Planning & Management

Oral

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
Co-Authors: 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.

Jonathan Richard. Lamontagne

Assistant Professor
Tufts University

Jon attended the University of New Hampshire, where he earned his Bachelor of Science in Civil Engineering in 29909. In fall 2009 he matriculated to Cornell University where he earned his MS and PhD in Civil and Environmental Engineering in 2014 and 2015 respectively. He is currently a post-doctoral researcher at Cornell University studying Integrated Assessment Models of the coupled climate-economic system.. His reasearch interests includes flood frequency analysis, optimization, senstivity analysis, hydropower operations, stochastic hydrology, and climate change impacts and adaptation.

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