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Hannah R. Dietterich

Publications and source records attributed to Hannah R. Dietterich.

At least 19 recordsLinked to original sources
USGS2026

Satellite observations, plume dynamics and volcanic lightning from the 2023 eruption of Shishaldin Volcano, Alaska

The 2023 eruption of Shishaldin Volcano, Alaska, provided an unusually well-monitored example of a “dry” (non-hydromagmatic) explosive basaltic eruption with sustained, buoyant plumes, and pyroclastic density currents. Here, we examine the 13 basaltic paroxysms that occurred between July and November 2023 using geostationary satellite observations, plume modeling, and volcanic lightning detection to characterize mass eruption rates and plume dynamics. We show that seven volcanic plumes reached 10–14 km above sea level, sustained for minutes to tens of minutes. Translating plume heights into mass eruption rates, using both empirical scaling and one-dimensional plume modeling, gives peak rates of 10 4 –10 7 kg·s −1 and a total erupted volume of ~ 0.05 km 3 (dense rock equivalent) across the eruption sequence. Despite the moderate intensity of this eruption, automated satellite detection algorithms generally failed to detect volcanic ash. We attribute this persistent challenge to the fines-poor grain size distribution of the eruption. Volcanic lightning provides an additional window into the eruption dynamics. A total of 491 volcanic lightning flashes were detected, concentrated during seven of the most energetic phases. The timing of this electrical activity suggests that lightning only became detectable by the global lightning monitoring network when plumes ascended to atmospheric levels favorable for ice formation. Based on the available data, we infer that the low fine ash content of the plumes, combined with minimal water and ice, led to the overall modest rates of lightning (1–13 flashes per minute) compared to “wet” eruptions of similar intensity. These results show that combining satellite observations, plume modeling, and lightning detection provides a valuable timeline of eruptive intensity, even for “dry” basaltic eruptions in remote locations.

Alaska↗
USGS2026

Volcanic activity in Alaska and the Northern Mariana Islands in 2024—Summary of events and response of the Alaska Volcano Observatory

In 2024, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 10 volcanic centers in Alaska and the Northern Mariana Islands. Eruptive activity in Alaska consisted of the eruption of a thick lava flow within the summit crater at Great Sitkin Volcano throughout the entire year and a small explosion at Atka volcano in March. Uplift, elevated seismicity, and the redevelopment of a summit meltwater lake was detected at Mount Spurr. A brief seismic swarm occurred at Mount Gareloi, while at Trident Volcano, Shishaldin Volcano, and Kanaga Volcano, seismicity declined to background levels, marking the end of unrest and volcanic activity for those volcanoes. Other activity that the Alaska Volcano Observatory responded to in 2024 included ash resuspension events at Mount Katmai, and a period of unrest at Mount Cleveland. Finally, at Ahyi seamount, in the Commonwealth of the Northern Marianas Islands, a plume of discolored ocean water observed in satellite data indicated underwater eruptive activity there.

Alaska↗
USGS2026

Growth and erosion of volcanic islands since 1963 analyzed by multi-sensor satellite data and historical records

Most volcanic activity is taking place in the oceans. Depending on water depth, eruption recurrence times and volumes, a new island can form. The power of erosional forces, the type of erupted material and the efficiency of secondary processes determine the island’s lifetime. Since the famous eruption of Surtsey (Iceland) in 1963, at least another 23 volcanic islands have appeared. Some islands remained intact for years or decades, whereas others disappeared within just weeks or months. In this study, we analyzed satellite data to determine growth and erosion rates of the volcanic edifices related to these 24 islands. We combined multi-sensor (optical, thermal, radar) satellite data time series with information from literature and the Global Volcanism Program database. We developed a comprehensive dataset, including 19 parameters, on the islands’ lifetime, shape, area, volume, eruption style and duration, environmental conditions, development of sedimentary deposits, and the geomorphic evolution of the island over time. Our dataset is available in a database format. This database allows us to test eight hypotheses about factors influencing the islands’ lifetime. Our results show that instead of one single critical factor, a combination of different factors influences the life history of volcanic islands. For instance, we show that larger islands do not necessarily live longer. The mechanical properties of the eruption products affect the island’s structural integrity. Irrespective of the material, a minimum initial area of around 50,000 m 2 seems to be a reasonable threshold to give the island a chance to exist longer.

