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

这项拟议的研究将发展出一种新的方法，使我们在大规模量化河流洪水风险的能力得到逐步改变，并将气候变化纳入其中。这将结合现有的和新兴的技术，在网格模型的基础上提供国家和区域的洪水风险估计，以改进对超过50年复发间隔的洪水风险的评估。连接网格降雨，径流，防洪性能和洪水淹没模型将显著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，包括新的情景，因为它们将从UKCIPnext提供。这将包括在大空间域上运行的空间和时间一致的网格化降雨模型，能够在国家尺度上建模的高分辨率网格化径流和流量路径模型，以及考虑到防御性能的洪水淹没的连续系统分析。由于这些模型将在时间上连续运行，因此将首次开发出一个连续的、关联的洪水风险分析系统。每个模型还可以利用推导出的未来气候变化来预测未来的洪水风险。此外，还将对模式和数据的不确定性进行评估，并对气候变化造成的不确定性进行估计。这些不确定性评估将包括不确定性在相关建模系统中的传播。该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上，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，以及使用集合场景集来表示不确定性。在区域或大流域尺度上的分析将包括一个基于网格的（5公里）降雨模型与一个（1公里）网格径流和路径模型相关联，以及防御系统的相关知识和开发的将降雨量转化为河流水位的新程序。这种建模系统最终适用于全国范围，这将在河流流量中得到证明。本演示的降水将来自观测到的降雨数据集，或模拟的时间序列，例如由再分析数据驱动的rcm提供的降水。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（GCMs）和区域气候模式（RCMs）中得出。还将分析多集合气候情景的应用，并生成未来洪水风险变化的概率情景。

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.