s11069-012-0410-3 Datadriven Mapping of Avalanche Release Areas Acase Study in SouthTyrol Italy

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O R I G I N A L P A P E R

Data-driven mapping of avalanche release areas: a case

study in South Tyrol, Italy

A. Pistocchi • C. Notarnicola

Received: 8 April 2011 / Accepted: 15 September 2012/ Published online: 7 October 2012 Springer Science+Business Media Dordrecht 2012

Abstract Avalanche hazard and risk mapping is of utmost importance in mountain areas in

Europe and elsewhere. Advanced methods have been developed to describe several aspects

of avalanche hazard assessment, such as the dynamics of snow avalanches or the intensity of

snowfall to assume as a reference meteorological forcing. However, relatively little research

has been conducted on the identification of potential avalanche release areas. In this paper,

we present a probabilistic assessment of potential avalanche release areas in the Italian

Autonomous Province of Bolzano, eastern Alps, using the Weights of Evidence and LogisticRegression methods with commonly available GIS datasets. We show that a data-driven

statistical model performs better than simple, although widely adopted, screening criteria

that were proposed in the past, although the complexity of observed release areas is only

partly captured by the model. In the best case, the model enables predicting about 70 % of

avalanches in the 20 % of area classified at highest hazard. Based on our results, we suggest

that probabilistic identification of potential release areas could provide a useful aid in the

screening of sites for subsequent, more detailed hazard assessment.

Keywords Avalanche release areas Weights of evidence Logistic regression Alps

1 Introduction

Avalanches represent a major concern in all mountain regions of the world (e.g., Schmidt-

Thome 2006), where they may generate high impact on settlements and infrastructures.

Avalanches are a threat in the practice of mountain sports (see McCammon and Haegeli

2007), in turn a significant source of income for mountain economies. Therefore, public

authorities in all European Alpine regions invest on the study of avalanche dynamics and

their spatial distribution, and on the management of their potential impacts.

A. Pistocchi (&)

GECOsistema srl, R&D Unit Botengasse, 27, 39050 Jenesien/Bolzano, Italy

e-mail: [email protected]

C. Notarnicola

EURAC Research, Viale Druso, 1, 39100 Bolzano, Italy

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Nat Hazards (2013) 65:1313–1330

DOI 10.1007/s11069-012-0410-3



In support of spatial planning of human activities, maps of avalanche hazard at a more

detailed level have been produced. These maps ground on the analysis of past avalanche

events and their expert interpretation with reference to local morphology, land cover, and

climate conditions, followed by the delineation of the runout areas (e.g., Barbolini et al.

2001; Mears 1992; Hervas 2003).The delineation of runout areas is typically performed with dynamic or statistical

models with different degrees of complexity, for which a broad body of research (e.g.,

Barbolini et al. 2000, 2001; Barpi 2004; Straub and Gret-Regamey 2006; Delparte et al.

2008; Eckert et al. 2007a, b; Keylock 2005; Gruber and Bartelt 2007) and application

guidance (e.g. Barbolini et al. 2001; Mears 1992; Gruber and Bartelt 2007) exists.

Extensive research has been developed on the identification and probabilistic assessment of

weather and snow cover conditions influencing avalanche activity (e.g., McClung et al.

2006; Bocchiola et al. 2006; Hendrikx et al. 2005; Schweizer et al. 2009; Davis et al. 1999;

Jomelli et al. 2007). On the other hand, relatively little research has been conducted on the

identification of potential avalanche release areas (PARAs). In several mountain areas,

documented events are abundant, so that it is typically assumed that future events will

predominantly occur at sites where they have been observed in the past. For instance,

Hendrikx et al. (2005) examine the frequency in time and the characteristics of several

avalanches occurring at a site which is known to be avalanche-prone. This situation is

rather common in the European Alps (Hebertson and Jenkins 2003), where avalanche

frequency analysis at a site is the recommended first step in avalanche hazard mapping

(e.g., Barbolini et al. 2001). However, there is sometimes a need to map avalanche hazards

over large and not fully documented regions (e.g., Eckert et al. 2007a, b), including other

European mountain environments where a sufficient dataset of historically recorded ava-lanches is not available (Barbolini et al. 2011). Under these circumstances, it may be

important to identify PARAs using indirect evidence. Another reason for spatial studies on

PARAs is in a certain explanatory capacity that the spatial distribution of avalanches over a

region has been suggested to retain with respect to avalanche temporal frequency (Heb-

ertson and Jenkins 2003). Last but not least, correct and exhaustive spatial identification of

PARAs may be critical in predicting accurate avalanche runout distances with dynamic

models (Barbolini et al. 2002; EEA 2010).

