INFLUENCE OF DEM RESOLUTION AND DOMAIN DISCRETIZATION ON THE
SNOW AVALANCHE DYNAMICS MODELLING
Ma cos Sanz-Ramos1*, Pe e Olle 2,3, and E nes Bladé1
1 Flumen Resea ch Ins i u e (Uni e si a Poli ècnica de Ca alunya [UPC Ba celonaTech] – In e na ional Cen e o Nume ical
Me hods in Enginee ing [CIMNE]), Ba celona, Spain
2 GeoNewRisk, Ba celona, Spain
3 Riskna g oup (Uni e si y o Ba celona), Ba celona, Spain
ABSTRACT: Nume ical modelling o snow a alanche dynamics equi es necessa ily he u ilisa ion o
e ain opog aphy o upda e he ele a ion o he calcula ion meshF ee dis ibu ed opog aphical da a is
cu en ly a ailable wo ldwide h ough digi al e ain models (DTM), om coa se o ine esolu ions. This
wo k aims on in es iga ing he in luence o he DTM esolu ion, bo h in he ho izon al and e ical accu-
acy, on he esul s o he bulk dynamics o dense snow a alanches. To ha end, he nume ical ool
Ibe , a dep h-a e aged hyd odynamic nume ical ool ecen ly enhanced o he simula ion o non–New-
onian shallow lows such as snow a alanches, was used. Se e al calcula ion scena ios, based on wo
well-documen ed e en s, we e ca ied ou u ilising combina ions o di e en mesh sizes and DTMs. The
indings e eal he impo ance o DTM esolu ion o mid-low size a alanches. When compa ing iden i-
cal mesh esolu ions, he e ical p ecision o he DTM has a mo e signi ican impac on he a alanche
dynamics han he ho izon al esolu ion o he DTM. E en wi h a i e- old imp o emen in spa ial eso-
lu ion, which cu en ly includes LiDAR echniques, he ou comes de i ed om he 5-me e DTM closely
esembled hose o he 2-me e DTM, he compu a ional cos being educed no ably. LiDAR-based
opog aphical da a allows he gene a ion o DTM and DEM (digi al ele a ion models), being he la es
use ul o ep esen he obs uc ions on he low dynamics due o he ege a ion and p o iding a closes
ep esen a ion o he a alanche dynamics o he obse a ions.
KEYWORDS: DTM/DEM, nume ical modelling, Ibe , smoo hing, ill sinks.
1. INTRODUCTION
Snow a alanches a e apid lows o snow down a
slope, posing signi ican isks o li e, in as uc-
u e, and ecosys ems in moun ainous egions
(CCA, 2016; McClung e al., 2002). Unde s and-
ing and p edic ing hese e en s a e c ucial o e -
ec i e isk managemen and mi iga ion s a e-
gies. Snow a alanche modelling is a scien i ic ap-
p oach ha aims o simula e he dynamics o a -
alanches o p edic hei beha iou , pa h, and po-
en ial impac a eas. These models ange om
simple empi ical o mulas o sophis ica ed nume -
ical simula ions ha conside a ious physical
p ocesses in ol ed in a alanche ini ia ion, low,
and deposi ion (Egli e al., 2020).
A undamen al componen o a alanche model-
ling is he in eg a ion o opog aphical da a, which
desc ibes he e ain o e which a alanches oc-
cu . Topog aphy in luences many aspec s o a -
alanche dynamics, including he s a ing zone,
low pa h, and un-ou dis ance (Maggioni and
G ube , 2003). Accu a e opog aphical da a, ob-
ained om sou ces such as digi al e ain/ele a-
ion models (DTM/DEMs), ae ial pho og aphy,
and sa elli e image y, p o ides c i ical in o ma ion
abou slope angles, aspec , cu a u e, and ough-
ness, all o which a e essen ial pa ame e s in
modelling e o s (G ube and Hae ne , 1995;
Maggioni e al., 2013).