Bulletin of Volcanology↗
USGS2026

Lava flow emplacement dynamics during the Mauna Loa 2022 eruption

On November 27, 2022, Mauna Loa erupted for the first time in 38 years, threatening a major highway on the Island of Hawaiʻi. The opening phases of the eruption were focused at the volcano’s summit, before magma propagated into the upper Northeast Rift Zone to erupt at four fissure segments that localized at fissure 3 by December 2. This fissure produced a braided ʻaʻā flow that traversed the steep north flank at >1 km/hr, before slowing as slopes shallowed. Effusion rates peaked in the early hours of the eruption and then steadied at around 100 m3/s at fissure 3. Lava effusion became variable and dropped abruptly on December 7. The eruption ended on December 10 with a final flow length of 18 km. We tracked lava effusion rates, flow advance, channel dynamics, and 3-D flow field development with field and remote sensing observations throughout this eruption to inform lava flow forecasts and investigate ʻaʻā flow dynamics. Flows were mapped using optical and infrared imagery, uncrewed aircraft systems, and airborne single-pass InSAR. Video- and time-lapse cameras were deployed to record lava fountaining and channelized flow at frequent intervals, supplemented by field measurements. Analyses of these data built a detailed record of effusion rates and associated flow dynamics. We combine these high frequency observations of the proximal flow field with broader thermal and 3-D mapping of the evolution in the medial and distal flow field to examine ʻaʻā flow dynamics from source to toe. Within the steep, medial section of the flow field, channel blockages caused back-ups and overflows as the eruption progressed. Disrupted lava supply to the distal flow field and low slopes resulted in stalling, widening, and thickening at the toe. Overall, 142 million m3 of lava covered 36 km2 over 13 days. Our results inform the controls on development and evolution of lava flow fields, with implications for forecasting primary flow advance and secondary flow field growth.

Hawaii↗
USGS2026

Syn-emplacement crystallization of Mauna Loa 2022 lava flows, Hawaiʻi

Advances in near-real-time forecasts of lava flow advance rates, final travel distance, and areal coverage are transforming hazard response, particularly in locations such as Hawaiʻi that experience frequent lava flow activity. The most severe threats are posed by rapidly advancing channelized ʻaʻā flows, such as those that characterized the November–December 2022 eruption of Mauna Loa volcano. To constrain rheological inputs to flow forecasting models during the eruption, samples were collected from the two most persistent lava flows and rapidly assessed for downflow changes in crystallinity and vesicularity. As observed in other channelized lava flows, the lava lost bubbles and cooled during transport, with consequent increases in groundmass crystallinity and bulk viscosity. Lava rheology, however, is controlled by more than bulk bubble and crystal contents; also important are the melt viscosity and the size and shape distributions of bubbles and crystals. Here we further interrogate the sample suite to document a down flow increase of almost two orders of magnitude in melt viscosity alone (a function of cooling combined with compositional change) and assess the extreme anisotropy of the plagioclase crystals as a function of size and flow type (pāhoehoe or ʻaʻā). When combined with previous studies, these data constrain observed relations between surface morphology, flow temperature and crystal content and indicate that remote assessments of morphological transitions and core lava temperature could be used for channel-wide monitoring of rheological evolution for assimilation into flow models.

Hawaii↗
USGS2026

Dynamics and evolution of the Kı̄lauea lower East Rift Zone 2018 fissure 8 lava flow and implications for multiphase magma properties

