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

目前最好的估计表明，在英国，大约有500万人居住在200万处房产中，面临极端风暴造成的洪水风险。在这些家庭中，约有20万户家庭没有得到保护，不受政府建议的最低保护水平——每75年发生一次的灾害的影响。当大洪水真的发生时，总损失是很高的（2007年夏季洪水的损失是35亿英镑），每年用于防洪的总支出接近8亿英镑。保护这一人口并在未来将这些成本降至最低，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）。然后，这些影响预测可以用于规划投资决策、提供实时警报、设计防洪方案，并通常有助于更好地管理风暴风险和减轻危险的气候变化的影响。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）项目的一部分，在联盟成员开发的基础上，我们提出了一个综合研究项目，这将导致我们在短期和长期量化风暴影响的能力的逐步改进。基于在上述项目中获得的知识，我们建议可以通过四个相互关联的目标来改进风暴影响建模，我们有独特的优势来实现这些目标。具体来说，它们是：1。水文模拟结构的降尺度、不确定性传播与评价。2. 发展资料同化和遥感方法以加强风暴影响模式的预测。3. 完全动态耦合极端风暴潮和河流模拟。4. 开发一种新型水力模型，可用于将降雨径流或沿海极端水位的预测转换为整个城市地区的激光雷达数据分辨率（~1 - 2米水平分辨率）的洪水范围和深度估计，使用真正的动量守恒方法。在本提案中，我们评估了上述四种方法的潜力，以减少风暴影响集合预测的不确定性，因为NWP和气候模式输出的典型误差被用作影响模拟链的边界强迫。我们对目前实施的NWP和气候模型在预测风暴特征（空间降雨和风速场）方面的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。随着项目的进行，我们将利用为本次会议的可交付成果1和2开发的风暴建模方面的进展来增强我们的误差特征，并确保我们开发的技术适用于当前和未来的气象建模技术。我们将通过使用布里斯托尔大学和雷丁大学开发的独特的风暴影响基准数据集，严格评估我们提出的方法的成功。

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.