Scenario analysis of local storylines to represent uncertainty in complex human-water systems

Alizadeh, Mohammad Reza, Adamowski, Jan and Inam, Azhar, (2024). Scenario analysis of local storylines to represent uncertainty in complex human-water systems. Journal of Hydrology, 635 131186-n/a

Document type:
Article

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author Alizadeh, Mohammad Reza
    Adamowski, Jan
    Inam, Azhar
    Title Scenario analysis of local storylines to represent uncertainty in complex human-water systems
    Appearing in Journal of Hydrology
    Volume 635
    Publication Date 2024-01-05
    Place of Publication Amesterdam
    Publisher Elsevier B.V.
    Start page 131186
    End page n/a
    Language eng
    Abstract Storylines are important in evaluating the uncertainty inherent in complex human-water systems. The interrelated nature of qualitative and quantitative scenarios can enhance our ability to address the uncertainty of integrated modelling of complex systems. This study proposes a transdisciplinary approach that integrates social and environmental sciences to characterize and comprehend uncertainty in the dynamic interactions of key factors affecting a human-water system. We introduce a framework for representing uncertainty through linguistic and epistemic uncertainty quantification using storyline narratives in the context of a regional integrated dynamic model. A systematic exploration of uncertainty space is performed using storytelling, fuzzy sets, and low discrepancy sequences sampling methods. Scenario analysis is applied to the generated uncertain ensemble of projections to discover predominant storylines of interest. As a representative case of a human-water system operating in a developing country, we
    Copyright Holder Elsevier B. V.
    Copyright Year 2024
    Copyright type All rights reserved
    ISSN 0043-1397
    DOI 10.1016/j.jhydrol.2024.131186
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 44 Abstract Views  -  Detailed Statistics
    Created: Thu, 26 Sep 2024, 03:21:57 JST by Haideh Beigi on behalf of UNU INWEH