Developing enhanced impact models for integration with next generation NWP and climate outputs

开发增强的影响模型以与下一代数值天气预报和气候输出相结合

• 批准号: NE/I005358/2
• 负责人: Hannah Cloke
• 金额: $ 2.01万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI005358%2F2
• 关键词: Developing; enhanced; impact; models; integration

Current best estimates indicate that approximately 5M people living in 2M properties are at risk of flooding resulting from extreme storms in the UK. Of these approximately 200,000 homes are not protected against a 1 in 75 year recurrence interval event, the Government's minimum recommended level of protection. When major floods do occur then total damage costs are high (£3.5Bn for the summer 2007 floods) and the total annual spending on flood defence approaches £800M. Protecting this population and minimizing these costs into the future requires the development of robust hydrologic and hydraulic models to translate the outputs from Numerical Weather Prediction (NWP) and climate models into meaningful estimates of impact (with uncertainty). These predictions of impact can then be used to plan investment decisions, provide real-time warnings, design flood defence schemes and generally help better manage storm risks and mitigate the effects of dangerous climate change. Building on foundations developed by consortium members as part of the NERC Flood Risk from Extreme Events (FREE) and EPSRC/NERC Flood Risk Management Research Consortium (FRMRC) Programmes, we here propose an integrated programme of research that will lead to step change improvements in our ability to quantify storm impacts over both the short and long term. Based on the knowledge gained in the above programmes, we suggest that improvements in storm impact modelling can be achieved through four linked objectives which we are uniquely positioned to deliver. Specifically, these are: 1. Downscaling, uncertainty propagation and evaluation of hydrologic modelling structures. 2. The development of data assimilation and remote sensing approaches to enhance predictions from storm impact models. 3. Fully dynamically coupled extreme storm surge and fluvial modelling. 4. The development of a new class of hydraulic model that can be used to convert predictions of rainfall-runoff or coastal extreme water levels to estimates of flood extent and depth at the resolution of LiDAR data (~1 - 2m horizontal resolution) over whole city regions using a true momentum-conserving approach. In this proposal we evaluate the potential of the above four approaches to reduce the uncertainty in ensemble predictions of storm impact given typical errors in the NWP and climate model outputs which are used as boundary forcing for impact modelling chains. Our initial characterization of the errors in predicted storm features (spatial rainfall and wind speed fields) in current implementations of NWP and climate models will be based on existing studies conducted by the UK Met Office and the University of Reading. As the project proceeds we will use the advances in storm modelling being developed for Deliverables 1 and 2 of this call to enhance our error characterizations and ensure that the techniques we develop are appropriate for current and future meteorological modelling technologies. We will rigorously evaluate the success of our proposed methods through the use of unique benchmark data sets of storm impact being developed at the Universities of Bristol and Reading.

当前的最佳估计表明，大约500万人居住在2M物业中的人面临着由英国极端风暴造成的洪水风险。在这些大约200,000户的房屋中，没有受到75年复发间隔事件的保护，这是政府建议的最低保护水平。当发生重大洪水时，总损害成本很高（2007年夏季楼层为35亿英镑），洪水防御的年度支出近8亿英镑。保护该人群并将这些成本最小化为未来，需要开发可靠的水文和水解模型，以将数值天气预测（NWP）的输出转化为有意义的影响（不确定性）。然后，这些对影响的预测可用于计划投资决策，提供实时警告，设计防洪计划，并通常有助于更好地管理风险风险并减轻危险气候变化的影响。在财团成员开发的基础基础上，作为极端事件（免费）和EPSRC/NERC洪水风险管理研究联盟（FRMRC）计划的NERC洪水风险的一部分，我们在这里提出了一项综合研究计划，将导致我们在短期和长期量化风暴影响的能力方面的逐步改善。根据上述程序中获得的知识，我们建议通过四个链接的目标来改善风暴影响建模的改进，我们将其独特地定位为实现。具体而言，这些是：1。水文建模结构的降压，不确定性传播和评估。 2。数据同化和遥感方法的发展，以增强风暴影响模型的预测。 3。完全动态的极端风暴潮和河流建模。 4。开发了一类新的氢化模型，可用于使用真正的动量稳定方法在整个城市地区对雨水量或沿海极端水位的预测转换为洪水范围和深度的估计。在此提案中，我们评估了上述四种方法的潜力，以减少风暴影响的整体预测的不确定性，因为NWP和攀爬模型输出的典型错误被用作冲击建模链的边界强迫。我们对NWP和气候模型的当前实施中预测的风暴特征（空间降雨和风速场）中错误的最初表征将基于英国大都会队和雷丁大学进行的现有研究。随着项目的进行，我们将使用该呼叫的可交付成果1和2开发的风暴建模的进步来增强我们的误差特征，并确保我们开发的技术适合当前和未来的气象建模技术。我们将通过使用在布里斯托尔大学和阅读大学开发的独特基准数据集来严格评估我们提出的方法的成功。

项目成果

The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images

• DOI: 10.3389/feart.2015.00043
• 发表时间: 2015-01-01
• 期刊: FRONTIERS IN EARTH SCIENCE
• 影响因子: 2.9
• 作者: Mason, David C.;Garcia-Pintado, Javier;Dance, Sarah L.
• 通讯作者: Dance, Sarah L.

Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering

• DOI: 10.1016/j.jag.2013.12.002
• 发表时间: 2014-05-01
• 期刊: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
• 影响因子: 7.5
• 作者: Mason, D. C.;Giustarini, L.;Cloke, H. L.
• 通讯作者: Cloke, H. L.

Recent climatic trends and linkages to river discharge in Central Vietnam

最近的气候趋势以及与越南中部河流流量的联系

• DOI: 10.1002/hyp.9693
• 发表时间: 2013
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: Souvignet M

Satellite-supported flood forecasting in river networks: A real case study

• DOI: 10.1016/j.jhydrol.2015.01.084
• 发表时间: 2015-04-01
• 期刊: JOURNAL OF HYDROLOGY
• 影响因子: 6.4
• 作者: Garcia-Pintado, Javier;Mason, David C.;Bates, Paul D.
• 通讯作者: Bates, Paul D.

Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI).

• DOI: 10.1007/s00484-014-0843-3
• 发表时间: 2015-03
• 期刊: INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
• 影响因子: 3.2
• 作者: Pappenberger, F.;Jendritzky, G.;Staiger, H.;Dutra, E.;Di Giuseppe, F.;Richardson, D. S.;Cloke, H. L.
• 通讯作者: Cloke, H. L.