# IJEGE-11_BS-Avolio-et-alii

*Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza*

*DOI: 10.4408/IJEGE.2011-03.B-044*

**DEBRIS FLOWS SIMULATION BY CELLULAR AUTOMATA:**

**A SHORT REVIEW OF THE SCIDDICA MODELS**

**K**

**ey**

**words****:**

*debris flows, modelling & simulation, cellular*

*automata, susceptibility analysis*

**INTRODUCTION**

slides. Debris flows (DF) (i

*et*

*alii*, 2001) are one of the most common and dan-

gerous types of landslides and may be classified as

complex systems of fluid-dynamical type. DF are

extremely rapid channeled landslides, composed of

slurry of rock, mud, organic matter and water, usually

originated by soil detachments in relation with intense

rainfalls, snow melt etc. DF may occur in subaerial,

subaqueous or mixed environment and their size can

vary from 10

the detached volume (also 100 times larger) due to

the erosion and consequent entrainment of material

along the path (H

*et alii*, 2005). Furthermore, in

landslides along the channel, thus furtherly increasing

the final volume of mobilized material. Hence, rheo-

logical properties (m

during propagation by water loss or inclusion. Addi-

tional mechanisms, such as impulsive loss of matter

(water and finer grains) and energy dissipation at im-

pact, must be considered in the cases of coastal DF.

Differently, buoyancy effects, drag forces and peculiar

**ABSTRACT**

a 3D topography. In this paper, an extensive review

of SCIDDICA, a Cellular Automata model based on

the equivalent fluid approach, is presented. Moreover,

the main steps in the development of SCIDDICA are

described with a chronological criterion. The last ver-

sion of SCIDDICA (SS2) is suitable for the simulation

of completely subaerial, completely subaqueous and

combined subaerial-subaqueous debris flows. Main

features of a debris flow are accounted by the SS2 ver-

sion such as erosion and deposition and triggering of

secondary landslides along the path, presence of struc-

tures and buildings, run-up effects and, in the case of

coastal landslides, impulsive loss of matter (water and

finer grains) and energy dissipation at water impact.

Moreover, buoyancy effects, drag forces and peculiar

mechanisms like hydroplaning are also modeled for

submerged events. Several past debris-flows like the

1998-1999 Campanian debris flows (Italy), the 1997

debris flow at Lake Albano (Italy) and the 2008 sub-

marine debris flow at Bagnara Calabra (Italy) have

been simulated in the last years by SCIDDICA. A

short review of these case studies will be presented

and the main limitations encountered will be also

discussed, thus suggesting future improvements and

perspectives, such as susceptibility analysis and inter-

action with man made structures

*M.V. AVOLIO, F. BOZZANO, D. D’AMBROSIO, S. DI GREGORIO, V. LUPIANO, P. MAZZANTI, R. RONGO & w. SPATARO*

*5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011*

ducing other “elementary processes” in order to model

more complex real cases.

paper, together with the results of their application to

real cases. However, the detailed description of physi-

cal laws controlling the models is not presented in this

review paper and we refer to specific references cited

in the text for this aspect.

its application to DF analysis.

whose evolution depends mainly on the local inter-

actions of their constituent parts (e.g. Di Gregorio &

Serra, 1999). CA evolves in a discrete space-time con-

text; they are based on a regular division of the space

in cells (or, equivalently, a regular lattice, whose sites

correspond to the cell centres), each one embedding

an input/output computing unit: a finite automaton

(

*fa*). S is the finite set of fa states, that the cell may

assume. The fa input of a cell c is given by the states

of m neighbouring cells, including the cell c. The

neighbourhood conditions are determined by a pat-

tern which is invariant in time and constant over the

cells. The fa have an identical state transition function,

which is simultaneously applied to each cell. At step

0, fa are in arbitrary states, describing the initial con-

ditions of the system; then, the CA evolves changing

the state of all cells simultaneously at discrete times

(CA step), according to the transition function

Macroscopic Cellular Automata were thus defined in

order to fit the modelling requirements of many mac-

roscopic phenomena, from a CA viewpoint, by con-

sidering:

in the submerged path.

problems arise due to the extreme complexity of these

events, which lead to difficulties in estimating kine-

matic geotechnical soil parameters for real phenomena

(a

differential equations), and several models have been

developed in the last years by using this approach

(H

is represented by MCA (Macroscopic Cellular Au-

tomata). MCA are an extension of classical Cellular

Automata (CA), developed for overcoming some of

the limitation affecting conventional CA frames such

as the modeling of large scale complex phenomena.

