Integrating X-MP radar data to estimate rainfall induced debris flow in the Merapi volcanic area

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

doi:10.1016/j.advwatres.2017.10.017

Cited by 17 publications

(21 citation statements)

References 28 publications

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“…Despite the continuous improvement of radar technology and calibration algorithms, predictions based on realtime monitoring of rainfall by radar is still problematic for debris-flow applications. Nevertheless, the use of weather radars may be especially valuable in specific areas such as active volcanos, where no rain gauges are available (e.g., Syarifuddin et al, 2017).…”

Section: Precipitation Monitoringmentioning

confidence: 99%

Debris-flow monitoring and warning: Review and examples

Hürlimann

Coviello

Bel

et al. 2019

Earth-Science Reviews

149

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Debris flows represent one of the most dangerous types of mass movements, because of their high velocities, large impact forces and long runout distances. This review describes the available debris-flow monitoring techniques and proposes recommendations to inform the design of future monitoring and warning/alarm systems. The selection and application of these techniques is highly dependent on site and hazard characterization, which is illustrated through detailed descriptions of nine monitoring sites: five in Europe, three in Asia and one in the USA. Most of these monitored catchments cover less than ~10 km 2 and are topographically rugged with Melton Indices greater than 0.5. Hourly rainfall intensities between 5 and 15 mm/h are sufficient to trigger debris flows at many of the sites, and observed debris-flow volumes range from a few hundred up to almost one million cubic meters. The sensors found in these monitoring systems can be separated into two classes: a class measuring the initiation mechanisms, and another class measuring the flow dynamics. The first class principally includes rain gauges, but also contains of soil moisture and pore-water pressure sensors. The second class involves a large variety of sensors focusing on flow stage or ground vibrations and commonly includes video cameras to validate and aid in the data interpretation. Given the sporadic nature of debris flows, an essential characteristics of the monitoring systems is the differentiation between a continuous mode that samples at low frequency ("non-event mode") and another mode that records the measurements at high frequency ("event mode"). The event detection algorithm, used to switch into the "event mode" depends on a threshold that is typically based on rainfall or ground vibration. Identifying the correct definition of these thresholds is a fundamental task not only for monitoring purposes, but also for the implementation of warning and alarm systems.

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Section: Precipitation Monitoringmentioning

confidence: 99%

Debris-flow monitoring and warning: Review and examples

Hürlimann

Coviello

Bel

et al. 2019

Earth-Science Reviews

149

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“…A small-compact X-band dual-polarization Doppler weather Multi-Parameter (X-MP) of WR2100 type radar, manufactured by Furuno Electric Co, was installed and operated at Merapi Museum (7.5 km SW from Merapi's vent) in 2014-2019 (Figure 1). The main objective of the installation was for research in volcano hazards such as rain-triggered lahar [16,20] and real-time tephra fallout rate [19]. Figure 1 presents the area covered by the 360 radar scan of plan position indicator (PPI) strategy.…”

Section: Radar Setting and Study Casementioning

confidence: 99%

“…Radar data were heavily affected by ground clutter up to 13 elevation angle scan (Figure 1). While the default clutter cancellation routine by radar can recognize the beam blockage [20], ground clutter still occurred as the result of direct contact between surface and sidelobes due to the presence of Merapi and Turgo Hill in the SW sector (Figure 1, [19]). Sidelobes are unwanted returns from a direction outside the main lobe, which can bias the reflectivity intensity, Doppler velocity, and spectrum width estimates.…”

Section: Radar Setting and Study Casementioning

confidence: 99%

“…First, we overcome previous limitations of unwanted clutter contaminating radar scanning data [19] by applying an automatic noise cancellation. Then, we coupled the modified tephra-radar retrieved model [8][9][18][19] with the EPS of radar nowcasting. The evaluation was done by comparing the radar-based tephra properties (i.e., concentration and grain size) with groundbased data and satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Syarifuddin¹,

Jenkins²,

Hapsari³

et al. 2021

Preprint

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Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

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“…Some studies on the debris behavior on Merapi Volcano were aimed at developing the mitigation system (Bélizal et al 2013;Solikhin et al 2015b). Modeling of the debris transport and integrating high-resolution rainfall observations with a lahar model were conducted by Syarifuddin et al (2017). It was considered that a fine spatial rainfall measurement from X-band polarimetric weather (X-MP) radar in volcanic rivers would usefully extend the observation of small-scale rainfall in the typically narrow watershed that the debris flow is triggered in.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers, Indonesia, Using X-band Polarimetric Radar

Hapsari

Sugna

Novianto

et al. 2020

Int J Disaster Risk Sci

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Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster. The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence. In this study, a debris flow disaster warning system was developed by applying the Naïve Bayes Classifier (NBC). The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar, a topographic factor, and soil type as predictors. The study was conducted in the Gendol River Basin of Mount Merapi, one of the most active volcanoes in Indonesia. Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets. The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors. The system verification was performed by quantitative dichotomous quality indices along with a contingency table. Using the validation datasets, the advantage of the NBC for estimating debris flow occurrence is confirmed. This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach. Despite the existence of predictive uncertainty, the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

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Integrating X-MP radar data to estimate rainfall induced debris flow in the Merapi volcanic area

Cited by 17 publications

References 28 publications

Debris-flow monitoring and warning: Review and examples

Debris-flow monitoring and warning: Review and examples

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Naïve Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers, Indonesia, Using X-band Polarimetric Radar

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