The 2018 Kı̄lauea lower East Rift Zone (KLERZ) eruption was one of the most voluminous eruptions on the Island of Hawai’i in the past 200 years, leading to major disruption and destroying over 700 homes and structures. The majority of the erupted magma was emitted as a lava flow from Ahu’ailā’au (fissure 8), which was active from late May to early August. To better understand the evolution of long-lived channelized lava flows, we examined the evolution of velocity, texture, and inferred rheology of the fissure 8 lava in space and time. We quantified lava flow surface velocities using particle image velocimetry in more than 200 aerial videos that span the lava flow duration and length. Velocity measurements were analyzed together with vesicularity and crystallinity measurements from 9 co-located post-eruptive field samples to understand the textural evolution of this flow and its impact on lava rheology and flow velocity. The fissure 8 flow was highly vesicular, with 79%–88% vesicularity at the vent, decreasing to 16%–26% vesicularity 12.5 km from the vent. The volume fraction occupied by crystals 50 in size increased from 6% at the vent to about 18% at 12.5 km downstream. We find that the effective flow viscosity increased at a quadratic rate with distance. Using experimentally determined liquid viscosity and applying established models to account for the effect of crystals and bubbles, we attribute this increase primarily to textural evolution driven initially by the near-vent loss of deformable bubbles and later by cooling and crystal growth. We demonstrate the importance of accounting for the evolution of vesicularity and the role of vesicles by showing that utilizing this capability in the open-source thermo-rheological lava flow propagation model PyFLOWGO allows for more accurate predictions of the observed flow velocities. Our modeling results suggest that small bubbles behaving rigidly are required to simulate the observed flow length, speed, and viscosities. Flow velocities of the fissure 8 lava also varied with time, driven by near-daily collapse events of the summit caldera. Temporal velocity changes were characterized by a period of steep acceleration, with the volumetric flux peaking around 4 h after a caldera collapse, followed by a period of gradual deceleration lasting up to 40 h or until the next collapse event. We use this temporal behavior to estimate the compressibility of the magma inside the plumbing system between the summit reservoir and the lower East Rift Zone. Overall, quantifying the spatial and temporal evolution of the KLERZ eruption provides information about magma and lava properties that can inform predictive modeling and hazard assessment during an eruption.

Hawaii↗
USGS2026

Seismoacoustic analysis of possible vent-drying sequence during phreatomagmatic activity on 13 July 2021 at Semisopochnoi Island, Alaska

Pulse-like infrasound accompanying volcanic phenomena sometimes suggests interaction with water at the crater. We focus on pulse-like infrasound observed at Semisopochnoi Island in the Aleutian Arc, Alaska, during the phreatomagmatic activity on 13 July 2021. Overflight observations confirmed a small, ephemeral water lake on the crater floor in June 2021. We examined temporal variations in pulse-like infrasound activity, seismoacoustic amplitudes, and spectral characteristics. Pulse-like infrasound activity gradually became intermittent and waned at the end of the day. Seismic-infrasonic amplitude ratio decreased, while the infrasound frequency index increased with time. These results suggest less interaction with water due to drying out of the vent. Satellite observations also show increased ground surface temperatures inside the crater from 13 to 14 July. Numerical simulation suggests a significant topographic effect on the waveform and spectrum of the pulse-like infrasound, which should be considered before interpreting the source process.

Alaska↗
USGS2025

Growth of a passive lava lake during the 2020–2021 eruption of Kīlauea volcano, Hawaii

We investigate the growth of a passive lava lake in Halemaʻumaʻu crater during the December 2020 to May 2021 eruption of Kīlauea volcano, Hawaii. Fed by vents above their surfaces, the formation of passive lava lakes in topographic lows is an important process in the growth of basaltic volcanoes. We captured visible and thermal images during 17 helicopter overflights and applied structure-from-motion photogrammetry to create digital elevation models and orthomosaics of Halemaʻumaʻu. These data products allowed us to track eruptive activity and processes. The bulk time-averaged discharge rate (TADR) in December 2020 initially exceeded 100 m 3 s –1 but decreased to < 10 m 3 s –1 within seven days. By February 2021, TADR was < 2 m 3 s –1 and continued to decrease until the eruption ended in May 2021. A total volume of 40.6 ± 0.5 × 10 6 m 3 of lava filled Halemaʻumaʻu to a depth of 225 m. As TADR decreased, the lake progressively developed an immobile, solidified crust, beginning with surfaces farthest from the vent. This immobile surface rose endogenously, whereas exogenous surface rise occurred near the vent. Eruptive activity at a vent ended when the level of the lake surface exceeded that of the vent, which we attribute to the effects of lava sitting above the vent on ascending magma. Regular helicopter overflights, combined with field observations and the extensive monitoring network at Kīlauea, generated an unprecedented density of observations that provide insights into the emplacement of passive lava lakes and how these eruptions wane and end.

