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



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



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



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



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



LANDSLIDE RISK MANAGEMENT

2 1

Fell et al. (2005)



Landslide risk management and warning systems

Risk mitigation by early warning

2 1

Fell et al. (2005)



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


Vienna (Austria): 25 April 2017 3 2 1

Territorial systems

Local systems

LEWS EXAMPLES



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)



LEWS TYPOLOGY

4 3 2 1



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



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



MONITORING STRATEGY

5 4 3 2 1



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.



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



LEWS COMPONENTS

6 5 4 3 2 1

for weather-induced landslides

Managers

People Researchers



Warning dissemination

Communication & Education

Emergency plan

Community involvement

WARNING SYSTEM

WARNING SYSTEM




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




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


LANDSLIDE MODEL

WARNING MODEL



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



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



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



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



dij = timeijDT

å

7 6 5 4 3 2 1

The EDuMaP method: the “duration matrix”



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



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



Emergency plan

Warning event

Warning criteria

LANDSLIDE MODEL


WARNING MODEL

WARNING SYSTEM

Monitoring

Landslide event

Weather




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



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



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



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)




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



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%)




THANK YOU for your attention

[email protected] https://michelecalvello.wordpress.com/

LANDSLIDE RISK MITIGATION BY MEANS OF EARLY … · Prof. Michele Calvello – University of...

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