1

SPATIAL MODELS FOR THE DEFINITION OF LANDSLIDE SUSCEPTIBILITY AND

LANDSLIDE HAZARD

FORM-OSE POST-GRADUATE TRAINING SCHOOLLiving with hydro-geomorphological risks: from theory to practice14-19 September 2004, Strasbourg - France

Centre of Geographical StudiesUniversity of Lisbon

J.L. Zêzere

1. BASIC CONCEPTS

Landslide Hazard:“the probability of occurrence of a potentially

damaging phenomenon [landslide] within a given area and in a given period of time.”

(Varnes et al., 1984)

Rt = (E) (Rs) = (E) (H x V)

H – Hazard

V – Vulnerability (degree of loss; 0-1)

E – Vulnerable Elements (Value of...)

Rs – Specific Risk (H x V)

Rt – Total Risk

CONCEPTUAL MODEL OF LANDSLIDE RISK

Dangerous PhenomenaRockfallToppleSlideSpreadFlow….

Vulnerable ElementsPopulationBuildingsInfrastructuresEconomic activitiesCultural and environmental values….

Landslide hazard Vulnerability

LANDSLIDE RISK

adapted from Panizza (1990 )

Landslide Hazard = probability of occurrence of a potentially damaging phenomenon [landslide] within a given area and in a given period of time.

1) SPATIAL LOCATION (“WHERE”?)

2) TIME RECURRENCE (“WHEN”?)

3) INTENSITY / MAGNITUDE (“HOW POWERFUL”?)

CRUCIAL ELEMENTS IN THE PREDICTION OF FUTURE LANDSLIDE BEHAVIOUR:

TYPICAL METHODS USED TO DEFINE LANDSLIDE PRONE ZONES AT A REGIONAL SCALE:

1)DIRECT APPROACH (Geomorphological)

2) INDIRECT APPROACHES (Quantitative and Semiquantitative)

Knowledge-based (index)Statistical (data-driven)Deterministic

Applied overpredefinedterrain units

2

BASIC ASSUMPTION OF BOTH DIRECT AND INDIRECT APPROACHES:

FUTURE LANDSLIDES ARE MORE LIKELY TO OCCUR UNDER THE SAME GEOLOGICAL AND GEOMORPHOLOGICAL CONDITIONS THAT LED TO PAST SLOPE INSTABILITY.

“PAST AND PRESENT ARE KEYS TO THE FUTURE”

KEYS FOR PREDICTION OF FUTURE LANDSLIDES:

MAPPING PAST AND RECENT SLOPE MOVEMENTS

IDENTIFICATION AND MAPPING OF THECONDITIONING OR PREPARATORY FACTORS OF SLOPE INSTABILITY

ASSESSMENT OF LANDSLIDE SUSCEPTIBILITY

LANDSLIDE SUSCEPTIBILITY

Expression of the likelihood that a landslide will occur in an area based on the local terrain conditions, not including the return period or the probability of occurrence of the instability processes.

Most regional landslide ‘hazard’ assessments (both direct and indirect) provide a ranking of terrain units only in terms of susceptibility (“spatial probability”), not including the temporal component of the hazard.

SUSCEPTIBILITY VS HAZARD

Gabbione, PaviaItalyRossetti (1997)

DIRECT APPROACH

Direct Landslide Susceptibility Assessment (Calhandriz Area)

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Indirect Landslide Susceptibility Assessment (Information Value Method)

ITC, ILWIS Manual

By definition, a hazard map should include an evaluation of the probability of occurrence of new landslides, thus implying the consideration of a time dimension.

FROM LANDSLIDE SUSCEPTIBILITY TO LANDSLIDE HAZARD

In the case of rainfall induced landslides the statistical analysis of rainfall data may enable both the definition of the triggering threshold and calculation of the recurrence interval.

