BIO Web Conf.
Volume 62, 20232023 5th International Conference on Environment, Resources and Energy Engineering (EREE 2023)
|Number of page(s)||8|
|Section||Climate Change and Precipitation Prediction|
|Published online||07 July 2023|
A comparison of generalized extreme value, gumbel, and log-pearson distributions for the development of intensity duration frequency curves. A case study in Costa Rica
1-4 Escuela de Ingeniería en Construcción, Instituto Tecnológico de Costa Rica, 159-7050, Cartago, Costa Rica
2 Escuela de Ingeniería en Computación, Instituto Tecnológico de Costa Rica, 159-7050, Cartago, Costa Rica
3 Gerencia Ambiental, Investigación y Desarrollo, Instituto Costarricense de Acueductos y Alcantarillados, 10109, San José, Costa Rica
* Corresponding author: firstname.lastname@example.org
Global warming has already affected frequency and intensity of extreme rainfall events. This makes the evaluation of current and alternative statistical distributions used in the formulation of Intensity Duration Frequency curves (IDF) curves highly relevant. This study aims to evaluate the suitability of applying the Generalized Extreme Value (GEV) and the Log-Pearson type 3 (LP3) probability distributions against the traditionally used Gumbel (EV1) distribution to derive IDF curves for a flood prone area located in northern Costa Rica. A ranking system based on a normalized total-score from five metrics was implemented to identify the best distribution. GEV proved to be the most suitable distribution for most storm-durations and was therefore selected for development of the IDF curves with return periods ranging from 2 to 100 years. As return periods get longer however, deviations between rainfall estimates obtained get more prominent. Hence, a meticulous analysis of adjustment to select the most adequate probability distribution to estimate extreme events with return periods of 50 years or more should be undertaken, regardless of GEV or any other distribution. Results also reinforce the need to identify the distribution that best fits observed data for a particular weather station, especially when time-series are asymmetric.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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