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An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation

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Abstract

Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model.

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Availability of Data and Material

All input data used in this research can be found from the publicly available sources.Code Availability The codes are available and can be presented if needed..

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Authors and Affiliations

  1. Urban Water Group, San Diego State University, San Diego, CA, USA

    Reyhaneh Rahimi & Hassan Tavakol-Davani

  2. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohsen Nasseri

Authors
  1. Reyhaneh Rahimi
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  2. Hassan Tavakol-Davani
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  3. Mohsen Nasseri
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Correspondence to Reyhaneh Rahimi.

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Rahimi, R., Tavakol-Davani, H. & Nasseri, M. An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation. Water Resour Manage 35, 2503–2518 (2021). https://doi.org/10.1007/s11269-021-02844-0

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  • DOI: https://doi.org/10.1007/s11269-021-02844-0

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