Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches
2016 (English)In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 84, 155-164 p.Article in journal (Refereed) Published
Computer simulation models can generate large numbers of scenarios, far more than can be effectively utilized in most decision support applications. How can one best select a small number of scenarios to consider? One approach calls for choosing scenarios that illuminate vulnerabilities of proposed policies. Another calls for choosing scenarios that span a diverse range of futures. This paper joins these two approaches for the first time, proposing an optimization-based method for choosing a small number of relevant scenarios that combine both vulnerability and diversity. The paper applies the method to a real case involving climate resilient infrastructure for three African river basins (Volta, Orange and Zambezi). Introducing selection criteria in a stepwise manner helps examine how different criteria influence the choice of scenarios. The results suggest that combining vulnerability- and diversity-based criteria can provide a systematic and transparent method for scenario selection.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 84, 155-164 p.
Climate change, Robust decision making, Scenario discovery, Scenario diversity analysis, Vulnerability based scenario analysis, Decision making, Decision support systems, Decision support applications, Diverse range, Diversity analysis, Optimization based methods, Robust decisions, Scenario analysis, Selection criteria
Environmental Sciences Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-195277DOI: 10.1016/j.envsoft.2016.06.011ISI: 000385595200013ScopusID: 2-s2.0-84979085712OAI: oai:DiVA.org:kth-195277DiVA: diva2:1046051
FunderMistra - The Swedish Foundation for Strategic Environmental ResearchEU, FP7, Seventh Framework Programme, 603416
QC 201611112016-11-112016-11-022016-11-11Bibliographically approved