Data_Sheet_2_Assessing Flood Risk Dynamics in Data-Scarce Environments—Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin.docx

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.

河流洪水是一种常见的环境灾害，在全球范围内常造成严重财产损失、人员伤亡及生计影响。多哥与贝宁的跨界下莫诺河流域（Lower Mono River Basin）亦不例外，频繁遭受河流洪水侵袭。为在洪水风险管理场景下制定合理决策，明晰风险驱动因子、其相互关联机制，以及二者如何共同催生洪水风险与相关不确定性，至关重要。然而，在数据稀缺的环境中，亟需方法学层面的进展，以更好地满足风险评估中的上述需求。针对上述问题，本研究通过案头调研与专家咨询构建了影响链，以揭示贝宁境内下莫诺河流域农业生计相关洪水风险的关键驱动因子及其相互关联。研究特别强调了脆弱性的动态形成过程，及其与致灾因子、暴露度之间的相互作用。为进一步探究这些相互作用，本研究基于影响链构建了α级贝叶斯网络（Bayesian Network），并将其应用于典型假设情景（what-if scenario），以模拟特定风险驱动因子变化时的风险变动情况。基于上述工作，本文批判性地评估了整合两种方法学路径的优势与局限，以在数据稀缺环境中理解并模拟风险动态变化。研究表明，影响链是一种用于概念化风险驱动因子相互作用的有效模型方法。尤其是与贝叶斯网络方法结合使用时，该方法可进一步增进对不同风险驱动因子在系统内相互作用机制的理解，并支持假设情景的动态模拟，例如助力适应规划工作。