E3S Web of Conferences 415, 04009 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504009

Post-wildfire erosion rates and triggering of debris flows: a casestudy in Susa Valley (Bussoleno).
Giuseppe Mandrone1*, Damiano Vacha1, and Jessica Chicco1
1DIST – University of Torino, 10125 Viale Pier Andrea Mattioli, 39 Torino, ITALY

Abstract. Post-wildfires geological hazards are an emerging problem in many places, including areas not
typically associated with these events such as the Alpine Region. Hazards connected with post-fire processes
such as debris-flows and flood-type events threatens people, infrastructures, services and economicalactivities. Apart from a few examples, there is a lack of models available to quantify the increase insusceptibility as a result of the modification induced by the wildfires. In this work we test the application ofa modified version of the RUSLE, on GIS, to quantify the post-fire erosive phenomena for a case study inthe north-western Italian Alps. The results of its application, taking advantage of high-resolution rainfall
series and data deriving from field surveys, highlight the marked increase (more than 20 times) in erosion
rates, quantified by expressing both the EI (erodibility index), the A (monthly soil loss) and the SL (monthlysediment loss) rise. The months of April, May and June represents the larger share of the total quantities.
This is a consequence of the noticeable increase of the Erodibility Index EI, which for the post-fire scenario
is more than one order of magnitude higher than the pre-fire one.

1 Introduction
Recent estimates for the Alpine region, forecasting anincreased impact of climate change effects, suggest
wildfires and post-wildfire geological hazards torepresent a looming issue soon [12-10-5]. Forest fires
lead to new avalanche-prone slopes, and to a higher
probability of rockfall, debris-flow, mudslides, soil
erosion and water quality problems.
Amongst other hydrological hazards, debris-flow
and flood-type events represents the most serious
concern, as can be seen in the reports and the scientific
literature of regions (USA, Australia) which are facing
the problem nowadays [2]. Modifications ofhydrological properties, due to litter and vegetation
removal, ash deposition, alteration of physicalproperties of soil and rocks results in an increase ofavailability of easily erodible materials on hillslopes andof runoff rates [7].
Currently very few models are available to estimate
the impacts of these phenomena. The USGS preliminary
hazard assessment relies on empirical models to assess
likelihood, volume and combined hazard of debris flows
for selected watersheds in response to a design storm.
These models rely on historical debris–flow occurrence
and magnitude data, rainfall storm conditions, terrain
and soils information, and burn–severity maps [8].

2 Study area
This study focus on the application and validation of amodified version of the RUSLE model (RevisedUniversal Soil Loss Equation) [11] to quantify the postfire erosive phenomena for a case study in the northwestern Italian Alps (fig. 1).
In this area (fig. 2), about 50 km W of Torino, in October
2017, ten wildfires occurred, burning a total area of
10,000 hectares of which 7,000 were forests; this value
far exceeds the average regional forest burned area (600ha/year between 2005 and 2013) [6]. These fires werefavoured by exceptionally dry conditions, hightemperatures and occurrence of several days with hotand dry winds. The largest and severe wildfire - almost
4,000 ha - occurred in Susa Valley, where many
catchments on the left of the Dora Riparia River wereinvolved [1]. Starting from late April 2018 until theearly June several flow events originated from theburned catchments. Damages were recorded especiallyat the outlet of the Comba delle Foglie, a small drainage
basin overhanging the Bussoleno municipality. Groundevidence highlighted a remarkable increase in erosion
rates exerted by the surface runoff in many sectors
within the fire perimeter, in agreement with literaturefindings [4].

3 Methods
The structure of the RUSLE model (Revised Universal
Soil Loss Equation) [11] proved to be the most suitable
framework to be adopted based on the assumption thatthese processes represent the key aspect governing theavailability of sediments to be entrained during rainfalls
and considering the available spatial data.

* Corresponding author: giuseppe.mandrone@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, 04009 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504009

Fig. 1 Perimeter of the Susa Valley wildfire and location of the Comba delle Foglie watershed.

Fig. 2 Erosional features on low order drainage a), and open slopes b), bottom of the channel c). Deposition area and one of theevacuated houses d).