Due to its particulate nature and local dynamics, MCA

are very powerful in dealing with complex bounda-

ries, incorporating of microscopic interactions, and

parallelization of the algorithm.

rheological features cannot be measured through labo-

ratory or in situ testing, but can only be obtained by

the back-analysis of real past events”.

tended systems, and firstly applied to the simulation

of basaltic lava flows (C

*et alii*, 1982). Since

natural phenomena: pyroclastic flows (a

*et alii*,

*et alii*, 2007; a

*et al*., 2010b) and, in particular, flow type land-

*et alii*, 2010a). Among existing Cellu-

a family of deterministic MCA models, specifically

developed for simulating debris flows. This model has

been developed according to an incremental strategy,

permitted by the underlying CA properties, that allow

to build a model by the composition of “elementary

processes”. This permits to consider first models of

the family for less complex case studies. Subsequent-

**DEBRIS FLOWS SIMULATION BY CELLULAR AUTOMATA: A SHORT REVIEW OF THE SCIDDICA MODELS**

*Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza*

set of natural numbers);

hexagonal tessellation;

*fa*(the list of all

is given in Tab. 1);

*P*is the finite set of parameters (e.g. pap and pt are

ing to a step of the CA). Specific parameters will be

presented in the following sections;

In the following, variables concerning sub-states

the specification of the neighborhood cell, an index at

subscript is used. Furthermore, nQ and ∆Q represent,

respectively, the new value and variation of the sub-

state

*S*

*Q*

**BASIC ASSUMPTIONS AND INITIAL CON-**

DITIONS

DITIONS

TH at altitude A (cf. example in fig. 1 for hexagonal

cells). If g is gravity acceleration, the sub-state total

energy,

*E*, is defined as

*E=ρ-g-B-TH-(TH/2+A+kH),*

while the sub-state run-up,

*R*, is defined as

*R=TH+kH.*

Velocity v is related to

*kH*by KH=

*v*

*2*

*/2g*(d’a

*et alii*, 2003).

*S*

*TH*

evolution of the system (each characteristic corre-

sponds to a sub-state). Moreover, thanks to the sub-

state “altitude of the cell” the model can be considered

fully 3D.

that are assumed to be relevant to the system evolu-

tion.

the purely local CA approach in order to minimise the

differences in “height” in the CA neighbourhood. The

general problem may be stated for a generic quantity

q according to the following definitions:

*q*

*d*

*q*

*0*

*q*

*i*

*f*

*0*

*f*

*i*

*q*

*i’*

*= q*

*i*

*+ f*

*i*

*q*

*d*

*f*

*i*

ences among all cells in the neighbourhood ∑

*{(i,j)|0≤i<j≤n}*

*(|q*

*i*

*’- q*

*j*

*’|)*is minimised. A surface flow evolves towards

cal level as a “pure” gravitational flow tending to the

hydrostatic equilibrium (Avolio et al., 2000). In this

framework, run-up effects of flow may be managed

by introducing a component of the kinetic head in the

“height” of the central cell (e.g., d’a

*et alii*,

portune alteration of the “height” in cells along the mo-

tion direction (d’a

*et alii*, 2006). As a general

simple local laws, observing the conservation laws of

physics in an approximation context.

**SCIDDICA MODEL: GENERAL FRA-**

**MEWORK**

SCIDDICA = (

*R, X, S, P, τ*)

where:

*Tab. 1 - SCIDDICA sub-states.*

*M.V. AVOLIO, F. BOZZANO, D. D’AMBROSIO, S. DI GREGORIO, V. LUPIANO, P. MAZZANTI, R. RONGO & w. SPATARO*

*5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011*

remaining debris in the central cell plus inflows. The

elementary process “energy dissipation” is specified by

a run-up reduction of a constant value (parameter rl):

*nR*

*0*

*=R*

*0*

*-rl if rl>R*

*0*

*else nR*

*0*

*=0*.

not suitable for the simulation of debris flows which

are characterized by strong soil erosion and entrain-

ment along the landslide path. Hence, the (

**S**

**x**

*et alii*, 2002,

Campanian debris flows event (i

*et alii*, 2003a;

*et alii*, 2007).

