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In permafrost valleys, artesian wells often penetrate highly pressurized aquifers beneath confining subpermafrost. These wells must be quickly brought under control at installation, and heated through their operational life to prevent them from freezing in winter. However, water breakthrough of the permafrost barrier around the casing results in loss of control of the well. Seepage icing impacts to property and infrastructure can be catastrophic, and the costs to regain well control and mitigate damages high. Methods to regain control of artesian wells in permafrost are few and risky. A new method, cryogenic freezeback, was successfully used to mitigate an uncontrolled artesian well in a permafrost valley north of Fairbanks, Alaska. This paper documents the freezeback method, seepage and thermal analyses, and well abandonment and retrofit. Thermal monitoring results are presented to demonstrate the long-term effectiveness of the method.

To elucidate the early stages of heave, settlement and fracture of intact frost-susceptible rock by temperature cycling above and below 0 °C, two physical modelling experiments were performed on 10 rectangular blocks 450 mm high of fine-grained, soft limestone. One experiment simulated 21 cycles of bidirectional freezing (upward and downward) of an active layer above permafrost, and the other simulated 26 cycles of unidirectional freezing (downward) of a seasonally frozen bedrock in a non-permafrost region. Heave and settlement of the top of the blocks were monitored in relation to rock temperature and unfrozen water content, which ranged from almost dry to almost saturated. In the bidirectional freezing experiment, heave of the wettest block initially occurred abruptly at the onset of freezing periods and gradually during thawing periods (summer heave). After the crossing of a threshold marked by the appearance of a macrocrack in the upper layer of permafrost, summer heave increased by an order of magnitude as segregated ice accumulated incrementally in macrocracks, interrupted episodically by abrupt settlement that coincided with unusually high air temperatures. In the unidirectional freezing experiment, the wet blocks heaved during freezing periods and settled during thawing periods, whereas the driest blocks showed the opposite behaviour. The two wettest blocks settled progressively during the first 15 freeze-thaw cycles, before starting to heave progressively as macrocracks developed. Four processes, operating singly or in combination in the blocks account for their heave and settlement: (1) thermal expansion and contraction caused heave and settlement when little or no water-ice phase change was involved; (2) volumetric expansion of water freezing in situ caused short bursts of heave of the outer millimetres of wet rock; (3) ice segregation deeper in the blocks caused sustained heave during thawing and freezing periods; and (4) freeze-thaw cycling caused consolidation and settlement of wet blocks prior to macrocracking in the unidirectional freezing experiment. Rock fracture developed by growth of segregated ice in microcracks and macrocracks at depths determined by the freezing regime. Overall, the heave, settlement and fracture behaviour of the limestone is similar to that of frost-susceptible soil.

Volcanic ash clouds are a natural hazard that poses direct threats to aviation safety. Many volcanic ash transport and dispersion (VATD) models have been developed to forecast trajectories of volcanic ash clouds and to plan safety measures in the events of eruptions. Predictions based on these models are heavily dependent on accuracy of wind fields and initial parameters of ash plumes. However, these data of high accuracy are usually difficult to obtain, leading to possible inaccurate predictions of ash clouds trajectories using VATD models. In this study, a new method is developed to predict volcanic ash transport. In contrast to many existing VATD models that simulate the evolution of volcanic ash clouds, the new method focuses on the overall properties of the wind field in which volcanic particles are transported and correlates particle motion to the attracting structures that dictate the transport. As demonstrated in the study of Eyjafjallajokull eruption in Iceland in April, 2010, these structures act as attractors in the atmosphere towards which volcanic ash particles are transported. These attracting structures are associated with hazard zones with high concentrations of volcanic ash. The advantages of the method are that the attracting structures are independent of particle source parameters and are less prone to inaccuracy in the wind field than particle trajectories. The new approach provides the hazard maps of volcanic ash, and is able to help improve long-term predictions and to plan flight route diversions and ground evacuations.

