a34 - relationships between selected baker river ... · the baker river hydroelectric project...

Unpublished work, Copyright 2001, Puget Sound Energy, Inc.

RELATIONSHIPS BETWEEN SELECTED

BAKER RIVER HYDROELECTRIC PROJECT

VARIABLES AND DOWNSTREAM FISH PASSAGE

DRAFT REPORT

Prepared for

Puget Sound Energy

By

February 2002

Project Effects on Downstream Fish Migration Study

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EXECUTIVE SUMMARY

Historical data describing Project operations were analyzed in an effort to document past

relationships between the Baker River Hydroelectric Project and downstream fish migration. Fish

migration was represented by daily juvenile salmonid abundance data obtained from fish collection

barges in the forebays of both Upper Baker Lake and Lower Baker Lake (Lake Shannon). Fish

abundance data were analyzed as a function of both Project operation data and meteorology data.

Project operation data included natural inflow, generator and spillway outflows from both operations,

lower intake water temperature, and water surface elevation from both reservoirs. Meteorology data

included windspeed, barometric pressure, air temperature, precipitation, solar radiation, lunar hours,

moon illumination, and photoperiod. The data were entered into a Microsoft Access database. Data

modifications and new data derived from existing data sets were documented.

Both descriptive and statistical analyses were conducted. For all analyses, fish abundance was the

dependent variable and project operation and meteorology variables were the predictive, independent

variables. Descriptive analyses included data descriptions, descriptive graphics showing fish

migration and project operation patterns over time, and Pearson’s correlation analysis. Statistical

analyses included independent regression to corroborate the results of the Pearson’s correlations, and

linear regression to assess the influence of combinations of variables for all Project operation data, all

meteorology data, and for a combination of the most promising variables from both Project operation

data and meteorology data. All analyses resulted in the same conclusion: project operations at both

Upper Baker Lake and Lower Baker Lake (Lake Shannon) did not display significant physical or

biological relationships to downstream fish migration, as represented by juvenile salmonid

abundance.


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TABLE OF CONTENTS

EXECUTIVE SUMMARY .................................................................................................................... i

FOREWORD ......................................................................................................................................... 1

1.0 PROJECT EFFECTS ON DOWNSTREAM FISH MIGRATION STUDY.................................. 2

BAKER RIVER RELICENSING.......................................................................................................... 2

1.1 Project Background .................................................................................................................. 2

1.2 Baker River Migratory Fishes .................................................................................................. 5

1.3 Patterns of Downstream Migration........................................................................................... 71.3.1 Sockeye Salmon .............................................................................................................. 71.3.2 Coho Salmon ................................................................................................................. 101.3.3 Chinook Salmon ............................................................................................................ 11

2.0 METHODOLOGY ....................................................................................................................... 13

2.1 Introduction............................................................................................................................. 13

2.2 Objective................................................................................................................................. 13

2.3 General Approach................................................................................................................... 13

2.4 Discussion of the Historic Data Sets and Modeling Variables............................................... 142.4.1 Project Data ................................................................................................................... 152.4.2 Meteorology Data.......................................................................................................... 162.4.3 Fish Migration Data....................................................................................................... 18

2.5 Assumptions ........................................................................................................................... 20

2.6 Data Storage............................................................................................................................ 23

2.7 Methods of Analysis ............................................................................................................... 232.7.1 Fish Migration over Time.............................................................................................. 232.7.2 Correlation Analysis...................................................................................................... 232.7.3 Regression Analysis ...................................................................................................... 24

3.0 RESULTS AND DISCUSSION ................................................................................................... 25

3.1 Introduction............................................................................................................................. 25

3.2 Descriptive Results and Discussion........................................................................................ 253.2.1 Project Operation Data ................................................................................................... 253.2.2 Meteorology Data ....................................................................................................... 283.2.3 Fish Abundance Data.................................................................................................. 29

3.3 Fish Migration Results and Discussion .................................................................................. 32

3.4 Pearson’s Correlation Results and Discussion........................................................................ 35

3.5 Regression Results and Discussion ........................................................................................ 383.5.1 Independent Regression Results..................................................................................... 383.5.2 Multivariate Regression Results for Project Operation Data ......................................... 393.5.3 Multivariate Regression Results for Meteorology Data ................................................. 403.5.4 Multivariate Regression Results for Best-Fit Data......................................................... 41


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3.6 Summary Discussion .............................................................................................................. 44

3.7 Statistical Limits ...................................................................................................................... 45

3.8 Future Monitoring and Evaluation.......................................................................................... 45

4.0 SUMMARY.................................................................................................................................. 48

5.0 REFERENCES ............................................................................................................................. 52


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LIST OF APPENDICES

APPENDIX A. ANNUAL FISH ABUNDANCE ..................................................................... A-1

APPENDIX B. PROJECT DATA OVER TIME … ..................................................................B-1

APPENDIX C. PEARSON’S CORRELATION MATRICES...................................................C-1

APPENDIX D. INDEPENDENT REGRESSION RESULTS .................................................. D-1

APPENDIX E. MULTIVARIATE REGRESSION RESULTS ................................................E-1

LIST OF FIGURES

FIGURE 1 LOCATION OF THE BAKER RIVER HYDROELECTRIC PROJECT ........................ 3

FIGURE 2 PERIOD OF RECORD FOR DATA SETS USED IN ANALYSIS................................ 17

FIGURE 3 INTAKE TEMPERATURE VERSUS DATE ................................................................. 19

FIGURE 4 FISH MIGRATION PATTERNS OVER TIME.............................................................. 29

FIGURE 5 TOTAL FISH ABUNDANCE BY SPECIES OVER TIME............................................ 30

LIST OF TABLES

TABLE 1 SUMMARY TABLE FOR DESCRIBING THE EFFECTS OF VARIOUSENVIRONMENTAL VARIABLES ON DOWNSTREAM MIGRATION OF SOCKEYESALMON SMOLTS........................................................................................................................ 9

TABLE 2 SUMMARY TABLE FOR DESCRIBING THE EFFECTS OF VARIOUSENVIRONMENTAL VARIABLES ON DOWNSTREAM MIGRATION OF SOCKEYESALMON SMOLTS...................................................................................................................... 10

TABLE 3 SUMMARY TABLE FOR DESCRIBING THE EFFECTS OF SEVERAL VARIABLESON DOWNSTREAM MIGRATING CHINOOK SALMON SMOLTS...................................... 12

TABLE 4 SUMMARY OF STATISTICAL VARIABLES ............................................................... 27

TABLE 5 PEARSON’S CORRELATION SUMMARY ................................................................... 33

TABLE 6 MULTIVARIATE REGRESSION SUMMARY FOR PROJECT OPERATION DATA 35

TABLE 7 MULTIVARIATE REGRESSION SUMMARY FOR METEOROLOGY DATA........... 35

TABLE 8 MULTIVARIATE REGRESSION SUMMARY FOR BEST-FIT INDEPENDENTVARIABLES................................................................................................................................. 35


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1

FOREWORDThis document is in support of the Baker River Hydroelectric Project (Project) evaluation required

for the Federal Energy Regulatory Commission [FERC] relicensing.

Authorization

Project: Puget Sound Energy -Baker River # 20636

Scope

The objective of this study was to evaluate potential relationships between selected Project operation

variables and fish abundance data at the Project. Historic data on past Project operations and

environmental conditions were compiled, correlated and statistically analyzed with respect to

abundance of juvenile anadromous salmonids collected at the upper and lower surface barge

collection facilities.



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1.0 PROJECT EFFECTS ON DOWNSTREAM FISH MIGRATION STUDY

BAKER RIVER RELICENSINGThe Baker River Hydroelectric Project (Project) is owned and operated by Puget Sound Energy, Inc.

(PSE). The Project consists of the Lower Baker and the Upper Baker Developments. The

construction, operation, and maintenance of these facilities was licensed by the Federal Power

Commission (now known as the Federal Energy Regulatory Commission [FERC]) in 1956. The

Lower Baker Development was originally constructed prior to federal licensing in 1925, while the

Upper Baker Development was completed in 1959. The issuance of the license in 1956 combined

the operations of the Upper Baker and Lower Baker Developments into one single license. FERC

regulations require FERC-licensed hydroelectric projects to undergo a re-evaluation process, known

as “relicensing,” prior to the date the original license expires. PSE’s existing 50-year license for the

Baker River Project expires on May 1, 2006.

1.1 Project BackgroundThe Baker River Hydroelectric Project consists of the Lower Baker and the Upper Baker

Developments (Figure 1). The Lower Baker Dam impounds Lake Shannon, a reservoir

approximately 7 miles long and 160,000 acre-ft of water at normal full pool (elevation 438.6 ft above

mean sea level) (PSE 2000). The Upper Baker Dam impounds Baker Lake, a reservoir

approximately 9 miles long, with a surface area of 285,000 acre-ft at normal full pool (elevation

724.0 ft) (PSE 2000). Before the Upper Baker Dam was built, Baker Lake existed as a natural lake

that occupied about 600 acres of the valley bottom within the northern half of the current footprint of

the Baker Lake reservoir. Both of these reservoirs are operated for hydropower and flood control.



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Figure 1 Location of the Baker River Hydroelectric Project

Washington State, USA

N



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The Lower Baker Development is located at river mile (RM) 0.5. The development includes a

concrete dam, intake gatehouse, pressure tunnel, surge tank, powerhouse, substation, office and

visitor center, barrier dam, upstream and downstream fish passage facilities, and miscellaneous

maintenance buildings. The powerhouse contains a single Francis-style turbine generator with a total

hydraulic capacity of 4,100 cfs at full gate. The barrier dam, located below the powerhouse, is used

to guide adult migrating fish to the associated upstream fish passage facility located on the east bank

of the river. The downstream passage facility consists of a surface collector barge located in the

Lake Shannon forebay. The surface collector barge uses a pump to create attraction flow within an

entrance channel. The surface collector is augmented by barrier nets that help guide fish into the

entrance. These nets span the width and depth of the reservoir in an attempt to provide a complete

barrier to passage outside of the barge collector. Fish entering the channel are guided over a weir and

into a hopper that directs them into a pipe leading to the fish trap. Once in the fish trap the fish are

sampled, counted and then transported downstream for release into the free flowing Baker River

below the Project.

