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

Institute

  • University of Bristol

Background

Flood hazard and risk maps form the evidence base for decision making 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 innovative, 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 datasets 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 advancement 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.

Aims

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.

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.

Approach

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 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.