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Improving the RESCON approach
Published in Silke Wieprecht, Stefan Haun, Karolin Weber, Markus Noack, Kristina Terheiden, River Sedimentation, 2016
N. Efthymiou, S. Palt, P. Pintz, P.K. Thapa, G.W. Annandale, P. Karki
It is important to note that the quantification of the probability of appearance of a specific climate future is not possible due to the lack of agreement between climate change scientists. Therefore, the climate change effects cannot be predicted and quantified in a deterministic manner, rather they are associated with high uncertainty. The incorporated tool/approach shall demonstrate the effect of climate change on the economic performance of the investment for different sediment management configurations over a representative range of potential climate futures. On these grounds RESCON 2 facilitates a sensitivity analysis which has the following objectives: – Climate “stress test”Indication of how vulnerable different project configurations might be across a sensible range of potential climate change effects. The project configurations are differentiated by the applied sediment management method.– Robust Decision Making (RDM)Identification of one or more robust project configurations, i.e. designs capable of delivering acceptable performance under a wide range of climate scenarios.
Decision making under deep uncertainty for adapting urban drainage systems to change
Published in Urban Water Journal, 2018
Filip Babovic, Ana Mijic, Kaveh Madani
Robust Decision Making (RDM) can be thought of as stress testing a given strategy through the use of many model runs (RAND 2013). In a RDM analysis the current system is modelled and its performance evaluated across numerous potential futures. A meta-analysis can then be performed on these scenarios in order to identify which are the key parameters that result in failure. Once these vulnerabilities are known, potential vulnerability reducing measures can be suggested, delivering a solution that performs adequately across all or most futures (Lempert, Groves, and Fischbach 2013). The process can then be repeated on the more robust system; this iterative process ‘enables systematic quantitative reasoning about the consequences of and trade-offs among alternative decision options using multiple values and multiple expectations about the future’ (Lempert, Groves, and Fischbach 2013, 10). As such, RDM seeks strategies whose performance is insensitive to the most significant uncertainties.
A system-of-systems framework for exploratory analysis of climate change impacts on civil infrastructure resilience
Published in Sustainable and Resilient Infrastructure, 2018
Exploratory analysis has been utilized in different studies (e.g. Hristov, 2015; Lempert, Nakicenovic, Sarewitz, & Schlesinger, 2004; Mohor, Rodriguez, Tomasella, & Júnior, 2015) for evaluation of climate change impacts. However, the use exploratory analysis in the context of CIS resilience under climate change impacts is rather limited. In this context, exploratory analysis can provide novel insights regarding how CIS performance will evolve under different scenarios of climate change impacts and adaptation actions. Unlike the existing approaches for assessment of CIS resilience, exploratory analysis does not aim to predict the behavior of a system and does not intend to optimize a system. Instead, exploratory analysis focuses primarily on considering different resilience and adaptation scenarios based on changes in system behavior and future uncertainty. Although the existing literature related to Robust Decision-Making involves frameworks (e.g. Lempert et al., 2004) for assessment of climate change impacts, the existing frameworks are not developed specifically for assessment of infrastructure resilience. Shortridge, Guikema, and Zaitchik (2017) is one of limited studies that adopted a RDM framework for dealing with data scarcity in assessment of infrastructure resilience. The current study complements the current literature related to Robust Decision-Making (RDM) through: (1) establishing a RDM framework in the context of infrastructure resilience; and (2) specifying the important mechanisms and relationships that need to be abstracted and modeled in order to assess infrastructure resilience to climate change impacts.