UNCERTAINTIES AND DRAWBACKS

1) DATA LIMITATIONS

UNCERTAINTIES IN LANDSLIDE IDENTIFICATIONAND MAPPING

QUALITY, QUANTITY AND RELEVANCE OF THEAVAILABLE INFORMATION:

The discontinuous nature in space and time of slopefailuresThe lack of a complete historical data concerningthe frequency of landslidesThe difficulty of identifying the causes, the triggeringfactors and cause-effect relationship

2) MODEL SHORTCOMINGS

EFFECTIVENESS AND RELIABILITY OF THE AVAILABLE MODELS

DIFFICULTY IN EXTRAPOLATION OF LOCAL DATATO LARGER AREAS

4

3) ADDITIONAL PROBLEM

THE DIFFERENT SPATIAL INCIDENCE OF DIFFERENT TYPES OF SLOPE MOVEMENTS,NORMALLY RELATED TO DISTINCT THRESHOLDSCONDITIONS CONCERNING PREPARATORY ANDTRIGGERING FACTORS.

ASSESSMENT OF LANDSLIDE HAZARDFOR EACH TYPE OF SLOPE MOVEMENTS

Indirect Landslide Susceptibility Assessment (Information Value Method)

Rotational movements susceptibility assessment (Information Value Method)

Translational movements susceptibility assessment (Information Value Method)

Shallow translational slides susceptibility assessment (Information Value Method)

VALIDATION OF SUSCEPTIBILITY AND HAZARD

WHY TO VALIDATE ?

Evaluation of the predictive power of models with respect to future slope movements

Strictly speaking, validation of the prediction of future landslides is only possible with the ‘wait and see’ procedure.

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0 200 m

I

II

III

IV

1

2

120

80

160

200

240

280

120

16020

0240

280

240

200

160

120

160 200

240

EDP

Landslide susceptibility assessment in the Trancãoriver valley (Direct approach, 1988)

Very high

High

Moderate

Low

CREL motorway (1995)

December 1995 / January 1996 landslides

Susceptibility classes:

VALIDATION OF SUSCEPTIBILITY AND HAZARD

VALIDATION FOR WHAT ?

Evaluation of the predictive power of models with respect to future slope movements

Strictly speaking, validation of the prediction of future landslides is only possible with the ‘wait and see’ procedure.

Proposed method: spatial/ time partitioning of the spatial landslide databases.

2. LANDSLIDE SUSCEPTIBILITY, HAZARD ASSESSMENT AND ZONATION

Supporting patternsselected by expert

Prediction map:resource potential,hazard or impactExpert

Mathematician

Validation supporting pattern

geol

ogy

slope

Land

use

know

n ha

zard

s,

reso

urce

s or i

mpa

cts

Assessment of Landslide Risk and Mitigation in Mountain Areas EVG1-2001-00018

REGIONAL FRAMEWORK

Tagu

s river

Oporto

Lisbon

Atla

ntic

Oce

an

Studyarea

Spain

• Monocline structure dipping 5° to 25° to S and SE.• Heterogeneous bedrock (limestones, sandstones, basalts, volcanic

tuffs, marls, clays) dating from upper Jurassic to Miocene.• Cuestas, strongly dissected by fluvial cutting.• Maximum altitude = 350m; steep slopes on catacline valleys.

ALARM PROJECTFANHÕES-TRANCÃO

TEST SITE

Trancão valleyFanhões valley

Lithology Superficial Geomorph. Land use Slope Slope Slope deposits units profile angle aspect

landslides landslides landslides landslides landslides landslides landslides

Documentation

Air-photo interpretation

Field work

Altitude points

Contour lines (5 m)

DEM (pixel: 5m)

Verification Rectification

Slope angle(continuous)

Slope aspect(continuous)

algorithms

DATA CAPTURE AND DATA TREATMENT

Lithology

Superficial

deposits

Geomorph.

units

Landslide

map

Land use

Slope aspect

Slope profile

Slope angle

Independent data layers (categorical)

CARTHOGRAPHIC DATABASE

DATA INTEGRATION

LANDSLIDE SUSCEPTIBILITY MAP

Partition (temporal criteria)

Validation group[age > 1979]

(54 cases)

Estimation group [age <= 1979]

(46 cases)

Data integration

Susceptibility prediction

image

PREDICTION-RATE CURVE

CLASSIFICATIONINTERPRETATION

SUSCEPTIBILITY ASSESSMENT AND VALIDATION

General methodology from data capture and treatment to landslide susceptibility assessment and validation

6

Documentation

Air-photo interpretation

Field work

Altitude points

Contour lines (5 m)

