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Regionalization and parameterization of a hydrologic model significantly affect the cascade of uncertainty in climate-impact projections

  • Saeid Ashraf VaghefiEmail author
  • Majid Iravani
  • David Sauchyn
  • Yuliya Andreichuk
  • Greg Goss
  • Monireh Faramarzi
Article

Abstract

Climate-impact projections are subject to uncertainty arising from climate models, greenhouse gases emission scenarios, bias correction and downscaling methods (BCDS), and the impact models. We studied the effects of hydrological model parameterization and regionalization (HM-P and HM-R) on the cascade of uncertainty. We developed a new, widely-applicable approach that improves our understanding of how HM-P and HM-R along with other uncertainty drivers contribute to the overall uncertainty in climate-impact projections. We analyzed uncertainties arising from general circulation models (GCMs), representative concertation pathways, BCDS, evapotranspiration calculation methods, and specifically HM-P and HM-R. We used the Soil and Water Assessment Tool, a semi-physical process-based hydrologic model with a high capability of parameterization, to project blue and green water resources for historical (1983–2007), near future (2010–2035) and far future (2040–2065) periods in Alberta, a western province of Canada. We developed an Analysis of Variance (ANOVA)-Sequential Uncertainty Fitting Program approach, to decompose the overall uncertainty into contributions of single drivers using the projected blue and green water resources. The monthly analyses of projected water resources showed that HM-P and HM-R contribute 21–51% and 15–55% to the blue water, and 20–48% and 15–50% to the green water overall uncertainty in near future and far future, respectively. Overall, we found that in spring and summer seasons uncertainty arising from HM-P and HM-R dominates other uncertainty sources, e.g. GCMs. We also found that global climate models are another dominant source of uncertainty in future impact projections.

Keywords

Uncertainty analysis Uncertainty decomposition Climate change Natural climate variability SWAT ANOVA-SUFI-2 

Notes

Acknowledgements

We gratefully acknowledge the funding from Alberta Innovates (Grant # RES0030781) and the grant awarded by the Campus Alberta Innovation Program Chair (Grant #RES0030781). The authors would like to acknowledge the anonymous reviewer who made a detailed evaluation on the paper.

Supplementary material

382_2019_4664_MOESM1_ESM.docx (1.7 mb)
Supplementary material 1 (DOCX 1694 KB)

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Authors and Affiliations

  1. 1.Watershed Science and Modelling Laboratory, Department of Earth and Atmospheric SciencesUniversity of AlbertaEdmontonCanada
  2. 2.Alberta Biodiversity Monitoring InstituteUniversity of AlbertaEdmontonCanada
  3. 3.Prairie Adaptation Research CollaborativeUniversity of ReginaReginaCanada
  4. 4.Department of Biological SciencesUniversity of AlbertaEdmontonCanada

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