Massey Research Online - Browsing by Author "Bebbington M"

Browsing by Author "Bebbington M"

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A new perspective on eruption data completeness: insights from the First Recorded EruptionS in the Holocene (FRESH) database
(Elsevier BV, 2022-11) Burgos V; Jenkins SF; Bebbington M; Newhall C; Taisne B
Identifying the most complete (best recorded) portion of an eruption record is essential before estimating eruption recurrence and probability. This is typically achieved by plotting cumulative eruptions through time. Here, we evaluate eruption data completeness from a new perspective, by compiling the first dated Holocene eruption from each volcano in the Volcanoes of the World (VOTW) database (i.e., First Recorded EruptionS in the Holocene (FRESH)). In our first analysis, we compared the subregional distribution of FRESH with time using Kolmogorov-Smirnov (K[sbnd]S) test. We found that the eruption record was best categorised into 31 regions containing subregions with similar degrees of completeness. This opened the way to define new Relative Completeness Date(s) (RCD) as a function of eruption size, volcanic characteristics, and region, by identifying multiple points in the record where the root-mean-square (RMS) level changes abruptly, corresponding to a gap, a decrease or increase in the FRESH rate. Regional RCDs in the Common Era (CE) range from as recently as 1964 CE in the Indian Ocean (southern) to 200 CE in Middle East and Western Indian Ocean. In contrast, some regions like Kamchatka and Mainland Asia have near-constant rates of FRESH over the last 12,000 years, making RCDs impossible to assign. We present and make available our FRESH database, and describe and implement an automatic approach to detect RCDs across our newly defined volcanic regions. We suggest that the different degrees of completeness observed at a regional scale can be explained by: socio-historical events, access to geological studies, submarine volcanism, and/or remoteness. The FRESH database, together with the new regions and proposed RCDs can be used in future studies to estimate eruption probabilities at volcanoes without Holocene records and identify which subregions are most likely to produce a FRESH in the future.
A Statistical Model for Earthquake And/Or Rainfall Triggered Landslides
(Frontiers Media S.A., 2021-02-04) Frigerio Porta G; Bebbington M; Xiao X; Jones G; Xu C
Natural hazards can be initiated by different types of triggering events. For landslides, the triggering events are predominantly earthquakes and rainfall. However, risk analysis commonly focuses on a single mechanism, without considering possible interactions between the primary triggering events. Spatial modeling of landslide susceptibility (suppressing temporal dependence), or tailoring models to specific areas and events are not sufficient to understand the risk produced by interacting causes. More elaborate models with interactions, capable of capturing direct or indirect triggering of secondary hazards, are required. By discretising space, we create a daily-spatio-temporal hazard model to evaluate the relative and combined effects on landslide triggering due to earthquakes and rainfall. A case study on the Italian region of Emilia-Romagna is presented, which suggests these triggering effects are best modeled as additive. This paper demonstrates how point processes can be used to model the triggering influence of multiple factors in a large real dataset collected from various sources.
Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano
(Elsevier BV, 2022-12) Scott E; Bebbington M; Wilson T; Kennedy B; Leonard G
Taupō volcano, located within the Taupō Volcanic Zone (TVZ) in the central North Island of Aotearoa-New Zealand, is one of the world's most active silicic caldera systems. Silicic calderas such as Taupō are capable of a broad and complex range of volcanological activity, ranging from minor unrest episodes to large destructive supereruptions. A critical tool for volcanic risk management is eruption forecasting. The Bayesian Event Tree for Eruption Forecasting (BET_EF) is one probabilistic eruption forecasting tool that can be used to produce short-term eruption forecasts for any volcano worldwide. A BET_EF model is developed for Taupō volcano, informed by geologic and historic data. Monitoring parameters for the model were obtained through a structured expert elicitation workshop with 30 of Aotearoa-New Zealand's volcanologists and volcano monitoring scientists. The eruption probabilities output by the BET_EF model for Taupō volcano's 17 recorded unrest episodes (between 1877 and 2019) were examined. We found time-inhomogeneity in the probabilities stemming from both the changes over time in the monitoring network around Taupō volcano and increasing level of past data (number of non-eruptive unrest episodes). We examine the former issue through the lens of the latest episodes, and the latter by re-running the episodes assuming knowledge of all 16 other episodes (calibration to 2021 data). The time variable monitoring network around Taupō volcano and parameter weights had a substantial impact on the estimated probabilities of magmatic unrest and eruption. We also note the need for improved monitoring and data processing at Taupō volcano, the existence of which would prompt updates and therefore refinements in the BET_EF model.
Forecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes
(John Wiley and Sons, Inc on behalf of the American Geophysical Union, 2022-06-28) Wang T; Bebbington M; Cronin S; Carman J
Forecasting future destructive eruptions from re-awakening volcanoes remains a challenge, mainly due to a lack of previous event data. This sparks a search for similar volcanoes to provide additional information, especially those with better compiled and understood event records. However, we show that some of the most obviously geologically comparable volcanoes have differing statistical occurrence patterns. Using such matches produces large forecasting uncertainties. We created a statistical tool to identify and test the compatibility of potential analogue volcanoes based on repose-time characteristics from world-wide datasets. Selecting analogue volcanoes with compatible behavior for factors being forecast, such as repose time, significantly reduces forecasting uncertainties. Applying this tool to Tongariro volcano (NZ), there is a 5% probability for a Volcanic Explosivity Index (VEI) ≥ 3 explosive eruption in the next 50 years. Using pre-historic geological records of a smaller available set of analogs, we forecast a 1% probability of a VEI ≥ 4 eruption in the next 50 years.
Identifying analogues for data-limited volcanoes using hierarchical clustering and expert knowledge: a case study of Melimoyu (Chile)
(Frontiers Media S.A., 2023-05-24) Burgos V; Jenkins SF; Bono Troncoso L; Perales Moya CV; Bebbington M; Newhall C; Amigo A; Prada Alonso J; Taisne B; Fournier N
Determining the eruption frequency-Magnitude (f-M) relationship for data-limited volcanoes is challenging since it requires a comprehensive eruption record of the past eruptive activity. This is the case for Melimoyu, a long-dormant and data-limited volcano in the Southern Volcanic Zone (SVZ) in Chile with only two confirmed Holocene eruptions (VEI 5). To supplement the eruption records, we identified analogue volcanoes for Melimoyu (i.e., volcanoes that behave similarly and are identified through shared characteristics) using a quantitative and objective approach. Firstly, we compiled a global database containing 181 variables describing the eruptive history, tectonic setting, rock composition, and morphology of 1,428 volcanoes. This database was filtered primarily based on data availability into an input dataset comprising 37 numerical variables for 438 subduction zone volcanoes. Then, we applied Agglomerative Nesting, a bottom-up hierarchical clustering algorithm on three datasets derived from the input dataset: 1) raw data, 2) output from a Principal Component Analysis, and 3) weighted data tuned to minimise the dispersion in the absolute probability per VEI. Lastly, we identified the best set of analogues by analysing the dispersion in the absolute probability per VEI and applying a set of criteria deemed important by the local geological service, SERNAGEOMIN, and VB. Our analysis shows that the raw data generate a low dispersion and the highest number of analogues (n = 20). More than half of these analogues are in the SVZ, suggesting that the tectonic setting plays a key role in the clustering analysis. The eruption f-M relationship modelled from the analogue’s eruption data shows that if Melimoyu has an eruption, there is a 49% probability (50th percentile) of it being VEI≥4. Meanwhile, the annual absolute probability of a VEI≤1, VEI 2, VEI 3, VEI 4, and VEI≥5 eruption at Melimoyu is 4.82 × 10−4, 1.2 × 10−3, 1.45 × 10−4, 9.77 × 10−4, and 8.3 × 10−4 (50th percentile), respectively. Our work shows the importance of using numerical variables to capture the variability across volcanoes and combining quantitative approaches with expert knowledge to assess the suitability of potential analogues. Additionally, this approach allows identifying groups of analogues and can be easily applied to other cases using numerical variables from the global database. Future work will use the analogues to populate an event tree and define eruption source parameters for modelling volcanic hazards at Melimoyu.
Probabilistic Volcanic Hazard Assessment for National Park Infrastructure Proximal to Taranaki Volcano (New Zealand)
(Frontiers Media S.A., 2022-03-28) Mead S; Procter J; Bebbington M; Rodriguez-Gomez C; Fontijn K
Hazard assessment for infrastructure proximal to a volcanic vent raises issues that are often not present, or not as severe in hazard assessments for more distal infrastructure. Proximal regions are subject to a greater number of hazardous phenomena, and variability in impact intensity increases with the hazard magnitude. To probabilistically quantify volcanic hazard to infrastructure, multiple volcanic hazards and their effects on exposed elements need to be considered. Compared to single-hazard assessments, multi-hazard assessments increase the size and complexity of determining hazard occurrence and magnitude, typically introducing additional uncertainties in the quantification of risk. A location-centred approach, focusing on key locations rather than key hazards, can simplify the problem to one requiring identification of hazards with the potential to affect the location, followed by assessment of the probability of these hazards and their triggering eruptions. The location-centred approach is more compatible to multi-source hazards and allows for different hazard estimation methodologies to be applied as appropriate for the infrastructure type. We present a probabilistic quantification of volcanic hazard using this location centred approach for infrastructure within Te Papakura o Taranaki National Park, New Zealand. The impact to proposed park infrastructure from volcanic activity (originating from Mt. Taranaki) is quantified using a probability chain to provide a structured approach to integrate differing hazard estimation methods with eruption probability estimates within asset lifetimes. This location-centered approach provides quantitative estimates for volcanic hazards that significantly improve volcanic hazard estimates for infrastructure proximal to the Taranaki summit vent. Volcanic mass flows, predominantly pyroclastic surges or block and ash flows, are most likely (probability >0.8) to affect walking tracks if an eruption occurs. The probability of one or more eruption(s) in the next 50 years is estimated at 0.35–0.38. This use of probability chains and a location centered assessment demonstrates a technique that can be applied to proximal hazard assessments globally.
