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The tectonic complexity in Indonesia has made it one of the most interesting targets for studies on seismic tomography. The Indian oceanic plate sunk beneath the Eurasian continental plate, forming the subduction zone in Southern Indonesia. This activity led to the formation of volcanoes along the Sunda Arc, including East Java, the research area covered in this study. This research is mainly aimed at identifying the influence of the volcanic activities by tomography analysis. The data of the earthquakes was recorded by 22 seismic stations of the Indonesia Tsunami Early Warning System (InaTEWS) seismic network in the period of 2009–2017. The tomographic image was analysed by exploring the anomalies of primary (P)- and secondary (S)-wave velocities and Vp/Vs ratio. The result shows the presence of a low-velocity zone with a high Vp/Vs ratio found around the volcanic area, which is correlated with the partial melting zone or magma chamber. The low-velocity zone was observed at the depth range of 27–155 km, which was also correlated with the subducted slab beneath Java Island. This leads to an assumption that there is an interlinked volcanic system which extends from west to east of Java.
This paper presents a site-specific seismic ground response evaluation through convolution–deconvolution analysis in the Balaroa–Petobo area during the 2018 Palu–Donggala Indonesia earthquake. The equivalent-linear ground response analysis for the earthquake time history recorded at Balaroa was carried out using DEEPSOIL software. The results of the analysis indicate that the EW component of the earthquake motion was amplified more severely (amax) than was the NS component, as it propagated to the Petobo surface. The amplification of the bedrock motion on the Petobo surface was more serious than that on the Balaroa surface, which appears to be due to the differences in the subsurface stratification and material properties of the two sites. The Fourier spectrum and response spectra also showed greater maximum spectral accelerations (Sa,max) and maximum Fourier amplitudes (Af) at the Petobo site than at the Balaroa site. The frequency of surface soil both the Petobo and Balaroa sites computed by using comparison between response spectra analysis and the local modes analysis VS/4*H was indicated the potential decline of surface soil stiffness at Petobo area appear to account for the structural damage and liquefaction flow slides during the 2018 incident.
Tropical Cyclone (TC) Kimi was active from January 15 to 19, 2021 in Australian waters. TC Kimi activity does influence the atmosphere and ocean dynamics around it, including in central and eastern Indonesian waters, with the highest increase in local winds occurring in Sangihe (315.8%), Gorontalo (236.3%), Seram (236.3%) dan Manado (225.8%). On the other hand, Gorontalo experienced the highest increase of significant wave height during TC Kimi active, with 921.4% increase. In this study, we analyze wave height change in central and eastern Indonesian waters before TC Kimi was active, at the peak intensity of TC Kimi, and after TC Kimi dissipated by employing Simulating Waves Nearshore (SWAN) wave model. From spatial lagged correlation analysis between wind from TC Kimi and local winds in Indonesia, we obtained 12 locations that have positive lag and correlation, namely: Denpasar, Waingapu, Rote, Majene, Gorontalo, Manado, Sangihe, Sanana, Seram, Raja Ampat, Agats dan Merauke. From time series lagged correlation, the locations that have negative lag are Denpasar (-6 h) and Rote (-1 h), those with 0 h lag are Raja Ampat and Agats, and those with positive lag are Waingapu (+ 8 h), Majene (+ 10 h), Gorontalo (+ 14 h), Manado (+ 6 h), Sangihe (+ 15 h), Sanana (+ 7 h), Seram (+ 5 h) and Merauke (+ 6 h). Surface wind analysis during the development and early phase of TC Kimi shows wind flows from Sulawesi Sea, Maluku Sea, Halmahera Sea, and Banda Sea towards the TC Kimi system. When TC Kimi approaches its strongest intensity, there are low-pressure areas (Low) that are also active, including Low in the Philippines and in the Gulf of Carpentaria, while the wind flow towards TC Kimi appears to be disconnected. Low in the Philippines and the Gulf of Carpentaria, respectively, play a role in maintaining the high waves in the northern and southern waters of Indonesia. It indicates that TC Kimi plays a role in the initial increase of wind speed in Indonesia, which is continued by the presence of Low in their respective local areas.
