Efficient Statistical Approach to Multisite Downscaling of Extreme Temperature Series Using Singular-Value Decomposition Technique
Publication: Journal of Hydrologic Engineering
Volume 23, Issue 6
Abstract
Downscaling techniques are required to describe the linkages between global climate model (GCM) outputs at coarse grid resolutions and surface variables at suitable finer scales for climate change impact and adaptation studies. The present paper proposes an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new approach is based on a combination of a multiple-regression model and the modeling of its stochastic component by the singular-value decomposition (SVD) technique to represent more effectively and accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using data from a network of 10 weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada and from the available National Centers for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data set indicated the effectiveness and the accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin time series, including their mean, standard deviation, Tmax 90th percentile, and Tmin 10th percentile. In addition, the at-site autocorrelations, interstation correlations, and intervariable correlations of the daily Tmax and Tmin series have been accurately reproduced. Furthermore, the proposed approach was able to adequately reproduce the interannual variability of the Tmax and Tmin.
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Acknowledgments
The authors would like to acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (Special Research Opportunity Program) for the project entitled “Probabilistic assessment of regional changes in climate variability and extremes.” Also, the authors acknowledge the “Fond Québécois de Recherche sur la Nature et les Technologies” for its funding of this research, and the Data Access Integration (DAI) Team for providing the data. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division (AIRD) of Environment Canada, and the Drought Research Initiative (DRI).
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Published In
Journal of Hydrologic Engineering
Volume 23 • Issue 6 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jun 21, 2017
Accepted: Dec 5, 2017
Published online: Mar 30, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 30, 2018
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