IOS: A modeling tool for evaluating environmental and water project effects on

salmon populations

Funded byDepartment of Water ResourcesCalifornia Urban Water Agencies

Metropolitan Water District

All Models Must Have Snappy Acronyms

Interactive Object-oriented Salmon Simulation (IOS)

Alternatives…

Object Oriented Population Simulation (OOPS)

Graphical Integrated Modeling Process (GIMP)

North-of-Delta-Offstream-Storage Project (NODOS)

• Increase water supply and reliability• Improve Delta water quality• Improve system flexibility• Environmental benefits

– Improve cold water reliability– Improve fish passage at RBDD– Reduce May-Sep agricultural diversions – Pulse flows

•• Increase water supply and reliabilityIncrease water supply and reliability•• Improve Delta water qualityImprove Delta water quality•• Improve system flexibilityImprove system flexibility•• Environmental benefitsEnvironmental benefits

–– Improve cold water reliabilityImprove cold water reliability–– Improve fish passage at RBDDImprove fish passage at RBDD–– Reduce MayReduce May--Sep agricultural diversions Sep agricultural diversions –– Pulse flowsPulse flows

Artist’s Conception of Sites Reservoir

NODOS Fish Modeling Needs

• NODOS Project is complexNeed a model integrating a diverse array of potential project effects (discharge, temperature, diversions, habitat improvements, fish passage)

• Critical project attributes undefined, many alternative and competing project configurations

Need tool to evaluate and compare NODOS operation and enhancement alternatives

• Project effects may be gradual and may differ by location

Need model that can capture temporal and spatial complexityNeed a model than can evaluate long term, cumulative effects of potential NODOS actions

Don’t we have reliable fish modeling tools?

Some fish modeling tools are available, but generally suffer from one or more significant deficiencies

• Most models emphasize physical factors and do not represent best available ecological data (e.g. PHABSIM)

• Most models are a “black box” (e.g. SALMOD)

• Most models are built by modelers (not biologists)

• Most models provide an answer, but no context population effects? cumulative effects? uncertainty?

Life Cycle Modeling Framework Guidelines

• Build on Existing Analytical Foundations

• Rely on Demonstrated Cause-Effect Relationships

• Use Readily Available Data

• Focus on Key Factors

Life Cycle Modeling

IOS Model: Geographic Overview

View within Segment 1 “container” (Reaches 1-10)

View within a reach “container”

IOS Model: Life stages within reaches

IOS Model: Fry rearing

Juvenile salmoncontainers

View within fry life stage container

Salmon Fry Within Reach: Conceptual

Water Temperature

Fry Capacity

Mesohabitat

Ration

Capacity-induced Migration

Fry Growth Rate

MWAT (oC)

12-14 14.1-16 16.1-18 18.1-20 20.1-22 22.1-24 24.1+

Mea

n co

ho d

ensi

ty (n

o./m

2 )

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Juvenile Coho Density vs. Temperature 260 Oregon Coast Sample Sites

Extended Bar shows 2 Standard Errors

Growth depends on Temperature & Ration

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20 25Temperature (0C)

Gro

wth

Rat

e (%

/day

)100% Ration

80%

60%

Fry

Parr

adapted from Brett et al. (1982) and Shelbourne et al. (1973)

Temperature Dependent Processes in IOS

• Egg and Alevin Incubation Time

• Egg and Alevin Mortality Rate

• Rearing Habitat Capacity (fry, parr)Higher river temperatures decrease rearing habitat capacity

• Emigration Survival (fry, parr, and smolts)

• Growth Rate (fry and parr)Both high and low water temperatures decrease growth rate,

intermediate temperatures optimize growth

• Delta Survival (all juveniles in the Delta)

IOS Model Calibration: RBDD passageJu

veni

les

pass

ing

RB

DD

0.0E+00

5.0E+05

1.0E+06

1.5E+06

2.0E+06Ju

l-96

Sep

-96

Nov

-96

Jan-

97

Mar

-97

May

-97

Jul-9

7

Sep

-97

Nov

-97

Jan-

98

Mar

-98

May

-98

Jul-9

8

Sep

-98

Nov

-98

Jan-

99

Mar

-99

May

-99

Jul-9

9

Sep

-99

Nov

-99

Jan-

00

Mar

-00

May

-00

Historical FryHistorical Parr & SmoltsSimulated FrySimulated Parr & Smolts

IOS Winter Run Hindcast ResultsE

scap

emen

t (Th

ousa

nds)