Hawaii↗
USGS2025

Case study of deep learning image segmentation for the purposes of rapid 2D petrographic analysis in volcanic rocks

Automation using deep learning methods is a useful alternative to manual methods of petrographic segmentation, but often requires user familiarity with coding and/or algorithms. We examine the Dragonfly TM program's deep learning tools for application by users with a variety of skill levels as a method for petrographic image segmentation. An image processing methodology, bimodal image stacking, was created for low-input-data, high-efficacy training of models which can then be applied to varied samples. Using backscatter electron images we show that the resulting model segmentations agree with manual segmentation total and modal crystallinity values within 5%, and calculated plagioclase crystal size distribution (CSD) values within 2σ, despite limitations in discriminating mafic phases. Model creation and training takes <24 hours, 1–3 hours of which are supervised, and the resultant model can then be applied to new uncharacterized samples in <15 minutes per image. This allows for non-experts to create and utilize deep learning models to segment images of variable brightness and texture, at low user-time cost and resulting in size and shape data which are within uncertainty of manual segmentation. While some limitations are noted (for example, sieve-textured phases may need manual correction, and different minerals with similar BSE intensity may not be resolved as separate phases), this methodology can be utilized for general application of models to wide ranges of volcanic crystalline and bubble textures, and to create a library of models for rapid petrological analysis during volcanic eruptions.

Alaska↗
USGS2025

Catastrophic lava flow levee failure: Precursors, processes, and implications

During an effusive eruption crisis the initial advance of a lava flow is typically the primary focus of model forecasts and hazard management efforts. Flow branching and lateral expansion of lava flows can pose significant dangers within evolving flow fields throughout the duration of an eruption and are an underappreciated hazard. We use field monitoring, infrasound, time lapse imagery, and lidar data collected during the 2018 lower East Rift Zone eruption of Kīlauea (Hawai‘i) to track the origins, progression, and implications of a flow branching event caused by catastrophic levee failure. Our analyses show that surges in effusion rate, rheologic transitions between pāhoehoe and ‘a‘ā flow regimes, slope-breaks, pre-existing topographic highs, and the structure of perched levee walls all played a role in the failure of the levee and subsequent re-routing of the lava flow. Failure of perched lava structures leads to an acutely hazardous situation because lava impounded by the structure can rapidly inundate the landscape. This is the first time a levee failure event has been observed in such detail with numerous monitoring techniques; this unprecedented level of observation provides quantifiable insights into levee failure processes that have important implications for hazard mitigation and an improved understanding of lava flow emplacement dynamics.

Hawaii↗
USGS2025

2022 Volcanic activity in Alaska and the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory

In 2022, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 11 volcanic centers in Alaska and in the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; the eruption of a thick lava flow within the summit crater at Great Sitkin Volcano; and weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano. Uplift and an increase in seismicity were detected at Mount Edgecumbe, a long-dormant volcano in southeastern Alaska. Anomalous seismicity was also detected at three other volcanoes, including Trident Volcano, Takawangha volcano, and Davidof volcano. Other activity documented in 2022 includes ash resuspension events at Mount Katmai and Aniakchak Crater, and Mount Cleveland had a period of unrest, but no eruptive activity took place. In the Commonwealth of the Northern Marianas Islands, hydroacoustic detections and a submarine plume observed in satellite data at Ahyi seamount indicated underwater eruptive activity there.

