E3S Web of Conferences 415, 04007 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504007

Future global debris flow susceptibility considering climatechange, wildfire probability, and glacier retreat
Laurie J. Kurilla1* and Giandomenico Fubelli1
1University of Torino, Earth Sciences Department, 10123 Torino, Italy

Abstract. The present-day impact of climate changes on debris flow magnitude, frequency, andsusceptibility has been demonstrated in North and South America, Europe, Asia, and New Zealand. Such
impacts are expected to increase under future emission scenarios. Future global debris flow susceptibility
models provide an international perspective on areas worthy of further, more detailed analyses with regard
to geographic changes in global debris flow susceptibility. In this study, future global debris flowsusceptibility models are developed under RCP 2.6 and 8.5 IPCC Climate Change Scenarios. These models
were further augmented with wildfire probability, and areas of potential glacier retreat, both of which can
act as amplifiers to debris flow susceptibility. The results are projected against future urban centers, for a
spatial view on potential human vulnerability. Key findings are (1) wildfire acts as a significant amplifierin area and magnitude of debris flow susceptibility in all modeling scenarios, (2) greater than 50% of thestudied glaciers reside within higher susceptibility zones when wildfire is not considered, and greater than
75% when wildfire probability is considered, (3) 76 of the studied glaciers are within 5 km of eleven urban
centers, (4) 11% of these “urban” glaciers are in higher susceptibility zones when wildfire probability is
not considered, and 51% are in higher susceptibility zones when wildfire is considered, (5) about 12% of
future urban centers will reside within higher susceptibility zones under both future climate change
scenarios. Consideration of these factors, together with traditional environmental factors and triggers, andfindings by local and regional glacier-related debris flow researchers, suggests a new paradigm in modelingdebris flow susceptibility, at any scale.

1 Introduction
Climate conditions (precipitation, temperature, aridity)
may experience major changes in magnitude andgeographic extent in the future. “The frequency andintensity of some extreme weather and climate events haveincreased as a consequence of global warming and willcontinue to increase under medium and high emission
scenarios (high confidence)” [1]. Climatic variability will
be manifested as significantly enhanced rainfall in someareas and marked desertification and enhanced winderosion in others, operating at scales of tens to hundreds ofyears [2, 3]. The frequency and intensity of droughts areprojected to increase in southern Africa, and theMediterranean region, while frequency and intensity of
extreme rainfall events are projected to increase in manyother regions across the world.
Both changes in temperature and precipitation affect slope
stability and potential debris flow susceptibility [4] byincreasing sediment availability and from intense and/or
prolonged rainfall triggers, rainfall totals, antecedent
rainfall, and rain on snowmelt [1].
Changes in regional precipitation and temperature levels
and patterns are the primary climate changes with a direct
impact on soils, geomorphology, land use/landcover, andother environmental factors which influence the potential
for debris flows. “Magnitude of debris flows could becomelarger due to larger amounts of sediment delivered to thechannels and as a result of the predicted increase in heavy
precipitation events.” [5]. These factors can subsequently
lead to debris flows in areas of frequent or intense rainfall[6] [4] [7] [8] [9].

Local and regional researchers have demonstrated therecent impacts of climate change on increased debris flow
magnitude and frequency, and the correlation of increased
debris flows with changes in seasonality, in North andSouth America, Europe, New Zealand, and Asia [4, 5, 1014]. These recent observable results provide credibility forthe potentiality of future impacts.
The future debris flow susceptibility models, developed
herein, are based on projected climate trends worldwide forthe decade 2100. The objective is to identify areas globallywith the highest potential for debris flows. The purpose is
not to predict, or provide early warning systems, but rather
as a high-level preliminary view of the potential impact tosociety that can be used to determine areas to drill-down
for more regional and localized research, and planning.
Frequently, debris flows occur post-fire from runoffdominated progressive bulking of storm runoff withsediment eroded from hillslopes and channels, and to alesser extent by infiltration-triggered failure andmobilization. In such areas, slope failures are in response
to prolonged periods of rainfall, or prolonged rain fall in
combination with rapid snowmelt [15-17]. With climatechange and increasing temperatures, the likelihood of
increased forest fires leads to an increased likelihood of
post-fire debris flow frequency, in those burn areas whereother debris flow predisposing factors exist [15] [17]; andrainfall amounts required to induce debris flows decrease[16, 18]. Given that debris flows may occur during the firstrainy season post-fire, one to two years post-fire, and evenup to 10 to 30 years [16], burned watersheds pose asignificant potential threat to humans living in close
proximity [19]. Thus, present-day, and future wildfire
probability models from Moritz et al. [20] are coupled with* Corresponding author: Laurie.Kurilla@edu.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, 04007 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341504007

without wildfire probability. These slopes are those mostcommonly associated with debris flows. ArcGIS “Buffer”
and “Near” tools were used to provide a 5-km buffer
around each glacier, and to determine the proximity tofuture urban centers. Future urban centers (a proxy for
impact on both humans and economies), based on SSP5
(Shared Socioeconomic Pathway 5) data at 1-km resolution[27] are used as a spatial overlay in ArcGIS for the glacierproximity analysis.

