Geology topics

Hans F. Schwaiger

Publications and source records attributed to Hans F. Schwaiger.

7 recordsLinked to original sources

USGS2024

2021 Volcanic activity in Alaska and the Commonwealth of the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory

In 2021, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 15 volcanic centers in Alaska and the Commonwealth of the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; an explosion at Great Sitkin Volcano followed by the eruption of a thick lava flow that filled and overflowed the summit crater; weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano; and a short-lived eruption at Mount Veniaminof that produced ash emissions from an intracaldera cone, as well as lava flows confined to a melt pit in the ice mantling the cone’s flank. Mount Cleveland had a period of unrest, but no eruptive activity took place there. Anomalous seismicity was also detected at Atka volcanic complex, Mount Gareloi, and Davidof volcano. New warm springs opened and deposited mud at the summit and north base of Shrub mud volcano. Other activity of note in Alaska consisted of large ice and rock avalanches at Iliamna Volcano and Mount Spurr, ash resuspension events at Mount Katmai and Aniakchak Crater, and anomalous deformation at Mount Okmok that was consistent with a shallow intrusion of magma. In the Commonwealth of the Northern Marianas Islands, a brief, ash-producing eruption occurred at Mount Pagan.

Alaska↗

USGS2020

A probabilistic assessment of tephra-fall hazards at Hanford, Washington, from a future eruption of Mount St. Helens

Hanford, Washington (USA) is the construction site of a multi-billion-dollar high-level nuclear waste treatment facility. This site lies 200 kilometers (km) east of Mount St. Helens (MSH), the most active volcano in the contiguous United States. Tephra from a future MSH eruption could pose a hazard to the air intake and filtration systems at this plant. In this report, we present a probabilistic estimate of the amount of tephra that could fall, and the concentrations of airborne ash that could occur at the Hanford Site during a future eruption. Mount St. Helens has produced four large explosive eruptions in approximately the past 500 years, suggesting that its annual probability of eruption ( P 1 ) is roughly 4/500=0.008. Assuming that a large eruption occurs, we calculate the probability ( P 3|1 ) of a given fall deposit thickness or airborne concentration at Hanford by running about 10,000 simulations of ash-producing eruptions using the atmospheric transport model Ash3d. In each simulation, we calculate the pattern of tephra dispersal, deposit thickness at Hanford, and airborne ash concentration at ground level. As input for each simulation, we choose meteorological conditions from a randomly chosen time in the historical record between 1980 and 2010, using data from the European Centre for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim model. The volume (dense-rock equivalent) of each simulated eruption is randomly chosen from a uniform probability distribution on a log scale from the range of magma volumes (0.008–2.3 cubic kilometers [km 3 ]) estimated for late Holocene eruptions at MSH. Plume heights and durations of each eruption are chosen using empirical correlations between volume, height, and eruption rate, which account for the fact that larger eruptions have higher plumes and last longer. We construct summary tables of final deposit thickness ( T ), maximum ground-level airborne concentration ( C max ), and average ground-level airborne concentration ( C avg ) during tephra-fall for each run. Each table is sorted and ranked by decreasing value of T , C max , or C avg . Conditional probabilities ( P 3|1 ) are derived by dividing rank by n+1, where n is the total number of successful runs. For example, a deposit thickness of 5.10 centimeters (cm) from run 446 is ranked 123 of 9,785 successful runs, yielding P 3|1 =123/9,786=0.01257. Its annual probability is P = P 1 · P 3|1 =0.008×0.01257=0.000101. By interpolation, the deposit thickness ( T 10k ) having an annual probability of 1 in 10,000 ( P = 0.0001) is 5.11 cm. Analogous concentration values are C max,10k =3,819 and C avg,10k =1,513 milligrams per cubic meter (mg/m 3 ), respectively. Independent calculations using the known mass accumulation rate of the deposit (=0.001–0.006 kilograms per square meter per second [kg/m 2 /s]), aggregate fall velocities ( u =0.3–0.8 meters per second [m/s]), and the simple formula , yield similar results, although highly variable fall velocities add significant uncertainty. This formula implies that deposit accumulation rates of millimeters (mm) to greater than 1 cm per hour, which are not uncommon during heavy ash fall, are associated with airborne concentrations of 10 2 –10 3 milligrams per cubic meter (mg/m 3 ). These concentrations are much higher than published measurements (10 -3 –10 1 mg/m 3 ), which record only suspended particles sampled in sheltered areas. During heavy ashfall, most fine ash falls as aggregates. Whether such aggregates will be ingested into air ducts will depend on the aggregate size and fall rate, the fragility of the aggregates, the air duct geometry, intake velocity, and other factors.

Washington↗

USGS2015

Hail formation triggers rapid ash aggregation in volcanic plumes

During explosive eruptions, airborne particles collide and stick together, accelerating the fallout of volcanic ash and climate-forcing aerosols. This aggregation process remains a major source of uncertainty both in ash dispersal forecasting and interpretation of eruptions from the geological record. Here we illuminate the mechanisms and timescales of particle aggregation from a well-characterized ‘wet’ eruption. The 2009 eruption of Redoubt Volcano in Alaska incorporated water from the surface (in this case, a glacier), which is a common occurrence during explosive volcanism worldwide. Observations from C-band weather radar, fall deposits, and numerical modeling demonstrate that volcanic hail formed rapidly in the eruption plume, leading to mixed-phase aggregation of ~95% of the fine ash and stripping much of the cloud out of the atmosphere within 30 minutes. Based on these findings, we propose a mechanism of hail-like aggregation that contributes to the anomalously rapid fallout of fine ash and the occurrence of concentrically-layered aggregates in volcanic deposits.

