THE EVOLUTION OF GLOBAL FLOOD HAZARD AND RISK [EVOFLOOD]

洪水是地球上最致命、代价最高的自然灾害，影响着全球各地的社会。近10亿人一生中面临着洪水的风险，每年约有3亿人受到洪水的影响。对个人和社会的影响是极端的：每年有6000多人死亡，经济损失超过600亿美元。这些问题在未来将变得更加严重。现在有一个明显的共识，即气候变化将在全球许多地区导致极端降雨事件的发生频率大幅增加，这反过来将导致洪峰流量增加，从而淹没大片土地。与此同时，由于人口增长以及人们和关键基础设施对泛滥平原的侵占，社会对这一危险的暴露进一步加剧。面对这一紧迫的挑战，需要可靠的工具来预测未来洪水风险和暴露情况将如何变化。现有最先进的全球洪水模型(GFMS)被用来模拟全球洪水泛滥的可能性，但不幸的是，它们受到两个基本限制的高度限制。首先，目前的GFMS以高度简化的方式表示河道和泛滥平原的地形和粗糙度，其相对较低的分辨率不足以表示河道和泛滥平原之间的自然连通性。这严重限制了他们预测洪水淹没程度和频率、洪水在空间上如何变化以及如何取决于洪水大小的能力。第二个限制是，目前的GFMS基本上将河流及其泛滥平原视为随着时间的推移保持不变的“静态管道”。事实上，在不同环境变化(如气候和土地利用变化、大坝建设)的影响下，河道通过侵蚀和沉积过程演变，并导致河道水流输送能力和泛滥平原连通性的变化。在GFMS能够解释这些变化之前，它们将从根本上不适合预测未来洪水风险的演变，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，我们将开发全新的新一代全球洪水模型，具体做法是：(1)利用大数据集和新的方法大幅增强模型对河道及滩地形态和粗糙度的描述，从而使全球洪水预报系统更具地貌意识；(2)包括采用新的方法来描述河道形态和河道-洪泛区的连通性；(3)将这些研究成果与预测21世纪集水区水流和泥沙供应制度变化的工具相结合。这些进展将使我们能够对气候、水文和河道地貌动力学之间的反馈如何推动洪水输送和未来洪水的变化产生新的理解。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集成的创新人口模型连接起来。我们将在未来一系列气候、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对不断变化的洪水灾害和风险的暴露程度，并动态做出反应。总体而言，该项目将在量化、测绘和预测河道-泛滥平原地貌和连通性之间的相互作用以及世界各河流流域的洪水风险方面产生根本性的变化。我们将在开源平台上共享模型和数据。项目成果将纳入科学家、全球数值模拟小组、政策制定者、人道主义机构、流域利益攸关方、易受常规或极端洪灾影响的社区、普通公众和学童。

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.