Quantitative evaluation of volcanic activity at Hakone Volcano, Japan, based on multiple observational datasets—Application of the Volcanic Unrest Index

Quantitative evaluation of volcanic activity at Hakone Volcano, Japan, based on multiple observational datasets—Application of the Volcanic Unrest Index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative evaluation of volcanic activity at Hakone Volcano, Japan, based on multiple observational datasets—Application of the Volcanic Unrest Index Ryo Kurihara, Kazutaka Mannen, Kotaro Toyama, Ryou Honda, Yuki Abe, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7709975/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Various phenomena occur in volcanic areas, including volcanic earthquakes, crustal deformation, and volcanic gas emissions. Observations are commonly conducted to understand this diverse activity; however, it is challenging even for experts to interpret multiple types of observational data comprehensively because volcanic activity is complex and these phenomena do not always change together. It is even more difficult for members of local government and non-scientists to understand volcanic activity based on observational data. The Volcanic Unrest Index (VUI) has been proposed as a tool to evaluate volcanic activity based on multiple types of observational data and to communicate the intensity of volcanic unrest to local government and non-scientists. We adapted the VUI and quantified the volcanic activity at Hakone Volcano, Japan, to aid future communication with multiple stakeholders. Although the original VUI uses only integer values, we use a precision of one decimal place to analyze temporal changes in volcanic activity in greater detail. The VUI was retrospectively applied to data from 2011 onward, and a system was developed to automatically calculate the index each day. We chose threshold values for each parameter based on the small hydrothermal eruption of Hakone in June 2015, which corresponds to a VUI of 3. We calculated the daily VUI by shifting a 45-day time window one day at a time. During the periods of unrest in 2019 and 2023, the VUI reached peaks of 1.7 and 1.6, respectively. Our results also quantified the complex activity that occurred from 2023 to 2024. Since the system calculating the VUI was launched in January 2024, it has contributed greatly to understanding volcanic activity and has stimulated discussions among researchers from different fields within our institute. In the future, we expect to use the system as a tool for communication with all stakeholders at Hakone. Hakone Volcano Phreatic eruption Volcanic Unrest Index Volcano monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Volcanic activity involves a range of geophysical and geochemical processes, including earthquake swarms, ground deformation, and fumarolic emissions. In recent years, advances in observation technology have enabled multiparameter monitoring in volcanic regions, including observing seismicity, crustal deformation, and volcanic gas composition. This progress has increased the need for comprehensive assessments and integrated interpretations of diverse datasets; however, different observations often send conflicting signals. For example, some unrest episodes only include increased seismicity, whereas others have clear changes in gas chemistry with less pronounced variations in seismic activity. In addition, different observation methods have different measurement frequencies, units, and accuracy, and are typically carried out by specialists from distinct disciplines. As a result, achieving a mutual understanding and synthesizing these data into a coherent interpretation remain challenging, even among experts. This study aims to provide a quantitative assessment of volcanic activity at Hakone Volcano, Japan. A minor magmatic hydrothermal eruption occurred at Hakone in 2015. In the months before and after the eruption, various phenomena were observed, including crustal deformation, earthquake swarms and an increase in deep low-frequency earthquakes, blowout of steam wells, and changes in volcanic gas composition (Mannen et al. 2018b ). Hakone Volcano is one of the most famous hot spring resorts in Japan, with more than three million people visiting Owakudani, the center of volcanic activity, annually. During the peak season, as many as 3000 tourists stay in the area. Given this high concentration of people near the eruption source, even a small-scale eruption poses a significant risk. On the other hand, overestimating volcanic activity may result in economic losses. To mitigate these risks, accurate eruption forecasting is crucial; however, we cannot currently make highly reliable predictions, especially for small eruptions. Volcanic disaster management in Japan relies on the volcanic alert level (VAL) issued by the Japan Meteorological Agency (JMA). The VAL is integrated with response measures, including criteria for the evacuation of residential areas (Kato and Yamasato 2013 ). Given that access to areas near the crater is restricted from VAL 2 upward, the administrative response changes markedly between VAL 1 and VAL 2. As a result, it is difficult to communicate minor volcanic unrest that falls below the threshold of VAL 2 effectively (Fujii 2016 ). The Hot Springs Research Institute of Kanagawa Prefecture (HSRI) has conducted multiple types of observations and field surveys for many years, including earthquake, global navigation satellite system (GNSS), and volcanic gas surveys, with a focus on the volcanic activity of Hakone Volcano. Some observation stations have also been installed by other organizations, including the JMA, the National Research Institute for Earth Science and Disaster Resilience (NIED), and the Geospatial Information Authority of Japan, creating dense seismic and geodetic networks on the volcano. Hakone has experienced several episodes of unrest in recent decades, characterized by increased seismicity, ground deformation, and anomalous steam emissions. These include the hydrothermal eruption in 2015 and a major unrest episode in 2019 characterized by crustal deformation. Both of these events led to the issuance of VAL 2 or higher. Such episodes of unrest were regarded solely as earthquake swarms until the introduction of GNSS observations in the late 20th century; however, they have been detected repeatedly in the 21st century using the well-established observation network developed in recent years, and are now recognized as unrest originating from magmatic activity deep below the volcano. The Volcanic Unrest Index (VUI; Potter et al. 2015 ) has been proposed to quantify volcanic activity and facilitate communication with non-scientists. However, this index represents the state of volcanic activity at a specific time and is not a tool for predicting volcanic activity. Worksheets based on past observational data and the current state of the volcanic activity are used to decide the index values for each data set, which are integrated to quantify volcanic activity. This index makes it easier to compare current activity with past states of the volcano during periods of unrest and quiescence, assisting in interpreting whether the volcano is currently active. Furthermore, the index is based purely on scientific observational data and excludes social impacts. As the VUI evaluates the activity of individual volcanoes, the criteria for defining the activity index are different for each site. We evaluated quantitatively the extent of deviation from the baseline level of volcanic activity since 2011, when comprehensive monitoring network established. We use the VUI to describe the deviation from the baseline level of volcanic activity to facilitate future communication with various stakeholders, including local government and residents. We report the details of its application to Hakone and provide a preliminary evaluation of its effectiveness. Before beginning our discussion, we would like to clarify the definitions of phreatic and hydrothermal eruptions. In previous papers, our team and many scientists from other groups referred to the 2015 eruption of Hakone as a phreatic eruption based on the definition of (Barberi et al. 1992 ), which includes all explosions involving confined steam generated in aquifers and hydrothermal systems; however, in VUI terminology, a phreatic eruption only refers to an eruption that occurs when ascending magma comes into contact with an aquifer and generates an explosion without ejecting juvenile magmatic material. The 2015 eruption of Hakone occurred in a pre-existing steaming area and was triggered by a sudden injection of hydrothermal fluid at shallow levels during a period of volcanic unrest caused by the deep supply of magmatic fluid (Mannen et al. 2018b , 2025 ). Following the VUI terminology and the definition by (Browne and Lawless 2001 ), this type of eruption should be classified as a hydrothermal eruption. 2 Volcano Unrest Index The VUI was developed as a semi-quantitative tool for characterizing the intensity of volcanic unrest in a consistent and communicable manner (Potter et al. 2015 ). The index was refined iteratively, incorporating feedback from international volcanological experts and civil defense personnel in New Zealand. Structured as an integer scale from 0 (no unrest) to 4 (heightened unrest), the VUI integrates multi-parameter monitoring data, including seismicity, deformation, and geothermal activity, into a single interpretable metric. The VUI is designed specifically to evaluate unrest intensity and is not intended for eruption forecasting. A key feature of the framework is its adaptability, as parameter thresholds are calibrated to background activity at each volcano, enabling consistent evaluation across a range of volcanic systems. The VUI was first applied to the Taupō Volcanic Centre in New Zealand, where it was used successfully to reconstruct a coherent chronology of historical unrest episodes using both qualitative and quantitative data. This early application illustrated the index’s potential to support scientific interpretation and stakeholder communication, particularly in settings where non-eruptive unrest might still represent a significant hazard. Implementing the VUI requires a structured five-step procedure to ensure consistency in assessing and comparing episodes of volcanic unrest across time and between different volcanic systems. This procedure can be divided conceptually into two phases: a preparatory phase consisting of the first three steps and an implementation phase comprising the final two steps. The preparatory phase involves establishing the fundamental parameters that define the scope and criteria for evaluation. The first step involves the definition of the geographical area surrounding the volcano from which relevant observations will be included. Although the selection of this area is necessarily subjective and volcano-specific, it must be clearly defined and consistently applied in all subsequent evaluations, with a rationale provided to support the choice. The second step involves establishing an appropriate time window over which unrest phenomena will be evaluated. A sliding time window is recommended for real-time assessment, ideally matched to the interval between routine observations to incorporate the most complete set of available data. Third, volcano-specific parameter ranges must be determined for each of the unrest indicators included in the VUI framework. These ranges are based on the historical behavior of the volcano in question and reflect the relative, rather than absolute, intensity of each phenomenon. All parameter values are scaled within a standardized low-to-high framework to ensure comparability between volcanoes. The application of the VUI procedure consists of two steps. First, monitoring data are included in the VUI framework by assigning a score to each parameter based on observations. Parameters for which no data are available are excluded from the calculation. Second, the VUI is obtained by computing the mean of the assigned values across all applicable parameters, with the result rounded to the nearest integer. This yields a value from 0 (no unrest) to 4 (heightened unrest), offering a semi-quantitative index of unrest intensity that is both reproducible and communicable. However, in this study we calculated the VUI to one decimal place to capture more detailed temporal variations in volcanic unrest. The implications and outcomes of this approach are discussed in Section 6.3 . To enable comparison with the VUI that is already applied in New Zealand and is expected to be adopted in other countries, and to evaluate the applicability of the VUI to Hakone, we applied the framework proposed by Potter et al. ( 2015 ) to Hakone as closely as possible. We divided the preparatory phase into two parts: geographical and time settings at Hakone are addressed in Section 3 , and the choice of each observational parameter is discussed separately in Section 4 . The application of the VUI to Hakone is considered in Section 5 . 3 Choosing the geographical range and time window 3.1 Geographical range Hakone has a caldera measuring 11 km (north–south) by 8 km (east–west), and lava flows extend ~ 16 km from the center of the volcano. The lava that has erupted over the last 60,000 y has formed a post-caldera edifice called the Younger Central Cones (YCC), which spans 2 km (NW–SE) by 1.4 km (NE–SW). The 2015 eruption occurred at Owakudani in the northern part of the YCC, and numerous fresh volcanic craters have been identified in the YCC region. The inflation source observed during unrest is also located in the YCC region (Harada et al. 2018 ); therefore, future eruptions are expected to occur in the YCC area. However, inflation is evident in data obtained from GNSS stations outside of the caldera. More importantly, during periods of unrest, including in 2015 and 2019, frequent earthquakes were observed near the Lake Ashi at the west part of caldera (Yukutake et al. 2022 ; Itadera and Yoshida 2023 ; Kawai et al. 2024 ). Therefore, we define earthquakes related to Hakone as those occurring within the broader caldera region, specifically with latitudes of 138.93°E to 139.09°E, longitudes of 35.14°N to 35.32°N, and depths of < 10 km (Fig. 1 ). 