This study compares and evaluates one-dimensional (1D) and three-dimensional (3D) numerical models of volcanic eruption columns in a set of different inter-comparison exercises. The exercises were ...designed as a blind test in which a set of common input parameters was given for two reference eruptions, representing a strong and a weak eruption column under different meteorological conditions. Comparing the results of the different models allows us to evaluate their capabilities and target areas for future improvement. Despite their different formulations, the 1D and 3D models provide reasonably consistent predictions of some of the key global descriptors of the volcanic plumes. Variability in plume height, estimated from the standard deviation of model predictions, is within ~20% for the weak plume and ~10% for the strong plume. Predictions of neutral buoyancy level are also in reasonably good agreement among the different models, with a standard deviation ranging from 9 to 19% (the latter for the weak plume in a windy atmosphere). Overall, these discrepancies are in the range of observational uncertainty of column height. However, there are important differences amongst models in terms of local properties along the plume axis, particularly for the strong plume. Our analysis suggests that the simplified treatment of entrainment in 1D models is adequate to resolve the general behaviour of the weak plume. However, it is inadequate to capture complex features of the strong plume, such as large vortices, partial column collapse, or gravitational fountaining that strongly enhance entrainment in the lower atmosphere. We conclude that there is a need to more accurately quantify entrainment rates, improve the representation of plume radius, and incorporate the effects of column instability in future versions of 1D volcanic plume models.
•We present the main results of an eruptive column model inter-comparison exercise.•Simulations with standard inputs for strong and weak eruptive plumes were performed.•We compare results of empirical, one-dimensional, and three-dimensional models.•Results allowed for evaluating model capabilities and areas for model improvement.
Mathematical models of natural processes can be used as inversion tools to predict unobserved properties from measured quantities. Uncertainty in observations and model formulation impact on the ...efficacy of inverse modelling. We present a general methodology, history matching, that can be used to investigate the effect of observational and model uncertainty on inverse modelling studies. We demonstrate history matching on an integral model of volcanic plumes that is used to estimate source conditions from observations of the rise height of plumes during the eruptions of Eyjafjallajökull, Iceland, in 2010 and Grímsvötn, Iceland, in 2011. Sources of uncertainty are identified and quantified, and propagated through the integral plume model. A preliminary sensitivity analysis is performed to identify the uncertain model parameters that strongly influence model predictions. Model predictions are assessed against observations through an implausibility measure that rules out model inputs that are considered implausible given the quantified uncertainty. We demonstrate that the source mass flux at the volcano can be estimated from plume height observations, but the magmatic temperature, exit velocity and exsolved gas mass fraction cannot be accurately determined. Uncertainty in plume height observations and entrainment coefficients results in a large range of plausible values of the source mass flux. Our analysis shows that better constraints on entrainment coefficients for volcanic plumes and more precise observations of plume height are required to obtain tightly constrained estimates of the source mass flux.
Observations of volcanic lightning made using a lightning mapping array during the 2010 eruption of Eyjafjallajökull allow the trajectory and growth of the volcanic plume to be determined. The ...lightning observations are compared with predictions of an integral model of volcanic plumes that includes descriptions of the interaction with wind and the effects of moisture. We show that the trajectory predicted by the integral model closely matches the observational data and the model well describes the growth of the plume downwind of the vent. Analysis of the lightning signals reveals information on the dominant charge structure within the volcanic plume. During the Eyjafjallajökull eruption both monopole and dipole charge structures were observed in the plume. By using the integral plume model, we propose the varying charge structure is connected to the availability of condensed water and low temperatures at high altitudes in the plume, suggesting ice formation may have contributed to the generation of a dipole charge structure via thunderstorm-style ice-based charging mechanisms, though overall this charging mechanism is believed to have had only a weak influence on the production of lightning.
The mass eruption rate (MER) of an explosive volcanic eruption is a commonly used quantifier of the magnitude of the eruption, and estimating it is important in managing volcanic hazards. The ...physical connection between the MER and the rise height of the eruption column results in a scaling relationship between these quantities, allowing one to be inferred from the other. Eruption source parameter datasets have been used to calibrate the relationship, but the uncertainties in the measurements used in the calibration are typically not accounted for in applications. This can lead to substantial over- or under-estimation. Here we apply a simple Bayesian approach to incorporate uncertainty into the calibration of the scaling relationship using Bayesian linear regression to determine probability density functions for model parameters. This allows probabilistic prediction of mass eruption rate given a plume height observation in a way that is consistent with the data used for calibration. By using non-informative priors, the posterior predictive distribution can be determined analytically. The methods and dataset are collected in a python package, called merph. We illustrate their use in sampling plausible MER—plume height pairs, and in identifying usual eruptions. We discuss applications to ensemble-based hazard assessments and potential developments of the approach.
•A Bayesian linear regression is applied to eruption source parameter datasets.•Inference of mass eruption rate from plume height can be obtained with a quantification of uncertainty.•Analytical forms for the Posterior Predictive distributions facilitate analysis and sampling.•Observational uncertainty can be included in prediction.
