UC Berkeley

Abstract Storylines are a useful approach for understanding and preparing for impactful events. For instance, resource managers often use “design droughts” or “floods of record” to stress-test system vulnerabilities and consider alternative responses. While plausible storylines are sufficient for many applications, there is a growing interest in rigorously evaluating storyline probabilities to inform risk-based adaptation investments, especially in cases where preparing for storylines is costly or where storyline probabilities are changing over time. However, assigning actionable probabilities to storylines presents conceptual, methodological, computational, and data challenges. One key challenge is how to reconcile deeply uncertain aspects of the future arising from different scenarios and model structural biases with those aspects that are more amenable to statistical characterization, such as stochastic variability. Another challenge is translating the idiosyncratic features of specific storylines into decision-relevant metrics that can be understood statistically, ideally delivering probability distributions of salient events. Informed by our experience coproducing storyline simulations with resource managers and Earth system scientists, and by a critical literature review, we propose a conceptual framework for rigorously incorporating probabilities into storyline-based analysis and planning. The framework isolates components of uncertainty addressable by extreme value analysis and encourages context-specific methods for evaluating variation across scenarios and models including methods from the field of decision-making under deep uncertainty. We present use cases for the framework that demonstrate how it might be applied for practical management purposes and highlight promising approaches from the literature that can overcome methodological, computational, and data challenges to implementation. Significance Statement Resource management practitioners often use impactful event sequences, or storylines, for stress-testing system resilience and considering alternative options for addressing vulnerabilities. Assigning probabilities to such storylines would increase their management value, especially if preparing for the storylines is costly. However, assigning probabilities to storylines presents significant conceptual, methodological, computational, and data challenges. Here, we present a framework for understanding the different kinds of uncertainty that contribute to storyline probabilities and for using appropriate methods to address each type of uncertainty as part of a comprehensive approach. We also highlight emerging opportunities for improving the methods and data available for calculating storyline probabilities and their uncertainties.