Abstract Rainfall in the mountainous region has unique characteristics related to time-varying and spatial distribution. In Mt. Merapi region, located at the border of Special Region of Yogyakarta Province and Central Java Province, Indonesia, rainfalls are typically classified as the deep-convective type, which occurs in a short period with high intensity. Therefore, short term rainfall predictions in a proper way remain challenging tasks. The use of remote monitoring instruments such as the GNSS (Global Navigation Satellite System) is believed to provide a better measurement accuracy through the identification of water vapor variation in the process of deep convection weather. GNSS observes the geodetic position of the GNSS antenna or receiver while it broadcasts microwave signals continuously through the atmosphere to the ground-based receivers. As they travel through the atmosphere, the microwave signals are mostly influenced by ionospheric and neutral atmospheric effects, which cause some delays. By using a sufficiently dense network of GNSS receivers, the impact of the neutral atmosphere delay can be estimated as a by-product of the geodetic processing. These delays can be regarded as an integrated water vapor along the path, namely Precipitable Water Vapor (PWV), which can be indirectly measured by Zenith Total Delay (ZTD). By studying the relationship between time-varying PWV and rainfall, it can be found that the PWV level increases sharply before raining. Through the deployment of GNSS receivers, the spatial feature of rainfall characteristics is also depicted. The initial results showed that the increase of PWV is strongly correlated to rainfall occurrence based on the rain gauge measurement around Mt. Merapi region. The results show that the correct forecasted rate is about 47%-62% with the PWV increment time is three hours.
Volcanoes can produce a range of eruptive behavior even during a single eruption, changing quickly from effusive to explosive style, and the other way around. The changes in eruption phases (e.g. phreatic explosion, magmatic explosion, lava extrusion, etc.) can lead to different volcanic hazards and require timely assessment for the implementation of mitigation measures. Here we explore how to correlate a given eruption phase with changes in the monitoring data using statistical analysis and conditional probabilities. We calculate the success of detection of an eruption phase using a threshold of monitoring data, which includes the uncertainty on the eruption phase dates with a Monte Carlo simulation. We apply the method to dome forming eruptions of Mt. Merapi (Indonesia) and evaluate their time occurrence using an exceptionally long monitoring time series (from 1993 to 2012, over nineteen years) of Multiphase (Hybrid) Seismic Energy. We identify the seismic energy threshold that is associated with the lava extrusion phase with an accuracy of 90 ±2%, precision of 73 ± 2%, specificity of 96 ± 1%, and sensitivity of 56 ± 1%. We further test our method with the recent 2018 eruption (not used in the thresholds calculations) and we identify the lava extrusion with a precision of 67%, specificity of 70%, and sensitivity of 92%. We also seismically detected the 2018′s onset of the lava extrusion phase 14 days earlier than the visual observation. Given the link between dome-collapse pyroclastic flows and growth episodes of the lava dome at Merapi, our analysis also allows us to establish that 83% of the most energetic pyroclastic flows occur within the first 3 months after the onset of lava extrusion phase. Our method can be applicable to a range of time series of monitoring data (seismic, deformation, gas) and to other volcanoes that have a significant number of past events.
Merapi has become one of the most enticing volcanoes due to its activity over the past century. Although we have to agree that the 2010 VEI = 4 (Volcanic Explosivity Index, [1]) eruption is the greatest in its recorded history, Merapi is more famous for its shorter cycle of smaller scale, making it one of the most active volcanoes on Earth. Many mechanisms are involved in an eruption, and pyroclastic flow is the most dangerous occurrence in terms of volcanic hazard. A pyroclastic flow is defined as a high-speed avalanche consisted of high temperature mixture of rock fragments and gas, resulted from lava dome collapse and/or gravitational column collapse. Researchers have studied Merapi’s history and behavior, and numerical simulations are an important tool for future hazard mitigation. By utilizing numerical simulation on basal part of pyroclastic flow, we investigated the applicability of the simulation on pyroclastic flows from historical eruptions of Merapi (1994, 2001, and 2006). Herein, we present a total of 32 simulations and discuss the areas affected by pyroclastic flows and the factors that affect the simulation results.
An X-band radar was installed in 2014 at Merapi Museum, Yogyakarta, Indonesia, to monitor pyroclastic and rainfall events around Mt. Merapi. This research aims to perform a reliability analysis of the point extracted rainfall data from the aforementioned newly installed radar to improve the performance of the warning system in the future. The radar data was compared with the monitored rain gauge data from Balai Sabo and the IMERG satellite data from NASA and JAXA (The Integrated Multi-satellitE Retrievals for GPM), which had not been done before. All of the rainfall data was compared on an hourly interval. The comparisons were conducted based on 11 locations that correspond to the ground rainfall measurement stations. The locations of the rain gauges are spread around Mt. Merapi area. The point rainfall information was extracted from the radar data grid and the satellite data grid, which were compared with the rain gauge data. The data were then calibrated and adjusted up to the optimum state. Based on January 2017–March 2018 data, it was obtained that the optimum state has a NSF value of 0.41 and R 2 value of 0.56. As a result, it was determined that the radar can capture around 79% of the hourly rainfall occurrence around Mt. Merapi area during the chosen calibration period, in comparison with the rain gauge data. The radar was also able to capture nearby 40–50% of the heavy rainfall events that pose risks of lahar. In contrast, the radar data performance in detecting drizzling and light rain types were quite precise (55% of cases), although the satellite data could detect slightly better (60% of cases). These results indicate that the radar sensitivity in detecting the extreme rainfall events must receive higher priority in future developments, especially for applications to the existing Mt. Merapi lahar early warning systems.
We propose a method to evaluate the potential volume of eruptive material using the seismic energy of volcanic earthquakes prior to eruptions of Merapi volcano. For this analysis, we used well-documented eruptions of Merapi volcano with pyroclastic flows (1994, 1997, 1998, 2001, 2006, and 2010) and the rates and magnitudes of volcano-tectonic A-type, volcano-tectonic B-type, and multiphase earthquakes before each of the eruptions. Using the worldwide database presented by White and McCausland [1], we derived a log-linear formula that describes the upper limit of the potential volume of erupted material estimated from the cumulative seismic energy of distal volcano-tectonic earthquakes. The relationship between the volume of pyroclastic material and the cumulative seismic energy released in 1994, 1997, 1998, 2001, 2006, and 2010 at Merapi volcano is well-approximated by the empirical formula derived from worldwide data within an order of magnitude. It is possible to expand this to other volcanic eruptions with short (< 30 years) inter-eruptive intervals. The difference in the intruded and extruded volumes between intrusions and eruptions, and the selection of the time period for the cumulative energy calculation are problems that still need to be addressed.
Merapi, the dangerous active volcano in Indonesia, has been monitored since the 1920s by applying several methods and tools. The monitoring data from earlier times are stored well and can be used as reference for any precursors and signs before each eruption. This article evaluates the long-term activity of Merapi from the monitoring data for 1992–2011 to obtain the trends and patterns before the eruption period by combining the seismicity, deformation, volcanic gas, and temperature data in the same time span. Several characteristics are exhibited before effusive and explosive eruptions, i.e., a significant level up in volcano-tectonic energy and increased CO 2 gas concentration indicating an explosive eruption. Effusive eruption is characterized by a significant multiphase earthquake with less occurrence of deep and shallow volcano-tectonic events. Deformation data from a tiltmeter and electronic distance measurement are important in understanding the dynamics of the lava dome and the eruption direction.