Nusantara, the new capital city of Indonesia, and its surrounding areas experienced intense heavy rainfall on 15–16 March 2022, leading to devastating and widespread flooding. However, the factors triggering such intense heavy rainfall and the underlying physical mechanisms are still not fully understood. Using high-resolution GSMaP (Global Satellite Mapping of Precipitation) data, we show that a mesoscale convective system (MCS) was the primary cause of the heavy rainfall event. The rainfall peak occurred during the MCS’s mature stage at 1800 UTC 15 March 2022, and diminished as it entered the dissipation stage. To understand the large-scale environmental factors affecting the MCS event, we analyzed contributions from the MJO, equatorial waves, and low-frequency variability to column water vapor and moisture flux convergence. Results indicate a substantial influence of the MJO and equatorial waves on lower-level (boundary layer) meridional moisture flux convergence during the pre-MCS stage and initiation, with their contributions accounting for up to 80% during the growth phase. Moreover, while La Niña and the Asian monsoon had negligible impacts on MCS moisture supply, we find a large contribution from the residual term of the water vapour budget during the maturation and decay phases of the MCS. This suggests that local forcing (such as small-scale convection, local evaporation, land-surface feedback, and topography) also contributed to modulation of the intensity and duration of the MCS. The results of this study can help in our understanding of the potential causes of extreme rainfall in Nusantara and could be leveraged to improve rainstorm forecasting and risk management across the region in the future.
On 18 November 2022, a large earthquake struck offshore southern Sumatra, generating a tsunami with 25 cm peak amplitude recorded at tide gauge station SBLT. Our W‐phase solution indicates a shallow dip of 6.2°, compatible with long‐period surface wave radiation patterns. Inversion of teleseismic body waves indicates a shallow slip distribution extending from about 10 km deep to near the trench with maximum slip of ∼4.1 m and seismic moment of 1.05×1020 Nm (MW 7.3). Joint modeling of seismic and tsunami data indicates a shallow rigidity of ∼23 GPa. We find a low moment‐scaled radiated energy of 4.15×10−6 , similar to that of the 2010 MW 7.8 Mentawai event (3.1×10−6 ) and other tsunami earthquakes. These characteristics indicate that the 2022 event should be designated as a smaller moment magnitude tsunami earthquake compared to the other 12 well‐documented global occurrences since 1896. The 2022 event ruptured up‐dip of the 2007 MW 8.4 Bengkulu earthquake, demonstrating shallow seismogenic capability of a megathrust that had experienced both a deeper seismic event and adjacent shallow aseismic afterslip. We consider seismogenic behavior of shallow megathrusts and concern for future tsunami earthquakes in subduction zones globally, noting a correlation between tsunami earthquake occurrence and subducting seafloor covered with siliceous pelagic sediments. We suggest that the combination of pelagic clay and siliceous sediments and rough seafloor topography near the trench play important roles in controlling the genesis of tsunami earthquakes along Sumatra and other regions, rather than the subduction tectonic framework of accretionary or erosive margin.
Sumatra-Java megathrust has been a host of six earthquake-triggered tsunamis in recent decades, which caused severe damage. Cilegon industrial area is a tsunami-prone zone since it is located on the Sunda Strait coast, facing a possibility of large-magnitude earthquake occurrence in the transition zone of Sumatra and Java megathrust. In this study, we assess the tsunami hazard in this area using a deterministic approach from a worst-case earthquake scenario (Mw 8.9). The assessment was done using numerical tsunami modeling, considering various fault source models and digital elevation model (DEM) datasets to accommodate the uncertainty of those factors in the modeling. Two source models (non-uniform and uniform fault slips) and three DEM datasets (global, regional, and local data) were employed. Uniform slip affected the smaller maximum tsunami amplitude than the non-uniform slip, but the travel time from these two source models is similar. Differences in water depth and slope bathymetry profile also strongly influence the tsunami propagation characteristics, particularly in the finest layer model. Generally, the Cilegon coast is consistently hit by up to 9 m of tsunami height from all used scenarios. The estimated tsunami arrival time is more than 60 min, providing enough time for the coastal community to evacuate to the higher ground level. However, the Cilegon industrial area is still categorized as highly hazardous since the tsunami strike can damage industrial buildings, infrastructure, and factory equipment, leading to economic losses.
