PYRAMID: Platform for dYnamic, hyper-resolution, near-real time flood Risk AssessMent Integrating repurposed and novel Data sources

PYRAMID：动态、超分辨率、近实时洪水风险评估平台，集成重新利用和新颖的数据源

• 批准号: NE/V00378X/1
• 负责人: Hayley Jane Fowler
• 金额: $ 100.94万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV00378X%2F1
• 关键词: PYRAMID; Platform; dYnamic; hyper; resolution

Flooding has been identified by the government as the number one priority and risk to the UK. Flooding already causes millions of pounds worth of damage to people's homes, infrastructure and the economy every year, and is projected to become even more severe under climate change. Being able to plan for, respond to and manage flooding effectively is therefore essential.We are lucky to have a tradition of flood management in the UK led by the Environment Agency. Operational flood models use meteorological data combined with elevation data to show us where flooding will occur. These models produce flood risk maps for planning and forecasting purposes and have helped us design flood defences for many areas.However, flooding is not only dependent on the topography of an area. There are many other factors at play that evolve over time: culverts can get blocked, flood gates are left open and flood walls can fall into disrepair. This can dramatically alter the extent and depth of a flood. Not only that, but our exposure to flood risk changes too. Far less disruption occurs from a flood overnight than during rush hour traffic. A prime example of this is the flooding of Boscastle in 2004. During the event, 116 cars parked in a carpark were washed downstream, blocking a bridge, causing water to back up and flood unexpected areas. If the rain had fallen in the evening, the cars would not have been in the carpark and the impact of the flood would have been smaller. Could we have predicted this? Can we reduce the impact of flooding for similar future events? We think that with the right data and tools, we can.We will build a tool that will change how we respond to flood risks as they evolve. The tool will allow flood risk managers to deploy just-in-time maintenance and alleviation measures, such as clearing critical blocked culverts or setting up mobile flood defences. To achieve this, the tool will incorporate brand new types of data and cutting edge flood models into an easy-to-use online platform that allows users to visualise evolving flood risks. The platform (called PYRAMID) will be developed in conjunction with the Environment Agency, local authorities and community groups to ensure that it delivers relevant information for critical decision-making in near-real time. The platform will have toolkits to make it easy for communities to incorporate their data, providing essential local information.The new data driving this modelling will be key. The data that we need are available but sit fragmented across a range of organisations in difficult-to-use formats. We will use artificial intelligence to extract this useful information from hidden datasets, such as old reports, flood asset registers and various types of satellite imagery. In addition, we want to incorporate brand new information from novel sensors that are being deployed as part of Newcastle University's Urban Observatory. These sensors monitor things like soil moisture and rainfall at very high resolutions, as well as other factors like traffic and congestion. We can also monitor the condition of specific factors affecting flood risk, such as whether particular culverts are blocked or whether certain flood walls are in poor condition. These factors can be monitored by looking at a combination of satellite remote sensing and sensors deployed on lorries and other vehicles. We will also harness data collected communities and citizens.All of this information will be put into our flood models. We have a hyper-resolution hydrodynamic flood model that can accurately simulate the movement of debris in flood flows at a centimetre scale. This model will work in conjunction with a broader catchment model, which will provide information on the hydrological conditions in the wider area. The platform will be trialled in Newcastle to take advantage of existing government investments in the Urban Observatory and a legacy of flood research conducted here.