The Upper Baker Development is located at RM 9.5 and consists of a primary concrete dam, an

earthen dam, a powerhouse, fish passage facilities, a substation, and artificial spawning beaches. The

powerhouse contains two Francis-style turbines with total hydraulic capacity of approximately 5,100

cfs at full gate. The intake providing water to the to the two penstocks is located in the center of the

dam. A fish baffle is suspended from two pontoons in front of the intake to prevent fish from

entering the intake. Fish facilities at Upper Baker include spawning beaches and downstream

passage facilities. Downstream passage is accomplished with a surface collector barge similar to that

described above for the Lower development.



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PSE has operated the Baker River Project as a peaking facility to take advantage of higher power

values that are associated with daily, weekly and seasonally variable power demands. Although

somewhat variable with the seasons, daily demand periods have resulted in increased turbine

operation during early morning and evening hours. Weekly demands have resulted in reduced

operation on weekends. Seasonal demand patterns have resulted in increased operation from October

through March. As a result of these peak periods the reservoirs are normally drafted on a daily and

weekly basis during the increased demand period. In the past, these normal operating conditions

combined with additional considerations (i.e. flood control) for reservoir operation have resulted in a

generalized pattern of reservoir operations. Lower reservoir levels are maintained during winter to

provide for increased power demand and create space for storage during high flow events, while

higher reservoir levels are maintained during summer to maximize hydraulic head for power

generation, maximize the reservoir surface area for recreation, and supplement the Skagit River low

flows.

1.2 Baker River Migratory FishesDownstream passage of migratory fishes has been identified as a key issue for consideration during

the Baker River relicensing process. The Baker River is home to numerous fish species including

five species of Pacific salmon, cutthroat trout, and bull trout. The migratory pathway of these fishes

was cut off with the closure of the Lower Baker Dam in 1925. The completion of the Upper Baker

Dam in 1959 further reduced the available migratory corridor for these anadromous species. In order

to accommodate the migratory life histories of these species, PSE constructed fish passage facilities,

starting with the Lower Baker upstream barrier dam and fish trap in 1954. In 1958 and 1959,

downstream passage facilities were constructed at both Upper and Lower Baker dams



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Even with fish passage facilities operating, the Baker River Project has the potential to directly and

indirectly impact passage of downstream migrating fish. Potential direct effects are generally a result

of the dam and/or fish passage facilities and their operation. The direct effects would include: delay

or reduced numbers of successfully migrating fish as a result of reduced effectiveness of passage

facilities, injury/mortality associated with fish passage facilities, and injury and mortality associated

with passage through turbines or via the spillway. Potential indirect effect can result from stress,

injury, or mortality associated with passage through the dam passage facilities. In addition, the

reservoir habitats created by the dams may result in indirect effects to fish migrants such as delay in

travel time through the system, increased mortality by predators, and increased rates of residuals (fish

that opt not to migrate to the ocean, but rather remain in the reservoir). Both direct and indirect

effects are a result of the physical structure of the hydroelectrical facilities and reservoirs, but these

effects can be further complicated by project operations. Operation variables that affect flow, water

temperature, and reservoir level have potential to intensify, or ameliorate, project impacts.

Improved downstream passage at both the Lower and Upper dams is a goal of PSE and the

relicensing participants. The existing data on the effectiveness of the current downstream passage

facilities is equivocal. To better understand the potential past project effects on downstream

migrating salmonids and to guide the development of passage improvements, PSE and Participants

are undertaking several studies of downstream fish passage at the Project. The first of these

undertakings was a desktop analysis to evaluate the potential relationships between past Project

operations and the outmigration of juvenile salmonids as indicated by the abundance of fish

enumerated at the Lower and Upper fish collection facilities. Coho and sockeye salmon currently are

the dominant stocks in the system and average 94% of the adults returning to the upstream Baker

River trap. Although other species of salmonids are of concern to PSE and the Project Relicensing



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Participants, the preponderance of data is available for coho, sockeye and chinook salmon, and thus,

they are the focus of this correlative analysis.

1.3 Patterns of Downstream MigrationA suite of environmental cues are thought to trigger the downstream migration juvenile of salmonids.

Prior to migration, salmon parr begin to experience physiological changes in coloration, shape, and

salinity tolerance that will allow them to survive in the ocean (Groot and Margolis 1991). These

changes are primarily influenced by temperature and photoperiod, although other factors also may

play a role. Photoperiod is thought to influence the onset of the parr-smolt change while temperature

affects the rate at which smolts respond to physiological changes (Kreeger and McNeil 1992). Parr

transition into smolts after undergoing these physiological processes. From recent genetic studies, it

is clear that salmonid species exhibit race-specific variation in their genetic makeup (Groot and

Margolis 1991). The timing of downstream migration appears to be a complex process involving the

interaction between the genetic makeup of the stock and specific environmental variables.

Variability in migration characteristics among salmonid species and race-specific differences make

generalizing downstream migration patterns difficult across species. However, several

environmental variables, such as temperature, photoperiod and flow, appear to be important cues to

multiple salmonid species. Relationships between specific environmental cues and migration are

more clear when considered by species.

1.3.1 Sockeye SalmonFor many salmonids, discharge has a large influence on the speed and/or duration of smolt

outmigration (Kreeger and McNeil 1992). However, lake-adapted sockeye salmon typically begin

their seaward migration as ice-covered lakes begin to unfreeze and water temperatures rise in the

spring (Groot and Margolis 1991). The oldest and largest smolts migrate first and have the fastest



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migration times (Kreeger and McNeil 1992). The majority of downstream migration occurs when

water temperatures are between 5-10°C (Hartman et al. 1967; Foerster 1968; Groot and Margolis

1991). As they move downstream through reservoirs, environmental factors such as cloud cover,

wind, and temperature affect their migration rates (Quinn and Brannon 1982; Foerster 1968; Kreeger

and McNeil 1992). Sockeye salmon smolts typically migrate at dusk, during the night, and in the

pre-dawn hours. Their peak migration time usually is between approximately 2200-0200h. (Groot

and Margolis 1991) Sockeye salmon smolts actively swim downstream. Active swimming and

migrating at night are behaviors to help avoid predation. The mechanism for this internal compass is

not well understood, but is thought to be related to the Earth’s magnetic fields. Periods of intense

winds have been shown to affect migration rates of sockeye salmon in reservoirs. High winds can

affect turbidity and “build-up” areas of warmer waters in parts of the reservoir that in turn increase

the migration rate for smolts in these warm-water areas. In summary, sockeye salmon smolts begin

their seaward migration in response to rising temperatures, breakup of ice, and longer days, they

migrate predominantly at night and orient using celestial or internal navigational cues



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Table 1 Summary table for describing the effects of various environmental variables ondownstream migration of sockeye salmon smolts.

Note: references summarized for selected topics from Groot and Margolis 1991.

Variable Comments Selected References

Light (photoperiod) Majority of smolts migrating

between approximately 2200-

0200h

Hartman et al. 1967; Groot

1972; Groot and Margolis

1991; Kerns 1961; Burgner

1962; Groot 1965; Warner 1997

Temperature Most migration occurs in water

between about 5-10°C

Hartman et al. 1967; Foerster

1968; Groot and Margolis 1991

Cloud Cover Increases in cloud cover may

cause shift in smolts’ direction

finding mechanisms

Quinn 1982; Quinn and

Brannon 1982; Foerster 1968

Wind Intensity and Direction Wind can affect turbidity and

“build-up” areas of warmer

waters, which raise intensity of

migration rate

Hartman et al. 1967; Krogius

and Krokhin 1948; Foerster

1968; Burgner 1991

Ice breakup Slower ice breakup in northern

latitudes cause delays in

migration

Burgner 1962, 1991; Groot and

Margolis 1991

Sun Used to orient migrating fish

during night hours or cloudy

conditions

Groot 1965; 1972; Hoar 1976;

Quinn 1982; Quinn and

Brannon 1982; Healy and Groot

1987

Size and age composition of

smolts

Older and largest smolts

migrate first

Gilbert 1916, 1918; Barnaby

1944; Burgner 1962; Foerster

1968; Dombroski 1954;

Burgner 1991; Pauley et al.

1989

Genetics Race-specific cues for

downstream migration

Groot and Margolis 1991



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1.3.2 Coho SalmonThe outmigration timing of coho salmon smolts is affected by many of the same factors as sockeye

salmon smolts. However, perhaps due to the fact that coho salmon use rivers during spawning and

rearing, their migration timing is more closely related to stream discharge. Peak migration of coho

salmon smolts usually occurs during periods of maximum discharge (Tripp and McCart 1983). The

onset of downstream migration also is largely influenced by temperature and photoperiod

(Shapovalvo and Taft 1954). Coho salmon smolts predominantly migrate downstream through the

night, with the peak migration period between about 2300-0300 h. They generally undertake their

seaward migrations when water temperatures are less than 10°C, although a large amount of variation

has been documented. Correlating water temperatures with migration timing is influenced by

latitude, altitude, and is often confounded by the interactions with other environmental variables. As

with sockeye smolts, the tendency is for the largest and oldest smolts to migrate first, and as they

migrate downstream, coho smolts exhibit schooling behavior (Shapovalvo and Taft 1954; Groot and

Margolis 1991).

