J. Dehn

The reawakening of Alaska's Augustine volcano

Augustine volcano, in south central Alaska, ended a 20-year period of repose on 11 January 2006 with 13 explosive eruptions in 20 days. Explosive activity shifted to a quieter effusion of lava in early February, forming a new summit lava dome and two short, blocky lava flows by late March (Figure 1). The eruption was heralded by eight months of increasing seismicity, deformation, gas emission, and small phreatic eruptions, the latter consisting of explosions of steam and debris caused by heating and expansion of groundwater due to an underlying heat source.

Heat loss measured at a lava channel and its implications for down-channel cooling and rheology

During May 2001 we acquired 2016 thermal images over an ~8-h-long period for a section of active lava channel on Mount Etna (Italy). We used these to extract surface temperature and heat-loss profi les and thereby calculate core cooling rates. Flow surface temperatures declined from ~1070 K at the vent to ~930 K at 70 m. Heat losses were dominated by radiation (5 × 10 W m) and convection (~10 W/m). These compare with a heat gain from crystallization of 6 × 10 W/m. The imbalance between sinks and sources gives core cooling (δT/δx) of ~110 K/km. However, cooling rate per unit distance also depends on fl ow conditions, where we distinguished: (1) unimpeded, high-velocity (~0.2 m/s) fl ow with low δT/δx (0.3 K/m); (2) unimpeded, low-velocity (~0.1 m/s) fl ow with higher δT/δx (0.5 K/m); (3) waning, insulated fl ow at low velocity (~0.1 m/s) with low δT/δx (0.3 K/m); and (4) impeded fl ow at low velocity (<0.1 m/s) with higher δT/δx (0.4 K/m). Our data allow us to defi ne three thermal states of fl ow emplacement: insulated, rapid, and protected. Insulated is promoted by the formation of hanging blockages and coherent roofs. During rapid emplacement, higher velocities suppress cooling rates, and δT/δx can be tied to mean velocity (V mean ) by δT/δx = aV mean . In the protected case, deeper, narrow channels present a thermally effi cient channel, where δT/δx can be assessed using the ratio of channel width (w) to depth (d) in w/d = aδT/δx.

The 1997 eruption of Okmok Volcano, Alaska: a synthesis of remotely sensed imagery

Abstract Okmok Volcano, in the eastern Aleutian Islands, erupted in February and March of 1997 producing a 6-km-long lava flow and low-level ash plumes. This caldera is one of the most active in the Aleutian Arc, and is now the focus of international multidisciplinary studies. A synthesis of remotely sensed data (AirSAR, derived DEMs, Landsat MSS and ETM+ data, AVHRR, ERS, JERS, Radarsat) has given a sequence of events for the virtually unobserved 1997 eruption. Elevation data from the AirSAR sensor acquired in October 2000 over Okmok were used to create a 5-m resolution DEM mosaic of Okmok Volcano. AVHRR nighttime imagery has been analyzed between February 13 and April 11, 1997. Landsat imagery and SAR data recorded prior to and after the eruption allowed us to accurately determine the extent of the new flow. The flow was first observed on February 13 without precursory thermal anomalies. At this time, the flow was a large single lobe flowing north. According to AVHRR Band 3 and 4 radiance data and ground observations, the first lobe continued growing until mid to late March, while a second, smaller lobe began to form sometime between March 11 and 12. This is based on a jump in the thermal and volumetric flux determined from the imagery, and the physical size of the thermal anomalies. Total radiance values waned after March 26, indicating lava effusion had ended and a cooling crust was growing. The total area (8.9 km 2 ), thickness (up to 50 m) and volume (1.54×10 8 m 3 ) of the new lava flow were determined by combining observations from SAR, Landsat ETM+, and AirSAR DEM data. While the first lobe of the flow ponded in a pre-eruption depression, our data suggest the second lobe was volume-limited. Remote sensing has become an integral part of the Alaska Volcano Observatory’s monitoring and hazard mitigation efforts. Studies like this allow access to remote volcanoes, and provide methods to monitor potentially dangerous ones.

