Improved physical process representation of river networks in Global Flood Models

量化洪水灾害是复原力规划、应急响应和保险的重要组成部分。最近，人们加大了努力，用全球洪水模型（GFM）来估计整个地球的洪水灾害。这些全球森林模型改进的一个关键领域是它们对全球河流网络的代表性。目前所有的GFM都使用一个自动从地形分析中得出的简化排水网络。这种简化的网络易于使用，但误差会导致与真实的河流不匹配。新的全球高分辨率数字高程模型和遥感数据集的出现有助于解决这些问题，但如何将这些新数据纳入全球高程模型仍存在问题。这个博士学位将旨在回答两个关键问题：如何识别，收集和整合相关的新数据，以及这是否会导致模型输出的改进？在审查了当前的河流数据集后，研究人员将开发识别和收集这些数据的方法，并将其纳入当前的GFM河流网络。由于数据集的全局性，这将需要自动化和一致的方法，同时捕获最相关的液压过程细节并将其集成到一个数据集中。然后将在不同的河流和气候条件下对方法进行测试，以确保全球适用性，并量化产量的改善。研究人员将直接访问Fathom Global Ltd。的最先进的GFM框架，并在4个月的借调期间与他们经验丰富的开发人员一起工作。在这个博士学位进行的工作有可能提高GFM的性能，从而提高其适用性。研究人员将在公开的科学出版物上发表所有发现。这个多学科项目包括：遥感，计算机方法，统计，机器学习，河流水力学，地貌学和洪水管理。

Quantifying flood hazard is an essential component of resilience planning, emergency response, and insurance. Efforts have recently intensified to estimate flood hazard for the whole planet with global flood models (GFMs). A key area of improvement for these GFMs is in their representation of global river networks. All current GFMs use a simplified drainage network derived automatically from analysis of the topography. This simplified network is easy to use, but errors result in mismatches with real rivers. The emergence of new, global, high resolution Digital Elevation Models (DEMs) and remote sensing data sets can help address these issues, but questions remain about how to operationalize the incorporation of this new data in the GFMs. This PhD will aim to answer two key questions: How to identify, collect, and incorporate the relevant new data and, does this result in model output improvements? After reviewing current river datasets, the researcher will then develop methods to identify and collect this data, and incorporate it into the current GFM river networks. Due to the global nature of the datasets, this will require automated and consistent methods, while capturing the most relevant hydraulic process details and integrating these into one dataset. Methods will then be tested under different rivers and climates to ensure global applicability and quantify the improvements to output. The researcher will have direct access to a Fathom Global Ltd.'s state-of-the-art GFM framework, and work alongside their experienced developers during a 4 months secondment. The work carried out in this PhD has the potential to improve the performance of GFMs and as a result increase their applicability. The researcher will publish all findings in open scientific publications. This multidisciplinary project includes; remote sensing, computer methods, statistics,machine learning, river hydraulics, geomorphology and flood management.