Munir et al. (2025) Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation

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Identification

• Journal: Climate
• Year: 2025
• Date: 2025-11-16
• Authors: Said Munir, Turki M. Habeebullah, Arjan O. Zamreeq, Muhannad Mohammed Alfehaid, Muhammad Ismail, Alaa A. Khalil, Abdalla A. Baligh, M. Nazrul Islam, Samirah Jamaladdin, Ayman S. Ghulam
• DOI: 10.3390/cli13110233

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study analyzed daily rainfall data (1985-2023) from 26 stations in Saudi Arabia to detect long-term trends, characterize annual cycles, and establish objective extreme rainfall thresholds, revealing considerable spatial variability and a higher likelihood of intense, infrequent events in the future.

Objective

• To detect long-term trends in daily rainfall across Saudi Arabia using data from 1985 to 2023.
• To characterize annual rainfall cycles across different regions of Saudi Arabia using k-means clustering.
• To identify site-specific, objective thresholds for extreme rainfall using the Peaks Over Threshold (POT) approach and Generalized Pareto Distribution (GPD).
• To estimate return levels for extreme rainfall events for various future return periods (2-, 5-, 10-, 20-, 50-, and 100-year).

Study Configuration

• Spatial Scale: Saudi Arabia, covering 26 monitoring stations.
• Temporal Scale: Daily rainfall data from 1985 to 2023 (39 years). Future return levels estimated for periods up to 100 years.

Methodology and Data

• Models used: Mann–Kendall test (for trend detection), k-means clustering (for annual cycle characterization), Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT), Generalized Pareto Distribution (GPD) (for extreme rainfall thresholds and return levels).
• Data sources: Ground-level daily rainfall data collected from 26 monitoring stations across Saudi Arabia.

Main Results

• Long-term trends in daily rainfall were detected across Saudi Arabia using robust statistics.
• Annual rainfall cycles were characterized across different regions using k-means clustering.
• Site-specific extreme rainfall thresholds were computed, ranging from approximately 16 mm (Arar) to 47 mm (Jazan).
• The frequency and intensity of extreme rainfall events were calculated using these derived thresholds.
• Return levels (RLs) for extreme rainfall events were estimated for various return periods (2-, 5-, 10-, 20-, 50-, and 100-year) using fitted GPD parameters.
• The study revealed considerable spatial variability in extreme rainfall behaviour across Saudi Arabia.
• Results indicate a higher likelihood of intense and infrequent precipitation events in Saudi Arabia in the coming decades.

Contributions

• Introduction and application of an automated, data-driven method (POT/EVT with GPD) for objective and reproducible site-specific extreme rainfall threshold selection, offering an improvement over ad hoc percentile-based methods.
• Comprehensive assessment of long-term rainfall trends, annual cycles, and future extreme rainfall return levels across Saudi Arabia.
• Provides critical insights into the spatial variability and future projections of extreme rainfall events in the region.

Funding

Not explicitly stated in the provided text.

Citation

@article{Munir2025Understanding,
  author = {Munir, Said and Habeebullah, Turki M. and Zamreeq, Arjan O. and Alfehaid, Muhannad Mohammed and Ismail, Muhammad and Khalil, Alaa A. and Baligh, Abdalla A. and Islam, M. Nazrul and Jamaladdin, Samirah and Ghulam, Ayman S.},
  title = {Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation},
  journal = {Climate},
  year = {2025},
  doi = {10.3390/cli13110233},
  url = {https://doi.org/10.3390/cli13110233}
}