洪水已被政府确定为英国的头号优先事项和风险。洪水每年已经给人们的家园、基础设施和经济造成了数百万英镑的损失，预计在气候变化的情况下会变得更加严重。因此，能够有效地规划、应对和管理洪水是至关重要的。我们很幸运，英国有一个由环境署领导的洪水管理传统。业务洪水模型使用气象数据结合高程数据，向我们展示洪水将发生的地方。这些模型可绘制洪水风险图，以供规划和预测之用，并帮助我们为许多地区设计防洪设施。然而，洪水并不仅仅取决于一个地区的地形。随着时间的推移，还有许多其他因素在起作用：涵洞可能被堵塞，防洪闸可能被打开，防洪墙可能失修。这可以极大地改变洪水的范围和深度。不仅如此，我们面临的洪水风险也会发生变化。夜间洪水造成的破坏远小于交通高峰期。这方面的一个主要例子是2004年博斯卡斯尔的洪水。在活动期间，停在一个停车场的116辆汽车被冲到下游，堵塞了一座桥梁，导致水倒流，淹没了意想不到的地区。如果晚上下雨，汽车就不会停在停车场，洪水的影响也会小一些。我们能预测到这一点吗？我们能否减少洪水对未来类似事件的影响？我们认为，有了正确的数据和工具，我们可以做到。我们将建立一个工具，随着洪水风险的演变，它将改变我们应对洪水风险的方式。该工具将使洪水风险管理人员能够及时部署维护和缓解措施，例如清理关键的堵塞涵洞或建立移动的防洪设施。为了实现这一目标，该工具将把全新类型的数据和最先进的洪水模型整合到一个易于使用的在线平台中，使用户能够可视化不断变化的洪水风险。该平台（称为PYRAMID）将与环境局、地方当局和社区团体共同开发，以确保它能够近实时地为关键决策提供相关信息。该平台将提供工具包，使社区能够轻松整合其数据，提供必要的本地信息。驱动这种建模的新数据将是关键。我们需要的数据是可用的，但以难以使用的格式分散在一系列组织中。我们将使用人工智能从隐藏的数据集中提取这些有用的信息，例如旧报告，洪水资产登记册和各种类型的卫星图像。此外，我们希望将来自新型传感器的全新信息纳入纽卡斯尔大学城市观测站。这些传感器以非常高的分辨率监测土壤湿度和降雨量等情况，以及交通和拥堵等其他因素。我们还可以监控影响洪水风险的特定因素的状况，例如特定涵洞是否堵塞或某些防洪墙是否状况不良。这些因素可以通过观察卫星遥感和部署在卡车和其他车辆上的传感器的组合来监测。我们还将利用收集到的社区和公民的数据，所有这些信息都将被纳入我们的洪水模型。我们有一个高分辨率的流体动力学洪水模型，可以精确地模拟厘米级洪水中碎片的运动。该模型将与一个更广泛的集水模型一起工作，后者将提供更广泛地区的水文条件信息。该平台将在纽卡斯尔进行试验，以利用政府对城市观测站的现有投资和在这里进行的洪水研究的遗产。

项目成果

Rapidly intensifying extreme weather events in a warming world: how important are large-scale dynamics in generating extreme floods?

在变暖的世界中迅速加剧的极端天气事件：大规模动态对于产生极端洪水有多重要？

• DOI: 10.5194/egusphere-egu24-22472
• 发表时间: 2024
• 作者: Fowler H

A second-order attention network for glacial lake segmentation from remotely sensed imagery

• DOI: 10.1016/j.isprsjprs.2022.05.007
• 发表时间: 2022-07
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: Shidong Wang;M. Peppa;W. Xiao;S. B. Maharjan;S. Joshi;J. Mills

Quality control of a global hourly rainfall dataset

• DOI: 10.1016/j.envsoft.2021.105169
• 发表时间: 2021-08-27
• 期刊: ENVIRONMENTAL MODELLING & SOFTWARE
• 影响因子: 4.9
• 作者: Lewis, Elizabeth;Pritchard, David;Fowler, Hayley J.
• 通讯作者: Fowler, Hayley J.

World Weather Attribution: Rapid attribution of heavy rainfall events leading to the severe flooding in Western Europe during July 2021.

世界天气归因：对导致 2021 年 7 月西欧严重洪水的强降雨事件的快速归因。

• 发表时间: 2021
• 作者: Kreienkamp F

Intensification of short-duration rainfall extremes and implications for flood risk: current state of the art and future directions.

• DOI: 10.1098/rsta.2019.0541
• 发表时间: 2021-04-19
• 期刊: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
• 作者: Fowler HJ;Wasko C;Prein AF
• 通讯作者: Prein AF