SPATIAL MODELS FOR THE DEFINITION OF LANDSLIDE SUSCEPTIBILITY AND LANDSLIDE HAZARD J.L. Zêzere Centre of Geographical Studies University of Lisbon CONCEPTUAL MODEL OF LANDSLIDE RISK Dangerous Phenomena Rockfall Topple Slide Spread Flow. hazard Vulnerable Elements Population Buildings Infrastructures Economic activities Cultural and environmental values. Vulnerability FORM-OSE POST-GRADUATE TRAINING SCHOOL Living with hydro-geomorphological risks: from theory to practice 14-19 September 4, Strasbourg - France LANDSLIDE RISK adapted from Panizza (1990 ) 1. BASIC CONCEPTS Hazard = probability of occurrence of a potentially damaging phenomenon [landslide] within a given area and in a given period of time. 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) CRUCIAL ELEMENTS IN THE PREDICTION OF FUTURE LANDSLIDE BEHAVIOUR: 1) SPATIAL LOCATION ( WHERE?) 2) TIME RECURRENCE ( WHEN?) 3) INTENSITY / MAGNITUDE ( HOW POWERFUL?) Rt = (E) (Rs) = (E) (H x V) TYPICAL METHODS USED TO DEFINE LANDSLIDE PRONE ZONES AT A REGIONAL SCALE: H Hazard V Vulnerability (degree of loss; 0-1) E Vulnerable Elements (Value of...) Rs Specific Risk (H x V) Rt Total Risk 1)DIRECT APPROACH (Geomorphological) 2) INDIRECT APPROACHES (Quantitative and Semiquantitative) Knowledge-based (index) Statistical (data-driven) Deterministic Applied over predefined terrain units 1
BASIC ASSUMPTION OF BOTH DIRECT AND INDIRECT APPROACHES: SUSCEPTIBILITY VS HAZARD FUTURE LANDSLIDES ARE MORE LIKELY TO OCCUR UNDER THE SAME GEOLOGICAL AND GEOMORPHOLOGICAL CONDITIONS THAT LED TO PAST SLOPE INSTABILITY. 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. PAST AND PRESENT ARE KEYS TO THE FUTURE KEYS FOR PREDICTION OF FUTURE LANDSLIDES: MAPPING PAST AND RECENT SLOPE MOVEMENTS DIRECT APPROACH Gabbione, Pavia Italy Rossetti (1997) IDENTIFICATION AND MAPPING OF THE CONDITIONING OR PREPARATORY FACTORS OF SLOPE INSTABILITY ASSESSMENT OF LANDSLIDE SUSCEPTIBILITY Direct Susceptibility Assessment (Calhandriz Area) 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. 2
Indirect Susceptibility Assessment (Information Value Method) FROM LANDSLIDE SUSCEPTIBILITY TO LANDSLIDE HAZARD 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. 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 IDENTIFICATION AND MAPPING QUALITY, QUANTITY AND RELEVANCE OF THE AVAILABLE INFORMATION: The discontinuous nature in space and time of slope failures The lack of a complete historical data concerning the frequency of landslides The difficulty of identifying the causes, the triggering factors and cause-effect relationship 2) MODEL SHORTCOMINGS EFFECTIVENESS AND RELIABILITY OF THE AVAILABLE MODELS DIFFICULTY IN EXTRAPOLATION OF LOCAL DATA TO LARGER AREAS ITC, ILWIS Manual 3
Translational movements susceptibility assessment (Information Value Method) 3) ADDITIONAL PROBLEM THE DIFFERENT SPATIAL INCIDENCE OF DIFFERENT TYPES OF SLOPE MOVEMENTS, NORMALLY RELATED TO DISTINCT THRESHOLDS CONDITIONS CONCERNING PREPARATORY AND TRIGGERING FACTORS. ASSESSMENT OF LANDSLIDE HAZARD FOR EACH TYPE OF SLOPE MOVEMENTS Indirect Susceptibility Assessment (Information Value Method) Shallow translational slides susceptibility assessment (Information Value Method) Rotational movements 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. 4
