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/S015817/1
• 负责人: Stephen Darby
• 金额: $ 87.26万
• 依托单位: University of Southampton
• 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%2FS015817%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、代价最高的自然灾害，影响着地球仪的各个社会。近10亿人在其一生中面临洪水风险，每年约有3亿人受到洪水影响。对个人和社会的影响是极端的：每年有6,000多人死亡，经济损失超过600亿美元。这些问题将来会变得更糟。现在有一个明确的共识，即气候变化将在地球仪的许多地方造成极端降雨事件发生频率的大幅增加，这反过来又将导致洪水峰值的增加，从而淹没大片土地。与此同时，由于人口增长以及人口和关键基础设施对洪泛区的侵蚀，社会面临的这一危险进一步加剧。面对这一紧迫的挑战，需要可靠的工具来预测未来洪水灾害和风险的变化。现有的最先进的全球洪水模型（GFM）用于模拟全球洪水的概率，但不幸的是，它们受到两个基本限制的高度约束。首先，目前的GFM代表地形和粗糙度的河道和洪泛区高度简化的方式，其相对较低的分辨率不足以代表渠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范围和频率的能力，它如何在空间上变化，以及它如何取决于洪水的大小。第二个限制是，目前的GFMs处理河流及其洪泛区基本上是“静态管道”，随着时间的推移保持不变。实际上，河道是在各种环境变化（例如，气候和土地利用变化、大坝建设），并导致河道水流输送能力和洪泛区连通性的变化。在GFM能够解释这些变化之前，它们从根本上仍然不适合预测未来洪水灾害的演变，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，我们将通过以下方式开发全新一代的全球洪水模型：（i）使用大数据集和新方法，大幅增强其对河道和洪泛区形态和粗糙度的表示，从而使GFM在形态上更加敏感;（ii）包括表示河道形态和河道-洪泛区连通性演变的新方法;以及（iii）将这些发展与预测世纪流域水流和沉积物供应状况变化的工具相结合。这些进展将使我们能够对气候，水文和通道形态动力学之间的反馈如何驱动洪水输送和未来洪水的变化提供新的理解。此外，我们还将把我们的下一代GFM与基于卫星、调查、手机和人口普查数据整合的创新人口模型联系起来。我们将在一系列未来气候，环境和社会变化情景下应用耦合模型系统，使我们能够充分询问和评估人们暴露的程度，并动态应对不断变化的洪水灾害和风险。总的来说，该项目将在世界河流流域的河道-洪泛区形态和连通性以及洪水灾害之间的相互作用的量化、绘图和预测方面带来根本性的变化。我们将在开源平台上共享模型和数据。项目成果将嵌入科学家、全球数字建模小组、决策者、人道主义机构、河流流域利益攸关方、经常或极端洪水易发社区、公众和学童。

项目成果

Beyond just floodwater

不仅仅是洪水

• DOI: 10.1038/s41893-022-00929-1
• 发表时间: 2022
• 期刊: Nature Sustainability
• 影响因子: 27.6
• 作者: Best J

Global scale evaluation of precipitation datasets for hydrological modelling

用于水文建模的降水数据集的全球尺度评估

• DOI: 10.5194/hess-2023-251
• 发表时间: 2023
• 作者: Gebrechorkos S