SPLASH: digital approaches to predict wave hazards

SPLASH：预测海浪灾害的数字方法

• 批准号: NE/Z503423/1
• 负责人: Nieves Garcia Valiente
• 金额: $ 24.3万
• 依托单位: Plymouth University
• 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%2FZ503423%2F1
• 关键词: SPLASH; digital; approaches; predict; wave

With sea level rise accelerating and coastal populations increasing, the requirement of accurate tools to predict natural hazards and mitigate damages to infrastructure, property and human life is ever more urgent. Our project sits in the frame of assessing impacts of changing environmental conditions, particularly extremes, on the state of the natural world, affected by both natural variability and impact of human activity. Coastal flooding is normally caused by wave overtopping that occurs when water is discharged by waves over a coastal structure such as a breakwater. There are multiple methods to forecast coastal overtopping, and most of them demonstrate a lack of precision and large dependency to local processes. Statistical analysis of Earth Observations (EO) will provide a method to assess wave fields to better understand how processes (winds, tides, coastal sheltering, swell and wind waves) interact across a coastal area to change the coastal wave hazard through, for example, (depth and current) refraction, wind shadowing and bimodal wave contributions. From March 2021-2022 monitoring of the wave overtopping at Dawlish and Penzance provided in-situ hazard alerts, indicating when overtopping starts and stops, along with a measure of the severity (WireWall data). Such observations can be used alongside national monitoring networks of waves, water levels and Earth Observations (EO) data to develop an environmental digital twin pilot, and ultimately, improve operational hazard management and increase UK resilience to natural hazards. The principal aim of this proposal is to build a deployable coastal overtopping warning tool (SPLASH) with the vision of transforming weather and climate research and services through transformative technologies. The main outcomes of the proposal will be (1) a method to analyse coastal wave fields from EO to determine regular asymmetries in hazards conditions, (2) a digital twin of wave overtopping in which machine learning has been applied to produce a warning tool using model predictions of wind, waves and water level, and (3) coastal overtopping projections to assess future changes in hazard frequency. The proposed project will use: (i) overtopping Dawlish/Penzance WireWall data (observations) to train and validate machine learning algorithms based on model predictions (wind, waves and water levels); (ii) camera images for calibration and validation; and (iii) satellite images to study variability in wave field indicators. Met Office reanalysis and analysis model data will be obtained from freely available data portals (e.g., Copernicus Marine Service) and through the UK Marine and Climate Advisory Service. Furthermore, case studies will be used as a demo and the approach will be tested in other wave hazard hotspot locations along the UK coastline where there are CCTV cameras or webcams (e.g., Chesil, Teignmouth). Reliable warning tools such as SPLASH provide essential information to those coastal communities that are currently experiencing wave related hazards. The combined application of SPLASH as a forecasting and a projection tool will facilitate coastal practitioners' decision making, helping mitigate the effects of climate change in already vulnerable locations.

随着海平面上升加速和沿海人口增加，对预测自然灾害和减轻对基础设施、财产和人类生命造成的损害的准确工具的需求变得更加迫切。我们的项目处于评估不断变化的环境条件（特别是极端环境条件）对自然世界状况的影响的框架中，自然世界状况受到自然变化和人类活动的影响。沿海洪水通常是由波浪漫溢引起的，当波浪冲过防波堤等沿海结构时，就会发生波浪漫溢。预测沿海漫溢的方法有多种，但大多数方法缺乏精确性并且对当地过程有很大依赖性。地球观测（EO）的统计分析将提供一种评估波场的方法，以更好地了解过程（风、潮汐、沿海遮挡、涌浪和风浪）如何在沿海地区相互作用，从而通过（深度和海流）折射、风影和双峰波贡献等方式改变沿海波浪危害。从 2021 年 3 月到 2022 年，对道利什和彭赞斯波浪漫溢的监测提供了现场危险警报，指示漫溢何时开始和停止，以及严重程度的衡量（WireWall 数据）。这些观测结果可以与波浪、水位和地球观测（EO）数据的国家监测网络一起使用，以开发环境数字孪生试点，并最终改善操作灾害管理并提高英国对自然灾害的抵御能力。该提案的主要目的是建立一个可部署的沿海溢水预警工具（SPLASH），其愿景是通过变革性技术改变天气和气候研究和服务。该提案的主要成果将是（1）一种分析来自 EO 的沿海波浪场的方法，以确定灾害条件中的规则不对称性；（2）波浪漫溢的数字孪生，其中应用机器学习来使用风、波浪和水位的模型预测来生成预警工具；以及（3）沿海漫溢预测，以评估灾害频率的未来变化。拟议项目将使用：(i) 超越 Dawlish/Penzance WireWall 数据（观测）来训练和验证基于模型预测（风、波浪和水位）的机器学习算法； (ii) 用于校准和验证的摄像机图像； (iii) 用于研究波场指标变化的卫星图像。气象局再分析和分析模型数据将从免费提供的数据门户（例如哥白尼海洋服务）以及英国海洋和气候咨询服务获得。此外，案例研究将用作演示，并且该方法将在英国海岸线沿线有闭路电视摄像机或网络摄像头的其他波浪灾害热点地区（例如切西尔、廷茅斯）进行测试。 SPLASH 等可靠的预警工具为目前正在遭受海浪相关危害的沿海社区提供重要信息。 SPLASH 作为预测和预测工具的综合应用将有助于沿海从业者的决策，帮助减轻气候变化对本已脆弱地区的影响。