Topog aphical da a allows o he de ailed map-
ping o po en ial a alanche elease a eas and
pa hs, enhancing he p ecision o a alanche haz-
a d assessmen s. As compu a ional echnologies
and emo e sensing me hods ad ance, he accu-
acy and eliabili y o a alanche models con inue
o imp o e, making hem indispensable ools in
he ield o snow science and haza d manage-
men .
The cu en wo k explo es he in luence o he
opog aphical da a (xy- esolu ion and z-accu acy)
and he domain disc e iza ion on he modelling o
he a alanche dynamics. To ha end, he nume -
ical model Ibe (Bladé e al., 2014) ecen ly en-
hanced o simula e non–New onian shallow lows
(Sanz-Ramos e al., 2024), such as dense snow
a alanches (Sanz-Ramos e al., 2023c), was u i-
lised o simula e se e al well-documen ed snow
a alanche e en s. The simula ions we e ca ied
ou by conside ing i e ypes o opog aphic da a,
oge he wi h pa icula op ions o Ibe o imp o e
he ep esen a ion o he e ain.
2. MATERIALS AND METHODS
2.1 S udy si es and e en s
Di e en s udy si es and a alanche e en s we e
chosen o analyse he in luence o he opog aph-
ical da a in he snow a alanche dynamics, o-
ge he wi h some p ocedu es o enhance he ele-
a ion da a in he nume ical model. All s udy
cases a e loca ed on he sou he n side o he Py -
enees ange (Figu e 1).
On Janua y 2014 a slab a alanche occu ed in
Bonaigua alley, c ossed a oad and s opped ew
me e s below wi h a unou dis ance o a ound
650 m. The a alanche spli in o b anches a he
deposi ion a ea. A wide desc ip ion o he e en
besides da a u ilised in he nume ical model is de-
ailed in Sanz-Ramos e al. (2023b).
Figu e 1: Loca ion o he s udy si es.
On Feb ua y 2018 an a alanche occu ed nea o
Coll de Pal pass, be ween 1900 and 2200 m.a.s.l
and s opped on a oad and ew me e s below.
The e en was cha ac e ized ew days a e he
e en showing a unou dis ance o 370 m wi h
e ical d op o 215 m. A ull desc ip ion o he
su ey and he nume ical pa ame e s a e de-
sc ibed in Sanz-Ramos e al. (2021b).
The las case s udy is he a alanche occu ed in
in 1996 in Bo des d'À eu, a small illage ha was
pa ially des oyed in 1803 by ano he a alanche
e en (Olle e al., 2020).
2.2 Topog aphical da a
All opog aphical da a u ilised come om he In-
s i u Ca og à ic i Geològic de Ca alunya (ICGC,
2021), who p o ide DTMs o di e en ho izon al
and e ical esolu ions and LiDAR da a, among
o he p oduc s.
The DTM o 15x15m and 5x5m o cell size a e
gene a ed om he opog aphical base a 1:5000
scale. The 2x2m o cell size DTM is based on he
2nd e sion o LiDAR. Finally, he cloud o poin s
come om LiDAR da a, being he 1s e sion ob-
ained om 2008 o 2011 while he 2nd e sion
om 2016 o 2017. LiDAR da a we e il e ed aim-
ing o ob ain he g ound, key poin s and, when is
necessa y, he ege a ion ( om low o high
heigh ), and hen con e ed in o a DTM o DEM
in as e o ma .
Table 1 summa izes he main speci ica ions o
he opog aphical da a employed o upda e he el-
e a ions o he mesh a each nume ical model.
Table 1. Speci ica ions o he opog aphical da a.
Name
Resolu ion
Accu acy
Sou ce
DTM15x15
15m
0.90m
1:5000
DTM5x5
5m
0.90m
1:5000
DTM2x2
2m
0.15m
LiDAR
LiDAR 1*
0.5p/m2
0.06m
LiDAR
LiDAR 2*
0.5p/m2
0.06m
LiDAR
*Wi h and wi hou ege a ion.