the debris flow susceptibility models of this project, as apotential debris flow susceptibility amplifier.
While there are numerous factors which contribute tolandscape instability and landslide susceptibility, such as
precipitation, snow melt, temperature, seismic andvolcanic activity, and anthropogenic changes to landscape,precipitation and snow melt are the most significant
triggers for debris flows [9]. In mountainous regions,which are most susceptible to slope instabilities, increased
temperatures can result in thawing of permafrost andinterstitial ice in rock mass cracks, reducing the shear
strength, and increasing the frequency and magnitude ofrock falls and debris flows [14]. Glacier-related debris
flows may occur during glacier retreat due to theavailability and exposure of large quantities of
unconsolidated sediments, which are mobilized withwarming trends, increased precipitation, snowmelt, andglacial lake outbursts as triggering mechanisms [21]. The
proximity of glaciers to populations within debris flowsusceptible areas is modeled as an additional proxy forhuman vulnerability.

Table 1. Susceptibility factors andsignificance values/weights

Environmental Factor
Slope (deg)Köppen-Geiger Climate Class

2 Methodology
A Maximum Entropy “present-day” global debris flowsusceptibility model developed by Kurilla and Fubelli 2022
[22], with MaxEnt software [23] was used for derivation offactors and factor classes associated with an inventory of5695 historic debris flow events. The “present-day”
Maximum Entropy model is used to extract those factors
with significance values > 1% and their factor classes
which exhibit >= 90% predicted probability, as suitabledebris flow environments. Table 1 lists the factors which
met these criteria. These factors are used as input to the
future models. The future models are developed in ArcGIS
Pro. Representative Concentration Pathways (RCP) 2.6
and 8.5 projected monthly average precipitation [24] andKöppen-Geiger climate classification [25] data are used forthe future models, extracting a subset based on the climate
and precipitation factor classes which exhibited >=90%
predicted probability in the “present-day” Maximum
Entropy model. Each of the factor layers were weighted
based on their “present-day” model Maximum Entropy
significance value (relative contribution to susceptibility)
and summed over each pixel. Each pixel of the resultant
susceptibility map is equal to the sum of the weights of allenvironmental factors present at that pixel, therefore
ranging from 0 (no factors present) to 99 (all factors
present). Pixels with higher weighted sums represent
higher susceptibility. The maps are classified using fiveequal intervals and qualitative labels of “Very Low”,“Low”, “Moderate”, “High”, and “Very High”
susceptibility.
Next, future (2070-2099) projected wildfire probability
data [20], at 0.5o resolution, are summed with debris flowsusceptibility values for each model, on a pixel basis, toidentify areas where wildfire probability may increase,decrease, or have no impact on debris flow susceptibility.
214,429 glaciers with slopes >=20 o and <=35o from theRandolph Glacier Inventory [26] were used for spatial
overlay with future debris flow susceptibilities, with andSignificanceValue/Factor
Weight27
20

Landform

18

Soil Drainage
Soil Type
Fault Density (km/sq km)
Land CoverPrecipitation (Avg mo mm)
Soil Thickness (m)Lithology

9
9
6
4
2
2
2

3 Results
The percentage area of each susceptibility zone for both
future scenarios, with and without consideration of wildfire
probability, is provided in Table 2, along with a referencecomparison to the susceptibility zonation of the presentday model.
Table 2. Percent area of global debris flow susceptibility
classification by model, with and without wildfire probability.
% Areasusceptibility
class bymodel

without wildfire /with wildfire

Present-day

RCP 2.6

RCP 8.5

Very Low
Low

91.6%/27.6%
5.2%/9.6%

92.5%/21.4%
5.2%/8.2%

91.0%/23.7%
6.7%/6.4%

Moderate

2.5%/27.8%

1.9%/32.4%

1.9%/32.1%

High
Very High

0.6%/23.4%
0.05%/7.4%

0.4%/25.6%
0.03%/8.7%

0.4%/23.7%
0.04%/10.9%

Extreme0/4.2%

0/3.7%

0/3.2%

An additional susceptibility classification (Extreme) was
introduced to accommodate the increase (> 100%
probability) when wildfire probability is included. All
susceptibility classifications increased in area, for allmodels, except “Very Low” when wildfire probability is
considered. The change in the percentage area of eachsusceptibility classification per model, when wildfire isconsidered, is presented in Table 3.
Figures 1a and 1b show future global debris flowsusceptibility without considering wildfire probability for
RCP 2.6 and 8.5, respectively. Figures 1c and 1d show
future susceptibilities with wildfire probability for RCP 2.6
and 8.5, respectively.

