PHASE 2 REPORT - REVIEW COPY
FURTHER SITE CHARACTERIZATION AND ANALYSIS
VOLUME 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
MAY 1999
For
U.S. Environmental Protection Agency
Region 2
and
U.S. Army Corps of Engineers
Kansas City District
Volume 2D - Book 1 of 4
Fate and Transport Models
Limno-Tech, Inc.
Menzie-Cura & Associates, Inc.
Tetra Tech, Inc.
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
REGION 2
290 BROADWAY
NEW YORK, NY 10007-1866
May 18, 1999
To All Interested Parties:
The U.S. Environmental Protection Agency (EPA) is pleased to release the Baseline Modeling
Report for the Hudson River PCBs Superfund site. This report presents results and findings
from the application of mathematical models for PCB transport and fate and bioaccumulation in
the Upper Hudson River.
The Baseline Modeling Report is the fourth of six volumes of the Phase 2 Report (Volume 2D)
for the Hudson River PCBs Reassessment. The baseline modeling effort was broken out as a
separate report to allow interested parties to comment on the modeling results that would be
utilized in the Human Health and Ecological Risk Assessments, and subsequently, the Phase 3
Report or Feasibility Study. As with the previous Phase 2 Reports, it is important to recognize
that the conclusions in this report do not yet determine whether or not remedial action is
necessary for the PCB-contaminated sediments of the Upper Hudson. EPA must complete the
Reassessment before an appropriate remedial decision can be made.
In their present forms, the models are useful tools for providing information on PCB exposure
concentrations for the Human Health and Ecological Risk Assessments. Additional modeling
efforts will be conducted to fine tune the model for predicting the time it takes for the system to
recover. The results of these additional modeling efforts will be made available as part of the
Responsiveness Summary for this report.
EPA will accept comments on the Baseline Modeling Report until Wednesday, June 23,1999.
Comments should be marked with the name of the report, the book number, and should include
the report section and page number for each comment. Comments should be send to:
As with previous Reassessment reports, EPA will hold a Joint Liaison Group meeting on the
date of release of this report, May 18, 1999, to discuss findings of the Baseline Modeling
Report. The meeting is being held at 7:30 p.m. at the Marriott Hotel at 189 Wolf Road in
Albany, New York, and is open to the general public. Notification of this meeting was sent to
liaison group members, interested parties and the press several weeks prior to the meeting.
During the public comment period, EPA will hold a public availability session to answer
questions from the public regarding the Baseline Modeling Report. The availability session will
be held on Tuesday, June 15, 1999 at the Marriott Hotel in Albany, New York from 2:30 to
4:30 p.m. and from 6:30 to 8:30 p.m.
Douglas Tomchuk
USEPA - Region 2
290 Broadway - 19th Floor
New York, NY 10007-1866
Attn: BMR Comments
Internet Address (URL) • http://www.epa.gov
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If you need additional information regarding the Baseline Modeling Report, the availability
session or the Reassessment in general, please contact Ann Rychlenski, the Community
Relations Coordinator for this site, at (212) 637-3672.
Sincerely yours,
Richard L. Caspe, Director
Emergency and Remedial Response Division
-------
PHASE 2 REPORT - REVIEW COPY
FURTHER SITE CHARACTERIZATION AND ANALYSIS
VOLUME 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
MAY 1999
S7^
i sfel
*1 PRO&
For
U.S. Environmental Protection Agency
Region 2
and
U.S. Army Corps of Engineers
Kansas City District
Volume 2D - Book 1 of 4
Fate and Transport Models
Limno-Tech, Inc.
Menzie-Cura & Associates, Inc.
Tetra Tech, Inc.
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Table of Contents
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
CONTENTS
BOOK 1 of 4
LIST OF TABLES iv
LIST OF FIGURES vi
LIST OF PLATES x
ACRONYMS xi
EXECUTIVE SUMMARY ES-1
1. INTRODUCTION 1
1.1. Purpose of Report l
1.2. Report Format and Organization 2
1.3. Project Background 2
1.3.1. Site Description 2
1.3.2. Site History 2
1.4. Modeling Goals and Objectives 3
2. MODELING APPROACH 5
2.1. Introduction 5
2.2. Conceptual Approach 5
2.3. Hydrodynamic Model 5
2.4. Depth of Scour Model 6
2.5. Mass Balance Model 6
2.6. Mass Balance Model Applications 7
2.7. Hudson River Database 8
3. THOMPSON ISLAND POOL HYDRODYNAMIC MODEL 9
3.1. Introduction 9
3.2. Modeling Approach 10
3.2. 1. Governing Equations 10
3.2.2. Computational Procedure 11
3.3. Model Input Data 12
3.3.1. Model Grid 12
3.3.2. Manning's 'n' 12
3.3.3. Forcing Functions 12
3.3.4. Boundary Conditions 13
3.4. Model Calibration 13
3.5. Model validation 14
3.5.1. Rating Curve Velocitx- Measurements 14
3.5.2. FEMA Flood Studies 15
3.5.3. 100-Year Peak Flow Model Results 15
3.6. Sensitivity Analyses 15
3.6.1. Sensitivity to Manning j n' 16
3.6.2. Turbulent Exchange Coefficient 16
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
CONTENTS
3.7. Conversion of Vertically averaged Velocity to Shear Stress 16
3.8. Discussion 19
4. THOMPSON ISLAND POOL DEPTH OF SCOUR MODEL 21
4.1. Introduction 21
4.2. DOSM Model Development 22
4.2.1. Conceptualization 22
4.2.2. Formulation for Cohesive Sediments 22
4.2.3. Formulation for Non-cohesive Sediments 24
4.2.4. Temporal Scale 25
4.3. DOSM Parameterization 25
4.3.1. Data 26
4.3.2. Parameterization for Cohesive Sediments 28
4.3.3. Parameterization for Non-cohesive Sediments 28
4.4. DOSM Application 29
4.4.1. Application Framework 29
4.4.2. Model Application to High Resolution Coring Sites 29
4.4.3. Model Application Poolwide 30
5. MASS BALANCE MODEL DEVELOPMENT 33
5.1. Introduction 33
5.2. Model approach 33
5.2.1. Introduction 33
5.2.2. Conceptual Framework 33
5.2.3. Governing Equations 34
5.3. Model Spatial Segmentation 48
5.4. Model Implementation 50
6. DATA DEVELOPMENT 51
6.1. Introduction 51
6.2. Hudson Rp/er Database 51
6.3. Model Application Datasets 52
6.3.1. Water Column Datasets 52
6.3.2. Sediment Datasets 53
6.4. External Loadings and Mainstem Mass Fluxes 54
6.4.1. Water Balance 54
6.4.2. Mainstem and Tributary Solids Loads 57
6.4.3. Development of Long-Term Average Solids Balance 59
6.4.4. Mainstem and Tributary PCB Loads 62
7. MASS BALANCE MODEL CALIBRATION 67
7.1. Introduction 67
7.2. Calibration Strategy 67
7.2.1. Solids Calibration Strategy 68
7.2.2. PCB Calibration Strategy 69
7.3. Calibration Parameters 70
7.3.1. Solids Dynamics Parameters 70
7.3.2. PCB Model Parameters 72
7.4. Calibration Results 75
7.4.1. Spring 1994 Solids Results 75
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PHASE 2 REPORT
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
CONTENTS
7.4.2. Results for 1991-1997 Calibration Period 76
7.4.3. Results for the 1977-1997 Calibration Period 78
7.5. Component analysis 80
7.6. Conclusions 83
8. MASS BALANCE MODEL FORECAST SIMULATIONS 84
8.1. Introduction 84
8.2. No Action 8-
8.2.1. Approach S4
8.2.2. Results 85
8.3. 100-Year Peak Flow 86
8.3.1. Approach 86
8.3.2. Results 87
8.4. Discussion 87
REFERENCES 91
Note: Book J and Book 4 Table of Contents is located in the respective books.
in
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF TABLES
TABLE TITLE
3-1 Comparison of Manning's 'n' from Previous Studies
3-2 Modeled Hudson River Flows at the Upstream Boundary of the TIP
3-3 Comparison of Model Results with Rating Curve Data
3-4 Effect of Manning's 'n' on Model Results for 100-Year Flow Event
3-5 Effect of Turbulent Exchange Coefficients on Model Results
4-1 Summary of Inputs for Depth of Scour Model at Each High Resolution Core
4-2 Predicted Depth of Scour Range for 100 Year Flood at Each High Resolution Core Location
4-3 TIP Cohesive Sediment Expected Values of Solids Erosion and Mean Depth of Scour for 100-
Year Flood, from Monte Carlo Analysis
5-1 a HUDTOX Water Column Segment Geometry in Thomspon Island Pool (2-Dimensional
Segmentation).
5-1 b HUDTOX Water Column Segment Geometry Below Thomspon Island Pool (1 -Dimensional
Segmentation).
5-2 a HUDTOX Sediment Segment Geometry in Thomspon Island Pool for Surficial Sediment
Segments (2-Dimensional Segmentation).
5-2 b HUDTOX Sediment Segment Geometry Downstream of Thomspon Island Pool for Surficial
Sediment Segments (i-Dimensional Segmentation).
6-1 Sediment Data Sets Used in Development and Application of the HUDTOX Model
6-2 Sediment Areas Used for Computing HUDTOX Sediment PCB Calibration Targets
6-3 USGS Gauge Information for Gauges Used in Flow Estimation
6-4 Drainage Areas and Reference Tributaries Used in the LTI Tributary Flow Estimation
6-5 Mean Seasonal USGS Flows for the Selected Flow Gauges in the Study Area for the Period 3/1/77
- 6/30/92
6-6 Seasonal Tributary Flow Adjustment Factors Applied to Tributaries between For' Edward and
Stillwater, and between Stillwater and Waterford
6-7 Summary of Available Solids Data for Mainstem Stations; Number of Samples and Sources of SS
Sample Data by Station
6-8 Summary of Available Solids Data for Tributaries: Number of Samples and Source of SS Sample
Data by Station
6-9 Reference Tributaries for Unmonitored Tributaries
6-10 Cumulative Mainstem SS Loads and Yields
IV
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PHASE 2 REPORT
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF TABLES (cont.)
TABLE TITLE
6-11 Cumulative SS Loads and Corresponding Yields by Reach (10/1/77 - 9/30/97)
6-12 Tributary' Drainage Areas Used in Tributary Load Adjustment
6-13 Inputs to SS Trapping Efficiency Calculations
6-14 SS Trapping Efficiency Estimates for Specific Reaches
6-15 Final Tributary SS Concentration Equations, Considering Deposition
6-16 Estimated Average Annual Tributary SS Loads to HUDTOX (10/1/77 - 12/31/97)
6-17 Seasonal Suspended Solids Load Difference by Reach Based on Preliminary Load Estimates for
the period 10/1/77 to 12/31/96
6-18 Number of Days on which PCB Data were Available for Batten Kill. Hoosic River, and Mohawk
River
6-S9 Percent of PCB Transport Past Mainstem Upper Hudson River PCB Sampling Stations Under
High and Low How (4/1/91 - 9/30/97)
6-20 Percent of PCB Load at Fort Howard for Suspended Solids Concentration Above and Below 10
mg/1 (4/1/91 - 9/30/97)
6-21 Comparison of Annual Tri-^ PCB Loads Estimates at Fort Edward, Schuylerville. Stillwater and
Waterford Presented in the DEIR (TAMS 1997) and this Report
7-1 HUDTOX Solids Model Calibration Parameter Values
7-2 Transition Levels by Reach for Flow-dependent Settling in HUDTOX
7-3 HUDTOX Sediment Rcsuspension and Armoring Parameters
7-4 HUDTOX Fraction Organic Carbon and Dissolved Organic Carbon Parameterization by Reach
7-5 HUDTOX PCB Model Calibration Parameter Values
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF FIGURES
FIGURE TITLE
1-1 Location Map for Hudson River Watershed
1 -2 Upper Hudson River Watershed
1-3 Thomspon Island Pool
2-1 Upper Hudson Reassessment Modeling Framework
3-1 Thomspon Island Pool Study Area
3-2 Thomspon Island Pool RMA-2V Model Mesh
3-3 Thomspon Island Pool Velocity Vectors 100-year Flow Event
3-4 Shear Stress Computed from Vertically Averaged Velocity
4-1 Erosion versus Shear Stress
4-2 Armoring Depth versus Shear Stress
4-3 Armoring Depth versus Shear Stress Above 5 dvnes/cm"
--4 Core HR-19: Likelihood of PCB Scour
4-5 Core HR-20: Likelihood of PCB Scour
4-6 Core HR-23: Likelihood of PCB Scour
4-7 Core HR-25: Likelihood of PCB Scour
4-8 Core HR-26: Likelihood of PCB Scour
4-9 Cumulative Percent versus Mean Depth of Scour
4-10 Cumulative Percent versus Total Solids Scoured
5-1 Conceptual Framework for the HUDTOX PCB Model
5-2 Illustration of Sediment Scour in the HUDTOX Model
5-3 Illustration of Sediment Burial in the HUDTOX Model.
5-4 (a-b) HUDTOX Model Water Column Segmentation Grid for Upper Hudson River
5-4 (c-d) HUDTOX Model Water Column Segmentation Grid for Upper Hudson River
5-5 Thomspon Island Pool Study Area
5-6 Schematic of the HUDTOX Water Column Segmentation Grid
5-7 HUDTOX Water Column Segment Depths by River Mile
5-8 Percent Cohesive Sedimeni Area Represented in HUDTOX by Riser Mile
6-1 Upper Hudson River Basin USGS Flow Gage Stations Used in HUDTOX Modeling
vi
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PHASE 2 REPORT
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF FIGURES (cont.)
FIGURE TITLE
6-2 Comparison of Sources of Flow as a Percentage of the Flow at the Federal Dam
6-3 Estimated Daily Average Mainstem and Tributary Flows for the Upper Hudson River between Fort
Edward and Federal Dam (1/1/97 - 9/30/97)
6-4 Upper Hudson River Basin Primary Long-Term Sampling Locations for Solids used in HUDTOX
Modeling
6-5 Subwatersheds Monitored for Solids between Fort Edward and Waterford
6-6 Log Flow vs. Log TSS Concentration at Fort Edward, Stillwater, and Waterford
6-7 Mainstem and Tributary Suspended Solids Watershed Yield based on HUDTOX Suspended Solids
Loading Estimates (10/1/77 - 9/30/97)
6-8 Relative Contribution of Estimated External Suspended Solids Loads to the Upper Hudson River
1/1/77 to 9/30/97
6-9 Upper Hudson River Basin Primary Long-Term Sampling Locations for PCB Data Used in
HUDTOX Modeling
6-10 Available Mainstem Upper Hudson River PCB Data from The Hudson River Database, Release
4.1
6-11 Estimated Annual Tri- PCB Load at Historical PCB Sampling Stations on the Upper Hudson
River
6-12 Estimated Annual Load of Total PCB, BZ#4, and BZ#52 past Fort Edward 4/1/91 to 9/30/97
6-13 Relative Contribution of Estimated External PCB Loads to the Upper Hudson River 1/1/77 to
9/30/97
6-14 Estimated Cumulative Tri+ PCB Load passing Fort Edward. Schuylerville. Stillwater, and
Waterford compared to DEIR Estimates
6-15 Estimated Annual Tri- PCB Load at Fort Edward. Stillwater, and Waterford compared to DEIR
Estimates
7-1 Flow Transition Levels for Gross Settling in the Thompson Island Pool Specified for HUDTOX
Calibration
7-2 Spring 1994 High Flow Event Settling and Resuspension Rates in HUDTOX
7-3 Monthly Air Temperature at Glens Falls. New York for 1977-1997
7-4 Monthly Average Water Temperature by Reach in the Upper Hudson River
7-5 Specification of Historical Atmospheric Gas-Phase PCB Boundary Concentrations for the 1977 -
1997 HUDTOX Calibration Period
7.6a HUDTOX Annual Surficial Sediment Porewater Diffusive Mass Transfer Rates for Cohesive and
Noncohesive Sediment
VII
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Volume 2D - BASELINE MODELING REPORT
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BOOK 2 of 4
LIST OF FIGURES (cont.)
FIGURE TITLE
7.6b HUDTOX Reach-Specific Annual Particulate Chemical Mass Transfer Rates
7-7 (a-c) Spring 1994 Total Suspended Solids HUDTOX Calibration Results
7-8 Spring 1994 HUDTOX-Computed versus Estimated Cumulative TSS Flux
7-9 (a-c) 1977 - 1997 Total Suspended Solids HUDTOX Calibration Results
7-10 (a-c) 1993 Total Suspended Solids HUDTOX Calibration Results
7-11 1991-1997 HUDTOX-Computed versus Estimated Cumulative TSS Flux
7-12 1977 - 1997 Cumulative HUDTOX-Computed versus Estimated Solids Transport at High and
Low Flows* past Mainstem Hudson River Sampling Stations
7-13 1991 - 1997 HUDTOX-Computed versus Observed Total PCB Concentrations
7-14 1991 - 1997 HUDTOX-Computed versus Observed BZ#4 Concentrations
7-15 1991 - 1997 HUDTOX-Computed versus Observed BZ#52 Concentrations
7-16 Observed versus Computed Water Column BZ#4 to BZ#52 Concentration Ratios
7-17a Computed versus Observed Total PCB Concentrations through TIP durmg the GE September 24.
1996 Time of Travel Survey
7-17b Computed versus Observed Total PCB Concentrations through TIP during the GE September 25.
1996 Time of Travel Survey
7-17c Computed versus Observed Total PCB Concentrations through TIP during the GE June 4. 1997
Time of Travel Survey
7-17d Computed versus Observed Total PCB Concentrations through TIP during the GE June 17. 1997
Time of Travel Survey
7-18 HUDTOX-Computed vs. Data-Estimated Total PCB Load Gain across Thompson Island Pool
1/1/91 to 9/30/97
7-19 Cumulative Total PCB In-River Fluxes Past Mainstem Hudson River Sampling Stations from
4/1/91 to 9/30/97
7-20 Percent of Total PCB Transport Occurring at High* and Low Flow at Mainstem Hudson River
Sampling Stations
7-21 Cumulative Tri-t- PCB Flux at Schuvlerville 1977 - 1984 with and without Additional External
PCB Load Specified at Fort Edward
7-22 Calibration Results of Total Suspended Solids 21-Year Cumulative Mass Flux in the Hudson River
7-23 (a-b.i 1977 - 1997 Computed versus Observed Water Column Tri-t- Concentrations
vm
Limno-Tech, Inc.
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PHASE 2 REPORT
FURTHER SITE CHARACTERIZATION AND ANALYSIS
Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF FIGURES (cont.)
FIGURE TITLE
7-24 1977 -1997 HUDTOX Computed versus Estimated Cumulative Tri-t- Flux past Schuylerville.
Stillwater, and Waterford
7-25 (a-b; 1977-1997 HUDTOX-Computed versus Observed Surficial (0 - 4 cm) Bulk Tri+ Concentrations in
Cohesive Sediments
7-25 (c-d) 1977-1997 HUDTOX-Computed versus Observed Surficial (0 - 4 cm) Bulk Tri- Concentrations in
Non-Cohesive Sediments
7-26 Computed (HUDTOX) TSS Mass Balance by Reach for 1977-1997
7-27 HUDTOX-Computed Long-Term Average Burial Velocity for Cohesive and Noncohesive
Sediment in Thompson Island Pool for 1977-1997
7-28 HUDTOX-Computed Cumulative Bed Elevation Change in Thompson Island Pool during 1977-
1997
7-29 Computed (HUDTOX) Total PCB Mass Balance by Reach for Period from 4/1/91 to 9/30/97
7-30 Cumulative HUDTOX-Computed Contribution of Sediment-Water Total PCB Flux to Cumulative
PCB Load Gain between Mainstem Hudson River Sampling Stations from 4/1/91 to 9/30/97
7-3 1 Cumulative HUDTOX-Computed Sediment Contribution to Load Gain across Thompson Island
Pool from 1991-1997 for TSS. Tot-PCB, BZ#4 and BZ#52
S-l Water Column PCB Concentrations at Thompson Isiand Dam and Waterford (Lock 1) for the No
Action Forecast Simulations (1998 - 2018)
8-2 1998 - 2018 Summer Average (June through September; Total PCB Concentrations at Thompson
Island Dam and Waterford for the No Action Forecast Simulations
8-? Annual Total PCB Flux at Thompson Island Dam and Waterford for the 1998 - 2018 No Action
Forecast Simulations
S-4 Predicted Reach Average Surficial Sediment Total PCB Concentrations for the No Action Forecast
Simulations (1998-2018)
8-5 Adjustment of 1977 Fort Edward Hydrograph to Include the 100-Year Peak Flow (47.330 cfsj
8-6 HUDTOX-Computed PCB Concentrations at Thompson Island Dam for the 100-Year Flow and
1977 Flow Forecast Simulations
8-7 Cumulative Total PCB Flux over Thompson Isiand Dam for the 1st Year of the 1998 - 2018
Forecast Simulations with the 100-Y'ear Flow and 1977 Flow
IX
Limno-Tech. Inc.
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PHASE 2 REPORT
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
BOOK 2 of 4
LIST OF PLATES
PLATE TITLE
3-1 Thompson Island Pool Bottom Shear Stress 100-year Flow Event
i 01 r»i
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Volume 2D - BASELINE MODELING REPORT
HUDSON RIVER PCBs REASSESSMENT RI/FS
ACRONYMS
BAF
Biota Accumulation Factor
BMR
Baseline Modeling Report
BSRE
Beale's Stratified Ratio Estimator
BURE
Beale's Unstratified Ratio Estimator
CEAM
Center for Exposure Assessment Modeling
CD-ROM
Compact Disc - Read Only Memory
cfs
Cubic feet per second
cm
Centimeter
Corp.
Corporation
DAR
Drainage Area Ratio
deg. C
Degree Celsius
DEIR
Data Evaluation and Interpretation Report
DOC
Dissolved Organic Carbon
DOSM
Depth of Scour Model
e-g.
For example
et al.
and others
FA
Flow Average (Phase 2 Water Column Monitoring Program)
FEMA
Federal Emergency Management Agency
foe
Fraction organic carbon
fps
Feet per second
g
Gram
GBTOX
Green Bay Toxic Chemical Model
GE
General Electric
GIS
Geographic Information System
GLI
Great Lake Initiative
HEC-2
US Army Corps of Engineers. Hydraulic Engineering Center.
Surface Water Profile Model
HOC
Hydrophobic Organic Chemicals
HUDTOX
Hudson River Toxic Chemical Model
i.e.
That is
IADN
Integrated Atmospheric Deposition Network
kg
Kilogram
LDEO
Lamont-Doherty Earth Observatory
LRSCR
Low Resolution Sediment Coring Report
m/s
Meters per second
mg/1
Milligrams per liter
mi2
Square miles
XI
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HUDSON RIVER PCBs REASSESSMENT RI/FS
ACRONYMS
MT
Metric Ton
MVUE
Minimum Variance Unbiased Estimator
NAPL
Non-aqueous Phase Liquid
NPDES
National Pollutant Discharge Elimination System
ng/mJ
Nanograms per cubic meter
ng/1
Nanograms per liter
NGVD
National Geodetic Vertical Datum
NO A A
National Oceanic and Atmospheric Administration
NWS
National Weather Service
NYSDEC
New York State Department of Environmental Conservation
NYSDOH
New York State Department of Health
NYSDOT
New York State Department of Transportation
OC
Organic Carbon
PCBs
Polychlorinated Biphenyls
PMCR
Preliminary Model Calibration Report
RMA-2V
Thompson Island Pool Hydrodynamic Model
ROD
Record of Decision
RPI
Rensselaer Polytechnic Institute
SS
Suspended Solids
TID
Thompson Island Dam
TIN
Triangulated Irregular Network
TIP
Thompson Island Pool
TSCA
Toxic Substances Control Act
TSF (tsf)
Temperature slope factor
TSS
Total Suspended Solids
ug/'g (ppm)
Micrograms per gram (parts per million)
Og/L
Micrograms per liter
USACE
United States Army Corps of Engineers
USEPA
United States Environmental Protection Agency
USGS
United States Geological Survey
WASP5
(USEPA) Water Quality Analysis Simulation Program. Version 4
TOXI5
Toxic Chemical Module in WASPS
WY
Water vear
xn
Limno-Tech. Inc.
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Executive Summary
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BASELINE MODELING REPORT
EXECUTIVE SUMMARY
May 1999
This report presents results and findings from the application of mathematical models for PCB
transport and fate and bioaccumulation in the Upper Hudson River. The modeling effort for the
Hudson River PCBs Site Reassessment has been designed to predict future levels of PCBs in
Hudson River sediment, water and fish. This report provides predictions under baseline
conditions, that is, without remediation (equivalent to a No Action scenario). The outputs from
the models, baseline sediment, water and fish PCB concentrations will be used as inputs in the
Human Health and Ecological Risk Assessments. Subsequently, the models will also be used in
the Feasibility Study (the Phase 3 Report) to evaluate and compare the impacts of various
remedial scenarios.
The Baseline Modeling Report (BMR) consists of four books. Books 1 and 2 are on the
transport and fate models, with Book 1 containing the report text and Book 2 containing the
corresponding tables, figures and plates. Similarly, Books 3 and 4 are on the bioaccumulation
models, with Book 3 containing the report text and Book 4 containing the corresponding tables,
figures and plates. Predictions from the transport and fate models are used as input values for the
bioaccumulation models.
Modeling Objectives - The overall goal of the modeling is to develop and field validate
scientifically credible models in order to answer the following principal questions:
1. When will PCB levels in fish populations recover to levels meeting human health
and ecological risk criteria under continued No Action?
2. Can remedies other than No Action significantly shorten the time required to
achieve acceptable risk levels?
3. Are there contaminated sediments now buried that are likely to become
"reactivated" following a major flood, possibly resulting in an increase in
contamination of the fish population?
The work presented in this Baseline Modeling Report provides information relevant to the first
and third questions. Predictions regarding the potential impacts of various remedial scenarios,
the second question, will be conducted in the future and be presented in the Feasibility Study (the
Phase 3 Report).
Model Development
A large body of information from site-specific field measurements (as found in Hudson River
Database Release 4.1), laboratory experiments and the scientific literature was synthesized within
the models to develop the transport and fate and bioaccumulation models. Data from numerous
ES -1
LTI/MC A/T etraT ech
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sources were utilized including USE PA. the New York State Department of Environmental
Conserv ation, the National Oceanic and Atmospheric Administration, the US Geological Survey
and the General Electric Company.
The proposed modeling approach, a description of the data sets to be used for calibration, and
demonstrations of model outputs were made available for public review in the Preliminary
Model Calibration Report (PMCR), which was issued in October 1996. In addition, in
September 1998, an independent peer review was held on the modeling approach. The
modeling framework of the PMCR was revised based on the peer review and public comment. A
major revision was the addition of a mechanistic bioaccumulation model, the Gobas Model, as
described below. Because of the many uncertainties inherent in modeling bioaccumulation, EPA
has used a weight-of-evidence approach employing three different bioaccumulation models at
varying levels of complexity, ranging from empirical to mechanistic.
The following models were developed and calibrated for the Baseline Modeling Report:
HUDTOX - The backbone of the modeling effort is the Upper Hudson River Toxic Chemical
Model (HUDTOX). The HUDTOX model covers the Hudson River from Fort Edward to Troy,
New York. HUDTOX is a transport and fate model, which is based on the principle of
conservation of mass. It balances inputs, outputs and internal sources and sinks for the Upper
Hudson River. Mass balances are constructed first for water, then sediment and then PCBs.
External inputs of water, sediment and PCBs are specified from field observations. Once
external inputs are specified, the internal model and system outputs, can be calibrated against
field observations. Outputs of PCB concentrations in water and sediment from HUDTOX are
used as inputs for the forecasts of the bioaccumulation models.
Depth of Scour Model (DQSM) - The Depth of Scour Model was developed to provide
spatially-refined information on sediment erodibility in response to high-flow events such as a
100-year flood. The DOSM model is a two-dimensional, GIS-based sediment erosion model
that was applied to the Thompson Island Pool. It is linked with the output from a hydrodynamic
model that predicts the velocity and shear stress (force of the water acting on the sediment
surface) during a flood. The model was also used to develop relationships between river flow
and cohesive sediment resuspension. These relationships were used in the HUDTOX model for
evaluating flow-dependent resuspension.
Bivariate BAF Analysis - The Bivariate BAF (Bioaccumulation Factor) Analysis for fish body
burdens looks at the data for sediment and summer average water-column PCB concentrations
(two variables or "bivariate") and compares them to measured PCB levels in fish tissue. This
allows for the interpretation of the relative importance of water and sediment sources to a
particular species of fish, in turn reflecting its feeding behavior. As the BAF calculated from this
model does not take into account causal relationships, this analysis has limited predictive
capabilities compared with the more mechanistic models, described below.
Empirical Probabilistic Food Chain Model - The Empirical Probabilistic Food Chain Model
relies upon feeding relationships to link fish body burdens to PCB exposure concentrations in
water and sediments. The model combines the information from available PCB exposure
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measurements with knowledge about the ecology of different fish species and the relationships
among larger fish, smaller fish, and invertebrates in the water column and sediments. The
Empirical Probabilistic Food Chain Model provides information on the expected range of
uncertainty and variability around average body burden estimates (in contrast to the Bivariate
BAF Analysis, which just provides the average body burden estimates).
Gobas Mechanistic Time-Varving Model (FISHPATH and FISHRAND) - As a result of the
peer review for the Modeling Approach held in September 1998, it was determined that a time-
varying, mechanistic model should be included in the suite of models being used to evaluate the
potential for PCB uptake into fish tissue. Consequently, two additional mechanistic models were
developed describing the uptake, absorption and elimination of PCBs in fish over time. The
models are based on the approach of the peer-reviewed uptake model developed by Gobas (1993
and 1995). This is the same form of the model that was used to develop criteria under the Great
Lakes Initiative (USEPA, 1995). Two versions of the model were developed for the
Reassessment, a deterministic version (average body burdens) referred to as FISHPATH, and a
probabilistic version (the average body burdens including estimates of uncertainty and
variability, predominantly variability) referred to as FISHRAND. The predictions of future fish
tissue concentrations from FISHRAND will be utilized for estimating potential risk in the
Human Health and Ecological Risk Assessments.
