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.

极端的降雨和洪水会导致所有气象事件中对社会的一些最大影响，并且预计气候变化会严重加剧这些影响。在英国，洪水防御计划需要了解在100年回报水平上及以后的事件的严重性（即预计平均每100年一次超过一次的幅度）。现有的预测是不充分的，依赖于对粗分辨率气候模型输出和简单统计洪水预测方法的小样本的降雨变化的模拟。这些方法不会捕获重要的效果，例如在温暖的气候下，降雨变得更加集中在较短的爆发中。洪水预测数据集是不透明的，导致了由于洪水观察到的三倍而导致的财务损失的预测，几乎没有信心量化极端阈值和项目气候变化的影响。这些问题可以通过基于基本法律的高分辨率降雨和洪水的基于物理的建模来解决。这结合了全球气候模型，这些模型模拟了大规模的天气状态，局部规模的天气模型，以预测各个河流集水区的详细降水，水文模型，以预测河流的水流和洪水模型，以确定洪水范围和损失。由于对这些罕见事件进行了足够的计算费用，因此以前无法使用这种方法研究极端的降雨和洪水事件。我们最近的进步已经克服了这一点。我们将首次在英国首次以〜100 - 1000年的回报水平量化极端高分辨率降雨，流量和洪水风险，以解决关键的与政策相关的事件。我们将为目前的2080年和与政策相关的全球变暖水平做到这一点。我们还将首次展示跨不同模型的预测的鲁棒性。我们将使用项目团队的以下开创性进步来做到这一点： - 以“ UKCP本地”英国气候模型生产的地方规模（2.2公里）的降水预测，该模型可以捕获强烈的对流降雨系统，在洪水风险中起着至关重要的作用。这些最近在降雨模拟的质量方面产生了巨大的进步。-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。这使得根据现有的多千年的粗分辨率气候模型运行，可以生产大量预测，以低成本研究极端。与以前的统计方法不同，该方法可以产生具有逼真的空间结构的降雨预测，这是逼真的洪水模型所必需的。-国家规模的水文和洪水模型以约20m的分辨率结合在一起，再加上暴露和脆弱性数据，这可以将这些降雨量预测转化为河流的风险和洪水的风险，从而在当地尺度上进行差异。我们将在当地的尺度上进行差异。我们将在降低范围内进行差异。水文模型首次是预期最严重的可能结果并减轻相关风险所必需的。一旦证明，我们的方法可以应用于其他各种现象和位置，从而大大增加了对局部规模的气候影响建模的机会。我们将与我们的大都会办公室，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洪水风险量化和缓解行业和政府。