Jailolo, the capital of West Halmahera Regency, faces earthquake risks due to its proximity to a Jailolo volcano and a double subduction zone in Eastern Indonesia. This study is aimed at investigating the seismic hazard characteristics in Jailolo through the analysis of statistic and signal parameters. Analyzing seismic hazard characteristics in this region is crucial for assessing earthquake risks and understanding how local soil conditions influence ground shaking. Earthquake catalogs from several databases spanning from 1970 to 2018 were utilized. Additionally, 40 locations around Jailolo were surveyed using a short-period seismometer to gather microtremor data. The earthquake catalogs underwent b value analysis using the maximum likelihood method, while microtremor data were examined for f0 and A0 using the horizontal-to-vertical spectrum ratio (HVSR) method. VS30 profiles were derived using the H/V curve inversion method. The b value characterizes earthquake size distribution, HVSR evaluates ground properties, especially the shallow subsurface, and VS30 represents the time-averaged shear wave velocity in the upper 30 m of the Earth’s surface. The findings revealed the average b value around Jailolo is 1.36 with a 15% deviation, categorizing the area as earthquake prone and volcanic. The values of f0 and A0 ranged from 0.536 to 2.44 Hz and 8.97 to 21.2, respectively. The results of f0 and A0 suggested that the Jailolo area is predominantly characterized by thick sediment. The H/V curve analysis provided a VS30 profile ranging from 109 to 460 m/s, indicating that the area is dominated by sediment consisting of stiff soil, very dense soil, and soft rock. In conclusion, Jailolo is highly prone to earthquakes due to its seismic and volcanic activity, supported by soil characteristics that can amplify seismic waves and increase ground shaking intensity during earthquakes. These findings stress the need for long-term seismic monitoring and community involvement to mitigate seismic risks.
The Sunda–Banda arc transition zone features the collision of the Indo-Australian oceanic plate and the Australian continent, resulting in intricate geological and geodynamic conditions. Tectonic activity in this region is shaped by the convergence of multiple major plates, including the Indo-Australian oceanic plate and the Eurasian plate. The crustal structure along the Sunda–Banda arc transition zone is complex and influenced by various factors such as subduction, continental collision, and volcanic activity. The tectonic complexity of the region in eastern Indonesia makes it an interesting area for study. In this research, International Seismological Centre-Engdahl-van der Hilst-Buland catalogue data from 1964 to 2020 were used, which include recorded information on 69.705 earthquake events from 1.185 recording stations and consist of 2.943.974 P phases. Resolution testing was performed using various velocity grids, and optimal results were obtained with a medium resolution of ∼100 km × 100 km × 80 km for the inversion process. The tomographic inversion analysis provided valuable insights into subsurface structures within Earth’s crust and mantle up to a depth of approximately 750 km. The occurrence of deep earthquakes in the study area has provided valuable insights into complex dynamics associated with subduction and plate tectonics. The results of the tomographic inversion analysis reveal that earthquakes are concentrated in areas with high-velocity anomalies, indicating intense tectonic activity near the subduction zone. This study offers the perspective on the structural complexities and earthquake origins in the Sunda–Banda arc transition zone following the 2023 Mw 7.1 Bali Sea earthquake, which occurred on August 29, 2023, at 02:55:32 UTC + 7, approximately 163 km northeast of Lombok, Indonesia. This earthquake was caused by slab pull activity from the Australian Plate and involved a combination of downward and oblique-normal movement. These characteristics indicate the convergence and interaction between tectonic plates in the subduction process occurring in the Bali Sea area. As a result, there have been frequent occurrences of various tectonic and volcanic activities including earthquakes of different magnitudes. These results highlight the significance of the high-velocity anomaly connected to this occurrence, offering valuable insights into seismic behaviour and tectonic phenomena in the region. The findings of this study indicate that the deep earthquakes in the Bali Sea may be induced by faulting due to the transformation of metastable olivine into denser spinel at significant depths, along with shear instability caused by phase transitions within Earth’s mantle layers. This theory proposes that stress-induced changes in phase can initiate shear instabilities and subsequently lead to deep earthquakes.
