Real Options, Optimisation Methods and Flood Risk Management Michelle Woodward - HR Wallingford and Exeter University Ben Gouldby – HR Wallingford Zoran Kapelan – Exeter University Soon-Thiam Khu – Exeter University Michelle Woodward - HR Wallingford and Exeter University Ben Gouldby – HR Wallingford Zoran Kapelan – Exeter University Soon-Thiam Khu – Exeter University
Page 2 Objective of PhD Objective: To investigate optimum flood risk intervention strategies taking into account the possible effects of climate change Title: Real options based optimum selection of flood risk mitigation options
Page 3 Presentation outline Overview of Risk Analysis tool Calculating Benefits of interventions Optimisation Techniques Evolutionary Algorithms Dynamic Programming Real Options Valuing flexibility for climate change adaptation strategies Outline of computational framework
Page 4 Background to RASP Risk Assessment for System Planning Research Project funded by the UK Environment Agency ( )
Page 5 RASP is a framework for flood risk analysis Common database (NFCDD) Common input/output Catchment / Coastal Cell Level Strategic planning Development regulation Site / System Level Scheme appraisal Site / System Level National Level - National justification, regional prioritisation, long term outlook -
Page 6 Conceptual model Utilises a structured definition of the flood system
Page 7 The system model Determining flood depth versus probability The system model: Recognises that levees behave as “defence systems” A flood depth versus probability distribution is established by considering multiple combinations of storm loading and possible levee failure
Page 8 Model has been compared to hydrodynamic models like Infoworks-RS2D All inundation scenarios A new super fast inundation model ( HR RSFM ) enables 10000s of inundation scenarios to be realised Runtime: <0.1 sec
Page 9 The system model Estimating flood damages Three steps are used to calculate risk 1.Depth damage curves are used to assess the damage associated with each possible flood scenario 2.By combining the scenario damage with the probability of the scenario occurring a scenario risk is estimated 3.By integrating across all scenarios the expected annual damages (risk) is determined Source: Flood Hazard Research Centre, 2003
Page 10 Investigating intervention strategies
Page 11 Optimisation Techniques - Dynamic Programming Enumerative Scheme - Evolutionary Algorithms Inspired by Darwin’s theory of evolution Survival of the fittest Genetic operators Reproduction (crossover) Mutation Selection
Page 12 Structure of a Simple Genetic Algorithm START Generate initial population Application Model Evaluate objective function Are optimisation criteria met? Best individual RESULT SelectionCrossover Mutation Generate new population
Page 13 Genetic Algorithm Operators Two Parent Chromosomes Two new Offspring Mutation Crossover
Page 14 Multi-objective optimisation Multi objective optimisation methods seek solutions that are “optimum” with respect to all objectives. Invariably a set of optimal solutions is discovered (known as a Pareto set )
Page 15 The Pareto Front
Page 16 The Pareto Front
Page 17 The Pareto Front
Page 18 Optimisation Problem Objectives: Maximise Benefit: EAD without interventions – EAD with interventions n Minimise total cost: ∑C i C i = costs per intervention i = 1 Subject to: Realistic and available intervention options
Page 19 Cost (£’s) Benefit (£’s) The Pareto Front Identification of most appropriate option/s given fixed budget Identification of costs associated with specified benefit level Identification of transition, where significantly more investment yields little benefit (incremental benefit cost) Multi-objective optimisation
Page 20 Real options overview “ A Real Option is a choice that becomes available through an investment opportunity or action”
Page 21 Real Option Overview Current Defence Maximum height increase for current defence Maximum height increase for widened defence Widening of Base Present Day extreme water level Plausible range of future extreme water levels
Page 22 Framework for Optioneering Features include Analysis of Real Options Automated option searching techniques using evolutionary optimization processes (multi-objective optimization) Automated option cost generation Economic discounting of benefits and costs Temporally evolving risk analysis (a fastRASP) – risk is a function of future climate change scenario, future socio- economic scenarios Range of decision making methods
Page 23 Overview of framework Decision variables include: Standard of maintenance Raise crest level (Each defence) Widen defence (each defence) Non structural measures (flood proofing)
Page 24 Thank you for listening