In general, PARAs can be related to morphology, which is rather intuitive, and can be

included in practical hazard ranking systems as discussed, for example, in Maggioni and

Gruber (2003). These authors suggest that PARAs can be preliminarily identified by simplebinary morphological criteria for slope (values between 30 and 60) and plan curvature

(values\-0.002 m-1). Their concept has been broadly used as a screening criterion to

identify PARAs (e.g., Eckert et al. 2007a, b) and has been shown to correspond to observed

avalanches in a few cases. However, to the best of our knowledge, a comprehensive test of

this simple rule against observed avalanche releases has not been undertaken.

Ghinoi and Chung (2005), after reviewing available documented approaches to the

mapping of PARAs, have applied probabilistic methods, broadly used in contexts such as

mineral deposit exploration (Agterberg 1989) and landslide hazard identification (Bonham

Carter 1994; Chung and Fabbri 1993), to map the favorability for avalanche release in avalley of the Italian Alps. They predict more than 80 % of avalanche releases in the 20 %

of the area classified at highest hazard, in a relatively narrow and well-documented region,

by using detailed information on weather and snow cover conditions. Their approach is

fully data-driven, that is, independent of expert judgment, which makes it reproducible and

applicable in a homogeneous way over large regions. Moreover, they stress the importance

of hazard map validation, which is often overlooked in expert (knowledge-driven) mapping

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exercises. However, to the best of our knowledge, the approach presented in that paper has

not been further applied in avalanche hazard mapping.

In this work, we test the performance of the popular data-driven Weights of evidence

(WofE) and Logistic regression (LR) methods, for the identification of PARAs in South

Tyrol, Italy. To this end, we exploit standard and broadly available datasets on land cover,morphology, snow cover, and historical records of avalanches.

Through these methods, we investigate the relationship between morphology, forest and

snow cover and the spatial distribution of historically observed avalanche releases: using

the variables showing highest association with avalanches, we generate maps of the

favorability to avalanche release on the basis of observed events.

These maps are subsequently tested using independent observations, enabling the

assessment of their predictive ability. The analysis identifies the most relevant factors that

explain observed avalanches, and conclusions are drawn about the advantages and limi-

tations of using data-driven integration methods in avalanche hazard mapping, also in

comparison with similar exercises in related geoscientific applications.

2 Materials and methods

2.1 Weights of evidence and logistic regression

The well-known weights of evidence (WofE) method (Bonham Carter et al. 1989) is used

to calculate a probability that an avalanche D is released in the presence of a set of

conditions F 1, …

, F n that we denote with Prob ð D F 1; . . .;

F nj Þ.Weights of evidence for the presence of each condition F i can be defined as:

W i ¼ logProb ðF i Dj Þ

Prob ðF i Dj Þð1Þ

where the symbol D denotes the absence of an avalanche. When the presence of condition

F i is more likely inside than outside of an avalanche, W i is positive and vice versa it is

negative when the condition is more likely outside than inside.

The probability of an avalanche in the presence of the n conditions can be expressed

through the weights of evidence as (Bonham Carter et al. 1989):

log Prob ð DjF 1; . . .; F nÞ

1 Prob ð DjF 1; . . .; F nÞ

¼Xn

i¼1

W i þ log Prob ð DÞ

1 Prob ð DÞ: ð2Þ

In the above expression, the weight of a given condition F i contributes to increasing the

summation at the right hand side if the condition is associated with avalanches, and vice

versa if it is not associated. From Eq. (2), the probability of the occurrence of an avalanche

given the conditions, Probð DjF 1; . . .; F nÞ, can be computed. The a priori probability

Prob( D) in Eq. (2) is usually estimated as the ratio of the number of sites where an

avalanche release has been observed, divided by the total number of sites, N , over the study

area. It is apparent that this is a scaling constant for the problem, so if one is not interested

in the absolute probability, but rather in the ranking of sites according to the respective

probabilities (‘‘relative’’ probability), the choice of Prob( D) is not influential.

Usually, probabilities used in Eq. (1) are computed on the basis of a known number of

cases of avalanches, in which conditions F 1, …, F n were known to be present or absent.

The variance of a weight can be computed as (Bonham Carter et al. 1989):

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r2W i

¼ 1

F i \ D þ

1

F i \ Dð3Þ

where denominators indicate the total number of cases where condition F i is present

together with an avalanche or in the absence of an avalanche, respectively.

It can be shown that the variance of Prob ð D F 1; . . .; F nj Þ is (Bonham Carter et al. 1989):

rpost ¼ r2prior þ

Xn

i¼1

r2W i

!0:5

ð4Þ

where r2prior is the a priori avalanche probability variance that can be approximated by

Prob( D)/ N , that is, the ratio of a priori probability and the number of sites, N , used for its

estimation. A weight of evidence for the absence of a condition can be similarly defined as:

W 0i ¼ log Prob ðF i Dj ÞProb ðF i Dj Þ

ð5Þ

where F i denotes the absence of the condition F i. Opposite to W i, W 0i is positive when the

absence of condition F i is more likely in the presence than in the absence of avalanches.

The contrast:

C i ¼ W i W 0i ð6Þ

is a measure of the strength of association between avalanches and a given condition.