DEM(pixel: 5m)

Verification Rectification

Slope angle (continuous)

Slope aspect (continuous)

algorithms

DATA CAPTURE AND DATA TREATMENT

Lithology

Superficial

deposits

Geomorph.

units

Landslide

map

Land use

Slope aspect

Slope profile

Slope angle

Independent data layers (categorical)

CARTHOGRAPHIC DATABASE

CONSTRUCTION OF A CARTHOGRAPHIC DATABASE

SLOPE ANGLE

(degrees)

SLOPE ASPECT

Flat areasNNEESESSWWNW

LITHOLOGY

SUPERFICIAL DEPOSITS

GEOMORPHOLOGICAL UNITS

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LAND USE /VEGETATION COVER

TRANSVERSAL SLOPEPROFILE

Fan

hõe

s Ri ver

Tranc

ão

Rive

r

Rotational slides Translational sl ides Shallow translational slides

LANDSLIDES

Landslide Landslide morphometricmorphometric parameters of the parameters of the FanhõesFanhões--TrancãoTrancão test sitetest site

Landslide types

N

(%)

Mean depth (m)

Mean area (m2)

Total area (m2)

Mean volume

(m3)

Total volume

(m3) Rotational slides

21 14.3 5.3 6,544 137,415 14,650 307,653

Translational slides

26 17.7 3.4 6,429 167,151 6,699 174,185

Shallow transl. slides

100 68.0 1.0 1,422 142,176 357 35,357

Total 147 100.0 2.1 3,039 446,742 3,542 517,195

General assumption:

Future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the conditioning factors (e.g. slope, aspect, slope profile, geomorphology, lithology, superficial deposits, land use, etc.).

SUSCEPTIBILITY ASSESSMENT

Lithology Superficial Geomorph. Land use Slope Slope Slope deposits units profile angle aspect

landslides landslides landslides landslides landslides landslides landslides

DATA INTEGRATION

DATA INTEGRATION

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DATA INTEGRATION (BAYESIAN PROBABILITY)

affected area/total area

class area/total area

classtheinareaaffected

areaclass ⎟⎟⎠

⎞⎜⎜⎝

⎛−−

111

DATA INTEGRATION (BAYESIAN PROBABILITY)

Prior probability of finding a landslide

Prior probability of finding a class of a layer

Conditional probability of finding a landslide in each class, for each layer

where L1, L2, ..., Ln are the several data layers used as independent variables, (L1×L2×

... ×Ln) represents the prior probability of finding the n data layers in the test site, Cp

is the conditional probability of finding a landslide in a class of each layer, and Pp and

Ppslide are the prior probabilities of finding, respectively, a class and a landslide in

the study area.

DATA INTEGRATION (BAYESIAN PROBABILITY)

Probability of finding a landslide, given n data layers, usingthe conditional probability integration rule (Chung & Fabbri, 1999):

( )( )( )

PpL PpL PpLn CpL CpL CpLn

PpslideLn L L Ln

1 2 1 21

1 2

× × × × × ⋅ ⋅ ⋅ ×

− × × × ⋅ ⋅ ⋅ ×

. . .

0.0767

Non-classified susceptibility map

(based on the complete landslide data set -100 cases).

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

Success-rate curve of the susceptibility assessmentbased on the complete landslide data set

LANDSLIDE SUSCEPTIBILITY MAP

Partition (temporal criteria)

Validation group[age > 1979]

(54 cases)

Estimation group[age <= 1979]

(46 cases)

Data integration

Susceptibility prediction

image

PREDICTION-RATE CURVE

CLASSIFICATION INTERPRETATION

SUSCEPTIBILITY ASSESSMENT AND VALIDATION

LandslidesData set

LANDSLIDE SUSCEPTIBILITY VALIDATION AND CLASSIFICATION

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0.0634

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

Non-classified susceptibility map(based on Estimation Group landslides - age ≤ 1979, 46 cases)

Prediction-rate curve of the susceptibility assessment based on Estimation Group landslides (age ≤ 1979, 46 cases) and compared with Validation Group

landslides (age > 1979, 54 cases).