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E3S Web of Conferences 415, 04009 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504009

different compared with the one proposed by Arpa
Piemonte (2018b) which, after expeditive surveys,
estimates a total event volume of about 20000 m3.
Erodibility Index values for the pre-fire and post-firesituation (table 1) has been calculated and finallymonthly mean soil loss A [Mg ha-1 m-1] and averaged
monthly sediment loss SL [Mg m-1] for the entire
watershed have been computed for both burned andunburned condition. The post-fire mean Erodibility
Index is more than one order of magnitude higher thanthe pre-fire one, having a pre-fire value of 4.63E-04 Mg
MJ-1 mm-1 h and a post fire value of 1.21E-02 Mg MJ-1
mm-1 h. Also, the maximum values show a rise of about
the same order.

4 Methods
The structure of RUSLE model (Revised Universal Soil
Loss Equation) [11] proved to be the most suitable
framework to be adopted based on the assumption thatthese processes represent the key aspect governing theavailability of sediments to be entrained during rainfalls
and considering the available spatial data. RUSLE
empirical model estimates the average annual soil loss
caused by surface water erosion through the following
equation: 𝐴 = 𝑅·𝐾·𝐿·𝑆·𝐶·𝑃
where
A = mean soil loss per year [Mg ha-1 y-1],
R = rainfall erosivity factor [MJ mm h-1 ha-1 y-1],
K = soil erodibility factor [Mg MJ-1 mm-1 h],
LS = topographic factor or slope length factor [-],
C = soil coverage [-], andP = erosion control practices factor [-].
RUSLE model is intended to quantify soil losses in
the long term, so that processes such as gully andchannel erosion and sediment transport cannot bemodelled. Prediction accuracy for individual storm isvery low, as controversial is the application on large
spatial scale. Despite this, the model can be used as asolid framework to quantify high-risk erodible areas.
The approach used is deliberately simple, replicable,improvable and easy to implement in a GIS
environment. The model has been applied and validated
on the Comba delle Foglie catchment, for which a
detailed temporal reconstruction of the processes andquantification of the volume of mobilized material has
been carried out in a previous work [9].
The burn severity map of the Bussoleno andMompantero Wildfire [6] was adopted in this work. Thismap was produced through satellite imagery and fieldsurveys, following a methodology based on US
FIREMON framework [3]. The analysis of spectralchanges caused by 2017 wildfires was carried out using
multispectral images acquired by the MultiSpectral
Instrument (MSI) onboard Sentinel-2 A/B satellites(European Space Agency).

5 Results

Fig. 3 Deposition area and deposit thickness reconstructed byphotogrammetric modelling.

The volume of coarser sediments coming from thewatershed was estimated in 1300 m3 (fig. 3). Volumes
were calculated by difference between DEM available
before the event (Piedmont Region data) and thatreconstructed using a "structure from motion" approachthrough photogrammetric processing of ground andhelicopter images after the event. By applying a simple
rule of thumb, considering a bulk density of 1500
Kg/m3, the 7 June flow mass can be estimated in 1950
Mg. Because other 4 minor events (1 debris/mud flow inApril and 3 floods in May) happened before the 7 June,is reasonable to presume that the remnant part of thetotal sediment loss estimated by the model could be
related to those events.
Some non-negligible aspects undermine the model
robustness and accuracy: in fact, the 7 June event
volume estimated via photogrammetrical modeling is6 Conclusions
Piedmont region experienced an unusually severe
wildfire season in 2017. Fires occurred in late autumn
and, after a snowy winter, were followed by spring rains.
Some of the catchments burned in the Susa Valley
wildfire were interested in May and June 2018 bydebris/mud flows and flood type events. The major
debris-flow involved Comba delle Foglie catchment andstruck the Bussoleno municipality. Based on field
evidence it was found that the flows mobilized materials
and sediments which was eroded from the burned
hillslopes and subsequently deposited in the channels.

