E3S Web of Conferences 415, 03030 (2023)
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The Territorial Debris Flow Early Warning System of Piemonte(North-western Italy)
Davide Tiranti1*, Paolo Bertolotto1, Roberto Cremonini1, Armando Riccardo Gaeta1 and Naima Vela1
1ARPA Piemonte, Department of Natural and Environmental Risks, 10135 Turin, Italy

Abstract. Debris flows are one of the most frequent and dangerous phenomena affecting the Alpine
environment; they are responsible of 36% of casualties due to gravitational phenomena in the Italian Alps
during the last century. In the Western Italian Alps (Piemonte, Italy) a Territorial Debris Flow Early Warning
System (Te-DFEWS) aimed to forecast and predict the occurrence of sediment mass-transport has beendeveloped, based on the characterization of small Alpine catchments (< 50 km2) and the processes that takeplace in these. The Te-DFWES is based on the identification of predisposing and triggering factors that
determinate debris flow occurrence. The Te-DFEWS operates from 2010 in present-time and nowcasting
using weather radar observations as input data. Recently (2021), the Te-DFEWS has been improved
extending the forecasting window (up to 48h from simulation) by the introduction of Quantitative
Precipitation Estimate/Forecast (QPE/QPF) input from COSMO-2I and ICON-IT, local high-resolution
weather models. The Te-DFEWS, named DEFENSE (DEbris Flows triggEred by storms - Nowcasting
SystEm) and the related warning procedures are presented as an operational tool integrated in the Regional
Warning System for Geo-hydrological and Hydraulic Risk at the Functional Centre of Piemonte, managed
by the Regional Agency for Environmental Protection of Piemonte (Arpa Piemonte).

1 INTRODUCTION
Channelized debris flows affect Alpine valleys,causing 36% of casualties by gravitational phenomenain the Italian Alps during the last century and economic
losses. In Piemonte (Western Italian Alps) a Te-DFEWS[1] has been developed in 2010 starting from an
innovative classification of Alpine catchments and using
the precipitation estimates from weather radar as model
input to identify the triggering rainfall amount anddistribution. In 2021, weather radar rainfall estimates,
which provide input for the present-time observationand the nowcasting (rainfall intensity and distribution
expected within the next hour), has been completed with
the integration of the Quantitative Precipitation
Estimate/Forecast (QPE/QPF) from COSMO-2I (theItalian version of the COSMO-MODEL) and ICON-IT(ICOsahedral Nonhydrostatic) high-resolution models
to lengthen the forecasting window (next 48h).
The Te-DFEWS is focused on 2100 Alpine
catchments classified by the Clay Weathering Index(CWI) based on the propensity of lithologies to produce
different quantity of clay or clay-like minerals as loosematerial that directly affect the behaviour of debris
flows (Fig. 1).

Fig. 1. The 2100 Alpine catchments classified by CWI.

2 The DEFENSE Te-DFEWSThe DEFENSE Te-DFEWS is based on theclassification of catchments using the CWI [2], anindex used to define the propensity of lithotypes which
forming the bedrock of a catchment to produce clay or* Corresponding author: davide.tiranti@arpa.piemonte.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/).

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other fine minerals with clay-like rheologicalbehaviour (e.g., phyllosilicates in general). This was
based on evidence of the role played by the amount offine particles in the loose material in the initiation andevolution of debris flows. Through the CWI three typesof catchments are distinguished based on the prevailinglithology that forms the catchments’ bedrock:
a.

b.

c.

Table 2. CWI catchments’ alluvial fanscharacteristics.
AlluvialCWI
class

Excellent Clay-Maker (ECM) catchments
formed mainly by rocks with an excellent
propensity to produce clay as loosematerial, such as thin foliated and/or finegrained metamorphic rocks or clayey
sedimentary rocks.
Good Clay-Maker (GCM) catchments
formed mainly by rocks with a good
propensity to produce clayey silt as loosematerial, such as massive limestone or
dolostones.
Bad Clay-Maker (BCM) catchments
formed mainly by rocks with a badpropensity to produce clay or clay-likeminerals as loose material, such as
intrusive igneous rocks or massive andcoarse-grained metamorphic rocks.

fan/Catchment area
ratio

Alluvial
fan shape

Alluvial

Alluvial fan

fan surface

grain-size

slope

distribution[%]
Gentle withirregular
ECM

5Strongly

distribution

irregularof local

Regularslope
gradients
Very gentle
with
GCM

20

Regularregularfan-distribution

shapedof local

Regularslope
gradients
Strongly irregular:

The three catchment classes show markedly different
characteristics in debris flow behaviour and are clearly
distinguishable by the sedimentological andmorphological characteristics of debris flow deposits
and alluvial fans, as shown in Tab. 1 and 2.

