FRACAS: a next generation national Flood Risk Assessment under climate ChAnge Scenarios

这项拟议的研究将开发新的方法，使我们能够在大规模量化河流洪水风险，并纳入气候变化。这将结合联合收割机现有和新兴技术，根据网格模型提供国家和区域洪水风险估计，以改进对50年以上重现期洪水风险的评估。将网格化的降雨、径流、防洪性能和洪水淹没模型联系起来，将大大提高我们评估极端事件洪水风险的能力，并探索气候变化的潜在影响，包括UKCIPnext提供的新情景。这将包括一个在大空间范围内运行的时空一致的网格降雨模型，一个能够在全国范围内建模的高分辨率网格径流和流量路由模型，以及一个考虑到防御性能的洪水淹没连续系统分析。由于这些模型中的每一个都将在时间上连续运行，因此将首次开发一个连续的、相互关联的洪水风险分析系统。每个模型还将能够使用衍生的未来气候变化来预测未来的洪水风险。此外，还将评估模型和数据的不确定性，以及对气候变化造成的不确定性的估计。这些不确定性评估将包括不确定性通过链接的建模系统的传播。该研究将利用许多现有的数据来源，并建立在一些既定的模型和技术，如奈曼-斯科特矩形脉冲（NSRP）随机降雨在纽卡斯尔大学，CEH网格到网格（G2 G）模型，RASP系统模型，并使用集合情景集来表示不确定性。在区域或大流域范围内，分析将包括一个与一个（1公里）网格化径流和路由模型相联系的网格化（5公里）降雨模型，以及防御系统的相关知识和为将降雨转化为河流水位而开发的新程序。这种模拟系统最终将在全国范围内适用，并将在河流流量方面得到证明。本次演示的降水量将来自观测到的降雨量数据集或模拟的时间序列，例如由再分析数据驱动的区域协调机制提供的数据。未来降雨量、径流量和河流水位变化对洪水风险的影响将在HR Wallingford RASP HLMplus模型的增强版中进行评估。气候变化情景将从一系列全球和区域气候模型中得出。还将分析多集合气候情景的应用和未来洪水风险变化概率情景的生成。

This proposed research will develop the new methodology required to make a step-change in our ability to quantify fluvial flood risk at large scales, incorporating climate change. This will combine existing and emerging technologies, to provide national and regional estimates of flood risk based on gridded models for improved assessment of flood risk to recurrence intervals in excess of 50 years. Linking gridded rainfall, runoff, flood defence performance and flood inundation models will significantly improve our ability to assess flood risk from extreme events and explore the potential impacts of climate change, including new scenarios, as they become available from UKCIPnext. This will include a spatially and temporally consistent gridded rainfall model operating over large spatial domains, a high resolution gridded runoff and flow routing model capable of modelling at the national scale and a continuous system analysis of flood inundation, taking account of defence performance. As each of these models will be run continuously in time, a continuous, linked flood risk analysis system will be developed for the first time. Each model will also be able to use derived future changes in climate to produce predictions of future in flood risk. Moreover there will be an assessment of the model and data uncertainties, as well as estimates of uncertainty due to climate change. These uncertainty assessments will include the propagation of uncertainty through the linked modelling system. The research will utilise many existing sources of data and build upon some established models and techniques, such as the Neyman-Scott Rectangular Pulses (NSRP) stochastic rainfall at the University of Newcastle, the CEH Grid-to-Grid (G2G) model, the RASP system models, and the use ensemble scenario sets to represent uncertainty. At the regional or large basin-scale analyses will include a grid-based (5km) rainfall model linked to a (1km) gridded runoff and routing model and associated knowledge of defence systems and new routines developed to translate rainfall to river levels. Such a modelling system is ultimately applicable at a national scale and this will be demonstrated for river flows. The precipitation for this demonstration will be sourced from observed rainfall datasets, or modelled time series, such as those available from RCMs driven with re-analysis data. The impact of future changes in rainfall, runoff and river levels on flood risk will be assessed within an enhanced version of the HR Wallingford RASP HLMplus model. Scenarios of climate change will be derived from a range of both global (GCMs) and regional climate models (RCMs). There will also be an analysis of the application of multi-ensemble climate scenarios and the generation of probabilistic scenarios of change in future flood risk.