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E3S Web of Conferences 415, 04009 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504009

Table 1 Spatially averaged mean soil loss A and averaged
monthly sediment loss SL comparison for the burned andunburned situation.
BurnedReferences
1. Arpa Piemonte. Rapporto evento del 07/06/2018,
Colata detritica nel comune di Bussoleno (2018)
2. J.V. De Graff, Geoenviron Disasters 1, 5 (2014)
3. C.H. Key, N.C. Benson. Landscape Assessment(LA). Sampling and analysis methods. USDA ServGen Tech Rep RMRS-GTR-164-CD 1–55 (2005)
4. J.A. Moody, P.A. Martin, Earth Surface Processes
and Landforms 26 (2001)
5. F. Moreira, O. Viedma, M. Arianoutsou, T. Curt, N.
Koutsias, E. Rigolot, A. Barbati, P. Corona, P. Vaz,
G. Xanthopoulos, F. Mouillot, E. Bilgili, Journal ofEnvironmental Management 92 (2011)
6. D. Moresi, R. Marzano, E. Lingua, R. Motta, M.
Garbarino, Remote Sensing of Environment 269(2022)
7. M. Parise, S.H. Cannon, Natural Hazards 61 (2012)
8. D.M. Staley, J.A. Negri, J.W. Kean, J.L. Laber,
A.C. Tillery, A.M. Youberg, Geomorphology 278(2017)
9. D. Vacha, G. Mandrone, M. Garbarino, D. Morresi.
First Consideration About Post 2017 Wildfire
Erosion Response and Debris Flow in Susa Valley(NW Italy), in Tiwari B., Sassa K., Bobrowsky P.T.,Takara K. (eds) Understanding and Reducing
Landslide Disaster Risk. WLF 2020. Springer,Cham (2021)
10. C. Wastl, C. Schunk, M. Leuchner, B. Pezzatti, A.
Menzel, Agricultural and Forest Meteorology 162–163 (2012)
11. W.D. Wischmeier, D.D. Smith. Predicting rainfall
erosion losses: a guide to conservation planning.
Agriculture Handbook n. 537, USDA, Washington
DC, USA, pp. 13-27 (1978)
12. T. Zumbrunnen, H. Bugmann, M. Conedera, M.
Bürgi, Ecosystems 12 (2009)

UnburnedMonthyearASLASL[Mg/ha*m][Mg/m][Mg/ha*m][Mg/m]

9-17

0.000

0.00

0.000

0.00

10-17

0.000

0.00

0.000

0.00

11-17

0.133

17.32

0.005

0.66

12-17

0.208

27.07

0.008

1.03

1-18

3.342

434.36

0.127

16.48

2-18

0.014

1.82

0.001

0.07

3-18

0.310

40.28

0.012

1.53

4-18

4.223

548.87

0.160

20.83

5-18

8.081

1050.28

0.307

39.86

6-18

2.371

308.14

0.090

11.69

7-18

0.981

127.55

0.037

4.84

8-18

1.014

131.75

0.038

5.00

TOT.

20.6772687.450.785

101.98A modified version of the RUSLE model wasapplied in that area to quantify the erosive processes ona monthly scale. The results of this application,
incorporating high resolution rainfall series and data
deriving from field surveys, made it possible toreproduce and highlight the marked increase in erosion
rates, quantified by expressing both the EI (erodibilityindex), the A (monthly soil loss) and the SL (monthlysediment loss) rise. Overall, A and SL increased morethan 20 times in post-fire scenario, the months of April,
May and June representing the larger share of the total
quantities. This is a consequence of the noticeable
increase of the Erodibility Index EI, which for the postfire scenario is more than one order of magnitude higher
than the pre-fire one. The intrinsic uncertainties of themodel are related to the fact that it does not consider thestream-flow erosion in the channels, it does not accountfor the material eroded by the debris-flow during its
passage, and it does not incorporate the eroded volumeof ash and combustion residues.
This methodology can provide a useful guidance torank the post-fire debris-flow susceptibility and toestablish intervention priorities. It can be applied
everywhere on the regional territory because the model
make use, for the most, on open-source spatialized data
and thanks to its structure it can be easily implemented
into a GIS for thematic map production.
Another aspect which should be considered when
dealing with the model validation is the remarkable
erosion exerted by the debris-flows along all their path,
which may have increased their volume considerably.
The results of the model are not suitable to predict
streamflow erosion, so when the estimated value iscompared to the available surveyed data, this aspect may
also increase the uncertainty. Finally, the current model
does not consider the ash and combustion residues
which, for sure, contribute to the overall sedimentavailability to be entrained.

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