Local hydrological and hydrodynamic modeling for flood forecasting in Burkina Faso

Burkina Faso climate is characterized by a short rainy season and  high rainfall variability, characteristic of tropical-equatorial regions, resulting in extreme rainfall events and high flood risks in its watersheds and cities. In the capital Ouagadougou, rapid urban development associated with low-permeability soils and high precipitation intensity lead to major flooding events (e.g. in 2009, 2016, 2020) affecting households and economy. This vulnerability to flooding also affects other strategic points in Burkina Faso, such as crossroads between national roads and rivers, where overflows almost every year lead to limited road access and hinder economical transportation.

This study presents an innovative integrated framework to improve forecasting capacity and manage flood risks at the local scale, for both (i) pluvial flooding over Ouagadougou city and (ii) fluvial flooding at six points of interest (POIs) across Burkina Faso. The methodology is based on a 2D hydrodynamic modeling using the DassHydro [1] framework and only publicly available data (soil properties, land cover, etc.). For pluvial flooding (Ouagadougou case), this model is forced with operational precipitation products. For fluvial flooding,  daily real-time discharge data computed with the MGB hydrological model [2] are used as boundary conditions for the hydrodynamic model set at the POIs. Both approaches produce local flood maps for different warning levels, based on precipitations  and/or discharge thresholds. Flood maps produced for each POI were validated through comparisons to Sentinel-2 images of  historical floods, on-site flood marks analysis and spatial altimetry.  Additionally, comparisons with previous studies conducted in Ouagadougou as well as historical informations,  demonstrated the relevance and reliability of the results obtained through our methodology at both local scale.

This preliminary approach showed the efficiency of the methodology for a flood risk warning and forecasting system in a data-sparse context and highlighted the strong need for in-situ data and finer-grained topology data, among others, in those regions. Further consideration of new in situ data provided by local agencies should permit increasing the accuracy of forecasts and provide refined risk analysis.

[1] https://dasshydro.github.io/

[2] https://www.ufrgs.br/lsh/mgb/what-is-mgb-iph/