Planning & Management
395348 - Untangling Consequential Futures: Discovering Self-Consistent Regional and Global Multi-Sectoral Change Scenarios
Wednesday, June 6
2:00 PM - 3:30 PM
Location: Greenway GH
Patrick Reed, Ithaca, NY – Cornell University
Water resources management largely occurs at regional scales, yet water systems are shaped global change through the interdependent evolution of climate, energy, agriculture, and industrial systems. Therefore it is important for regional actors to account for the impacts of global changes on their systems in a globally consistent but regionally relevant way. This can be challenging because emerging global reference scenarios may not reflect regional sources of challenge; likewise, regional scenarios may miss important global feedbacks. In this work, we contribute a scenario discovery framework to identify regionally-specific, decision relevant scenarios from an ensemble of global change scenarios. To this end, we generated a large ensemble of time evolving regional, multi-sector global change scenarios by sampling the underlying assumptions of the shared socio-economic pathways (SSPs), using the Global Change Assessment Model (GCAM). Statistical and visual analytics were then used to discover which SSP assumptions are particularly consequential for various regions, considering a broad range of time-evolving metrics that encompass multiple spatial scales and sectors. We identify the most important global change narratives to inform water resource scenarios for several geographic regions using the proposed scenario discovery framework. Our results highlight the relative importance of demographic and agricultural evolution compared to technical improvements in the energy sector. We find that narrowly sampling a few canonical reference scenarios provides a narrow view of the consequence space, increasing the risk of tacitly ignoring major impacts. Formulating consequential scenarios of deeply and broadly uncertain futures requires a better exploration of which quantitative measures of consequences are important, for whom are they important, where, and when. To this end, we have contributed a large database of climate change futures that can support ‘backwards’ scenario generation techniques, that capture a broader array of consequences than those that emerge from limited sampling of a few reference scenarios.