3.2 Time-window and calculation procedure At Hakone, field surveys, including volcanic gas observations, are typically conducted about once a month, with the interval between observations occasionally extending to ~ 45 days; therefore, we set the time window covering the past 45 days. Potter et al. ( 2015 ) used a worksheet for calculations; however, in practice, manual computation is burdensome, so we developed the following automated system. We developed a computer program to calculate the VUI at 8:00 a.m. (Japan Standard Time) each day using data from the past 45 days. The day preceding the calculation date is used as the reference day, and the latest VUI is calculated using data from the 45 days leading up to and including the reference day. However, due to potential updates from manual earthquake picking and data review, the VUI values are retroactively updated for the following 30 days. The data used are acquired by the HSRI; ~10 y of data are available and complex calculations are not required. These data are detailed in the Section 4 . 3.3 Range of VUI values The observational history of Hakone places constraints on VUI values. Typically, at least 10 volcano-tectonic (VT) earthquakes are detected at Hakone each month, and the volcano is characterized by persistent fumarolic activity. These observations suggest that it is a “warmer” volcano with minor unrest even during quiescence. As such, VUI 1 represents the baseline state for Hakone. By definition, the hydrothermal eruption that occurred in 2015 is classified as VUI 3, and the lower threshold for VUI 3 can be defined empirically based on observations of the 2015 eruption. The thresholds for other index values are tailored through observations made during other periods and using expert elicitation. 4 VUI parameters at Hakone The VUI classifies unrest phenomena into three categories: local earthquakes, local deformation, and geothermal systems including degassing. Each category includes several parameters (e.g., earthquake swarm duration, deformation rate, and gas flux) that are used to evaluate the intensity of unrest. Although some parameters are observable at many volcanoes, others depend on monitoring conditions or volcanic characteristics. In addition, parameter thresholds must be defined for each volcano individually, reflecting the monitoring context and typical background activity. In the following sections, “category” is used to refer to the types of unrest phenomena, and "parameter" is used to denote the individual phenomenon within each category. In this section, we present an overview of each parameter and discuss the basis for the chosen threshold values. Table 1 lists the parameters and threshold values adopted here to calculate the VUI at Hakone and Fig. 2 shows the stations that collect the data used for the calculation, including seismic monitoring, GNSS, tilt measurements, and volcanic gas. Table 1 Threshold values for each parameter used in calculating the Volcanic Unrest Index for Hakone Volcano. Category Parameter Index 1 Index 2 Index 3 Index 4 Earthquakes Total duration of swarms No swarm 96 h Number of earthquakes 10,000 Low frequency earthquakes (LFEs) and tremor No LFEs or tremor Small number of LFEs Large number of LFEs or small tremor Many LFEs Large tremor Crustal deformation Deep crustal deformation 12.0 mm Shallow deformation (tilt, GNSS, InSAR) No deformation Deformation in one observation Deformation in multiple observation Strong deformation Thermal and gas Thermal phenomena No blowout Small blowout Blowout Strong blowout Gas flux (DOAS) 200 ton/day Gas composition Owakudani (SO 2 /H 2 S) 4.5 Kamiyu (CO 2 /H 2 S) 60 4.1 Earthquake-related parameters Potter et al. ( 2015 ) included four parameters in the earthquake category: (1) the duration of earthquake swarms, (2) the location of earthquakes (depth and distance from the likely vent), (3) the rate of VT earthquakes, and (4) the occurrence of tremor and low-frequency earthquakes (LFEs). Shallower hypocenters would typically be assigned a higher index value when using the approach of Potter et al. ( 2015 ); however, earthquakes at Hakone commonly occur at depths of < 2 km and the accuracy in determining depths, especially for earthquakes occurring above sea level, is poor; therefore, we decided to exclude hypocenter depths from our calculation of the VUI at Hakone. We use the following three parameters: (1) earthquake swarm duration, (2) VT earthquake rate, and (3) the occurrence of LFEs or tremor. 4.1.1 Duration of earthquake swarms Large earthquake swarms were observed in 2001 and 2015, and moderate swarms were observed immediately after the 2011 M w 9.0 Tohoku Earthquake (Yukutake et al. 2011 , 2017 ). Prior to the development of the VUI framework, HSRI had defined earthquake swarms as sequences of ≥ 10 earthquakes at depths of < 10 km inside Hakone caldera within 1 h. The swarm period is defined as a continuous sequence of seismic events before and after the initial 1-hour window, provided there is no period of ≥ 3 h without earthquakes. The same criteria are used in the present study. In this case, the minimum earthquake swarm duration is 0 h and the maximum is the 45-day period of the moving window (i.e., 1080 h). In practice, earthquake swarms are absent during quiescent periods, resulting in durations of 0 h. On the other hand, a long-lived swarm lasting 622 h was recorded before the 2015 eruption, from 16:16 on May 7 to 14:52 on June 2. Moreover, ~ 3 h after the eruption (from 17:56 on June 2), earthquake swarms restarted and continued for 1 week. Thus, in time windows including this period, nearly the entire time window is occupied by earthquake swarms. These prolonged swarms are assigned the maximum index of 4, and we define the other indices as follows: index 1 is defined as 0 h, index 2 is defined as durations 96 h. A time series of this index is shown in Fig. S1 (see Additional file 1). 4.1.2 Earthquake occurrence rate We counted the number of earthquakes occurring within Hakone caldera. The earthquake counts increase by several orders of magnitude during periods of unrest. During quiescent periods, ~ 20 events occur over 45 days. In contrast, 8,568 earthquakes were observed in a 45-day window prior to the 2015 eruption; therefore, we defined the thresholds logarithmically rather than linearly: 10,000 events: index value of 4. Although Potter et al. ( 2015 ) included a “sudden decrease” as index value 4, this phenomenon was not observed during the 2015 eruption at Hakone, making it impossible to establish a quantitative threshold. Accordingly, “sudden decrease” was excluded from our analysis. A time series of this parameter is shown in Fig. S2 . 4.1.3 Low-frequency earthquakes and tremor This parameter means shallow events and excludes deep low-frequency earthquakes (LFEs). Shallow LFEs and volcanic tremor are rarely observed at Hakone. Volcanic tremor was only observed from June 29 to July 1, 2015, during the hydrothermal eruption (Yukutake et al. 2017 ). Consequently, only the period encompassing this tremor is assigned an index value of 3, with all other periods assigned a value of 1. The index is defined as follows: index value of 1 when there are no LFEs and tremor, value of 2 when LFEs occur, value of 3 during swarms of LFEs or volcanic tremor, and value of 4 if there is a substantial increase in LFEs or high-amplitude volcanic tremor. For example, 10 events over a short period corresponds to an index value of 3, whereas ~ 100 LFEs or tremor lasting > 5 days corresponds to an index value of 4. Tremor amplitudes of > 500 µm/s at the Ninotaira observation station, corresponding to the criteria of VAL 5 by the JMA, result in an index value of 4. A time series of this index is shown in Fig. S3 . 4.2 Parameters related to crustal deformation Potter et al. ( 2015 ) established three parameters for crustal deformation: (1) local deformation, (2) the location of the deformation source, and (3) groundwater level and spring flow. However, estimating the deformation source requires complex calculations that are difficult to perform automatically. In addition, quantitative groundwater level and flow rate observations that reflect volcanic crustal deformation have not been established at Hakone. Therefore, for the crustal deformation category, we combined parameters (1) and (2) from Potter et al. ( 2015 ) and employed two parameters: (1) changes in long GNSS baseline lengths, thought to indicate deep inflation; and (2) shallow crustal deformation. 4.2.1 Changes in long GNSS baseline lengths We use changes in the lengths of GNSS baselines spanning Hakone as the first crustal deformation parameter. During the activation of Hakone, changes occur in the lengths of east–west baselines crossing Hakone alongside increased seismicity and earthquake swarms. The distance between the Odawara and Susono2 GEONET stations is often used as a baseline (Harada et al. 2018 ). The baselines are thought to record the inflation of a deep source beneath Hakone. Although both observation stations are from the Geospatial Information Authority of Japan, we use the results calculated by Bernese 5.2 at the HSRI (Doke et al. 2020 ). Since the GNSS analyses have large daily errors, we use the difference between the median baseline length over the most recent 45 days and the median from 46–90 days prior, calculated as follows: $$\:d\:=\:\text{M}\text{e}\text{d}\text{i}\text{a}\text{n}\left(l\right(1),l(2),...,l(45\left)\right)-\text{M}\text{e}\text{d}\text{i}\text{a}\text{n}\left(l\right(46),l(47),....l(90\left)\right)$$ , where l ( n ) is the length of the Odawara–Susono2 baseline n days ago. When d is 12.0 mm the value is 4. A time series of this index is shown in Fig. S4 , and the long-term trend since 2011 is shown in Fig. S5. 4.2.2 Shallow crustal deformation Crustal deformation at Hakone often involves deep sources that are measured using long baselines, but the deformation of shallow sources has also been observed during two periods of unrest that included steam well blowouts in 2001 and 2015 (Harada and Yoshida 2024 ). Around the time of the 2015 hydrothermal eruption, deformation observed by tiltmeters (Honda et al. 2018 ) and GNSS (Doke et al. 2018b ; Harada and Yoshida 2024 ) appeared to be associated with crack-like intrusions. Localized uplift was observed in the Owakudani fumarolic area by satellite synthetic aperture radar (SAR; (Doke et al. 2018a ) and ground-based interferometric SAR (GB-InSAR; (Kuraoka et al. 2018 ). This parameter evaluates the shallow deformation. However, automatic detection is difficult because tiltmeter readings are influenced by rainfall, it is difficult to automatically analyze satellite SAR data, and the short baseline lengths observed by GNSS yield large errors and are potentially affected by landslides. In addition, the ground-based SAR installation was temporary and the instrument is no longer in place. Therefore, this parameter must be determined by a comprehensive evaluation of GNSS, tiltmeter, and satellite SAR results. Previous data for the deformation from May to June 2015 corresponds to an index value of 3. An index value of 1 corresponds to periods without shallow crustal deformation, a value of 2 corresponds to periods when deformation is observed in one dataset, a value of 3 is when deformation is observed in multiple datasets, and a value of 4 is when shallow crustal deformation greater than that observed before the 2015 eruption is identified in every dataset. A time series of this index is shown in Fig. S6. 4.3 Geothermal and volcanic gas phenomena Potter et al. ( 2015 ) established three parameters for geothermal and volcanic gas phenomena: (1) surface temperature, heat flow, and physical manifestations; (2) gas flux; and (3) gas and fluid composition. We employed all of these parameters at Hakone. 4.3.1 Surface and geothermal phenomena Before the 2015 eruption there was no steam emitted at temperatures above the boiling point of water at Owakudani. Since the 2015 eruption, numerous new fumaroles have appeared around the eruption center, releasing steam at temperatures above the boiling point of water. We measure the temperature of several fumaroles approximately once a month, but no temperature changes corresponding to unrest have been observed (Fig. S7). In addition, there is currently no way to accurately monitor heat release rates. However, significant changes in surface phenomena at Owakudani, including blowouts of steam wells, were observed in 2001 and 2015 (Fig. 3 ; (Mannen et al. 2021 ); therefore, an index value of 1 is defined as periods of normal conditions with no blowouts, a value of 2 is defined as small blowouts, a value of 3 is defined as blowouts, and a value of 4 is defined as fumarolic activity and more substantial blowouts than those during the 2015 event. A time series of this index is shown in Fig. S8. 4.3.2 Volcanic gas flux We measure the flux of SO 2 using differential optical absorption spectroscopy (DOAS), based on the absorption of certain wavelengths of ultraviolet light by SO 2 (cf. Mori et al. 2007 ). We conduct observations by mounting a spectrometer on a vehicle, driving, and estimating the amount of sulfur dioxide above the road (Abe et al. 2018 ). We drive from near the Kamiyu bus stop north of Owakudani to near the Sounzan station of the Hakone Ropeway (Fig. 2 d). The JMA and Meteorological Research Institute also conducted DOAS observations during the 2015 hydrothermal eruption (Japan Meteorological Agency 2016 ; Meteorological Research Institute 2016 ), although using a different measurement method. Based on these observations, we defined the index as follows: 200 tons/day to a value of 4. A time series of this index is shown in Fig. S9. 4.3.3 Gas composition We conduct gas surveys at the fumarolic areas of Owakudani and Kamiyu approximately once a month. At Owakudani, we have been conducting gas composition surveys in the fumarolic area of the crater, which formed in 2015, since March 2018 (Mannen et al. 2018a , 2020 , 2022 ). We measure the SO 2 /H 2 S ratio at the 15 − 1 and 15 − 2 craters using diffusive tubes. We analyze both the raw gas from each crater and gas dried with silica gel. In this study, we use the mean of all four measurements taken during the corresponding period. As these observations started in 2018, we set the thresholds based on data from the 2019 and 2023 unrest events. The index is defined as follows: an index value of 1 corresponds to SO 2 /H 2 S 4.5. A time series of this index is shown in Fig. S10. We measure the CO 2 /H 2 S ratio at the Kamiyu fumarolic area located ~ 500 m north of the Owakudani fumarolic area (Daita et al. 2021 ). These observations started in March 2012. Rapid increases in the CO 2 /H 2 S ratio corresponding to earthquake swarms and other events were observed in 2013 and 2015. We defined the index as follows: an index value of 1 corresponds to a CO 2 /H 2 S ratio of 60. A time series of this index is shown in Fig. S11. As the above two parameters are the ratios of components in gas, we use the mean value of the two indices with an equal weighting for the gas composition parameter. 