Synthetic Drought Scenario Generation to Support Bottom-Up Water Supply Vulnerability Assessments

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

doi:10.1061/(asce)wr.1943-5452.0000701

Cited by 87 publications

(69 citation statements)

References 81 publications

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“…In this study the Classification and Regression Tree (CART) method (Breiman et al, ) was used for scenario discovery. Alternative scenario discovery methods include the Patient Rule Induction Method (PRIM) (Guivarch et al, ; Kwakkel, ; Kwakkel & Jaxa‐Rozen, ), support vector classification (Herman et al, ), and logistic regression (Quinn, ). Lempert et al () provide a detailed description of CART and PRIM and evaluate their performance for different test cases.…”

Section: Methodology and Exploratory Modelingmentioning

confidence: 99%

Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios

et al. 2018

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An analytic scenario generation framework is developed based on the idea that the same climate outcome can result from very different socioeconomic and policy drivers. The framework builds on the Scenario Matrix Framework's abstraction of “challenges to mitigation” and “challenges to adaptation” to facilitate the flexible discovery of diverse and consequential scenarios. We combine visual and statistical techniques for interrogating a large factorial data set of 33,750 scenarios generated using the Global Change Assessment Model. We demonstrate how the analytic framework can aid in identifying which scenario assumptions are most tied to user‐specified measures for policy relevant outcomes of interest, specifically for our example high or low mitigation costs. We show that the current approach for selecting reference scenarios can miss policy relevant scenario narratives that often emerge as hybrids of optimistic and pessimistic scenario assumptions. We also show that the same scenario assumption can be associated with both high and low mitigation costs depending on the climate outcome of interest and the mitigation policy context. In the illustrative example, we show how agricultural productivity, population growth, and economic growth are most predictive of the level of mitigation costs. Formulating policy relevant scenarios of deeply and broadly uncertain futures benefits from large ensemble‐based exploration of quantitative measures of consequences. To this end, we have contributed a large database of climate change futures that can support “bottom‐up” scenario generation techniques that capture a broader array of consequences than those that emerge from limited sampling of a few reference scenarios.

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Section: Methodology and Exploratory Modelingmentioning

confidence: 99%

Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios

et al. 2018

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“…Natural variability is commonly represented as synthetic climate time-series data obtained from a stochastic weather generator (Steinschneider & Brown, 2013). However, in some cases, it can be desirable to sample natural variability in hydrological conditions directly using a synthetic streamflow generator (Borgomeo, Farmer, & Hall, 2015;Henley, 2012;Herman et al, 2010) , 1979). LHS samples are be obtained by dividing the continuous uncertainty range of each factor into n equal intervals and generating an n x m matrix, where each factor interval is sampled exactly once.…”

Section: Phase 2: Vulnerability Analysismentioning

confidence: 99%

Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

Taner

Ray

Brown

2019

Water Resources Research

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The widespread uncertainty regarding future changes in climate, socioeconomic conditions, and demographics have increased interest in vulnerability‐based frameworks for long‐term planning of water resources. These frameworks shift the focus from projections of future conditions to the weaknesses of the baseline plans and then to options for reductions in those weaknesses across a wide range of futures. A consistent challenge for vulnerability‐based planning is how to assess the relative likelihood of the occurrence of the multidimensional and codependent uncertainties to which the system or plan is vulnerable. This work proposes a methodological solution to the problem, demonstrated in this case as an extension to Decision Scaling framework. The proposed approach first generates a wide range of futures using stochastic simulators, and then stress tests the system across those futures to identify vulnerabilities relative to stakeholder‐defined performance thresholds. The relative likelihood of the vulnerabilities is then explored using a Bayesian belief network of the knowledge domain of the water resources system. The Bayesian network provides a formal representation of the joint probabilistic behavior of the system conditioned on the uncertain but potentially useful sources of information about the future, including historical trends, expert judgments, and model‐based projections. The proposed approach is applied to compare four design options for a dam project in the Coastal Province of Kenya with respect to the reliability and net present value metrics. Results show that incorporation of belief information helps better distinguishing of the available options, principally by magnifying the differences between the computed net present values.

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“…Reporting metrics of risk requires extensive and explicit simulation of hydrological variability, extending well beyond historic droughts to include worse than observed conditions. The methodologies are available to do this either via stochastic streamflow (Borgomeo et al, ; Herman et al, ) and groundwater (Mackay et al, ; Prudhomme et al, ) simulation, or via regional climate simulations (Guillod et al, ) or weather generators (Glenis et al, ; Steinschneider & Brown, ) coupled with rainfall‐runoff and groundwater models. For spatially extensive systems this will involve consideration of spatial variability in the simulations (Serinaldi, ).…”

Section: The Frameworkmentioning

confidence: 99%

Risk, Robustness and Water Resources Planning Under Uncertainty

et al. 2018

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Risk‐based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost‐benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk‐based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision‐theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade‐off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state‐of‐the ‐art regional climate simulations to inform the estimation of risk and robustness.

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Synthetic Drought Scenario Generation to Support Bottom-Up Water Supply Vulnerability Assessments

Cited by 87 publications

References 81 publications

Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios

Large Ensemble Analytic Framework for Consequence‐Driven Discovery of Climate Change Scenarios

Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

Risk, Robustness and Water Resources Planning Under Uncertainty

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