed for that run. 5

In assigning distributions, each parameter was first characterized, based upon whether it reflected 6

soil, climate, vegetation, sediment, general site characteristics, or some combination of these 7

categories. Then an LN distribution was assigned for the soil- and sediment-related parameters, 8

and NO distributions were assigned for the others. A number of articles on soil hydrologic and 9

hydraulic characteristics (Carsel and Parrish, 1988; Boll et al., 1997; van Genuchten et al., 1991; 10

Carsel et al., 1988; Brejda et al., 2000) clearly confirm a consensus that soil properties more 11

often demonstrate LN distributions, and the LN generally is preferred over an NO distribution. 12

To address the issue of parameter correlation, major parameters that would be correlated because 13

they represented similar soil, sediment, or vegetation characteristics of the watershed were 14

identified. Based on professional experience with the HSPF model and the processes/properties 15

represented, the model parameters listed below were arranged into five groups representing 16

different processes or properties of the system: 17

1. Soil moisture capacity: LZSN, UZSN 18 2. Shear stress threshold: TAUCD, TAUCS 19 3. Particle settling velocity: Wsilt, Wclay 20 4. Soil heat terms: BSLT, ULTP2, LGTP1 21 5. Vegetation/cover: CEPSC, COVER, LZETP, SHADE 22

23 An appropriate correlation structure was incorporated into the parameter perturbations generated 24

for each model run to reflect the relationships within each parameter group. The LHS software 25

used for the Monte Carlo analysis allows the user to specify which model parameters to correlate 26

within a sample, based on nonparametric rank correlation methods. The method preserves the 27

sampling scheme, i.e., the same numbers originally selected as input values are retained, only 28

their pairing is affected to achieve the desired rank correlations (Iman and Conover, 1982). 29

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Thus, within each of the five groups listed above, the parameter perturbations were correlated so 1

that their values changed in the same direction and with similar magnitudes for each model run. 2

For example, all the parameters that reflect vegetation conditions (Group 5 above) would 3

increase or decrease together for each model run because they each reflect some aspect or 4

characteristic of the landscape vegetation that is positively correlated with the other parameters 5

in the group. 6

The input parameters required by the LHS software (Wyss and Jorgensen, 1998) to generate a 7

vector of n values that demonstrate a bounded NO or bounded LN distribution are shown below 8

in Table 5.2-2. 9

Table 5.2-2 10 11

LHS Input Parameters for Bounded Normal and Lognormal Distributions 12

Normal Distribution Lognormal Distribution

Mean Mean

Standard Deviation Error Factor

Lower Bound Lower Bound

Upper Bound Upper Bound

13

For the NO distribution, the mean was set as the mean of the calibrated values for the watershed. 14

The standard deviation was approximated as the range into which 90% of the values were 15

expected to fall divided by 3.3 (i.e., ±1.65 standard deviations from the mean of a normal 16

distribution contains 0.90 of the area). 17

The error factor for the LN distribution was defined as the ratio of the 95th percentile to the 18

median. Because this is not a commonly used statistic, the discussion below is presented to 19

describe how this statistic and the mean were calculated. For this discussion, the subscripts X 20

and Y are used to specify if a given metric or variable applies to the underlying normal 21

distribution or the lognormal distribution, respectively; i.e., X is considered a normally 22

distributed variable and Y is considered its lognormally distributed counterpart, where X = 23

Ln(Y) and Y = e^X. 24

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The error factor was calculated using the following steps: 1

1. Define the upper 95th percentile (UBY) and lower 5th percentile (LBY) for the 2 lognormally distributed variable. This range was based on professional experience with 3 each of the associated HSPF parameters (i.e., the parameters that were assumed to 4 follow an LN distribution) and the calibrated value for the Housatonic River watershed. 5 In effect, a range was defined that was believed to encompass 90% of the values that 6 may be appropriate for the watershed. 7

2. Calculate the standard deviation of the underlying normal distribution (sigma X), where: 8 sigma X = Ln(UBY / LBY ) / 3.3. 9

