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Smart forecasting: joined-up flood forecasting (FF) infrastructure with uncertainties

智能预报：具有不确定性的联合洪水预报（FF）基础设施

基本信息

批准号：
EP/R007349/1
负责人：
Georges Kesserwani
金额：
$ 139.11万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR007349%2F1
关键词：
Smart forecasting joined up flood

项目摘要

Reliable and comprehensive flood forecasting is crucial to ensure resilient cities and sustainable socio-economic development in a future faced with an unprecedented increase in atmospheric temperature and intensified precipitation. Floodwaters from the areas surrounding a city can heavily affect flood cycle behaviour across urban areas, introducing uncertainties into the forecast that are often non-negligible. However, currently the extent to which we can predict flood hazards is limited, and existing methods cannot for example deal with inter-regional dependencies (e.g. as was seen when floods affected nine different countries across Central and Eastern Europe). Presently in the UK approx. 25% of yearly flood insurance claims are from areas outside the zones forecast to be at flood risk, and annual flood damage costs are already high (approx. £1.5 billion). Also more than 20,000 houses per year continue to be built on floodplains.The need to transform flood forecasting for a range of applications and scales has already been recognised by various parties. The UK Climate Change Risk Assessment 2017 Evidence Report prioritises flooding as the greatest direct climate change related threat for UK cities now and in the future, and urges urgent action to be taken, including the development of new solutions over the next 5 years. The hydraulic software industry and consultancy firms have expressed a desire for more reliable and sophisticated flood forecasting approaches, which can also reduce the manual labour required. In addition, mathematics and engineering research communities are still searching for forecasting models that are joined-up, reliable and efficient, as well as versatile and adaptable. To address this need, 'Multi-Wavelets' technology will be employed in this fellowship with a view to transforming flood forecasting routines from a disparate set of activities into a unified automatic framework. The applicant's vision is to exploit the innate capability of Multi-Wavelets technology to reformulate flood forecasting methods by providing a smart modelling foundation for the delivery of timely and accurate flood maps, alongside statistically quantified uncertainties. This research presents a unique opportunity for the applicant, UK academia and UK industry, to establish a world leading capability in a nascent field while addressing Living With Environmental Change (LWEC) priorities for improved forecasting of environmental change. The fellowship research will stimulate the creation of new software infrastructure capable of significantly improving our flood forecasting ability across length scales and under multiple uncertainties, helping us to better design infrastructure against flood risk and to plan for the consequences.

在未来面临前所未有的气温升高和降水加剧的情况下，可靠、全面的洪水预报对于确保城市的韧性和可持续的社会经济发展至关重要。来自城市周边地区的洪水会严重影响整个城市地区的洪水周期行为，给预测带来往往不可忽视的不确定性。然而，目前我们预测洪水灾害的程度有限，并且现有方法无法处理区域间依赖性（例如洪水影响中欧和东欧九个不同国家时所见）。目前在英国大约。每年 25% 的洪水保险索赔来自预测存在洪水风险的区域以外的地区，每年的洪水损失成本已经很高（约 15 亿英镑）。此外，每年还有超过 20,000 所房屋继续在洪泛区上建造。各方已经认识到需要改变各种应用和规模的洪水预报。《2017 年英国气候变化风险评估证据报告》将洪水列为英国城市现在和未来与气候变化相关的最大直接威胁，并敦促采取紧急行动，包括在未来 5 年内制定新的解决方案。水利软件行业和咨询公司表达了对更可靠和更复杂的洪水预报方法的渴望，这也可以减少所需的体力劳动。此外，数学和工程研究界仍在寻找联合、可靠、高效、通用、适应性强的预测模型。为了满足这一需求，本研究金将采用“多小波”技术，以期将洪水预报例程从一组不同的活动转变为统一的自动框架。申请人的愿景是利用多小波技术的固有能力，通过提供智能建模基础来重新制定洪水预报方法，以提供及时、准确的洪水地图以及统计量化的不确定性。这项研究为申请人、英国学术界和英国工业界提供了一个独特的机会，可以在新兴领域建立世界领先的能力，同时解决与环境变化共存（LWEC）的优先事项，以改进对环境变化的预测。该奖学金研究将刺激新的软件基础设施的创建，能够显着提高我们在多个长度尺度和多种不确定性下的洪水预报能力，帮助我们更好地设计基础设施以应对洪水风险并为后果做好规划。