EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Flood modelling using disaggregated rainfall time series and estimating the probability of cholera infection from post-flood ponds in Accra, Ghana 

Auður Eva Jónsdóttir1, Hannes Müller-Thomy2, Jorge Leandro3, and Jingshui Huang1
Auður Eva Jónsdóttir et al.
  • 1Chair of Hydrology and River Basin Management, Technical University of Munich, Munich, Germany (
  • 2Leichtweiß-Institute for Hydraulic Engineering and Water Resources, Technical University Braunschweig, Braunschweig, Germany
  • 3Chair of Hydromechanics and Hydraulic Engineering, University of Siegen, Siegen, Germany

Extreme weather events magnified by climate change will likely increase the frequency of severe flooding. In this work, we studied the effects of climate change on flooding and cholera infections associated with contaminated floodwater in the Alajo neighbourhood in Accra, Ghana, by considering projected rainfall from different climate scenarios of the GFDL-ESM4 climate model, SSP1-2.6, SSP3-7.0 and SSP5-8.5.

Rainfall of daily resolution projected by the climate scenarios was disaggregated into five-minute resolution time series using a multiplicative microcanonical cascade model, and resulting extreme events were simulated using a 1D SWMM model of the subcatchments coupled with a 2D parallel diffusive wave model (P-DWave) of Alajo. The concentration of V. cholerae in the floodwater was further simulated as coming from open drains in the neihbourhood.  

Following the flood simulation, the post-flood phase was further simulated, where the V. cholerae concentration was estimated using a constant pathogen die-off rate, and infiltration and evaporation of the post-flood ponds.

Using a quantitative microbial risk assessment (QMRA), the probabilities of infection for both adults wading and young children playing or swimming in the post-flood ponds was estimated with a Beta-Poisson dose response model for the El Tor V. Cholera biotype. The QMRA was integrated into the flood risk assessment framework, by replacing the consequence component with infection probability. The expected annual probability of infection (EAPI) for each climate scenario was then found by numerically integrate over the precedence probability.

It was found that the mean estimated EAPI is higher for young children than for adults in the study area, but only differs slightly between climate scenarios. This study highlighted the areas most vulnerable to flooding and associated cholera outbreaks, and further development of these techniques could help with decision making on preventative measures for affected areas.

How to cite: Jónsdóttir, A. E., Müller-Thomy, H., Leandro, J., and Huang, J.: Flood modelling using disaggregated rainfall time series and estimating the probability of cholera infection from post-flood ponds in Accra, Ghana , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16261,, 2023.