Spatio-temporal description of the rainfall in the Andean city of Manizales (Colombia) for storm design

David Rincon, Jorge Vélez, Philippe Chang

Thursday 2 july 2015

9:45 - 10:00h at Central America (level 0)

Themes: (T) Water resources and hydro informatics (WRHI), (ST) Catchment hydrology

Parallel session: 10H. Water resources - Catchment

This study investigated a spatio-temporal description of various rainfall events in Manizales (Colombia) using nine meteorological stations located in various parts of the city. The chosen stations were those with the greatest number of available historical data. The selected time period from January 2006 to July 2014, ensures homogeneity of the data. Raw data was extracted, processed and analyzed over intervals of five minutes with a minimum time period between rainfall events of fifteen minutes. The location and topography of the city are responsible for the climate variability, in particular due to the Intertropical Confluence Zone and the El Niño South Oscillation effect. High intensity and cumulative rainfall in the region are responsible for triggering landslides. Hence, a detailed description and understanding of the rainfall pattern is required to enable decision makers to reduce the risk associated with floods and landslides, which are the most important issue in the Andean region. This rainfall analysis also improves the early warning systems; the identification of vulnerable zones and the design methods for protective civil works. The results indicate a high spatio-temporal variability, especially in the spatial distribution of the rainfall. The dimensionless temporal distribution of the observed rainfall does not meet the recommended standards given by hydrology manuals and the observations for the region indicate a uniform distribution of rainfall over time. Such information is required by engineers for proper storm design. The rainfall reduction factor analysis indicates that daily rainfall used to estimate the design storm for different durations are highly variable in the entire city.