Empirical Model for Remote Monitoring of Rain-Triggered Lahar at Mount Merapi

Syarifuddin, Magfira; Oishi, Satoru; Hapsari, Ratih Indri; Legono, Djoko

doi:10.2208/jscejhe.74.i_1483

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…Meteorological radar systems such as XMP are more accurate in acquiring data in physically inaccessible areas (Guo et al, 2016;Syarifuddin et al, 2020) such as at Sakurajima, where reaching altitudes higher than 400 m is restricted. Similar results were obtained by using XMP radars rather than rain gauges at Merapi volcano (Hairani et al, 2019;Hapsari et al, 2019;Putra et al, 2019;Syarifuddin et al, 2018).…”

Section: Data Acquisitionsupporting

confidence: 81%

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Temporal changes in lahar sediment run‐off characteristics and run‐off coefficients in the Arimura River basin of Sakurajima volcano, Japan

Ersöz,

Haneda,

Kuribayashi

et al. 2023

Earth Surf Processes Landf

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The rapid flowing nature of lahars can cause serious hazards in the proximal to distal areas of volcanoes. The accumulation of ash can form an impermeable layer on the ground and prevent the infiltration of precipitation. The surface water thus formed erodes the ash layer and causes the surface permeability to rise again. Over time, this accumulation and erosion sequence changes the lahar risk. Large numbers of lahars are observed each year around Sakurajima, which is known as one of the most active volcanoes in Japan. In this study, the temporal change of lahar sediment run‐off properties and run‐off coefficients have been investigated between 2015 and 2020 in the Arimura River basin of Sakurajima volcano, Japan. X Band Multi‐Parameter (XMP) radars are used to gather precipitation data in the basin. The sediment data have been obtained by Load, Vibration, Pressure (LVP) sensors, and a kinematic wave model is adopted to calculate surface run‐off. Lahars are categorized as regular and irregular considering the correspondence of measured and calculated waveforms of the hydrographs based on the kinematic wave model. Analyses reveal that run‐off coefficient changes depend on the volcanic activity and decreases after an irregular lahar compared with a previous regular lahar. LVP sensor analysis results show that sediment concentrations of the lahars change in time. It is found that irregular and regular lahars are distinct in terms of velocity and sediment concentrations. It is estimated that irregular lahars carry larger material than regular lahars based on the vibration data. This study underlines that the properties of lahars change over time under the influence of ash fall thickness, and these changes can be detected in detail with our kinematic wave model and LVP sensors.

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Section: Data Acquisitionsupporting

confidence: 81%

“…The kinematic wave model (Lighthill & Whitham, 1955) is an approximation of the dynamic‐wave model for 1D shallow water waves (Syarifuddin et al, 2018). It uses an empirical relationship between fluid discharge and flow depth in order to understand flow mechanisms and propagation (Arattano & Savage, 1994; Vigneaux & Weir, 1990).…”

Section: Methodsmentioning

confidence: 99%

Temporal changes in lahar sediment run‐off characteristics and run‐off coefficients in the Arimura River basin of Sakurajima volcano, Japan

Ersöz,

Haneda,

Kuribayashi

et al. 2023

Earth Surf Processes Landf

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“…Pengamatan dan peramalan atas kejadian, intensitas, dan evolusi bencana hidrometeorologi merupakan komponen penting dalam upaya mitigasi non-struktural. Dalam analisa respon permukaan terhadap hujan, model hidrologi dengan input data hasil prediksi curah hujan yang akurat menjadi syarat utama dalam setiap sistem deteksi dini bencana hidrometeorologi (Syarifuddin, 2018;Syarifuddin et al, 2009;2017a;Hapsari et al, 2012;Jayawardhena, 2015).…”

Section: Manajemen Bencana Hidrometeorologiunclassified

“…Dalam kasus ketidaktersediaan informasi hujan oleh alat penakar hujan, radar cuaca tipe X-band mampu menjadi alternatif dalam simulasi banjir dan aliran lahar dingin di Merapi (Hapsari et al, 2019;Syarifuddin et al, 2017b;Syarifuddin, 2018).…”

Section: Metode Ekstrapolasiunclassified

“…Pemutakhiran sistem observasi cuaca dan iklim adalah syarat mutlak dalam setiap program mitigasi bencana hidrometeorologi. Produk hujan hasil pengukuran satelit dan radar diharapkan mampu menjadi alternatif dan pelengkap terhadap penakar hujan, terutama di masa datang (Syarifuddin 2018;Syarifuddin et al, 2017a;Zhang et al, 2014). Dengan perkembangan sistem komputasi berkinerja tinggi, teknik asimilasi data, data mining, dan kecerdasan buatan, akan semakin memperkaya kajian prediksi iklim dan cuaca.…”

Section: Penutupunclassified

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Prediksi Hujan untuk Manajemen Bencana dan Risiko Pertanian

Syarifuddin¹

2023

Preprint

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Di antara tiga bencana alam utama, geologi, biologi, dan hidrometeorologi, bencana hidrometeorologi terjadi paling sering (~90%) dan berdampak paling merugikan. Bencana hidrometeorologi yang sering terjadi di Indonesia adalah banjir, tanah longsor dan aliran debris akibat hujan lebat, gelombang tinggi, kebakaran hutan dan lahan, kekeringan, serta cuaca ekstrim seperti angin puting beliung maupun siklon tropis. Selama 20 tahun terakhir, setidaknya terdapat rata-rata 42 kematian per 1,000,000 populasi akibat banjir di Indonesia. Sebagai negara kepulauan yang berada di Daerah Konvergensi Antar Tropik (DKAT), Indonesia memiliki kondisi iklim dan cuaca yang sangat kompleks dan dinamis. Berkurangnya wilayah resapan dan proyeksi peningkatan intensitas cuaca ekstrim dan frekuensi banjir sebagai dampak perubahan iklim, semakin menambah kekhawatiran terhadap risiko iklim dan cuaca. Tahun 2021, Nusa Tenggara Timur (NTT) bahkan tidak berdaya menghadapi siklon tropis Seroja. Sebanyak 67.1% dari Produk Domestik Bruto di Indonesia dihasilkan dari sektor yang tergantung kepada cuaca dan iklim dengan pertanian adalah sektor peringkat pertama. Dampak bencana hidrometeorologi pada sektor pertanian dapat berupa penurunan luasan area lahan produktif, gangguan dalam operasional serta kerusakan jaringan irigasi, bendungan, dan infrastruktur pertanian lainnya atau secara tidak langsung akibat kenaikan harga komoditi pertanian di pasar setelah bencana terjadi.

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