Potential Application of an Individu

Based Model to the Glen Canyon TrFisheryKirk LaGory, Steve Railsback, and John Hayse

Presentation to the Glen Canyon Dam Technical Working Group

April 22, 2015
Objectives of this Presentation

 Describe an Individual

‐Based Model (inSTREAM

‐SD) that has b

successfully to

a number of fisheries

‐related problems includin

the Colorado River Basin

 Illustrate inSTREAM’s application to Flaming Gorge

 Discuss potential application to Glen Canyon

– Evaluation and design of trout management flows

– Assessment of impacts of equalization flows and appropriate managemen– Understanding factors leading to current population status
Individual-Based Model Approach

 Computationally track individual fish

in space and time

 Use simple, mechanistic models

based on well

‐understood

relationships that are based on the

literature or site

‐specific information

 Estimate status of overall population

by aggregating information for all

individuals

 inSTREAM reproduces many

observed patterns of movement

response to risk, food, competition,

and temperature
Benefits of Individual-Based Models

 Identify critical uncertainties that affect model resul Understand consequences of beliefs and assumption

– Applying the model leads to understanding (e.g., identifyin

are incompatible with data)

 Plan adaptive management experiments and foresee

consequences

 Design good management policies
The inSTREAM-SD Family of Individual-Based 

Salmonid Models for River Management Developed by Steve Railsback (Humboldt State University) and Bret H

 15 years of development and use

 Applications at 40 sites

 Funding from

8 federal and power industry agencies

 Examples:

– EPA‐STAR: Application of fish IBMs to regional decision making

– Cutthroat trout at Little Jones Creek, California

– Effects of dam operations on rainbow and brown trout downstream of Fla

Dam

– Instream flow studies for McCloud River hydroelectric project, California

– Evaluation of Chinook salmon habitat restoration, instream flows, and tem

Clear Creek, California

 http://www2.humboldt.edu/ecomodel/
General Approach of inStream-SD

 Habitat represented as

2

‐

D array of cells each with velocity and 

specific to flow volume (channel geometry and hydraulic mode

 Multiple time steps per day: day, night, when flow changes

 Population dynamics over time and space emerge from individ

differences in:

– Location– Activity (feeding or hiding)

– Growth– Survival– Spawning
Factors Affecting Growth, Mortality, and Spa

 Growth

– Food availability

– Abundance of larger fish

– Velocity and depth– Turbidity and temperature– Fish size

 Mortality

– Aquatic and terrestrial predation, angling

– Starvation and disease– Stranding– High temperature

 Spawning

– Availability of habitat

– Extreme high or low temperature

– Stranding– Scouring– Superimposition
Application of inSTREAM-SD at Flaming Gorg

 Originally developed at Flaming Gorge Dam as

a tool to addres

management issues:

– What are the effects of fluctuations on trout?

– What are the effects of winter double‐peaking on trout?

– How do these effects vary according to hydrologic condition?

 www.langrailsback.com/

FlamingGorge
Flaming Gorge Model

 Three,

1‐km study sites below Flaming Gorge Dam

 Channel morphology data collected using acoustic

Doppler

 Study site divided into cells that differ in depth,

velocity, and other habitat characteristics

important to fish
Example Screenshot from Flaming Gorge Mo
Flaming Gorge Test Scenarios

 Three hydrologic conditions

– Dry (mean daily flow 34.6

m3/s,

3 hr on peak)

– Average (mean daily flow 51.0

m3/s,

5 hr on peak)

– Wet (mean daily flow 86.1

m3/s, 14 hr on peak)

 Six levels of fluctuation: 0, 12.5, 25, 50, 75, 100% of maximum

Single Daily Peak Double Daily Peak
Important Findings of Flaming Gorge Model 

Simulations—Single-Peak Fluctuation Effects Effects are site‐dependent (e.g., almost no effect at Indian Crossing si

 Effects are dependent on hydrologic conditions (greater effect in dry 

 Relatively minor effects until fluctuations reach 50% or more

Daily flow fluctuation, % of full load following

Simulated trout production

0 20% 40% 60% 80% 100%

0

10%20%30%40%50%

AverageDryWet

Little Hole

Daily flow fluctuation, % of ful

Simulated trout production

0 20% 40% 60% 

-50%-40%-30%-20%-10%

0

Indian Crossing
Important Findings of Flaming Gorge Model 

Simulations—Single-Peak Fluctuation Effects  Predicted effects on production result from:

– Site‐specific differences in habitat availability at low flows and the amoun

flows are in this range

– Fluctuation effect on feeding
Important Findings of Flaming Gorge Model 

Simulations—Double-Peak Fluctuation Effect Results were very similar to those of the single‐peak experiment

 Small (about 2%), but consistent decrease in production

 Modeled effect results from feeding disruption and increased movem

of fluctuation

Little Hole
Flaming Gorge Model Simulations—Applicatio

Adaptive Management Initial simulations prompted development of

a

5‐year study plan to:

– Perform analysis of condition data from 1990‐2014 to examine effects of 

condition

– Test effects of fluctuation on drift abundance

– Test effects of fluctuations on foraging behavior and diet

 Results of study were used to change operations and update the mod
inSTREAM-SD Could Be Used at Glen Canyon 

Evaluate a Number of Important Flow Effect Effects of hydropower operations on mortality of eggs and age

subsequent effects on adult population characteristics (numbe

distributions)

 Effects of trout management flows on mortality of redds and a

– Number and timing of cycles

– Magnitudes and durations of high flows and low flows

– Trigger levels
inSTREAM-SD Could Be Used at Glen Canyon 

Evaluate a Number of Important Flow Effect(Cont.) Effects of equalization flows on trout production

 Effects of low summer and steady flows (e.g., before and 

HFEs) on trout production

 Effects of HFEs on trout production

 Understanding factors leading to current population stat

– Sequence of flows

– Changes in food abundance and quality
Next Steps

 Determine level of interest in application at Glen Canyon

 Can the model be modified for application at the Glen Canyon 

 Identify reaches of interest within Glen Canyon

 Assess availability of data

– Channel geometry and hydraulic modeling

– Habitat variables (cover, spawning gravel, velocity shelters)

– Minimum age and length for spawning

– Range of dates for spawning

– Length‐weight regression parameters

– Angling pressure

 Identify important modeling questions to be addressed by the 

 Contacts:

– Kirk LaGory, lagory@anl.gov– Craig Ellsworth, ellsworth@wapa.gov– Jerry Wilhite, wilhite@wapa.gov