Saat Mubarrok EVA Talk Preview
From Anna Munro
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Name: Saat Mubarrok
Talk Title: Annual Maximum Precipitation in Indonesia linked to Climate Variability: Extreme Value Analysis
Abstract: Heavy rainfall frequently occurs in tropical climate countries, leading to a major disaster such as flood and landslide, especially in Indonesia. With climate change, the future change projection is important to reduce the impact of extreme rainfall. In this study, we investigated inter-annual variability of rainfall extremes and its relation with climate variability in Indonesia using a generalized extreme value (GEV) distribution based on daily rainfall data recorded from 1985 to 2014 at ten meteorological stations across the country. Maximum likelihood estimation was used to find the parameters of GEV distribution. We applied four GEV models whose location parameter consider climate variability, El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), and choose the best fit model using corrected Akaike Information Criterion (AICc) and likelihood ratio test. We used possible time-varying non-stationary behavior of the location parameter only and keep the scale and the shape parameter constant. Overall, trend-free prewhitening (TFPW) Mann-Kendall test shows a significant trend of annual maxima only in Surabaya station with averaged country trend is about 29.5 mm/day over 30 years period. Furthermore, two stations, Waingapu and Luwuk, covariate significantly with ENSO, while the other two stations, Perak and Jakarta, covariate insignificantly to IOD. Conversely, the MJO signal in annual maxima was less prominent in all stations and thus does not improve the stationary GEV model. Additionally, the estimated shape parameter at seven stations classified as the Gumbel distribution, but Jakarta, Perak, and Masamba classified as the Frechet distribution. It concludes that the annual precipitation maxima in Indonesia are well described by the Gumbel distribution rather than the Frechet or the Weibull distribution.
This talk is a contributed talk at EVA 2021. View the programme here.