Browsing by Author "Green, Michael"

Now showing 1 - 4 of 4
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    Coping with climate change uncertainty for adaptation planning for local water management
    (Cranfield University, 2014-05) Green, Michael; Weatherhead, E. Keith
    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.
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    Coping with climate change uncertainty for adaptation planning: An improved criterion for decision making under uncertainty using UKCP09
    (2014-03-01T00:00:00Z) Green, Michael; Weatherhead, E. K.
    Despite information on the benefits of climate change adaptation planning being widely available and well documented, in the UK at least relatively few real-world cases of scenario led adaptation have been documented. This limited uptake has been attributed to a variety of factors including the vast uncertainties faced, a lack of resources and potentially the absence of probabilities assigned to current climate change projections, thereby hampering conventional approaches to decision making under risk. Decision criteria for problems of uncertainty have been criticised for being too restrictive, crude, overly pessimistic, and data intensive. Furthermore, many cannot be reproduced reliably from sub-samples of the UKCP09 probabilistic dataset. This study critically compares current decision criteria for problems of uncertainty and subsequently outlines an improved criterion which overcomes some of their limitations and criticisms. This criterion, termed the GreenZ-score, is then applied to a simplified real-world problem of designing an irrigation reservoir in the UK under climate change. The criterion is designed to be simple to implement, support robust decision making and provide reproducible results from sub-samples of the UKCP09 probabilistic dataset. It is designed to accommodate a wide range of risk appetites and attitudes and thereby encourage its use by decision makers who are presently struggling to determine whether and how to adapt to future climate change and its potential impacts. Analyses using sub-samples of the complete probabilistic dataset showed that the GreenZ-score had comparable reproducibility to Laplace and improved reproducibility compared to other current decision criteria, and unlike Laplace is able to accommodate different risk attitudes. KeywordsDecision making; Adaptation; Uncertainty; UKCP09; WaSim; GreenZ-score
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    A critical comparison of using a probabilistic weather generator versus a change factor approach: irrigation reservoir planning under climate change
    (IWA, 2014-03-01) Green, Michael; Weatherhead, E. K.
    In the UK, there is a growing interest in constructing on-farm irrigation reservoirs, however deciding the optimum reservoir capacity is not simple. There are two distinct approaches to generating the future daily weather datasets needed to calculate future irrigation need. The change factor approach perturbs the observed record using monthly change factors derived from downscaled climate models. This assumes that whilst the climate changes, the day-to-day climate variability itself is stationary. Problems may arise where the instrumental record is insufficient or particularly suspect. Alternatively, probabilistic weather generators can be used to identify options which are considered more robust to climate change uncertainty because they consider non-stationary climate variability. This paper explores the difference between using the change factor approach and a probabilistic weather generator for informing farm reservoir design at three sites in the UK. Decision outcomes obtained using the current normal practice of 80% probability of non-exceedance rule and simple economic optimisations are also compared. Decision outcomes obtained using the change factor approach and probabilistic weather generators are significantly different; whether these differences translate to real-world differences is discussed. This study also found that using the 80% probability of non-exceedance rule could potentially result in maladaptation.
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    Irrigation demand modelling using the UKCP09 weather generator: lessons learned
    (IWA Publishing, 2013-12-03) Green, Michael; Weatherhead, E. K.
    The determination of irrigation demand is typically based on crop modelling using a long historic record of local daily weather data. However, there are rarely adequate weather station records near to given sites; often any local records cover a limited number of years, are incomplete, costly or are of poor quality. This paper examines whether version 1 of the UKCP09 weather generator can provide a simpler and effective method of calculating irrigation demand with sufficient accuracy for regulatory and design purposes. The irrigation demands at seven sites distributed around England were modelled using the UKCP09 baseline climatology and compared with results modelled using daily observed weather records. For the design dry year used for irrigation planning, the weather generator replicated the observed conditions with reasonable accuracy. The weather generator was however less successful at replicating extreme dry years. These results are encouraging but also provide a note of caution for the use of these generated datasets for studying current irrigation demand and by implication for modelling future needs under climate change. The study also demonstrated a simple sub-sampling approach for reducing the processing demands if using the dataset in more complex models, though this would not remove any underlying error.