Model Calibration
The HUDTOX model was calibrated for four different forms of PCBs: total PCBs, Tri+, BZ#4,
and BZ#52. Total PCBs represents the sum of all measured PCB congeners and represents the
entire PCB mass. Tri+ represents the sum of the trichloro- through decachlorobiphenvl
homologue groups. This allows for the comparison of data that was analyzed by congener-
specific methods with data analyzed by packed column methods that did not separate the various
PCBs as well and did not measure many of the mono- and dichlorobiphenyls. Therefore, use of
the operationally defined Tri+ term allowed for a consistent basis for comparison over the entire
period for which historical data were available. BZ#4 is a dichloro congener that represents a
final product of PCB dechlorination in the sediments. In addition, the physical and chemical
properties of BZ?4 are different from the other forms of PCBs (e.g.. it is more soluble and has a
lower partitioning coefficient), which adds to the rigor of the calibration. BZ#52 is a
tetrachlorobiphenyl that was selected as a normalizing parameter for congener patterns based on
its presence in Aroclor 1242, the main Aroclor used by General Electric at the Hudson River
capacitor plants, and due to its resistance to degradation or dechlorination in the environment.
A long-term hindcasting application was conducted for Tri- for the period of record, from 1977
to 1997. However, the period from 1977 to 1984 had limited PCB data for estimating external
Tri+ loadings. The uncertainty introduced by this limited PCB data required that an additional
PCB load be added in order for the model to match sediment concentrations in Thompson Island
Pool in 1984 and water column observations downstream. Consequently, the long-term hindcast
calibration for Tri+ was actually only conducted for the period from 1984 to 1997.
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The period from 1991 to 1997 was the principal focus of the calibration effort because this period
was relatively data rich in terms of parameters measured, spatial-temporal coverage and data
quality. Applications for this period included all four PCB forms: total PCBs, Tri+, BZ#4, and
BZ«52.
The HUDTOX model was successful in representing the hydraulics, solids and PCB dynamics of
the Upper Hudson River over the historical period of record. This period was characterized by a
significant transition from an early phase of high upstream PCB loads, followed by a long
declining phase to present-day conditions with upstream PCB loads now very close to detection
limits. Results from the HUDTOX calibration applications were consistent with the magnitudes
and trends of the best available data for the historical period.
Model Forecast
The models were run for a forecast period of 21 years beginning January 1, 1998. The 21-year
time frame was selected because it matched the time frame of the 1977 to 1997 hindcast. All
flows, solids loadings and other external forcing functions were the same as those used in the
hindcast, with the exception of PCB concentrations at Fort Edward. The initial PCB
concentrations for the forecast were the same as the final PCB concentrations from the 1991 to
1997 calibration simulation. Forecast simulations were run for two different assumptions for
PCB loadings at the upstream boundary at Fort Edward: first, water column PCB concentrations
were held constant at a level equal to the annual average PCB concentration that was observed in
1997 (9.9 ng/1); and second, water column PCB concentrations were held constant at zero. Note
that these simulations assume that there will be no future load increases from any upstream
sources. In particular, it was assumed that during the forecasts PCB migration from the GE
Hudson Falls Plant site would not increase and that there would not be any type of event similar
to the releases that occurred with the alleged partial failure of the Allen Mill gate structure in
1991. Based on the expectation that the PCB load from the GE Hudson Falls Plant site would
decrease in the future due to the implementation of remedial measures there, these forecasting
simulations were designed to bound the estimates of system responses.
Appropriate target levels for fish body burdens have not yet been established. In the Feasibility
Study, site-specific target levels to be protective of human health and the environment will be
developed from the risk assessments. However, it is beneficial at this time to compare forecasted
fish body burden levels against certain available criteria as a matter of perspective. These
include: the 2 ppm wet weight Action Level used by the Food and Drug Administration (FDA)
for regulating fish in commerce, and the Great Lakes Sport Fish Advisory Task Force values of
1.1 ppm wet weight for consumption of six fish meals per year, and 0.2 ppm wet weight for
consumption of one fish meal per month. Again, these are not endorsements of these values for
decision making, and appropriate values will be developed in the Feasibility Study for the site.
Forecasts using the mechanistic Gobas model, FISHRAND, were run only under the constant
upstream boundary condition because predicted sediment and water exposure concentrations
from HUDTOX were virtually the same for the constant and zero upstream boundary conditions,
which would result in virtually the same body burden predictions. Species modeled were
largemouth bass, brown bullhead, yellow perch, white perch and pumpkinseed. The reported
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time period for achieving target values for fish body burdens may extend beyond the 21-year
forecast period after consideration of the uncertainty around the best estimated values.
Major Findings
The primary objective in the modeling effort is to construct a scientifically credible tool to help
in the understanding of PCB transport and fate and bioaccumulation in the Upper Hudson River,
and to use that tool for making forecasts of what will happen in the future. As such, one of the
major findings was that it was possible to construct a suite of models that generate output that
matches the observed data reasonably well. Subsequent to this report, the model predictions can
be used to evaluate ecological and human health risks and to assess the time it takes for the river
to recover under various remedial scenarios.
There are numerous general observations about the river that are apparent from the mass balance
exercises. Some important observations that impact USEPA's understanding of the system
include: the tributaries along the length of the river contribute the vast bulk of the solids load
carried by the system; the river is net depositional in the Thompson Island Pool and apparently
also in the downstream reaches; and, the models indicate a gradual decline in the mass transport
of PCBs down river over time.
Beyond the general observations above, the development of the models and the analysis of model
outputs have provided USEPA with the following findings regarding PCBs in the Upper Hudson
River:
1. The future projection for PCB concentrations in the water column is controlled by
inputs from the sediment. Although the constant upstream PCB load in the
forecast simulations contributes to the PCB concentration in the water column, the
shape of the response curve is set by the sediment-to-water PCB fluxes.
• Predicted PCB concentrations in the surface sediments are not controlled by
PCB loads generated above Fort Edward. Sediment PCB concentrations are
controlled primarily by sediment-to-water flux and exchange between deep
and surface sediments.
• Water column PCB concentrations are influenced by upstream PCB loadings,
with the relative degree of influence increasing with time, due to declining
PCB concentrations in the surficial sediments.
2. A 100-year peak flow event would not be expected to have substantial impacts on
the recovery' rate of the Upper Hudson River.
• The models predict that approximately 60 kg (130 lbs.) of PCBs would be lost
from the Thompson Island Pool in response to a 100-year peak flow (47.330
cubic feet per second).
• Long-term, summer average PCB concentrations in the water column with and
without the 100-year peak flow are virtually indistinguishable one year after
the event. (Note that this does not account for potential impacts from PCBs
that moved into the Lower Hudson River.)
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3. Although there has been net deposition of sediment in the Thompson Island Pool
(as well as the entire upper Hudson), there have been losses of PCBs from the
sediment. In other words, net deposition does not mean that PCBs will be
unavailable to the water column. For example., from 1984 to 1994 (the same time
frame analyzed in the Low Resolution Sediment Coring Report) the model
estimated that 2000 kg of Tri+ were lost from the Thompson Island Pool sediment
inventory, while at the same time 1.6 cm of net sediment deposition occurred on a
poolwide basis.
4. There is a contribution of PCBs from the sediment that is not dependent on the
flow of the river. Some of the processes that may cause non-flow dependent
resuspension are: wind driven dispersion, bioturbation by benthic organisms,
bioturbation by demersal fish, mechanical scour by propwash, boats and floating
debris, and uprooting of macrophytes by flow, wind or biological action. Such a
non-flow dependent load is important because the model calibration suggests that
approximately 80 percent of the total PCB transport down the river from 1991 to
1997 took place during low-flow periods.
5. Forecasts for the FISHRAND model suggest that largemouth bass will achieve
2.0 ppm on an average wet weight basis between 2008 and 2014, with the best
estimate of 2011 for river mile 189 (within the Thompson Island Pool), and
between 2011 and 2019 (best estimate 2015) for river mile 168 (Stillwater) under
constant upstream boundary conditions. Largemouth bass average values will not
achieve target levels of 1.1 ppm or 0.2 ppm within the 21-year forecast period at
these locations. In addition, the 95th percentile value (a statistically important
value that is frequently used in evaluating a high-end risk and/or as part of the
evaluation of uncertainty around the range of predicted values) will not achieve
any of the target levels in the forecast period. Note that the target levels are for
comparison purposes only, and that appropriate levels will be determined in the
Feasibility Study.
6. Forecasts suggest that for river mile 189. average values for yellow perch will
achieve 2.0 ppm between 2007 and 2014 (best estimate 2010). and 1.1 ppm
between 2015 and 2021. 95th percentile values would not reach any of the targets
within the forecast period. Average yellow perch values will achieve 2.0 ppm
between 2008 and 2014 (best estimate 2011) for river mile 168, but the lower
target values and the 95!h percentile values will be not reached within the forecast
period.
7. For brown bullhead, the average fish body burden is forecasted to reach 2.0 ppm
between 2014 and 2020 (best estimate 2017) at river mile 168. Within the 21-
year forecast period, no other target levels will be achieved for average brown
bullhead at river mile 168. and none of the target levels are achieved at river mile
189.
8. At river miles 157 and 154. forecasts for all species modeled achieved the FDA
action level of 2 ppm by 2021. even at the 95m percentile value.
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9. For all locations and species modeled, predicted average body burdens did not fall
below 0.5 ppm within the 21-year forecast period.
Summary
The principal processes that control contemporary PCB dynamics in the Upper Hudson River are
hydraulics, external solids load, sediment-to-water fluxes, water-to-air fluxes and PCB fate in the
bedded sediments. It appears that the river is currently on the tail of a long PCB washout curve
controlled largely by the rate at which PCBs are being reduced in the upper mixed sediment
layer. Consequently, forecasts of system responses depend on an accurate representation of
processes controlling solids dynamics and PCB interactions across the sediment-water interface.
The forecasting results suggest that the water column and sediments of the Upper Hudson River
will not have reached steady-state by 2018 (the end of the forecast period). At that time, even
with constant upstream PCB loads, water concentrations still show a declining trend, suggesting
that the sediments continue to be a source of PCBs to the system.
In their present forms, the models are useful tools for providing information on PCB exposure
concentrations for the Human Health and Ecological Risk Assessments. Additional modeling
efforts will be conducted to fine tune the model for predicting the time it takes for the system to
recover. The results of these additional modeling efforts will be made available as part of the
Responsiveness Summary for this report.
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Chapter 1
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1. INTRODUCTION
1.1. Purpose of Report
This volume is the fourth in a series of reports describing the results of the Phase 2 investigation
of Hudson River sediment polychlorinated biphenyls (PCB) contamination. This investigation is
being conducted under the direction of the U.S. Environmental Protection Agency (USEPA).
This investigation is part of a three phase remedial investigation and feasibility study intended to
reassess the 1984 No Action decision of the USEPA concerning sediments contaminated with
PCBs in the Upper Hudson River. Figure 1-1 contains a location map for the Hudson River
watershed. For purposes of the Reassessment, the area of the Upper Hudson River considered
for remediation is defined as the river bed between the Fenimore Bridge at Hudson Falls (just
south of Glens Falls) and Federal Dam at Troy (Figure 1-2).
In December 1990. USEPA issued a Scope of Work for reassessing the No Action decision for
the Hudson River PCB site. The scope of work identified three phases:
• Phase 1 - Interim Characterization and Evaluation
• Phase 2 - Further Site Characterization and Analysis
• Phase 3 - Feasibility Study
The Phase 1 Report (TAMS/Gradient, 1991) is Volume 1 of the Reassessment documentation
and was issued by USEPA in August 1991. It contains a compendium of background material,
discussion of findings and preliminary assessment of risks.
The Final Phase 2 Work Plan and Sampling Plan (TAMS/Gradient, 1992) detailed the following
main data collection tasks to be completed during Phase 2:
• High- and low-resolution sediment coring;
• Geophysical surveying and confirmatory sampling;
• Water column sampling (including transects and flow-averaged composites); and.
• Ecological field program.
The Database Report (Volume 2A in the Phase 2 series of reports; TAMS/Gradient. 1995) and
accompanying CD-ROM database provides the validated data for the Phase 2 investigation. This
Baseline Modeling Report (BMR) utilized the Hudson River Database. Release 4.1b, which was
updated in Fall 1998 (TAMS et ai., 1998a) The BMR is Volume 2D of the Reassessment
documentation and is the fourth of a series of six reports describing the Phase 2 characterization
and analysis activities. It presents results and findings from application of mathematical models
for PCB transport and fate and bioaccumulation in the Upper Hudson River.
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1.2. Report Format and Organization
The information gathered and the findings of this phase are presented here in a format that is
focused on answering questions critical to the Reassessment, rather than report results strictly
according to Work Plan tasks. In particular, results are presented in a way that facilitates input to
other aspects of the project.
This report is presented in four books. Books 1 and 2 contain results and findings from the PCB
transport and fate models. Book 1 contains the report text and Book 2 contains all tables, figures
and plates for these models. Books 3 and 4 contain results and findings from the PCB
bioaccumulation models. Book 3 contains the report text and Book 4 contains all tables, figures
and plates for these models.
Books 1 and 2 contain results and findings for applications of PCB transport and fate models to
existing historical data and for forecast simulations designed to estimate long-term responses to
continued No Action and impacts due to a 100-year peak flow. Chapter 2 presents the overall
conceptual approach used for the mathematical models and the relationships among individual
models. Chapter 3 presents the hydrodynamic model used for Thompson Island Pool (TIP).
Chapter 4 presents the Depth of Scour Model (DOSM) used to estimate masses of solids and
PCBs eroded from cohesive and non-cohesive sediment areas in TIP for a given peak flow.
Chapter 5 presents the development of the Hudson River Toxic Chemical Model (HUDTOX)
including conceptual framework, governing equations and spatial-temporal scales. Chapter 6
presents results from data development tasks that were necessary to provide model inputs and to
support post-processing of model outputs. Chapter 7 presents results and findings from
application of the HUDTOX model to existing historical data, including data collected as part of
the Phase 2 investigation. Finally, Chapter 8 presents results and findings from forecast
simulations with the HUDTOX model designed to estimate long-term responses to continued No
Action and impacts due to a 100-vear peak flow.
1.3. Project Background
1.3.1. Site Description
The Hudson River PCBs Superfund site encompasses the Hudson River from Hudson Falls
(River Mile [RM] 198) to the Battery in New York Harbor (RM 0). a river distance of nearly 200
miles. Because of their different physical and hydrologic regimes, approximately 40 miles of the
Upper Hudson River, from Hudson Falls to Federal Dam (RM 153.9). is distinguished from the
Lower Hudson River below Federal Dam. Emphasis was placed on Thompson Island Pool
(TIP), a 6-mile portion of the river between Fort Edward and Thompson Island Dam (TID)
(Figure 1-3). because a substantial amount of PCB-contaminated sediment is contained in this
location.
1.3.2. Site History
Over a 30-year period ending in 1977, two General Electric (GE) facilities, one in Fort Edward
and the other in Hudson Falls. NY. used PCBs in the manufacture of electrical capacitors.
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Chapter 2
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2. MODELING APPROACH
2.1. Introduction
The overall modeling approach in this Reassessment is based on the principle of conservation of
mass. Models were developed for transport and fate of PCBs in the water column and bedded
sediments, and for PCB bioaccumulation in fish populations. The spatial domain of these
models was the Upper Hudson River between Fort Edward and Federal Dam at Troy (Figure 1-
2). Emphasis was placed on TIP, a 6-mile portion of the river between Fort Edward and
Thompson Island Dam (TID) (Figure 1-3), because a substantial amount of PCB-contaminated
sediment is contained in this location.
Section 2.2 presents the overall modeling framework used in this Reassessment. Sections 2.3
and 2.4 describe the hydrodvnamic model and the Depth of Scour Model (DOSM), respectively,
that were developed and applied to TIP. Section 2.5 describes the Hudson River Toxic Chemical
Model (HUDTOX) that was developed and applied to the Upper Hudson River between Fort
Edward and Federal Dam at Troy. Section 2.6 describes the various applications conducted with
the HUDTOX model. Section 2.7 presents an overview of the database used for model
development and applications.
2.2. Conceptual Approach
The overall conceptual framework for the Reassessment models is illustrated in Figure 2-1.
Depicted are the principal individual modeling components and their inter-relationships. The
hydrodvnamic model, the DOSM and HUDTOX comprise the transport and fate models. The
linkage module serves only to process output from the hydrodvnamic model and the DOSM for
use in HUDTOX. These transport and fate models are described in the following sections. The
Bivariate Biota Accumulation Factor (BAF) Model and the Food Chain Model quantify linkages
between PCB water column and sediment concentrations and fish body burdens. These models
are the subject of Books 3 and 4.
2.3. Hydrodvnamic Model
The hydrodvnamic model is time-variable, two-dimensional and vertically-averaged. It was
applied to TIP in steady state mode to provide hydraulic information for the HUDTOX model
and information on bottom shear stresses at the sediment-water interface for the DOSM. The
hydrodvnamic model includes explicit representation of the flood plain to account for overbank
flow during flood events.
The hydrodvnamic model was not directly integrated with the HUDTOX model. Output from
the hydrodvnamic model was spatially and temporally processed using a linkage module that
transformed water velocities into flows that were routed among the HUDTOX model spatial
segments in TIP. Water velocities were also transformed into applied shear stresses at the
sediment-water interface for use in DOSM and HUDTOX. The hydrodvnamic model was run to
steady-state for a range of different river flows, including the 100-vear peak flow.
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2.4. Depth of Scour Model
The DOSM is a two-dimensional, GIS-based model of sediment erosion that was applied to TIP.
It is a specialized tool for providing spatially-refined information on sediment erodibility in
response to flood events. Information on sediment physical properties and PCB concentrations
was provided to the DOSM from field measurements. Information on applied shear stresses at
the sediment-water interface was provided to the DOSM as output from the hydrodynamic
model.
The DOSM was used in two different ways. First, the DOSM was used as a stand-alone tool to
provide mass estimates of solids and PCBs eroded, and depth of sediment bed scour, in response
a 100-year peak flow. Second, the DOSM was used to develop relationships between river flow
and cohesive sediment resuspension. These relationships were used in the HUDTOX model in
the form of algorithms describing flow-dependent cohesive sediment resuspension. To develop
these relationships, the DOSM was run for a range of flows using output for applied shear
stresses from the hydrodynamic model.
2.5. Mass Balance Model
HUDTOX is the principal transport and fate modeling tool in this Reassessment. HUDTOX is a
time-variable, three-dimensional mass balance model. It is a fully-integrated representation of
solids and PCB concentrations in the water column and bedded sediments. HUDTOX was
applied to the entire Upper Hudson River from Fort Edward to Federal Dam at Troy. Because a
substantial amount of PCB-contaminated sediment is contained in TIP, the TIP portion of
HUDTOX included greater spatial resolution than the portion downstream of TID. In TIP,
HUDTOX is two-dimensional in the water column and three-dimensional in the sediments.
Between TID and Federal Dam it is one-dimensional in the water column and two-dimensional
in the sediments.
Three types of mass balances are represented in HUDTOX: (1) a water balance: (2) a solids
balance: and (3) a PCB mass balance. A water balance is necessary because PCB dynamics are
influenced by river flow rates and mixing rates. A solids balance is necessary because PCB
dynamics are influenced by the tendency of PCBs to sorb, or attach, to both suspended and
bedded solids in the river. Finally, a PCB mass balance itself is necessary to account for all
sources, losses and internal transformations of PCBs in the river.
Output from the hydrodynamic model was used to provide hydraulic routing information for
HUDTOX in TIP. Hydraulic routing downstream of TID was one-dimensional and was
specified using USGS flow gage data at Fort Edward and estimated flows from downstream
tributaries. Output from the DOSM was used to provide information on resuspension for
HUDTOX in the form of flow-resuspension algorithms that were part of its dynamic solids mass
balance. This linkage between the DOSM and HUDTOX ensured internal consistency in
representation of flow-dependent resuspension between these two models for cohesive
sediments. Output from HUDTOX for PCB concentrations in the water column and bedded
sediments provided PCB exposure information for the ecological and human health
investigations in the Reassessment.
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2.6. Mass Balance Model Applications
Developmental applications and model calibrations were conducted with HUDTOX using
historical data for the period January 1, 1977 to September 30, 1997. Differences in HUDTOX
model applications were determined by model calibration strategy and data availability. In broad
terms, the model calibration strategy involved testing HUDTOX over different physical
conditions in the river, different PCB physical-chemical properties and different time frames.
The calibrated model was used to conduct forecast simulations for a 21 -year period beginning in
1998. These forecast simulations were intended to estimate long-term system responses to
continued No Action and impacts due to a 100-year peak flow.
HUDTOX applications included mass balances for four different PCB forms: total PCBs, Tri+,
BZ#4 and BZ#52. Total PCBs represents the sum of all measured PCB congeners and is the
only PCB form that completely represents total PCB mass. A limitation to the use of total PCBs
is that data were available for only the period from 1991 to 1997. To extend the period of time
for the HUDTOX applications, Tri+ was used as a surrogate for total PCBs. Tri+ represents the
sum of only trichloro through decachloro homologue groups. Due to differences in analytical
methods among individual datasets, Tri+ was the only internally-consistent PCB form that could
be operationally defined to approximate total PCBs over the entire period from 1977 to 1997
(TAMS et al., 1998b). BZ#4 is a dichloro congener that represents a final product of PCB
dechlorination in the sediments (TAMS et al., 1997). BZ»52 is a tetrachloro congener that was
selected as a normalizing parameter for congener patterns based on its presence in Aroclor 1242,
the main Aroclor used by GE, and on its resistance to degradation or dechlorination in the
environment (TAMS et al., 1997). Because BZ#4 and BZ#52 have different physical-chemical
properties, especially partitioning, their inclusion imposes tighter constraints on the HUDTOX
model calibration and enhances its scientific credibility.
Most of the calibration effort was focused on the period 1991 to 1997 because this period was
relatively data-rich in terms of parameters measured, spatial-temporal coverage and data quality.
Applications for this period included all four PCB forms, total PCBs. BZ#4. BZ#52 and Tri-.
To strengthen the scientific credibility of the model, a long-term hindcasting application was
conducted for the period 1977 to 1997. This application included only Tri+ because this was the
only one of the four PCB forms for which data were available over a long historical period. The
principal emphasis in the hindcast application was on the period from 1984 to 1997 due to
uncertainties in sediment PCB concentrations in 1977 and PCB inputs at Fort Edward between
1977 and 1984. The 21-year forecast simulations were conducted for total PCBs because total
PCB concentrations are required for the ecological and human health risk assessments.
In the Preliminary Model Calibration Report (PMCR), short-term calibrations were conducted
for five PCB congeners or groups of co-eluting congeners: BZ#4, BZr?28. BZ#52, BZ#90-H01
and BZ#138. The period of simulation for these calibrations was from January 1 to September
30. 1993. In the present report the short-term calibrations for BZ#4 and BZ#52 were extended to
include the period from 1991 to 1997. The scientific credibility of the HUDTOX model can be
further strengthened by extending the short-term calibrations for BZ#28, BZ#90-101 and
BZ#138. Calibration work with these three additional congeners is planned.
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2.7. Hudson River Database
All modeling work in this report utilized the extensive database that was created to support this
Reassessment. The Database Report (TAMS/Gradient, 1995) and accompanying CD-ROM
database provides the validated data for the Phase 2 investigation. This Baseline Modeling
Report (BMR) utilized the Hudson River Database, Release 4.1b, which was updated in fall 1998
(TAMS et al., 1998a). This database contains information from a large variety of different
sources, including:
• New York State Department of Environmental Conservation (NYSDEC)
• New York State Department of Health (NYSDOH)
• New York State Department of Transportation (NYSDOT)
• General Electric Company (GE)
• Lamont-Doherty Earth Observatory (LDEO)
• Rensselaer Polytechnic Institute (RPI)
• U.S. Geological Survey (USGS)
• National Oceanic and Atmospheric Administration (NOAA)
• National Weather Service (NWS)
• U.S. Environmental Protection Agency (USEPA).
To supplement the database in Release 4.1b, a portion of the 1997 USGS flow, suspended solids
and PCB data were obtained directly from the USGS in Albany, New York. Where necessary
and appropriate, information from the scientific literature and various technical reports was also
used in this modeling work. These sources are cited in the report text.
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3. THOMPSON ISLAND POOL HYDRODYNAMIC MODEL
3.1. Introduction
The Thompson Island Pool (TIP) is defined as the reach of the Hudson River upstream from the
Thompson Island Dam at RM 188.5 and downstream from the former Fort Edward Dam. as
shown in Figure 3-1. The purpose of the hydrodynamic modeling effort for TIP was to provide
hydraulic routing information for the HUDTOX model and information on bottom shear stresses
at the sediment-water interface for the DOSM and HUDTOX. A hydrodynamic model was used
to calculate two-dimensional, vertically-averaged velocity fields for a range of different river
flows, including the 100-year peak flow in the Hudson River which is considered to be a flow of
47,330 cfs (Butcher, 1993). The computation of a two-dimensional vertically averaged velocity
field is necessary to account for the lateral variability of the flow, which in turn allows for the
estimation of a bed shear in the entire flow domain. The bed shear is used to compute the mass of
cohesive sediments eroded in the DOSM. Because sediment properties and PCB concentrations
are not uniformly distributed, the bottom shear stresses must be determined for each element
used in the river model to correctly estimate pool-wide resuspension of PCBs.
The hydrodynamic model used to compute the flow is the US Army Corps of Engineers RMA-
2V. RMA-2V uses the finite element method to compute vertically averaged velocities and
water surface elevations in the flow field. The model has been extensively studied and applied
widely (Berger 1990, Lin and Richards 1993, McAnally et. al. 1984, Richards (1990)). The
choice of a two-dimensional vertically averaged model, and the density of the grid mesh was
largely determined by the resolution needed to adequately define the flow field variations and
river bathymetry, and hence, shear stress variation. The shear stress exerted on the river bottom
is parameterized by the magnitude of the vertically averaged velocity and the depth of flow as is
described in Section 3.7
The hydrodynamic model was applied for a range of steady flow conditions in the TIP.
Transient effects due to storage and drainage were not included in the RMA-2V simulations
because the HUDTOX model applies constant flow routing. The historical flow record at Fort
Edward shows that the Hudson River high flow events occur over several days which gives the
TIP enough time to establish steady state conditions. The credibility of the numerical results was
established by applying the model to events where the flow in the river had been measured. The
model was run for the 100-year peak flow to provide the velocity field used by the DOSM.
The description of the hydrodynamic modeling effort is divided into eight sections. Section 3.2
describes the hydrodynamic modeling approach. Section 3.3 describes the input data required by
the model to simulate TIP. Section 3.4 describes the model calibration. Section 3.5 describes
model validation. Section 3.6 describes model sensitivity in response to changes in various
model inputs. Section 3.7 outlines the conversion of the vertically averaged velocities computed
by the model to the corresponding bed shear stresses. Finally, Section 3.8 contains a discussion
of the model results.
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3.2. Modeling Approach
A short summary of the modeling procedure is as follows. A finite element grid was first
constructed for the TIP section of the river and floodplain. The RMA-2V uses a finite element
procedure to solve the governing equations that describe the vertically averaged velocities and
water surface elevation. The boundary conditions consist of a specified upstream flow, the water
elevation downstream and the resistance to flow. The downstream boundary was obtained from
a rating curve developed for the stage-discharge gage near the Thompson Island Dam, and the
resistance to flow is parameterized by Manning's *n\
3.2.1. Governing Equations
The variables u and v, represent the vertically averaged velocity field in the downstream and
cross stream directions respectively. The depth of flow is given by the variable, h. To solve for
these three variables, three equations are needed. An additional relationship for the bottom stress
is used to close the set of equations. These are as follows:
1. Continuity
Sh + d{uh) 5{yh) _
St 5x by
(3-1)
2. Linear Momentum
a.
x-direction (longitudinal) momentum
cu cu cu
b. y-direction (transverse) momentum
(3-3)
3. Bottom Friction Coefficient
(English Units')
C - 2
(1.486):/?,:'3>
(3-4)
('Metric Units)
(3-5)
where:
h
water depth [L]
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u = vertically averaged flow velocity in the x-direction
(longitudinal) [L/T]
v = vertically averaged flow velocity in the y-direction (lateral)
[L/T]
x = distance in the longitudinal direction [L]
y = distance in the lateral direction [L]
t - time [T]
g = acceleration due to gravity [L/T2]
a0 = bottom elevation [L]
Cf = flow roughness coefficient [dimensionless]
n = Manning's 'n' channel roughness coefficient
[dimensionless]
Exx ~ normal turbulent exchange coefficient in the x direction [M/(LT)]
Exv = tangential turbulent exchange coefficient in the x
direction [M/(LT)]
Eyy = normal turbulent exchange coefficient in the y direction [M/(LT)]
Eyx = tangential turbulent exchange coefficient in the y
direction [M/(LT)]
p = water density [M/LJ]
q = velocity magnitude = (u2~ v2)1'2 [L/T].
The Coriolis apparent force and the force imposed by wind stress have been neglected here
because these forces are small compared to the other forces for the Hudson River.
3.2.2. Computational Procedure
The RMA-2V model was first calibrated to the measured hydrodvnamic data of the river, with
Manning's 'n' as the primary calibration parameter. River data, such as river stage-discharge
relations for the upstream (Lock 7) gaging station, were used to calibrate the model. Other data,
such as velocity measurements made by the USGS during high flow events, were also used to
validate the model results.
The specific steps used in the modeling procedure are as follows:
1) The flow field velocity and depth for each node were determined using RMA-2V;
2) The bed shear velocity u* for each node was determined from calculated velocity and
depth at each node;
3) The bottom shear stresses were then calculated from the bottom shear velocities using
the relation; and.
r = p ( u *)2
4) The calculated TIP velocities were used to determine the discharges for each
HUDTOX segment.
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3.3. Model Input Data
The hydrodynamic model RMA-2V requires specific input data describing the hydraulic
conditions of the system chosen for simulation. These input data consist of the grid used for the
computation, the use of Manning's 'n' to parameterize the bottom friction, the forcing functions
or upstream boundary conditions, and the downstream and side-channel boundary conditions.