When the contrast is zero, the probability of having an avalanche given the condition is the

same as in its absence. The more W i is high in absolute value and positive in sign, and W 0i is

high and negative, the higher (and positive) the contrast; also the higher and negative W iand the higher and positive W 0i , the higher (and negative) the contrast. In the former case,

F i tends to occur frequently at avalanche release areas, while in the latter case, its absence

tends to occur at avalanche release areas.

The standard deviation of the contrast is:

rC i ¼ r2W i

þ r2W 0

i

0:5

: ð7Þ

The ratio

C irC i can be used to assess whether a certain condition F i is significant or not,

through a Student’s t test: assuming a Student’s t distribution with a high number of

degrees of freedom, at a significance level of 98 %, C irC i

should be higher than 2 for contrast

to be significantly different from 0 (Bonham Carter et al. 1989; Bonham Carter 1994).

Conditions to be considered in order to identify the favorability for an avalanche release

at a point can be either binary patterns, that is, information on the presence or absence of a

certain feature, or a certain value of an attribute (e.g., slope). For practical reasons, con-

tinuous attributes such as slope, aspect, elevation, etc. were sliced to a limited number of

attribute classes (the classification of continuous attributes is a problematic issue as will be

discussed below). After classification, all conditions were in the form of multiclass orbinary maps. The weights of evidence for each class of the condition maps are computed

according to Eq. (1) using a set of known avalanche release areas: the probability of a

condition given the avalanche release area, ProbðF ij DÞ, can be computed as the percentage

of the release areas, in which the condition was met (10), while ProbðF ij DÞ is its com-

plement to 1.

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The LR method adopted here is a linear regression of the term log Prob ð DjF 1;...;F nÞ1Prob ð DjF 1;...;F nÞ

h icomputed for each combination of the conditions, against the condition values, weighted

by area (Bonham Carter et al. 1989; Agterberg 1989). The method does not assume

conditional independence of the different conditions and can be regarded as more flexible

than the WofE. In the research presented here, we used the Spatial Data Modeler package

for ArcGIS 9.3 (Sawatzky et al. 2009) to perform all calculations for both WofE and LR.

2.2 Data collection and processing

We aim at deriving maps of the probability of a release of avalanches in South Tyrol,

northeastern Italian Alps (Fig. 1). The region extends on approximately 7,400 km2

and has

typical mountainous morphology, with elevations ranging from about 200 m asl at the

southern valley bottoms, to around 3,900 m asl in the western upper ranges. Roughly

speaking, the lower elevation Southeastern part of the area (usually below 3,000 m asl)belongs to the Dolomites, sedimentary rock formations with rolling morphology inter-

rupted by sharp cliffs, while the rest is formed by metamorphic and igneous rocks with

steeper valley sides and higher elevations. The climate of the region is relatively dry, with

annual precipitation in the range of 500–1,500 mm. In the last decades, stable snow cover

during winter months (above 150 days/year) has been observed only above elevations of

1,200 m asl or higher. The analysis of PARAs in the region requires collecting data on

known past avalanches as well as the morphological, snow and land cover conditions that

might explain their distribution. The physical conditions of the snow are a key avalanche

hazard determinant. Knowledge of internal and external stresses on the snowpack, which

depends on a complex set of thermal and mechanical factors (e.g., McClung et al. 2006), is

essential in avalanche modeling, but information for this purpose is not systematically

available over large regions. Snowpack stress conditions vary significantly in time. For the

purposes of the spatial analysis targeted here, it would be necessary to define an indicator

of the snowpack stress conditions in a map form. However, to the best of our knowledge,

no such simple indicator has been identified so far. On the other hand, it may be argued that

snow conditions, under given weather over a region, are essentially controlled by the

morphology of release areas (elevation, curvature, slope, distance from crests, etc.).

Therefore, we limited our consideration to the 8 factors discussed hereafter.

Forests are known to operate a stabilizing effect on snow masses on hillslopes (e.g.,Barbolini et al. 2011). A binary (yes/no) map of forest cover (1st layer) derived from a

locally available land use map with 10-m resolution was considered.

The duration of snow cover may be included among descriptors potentially associated

with avalanches: although it clearly has no systematic relationship with snow physical

conditions, it can be argued that avalanches should be associated with those areas only,

where snow cover duration is sufficiently long. Moreover, it is quite logical that, within a

relatively homogeneous area, similar snow cover duration corresponds to roughly similar

conditions of snow depth, snow transformation processes, possibility of wind transport and

other processes. This consideration of course holds coeteris paribus, for example, at similar

slope, slope orientation, curvature, etc.Snow cover can be easily mapped from optical satellite images. Binary maps of snow

cover with a resolution of 250 m derived from MODIS images (Molg et al. 2010) were

used to compute maps of the average snow cover duration (i.e., the number of days each

pixel is covered by snow—2nd layer) for the period 2003–2009; although this resolution is

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too coarse to capture such local phenomena as avalanche release areas, the parameter was

retained as a potential descriptor of general avalanche-prone conditions.

