NHESSNatural Hazards and Earth System ScienceNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus GmbHGöttingen, Germany10.5194/nhess-15-1639-2015Landslide early warning based on failure forecast models: the example of the Mt. de La Saxe rockslide, northern ItalyManconiA.andrea.manconi@irpi.cnr.ithttps://orcid.org/0000-0003-2930-4422GiordanD.https://orcid.org/0000-0003-0136-2436Geohazard Monitoring Group, CNR IRPI, Strada delle Cacce 73, 10135 Turin, Italynow at: Swiss Federal Institute of Technology, Department of Earth Sciences, Zurich, SwitzerlandA. Manconi (andrea.manconi@irpi.cnr.it)29July20151571639164430January201523February201510July2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/15/1639/2015/nhess-15-1639-2015.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/15/1639/2015/nhess-15-1639-2015.pdf
We apply failure forecast models by exploiting near-real-time monitoring
data for the La Saxe rockslide, a large unstable slope threatening Aosta
Valley in northern Italy. Starting from the inverse velocity theory, we
analyze landslide surface displacements automatically and in near real time
on different temporal windows and apply straightforward statistical methods
to obtain confidence intervals on the estimated time of failure. Here, we
present the result obtained for the La Saxe rockslide, a large unstable
slope located in Aosta Valley, northern Italy. Based on this case study, we
identify operational thresholds that are established on the reliability of
the forecast models. Our approach is aimed at supporting the management of
early warning systems in the most critical phases of the landslide emergency.
Introduction
The use of analytical and numerical models to determine the occurrence of
natural hazards is a major research subject. For landslides, this topic not
only has great relevance in the scientific community but also strongly
affects best practices for efficient land planning and management. The
approaches used to forecast landslide occurrence mainly depend on the
spatial scale analyzed (regional vs. local) and the temporal range of
forecast (long term vs. short term), as well as the triggering factor and the
landslide type being considered. A certain proportion of landslides are
triggered by intense and prolonged rainfall events; thus, a large number of
studies have focused on the relationship between intensity/duration of
rainfall and the consequent activation (or re-activation) of landslides
(Wieczorek and Guzzetti, 1999). In general, the main inputs
for these analyses are retrieved from rain gauge data and historical
landslide catalogs. Models are used to identify and calibrate the intensity
and duration thresholds that, if exceeded during a rainfall event, indicate
the likely occurrence of landslides in a specific area with a specified
degree of uncertainty. Early warning systems (EWSs) based on this approach
rely on the acquisition of near-real-time data from rain gauges and consider
both measured precipitation and rain forecasts based on meteorological
models (Rossi et al., 2012). EWSs of this type are used worldwide and are
usually applied at regional scales; they constitute a suitable solution in
areas where the combination of climatic conditions, landslide
susceptibility, and dense population generates high-risk exposure.
By considering large slope instabilities, event forecasting may be
approached in a different manner. Large unstable slopes display a wide range
of failure behaviors, from slow slope deformations to rapid and catastrophic
rockslides. One of the most critical issues related to these phenomena is
their likelihood of evolving into impulsive gravitational events involving
some or all of the unstable mass (e.g., rockfalls and/or rock avalanches).
In this context, surface displacements and/or deep-seated deformation often
represent the key information for a proper understanding and interpretation
of the phenomenon (Wieczorek and Snyder, 2009).
When unstable slopes menace populations and/or important infrastructure,
monitoring networks are set up as the basis of EWSs. In such situations, EWSs
may rely on thresholds defined with respect to direct measurements of
physical parameters describing the landslide evolution over time, i.e., surface
and/or sub-surface displacement data (Michoud et al., 2013). If
thresholds are exceeded, specific actions are typically prescribed to reduce
the consequences of a potential landslide failure on the population and/or
exposed infrastructure (Medina-Cetina and Nadim, 2008). The identification
of thresholds for rockslide displacements (or velocities) is generally
approached by performing back analyses on the available monitoring data
and/or considering similarities to previous case studies in terms of geology
and volume of the material involved. However, this is not always possible.
