Adaptive flood risk management under climate change uncertainty using real options and optimization

This is the peer reviewed version of the following article: oodward, M., Kapelan, Z. and Gouldby, B. (2014), Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization. Risk Analysis, 34: 75–92, which has been published in final form at 10.1111/risa.12088. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving: http://olabout.wiley.com/WileyCDA/Section/id-820227.html#termsIt is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state-of-the-art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.Engineering and Physical Sciences Research Council (EPSRC)Department of Environment, Food and Rural Affairs/Environment Agency Joint Research Programme on Flood and Coastal DefenceUnited Kingdom Water Industry ResearchOffice of Public Works DublinNorthern Ireland Rivers Agenc

P5_1 Light-ning Rod

The following paper aims to introduce the reader to the potential application of lasers as generators of paths of ionised gas that will act as lightning conductors in the event of a storm. Using a simple model of laser operation, values for energy, pulse duration and ultimately, power required have been calculated. While yielding no revolutionary data in the field of weather manipulation, the subject of this paper remains a valid source of research

P5_6 A Hovering 747

The Halicarnassus is a highly modified 747, capable of hovering, featured in Matthew Reilly’s novel, Seven Ancient Wonders. The following paper examines this aircraft by first trying to validate its ability to hover with statistics from the components specified in the novel and then by proposing the use of solid rocket boosters instead. The feat accomplished in the novel of making the Halicarnassus hover is validated, an equation for the use of a solid fuel rocket is derived and an example of a solid rocket booster that exceeds the requirements placed upon it is found

P5_2 Fluid Dynamics of a Near Supersonic Train

This paper investigates the effect on wind behaviour of allowing the trains of the London underground to accelerate to near super-sonic speeds i.e. Mach 0.9. The paper calculates the magnitude of the piston effect of a deep underground train arriving at a platform on a human and finds this value to be 67.7N of constant force

A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

<p>Abstract</p> <p>Background</p> <p>Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity.</p> <p>Results</p> <p>The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data.</p> <p>Conclusion</p> <p>After carrying out the analysis and validation for three sequenced <it>Escherichia coli </it>strains, CGH microarray data from 19 <it>E. coli </it>O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes.</p

Lubricating Bacteria Model for Branching growth of Bacterial Colonies

Various bacterial strains (e.g. strains belonging to the genera Bacillus, Paenibacillus, Serratia and Salmonella) exhibit colonial branching patterns during growth on poor semi-solid substrates. These patterns reflect the bacterial cooperative self-organization. Central part of the cooperation is the collective formation of lubricant on top of the agar which enables the bacteria to swim. Hence it provides the colony means to advance towards the food. One method of modeling the colonial development is via coupled reaction-diffusion equations which describe the time evolution of the bacterial density and the concentrations of the relevant chemical fields. This idea has been pursued by a number of groups. Here we present an additional model which specifically includes an evolution equation for the lubricant excreted by the bacteria. We show that when the diffusion of the fluid is governed by nonlinear diffusion coefficient branching patterns evolves. We study the effect of the rates of emission and decomposition of the lubricant fluid on the observed patterns. The results are compared with experimental observations. We also include fields of chemotactic agents and food chemotaxis and conclude that these features are needed in order to explain the observations.Comment: 1 latex file, 16 jpeg files, submitted to Phys. Rev.

Draft Genome Sequence of Aneurinibacillus migulanus Strain Nagano

ACKNOWLEDGMENTS This project was funded by the Kuwait Government (to F.N.A.), the European Union’s Seventh Framework Programme under grant agreement no. 245268 (ISEFOR; to L.B. and S.W.). Further support came from the SwissBOL project, financed by the Swiss Federal Office for the Environment (grant holder, L.B.) and the Sciex–Scientific Exchange Programme NMS.CH (to L.B. and L.L.).Peer reviewedPublisher PD

Multiobjective optimization for improved management of flood risk

Journal ArticleEffective flood risk management requires consideration of a range of different mitigation measures. Depending on the location, these could include structural or nonstructural measures as well as maintenance regimes for existing levee systems. Risk analysis models are used to quantify the benefits, in terms of risk reduction, when introducing different measures; further investigation is required to identify the most appropriate solution to implement. Effective flood risk management decision making requires consideration of a range of performance criteria. Determining the better performing strategies, according to multiple criteria, can be a challenge. This article describes the development of a decision support system that couples a multiobjective optimization algorithm with a flood risk analysis model and an automated cost model. The system has the ability to generate potential mitigation measures that are implemented at different points in time. It then optimizes the performance of the mitigation measures against multiple criteria. The decision support system is applied to an area of the Thames Estuary and the results obtained demonstrate the benefits multiobjective optimization can bring to flood risk management. © 2014 American Society of Civil Engineers.Engineering and Physical Sciences Research Council (EPSRC)Department of Environment, Food and Rural Affairs/Environment Agency (Defra/EA) Joint Research Programme on Flood and Coastal DefenceUnited Kingdom Water Industry Research (UKWIR)Office of Public Works (OPW) DublinNorthern Ireland Rivers Agency (DARDNI