Taguemount et al. (2026) Assessing Probable Maximum Precipitation in the Soummam Basin (Algeria) Using Bias‐Corrected Climate Projections

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-10-07
• Authors: Rihab Taguemount, Ayoub Zeroual, Mohamed Meddi, Roberto Serrano‐Notivoli, Imene Senadi
• DOI: 10.1002/joc.70626

Research Groups

The abstract does not explicitly list the main research groups, labs, or departments involved in the study.

Short Summary

This study analyzes the future evolution of Probable Maximum Precipitation (PMP) in the Soummam watershed, Algeria, by applying multi-variable bias correction to CMIP6 models. It finds that PMP projections vary substantially depending on the bias correction method, with QDM and CDF-t projecting significant increases (+65% to +90%) while QM projects decreases (up to −40%), highlighting the critical influence of methodology on climate-driven hydrological risk assessments.

Objective

• To analyze the future evolution of Probable Maximum Precipitation (PMP) in the Soummam watershed, Algeria, by correcting biases across four variables (precipitation, minimum temperature, relative humidity, and wind speed) using three distinct methods: Quantile Mapping (QM), Quantile Delta Mapping (QDM), and CDF-t transformation.

Study Configuration

• Spatial Scale: Soummam watershed in northeastern Algeria; analysis based on daily observations from 56 rain gauge stations; spatial patterns observed in western and northwestern high-altitude sectors.
• Temporal Scale: Future evolution analyzed over two periods: 2026–2064 and 2065–2100, under SSP2-4.5 and SSP5-8.5 scenarios. Daily observations were used for calibration.

Methodology and Data

• Models used: CMIP6 climate models.
• Data sources: Daily observations from 56 rain gauge stations; ERA5-Land reanalysis data.
• Methods: Multi-variable bias correction applied to precipitation, minimum temperature, relative humidity, and wind speed using three distinct methods: Quantile Mapping (QM), Quantile Delta Mapping (QDM), and CDF-t transformation. Statistical analysis included ANOVA (p < 0.05) and calculation of Coefficient of Variation (CV).

Main Results

• PMP projections show substantial method-dependent variations.
• The QDM and CDF-t methods project PMP increases ranging from +65% to +90%, with large ensemble uncertainty (Coefficient of Variation: 60%–90%, statistically significant in 70% of comparisons, ANOVA p < 0.05). These increases are predominantly observed in the 2026–2064 period under the SSP2-4.5 scenario.
• In contrast, the QM method produces PMP decreases of up to −40%.
• The counterintuitive greater increases under SSP2-4.5 reflect the complex interplay between the climatic variables.
• Kernel Density Estimation (KDE) distributions exhibit bimodality with QDM and CDF-t, reflecting heterogeneous model and elevation-dependent responses across the CMIP6 ensemble, whereas QM produces narrower, unimodal distributions.
• Spatially, western and northwestern high-altitude sectors show the largest PMP increases, while other regions remain stable or decline.

Contributions

• Addresses a critical gap by employing multi-variable bias correction for PMP estimation and systematically comparing different correction methods.
• Demonstrates that univariate bias correction methodology fundamentally shapes PMP projections, leading to vastly different outcomes (increases vs. decreases).
• Highlights the necessity of ensemble-based uncertainty framing in climate-driven hydrological risk assessment, especially when PMP depends on multiple correlated variables.
• Provides specific insights into PMP evolution in the vulnerable Soummam watershed, Algeria.

Funding

The abstract does not provide information regarding funding projects, programs, or reference codes.

Citation

@article{Taguemount2026Assessing,
  author = {Taguemount, Rihab and Zeroual, Ayoub and Meddi, Mohamed and Serrano‐Notivoli, Roberto and Senadi, Imene},
  title = {Assessing Probable Maximum Precipitation in the Soummam Basin (Algeria) Using Bias‐Corrected Climate Projections},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70626},
  url = {https://doi.org/10.1002/joc.70626}
}