These are described below.
3.3.1. Model Grid
The RMA-2V model uses a six-node triangular element scheme to describe the physiography of
the TIP system. The model grid consists of approximately 6000 nodes defining 3000 elements.
Each node is defined by an x-v coordinate and its corresponding elevation. The depth associated
with each grid node for the main channel is based on the bathvmetric survey performed by
General Electric in 1991 (O'Brien & Gere, 1993b). Figure 3-2 shows the finite element grid
used in the model calibration. The finite element grid in the floodplain was constructed using
elevations taken from the USGS topographic maps. As seen in Figure 3-2, the grid in the
floodplain is much coarser than in the TIP channels. This is justified because velocities in the
floodplain are much smaller than in the TIP channels and do not vary as much. The nodes of the
finite element grid in the main channel are located approximately every 50 feet across the river
(laterally) and approximately 300 feet along the channel (longitudinally).
During the course of model calibrations and runs, it was necessary to refine the grid so that the
water mass was conserved at the various transects. Conservation was achieved within
approximately six percent and this was judged compatible with the requirements of the
HUDTOX model. The refining of the grid consisted of eliminating isolated nodes along the
sides of the flow and smoothing the bottom elevations. These changes were minor and it is
judged that these changes had little impact on the calculated overall velocity field.
3.3.2. Manning's 'n'
The input parameter. Manning's "n\ expresses the river's hydraulic resistance to flow.
Conceptually, resistance to flow reflects the character of the sediments and the nature of the flow
pathways. This parameter is commonly a calibration parameter, because its value cannot be
determined accurately from a measurement of the physical dimensions of the river or from a
description of the sediment type. Two site-specific hydraulic flow modeling studies. Zimmie
(1985) and FEMA (1982) had been conducted previously and the Manning's "n" values can be
expected to be near the values used in these studies. Table 3-1 contains the Manning "n" values
used in these two studies.
For this study, the values of Zimmie were used initially and then subsequently calibrated to best
fit the recorded observations of the river, especially those at high flow. The sensitivity of the
model to changes in this parameter is discussed below in Section 3.6.1.
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3.3.3. Forcing Functions
The principal forcing function of the model consists of the upstream boundary- condition, which
is the specified flow. The model was run for the eight different flows shown in Table 3-2. The
first four flows are of interest because the concentration of suspended sediment in the river was
sampled when they occurred. The fifth flow is of interest because it is the highest flow recorded
in TIP after the Fort Edward dam was removed in 1973. The final three flows are of interest
because they represent high flow events with a specified return period.
The model results for these eight flows were used in the DOSM to develop relationships between
river flow and solids resuspension. These flows were specified at the most upstream transect of
the model grid. This transect is approximately 500 feet upstream of Rogers Island.
3.3.4. Boundary Conditions
The boundary conditions of the model consist of the side-channel boundary' condition and the
downstream boundary condition. The side-channel boundary' condition is the requirement that
the velocity normal to the sides of the channel is zero. This is implicitly performed in the RMA-
2V model. The downstream boundary condition consists of specifying the water surface
elevation at the most downstream transect, which is the Thompson Island Dam. The downstream
boundary must be specified as an elevation in order to incorporate the backwater effects of the
dam into the model.
The downstream boundary surface elevation was taken from the rating curve for Gage 118.
which is located just above Thompson Island Dam. The rating curve was developed from a
regression analysis performed on the discharge-water level data accumulated during the 11 year
period of 1983 to 1993 (TAMS et aL 1997). Examination of this rating curve showed that the
regression is good for flows up to 30,000 cfs: how:ever, the third-order polynomial developed in
the regression fails to accurately predict increasing river elevations for flows above 30.000 cfs.
Refined extrapolation using engineering best judgment and a theoretical rating curve (Zimmie.
1985) was used to determine the water levels at Thompson Island Dam above these flows.
3.4. Model Calibration
The calibration approach consists of determining an appropriate value for the turbulent exchange
coefficients and then varying the Manning's 'n' so that the river levels computed by the model
agree with the river levels predicted by the upstream rating curve for each flow input at the
upstream transect of the grid. Note that only one value of Manning's "n' was used for the entire
length of the main channel, because there are no physical data on which to base a variation of
Manning's 'n\
The upstream rating curve used for comparing to model output during calibration was Gage 119.
near Lock Number 7, which is near the southern tip of Rogers Island (Figure 3-1).
Because this component of the study is primarily interested in larger flows on the Hudson River,
the calibration first focused on the flow of 30,000 cfs which is the highest flow for which the
rating curves for both Gage 119 (upstream) and Gage 118 (downstream) are substantiated. The
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Manning's 'n' values were calibrated for 30,000 cfs and were then used in the model to predict
water elevations for lesser flows. These predicted water elevations were then compared with the
elevations from the Gage 119 elevations.
The turbulent exchange coefficients were set to 4,790 Pa-sec (100 lb-sec/ft2), based on the
guidelines given in the RMA-2V manual (Thomas and McNally, 1990). Specifically, the
guidelines given in the manual suggest a range of values from 479 to 4. 790 Pa-sec (10 to 100 lb-
sec/ft2), and the stage and discharge results proved to be relatively insensitive to variations within
this range of values.
As described above, the model was primarily calibrated for the flow of 30,000 cfs. The
Manning's 'n' values for the final calibration were 0.020 for the main channel and 0.060 for the
floodplain. The model computed the same river water surface elevation as was observed at Gage
119 using these Manning's ;nr calibration values. Table 3-3 shows this result, along with the
comparison of model output vs. rating curve water levels for lesser flows. The elevations in the
table are listed in feet relative to the National Geodetic Vertical Datum (NGVD).
As seen when comparing the last two columns in Table 3-3, the model's results are slightly
higher than the rating curve for the smaller flows, implying that the calibrated Manning's 'n'
appears to be somewhat low for the lower-flow cases. Nevertheless, it was judged that a
higher value could not be justified, given the model's close fit for 30,000 cfs. because a higher
Manning's 'n' would increase the model's prediction of the upstream water surface in that case.
The excellent model fit at the calibration flow of 30,000 cfs, along with good results from two
validation exercises described below, provide confidence in using the model to simulate high-
flow events.
3.5. Model Validation
There were two additional and separate sources of information used to validate the calibration
results. The first source is the Hudson River velocity measurements made in the TIP by the
USGS. The second source is the flood study conducted by FEMA. A comparison between
model results with these sources of information is discussed below.
3.5.1. Rating Curve Velocity Measurements
The USGS periodically measures the flow in the Hudson River in the TIP to develop and update
the river's rating curves. For the rating curve located at Scott Paper, which is upstream of
Rogers Island, the flow is measured by measuring the depth and velocity at numerous points over
the cross-section of the river at Rogers Island. These data are taken at the bridges over the
Hudson River on both sides of Rogers Island. Using these data, the model's simulated velocities
can be compared to the measured velocities as a check on the accuracy of the model.
The model was run for the discharge (29.800 cfs) that was measured on April 18. 1993. The
velocities that were computed by the model for locations along the cross-section of the river were
approximately equal to or slightly lower than measured. For example, the river velocities
measured in the middle of the channel by the USGS were approximately 4.3 feet per second
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(fps), while the model computed velocities of approximately 4.1 fps. Even though these values
are sufficiently close for validation, it should be noted that the measured velocities were expected
to be slightly higher, because the bridges from which the velocity measurements are taken
constrict the flow, causing localized higher velocities. The model does not include the localized
effect of flow restriction due to bridge piers.
3.5.2. FEMA Flood Studies
The Federal Emergency Management Agency (FEMA) regularly conducts studies to predict the
flood elevations in rivers for flows of various return periods. The results of the study conducted
by FEMA in 1984 for the Upper Hudson River were used as an additional check of the
credibility of the model. The 100-year flow used by FEMA (52.400 cfs) is greater than the 100-
year flow used in this study (47,330 cfs) so that a direct comparison of 100-year flood elevations
was not initially possible. However, the model was eventually run for the 100-year FEMA flow
of 52,400 cfs, and the model predicted a river elevation at Fort Edward of 130.4 ft. NGVD
(National Geodetic Vertical Datum, formerly Sea Level Datum of 1929). The FEMA flood study-
using the HEC-2 program (with higher Manning ;n' values) predicted a river elevation of 130.7
ft. NGVD. These results are comparable and each model reflects a slightly different
representation of the river hydraulics.
The RMA-2V model developed here was also run for 52,400 cfs with a Manning's 'n" of 0.030
for the main channel and 0.075 for the floodplain (approximately the same as the FEMA study).
This resulted in a predicted river elevation of 131.7 ft. Most importantly, the river velocities do
not vary appreciably for the various representations therefore, the model results are judged to be
comparable to the FEMA flood studies.
3.5.3. 100-Year Peak Flow Model Results
The model was run for the 100-year peak flow of 47,330 cfs, and the predicted river elevation at
the downstream tip of Rogers Island was 128.6 ft. This elevation is slightly lower than the
extrapolated rating curve's elevation of 129.1.
The vertically averaged velocity field produced by RMA-2V for the 100-year peak flow is shown
in Figure 3-3. The velocity magnitudes are reflected by the length of the vectors in accordance
with the scale provided near the bottom of the figure. The vectors in the floodplain that have no
visible tail indicate slow moving water in the overbank area. A vector was printed where the
water depth was greater than zero, even if the velocity was small, to indicate the extent of the
flow. Examining the flow patterns provides a visual check on the model performance. This
velocity field was used to compute the shear stresses for the DOSM.
3.6. Sensitivity Analyses
The sensitivity of the model to the principal inputs was evaluated by varying the finite element
grid size, the Manning's 'n'. and the turbulent exchange coefficient. The model's sensitivity to
the grid size was checked by running the model for a flow of 40.000 cfs with a finite element
grid having approximately two times the number of elements as the baseline finite element grid.
The results obtained with the larger grid resolution were the same as the smaller grid and,
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therefore, it was concluded that the finite element grid used here was of sufficient resolution to
simulate the river flow.
The sensitivity of the model to the Manning's 'n' and the turbulent exchange coefficient was
measured by the effect on the predicted water elevations for the 100-year peak flow at the
downstream tip of Rogers Island (Gage 119). ' The sensitivity results are presented in the
following discussion.
3.6.1. Sensitivity to Manning's 'n'
The Manning's 'n' was varied over a reasonable range for the main channel and the floodplain.
The model was run for the 100-year peak flow of 47,330 cfs and the results are contained in
Table 3-4. These results indicate that changes in Manning's 'n' do not significantly affect results
from the calibrated model. It is also evident that the main channel Manning's 'n' generally
affects the results much more than the floodplain Manning's 'n\ as would be expected because
most of the flow occurs in the main channel. The model insensitivity to Manning's ;n' is due to
the fact that the flows are large and the system is strongly forced. The accurate prediction of
stages and velocities in this flow regime will depend more on having an accurate representation
of the depth of the main channel and the flood plains.
3.6.2. Turbulent Exchange Coefficient
The four turbulent exchange coefficients, Exx, Exy, E^, and E>y were all set to a value of 4,790
2
Pa-sec (100 lb-sec/ft* ) in the baseline run. The RMA-2V manual provides guidelines in
choosing values for these coefficients. These guidelines are: 1) in general, there is a tendency for
these coefficients to be assigned at values that are too high rather than too low; and 2) most rivers
without flow reversal will have coefficients in the range of 479 to 4,790 Pa-sec (10 to 100 lb-
sec/ft )• Table 3-5 shows the effects of varying these turbulent exchange coefficient values in the
calibrated model.
It can be concluded that variations turbulent viscosities do not affect the river elevation
dramatically, especially evidenced by the small increase in the river elevation for each doubling
of the coefficients. The model predicts higher elevations for higher turbulent exchange
coefficients. This means that if higher turbulent exchange coefficients were used in the
calibration, then a lower Manning's 'n" would be used to obtain equally good agreement with the
observed rating curve. Given these results, it was judged that a turbulent exchange coefficient of
100 was reasonable and that further calibration was not required.
3.7. Conversion of Vertically Averaged Velocity to Shear Stress
The conversion of the vertically averaged river velocities, as obtained from the RMA-2V model,
to shear stresses is required to compute resuspension of bed sediments in the TIP in the DOSM
and HUDTOX models. Several candidate conversion formulations were investigated. One of
these formulations computes shear stress directly from the vertically averaged velocity, while the
other three provide computed values of shear velocity u*. for use in computing shear stress as
r = p(u'):. The four methods, with a short description of each, are presented below.
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1) Smooth wall log velocity profile
This conversion method (Thomas and McNally, 1990; Schlichting, 1979) derives from the
assumption that the vertical velocity profile at any point in the river follows the smooth wall log
velocity profile. The following equation describes this velocity profile.
U =2.51nf3-32udl (3-6)
where:
u* v v J
u = vertically averaged velocity
u* = shear velocity
d = depth of flow
v = kinematic viscosity.
The applicability of this relation to the Hudson River is suspect, because it is known that the
bottom of the river is not hvdraulically smooth.
2) Gailani Method
This method was used by Gailani (Gailani et al.. 1991), for the Fox River
rb = 0.003 u2 (3-7)
where:
rb = bottom shear stress.
This relation is based on empirical approach.
3) Rough wall log velocity profile
— = 6.25-r2.51n(d/k) (3-8)
where:
u = vertically averaged velocity.
u* - shear velocity (friction velocity),
d = depth of flow,
k = equivalent Nikuradse roughness.
This relation (Thomas and McNally, 1990) describes the velocity profile for a rough wall river
flow, which is typically the condition for river flows. The only free parameter for this equation
is k, the roughness factor. This parameter can be estimated from the Manning's roughness
(Chow, 1960): for "n" = 0.02. k was determined to be 0.04 feet.
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4) Manning shear stress equation
u* ~ ,"6 (English Units) (3-9)
(1.486K'
"*= ^ 1" (Metric Units) (3-10)
d '
This shear stress conversion (Thomas and McNally, 1990) above is based on combining
equations which represent cross-section average velocity and bottom shear stress. Specifically,
the one-dimensional Manning equation for channel averaged velocity which is given below as,
^ _ L486 ^2/j ^1,2 (English Units) (3-11)
u = —R21'S1'2 (Metric Units) (3-12)
n
The definition of the cross-sectional average shear stress (x0), can be written as,
z0 - wRS = pgRS (3-13)
where:
u
channel averaged velocity,
n =
Manning's "n' ,
o =
acceleration due to gravity,
W
weight of the water (pg),
R
hydraulic radius.
S
the slope of the river.
The definition of the friction velocity u" can be combined with Equation 3-13 to yield;
= {gRS)U2 (3-14)
] P
For flow in a channel, wetted perimeter is approximated by the depth (R = d). Combining this
assumption with Equations 3-12 and 3-14 will yield Equation 3-10.
Note that although normally both equations are only valid for the whole cross-section of the
river, the finite element model, RMA-2V. actually uses the above formulations in local flow field
calculations.
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Figure 3-4 shows the variation of shear stress with the average vertical velocity for the four
different methods. In Figure 3-4 the depth used to calculate the conversion for methods 1.2 and
4 was 10 feet. As seen in Figure 3-4, Method 1. the smooth wall velocity profile, and Method 2,
the Gailani method, yields the smaller shear stresses especially at higher flows. Methods 3 and 4,
the rough wall and Manning's methods respectively, yield appreciably higher values for stress at
high velocity flows. Method 4 (Manning's) was chosen to estimate shear stress because it would
provide the most conservative estimate for the DOSM.
The shear stress field for the Thompson Island Pool 100-year peak flow as computed by Method
4 using the velocity field shown in Figure 3-3, is plotted out Plate 3-1. Maximum stresses are
observed in the flood plain which is to be expected since the depths of the flow are smaller and
the Manning's 'n' is 0.06 as compared to 0.02 in the main channel.
3.8. Discussion
The calibrated RMA-2V model is a reasonable representation of TIP hydraulics for various flow-
regimes. This conclusion is based on the good agreement found between model output for water
levels and rating curve results at Lock 7, and the good agreement between model output for
velocities and those measured by the USGS. The model's ability to simulate flows well above
the calibration flow, 30,000 cfs, is supported by the reasonable agreement between the 100-vear
peak flow predictions by this model and the FEMA model, and also by the lack of sensitivity of
high-flow results to changes in internal model parameters.
The sensitivity analyses show that the RMA-2V model is not appreciably sensitive to changes in
the calibration parameters. However, the analysis of the conversion of the flow field output
(vertically averaged velocity and depth) to river- bed shear stress shows that shear stress can vary
significantly, depending on the conversion method used. The most conservative method, that
method which predicts the largest shear stress given the magnitude of the vertically averaged
velocity was chosen to provide shear stress to the DOSM.
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Chapter 4
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4. THOMPSON ISLAND POOL DEPTH OF SCOUR MODEL
4.1. Introduction
The Depth of Scour Model (DOSM) is a two-dimensional. GIS-based sediment erosion model
that was applied to Thompson Island Pool. It is a specialized tool for providing spatially-refined
information on sediment erodibility in response to flood events. The DOSM was used as a stand-
alone tool to provide gross mass estimates of solids and PCBs eroded, and depth of sediment bed
scour, in response to a 100-year peak flow. The DOSM does not account for subsequent
transport or redeposition of eroded material. The primary use for the DOSM in this study was to
develop relationships between river flow and cohesive sediment resuspension. These
relationships were used in the HUDTOX model in the form of algorithms describing flow-
dependent resuspension.
The DOSM is based on separate empirical erosion equations for cohesive and non-cohesive
sediments; the equations are fully predictive for cohesive sediments but give only upper bound
estimates for non-cohesive sediments. The DOSM application for the 100-year peak flow was
designed to address the following questions:
1. What is the range of expected scour depths in TIP?,
2. How do these depth of scour ranges compare to observed depth profiles of PCB
concentrations at five Phase 2 high resolution coring sites?; and
3. What is the expected range of total PCB and solids mass eroded from cohesive
sediments throughout TIP?
The DOSM was designed to address the question of the potential risk of resuspension of PCBs
from the deeply buried sediments of TIP in response to a '"catastrophic'" flood event. The model
provides quantitative and qualitative information to estimate this risk. It was used to estimate the
total mass of solids and PCBs eroded for the 100-year peak flow (hydrodynamic modeling of this
peak flow is discussed in Chapter 3). In addition, more detailed estimates of local scour at
selected locations in TIP were conducted. As part of the Phase 2 monitoring program, sediment
cores were taken at five locations in areas containing cohesive sediments in TIP and analyzed at
a high vertical resolution. These sediment cores showed peak PCB concentrations in excess of
2.000 ug/g (dry weight). The vertical resolution of PCB data at these locations allows a more
detailed investigation of the potential risk of scour in response to large events. These analyses
were carried out. including an explicit consideration of the uncertainties in the resulting
estimates.
Section 4.2 describes the development of the DOSM, including model conceptualization and
formulation for cohesive and non-cohesive sediments. Section 4.3 describes the
parameterization of the DOSM, including a description of the data available to support the model
and how it was used for parameterization. Section 4.4 describes the application of the DOSM.
including 1) comparison of predicted ranges for depth of scour at each of the five Phase 2 high
resolution coring sites with observed PCB concentration profiles. 2) poolwide computations for
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total mass of PCBs and solids remobilized from cohesive sediments throughout TIP. and 3)
determination of the mean depth of scour in cohesive and non-cohesive sediments.
4.2. DOSM Model Development
4.2.1. Conceptualization
Two categories of information are necessary to compute the depth of erosion and total mass of
solids eroded from bedded sediments for a high-flow event. First, the hydrodvnamic conditions
at the sediment-water interface need to be specified. The primary forcing function for
entrainment is the shear stress exerted at the sediment-water interface by flowing water. The TIP
Hydrodynamic Model yields estimates of velocities at a fine spatial resolution. Bottom shear
stresses are computed from the velocities by a simple formula (Section 3.7). Second, the
physico-chemical properties of the bedded sediments greatly influence the magnitude and rate of
entrainment of sediments for a given event, and the resulting depth of scour.
Entrainment mechanisms can be classified into two distinct categories based on sediment bed
properties. The main parameters affecting the entrainment of non-cohesive sediments include
grain size and shape (and their distributions), the applied shear stress, bed roughness, and
specific weight. Bed sediments which are primarily fine grained and/or possess a high clay
content exhibit interparticle effects which are cohesive in nature. The resulting entrainment
properties are very different from non-cohesive sediments. Since the toxic contaminants of
interest (PCBs) are associated preferentially with fine grained sediments, this distinction is of
considerable importance.
4.2.2. Formulation for Cohesive Sediments
Background
Particle diameter has a significantly lower influence on the entrainment characteristics of
cohesive sediments compared to electrochemical influences. Relatively small amounts of clay in
the sediment-water mixture can result in critical shear stresses far larger than those in non-
cohesive materials of similar size distribution (Raudkivi, 1990).
Previous studies on the entrainment of cohesive sediments hypothesize that the scour magnitude
is primarily influenced by the excess applied shear stress (i.e.. the difference between the applied
shear stress and the critical shear stress of the surficial sediments), and the state of consolidation
(or age after deposition) of the bed sediments (Partheniades, 1965; Mehta et al., 1989; Xu. 1991).
The mass of material resuspended can be expressed in the following functional form:
M = f(x - xc; age. other sediment properties)
where M is the mass of material resuspended, x is the applied shear stress, and xc is the bed
critical shear stress. The function f has been expressed in a variety of different forms ranging
from linear, e.g. Partheniades (1965). exponential, e.g. Parchure and Mehta (1985), and the
power relationship developed by Lick and co-workers, e.g. Gailani et al. (1991).
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Basic Equations
Lick et al. (1995), based on statistical analysis of laboratory and field data, proposed an erosion
equation of the following form:
^x
t
' r-s- I (4-1)
r c )
where e is the net total amount of material resuspended (g/cnr); x is the applied shear stress and
xc is the bed critical shear stress (dvnes/cm2); td is the time after deposition: and a0, n. and m are
empirical constants.
The depth of scour can then be calculated as:
Ł
Z scour — — (4-2)
C bulk
where Zscour is the depth of scour (cm), and Cbulk is the dry bulk sediment density (g/cmJ).
These equations have been applied to site-specific data for several rivers (Fox. Detroit, and
Buffalo) by McNeil, 1994.
Reparameterization to a Probabilistic Model
If the value of xc is known or assumed, while the other parameters are unknown, then Equation
4-1 can be reduced from five parameters to two using a dimensionless shear stress parameter x':
e = A x (r')m (4-3)
where:
x' = (x-xc)x;.
A = VC
Equation 4-3 can be linearized as follows:
111(f) = In(.-4)+ m x ln(r') (4-4)
Therefore, a linear regression may be performed to fit a straight line to data for erosion vs.
dimensionless shear stress in "log-log" space. The slope obtained from this regression will
correspond to the exponent m from Lick's equation, while the intercept will correspond to the
logarithm of the lumped term ac/tdn. Characterization of the distribution of errors around this
regression will allow estimation of the uncertainty in erosion predictions.
Given a regression line with normally distributed residuals, prediction limits for new
observations (for a given value of the independent variable) fall on a Student-t distribution (Neter
et al., 1985). For large sample sizes, the Student-t distribution is approximately normal.
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Predicted values for new observations are therefore calculated as percentiles of normal
distributions, in log-log space. The resulting predicted distribution in ordinary space (again, for
given values of shear stress) is log-normal, and is calculated according to Equation 4-5.
s = exp(/J + mxIn(r')+«)
(4-5)
where:
u = Zx
MS Ex
1 (ln(r')-*„)•
1 + — + •
ns
and
= (T"TC) / Tcf
exp = exponentiation operator
Z = a value of the standard normal distribution variable
MSE = mean square error of regression
ns = number of data used in the regression
Xavg ~ mean of the natural log dimensionless shear stresses
X[ - a particular natural log dimensionless shear stress value.
Division of the erosion by the bulk density- gives the depth of scour in cm. as shown in Equation
4-2.
Calculation of PCB Erosion
Equations 4-2 and 4-5 define a probabilistic model for predicting mass erosion and depth of
scour as a function of shear stress and sediment physical properties. An estimate of the poolwide
PCB erosion from cohesive sediments can then be determined as a function of sediment PCB
concentration using Equation 4-6.
S x C ¦v,
P = 7 ^ (4-6)
lOOOm^e ;
where:
P = quantity of PCBs eroded from cohesive sediments (g)
5 = mass of solids eroded from cohesive sediments (kg)
Cpcb = average cohesive sediment surficial PCB concentration (mg/kg).
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4.2.3. Formulation for Non-cohesive Sediments
Background
Net erosion of non-cohesive sediments occurs when the sediment transport capacity of the flow
exceeds the actual sediment burden being carried by the flow. A flow will have transport
capacity for a particular particle diameter (size class) when the shear stress applied to those
particles as a result of the flow exceeds the critical shear stress of the particle size class. When
the flow continuously has excess transport capacity the bed is scoured as transportable particles
are entrained when exposed at the bed surface. Because the transport capacity of the flow is
inversely related to the particle size, differential scouring takes place with the smaller particles
being removed in greater proportion than the larger particles. The particle size distribution of the
bed surface shifts progressively towards larger particles. If sufficient large particles are present
that cannot be transported under the flow conditions, the bed surface will come to consist
primarily of the larger particles, with smaller particles sheltered underneath them. This layer of
coarse particles, called the armor layer, may persist until higher flows and their associated shear
stresses erode it, causing further coarsening and the establishment of a new armor layer. The
armor layer can be degraded by vertical mixing with the parent bed material and replenishment
of fine material via deposition from the water column.
Equations
Borah (1989) gives equations for the depth of scour that will occur before the establishment of an
armor layer, assuming a well mixed surface layer with constant particle specific gravity, and that
a monolayer of the smallest nontransportable particle present in the bed material will be created.
The formulation is conservative in that the potential for finer particles to be trapped (hiding) in
the armor layer is ignored. An active layer thickness is defined as:
T = (4-7)
(1-^
where T is the thickness of the active layer (cm); Da is the smallest armor size (cm); is the
porosity of the bed material; and Pa is the fraction of all the armor sizes present in the bed
material. Da is computed using a modified version of the Shields Curve ( Shields. 1936: van den
Berg and van Gelder. 1993). The scour depth is then computed as:
E ~T - Da (4-8)
where E is the scour depth. These equations have been applied and the results validated for
laboratory (Little and Mayer, 1972) and field (Karim and Kennedy. 1982) data.
4.2.4. Temporal Scale
The cohesive computations result in a mass estimate for the entire event assuming that the event
peak shear stress is established instantaneously. Experiments by Lick et al. (1995) indicate that
this mass is eroded over the time scale of approximately one hour. The non-cohesive
computations result in a mass estimate corresponding to scour down to the armoring depth. The
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temporal scale required to reach armoring depth cannot be directly calculated with the available
models. Model predictions for non-cohesive sediments should therefore be considered "upper
bound" estimates, as they are based upon the assumption that the flood event is of sufficient
duration to allow erosion to proceed all the way down to the armoring depth.
4.3. DOSM Parameterization
4.3.1. Data
Distribution of Types of Bottom Sediment
The bedded sediments in TIP were delineated as cohesive and non-cohesive based on side-scan
sonar profiles of fine and coarse sediments (Flood, 1993). The analysis of sonar and sediment
data suggested that the results of the 500 kHz digital image (i.e. mean digital number, or DN) can
be successfully correlated to mean grain size. It was found that DN values less than about 40
generally correspond to finer grain sizes (mean size less than about 4 phi) while DN values
greater than about 60 generally correspond to coarser sediments (coarse sand, gravel). For the
purpose of characterizing the sonar images, sediment type is described as "finer" for DN less
than 40, or as ''coarse'" or ''coarser'' for DN greater than 60.
The sonar maps were qualitatively divided into several categories including "coarser", "finer",
"island", and "rocky". These maps were digitized into a GIS coverage by TAMS Consultants.
Inc.. The two sediment categories considered for this analysis to be significant sources of
potentially erodible materials (due to magnitude of area and/or substrate type) were "coarser" -
representing non-cohesive sediments - and "finer" - representing cohesive sediments. No
sediments described as "coarse" were listed for Thompson Island Pool. The area of non-cohesive
sediments in TIP is approximately three times that of cohesive sediments.
Resuspension Experiments
Data used to parameterize the DOSM for cohesive TIP sediments were obtained from
resuspension experiments described in a report by HydroQual (1995). This report contained two
different sets of experimental data.
The first data set came from an annular flume study, wherein sediments from three different
locations in TIP were transported to a laboratory at the University of California at Santa Barbara
and subjected to two types of experiments involving shear stress. Multiple shear stress tests were
conducted by filling the flume with sediment, allowing it to compact for 1. 3, or 14 days with the
flume at rest, and running (i.e., rotating) the flume at successively higher levels of shear stress,
with steady state suspended sediment concentrations achieved (as indicated by concentration
measurements at 30 minute intervals) before each shear stress increase. A continuous flow test
was conducted by filling the flume with sediment and running it continuously for 47 days at a
shear stress of about one dyne/cm2, except that on several days the shear stress was increased to 5
dynes/cm: for two hours. Also, one multiple shear stress test similar to those described above
was conducted.
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The purpose of these experiments was to investigate the effects of bed compaction and to
estimate the value of the critical shear stress, within the framework of the Lick equation.
Equation 4-1. Based upon these laboratory flume experiments, HydroQual (1995) concluded
that: 1) the critical shear stress was approximately 1.0 dyne/cm2, 2) the maximum time since
deposition (tj was 7 days (i.e., after 7 days no further significant bed compaction takes place),
and 3) the exponent, n, for td was 0.5.
The second set of sediment resuspension measurements described in HydroQual (1995) consisted
of field studies using a portable resuspension device, commonly called a shaker. Surficial
sediment cores were collected at 20 cohesive sediment locations in TIP and 8 locations
downstream; each location had one (TIP) or two (downstream) sets of three cores each. Each
core was subjected to a shear stress in the shaker and the resulting resuspension potential was
determined. The field study produced 107 resuspension potential-shear stress data pairs for the
Hudson River, with 60 measurements specific to TIP. The shear stresses used in the field study
ranged from 5 to 11 dynes/cm2. Observed sediment erosion rates in TIP ranged from 0.06 to
28.84 mg/cm2.
