risk evaluation, detection and simulation during effusive eruption
TRANSCRIPT
Risk evaluation, detection and simulation during
effusive eruption disasters
ANDREW HARRIS1*, TOM DE GROEVE2, SIMON CARN3 & FANNY GAREL4
1Laboratoire Magmas et Volcans, Universite Blaise Pascal, 5 Rue Kessler,
63038 Clermont Ferrand, France2European Commission – Joint Research Centre, Institute for the Protection and the
Security of the Citizen, Via Enrico Fermi 2749, TP 680, 21027 Ispra (VA), Italy3Geological and Mining Engineering and Sciences, Michigan Technological University,
1400 Townsend Drive, Houghton, MI 49931, USA4Geosciences Montpellier, UMR 5243, Universite de Montpellier, Campus Triolet CC060,
Place Eugene Bataillon, 34095 Montpellier cedex 05, France
*Corresponding author (e-mail: [email protected])
Abstract: Lava ingress into a vulnerable population will be difficult to control, so that evacuationwill be necessary for communities in the path of the active lava, followed by post-event population,infrastructural, societal and community replacement and/or relocation. There is a pressing need toset up a response chain that bridges scientists and responders during an effusive crisis to allow near-real-time delivery of globally standard ‘products’ for a timely and adequate humanitarian response.In this chain, the scientific research groups investigating lava remote-sensing and modelling needto provide products that are both useful to, and trusted by, the crisis response community. Require-ments for these products include (a) formats that can be immediately integrated into a crisis man-agement procedure, and (b) in an agreed and stable standard. A review of current capability revealsthat we are at a point where the community can provide such a response, as is the aim of the REDSEED (Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters) workinggroup. This book is the first production of this group and is intended not only as a directory of cur-rent capabilities and operational service providers, but also as a statement of intent and need, whileproviding a simulation designed to demonstrate how a truly pan-disciplinary response to an effu-sive crisis could work.
Although volcanic eruptions do not often causelarge humanitarian disasters, they have the potentialto cause extreme ones (Alemanno 2011; Papaleet al. 2015). Volcanically induced mud flows caused21 800 deaths in Colombia in 1985 and CO2 poison-ing owing to the overturn of a crater lake caused1746 deaths in Cameroon in 1986. Both eventslasted minutes to hours and impacted a single catch-ment, but caused near-complete destruction withinthe impacted catchment. Explosive volcanic erup-tions also impact relatively small populations, forexample, 1 million people in 1991 in the Philip-pines, plus around 300 000 in Nicaragua (1992),Ecuador (2006) and Indonesia (1982) (source: Guha-Sapir et al. 2009). The impacting event is also muchshorter lived when compared with humanitariandisasters caused by conflicts and famines, but it caninflict locally severe loss over a very short periodof time. If such volcanic events are a major concernin terms of casualties, evacuation needs and dam-age, effusive eruptions also require an adequatehumanitarian response owing to the extreme hazard
they pose to human populations in their catchments(Blong 1984). Like any volcanic eruption, an effu-sive event cannot be stopped. However, being rela-tively slow in terms of propagation (lava flow frontstend to advance at a few to a few hundred metres perhour), there is time to respond to an advancing lavaflow once the event is underway. We may thus arguethat the effusive-event scenario is one that is rela-tively easy to prepare for, there being time to issuea call to scientific and civil protection respondersso as to set up event scenarios and response plansonce the event is underway.
The hazard
Although lava may threaten a community relativelyslowly and with due warning, lava ingress into avulnerable population will be at best difficult tocontrol, so that evacuation will be necessary forcommunities in the path of active lava flow (Harris2015). Upon passage, lava will burn and bury all
From: Harris, A. J. L., De Groeve, T., Garel, F. & Carn, S. A. (eds) 2016. Detecting, Modelling andResponding to Effusive Eruptions. Geological Society, London, Special Publications, 426, 1–22.First published online March 30, 2016, http://doi.org/10.1144/SP426.29# 2016 The Author(s). Published by The Geological Society of London. All rights reserved.For permissions: http://www.geolsoc.org.uk/permissions. Publishing disclaimer: www.geolsoc.org.uk/pub_ethics
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in its path (Blong 1984; Harris 2015), so that therewill be a need for post-event infrastructural, societaland community replacement and/or relocationif human intervention has not succeeded. Inter-vention may be possible using diversion, deflectionor damming barriers, bombs and explosives and/orwater, but all such measures require preparation,planning and allocation of money and resources.The deflection barriers constructed to protect vul-nerable infrastructure on the south flank of MountEtna during the 1983 eruption, for example, requiredthe construction of 10 km of service roads and deliv-ery of 750 000 m3 of rock to the construction siteover a period of 50 days at 13 h/day (Colombrita1984). Shipping 103 m3 of rock to the site each hourrequired 20 trucks per hour, with the total cost being3678 million Italian lira or 1.9 million Euros.
Because a number of large urban populationsare located on frequently active effusive centres,the problem is not trivial. Vesuvius, although wellknown for its explosive events, is also a frequentsource of effusive hazard, which required responsein the form of evacuation and property replacementas recently as March 1944 (Chester et al. 2007).On Vesuvius, if we consider the current populationeither on or in the paths of lavas erupted between1669 and 1944, we have a total of 337 500 peoplein a broad sector between the municipalities ofMassi di Somma–San Sebastiano in the NEthrough Ercolano–Leopardi–Trecase–Boscotrese–Boscoreale in the south to Terzigno in the SW(from ISTAT, 2015). On the flanks of Mount Etna,42 municipalities containing a population of933 354 (from ISTAT, 2008) are at risk, alongwith 369 km2 of agricultural land and 50 km2 ofurban land (Harris et al. 2011). On the island ofHawaii, 23 000 people and 66 000 housing unitslie on the 430 km2 lava flow field of Kilauea’sAD1445 eruption, which extends 40 km eastwardsfrom the caldera to the coast (Harris 2015). On LaReunion Island (France) any vent opening on theouter NE or NW flank of Piton de la Fournaise hasthe potential to impact the towns of La Plaine desCafres, Le Tampon and St Pierre to the NE (popula-tion: 156 500), and La Plaine des Palmistes, SainteBenoit and Sainte Anne–Sainte Rose to the NW(population: 42 825); the combined populations rep-resent almost 25% of La Reunion’s population(from INSEE, 2012). If we add to these the 1.1 mil-lion population at risk on the flanks of Nyiragongo(D.R. Congo), plus the populations of Kailua Kona(11 975) and Hilo (43 263) on Mauna Loa (fromUnited States Census Bureau, 2010), we arrive at atotal population of 2.65 million at risk on the flanksof these six effusive volcanoes alone.