Morphology is usually a good predictor of mass flows on hillslopes. Slope and curvature

are deemed the main drivers of avalanches (Maggioni and Gruber 2003; Ghinoi and Chung

2005). Aspect has been found to be associated with avalanche occurrence by Ghinoi and

Chung (2005) in Val Badia, Italy. The popular topographic wetness index of Beven and

Kirkby (1979) accounts for accumulation of water over the landscape and was considered

as a possible predictor of snow accumulation. Finally, the distance from the crest is

identified to reflect different potentials for wind effects on the snow cover (see e.g., Ghinoi

and Chung 2005). From a digital elevation model (DEM) with resolution of 10 m available

from the Province of Bolzano, morphological descriptors were computed, namely slope

(3rd), plan curvature (4th), profile curvature (5th), aspect (6th), the wetness index (7th), the

distance from the crest line or upstream flow length (8th). The 8 descriptors mentioned

above were used as condition maps to compute the probability of occurrence of avalanches.

Elevation, represented by the DEM itself, was considered as an additional 9th potential

explanatory variable.

Data on recorded past avalanches were made available by the local Province technical

offices; the database (avalanche register compiled for the preparation of the map of fre-quent avalanche sites, or ‘‘carta della localizzazione probabile delle valanghe—CLPV’’

according to the Italian procedures) is in the form of polygons corresponding to observed

avalanche bodies, mapped during field surveys or from aerial photographs after the events.

Over the area, 1963 polygons were retained for this analysis (Fig. 2). One problem with

this dataset is that polygons represent the avalanche body while they do not report specific

information on the release area, which is in turn a seldom reported feature. However, it

may be generally assumed that the uppermost part of the avalanche polygons corresponds

reasonably to the release area. As a practical procedure, we extracted automatically from

the DEM such uppermost portion of each polygon, by retaining only elevations above

Z avg ? 0.9 ( Z max - Z avg), where Z avg and Z max are the average and maximum elevation

within the polygon, as obtained from an automated statistical summary of the DEM values

within each avalanche polygon. These conventional release areas were obtained from the

10-m resolution DEM; each grid cell in the release areas was converted to a point. A total

of 77,050 points were created. Out of these, 2,827 points were randomly selected as

training sites for the calculation of the WofE, while the remaining points were used to test

Fig. 1 Location of the study region (source http://www.suedtirol.info/ )

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http://www.suedtirol.info/

http://www.suedtirol.info/



the performance of the model. This procedure implies the assumption that all points within

the uppermost part of an avalanche polygon are potentially part of the avalanche release

area. Therefore, it is assumed that all combinations of the 8 descriptors observed at these

points may be statistically representative of conditions at release. Often, mapped avalanche

polygons include more than one avalanche body; therefore, with this procedure, we sample

combinations of descriptors in the whole area of release of avalanches and cannot capture

the actual, local conditions of release of a specific avalanche body. The approximation can

be considered acceptable as long as we assume that each point at the upper edge of mapped

avalanche polygons might have originated an avalanche within the polygon.

2.3 Classification of the explanatory factors

In order to compute the WofE, all continuous maps of the condition layers used in the

analysis needed to be converted into discrete classes, except in the case of forest cover that

had already a binary pattern. For aspect, after a qualitative inspection in the distribution of

avalanches, we deemed sufficient to adopt a standard classification in eight principal

directions (north, northeast, east, etc.). For all other condition layers, an obvious classifi-

cation could not be figured out and a more systematic investigation was required. Generally

speaking, a higher number of classes accommodates for more flexible description of theassociation between avalanches and descriptors, but yields less robust estimates of the

weights of evidence as explained, e.g., in the documentation of Sawatzky et al. (2009). In

this work, an ad hoc multi-tiered classification procedure was followed: in the first

instance, a continuous field was divided into a high number of classes by either converting

floating point values to their integer part, or by slicing their range in equal intervals. One

Fig. 2 Distribution of avalanche release areas

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possible way to define class limits for a continuous field is multiclass slicing that consists

in inspecting the plot of the cumulative number of training sites (avalanche release points)

as a function of the cumulative area of each layer, ordered by increasing or decreasing

attribute value. When the cumulative plot shows significant changes in the slope, the

corresponding value may be taken as a class limit, as suggested in Sawatzky et al. ( 2009).Another procedure, often referred to in favorability studies, consists in extracting binary

patterns from a continuous field by setting a threshold where the contrast reaches a

maximum (see Bonham Carter et al. 1989; Bonham Carter 1994). According to this

approach, a continuous field is divided iteratively in two classes by setting a threshold;

weights of evidence and contrast are then computed for the two classes. By repeating these

steps for a sufficient number of threshold values in the range of the continuous field, it is

usually possible to identify the threshold yielding the highest contrast, which is in principle

the one indicating the optimal binary pattern for avalanche prediction. In this exercise, we

tried both the multiclass slicing approach based on the cumulative frequency of observed

avalanches, and the binary classification based on the maximum contrast, and we chose the

one yielding highest weights, on a case by case basis. Once a continuous field was clas-

sified, weights of evidence and the contrast were computed for each of the classes iden-

tified in the first instance. When the contrast was not significantly different from 0, the

class was discarded and considered as ‘‘other’’. Eventually, only classes with a significant

contrast were retained, and a weight of evidence for class ‘‘other’’ was recomputed.

