Evaluasi Kesesuaian Deteksi Hujan Ekstrem Bulanan Menggunakan Data GPM Di Kabupaten Tanah Laut

Extreme rainfall is one of the main causes of hydrometeorological disasters in Indonesia, such as floods and landslides, which significantly impact social, economic, and environmental sectors. Tanah Laut Regency, as the study area, faces challenges in rainfall monitoring due to the limited number of observation stations and reliance on manual recording systems. Satellite-based data, such as the Global Precipitation Measurement (GPM), offer an alternative to fill observation gaps, yet their accuracy in detecting extreme rainfall events must be validated locally. This study aims to evaluate the capability of GPM data in detecting monthly extreme rainfall in Tanah Laut during the 2020–2024 period. Extreme events were identified using the 90th percentile (P90) threshold from observed data and classified based on temporal agreement into four categories: true positive, false positive, false negative and true negative. The evaluation employed classification metrics including precision, recall and false alarm ratio (FAR). The results showed that GPM achieved a precision of 0.60, a recall of 0.50, and a FAR of 0.40. These values indicate moderate detection performance, suggesting that GPM data can serve as an alternative rainfall information source, although local validation remains necessary for its operational use in early warning systems and water resource management.