RegularBCM

CWI
classECM

GCM

BCM

Cohesivedebris flow
(viscoplastic)- steepasymmetricallevee and flat
lobe withclay matrix
Cohesivedebris flow
(viscoplastic)- flatsymmetricallevee and flat
lobe withclayey silt
matrix
Noncohesive
debris flow
(frictional/collisional) levee-likeboulder train
and fan-lobe
with gravelsand andsilty matrix

lobeshaped

Table 1. CWI catchments’ behaviour.
Dominantrheology depositional
style5

Mean
triggering
occurrence[years]Rainfallthreshold2

storm ofmoderateintensity(≥ 20mm/h)

summer5

storm ofhighintensity (≥30 mm/h)

late springearly fall

20

storm ofvery highintensity (≥50 mm/h)

fall andspring(rarely insummer)

Main
occurrence
seasonVerygrain-size

irregular

decrease fromwith steep

apex to toe. Grain-slope insize changes areapex zone

highlighted by

and flat

morphologicalslope in toe

steps inzone

longitudinal
section.

Moreover, according to [3] they have been divided
into catchments capable of generating debris flow or notbased on the area ratio of outcropping bedrock vs. plantcover, according to the Fig. 2.

Fig. 2. Outcropping bedrock thresholds for torrential process
type. Where: ROI is the Relative Occurrence Index, DF isDebris Flow and HF is Hyperconcentrated Flow (modifiedfrom [3]).

It has been demonstrated that the area ratio ofoutcropping bedrock vs. plant cover, and consequently
the capacity of a catchment to generate debris flow, is2

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strongly influenced by the occurrence of wildfires thatlargely involve a given catchment [4]. In that case, thedebris flows’ triggering thresholds should be loweredfrom 20% to 60% compared with the standard values inTab. 2.
Traditional DFEWSs (Territorial and Local) areusually based on rainfall rate thresholds derived byrecording of rain gauges, but rain gauge networks areoften inadequate to properly detect rain field inmountain areas [5], especially if thunderstorms happen,
even of small size (around 1 km in diameter) and very
localized. Operational polarimetric C-band weather
radar can provide reliable real-time rainfall estimation
with high spatial and temporal resolution. Arpa
Piemonte implemented an algorithm for storm
identification and tracking using the Tracking Radar
Echoes by Correlation (TREC) technique [6]. In thispattern recognition procedure, radar-echo data are
stored in arrays, and, on each iteration an array iscompared to all other arrays of same size for thesubsequent time step to determine which array exhibits
the highest correlation with the previous array [7]. In
this way, storm cells are localized, classified (e.g.,maxima echo, storm area, Vertical Integrated Liquid VIL) and tracked. Considering the overall path of thestorm is possible to forecast the next position of thestorm with nowcasting techniques (withing one hour).
Arpa Piemonte has developed since 2005 a WebGISbased system to disseminate real-time weather radar andweather stations data both for stakeholders and citizens.
This application is primarily focused on visualizing
meteorological and hydrological data for decision
support and situational awareness [8].
Arpa Piemonte moved to a new approach, oriented
to real-time analysis and nowcasting derived products,
using full GIS functionality by the choice of Linux
platform and GFOSS tools. The PostGIS extension
allows for the native storage of geometries in thedatabase and allows for various GIS queries to be madebetween data, including unions, area calculations, andfeatures within. Geometry features can then be
displayed by various GIS servers and client applications,allowing the database to act as a backend GeoSpatial
database for GIS servers.
Storms, represented by ellipses that reproduces theirreal size, with their parameters e.g., area, VIL, mean andmaximum reflectivity, cloud top, lifetime) and paths are
stored in the PostgreSQL/PostGIS database, wherecatchments polygons are also stored, using Python
scripts. Storm cells, with enough intensity to exceed thedebris flows trigger thresholds, that are intersecting or
will intersect the polygons representing the catchmentsin the next 60 minutes, are identified and a warning isautomatically displayed on the WebGIS interface (Fig.
3) and issued by e-mail to the regional EWS operatorsand stakeholders.

Fig. 3. DEFENSE WebGIS interface: storm cells arerepresented by coloured ellipses, according to their severity(instantaneous intensity), red lines are storms’ paths, numbers
at the ellipses’ centroids are the reflectivity values (dBZ)
estimate by weather radar; catchments delimitation are
represented in black, alluvial fans as beige polygons.

