How Should Robustness Be Defined for Water Systems Planning under Change?

Herman, Jonathan D.; Reed, Patrick M.; Zeff, Harrison B.; Characklis, Gregory W.

doi:10.1061/(asce)wr.1943-5452.0000509

Cited by 303 publications

(282 citation statements)

References 104 publications

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“…Define robustness based on stakeholders' performance requirements Several robustness metrics have been proposed to evaluate if a management action remains viable across alternative SOWs sampled for deeply uncertain factors (Starr 1963, Schneller and Sphicas 1983, Lempert et al 2003, Dixon et al 2007, Lempert and Collins 2007, Herman et al 2015. Lempert et al (2003:52) define robustness as one that "performs reasonably well compared to the alternatives across a wide range of plausible futures."…”

Section: Objectives and Constraintsmentioning

confidence: 99%

“…Collins (2007:1009) further compared various alternatives to assess robustness: "trading some optimal performance for less sensitivity to assumptions, satisficing over a wide range of plausible futures, and keeping options open." Herman et al (2015) provide a comprehensive assessment of robustness measures. Although most studies agree that robustness is related to the performance of a strategy across uncertain states of the world, to our knowledge only a few studies cast robustness as multicriterion regrets whereas others focus on a single objective measure ).…”

Section: Objectives and Constraintsmentioning

confidence: 99%

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Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Singh¹,

Reed²,

Keller³

2015

E&S

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ABSTRACT. Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers' preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty).

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Section: Objectives and Constraintsmentioning

confidence: 99%

Section: Objectives and Constraintsmentioning

confidence: 99%

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Singh¹,

Reed²,

Keller³

2015

E&S

Self Cite

View full text Add to dashboard Cite

show abstract

“…We implement a Bayesian calibration approach with an aim to adequately resolve the tails of the distribution of future sea levels because these low-probability areas represent highrisk events. In robust decision-making approaches, it can be favorable to be underconfident as opposed to overconfident, e.g., by applying conservative estimates in the sense of being risk-averse (Herman et al, 2015). We hence include in our Bayesian approach wide, mechanistically motivated prior parameter probability distributions (Bakker et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

et al. 2017

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Abstract. Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.

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“…One way of approaching such decisions has been to create an ensemble of future climate states (Giorgi and Francisco 2000), downscale these (Tebaldi and Knutti 2007;Wilby and Dessai 2010), then apply a range of climate, business, perhaps hydrologic (Blöschl and Montanari 2010;Cervigni et al 2015;Groves et al 2013;Harding et al 2012) and decision models (Herman et al 2015) to the human-climate system of interest. Are there other, perhaps simpler, tools that can be used to examine this intersection of human and natural systems?…”

Section: Introductionmentioning

confidence: 99%

Using an option pricing approach to evaluate strategic decisions in a rapidly changing climate: Black–Scholes and climate change

Sturm

Goldstein

Huntington³

et al. 2016

Climatic Change

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Nature provides critical ecosystem services on which society and businesses rely, but the effort and cost of utilizing those services can change with the climate. Both climatic trend and variance affect these efforts and costs, creating a complex decision space where uncertain future predictions are the rule. Here, we show how these problems mimic option payoffs and demonstrate a modified version of the Black-Scholes option pricing formula (widely used in finance) to analyze these types of business-climate decisions. We demonstrate the method by (1) examining the viability of building ice roads in the Northwest Territories of Canada, where a strong negative warming trend is underway, and (2) applying it to the problem of the ongoing California drought, estimating expected water costs with and without storage. The method is novel and provides a simple and accessible way to make such assessments to at least a first-order approximation. While our focus here is on business situations where decisions are usually based on money, we suggest that a similar approach could be used beyond the business world in examining risk and attributing that risk to climate variance vs. trend.

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How Should Robustness Be Defined for Water Systems Planning under Change?

Cited by 303 publications

References 104 publications

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

Using an option pricing approach to evaluate strategic decisions in a rapidly changing climate: Black–Scholes and climate change

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