CELLULAR AUTOMATA SIMULATION OF LAVA FLOWS

Applications to Civil Defense and Land Use Planning with a

Cellular Automata based Methodology

Rocco Rongo

, Valeria Lupiano

, Maria Vittoria Avolio

Donato D’Ambrosio

, William Spataro

and Giuseppe A. Trunﬁo

Department of Earth Sciences, University of Calabria, Via Pietro Bucci, I-87036 Rende, Italy

Department of Mathematics, University of Calabria, Via Pietro Bucci, I-87036 Rende, Italy

Department of Architecture, Planning and Design, University of Sassari, Piazza Duomo 6, 07041, Alghero, Italy

Keywords:

Lava ﬂows simulation, Cellular automata, Hazard maps, Land use planning, Mt Etna.

Abstract:

A large number of people live either on or in the surrounding areas of hundreds of worldwide active volcanoes.

For this reason, the individuation of those areas that are more likely to be affected by new eruptive events is

of fundamental importance for diminishing possible consequences in terms of loss of human lives and/or

material properties. We here illustrate a methodology for deﬁning ﬂexible high-detailed lava invasion hazard

maps which is based on a efﬁcient Cellular Automata computational model for simulating lava ﬂowson present

topographic data and on High Performance Parallel Computing for increasing computational efﬁciency. We

also show the application of the methodology to the entire area surrounding Mt Etna (Italy), Europe’s most

active volcano, showing its suitability for land use planning and civil defence applications. Furthermore,

speciﬁc applications to inhabited areas of the volcano are also shown, which demonstrate the methodology’s

applicability in this ﬁeld.

1 INTRODUCTION

Despite being the most active volcano in Europe, Mt

Etna (South Italy) is home to approximately one mil-

lion people (Behncke and Neri, 2003). Still, the ma-

jority of the events occurred in the last four cen-

turies report damage to human properties in numer-

ous towns on the volcano ﬂanks. In last decades,

the susceptibility of the Etnean area to lava invasion

has increased due to continued urbanization (Dibben,

2008), with the consequence that new eruptions may

involve even greater risks. Different countermeasures

based on embankments or channels were adopted in

recent crises to stop or deﬂect lava (Barberi et al.,

1993) (Barberi et al., 2003). However, such kinds

of interventions are generally performed while the

eruption is in progress, with the consequence of both

not guarantying their effectiveness, besides inevitably

putting into danger the safety of involved persons.

One response to such challenges is the numerical sim-

ulation of lava ﬂows (Ishihara et al., 1990), (DelNegro

et al., 2008), (Avolio et al., 2006), for the purpose of

individuating affected areas in advance. As a matter

of fact, in 2001 the path of the eruption that threat-

ened the town of Nicolosi on Mt Etna was correctly

predicted by means of a lava ﬂows simulation model

(Crisci et al., 2004), providing at that time useful in-

formation to local Civil Defense authorities. How-

ever, in order to be efﬁciently and correctly applied,

the above approaches require an a priori knowledge of

the degree of exposure of the volcano surrounding ar-

eas, to allow both the realization of preventive coun-

termeasures, and a more rational land use planning.

Based on an improved version of the SCIARA cel-

lular automata lava ﬂow model (Crisci et al., 2004),

we here illustrate a methodology for the deﬁnition

of ﬂexible high-resolution lava invasion hazard maps,

and show results related to Mt Etna. Furthermore, we

show speciﬁc applications that can be useful for civil

defense purposes and land use planning.

2 THE SCIARA MODEL

In order to be applied for land use planning and

civil defense purposes in volcanic regions, a com-

putational model for simulating lava ﬂows should be

Rongo R., Lupiano V., Avolio M., D’Ambrosio D., Spataro W. and Trunﬁo G..

CELLULAR AUTOMATA SIMULATION OF LAVA FLOWS - Applications to Civil Defense and Land Use Planning with a Cellular Automata based

Methodology.