From the TIP-specific data, HydroQual (1995) assumed a TIP-wide constant value of 3 for m,
and back-calculated core-specific values for ag necessary to produce the observed erosion. The
methodology used to determine the value for m was not provided. HydroQual reported a mean
value and standard deviation for ao of 0.071 (in units of mg- day!/2/cm2) and 0.062 respectively,
excluding certain results deemed to be outliers.
Non-Cohesive Particle Size Distributions
The Borah formulation described above requires sediment data on particle size distribution,
particle density, and wet bulk density (the last as a means to get porosity). An obstacle
encountered in using the core data was that some cores had missing or incomplete data for one or
more properties. This obstacle was overcome in two ways: 1) missing data on particle density
and bulk density were replaced by random deviates from the distributions found for the existing
data, and 2) particle size distributions, which were occasionally incomplete on the large-particle
end. were extrapolated by plotting the data for each core as ln(size) vs. ln(fraction) and extending
the curves smoothly (this was done for 81 cores with data to extrapolate). The distribution used
for particle density was normal with a mean of 2.438 g/cnv' and a standard deviation of 0.262.
The distribution used for wet bulk density was normal with a mean of 1.452 g/cnr and a standard
deviation of 0.212; random deviates greater than 1.8 or less than 1.04 were rejected on the
grounds of physical improbability and were replaced with new deviates. Particle size
distributions were extrapolated as far as size fraction 2.7% or size 20 mm.
Two limitations of the above method are: 1) extrapolations and data substitutions contribute to
model uncertainty, and 2) the method would require modification to handle correlations, if any.
between the physical property distributions.
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Sediment PCB Concentration
As an illustrative example, the DOSM was used to estimate a range for the mass of PCBs eroded
from the sediments of TIP in response to a specified peak flow. To use the DOSM for this
example, it was required that sediment PCB concentration be specified as a model input. The
median value of samples of the cohesive sediment surficial total PCB concentrations (TAMS et
al., 1998a) was found to be 32.5 mg/kg. This value was used to approximate the surficial
cohesive sediment PCB concentration throughout TIP, although it does not take into account
variation of PCB concentration with location and time.
4.3.2. Parameterization for Cohesive Sediments
There are several assumptions inherent in the application of Equations 4-2 and 4-5 to the shaker
data for parameterization of the DOSM. These include:
• The value for critical shear stress imputed from the annular flume study applies
throughout TIP.
• The sediment cores used in the resuspension studies represent an unbiased random
sample of TIP cohesive sediments.
• The experimental shear stress values are exact.
• The statistical model is valid for extrapolation to higher values of shear stress than
were used experimentally.
• The bulk density, at a specific location, used for converting erosion to depth of scour
can be represented as a single number.
All statistical analyses were conducted using SYSTAT® Version 6.0 for Windows® (SPSS,
1996), and only data from TIP were considered. A linear regression of natural log erosion (in
mg/cnr) vs. natural log x' produced an intercept (A) value of -3.829 and a slope (m) value of
2.906 (Figure 4-1). Of 60 TIP data points, two outliers were deleted: 58 data points were used.
The outliers were identified solely on the basis that their Studentized residuals were too large
(absolute value greater than 3.0). The outliers were: 1) erosion 0.06 at shear stress 5; and 2)
erosion 0.47 at shear stress 11. The regression R-squared value was 0.541. p-values for both the
regression constant and the slope were <0.00001. An analysis of the residuals strongly indicated
that they could be assumed to be normally distributed. It was concluded on the basis of these and
other statistical indications that the use of linear regression was supported by the data.
The value of 2.906 obtained for m is similar to the value of 3 reported by HvdroQual (1995).
Assuming from the flume studies that the maximum time since deposition (td) was 7 days, and
the exponent, n. for td was 0.5, the lumped term corresponds to a value of ap of 0.0575. This
value is well within one standard deviation of the value (Section 4.3.1.2) reported by HvdroQual.
4.3.3. Parameterization for Non-cohesive Sediments
The Borah formulation was used to calculate an armoring depth - shear stress data point for each
size fraction of each core with a particle size distribution (core CG-23 was excluded because it
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produced several outliers). That is to say, a given size fraction corresponds to a minimum
particle size which requires a minimum shear stress to scour, and for which an armoring depth is
calculated as the depth achieved when all smaller particles are scoured from the active layer. The
data points were plotted on a log-log plot. One linear relationship was found for shear stresses
below about 5 dynes/cm2, and another for shear stresses above 5 dynes/cm2 (Figure 4-2).
Data for determining particle size distributions are not available throughout TIP, but shear
stresses are available on a fine scale. A predictive relationship between armoring depth and
shear stress was sought. Assuming that the core particle size distributions are typical of particle
size distributions throughout TIP, the relationships between armoring depth and shear stress
discussed above can be considered predictive even where the particle size distribution is
unknown. Therefore, a linear regression was performed to fit the 355 data points above 5
dynes/cm2 (shear stresses lower than 5 would not be, of course, as significant in producing
erosion) to Equation 4-9.
In (Depth,cm) = A + mx In (ShearStress,dynes / cm') (4-9)
A constant (A) value of -1.6335 and a slope (m) value of 1.2407 were found. The R-squared
value was 0.5, and the p-values were less than 0.00001. The spread around the regression line is
considerable, encompassing approximately two orders of magnitude. This is not unexpected,
since a similarly large spread was observed for the cohesive sediment correlation. The graph of
armoring depth vs. shear stress, with the regression line shown, is provided in Figure 4-3.
4.4. DOSM Application
4.4.1. Application Framework
An ARC/INFO-based Geographical Information System (GIS) (ESRI. 1997) was utilized to
associate sediment and hydrodynamic properties with geographic locations and areas in TIP.
Computations made use of shear stresses estimated at the nodal locations where flow field
information was available from the TIP Hydrodynamic Model (Chapter 3). The sediments were
spatially differentiated into cohesive and non-cohesive areas, as described in Section 4.3.1. with
separate analyses conducted for each sediment type.
It is important to note that the DOSM. as a stand-alone model, has not been designed to simulate
the subsequent transport and redeposition of eroded sediments. It evaluates only the mass of
bottom sediments potentially mobilized at a specified peak flow. The HUDTOX mass balance
model includes a dynamic, fully-integrated representation of solids and PCB transport and fate in
the water column and bedded sediments. The DOSM was used to develop relationships between
river flow and solids resuspension that were subsequently used in the HUDTOX model in the
form of resuspension algorithms for flow-dependent resuspension. The relationship between the
DOSM and HUDTOX ensures internal consistency in representation of flow-dependent
resuspension for cohesive sediments between these two models.
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4.4.2. Model Application to High Resolution Coring Sites
The DOSM was applied to compare expected scour depths in TIP for the 100-vear peak flow to
observed depth profiles of PCB concentrations at five Phase 2 high resolution coring study sites.
Although these sites were not necessarily representative of PCB profiles in cohesive sediments in
the entire TIP, they were used because they were identified as cohesive sediment sites by the
TAMS/Gradient Team and because each site contained detailed measurements of sediment
physical-chemical properties that were required for a finely resolved analysis of resuspension
potential. Location-specific inputs consisted of predicted shear stress at each coring location and
sediment bulk density measured for each core. Table 4-1 lists location-specific input values for
each of the five cores. Average values of dry bulk density somewhat higher than the surficial (0-
2 cm) values shown in this table were used for calculating depths of scour greater than two cm.
Table 4-2 contains summary results for each of the five sediment core locations. The predicted
median depths of scour for the five locations, shown in the second column of Table 4-2, range
from less than 0.08 (HR-19) to almost 4 cm (HR-25). The third and fourth columns in Table 4-2
show the range of predicted scour depths encompassing the middle 90 percent of expected values
(i.e. 5th to 95th percentile) for each core location. By comparing the depth of scour estimates in
Table 4-2 with the input data in Table 4-1, one can see that bottom shear stress is a very strong
determinant of erodibility in these cohesive sediments.
Median predicted depth of scour provides information on quantities of solids that can potentially
resuspend during an event; however, this information alone does not tell us the quantity of PCBs
that can potentially resuspend. The last column in Table 4-2 contains the observed depth of the
total PCB peak at each of the five core locations. By comparing median predicted depths of
scour and observed depths of PCB peaks, a more complete picture of potential PCB erodibility
emerges. These results are depicted graphically in Figures 4-4 through 4-8. which show the total
PCB (as originally measured) profiles with depth for each of the five sediment cores, along with
the 5L\ 50!h and 95:h percentile predicted depth of scour for each of the five core locations.
Results indicate that Core HR-25 is likely to experience scour of sufficient magnitude to
substantially erode the PCB peak at that location. However, even if erosion occurs at the 95th
percentile depth. PCB peaks at the other four locations are predicted to be unscoured (i.e. the
PCB peaks are likely to stay intact after a 100-year peak flow event).
4.4.3. Model Application Poolwide
Cohesive Sediments
Equations 4-2 and 4-5 can conveniently be used to estimate the total mass of solids remobilized
from cohesive sediments throughout TIP, and the mean depth of scour in cohesive sediments, by
means of a Monte Carlo Analysis. The cohesive sediment areas of TIP were subdivided into
polygons of constant shear stress and dry bulk density by intersecting coverages for these
properties in the GIS system discussed in Section 4.4.1. The Monte Carlo technique was
employed to calculate the depth of scour and the mass scour as the values of random variables at
each location. Poolwide results for mass scour were obtained by summing the results at all
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locations, while an area-weighted average was calculated as the mean depth of scour. The
calculation was repeated many times to get a valid statistical distribution of results.
Monte Carlo calculations were performed with the Crystal Ball® computer program
(Decisioneering, Inc., 1996). Depth and mass of scour were computed together with 3000
repetitions; a sensitivity analysis of the number of repetitions demonstrated that 3000 repetitions
was adequate to produce consistent results. The results were plotted as cumulative percent vs.
mean depth of scour or mass scour, respectively. Expected values for mean depth and mass of
scour were estimated by the mean of the Monte Carlo trials and are shown in Table 4-3.
Figure 4-9 shows the results for mean depth of scour. Most of the predictions fall into the range
of about 0.3 to 0.4 cm. There is, therefore, a high probability that a future 100-year peak flow-
would result in a mean depth of scour of between 0.3 and 0.4 cm. Figure 4-10 shows the results
for total solids scoured. Most of these predictions fall into the range of about 1,500,000 to
2.000,000 kg. There is, therefore, a high probability that a future 100-year peak flow would
result in a mass scour of between 1,500,000 and 2,000,000 kg.
The total PCB concentration in TIP surficial sediments was estimated to be 32.5 mg/kg (TAMS
et al., 1998a). Using this concentration value with the above estimate of 1,500.000 to 2,000,000
kg of solids erosion in Equation 4-6 provides an approximate range of gross PCB erosion of
49.000 to 65.000 grams. The actual amount of PCBs eroded by a 100-year peak flow would
depend upon the amount of PCBs in the sediments at the time the flood occurred and upon areal
and depth variations in PCB concentration.
Non-Cohesive Sediments
Equation 4-9 was applied to estimated shear stresses in non-cohesive sediment areas. For the
100 year peak flow, the mean, non area-weighted TIP non-cohesive sediment armoring depth is
13.1 cm. Therefore 13.1 cm is an upper bound estimate of the expected average erosion from
non-cohesive sediment areas in TIP resulting from a 100-year peak flow. Upper bound
estimates of erosion at specific non-cohesive sediment locations throughout TIP ranged from 1.5
to 42 cm. This estimate of erosion in non-cohesive sediment areas is fundamentally different
from, and not directly comparable to, the above estimates of erosion in cohesive sediment areas.
Those cohesive estimates are predictive of the actual erosion that would occur under the
specified conditions, including an uncertainty band for the prediction. The non-cohesive
sediment erosion estimate is a value for which it is reasonably certain that the actual erosion
would be less than that value, perhaps much less. Given the difference in the nature of the
estimates, it is not surprising that the 13.1 cm upper bound on the average erosion from non-
cohesive sediment areas of TIP substantially exceeds the 0.317 cm expected value of the mean
depth of scour from cohesive sediment areas of TIP. Relative estimates of erosion from cohesive
and non-cohesive sediment areas can best be made using the HUDTOX model which contains a
dynamic, full-integrated representation of solids dynamics.
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Chapter 5
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5. MASS BALANCE MODEL DEVELOPMENT
5.1. Introduction
Chapter 5 contains the development of the Hudson River Toxic Chemical Model (HUDTOX),
the principal transport and fate modeling tool in this Reassessment. Section 5.2 presents the
model approach, including the conceptual framework and the governing equations for model
state variables and process mechanisms. Section 5.3 presents the spatial segmentation grid on
which the HUDTOX model was applied for the Upper Hudson River. Section 5.4 presents
information on model implementation, including details on the hardware and software operating
environment.
5.2. Model Approach
5.2.1. Introduction
HUDTOX is the principal transport and fate modeling tool in this Reassessment. HUDTOX is a
time-variable, three-dimensional mass balance model. It is a fully-integrated representation of
solids and PCB concentrations in the water column and bedded sediments. HUDTOX was
applied to the entire Upper Hudson River from Fort Edward to Federal Dam at Troy. Because a
substantial amount of PCB-contaminated sediments is contained in TIP, the TIP portion of
HUDTOX included greater spatial resolution than the portion downstream of TID. In TIP,
HUDTOX is two-dimensional in the water column and three-dimensional in the sediments.
Between TID and Federal Dam it is one-dimensional in the water column and two-dimensional
in the sediments.
Developmental applications and model calibrations were conducted with HUDTOX using
historical data for the period 1977 to 1997. Differences in HUDTOX model applications were
determined by model calibration strategy and data availability. In broad terms, the model
calibration strategy involved testing HUDTOX over different physical conditions in the river,
different PCB physical-chemical properties and different time frames. The calibrated model was
used to conduct forecast simulations for a 21-year period beginning in 1998. These forecast
simulations were intended to estimate long-term system responses to continued No Action and
impacts due to a 100-year peak flow.
5.2.2. Conceptual Framework
Three different mass balances are represented in HUDTOX: (1) a water balance; (2) a solids
balance; and (3) PCB mass balances. A water balance is necessary because PCB dynamics are
influenced by river flow rates and mixing rates. A solids balance is necessary because PCB
dynamics are influenced by the tendency of PCBs to sorb, or attach, to both suspended and
bedded solids in the river. Finally, a PCB mass balance itself is necessary to account for all
sources, losses and internal transformations of PCBs in the river.
HUDTOX represents PCBs in both the water column and bedded sediments. PCBs in each
medium are comprised of three phases: truly dissolved, bound to dissolved organic carbon
(DOC), and sorbed to total solids. Since organic carbon is the principal sorbent compartment for
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hydrophobic organic chemicals in aquatic systems, the approach was to first conduct a time-
dependent mass balance for the suspended and bedded sorbent solids, and then to assign organic
carbon fractions to these solids. Dissolved organic carbon (DOC) was not simulated in the mass
balance, rather, concentrations were held constant in the sediment and the water column.
HUDTOX computes time-dependent mass balances for two state variables: solids and PCBs
(total PCBs, Tri-, BZ#4, or BZ#52, depending on the particular application). It assumes that
within each model spatial segment a local equilibrium exists among the three different PCB
phases. It computes the PCB distribution among these phases by applying an organic carbon-
based partition coefficient to the organic carbon concentration of each sorbent. This local
equilibrium assumption allows the mass balance model to compute only a single PCB state
variable while still representing the specific process kinetics operating on each PCB phase. For
example, only the solids-sorbed PCBs will settle; therefore, the settling velocity determined
through the solids mass balance is applied to only the solids-bound phase of PCBs within each
spatial segment. On the other hand, only truly dissolved PCBs can exchange across the air-water
interface; hence, that process is applied to only dissolved phase PCBs in water column segments
at the air-water interface.
Figure 5-1 contains a conceptual diagram for HUDTOX that illustrates PCBs in the water
column and upper sediment spatial segments. This diagram displays the three phases into which
PCBs can be partitioned, as well as the model processes which are applied to either the whole
PCB form or to an individual PCB phase. Thus, each arrow into or out of a given control volume
(or spatial segment) represents a distinct source or sink flux process that operates on the PCB
state variable and forms its full mass balance equation for that segment. The simultaneous
solution of those mass balance equations permits quantification of the relationship between
external inputs and within-svstem concentrations of PCBs as a function of space and time.
5.2.3. Governing Equations
This section presents a summary of the state variables and processes in the HUDTOX mass
balance model. The HUDTOX model is a modified version of the USEPA WASP toxic
chemical model WASP5TOXI5. The HUDTOX model code was originally developed using an
earlier version of the WASP model (WASP4/TOXI4) that was updated by EPA to reflect coding
error corrections and various enhancements. The primary source for documentation of the
WrASP5/TOXI5 model is Ambrose et al. (1993). This document can be obtained via the Internet
by downloading it from the USEPA Center for Exposure Assessment Modeling (CEAM) web
site located at ~*http://\vw\v.epa.gov/epa_cearn/\vw\vhtml/ceamhome.htm'\ The HUDTOX model
description presented in this section is a summarized version of the WASP5/TOXI5
documentation contained in Ambrose et al. (1993). Details are presented for only those
HUDTOX processes that were modified from the original W'ASP5/TOXI5 model. Unless
specifically noted, the HUDTOX model processes are identical to those in the WASP5/TOXI5
model.
The mass balance for the HUDTOX model accounts for all material entering and leaving the
system by external loading, advective and dispersive transport, settling and resuspension, and
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physical, chemical, and biological transformations. The generalized HUDTOX mass balance
(partial differential) equation for an infinitesimally small fluid volume in three-dimensions is:
^ - ¦ ^(U.C)- |-(UVC). ^-(U,C)
Ct OX CV ' CZ
8 3 (r cC^
5x1 x dx
c\
V ^ J
c (^ 8C
- E« -
czy CZ J
+ SL S3 + SK (5-1)
where:
C = concentration of the water quality constituent state variable,
mg/L (g/m3) [M/L3]
t = time, days [T]
Ux. Uy, U2 = longitudinal, lateral, and vertical advective velocities, m/day [L/T]
Ex, Ey, Ez = longitudinal, lateral, and vertical diffusion (dispersion) coefficients,
m2/day [L2/T]
SL = direct and diffuse loading rate, g/mJ-day [M/L7T]
SB = boundary loading rate (including upstream, downstream, sediment,
and atmospheric), g./m7day [M/L3/T]
SK = total kinetic transformation rate; positive indicates a source,
negative indicates a sink, g/m7dav [M/L3/T].
By expanding the infinitesimally small control volumes into larger adjoining ''segments" and
specifying transport, loading, and transformation parameters. HUDTOX implements a finite-
difference form of Equation 5-1 to solve for the concentration of each water quality state variable
over time. A one-dimensional simplification of Equation 5-1 may be expressed by assuming
vertical (z-domain) and lateral (v-domain) homogeneity:
~(AC) = ^-\ -UxAOExA^ I-A(Sl-Sb)- AS, (5-2)
ct ox V ex)
where:
A = cross-sectional area, nr [L:]
This equation represents the three major classes of water quality processes: transport (term 1),
external loading (term 2), and transformation (term 3). These processes, which describe the fate
of each HUDTOX solids and PCB model state variable, are discussed in the following
paragraphs. The finite-difference derivation of the general WASP mass balance equations and the
specific solution technique implemented to solve these equations are described in Ambrose et al.
(1993).
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Water Transport
Advective water column flows directly control the transport of dissolved and particulate
pollutants in many water bodies. In addition, changes in velocity and depth resulting from
variable flows can affect such kinetic processes as reaeration, volatilization, and photolysis.
HUDTOX tracks each separate inflow specified by the user from its point of origin and through
each segment until it exits the model network. For each inflow, the user must supply a continuity
or unit flow response function and a time function. The continuity function describes the spatial
extent of the inflow response as it varies throughout the model network. The time function
describes the temporal variability of the inflow. The actual flow between segments that results
from a given inflow is the product of the time function and the continuity function. If several
inflow functions are specified between any segment pair, then the total flow between segments is
computed as the sum of the individual flow functions. In this manner, the effect of several
tributaries joining, density currents, and wind-induced flow patterns can be described in a simple
manner.
Discharge coefficients describing depth and velocity from stream flow are based on formulations
developed by Leopold and Maddox (1953) which describe empirical observations of the
velocity-depth-stream flow relationship. These relationships, which are used for determining
chemical air-water mass transfer rates (gas phase absorption and volatilization), are described in
Ambrose et al. (1993). For the TIP portion of the Upper Hudson River, the HUDTOX model
coefficients describing this relationship were developed from the RMA-2V hvdrodynamic model
described in Chapter 4. The relationship for downstream reaches was developed using
correlations between surface water elevations and flow (TAMS et al., 1997). Note that these
relationships are only used to affect chemical gain or loss within a water column model segment
(through volatilization); they do not affect advective or dispersive transport of chemicals between
model segments.
Dispersive water column exchanges significantly influence the transport of dissolved and
particulate pollutants in such water bodies as lakes, reservoirs, and estuaries. In rivers,
longitudinal dispersion can also be an important process in diluting peak concentrations that may
result from dynamic (unsteady) loads or spills. Natural or artificial tracers such as dyes, salinity,
conductivity or heat (temperature) are often used to calibrate dispersion coefficients for a model
network. The dispersive exchange between HUDTOX segments i and j at time t is given by:
(5-3)
where:
Mj = mass of constituent (state variable) in segment i. g [M]
C = total constituent (state variable) concentration. mg/L (g/m3) [M/L-3]
Ejj(t) = exchange / dispersion coefficient time function for exchange "ij",
m-/day [L- T]
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Ajj = interfacial area shared by segments 1 and j, m^- [L—]
Lcij = characteristic mixing length between segments i and j. m [L].
The exchange coefficient may also be expressed as a mass transfer velocity by dividing the
dispersion coefficient by the characteristic mixing length:
vij(t) = (5-4)
cij
where:
\'ij(t) = mass transfer rate for exchange "ij", m/day [L/'T] .
Solids Dynamics
HUDTOX uses a finite difference form of the mass balance relationship expressed by Equation
5-1 to calculate sediment and chemical mass and concentrations for every segment in a model
grid that includes surface water, surficial sediment bed, and underlying sediment bed layers.
During simulation, solids are treated as a conservative constituent that is advected and dispersed
through water column segments, settles to and resuspends from surficial sediment segments, and
moves through the subsurface bed through burial/scour of the surficial bed or through particle
mixing.
Solids Gross Settling
HUDTOX differs from WASP5/TOX15 with respect to gross settling of suspended solids from
the water column to the sediment bed. Gross settling in HUDTOX is represented as a flow-
dependent process. This approach attempts to capture well-known behavior in rivers whereby
increasingly larger, faster-settling particles are entrained as flow increases above a resuspension
threshold. The gross settling mechanism in HUDTOX is similar to the empirical relationship
used to model particle settling in other river systems (Gailani et al.. 1991: Gailani et al.. 1996;
Ziegler and Nisbet. 1994).
In HUDTOX, gross solids settling speed progresses from a constant low flow value (vsl . m/day)
to a constant high flow value (vsH) between user-specified low and high flow thresholds (qCTL and
qCTH, cms). These functions are implemented in HUDTOX on a water column segment-specific
basis so that reach-specific characteristics affecting the settling speed-flow relationship can be
incorporated within these flow-dependent functions.
Cohesive Sediment Flow-Driven Resuspension
The algorithm for flow-driven resuspension of cohesive sediments from the DOSM was
incorporated into the HUDTOX model. Total sediment erosion (s . mg/cnr) applied to the rising
limbs of the flood hvdrograph were converted into erosion rates. Non-linear correlations were
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developed relating the DOSM-predicted sediment erosion in each segment as a function of flow
measured at Fort Edward. The erosion was correlated to flow by fitting equations of the form:
s = a, +a;xQ^3 (5-5)
where:
Ł = cohesive sediment erosion, mg/cm2
Qx = advective flow in 1000's of cfs
a! = empirical constant fit to DOSM results, mg/cm:
a; = empirical constant fit to DOSM results, mg/cnr/1000 cfs
a;. = empirical constant fit to DOSM results, dimensionless.
The cohesive sediment erosion is converted to an effective resuspension rate (vrK, m/day) in the
HUDTOX model over each model time step during the rising limb of a flood hydrograph.
Because computational time steps in the model are on the order of 30 minutes or less, this
approach is consistent with observations by Lick et al. (1995) that most resuspendable material is
mobilized in approximately one hour. In the HUDTOX model, resuspension occurring over
previous model time steps during an increasing hydrograph is tracked such that total erosion
equals the amount computed using the maximum shear stress during the event. Tracking occurs
in an incremental fashion, dependent on the change in flow and the cohesive sediment solids
concentration (or dry bulk density). The total amount of sediment erosion is limited by the
maximum predicted erosion associated with the peak flow. This flow-driven resuspension
effectively stops once the peak flow is reached under the assumption that cohesive sediment
armoring has occurred.
HUDTOX also represents a recovery period (trcc, days) for the maximum erosion rate to prevent
subsequent near-term smaller floods from eroding cohesive sediments that have reached an
armored condition.
Non-Cohesive Sediment Flow-Driven Resuspension
The DOSM provides only an upper-bound estimate of non-cohesive resuspension occurring
during a flood event because it does not account for armoring as a function of time and
deposition of suspended material to the bed. Consequently, the algorithm for non-cohesive
resuspension in the DOSM was not incorporated into the HUDTOX model. Flow-driven
resuspension of non-cohesive sediments has been incorporated into the HUDTOX model in an
empirical manner under the assumption that the rate of erosion is controlled by: bottom shear
stress, the critical bottom shear stress, armoring of the sediment bed. and a recovery from
armoring of the non-cohesive sediment bed. Armoring occurs when the largest particles that can
be resuspended by a specific flow are removed from the surface layer, leaving larger non-
resuspendable particles. Armoring can persist until either a higher flow occurs that can
resuspend larger particles or the armored layer is degraded due to mixing with the parent bed or
deposition of fine material from the water column. Vertical mixing processes may also serve to
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mix the armored layer with the parent bed material and restore the particle size grading to pre-
armored conditions.
The resuspension rate when the bottom shear stress is greater than the critical shear in non-
cohesive sediments is described by:
VrH =
f ^
T- T „
(5-6)
V TC J
where:
VrH = high flow resuspension rate, m/day
t = bottom shear stress, dynes/cm2
xc = critical shear stress, dynes/cm"
P3 = empirical constant, m/day.
The resuspension rate when the bottom shear stress is less than or equal to the critical shear in
non-cohesive sediments is described by:
VrH = 0 t5"7)
The bottom shear is specified as a function of flow:
T = p:xQxP: (5-8)
where:
Qx = advective flow in 1000's of cfs
P, = empirical constant fit to hydrodynamic model results
P2 = empirical constant fit to hydrodynamic model results.
Armoring effectively increases the critical shear stress over time when bottom shear is above the
specified critical value. Degradation of the armored layer returns the critical shear stress to a
steady baseline value, Tc0. representative of the particle grading in the parent bed. Representation
of the armoring process incorporated in HUDTOX assumes that degradation of the armored layer
back to the composition of the parent bed occurs in a linear fashion with complete recover,' at a
constant time, t^. An empirical relationship describing this process is implemented in HUDTOX
as follows:
_ Q
dt .-s xmax(T-Tc.0j-Ł6 x(lc -"c0 (5-9)
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where:
Qx = advective flow in 1000s of cfs
P5 = critical shear "bed armoring" rate for non-cohesive sediments, day"1
P6 = critical shear "bed recovery" rate for non-cohesive sediments, day'1
xc0 = initial baseline (steady) critical bottom shear stress, dynes/cm"
xc = Tc0at time t = 0.0.
This relationship is used to adjust the bottom critical shear stress, Tc, in the non-cohesive
sediments at each time step during a model simulation. During non-event conditions, the critical
shear approaches the baseline value, Tc0, within a time period determined by the specified
"recovery"' rate.
Background fNon-Flow-Dependent) Sediment Resuspension
The HUDTOX model includes background, non-flow-dependent solids resuspension. There
were two reasons for including this process. The first reason was that even under a condition of
zero net advective flow, there are physical processes in the river that cause solids flux (and flux
of sorbed PCBs) from the sediment bed to the overlying water column. Some of these processes
include wind-driven dispersion, bioturbation by benthic organisms (Thomas et al., 1995;
Thibodeaux et al., 1990), bioturbation by demersal fish, mechanical scour by propwash, boats
and floating debris, and uprooting of macrophvtes by flow, wind or biological action.
The second reason was that most contemporary mass balance models (including HUDTOX)
represent flow-dependent resuspension of cohesive sediments using Equation 4-1 by Lick et al.
(1995). The parameters in this equation are empirically-derived. Using results from laboratory
flume experiments, HydroQual (1995) concluded that the critical shear stress parameter in
Equation 4-1 for cohesive sediments in TIP was 1.0 dynes/cm:. This value was used as the
critical shear stress for cohesive sediment resuspension in both DOSM and HUDTOX. Recent
experimental results by Zreik et al. (1998) claim to achieve better accuracy of critical shear
measurements at very low flows and suggest that critical shear stress for cohesive sediment
resuspension might actually be closer to 0.1 dynes/cm:. Consequently, the non-flow-dependent
background resuspension in HUDTOX may be considered to represent resuspension that occurs
under low flow conditions that generate bottom shear stresses between 0.1 and 1.0 dynes/cm\
There is precedent for using a non-flow-dependent background sediment resuspension rate in
contaminated sediment transport and fate models. A similar "background resuspension" process
was used by Velleux et al. (1996) to represent sediment resuspension in a PCB mass balance
model for the Fox River. Velleux. et al. suggest that the apparent resuspension rate for this
process can range from 0 to 0.3 cm/yr.
