In Cooperation with the U.S. Fish and Wildlife Service 

Assessment of Factors Limiting Klamath River Fall 

Chinook Salmon Production Potential Using Historical 

Flows and Temperatures 

By John M. Bartholow and James A. Henriksen 

Open File Report 2006–1249 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

P. Patrick Leahy, Director 

U.S. Geological Survey, Reston, Virginia 2006 

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imply endorsement by the U.S. Government 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

ii
Contents 

Executive Summary............................................................................................................................................................1

Introduction .........................................................................................................................................................................1

Objectives ............................................................................................................................................................................5

Methods ...............................................................................................................................................................................5

Model Selection..............................................................................................................................................................5

General Description of Salmod ................................................................................................................................6

Data and Parameter Sources for Salmod ..............................................................................................................8

Definition of Life-History Structure..............................................................................................................................9

Life Stage and Size Classes ......................................................................................................................................9

Weight:Length Data....................................................................................................................................................9

General Biological Year Timing..............................................................................................................................11

Physical Data.................................................................................................................................................................13

Study Area..................................................................................................................................................................13

Flow and Temperature Reaches ............................................................................................................................14

Flow and Water Temperature Data.......................................................................................................................15

Meso- and Microhabitat..........................................................................................................................................16

Geomorphic Segments.........................................................................................................................................16

Mesohabitat Types ...............................................................................................................................................16

Microhabitat Suitability Criteria .........................................................................................................................18

Microhabitat Quantification................................................................................................................................18

Model Processes..........................................................................................................................................................21

Spawning....................................................................................................................................................................21

Spawner Characteristics.....................................................................................................................................21

Fecundity ................................................................................................................................................................21

Redd Area and Superimposition ........................................................................................................................21

Spatial and Temporal Distribution of Spawners .............................................................................................22

Comments on Spawn Timing...............................................................................................................................23

Egg Development and Juvenile Growth ...............................................................................................................25

Egg Development Rate.........................................................................................................................................25

Emergence Temperature.....................................................................................................................................26

Emergent Length ...................................................................................................................................................27

Juvenile Growth Rates.........................................................................................................................................27

Movement and Associated Mortality....................................................................................................................28

Freshet Movement................................................................................................................................................28

Seasonal Movement Timing and Attributes.....................................................................................................29

Base Mortality Rates................................................................................................................................................31

Thermal Mortality Rates ..........................................................................................................................................31

Egg Thermal Mortality Rates...............................................................................................................................32

In Vivo Egg Mortality ............................................................................................................................................35

Juvenile and Adult Thermal Mortality Rates....................................................................................................38

Verification of Thermal Mortality Rates............................................................................................................39

iii
Uncertainty in Thermal Mortality Rates............................................................................................................42

Effect of Disease onJjuvenile Thermal Mortality............................................................................................44

Habitat Capacity........................................................................................................................................................47

Habitat-Induced Movement Rules.....................................................................................................................48

Distance Moved Mortality Rate..........................................................................................................................49

Exogenous Production.............................................................................................................................................49

Tributary Contributions ........................................................................................................................................50

Hatchery Contributions ........................................................................................................................................52

Summary of Model Parameters and Variables...........................................................................................................53

Calibration Targets...........................................................................................................................................................55

General Model Timing..................................................................................................................................................55

Juvenile Growth............................................................................................................................................................55

Index of Abundance .....................................................................................................................................................57

Overall Survival/Production........................................................................................................................................57

Discussion on Model Parameterization .......................................................................................................................58

Interpreting Model Results .........................................................................................................................................58

Uncertainty Inherent in Model Results.....................................................................................................................58

Drawing Inferences from Model Results .................................................................................................................59

Simulation Results............................................................................................................................................................61

Habitat Availability........................................................................................................................................................61

Life-History Timing........................................................................................................................................................62

Model Fit to Measured Data.......................................................................................................................................63

Annual Production Potential.......................................................................................................................................65

Causes of Mortality ......................................................................................................................................................66

Effects of Water Delivery on Simulated Production Potential .............................................................................70

Effects of Water Temperature on Simulated Production Potential.....................................................................72

Bottlenecks?..................................................................................................................................................................74

Summary of Important Modeling Inferences ..............................................................................................................74

Recommendations for Further Study ............................................................................................................................75

References.........................................................................................................................................................................77

Acknowledgments............................................................................................................................................................86

Appendix A. Salmod Input Files ....................................................................................................................................87

Appendix B. Habitat Suitability Criteria.......................................................................................................................87

Appendix C. Flow Versus Weighted Usable Area Relationships............................................................................90

Appendix D. Salmod Sensitivity Analysis..................................................................................................................103

Appendix D. Salmod Sensitivity Analysis..................................................................................................................104

Methods........................................................................................................................................................................104

Findings.........................................................................................................................................................................106

Discussion....................................................................................................................................................................106

Appendix E. Fahrenheit to Celsius, and Celsius to Fahrenheit Conversion Chart...............................................111

iv
Figures 

1. Klamath Basin, Oregon and California, and approximate location of power-producing facilities on the 

main stem.............................................................................................................................................................................4

2. Conceptual illustration of the variety of factors important in controlling salmon production throughout 

Salmod’s biological year. ..................................................................................................................................................7

3. Weight:length relations for the Klamath River, Trinity, and Sacramento Rivers. ............................................10

4. Approximate timing of the various ocean-type life-history phases for fall Chinook salmon.........................12

5. Salmon production model study area in northern California, ranging from Iron Gate Dam to the Scott 

River.. ..................................................................................................................................................................................14

6. Mean weekly discharges in cubic feet per second for two river reaches for biological years 1990–1995 

from SIAM..........................................................................................................................................................................15

7. Mean weekly water temperatures (°C) for two reaches for water years 1990–1995 from SIAM. ...............16

8. Comparison between measured water temperatures downstream from Iron Gate Dam and spawning 

activity through time.. ......................................................................................................................................................24

9. Egg and alevin development rate as a function of mean weekly water temperature. ..................................25

10. Juvenile growth rates for different weight fish (grams) as a function of mean weekly water 

temperature.. .....................................................................................................................................................................28

11. Index to the average number of juvenile Chinook trapped at each of three locations along the Klamath 

River, 2000 to 2004. ..........................................................................................................................................................30

12. Egg mortality from low constant water temperatures........................................................................................35

13. Fertility rate of lots of Iron Gate eggs plotted against measured main stem water temperature at the 

time of entry.......................................................................................................................................................................36

14. Fertility rate of lots of Iron Gate eggs plotted against simulated main stem water temperature at the 

time of entry. ....................................................................................................................................................................37

15. Mortality as a function of mean weekly water temperature used in Salmod simulations. .......................39

16. Data supplied by Bill Chesney for young of year Chinook outmigrants on the Shasta River......................50

17. Juvenile production index for Bogus Creek calculated from unpublished data. .........................................52

18. Young of year length measurements from Tom Shaw (USFWS) labeled to highlight initial, maximum, and 

minimum values. ...............................................................................................................................................................56

19. Average length of emigrating juvenile Chinook salmon trapped at each of three locations along the 

Klamath River, 2000 to 2004. Data courtesy of the Arcata Fish and Wildlife Office. ...........................................57

20. Gross habitat availability as a function of discharge through the entire study area.. .................................61

21. Comparison of spatial distribution of peak egg numbers (scaled to fit) relative to habitat availability for 

fry and presmolts during their respective peak weeks in a single simulation year.............................................62

22. Average simulated life-history timing through the biological year for fall Chinook salmon in the study 

area. ...................................................................................................................................................................................63

23. Comparison between Salmod’s estimate of average weekly outmigration just upstream from the Scott 

River and the field measurement index at the Kinsman trap site............................................................................64

24. Comparison between Salmod’s estimate of average weekly outmigrant length and field measurements 

taken at the Kinsman trap site. .....................................................................................................................................65

25. Time series of annual production potential by water year, both numbers of juveniles and their biomass, 

surviving to move downstream from the Scott River.................................................................................................66

26. Relationship between annual production potential and biomass for the 43-year simulation period........66

v
27. Average annual mortality by cause over the 43-year simulation period for eggs, fry, presmolts, and 

immature smolts. .............................................................................................................................................................67

28. Mortality irregularity index showing the relative infrequency (and its inverse, the relative predictability) 

of annual mortality due to different factors. ...............................................................................................................68

29. Probability of flow equaling or exceeding the given flow in the months of October and November 

immediately downstream from the Shasta River........................................................................................................69

30. Probability of flow equaling or exceeding the given flow in the months of March and April immediately 

downstream from the Shasta River...............................................................................................................................69

31. Time series of mortality from several of the less frequently consistent causes by water year.................70

32. Annual production portrayed as a function of water delivery downstream from Iron Gate Dam. ............71

33. Annual production compared with maximum weekly water temperature in the last 1two weeks of 

October...............................................................................................................................................................................73

34. Annual production compared with maximum weekly water temperature in the first two weeks of May.

..............................................................................................................................................................................................73

Tables 

1. Life stage and size class naming and break points. ...............................................................................................9

2. Weight:length relationship for Klamath River fall Chinook salmon....................................................................11

3. Correspondence between Salmod's weekly time-step number and the date during the biological year..13

4. Defined flow and temperature reaches for Klamath River study area as used in SIAM and Salmod. .......14

5. Attributes of the mesohabitat types in geomorphic segments 1 and 2. ............................................................17

6. Physical habitat study sites [and abbreviations], mesohabitat types sampled, number of transects, and 

hydraulic data collected in Segments 1 and 2. ...........................................................................................................18

7. Habitat suitability variables used for three Chinook life stages. ........................................................................18

8. Average proportion of main stem Klamath River spawning by week (left hand table) and spatial 

spawning segment derived from Magneson and others (2000)...............................................................................23

9. Attributes for seasonal outmigration for pre- and immature Chinook smolts for March 11 to May 12 of 

each year. ..........................................................................................................................................................................30

10. Calculation of mean weekly mortality rate as a function of mean daily water temperature (diel 

fluctuations of 3°F) for Chinook salmon........................................................................................................................34

11. A compilation of published information and summary of the observed relationships between water 

temperature and various attributes of spawning performance in Chinook salmon, with inferences on the 

sublethal elevated temperature range, derived from the scientific literature, agency reports, and interviews 

with fishery biologists and hatchery workers.............................................................................................................40

12. Estimates of thermal conditions known to support various life stages and biological functions of 

anadromous salmon.........................................................................................................................................................41

13. Comparison of juvenile Chinook mortality rates as a function of mean water temperature. .....................46

14. Comparison of maximum number of individuals per unit Weighted Usable Area used in different 

applications.. .....................................................................................................................................................................48

15. Maximum distance moved per week associated with 100-percent mortality during habitat-induced 

movement...........................................................................................................................................................................49

vi
16. Date of release, number and weight of juveniles released from Iron Gate Hatchery into the Klamath 

River since the beginning of the “early release” strategy........................................................................................53

17. Summary of important model structural elements, parameters, variables, and potential calibration data, 

with notes on their origin, status, and inherent uncertainty. ...................................................................................54

18. Progression of model development and application stages.............................................................................60

 

Conversion Factors 

Inch/Pound to SI 

Multiply By To obtain 

Length 

mile (mi) 1.609 kilometer (km) 

Area 

acre-foot (acre-ft) 1,233 cubic meter (m3) 

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3) 

Flow rate 

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s) 

Mass 

ounce, avoirdupois (oz) 28.35 gram (g) 

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

vii
SI to Inch/Pound 

Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

millimeter (mm) 0.03937 inch (in.) 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

Area 

square meter (m2) 10.76 square foot (ft2) 

square kilometer (km2) 0.3861 square mile (mi2) 

Flow rate 

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per second (m3/s) 22.83 million gallons per day (Mgal/d) 

Mass 

gram (g) 0.03527 ounce, avoirdupois (oz) 

kilogram (kg) 2.205 pound avoirdupois (lb) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8 

viii
Assessment of Factors Limiting Klamath River Fall 

Chinook Salmon Production Potential Using 

Historical Flows and Temperatures 

By John M. Bartholow and James A. Henriksen 

Executive Summary 

We parameterized and applied a deterministic salmon production model to infer the degree 

to which river flows and temperatures may limit freshwater production potential of the Klamath 

River in California. Specific parameter requirements, data sources, and significant assumptions 

are discussed in detail. Model simulations covered a wide variety of historical hydrologic and 

meteorologic conditions for 40+ years of environmental data. 

The model was calibrated only qualitatively, appearing to perform well in predicted 

outmigrant timing, but overestimating growth. Egg-to-outmigrant survival was near that 

reported for other rivers north of the Klamath River. 

Predicted production potential appeared to be determined by multiple causes involving both 

regularly occurring habitat-related constraints and irregularly occurring exposure to high water 

temperatures. Simulated production was greatest in years of intermediate water availability and 

was constrained in both dry and wet years, but for different reasons. Reducing mortality 

associated with limitations to juvenile habitat, if possible, would be expected to have the highest 

payoff in increasing production. Water temperature was important in determining predicted 

production in some years but overall was not predicted to be as important as physical 

microhabitat. No single mortality cause acted as a true “bottleneck” on production. 

Model uncertainty is addressed through a sensitivity analysis. Predicted habitat area may be 

a large source of model uncertainty and sensitivity, but collectively, model parameters associated 

with timing of events (for example spawning, fry emergence, and emigration) or related triggers 

control much of the model sensitivity. 

Though model uncertainty remains, one can begin to explore potential alternatives to reduce 

production limitations. Specific recommendations are made regarding future study and reducing 

uncertainty. 

Introduction 

Existing coho (Oncorhynchus kisutch) and Chinook (O. tshawytscha) salmon stocks from 

the lower Klamath River and its tributaries (Figure 1) are in continued decline or serious risk of 

extinction (Nehlsen and others 1991). Regional stock declines have been attributed to many 

factors, including habitat loss or damage, hydropower, water diversion (for example agricultural 

depletions and related water quality issues), logging, overfishing, and genetic alterations 

associated with hatchery augmentation, all of which are relevant concerns in the Klamath Basin. 

1
The U.S. Fish and Wildlife Service, several Indian nations, State fishery agencies, and other 

interested natural resource groups in the area have expressed a need to more fully understand the 

factors that appear to limit the freshwater production of anadromous stocks now found in the 

lower Klamath River. It is believed that a more complete understanding and analysis of how 

flow and water temperature regimes downstream from the impounded reaches of the main stem 

likely either enhance or retard production will enable development of long- and short-term flow 

recommendations to the appropriate water management and control agencies, specifically the 

Bureau of Reclamation managing the Klamath Irrigation Project and the electric utility provider, 

PacifiCorp, who has responsibility for managing several hydropower facilities on the main stem 

Klamath River. 

It is presumed by many that salmon production on the Klamath River is governed by the 

interaction of many physical and biological factors. Biological factors are complicated, and 

there is much to learn about how these factors regulate production. Physical factors, principally 

streamflow and physical facilities, are more subject to management control. Streamflow, in turn, 

affects the quantity of physical habitat in the river and, to varying degrees, Klamath River water 

temperatures. 

Fisheries science has long recognized the role physical habitat plays in providing living 

space for reproduction and rearing of fish (Stalnaker and others, 1995). At the riverscape scale, 

quantifying habitat availability as a metric to estimate maximum salmonid productive capacity 

has now become a standard fisheries analysis technique (for example Beechie and others, 1994). 

At smaller scales, quantifying microhabitat bottlenecks through time to provide insight to a 

population’s reproductive and rearing success has also become standard practice. However, use 

of microhabitat bottlenecks as metrics to evaluate a population’s reproductive and rearing 

potential should be used with caution as stated by Stalnaker and others (1995): 

 “Habitat bottlenecks are important, but sometimes poorly understood. The basic premise of 

the habitat bottleneck is that populations of aquatic organisms are related to the availability 

of habitat through time. This definition has been commonly misinterpreted to mean that ... 

fish populations must be instantaneously correlated with habitat. Such an interpretation 

logically requires a belief in instantaneous mortality and spontaneous generation, or the 

ability of fish to move quite quickly among habitats, in order for fish populations to increase 

and decrease at the same rate that habitat can change in a stream. In reality, habitat 

limitations affecting a population usually occur prior to the time when the population size is 

measured. Adult populations are frequently determined by recruitment, which is highly 

correlated with the amount of habitat available for early life stages of the species. Such 

“habitat events” usually affect recruitment via habitat types directly related to the production 

and survival of eggs, larvae, and fry (such as spawning habitat and young-of-year rearing 

habitat), or indirectly related to survival by the growth rates of age-0 fish (such as 

temperature regime, young-of-year rearing habitat, or microhabitat for invertebrate food 

supplies). These habitat bottlenecks typically occur 1 to 3 years prior to maturation, when 

their effects are detectable in the adult population.” 

As habitat quantification techniques have become more sophisticated, useful generalizations 

have resulted (Bovee and others, 1994): 

• There may be several consecutive, independent, habitat “events” that ultimately affect 

adult populations, and may involve spawning habitat, fry rearing habitat, thermal regime, 

and so forth. 

2
• Limiting events frequently occur over variable time scales, such as short-term events that 

limit spawning on a single occasion, compared to events that recur every year, such as 

juvenile crowding. 

• Habitat may be limited by both high and low flow events and by the rate of change of 

flow events. 

• The smallest amount of habitat available during the year may not necessarily be the 

limiting event (such as during the winter when fish are less active). 

• Habitat types not directly used by the species (such as macroinvertebrate habitat as it 

affects food supply for fish) may be more important than the habitat directly used by the 

species. 

Through time, we have become more adept at conceptualizing and understanding habitat 

bottlenecks and other limiting factors. We have also added to our technological toolbox that 

helps to identify management options, evaluate trade-offs, focus on spatial and temporal effects, 

and recognize counterintuitive consequences resulting from changing system operations in 

regulated rivers such as the Klamath. In this instance, we have applied a decision support system 

(the System Impact Assessment Model, or SIAM) and specifically its biological component 

(Salmod). Both of these models have been through multiple peer review processes (see 

Williamson and others 1993; Bartholow and others 1993; Bartholow 1996; Campbell and others, 

2001; Bartholow and others, 2004; and Bartholow, 2005). The goal of this report is to begin to 

understand the degree to which habitat, both microhabitat and macrohabitat (sensu Stalnaker and 

others 1995) may influence production of Chinook salmon on the Klamath River downstream 

from Iron Gate Dam. We do not believe we have enough high-quality data yet to do the same for 

other salmonids on this river. 

USGS has been involved in studies on the Klamath River for several years, working to 

improve data quality and predictive models of water quantity, water quality, and anadromous 

fish production. It must be clearly understood that the U.S. Geological Survey has performed this 

analysis solely to assist the resource agencies with a framework for making their 

recommendations to water management agencies. We make no specific water management 

recommendations. 

3
Figure 1. Klamath Basin, Oregon and California, and approximate location of power-producing 

facilities on the main stem. More study-area specific detail is provided in Figure 5. Map adapted 

with permission from the Water Education Foundation, Sacramento, California. [mi, miles; km, 

kilometers] 

4
Objectives 

This document addresses three relatively discrete tasks performed for this analysis in 

conjunction with a review committee of local Klamath River biologists: 

1. Develop evidence-based model parameters and output comparison metrics for a salmon 

freshwater production model. 

a. Evaluate and document historical physical variables (flow and water temperature) and 

other biological factors relevant to parameterize a Chinook salmon production model 

for the lower Klamath River downstream from Iron Gate Dam. 

b. Explore alternative production metrics and choose the most relevant to quantify the 

Chinook salmon production in response to the variety of hydro-meteorological years 

from 1961 to 2003. 

 2. Parameterize a salmon production model with best estimate of parameter values. 

 a. Conduct parameter sensitivity analysis to identify their inherent uncertainty, with 

attention to questioning and refining parameters demonstrating the most sensitivity. 

b. Establish a solid framework for future/continued model calibration 

3. Using the parameterized model to identify (and eventually overcome) limiting factors: 

a. Compare and contrast years and seasons of dissimilar hydrology and meteorology. 

b. Identify dominant freshwater limiting factors across the historical flow and 

temperature record. 

c. Determine effects of river flow on microhabitat availability. 

Note that rigorous calibration of this model is not yet possible. Field data techniques to 

enumerate the annual production of salmon on the Klamath River downstream from Iron Gate 

Dam are still being perfected on the main stem Klamath River. It is our hope that when several 

years of these data are available, the model can be more thoroughly tested and improved. 

Methods 

The modeling environment, including model selection and operation, and data requirements 

are outlined in the following sections. 

Model Selection 

Salmod (Version 3.74) is a component of the Instream Flow Incremental Methodology, or 

IFIM (Stalnaker and others 1995). Another component of the IFIM methodology, the Physical 

Habitat Simulation System (PHABSIM), has been criticized (for example Conder and Annear, 

1987) as demonstrating no relationship between microhabitat quantification (weighted usable 

area, or WUA, an index to suitable microhabitat) and fish standing crop. Yet many other 

researchers persist in developing and using these relationships to relate WUA and standing crop 

(for example Capra and others 1995; Heggenes and others, 1996). Like Stalnaker and others 

(1995) and Bovee and others (1994), Orth (1987) argued persuasively that it is illogical to expect 

any instantaneous relationship between habitat availability and fish density to hold true. Orth 

outlined the hypothesis that microhabitat availability may limit fish populations, but episodically, 

5
not continuously. In addition, he notes that other factors, such as water temperature, must be 

included in an analysis. In effect, Orth (1987) said that the PHABSIM models were incomplete. 

In response, the Salmod model was constructed to integrate habitat limitations to a population 

through time and space, both microhabitat and macrohabitat. Note that when we refer to habitat 

limitations, this does not necessarily mean that freshwater habitat is the ultimate factor limiting 

populations. Habitat constraints may simply reduce production while other factors, such as 

ocean conditions or fishing pressure may be the ultimate “bottleneck.” 

Salmod was chosen for the Klamath River for three reasons. First, Salmod was initially 

conceived and tested on the Klamath's largest tributary, the Trinity River, specifically for that 

river's Chinook salmon restoration (Williamson and others 1993), building on the foundation laid 

by similar models. Second, the continued development and application of Salmod fits into an 

ongoing research program at USGS. The objectives of much of the fieldwork we have helped 

design and assisted with have centered on collecting data sufficient to parameterize and confirm 

the model's predictive ability in response to changing flow and temperature regimes. Third, 

Salmod has been the “third leg” of the SIAM decision support system developed for the Klamath 

River (Campbell and others, 2001), on a parallel with a water quantity model to predict flows 

and a water quality model to predict water temperatures to evaluate the efficacy of alternative 

water management plans that may assist resource agencies with their task of recovery of 

anadromous fish in the basin. 

General Description of Salmod 

Salmod simulates population dynamics for freshwater (for example in-river) salmonids; no 

population dynamics are included for ocean habitat. Though the model is applicable for both 

anadromous and non-anadromous salmonids, this document will only discuss the anadromous 

life-history implementation. The model is fully described in Bartholow and others (1993 and 

2001); only an outline of the model is presented here. 

The model’s premise is that egg and fish mortality are directly related to spatially and 

temporally variable micro- and macrohabitat limitations, which themselves are related to the 

timing and amount of streamflow and other meteorological variables. Salmod is a spatially 

explicit model (sensu Dunning and others 1995) where habitat quality and carrying capacity are 

characterized by the hydraulic and thermal properties of individual mesohabitats, which serve as 

spatial computation units in the model. The model tracks a population of spatially distinct 

cohorts that originate as eggs and grow from one life stage to another as a function of water 

temperature in a computation unit. Individual cohorts either remain in the computational unit in 

which they emerged or move, in whole or in part, to nearby units. Model processes include 

spawning (with redd superimposition), incubation losses (such as redd scour or dewatering), 

growth (including egg maturation), mortality due to water temperature and other causes, and 

movement (freshet-induced, habitat-induced, and seasonal). 

The model is organized around events (Figure 2) occurring during a biological year 

(sometimes known as a production year) beginning with spawning and typically concluding with 

fish that are physiologically “ready” (for example presmolts) swimming downstream toward the 

ocean. It operates on a weekly time-step for one or more biological years. Input variables (for 

example streamflow, water temperature, number and distribution of adult spawners) are 

represented by their weekly average values. The study area is divided into individual 

mesohabitat types (for example pools, riffles, or runs) categorized primarily by channel structure 

6
and hydraulic geometry but modified by the distribution of features such as fish cover. 

(Microhabitat refers to small-scale physical features defining suitability for fish on a fish’s scale, 

for example one meter. In contrast, mesohabitat refers to the character of the channel that 

defines microhabitat, for example tens of meters.) Thus, habitat quality in all computation units 

of a given mesohabitat type changes similarly in response to discharge variation.

