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STORMS: Strategies and Tools for Resilience of Buried Infrastructure to Meteorological Shocks

STORMS: Strategies and Tools for Resilience of Buried Infrastructure to Meteorological Shocks
风暴:埋地基础设施抵御气象冲击的策略和工具
批准号:
ST/Y004035/1
负责人:
Andrew Hughes
金额:
$5.83万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
翻译
埋在地下的基础设施系统容易受到气象冲击或极端天气事件的影响,如极端降水造成的洪水和干旱,以及极端温度。这类事件可能会导致土壤移动、热收缩和膨胀以及天坑等问题。尽管迫在眉睫,但我们的社会还没有做好充分准备,以应对这些冲击对被掩埋的基础设施的影响。我们对风险在哪里以及风险有多大的了解仍然很差,因为现有的风险评估工具没有全面考虑洪水和地下湿度/温度变化的影响。目前尚不清楚英国被掩埋的基础设施能在多大程度上应对重大天气事件。该项目旨在为地下基础设施开发一个全面的天气相关风险评估框架,包括对城市和城市生活至关重要的电缆和管道。该框架将用于了解天气事件和气候变化对这些基础设施的潜在影响。项目组还将与利益攸关方共同制定适应措施,以提高对这些极端事件的复原力。这一目标将通过五个相互关联的一揽子工作来实现。这包括1)为水文条件创建广泛的建模方法;2)确定当前和未来的水文和气象情景,对地下基础设施构成风险;3)采用先进的流体动力学建模和脆弱性分析,以了解地下管道和电缆如何响应不同的条件;4)整合开发的模型和数据集,进行全面的风险评估;5)与利益相关者共同制定复原力和适应战略。该项目预计将产生重大的社会和经济影响。通过增强基础设施运营商和公用事业公司的决策能力,这项研究可以减少服务中断,潜在节省成本,并提高基础设施系统在面对气象冲击和气候变化时的弹性。该项目利用多个机构的专业知识,包括伯明翰大学、英国生态和水文中心以及英国地质调查局,来解决一个关键挑战--地下基础设施对气象冲击的弹性,通过利用在模型、设施和国家数据集方面的重大投资,展示了极高的性价比。这一研究计划的预期结果,包括将在Dafni平台上提供的工具和数据,具有长期价值。
英文摘要
Buried infrastructure systems are vulnerable to meteorological shocks or extreme weather events, such as floods and droughts due to extreme precipitation, as well as extreme temperatures. Such events can lead to soil movement, thermal contraction and expansion, and sinkholes, among other problems. Despite the urgency, our society is not well prepared for the impacts of these shocks on buried infrastructure. Our understanding of where the risk is and how much it is remains poor, because existing risk assessment tools do not comprehensively consider impacts from both flood water and subsurface moisture/temperature variations. The extent to which the UK's buried infrastructure can cope with a significant weather event, or 'shock', is unclear. Such understanding is crucial for developing effective resilience strategies.This project aims to develop a comprehensive weather-related risk assessment framework for buried infrastructure, which include cables and pipes vital to cities and urban lives. The framework will be applied to understand the potential impacts of weather events and climate change on these infrastructures. The project team will also co-develop adaptation measures with stakeholders to increase resilience to these extreme events. The aim will be accomplished through five interrelated work packages. This includes 1) creating a broad-scale modelling methodology for hydrological conditions; 2) identifying current and future hydrological and meteorological scenarios posing risks to buried infrastructure; 3) employing advanced hydrodynamic modelling and vulnerability analysis to understand how buried pipes and cables respond to varying conditions; 4) integrating the developed models and datasets for a comprehensive risk assessment, and 5) co-developing resilience and adaptation strategies with stakeholders.The project is expected to deliver significant societal and economic impacts. By enhancing decision-making capabilities among infrastructure operators and utility companies, the research can lead to fewer service disruptions, potential cost savings, and increased resilience of infrastructure systems in the face of meteorological shocks and climate change.The project leverages expertise across multiple institutions, including the University of Birmingham, UK Centre for Ecology and Hydrology, and British Geological Survey, to address a critical challenge - the resilience of buried infrastructure to meteorological shocks, demonstrating excellent value for money by capitalising on significant investments in models, facilities, and national datasets. The anticipated outcome of this research program, including the tools and data that will be made available on the DAFNI platform, promises long-term value.
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Mitigation potential of horizontal Ground Coupled Heat Pumps for current and future climatic conditions: UK environmental modelling studies
  • 批准号:
    NE/F018568/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $8.53万
  • 财政年份:
    2009
  • 负责人:
    Andrew Hughes
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis