Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

Shortridge, Julie; Guikema, Seth D.

doi:10.1111/risa.12582

Cited by 29 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We adapt a previously developed simulation approach to evaluate the performance of the planned water storage infrastructure under a range of climatic and economic conditions (Shortridge & Guikema, ; Shortridge et al., ). First, we resampled historical temperature and precipitation time series from the University of East Anglia CRU TS3.10 simulations of gridded meteorological fields (Harris et al., ), preserving two‐year blocks to maintain continuity with the seasonal cycle and observed autocorrelation (Kunsch, ; Shortridge et al., ), generating 2,000 50‐year monthly time series (projected as 2020–2069).…”

Section: Methodsmentioning

confidence: 99%

“…The COV parameter was used to decrease variability during years that received less than the median precipitation and to increase variability during wetter than median years, via a linear array from 0 to the sampled COV parameter multiplied by the precipitation variable. We then calculated evaporation from each reservoir using the Penman equation (Penman, ), using the perturbed temperature and precipitation time series and average data for wind speed, relative humidity, and solar radiation taken from the Bair Dar meteorological station (Kebede, Travi, Alemayehu, & Marc, ; Shortridge & Guikema, ).…”

Section: Methodsmentioning

confidence: 99%

“…More recently, projections in the region show increases in precipitation beginning mid‐21st century, yet these range ±20% (Niang et al., ; Taye, Ntegeka, Ogiramoi, & Willems, ). This uncertainty in precipitation changes complicates decisions in water storage infrastructure investment in the Ethiopian highlands (Block & Strzepek, ; Gashaw & Mahari, ), so that investigations of agriculture or infrastructure development in Ethiopia tend to consider a broad range of potential changes (Block, Souza Filho, Sun, & Kwon, ; Reis, Culver, Block, & McCartney, ; Shortridge & Guikema, ; Shortridge et al., ). Moreover, the unknowable future climate conditions undermine simple economic computations of water reservoir planning, making RDF essential to reservoir cost‐benefit analyses (Nassopoulos, Dumas, & Hallegatte, ).…”

Section: Case Study Backgroundmentioning

confidence: 99%

“…A satisficing decision criteria set a threshold of minimum acceptable performance, as a binary management objective, and characterize the robustness of different plans based on how well they meet this threshold under a range of uncertain conditions (Groves & Bloom, ; Lempert & Groves, ; Moody & Brown, ; Starr, ). Although RDFs sometimes use an aggregate of multiple criteria (Lempert et al., ), Shortridge and Guikema () found that robustness results depend on the method of aggregation and recommended performing scenario discovery over each performance criterion independently to elucidate the merits of various management plans.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this article is to quantify the impact that uncertainty parameter ranges and distributions have on the results of an RDM assessment. We take a calibrated model of water resources in the vicinity of Lake Tana, Ethiopia (Alemayehu, McCartney, & Kebede, ; Shortridge & Guikema, ), and simulate irrigation supply and net present value for the status quo and four alternatives of planned water storage infrastructure investments over the next 50 years. In particular, we identify how the range and statistical distribution of five climatic and economic parameters, reflecting exogenous uncertainty, impact the results of the RDM analysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Reis

Shortridge

2019

Risk Analysis

View full text Add to dashboard Cite

Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision‐making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Case Study Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Reis

Shortridge

2019

Risk Analysis

View full text Add to dashboard Cite

show abstract

Can exploratory modeling of water scarcity vulnerabilities and robustness be scenario neutral?

