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Quantifying the Uncertainty In Future Flood Hazard Assessment Assuming Climate Change Uncertainties

假设气候变化存在不确定性，量化未来洪水灾害评估的不确定性

基本信息

批准号：
1989537
负责人：
金额：
--
依托单位：
Heriot-Watt University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1989537
关键词：
Quantifying Uncertainty Future Flood Hazard

项目摘要

Floods have an enormous impact worldwide. In the UK the 2015/16 floods caused more than £1.3 billion worth of damage. The consensus of current research on climate change and its effect on future flooding hazards is that in the UK floods are going to become more frequent and more extreme. The ability to properly safeguard against such natural disasters will reduce the economical and social impacts occurring and protect future generations. In modelling flood hazards the process takes regional climate models (RCM) as input for hydrological models. These are then analysed using extreme value theorem giving inputs into the hydraulic model. Finally, the hydraulic model output is produces probabilistic maps of flooding in a given area. At each stage there is uncertainty in the form of input (RCM's), structure (hydraulic model), and parameters (friction coefficient). This results in a cascade of uncertainty through the system. The aim of uncertainty quantification is to identify sources of uncertainty and understand how it cascades through the model framework.When decisions are being made it is important to fully understand the total uncertainty associated with the system. Probabilistic maps are used to create regions in which there is a given probability of flooding. This results in a better understanding of possible future scenarios compared to deterministic methods. Floods are inherently uncertain and as such flood risk management must take uncertainty into account. Investigating and quantifying the uncertainty associated within probabilistic mapping has become an area of interest in flood inundation modelling. However, industrial and academic researchers alike have been validating and verifying hydraulic models but running a limited number of simulations. This does not account for structural or input uncertainty in the hydraulic model. Therefore, there is a need for Monte Carlo type approaches. Increasing the dimensionality of the hydraulic model reduces the structural uncertainty and creates a more realistic output but takes more time to run. Thus, a comparison of 1D/2D hybrid and fully 2D hydraulic model outputs and structural uncertainties will help with deciding which is best suited for different studies.Different sampling techniques and computational cost reduction methods can be implemented to speed up run time: Latin hypercube sampling, Multi-level Monte Carlo, Markov chain Monte Carlo, and Multi-level Markov chain Monte Carlo will all be investigated and compared. These approaches reduce the computational cost due to an increase in convergence rates while increasing accuracy compared to the simple Monte Carlo method. Utilising surrogate models will be explored as they can run faster while maintaining the required accuracy by extrapolating model outputs. The surrogate models can also be used for uncertainty propagation through the system and sensitivity analysis.The application of these improvements to the hydraulic modelling process and the variance reduction techniques will result in a more accurate probabilistic map of case studies in the UK, while being more accessible to practitioners who have limited computational time. The hydraulic model and total uncertainty will be quantified; creating a framework that sufficiently assesses total model uncertainties. With a changing and more unpredictable climate the ability to have a more clear understanding of uncertainties could prevent economical, social, and environmental losses. This research can be used for further studies across the UK and abroad.

洪水在世界范围内造成了巨大的影响。在英国，2015/16年度的洪水造成了超过13亿GB的损失。目前关于气候变化及其对未来洪水危害影响的研究的共识是，在英国，洪水将变得更加频繁和极端。适当防范这类自然灾害的能力将减少发生的经济和社会影响，并保护子孙后代。在对洪水灾害进行建模时，该过程将区域气候模型(RCM)作为水文模型的输入。然后利用极值定理对这些问题进行了分析，给出了水力模型的输入。最后，水工模型输出将生成给定区域的洪水概率图。在每个阶段都存在输入形式(RCM)、结构(水力模型)和参数(摩擦系数)的不确定性。这导致了整个系统的一连串不确定性。不确定性量化的目的是确定不确定性的来源，并了解它是如何通过模型框架级联的。在做出决策时，重要的是充分了解与系统相关的总不确定性。概率图用于创建存在给定泛洪概率的区域。与确定性方法相比，这可以更好地理解未来可能出现的情景。洪水本身是不确定的，因此洪水风险管理必须考虑到不确定性。研究和量化概率图中的不确定性已成为洪水淹没模型中的一个重要领域。然而，工业界和学术界的研究人员都在验证和验证水力模型，但进行的模拟数量有限。这不考虑水力模型中的结构或输入不确定性。因此，需要蒙特卡洛式的方法。增加水力模型的维度可以减少结构不确定性，并创建更逼真的输出，但运行时间会更长。因此，比较一维/二维混合水力模型和全二维水力模型的输出和结构不确定性将有助于确定哪一种最适合不同的研究。可以采用不同的采样技术和计算成本降低方法来加快运行时间：拉丁超立方体采样、多层蒙特卡罗、马尔可夫链蒙特卡罗和多层马尔可夫链蒙特卡罗都将被研究和比较。与简单的蒙特卡罗方法相比，这些方法在提高收敛速度的同时提高了精度，从而降低了计算成本。将探索使用代理模型，因为它们可以运行得更快，同时通过外推模型输出来保持所需的精度。代理模型还可以用于系统和灵敏度分析中的不确定性传播。将这些改进应用于水力建模过程和方差减少技术将产生更准确的英国案例研究的概率图，同时更便于计算时间有限的从业者访问。水力模型和总不确定性将被量化；创建一个充分评估总模型不确定性的框架。随着气候的变化和更加不可预测，更清楚地了解不确定性的能力可以防止经济、社会和环境损失。这项研究可以用于英国和国外的进一步研究。