Alaska↗

USGS2013

User’s guide and reference to Ash3d—A three-dimensional model for Eulerian atmospheric tephra transport and deposition

Ash3d is a three-dimensional Eulerian atmospheric model for tephra transport, dispersal, and deposition to study and forecast hazards of volcanic ash clouds and tephra fall. In this report, we explain how to set up simulations using a web interface, and how to view and interpret model output. We also summarize the architecture of the model and some of its properties.

Open-File Report↗

USGS2013

A Bayesian method to rank different model forecasts of the same volcanic ash cloud: Chapter 24

Volcanic eruptions often spew fine ash high into the atmosphere, where it is carried downwind, forming long ash clouds that disrupt air traffic and pose a hazard to air travel. To mitigate such hazards, the community studying ash hazards must assess risk of ash ingestion for any flight path and provide robust and accurate forecasts of volcanic ash dispersal. We provide a quantitative and objective method to evaluate the efficacy of ash dispersal estimates from different models, using Bayes theorem to assess the predictions that each model makes about ash dispersal. We incorporate model and measurement uncertainty and produce a posterior probability for model input parameters. The integral of the posterior over all possible combinations of model inputs determines the evidence for each model and is used to compare models. We compare two different types of transport models, an Eulerian model (Ash3d) and a Langrangian model (PUFF), as applied to the 2010 eruptions of Eyjafjallajökull volcano in Iceland. The evidence for each model benefits from common physical characteristics of ash dispersal from an eruption column and provides a measure of how well each model forecasts cloud transport. Given the complexity of the wind fields, we find that the differences between these models depend upon the differences in the way the models disperse ash into the wind from the source plume. With continued observation, the accuracy of the estimates made by each model increases, increasing the efficacy of each model’s ability to simulate ash dispersal.

Geophysical Monograph↗

USGS2013

Injection, transport, and deposition of tephra during event 5 at Redoubt Volcano, 23 March, 2009

Among the events of the 2009 eruption at Redoubt Volcano, Alaska, event 5 was the best documented by radar, satellite imagery, and deposit mapping. We use the new Eulerian tephra transport model Ash3d to simulate transport and deposition of event 5 tephra at distances up to 350 km. The eruption, which started at about 1230 UTC on 23 March, 2009, sent a plume from the vent elevation (estimated at 2.3 ± 0.1 km above sea level or a.s.l.) to about 16 ± 2 km above sea level in 5 min. The plume was a few kilometers higher than would be expected for the estimated average mass eruption rate and atmospheric conditions, possibly due to release of most of the eruptive mass in the first half of the 20-minute event. The eruption injected tephra into a wind field of high shear, with weak easterly winds below ~ 3 km elevation, strong southerly winds at 6–10 km and weak westerlies above ~ 16 km. Model simulations in this wind field predicted development of a northward-migrating inverted “v”-shaped cloud with a southwest-trending arm at a few kilometers elevation, which was not visible in IR satellite images due to cloud cover, and a southeast-trending arm at > 10 km elevation that was clearly visible. Simulations also predicted a deposit distribution that strongly depended on plume height: a plume height below 15 km predicted ash deposits that were located west of those mapped, whereas good agreement was reached with a modeled plume height of 15–18 km. Field sampling of the deposit found it to contain abundant tephra aggregates, which accelerated the removal of tephra from the atmosphere. We were able to reasonably approximate the effect of aggregation on the deposit mass distribution by two methods: (1) adjusting the grain-size distribution, taking the erupted mass < = 0.063 mm in diameter and distributing it evenly into bins of coarser size; and (2) moving 80–90% of the mass < = 0.063 mm into a single particle bin ranging in size from 0.25 to 1 mm. These methods produced an area inside the 100 g m − 2 isomass lines that was within a few tens of percent of mapped area; however they under-predicted deposit mass at very proximal (< 50 km) and very distal (> 250 km) locations. Modeled grain-size distributions at sample locations are also generally coarser than observed. The mismatch may result from a combination of limitations in field sampling, approximations inherent in the model, errors in the numerical wind field, and aggregation of particles larger than 0.063 mm.

Alaska↗

USGS2012

Ash3d: A finite-volume, conservative numerical model for ash transport and tephra deposition

We develop a transient, 3-D Eulerian model (Ash3d) to predict airborne volcanic ash concentration and tephra deposition during volcanic eruptions. This model simulates downwind advection, turbulent diffusion, and settling of ash injected into the atmosphere by a volcanic eruption column. Ash advection is calculated using time-varying pre-existing wind data and a robust, high-order, finite-volume method. Our routine is mass-conservative and uses the coordinate system of the wind data, either a Cartesian system local to the volcano or a global spherical system for the Earth. Volcanic ash is specified with an arbitrary number of grain sizes, which affects the fall velocity, distribution and duration of transport. Above the source volcano, the vertical mass distribution with elevation is calculated using a Suzuki distribution for a given plume height, eruptive volume, and eruption duration. Multiple eruptions separated in time may be included in a single simulation. We test the model using analytical solutions for transport. Comparisons of the predicted and observed ash distributions for the 18 August 1992 eruption of Mt. Spurr in Alaska demonstrate to the efficacy and efficiency of the routine.

Journal of Geophysical Research↗