DOI: 10.5220/0003575900370044

In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2011), pages

37-44

ISBN: 978-989-8425-78-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

well calibrated and validated against test cases to as-

sess its reliability, cf. e.g. (DelNegro et al., 2008)

(Rongo et al., 2008) (Vicari et al., 2007). Another

desirable characteristic should be the model’s efﬁ-

ciency since, depending on the extent of the consid-

ered area, a great number of simulations could be

required (D’Ambrosio et al., 2006), (Crisci et al.,

2010). A ﬁrst computational model of basaltic lava

ﬂows, based on the Cellular Automata computational

paradigm and, speciﬁcally, on the Macroscopic Cel-

lular Automata approach for the modeling of spatially

extended dynamical systems, was proposed in (Crisci

et al., 1982) called SCIARA. In the following years,

the SCIARA family of lava ﬂows simulation mod-

els have been improved and applied with success to

the simulation of different Etnean cases of study, e.g.

(Crisci et al., 2004) (Rongo et al., 2008).

Cellular Automata (CA) (Neumann, 1966) were

introduced in 1947 by the John von Neumann in

his attempt to understand and formalise the under-

ling mechanisms that regulate the auto-reproduction

of living beings. After the publication of his stud-

ies, Cellular Automata quickly came to the attention

of the Scientiﬁc Community both as powerful paral-

lel computational models and as convenient tools for

modelling and simulating several types of complex

physical phenomena (Chorpard and Droz, 1998).

Classical Cellular Automata can be viewed as an

n-dimensional space, R, subdivided in cells of uni-

forms shape and size. Each cell embeds an identical

ﬁnite automaton (fa), whose state accounts for the

temporary features of the cell; Q is the ﬁnite set of

states. The fa input is given by the states of a set

of neighbouring cells, including the central cell it-

self. The neighbourhood conditions are determined

by a geometrical pattern, X, which is invariant in time

and space. The fa have an identical state transition

function τ : Q

♯X

→ Q, where ♯X is the cardinality of

the set of neighbouring cells, which is simultaneously

applied to each cell. At step t = 0, fa are in arbitrary

states and the CA evolves by changing the state of all

fa simultaneously at discrete times, according to τ.

Macroscopic Cellular Automata (MCA) (DiGre-

gorio and Serra, 1999) introduce some extensions to

the classical CA formal deﬁnition. In particular, the

Q of state of the cell is decomposed in r substates,

, Q

,..., Q

, each one representing a particular

feature of the phenomenon to be modelled (e.g. for

lava ﬂow models, cell temperature, lava content, out-

ﬂows, etc). The overall state of the cell is thus ob-

tained as the Cartesian product of the considered sub-

states: Q = Q

× Q

× ... × Q

. A set of parameters,

P = {p

, p

,..., p

}, is furthermore considered, which

allow to “tune”the model for reproducing different

dynamical behaviours of the phenomenon of interest

(e.g. for lava ﬂow models, the Stephan-Boltzmann

constant, lava density, lava soldiﬁcation temperature,

etc). As the set of state is split in substates, also the

state transition function τ is split in elementary pro-

cesses, τ

,τ

,...,τ

, each one describing a particular

aspect that rules the dynamic of the considered phe-

nomenon. Eventually, G ⊂ R is a subset of the cel-

lular space that is subject to external inﬂuences (e.g.

for lava ﬂow models, the crater cells), speciﬁed by the

supplementary function γ. External inﬂuences are in-

troduced in order to model features which are not easy

to be described in terms of local interactions.

In the MCA approach, by opportunely discretiz-

ing the surface on which the phenomenon evolves, the

dynamics of the system can be described in terms of

ﬂows of some quantity from one cell to the neigh-

bouring ones. Moreover, as the cell dimension is a

constant value throughout the cellular space, it is pos-

sible to consider characteristics of the cell (i.e. sub-

states), typically expressed in terms of volume (e.g.

lava volume), in terms of thickness. This simple as-

sumption permits to adopt a straightforward but efﬁ-

cacious strategy that computes outﬂows from the cen-

tral cell to the neighbouring ones in order to minimize

the non-equilibrium conditions.

