U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2016–1104

Abstract Volume for the 2016 Biennial Meeting of the 

Yellowstone Volcano Observatory
Cover: Photograph of the 2016 deployment of eddy covariance tower (left) and Multi-GAS (right) for timeseries monitoring of gas chemistry and discharge. The equipment sits in acid-altered terrain at the north 

end of the Norris Geyser Basin. It was installed as part of research permit YELL-2016-SCI-5406.
Abstract Volume for the 2016 Biennial Meeting of the 

Yellowstone Volcano Observatory

Edited by Jacob B. Lowenstern

Open-File Report 2016–1104

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016 

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Suggested citation:

Lowenstern, J.B., ed., 2016, Abstract volume for the 2016 biennial meeting of the Yellowstone Volcano 

Observatory: U.S. Geological Survey Open-File Report 2016–1104, 46 p., 

http://www.dx.doi.org/10.3133/ofr20161104

ISSN: 2331-1258 (online)
iii

Contents

Biennial Meeting of the Yellowstone Volcano Observatory............................................................................ 1

By Jacob B. Lowenstern

The 2015 Upper Geyser Basin Seismic Imaging Experiment ........................................................................ 4

By Jamie Farrell, Fan-Chi Lin, Sin-Mei Wu, Bob Smith

Multiscale, Multi-property Geophysical Imaging of Hydrothermal Systems, Yellowstone National Park........ 8

By W. Steven Holbrook, Bradley Carr, Ken Sims, Sylvain Pasquet, Henry Heasler, and Cheryl 

Jaworowski

The Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE) Project: Responses to Tectonic, Magmatic, 

and Climatic Forcing...................................................................................................................................... 9

By Robert Sohn

What We’ve Learned From 12 Years of Sampling Gas and Water at Yellowstone...................................... 10

By Jacob B. Lowenstern, Deborah Bergfeld, William C. Evans, Shaul Hurwitz, and Andrew G. 

Hunt

Status and Future Directions of the Chloride Flux Program......................................................................... 13

By R. Blaine McCleskey and Jacob B. Lowenstern

Continuous Monitoring of Hydrothermal CO2 and Heat Emissions Using the Eddy Covariance Method at 

Norris Geyser Basin, Yellowstone National Park......................................................................................... 17

By Jennifer L. Lewicki

Satellite and Airborne Measurements of Thermal Emission at Yellowstone ................................................ 19

By R. Greg Vaughan

Yellowstone Hydrothermal Systems: Public Safety, Science and Recommendation................................... 21

By Bob Smith, Jamie Farrell, Keith Koper, and Fred Massin

What Do We Know About Earthquakes at Yellowstone: An Overview of Swarms, Magnitudes, Fault 

Interactions, Earthquake Families, and Why We Can’t Find Long-Period Events........................................ 23

By Jamie Farrell and Bob Smith

Recent InSAR Studies at Yellowstone......................................................................................................... 27

By Chuck Wicks, Daniel Dzurisin, Peter F. Cervelli, Jacob B. Lowenstern, Wendy K. Stovall, and 

David R. Shelly

Probabilistic Forecasting of Volcanic Eruptions Using Physics-based Models ............................................ 29

By Kyle R. Anderson

What Do We Know About Eruption Precursors at Large Silicic Caldera Systems? ..................................... 32

By Daniel Dzurisin

Tools for Decision Making in Eruption Forecasting...................................................................................... 36

By Sarah E. Ogburn, John S. Pallister, and Heather M. Wright

Using Global Data for Eruption Forecasting ................................................................................................ 39

By Sarah E. Ogburn, Christopher J. Harpel, Jeremy D. Pesicek, John J. Wellik, John S. Pallister, 

and Heather M. Wright

An Exercise to Obtain Expert Views on Precursors to Volcanic Eruptions at Yellowstone .......................... 43

By Jacob B. Lowenstern, Sarah E. Ogburn, and Jamie M. Farrell
Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2) 

acre 0.004047 square kilometer (km2)

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 6.290 barrel (petroleum, 1 barrel = 42 gal)

cubic meter (m3) 264.2 gallon (gal) 

cubic meter (m3) 0.0002642 million gallons (Mgal) 

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

cubic decimeter (dm3) 61.02 cubic inch (in3) 

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 1.308 cubic yard (yd3) 

cubic kilometer (km3) 0.2399 cubic mile (mi3) 

cubic meter (m3) 0.0008107 acre-foot (acre-ft) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

metric ton (t) 1.102 ton, short [2,000 lb]

metric ton (t) 0.9842 ton, long [2,240 lb]
Biennial Meeting of the Yellowstone Volcano Observatory

By Jacob B. Lowenstern1

Introduction

Every two years, scientists, natural resource managers, outreach specialists, and a variety 

of other interested parties get together for the biennial meeting of the Yellowstone Volcano 

Observatory (YVO). Each time, the theme varies. In past years, we have focused the meeting 

around topics including monitoring plans, emergency response, geodesy, and outreach. This 

year, we spent the first half-day devoted to recent research results, plans for upcoming studies, 

and geothermal monitoring. On the second day, our focus switched to eruption precursors, 

particularly as they apply to large caldera systems.

Very few large explosive eruptions from caldera systems have taken place in recorded 

history. Therefore, there are few empirical data with which to characterize the nature of volcanic 

unrest that might precede eruptions with volcano explosivity index (VEI) of six or greater. For 

this reason, we set up a series of talks that explore what we know and don’t know about large 

eruptions. We performed an informal expert elicitation (a frequently used method to characterize 

expert opinion) with a small number of our colleagues, which served as the basis for a productive 

discussion session. 

This short volume of abstracts and extended abstracts provides a summary of the 

presentations made at the YVO meeting held in Mammoth Hot Springs, Wyoming, on May 10–

11, 2016. 

Agenda for YVO Meeting May 2016

Meeting overview:

The first afternoon is a combination of research updates, research plans, and tools for 

improved monitoring. These talks should be of interest to all YVO participants and NPS staff. 

Hopefully, the presentations will all be targeted toward a general audience.

The second day is “continuing education” for the YVO scientific staff. It is a series of 

talks, exercises, and discussion related to understanding precursors to large (and smaller) 

volcanic eruptions. Most talks will extend beyond the interest levels of NPS staff. However, 

those with an interest in volcanology, or those who want a preview of discussions expected 

during the next intense earthquake swarm, are encouraged to attend. 

 

1

U.S. Geological Survey 
2

Schedule

[USGS, U.S. Geological Survey; YNP, Yellowstone National Park; WHOI, Woods Hole Oceanographic Institution; 

PBO, Plate Boundary Observatory; GPS, global positioning system]

Tuesday, May 10, 2016

1:00 p.m.—Jake Lowenstern: Welcome to meeting, Overview of talks, Quick Summary of 

last 2 years from YVO perspective. 

Recent and upcoming research

1:15 p.m.—Sarah Haas, YNP: News from the research permit office. 

1:30 p.m.—Jamie Farrell, University of Utah: Old Faithful seismic experiment. 

1:50 p.m.—Steve Holbrook and Brad Carr, University of Wyoming: Near surface 

geophysical work and upcoming plans. 

2:10 p.m.—Rob Sohn, WHOI: The HD-YLAKE project—plans and opportunities. 

2:35 p.m.—Dave Mencin, UNAVCO: UNAVCO, PBO, and the Yellowstone GPS and strain 

network. 

2:55 p.m.—Break

Methods for study of heat and fluid flow

3:10 p.m.—Jake Lowenstern, USGS: What we’ve learned from 12 years of sampling gas and 

waters. 

3:30 p.m.—Blaine McCleskey, USGS: Status and future directions for the chloride flux 

program. 

3:45 p.m.—Jen Lewicki, USGS: Using Eddy Covariance methods to quantify gas and heat 

flux at Yellowstone and beyond. 

4:00 p.m.—Peter Kelly, USGS: Long-term autonomous volcanic gas monitoring with MultiGAS. 

4:15 p.m.—Sean Scott and Ken Sims, University of Wyoming: Timescales of fluid 

movement in the Yellowstone hydrothermal system. 

4:35 p.m.—Greg Vaughan, USGS: Satellite and airborne measurements of thermal emission 

at Yellowstone. 

4:50 p.m.—Bob Smith, University of Utah: A plan for hydrothermal monitoring at 

Yellowstone. 

Wednesday, May 11, 2016

Unrest at Yellowstone

8:30 a.m.—Jamie Farrell, University of Utah: What do we know about earthquakes at 

Yellowstone. An overview of swarms, magnitudes, fault interactions, earthquake 

families, and why we can’t find LPs (long-period earthquakes). 

9:15 a.m.—Chuck Wicks, USGS: Insights from deformation preceding and subsequent to the 

March 30, 2014 M4.8 earthquake near Norris. 

Tools and knowledge applied to eruption forecasting 

9:35 a.m.—Kyle Anderson, USGS: Physics-based models for eruption forecasting. 

10:05 a.m.—Break
3

10:20 a.m.—Dan Dzurisin, USGS: Setting the stage for the afternoon. What happens before 

eruptions at calderas? How much do we know? 

10:55 a.m.—Sarah Ogburn, USGS: Tools for decision making in eruption forecasting (Event 

Trees, Expert Elicitation). 

11:40 a.m.—Lunch 

Eruption forecasting as applied to Yellowstone

1:00 p.m.—Sarah Ogburn, USGS: Volcano databases to improve probabilistic assessments. 

1:45 p.m.—Jake Lowenstern and Sarah Ogburn, USGS: Results of elicitation of monitoring 

thresholds. Slicing and dicing the results. 

3:00 p.m.—Break

3:15 p.m.—Moderated Discussion (Mike Poland [USGS] and Jamie Farrell):

a) What did we learn from the survey? 

b) How could we make it better?

c) What research or methodologies will help us to make progress? 

d) What tools need to be better integrated to monitoring streams so we can detect 

key precursors?
4

The 2015 Upper Geyser Basin Seismic Imaging Experiment

By Jamie Farrell,1 Fan-Chi Lin,

1 Sin-Mei Wu,1 Bob Smith1

The Yellowstone hydrothermal system comprises the largest concentration of geysers, 

hot springs, fumaroles, and hydrothermal explosion craters on the globe. Old Faithful, located in 

the Upper Geyser Basin of Yellowstone National Park, is the most famous geyser in the world 

and has been the focus of several geophysical studies in the past. In a series of in situ 

experiments from 1983 to 1993, probes with pressure and temperature sensors and a video 

camera were lowered into the Old Faithful conduit and the geometry of the conduit was modeled 

do to about 22 m beneath the surface (Hutchinson and others, 1997). Between 1991 and 1994, 

several temporary seismic array deployments (including a 96 station dense single component 

geophone array) were conducted near Old Faithful to investigate the origin of the pre-eruption 

harmonic tremor (Kieffer, 1984; Kedar and others, 1996; 1998). More recently, 

Vandemeulebrouck and others (2013) reinterpreted the dense geophone data and applied 

beamforming techniques to determine the source location of the geyser tremor noise. Two groups 

of source locations are identified with the first group within the conduit directly beneath the Old 

Faithful vent and the second group being slightly off to the side and is thought to be within a 

larger hot water and steam recharge cavity. However, the exact dimensions and physical 

properties of the cavity remain largely unclear.

In November 2015, the University of Utah conducted a seismic experiment where 133 

new autonomous three-component seismometers were deployed in the Upper Geyser Basin 

focused on Old Faithful geyser and the surrounding developed areas to evaluate the seismic 

structure the shallow geotechnical engineering properties (fig. 1). The instruments collected 

continuous seismic data for two weeks. Preliminary results show unexpected variations in the 

seismic signature of individual thermal features. The actual Old Faithful geyser eruptions have 

very little seismic energy, however, about 45 minutes prior to an Old Faithful eruption, 

hydrothermal tremor begins to develop across the geyser area with amplitudes slowly increasing 

in time until it reaches a peak about 25 minutes prior to the eruption with slowly decreasing 

amplitudes until the eruption onset (fig. 2). The seismic signal related to the buildup of Old 

Faithful reservoir is recorded at stations north, south and to the east of Old Faithful but is missing 

on stations to the northwest. This suggests a shallow subsurface feature that strongly attenuates 

the seismic signal immediately NW of the cone of Old Faithful.

Another of the more interesting signals comes from Doublet Pool on Geyser Hill that 

occurs regularly about every 38 minutes and coincides with visual pulsing of the surface of 

Doublet Pool. This signal has large seismic wave amplitudes and is recorded throughout the 

seismic array (fig. 3). The Geyser Hill signal is also affected by the aforementioned subsurface 

feature NW of the Old Faithful cone. 