Volcanic debris flows (lahars) are highly destructive volcanic phenomena and present significant challenges in numerical simulation. This manuscript tackles the three fundamental requirements for ...modelling gravitational flows: determining plausible source configurations; selecting suitable topographic data; and employing appropriate mathematical models to assess the current hazard posed by long-distance lahars at Cotopaxi volcano. After incorporating these elements, we successfully simulated the characteristics of a future 1877-type lahar under current conditions, accounting for glacier size and topography. For the source conditions, or “scenario”, we identified 27 equidistant source locations along the lower edge of the current glacier’s extent. Each source was assigned a hydrograph based on the weighted volume of water available on Cotopaxi’s current glacier. Additionally, we introduced a methodology for quantifying channel width when high-resolution digital elevation models (DEMs) are available. This method enabled us to determine the minimum pixel size required for accurate representation of ravine shapes. While higher resolution DEMs demand robust computational resources and extended computational timeframes, we upscaled Cotopaxi’s DEM from 3 m to 15 m to balance accuracy and efficiency, as a 15-m DEM capture over 90% of the topography and reduces computing time significantly. Optimizing DEM selection is crucial, especially when contemplating future ensemble approaches. After employing the dynamic-based model Kestrel, parameterised for large lahars, we obtained predictions closely aligned with field observations, historical flow conditions inferred for the 1877 lahar-event, and results from previous simulation studies. Notably, we observed higher depths and speeds in canyons compared to plains, consistent with historical reports and previous studies. Minor discrepancies in the inundation area, when compared with existing hazard maps, emphasize the importance of understanding flow dynamics and lahar trajectories for effective hazard assessment and mitigation strategies. Furthermore, our results contribute valuable information to current hazard maps and can aid in damage quantification and cost/benefit analyses, particularly when planning the construction of mitigation infrastructure.
In this paper, a state-of the art numerical weather prediction (NWP) model is used to simulate the near-field plume of a Plinian-type volcanic eruption. The NWP model is run at very high resolution ...(of the order of 100 m) and includes a representation of physical processes, including turbulence and buoyancy, that are essential components of eruption column dynamics. Results are shown that illustrate buoyant gas plume dynamics in an atmosphere at rest and in an atmosphere with background wind, and we show that these results agree well with those from theoretical models in the quiescent atmosphere. For wind-blown plumes, we show that features observed in experimental and natural settings are reproduced in our model. However, when comparing with predictions from an integral model using existing entrainment closures there are marked differences. We speculate that these are signatures of a difference in turbulent mixing for uniform and shear flow profiles in a stratified atmosphere. A more complex implementation is given to show that the model may also be used to examine the dispersion of heavy volcanic gases such as sulphur dioxide. Starting from the standard version of the weather research and forecasting (WRF) model, we show that minimal modifications are needed in order to model volcanic plumes. This suggests that the modified NWP model can be used in the forecasting of plume evolution during future volcanic events, in addition to providing a virtual laboratory for the testing of hypotheses regarding plume behaviour.
The objective of this study was to evaluate the impact of Minnesota's Return to Community Initiative (RTCI) on postdischarge outcomes for nursing home residents transitioned through the program.
...Secondary data were from the Minimum Data Set and RTCI staff (January 2015 to December 2016), state Medicaid eligibility files and death records. The sample consisted of 29,201 nursing home discharges in Minnesota occurring in 2015.
Cox proportional hazard models were used to compare 1-year postdischarge outcomes of nursing home readmission, mortality, and Medicaid conversion for RTCI assisted community discharges and a propensity-matched sample of unassisted community discharges.
The majority (60%) of RTCI assisted discharges remained alive, in the community and not having converted at Medicaid at 1 year after discharge. Time to mortality was significantly lower for the assisted group than the unassisted group, but time to readmission and Medicaid conversion were similar.
The RTCI assisted residents fared well postdischarge in their time to mortality, nursing home readmission, and Medicaid conversion; they lived longer than a propensity-matched sample of their peers.
Volcanic water-sediment flows, commonly known as lahars, can often pose a higher threat to population and infrastructure than primary volcanic hazardous processes such as tephra fallout and ...Pyroclastic Density Currents (PDCs). Lahars are volcaniclastic flows of water, volcanic debris and entrained sediments that can travel long distances from their source, causing severe damage by impact and burial. Lahars are frequently triggered by intense or prolonged rainfall occurring after explosive eruptions, and their occurrence depends on numerous factors including the spatio-temporal rainfall characteristics, the spatial distribution and hydraulic properties of the tephra deposit, and the pre- and post-eruption topography. Modeling (and forecasting) such a complex system requires the quantification of aleatory variability in the lahar triggering and propagation. To fulfill this goal, we develop a novel framework for probabilistic hazard assessment of lahars within a multi-hazard environment, based on coupling a versatile probabilistic model for lahar triggering (a Bayesian Belief Network: Multihaz) with a dynamic physical model for lahar propagation (LaharFlow). Multihaz allows us to estimate the probability of lahars of different volumes occurring by merging varied information about regional rainfall, scientific knowledge on lahar triggering mechanisms and, crucially, probabilistic assessment of available pyroclastic material from tephra fallout and PDCs. LaharFlow propagates the aleatory variability modeled by Multihaz into hazard footprints of lahars. We apply our framework to Somma-Vesuvius (Italy) because: (1) the volcano is strongly lahar-prone based on its previous activity, (2) there are many possible source areas for lahars, and (3) there is high density of population nearby. Our results indicate that the size of the eruption preceding the lahar occurrence and the spatial distribution of tephra accumulation have a paramount role in the lahar initiation and potential impact. For instance, lahars with initiation volume ≥105 m3 along the volcano flanks are almost 60% probable to occur after large-sized eruptions (~VEI ≥ 5) but 40% after medium-sized eruptions (~VEI4). Some simulated lahars can propagate for 15 km or reach combined flow depths of 2 m and speeds of 5–10 m/s, even over flat terrain. Probabilistic multi-hazard frameworks like the one presented here can be invaluable for volcanic hazard assessment worldwide.
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