Between 1905 and 2005, 19 population centreswere destroyed or damaged by lava (Table 1), thisbeing approximately two impacts per decade over
the twentieth century. Weisel & Stapleton (1992)recorded the losses and costs inflicted on Kalapana(Kilauea, Hawaii) owing to lava inundation in 1990as being:
† 175 residences destroyed;† removal of 21 uninsured homes;† 24 homes rendered uninhabitable;† damage costing US$50 million to private
property;† damage costing US$15 million to public
utilities;† damage costing US$32 million to public and
private roads;† US$3.5 million cost to rebuild water systems.
In addition, Bertile (1987) estimated the lossesowing to ingress of lava into Saint-Philippe (Ile deReunion) in March 1986 to be:
† agriculture – 1 661 414 French Francs (253 000Euros);
† housing – 1 250 000 French Francs (191 000Euros);
† household contents – 433 500 French Francs(66 100 Euros);
† other damage (roads, utilities, etc.) – 1 106 920French Francs (169 000 Euros).
Excluding the City of Goma (D.R. Congo) theevents of Table 1, if occurring today, would haveinvolved the displacement of almost 72 000 Euro-pean, American and/or Mexican citizens. In addi-tion to the needs, costs and logistics of evacuation,intervention, replacement and relocation for thesepopulations we have to consider the need for mentalhealth care, measures against the collapse of socialstructures and the maintenance of law and order.For example, while many residents of Kalapanasuffered from post-traumatic stress disorder owingto witnessing slow, piecemeal destruction of a tight-knit community (Weisel & Stapleton 1992), onesuicide was recorded during the invasion of SanSebastiano (Vesuvius) by lava during March 1944(Chester et al. 2007), in spite of San Sebastianobeing an extremely resilient population owing tothe experiences of the Second World War and anexcellent response organized by the Allied ControlCommand. In addition, while poor choice of landfor relocation of the population displaced by Paric-tuin’s 1943–44 lava flows led to mass emigrationto the USA or back the original volcanic zone, vio-lence and deaths resulted from land disputes inre-settled areas (Luhr & Simkin 1993).
During Nyiragongo’s 2002 effusive eruption,which invaded the city of Goma (population144 000), over US$40 million was committed to thehumanitarian response. Since then, funds were com-mitted in 2005, as well as in 2006 (US$8 million),2010 (US$2 million) and 2009 (source: Financial
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Tracking Service, https://fts.unocha.org/). Duringthe 2002 eruption, while the BBC reported lootingby some Congolese troops in a ‘completely de-serted’ town (Vesperini 2002), around 50 peoplewere killed when the petrol station from whichthey were taking fuel exploded (Harris 2015).Recently, Nyiragongo became active again and,because of a complex humanitarian crisis in thearea coupled with the breakdown of the local vol-cano observatory, space-based observation becamethe only alternative for monitoring the volcano.
RED SEED
This is the backdrop to the RED SEED (Risk Eval-uation, Detection and Simulation during EffusiveEruption Disasters) working group. The initial aimof this informal working group is to:
(1) review and collate all current capabilities inthe volcano hot spot remote-sensing and lavaflow modelling research communities;
(2) identify key issues that need to be addressedfor a near-real-time response to an effusivecrisis;
(3) identify standards and formats, linked to acommon database platform, to allow products
(i.e. remote-sensing or flow modellingoutputs) to be handed between each groupfor comparison, error testing, full probabilisticappraisals and ingestion into crisis responsemodels;
(4) create a common dataset to carry out tests dur-ing which data and products are fed throughthe chain from the remote sensor, throughthe modeller, to the operational responder;
(5) formalize a working group with a commoninterest in satellite-data-driven event detec-tion, tracking and modelling during effusiveeruptions.
The idea for RED SEED grew out of discussions inJune 2011 between the remote sensing and model-ling communities regarding the need to couple andcompare current volcano hot spot detection proce-dures, while providing outputs that could beingested into operational lava flow models. Such asystem could be of obvious benefit to real-time crisisresponse. Following discussions at the EuropeanCommission’s Joint Research Centre (JRC) duringthe VALgEO meeting, held at the JRC (Ispra,Italy) in October 2011, the theme grouping wasextended to the operational response community.That is, the remote sensing and modelling commu-nities would have to provide products that were
Table 1. Towns and villages completely or partially inundated by lava, 1905–2005
Town Volcano Eruption 2015Population
Source(date)
Casa Bianca/Boscotrese
Vesuvius 1906 10 353 dati.istat.it (2015 data)
Catena (nearLinguaglossa)
Etna 1923 5418 dati.istat.it (2015 data)
Hoopuloa Mauna Loa 1926 – No on-line data availableMascali Etna 1928 14 160 dati.istat.it (2015 data)Paricutin and
San Juan*Paricutin 1943–1952 5773 www.citypopulation.de (2010 data
for Angahuan)San Sebastiano/
MassaVesuvius 1944 9257 + 5491 dati.istat.it (2015 data)
Hookena Mauna Loa 1950 51 www.city-data.com (2007 data)Kapoho Kilauea 1960 579 www.city-data.com (2007 data)Vestmannaejar Eldfell 1974 4272 www.citypopulation.de (2015 data)Goma Nyiragongo 1977 (January) – Estimate given by Wikipedia:
1.1 million (14 October 2015)Piton Sainte-Rose Piton de la
Fournaise1977 (March) 6862 Insee, Recensement de la
population (2012)Sainte Philippe Piton de la
Fournaise1986 (March) 5129 Insee, Recensement de la
population (2012)Kalapana (and
Royal Gardens)†Kilauea 1990–1991 9986 www.city-data.com (2007 data for
Pahoa-Kalapana)Goma Nyiragongo 2002 – Estimate given by Wikipedia:
1.1 million (14 October 2015)
*Damage to three other villages: Zirosto, Angahuan and Zacan.†The last house in Royal Gardens sub-division was destroyed in 2010.
