Estimating return periods for daily precipitation extreme events over the Brazilian Amazon

This paper aims to model the occurrence of daily precipitation extreme events and to estimate the return period of these events through the extreme value theory (generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD)). The GEV and GPD were applied in precipitation...

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Bibliographic Details
Published in:Theoretical and applied climatology 2016-11, Vol.126 (3-4), p.585-595
Main Authors: Santos, Eliane Barbosa, Lucio, Paulo Sérgio, Santos e Silva, Cláudio Moisés
Format: Article
Language:English
Subjects:
Analysis
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Climate change
Climatology
Coastal zone
Distribution (Probability theory)
Earth and Environmental Science
Earth Sciences
Extreme weather
Meteorology
Original Paper
Precipitation
Precipitation (Meteorology)
Waste Water Technology
Water Management
Water Pollution Control
Weather forecasting
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Summary:This paper aims to model the occurrence of daily precipitation extreme events and to estimate the return period of these events through the extreme value theory (generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD)). The GEV and GPD were applied in precipitation series of homogeneous regions of the Brazilian Amazon. The GEV and GPD goodness of fit were evaluated by quantile–quantile (Q-Q) plot and by the application of the Kolmogorov–Smirnov (KS) test, which compares the cumulated empirical distributions with the theoretical ones. The Q-Q plot suggests that the probability distributions of the studied series are appropriated, and these results were confirmed by the KS test, which demonstrates that the tested distributions have a good fit in all sub-regions of Amazon, thus adequate to study the daily precipitation extreme event. For all return levels studied, more intense precipitation extremes is expected to occur within the South sub-regions and the coastal area of the Brazilian Amazon. The results possibly will have some practical application in local extreme weather forecast.
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-015-1605-9