Probability, uncertainty and risk in the natural environment

Lead Research Organisation: University of Oxford
Department Name: Smith School of Enterprise and the Env

Abstract

Natural hazards pose serious problems to society and to the global economy. Recent examples in the UK include the cold winters of 2009 and 2010 and the eruptions of the Grimsvotn and Eyjafjallajökull volcanoes with the consequent disruption to air travel. Moving further afield, the first half of 2011 saw major disasters in Australia (flood), New Zealand (earthquake), Japan (earthquake and tsunami) and the US (hurricanes).

It would be nice if scientists could provide precise information to help with the management of such events. This is unrealistic, however, for several reasons: data are usually incomplete (e.g. not available at all required locations) and measured with error; predictions are made using computer models that can at best approximate reality; and our understanding of some phenomena is limited by lack of experience (for example, the historical tsunami record is relatively limited). Therefore, natural hazard scientists must acknowledge the uncertainty in the information they provide, and must communicate this uncertainty effectively to users of the science. However, neither of these tasks is easy. Moreover, scientists do not always understand what users want and need; and users themselves often are uncomfortable with uncertainty.

Despite these problems, modern statistical methods are available for handling uncertainty in complex systems using probability theory. In parallel, social science researchers are interested in understanding how people react to and understand uncertainty. By bringing these two developments together, and linking with scientists from several hazard areas along with a variety of users, we aim (a) to demonstrate a generic framework for handling uncertainty across hazards; and (b) to develop improved tools for communicating uncertain information.

The generic framework considered here has three core components. The first is the treatment of uncertainties arising from our imperfect models and imperfect understanding of any complex system. The second is the combination of information from various sources that are all judged to be relevant: this is particularly important in event management situations where decision-makers must take rapid action based on multiple strands of evidence that might be apparently contradictory. The third is the treatment of uncertainties that are deemed to be "unquantifiable" or too hard to handle:an example from the insurance industry involves how much money to set aside to cover the cost of an event that is known to be possible but for which no historical loss data are available (such as an Atlantic tsunami caused by the collapse of the Cumbre Vieja volcano in La Palma). Five case studies will be used to illustrate the framework: (1) flood risk management in the UK; (2) earthquake hazard in the UK (relevant to the nuclear power industry) and in Italy; (3) tsunami hazard and risk assessment, including the development of methods to improve real-time warning systems; (4) the interpretation of days-ahead weather forecasts (focusing on wind speeds and cold weather); (5) volcanic ash dispersal, again including real-time warning systems.

A final, and critical, component of the proposed research relates to the communication and use of the uncertainty information derived from the three previous components. Working with industrial partners, we will demonstrate how an improved understanding of uncertainty in the hazard itself can be translated through into risk assessments (which focus on the consequence of the hazard, for example the economic loss or damage to infrastructure). We will also carry out research to understand better how people perceive and use risk information. The results will be used to inform the development of novel methods for communicating natural hazard risk information to specialist and non-specialist users; and also (in collaboration with the PURE Network) to produce a handbook of risk communication for natural hazards.

Planned Impact

The proposed research will benefit all individuals and organisations with an interest in understanding, responding to and planning for natural hazards and their consequences. Excluding academic beneficiaries, these include:

- Business and industry, in particular the financial (notably insurance), energy, aviation and built environment sectors;
- Organisations such as DEFRA, the Environment Agency and SEPA, with responsibility for natural hazard risk management in the UK and elsewhere;
- Agencies responsible for the provision of risk and hazard management information, such as the UK Meteorological Office (UKMO);
- The general public, including schoolchildren.

For these non-academic beneficiaries, the primary impact of the research will arise from improved communication between the science and user communities, so that the science becomes more relevant to the users and the users are better able to understand the science. The requirements here work both ways. Our engagement with users and industrial partners, and research on communication under Work Package D, aims to foster better understanding of user needs by scientists. Simultaneously however, we will help users to develop a better understanding of what science can and cannot be expected to provide, and to make effective use of uncertain information in decision-making. Apart from the direct engagement with our industrial partners, much of this work will be carried out via dissemination, engagement and training events organised in collaboration with the PURE Network.

Further details of the research impact can be found in our "Pathways to Impact" statement.
 
Description The key findings are the following:
(1) Eight models for predicting high wind speed over Europe were compared. Performance varies substantially between models and for different wind speeds. ECMWF model performs best overall.
(2) Ensemble prediction systems are generally poorly calibrated, particularly over land, and the mis-calibration is worst at short lead times and improves at longer lead times. Overall, the ECMWF model is the most reliable and the CPTEC model the least reliable.
(3) The European Extreme Windstorm catalogue was used to construct a catastrophe model to assess the risk of financial losses (economic, turbine damage and reduction of generation) and excellence probability curves based on the actual locations of European off-shore wind farms.
(4) A website portal, climate frontier.com, was developed using a business intelligence dashboard to provide an easy way to access, visualise, analyse and download historical climate data for the UK. This portal allows users to find weather extremes, identify trends and compare recent observations with historical distributions and has received positive feedback from private sector companies engaged in delivering climate change solutions.
(5) For index insurance products, satellite imagery was found to be superior to ground weather stations which suffers from data quality, missing data, sensor failure and geographical distance. Soil moisture, rainfall and root moisture were particularly important for forecasting productivity. There is a need for independent evaluation of index insurance products to quantify basis risk and avoid mis-selling of these complex products.
Exploitation Route From conversations with the Met Office, I hope that climate frontier.com could be scaled up to include weather and environmental observations from other regions of the world. Discussions with the Rwanda Meteo Agency suggest that this might be appropriate for their datasets and the government is working on an open data framework (data revolution policy). We have already identified a use for the weather data in understanding agricultural productivity and also hope to use it to analyse the incidents of malaria via the sales of drugs in a large network of pharmacies.
Sectors Agriculture