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Table 2. Review thermo-rheologically based models for terrestrial (sub-aerial) lava flows developed between 1970 and 1999, and the lava flow physical parametersaddressed by each
Model Heat sink Heat source Cooling Rheology Flow dynamics Notes
Danes (1972) qrad 3 T, m h, u Flow cooling and velocityHulme (1974) t0 u, Er, 1 Rheology and surface morphologyBorgia et al. (1983) Heat, kinetic and potential energy m, t0 X, u, 1 Channel-fed flow emplacementFink (1980) m 1 Flow surface foldingCigolini et al. (1984) f, m u, h, w, u Flow velocity profilePark & Iversen (1984) qrad 3 t0, m u, h, u Dynamics of Bingham fluidBaloga & Pieri (1986) qrad m X, h, u, Er, t Time-dependent flow profileDragoni et al. (1986) m, t0 u, h, w, u, Er, 1 Velocity profile for laminar flowPieri & Baloga (1986) qrad qadv 3 Er, X Effusion rate and cooling controls on flow areaBaloga (1987) m X, h, w, u Lava flow as kinematic waveMoore (1987) t0, m u, h, w, u, Er Channel flow and rheological modelBaum et al. (1989) m u Rhyolite: folds and Taylor instabilityDragoni (1989) qrad 3 T, m, t0 u, h, u, Er, 1 Temperature-dependent rheology and velocityHeslop et al. (1989) m, t0 u, h, w, u, Er, 1 Super-elevated flowBorgia & Linneman (1990) m, t0 u, X, h, Er, t Channel-fed flow field growthCrisp & Baloga (1990a, b) qrad qadv 3 t Surface thermal structure and qrad
Fink & Griffiths (1990) 3 Spreading of crusted viscous-gravity currentOppenheimer (1991) heat budget 3 Flow heat loss modelDragoni et al. (1992) qrad 3 T, m, t0 u, 1 Velocity profile for laminar flowFink & Griffiths (1992) qconv 3 u, Pe Flow surface morphologyManley (1992) 3 f, m u Silicic flow thermo-rheology modelGriffiths & Fink (1993) 3 Effects of surface cooling on the lava spreadingStasiuk et al. (1993) 3 Influence of cooling on lava-flow dynamicsCrisp & Baloga (1994) qrad, qent qadv, L 3 f Thermal effects of entrainment and LDragoni & Tallarico (1994) qrad T, f, m, t0 u, h, u f-dependent rheology and velocityKeszthelyi (1994) qrad 3 Ra Effect of vesicles on cooling
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Pinkerton & Wilson (1994) 3 m, t0 u, h, L, Er, Gz Flow length v. Er
Baloga et al. (1995) m u, h, w, u, Er, Re High velocity flowDragoni et al. (1995) qcond 3 m, t0 u, h, u, 1 Roofing over of a channelKeszthelyi (1995) heat budget qadv, L 3 Er Tube heat budgetBruno et al. (1996) m u, X, h, u, Er Gravity-driven flowDragoni & Tallarico (1996) 3 1 Flow front breakoutKeszthelyi & Denlinger (1996) heat budget qadv, L 3 Pahoehoe heat budgetBussey et al. (1997) qcond 3 Substrate heating and thermal erosionWooster et al. (1997) heat budget 3 Flow heat loss modelGregg & Fornari (1998) qrad, qconv L f, m u Submarine flow heat loss modelGregg et al. (1998) 3 m Er, 1 Flow surface foldingHarris et al. (1998) heat budget qadv, L 3 Er Tube-fed pahoehoe and ocean-entryKeszthelyi & Self (1998) heat budget qadv, L 3 m u, h, u, Re Flow heat budget/cooling rateNeri (1998) qrad, qconv Convective heat transferSakimoto & Zuber (1998) heat budget 3 u, Er, Re, Pr, Pe Tube flow and convective coolingWilliams et al. (1998) qcond 3 f, m u, Re, Pr Substrate heating and thermal erosionCashman et al. (1999) qrad L 3 f, m Lava cooling and crystallizationTallarico & Dragoni (1999) m, t0 u, h, u, Er, 1 Channelized laminar flow velocityKlingelhofer et al. (1999) heat budget 3 f, m u Submarine flow heat loss model
Collation is ordered chronologically (see Tables 3 & 4 for parameter definition).
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both useful to, and trusted by, the crisis responsecommunity, while also being provided in a formatthat could be immediately integrated into a crisismanagement procedure, and of an agreed and stablestandard.
A humanitarian perspective
The JRC of the European Commission has beenactive for over 10 years in the translation of scien-tific monitoring data into actionable informationfor its own humanitarian service (DG ECHO) andfor the wider humanitarian community. The JRCbegan a humanitarian alerting service in 2004 inthe framework of the Global Disaster Alert andCoordination System. The aim was to inform theresponse community in a timely manner about thelikely impact of sudden-onset disasters. With over20 000 professional first responders subscribed,the Global Disaster Alert and Coordination Systemservice is mature for earthquakes, tsunamis andcyclones. It is experimental for floods, but it isnot available at this point for volcanic eruptionsowing to the lack of globally consistent and near-real-time detection and measurement tools forvolcanic eruptions. In addition, the multiple waysin which volcanoes can impact local populationsand economies mean that developing an extensiveresponse model that is valid for all volcanoes inthe world, and all eruptive event types, is a challeng-ing task. Developing an operational service forvolcanic eruptions thus remains an outstandingchallenge.
The combination of increased satellite coverage,fast and accurate hot spot detection algorithms, plusnew ways to derive essential community-impactparameters led JRC to think about the potential forreal-time assessment of lava inundation duringan effusive eruption. These capabilities have thepotential to be combined for impact modelling,creating an opportunity to build services that mayhelp humanitarian and civil protection agencies –potentially in a similar way to the Volcano AshAdvisory Centers operate for the aviation commu-nity. Increased adoption of data that share commonstandards also helps to make observation and inter-pretation of data by local observatories easier,where it can be aggregated into a consistent globalsituational picture of volcano eruptions.
