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

Raúl Renteria1, Raúl Aquino2, Mayrén Polanco3

  • 1Facultad de Ingeniería Civil, Universidad de Colima, Coquimatlán 28400, Mexico.

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|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Extreme rainfall in Colima, Mexico, was analyzed using statistical modeling and spatial visualization. Northern and western regions show the highest frequency of intense rainfall events, aiding flood risk assessment.

Keywords:
GEVexceedance analysisextreme rainfallsensors networks

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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.