Still, owing to their intrinsic parallelism, both CA

and MCA models implementation on parallel com-

puters is straightforward, and the simulation dura-

tion can be reduced almost proportionally to the num-

ber of available processors (D’Ambrosio and Spataro,

2007).

In this work, the latest release of the SCIARA Cel-

lular Automata model for simulating lava ﬂows was

adopted. Speciﬁcally, a Bingham-like rheology has

been introduced for the ﬁrst time as part of the Min-

imization Algorithm of the Differences (DiGregorio

and Serra, 1999), which is applied for computing lava

outﬂows from the generic cell towards its neighbors.

Besides, the hexagonal cellular space adopted in the

previous releases (Crisci et al., 2004) of the model

for mitigating the anisotropic ﬂow direction problem

has been replaced by a square one, nevertheless by

producing an even better solution for the anisotropic

effect. The model has been calibrated by consider-

ing three important real cases of studies, the 1981,

2001 and 2006 lava ﬂows at Mt Etna (Italy), and on

ideal surfaces in order to evaluate the magnitude of

anisotropic effects. Even if major details of this ad-

vanced model can be found in (Spataro et al., 2010),

we brieﬂy outline it’s main speciﬁcations.

In formal terms, the SCIARA MCA model is de-

ﬁned as:

SCIARA =< R, L,X,Q,P,τ, γ > (1)

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

Applications

where:

– R is the set of square cells covering the bi-

dimensional ﬁnite region where the phenomenon

evolves;

– L ⊂ R speciﬁes the lava source cells (i.e. craters);

– X = {(0, 0),(0,1), (−1,0),(1,0),(0,−1), (−1,1),

(−1, −1),(1, −1), (1,1)} identiﬁes the pattern of

cells (Moore neighbourhood) that inﬂuence the

cell state change, referred to cells by indexes 0

(for the central cell) through 8;

– Q = Q

× Q

is the ﬁnite set of states,

considered as Cartesian product of “substates”.

Their meanings are: cell elevation a.s.l. (above

sea level), cell lava thickness, cell lava tempera-

ture, and lava thickness outﬂows (from the central

cell toward the eight adjacent cells), respectively;

– P = {w,t,T

sol

vent

Tsol

Tvent

,hc

Tsol

,hc

Tvent

,δ,

ρ,ε,σ,c

} is the ﬁnite set of parameters (invari-

ant in time and space) which affect the transition

function (please refer to (Spataro et al., 2010) for

their speciﬁcations);

– τ : Q

→ Q is the cell deterministic transition

function, applied to each cell at each time step,

which describes the dynamics of lava ﬂows, such

as cooling, solidiﬁcation and lava outﬂows from

the central cell towards neighbouring ones;

– γ : Q

× N → Q

speciﬁes the emitted lava thick-

ness, h, from the source cells at each step k ∈ N

(N is the set of natural numbers).

As stated before, the new SCIARA model intro-

duces a rheology inspired to the Bingham model and

therefore the concepts of critical height and viscos-

ity are explicitly considered (Park and Iversen, 1984),

(Dragoni et al., 1986). In particular, lava can ﬂow

out from a cell towards its neighbours if and only if

its thickness overcomes a critical value (i.e. the crit-

ical height), so that the basal stress exceeds the yield

strength. Moreover, viscosity is accounted in terms of

ﬂow relaxation rate, r, a the parameter of the distribu-

tion algorithm that inﬂuences the amount of lava that

actually leaves the cell, according to a power law of

the kind:

logr = a+ bT (2)

where T is the lava temperature and a and b coef-

ﬁcients determined by solving the system:



logr

Tsol

= a+ bT

sol

logr

Tvent

= a + bT

vent

where T

sol

and T

vent

are the lava temperature at so-

lidiﬁcation and at the vents, respectively. Similarly,

the critical height, hc, mainly depends on lava tem-

perature according to a power law of the kind:

