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Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
by
Baligh, Abdalla A.
, Zamreeq, Arjan O.
, Ismail, Muhammad
, Ghulam, Ayman S.
, Alfehaid, Muhannad M. A.
, Islam, M. Nazrul
, Jamaladdin, Samirah
, Habeebullah, Turki M. A.
, Khalil, Alaa A.
, Munir, Said
in
Annual rainfall
/ Automation
/ Climate change
/ Cluster analysis
/ Clustering
/ Daily rainfall
/ Damage
/ Drought
/ Economic impact
/ Extreme value theory
/ Extreme values
/ Extreme weather
/ Flash flooding
/ Flash floods
/ Flood damage
/ Floods
/ Hydrologic data
/ Maximum rainfall
/ Objectivity
/ Precipitation
/ Precipitation variability
/ Rain
/ Rain and rainfall
/ Rainfall
/ Rainfall cycles
/ Rainfall data
/ Regions
/ Reproducibility
/ Robustness (mathematics)
/ Spatial variability
/ Spatial variations
/ Statistical analysis
/ Statistical tests
/ Thresholds
/ Time series
/ Trends
/ Vector quantization
/ Water shortages
2025
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Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
by
Baligh, Abdalla A.
, Zamreeq, Arjan O.
, Ismail, Muhammad
, Ghulam, Ayman S.
, Alfehaid, Muhannad M. A.
, Islam, M. Nazrul
, Jamaladdin, Samirah
, Habeebullah, Turki M. A.
, Khalil, Alaa A.
, Munir, Said
in
Annual rainfall
/ Automation
/ Climate change
/ Cluster analysis
/ Clustering
/ Daily rainfall
/ Damage
/ Drought
/ Economic impact
/ Extreme value theory
/ Extreme values
/ Extreme weather
/ Flash flooding
/ Flash floods
/ Flood damage
/ Floods
/ Hydrologic data
/ Maximum rainfall
/ Objectivity
/ Precipitation
/ Precipitation variability
/ Rain
/ Rain and rainfall
/ Rainfall
/ Rainfall cycles
/ Rainfall data
/ Regions
/ Reproducibility
/ Robustness (mathematics)
/ Spatial variability
/ Spatial variations
/ Statistical analysis
/ Statistical tests
/ Thresholds
/ Time series
/ Trends
/ Vector quantization
/ Water shortages
2025
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Do you wish to request the book?
Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
by
Baligh, Abdalla A.
, Zamreeq, Arjan O.
, Ismail, Muhammad
, Ghulam, Ayman S.
, Alfehaid, Muhannad M. A.
, Islam, M. Nazrul
, Jamaladdin, Samirah
, Habeebullah, Turki M. A.
, Khalil, Alaa A.
, Munir, Said
in
Annual rainfall
/ Automation
/ Climate change
/ Cluster analysis
/ Clustering
/ Daily rainfall
/ Damage
/ Drought
/ Economic impact
/ Extreme value theory
/ Extreme values
/ Extreme weather
/ Flash flooding
/ Flash floods
/ Flood damage
/ Floods
/ Hydrologic data
/ Maximum rainfall
/ Objectivity
/ Precipitation
/ Precipitation variability
/ Rain
/ Rain and rainfall
/ Rainfall
/ Rainfall cycles
/ Rainfall data
/ Regions
/ Reproducibility
/ Robustness (mathematics)
/ Spatial variability
/ Spatial variations
/ Statistical analysis
/ Statistical tests
/ Thresholds
/ Time series
/ Trends
/ Vector quantization
/ Water shortages
2025
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Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
Journal Article
Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
2025
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Overview
The increasing occurrence of extreme rainfall events often leads to flash floods, infrastructure damage, loss of human life, and significant economic impacts. There is a pressing need for data-driven assessments and the application of robust analytical approaches to better understand these changes. Analyzing ground-level daily rainfall data from 1985 to 2023 from 26 monitoring stations, this study first employs the Mann–Kendall test using robust statistics including minimum, median, various quartiles, and maximum rainfall values for detecting long-term trends across Saudi Arabia. Next, the k-means clustering technique is applied to characterize the annual rainfall cycles across different regions of the country. Finally, the Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT) is employed to identify site-specific thresholds for extreme rainfall using the Generalized Pareto Distribution (GPD). This automated, data-driven method offers a more objective alternative to the commonly used ad hoc percentile-based threshold selection, thereby enhancing the rigour and reproducibility of extreme rainfall analysis. Local specific thresholds were computed ranging from about 16 to 47 mm from Arar and Jazan, respectively. These thresholds were then used to calculate the frequency and intensity of extreme rainfall events. The fitted GPD parameters were further used to estimate return levels (RLs) for different return periods (2-, 5-, 10-, 20-, 50-, and 100-year) into the future. The results underscore considerable spatial variability in extreme rainfall behaviour across Saudi Arabia, with a higher likelihood of intense and infrequent precipitation events in the coming decades.
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