Consortium on Risk in the Environment: Diagnostic, Integration, Benchmarking, Learning and Elicitation (CREDIBLE)

Lead Research Organisation: UNIVERSITY OF EXETER
Department Name: Engineering Computer Science and Maths

Abstract

Natural hazard events claim thousands of lives every year, and financial losses amount to billions of dollars. The risk of losing wealth through natural hazard events is now increasing at a rate that exceeds the rate of wealth creation. Therefore natural hazards risk managers have the potential, through well-informed actions, to significantly reduce social impacts and to conserve economic assets. By extension, environmental science, through informing the risk manager's actions, can leverage research investment in the low millions into recurring social and economic benefits measured in billions. However, to be truly effective in this role, environmental science must explicitly recognize the presence and implications of uncertainty in risk assessment.

Uncertainty is ubiquitous in natural hazards, arising both from the inherent unpredictability of the hazard events themselves, and from the complex way in which these events interact with their environment, and with people. It is also very complicated, with structure in space and time (e.g. the clustering of storms), measurements that are sparse especially for large-magnitude events, and losses that are typically highly non-linear functions of hazard magnitude. The tendency among natural hazard scientists and risk managers (eg actuaries in insurance companies) is to assess the 'simple' uncertainty explicitly, and assign the rest to a large margin for error.

The first objective of our project is to introduce statistical techniques that allow some of the uncertainty to be moved out of the margin for error and back into an explicit representation, which will substantially improve the transparency and defensibility of uncertainty and risk assessment. Obvious candidates for this are hazard models fitted on a catalogue of previous events (for which we can introduce uncertainty about model parameters, and about the model class), and limitations in the model of the 'footprint' of the hazard on the environment, and the losses that follow from a hazard event.

The second objective is to develop methods that allow us to assess less quantifiable aspects of uncertainty, such as probabilities attached to future scenarios (eg greenhouse gas emissions scenarios, or population growth projections). The third objective is to improve the visualisation and communication of uncertainty and risk, in order to promote a shared ownership of choices between actions, and close the gap between the intention to act (eg, to build a levee, or relocate a group of people living in a high-risk zone) and the completion of the act. In natural hazards this gap can be large, because the cost of the act is high, many people may be affected, and the act may take several years to complete.

Ultimately, everyone benefits from better risk management for natural hazards, although the nature of the benefits will depend on location. In the UK, for example, the primary hazard is flooding, and this is an area of particular uncertainty, as rainfall and coastal storm surges are likely to be affected by changes in the climate. A second hazard is drought, leading to heat stress and water shortages. Our project has explicit strands on inland flooding, wind-storms, and droughts. Other parts of the world are more affected by volcanoes or by earthquakes, and our project has strands on volcanic ash, debris flows as found in volcanic eruptions (ie lahars; avalanches are similar), and earthquakes. In the future, new hazards might emerge, such as the effect of space weather on communications. A key part of our project is to develop generic methods that work across hazards, both current and emerging.

Planned Impact

The uncertainty framework developed by the CREDIBLE project will bring new approaches into the field of natural hazards and adapt them to the specific needs of this field. It will create consistency and greater scientific rigour regarding the estimation of uncertainty in natural hazard risk assessment. Thus enhancing capacity, knowledge and skills of stakeholders from private and public sectors, and improve societal security through better and more consistently informed decision-making under uncertainty.

Beneficiaries of the proposed research include the whole range of sectors involved in risk assessment of natural hazards, which is reflected in our list of project partners. These included the insurance and finance sector (Willis Ltd., Lighthill Risk Network, Catlin Underwriting Agencies Ltd), consultants (HR Wallingford, JBA Ltd, RMS), and the UK government agencies (Environment Agency, Met Office, DEFRA).

The insurance industry will benefit from the more accurate pricing of contracts. As we explain elsewhere in this proposal, currently many uncertainties in natural hazards are acknowledged but not explicitly quantified, and enter into insurance premiums as a loading for risk, which is effectively a margin for error on top of the expected loss. Our intention in CREDIBLE is to move some of these uncertainties out of the margin for error and into explicit assessment, thus improving the pricing of risk premiums, and, ultimately, the economic performance of UK insurance companies connected to our project partners. We will also introduce standard statistical tools for improving efficiency in Monte Carlo simulations, and for quantifying variability in Monte Carlo estimates, which will be immediately taken up by CAT modelling companies (notably our project partner RMS), and feed through into regulation, for example through quantifying variability in the estimate of the 99.5th percentile, as required by the EU Solvency 2 directive.

Consultants and agencies will benefit from more powerful tools for assessing uncertainty, and for propagating it into the choice between actions. Our environmental consulting project partners (JBA Consulting and HR Wallingford) focus primarily on flooding, which is also a major concern for our agency project partners (the Environment Agency, the UK Met Office, and DEFRA). In flooding it is acknowledged that providing information about uncertainties is crucial, and this has been reflected in UK postcode-level flood maps produced by the EA (although technically these maps convey frequencies rather than probabilities). CREDIBLE's focus on (i) explicitly assessing more of the uncertainty, (ii) providing additional tools to represent less-quantifiable uncertainties, (iii) considering explicitly the link between potential actions, scenarios, uncertainties and risks, and (iv) visualisation and communication, will provide a more transparent and defensible assessment of different actions and consequences. This will promote a shared ownership of some of the very contentious issues that can arise in natural hazards, such as developments on flood-plains, or groups of people who choose to live in the high risk zones of active volcanoes.

This final aspect is an absolutely crucial part of CREDIBLE, and of our intention to have an enduring effect on natural hazards risk management, and the quality of life of people who are affected by natural hazards. Natural hazards interventions (eg building a levee or a barrage, changing building regulations, relocating a group of people) are almost always contentious, costly, and take several years. There is a large gap, therefore, between the intention to act, and the completion of the action. CREDIBLE can reduce the size of this gap by promoting a shared sense of ownership, of the science and of the decision.

Publications

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Dawkins L (2016) The 21st century decline in damaging European windstorms in Natural Hazards and Earth System Sciences

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Economou T (2016) On the use of Bayesian decision theory for issuing natural hazard warnings. in Proceedings. Mathematical, physical, and engineering sciences

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Roberts J (2014) The XWS open access catalogue of extreme European windstorms from 1979 to 2012 in Natural Hazards and Earth System Sciences

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Simpson M (2016) Decision Analysis for Management of Natural Hazards in Annual Review of Environment and Resources

 
Description The work led by Dr Theo Economou has successfully shown how Bayesian decision theory can be used in hazard warning systems to make more transparent and more optimal warnings. This has been illustrated using severe weather warnings kindly provided by the UK Met Office.

The PhD thesis work of Laura Dawkins has developed interesting new geostatistical models for windstorm footprints (maximum wind gust speeds over Europe over 3 day periods covering extreme storms). This has revealed that wind gusts at different locations are less dependent than previously thought. It has also shown that potential insured losses are more sensitive to the local wind speed properties than to spatial dependency characteristics. The abrupt 20% decrease in windstorm losses after 1996 can be attributed to wind speed changes rather than changes in spatial properties of storms.
Exploitation Route Three publications are currently being written up and will be submitted before April 2016.

A pilot study to implement the decision theory ideas operationally at the Met Office will be funded by this project over summer 2016.
Sectors Environment,Financial Services, and Management Consultancy