Interestingly, some of the largest amplitude signals come from cultural noise (cars, etc.) 

even though the experiment took place after the park was closed to visitors. We will attempt to 

quantify the cultural energy that is imparted on the hydrothermal system in comparison to natural 

 

1

University of Utah
5

signals. With record attendance in and around Yellowstone, and Old Faithful being the most 

popular stop in the park, it is possible that the seismic noise imparted by visitor activities, as well 

as visitor services, could have a slight impact on the hydrothermal resource. 

Figures 

Figure 1. Map of seismic deployment around Old Faithful in the Upper Geyser Basin. Yellow circles 

show deployed geophones with the numeric name. Red lines are trails and white lines are roads. The red 

star shows the location of the Old Faithful vent.
6

Figure 2. Seismic data for stations 01, 02, 03, and 05 (see fig. 1) with 3 Old Faithful eruptions and 

precursory signals. Old Faithful eruptions are labeled E1, E2, and E3. 
7

Figure 3. Seismic data for stations 103, 104, 45, 46, 47, 66, and 67 (see fig. 1) for the Geyser Hill signal.

Four tremor signals are shown during the ~2.75 hour time period.

References Cited

Hutchinson, R.A., Westphal, J.A., and Kieffer, S.W., 1997, In situ observations of Old Faithful 

Geyser: Geology, v. 25, p. 875–878. 

Kedar, S., Sturtevant, B., and Kanamori, H., 1996, The origin of harmonic tremor at Old Faithful 

Geyser: Nature, v. 379, p. 708–711. 

Kedar, S., Kanamori, H., and Sturtevant, B., 1998, Bubble collapse as the source of tremor at Old 

Faithful Geyser: Journal of Geophysical Research, v. 103, p. 24283–24299. 

Kieffer, W.W., 1984, Seismicity at Old Faithful Geyser—An isolated source of geothermal noise 

and possible analogue of volcanic seismicity: Journal of Volcanology and Geothermal 

Research, v. 22, p. 59–95. 

Vandemeulebrouck, J., Roux, P., and Cros, E., 2013, The plumbing of Old Faithful Geyser 

revealed by hydrothermal termor: Geophysical Research Letters, v. 40 no. 10, 1989–1993, 

doi:10.1002/grl.50422. 
8

Multiscale, Multi-property Geophysical Imaging of 

Hydrothermal Systems, Yellowstone National Park

By W. Steven Holbrook, 1 Bradley Carr,1 Ken Sims,1 Sylvain Pasquet,1 Henry Heasler,2 and Cheryl 

Jaworowski2

Yellowstone National Park hosts the most active terrestrial hydrothermal system on 

Earth, with ~10,000 individual features, including the geysers, fumaroles and hot pools that 

provide the natural beauty that attracts millions of visitors each year. While much is known about 

the chemistry of the hydrothermal features and about the deep magmatic heat sources that fuel 

them, a critical knowledge gap exists in the upper ~100 m beneath the surface. We know very 

little about the details of the plumbing system beneath the park’s iconic geysers and hot springs. 

Numerous conceptual models of the Yellowstone hydrothermal system attempt to explain 

geochemical analyses, but the structural underpinnings of these models are often based on little 

more than speculation and geologic reasoning. Geophysical methods provide a means to image 

Yellowstone’s hydrothermal system at depths ranging from 1 m to hundreds of meters, where 

validation of existing conceptual models is sorely needed. 

We examined sites in the Norris Geyser Basin and the Greater Obsidian Pool Thermal 

Area where neutral chloride and acid sulfate pools exist in close proximity enabling us to 

investigate the geometry and scale of phase separation. Numerous geophysical methods were 

acquired at both sites, including: 2D & 3D DC resistivity and self-potential imaging, refraction 

and surface wave seismic imaging, ground penetrating radar profiling, total field magnetics 

mapping, shallow multifrequency electromagnetic induction mapping, deep time-domain 

electromagnetic sounding, and nuclear magnetic resonance sounding. These geophysical 

methods were integrated to develop the first images of subsurface geophysical properties, 

groundwater flow pathways, and structural constraints. 

 

1

University of Wyoming 

2

Formerly National Park Service
9

The Hydrothermal Dynamics of Yellowstone Lake (HDYLAKE) Project: Responses to Tectonic, Magmatic, and 

Climatic Forcing

By Robert Sohn1

Yellowstone Lake hosts more than 250 sites of hydrothermal discharge on the lake floor, 

composing one of the major thermal basins within the Park. These vent fields are continually 

perturbed by a variety of processes spanning a large range of amplitudes and timescales. For 

example, the stress state of the subsurface matrix hosting hydrothermal flow is perturbed by lake 

waves with periods of several seconds, intermittent earthquakes and seismic swarms, annual lake 

level changes, and changing climate stretching back to the last glaciation. The lake also hosts a 

collection of hydrothermal explosion craters, which are formed when a large volume of 

hydrothermal fluid percolating through porous rocks flashes to steam in response to a rapid 

depressurization event, generating an explosion that excavates a large crater. HD-YLAKE, a 

multi-institutional, National Science Foundation-funded project, seeks to understand the complex 

cause-and-effect relationships between tectonic, magmatic, and climatic processes with 

hydrothermal flow beneath the lake by: (1) deploying instrumentation to monitor hydrothermal 

discharge and forcing mechanisms on the lake floor, (2) acquiring sediment cores that contain a 

record of forcing and response relationships throughout the post-glacial lake history, and (3) 

incorporating these results into system-scale models of hydrothermal flow. The project brings 

together experts in the study and instrumentation of deep-sea vent fields with experts on the 

Yellowstone hydrothermal system to acquire new types of data from the lake floor and generate 

new insights into this scientifically important resource that is hidden beneath the lake surface. 

The project has three field seasons from 2016–2018, and in this talk I will highlight plans for the 

inaugural 2016 fieldwork. 

 

1

Woods Hole Oceanographic Institution 
10

What We’ve Learned From 12 Years of Sampling Gas and 

Water at Yellowstone

By Jacob B. Lowenstern,1 Deborah Bergfeld,1 William C. Evans,1 Shaul Hurwitz,1 and Andrew G. Hunt1

In 2003, we started an extended effort to sample gas discharges at Yellowstone from 

features such as fumaroles, frying pans (bubbling ground), steaming ground, and hot springs. 

Surprisingly, very few complete chemical analyses of gas had been published over the 130 years 

since the area was first explored and preserved as a national park. Our goal was to characterize 

the geographic variability of gas chemistry, flux, and isotopic compositions. Since 2003, we’ve 

published a series of peer-reviewed journal articles and data compilations aimed at fulfilling that 

initial goal (Werner and others, 2008; Evans and others, 2010; Bergfeld and others, 2012, 2014; 

Lowenstern and others, 2012, 2014, 2015). In this extended abstract, we summarize some of the 

notable observations made by us and other geoscientists. Much of the discussion is based on the 

recent review by Lowenstern and others (2015), which provides full references and additional 

details for those seeking further information. 

Early work on geothermal phenomena at Yellowstone focused primarily on the 

chemistry, geothermometry, and stable isotope systematics of the hot waters found in geyser 

basins (Fournier, 1989; Nordstrom and others, 2009). The temperatures, mineral-water reactions, 

and geothermal solute flux were determined by systematic sampling and water chemistry 

analyses, complementary drilling, and river discharge measurements. A general model emerged 

of a meteoric-water-dominated groundwater system where long-term water–rock reactions take 

place between surface-sourced water and rhyolitic rocks present in and around the caldera. The 

prolific heat flow from the mantle and associated crustal magmatism generate boiling 

temperatures beneath most thermal areas. Fluid chemistry is controlled by water–rock reactions 

that create a parent thermal water at elevated temperatures (~340 °C). Boiling and mixing of 

waters during ascent to the surface could explain most of the variations in water chemistry 

around the caldera (Fournier, 1989). 

More recent studies of gas discharge are somewhat at odds with this simple model, 

primarily because the very high estimated CO2 flux (45 kilotons per day [kt/d]; Werner and 

Brantley, 2003) requires that large amounts of deep magmatic and metamorphic gases pass 

through the hydrothermal system. Thus, gas systematics do not relate simply to boiling, mixing, 

and other shallow geothermal phenomena (Lowenstern and others, 2015), but rather to opensystem throughput of deep gas through a shallow meteoric-dominated system. This process is 

evident in most acid-sulfate areas where minimal water is discharged at the surface, but diffuse 

and vent-related gas discharge is considerable (Werner and others, 2008). Though some thermal 

areas discharge “too much” gas (more than can come from shallow degassing), other areas 

appear gas-depleted, such as those associated with high-flow springs along the edges of young 

Central Plateau Member lavas (Morgan and Shanks, 2005; Hurwitz and Lowenstern, 2014). 

 

1

U.S. Geological Survey 
11

These gas-depleted waters likely experience long flow paths beneath the young lavas, allowing 

them to lose gas prior to emergence.

Steam is the dominant volatile species in fumaroles, frying pans, and steaming ground, 

followed by CO2, which typically makes up more than 90 percent of the remainder. Trace gases 

include H2, CH4, H2S and N2, each of which may dominate at different locales. Other analyzed 

gases include He, Ar, Ne, NH3 and C2H6. The dry gas to steam ratio (fig. 1) varies over orders of 

magnitude (~10 percent down to <0.01 percent), yet correlates negatively with the ratio of 

meteoric (Ar, N2) to deep (He, CO2) gas. We interpret the trend to reflect deep input of CO2-rich 

gas that is diluted by variable amounts of boiled meteoric geothermal water. Such a process is 

most likely in high-heat flow areas such as Yellowstone.

Figure 1. Bubble plot of results from gas collected from Yellowstone fumaroles with log (percent Gas) 

versus log (N2/CO2) and bubble size scaled to log (Ar/He). Steam-rich samples tend to have higher 

concentrations of atmospheric gases (N2 and Ar) relative to gases with deeper origin, CO2 and He. Fields 

for magmatic and meteoric fluids are interpretive, and are represented by Mud Volcano and Heart Lake, 

respectively. The overall trend of Yellowstone fumarole chemistry is inconsistent with generation of gases 

in different areas by progressive boiling of a parent thermal water (batch and Rayleigh degassing 

processes shown). From Lowenstern and others (2015).

Stable carbon isotope ratios and CO2/

3

He of collected gases reflect a mantle source of 

CO2 with δ13C of –3.4 per mil, somewhat heavier than mid-ocean ridge basalt (MORB) but 
12

consistent with other major hotspot volcanoes such as Etna and Nyiragongo (Lowenstern and 

others, 2015). Helium isotope ratios also reflect a hotspot origin: at Mud Volcano, they reach 16 

times the atmospheric ratio (Ra). However, ratios drop precipitously in some locations due to 

addition of crustal 4He. This radiogenic gas may have resided for hundreds of millions of years 

within Archaean crust that underlies Yellowstone, to then be degassed over the past 2 million 

years (Lowenstern and others, 2014). 

Future research should focus upon temporal variations in gas discharge and chemical 

composition. Geophysical models for swarms and uplift/subsidence cycles commonly 

incorporate pressurization (and release) of gas and aqueous fluid. It seems likely that a 

comprehensive effort to quantify variations in gas output, chemistry, and isotope systematics will 

allow us to test various hypotheses and understand cycles of caldera unrest. 

References Cited 

Bergfeld, D., Evans, W.C., Lowenstern, J.B., and Hurwitz, S., 2012, Carbon dioxide and 

hydrogen sulfide degassing and cryptic thermal input to Brimstone Basin: Chemical Geology, 

v. 330-331, p. 233–243. 

Bergfeld, D., Lowenstern, J.B., Hunt, A.G., Shanks, Evans, W.C., 2014, Gas and isotope 

chemistry of thermal features from Yellowstone National Park, Wyoming (ver. 1.1, September 

2014): U.S. Geological Survey Scientific Investigations Report 2011–5012, 26 p. 

Evans, W.C., Bergfeld, D., McGeehin, J.P., King, J.C., and Heasler, H., 2010, Tree-ring 14C 

links seismic swarm to CO2 spike at Yellowstone, USA: Geology, v. 38, p. 1075–1078. 

Fournier, R.O., 1989, Geochemistry and dynamics of the Yellowstone National Park 

Hydrothermal System: Annual Reviews of Earth and Planetary Science, v. 17, p. 13–53. 

Hurwitz, S., and Lowenstern, J.B., 2014, Dynamics of the Yellowstone hydrothermal system: 

Reviews of Geophysics, v. 52, no. 3, p. 375–411, doi:10.1002/2014RG000452. 

Lowenstern, J.B., Bergfeld, D., Evans, W.C., and Hurwitz, S., 2012. Generation and evolution of 

hydrothermal fluids at Yellowstone—insights from the Heart Lake Geyser Basin: 

Geochemistry, Geophysics, Geosystems, v. 13, Q01017, http://dx.doi.org/10.1029/ 

2011GC003835. 