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Table 3. Percent area change in susceptibility zone, by model,
when wildfire probability is considered.

Debris Flow
Susceptibility
Susceptibility
Class
Very Low
Low
ModerateHigh
Very HighSum of Moderate
to Very High% Susceptibility changewith addition of wildfire
Current-62.1%
27.0%
23.6%
10.3%
1.2%
43.8%

RCP
2.6
-75.8%
33.3%
26.9%
10.8%
4.8%
42.5%

RCP
8.5
-74.2%
30.5%
25.5%
17.3%
0.9%
35.1%

Fig. 1. Future susceptibility with and without wildfire
consideration. a) RCP 2.6 without wildfire, b) RCP 8.5 without
wildfire, c) RCP 2.6 with wildfire, RCP 8.5 with wildfire
consideration

In studying the proximity of glaciers to debris flowsusceptibility and urban centers, seventy-six of the studied
glaciers were found to be within 5 km of eleven urban
centers. 11% of these “urban” glaciers are in Moderate to
Very High susceptibility zones under both futurescenarios without wildfire consideration (Table 4), andgreater than 50% when considering wildfire probability(Table 5).

Table 4. Glaciers vis-à-vis susceptibility zones
and urban centers, without wildfire

Debris Flow
Susceptibility Class
without wildfire
probability(RCP 2.6 and 8.5)
Very Low
Low
ModerateHigh
Very HighTOTALS# Glaciers

Population
within 5km

22
46
8
0
0
76

200,815
218,358
83,05300502,226

Table 5. Glaciers vis-à-vis susceptibility zones
and urban centers, with wildfire

Debris Flow
Susceptibility Class withwildfire(RCP 2.6 and 8.5)
Very Low
Low
ModerateHigh
Very High
ExtremeTOTALS# Glaciers

Population
within 5km

0
37
19
12
4
4
76

n/a
184,996
215,472
59,618
8,623
46,786515,495

4 Conclusions
Traditional environmental factors and triggers used fordebris flow susceptibility modeling, based on historicdebris flow events to-date, may no longer be sufficient for
determining susceptibility under changing climaticconditions worldwide. A new paradigm may be needed
which considers wildfire probability as well as potential
cryosphere-related warming, when considering thepotential human vulnerability.
The percentage of total land area with an increase in debris
flow susceptibility classification under future scenarios
RCP 2.6 and RCP 8.5 is small (1.49% and 3.14%,

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respectively) until wildfire probability is considered.
When considering future wildfire probability, global debris
flow susceptibilities are amplified in both magnitude andarea across all models, present-day and future.
Furthermore, it is demonstrated that human vulnerability
may further increase where glaciers are coincident withhigher debris flow susceptibility zones and within close
proximity to urban centers.
Only time can be the real test of validity of this type of
projection. There are many types and sources of
uncertainty in any modeling effort, potentially resulting in
spatial and/or classification uncertainty of the model.
Given that there are few models at the global level, there
are fewer opportunities to compare models. The credibility
and known uncertainties of the underlying data (climate
projections, wildfire probability, future urbanization) is
critical in trusting this type of model. The future climate,urbanization, and wildfire researchers all constructed their
final results through numerous (from 37 to 100)
simulations and model comparisons, choosing thosefactors which had the highest confidence level across all
simulations, on a pixel basis, and comparative analyses
with other researchers’ results at regional levels. They also
consider coarser, long-term norms rather than short-term
environmental fluctuations. One significant uncertainty inthe future urbanization projections is not considering theimpacts of climate change on urban growth [27].
Performing additional future global debris flowsusceptibility models using different modeling approaches
can allow for comparative analyses, and fine tuning. This
research is based on the best (RCP 2.6) and worst (RCP
8.5) projections for greenhouse gas concentration, but only
the worst case (SSP5) socio-economic scenario, of rapid
and unconstrained growth in economic output and energy
use. Including SSP1, which represents sustainabilityfocused growth and equality, would be a more complete
representation of the two ends of a spectrum of potential
outcomes