Problems with the identification of these thresholds are well known, mainly
resulting from the complexity of the phenomena analyzed, as well as from the
large number of variables involved (Crosta and Agliardi, 2002). Moreover, an
additional limitation of this approach is that the efficacy of EWSs is lost
when the last threshold is exceeded. Once this condition is reached, the
time before a slope failure produces a (partial or total) landslide
occurrence is still unknown, and thus the critical situation can be
protracted for long periods. This is usually the most critical stage of the
landslide emergency.
In the last decades, several modeling procedures have been proposed for the
estimation of the time of failure (ToF) for landslide phenomena. These
approaches, hereafter referred to as failure forecast methods (FFMs),
analyze the evolution of the landslide deformation over time (i.e., the
strain rate) and are based on the assumption that under constant stress
conditions, landslide materials follow the creep mechanism. After the
pioneering work of Saito (1965), a number of authors have attempted to
estimate ToF using different approaches, including simplified empirical
and/or graphical solutions, analytical models known as “regression-only”
methods, and physically consistent methods (see Federico et al., 2012, and
references therein). The “inverse-velocity” method proposed by Fukuzono (1985)
has been widely considered and has led to successful applications both
in large-scale laboratory experiments and in real landslide scenarios (Dick
et al., 2015; Mazzanti et al., 2015; Rose and Hungr, 2007). This approach
exploits the evolution over time of the inverse value of the surface
velocity (v) by assuming that failure approaches as v-1 tends to zero.
Recently, starting from Fukuzono's method, Manconi and Giordan (2014)
proposed a new approach to achieve landslide ToF forecast by considering
near-real-time monitoring data. While in Manconi and Giordan (2014) we
presented the details on the failure forecast modeling approach, in this
paper we aim to define operative thresholds based on the results of the
failure forecast models. Our goal is to contribute to filling an important
gap, i.e., supporting authorities and decision makers during the time frame
between the point when thresholds set on displacements (or its derivatives)
are exceeded and the occurrence of a (partial or total) landslide failure.
Schematic representation of the evolution over time of landslide
velocity prior to a failure event, by considering materials behaving under
creep conditions. The evolution towards failure may have different phases
characterized by non-linear accelerations. While thr1 and thr2 are
static thresholds defined from a priori information on the landslide
behavior, thr3 is based on the results from the failure forecast
modeling obtained in near real time.
Failure forecast in near real time
Let us assume that an active monitoring network is deployed on the landslide
area and that the information on the deformation field is delivered in near
real time. Figure 1 depicts an example of the temporal evolution that might
be observed in landslide surface velocity prior to failure occurrence. Under
these conditions, the monitoring network is usually coupled to an EWS based
on three stages associated with two predefined velocity (v) thresholds:
(i) v< thr1= landslide velocity is below values considered critical;
(ii) v> thr1= warning conditions; (iii) v> thr2= alarm.
When thr1 or thr2 are exceeded at a specific measurement point (or
area), the EWS can be set to send alert messages (e.g., via SMS and/or
email) to the responsible authorities. The latter must evaluate the
situation and eventually activate specific civil protection procedures
(Allasia et al., 2013; Intrieri et al., 2012). An EWS using thresholds based
only on the actual measured deformation values does not provide any
information about the possible evolution of the landslide towards failure.
Indeed, the time between the passing of thr2 to the slope failure is
unknown, posing serious concerns for the management of emergency scenarios.