2.3 Nume ical ool: Ibe
The case s udies we e simula ed wi h Ibe (Bladé
e al., 2014), a ee dis ibu ed wo-dimensional
hyd odynamic ool ecen ly enhanced o simula e
non–New onian shallow lows such as dense
snow a alanches, mud lows, laha s, wood laden
lows, e c. (Ruiz-Villanue a e al., 2019; Sanz-
Ramos e al., 2023b, 2023c, 2024).
Ibe sol es he shallow wa e equa ions (2D-
SWE) h oughou a pa icula nume ical scheme
based on he Roe scheme (Roe, 1986). I ensu es
he balance be ween he lux and p essu e g adi-
en s and he ic ion sou ce e m, a oiding nume -
ical ins abili ies and achie ing non–ho izon al ee
su ace acco ding o he heology o he luid e en
in i egula geome ies and sloping e ain (Sanz-
Ramos e al., 2023c).
The cha ac e iza ion o he esis ance o ces can
be done by means o di e en heological models,
such as Voellmy (1955) join ly o no wi h cohe-
sion (Ba el e al., 2015), simpli ied Bingham
(Bingham, 1916; Chen and Lee, 2002; Nae e al.,
2006), Manning (Chow, 1959), dila an - and is-
cous-like (Macedonio and Pa eschi, 1992), quad-
a ic (O’B ien and Julien, 1988), and (He schel
and Bulkley, 1926). Based on p e ious s udies,
he Voellmy-Ba el heological model was u i-
lized in he simula ions.
Addi ionally, Ibe includes se e al ea u es o i-
en ed o imp o e he nume ical pe o mance o
he model. Specially hose o opog aphical da a
ea men in hyd ological modelling (Cea and
Bladé, 2015; Ga cía-Alén e al., 2022; Sanz-
Ramos e al., 2020, 2021a), such as ‘ ill sinks’ and
‘smoo hing’ can be also use ul o snow a a-
lanche modelling.
* Co esponding au ho add ess:
C/ G an Capi à s/n, Flumen Resea ch Ins i u e, Uni e si a
Poli ècnica de Ca alunya (UPC Ba celonaTech) – Cen e In-
e nacional de Mè odes Numè ics en Enginye ia (CIMNE),
08034 Ba celona, Spain;
el: +34 934054251;
email: ma cos.sanz- am[email p o ec ed],
[email protected]
25 km
N
B ie ly, he ‘smoo hing’ op ion adjus he ele a ion
o a node acco ding o he nodes ele a ion o i s
icini y; while he ‘ ill sinks’ op ion inc ease he el-
e a ion o he dep essed nodes o he op ele a-
ion o he su ounding o dele e na u al o unna -
u al dep essions.
2.4 Scena ios and domain disc e iza ion
Se e al simula ions we e ca ied ou pe each
s udy si e by eeding he model wi h he o iginal
da a, conside ing wo il e s o he opog aphical
smoo hing ha Ibe inco po a es (2 and 10
passes) and he u ilisa ion o DTM (no ege a ion
land co e ) o DEM ( ege a ion land co e ) in Li-
DAR based simula ions. Thus, h ee simula ions
we e done pe each opog aphical sou ce, excep
o LiDAR in which h ee addi ional simula ions
we e ca ied ou conside ing he ‘ ill sinks’ op ion
o Ibe .
The domain was disc e ized by means o iangu-
la elemen s, de ining a side leng h equal o he
esolu ion o he DTM in a e age. Thus, he ele-
men side anges om 1 o 15 m.
3. RESULTS
Due o he la ge numbe o simula ions, he mos
ele an esul s o he in luence o he opog aph-
ical da a in he a alanche dynamics is p esen ed.
3.1 Coll de Pal 2018
The pa ame e s o he heological model
(Voellmy-Ba el ) we e selec ed acco ding o
Sanz-Ramos e al. (2021b), being he u bulen
coe icien o 1250 m/s2, he Coulomb ic ion co-
e icien o 0.34, and he cohesion o 100 Pa.
Figu e 2 shows he map o dep h when he a a-
lanche s opped. As expec ed, as he elemen size
is educed and he e o e he accu acy o he opo-
g aphic da a is inc eased, he esul s i mo e
closely o wha was obse ed ( anspa en whi e
polygon).