Prediction-Rate Curve

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

Percentage of studyarea predicted as susceptible usingestimation group landslides

Per

cen

tag

eo

fp

red

icte

dva

lidat

ion

gro

up

lan

dsl

ides

I

IIIII IV V

8%; 41%

18%; 70%

35%; 86%84%; 100%

Susceptibility map

classified according to the prediction-rate curve.

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

I – Top 8%II – 8-18%III – 18-35%IV – 35-84%V – 84-100%

I 41II 29III 16IV 14V 0

Susceptibility class %

Predictive value ofsusceptibility classes

Landslides in the study area have a clear climatic signal.

TRIGGERING ASSESSMENT

0

200

400

600

800

1000

1200

1400

R (m

m) MAP

S.Julião do Tojal Annual precipitationSlope instability events

Archive investigation

Fieldwork

Interviews

Reconstruction of past landslide activity dates

Rainfall analysis(daily data)

Landslide type A

Landslide type B

Landslide type …..

Critical thresholds of rainfall (quantity-duration)

responsible for landslide events

Reconstruction of antecedent rainfall from 1 to 90 days

(Pxn = P1 + P2 +…Pn)

Return periods

(Gumbellaw)

Types oflandslides

Methodology for rainfall triggeringof landslides analysis

Episode Date (yy/mm/dd) Critical rainfall amount/duration

mm (dd)

Return period (years)

Landslide typology

1 1958/12/19 149 (10) 2.5 a

2 1959/03/09 175 (10) 4 a

3 1967/11/25 137 (1) 60 a,d

4 1968/11/15 350 (30) 6.5 b

5 1978/03/04 204 (15) 3.5 d

6 1979/02/10 694 (75) 20 b,c,e

7 1981/12/30 174 (5) 13 a,d

8 1983/11/18 164 (1) 200 a,d

9 1987/02/25 52 (1) 2 a,d

10 1989/11/22 164 (15) 2 a,d

11 1989/11/25 217 (15) 4.5 a,d

12 1989/12/05 333 (30) 5.5 b,c,e

13 1989/12/21 495 (40) 20 b,c,e

14 1996/01/09 544 (60) 10 b,c,e

15 1996/01/23 686 (75) 18 b,c,e

16 1996/01/28 495 (40) 20 b,c,e

17 1996/02/01 793 (90) 24 b,c,e

18 2001/01/06 447 (60) 5 c

19 2001/01/09 467 (60) 5.5 c

Landslide typology: a) shallow translational slidesb) deep translational slidesc) rotational slidesd) slope movements triggered

by bank erosione) complex and composite

slope movements

Temporal occurrence of rainfall triggered landslides in Lisbon area

1956 - 2001

10

0100200300400500600700800900

0 20 40 60 80 100

Duration (days)

Cum

ulat

ive ra

infa

ll (m

m)

landslides no landslides Cr = 6.3D + 70

Rotational slidesTranslational slidesComplex slope movements

Shallow translational slidesSlope movements triggered by bank erosion

Duration (consecutive days)

Age of and total affected areas by shallow translational slideswithin the Fanhões-Trancão test site.

100.0142,176100.0100Total

0.71,0004.041996, January0.79963.031989, December0.91,3153.031989, November2.33,2834.041987, February

33.147,12540.0401983, November31.344,44024.0241979, February31.044,01722.0221967 or prior(%)Total affected area (m2)(%)NAge

General assumption:

The rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area).

Three scenarios for future landslide activity within the Fanhões-Trancão test site based on past landslide events

1,3154.5217/15(3) 1989, November47,125200164/1(2) 1983, November44,4408.5128/3(1) 1979, February

Affected area by shallow translational

slides (m2)

Return Period (years)

Critical rainfall amount/duration

(mm/days)

Scenario

HAZARD ASSESSMENT AT A PROBABILISTIC BASIS

For each particular triggering scenario, the conditionalprobability that a pixel will be affected by a shallowtranslational slide in the future is estimated by:

⎟⎟⎠

⎞⎜⎜⎝

⎛−−= pred

TyTaffectedP .11

Where:

Taffected = Total area to be affected by landslides in a scenario (x);

Ty = Total area of susceptibility class y

pred = prediction value of susceptibility class y.