For example, if the civil protection procedures associated with the stage
“v> thr2” are “evacuation of inhabited buildings” or
“closure of the access roads”, the main question of decision makers under
these conditions is “how long should we keep buildings empty and/or roads
closed?”. In several scenarios, due to the high variability of landslide
behavior, uncertainty over which protection procedures to adopt can last for
several days or even weeks, causing discomfort to the population and
economic loss. Adoption of failure forecast models during this critical
phase could mitigate these problems. More specifically, here we apply
Fukuzono's inverse-velocity method by considering several calculation time
windows (CTWs, data acquired over the last 12 h, 24 h, 48 h,
1 week, etc.) and iterate the procedure several times
(e.g., N= 1000 iterations) within a bootstrap resampling strategy (readers are
referred to Manconi and Giordan, 2014, for more detail). This approach is
aimed at evaluating the evolution of landslide status by considering data
over different periods, as well as deriving robust assessments of errors
associated with the ToF estimate. In addition, the fit of the forecast to
the observations is evaluated by calculating Pearson's correlation
coefficient (CC) between the model and the data. Normalized CC values, when
statistically significant, can be interpreted as a measure of the
reliability (R) of the computed forecast model. At this stage, we consider a
number of R ranges, as follows: (i) 50 % <R< 60 % = model
reliability is low, failure is unlikely but the situation must be
surveyed; (ii) 60 % <R< 75 % = model reliability is
higher, a failure within the estimated ToF range starts to be more likely;
(iii) 75 % <R< 90 % = model reliability is high, a
failure within the estimated ToF range is likely; (iv) R> 90 % = model
reliability is very high, a failure within the estimated ToF
range is highly probable. In general, the results of the failure forecast
procedure presented herein must be read as follows: if the landslide
velocity continues to increase as in the last CTW, the probability of
observing a failure within the estimated ToF range is R %.
Additional information to take into account when interpreting the FFM
results is the consistency of the forecast among different CTWs as well as
the evolution tendency of R. For example, if R progressively increases
and/or remains stable over high values (e.g., R> 75 %), the
probability of observing a failure is higher.
To facilitate the exploitation of this information based on failure
forecasting as well as to provide a straightforward understanding of the
modeling results to people without detailed knowledge of the
inverse-velocity theory, we designed specific representations aimed at
summarizing the obtained results (see Fig. 2). We have implemented this
procedure within the ADVICE system (Allasia et al., 2013), and failure
forecast plots are generated automatically when monitored target
velocities exceed v> thr2.
Example of the failure forecast plots. The x axis represents the
different computational time windows (CTWs), while the y axis indicates
the predicted time to failure (TTF = ToF-now, where now is the time of the
current computation). The bar length is a function of the TTF range between
5 and 95 percentiles computed with the bootstrap procedure (see text for
details). The bar colors depend on the forecast model reliability values (R).
Black triangles indicate the reliability tendency with respect to the
previous model: an increase (or decrease) occurs when current R is higher (or
lower) by 1 %. N/A indicates that the modeling results are not reliable;
thus the failure forecast model is not applicable.
Application to Mont de La Saxe rockslide
Active mass movement affects a large portion of the southern flank of the
Mont de la Saxe, in the northwestern part of Aosta Valley, northern Italy.
The rockslide, hereafter referred to as La Saxe, involves an unstable volume
of ca. 8 × 106 m3 (Crosta et al., 2014, 2015) and
poses a hazard to part of the Courmayeur municipality, i.e., Entreves and La
Palud villages. In addition, the landslide threatens a crucial point of
route E25, an important highway connection crossing Europe from north to
south and ensuring commercial activities between Italy and transalpine
countries. Continuous monitoring of surface displacements started in 2009
and showed that spring snowmelt causes progressive acceleration of the
surface displacements, which may locally reach up to several decimeters or
even meters per day. Over the years, these acceleration phases have led to
failures of portions of the landslide body, with volumes ranging from minor
rockfalls up to relatively larger mass wasting (> 1 × 104 m3).
The monitoring network deployed includes several instruments to
measure surface displacements (Crosta et al., 2014), as follows: (i) a
robotized total station (RTS) measuring every hour the 3-D position of
approximately 30 optical targets installed on the landslide body; (ii) a
ground-based synthetic aperture radar (GB-SAR), measuring at time intervals
ranging from a few minutes to approximately 1 h, adapted depending on
the current landslide velocity; (iii) 8 continuous GPS receivers, also
installed on the landslide body. However, the EWS thresholds are based
mainly on measurements performed via the RTS. When one or more RTS point
targets overcome predefined warning and/or alarm levels (1 and 2 mm h-1,
respectively, considered over a 24 h observation window), specific civil
protection procedures are activated, including the interruption of road
traffic and evacuation of inhabitants from edifices located in areas
potentially involved in a failure event.
