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 and Hagenlocher, Michael, (2022). Assessing Flood Risk Dynamics in Data-Scarce Environments— Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin. Frontiers in Water, 4 1-16

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  • Sub-type Journal article
    Author Wetzel, Mario
    Schudel, Lorina
    Almoradie, Adrian
    Komi, Kossi
    Adounkpè, Julien
    Walz, Yvonne
    Hagenlocher, Michael
    Title Assessing Flood Risk Dynamics in Data-Scarce Environments— Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin
    Appearing in Frontiers in Water
    Volume 4
    Publication Date 2022-03-11
    Place of Publication Lausanne
    Publisher Frontiers
    Start page 1
    End page 16
    Language eng
    Abstract River floods are a common environmental hazard, often causing severe damages, loss of lives and livelihood impacts around the globe. The transboundary Lower Mono River Basin of Togo and Benin is no exception in this regard, as it is 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. However, methodological advances to better account for these necessities in risk assessments, in data-scarce environments, are needed. Addressing the above, we developed an impact chain via desk study and expert consultation to reveal key drivers of flood risk for agricultural livelihoods and their interlinkages in the Lower Mono River Basin of Benin. Particularly, the dynamic formation of vulnerability and its interaction with hazard and exposure 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 to simulate changes in risk if certain risk drivers change. Based on the above, this article critically evaluates the benefits and limitations of integrating the two methodological approaches to understand and simulate 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 simulations of what-if scenarios, for example, to support adaptation planning.
    UNBIS Thesaurus RISK ASSESSMENT
    BENIN
    Keyword Drivers of risk
    Conceptual model
    Flood risk
    Vulnerability
    Copyright Holder The Authors
    Copyright Year 2022
    Copyright type Creative commons
    DOI 10.3389/frwa.2022.837688
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    Created: Tue, 15 Mar 2022, 18:45:25 JST by Aarti Basnyat on behalf of UNU EHS