Having worked for several years in multi-disciplinary environments to bridge the gap betweenscience and operations, the vision of the JRC was,through initiation of RED SEED, to kick-start adialogue between different communities aimed atdeveloping new products and services that are ofdirect benefit to future humanitarian response duringeffusive crises, as described in Andredakis & DeGroove (2015).
The science perspective
One of the first attempts to numerically describeflow in molten basalt may have been by Recupero(1815) in the notes section to the first volume ofhis Storia Naturale e Generale Dell’Etna. In thisvolume Recupero derived a relation for ‘the resis-tance that opposes fluid motion’, which he thenused to calculate the time a bubble will ascend163.717 feet (i.e. 50 m) through a dense fluid, theresult being 11 minutes. Development of modernlava flow numerical models aimed at improvingour understanding of the physics and mathematicsof lava flow cooling, rheology and motion may betraced to the pioneering work of Danes (1972) andHulme (1974). A further 12 studies were publishedin the 1980s (Table 2), which began to considerthe physics-based and empirical relations betweenlava heat loss, rheology and dimensions of the flow,and lava dynamics (Tables 3 & 4). Around 30 werepublished in the 1990s (Table 2). During this decadea number of key analogue and theoretical models forlava flow cooling were published, including Crisp &Baloga (1990a, b), Fink & Griffiths (1990), Dragoniet al. (1992), Griffiths & Fink (1993) and Stasiuket al. (1993). These physical, analogue and empiri-cal models laid the foundation for more sophisti-cated lava flow simulation models that began to bedeveloped in the 1980s and have become increas-ingly advanced as computing capabilities haveproliferated. As a result, today, around 12 modellingoptions exist, culminating with models run onGraphics Processing Unit using the Smoothed Parti-cle Hydrodynamics model of Monaghan (1994,2005) for free surface flows (Table 5). These usevarious combinations of flow path, fluid dynamic,heat loss and rheological relations to model theflow advance and spread of active lava (Table 6).
Thermal remote sensing of volcanic emissionshas a similarly rich heritage that does not necessar-ily begin with the launch of the first vehicle armedwith sensor technology designed to capture therequired data. We can trace the invention of thefirst thermal remote sensing device back to theinvention of the gas thermometer by Amonton in1703 (Krutikov 2002), with William Herschel dis-covering emission in the infrared (0.7–20 mm)region of the spectrum in 1800, and his son, John,making the first thermal image in 1840. Then, in1957, the first man-made satellite was launchedinto Low Earth Orbit. The first platforms to carrythermal sensors were launched shortly thereafter,and ways and means to apply the resulting datadeveloped in parallel (Harris 2013).
The first thermal detection capability followedwith the launch of a radiometer on TIROS-2 in1960. Near-real-time hot spot response systemsusing thermal data have thus been within our
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reach since Gawarecki et al. (1965) reported ther-mal anomalies apparent in TIROS images recordedover Kilauea. As we argue throughout this book,
volume and mass flux is probably the most usefulmeasure – and deliverable – that the remote sens-ing community can provide to the modeller. Mass
Table 3. Parameters and symbols used in Table 2
Parameter Definition Units
h Flow depth mEr Effusion rate m3 s21
L Latent heat of crystallization J kg21
qadv Advected heat flux W m22
qcond Conductive heat loss W m22
qconv Convective heat loss W m22
qent Heat loss owing to entrainment W m22
qrad Radiative heat loss W m22
t Emplacement time sT Flow core temperature Ku Velocity m s21
w Channel width mX Flow length m1 Strain rate s21
f Crystallinity % (or volume fraction)u Slope degreesm Viscosity Pa st0 Yield strength Pa
Table 4. Summary of dimensionless numbers parameters used in modelling and their constitutive parameters,as used in Table 2
Parameter Definition Derivation/units
Gr Grashof number: ratio of buoyancy to viscosity Gr ¼ gb(T1–T0) L*3/y 2
Gz Gratz number: balance between mass diffusivity andthermal diffusivity
Gz ¼ ude2/kL*
Pe Peclet number: ratio of energy transport by convectionto that by conduction
Pe ¼ Re PrPe ¼ uL*/k
Pr Prandtl number: ratio of momentum diffusivity and tothermal diffusivity
Pr ¼ y/k
Ra Rayleigh number: describes the relationship betweenbuoyancy forces and thermal and momentumdiffusivities. Below a critical Ra heat transfer isconduction dominated, above the critical Ra heattransfer is convection dominated
Ra ¼ Gr PrRa ¼ (gb/ku) (T1–T0)L*3
Re Reynolds number: ratio of inertial forces to viscousforces. Below a critical Re flow is laminar, above thecritical Re flow is turbulent
Re ¼ uL*/y
cp Specific heat capacity J kg21 K21
de ¼ 2wd/(w + d ) Flow equivalent diameter mg Acceleration owing to gravity m2 s21
k Thermal conductivity W m21 K21
L* Characteristic length scale mT1 Hot fluid temperature KT0 Cold reference temperature (surrounding cool medium) Ku Velocity m s21
w Flow width mb Thermal expansion coefficient K21
k (¼ k/r cp) Thermal diffusivity m2 s21
m Viscosity Pa sr Density kg m23
y (¼m/r) Kinematic viscosity m2 s21
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Table 5. Review of models (in chronological order) for terrestrial lava flow emplacement simulation, and the physical basis of each (updated from Harris 2013)
Model and/or source references Application sites Flow pathmethod
Flowmodeltype
Velocitymodel
Heat lossmodel
Yieldstrengthmodel
Viscositymodel
Output
SCIARA Crisci et al. (1986) CA Cool H qrad 4(T) 4(T) First application of CA to lavaBarca et al. (1993) Etna 1986–87 Simulation of 1986–87 flowCrisci et al. (1999) Etna 1989 Risk assessment for three Etnean
townsCrisci et al. (2003) Etna 1991/2001 Simulation of 1991 and 2001
flowsCrisci et al. (2004) Etna 1669 Hazard assessment for Catania
based on 1669FLOWFRONT Young & Wadge