5 Results Based on the criteria defined in Sections 2 and 3 , calculations were performed using a moving window from 1 January 2011 onwards to create daily VUI values. Potter et al. ( 2015 ) proposed using integers, but in this study, we give VUI values to one decimal place to test whether a more detailed evaluation of volcanic activity is possible (Fig. 4 ). The results show that periods of unrest (VUI ≥ 1.5) occurred every few years. Unrest occurred in 2011, 2013, 2015 (accompanied by a hydrothermal eruption), 2019, and 2023. The peak VUI in 2015 was 3.2, whereas peak values for the unrest in 2019 and 2023 were 1.7 and 1.6, respectively. Figure 5 shows the changes in VUI around 2015 alongside the JMA VAL. The 2015 hydrothermal eruption occurred on June 29. An increase in the VUI was observed from April, two months before the hydrothermal eruption. Since the index values for parameters with high sensitivity increased through time, a smooth upward trend is seen in the overall index; however, in cases where rapid seismic activity occurs, the VUI may increase suddenly, as seen at the end of June when the eruption occurred. The VUI then decreased rapidly from July to August. We show the 2019 activity in detail in Fig. 6 . In 2019, earthquake swarms and other events occurred, and the VAL was raised to 2 from May 19 to October 7. The change in VUI during this period was gradual, with a slow increase from late March, peaking in June, and then slowly decreasing back to the original VUI by late September. Changes in the observed activity from 2023 to 2024, when earthquake swarms and other events occurred intermittently and there is no clear trend, are shown in Fig. 7 . The VUI started to increase from May 2023, but there were repeated small increases and decreases. Peaks are seen around September 2023, November 2023, and May 2024, which were influenced mainly by earthquake swarms. During this unrest, the activity repeatedly transitioned between elevated and quiescent, and the overall duration of the activity was long (~ 2 y). Although we found sudden changes in the VUI on certain days (e.g., June 29, 2015, when the hydrothermal eruption occurred), in most cases the VUI changed gradually over periods of 1–2 months. 6 Discussion 6.1 Temporal variations in observational data We analyzed the temporal variation in each parameter to investigate how each index is reflected in the overall VUI (Fig. 8 ). During the 2015 unrest, all parameters had high index values. Although LFEs and tremors, shallow deformation, and thermal anomalies (blowouts) were not observed during the periods of unrest in 2019 and 2023–2024, the indicators derived from numerical data showed synchronous increases. Looking in more detail at 2015, all indices rose almost simultaneously when considering observation frequency and other factors (Fig. S12). In contrast, in 2019, changes in the gas composition at the Kamiyu fumarole area lagged 2 months during both the initial increase and the later decrease in activity (Fig. S13). It is essential to check the original data and trends for each index when the VUI rises, because transitions in volcanic activity vary between unrest periods. 6.2 Addition of observational parameters and automated data collection In this study, we use a total of nine observational parameters, which are adapted from the indices proposed by Potter et al. ( 2015 ); however, several additional indices based on the monitoring parameters implemented at Hakone and previously observed phenomena can be considered. One potential candidate is activity of deep low-frequency earthquakes (DLFEs). At Hakone, DLFEs occur at a depth of ~ 20 km beneath the northern edge of the caldera. DLFEs became more frequent before the 2015 unrest (Yukutake et al. 2019 ), suggesting that they represent deep magma supply and the onset of volcanic unrest. During the 2019 unrest, however, DLFEs occurred predominantly around October, after the unrest had finished; therefore, we will consider adding this parameter once its relationship with shallow volcanic activity becomes clearer. We did not use the groundwater level and spring flow parameters proposed by Potter et al. ( 2015 ). In their study, these indices are determined based on factors that include changes in spring flux, temperature, and the occurrence of lahars, which are associated with crustal compression and extension. At Hakone, no significant changes in spring flux have been observed in relation to volcanic activity. Although variations in the temperature of hot springs have been recorded and were once considered indicative of increased volcanic activity (Oki and Hirano 1970 ), more recent studies suggest that these changes reflect long-term trends rather than being linked directly to specific volcanic unrest events (Machida et al. 2007 ). During the unrest in 2001, only two wells recorded temperature increases of several degrees (Mannen 2008 ). This change is not significant, given that there are hundreds of hot springs at Hakone. Moreover, no temperature perturbations were observed in these wells during other unrest events, including the 2015 eruption. A crater-outflow lahar occurred in 2015, but this was during the eruption itself. Given that no observations have linked the flow from hot springs to unrest levels, we decided not to include spring flux for now; however, we will consider adding the parameter if relevant observations indicate a relationship between hot springs and volcanic activity. We also monitor the chemical composition of hot-spring water; however, changes in chemical composition cannot currently be incorporated into the VUI because of dilution by rainfall. It may be possible to use these data when high-frequency and automatic measurements of hot-spring chemistry become feasible. For volcanic activity assessments and disaster resilience, rapid automatic calculations are necessary; however, in the current iteration of the VUI, some data acquisition and index calculations still require manual processing. Manual steps include earthquake detection and removing false detections, identifying low-frequency earthquakes and tremor, determining crustal deformation from tilt and SAR data, visually confirming surface activity, and collecting and analyzing volcanic gas samples. It is important that we develop automatic analysis methods (e.g., machine learning). Advances in these technologies will allow us to replace or add observational parameters to the VUI, further improving the accuracy of this approach and reducing the delay in calculating index values. 6.3 Current operation and potential for communication with stakeholders We present the VUI to one decimal place to capture detailed changes in volcanic activity; however, for communication with some stakeholders, including local government, it is essential to present information in an easily understandable format. As suggested by Potter et al. ( 2015 ), the VUI is likely to be communicated using integer levels. Figure 9 shows integer VUI values for Hakone, which provide a clear representation of unrest levels and make it suitable for communication with local government; however, for specialized institutions monitoring and researching volcanic activity, the decimal VUI values shown in Fig. 4 allow for a more detailed and real-time assessment of volcanic conditions. The HSRI, to which the authors of this study belong, is one of the offices of the Kanagawa Prefectural Government. We have already discussed the volcanic activity with some stakeholders, including local government, the JMA, and other institutions. There are many agencies responsible for responding to increased volcanic activity, including the prefectural government, town offices, police, and the tourism bureau. In addition, volcanic unrest can have significant economic impacts, including the temporary closure of tourist facilities. Discussions that make use of this index have been incorporated into monthly volcanic activity assessment meetings in HSRI since 2024, allowing researchers from different specialties to discuss volcanic activity quantitatively. The automatic generation of worksheets (Fig. 10 ) and graphs has also made it possible to monitor the current volcanic activity without daily manual data verification, reducing the workload for routine monitoring. There is also a demand from non-experts in local government for a clear understanding of volcanic activity. Moving forward, HSRI is considering using this index not only during periods of unrest, but also for routine activity monitoring, to facilitate information sharing with related organizations and improve preparedness. 7 Conclusions We applied the Volcanic Unrest Index (VUI) to assess the activity of Hakone Volcano quantitatively using various observational data. As a result, we were able to represent numerically the recurring small-scale unrest events that occur every few years at Hakone. The 2015 hydrothermal eruption corresponded to a maximum VUI of 3.2, whereas the peak VUI values during the unrest events in 2019 and 2023 were 1.7 and 1.6, respectively. By presenting values to one decimal place, it is possible to monitor unrest in detail in real time. The VUI is highly effective at providing a comprehensive assessment of volcanic activity and has proven useful in facilitating discussions among researchers from different fields. In the future, we expect that the VUI will be used as a communication tool for non-experts, including local government and residents. Abbreviations VUI Volcanic Unrest Index JMA Japan Meteorological Agency NIED National Research Institute for Earth Science and Disaster Resilience HSRI Hot Springs Research Institute of Kanagawa Prefecture GNSS global navigation satellite system GEONET GNSS Earth Observation Network System VT earthquake volcanic-tectonic earthquake LFE low-frequency earthquake SAR synthetic aperture radar DOAS differential optical absorption spectroscopy Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The data for the Volcanic Unrest Index for each day are included in Additional file 2 and raw data are included in Additional file 3. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Integrated Program for Next Generation Volcano Research and Human Resource Development of the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT), and by the MEXT under the third Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research). This study was also partially supported by a Japan Society for the Promotion of Science KAKENHI grant (22K14113). Authors’ contributions RK designed and analyzed the data and wrote the paper. KM supervised the research. Other authors advised and discussed the contents of this paper and contributed to various observations of the volcano. Acknowledgements We used Generic Mapping Tools (Wessel and Smith 1998) for drawing figures and used Hi-net seismic observation data (http://www.hinet.bosai.go.jp) from NIED (National Research Institute for Earth Science and Disaster Resilience 2019). We used GNSS observation data from the Japan Meteorology Agency and Geospatial Information Authority of Japan. All members of the Hot Spring Research Institute at any time after 2011 were involved in collecting the observational data. Authors’ information Not applicable. Endnotes Not applicable. References Abe Y, Harada M, Itadera K, Takehiko Mori, Akimichi Takagi (2018) Emission rate of sulfur dioxide at Owakudani, Hakone Volcano, Japan—Observation, analysis, and temporal transition of emission rate to June 2018. Bull Hot Spring Res Inst Kanagawa Prefecture 50:1–18 (in Japanese) Barberi F, Bertagnini A, Landi P, Principe C (1992) A review on phreatic eruptions and their precursors. J Volcanol Geotherm Res 52:231–246. https://doi.org/10.1016/0377-0273(92)90046-G Browne PRL, Lawless JV (2001) Characteristics of hydrothermal eruptions, with examples from New Zealand and elsewhere. Earth Sci Rev 52:299–331. https://doi.org/10.1016/S0012-8252(00)00030-1 Daita Y, Ohba T, Yaguchi M, Sogo T, Harada M (2021) Volcanic activity forecast based on volcanic gas composition of Hakone Volcano, Japan: Utilization for volcanic disaster prevention. J of Geogr (Chigaku Zasshi) 130:783–796 (in Japanese with English abstract) Doke R, Harada M, Mannen K, Itadera K, Takenaka J(2018a) InSAR analysis for detecting the route of hydrothermal fluid to the surface during the 2015 phreatic eruption of Hakone Volcano, Japan. Earth Planets Space 70:63. https://doi.org/10.1186/s40623-018-0834-4 Doke R, Harada M, Miyaoka K (2018b) GNSS Observation and monitoring of the Hakone Volcano and the 2015 unrest. J Disaster Res 13:526–534. https://doi.org/10.20965/jdr.2018.p0526 Doke R, Harada M, Itadera K, Kato T, Nakamura Y (2020) A new strategy of GNSS analysis in the Hot Springs Research Institute of Kanagawa Prefecture. Bull Hot Spring Res Inst Kanagawa Prefecture 52:63–68 (in Japanese) Fujii T (2016) Present situation and issues to be concerned on the prediction of volcanic eruption in Japan. Bull Volcanol Soc Jpn 61:211–223 (in Japanese) Harada M, Yoshida A (2024) Depth of pressure source of activity at Hakone Volcano: From the viewpoint of monitoring volcanic activity. J Geogr 133:91–100. https://doi.org/10.5026/jgeography.133.91 Harada M, Doke R, Mannen K, Itadera K, Satomura M (2018) Temporal changes in inflation sources during the 2015 unrest and eruption of Hakone Volcano, Japan. Earth Planets Space 70:152. https://doi.org/10.1186/s40623-018-0923-4 Honda R, Yukutake Y, Morita Y, Sakai S, Itadera K, Kokubo K (2018) Precursory tilt changes associated with a phreatic eruption of the Hakone Volcano and the corresponding source model. Earth Planets Space 70:117. https://doi.org/10.1186/s40623-018-0887-4 Itadera K, Yoshida A (2023) Earthquake swarms in May 2015 at the northern shore of Lake Ashi, Hakone, Japan: What caused the activity? J Geogr 132:465–482(in Japanese with English abstract). https://doi.org/10.5026/jgeography.132.465 Japan Meteorological Agency (2016) Hakoneyama. report of coordinating committee for prediction of volcanic eruption 135:58–66 (in Japanese) Kato K, Yamasato H (2013) The 2011 eruptive activity of Shinmoedake Volcano, Kirishimayama, Kyushu, Japan—Overview of activity and volcanic alert level of the Japan Meteorological Agency—. Earth Planets