3. Calculate the error factor (Wyss and Jorgensen, 1998) , where: 10 error factor = e ^ (1.645 * sigma X ). 11

For calculating the mean of the lognormal distribution (mean Y), the following steps were 12

performed: 13

1. Calculate the mean of the underlying normal distribution (mean X) , where: 14 mean X = LN(LB Y) + 1.645 * sigma X. 15

2. Calculate mean Y (Wyss and Jorgensen, 1998) , where: 16 mean Y = exp(mean X + sigma X ^ 2 / 2). 17

The LHS software includes plotting capabilities that provide the mechanism to visually inspect 18

the parameter distributions developed by the LHS software. Figures 5.2-1 and 5.2-2 present 19

example plots for the MFACTs for NO model parameters (CEPSC, SHADE, COVER) and LN 20

model parameters (LZSN, UZSN, INFILT). The figures present probability density curves for 21

the MFACTs applied to the calibrated model parameters. 22

23

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5-8

Figure 5.2-1 Probability Density Plots for MFACTs for NO Model Parameters 1

0.0 0.5 1.0 1.5 2.0 2.5MFACT

0.0

0.2

0.4

0.6

0.8

1.0

1.2Pr

obab

ility

NO Probability Model ParametersCEPSCSHADECOVER

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Figure 5.2-2 Probability Density Plot for MFACTs for LN Model Parameters 1

The actual parameter distributions are shown in Figure 5.2-3 for the same parameters whose 2

MFACTs are shown in Figures 5.2-1 and 5.2-2. The plots confirm that the parameter values 3

follow the normal and lognormal distributions specified for the corresponding parameter values. 4

5.2.1.1.3 Output to be Analyzed/Processed 5

Model results were generated for each of the following locations in the watershed to assess the 6

stability of the Monte Carlo simulation methodology and the uncertainty in the model results. 7

These are the same locations that were used in the sensitivity analysis (see Section 5.1). 8

Coltsville 9 Pomeroy Avenue 10 West Branch 11 New Lenox Road 12 Woods Pond Dam 13 Great Barrington 14

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0MFACT

0.0

0.2

0.4

0.6

0.8

1.0

1.2Pr

obab

ility

LN Probability Model ParametersLZSNUZSNINFILT

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NO Probability Parameters LN Probability Parameters 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Figure 5.2-3 Probability Density Plots for NO and LN Model Parameters17

0.0 1.0 2.0 3.0Parameter Value

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Prob

abili

ty

UZSNUZSN

0.0 0.05 0.1 0.15 0.2 0.25 0.3Parameter Value

0.0

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Prob

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ty

CEPSCCEPSC

0.2 0.4 0.6 0.8 1.0Parameter Value

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Prob

abili

ty

COVERCOVER

0.0 0.1 0.2 0.3 0.4 0.5 0.6Parameter Value

0.0

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Prob

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ty

INFILTINFILT

2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0Parameter Value

0.0

0.2

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1.2

Prob

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ty

LZSNLZSN

0.0 0.2 0.4 0.6 0.8Parameter Value

0.0

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1.0

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Prob

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SHADESHADE

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The output analyzed at each of these locations included: 1

Mean annual streamflow, cfs 2 Mean annual runoff, inches 3 10% highest flows (i.e., mean flow exceeded 10% of the time), cfs 4 25% lowest flows (i.e., mean flow exceeded 75% of the time), cfs 5 Mean annual TSS loadings, tons/year 6 Mean annual water temperature, oF 7 Mean summer water temperature (June–August), oF 8

9 The annual results refer to a water year, i.e., October 1 to September 30. 10

5.2.1.1.4 Stability of the Monte Carlo Simulation 11

The number of runs performed for a Monte Carlo analysis (MCA) should be sufficient to ensure 12

that the simulation reaches an appropriate solution. As additional runs are performed, the results 13

converge and the mean output(s) of the analys