Holding/Spawning

Adults

by river segment

Post-spawning mortality

Natality

Timing of spawning Water temperature

Number and sex ratio of adults

Fecundity (by size group)

Spawning habitat capacity

Eggs and alevins

in gravel

Base mortality

Water temperature-related mortality

Pre-spawning adult mortality

In vivo egg mortality

“Lost” eggs

Water temperature Maturation rate

Fry

by size group

Egg and alevin in-gravel mortality Hatchery/tributary supplementation

Water temperature Growth rate In-movement from upstream

Base mortality

Water temperature-related mortality

Fry mortality by size group

Pre-smolts

by size group Water temperature Growth rate

Base mortality

Water temperature-related mortality

Pre-smolt mortality by size group

Imm. smolt

by size group Water temperature Growth rate

Base mortality

Water temperature-related mortality

Imm. smolt mortality by size group

Base mortality

Water temperature-related mortality

Habitat capacity-effective incubation

Habitat capacity-redd superimposition

Freshet-induced movement

Habitat-induced movement

Seasonally-induced movement

Movement mortality

Out-reach movers

allocated to 

downstream reaches

or exit study area

Hatchery/tributary supplementation

In-movement from upstream

Freshet-induced movement

Habitat-induced movement

Seasonally-induced movement

Movement mortality

Out-reach movers

allocated to 

downstream reaches

or exit study area

Hatchery/tributary supplementation

In-movement from upstream

Freshet-induced movement

Habitat-induced movement

Seasonally-induced movement

Movement mortality

Out-reach movers

allocated to 

downstream reaches

or exit study area

Figure 2. Conceptual illustration of the variety of factors important in controlling salmon 

production throughout Salmod’s biological year. [Imm. Smolt, immature smolt]

7
Fish cohorts are tracked by life stage and size class within the spatial computation units. 

Streamflow and habitat type determine available habitat area for a particular life stage for each 

time-step and computation unit. Habitat area (quantified as weighted usable area, or WUA) is 

computed from flow:microhabitat area functions developed empirically or by using the Physical 

Habitat Simulation System (PHABSIM; Milhous and others, 1989) or similar model. Habitat 

capacity for each life stage is a fixed maximum number (or biomass) per unit of habitat area 

available estimated from literature or empirical data. Thus, the maximum number of individuals 

that can reside in each computation unit is calculated for each time-step based on streamflow, 

habitat type, and available microhabitat. Fish from outside the model domain (from stocking, 

hatchery production, or tributaries) may be added to the modeled stream at any point in their life 

cycle. 

Models like Salmod are attaining confirmation in the scientific literature. For example, 

Capra and others (1995) has demonstrated that spawning habitat availability reductions over 

continuous 20-day periods correlates well with production of juvenile trout. Building on Capra’s 

work, Sabaton and others (1997) and Gouraud (2001) have further explored the field of limiting 

factors, both microhabitat and macrohabitat, using population models markedly similar to 

Salmod, with some promising results. 

Data and Parameter Sources for Salmod 

In this report, we detail all the sources of data and parameter values developed or adopted for 

the Klamath River for fall Chinook salmon. There are three primary sources for initial parameter 

values for fall Chinook modeling on the Klamath River. The first is from the Trinity River flow 

evaluation (U.S. Fish and Wildlife Service and Hoopa Valley Tribe, 1999), which in turn was an 

outgrowth of the work done by Williamson and others (1993) and Bartholow and others (1993). 

These values were reinforced by Kent (1999) and Bartholow (2003) who applied Salmod for fall 

Chinook salmon (and other races) on the Sacramento River downstream from Shasta Dam. Both 

of these applications added credence to parameter values, strengthened confidence in the model's 

predictive utility, and supplemented the analysis toolbox. Somewhat less relevant, but still a 

useful part of the modeling foundation, is another application by Bartholow and Terrell (2001) 

for Atlantic salmon in the State of Maine. 

Second, because there is never a full complement of values available for any site-specific 

model application, literature values developed for other rivers or related species are used. By 

necessity, data were obtained from unpublished material when this was the best source available 

to represent the life-history of Klamath River Chinook. Where relevant, significant assumptions 

are included when data are borrowed from other species, locales, or races. A summary of the 

important model input values and assessment of their relative certainty or uncertainty is 

provided. 

Third, a great deal of biological information is beginning to become available for the 

Klamath River. Quite a bit of this information is, for the time being, found in unpublished 

reports and databases, but has been used extensively in developing parameters for this modeling 

effort. 

There are two things that may enhance the readers’ understanding of this report. The first is 

patience; there is a fundamental difficulty inherent in explaining a complex model that makes it 

hard to understand some portions of the model until other portions have been explained. The 

second is to understand that data input for many of the parameters are sets of paired values. For 

8
example, the thermal mortality values are described by a set of values for the temperature and 

corresponding life stage mortality rate (for example temperature1, mortality rate1, ... 

temperaturen, mortality raten). Salmod always performs a piece-wise interpolation between userspecified values to derive intermediate results, or if outside the range of supplied values, extends, 

but does not extrapolate, the terminal values. The availability of Salmod’s 10 input files for the 

Klamath River Chinook salmon is described in Appendix A. 

Definition of Life-History Structure 

Life Stage and Size Classes 

The naming of life stages and size classes is flexible in Salmod and generally reflects the 

nomenclature used by the local biologists. The egg class covers both eggs and in-gravel alevins 

(larvae or pre-emergent fry) with a developmental index roughly dividing the two equally in 

time. We refer to smolts as immature solely because these fish may be of a size indicative of a 

smolt but are not yet tolerant to saltwater, and they are still many kilometers from the ocean. 

Table 1 lists the class attributes chosen for the Klamath River and is a modification of the 

categorization used on the Trinity and Sacramento Rivers. 

Table 1. Life stage and size class naming and break points. 

Salmod life stage Sometimes known as Development index for eggs, 

Length class (mm) for juveniles 

Minimum Maximum 

Eggs/Alevins Eggs 0.0 0.6 

Alevins 0.6 1.0 

Fry Yolk-sac fry F1 30 35 

Fry F2 35 55 

Presmolts Parr P1 55 65 

Silvery parr P2 65 80 

Immature smolts Smolts S1 80 90 

S2 90 110 

S3 110 200 

Weight:Length Data 

Tom Shaw, U.S. Fish and Wildlife Service, supplied a spreadsheet containing length and 

weight data for the Klamath River (unpublished data). He had performed a standard regression 

on about 237 samples ranging from 43 to 116 mm fork length (FL). The regressions, compared 

to the values previously used on the Trinity, look like: 

Klamath Weight(g) = 10(-5.15 + 3.110 * Log10(FL-mm)) (R = 0.99) 

Trinity Weight(g) = 10(-4.86 + 2.908 * Log10(FL-mm)) (R = 0.74) 

The above formulae may be contrasted with the method reported for the Sacramento River 

by Kent (1999) that used an alternate formula based on a cubic regression of fork length and wet 

weight of developed for naturally reared fall Chinook salmon with lengths between 30 and 100 

9
mm. A cubic regression was used because the length and weight relationship for fish is 

approximately cubic (Busacker and others, 1990). Accordingly: 

Sacramento WW(g) = – 0.67 + 0.0282FL – 0.000491FL2 + 0.0000141FL3 

(R unspecified) 

where WW = wet weight (grams), and 

FL = fork length (mm). 

All three relations are shown in Figure 3 for the length ranges from which the data were 

derived. Whereas variability in the wet weight of individual fish of the same fork length may be 

due to true variation in weights, some variability may simply be explained by differences among 

individuals in fullness of the stomach or presence of water in the buccal (mouth) cavity. On the 

other hand, one might reasonably conclude that Klamath Chinook salmon have a better condition 

factor than those from the Trinity, at least for the time periods from which these fish were 

collected and relations developed. Klamath fish also appear to be slightly heavier than 

Sacramento fish of the same length. Note, however, that diseased juveniles can appear to have 

higher condition factors (Nick Hettrick, USFWS Arcata, written comm., 2006). Klamath River 

values dovetail well with “standard” values from the Piper and others (1982) hatchery handbook 

that were used to extend Tom Shaw’s relationship above 120 mm. 

Fry & Juvenile Weight:Length Relations

0.000

5.000

10.000

15.000

20.000

30 50 70 90 110

Fork Length (mm)

Wet Weight (g)

Klamath Trinity Sacramento

Figure 3. Weight:length relations for the Klamath River (Tom Shaw, U.S. Fish and Wildlife Service, 

written comm., 2005), Trinity River (Bartholow and others, 1993), and Sacramento River (Kent, 

1999). 

The weight:length relationship is used in Salmod to convert from one metric to the other. 

Fish grow in body mass (weight) and are then assigned the appropriate length. The exception to 

10
this is if fish lose weight; if so, they retain their previous length but must regain lost weight to 

add length. The weight:length relationship supplied to Salmod for the Klamath River is given in 

tTable 2. 

Table 2. Weight:length relationship for Klamath River fall Chinook salmon. Values for fish less 

than or equal to 120 mm were derived from Tom Shaw (written comm., 2005) using the formula 

given in the text; values for fish > 120 mm were derived from Piper and others (1982). The number 

of decimal points reflects the need to convert back and forth accurately and should not be 

construed to imply precision. 

Weight (g) Fork length (mm) Weight (g) Fork length (mm) 

0.278 30 92.00 200 

0.680 40 179.69 250 

1.361 50 310.50 300 

2.399 60 736.00 400 

3.875 70 1,437.50 500 

5.870 80 2,484.00 600 

8.466 90 3,944.50 700 

11.749 100 5,888.00 800 

15.803 110 7,062.44 850 

20.714 120 8,383.50 900

38.81 150 

General Biological Year Timing 

The fall Chinook life-history timing is illustrated in Figure 4. Adults begin entering the 

main stem in early August and are present near Iron Gate Dam by early September. Spawning 

commences in mid-October, when water temperatures cool, and is concentrated within a few 

weeks. Emergence is noted to begin in early February with essentially simultaneous emigration. 

The bulk of the ocean-type emigration from our Iron Gate to Scott study area has occurred by 

early June, tailing off completely by July. 

11
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Adult immigration (AI) General

AI - Klamathon (historical)

AI - Iron Gate

AI - Bogus Creek weir

AI - Shasta River weir

AI - Scott River weir

Spawning (fall)

Emergence

Emigration (general YOY)

Emigration Bogus Creek (YOY)

Emigration Shasta River (YOY)

Figure 4. Approximate timing of the various ocean-type life-history phases for fall Chinook 

salmon, adapted from Shaw and others (1997). Other values from Shaw and others have been 

omitted, for example spring Chinook spawning, over-summer juvenile outmigration, and areas 

downstream from the Shasta River. YOY is young of year.

Salmod is a weekly time-step model that, when used for an anadromous species with a single 

season in freshwater, most frequently begins with the onset of spawning and continues through 

the duration of outmigrating juveniles. Because fall run Chinook spawning on the Klamath 

River begins in mid-October, we have set the biological year to begin 1 October; this is 

convenient because this it is also a water year boundary. The simulation time-steps needed in 

some of Salmod's input files are simply chronological week numbers (Table 3). Note that 

simulation processes are initiated on the first day of the week, but simulation results are tabulated 

on the last day. This can be a cause for confusion when reviewing the output. 

Linda Prendergast supplied an alternative characterization of the life-history periodicity 

(PacifiCorp, 2003) for comparison with the Shaw and others (1997) report. Because there were 

notable differences, especially for outmigrant timing, we decided to rely on data collected at 

outmigrant trapping locations for modeling purposes. These data will be discussed in a 

subsequent section of this report. 

12
Table 3. Correspondence between Salmod's weekly time-step number and the date during the 

biological year. 

Simulation week Beginning date Simulation week Beginning date 

1 10/1 27 4/1 

2 10/8 28 4/8 

3 10/15 29 4/15 

4 10/22 30 4/22 

5 10/29 31 4/29 

6 11/5 32 5/6 

7 11/12 33 5/13 

8 11/19 34 5/20 

9 11/26 35 5/27 

10 12/3 36 6/3 

11 12/10 37 6/10 

12 12/17 38 6/17 

13 12/24 39 6/24 

14 12/31 40 7/1 

15 1/7 41 7/8 

16 1/14 42 7/15 

17 1/21 43 7/22 

18 1/28 44 7/29 

19 2/4 45 8/5 

20 2/11 46 8/12 

21 2/18 47 8/19 

22 2/25 48 8/26 

23 3/4 49 9/2 

24 3/11 50 9/9 

25 3/18 51 9/16 

26 3/25 52 9/23 

Physical Data 

Study Area 

The study area (Figure 5) covers a 75-km (46-mile) stretch of the Klamath River from Iron 

Gate Dam (RM 191.7) to just upstream from the Scott River (RM 145.3). Iron Gate forms the 

current upstream boundary of anadromous migration in the Klamath River, and the Scott River 

marks the current downstream limit of habitat that has been evaluated using PHABSIM. 

13
Figure 5. Salmon production model study area in northern California, ranging from Iron Gate Dam 

to the Scott River. This map, taken from SIAM, shows the location of several of the larger 

tributaries entering the Klamath River in the study area, many of which also contribute to the 

basin's existing salmon production. For reference, Interstate 5 crosses the Klamath River 

immediately upstream from the Shasta River. 

Flow and Temperature Reaches 

The study area was subdivided into 11 river reaches, each with its own relatively 

homogeneous flow and thermal regime. These reaches are described in Table 4. 

Table 4. Defined flow and temperature reaches for Klamath River study area as used in SIAM 

and Salmod.

Reach Length (km) Reach name 

1 0.32 Iron Gate Dam to Bogus Creek 

2 3.70 Bogus Creek to Dry Creek 

3 3.21 Dry Creek to Willow Creek 

4 5.63 Willow Creek to Cottonwood Creek 

5 8.69 Cottonwood Creek to Shasta River 

6 6.43 Shasta River to Humbug Creek 

7 10.46 Humbug Creek to Empire Creek 

8 8.85 Empire Creek to Beaver Creek 

9 8.85 Beaver Creek to McKinney Creek 

10 11.74 McKinney Creek to Horse Creek 

11 9.79 Horse Creek to Scott River 

14
Flow and Water Temperature Data 

For this analysis, mean weekly flows and water temperatures for each of the reaches listed in 

Table 4 were derived from SIAM for the Klamath River. 

Klamath flows

0

2,000

4,000

6,000

8,000

 10/ 6/1990 

 4/13/1991 

 10/19/1991 

 4/26/1992 

 11/ 1/1992 

 5/ 9/1993

11/14/1993

 5/22/1994 

 11/27/1994 

 6/ 4/1995

Mean Weekly 

Discharge (cfs) 

Segment 1 Segment 11

Figure 6 and Figure 7 illustrate a portion of the historical record supplied by SIAM. 

Klamath flows

0

2,000

4,000

6,000

8,000

 10/ 6/1990 

 4/13/1991 

 10/19/1991 

 4/26/1992 

 11/ 1/1992 

 5/ 9/1993

11/14/1993

 5/22/1994 

 11/27/1994 

 6/ 4/1995

Mean Weekly 

Discharge (cfs) 

Segment 1 Segment 11

Figure 6. Mean weekly discharges in cubic feet per second (ft3/s) for two river reaches for 

biological years 1990–1995 from SIAM. Reach boundaries are defined in Table 4.

15
Klamath temperatures

0

5

10

15

20

25

10/ 8/1990

2/25/1991

7/15/1991

12/ 2/1991

4/21/1992

9/ 8/1992

1/26/1993

6/15/1993

11/ 2/1993

3/22/1994

8/ 9/1994

12/27/1994

5/16/1995

Mean Weekly Water 

Temperature (°C)

Segment 1 Segment 11

Figure 7. Mean weekly water temperatures in degrees Celsius (°C) for two reaches for water 

years 1990–1995 from SIAM. Reach boundaries are defined in Table 4. 

Meso- and Microhabitat 

Geomorphic Segments 

For the physical microhabitat analysis, the study area was segmented into two geomorphic 

units. Segment 1 extended from Iron Gate Dam to 2.7 km downstream from Cottonwood Creek 

(total length 15.2 km), the approximate end of the broad valley. Segment 2 extended from 2.7 

km downstream from Cottonwood Creek to the Scott River (total length 66.6 km). Channel 

slope and valley confinement–a broad valley in Segment 1 compared to a confined canyon in 

Segment 2–were the physical characteristics used to define the subsegments. An examination of 

the flow versus WUA relationships for the two geomorphic subsegments confirmed that 

meaningful differences existed for the mesohabitat types in the two geomorphic subsegments. 

Mesohabitat Types 

Carefully distinguishing microhabitat and mesohabitat is important. Microhabitat refers to 

the collection of physical characteristics (depth, velocity, substrate, cover) that determine 

suitability of a given river’s “space” for fish of a given life stage (for example adults, juveniles), 

essentially on a square meter or finer scale. By contrast, mesohabitat refers to larger channel 

forms such as riffles, pools, or runs that tend to respond similarly to changes in flow. Morhardt 

and others (1983) argued that collecting data for a PHABSIM microhabitat study was best done 

at the mesohabitat unit (also known as a channel geomorphic unit) level where microhabitat is 

characterized by multiple samples of each mesohabitat type within each subsegment. Salmod 

carries this process further by retaining the exact sequence and length of each mesohabitat type 

as computation units within the model. 

A complete inventory of the mesohabitat types was conducted for the study area. 

Mesohabitat types were classified into two primary types and several subtypes to distinguish 

differences in hydraulic geometry (Table 5). The two primary types were backwaters (pools with 

16
a downstream hydraulic control, sometimes referred to as pools elsewhere in this document) and 

slopes (not influenced by a hydraulic control and commonly referred to as runs, riffles, and so 

forth). Slope habitats were further classified into three subtypes: low, moderate, and steep. The 

terms low, moderate, and steep slope are relative to each other at a reference flow. In Segment 1, 

all four mesohabitat types occurred in three channel configurations: main channel, split channels 

(perennial vegetation, rarely inundated, island present) and side channels (no vegetation or 

annual vegetation, inundated annually, sand or gravel bar). In Segment 2, however, the four 

mesohabitat types occurred only in the main channel and side channels; there were no split 

channels in Segment 2. The explicit sequence of mesohabitats and their respective lengths may 

be found in Salmod’s Stream.Dat input file (see Appendix A). 

Table 5. Attributes of the mesohabitat types in geomorphic segments 1 and 2. Note that side 

channels and split channels “double count” the length of the stream. Actual length of the study 

area was approximately 75 kilometers. Some rounding may occur. 

Mesohabitat type Units Mean length 

(meters) 

Cumulative length 

(kilometers) 

Percent length 

of segment 

Segment 1 

Low Slope (LS) 28 193.8 5.4 36 

Moderate Slope (MS) 24 127.0 3.0 20 

Steep Slope (SS) 3 87.5 0.3 2 

Backwater (BW) 27 182.4 4.9 32 

Side Channel (SC) 

Split Channel (SP) 

Subtotal 

Segment 2 

Low Slope (LS) 

Moderate Slope (MS) 

Steep Slope (SS) 

Backwater (BW) 

Side Channel (SC) 

Subtotal 

8 

3 

93 

119 

83 

40 

154 

37 

433 

129.3 

182.6 

-- 

134.1 

136.8 

104.5 

162.6 

160.5 

-- 

1.0 

0.5 

15.2 

16.0 

11.4 

8.4 

25.0 

5.9 

66.6 

7 

4 

100 

24 

17 

13 

38 

9 

100 

Total 526 -- 81.8 -- 

Hydraulic and microhabitat data for PHABSIM were collected following the protocol 

described in Bovee (1997). Table 6 shows the number of study sites in each segment, 

mesohabitat types sampled, transects per mesohabitat type, and hydraulic data collected. 

Transect data representing individual mesohabitats were averaged to create a single description. 

The calibration flows at which the hydraulic data were collected were approximately 1,300 and 

3,300 cubic feet per second (ft3/s) for the full channel velocity measurements and 8,500 ft3/s for 

the edge velocity measurements. Water surface elevations were measured at all three of the 

calibration discharges. 

17
Table 6. Physical habitat study sites [and abbreviations], mesohabitat types sampled, (number of 

transects), and hydraulic data collected in Segments 1 and 2. 

Attributes Number in Segment 1 Number in Segment 2 

Study sites 

 Main Channel [MC] 5 5 

 Split Channel [SP] 

 Side Channel [SC] 

Mesohabitat types sampled (# transects) 

 Low Slope [LS] 

 Moderate Slope [MS] 

 Steep Slope [SS] 

 Pool/Backwater [P] 

Mean column velocities (entire channel) 

Edge velocities 

Water surface elevations sets (all transects) 

2 

1 

 

8 (16) 

6 (8) 

0 

6 (28) 

2 

1 

3 

0 

0 

3 (4) 

3 (6) 

0 

3 (6) 

2 

1 

3 

Microhabitat Suitability Criteria 

Microhabitat quality in PHABSIM is defined by habitat suitability criteria for depth, 

velocity, and substrate or cover depending on the life stage. Table 7 shows which variables were 

used for spawning females, fry, and presmolts. The habitat suitability criteria for each 

microhabitat variable, for each life stage, may be found in Appendix B–Figures 1–3. These 

criteria were developed from observational data presented in Hardin-Davis, Inc. (2001; 

eventually published as Hardin and others, 2005), a habitat suitability criteria study conducted 

within the study area. We assumed rearing criteria are identical between pre- and immature 

smolts, an assumption supported by Hoffman and Deibel (1984), though they did note some 

differences. Cover was incorporated in the analysis for fry through the use of appropriate 

channel index codes and by restricting the available area to within ~3.5 m (12 feet) of the 

shoreline. The geometric mean was used as the compositing algorithm within PHABSIM. We 

note that the Chinook fry WUA curves for each habitat type do not reflect “irrationally” high 

habitat availability at low discharges experienced by some practitioners (Appendix C). 

Table 7. Habitat suitability variables used for three Chinook life stages. 

Life stage Depth Velocity Substrate Cover 

Spawning females 

Fry 

Presmolts 

 X X X 

 X X X 

 X X X 

Microhabitat Quantification 

Weighted Usable Area. The Physical Habitat Simulation Model (PHABSIM) was used to 

quantify flow:WUA relationships for each mesohabitat type for four life stages of Chinook 

salmon–spawning females, eggs/alevins, fry, and presmolts. WUA is expressed in ft2

/1000 ft of 

each mesohabitat type. Habitat relationships are provided in Appendix C, with figures 1–23 

representing Segment 1 and figures 24–41 representing Segment 2. 

18
Several noteworthy decisions were made regarding the quantification of suitable 

microhabitat (WUA), mesohabitat types, and the special situation of split and side channels. 

First, because steep slope mesohabitat types were not sampled for safety reasons (high velocities) 

the assumption was made that this mesohabitat type was equivalent to the minimum habitat 

found for all moderate slopes in each segment respectively. This assumption resulted in zero 

habitat for spawning. During the fall of 1999, Hardin-Davis, Inc. (2001) located 290 redds in the 

study area. Eighty-seven percent of the observed redds were in pools or low slope mesohabitat 

types. No redds were observed in steep slopes (or midchannel), appearing to substantiate the 

assumption that no suitable spawning habitat is available in steep-slope mesohabitats. Note also 

that steep slope mesohabitats account for only 2 percent of the total length of the upper segment 

(see Table 5). 

Second, Salmod expects only a linear series of habitat types, but parallel habitat types, split 

and side channels, necessitated some adjustments. Split channel (SP) and side channel (SC) 

habitats in Segment 1 were quantitatively combined by computing their relative proportions 

based on total lengths (after eliminating types classified as "unknown"). Two new habitat types 

(named SP1 and SC1) were created by weighting the mean WUA value of each of its 

components (LS, P, and so forth) with the parallel main channel component proportions. This 

combined habitat value was doubled for these two new habitat types for fry and juveniles 

because a given length of river has four edges instead of two for these habitat types. This 

process was not necessary in Segment 2. 

In a related step, the length of both split channels and side channels was summed and 

subtracted from the main channel habitat type. The main channel type was then followed by 

replacing all appropriate side channel types with the composite SP or SC for the total length of 

all side channel components. This keeps the river roughly the "correct" length as would be 

experienced by any floating particle, but with the doubling of the WUA shows that roughly twice 

as many fish per unit length could be found in the habitat complex represented by the split or 

side channels. Occasionally, two main channel types needed to be shortened to account for the 

total length of the SP or SC. Using this procedure, the main channel habitat type once or twice 

"disappeared" in the sense that this type was entirely eliminated, perhaps in conjunction with the 

unit downstream from the split channel. This process did not completely assimilate the main 

channel habitat type, but again, it is a small section of river in total. (Note that the length of river 

compiled from survey field notes agrees favorably with the length of river from digitized 7.5'-

minute quad maps. There were individual differences of plus or minus about 7 percent, but on 

the average for the whole length of river, the two methods agreed within 2 percent. The 

mesohabitats begin at the hatchery bridge, not at the dam, so the two systems were never 

completely compatible, but this length difference remains insignificant for Salmod.) 

We divided PHABSIM-generated WUA values by 1000 because Salmod expects ft2

/ft 

instead of ft2/1,000 feet. This step was done for each life stage and segment and then formatted 

to create Salmod’s WUA.dat input file. Note that we also inserted a zero, zero (0,0) pair for 

various life stage/segments to signify zero habitat at zero flow, if appropriate. These breakpoints 

are evident in Appendix C. 

Though we did not develop egg/alevin incubation curves directly using PHABSIM, we did 

derive them from the simulated spawning relationship. This was accomplished by retaining the 

rising limb of the spawning curve with increasing discharge but then holding the maximum 

WUA value constant with increasing flow. This is equivalent to using a criterion of “keep the 

eggs wet regardless of depth.” However, as is evident in Appendix C, we truncated this 

19
maximum value when flows exceed 13,000 ft3

/s, depressing the habitat value to zero at 14,000 

ft3

/s, due to increasing probability of redd-destroying bed scour or entombment (Bob Milhous, 

U.S. Geological Survey, unpub. data). Zero habitat above 14,000 ft3/s assumes that redd scour or 

entombment causes 100 percent egg mortality, an assumption for which we have little guidance. 

Lapointe and others (2000) estimated that scour would indeed “destroy” a redd, but they also 

estimated that flooding would scour a maximum of only 20 percent of a Canadian Shield stream. 

However, according to Bob Milhous (oral comm., 2005), their method only considered “net 

scour,” that is, what had changed from pre- to post-flood. Such a technique risks ignoring the 

during-flood maximum scour extent. Montgomery and others (1996) speculated much higher 

mortality when scouring occurs at only modest egg burial depths, for example 80 percent at 30 

cm. Note that Salmod’s weekly time-step may underestimate the frequency of scour from daily 

peak-flow events, especially if those flows were derived from SIAM’s monthly flow submodel. 