Food and Drink

Communities and Social Services/Policy

Energy

Environment

Financial Services

and Management Consultancy

Healthcare

URL http://www.mcsharry.net
 
Description I have been studying how weather information affects different economic sectors such as energy environment and agriculture. The use of probabilistic weather forecasting has been useful for improving our understanding of wind power generation and this has been communicated through a consortium supported by the EU. In addition, my work on weather and agriculture has identified the benefits of using satellite imagery in addition to ground weather stations in order to reduce basis risk for index insurance products. Finally the work on classifying rainfall patterns for forecasting should lead to improvements in the operation of an urban water system in Copenhagen.
First Year Of Impact 2016
Sector Agriculture, Food and Drink,Energy,Environment
Impact Types Economic

Policy & public services

 
Description Feasibility study of index insurance
Amount £17,000 (GBP)
Organisation International Growth Centre (IGC) 
Sector Charity/Non Profit
Country United Kingdom
Start 01/2016 
End 04/2016
 
Description Index insurance 
Organisation Government of the UK
Department Department for International Development (DfID)
Country United Kingdom 
Sector Public 
PI Contribution In collaboration with DFID, I have been helping to assess the viability of index insurance products. My role has been to share insights from assessment of data quality, data availability and relevance of multiple satellite products.
Collaborator Contribution DFID has contributed time for meetings, shared documents and helped to organise meetings and make introductions to relevant people working in the private and public sectors.
Impact Outputs are in the form of recommendations supported by quantitative analysis with regard to index insurance. These recommendations have been provided to DFID and two challenge funds created by DFID (Access to Finance and the Environment and Climate Change fund).
Start Year 2015
 
Title Climate Frontier 
Description Many governments are adopting open data programs with varying levels of success. Despite the substantial resources required for open data, there is an expectation that making data accessible will drive innovation and strengthen the economy. The Open Data Institute (ODI) and Nesta, the UK's innovation charity, claim that open data could strengthen the UK economy with a five to ten fold return on every pound invested. The impetus for this project was the realisation there remains a significant gap between the availability of raw data from UK government websites and the ability to use this data in a research or business context. Substantial resources are currently being wasted through the need for individuals to carry out numerous repetitive manual tasks in order to obtain data in an appropriate format. By providing suitable data science techniques, we show that it is possible to bring raw data to life by offering summary statistics, extremes, distributions and trends. We also offer a user-friendly download portal and application programming interface (API) to facilitate developers. The Met Office is the national weather service in the UK. Weather, climate and environmental data have been identified as a key priority for the UK's open data program. DataPoint is a service to access freely available Met Office data feeds in a format that is suitable for application developers. We focus on long-term historical weather observations at a monthly time scale for 33 stations in the UK. The Climate Historic database provides observations on maximum and minimum temperature, rainfall, sunlight and air frost. This project demonstrates how to bridge the gap between raw data and usable data in a refined and structured format. We welcome feedback on how this process could be improved and scaled up to include other datasets. Input from practitioners that need to use weather and climate data would be particularly useful. 
Type Of Technology Webtool/Application 
Year Produced 2015 
Impact Private sector practitioners have used the website to access climate data The media has used it to assess trends in rainfall data in relation to flooding in the NW of the UK Scientists Divided Over UK Flooding | The Global Warming: www.thegwpf.com/scientists-divided-over-uk-flooding/ 
URL http://www.climatefrontier.com
 
Description Forecasting changes in the rainy seasons 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Policymakers/politicians
Results and Impact Anecdotal evidence suggests that climate change is already affecting the start dates and duration of the rainy seasons in East Africa. This makes it difficult for farmers to decide when to plant seeds as past behaviour is no longer an accurate guide. I am working with daily weather data to date the seasons and quantitatively understand the climate change patterns and to identify the best means of providing forecasts for helping to improve decisions. This work is in collaboration with the International Potato Center.
Year(s) Of Engagement Activity 2016
 
Description Index insurance for tea 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact Tea in Rwanda currently is not protected by insurance. I have been working with a number of managers of tea estates in Rwanda to understand the drivers of tea productivity at a daily time scale. This has involved modelling the relationship between weather measured at ground stations (public and private) and a number of satellite products. The relationship between weather and tea productivity is important for understanding the likely impacts of climate change (collaboration with DFID) and for deciding on the best portfolio of strategies to mitigate against adverse effects (e.g. insurance).
Year(s) Of Engagement Activity 2016
 
Description Objective classification of rainfall for online operation of urban water systems 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This study evaluated methods for automated classification of rain events into groups of "high" and "low" spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities.
A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of "high" variability. This work was carried out in collaboration with the Danish Water Pollution Committee (SVK) using rain gauge observations from 34 gauges in the Copenhagen area.
Year(s) Of Engagement Activity 2016