Tephra4D: A Python-Based Model for High-Resolution Tephra Transport and Deposition Simulations—Applications at Sakurajima Volcano, Japan

Vulcanian eruptions (short-lived explosions consisting of a rising thermal) occur daily in volcanoes around the world. Such small-scale eruptions represent a challenge in numerical modeling due to local-scale effects, such as the volcano’s topography impact on atmospheric circulation and near-vent plume dynamics, that need to be accounted for. In an effort to improve the applicability of Tephra2, a commonly-used advection-diffusion model, in the case of vulcanian eruptions, a number of key modifications were carried out: (i) the ability to solve the equations over bending plume, (ii) temporally-evolving three-dimensional meteorological fields, (iii) the replacement of the particle diameter distribution with observed particle terminal velocity distribution which provides a simple way to account for the settling velocity variation due to particle shape and density. We verified the advantage of our modified model (Tephra4D) in the tephra dispersion from vulcanian eruptions by comparing the calculations and disdrometer observations of tephra sedimentation from four eruptions at Sakurajima volcano, Japan. The simulations of the eruptions show that Tephra4D is useful for eruptions in which small-scale movement contributes significantly to ash transport mainly due to the consideration for orographic winds in advection.

Modelling wildfire ash transport by concentrated flow

<p>Wildfire ash is a mixture of pyrogenic organic and inorganic materials with high concentration in nutrients and potential contaminants that is easily mobilized by runoff. Ash has been identified as a major threat to water quality since it can impact aquatic life and disrupt water treatment operations when is washed off into water bodies. The ability to modelling ash transport, however, is in its infancy.  One reason for this is that the relationship between runoff and ash transport for concentrated flow has not been described yet, limiting the capabilities of current runoff-erosion models to predict ash transport and delivery in fire-affected areas.</p><p>To fill this knowledge gap, we conducted a series of laboratory experiments on ash transport using concentrated flow on flumes. Ash collected from US conifer forest burned at high severity was applied at two different rates (corresponding to layers of 1 and 3 cm thickness) to an artificial substrate of gravel and sand attached to the bottom of the flumes that simulates soil roughness. Three different flow rates were consecutively applied to each flume in all possible combinations (6 flow rate combinations and 6 replicates per combination and ash thickness).</p><p>The results show that ash is easily transported by concentrated flow, confirming previous observations on ash mobility. The runoff rate required to start transporting the ash was close to 0 (0.005 L min<sup>-1 </sup>for both 1 and 3 cm ash layers) and the average concentration of ash in the runoff once this process started was considerably high (120 and 176 g L<sup>-1</sup> for 1 and 3 cm ash layers respectively), probably due to the low density and cohesion of this fire by-product. The results also show that ash depletion is a critical process when modelling ash transport and, thus, that ash transport by concentrated flow is better modelled using a variable sediment transport rate to account for ash decay with consecutive rainfall events. This is especially true for the 1 cm ash layer. The relationships between runoff and ash transport for concentrated flow obtained here for the evaluated ash type and loads are critical parameters to predict ash transport and will be used to refine ash transport capabilities of WEPPcloud-WATAR, a new tool aimed at predicting ash contamination risks after wildfires.</p>

Impacts of Volcanic Eruptions around the Korean Peninsula: A Long-term Simulation Study for Hypothetical Eruption Scenarios

To build a response against potential volcanic risks around Korea, we developed a three-dimensional Volcanic Ash Transport and Dispersion Model (VATDM), known as the Lagrangian Atmospheric Dose Assessment System-Volcanic Ash (LADAS-VA) model. Using the LADAS-VA model, we performed numerous simulations for multiple year-round hypothetical eruptions of several representative volcanoes around the Korean peninsula. We analyzed the simulation results and revealed the impacts of hypothetical volcanic eruptions on the Korean peninsula as counting the number of days influenced by the season. Overall simulations for hypothetical volcanic eruptions around the Korean peninsula revealed that the most impactful eruptions would potentially occur during the summer season. Long-term simulations examining hypothetical eruption scenarios at least over a decade must be conducted to enable the analysis of deviations on a year-on-year basis, in comparison with the climatological normals.

Analysis of the ETNA 2015 Eruption Using WRF–Chem Model and Satellite Observations

The aim of the present work is to utilize a new functionality within the Weather Research and Forecasting model coupled with Chemistry (WRF–Chem) that allows simulating emission, transport, and settling of pollutants released during the Etna 2015 volcanic activities. This study constitutes the first systematic application of the WRF–Chem online-based approach to a specific Etna volcanic eruption, with possible effects involving the whole Mediterranean area. In this context, the attention has been focused on the eruption event, recorded from 3–7 December 2015, which led to the closure of the nearby Catania International Airport. Quantitative meteorological forecasts, analyses of Etna volcanic ash transport, and estimates of the ash ground deposition have been performed. In order to test the performance of the proposed approach, the model outputs have been compared with data provided by satellite sensors and Doppler radars. As a result, it emerges that, as far as the selected eruption event is concerned, the WRF–Chem model reasonably reproduces the distribution of SO2 and of volcanic ash. In addition, this modeling system may provide valuable support both to airport management and to local stakeholders including public administrations.

