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Presentation . 2023
License: CC BY
Data sources: Datacite
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Other literature type . 2023
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Advancing scenario discovery to identify impacts and consequential dynamics for complex multi-actor human-natural systems

Authors: Hadjimichael, Antonia; Reed, Patrick M.; Quinn, Julianne D.; Vernon, Chris R.; Thurber, Travis;

Advancing scenario discovery to identify impacts and consequential dynamics for complex multi-actor human-natural systems

Abstract

Abstract: Scenarios are widely used tools in both assessing the potential impacts of future uncertain conditions and in informing adaptive management. Yet, traditional “top down” narrative scenario approaches face criticisms when they rely on a a small number of prespecified scenarios that can inadvertently exclude consequential dynamics, extreme conditions, and diverse stakeholder impacts. Exploratory modeling and “bottom up” approaches on the other hand, advocate for the use of ensembles that investigate large numbers of hypothetical futures to discover the ones most consequential to a system and its stakeholders. Exploratory methods introduce more rigor into how the space of potential uncertainty is explored, but, at the same time, cause users to struggle with conveying actionable information and guiding follow-on adaptive actions. This talk introduces the FRamework for Narrative Scenarios and Impact Classification (FRNSIC; pronounced “forensic”) bridging the gap between these two approaches. The framework uses hierarchical classification sets to discover narrative scenarios within a broad exploratory ensemble. The identified classes of narrative scenarios summarize both critical impacts and the consequential system dynamics that produce them. We build on prior work to demonstrate FRNSIC on the Upper Colorado River Basin, focusing on decadal drought conditions and their impacts to the basin’s water users as well as the basin’s downstream deliveries. Our results show how FRNSIC can be used to guide future adaptive management and iterative (re-)analysis on adaptation options under consequential dynamic storylines.

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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