Rapid response petrology for the opening eruptive phase of the 2021 Cumbre Vieja eruption, La Palma, Canary Islands

How and why magmatic systems reactivate and evolve is a critical question for monitoring and hazard mitigation efforts during initial response and ongoing volcanic crisis management. Here we report the first integrated petrological results and interpretation provided to monitoring authorities during the ongoing eruption of Cumbre Vieja, La Palma, Canary Islands, Spain. The first eruptive products comprised simultaneous Strombolian fountain-fed lava flows and tephra fall from near-continuous eruption plumes. From combined field, petrographic and geochemical analyses conducted in the 10 days following sample collection, we infer low percentage mantle melts with a variably equilibrated multimineralic crystal-cargo and compositional fractionation by winnowing during eruptive processes. Hence ‘rapid response’ petrology can untangle complex magmatic and volcanic processes for this eruption, which combined with further study and methodological improvement can increasingly assist in active decision making.

Agriculture and forestry impact assessment for tephra fall hazard: fragility function development and New Zealand scenario application

Developing approaches to assess the impact of tephra fall to agricultural and forestry systems is essential for informing effective disaster risk management strategies. Fragility functions are commonly used as the vulnerability model within a loss assessment framework and represent the relationship between a given hazard intensity measure (e.g., tephra thickness) and the probability of impacts occurring. Impacts are represented here using an impact state (IS), which categorises qualitative and quantitative statements into a numeric scale. This study presents IS schemes for pastoral, horticultural, and forestry systems, and a suite of fragility functions estimating the probability of each IS occurring for 13 sub-sectors. Temporal vulnerability is accounted for by a ‘seasonality coefficient,’ and a ‘chemical toxicity coefficient’ is included to incorporate the increased vulnerability of pastoral farming systems when tephra is high in fluoride. The fragility functions are then used to demonstrate a deterministic impact assessment with current New Zealand exposure.

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band multi-parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was performed by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity factor. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of an ensemble prediction system (EPS). Cross-validation was performed using field-survey data, radar observations, and Himawari-8 imageries. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results are in agreement with ground-based data, where the radar-based estimated grain size distribution falls within the range of in situ grain size. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

A unified probabilistic framework for volcanic hazard and eruption forecasting

The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seek to quantify the deep uncertainties that pervade the modeling of pre-, sin-, and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi and Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards.

Petrology of the opening eruptive phase of the 2021 Cumbre Vieja eruption, La Palma, Canary Islands

The first products of the current Cumbre Vieja eruption comprise simultaneous tephra fall from near-continuous, gas-rich eruption plumes and lava flows. From combined field, petrographic and geochemical analyses we identify: low percentage mantle melts with a variably-equilibrated multimineralic crystal-cargo and compositional fractionation by eruptive processes. Hence petrology can untangle complex magmatic and volcanic processes for this eruption, which through further study can assist in active decision making.

A Unified Probabilistic Framework for Volcanic Hazard and Eruption Forecasting

The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seeks to quantify the deep uncertainties that pervade the modeling of pre-, sin- and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi & Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards.

How rainfall influences tephra fall loading — an experimental approach

The load a tephra fall deposit applies to an underlying surface is a key factor controlling its potential to damage a wide range of assets including buildings, trees, crops and powerlines. Though it has long been recognised that loading can increase when deposits absorb rainfall, few efforts have been made to quantify likely load increases. This study builds on previous theoretical work, using an experimental approach to quantify change in load as a function of grainsize distribution, rainfall intensity and duration. A total of 20 laboratory experiments were carried out for ~ 10-cm thick, dry tephra deposits of varying grainsize and grading, taken to represent different eruptive scenarios (e.g. stable, waxing or waning plume). Tephra was deposited onto a 15° impermeable slope (representing a low pitch roof) and exposed to simulated heavy rainfalls of 35 and 70 mm h−1 for durations of up to 2 h. Across all experiments, the maximum load increases ranged from 18 to 30%. Larger increases occurred in fine-grained to medium-grained deposits or in inversely graded deposits, as these retained water more efficiently. The lowest increases occurred in normally graded deposits as rain was unable to infiltrate to the deposit’s base. In deposits composed entirely of coarse tephra, high drainage rates meant the amount of water absorbed was controlled by the deposit’s capillary porosity, rather than its total porosity, resulting in load increases that were smaller than expected. These results suggest that, for low pitch roofs, the maximum deposit load increase due to rainfall is around 30%, significantly lower than the oft-referenced 100%. To complement our experimental results, field measurements of tephra thickness should be supplemented with tephra loading measurements, wherever possible, especially when measurements are made at or near the site of observed damage.

