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，有640万人以及关键的基础设施（例如道路，铁路和电力网络）在英国面临洪水风险，预计到2080年，这将增加到1,080万人，并涵盖了2080年的进一步关键资产。2020年的国家风险登记处将洪水泛滥，仅次于大流浪汉和大规模攻击，这是对英国的最大巨大风险。尽管如此，用于计划，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，使用基于水力的过程模型，这些模型在计算上很重（〜小时至〜周运行时间）。这个建立的过程未能承认，量化和捕获该过程中固有的层叠式不确定性，这些不确定性来自广泛的来源，包括气候场景，流量测量值，极值估计和水文模型。根据当前和未来的洪水危害的估计，可能导致政府的气候变化风险评估（CCRA）术语“锁定”和设计不足的适应措施，而过度估计可能会导致经济上不可活力的计划和不适当的发展。洪水分析行业必须紧急朝着承认和量化级联不确定性的概率方法迈进。但这需要尚未开发的算法，该算法捕获过程中的关键不确定性，并减少与前进不确定性量化（UQ）相关的计算负担。该项目将通过开发新颖和定制的不确定性定量算法来实现强大的洪水危害不确定性所需的速度；通过证明其适用于在一系列尺度上预测当前和未来洪水危害的能力，并在建模链中纳入了各种不确定性。成功将带来洪水分析行业所需的步骤变化，以采用必要的过渡到UQ评估，从而使英国处于洪水研究的最前沿以及未来证明气候变化的适应性。