Space 65:489–504. https://doi.org/10.5047/eps.2013.05.009 Kawai T, Yukutake Y, Doke R, Honda R (2024) Contribution of aseismic slips to earthquake swarms at the Hakone Volcano. Earth Planets Space 76:152. https://doi.org/10.1186/s40623-024-02098-1 Kuraoka S, Nakashima Y, Doke R, Mannen K (2018) Monitoring ground deformation of eruption center by ground-based interferometric synthetic aperture radar (GB-InSAR): a case study during the 2015 phreatic eruption of Hakone Volcano. Earth Planets Space 70:181. https://doi.org/10.1186/s40623-018-0951-0 Machida I, Itadera K, Mannen K (2007) Source area of heat and NaCl for hot springs in Gora region, Hakone. J Groundwater Hydrol 49:327–339. https://doi.org/10.5917/jagh1987.49.327 Mannen K (2008) Hakone Caldera: Structure, mode of formation, and role in present-day volcanism. Research report of the Kanagawa Prefecutral Museum, Natural History 61–76 Mannen K, Kikugawa G, Miyashita Y, Yamaguchi T, Tanbo T, Honma N (2018a) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature. Bull Hot Spring Res Inst Kanagawa Prefecture 50:19–44 (in Japanese with English abstract) Mannen K, Yukutake Y, Kikugawa G, Harada M, Itadera K, Takenaka J (2018b) Chronology of the 2015 eruption of Hakone Volcano, Japan: geological background, mechanism of volcanic unrest and disaster mitigation measures during the crisis. Earth Planets Space 70:68. https://doi.org/10.1186/s40623-018-0844-2 Mannen K, Kikugawa G, Miyashita Y, Kato T (2020) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature (Part II). Bull Hot Spring Res Inst Kanagawa Prefecture 52:1–14 (in Japanse with English abstract) Mannen K, Abe Y, Daita Y, Doke R, Harada M, Kikugawa G, Honma N, Miyashita Y, Yukutake Y (2021) Volcanic unrest at Hakone Volcano after the 2015 phreatic eruption: reactivation of a ruptured hydrothermal system? Earth Planets Space 73:80. https://doi.org/10.1186/s40623-021-01387-3 Mannen K, Miyashita Y, Fujimatsu J, Ninomiya R, Toyama K (2022) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature (Part III: 2020–2022). Bull Hot Spring Res Inst Kanagawa Prefecture 54:1–17 (in Japanese with English abstract) Mannen K, Doke R, Johmori A, Kikugawa G, Minami T, Takahashi T, Utsugi M, Fujimoto K (2025) Anatomy of the fumarole field of Hakone Volcano, Japan: Interpretation of its resistivity structure and inferences for the steaming activity and recent hydrothermal eruption. J Volcanol Geotherm Res 465:108363. https://doi.org/10.1016/j.jvolgeores.2025.108363 Meteorological Research Institute (2016) Sulfur dioxide emission rate from Hakone Volcano (Owakudani). Report of Coordinating Committee for Prediction of Volcanic Eruption 135:67 (in Japanese) Mori T, Hirabayashi J, Kazahaya K, Mori T, Ohwada M, Miyashita M, Iino H, Nakahori T (2007) A compact ultraviolet spectrometer system (COMPUSS) for monitoring volcanic SO 2 emission: Validation and preliminary observation. Bull Volcanol Soc Jpn 52:105–112 National Research Institute for Earth Science and Disaster Resilience (2019) NIED Hi-net. https://doi.org/10.17598/nied.0003 Oki Y, Hirano T (1970) The geothermal system of the Hakone Volcano. Geothermics 2:1157–1166. https://doi.org/10.1016/0375-6505(70)90428-1 Potter SH, Scott BJ, Jolly GE, Neall VE, Johnston DM (2015) Introducing the Volcanic Unrest Index (VUI): a tool to quantify and communicate the intensity of volcanic unrest. Bull Volcanol 77:77. https://doi.org/10.1007/s00445-015-0957-4 Wessel P, Smith WHF (1998) New, improved version of generic mapping tools released. Eos, Trans Am Geophys Union 79:579–579. https://doi.org/10.1029/98EO00426 Yukutake Y, Honda R, Harada M, Aketagawa T, Ito H, Yoshida A(2011) Remotely-triggered seismicity in the Hakone Volcano following the 2011 off the Pacific coast of Tohoku earthquake. Earth Planets Space 63:737–740. https://doi.org/10.5047/eps.2011.05.004 Yukutake Y, Honda R, Harada M, Doke R, Saito T, Ueno T, Sakai S, Morita Y (2017) Analyzing the continuous volcanic tremors detected during the 2015 phreatic eruption of the Hakone Volcano. Earth Planets Space 69:164. https://doi.org/10.1186/s40623-017-0751-y Yukutake Y, Abe Y, Doke R (2019) Deep low‐frequency earthquakes beneath the Hakone Volcano, central Japan, and their relation to volcanic activity. Geophys Res Lett 46:11035–11043. https://doi.org/10.1029/2019GL084357 Yukutake Y, Yoshida K, Honda R (2022) Interaction between aseismic slip and fluid invasion in earthquake swarms revealed by dense geodetic and seismic observations. J Geophys Res B: Solid Earth 127:e2021JB022933. https://doi.org/10.1029/2021JB022933 Supplementary Files AdditionalFile2.dat AdditionalFile3.dat additionalfiguresHakoneVUI.docx graphicalabstract.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 13 Nov, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor assigned by journal 06 Oct, 2025 First submitted to journal 02 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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09:19:07","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117402,"visible":true,"origin":"","legend":"","description":"","filename":"EPSPD25002901structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/03d999252b292b6ae0059323.xml"},{"id":94649034,"identity":"c24e4bbd-efff-418d-83b3-d457cafb191e","added_by":"auto","created_at":"2025-10-29 09:19:07","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126242,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/51fe746430dc9d8f088f6ae4.html"},{"id":94649000,"identity":"12b3504f-7354-4946-acac-a2ae7ecf3542","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":379499,"visible":true,"origin":"","legend":"\u003cp\u003e(Left) Location of Hakone Volcano in Japan and (right) distribution of earthquakes from 2014 to 2024. The red rectangle indicates the geographic range for earthquakes related to Hakone, as defined in this study. Black dots show earthquake epicenters.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/e40a668f0a84343065ea942c.jpg"},{"id":94672185,"identity":"29165fad-c0f3-42b3-a927-35397df6fe4e","added_by":"auto","created_at":"2025-10-29 13:39:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":780872,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of observation stations at Hakone. (a) Seismic stations. Blue triangles indicate the locations of seismometers operated by the HSRI and white triangles indicate the locations of seismometers operated by the NIED and JMA. (b) GNSS stations. Orange diamonds indicate the locations of GNSS stations operated by the HSRI and white diamonds indicate the locations of GNSS stations in the GNSS Earth Observation Network System (GEONET) operated by the Geospatial Information Authority of Japan and a station operated by the JMA. (c) Location of tiltmeters operated by the HSRI. (d) Volcanic gas observation sites. Differential optical absorption spectroscopy (DOAS) observations are acquired along road 734 of Kanagawa Prefecture, plotted as a red line.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/4a9c60c11f9d52172e6d45bd.jpg"},{"id":94672151,"identity":"f154303d-da40-4247-94b6-82ac8b14001b","added_by":"auto","created_at":"2025-10-29 13:39:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7705084,"visible":true,"origin":"","legend":"\u003cp\u003eBlowout of a hot spring in 2015.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/cde9d17d5be8cfd578d47a17.jpg"},{"id":94648996,"identity":"15afa5f3-224e-41bb-a3ad-6ccd0d39556f","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191935,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the Volcanic Unrest Index for Hakone from 2011 to 2024.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/1e7f457f3fb16660c7944a8d.jpg"},{"id":94672215,"identity":"a2db0448-f4a0-4698-9a71-a4f2046000ba","added_by":"auto","created_at":"2025-10-29 13:39:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171215,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the VUI for Hakone in 2015. The yellow background shows periods when the JMA VAL was 2, and the red background shows when the VAL was 3. A red arrow indicates the time of the hydrothermal eruption.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/cd9d0774b7612d3603045dd1.jpg"},{"id":94648998,"identity":"4c6b297e-d61f-453f-b59b-938e0b0cea76","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":151502,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the VUI in 2019. The yellow background shows the duration of the JMA VAL 2.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/b992f3b6c4cb4cf06de1655a.jpg"},{"id":94649003,"identity":"5d0bd7f6-ce49-4c06-8fc0-885d489c7a75","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":150771,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the VUI for Hakone in 2023–2024.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/af6ac474fda83a4f2544decc.jpg"},{"id":94672186,"identity":"4dd492e2-3c92-4cde-92cf-a7393f86b9e3","added_by":"auto","created_at":"2025-10-29 13:39:43","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":225225,"visible":true,"origin":"","legend":"\u003cp\u003eIndex value for each observational parameter. Gray hatched areas show periods with no observations during the 45-day window.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/971c1ed2c55a090aaac2a3ca.jpg"},{"id":94672150,"identity":"3fedfb76-0269-4ba5-859a-0fc82ca6cbb6","added_by":"auto","created_at":"2025-10-29 13:39:32","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":141759,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of VUI values for Hakone from 2011 to 2024, rounded to the nearest integer.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/a59b60cd356c82027aee823e.jpg"},{"id":94649007,"identity":"9d53978e-62ed-4d66-b693-f267eb72cab4","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":323659,"visible":true,"origin":"","legend":"\u003cp\u003eWorksheet showing the VUI for the time window between January 19 and March 04, 2025. The large numbers (1, 2, 3, and 4) are the index for each parameter, and the small numbers below the index are the thresholds.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/a44443c93fba70d22a791632.jpg"},{"id":94728020,"identity":"0705224a-8d80-4391-82d4-ac8da4ede896","added_by":"auto","created_at":"2025-10-30 07:02:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11574425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/f4d98842-718c-4965-bdef-c04f1543dddc.pdf"},{"id":94672997,"identity":"f81e292d-eceb-4eb2-a9ea-fbc438cb731d","added_by":"auto","created_at":"2025-10-29 13:41:09","extension":"dat","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":169178,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2.dat","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/2632e66394825ae01ea2b353.dat"},{"id":94649014,"identity":"03041499-eed1-48f3-b813-f13211afe29d","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"dat","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":244579,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile3.dat","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/64d63735c21c94eb6515ee7a.dat"},{"id":94672877,"identity":"e05aa3f4-402b-439e-80f6-b07afafa7191","added_by":"auto","created_at":"2025-10-29 13:41:03","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":1913589,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfiguresHakoneVUI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/f9f1d7f71a6d9593003b5098.docx"},{"id":94649011,"identity":"34e41940-ab6f-46d7-b19d-fc3ccd513695","added_by":"auto","created_at":"2025-10-29 09:19:06","extension":"jpg","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":1208507,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7709975/v1/d182f150bcb9ae2c4b077ca1.jpg"}],"financialInterests":"","formattedTitle":"Quantitative evaluation of volcanic activity at Hakone Volcano, Japan, based on multiple observational datasets—Application of the Volcanic Unrest Index","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eVolcanic activity involves a range of geophysical and geochemical processes, including earthquake swarms, ground deformation, and fumarolic emissions. In recent years, advances in observation technology have enabled multiparameter monitoring in volcanic regions, including observing seismicity, crustal deformation, and volcanic gas composition. This progress has increased the need for comprehensive assessments and integrated interpretations of diverse datasets; however, different observations often send conflicting signals. For example, some unrest episodes only include increased seismicity, whereas others have clear changes in gas chemistry with less pronounced variations in seismic activity. In addition, different observation methods have different measurement frequencies, units, and accuracy, and are typically carried out by specialists from distinct disciplines. As a result, achieving a mutual understanding and synthesizing these data into a coherent interpretation remain challenging, even among experts.\u003c/p\u003e\u003cp\u003eThis study aims to provide a quantitative assessment of volcanic activity at Hakone Volcano, Japan. A minor magmatic hydrothermal eruption occurred at Hakone in 2015. In the months before and after the eruption, various phenomena were observed, including crustal deformation, earthquake swarms and an increase in deep low-frequency earthquakes, blowout of steam wells, and changes in volcanic gas composition (Mannen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Hakone Volcano is one of the most famous hot spring resorts in Japan, with more than three million people visiting Owakudani, the center of volcanic activity, annually. During the peak season, as many as 3000 tourists stay in the area. Given this high concentration of people near the eruption source, even a small-scale eruption poses a significant risk. On the other hand, overestimating volcanic activity may result in economic losses. To mitigate these risks, accurate eruption forecasting is crucial; however, we cannot currently make highly reliable predictions, especially for small eruptions.