Pendekatan Peaks Over Threshold dan Gamma Distribution Mapping untuk Prediksi Hujan Ekstrem Subseasonal di Jawa Timur
DOI:
https://doi.org/10.29303/goescienceed.v7i4.3283Keywords:
Bias Correction, Contingency Table, Extreme Value Theory, Generalized Pareto Distribution, Subseasonal Prediction.Abstract
Extreme rainfall triggers most floods and landslides in East Java, yet its predictability two to six weeks ahead is rarely tested against documented disaster events. This study evaluates the ECMWF Subseasonal to Seasonal (S2S) model in detecting extreme rainfall linked to floods and landslides in East Java during 2025. Thresholds were derived from a 95th-percentile Peaks Over Threshold approach and the Generalized Pareto Distribution at six BMKG stations (1991-2024). Control forecast output at lead times of 14, 21, 28, 35, and 42 days was corrected sequentially by wet-day correction and Gamma Distribution Mapping, with parameters built per station and lead time. BNPB disaster records were screened by rain-gauge rainfall on the event day or the preceding day and by a 30 km distance limit, then verified through contingency tables. Bias correction cut the modelled wet-day fraction from 79-83% to 37-41% and brought intensity distributions closer to observations. Nevertheless, 205 event-lead time pairs yielded only 12 hits against 83 misses. Sensitivity peaked at the 28-day lead time (POD 0.21; CSI 0.17; FAR 0.50), and all BIAS values fell below one. The corrected model still underestimates extreme rainfall and suits an early-signal role rather than a sole determinant of disaster occurrence.
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