Articles | Volume 19, issue 1
https://doi.org/10.5194/hess-19-361-2015
© 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-19-361-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Jan 2015
Research article |
| 21 Jan 2015
Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation
T. A. McMahon
Department of Infrastructure Engineering, University of Melbourne, Victoria, 3010, Australia
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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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
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Hydrol. Earth Syst. Sci., 19, 2821–2836, https://doi.org/10.5194/hess-19-2821-2015, https://doi.org/10.5194/hess-19-2821-2015, 2015
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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
Z. K. Tesemma, Y. Wei, M. C. Peel, and A. W. Western
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Revised manuscript not accepted
T. A. McMahon, M. C. Peel, and J. Szilagyi
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Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
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M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
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S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
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Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
Here we assess GCM performance from a hydrologic perspective. We identify five better performing...