Over the last 40 years, there have been numerous volcanic eruptions across the North Pacific (NOPAC) region that posed a potential threat to both local communities and transcontinental aircraft. The ability to detect these volcanic clouds using satellite remote sensing and predict their movement by dispersion modeling is a major component of hazard mitigation. The Puff volcanic ash transport and dispersion model, used by the Alaska Volcano Observatory, was used to illustrate the impact that these volcanic ash clouds have made across the NOPAC and entire Polar region over the past 40 years. Nearly, 400 separate ash clouds were analyzed that were either reported or detected to have reached above 6 km (20,000 ft) above sea level, an average of one ash cloud every 1.25 months. Particular events showed that ash clouds can be tracked from Alaska to Greenland (Crater Peak, Mount Spurr in 1992), from Kamchatka to Alaska (Kluvicheskoi Volcano in 1994), from Alaska to California (Mount Cleveland Volcano in 2001) and from multiple events within 1 day (Mount Augustine Volcano in 2006). This study showed the vast number of events that have impacted this Polar region and how tracking them is useful for hazard mitigation.

In this paper we present a new statistical approach which provides tephra deposit load and ash concentration using PUFF, a Lagrangian model widely used to forecast volcanic ash dispersal during volcanic crisis. We perform a parametric study in order to analyze the influence of each input parameter on model outputs. For this test, we simulate two eruptive scenarios similar to the 2001 ( Scenario 1 ) and 1998 ( Scenario 2 ) Etna eruptions using high resolution weather data and a domain of 170 × 170 km. Results show that for both scenarios, we are able to calculate the tephra deposit load and ash concentration but the use of millions of particles is required. Specifically, up to 33 and 220 millions of particles were necessary to accurately predict the tephra deposit and ash concentration in air, respectively. This is approximately two orders of magnitude larger than the values typically considered running PUFF. The parametric study shows that the horizontal diffusion coefficient, the time step of the simulations, the topography and the standard deviation of the particle distribution greatly affect the model outputs. We also validate the model by best-fit procedures. Results show a good comparison between field data of the 2001 Etna eruption and PUFF simulations, being inside 5 and 1/5 times the observed data, comparable with results of Eulerian models. This work will allow to reliably outline the areas of contaminated airspace using PUFF or any other Lagrangian model in order to define the No Fly Zone and ensure the safety aviation operations as required after the Eyjafjallajokull eruption.

There are over 100 active volcanoes in the North Pacific (NOPAC) region, most of which are located in sparsely populated areas. Dispersion models play an important role in forecasting the movement of volcanic ash clouds by complementing both remote sensing data and visual observations from the ground and aircraft. Puff is a three-dimensional dispersion model, primarily designed for forecasting volcanic ash dispersion, used by the Alaska Volcano Observatory and other agencies. Since early 2007, the model is in an automated mode to predict the movement of airborne volcanic ash at multiple elevated alert status volcanoes worldwide to provide immediate information when an eruption occurs. Twelve of the predictions are within the NOPAC region, nine more within the southern section of the Pacific ring of fire and the others are in Europe and the Caribbean. Model forecasts are made for initial ash plumes ranging from 4 to 20 km altitude above sea level and for a 24-h forecast period. This information is made available via the Puff model website. Model results can be displayed in Virtual Globes for three-dimensional visualization. Here, we show operational Puff predictions in two and three-dimensions in Google Earth®, both as iso-surfaces and particles, and study past eruptions to illustrate the capabilities that the Virtual Globes can provide. In addition, we show the opportunity that Google Maps® provides in displaying Puff operational predictions via an application programming web interface and how radiosonde data (vertical soundings) and numerical weather prediction vertical profiles can be displayed in Virtual Globes for assisting in estimating ash cloud heights.