As is always necessary, we had to make assumptions about how to extrapolate PHABSIM 

model results both above and below our measured discharges. Some of these assumptions 

resulted in odd-looking breakpoints seen in Appendix C but are the best we can do at present 

without collecting data using different methods. 

Finally, we inserted a duplicate habitat unit, and halved the length of each, for units longer 

than 500 meters. This was because we were unsure whether Salmod would properly account for 

fish movement through long computation units when forced movement might be less than 500 

m. This approach did not change the length of the stream, it simply added computation units. 

There are two assumptions to note regarding our treatment of physical micro/ mesohabitat. 

First, in assessing the effects of alternative flows and water temperatures on different life stages 

of salmon, we are making the assumption that they do not use–and compete for–the same 

microhabitat at the same time, an assumption supported by Chapman and Bjornn (1969), Fraser 

(1969), and Mundie (1974). Although more than one juvenile life stage (for example fry and 

presmolts) may be present in the river at the same time, juvenile Chinook salmon use 

progressively deeper and faster water as they grow (Chapman and Bjornn, 1969). Therefore, it is 

reasonable to assume that there is minimal competitive interaction. The same holds true with the 

assumption that juveniles are not competing with those of other species (for example steelhead). 

Obviously, these are ecological niche assumptions that could be strengthened or challenged by 

additional research. 

Second, the quantification of WUA as a function of discharge is static. That is, we assumed 

that none of the flows we simulate result in changes to the channel geometry, substrate 

composition (gravel quantity or quality), or cover availability. We know that the Klamath River 

does change its channel morphology during high-flow events (Ayres Associates, 1999), but the 

assumption we are making is that such changes are tantamount to dynamic equilibrium, in other 

words habitat types remain in approximately the same proportion before and after channelchanging events. The geomorphic assessment by Ayres Associates (1999) supports the 

assumption that although channel forming events occur every 3 years or less, there has been very 

little overall aggradation or degradation since 1964. 

20
Model Processes 

Spawning 

Model specification of spawning involves their number, sex ratio, fecundity, habitat use 

characteristics, and timing. 

Spawner Characteristics 

Salmod requires the specification of the number and attributes of adults to “seed” the model. 

Magneson and others (2000) provide data showing 367 grilse and 1,978 adult spawners in the 

main stem Klamath River for 1999, resulting in a 45 percent spawning female to 55 percent nonspawner ratio. Mean spawner length was 694 mm (approximately 4 kg) from a historical data set 

reported by Healey and Heard (1984). However, recent data collected and summarized by the 

USFWS and USGS (Tom Shaw, U.S. Fish and Wildlife Service, and Sam Williamson, U.S. 

Geological Survey, written comm., 2006) indicate that the redd counts underestimate actual 

spawners several fold. After consultation, we increased the number of adults to 12,000 to better 

reflect this more recent information and used this value to seed the simulation study area for each 

year of all simulations unless otherwise stated. 

Fecundity 

Salmod uses a simple relationship for the number of eggs per gram of spawning female 

weight. We used average fecundity from the Lewiston Hatchery on the Trinity River of 3,732 

eggs for a 4-kg fish. This is in good agreement with an older mean value from the Klamath 

River of 3,634 listed by Healey and Heard (1984), who also noted that Klamath stocks appeared 

to be only half as fecund as Sacramento River fish of the same length. 

Redd Area and Superimposition 

Salmod calculates the amount of spawning habitat required each week for the number of 

female spawners ready to spawn given the value supplied for the area of an average redd's egg 

pocket. The model also calculates the probability of redd superimposition for previously 

constructed and undefended redds (McNeil, 1967) by knowing the area already occupied by preexisting redds. The model does not allow superimposition of redds created within one weekly 

time-step; in effect, this means that redds are defended for one week. 

A female spawner typically excavates multiple egg pockets by repeatedly digging in an 

upstream direction and depositing newly swept material on top of downstream egg pockets; the 

total area of disturbance may be more than 10 m2

 (Neilson and Banford, 1983). However, input 

values to Salmod specify only the approximate area of just the egg pockets for its calculation of 

superimposition mortality. The egg pocket refers to that area where deep streambed disturbance 

is at a maximum, indicative of essentially complete destruction of any previously deposited eggs. 

The egg pocket area is typically a value much smaller than the total area of disturbance. 

21
Tom Shaw (unpub. Data, 2004) supplied a spreadsheet providing Klamath River redd area 

measurements. We calculated values from approximately 85 individual records, each record 

being a unique redd followed over a maximum 4-week span. The results were as follows: 

Average maximum redd disturbance area = 15 ± 6.7 m2

Average maximum redd mound area = 5.9 ± 2.2 m2

Average redd egg-pocket area = 4.25 ± 1.4 m2

We chose to parameterize the model with the 4.25-m2 average value because it is close to 

that used by Bartholow and others (1993) for the Trinity River (4.5 m2). Salmod can simulate 

superimposition using three distinct probability algorithms. For this application, we have chosen 

the “random” option, signifying that current spawners neither seek nor avoid previously 

constructed redds in agreement with Bartholow's (1996) recommendation. 

Spatial and Temporal Distribution of Spawners 

Salmod allocates adult spawners to designated segments of the river at the beginning of each 

simulation year; these segments may be defined differently from the flow and temperature 

division points described previously. This information is typically available from carcass or redd 

counts. Required data include the number of adults spawning in each section of river, the 

proportion of female spawners to non spawners, and their weights. 

Magneson and others (2000) describes the seventh annual USFWS survey from 13 October 

to 19 November, 1999, covering 135 river kilometers between Iron Gate Dam and the confluence 

of Indian Creek at Happy Camp. A total of 989 redds were observed that year, but the report 

also summarizes data collected back to calendar year 1993, a total of seven years. The surveys 

have been conducted through six standardized river reaches, each with its own specified length. 

The exact dates sampled and reported have not been identical each year (in terms of week one 

being from October X to Y) but they are similar and useful. Although the peak of the spawning 

run on the Klamath River is known to be slightly variable from year to year (Magneson and 

others, 2000), evidence from many West Coast rivers suggests that spawn time is more a 

function of stock genetics (Burger and others, 1985; Quinn and Adams, 1996). However some 

of this evidence (Quinn and Adams, 1996) comes from sockeye salmon that spawn in headwaters 

whose conditions may be more hydrologically and thermally stable than many near-ocean 

conditions. 

The Magneson and others (2000) report comments on an observed trend in spawning 

location from year to year. For example, in 1999, 73 percent of redds lay between Iron Gate and 

the Shasta River, a proportion that has been increasing through time. Though it is obviously 

possible to develop separate spatial and temporal values for each year, we chose to aggregate 

them as a starting point, relying on a simple annual average. To accomplish this, we computed 

the relative proportions spawning through time and space from the Magneson and others (2000) 

data. We assumed that values in Magneson and others’s (2000) table 1 represented newly 

constructed redds in all cases, as that seemed to be the intent, though it was not explicitly stated. 

It was also unclear whether the first week’s measurement included redds that had been 

constructed more than one week in the past. Looking at the “skew” of the temporal distribution, 

it would appear that the spawning survey probably did not pick up the first week (or two?) of 

spawning. For this reason, we arbitrarily divided the first week's tally into two equal groups. In 

22
other words, we assumed spawning to have begun one week prior to the survey's start with onehalf the number of redds. Though it is likely that some new redds may have been missed at the 

end of the reported survey period, we did not similarly divide the last week reported because the 

values were so low. We also forced the week boundaries into those used by SIAM/Salmod, 

which always begins the biological year on October 1. The average first date of spawning was 

October 8. 

In some cases reported by Magneson and others data were missing through space for certain 

weeks. The average values we derived simply ignored those missing observations. In no case 

were the Iron Gate to Ash Creek (Reach 1) data missing however, so at least the majority of 

redds was counted because this is the highest density spawning area. The following table 

summarizes the results, which we have used to apportion spawners among simulation weeks and 

river segments. For example, peak spawning occurs the week of October 22. Within that week, 

and every week, 51+ percent of the spawning activity will be in the first 24 km downstream from 

Iron Gate dam. 

Table 8. Average proportion of main stem Klamath River spawning by week (left hand table) and 

spatial spawning segment derived from Magneson and others (2000) (right hand table). Only the 

first three spawning segments fall within the Salmod study area; proportions of adults were 

recomputed for these three segments alone and supplied to Salmod. 

Week beginning Percentage of 

spawners 

Spawning 

segment 

Segment ending 

(km downstream 

from Iron Gate Dam) 

Percentage of 

spawners 

8-October 10.72 

15-October 10.73 1 24.41 51.43 

22-October 32.32 2 48.91 5.59 

29-October 30.22 3 76.01 11.72 

5-November 11.69 4 94.81 6.63 

12-November 3.60 5 117.51 7.42 

19-November 0.72 6 136.61 17.22 

Total 100.00 Total 100.00 

The model does not account for “green” spawners directly, but does so indirectly by 

allocating spawning activity through time based on "new" redds identified in the redd counts. 

Thus, it does not matter if spawning occurs only in one week or is spread out over two months or 

more. You tell the model what proportion of the adults are "ready" to spawn each week of the 

designated period. These proportions will hold unless other things preclude spawning, such as 

temperatures being too high – they wait – or not enough spawning habitat to go around even with 

superimposition – the adults shed their eggs and die. Adult mortality will be discussed later, but 

suffice it to say that adults may suffer prespawn mortality from various causes, for example high 

water temperatures. 

Comments on Spawn Timing 

Spawn timing in Salmod is set to occur within a certain time window and is not specifically 

a function of streamflow or habitat availability, though it does depend on water temperature 

being within certain bounds. If outside the specified bounds, fish that are ready to spawn will 

wait for the next time-step and reevaluate the temperature. 

23
Some biologists believe that spawn timing may be more a function of habitat availability 

rather than water temperature. For example, if Klamath River flows are in the 6,000 ft3

/s range 

there may be little spawning habitat, whereas when flows are reduced to about 1,000 ft3/s, 

spawning habitat is abundant. Though spawning in Salmod does not directly respond to a habitat 

cue, limited spawning habitat will result in the spawners above the spawning habitat’s capacity 

shedding their eggs or dying unspawned. Thus, Salmod does indirectly consider habitat 

availability. 

The USGS committed to closely examine redd and carcass count records to see what water 

temperatures accompanied main stem spawning. We compared the measured weekly water 

temperatures downstream from Iron Gate Dam with carcass counts from an unpublished report 

supplied by Tom Shaw (USFWS). Figure 8 is the result. 

Iron Gate Water Temperatures Relative to Spawning Activity

Temperature Data from Bureau of Reclamation & U.S. Fish and Wildlife Service

1993-99 Spawning Data from USFWS (and estimated by USGS)

0

2

4

6

8

10

12

14

16

18

20

10/7

10/14

10/21

10/28

11/4

11/11

11/18

11/25

12/2

12/9

12/16

12/23

12/30

Week Ending

Mean Weekly Measured Water Temps (°C) 

0%

5%

10%

15%

20%

25%

30%

35%

Percentage of Total Spawners 

Mean spawn activity

1996

1997

1998

1999

2000

2001

2002

Range: 16.5 - 17.5°C

Range: 9.0 - 10.1°C

Figure 8. Comparison between measured water temperatures downstream from Iron Gate Dam 

and spawning activity through time. Vertical bars represent mean proportion of total spawning 

activity as measured by carcass counts.

It appears that main stem Klamath fall Chinook spawning occurs in a range from 9 to 17.5°C 

(48.2–63.5°F). Of course, this is no guarantee that temperatures outside this range limit 

spawning but are just associated with spawning at this location. The spawning temperature 

range we have been using in Salmod was 5.6 to 18.9°C (42.8–66°F) per Bell (1973) and 

McCullough (1999). In Salmod, if temperatures are outside these bounds, no spawning will 

occur that week. Because the range observed on the Klamath is within these wider bounds, we 

have decided to retain the broader range so that the model will be more likely to faithfully 

24
simulate the full range of historical conditions from 1961 to 2003. The broader range will likely 

also do a better job of simulating a variety of out-of-the-ordinary reservoir operations that might 

result in a wider thermal response to temperatures downstream from Iron Gate than we have seen 

in the historical record. 

Note also that the timing of spawners arriving at the hatchery can be slightly earlier than the 

first week in October. Historical hatchery data supplied by Gary Curtis, Yreka Calif., FWS 

(written comm., 07/19/04), shows fish entering the hatchery as early as 28 September, though in 

small numbers. 

Egg Development and Juvenile Growth 

Egg Development Rate 

After deposition, eggs incubate and hatch in approximately 6–12 weeks, depending on local 

river temperatures. Alevins remain in the gravel for an additional period, living off the stillattached yolk sac, and emerge when 100 percent of the development accumulation is reached. 

Crisp's (1981) quadratic equation was used to calculate each day's thermal contribution from 

deposition to hatch. The resulting rate values were decreased by one-half to account for the time 

from hatch to pre-emergence (a slight modification of Crisp, 1988), as was done for the Trinity 

River (Bartholow and others 1993). The resulting rate function supplied to Salmod is shown in 

Figure 9. This function shows that eggs will mature more rapidly at 10°C than at 2°C. Note that 

thermal accumulation begins with egg deposition and does not account for any ova maturation that 

may have occurred in vivo. 

Chinook Salmon Egg Deposition to Emergence

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Water Temperature (°C)

Percent Contribution to Development 

(/day) 

Derived from Crisp (1981) and Crisp 

(1988)

Figure 9. Egg and alevin development rate as a function of mean weekly water temperature. 

Each week adds to the percent development until 100 percent is reached. 

25
Emergence Temperature 

Salmod does not allow fry to emerge from the gravel until mean weekly water temperature 

exceeds a user-specified threshold. Previous applications have used a minimum of 8°C (46.4°F) 

based on studies of Atlantic salmon (Jensen and others, 1991), though it is known that in-gravel 

feeding for Chinook alevins may still be underway (Heming and others, 1982). 

When fish are starting to be caught in rotary screw and frame net traps on the Klamath River, 

measured main stem water temperatures given by SIAM have averaged 6.2°C (confidence 

interval [CI] ±0.5°C) since 1961. However, it is always possible that warmer, spring-fed 

tributaries may have contributed fry to the main stem, biasing any estimate of true emergence 

temperature. Therefore, we looked for other sources of basin-specific data. 

Jim Kilgore gave us records from an unpublished 1994 Scott River redd-monitoring study 

indicating that emergence was indeed highly correlated with 8°C. The Bogus Creek outmigrant 

study (Jong and Mills, 1993 draft) only recorded weekly maximum and minimum temperatures, 

but was helpful, if inconsistent. Weekly mean temperatures accompanying significant spikes in 

early outmigration were 10.9°C in 1986, 2°C in 1987, 5.9°C in 1988, 6°C in 1989, and 8.8°C in 

1990. These values average 6.7°C if all values are used and 7.9°C if the 2°C value is considered 

an anomaly. We also looked at dates for Chinook fry observed emigrating from Fall Creek when 

that stream was still accessible to anadromous salmon. Data from an old California Department 

of Fish and Game report (Coots, 1957) show fry leaving that system as early as the week ending 

January 2, though large numbers did not typically outmigrate until the week ending January 23. 

Peak outmigration from Fall Creek did not typically occur until late February to early March. 

Temperature data from Coots (1957) are limited but do characterize water temperatures in Fall 

Creek in the range of 3.9 to 11.1°C (39 to 52°F) during the 1950–1951 spawning and rearing 

period. Finally, from charts presented in Chesney and others (2004, Chart 19), the mean weekly 

water temperature associated with significant early Chinook fry outmigration on the Shasta River 

was approximately 8.3°C (47°F). 

We have consulted with others on this issue, and opinions vary. Thomas Quinn (University 

of Washington, written comm., 2006) believes there may indeed be a threshold emergence 

temperature, though it might vary from river to river or area to area. He cites anecdotal 

information related to ice-out conditions and to late-season temperatures being the best predictor 

of emergence timing. Others are not so sure. Nick Beer (also University of Washington, written 

comm., 2006) believes that the suite of simultaneous environmental cues is tricky to decouple, 

but most likely fish will synchronize spawn timing to “optimize” production and development 

rate is purely mechanistic. Ernie Brannon (University of Idaho, written comm., 2006) says that 

he knows of no situation in the field or laboratory where there was an emergence threshold 

below which emergence would not occur. However, he also stated that, unlike other species, 

Chinook can feed in the gravel and remain there after their yolk is absorbed if conditions require 

it. 

In summary, we have “data” or observations that suggest that 7–8°C is not unreasonable. 

We have left the threshold value at 8°C (46.4°F) until more main stem-specific evidence may be 

brought to bear on the issue. Salmod has no upper temperature threshold. If temperatures are 

too hot, fry will die due to thermal mortality. 

26
Emergent Length 

Salmod initializes newly emerged fish with a length and weight. Because Tom Shaw's (U.S. 

Fish and Wildlife Service, written comm., 2005) juvenile measurements began around March 1, 

presumably after significant emergence had taken place, it was unclear whether his 

measurements could provide accurate initial estimates for newly emerged fry. For example, 

Shaw's early measurements (March 9–13; 98 observations) have a mean of 38 mm with a 

standard deviation of 1.8 mm and a total range of 7.0 mm (35.0–42.0 mm). The single smallest 

fish measured was 27 mm. Because Salmod initializes emerging cohorts with a uniform 

deviation around the specified mean, not a normal distribution, we decided to start with a mean 

emergence size of 35 mm ± 4.5 mm. Correspondingly, the initial mean weight of emergents was 

set to be 0.449 g, per the formula previously described. 

Juvenile Growth Rates 

Growth rates for juvenile fish are important because the size fry and presmolts achieve 

provides a competitive advantage to all subsequent life stages, being correlated with survival, 

smoltification, and reproductive success (Dill and others, 1981; Holtby and Scrivener, 1989; 

Quinn and Peterson, 1996). Growth rate is the most frequently reported measure of fish health 

(Sullivan and others, 2000), as it appears to integrate the full range of physiological responses to 

water temperature. In Salmod, growth is (almost) solely a function of mean weekly water 

temperature. Although the weekly time-step has been questioned regarding its adequacy in 

handling thermal mortality (but see discussion below), a mean weekly temperature approach for 

growth appears well justified. Several authors have investigated the effects of fluctuating 

temperatures on growth. Fortunately, a time-weighted mean provides essentially the same 

results as integration over much smaller time increments (Sullivan and others, 2000). 

Growth as a function of water temperature for juvenile life stages was obtained from 

Shelbourne and others (1973) and is the same function used on the Trinity and Sacramento 

Rivers. Note that this function (Figure 10) assumes a constant food supply with juveniles fed to 

excess, potentially making it representative for the Klamath River, which has been preliminarily 

classified as mesotrophic-eutrophic (Caryn Woodhouse, California Regional Water Quality 

Control Board, written comm., 2005). The growth rates we use are consistent with findings from 

Marine and Cech (2004) who did not observe significant reductions in juvenile growth rates until 

daily temperatures, either means or maxima, exceeded 20°C (68°F). However, because the 

growth rates were derived from optimal experimental conditions, lowering them to some degree 

may be warranted. 

The exception to the statement that growth is solely a function of water temperature is that 

Salmod can control whether fish that are forced to move due to a habitat/density constraint will 

be allowed to grow or not. There is scant literature to support one view or the other, but Titus 

and Mosegaard (1991) concluded the newly emerged trout fry that successfully established 

feeding territories grew well in contrast to those forced into downstream movement. In fact, they 

characterized the emigrants as “starved” based on otolith measurements. For this reason, we 

have set Salmod to allow growth only for juveniles not forced to move during any time-step, the 

assumption being that energy is preferentially expended by movers in search for new territory 

and is then not available for growth. In contrast, we set Salmod to allow growth during 

volitional seasonal downstream movement (discussed in the following section) as reported by 

Mikulich and Gavrenkov (1986). 

27
Juvenile Growth Rate

-1

0

1

2

3

4

5

6

7

0 5 10 15 20 25

Mean Weekly Water Temperature (°C)

Specific Growth Rate (%/day)

0.5-1.5 g 1.9-5.2g 4.2-30.1g

Figure 10. Juvenile growth rates for different weight fish (grams) as a function of mean weekly 

water temperature. Values are from Shelbourne and others (1973). 

Movement and Associated Mortality 

Freshet Movement 

Freshets (sudden increases in discharge) have been associated with displacement of fry in 

some rivers (Godin, 1981; Irvine, 1986; Saltveit and others, 1995). It is not clear whether such 

displacement is due to volitional movement, or is entirely involuntary, or some combination of 

the two. Nor is it clear whether the stimulus is discharge, turbidity, temperature, or some 

combination (and note that a water temperature “signal” may not occur in regulated rivers 

immediately downstream from sizable impoundments). Salmod can displace stages/classes 

according to user-specified parameters governing proportion of fish moved per weekly time 

period, the distance they are displaced downstream, and any associated mortality. There are 

currently three options in Salmod for defining a freshet: (1) when the current time-step’s flow is 

greater than or equal to twice the previous time-step’s flow or is greater than or equal to twice 

the average of the previous three flows; (2) when the current time-step's flow is greater than or 

equal to twice the previous time-step's flow and is greater than or equal to twice the average of 

the three previous time-step's flows; or (3) user specified in the Flow.Dat input file. 

Freshet movement was used initially in the model for the Trinity River but was discontinued 

due to lack of direct evidence for movement stimulus. The freshet option is currently disabled 

28
for the Klamath River because contemporary trapping data have shown no indication of freshetinduced movement in our study area (Sam Williamson, oral comm., 2005). However, because 

flow fluctuations downstream from Iron Gate are minimal, especially in dry years, there may be 

little opportunity to witness a “freshet.” Trapping data downstream from the Scott River did 

appear to stimulate a large number of juveniles to emigrate in 1994, but only late in the season 

well after June 15, after hatchery-released fish were present, and after fish had achieved a mean 

size of 75–80 mm (Craig, 1994). Field personnel continue to monitor the situation. 

Note that a corollary to the previous discussion is that a lack of freshet stimulations may 

“encourage” juveniles to remain longer in freshwater than they might otherwise do (Irvine, 

1986). 

Seasonal Movement Timing and Attributes 

Salmod moves juveniles a specified distance downstream through a specified time period. 

The assumption is that these fish are physiologically “ready” and that some combination of 

external timing cues (water temperature, moon phase, discharge, and so forth) trigger 

downstream volitional movement of (pre)smolts (McDonald, 1960; Bjornn, 1971). 

Though the entire period for seasonal outmigration was shown in Figure 4, recent trapping 

data reveal a more concentrated timing (Figure 11) extending from the weeks beginning March 

11 and ending May 12 (weeks 24–32), though some outmigration at the lowest site (Kinsman) 

persists longer. Trapping data were of two forms, rotary screw trap and frame netting. The two 

data sets have been combined in figure 11 by simple averaging because, per Tom Shaw (written 

comm., 2005), they both have their unique biases. Frame nets likely have problems under turbid 

conditions and in catching larger Chinook (>55 mm); screw traps likely have problems catching 

smaller fish. Screw traps must be set in deep water with high velocity, and frame nets must be 

set in fairly shallow water with moderate velocity. Debris load and variable flows are constant 

problems with both kinds of traps. Screw traps can ride out most events (but have mechanical 

breakdowns for other reasons), whereas frame nets wash out more often. 

Note that these data have not yet been “expanded” to account for trapping method 

efficiencies, so they cannot be used for quantitative validation of Salmod. Any outmigration 

occurring earlier in the biological year may be explained by habitat-constrained movement (or 

freshets) and not physiological readiness. It should also be remembered that these data may 

reflect recent evidence of juvenile disease that may “abnormally” abbreviate the outmigration 

season. Please refer to the section on disease herein. 

29
Main stem Outmigrant Index (2000-2004)

0

1000

2000

3000

4000

03/1103/1803/2504/0104/0804/1504/2204/2905/0605/1305/2005/2706/0306/1006/1706/2407/0107/08

Last Day of Week

Index Value

Below Bogus Interstate 5 Kinsman

Figure 11. Index to the average number of juvenile Chinook trapped at each of three locations 

along the Klamath River, 2000 to 2004. This is an index only and should not be construed as 

absolute numbers. The Kinsman trap is immediately upstream from the Scott River. Data 

courtesy of the Arcata Fish and Wildlife Office. 

To achieve the approximate shape of the outmigrant temporal distribution shown in figure 

11, Salmod also requires a description of the proportions of each size class moving per week. 

This is used to roughly produce the number and length distribution in outmigrants as noted in the 

field trapping data. Parameter values describing this movement as currently implemented are 

given in Table 9, indicating that larger fish move farther than smaller fish. This fact was verified 

by California Department of Fish and Game (2003) who found that the average travel times for 

Iron Gate hatchery-reared Chinook to the estuary ranged from 26 to 40 days, depending on size. 

This translates into approximately 82 to 53 km per week, respectively; but of course there is no 

guarantee that the movement rates are constant either through time or space. We assumed that 10 

percent of all emigrating juveniles died each week as they navigated the main stem Klamath 

River, and we believe this mortality may be largely associated with predation, but offer no proof. 

This value (10 percent) is questionable and sensitive (see Appendix D) and should be scrutinized 

closely.

Table 9. Attributes for seasonal outmigration for pre- and immature Chinook smolts for March 11 

to May 12 of each year. Parameter values are preliminary calibration values. 