5. M. Stoffel, D. Tiranti, C. Huggel, Sci Total Environ
493 (2014)
6. M. Li, C.-S. Tian, Y.-K. Wand, Q. Liu, Y.-F. Lu, W.
Shan, Journal of Mountain Science 15, 8 (2018)
7. M. Stoffel, T. Mendlik, M. SchneuwlyBollschweileret, A. Gobiet, Climatic Change 122(2014)
8. M. Alvioli, M. Melillo, F. Guzzetti, M.Rossi, E.
Palazzi, J.von Hardenberg, M.T. Brunetti, S.
Peruccacci, Sci Total Environ 630 (2018)
9. S.L. Gariano, F. Guzzetti, Earth-Science Reviews 162(2016)
10. M. Keiler, J. Knight, S. Harrison, Philos Trans A
Math Phys Eng Sci 368, 1919 (2010)
11. S.M. Moreiras, S.A. Sepúlveda, M. Correas-González,C. Lauro, I. Vergara, P. Jeanneret, S. JunqueraTorrado, J.G. Cuevas,A. Maldonado, J.L. Antinao, M.
Lara, Geosciences 11, 2 (2021)
12. N.T. Legg, A.J. Meig, G.E. Grant, P. Kennard,
Geomorphology 226 (2014)
13. C. Huggel, J.J. Clague, O. Korup, Earth Surface
Processes and Landforms 37, 1 (2012)
14. C. Huggel, N. Salzmann, S. Allen, J. CaplanAuerbach, L. Fischer, W. Haeberli, C. Larsen, D.
Schneider, R. Wessels, Philos Trans A Math Phys Eng
Sci 368, 1919 (2010)15. S.H. Cannon, J.E. Gartner, M.G. Rupert, J. A.
Michael, A. H. Rea, C. Parrett, Geological Society ofAmerica Bulletin 122, 1-2 (2009)
16. M. Parise, S.H. Cannon, Natural Hazards 61, 1 (2011)17. S.H. Cannon, J.V. DeGraff. Cascading consequencesof climate change and expanding population on thethreat of wildfire and post fire debris-flow hazards,
Western U.S. (Invited). in American GeophysicalUnion. 2009.
18. J.A. Moody, R.A. Shakesby, P.R. Robichaud, S.H.
Cannon, D.A. Martin, Earth-Science Reviews 122(2013)
19. J.V. De Graff, Geoenvironmental Disasters 5, 1 (2018)20. M.A. Moritz, M.-A. Parisien, E. Batllori, M.A.
Krawchuk, J. Van Dorn, D.J. Ganz, K. Hayhoe,
Ecosphere 3 , 6 (2012)
21. M. Chiarle, Global and Planetary Change 56, 1-2(2007)
22. L.J. Kurilla, G. Fubelli, Natural Hazards (2022)
23. S. Phillips, M. Dudik, R.E. Schapire, Maxent software
for modeling species niches and distributions (Version
3.4.1). 2021.
24. S.E. Fick, R.J. Hijmans, International Journal of
Climatology (2017)25. H.E. Beck, N.E. Zimmermann, T.R. McVicar, N.
Vergopolan, A. Berg, E.F. Wood, Sci Data 5 (2018)
26. RGI Consortium, Randolph Glacier Inventory - A
Dataset of Global Glacier Outlines: Version 6.0, inTechnical Report, Global Land Ice Measurementsfrom Space, R. Consortium, Editor. 2017: Boulder,
Colorado, USA.
27. G. Chen, X. Li, X. Liu, Y. Chen, X. Liang, J. Leng, X.
Xu, W. Liao, Y. Qiu, Q. Wu, K. Huang Nat Commun
11, 1 (2020)

References
1. IPCC, Climate Change and Land: an IPCC special
report on climate change, desertification, landdegradation, sustainable land management, foodsecurity, and greenhouse gas fluxes in terrestrial
ecosystems, P.R. Shukla, et al., Editors. 2019.
2. N. Middleton, D.S. Thomas, World Atlas ofDesertification - Second Edition. 1997, United
Nations Environment Program: London.
3. M. Cherlet, C. Hutchinson, J. Reynolds, J. Hill, S.
Sommer, G. Von Maltitz, World Atlas ofDesertification, Office of the European Union:
Luxembourg. (2018).
4. C. Huggel, N. Khabarov, O. Korup, M. Obersteiner,
Physical impacts of climate change on landslide
occurrence and related adaptation, in Landslides:
Types, Mechanisms, and Modeling, J.J. Clague and D.
Stead, Editors. 2012, Cambridge University Press:4