3 Results

3.1 Classification of the condition layers

3.1.1 Forest cover

This was a binary map not requiring any processing. The weights reflect a high association

with avalanche release points, with a weight for presence (W i) of about 0.4 for non-forest

and a weight for presence of about -0.9 for forest, in both cases with high and significant

contrast at the 98 % confidence level.

3.1.2 Snow cover duration

The plot of the cumulative number of points as a function of cumulative area indicates a

threshold around 150 days of snow cover (according to the MODIS data), with most of the

avalanche release points occurring above. The maximum significant contrast at the 98 %

confidence level occurs around 50 days. This threshold would not have been sufficiently

selective as it would have included also a large share of low valley bottoms, and we

retained the binary pattern corresponding to the 150 days threshold.

3.1.3 Slope

While the maximum significant contrast at the 98 % confidence level is identified at low

slope angles (around 5), most of the avalanche release points occur between 30 and 60,

as already pointed out by Maggioni and Gruber (2003). Therefore, a binary map with 1 for

slopes between these values and 0 elsewhere has been retained.

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3.1.4 Plan curvature

This indicator did not show any significant association with avalanche release points: the

area-number of points cumulative appeared rather close to a straight line, and the number

of avalanche release points was evenly distributed. The maximum significant contrastcorresponded to the threshold of -16.1, which is of no practical use as it identifies only a

few highly convex locations. Instead of the classification provided by the maximum

contrast, we tested a binary criterion (separation of positive/negative curvatures); however,

this approach yielded no significant contrast and weights of evidence close to zero.

Although the release of avalanches is known to be associated with concave slopes, the

distribution of release points used for computing weights apparently does not show a tight

correspondence with concave slopes computed on a 10-m resolution digital elevation

model. This may partly owe to the method used to select the release area, which samples

from the whole upper part of the avalanche body and may include points in convex slopes

as well, by this introducing noise.

We repeated the calculation using a coarser map (resolution of 100 m) obtained from

resampling of the well-known SRTM digital elevation model (http://www2.jpl.nasa.gov/

srtm/ ; http://srtm.csi.cgiar.org/ ) which yielded higher weights (0.18 for concave and -0.22

for convex slopes) and significant contrast at the 98 % confidence level.

3.1.5 Profile curvature

Practically, all avalanche release points lay at profile curvatures between 0 and -9,

although the maximum contrast occurs at -66, where the first point is found, which makesno practical sense. A binary pattern using the range -9–0 yields weights very close to 0

and no significant contrast. Excluding 0, however, yields a weight of 0.38 inside the range

and -0.125 outside.

3.1.6 Aspect

Five of the eight main classes of aspect showed significant relationships with avalanche

release points, namely southeast and southwest (positive weights equal to 0.29 and 0.16,

respectively) indicating favorability for avalanches, and west, northwest and north (posi-

tive weights equal to -0.11, -0.14, and -0.26, respectively), indicating lack of favor-ability. The other main aspect classes (south, east and northeast) did not show significant

relationships.

3.1.7 Topographic wetness index

The maximum contrast threshold was at the value of 9, which allowed separating the lower

parts of the hillslopes and produced a positive weight equal to -2.30 for the index above 9,

but a weight close to 0 for the upper parts where it assumes values\10. However, as most of

the avalanche release points correspond to the range -1: 4 of the index, a binary map was

built considering this range, obtaining positive weights of 0.79 inside and -0.90 outside.

3.1.8 Upstream flow length (distance from the crests)

This parameter behaves in a way very similar to the topographic wetness index. The

maximum contrast threshold was 3,080 m, which yielded a positive weight equal to -2.51

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http://srtm.csi.cgiar.org/

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for higher distances, and close to 0 below. However, as most of the avalanche release

points are within 100 m from the crest, this threshold was used to create a binary pattern.

This yielded a positive weight of 0.17 below the threshold and -0.56 above.

3.1.9 Elevation

This layer has a pattern very similar to snow cover duration. In order to compute weights,

the continuous elevation field has been sliced into 40 equal-area slices, and the maximum

contrast corresponded to the 12th class (a threshold of approximately 1,200 m asl).

However, an analysis of the cumulative distribution of avalanche release points suggested

that most of them lay between the 19th and the 31st classes, approximately in the range

1,800–3,000 m asl). With the binary pattern using the maximum contrast threshold,

positive weights were approximately 0.2 and -5, above and below the threshold elevation

respectively. With the binary pattern of the range, positive weights were 0.59 and -1.69

inside and outside of it, respectively. This pattern was therefore preferred.