Frontal view of the La Saxe rockslide (April 2013). Red dashed
line is the limit of the unstable slope, while blue dashed line defines the
most active landslide sector. B4 and B6 are the location of the RTS targets
considered for the failure forecast. Colored areas represent the zones
collapsed on 21 April 2013 (reddish), 17 April 2014 (orange), and 21 April 2014
(yellow), respectively.
Results of the failure forecast procedure obtained in near real
time during the 2014 emergency scenario. Note how the TTF predicted varies
depending on the CTW considered.
Starting at the end of March 2014, a specific sector of the La Saxe
rockslide started to accelerate (see Fig. 3), with surface velocities
reaching values up to 5–6 cm per day. This acceleration phase has
caused a large number of minor rockfalls as well as two main failure
events: (i) 17 April 2014, 20:00 CET, ca. 5 × 103 m3 and
(ii) 21 April 2014, 23:00 CET, ca. 3 × 104 m3. Figure 4 shows examples of the
failure forecast plots generated in near real time from RTS measurements on
target B4 during this particular phase. Target B4 was installed close to the
zone characterized by the largest displacements and at that moment was
considered to be one of the most representative for understanding the
evolution of this kinematic domain. We noticed that from 31 March
to 15 April, the reliability of the FFM progressively increased for all the
CTWs considered. At this stage, landslide material had reached surface
displacement rates larger than several centimeters per hour, and a failure
was considered highly probable.
Discussion and conclusions
We presented an approach aimed at updating operational EWS thresholds by
including values based on the results of the failure forecast method. Our
approach has been applied to forecast landslide events associated with the
evolution of the La Saxe rockslide during the 2014 emergency scenario. The
results show that reliability thresholds applied to FFM results can be used
to help the interpretation of the evolution of the landslide body towards a
failure and to provide additional support for early warning purposes.
Despite the limited number of events observed so far, we evaluated the
performance of the proposed methodology by building contingency tables
(Jolliffe and Stephenson, 2012). For this purpose, we have taken into
account the failure forecast results for the La Saxe failure event of 21 April 2013
(see Manconi and Giordan, 2014) and the two major events that
occurred in 2014. In particular, the analysis was performed by using for
“event forecasting” only those models with reliability (R) higher than a
predefined value. Among them, models predicting a ToF range that included
the time of the real events observed have been considered to be “true
positive”, while “false alarms” are models predicting a ToF range earlier
than the real event occurrence, and “missed alarms” are models predicting
a ToF range later than the real event occurrence. Models with R below the
predefined reliability threshold have been considered to be “non-event
forecasts” and thus as true negatives. The analysis was performed on
forecast models producing reliability thresholds R> 75 % and
R> 90 % in the week preceding the failure (see Supplement).
We note that the model hit rate for the 2013 event is on the
order of 0.8 (see Table S7) and depends highly on the computational time
windows considered. However, the modeling procedure yields a consistent
number of false alarms, although among these, the mean distance between the
predicted and the real event is on the order of 2.5–3 days. Moreover, we
note that by considering only the forecast models with R> 90 %,
the number of missed alarms approaches zero. For the 2014 events, the
evaluation of the model performance with standard contingency estimators is
difficult to interpret. The event of 21 April 2013 occurred after a
straightforward evolution towards failure, and the target analyzed was
installed right on top of the collapsed landslide sector (see Fig. 3). By
contrast, the 2014 emergency scenario was characterized by a different
evolution. In particular, in the period from 15 to 21 April 2014, a
progressively increasing number of rockfalls and minor collapses
were observed (Bertolo and Arrighetti, 2014), and the landslide acceleration
was highly non-linear. In addition, while the landslide acceleration trend
was recorded by several RTS targets, none of them was located right on the
sectors that finally collapsed (see Fig. 3). This is a main limitation of
using this type of failure forecast model on point data: if the point is not
representative of the collapsing sector, the forecasted time of failure can
be inaccurate. Under these conditions, the use of time series retrieved from
GB-SAR, which provide a spatially distributed map of surface displacements,
can be helpful; however, in this specific case, SAR data accuracy suffered
from the occurrence of very large and/or rapid deformation, hindering its
measuring capabilities due to signal decorrelation (see Casu et al., 2011).