Size class Distance moved 

(kilometers/week) 

Proportion moving (graded from 

first week to last, respectively) 

Mortality 

(%) 

F1 60 2–10% 10 

F2 60 2–10% 10 

P1 70 2–10% 10 

P2 70 2–10% 10 

S1 80 2–10% 10 

S2 80 2–10% 10 

S3 80 2–10% 10 

Note that Salmod does not adjust movement distance based on the river’s discharge, as has 

been documented for the Columbia and Snake Rivers (Berggren and Filardo, 1993). This is an 

30
area of potential improvement in the model, though we would need reasonable estimates of 

travel time relative to discharge for the mobile life stages. Movement rates found by Berggren 

and Filardo (1993) would not be applicable because in that study, movement rates were 

computed for fish moving through impoundments as well as riverine reaches and were on rivers 

substantially larger than the Klamath. 

Base Mortality Rates 

Base, or background, rates of mortality cover all causes of death not otherwise modeled by 

Salmod. For example, "normal" or “background level” predation fall into this category, as 

would mortality due to chronically low dissolved oxygen and egg survival. The fractional rates 

we used came from the calibrated Trinity River model. The weekly base mortality rates were: 

eggs 0.035, fry 0.025, presmolts 0.025, and immature smolts 0.025. The adult rate was 0.002 

based on judgment. Note that “catastrophic” thermally induced disease has been handled 

differently in Salmod and is discussed in the following section. 

Thermal Mortality Rates 

Thermal effects on salmon have long been recognized as important on the Klamath River 

(CH2M Hill, 1985). Thermal concerns span the range from (1) physiological changes, including 

direct or indirect mortality, growth rate, embryonic development, and susceptibility to parasites 

and disease; (2) changes to behavior, including seeking special habitat such as thermal refugia, 

altering feeding activity, shifting fish spatial distributions, and altered species interaction; (3) 

changes to periodicity, including duration of incubation, onset of spawning, onset of migration, 

and gonad maturation; and (4) interaction with other water quality constituents, including 

dissolved oxygen. Most of the temperature focus on West Coast rivers has been high 

temperatures, with both the Central Valley of California and the Columbia River getting the 

largest share of attention. However, there is growing concern on East Coast rivers as well as 

selected interior habitats, for example Ozark and Appalachian Mountains. 

Thermal mortality values for Salmod are meant to reflect 7-day exposure-related effects of 

water temperature. Acute mortality is generally defined as anything up to 96 hours, but 

Salmod’s 7-day (168-hour) time-step encompasses both acute and longer term (chronic) 

mortality. The reason that Salmod uses mean weekly water temperatures instead of maximum 

daily temperatures is that there is a growing consensus that chronic, sublethal temperatures are 

often more significant than acute lethal temperatures, with the effects being both cumulative and 

positively correlated with the duration and severity of exposure (Ligon and others, 1999). Brett 

(1956) concludes that sublethal thermal stress is as decisive as lethal temperatures to survival. 

Sublethal effects are also associated with suboptimal growth rates, reduced swimming 

performance and associated predation, increased disease risk, and impaired smoltification (U.S. 

Environmental Protection Agancy, 2003, Marine and Cech, 2004). 

Salmod deals with thermal mortality for each life stage: egg and alevin, fry, juvenile, and 

adult. There is also a special in vivo category for eggs inside female spawners. Literature 

suggests that exposure of eggs to high temperatures in vivo may not directly kill the eggs, but 

rather result in unviable fry that have high mortality. Salmod, however, calculates in vivo 

mortality as if it occurred prespawn. (Note that in vivo egg mortality is calculated independently 

of other adult mortality; if an adult female dies for any reason, her eggs also die.) Thermal 

31
mortality tends to be sensitive for fall Chinook salmon during the early fall (October) for eggs 

and late spring/early summer for presmolts (National Biological Service, 1995). Obviously, any 

residual (stream type) presmolts that over-summer in the main stem Klamath River would also 

be exposed to high temperatures. 

Egg Thermal Mortality Rates 

The basis for egg and embryo (including in vivo egg) mortality rates used in Salmod was 

work done by the U.S. Fish and Wildlife Service and the Bureau of Reclamation to evaluate the 

effectiveness of adding temperature control to Shasta Dam on the Sacramento River. For that 

project, Bureau of Reclamation (1991) built a salmon mortality model parameterized with values 

supplied by the USFWS (Richardson and Harrison, 1990) in collaboration with the California 

Department of Fish and Game. The exact origin of the rate values supplied by Richardson and 

Harrison is somewhat obscure, but they cite Hinze and others (1956) and Boles (1988), among 

others. 

Unfortunately, the USFWS calculated what is called "crude" mortality rates because for 

most, but not all, of the rates they presented (Table 10), they took the percent mortality and 

divided it by the number of days in the reference period to get the average daily mortality. Crude 

mortality rates would not be correct for Salmod or similar models because the model's mortality 

rates operate sequentially. For example, the egg mortality rate given by Richardson and Harrison 

(1990) for a temperature of 61°F is 80 percent at 15 days. Using their "crude" averaging method 

resulted in an average daily rate of 5.33 percent (they report 5.3 percent). But if one applied 

such a crude rate for 15 consecutive days, the resulting mortality rate would be: 

 15 day mortality (M15) = 1 - (1 - 0.0533)15 = 1 - 0.44 = 0.56 

far different from the 80 percent they expected and that Salmod requires. 

We have corrected the values reported by Richardson and Harrison (1990) using a formula to 

calculate what is called an "absolute" or "instantaneous" mortality rate and then converting those 

rates to the reference time period, namely one week for Salmod. Continuing with the same 

example for illustration, we use: 

 M1 = 1 - (1 - Mn)

1/n

where n is the number of days in the reference period. Thus we have: 

 M1 = 1 - (1 - M15)

1/15 = 1 - (1 - 0.8)1/15 = 1 - 0.898 = 0.102 

Then a seven-day mortality rate would be calculated as: 

 M7 = 1 - (1 - 0.102)7 = 1 - 0.472 = 0.528 

Regrettably, the 100 percent mortalities for temperatures over 62°F given in Richardson and 

Harrison (1990) present a challenge for this technique. The best we can do is to assume a 1 

percent survival for mathematical convenience. Thus a single-day mortality rate that would 

result in 99 percent mortality at 12 days could be calculated as: 

32
33

 1 - (1 - M1)

12 = 0.99 

 1 - M1 = 0.011/12

 M1 = 1 - 0.6812 = 0.3187 

We also averaged the mortality rates Richardson and Harrison (1990) used for eggs and sac 

fry (embryos) to be consistent with the combined life-history simulated in Salmod for the 

Klamath River. This was done by first calculating the absolute weekly mortality rate for both 

egg and sac fry. We then averaged these two rates by taking the geometric mean of their 

respective survival rates (analogous to what we were doing above). We have complicated this 

somewhat by weighting the two survival rates by their respective durations. That is, the egg 

stage lasts about 2/3 of the whole egg-alevin life stage whereas the sac-fry stage lasts about 1/3. 

Thus, these two survival rates were weighted accordingly. This method assumes independence, 

which is probably not true, but we do not know a better alternative. 

With one exception, the last column of Table 10 then records the in-gravel egg mortality rates 

used in the model. Richardson and Harrison (1990) did not evaluate temperatures below 13°C 

(55.4°F), but Combs and Burrows (1957) supply relevant data for egg mortality under low 

constant water temperatures (Figure 12). Data from their study indicate substantial mortality 

below about 4.5°C (41°F). However, because SIAM tends to underestimate the coldest wintertime water temperatures by about 1.5–1.7°C (unpublished model results), we adjusted the low 

temperature egg mortality rates to keep from overestimating total mortality. 
Table 10. Calculation of mean weekly mortality rate as a function of mean daily water temperature (diel fluctuatio

salmon. Values on the left side of the table were given by Richardson and Harrison (1990); those shaded on the rireplacement calculations. [Temp, temperature; °F. degrees Fahrenheit; °C, degrees Celsius; %, percent; frct, fracnot applicable] 

Temp 

(°F) 

Temp 

(°C) 

Given 

egg 

mortality 

(%/days)1

Given 

egg avg. 

mortality 

(%/day) 

Given 

sac-fry 

mortality 

(%/days) 

Egg 

mortality 

(frct/day)3

Sac-fry 

mortality 

(frct/day) 

Egg 

mortality 

(frct/week) 

Sac-fry 

mortality 

(frct/week) 

34

<56 13.33 Natural2 0.00 Natural 0.000 0.000 0.000 0.000 57 13.89 8 / 24 0.40 Natural 0.003 0.000 0.024 0.000 58 14.44 15 / 22 0.70 Natural 0.007 0.000 0.050 0.000 59 15.00 25 / 20 1.25 10 / 14 0.014 0.007 0.096 0.051 60 15.56 50 / 12 4.16 25 / 14 0.056 0.020 0.333 0.134 61 16.11 80 / 15 5.30 50 / 14 0.102 0.048 0.528 0.293 62 16.67 100 / 12 8.30 75 / 14 0.319 0.094 0.932 0.500 63 17.22 100 / 11 9.00 100 / 14 0.342 0.280 0.947 0.900 64 17.78 100 / 7 14.00 NA 0.482 NA 1.000 NA 

2. Natural implies not elevated above normal background levels. 1. Percent mortality for the number of days indicated. 3. Mortality expressed as a fraction. 
Chinook Egg Mortality

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

Water Temperature (°C)

Percent Mortality

Figure 12. Egg mortality from low constant water temperatures, from Combs and Burrows (1957). 

In Vivo Egg Mortality 

Donaldson (1990) compiled an extensive list of likely potential effects of stressors (not just 

water temperature) on sexually maturing adults, including changes in gonad development, 

changes in the endocrine control system, and changes in gametes, all of which may reduce 

reproductive success or ultimate recruitment. In Salmod, these effects due to temperature have 

been lumped into the in vivo egg mortality category. In previous model applications, Salmod 

has been parameterized using an in vivo mortality rate as a function of water temperature 

identical to the rate used for in-gravel eggs. 

Though not cited by the USFWS, probably the strongest evidence for in vivo gamete 

mortality has been presented by Billard (1985, his Figure 7) citing his own published work (but 

in French), Berman (1990), Berman and Quinn (1991) and Leitritz and Lewis (1980). Berman 

held adult spring Chinook salmon at 14°C and 19°C. The group held at 19°C produced a greater 

number of pre-hatch mortalities and developmental abnormalities as well as smaller eggs and 

alevins. As with Berman and Quinn (1991), sample size was too small to permit statistical 

analysis, and disease was an issue. Leitritz and Lewis (1980, p. 33) dealt primarily with hatchery 

methods, stating that young rainbow trout should be reared at around 15.5°C (60°F) for good 

growth, but then maturing rainbows (including Chinook) should be held at water temperatures 

not exceeding 13.3°C (56°F), and preferably not above 12.2°C (54°F), for a period of at least 6 

months before spawning. Flett and others (1996) speculated that low egg survival of coho 

swimming through warm lake surface water to spawn in tributaries was due to “overripening” in 

females exposed to high, but not lethal, temperatures. Unfortunately, exact thermal exposure 

was unknown. Smith and others (1983) showed that cutthroat trout (Salmo clarki lewisi) whose 

holding temperatures ranged from 2 to10°C produced better quality eggs than those fish held at a 

constant 10°C, but the water sources were different. 

35
Gary Curtis, USFWS Yreka (07/19/04), supplied historical Iron Gate hatchery egg fertility 

rate data summarized by temporal lot number over the years 1970 to 2001. These data showed a 

depression in fertility rate in the early portion of the run compared with the later arrivals that 

could be related to water temperature or some other factors. The linear trend was not high (r2

 = 

0.5), but the pattern was pervasive and collectively represented many eggs, and therefore smolts. 

Curtis commented that the early lots are composed of few fish and that the Klamath stock may 

be more thermally tolerant than other stocks. Though data were not definitive, they were 

consistent with the earlier portion of the run generally being exposed to higher temperatures. On 

the other hand, we generally seed the Salmod study area with the full complement of adults in 

early October, resulting in a potential overestimate of in vivo mortality if all adults have not 

actually been exposed to the same water temperatures. 

We looked more closely at the Iron Gate hatchery fertility data by examining temperatures 

that each individual lot was exposed to rather than across all lots combined. Plotting the lot 

fertility rate as a function of hatchery-entry water temperature resulted in the two charts shown in 

figures 13 and 14. There are two charts because we have a complete record of simulated 

temperatures but only a spotty record of measured temperatures. Data are sparse on both graphs 

above 17°C, presumably because hatchery personnel do not start letting Chinook into the 

hatchery until about October 1, which means that the data set has been abnormally truncated. 

Iron Gate Hatchery

0.0

0.2

0.4

0.6

0.8

1.0

8 10 12 14 16 18

Temp at Entry (measured °C)

Fertility Rate (fraction)

20

Figure 13. Fertility rate of lots of Iron Gate eggs plotted against measured main stem water 

temperature at the time of entry. 

36
Iron Gate Hatchery

0.0

0.2

0.4

0.6

0.8

1.0

8 10 12 14 16 18 2

Temp at Entry (simulated °C)

Fertility Rate (fraction)

0

Figure 14. Fertility rate of lots of Iron Gate eggs plotted against simulated main stem water 

temperature at the time of entry. We have a complete record of simulated temperatures 

compared with measured temperatures. 

At entry temperatures up to about 14°C, most but not all fertility rates are quite high. At 

entry temperatures above 14°C, the maximum fertility rate begins to decline steadily and low 

fertility rates become much more common. It looks like the maximum fertility rate could have a 

"ceiling" at about 75–80 percent when temperatures exceed 18°C. 

We consulted with Keith Marine (North State Resources, Redding, California) on this 

subject. Dr. Marine noted that the principal shortcoming of this data set is that we cannot 

decompose the effect of temperature on egg fertility into its component effects on the gametes 

(pre-spawning), sperm motility/ovum membrane function/fertilization/nuclear fusion (spawning), 

and the initial stages of embryogenesis (incubation), or its effects on the egg at least until eye-up 

or the customary stage when hatcheries addle eggs to determine fertility. The fertility pattern 

exhibited in figures 13 and 14 could be affected by temperature during one or all of these stages. 

The important thing as far Salmod is concerned is that the model captures the cumulative 

effect of thermal exposure regardless of the exact sequence of events or causes. However, our 

preference is certainly to mimic "reality" to the degree possible and not postulate a relationship 

that we cannot stand behind. Because there is a considerable body of published literature that 

suggests that there is a real in vivo thermal effect, we have chosen a compromise. We will 

assume that the in-gravel egg thermal mortality rates apply for in vivo eggs, but we will also 

assume that adults are behaviorally capable of buffering themselves (and their eggs) from the 

warmest in-river temperatures. For lack of any other value, we will use the 2.5°C difference 

found by Berman and Quinn (1991) in the Yakima River. We recognize that this compromise is 

not supported by McIntosh and Li (1998) for the Klamath (see next section), but it is bolstered 

by Mike Belchik’s (Yurok Tribe) associate, Josh Strange, who has unpublished data showing 

that at least some adults use water (refugia) that is 2–4°C cooler than the ambient main stem 

temperature in the lower Klamath River. Because we have not been able to track down the data 

from Josh Strange, and because of continued uncertainty, this topic could be a priority for future 

research. 

37
Juvenile and Adult Thermal Mortality Rates 

Thermal mortality rates for juvenile and adult life stages were derived from Baker and others 

(1995) who used coded-wire tag data to conclude that hatchery-raised fall run Chinook salmon 

migrating through the Sacramento-San Joaquin Delta had an upper incipient lethal temperature 

(LT50) of 23.01±1.08°C (73.4±1.9°F). This value is slightly lower than well-recognized 

laboratory data with established acclimation temperatures, but was pragmatically estimated in the 

field from trawl runs 2 to 5 days after hatchery releases. One can use the Baker and others 

(1995) data to estimate a survival curve from a quasi-likelihood function the authors fitted: 

Survival rate = 1_____

 1 + e –a-bT

where a = 15.56 

b = -0.6765, and 

T = mean daily water temperature for the sampling period 

This method is appealing because it avoids problems associated with applying laboratory 

results to field situations and has an exposure period roughly equal to Salmod's. We assumed 

that mortality rates for juveniles derived from Baker at al. (1995) also represent adult thermal 

mortality. 

Though there are other data sets in the literature for adults, we wanted to retain the best 

estimate from field methodology. However, as has been discussed for in vivo eggs, adults may 

also be buffered from ambient thermal mortality. The study by Berman and Quinn (1991) 

demonstrated that adult spring Chinook salmon could maintain an average internal body 

temperature 2.5°C (4.5°F) below ambient river temperatures through a combination of specific 

cool-water habitat selection and behavioral timing. Though their study was for the Yakima 

River in Washington, some areas of cool-water refuges generally associated with tributary 

mouths are known to exist in the Klamath River (Belchik, 1997). Though limited monitoring of 

adults by McIntosh and Li (1998) found no evidence that upstream-migrating adult fall Chinook 

salmon were behaviorally thermoregulating, other researchers (Josh Strange) have now found 

evidence for a 2–4°C buffer in the lower Klamath (unpub. Data, 2005). For this reason, and to 

be consistent with our in vivo mortality compromise, we have chosen to buffer adults by using 

the same 2.5°C value. In other words, the model would treat an ambient water temperature of 

17.5°C as if it were only 15°C for adults in calculating thermal mortality. The mortality curves 

we used are shown in Figure 15. 

38
39

Fall Chinook Thermal Mortality

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30

Mean Weekly Water Temperature (°C)

Weekly Mortality 

Rate (fraction)

In Vivo Eggs In-Gravel Eggs

Juveniles Adults

Figure 15. Mortality as a function of mean weekly water temperature used in Salmod simulations. 

See text for a description of data sources and assumptions. Mortality values used for in vivo 

eggs and adults have been shifted to the right by 2.5°C to reflect assumed adult behavioral 

“thermoregulation.” 

Verification of Thermal Mortality Rates 

Because Salmod was sensitive to thermal mortality rates for all life stages, it was appropriate 

to seek independent verification. Representative values from the literature follow. In general, 

the authors are referring to constant temperature experiments, but occasionally their metrics are 

not specific: 

Healey (1977) examined egg-to-fingerling mortality at the Coleman National Fish Hatchery 

and concluded that main stem Sacramento River temperatures should not exceed 14.2°C (57.6°F) 

to prevent abnormally high (~80 percent) mortality. 

Boles (1988) reviewed thermal requirements for each Chinook life stage. Though not 

quantified in a manner suitable for direct comparison, his findings include: (1) adults held at 

temperatures in excess of 15.5°C (60°F) exhibited "poor" survival and "reduced" egg viability; 

(2) eggs incubated at temperatures in excess of 15.5°C (60°F) suffer "high" mortality; (3) eggs 

incubated in the range of 12.8–14.2 (55–57.5°F) experienced sac-fry mortality in excess of 50 

percent; (4) fingerlings appear to have an upper lethal temperature of approximately 25.8°C 

(78.5°F) for long-term exposure. 

Marine (1992) explored a wide variety of thermal effects with an emphasis on adults and 

their progeny. His findings are summarized in Table 11. 
40

Table 11. A compilation of published information and summary of the observed relationships between water temp

attributes of spawning performance in Chinook salmon, with inferences on the sublethal elevated temperature rascientific literature, agency reports, and interviews with fishery biologists and hatchery workers. Reproduced fro

degrees Fahrenheit; °C, degrees Celsius; <, less than; >, greater than] 

Temperature range Effect on adult salmon and reproduction Sources cited by M

< 6°C (< 42.8°F) 

Increased adult mortality, retarded gonad development and maturation, infertility. 

Leitritz and Lewis (1976); Piper and oth

10°C–18°C (50–64.4°F) 

Physiological and behavioral optimum temperature range for non-gravid adult salmon. 

Coutant (1977); Piper and others (1982)(1986). 

6°C–14°C (42.8–57.2°F) 

Optimal pre-spawning broodstock survival, maturation, and spawning temperature range. 

Leitritz and Lewis (1976); Piper and oth

15°C–17°C (59–62.6°F) 

For chronic exposure, inferred range of incipient sublethal elevated water temperature for broodstock, increased infertility, and embryonic developmental abnormalities. 

See text for derivation of this temperatur

17°C–20°C (62.6–68°F) 

For chronic exposure, incipient range of upper lethal water temperature for pre-spawning adult Chinook salmon (primarily derived from observations of captive broodstock). 

Hinze and others (1956); Rice (1960); BBerman (1990); and personal communic

13°C–27°C (55.4–80.6°F) 

Increased pathogenesis of many of the important salmonid disease organisms with potential for impairing reproduction in Chinook salmon. 

Fryer and Pilcher (1974); Becker and Fu(1987). 

25°C–27°C (77–80.6°F) 

Range of highest elevated temperatures observed to be transiently passed through during migrations or tolerated for short-term by adult Chinook salmon. 

Moyle (1976); Piper and others (1982); Resources (1988). 
Myrick and Cech (2001) provide a recent comprehensive review for Central Valley salmon. 

They conclude that eggs can survive between 1.7 and 16.6°C (35.1–61.9°F), but with increased 

mortality below 4°C (39.2°F) or above 12°C (53.6°F). The chronic upper lethal level is 

approximately 25°C (77°F) with higher temperatures, up to 29°C (84.2°F), tolerated for short 

periods. Marine and Cech (2004) provide the latest information for juveniles. They conclude 

that juvenile fall Chinook salmon can withstand chronic (> 60 day) exposure to temperatures in 

the range 21–24°C (69.8–75.2°F) (with diel fluctuations) and even grow when fed ad lib, albeit 

at reduced rates. At these temperatures, smoltification was impaired and the smaller fish were at 

increased vulnerability to predation. Fish reared at 17–20°C (62.6–68°F) grew well, but 

experienced variable smoltification impairment and higher predation rates than fish reared at 13–

16°C (55.4–60.8°F). Although Marine and Cech (2004) conclude that the Baker and others 

(1995) results likely represented indirect thermal effects as opposed to direct upper incipient 

lethal thermal effects, for Salmod’s purposes, the distinction is unimportant because thermal 

mortality covers both direct and indirect effects. 

Olson and Foster (1955) showed that Columbia River Chinook eggs suffered a total of 79 

percent mortality through the fingerling stage if initial incubation temperatures were 18.4°C 

(65.2°F), but only 10.4 percent mortality if the temperature was 16°C (60.9°F). The latest 

compilation of information appears in information assembled in support of thermal criteria 

developed by USEPA primarily for use in Total Maximum Daily Load (TMDL) analyses (Poole 

and others, 2001). This compilation drew heavily from the work of McCullough (1999) and is 

summarized in Table 12. 

Table 12. Estimates of thermal conditions known to support various life stages and biological 

functions of anadromous salmon. These numbers do not represent rigid thresholds, but rather 

represent temperatures above which adverse effects are more likely to occur. In the interest of 

simplicity, important differences between various species of anadromous salmon are not 

reflected in this table. Likewise, important differences in how temperatures are expressed are 

not included (for example instantaneous maximums, daily averages, and so forth). Adapted from 

Poole and others (2001). [°C, degrees Celsius; <, less than; >, greater than] 

Consideration Anadromous salmon 

Temperature of common summer habitat use 10–17°C 50–62.6°F 

Lethal temperatures (one week exposure) Adults: >21–22°C 

Juveniles: >23–24°C 

 

>69.8–71.6°F 

>73.4–75.2°F 

Adult migration Blocked: >21–22°C >69.8–71.6°F 

Swimming speed Reduced: >20°C 

Optimal: 15–19°C 

>68°F 

59–66.2°F 

Gamete viability during holding Reduced: >13–16°C >55.4–60.8°F 

Disease rates Severe: >18–20°C 

Elevated: 14–17°C 

Minimized: <12–13°C 

>64.4–68°F 

57.2–62.6°F 

<53.6–55.4°F 

Spawning Initiated: 7–14°C 44.6–57.2°F 

Egg incubation Optimal: 6–10°C 42.8–50°F 

Optimal growth Unlimited food: 13–19°C 

Limited food: 10–16°C 

55.4–66.2°F 

50–60.8°F 

Smoltification Suppressed: >11–15°C >51.8–59°F 

Finally, a relatively new report (Richter and Kolmes, 2005) synthesizes numeric water 

temperature criteria on a mean weekly basis as follows: spawning and incubation, 10°C (50°F); 

41
juvenile rearing, 15°C (59°F); adult migration, 16°C (61°F); smoltification, 15°C (59°F). So, in 

short, there does not appear to be any information that provides more temperature dose-response 

quantification than that developed from Richardson and Harrison (1990), Combs and Burrows 

(1957), and Baker and others (1995) with the modifications we have applied. However, it is 

apparent that much of the emphasis has been on developing thermal standards (thresholds), not 

examining exposure-related mortality. To corroborate the estimates we derived from Baker and 

others (1995) we examined the more “classic” approach to calculate mortality given exposure 

time and acclimation temperature. Armour (1991) summarizes parameters for an equation that, 

if evaluated to be greater than 1.0, mortality is expected to occur: 

 1 ≥ minutes____

 10[a + b (temperature°C +2°C)] 

where a = 22.9065 and b = -0.7611 for an acclimation temperature of 20°C (68°F). 

Using this equation and a weekly exposure (10,080 minutes), a temperature of 23°C (73.4°F) 

is expected to result in 50 percent mortality, in remarkably exact agreement with the Baker and 

others (1995) formula (see Figure 15). Thus, using multiple lines of evidence, relevant data and 

accepted methods point to the conclusion that the relationships given in Figure 15 are acceptable 

for modeling, if not universally subscribed to. 

Uncertainty in Thermal Mortality Rates 

Having said that we are comfortable with our initial compilation of mortality rates, we also 

acknowledge room for evaluation and adjustment. Let us briefly revisit both the egg and 

juvenile/adult criteria. 

