EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

The use of impact chains and Bayesian Network Analysis to assess flood risk dynamics in the Lower Mono River Basin, Benin

Mario Wetzel1, Lorina Schudel1, Adrian Almoradie2, Kossi Komi3, Julien Adounkpe4, Yvonne Walz1, and Michael Hagenlocher1
Mario Wetzel et al.
  • 1United Nations University, Institute for Environment and Human Security, Bonn, Germany
  • 2Working Group Eco-Hydrology and Water Resource Management, Department of Geography, University of Bonn, Bonn, Germany
  • 3Working Group Hydro-climatology and Water Resource Management, Research Laboratory on Spaces, Exchanges and Human Security, Department of Geography, University of Lomé, Lomé, Togo
  • 4Graduate Research Program Climate Change and Water Resource, West Africa Science Service Centre on Climate Change and Adapted Land Use, University of Abomey-Calavi, Abomey-Calavi, Benin

River floods are a common and often devastating environmental hazard causing severe damages, loss of lives and livelihoods, notably for the most vulnerable. Understanding the root causes, drivers, patterns and dynamics of flood risks and associated uncertainties is important to inform adequate risk management. Yet, a lack of understanding the highly dynamic processes, interactions, uncertainties, and the inclusion of participatory methods and transdisciplinary approaches in risk assessments remains a limiting factor. In many flood-prone regions of the world, data scarcity poses another serious challenge for risk assessments. Addressing the above, we developed an impact chain via desk study and expert consultation to reveal key drivers of flood risk for agricultural livelihoods in the Lower Mono River Basin of Benin and their interlinkages – a region that is both highly prone to flooding and can be considered data-scarce. Particularly, the dynamic formation of vulnerability and its interplay with hazard and exposure components is highlighted.

Based on a simplified version of the impact chain which was validated in a participatory manner during a virtual expert workshop, an alpha-level Bayesian Network was created to further explore these interactions. The model was applied to an exemplary what-if scenario for the study area in Benin. Based on the above, this study critically evaluates the benefits and limitations of integrating the two methodological approaches to better understand and simulate risk dynamics in data scarce environments. The study finds that impact chains are a useful approach to conceptualize interactions of risk drivers. Particularly in combination with a Bayesian Network approach the method enables an improved understanding of how different risk drivers interact within the system and allows for dynamic assessments of what-if scenarios, for example, to inform resilience building strategies.

How to cite: Wetzel, M., Schudel, L., Almoradie, A., Komi, K., Adounkpe, J., Walz, Y., and Hagenlocher, M.: The use of impact chains and Bayesian Network Analysis to assess flood risk dynamics in the Lower Mono River Basin, Benin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11251,, 2022.