Assessing Flood Risk Dynamics in Data-Scarce Environments—Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin - Wetzel, Mario; Schudel, Lorina; Almoradie, Adrian; Komi, Kossi; Adounkpè, Julien; Walz, Yvonne; Hagenlocher, Michael (Frontiers in Water, 2022)
River floods are a common and often devastating environmental hazard causing severe damages, loss of lives and livelihoods around the globe. The transboundary Lower Mono River Basin of Togo and Benin is no exception in this regard, being frequently affected by river flooding. To enable adequate decision-making in the context of flood risk management it is crucial to understand the drivers of risk, their interconnections and how they co-produce flood risks as well as associated uncertainties. Yet, methodological advances to better account for these necessities in risk assessments, particularly against the background of data-scarcity, are needed. Addressing the above, we developed a participatory 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. Particularly, the dynamic formation of vulnerability and its interaction with hazard and exposure components is highlighted. To further explore these interactions, an alpha-level Bayesian Network was created based on the impact chain and applied to an exemplary what-if scenario. Based on the above, this paper critically evaluates the benefits and limitations of integrating the two methodological approaches to understand risk dynamics in data scarce environments. The study finds that impact chains are a useful model 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 support adaptation planning.