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Multi-actor, multi-impact scenario discovery of consequential narrative storylines for human-natural systems planning
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  • Antonia Hadjimichael,
  • Patrick M. Reed,
  • Julianne D. Quinn,
  • Chris R Vernon,
  • Travis Thurber
Antonia Hadjimichael
Penn State University

Corresponding Author:[email protected]

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Patrick M. Reed
Cornell University
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Julianne D. Quinn
University of Virginia
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Chris R Vernon
Pacific Northwest National Laboratory
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Travis Thurber
Pacific Northwest National Lab
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Scenarios have emerged as valuable tools in managing complex human-natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or narrative storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Scenarios and Impact Classification (FRNSIC; pronounced “forensic’): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of narrative storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin’s diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify narrative drought storylines, that can be used to inform future adaptation planning.
21 Nov 2023Submitted to ESS Open Archive
22 Nov 2023Published in ESS Open Archive