Eggs 

It well could be that the egg mortality rates derived from hatchery studies are too high at 

moderate temperatures because eggs, and presumably embryos, remain buried in approximately 

10–30 cm of gravel and may be buffered from in-channel water temperatures that would 

otherwise be too hot, or too cold, for optimum survival. Shepherd and others (1986) showed that 

intragravel temperatures approximately 10 cm into the streambed causes parallel but lagged and 

buffered heating and cooling trends in infiltration-source intragravel water compared with 

surface water. Such waters were generally 0.5–1.0°C warmer in winter and 0.5–1.5°C cooler in 

summer, with crossovers around March and October. Hannah and others (2004) showed that ingravel incubation temperatures were, on average, 1.97°C warmer than water-column 

temperatures in a coastal Scottish salmon stream. However, Geist and others (2002) found that 

Chinook, unlike chum salmon (Oncorhynchus keta), in the Columbia River tended to spawn in 

zones of downwelling water where, presumably, a redd’s thermal environment would be more 

like that of the main river. 

Linda Prendergast (PacifiCorp) said that she could make data for intragravel temperatures 

downstream from Iron Gate Dam available. USGS was contacted by a PacifiCorp consultant 

(Richard Raymond, ES Environmental Services) who had attempted to conduct a study of 

intragravel dissolved oxygen in October 2004. He used methods similar to those used previously 

on the Deschutes River and supplied a report of his work. At one site downstream from Iron 

42
Gate dam approximately 100 m upstream from the hatchery bridge, they simultaneously 

deployed two multi-parameter probes. One was in a standpipe in an artificially created gravel 

redd and the other was in the river at about mid-depth. Unfortunately, the effort was “sabotaged” 

by salmon attempting to spawn on top of their artificially created redd. The salmon repeatedly 

rearranged the redd’s gravel, knocking down and mostly burying the standpipe. A spreadsheet of 

data capture was supplied and examined. Despite the apparent problem with the data, they 

appeared somewhat useful. There was a characteristic lag between ambient river temperatures 

and what occurred in the gravel, but no significant difference between the two on a daily average 

basis, at least at this site. 

During the Yreka workshop, it was also suggested that we need to revisit egg mortality at 

cold water temperatures (< 5°C) per the Combs and Burrows (1957) data. Scant data exist on 

this because these temperatures are so rarely encountered under hatchery conditions, but a recent 

published review (Myrick and Cech, 2004) confirms that temperatures below 5.6°C are 

associated with increasingly high egg mortality, so we have retained the Combs and Burrows 

(1957) portion of the mortality curve. 

In summary, Klamath-specific data did not appear to justify any adjustment to egg thermal 

mortality rates in either cold or hot conditions. We have chosen to assume (per Geist and others, 

2002) that intragravel egg temperatures are likely to be little different from main-channel water 

temperatures. This may be an appropriate area for research in the future. 

Juveniles and Adults 

There may be problems using the Baker at al. (1995) technique applied previously. The data 

were collected from fall-run hatchery fish traversing the sometimes-brackish waters of the 

Sacramento bay-delta system. Fish recoveries were made from mid-water trawls that may bias 

the interpretation for fish not actively (or passively) outmigrating. There are a variety of 

mathematical assumptions implicit in the curve fitting that Baker and others (1995) did. 

Exposure times were not uniform and may or may not conform to Salmod's weekly time-step. 

Finally, the data represent only smolts, yet we have applied the results to all juvenile and adult 

life stages. In spite of these limitations, we feel that this approach is a step forward from the 

more simplistic habitat suitability index (HIS)-type method used in previous Salmod applications 

and helps avoid using unmodified laboratory-derived data in real-world applications (Ligon and 

others, 1999). 

There has always been speculation that the Klamath River stocks may exhibit higher thermal 

thresholds than other West Coast stocks. However, during the course of our literature review, we 

found no conclusive evidence that this is true. McCullough (1999) investigated the issue of 

stock-specific thermal adaptation as part of his comprehensive review and found that, although 

there are well recognized genetic adaptations to temperature that appear to tailor the fitness of 

stocks to their environment, absolute differences are small, generally attributable to 

morphological distinctions, and never result in a conclusion that thermal standards should be 

stock specific. Myrick and Cech (2001) comment that Central Valley Chinook salmon, despite 

their southerly distribution, do not appear to have any greater thermal tolerance than more 

northerly races. Further, thermal tolerance is a function of acclimation history that is, of course, 

an implicit consequence of each unique physical setting and time series of thermal exposure. 

It is reasonable to speculate that fish survive thermal extremes best with abundant food 

availability. Brett and others (1982) and others have shown that both optimum growth 

temperatures and the point of zero net growth shift toward lower temperatures as ration declines 

43
and sublethal growth stress increases. The Klamath River may be classified as an enriched river 

(Caryn Woodhouse, California Regional Water Quality Control Board, written comm., 2005) 

and therefore Klamath fish may be expected to exhibit some of the highest levels of growth in 

response to otherwise non-lethal high temperatures. Speculation about the uniqueness of 

Klamath stocks relative to thermal mortality, however, will remain an issue. 

In sum, we feel comfortable that we have identified suitable sets of thermal mortality rates 

for each of the Chinook life stages, at least initially. Remaining uncertainty leaves some room 

for adjusting those rates, up or down, as we learn and adapt during model calibration. Further 

research would always be advisable. 

Effect of disease on juvenile thermal mortality 

Disease is emerging as a potentially significant issue for juvenile Chinook on the Klamath 

River, and we treat it here as a unique concern. At a review workshop held in Yreka in January 

2005, participants reached no consensus about river reaches involved in high-incidence disease 

outbreaks, likely triggers of the three juvenile diseases, or related issues except that the incidence 

of disease appears low when water temperatures are below 10°C. 

To better capture information from others not present at the workshop, a conference call was 

convened on March 17, 2005, with several individuals, notably researchers Jerry Bartholomew 

(Center for Fish Disease Research, Oregon State University) and Scott Foott (USFWS CA-NV 

Fish Health Center, Anderson, California). The stage was set by generally describing the 

Salmod model (including that thermal exposure was handled on a mean weekly basis) and 

outlining several options for how one might account for what is known about juvenile Chinook 

disease-induced mortality into the Salmod model. Options ranged from (1) just incorporating 

disease as a “background” mortality rate, (2) considering a disease “window” though time and 

space where disease prevalence or virulence were especially high, (3) considering that diseaseinduced mortality was a function of water temperature or discharge, or both, and (4) any of the 

above with a fish density component. 

Though the conversation ranged widely, it was clear from the beginning that the immediate 

focus was Ceratomyxa Shasta. The researchers were confident that the entire stretch of river 

between Iron Gate Dam and the Scott River was problematic for this disease infecting juvenile 

Chinook salmon. High mortalities to relatively short exposures were commonly observed, 

depending on water temperatures, particularly those over 10°C. 

There was also speculation that there may be a higher incidence of disease during years of 

lower flow, on the Klamath and on other rivers in Oregon. Low flows may act by (A) not 

adequately flushing substrates, (B) concentrating actinospores released by polychaete 

populations, or (C) stimulating an overgrowth of Cladophora and related species thought to 

provide quality habitat for polychaete rearing. In addition, although C. Shasta itself is not a 

contagious disease, other co-occurring diseases are, and when they act synergistically, there may 

be a reason to believe that disease-induced mortality from the complex of hosts is density 

dependent. It was speculated that low river flows may simultaneously restrict fish habitat, thus 

concentrating fish. Low flows may also render some microhabitats unavailable that could have 

provided some protection (refuge) from water-borne diseases. Thus, in low water years, both 

fish and intermediate hosts may be in closer proximity simply because habitat is limited. 

A report by the Oregon Department of Fish and Wildlife (2000) demonstrated a clear 

relationship between Willamette River discharge and downstream water temperatures. However, 

the relationship between flow and water temperature downstream from Iron Gate Dam is neither 

44
as clear cut nor as intuitive as on the Willamette River. We (USGS) have carefully examined the 

relationship between discharge and water temperature in all months (unpublished data) and can 

confidently state that varying Iron Gate discharge in April between 500 and 2,500 ft3

/s has 

virtually no effect on mean daily water temperature because temperatures throughout the river 

are essentially uniform and the reservoirs have little or no hypolimnetic storage. Varying the 

discharge in May from 500 to 2,500 ft3

/s actually increases water temperatures, but by less than 

2°C, because you are simply bringing more ambient ("hot") water down from Upper Klamath 

Lake. Increasing the flows during the summer months results in similar temperature increases. 

Annual variation in meteorology over the 40+ year period we have looked at is far more 

influential in controlling water temperatures, accounting for about 4–6°C in year-to-year (and 

month-to-month) water temperature variation. This does not mean that flow is unimportant in 

disease-induced salmon mortality given the hypothesis that spore concentration is increased at 

low flow. It does mean, however, that until we have a more definitive (and defensible) base of 

evidence on the Klamath, it would be unwise to make the juvenile Chinook 

temperature:mortality function in Salmod flow dependent – except to further explore this as a 

hypothesis. 

Dealing for the moment with water temperature alone, it is clear that the parasites replicate 

as a function of water temperature and that the infection process is therefore temperaturedependent. Though infection has been observed as low as 4°C, 10°C is the commonly cited 

threshold above which the infection rate and degree of mortality both increase rapidly regardless 

of life stage, size class, or whether the fish were of “wild” or hatchery origin. Several references 

were cited by participants that pointed to 10°C as the approximate threshold (Ratliff, 1981; 

Ratliff, 1983; Hendrikson and others, 1989; Bartholomew, 1998; and Udey and others, 1975) 

with an increasing incidence of infection, ultimate mortality rate and reduction in time to death 

above that temperature. [For an excellent discussion of diseases affecting West Coast streams, 

see Washington State Department of Ecology (2002).] 

Spatially, it was noted that Iron Gate Reservoir, or at least the hatchery withdrawal below 

Iron Gate’s surface, did not appear to contain C. Shasta because both hatchery and sentinel fish 

placed immediately downstream from the dam had a low rate of infection relative to downstream 

locations. For example, only a few river kilometers downstream near the I-5 Bridge, infection 

rates were high. This also seems to be true in the Upper Klamath area near Keno Dam, Oregon. 

There was some speculation that adult Chinook salmon in the main stem transport the spores 

upstream to spawning areas and that high density spawning areas might themselves be “hot 

spots” for infection. It was also noted that there must be some degree of disease resistance in 

Klamath stocks because at least a small proportion of hatchery fish return each year to spawn and 

these fish are clearly exposed but have not succumbed to the disease. 

Given this information, we discussed several possibilities to describe the functional 

relationship between water temperature and mortality in Salmod. The following table compares 

current Salmod values, proposed values from the phone conversation, and values being used in 

the Ecosystems Diagnostics and Treatment (EDT) model. As discussed elsewhere, current 

weekly values were derived from the Sacramento River (Baker and others, 1995) with no major 

mortality attributable to C. Shasta in that river. Note that proposed mortality estimates are given 

as a range (uncertainty arising from other as yet unquantified factors) derived from expert 

opinion and comparable to the relationship being used in the EDT model. Mortalities represent 

not just those due to C. Shasta alone, but also other synergistic diseases. It seems clear that the 

mortality rates currently being used in Salmod are too low to account for the recently observed 

45
disease prevalence. (It would also appear that the EDT values may be too low, especially 

considering the monthly time-step.) 

Table 13. Comparison of juvenile Chinook mortality rates as a function of mean water 

temperature. Shown are the values used in Salmod with and without disease mediation and 

current EDT model values. The disease-mediated values were proposed by Jerri Bartholomew 

(Oregon State University) and Scott Foott (USFWS), whose opinions were supplemented by 

experimental results and field data collections (Bartholomew, 1998; Foott and others, 1999; Foott 

and others, 2002; Foott and others, 2003; Foott and others, 2004) and are the values applied in this 

analysis. [Temp, temperature; 5, percent; °C, degrees Celsius; <, less than; >, greater than; EDT, 

Ecosystems Diagnostics and Treatment] 

Salmod No Disease Salmod with Disease EDT 

Temp (°C) Weekly 

Mortality 

(%) 

Temp (°C) Weekly 

Mortality 

Range 

(%) 

Temp (°C) Monthly 

Mortality 

(%) 

10 0–20 <10 0 

12 20–40 

15 >50 

... ... 10–16 20–60 

<16 0.0 ... ... 

16 0.9 Interpolated Interpolated 16 20–60 

17 1.7 ... ... 17 50->90 

18 3.3 ... ... 18 50->90 

19 6.3 ... ... 19 50->90 

20 11.6 20 80–100 20 50->90 

21 20.5 ... ... 21 50->90 

22 33.7 ... ... 22 50->90 

23 50.0 23 ~100 23 >90 

24 66.3 >23 100 24 >90 

25 79.5 25 >90 

26 88.4 26 >90 

27 93.7 27 >90 

28 96.7 28 >90 

29 98.3 29 100 

30 99.1 30 100 

After much consideration, we decided that for the historical analysis we will apply only the 

post-1994 mortality values because these values reflect the current and foreseeable management 

environment. In other words, the historical analysis relies on the historical flow and temperature 

time series but estimated current biological phenomena. Any model calibration meant to reflect 

true historical conditions should apply the pre-1994 values as appropriate because it appears 

likely that high C. Shasta mortality rates might not have been associated with Klamath stocks 

before that time (Foott and others, 1999). 

46
Habitat Capacity 

Salmod assumes a relatively fixed “capacity” per unit of available physical habitat for adult 

and juvenile fish (Chapman, 1962; Mesick, 1988; Beechie and others, 1994). Capacity is 

computed by knowing the flow in each computation unit, translating that into square meters of 

available habitat for each life stage, and knowing the maximum biomass or number of 

individuals for that life stage that can occupy a square meter of optimum habitat. The model 

moves juvenile and adult fish that exceed capacity to a downstream computation unit. 

In previous Salmod applications, we have used either the maximum number of fish or 

maximum biomass per unit area. On the Trinity River, for example, the biologists preferred the 

maximum number because it best matched the data they had collected from systematic snorkel 

observations. Kent (1999) subsequently applied the Trinity River derived values to the initial 

Sacramento River model but did not calibrate the model. Burns (1971) preferred to quantify 

habitat capacity in terms of the average weight of fish per unit of surface area or, in our case, 

habitat area (and as a note, Salmod is to a degree based on Burns’ early work). Bartholow 

(2005) used the maximum biomass approach rather than numbers of individuals because he 

believed that (1) it is more consistent with what we understand in terms of bioenergetic 

requirements, (2) measuring density with numbers per unit area has the problem that two 

individuals of different body size should not count equally, and (3) because biomass increases as 

fish grow in length and weight, such growth would result in a somewhat constant “pressure” for 

some individuals to move (Grant and Kramer, 1990; Bohlin and others, 1994; though see Grant 

and others, 1998, for a critique). Another approach used in France is to choose the median 

biomass per unit area. 

Regardless of the technique used, it is apparent that one can obtain vastly different density 

estimates in different riverine settings, and great care must be used if one transfers site-specific 

density values from another river to the Klamath unless verified. Density estimates described by 

Grant and Kramer (1990) were largely from small “natural” streams; the Klamath River with 

several in-line reservoirs is anything but natural and small. Further, Salmod assumes that 

maximum habitat capacity is per unit of ideal habitat (WUA), and the quality of ideal habitat 

may not be transferable from small streams to large rivers (Grant and others, 1998). The factor 

most likely to influence the currency, and therefore lack of transferability from one stream to 

another, is food availability because food productivity is thought to directly affect minimum 

territory size (Grant and others, 1998). For example, Allen (1969) cites an average salmonid 

density of 1.7 g/m2

 for New Zealand rivers, an order of magnitude lower than the values from 

Grant and Kramer (1990). Hume and Parkinson (1987) cite stocking densities as low as 0.3–0.7 

fry/m2 in low-productivity British Columbia streams. 

In consultation with field biologists, it was agreed that we would use numbers of fish, rather 

than biomass, as the best way to express this value on the Klamath River. Tom Shaw (USFWS) 

sent spreadsheets compiled from their 1998–2004 database of juvenile density estimates derived 

from a Moran-Zippin multi-pass calculation method and summarized as fish/m2

. The 90-

percentile density was calculated after eliminating data records inappropriate for this method, 

under the assumption that values in excess of the 90-percentile are aberrations above carrying 

capacity. Although the data base did include some fish above 55 mm, the vast majority (>97 

percent), especially early in the year when high densities are expected, were for fish below 55 

mm. The 90-percentile value was 15.3 fry/m2, remarkably close to the literature value we have 

used previously of 15.48 fry/m2

 (Grant and Kramer, 1990). 

47
There was no clear evidence that the average density has varied through time (across years), 

but there was a clear gradient along the river from the high-density spawning areas to low 

densities farther downstream, as would be expected. There was variation in density by habitat 

type (for example low slope, mod slope, and so forth), but not extreme. In the future, we may 

wish to consider trying to correct for any assumed differences in habitat “quality” between 

habitat types. For this report we have assumed 15.3 fry/m2. Because this value is so close to 

Grant and Kramer (1990), and there were not enough samples in the Arcata FWS database for 

fish > 55 mm, we have used Grant and Kramer’s estimates of 3.23 presmolts/m2

 and 0.93 

immature smolts/m2

 as well. As with thermal mortality rates, we must acknowledge considerable 

uncertainty in these estimates of maximum density per unit usable area. The values will be 

sensitive in controlling relative Chinook salmon production. However, the good news is that 

these values have generally been found to only shift (scale) the relative magnitude of production 

rather than alter the absolute ranking of one flow scenario over another (unpublished modeling 

results). 

Table 14 compares fish density values used in some previous Salmod applications. As 

mentioned, the Kent (1999) density values were used on the Trinity and initial Sacramento River 

models. The Grant and Kramer (1990) values have been used for the Atlantic salmon model 

because we had nothing better to apply. Differences between rivers remain unexplained but 

likely represent differences in methodologies. 

Table 14. Comparison of maximum number of individuals per unit weighted usable area (WUA; 

m2

) used in different applications. The values for Grant and Kramer (1990) were calculated by 

using the mean weight for each respective life stage. 

Life stage 

Maximum 

number/m2

 WUA 

(Kent, 1999) 

Maximum 

number/m2

 WUA 

(Grant and 

Kramer, 1990) 

Maximum number/m2

WUA 

For the Klamath River 

(fry value from Arcata 

Fish and Wildlife 

Service) 

Fry 86.0 15.48 15.3 

Presmolts 11.8 3.23 3.23 

Immature 

smolts 

11.8 0.93 0.93 

Adults 0.01 0.01 0.01 

Habitat-Induced Movement Rules 

In the event that fry in a computation unit exceed the computed habitat capacity, we set 

Salmod to first move the most recent arrivals out of that computation unit under the supposition 

that moving, non-territorial fry are more likely to continue to move. In contrast, the model moves 

the more territorial presmolts and immature smolts with the lowest condition factor first, 

assuming that more robust fish have a territorial advantage. These two methods operate only 

within in a life stage category, in other words fry only compete with fry, and so forth. It is 

possible to set Salmod to be even more size selective within a life stage. In other words, one 

could move the smallest, most recently arrived fry first, but we have not done so for this Klamath 

application as it does not appear to significantly affect the results. On the Klamath River, all 

habitat-induced movement is set to be downstream only. 

48
Distance Moved Mortality Rate 

There is a mortality rate associated with habitat-induced movement–the farther fish must 

travel to encounter unoccupied habitat, the greater their mortality. Though there are a variety of 

ways to quantify this mortality in Salmod, we find it conceptually easiest to specify the 

maximum distance that can be moved in one week before 100 percent mortality, linearly 

interpolating back to zero mortality at zero distance, though it is possible to describe any 

curvilinear function one can support or hypothesize. 

Kent (1999) used 3 km as the maximum distance regardless of life state/size class on the 

Sacramento River, stating: 

No studies have been performed to find the average distance juveniles move over a specific 

time period while rearing. Bill Snider of the California Department of Fish and Game 

reports that juvenile Chinook salmon migrate long distances while rearing, such that a fry 

migrating 3 kilometers downstream or more in the course of one week is not unusual. Snider 

also reports seeing juveniles that have physically matured faster than juveniles of similar 

length and age in other river systems. The process for this is unknown. 

Because no studies have been performed on juvenile migration, we used the expert opinion 

of Snider to set the upper limit of weekly juvenile movement without mortality at a 

conservative value of 3 kilometers. Juveniles which must move more than 3 kilometers in a 

week due to lack of suitable rearing habitat will die. 

In contrast, values applied for the Trinity River are shown in Table 15, illustrating the 

assumption that smaller fish may be more vulnerable to mortality during a forced movement. 

These are the values currently used in the Klamath River Salmod model. Note that these values 

only apply during a forced movement. Mortality associated with seasonal emigration are 

specified independently. 

Table 15. Maximum distance moved per week associated with 100 percent mortality during 

habitat-induced movement (from Bartholow and others, 1993). [m, meters] 

Stage Size class Maximum distance (m) moved 

before 100% mortality 

Fry F1 800 

F2 1,000 

Presmolts P1 1,500 

P2 2,500 

Immature smolts S1 3,000 

S2 3,500 

S3 4,000 

Exogenous Production 

Chinook salmon production in the Klamath River downstream from Iron Gate is not isolated 

to the main stem. Several tributaries and the Iron Gate hatchery also produce fish that 

supplement main stem production, entering the main stem at specific locations and specific 

times. It was agreed at the Yreka workshop that we needed to get an idea of the magnitude, 

timing, and length classes for juveniles emerging from the monitored Shasta River and Bogus 

49
Creek and other tributaries in the Iron Gate to Scott River modeling area. These additional 

contributions then become part of the standard Salmod input file (Suplement.Dat) for use in 

model simulations to shed light on the effects that these tributary fish may have on other main 

stem-spawned juveniles. However, it should be understood that these tributaries are not 

modeled; rather the tributary production was simulated as a constant for each year. 

Several steps were involved in doing this and we lay these steps out below, including 

important assumptions that accompany the process. (We also note that there was some 

discussion at the workshop that historical tributary production might have been considerably 

larger than at present.) 

Tributary Contributions 

Bill Chesney supplied copies of the CDFG Shasta River Chinook fry outmigrant studies for 

2001–2004, the later year being draft only, with accompanying spreadsheets (Chesney, 2002; 

Chesney and others, 2003; Chesney and others, 2004). The summary data Chesney supplied had 

already been corrected for trap efficiency and river volume sampled. We took a simple average 

of the 2001–2004 data by week, converted these weekly averages from 6-day per week estimates 

to 7-day per week estimates, and summarized the data as shown in Figure 16. Because part of 

the objective is to “test” the main stem model with a substantial number of tributary juveniles, 

we elected to use the higher average rather than the median number of juveniles per week, 

recognizing that the average, or even the 2001 number, may be small relative to historical 

estimates of production. 

Shasta River Outmigrating Juveniles

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Julian week (Jan 1 = 1)

Number of YOY (adjusted)

2001 2002 2003 2004(draft) Average Median

2/5 - 2/11

4/9 - 4/15

5/28 - 6/3

Figure 16. Data supplied by Bill Chesney (California Department of Fish and Game) for young-ofyear (YOY) Chinook outmigrants on the Shasta River. 

50
“Raw” trap data supplied by Chesney were used to compute the relative proportion of 

juveniles in each of several size classes for each sampling week. These data were “raw” in that 

they had not been corrected for sampling efficiency, but they were suitable for computing size 

class proportions to be applied to the average weekly corrected estimates. No data were 

available to suggest that one size class was more or less susceptible to trapping, so no further 

adjustments were made. The raw trap data Chesney supplied recorded a few juveniles in weeks 

not represented in his outmigrant summaries; because these represented only trivial tails of each 

annual distribution, we ignored these small numbers. 

Chinook salmon production data for Bogus Creek were also compiled from California 

Department of Fish and Game reports (Jong and Mills, 1993, unpublished data). Unlike data 

available for the Shasta River, the Bogus Creek data were not true production estimates, instead 

representing an unexpanded index. Thus we had no computed estimate of total Bogus Creek 

production. Bill Jong and Mark Pisano (California Department of Fish and Game, written 

comm., 2005) offered their speculation that the Shasta River produces more total juveniles than 

Bogus Creek. They stated that Bogus Creek likely has more adult escapement given its 

proximity to the hatchery (and that many, if not most, adults ascending Bogus Creek are likely of 

hatchery origin). Redd superimposition probably occurs more often in Bogus Creek than in the 

Shasta River, but abundance of spawning substrate is greater in Bogus Creek than at least the 

canyon portion of the Shasta River, which argues for higher hatching success in Bogus Creek. 

However, the Shasta River is longer (64 km compared to 24 km), has a generally lower gradient 

(1.6 compared to 7.2 percent), and has a higher flow during the spawning season (35 to 208 ft3

/s 

compared to 14 to 21) (Jong and Mills, 1993, unpublished data). The Shasta River also has a 

greater abundance of suitable spawning gravels and higher water quality (temperature) in its 

upper reaches. For these reasons, we (USGS) have assumed that Bogus Creek produces one-half 

the YOY juveniles that the Shasta River produces, on average. 

The draft report on Bogus Creek production (Jong and Mills, 1993, unpublished data) 

recorded the total weekly catch for 1986–1990 from which we could once again compute an 

overall weekly average production index. These weekly index values were then totaled and 

adjusted as discussed above to equal 1⁄2 of the Shasta production. Though the Bogus Creek report 

did not contain length:frequency data, it did contain a weekly mean length and standard 

deviation. Therefore, we divided the weekly juvenile production computed into two 

length/weight categories, mean ± standard deviation to reflect the variance to the degree 

supported by the data. Both the average weekly production and length classes are shown in 

Figure 17. 