(1990)Etna Grid Vol – – t0 – Lava inundation zone
Wadge et al. (1994) LonquimayEtna
Lava inundation zone for1988–89 flowLava flow hazard map for Etna
Ishihara et al. (1990) Miyakejima 1983Izu-Oshima 1986Sakurajima 1914
CA Cool NS qrad t0(T) m(T) Simulation of the three flow fieldsOriginal cooling limited CA
Kauahikaua et al. (1995) Mauna Loa Line-C Prob – – – – Lava flow hazard map for MaunaLoa
Miyamoto & Sasaki (1997)Miyamoto & Sasaki (1998)Miyamoto & Papp (2004)
Miyakejima 1983Okmok 1997
CA Cool NS qrad t0(T) m(T) Simulation of 1983 flowApplication to flood basaltsSimulation of 1997 flow
Felpeto et al. (2001) Lanzarote Line-S Prob – – 2 2 Lava flow hazard map forLanzarote
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FLOWGO Harris & Rowland(2001)
Mauna LoaKilaueaEtna
Line-C Cool J Q+ t0(T,f) m(T,f) Simulation of 1984 channelconditions
Simulation of 1997 channelconditions
Simulation of 1998 channelconditions
Rowland et al.(2005)
Mauna Loa Effusion rate contour-based hazardmap for ML
Harris et al. (2007) Etna 2004 Validation using LIDAR-derivedchannel data
Costa & Macedonio (2005) Etna 1992 Grid Cool SWE Q+ 2 m(T) Simulation of 1992 flowDOWNFLOW Favalli et al. (2005) Etna 1992/2001/2004 Line-S Prob(D) E 2 2 Lava flow inundation assessment
for each flowFavalli et al. (2006) Nyiragongo 2002 Flow path simulations through
GomaTarquini & Favalli
(2013)Etna Mapping simulation of recent lava
flow fieldsLavaSIM Hidaka et al. (2005) Izu-Oshima 1986 CA Cool NS Q+ t0(m) m(T) Simulation of 1986 flowMAGFLOW Del Negro et al.
(2005)Etna 2001 CA Cool NS qrad t0(T) m(T) Introduction to Cellular Nonlinear
NetworksVicari et al. (2007) Etna 2001 Simulation of 2001 flowHerault et al. (2009) Etna 2006 Simulation of 2006 flowDel Negro et al.
(2008)Etna 2004 Simulation of 2004 flow
SPH-flow Herault et al. (2011) Etna CA Cool NS qrad t0(T) m(T) 1st application of GPU SPH(Smoothed ParticleHydrodynamics) model for freesurface flows.
See Tables 3 and 4 for definition of terms; GPU ¼ Graphics Processing Unit.
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flux derived from the satellite perspective also rep-resents a near-real-time assessment of eruptionintensity than can be updated at frequent, regularand known times, thus being a key measureablethat can be delivered for assessment and reportingduties during a volcanic crisis. The time-line forthe development of the methodology used in ther-mal remote sensing to obtain mass flux can betraced back to the pioneering work of Jeffreys(1924), who linked heat flowing into a mass ofrock to changes in temperature and pressure in
the rock mass. This principle has subsequentlybeen developed and debated, increasing our under-standing as to how at-sensor spectral radiance canbe related, theoretically or empirically, to themass or volume of cooling lava present in theanomalous pixel (Fig. 1). The aim has been to pro-vide a valid, reliable, operational product to model-lers and responders. Given all of these needs,capabilities and advances we are today in a posi-tion to generate the ideal response chain sketchedin Figure 2.
Table 6. Key to abbreviations used in Table 5 (from Harris 2013).
Abbreviation Description
(1) Flow pathLine-C Single downhill flow path is projected down a map or DEM as a line, in a method identical to that used
to identify stream flow lines and water catchments.Line-S Multiple (n) flow paths are projected using a stochastic model that projects a downhill flow path down a
DEM, adds random noise to the DEM, projects a new path and repeats n times to produce a field offlow paths.
Grid Flow spreads across a gridded topography.CA Cellular automata.
(2) Flow model typeProb Probabilistic – assessment of the probability that a lava flow will inundate a given location.Prob(D) Probabilistic (DOWNFLOW) – probability of lava invasion for any given pixel downflow of a given
vent is based on the ratio of the number of times a pixel is crossed by a flow path divided by thenumber of runs. Runs are projected to the edge of the DEM with no length limit.
Cool Cooling-limited – lava control volume cools with time and distance, so that the core temperaturedeclines and the flow rheology evolves until further spreading/advance is no longer possible.
Vol Volume-limited – a fixed volume of lava is spread across the DEM until the volume is used up.
(3) Velocity model– No velocity model applied.NS Navier–Stokes.H Flow across each cell is driven by hydrostatic pressure gradients set up by differences in lava thickness
within surrounding cells, and the rheology of lava in the cell.J Jeffreys equation for mean velocity of flow in a channel.SWE Use of shallow water equations as introduced by de Saint-Venant (1871) and Boussinesq (1872) and
used for simulations of floods and tsunami propagation. Assumes an incompressible, homogeneousfluid, a hydrostatic pressure distribution and uniform or gradually varying flow.
E Flow length is based on empirical laws that relate flow length on Etna to vent altitude or effusion rate.
(4) Heat loss model– No heat loss model applied.qrad Heat loss considers solely radiative heat loss (qrad).Q+ Heat loss model considers: radiation (qrad) and forced and/or free convection (qconv), plus conduction
through the flow base (qcond), as well as heat loss owing to boiling and vaporization of rainwater, and/or surface crust entrainment (qent).
(5) Yield strength model– No yield strength model applied.t0 A constant yield strength is used to define the critical thickness of lava remains in a cell.t0(T) A temperature-dependent yield strength model is used.t0(T,f) A temperature- and crystal-dependent yield strength model is used.t0(m) An empirical approach to set yield strength as a function of viscosity.
(6) Viscosity model– No viscosity model applied.m(T) A temperature-dependent viscosity model is used as set for the appropriate composition.m(T,f) A temperature- and crystal-dependent viscosity model is used.4 (T) Temperature-dependent adherence parameter that defines the thickness of lava that cannot flow out of a
cell owing to rheological resistance.
