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Downscaling Techniques in Climate Modeling

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Impact of Climate Change on Water Resources

Part of the book series: Springer Climate ((SPCL))

Abstract

Describes downscaling techniques where GCM outputs are interpolated to the scale of hydrological modeling or local scale requirement. Statistical downscaling techniques that facilitate statistical relationships that metamorphose large-scale atmospheric variables/predictors simulated by GCMs to local scale variables/predictand are discussed in detail. Techniques include Linear and Non-linear regression, Artificial Neural Networks, Statistical Downscaling Model (SDSM), Change Factor, Least-Square, and Standard Support Vector Machines. Detailed discussion about Artificial Neural Networks that includes information about preprocessing, weights, epoch, activation function, training, learning rate, momentum factor, weight updation procedures, and challenges are also presented. SDSM, combination of regression and conditional weather generator techniques, Change Factor, and Support Vector Machine are also briefed. Nested Bias Correction technique which addresses bias across prespecified multiple timescales is also part of this chapter. Reader is expected to understand various statistical downscaling techniques by studying this chapter.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India

    Komaragiri Srinivasa Raju

  2. Department of Civil Engineering, Centre for Earth Sciences, Divecha Centre for Climate Change, Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore, Karnataka, India

    Dasika Nagesh Kumar

Authors
  1. Komaragiri Srinivasa Raju
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  2. Dasika Nagesh Kumar
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Corresponding author

Correspondence to Komaragiri Srinivasa Raju .

3.1 Electronic Supplementary Material

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Srinivasa Raju, K., Nagesh Kumar, D. (2018). Downscaling Techniques in Climate Modeling. In: Impact of Climate Change on Water Resources . Springer Climate. Springer, Singapore. https://doi.org/10.1007/978-981-10-6110-3_3

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