Alodah (2026) Bayesian quantile mapping and schaake shuffle for precipitation downscaling in arid environments

Identification

• Journal: Frontiers in Environmental Science
• Year: 2026
• Date: 2026-09-17
• Authors: Abdullah Alodah
• DOI: 10.3389/fenvs.2026.1773597

Research Groups

• Department of Civil Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia

Short Summary

This study introduces an enhanced statistical downscaling framework that combines Bayesian quantile mapping with the Schaake Shuffle (BQM+SS) to address precipitation modeling challenges in arid environments. The methodology accounts for parameter uncertainty through Bayesian inference and improves the representation of inter-station spatial coherence.

Objective

• To develop a robust statistical downscaling method for precipitation in arid regions, incorporating Bayesian quantile mapping with the Schaake Shuffle (BQM+SS).

Study Configuration

• Spatial Scale: Station-based climate projections for Qassim region, Saudi Arabia.
• Temporal Scale: Daily precipitation series from 1986 to 2014 and future windows: short-term (2025–2049), mid-term (2050–2074), and long-term (2075–2099).

Methodology and Data

• Models used: Bayesian quantile mapping with the Schaake Shuffle (BQM+SS), k-nearest neighbors (KNN) analog resampling, weather generator (WG), standard quantile mapping (QM), and Bayesian quantile mapping (BQM).
• Data sources: Observed daily precipitation records from six meteorological stations in Qassim region and CMIP6 GCM/ESM outputs.

Main Results

• BQM+SS achieved the most consistent performance across evaluation metrics, with a competitive MAE of 0.525 mm/day and minimal bias of 0.0045.
• The method showed the strongest distributional fidelity with the lowest KS-statistic of 0.019.
• Future projections under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) indicate a clear scenario-dependent intensification of precipitation extremes.

Contributions

• The study advances precipitation downscaling for water resource planning in data-sparse arid environments.
• The methodology offers a methodologically consistent framework for climate impact assessment and adaptation strategies essential for sustainable water management.

Funding

• This research was funded by Qassim University, Saudi Arabia.

Citation

@article{Alodah2026Bayesian,
  author = {Alodah, Abdullah},
  title = {Bayesian quantile mapping and schaake shuffle for precipitation downscaling in arid environments},
  journal = {Frontiers in Environmental Science},
  year = {2026},
  doi = {10.3389/fenvs.2026.1773597},
  url = {https://doi.org/10.3389/fenvs.2026.1773597}
}