Table 1 summarizes the statistical properties of the condition layers selected for the

analysis.

3.2 Prediction using all explanatory factors

As a first step, we computed the probability of avalanche release with Eq. (2) and with the

corresponding logistic regression using all the above layers. It is worth noting that these

layers are very likely to be conditionally dependent, which would imply that the WofE

would not yield a ‘‘true’’ probability [see documentation in (38)]. However, in our cal-culation, we are interested in ranking the study area by probability, that is, in a relative

scale of probability and not in an absolute measure of probability, a reason why we ignored

the issue. For the calculation, we used the set of training sites (avalanche release points)

that was previously used for the classification of layers. In order to compute the prior

probability that appears in Eq. (2), we need to assign a unit area to the training sites. This

can be a difficult choice if one aims at estimating ‘‘true’’ probabilities, while we regard the

priori probability as a scaling factor that is filtered out by ranking. Therefore, we assigned

invariably a unit area equal to 0.01 km2 (1 ha) for all calculations. In this way, we obtained

two maps of favorability to avalanche release (the one with the LR method is shown in

Fig. 3), that is, conventionally computed probabilities which represent the rank of eachlocation on a scale of likelihood, plausibility or probability of occurrence of an avalanche,

as discussed in Chung and Fabbri (1993). Such probability maps highlight areas more or

less prone to avalanche release, following the patterns of the condition layers used for the

prediction. The LR method yields a more ‘‘diffusive’’ picture, while critical areas

according to the WofE appear slightly more contained (results not shown here for sim-

plicity). The general qualitative impression that can be obtained by overlaying the known

avalanche events to the prediction maps is quite encouraging (see Fig. 4 as an example):

avalanches tend to recur in areas systematically predicted at higher hazard, and lower-

hazard zones are most of the times free from avalanches. However, a favorability mapneeds to be tested more rigorously in order to evaluate its predictive power. An indicator of

the quality of the prediction is the prediction rate plot obtained from the subset of the

known avalanches which were not used to compute probabilities (Chung and Fabbri 1999,

2003). This plot displays the cumulative frequency of avalanches, as a function of the

cumulative area of the study region ranked in decreasing order of probability. The closer

this plot is to the y-axis, the more powerful the prediction. On the contrary, a plot

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approaching the 1:1 line indicates pure random distribution of the avalanches with respect

to the computed probability. We drew a prediction rate plot for both the WofE and the LR

predictions, which is reproduced in Fig. 5. The avalanche data used on purpose were the

74,223 points of the test set not used to train the WofE and LR models. From the plot of theprediction rate, it is apparent that the performance of the two methods is not dramatically

different. However, the LR method performs systematically better than the WofE in this

specific case. The WofE and LR models predict about 65 and 70 % of the test points of

avalanche release in the 20 % highest probability of area, respectively. It is useful to

compare the predictions of Fig. 3 with the one that may be obtained from a simpler

criterion widely adopted in practice, which was originally suggested by Maggioni and

Gruber (2003) and is basically retained as the default model of avalanche release areas in

the procedure proposed by Barbolini et al. 2011. According to this criterion, we crossed the

plan curvature map, slope and forest cover to obtain a boolean selection of areas with

favorability to avalanche release. The locations with slope between 30 and 60, with

negative plan curvature and absence of forest correspond to about 10 % of the study area.

Only approximately 26 % of the test points fall in this 10 %, as shown in Fig. 5, indicating

that the performance of this simpler model in South Tyrol is much less effective than WofE

and, a fortiori, LR.

Table 1 Weights of evidence computed for the layers used in the present analysis

Layer Pattern

considered

W ? (pattern

present) [sd]

W ? (pattern

absent) [sd]

# of training

points within

the pattern

Area in

the

pattern,

Km

2

1. Elevation Elevations

1,800–3,000 m

asl

0.59 [0.02] -1.69 [0.06] 2,568 4,002.65

2. Forest cover Absence of forest 0.40 [0.02] -0.90 [0.04] 2,306 4,334.89

3. Slope Slope 30–60 0.57 [0.02] -0.81 [0.04] 2,104 3,364.82

4. Mean annual

snow cover

duration (SCD)

SCD C 150 days/

year

0.62 [0.02] -1.38 [0.051] 2,449 3,701.31

5. Plan curvature Concave slopes

(100 mresolution

DEM)

0.18 [0.02] -0.22 [0.03] 1,683 3,719.83

6. Profile

curvature

[-9 and\0 0.38 [0.03] -0.13 [0.02] 839 1,612.24

7. Aspect SE 0.29 [0.05] Not considered

for multiclass

patterns

461 957.10

SO 0.16 [0.05] 382 904.27

W -0.11 [0.06] 324 1,006.46

NW -0.14 [0.06] 286 910.53

N -0.26 [0.06] 279 1,000.02

Other -0.01 [0.03] 312 813.06

8. Topographic

wetness index

-1 to 4 0.79 [0.02] -0.90 [0.04] 2,055 5,432.16

9. Upstream flow

length

\100 m 0.17 [0.02] -0.56 [0.05] 2,350 5,584.88

Nat Hazards (2013) 65:1313–1330 1323

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3.3 Sensitivity analysis and exploration of other combinations of factors

In order to understand which parameters were more important in computing avalanche

probability, a sensitivity analysis was conducted by repeating the calculation excluding one

parameter at a time. The resulting maps were examined in terms of their prediction rate.

