Ng et al. (2025) Evaluation of Bias Correction Methods for Coupled Model Intercomparison Project Phase 6 Model and Future Rainfall Projections over Muda River Basin

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Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2025
• Date: 2025-11-20
• Authors: Cia Yik Ng, Eugene Zhen Xiang Soo, Wan Zurina Wan Jaafar, Faridah Othman, Jingyi Khor, Nurmin Bolong
• DOI: 10.1051/e3sconf/202565902008/pdf

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study evaluates three bias correction methods for CMIP6 rainfall projections in the Muda River Basin, finding that Local Intensity Scaling (LOCI) significantly improves accuracy and reveals a nonlinear relationship between future rainfall and emission scenarios, with increased annual rainfall under higher emissions by the century's end.

Objective

• To evaluate the performance of Linear Scaling (LS), Local Intensity Scaling (LOCI), and Empirical Quantile Mapping (EQM) bias correction methods applied to CMIP6 MPI-ESM1.2-HR rainfall simulations for accurate rainfall projections in Malaysia's Muda River Basin.

Study Configuration

• Spatial Scale: Muda River Basin, Malaysia.
• Temporal Scale: Historical period: 1989–2014. Future projection period: 2015–2100, analyzed in sub-periods: near-term (2026–2050), mid-term (2051–2075), and end-of-century (2076–2100).

Methodology and Data

• Models used: CMIP6 MPI-ESM1.2-HR.
• Data sources: Historical rainfall observations (1989–2014) for calibration and validation; CMIP6 model rainfall simulations under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios.

Main Results

• Local Intensity Scaling (LOCI) achieved the highest overall skill with errors less than 10% for wet-day frequency, mean wet-day intensity, and the 90th percentile of wet-day rainfall.
• Empirical Quantile Mapping (EQM) showed 10–16% errors, while Linear Scaling (LS) showed 21–36% errors.
• LOCI-corrected projections indicate a nonlinear relationship between rainfall response and emission scenarios in the near- (2026–2050) and mid-term (2051–2075) future.
• By the end of the century (2076–2100), higher emissions (SSP5-8.5) are associated with increased annual rainfall.
• Mid-term rainfall projections are relatively stable across scenarios, but greater deviations emerge toward the end of the century.

Contributions

• Demonstrates the substantial improvement in CMIP6 projected rainfall accuracy through appropriate bias correction methods.
• Provides a comparative evaluation of three common bias correction techniques (LS, LOCI, EQM) for rainfall projections in the Muda River Basin, identifying LOCI as the most effective.
• Offers refined rainfall projections for the Muda River Basin under CMIP6 Shared Socioeconomic Pathway (SSP) scenarios, highlighting nonlinear responses and end-of-century trends crucial for regional water resource planning.

Funding

Not explicitly stated in the provided text.

Citation

@article{Ng2025Evaluation,
  author = {Ng, Cia Yik and Soo, Eugene Zhen Xiang and Jaafar, Wan Zurina Wan and Othman, Faridah and Khor, Jingyi and Bolong, Nurmin},
  title = {Evaluation of Bias Correction Methods for Coupled Model Intercomparison Project Phase 6 Model and Future Rainfall Projections over Muda River Basin},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2025},
  doi = {10.1051/e3sconf/202565902008/pdf},
  url = {https://doi.org/10.1051/e3sconf/202565902008/pdf}
}