Modeling volcanic ash aggregation processes and related impacts on the April–May 2010 eruptions of Eyjafjallajökull volcano with WRF-Chem

Volcanic eruptions eject ash and gases into the atmosphere that can contribute to significant hazards to aviation, public and environment health, and the economy. Several volcanic ash transport and dispersion (VATD) models are in use to simulate volcanic ash transport operationally, but none include a treatment of volcanic ash aggregation processes. Volcanic ash aggregation can greatly reduce the atmospheric budget, dispersion and lifetime of ash particles, and therefore its impacts. To enhance our understanding and modeling capabilities of the ash aggregation process, a volcanic ash aggregation scheme was integrated into the Weather Research Forecasting with online Chemistry (WRF-Chem) model. Aggregation rates and ash mass loss in this modified code are calculated in line with the meteorological conditions, providing a fully coupled treatment of aggregation processes. The updated-model results were compared to field measurements of tephra fallout and in situ airborne measurements of ash particles from the April–May 2010 eruptions of Eyjafjallajökull volcano, Iceland. WRF-Chem, coupled with the newly added aggregation code, modeled ash clouds that agreed spatially and temporally with these in situ and field measurements. A sensitivity study provided insights into the mechanics of the aggregation code by analyzing each aggregation process (collision kernel) independently, as well as by varying the fractal dimension of the newly formed aggregates. In addition, the airborne lifetime (e-folding) of total domain ash mass was analyzed for a range of fractal dimensions, and a maximum reduction of 79.5 % of the airborne ash lifetime was noted.

Novel statistical emulator construction for volcanic ash transport model Ash3d with physically motivated measures

Statistical emulators are a key tool for rapidly producing probabilistic hazard analysis of geophysical processes. Given output data computed for a relatively small number of parameter inputs, an emulator interpolates the data, providing the expected value of the output at untried inputs and an estimate of error at that point. In this work, we propose to fit Gaussian Process emulators to the output from a volcanic ash transport model, Ash3d. Our goal is to predict the simulated volcanic ash thickness from Ash3d at a location of interest using the emulator. Our approach is motivated by two challenges to fitting emulators—characterizing the input wind field and interactions between that wind field and variable grain sizes. We resolve these challenges by using physical knowledge on tephra dispersal. We propose new physically motivated variables as inputs and use normalized output as the response for fitting the emulator. Subsetting based on the initial conditions is also critical in our emulator construction. Simulation studies characterize the accuracy and efficiency of our emulator construction and also reveal its current limitations. Our work represents the first emulator construction for volcanic ash transport models with considerations of the simulated physical process.

Comparing Simulations of Umbrella-Cloud Growth and Ash Transport with Observations from Pinatubo, Kelud, and Calbuco Volcanoes

The largest explosive volcanic eruptions produce umbrella clouds that drive ash radially outward, enlarging the area that impacts aviation and ground-based communities. Models must consider the effects of umbrella spreading when forecasting hazards from these eruptions. In this paper we test a version of the advection–dispersion model Ash3d that considers umbrella spreading by comparing its simulations with observations from three well-documented umbrella-forming eruptions: (1) the 15 June 1991 eruption of Pinatubo (Philippines); (2) the 13 February 2014 eruption of Kelud (Indonesia); and (3) phase 2 of the 22–23 April 2015 eruption of Calbuco (Chile). In volume, these eruptions ranged from several cubic kilometers dense-rock equivalent (DRE) for Pinatubo to about one tenth for Calbuco. In mass eruption rate (MER), they ranged from 108–109 kg s−1 at Pinatubo to 9–16 × 106 kg s−1 at Calbuco. For each case we ran simulations that considered umbrella growth and ones that did not. All umbrella-cloud simulations produced a cloud whose area was within ~25% of the observed cloud by the end of the eruption. By the eruption end, the simulated areas of the Pinatubo, Kelud, and Calbuco clouds were 851, 53.2, and 100 × 103 km2 respectively. These areas were 2.2, 2.2, and 1.5 times the areas calculated in simulations that ignored umbrella growth. For Pinatubo and Kelud, the umbrella simulations provided better agreement with the observed cloud area than the non-umbrella simulations. Each of these simulations extended 24 h from the eruption start. After the eruption ended, the difference in cloud area (umbrella minus non-umbrella) at Pinatubo persisted for many hours; at Kelud it diminished and became negative after 14 h and at Calbuco it became negative after ~23 h. The negative differences were inferred to result from the fact that non-umbrella simulations distributed ash over a wider vertical extent in the plume, and that wind shear spread the cloud out in multiple directions. Thus, for some smaller eruptions, wind shear can produce a larger cloud than might be produced by umbrella spreading alone.

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.

Operational Modelling of Umbrella Cloud Growth in a Lagrangian Volcanic Ash Transport and Dispersion Model

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available.