Sediment Particle Mixing
Bioturbation and other physical processes discussed above can result in mixing of solids (and
sorbed chemicals) between different layers within the bedded sediment. Investigators in other
studies have estimated effective biodiffusion coefficients ranging from approximately 1-100
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cm2/yr (Matisoff, 1982). More specifically. Aller (1982) estimated bioturbation-induced particle
mixing rates in Narragansett Bay to range from 5 to 32 cmvyr, Brownawell (1986) estimated a
biodiffusion coefficient of 9.4 cm2/yr in Buzzards Bay, and Thibodeaux et al. (1990) estimated
biodiffusion coefficients of 9-13 cm2/yr. These authors suggest that bioturbation-induced particle
mixing can occur to a depth of 6-10 cm and that benthic organism density and associated mixing
generally decreases with depth from the sediment surface. These sediment mixing processes are
represented in HUDTOX by particle mixing exchange coefficients which range from 36.5 cm2/yr
between the top two sediment layers (0-2 and 2-4 cm) to 0.365 cnr/yr between sediment layers
three and four (4-6 and 6-8 cm) (see Table 7-1). Operationally, some degree of sediment particle
mixing occurs to a depth of 8 cm in HUDTOX. The form of this particle mixing is similar to that
represented by Equation 5-3, but with the concentration gradient expressed in terms of the solids
concentrations (and sorbed chemical concentrations) in the sediment layers across which the flux
takes place.
Scour and Burial
The HUDTOX model uses an improved sediment bed handling approach from that in
WASP/TOXI5. The HUDTOX approach maintains and allows the formation of a distinct
vertical chemical profile through the bedded sediments. This modified sediment bed handling
routine is a better representation of transport of PCB mass through the sediment bed because it
maintains the integrity of the deeply buried sediment layers as buriai or scour occurs. The
standard WASP5/TOXI5 model can exhibit significant numerical dispersion over long
simulation periods, leading to a "smearing" of vertical contaminant profiles.
To insure the maintenance and formation of a distinct vertical profile, the following
modifications were made to WASP/TOXI5: 1) implementation of an alternative sediment bed
handling routine; and. 2) implicit specification of a dynamic boundary condition in an archival
stack of deep sediment layers. The following paragraphs describe the implementation of this
alternative bed handling through a set of modifications to the WASP5/TOXI5 scour and burial
processes.
In simple terms, the revised sediment bed handling routine eliminates the sedimentation time
step as a "burial/scour" trigger and maintains the integrity of the deeply buried sediments as
sedimentation and erosion occur. With the revised framework, the surficial sediment layer
volume varies until either erosion or burial is triggered based on the volume (or equivalently. the
thickness) reaching a specified minimum or maximum level. For burial, the trigger is based on a
doubling of the surficial sediment thickness. Erosion is triggered by depletion (or near
depletion) of the original surficial sediment volume.
Figures 5-2 and 5-3 illustrate the manner in which HUDTOX implements scour and burial of
surficial sediment segments. In the HUDTOX bed handling framework, burial results in no
numerical mixing of chemicals to deeper sediments, because the surficial sediment segment is
simply split into two and renumbering of the segments is triggered whenever its volume doubles.
Erosion of the surficial sediments still provides a degree of mixing between the surface
sediments and the immediate segment below. The degree of this mixing is dependent on the
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amount of sediment remaining in a surficial segment once it has been effectively depleted.
However, no additional mixing occurs through the deeper sediment segments. These deeper
segments are subject to renumbering when erosion occurs, but they still maintain their original
pre-erosion characteristics.
In order to provide long-term tracking of sediment layer PCB concentration, and allow possible
future exposure of deeply buried PCBs, a second modification of the WASP/TOXI5 framework
maintains an "archival" stack of deep sediment layers beneath the existing simulated bed
segments. A user-defined reserve stack of deep sediment layers can be specified to underlie the
existing simulated bed segments with distinct stacks for each surficial sediment segment. In
essence, the archive stacks provide a dynamic boundary condition for the bottom sediments. The
stacks are not part of the computational grid, except to the extent that layers are moved between
the stack and the model grid to compensate for burial or erosion of the surficial sediment
segments. The process of decay is not currently allowed in the archive stack, but it could easily
be added in future applications because the time at which a segment is added to the archival stack
is also tracked. Although the HUDTOX model framework allows for dechlorination and other
degradation processes, these loss processes were assumed to be zero in the HUDTOX
applications presented herein.
When erosion results in a surface sediment segment being depleted, then "renumbering" of the
segments is triggered as previously described. Additionally, the top layer of the archival stack is
then incorporated within the computational grid as a new bottom sediment segment. During
periods of deposition, the surficial layer is allowed to grow in thickness (the bed solids density is
kept constant) until renumbering is triggered, based on a doubling of the surficial sediment
volume. The surficial segment is then split into two layers and the sediment segments are
renumbered accordingly. Additionally, the bottom sediment layer is removed from the
computational grid and placed on the top of the archive sediment stack. The archive stack is
allowed to grow or shrink as needed in response to burial or erosion of the surficial sediment
segments. A significant advantage of using the sediment archive stack relates to its minimal
effect on the computational requirements and execution speed of the model. This allows for
improved vertical resolution of the sediment bed without excessively increasing memory and
runtime requirements. Essentially, the HUDTOX model bed handling implements a quasi-
Lagrangian (or floating frame of reference) approach to burial and scour versus the
\VASP/TOXi5-based quasi-Eulerian (fixed frame of reference) approach.
The HUDTOX scour/burial approach also requires the computational upper portion of the bed to
be composed of layers of equal thickness. This insures that long periods of scour and deposition
will not result in a change to the basic physical characteristics (e.g. the original volume and
thickness) of the surface sediments.
PCB Dynamics
In the environment, organic chemicals may transfer across the different environmental media
(air. water, and sediment) and may be degraded and/or transformed by a number of physico-
chemical and biological processes. Cross-media PCB transfer processes within the HUDTOX
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model framework include equilibrium sorption and volatilization (air-water exchange). PCBs
may also be transformed within HUDTOX through degradation as expressed by a first-order rate
equation to represent the effect of dechlorination and/or destruction as a net mass loss over time.
PCB dechlorination or degradation processes are not currently represented in the HUDTOX
model. Other chemical transformation processes (hydrolysis, photolysis, and chemical
oxidation) are included within the overall WASP5/TOXI5 framework. Detailed descriptions of
these processes are contained in Ambrose et al. (1993).
Equilibrium Sorption
Sediment particle dynamics are important in controlling the transport, transformation and fate of
PCBs in aquatic systems due to the tendency of PCBs to sorb, or bind, to both suspended and
bedded solids (Eadie and Robbins, 1987). Karickoff (1979; 1984) has shown that organic carbon
is the principal sorbent compartment for hydrophobic organic chemicals, such as PCBs, in
aquatic systems. In addition-to organic carbon in particulate form, dissolved organic carbon
(DOC) can also be an important sorption compartment in determining PCB fate (Eadie et al.,
1990; Bierman et al., 1992). Partition coefficients are used to characterize the distribution of
chemical among three apparent phases: truly dissolved, particulate-bound. and DOC-bound.
The partition coefficients depend upon characteristics of the chemical and the sediments or DOC
on which sorption occurs. PCBs are non-polar, hydrophobic, organic compounds. The sorption
of these compounds correlates well with the organic carbon fraction (foe) of the sediment. Rao
and Davidson (1980) and Karickhoff et al. (1979) developed empirical expressions relating
equilibrium coefficients to laboratory measurements, leading to reliable means of estimating
appropriate values. Dissolved organic materials are typically assumed to be composed entirely
of organic carbon (f0c = 1)- The partitioning expressions used in HUDTOX are:
*S = focXfCo, (5-10)
Kb= 1.0xKCoc (5-11)
where:
Kp = Solids partition coefficient. Lw/kgsolid [L^/VI],
Kpoc = particulate organic carbon partition coefficient. Lw/kgoc [L^/M]
fx = organic carbon fraction of sediment. kgoc/kgSolid [M/M].
Kdoc = organic carbon partition coefficient, Lw/kgdoc [L^/M].
The dissolved organic carbon (DOC) partition coefficient. Kdoc, is typically estimated as Kp0C
times a binding efficiency factor based on analysis of field data measurements of each chemical
phase.
HUDTOX differs from WASP5/TOXI5 in that it includes temperature-dependent partitioning.
This dependence was developed and presented in the DEIR (TAMS et al.. 1997). The general
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form of the resulting empirical relationship, applicable to both the paniculate and DOC partition
coefficients, is represented by:
\
25 -T
Ao y
(5-12)
V1 o
where:
K? ,5 = partition coefficient at 25°C, L/kg
T = water temperature, °C
T0 = Absolute zero temperature (0 °K) = -273.15 °C
tsf = temperature slope factor, °K.
The HUDTOX model can include particle interaction effects on solids partition coefficients
using the approach proposed by Di Toro (1985). This approach is described in Ambrose et al.
(1993). .Analysis of site-specific data for the Upper Hudson River indicated that particle
interaction effects on PCB partitioning were minimal (TAMS et al., 1997). Consequently, none
of the present HUDTOX applications included particle interaction effects on PCB partitioning.
The total chemical concentration is the sum of the three phase concentrations:
Cw = concentration of dissolved chemical in water, mg/kg water
n = porosity (Volumewater' Volumewater - solids): Lwater'L
C. = concentration of solids-sorbed chemical on a mass basis, mg/kg solid
Ms = concentration of solids. kgsolids'T-
CB = concentration of DOC-bound chemical on a mass basis, mg/kg DOC
B = concentration of DOC. kg /L.
The dissolved fraction fd is given by:
f Cwn 1
fd = = (5-1
C 1 + KbB +kpms
The particulate (solids-sorbed) and DOC-bound fractions, respectively fp and fb, are given by:
C= C„n-r- Cs Ms - CB B
(5-13)
where:
(5-15)
1^KbB +k:ms
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fb = Łb^ = KbB
C l + KBB'rKpM's
(5-16)
where:
Ms= Ms/n = solids concentration on a water volume basis, kgsolid/'Lvv
B = B/n = DOC concentration on a water column basis, kg /Lw
' cDOC
These fractions are determined in time and space throughout a simulation from the partition
coefficients, internally calculated porosities, simulated solids concentrations, and externally-
specified DOC concentrations. Bulk volumetric concentrations for each phase (Cw for dissolved,
Cp for particulate chemical, and CB for DOC-bound chemical) are simply determined from the
product of each relative fraction and the total chemical concentration.
Air-Water Exchange
Air-water exchange is the mass transfer of a chemical across the air-water interface as dissolved
chemical attempts to equilibrate with the gas phase concentration of that chemical in the
atmosphere. Equilibrium occurs when the ratio of the atmospheric partial pressure of a chemical
to its dissolved concentration in the water column equals its temperature-corrected Henry's Law
constant. Atmospheric partial pressure is expressed as a boundary condition in HUDTOX and
the determination of its value is described in Chapter 7.
HUDTOX employs the same two-layer resistance model (Whitman. 1923) utilized by
WASP5/TOXI5 to calculate the air-water exchange rate. This model assumes that two "stagnant
films'" exist at the air-water interface, bounded by well-mixed compartments on either side. The
air-water mass transfer rate is controlled by the combined effect of liquid and gas phase
resistance described by the following equation:
K,. = Air-water chemical transfer rate, m/dav
V -
Rl = liquid phase resistance, day/m
Rg = gas phase resistance, day/m
Kl = liquid phase transfer coefficient, m/day
Kg = gas phase transfer coefficient, m/day
R = universal gas constant. 8.206x10"' atm mVmole CK
Tk = water temperature, °K
Ht = Henry's Law constant at temperature T (°C), atm nrVmole.
Kv = (Rl-Rq)"'-
V K /
j
(5-17)
where:
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Diffusion of chemical through the liquid (water) layer is driven by concentration differences,
whereas the gas (air) layer diffusion is controlled by partial pressure differences. The Henry's
Law constant generally increases with increasing vapor pressure and decreases with increasing
solubility of a compound. Therefore, highly volatile compounds that have low solubility are
likely to exhibit mass transfer limitations in water (i.e., high liquid phase resistance). Similarly,
mass transfer in air is limited (i.e., high gas phase resistance) when chemical compounds are
relatively nonvolatile and have high solubility.
Air-water exchange is usually smaller in lakes and reservoirs than in relatively turbulent rivers
and streams. Gas exchanges in rivers and river-reservoir systems can also be significantly
enhanced by the highly turbulent conditions created as water flows through and/or over dams.
The present HUDTOX model does not account for the possible gas exchange losses of PCBs to
the atmosphere as water flows through the various run-of-the-river dams along the Upper Hudson
River between Fort Edward and Federal Dam at Troy. Future work is planned to investigate the
significance of gas exchange at dams on PCB dynamics in the Upper Hudson River.
Air-water exchange in HUDTOX is the same as in WASP5/TOXI5 with two exceptions that are
described in the following paragraphs.
The chemical-specific Henry's Law constant (H) is assumed to describe the equilibrium
between the gas phase and dissolved liquid phase at the boundary between the two layers. In
HUDTOX, the Henry's Law constants are temperature corrected according to the empirical
relationship presented by Achman et al. (1993) in the following equation:
As in WASP5/TOXI5. HUDTOX uses a constant gas film transfer coefficient of 100 m/day
typically used for flowing waterbodies such as the Upper Hudson River. HUDTOX differs from
WASP5/TOXI5 in that it directly adapts the O'Connor-Dobbins oxygen reaeration formula, as
opposed to the Covar method which selects rates from a range of formulation (including
O'Connor-Dobbins) depending on predicted water depth and current velocity within a river
cross-section:
log Hr = log H:5
(5-18)
where:
HT = Henry's Law constant at temperature T, atm nr/mole
H-k = Henry's Law constant at 25 °C, atm nr/mole
T0 = Absolute zero temperature = -273.15 CC
T = Temperature. °C.
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X 8.64x10''
(5-19)
where:
Ka = reaeration velocity, m/'day
D = water depth, m
u = water velocity, m/sec
D„. = diffusivitv of oxvgen in water, nr/sec.
W ^ y C '
The computed rearation rate is adjusted to determine a chemical-specific liquid film air-water
transfer rate based on the ratio of molecular weights:
where:
MW = molecular weight of the chemical, g/mole
MWq = molecular weight of the oxygen molecule (as O9) = 32 g/mole.
Tsivoglu and Wallace (1972) show this ratio to be constant regardless of the level of turbulence
in the receiving water body. A detailed description of the two-layer resistance model used in
HUDTOX and WASP5/TOXI5 is contained in Ambrose et al. (1993).
Sediment-Water Mass Transfer of PCBs
In the absence of any physical disturbance of the upper sediment layer (e.g., bioturbation.
advection or dispersion), exchange of PCBs between the sediments and water takes place by-
molecular diffusion (for dissolved material) or Brownian diffusion (for colloidal bound material).
Valasaraj et al. (1997), using a water diffusivitv of 5.6 x 10'" cnr/sec. estimated that mass
transfer rates due to molecular diffusion applied to the dissolved phase of a chemical in sediment
pore water would be on the order of 0.02 cm/day. Application of this mass transfer rate to pore
water concentrations of PCBs will result in a relatively small mass flux from sediments to water.
Numerous sediment studies have shown that molecular or Brownian diffusion is not the only
mechanism driving sediment-water exchange. Greatly enhanced sediment-water mass transfer of
chemicals like PCBs has been shown to occur as a consequence of mixing processes within the
upper 2-10 cm of bottom sediments (see above discussion on sediment particle mixing). Several
authors have shown that by continually replacing sediment particles at the interfacial boundary
with new sediment particles and associated pore water from deeper layers, these mixing
processes can increase effective chemical mass fluxes across the sediment-water interface by a
factors of 10-1000 (e.g.. Thibodeaux. 1996;Nadal, 1998: Thorns et al., 1995; Reible et al.. 1991).
For example, in comparison to their calculation of molecular diffusion mass transfer of 0.02
cm/day, Valasaraj et al. (1997) estimated that a biodiffusion (bioturbation-induced mass transfer
of pore water chemical) mass transfer rate would be approximately 12 cm/day.
In river systems, sediment mixing can result in sediment-water mass transfer of both pore water
and particulate phase PCBs. HUDTOX represents diffusive exchanges of dissolved and DOC-
Kl =Ka(MWa/MW)1/2
(5-20)
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bound PCBs between sediment pore water and the overlying water column with a diffusion
equation similar to Equation 5-3, but with the concentration gradient expressed in terms of the
dissolved and DOC-bound PCB concentrations in the pore water. Depending on the PCB
concentration gradients, pore water diffusion may be a source or sink for the water column. .
Sediment mixing in rivers can also result in sediment-water mass transfer of particulate phase
PCBs via the following sequence of processes: first, particles can be transported by mixing
processes from depth to the sediment-water interface; second, while residing briefly at this
interface, particles can desorb a fraction of the sorbed PCB before being mixed back into deeper
sediments; and finally, desorbed PCB can move through the benthic boundary layer into the
overlying water column (Portielje and Lijklema, 1999; Thibodeaux, 1996). HUDTOX represents
this net mass transfer of PCBs from the particulate phase in the sediment to the overlying water
column, without net mass transfer of associated solids, via application of a mass transfer
coefficient applied directly to the particulate phase PCBs in the upper sediment layer (Table 7-5).
Horn et al. (1979) suggested that this non-flow-dependent sediment-water exchange process is
important for PCBs in the Hudson River. They further suggested that approximately half of PCB
transport in the Hudson River occurs at low to moderate flows and is not the result of solids
scour from the sediment bed.
5.3. Model Spatial Segmentation
The HUDTOX water column spatial segmentation was developed to capture the effects of the
principal factors that influence spatial patterns of water column and sediment PCB
concentrations within the Upper Hudson River. A total of 47 water column segments was
represented from Rogers Island (RM 194.6) to Federal Dam (RM 153.9) at Troy (Figure 5-4,
Parts A through D).
The criteria for developing the water column segmentation grid were driven by locations of:
• Major tributaries to the Upper Hudson River:
• Lock and dam structures along the river;
• Known, significant sources of direct PCB loading to the river:
• Phase 2 and historical water quality sampling stations;
• USGS gaging stations: and.
• Sediment PCB "hot spots" along the river.
Hydrographic survey data collected by GE during 1991 (O'Brien & Gere. 1993b) were used to
estimate HUDTOX model segment cross-sections. The TAMS/Gradient Team also conducted
hydrographic measurements within a portion of the Upper Hudson River; however, the GE data
provides more complete coverage. No significant differences were found between the two data
sets in reaches of the river covered by both surveys, including TIP. Consequently the GE data
were used exclusively in determining river cross-section geometry for HUDTOX.
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A two-dimensional segmentation for the water column was developed within TIP to better
resolve potential differences in impacts from cohesive and non-cohesive sediment areas. The 28
water column segments within TIP are configured as three lateral segments across the river,
except at Rogers Island, with longitudinal resolution on the order of Vi to V* of a mile (Figure 5-
5). At Rogers Island the east and west river channels are each represented by one lateral
segment. Figure 5-6 presents a schematic representation of the HUDTOX model grid that
includes references to geographical locations. Output from the RMA-2V hvdrodynamic model
for a flow of 8,000 cfs at Fort Edward was used to provide flow-routing information for this two-
dimensional segmentation grid within TIP. The flow routing pattern was held constant over the
entire range of flows simulated.
The 19 one-dimensional water column segments between TIP and Federal Dam were developed
to capture the impacts of hydrologic features of the river, including dams, as well as sediment
PCB "hot spots". Consequently, the longitudinal resolution of these segments is variable,
ranging from less than one mile to greater than four miles.
The geometry of the HUDTOX water column segmentation is presented in Tables 5-1A and 5-
1B. Tables 5-2A and 5-2B present the spatial configuration and geometry of the HUDTOX
sediment segmentation, including the assignment of cohesive and non-cohesive sediment areas.
Figure 5-7 illustrates how the HUDTOX water column segment depths vary from upstream to
downstream, indicating the important impacts of the lock and dam systems on river geometry.
The longitudinal variation in cohesive sediment abundance in the HUDTOX model is depicted in
Figure 5-8.
Surficial sediment segment surface areas for the HUDTOX model were computed using two GIS
coverages. First, a GIS coverage developed from side scan sonar studies conducted as part of the
USEPA Phase 2 investigation (TAMS et al., 1997) was used to define sediment segments within
TIP and downstream to the Northumberland Dam (RM 183.4). The side scan sonar
measurements were used to distinguish river bottom areas of finer (representing cohesive solids)
and coarser (representing non-cohesive solids) sediments. Rocky and mounded bed areas
identified by the river bottom coverage were excluded from the sediment segmentation grid, as
were all islands.
Two additional criteria were used in developing the sediment segmentation from the side scan
sonar data:
• Water column segments underlain by 15 % or more cohesive sediment area were
assigned both cohesive and non-cohesive sediment segments, unless they contained
more than 85 % cohesive sediment area, in which case only a cohesive sediment
segment was assigned; and.
• Water column segments underlain by less than 15 % cohesive sediment area were
assigned only non-cohesive sediment segments.
The second GIS coverage was based on GE"s 1997 sediment bed type sampling between
Northumberland Dam and Federal Dam (QEA. 1998). This coverage was used to define the
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HUDTOX sediment segmentation in reaches of the Upper Hudson River that were not covered
by the side scan sonar surveys.
These two GIS coverages of sediment type were intersected with the HUDTOX water column
segments to develop a two-dimensional picture of the surficial sediments, and to define 27
cohesive and 43 non-cohesive sediment segments for the Upper Hudson River between Fort
Edward and Federal Dam. Figure 5-4 (Parts A through D) depicts the two sediment types
underlying each water column segment for the entire upper river. Figure 5-5 provides a large-
scale view of the same information within just TIP, which was represented with 15 cohesive and
27 non-cohesive surficial sediment segments. The longitudinal variation in cohesive sediment
abundance within the Upper Hudson River is depicted in Figure 5-8.
A vertical discretization of two centimeters was used for the HUDTOX sediment segmentation to
provide adequate resolution of vertical PCB profiles for simulating sediment-water interactions
and long-term system responses. This resolution also provides flexibility in the use of HUDTOX
model output for PCB sediment exposures in terms of an "active" surface sediment layer for the
bioaccumulation models. A summary of the HUDTOX surficial sediment segmentation
geometry' is provided in Tables 5-2A and 5-2B. The model grid includes sediments down to 26
cm (13 layers), resulting in a total of 1035 water column and sediment segments in the entire
model grid.
5.4. Model Implementation
The HUDTOX model was developed from the USEPA WASP toxic chemical model framework.
The model was originally constructed from the WASP4/TOXJ4 version of the code and
subsequently modified to include relevant code corrections and changes that were implemented
by USEPA in the WASP5/TOXI5 version. The WASPS model is documented in Ambrose et al.
(1993) and is distributed by the Center for Exposure Assessment Modeling (CEAM) at the
USEPA Environmental Research Laboratory. Athens. Georgia.
The HUDTOX model FORTRAN source code was compiled and run using Lahey FORTRAN
90 (Version 4.50b. Lahey Computer Systems. Inc.) for personal computers running Microsoft
DOS or Windows (95, 98 or NT) operating systems. Development, testing and application of the
HUDTOX model was conducted on IBM-PC compatible personal computers. The computer
hardware system requirements vary, depending on the type of HUDTOX model simulations
being conducted. A Pentium II microprocessor (266 Mhz or higher), 64 Megabytes of RAM, and
available disk storage space of 1.0 Gigabyte are minimum requirements for the simulations
presented in this report. As a general indication of model execution speed, a 21-year simulation
from 1977 to 1997 required on the order of 10+ hours of real time on a 450 Mhz Pentium II
personal computer. This simulation included a model grid consisting of 1035 spatial segments
and computational time steps ranging from 0.0027 to 0.019 days over the 21-year simulation
period.
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Chapter 6
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6. DATA DEVELOPMENT
6.1. Introduction
The applications of the HUDTOX mass balance model required a large effort to organize and
process primary data for use as model input and for model calibration targets. This chapter
contains a summary' of the data development effort. Section 6.2 presents an overview of the
Hudson River Database. Section 6.3 presents summaries of the principal water column and
sediment datasets used in the modeling analysis. Section 6.4 describes synthesis of river flow-
data and development of external loadings and in-river mass fluxes for solids and PCBs.
External loadings were the most important model inputs and in-river mass fluxes were important
model calibration targets.
6.2. Hudson River Database
All modeling work in this report utilized the extensive database that was created to support this
Reassessment. The Database Report (TAMS/Gradient, 1995) and accompanying CD-ROM
database provides the validated data for the Phase 2 investigation. This Baseline Modeling
Report (BMR) utilized Release 4.1b. which was updated in fall 1998 (TAMS et al.. 1998a). This
database contains information from a large variety of different sources, including:
• New York State Department of Environmental Conservation (NYSDEC)
• New York State Department of Health (NYSDOH)
• New York State Department of Transportation (NYSDOT)
• General Electric Company (GE)
• Lamont-Doherty Earth Observatory (LDEO)
• Rensselaer Polytechnic Institute (RPI)
• U.S. Geological Survey (USGS)
• National Oceanic and Atmospheric Administration (NOAA)
• National Weather Service (NWS)
• U.S. Environmental Protection Agency (USEPA).
To supplement the database in Release 4.1b. a portion of the 1997 USGS flow, suspended solids
and PCB data were obtained directly from the USGS in Albany, New York. Where necessary
and appropriate, information from the scientific literature and various technical reports was also
used in this modeling work. These sources are cited in the report text.
The Data Evaluation and Interpretation Report (DEIR) (TAMS et al.. 1997) and the Low
Resolution Sediment Coring Report (LRSCR) (TAMS et al., 1998b) are companion reports to
this Baseline Modeling Report (BMR). The DEIR contains a literature review of current and
historical PCB water column data, and an evaluation of geochemical fate of PCBs in the
sediments of the Upper Hudson River. The LRSCR contains an assessment of current and
historical inventories of sediment PCBs in the Upper Hudson River. The reader is referred to
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these companion reports for complete details on the available datasets for PCBs and associated
parameters in this Reassessment.
6.3. Model Application Datasets
From the standpoint of mass balance model applications, the most comprehensive datasets for
the Upper Hudson River were acquired during 1991 to 1997. Data for tributaries are very sparse
compared to data for the mainstem. The most extensive long-term monitoring for solids and
PCB concentrations was conducted at Fort Edward, Thompson Island Dam, Schuylerville,
Stillwater and Waterford. Data for solids and PCBs were also collected at other locations as part
of specialized, short-term studies. The principal sediment datasets were collected by NYSDEC
in 1976-1978 and 1984, by GE in 1991-1997 and by USEPA in 1992 and 1994.
6.3.1. Water Column Datasets
The principal water column datasets used for solids and PCBs were the following:
• Long-term monitoring data collected Fort Edward, Schuylerville, Stillwater and
Waterford from 1977 to 1997 (collected by USGS, USEPA and GE)
• Thompson Island Dam data from 1991 to 1997 (collected by USEPA and GE)
• Mainstem and tributarv solids data from the spring 1994 hieh flow survev (collected
by USEPA)
• Mainstem data from the USEPA Phase 2 monitoring program in 1993
• High flow sampling data in 1997 (collected by GE)
• Thompson Island Pool float study data in 1996 and 1997 (collected by GE)
• Thompson Island Dam bias study data (collected by GE).
Data from these various studies is included in the TAMS''Gradient Phase 2 database. Release
4.1b (TAMS et al.. 1998a).
These water column data were used to estimate external loadings of solids and PCBs at the
upstream boundary (Fort Edward) and for tributaries to the mainstem portion of the river, both of
which are required for HUDTOX model input. In-river mass fluxes of solids and PCBs were
also estimated for use as model calibration targets. Concentration time series throughout the
calibration period and down-river concentration profiles on specific days were also derived from
the data for comparisons between model output and field observations. These data were also
used to specify model initial conditions.
Bias in Thompson Island Dam PCB Data
As summarized in QEA (1998). an apparent sampling bias was discovered in fall of 1997 in PCB
measurements from the routine monitoring station located on the west shore of TID. The
samples collected at this station are not always representative of the average PCB concentration
leaving the pool due to apparent influences of a local PCB hot spot near the west shore just above
the dam, hence the term bias. The bias appears to be related to incomplete lateral mixing
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between the west shore and center channel of the river during periods of low flow. The
magnitude of the bias in terms of percent difference between the west shore and center channel
locations is related to flow conditions and upstream loading. During high flow periods and/or
periods of high PCB loading at Fort Edward, the localized contribution of the hot spot upstream
of the west shore station appears to be smaller. Presumably, during high flow periods, sufficient
lateral mixing occurs to destroy any significant lateral gradients in the river. After discover.' of
the west shore station bias, GE continued to conduct a monitoring program designed to better
quantify the magnitude of the bias. This monitoring program included collection of samples
further upstream and downstream of TID and on lateral transects.
When the TID PCB data were processed for use in model calibration, 34 paired west shore-center
channel samples collected by GE were available. These data pairs were used to develop a
method for bias-correcting PCB concentrations measured at the west shore monitoring station in
order to quantify PCB mass leaving TIP. Monthly bias-correction factors were computed as
monthly average percent differences between west shore and corresponding center channel or
downstream concentrations for total PCBs and Tri-. No sample pairs were available to establish
the existence of a sampling bias at high flows, thus the bias was assumed not to exist at flows
above 10,000 cfs. The computed monthly average bias correction factors were applied to
individual west shore data collected at flows below 10,000 cfs. This analysis did not consider
the effect of upstream load in controlling the magnitude of the bias. Further investigation of the
TID bias is planned. Specifically, the magnitude and temporal pattern of the bias will be
investigated as a function of both upstream flow and PCB load at Fort Edward.
Potential Bias in USGS Water Column PCB Data
An analytical bias exists in the USGS water column dataset that was not accounted for in
development of historical PCB loads and model calibration targets. The bias is inherent in the
analytical technique used by USGS to measure PCBs. The USGS methods use manufactured
Aroclor standards that contain small percents by weight of mono- and di-homologues. This
introduces bias into samples containing a relatively high percentage of these homologues. The
bias affects samples collected downstream of Fort Edward more than the Fort Edward samples
due to the higher fractions of mono- and di-homologues in downstream samples resulting from
dechlorination in the sediments. Samples collected at Fort Edward are more similar to the source
.Aroclor material than downstream samples because they have not undergone the same degree of
dechlorination.
General Electric provided USEPA with their quantification of this analytical bias (Rhea and
Werth, 1999). These GE results suggest that the USGS data at stations downstream of Fort
Edward are biased low by 4 to 70 percent. The impact of this bias might not be large, however,
due to the high number of samples near detection limits. Future work is planned to further
investigate this bias and its significance to the HUDTOX model results.