loghc = c+ dT (3)

whose coefﬁcients c and d are obtained by solving

the system:



loghc

Tsol

= c+ dT

sol

loghc

Tvent

= c+ dT

vent

It is known that, in general, deterministic CA

for the simulation of macroscopic ﬂuids present a

strong dependence on the cell geometry and direc-

tions of the cellular space. In order to solve the prob-

lem, different solutions have been proposed in liter-

ature, such as the adoption of hexagonal cells (Crisci

et al., 2004), (Avolio et al., 1998), (D’Ambrosio et al.,

2003) or Monte Carlo approaches (Miyamoto and

Sasaki, 1997), (Vicari et al., 2007). The ﬁrst solu-

tion, however, does not perfectly solve the problem

on ideal surfaces, while the second one has the dis-

advantage of giving rise to non-deterministic simula-

tion models. In order to solve the anisotropic prob-

lem, which is typical of deterministic Cellular Au-

tomata models for ﬂuids on ideal surfaces, a ﬁctitious

topographic alteration along diagonal cells is consid-

ered with respect to those “individuated” by the DEM

(Digital Elevation Model). As a matter of fact, in a

standard situation of non-altered heights, cells along

diagonals result in a lower elevation with respect to

the remaining ones (which belong to the von Neu-

mann neighborhood), even in case of constant slope.

This is due since the distance between the central cell

and diagonal neighbors is greater than of the distance

between the central cell and orthogonal adjacent cells

(cf. Figure 1). This introduces a side effect in the

distribution algorithm, which operates on the basis of

height differences. If the algorithmperceivesa greater

difference along diagonals, it will erroneously privi-

lege them by producing greater outﬂows. In order to

solve this problem, we consider the height of diag-

onal neighbors taken at the intersection between the

diagonal line and the circle with radius equal to the

cell side and centered in the central cell, so that the

distance with respect to the centre of the central cell

is constant for each cell of the Moore neighbourhood

(Figure 1). Under the commonly assumed hypothesis

of inclined plane between adjacent cells (Vicari et al.,

2007), this solution permits to have constant differ-

ences in level in correspondence of constant slopes,

and the distribution algorithm can work “properly”.

Refer to (Spataro et al., 2010) for other speciﬁcations

on this issue.

CELLULAR AUTOMATA SIMULATION OF LAVA FLOWS - Applications to Civil Defense and Land Use Planning

with a Cellular Automata based Methodology

Figure 1: Reference schema for cells altitude determination

in the Moore neighbourhood. Altitudes of cells belonging

to the von Neumann neighbourhood correspond to normal

DEM values, while those along diagonals are taken at the

intersection between the diagonal line and the circle with

radius equal to the cell side, so that the distance with respect

to the centre of the central cell is constant for each adjacent

neighbour.

3 A METHODOLOGY FOR

CREATING HAZARD MAPS

While a reliable simulation model is certainly a valid

instrument for analyzing volcanic risk in a certain area

by simulating possible single episodes with different

vent locations, e.g. (Crisci et al., 1999), the methodol-

ogy for deﬁning high detailed hazard maps here pre-

sented is based on the application of the SCIARA lava

ﬂows computational model for simulating an elevated

number of new events on topographic data. In par-

ticular, the methodology requires the analysis of the

past behavior of the volcano, for the purpose of clas-

sifying the events that historically interested the re-

gion. In such a way, a meaningful database of plau-

sible simulated lava ﬂows can be obtained, by char-

acterizing the study area both in terms of areal cover-

age, and lava ﬂows typologies. Once the simulation

database has been completed (i.e., an adequate, usu-

ally elevated, number of simulations have been car-

ried out), data is processed by considering a proper

criterion of evaluation. A ﬁrst solution could simply

consist in considering lava ﬂows overlapping, by as-

signing a greater hazard to those sites interested by

a higher number of simulations. However, a similar

choice could be misleading. In fact, depending on

their particular traits (e.g., location of the main crater,

duration and amount of emitted lava, or effusion rate

trend), different events can occur with different prob-

abilities, which should be taken into account in evalu-

ating the actual contribution of performed simulations

with respect to the deﬁnition of the overall hazard of

the study area. In most cases, such probabilities can

be properly inferred from the statistical analysis of

past eruptions, allowing for the deﬁnition of a more

reﬁned evaluation criterion. Accordingly, in spite of

a simple hitting frequency, a measure of lava invasion

hazard can be obtained in probabilistic terms. In the

following, we show how such approach was applied

to Mt Etna.

