E3S Web of Conferences 415, 01017 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341501017

Multi-component avalanches for rock- and ice-falls to potential
debris flow transition modelling
Jessica Munch1*, Perry Bartelt1, and Marc Christen1
1WSL Institute for snow and avalanche research SLF, Flüelastrasse 11, 7270 Davos-Dorf, Switzerland

Abstract. Rock/ice avalanches are complex gravitational flows involving three important
physicalprocesses: entrainment, phase-changes, and flow transitions. The basic complexity of modellingrock/ice avalanches lies in the fact that the flow is a mixture of rock, ice, water, snow, soil sediments. Theinteraction of these components can lead to flow transitions which is difficult to model with simplified flowrheologies. For example, a flow initially composed of rocks and ice can transition into a long-runout debris
flow, dependent on the entrainment of water, water-saturated sediments and/or snow. The dynamic
behaviour of rock/ice avalanches is therefore highly difficult to predict because the flow mixture isdependent on the hydrological and geomorphological properties along the avalanche track. These properties
can vary from year to year but even from season to season. The problem is intensified by the fact thatfrictional shearing can produce enough heat energy to melt ice and snow, leading to additional water in theflow. In this contribution, we present a new RAMMS module specifically designed to simulate single- andmulticomponent avalanches of rock, ice, water, snow, and ice. The rheology of the flow is treated by using
the concept of component activation energy. The model includes both snow and sediment/water/ice
entrainment modules. A unique feature of the model is that it tracks the temperature of the rock, ice andwater phases and therefore can treat phase changes. We apply the model on the Chamoli case-study tohighlight the potential and present limitations of the model..

1 Introduction
Multi-component rock/ice avalanches occur at a lower
frequency than snow avalanches or debris flows, but
usually lead to larger consequences in terms ofinfrastructure destruction and human loss of life. Some
events that occurred over the last years include the Piz
Cengalo, 3368 m a.s.l, rock and ice avalanche that
occurred in August 2017. It initiated with the release of
3.106 m3 of rock which fell on to an ice-glacier, leading
to the entrainment of 0.6 million cubic meters of ice [1,2]. The flow then transitioned into a debris flow thatreached the village of Bondo in the Grisons,
Switzerland, causing casualties and infrastructure
destruction [2] . Another major example of this kind of
event it the rock and ice avalanche that took place in
Chamoli area, in India, in February 2021. In this case,27.106 m3 of material fell from Ronti Peak, 5600
m.s.l,travelled through the Rishiganga and thenDhauliganga valleys, leading to the destruction of twohydroelectrical projects and 204 casualties [3-5] .
These events have been widely studied from a field
perspective, but also using seismic data, numericalmodels etc. However, most the of the numerical models
used so far focused on one-phase flow [5-7]. To
accurately model multi-component avalanches, we holdthat it is necessary to consider all the avalanche
components in the form of separate phases, such as
snow/ice, rocks, and water. Indeed, it has beensuggested [8, 9] , that the water content of gravitational
flow strongly influences the rheological properties ofthe flow and therefore the final runout distance andhazard potential. The water can arise from manydifferent sources. Indeed, water is not necessarily
present in the flow at the beginning, however, when theice melts, or when ground water/river/glacier water isentrained, the water content can increase dramatically.
Hence, the different materials present in the avalancheshould be allowed to interact, and phase changes should
be considered when attempting to model suchphenomena. Moreover, the importance of entrainment innumerical models is common to various gravitationalflows [10, 11], and we think that it is crucial tomulticomponent avalanches as, depending on the flow
composition, which is likely to be strongly affected byentrained material, the runout distances and potential todebris flow transition will vary.
In this work, we are introducing a new module of
the RAMMS software [12] specifically built to model
avalanches that contain rocks, ice, snow and water. We
will first shortly describe the governing equations before
applying the model to the Chamoli event from 2021 andassess which are the controlling parameters for the watercontent in the flow.