\u003c/p\u003e\u003cp\u003eVolcanic disaster management in Japan relies on the volcanic alert level (VAL) issued by the Japan Meteorological Agency (JMA). The VAL is integrated with response measures, including criteria for the evacuation of residential areas (Kato and Yamasato \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given that access to areas near the crater is restricted from VAL 2 upward, the administrative response changes markedly between VAL 1 and VAL 2. As a result, it is difficult to communicate minor volcanic unrest that falls below the threshold of VAL 2 effectively (Fujii \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Hot Springs Research Institute of Kanagawa Prefecture (HSRI) has conducted multiple types of observations and field surveys for many years, including earthquake, global navigation satellite system (GNSS), and volcanic gas surveys, with a focus on the volcanic activity of Hakone Volcano. Some observation stations have also been installed by other organizations, including the JMA, the National Research Institute for Earth Science and Disaster Resilience (NIED), and the Geospatial Information Authority of Japan, creating dense seismic and geodetic networks on the volcano.\u003c/p\u003e\u003cp\u003eHakone has experienced several episodes of unrest in recent decades, characterized by increased seismicity, ground deformation, and anomalous steam emissions. These include the hydrothermal eruption in 2015 and a major unrest episode in 2019 characterized by crustal deformation. Both of these events led to the issuance of VAL 2 or higher. Such episodes of unrest were regarded solely as earthquake swarms until the introduction of GNSS observations in the late 20th century; however, they have been detected repeatedly in the 21st century using the well-established observation network developed in recent years, and are now recognized as unrest originating from magmatic activity deep below the volcano.\u003c/p\u003e\u003cp\u003eThe Volcanic Unrest Index (VUI; Potter et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) has been proposed to quantify volcanic activity and facilitate communication with non-scientists. However, this index represents the state of volcanic activity at a specific time and is not a tool for predicting volcanic activity. Worksheets based on past observational data and the current state of the volcanic activity are used to decide the index values for each data set, which are integrated to quantify volcanic activity. This index makes it easier to compare current activity with past states of the volcano during periods of unrest and quiescence, assisting in interpreting whether the volcano is currently active. Furthermore, the index is based purely on scientific observational data and excludes social impacts. As the VUI evaluates the activity of individual volcanoes, the criteria for defining the activity index are different for each site.\u003c/p\u003e\u003cp\u003eWe evaluated quantitatively the extent of deviation from the baseline level of volcanic activity since 2011, when comprehensive monitoring network established. We use the VUI to describe the deviation from the baseline level of volcanic activity to facilitate future communication with various stakeholders, including local government and residents. We report the details of its application to Hakone and provide a preliminary evaluation of its effectiveness.\u003c/p\u003e\u003cp\u003eBefore beginning our discussion, we would like to clarify the definitions of phreatic and hydrothermal eruptions. In previous papers, our team and many scientists from other groups referred to the 2015 eruption of Hakone as a phreatic eruption based on the definition of (Barberi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), which includes all explosions involving confined steam generated in aquifers and hydrothermal systems; however, in VUI terminology, a phreatic eruption only refers to an eruption that occurs when ascending magma comes into contact with an aquifer and generates an explosion without ejecting juvenile magmatic material. The 2015 eruption of Hakone occurred in a pre-existing steaming area and was triggered by a sudden injection of hydrothermal fluid at shallow levels during a period of volcanic unrest caused by the deep supply of magmatic fluid (Mannen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Following the VUI terminology and the definition by (Browne and Lawless \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), this type of eruption should be classified as a hydrothermal eruption.\u003c/p\u003e"},{"header":"2 Volcano Unrest Index","content":"\u003cp\u003eThe VUI was developed as a semi-quantitative tool for characterizing the intensity of volcanic unrest in a consistent and communicable manner (Potter et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The index was refined iteratively, incorporating feedback from international volcanological experts and civil defense personnel in New Zealand. Structured as an integer scale from 0 (no unrest) to 4 (heightened unrest), the VUI integrates multi-parameter monitoring data, including seismicity, deformation, and geothermal activity, into a single interpretable metric. The VUI is designed specifically to evaluate unrest intensity and is not intended for eruption forecasting. A key feature of the framework is its adaptability, as parameter thresholds are calibrated to background activity at each volcano, enabling consistent evaluation across a range of volcanic systems. The VUI was first applied to the Taupō Volcanic Centre in New Zealand, where it was used successfully to reconstruct a coherent chronology of historical unrest episodes using both qualitative and quantitative data. This early application illustrated the index\u0026rsquo;s potential to support scientific interpretation and stakeholder communication, particularly in settings where non-eruptive unrest might still represent a significant hazard.\u003c/p\u003e\u003cp\u003eImplementing the VUI requires a structured five-step procedure to ensure consistency in assessing and comparing episodes of volcanic unrest across time and between different volcanic systems. This procedure can be divided conceptually into two phases: a preparatory phase consisting of the first three steps and an implementation phase comprising the final two steps.\u003c/p\u003e\u003cp\u003eThe preparatory phase involves establishing the fundamental parameters that define the scope and criteria for evaluation. The first step involves the definition of the geographical area surrounding the volcano from which relevant observations will be included. Although the selection of this area is necessarily subjective and volcano-specific, it must be clearly defined and consistently applied in all subsequent evaluations, with a rationale provided to support the choice.\u003c/p\u003e\u003cp\u003eThe second step involves establishing an appropriate time window over which unrest phenomena will be evaluated. A sliding time window is recommended for real-time assessment, ideally matched to the interval between routine observations to incorporate the most complete set of available data.\u003c/p\u003e\u003cp\u003eThird, volcano-specific parameter ranges must be determined for each of the unrest indicators included in the VUI framework. These ranges are based on the historical behavior of the volcano in question and reflect the relative, rather than absolute, intensity of each phenomenon. All parameter values are scaled within a standardized low-to-high framework to ensure comparability between volcanoes.\u003c/p\u003e\u003cp\u003eThe application of the VUI procedure consists of two steps. First, monitoring data are included in the VUI framework by assigning a score to each parameter based on observations. Parameters for which no data are available are excluded from the calculation. Second, the VUI is obtained by computing the mean of the assigned values across all applicable parameters, with the result rounded to the nearest integer. This yields a value from 0 (no unrest) to 4 (heightened unrest), offering a semi-quantitative index of unrest intensity that is both reproducible and communicable. However, in this study we calculated the VUI to one decimal place to capture more detailed temporal variations in volcanic unrest. The implications and outcomes of this approach are discussed in Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e6.3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTo enable comparison with the VUI that is already applied in New Zealand and is expected to be adopted in other countries, and to evaluate the applicability of the VUI to Hakone, we applied the framework proposed by Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to Hakone as closely as possible. We divided the preparatory phase into two parts: geographical and time settings at Hakone are addressed in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the choice of each observational parameter is discussed separately in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The application of the VUI to Hakone is considered in Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e"},{"header":"3 Choosing the geographical range and time window","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Geographical range\u003c/h2\u003e\u003cp\u003eHakone has a caldera measuring 11 km (north\u0026ndash;south) by 8 km (east\u0026ndash;west), and lava flows extend\u0026thinsp;~\u0026thinsp;16 km from the center of the volcano. The lava that has erupted over the last 60,000 y has formed a post-caldera edifice called the Younger Central Cones (YCC), which spans 2 km (NW\u0026ndash;SE) by 1.4 km (NE\u0026ndash;SW). The 2015 eruption occurred at Owakudani in the northern part of the YCC, and numerous fresh volcanic craters have been identified in the YCC region. The inflation source observed during unrest is also located in the YCC region (Harada et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); therefore, future eruptions are expected to occur in the YCC area. However, inflation is evident in data obtained from GNSS stations outside of the caldera. More importantly, during periods of unrest, including in 2015 and 2019, frequent earthquakes were observed near the Lake Ashi at the west part of caldera (Yukutake et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Itadera and Yoshida \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kawai et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, we define earthquakes related to Hakone as those occurring within the broader caldera region, specifically with latitudes of 138.93\u0026deg;E to 139.09\u0026deg;E, longitudes of 35.14\u0026deg;N to 35.32\u0026deg;N, and depths of \u0026lt;\u0026thinsp;10 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Time-window and calculation procedure\u003c/h2\u003e\u003cp\u003eAt Hakone, field surveys, including volcanic gas observations, are typically conducted about once a month, with the interval between observations occasionally extending to ~\u0026thinsp;45 days; therefore, we set the time window covering the past 45 days. Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used a worksheet for calculations; however, in practice, manual computation is burdensome, so we developed the following automated system.\u003c/p\u003e\u003cp\u003eWe developed a computer program to calculate the VUI at 8:00 a.m. (Japan Standard Time) each day using data from the past 45 days. The day preceding the calculation date is used as the reference day, and the latest VUI is calculated using data from the 45 days leading up to and including the reference day. However, due to potential updates from manual earthquake picking and data review, the VUI values are retroactively updated for the following 30 days. The data used are acquired by the HSRI; ~10 y of data are available and complex calculations are not required. These data are detailed in the Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Range of VUI values\u003c/h2\u003e\u003cp\u003eThe observational history of Hakone places constraints on VUI values. Typically, at least 10 volcano-tectonic (VT) earthquakes are detected at Hakone each month, and the volcano is characterized by persistent fumarolic activity. These observations suggest that it is a \u0026ldquo;warmer\u0026rdquo; volcano with minor unrest even during quiescence. As such, VUI 1 represents the baseline state for Hakone. By definition, the hydrothermal eruption that occurred in 2015 is classified as VUI 3, and the lower threshold for VUI 3 can be defined empirically based on observations of the 2015 eruption. The thresholds for other index values are tailored through observations made during other periods and using expert elicitation.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 VUI parameters at Hakone","content":"\u003cp\u003eThe VUI classifies unrest phenomena into three categories: local earthquakes, local deformation, and geothermal systems including degassing. Each category includes several parameters (e.g., earthquake swarm duration, deformation rate, and gas flux) that are used to evaluate the intensity of unrest. Although some parameters are observable at many volcanoes, others depend on monitoring conditions or volcanic characteristics. In addition, parameter thresholds must be defined for each volcano individually, reflecting the monitoring context and typical background activity. In the following sections, \u0026ldquo;category\u0026rdquo; is used to refer to the types of unrest phenomena, and \"parameter\" is used to denote the individual phenomenon within each category. In this section, we present an overview of each parameter and discuss the basis for the chosen threshold values. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the parameters and threshold values adopted here to calculate the VUI at Hakone and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the stations that collect the data used for the calculation, including seismic monitoring, GNSS, tilt measurements, and volcanic gas.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold values for each parameter used in calculating the Volcanic Unrest Index for Hakone Volcano.