Lowenstern, J.B., Evans, W.C., Bergfeld, D., and Hunt, A.G., 2014. Prodigious degassing of a 

billion years of accumulated radiogenic helium at Yellowstone: Nature, v. 506, p. 355–358. 

Lowenstern, J.B., Evans, W.C., Bergfeld, D., and Hunt, A.G., 2015, Origin of Geothermal Gases 

at Yellowstone: Journal of Volcanology and Geothermal Research, v. 302, p. 87–101. 

Morgan, L. A., and Shanks, W.C., 2005, Influences of rhyolitic lava flows on hydrothermal 

processes in Yellowstone Lake and on the Yellowstone Plateau, in Inskeep, W.P. and 

McDermott, T.R., eds., Geothermal Biology and Geochemistry in Yellowstone National Park: 

Bozeman, Mont., Montana State University Thermal Biology Institute, p. 31–52. 

Nordstrom, D.K., McCleskey, R.B., and Ball, J.W., 2009, Sulfur geochemistry of hydrothermal 

waters in Yellowstone National Park—IV Acid-sulfate waters: Applied Geochemistry, v. 24, 

p. 191–207. 

Werner, C., and Brantley, S., 2003, CO2 emissions from the Yellowstone volcanic system: 

Geochemistry, Geophysics, Geosystems, v. 4, no. 7, http://dx.doi.org/10.1029/2002GC000473. 

Werner, C., Hurwitz, S., Evans, W.C., Lowenstern, J.B., Bergfeld, D., Heasler, H., Jaworowski, 

C., and Hunt, A., 2008, Volatile emissions and gas geochemistry of Hot Spring Basin, 

Yellowstone National Park, USA: Journal of Volcanology and Geothermal Research, v. 178, p. 

751–762. 
13

Status and Future Directions of the Chloride Flux Program

By R. Blaine McCleskey1 and Jacob B. Lowenstern1

Yellowstone National Park (YNP) was established due to its large number of geysers, hot 

springs, mud pots, and steam vents. Monitoring Yellowstone’s geothermal system is difficult 

owing to the presence of over 10,000 geothermal features distributed across 2.2 million acres. 

However, the chloride flux in the major rivers in YNP has been used as a surrogate for 

estimating the integrated heat flow in geothermal systems (Ellis and Wilson, 1955; Fournier, 

1989) because geothermal waters contain high concentrations of chloride and water discharged 

from these geothermal features eventually enters major rivers. The U.S. Geological Survey 

(USGS) and the National Park Service (NPS) have collaborated on monitoring chloride flux in 

YNP rivers since the 1970s (Fournier and others, 1976; Norton and Friedman, 1985; Hurwitz and 

others, 2007a; McCleskey and others, 2012). 

Monitoring thermal activity is an important tool for managing YNP. The Yellowstone 

Supervolcano is located in YNP and having a long-term chloride flux baseline and timely 

monitoring is central to hazard assessment. Monitoring YNP’s geothermal activity potentially 

yields insights into a variety of research, management, and safety topics including the effects of 

earthquakes (Norton and Friedman, 1985), climate change, and the effect of inflation/deflation of 

the magma chamber on geothermal activity. In addition, the amount of water and heat released 

during geyser eruptions and the effects of storm events can be quantified by continuously 

monitoring the chloride flux in rivers (McCleskey and others, 2012). 

There are several long-term chloride flux monitoring sites (fig. 1) located at or near 

USGS gages. In the past, chloride concentration has been determined in discrete water samples 

collected by researchers and volunteers. However, the great distances between sites and 

accessibility challenges, especially during the winter, limit the number of samples collected 

annually to about 28 per site. Staffing changes and funding limitations have created large data 

gaps at some of the monitoring sites (Hurwitz and others, 2007b). 

Since 2010, USGS and NPS researchers have been collaborating on a study to quantify 

the relationship between electrical conductivity and chloride at the Madison, Gibbon, Firehole, 

Gardner, Yellowstone, and Snake River monitoring sites (fig. 1). The electrical conductivity–

chloride correlations (R2

>0.97) in the Madison, Firehole, Gibbon, Snake, Gardner, and 

Yellowstone Rivers are shown figure 2. Electrical conductivity also correlates (R2>0.9) with Na, 

SO4, F, HCO3, SiO2, K, Li, Ca, B and As in several YNP rivers. In effect, one simple 

measurement of electrical conductivity gives an excellent estimate of the overall water 

composition within the correlations demonstrated in this study. Finally, the annual chloride flux 

can be calculated using electrical conductivity as a surrogate for chloride (fig. 3). 

Determining the geothermal flux using electrical conductivity provides a cost- and laboreffective alternative to previous protocols whereby chloride flux was estimated through manual 

collection of numerous water samples and subsequent chemical analysis. Continuous 

conductivity measurements are relatively straight forward, reliable, and inexpensive. There are 

 

1

U.S. Geological Survey 
14

several potential advantages of using electrical conductivity monitoring in YNP: (1) real-time 

flux estimations can be made (no delay for chemical analyses); (2) high-frequency data (35,000 

measurements per year) allows chemical effects of short term events such as geyser eruptions, 

rain events, and earthquakes on rivers to be quantified; (3) it functions as a surrogate for several 

geothermal solutes, in addition to chloride, providing a much greater understanding of the 

thermal discharge and its effects on the Yellowstone ecosystem and downstream communities; 

and (4) water-quality in two popular swimming holes (Firehole Canyon and Gardiner River at 

the Boiling River confluence) can readily be determined.

Figure 1. Map of long-term chloride flux monitoring sites. R., River; Cr., Creek.

Future work will focus on the main rivers draining southwest Yellowstone. The Falls 

River and Henrys Fork are the two remaining sites where the electrical conductivity method 

needs to be developed. Because southwestern YNP is comparatively inaccessible (fig. 1) and 

much of the area is remote, electrical conductivity–chloride correlations have not yet been 

developed and continuous electrical conductivity monitoring has not been initiated. Furthermore, 

the monitoring sites in southwest YNP have the largest data gaps despite the chloride flux 

emanating from southwest YNP being a substantial portion (~12 percent) of the total chloride 

leaving the park (Hurwitz and others, 2007a). 
15

0

10

20

30

40

50

60

70

80

90

0

10

20

30

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0 100 200 300 400 500 600 700 800 900

0

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0 100 200 300 400 500 600 700 800 900 1000

Chloride (mg/L)

Madison River

R2 = 0.998

 Chloride (mg/L)

Firehole River

R2 = 0.997

 Electrical Conductivity (mS/cm)

Chloride (mg/L)

Gibbon River

R2 = 0.999

Yellowstone River at Corwin Springs

R2 = 0.974

Gardner River

R2 = 0.983

Electrical Conductivity (mS/cm)

Snake River

R2 = 0.987

Figure 2. Plots of chloride-electrical conductivity correlations for six Yellowstone National Park river 

monitoring sites.
16

1980 1985 1990 1995 2000 2005 2010 2015

0

5

10

15

20

25

30

 Hurwitz and others (2007) Electrical Conductivity Method

Madison River

Yellowstone River

 Friedmand and Norton (2007)

Chloride Flux (kt/yr)

Snake River

Figure 3. Plot of annual chloride flux in the Madison, Yellowstone, and Snake Rivers. kt/yr, kilotons per 

year; R., River.

References Cited

Ellis, A.J., and Wilson, S.H., 1955, The heat from the Wairakei–Taupo thermal region calculated 

from the chloride output: New Zealand Journal of Science and Technology, v. B36, p. 622–

631. 

Fournier, R.O., 1989, Geochemistry and dynamics of the Yellowstone National Park 

hydrothermal system: Annual Reviews of Earth and Planetary Science, v. 17, p. 13–53. 

Fournier, R.O., White, D.E., and Truesdell, A.H., 1976, Convective heat flow in Yellowstone 

National Park, in Proceedings of the second United Nations Symposium on the Development 

and Use of Geothermal Resources, May 20–29, 1975, San Francisco: Washington, D.C., U.S. 

Government Printing Office, p. 731–739. 

Friedman, I., and Norton, D.R., 2007, Is Yellowstone losing its steam? Chloride flux out of 

Yellowstone National Park, in Morgan, L.A., ed., Integrated geoscience studies in the Greater 

Yellowstone Area—Volcanic, Hydrothermal and tectonic Processes in the Yellowstone 

Geoecosystem: U.S. Geological Survey Professional Paper 1717, p. 275–297. 

Hurwitz, S., Eagan, S., Heasler, H., Mahony, D., Huebner, M.A., and Lowenstern, J.B., 2007a, 

River chemistry and solute flux in Yellowstone National Park. U.S. Geological Survey Data 

Series 278, Version 3.0. 

Hurwitz, S., Lowenstern, J.B., and Heasler, H.P., 2007b, Spatial and temporal geochemical 

trends in the hydrothermal system of Yellowstone National Park—Inferences from river solute 

fluxes: Journal of Volcanology and Geothermal Research, v. 162, p. 149–171. 

McCleskey, R.B., Clor, L.E., Lowenstern, J.B., Evans, W.C., Nordstrom, D.K., Heasler, H., and 

Huebner, M.A., 2012, Solute and geothermal flux monitoring using electrical conductivity in 

the Madison, Firehole, and Gibbon Rivers, Yellowstone National Park: Applied Geochemistry, 

v. 27, no. 12, p. 2370–2381. 

Norton, D.R., and Friedman, I., 1985, Chloride flux out of Yellowstone National Park. Journal of 

Volcanology and Geothermal Research, v. 26, no. 3–4, p. 231–250. 
17

Continuous Monitoring of Hydrothermal CO2 and Heat 

Emissions Using the Eddy Covariance Method at Norris 

Geyser Basin, Yellowstone National Park

By Jennifer L. Lewicki1

Eddy covariance (EC) is a micrometeorological technique traditionally used to measure 

fluxes of gas and heat between a plant canopy and the atmosphere. Studies over the past decade, 

however, have demonstrated that EC is a viable method to measure magmatic and hydrothermal 

CO2 emissions from soil (diffuse) and vent (point) sources. Preliminary findings have also 

suggested that EC may be used to quantify the hydrothermal component of total heat flux, but 

more work is required to refine the technique. EC determines the net vertical turbulent flux of a 

scalar (for example, gas, heat) by measuring the temporal covariance of the scalar and vertical 

wind velocity measured at a fixed height above the ground surface. The method utilizes a fastresponse (≥10 Hertz) CO2–H2O analyzer and three-dimensional sonic anemometer to measure 

fluctuations in atmospheric CO2 and H2O concentrations, temperature, and wind speed in three 

directions, from which average half-hourly CO2 and heat fluxes are calculated. Using CO2 flux 

as an example, each value in a time series is a weighted sum of surface CO2 fluxes over an 

upwind land area. A unique “source weight function” describing the relative contribution of each 

element of upwind surface CO2 flux to the total measured EC CO2 flux can be modeled based on 

measured atmospheric parameters. Finally, by carrying out an inversion of the time series of the 

measured CO2 fluxes and corresponding modeled source weight functions, it is possible to model 

the “best-fit” distribution of surface CO2 fluxes and calculate a total CO2 emission rate (in tons 

per day) from the study area. Overall, the EC technique uniquely provides the attributes of 

automated, continuous, and time- and space-averaged measurements of CO2 and heat fluxes from 

moderate-size (square meter- to square kilometer-scale) land areas in volcanic and hydrothermal 

systems.

An EC system was deployed on May 14, 2016 in the northeastern part of the Norris 

Geyser Basin in Yellowstone National Park as part of a study intended to quantify gas and heat 

emissions and their temporal variability. The EC system will measure CO2 and sensible and 

latent heat fluxes, along with a set of ancillary environmental parameters, on a half-hourly basis 

over a period of approximately 5 months. Based on preliminary analysis of the first 3 weeks of 

EC data, the mean plus or minus the standard deviation of hydrothermal CO2 was 171.1±72.6 

grams per square meter per day and the total (hydrothermal and background solar) sensible and 

latent heat fluxes were 130.6±58.7 watts per square meter (W/m2) and 249.2±109.4 W/m2, 

respectively. Diurnal variations in the time series were characterized. The ~5-month-long time 

series will be used to: (1) characterize diurnal to seasonal variations in gas and heat emissions 

and their correlation with changes in environmental parameters and potential regional seismic or 

hydrothermal activity; (2) develop a method to quantify the hydrothermal components of the 

 

1

U.S. Geological Survey 
18

total measured sensible and latent heat fluxes; (3) integrate the EC CO2 flux time series with 

atmospheric H2S/CO2 ratios measured by a nearby Multi-GAS system to quantify the temporal 

variability of H2S fluxes; and (4) calculate the average (~5-month) total hydrothermal CO2 and 

sensible and latent heat emission rates from the study area.
19

Satellite and Airborne Measurements of Thermal Emission

at Yellowstone

By R. Greg Vaughan1

The largest geothermal system in the world, located at Yellowstone National Park, is the 

surface manifestation of a partly molten magma reservoir that exists beneath the 630,000-yearold Yellowstone Caldera. Magmatic heat and volatiles transferred into the overlying rock and 

groundwater reservoirs, combined with abundant seismic activity, result in an impressive display 

of >10,000 thermal features at the surface. The purpose of thermal monitoring in Yellowstone is 

to document baseline geothermal activity so that significant changes can be recognized should 

they ever occur—changes that may be related to tectonic or magmatic processes, seasonal 

hydrologic cycles, or human activities. This information is relevant to (1) monitoring a 

potentially active volcanic system and (2) supporting decisions for park development, resource 

protection, and visitor safety. Thermal monitoring is a challenge in Yellowstone because there 

are so many thermal features that range widely in size and temperature, and they are spread out 

over a large and mostly inaccessible area.