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1950 1960 1970 1980 1990 2000 2010
Yokoyama (1957): Converts between mass and heat carried by lava flows for Mihara’s 1950-
1952 eruption
Hérdervári (1963): Converts between mass and heat carried by lava
flows for 94 eruptions
Friedman & Williams (1968) Williams & Friedman (1970):
Converts between lava volume and heat flux for Surtsey’s
1967 activity
Scandone (1979): Converts between lava mass and heat carried
by lava for Paricutin’s 1943-52 eruption
Pieri & Baloga (1986): Explores empirical relation
between lava flow eruption rate, area, length and heat flux.
Data for Hawaiian lavas used
Le Guern (1987): Uses radiometer-derived heat flux
for Niragongo’s lava lake to convert to mass flux (of magma involved in convection)
Wooster et al. (1997): Converts volume of Etna’s 1991-1993 lava flow field to heat released in cooling from molten to ambient
Crisp & Baloga (1990): Presents a method that uses heat flux to convert to eruption rate -
proposed as a method for estimating eruption rates for planetary lava flows
Harris et al. (1997):Adapts approach of Pieri & Baloga (1986) to convert AVHRR-derived heat fluxes to lava discharge rates for Etna’s 191-1993 eruption
Francis et al. (1993): Uses previously TM-derived heat fluxes to convert to mass fluxes
(of magma involved in convection) at Erta Ale and Erebus lava lakes, as well as Lascar’s dome
Harris et al. (1998; 1999):Uses TM-derived heat fluxes to derive lava discharge rates at Kilauea, as well as mass fluxes for lava lakes at Erebus, Erta Ale & Nyiragongo
Wright et al. (2001): Clarification of the of the discharge rate conversion technique as applied to satellite IR data
Dragoni & Tallarico (2009) Harris & Baloga (2009)
Harris et al. (2010):Clarification and testing of
the relation used to convert lava area to discharge rate
Garel et al. (2012):Theoretical and experimental
attempts to link discharge rate and heat flux
Ganci et al. (2012):Use of cooling curves derived
from SEVIRI data to integrate heatflux through time, and then
convert total power released tolava volume for short-durationfountain-fed lava flows at Etna
21 published IR data based TADR estimatesSee: Table S5.2, Electronic Supplement 5 of Harris (2013)
Fig. 1. Time line for studies involving conversion of at-sensor spectral radiance to lava mass or volume (or vice versa), with a special emphasis on use of thermal sensors toestimate time-averaged discharge rate and lava volume. Modified from Electronic Supplement 5 of Harris (2013).
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The RED SEED working group: initiation
and activities
In mid-2012, an application was submitted to theEuropean Science Foundation MeMoVolc network(Measuring and Modelling of Volcano EruptionDynamics, http://www.esf.org/index.php?id=9263)to secure funds to support a workshop with thegoal of gathering, for the first time, the main actorsin satellite volcano hot spot detection, lava flowmodelling and effusive crisis response together inone room. The initiative was funded to cover16 000 Euros in delegate costs in December 2012.Invitations went out shortly afterwards, parallelwith a scramble to raise funds to support attendanceof delegates from countries that were not MeMo-Volc members. Remaining support came from theJRC (3000 Euros), Universite of Blaise Pascal(2600 Euros), the Laboratoire Magmas et Volcans(2000 Euros), l’Observatoire de Physique du Globede Clermont-Ferrand (1500 Euros), as well as LabexCLERVOLC (Clermont-Ferrand Centre for Vol-cano Research, http://clervolc.univ-bpclermont.fr/).CLERVOLC contributed not only 4200 Euros forworkshop support, but also a further 8000 Euros tohire a technician to aid in book preparation and pro-ject completion. Together these funds ensured acomprehensive gathering of the volcano hot spotdetection and lava flow modelling communities.
The workshop was held between 28 and 30 May,2013, at the Maison Internationale Universitaire ofClermont Universite (Clermont Ferrand, France),and the list of attendees is given in Table 7. Prepa-ration of the abstract volume allowed us to attain
our first objective, that being ‘to review and collateall current capabilities in the volcano hot spotremote sensing and lava flow modelling communi-ties’. This collation is given here in Appendix A(for volcano hot spot detection) and Appendix B(for lava flow modelling) of the concluding chap-ter of this book (Harris et al. 2016). Objectivestwo and three, identification of ‘key issues thatneed to be addressed for a near-real-time responseto an effusive crisis’ and ‘standards and formatsthat allow effective product communication duringan effusive crisis’, were met by group reporting,which is given here in the same concluding chapter(Harris et al. 2016).
The fourth objective was ‘to agree on a commondataset and carry out a test during which data andproducts are fed through the chain from remote sen-sor through modeller to operational responder’. Tocomplete this test, the group agreed to follow theresponse flow of Figure 2. To test this flow, webegan by comparing volcano hot spot detectionalgorithm output for two cases for which quality sat-ellite thermal sensor data exist:
(1) a short-lived high-intensity effusive event,this being the 12–13 January 2011 fountain-ing event from Etna’s SE Crater as recordedby SEVIRI;
(2) a long-lived low-intensity effusive event cap-tured by MODIS, this being Etna’s 2008–09east flank eruption.
All detection algorithm outputs for these two casesare collated in Appendix C of Harris et al. (2016).For linking with lava flow models, we agreed to
Fig. 2. Flow chart showing information chain from detection of infrared data, through provision of source terms(vent location and time-averaged discharge rates) to lava simulation models, which provide inputs for impactassessments into a GIS for humanitarian response.
