This paper explores the concept of “institutional readiness” (IR) applied to the adoption and mainstreaming of Nature-based solutions (NBS) to deal with climate related risks. We argue that barriers towards up-scaling and mainstreaming of NBS are a manifestation of uncertainty, and are often associated with the ‘readiness’ of the institutional setting rather than with the readiness of the NBS technology itself. We align the concepts of Institutional Readiness (IR) to the more widely used concept of Technology Readiness Level (TRL) to understand drivers and barriers for adoption of NBS and analyse the role of institutional capacity. We illustrate this with the case study of the Urban Water Buffer Spangen in Rotterdam, the Netherlands, which is an NBS with high TRL. To do so, we constructed a timeline of the design and implementation process of the NBS, identifying and classifying key uncertainties as well as the strategies applied to deal with these uncertainties, particularly in the institutional context. Our results indicate that for mainstreaming of NBS, Institutional Readiness (IR) should be at a degree where strategies to deal with uncertainties in institutional, organizational and governance contexts can be integrated in the design and planning process. We claim that the concept of IR should be considered in its role to deal with uncertainty, in order to close the documented gap of NBS implementation and mainstreaming.

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The complexity and uncertainty inherent in environmental models must be considered and managed in an appropriate manner. This chapter presents a conceptual approach to deal with uncertainty, which considers the context of the purpose for which the model is developed. The four major modelling purposes identified - prediction, exploratory analysis, communication and learning - each focus on different modelling characteristics and roles of uncertainty. The notion of uncertainty is broadened, from being an attribute associated with the quality of information to also comprise modellers' beliefs and experiences. The chapter proposes ways in which uncertainty should be handled for each of the four modelling purposes and presents examples that illustrate the concepts. Various sources of uncertainty are considered relevant when modelling complex systems, and each source manifests differently in the data, structure or frame of the model.@en