6.3.2. Sediment Datasets
The principal sediment datasets used for solids and PCBs. and for physical characterization, were
the following:
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1976-1978 NYSDEC data
• 1984 NYSDEC data
• 1991 GE data
• 1992 USEPA high resolution coring data
• 1994 USEPA low resolution coring data.
These datasets were used to specify model initial conditions and model calibration targets. Table
6-1 summarizes the uses of these primary datasets in development and application of the
HUDTOX model. Sediment PCB concentrations were computed for cohesive and non-cohesive
sediments. Table 6-2 presents the areas of cohesive and non-cohesive sediments that were
estimated for different reaches in the river.
6.4. External Loadings and Mainstem Mass Fluxes
The HUDTOX model is based on the principle of conservation of mass. It balances inputs,
outputs and internal sources and sinks for the Upper Hudson River. Three separate mass
balances are represented in HUDTOX: (1) a water balance; (2) a solids balance; and (3) a PCB
balance. Before the HUDTOX model can be applied, all external inputs for water, solids and
PCBs must be specified from field observations. During the calibration process, internal model
parameters are adjusted in order to balance these external inputs against field observations for
internal sources, sinks and reservoirs, and for system outputs. The purpose of this section is to
describe the development of external inputs for water, solids and PCBs, and of mainstem solids
and PCB mass fluxes required for the model calibration process.
Daily average river flow estimates were developed for the mainstem and tributaries in the Upper
Hudson River between Fort Edward and Federal Dam at Troy for the period January 1. 1977
through September 30, 1997. Daily average suspended solids (SS) and Tri+ load estimates were
also developed in association with these historical flow estimates. Daily average loads for total
PCBs and congeners BZ?4 and BZŁ52 were estimated for the period April 1, 1991 through
September 30. 1997.
6.4.1. Water Balance
The HUDTOX model required specification of all hydraulic inflows in the form of daily time
series. These inflows included upstream flow at Fort Edward and flows from all important
tributaries between Fort Edward and Federal Dam at Troy. These time series were developed for
mainstem Hudson River locations and 12 tributaries for the period January 1, 1997 to September
30, 1997. The daily flow estimates were based on available USGS flow gage data.
Mainstem and tributary flow gages in operation during the study period are summarized in Table
6-3. The locations for these flow gages are shown in Figure 6-1. The Fort Edward gaging
station was operational for almost the entire study period (March. 1977 to the present), whereas
major gaps exist in daily flow records for the other mainstem stations. Final USGS daily flows
for the Stillwater and Waterford stations are flagged as estimated values from October 1992
onward due to construction activities that began that year and continued through at least 1995
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(Charles Fluelling, NYS Thruway, personal communication, February 27, 1997). The daily
flows at Stillwater continued to be reported as estimates through the end of 1997 because this
gage remained out of operation until that time. The only tributaries gaged for the entire study
period were located on the Hoosic and Mohawk Rivers.
Methods
Gaged daily average USGS flows were used directly where available, and when reported to be
accurate, to estimate upstream and tributary flows. These included flow gage data from USGS
stations at Fort Edward, the Mohawk River (sum of daily flows at Cohoes and the Crescent Dam
diversion), and the Hoosic River at Eagle Bridge. The entire Fort Edward flow time series
reported by USGS was used without modification. Ungaged tributary flows were estimated by
applying average flow per unit area from nearby, gaged tributaries that drained similar
watersheds. Because the Hoosic River flows were gaged at Eagle Bridge, upstream of the
mouth, these flows were adjusted to the mouth to account for the ungaged portion of the Hoosic
River watershed using a drainage area ratio (DAR) approach (Equation 6-1):
r DA ^
nj 'nbX 6-1
^ Gagedtnb j
where:
QtribX~ ungaged drainage tributary flow
QGagedtrib = gaged tributary flow
DAtribX= ungaged tributary drainage area
DA-Gagedtrib ~ gaged tributary drainage area.
Equation 6-1 was used to estimated all ungaged tributary flows. Similarities in land use.
topography and location were considered when selecting a reference tributary. Tributary-
drainage areas were estimated by digitizing the watershed boundaries in a GIS and are presented
in Table 6-4. Also presented in this table are the gaged reference tributaries used in the DAR
approach for each ungaged tributary.
The DAR approach does not result in flows from individual tributaries that are mutually
constrained. Long-term USGS flow estimates at Stillwater and Waterford were used to check the
internal consistency of flows in the Upper Hudson River. The seasonal mean flow computed by-
summing the Fort Edward and estimated tributary flows was compared to the seasonal mean flow
from the USGS gages at Stillwater and Waterford over the period from March 1, 1977 to June
30. 1992 (Table 6-5). This period was used because all three gages (Fort Edward, Stillwater and
Waterford) were operational. After October 1992. the gages at Stillwater and Waterford were
influenced by dam construction activity. The ungaged tributary flows estimated by the DAR
method were scaled, as necessary, by their percent contributions to the total ungaged area in
order to be consistent with the long-term seasonal average flows at Stillwater and Waterford.
This approach achieved a seasonal mean flow balance between Fort Edward and Stillwater and
between Stillwater and Waterford. The daily flow estimates assigned to direct drainage and
ungaged portions of tributaries between Fort Edward and Waterford represent approximately 27
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percent (1,258 mi: of a total 4,611 mi2) of the Upper Hudson River drainage basin area between
these locations.
The mean seasonal flows presented in Table 6-5 were used to calculate a factor, a, by which the
DAR-estimated tributary flows were multiplied so that the sum of the seasonally-averaged
tributary flows equaled the difference in the seasonally-averaged, gaged USGS inflows
(upstream flow + gaged tributary flows) and seasonally-averaged, USGS outflow (downstream
flow) between the main gauging stations (Fort Edward, Stillwater, and Waterford. The seasonal
flow adjustment factor, a, was calculated for the Fort Edward to Stillwater reach based on the
seasonal average flow difference and tributary DARs. This factor was then applied to the DAR-
estimated tributary flows in this reach to give the seasonally-adjusted flows. The same
calculations were performed for the Stillwater to Waterford reach. The reach-specific seasonal
flow adjustment factors, a, are summarized in Table 6-6.
The required adjustment for the ungaged tributary flow between Stillwater and Waterford was
much less than 1.0 in the summer and fall. This indicates that the extrapolation of the Hoosic
River flows gaged at Eagle Bridge using the DAR approach gives a significant overestimate of
incremental flows during summer and fall from Stillwater to Waterford, and possibly the Hoosic
River at the mouth. It is possible that differences in watershed geology may cause different base
flow behavior relative to higher flows in the Hoosic River than in the smaller tributaries draining
directly to the Hudson River. Evaporative and other losses from the Hoosic River between Eagle
Bridge and the Hudson may be significant during the summer and fall, which may result in an
overestimate of the Hoosic River flows to the Hudson River for these periods.
Note that while the above tributary and mainstem flow balance was determined for the period
from March 1, 1977 to June 30, 1992, the adjustment factors in Table 6-6 were applied to the
DAR-estimated tributary flows for the entire HUDTOX application period from January 1. 1977
to September 30, 1997.
Results
The tributary flows estimated in the mariner described above were summed and added to the Fort
Edward flows to develop an estimated daily flow time series for comparison to the measured
USGS flows at Stillwater and Waterford. In general, the estimated values compared well:
however, during some high flow events the LTI flow estimate differed by over 30 percent from
the USGS flow. This was not unexpected because the seasonal correction applied to the tributary-
flows can not capture all events due to localized precipitation or snowmelt in the ungaged
tributaries that might not have occurred in the gaged tributaries or vice versa. Because the USGS
gage readings during the 1977 to 1992 period were assumed to be accurate within 30 percent
during high flow events, the LTI-estimated tributary flows were adjusted to within 30 percent of
the USGS gaged flows where differences greater than 30 percent occurred.
In summary, although approximately 67 percent of the tributary flows were estimated, estimates
of daily mainstem flows at Stillwater and Waterford differ by no more than 30 percent from the
measured daily USGS flows and in general, agreement was within 10 percent. For the period
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between March 1, 1977 and June 30, 1992. estimated cumulative flows equal 100 percent and
99.7 percent, respectively, of the measured cumulative USGS flows at Stillwater and Waterford.
Analysis of the estimated flow contributions from each source indicate that most of the flow
volume measured at Federal Dam enters the Upper Hudson River from tributaries between Fort
Edward and Federal Dam. Of the total flow over the 21-year study period, approximately 38
percent of the flow at Federal Dam enters the system at Fort Edward with 62 percent coming
from tributaries (Figure 6-2). The Hoosic and Mohawk Rivers contribute most of the tributary
flow (10 percent and 41 percent, respectively). Figure 6-3 presents a summary of average daily
flows for the study period, by tributary and mainstem station.
The estimated daily flow time series at Stillwater and Waterford. instead of the measured USGS
flows, were used to compute in-stream fluxes of solids and PCBs for the HUDTOX model. This
was done to maintain consistency with upstream and tributary flow inputs to the model. The
estimated flows at Stillwater and Waterford after October 1992 were assumed to be reasonable,
based on the flow balance achieved from March 1, 1977 to June 30, 1992. Comparison of the
estimated flows at Stillwater and Waterford for 1993 to the flow estimates presented in the DEIR
(TAMS et al., 1997) showed the DEIR estimates to be substantially higher during low flow.
Correlation of the DEIR summer average flow estimates with cumulative precipitation data
revealed that the DEIR estimates were biased high. Consequently, the DEIR flow estimates were
not used in any of the HUDTOX model applications.
6.4.2. Mainstem and Tributary Solids Loads
Daily average suspended solids (SS) load estimates were developed for the mainstem and
tributaries in the Upper Hudson River for the period January 1, 1977 through September 30,
1997. Because suspended solids loads were estimated based on both flow measurements and
solids concentration data, the stations selected for load estimation were either nearby or the same
as those selected for flows. The available solids data for the mainstem and tributary stations are
summarized in Tables 6-7 and 6-8, respectively. The locations of these solids sampling stations
are shown in Figure 6-4.
More frequent solids concentration data were available for mainstem stations than tributary
stations, with no tributary solids data available prior to 1988. In addition, as illustrated in Figure
6-5. only 71 percent of the watershed area between Fort Edward and Waterford was monitored
for solids, thus requiring estimation of solids loads from 29 percent of the total watershed area in
the Upper Hudson River. Furthermore, for the 71 percent of the watershed area that was
monitored, only very limited data are available for most of the tributaries. Generally, tributary
samples were collected for only a short period of time during the 21-year study period. Solids
samples were collected for mainstem and tributary stations on only 24 percent and 1 percent,
respectively, of the total days in the 21-year simulation period.
Methods for Mainstem Solids Loads
An extensive record of suspended solids concentration data are available at Fort Edward.
Stillwater and Waterford over the 21-year study period. Although numerous measurements are
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available, sampling frequency was sporadic during certain time periods. To develop accurate
estimates of solids loads, the following three methods were used in different combinations:
1) A modified ratio estimator approach;
2) A flow-stratified minimum variance unbiased estimator (MVUE) regression
approach; and,
3) Use of monthly average suspended solids concentrations with daily average flow.
The modified ratio estimator approach was based on Beale's Stratified Ratio Estimator (BSRE)
(Beale, 1962) which computes ratios of load to flow and then stratifies by flow to separate
periods with similar ratios. The MVUE (Cohn et al., 1989) develops a statistical relationship
between measured concentrations and flows and then uses this relationship to estimate
concentrations on days for which only a flow measurement is available. Loads were estimated
using monthly average concentrations and daily average flows for periods that did not contain
sufficient data to apply the BSRE or during which there w-ere no significant relationships
between concentration and flow. In general, the approach to load estimation in the Upper
Hudson River followed Preston, et al. (1989. 1992) who conducted retrospective studies with
comprehensive sets of field measurements to evaluate various ratio estimators, regression
methods and averaging methods.
Results for Mainstem Solids Loads
Suspended solids concentrations were generally well-correlated with flow at Fort Edward.
Stillwater and Waterford. with stronger correlations observed at higher flows (Figure 6-6).
Flow-stratified regressions were also conducted at these three stations. First, high and low flow
data were separated based on the approximate breakpoint observed in the concentration-flow
correlation plots. Then a flow-stratified regression analysis was conducted for each station by
calculating the mean concentration for all solids concentration data collected below a flow
cutpoint and then conducting a MVUE regression analysis on the mean daily flow (Q) and solids
concentration data above the cutpoint flow. The equations developed for the mainstem stations
are summarized below, with the high flow regression equations incorporating the MVUE bias
correction factor.
1) Fort Edward SS (mg/L) = 3.6 when Q < 11.000 cfs
= 9E-09 x Q: 15 when Q > 11.000 cfs
2) Stillwater SS (mg/L) =5.1 when Q < 13,000 cfs
= 1E-06 xQ' "J when Q > 13.000 cfs
3) Waterford SS (mg/L) = 7.5 when Q < 16.000 cfs
= 2E-08 x Q: i: when Q > 16,000 cfs
Daily solids load estimates were obtained by multiplying estimated daily solids concentrations,
computed as shown above, by daily average flow. Measured USGS flows were used at Fort
Edward and the estimated flows (see Section 6.4.1) were used at Stillwater and Waterford.
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Based on comparisons of results among different methods, solids loads at Fort Edward were
estimated using a MVUE regression approach, while the modified ratio estimator was used to
estimate solids loads at Stillwater and Waterford. It should be noted that these methods were
used to estimate loads only on days for which no concentration measurements were available. In
constructing the actual daily loading time series used for input to the HUDTOX model, estimated
loads were replaced with observed loads on days where paired measurements of flow and
concentration were available.
Methods for Tributary- Solids Loads
A major obstacle to estimation of tributary solids loadings was that available data were very
limited, especially for solids concentrations. Many tributaries had little or no suspended solids
concentration data. For those tributaries having data, a correlation between concentration and
flow was sought in order to estimate concentration as a function of flow. This sort of regression
analysis is also referred to as a rating curve approach. The tributary data are too sparse to
support application of the ratio estimator approach that was used in calculation of the mainstem
solids fluxes. Similar to the pattern observed at the mainstem stations, tributary solids
concentrations were positively correlated with flow and the tributary rating curves generally-
exhibited a breakpoint above which the slope of the relationship increases. For each monitored
tributary, the average solids concentration was calculated below the average flow and a MVUE
regression was developed between flow and solids concentration above average flow, which
generally approximated the observed flow breakpoint.
Unmonitored tributaries comprise 29 percent of the drainage area between Fort Edward and
Waterford. Each unmonitored tributary was matched with a monitored tributary' that had a
watershed with similar land use distribution, topography and location (Table 6-9). Watershed
size was also considered. The rating curve for the reference tributary was then applied to the
matched unmonitored tributary using flows specific to the unmonitored tributary.
Results for Tributary Solids Loads
The cumulative mainstem solids loads and annual average mainstem solids yields for the
drainage area at Fort Edward. Stillwater and Waterford were computed (Table 6-10) and
compared to the tributary load and yield (Table 6-11). Results show larger solids load gain
between mainstem stations than contributed by the estimated tributary solids loads. The
computed watershed yields based on solids load gain between the mainstem stations is nearly a
factor of two larger than the estimated tributary yields. This implies that more solids are passing
Stillwater and Waterford than can be explained by upstream loads at Fort Edward plus estimated
tributar> solids loads based on the rating curves. This large discrepancy in the Upper Hudson
River solids balance required a resolution in order to develop consistent external solids load
inputs for the HUDTOX model.
6.4.3. Development of Long-Term Average Solids Balance
In order to calibrate the HUDTOX mass balance model for solids, it was necessary to achieve a
long-term solids balance that reconciles external solids loads, outgoing solids fluxes, and internal
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sources (resuspension and primary production) and sinks (deposition). If external loads and
outgoing fluxes are reasonably well known, the mass balance model can be used to reconcile
internal processes with these inputs and outputs. Without good estimates of incoming and
outgoing solids loads, the internal solids dynamics (source and sink processes) will be
unconstrained. In order to achieve a long-term solids balance for the Upper Hudson River, it was
necessary to determine whether the estimated upstream and tributary solids loads could be
reconciled with those at Stillwater and Waterford to produce an internally consistent
representation of solids dynamics in the system.
Methods
In determining the most probable explanation for the solids load discrepancy, estimated loads
passing Fort Edward, Stillwater and Waterford were assumed to be accurate. Based on results in
the PMCR, the contribution to solids loading by internal primary' production was assumed to be
insignificant. Remaining possible explanations are underestimation of external loads, including
tributary' and possible bank erosion loads, or net erosion of the sediment bed. Experience with
other similar river systems suggests that the impounded reaches in the Upper Hudson River are
net depositional over long periods of time, even if there might be localized areas that are net
erosional. Nevertheless, the possibility of net solids erosion as an explanation for the solids load
discrepancy has not been dismissed. Future work is planned to investigate alternate scenarios for
solids dynamics in the Upper Hudson River, including net erosion as a source for the observed
gain in solids loadings.
To develop tributary solids loads for the HUDTOX model applications presented herein, it was
assumed that tributary loads developed from the limited available data were underestimates of
the true tributary solids loads. As a global constraint, it was assumed that the Upper Hudson
River from Fort Edward to Waterford was net depositional over the historical study period. It
should be noted that a solids balance for TIP based solely on available measurements confirmed
that the pool was, in fact, net depositional. Available data for tributaries to TIP (Snook Kill and
Moses Kill) were more extensive than available data for downstream tributaries, especially
during high flows. Consequently, solids loads for these tributaries were assumed to represent
true solids loads.
The approach used to develop tributary solids loads for the HUDTOX model was to assume that
the net depositional condition observed in TIP also existed for reaches downstream of TID.
Tributary solids loads developed from the limited available data were adjusted upward to be
consistent with this global constraint. The incremental loads were apportioned to each
subwatershed within a reach based on tributary drainage area (Table 6-12). These upward
adjustments were applied only to tributaries downstream of TID and not to any external solids
loads to TIP.
The first step in the approach was to assume a range of net deposition rates for cohesive and non-
cohesive sediment in each reach downstream of TIP (Table 6-13). A reach-wide average long-
term sediment burial velocity was estimated using the range and the average of assumed
sediment burial velocities and the areas of cohesive and non-cohesive sediments. Depositional
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solids loads were then computed using sediment bulk density (Table 6-13). The depositional
loads were used to compute the solids trapping efficiency of each reach below TIP by dividing
by total upstream load. The trapping efficiency in TIP was computed directly from observations
and no adjustment of TIP tributary loads was conducted. During data-rich periods in 1993. 1994,
and 1997 reasonable solids mass balances could be constructed for TIP based on measured
concentrations and estimated flows at Fort Edward, Snook Kill, Moses Kill and TID. Based on
the solids trapping efficiencies for these three periods, which ranged from 9 to 28 percent, a
solids trapping efficiency of 15 percent was specified for TIP (Table 6-14). For the TID to
Stillwater portion of the river, the total external solids loads were computed as the sum of direct
tributary loads plus the TID load. The TID load was computed as the sum of all external loads to
TIP times 0.85. The trapping efficiency for this reach was computed as the depositional load
divided by the sum of tributary loads and the TID load. The trapping efficiency of the Stillwater
to Waterford portion of the river was computed in a similar manner (Table 6-14).
Using the solids trapping efficiency estimates presented in Table 6-14, the incremental solids
loads required between TID and Stillwater and between Stillwater and Waterford were
computed. The required load increments were then allocated to the tributaries on a drainage area
basis by increasing the high flow slope coefficient on the rating curves as shown in Table 6-15.
In summary, the tributary suspended solids load adjustments were performed for tributaries
entering the Upper Hudson River between Thompson Island Dam and Waterford. The tributary
loads to Thompson Island Pool were not adjusted because sufficient suspended solids data exist
for both Snook and Moses Kill to define the suspended solids rating curve sufficiently well that
the loads are considered accurate. The Mohawk River suspended solids loads were not adjusted
because insufficient data exist at Federal Dam to evaluate the solids balance between Waterford
and Federal Dam.
Following the adjustment to tributary loads, the resulting equations in Table 6-15 were used to
compute tributary solids loads for input into HUDTOX based on flows estimated in Section
6.4.1. In constructing the input load time series, measured suspended solids loads were used in
place of predicted loads on days where paired flow and concentration data were available.
Results
Using the tributary solids load time series constructed as described above, average annual
tributary loads were computed (Table 6-16). Results show that in general, tributary loads
between TID and Waterford were adjusted upward by a factor of 2.5. relative to load estimates
based on the unmodified rating curves. This required adjustment is large and it illustrates the
large uncertainty regarding solids dynamics in the Upper Hudson River below Thompson Island
Dam. To evaluate the reasonableness of the adjustments, the resulting tributary watershed yields
were computed and compared to available information. WTiile the yields are relatively large,
they are well within ranges reported in the literature.
Tributary yields were also compared to the yield computed at stations on the mainstem Upper
Hudson River (Figure 6-7). This comparison reveals an increase in yield moving from Fort
Edward downstream. The mainstem yields computed based on in-river data are much lower than
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the tributary yields; however, this is expected considering the ability of the mainstem to capture
solids via deposition. Apparent yields computed at these stations do not reflect the total loading
due to the large degree of deposition that might occur.
Evaluation of the relative contribution of tributary suspended solids loads to the Upper Hudson
River indicates the large contribution of the tributaries (Figure 6-8). Only 5 percent of the
suspended solids load at Federal Dam enters the system at Fort Edward, with 95 percent entering
from tributaries. Table 6-17 presents a temporal summary of solids loads that shows seasonality
of load differences from TID to Waterford, based on mainstem load calculations. The
comparison is made to the seasonal tributary loads, estimated using the equations in Table 6-15.
Results in this table confirm that application of a correction factor to the exponent on the high
flow regression equation caused the additional tributary loads to be delivered during the season
in which the load differences between mainstem stations were actually observed.
6.4.4. Mainstem and Tributary- PCB Loads
Application of the HUDTOX model requires specification of all external inputs of water, solids
and PCBs. Just as loading time series were developed for water (Section 6.4.1) and suspended
solids (Section 6.4.2). external loading time series were developed for the four PCB state
variables: total PCB, Tri-K BZ#4 and BZ#52. In order to apply the HUDTOX model, daily
average loading estimates were developed for Fort Edward and the 12 tributaries represented in
the model. Tri-r loads were estimated over the hindcast calibration period, January 1, 1977
through September 30, 1997. Total PCB, BZ#4, and BZ#52 loads were estimated over the April
1, 1991 through September 30. 1997 calibration period. To aide in model calibration, in-river
fluxes of PCBs were developed at mainstem Upper Hudson River stations where data availability
permitted. The in-river flux estimates were calculated solely for the purpose of model calibration
and were not used as external loads to the model.
It should be noted that Tri- and total PCB concentrations measured at the west shore of TID
were bias-corrected (Section 6.3.1) prior to estimating mass fluxes across TID. The bias
correction applied did not take into account the effect of the upstream load measured at Fort
Edward in determining the magnitude of the bias. Future work is planned to further investigate
this bias. Furthermore, the USGS data were not adjusted to account for the analytical bias in
these data (Section 6.3.1) and future work is also planned to investigate the significance of this
bias.
Summaries of PCB data availability for tributary stations are presented in Table 6-18. PCB data
sources consisted of the USGS (1977-present), the USEPA Phase 2 investigation (1993) and GE
(1991-present). Table 6-18 highlights the fact that tributary data are very limited, wiu only the
Batten Kill, Hoosic River and Mohawk River being sampled for PCBs. In addition, no tributary
PCB data exist prior to 1991. Figure 6-9 presents the location of the PCB sampling stations
within the study area.
Several data processing steps were taken prior to PCB load estimation. The first step wras to
assign a value equal to one-half the detection limit for any values reported as being less than the
detection limit. Second, for the data from the USEPA Phase 2 flow-average surveys (16 days
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duration), reported concentrations were assigned to each of the days over which the sample was
collected. Finally, for days with multiple concentrations, an average daily value was used for
load estimation.
Methods
Similar to the development of external solids loads for the HUDTOX model, several different
methods were also considered for estimating daily average external PCB loads. The scarcity of
measured PCB concentrations over the historical calibration period required estimation of the
external loads and in-river fluxes of PCBs during a major part of the calibration period. This
introduces substantial uncertainty into the calibration of the model during data-poor periods,
especially from 1977 to 1984.
Regression methods were eliminated because no significant relationships were found among
flow, PCB concentration and suspended solids concentration. Beale's ratio estimators, both
stratified (BSRE) and unstratified (BURE), and the seasonal average methods were eliminated
because none of these methods reproduced actual observed PCB loads with sufficient accuracy
when predicted and observed loads were compared. Furthermore, data frequency was too low for
application of the BURE during large portions of the study period. A combination of linear
interpolation and year-specific, seasonal average concentrations was adopted as the method of
choice to construct both daily PCB loading time series at Fort Edward and in-river fluxes at
Thompson Island Dam, Schuvlerville, Stillwater and Waterford.
The available time series of water column PCB data were reviewed for each station and separated
into time periods where sufficient data frequency existed to support interpolation. For those
periods where interpolation did not appear reasonable, seasonal average concentrations were
computed during each year and applied in the respective individual years. The periods over
which interpolation was used varied among stations due to variations in sampling frequency
among stations.
One complication in the use of linear interpolation is the apparent occurrence of random "pulse"
loads of PCBs at Fort Edward. These pulse loads appear to be largely unrelated to flow and they
can contribute significant mass of PCBs to TIP. The use of linear interpolation during periods of
infrequent sampling sometimes exaggerated the apparent contribution of pulse loads that were
characterized by only one or two data points. Interpolation in these situations caused incoming
concentrations to be strongly affected by individual high concentration measurements for long
periods of time prior to and following the measurements. To account for this effect, the
approximate duration of these pulse loads was estimated by inspection of the concentration time
series. It appeared that the typical time scale for high-concentration events was on the order of
only several days. Therefore, during periods of infrequent sampling, best professional judgment
was used in substituting year-specific, seasonal average concentrations for interpolated
concentrations several days prior to and following individual high concentration measurements.
Year-specific seasonal average concentrations were used where necessary in place of
interpolation because the data show both seasonal trends within a year and decreasing
concentrations over time (Figure 6-10). It should be noted that substitution of seasonal average
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concentrations was necessary only for Tri-. Sufficient data frequency was available for total
PCBs, BZ#4 and BZ#52 and the interpolation-based loads were used without modification.
Due to extremely limited data, tributary PCB loads were estimated in a different manner from
mainstem locations in the Upper Hudson River. For the monitored tributaries. Batten Kill,
Hoosic River and Mohawk River, the average PCB concentration was calculated and the
assumption was made that this concentration remained constant for the entire study period.
Measured concentrations were substituted when available. Because the three monitored
tributaries were also the only tributaries with known PCB dischargers, it was assumed that these
tributaries would have higher PCB concentrations than the other tributaries in the study area.
The PCB concentrations in the unmonitored tributaries were assumed to equal the lowest
recorded PCB concentration from the three monitored tributaries. These concentrations were
0.17 ng/1 for Tri+, 0.51 ng/1 for total PCBs, 0.0 ng/1 for BZ#4 and 0.03 ng/1 for BZ#52. These
values were assumed to represent background concentrations for the unmonitored tributaries. It
is likely that historical tributary PCB concentrations were higher; however, the relative
contribution of tributary PCB loads compared to the upstream PCB load at Fort Edward is very
small and has negligible impact on the HUDTOX model calibration.
Results
The resulting daily average PCB loads estimated as described above were used to develop input
time series of PCB loads for the HUDTOX model at Fort Edward and all 12 tributaries. Most of
the total external load of PCBs to the system is upstream loading at Fort Edward, with tributaries
contributing only a few percent of the total load (Figure 6-13). Cumulative PCB load profiles
past TID, Schuvlerville, Stillwater and Waterford were also developed for use as calibration
targets for the HUDTOX model.
The PCB loads estimated here for the HUDTOX model compare favorably with previous PCB
load estimates developed in the DEIR (TAMS et al.. 1997). The DEIR estimates were developed
at an annual time scale, whereas the estimates developed for the HUDTOX model were daily
average time series. The DEIR estimates included Fort Edward (1977 to 1994). Schuvlerville
(1977 to 1989). and Stillwater and Waterford (1977 to 1993). When the present daily loading
time series are expressed in terms of cumulative loads (Figure 6-14) and annual loads (Table 6-
21 and Figure 6-15) they agree to within approximately 12 percent of the DEIR loads for
corresponding years and station locations.
Several important observations can be made from inspection of the annual Tri- loads over the
simulation period. First, a significant overall declining trend in TrB- loads past all of the
mainstem stations is evident over the period 1977 to 1997 (Figure 6-11). Second, the trend is not
monotonic and during some years, loads are much larger than the previous year. Of particular
note are Tri - loads in 1983-84 and 1991-92. The large, temporary increase in PCB load in
1991-92 is probably associated with the failure of the Allen Mill gate structure in September
1991 (TAMS et al.. 1997). Consistent with the long-term declining trend in Tri+ loads observed
at all mainstem stations, total PCBs, BZ#4. and BZ#52 loads at Fort Edward are observed to
decline over the period 1992-1997 following the increase in load associated with the Allen Mill
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gate structure event (Figure 6-12). Third, the estimated Tri+ load passing Fort Edward is much
lower than estimated PCB loads passing Schuylerville, Stillwater and Waterford in 1977, 1978,
and 1979, relative to the remainder of the simulation period. This suggests that either the
contribution of Tri-s- from sediments between Fort Edward and Schuylerville was very large
during this period, or the external Tri+ loads are underestimated either at Fort Edward or to the
reach between these two locations. Both of these are likely possibilities and are discussed in
presentation of the HUDTOX calibration (Chapter 7).
An important understanding gained from interpreting the estimated daily PCB loads is that the
majority of PCB transport occurs during low flow periods. Low flow periods are characterized
by relatively low sediment scour and transport as opposed to high flow events. During 1991 to
1997, between 81 and 94 percent of the estimated daily PCB load is delivered at flows less than
11,000 cfs at Fort Edward (Table 6-19). A similar pattern is observed when PCB load at Fort
Edward is stratified by suspended solids concentrations (Table 6-20). Based on the estimated
PCB loads at the various mainstem stations, an important conclusion is that most of the PCB
transport in the Upper Hudson River occurs during periods of low flow and low suspended solids
concentrations. This focuses attention on the importance of non-flow-dependent sediment-water
mass transfer processes. This observation does not diminish the significance of high flow events
in mobilizing PCBs due to flow-dependent resuspension, however, it does suggest that flow-
dependent resuspension is not the most important process controlling long-term, in-river PCB
mass fluxes in the Upper Hudson River.