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Table 7. Workshop on Satellite-Data-Driven Detection, Tracking and Modelling of Volcanic Hot Spots (28–30May, 2013, Clermont Ferrand): participant list broken down by working group
No. Name Affiliation (as at time of workshop)
(1) Hot Spot Detection and Deliverables1 Talfan Barnie Cambridge University, UK2 Diego Coppola University of Turin, Italy3 Jonathan Dehn AVO, University of Alaska Fairbanks, USA4 Fanny Garel Imperial College London and Cardiff University, UK5 Yannick Guehenneux LMV, Universite Blaise Pascal, France6 Andrew Harris LMV, Universite Blaise Pascal, France7 Valerio Lombardo INGV – Rome, Italy8 Peter Miller NERC Remote Sensing Data Analysis Service9 Nicola Pergola University of Basilicata, Italy
10 Robert Wright University of Hawaii, USA11 Klemen Zaksek University of Hamburg, Germany
(2) Towards Operational Tracking and Dissemination Systems12 Simon Carn Michigan Technological University, USA13 Thibault Catry Station SEAS-OI, La Reunion, France14 Ashley Davies Jet Propulsion Laboratory (USA)15 Gaetana Ganci INGV – Catania, Italy16 Mathieu Gouhier OPGC, Universite Blaise Pascal, France17 Matthew Patrick USGS – Hawaiian Volcano Observatory, USA18 Michael Ramsey University of Pittsburgh, USA
(3) Lava Flow Modelling and Deliverables19 Noe Bernabeu University of Grenoble, France20 Benoıt Cordonnier ETH Zurich, Switzerland21 Eisuke Fujita National Research Institute for Earth Science and
Disaster Prevention, Japan22 Karim Kelfoun LMV, Universite Blaise Pascal, France23 Ciro del Negro INGV – Catania, Italy24 Rocco Rongo University of Calabria, Italy25 Simone Tarquini INGV – Pisa, Italy
(4) Crisis Management: Requirements26 Sonia Calvari INGV – Catania, Italy27 Tom De Groeve IPSC, European Commission Joint Research Centre28 Anthony Finizola University of Reunion, La Reunion, France29 Magnus Guðmundsson University of Iceland, Iceland30 James Kauahikaua USGS – Hawaiian Volcano Observatory, USA31 Giovanni Macedonio INGV – Osservatorio Vesuviano, Italy32 Jose Pacheco University of the Azores, Portugal33 Maurizio Ripepe University of Florence, Italy34 Kay Smith British Geological Survey, UK
(5) Laboratoire Magmas et Volcans: RepresentativesPierre Schiano LMV and CLERVOLC DirectorOlivier Roche LMV – Assistant DirectorTim Druitt CLERVOLC Scientific Manager and MeMoVolc
CoordinatorJean-Francoise Lenat Head of the LMV Volcanology GroupPatrick Bachelery OPGC, DirectorPhilippe Labazuy Head of OPGC Remote Sensing GroupJean-Luc Le Pennec IRD, directorJudicael Decriem HOTVOLC (LMV/OPGC)Jean Battaglia Seismology (LMV/CNRS)
(6) Laboratoire Magmas et Volcans: StudentsMaxime Bombrun PhD (Advanced alogorithms for hot spot tracking)Benjamin Latutrie M2 (Texture and rheology of thick, silicic lavas)Benedict Robert M2 (Texture and rheology of Hawaiian lavas)Marina Valer M2 (Geochemistry of porphrytic basaltic lavas)
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run all models on a neutral site using the time-averaged discharge rate output form the two hotspot detection test cases. The neutral test site wasselected as the Chaıne des Puys (Auvergne, France),this being an effusive centre local to the workshopvenue and a major urban centre (Clermont Ferrand)at which no model had previously been initialized,applied or tested, and at which no group had avested interest. Two eruption cases were selectedas being:
(1) a short-lived high-intensity effusive eventfrom the Petit Puy de Dome;
(2) a long-lived low-intensity effusive event fromthe Grave Noire.
The location of these vents and flow fields in relationto Clermont Ferrand are given in Figure 3. Locatedin the centre of France, immediately to the east ofthe Chaıne des Puys, this urban area covers42.67 km2, has a population of 142 948, and con-tains 10 240 industrial, commercial and serviceestablishments (source: INSEE 2007 and ClermontCommunaute en Chiffres 2008). While the resultsof these tests are collated in Appendix D of Harriset al. (2016). Benchmarking of the same flow modelsis presented in Cordonnier et al. (2015) for the Etna2001 eruption, for which the dynamics and chronol-ogies of flow emplacement are better known than forthe Chaıne des Puys cases. Following the work-flowof Figure 2, our final objective was to put all ofthese results together in a GIS for humanitarianresponse. That is, to create a central data collectionhub that also allows assessment of the potential forpopulation displacement and material loss on thebasis of the satellite-data-driven, model-based lavainundation projections. The overall aim of this exer-cise was to assess how the community can unite toprovide a timely, effective, appropriate and unitedresponse to a humanitarian crisis during an effusiveeruption. The result of our group test is given inLatutrie et al. (2015).
In terms of formalizing the working group, weneed funds and a documented statement of intentand capability. For funds, we are currently seekinglong-term financial support for cooperation, net-working and exchange from the European Commis-sion’s COST (European Cooperation in Scienceand Technology) framework. The key argumentfor the COST submission has been that, becauselava flows will impact heavily urbanized areas ofEurope (and the world), such an action is timelybecause
the relevant tools for mitigation are now being per-fected and need to be pooled to provide an effectiveresponse. Huge advances in technology and computingpower over the last two decades have meant that weare, today, in a position to greatly update and enhancescience and technology as it applies to research in
volcanology. In addition, one day a destructive, effu-sive event will enter a major urban or industrial areainflicting complete socio-economic loss and displace-ment in the inundated zone. Volcanic hazards such aslava flows are often considered binary, with total dam-age in the areas impacted and zero damage in areas notimpacted, regardless of building type. However, thismay be simplistic in some cases because, for example,lava flows can cause fires outside of the zone ofimpact. In addition, acid gases can impact distantcommunities.
The aim is to unite the community in preparation, sothat we are ready and able to respond to such anevent when it occurs. We are seeking further supportthrough a proposal to the French Agence Nationalede la Recherche (generic project 2015 challenge 9,‘Liberty and security of Europe, its citizens and itsresidents’). This title describes well the niche intowhich RED SEED now needs to fit, our argumentbeing that lava flow surveillance and modellingare at a point today where results can be input intoreal-time risk assessment models. Models lack,however, real-time ingestion of source term condi-tions (i.e. the volumetric, textural and rheologicalproperties of the lava) as well as consideration ofnatural and man-made surfaces over which modelswill be run. We thus have a fundamental need forprocess-based research to allow cross-disciplinaryinteraction. This is required to, first, achieve a fullunderstanding of the complex physico-chemicalprocesses that control lava propagation and, second,to develop information collation, integration anddistribution nodes. The overall objective is to gener-ate a system for intelligent, state-of-the-art, real-time risk assessment. Our objective is to buildsuch a system, this being a GIS-based database thatcan store lava flow simulation results and eventscenarios to allow full assessment of risk and loss, aswell as mitigation, evacuation and replacementneeds, plus implementation of cost–benefit analysis.