The DTM15x15 p o ided coa se esul s due o
he low esolu ion and accu acy o he opog aphy
and he size o he a alanche. A 5x5m DTM
shown a good ep esen a ion o he a alanche,
wi h a de en ion a ea shi ed o he eas as ob-
se ed. When a 2x2m esolu ion is u ilised, he
esul s adjus ed o he obse a ions, no only in
he a alanche’s ex en bu also in he snow accu-
mula ed on he oad (Sanz-Ramos e al., 2021b).
Models ed wi h LiDAR da a also pe o med ade-
qua ely, wi h di e ences in bo h scena ios ela ed
o ege a ion g ow h (no ably highe in LiDAR 2
e sus LiDAR 1). In such cases, he shape o he
ege a ion was included in he mesh ac ing as an
obs acle o he low, gene a ing accumula ion up-
s eam o he ees and expanding he de en ion
zone (Naaim e al., 2004).
MDT15x15
MDT15x15_s2
MDT15x15_s10
MDT5x5
MDT5X5_s2
MDT5X5_s10
MDT2X2
MDT2X2_s2
MDT2X2_s10
LiDAR 1
LiDAR 1_s2
LiDAR 1_s10
LiDAR 2
LiDAR 2_s2
LiDAR 2_s10
Figu e 2: Coll de Pal. Map o dep h when he a -
alanche s opped.
3.2 Bonaigua 2014
The s udy a ea was disc e ized in 3705 elemen s
o he coa se DTM o 15x15, while he numbe o
elemen s almos each 750,000 o he LiDAR-
based models. The pa ame e s o he heological
model ha bes i o he obse a ions a e
500 m/s2 o he u bulen coe icien o and 0.125
o he Coulomb ic ion coe icien (Sanz-Ramos
e al., 2023c).
This case highligh s he bene i o using he ‘ ill
sinks’ (_ s) op ion o Ibe since al eady exis s
some poin s o he LiDAR cloud wi h w ong ele-
a ion da a despi e he da a ha e been ea ed
p e iously. This gene a es un eal dep essions on
he e ain (Figu e 3, uppe ) ha , some imes, a e
di icul o de ec e en using ad hoc LiDAR o /and
GIS so wa e.
Figu e 3: Bonaigua. Map o e ain: LiDAR 1 (up-
pe ) and LiDAR 1_ s (lowe ). Values lowe ha
1975 m a e plo ed in black, which ep esen s he
dep ession (highligh ed in a ci cle).
Figu e 4: Bonaigua. Map o maximum snow ele-
a ion: LiDAR 1 (uppe ) and LiDAR 1_ s (lowe ).
Values lowe ha 1975 m a e ep esen ed in
whi e (highligh ed in a ci cle).
As expec ed, his ab up change in he opog a-
phy no ably modi ied he dynamics o he a a-
lanche because he dep ession ends o be illed.
An un eal inc ease o eloci y was p oduced due
o he change o slope, while he dep ession could
no be illed depending on he a alanche dynam-
ics gene a ing an un eal snow ele a ion p o ile
(Figu e 4, uppe ).
The ‘ ill sinks’ op ion o Ibe nume ically ill hese
dep essed a eas, sol ing he a o emen ioned is-
sues and p o iding a eliable snow a alanche dy-
namic modelling.
Rega ding he pe o mance o he model wi h he
di e en opog aphical da a, in all cases he e-
sul s show a de en ion a ea spli in wo b anches,
e en o he coa se DTM o 15x15m.
3.3 À eu 1996
The snow a alanche o À eu 1996 was well e-
p oduced wi h all opog aphical da a due o he
size o he a alanche. The de en ion zone was
p oduced in he Mona s Go ge and À eu Ri e
junc ion. Figu e 5 (uppe maps) shows he a a-
lanche a he end o he simula ion when using a
15x15 (up), 5x5 (middle), and 2x2 (down) DTM.
