Preprints
https://doi.org/10.5194/hessd-6-6535-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hessd-6-6535-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
27 Oct 2009
Status: this preprint was under review for the journal HESS but the revision was not accepted.
Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment
Abstract. Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. However, the downscaling results may contain considerable uncertainty which needs to be quantified before making the results available. Among the variables usually downscaled, precipitation downscaling is quite challenging and is more prone to uncertainty issues than other climatological variables. This paper addresses the uncertainty analysis associated with statistical downscaling of a watershed precipitation (Clutha River above Balclutha, New Zealand) using results from three well reputed downscaling methods and Bayesian weighted multi-model ensemble approach. The downscaling methods used for this study belong to the following downscaling categories; (1) Multiple linear regression; (2) Multiple non-linear regression; and (3) Stochastic weather generator. The results obtained in this study have shown that this ensemble strategy is very efficient in combining the results from multiple downscaling methods on the basis of their performance and quantifying the uncertainty contained in this ensemble output. This will encourage any future attempts on quantifying downscaling uncertainties using the multi-model ensemble framework.
How to cite. Hashmi, M. Z., Shamseldin, A. Y., and Melville, B. W.: Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment, Hydrol. Earth Syst. Sci. Discuss., 6, 6535–6579, https://doi.org/10.5194/hessd-6-6535-2009, 2009.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
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RC C2961: 'referee comment', Anonymous Referee #1, 20 Dec 2009
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AC C3372: 'Final Response to Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
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AC C3372: 'Final Response to Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
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RC C2979: 'Statistical downscaling of climate model results. Bayesian uncertainty assessment.', Anonymous Referee #2, 25 Dec 2009
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AC C3373: 'Final Response to the Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
-
AC C3373: 'Final Response to the Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
-
RC C2961: 'referee comment', Anonymous Referee #1, 20 Dec 2009
-
AC C3372: 'Final Response to Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
-
AC C3372: 'Final Response to Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
-
RC C2979: 'Statistical downscaling of climate model results. Bayesian uncertainty assessment.', Anonymous Referee #2, 25 Dec 2009
-
AC C3373: 'Final Response to the Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
-
AC C3373: 'Final Response to the Referee comments', Muhammad Zia ur Rahman Hashmi, 22 Feb 2010
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Latest update: 23 Apr 2024
M. Z. Hashmi
Department of Civil and Environmental Engineering, School of Engineering, The University of Auckland, New Zealand
A. Y. Shamseldin
Department of Civil and Environmental Engineering, School of Engineering, The University of Auckland, New Zealand
B. W. Melville
Department of Civil and Environmental Engineering, School of Engineering, The University of Auckland, New Zealand