PREDIKSI CURAH HUJAN BULANAN UNTUK PERINGATAN DINI LONGSOR DI BANJARNEGARA BAGIAN SELATAN DENGAN STATISTICAL DOWNSCALING DAN ENSEMBLE
DOI:
https://doi.org/10.31172/jmg.v21i2.698Keywords:
Prediksi Curah hujan, Ensemble, Statistical Downscaling, Peringatan dini dan IKLAbstract
Banjarnegara merupakan wilayah pegunungan sehingga sering terjadi longsor. Curah hujan sebagai salah satu parameter cuaca dengan kondisi tertentu mampu memicu terjadinya longsor. Keberadaan prediksi hujan sangat diperlukan untuk informasi berbasis dampak (Impact Based Forecasting) sebagai media untuk mitigasi bencana. Tujuan penelitian ini untuk membuat peringatan dini potensi bahaya longsor bulanan dengan input prediksi curah hujan bulanan (faktor dinamis) dengan metode ensemble dan statistical downscaling (SD). Prediktor yang digunakan terdiri dari CAPE, PW, U850 dan V850 dan SST sebagai parameter atmosfer yang terkait fisis dan dinamis dengan curah hujan. Indeks kerawanan longsor (IKL) yang digunakan sebagai faktor statis untuk peringatan dini bahaya longsor meliputi parameter curah hujan tahunan, kemiringan lereng dan penggunaan lahan. Hasil IKL selanjutnya di-overlay dengan prediksi curah hujan dengan tiga kategori persentil, yaitu Curah Hujan < P33 (persentil 33%) sebagai curah hujan rendah, P33-P66 (sedang) dan >P66 (tinggi). Hasil prediksi model ensemble menunjukkan pola curah hujan mengikuti pola musim kemarau dan awal musim hujan (curah hujan prediksi sesuai dengan observasi). Hasil korelasi yang tinggi menunjukkan bahwa model prediksi layak digunakan sebagai masukan model untuk peringatan dini longsor. Nilai IKL menunjukan bahwa Wilayah Kecamatan Banjarnegara dan Wanadadi merupakan lokasi paling rawan longsor (3,625) kemudian Wanadadi (3,188) dan agak rawan Mandiraja (2,875). Hasil prediksi curah hujan kemudian dioverlay dengan tingkat IKL digunakan sebagai indikator peringatan dini. Hasil validasi dengan data observasi menunjukkan bahwa peringatan dini longsor mempunyai akurasi yang cukup baik (informasi peringatan dini sesuai umumnya dengan kejadian longsor).
Banjarnegara is a mountainous region so landslides often occur. Rainfall is one of the weather parameters with certain conditions that can trigger landslides. The presence of rain predictions is really crucial for impact-based information (Impact Based Forecasting) as a mechanism for disaster reduction. The purpose of this paper is to make an early warning of potential monthly landslides with monthly rainfall prediction input (dynamic factors) with the ensemble and statistical downscaling (SD) methods. Predictors used consisted of CAPE, PW, U850, and V850, and SST as atmospheric parameters related to physical and dynamic rainfall. To build an early warning of landslide hazards, the landslide susceptibility index (IKL) was employed using annual rainfall, slope, and land use parameters. The results of IKL are then overlaid with predictions of rainfall with three percentile categories namely Rainfall <P33 (percentile 33%) as low rainfall, P33-P66 (moderate), and > P66 (high). The results of the ensemble model predictions show that rainfall patterns follow the pattern of the dry season and the beginning of the rainy season (predicted rainfall is in accordance with observations). The IKL value shows that the Districts of Banjarnegara and Wanadadi are the most prone to landslides (3,625) than Wanadadi (3,188) and somewhat vulnerable to Mandiraja (2,875). The rainfall prediction results are then overlaid with the IKL level producing an index as an early warning indicator. The results of the validation with observational data indicate that early warning landslides have quite a good accuracy (early warning information is generally in accordance with landslide events).
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