E3S Web of Conferences 415, 03028 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341503028

The RES approach for debris flow susceptibility analysis: a casestudy
Battista Taboni1*, Davide Vianello1, Federico Vagnon2, Anna Maria Ferrero1 and Sabrina Maria Rita Bonetto1
1Department of Earth Sciences, University of Turin, via Valperga Caluso 35, 10125 Turin, Italy
2Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Turin,Italy

Abstract. Climate change has increased the occurrence and magnitude of debris flow events, especially inmountain areas. Moreover, their unpredictability requires to develop reliable methodologies for theevaluation of debris flow susceptibility, which is the starting point for risk assessment and management. In
this paper, a modified version of the Debris flow Propensity Index (DfPI) is developed for the debris flowsusceptibility estimation at basin scale. Bedrock lithology, fracture network, quaternary deposits, slopeangle, channel network, and land use were identified as debris flow predisposing factors and were indexedby using open-access data and geodatabases. The objective of the proposed study is to develop a simple andeconomic procedure for the susceptibility estimation, easily to implement in GIS-based software for furtheranalyses, such as propagation simulations or hazard scenarios, useful for planning mitigation strategies. TheCanè Valley, a small valley located in the more famous Camonica Valley, (Lombardia Region, NorthernItaly), was used as a case study for developing and testing the proposed approach.

1 Introduction
Debris flows are rapid to extremely rapid phenomenaable to mobilize a large amount of soil, sediments ordebris, usually following the path of a pre-existing
channel. Due to these aspects and their unpredictability,
these phenomena are generally deemed as highly
dangerous [1]. Mountain regions are heavily affected bythese events: the permafrost degradation and the glacierretreat, in addition to the increasing frequency ofextreme rainfall events require to undertake mitigation
strategies for managing the debris flow risk. The starting
point of risk analysis is the detection of the mostsusceptible areas and the possible propagation
scenarios.
A correct risk assessment requires a quantitative
approach through “Quantitative Risk Analysis (QRA)”[2,3] routines, based on a multi-level characterization of
the study area starting from the definition ofsusceptibility maps. Many methodologies have beendeveloped for the susceptibility mapping: inventorybased, heuristic, statistical or deterministic methods [4].
The main limitation of the proposed methods [5-7] isrelated to data availability, which usually appears to be
a complex problem.
Bonetto et al. [8] applied the matrix approachsuggested by the Rock Engineering System (RES) [9]method for the evaluation of the debris flowsusceptibility at regional scale in a rapid and economicalway. The authors identified five predisposing factors forthe evaluation of the Debris flow Propensity Index(DfPI) at the basin scale: (i) bedrock lithology, (ii)fracture network, (iii) quaternary deposits, (iv) slope
angle, and (v) channel network.
In this research, starting from the innovative
approach developed by [8], a modified version of theDfPI was proposed by introducing the land use as apredisposing factor, given that it plays an important rolein controlling the interaction between surface runoff andsoil or sediment deposits. The methodology was applied
to the Canè Valley, located in the Central Alps
(Northern Italy). The obtained DfPI was validated bycomparing the occurred phenomena recorded in thestudy area. Strong points, limitations and future
developments of the proposed approach will be also
discussed.

2 Study area
The Canè Valley (Figure 1) is a glacial valley located inthe northern sector of Camonica Valley (Central Alps,Lombardia Region, Italy) and covers an area ofapproximately 14 km2.
The geology of the area is well described by theItalian Geological Cartography at 1:50.000 scale [10]:the bedrock is composed by rocks of the Austroalpine
Domain, belonging to the Peio Unit, which is composedby metamorphic rocks (mainly micaschists andparagneisses, with local bodies of metapegmatites,anphibolites, marbles and quarzites) and igneous
intrusions located in the northern portion of the valley.

