Modeling Extreme Rainfall Using the Generalized Extreme Value Distribution and Exceedance Analysis in Colima, Mexico

Area of Science:

• Environmental Science
• Hydrology
• Data Science

Background:

• Extreme rainfall events pose significant risks to Colima, Mexico, necessitating robust analytical frameworks.
• Accurate assessment of precipitation patterns is crucial for effective flood risk management and territorial planning.

Purpose of the Study:

• To develop a statistical and technological framework for analyzing extreme rainfall in Colima, Mexico.
• To identify high-intensity rainfall events and model their recurrence for flood risk assessment.

Main Methods:

• Utilized historical precipitation data from CONAGUA meteorological stations.
• Applied Generalized Extreme Value (GEV) distribution for probabilistic modeling of rainfall recurrence and return periods.
• Employed spatial interpolation in Python (version 3.13) and QGIS (version 3.38) for creating exceedance maps.

Main Results:

• Identified key return periods for extreme rainfall events in Colima.
• Spatial visualization revealed geographic variations in rainfall intensity across the state.
• Northern and western areas of Colima exhibit the highest frequencies of extreme rainfall events.

Conclusions:

• The developed framework effectively supports flood-risk assessment and territorial planning in Colima.
• Exceedance maps highlight critical areas for targeted flood mitigation strategies.
• Integration of real-time sensor technologies and satellite observations can enhance future flood monitoring and risk management.