Probabilistic volcanic mass flow hazard assessment using statistical surrogates of deterministic simulations
(Elsevier Ltd., 2023-09-01) Mead SR; Procter J; Bebbington M
Probabilistic volcanic hazard assessments require (1) an identification of the hazardous volcanic source; (2) estimation of the magnitude-frequency relationship for the volcanic process; (3) quantification of the dependence of hazard on magnitude and external conditions; and (4) estimation of hazard exceedance from the magnitude-frequency and hazard intensity relationship. For volcanic mass flows, quantification of the hazard is typically undertaken through the use of computationally expensive mass flow simulators. However, this computational expense restricts the number of samples that can be used to produce a probabilistic assessment and limits the ability to rapidly update hazard assessments in response to changing source probabilities. We develop an alternate approach to defining hazard intensity through a surrogate model that provides a continuous estimate of simulation outputs at negligible computational expense, demonstrated through a probabilistic hazard assessment of dome collapse (block-and-ash) flows at Taranaki volcano, New Zealand. A Gaussian Process emulator trained on a database of simulations is used as the surrogate model of hazard intensity across the input space of possible dome collapse volumes and configurations, which is then sampled using a volume-frequency relationship of dome collapse flows. The demonstrated technique is a tractable solution to the problem of probabilistic volcanic hazard assessment, with the surrogates providing a good approximation of the simulator, and is generally applicable to volcanic hazard and geo-hazard assessments that are limited by the demands of numerical simulations and changing source probabilities.
PROMETHEUS: Probability in the Mediterranean of Tephra dispersal for various grain sizes. A tool for the evaluation of the completeness of the volcanic record in medial-distal archives
(Elsevier BV, 2024-03) Billotta E; Sulpizio R; Selva J; Costa A; Bebbington M
PROMETHEUS is a statistical tool that allows creating maps showing the probability of finding tephra deposits of different grain sizes, originating from eruptions of a specific volcanic source, at any location around the vent. It couples wind profiles at different heights in the Mediterranean area with terminal velocity of volcanic particles. The input parameters include the height of the eruption column (which characterizes the intensity of the eruption), wind statistics (directions and intensities), and tephra deposits of a selected grain size. In particular, we used the parameterizations provided by Costa et al. (2016) and performed simulations using the HAZMAP tephra dispersal model to determine the maximum reachable distances that tephra can cover under weak, medium, and strong wind conditions (e.g. 7, 30, and 70 m/s velocities at the tropopause) and with column heights of 10, 20, and 30 km, depositing of at least the loading corresponding to 0.1 mm (corresponding to cryptotephra). Three alternative configurations of the model are validated analyzing first the eruptive source of Somma Vesuvius, with the related explosive eruptions from 22 ka Pomici di Base to the 1944 eruption. A further validation is made by comparing the probabilistic maps with the tephrostratigraphy of known marine and terrestrial cores using standard test of proportions (binomial distributions) and the binary logistic regression model, statistically quantifying the effectiveness of the model against the tephrostratigraphy recorded within this time frame. Based on this validation, a preferred configuration of PROMETHEUS is selected. PROMETHEUS probability maps will guide the selection of sampling sites for specific tephra deposits and could also support the study of the completeness of overall eruption catalogs over time.
What is the probability of unexpected eruptions from potentially active volcanoes or regions?
(Springer Nature Switzerland AG on behalf of the International Association of Volcanology and Chemistry of the Earth's Interior, 2022-11) Burgos V; Jenkins SF; Bebbington M; Newhall C; Taisne B; Sandri L
Since the start of the twentieth century, 101 potentially active volcanoes have produced their first Holocene eruption, as recorded in the volcanoes of the world (VOTW) database. The reactivation of potentially active volcanoes is often a surprise, since they tend to be less well-studied and unmonitored. The first step towards preparing for these unexpected eruptions is to establish how often potentially active volcanoes have erupted in the past. Here, we use our previously developed FRESH (First Recorded EruptionS in the Holocene) database to estimate the past regional Average Recurrence Interval (ARI) of these unexpected events. Within the most complete portions of the FRESH database, a FRESH (i.e., the first recorded eruption from a potentially active volcano) has occurred as frequently as every ~ 7 years in the Pacific Ocean region (~ 50 years of relatively complete record) and ~ 8 years in Izu, Volcano, and the Mariana Islands region (~ 150 years of relatively complete record). We use the regional frequency to estimate the annual probability of a FRESH at individual potentially active volcanoes in selected regions of Asia–Pacific, which ranged from 0.003 for Izu, Volcano, and Mariana Islands to 1.35 × 10−5 for Luzon. Population exposure around potentially active volcanoes showed that at volcanoes such as Kendeng (Indonesia) and Laguna Caldera (Philippines), more than 30 million people reside within 100 km of the summit. With this work, we hope to establish how often potentially active volcanoes erupt, while identifying which regions and which potentially active volcanoes may require more attention.

Browsing by Author "Bebbington M"

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