* Corresponding author: battista.taboni@unito.it© 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, 03028 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341503028

The bedrock is covered by discontinuous quaternary
deposits, mostly associated with gravitational
phenomena involving the steep slopes or with glacial
processes active in the past, which have shaped thevalley. The lower sector of the valley is covered by
alluvial deposits and mixed-origin fans, mainly
generated by debris flow phenomena.

value for a cell of the interaction matrix (in this case isequal to 4 according ESQ approach).
For each parameter of the diagonal matrix, a specificindex Pik was defined. These indexes describe theinfluence of each predisposing factor to generate debris
flow events. In particular, they were calculated bydividing into homogeneous classes the values of theconsidered
predisposing
factor,assigning
arepresentative value and evaluating the percentage of its
areal distribution into the whole basin. The Piks weredefined by using open access data available on
geodatabases [10-13].
A detailed description of the considered indexes andtheir evaluating procedure is provided in [8]. As statedin the Introduction, the land use has been addressed forthe DfPI evaluation. Land use plays a relevant role in
the erosion control, since the presence and type ofvegetation influences the mechanical behaviour of thecover deposits and soils. By using the land use map
provided by the regional administration [12], land
covers were grouped into 4 classes: i) rock and deposit,ii) grassland, iii) vegetated areas (mainly forests), iv)
lakes or dams, v) urbanized areas. For each class, thequalitative rating SUi was assigned based on thepropensity to generate debris flow. SUi can assume
values from 0 to 4, and allows the evaluation of the SUBi
index, which provide the weight of each land use classwithin the basin and it was defined as follows:

Fig. 1. Location, debris flow activity map and geologicalsketch of the Canè Valley.

3 Methodology

𝑆𝑈𝐵𝑖 =The methodology proposed in [8] was modified byadding the land use to the five predisposing factors(lithology, fracture network, Quaternary deposit, slope
angle and channel network). These factors, considered
as independent parameters, have a significant influenceon the triggering processes of debris flow phenomena.
Their spatial distribution allows to perform awidespread analysis throughout the territory. Moreover,in debris flow analysis the land use is important in thecontrol of slope behaviour during rainfall and it reflects
the different soil conditions related to erodibility orresilience to the impact of rainfall and surface runoff.
Figure 2 shows the new proposed matrix: on the main
diagonal there are the predisposing factors, while theoff-diagonal terms represent the mutual interactions,scored using the Expert Semi-Quantitative approach(ESQ), which assigns a value ranging between 0(minimum interaction) and 4 (maximum interaction).
The ai values represent the weight of each parameter on
the susceptibility of the phenomenon and how theparameter influences the system. It can be obtained by
the following equation:𝑎𝑖 =1max(𝐼𝑖𝑗𝑡ℎ )× (∑𝐶+𝐸𝐶+ ∑ 𝐸× 100)

𝐴𝑈𝐼𝐴𝑏∙ 𝑆𝑈𝑖(2)where AUi is the area associated with the ith class, Ab thearea of the basin and to that class. The sum of all thecontributions given by each class defines the global
index, iU:
𝑖𝑈 = ∑ 𝑆𝑈𝐵𝑖
(3)
The susceptibility is finally quantified using the Debris
flow Propensity Index (DfPI), ranging from 0 to 100:
𝐷𝑓𝑃𝐼 = ∑(𝑎𝑖 × 𝑃𝑖𝑘 )(4)

The evaluation of the level of susceptibility associatedwith the studied area can by qualitatively expressedusing the following Table 1, derived from [8].
Table 1. DfPI index values and related susceptibility classes;
form [8].

(1)

where C is the sum of all cells on the same row anddescribes the effects of the system on the parameter(Causes), E is the sum of all cells on the same column
and quantifies the effects that the parameter has on thesystem (Effects), max(Iijth) is the maximum possible

2CLASS

DfPI

Susceptibility

I

0-1

None

II

2-8

Very Low

III

9 - 25Low

IV

26 - 42

MediumV

43 - 53

HighVI

54 - 70

Very HighVII

71 - 100ExtremelyHighE3S Web of Conferences 415, 03028 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341503028

The type of substrate in relation toThe composition of the substrate
its mineral-petrographicalThe slope is the morphologicalaffects the alterability of the rockcomposition and strength
expression of rock weathering.
and therefore the susceptibility toconditions, considering the sameStrength, fabric and rock texture
generate debris, which can bestress state, will affect the degreeaffect slope morphology.
easily mobilized.
of fracturing.

Bedrock lithology

Faulting and structural elemnts
can cotrol juxtaposition ofdifferent types of rock.