2 Methods* Corresponding author: jessica.munch@slf.ch© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 01017 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341501017

The module is built on the depth-averaged conservationof mass and momentum for all the phases involved inthe flow:̂Φ + 𝛁 ⋅ (𝐻Φ 𝑽𝚽 ) = 𝑀̇Σ→Φ − 𝑀̇Σ→Π𝜕𝑡 𝐻

(1)

With
̂Φ = 𝜌𝑖 (ℎ𝑖 + 𝜌𝑠 ℎ𝑠 ) + 𝜌𝑟 ℎ𝑟 + 𝜌𝑤 ℎ𝑤
𝐻(2)

𝜌̂Φ

𝜌𝑖𝜌̂Φ𝜌̂
Φ

occurred in 2016 ([5]), as well as sediments in the valley
and water from incoming rivers. Several sources ofwater can be identified to explain the transition from theinitial purely rock and ice flow to the debris flow thatdamaged infrastructures and caused human fatalities.
The sources likely to be 1) water produced from themelting of the ice initially present in the flow, 2) water
entrainment from the ground or coming from asecondary release, 3) water produced from ice entrained
and melting.
In this work, we run simulations for the Chamoliarea, using the proportions of rock and ice suggested forthe event, and vary the heat transfer coefficients, thepresence or absence of a secondary release zone that
could be triggered by the avalanche when it crosses
unconsolidated deposits from previous multicomponents avalanches, and composition of theentrained material. We want to assess how thesedifferent elements influence the amount of water present
in the flow and the potential for a transition to debrisflow.
We performed 7 sets of simulations that are detailedin Table 1. We used a resolution of 20 m and ran themfor 1500s. We defined a main release zone at Ronti
Peak, 66 m deep, of about 25.106 m3, made of 80% rocks
and 20% ice, with a density of 2500 kg/m3. In somesimulations, we defined a secondary release zone in thedeposits area of the 2016 avalanche, we set it made of70% rocks and 30% ice, its volume is about 3.7 millionsof cubic meters, and we set it to release 80 s after the
initial release, at the same time as the flow reaches theconcerned area. All the releases are done at atemperature of 0C. We ran the simulations with various
heat coefficients and with and without ground materialentrainment. The entrainment layer has been defined
uniformly over the computation domain, with a depth of
maximum 10 m at 5200 m high and a loss of snow layer
thickness of 0.03 m per 100m elevation loss, and at atemperature of 0 C.

̂Φ is the height of the flow, h is the height ofWhere 𝐻
each material in the flow, and 𝜌 is the density for eachmaterial, the subscript r stands for rock, 𝑖 for ice, 𝑤 for
water, and s for snow. 𝜌̂Φ is the density of the co-volumeof the flow [13]. The velocity of the avalanche core isgiven by 𝑽𝚽  𝑀̇Σ→Φ represents the mass entrainment
rate by the core and 𝑀̇Σ→Π is the mass loss rate from thecore to the powder cloud.
The momentum conservation in the slope parallel
direction is written as:̂Φ 𝑽𝚽 ) + 𝛁 ⋅ (𝐻̂Φ 𝑽𝚽 ⊗ 𝑽𝚽 +𝜕𝑡 (𝐻= 𝑮 −𝑽Φ
||𝑽Φ ||

̂2𝑔𝐻Φ𝐼)
2[𝑺Φ + ((1 + 𝑟)𝑀̇Σ→Γ + 𝑀̇Σ→Π ||𝑽𝚽 ||)] (3)with G the gravitational constant, g its verticalcomponent, I the identity matrix, S the shearing forces
acting on the core, r is the splashing restitutioncoefficient, 𝑀̇Σ→Γ the mass splashing rate, 𝑀̇Σ→Π themass of entrained material that is transferred to thepowder cloud. The complete set of differential equations
is given [13].
An important aspect of the code is the heat transfer
between the different phases that potentially leads to themelting of the ice contained in the flow. Heating comesfrom two different sources: shear heating of thematerials and sensible heat transfer between the rock,ice/snow, and water components.
The temperature of the flow 𝑇Φ is related to theinternal heat energy 𝐸Φ via the specific heat capacity 𝑐Φ
of the materials by:̂Φ𝐸Φ = 𝜌̂Φ 𝑐Φ 𝑇Φ 𝐻
(4)This leads to the following energy equation:
𝜕𝑡 en + 𝛁 ⋅ (𝑒n 𝑽𝚽 ) = 𝑓ṅ