3.1 Application of the Methodology to

the Mt. Etna Volcano Area

By adopting a procedure well described in (Crisci

et al., 2010) and (Avolio et al., 2010), which referred

to the Eastern sector of Mt. Etna and which was ap-

plied by employing a previousversion of the SCIARA

CA model, we here show the application to the entire

area of the volcano using the new SCIARA model de-

scribed in Section 2. Firstly, based on documented

past behavior of the volcano, the probability of new

vents forming was determined, resulting in a charac-

terization (thus, a Probability Density Function - PDF

- map) of the study region into areas (Figure 2), that

represent different probabilities of new vents opening

(Cappello et al., 2011), assessed by employing a Pois-

son distribution which considers a spatial density and

a temporal component. The spatial probability den-

sity function was estimated through a Gaussian ker-

nel by considering the main volcanic structures at Mt

Etna, while the temporal rate was evaluated by using

an approach based on the “repose-time method” (Ho

et al., 1991).

Figure 2: The characterization of new vents forming of the

study region on the basis of historical data (see text), rep-

resenting different probabilities of activation, considered in

this work, together with the grid of 4290 hypothetical vents

deﬁned as the source for the simulations to be carried.

Subsequently, all ﬂank eruptions of Etna since

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

Applications

1600 AD were classiﬁed according to duration and

lava volume (Crisci et al., 2010) and a representa-

tive effusion rate trend taken into account in order

to characterize lava temporal distribution for the con-

sidered representative eruptions, basically reﬂecting

the effusive mean behavior of Etnean lava ﬂows. In

fact, with the exception of few isolated cases, a typ-

ical effusive behavior was strongly evidenced by the

analysis of the volcano past activity (Behncke et al.,

2005). As a consequence, it is not a hasty judgment

to suppose that such behavior will not dramatically

change in the near future and thus that the SCIARA

lava ﬂows simulation model, calibrated and validated

on a set of effusive eruptions, be adequate for simulat-

ing new events on Mt Etna. An overall probability of

occurrence, p

, was thus deﬁned for each scenario, by

considering the product of the individual probabilities

of its main parameters:

= p

· p

(4)

where p

denotes the probability of eruption from

a given location (i.e., based on the PDF map), p

the

probability related to the event’s membership class

(i.e., emitted lava and duration), and p

the probability

related to its effusion rate trend.

Once representative lava ﬂows were devised as

above, a set of simulations were planned to be exe-

cuted in the study area by means of the SCIARA lava

ﬂows simulation model. At this purpose, a grid com-

posed by 4290 craters, equally spaced by 500m, was

deﬁned on the considered as a covering for Mt Etna,

as shown in Figure 2. This choice allowed to both

adequately and uniformly cover the study area, be-

sides considering a relatively small number of craters.

Speciﬁcally, a subset of event classes which deﬁne

6 different effusion rates probabilities, derived from

historical events considered in (Crisci et al., 2010),

were taken into account for each crater, thus resulting

in a total of 25740 different simulations to be carried

out. Owing to the elevated number of SCIARA simu-

lations to be carried out, thanks to the adoption of Par-

allel Computing each scenario was simulated for each

of the vents of the grid. Simulations were performed

on an 80-node Apple Xserve Xeon-based cluster and

were performed in ca. 10 days.

