LANDSLIDE RISK MITIGATION BY MEANS OF EARLY … · Prof. Michele Calvello – University of...
Transcript of LANDSLIDE RISK MITIGATION BY MEANS OF EARLY … · Prof. Michele Calvello – University of...
LANDSLIDE RISK MITIGATION BY MEANS OF EARLY WARNING SYSTEMS
Prof. Michele Calvello
University of Salerno (Department of Civil Engineering)
European Geosciences Union
General Assembly 2017
Vienna | Austria | 23–28 April 2017
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
People-centred early warning systems include 4 key elements:
1. Risk knowledge
2. Monitoring & Warning
3. Dissemination & Communication
4. Response capability
Early warning systems (EWS): elements
UNISDR (2006, 2009)
1 2
3 4
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Landslide early warning systems (LEWS): activities, components
Di Biagio and Kjekstad (2007)
Fou
r act
ivit
ies
Intrieri et al. (2013)
A
B
C
D
I II
III IV
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Sections of presentation
1. Landslides
2. Landslide risk management
3. LEWS examples
4. Typology of LEWS
5. Monitoring strategy
6. Components of LEWS
7. Warning model
Performance assessment
8. Risk perception
Communication and education
People’s participation
Emergency plans (resilience)
9. Concluding remarks
I 1
I 1
A II 2
B III 2
D
IV
4
3
IV
Different scales of operation:
local and territorial systems
Multidisciplinary approach
4 IV
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
LANDSLIDES
Movement of a mass of rock, earth or debris
down a slope (Cruden 2001)
1
Photos
Hungr, O., Leroueil, S.,
Picarelli, L. (2014).
Varnes classification of
landslide types, un update.
Landslides, 11(2):167-194
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Landslide classification systems Example of landslide classification scheme (Calvello 2017) useful for defining monitoring and modelling strategies for early warning purposes
Variables From classification systems
Typology, Material Varnes (1978), Hungr et al. (2014), state of practice
Phase of activity Skempton & Hutchinson (1969), Leroueil et al. (1996)
Velocity e.g., Cruden & Varnes (1996)
Volume (magnitude) e.g., Fell (1994) velocity
volume
Reactivation
First failure
Shallow slides
(coarse-grained)
Rapid Mass Movements
Deep-seated
slides
Rock falls Rock slides
Earth slides / Earth flows
Creep
Rock avalanches
Debris flows/avalanches
Shallow slides
(fine grained)
1
Hyperconcentrated flows
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
LANDSLIDE RISK MANAGEMENT
2 1
Fell et al. (2005)
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Landslide risk management and warning systems
Risk mitigation by early warning
2 1
Fell et al. (2005)
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Researchers (model efficiency)
ISO 31000 (2009). Risk defined as “the effect of uncertainty on objectives”
Risk mitigation by early warning
People (risk perception)
Managers (system effectiveness)
2 1
Landslide risk management and warning systems
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017 3 2 1
Territorial systems
Local systems
LEWS EXAMPLES
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Examples in the world
At SLOPE scale
La Saxe – Italy ”rockslide”, ca. 8 106 m3 (Crosta et al. 2014) weather-dependent landslide activity evacuation of 80 residents 8/4-5/5 2014
Aknes – Norway (Blikra et al. 2013) ”rockslide”, ca. 54 Mm3, from 2005 risk scenario with tzunami
Illgraben – Switzerland (Bardoux et al. 2009) “debris flows”, from 2000
Three-gorges reservoir – China (e.g., Yin et al. 2010) many ”active slides”, from 1999
At REGIONAL scale
Hong Kong – China (Chan et al 2003, Wong et al 2014) 1100 km2, from 1977
Rio de Janeiro – Brazil (d’Orsi et al 2004, Calvello et al 2015) 1255 km2, from 1996
Seattle – USA (Chleborad 2004, Baum and Godt 2010) 370 km2, from 2002
Japan (Osanai et al 2010, Okamoto et al 2013) 372000 km2, from 2005
Norway (Boje et al 2014, Piciullo et al 2017) 385000 km2, from 2013
3 2 1
@EGU 2017, SSS9.5/NH3.13, Poster session:
Main components and characteristics of landslide early warning systems operational worldwide (Piciullo and Cepeda)
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
LEWS TYPOLOGY
4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
from the LITERATURE > LEWS typology
Stahli et al. (2015)
Monitoring and prediction in early warning systems for rapid mass movements. NHESS, 15:905–917.
