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

[Display omitted] • Uncertainty in complex human-water systems is represented by storyline discovery. • A transdisciplinary framework is presented for uncertainty quantification using storylines. • Findings highlight the significance of narratives in communicating uncertainty. • The soil salinity and crop yield were the most uncertain across all local storylines. • 95th percentile of soil salinity is estimated to be nearly 60% higher by the year 2100. 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 examine the uncertainty effects of a variety of drivers of climatic and socio-economic changes on key agriculture and water-related sectors in Pakistan's Rechna Doab region. The findings revealed soil salinity and crop yield indices were the most uncertain and showed significant variance across all developed storylines. The 95th percentile for soil salinity in year 2100 was estimated to be nearly 60 % higher than the baseline level (year 2020). There was, however, considerable overlap in different socio-economic scenarios at the local scale, indicating that change in socio-economic conditions could not fully offset climate-related uncertainty. Our analysis provides better quantification and a deeper understanding of the uncertainty in integrated assessment modelling of coupled human-water systems and the complex relationships between inputs and outcomes. [ABSTRACT FROM AUTHOR]