4 Results
To start with general observations, we note thatsimulations which do not involve material entrainment
stop before the maximum time allowed for thesimulation, due to a lack of momentum. The flow comes
down at up to 90 m/s and goes through the Rishiganga
valley. By the time it reaches the point A (30.466524,79.733131, red arrow Fig. 1) its velocity decreases to 50
m/s and keeps on decreasing until stopping when noentrainment. Water starts being produced as soon as thefalling material reaches the valley floor. A large amountof water is then produced around the point A (Fig. 1).
Over time, the water content in the flow increases,
especially when material is entrained, and it
concentrates at the front of the flow (Fig. 1.).
When looking at the water content evolution in theflow with time for the different scenarios, the highest
amount of water is produced in the simulation involving
a high heat transfer between rocks and ice, a secondary
release as well as material entrainment, then come thesimulation with a high heat transfer between rocks and(5)Again, Eq. 5 contains n=3 equations, 𝑒𝑛 being theinternal energy for each phase: n=rock, water, and iceand snow that are treated together. 𝑓𝑛̇ is the source term
for each material.
The entrainment is computed as in ([11]), different areas
with different compositions in terms of rock, ice, snow,
water and subsequent rheologies and maximum erosion
depth are defined.

3 Chamoli event, February 2021
On February 7th, 2021, 27.106 m3 of rock (80%) and ice(20%) ([5]) fell from Ronti Peak, 5600 m a.s.l, in
Chamoli, Uttharakhand district, India. The flow went
down in the Rishiganga and then Dhauliganga valleys,potentially remobilizing material from an avalanche that2

E3S Web of Conferences 415, 01017 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341501017

amount in the flow at 80s, up to 1.106 m3 right after thesecondary release (Fig. 2.).

ice, entrainment but no secondary release, then the one
with a low heat transfer between rocks and ice, nosecondary release and material entrainment.

5 Discussion

Table 1: Simulation settings. In the release compositions, R
stands for rock, I for ice, S for snow, W for water, thecomposition is given in percentages of the different materials.
SimulationHeat
transferrockiceHeattransfer
rockwater

Heattransferwaterice

Secondaryreleasecomposition(in %)

Material
entrainment
andcomposition(in %)

1234

103
106
106
103

103
103
106
103

103
103
106
103

xxxxxxx
R60I30W10

5106

103

103

x

R60I30W10

67

106
106

103
103

103
103

R 70 I 30
R 70 I 30

X
R60I30W10

The water starts being produced when the avalanchecore reaches the valley floor, which suggests that theshear heating produced is enough to start melting the ice
at that point. The important water production around thepoint A can also be explained by shear heating which islikely on increase due to lateral curvature of the valley.
Heat transfer within the flow, entrainment andsecondary release are all responsible for waterproduction as the flow propagates. Heat transfer appears
to be the predominant process at the very beginning of
the avalanche, until the secondary release occurs.
Material entrainment is largely contributing to water
input in the flow, especially on the long run, but thisobservation must be considered with caution as it islikely to strongly depend on the composition of theentrainment layer.

Fig. 2 : Evolution of the water content in the flow with time.
Simulation numbers are given in the legend.
Fig. 1: Maximum water content in the flow (in mm) at 1500s
after avalanche start for simulation 1. Water height in thecore is up to 16 m in some locations towards the front of theflow.

The water content we observe in our simulations arelower, within the range and higher than the ones givenby ([3, 5]), depending on the presence of a secondary
release, and on material entrainment. The melting
cannot explain alone the final amount of water in theflow; however, depending on the composition of theentrainment layer and/or of the secondary release, theamount of water computed can dramatically change(Fig. 2). This emphasizes the importance of constraints
regarding the composition and thickness of theentrainment layer, as well as of potential secondaryreleases. Progress could be made in this domain via fielddata and remote sensing, as well as observations of pastevents.
Concerning the evolution of melt water production
via heat transfer, we note that water production rate
decreases when the flow travels between the Rishiganga
and Dhauliganga rivers, in simulations without

All those simulations were stopped because of themaximum time allowed for the simulation to run. The
simulations without entrainment stopped before, due toa lack of momentum, the highest water quantities being
found in the scenario with a secondary release and a highheat transfer between rocks and ice, then quite close
together the ones with no secondary release and 1) ahigh heat transfer between all of the materials and 2) ahigh heat transfer between rocks and ice, which suggeststhat the main cause for melting the ice comes from theheat transfer between rocks and ice. The simulation witha low heat transfer between all the materials was the first
one to stop but also the one producing the least amountof water. We also observe an increase of the water3

