Return Levels on Stationary Extreme Rainfall Series: A Comparative Study of Generalized Extreme Value and Generalized Pareto Distributions

Extreme rainfall events often result in destructive weather conditions, as they frequently lead to flooding. The assessment of return levels, which represent the maximum rainfall that is expected to be exceeded within a given time frame, is crucial for effective flood planning. This study aims to compare the accuracy of return level estimations using two statistical distributions: the stationary Generalized Extreme Value distribution (GEVD) and the stationary Generalized Pareto distribution (GPD). The analysis utilized daily rainfall data from Makassar city, obtained at the Hasanuddin rain gauge station, spanning the period from 1980 to 2022. Two approaches were employed to assess the accuracy of return level estimation: the block maxima (BM) approach with GEVD and the peaks over threshold (POT) approach with GPD. Return levels were estimated for return periods of 2, 3, 4, and 5 years. The root mean square error (RMSE) was used as a metric for comparing the accuracy of the two models. The findings indicate that the GPD outperforms the GEVD in predicting the return level of extreme rainfall for each return period in Makassar city. Furthermore, the study predicts the maximum rainfall expected in the following year. In 2023, based on the GEVD, the maximum rainfall is projected to exceed 144,675 mm/day with a 50% chance of occurrence, while based on the GPD, it is expected to surpass 167,320 mm/day with a 14% chance of occurrence. These predictions provide valuable insights for understanding the potential severity of extreme rainfall events and can assist in planning and managing flood risks in Makassar city.