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

这项拟议的研究将开发所需的新方法，使我们在考虑气候变化的情况下，在大范围量化河流洪水风险的能力方面取得阶段性变化。这将结合现有和新兴技术，以网格模型为基础提供国家和区域洪水风险估计，以改进对50年以上复发间隔的洪水风险的评估。将网格降雨、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的洪水风险和探索气候变化的潜在影响的能力，包括新的情景，因为它们可以从UKCIPNEXT获得。这将包括一个在大空间范围内在空间和时间上保持一致的网格降雨模型，一个能够在全国范围内模拟的高分辨率网格径流和径流模型，以及一个考虑到防御性能的洪水淹没连续系统分析。由于每个模型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。每个模型还将能够利用推导出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。这些不确定度评估将包括通过关联建模系统传播不确定度。这项研究将利用许多现有的数据来源，并建立在一些已建立的模型和技术的基础上，例如纽卡斯尔大学的Neyman-Scott矩形脉冲(NSRP)随机降雨、CEH网格到网格(G2G)模型、RASP系统模型以及使用集合情景集来表示不确定性。在区域或大型流域，分析将包括与(1公里)网格径流和路线模型相联系的基于网格的(5公里)降雨模型，以及防御系统的相关知识和为将降雨量转换为河流水位而制定的新程序。这种模拟系统最终适用于全国范围，这将在河流流量中得到示范。本次演示的降雨量将来自观测的降雨量数据集，或模拟的时间序列，例如由再分析数据驱动的RCM提供的数据。未来降雨、径流和河流水位变化对洪水风险的影响将在HR Wallingford Rasp HLMplus模型的增强版本中进行评估。气候变化情景将从一系列全球(全球气候模型)和区域气候模型(区域气候模型)中得出。还将分析多种气候集合情景的应用，以及未来洪水风险变化的概率情景的产生。

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.