The book
This book, published as part of the Geological Soci-ety of London Lyell Series, is our documented state-ment of intent and capability. Our intention is, toprovide documentation to support an argumentwhereby a fully integrated approach involving allactors and stakeholders allows the best possibleresponse in terms of citizens and residents of dan-gerous volcanic terrains. Bringing together 96authors from France, Germany, Iceland, Italy,Japan, Portugal, Switzerland, UK and the USA. Thebook is split into four thematically grouped parts:
(1) Volcano Hot Spot Detection, Tracking andTargeting;
(2) Towards Operational Tracking and Dissemi-nation Systems;
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Fig. 3. Mapped extent of the Petit Puy de Dome (light green) and Grave Noire (dark green) lava flow fields modified from Latutrie et al. (2015). These two flow fields are hereoverlain on the shaded relief, height map (warmer colours ¼ greater elevations) and aerial photograph covering the City of Clermont Ferrand. Brown zone around the whitestar marks the area of the Grave Noire scoria mound. This flow field was emplaced around 60 + 5 ka and now underlies the towns of Chamalieres (NE branch) plus Beaumontand Aubiere (east branch). Dashed green circle gives approximate location of the source of the Petit Puy de Dome lavas. This flow field was erupted around 42 + 2 ka and flowacross the plateau to the east of the Puy de Dome. Between La Font de L’Arbre and Royat, the Petit Puy de Dome lava follows the valley of the River Tiretaine, beforemeeting ponding behind a barrier formed by the earlier Grave Noire flows. The root of the workshop field excursion is marked in red (departure and arrival points are given asred stars), along with the main features viewed during this excursion to set the field context for the response exercise.
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(3) Lava Flow Modelling;(4) Application in Crisis-mode: Experiences and
Requirements.
This sequence is designed to flow logically throughan information chain that begins with volcano hotspot detection and provision of lava flow modelsource terms, though modelling, to input of model-based assessments of potential lava inundationzones as a final layer in a humanitarian GIS-basedresponse to an effusive volcanic crisis (Fig. 2). Inaddition, all contributions were invited as review-style submissions that focus on entirely effusivethemes so as to build a manual, inventory and direc-tory as to the current state of the art in operationallava flow tracking and modelling.
Part 1 begins with a review of three well-established hot spot detection algorithms: MOD-VOLC (Wright 2015), RST-Volc (Pergola et al.2015) and AVHotRR (Lombardo 2015), plus anew algorithm type based on application of the Kal-man Filter (Zaksek et al. 2015). We also reviewmeans of near-real-time multiple sensor targetingto obtain high spectral, spatial and temporal resolu-tion event coverage (Ramsey 2015) plus the use of asensor web-based networking approach to target allavailable tracking devices on an effusive crisis(Davies et al. 2015).
Part 2 turns to operational hot spot tracking anddata dissemination systems, such as MYVOLC(Ferrucci & Hirn 2016), MIROVA (Coppolaet al. 2015), HOTSAT (Ganci et al. 2015) andHOTVOLC (Gouhier et al. 2016). We also includea consideration of two new perspectives on how tointerpret thermal radiance time series extractedfrom satellite data in terms of lava physical param-eters using laboratory analogue experiments (Garelet al. 2015) and independent component analysis(Barnie & Oppenheimer 2015). Part 2 finishes byexploring means to synergistically use infrared andultraviolet data to allow coupled tracking of heatand gas emissions during an effusive crisis (Carn2016).
In Part 3 we consider operational lava flowmodels. Beginning with a stochastic model,DOWNFLOW (Tarquini & Favalli 2015), and aone-dimensional model for the downflow thermo-rheological evolution of a channel-contained lavavolume, FLOWGO (Harris et al. 2015), we moveto the use of depth-average equations for lava flowmodelling (Costa & Macedonio 2005), as appliedby VOLCFLOW (Kelfoun & Vargas 2015). Wethen turn to a review of the original cellular autom-ata, SCIARA (Rongo et al. 2015), followed bythe cellular automatas of MAGFLOW (Cappelloet al. 2015) and LavaSIM (Fujita & Nagai 2015).We then move to two new approaches being devel-oped at INGV-Catania (Bilotta et al. 2015) and at
the Universite Joseph Fourier – Grenoble (Berna-beu et al. 2016).
Part 4 begins by considering case studies of effu-sive crisis responses taken from Etna (Bonaccorsoet al. 2015; Miller & Harris 2016) and Hawaii(Patrick et al. 2015). These both review and assesshow near real-time deliverables from the remotesensing and modelling communities can be inte-grated into an effective crisis assessment, responseand communication system during an effusiveevent. Operational radar-based systems are alsoconsidered to show their potential use for pre- andpost-effusive eruption deformation patterns (Catryet al. 2015), as well as their combined use withsatellite-sensor thermal data to obtain lava areas,thicknesses, volumes and post-eruption flow fieldsubsidence (Bato et al. 2016).
The concluding section first gives the recom-mendations and findings of the RED SEED workinggroup, following the round-table discussions ofthe May 2013 workshop (Harris et al. 2016). Thisis followed by the results of the exercise designedto feed output from the hot spot detection systems,through the lava flow models, to generate real-time,up-to-the-minute, lava flow inundation layers forinput into a crisis-response GIS (Latutrie et al.2015).
Summary
The core objective of this book is to collate, as of2016, the state-of-the-art regarding the use ofsatellite-based sensors for volcano hot spot detec-tion and derivation of lava flow source term param-eters, and to then review the lava flow simulationmodels that require these source terms. The ultimateaim is to understand how all current capabilities canbest be assembled so as to ensure a timely and unitedhumanitarian response during a high-impact effu-sive event, especially at a site lacking local monitor-ing and response systems where this book can beused as a directory of capabilities and operationalservice providers.
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