Table 2 Summary table for describing the effects of various environmental variables ondownstream migration of sockeye salmon smolts.



Light (photoperiod) Large portion of outmigration occurs at

night between 2300-0300h

Sharpovalov and Taft 1954; Meehan

and Siniff 1962; Mace 1983

Flow (discharge, current) Peak migration coincides with

maximum discharge

Churikov 1975; Tripp and McCart

1983;

Temperature Majority of coho migrate in

temperatures < 10°C

Logan 1967; Drucker 1972; Holtby

et al. 1989


smolts

Migration typically occurs after smolt

reaches 10 cm in size; usually age 1 with

one winter of growth

Gribanov 1948; Sumner 1953;

Logan 1967; Andersen and Narver

1975; McHenry 1981



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1.3.3 Chinook SalmonThe large amount of variation within run timing for chinook salmon makes generalizing outmigration

trends difficult. Ocean-type chinook salmon juveniles often are distributed downstream by

prevailing flows and migrate seaward shortly after emergence (Groot and Margolis 1991; Kjelson et

al. 1982; Healy 1980). Stream-type chinook salmon juveniles take up residence in stream reaches for

a period of a year or more (Groot and Margolis 1991). The migration timing of stream-type chinook

salmon is closely related to the stream discharge with peak migration periods associated with the

occurrence of spring and fall freshets. The onset of chinook migration also is largely influenced by

temperature and photoperiod (Mains and Smith 1964; Reimers 1971). Chinook salmon smolts

generally migrate downstream through the night. They occupy habitats along the shoreline and on

the surface where the main body of flow is present during their migration. (Groot and Margolis 1991)

Their outmigration timing often is variable from year-to-year in the same river system, and the time

of peak migration is extremely variable. Generally, the rate of migration for chinook smolts

increases as the season progresses. Chinook salmon migrate in a wider temperature range, with more

variability, than coho or sockeye smolts; in general, they undertake their seaward migrations when

temperatures are between 4-15°C. Correlating temperatures with migration timing is influenced by

latitude, altitude, and is often confounded by the interactions with other environmental variables.

Genetic factors also may determine when chinook salmon migrate downstream by cueing migrations

when optimal habitat conditions are present (i.e., food) and competitive interactions between species

are most likely to be minimized (Kreeger and McNeil 1992).



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Table 3 Summary table for describing the effects of several variables on downstreammigration of chinook salmon smolts.



Light (photoperiod) Large portion of outmigration

occurs at night in the hours

around midnight

Reimers 1971; Lister et al.

1971; Mains and Smith 1964

Flow (discharge, current) Peak migration coincides with

maximum discharge or

occurrence of freshets

Kjelson et al. 1981; Healey

1980; Groot and Margolis

1991; Healey and Jordan 1982

Temperature Majority of chinook migrate in

temperatures between 4 - 15°C

Healey 1980; Irving 1986;

Mains and Smith 1964

Genetics Genotype determines

downstream migration timing

after emergence

Taylor and Larkin 1986; Taylor

1988; Groot and Margolis 1991


smolts

Migration typically occurs after

smolt reaches 10 cm in size;

usually age 1 with one winter of

growth

Everest and Chapman 1972



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2.0 METHODOLOGY

2.1 IntroductionThis chapter describes the modeling variables and the methods of analysis used to assess

relationships between the Baker River Hydroelectric Project (Project) and downstream fish passage.

The primary data consisted of both Project operation data and fish migration data. Secondary data

included meteorology data. Both correlation and regression analyses were used to analyze the data.

The term ‘Upper’ refers to the Upper Baker reservoir and operations and ‘Lower’ refers to the Lower

Baker development, which is also called Shannon Lake.

2.2 ObjectiveThe objective of this study was to evaluate potential relationships between selected Project variables

and fish abundance data at the Project. Historic data on past Project operations and environmental

conditions were compiled and correlated with abundance of juvenile anadromous salmonids collected

at the upper and lower surface barge collection facilities.

2.3 General ApproachHistoric data on Project operations and environmental conditions of the Baker River Project were

obtained from PSE. Historic data were formatted in a Microsoft Access database and other data sets

were derived from the original data, including weekly summaries and daily changes. As described

later in detail, a quality analysis procedure was applied to the data prior to analysis to account for

missing data. Photoperiod (solar day) and lunar period data were calculated.

The data were divided into two sets, Project operation data and fish abundance data for both

reservoirs and regional meteorology data and fish abundance data. Project data included inflow;

upper and lower reservoir outflow; reservoir water surface elevations; lower intake temperature; and



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daily change in flows, temperature, and water surface elevations. Meteorology data included

photoperiod, lunar period, windspeed, barometric pressure, precipitation, and solar radiation. Fish

abundance data consisted of counts of juvenile salmonids taken from surface barge collection

facilities at both the upper and lower forebays. These data included daily total fish counts for

salmonid species and age classes collected.

For coho, sockeye and chinook salmon, the annual fish abundance data were graphed over time by

species in order to visualize fish migration patterns. Correlation matrices were used to analyze all of

the modeling data for patterns and relationships. In particular, Pearson’s correlation coefficients

were used to define the degree to which changes in the value of one variable were repeated in the

behavior of another variable. The outcomes of the correlation analyses provided information as to

which modeling variables were best suited for use in regression analyses. While the correlation

coefficients measured the strength of the association between two variables, regression was used to

define the mathematical function that linked these variables. Both independent and multivariate

regressions were performed. Specifically, multivariate regressions were used to model the

relationship 1) between the project data and fish abundance, 2) between the meteorology data and

fish abundance data, and 3) between the most promising correlations within the project and

meteorology data and fish abundance.

2.4 Discussion of the Historic Data Sets and Modeling VariablesThe historic data consisted of Project operation data, meteorology data and fish migration data. The

Project data included flow data, reservoir levels, and water temperature data for both the upper and

lower reservoirs. Meteorology data consisted of air temperature, barometric pressure, windspeed,

precipitation, and photon data. Fish migration data included juvenile salmonid counts, salmon and

trout releases, rearing environment, release locations, and fish marks, although only the juvenile



15

migrant collection data was statistically useful for this project. These data were originally obtained

in differing formats, such as documents, graphs, and Microsoft Excel spreadsheets and were

comprised of both categorical data and continuous data, as noted in each description. The data were

restructured in a consistent format and converted to a Microsoft Access database. A quality analysis

was performed to account for missing data in a statistically relevant manner as described in detail

below. Puget Sound Energy, Inc (PSE) supplied the historical data, unless noted otherwise. All data

were correlated by date, Julian day, and Julian week, where the first Julian week spanned from

January 1st through January 7th.

2.4.1 Project DataAll of the Project data were continuous data describing various flows (discharge), reservoir levels,

intake temperature and meteorology data. Daily changes for outflow, reservoir levels, and

temperature were derived from the original data set. The letter ‘L’ represents lower data, the letter

‘U’ represents Upper data, the letter ‘Q’ represents flow, and the letter ‘D’ represents change. All

record total numbers refer to the number of original records. Note: To fully understand the data

limits and project operations, this section would benefit from more metadata, if available, from PSE.

Natural Inflow (Qn) data represent the calculated mean daily flows in the Lower Baker River (values

reformatted from K. Brettmann 4x8 format). These were equivalent to the estimated mean daily flow

in the Lower Baker River as if no reservoirs were present in the basin. All flows were given in sfd (a

volume unit representing cubic feet per second for one day). The records span from January 1, 1926

to December 6, 2001, although the last two weeks of December 2000 were missing. There were

27,734 records. The five-day running average of the natural flows were calculated (QnRAvg).



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Upper and Lower Baker Generation and Spillway data were collected from June 1, 1989 through

December 4, 2001. There were a total of 4,570 records for each operation. These data represent the

daily discharge in sfd. The source is FERC reports and generation records compiled by Carol

Hoerner and Tom Le. Categories compiled and derived include generation flow (Q), spillway flow

(Qs), total flow Qt), and daily change in flows (DQ).

Upper and Lower Water Surface Elevation (WSE) data representing reservoir levels were given as

feet above mean sea level. These data records were collected from June 1, 1989 through December

4, 2001. There were a total of 4,570 records for each operation.

Lower Water Intake Temperature Data (temp) was taken at the intake leading from the lower forebay

to the turbines. This data record spanned from January 1, 1996 to May 20, 2000 and comprised

1,602 total records. Temperature data is recorded in degrees Fahrenheit. While this flow also

reflects meteorology data, it is included in the Project operation data set because it is influenced by

the project.

2.4.2 Meteorology DataMeteorology data were collected in the Mount Baker area from October 1, 1990 to December 31,

2001 (no specific collection site given). Claire Yoder created these records on January 8, 2002.

They include windspeed (WSpeed) recorded as mph (4,083 records), barometric pressure (BPress),

given in inches of mercury (4,082 records), average air temperature (ATemp) registered in degrees

Fahrenheit (4,083 records), precipitation (Precip) given in inches (4,049 records), and solar radiation

(SRad) recorded in watts/square meter (4,083 records). Photoperiod (PhotoP), lunar hours

(LunarHrs), and lunar illumination (Millum) were calculated and contain 4,635 total records each.



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Photoperiod and lunar hours are recorded in decimal hours and lunar illumination is recorded as

percent surface illuminated.

Photoperiod (PhotoP) data were calculated using the following methodology. The photoperiod

(length of day) was calculated for each day for the period of record spanning the length of record for

the gulper count data. The calculation was made by computing the sunrise and sunset times at the

project site in decimal hours, and subtracting the former from the latter. The calculation used a suite

of standard equations that included corrections for the sun's diameter, parallax and atmospheric

refraction (Duffett-Smith, 1981). Corrections for site variables like shading from mountains and

twilight were not incorporated into this calculation of photoperiod.