0

10

20

30

40

50

60

70

80

9019

72

1976

1980

1984

1988

1992

1996

2000

HistoricalPredicted

Esc

apem

ent (

Thou

sand

s)

IOS Model Dashboard

IOS Model Recap: Key Features

• Create complex, but useful life cycle models by assimilating and integrating studies

• Explore sensitivity and uncertainty within the modelFocus future studies, improve understanding Evaluate alternative operation scenariosUse probability distributions and stochastic events to quantify uncertainty

• Explain and defend decisions to stakeholders

IOS Model: Where to next?• Interagency review of winter run Chinook IOS is

underway

• Spring run model is on deck

• Preparing for analysis of North-of-Delta-Offstream-Storage (NODOS) alternatives

• Working to incorporate new informationOcean effectsAcoustic tag studies, juvenile salmon

OceanEntryFlow Export

Turbidity DCCSpawners Delta EntryFry

fry/egg=0.29

Rotary Screw Trap JPI (1000's)

0 2000 4000 6000 8000 10000

Carc

ass

JPE

(100

0's)

010002000300040005000600070008000

r2 = 0.981P = 0.001

1998 and 1999 Broods

% P

assa

ge b

y M

onth

01530456075

30

60

90

120

150

PassageLength

RBDD

GCID

AUG SEP OCT NOV DEC JAN FEB MAR APR0

1530456075

30

60

90

120

150Chipps Island

01530456075

Mean Length (m

m)

306090120150

Knights Landing

01530456075

306090120150

Fry Delta EntryOceanEntryFlow Export

Turbidity DCC

Fry (millions)

0 20 40 60 80 100 120 140

Smolts

(millions

)

0

1

2

3

4

5

6

7

Beverton-Holtr2 = 0.76

Spawners

Recent Life Cycle Modeling Projects

• Sacramento Spring Chinook• Sacramento Winter Chinook• Klamath River Coho• Clackamas River Chinook, Coho, and

Steelhead• San Joaquin Fall Chinook• Deschutes River Steelhead• Yakima Basin Bull Trout

IOS Physical Input Parameters

• River Flow (daily)• River Temperature

(daily)• Diversion Flow (daily)• Mesohabitat• Spawning Gravel• Migration Barrier status

(daily)

• Delta Flow (weekly) • Delta Temperature

(weekly)• Delta Exports (weekly)• Delta Salinity (weekly)• DCC Gate Position

(weekly)

IOS Model: Data

Reach 1 temperature input data, linked from Excel spreadsheet “Reaches”

Note daily temperature data, model has daily time step.

Juvenile Salmon: Conceptual

MigrationEmigration Rate

Fry Growth Rate

Maturation Rate

Diversion Mortality

Fry Emigration Mortality

Reach Length

Fry Migration Speed

Fry Capacity-induced Migration

Fry Flow-induced Migration

Fry Volitional Migration

Size Scalar

Temperature Scalar

Reach Length

Diversion Flow

Time of Year Flow

Temperature

Fry Capacity Mesohabitat

Fry Density by

Mesohabitat

Fry in Delta

Temperature

Fry in River

Parr in River

Ration

IOS Winter Run Hindcast Results

4244464850525456586062

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

0

5

10

15

20

25

30

1968

1970

1972

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1978

1980

1982

1984

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1988

1990

1992

1994

1996

1998

2000

Mea

n M

onth

ly T

empe

ratu

re

(o F) a

t Bal

l’s F

erry

Egg-

to-F

ry S

urvi

val (

%)

The Problem: disconnected fishery science

Fisheries data are often difficult to interpret and put into a useful context.

• Studies are often general and descriptivedon’t explicitly target critical management questions

• Quantitative studies are usually very focused on a specific location or a detailed question

• Results are not readily amenable for use by physical modelers or managers.

The Problem: disconnected operations models

CALSIM or similar physical models…

• Generate lots of data, but often not high value data for biological assessment

e.g. monthly means, rather than daily maximums

• Need feedback from population modelsfocus on biologically significant outputsinform boundaries on operational alternatives

Decision makers need reliable toolsManagers require tools integrating disparate data sources and best available information.

Managers need a “blackboard” where:

• Alternative project operations can be evaluated and compared.

• Restoration benefits can be assessed.

• Critical uncertainties can be quantified.

• Management decisions can be explained and defended to stakeholders.

IOS Model: Mesohabitat

Complex Channel

Simple Channel