An unresolved issue in estimating PCB loads in the Upper Hudson River is that of stochastic
pulse loads due to apparent releases from the GE Hudson Falls site and loads from flow-driven
resuspension between Hudson Falls and Fort Edward. It is not clear that sampling frequencies
for water column PCBs were sufficiently high to capture all of these loads, especially before
1991 when most of the historical cumulative mass loading occurred. There could have been
significant additional loads delivered to the system during periods of low sampling frequency
that were not captured by the sampling. The significance of these potential "missing loads" is
difficult to determine; however, it is noteworthy that a single measured pulse load in 1992 was
responsible for 19 percent of the total PCB load in that year aione. Recent monitoring data
(O'Brien & Gere, 1999) for a high flow event that occurred in the Upper Hudson River in
January 1998 appear to indicate that there are PCB sources above Rogers Island that can be
activated by high river flows. Future work is planned to investigate the significance of these
sources and their potential implications for forecast simulations with the HUDTOX model.
The issue of pulse loads is further exacerbated by the fact that when PCB loads to the Upper
Hudson River were at their highest levels, PCB water column sampling frequencies were at their
lowest. Most of the historical cumulative Tri+ load occurred between 1977 and 1983 (Figure 6-
11): however, of the total number of water column PCB samples taken between 1977 and 1997 at
Fort Edward (801). only 22 percent (173) were taken between 1977 and 1983. Furthermore, only
three samples were taken during 1977, a year in which PCB loads were estimated to be the
second-highest in the entire historical period of record.
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Chapter 7
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7. MASS BALANCE MODEL CALIBRATION
7.1. Introduction
Before applying a site-specific model such as HUDTOX in a forecast mode, it is necessary- to
first calibrate the model to existing field observations. Model calibration involves adjusting a
process model's coefficients within an acceptable range of values - this acceptable range
generally depends on experience with other similar systems and reported literature values - until
the model captures the observed spatial and temporal behavior of the state variables and
processes (system observables) in the system. Deterministic, process-oriented mass balance
models are a simplified representation of the full complexity of the actual system. In that regard,
models of this type are designed to describe the behavior of those processes and state variables
that are important to the problem under consideration. If such a site-specific model can be
formulated and parameterized (i.e., calibrated) to simulate the system observables of interest for
a period approaching that of the desired forecast period, then this model is considered usable for
making forecasts of system behavior in response to different remedial alternatives.
This section reports on the approach and results of the calibration exercise. Section 7.2 describes
the calibration strategy and resulting metrics for model calibration. Section 7.3 presents a
detailed summary of model parameters which, in combination with model configuration to the
physical system, flows, and loads of solids and PCBs, provided the most scientifically justifiable
and internally consistent calibration to available data. Sections 7.4 and 7.5 present the
calibration results and a diagnostic component analysis of those results, respectively.
7.2. Calibration Strategy
The calibration strategy was driven by a desire to use available observations in the model domain
in a way that would optimize our confidence in using the model to make accurate long-term
forecasts of the behavior of PCBs in the river in response to both No Action and potential
remediation alternatives. The strategy also followed a generally accepted principle of proceeding
sequentially from balancing water to solids (sorbents) and finally to the PCBs. The long-term
flow balance was based on USGS measurements at Fort Edward and estimated tributary flows
(Section 6.4.1). Once the solids dynamics were calibrated, an attempt was made to calibrate the
PCB dynamics without changing the parameters that govern solids dynamics. Ultimately,
however, it is desirable to obtain a scientifically credible and internally consistent calibration for
both solids and PCBs. Achieving this goal often requires some iteration between solids and
PCB calibration. In effect, PCBs can serve as a "tracer'' for solids because under many
conditions solids and PCBs tend to be mutually constrained, especially during resuspension
events.
The calibration proceeded from short-term applications to longer-term applications. If used
judiciously, high-resolution, short-term data sets can provide useful information to sufficiently
constrain parameters that control the long-term behavior of a system. Small biases that are not
detectable in short-term simulations can manifest themselves when simulations are carried out
over long time periods, which is the requirement of this overall modeling analysis.
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An important reality of model calibration should be recognized. Virtually all process-oriented
water quality models are under-determined (i.e.. they have more calibration coefficients (degrees
of freedom) than state variables); therefore, for complex models such as HUDTOX often there is
not a unique set of model coefficients that will give the "best" fit to the observed data. Trade-
offs often exist between two processes that cause the same change in a given state variable. For
example, sediment resuspension and pore-water diffusion can both transfer PCBs from the
sediments to the water column. If water column PCB concentration is the only calibration
metric, then the same transfer rate can be achieved with a range of parameter values for these two
processes. On the other hand, if water column suspended solids are also included as a calibration
metric, then the range of parameter values for these two processes is more tightly constrained
because one of them transfers solids to the water column and the other does not. With this
understanding, the model calibration strategy was conceived to use metrics that provided the
most constraint on the calibration possible with the available data. For example, system-specific
process rate measurements (e.g., field shaker resuspension experiments) were used to derive
estimates of process parameters governing resuspension. The model calibration process was also
constrained by formulating model processes in theoretically sound ways, so that the process
coefficients had as much physical meaning as possible. Even with the constraints imposed by
mechanistic process descriptions, calibration of the HUDTOX model required scientific
judgment in specifying process coefficients and many sensitivity analyses were conducted to
understand state variable responses to each parameter.
7.2.1. Solids Calibration Strategy
Once the flows in the system have been specified, the first step in the calibration process is to
calibrate the solids dynamics. Calibration of solids dynamics in HUDTOX required a priori
specification of all external solids loading rates (Section 6.4.2). including the upstream load at
Fort Edward. Given flows and solids loads, the parameters controlling water column settling,
bottom sediment resuspension from both cohesive and non-cohesive sediment areas, and
resulting bottom sediment accumulation and/or erosion were adjusted within scientifically
reasonable values until suspended solids concentration, mass fluxes, and sediment accumulation
rates in the modeled system compared best with observations.
The first step in the solids calibration was to parameterize solids settling and resuspension using
the short-term, high-frequency TSS data collected by RPI during the spring of 1994. This
sampling program was designed to capture daily variation in solids loads and concentrations in
the system in response to spring high flow events associated with snovvmelt and rainfall runoff
events. There are also relatively high-frequency data for Fort Edward. TID. Stillwater and
Waterford during the entire 1991-1997 period (and for Schuylerville in 1991 only). Therefore,
by next modeling the entire 1991-1997 period, we were able to compare model predicted
concentrations with available data during both high-flow and summer low-flow periods in this
data set in order to verify' appropriate behavior of the solids dynamics during a range of flow-
conditions occurring in sequence.
Once a satisfactory short-term calibration for solids was obtained, the model with the same solids
parameters was applied to the long-term solids data set between 1977-1997 (a 21-year hindcast).
This long-term historical simulation is driven by daily flow and estimated daily external
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(tributaries and upstream boundary) loadings of TSS. The cumulative solids flux profiles at
several points below Fort Edward was an important metric for this part of the calibration. The
model was also tested for its ability to capture the long-term net solids accumulation or erosion
rates in each reach of the river in cohesive and non-cohesive sediment areas; these rates of solids
sediment accumulation must be reasonable relative to observed burial of historical inputs of
PCBs to the system. These two additional constraints were used to refine the settling and
resuspension parameterization so that river solids dynamics were captured during both low-flow
and high-flow event periods. This accurate determination of the long-term sediment-water solids
exchange rate is very important in modeling long-term PCB dynamics.
In summary the metrics used in the solids calibration step included:
1. Water column concentration time series of TSS at several locations within the mainstem
of the river;
2. Cumulative TSS mass flux time profiles at major locations along the mainstem (TID,
Stillwater, Waterford); and.
3. Solids burial rates in cohesive and non-cohesive sediment areas.
This phased data-model comparison approach presented the best opportunity to parameterize
how settling and resuspension from both cohesive and non-cohesive sediment regions vary as a
function of flow and season.
7.2.2. PCB Calibration Strategy
Following the TSS model calibration, an attempt was made to calibrate HUDTOX to the PCB
data by adjusting PCB fate and transport process parameters without altering the solids
dynamics. However, as mentioned above, since the transport and fate of hydrophobic organic
compounds is closely tied to solids dynamics, minor readjustments to the solids calibration were
made in order to produce the most scientifically credible and internally consistent calibration for
both TSS and PCBs.
Once the solids dynamics of the system and external loads of PCBs have been specified (Section
6.4.4). there are a limited number of processes available for calibration of the PCB model: air-
water exchange; sediment-water exchange by mass transfer and resuspension: partitioning;
panicle mixing rates in sediments; and sediment dechlorination rates (a consideration in
modeling historical hindcast for Tri-). Among other environmental factors, these process rates
are dependent on the physical-chemical properties (molecular weight. Koc, Henry's Law
Constant) of the chemical under investigation. Therefore, average properties were attributed to
the congener mixture under investigation when modeling either total PCBs or Tri-.
For the calibration of HUDTOX. four different PCB state variables were used in conjunction
with the data sets described in Chapter 6. During the period from 1991-1997 total PCBs, BZ#4,
BZ?52 and Tri-f- were analyzed. Because of the different properties of the two congeners
measured in the data sets from this period, we were able to constrain individual process
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(sediment-water exchange, air-water exchange, partitioning, solids settling and resuspension) that
might otherwise not be constrained when simply modeling total PCBs or Tri-r.
For the long-term hindcast runs (1977-1997), the state variable was Tri-r. This is the only PCB
state variable for which data are available over the entire historical period. Unfortunately, as
described in Section 6.4.4, data for estimating external loading of Tri- was most scarce between
1977 and 1984, a period when loadings entering the river above Rogers Island were still very
high compared to post-1990s loadings.. This important data uncertainty required special
consideration for the 1977-1984 model period. The HUDTOX model was run for this period
simply as a means of generating initial sediment concentrations in the reaches of the river
downstream of Thompson Island pool (which were not measured in the 1984 NYSDEC sediment
sampling) for the actual historical calibration period of 1984-1997. This analysis approach
required an upward adjustment in data-based PCB loading at Fort Edward during the 1977-84
period by an amount that was necessary to match the measured cumulative Tri+ flux at
Schuvlerville over the period. Consequently, the long-term hindcast calibration for Tri~ was
actually conducted only for the period from 1984 to 1997.
In summary, the PCB calibration strategy has been an iterative process that produced internally
consistent simulations of the solids model metrics mentioned above as well as the following PCB
metrics:
1. Water column concentration space and time profiles;
2. PCB mass cumulative flux profiles at key locations along the mainstem of the river (TID.
Schuvlerville. Stillwater. Waterford) and under both high and low flow conditions; and.
3. Sediment PCB concentration profiles (surficial sediment changes over time and vertical
profiles at selected locations).
Again, the juxtaposition of PCB behavior during high and low flow periods in more recent, high-
frequency data sets (1991-1997) was also important for the iterative PCB calibration.
7.3. Calibration Parameters
This section presents the values of parameters for both solids and PCBs used in the base model
calibration. Datasets used to develop water column and sediment initial conditions were
described in Section 6.3. Flows and loadings of solids and PCBs for model calibration were
presented in Section 6.4. A presentation of the results of the calibration process relative to the
above metrics will follow in Section 7.4.
7.3.1. Solids Dynamics Parameters
The solids dynamics calibration deals with establishing those parameters that govern the
processes of settling, resuspension. and sediment burial in the system. As discussed in Chapter 5.
the solids dynamics sub-model within HUDTOX is a time-dependent mass balance of a single
suspended solids type in the water column (represented by average particle properties of the
river's suspended solids). But in the bottom sediments, the model does distinguish between
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cohesive and non-cohesive sediment areas; of course, this distinction, along with hydraulic
conditions, impacts the spatial distribution of sediment resuspension and burial rates.
Presented in Table 7-1 are the HUDTOX solids model calibration parameters and the values
obtained for the base calibration. Gross settling from the water column into the sediments is
modeled as a flow-dependent process, as described in Section 5.2.3. The shape of the
functionality is shown in Figure 7-1; gross settling velocities and flow thresholds presented in
this figure are those determined for the Thompson Island Pool (upstream of RM 182.3). This
functionality attempts to capture well-known river behavior, where increasingly larger, faster
settling particles are entrained in the flow as it increases above a resuspension threshold. There is
an upper bound on settling velocity that is essentially limited by the size distribution of particles
being washed into the system from its drainage basin. Since the flow-velocity relationships
change as one moves downstream, these parameters have been established on a reach-specific
basis according to cumulative drainage area as indicated in Table 7-2.
There are two types of bottom sediment resuspension in HUDTOX: flow-driven resuspension
(large in magnitude, but short in duration) and low-level continuous background resuspension
that occurs as a result of a variety of processes that generate turbulence at the sediment-water
interface in the absence of high flows. The mechanisms giving rise to this process and its
potential rate have already been discussed in Section 5.2.3. The background resuspension is
included as a constant value that is different for cohesive and non-cohesive sediment areas (Table
7-1). The background resuspension values (0.2 mm/yr for cohesive sediments and 0.4 mm/yr for
non-cohesive sediments) are small relative to the maximum potential value of 3.0 mm/yr
suggested in Velleux et al. (1996). While the rates used here have very little impact on the solids
calibration, they provide an important source of PCB mass flux from the sediments that is
necessary to calibrate observed water column concentrations under low flow conditions.
Flow-driven resuspension is formulated differently for cohesive and non-cohesive sediment, as
described in Section 5.2.3. Table 7-3 contains the calibration parameters for these two event-
driven resuspension processes. The mechanistic description for flow-driven resuspension from
cohesive sediments is the same as that used in the Depth of Scour Model (Chapter 4). .An
empirical equation was formulated for non-cohesive sediment to describe resuspension as a
function of applied shear stress and its time history. This equation simulates the process of
sediment armoring during high flow and "recovery" from armoring (Chapter 5).
In order to visualize the different responses of cohesive and non-cohesive sediments to a high-
flow period, the parameterization in Table 7-3 has been used to plot the model-computed gross
settling velocity and resuspension velocities for both cohesive and non-cohesive sediments in
model Segment 21 (TIP - see Figure 5-6) during the spring 1994 high-flow period (Figure 7-2).
Cohesive sediment resuspension responds to an increase in bottom shear stress above a critical
value by eroding a fixed amount that is a function of the incremental shear stress above the
previous value. No resuspension from cohesive sediments takes place on the falling limb of a
hydrograph within a specified period that was determined through calibration. For non-cohesive
sediments increases in the shear stress above the critical shear stress also produce resuspension;
however, the critical shear stress for non-cohesive sediments is time-variable and depends on the
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previous history of erosion and deposition in the bed. As smaller particles get eroded and only
larger particles are left behind, the effective critical shear stress for resuspension increases (i.e.,
armoring takes place). This armored bed takes some time to recover (i.e., for effective critical
shear stress to decline as a result of smaller particle deposition); and overlapping events may-
produce a smaller resuspension velocity for the second event even though its flow is higher than
the first. This phenomenon is displayed in Figure 7-2 during the April 14-18 period.
7.3.2. PCB Model Parameters
Parameters for the PCB mass balance model - those required in addition to the solids dynamics
parameters described above - are presented in Tables 7-4 and 7-5. Two important forcing
functions (foc and DOC concentrations) that have a bearing on PCB transport and fate must be
specified a priori in HUDTOX. The values specified for these parameters in water and
sediments are presented in Table 7-4; these values have been specified on the basis of field
observations as listed in the table.
Several of the PCB model parameters are temperature dependent: therefore, two important
forcing functions in the model are air and water temperature. Air temperature was determined
over the 1977-1997 model period from monthly average data at Glens Falls. NY as depicted in
Figure 7-3. Shown in Figure 7-4 are the monthly average water temperatures in the four reaches
of the model domain; these temperatures were determined from the TAMS/Gradient Phase 2
Database TAMS et al. (1998a).
The first four items in Table 7-5 (molecular weight, Henry's Law constant, and organic carbon
normalized partition coefficient) can be thought of as fundamental chemical properties, although
the organic carbon normalized partition coefficients are somewhat dependent on the nature of the
organic carbon in the system. Initial estimates of these attributes were based on the
recommendations from the congener distribution analysis conducted by TAMS/Gradient (1996);
however, some adjustment, especially for partitioning, was necessary to account for site-specific
conditions.
The molecular weight for the two congeners was computed on the basis of knowing their
elemental composition. The molecular weights for Tri- and total PCB were based on
assumptions regarding their congener distributions relative to Aroclors 1242 and 1248
(TAMS/Gradient. 1996: Mackay et al. 1992). Since Tri- does not contain mono- or di-
chlorobiphenyls. its molecular weight (set at the mid-point between 1242 and 1248) is larger than
total PCBs.
Henry's Law Constants were also based on measured values reported in the literature. Values for
BZ??4 and BZ#52 were taken from congener-specific measurements made by Brunner et al.
(1990). The Tri+ value was again determined by selecting a value mid-way between those
reported by Mackay et al. (1992) for Aroclor 1242 and 1248. The Henry's Law Constant for
total PCBs was the same value used for the PMCR.
Some calibration flexibility is available for setting the organic carbon normalized partition
coefficients. The approach was to begin with the values arrived at by the theoretical analysis
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performed in the DEIR (TAMS et al., 1997). It was then recognized that some adjustment of
these values could be made during the calibration process, especially for total PCBs and Tri-K A
range of ±0.5 log units for the congener mixtures was permitted in the calibration, so long as the
theoretical relationships among congeners and congener mixtures was not violated. In other
words, the ranks for in decreasing order should be BZ#52 > Tri+ > total-PCB > BZ#4. In
the absence of empirical measurements for Kdoc the DOC binding constants were set to 10
percent of the respective Kpoc; this practice is within the range of values (1 to 10 percent)
obtained for PCBs in natural systems by other researchers (Eadie et al. 1992; Capel and
Eisenreich, 1990). All partition coefficients were assumed to be temperature dependent with the
same temperature slope factor (tsf) determined by TAMS et al. (1997).
Air-water mass transfer rates were determined using a Whitman two-film theory, with the liquid
film mass transfer rate being dependent on oxygen mass transfer as determined according the
O'Connor-Dobbins formulation (Section 5.2.3) and adjusted for the PCB molecular weight and
for variation in water temperature (Chapra, 1997; Thomann and Mueller, 1987). The gas film
mass transfer rate was set to a constant value of 100 m/day, as recommended by Ambrose et al.
(1993) for fluvial systems.
Given the air-water mass transfer rates, air-water flux depends on the gradient between the
dissolved water phases and the atmospheric gas phase (Section 5.2.3); therefore, computation of
this flux requires specification of the atmospheric gas phase boundary condition. For this
boundary condition an annual average value was estimated for Tri- from 1977-1997 and for total
PCBs and the two congeners from 1991-1997. The procedure for setting this boundary condition
involved establishing a recent reference concentration based on measurement of total PCBs in
the atmosphere and back projecting from that reference value to obtain estimates of historical
levels. The nearest and most recent reference value was the 1992 annual average atmospheric
gas phase total PCB value of 170 = 86 pg/nr determined by Hoff et al. (1996) at the Integrated
Atmospheric Deposition Network (IADN) station at Point Petre, Ontario. Historical
concentrations were determined by scaling this value to a curve developed using PCB profiles
collected in dated (1940-1981), ombrotrophic peat bogs (Rapaport and Eisenreich. 1988) and
observed water column PCB load decay rates for rivers draining Lake Michigan watersheds from
1981-present (Marti and Armstrong. 1990). This scaling process produced a curve which reflects
the synthesized time series of atmospheric total PCB concentration from 1977-1997 (Figure 7-5).
Also included in Figure 7-5 as a check on this approach, are seasonal data reported by NYSDEC
(undated) and data from Buckley and Tofflemire (1983), both of which represent air sampled in
the vicinity of the upper Hudson River. Additionally, the line representing historical atmospheric
PCB concentrations estimated by Mackay (1989) in conducting a modeling analysis for Lake
Ontario is included.
Ideally, the estimate of historical atmospheric concentrations for congeners or the Tri-rnixture
would be made by applying measured ratios of these constituents to the hindcast total PCBs.
This was possible for estimating BZ#4 and BZ#52 levels by using ratios reported by Hornbuckle
(personal communication. 11/18/98) for samples collected over Lake Michigan. For Tri-K a ratio
was determined by assuming the atmospheric gas phase concentrations for both Tri- and total
PCBs in 1992 were in equilibrium with the dissolved phase in the water column and computing a
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gas phase Tri-r/total PCB ratio for 1992 on that basis. Then Tri- was hindcast using the same
scaling curve as was used for total PCBs in Figure 7-5. The resulting HUDTOX boundary-
condition values used for these PCB state variables are presented in tabular form on Figure 7-5.
Sediment dechlorination rates were not included in this modeling exercise, primarily for two
reasons. First, because of the uncertainty in determining the external PCB loading from 1977-
1984, any loss of sediment PCBs due to dechlorination during that period would be masked by
the arbitrary load increment required to attain 1984 sediment inventories in TIP. Second, TAMS
et al. (1998b) estimated that the sediment reservoir in TIP had lost approximately 10 percent of
its mass between 1984-1994 due to dechlorination. Sensitivity analysis in combination with an
estimate of the uncertainty in PCB loading during this period suggested that data were
insufficient to discern between no dechlorination and a sediment decay rate of approximately 1
percent per year. In the interest of parsimony, it was therefore decided to neglect the effect of
sediment dechlorination on Tri- and total PCBs for this analysis. The need to include this
process in model forecast simulations will be further investigated.
Finally, HUDTOX contains two processes that serve to exchange PCBs between the sediments
and the overlying water and between upper mixed sediment layers without causing any net solids
transport. The two processes are: diffusive mass transfer of pore water PCBs and sediment-to-
water mass transfer from particulate phase PCBs. Based on the assumption that bioturbation,
mechanical scour and other turbulence-generating phenomena in the surface sediments are the
driving forces for both of these processes (Section 5.2.3). the following constraints were
established in calibrating these rates:
1. The maximum rates should occur for the PCB exchange between the upper sediment
layer and the overlying water; PCB exchange between layer 1 (0-2 cm) and layer 2 (2-4
cm) should be lower than across the sediment-water interface and it should decrease to
strictly molecular diffusion of pore water PCBs below the bioturbation zone;
2. The rates should be seasonally variable with highest rates during summer, biologically
active periods:
3. Based on the reanalvsis of the TIP sediment source congener signature (Tetra-Tec. Inc.
and TAMS Consultants. Inc., 1998), PCBs in both pore water and the particulate phase
should contribute to sediment-water mass transfer; and.
4. The final calibration values should be within the range of values employed in other
modeling applications to similar systems (see Section 5.2.3V
The first PCB sediment-water exchange process is pore water diffusive mass transfer between the
upper mixed sediment layers and between the top layer and overlying water. This transport
process acts on the concentration gradient between dissolved and DOC-bound PCBs in the
sediment pore water and in the overlying water. Achman et al. (1996) have measured this
phenomenon in the Hudson River Estuary and have determined that in this system it is
potentially as important as resuspension and subsequent desorption of particle bound PCBs.
Because of bioturbation. this process can be 2-3 orders of magnitude greater than molecular
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diffusion near the sediment-water interface (Portielje and Lijklema. 1999; Thibodeaux, 1996).
Deeper than four centimeters (top two layers), the pore water diffusion was set to a value
approximately equal to molecular diffusion for compounds of their relative size of dissolved and
DOC-bound PCBs. The final HUDTOX calibration values for diffusive mass transfer rates and
their seasonal variability, based on an Arrhenius temperature dependence, are presented
graphically in Figure 7-6a. This dependence was driven by the same temperature time series
developed for the corresponding overlying water column segments (see Figure 7-4) because
measurements were not available to specify sediment temperatures on a seasonal basis in the
Upper Hudson River.
At the same time as upper sediment mixing processes are enhancing pore water diffusive mass
transfer, they are also causing an increased exposure of sediment particulate PCBs to overlying
water. This process, which has been formulated in HUDTOX as a sediment-water mass transfer
of particulate phase PCBs, is a simplified representation of the very complex and fine-scale
dynamics in which sub-surface sediments are transported by mixing processes to the sediment-
water interface, desorb PCBs, and then are re-mixed back into deeper sediments without
contributing measurable suspended solids to the water column (see Section 5.2.3). An analysis
of congener distribution patterns in TIP (Tetra-Tech, Inc. and TAMS. 1998) indicated that PCB
sediment-water mass transfer from only pore water sources could not account for observed
congener patterns in the water column during low flow conditions and that PCB mass transfer
must also be occurring from particulate phase sources. This finding was confirmed during the
HUDTOX calibration. Accordingly, values for sediment-water mass transfer rates for PCBs
from the particulate phase were determined by calibration to PCB observations after all other
sediment-water exchange fluxes were specified. The sediment-water mass transfer rates for
particulate PCBs have also been parameterized as seasonaliy-variable and reach-specific as
depicted in Figure 7-6b.
7.4. Calibration Results
7.4.1. Spring 1994 Solids Results
In keeping with the model calibration strategy discussed above, the first calibration analysis was
conducted on short-term solids dynamics using the high frequency data set obtained during the
spring of 1994. This data set permitted the testing and refinement of short-term resuspension rate
coefficients in response to flows that generated shear stresses in excess of critical shear stresses
for both cohesive and non-cohesive sediments. Shown in Figure 7-7 are the calibration results
for total suspended solids (TSS) concentration versus time for each sampling station during the
study period. The series of plots begins at the upstream boundary at Fort Edward and moves
downstream in successive plots. Tne plots demonstrate that HUDTOX did quite a good job of
capturing the river solids dynamics during the course of three major flow events.
Cumulative TSS flux versus time plots (both model generated and data-base estimated) for three
important river cross-sections are shown Figure 7-8. The accurate simulation of mainstem flux
profiles was another important calibration metric. Again, the model produced quite a good
simulation of cumulative river flux profiles during both high and lower flow periods.
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It is important to notice the considerably higher suspended solids concentrations (Figure 7-7) and
TSS fluxes (Figure 7-8) in the river below Schuylerville; this is largely the result of high
tributary' solids loadings from Batten Kill, Hoosic River, and the Mohawk River in these lower
reaches as discussed in Section 6.4.2. These large tributary solids loadings below the
Northumberland Dam have important implications for the response of sediments to PCB
loadings from upstream.
7.4.2. Results for 1991-1997 Calibration Period
The period from 1991-97 represented the best multiple-year data set for calibrating and testing
the behavior of HUDTOX during both high and low flow conditions; therefore, this period was
used as the next data set for calibration of TSS and all four PCB state variables. The results of
the model application for TSS, using the parameterization described in Section 7.3.1, is presented
in Figures 7-9 and 7-10 for all seven years and for 1993, respectively. Note that a logarithmic
scale was used for these plots, because of the wide fluctuations in TSS concentrations that occur
in the river. Even so, these figures demonstrate that the solids dynamics model is capable of
capturing both high-flow event peaks of TSS and low-flow depositional periods. The expansion
of 1993 in Figure 7-10 was selected for presentation because this year had the highest flows
during the spring runoff period. Measured suspended solids concentrations reached as high as
200 mg/1 at Stillwater and 300 mg/1 at Waterford, yet HUDTOX was still able to simulate these
events.
The success of the model at simulating the cumulative TSS flux at TID. Stillwater, and
Waterford (Figure 7-11) suggests that a good closure of the solids mass balance has been attained
and. therefore, an accurate estimate of net solids burial rates in these reaches has also been
achieved. Again, much higher solids fluxes at Stillwater and higher still at Waterford confirm
the importance of tributary TSS loads below TID. Note also in this figure the large, abrupt
increases in cumulative solids flux during the spring 1993 runoff period.
Finally, a comparison of the seven year total solids load through the mainstem transects during
both high (Q > 2*average flow at a given station) and low (Q < 2*average flow) flow conditions
is presented in Figure 7-12. A cut-off point between high and low flow conditions of twice the
average flow was chosen because it was approximately at this point that the "break" in the TSS
concentration versus flow rating curve occurred. This mass load metric demonstrates the
capability of the model to accurately capture solids fluxes at both high and low flows. It also
demonstrates that about 55 percent of the solids flux over the Thompson Island Dam occurred
during low-flow periods. A similar analysis for the entire 21 period from 1977-1997 gave the
consistent result that approximately 50 percent of the solids flux at TID occurred during low-
flow periods. This result demonstrates that the Hudson River is not a "flashy" system, and,
therefore, it does not depend exclusively on high flow events mobilize solids.
The PCB concentration versus time calibration results at mainstem Hudson River stations are
presented in Figures 7-13 to 7-15. The model has done a good job of tracking PCB loadings at
Fort Edward through the system. It has also demonstrated that it captured the seasonal
variability in the system as well as the long term gradual decline in water column PCB levels
throughout the model domain.
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The fact that the model has done a reasonably good job of simulating the BZ#4 and BZ#52
congener profiles with the same flow, solids, and mass transfer rate parameterization (only
chemical-specific parameters have been adjusted) is an excellent check on the scientific validity'
and internal consistency of our conceptualization and parameterization of how the Upper Hudson
River system behaves relative to fate and transport of hydrophobic chemical like PCBs. It is
apparent in careful scrutiny of Figure 7-14 that the model tends to overestimate BZ#4
concentrations downstream of TID. This is manifested and even more apparent in Figure 7-16.
which is a calibration results plot of the BZ#4/BZ#52 ratios at mainstem stations; the modeled
ratios at Stillwater and Waterford tend to be about a factor of two higher than the observed data.
Given this result, it is quite possible that the model is underestimating the loss of BZ#4 to the
atmosphere by volatilization in these lower reaches below TID. Currently, HUDTOX does not
estimate volatilization losses over the TID or any of the other low-head dams in the lower
reaches of the river. Future work is planned to investigate the significance of water-air
exchanges at dams on PCB dynamics in the Upper Hudson River.
One other observation from Figures 7-14 - 7-16 is that, while the TIP sediments contribute to an
increase of both congeners across the pool, the increase is much more pronounced for BZ#4.
