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/S015728/1
• 负责人: Louise Slater
• 金额: $ 34.15万
• 依托单位: University of Oxford
• 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%2FS015728%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，影响着全球社会。近 10 亿人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：每年有超过 6,000 人死亡，经济损失超过 600 亿美元。这些问题在未来将会变得更加严重。现在人们已经达成明确的共识，即气候变化将在全球许多地区导致极端降雨事件发生频率大幅增加，进而导致洪水峰值流量增加，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占，社会面临的这一危害进一步加剧。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暴露程度将如何变化。现有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限制。首先，当前的 GFM 以高度简化的方式表示河道和洪泛区的地形和粗糙度，其相对较低的分辨率不足以代表河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、洪水在空间上如何变化以及洪水强度如何变化的能力。第二个限制是，当前的 GFM 将河流及其洪泛区本质上视为“静态管道”，随着时间的推移保持不变。事实上，河道在各种环境变化（例如气候和土地利用变化、大坝建设）的影响下，通过侵蚀和沉积过程而演变，并导致河道流量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，它们从根本上来说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）使用大数据集和新颖的方法来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 更具形态意识； (ii) 包括表示河道形态演变和河道-洪泛区连通性的新方法； (iii) 将这些进展与预测 21 世纪流域流量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、水文和河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认识。此外，我们还将把下一代 GFM 与基于卫星、调查、手机和人口普查数据集成的创新人口模型连接起来。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面临的程度，并动态应对不断变化的洪水灾害和风险。总体而言，该项目将为河道洪泛区形态和连通性之间的相互作用以及世界各河流流域的洪水灾害之间的相互作用的量化、绘图和预测带来根本性的改变。我们将在开源平台上共享模型和数据。项目成果将融入科学家、全球数值建模小组、政策制定者、人道主义机构、流域利益相关者、经常遭受或极端洪水袭击的社区、公众和学童的影响。

项目成果

Increase in ocean-onto-land droughts and their drivers under anthropogenic climate change

• DOI: 10.1038/s41612-023-00523-y
• 发表时间: 2023-11
• 期刊: npj Climate and Atmospheric Science
• 影响因子: 9
• 作者: Yansong Guan;Xihui Gu;Louise J. Slater;Jiabo Yin;Jianfeng Li;S. Gebrechorkos;Xiang Zhang

Supplementary material to "Afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain"

“与英国气候变化相比，造林对陆地水文的影响微不足道”的补充材料

• DOI: 10.5194/hess-2023-138-supplement
• 发表时间: 2023
• 作者: Buechel M

Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes

• DOI: 10.1007/s00704-023-04807-4
• 发表时间: 2023-12
• 期刊: Theoretical and Applied Climatology
• 影响因子: 3.4
• 作者: Fatih Tosunoğlu;Louise J. Slater;Katie Kowal;Xihui Gu;Jiabo Yin

Indirect and Direct Impacts of Typhoon In-Fa (2021) on Heavy Precipitation in Inland and Coastal Areas of China: Synoptic-Scale Environments and Return Period Analysis

台风“英发”（2021）对中国内陆和沿海强降水的间接和直接影响：天气尺度环境和重现期分析

• DOI: 10.1175/mwr-d-22-0241.1
• 发表时间: 2023
• 期刊: Monthly Weather Review
• 影响因子: 3.2
• 作者: Wang L

Hybrid forecasting: blending climate predictions with AI models

• DOI: 10.5194/hess-27-1865-2023
• 发表时间: 2023-05
• 期刊: Hydrology and Earth System Sciences
• 影响因子: 6.3
• 作者: L. Slater;L. Arnal;M. Boucher;An Chang;S. Moulds;C. Murphy;G. Nearing;Guy Shalev;Chaopeng Shen;L. Speight;G. Villarini;R. Wilby;A. Wood;M. Zappa