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/I005242/1
• 负责人: Sarah Dance
• 金额: $ 39.34万
• 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: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI005242%2F1
• 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. Better error propagation through coastal storm surge models. 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.

目前的最佳估计表明，居住在 200 万处房产中的大约 500 万人面临着英国极端风暴造成的洪水风险。其中大约 200,000 户家庭没有受到 75 年一遇事件的保护，这是政府建议的最低保护水平。当大洪水确实发生时，总损失成本很高（2007 年夏季洪水为 35 亿英镑），每年防洪总支出接近 8 亿英镑。保护这一群体并最大限度地减少未来的成本需要开发强大的水文和水力模型，将数值天气预报 (NWP) 和气候模型的输出转化为有意义的影响估计（具有不确定性）。这些影响预测可用于规划投资决策、提供实时预警、设计防洪计划，并通常有助于更好地管理风暴风险并减轻危险气候变化的影响。作为 NERC 极端事件洪水风险 (FREE) 和 EPSRC/NERC 洪水风险管理研究联盟 (FRMRC) 计划一部分的联盟成员建立的基础，我们在此提出一项综合研究计划，该计划将导致我们量化短期和长期风暴影响的能力逐步提高。基于在上述计划中获得的知识，我们建议可以通过我们具有独特优势的四个相互关联的目标来实现风暴影响模型的改进。具体来说，这些是： 1. 水文模型结构的降尺度、不确定性传播和评估。 2. 开发数据同化和遥感方法，以增强风暴影响模型的预测。 3. 通过沿海风暴潮模型更好地传播误差。 4. 开发一类新型水力模型，可使用真正的动量守恒方法，将降雨径流或沿海极端水位的预测转换为对整个城市区域的 LiDAR 数据分辨率（~1 - 2m 水平分辨率）的洪水范围和深度的估计。在本提案中，考虑到 NWP 和气候模型输出中的典型误差（用作影响建模链的边界强迫），我们评估了上述四种方法减少风暴影响集合预测不确定性的潜力。我们对当前 NWP 和气候模型实施中预测风暴特征（空间降雨和风速场）误差的初步描述将基于英国气象局和雷丁大学进行的现有研究。随着项目的进展，我们将利用为本次通话的可交付成果 1 和 2 开发的风暴建模方面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。我们将通过使用布里斯托大学和雷丁大学正在开发的独特的风暴影响基准数据集来严格评估我们提出的方法的成功与否。

项目成果

Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data

• DOI: 10.1016/j.jag.2015.02.004
• 发表时间: 2016-03
• 期刊: Int. J. Appl. Earth Obs. Geoinformation
• 作者: D. Mason;J. García-Pintado;H. Cloke;S. Dance

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.

Floodwater detection in urban areas using Sentinel-1 and WorldDEM data

• DOI: 10.1117/1.jrs.15.032003
• 发表时间: 2021-02
• 期刊: Journal of Applied Remote Sensing
• 影响因子: 1.7
• 作者: D. Mason;S. Dance;H. Cloke

A real-time measurement system for long-life flood monitoring and warning applications.

• DOI: 10.3390/s120404213
• 发表时间: 2012
• 期刊: Sensors (Basel, Switzerland)
• 作者: Marin-Perez R;García-Pintado J;Gómez AS
• 通讯作者: Gómez AS

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.