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.

<p>Floods in West Africa repeatedly cause devastating impacts on human life and livelihoods, infrastructure and the environment and they are expected to increase in frequency and severity under a changing climate. Ecosystem-based approaches can be a cost-effective, efficient way to reduce flood risk while at the same time providing co-benefits. However, qualitative and quantitative assessments of ecosystem-based approaches that are suitable for the climatic conditions and socio-ecological system of the region are scarse. This study therefore identifies and evaluates climate-sensitive ecosystem-based approaches for the transboundary Lower Mono River Basin in Benin and Togo. The identification of ecosystem-based approaches has been done based on a review of scientific literature and complemented by a participatory approach with experts from the catchment. During focus group discussions, national stakeholders and policy makers identified, prioritized and mapped existing measures and provided their perspectives on prospective measures to reduce flood risk in the transboundary catchment. They include measures to reduce flow velocity, increase soil infiltration and improve water management. In a next step, we used a multi-criteria analysis considering ecological, climatic and flood hazard data to create suitability maps for different clusters of identified ecosystem-based approaches. This study is part of the CLIMAFRI project, which aims at creating a river basin information system for the Lower Mono Basin as well as creating a flood risk management plan. Through the integration of the suitability maps into the flood risk assessment tool, which has been created in the scope of this project, the ecosystem-based approaches are evaluated quantitatively. In a second round of focus group discussions with representatives from the local communities, feasibility of selected ecosystem-based approaches, co-benefits and trade-offs of the measures are discussed. Through the combination of qualitative and quantitative data, a holistic evaluation of ecosystem-based approaches and their contribution to hazard mitigation, increase of coping capacity, ecosystem resilience and overall flood risk reduction can be achieved.</p>

<p>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.</p><p>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.</p>