The northern coast of Central Java is a strategic development area, but it has the potential for high wave disasters. Planning activities and mitigating maritime disaster risks requires an understanding of wave characteristics. This study analyzes wave characteristics in the northern waters of Central Java using the Wavewatch III numerical model. Significant wave height and wave direction were averaged from 2014 to 2023. The results showed that wave conditions in the northern waters of Central Java tend to be higher in December, January, February, July, August, and September. Meanwhile wave conditions tend to be lower in March, April, May, June, October, and November.
Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.
In the past decades, the flooding in Jakarta has become a huge issue with major floods in 2002, 2007, 2013, 2014, and 2020 causing billions of dollars of direct and indirect economic damage, and huge social upheaval. In this context, case study author Azhari Cempaka undertakes a hazard, exposure and vulnerability analysis to estimate a median increase in flood risk of 183% in 2030 compared to baseline conditions and synthesizes data to present a forward look at risk drivers. The author employs a Damagescanner model to assess the impacts of changes in physical and socioeconomic drivers on flood risk. In addition, the author identifies the ways in which land subsidence and land use change impact the likelihood of flooding disasters and explores the potential benefits of an SMS-based flood early warning system as a high-value hazard reduction measure. From this analysis, it was found that an SMS-based flood early warning system could reduce annual flood risk in Jakarta by US 22.6 million (12%) under an optimistic scenario.
Wind forecasting is an integral part of wind energy management as a crucial instrument for predicting wind patterns in coastal areas. One common technique to predict the wind field in a specific area is the dynamical downscaling method, which is based on a physical model and requires a substantial computational cost. Instead, this study proposes a novel approach for wind downscaling based on deep learning techniques as a substitution for a dynamical downscaling method. Our methodology starts with generating a high-resolution wind dataset by dynamically downscaling global climate data using RegCM4.7. Then, we employ a feature selection technique to identify the optimal global wind data points that exhibit a strong spatial correlation with the local wind data of interest. The selected features from global climate data and the target from the high-resolution wind data are used to develop a machine learning-based model to predict wind variability in a specific location. We consider various models, namely multilayer perceptron (MLP), AdaBoost, XGBoost, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), and conduct performance analysis to find an optimum model. The BiLSTM model has been shown to be the most optimal algorithm for wind downscaling among various machine learning models. We also evaluated the model’s performance by conducting a comparative analysis between its predictions and the observed wind data gathered from Jakarta and Meulaboh. This analysis yields significant insights into the accuracy and applicability of our methodology. Our approach reveals a strong correlation coefficient of 0.963 and a low root mean square error (RMSE) of 0.476. These results highlight the efficacy of our method in correctly downscaling wind data.
The Biak tsunami event on February 17, 1996, was triggered by a Mw 8.2 earthquake at 5:59 UTC (14:59 local time). Based on the field survey, the maximum tsunami height was not located on the coast that directly faces the earthquake epicenter. The maximum tsunami of up to 7.7 m was recorded at Farusi village on the opposite coast. In addition to the high tsunami hit, the fast arrival time in this village was an anomaly that raised questions regarding the multiple tsunami sources. Previous studies suspected a landslide when a rupture occurred, but no one had yet identified the dimensions and mechanism of the landslide. The purpose of this research is to increase understanding of tsunami generators and answer that question. The COMCOT software is used to perform tsunami simulations, integrating fault and landslide sources simultaneously. This study obtains the Biak tsunami generator from a fault source model with a length of 272 km, a width of 110 km, an average dislocation of 8 m, and a maximum slip of 10.6 m. Also, there are three landslides occurred in the south coast. One of the major landslide source model has dimensions length and width of 5.629 km and 14.595 km, respectively, and a thickness of landslide material of 50 m, with an average slope of the slip plane of 10° located in the Ramardori. These two source models answer the particular questions of the Biak tsunami incident.
This chapter provides summaries of the 2023 temperature and precipitation conditions across seven broad regions: North America, Central America and the Caribbean, South America, Africa, Europe and the Middle East, Asia, and Oceania. In most cases, summaries of notable weather events are also included. The base period used for these analyses is 1991–2020, unless otherwise stated.
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Prof. Ir. Dwikorita Karnawati, M.Sc. Ph.D
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