Through this procedure, it was highlighted that the only individually relevant parameter

was slope, excluding which the prediction rate dropped, for example, from about 70 % to

slightly more than 60 % of predicted avalanches within the area with 20 % highest

computed probability in the case of LR, and similarly from about 65 % to about 55 % inthe one of WofE (Fig. 6). In this case, there is a more marked sensitivity of the model to

the snow cover duration at high cumulate percentages of areas. This does not influence,

however, the initial part of the prediction rate curve, which is the most interesting one for

predictions.

From the sensitivity analysis, it seems that each individual layer except slope has very

limited influence on the prediction. However, joint consideration of all layers yields sig-

nificantly better predictions than using slope only.

4 Discussion and conclusions

We have applied a statistical method to extract from available data as much information as

possible on the spatial distribution of avalanche release areas. The result is a computed

map of probability of avalanche release. This should not be read as a ‘‘true’’ probability

map, but rather in terms of ‘‘relative’’ probability or ‘‘favorability’’, allowing identification

Fig. 3 Prediction maps with all condition layers using LR. The one obtained with WofE is visually similar

to the LR one. Frames marked with A and B refer to sub-domains portrayed in Fig. 4

1324 Nat Hazards (2013) 65:1313–1330

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of areas most prone to avalanche release. It is essential to clearly focus the nature, intended

use, and goal of a screening level map of potential avalanche release areas: the methodapplied here should be considered when documentation is limited and an expert cannot go

on the field to check all sites in detail. This approach may be useful in undocumented or

poorly documented areas, for the screening of avalanche potentials. Predicting avalanche

release areas on purely data-driven statistical methods with a confidence of the order

shown here (i.e., about 70 % in the top 20 %) may be a valuable piece of information in the

Fig. 4 a A stereo plot of an example portion of the LR prediction with the observed avalanche polygons

overlaid; b zoom on the map for comparison with mapped (‘‘observed’’) avalanches. The two portions are

identified in Fig. 3

Nat Hazards (2013) 65:1313–1330 1325

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planning of surveys, monitoring, and even adaptation to avalanches in land management.

Of course, expert’s judgment based on field surveys is necessary in more detailed

implementation phases: it would make no sense to use our maps, for example, to design

avalanche protection measures or to predict avalanche trajectories at a fine scale from a

given release area. In that case, an expert judgment is apparently essential.

The probabilistic model applied to our case study highlights a prediction rate that is in

the typical range for other geoscientific phenomena (e.g., Dahal et al. 2008; Porwal et al.

2010; Oh and Lee 2010; Neuhauser and Terhorst 2007; Regmi et al. 2010). It must be

stressed that, although certain applications of similar methods have obtained higher pre-

diction rates in several areas of geosciences, others have been considered successful with

prediction rates similar to ours, or lower (e.g., Regmi et al. 2010).

Ghinoi and Chung (2005) obtained with a similar method a prediction of up to 95 % of

the avalanches in the 20 % area with highest probability. However, their results refer to a

well-delimited area with site-specific data and a differentiation of avalanches by meteo-rological scenarios, and their prediction rates vary considerably with the different mete-

orological scenarios, in some cases remaining similar to ours. High prediction rates are not

the rule for this type of methods, and generally lower prediction rates are obtained when

generic data are used without specific fieldwork and refinement of the input data (Chung

and Fabbri 1999; Pistocchi et al. 2002).

Prediction rates might be improved by referring to more specific information on ava-

lanches: for instance, a critical assumption of the present study is that the uppermost part of

an avalanche body is a potential release area, while information on the exact location and

extent of release areas might yield more specific insights on the weights of evidence of the

different factors considered here. Additionally, separating avalanches into classes corre-sponding to different triggering conditions [such as the meteorological scenarios used in

Ghinoi and Chung (2005)] is likely to yield improved prediction rates. Unfortunately,

information currently available in the study area as well as in many other mountain areas

goes seldom beyond the data used in this study.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

c u m u l a t i v e a r e a

cumulative avalanches

WofE, all layersLR, all layersrandomMaggioni and Gruber

Fig. 5 Prediction rates for the 9 condition layers

1326 Nat Hazards (2013) 65:1313–1330

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Although accurate probability maps require accurate input data, we have shown that a

probabilistic model may enable to obtain a higher prediction capability than one would get

out of rule-based modeling as in Maggioni and Gruber (2003), suggesting that it may be

used as a tool to support expert judgment in mapping avalanche release areas, although

careful assessment for specific hazard mapping cannot be replaced by an automated

procedure.