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndex 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndex 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndex 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndex 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eEarthquakes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTotal duration of swarms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo swarm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;48 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48\u0026ndash;96 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;96 h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNumber of earthquakes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u0026ndash;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u0026ndash;10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLow frequency earthquakes (LFEs) and tremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo LFEs or tremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmall number of LFEs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge number of LFEs or small tremor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMany LFEs\u003c/p\u003e\u003cp\u003eLarge tremor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCrustal deformation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeep crustal deformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2.0 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.0\u0026ndash;6.0 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.0\u0026ndash;12.0 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;12.0 mm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eShallow deformation\u003c/p\u003e\u003cp\u003e(tilt, GNSS, InSAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo deformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeformation in one observation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDeformation in multiple observation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStrong deformation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eThermal and gas\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eThermal phenomena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo blowout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmall blowout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlowout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStrong blowout\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGas flux (DOAS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30 ton/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u0026ndash;80 ton/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80\u0026ndash;200 ton/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200 ton/day\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGas composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOwakudani (SO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5\u0026ndash;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0\u0026ndash;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKamiyu (CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Earthquake-related parameters\u003c/h2\u003e\u003cp\u003ePotter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) included four parameters in the earthquake category: (1) the duration of earthquake swarms, (2) the location of earthquakes (depth and distance from the likely vent), (3) the rate of VT earthquakes, and (4) the occurrence of tremor and low-frequency earthquakes (LFEs).\u003c/p\u003e\u003cp\u003eShallower hypocenters would typically be assigned a higher index value when using the approach of Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); however, earthquakes at Hakone commonly occur at depths of \u0026lt;\u0026thinsp;2 km and the accuracy in determining depths, especially for earthquakes occurring above sea level, is poor; therefore, we decided to exclude hypocenter depths from our calculation of the VUI at Hakone. We use the following three parameters: (1) earthquake swarm duration, (2) VT earthquake rate, and (3) the occurrence of LFEs or tremor.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Duration of earthquake swarms\u003c/h2\u003e\u003cp\u003eLarge earthquake swarms were observed in 2001 and 2015, and moderate swarms were observed immediately after the 2011 M\u003csub\u003ew\u003c/sub\u003e 9.0 Tohoku Earthquake (Yukutake et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prior to the development of the VUI framework, HSRI had defined earthquake swarms as sequences of \u0026ge;\u0026thinsp;10 earthquakes at depths of \u0026lt;\u0026thinsp;10 km inside Hakone caldera within 1 h. The swarm period is defined as a continuous sequence of seismic events before and after the initial 1-hour window, provided there is no period of \u0026ge;\u0026thinsp;3 h without earthquakes. The same criteria are used in the present study. In this case, the minimum earthquake swarm duration is 0 h and the maximum is the 45-day period of the moving window (i.e., 1080 h).\u003c/p\u003e\u003cp\u003eIn practice, earthquake swarms are absent during quiescent periods, resulting in durations of 0 h. On the other hand, a long-lived swarm lasting 622 h was recorded before the 2015 eruption, from 16:16 on May 7 to 14:52 on June 2. Moreover, ~\u0026thinsp;3 h after the eruption (from 17:56 on June 2), earthquake swarms restarted and continued for 1 week. Thus, in time windows including this period, nearly the entire time window is occupied by earthquake swarms. These prolonged swarms are assigned the maximum index of 4, and we define the other indices as follows: index 1 is defined as 0 h, index 2 is defined as durations\u0026thinsp;\u0026lt;\u0026thinsp;48 h, index 3 is defined as durations of 48\u0026ndash;96 h, and index 4 is defined as durations of \u0026gt;\u0026thinsp;96 h. A time series of this index is shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (see Additional file 1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Earthquake occurrence rate\u003c/h2\u003e\u003cp\u003eWe counted the number of earthquakes occurring within Hakone caldera. The earthquake counts increase by several orders of magnitude during periods of unrest. During quiescent periods, ~\u0026thinsp;20 events occur over 45 days. In contrast, 8,568 earthquakes were observed in a 45-day window prior to the 2015 eruption; therefore, we defined the thresholds logarithmically rather than linearly:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100 events: index value of 1,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e100\u0026ndash;1000 events: index value of 2,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e1000\u0026ndash;10,000 events: index value of 3, and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10,000 events: index value of 4.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAlthough Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) included a \u0026ldquo;sudden decrease\u0026rdquo; as index value 4, this phenomenon was not observed during the 2015 eruption at Hakone, making it impossible to establish a quantitative threshold. Accordingly, \u0026ldquo;sudden decrease\u0026rdquo; was excluded from our analysis. A time series of this parameter is shown in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3 Low-frequency earthquakes and tremor\u003c/h2\u003e\u003cp\u003eThis parameter means shallow events and excludes deep low-frequency earthquakes (LFEs). Shallow LFEs and volcanic tremor are rarely observed at Hakone. Volcanic tremor was only observed from June 29 to July 1, 2015, during the hydrothermal eruption (Yukutake et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, only the period encompassing this tremor is assigned an index value of 3, with all other periods assigned a value of 1. The index is defined as follows: index value of 1 when there are no LFEs and tremor, value of 2 when LFEs occur, value of 3 during swarms of LFEs or volcanic tremor, and value of 4 if there is a substantial increase in LFEs or high-amplitude volcanic tremor. For example, 10 events over a short period corresponds to an index value of 3, whereas ~\u0026thinsp;100 LFEs or tremor lasting\u0026thinsp;\u0026gt;\u0026thinsp;5 days corresponds to an index value of 4. Tremor amplitudes of \u0026gt;\u0026thinsp;500 \u0026micro;m/s at the Ninotaira observation station, corresponding to the criteria of VAL 5 by the JMA, result in an index value of 4. A time series of this index is shown in Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Parameters related to crustal deformation\u003c/h2\u003e\u003cp\u003ePotter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) established three parameters for crustal deformation: (1) local deformation, (2) the location of the deformation source, and (3) groundwater level and spring flow. However, estimating the deformation source requires complex calculations that are difficult to perform automatically. In addition, quantitative groundwater level and flow rate observations that reflect volcanic crustal deformation have not been established at Hakone. Therefore, for the crustal deformation category, we combined parameters (1) and (2) from Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and employed two parameters: (1) changes in long GNSS baseline lengths, thought to indicate deep inflation; and (2) shallow crustal deformation.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Changes in long GNSS baseline lengths\u003c/h2\u003e\u003cp\u003eWe use changes in the lengths of GNSS baselines spanning Hakone as the first crustal deformation parameter. During the activation of Hakone, changes occur in the lengths of east\u0026ndash;west baselines crossing Hakone alongside increased seismicity and earthquake swarms. The distance between the Odawara and Susono2 GEONET stations is often used as a baseline (Harada et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The baselines are thought to record the inflation of a deep source beneath Hakone. Although both observation stations are from the Geospatial Information Authority of Japan, we use the results calculated by Bernese 5.2 at the HSRI (Doke et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since the GNSS analyses have large daily errors, we use the difference between the median baseline length over the most recent 45 days and the median from 46\u0026ndash;90 days prior, calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:d\\:=\\:\\text{M}\\text{e}\\text{d}\\text{i}\\text{a}\\text{n}\\left(l\\right(1),l(2),...,l(45\\left)\\right)-\\text{M}\\text{e}\\text{d}\\text{i}\\text{a}\\text{n}\\left(l\\right(46),l(47),....l(90\\left)\\right)$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003el\u003c/em\u003e(\u003cem\u003en\u003c/em\u003e) is the length of the Odawara\u0026ndash;Susono2 baseline \u003cem\u003en\u003c/em\u003e days ago. When \u003cem\u003ed\u003c/em\u003e is \u0026lt;\u0026thinsp;2.0 mm the index value is 1, when \u003cem\u003ed\u003c/em\u003e is 2.0\u0026ndash;6.0 mm the value is 2, when \u003cem\u003ed\u003c/em\u003e is 6.0\u0026ndash;12.0 mm the value is 3, and when \u003cem\u003ed\u003c/em\u003e is \u0026gt;\u0026thinsp;12.0 mm the value is 4. A time series of this index is shown in Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, and the long-term trend since 2011 is shown in Fig. S5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Shallow crustal deformation\u003c/h2\u003e\u003cp\u003eCrustal deformation at Hakone often involves deep sources that are measured using long baselines, but the deformation of shallow sources has also been observed during two periods of unrest that included steam well blowouts in 2001 and 2015 (Harada and Yoshida \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Around the time of the 2015 hydrothermal eruption, deformation observed by tiltmeters (Honda et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and GNSS (Doke et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Harada and Yoshida \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) appeared to be associated with crack-like intrusions. Localized uplift was observed in the Owakudani fumarolic area by satellite synthetic aperture radar (SAR; (Doke et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) and ground-based interferometric SAR (GB-InSAR; (Kuraoka et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis parameter evaluates the shallow deformation. However, automatic detection is difficult because tiltmeter readings are influenced by rainfall, it is difficult to automatically analyze satellite SAR data, and the short baseline lengths observed by GNSS yield large errors and are potentially affected by landslides. In addition, the ground-based SAR installation was temporary and the instrument is no longer in place. Therefore, this parameter must be determined by a comprehensive evaluation of GNSS, tiltmeter, and satellite SAR results. Previous data for the deformation from May to June 2015 corresponds to an index value of 3. An index value of 1 corresponds to periods without shallow crustal deformation, a value of 2 corresponds to periods when deformation is observed in one dataset, a value of 3 is when deformation is observed in multiple datasets, and a value of 4 is when shallow crustal deformation greater than that observed before the 2015 eruption is identified in every dataset. A time series of this index is shown in Fig. S6.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Geothermal and volcanic gas phenomena\u003c/h2\u003e\u003cp\u003ePotter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) established three parameters for geothermal and volcanic gas phenomena: (1) surface temperature, heat flow, and physical manifestations; (2) gas flux; and (3) gas and fluid composition. We employed all of these parameters at Hakone.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Surface and geothermal phenomena\u003c/h2\u003e\u003cp\u003eBefore the 2015 eruption there was no steam emitted at temperatures above the boiling point of water at Owakudani. Since the 2015 eruption, numerous new fumaroles have appeared around the eruption center, releasing steam at temperatures above the boiling point of water. We measure the temperature of several fumaroles approximately once a month, but no temperature changes corresponding to unrest have been observed (Fig. S7). In addition, there is currently no way to accurately monitor heat release rates. However, significant changes in surface phenomena at Owakudani, including blowouts of steam wells, were observed in 2001 and 2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; (Mannen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); therefore, an index value of 1 is defined as periods of normal conditions with no blowouts, a value of 2 is defined as small blowouts, a value of 3 is defined as blowouts, and a value of 4 is defined as fumarolic activity and more substantial blowouts than those during the 2015 event. A time series of this index is shown in Fig. S8.