Lava ﬂow hazard was then punctually evaluated

by considering the contributions of all the simulations

which affected a generic cell in terms of their proba-

bility of occurrence. Formally, if a given DEM cell

(and thus, a CA cell) of co-ordinates (x,y) is affected

by n

x,y

≤ N simulations, being N the overall number

of performed simulations, its hazard h

x,y

can be de-

ﬁned as the sum of the probabilities of occurrence of

involved lava ﬂows, p

(i) (i=1, 2, ..., n

x,y

Figure 3: Hazard map of the study area based on the 25740

simulations. As a compromise between map readability and

accuracy, 5 classes are reported (grey colouring), in increas-

ing order of susceptibility (probability of lava invasion).

x,y

∑

i=1

(i) (5)

The obtained lava ﬂow hazard map resulting from

these simulations and the application of equation 5 is

presented in Figure 3, and represents the probability

that future eruptions will affect the entire Etnean area.

Importantly, the methodology for the compilation

of lava ﬂows invasion hazard maps here proposedpro-

vides for, as integrant part, a process for the veriﬁca-

tion of results. A validation procedure was thus con-

templated for the produced hazard map, consisting in

a technique which produces statistical indicators on

which one can quantify the reliability of the results

and, therefore, assess whether the ﬁnal product can be

conﬁdently used for Civil Protection, for example, for

setting in safety particularly vulnerable areas, and for

Land Use Planning. Refer to (Crisci et al., 2010) for

major details on the methodology validation process.

4 APPLICATIONS FOR CIVIL

DEFENSE AND LAND USE

PLANNING

Apart leading for the deﬁnition of general hazard

maps, as the one reported in Figure 3, the method-

ology permits further Civil Defense oriented appli-

cations. For this purpose, the calibrated and vali-

dated SCIARA simulation model has been integrated

CELLULAR AUTOMATA SIMULATION OF LAVA FLOWS - Applications to Civil Defense and Land Use Planning

with a Cellular Automata based Methodology

Figure 4: Map showing vents, belonging to the simulation

grid of Fig. 2, which can produce eruptions capable of af-

fecting the urban area of the town of Belpasso, together with

the resulting susceptibility scenario, allowing to immedi-

ately assess the threat posed by an eruption exclusively on

the basis of its source location.

in a GIS (Geographic Information System) applica-

tion that permits to take also into account the effects

of “virtual” embankments, channels, barriers, etc. In

particular, the availability of a large number of lava

ﬂows of different eruption types, magnitudes and lo-

cations simulated for this study allows the instanta-

neous extraction of various scenarios. This is espe-

cially relevant once premonitory signs indicate the

possible site of imminent eruptions, and thus permit-

ting to consider hazard circumscribed to a smaller

area.

A ﬁrst Civil Defense oriented application regards

the possibility to identify all source areas of lava ﬂows

that are capable of affecting a given area of interest,

such as a town or a major infrastructure. In this case,

this application is rapidly accomplished by querying

the simulation database, by selecting the lava ﬂows

that affect the area of interest and by circumscrib-

ing their sources. For this application we have cho-

sen the towns of Belpasso and Zafferana Etnea, two

important historical and cultural sites, with many ad-

ministrative buildings and tourist facilities. Figure 4

and Figure 5 show vents which can originate erup-

tions capable of affecting the urban areas of Belpasso

and Zafferana Etnea, respectively, together with the

resulting hazard scenario, allowing to immediately as-

sess the threat posed by an eruption exclusivelyon the

basis of its source location.

Etnean eruptions can even comprise complex

events, which for Etna are fairly typical, such as lava

Figure 5: A second example of application of hazard zona-

tion referred to the town of Zafferana Etnea. Even in this

case, the map shows vents which can produce eruptions ca-

pable of affecting the urban area of the town. As before, an

elevated risk is present for the center of the inhabited area

emission from an extensive system of eruptive ﬁs-

sures propagating downslope over a length of several

kilometers. We have performed an analysis for such

an eruption on the east-northeast ﬂank of Etna, not far

from the 1928 eruption site, with lava emission from

a ﬁssure system about 7km long. The eruptive system

was approximated by a subset of vents of the simula-

tion grid and all lava ﬂows originated from them se-

lected from the simulation database (without needing

to perform new simulations). For this application, the

resulting map is shown in Figure 6. The same ﬁgure

refers also to a further application regarding temporal

hazard mapping, by evidencing the evolution of the

involved area in time. This application could be of

fundamental importance for assessing, from a tempo-

ral pointof view, howhazard of a speciﬁc area evolves

in time (e.g. day by day), so that more speciﬁc coun-

termeasures can be considered by responsible author-

ities.

