Probability, Uncertainty and Risk in the Natural Environment

自然环境中的概率、不确定性和风险

• 批准号: NE/J016578/1
• 负责人: Neil McIntyre
• 金额: $ 26.48万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ016578%2F1
• 关键词: Probability; Uncertainty; Risk; Natural; Environment

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.

自然灾害给社会和全球经济带来严重问题。英国最近的例子包括2009年和2010年的寒冷冬季以及格里姆斯沃顿火山和艾雅法拉火山的喷发，从而扰乱了航空旅行。从更远的地方来看，2011年上半年，澳大利亚(洪水)、新西兰(地震)、日本(地震和海啸)和美国(飓风)都发生了重大灾难。如果科学家能提供准确的信息来帮助管理这些事件，那就太好了。然而，这是不现实的，原因有几个：数据通常不完整(例如，并非所有要求的地点都有)，而且测量有误差；预测是使用充其量只能接近现实的计算机模型做出的；我们对某些现象的了解因缺乏经验而受到限制(例如，历史海啸记录相对有限)。因此，自然灾害科学家必须承认他们提供的信息中的不确定性，并必须将这种不确定性有效地传达给科学的用户。然而，这两项任务都不容易。此外，科学家并不总是理解用户想要什么和需要什么；用户本身往往对不确定性感到不舒服。尽管存在这些问题，但现代统计方法仍可用于利用概率论来处理复杂系统中的不确定性。与此同时，社会科学研究人员感兴趣的是了解人们对不确定性的反应和理解。通过将这两项发展结合在一起，并与来自几个危险领域的科学家以及各种用户联系起来，我们的目标是(A)展示一个处理各种危险的不确定性的通用框架；以及(B)开发用于交流不确定信息的改进工具。这里考虑的通用框架有三个核心组件。首先是如何处理由于我们不完美的模型和对任何复杂系统的不完美理解而产生的不确定性。第二是综合各种来源的信息，这些信息都被认为是相关的：这在事件管理情况下尤其重要，因为决策者必须根据可能明显相互矛盾的多条证据迅速采取行动。第三是如何处理被认为“无法量化”或太难处理的不确定性：保险业的一个例子是，在已知有可能发生但没有历史损失数据的事件(例如拉帕尔马的坎布雷·维埃哈火山坍塌引发的大西洋海啸)，需要拨出多少钱来支付成本。将使用五个案例研究来说明该框架：(1)英国的洪水风险管理；(2)英国(与核电行业有关)和意大利的地震危险；(3)海啸危险和风险评估，包括改进实时警报系统的方法的发展；(4)对未来几天天气预报的解释(重点是风速和寒冷天气)；(5)火山灰扩散，同样包括实时警报系统。拟议研究的最后一个关键组成部分涉及传播和利用从前三个组成部分得出的不确定信息。我们将与工业伙伴合作，展示如何通过风险评估(重点关注灾害的后果，例如经济损失或对基础设施的破坏)，将对危险本身不确定性的更好理解转化为风险评估。我们还将开展研究，更好地了解人们如何感知和使用风险信息。这些成果将用于为开发向专业和非专业用户传达自然灾害风险信息的新方法提供信息；还将(与PURE网络合作)编写一本自然灾害风险信息通报手册。

项目成果

Modelling socio-hydrological systems: a review of concepts, approaches and applications

• DOI: 10.5194/hessd-12-8761-2015
• 发表时间: 2015-09
• 期刊: Hydrology and Earth System Sciences Discussions
• 作者: P. Blair;W. Buytaert

Web technologies for environmental Big Data

• DOI: 10.1016/j.envsoft.2014.10.007
• 发表时间: 2015
• 期刊: Environ. Model. Softw.
• 作者: C. Vitolo;Yehia El-khatib;D. Reusser;C. Macleod;W. Buytaert

Combining information from multiple flood projections in a hierarchical Bayesian framework

在分层贝叶斯框架中组合来自多个洪水预测的信息

• DOI: 10.1002/2015wr018143
• 发表时间: 2016
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Le Vine N

rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive

rnrfa：用于从英国国家河流流量档案馆检索、过滤和可视化数据的 R 包

• DOI: 10.32614/rj-2016-036
• 发表时间: 2016
• 期刊: The R Journal
• 作者: Vitolo C

Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

考虑区域化签名中的依赖性以进行未计量流域的预测

• DOI: 10.5194/hess-20-887-2016
• 发表时间: 2016
• 期刊: Hydrology and Earth System Sciences
• 影响因子: 6.3
• 作者: Almeida S