Lunar Hour (LunarHrs) data are analogous to photoperiod except that they apply to the number of

decimal hours that the moon is visible at night. This variable was calculated in a similar manner as

Photoperiod but includes a certain modification to account for the fact that moonrise or moonset may

occur anytime before or after sunrise or sunset, in timing with the lunar and solar cycles. Modified

moonrise and moonset times consist of setting the moonrise time to the sunset if the moonrise occurs

before sunset and setting moonset to the time of sunrise if it occurs later. This modification assumes

that light reflected from the moon during daylight hours is very much less and comparatively

insignificant to the sun. The purpose for doing this was to explore migration patterns correlated to

the nighttime visibility, excluding weather phenomena.

Moon Illumination (Millum) data represent moon illumination and express the percentage of the

moon's surface that is visible from earth; it is directly correlated to the lunar phase. The value is

expressed as a decimal fraction with 100% set to unity.



18

2.4.3 Fish Migration DataFish migration data were collected by a cooperative effort of the Steelhead Program, the Skagit

System Cooperative, the Washington Department of Fish and Wildlife, and Puget. Juvenile

Salmonid Abundance data were used to represent the dependant fish migration variable. Note: This

section would benefit from input from PSE or other Participants. This metadata would provide

valuable insight as to how this information was obtained and what its strengths and weaknesses are.

Upper and Lower Juvenile Salmonid Abundance data were collected from fish collection facilities

located in the forebays of both upper and lower Baker developments. The upper gulper record spans

from March 27, 1985 to August 7, 1997, although both 1992 and 1993 data were missing. There

were a total of 1,255 records. The lower gulper data were collected from April 24, 1985 to July 30,

1997, with a total of 1,104 records. In both cases, some of the records have missing data and the

records were seasonal. The data were presented by species and age class. For this study, the age

class data were combined to represent total number of species. Coho, sockeye, and chinook salmon

were the species used for this study. Other species included chum and pink salmon, Steelhead, Dolly

varden and Cutthroat trout, which had sparse records. All age classes for all species counts were

totaled to represent total fish. The compiled data were thus, date, Julian day, Julian week, total

number of coho salmon per day, total number of sockeye salmon per day, total number of chinook

salmon per day, and total number of all species per day. The data was categorical in the context of

fish species, and continuous in the context of fish counts.



19

Period of Record for Data Sets Used in Correlation and Regression Analyses

0

1

2

3

4

5

6

7

Dec-88

Jun-89

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Jun-00

Dec-00

Jun-01

Dec-01

Date

Upper Gulper‡ Lower Gulper‡ Natural Flows (modeled)‡Upper Reservoir Operations*‡ Lower Reservoir Operations*‡ Temperature‡Simulated Temperature†‡

* - Operat ions data includes turbine f lows, spillway f lows, total f lows, water surface elevat ions, change in total f lows and change in water surface elevat ion; all in daily mean values.‡ - Only reocrds containing gulper data were used in regression analysis.† - Simulated temperature values used only in regression analysis.

Figure 2 Period of Record for Data Sets Used in Analyses



20

2.5 AssumptionsMissing outflow data, as represented by the value zero or no entry, were accounted for in the

following manner. If zero sfd was recorded at the generator and 80 sfd at the spillway, and the

inflow did not increase significantly, and the water surface elevation increased, then it was assumed

that the turbines were shut down for maintenance, and the spillways discharged at the 80 sfd

recorded. If however, the water surface elevation did not increase, it was assumed that the data were

not recorded. In this case, the missing data were filled by interpolating the values between the last

and next recorded values.

Missing juvenile gulper data were accounted for in the following manner. If there were no counts

(zero or missing data), and the next recorded count was similar to the previous counts, then it was

assumed that the gulper was not operational during that time and the value was left as zero. If

however, the next recorded value reflected a cumulative number of species, then that value was

divided over the day of record and the missing days. There was no accountability for periods when

spill occurred and the guide net was damaged as this information was not given.

Missing temperature data was extrapolated to account for missing years. Given that there was a

short period in which temperature data overlapped with gulper data, roughly 2.5 seasons, it was

deemed necessary to develop a series of simulated temperature data for the sake of regressing 11

season (years) of fish abundance with a known temperature variable. It was shown that intake water

temperature was highly correlated to the day of the year, so it was assumed that the simulated

temperature can be based on the time of year. This was reasonable given that water temperature is

directly governed by, temporally varying, seasonal weather cycles. Given this annual cyclicity, the

lower intake temperature data was used as an analog for upper intake temperature because no upper



21

intake temperature records were available. This derived temperature data set was used for the

regression analysis.

Temperature data was simulated for two periods totaling roughly 8.5 years using a process

employing the ratio-of-uniforms method (Devroye, 1986) Figure 3. The ratio-of-uniforms method

was programmed as a function that takes a mean value and a standard deviation as arguments and

returns any specified number of random values having a normal distribution about the mean value.

The roughly 4.5 years of temperature data were tagged with a Julian Day values (JDay: day number

of year) and the means and standard deviations were calculated for each JDay. These results were

then used with the ratio-of-uniforms method to calculate simulated temperature values for each day,

for the periods without temperature records. The values were kept as time series and then smoothed

using a 14-day running average to pull out a certain level of white-noise that produced average daily

fluctuations in temperature that were a little above normal. The simulated temperature data were

then inserted in the temperature field, registered to the appropriate JDay, of the database.



22

Intake Temperature vs Date,Data Used in Regression Analysis

0

2

4

6

8

10

12

14

16

18

20

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Jun-89

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Dec-99

Jun-00

Dec-00

Jun-01

Dec-01

Date

Inta

ke T

empe

ratu

re (

°C )

Actual DataSimulated Data

Figure 3 Intake Temperature versus Date



23

2.6 Data StorageData storage included both an original, unedited data set and an edited data set. Both are formatted

in a Microsoft Access database. The edited data set can be used to repeat these analyses or to

conduct further analyses using the assumptions presented in this chapter. The unedited version can

be used as baseline data in the event that a well-formatted data set is needed, but different

assumptions are presented.

2.7 Methods of Analysis

2.7.1 Fish Migration over TimeFish migration, as represented by the juvenile salmonid abundance data, was graphically displayed

by species as both the weekly total counts versus Julian week for each year and as the percent of total

count over time (Julian week). This enabled detection of migration patterns over time. The average

of all of the years was also imposed on the graphs for reference.

2.7.2 Correlation AnalysisPearson’s correlation analysis was used to define the degree to which changes in the value of a

project variable were repeated in the fish migration data, represented by juvenile gulper data. The

“correlation coefficient determines the extent to which values of two variables were ‘proportional’ to

each other. For example, let us suppose that a graph is drawn of a Project Data variable such as

Lower Spillway Flow (X-axis) against the number of total fish found in the Lower Baker River

Juvenile Gulper (Y-axis) and a set of data values is plotted. If all of the data points lie along a

straight line, then there is a perfect correlation between the lower spillway flow variable and total

number of fish. The two variables were changing together at the same pace. Given this, the Pearson

correlation coefficient, r, will equal +1 (positive correlation). On the other hand, if the variable

increases in exact ratio to decreases in the total score, then r would equal –1 (negative correlation).



24

Typically, the Pearson Correlation Coefficient, r, represents a level of correlation between variables

between +1 and –1. As r approaches zero, absolutely no correlation occurs (e.g., a cloud of points).

2.7.3 Regression AnalysisWhile correlation coefficients measure the strength of the association between two variables,

regression defines the mathematical function linking these variables. This function can then be

expanded to predict the value of one variable (Y) from the other (X). Thus, regression is the

statistical relationship between variables and is a common modeling method.

The two types of variables involved in regression analysis were dependent variables (response

variables) and independent variables (predictor variables). Dependent variables were labeled as Y1,

Y2, … Yr, and independent variables were labeled as X1, X2, … Xs. In its simple form, regression

takes o the formula of a line, Y = a + bX, where Y is the dependent variable, X is the independent

variable, a is the Y-axis intercept for the value X=0, and b is the regression coefficient. A stepwise

linear regression would include multiple variables, such as Y = a + bX + cX + dX and so on. This

can be also be shown as Y = a0 + a1X1 + …+anXn. As the position of the line changes due to

different data sets, the equations defining the statistical relationship between the variables changes to

describe the new line.

The regression analyses used in this study were independent regression and stepwise linear

regression. In both cases, the juvenile salmonid abundance species data were the dependent variables

and the independent variables were the Project operation variables and the meteorology variables.



25

3.0 RESULTS AND DISCUSSION

3.1 IntroductionSeveral methods were used to analyze the relationships between the Project operation and

meteorology modeling variables and downstream fish abundance, including methods aimed at

explanatory and analytical evaluation. Explanatory variables included descriptive statistics,

explanatory graphics, and Pearson’s correlation coefficient calculations. Analytical methods

included regression analysis. The results of these analyses are presented in this chapter.

3.2 Descriptive Results and DiscussionThe descriptive statistics are summarized in Table 4 and are explained below.

3.2.1 Project Operation DataNatural Inflow data exhibited a mean of 2,666 sfd and a median of 2,087 sfd. The minimum value

was 449 sfd and the maximum value was 38,552 sfd. The standard deviation was 2,283. What

appeared to be outliers from a statistical perspective were actually rare high flow events from a

hydrologic perspective, and thus were considered valid data. The total number of records was 4,635.

Change In Natural Inflow data displayed a mean of -0.08 sfd and a median of -56. The minimum

value was –23,875 sfd and the maximum value was 23,992 sfd. The standard deviation was 1,669.

The total number of records was 4,635.