Fracture network

The presence of brittle
Brittle deformation influences the Higly fractured substrates are
deformation affects the erodibility
rock fracturing, increasing thesubject to alteration, erosion andand alterability of the substrate,
The presence of fracturesrock weathering. These processes decrease of mechanical properties
increasing the erosive power of influences the erodibility and theincrease the availability of loose (friction angle and cohesion) withstreams. Moreover, fault zones
water circulationmaterial that change theconsequent influence on slopes
may allow the formation of new
magnitude of the deposit.
(minor slopes).
stream.

No direct conditioning on thedevelopment and localization offragile structures. They protect theQuaternary deposits define areal
fractured substrate from the action
distribution of bedrock at surface.
of atmospheric agents, limiting
the weathering and decay of theresistance parameters
No influence

No influence

No influence

No influence

No influence

No influence

Compositional and texturalfeatures affect the erodibility of
The type of substrate influence thethe stream. For this reason thetype of land coverhydrographical network isinfluenced.

Slopes on deposits have different
Type, thickness and textural
acclivity depending on the types,
characteristics of the deposits
The type of depositis and their
thickness and textural
influence the morphology around thicknesses influence the land use
characteristics of the loose
the stream.
deposits that characterize them

Quaternary deposits

The slope affects the presence,stability and thickness of any
deposits
The channel can determine theaccumulation or movement of thedeposits by changing thedistribution and texturalcharacteristics of deposits.

Slope
Depending on the energy of thewatercourse and its erosive
capacity, the hydrographic
network can reshape the slopeThe slope affects the energy of theThe slope influence the land use
watercourse and the erosive power

Hydrographic network

Hydrografic network has a great
influence on the land use

No influenceLand Use

Land cover influences distribution Land cover influences the slopeand characteristics of the deposit
stability

Fig. 2. Interaction matrix defined for the case study herepresented.

4 Results and Discussionof the debris flow susceptibility into a basin by using
available and easy-to-access data.
The main limitation of this approach is the use of asingle value for quantifying the whole basinsusceptibility. This can be useful for a rough preliminary
estimation and for a hierarchical risk management atregional scale (i.e. in defining areas of interest for future
defensive works). However, no information is provided
on the spatial distribution inside the basin, which could
be useful for further risk assessment steps, such ashazard areas identification.

The encoded 6x6 matrix (Figure 3) shows the ESQ
values used for off-diagonal terms and the ai x Pik resultsfor each predisposing factor. The proposed matrix is auseful standardized tool, universally applicable, since it
is independent of type and characteristic of the basin.
The results highlighted that the Canè Valley has a very
high susceptibility to debris flow events since the DfPI
obtained with the proposed methodology is equal to64.72. This mirrors the data available in the landslide
inventory [14, 15] where 30 debris flow-like phenomena
were recorded in the last 50 years. Moreover, byanalysing the regional orthophotos and satellite images,
the morphological effects of about 10 past events arestill visible (Figure 4). Consequently, for the Cané
Valley (14 km2) it is possible to estimate a debris flow
density of 2.71 events per km2. Actually, this spatial
distribution is mainly concentrated in the middle sectorof the valley, where most of the active fans are located(Figure 4).
CAUSE
[C]

Ci+Ei

P1

14231

1115

3P2344115

1600P3

22

1

5

20

00

4P4

43

1123

0033P52823

101

12P6513EFFECT4[E]115 12 158110

ParameterBedrock
Lithology [P1]
FractureNetwork [P2]
Quaternary
Deposit [P3]
Slope Angle[P4]
ChannelNetwork [P5]Land Use [P6]

Pikai

DfPIi

0.84

3.41

2.86

4

The approach presented in this study is easily applicableand it requires input data which can be found on open
access geodatabases.
For the considered case study, the RES method yielded
a numerical value of DfPI index corresponding to a very
high susceptibility class. This outcome is confirmed by
the current distribution of debris flow phenomena in thestudy area.
At regional scale or when we analyse large portion ofthe territory, if the susceptibility analysis is carried out
to focus the attention on critical area detected by themodel, it is then possible to model the runout of possible
events.
This approach has still quite visible limits, consideringthat it provides a simple numerical value that describe awhole basin without considering the effective spatialdistribution. In the present form, global DfPI has beenvalidated using existing landslide inventory and satellite
or aerial images, but the natural next step will be themapping of the potential source distribution inside theconsidered basin.

