Articles | Volume 19, issue 4
https://doi.org/10.5194/hess-19-1615-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-1615-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Apr 2015
Research article |
| 08 Apr 2015
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
Department of Infrastructure Engineering, University of Melbourne, Victoria, 3010, Australia
R. Srikanthan
Environment and Research Division, Bureau of Meteorology, Melbourne, Victoria, 3001, Australia
T. A. McMahon
Department of Infrastructure Engineering, University of Melbourne, Victoria, 3010, Australia
D. J. Karoly
School of Earth Sciences and ARC Centre of Excellence for Climate System Science, University of Melbourne, Victoria, 3010, Australia
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Short summary
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary...