Satellite-based thermal infrared (TIR) remote sensing data from the Advanced 

Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Landsat 8 Thermal 

Infrared Sensor have been used to map the location and spatial extent of active thermal areas, to 

generate thermal anomaly maps, and to quantify thermal monitoring metrics (for example, 

surface temperature, geothermal radiant emittance, and geothermal radiant heat output), both 

locally and for the entire geothermal system. In addition, high-spatial-resolution airborne TIR 

data (1-m pixels) that are occasionally acquired over individual thermal areas provide more 

spatial details and more accurate thermal metrics than can be achieved with moderate spatial 

resolution satellite TIR data (90- to 100-m pixels).

Thermal anomaly maps derived from the remote sensing data have been used to assess 

and update field-based thermal area maps, identify and map gradations in heat flux within 

thermal areas, and differentiate between thermally emissive and cold degassing thermal areas; 

they could be used to detect future changes in thermal areas. These data have also been used to 

estimate the geothermal radiant emittance from each thermal area, which ranges from 7 to 70 

watts per square meter (W/m2

), and the total geothermal radiant heat output from the entire 

geothermal system, which is about 2 gigawatts (GW).

Landsat 8 and ASTER TIR data have also been compared, to assess the extent to which 

they can produce similar thermal monitoring metrics. ASTER was launched on the Terra satellite 

in December 1999, and has, to date, acquired ~270 scenes from Yellowstone. About 25 percent

of these are nighttime scenes. Nighttime TIR data are important for studying sub-boiling, subpixel-scale thermal features because at night the effects of solar radiance on surface thermal 

emission are minimized. Landsat 8 was launched in February 2013. Since August 2013, it has 

been acquiring nighttime TIR data regularly (every 16 days) over Yellowstone. As of May 9, 

 

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U.S. Geological Survey 
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2016, Landsat 8 had acquired ~130 scenes over Yellowstone, about 50 percent of which are 

nighttime scenes. This regular data acquisition schedule provides nighttime data that, for the first 

time, can be used for time series analysis and change detection at moderate spatial scales.

Landsat 8 TIR data have a swath (170 km × 183 km) that encompasses the entire park in a single 

scene (path 132, row 215, for the nighttime data). With a 60-km swath, ASTER data from 

multiple overpasses from different times have to be mosaicked together to cover the entire park. 

Landsat 8 nighttime TIR data have exceptionally good pixel geolocation accuracy (within 41 m). 

ASTER nighttime TIR pixels have been observed to be mis-geolocated by up to 9-pixels (~800 

m) in the cross track (east-west) direction. This can be corrected without resampling the pixel 

values, but requires extra data processing steps. The pixel sizes of ASTER and Landsat 8 TIR 

data are 90-m/pixel and 100-m/pixel, respectively. However, the Landsat 8 TIR data are 

resampled to 30-m/pixel to match the spatial resolution of the other Landsat 8 channels. Because 

ASTER is a multispectral instrument, with five spectral channels in the TIR wavelength region, 

temperature–emissivity separation methods are more robust and ASTER generally retrieves more 

accurate surface pixel temperatures, particularly over land. Simple radiometric temperature 

retrievals from the TIR at-sensor radiance data (for example, via emissivity normalization) are 

comparable between ASTER and Landsat 8. The difference in spatial resolution between 

ASTER and Landsat 8 TIR data is not significant with respect to sub-pixel heterogeneity issues 

in retrieving pixel temperatures. However, there is an issue with some Landsat 8 TIR-derived 

data products related to the fact that Landsat 8 TIR data are acquired with 100-m pixels, but 

resampled and delivered as 30-m pixels. As pixel size increases, retrieved pixel temperature 

decreases due to sub-pixel thermal mixing. Calculated radiant emittance (in watts per unit area; 

derived from the Stefan-Boltzmann equation) also decreases with increasing pixel size because 

this metric is a function of the retrieved pixel temperature. This is true for total radiant emittance 

and for the background-subtracted geothermal component of radiant emittance. As pixel size 

increases, the estimated area of any given target also increases. This is because larger pixels do 

not resolve the smooth boundaries of natural features as accurately as smaller pixels. Therefore, 

the calculated total radiant heat output (in watts) also increases with increasing pixel size because 

this metric is a function of the estimated area of a given thermal target. This overestimate of 

target area and underestimate of target radiant emittance can be normalized by using a simple 

background subtraction method, resulting in radiant heat output values that are accurate in 

comparison to those derived using higher spatial-resolution airborne data or theoretical 

calculations. However, this only holds true if the data used have a spatial resolution that is 

consistent with respect to deriving both geothermal radiant emittance and target area. If the 

image data product has a pixel size that is not commensurate with the actual measurements, then 

the resulting radiant heat output values will not be consistent with what is expected based on 

theoretical calculations or higher resolution measurements. In other words, Landsat 8 provides 

30-m TIR pixels spatially, but the actual pixel values are reflective of 100-m pixels. Therefore, 

total geothermal radiant heat output values (in watts) are significantly underestimated. Using unresampled 100-m Landsat 8 TIR data solves this issue.

In summary, Landsat 8 TIR data have several advantages over ASTER with respect to 

mapping surface thermal features (for example, regularly acquired nighttime TIR data, image 

swath, and pixel geolocation accuracy). ASTER, on the other hand, retrieves more accurate 

surface temperatures, which improves some of the other thermal monitoring metrics.
21

Yellowstone Hydrothermal Systems: Public Safety, Science 

and Recommendation

By Bob Smith,1 Jamie Farrell,

1 Keith Koper,1 and Fred Massin2

Thousands of persons visit Yellowstone’s famous geysers, hot springs, mud pots, and 

fumaroles and are therefore exposed to the boiling water, steam eruptions and explosion hazards. 

Hydrothermal explosions, dangerously hot ground temperatures on footpaths, and development 

of new thermal features are commonly observed in Yellowstone in addition to the potential for 

the release of toxic quantities of carbon dioxide and/or hydrogen sulfide gas. The University of 

Utah recommends the installation of hydrothermal monitoring systems at front country 

hydrothermal areas that will be able to detect unusual thermal, seismic, deformation, gas, etc. 

activity and provide real-time data to the National Park Service to respond appropriately in case 

of unusual observations and features. The data will locally recorded by MEMS, 

microelectromechanical sensor nodes, telemetered via real-time on L1 and L2 GPS systems at 

UNAVCO and telemetered via the UUSS seismic telemetry system (fig. 1). This will allow for 

the long-term determination of long term background activity associated with geyser eruptions, 

precipitation, and seasonal water cycles that can be compared with unexpected changes in the 

above parameters. Moreover the acquired data can be used to model and interpret hydrothermal 

processes advancing an understanding of the physics and chemistry of hydrothermal features that 

are necessary to develop working models of Yellowstone hydrothermal systems. An example of 

a hydrothermal monitoring system for Norris Geyser Basin is shown in figure 2. The seismicacoustic data can be telemetered using a similar telemetry system as the University of Utah 

Seismograph Station telemetry network and GPS data can be telemetered through the PBO 

network to be sent to UNAVCO for processing and archiving. The gas sensor systems can be 

transmitted to the USGS, Menlo Park for recording and analysis. MEMS logging systems 

provide low-cost, with minimum environmental footprints, with independent photovoltaic power 

supplies and easily retrievable data. We therefore recommend that a joint YVO-NPS working 

group, and other interested parties, be convened to formulate a work plan and seek funding 

sources for the implementation of a real-time hydrothermal monitoring plan for Yellowstone 

National Park. 

 

1

University of Utah

2

Swiss Federal Institute of Technology 
22

Figures

Figure 1. Example of a MEMS setup for hydrothermal monitoring.

Figure 2. Example of a typical seismic, acoustic and GPS network layout for hydrothermal monitoring at 

Norris Geyser Basin with a local MEMS recording system and links to UUSS telemetry at the Norris seismic 

station.
23

What Do We Know About Earthquakes at Yellowstone: An 

Overview of Swarms, Magnitudes, Fault Interactions, 

Earthquake Families, and Why We Can’t Find Long-Period 

Events

By Jamie Farrell1 and Bob Smith1

The Yellowstone area averages about 1,500 to 2,000 earthquakes per year, making it one 

of the most seismically active regions in the western U.S. interior. People have been reporting 

felt earthquakes in Yellowstone since the early explorers started visiting the region in the 1800s, 

so intense that geologist F.V. Hayden in 1871 named northern Yellowstone Lake, earthquake 

camp. With today’s modern seismic network, we are able to record and locate earthquakes in 

real-time and disseminate the information to stakeholders such as Yellowstone National Park 

(YNP) and the U.S. Geological Survey (USGS) as well as scientists interested in research in 

addition to the general public. 

The seismic network in Yellowstone was originally installed and operated by the USGS 

in the early 1970s until it was shut down in the early 1980s. The University of Utah took over the 

network in 1983 and has operated it ever since with continuous upgrades in number, quality, and 

reliability of stations. Today the Yellowstone Seismic Network (YSN) is one of the most modern 

volcano centric seismic networks in the world. 

From 1972 to 2016, Yellowstone has experienced over 44,000 earthquakes including one 

M6 event, two M5 events, 28 M4 events, and 364 M3 events (fig. 1). This includes the 1975 

M6.1 Norris event located just SE of Norris Geyser Basin on the 0.64 Ma Yellowstone Caldera 

boundary, which caused numerous rock-falls and affected the Yellowstone hydrothermal system 

including the formation of two new geysers. Prior to the installation of the YSN, the region 

experienced the 1959 M7.3 Hebgen Lake earthquake located just west of YNP near Hebgen 

Lake, Montana. The Hebgen Lake earthquake, the largest in the Intermountain West, ruptured 

the Hebgen Lake and the Red Mountain faults and triggered the Madison Canyon landslide, 

which damned the Madison River and formed Earthquake Lake. In total, 28 people died due to 

this earthquake with most being buried beneath the above-mentioned landslide. The shaking had 

profound effects on nearby YNP including changes to the hydrothermal system, major rock-falls 

along the roadways, and major damage the famous Old Faithful Inn. 

About half of Yellowstone earthquakes occur during earthquake swarms, a common 

mode of seismicity in volcanic areas where there is a burst of earthquakes in a relatively small 

time-period and geographic area. From 1985 to 2016, we have identified 389 individual swarms 

comprised of 20,290 swarm events (figs. 1, 2). Some of the largest swarms coincide with a 

change in caldera deformation such as the 1985 swarm (the largest swarm identified in 

Yellowstone), the 2008–2009 Yellowstone Lake swarm, the 2010 Madison Plateau swarm, and 

 

1

University of Utah
24

the 2013–2014 Norris area swarm sequence (fig. 2). Many of these aforementioned swarms have 

been attributed to the movement of fluids in the subsurface. 

This is demonstrated during the 2013–2014 Norris area swarm sequence where swarm 

activity began west of Norris Geyser Basin (NGB) in September 2013 and continued around the 

NGB through June 2014. During this time period, 16 individual swarms were identified with the 

largest one occurring during March 27–April 1, 2014 north of the NGB. This swarm included the 

March 30 M4.8 Norris area event, the largest earthquake in Yellowstone for over 30 years. Prior 

to this event, the Norris area had been inflating at high rates for ~6 months with a drastic change 

to subsidence coincident with the M4.8 earthquake (fig. 2). In addition, during this time period, 

the caldera changed from subsidence to renewed inflation showing the relationship between 

seismicity and deformation in Yellowstone both at local scales (Norris area) and over broader 

regions (caldera wide).

