INSURANCE and WATER: Estimating uncertainty in future flood risk analysis for insurance and re-insurance markets

保险和水：估计保险和再保险市场未来洪水风险分析的不确定性

• 批准号: NE/H017836/1
• 负责人: Paul Bates
• 金额: $ 8.64万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH017836%2F1
• 关键词: INSURANCE; WATER; Estimating; uncertainty; future

A forward view of likely future changes in flood risk resulting from predicted climate change is critically required by the insurance industry in order to take long term strategic decisions on risk management, allow future financial planning and improve business resilience to any increased frequency of large events. Typically, flood insurance losses are regarded as attritional events that impact on profit margins but which are unlikely to result in company bankruptcies and an inability to meet both insurance and reinsurance policy obligations. However, changes to the frequency of large flood events and event clustering may alter this situation and increase the potential for severe disruption to insurance markets. The re-prioritization of insurance portfolios and negotiation of new re-insurance contracts to deal with such threats must proceed on the basis of available evidence and requires a long term view as such changes take considerable time to implement and may incur substantial additional costs. A robust scientific understanding of the impact of climate change on future flood risk is, however, currently lacking because the impact of uncertainty in Global and Regional Climate Model output variables (rainfall, temperature and evapotranspiration) has yet to be cascaded all the way through to flood inundation models. In particular, such analyses have yet to be attempted for urban settings where the majority of at-risk assets are located because of the computational cost of the fine scale hydraulic simulations that are here required. Moreover. whilst methods have been developed to assign likelihoods to uncertain RCM and GCM outputs (e.g. Rougier, 2007) these require extension to cascade likelihoods through to hydraulic predictions such that likelihood-weighted inundation maps can be produced. For this reason University of Bristol and King's College London were approached by Willis Re, a leading global re-insurance firm based in London, to consider ways to research this issue. Such work is ideally suited to CASE studentship funding as it builds on existing research which has confirmed feasibility. It also requires both the substantial period of focussed individual research and close collaboration between industry and academics that a CASE award facilitates. The next steps for such research are therefore to: 1. Undertake further analysis of GCM and RCM output to better understand the strengths and limitations of such models for simulating current and future extreme rainfalls and determine best possible methods to assign likelihoods to these. 2. To test whether bias correction can reduce systematic spatio-temporal errors in climate model predicted rainfalls and increase confidence in these outputs. 3. To cascade uncertain rainfall estimates through catchment hydrology and hydraulic models for urban settings and at regional scales, and test the use of likelihood weighted methods to produce uncertain future flood risk maps. 4. To work closely with the insurance industry to ensure that such complex analyses are communicated in the most appropriate form and analyse the potential impact of such maps on long term strategic decision making. The outputs from the studentship will be: (1) new tools for assessing future flood risk with applicability to a wide range of problems including, primarily, the insurance sector; (2) new scientific understanding of likely future changes in flood risk and their associated uncertainty; and (3) a focus on visualization and communication of complex model outputs to determine how the developed understanding can be most effectively transferred to industry users. This represents high quality science in its own right, with potentially significant impact for UK industry. The student will be supervised by Professor Paul Bates and Dr. Jim Freer of University of Bristol, Dr. Hannah Cloke of King's College London and Matt Foote of Willis Re.

保险业迫切需要对预测的气候变化导致的洪水风险未来可能发生的变化进行前瞻性分析，以便就风险管理做出长期战略决策，制定未来财务规划，并提高业务对大型事件频率增加的适应能力。通常，洪水保险损失被视为损耗事件，影响利润率，但不太可能导致公司破产和无法履行保险和再保险政策义务。然而，大洪水事件发生频率和事件聚集的变化可能会改变这种情况，并增加对保险市场造成严重破坏的可能性。重新确定保险组合的优先次序和谈判新的再保险合同以应对这些威胁，必须根据现有证据进行，并需要有长远的眼光，因为这些变化需要相当长的时间才能实施，并可能产生大量额外费用。然而，目前缺乏对气候变化对未来洪水风险影响的科学认识，因为全球和区域气候模型输出变量（降雨量、温度和蒸散量）的不确定性影响尚未一路级联到洪水淹没模型。特别是，这种分析尚未尝试为城市环境中的大多数风险资产所在的，因为这里所需的精细规模的水力模拟的计算成本。而且。虽然已经开发了将似然分配给不确定的RCM和GCM输出的方法（例如Rougier，2007），但这些方法需要扩展到级联似然，直到水力预测，以便可以生成似然加权淹没图。出于这个原因，总部设在伦敦的全球领先再保险公司Willis Re与布里斯托大学和伦敦国王学院进行了接触，以考虑研究这一问题的方法。这项工作非常适合CASE助学金资助，因为它建立在已确认可行性的现有研究基础上。它还需要大量的集中个人研究和行业与学术界之间的密切合作，这是一个案例奖的促进。因此，这种研究的下一步是：1。对GCM和RCM输出进行进一步分析，以更好地了解这些模型在模拟当前和未来极端灾害方面的优势和局限性，并确定为这些灾害分配可能性的最佳方法。2.测试偏差校正是否可以减少气候模式预测结果中的系统时空误差，并增加这些输出的置信度。3.通过流域水文学和城市环境和区域尺度的水力模型级联不确定的降雨量估计，并测试使用似然加权方法来制作不确定的未来洪水风险图。4.与保险业密切合作，确保以最适当的形式传达此类复杂的分析，并分析此类地图对长期战略决策的潜在影响。该奖学金的成果将是：（1）评估未来洪水风险的新工具，适用于广泛的问题，主要包括保险部门;（2）对洪水风险及其相关不确定性的未来可能变化的新科学认识;以及（3）重点关注复杂模型输出的可视化和交流，以确定如何最有效地将已开发的理解转移到行业用户.这本身就代表着高质量的科学，对英国工业有着潜在的重大影响。该学生将由布里斯托大学的保罗·贝茨教授和吉姆·弗里尔博士、伦敦国王学院的汉娜·克洛克博士和威利斯再保险公司的马特·富特监督。