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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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  • Reyhaneh Rahimi

    (San Diego State University)

  • Hassan Tavakol-Davani

    (San Diego State University)

  • Mohsen Nasseri

    (University of Tehran)

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.

Suggested Citation

  • Reyhaneh Rahimi & Hassan Tavakol-Davani & Mohsen Nasseri, 2021. "An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2503-2518, June.
  • Handle: RePEc:spr:waterr:v:35:y:2021:i:8:d:10.1007_s11269-021-02844-0
    DOI: 10.1007/s11269-021-02844-0
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    References listed on IDEAS

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    1. Muhammad Azmat & Muhammad Uzair Qamar & Shakil Ahmed & Muhammad Adnan Shahid & Ejaz Hussain & Sajjad Ahmad & Rao Arsalan Khushnood, 2018. "Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3155-3174, July.
    2. Alan F. Hamlet & Kyuhyun Byun & Scott M. Robeson & Melissa Widhalm & Michael Baldwin, 2020. "Impacts of climate change on the state of Indiana: ensemble future projections based on statistical downscaling," Climatic Change, Springer, vol. 163(4), pages 1881-1895, December.
    3. Hadi Galavi & Majid Mirzaei, 2020. "Analyzing Uncertainty Drivers of Climate Change Impact Studies in Tropical and Arid Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2097-2109, April.
    4. Shadi Arfa & Mohsen Nasseri & Hassan Tavakol-Davani, 2021. "Comparing the Effects of Different Daily and Sub-Daily Downscaling Approaches on the Response of Urban Stormwater Collection Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 505-533, January.
    5. S. Samadi & Gregory Carbone & M. Mahdavi & F. Sharifi & M. Bihamta, 2013. "Statistical Downscaling of River Runoff in a Semi Arid Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 117-136, January.
    6. Roja Najafi & Masoud Reza Hessami Kermani, 2017. "Uncertainty Modeling of Statistical Downscaling to Assess Climate Change Impacts on Temperature and Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1843-1858, April.
    7. Daniel S. Wilks, 2010. "Use of stochastic weathergenerators for precipitation downscaling," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 1(6), pages 898-907, November.
    8. Subbarao Pichuka & Rajib Maity, 2020. "Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1809-1826, March.
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