51
Bogus Creek, 1986-90 Average

20

30

40

50

60

70

80

1/141/282/112/253/113/25

4/8

4/22

5/6

5/20

6/3

6/17

Week ending

Fish Length 

(mm) 

0

5,000

10,000

15,000

20,000

25,000

Production 

 Index 

Fish High Low

Figure 17. Juvenile production index for Bogus Creek calculated from unpublished data (Jong 

and Mills, 1993 draft). Fish refers to numbers of juvenile Chinook, whereas High and Low refer to 

the mean plus and minus one standard deviation for length classes. 

We assumed that the total juvenile contribution from the Shasta River and Bogus Creek 

combined represented 80–90 percent of the total tributary juvenile contribution between Iron 

Gate Dam and the Scott River, on average. Though it would have been possible to allocate this 

remaining percentage among the other minor tributaries according to their location along the 

modeling reach, for simplicity we added 15 percent to the total Bogus and Shasta contributions, 

respectively, (by dividing each by 0.85) because we believed that such few juveniles distributed 

elsewhere would not result in a noticeably different effect in Salmod. 

Conceptually, tributaries enter the simulation model’s virtual river at one computation unit. 

Adding one week’s tributary contribution to a single computation unit would potentially permit 

disproportionate crowding in that unit. An alternative would be to distribute these fish for a 

distance equal to one week’s travel time downstream, but this would essentially permit 

distribution throughout the entire study area. We chose a compromise by assuming that tributary 

fish, like hatchery fish discussed in the next section, would be distributed throughout a 5-km 

“mixing zone” downstream from each tributary. Juveniles entering the main stem are treated just 

like main stem cohorts; if they are moving seasonally, they will continue to do so. 

Hatchery Contributions 

Mark Hampton supplied a spreadsheet of Iron Gate hatchery data so that we could develop 

estimates of juvenile releases into the main stem (Table 16). Unlike the values we computed for 

the Shasta River and Bogus Creek, these releases are so “late” and generally homogeneous in 

52
length and weight, we chose not to divide them into length classes, which would have been 

difficult from the available data anyway. 

Table 16. Date of release, number and weight of juveniles released from Iron Gate Hatchery into 

the Klamath River since the beginning of the “early release” strategy. All values were computed 

from spreadsheet supplied by Mark Hampton (CDFG, written comm., 2005). Averages at the 

bottom of the table are meant to be representative and are the values used in Salmod. Values for 

yearlings released in November were ignored. 

Year Release date Salmod week Number of fry Weight 

(grams) 

2001 5/21 34 868,165 5.04 

2001 5/26 34 869,265 4.93 

2001 5/26 34 1,738,503 3.94 

2001 5/26 34 1,464,064 2.77 

2002 5/10 32 862,908 4.98 

2002 5/21 34 854,456 5.04 

2002 5/28 35 1,699,858 5.34 

2002 6/5 36 1,549,867 4.32 

2003 5/13 33 896,871 4.93 

2003 5/23 34 896,410 4.98 

2003 5/29 35 1,775,835 5.15 

2003 6/4 36 1,547,049 3.84 

2004 5/13 33 1,021,023 4.77 

2004 5/20 34 1,037,860 4.77 

2004 5/24 34 1,012,623 4.77 

2004 5/28 35 1,008,388 4.54 

2004 6/3 36 1,102,198 3.78 

Average 5/14 33 912,242 4.93 

Average 5/22 34 914,498 4.93 

Average 5/26 34 1,556,705 4.80 

Average 5/31 35 1,334,313 3.85 

Summary of Model Parameters and Variables 

Obviously Salmod has many input requirements. To the degree possible, we have 

endeavored to derive evidence-based inputs from Klamath-specific sources. However, some 

values were derived from literature sources, previous model applications, and assumptions. 

Table 17 summarizes these values. In addition, please refer to the Sensitivity Analysis provided 

in Appendix D. Both the initial parameters and the initial sensitivity analysis were reviewed by 

an interagency team at a workshop held in Yreka, California, January 11–12, 2005. Consensus 

suggestions have been incorporated into this document. 

53
Table 17. Summary of important model structural elements, parameters, variables, and potential 

calibration data, with notes on their origin, status, and inherent uncertainty. [NA, not applicable; 

WUA, weighted usable area] 

Element, parameter or 

variable 

Klamath- specific Status Uncertainty 

Study area Yes Fixed at present NA 

Flow and temperature 

 reaches 

Yes Eleven reaches, well 

 matched to 

 hydrology and 

 thermal 

 characteristics 

 of the river 

Minor 

Flow and water 

 temperature data 

Yes Coming from SIAM Masks peaks; need measured data if 

 calibrating 

Mesohabitat typing data 

 and sequence 

Yes Good Unknown 

PHABSIM WUA 

 quantification 

Yes Available, with 

 assumptions 

Hydraulics validated, remainder 

 untested 

Biological year timing Yes Good Minor 

Life stage nomenclature 

 and size class 

 breakpoints 

Yes Good Unknown 

Weight:length 

 relationship 

Yes Well defined Agrees well with Sacramento River 

Spawning spatial and 

 temporal distribution 

Yes Well defined, but 

 using multi-year 

 average 

Minor 

Spawning initiation 

 temperature 

Yes Reasonably well 

 defined 

Unknown 

Spawner density and 

 characteristics 

Yes Have some data May not all be specific for main stem 

 study area 

Fecundity Partially From Lewiston 

 Hatchery 

Could perhaps improve with values 

 from Iron Gate Hatchery 

Redd area and 

 superimposition 

Yes Well defined Some room for variation around 4.25 

 ± 1.4 m2

Egg development rate No From reliable 

 literature 

Some uncertainty in hatch to pre- 

 emergent timing; may need to 

 adjust during calibration 

Emergent length Yes From field 

 measurements, but 

 not explicitly for this 

 measure 

Contains some uncertainty 

Emergence temperature Yes Reasonable estimate Some uncertainty 

Juvenile growth rates No Well defined literature 

 values that have 

 worked well on 

 other rivers 

Some uncertainty because values 

 derived from ad lib feeding 

Freshet movement 

 attributes 

Not used on 

Klamath 

Monitor Largely stable flows in dry years 

 have precluded measurement 

Seasonal movement 

 timing and attributes 

Yes for timing and 

distance 

Other values derived 

 from other 

 applications 

Values may have been influenced by 

 recent disease 

54
Base mortality rates No Values derived from 

Trinity 

 River 

Uncertainty due to endemic disease 

Thermal mortality rates No Values composite 

from 

 multiple literature 

sources 

Uncertainty due to many causes, and 

will be sensitive to model behavior 

Habitat capacity Partial Based on extensive 

sampling 

Uncertainty from multiple causes 

Habitat capacity 

 movement rules 

No Literature and 

previous model 

 based 

Unknown 

Distance moved 

 mortality rate 

No Derived from Trinity 

 and Sacramento 

River 

 models 

Much uncertainty 

Exogenous production Yes Derived from 

 hatchery, Bogus 

 Creek, and the 

 Shasta River data 

Bogus Creek is an estimate with 

 considerable uncertainty 

Calibration metrics 

Juvenile growth Yes Review possibilities Uncertainty due to tributary 

contribution and sampling gear 

Overall survival/production Review Need best information Potential gear biases; may be 

influenced by recent diseases 

Calibration Targets 

As mentioned, it is not yet possible to rigorously calibrate this model. We have few years of 

estimated outmigrants, the estimates have not yet been expanded for trap efficiency, and when 

expanded, confidence intervals–and uncertainty–will likely be high. However, we can make 

some judgments about whether the model is behaving properly and producing reasonable, or at 

least not irrational, results. 

General Model Timing 

Generally, the first thing to do in approaching Salmod model calibration is to verify that the 

model timing is approximately correct. Some timing elements are fixed as inputs, such as spawn 

initiation and duration, and general seasonal outmigration timing. But others, such as time of 

first and peak emergence, depend on other model processes operating (computing) correctly. 

Figure 11 (previously presented) serves as the best reference for outmigration timing. The length 

of instream and outmigrant fish is presented below. 

Juvenile Growth 

There are at least two different ways to characterize measured juvenile growth data. Tom 

Shaw (USFWS) supplied a spreadsheet recording length measurements from 3,217 young-ofyear fish. [The spreadsheet also contained data for a few yearling fish (8), not enough to yield 

reliable estimates.] Three different sampling techniques–electrofishing, fyke net, and seine–were 

55
used to gather the data. The data appear to show (Figure 18) that (1) there may be considerable 

sampling differences among the techniques, (2) the length of the smallest fish changes little 

through time, probably due to entrants from tributaries with timings that differ from the main 

stem, and (3) the variance in length expands considerably through time, another potential 

indicator of tributary or hatchery influence. We will refer to these fish as “instream” fish, though 

some of them may be emigrating. 

Figure 19, in contrast, shows the average length of emigrating Chinook trapped at the same 

locations previously illustrated in Figure 11. As expected, the average length generally increases 

the farther downstream you sample. Initial “calibration” will rely on these data to see if, or how 

well, the model reflects the relative trend and magnitude in outmigrant length. 

Young of Year

0

20

40

60

80

100

120

01-Mar-99 21-Mar-99 10-Apr-99 30-Apr-99 20-May-99 09-Jun-99 29-Jun-99

Sample Date

Fork Length (mm)

Electrofish Fyke Seine

105 mm

27 mm

37 mm

Figure 18. Young of year (YOY) length measurements from Tom Shaw (USFWS, written comm., 

2005) labeled to highlight initial, maximum, and minimum values. [mm, millimeters] 

56
Main stem Outmigrant Length (2000-2004)

30

40

50

60

70

80

03/1103/1803/2504/0104/0804/1504/2204/2905/0605/1305/2005/2706/0306/1006/1706/2407/0107/08

Last Day of Week

Average Length 

(mm)

Below Bogus Interstate 5 Kinsman

Figure 19. Average length of emigrating juvenile Chinook salmon trapped at each of three 

locations along the Klamath River, 2000 to 2004. Data courtesy of the Arcata Fish and Wildlife 

Office. 

Note that Salmod produces variability in the predicted length of rearing and outmigrating 

fish for several reasons. First, recall that the model introduces an initial random deviation to the 

length of emergent fry. Second, emergence is spread through time, introducing an inherent, 

temporally-induced length variance. Third, juveniles forced to move due to habitat limitations 

do not grow, fueling a gap with cohorts of similar age and thermal history that do grow. It 

should also be noted that fish moving out of the system often result in a bimodal length 

distribution because outmigration arises from two processes, (A) the often severe habitat 

limitations immediately after the peak of emergence forcing newly emerged fish out of the study 

area (Unwin, 1997), and (B) subsequent seasonally-induced movement (due to assumed 

physiological readiness) later in the biological year. These factors, along with tributary and 

hatchery contributions, collectively account for variance and bimodality, not some more 

elaborate bioenergetics explanation dealing with food availability and individual competition 

(Elliott, 1990). 

Index of Abundance 

On the Trinity River, we had bi-weekly indices of abundance at several reference sites along 

the study area. These were quite helpful in “monitoring” the abundance (as well as 

length/weight) of juveniles through time, both in terms of time of emergence and in the 

longitudinal progression of slowly outmigrating juveniles. We have not yet pursued using this or 

similar type of monitoring on the Klamath River; this step remains for subsequent calibration. 

Overall Survival/Production 

Finally, of course, we would like the model to predict outmigrant production correctly, but 

this will undoubtedly be a challenge. Unbiased estimates of main stem production are difficult to 

obtain from trapping data and may be “contaminated” with tributary production. It is worth 

stressing again that the field data itself contains considerable uncertainty. 

57
Coronado and Hilborn (1998) reviewed coded wire tag return data for 1971 to 1988 from 

multiple hatcheries in the Pacific Northwest, Iron Gate among them. Region-wide, Chinook 

salmon survival declined in the mid-1970s and has not rebounded. Fall Chinook survival to age 

3 returning to the Iron Gate hatchery in 1988 was given as approximately 1.75 percent. Perhaps 

we could derive an estimate of outmigrant production from the Iron Gate to Scott study area 

based on the Coronado and Hilborn (1998) estimate, but this will be complicated given our 

assumptions about what constitutes successful production (see following section). 

As a last resort, we may be able to develop a relationship with subsequent year escapement, 

but of course this introduces all of the ocean dynamics, as well as harvest and straying, that are a 

undescribed processes. 

Discussion on Model Parameterization 

Interpreting Model Results 

Because no true calibration is yet possible for this Klamath Salmod model application, the 

reader is urged to remember that simulated outmigration numbers and their attributes are best 

used not as absolute values, but rather as relative values (Prager and Mohr, 1999). Even if the 

model were fully calibrated, measurements for outmigrating salmon are imprecise and subject to 

poorly understood biases; therefore, the model might be expected to reflect any biases. Further, 

because this is not a full life cycle model including complex estuarine and ocean dynamics, 

nothing is known about what happens to salmon successfully migrating downstream from the 

Scott River, where other density-dependent phenomenon may constrain the populations. Salmod 

is clearly not an ecosystem model (sensu Link, 2002), but instead a single species model whose 

“predictions” are limited to that target species. 

Uncertainty Inherent in Model Results 

Models can be misused (Radomski and Goeman, 1996; Schnute and Richards, 2001). We 

have spoken at some length about the uncertainty and assumptions bound up in this application. 

Parameter values have come from a variety of sources representing studies in different locations 

and river settings, have been "extrapolated" across salmon runs, and in some cases, borrowed 

across species. One must be forever critical of what has been published. We are rather fond of a 

statement from Healey and Heard (1984), to wit: "Much of the work that has been done ... is 

tantalizing rather than conclusive. Most of the studies were undertaken to describe consequences 

rather than to test specific hypotheses .... Unfortunately, sampling and analysis methods were 

sometimes inadequate and replication was usually insufficient." 

Model formulations are inexact approximations of the processes we believe to be governing 

populations, not necessarily the "truth." Models act as metaphors of reality and also as filters to 

isolate a signal from background noise in the data. But there are three types of potential errors 

inherent in fisheries models that frustrate this signal extraction (Schnute and Richards, 2001). 

The first is process error, referring to the model’s inability to capture the full range of dynamism 

in birth, death, and growth rates. The second is measurement error, referring to our inability to 

precisely measure what it is we are trying to model. The third element is model uncertainty, 

58
referring in the authors’ context to our occasional inability to know whether the model does in 

fact cover the full range of possible phenomena that may occur to a fish stock. Collectively, 

these three types of potential errors indicate that there may be multiple, equally valid 

explanations to account for what we believe we have witnessed. As has been pointed out by 

modelers investigating the dynamics of Klamath River fall Chinook in the ocean, relationships 

can be spurious and fail with the addition of new data, relationships can be real, but 

environmental or recruitment stochasticity masks the relationship, or relationships may not be 

stationary, but change over time for unclear reasons, making that relationship exceedingly 

difficult to determine (Prager and Mohr, 1999). 

Suggested remedies to these problems include vigilant skepticism, continued data collection 

to “disprove” the model, applying common sense, and implementing precautionary management 

strategies that are robust to fish stock failure (Schnute and Richards, 2001). 

Drawing Inferences from Model Results 

Walters (1986) reasons that we are always in the mode of needing to make policy choices, 

even when field experimentation is impossible or extremely difficult. Thus, choices will 

continue to be made based on inference. Inference is fine as long as we make our assumptions 

explicit–and that's what a model is all about. The Achilles heel, however, is that our 

assumptions, however carefully considered, may still be wrong (Schnute and Richards, 2001). 

For this reason, Walters (1986) further argues that there should always be an opportunity to 

rethink, revise, and expand the model. 

With this in mind, we have given some thought to the evolutionary progression of model 

development and application (Table 18) shows that modeling, like any investigation, moves from 

general and suggestive to specific and credible (Holling and Allen, 2002). The reader will notice 

from Table 18 that validity is always provisional rather than essential for model utility (Rykiel, 

1996). In our estimation, Salmod for the Klamath River is currently cycling between Stages 5 

and 6, indicating that one can begin to explore “solutions” to management issues as long as one 

is clear that the model remains a hypothesis and skepticism is fostered. We believe the model 

rests on a sound theoretical footing, and most, but not all, of its parameters are tied to sound 

empirical data. 

59
Table 18. Progression of model development and application stages. 

Model development stage Attributes Model capabilities may be used in decisions to: 

(9+) repeated calibration/ 

verification loop 

Confidencedriven Refine estimate of uncertainty/ 

Evaluation is ongoing/ 

Model becomes ever more trustworthy 

• • • 

(8) verification Understandingdriven “confirm”/strengthen/predict/or falsify; 

Continue to accumulate evidence; 

Uncertainty is poorly defined 

• • •

(7) calibration Knowledgedriven “suggest” (assuming model is “calibratable”); gain precision 

• • •

(6) parameterized using best 

river-specific data 

“fact”-driven “imply or infer” 

Can begin to explore “solutions” to issues, but must be clear 

that model remains a hypothesis 

• • •

(5) testing Plausibility? Question perceptions; gain insight by identifying patterns 

Revise data and implementation 

• • •

(4) parameterized from 

literature or general 

knowledge 

Data-driven “deduce” based on estimates and assumptions; 

Continue consensus building on model structure and expected 

behavior; gain realism 

• • •

(3) formalization and 

implementation 

Box-and-arrowdriven Stimulate concrete thought about variables, relationships, 

constraints, temporal and spatial scale, and so forth; 

speculation 

• • •

(2) conceptual formulation Hypothesisdriven “reason” 

• • •

(1) opinion Experiencedriven No real model 

Though skepticism is always warranted, modeling applications can often point to interesting 

and potentially useful results. For example, on the Trinity River, Salmod has suggested 

pragmatic flow regimes (Bartholow, 1996; Bartholow and Waddle, 1995). A well-designed 

model can greatly aid one’s thinking about the complexities of the real world and the attendant 

consequences of our collective assumptions (Schnute and Richards, 2001). What is essential is 

to set the model at risk and be vigilant in its adaptation to new understandings, which brings up 

another modeling value. This may be referred to as the “lie detector” benefit of modeling. 

When the model and measured data disagree, we know that one or the other–or both–are wrong. 

The ensuing process of questioning can be quite instructive, as we will see in reviewing the 

results presented in the following section. 

60
Simulation Results 

Habitat Availability 

Salmod may be used to calculate the aggregate microhabitat availability throughout the 

entire 75-km study area by applying the results of the PHABSIM analysis. We have done so in a 

simplified manner by assuming that discharge was uniform throughout the Iron Gate to Scott 

River study reach. The resulting curves for each life stage (Figure 20) are comfortingly smooth, 

presumably because of the large number of transects used to develop each mesohabitat type’s 

relationship and the smoothing we did. They indicate that, if water supply permits and 

management can control, fall and winter flows in the range of about 1,700 to 2,100 ft3/s provide 

the greatest availability of spawning and incubation habitat, while spring flows in the range of 

5,500 to 5,900 ft3

/s provide the greatest amount of juvenile rearing habitat. Incubation habitat 

does not decline with increasing flow due to our assumption that the eggs must simply remain 

wet, though scouring flows would eliminate incubation habitat above 13,000 to 14,000 ft3/s (not 

shown on the graph). The curve set also indicates that fry habitat is more limited than pre- or 

immature smolt habitat, but recall from Table 14 that fry require less space per individual than do 

larger juveniles. 

Gross Habitat Availability

0

100

200

300

400

500

600

500 1500 2500 3500 4500 5500 6500 7500 8500 9500 10500

Discharge (cfs)

Area (1,000 m2) 

Egg incubation Fry Pre- and immature smolts Spawning

Figure 20. Gross habitat (m2) availability as a function of discharge through the entire study 

area. Accretions and effects of water temperature are ignored in this graph. 

Note, however, that the portrayal in Figure 22 is oversimplified because it assumes that there 

are no accretions from Iron Gate to the Scott River, it completely ignores any effects that 

sustained flows may have on water temperature, and it does not account for the nonhomogeneous distribution of habitats. In reality, median accretions from Iron Gate to the Scott 

River are about 450 ft3

/s, approximately one-half of which arises upstream of or from the Shasta 

River. Thus one might reasonably conclude that the curves in Figure 20 should be “shifted to the 

left” by about one half of the median accretion, or about 225 ft3

/s, if applied in a rudimentary 

manner. 

61
The non-homogeneous nature of habitat availability is worth emphasizing. Figure 21 depicts 

the spatial distribution of eggs, fry habitat, and presmolt habitat throughout the study area during 

one year, each a weekly snapshot taken when their numbers peaked during that year. Though fry 

and presmolt habitat are well correlated, most of the eggs (and redds) are close to Iron Gate 

Dam. Redistribution of emergent juveniles is required to reduce habitat capacity constraints. 

17

142026333842485460667277

0

200

400

600

800

1000

1200

Incubating eggs or 

habitat area (m2) 

Kilometers downstream of Iron Gate Dam

Spatial Distributions

Incubating Eggs/1000 Fry habitat PS habitat

Figure 21. Comparison of spatial distribution of peak egg numbers (scaled to fit) compared to 

habitat availability (m2) for fry and presmolts (PS) during their respective peak weeks in a single 

simulation year. The values have been smoothed by averaging over 10 computation units. 

Life-History Timing 

The Salmod model was run for a 43-year period covering water years 1961 to 2003. For this 

period, the average life-history timing is shown in Figure 22; individual years may look quite 

different. As will be seen, adults seed the study area and then die as they spawn. Eggs 

accumulate as adults spawn and die, and then decline due to natural and environmental causes 

throughout the lengthy incubation period. The earliest fry emergence overlaps the egg 

incubation period. Two factors obscure clear discrimination of main stem fry emergence. First, 

water temperatures are not uniform throughout the Iron Gate to Scott reach; downstream water 

temperatures can average less than 1°C cooler near the Scott River during the spawning period. 

Second, juveniles, including fry, are added from tributaries (and the hatchery) throughout the 

spring period. Newly emerged fry grow and progress normally into the presmolt and immature 

smolt stages. Ultimately, most surviving juveniles exit the study area through seasonal 

outmigration. 

62
Average Phenology

0

0.2

0.4

0.6

0.8

1

10/711/712/7

1/72/73/74/75/76/77/7

Relative Abundance 

Adults Eggs Fry Pre&ImSmolts Exiters

Figure 22. Average simulated life-history timing through the biological year for fall Chinook 

salmon in the study area. The scale is based on the maximum abundance of individuals in each 

stage. 

Note that in the absence of a dynamic model, one might propose flow scenarios based solely 

on knowledge of the life-history timing and the PHABSIM results. This will be discussed in 

more detail later. 

Model Fit to Measured Data 

During initial simulations, small adjustments were made in the proportion of each life stage 

moving during each week during the seasonal outmigration period to roughly mimic the field 

measurements made at the most downstream trapping location (Kinsman) in our study area. 

Because these trapping data have not been expanded, we cannot compare the absolute 

magnitudes, but we can compare the values as an index (Figure 23). Though we did not expect 

an exact replication due to differences in averaging of the data sources, the model fit appears to 

capture the temporal trend in outmigration well, especially in the first several weeks of the 

period. The model does not reflect the oscillation apparent in the measured late April and early 

May timeframe, nor does it seem to reflect the “blip” measured in mid-May. We offer no 

explanation for these differences. 

63
Outmigrant Abundance Comparison

0

50

100

150

3/113/183/25

4/14/8

4/154/224/29

5/6

5/135/205/27

6/3

6/106/176/24

7/1

Last Day of Week

Salmod Index 

0

200

400

600

800

1,000

1,200

1,400

Kinsman Index 

Salmod Kinsman Trap

Figure 23. Comparison between Salmod’s estimate of average weekly outmigration just 

upstream from the Scott River and the field measurement index at the Kinsman trap site. Data 

have been averaged for the period 2000 to 2004. 

Figure 24 compares the length of outmigrants predicted by Salmod with the Kinsman trap 

measurements. Though the temporal trend in lengths agrees well until the last few weeks when 

there are few fish migrating, the absolute magnitudes diverge significantly by late May. We 

have not been able to satisfactorily explain this discrepancy. Obviously the difference may arise 

from one or more causes, including: (A) the model over predicts the juvenile growth rate, (B) the 

trapping data under sample larger fish (or over samples smaller fish), (C) we have not captured 

or have misrepresented some form of size-selective mortality, (D) juvenile emigration rates 

differ substantially from the parameter values we used, or (E) we made some other error, 

especially in interpreting tributary or hatchery fish lengths. 

Because we have not seen such a large divergence in fish lengths in other modeling studies, 

we do not initially suspect Salmod’s growth-rate parameterization, though we may have slightly 

overestimated the length of newly emerged fry, as shown in the first week’s values in Figure 24. 

There is no evidence that mortality associated with disease affects larger fish more than smaller 

ones; if anything, the opposite is true (Scott Foott, written comm., 2006). The other possibilities 

remain to be further evaluated. However, though it is clear that Salmod may over-estimate 

production biomass, differences in juvenile growth alone are unlikely to significantly affect 

inferences about relative annual production potential. 

64
Outmigrant Length Comparison

0

20

40

60

80

100

3/113/183/25

4/14/8

4/154/224/29

5/6

5/135/205/27

6/3

6/106/176/24

7/1

Last Day of Week

Length (mm) 

0

20

40

60

80

100

Length (mm) 

Salmod Kinsman Trap

Figure 24. Comparison between Salmod’s estimate of average weekly outmigrant length and 

field measurements taken at the Kinsman trap site. Data have been averaged for the period 2000 

to 2004. 