Moreover, hundreds of earthquakes were triggered in the Yellowstone area due to the 

passage of the surface waves from the 2002 M7.9 Denali Fault earthquake that occurred in 

Alaska ~3,000 km away. In addition to triggered earthquakes, the Denali Fault earthquake 

surface waves also changed the nature of many hydrothermal features in Yellowstone. Eruption 

intervals at many geysers were altered after the Denali Fault earthquake intervals at Riverside 

geyser and Daisy geyser decreasing by ~15 minutes and ~32 minutes respectively after the 

passage of the surface waves. These eruption intervals slowly recovered during the following 

months. 

Even though long period (LP) earthquakes, events that are deficient in energy above 5 

Hz, have been recorded in other active volcanic areas, including the Long Valley caldera, there is 

no evidence of LP earthquakes in Yellowstone. 

Overall, earthquakes play a major role in the formation and continued existence of 

Yellowstone’s world famous hydrothermal systems. Without periodic ground shaking, these 

systems would clog due to mineral precipitation from saturated hydrothermal waters.

The hazard of large amplitude ground shaking from an earthquake in or nearby 

Yellowstone remains the dominant geologic hazard for the region. Effects from seismic activity 

in Yellowstone does not only apply to human activity but also the natural resource itself and the 

more we can monitor and study seismic activity the better we can understand and mitigate its 

resulting effects in Yellowstone.
25

Figures

Figure 1. Yellowstone area earthquakes from 1972–2016. Earthquakes are sized based on magnitude 

with red circles representing the total seismicity during the time period and blue circles representing swarm 

earthquakes. The red star shows the location of the 1959 MW7.3 Hebgen Lake earthquake.
26

Figure 2. Histogram showing the amount of total earthquakes (red) and swarm earthquakes (blue) from 

1972–2016. Black and green lines show vertical deformation through time in the caldera and Norris area, 

respectively. 
27

Recent InSAR Studies at Yellowstone

By Chuck Wicks,1 Daniel Dzurisin,1 Peter F. Cervelli,1 Jacob B. Lowenstern,1 Wendy K. Stovall,1 and David 

R. Shelly1

We present ongoing InSAR related research covering three topics in the Yellowstone 

volcanic system: (1) uplift near the north rim of the caldera related to the March 30, 2014 MW 4.8 

earthquake: (2) deformation near the north end of Yellowstone Lake related to snow and water 

loading: and (3) a persistent deformation anomaly near Hebgen Lake.

Uplift of the Norris area that began in the autumn of 2013 (with a possible precursor in 

midsummer of 2013) switched to subsidence after the March 30, 2014 MW 4.8 earthquake that 

occurred near the area of peak uplift. We model the deformation measured by global positioning 

system (GPS) instruments (continuous and semi-continuous) and TerraSAR-X (TSX) 

interferograms spanning 2012 to June 2014 with a lens-like source near 6-km depth, north of the 

north caldera rim and beneath Norris Geyser Basin. We interpret this source as an accumulation 

of fluids derived from a deeper magma body. We speculate that the fluids accumulated near the 

top of the brittle/ductile transition and the MW 4.8 earthquake caused the fluid reservoir seal to 

rupture. Only the GPS data measure the peak inflation of the reservoir, whereas the InSAR data 

measure about 60 percent of the inflation. We interpret anomalous short-spatial-wavelength 

signals in one of the TSX interferograms to be the result of fluid migration from the reservoir 

into the crust. In this interferogram, the fluid appears to have migrated mostly to the west of the 

uplift area, and some InSAR signals indicate fluid intrusion into mapped faults. 

An ERS2 interferogram from June 5, 2011, to July 2, 2011, shows a brief excursion to 

caldera uplift during a long period (2010 through 2013) of caldera subsidence. The uplift, locally 

more than 2 cm, was concentrated on and near the resurgent domes. During this same time 

period the land near the north end of Yellowstone Lake appears to have subsided. Continuous 

GPS (CGPS) station WLWY, on the east side of the Sour Creek dome, shows over 10 mm of 

uplift in the time spanned by the interferogram, and CGPS station LKWY, about 1 km north of 

the Yellowstone Lake shoreline, shows about 10 mm of subsidence. By comparing the GPS 

signals to the water level gage at the outlet of Yellowstone Lake, the anomalous uplift seen in the 

interferogram and at station WLWY is interpreted to be the result of unloading from snow melt. 

The subsidence signal around the north end of Yellowstone Lake is interpreted to be the result of 

loading from the increased volume of water (from snow melt) in the lake. The spring of 2011 

saw a heavy accumulation of snow with over 75 cm of water-equivalent snow depth over most of 

the caldera by late May and the second highest lake level ever recorded (since 1923). The lakeloading signal is prominent on the LKWY vertical and north components every year. We hope to 

install additional semi-continuous GPS stations between LKWY and Yellowstone Lake and use 

the lake-loading signal to investigate the shallow crustal structure.

A series of ERS2 and ENVISAT interferograms spanning multiple years indicates that an 

area just east of Hebgen Lake is inflating at a rate of about 5 mm/yr. The interferograms span 

from 3 to 6 years between 1997 and 2009. The area of inflation, about 30 km in diameter, is on 

 

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U.S. Geological Survey 
28

the west end of the most seismically active area in Yellowstone. This area appears to be inflating 

at a near-constant rate, independent of the uplift/subsidence episodes of the Yellowstone caldera 

and the deformation center on the north caldera rim. We hope to install a semi-continuous GPS 

station near Hebgen Lake to better characterize the deformation.
29

Probabilistic Forecasting of Volcanic Eruptions Using 

Physics-based Models

By Kyle R. Anderson1

Magma physics are the mathematical “glue” which binds together our diverse 

observations of volcanic processes. Yet, almost all eruption forecasting is observational, based 

on recognizing patterns of activity or changes in the rates of key indicator signals (earthquake 

counts, distance between geodetic instruments, type and quantity of gas emissions, and so on). 

Despite the success of many such forecasts (for example, Swanson and others, 1983), this 

approach faces obvious limitations. 

Basing eruption forecasts on the physical laws governing volcanic processes and 

quantitatively constraining such models using time-evolving observations should, in principle, 

offer some advantages. Physics-based eruption models are (generally) predictive—that is, given 

a known set of initial conditions, they may be used to predict the state of the volcano at future 

times. Given a suitable volcano model and some observations, it should be possible to make 

quantitative, deterministic forecasts of future behavior.

Although physics-based eruption models have indeed been used to make forecasts (for 

example, Mastin and others, 2009), the approach has not been widely adopted for a number of 

reasons. Volcanic systems can be extraordinarily complex, even chaotic (with small changes in 

initial conditions leading to very different outcomes) (Sparks, 2003), and many fundamental 

principles of magma physics are still poorly understood. Models can only imperfectly represent 

reality, but the degree to which the model is “wrong” may not be known until too late. This 

difficulty is compounded for volcanoes that have not erupted in historical times, because it 

leaves modelers with no observations on which to base model designs, and for volcanoes that 

erupt with a wide variety of styles (such as Yellowstone). Physics-based eruption models can be 

very time consuming to design and develop, which presents a challenge given the constraints 

imposed by a rapidly evolving crisis. Similarly, the models themselves must not be too 

computationally intensive, and the forecasting framework must allow for the incorporation of 

new observations as they become available, possibly in close to real time. Finally, the current 

state of the volcanic system (volume of stored magma, volatile content, and so on), from which 

forecasts are extrapolated, is imperfectly known, leading to corresponding forecast uncertainty. 

Yet, ongoing research suggests cause for optimism. In particular, incorporating physicsbased eruption models into probabilistic frameworks can go a long way towards addressing some 

of the challenges listed above. If an eruption model is capable of predicting a diverse range of 

observations (Anderson and Segall, 2011), then a Bayesian inverse technique allows those 

observations, together with any available independent a priori information (derived from the 

results of previous studies) to be used to constrain model parameters (Anderson and Segall, 

2013). This approach can be extended in a straightforward manner to yield a mixed 

deterministic-probabilistic eruption-forecasting framework (Segall, 2013), suitable for use with 

 

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data assimilation techniques to incorporate evolving observations (Gregg and Pettijohn, 2016), 

which yields forecasts whose uncertainties are derived from uncertainties in initial conditions. 

Applying such a technique to a poorly understood volcano remains a challenge, but some 

assistance may come from information derived in advance from observations and statistics of 

many other volcanoes around the world (Ogburn and others, 2012). This Bayesian physics-based 

modeling and forecasting framework may also be incorporated into a larger Bayesian event tree 

(for example, Newhall and Hoblitt, 2002), such that volcano physics can be used where 

appropriate and other approaches (expert elicitation, pattern matching, and thresholding) can be 

used to inform probabilities in other cases. In the case of a rapidly evolving crisis, it is possible 

that generic eruption models could be utilized, much as generic event trees might be developed 

in advance for various classes of eruptive behavior (Newhall and Pallister, 2015). Finally, and 

encouragingly, we note that our understanding of magma physics is continually improving, our 

models are gaining in sophistication, computational speed is increasing, and fast sophisticated 

mathematical emulators are being developed that may be used in place of computationally

expensive models (Bayarri, 2009). 

We are likely many years from the point where physics-based models may be used to 

independently and reliably forecast activity at most volcanoes. However, forecasting the 

evolution (rather than the onset) of some eruption types—such as those whose evolution is 

controlled largely by deflation of an elastic reservoir rather than highly nonlinear conduit 

processes—may already be feasible. Thus, while one would not yet want to use a physics-based 

model to forecast the timing of a Yellowstone caldera-forming eruption, one might feel 

somewhat more confident using such a model to help forecast the ultimate volume of an active 

lava flow within the caldera.

References Cited

Anderson, K. and Segall, P., 2011, Physics-based models of ground deformation and extrusion 

rate at effusively erupting volcanoes: Journal of Geophysical Research—Solid Earth, v. 116, p. 

B07204, doi:10.1029/2010JB007939. 

Anderson, K., and Segall, P., 2013, Bayesian inversion of data from effusive volcanic eruptions 

using physicsbased models: Application to Mount St. Helens 2004–2008: Journal of 

Geophysical Research: Solid Earth, v. 118, p. 2017–2037, doi:10.1002/jgrb.50169. 

Bayarri, M.J., Berger, J.O., Calder, E.S., Dalbey, K., Lunagomez, S., Patra, A.K., Pitman, E.B., 

Spiller, E.T., and Wolpert, R.L., 2009, Using statistical and computer models to quantify 

volcanic hazards: Technometrics, v. 51, p. 402–413, doi:10.1198/TECH.2009.08018. 

Gregg, P.M., and Pettijohn, J.C., 2016, A multi-data stream assimilation framework for the 

assessment of volcanic unrest: Journal of Volcanology and Geothermal Research, v. 309, p. 

63–77, 2016, doi:10.1016/j.jvolgeores.2015.11.008. 

Mastin, L.G., Lisowski, M., Roeloffs, E., and Beeler, N., 2009, Improved constraints on the 

estimated size and volatile content of the Mount St. Helens magma system from the 2004–

2008 history of dome growth and deformation: Geophysical Research Letters, v. 36, p. 

L20304, doi:10.1029/2009GL039863. 

Newhall, C.G. and Hoblitt, R.P., 2002, Constructing event trees for volcanic crises: Bulletin of 

Volcanology, v. 64, p. 3–20, doi:10.1007/s004450100173. 
31

Newhall, C.G., and Pallister, J.S., 2015, Using multiple data sets to populate probabilistic 

volcanic event trees, in Papale, P, and Shroder, J., eds., Volcanic Hazards, Risks and Disasters: 

Elsevier, p. 203–232, doi:10.1016/B978-0-12-396453-3.00008-3. 

Ogburn, S.E., Loughlin, S.C., and Calder, E.S., 2012, DomeHaz—Dome-forming eruptions 

database v2.4: VHub database, available at https://vhub.org/groups/domedatabase. 

Segall, P., 2013, Volcano deformation and eruption forecasting: Geological Society, London, 

Special Publications, v. 380, p. 85–106, doi:10.1144/SP380.4. 

Sparks, R.S.J., 2003, Forecasting volcanic eruptions: Earth and Planetary Science Letters, v. 210, 

p. 1–15, doi:10.1016/S0012-821X(03)00124-9. 

Swanson, D.A., Casadevall, T.J., Dzurisin, D., Malone, S.D., Newhall, C.G., and Weaver, C.S., 

1983, Predicting eruptions at Mount St. Helens, June 1980 through December 1982: Science, 

v. 221, p. 1369–76, doi:10.1126/science.221.4618.1369. 
32

What Do We Know About Eruption Precursors at Large 

Silicic Caldera Systems?

