Presentation on theme: "How ESSA has successfully used Decision Analysis to overcome challenges in multi-objective resource management problems Developed by ESSA Technologies."— Presentation transcript:
1How ESSA has successfully used Decision Analysis to overcome challenges in multi-objective resource management problemsDeveloped byESSA Technologies Ltd.General overviewJanuaryDavid Marmorek,Calvin Peters,Ian Parnell,Clint Alexander
2Common challenges in resource management Getting stakeholder groups to agree on a course of action, given multiple values and objectivesGetting scientists to agree on which uncertainties most critically affect management decisions, and what decisions are most robust to these uncertaintiesEvaluating the costs and benefits of adaptive management - is it worth it?
3How decision analysis can help with these challenges It provides a toolbox for handling multiple objectives / values, and analyzing tradeoffs among these objectivesIt systematically analyzes the impacts of uncertainties on decisionsIt can be used to evaluate the ability of Adaptive Management experiments to improve decisionsIt provides a helpful way to integrate many techniques employed by managers and scientists (i.e. models, interactive workshops, sensitivity analysis) into products that better clarify management decisions
4Three examples Getting scientists to agree: PATH Getting stakeholders to agree: CheakamusEvaluating adaptive management: Keenleyside
5PATH: Decision Context Multiple historical changes in Columbia and Snake River ecosystems and fisheries management practicesEndangered species listings for Snake River salmon populationsMultiple hypotheses and uncertainties held by different groups of scientistsDuelling models representing these hypotheses and uncertaintiesBest management policies for species recovery?
7Decision Analysis: 8 elements 1. List of alternative management actions2. Management objectives composed of performance measures (to rank management actions)3. Uncertain states of nature (different hypotheses)4. Probabilities of those states (to account for uncertainty);5. Model to calculate outcomes of each combination of management action and hypothesised state of nature;6. Decision tree;7. Rank actions based on expected value of the performance measures; and,8. Sensitivity analyses.
10Benefits of decision analysis in PATH Allowed evaluation of multiple hypotheses for 14 uncertainties - scientists did not have to agree!Only 3 of these turned out to make a difference to the decision - created a common focus for AM, researchPreferred actions were those which were most robust to the critical uncertainties (drawdown A3)Sensitivity analyses defined how much belief you would have to have in a given hypothesis to change decision
11Recent Publications on PATH Marmorek, David R. and Calvin Peters Finding a PATH towards scientific collaboration: insights from the Columbia River Basin. Conservation Ecology 5(2): 8. [online] URL: <http://www.consecol.org/vol5/iss2/art8>Deriso, R.B., Marmorek, D.R., and Parnell, I.J Retrospective Patterns of Differential Mortality and Common Year Effects Experienced by Spring Chinook of the Columbia River. Can. J. Fish. Aquat. Sci. 58(12)Peters, C.N. and Marmorek, D.R Application of decision analysis to evaluate recovery actions for threatened Snake River spring and summer chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 58(12): <same web site as above>Peters, C.N., Marmorek, D.R., and Deriso, R.B Application of decision analysis to evaluate recovery actions for threatened Snake River fall chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 58(12): <same web site as above>
12Cheakamus WUP: Decision Context British Columbia Hydro, Water Use Planning: Stakeholder driven multi-objective consultation / decision process.No formal incorporation of uncertainty as for PATHEmphasis: values, objectives, performance measures, trade off analysis (DA steps 1, 2, 5 and 7).Used PrOACT approach (Smart Choices, Hammond et al 1999)
14Cheakamus WUP:Decision Problem Select operating alternatives for Daisy Lake Dam that:1) recognize multiple water uses in the Cheakamus and Squamish Rivers, and2) achieve a balance between competing interests and needs.
15Cheakamus WUP:Objectives and PMs PowerFirst NationsRecreationFloodingFishAquatic Ecosystem
16Cheakamus: WUP Alternatives Consultative Committee specifies operating alternatives for Hydro operations model (AMPL).Basic constraints: minimum flow at Brackendale gauge, minimum dam release.AMPL model produces 32 water years of flow data for these control pointsFlow data and other models used to calculate performance measures.Performance measures summarize consequences of alternatives for objectives.
19Cheakamus WUP: Filtering Use PMs to Eliminate clearly inferior alternatives.Drop insensitive PMs (e.g., rafting).Drop Objectives that don’t help the decision (e.g., flooding).Tradeoff analysis: Even SwapsElicit values behind decisions (e.g., rating exercises)Develop new alternatives to address concerns (e.g., chum spawning vs. rainbow trout rearing).
20Keenleyside Problem : Increased egg mortality from dam operation Flow during spawningFlow during incubation stageProportion eggs in de-watered areaRiskBiologicalflows too high reduce productive capacity, may drive population towards extinctionEconomicsmaller flows may reduce de-watering mortality but reduce potential $ and operational flexibility
21Problem II: Uncertainty True whitefish recruitment dynamics? Given typicalegg mortality,LARGE differencesin abundanceassociated withthese curvesNo reliable baseline information
22Stage 1 - Decision Analysis w current uncertainty
23Stage 1 Results: Current Uncertainty Objective:Maintain “least cost” whitefish population nearest to or greater than 45,000 adults
24Stage 2 - Simulated learning from flow experiments and monitoring Uses same model and uncertain components but...Actions are now alternative experimental flow regimes + monitoring programsAssume a true relationship for population dynamics with process error
25What would you change if you knew the “truth” What would you change if you knew the “truth”? If population insensitive, then maximize power revenues (85 kcfs) If population sensitive, then minimize biological risk (~60 kcfs)1052.57.5$Cnd milMax. potential power revenues (per yr)
26Example Stage 2 Results: Good monitoring is critical for differentiating hypotheses; flow manipulation had less effect than expected.
28Is AM and monitoring worth it? “Yes” IfNew information leads to choice of a different management action that better satisfies a particular objective,orrigorously confirms that current management action is appropriate.
29No definitive “yes/no” Under AM practitioners controlCan evaluate implications using decision analysis?FactorManagement objective(fish vs. power $)Ability to do well designed experimentsInitial level of uncertainty in alternative hypothesesMagnitude of natural variability in the systemWhat “truth” really isInherent sensitivity of best action to uncertaintyYesMaybeNoNo (can’t know without doing the experiment)Yes
30General 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 benefitsDetermine which uncertainties have strongest effect on choice of “best” management decisionDecisions more robust to uncertainties (reduces risk - integrates broader range of possible outcomes included)Include new information as revised probabilities on hypotheses