Upper Baker Generation data displayed a mean of 1,961 sfd and a median of 2,015. The minimum

value was 0.00 sfd and the maximum value is 5,302 sfd. The standard deviation was 1,225. The

total number of records was 4,570.



26

Lower Baker Generation data displayed a mean of 2,358 sfd and a median of 2,693. The minimum

value was 0.00 sfd and the maximum value was 4,245 sfd. The standard deviation was 1,287. The

total number of records was 4,570.

Upper Baker Spillway data exhibited a mean of 83 sfd and a median of 0.00. The minimum value

was 0.00 sfd and the maximum value was 13,475 sfd. The standard deviation was 680. The total

number of records was 4,570.

Lower Baker Spillway data displayed a mean of 189 sfd and a median of 10. The minimum value

was -427 sfd and the maximum value was 16,750 sfd. The standard deviation was 991. The total


Upper Baker Water Surface Elevation data exhibited a mean of 709 feet above mean sea level and a

median of 709 ft. The minimum value was 675 ft and the maximum value was 724 ft. The standard

deviation was 11. The total number of records was 4,570.

Lower Baker Water Surface Elevation data exhibited a mean of 423 feet above mean sea level and a

median of 428 ft. The minimum value was 371 ft and the maximum value was 439 ft. The standard

deviation was 14. The total number of records was 4568.

Discharge Total for Upper Baker data displayed a mean of 2,044 sfd and a median of 2,019 sfd. The

minimum value was 0.00 sfd and the maximum value was 18,173 sfd. The standard deviation was

1,494. The total number of records was 4,570.



27

Discharge Total for Lower Baker data showed a mean value of 2,546 sfd and a median of 2,713 sfd.

The minimum value was 0.00 sfd and the maximum value was 20,445 sfd. The standard deviation

was 1,652. The total number of records was 4,570.

Daily Change in Discharge Total Upper Baker data displayed a mean of 0.28 sfd and a median of

0.00 sfd. The minimum value was 9,492 sfd and the maximum value was 14,617 sfd. The standard

deviation was 1,048. The total number of records was 4,570.

Daily Change in Discharge Total Lower Baker data displayed a mean of 0.20 sfd and a median of

0.00 sfd. The minimum value was –11,135 sfd and the maximum value was 11,076 sfd. The

standard deviation was 962. The total number of records was 4,570.

Daily Change in Upper Water Surface Elevation data exhibited a mean of 0.00 feet above mean sea

level and a median of –0.06 ft. The minimum value was -12 ft and the maximum value was 11 ft.

The standard deviation was 0.91. The total number of records was 4,570.

Daily Change in Lower Water Surface Elevation exhibited have a mean of 0.00 feet above mean sea

level and a median of 0.00 ft. The minimum value was -433 ft and the maximum value was 432 ft.

The standard deviation was 9. The total number of records was 4,570.

Lower Intake Water Temperature data displayed a mean of 5 degrees Fahrenheit and a median of 5oF

The minimum value was 0.00oF and the maximum value was 20oF. The standard deviation was 4.




28

Change in Lower Intake Water Temperature data showed a mean value of 0.00 degrees Fahrenheit

and a median of 0.00oF. The minimum value was –7.5oF and the maximum value was 8oF. The

standard deviation was 0.66. The total number of records was 1,602.

3.2.2 Meteorology DataPhotoperiod exhibited a mean of 12.28 decimal hours and a median of 12.38 hours. The minimum

value was 8.27 hours and the maximum value was 16.08 hours. The standard deviation was 2.66.


Lunar Hours exhibited a mean of 5.91 decimal hours and a median of 5.53 hours. The minimum

value was 0.00 hours and the maximum value was 15.73 hours. The standard deviation was 3.83.


Moon Illumination exhibited a mean of 0.50 decimal hours and a median of 0.50 hours. The

minimum value was 0.00 hours and the maximum value was 1.0 hours. The standard deviation was

0.35. The total number of records was 4,635.

Solar Radiation exhibited a mean of 3,192 watts/square meter and a median of 2,573 watts/square

meter. The minimum value was 12 watts/square meter and the maximum value was 14,001

watts/square meter. The standard deviation was 2510. The total number of records was 4,083.

Air Temperature exhibited a mean of 47.92oF and a median of 47.0 oF. The minimum value was

6.5oF and the maximum value was 77.5 oF. The standard deviation was 11.64 oF. There were 4,083

total records.



29

Barometric Pressure exhibited a mean of 29.99 inches of mercury and a median of 30.01 inches of

mercury. The minimum value was 28.64 inches of mercury and the maximum value was 33.52

inches of mercury. The standard deviation was 0.31. The total number of records was 4,082.

Windspeed exhibited a mean of 8.43 miles per hour and a median of 7.95 miles per hour. The

minimum value was 0.24 miles per hour and the maximum value was 37.89 miles per hour. The

standard deviation was 4.20. The total number of records was 4,083.

Precipitaion exhibited a mean of 0.29 inches and a median of 0.02 inches. The minimum value was

0 inches and the maximum value was 5.3 inches. The standard deviation was 0.54 inches. The total


3.2.3 Fish Abundance DataUpper Juvenile Gulper data are as follows: Coho data displayed an overall daily mean of 259 total

fish counts and a daily median of 54 counts for the period of collection. The minimum value

representing species abundance was 0.00 and the maximum value was 4,556. The standard deviation

was 493 fish. The total number of records was 1,511. Chinook data exhibited a daily mean of 12

total fish counts and a median of 4 counts. The minimum value representing species abundance was

0.00 and the maximum value was 262 fish. The standard deviation was 23. The total number of

records was 1,511. Sockeye data displayed a daily mean of 593 total fish counts and a median of 7

counts. The minimum value representing species abundance was 0.00 and the maximum value was

22,949 fish. The standard deviation was 2,113. The total number of records was 1,511.

Lower Juvenile Gulper data are as follows: Coho data exhibited a daily mean of 30 total fish counts

and a median of 2 counts. The minimum value representing species abundance was 0.00 and the



30

maximum value was 1,674 fish. The standard deviation was 108. The total number of records was

1,598. Chinook data displayed a daily mean of 0.11 total fish counts and a median of 0.00 counts.

The minimum value representing species abundance was 0.00 and the maximum value was 7 fish.

The standard deviation was 0.49. The total number of records was 1,598. Sockeye data exhibited a

daily mean of 48 total fish counts and a median of 0.00 counts. The minimum value representing

species abundance was 0.00 and the maximum value was 6,773 fish. The standard deviation was

300. The total number of records was 1,598.



31

Table 4 Summary of Statistical Variables

Natural Inflow Upper Outflow Upper WaterSurface Elevation

StatisticalProperty

Qn DQn QU QsU QtU DQtU WSEU DwseUMin 449.00 -23875.00 0.00 0.00 0.00 -9492.00 674.74 -12.06

Max 38522.00 23992.00 5302.00 13475.00 18173.00 14617.00 723.95 11.09

Mean 2665.52 -0.08 1961.26 83.18 2044.44 0.28 708.53 0.00

Median 2087.00 -56.00 2014.50 0.00 2019.00 0.00 709.52 -0.06

Count 4635.00 4635.00 4570.00 4570.00 4570.00 4570.00 4570.00 4570.00

St Dev 2282.68 1668.88 1225.02 679.73 1493.94 1048.45 11.45 0.91

Lower IntakeTemperature Lower Outflow

Lower WaterSurface

Elevation

StatisticalProperty

Temp DTemp QL QsL QtL DQtL WSEL DwseLMin 0.00 -7.50 0.00 -427.00 0.00 -11135.00 370.58 -433.21

Max 20.00 8.00 4245.00 16750.00 20445.00 11076.00 438.81 432.31

Mean 5.41 0.00 2357.80 188.60 2545.91 0.20 423.02 0.00

Median 5.00 0.00 2693.00 10.00 2713.00 0.00 427.82 0.00

Count 1602.00 1602.00 4570.00 4558.00 4570.00 4570.00 4568.00 4570.00

St Dev 3.83 0.66 1287.12 990.72 1651.54 961.52 14.06 9.14

StatisticalProperty

Photo-period

Lunarhours

SolarRadiation

AirTemp

BarometricPressure

WindSpeed

Precip

Min 8.27 0.00 12 6.5 28.64 0.24 0.00

Max 16.08 15.73 14001 77.5 33.52 37.89 5.33

Mean 12.28 5.91 3192 47.92 29.99 8.43 0.29

Median 12.28 5.53 2573 47.0 30.01 7.95 0.02

Count 4635 4635 4083 4083 4082 4083 4049

St Dev 2.66 3.83 2510 11.64 0.31 4.20 0.54

StatisticalProperty

CohoUpper

SockeyeUpper

ChinookUpper

GulperUpper

CohoLower

SockeyeLower

ChinookLower

GulperLower

Min 0 0 0 0 0 0 0 0

Max 4556 22949 262 26569 1674 6773 7 6844

Mean 259.3 593.5 12.1 875.1 30.3 48.3 0.1 82.1

Median 54 7 4 91 2 0 0 4

Count 1511 1511 1511 1511 1598 1598 1598 1598

St Dev 493.3 2113.2 23.2 2382 107.7 300.4 0.5 329.9



32

3.3 Fish Migration Results and DiscussionTotal fish abundance was graphed against time for each fish species in order to visualize fish

migration patterns over time (Figures 3 and 4, Appendix A). These were graphed by year for the

period of record for both the upper gulper and the lower gulper. As noted in Figure 4, time is

represented by Julian week (week within 52 week year); Julian weeks 10 through 32 begin about

March 6th and end about August 13th for any given year. Fish abundance and the cumulative percent

of the fish detected over time are both displayed for three species, coho salmon, sockeye salmon, and

chinook salmon. The upper gulper fish abundance was, on average, approximately an order of

magnitude greater than the lower gulper fish abundance. This was likely related to the fact that only

a minority of fish that leave Baker Lake pass downstream, via turbines or the spillway and the fact

that Lower Baker (Shannon) Lake has fewer tributaries supporting salmonid populations (NICK-

PLEASE CONFIRM THIS STATEMENT IS ACCURATE).

