Assessing the socioeconomic footprint of tornado in Indonesia: A 15 year spatiotemporal impact analysis

Abstract

Puting beliung, or small-scale tornadoes, pose significant threats to infrastructure and human lives in tropical island environments. This study analyzes the socioeconomic impacts of these extreme weather events in Indonesia over a 15-year period from 2010 to 2024. A database of incidents was compiled by collecting reports from government agencies, scientific journals, online media, and YouTube, applying a multi-layered filtering method to verify 267 confirmed incidents. The epicenter of this extreme weather phenomenon is concentrated on the island of Java (63.3%), with peak activity occurring during the rainy season in most parts of Indonesia (December through February). Nationwide, these whirlwinds caused 184 fatalities and total economic losses of 25.8 million USD, with residential damage accounting for approximately 66% of the total losses. Over the study period, overall impacts increased substantially, with a 225% increase in fatalities and a 189% increase in infrastructure damage. These findings highlight the growing severity of tropical tornado impacts and demonstrate the critical need for public education in vulnerable areas. The results provide a scientific foundation for enhancing early warning systems, recommending wind-resistant building standards in highrisk regions, and offering a valuable analytical framework for other tropical island nations facing similar risks.

1 Introduction

Indonesia is the world's largest maritime archipelago, with the longest coastline and the highest level of convective activity in Asia (Alfahmi et al., 2017). It has a unique geographical position: it lies on the equator and is flanked by two continents and two major oceans—Asia and Australia, as well as the Indian and Pacific Oceans. This unique position results in Indonesia having a surplus of convective energy, driven by year-round solar radiation and a supply of water vapor from surrounding waters. Indonesia's high population density in certain regions makes it vulnerable to the socioeconomic impacts of hydrometeorological disasters such as heavy rain, hail, strong winds, and even tornadoes—known locally as “puting beliung.” Although tornadoes occur more frequently in mid-latitude regions, records of this destructive wind phenomenon have been documented on every continent except Antarctica (Goliger and Milford, 1998).

Intense local convection caused by significant solar radiation and orographic lifting is among the mechanisms capable of triggering the growth of tornado-producing parent clouds in Indonesia. Several previous studies on tornadoes in Indonesia have reported an increase in frequency over the past few years (Nurjani et al., 2013; Ali and Hidayati, 2016; Darman, 2019; Kiki et al., 2025), suggesting a corresponding rise in potential socioeconomic impacts, particularly in highly vulnerable regions such as Java. The impacts of this extreme phenomenon generally manifest as structural damage, particularly to semi-permanent homes. Damage is primarily concentrated on the roofs of these homes. Fatalities and injuries are also frequently reported in some cases, including psychological impacts that require professional intervention for tornado victims (Nikmah and Hazim, 2023).

Although research on this extreme phenomenon has increased in both volume and methodological complexity, most studies remain single-case analyses. These focus primarily on environmental and meteorological conditions, as demonstrated by the research conducted by Siswanto and Supari (2012); Molle and Wardoyo (2015); Prasetyo et al. (2020); Saragih (2020); Anam and Amri (2021); Pratama and Mulya (2022) and Agung and Darmawan (2024). Other studies analyze tornado risk levels in selected regions (Darmawan et al., 2020; Fadillah and Nurdin, 2021; Rizal and Paharuddin, 2012; Syafitri et al., 2021; Tiara et al., 2023) or predict tornadoes using a statistical approach (Mustakim et al., 2018; Aliftha et al., 2020).

Significant research gaps remain, especially in the analysis of socioeconomic impacts on a national scale and over long time periods. Addressing these gaps is critical. This study aims to to meet these needs by comprehensively analyze the socioeconomic impacts of tornadoes in Indonesia at the national level from 2010 to 2024. The findings of this study are expected to contribute to the scientific understanding of this local-scale extreme phenomenon and to serve as a data-driven reference for policymakers in Indonesia and other tropical island nations that may face similar threats, helping build communities resilient to extreme weather and hydrometeorological disasters.

2 Materials and methods

Data on tornadoes in Indonesia for the period 2010–2024 were collected from various sources, namely the National Disaster Management Agency (BNPB), the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), and the Health Crisis Center of the Ministry of Health of the Republic of Indonesia (Kemenkes RI). To minimize the risk of information bias in remote areas, secondary data collection was conducted through a review of reputable scientific journals, online local media reports, and video-based visual documentation from the YouTube platform.