Stähli M. Swiss Fed. Institute Forest, Snow and Landscape Research WSL
Sättele M. WSL Institute Snow and Avalanche Research SLF
Huggel C. Department of Geography, University of Zurich
McArdell BW. Swiss Fed. Institute Forest, Snow and Landscape Research WSL
Lehmann P. Soil and Terrestrial Environmental Physics, ETH Zurich
Van Herwijnen A. WSL Institute Snow and Avalanche Research SLF
Berne A. Environmental Remote Sensing Laboratory, EPF Lausanne
Schleiss M. Environmental Remote Sensing Laboratory,EPF Lausanne
4 3 2 1
Ferrari A. Soil Mechanics Laboratory, EPF Lausanne
Kos A. Institute for Geotechnical Engineering, ETH Zurich
Or D. Soil and Terrestrial Environmental Physics, ETH Zurich
Springman SM. Institute for Geotechnical Engineering, ETH Zurich
Switzerland
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Stahli et al. (2015)
Monitoring and prediction in early warning systems for rapid mass movements. NHESS, 15:905–917.
EWSs classification
(i) Alarm systems detect process parameters of ongoing hazard events to initiate an alarm
automatically, e.g., in the form of red flashing lights accompanied by sirens.
(ii) Warning systems aim to detect significant changes in the environment (time-dependent factors
determining susceptibility with respect to mass release), e.g., crack opening, availability of loose
debris material and potential triggering events (e.g., heavy rain), before the release occurs and
thus allow experts to analyze the situation and implement appropriate intervention measures.
(iii) Forecasting systems predict the level of danger of a RMM process, typically at the regional scale
and at regular intervals. In contrast to warning systems, the data interpretation is not based on a
threshold but is conducted on a regular basis, e.g., daily.
System Lead time Detect Alarm
Alarm short Parameters of ongoing
event
Automatic
Warning extended Factors of susceptibility (t) Predefined thresholds
Forecasting regular intervals Sensor data & forecasts Data interpretation
SLOPE scale
REGIONAL scale
4 3 2 1
from the LITERATURE > LEWS typology
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
MONITORING STRATEGY
5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Choosing the measuring device
(e.g., Michoud et al., 2013)
Ease of implementation
Robustness
Precision and accuracy
Costs (inst., main., readings, repair)
Scale of operation
LOCAL LEWS
TERRITORIAL LEWS
Typology, Material
Phase of activity
Velocity
Volume
Function of…
5 4 3 2 1
Monitoring networks for weather-induced landslides
Geo
tech
Hyd
ro
Geo
ph
ys
Geo
de
tic
Rem
ote
sen
sin
g
Mete
o
Deformation activity
Displacements
Inc
BExt
DMS
Tilt
GPS
Int
TotS
Cam
GbLiD
ALiD
GbSAR
InSAR
UAV
Strains OptF
EExt Geoph
Cracking Crack GbLiD
ALiD
Microseismicity and
acoustic emission
Acc
Seis
Geoph
GPR
Rockfall event
frequency
GbLiD
ALiD
Mass balance GbLiD
ALiD
Groundwater
Pore water pressure Piez
DMS
Suction Tens
TPsy
ElCS
ThCS
Soil humidity TDR Sat
Water quality SprS
Trigger Weather Sat RainG
WS
Predisposing factor
Atmospheric tides Bar
Stream flow WLM
Hyd
Monitoring parameter
Monitoring method
Legend: Inc=Inclinometer; BExt=Borehole extensometer; DMS=“Differential monitoring of stability” column; Tilt=Tiltmeter; GPS=Global positioning satellite; Int=Interferometer; TotS=Total
station; Cam=Camera; GbLID=Ground-based LIDAR; ALID=Airborne LIDAR; GbSAR=Ground-based synthetic aperture radar; InSAR=Interferometric synthetic aperture radar;
UAV=Unmanned air vehicle; OptF=Optic fiber; EExt=Embedded extensometer; Gp=Geophone; Crack=Crackmeter; Acc=Accelerometer; Seis=Seismometer; GPR=Ground penetrating
radar; Piez=Piezometer; Tens=Tensiometer; TPsy=Thermocouple psychrometer; ElCS=Electrical conductivity sensor; ThCS=Thermal conductivity sensor; TDR=Time domain
reflectometer; Sat=Satellite sensor; SprS=Spring sampling; RainG=Rain gauge; WS=Weather Station; Bar=Barometer; WLM=Water level meter; Hyd=Hydrometer.