Figu e 5: À eu. Map o dep h when he a a-
lanche s opped (uppe maps). Map o slopes
(lowe map), colou ed map il e ed om 0 o
1 m/m (black colou ep esen s slopes highe
han 1 m/m).
This ag ees wi h he selec ed heological p ope -
ies o he a alanche and he slope, he Coulomb
ic ion coe icien being o 0.35 (Figu e 5, low
map).
As he DTM cell size is educed, he de ini ion o
he junc ion, as well as he es o he alley, im-
p o es, p o iding a mo e de ailed and accu a e
desc ip ion o he a alanche dynamics. The junc-
ion is almos pe pendicula ; hus, when he a a-
lanche a i es ends o con inue lowing in he
o iginal di ec ion gene a ing an accumula ion a
highe al i udes o he i e bed.
À eu case s udy also p esen ed p oblem wi h he
opog aphical da a, especially in LiDAR da a wi h
some poin s below o he eal opog aphy. This
issue was also sol ed wi h he ‘ ill sinks’ op ion o
Ibe .
4. DISCUSSION
4.1 On he da a sou ce and pe o mance
All da a u ilised come om cu en echniques
and ollowed se e al s anda ds ha modelle s u i-
lise o eed nume ical models aiming o simula e
di e en en i onmen al lows.
Pa icula ly o dense snow a alanches, cu en ly
exis s a wide ange o esolu ions hanks o he
con inuous e olu ion in he acquisi ion o opo-
g aphical da a. Fine esolu ion commonly implies
highe accu acy, being his las ac o a key in nu-
me ical modelling (Chojnacki e al., 2010; Do o i
e al., 2013), especially in moun ain a eas whe e
he accu acy is limi ed by he echnique u ilised o
ob ain i .
This also implies he possibili y o building up
mo e de ailed nume ical models. The s udy cases
we e disc e ised using a mesh o iangula ele-
men s, which i in ol es a densi y o elemen s pe
hec a e anging om ~88 o 20000. The compu-
a ional e o when ine meshes a e u ilised in-
c eases no ably o nume ical models based on a
nume ical scheme explici in ime (Cou an e al.,
1967). Thus, he applica ion o gene al-pu pose
compu ing on g aphics p ocessing uni s being
manda o y o ca y ou simula ions in a easible
compu a ional ime, such is al eady done in he
hyd odynamic and sedimen anspo module o
Ibe (Dehghan-Sou aki e al., 2024; Sanz-Ramos
e al., 2023a) and i will be ealized in u u e e -
sion o he non–New onian module, eaching
speed-up abo e 100- imes.
4.2 On he land co e (DEM s. DTM)
LiDAR da a is a cloud o poin s usually classi ied
acco ding o he LiDAR e u n/in ensi y as
g ound, low-mid-high ege a ion, buildings, wa-
e , e c. Depending on he p ocedu e o ob ain
his cloud o poin s, and la e ea men , LiDAR
da a can be di ec ly used o upda e he ele a ion
o he nodes o a calcula ion mesh.
Figu e 6 exempli ies he di e ences in he u ilisa-
ion o opog aphical da a as DTM (le , wi hou
ege a ion land co e ) and as DEM ( igh , wi h
ege a ion land co e ) o he simula ion o he
e en o Coll de Pal. In bo h cases he unou ob-
ained was simila , bu conside able di e ences
we e obse ed in he dynamic and s a ic phases,
specially below he oad.
Figu e 6: Coll de Pal e en simula ed wi h Li-
DAR 1: DEM (uppe le ) and DTM (uppe igh );
map o maximum dep h wi h opog aphical da a
as DEM (lowe le ) and as DTM (lowe igh ).
This a ea is co e ed by mid-low dense ege a ion
ha co esponds o a ela i ely young o es .
When he DEM is u ilised, he ege a ion is inco -
po a ed in o he model as an addi ional ele a ion
ha ac s as mac o- oughness. This has an e ec
on he a alanche pa h and de en ion since lows
wi h enough ene gy pa ially accumula es up-
s eam o he ege a ion; while he es con inue
lowing gene a ing an i egula de en ion zone in
compa ison wi h he DTM simula ion, which is
smoo h. In such case, he esul s ha bes i wi h
he obse a ion may be in an in e media e si ua-
ion: DTM wi h ege a ion in he g ea es ees
and an inc ease o oughness in he es .