In terms of fish abundance averaged for the period of record, sockeye salmon are the predominant

species in the system, followed by coho salmon, with only a few chinook salmon present for a given

time. However, this is not always the case when viewed on a yearly basis (Appendix A, Figure 5).

While the sockeye salmon population has increased over time, there are still five years at Upper

Baker Lake and seven years at Shannon Lake where coho salmon populations exceed or are equal to

sockeye salmon populations.

As noted in Figure 4, the upper gulper weekly fish counts displayed a fairly normal, unimodal

distribution pattern, where the sockeye salmon abundance peaked in the 20th Julian week and the

coho salmon abundance peaked in the 21st week of the year. Chinook salmon abundance was

relatively low in comparison with a peak evident during week 25. Unlike the upper gulper fish

abundance data, the lower gulper weekly counts displayed a bimodal distribution.



33

Figure 4 Fish Migration Patterns Over Time

(Fish Abundance averaged from 1989-2001 records)

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

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10 12 14 16 18 20 22 24 26 28 30 32

Week of Year

Mea

n Fi

sh A

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ance

0%

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ulat

ive

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etec

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Coho Sockeye Chinook %Coho %Sockeye %C

Upper Gulper

0

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ance

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etec

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Lower Gulper



34

Upper Gulper -Total Fish Abundance

0

50000

100000

150000

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250000

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Time (years)

Fish

Abu

ndan

ce

ChinookSockeyeCoho

Upper Gulper -Total Fish Abundance

0

50000

100000

150000

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250000

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Time (years)

Fish

Abu

ndan

ce

ChinookSockeyeCoho

Figure 5 Total Fish Abundance by Species Over Time



35

The predictive variables selected to describe Project operations were averaged over the period of

record and graphed against the same Julian time scale as the fish abundance in an effort to detect

pattern similarities that might have helped to explain the behavior of the dependent variables

(Appendix B). No pattern was found. In looking at the yearly graphs displaying fish abundance by

species over Julian week (Appendix A), it was evident that for both the upper and lower gulpers,

peaks in fish counts occurred at weeks 20 through 24 for coho salmon, weeks 18 through 23 for

sockeye salmon. Chinook salmon peak ranges were difficult to ascertain. Both the lower and upper

reservoir abundance records exhibited unimodal and multimodal distributions throughout the years of

record. The upper gulper record displayed 30% multimodal years and 70% unimodal years for both

sockeye salmon and coho salmon. The lower gulper record exhibited 50% of each modality for coho

salmon, and displayed a unimodal distribution 60% of the years for sockeye salmon. Based on

graphical observations, there was no evidence of a relationship between fish abundance data patterns

and project operations data over time.

3.4 Pearson’s Correlation Results and DiscussionTable 5 and Appendix C give the results for the Pearson’s correlation analysis correlating fish

abundance data, Project operation data, and meteorology data. Significant interactions at or above

the 5% confidence level were highlighted in bold text, and significant interactions at or above the

10% confidence level were italicized and underlined. It is important to note that a statistically

significant value does not imply a biologically or physically significant relationship between Project

operation or meteorology variables and the fish abundance variables. With large sample sizes, such

as those in this study, it was possible to quantify very small effects with statistical significance. For

example, a Pearson’s r-value of 0.01 shows that 1% of the variability in the dependent variable was

attributed to the independent variable. Although it was statistically significant, it explained only 1%

of the variability of the fish abundance data and did not show a strong, obvious biological



36

relationship. If an r-value is highlighted in bold, there is a 95% probability that the stated

relationship is correct, even though it may not be physically or biologically meaningful.

As noted in the methodology section, a Pearson’s r-value at or close to 1 or –1 represents a strong

correlation and a Pearson’s r-value at or close to 0 represents no correlation. As noted in Table 5, the

majority of the values were close to zero, indicating little if any mathematical relationship between

the dependent and independent variables. The only value over 50% in Table 5 was a relationship

between upper chinook salmon abundance and lower intake temperature. While upper chinook

salmon were collected in the Upper Baker Lake and the temperature was taken from the Lower Baker

Lake (no Upper Lake temperature data were provided), the temperature cycles over time are likely to

be similar. Photoperiod had a Pearson’s r-value of 33% and 28% with respect to upper chinook and

upper coho salmon, respectively and water surface elevation had a Pearson’s r-value of 22% with

respect to upper chinook salmon. The values for chinook salmon are less reliable than for coho and

sockeye salmon due to the lower number of chinook salmon present in the system. The mean daily

upper chinook count was 12.1. Natural flow, lower intake water temperature, photoperiod, and solar

radiation had the most consistent pattern of Pearson’s r-values greater than 10%. The strength of

these relationships, in contrast to the other Project variables, support the idea that natural stream and

solar variables may be the critical factors motivating fish migration. Given the correlation data

presented in Table 5, no strong correlations were detected between Project operations and

downstream fish abundance.



37

Table 5 Pearson’s Correlation StatisticsSignificant interactions at the 5% confidence level are in bold text and significant interactions at the 10% confidencelevel are italicized and underlined. A Pearson’s r-value at or close to +/- 1 indicates a strong correlation; a r-value ator close to zero indicates no physical or biological relationship between variables.

Fish Abundance Variables

CohoU SockU ChinU CohoL SockL ChinL

Qn 0.16 0.03 0.09 0.1 0.1 0.08

DQn 0 0.02 0 -0 -0 -0

QU 0.08 -0.1 0.08 0.06 -0.1 0.02

QsU -0 -0 -0 0 -0 -0

QtU 0.07 -0.1 0.07 0.05 -0.1 0.01

DQtU -0 0.01 0.02 0.01 0.01 0.03

WSEU 0.09 -0.1 0.22 0.08 -0 0.02

DwseU 0.07 0.08 0.04 0.06 0.14 0.08

QL 0.1 -0 0.06 0.06 -0.1 -0.1

QsL 0.01 0.06 0.02 0.03 -0 0.02

QtL 0.11 0.02 0.08 0.08 -0.1 -0

DQtL -0 -0 -0 0 -0 -0

WSEL 0.06 -0 0.22 0.1 -0.1 0.06

DwseL -0 -0.1 -0 -0 0.01 0.01

Temp 0.18 -0 0.51 0.16 -0 0.03

Proj

ect O

pera

tion

Var

iabl

es

DTemp 0.02 0.05 -0 -0 0 -0

PhotoP 0.28 0.07 0.33 0.16 0.03 0.08

LunarHrs -0.13 -0.05 -0.13 -0.04 0.06 0.02

Millum -0.07 -0.02 -0.05 0.00 0.05 0.04

SRad 0.12 0.12 0.19 0.12 -0.02 0.00

ATemp 0.08 -0.02 0.19 0.05 0.04 0.02

BPress -0.01 0.07 0.04 -0.01 -0.04 0.02

Wspeed -0.03 -0.09 -0.01 0-.01 0.05 -0.02Met

eoro

logy

Var

iabl

es

Precip -0.08 -0.03 -0.07 -0.01 -0.06 -0.01



38

3.5 Regression Results and DiscussionAlthough few independent variables showed a strong correlative relationship to the dependent fish

abundance variables, we chose to run a regression analyses to determine if a combination of variables

was better at explaining the dependent variable. For example, while natural inflow itself did not

explain Upper Baker coho salmon abundance and intake water temperature itself did not explain

Upper Baker coho salmon abundance, there was still a possibility some combination of natural

inflow and intake temperature or other combinations of Project operation and/or meteorology

variables might have explained Upper Baker coho salmon abundance. Given this possibility,

multivariate regression analyses were conducted for coho salmon, sockeye salmon, and chinook

salmon, and for all total fish abundance for both the lower and upper lakes. Independent analyses

also were applied to the same dependent variables to verify the correlation results. Specifically, four

regression analyses were conducted: 1) independent analyses for each independent variable against

each fish species and the total fish abundance for both locations, 2) multivariate analyses for all

Project operation variables against each fish species and the total fish abundance for both locations,

3) multivariate analyses for all meteorology variables against each fish species and the total fish

abundance for both locations, and 4) multivariate analyses using the independent project variables

that showed the strongest correlative relationships (anything greater or equal to 10%) to the fish

abundance variables for each fish species and the total fish abundance for both locations.

3.5.1 Independent Regression ResultsIndependent (univariate) regression analyses were conducted for all independent (predictor)

variables, represented by Project operation and meteorology data, regressed against fish abundance.

Independent analyses are similar to correlation analyses in terms of noting relationships one variable

at a time. In the independent regression analyses, the adjusted R square shows how much of the



39

variation is explained by the regression equation. The regression coefficients from independent

regression analyses are displayed in Appendix D.

For the Project operation data regressed against all fish species and all fish totaled in both locations,

the adjusted R square value ranged from 0 to 4.2%. This indicated that 0 to 4% of the original

variability was explained by the relationship between fish abundance and any of the predictive

Project operation variables. Therefore, 95.8% to 100% of the variability was unexplained. The

univariate analysis results support the correlation results. No independent relationships were evident

between fish abundance and Project operations variables as represented by inflow, outflows, water

surface elevation, or temperature.