This diversification of data sources is crucial given the methodological challenges in disaster documentation in Indonesia. Historically, there has been overlap in reporting between tornadoes and non-tornadic strong wind events. Therefore, all collected data subsequently undergoes a screening process. This step aims to validate and classify events that meet the physical criteria for tornadoes, while eliminating duplicate or erroneous records. Details on the screening criteria, physical indicators, and validation parameters for each data source are presented in Table 1.

The filtering method used to build a verified database of tornadoes in Indonesia is based on Rauhala et al. (2012) and Kahraman and Markowski (2014), who also developed tornado databases in Finland and Turkey, respectively. This filtering method involves searching for visual documentation of tornado vortices in the form of photos or videos, eyewitness reports stating that a tornado vortex was seen, keywords in news reports of the event such as “vortex,” “spinning,” etc., and analysis of damage patterns typical of tornadoes, which exhibit a circular pattern within a relatively narrow area. Tornado intensity was not analyzed in this study because this data has not been included in tornado records, and it is difficult to determine solely from quantitative damage data, as most events lack clear visual documentation. The filtering criteria applied in this study are presented in full in Table 2.

The method used to analyze the spatio-temporal patterns of tornado occurrences and impacts in Indonesia employed a simple statistical approach using Python programming language version 3.11.9. An analysis of tornado density using the Kernel Distribution method was conducted using ArcGIS Desktop version 10.6. Data on building damage caused by tornadoes was obtained from the BNPB.

The criteria for determining the severity of building damage are based on Implementation Guideline No. 7 of 2023 on Standards for Disaster Incident and Impact Data, as presented in Table 3. Economic loss estimates from building damage caused by tornadoes were calculated based on the BNPB Regulation No. 5 of 2017 concerning the Preparation of Post-Disaster Rehabilitation and Reconstruction Plans (BNPB, 2017).

Economic loss estimates were calculated in accordance with BNPB guidelines, with the following values: for a single residential home, Rp. 25 million; for an educational or office facility, Rp. 3 million per square meter, assuming a building area of 300 square meters; and for places of worship and healthcare facilities, Rp. 3 million per square meter, assuming an area of 500 square meters. Nurjani also used similar estimates to calculate the economic losses caused by tornadoes in Central Java for the period 1990–2011.

3 Results and discussion

3.1 Indonesian tornadoes climatology

Based on the filtering criteria in Table 2, 267 tornado events were obtained for the Confirmed and Probable tornado categories throughout the 2010-2024 period from the six data sources used (Table 1). Data from these two categories were subsequently analyzed to determine the spatiotemporal patterns of tornado occurrences and assess their impacts. The results are presented as follows.

3.1.1 Annual dan seasonal distribution

During the 2010–2024 period, a total of 267 tornado events were validated in Indonesia, specifically those meeting the Confirmed and Probable criteria (available in the Supplementary file). Figure 1 shows the annual frequency of tornadoes in Indonesia, ranging from 7 to 37. The highest frequency (37) was recorded in 2024. Although annual variability is evident, a significant upward trend in frequency is also apparent from 2020 through the end of the study period. This increase may be related to the active triple-dip La Niña phenomenon (2020–2022), which increased convective activity in parts of Indonesia (Weather, 2026). The decrease in frequency in 2023 coincided with the active El Niño phenomenon that year, which is known to reduce convective activity in Indonesia. The Madden-Julian Oscillation (MJO) was also identified as active in 2021–2022 in the Indonesian region (Quadrant 3), where this phenomenon is known to increase convective potential, leading to rainfall and triggering hydrometeorological disasters (Wijayanti et al., 2021).

Figure 1

In addition, the increase in frequency since 2020 may also be due to advancements in digital technology and the use of social media in recent years. Technological advancements, social media, and high population density in certain regions can facilitate the reporting of these extreme phenomena, making it increasingly easier. An analysis of tornado occurrence frequency on a seasonal scale shows that the rainy season, specifically December–January–February (DJF), has the highest occurrence rate throughout the study period (37.8%). This is followed by the second transitional season, namely September-October-November (SON) (27.3%), the second transitional season, namely March-April-May (24.7%), and the dry season, June-July-August, which shows the lowest frequency (10.1%).

Consistent results were also reported by Firdaus et al. (2025) who compiled data on tornado incidents from online news sources and social media (X and YouTube), indicating that the average annual frequency of tornadoes in Indonesia is approximately 16.2 incidents, concentrated on the island of Java, and that the rainy season marks the peak of tornado activity.