For the above-discussed reasons, it is difficult to identify proper failure
events for cases like those encountered during the La Saxe 2014 emergency
phase. In these specific cases, instead of failure events it is more
appropriate to define a “critical time range” when failure may occur.
Based on the modeling results obtained for the La Saxe case study, we can
consider thr3=R> 75 % to be a good compromise to catch in
advance the occurrence of the critical time range (see Fig. 1). We
emphasize that, as for forecast models relevant to other natural phenomena
(e.g., meteorological events), our results are based on statistical
inference and must always be considered in terms of probability. Moreover,
unpredictable changes of the boundary conditions, as well as deviations in
the material behavior from the classical creep theory, may deeply affect the
results of the forecast model (Mazzanti et al., 2015).
The main advantage of the method presented herein is that additional
thresholds are based on the results of failure forecast models computed in
near real time and thus rely only on the status of the landslide as defined
by the measurements currently available, requiring neither a calibration
period nor back analyses. It is worth mentioning that our method has been
developed to achieve reliable short-term failure forecasts and is not
intended for medium- and long-term predictions of the ToF. On the contrary,
we aim to provide a supporting toolbox to manage EWSs in critical situations,
especially when predefined early warning thresholds are exceeded. EWS
managers can benefit from the additional information provided by the FFM
because when the reliability of the forecast is high and a landslide failure
thus more likely, authorities can be informed in advance (in an automatic
and/or semi-automatic manner) and thus have the time to take
countermeasures. The final interpretation of landslide failure potential
must be provided by experienced users who have a deep knowledge of landslide
phenomena, have access to additional data on landslide status, and are
conscious of the limitations of FFM. Thus, the FFM information can be better
interpreted by taking carefully into account additional evidence from other
data sources, depending on the specific context. Further investigation on
the reliability and accuracy of the method presented herein will be
performed, mainly by considering different data sources as well as
performing tests on a larger number of case studies.
The Supplement related to this article is available online at doi:10.5194/nhess-15-1639-2015-supplement.
Acknowledgements
We thank D. Bertolo of Geological Survey of the Aosta Valley region, for the
data availability and for fruitful discussions. The comments provided by two
anonymous reviewers and by the editor F. Catani helped to improve the manuscript.
Edited by: F. Catani
Reviewed by: two anonymous referees
ReferencesAllasia, P., Manconi, A., Giordan, D., Baldo, M., and Lollino, G.: ADVICE: A
New Approach for Near-Real-Time Monitoring of Surface Displacements in
Landslide Hazard Scenarios, Sensors, 13, 8285–8302, 10.3390/s130708285, 2013.
Bertolo, D. and Arrighetti, S.: Early detection of rockfalls and trajectory
evaluation by “state-of-the art” computer vision technologies, in:
Proceedings of the ROCEXS Meeting 2014, Lecco, Italy, 2014.Casu, F., Manconi, A., Pepe, A., and Lanari, R.: Deformation Time-Series
Generation in Areas Characterized by Large Displacement Dynamics: The SAR
Amplitude Pixel-Offset SBAS Technique, IEEE T. Geosci. Remote,
49, 2752–2763, 10.1109/TGRS.2010.2104325, 2011.Crosta, G. B. and Agliardi, F.: How to obtain alert velocity thresholds for
large rockslides, Phys. Chem. Earth Pt. ABC, 27, 1557–1565, 10.1016/S1474-7065(02)00177-8, 2002.Crosta, G. B., Prisco, C., Frattini, P., Frigerio, G., Castellanza, R., and
Agliardi, F.: Chasing a complete understanding of the triggering mechanisms
of a large rapidly evolving rockslide, Landslides, 11, 747–764,
10.1007/s10346-013-0433-1, 2014.