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Volcanic gas flux\u003c/h2\u003e\u003cp\u003eWe measure the flux of SO\u003csub\u003e2\u003c/sub\u003e using differential optical absorption spectroscopy (DOAS), based on the absorption of certain wavelengths of ultraviolet light by SO\u003csub\u003e2\u003c/sub\u003e (cf. Mori et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We conduct observations by mounting a spectrometer on a vehicle, driving, and estimating the amount of sulfur dioxide above the road (Abe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We drive from near the Kamiyu bus stop north of Owakudani to near the Sounzan station of the Hakone Ropeway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The JMA and Meteorological Research Institute also conducted DOAS observations during the 2015 hydrothermal eruption (Japan Meteorological Agency \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meteorological Research Institute \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), although using a different measurement method. Based on these observations, we defined the index as follows: \u0026lt;30 tons/day corresponds to an index value of 1, 30\u0026ndash;80 tons/day to a value of 2, 80\u0026ndash;200 tons/day to a value of 3, and \u0026gt;\u0026thinsp;200 tons/day to a value of 4. A time series of this index is shown in Fig. S9.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Gas composition\u003c/h2\u003e\u003cp\u003eWe conduct gas surveys at the fumarolic areas of Owakudani and Kamiyu approximately once a month. At Owakudani, we have been conducting gas composition surveys in the fumarolic area of the crater, which formed in 2015, since March 2018 (Mannen et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We measure the SO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS ratio at the 15\u0026thinsp;\u0026minus;\u0026thinsp;1 and 15\u0026thinsp;\u0026minus;\u0026thinsp;2 craters using diffusive tubes. We analyze both the raw gas from each crater and gas dried with silica gel. In this study, we use the mean of all four measurements taken during the corresponding period. As these observations started in 2018, we set the thresholds based on data from the 2019 and 2023 unrest events. The index is defined as follows: an index value of 1 corresponds to SO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS\u0026thinsp;\u0026lt;\u0026thinsp;1.5, a value of 2 corresponds to 1.5\u0026ndash;3.0, a value of 3 corresponds to 3.0\u0026ndash;4.5, and a value of 4 corresponds to \u0026gt;\u0026thinsp;4.5. A time series of this index is shown in Fig. S10.\u003c/p\u003e\u003cp\u003eWe measure the CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS ratio at the Kamiyu fumarolic area located\u0026thinsp;~\u0026thinsp;500 m north of the Owakudani fumarolic area (Daita et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These observations started in March 2012. Rapid increases in the CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS ratio corresponding to earthquake swarms and other events were observed in 2013 and 2015. We defined the index as follows: an index value of 1 corresponds to a CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eS ratio of \u0026lt;\u0026thinsp;30, a value of 2 corresponds to a ratio of 30\u0026ndash;45, a value of 3 to a ratio of 45\u0026ndash;60, and a value of 4 to a ratio of \u0026gt;\u0026thinsp;60. A time series of this index is shown in Fig. S11.\u003c/p\u003e\u003cp\u003eAs the above two parameters are the ratios of components in gas, we use the mean value of the two indices with an equal weighting for the gas composition parameter.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5 Results","content":"\u003cp\u003eBased on the criteria defined in Sections \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, calculations were performed using a moving window from 1 January 2011 onwards to create daily VUI values. Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) proposed using integers, but in this study, we give VUI values to one decimal place to test whether a more detailed evaluation of volcanic activity is possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results show that periods of unrest (VUI\u0026thinsp;\u0026ge;\u0026thinsp;1.5) occurred every few years. Unrest occurred in 2011, 2013, 2015 (accompanied by a hydrothermal eruption), 2019, and 2023. The peak VUI in 2015 was 3.2, whereas peak values for the unrest in 2019 and 2023 were 1.7 and 1.6, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the changes in VUI around 2015 alongside the JMA VAL. The 2015 hydrothermal eruption occurred on June 29. An increase in the VUI was observed from April, two months before the hydrothermal eruption. Since the index values for parameters with high sensitivity increased through time, a smooth upward trend is seen in the overall index; however, in cases where rapid seismic activity occurs, the VUI may increase suddenly, as seen at the end of June when the eruption occurred. The VUI then decreased rapidly from July to August.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe show the 2019 activity in detail in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In 2019, earthquake swarms and other events occurred, and the VAL was raised to 2 from May 19 to October 7. The change in VUI during this period was gradual, with a slow increase from late March, peaking in June, and then slowly decreasing back to the original VUI by late September.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eChanges in the observed activity from 2023 to 2024, when earthquake swarms and other events occurred intermittently and there is no clear trend, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The VUI started to increase from May 2023, but there were repeated small increases and decreases. Peaks are seen around September 2023, November 2023, and May 2024, which were influenced mainly by earthquake swarms. During this unrest, the activity repeatedly transitioned between elevated and quiescent, and the overall duration of the activity was long (~\u0026thinsp;2 y).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlthough we found sudden changes in the VUI on certain days (e.g., June 29, 2015, when the hydrothermal eruption occurred), in most cases the VUI changed gradually over periods of 1\u0026ndash;2 months.\u003c/p\u003e"},{"header":"6 Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Temporal variations in observational data\u003c/h2\u003e\u003cp\u003eWe analyzed the temporal variation in each parameter to investigate how each index is reflected in the overall VUI (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). During the 2015 unrest, all parameters had high index values. Although LFEs and tremors, shallow deformation, and thermal anomalies (blowouts) were not observed during the periods of unrest in 2019 and 2023\u0026ndash;2024, the indicators derived from numerical data showed synchronous increases.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLooking in more detail at 2015, all indices rose almost simultaneously when considering observation frequency and other factors (Fig. S12). In contrast, in 2019, changes in the gas composition at the Kamiyu fumarole area lagged 2 months during both the initial increase and the later decrease in activity (Fig. S13). It is essential to check the original data and trends for each index when the VUI rises, because transitions in volcanic activity vary between unrest periods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Addition of observational parameters and automated data collection\u003c/h2\u003e\u003cp\u003eIn this study, we use a total of nine observational parameters, which are adapted from the indices proposed by Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); however, several additional indices based on the monitoring parameters implemented at Hakone and previously observed phenomena can be considered.\u003c/p\u003e\u003cp\u003eOne potential candidate is activity of deep low-frequency earthquakes (DLFEs). At Hakone, DLFEs occur at a depth of ~\u0026thinsp;20 km beneath the northern edge of the caldera. DLFEs became more frequent before the 2015 unrest (Yukutake et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suggesting that they represent deep magma supply and the onset of volcanic unrest. During the 2019 unrest, however, DLFEs occurred predominantly around October, after the unrest had finished; therefore, we will consider adding this parameter once its relationship with shallow volcanic activity becomes clearer.\u003c/p\u003e\u003cp\u003eWe did not use the groundwater level and spring flow parameters proposed by Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In their study, these indices are determined based on factors that include changes in spring flux, temperature, and the occurrence of lahars, which are associated with crustal compression and extension. At Hakone, no significant changes in spring flux have been observed in relation to volcanic activity. Although variations in the temperature of hot springs have been recorded and were once considered indicative of increased volcanic activity (Oki and Hirano \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), more recent studies suggest that these changes reflect long-term trends rather than being linked directly to specific volcanic unrest events (Machida et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). During the unrest in 2001, only two wells recorded temperature increases of several degrees (Mannen \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This change is not significant, given that there are hundreds of hot springs at Hakone. Moreover, no temperature perturbations were observed in these wells during other unrest events, including the 2015 eruption. A crater-outflow lahar occurred in 2015, but this was during the eruption itself. Given that no observations have linked the flow from hot springs to unrest levels, we decided not to include spring flux for now; however, we will consider adding the parameter if relevant observations indicate a relationship between hot springs and volcanic activity.\u003c/p\u003e\u003cp\u003eWe also monitor the chemical composition of hot-spring water; however, changes in chemical composition cannot currently be incorporated into the VUI because of dilution by rainfall. It may be possible to use these data when high-frequency and automatic measurements of hot-spring chemistry become feasible.\u003c/p\u003e\u003cp\u003eFor volcanic activity assessments and disaster resilience, rapid automatic calculations are necessary; however, in the current iteration of the VUI, some data acquisition and index calculations still require manual processing. Manual steps include earthquake detection and removing false detections, identifying low-frequency earthquakes and tremor, determining crustal deformation from tilt and SAR data, visually confirming surface activity, and collecting and analyzing volcanic gas samples. It is important that we develop automatic analysis methods (e.g., machine learning). Advances in these technologies will allow us to replace or add observational parameters to the VUI, further improving the accuracy of this approach and reducing the delay in calculating index values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Current operation and potential for communication with stakeholders\u003c/h2\u003e\u003cp\u003eWe present the VUI to one decimal place to capture detailed changes in volcanic activity; however, for communication with some stakeholders, including local government, it is essential to present information in an easily understandable format. As suggested by Potter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the VUI is likely to be communicated using integer levels. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows integer VUI values for Hakone, which provide a clear representation of unrest levels and make it suitable for communication with local government; however, for specialized institutions monitoring and researching volcanic activity, the decimal VUI values shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e allow for a more detailed and real-time assessment of volcanic conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe HSRI, to which the authors of this study belong, is one of the offices of the Kanagawa Prefectural Government. We have already discussed the volcanic activity with some stakeholders, including local government, the JMA, and other institutions. There are many agencies responsible for responding to increased volcanic activity, including the prefectural government, town offices, police, and the tourism bureau. In addition, volcanic unrest can have significant economic impacts, including the temporary closure of tourist facilities.\u003c/p\u003e\u003cp\u003eDiscussions that make use of this index have been incorporated into monthly volcanic activity assessment meetings in HSRI since 2024, allowing researchers from different specialties to discuss volcanic activity quantitatively. The automatic generation of worksheets (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) and graphs has also made it possible to monitor the current volcanic activity without daily manual data verification, reducing the workload for routine monitoring. There is also a demand from non-experts in local government for a clear understanding of volcanic activity. Moving forward, HSRI is considering using this index not only during periods of unrest, but also for routine activity monitoring, to facilitate information sharing with related organizations and improve preparedness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"7 Conclusions","content":"\u003cp\u003eWe applied the Volcanic Unrest Index (VUI) to assess the activity of Hakone Volcano quantitatively using various observational data. As a result, we were able to represent numerically the recurring small-scale unrest events that occur every few years at Hakone. The 2015 hydrothermal eruption corresponded to a maximum VUI of 3.2, whereas the peak VUI values during the unrest events in 2019 and 2023 were 1.7 and 1.6, respectively. By presenting values to one decimal place, it is possible to monitor unrest in detail in real time. The VUI is highly effective at providing a comprehensive assessment of volcanic activity and has proven useful in facilitating discussions among researchers from different fields. In the future, we expect that the VUI will be used as a communication tool for non-experts, including local government and residents.