Unlocking the potential of surface water flood nowcasting for emergency services in a changing climate
Unlocking the potential of surface water flood nowcasting for emergency services in a changing climate
批准号:
NE/S017186/1
负责人:
Dapeng Yu
金额:
$31.5万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
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英文摘要
Surface water flooding affects 3.2 million properties in England, and is seen as "the biggest flood risk of all" by the Environment Agency. Average annual damage due to surface water flooding in the UK exceeds £290 million. Climate change is expected to increase the intensity of heavy bursts of rainfall, and weather is expected to become more 'uncertain' and 'unfamiliar'. The cost of associated damage under climate change could rise by 40% by the 2050s if current management approaches remain unchanged. In the UK, Ambulance and Fire & Rescue Services are the primary emergency responders to extreme flood events, during which demands for services often peak. For example, London Fire & Rescue Service saw more than a three-fold increase in emergency 999 calls attended during flooding on the EU Referendum Day (23 June 2016). Surface water flooding such as this affects the operation of emergency responders who have to operate under flood conditions, while meeting their mandatory response time target. For example, the majority of Fire & Rescue Services in the UK aims to reach incidents within 8-10 minutes. Flood incidents compound the challenges of meeting their response time targets. For example, during the 23 June 2016 flooding, the average response time of London Fire Brigade increased from 6 minutes under normal days to 16 minutes, thereby missing the response time target. 40% of incidents were reported to be delayed due to weather, flooded roads, and congestion. To combat this and enable effective decision making, knowing where and when it could flood is important.The operational decision making of emergency responders often involve determining when and where to allocate resources (e.g. closing flooded roads; pumping road sections to allow access; dispatching sandbags; positioning emergency vehicles; arranging cross-boundary and multi-agency operations). However, current system for predicting surface water flooding is not designed for operational purpose. The only (based on our knowledge) system exists is the UK Flood Forecast Centre's daily service, which provides 5-day outlook of flood risks including surface water flooding. A flood guidance document is issued daily and surface water flood risk is provided at the county-level, based on a pre-fun library of impact scenarios, rather than real-time analysis of risks. The spatial resolution and temporal frequency of the existing system means that such detailed decisions cannot be supported. Fundamentally, episodes of surface water flooding are typically less than 2-3 hours in the UK and associated with convective weather systems. Daily forecasts of surface water flooding are not able to capture the spatiotemporal dynamics of the fast-developing convective storms associated with such events.Under two NERC projects (2016, 2018), we developed the first nowcasting technology for surface water flooding. This approach involves around the clock real-time modelling of surface water flooding at street-level resolution for the next 3-6 hours, updated every 2-3 hours. In this way we use the latest weather forecast from the Met Office and capture every short duration intense rainfall events that cause flooding. Our stakeholder-driven research and innovation in surface water flood nowcasting have opened up several new, untapped research and innovation opportunities. The proposed project aims to address two key research questions in order to unlock the potential of surface water flood nowcasting for emergency services to support their operational decision making in a changing climate. These include: (i) uncertainty propagation from precipitation nowcasting and forecasting products to high-resolution surface water flood predictions; and (ii) effective communication of complex surface water flood risk information to support emergency responders' operational decision making.
期刊论文(10)
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Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places
数据稀疏地区次日极端降雨量的时空尺度
DOI:
10.1007/s00382-022-06528-2
发表时间:
2022
期刊:
Climate Dynamics
影响因子:
4.6
作者:
[Wilby R]
通讯作者:
Wilby R
Long-term flood-hazard modeling for coastal areas using InSAR measurements and a hydrodynamic model: The case study of Lingang New City, Shanghai
使用 InSAR 测量和水动力模型对沿海地区进行长期洪水灾害建模:以上海临港新城为例
DOI:
10.1016/j.jhydrol.2019.02.015
发表时间:
2019-04
期刊:
Journal of Hydrology
影响因子:
6.4
作者:
[Yin Jie, Zhao Qing, Yu Dapeng, Lin Ning, Kubanek Julia, Ma Guanyu, Liu Min, Pepe Antonio]
通讯作者:
Pepe Antonio
Quantifying uncertainty in heavy rainfall forecasts used to support flood modelling and emergency responders
量化强降雨预报的不确定性,用于支持洪水建模和应急响应人员
DOI:
--
发表时间:
2019
期刊:
影响因子:
--
作者:
[Camacho Suarez Vivian]
通讯作者:
Camacho Suarez Vivian
Strategic flood evacuation planning facilitates effective transfer of vulnerable population in coastal megacities
战略性洪水疏散规划有助于沿海特大城市弱势群体的有效转移
DOI:
10.21203/rs.3.rs-2914003/v1
发表时间:
2023
期刊:
影响因子:
--
作者:
[Yin J]
通讯作者:
Yin J
A city-scale assessment of emergency response accessibility to vulnerable populations and facilities under normal and pluvial flood conditions for Shanghai, China
中国上海在正常和雨洪条件下对脆弱人群和设施的应急响应可达性的城市规模评估
DOI:
10.1177/2399808320971304
发表时间:
2020-11
期刊:
Environment and Planning B: Urban Analytics and City Science
影响因子:
--
作者:
[Yin Jie, Yu Dapeng, Liao Banggu]
通讯作者:
Liao Banggu
共 6 条
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