UQ4FM: Uncertainty Quantification for Flood Modelling

UQ4FM：洪水建模的不确定性量化

• 批准号: EP/X041093/1
• 负责人: Lindsay Beevers
• 金额: $ 81.05万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX041093%2F1
• 关键词: UQ4FM; Uncertainty; Quantification; Flood; Modelling

Currently 6.4 million people, as well as critical infrastructure such as road, rail and power networks, are exposed to flood risk across the UK, and this is expected to rise to 10.8 million people and encompass further critical assets by 2080. The 2020 National Risk Register places flooding behind only pandemics and large-scale attacks as the most significant risks to the UK. Despite this, routine flood risk assessments for planning, development and adaptation purposes use deterministic methods to assess flood hazard, using hydro-dynamic process-based models which are computationally heavy (~hours to ~weeks run time). This established process fails to acknowledge, quantify and capture the cascading uncertainties inherent in the process, which manifest from a wide range of sources including climate scenarios, flow gauging, extreme value estimates and hydrological models. Under estimation of current and future flood hazard could lead to what the Government's Climate Change Risk Assessment (CCRA) terms 'lock-in' and under-engineered adaptation measures, whilst over-estimation could lead to financially non-viable schemes and inappropriate development. The flood analytics industry must urgently move towards probabilistic methods which acknowledge and quantify cascading uncertainties; but this requires yet-to-be developed algorithms which capture the critical uncertainties within the process and reduce the computational burden associated with forward Uncertainty Quantification (UQ). This project will deliver the speed up required to robustly assess flood hazard uncertainty through the development of novel and bespoke uncertainty quantification algorithms for inundation modelling; and by demonstrating their applicability to the prediction of current and future flood hazards at a range of scales, incorporating a wide range of uncertainties in the modelling chain. Success will deliver the step change needed by the flood analytics industry to embrace the necessary transition to UQ assessment, thus placing the UK at the forefront of flooding research, and future proofing climate change adaptation.

目前，英国各地有 640 万人以及公路、铁路和电力网络等关键基础设施面临洪水风险，预计到 2080 年，这一数字将增至 1,080 万人，并涵盖更多关键资产。 2020 年国家风险登记册将洪水列为英国面临的最重大风险，仅次于流行病和大规模袭击。尽管如此，出于规划、开发和适应目的的常规洪水风险评估使用确定性方法来评估洪水危害，使用计算量很大的基于水动力过程的模型（〜几小时到〜几周的运行时间）。这一既定流程未能承认、量化和捕捉流程中固有的级联不确定性，这些不确定性来自多种来源，包括气候情景、流量测量、极值估计和水文模型。对当前和未来洪水灾害的低估可能会导致政府气候变化风险评估（CCRA）所称的“锁定”和设计不足的适应措施，而高估可能会导致财务上不可行的计划和不适当的开发。洪水分析行业必须紧急转向概率方法，承认并量化级联不确定性；但这需要尚未开发的算法来捕获过程中的关键不确定性并减少与前向不确定性量化（UQ）相关的计算负担。该项目将通过开发用于洪水建模的新颖且定制的不确定性量化算法，提供稳健评估洪水灾害不确定性所需的速度；并通过展示其在各种规模下预测当前和未来洪水灾害的适用性，在建模链中纳入广泛的不确定性。成功将实现洪水分析行业所需的阶跃变革，以接受向昆士兰大学评估的必要过渡，从而使英国处于洪水研究和面向未来的气候变化适应的前沿。