Alaska↗
USGS2024

Real-time lava flow forecasting during the 2022 Mauna Loa eruption response

On November 27, 2022, Mauna Loa (Hawai‘i) erupted for the first time in 38 years, initially producing lava flows that covered the floor of its summit caldera, Moku‘āweoweo. Over the first 12 h following the summit eruption, four main fissures opened on Mauna Loa’s Northeast Rift Zone, with “fissure 3” quickly becoming the dominant source of lava flows. For the next 12 days, fissure 3 produced a 19-km-long lava flow to the north, crossing the Mauna Loa Weather Observatory access road and coming within 2.8 km of inundating the Daniel K. Inouye Highway (Saddle Road). Within 40 min of fissure 3 opening, inundation modeling efforts had begun. For the duration of the eruption, the computational fluid dynamics model Lava2d was run in real time, using flow front locations and other field observations to make sequential forecast improvements, eventually producing a set of models which accurately predicted the routing and arrival times of lava from fissure 3. These models were used to inform timing estimates of possible future inundation of the Saddle Road. As the eruption progressed, almost 4000 Lava2d models were made using high-performance computing resources, providing critical information on uncertainty in multi-week forecasts. To our knowledge, this was the first ever real-time physics-based ensemble lava flow modeling and forecasting effort. Here, we present a chronology of these real-time efforts, focusing on the successes and limitations of this approach.

Hawaii↗
USGS2024

Tracking surface changes caused by volcanic activity

Introduction Dynamic volcanic landscapes produce various changes at the surface of volcanic edifices. For example, rising magma can induce thermal emissions, formation of ground cracks, and variations in glacier and edifice morphology; volcanic deposits from eruptions can transform the land surface with tephra fall, pyroclastic flows, lava flows and domes, and lahars; and geomorphic changes from landslides and lahars can occur in the absence of unrest or eruption. The best way to detect these changes is with imagery obtained via satellite, aircraft (including unoccupied aircraft systems, or UAS), and ground-based imaging. Rapid advances in imaging technologies have been leveraged by the U.S. Geological Survey (USGS) Volcano Hazards Program to improve the ability to monitor volcanoes. To this end, the guidance outlined here provides a framework for tracking volcanic unrest and the emplacement and evolution of volcanic deposits, further elucidating the processes associated with volcanic eruptions. The techniques currently used include (1) various telemetered and non-telemetered cameras, (2) high-resolution ground-based optical (visible to short-wave infrared wavelengths) and thermal infrared photography, (3) satellite and airborne thermal, optical, and synthetic aperture radar (SAR) imagery, and (4) light detection and ranging (lidar) surveys from airborne and ground-based platforms. Given that similar or overlapping techniques are applied to meet the capabilities listed in this chapter, we first provide an overview of remote sensing techniques. The use of UAS in monitoring surface change is briefly mentioned in this chapter and described in more detail in the dedicated UAS chapter ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–L, 5 p., https://doi.org/10.3133/sir20245062l.">chapter L , this volume; Diefenbach, 2024).

Scientific Investigations Report↗
USGS2024

From field station to forecast: Managing data at the Alaska Volcano Observatory

The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.

Alaska↗
USGS2024

Pulsing in the Ahu‘ailaʻau pond-spillway system during the 2018 Kilauea Eruption: A dynamical systems perspective

During the 2018 K &#x012B; "> ī ī lauea lower East Rift Zone eruption, lava from 24 fissures inundated more than 8000 acres of land, destroying more than 700 structures over three months. Eruptive activity eventually focused at a single vent characterized by a continuously fed lava pond that was drained by a narrow spillway into a much wider, slower channelized flow. The spillway exhibited intervals of ‘pulsing’ behaviour in which the lava depth and velocity were observed to oscillate on time scales of several minutes. At the time, this was attributed to variations in vesiculation originating at depth. Here, we construct a toy fluid dynamical model of the pond–spillway system, and present an alternative hypothesis in which pulsing is generated at the surface, within this system. We posit that the appearance of pulsing is due to a supercritical Hopf bifurcation driven by an increase in the Reynolds number. Asymptotics for the limit cycle near the bifurcation point are derived with averaging methods and compare favourably with the cycle periodicity. Because oscillations in the pond were not observable directly due to the elevation of the cone rim and an obscuring volcanic plume, we model the observations using a spatially averaged Saint-Venant model of the spillway forced by the pond oscillator. The predicted spillway cycle periodicity and waveforms compare favourably with observations made during the eruption. The unusually well-documented nature of this eruption enables estimation of the viscosity of the erupting lava.

Hawaii↗
USGS2024

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: The hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

Alaska↗