Future Rainfall and Flood Extremes (FURFLEX)

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，预计气候变化将大大加剧这些影响。在英国，防洪规划要求了解100年回归水平及以上事件的严重程度(即预计平均每100年超过一次的震级)。现有的预测是不充分的，依赖于对未来降雨量变化的模拟，这些模拟来自粗分辨率气候模式输出的小样本和简单的统计洪水预测方法。这些方法没有捕捉到重要的影响，例如在气候变暖的情况下，降雨变得更加集中在较短的暴发中。洪水预测数据集不透明，导致对洪水造成的经济损失的预测是观测数据的三倍，这让人对使用它们来量化极端阈值和预测气候变化影响缺乏信心。这些问题可以通过使用基于物理的、基于基本定律的高分辨率降雨和洪水模拟来解决。它结合了模拟大规模天气状态的全球气候模型、预测个别河流集水区详细降雨量的局部天气模型、预测河流径流的水文模型和确定洪水范围和损失的洪水模型。以前用这种方法研究极端降雨和洪水事件是不可能的，因为对这些罕见事件进行足够的采样需要计算费用。我们最近的进展已经克服了这一点。我们将产生基于物理的模拟，首次量化英国各地约100-1000年回归水平的极端高分辨率降雨、径流和洪水风险，解决与政策相关的关键事件。目前到2080年，我们将在与政策相关的全球变暖水平上做到这一点。我们还将首次展示不同模型中预测的稳健性。我们将利用项目组取得的以下开创性进展来实现这一点：-用“UKCP Local”英国气候模式制作的局地尺度(2.2公里)降水预测，可以捕捉到对洪水风险具有关键作用的强对流降雨系统。这些最近在降雨模拟的质量方面产生了很大的进步。-基于尖端机器学习的这些高分辨率降雨模拟的非常有效的模拟器。这使得能够在现有数千年、粗分辨率气候模型运行的基础上，以低成本产生用于研究极端情况的大样本预测。与以前的统计方法不同，该方法可以产生具有真实空间结构的降雨预测，这是现实洪水建模所需的。-以~20米的分辨率进行全国范围的水文和洪水建模，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，从而能够在地方尺度上进行决策。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可能发生的极端变化的范围，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其他各种现象和地点，大大增加了当地规模气候影响建模的机会。我们将与英国气象局、环境局和Fathom风险咨询合作伙伴合作，利用我们的发现来改善行业和政府的洪水风险量化和缓解。

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.