By Daniel Dzurisin1

Recent unrest at the Yellowstone (Wyoming) and Long Valley (California) calderas 

raises the question of what precursors might be expected to occur before an eruption at one of 

these large silicic magma systems. Neither has erupted during historical time, so to address the 

question we are left with three types of information: (1) negative occurrences, in other words, 

events that have already occurred without an ensuing eruption, (2) evidence from other restless 

silicic calderas, including some with historical eruptions, and (3) evidence from petrologic and 

modeling studies of potential triggering mechanisms for caldera eruptions. 

At Yellowstone, historical unrest that has not culminated in an eruption includes 

seismicity, ground deformation, hydrothermal activity, and gas emissions. Large tectonic events 

in the vicinity include the 1959 MS 7.5 Hebgen Lake earthquake and the 1975 ML 6.1 Norris 

earthquake. Both shook the magmatic system violently and caused substantial changes in the 

overlying hydrothermal system, but neither triggered an eruption. Swarms of smaller earthquakes 

are common at Yellowstone and there is direct evidence for fluid migration in at least two cases 

(October 1985, Waite and Smith, 2002; December 2008, Farrell and others, 2010). The October 

1985 swarm was accompanied or soon followed by two small steam explosions, increased 

ground temperatures, and formation of new fumaroles and a mud volcano at three widely 

separated sites near the caldera rim (Dzurisin and others, 1994, p. 266). Another notable swarm, 

the largest since October 1985, began in January 2010 and, like the 1985 swarm, coincided with 

a reversal from caldera-floor uplift to subsidence—circumstantial evidence for a third case of 

fluid migration not followed by an eruption. 

Ground deformation, another common occurrence at Yellowstone during historical time, 

likewise has not been indicative of impending eruptive activity. For example, the 1959 MS 7.5 

Hebgen Lake earthquake produced a 26-km-long zone of surface faulting with scarps as high as 

6 m within 25 km of the northwest caldera rim, and postseismic deformation extended well into 

the caldera (Savage and others, 1993). New geysers formed and there were dramatic changes in 

some of Yellowstone’s preexisting hydrothermal features, but no eruption ensued. Less dramatic 

but more typical are bradyseisms, in other words, episodes of gradual (few centimeters per year) 

surface uplift or subsidence that typically last a few months to several years. The primary 

deformation loci are the center of the caldera and north caldera rim near Norris Geyser Basin, 

with secondary loci at each of the caldera’s two resurgent domes (for example, Wicks and others, 

2006; Dzurisin and others, 2012). Net uplift of the caldera floor since the first leveling survey in 

1923 has been nearly 1 m at rates as fast as 7–8 cm/yr (2004–2010; Chang and others, 2010), 

with intervening periods of subsidence lasting at least a decade (1985–1995, Dzurisin and others, 

1990, 2012). Near Norris, surface displacement rates as fast as 20 cm/yr have been observed for 

periods of several months (early 2014).

 

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Like seismicity and ground deformation, dynamic hydrothermal activity has been 

characteristic of the historical period of eruptive quiescence at Yellowstone. New features have 

formed while others have evolved or gone dormant. Examples include the changes mentioned 

above that occurred in association with large earthquakes or earthquake swarms. Even small 

dynamic stresses from regional or more distant earthquakes, such as the 1959 MS 7.5 Hebgen 

Lake, 1975 ML 6.1 Norris, 1983 MW 6.9 Borah Peak, and 2002 MW 7.9 Denali events have 

produced changes in the interval between eruptions at Daisy and Old Faithful geysers (Hurwitz 

and others, 2014). 

Volcanic gas emission at sustained, very high rates is another of Yellowstone’s 

characteristics during eruptive quiescence. With an estimated CO2 flux of 45,000±16,000 tons 

per day (t/d) (Werner and Brantley, 2003), Yellowstone is responsible for ∼5 percent of the 

global volcanogenic CO2 budget. It is unequivocal that a substantial fraction of the CO2 flux 

derives from outgassing of basaltic magma accumulating beneath the silicic system. Otherwise, 

the reservoir of silicic magma thought to exist beneath the caldera would be purged of CO2 in 

∼1,000 years. The same conclusion can be drawn from the relative proportions of CO2, chlorine, 

fluorine, and sulfur emitted at Yellowstone, which are inconsistent with degassing of silicic 

magma and, combined with the flux values, require basaltic input at a rate of ∼0.3 km3/yr 

(Lowenstern and Hurwitz, 2008; Hurwitz and Lowenstern, 2014). 

Given that the variety and intensity of unrest at Yellowstone during historical time has 

not been precursory to an eruption, we can look to calderas elsewhere for an answer to the 

question posed in the title of this abstract. Newhall and Dzurisin (1988) compiled information 

about more than 1,000 episodes of historical unrest at more than 100 calderas worldwide. Among 

their conclusions were (1) few very large earthquakes have occurred near calderas in association 

with unrest, and none were immediate eruption precursors; (2) eruptions at calderas are relatively 

common: more than 1,000 eruptions have occurred at calderas (all types) during historical time, 

about 18 percent of the roughly 6,000 eruptions from all types of volcanoes; (3) most explosive 

eruptions at calderas are small (75 percent were volcano explosivity index [VEI]) 1 or 2, 14 

percent were VEI 3, 3 percent were VEI 4+)—“Large calderas hold greater potential for 

catastrophic eruptions than do small centers, but most eruptions at calderas are no larger than 

eruptions at other volcanic centers” (Newhall and Dzurisin, 1988); and (4) good indicators of an 

impending eruption at silicic calderas are tremor, surface uplift, ground deformation, local 

seismicity, and thermal/hydrothermal changes. With the exception of tremor (and long-period 

earthquakes), Yellowstone routinely experiences all of the precursors that typically accompany 

eruptions at other calderas, but it hasn’t erupted for the past ∼70,000 years. 

If historical unrest is indicative of inter-eruption quiescence, what might be required to 

trigger an eruption at Yellowstone? Recent petrologic and modeling studies suggest several 

possibilities. Wotzlaw and others (2014), based on petrologic evidence, concluded that the 4.5 

Ma, ∼1,800 km3 Kilgore Tuff eruption at the Heise volcanic field in the eastern Snake River 

Plain, Idaho, followed a relatively short period (103

–104 years) of shallow crustal melting and 

assembly of isolated magma batches into a supervolcanic magma reservoir. They hypothesized 

that “...connection of magma batches vertically distributed over several kilometers in the upper 

crust would cause a substantial increase of buoyancy overpressure, providing an eruption trigger 

mechanism” (internal overpressure scenario). Gregg and others (2015) came to a different 

conclusion based on a numerical model of subcaldera magma bodies, which indicated that “...the 

ductile halo created around the hot magma chamber buffers increasing overpressures and 

prevents pressure relief via magmatic injection from the magma chamber.” Instead, according to 
34

the model, eruptions from the largest magma reservoirs are triggered when reservoir 

overpressure produces uplift and consequent faulting in the overlying roof (external faulting 

scenario). Barker and others (2016) argued from magma chemistry and mineral diffusion 

modeling that elevated mafic magma supply to a silicic mush zone beneath Taupo volcano 

resulted in rapid melt accumulation, and that high differential tectonic stress built up and 

culminated in the ∼105 km3 232 C.E. eruption (hybrid internal/external triggering scenario). 

If an eruption at Yellowstone could be triggered internally by basalt injection or magma 

reservoir integration, externally by faulting, or a combination of both, and if contemporary unrest 

at Yellowstone is not indicative of an impending eruption but instead typifies inter-eruption 

periods, then I suggest that the following unresolved questions need to be addressed in order to 

better assess the likelihood of an eruption in the foreseeable future. 

Does an eruptible body of silicic melt exist beneath the caldera? This is paramount. 

Potential eruption triggers matter little if the state of the subcaldera silicic magma body is such 

that neither an injection of basalt nor a through-going fault could intersect a body of eruptible 

partial melt.

How often do basaltic intrusions penetrate the subcaldera silicic magma body? It’s clear 

that basalt is underplating the silicic system, perhaps at a depth of ∼20 km, but how often do 

mafic and silicic magmas intermingle? Even if the answer to the first question is yes, triggering 

an eruption with a basaltic intrusion is unlikely if such intrusions seldom encounter eruptible 

magma.

Do the ∼150–70-ka caldera-filling rhyolite flows represent the end-stage of the third 

Yellowstone Plateau caldera cycle, a completed fourth cycle that did not include caldera 

formation, or the beginning-stage of a fourth cycle? Eruptions in the foreseeable future are most 

likely in the third scenario, but it is not known whether a fourth cycle is already over, just getting 

started, or will never occur. 

What do we know about eruption precursors at large silicic caldera systems? Not enough, 

clearly. But at Yellowstone, focusing on the three questions above might be a productive way 

forward.

References Cited

Barker, S.J., Wilson, C.J.N., Morgan, D.J., Rowland, J.V., 2016, Rapid priming, accumulation, 

and recharge of magma driving recent eruptions at a hyperactive caldera volcano: Geology, v. 

44, no. 4, p. 323–326, doi:10.1130/G37382.1. 

Chang, W-L., Smith, R.B., Farrell, J.M., and Puskas, C.M., 2010, An extraordinary episode of 

Yellowstone caldera uplift, 2004–2010, from GPS and InSAR observations: Geophysical 

Research Letters, v. 37, L23302, doi:10.1029/2010GL045451. 

Dzurisin, D., Savage, J.C., and Fournier, R.O., 1990, Recent crustal subsidence at Yellowstone 

Caldera, Wyoming: Bulletin of Volcanology, v. 52, p. 247–270. 

Dzurisin, D., Yamashita, K.M., and Kleinman, J.W., 1994, Mechanisms of crustal uplift and 

subsidence at the Yellowstone caldera, Wyoming: Bulletin of Volcanology, v. 56, p. 261–270. 

Dzurisin, D., Wicks, C.W, and Poland, M.P., 2012, History of surface displacements at the 

Yellowstone Caldera, Wyoming, from leveling surveys and InSAR observations, 1923–2008: 

U.S. Geological Survey Professional Paper 1788, 68 p. and data files, available at 

http://pubs.usgs.gov/pp/1788/. 
35

Farrell, J., Smith, R.B., Taira, T., Chang, W.L., and Puskas, C.M., 2010, Dynamics and rapid 

migration of the energetic 2008–2009 Yellowstone Lake earthquake swarm: Geophysical 

Research Letters, v. 37, L19305, doi:10.1029/2010GL044605. 

Gregg, P.M., Grosfils, E.B., and de Silva, S.L., 2015, Catastrophic caldera-forming eruptions 

II—The subordinate role of magma buoyancy as an eruption trigger: Journal of Volcanology 

and Geothermal Research, v. 305, p. 100–113, doi:10.1016/j.jvolgeores.2015.09.022. 

Hurwitz, S., and Lowenstern, J.B., 2014, Dynamics of the Yellowstone hydrothermal system: 

Reviews of Geophysics, v. 52, no. 3, p. 375–411, doi:10.1002/2014RG000452. 

Hurwitz, S., Sohn, R.A., Luttrell, K., and Manga, M., 2014, Triggering and modulation of geyser 

eruptions in Yellowstone National Park by earthquakes, Earth tides, and weather: Journal of 

Geophysical Research, v. 119, p. 1718–1737, doi:10.1002/2013JB010803. 

Lowenstern, J.B., and Hurwitz, S., 2008, Monitoring a supervolcano in repose—Heat and 

volatile flux at the Yellowstone caldera: Elements, Special Issue on Supervolcanoes, v. 4, p. 

35–40, doi:10.2113/GSELEMENTS.4.1.35. 

Newhall, C.G., and Dzurisin, D., 1988, Historical unrest at large calderas of the world: U.S. 

Geological Survey Bulletin 1855, 1108 p. 

Savage, J.C., Lisowski, M., Prescott, W.H., and Pitt, A.M., 1993, Deformation from 1973 to 

1987 in the epicentral area of the 1959 Hebgen Lake, Montana, earthquake (Ms=7.5): Journal 

of Geophysical Research, v. 98, p. 2145–2153. 

Waite, G.P., and Smith, R.B., 2002, Seismic evidence for fluid migration accompanying 

subsidence of the Yellowstone Caldera: Journal of Geophysical Research, v. 107, no. B9, p. 

2177, doi:10.1029/2001JB000586. 

Werner, C., and Brantley, S., 2003, CO2 emissions from the Yellowstone volcanic system: 

Geochemistry Geophysics Geosystems, v. 4, no. 7, p. 1061, doi:10.1029/2002GC000473. 

Wicks, C.W., Thatcher, W., Dzurisin, D., and Svarc, J., 2006, Uplift, thermal unrest, and magma 

intrusion at Yellowstone Caldera, observed with InSAR: Nature, v. 440, no. 7080, p. 72–75, 

doi:10.1038/nature04507. 