For the meteorology data regressed against all fish species and all fish totaled in both locations, the

adjusted R square value ranged from 0 to 12.2%. Specifically, 0 to 12.2% was the upper gulper

range and 0 to 2.8 % was the range within the lower gulper. Within the upper gulper, the range was

0 to 3.8 when excluding photoperiod. Photoperiod and solar radiation displayed the strongest

independent regression values. Still, 87.8% to 100% of the variability was unexplained. The

univariate analysis results support the correlation results. No strong independent relationships were

evident between fish abundance and meteorology variables, although photoperiod displayed a

relatively strong regression, particularly with upper coho salmon (7%) and upper chinook salmon

(12.5%).

3.5.2 Multivariate Regression Results for Project Operation DataFor the multivariate analyses (Appendix E), Upper Baker Lake fish abundance was modeled as a

function of natural flow, total upper outflow, upper water surface elevation, and lower intake

temperature. Lower intake temperature was used as an analog for the missing Upper intake



40

temperature data because of the assumption that it is highly likely that the temperature cycles would

be similar between the two lakes. Lower Baker Lake fish abundance was modeled as a function of

natural flow, total upper outflow, total lower outflow, lower water surface elevation, and lower intake

temperature. Total Upper Baker Lake outflow approximates Lower Baker Lake inflow. The

multivariate data is summarized in Table 6.

In order to develop a strong predictive model using multivariate analyses, the regression equation

that describes the relationship between the variables should have a strong fit. As displayed in Table

6, the multiple R-squared values for fish abundance for each fish species ranged between 2.8% to

5.8%, meaning that less than 5.8% of the original variability can be explained by the data sets

analyzed. While this fit is better than the independent regression analyses for Project operation data,

it is still a poor fit in terms of defining a relationship. Given this, a reliable model linking Project

operation data and fish abundance cannot be developed.

3.5.3 Multivariate Regression Results for Meteorology DataUpper and Lower Baker Lake fish abundance was modeled as a function of the following

meteorology variables: photoperiod, lunar hours, moon illumination, solar radiation, air temperature,

barometric pressure, windspeed and precipitation. The multivariate data is summarized in Table 7.

The multiple R-squared values for fish abundance for each fish species were between 1.2% and

14.5%. Specifically, the Upper Baker River fish abundance multiple R-squared values ranged from

4.1% to 14.5% and the Lower Baker River fish abundance values ranged from 1.5% to 4.5%. Given

this, 4.1% to 14.5% of the original variability in the Upper Baker fish abundance data and 1.5% to

4.5% of the original variability in the Lower Baker fish abundance data can be explained by the data

sets analyzed.



41

Table 6 Multivariate Regression Analyses Summary for Project Operation Data

(Multiple R-squared values at or close to zero indicate a poor fit in terms of defining a relationship)

Upper Baker Lake Gulper Lower Baker Lake GulperCoho Sockeye Chinook Total

Fish Coho Sockeye Chinook TotalFish

MultipleR-Squared

.035 .028 .058 0.022 .033 .047 .029 .039

Project Operation Coefficients

Intercept -3185 21114.7 -195.7024 18205.4 -629.1081 960.0319 -1.73 303.9529

QnRAvg 0.060 0.129 0.0011 0.1922 0.0089 0.0368 0.0 0.0467

QtU -0.0062 -0.1302 -0.0003 -0.1344 -0.007 -0.002 0.0 -0.0069

QtL - - - - 0.0074 -0.0424 -0.001 -0.0359

WSEU 4.72 -29.2295 0.2805 -24.9106 - - - -

WSEL - - - - 1.5635 -2.2533 0.0045 -0.06171

Temp -13.38 34.3023 1.0327 23.9854 -4.9548 4.3878 -0.0157 -0.7558

3.5.4 Multivariate Regression Results for Best-Fit DataMultivariate regression equations are usually built using the independent variables that showed the

strongest correlative relationship to the dependent fish abundance variable. In this case, all Pearson’s

r-values greater or equal to 10% were selected to build the regressions. These ‘best-fit’ data, in

combination, provided the maximum opportunity to explain the behavior of the dependent fish

abundance variable.

Upper Coho salmon abundance was run as a function of the natural flow 5-day running average,

lower intake water temperature, solar hours, lunar hours, and solar radiation. Upper sockeye salmon

abundance was run as a function of upper water surface elevation and solar radiation. Upper chinook

salmon abundance was run as a function of the natural flow 5-day running average, upper water

surface elevation, lower intake water temperature, solar hours, lunar hours, solar radiation and air

temperature. Upper Baker combined fish abundance was run as a function of solar hours and solar



42

radiation. Lower Coho salmon abundance was run as a function of the natural flow 5-day running

average, lower intake water temperature, solar hours, and solar radiation. Lower Sockeye salmon

abundance was run as a function of the natural flow 5-day running average and change in upper

water surface elevation. Lower chinook salmon abundance did not display multiple correlations at or

in excess of 10%. The results of these regressions are summarized in Table 8.

These multivariate regressions yielded multiple R-squared values ranging from 2.1% to 15.4%.

Upper Baker values ranged from 2.7% to 15.4%, where both coho and chinook salmon multiple R-

squared values were around 15%, and sockeye salmon was at 4.2%. Lower Baker multiple R-

squared values were 2.1% for sockeye salmon and 6.6% for coho salmon; chinook salmon did not

have enough strong independent variables to run a multivariate regression. Given this, 84.6% to

97.3% of the original variability of the Upper Baker Lake fish abundance data and 94.4% to 97.9%

of the original variability of the Lower Baker Lake fish abundance data could not be explained by the

best-fit data selected for these analyses.



43

Table 7 Multivariate Regression Analyses Summary for Meteorology Data



FishCoho Sockeye Chinook Total

FishMultipleR-Squared

0.114 0.041 0.145 0.058 0.045 0.015 0.017 0.019

Meteorology Coefficients

Intercept -2250.74 -12659.35 -316.8202 -15386 -69.6781 396.2495 -3.8269 226.7478

PhotoP 161.6095 288.5507 7.5622 463.8791 20.4273 11.2867 0.0706 36.4538

LunarHr -10.2745 -55.1175 -0.4521 -64.9639 -1.9883 11.8765 0.0042 11.0907

Millum -28.4544 275.1719 0.2811 235.9732 15.6358 -52.4763 0.0277 -45.5523

SRad 0.0082 0.0769 0.0006 0.0858 0.0041 -0.0038 0.0000 0.0002

ATemp -14.2547 -42.9215 -0.2789 -57.9986 -1.6664 0.1915 -0.0051 -1.9842

BPress 32.9557 388.9062 7.8035 433.2469 -3.9543 -19.2161 0.1074 -21.5545

Wspeed -6.2471 -59.9895 -0.2212 -66.0987 -0.7985 7.482 -0.0053 6.9432

Precip -147.359 -181.1758 -2.7487 -335.0585 -1.0823 -78.699 -0.0008 -85.2016

Table 8 Multivariate Regression Analyses Summary for Best-Fit Independent Variables



FishCoho Sockeye Chinook Total

FishMultipleR-Squared

0.1536 0.042 0.152 0.027 0.066 0.021 - 0.034

Best-Fit Coefficients

Intercept -2427.49 22182.17 -290.0756 -2511.479 -300.149 -34.477 - -51.5757

QnRAvg 0.0092 - -0.0004 - 0.0102 0.273 - 0.0472

WSEU - -31.174 0.2856 - - - - -

DWSEL - - - - - 22.7944 - 17.3899

Temp -83.4635 - -1.7161 - -9.0758 - - -

PhotoP 211.9828 - 8.2308 201.9484 22.6172 - - -

LunarHr -13.3788 - -0.4243 - - - - -

SRad 0.0101 0.1366 0.0009 0.1025 0.0043 - - -

ATemp - - -0.2467 - - - - -



44

3.6 Summary DiscussionSeveral methodologies were used to analyze the relationship between Project operations and

downstream fish abundance. Descriptive statistics, including graphical displays comparing

downstream fish migration patterns to project operation patterns did not yield observable

relationships. Pearson’s Correlation results did not yield biological or physical statistical

significance between the dependent and independent variables, but suggested that natural flow,

photoperiod, water temperature and solar radiation may motivate downstream migration. Linear

regression did not yield meaningful physical or biological relationships between project operations

and downstream fish abundance, yet often showed strong photoperiod coefficient values. Overall,

multiple regressions showed that, in combination, meteorology variables had better fits than Project

operation variables explaining Upper Baker Lake fish abundance. Conversely, all Project operation

variables had better fits than meteorology variables when explaining Lower Baker Lake fish

abundance. Best-fit combinations, however, had stronger fits than either Project operation variables

in combination or meteorology variables in combination. In all cases, the relationships did not

explain the vast majority (>85%) of the original variability in the data. Given these analyses, no

physical or biological relationships were detected linking project operations and downstream fish

migration as represented by fish abundance.

While the data that this study was based on were associated with several uncertainties, the findings

are consistent with other natural fish migration studies. As detailed in the introductory chapter, past

studies have shown that natural meteorological phenomena, such as solar day, are the dominant

factors motivating fish migration. Similarly, in this study, photoperiod, solar radiation, water

temperature and natural flow were the strongest factors in the correlations relating to fish abundance.



45

3.7 Statistical LimitsIdeally, the data used in a statistical analysis would be unequivocal, yielding confident results. This

particular data set elicited low confidence; it contained missing values, data were not collected to

represent conditions detectable to fish, data were not well documented (contained few metadata), and

data were often discontinuous and sporadic. Because of these limitations, caution should be used

when drawing conclusions from the study. To avoid this situation in the future, monitoring and

evaluation methods should be designed to acquire high confidence data in support of future analyses.