3.1.2 Spatial distribution by provinces

The analysis results show that tornadoes occur in all provinces of Indonesia, as presented in Figure 2. Java Island was identified as the epicenter of tornado activity with 169 occurrences, or 63% of the national total, with West Java, East Java, and Central Java being the provinces with the highest tornado frequency, totaling 81, 39, and 34 occurrences, respectively. The islands of Sumatra and Sulawesi show moderate occurrence frequencies, with 49 and 21 occurrences, respectively. The regions of Bali, Nusa Tenggara, Maluku, and Papua show low occurrence frequencies, ranging from 5 to 12 occurrences. These differences in the spatial distribution of occurrence frequencies may be influenced by disparities in population density between western and eastern Indonesia, leading to reporting biases, such as underreporting in areas with low population density.

Figure 2

3.2 Spatiotemporal analysis of tornado hotspots

An analysis of tornado occurrence density over the study period was conducted using the Kernel density method, as shown in Figure 3. The analysis indicates that the highest tornado occurrence density (>25 tornadoes per 10⁴ km²) is in the western part of West Java. This value is significantly higher compared to most other regions in Indonesia, which range from 1 to 5 tornado events per 10⁴ km². Central Java and East Java provinces also exhibit fairly high-density values, ranging from 5 to 15 tornado events per 104 km², except for North Sumatra, which shows a density of up to 10 tornado events per 10⁴ km².

Figure 3

Figure 4 shows the monthly distribution of tornado occurrences across 34 provinces in Indonesia. The three provinces with the highest tornado frequency nationwide (West Java, East Java, and Central Java) experience significant activity from October through March, with January recording the highest frequency (42 occurrences). Other provinces also exhibit a similar pattern but with significantly lower occurrence rates, except for North Sumatra, which shows a different pattern, with significant tornado activity occurring during the mid-year period, specifically between May and August.

Figure 4

Tornado seasons vary significantly around the world. In the United States, the world's primary tornado hotspot, the tornado season runs from April through June (Tippett et al., 2016). Similarly, in South America, the tornado season runs from spring through early summer (Brooks et al., 2003). In Southern Europe and the Mediterranean region, the tornado season runs from fall through winter, while in Northern and Central Europe it generally occurs during the summer and early fall (Antonescu et al., 2016). In Australia, the tornado season is similar to that in Indonesia, spanning from October through April (Zovko-Rajak et al., 2023). In South Africa, tornado season runs from November to February, during the summer (Milford and Goliger, 1994). In parts of Asia, such as China and South Korea, tornado season runs from June to August (Lim et al., 2018; Bai et al., 2019). Meanwhile, in Japan, tornadoes frequently occur from September to November (Niino et al., 1997). In Bangladesh and India, tornado season runs from March to May, which is the pre-monsoon period (Bhan et al., 2016; Masum, 2019).

3.3 Socioeconomic impact assessment

Figure 5 shows the number of casualties, including both injuries and fatalities, resulting from tornadoes in Indonesia throughout the study period. Consistent with the concentration of occurrences on the Java Island, the distribution of casualties also exhibits a similar spatial pattern. A total of 11 provinces reported casualties resulting from being struck by collapsed buildings or fallen trees caused by tornadoes. The highest number of casualties was recorded in West Java Province, with 102 casualties (3 fatalities; 99 injuries) from a total of 12 incidents. At the national level, the total number of tornado-related fatalities during the 2010–2024 period reached 184, averaging 12 casualties (injured or deceased) per year, or 1 victim per tornado event.

Figure 5

Figure 6 presents a summary of building damage caused by tornadoes throughout the study period. The damage was classified into three categories: residential areas, social facilities (educational institutions, religious facilities, and health facilities), economic infrastructure (offices, and factories). Throughout the study period, 176 tornado events (66%) caused damage in residential areas, with 25% light, 13% moderate, and 62% severe. Social facilities suffered the second-highest number of damage incidents, with 14 events (5%). These damage severity data were obtained from the BNPB based on field surveys of tornado incident sites and descriptions in news reports covering the events.

Figure 6

Estimates of economic losses caused by tornadoes during the 2010–2024 period is presented in Figure 7. Of the total 267 tornado events, 224 occurred across 32 provinces, resulting in losses of up to Rp417.925 billion (~25.8 million USD) nationwide, or approximately Rp13.1 billion (~0.734 million USD) per province and Rp1.865 billion (~105 thousand USD) per tornado event. The median economic loss was Rp0.25 billion (~14 thousand USD). The median value is lower than the average per tornado event, indicating that most events caused losses below the average; however, several tornado events with extremely large economic losses raised the average.

