THE EVOLUTION OF GLOBAL FLOOD HAZARD AND RISK [EVOFLOOD]

全球洪水灾害和风险的演变 [EVOFLOOD]

• 批准号: NE/S015655/1
• 负责人: Philip Ashworth
• 金额: $ 38.41万
• 依托单位: University of Brighton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS015655%2F1
• 关键词: EVOLUTION; GLOBAL; FLOOD; HAZARD; RISK

Flooding is the deadliest and most costly natural hazard on the planet, affecting societies across the globe. Nearly one billion people are exposed to the risk of flooding in their lifetimes and around 300 million are impacted by floods in any given year. The impacts on individuals and societies are extreme: each year there are over 6,000 fatalities and economic losses exceed US$60 billion. These problems will become much worse in the future. There is now clear consensus that climate change will, in many parts of the globe, cause substantial increases in the frequency of occurrence of extreme rainfall events, which in turn will generate increases in peak flood flows and therefore flood vast areas of land. Meanwhile, societal exposure to this hazard is compounded still further as a result of population growth and encroachment of people and key infrastructure onto floodplains. Faced with this pressing challenge, reliable tools are required to predict how flood hazard and exposure will change in the future. Existing state-of-the-art Global Flood Models (GFMs) are used to simulate the probability of flooding across the Earth, but unfortunately they are highly constrained by two fundamental limitations. First, current GFMs represent the topography and roughness of river channels and floodplains in highly simplified ways, and their relatively low resolution inadequately represents the natural connectivity between channels and floodplains. This restricts severely their ability to predict flood inundation extent and frequency, how it varies in space, and how it depends on flood magnitude. The second limitation is that current GFMs treat rivers and their floodplains essentially as 'static pipes' that remain unchanged over time. In reality, river channels evolve through processes of erosion and sedimentation, driven by the impacts of diverse environmental changes (e.g., climate and land use change, dam construction), and leading to changes in channel flow conveyance capacity and floodplain connectivity. Until GFMs are able to account for these changes they will remain fundamentally unsuitable for predicting the evolution of future flood hazard, understanding its underlying causes, or quantifying associated uncertainties. To address these issues we will develop an entirely new generation of Global Flood Models by: (i) using Big Data sets and novel methods to enhance substantially their representation of channel and floodplain morphology and roughness, thereby making GFMs more morphologically aware; (ii) including new approaches to representing the evolution of channel morphology and channel-floodplain connectivity; and (iii) combining these developments with tools for projecting changes in catchment flow and sediment supply regimes over the 21st century. These advances will enable us to deliver new understanding on how the feedbacks between climate, hydrology, and channel morphodynamics drive changes in flood conveyance and future flooding. Moreover, we will also connect our next generation GFM with innovative population models that are based on the integration of satellite, survey, cell phone and census data. We will apply the coupled model system under a range of future climate, environmental and societal change scenarios, enabling us to fully interrogate and assess the extent to which people are exposed, and dynamically respond, to evolving flood hazard and risk. Overall, the project will deliver a fundamental change in the quantification, mapping and prediction of the interactions between channel-floodplain morphology and connectivity, and flood hazard across the world's river basins. We will share models and data on open source platforms. Project outcomes will be embedded with scientists, global numerical modelling groups, policy-makers, humanitarian agencies, river basin stakeholders, communities prone to regular or extreme flooding, the general public and school children.

洪水是地球上最致命，最昂贵的自然危害，影响了全球的社会。在任何一年中，近十亿人处于一生中遭受洪水泛滥的风险，约有3亿人受洪水的影响。对个人和社会的影响是极端的：每年有超过6,000人死亡，经济损失超过600亿美元。这些问题将来会变得更糟。现在有明确的共识，气候变化将在全球许多地方导致极端降雨事件发生的频率大幅增加，这反过来又会在峰值洪水流中增加，因此淹没了广阔的土地。同时，由于人口增长和人口增长和关键基础设施对洪泛区的侵占，社会对这种危害的暴露越来越复杂。面对这一紧迫的挑战，需要可靠的工具来预测将来洪水危害和暴露方式将如何变化。现有的最新全球洪水模型（GFM）用于模拟整个地球洪水的可能性，但不幸的是，它们受到了两个基本限制的高度限制。首先，当前的GFMs以高度简化的方式代表了河道和洪泛区的地形和粗糙度，其相对较低的分辨率不足代表了通道和洪泛区之间的自然连通性。这严重限制了他们预测洪水淹没程度和频率，空间的变化以及它如何取决于洪水量的能力。第二个限制是，当前的GFM将河流及其洪泛区视为随着时间的流逝保持不变的“静态管道”。实际上，河道通过侵蚀和沉积过程发展，这是在各种环境变化（例如，气候和土地使用变化，大坝建设）的影响的驱动下，导致通道流通运输能力和洪泛区连接的变化。在GFM能够考虑这些变化之前，它们将根本不适合预测未来洪水危害的演变，了解其根本原因或量化相关的不确定性。为了解决这些问题，我们将通过：（i）使用大数据集和新颖方法来开发全新的全球洪水模型，从而大大增强其对渠道和洪泛区形态和粗糙度的代表，从而使GFMS在形态上更加意识到； （ii）包括代表通道形态和通道泛滥连接的发展的新方法； （iii）将这些发展与21世纪的集水流和沉积物供应制度的变化相结合。这些进步将使我们能够对气候，水文学和渠道形态动力学之间的反馈如何推动洪水运输和未来洪水的变化有了新的了解。此外，我们还将将下一代GFM与基于卫星，调查，手机和人口普查数据的整合的创新人群模型联系起来。我们将在一系列未来的气候，环境和社会变化方案中应用耦合模型系统，使我们能够充分询问并评估人们暴露的程度，并动态地响应不断发展的洪水危害和风险。总体而言，该项目将在世界河流盆地的频道泛滥形态和连通性以及洪水危害之间的量化，映射和预测进行基本变化。我们将在开源平台上共享模型和数据。项目成果将与科学家，全球数值建模小组，政策制定者，人道主义机构，河流利益相关者，容易常规或极端洪水的社区，公众和学童。