IRP Context: State-of-the-art in water

systems planning under change Patrick Reed

Professor of Civil Engr., Cornell University

patrick.reed@cornell.edu

Image Credit: The Atlantic, Mar. 2014, Reuters/Robert Galbraith

A qualitative starting point…

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13 Case Studies

-UK & Australia -Denver Water -Bureau of Rec. -CA DWR -MWD -many more Seeking robustness across possible futures

“Bottom-up” decision frameworks

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(Decision Scaling, Info-Gap, RDM, MORDM, Dynamic Adaptive Policy Pathways …)

Planning under uncertainty: Some context

What do robustness-based decision frameworks have in common?

How do methodological choices impact decision recommendations?

How does the MWD IRP link to the state-of-the-art?

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Evaluate alternatives in multiple

states of the world…

Quantify robustness measures and

determine sensitive uncertainties

What do robustness analyses have in common?

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What actions?

What causes

failures?

What world?

Acceptable in

many worlds?

Planning under uncertainty: Some context

What do robustness-based decision frameworks have in common?

How do methodological choices impact decision recommendations?

How does the MWD IRP link to the state-of-the-art?

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Eastern U.S. example of four water

utilities through 2025

Four regional objectives:

• Volumetric reliability

• Restriction frequency

• Average cost

• Worst-case cost

(Zeff et al. 2014)

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The worst-performing utility is optimized such that others will perform as well or better.

What risk-of-failure should trigger demand restrictions?

5%

0%

10% 10%

0%

4%

5%

4%

What risk-of-failure should trigger water transfers?

Risk-based triggers for drought management

10%

0%

3%

1%

What percent of revenue should each utility save as self-insurance?

10%

0%

3%

6%

What percent of revenue should each utility put toward third-party insurance?

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(Must be specified for every utility)

Avoiding Tunnel Vision: Scenarios vs. Exploratory Modeling

http://www.hockscqc.com/articles/tunnelvision/tunnel-vision.jpg

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Which solutions are robust? How to decide?

Sample deeply uncertain states of the world (inflows, demand, etc.)

0

5

10

15

20

25

30

1965 1975 1985 1995 2005 2015 2025 2035 2045 2055

Raw

Wat

er D

eman

d (m

gd)

OWASA Demand Projections

1969

1987

1998

20082011

Actual

Previous OWASA Demand Projections 7/29/2015 11

It is very common to make poor predictions

Construct alternative states of the world for different

combinations of uncertainties

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2 of 13 uncertainties including demand seasonality, reservoir evaporation, transfer prices, etc.

Given/assumed

(classic scenarios) Discovered via modeling (scenario discovery—RDM)

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An assumed focus on climate/hydrologic factors

may fail to capture system vulnerabilities

? Rate of demand growth controls success more so than hydrology

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Acceptable in

many worlds?

References: Schneller and Sphicas (1983)

Number of successes Number of failures

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What causes

failures?

Discovering sensitive uncertainties

improves robustness of alternatives

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Planning under uncertainty: Some context

What do robustness-based decision frameworks have in common?

How do methodological choices impact decision recommendations?

How does the MWD IRP link to the state-of-the-art?

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Some summative comments

MWD is serving as a national/international leader in planning

Climate change is not the only concern, RDM allows the consideration of many uncertainties

In the future it would interesting to more fully link the uncertainties in the “scoping” and “implementation/policy” phases of the IRP

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Thanks!

Acknowledgements

NOAA SARP #NA110AS2310144

NSF SCRiM #GEO-1240507

USDA WSC #2014-67003-22076

References

Herman et al. (2015) How should robustness be defined for water systems planning under change? JWRPM, 04015012.

Herman et al. (2014) Beyond Optimality: Multi-stakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty. WRR, 50, 7692-7713.

Zeff et al. (2014) Navigating financial and supply reliability tradeoffs in regional drought portfolios. WRR, 50, 4906-4923.

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