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

目前最好的估计表明，英国约有500万人生活在200万处房产中，面临着极端风暴导致的洪水风险。在这大约200,000户家庭中，没有针对每75年复发一次的事件提供保护，这是政府建议的最低保护水平。当发生大洪水时，总损失很高(2007年夏季洪水的总损失为35亿英镑)，每年用于防洪的总支出接近8亿英镑。保护这些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，这些对影响的预测可用于规划投资决策、提供实时警告、设计洪水防御方案，通常有助于更好地管理风暴风险，减轻危险气候变化的影响。在联盟成员开发的基础上，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)计划的一部分，我们在此提出一个综合研究计划，将导致我们在短期和长期量化风暴影响的能力方面的阶段性变化。基于从上述方案中获得的知识，我们建议可以通过四个相互关联的目标来改进风暴影响模拟，而我们具有独特的能力来实现这些目标。具体来说，它们是：1.水文模型结构的缩尺、不确定性传播和评估。2.开发数据同化和遥感方法，以加强风暴影响模型的预测。3.极端风暴潮与河流的全动力耦合模拟。4.发展了一类新的水力学模型，它可以用真正的动量守恒方法将降雨径流或沿海极端水位的预报转化为整个城市区域激光雷达资料分辨率(~1-2m水平分辨率)下的洪水范围和深度估计。在这项建议中，我们评估了上述四种方法的潜力，以减少风暴影响集合预报的不确定性，考虑到NWP和气候模式输出中的典型误差，这些输出被用作影响模型链的边界强迫。我们对当前实施的NWP和气候模式中预测的风暴特征(空间降雨量和风速场)的误差的初步表征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，我们将利用为本次呼吁的成果1和2开发的风暴模拟方面的进展，以加强我们的错误特征，并确保我们开发的技术适用于当前和未来的气象模拟技术。我们将通过使用布里斯托尔大学和雷丁大学正在开发的风暴影响独特基准数据集，严格评估我们提议的方法的成功。

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.