Developing and evaluating Impact-based Forecasting (IbF) for flooding

概述：洪水比任何其他自然灾害影响更多的人，并且事件的数量正在增加，最近20年（2000-2019年）记录的洪水事件数量是前20年的两倍多。在印度，极端风暴的频率在过去70年中增加了三倍，风暴强度和洪水发生率也增加了。再加上人口增长、城市快速扩张和面临洪水风险的资产价值，洪水的社会经济影响在近几十年来急剧增加，在气候变化的情况下只会恶化。改进洪水（风险）预报已被确定为提高印度洪水复原力和支持有效洪水风险管理的一个关键优先事项。国际研究界和研究机构一直在转向基于影响的预测方法，不仅预测灾害，而且预测灾害的潜在影响，以帮助决策-正如世界气象机构所建议的那样。关键的挑战是如何考虑水文气象预报的不确定性，以及如何开发和评估IbF端到端系统，以确保用户可以采取行动。集合水文气象灾害预报的使用是一个基本组成部分，但如何最好地形成这些集合预报和如何评估它们，还有一些开放的研究问题。其次，评估和交流基于影响的预测技能是一个新兴的研究领域，需要新的技术。印度洪水泛滥的一个特别热点是西南沿海的喀拉拉邦，那里有多山的西高止山脉和向西流入阿拉伯海的集水区。在季风季节（6月至9月），强降雨可能会造成严重的洪水，2018年，2019年和2020年的近期事件会造成重大影响。监督小组一直在与英国气象局和业务合作伙伴NCMRWF（国家中期天气预报中心）和IMD（印度气象局）合作，为喀拉拉邦开发一个原型IbF系统，该系统将成为研究的基础，并提供一个令人兴奋的机会，与国家气象机构合作，并为研究提供现实世界的好处。 方法学：由监督小组和印度合作伙伴共同开发的印度洪水灾害影响模型（FHIM-India）将为研究提供框架。它使用网格到网格（G2 G）分布式水文模型配置在印度南部约1公里的规模，并结合高分辨率（<30米）的HiPIMS（高性能综合水动力模拟系统）模拟城市规模的洪水淹没。由此产生的水深和流速估计数可直接与计算撞击严重程度时的受体联系起来。洪水风险矩阵框架结合了集合降雨预报的不确定性，如NEPS-R（NCMRWF区域EnergyPrediction System），以及洪水预报的严重性。然后，在空间（例如，病房或村庄区域）和时间（例如，预测范围）的适当整合水平上总结风险。该研究将探索和评估替代集合预报方法，并为IbF输出开发新的验证方法。需要使用HPC环境（例如伯明翰的JASMIN或BEAR）。

Overview: Flooding affects more people globally than any other natural hazard and the number of events is increasing with the recent 20 year period (2000-2019) recording more than double the number of flood event than the previous 20 years. Within India, the frequency of extreme storms have increased three-fold during the last 70 years with the storm magnitudes and occurrence of flooding increasing too. When combined with population growth, rapid urban expansion and the value of assets exposed to the risk of flooding, the social-economic impacts of flooding have increased dramatically over recent decades with this only set to worsen under climate change. Improved flood (risk) forecasting has been identified as a key priority for increasing flood resilience and supporting effective flood risk management in India. The international research community and research agencies have been moving towards Impact- based Forecasting approaches that not only forecast the hazard but also the potential impact of the hazard to help with decision making - as recommended by the World Meteorological Agency. Key challenges are how to account for uncertainty in the hydro-meteorological forecasts, and how to develop and evaluated the IbF end-to-end system to ensure the outputs are actionable by users. Use of ensemble hydro-meteorological hazard forecasts are a fundamental component but there are open research questions as to how best to form these ensemble forecasts and how to evaluated them. Secondly, evaluating and communicating the skill in Impact-based Forecasts is a new and emerging research area that requires novel techniques. A particular hotspot for flooding in India is the south west coastal state of Kerala that has the mountainous Western Ghats and catchments draining westward to the Arabian sea. During the monsoon season (June-September), heavy rainfall can produce serious flooding with recent events in 2018, 2019 and 2020 causing major impacts. The supervising team have been collaborating with the UK Met Office and operational partners NCMRWF (National Centre of Medium Range Weather Forecasting) and IMD (Indian Meteorological Department) to develop a prototype IbF system for Kerala that will form the basis for the research and provide an exciting opportunity to work with national Meteorological Agencies and for the research to have real-world benefits. Methodology: The Flood Hazard Impact Model for India (FHIM-India), co-developed by the supervising team and Indian partners, will provide the framework for the research. It uses the Grid-to-Grid (G2G) distributed hydrological model configured at ~1km scale over southern India and combined with the high-resolution (<30m) HiPIMS (High-Performance Integrated hydrodynamic Modelling System) to model city-scale flood inundation. The resulting estimates of water depth and velocity can be linked directly to receptors in the calculation of impact severity. The Flood Risk Matrix framework combines uncertainty from ensemble rainfall forecasts, such as NEPS-R (NCMRWF Regional Ensemble Prediction System), together with the severity of flooding forecast. The risk is then summarises at appropriate levels of integration in space (e.g. Ward or Village area) and time (e.g. forecast horizon). The research will explore and evaluate alternative ensemble forecasting methods and develop novel verification approaches for IbF outputs. Use of HPC environments (e.g. JASMIN or BEAR at Birmingham) will be required.