A technique has been developed that uses Puff, a volcanic ash transport and dispersion (VATD) model, to forecast the relative exposure of aircraft and ground facilities to ash from a volcanic eruption. VATD models couple numerical weather prediction (NWP) data with physical descriptions of the initial eruptive plume, atmospheric dispersion, and settling of ash particles. Three distinct examples of variations on the technique are given using ERA-40 archived reanalysis NWP data. The Feb. 2000 NASA DC-8 event involving an eruption of Hekla volcano, Iceland is first used for analyzing a single flight. Results corroborate previous analyses that conclude the aircraft did encounter a diffuse cloud of volcanic origin, and indicate exposure within a factor of 10 compared to measurements made on the flight. The sensitivity of the technique to dispersion physics is demonstrated. The Feb. 2001 eruption of Mt. Cleveland, Alaska is used as a second example to demonstrate how this technique can be utilized to quickly assess the potential exposure of a multitude of aircraft during and soon after an event. Using flight tracking data from over 40,000 routes over three days, several flights that may have encountered low concentrations of ash were identified, and the exposure calculated. Relative changes in the quantity of exposure when the eruption duration is varied are discussed, and no clear trend is evident as the exposure increased for some flights and decreased for others. A third application of this technique is demonstrated by forecasting the near-surface airborne concentrations of ash that the cities of Yakima Washington, Boise Idaho, and Kelowna British Columbia might have experienced from an eruption of Mt. St. Helens anytime during the year 2000. Results indicate that proximity to the source does not accurately determine the potential hazard. Although an eruption did not occur during this time, the results serve as a demonstration of how existing cities or potential locations of research facilities or military bases can be assessed for susceptibility to hazardous and unhealthy concentrations of ash and other volcanic gases.

Satellite data were the primary source of information for the eruption of Mt. Cleveland, Alaska on 19 February, and 11 and 19 March 2001. Multiple data sets were used pre-, syn- and post-eruption to mitigate the hazard and determine an eruption chronology. The 19 February eruption was the largest of the three, resulting in a volcanic cloud that formed an arc over 1000 km long, moved to the NE across Alaska and was tracked using satellite data over more than a 50-h period. The volcanic cloud was “concurrently” detected on the GOES, AVHRR and MODIS data at various times and their respective signals compared. All three sensors detected a cloud that had a very similar shape and position but there were differences in their areal extent and internal structural detail. GOES data showed the largest volcanic cloud in terms of area, probably due to its oblique geometry. MODIS bands 31 and 32, which are comparable to GOES and AVHRR thermal infrared wavelengths, were the least effective single channels at detecting the volcanic cloud of those investigated (MODIS bands 28, 29, 31 and 32). MODIS bands 28 and 29 detected the largest volcanic clouds that could easily be distinguished from weather clouds. Of the split-window data, MODIS bands 29 minus band 32 detected the largest cloud, but the band 31 minus band 32 data showed the volcanic cloud with the most internal structural detail. The Puff tracking model accurately tracked the movement, and predicted the extent and shape of this complex cloud even into areas beyond satellite detection. Numerous thermal anomalies were also observed during the eruption on the twice-daily AVHRR data and the high spatial-resolution Landsat data. The high-resolution Radarsat data showed that the AVHRR thermal anomalies were due to lava and debris flow features and a newly formed fan along the west coast of the island. Field observations and images from a hand-held Forward Looking Infrared Radiometer (FLIR) showed that the flow features were ′a′a lava, debris flows and a warm debris fan along the west coast. Real-time satellite data were the primary tool used to monitor the eruption, track changes and to mitigate hazards. High-resolution data, even though coverage is infrequent, were critical in helping to identify volcanic processes and to compile an eruption chronology.

A volcanic eruption is an amazing event. The associated earthquakes, lava flows, and ash clouds are both intriguing yet potentially dangerous features that can cause enormous changes to the landscape, damage to infrastructure, and even loss of life. Obviously, the ability to predict the occurrence and dynamics of an eruption is both desirable and necessary for public safety. Despite many advances in the understanding of what leads to volcanic eruptions, predicting the commencement of an eruption remains difficult. Once an eruption has begun, predicting its behavior is equally if not more important in order to minimize the potential financial and human costs.

International audience; The volumetric expansion of freezing pore water is widely assumed to be a major cause of rock fracture in cold humid regions. Data from experiments simulating natural freezing regimes indicate that bedrock fracture results instead from ice segregation. Fracture depth and timing are also numerically simulated by coupling heat and mass transfer with a fracture model. The depth and geometry of fractures match those in Arctic permafrost and ice-age weathering profiles. This agreement supports a conceptual model in which ice segregation in near-surface permafrost leads progressively to rock fracture and heave, whereas permafrost degradation leads episodically to melt of segregated ice and rock settlement