Speciﬁcally, Figure 6 also shows the result rela-

tive to 1, 3, 6 and 9 days respectively, of the invaded

areas, with relative probability values of occurrence,

in the case of the activation of the considered ﬁssure

system. This application regards a real-time assess-

ment of lava invasion in conﬁned areas, since the pro-

duced map indicates a temporal evolution of hazard,

in terms of probability, which can be useful in case of

an imminent/new event to Civil Protection to moni-

tor, and eventually intervene, in areas with higher val-

ues of lava hazard, without having information on the

event’s duration and emission rate that take place.

Other examples of applications referred to risk

mitigation can be considered for testing the impact

of human intervention during an eruption, in order to

assess the effect of possible human interventions with

the aim for decreasing hazard in critical areas. For in-

stance, it is possible to evaluate the effect, in terms

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

Applications

Figure 6: A hypothetical hazard scenario for a system of

eruptive ﬁssures propagating downslope over a length of 7

km on the east-northeast ﬂank of Mount Etna. The ﬁgure

refers to lava hazard of the area after 1, 3, 6 and 9 days (a,

b, c, d), respectively. Note that the same scenarios can be

considered as temporal scenario for the area.

of hazard decrease, of barriers or channels built to

protect inhabited areas. Please refer to (Crisci et al.,

2010) for speciﬁc examples regarding a case study

performed for evaluating the impact of a barrier to

protect the town of Nicolosi, a strategic town of the

Etnean area.

5 CONCLUSIONS

We have presented an application of Cellular Au-

tomata for deﬁning high-detailed hazard maps which

can be fruitfully applied for land use planning and

civil defense purposes. Based on the adoption of the

latest release of the Macroscopic Cellular Automata

model SCIARA for simulating lava ﬂows, which con-

siders a Bingham-like rheology, the methodology per-

mits the creation of general lava ﬂow hazard maps,

which can be fundamental for land-use and civil

defense planning in the long-term. Regarding the

adopted new computational model, which is at the

basis of the methodology, SCIARA re-introduces a

square tessellation of the cellular space instead of the

previously adopted hexagonal one, which was con-

sidered in the earlier versions to limit the effect of the

anisotropic ﬂow direction problem.

A novelty of the presented methodology, besides

the possibility of assessing the efﬁciency of protective

measures for inhabited areas and/or major infrastruc-

tures, is that the simulation data permits to produce

general susceptibility maps in unprecedented detail,

and contains each single scenario out of a total of over

thousands of simulated cases. It is therefore no longer

necessary to wait for the next eruption and know its

eruptive parameters and location in order to run ad-

hoc simulations, as has been the practice until now.

Instead, virtually all possible eruption scenarios can

be simulated a priori, from as dense a network of hy-

pothetical vent locations as possible, and extracted in

real time as soon as the need arises, as in the case of

an imminent or incipient eruptions.

The methodology here described can therefore

represent a substantial advance in the ﬁeld of lava ﬂow

impact prediction and can also have immediate, far

reaching implications both in land use and civil de-

fense planning.

ACKNOWLEDGEMENTS

Authors thank Dr. B. Behncke and Dr. M. Neri from

the INGV - Istituto Nazionale di Geoﬁsica e Vul-

canologia of Catania (Sicily, Italy), who provided to-

pographic maps and the volcanological data. The au-

thors are also grateful to Prof. G.M. Crisci and Prof.

S. Di Gregorio for their precious comments and the

common researches.

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