Wotzlaw, J.F., Bindeman, I.N., Watts, K.E., Schmitt, A.K., Caricchi, L., and Schaltegger, R., 

2014, Linking rapid magma reservoir assembly and eruption trigger mechanisms at evolved 

Yellowstone-type supervolcanoes: Geology, v. 42, p. 807–810, doi:10.1130/G35979.1. 
36

Tools for Decision Making in Eruption Forecasting

By Sarah E. Ogburn,1 John S. Pallister,1 and Heather M. Wright1

This work reviews and compares three popular methods for eruption forecasting: the 

Classical Model for Expert Judgment Elicitation; Probabilistic Event Trees and the U.S. 

Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) method of multiple 

datasets; and the Bayesian Event Tree-Eruption Forecasting (BET-EF) method and software.

Expert Judgment Elicitation using the Classical Model (also known as Structured Expert 

Judgment or Expert Opinion Elicitation; with procedures for accomplishing the elicitation also 

known as the Cooke-Aspinall Method, or the Delft Procedure) is a method of weighting and 

combining expert opinions to arrive at a pooled expert opinion for questions of interest (Cooke, 

1991; Aspinall, 2006; Aspinall and Cooke, 2013). Experts first give their median estimate, along 

with 5th and 95th percentile confidence bounds for a series of “seed” questions with known 

answers, which are relevant to the questions of interest. The experts are then weighted according 

to a measure of calibration, which measures the degree to which the data support the hypothesis 

that the experts’ given probability estimates are accurate for the seed questions (Aspinall, 2006). 

Experts are also scored based on the information in their distributions, which is related to the 

width of their distributions and the location of their median choices (Aspinall, 2006). Thus there 

exists a tradeoff between inaccurate over-confident estimates (narrow confidence bounds that 

miss the true value; bad calibration but high information) and accurate but imprecise estimates 

(true value is contained within wide confidence bounds; good calibration but low information). 

The experts then give bounded median estimates for target questions of interest. In this case, 

these are the questions regarding eruption forecasting that are unknown and have uncertainty that 

is difficult to quantify with historical or measured data. Finally, a synthetic “decision maker” is 

constructed using the expert weights and opinions to arrive at an assessment of variables of 

interest along with associated uncertainties (Aspinall, 2006; Aspinall and Cooke, 2013). Both the 

weighting of experts and the construction of the synthetic “decision maker” are accomplished 

using a software package (Excalibur; Cooke and Solomatine, 1992). This method is often 

employed in contentious situations where consensus cannot be reached; it is unbiased, inclusive, 

and confidential; and it formally quantifies uncertainties (Aspinall, 2006; Aspinall and Cooke, 

2013). The method, however, requires a trained facilitator and a software package. Weighting 

experts and creating appropriate seed questions can also be time intensive, although this can be 

done in advance of a crisis. Also, it can be difficult to see the rationale behind forecasts, as the 

method is confidential and anonymous. 

Probabilistic event trees are frequently used to facilitate discussion, reach consensus, and 

produce probabilistic forecasts of volcanic activity (Newhall and Hoblitt, 2002; Newhall and

Pallister, 2015). Event trees are graphical tree-like representations of successive possible events 

or outcomes, in this case, events during volcanic unrest. The branches of an event tree lead from 

general prior events (for example, volcanic unrest) to increasingly specific events (for example, 

volcano explosivity index [VEI] 3 explosion, lahars) to final outcomes (for example, areas 

 

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impacted or vulnerability). Each node of the tree is assigned both a nodal and conditional 

probability (based on prior nodal probabilities along a path). Expert Judgment Elicitation can be 

employed to assign these probabilities, or other, less formal, methods can be used. VDAP uses a 

“method of multiple data sets” (Newhall and Pallister, 2015) to combine conceptual and physical 

models of volcanic processes, current and past monitoring data, local and global patterns of prior 

occurrence, and expert judgment from multiple disciplines in order to assign probabilities via 

group discussion and consensus for each node of an event tree (Newhall and Pallister, 2015). 

Event trees provide a means to elicit communication and debate, a structure to help reach 

consensus, and a documentation of the rationale behind eruption forecasts (Newhall and Pallister, 

2015). This method requires little time to prepare and does not require software or a specially 

trained facilitator, though it is useful to have a moderator to keep the group on task and to record 

the rationale behind group decisions. The basis for each probability assignment is easy to trace 

and well documented. However, because it relies on consensus to assign probabilities, it greatly 

depends upon the group dynamic. Additionally, VDAP only quantifies event tree uncertainties in 

an informal way, through assigning high, median, and low probability estimates to each node; 

though uncertainty could be more fully quantified, depending on time constraints. 

The BET-EF software package also relies upon the basic event tree structure, but assesses 

nodal probabilities and uncertainties in a more formal way (Marzocchi and others, 2004; 

Marzocchi and others, 2008). First, a prior probability distribution for a particular node is 

estimated using theoretical knowledge. Then, the probability distributions are updated (to 

posterior probability distributions) based on historical data. Finally, the new distributions are 

calculated if monitored parameters exceed thresholds predetermined through an informal expert 

elicitation (Marzocchi and others, 2004; Marzocchi and others, 2008). Because probabilities at 

each node are represented by probability distributions, this method has the advantage of fully 

characterizing uncertainties in an eruption forecast. Additionally, different data sources are 

combined in a formal manner through a Bayesian framework (Marzocchi and others, 2004; 

Marzocchi and others, 2008), and thus it is possible to see the rationale for particular forecasts, 

given some statistical understanding. However, this method requires specialized software and 

training, and requires effort before a volcanic crisis to decide upon prior distributions, 

monitoring thresholds, and other software parameters. 

These three methods for eruption forecasting differ mainly in their formality (table 1). 

Expert Judgment Elicitation and BET-EF both formally weight experts, require expert facilitators 

and software packages, and report formal statistical uncertainties; while the event tree method, as 

practiced by VDAP, does not (Newhall and Pallister, 2015). The event tree method utilized by 

VDAP, however, is quicker to implement when faced with sudden volcanic crises, making it 

well suited to VDAP operations. Finally, it is important to note that all of these methods, 

regardless of formality, are ultimately based on the judgment of experts. 
38

Table 1. Comparison of methods. Modified from Newhall and Pallister (2015). 

Expert Judgment 

Elicitation

Event trees 

(VDAP) BET-EF

Weights experts Yes No, but subconsciously? Yes

Weights monitoring 

parameters

Informally Informally Formally

Formal statistical uncertainty Yes, through the 

weighting and 

combination of the 

probability 

distributions given by 

experts

No, high-medium-low 

consensus values are 

given for each node

Yes, through the use of 

updatable probability 

density functions at 

each node 

Requires expert facilitator 

and/or software package

Yes, both No Yes, both

Appropriate for sudden crisis 

at poorly studied volcano

Yes, if experts are 

weighted using seed 

questions in advance

Yes No; priors and 

monitoring thresholds 

must be determined in 

advance

References Cited

Aspinall, W.P., 2006, Structured elicitation of expert judgment for probabilistic hazard and risk 

assessment in volcanic eruptions, in Mader, H.M, Coles S.G., Connor C.B., and Connor L.J., 

eds., Statistics in Volcanology (Special Publications of IAVCEI, No. 1): Geological Society 

for IAVCEI, p. 15–30. 

Aspinall, W.P., and R.M. Cooke, 2013, Quantifying scientific uncertainty from expert judgement 

elicitation, in Rougier, J., Sparks, S., and Hill, L.J., eds., Risk and Uncertainty Assessment for 

Natural Hazards: Cambridge University Press., p. 64–99. 

doi:10.1017/CBO9781139047562.005. 

Cooke, R.M., 1991, Experts in Uncertainty—Opinion and Subjective Probability in Science: 

Oxford, England, Oxford University Press, 336 p. 

Cooke, R.M., and Solomatine, D., 1992, EXCALIBR User’s Manual: Delft, Netherlands, Delft 

University of Technology/SoLogic Delft.

Marzocchi, W., Sandri, L., Gasparini, P., Newhall, C., and Boschi, E., 2004, Quantifying 

probabilities of volcanic events—The example of volcanic hazard at Mount Vesuvius: Journal 

of Geophysical Research—Solid Earth, v. 109, doi:10.1029/2004JB003155. 

Marzocchi, W., Sandri, L., and Selva, J., 2008, BET_EF—A probabilistic tool for long- and 

short-term eruption forecasting: Bulletin of Volcanology, v. 70, p. 623–632, 

doi:10.1007/s00445-007-0157-y. 

Newhall, C., and Hoblitt, R., 2002, Constructing event trees for volcanic crises: Bulletin of 

Volcanology, v. 64, p. 3–20, doi:10.1007/s004450100173. 

Newhall, C.G., and Pallister, J.S., 2015, Using multiple data sets to populate probabilistic 

volcanic event trees, in Papale, P., and Shroder, J., eds., Volcanic Hazards, Risks and 

Disasters: Elsevier, p. 203–232, doi:10.1016/B978-0-12-396453-3.00008-3. 
39

Using Global Data for Eruption Forecasting

By Sarah E. Ogburn,1 Christopher J. Harpel,1 Jeremy D. Pesicek,1 John J. Wellik,1 John S. Pallister,1 and 

Heather M. Wright1

To prevent volcanic crises from becoming disasters, the U.S. Geological Survey-USAID 

Volcano Disaster Assistance Program (VDAP) helps foreign counterparts to assess volcanic 

unrest, activity, and hazards before and during crises. Probabilistic event trees are frequently 

used to facilitate discussion, reach consensus, evaluate uncertainty, and produce probabilistic 

forecasts of volcanic activity. Event trees (fig. 1) are graphical tree-like representations of 

successive possible events or outcomes, with the branches leading from general prior events to 

increasingly specific events to final outcomes (Newhall and Hoblitt, 2002). VDAP uses a 

“method of multiple data sets” (Newhall and Pallister, 2015) to assign probabilities to each node 

(nodal probabilities) and to calculate the conditional probabilities (the probability of a node 

given the occurrence of the previous node) along a path. This method combines conceptual and 

physical or empirical models of volcanic processes (for example, LAHARZ, energy cone), 

current and past monitoring data, local and global patterns of prior occurrence, and expert 

judgment from multiple disciplines to assign probabilities for each node of the event tree. Of 

particular importance is the global record of volcanic unrest, which can be used to inform our 

conceptual models, to fill in gaps where local information is sparse, to explore the full range of 

possible behavior, to compare volcanic activity to that at analogous volcanoes, and to improve 

uncertainty estimates by leveraging larger datasets. 

A variety of data sources are used to populate event trees, including scientific literature, 

the Smithsonian Institution’s Global Volcanism Program (GVP) Volcanoes of the World 

(VOTW) database of volcano and eruption information (Global Volcanism Program, 2013), the 

World Organization of Volcano Observatories (WOVO) WOVOdat database of monitoring data 

(http://www.wovodat.org), the Geologic Database of Information on Volcanoes in Alaska 

(GeoDIVA; Cameron and others, 2013), DomeHaz (Ogburn and others, 2012, 2015), and 

FlowDat (Ogburn, 2012, 2014; Ogburn and others, 2016). 

The Eruption Forecasting Information System (EFIS) is a new VDAP initiative with the 

goal of enhancing VDAP’s ability to forecast the outcome of volcanic unrest. The EFIS project 

seeks to (1) move away from relying on collective memory and towards probability estimation 

using databases; (2) create databases useful for pattern recognition and for answering common 

VDAP questions (for example, how commonly does unrest lead to eruption?); (3) create generic 

probabilistic event trees using global data for different volcano types; (4) create background, 

volcano-specific probabilistic event trees for frequently active or particularly hazardous 

volcanoes in advance of a crisis; (5) quantify and communicate uncertainty in probabilities; and 

(6) support, complement, and leverage existing databases. 

 

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Figure 1. Typical layout for a probabilistic event tree. Both nodal and conditional probabilities are 

assigned to each node of the tree. The Volcano Disaster Assistance Program (VDAP) typically focuses on 

nodes 1–7. After column 4, not all nodes are shown.

A major component of the project is the global EFIS relational database, which contains 

multiple modules designed to aid in the construction of probabilistic event trees and to answer 

common questions that arise during volcanic crises. The primary module contains chronologies 

of volcanic unrest—for instance, the timing of phreatic eruptions, column heights, and eruptive 

products. This module allows us to query eruption chronologies, monitoring data, descriptive 

information, operational data, and eruptive phases alongside other global databases, such as 

WOVOdat and the GVP-VOTW (see Redoubt 2008–2009 example, fig. 2). The relational 

database will be populated initially using chronologies of historical eruptive activity from 

eruptions in Alaska. 
41

Figure 2. Plots of the Redoubt Volcano 2008–2009 eruption chronology, including eruptive phases, alert 

level changes, eruptive products, and continuous monitoring data. Data were drawn from WOVOdat; 

Alaska Volcano Observatory AQMS Earthquake Catalog; ANSS Earthquake Catalog; Power and others, 

2013; Schaefer, 2011; Werner and others, 2013. Special acknowledgment to Kristi Wallace for her helpful 

eruption chronology data sheets.