Annual Production Potential 

Figure 25 presents the annual time series of fall Chinook salmon production from the model 

simulation. Remembering that each simulation year begins with the same number of adult 

spawners and gets the same tributary and hatchery contribution, the model suggests that 

production potential has varied by a factor of about four to five regardless of whether one is 

more interested in numbers of fish or biomass. Interestingly, the period of the early 1990s was 

characterized by wide every-other-year swings that mark the highest and lowest values for the 

43-year period. Clearly, accurate but unmodeled annual variation in the number of main stem 

spawners and tributary fish would serve to amplify these potential production estimates. 

Biomass can be seen to roughly parallel the numbers of outmigrants, but not perfectly. This 

is a function of differing growth rates between years, largely attributable to the seasonal thermal 

regime. Figure 26 further illustrates the relationship between simulated production numbers and 

biomass and signifies that generalizations that one may develop using one measure apply well to 

both. 

During the 1961–2003 period we simulated, the minimum production was approximately 

330,000 and the maximum production was 1,580,000 fish. The median is slightly above the 

mean at 933,000 fish, signaling little variation in annual production–the occasional “busts” are 

not counterbalanced by exceptional “banner years.” Overall, the average egg to main stemorigin smolt survival rate predicted by the Salmod model was about 5 percent, slightly lower 

than the value of 7 percent reported in the literature from multiple salmon streams north of the 

Klamath River (Bradford, 1995). To be truly comparable, however, it must be remembered that 

presmolts and smolts moving downstream from the Scott River still have another 233 km (145 

mi) of river and estuary to negotiate, so it would be fair to assume that the model-predicted 

survival rate is even lower. 

65
Annual Production

0

500

1,000

1,500

2,000

196119631965196719691971197319751977197919811983198519871989199119931995199719992001

Thousands of Fish 

0

500

1,000

1,500

2,000

2,500

3,000

Biomass (kg) 

Numbers Biomass

Figure 25. Time series of annual production potential by water year, both numbers of juveniles 

and their biomass, surviving to move downstream from the Scott River. 

R2 = 0.8649

0

500

1,000

1,500

2,000

2,500

3,000

0 500 1,000 1,500 2,000

Thousands of Fish

Biomass (kg) 

Figure 26. Relationship between annual production potential and biomass for the 43-year 

simulation period. 

Causes of Mortality 

Over the course of the 43-year simulation, production potential was reduced from the 

maximum possible by a variety of causes. A simple average across the simulation period 

suggests that fry and presmolt habitat-related mortality were the largest contributors to annual 

mortality after a somewhat expected high base mortality for eggs, followed by egg and fry 

temperature-related mortality (Figure 27). Remaining mortality causes other than 

superimposition begin to rank well down the list, in marked contrast to Salmod applications for 

other study areas, presumably because of the large number of juvenile Chinook salmon 

exogenously added to the model domain from the Shasta River and Bogus Creek. Said another 

66
way, since the main stem Klamath River has only about 22 percent of the adults estimated to 

spawn in either the main stem, Bogus Creek, or the Shasta River (California Dept. of Fish and 

Game, no date; though note that the recent conclusion that there are more main stem spawners 

than previously recognized may change this estimate), these other tributaries tend to overwhelm 

main stem production. Large numbers of tributary fish result in high habitat-related mortality 

estimated by the model occurring downstream from these tributary mouths. Predicted egg 

superimposition mortality, though infrequent through the years (see fig. 27), is higher than we 

expected and should receive some scrutiny for reasonableness in the future. 

Average Mortality by Cause

0 2,000 4,000 6,000 8,000 10,000

IS Seasonal

IS Base

IS Habitat

PS Base

PS Seasonal

E Lost

IS Temperature

F Seasonal

E Incubation

F Base

E In vivo

PS Temp.

E Superposition

F Temperature

E Temperature

PS Habitat

F Habitat

E Base

Thousands of Fish

Figure 27. Average annual mortality by cause over the 43-year simulation period for eggs (E), fry 

(F), presmolts (PS), and immature smolts (IS). No mortality due to habitat or water temperature 

was predicted for adult spawners in the study area, but some ripe females did die unspawned. 

It is also instructive to look at annual mortality in another way. Figure 28 shows the relative 

frequency or predictability of various forms of mortality, but presented in a somewhat unusual 

fashion. At the top of the graph appear those mortality events that occur with the lowest 

frequency or consistency. For example, “lost eggs” refers to mortality that occurs when many 

ripe females died prior to spawning due to a shortage of available spawning habitat. This was a 

relatively rare occurrence during the 43-year simulation in contrast to those forms of mortality 

that appear at the bottom of Figure 28 that take place with great regularity from year to year at 

roughly the same magnitude. Presmolt habitat-related mortality is the most consistent form of 

mortality predicted by the model. 

67
0123456

PS Habitat

PS Seasonal

F Habitat

IS Seasonal

IS Temperature

IS Base

PS Temperature

F Seasonal

F Base

E Base

F Temperature

PS Base

IS Habitat

E Temperature

E Superposition

E In vivo

E Incubation

E Lost

Mortality Irregularity Index

7

Figure 28. Mortality irregularity index showing the relative infrequency (and its inverse, the 

relative predictability) of annual mortality due to different factors. This index is the coefficient of 

variation (standard deviation divided by the mean) for the 43-year simulation. In other words, 

mortality to presmolts due to habitat constraints occurs year in and year out at approximately the 

same magnitude, whereas lost eggs was a very rare event of inconsistent magnitude.

Because habitat-related mortality appears to be a significant limiting factor in model 

simulations, it may be instructive to more closely examine past flow regimes associated with 

these limits. It is difficult to look at this issue comprehensively because of the dynamic nature of 

flows, their seasonal consistency (or lack thereof), and flow transitions from month to month or 

week to week. One way to synthesize the large data set is to look at exceedance plots depicting 

the probability of historical flows in different periods relevant to Chinook life-history. The 

following two graphs capture this information, Figure 29 for the spawning and early incubation 

phases and Figure 30 for the bulk of the juvenile rearing phase. Referring back to Figure 20, we 

can see that fall spawning flows from 1,000 to 2,000 ft3

/s, indicative of maximum habitat 

availability, occur frequently in Figure 29, but flows outside this range occur approximately 36 

percent of the time. In contrast, spring rearing flows in the range of approximately 5,000 to 

6,500 ft3

/s are quite rare (only about 20 percent of the time; Figure 30). This flow frequency 

perspective may shed some light on the dominance that juvenile rearing habitat constraints seem 

to play relative to spawning and incubation habitat limitations. A more extensive time-series 

analysis is outside the scope of this report, except insofar as Salmod captures these dynamics. 

68
October-November Flows

0

1,000

2,000

3,000

4,000

5,000

6,000

0 0.2 0.4 0.6 0.8 1

Exceedance Probability

Discharge Downstream 

from Shasta River (cfs) 

Figure 29. Probability of flow equaling or exceeding the given flow in the months of October and 

November immediately downstream from the Shasta River. 

March-April Flows

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 0.2 0.4 0.6 0.8 1

Exceedance Probability

Discharge Downstream 

from Shasta River (cfs) 

Figure 30. Probability of flow equaling or exceeding the given flow in the months of March and 

April immediately downstream from the Shasta River. 

Figure 31 depicts some of the least consistent forms of mortality over time and is a good 

illustration of how total mortality is composed of sub elements of variable magnitude and 

frequency. Just because habitat limitations appear to dominate overall mortality, less frequent or 

severe causes should not be completely discounted as potentials for mitigation. 

69
Mortality by Selected Cause

0

5,000

10,000

15,000

20,000

19621965196819711974197719801983198619891992199519982001

Thousands of Fish 

E Lost E In vivo E Incubation E Superposition

E Temperature F Temperature PS Temperature

Figure 31. Time series of mortality from several of the less frequently consistent causes by 

water year. Shading progresses from the bottom of each bar to match the legend from left to 

right. 

Effects of Water Delivery on Simulated Production Potential 

One important variable to investigate has to do with the potential effect of annual water 

delivery downstream from Iron Gate Dam on simulated fall Chinook production. We use the 

term “water delivery” instead of “water supply” to indicate that this analysis draws no conclusion 

regarding variable water supply and consumptive use upstream of Iron Gate Dam. In relating 

annual production to water released from Iron Gate, we are oversimplifying any effects that arise 

from the variable accretions of the Shasta River and other minor tributaries, especially if the 

accretions do not mirror Iron Gate releases. As will be seen, we know that these accretions do 

affect the model’s predicted production. We also acknowledge that quantifying water delivery as 

an annual value ignores important distinctions between intra-annual delivery patterns. However, 

we will look more closely at these seasonal phenomena when we examine the discrete causes of 

mortality. 

Figure 32 displays annual production against the backdrop of annual Iron Gate discharge. 

When conditions are either “dry” or “wet,” production appears to be diminished. Production 

may be high when water delivery is “average,” but average conditions are no guarantee of 

maximizing production; production can be lower in an average year than it is under drier or 

wetter conditions. (Note that we are, for the moment, ignoring important seasonal differences in 

discharge.) 

70
Production Relative to Water Delivery

95

93

88

81

91

90

03

87

66

02

77

79

62

61

01

73

80

64

00

76

68

83 72 96

97

86

85

70

78

67

63

75

98

82

74

74

83

84

99

89

69

92

94

100

300

500

700

900

1,100

1,300

1,500

1,700

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Average Annual Iron Gate Discharge (cfs)

Thousands of Fish 

A

C

D

B

Figure 32. Annual production portrayed as a function of water delivery downstream from Iron 

Gate Dam. Numbers associated with each annual point refer to the water year. See text for an 

explanation of groupings. 

Using figure 32, we examined several water years with relatively low simulated production, 

some associated with low annual delivery of water downstream from Iron Gate Dam and some 

with high delivery. These years were (Group A) 1992 and 1994, and (Group B) 1972, 1983 and 

1984, respectively. We also chose three water years with relatively high simulated production 

clustered in the middle range of water delivery downstream from Iron Gate Dam. These years 

were (Group C) 1967, 1978, and 1995. Finally, we looked closely at four other years, (Group D) 

1966, 1976, 1985, and 2000, also falling in the mid range of water delivery but exhibiting lower 

production potential than other mid delivery years. Our objective was to learn whether there was 

any commonality in these groupings. 

Group A characterizes years of relative drought from the historical record. Careful 

examination of the simulated annual mortality for these years shows some similarities and some 

differences. Both years (1992 and 1994) had three to five times the average egg thermal 

mortality, both in vivo and during incubation, due to mean weekly water temperatures above 

16.5°C before and during the initiation of the spawning period. Both years also had more than 

2.5 times the average egg incubation mortality primarily associated with reductions in February 

flows significantly below flows available during the fall spawning period sufficient to dewater 

much of the previously used spawning habitat. Remaining causes of mortality were generally 

average. 

71
Group B (1972, 1983, and 1984) characterizes years of relatively high water delivery. Like 

the drought years, these years had somewhat higher egg mortality than average, considering all 

forms of egg mortality. Some mortality was associated with redd-scouring flood flows 

downstream from the Shasta River instead of the flow reductions noted for Group A and some 

for superimposition. The year 1984 exhibited high thermal mortality on immature smolts. Mean 

weekly water temperatures in this year often exceeded 17°C by mid-June. It should be noted that 

temperatures regularly exceeded 17° in other years as well, but 1984 had a relatively cool spring, 

which may have delayed juvenile growth, which was then followed by a relatively hot summer. 

But fundamentally, Group B simply had average mortality from almost all causes. Thus, their 

production was simply average. 

Group C (1967, 1978, and 1995) characterizes the mid range of water delivery and higher 

than average annual production. As might be expected, the commonality among these years was 

a general lack of higher than normal mortality. Some of these years exhibited slightly higher 

than average thermal mortality on either presmolts or immature smolts, or both, but none of these 

years was marked by high mortality on multiple life stages, especially the early life stages. Fall 

and spring flows in these years were not necessarily ideal (as judged by Figure 20), but did share 

some common characteristics. Flows generally increased steadily during the spawning season, 

giving rise to progressively more spawning habitat as the season advanced. Spring rearing flows 

were most often above 2,000 ft3

/s, and generally peaked in the neighborhood of 4,000 to 5,000 

ft3/s. All of these high production years did have higher than normal base mortality. This should 

be expected because there were simply more fish suffering the same mortality rate. 

Group D (1966, 1976, 1985 and 2000) characterizes mid-range water delivery but depressed 

production and is perhaps the most interesting group of all. These years were unremarkable with 

respect to the “normal” thermal and habitat-related mortality noted for Groups A and B. Instead, 

the unifying factor for these years was depression due to the relatively infrequent forms of 

mortality. Each had two or more incidents of high mortality due to “lost eggs,” in vivo egg 

mortality, egg incubation mortality, redd superimposition, egg thermal mortality, or immature 

smolt habitat-related mortality. The lowest production year, 1985, showed significant decreases 

in spawning habitat availability that led to exceptionally high lost eggs and superimposition 

mortality. It should be noted that Group D may or may not have had the same seasonal delivery 

pattern as years included in Group C. In the future, it would be intriguing to pursue this same 

sort of analysis, but with water delivery broken into seasonal groups, for example fall, winter, 

spring. 

Effects of Water Temperature on Simulated Production Potential 

Because of the predictable annual thermal cycle in the Klamath River, we know that high 

temperatures are likely to affect both the period of early fall adult upstream migration and egg 

incubation as well as late spring juvenile rearing (Bartholow and others, 2004). For this reason, 

and because we discussed water temperature effects to some degree in the previous section, we 

chose not to rely on a single annual index for water temperature. Instead, Figure 33 and Figure 

34 attempt to isolate the individual effects of fall and spring temperatures on production. 

Figure 33 illustrates the relationship, or lack thereof, between maximum weekly 

temperatures in the last two weeks of October and Chinook production. There may be a 

relationship, but this relationship does not appear to be statistically significant, though from 

model parameterization we would not expect to see much effect until temperatures exceeded 

72
15°C. An analysis using quantile regression might be in order as this technique may better 

elucidate the true “limit” here. In other words, imagine fitting a “regression” line not through the 

middle of the cloud in Figure 33 but rather as a “cap” or ceiling on the cloud. Such a limiting 

cap would be apparent in the production decline evident above 14 or 15°C. A similar relationship 

appears in Figure 34. Again this relationship is not strong, but the effect of the increasing water 

temperatures above 10°C on the rate of disease-induced mortality does come through. In neither 

case would one conclude that water temperature is the decisive factor limiting freshwater 

production. Instead, one would surmise that many limitations are involved, both habitat and 

water quality. 

R2 = 0.1332

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

12 13 14 15 16

Maximum Weekly Fall Water Temperature (°C)

Thousands of Fish 

Figure 33. Annual production compared with maximum weekly water temperature in the last 

two weeks of October. 

R2 = 0.0644

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

10 11 12 13 14 15 16

Maximum Weekly Spring Water Temperature 

(°C)

Thousands of Fish 

Figure 34. Annual production compared with maximum weekly water temperature in the first 

two weeks of May.

73
Bottlenecks? 

So far we have sidestepped at least one important issue. We have seen that overall 

production seems to be related to the number and severity of the various forms of mortality that 

can occur to multiple life stages within a biological year. We have also seen that fry and 

presmolt habitat-related mortality are predicted to be the most significant and regularly acting 

forms of mortality. The reader may also recall that habitat-related mortality is a direct function 

of the specified habitat capacity because there is a maximum number of individuals that can 

occupy the habitat available each week for each non-egg life stage. This fixed limit could act as 

a so-called bottleneck, setting an absolute limit to the maximum production that could ever occur 

given the computed habitat availability. Bartholow (1996) found some evidence for this on the 

Trinity River. However, the specified habitat capacity is not as fixed as it sounds because of the 

model’s movement algorithm, the dynamic nature of juvenile growth, the ever-changing habitat 

availability as a function of flow, and other factors (Bartholow and others, 1993). So the 

question becomes, “How much would any increased survival at the egg or fry stage likely persist 

through any subsequent habitat bottleneck at the fry and presmolt stages to increase production?” 

More simply, if you could save eggs or fry from thermal or incubation losses, would such action 

be effective in increasing juvenile production, or would subsequent juvenile habitat constraints 

preclude any increase? 

In addition to the benefits of a sensitivity analysis listed in Appendix D, one may also infer 

which limitations, if relaxed, are likely to have payoff in terms of production. We have already 

seen from the sensitivity analysis (Appendix D) that egg thermal mortality has only moderate 

bearing on overall production, but it is not zero. Fry thermal mortality is far more sensitive, so 

we have some reason to believe that juvenile habitat constraints do not act as absolute limits. 

Fortunately, a model such as Salmod can be used to directly answer this question by “turning 

off” egg mortality due to high temperatures. We ran the model after eliminating egg thermal 

mortality to see what effect this would have on overall production and found that between 0.5 

percent and 7.5 percent of eggs so “saved” would contribute to outmigrant production, 

depending on the year, as currently parameterized. In other words, habitat limits still largely 

govern production potential but do not act as absolute bottlenecks–thermal effects can adjust 

production potential somewhat, especially in some years. Whether managing water to reduce 

egg or fry thermal mortality would be feasible or worth the effort remains an open question. 

Summary of Important Modeling Inferences 

Perhaps it is common sense that low production years are characterized by mortality 

affecting multiple life stages and involve both habitat-related constraints and higher than normal 

water temperatures. Neither fall nor spring water temperatures were decisively associated with 

limits on annual production, though in both cases the highest temperatures did appear to depress 

production. We believe this underscores the conclusion that reduced production is most closely 

associated with multiple causes involving both water temperature and habitat constraints. 

Neither alone is a sufficient explanation for significant decreases in simulated production. 

It may be less obvious that both dry years and wet years were predicted to negatively impact 

egg incubation. Dry years were also associated with higher egg thermal mortality and wet years 

were unexpectedly associated with higher juvenile thermal mortality. We must make it clear that 

74
we draw no conclusion from this analysis that water delivery significantly affects or controls 

water temperature. Instead, it is likely that water temperature is primarily associated with 

seasonal meteorological conditions instead of water management (Campbell and others, 2001). 

Nonetheless, this remains for further investigation. 

All things being equal, the model suggests that any action successful in mitigating forms of 

mortality that are both common and substantial may be expected to result in improved Chinook 

production. Mortality associated with limitations to presmolt and fry habitat fits this 

classification. Mortality associated with high water temperature also can be substantial but 

occurs far less predictably. It is clear that main stem flows, if available and properly regulated, 

have the ability to control the amount of habitat available for fry and presmolts precisely through 

time. The role that flow regulation may play in controlling water temperatures is far less clear. 

The feasibility and relative effectiveness of alternative water management scenarios 

designed to minimize the collective constraints on Chinook salmon production remain for 

additional study. However, the principal hypothesis emerging from this analysis is that spring 

flows, which govern fry and pre-smolt habitat availability, may have the greatest utility in 

moderating production limitations. 

Recommendations for Further Study 

Several lines of investigation were highlighted as important for future study. Some apply to 

model parameter uncertainty, some to model process uncertainty or assumptions, and some to 

water management. In no particular order, let us try to summarize these important issues. 

The effect of disease on juvenile mortality must be better understood. Fortunately, 

considerable effort is ongoing by others to study and quantify disease dynamics. Better ways to 

relate disease incidence and virulence to water temperature will likely prove worthwhile. 

The model currently overestimates the juvenile growth rate. We have discussed potential 

reasons for this problem and suggest that some effort be devoted to pursuing a differential 

diagnosis to eliminate the source of this inaccuracy. 

The model suggests that fry and presmolt habitat constraints collectively are major 

impediments to increased production. The model further suggests that habitat-related mortality 

should be the greatest in the few kilometers downstream from each of the major tributaries and 

the hatchery release site. A carefully executed field study may be able to confirm or reject this 

hypothesis. 

Because presmolt habitat appears to be a major limiting factor, and we do not have a 

measured habitat capacity for presmolts in the Klamath River, we recommend that more effort be 

devoted to sampling juveniles larger than 55 mm in order to better estimate a river-specific 

capacity value. 

The model suggests that tributary production significantly boosts overall production from the 

study area and that management of springtime limiting factors may be more important overall 

than those in the fall. However, we did not explicitly model these tributaries whose production 

in reality would also be influenced by fall river conditions, especially in vivo egg mortality. We 

may wish to consider a way of incorporating dynamic tributary production, especially as we 

move closer to model calibration. 

The exact relationships between managed Iron Gate releases and downstream water 

temperature remain for investigation. We have at present unpublished modeling results that 

75
indicate that there is such a relationship, but it is simply not straightforward. We believe that this 

issue is worthy of further study and in fact are in the process of working on it. 

It is important to resolve the issue with in vivo egg mortality. Does it exist? Is it significant 

on the Klamath River? Though this report suggests that flow (and possibly temperature) 

management in the spring is likely the key to improving Chinook salmon production, we 

continue to believe that we cannot completely overlook flow and temperature management in the 

fall. It may take some investigation of the Iron Gate hatchery records to be successful at putting 

this issue at rest, but it may well be important because any effect on holding adults will affect not 

only main stem spawners, but also tributary and hatchery spawners. In addition, opportunities 

for multiple-level temperature control, even if the volumes are small, remain for investigation. 

Naturally, data collection sufficient to further calibrate and then validate, or invalidate, the 

Salmod model is important. It is clear from the data already collected by the USFWS at multiple 

locations that there is considerable mortality occurring between Iron Gate Dam and the Shasta 

River. This may be related to disease or other factors. We fully support continued efforts to 

understand and quantify outmigrant dynamics as well as fish abundance along the length of the 

study area. 

The ongoing data-collection effort by the USFWS and others should continue to look for 

evidence for or against the influence that sudden changes in discharge (freshets) may have in 

stimulating juvenile outmigration. This phenomenon is certainly present in other river systems 

and we would be surprised if it is not true on the Klamath River. If such stimulation is evident, 

capturing the phenomenon in the model would add realism and potentially be quite valuable in 

designing effective flow regimes. 

Because Bogus Creek appears to be a significant factor in Klamath River Chinook 

production, it is important to better quantify its contribution to the main stem. We have made 

what we believe is a reasonable assumption on the numbers of juveniles emigrating the main 

stem, but our assumption should be confirmed or replaced. 

Some of our recommendations indirectly address the relative importance of Klamath River 

main stem production compared to the tributaries (principally the Shasta River and Bogus Creek 

in our study area) and the hatchery. There is no evidence that the main stem segment of the 

spawning population is declining relative to the tributaries. However, because main stemspecific production is small relative to these other sources, we raise the question of whether nearfuture river management may ultimately marginalize main stem production. This is not a 

question that can be answered effectively using Salmod or similar production models but instead 

must be addressed within the larger context of conservation biology because man-made 

alterations to the Klamath River Basin have progressively pruned one tributary or main stem 

segment’s subpopulation after another from the genetic pool. To the degree that these 

subpopulations represent population “insurance” to the variety of hydrometeorological and 

channel-forming events that can occur, the issue should not be considered trivial lest cumulative 

effects put the entire population at risk of extinction. 

An additional avenue of investigation that we have not pursued is to better understand the 

likelihood of ensuring two high production years in a row. Though not necessarily beneficial for 

ocean-type fall Chinook, developing a familiarity with the potential management implications for 

stream-type Chinook and coho salmon, both of which require favorable living conditions over 

the summer and through the next fall and winter, may be important. 

We have not kept up on other salmonid modeling efforts underway on the Klamath River, 

specifically KlamRas and EDT. It might be fruitful to compare results among all three studies, 

76
identifying similarities and differences. Such an effort might reveal promising lines of 

investigation. 

We offer one further observation. During the modeling to produce this report, we increased 

the number of spawners. Unfortunately we also changed some other parameters at the same time, 

making clear discrimination of the changes attributable to each problematic. Nonetheless, the 

increased population gave some indication that the relative magnitude of bottlenecks changed. 

This was also apparent in the sensitivity analysis. This model prediction could bear additional 

attention. 

Finally, our objective in this study has been to better understand the factors that have likely 

been instrumental in controlling freshwater production for Klamath River Chinook salmon. We 

have not yet addressed the full potential that alternative flow regimes may have for increasing 

production. Techniques to “design” favorable flow regimes would be capable of further 

illuminating the tradeoffs involved in how to “spend” a limited quantity of water throughout the 

biological year. This is an area of investigation of great potential utility and could target either 

flow regimes that are tailored to individual water supply or delivery scenarios, or toward flow 

regimes that may be of benefit regardless of water availability, or both. We do not believe that 

Salmod must be rigorously validated to begin this task. 

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Acknowledgments 

We owe enormous thanks to many people for their help in this endeavor. Among them are 

Jerri Bartholomew, Cara Berman, Randy Brown, Mike Belchik, Sharon Campbell, Ian Chane, 

Bill Chesney, Michael Cunanan, Gary Curtis, Marshall Flug, Scott Foott, Mark Gard, John 

Hamilton, Mark Hampton, John Heasley, Nick Hettrick, Bill Jong, Steve MacDonald, Steve 

Mamoyac, Keith Marine, Dean Marston, Bob Milhous, Mark Pisano, Linda Prendergast, Steve 

Railsback, Richard Raymond, Mike Rode, Tom Shaw, Gary Smith, Clair Stalnaker, Mike 

Wallace, Sam Williamson, and many more we may have unintentionally forgotten.

85

Appendix A. Salmod Input Files 

We will continue to update the Salmod model’s parameter files as warranted by newly 

collected data. Complete Salmod input files accompanying the SIAM model may be 

downloaded from the Internet at http://www.fort.usgs.gov/Products/Software/SIAM/

Appendix B. Habitat Suitability Criteria 

4 = Object plus overhead 

Channel Index Codes – 

2 = Cover Object 

Figure B-1. Habitat suitability criteria for fry Chinook. 