Regarding the shorth-medium forecast (from 2 to 48hours), recently (2021) the Te-DFEWS DEFENSE has
been improved with a new input module for QPE andQPF, deriving from COSMO-2I high-resolution
meteorological model (00 UTC run) interpolated overthe catchments’ areas. The model itself is experimenting
a transition toward ICON-IT model and will further
migrate under the new framework.
Some verifications have been done over significant
case studies, showing a discreet predicting potential of
the technique; verifications were mainly carried out
using fuzzy verification strategies [9] and plotting
general scores like FSS (Fraction Skill Score) [10].
CWI catchments have been inscribed in larger areas(Macrobasins) where the quantitative rainfall forecast
is interpolated. For each Macrobasin a set of thresholds
are assigned, depending on the CWI catchments classes
included in the Macrobasin (Fig. 4).

Fig. 4. Example of CWI triggering thresholds within aMacrobasin.

When one or more thresholds are exceeded in one ormore Macrobasins a warning is issued in the same way
previously described. Grouping of individual
catchments into Macrobasins was adopted to meet the3E3S Web of Conferences 415, 03030 (2023)
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spatial resolution of the COSMO-2I and ICON-IT
models, which is lower than that obtained from weather
radar estimates, and to better synthesise and visualize
the warning scenarios by an interactive bulletin on theArpa Piemonte’s institutional website.

3.

D. Tiranti, R. Cremonini, I. Asprea, F. Marco,
Front. Earth Sci. 4,16 (2016)
4. D. Tiranti, R. Cremonini, D. Sanmartino,Geosciences 11, 422 (2021)
5. M.R. Duncan, B. Austin, F. Fabry, G.L. Austin,
Journal of Hydrology 142, 1-4 (1993)
6. R.E. Rinehart, Internal storm motions from asingle non-Doppler weather radar, NCAR/TN146+STR, 262 pp (1979)
7. J. D. Tuttle, G. B. Foote, J. Atmos. Oceanic
Technol. 7 (1990)
8. B. P. Walawender, M. W. Davis, Development ofweb-based GIS applications for decision supportand situational awareness, in Proceeding of the26th Conference on Interactive Information andProcessing Systems (IIPS) for Meteorology,Oceanography, and Hydrology (2010)
9. M. Mittermaier, R. Nigel, S. A. Thompson,
Meteorological Applications 20, 2 (2013)
10. E. E. Ebert, Fuzzy verification of high-resolution
gridded forecasts: a review and proposedframework. Meteorological Applications: A
journal of forecasting, practical applications,training techniques and modelling 15,1 (2008)

3 Conclusions
The DEFENSE Te-DFEWS has been described as anexample of regional EWS that tries to integrate in themost comprehensive way all the predisposing andtriggering factors for debris flow occurrence in small
Alpine catchments. The presented methodology,
adopted to forecast and predict the debris flows
triggering considering the characteristics of eachcatchment and related processes that in these take place,has demonstrated that the prediction of debris flows is
not linked to the identification of rainfall thresholds withgeneric value effective for all the catchments. On thecontrary, the triggering thresholds must be determinedtaking into consideration the greater number of aspects
that distinguish a given catchment, which de facto
represent the set of predisposing factors that drive theoccurrence of debris flows and their features. In thispaper it was also pointed out that it is extremely
important to choose the most suitable tool to observe andforecast the initiating causes (rainfall, in this specificcase) and understanding which types of rainfall are mosteffective in determining the critical conditions for thetriggering of such phenomena. On the conceptualmodel, an operational Te-DFEWS has been developed
aimed to predict the likely occurrence of a debris flowat different time scales (present-time, nowcasting andshort-medium forecast period) using diversified
approaches and input sources for each time scale toensure the best system performance. The Te-DFEWS
has been realized to be upgradable, easy to checkthrough a user-friendly WebGIS interface accessible atany time and from any device (smartphones, tablets andcomputers with every operating system and webbrowser). The automatic warnings issued whenthresholds are exceeded, via the WebGIS interface, viae-mail and through an interactive bulletin published on
the institutional site, also contribute to make this EWS
an effective and indispensable tool for the mitigation of
the hazard and risk induced by debris flows at regional
scale. An exhaustive evaluation of the Te-DFEWS
performance (missed and false alarms quantification) is
still under analysis, considering the youth of this EWS
in its last updated version (2021).
This paper is supported by the 2022-2023 Agreement
between the Department of National Civil Protection and Arpa
Piemonte as Competence Centre.

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