Environmental management is plagued with uncertainty, despite this, little attention has until recently been given to the sensitivity of management decisions to uncertain environmental projections. Assuming that the future climate is stationary is no longer considered valid, nor is using a single or small number of potentially incorrect projections to inform decisions. Instead, it is recommended that decision makers make use of increasingly available probabilistic projections of future climate change, such as those from perturbed physics ensembles like United Kingdom Climate Projections 2009 (UKCP09), to gauge the severity and extent of future impacts and ultimately prepare more robust solutions. Two case studies focussing on contrasting aspects of local water management; namely irrigation demand and urban drainage management, were used to evaluate current approaches and develop recommendations and improved methods of using probabilistic projections to support decision making for climate change adaptation. A quantitative understanding of the impact of uncertainty to decision making for climate change adaptation was obtained from a literature review; followed by a comparison of using (1) the low medium and high emission scenarios, (2) 10,000 sample ensemble and 11 Spatially Coherent Projections (11SCP), (3) deterministic and probabilistic climate change projections, (4) the complete probabilistic dataset and sub-samples of it using different sampling techniques, (5) the change factor (or delta change) and stochastic (or UKCP09 weather generator) downscaling techniques and (6) different decision criteria using two contrasting case studies at three UK sites. This research provides an insight into the impact of different sources of uncertainty to real-world adaptation and explores whether having access to more data and a greater appreciation of uncertainty alters the way we make decisions. The impact of the “envelope of uncertainty” to decision making is explored in order to identify those factors and decisions that have the greatest impact on what we perceive to be the “best” solution. An improved novel decision criterion for use with probabilistic projections for adaptation planning is presented and tested using simplified real-world case studies to establish whether it provides a more attractive tool for decision makers compared to the current decision criteria which have been advocated for adaptation planning. This criterion explicitly incorporates the unique risk appetite of the individual into the decision making process, acknowledging that this source of uncertainty and not necessarily the climate change projections, had the greatest impact on the decisions considered by this research. This research found the differences between emission scenarios, projection datasets, sub-sampling approaches and downscaling techniques, each contributing a different source of uncertainty, tended to be small except where the decision maker already exhibited an extremely risk seeking or risk adverse appetite. This research raises a number of interesting questions about the “decision significance” of uncertainty through the systematic analysis of several different sources of uncertainty on two contrasting local water management case studies. Through this research, decision makers are encouraged to take a more active role in the climate change adaptation debate, undertaking their own analysis with the support of the scientific community in order to highlight those uncertainties that have significant implications for real world decisions and thereby help direct future efforts to characterise and reduce them. The findings of this research are of interest to planners, engineers, stakeholders and adaptation planning generally.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:613545 |
| Date | January 2014 |
| Creators | Green, Michael |
| Contributors | Weatherhead, E. K. |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/8649 |
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