Future Rainfall and Flood Extremes (FURFLEX)

未来降雨量和极端洪水 (FURFLEX)

• 批准号: NE/Z000076/1
• 负责人: Peter Watson
• 金额: $ 108.29万
• 依托单位: University of Bristol
• 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=NE%2FZ000076%2F1
• 关键词: Future; Rainfall; Flood; Extremes; FURFLEX

Extreme rainfall and flooding cause some of the largest impacts on society out of all meteorological events, and these are predicted to be strongly exacerbated by climate change. In the UK, flood defence planning requires understanding the severity of events at and beyond the 100 year return level (i.e. the magnitude that would be expected to be exceeded once per 100 years on average). Existing predictions are inadequate, relying on simulations of future rainfall changes from small samples of coarse-resolution climate model output and simple statistical flood prediction methods. These approaches do not capture important effects such as rainfall becoming more concentrated in shorter bursts in a warmer climate. The flood prediction datasets are opaque and lead to predictions of financial losses due to flooding three times those observed, giving little confidence in their use to quantify extreme thresholds and project climate change impacts. These problems can be addressed by using physically-based modelling of high-resolution rainfall and flooding, based on fundamental laws. This combines global climate models that simulate large-scale weather states, local-scale weather models to predict detailed precipitation for individual river catchments, hydrological models to predict streamflow in rivers and flood models to determine flood extent and losses. It has not previously been possible to study extreme rainfall and flood events with this approach due to the computational expense of sampling enough of these rare events. Our recent advances have overcome this.We will produce physically-based simulations that quantify extreme high-resolution rainfall, streamflow and flood risks at ~100-1000 year return levels across the UK for the first time, addressing the key policy-relevant events. We will do this for the present up to 2080 and at policy-relevant global warming levels. We will also show the robustness of projections across different models for the first time. We will do this using the following groundbreaking advances made by the project team: - local-scale (2.2km) precipitation projections produced with the "UKCP Local" UK climate model that can capture strongly convective rainfall systems, which have a critical role in flood risk. These have recently produced a great advance in the quality of rainfall simulations.- a very efficient emulator of these high-resolution rainfall simulations based on cutting edge machine learning. This enables large samples of predictions to be produced for studying extremes at low cost, based on existing multi-thousand year, coarse-resolution climate model runs. Unlike previous statistical approaches, the method can produce rainfall predictions with realistic spatial structure, as required for realistic flood modelling.- national-scale hydrological and flood modelling at ~20m resolution, combined with exposure and vulnerability data, which can translate these rainfall predictions into river flows and flood risk, enabling decision-making at the local scale.We will also use our rainfall emulator to show the range of plausible changes in extremes across different climate and hydrological models for the first time, which is necessary for anticipating the most severe possible outcomes and mitigating the associated risks. Once demonstrated, our methods could be applied to a wide range of other phenomena and locations, greatly increasing access to local-scale climate impacts modelling. We will work with our Met Office, Environment Agency and Fathom risk consultancy partners to use our findings to improve flood risk quantification and mitigation for industry and government.

在所有气象事件中，极端降雨和洪水对社会造成的影响是最大的，据预测，气候变化将严重加剧这些影响。在英国，防洪规划需要了解事件的严重程度，达到或超过100年的回报水平（即平均每100年预计会超过一次的程度）。现有的预测是不充分的，依赖于从小样本的粗分辨率气候模式输出和简单的统计洪水预测方法对未来降雨变化的模拟。这些方法没有捕捉到重要的影响，比如在气候变暖的情况下，降雨会更集中在更短的时间内。洪水预测数据集是不透明的，导致对洪水造成的经济损失的预测是观测数据的三倍，这使人们对利用这些数据量化极端阈值和预测气候变化影响缺乏信心。这些问题可以通过基于基本定律的高分辨率降雨和洪水的物理建模来解决。它结合了模拟大尺度天气状态的全球气候模型、预测单个河流集水区详细降水的局地尺度天气模型、预测河流流量的水文模型和确定洪水范围和损失的洪水模型。由于对这些罕见事件进行足够采样的计算费用，以前不可能用这种方法研究极端降雨和洪水事件。我们最近的进步已经克服了这一点。我们将产生基于物理的模拟，量化极端高分辨率的降雨，流量和洪水风险，在约100-1000年的回报水平在英国首次，解决关键的政策相关事件。我们将在目前到2080年以及与政策相关的全球变暖水平上这样做。我们还将首次展示跨不同模型的预测的稳健性。我们将利用项目团队取得的以下突破性进展：-利用“UKCP Local”英国气候模型进行局地尺度（2.2公里）降水预测，该模型可以捕捉在洪水风险中起关键作用的强对流降雨系统。这些方法最近在模拟降雨的质量方面取得了很大的进步。-基于尖端机器学习的高分辨率降雨模拟的非常有效的模拟器。这使得基于现有的几千年的粗分辨率气候模型运行，能够以低成本产生大量预测样本，用于研究极端事件。与以往的统计方法不同，该方法可以产生具有现实空间结构的降雨预测，这是现实洪水建模所需要的。- 20米分辨率的国家尺度水文和洪水模型，结合暴露和脆弱性数据，可以将这些降雨预测转化为河流流量和洪水风险，从而实现地方尺度的决策。我们还将首次使用我们的降雨模拟器来显示不同气候和水文模型中极端事件的可能变化范围，这对于预测最严重的可能结果和减轻相关风险是必要的。一旦得到证明，我们的方法可以应用于广泛的其他现象和地点，大大增加了对局地尺度气候影响建模的获取。我们将与气象局、环境署和Fathom风险咨询公司合作，利用我们的研究结果，为行业和政府改善洪水风险量化和缓解措施。