Conside ing he ege a ion o he DEM in he
snow a alanche modelling could no be ep e-
sen a i e o he eal beha iou : he sh ub ege a-
ion is ei he co e ed wi h snow, o has a lexible
beha iou owa ds he a alanche as occu in lu-
ial loods (Cheng, 2011; Nep , 2012; Sanz-
Ramos e al., 2018; S ephan and Gu knech ,
2002), and some ees will wi hs and he impac ,
bu o he s will b eak. Thus, DEM da a conside s
all hese elemen s as a pe manen and in a ian
mac o- oughness h oughou he simula ion, no
being as ealis ic as wi h DTM da a. The use o

DEM can be mo e use ul o small a alanches,
which do no ha e enough ene gy o b eak ees,
han o la ge a alanches, whe e he e is a lo o
o es des uc ion. Howe e , u he in es iga ion
is needed o ully unde s and he ole o DEM in
snow a alanche modelling.
4.3 Fine- uning opog aphy wi h Ibe
Addi ionally, despi e he classi ica ion and ea -
men o LiDAR da a, which is done by he p o ide
o he sou ce ollowing s ic s anda ds, some e -
o s and ou lie s can emain. This was he case in
Bonaigua case s udy, in which a ew poin s o he
cloud ha e an ele a ion e y a om he poin s
o i s icini y, e en a e applying he same ea -
men as o he es o LiDAR da a. In such cases,
he ‘ ill sinks’ op ions o Ibe allowed o co ec he
opog aphical da a illing he sink wi h an ele a-
ion equal o he lowes node o he su oundings.
This is also use ul o ill dep essed a eas ha usu-
ally cumula es snow du ing snow e en s (e.g. up-
s eam o b idges, cul e s, e c.) and migh condi-
ion he a alanche dynamics.
The op ion ‘smoo hing’ o Ibe plays a ole simila
o hose ob ained when simula ing an a alanche
wi h coa se DTMs. Howe e , his op ion allows
modelle s o use e y ine esolu ions and ob ain
a smoo h ep esen a ion o he e ain, e en when
using summe opog aphy. Fu he in es iga ion
on he u ilisa ion o ‘smoo hing’ o Ibe is neces-
sa y, mainly o ien ed o compa e he esul s o
summe opog aphy wi h ‘smoo hing’ and he win-
e opog aphy (Maggioni e al., 2013).
These op ion helps he modelle o deal, wi hou
addi ional ea men s and in he simula ion p o-
cess, wi h some p oblems in he opog aphical
da a when simula ing dense snow a alanches.
5. CONCLUSIONS
Se e al opog aphical sou ces we e u ilised o e-
p oduce well-documen ed snow a alanche
e en s wi h Ibe , a dep h-a e aged hyd odynamic
nume ical ool ecen ly enhanced o simula e
non–New onian en i onmen al lows.
Depending on he size o he a alanche, coa se
mesh and opog aphical da a (e.g. 15m) canno
be able o ob ain a sui able ep oduc ion o an a -
alanche e en . Fine meshes and de ailed opo-
g aphical da a, despi e inc ease he compu a-
ional e o , p o ide high esolu ion esul s. The
u ilisa ion o DEMs (DTMs wi h ege a ion) can be
u ilised conside ing he ege a ion as mac o-
oughness, especially o small and medium a a-
lanches, bu u he esea ch is needed.
A di ec u ilisa ion o DTM/DEMs o 15, 5, 2, and
1 m o as e cell size is possible, bu some issues
migh be gene a ed especially wi h LiDAR da a.
To ha end, he op ions o Ibe ‘smoo hing’ and
‘ ill sinks’ can help modelle s o sol e by gene a -
ing a smoo he opog aphical da a and illing na -
u al o unna u al dep essed a eas. This imp o es
he simula ion o he dynamics o he a alanche.
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