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Abstract

Papua, known as one of the wettest regions in the world with annual precipitation ranging from 2400 to 4500 mm, faces a high risk of flooding, especially during La Niña, as observed in 2018 and 2022. Conversely, the region also experiences forest fires, mainly in the southern areas of Papua, during periods of extreme dry conditions brought about by El Niño events, as seen in 2002, 2004, and 2015. Given the increasing frequency of extreme climate events in the context of climate change, understanding the impact of global climate phenomena such as El Niño Southern Oscillation (ENSO) on Papua is crucial. This research aims to analyze the influence of ENSO and the Indian Ocean Dipole (IOD) on forest fires and flood risk in Papua, Indonesia. The analysis of forest fires utilizes MODIS hotspot and ERA5 precipitation data, employing quantitative modeling techniques such as Lasso and Elastic Net Regression. It integrates both ENSO and IOD indices into the precipitation indicators. In contrast, flood analysis is carried out through distribution and joint pattern analysis. The Elastic Net Regression yields promising results in modeling, with more than 96% of the tested models successfully predicting the total annual hotspots for each year, achieving an R-squared value of 90%. This suggests that the method and algorithm used can serve as a robust model for early hotspot prediction in the analyzed area. The warm phases of ENSO and IOD consistently exhibit a positive correlation with the dry season. However, the cold phases of these phenomena do not significantly impact heavy and extreme precipitation indicators in the studied region. The flood analysis reveals that La Niña has only a slight effect on all three precipitation patterns in the analyzed area, primarily by increasing the risk of extreme precipitation indicators. Conversely, negative IOD demonstrates inconsistency across all three precipitation patterns in Papua.