The screening of potential release areas is the basis for subsequent hazard and risk analysis (e.g., Barbolini et al. 2001; Barbolini et al. 2011). For instance, a map of ava-

lanche release favorability may support the elicitation of avalanche release scenarios as

boundary conditions for an analysis of potential avalanche runout in order to identify

buildings or infrastructures likely exposed to avalanche hazard. Although the complexity

of avalanche dynamics plays a key role in avalanche effects, it is also critical to the

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

c u m u l a t i v e a v a l a n c h e s

cumulative area

prof_curv

plan_curv

upstream flow length

wetness index

forest

slope

snow

aspect

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

c u m u l a t i v e a

v a l a n c h e s

cumulative area

prof_curv

plan_curvupstream flow length

wetness index

forest

slope

snowaspect

all factors

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

A

B

Fig. 6 Sensitivity of the prediction rate to the 9 condition layers using WofE (a) and LR (b)

Nat Hazards (2013) 65:1313–1330 1327

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assessment that PARAs are considered in a comprehensive way. Therefore, the first nec-

essary step in avalanche risk mapping is always to represent in a GIS their spatial distri-

bution. The approach that we discuss here may help in early stages of avalanche risk

mapping, as an improvement to simple screening methods based on morphological criteria

(e.g., Maggioni and Gruber 2003): the use of statistical methods to identify hazardous sitesperforms better than a simple curvature/slope threshold, as discussed with reference to

Fig. 5, and has been already recommended for the improvement of the Location Maps of

Probable Snow Avalanches (Ghinoi 2008).

The type of map that can be obtained is based on landscape characteristics, hence

inherently static: it only reflects favorability of sites to avalanches, but not hazard con-

ditions under a certain meteorological and snow configuration. Direct identification of

PARAs based on inventories of past events may be appropriate where these are highly

representative of avalanches occurred during the time span covered by the survey; how-

ever, avalanches of high return period may occur in areas not included in recent or limited

inventories. The regions likely to benefit the most from application of data-driven analyses

are those with limited inventories of past avalanche events available that can be exploited

to calibrate a statistical model such as those presented here. This is the case of mountainous

regions in Southern Europe (e.g., non-Alpine Italy, Greece) and Asia, where avalanche

mapping has received increasing attention in the last years also due to growing mountain

tourism on snow, or development of mountain areas for tourism and transport. Typically,

mapping PARAs is a task of technical services in the public administration and is required

at the strategic level both for purposes of land planning and civil protection. PARAs serve

a screening of hazardous situations. Based on this screening, identified PARAs need to be

inspected and evaluated by experts in the field, on the basis of field surveys, in order todefine and quantify appropriate boundary conditions for dynamic avalanche models to be

used as a next step in the delineation of avalanche runout areas, hence risks for settlements

and infrastructures. The delineation of PARAs through data-driven methods needs to be

considered in terms of capacity to correctly select areas at risk. We have shown that, for the

case of South Tyrol, the 20 % highest favorability area captures approximately 70 % of

observed avalanche release areas, which means that about 30 % of known avalanche

release areas may fall in areas classified at lower favorability. This does not necessarily

mean that 30 % of avalanches are not correctly predicted: for instance, if we would

consider the 40 % highest favorability area, more than 90 % of avalanches would be

correctly predicted (Fig. 5). However, the prediction rate curve provides a qualitativeindication of the capacity of the model to identify priorities, which is always imperfect.

Therefore, while a screening map of PARAs may be a valuable screening tool in order to

prioritize sites for further survey, an expert assessment of the results and careful follow-up

field investigation remain essential ingredients of a good PARA mapping exercise. In this

direction, mapping a continuous score of favorability instead of a crisp classification (e.g.,

‘‘hazard’’ or ‘‘non-hazard’’ based on the 20 % highest favorability) as done in Fig. 3 may

be more informative for the practitioner in charge of field verification.

Acknowledgments The research was partly funded within the Interreg IVb Project CLISP (www.clisp.eu).

Geographical data, and particularly the CLPV avalanche register data, were provided by the Avalancheservice of the Civil Protection Department of the Province of Bolzano. S. Kass, M. Zebisch and S.

Schneiderbauer of the EURAC Institute for Applied Remote Sensing are gratefully acknowledged for

technical help and discussion. S. Lermer helped performing some preliminary calculations during his

internship at EURAC under the supervision of A. Pistocchi; results obtained in that context inspired parts of

the present paper and will be a subject of a dedicated, upcoming contribution. Avalanche data were provided

by the Civil Protection Service of the Autonomous Province of Bolzano—Alto Adige (Italy).

1328 Nat Hazards (2013) 65:1313–1330

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http://www.clisp.eu/

http://www.clisp.eu/



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s11069-012-0410-3 Datadriven Mapping of Avalanche Release Areas Acase Study in SouthTyrol Italy