E3S Web of Conferences 415, 01017 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341501017

entrainment. There, we observe a general decrease of theflow velocity, which would imply a lower shear heating
possibly leading to a general decrease in heat transfer.
Shear heating and heat transfer between the phases mustbe further investigated, especially regarding the values
for the heat coefficients, as it seems to strongly influencewater production within the flow and remains poorly
constrained by experimental data.
The observation about the flow travelling further inthe simulations involving a secondary release andentrainment fits the findings of [8, 9] regarding the
lubricating effect of water in the flow but that increase
might also be related to the fact that there is morematerial in the flow in general.
Finally, our simulations show that the water mostly
accumulates at the front of the flow. In our model, thewater is bonded to the flow, there is no phase separation
possible for now, which means that we cannot have atransition to a debris flow. We can only suggest that thisaccumulation of water could be responsible for thattransition. The result as such could be then used as aninitial condition for a debris flow model to thensimulation the runout of the debris flow resulting fromthe multi-components avalanche.

3.

6 Conclusions

10.

4.

5.

6.

7.

8.

9.

We are developing a new module for the RAMMS
software that is designed to model multi-component
avalanches. The program allows for the definition ofdifferent phases which interact with each other, leading
to heat transfer and phase changes. It allows for thequantification of water production within the flow due
to ice melting, ground material entrainment andsecondary releases in areas that could be massively
remobilised by the passing avalanche. The module
allows for tracking the thermo-mechanical evolution of
the different phases involved in the flow. Preliminary
results show that the water content of the flow increaseswith time due to all the factors mentioned previously,
the main ones being secondary releases and materialentrainment. Depending on the composition of thesecondary release and entrainment layer, the final
amounts of water in the flow vary substantially, whichhighlights the importance of data as accurate as possibleon the areas that could potentially be on the track ofrock-ice/multiphase avalanches. Further work isnecessary to calibrate the heat transfer coefficients
responsible for sensible heat transfer between thephases, to assess the amount of water that can beproduced by melting the ice present in the flow.

11.

12.
13.

References
1.

2.

F. Walter, F. Amann, A. Kos, R. Kenner, M.
Phillips, A. de Preux, M. Huss, C. Tognacca, J.
Clinton, T. Diehl, Geomorphology 351 (2020)
W.-I.f.S.-u. L. SLF, WSL, WSL Gutachten
G2017.20: Modellierung des Cengalo Bergsturzes
mit verschiedenen Tahmenbedingungen, Bondo,
GR. (2017)4

P. Rautela, , S. Khanduri1 S. Kundalia, GC Joshi,
R. Jugran, Journal of Environmental & EarthSciences 3 (2021)
G. o I.Disaster Management Division (NationalEmergency Response Centre). Ministry of Home
Affairs. Situation report on the glacial outburst atReni in Chamoli District Uttarakhand (2021)
D.H. Shugar, M. Jacquemart, D. Shean, S.
Bhushan, K. Upadhyay, A. Sattar, W.
Schwanghart, S. McBride, M. Van Wyk de Vries,
M. Mergili. Science 373, 6552 (2021)
T. Zhang, Y. Yin, B. Li, X. Liu, M. Wang, Y. Gao,
J. Wan, K.R. Gnyawali, Journal of RockMechanics and Geotechnical Engineering 15, 2(2022)
M. Mergili, M. Jaboyedoff, J. Pullarello, S. PPudasaini, Natural Hazards and Earth System
Sciences 20 (2020)
Q. Yang, Z. Su, Z. Li, H. Liu. Influence of ice
content on the run-out of rock-ice avalanches. inWorkshop on World Landslide Forum, 587-91.
Springer (2017)
R. Sosio, G.B. Crosta, J.H. Chen, O. Hungr,
Quaternary Science Reviews 47 (2012)
C. Kang, D. Chan, F. Su, P. Cui, Landslides 14(2017)
F. Frank, B. W McArdell, C. Huggel, A. Vieli,
Natural Hazards and Earth System Sciences 15(2015)
M. Christen, J. Kowalski, P. Bartelt, Cold Regions
Science and Technology 63 (2010)
P. Bartelt, M: Christen, Y. Bühler, O. Buser,
Numerical Methods in Geotechnical Engineering 9(2018)