3.64 14.55

1.34

4.55

3.14

5.23 16.41

4

5.23 20.91

1.32

5 Conclusions

2.95

6.09

3.90
64.72

Fig. 3. Interaction matrix after coding using the indexes for thesix parameters and their interactions.

The results confirm the reliability of the GIS-based
approach proposed for the quantitative estimation

3

E3S Web of Conferences 415, 03028 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341503028

12. Regione Lombardia, Destinazione d’uso dei suoli
agricoli e forestali, aggiornamento del 2018 –DUSAF 6.0., (2018),
https://www.geoportale.regione.lombardia.it/metad
ati?p_p_id=detailSheetMetadata_WAR_gptmetada
taportlet&p_p_lifecycle=0&p_p_state=normal&p_
p_mode=view&_detailSheetMetadata_WAR_gpt
metadataportlet_uuid=%7B18EE7CDC-E51B4DFB-99F8-3CF416FC3C70%7D
13. Regione Lombardia, Evoluzione temporale delle
Ortofoto regionali dal 1954 al 2018, (2018),
https://www.geoportale.regione.lombardia.it/metad
ati?p_p_id=detailSheetMetadata_WAR_gptmetada
taportlet&p_p_lifecycle=0&p_p_state=normal&p_
p_mode=view&_detailSheetMetadata_WAR_gpt
metadataportlet_uuid=%7B6815A8C0-6E274F2C-907C-2E52C9BC57C9%7D
14. Regione Lombardia, Inventario dei Fenomeni
Franosi d’Italia (IFFI) per la Regione Lombardia.
(2017),
https://www.geoportale.regione.lombardia.it/metad
ati?p_p_id=detailSheetMetadata_WAR_gptmetada
taportlet&p_p_lifecycle=0&p_p_state=normal&p_
p_mode=view&_detailSheetMetadata_WAR_gpt
metadataportlet_uuid=%7B1D4AAE9F-EB7B4E3E-AB8A-EE29A2211593%7D
15. Comunità Montana di Valle Camonica,Censimento dei Fenomeni Franosi, (2022),
https://cmvallecamonica.comuniweb20.apps.ckube.it/scheda-ist/censimento-fenomeni-franosi

Fig. 4. Debris flow inventory of the Canè Valley.

References
1.

M. Jakob, O. Hungr, & D.M. Jakob, Debris-flowhazards and related phenomena, 739. Berlin:Springer (2005)
2. J. Corominas, C.J. van Westen, P. Frattini et alii,
Bull. Eng. Geol. Environ. 73 (2014)
3. C.J. van Westen, T.W.J van Asch, R. Soeters, Bull
Eng Geol Environ. 65 (2006)
4. P. Reichenbach, M. Rossi, B.D. Malamud, M.
Mihir, F. Guzzetti, Earth-Science Reviews 180(2018).
5. C.J. van Westen, E. Castellanos, S.L. Kuriakose,Eng. Geol. 102, (3–4) (2008)
6. D. Hansen, V.K. Garga, D.R. Townsend, Canadian
Geotechnical Journal 32, 2 (1995)
7. G.G. Anagnostopoulos, P. Burlando,
Environmental modelling & software 38 (2012)
8. S. Bonetto, P. Mosca, F. Vagnon, D. Vianello,
Journal of Mountain Science 18, 12 (2021)
9. J.A. Hudson, Rock Engineering Systems: Theory& Practice, High Plains Press (JAH), Chicester,UK, (1992)
10. S. Chiesa, P. Micheli, M. Cariboni, P. Tognini, D.
Motta, M. Longhin, G. Zambotti, E. Marcato, A.
Ferrario, C. Ferliga, Carta Geologica d’Italia scala
1:50.000 - Foglio 041 Ponte di Legno, Servizio
Geologico d’Italia, Organo Cartografico dello
Stato, (2011)
11. Regione Lombardia, Modello Digitale del Terreno(DTM) 5x5, (2015)
https://www.geoportale.regione.lombardia.it/metad
ati?p_p_id=detailSheetMetadata_WAR_gptmetada
taportlet&p_p_lifecycle=0&p_p_state=normal&p_
p_mode=view&_detailSheetMetadata_WAR_gpt
metadataportlet_uuid=%7BFC06681A-2403481F-B6FE-5F952DD48BAF%7D4