**S**

**3-hex**

**(d’a**

*et alii*, 2003b), was

HEX (for obtaining six flow directions that are the

maximum number permitted in a regular plane tes-

sellation),

*S=S*

*A*

*×S*

*TH*

*×S*

*O*

*6*

*×S*

*kH*

*×S*

*D*

*,*AMD is specified

*q*

*0*

*=A*

*0*

*+kH*

*0*

*+adh*,

*q*

*d*

*=T*

*H0*

*-adh*,

*q*

*i*

*=A*

*i*

*+TH*

*i*

*1≤i≤6*

*nTH*

*0*

*=TH*

*0*

*+∑*

*1≤i≤6*

*(I*

*i*

*-O*

*i*

*)*is the new thickness.

*nE*

*0*

flows (remaining energy) and inflows (acquired ener-

gy). The energy dissipation is the same as O version.

The erosion occurs when E>mt (where mt is a thresh-

old parameter); the eroded quantity of the soil cover is

*-∆D=(E−mt)−er*if

*(E−mt)−er<D*else

*-∆D=D*(where

er is the erosion parameter).

lated to any traditional governing equations for erosion

(e.g., e

of the model.

*S*

**4***et alii*, 2006), holds the

agement of inertial effects by introducing indicators of

momentum

*S*

*px*

sociated kinetic head, by formula

*kH=v*

*2*

*/2g*, and their

mass. Two components

*PX*and

*PY*(respectively along

axes x and y) are computed by the sum of all the contri-

butions. A “height” alteration, based on

*PX*and

*PY*, in

the neighbouring cells was introduced in AMD for

*S*

**4**the motion directions.

*S*

*A*

*S*

*D*

regard must be pointed out that in the case of the de-

tachment area, the thickness of the landslide mass is

subtracted from the value of both the morphology and

detrital cover;

*S*

*R*

*E=ρ-g-B-TH-(TH/2+A)*(potential energy

*EVOLUTION OF SCIDDICA OVER THE TIME*

latest versions include all the improvement of previous

ones. In the following, a brief review of the evolution

of the model in time is presented.

**) of SCIDDICA was a simple**

*T*validated on the 1992 Tessina (Italy) earth flow (a

*et alii*, 2000), characterised by a velocity up to few

*S=S*

*A*

*×S*

*TH*

*×S*

*O*

*4*,

*q*

*0*

*=A*

*0*

*+adh*,

*q*

*d*

*=TH*

*0*

*-adh*,

*q*

*i*

*=A*

*i*

*+TH*

*i*

thickness of the mud quantity, that becomes adherent to

soil). The balance of inflows and outflows in the cells is

defined as

*nTH*

*0*

*=TH*

*0*

*∑*

*1≤i≤4*

*(I*

*i*

*-O*

*i*

*)*.

a run up effect, and was tested on the 1984 Mt. Ontake

landslide occurred in Japan (d

*et alii*, 1999).

*S=S*

*A*

*S*

*O*

*4*

*S*

*R*

*q*

*d*

*TH*

*0*

*q*

*0*

*=A*

*0*

*+R*

*0*

*-TH*

*0*

*+adh,*

*q*

*i*

*=A*

*i*

*+R*

*i*

*nTH*

*0*

*=TH*

*0*

*+∑*

*1≤i≤4*

*(I*

*i*

*-O*

*i*

*).*The re-

*Fig. 1 - 3D visualization of some sub-states for a hexago-*

*nal cell together with an ideal section of a debris*

*flow along a slope*

**DEBRIS FLOWS SIMULATION BY CELLULAR AUTOMATA: A SHORT REVIEW OF THE SCIDDICA MODELS**

*Italian Journal of Engineering Geology and Environment - Book www.ijege.uniroma1.it © 2011 Casa Editrice Università La Sapienza*

*-∆kH= tdkH*, where td is the turbulence dissipation

parameter. The resulting new mass, barycentre and

energy (related to velocity) are computed by the com-

position of all the inflows from the neighbors and the

residual quantities inside the cell. Air-water interface

is managed only for external flows from air to water.

An external flow from an air cell (altitude higher than

water level) to water cell (altitude lower than water

level) implies always a loss of matter (water inside de-

bris and components lighter than water) proportional

to debris mass, specified by a parameter ml.

