Top Banner
ESSA Technologies AFS - Spokane Apr 27 to May 1, 2002 A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties? An application to Columbia River whitefish management AFS - Spokane; Apr 27 to May 1 2002 Developed by ESSA Technologies Ltd. Clint Alexander, Paul Higgins*, David Marmorek, and Calvin Peters * Funded by BC Hydro Power Supply & Watershed Management
23

AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

Mar 26, 2015

Download

Documents

Emily Cobb
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

A decision analysis of adaptive management experiments: Is it worth

varying flows to reduce key uncertainties?

An application to Columbia River whitefish management

AFS - Spokane;

Apr 27 to May 1 2002

Developed byESSA Technologies Ltd.

Clint Alexander, Paul Higgins*, David Marmorek, and Calvin Peters

* Funded by BC Hydro Power Supply & Watershed Management

Page 2: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Outline

• Study Area / Problem• Objective• Methods

– Decision analysis

– Model

• Results: Is it worth it?• General conclusions

Page 3: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Problem IProblem I: Increased egg mortality from dam operation

Flow during spawningFlow during spawning

Flow during Flow during incubationincubation

stage

Proportion eggs in de-watered area

Some flexibility to regulate flows during spawning (January 1 - 21)

Page 4: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Problem IIProblem II: Uncertainty True whitefish recruitment dynamics?

No reliable baseline information

Alternative Hypotheses

-

5,000

10,000

15,000

20,000

25,000

0 5 10 15 20 25

Eggs Just Prior to Hatching (millions)

Age

4 W

hite

fish

Very Sensitive

Sensitive

Neutral

Insensitive

Very Insensitive

Page 5: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Study Objective

Use Decision Analysis to evaluate benefits and costs of alternative

spawning flows & monitoring programs

Page 6: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Methods

• Decision Analysis

• Age-structured simulation model– 1) population dynamics– 2) simulated mark-recapture egg & age4 abundance

estimates

– site specific stage and wetted-area at depth data generated from hydraulic flow simulations (HEC2-RAS model)

Page 7: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Management Objective

• Maintain “least cost” whitefish population nearest

to or greater than 45,000 adults

Page 8: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Stage 1 - Base Decision Analysis

Columbia RiverFlows During

WhitefishSPAWNING

Kootenay RiverFlows During

WhitefishSPAWNING

Min. Columbia RiverFlows Prior to

WhitefishHATCHING

Egg-Age4RecruitmentRelationship

EggAbundance

Abundance4+ Recruits

50 kcfs Model

30 kcfs

20 kcfs

85 kcfs

80 kcfs

20 kcfs

15 kcfs

10 kcfs

55 kcfs

25 kcfs

20 kcfs

15 kcfs

85 kcfs

a3, b 3

a2, b 2

a1, b 1

a5, b 5

a4, b 4

ForegonePower

Revenues

......

ManagementActions States of Nature and their Probabilities Outcomes

Min. KootenayRiver Flows Prior

to WhitefishHATCHING

20 kcfs

15 kcfs

10 kcfs

55 kcfs

...

40 kcfs

45 kcfs

55 kcfs

60 kcfs

65 kcfs

70 kcfs

Natural variability in flow Uncertainty due to lackof understanding / data

Page 9: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Stage 1 Results: Current Uncertainty

HKD Spawning Q

(kcfs)

Expected Adults (yr50)

Expected de-watering mortality

Annual Foregone Revenue

($mil)

Maximum Annual

Potential Revenue ($mil)

Expected Egg Abundance (millions)

20 30,190 0.003 3.75 0.02 6.430 36,143 0.010 1 0.5 9.240 41,754 0.034 0.4 1.25 12.445 43,967 0.054 0.15 1.61 13.850 45,286 0.087 0 1.95 14.755 45,827 0.127 0 2.15 15.360 45,776 0.172 0 2.4 15.565 44,768 0.230 0 2.6 15.070 43,274 0.292 0 3 14.280 38,707 0.429 0 5.75 11.785 36,309 0.492 0 9 10.3

