Next generation flood hazard mapping for the African continent at hyper-resolution

洪水风险和风险地图构成了土地利用规划、保险和资本供应、应急和备灾等问题决策的证据基础。如果没有这样的数据，这些基本活动都不可能得到适当的规划，这一点得到了欧盟洪水指令、仙台框架以及英国洪水和水资源管理法案等高层政策的承认。然而，在撒哈拉以南非洲的大部分地区，这些数据都没有，这给灾害风险管理人员带来了巨大的挑战。编制洪水风险地图所需的高昂费用和专业知识是在许多撒哈拉以南国家提供这类地图的障碍，这意味着，如果要普及提供这种地图和风险管理的相关好处，就需要创新、低成本的解决办法。一种解决方案是使用过去五年出现的全球洪水模型的数据，以填补覆盖范围的众多空白。这些模型根据未测量流域的水文预报技术，结合关于集水区地形、河流大小和位置的遥感数据集，在任何地方进行预测。不幸的是，所有全球洪水模型都有很大的局限性，以至于它们产生的数据通常只被认为足够准确，用于高级别的国家和跨国风险评估。这妨碍了它们支持广泛的灾害风险管理活动的能力。因此，需要有足够的预测技能和不确定性量化的第二代全球洪水模型，以区分区域甚至社区范围内的风险水平。只有这样的进步才有可能改变我们对风险的理解，并确定最能集中区域和社区层面减少风险工作的风险热点。HYFLOOD将以现有的全球洪水模型为基础，以前所未有的详细程度提高我们对洪水发生、位置和强度的了解，以制定区域到社区范围的洪水风险地图。我们将使用几颗卫星的洪水发生遥感数据记录来实现这一点，将河流河段分解为我们认为或多或少会漫过岸的河段。这些信息将被用来局部改变河道特性，从而影响极端事件的模拟洪水淹没范围、深度和持续时间。通过叠加有关人口和土地使用的信息，我们将改进对谁和什么人面临洪灾的估计。我们将通过布里斯托尔大学和金沙萨大学之间的现有合作，向刚果民主共和国的最终用户试用我们的方法，这两个大学是刚果盆地水资源研究和能力发展网络的东道国。该项目的成果将是改进的非洲大陆洪水风险图，该图第一次可以包括河流特征的地方尺度变异性和预测不确定性的量化。这将伴随着对大陆尺度河流水深的第一次估计，可供其他洪水风险和风险建模小组使用。因此，HYFLOOD将提高我们对决定洪水发生、持续时间和影响的水文和形态因素的了解。

Flood hazard and risk maps form the evidence base for decision-marking regarding issues such as land use planning, insurance and capital provision, emergency response and disaster preparedness. None of these essential activities could be planned properly without such data and this is recognised by high level policy such as the EU Floods Directive, the Sendai framework and the flood and water management act in the UK. However, across most of sub-Saharan Africa such data are absent posing a huge challenge to disaster risk managers. The high cost and expertise needed to create flood hazard maps is a barrier to their provision in many sub-Saharan countries meaning that innovation low cost solutions are needed if the provision of such maps and associated benefits for risk management are to become universal. One solution is to use data from global flood models, which have emerged in the last five years, to fill the numerous gaps in coverage. These models make predictions everywhere based on techniques for hydrological prediction in ungauged basins combined with remotely sensed data sets on catchment topography and river size and location. Unfortunately, all global flood models have substantial limitations, such that, the data they produce are usually only considered accurate enough for high level national and transnational risk assessment. This hampers their ability to support a wide range of disaster risk management activities. A second generation of global flood models is therefore needed with sufficient predictive skill and quantification of uncertainty to discriminate risk levels at regional or even community scales. Only with such an advancements will it be possible to transform our understanding of risk and to identify risk hotspots where regional and community level risk reduction efforts would be best focused.HYFLOOD will improve our understanding of the occurrence, location and intensity of flooding with unprecedented detail by building on an existing global flood model to develop regional to community scale flood hazard maps. We will do this by using the remotely sensed data record on flood occurrence for several satellites to disaggregate river reaches into those that we think go overbank more or less often. This information will be used to locally change the river channel characteristics that will then influence the simulated flood inundation extents, depth and duration for extreme events. By overlaying information on population and land use we will make improved estimates of who and what is exposed to flooding. We will trial our approach with end-users in the Democratic Republic of the Congo via an existing collaboration between the University of Bristol and the University of Kinshasa who host the Congo Basin Network for Research and Capacity Development in Water Resources. The outcome of the project will be an improved flood hazard map for the African continent that for the first time can include local scale variability in river characteristics and a quantification of prediction uncertainty. This will be accompanied by the first estimate of river bathymetry at continental scale that can be used by other flood hazard and risk modelling groups. Therefore, HYFLOOD will improve our understanding of the hydrological and morphological factors that determine the occurrence, duration and impact of floods.