Calculation of probabilities for landslide hazard assessmentworking on a scenario basis

0.000020.00000

0.00070.0000

0.00060.0000

0.13970.0000

382114127459

IV - 35-84%V – 84-100%

0.000060.00220.00210.1647142342III - 18-35%0.000190.00660.00620.288582044II – 8-18%0.000340.01200.01130.407164150I - Top 8%

(3)November

1989

(2)November

1983

(1)February

1979

Predictive value of susceptibility class

Area (number of pixels)

(pixel= 5m)

Landslide susceptibility

class

Scenarios

Probability to each pixel to be affected by a landslide

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Hazard map

Triggering scenario (1)128 mm / 3 days (RP=8.5 years)

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

I 0.0113 II 0.0062 III 0.0021IV 0.0006 V 0.0000

Class Probability by pixel

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

Hazard map

Triggering scenario (3)217mm / 15 days(RP=4.5 years)

I 0.00034II 0.00019III 0.00006 IV 0.00002 V 0.00000

Class Probability by pixel

House(500 m2)

Shallow translational slidesFanhões-Trancão test site

Joint Conditional Probability Function

Hazard map

( ) ( )( )houseofsize

predITI

TaffectedP=

⎟⎟⎠

⎞⎜⎜⎝

⎛=

=−−=

2041.0.

150,6411'

Taffected = Total area to be affected by landslides in a scenario (x);

TI = Total area of susceptibility class I;PredI = prediction value of susceptibility class I.

Probability that a part of the house will be involved

in landslide activity

Scenario (1) 20.4% Scenario (2) 21.5% Scenario (3) 0.7%

Prediction-Rate Curve

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

Percentage of study area predicted as susceptible using estimation group landslides

Per

cen

tage

of p

redi

cted

va

lidat

ion

grou

p la

ndsl

ides

I

IIIII

IV V

I 13II 44III 11IV 32V 0

Susceptibility class %

ROTATIONALSLIDES

Predictive value ofsusceptibility classes

Prediction-Rate Curve

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

Percentage of study area predicted as susceptible usingestimation group landslides

Perc

enta

geof

pred

icte

dva

lidat

ion

grou

pla

ndsl

ides

I

IIIII IV V

I 61II 14III 4IV 21V 0

Susceptibility class

%

TRANSLATIONALSLIDES

Predictive value ofsusceptibility classes

WORKING ON A SCENARIO BASISScenario January 1996:Critical rainfall amount/duration - 495mm/40 daysReturn period - 20 years

Rotational slides 48,127Translational slides 6,552Shallow translational slides 1,000

Affected area by landslides (m2)

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I - Top 1% 8122 0.1275 0.0302II - 1-12% 88934 0.4448 0.0096III - 12-17% 40334 0.1129 0.0054IV - 17-70% 423765 0.3148 0.0014V - 70-100% 236954 0.000 0.0000

Probability to each pixel to be

affected by a landslide

Predictive value of susceptibility

class

Area (# pixels)

Landslide susceptibility

class

I - Top 9% 72518 0.6080 0.0022II - 9-13% 32423 0.1418 0.0011III - 13-17% 33071 0.0426 0.0003IV - 17-75% 465020 0.2076 0.0001V - 75-100% 195077 0.000 0.0000

Probability to each pixel to be

affected by a landslide

Landslide susceptibility

class

Area (# pixels)

Predictive value of susceptibility

class

I - Top 8% 64150 0.4071 0.00025II - 8-18% 82044 0.2885 0.00014III - 18-35% 142342 0.1647 0.00005IV - 35-84% 382114 0.1397 0.00001V - 84-100% 127459 0.000 0.00000

Landslide susceptibility

class

Area (# pixels)

Predictive value of susceptibility

class

Probability to each pixel to be

affected by a landslide

ROTATIONALSLIDES

TRANSLATIONALSLIDES

SHALLOWTRANSLATIONALSLIDES

Susceptibility

Causes Effects

Vulnerable elements

Typology Value

Hazard Intensity Vulnerability

Specific Risk Potential loss

TOTAL RISK

RISK MANAGEMENT

Acceptable Risk

Temporal dimension

TOWARDS LANDSLIDE RISK ASSESSMENT AND MANAGEMENT

adapted from Canuti & Casagli (1994 )