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Multiscalar monitoring strategy
Integration between local and territorial LEWSs
5 4 3 2 1
SLOPE scale
Susceptibility
zoning
Elements at risk
Classification
of slopes
Warning
zone
SLOPES
to monitor
REGIONAL scale Thematic information
and past landslides
Significant
SLOPES
Geotechnical
variables Geotechnical slope
characterization
Slope
response
Parameters
to monitor
Real time
monitoring
(multiparametric)
Real time
monitoring
(e.g. rainfall)
Territorial
early
warning
Local
early
warning
Territorial
model
Local
model
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
LEWS COMPONENTS
6 5 4 3 2 1
for weather-induced landslides
Managers
People Researchers
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Warning dissemination
Communication & Education
Emergency plan
Community involvement
WARNING SYSTEM
WARNING SYSTEM
Warning dissemination
Communication & Education
Community involvement
Emergency plan
WARNING MODEL
Warning event
Warning criteria
LANDSLIDE MODEL
Weather
Monitoring
GEO characterization
Landslide event
Calvello (2017)
EWS for weather-induced landslides: components
6 5 4 3 2 1
Warning event
Warning criteria
WARNING MODEL
LANDSLIDE MODEL
Monitoring
Landslide event
Weather
GEO characterization
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
WARNING MODEL Model efficiency
7 6 5 4 3 2 1
Hyogo Framework for Action (2005) In the “priority for action 2”–identify, assess and monitor disaster risks and enhance early warning– the following key activity is identified: establish institutional capacities to ensure that early warning systems are subject to regular system testing and performance assessments.
Warning event
Warning criteria
Monitoring
Landslide event
Weather
GEO characterization
LANDSLIDE MODEL
WARNING MODEL
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
from the LITERATURE > Warning criteria and performance evaluation
Cloutier et al. (2015) The first international workshop on warning criteria for active slides: technical issues, problems and solutions for managing early warning systems. Landslides, 12:205-212.
SLOPE scale
The workshop turned out to point out more to the problems related to their definition than to tools and solutions. [..] EWSs are relatively new in natural hazard protection [..] tools for warning criteria definition are limited.
Sattele et al. (2016) Forecasting rock slope failure: how reliable and effective are warning systems? Landslides, 13(4):737-750.
A reliability analysis for warning systems must address
technical reliability, accounts for failures of technical system components due to
aging and external causes such as lightning and destruction
inherent reliability, describes the general ability of the system to detect an event, it
is primarily a function of the warning thresholds, the model forecast accuracy of
models, and human decision-making.
7 6 5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
REGIONAL scale
Two approaches for quantifying the model performance
analysis of the time frames during which significant high-consequence
landslides occurred in the test area (Keefer et al 1987; Aleotti 2004; Baum
and Godt, 2010; Capparelli and Tiranti 2010)
evaluation based on 2 by 2 contingency tables computed for the joint
frequency distribution of landslides and alerts, both considered as
dichotomous variables (Yu et al 2003; Cheung et al 2006; Godt et al 2006;
Restrepo et al 2008; Tiranti and Rabuffetti 2010; Kirschbaum et al 2012;
Martelloni et al 2012; Giannecchini et al 2012; Peres and Cancelliere 2012;
Staley et al 2013; Lagomarsino et al 2015; Greco et al 2013; Segoni et al
2014; Gariano et al 2015; Rosi et al 2015; Stähli et al 2015)
7 6 5 4 3 2 1
from the LITERATURE > Warning criteria and performance evaluation
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
Warning criteria and performance evaluation
REGIONAL Scale Warning level
time
(landslides)
Was the
performance of the
warning model
satisfactory?
Red alert
Orange alert
Yellow alert
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
The EDuMaP method
1
2
3
7 6 5 4 3 2 1
Calvello M, Piciullo L (2016)
Assessing the performance of regional landslide early warning models: the EDuMaP method
NHESS, 16(1):103-122
The EDuMaP methods evaluates
the performance of a warning model
used within a territorial LEWS
employing a 3-step procedure
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
dij = timeijDT
å
7 6 5 4 3 2 1
The EDuMaP method: the “duration matrix”
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
EDuMaP applications @ EGU 2017
Emilia Romagna (Italy) Campania (Italy) Norvegia
SSS9.5/NH3.13, Poster session:
Using a landslide inventory from online news to evaluate the performance of warning models for
rainfall-induced landslides in Italy (Pecoraro,Calvello)
Design of a reliable and operational landslide early warning system at regional scale
(Calvello, Piciullo, Gariano, Melillo, Brunetti, Peruccacci, Guzzetti)
Performance evaluation of the national early warning system for shallow landslides in Norway
(Dahl, Piciullo, Devoli, Colleuille, Calvello)
7 6 5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
RISK PERCEPTION Risk communication and risk education strategies
People’s involvement (resilience of community at risk)
Emergency plans (resilience of community at risk)
8 7 6 5 4 3 2 1
Warning dissemination
Communication & Education
Emergency plan
Warning event
Warning criteria
LANDSLIDE MODEL
Community involvement
WARNING MODEL
WARNING SYSTEM
Monitoring
Landslide event
Weather
GEO characterization
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
A valid theory of risk perception should be able to predict/explain “what kinds of people will perceive which potential hazard to be how dangerous” (Wildavsky and Dake 1990).