**was tested on the 1997 subaeri-**

*SS2*ly) and on a recent completely submarine debris-flow,

in the nearshore of Bagnara Calabra (Italy).

characterized by the presence of large blocks inside

the main landslide debris. Blocks are idealised as cyl-

inders; they move along the line of maximum slope

when there is no fluid matter in all the cells or in part

of the cells occupied by it; the shift of blocks is com-

puted by classic motion equations (see at a

*et*

*alii*, 2009 for further details).

flow and different types of man-made structures (i.e.

houses, cables, pipelines, and defenses structures)

both in subaerial and in submerged environment

*SUSCEPTIBILITY ANALYSIS*

ment for risk assessment and reduction strategies.

Simple susceptibility analyses have been performed

by SCIDDICA

**in recent years (Iovine et al.**

*S3−hex*2002; 2003a; 2003b; 2005; 2007).

ible soil cover overlying the bedrock; (iii) location

of landslide sources; (iv) areal and volumetric size of

landslide sources; (v) back analysis simulation of real

landslides occurred in the same area necessary for the

calibration of model parameters; (vi) validation of the

model by the comparison of real and simulated events.

fected by a relevant limitation: the flow velocity could

be only deduced “a posteriori” by averaging in space

(i.e. considering clusters of cells and computing the

resulting velocity for all cluster flows) or in time (e.g.

considering the average velocity of the advancing flow

front in a sequence of CA steps) since the flow moves

from a cell to another one in a CA step (which corre-

sponds to a constant time). Hence, in the CA context of

discrete space/time, “velocity” is constant.

**SS2**) (a

*et alii*, 2008, 2009; m

*et alii*,

veloped for the lava flow model SCIARA (a

*et*

*alii*, 2006b). This approach is based on the introduc-

tion of barycentre co-ordinates for debris flows, thus

obtaining “explicit” velocity. Furthermore, this ver-

sion was significantly improved, in order to simulate

combined subaerial-subaqueous debris flows, by in-

troducing different parameters and different transition

functions for the modelling of both the subaerial and

the submerged path at the same time. Effects like loss

of energy and loss of material at the air-to-water tran-

sition were added instead.

nates X and Y; its

next edge of the hexagonal cell. Outflows move on

such ideal path, whose shift are computing according

to the simple kinetic formula which depends whether

the flow is subaerial or subaqueous. In fact, the shift

formula for subaqueous debris considers also a water

resistance parameter, using modified Stokes equations

(a

*et alii*, 2008) with a form factor parameter

accounting for buoyancy. The motion involves three

possibilities: (1) only internal flow (the entire debris

remain inside the cell); (2) only external flow (all the

debris moves to the adjacent cells); (3) the flow is di-

vided between the central and the adjacent cell (a part

of the debris remains inside the cell and a part of it

moves towards adjacent cells) . The kinetic head vari-

ation is computed according to the new position of in-

ternal and external flows; furthermore, a contribution

*M.V. AVOLIO, F. BOZZANO, D. D’AMBROSIO, S. DI GREGORIO, V. LUPIANO, P. MAZZANTI, R. RONGO & w. SPATARO*

*5th International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment Padua, Italy - 14-17 June 2011*

nomena. Specifically, areas affected by only one of the

simulated landslides are considered less susceptible

(light gray) than zones affected by more landslides

(dark gray). More sophisticated and powerful ap-

proaches for susceptibility analysis have been devel-

oped by our research group and tested on lava flows

(C

*et alii*, 2010). A similar approach could be ap-

analysis of debris flows.

**DEBRIS FLOW SIMULATION BY SCID-**

**DICA**

ed below. Specifically, simulations of two completely

subaerial debris flows (Chiappe di Sarno-Curti, 1998

and San Martino Valle Caudina, 1999), one submarine

debris flow (Bagnara Calabra, 2008) and one mixed

subaerial-submerged debris flow (Lake Albano, 1997)

are shown. A short description of simulation results

are also presented together with a quantitative evalu-

ation of simulations according to a fitness function

based on the areal comparison of real and simulated

event (D’Ambrosio & Spataro, 2007). Specifically,

the considered evaluation function is

*R*is the set of cells involved in the real

*S*the set of cells involved in the simulated

event. A value between 0 (total failure) and 1 (perfect

simulation) is obtained, with values greater than 0.7

considered as satisfactory for landslide simulations

*CHIAPPE DI SARNO–CURTI*

On May 5–6 1998, about 150 debris flows were

ern Italy), mostly on the slopes of Pizzo d’Alvano

massif. Hundreds of small debris slides originated in

the volcanoclastic mantle overlying carbonate bed-

rock and propagated downslope as an extremely rap-

id, highly erosive debris flow, dramatically increasing

their volume (z

*et alii*, 2004). One of these

size of the detachment area was approximately 100

m

running down the slope along a smooth pre-existing

channel, for about 375 m, and eroding the available

detrital cover. The debris flowed for a distance of

about 325 m, triggering some minor debris slides on

both flanks of the channel. After that, influenced by

the pre-existing morphology, it made a left turn, en-

larged, and subdivided into two distinct flows.