Apocalypse then! Apocalypse now? Using the Laacher See eruption (13ka BP) for Realistic Disaster Scenario design

<p>Approximately 13ka BP, the Laacher See volcano (East Eifel volcanic field, Rhenish Shield) erupted cataclysmically<sup>1</sup>. The details of this eruption as well as its impact on climate, environments and human in the near and far fields have been intensely researched offering rich data for designing Realistic Disaster Scenarios that consider, specifically, the potential consequences of renewed volcanic activity in the Eifel and, more generally, the consequences of similar extreme events/natural hazards on societies in Europe<sup>2</sup>. In this paper, I review the available evidence relating to the Late Pleistocene eruption with particular focus on (i) new climate modelling<sup>3</sup>, (ii) the impacts of the tephra-fall on ecosystem services<sup>4,5</sup> and (iii) the disruption to contemporaneous forager migration and communication networks<sup>6,7</sup>. Building on this, I reflect on how this evidence has recently fed into a special museum exhibition that places a Laacher See-type eruption in the year 2100 (https://www.moesgaardmuseum.dk/en/exhibitions/after-the-apocalypse/). Combing principles of evidence-based climate communication<sup>8–10</sup>, Realistic Disaster Scenario thinking<sup>11,12</sup> and state-of-the-art exhibition design, the exhibition addresses likely impacts on economy, travel/communication networks, politics and culture within the context of Anthropocene warming as projected by the IPCC scenarios.</p><p> </p><p> </p><p><strong>References:</strong></p>

Remotely assessing tephra fall building damage and vulnerability: Kelud Volcano, Indonesia

Tephra from large explosive eruptions can cause damage to buildings over wide geographical areas, creating a variety of issues for post-eruption recovery. This means that evaluating the extent and nature of likely building damage from future eruptions is an important aspect of volcanic risk assessment. However, our ability to make accurate assessments is currently limited by poor characterisation of how buildings perform under varying tephra loads. This study presents a method to remotely assess building damage to increase the quantity of data available for developing new tephra fall building vulnerability models. Given the large number of damaged buildings and the high potential for loss in future eruptions, we use the Kelud 2014 eruption as a case study. A total of 1154 buildings affected by falls 1–10 cm thick were assessed, with 790 showing signs that they sustained damage in the time between pre- and post-eruption satellite image acquisitions. Only 27 of the buildings surveyed appear to have experienced severe roof or building collapse. Damage was more commonly characterised by collapse of roof overhangs and verandas or damage that required roof cladding replacement. To estimate tephra loads received by each building we used Tephra2 inversion and interpolation of hand-contoured isopachs on the same set of deposit measurements. Combining tephra loads from both methods with our damage assessment, we develop the first sets of tephra fall fragility curves that consider damage severities lower than severe roof collapse. Weighted prediction accuracies are calculated for the curves using K-fold cross validation, with scores between 0.68 and 0.75 comparable to those for fragility curves developed for other natural hazards. Remote assessment of tephra fall building damage is highly complementary to traditional field-based surveying and both approaches should ideally be adopted to improve our understanding of tephra fall impacts following future damaging eruptions.

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model TEPHRA2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works, and to show how inputs affect its performance. Results are presented as sample posterior distribution estimates for variables of interest. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; and iii) capture correlations between variables of interest in the estimated Eruption Source Parameters. A limitation is that some of the inputs need to be specifed subjectively. How and why such inputs affect the performance of the algorithm and how to specify them properly are explained and listed. Correlation between variables of interest are well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters, and wind direction and speed at each elevation level, as this increases the number of variables to be estimated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from TEPHRA2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from the deposit.