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Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables

Author

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  • Taesam Lee

    (Gyeongsang National University)

  • Taha B. M. J. Ouarda

    (INRS-ETE)

Abstract

The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled.

Suggested Citation

  • Taesam Lee & Taha B. M. J. Ouarda, 2018. "Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables," Climatic Change, Springer, vol. 150(3), pages 163-180, October.
  • Handle: RePEc:spr:climat:v:150:y:2018:i:3:d:10.1007_s10584-018-2276-1
    DOI: 10.1007/s10584-018-2276-1
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    References listed on IDEAS

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    1. D. Jeong & A. St-Hilaire & T. Ouarda & P. Gachon, 2012. "Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator," Climatic Change, Springer, vol. 114(3), pages 567-591, October.
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    Cited by:

    1. Taesam Lee & Younghwan Choi & Vijay P. Singh, 2023. "Stochastic Spatial Binary Simulation with Multivariate Normal Distribution for Illustrating Future Evolution of Umbrella-Shape Summer Shelter under Climate Change," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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