With the exception of one abnormally large peak in the spring of 1993. the increase in the
BZ#4/BZ#52 ratio across the TIP is striking. The ability of HUDTOX to capture this chemical-
specific behavior is further evidence of the validity of the model formulation.
Another dataset which can serve as a metric of the ability of HUDTOX to faithfully capture the
behavior of the TIP was the time of travel studies conducted by Genera! Electric in June and
September. 1997. Essentially, plug-flow monitoring was conducted by allowing a boat to be
carried by the current along the length of the TIP while sampling periodically what presumably
would be the same control volume of water. The four such surveys (two in June and two in
September. 1997) demonstrated the increase in total PCBs that occurred as water flowed over
contaminated sediments in the TIP. As shown in Figure 7-17(a-d). HUDTOX. with its various
calibrated sediment-water exchange processes, did a reasonable job of capturing the rate of flux
of PCBs from sediments to overlying water that obviously occurs in the TIP.
Another way of looking at the importance of the sediment-water exchange processes in the TIP is
to compute the load increment across the TIP. Shown in Figure 7-18 is the monthly average total
PCB load gain across the pool, both as computed by HUDTOX and estimated from the time
series of data at Fort Edward and at the TID from 1991-1997. Aside from the somewhat
complicated response of TIP to the Allen Mill gate structure failure in September 1991. the
comparison between model and data is very good. HUDTOX was able to capture the increases
across the pool that typically occurred in spring and summer months: however, it does not
describe the apparent loss of PCB flux across the pool estimated for November and December of
several years during the period. The average daily load increment across the TIP (0.5 kg/d)
computed by HUDTOX is very close to that estimated from available data (0.43 kg/d).
Finally, the cumulative mass load of PCBs past mainstem stations in the river was another
calibration metric during the 1991-1997 period. Shown in Figure 7-19 are the model-computed
and data estimated cumulative total PCB mass transport past TID. Stillwater and Waterford.
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Note that PCB data for the Stillwater and Waterford stations were only available between April.
1991 and May, 1992. Still, where data were available, model and data cumulative loads compare
quite well.
The trends in Figure 7-19 show a continuous increase in the cumulative load at all three stations,
with the rate of load increase slowing over time. There are some sharp increases due to major
spring flow events during this period, but it is obvious that the increase in cumulative load at
other, low-flow times during this period is significant. This observation is more graphically
illustrated in Figure 7-20, which indicates that approximately 80 percent of the total PCB
transport during this period occurs during low-flow periods when the flow at a given station is
less than twice its average flow. This is consistent with the earlier solids load results, and it
confirms that the Upper Hudson River is not a very flow-responsive system.
7.4.3. Results for the 1977-1997 Calibration Period
The hindcast calibration for 1977-1997 was conducted for TSS and Tri-K Tri- is the only PCB
form for which a continuous data set was available for the entire 21-year period. Initial efforts to
calibrate the HUDTOX model over the period from 1977 to 1997 were unable to describe the
large increase in PCB load between Fort Edward and Schuylerville between 1977 and
approximately 1984. This suggests that either the contribution of Tri- from sediments between
Fort Edward and Schuylerville was very large during this period, or the external TrH- loads are
underestimated either at Fort Edward or to the reach between these two locations.
Underestimation of TrH loads at Fort Edward is a possibility because water column sampling
frequency was low and PCB loads were high during this period. A large contribution of Tri+
from the sediments is also a possibility due to unstable conditions resulting from removal of the
Fort Edward dam in 1973.
The need for an additional load during this period is evident from examining the cumulative Tri^-
load at Schuylerville with and without an additional load at Fort Edward. Illustrated in Figure 7-
21 are the data-based estimate of the TID cumulative Tri-1- load, along with the HUDTOX
predicted cumulative loads for two load estimates at Fort Edward: the data-based load estimate at
Fort Edward and a run with an additional load added at Fort Edward that was constrained to meet
the cumulative load observed at Schuylerville. Consequently, the actual hindcast calibration for
Tri- focused only on the period 1984 to 1997. not the entire period from 1977 to 1997. due to
uncertainty in PCB loads during this earlier period.
Just as a frame of reference, it is worth confirming that the model solids dynamics parameters
determined by calibration to spring, 1994 and the 1991-1997 period are also valid for the entire
21-year hindcast period. Figure 7-22 shows quite good agreement between the model-generated
and data-derived cumulative TSS loads at mainstem stations over the entire 21-year period. The
results suggest that not enough solids are being deposited in the reach from TID to Stillwater,
particularly from the mid-80s forward. It is difficult, however, to distinguish whether this minor
difference is the result of parameterization of net solids deposition to sediments or due to a slight
overestimation of solids loading (see Section 6.4.2 for discussion of solids load calculations)
during this period.
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Given the 1977-1984 load revision and the calibration of solids dynamics, it is now possible to
apply the three PCB calibration metrics to the entire TRI4- hindcast. The concentration versus
time profiles at several mainstem stations is shown in Figure 7-23. The richest data for Tri+
were available at Schuvlerville, Stillwater, and Waterford; however, a very good Phase 2 data set
was available at the TID. This model metric shows a very good comparison between model and
data at all mainstem stations. In addition to capturing spatial and seasonal trends in Tri+ for the
system, HUDTOX has also been able to accurately simulate the long-term, approximately
exponential, decline in water column concentration over the hindcast period. This bodes well for
the model's capability to make an accurate estimate of the system response to continued No
Action.
Although necessary, water column concentration profiles alone are not sufficient to assure an
accurate model forecast. Mass fluxes through the system and accurate sediment trend
simulations - the other two metrics described earlier - are also very important. The 21-year
cumulative mass load comparisons for Tri+ at mainstem stations with sufficient data are shown
in Figure 7-24. This model result is consistent with earlier cumulative mass load calculations for
other PCB parameters in that it indicates a gradual decline in the mass transport downriver over
time, an obvious result of concentration declines in the water column. Note the much greater
slope of these lines (greater mass load carried by the river) during the period from the beginning
of this simulation through the mid-1980s. The loading from Fort Edward early in this hindcast
period is likely the "declining tail" of the peak loading period to the river, which no doubt
occurred prior to 1977.
Comparison with the data-estimated cumulative mainstem load in Figure 7-24 indicates a
divergence between model and data that begins in the mid-1980s. The HUDTOX model begins
to overestimate cumulative load at Schuvlerville and it continues to do so downstream,
suggesting that the divergence is occurring upstream of Schuvlerville in TIP or just downstream
of the TID. It is possible that this divergence is a consequence of not including dechlorination of
Tri+ in the model, thus leaving sediment Tri+ concentrations higher than they would be in the
presence of even a small rate of decay. This decay process was probably not as important earlier
in the hindcast because the load at TID was more strongly controlled by upstream PCB loading
at Fort Edward. Later in the hindcast (after the mid-1980s) the mass flux over the TID becomes
more dependent on sediment-water mass transfers within TIP.
An important metric for a system whose response time is largely controlled by sediment-water
interactions is the ability of the model to accurately simulate sediment concentrations. The
results of this metric for the Tri+ hindcast are shown in Figure 7-25. Figure 7-25 shows the long-
term simulation of time trends in Tri- solids-normalized concentrations in surficial (upper mixed
layer at 0-4 cm depth) sediments along the entire length of the model domain. In consultation
with Menzie-Cura and Associates, Inc., it was judged that a sediment depth of 4 cm was the most
appropriate spatial scale for representation of sediment PCB exposures to benthic organisms.
Simulations and data estimates are separated into cohesive and non-cohesive areas of the various
reaches. Also, much more longitudinal resolution is presented in TIP where data and model
output are separated into six sections. Four sediment surveys are available for comparison with
model forecasts. The results suggest an outstanding capacity of the HUDTOX model to capture
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both spatial and temporal trends in surficial sediment concentrations. Again, in a system for
which sediment-water interactions are very important, the capacity to reproduce trends in surface
sediment concentrations is crucial.
It should be noted that accurate simulation of surface sediment concentrations can be obtained by
either "pulling" PCBs up from deeper sediment layers at a higher rate, or by burying PCBs into
deeper sediment layers at a higher rate. Future work is planned to further investigate the
sensitivity of surficial sediment PCB concentrations to dechlorination and vertical mixing in the
sediment bed.
7.5. Component Analysis
The results of the HUDTOX calibration presented in the previous section have demonstrated that
it has accurately captured the historical behavior of the Upper Hudson River for both solids and
PCBs. It is now very instructive to utilize the calibrated model to determine how the interaction
of processes included in the model have led to simulation of that behavior. This can most
effectively be accomplished by developing model-generated mass balance diagrams that show
the relative mass flows by each model processes, many of which cannot be directly measured in
the system.
Presented in Figure 7-26 is a HUDTOX-computed TSS mass balance diagram for the period
from 1977 to 1997; water column mass balances are shown for four reaches extending from the
Thompson Island Pool down to Waterford. Several observations can be made about the river
behavior with respect to solids dynamics from inspection of this diagram. First, one can see that
tributaries along the length of the river contribute the vast bulk of the solids load carried by this
system; the tributary loadings from Batten Kill and Fish Creek (Reach 2) and the Hoosic River
(Reach 4) are the most significant tributaries. A second, very significant observation is that the
river is net depositional throughout; gross TSS settling exceeds resuspension in all reaches.
Given its length, the TIP is relatively efficient at trapping solids in its sediments. Based on this
diagram 10.5 percent of incoming solids (upstream+tributary) are trapped in the bottom
sediments of the pool. The Schuylerville to Stillwater reach (Reach 3) also traps approximately
10.5 percent of incoming solids but it is more than twice as long as the TIP reach (Reach 1).
A closer analysis of the solids dynamics in the TIP yields information about the relative net
burial rates of solids in cohesive versus non-cohesive sediments in the system. Shown in Figure
7-27 are the model-generated average annual solids burial rates along the TIP for the 21-year
simulation. It is quite apparent that more net burial occurs in the cohesive sediments than in non-
cohesive areas. Annual burial rates in cohesive sediments range from 0.24-0.62 cm/yr along the
pool, while burial in non-cohesive areas never exceeds 0.1 cm/yr (very little net deposition). Of
course, this is an important consistency check on the model since cohesive sediments would not
be cohesive if they were not located in higher deposition areas. Based on these model
computations, the cumulative pool-wide average bed elevation change can be determined.
Shown in Figure 7-28 is the result of this calculation, which indicates that the average bed
elevation change in TIP over the 21-year hindcast period is approximately 4 cm. a very
reasonable estimate for a dammed river.
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In a similar fashion to what was done with solids, a mass balance diagram for total PCBs has
been produced for the period from 1991-1997 (Figure 7-29). This more recent period was
selected so as not to be misleading about PCB sediment-water fluxes, which have changed
significantly over the 21-year historical period of record. Shown in Figure 7-29 on a reach-
specific basis are the seven-year relative mass flows for all five sediment-wrater exchange
processes included in HUDTOX. In the first three reaches the sediments are a net source of
PCBs to the water column over this period, with the TIP sediments representing by far the largest
net source. This occurs in spite of the fact that all reaches are net sinks for sorbing solids (see
Figure 7-26); this type of behavior is not unusual for PCBs when sediment levels are high due to
very large historical loadings. In Reach 4 (Stillwater to Waterford), this mass budget indicates
the sediments acting as a very small (151 kg) sink of total PCBs. Nonetheless, over the entire
upper river from Fort Edward to Waterford there has been a net increase of 1063 kg (a 45 percent
increase of the Fort Edward load) in total PCB mass being carried in the river flow. Virtually all
of this increase came from releases from the sediment pool.
The cumulative time trend of the sediment-water flux discussed in the above paragraph is shown
in Figure 7-30. It shows that on a yearly basis the sediments in the first two reaches were always
net sources. Sediments in Reach 3 began the seven year period as a very slight net sink, but by
1993 became a net source. Reach 4 sediments are a very small net sink over the entire seven
year period. During certain short-term events, the TIP sediments become a net sink for total
PCBs (the Allen Mill gate structure failure in fall, 1991 being the most evident); however, on a
yearly time scale TIP sediments were always a net source.
Referring back to Figure 7-29, of the four processes by which PCBs can be transported from
sediments to water, the largest sediment to water mass transfer in the TIP is the result of
particulate-PCB mass transfer (refer to Section 5.2.3 for discussion of this process).
Resuspension and diffusive mass transfer of DOC-bound PCBs are also important in the pool.
Downstream of the TID resuspension tends to dominate the sediment to water mass transfer
processes.
Focusing on sediment contributions to water column PCBs in the TIP. it is instructive to compare
the cumulative HUDTOX-computed net sediment contribution to load gain across the pool
(Figure 7-31). One can see that BZ#4 is responsible for almost half of the net sediment release
of total PCB between 1991-1997. BZ#52 makes a much smaller contribution, because its total
concentration is smaller (see Figure 7-16) in combination with the fact that it is more
hydrophobic, hence less mobile, than BZ#4. The negative net contribution of TSS from
sediment in the TIP is included in this figure as a reminder that sediments can still be a net
source of PCBs at the same time as there is a net deposition of solids.
Comparison of HUDTOX and Low Resolution Coring Report (LRCR) Results
As part of the Reassessment. TAMS et al. (1998b) conducted an investigation of the change in
sediment PCB inventories in TIP between 1984 and 1994. This investigation involved a
comparison of results from the extensive 1984 NYSDEC survey with results from a series of
matched sediment cores collected by USEPA in 1994. Inventories from a set of 60 sampling
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locations in TIP were compared on a point-to-point basis to provide a quantitative indication of
the direction and magnitude of change in the sediment PCB inventory. This analysis was
subsequently revised to include comparisons based on sediment areas as opposed to point-to-
point comparisons (TAMS and Tetra-Tech, 1999). Results from the revised analysis indicated
that the best unbiased mean estimate of mass loss of Tri- from the sediments within historic
hotspot areas was 45 percent, with an uncertainty range from 4 to 59 percent. It was estimated
that dechlorination was responsible for approximately 5 percent of the mean mass loss. The
remaining loss was interpreted as a loss of the Tri+ hotspot inventory either to the overlying
water column or through redistribution of contaminated sediments within TIP. Another
conclusion from this analysis was that there was no evidence of extensive widespread burial of
historically contaminated sediments in TIP.
A direct comparison of results from the HUDTOX model with results from the LRCR is not
possible due to the different assumptions in these two approaches. The LRCR analysis included
only cohesive sediment areas that were historically known to be more contaminated than average
TIP sediments, whereas the HUDTOX model included both cohesive and non-cohesive sediment
areas over the full range of sediment inventories found in TIP. The LRCR analysis did not
account for Tri-f- mass loss that would be transported downstream of TIP or redeposited in TIP in
non-cohesive sediment areas or in less contaminated cohesive sediment areas. The HUDTOX
model accounts for the full mass balance cycle including transport and fate downstream of TIP,
and redeposition in TIP.
An approximate comparison of results suggests consistency among the HUDTOX. DEIR and
LRCR analyses. A components analysis of the Tri+ hindcasting calibration indicated that 2,000
kg of Tri+ was lost from the TIP sediment inventory between 1984 and 1994. Most of this loss
was due to Tri- mass flux across TID and a small portion was due to volatilization. If the Tri+
inventory in 1984 is taken to be approximately 14.500 kg (TAMS et al., 1997). then this mass
loss out of the pool correspond to approximately 14 percent. This value is within the range of the
4 to 59 percent estimate of mass loss from historical hotspots in the LRCR analysis. As an
independent check on both of these approaches, the annual rate of net export of Tri- from TIP
was estimated to range between 0.36 and 0.82 kg/dav over the period April 1991 to October 1995
(TAMS et al.. 1997). Assuming a value of 0.60 kg/day, the net export of Tri-1- from TIP
sediments between 1984 and 1994 would be 2,190 kg which corresponds to a mass loss of 15
percent of the 1984 inventory. Because of its focus on hotspots, the LRCR is not able to
distinguish between loss from TIP and redistribution to less contaminated areas within the pool.
When coupled with the LRCR findings. HUDTOX and the DEIR suggest that there has also been
a significant amount of redistribution of Tri+ mass within TIP from historical hotspots.
With respect to lack of extensive widespread burial of historically contaminated sediments in
TIP, the HUDTOX model results are again consistent with results from the LRCR analysis.
Results in Figure 7-28 indicate that the increase in sediment bed elevation in TIP between 1984
and 1994 computed by the HUDTOX model is approximately 1.6 cm. This is a poolwide result
and it should be understood that there are differences between cohesive and non-cohesive
sediment areas within TIP (Figure 7-27). Furthermore, it should be understood that in the actual
river there is variability within the individual model spatial segments and that certain areas can
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be erosional and not depositional. Nonetheless, on a poolwide basis a net sedimentation rate of
1.6 cm over 10 years is small compared to the surface layer depth of 23 cm (9 in) in the LRCR
sediment cores. Considering the differences in spatial and temporal scales of the two
approaches, it can be concluded that the HUDTOX model and the LRCR are in qualitative
agreement with respect to the question of widespread burial of historically contaminated
sediments in TIP.
7.6. Conclusions
The above presentation of the calibration of HUDTOX has demonstrated that the model has been
successful at representing the hydraulics, solids, and PCB dynamics of the Upper Hudson River
over a long historic time period. This period of record was characterized by a significant
transition from an early phase of high upstream PCB loads through a long declining phase to
present-day conditions where upstream PCB loads are now very small (at or near current
detection limit). The fact that HUDTOX has been able to successfully simulate this decadal-
scale transition indicates that it is a reasonable, scientifically credible representation of the water,
solids and PCB dynamics in the Upper Hudson River. The HUDTOX calibration also
demonstrates that the model is internally consistent with the major trends and magnitudes of the
best available data during this period.
With regard to the Upper Hudson River recovery, the controlling processes today and in the
future are system hydraulics, external solids loads, sediment-water fluxes, water-air fluxes, and
PCB fate in the sediments. It has been determined that this system is currently in the tail of a
long PCB washout curve controlled largely by the rate at which PCBs are depurating from the
upper mixed sediment layer. The surficial sediment depuration rate is crucial to the estimation of
the river's recovery time under a continued No Action scenario. The surficial sediment
depuration takes place by a combination of sediment to water feedback flux (resuspension and
other mass transfer processes discussed above) and burial to deep sediments by net accumulation
of'"clean" solids from the watershed. Over the 21-year simulation period, external control of the
depuration rate has shifted from upstream PCB loading to loading of "clean" solids from the
watershed. Therefore, simulation of the river's recovery trajectory depends on an accurate
representation of the processes controlling sediment-water interactions of PCBs and dynamics of
solids in the system. The above HUDTOX calibration and diagnostic analysis has served to
demonstrate that these important processes are well-characterized and well-parameterized by the
calibrated HUDTOX model.
That is not to say that there are no unresolved scientific issues. There does not yet exist a
complete understanding of all the physical, chemical, and biological processes that control PCB
dynamics in the Upper Hudson River. PCB partitioning (particularly in sediments), non-flow-
dependent sediment-water PCB fluxes (both dissolved and particulate), and sediment
dechlorination and biodegradation are examples of processes for which a complete understanding
is lacking. Nevertheless, the success of this model calibration confirms that HUDTOX is a
credible and useful model for providing information on the principal Reassessment questions. In
its present form, HUDTOX is a valid tool for providing information on system responses to
continued No Action, impacts of a 100-year peak flow event, and for comparing responses
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among different remedial alternatives. Model forecasts for continued No Action and No Action
with a superimposed 100-year peak flow event are presented in Chapter 8.
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Chapter 8
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8. MASS BALANCE MODEL FORECAST SIMULATIONS
8.1. Introduction
In 1984 USEPA issued an interim decision of No Action concerning sediments contaminated
with PCBs in the Upper Hudson River. This Reassessment is being conducted to provide
technical information for reassessment of that No Action decision. As a first step in providing
that information, the calibrated HUDTOX mass balance model was used to conduct forecast
simulations designed to estimate long-term system responses to continued No Action and
impacts due to a 100-year peak flow. The forecast simulations extend 21 years into the future
from January 1, 1998. Results of these forecast simulations include PCB concentrations in the
water column and sediments. These PCB exposure concentrations will be used as inputs to the
ongoing ecological and human health investigations in the Reassessment.
8.2. No Action
Before conducting forecast simulations with any mass balance model, model inputs must be
specified. In essence, model inputs themselves must be predicted before the model can be used
to make predictions. It is not possible to make absolute predictions of river flows, solids loads.
PCB loads or other model inputs. Consequently, it is not possible to use a mass balance model to
make absolute predictions of the future. Results from forecast simulations can provide useful
information on trends and approximate magnitudes of system responses to continued No Action
under a specified set of assumptions. Mass balance models are most useful for comparing
responses among different remedial alternatives and between remedial alternatives and continued
No Action.
8.2.1. Approach
Forecast simulations were conducted for total PCBs because total PCB concentrations are
required for the ecological and human health investigations in the Reassessment. The HUDTOX
model was run for a 21-year forecast simulation period beginning on January 1. 1998 and
extending through 2018. The initial water column and sediment PCB concentrations for forecast
simulations were the same as the final PCB concentrations computed by the HUDTOX model in
the 1991-1997 calibration simulation. The reason for selecting a 21-year simulation period was
strictly operational. The most simple and direct approach for specifying the required model
inputs was to re-use model inputs for the 1977 to 1997 hindcasting application and project them
forward in time. Longer-term forecast simulations are planned as part of future work. With the
exception of upstream PCB concentration at Fort Edward, all flows, solids loads and other
external forcing functions in the forecast simulations were the same as those used for the 1977-
1997 hindcasting application. In addition, all internal model parameters in the forecast
simulations were the same as those determined in the calibration applications. The forecast
simulations used the same model input file as the 1977-1997 hindcasting application with the
exception of initial conditions for PCB water and sediment concentrations, and PCB loads at Fort
Edward.
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While water column PCB concentrations measured at Fort Edward show a declining trend
through 1997, it is impossible to predict whether these concentrations will continue to decline,
remain constant or increase in the future. A complicating factor is that stochastic pulse loads
have occurred during the historical period. To conduct the forecast simulations, two different
assumptions were made for upstream PCB concentrations at Fort Edward: first, water column
PCB concentrations were held constant at a value equal to the observed annual average PCB
concentration for 1997 (9.9 ng/1); and second, water column PCB concentrations were held
constant at zero. Note that 9.9 ng/1 is below the detection limit (11 ng/1) because many non-
detects occurred in 1997 and these were assigned a value of one-half the detection limit. This
approach to the forecast simulations constituted a single factor experiment in which upstream
PCB load at Fort Edward was the only variable. It assumes that there will be no future load
increases from any sources upstream of Fort Edward. In particular, it assumes no PCB releases
from the GE Hudson Falls site other than those that might have existed during 1997 and no large
releases such as occurred with the partial failure of the Allen Mill gate structure in 1991. In
future work, other scenarios for continued No Action will be designed and additional forecast
simulations will be conducted as part of the Feasibility Study.
It should be noted that after preparation of the Baseline Modeling Report, data became available
for solids and PCBs from a high flow event that occurred in the Upper Hudson River in January,
1998 (O'Brien & Gere, 1999). The instantaneous peak flow during this event reached 35,300 cfs
at Fort Edward, a flow that exceeded the maximum recorded USGS daily average flow for the
entire 1977 to 1997 hindcast simulation period. A flood event with a peak flow of similar
magnitude (35,200 cfs) was recorded during May of 1983. Both of these events represented a
recurrence interval of approximately once in 15 years.
Results for the January 1998 event indicated that water column PCB concentrations were
substantially higher than those observed during 1997. High PCB concentrations were observed
between Hudson Falls and Fort Edward, in TIP and down to Schuylerville. These results appear
to indicate that there are PCB sources above Rogers Island that can be activated by high river
flows. Future work is planned to investigate the significance of these sources and their potential
implications regarding assumptions about upstream boundary PCB loadings during model
forecast simulations.
8.2.2. Results
Results are expressed in terms of PCB concentrations and mass fluxes in the water column at
Thompson Island Dam and Waterford, and PCB concentrations in surficial sediments (0-4 cm) in
Thompson Island Pool and in downstream reaches between Northumberland Dam and
Waterford. In consultation with Menzie-Cura and Associates. Inc.. it was judged that a sediment
depth of 4 cm was the most appropriate spatial scale for representation of sediment PCB
exposures to benthic organisms. PCB concentrations are presented at daily average and summer
average (June through September) time scales.
Water column PCB concentrations appear to decline for both constant and zero upstream
boundary cases (Figure 8-1): however, it is difficult to discern relative trends on the log scale in
this figure. Results for summer average PCB concentrations (Figure 8-2) are more indicative of
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long-term response trends because much of the variability due to high flow events is removed
and results can be compared on a linear scale. Summer average PCB concentrations decline at
both locations and there is separation between results for the constant and zero upstream
boundary cases. The rate of decline appears to be greater at TID than at Waterford. Annual PCB
fluxes show trends similar to those for summer average PCB concentrations in the water column
(Figure 8-3).
Surficial sediment PCB concentrations decline for both constant and zero upstream boundary
cases (Figure 8-4). In absolute terms, declines in concentrations are greater in Thompson Island
Pool than in downstream reaches (Northumberland Dam to Waterford). In sharp contrast to
results for water column PCB concentrations (Figure 8-2). there appears to be no difference
between constant and zero upstream PCB boundary cases in any portion of the river during the
entire 21 -year forecast.
8.3. 100-Year Peak Flow-
There is concern that deeply buried contaminated sediments might become "reactivated" during a
major flood, possibly resulting in an increase in PCB contamination of the fish population. The
available historical data for the Upper Hudson River can not provide a direct answer to this
question because there were no very large floods during the period 1977 to 1997. As mentioned
above, the peak flow during this period was 35,200 cfs in May of 1983, a 15-year peak flow.
The 100-year peak flow in the Upper Hudson River at Fort Edward is estimated to be 47,330 cfs
(Butcher, 1993).
8.3.1. Approach
The HUDTOX model was run for a 21-year forecast simulation with a peak flow corresponding
to the peak flow in a 100-vear flood imposed in spring of the first year. This peak flow was
imposed on the forecast simulation for continued No Action with zero PCB concentration at Fort
Edward. External solids loads were not increased during simulation of the 100-year peak flow,
but remained the same as in the continued No Action simulation. This design was a single factor
experiment in which the only differences between continued No Action and the 100-year peak
flow would be due to changes in flow-dependent sediment resuspension. This design was also a
worst-case scenario because the peak flow was placed in the first year of the simulation when
sediment contamination was greatest and because there was no increase in external loads of
"clean" solids to sorb PCBs in the water column or enhance PCB burial rates.
Figure 8-5 illustrates the base flow at Fort Edward during the continued No Action simulation
and the scaling of the first spring peak to match the 100-year peak flow. Daily flows at Fort
Edward were scaled up by a factor of 1.972 from March 13 through March 29. The 1977 peak
spring flow in the time series was scaled up from 24.000 cfs to a value of 47.330 cfs on March
15. The duration, rise and fall of the 100-year flow in this modified hydrograph was generally
consistent with other observed peak flows in the 21-year historical period. The value of 24.000
cfs for the first spring peak in 1977 was close to the historical average spring peak flow of
21.339 cfs at Fort Edward. This modified hydrograph represents a 100-year peak flow but does
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not necessarily represent the duration, rise and recession characteristics of a 100-vear flood
event.
8.3.2. Results
Differences in water column PCB concentrations between the base No Action case and
imposition of a 100-year peak flow are relatively small and of short duration (Figure 8-6).
Forecasted PCB concentrations are actually lower for during the 100-vear peak flow due to
dilution, and then remain lower due to deposition by increased external loadings of less-
contaminated sediments due to the event. Results are shown only at TID because this location is
immediately downstream of the most contaminated sediments in the Upper Hudson River.
Imposition of a 100-year peak flow on the base No Action case causes an increase of
approximately 59 kg in cumulative total PCB flux across TID (Figure 8-7). Most of this flux
increase occurs over a very short period of time during the peak flow event.
8.4. Discussion
A verv significant result is that surficial sediment PCB concentrations are not controlled bv
upstream PCB loads, at least for the loading range between the constant and zero upstream
boundary cases in these forecast simulations. .Another significant result is that water column
PCB concentrations are influenced by upstream PCB loadings (Figure 8-2). and the relative
degree of influence increases with time over the 21-year forecast as PCB concentrations in the
surficial sediments decline (Figure 8-4).
These responses suggest that sediment PCB concentrations are controlled primarily by sediment-
water flux and exchange between surface and deep sediments. They also suggest that water
column PCB concentrations are controlled by a combination of sediment-water exchange and
upstream loading. For the constant upstream boundary assumptions in these forecasting
simulations, the rate of decline of PCB concentrations in the water column is controlled by the
rate of decline of PCB concentrations in the surficial sediments. For example, the apparent first-
order decay rate for summer average water column PCB concentrations at TID (Figure 8-2) is
0.073/yr. The corresponding decay rate for PCB concentrations in the surficial sediments of TIP
is 0.069/'yr. This suggests that although a constant upstream PCB load contributes to PCB
concentration in the water column, the response trajectory of the water column PCB
concentration is controlled by sediment-water fluxes.
Another conclusion that can be drawn from the above results is that the water column and
sediments of the Upper Hudson River have not reached steady-state with the present upstream
PCB loads. For example, results in Figure 8-2 indicate that even after 21 years with constant
upstream boundary concentrations, water column PCB concentrations still continue to decline
and have not yet dropped below the 1997 upstream boundary concentration.
Results of the forecast simulation for the 100-vear peak flow indicated that this event causes a
relatively minor and short-lived perturbation to the long-term response trajectory for continued
No Action. Although not shown, forecasted summer average PCB concentrations in the water
column with and without the 100-year peak flow are virtually indistinguishable one year after the
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event. The estimated additional PCB flux of approximately 59 kg over Thompson Island Dam
for this event is a small fraction of the total sediment PCB inventor.' in TIP. The size of this
inventory in 1984 (in terms of Tri+) was estimated at 14,500 kg (TAMS et al.. 1997).
Results from this baseline modeling effort are necessary but not sufficient to guide a decision on
continued No Action versus remedial scenarios. Information on PCB exposure concentrations
will provide input to the ecological and human health investigations in the Reassessment.
Remediation decisions will ultimately be based on the desired ecological and human health
endpoints, and the desired schedule for attaining these endpoints.
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