Skip to main content

Successful trip to Japan: workshop on probabilistic hazard assessment

DPRI Kyoto and Cabot Institute
Over the last two days a group from the Cabot Institute has been holding a workshop with colleagues from Kyoto University’s Disaster Prevention Research Institute (or DPRI) on the topic of probabilistic hazard analysis.  On the face of it Japan and the UK are very similar: highly urbanised and complex island societies with high population densities and therefore the potential for serious disruption if natural hazards occur.  Mind you, the earthquake, tsunami and volcano hazards do put Japan in a different league when it comes to potential impacts.  In both countries, robust hazard analysis, planning and decision making is therefore essential to protecting society.  Both countries have a lot to learn from each other, and our recent paper on lessons for the UK from the Fukushima disaster is a case in point. 

Cabot members Wendy Larner, Colin Taylor, Susanna Jenkins, Jeremy Phillips, Katsu Goda, Philippa Bayley and myself (Paul Bates) spent two days working with around 30 Japanese colleagues, with Skype presentations from the UK delivered by WillyAspinall, Jonty Rougier and Tamsin EdwardsA full programme of the meeting is on our website, and includes pdfs of the presentations for download.  We learned a huge amount about hazard research in Japan and have hopefully begun a large number of research collaborations that will be important for Bristol University for many years.  Our profound thanks go to our Japanese hosts Prof. Eiichi Nakahita and Prof. Hirokazu Tatano, and to the Director of DPRI, Prof. Nakashima.  The photos here give a flavour of the wonderful time we had.

Possibly the most important theme to emerge from the workshop was that whilst probabilistic analysis of hazards (where we give the chance of an event occurring rather than a definite yes/no prediction) is now commonplace in science, there is still a major issue in educating decision makers, governments and the public in how to use such forecasts to take decisions.  Indeed the Daily Mail in the UK has recently been giving the Met Office a hard time for wanting (very sensibly) to move to a probabilistic forecast of rainfall. This shouldn’t be such a big problem, but the fact that it is tells us an awful lot.  Intrinsically people deal with probability information all the time: betting and insurance, for example, are both examples of probabilistic contracts that are well understood by the public. So why do we resist being told about other risks in a similar way.  My gut feeling is that it is to do with the question of responsibility. A probabilistic forecast of risk forces the decision maker (be they Ministers, civil servants or the public) to deal with uncertainty in predictions, whilst insistence on a deterministic forecast puts the responsibility for this onto the scientists who can then be blamed if things go wrong.

Popular posts from this blog

Converting probabilities between time-intervals

This is the first in an irregular sequence of snippets about some of the slightly more technical aspects of uncertainty and risk assessment.  If you have a slightly more technical question, then please email me and I will try to answer it with a snippet. Suppose that an event has a probability of 0.015 (or 1.5%) of happening at least once in the next five years. Then the probability of the event happening at least once in the next year is 0.015 / 5 = 0.003 (or 0.3%), and the probability of it happening at least once in the next 20 years is 0.015 * 4 = 0.06 (or 6%). Here is the rule for scaling probabilities to different time intervals: if both probabilities (the original one and the new one) are no larger than 0.1 (or 10%), then simply multiply the original probability by the ratio of the new time-interval to the original time-interval, to find the new probability. This rule is an approximation which breaks down if either of the probabilities is greater than 0.1. For example

1-in-200 year events

You often read or hear references to the ‘1-in-200 year event’, or ‘200-year event’, or ‘event with a return period of 200 years’. Other popular horizons are 1-in-30 years and 1-in-10,000 years. This term applies to hazards which can occur over a range of magnitudes, like volcanic eruptions, earthquakes, tsunamis, space weather, and various hydro-meteorological hazards like floods, storms, hot or cold spells, and droughts. ‘1-in-200 years’ refers to a particular magnitude. In floods this might be represented as a contour on a map, showing an area that is inundated. If this contour is labelled as ‘1-in-200 years’ this means that the current rate of floods at least as large as this is 1/200 /yr, or 0.005 /yr. So if your house is inside the contour, there is currently a 0.005 (0.5%) chance of being flooded in the next year, and a 0.025 (2.5%) chance of being flooded in the next five years. The general definition is this: ‘1-in-200 year magnitude is x’ = ‘the current rate for eve

Coconuts and climate change

Before pursuing an MSc in Climate Change Science and Policy at the University of Bristol, I completed my undergraduate studies in Environmental Science at the University of Colombo, Sri Lanka. During my final year I carried out a research project that explored the impact of extreme weather events on coconut productivity across the three climatic zones of Sri Lanka. A few months ago, I managed to get a paper published and I thought it would be a good idea to share my findings on this platform. Climate change and crop productivity  There has been a growing concern about the impact of extreme weather events on crop production across the globe, Sri Lanka being no exception. Coconut is becoming a rare commodity in the country, due to several reasons including the changing climate. The price hike in coconuts over the last few years is a good indication of how climate change is affecting coconut productivity across the country. Most coconut trees are no longer bearing fruits and thos