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

洪水对全世界都有巨大的影响。在英国，2015/16年的洪水造成了超过13亿英镑的损失。目前关于气候变化及其对未来洪水灾害影响的研究一致认为，在英国，洪水将变得更加频繁和极端。妥善防范这类自然灾害的能力将减少所发生的经济和社会影响，并保护子孙后代。在洪水灾害模拟过程中，将区域气候模型（RCM）作为水文模型的输入。然后使用极值定理对这些进行分析，为水力模型提供输入。最后，水力模型输出是在给定区域产生洪水的概率图。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。这在整个系统中导致了一连串的不确定性。不确定性量化的目的是确定不确定性的来源，并了解它如何通过模型框架级联。在做出决策时，充分理解与系统相关的全部不确定性是很重要的。概率图用于创建洪水发生概率给定的区域。与确定性方法相比，这可以更好地理解可能的未来情景。洪水本身具有不确定性，因此洪水风险管理必须考虑到不确定性。调查和量化与概率映射相关的不确定性已成为洪水淹没建模的一个感兴趣的领域。然而，工业和学术研究人员都在验证和验证水力模型，但运行的模拟数量有限。这没有考虑水力模型中结构或输入的不确定性。因此，需要蒙特卡罗类型的方法。增加水力模型的维度减少了结构的不确定性，创造了更真实的输出，但需要更多的时间来运行。因此，比较1D/2D混合和全2D水力模型输出和结构不确定性将有助于决定哪种模型最适合不同的研究。不同的采样技术和降低计算成本的方法可以加快运行时间：拉丁超立方体采样，多层次蒙特卡罗，马尔可夫链蒙特卡罗和多层次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，这些方法由于收敛速度的提高而降低了计算成本，同时提高了精度。将探索利用代理模型，因为它们可以运行得更快，同时通过外推模型输出保持所需的准确性。代理模型也可用于系统的不确定性传播和灵敏度分析。将这些改进应用于水力建模过程和方差减少技术，将在英国产生更准确的案例研究概率图，同时使计算时间有限的从业者更容易获得。水力模型和总不确定性将被量化；创建一个充分评估整个模型不确定性的框架。随着气候的变化和更加不可预测，对不确定性有更清晰的认识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。

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.