**. A graphic comparison between the map**

*S3-hex*of the best simulation, and the real case is shown in

*Fig. 2 - Debris-flow susceptibility map of the southern*

*slope of the Pizzo d’Alvano massif: ten soil-slip*

*sources have been considered (case a, b, c, d cor-*

*respond to the may 1998 debris flows) along the*

*southern slope of Pizzo d’Alvano. Area “poten-*

*tially” affected by one (1), two (2), three-four (3),*

*and five-six (4) simulated landslides; 5) wall; 6)*

*secondary soil slips; 7) border of the area consid-*

*ered in simulations*

*Fig. 3 - The 1998 Curti DF: comparison between the real*

*case and the best simulation. (1) area affected by*

*real landslide, (2) simulated landslide, (3) both*

*cases; and (4) border of the area considered for*

*comparison*

**DEBRIS FLOWS SIMULATION BY CELLULAR AUTOMATA: A SHORT REVIEW OF THE SCIDDICA MODELS**

(Figure 5) which is a rare case of combined subaer-

ial-subaqueous debris-flow. This landslide occurred

in the eastern slope of the Albano lake on the 7th of

November 1997 after an intense rainfall event (128

mm in 24 hours), and it began as a soil slide, mobi-

lizing about 300 m

impluvium (about 40°) and thus evolved as a debris

flow which entrained a large amount of debris mate-

rial along the bottom of the channel and reached an

estimated volume of some thousands of m3 at the

coastline. A few amount of material was deposited at

the coastline while a greater quantity entered in wa-

ter generating a little tsunami wave. Simulation re-

sults are quite satisfactory since the achieved fitness

function ev value was 0.85 (a

*et alii*, 2008).

terms of erosion and deposits on both subaerial and

subaqueous parts and reasonable values of the land-

slide velocity (up to 15 m/s).

*BAGNARA CALABRA*

paring detailed bathymetries coming from two sonar

multibeam surveys carried out in November 2007

and in September 2008. Landslide detachment area

was located between 10 m and 20 m b.s.l., about 100

m far from the coastline. Initial landslide volume

the DF and depth of regolith erosion along the path

are in good accordance with surveyed evidences; the

branching of the flow at the base of the slope is fairly

well simulated. The obtained ev fitness value was

0.76-0.78 (d’a

*et alii*, 2003a-b)

*SAN MARTINO VALLE CAUDINA*

gering numerous debris flows on the slopes of the

Monti di Avella massif (v

*et alii*, 2000). Sev-

Castello (Cervinara) and Mt. Pizzone (San Martino

V.C.), within the weathered volcaniclastic mantle.

These landslides turned into fast flowing mixtures

of matrix and large blocks, downslope eroding the

soil cover and increasing their original volume. At

the base of the slopes, debris flows impacted on the

cited urban areas, causing one casualty and severe

destruction. Simulation of this event was performed

by SCIDDICA S4. A graphic comparison among real

landslides and “best” simulations is shown in Fig.4

(i

*et alii*, 2003b). The achieved ev fitness val-

C respectively (Fig.4). These results demonstrate the

efficacy of the SCIDDICA models to simulate also

cases of open channel debris flows (mudflows).

*ALBANO LAkE*

*Fig. 4 - The 1999 San Martino Valle Caudina DF: com-*

*parison between the real case and the best simula-*

*tion. (1) area affected by real landslide, (2) simu-*

*lated landslide, (3) both cases; and (4) border of*

*the area considered for comparison.*

*Fig 5 - The 1997 Albano lake subaerial-subaqueous*

*debris flow as simulated by the SCIDDICA SS2*

*model. key: (1) real event, (2) simulated event, (3)*

*intersection between real and simulated event, (4)*

*water level*

**TION WITH OTHER MODELS**

flow models. In particular, the 1997 Lake Albano

debris flow has been simulated by SCIDDICA SS2

as well as by

*DAN-w*(H

*DAN3D*

*DAN3D*and SCIDDICA were surprisingly similar in

terms of areal debris distribution, velocity and time

of propagation (m

*et alii*, 2009) while results

*DAN-w*show a significant discrepancy.

