Despite efforts to reduce disaster risk, losses due to flood events have been increasing in sub-Saharan Africa over the last half century. Such losses are occurring amidst a combination of increasing vulnerability due to population growth, informal urbanisation and political instability, more uncertain hazard probabilities as a result of climate change and a rapidly declining network of already scarce on-the-ground hydrological observations. We currently possess a rather limited understanding and poor mapping capability regarding flood hazard and risk, which undermines the effectiveness of on-going disaster risk management efforts. This data scarcity becomes especially acute as the spatial resolution of a risk analysis increases to regional and community levels.
The vision underpinning HYFLOOD is that future disaster risk management and planning will be based on modelling systems that provide flood risk information everywhere, conditioned on local data if available, therefore ending the current fragmented and unequal provision of information. Such a system has become feasible over the past five years with the development of global flood models. However, although these have proved useful tools for high-level planning, they have yet to attain a level of precision and accuracy that can discriminate between areas of greater and lesser risk at national to community scales.
The overall aim of HYFLOOD is to develop a modelling framework where hazard predictions have local fidelity and to improve the availability, value and uptake of flood risk information at the sub-national level as a critical foundation for decision making in the context of disaster risk management. For this effort to be successful, estimates of uncertainty in global flood model predictions are also needed to avoid poor decisions being made on the basis of flood edges on hazard maps. To achieve this aim, HYFLOOD will pursue the following objectives:
O1: Create an improved flood hazard map for sub-Saharan Africa, with greater accuracy and unprecedented resolution.
O2: Develop an approach to estimate river bank overtopping frequency from the remotely sensed record that for the first time will allow uniqueness of place in river conveyance estimates.
O3: Develop a method to invert river bathymetry from observed bank elevation, river width and overtopping frequency combined with flow frequency from the same data source as the extreme flow estimates.
O4: Evaluate new global flood model estimates from O3 bathymetry data in the Congo basin using data from our Royal Society Congo River users Hydraulics and Morphology project and undertake an analysis of model sensitivity to overtopping frequency.
O5: Build capacity at the University of Kinshasa to provide local expertise in the interpretation and analysis of GFM outputs from our system and others in the global floods partnership.
In collaboration with our project partner Dr Andrew Smith at Fathom we will aim to share our flood hazard and risk data with international organisations such as GFDRR using pre-existing links. We will also collaborate with stakeholders via the Congo Basin Network for Research and Capacity Development in Water Resources headed by Co-Investigator Dr Raphael Tshimanga. The outcomes of this collaboration will include increased capacity at the University of Kinshasa to analysis, visualise, manipulate and disseminate GFM outputs to stakeholders and an understanding of stakeholder needs and preferred dissemination approach. Risk information and training provided by HYFLOOD in DR Congo will support: Risk identification - Understanding, communicating and raising awareness of flood disaster risk; Risk reduction - Informing policies, investments, and structural and non-structural measures intended to reduce risk; and Preparedness - Informing early warning systems and emergency measures and supporting preparedness.
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.