Crosta, G. B., Lollino, G., Frattini, P., Giordan, D., Tamburini, A.,
Rivolta, C., and Bertolo, D.: Rockslide Monitoring Through Multi-temporal
LiDAR DEM and TLS Data Analysis, in: Engineering Geology for Society and
Territory – Volume 2, edited by: Lollino, G., Giordan, D., Crosta, G.,
Corominas, J., Azzam, R., Wasowski, J., and Sciarra, N., Springer
International Publishing, 613–617, 2015.Dick, G. J., Eberhardt, E., Cabrejo-Liévano, A. G., Stead, D., and Rose,
N. D.: Development of an early warning time-of-failure analysis methodology
for open pit mine slopes utilizing ground-based slope stability radar
monitoring data, Can. Geotech. J., 52, 515–529, 10.1139/cgj-2014-0028, 2015.Federico, A., Popescu, M., Elia, G., Fidelibus, C., Internò, G., and
Murianni, A.: Prediction of time to slope failure: a general framework,
Environ. Earth Sci., 66, 245–256, 10.1007/s12665-011-1231-5, 2012.
Fukuzono, T.: A New Method for Predicting the Failure Time of a Slope,
University Press, Tokyo, 1985.Intrieri, E., Gigli, G., Mugnai, F., Fanti, R., and Casagli, N.: Design and
implementation of a landslide early warning system, Eng. Geol., 147–148,
124–136, 10.1016/j.enggeo.2012.07.017, 2012.
Jolliffe, I. T. and Stephenson, D. B.: Forecast Verification: A
Practitioner's Guide in Atmospheric Science, John Wiley & Sons, Oxford, UK, 2012.Manconi, A. and Giordan, D.: Landslide failure forecast in near-real-time,
Geomat. Nat. Hazards Risk, 10.1080/19475705.2014.942388, in press, 2014.Mazzanti, P., Bozzano, F., Cipriani, I., and Prestininzi, A.: New insights
into the temporal prediction of landslides by a terrestrial SAR
interferometry monitoring case study, Landslides, 12, 55–68, 10.1007/s10346-014-0469-x, 2015.Medina-Cetina, Z. and Nadim, F.: Stochastic design of an early warning
system, Georisk Assess. Manage. Risk Eng. Syst. Geohaz., 2, 223–236,
10.1080/17499510802086777, 2008.Michoud, C., Bazin, S., Blikra, L. H., Derron, M.-H., and Jaboyedoff, M.:
Experiences from site-specific landslide early warning systems, Nat. Hazards
Earth Syst. Sci., 13, 2659–2673, 10.5194/nhess-13-2659-2013, 2013.Rose, N. D. and Hungr, O.: Forecasting potential rock slope failure in open
pit mines using the inverse-velocity method, Int. J. Rock Mech. Min. Sci.,
44, 308–320, 10.1016/j.ijrmms.2006.07.014, 2007.
Rossi, M., Peruccacci, S., Brunetti, M. T., Marchesini, I., Luciani, S., Ardizzone, F., Balducci,
V., Bianchi, C., Cardinali, M., Fiorucci, F., Mondini, A. C., Reichenbach, P., Salvati,
P., Santangelo, M., Bartolini, D., Gariano, S. L., Palladino, M., Vessia, G., Viero, A., Antronico,
L., Borselli, L., Deganutti, A. M., Iovine, G., Luino, F., Parise, M., Polemio, M.,
Guzzetti, F., Luciani, S., and Tonelli, G.: “SANF: a national warning
system for rainfall-induced landslides in Italy” in Landslides and
Engineered Slopes: Protecting Society through Improved Understanding, Vol. 2, edited
by: Eberhardt, E., Froese, C., Turner, K., and Leroueil, S., ISL NASL 2012,
Banff, Alberta, Canada, 1895–1899, 2012.
Saito, M.: Forecasting the time of occurrence of a slope failure, vol. 11, Montreal, 537–541, 1965.
Wieczorek, G. F. and Guzzetti, F.: A review of rainfall thresholds for
triggering landslides, in: Proc. of the EGS Plinius Conference, Maratea,
Italy, 407–414, 1999.
Wieczorek, G. F. and Snyder, J. B.: Monitoring Slope Movements, in: Geological
Monitoring, edited by: Young, R. and Norby, L., Geological Society of America,
Boulder, Colorado, 245–271, 2009.