Evaluación de los umbrales de precipitación extrema, sus conexiones con variables metereológicas locales y teleconexiones con oscilaciones océanicas en la ciudad de Quito

Abstract

The present study aims to determine the thresholds of extreme precipitation in the city of Quito and also to evaluate the relationship and correlation with other local meteorological variables and tele connections with ocean oscillations, through the use of statistical analysis and Wavelets. For this analysis, precipitation, relative humidity, temperature, wind speed, wind direction and atmospheric pressure data from 9 weather stations were considered. Six belong to the REMMAQ, which took into account 16 years of hourly data ranging from 2004 to 2020. And three belong to INAMHI, where 39 years of daily data ranging from 1980 to 2019 were considered. RStudio scripts and Excel commands were used to manage the data. Also with the help of the ArcGis tool, a regionalization of intense precipitation values P90 extreme P99 was performed by Kriging interpolation. On a temporal level, the months of greatest precipitation are: March-April. And the hours of the day in which most rains are recorded are between 16:00h and 18:00h. It is found that the values of intense rain vary between 2.7mm/h and 4.2mm/h, and the extreme rain varies between 9.9mm/h and 16.4mm/h, the most vulnerable areas of Quito are Valle de Los Chillos and the South of the City. When analyzing cases of study of the most intense events in Quito, the strongly convective nature of rain is evidenced, which is related to the decrease of atmospheric pressure, increase of wind speed and change of wind direction. When studying its behavior in the face of ocean oscillations, it was evident that there is an influence of the ENSO oscillation, but only in intense periods. Thus precipitation presents a behavior that depends on the time of year, and the time of day, so this research seeks to contribute the understanding of the same in the city of Quito and so in the future through prediction models help prevent disasters that affect the population and its environment.