Preliminary data are already providing insight into a variety of research areas. For 

example, VDAP provided a forecast of the likely remaining eruption duration for Sinabung 

volcano, Indonesia (which has been erupting a lava flow and dome since 2013), using global data 

taken from similar volcanoes described in the DomeHaz database (Wolpert and others, 2016), in 

combination with local monitoring streams (current and past eruptive volumes and extrusion 

rates, and so on). In addition, EFIS seismologists used the beta-statistic test and empirically

derived thresholds to identify distal volcano-tectonic earthquake anomalies preceding volcanic 

eruptions in Alaska during 1990–2015 to retrospectively evaluate Alaska Volcano Observatory 

(AVO) forecasts. 
42

References Cited

Cameron, C., and AVO staff, 2013, Geologic Database of Information on Volcanoes in Alaska 

(GeoDIVA): Fairbanks, Alaska Volcano Observatory.

Global Volcanism Program, 2013, Volcanoes of the World, v. 4.5.0: Venzke, E., ed.,

Smithsonian Institution, available at http://volcano.si.edu .

Newhall, C., and Hoblitt, R., 2002, Constructing event trees for volcanic crises: Bulletin of 

Volcanology, v. 64, p. 3–20, doi:10.1007/s004450100173.

Newhall, C.G., and Pallister, J.S., 2015, Using multiple data sets to populate probabilistic 

volcanic event trees, in Papale, P., and Shroder, J., eds., Volcanic Hazards, Risks and 

Disasters: Elsevier, p. 203–232, doi:10.1016/B978-0-12-396453-3.00008-3.

Ogburn, S.E., 2012, FlowDat—Mass flow database: Vhub database, available at 

https://vhub.org/groups/massflowdatabase.

Ogburn, S.E., Loughlin, S.C., and Calder, E.S., 2012, DomeHaz—Dome-forming eruptions 

database v2.4: Vhub database, available at https://vhub.org/groups/domedatabase.

Ogburn, S.E., 2014, Reconciling field observations of pyroclastic density currents with 

conceptual and computational analogs using a GIS and a newly developed global database:

Buffalo, New York, State University of New York, University at Buffalo, Ph.D. dissertation, 

318 p.

Ogburn, S.E., Loughlin, S.C., and Calder, E.S., 2015, The association of lava dome growth with 

major explosive activity (VEI ≥ 4)—DomeHaz, a global dataset: Bulletin of Volcanology, v. 

77, p. 1–17, doi:10.1007/s00445-015-0919-x.

Ogburn, S.E., Berger, J., Calder, E.S., Lopes, D., Patra, A., Pitman, A.B., Rutarindwa, R., 

Spiller, E., and Wolpert, R.L., 2016, Constraining sparse datasets using hierarchical Bayesian 

analysis, with application to assessment of pyroclastic density current mobility metrics:

Statistics in Volcanology, v. 2, p. 1–26, doi:10.5038/2163-338X.2.1.

Power, J.A., Stihler, S.D., Chouet, B.A., Haney, M.M., and Ketner, D.M., 2013, Seismic 

observations of Redoubt Volcano, Alaska—1989–2010 and a conceptual model of the Redoubt 

magmatic system: Journal of Volcanology and Geothermal Research, v. 259, p. 31–44,

doi:10.1016/j.jvolgeores.2012.09.014.

Schaefer, J.R.,ed., 2011, The 2009 Eruption of Redoubt Volcano, Alaska: Alaska Division of 

Geological & Geophysical Surveys Report of Investigation 2011-5, 45 p., doi:10.14509/23123.

Werner, C., Kelly, P.J., Doukas, M., Lopez, T., Pfeffer, M., McGimsey, R., and Neal, C., 2013,

Degassing of CO2, SO2, and H2S associated with the 2009 eruption of Redoubt Volcano, 

Alaska: Journal of Volcanology and Geothermal Research, v. 259, p. 270–284,

doi:10.1016/j.jvolgeores.2012.04.012.

Wolpert, R.L., Ogburn, S.E., and Calder, E.S., 2016, The longevity of lava dome eruptions:

Journal of Geophysical Research, v. 121, p. 676–686, doi:10.1002/2015JB012435.
43

An Exercise to Obtain Expert Views on Precursors to 

Volcanic Eruptions at Yellowstone

By Jacob B. Lowenstern,1 Sarah E. Ogburn,1 and Jamie M. Farrell2

Introduction

The last volcano explosivity index (VEI) 7 eruption on Earth occurred in 1815 at 

Tambora, Indonesia. The last VEI 8 occurred 26,500 years ago from the Taupo Caldera in New 

Zealand. Clearly, neither eruption was recorded with modern scientific instrumentation;

descriptions of precursors are minimal for the Tambora eruption and non-existent for Oruanui 

(Taupo) eruption. Volcanologists have abundant familiarity with the precursors to VEI 3, 4, and 

5 eruptions, but how can we know if something larger is in the offing? At large calderas, we have 

minimal instrumental records of the volcanic behavior prior to VEI 5 and larger eruptions. What 

would activity be like if Yellowstone Caldera were to erupt again? How could we tell the 

difference between a small and a large eruption? With minimal empirical data to answer such 

questions, a useful approach is to canvas experts and identify their prevailing views.

As part of the 2016 biennial meeting of the Yellowstone Volcano Observatory (YVO), 

we created a survey to obtain views on likely precursors to several eruptive scenarios at 

Yellowstone and its volcanic system. We did this to gain insight on the following questions: (1) 

What are the kinds and magnitudes of precursory phenomena that experts predict prior to a 

Yellowstone eruption? (2) What is the timeframe in which we expect those phenomena? (3) Are 

there any thresholds (in other words, amount of deformation or number of earthquakes) that 

experts tend to trust more than others? (4) How varied are scientists’ expectations about eruption 

precursors at a large caldera like Yellowstone? (5) Given a certain amount of unrest, what 

fraction of the group is likely to think an eruption is inevitable?

The survey included three scenarios: (1) a large lava flow, (2) a sizeable pyroclastic 

eruption, and (3) a hydrothermal explosion. In each case, respondents were asked to envision the 

entire range of precursors that might take place, including earthquakes, ground deformation, 

phreatic explosions, long-period earthquakes, and so on. Different respondents might envision

different timeframes for the unrest, and the questions were worded in a manner to reflect the 

different timeframes. That is, respondents could predict when precursors would occur as well as 

which types. Questions were posed about the nature of those precursors: some were multiple 

choice and others required numerical entries. We worded questions in a way that they would be 

understandable and intuitive to a diverse range of scientists. One weakness of the survey was that 

it failed to accommodate the “rates of change” of precursors.

Participants were invited to fill out the survey in March of 2016. This would give 

sufficient time to organize the data for a presentation at the May YVO meeting. Sixteen YVO 

scientists responded to the survey, including geologists, geodesists, and seismologists (self-

 1

U.S. Geological Survey

2

University of Utah
44

identified). There were insufficient responses to provide useful quantitative results, but 

qualitatively, the survey provided important insights that can help the observatory respond to 

future unrest. In particular, it created context for expectations, predictions, and thresholds 

envisioned by YVO staff.

The Questions

Scenario 1: Lava erupts from a vent near Mary Lake on the Central Plateau. The eruption lasts 

for about 5 years and erupts a total of 10 km3 of material, comparable to the size of the 

Mallard Lake Flow.

Scenario 2: A medium-sized caldera-forming eruption occurs from a vent near Shoshone Lake. 

Up to 10 cm of ash falls on Cody, Wyoming, between 10 and 50 km3 of magma are erupted, 

and there is widespread damage in Yellowstone National Park. All explosive eruptive 

activity is complete within about 1 month.

Scenario 3: A hydrothermal explosion creates a 250-m-wide crater at the Norris Geyser Basin 

over a several-hour-long series of explosive bursts, some of which are quite strong. Traces of 

ash reach Gardiner and Cooke City, Montana.

For each of these scenarios, the following questions (or ones slightly reworded) were asked: 

Number of earthquakes (M>1) within 50 km of the vent during the pre-eruption unrest 

Minimum

Maximum

Best Guess

Maximum earthquake magnitude within 50 km of the vent during the pre-eruption 

unrest 

Minimum

Maximum

Best Guess

Amount of maximum uplift (in centimeters) during the pre-eruption unrest

Minimum

Maximum

Best Guess

Number of phreatic explosions (plume >300 m) during the eruptive unrest

Minimum

Maximum

Best Guess

Please include any comments about these answers in the space below:

Below, please answer the timeframe that you think is most likely, with respect to 

monitoring parameters. 

a) Assuming one could detect the beginning of an earthquake swarm that relates to the 

eruption, how long before the eruption would the swarm reach 1,000 earthquakes 

(M>1)?

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption
45

• <1 month prior to eruption

• a swarm of that size would not occur prior to the eruption

• Comments _________________________________________

b) If you believe that a M>6 earthquake could occur, when would it be most likely? 

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption 

• <1 month prior to eruption

• only during the eruption itself

• an earthquake of that magnitude would not occur

• Comments _________________________________________

c) If >10 cm of maximum uplift occurs, when would it reach this threshold?

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption

• <1 month prior to eruption

• This threshold would not be exceeded

• Comments _________________________________________

d) If >1 m of maximum uplift occurs, when would it reach this threshold?

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption

• <1 month prior to eruption

• This threshold would not be exceeded

• Comments _________________________________________

e) With current available technologies, when would anomalous thermal activity near 

Mary Lake (for example, Highland Hot Springs) first be recognized?

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption 

• <1 month prior to eruption

• Not until the eruption

• Comments _________________________________________

f) When would we reach five major (plume >300 m) phreatic explosions?

• 1 year prior to eruption

• 6 months to 1 year prior to eruption

• 3 months to 6 months prior to eruption

• 1 month to 3 months prior to eruption

• <1 month prior to eruption

• There would not be that many phreatic explosions
46

• Comments _________________________________________

g) Could this eruption occur without any major (>300 m plume) phreatic explosions?

YES NO

The Results

YVO meeting survey respondents consistently predicted greater extent of unrest prior to 

Scenario 2 than Scenario 1, and considerably more unrest than typically occurs during historical 

episodes of unrest at Yellowstone. For example, a median of 6,000 M>1 earthquakes were 

expected prior to the lava flow, whereas a median of 10,000 earthquakes were postulated prior to 

a VEI 6 caldera-forming eruption. In contrast, only a median of 30 earthquakes was expected 

prior to a large hydrothermal explosion (Scenario 3). These values characterize the median of the 

“best guess” for the entire group of participants. Similarly, respondents expected 100 cm of 

cumulative maximum uplift with Scenario 1 (median), 150 cm associated with Scenario 2, yet 

only 3 cm for Scenario 3.

It is clear that most respondents feel that phreatic explosions will accompany future 

volcanic eruptions. Prior to Scenario 1, respondents expect a median of 10 events with an 

eruptive plume >300 m. That value rises to 30 prior to the VEI 6 caldera-forming eruption. A 

majority of respondents felt that phreatic eruptions were required prior to either type of eruption.

In contrast, most respondents felt that phreatic eruptions prior to the “climactic” hydrothermal 

explosion (Scenario 3) were not necessary and that the large final eruption could occur without 

smaller precursors.

The timescale of eruptions generated a diversity of viewpoints, though the great majority 

of respondents thought that precursors for Scenario 2 would be substantial over 6 months prior to 

the eruption. For example, more than 75 percent of respondents concluded that over 1,000 M>1 

earthquakes would be recorded over 6 months prior to the eruption, and more than 50 percent felt 

that long-period earthquakes (never before recorded at Yellowstone) would be evident more than 

3 months before the eruption. More than 60 percent thought that maximum uplift would exceed 1 

m more than 6 months prior to an eruption.

Such a long period of unrest prior to final eruption was also expected prior to Scenario 1, 

though all responses were shifted 1–3 months closer to the eruption. In contrast, few respondents 

had confidence that a large hydrothermal explosion (Scenario 3) would be accompanied by 

detectable precursors.

Some questions asked about thermal monitoring, including satellite-based infrared sensors as on 

Landsat 8. Assuming that there was regular quantification of geothermal heat flow, most 

respondents thought that rising magma could cause increased heat flow at the future site of 

eruption over 3 months prior to the eruption.

ISSN 2016-1104 (online)

http://dx.doi.org/10.3133/ofr

20161104