Page 10: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Experimental flowsduring whitefish

spawning + Populationmonitoring

Kootenay RiverFlows During

WhitefishSPAWNING

Min. Columbia RiverFlows Prior to

WhitefishHATCHING

Assumed truestate,

includingprocess error

eA+10meH

ModeleA+10meL

eP10meL

eC10meH

20 kcfs

15 kcfs

10 kcfs

55 kcfs

25 kcfs

20 kcfs

15 kcfs

85 kcfs

a1, b 1

......

AdaptiveManagementExperiments States of Nature and their Probabilities Outcomes

Min. KootenayRiver Flows Prior

to WhitefishHATCHING

20 kcfs

15 kcfs

10 kcfs

55 kcfs

...

a 3, b 3

a 2, b 2

a 1, b 1

a 5, b 5

a 4, b 4

1. Using updated probabilities, repeatdecison analysis

2. Compare value of what youwould do with and without the newinformation from AM

3. Assess trade-offs, and "pick" thebest experiment!

UpdatedProbabilities

for hypotheses

Cost of Experiment(collecting information

+ foregone powergeneration)

Revised Probabilities

Sensitive - Low natural varation

0

0.2

0.4

0.6

H1 H2 H3 H4 H5

P

Current knowledge

eP10meL-$2.3

eA+10meL-$20.3

eA+10meH-$19.23

eA10meL-$4.55

eC10meL-$1.55

eP10meH-$1.23

eA10meH-$3.48

eC10meH-$0.48

...

Repeat stage 1...

Page 11: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Stage 2 - Simulated learning from flow experiments and monitoring

Uses same model and uncertain components but...

Actions are now alternative experimental Actions are now alternative experimental flow regimes + monitoring programsflow regimes + monitoring programs

Assume a true relationship for population Assume a true relationship for population dynamics with process errordynamics with process error

Page 12: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

4 experimental flow regimes4 experimental flow regimes

Year Active Constant Active+1 35 55 202 65 55 75

etc… … … …

Average January flows used in "passive" experiments

60

75

60

7580

70

35

5550

45

30

50

70

90

1 2 3 4 5 6 7 8 9 10

Simulation year

Q (kcfs)

Page 13: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Learning from AM experiments: a function of what practitioner can and cannot control

Spatial / temporalcontrast in mgmt.

actions(e.g., flow)

Level precision/investment in

monitoringNatural variability

(added noise)

Ability to distinguish alternative hypotheses w AMexperiments

Value of information for decisions

Under AM practitioners control

Page 14: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Would you change if you knew the “truth”?Expected adult N, year 50

25,000

30,000

35,000

40,000

45,000

50,000

20 30 40 45 50 55 60 65 70 80 85

HKD Spawning Q (kcfs)

N

Minimum desired

Current Uncertainty

Sensitive

Insensitive

10

5

2.5

7.5

$Cnd mil

Max. potential power revenues (per yr)

Page 15: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Stage 2 Results: Good monitoring is critical for learning; flow manipulation has less effect than

expected.

Flow manipulation

High Meas. Error

Low Meas. Error

High Meas. Error

Low Meas. Error

Constant 0.55 ($0.48) 0.88 ($1.55) 0.51 ($0.48) 0.74 ($1.55)Passive 0.60 ($1.23) 0.92 ($2.3) 0.57 ($1.23) 0.85 ($2.3)

Active 0.63 ($3.48) 0.92 ($4.55) 0.54 ($3.48) 0.85 ($4.55)

$ CDN MillionsBlue = things under AM practitioners controlRed = beyond AM practitioners control

Probability identify insensitive population (10-year experiments)

Low Nat Variability High Natural Variability

Natural Variability and Measurement Error

Page 16: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Stage 2 Results: It’s easier to identify a sensitive population than an insensitive one.

