Structuring uncertainty to improve climate change management success

Alfonso Arroyo-Santos , Luis A. Bojorquez-Tapia, Yosune Miquelajauregui

This paper advances the field of climate adaptation by addressing two persistent challenges: navigating multiple forms of uncertainty and enabling the construction of actionable future scenarios. Using a methodology grounded in Decision Making under Deep Uncertainty (DMDU), we combine computational modeling with stakeholder-informed metanarratives to connect abstract analysis with grounded, context-specific knowledge. Our study introduces a novel simulation approach to water scarcity vulnerability in Mexico City, revealing that no amount of budget allocation alone can solve the persistent vulnerability of areas like Iztapalapa. This counterintuitive finding, generated through model-based scenarios, was contextualized and explained by community-derived metanarratives that surfaced deep social, political, and historical uncertainties. In doing so, we highlight how simulations and narratives together offer a more robust means of identifying adaptation pathways than either can alone. Our vulnerability model integrates exposure, sensitivity, and adaptive capacity, drawing from both quantitative service indicators and community knowledge. We argue that addressing climate challenges requires cognitive and methodological tools capable of holding plural uncertainties, enabling diverse futures to be imagined and evaluated.