Beyond pipes and budgets: Mapping uncertainty and crafting scenarios for transformative water adaptation

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.Author summary: Our study introduces a novel approach to climate adaptation planning, through a case study of water scarcity in Mexico City. We argue that traditional adaptation planning often falls short because it treats uncertainty as a single technical problem, overlooking the complex social and political roots that generate it. To address this gap, we developed a computational tool that integrates hard data with the lived stories or “meta-narratives” of local communities. With this information we model distinct roots of uncertainty by simulating thousands of possible futures. Our goal is not predictive accuracy but constructing guiding principles for making robust decision-making principles that remain effective across different possible futures. This process uncovered a critical, counterintuitive insight in the borough of Iztapalapa: even with maximum budget allocations for new infrastructure, water vulnerability remained critically high. The model shows that the true drivers of scarcity are not physical infrastructure gaps, but deep-seated historical and political inequalities. Overall, this framework allows researchers to gain insights that would not be possible using traditional statistical methods. By making visible these often-hidden barriers, the study proves the value of advanced modeling to identify when and why proposed solutions might fail before they are implemented.