Flow manipulation

High Meas. Error

Low Meas. Error

High Meas. Error

Low Meas. Error

Constant 0.84 ($0.48) 0.90 ($1.55) 0.81 ($0.48) 0.82 ($1.55)Passive 0.90 ($1.23) 0.99 ($2.3) 0.88 ($1.23) 0.97 ($2.3)

Active 0.84 ($3.48) 0.90 ($4.55) 0.84 ($3.48) 0.85 ($4.55)

$ CDN MillionsBlue = things under AM practitioners controlRed = beyond AM practitioners control

Probability identify sensitive population (10-year experiments)

Low Nat Variability High Natural Variability

Natural Variability and Measurement Error

Page 17: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Low measurement error critical to differentiating hypotheses

Inensitive - eC

0

0.2

0.4

0.6

H1 H2 H3 H4 H5

Current knowledgeNatVar-L - MErr-L-$1.55NatVar-H - MErr-L-$1.55NatVar-L - MErr-H-$0.48NatVar-H - MErr-H-$0.48

Inensitive - eA

0

0.2

0.4

0.6

H1 H2 H3 H4 H5

Current knowledgeNatVar-L - MErr-L-$4.55NatVar-H - MErr-L-$4.55NatVar-L - MErr-H-$3.48NatVar-H - MErr-H-$3.48

Page 18: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

AM can “pay for itself”

Flow manipulation

High Meas. Error

Low Meas. Error

High Meas. Error

Low Meas. Error

Constant $0.2 (2.4) $0.6 (2.6) $0.2 (2.4) $0.2 (7.7)

Passive $0.2 (6) $0.6 (3.8) $0.2 (6.15) $0.6 (3.8)

Active $0.6 (5.8) $0.6 (7.6) $0.2 (17.4) $0.6 (7.6)

$Cnd millionsNumbers in brackets = experimental pay-back interval in yearsBlue = things under AM practitioners controlRed = beyond AM practitioners control

I ncrease in annual power revenues from operating with experimental information (insensitive population only, 10-year experiments)

Low Nat Variability High Natural Variability

Natural Variability and Measurement Error

Page 19: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Caveats about the Keenleyside Results

• Small difference in performance of alternative experiments was surprising. Why?

– Large uncontrollable natural variation in flows, at both spawning and hatching, creates year-to-year variability in egg mortality (Kootenay R influence)

– “Passive” flows not actually that passive (large spawning flows informative)

– Model added too much measurement error (true detection probability higher than shown)

Page 20: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Is AM worth it?Is AM worth it?

“Yes” IfNew information leads to choice of a

different management action that better satisfies a particular objective

Page 21: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

FactorUnder AM practitioners control

Benefits of AM decision analysisBenefits of AM decision analysis

Management objective(fish vs. power $)

Ability to do well designed experiments

Initial level of uncertainty in alternative hypotheses

Magnitude of natural variability in the system

What “truth” really is

Inherent sensitivity of best action to uncertainty

Yes

Yes

May be known

No

No (can’t know without doing the experiment)

No

Yes

Yes

Yes

Yes

Yes

Yes

Can evaluate implications using decision analysis?

Page 22: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

Natural variation in recruitmentNatural variation in recruitment

Low natural variation: sigma = 0.25

0

20000

40000

60000

80000

1 11 21

High natural variation: sigma = 0.45

0

20000

40000

60000

80000

1 11 21

Page 23: AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties?

ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002

General ConclusionsGeneral Conclusions

• Value of AM potentially large

• Whether to proceed depends on “the kind” of system you are in (i.e. previous factors)

• Decision Analysis is very helpful for evaluating these benefits

– Decisions more robust to uncertainties (reduces risk - explicitly accounts for uncertainties)

– forces clarification of problem & uncertainties

– Determine which uncertainties have strongest effect on choice of “best” management decision (identify research priorities)