Quinn

Hadjimichael

Reed

2020

Preprint

View full text Add to dashboard Cite

Planning under deep uncertainty, when probabilistic characterizations of the future are unknown, is a major challenge in water resources management. Many planning frameworks advocate for "scenario-neutral" analyses in which alternative policies are evaluated over plausible future scenarios with no assessment of their likelihoods. Instead, these frameworks use sensitivity analysis to discover which uncertain factors have the greatest influence on performance. This knowledge can be used to design monitoring programs and adaptive policies that respond to changes in the critical uncertainties. However, scenario-neutral analyses make implicit assumptions about the range and independence of the uncertain factors that may not be consistent with the coupled human-hydrologic processes influencing the system. These assumptions could influence which factors are found to be most important and which policies are most robust, belying their neutrality; assuming uniformity and independence could have decision-relevant implications. This study illustrates these implications using a multistakeholder planning problem within the Colorado River Basin, where hundreds of rights holders vie for the river's limited water under the law of prior appropriation. Variance-based sensitivity analyses are performed to assess users' vulnerabilities to changing hydrologic conditions using four experimental designs: (1) scenario-neutral samples of hydrologic factors, centered on recent historical conditions, (2) scenarios informed by climate projections, (3) scenarios informed by paleohydrologic reconstructions, and (4) scenario-neutral samples of hydrologic factors spanning all previous experimental designs. Differences in sensitivities and user robustness rankings across the experiments illustrate the challenges of inferring the most consequential drivers of vulnerabilities to design effective monitoring programs and robust management policies.Plain Language Summary How we should best manage our water resources depends on future water supply and demand, both of which are changing in uncertain ways as a consequence of climate change, population growth, and sectoral change. This makes it challenging to decide between alternative management plans that may be most favorable under different conditions. Given this uncertainty, many planning frameworks advocate for implementing "robust" management plans that perform reasonably well over a wide range of conditions. These plans can then be modified adaptively to cater to particular climate and socioeconomic conditions as our uncertainty about the future is reduced. Unfortunately, we find that the determination of which plan is most robust, and what conditions should trigger adaptation, is an additional challenge. In assessing the vulnerabilities of hundreds of water users in the Upper Colorado River Basin to different possible drought conditions, we find different users to be robust depending on which scenarios are considered. The sensitivities of these users' water shortages to different climate conditio...

show abstract

Application of the patient rule induction method to detect hydrologic model behavioural parameters and quantify uncertainty

Shokri

Walker

Dijk

et al. 2018

Hydrological Processes

View full text Add to dashboard Cite

Finding an operational parameter vector is always challenging in the application of hydrologic models, with over-parameterization and limited information from observations leading to uncertainty about the best parameter vectors. Thus, it is beneficial to find every possible behavioural parameter vector. This paper presents a new methodology, called the patient rule induction method for parameter estimation (PRIM-PE), to define where the behavioural parameter vectors are located in the parameter space. The PRIM-PE was used to discover all regions of the parameter space containing an acceptable model behaviour. This algorithm consists of an initial sampling procedure to generate a parameter sample that sufficiently represents the response surface with a uniform distribution within the "good-enough" region (i.e., performance better than a predefined threshold) and a rule induction component (PRIM), which is then used to define regions in the parameter space in which the acceptable parameter vectors are located. To investigate its ability in different situations, the methodology is evaluated using four test problems. The PRIM-PE sampling procedure was also compared against a Markov chain Monte Carlo sampler known as the differential evolution adaptive Metropolis (DREAM ZS ) algorithm. Finally, a spatially distributed hydrological model calibration problem with two settings (a three-parameter calibration problem and a 23-parameter calibration problem) was solved using the PRIM-PE algorithm. The resultsshow that the PRIM-PE method captured the good-enough region in the parameter space successfully using 8 and 107 boxes for the three-parameter and 23-parameter problems, respectively. This good-enough region can be used in a global sensitivity analysis to provide a broad range of parameter vectors that produce acceptable model performance. Moreover, for a specific objective function and model structure, the size of the boxes can be used as a measure of equifinality.

show abstract

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

Cited by 29 publications

References 38 publications

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Can exploratory modeling of water scarcity vulnerabilities and robustness be scenario neutral?

Application of the patient rule induction method to detect hydrologic model behavioural parameters and quantify uncertainty

Contact Info

Product

Resources

About