Studied in relation to numerous anthropic activities
Technological and industrial threats (e.g., Slovic 1987; Hornig 1993)
Virus outbreaks (e.g., Rubin et al. 2009)
Medical treatments (e.g., Slovic et al. 2007; Freudenberg and Beyer 2011)
Social problems (e.g., Quillian and Pager 2010)
Economic problems (e.g., Chassagnon and Villeneuve 2005, Sari et al. 2011)
Transportation and aviation (e.g., Hayakawa et al. 2000, Thomson et al. 2004)
Slovic P (1987). Perception of risk. Science, 236(4799):280-285
Finlay PJ, Fell R (1997) Landslides: risk perception and acceptance. Can Geotech J, 34(2):169–188
… and to the risk posed by “natural hazards”
Earthquakes (e.g., Lindell and Perry 2000)
Floods (e.g., Rogers et al. 1983, Grothmann and Reusswig 2006)
Floods and landslides (e.g., Lin et al. 2008, Wagner 2007, Plattner et al. 2006, Wachinger and Renn 2010)
Landslides (Finlay and Fell 1997, Solana and Kilburn 2003, Nathan 2008, Scolobig et al. 2011, Salvati et al. 2014, Hernández-Moreno and Alcántara-Ayala 2016, Thiene et al., 2016).
8 7 6 5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
from the LITERATURE > Risk perception, people’s participation and warning
Baudoin et al. (2016)
From Top-Down to “Community-Centric” Approaches to Early Warning Systems: Exploring Pathways to
Improve Disaster Risk Reduction Through Community Participation. Int J Disaster Risk Science, 7:163–174.
There is no single approach to designing and implementing CCEWS.
Common principle: need to understand local context, integrate local knowledge, and take account
of individual motivations
The system should be embedded within a community rather than conceived as a technological tool that detects risks and issues warnings (Kelman and Glantz 2014). [..] Participatory EWS should not be built on the rejection of modern science and technology. Rather, coupling knowledge systems—traditional and science-based—can contribute to improving risk detection and monitoring.
Need for greater involvement of earth science and engineering researchers in CCEWSs
(not only for traditional communities in developing countries but also for cities and regions of developed countries)
Case study 3: Tsunamis and landslides
8 7 6 5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
A survey on landslide risk perception (and landslide warning systems)
Calvello M, Papa MN, Pratschke J, Nacchia Crescenzo M (2016)
Landslide risk perception: a case study in Southern Italy. Landslides, 13(2): 349-360.
Study amongst residents living in the Sarno municipality, a relatively small town that experienced in
1998 enormous damage and loss of life—137 deaths—as a result of a landslide event of great
magnitude, with numerous landslides of the flow-type occurring within a few hours after two days of
exceptional rainfall.
All’interno del territorio a rischio residuo
Al di fuori del territorio a rischio residuo
Luogo di residenza degli intervistati
100 interviews
Semi-structured interviews based on a
questionnaire, lasting from 15 to 30 minutes.
60 individuals living inside the so-called “red zone”,
the urbanised area considered to be exposed to
residual risk soon after the 1998 landslides; and 40
individuals living outside this area.
8 7 6 5 4 3 2 1
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
CONCLUDING REMARKS (1/3)
Whether traditional or technology-
based, EWS are only as good as their
weakest link. They can, and frequently
do, fail for a number of reasons.
(Maskrey 1997)
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
CONCLUDING REMARKS (2/3)
Geography (physical and social)
land management engineering
Physics (meteorology, geophysics)
Earth sciences (geology, geomorphology, hydrogeology)
«Engineering geology»
Sociology
Psicology
Economy
Law
Civil, environmental and
Other engineering
(geotechnics)
Statistics
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
LandAWARE - Landslide early warning systems as tools for community resilience COST Action project (currently under review, outcome of call expected in June 2017)
MAIN AIM
To create a multidisciplinary pan-European network of researchers and stakeholders for defining a
set of interdisciplinary methods to operate effective and efficient LEWSs, thus increasing the
resilience of communities exposed to landslide risk
NETWORK (at proposal stage)
Main proposer
Michele Calvello
Co-proposers
41 experts from 29 different countries
Core expertise of proposers
Earth and related environmental sciences (57%)
Civil and environmental engineering (26%)
Economics and sociology (10%)
Other (7%)
CONCLUDING REMARKS (3/3)
Prof. Michele Calvello – University of Salerno, Italy (Dep. Civil Engineering)
Vienna (Austria): 25 April 2017
THANK YOU for your attention
[email protected] https://michelecalvello.wordpress.com/