87
1 = Cover present 

Channel Index Codes – 

0 = No cover 

Figure B-2. Habitat suitability criteria for presmolt Chinook. 

88
 

16 = Small cobble 

Channel Index Codes – 

14 = Medium gravel 

15 = Large gravel 

Figure B-3. Habitat suitability criteria for spawning females–Chinook. 

89
Appendix C. Flow in Relation to Weighted Usable Area 

Figure C-1. Weighted Usable Area (WUA) for Fry in Low Slope 

Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

Discharge - Cubic Feet/second

Figure C-2. Weighted Usable Area(WUA) for Presmolts in Low Slope 

Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Discharge - Cubic Feet/second

90

Figure C-3. Weighted Usable Area(WUA) for Spawning Females in Low 

Slope Mesohabitat Type - Segment 1

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

Discharge - Cubic Feet/second
Figure C-4. Weighted Usable Area(WUA) for Eggs/Alevins in Low Slope 

Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 100 200 300 400 500 600 700 800 900 1000 1200 1400 1600 13000 14000

Discharge - Cubic Feet/second

Figure C-5. Weighted Usable Area(WUA) for Fry in Moderate Slope 

Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Discharge - Cubic Feet/second

Figure C-6. Weighted Usable Area(WUA) for Presmolts in Moderate 

Slope Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Discharge - Cubic Feet/second

91
Figure C-7. Weighted Usable Area(WUA) for Spawning Females in 

Moderate Slope Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Discharge - Cubic Feet/second

Figure C-8. Weighted Usable Area(WUA) for Eggs/Alevins in Moderate 

Slope Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 100 200 300 400 500 600 700 800 900 1000 1200 13000 14000

Discharge - Cubic Feet/second

Figure C-9. Weighted Usable Area(WUA) for Fry in Backwater

Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

Discharge - Cubic Feet/second

92
Figure C-10. Weighted Usable Area(WUA) for Presmolts in Backwater

Mesohabitat Type - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

Discharge - Cubic Feet/second

Figure C-11. Weighted Usable Area(WUA) for Spawning Females 

in Backwater Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Discharge - Cubic Feet/second

Figure C-12. Weighted Usable Area(WUA) for Eggs/Alevins in

Backwater Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100 200 300 400 500 600 700 800 900 1000 13000 14000

Discharge - Cubic Feet/second

93
Figure C-13. Weighted Usable Area(WUA) for Fry in Steep Slope 

Mesohabitat Type - Segment 1

0

1000

2000

3000

4000

5000

6000

Discharge - Cubic Feet/second

Figure C-15. Weighted Usable Area(WUA) for Spawning Females in 

Steep Slope Mesohabitat Type - Segment 1

0

100

200

300

400

500

600

700

800

900

1000

Discharge - Cubic Feet/second

Zero Habitat

94
Figure C-16. Weighted Usable Area(WUA) for Fry in Split Channel

Combined Mesohabitat Types - Segment 1

0

5000

10000

15000

20000

25000

30000

Discharge - Cubic Feet/second

Figure C-17. Weighted Usable Area(WUA) for Presmolts in Split 

Channel Combined Mesohabitat Types - Segment 1

0

5000

10000

15000

20000

25000

30000

35000

40000

Discharge - Cubic Feet/second

Figure C-18. Weighted Usable Area(WUA) for Spawning Females in 

Split Channel Combined Mesohabitat Types - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Discharge - Cubic Feet/second

95
Figure C-19. Weighted Usable Area(WUA) for Eggs/Alevins in Split 

Channel Combined Mesohabitat Types - Segment 1

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0 100 200 300 400 500 600 700 800 900 1000 1200 1400 13000 14000

Discharge - Cubic Feet/second

Figure C-20. Weighted Usable Area(WUA) for Fry in Side Channel

Combined Mesohabitat Types - Segment 1

0

5000

10000

15000

20000

25000

Discharge - Cubic Feet/second

Figure C-21. Weighted Usable Area(WUA) for Presmolts in Side 

Channel Combined Mesohabitat Types - Segment 1

0

5000

10000

15000

20000

25000

30000

Discharge - Cubic Feet/second

96
Figure C-22. Weighted Usable Area(WUA) for Spawning Females in 

Side Channel Combined Mesohabitat Types - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Discharge - Cubic Feet/second

Figure C-23. Weighted Usable Area(WUA) for Eggs/Alevins in Side

Channel Combined Mesohabitat Types - Segment 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Discharge - Cubic Feet/second

Figure C-24. Weighted Usable Area(WUA) for Fry in Low Slope

Mesohabitat Type - Segment 2

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Discharge - Cubic Feet/second

97
Figure C-25. Weighted Usable Area(WUA) for Presmolts in Low Slope 

Mesohabitat Type - Segment 2

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Discharge - Cubic Feet/second

Figure C-26. Weighted Usable Area(WUA) for Spawning Females in 

Low Slope Mesohabitat Type - Segment 2

0

100

200

300

400

500

600

Discharge - Cubic Feet/second

Figure C-27. Weighted Usable Area(WUA) for Eggs/Alevins 

in Low Slope Mesohabitat Type - Segment 2

0

100

200

300

400

500

600

0 1000 1100 13000 14000

Discharge - Cubic Feet/second

98
Figure C-28. Weighted Usable Area(WUA) for Fry 

in Moderate Slope Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

30000

Discharge - Cubic Feet/second

Figure C-29. Weighted Usable Area(WUA) for Presmolts 

in Moderate Slope Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

30000

35000

40000

Discharge - Cubic Feet/second

Figure C-30. Weighted Usable Area(WUA) for Spawning Females

in Moderate Slope Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

Discharge - Cubic Feet/second

99
Figure C-31. Weighted Usable Area(WUA) for Eggs/Alevins

in Moderate Slope Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

0 1000 1100 1200 1300 13000 14000

Discharge - Cubic Feet/second

WUA - Sq. Ft./1000 Ft

Figure C-32. Weighted Usable Area(WUA) for Fry in Backwater

Slope Mesohabitat Type - Segment 2

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Discharge - Cubic Feet/second

Figure C-33. Weighted Usable Area (WUA) for Presmolts in 

Backwater Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

100
Figure C-34. Weighted Usable Area (WUA) for Spawning Females

 in Backwater Mesohabitat Type - Segment 2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Zero Habitat

101
Figure C-37. Weighted Usable Area (WUA) for Spawning Females in 

Steep Slope Mesohabitat Type - Segment 2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Discharge - Cubic Feet/second

Zero Habitat

Figure C-38. Weighted Usable Area (WUA) for Fry in Split/Side

Channel Combination Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

30000

35000

Discharge - Cubic Feet/second

Figure C-39. Weighted Usable Area (WUA) for Presmolts in 

Split/Side Channel Combination Mesohabitat Type - Segment 2

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Discharge - Cubic Feet/second

102
Figure C-40. Weighted Usable Area (WUA) for Spawning Females in 

Split/Side Channel Combination Mesohabitat Type - Segment 2

0

2000

4000

6000

8000

10000

12000

Discharge - Cubic Feet/second

Figure C-41. Weighted Usable Area (WUA) for Eggs/Alevins in 

Split/Side Channel Combination Mesohabitat Type - Segment 2

0

2000

4000

6000

8000

10000

12000

0 1000 1100 1200 1300 13000 14000

Discharge - Cubic Feet/second

103
Appendix D. Salmod Sensitivity Analysis 

Salmod is a mathematical model constructed from a series of variable inputs, equations, and 

parameters that attempts to describe and quantify fall Chinook salmon production potential on 

the Klamath River downstream from Iron Gate Dam. Variables are defined as those external 

driving factors (flow, water temperature, and spawner seeding density) that vary from time step 

to time step or year to year. Parameters are essentially fixed values controlling internal model 

computations. It is important to understand uncertainties in both model variables and 

parameters, but in this initial sensitivity analysis (SA) we are targeting model parameters. 

Sensitivity to flow and temperature variability has been addressed in another stage of the 

historical analysis. 

Model parameters are subject to many sources of uncertainty including errors of 

measurement, absence of information, and poor or partial understanding of important biological 

mechanisms. These limitations necessarily tax our confidence in model predictions. Good 

modeling practice requires that the modeler provide an evaluation of his or her confidence in the 

model, a portion of which involves assessing uncertainties associated with all model inputs. 

Sensitivity analysis is one tool that can be used to: 

• Apportion the relative variation in model output to variation in model inputs, 

qualitatively or quantitatively. 

• Identify those parameters that may be in the greatest need of additional empirical data 

collection. 

• Identify factors that may prove useful in subsequent model calibration. 

• Identify insensitive variables that require little further attention. 

• Establish defensibility in the sense that reviewers are increasingly asking for sensitivity 

analysis as a component of a thorough modeling analysis. 

Methods 

These are the general steps followed in conducting a SA for Salmod on the Klamath River: 

1. Specify the model output of interest. It is important to select only one or a few of the 

many outputs produced by a model and identify this as the output of interest. In our case, the 

key value chosen was the total annual number of fall Chinook outmigrating downstream from the 

Scott River. Though we could have chosen biomass, we elected to choose numbers of fish 

because we felt that this would be more widely understood by all stakeholders and we relied on 

this metric during subsequent modeling analysis. 

2. Select the inputs of interest from the full suite of possibilities, focusing on the most likely 

sensitive factors. Salmod has literally many hundreds of input values. If every single value were 

subject to variation, it would be very difficult to make sense of the voluminous results. For this 

reason, we grouped values into sets that were subsequently treated as single factors. For 

example, Salmod has a set of x,y coordinates that describe the relationship between mean weekly 

thermal exposure and mortality rate for each life stage. Rather than test the sensitivity of each 

coordinate pair, we shifted the whole set of coordinates “left and right” by 2°C for each life 

stage. 

3. Choose the amount of variability for the selected factors. There is no single standard 

technique in performing a sensitivity analysis. Parameter variation is typically specified either as 

104
proportionate (for example ± 10 percent) or through a “reasonable range” (for example from a 

low to high “probable” or “expected” value). We have chosen the reasonable range approach 

for most parameters, but we also used the proportionate approach when could not clearly identify 

the reasonable range. 

Note that using both techniques can result in measures of sensitivity that are difficult to 

compare. For example, adjusting the calendar date of downstream pre-smolt migration by ±1 

week may not be directly comparable to varying the temperature that initiates spawning by ±2°C 

because the units of variation differ. In addition, it should be clear that we may have 

overestimated the variability range for some parameters and underestimated the range for others, 

regardless of the approach. A comprehensive list of parameters and the variability assigned to 

them, along with other information, is given in the table. 

4. Choose variation technique. The simplest and most common sensitivity analysis varies 

one parameter at a time, executing the model repeatedly to quantify any differences in key model 

outputs. The next level of complexity calls for variation of more than one parameter at a time, 

typically from a joint probability distribution that attempts to describe how the parameters might 

vary in tandem. However, it is often the case that such a joint probability distribution is itself 

unknown. We chose the single factor approach due to its simplicity. Under the presumption that 

all uncertain factors are susceptible to “correct” determination, and have the same cost to remove 

uncertainty, this so-called first-order sensitivity analysis identifies the factor(s) most deserving of 

better field or experimental measurement. 

5. Generate a matrix showing the maximum sensitivity in model outputs from parameter 

variation. Again, we have chosen a simple design. We begin with the base simulation which 

contains our current best estimate of parameters. Then we make two other simulation runs, one 

with the high estimate and one with the low. Computing the biggest percentage change in 

outmigrant numbers (high minus base or low minus base) provides a measure of the maximum 

sensitivity for this parameter. In addition, having three points for each parameter (high, base, 

low) enables us to examine whether variation in each parameter causes a linear or non-linear 

response. We do not discuss this last point further here. 

6. Repeat Step 5 for a variety of year types. Following the philosophy of looking for the 

maximum possible sensitivity, we also wanted to make sure that we examined a variety of 

different year types, from wet to dry and hot to cold. Thus, we repeated the sensitivity 

calculation for a three-by-three matrix representing these conditions by drawing on categorized 

years. Years were chosen using SIAM’s yearly sort function and a previous analysis that 

provided maximum June air temperature and inflow to Upper Klamath Lake. The selected years 

are shown in the following table: 

 Hot Avg Cool 

Wet 1998 1982 1971

Avg 1985 1995 1976

Dry 1977 1988 1981

As before, we chose the maximum sensitivity for each parameter across all nine year types. 

7. As previously noted, the single factor variation scheme prevents the detection of 

interactions among parameters. That is, in examining the sensitivity of one parameter, we accept 

the risk of remaining ignorant about important model dynamics if some or all of the remaining 

parameters were different than their base value. A prime example of this in our case is that the 

model might behave very differently, depending on the number of adult spawners used to seed 

105
the system. With low numbers there might be little in the way of juvenile habitat-related 

mortality, but with higher seeding levels there may be a lot. Because of this, we repeated Steps 5 

and 6 for the case where there were two times the average number of spawners used in the base 

case, once again choosing the maximum sensitivity values. 

To summarize, we choose the maximum parameter sensitivity across three different cases: 

(a) base compared with high and low parameter estimates, (b) across nine year types, and (c) 

with a base and high number of spawners. Finally, we have displayed each parameter’s relative 

sensitivity by scaling all sensitivity values to a maximum value of 100 for simplicity. 

Findings 

The following figure summarizes the findings. Parameters are arrayed in order of their 

relative sensitivity and arbitrarily divided into high, medium, and low categories. Highly 

sensitivity parameters were judged as those essentially demanding extra scrutiny. Parameters of 

lesser sensitivity are still important but are not likely to dominate Salmod’s predictive ability. 

Parameters with low sensitivity warrant little scrutiny at this time. 

Discussion 

We were not surprised by the sensitivity of fry and presmolt weighted usable area (WUA). 

There is inherent uncertainty here (Gard 2005), and the results reflected our liberal 0.5 to 2X 

weighting. Similarly, habitat capacity values in essence reflect exactly the same uncertainty and 

must be accurately calculated from the best data available. Juvenile thermal mortality rates are 

also well described as highly sensitive. Fortunately, these are known with greater certainty. Fry 

and presmolt weekly seasonal movement distance are likely sensitive solely because they are, by 

default, just less than the study area length, meaning that outmigrating fry exit in either one or 

two weeks. (In fact, we reduced the computed presmolt sensitivity, believing it to be an artifact 

of the study area, not the actual parameter.) 

More surprising were the several factors that in one way or another relate directly to species 

life-history timing, specifically: 

• Egg development rate 

• Spawning initiation week 

• Fry seasonal movement initiation 

These factors, collectively, account for much model sensitivity (variance) and highlight the 

need to carefully verify event timing and triggers. Perhaps this should not have been surprising 

because Bartholow (2005) had shown that timing was a key determinant in predicting relative 

survival for the four races of Chinook salmon in the Sacramento River. Clearly establishing 

these timing factors will reduce the model’s uncertainty. 

Sensitivity analysis by itself does not address the issue of model realism. In other words, 

one might identify a parameter that has little influence on simulated model outcomes, but if the 

value is “wrong” it will detract from the believability and trust in model results regardless. In 

addition, one must be cognizant that in complicated, multi-parameter models, errors in one 

parameter may be masked by errors in other parameters without significantly affecting model 

behavior. Should one choose an apparently sensitive parameter as a management focus, it would 

be wise to test that sensitivity as a hypothesis before a full-scale effort. 

106
Sensitivity analysis also can be used to address the model’s internal structure. In our 

opinion, that is not our principal objective here. However, we must not lose sight of the fact that 

Salmod attempts solely to represent the freshwater dynamics and is not a full life-cycle model. 

It is important to remember that sensitivity analysis does not in any way identify parameters 

that are wrong. The model may well be, and should be, sensitive to parameter changes. A 

different form of sensitivity analysis that could be pursued is what might be called the ultimate 

sensitivity analysis where one would examine how parameter variation might lead to a change of 

decision in using the model. This would require much additional work, but certainly what we 

have been doing is a sensitivity analysis of the variables, flow and water temperature, and how 

that variation may have affected historical salmon production. 

107
108

Relative Parameter Sensitivity

0 20 40 60 80 100

Fry seas. move. mortality Egg development rate Spawning initiation week Fry temp. mortality

Fry flow vs WUA Fry habitat capacity

Fry seas. move. initiation

Spawner flow vs WUA

PS seas. move. distance

Fry seas. move. distance PS temp. mortality

PS flow vs WUA

Fry distance mortality PS habitat capacity

Egg temp. mortality

Redd Area

PS seas. move. initiation

Fecundity PS distance mortality Fry growth rate Number of spawners Sex ratio

Juv. supplement weights Egg base mortality Juv. supplement numbers Emergence temp.

Emergent length

Spawner weight Spawn initiation temp. range Spawn distribution

IS temp. mortality

IS habitat capacity

Fry base mortality IS seas. move. initiation

IS distance mortality PS growth rate IS seas. move. distance

PS base mortality PS seas. move. mortality IS base mortality IS growth rate Adult base mortality IS seas. move. mortality

IS seas. move. duration

Eggs flow vs WUA PS seas. move. duration

Adult temp. mortality

Adult habitat capacity Fry seas. move. duration
Structural Elements Klamathspecific Status Uncertainty 

Study Area Yes Considered fixed, IGD to the Scott River. Downstream fate (including estuary 

ocean) are considerable. 

Flow and Temperature Reaches 

Yes Considered fixed. Eleven reaches, well 

matched to hydrology and thermal characteristics of the river. 

Relatively minor. 

Mesohabitat Typing Data and Downstream Sequence 

Yes Considered fixed. Derived from detailed 

habitat mapping. 

Any misclassifications considered random. 

Life Stage Nomenclature and Length Class Breakpoints 

Yes Well defined. Some investigators may use 

slightly different values. 

Considered minor. May be explorelater. 

Initiation of Biological Year Yes Begins October 1. Some adults may be in study area 

somewhat prior to October 1. 

Hatchery Supplementation Yes Recent average defined by California 

Department of Fish and Game 

Is not dynamic across years/conditi

Tributary Supplementation Yes Recent average defined by California 

Department of Fish and Game 

Is not dynamic across years/conditiUncertainty regarding Bogus Creek values. 

Driving Environmental 

Variables 

Klamathspecific Status Uncertainty 

Flow and Water Temperature Values 

Yes Come from SIAM (MODSIM and HEC5Q). Aggregation to weekly time-step m

peaks. May need to test with measdata to the degree possible. 

Parameters Klamathspecific Status Uncertainty 

Q:WUA Quantification (Life Magnitude (y-axis) stage-specific) 

Yes Available from PHABSIM. 

Flow-dependence (x-axis) 

Weight:Length Relationship Yes Well defined. Agrees well with Sacramento RiverSpawning Initiation Temperature 

No Klamath values are a subset of literaturederived values. Upper value could be important, no

lower value on Klamath 

Distribution through study area Initiation timing (x-axis) 

Spawning Spatial and Temporal Distribution 

Yes Well defined, but using multi-year average 

for all attributes. Could differ given differences between carcass and redd counts. Duration or “peakedness” (x-axis) 

Spawner Density and Number of adults Characteristics 

Yes Have hatchery and carcass count data. 

Likely to be representative of main stem study area. Sex Ratio (actually spawners to non

spawner ratio) 

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110

Size (weight) 

Fecundity No From Lewiston Hatchery, but agrees well 

with older Klamath-specific data. 

Could perhaps improve with values Iron Gate Hatchery. 

Redd Area Yes Well defined. Calculated from data. Superimposition Option No From Trinity. Considered random. Egg Development Rate No From reliable literature. Some uncertainty in hatch to preemergent timing; may need to adju

during calibration. 

Emergent Length Yes From field measurements, but not explicitly 

to estimate this parameter. 

Contains some uncertainty. 

Emergence Temperature No Literature derived, but for Atlantic salmon. 

Experts disagree. 

Fair uncertainty. 

Juvenile Growth Rates (Life stage-specific) 

No Well defined literature values that have 

worked well on other rivers. 

Some uncertainty because values dfrom ad lib feeding. 

Trigger Distance moved 

Freshet Movement Attributes (Life stage-specific) 

Not yet used on Klamath 

Other rivers see demonstrable effects, both for spikes and drops. 

Mortality 

Initiation timing Duration Distance moved 

Seasonal Movement Attributes (Life stagespecific) 

Yes, for timing and distance 

Values derived from other applications and may be adjusted during calibration. 

Mortality 

Base Mortality Rates (Life stage-specific) 

No Values derived from Trinity River. Uncertainty due to endemic disease

Thermal Mortality Rates (Life stage-specific) 

No Values are composites from multiple 

literature sources. 

Uncertainty due to many causes. 

Habitat Capacity (Juvenile Life stage-specific) 

Partial Klamath-specific for fry; literature for other 

juveniles; guess for adults. 

Uncertainty from multiple causes. 

Habitat Capacity Movement Dynamics 

No Several assumptions. Considered fixed assumption of the 

model. 

Habitat-related Distance Moved Mortality Rate (Life stage-specific) 

No Derived from Trinity and Sacramento 

Rivers. 

Much uncertainty. Will vary only tdistance to 100% mortality. 
Appendix E. Fahrenheit to Celsius, and Celsius to Fahrenheit 

Conversion Chart. 

°F °C °F °C °F °C °F °C °C °F °C °F 

32.0 0.0 52.5 11.4 73.0 22.8 93.5 34.2 0.0 32.0 20.5 68.9 

32.5 0.3 53.0 11.7 73.5 23.1 94.0 34.4 0.5 32.9 21.0 69.8 

33.0 0.6 53.5 11.9 74.0 23.3 94.5 34.7 1.0 33.8 21.5 70.7 

33.5 0.8 54.0 12.2 74.5 23.6 95.0 35.0 1.5 34.7 22.0 71.6 

34.0 1.1 54.5 12.5 75.0 23.9 95.5 35.3 2.0 35.6 22.5 72.5 

34.5 1.4 55.0 12.8 75.5 24.2 96.0 35.6 2.5 36.5 23.0 73.4 

35.0 1.7 55.5 13.1 76.0 24.4 96.5 35.8 3.0 37.4 23.5 74.3 

35.5 1.9 56.0 13.3 76.5 24.7 97.0 36.1 3.5 38.3 24.0 75.2 

36.0 2.2 56.5 13.6 77.0 25.0 97.5 36.4 4.0 39.2 24.5 76.1 

36.5 2.5 57.0 13.9 77.5 25.3 98.0 36.7 4.5 40.1 25.0 77.0 

37.0 2.8 57.5 14.2 78.0 25.6 98.5 36.9 5.0 41.0 25.5 77.9 

37.5 3.1 58.0 14.4 78.5 25.8 99.0 37.2 5.5 41.9 26.0 78.8 

38.0 3.3 58.5 14.7 79.0 26.1 99.5 37.5 6.0 42.8 26.5 79.7 

38.5 3.6 59.0 15.0 79.5 26.4 100.0 37.8 6.5 43.7 27.0 80.6 

39.0 3.9 59.5 15.3 80.0 26.7 100.5 38.1 7.0 44.6 27.5 81.5 

39.5 4.2 60.0 15.6 80.5 26.9 101.0 38.3 7.5 45.5 28.0 82.4 

40.0 4.4 60.5 15.8 81.0 27.2 101.5 38.6 8.0 46.4 28.5 83.3 

40.5 4.7 61.0 16.1 81.5 27.5 102.0 38.9 8.5 47.3 29.0 84.2 

41.0 5.0 61.5 16.4 82.0 27.8 102.5 39.2 9.0 48.2 29.5 85.1 

41.5 5.3 62.0 16.7 82.5 28.1 103.0 39.4 9.5 49.1 30.0 86.0 

42.0 5.6 62.5 16.9 83.0 28.3 103.5 39.7 10.0 50.0 30.5 86.9 

42.5 5.8 63.0 17.2 83.5 28.6 104.0 40.0 10.5 50.9 31.0 87.8 

43.0 6.1 63.5 17.5 84.0 28.9 11.0 51.8 31.5 88.7 

43.5 6.4 64.0 17.8 84.5 29.2 11.5 52.7 32.0 89.6 

44.0 6.7 64.5 18.1 85.0 29.4 12.0 53.6 32.5 90.5 

44.5 6.9 65.0 18.3 85.5 29.7 12.5 54.5 33.0 91.4 

45.0 7.2 65.5 18.6 86.0 30.0 13.0 55.4 33.5 92.3 

45.5 7.5 66.0 18.9 86.5 30.3 13.5 56.3 34.0 93.2 

46.0 7.8 66.5 19.2 87.0 30.6 14.0 57.2 34.5 94.1 

46.5 8.1 67.0 19.4 87.5 30.8 14.5 58.1 35.0 95.0 

47.0 8.3 67.5 19.7 88.0 31.1 15.0 59.0 35.5 95.9 

47.5 8.6 68.0 20.0 88.5 31.4 15.5 59.9 36.0 96.8 

48.0 8.9 68.5 20.3 89.0 31.7 16.0 60.8 36.5 97.7 

48.5 9.2 69.0 20.6 89.5 31.9 16.5 61.7 37.0 98.6 

49.0 9.4 69.5 20.8 90.0 32.2 17.0 62.6 37.5 99.5 

49.5 9.7 70.0 21.1 90.5 32.5 17.5 63.5 38.0 100.4 

50.0 10.0 70.5 21.4 91.0 32.8 18.0 64.4 38.5 101.3 

50.5 10.3 71.0 21.7 91.5 33.1 18.5 65.3 39.0 102.2 

51.0 10.6 71.5 21.9 92.0 33.3 19.0 66.2 39.5 103.1 

51.5 10.8 72.0 22.2 92.5 33.6 19.5 67.1 40.0 104.0 

52.0 11.1 72.5 22.5 93.0 33.9 20.0 68.0 

111