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVUI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVolcanic Unrest Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJapan Meteorological Agency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIED\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Research Institute for Earth Science and Disaster Resilience\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHSRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHot Springs Research Institute of Kanagawa Prefecture\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGNSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglobal navigation satellite system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGEONET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGNSS Earth Observation Network System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVT earthquake\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evolcanic-tectonic earthquake\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLFE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow-frequency earthquake\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esynthetic aperture radar\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDOAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edifferential optical absorption spectroscopy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for the Volcanic Unrest Index for each day are included in Additional file 2 and raw data are included in Additional file 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Integrated Program for Next Generation Volcano Research and Human Resource Development of the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT), and by the MEXT under the third Earthquake and Volcano Hazards Observation and Research Program (Earthquake and Volcano Hazard Reduction Research). This study was also partially supported by a Japan Society for the Promotion of Science KAKENHI grant (22K14113).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRK designed and analyzed the data and wrote the paper. KM supervised the research. Other authors advised and discussed the contents of this paper and contributed to various observations of the volcano.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Generic Mapping Tools (Wessel and Smith 1998) for drawing figures and used Hi-net seismic observation data (http://www.hinet.bosai.go.jp) from NIED (National Research Institute for Earth Science and Disaster Resilience 2019). We used GNSS observation data from the Japan Meteorology Agency and Geospatial Information Authority of Japan. All members of the Hot Spring Research Institute at any time after 2011 were involved in collecting the observational data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbe Y, Harada M, Itadera K, Takehiko Mori, Akimichi Takagi (2018) Emission rate of sulfur dioxide at Owakudani, Hakone Volcano, Japan\u0026mdash;Observation, analysis, and temporal transition of emission rate to June 2018. Bull Hot Spring Res Inst Kanagawa Prefecture 50:1\u0026ndash;18 (in Japanese)\u003c/li\u003e\n\u003cli\u003eBarberi F, Bertagnini A, Landi P, Principe C (1992) A review on phreatic eruptions and their precursors. J Volcanol Geotherm Res 52:231\u0026ndash;246. https://doi.org/10.1016/0377-0273(92)90046-G\u003c/li\u003e\n\u003cli\u003eBrowne PRL, Lawless JV (2001) Characteristics of hydrothermal eruptions, with examples from New Zealand and elsewhere. Earth Sci Rev 52:299\u0026ndash;331. https://doi.org/10.1016/S0012-8252(00)00030-1\u003c/li\u003e\n\u003cli\u003eDaita Y, Ohba T, Yaguchi M, Sogo T, Harada M (2021) Volcanic activity forecast based on volcanic gas composition of Hakone Volcano, Japan: Utilization for volcanic disaster prevention. J of Geogr (Chigaku Zasshi) 130:783\u0026ndash;796 (in Japanese with English abstract)\u003c/li\u003e\n\u003cli\u003eDoke R, Harada M, Mannen K, Itadera K, Takenaka J(2018a) InSAR analysis for detecting the route of hydrothermal fluid to the surface during the 2015 phreatic eruption of Hakone Volcano, Japan. Earth Planets Space 70:63. https://doi.org/10.1186/s40623-018-0834-4\u003c/li\u003e\n\u003cli\u003eDoke R, Harada M, Miyaoka K (2018b) GNSS Observation and monitoring of the Hakone Volcano and the 2015 unrest. J Disaster Res 13:526\u0026ndash;534. https://doi.org/10.20965/jdr.2018.p0526\u003c/li\u003e\n\u003cli\u003eDoke R, Harada M, Itadera K, Kato T, Nakamura Y (2020) A new strategy of GNSS analysis in the Hot Springs Research Institute of Kanagawa Prefecture. Bull Hot Spring Res Inst Kanagawa Prefecture 52:63\u0026ndash;68 (in Japanese)\u003c/li\u003e\n\u003cli\u003eFujii T (2016) Present situation and issues to be concerned on the prediction of volcanic eruption in Japan. Bull Volcanol Soc Jpn 61:211\u0026ndash;223 (in Japanese)\u003c/li\u003e\n\u003cli\u003eHarada M, Yoshida A (2024) Depth of pressure source of activity at Hakone Volcano: From the viewpoint of monitoring volcanic activity. J Geogr 133:91\u0026ndash;100. https://doi.org/10.5026/jgeography.133.91\u003c/li\u003e\n\u003cli\u003eHarada M, Doke R, Mannen K, Itadera K, Satomura M (2018) Temporal changes in inflation sources during the 2015 unrest and eruption of Hakone Volcano, Japan. Earth Planets Space 70:152. https://doi.org/10.1186/s40623-018-0923-4\u003c/li\u003e\n\u003cli\u003eHonda R, Yukutake Y, Morita Y, Sakai S, Itadera K, Kokubo K (2018) Precursory tilt changes associated with a phreatic eruption of the Hakone Volcano and the corresponding source model. Earth Planets Space 70:117. https://doi.org/10.1186/s40623-018-0887-4\u003c/li\u003e\n\u003cli\u003eItadera K, Yoshida A (2023) Earthquake swarms in May 2015 at the northern shore of Lake Ashi, Hakone, Japan: What caused the activity? J Geogr 132:465\u0026ndash;482(in Japanese with English abstract). https://doi.org/10.5026/jgeography.132.465\u003c/li\u003e\n\u003cli\u003eJapan Meteorological Agency (2016) Hakoneyama. report of coordinating committee for prediction of volcanic eruption 135:58\u0026ndash;66 (in Japanese)\u003c/li\u003e\n\u003cli\u003eKato K, Yamasato H (2013) The 2011 eruptive activity of Shinmoedake Volcano, Kirishimayama, Kyushu, Japan\u0026mdash;Overview of activity and volcanic alert level of the Japan Meteorological Agency\u0026mdash;. Earth Planets Space 65:489\u0026ndash;504. https://doi.org/10.5047/eps.2013.05.009\u003c/li\u003e\n\u003cli\u003eKawai T, Yukutake Y, Doke R, Honda R (2024) Contribution of aseismic slips to earthquake swarms at the Hakone Volcano. Earth Planets Space 76:152. https://doi.org/10.1186/s40623-024-02098-1\u003c/li\u003e\n\u003cli\u003eKuraoka S, Nakashima Y, Doke R, Mannen K (2018) Monitoring ground deformation of eruption center by ground-based interferometric synthetic aperture radar (GB-InSAR): a case study during the 2015 phreatic eruption of Hakone Volcano. Earth Planets Space 70:181. https://doi.org/10.1186/s40623-018-0951-0\u003c/li\u003e\n\u003cli\u003eMachida I, Itadera K, Mannen K (2007) Source area of heat and NaCl for hot springs in Gora region, Hakone. J Groundwater Hydrol 49:327\u0026ndash;339. https://doi.org/10.5917/jagh1987.49.327\u003c/li\u003e\n\u003cli\u003eMannen K (2008) Hakone Caldera: Structure, mode of formation, and role in present-day volcanism. Research report of the Kanagawa Prefecutral Museum, Natural History 61\u0026ndash;76\u003c/li\u003e\n\u003cli\u003eMannen K, Kikugawa G, Miyashita Y, Yamaguchi T, Tanbo T, Honma N (2018a) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature. Bull Hot Spring Res Inst Kanagawa Prefecture 50:19\u0026ndash;44 (in Japanese with English abstract)\u003c/li\u003e\n\u003cli\u003eMannen K, Yukutake Y, Kikugawa G, Harada M, Itadera K, Takenaka J (2018b) Chronology of the 2015 eruption of Hakone Volcano, Japan: geological background, mechanism of volcanic unrest and disaster mitigation measures during the crisis. Earth Planets Space 70:68. https://doi.org/10.1186/s40623-018-0844-2\u003c/li\u003e\n\u003cli\u003eMannen K, Kikugawa G, Miyashita Y, Kato T (2020) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature (Part II). Bull Hot Spring Res Inst Kanagawa Prefecture 52:1\u0026ndash;14 (in Japanse with English abstract)\u003c/li\u003e\n\u003cli\u003eMannen K, Abe Y, Daita Y, Doke R, Harada M, Kikugawa G, Honma N, Miyashita Y, Yukutake Y (2021) Volcanic unrest at Hakone Volcano after the 2015 phreatic eruption: reactivation of a ruptured hydrothermal system? Earth Planets Space 73:80. https://doi.org/10.1186/s40623-021-01387-3\u003c/li\u003e\n\u003cli\u003eMannen K, Miyashita Y, Fujimatsu J, Ninomiya R, Toyama K (2022) Steaming area formed after the 2015 eruption of Hakone Volcano, Japan and sequential changes of fumarolic temperature (Part III: 2020\u0026ndash;2022). Bull Hot Spring Res Inst Kanagawa Prefecture 54:1\u0026ndash;17 (in Japanese with English abstract)\u003c/li\u003e\n\u003cli\u003eMannen K, Doke R, Johmori A, Kikugawa G, Minami T, Takahashi T, Utsugi M, Fujimoto K (2025) Anatomy of the fumarole field of Hakone Volcano, Japan: Interpretation of its resistivity structure and inferences for the steaming activity and recent hydrothermal eruption. J Volcanol Geotherm Res 465:108363. https://doi.org/10.1016/j.jvolgeores.2025.108363\u003c/li\u003e\n\u003cli\u003eMeteorological Research Institute (2016) Sulfur dioxide emission rate from Hakone Volcano (Owakudani). Report of Coordinating Committee for Prediction of Volcanic Eruption 135:67 (in Japanese)\u003c/li\u003e\n\u003cli\u003eMori T, Hirabayashi J, Kazahaya K, Mori T, Ohwada M, Miyashita M, Iino H, Nakahori T (2007) A compact ultraviolet spectrometer system (COMPUSS) for monitoring volcanic SO\u003csub\u003e2\u003c/sub\u003e emission: Validation and preliminary observation. Bull Volcanol Soc Jpn 52:105\u0026ndash;112\u003c/li\u003e\n\u003cli\u003eNational Research Institute for Earth Science and Disaster Resilience (2019) NIED Hi-net. https://doi.org/10.17598/nied.0003\u003c/li\u003e\n\u003cli\u003eOki Y, Hirano T (1970) The geothermal system of the Hakone Volcano. Geothermics 2:1157\u0026ndash;1166. https://doi.org/10.1016/0375-6505(70)90428-1\u003c/li\u003e\n\u003cli\u003ePotter SH, Scott BJ, Jolly GE, Neall VE, Johnston DM (2015) Introducing the Volcanic Unrest Index (VUI): a tool to quantify and communicate the intensity of volcanic unrest. Bull Volcanol 77:77. https://doi.org/10.1007/s00445-015-0957-4\u003c/li\u003e\n\u003cli\u003eWessel P, Smith WHF (1998) New, improved version of generic mapping tools released. Eos, Trans Am Geophys Union 79:579\u0026ndash;579. https://doi.org/10.1029/98EO00426\u003c/li\u003e\n\u003cli\u003eYukutake Y, Honda R, Harada M, Aketagawa T, Ito H, Yoshida A(2011) Remotely-triggered seismicity in the Hakone Volcano following the 2011 off the Pacific coast of Tohoku earthquake. Earth Planets Space 63:737\u0026ndash;740. https://doi.org/10.5047/eps.2011.05.004\u003c/li\u003e\n\u003cli\u003eYukutake Y, Honda R, Harada M, Doke R, Saito T, Ueno T, Sakai S, Morita Y (2017) Analyzing the continuous volcanic tremors detected during the 2015 phreatic eruption of the Hakone Volcano. Earth Planets Space 69:164. https://doi.org/10.1186/s40623-017-0751-y\u003c/li\u003e\n\u003cli\u003eYukutake Y, Abe Y, Doke R (2019) Deep low‐frequency earthquakes beneath the Hakone Volcano, central Japan, and their relation to volcanic activity. Geophys Res Lett 46:11035\u0026ndash;11043. https://doi.org/10.1029/2019GL084357\u003c/li\u003e\n\u003cli\u003eYukutake Y, Yoshida K, Honda R (2022) Interaction between aseismic slip and fluid invasion in earthquake swarms revealed by dense geodetic and seismic observations. J Geophys Res B: Solid Earth 127:e2021JB022933. https://doi.org/10.1029/2021JB022933\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hakone Volcano, Phreatic eruption, Volcanic Unrest Index, Volcano monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7709975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7709975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVarious phenomena occur in volcanic areas, including volcanic earthquakes, crustal deformation, and volcanic gas emissions. Observations are commonly conducted to understand this diverse activity; however, it is challenging even for experts to interpret multiple types of observational data comprehensively because volcanic activity is complex and these phenomena do not always change together. It is even more difficult for members of local government and non-scientists to understand volcanic activity based on observational data. The Volcanic Unrest Index (VUI) has been proposed as a tool to evaluate volcanic activity based on multiple types of observational data and to communicate the intensity of volcanic unrest to local government and non-scientists. We adapted the VUI and quantified the volcanic activity at Hakone Volcano, Japan, to aid future communication with multiple stakeholders. Although the original VUI uses only integer values, we use a precision of one decimal place to analyze temporal changes in volcanic activity in greater detail. The VUI was retrospectively applied to data from 2011 onward, and a system was developed to automatically calculate the index each day. We chose threshold values for each parameter based on the small hydrothermal eruption of Hakone in June 2015, which corresponds to a VUI of 3. We calculated the daily VUI by shifting a 45-day time window one day at a time. During the periods of unrest in 2019 and 2023, the VUI reached peaks of 1.7 and 1.6, respectively. Our results also quantified the complex activity that occurred from 2023 to 2024. Since the system calculating the VUI was launched in January 2024, it has contributed greatly to understanding volcanic activity and has stimulated discussions among researchers from different fields within our institute. In the future, we expect to use the system as a tool for communication with all stakeholders at Hakone.\u003c/p\u003e","manuscriptTitle":"Quantitative evaluation of volcanic activity at Hakone Volcano, Japan, based on multiple observational datasets—Application of the Volcanic Unrest Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 09:19:01","doi":"10.21203/rs.3.rs-7709975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-11-13T18:40:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-16T00:30:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T07:26:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T10:57:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth, Planets and Space","date":"2025-10-02T08:35:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f802b1a-d778-437d-a079-5878162605f9","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T02:14:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 09:19:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7709975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7709975","identity":"rs-7709975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}