This is once more a confirmation of the limitations af-

fecting 2D models, particularly for the simulation of

channeled events like debris flows.

**CONCLUSION REMARKS AND OUTLO-**

**OKS**

presented together with some examples of applica-

tion to real events occurred in Italy in recent years.

Thanks to the significant improvements made in the

latest years, and briefly summarized in this paper in a

chronological way (Tab.2), Cellular Automata models

can now be considered as a powerful and reliable tool

for the simulation of debris flows and hyper-concen-

trated flows. In particular, the introduction of explicit

velocity (in the SCIDDICA version SS2) has brought

these models to the same level of the most accepted

codes as

*DAN3D*

introduction of explicit velocity, the management of

momentum (even if yet in a rough way) and the intro-

duction of turbulence forces, which can be found in

the latest versions of SCIDDICA, the main require-

ments of the “equivalent fluid” approach (H

recorded along the pathway between 20 m and 60

m b.s.l.. Final deposit is partly distributed between

60 m and 90 m b.s.l. and partly below 100 m with a

maximum thickness of 5 m.

*et alii*, 2009). Furthermore, de-

very satisfactorily with the real event; moreover,

deposit thickness and erosion depth values do not

differ substantially. The detachment area was com-

pletely emptied after about one minute and the flow

propagated until its final position in few minutes.

Landslide velocity was up to 6 m/s in the upper part

of the slope, immediately after the mass release, and

dropping below 4 m/s in the following stages. Such

values of velocity are considered reasonable for the

type and volume of landslide and the slope gradi-

ent (up to 12°). Fig. 6 shows an example of possible

output layers than can been obtained by SCIDDICA

SS2 and specifically: final deposit thickness, erosion

thickness and maximum velocity.

**COMPARISON OF SCIDDICA SIMULA-**

*Fig 6 - simulation of Bagnara Calabra subaqueous*

*landslide: a) deposit thickness, b) erosion, c)*

*maximum occurred debris thickness; d) maxi-*

*mum velocity; contour lines are referred to: 1*

*perimeter of the simulated event; 2 perimeter*

*of real event; 3 probable real event perimeter*

*in missing data area*

*Tab.2 - features of the SCIDDICA versions*

**DEBRIS FLOWS SIMULATION BY CELLULAR AUTOMATA: A SHORT REVIEW OF THE SCIDDICA MODELS**

C

*et alii*, 2010; C

*et alii*, 2010).

possible. First of all, a new solution for the manage-

ment of the momentum is needed, as the importance

of non isotropic internal earth pressure (s

*et alii*, 2007). This is one of the

tion of hydrostatic internal earth pressures does not

lead to significant mistakes in the case of debris flows

with a high percentage of water (C

*et alii*, 2009).

the erosion parameters.

between solid and fluid-phases on the debris as the rel-

evance of inner fluid pressures in the propagation of a

debris flow has been demonstrated (i

First preliminary results have been achieved for test-

ing the resistance of submarine cables, even if signifi-

cant improvements are still necessary.

stable, well-performing and suitable for the simulation

of different types of debris flows in different environ-

ments. In other words, SCIDDICA can be considered

a valid and efficient tool for the susceptibility analysis

of debris flow run-out at a local or global scale.

a complex mixtures of water and heterogeneous debris

(which often changes its features during the propaga-

tion) to behave as an equivalent and homogeneous

fluid (usually integrated in depth) controlled by only 2

or 3 parameters. It is evident that this is a very strong

approximation; however, at this time, models based

on the equivalent fluid approach are probably the only

ones able to give reasonable results (in a reasonable

time of computation).

DICA SS (i.e. m

of debris flows, such as:

(iv) immersion in water (in the case of costal DF).

Nevertheless, it must be pointed out that users

must be completely conscious of these limitations.

give good results also in terms of forecasting analy-

ses. In particular, forecasting instruments for DF im-

pact are a fundamental tool for the society in order to

produce hazards maps which must become the basic

data for the landscape management. The first step in

this direction has been already carried out with the

SCIDDICA models by using a simple approach for

susceptibility analysis. However, more sophisticated

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