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions - height, profile of particle location, volcanic vent parameters - are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery.The April 2010 eruption of the Eyjafjallajokull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14-16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

A DDDAS framework for volcanic ash propagation and hazard analysis

Abstract In this paper we will present early work on using a DDDAS based approach to the construction of probabilistic estimates of volcanic ash transport and dispersal. Our primary modeling tools will be a combination of a plume eruption model BENT and the ash transport model PUFF. Data from satellite imagery, observation of vent parameters and windfields will drive our simulations. We will use uncertainty quantification methodology – polynomial chaos quadrature in combination with data integration to complete the DDDAS loop.

Polynomial Chaos Quadrature-based Minimum Variance Approach for Source Parameters Estimation

Abstract We present a polynomial chaos based minimum variance formulation to solve inverse problems. The utility of the proposed approach is evaluated by considering the ash transport problem arising due to volcanic eruption. Volcanic ash advisory centers generally makes use of mathematical models for column eruption and advection and diffusion of ash cloud in atmosphere. These models require input data on source conditions such as vent radius, vent velocity and distribution of ash-particle size. The inputs are usually not well constrained, and estimates of the uncertainty in the inputs is needed to make accurate predictions of cloud motion. The recent eruption of Eyjafjallajokull, Iceland in April 2010 is considered as test example. For validation, the puff advection and diffusion model is used to hindcast the motion of the ash cloud through time concentrating on the period 14-16 April 2010. Variability in the height and loading of the eruption is introduced through the volcano column model bent. Output uncertainty due to uncertain input parameters is determined with a polynomial chaos quadrature (PCQ)-based sampling of the multidimensional puff input vector space. Furthermore, the posterior distribution for input parameters is obtained by assimilating satellite imagery data with PCQ predictions using a minimum variance approach.

Challenges in developing DDDAS based methodology for volcanic ash hazard analysis - Effect of numerical weather prediction variability and parameter estimation

In this paper, we will present ongoing work on using a dynamic data driven application system (DDDAS) based approach to the forecast of volcanic ash transport and dispersal. Our primary modeling tool will be a new code puffin formed by the combination of a plume eruption model Bent and the ash transport model Puff. Data from satellite imagery, observation of vent parameters and windfields will drive our simulations. We will use ensemble based uncertainty quantification and parameter estimation methodology – polynomial chaos quadrature in combination with data integration to complete the DDDAS loop.

Conclusion: recommendations and findings of the RED SEED working group

Abstract RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.

Forecasting large explosions at Bezymianny Volcano using thermal satellite data

Abstract Large volcanic explosions pose a severe risk to life and cargo by injecting ash into local and international air traffic routes. Prior to exploding, Bezymianny (Kamchatka) commonly shows an increase in lava extrusion rate, which can be detected by satellites as an increase in thermal radiance. Here we present the first method of forecasting explosive eruptions based solely on satellite data. A pattern recognition algorithm using Advanced Very High Resolution Radiometer (AVHRR) data has been developed based on known precursory trends of increasing radiance prior to 19 explosions at Bezymianny Volcano in 1993–2008. The algorithm retrospectively forecasts 89% of the explosions (100% of the explosions that show precursory increases in thermal radiance), with 71% of alerts issued in the 30 days beforehand. The method also provides the probability of an explosion occurring within a given number of days after an alert is triggered by the algorithm. When applied to independent data, the algorithm correctly provided alerts before the 16 December 2009, 31 May 2010 and 13 April 2011 explosions.

Temporal, probabilistic mapping of ash clouds using wind field stochastic variability and uncertain eruption source parameters: Example of the 14 April 2010 Eyjafjallajökull eruption

Uncertainty in predictions from a model of volcanic ash transport in the atmosphere arises from uncertainty in both eruption source parameters and the model wind field. In a previous contribution, we analyzed the probability of ash cloud presence using weighted samples of volcanic ash transport and dispersal model runs and a reanalysis wind field to propagate uncertainty in eruption source parameters alone. In this contribution, the probabilistic modeling is extended by using ensemble forecast wind fields as well as uncertain source parameters. The impact on ash transport of variability in wind fields due to unresolved scales of motion as well as model physics uncertainty is also explored. We have therefore generated a weighted, probabilistic forecast of volcanic ash transport with only a priori information, exploring uncertainty in both the wind field and the volcanic source.