Figure 7

The variation in the level of economic losses caused by these tornadoes is evident from a tornado incident that resulted in losses of up to Rp16 billion (~0.987 million USD), specifically, the tornado that struck Rancaekek, Bandung, West Java, on January 11, 2019, in a densely populated residential area. It was reported that approximately 640 homes sustained damage ranging from minor to severe. When analyzed by building type, the highest losses from the tornado occurred in residential areas, totaling Rp348.625 billion (83.4%). This was followed by educational facilities at Rp31.5 billion (7.5%), religious facilities at Rp27.9 billion (6.7%), office and factory facilities at Rp6 billion (1.4%), and health facilities at Rp3.9 billion (0.9%).

The three provinces with the highest economic losses are West Java at 8.42 million USD, East Java at 4.14 million USD, and Central Java at IDR 3.18 million USD. Meanwhile, the other 29 provinces generally show significantly lower economic impacts than these three Javanese provinces, with losses ranging from 1.4 K USD to 0.556 million USD. Complete details are presented in Figure 8.

Figure 8

The socioeconomic impacts of tornadoes were analyzed over 5-year periods to evaluate trends across these three-time frames. The three periods are 2010–2014, 2015–2019, and 2020–2024. All three impact aspects showed an upward trend from Period 1 through Period 3. The impact in terms of tornado-related casualties—including both fatalities and injuries—showed a 225% increase from the first period (Figure 9a). The impact on the number of damaged buildings also increased by up to 189%, as shown in Figure 9b. A similar increase is also evident in economic losses, which rose by 235% (Figure 9c).

Figure 9

3.4 Policy recommendation

Based on the analysis of the socio-economic impacts of tornadoes in Indonesia over the past 15 years, several disaster mitigation measures can be implemented by relevant policymakers. The central and local governments must revise building codes to make homes more wind-resistant in tornado-prone areas such as West Java and East Java. Subsidies should be provided to reinforce building roofs. The National Disaster Management Agency (BNPB) and Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG) can strengthen the tornado early warning system by expanding the weather radar observation network, increasing monitoring during the tornado season (the DJF season), utilizing social media for incident reporting and post-event impact analysis, and educating the public about the vulnerability of buildings to tornadoes and disseminating tornado emergency response protocols.

4 Conclusion

A total of 267 tornado events were identified between 2010 and 2024. The analysis results indicate that Java has been the epicenter of tornado activity in Indonesia over the past 15 years, with the highest event frequency, fatalities, building damage, and economic losses. The rainy season, from December through February, had the highest tornado frequency. An increase in tornado frequency was observed from 2020 through the end of the study period, which may also be attributed to advancements in digital technology and the use of social media. The average annual impact of tornadoes was 12 people (injured and killed), the majority of whom were struck by collapsing buildings or falling trees. Residential areas had the highest percentage of building damage at 66%, followed by educational, religious, office, and healthcare facilities. The high level of damage to residential areas underscores the urgent need to improve the structural standards of residential buildings to make them wind-resistant, particularly in tornado-prone areas such as West Java, Central Java, and East Java.

Some limitations of this study include the exclusion of tornado intensity data due to insufficient supporting data for intensity determination, as well as inconsistencies in tornado reporting practices across agencies. Another limitation is the potential for reporting bias, particularly for events during the early stages of the study, when the use of digital technology and social media was not yet widespread. Reporting bias may also occur in sparsely populated regions, such as eastern Indonesia.

Future studies could expand the criteria for filtering events, such as analyzing tornado signatures in weather radar imagery, assessing damage impacts using satellite imagery, or identifying tornado-prone environmental conditions in numerical reanalysis data. Given the trend of increasing socioeconomic impacts from tornadoes over the past 15 years, it is important for future research to develop machine learning or deep learning models to predict tornado damage. There is also a need to standardize tornado reporting in Indonesia so that data from various agencies can be properly compiled. Additionally, there is a need to develop a tornado intensity scale specific to Indonesia, where tornadoes exhibit different intensities compared to classic mid-latitude tornadoes, and where building types and topography also differ. This intensity scale may eventually be applicable to tornadoes in other tropical regions as well.

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