In general, statistical techniques are limited in their ability to detect pattern in data that does not

match their assumptions. For example, linear regression can accurately represent relationships

among variables only if the variables follow a linear relationship. The project operation data were

not normally distributed over the period of record coinciding with fish abundance (Julian weeks 10

through 32) and the data had many limitations as described above. It is often the case that non-linear

and complex data are not well suited for statistical analysis. Patterns not detected by statistical

analysis may be detected using pattern recognition techniques borrowed from the field of machine

learning, within the field of artificial intelligence (Moret, 2001). However, these techniques are still

in the research phase.

3.8 Future Monitoring and EvaluationEvaluation and monitoring design should emphasize consistency between and within years. Thought

should be given as to what data is needed to describe each project operation or natural phenomena

that might explain differences in fish migration. Throughout the collection process, metadata

describing daily conditions should be documented (e.g., hours that the collection trap was closed or

turbines were shut down). Data that could be interpreted to be values of zero should be noted to

avoid missing data being interpreted as values of zero. Data should be downloaded often to

minimize the potential of losing data.



46

Data collection methods describing the number and characteristics of fish captured at the gulpers

should be done in a systematic manner. In addition to collecting information on the fish, it is crucial

that information be collected to describe trapping operations. This should include recording the

timing of the beginning and ending of each trapping event and noting situations that may have caused

inaccurate counts (e.g., escape of fish, clogging of trap, or raising of guide nets).

Water temperature data should be recorded hourly from at least the first day that the fish collection

facilities are in operation to at least the last day of their operation. Temperature recorders might be

placed at the mainstem above the upper reservoir, in the tailraces (for upstream migration records), in

the forebays below the fish collection barges at the same depth as the gulper (this depth should be

recorded and maintained constant), and at the reservoir intakes. The floating fish collection facilities

will be the same distance from the surface and may reflect air temperature influences, whereas the

distance between the water surface elevation and the intake will vary over time. Daily maximum,

minimum, and average temperature and the timing of maximum and minimum values should be

noted, as should the magnitude of diurnal variations. Ideally, these values should be subsequently

compiled into Julian week data sets.

Because the literature suggests that meteorological conditions influence fish migration of lake-

rearing species, data should be collected on an hourly interval to describe meterologic conditions in a

similar manner to that described for water temperature. Data collected should include windspeed,

wind direction, barometric pressure, air temperature, precipitation, and solar radiation.

Meteorological stations could be placed at the fish collection facilities in both reservoirs. Collection

of meterologic data at these stations may also be advantageous for water temperature modeling

efforts if needed. It would also be advantageous to collect relative humidity data if a water



47

temperature model will be developed. Data assimilation should be similar to that described for water

temperature.

Generation and spillway data should incorporate more metadata or be tracked on an hourly basis.

For example, peaking might increase downstream migration, thus the same sfd value recorded for a

24-hour period would have a different effect upon fish than the same value recorded for a 6-hour

period of operation. Changes in flows and water surface elevations within each day would be useful.

It would also be beneficial to maintain a record that designates the difference between data not being

collected and zero values. For example, noting when zero value flows are due to the project being

off-line or if they exist because the flow was not recorded (e.g., holidays).

Future studies should be anticipated and discussed in an effort to identify other valuable predictive

variables that may need to be collected. Potential research or monitoring and evaluation objectives

should be considered. For a given objective, the data that would be needed to adequately assess that

objective could be determined. Statistical analyses design plans should be conducted by a

professional statistician before the data is collected to make sure that the analyses will be statistically

relevant based on 1) what data is collected, 2) where it is collected, 3) when it is collected, 4) how

many data points are collected, 5) how it is collected and 6) how it is formatted in reference to future

analysis. A study design should incorporate a plan for assessing confidence in the data. By

following this format, future analyses should be easy to evaluate and should result in data with high

confidence.



48

4.0 SUMMARYHistorical data describing Project operations were analyzed in an effort to document past

relationships between the Baker River Hydroelectric Project and downstream fish migration. Fish

migration was represented by daily juvenile salmonid abundance data obtained from fish collection

barges in the forebays of both Upper Baker and Lower Baker dams. Project operation data included

natural inflow, generator and spillway outflows from both operations, lower intake temperature, and

water surface elevation from both reservoirs. Meteorology data included windspeed, barometric

pressure, air temperature, precipitation, solar radiation, and photoperiod. Fish abundance data

consisted of counts of juvenile salmonids taken from surface barge collection facilities at both the

upper and lower forebays. These data included daily total fish counts for salmonid species and ages

classes collected. The data were entered into a Microsoft Access database. Data modifications and

new data derived from existing data sets were documented. The data were analyzed to assess

relationships between Project operations and fish abundance.

Both descriptive and statistical analyses were conducted. For all analyses, fish abundance was the

dependent variable and project operation and meteorology variables were the predictive, independent

variables. Descriptive analyses included data descriptions, descriptive graphics showing fish

migration and project operation and meteorology patterns over time, and Pearson’s correlation

analysis. Statistical analyses included independent regression to corroborate the results of the

Pearson’s correlations, and linear regression to assess the influence of combinations of variables. All

analyses resulted in the same conclusion: project operations at both Upper Baker Lake and Lower

Baker Lake (Lake Shannon) did not display significant physical or biological relationships to

downstream fish migration, as represented by juvenile Salmonid abundance.



49

Table 5 and Appendix C give the results for the Pearson’s correlation analysis. Significant

interactions at or above the 5% confidence level were highlighted in bold text, and significant

interactions at or above the 10% confidence level were italicized and underlined. It is important to

note that a statistically significant value does not imply a biologically or physically significant

relationship between Project operation and the fish abundance variables. With large sample sizes,

such as those in this study, it was possible to quantify very small effects with statistical significance.

For example, a Pearson’s r-value of 0.01 shows that 1% of the variability in the dependent variable

was attributed to the independent variable. Although it was statistically significant, it explains only

1% of the variability of the fish abundance data and did not show a strong, obvious biological

relationship. If an r-value is highlighted in bold, there is a 95% probability that the stated

relationship is correct, even though it may not be physically or biologically meaningful.

As noted in the methodology section, a Pearson’s r-value at or close to 1 or –1 represents a strong

correlation and a Pearson’s r-value at or close to 0 represents no correlation. As noted in Table 5, the

majority of the values were close to zero, indicating little if any mathematical relationship between

the dependent and independent variables. The only value over 50% in Table 5 was a relationship

between upper chinook salmon abundance and lower intake temperature. While upper chinook

salmon were collected in the Upper Baker Lake and the temperature was taken from the Lower Baker

Lake (no Upper Lake temperature data were provided), the temperature cycles over time are likely to

be similar. However, while chinook salmon records were plentiful, chinook salmon abundance was

limited, weakening the correlative association. Photoperiod had a Pearson’s r-value of 33% and 28%

with respect to upper chinook and upper coho salmon, respectively. Solar radiation and natural flow

values were also consistently high relative to other independent variables. The strength of these

relationships, in contrast to the other Project variables, support the idea that natural stream and/or

meteorology variables may be the critical factors motivating fish migration. Specifically, in this



50

study, natural flow, lower intake water temperature, photoperiod, and solar radiation had the most

consistent pattern of Pearson’s r-values greater than 10%. Given the correlation data presented in

Table 5, no strong correlations were detected between Project operations and downstream fish

abundance.

The regression coefficients from independent regression analyses are displayed in Appendix D. For

all fish species and all fish totaled in both locations, the adjusted R-squared value ranged from 0 to

0.042 for all project operation variables. This indicated that 0 to 4.2% of the original variability was

explained by the relationship between fish abundance and any of the predictive Project operation

variables. Therefore, 95.8% to 100% of the variability was unexplained. For meteorology data,

these values were 0 to 12.2%. Of this, the range for Lower Baker Lake was 0 to 2.8%, and the range

was 0 to 3.8 when excluding photoperiod. Photoperiod and solar radiation displayed the strongest

fits. The univariate analysis results support the correlation results. No independent relationships

were evident between fish abundance and Project operations variables as represented by operation

flows, water surface elevation, or temperature.

Three multivariate analyses were conducted: 1) all Project operation variables against each fish

species and the total fish abundance for both locations, 2) all meteorology variables against each fish

species and the total fish abundance for both locations, and 3) multivariate analyses using the

independent project variables that showed the strongest correlative relationships (anything greater or

equal to 10%) to the fish abundance variables for each fish species and the total fish abundance for

both locations.

In order to develop a strong predictive model using multivariate analyses, the regression equation

that describes the relationship between the variables should have a strong fit. The multiple R-



51

squared values for the Project operation analyses ranged from 2.8% to 5.8%. These values were

1.2% to 14.5% for the meteorology data, where the best fits occurred in the Upper Baker Lake data.

The multiple R-squared values for the best-fit data ranged from 2.7% to 15.4%, where Upper Coho

and Upper Sockeye salmon regression fits were both slightly over 15%. While all of these fits are

better than the independent regression analyses, they still represent poor fits in terms of defining a

relationship. Overall, photoperiod was the strongest factor, followed by solar radiation. Upper

Baker Lake findings had more strength, overall, than Lower Baker Lake findings. Meteorology data

had better fits than Project operation data on the Upper Baker Lake salmon abundance and the

opposite was true for the Lower Baker Lake salmon abundance.

As noted, for the multivariate regression analyses, less than 15.4% of the original variability in the

fish abundance data can be explained by the data sets analyzed and less than 5.8% of the original

variability in the fish abundance data can be explained by the Project operation data sets analyzed.

Given this, a reliable model linking Project operation data and fish abundance cannot be developed.

While the data used in this study had some uncertainties associated with them and required several

assumptions, the findings are consistent with other natural fish migration studies. As detailed in the

introductory chapter, past studies have shown that natural meteorological phenomena, such as

photoperiod, solar radiation and water temperature, are the dominant factors motivating fish

migration. Similarly, in this study, photoperiod and temperature were the strongest factors in the

correlation relating to fish abundance.



52

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