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Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

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Abstract

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16 % ~ 22 % of total variance in daily precipitation occurrence series and 13 % ~ 25 % of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.

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Acknowledgments

We acknowledge the financial support provided by the National Science and Engineering Research Council (NSERC) of Canada. We are also grateful to Eva Mekis from Environment Canada for providing observed data sets of rehabilitated precipitation. The authors would like to acknowledge also the Data Access and Integration (DAI, see http://loki.qc.ec.gc.ca/DAI/login-e.php) team for providing the predictors data and technical support. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Section (AIRS) of Environment Canada, and the Drought Research Initiative (DRI). The authors would like to acknowledge the two anonymous reviewers for their useful and constructive comments in order to improve the paper.

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Authors and Affiliations

  1. INRS-ETE, University of Québec, 490 de la Couronne, Québec, G1K 9A9, Canada

    D. I. Jeong, A. St-Hilaire & T. B. M. J. Ouarda

  2. Atmospheric Science and Technology Directorate, Canadian Centre for Climate Modelling and Analysis (CCCMA) Section, Climate Research Division, Environment Canada, Montréal, Québec, Canada

    P. Gachon

  3. Water & Environmental Engineering, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

    T. B. M. J. Ouarda

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  1. D. I. Jeong
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  2. A. St-Hilaire
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  3. T. B. M. J. Ouarda
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  4. P. Gachon
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Correspondence to D. I. Jeong.

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Jeong, D.I., St-Hilaire, A., Ouarda, T.B.M.J. et al. Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator. Climatic Change 114, 567–591 (2012). https://doi.org/10.1007/s10584-012-0451-3

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