EDP 240 280 240 160 120 120 160 240 susceptibility assessment in the Trancão river valley (Direct approach, 1988) Susceptibility es: I Very high Atlantic Ocean Study area Lisbon Oporto Tagus river Spain REGIONAL FRAMEWORK ALARM PROJECT FANHÕES-TRANCÃO TEST SITE II High 280 IV III Moderate Low 1 CREL motorway (1995) 160 120 80 240 160 2 0 m December 1995 / January 1996 landslides 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. VALIDATION OF SUSCEPTIBILITY AND HAZARD Fanhões valley Trancão valley 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 slope Supporting patterns selected by expert geology Land use Expert Prediction map: resource potential, hazard or impact General methodology from data capture and treatment to landslide susceptibility assessment and validation map DATA CAPTURE AND DATA Altitude Contour Verification TREATMENT points lines Rectification (5 m) DEM Documentation (pixel: 5m) Air-photo interpretation algorithms Field work Slope angle Slope aspect (continuous) (continuous) CARTHOGRAPHIC DATABASE Lithology Superficial Geomorph. Land use Slope Slope Slope deposits units profile angle aspect Independent data layers (categorical) DATA INTEGRATION Lithology Superficial Geomorph. Land use Slope Slope Slope deposits units profile angle aspect known hazards, resources or impacts Mathematician Validation supporting pattern landslides landslides landslides landslides landslides landslides landslides Partition LANDSLIDE (temporal criteria) SUSCEPTIBILITY MAP Validation group [age > 1979] (54 cases) Estimation group [age <= 1979] (46 cases) Data integration Susceptibility prediction image CLASSIFICATION INTERPRETATION Assessment of Risk and Mitigation in Mountain Areas EVG1-1-00018 SUSCEPTIBILITY ASSESSMENT AND VALIDATION PREDICTION-RATE CURVE 5
CONSTRUCTION OF A CARTHOGRAPHIC DATABASE LITHOLOGY DATA CAPTURE AND DATA TREATMENT Verification Rectification Altitude points Contour lines (5 m) Documentation DEM (pixel: 5m) Air-photo interpretation Field work algorithms Slope angle (continuous) Slope aspect (continuous) CARTHOGRAPHIC DATABASE map Lithology Superficial deposits Geomorph. units Land use Slope profile Slope angle Slope aspect Independent data layers (categorical) SLOPE ANGLE SUPERFICIAL DEPOSITS (degrees) SLOPE ASPECT GEOMORPHOLOGICAL UNITS Flat areas N NE E SE S SW W NW 6
Fanhões River Trancão River LAND USE / VEGETATION COVER morphometric parameters of the Fanhões-Trancão test site types N (%) Mean depth (m) Mean area (m 2 ) Total area (m 2 ) Mean volume (m 3 ) Total volume (m 3 ) Rotational 21 14.3 5.3 6,544 137,415 14,650 307,653 slides Translational 26 17.7 3.4 6,429 167,151 6,699 174,185 slides Shallow transl. 100 68.0 1.0 1,422 142,176 357 35,357 slides Total 147 100.0 2.1 3,039 446,742 3,542 517,195 TRANSVERSAL SLOPE PROFILE SUSCEPTIBILITY ASSESSMENT 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.). LANDSLIDES DATA INTEGRATION DATA INTEGRATION Lithology Superficial Geomorph. Land use Slope Slope Slope deposits units profile angle aspect landslides landslides landslides landslides landslides landslides landslides Rotational slides Translational slides Shallow translational slides 7
DATA INTEGRATION (BAYESIAN PROBABILITY) Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Non-ified susceptibility map (based on the complete landslide data set -100 cases). 0.0767 DATA INTEGRATION (BAYESIAN PROBABILITY) Prior probability of finding a landslide affected area/total area Success-rate curve of the susceptibility assessment based on the complete landslide data set Prior probability of finding a of a layer area/total area Conditional probability of finding a landslide in each, for each layer 1 1 1 area affected area in the DATA INTEGRATION (BAYESIAN PROBABILITY) LANDSLIDE SUSCEPTIBILITY VALIDATION AND CLASSIFICATION Probability of finding a landslide, given n data layers, using the conditional probability integration rule (Chung & Fabbri, 1999): ( )( ) Pp L 1 Pp L 2... Pp Ln Cp ( L 1 Cp L 2 Cp ) Ln Ppslide L n 1 L L L n 1 2 s Data set Validation group [age > 1979] (54 cases) Partition (temporal criteria) Estimation group [age <= 1979] (46 cases) LANDSLIDE SUSCEPTIBILITY MAP Data integration Susceptibility prediction image CLASSIFICATION INTERPRETATION 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 of each layer, and Pp and Ppslide are the prior probabilities of finding, respectively, a and a landslide in the study area. SUSCEPTIBILITY ASSESSMENT AND VALIDATION PREDICTION-RATE CURVE 8
Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Non-ified susceptibility map (based on Estimation Group landslides - age 1979, 46 cases) 0.0634 TRIGGERING ASSESSMENT s in the study area have a clear climatic signal. S.Julião do Tojal Annual precipitation R (mm) 1400 1 1000 800 MAP 600 400 Slope instability events 0 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). Archive investigation Field work Interviews Rainfall analysis (daily data) Percentage of predicted validation group landslides 100 90 80 70 60 50 40 30 20 10 0 I II Prediction-Rate Curve III 8%; 41% 18%; 70% IV 35%; 86% 0 10 20 30 40 50 60 70 80 90 100 Percentage of studyarea predicted as susceptible using estimation group landslides V 84%; 100% Reconstruction of past landslide activity dates Types of landslides type A type B type.. Reconstruction of antecedent rainfall from 1 to 90 days (Px n = P1 + P2 + Pn) Critical thresholds of rainfall (quantity-duration) responsible for landslide events Return periods (Gumbel law) Methodology for rainfall triggering of landslides analysis Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Susceptibility map ified according to the prediction-rate curve. I Top 8% II 8-18% III 18-35% IV 35-84% V 84-100% Predictive value of susceptibility es Susceptibility % I 41 II 29 III 16 IV 14 V 0 Temporal occurrence of rainfall triggered landslides in Lisbon area Critical rainfall Return period Episode Date (yy/mm/dd) amount/duration (years) typology mm (dd) 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) 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 1/01/06 447 (60) 5 c 19 1/01/09 467 (60) 5.5 c 1956-1 typology: a) shallow translational slides b) deep translational slides c) rotational slides d) slope movements triggered by bank erosion e) complex and composite slope movements 9
Shallow translational slides Slope movements triggered by bank erosion Rotational slides Translational slides Complex slope movements Three scenarios for future landslide activity within the Fanhões-Trancão test site based on past landslide events Cumulative rainfall (mm) 900 800 700 600 500 400 300 100 0 0 20 40 60 80 100 Duration (consecutive (days) days) Scenario (1) 1979, February (2) 1983, November (3) 1989, November Critical rainfall amount/duration (mm/days) 128/3 164/1 217/15 Return Period (years) 8.5 4.5 Affected area by shallow translational slides (m 2 ) 44,440 47,125 1,315 landslides no landslides Cr = 6.3D + 70 Age of and total affected areas by shallow translational slides within the Fanhões-Trancão test site. HAZARD ASSESSMENT AT A PROBABILISTIC BASIS Age 1967 or prior 1979, February 1983, November 1987, February 1989, November 1989, December 1996, January N 22 24 40 4 3 3 4 (%) 22.0 24.0 40.0 4.0 3.0 3.0 4.0 Total affected area (m 2 ) 44,017 44,440 47,125 3,283 1,315 996 1,000 (%) 31.0 31.3 33.1 2.3 0.9 0.7 0.7 For each particular triggering scenario, the conditional probability that a pixel will be affected by a shallow translational slide in the future is estimated by: Taffected P = 1 1. pred Ty Where: Taffected = Total area to be affected by landslides in a scenario (x); Total 100 100.0 142,176 100.0 Ty = Total area of susceptibility y pred = prediction value of susceptibility y. Calculation of probabilities for landslide hazard assessment working on a scenario basis General assumption: Probability to each pixel to be affected by a landslide Scenarios 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). susceptibility I - Top 8% II 8-18% III - 18-35% IV - 35-84% V 84-100% Area (number of pixels) (pixel= 5m) 64150 82044 142342 382114 127459 Predictive value of susceptibility 0.4071 0.2885 0.1647 0.1397 0.0000 (1) February 1979 0.0113 0.0062 0.0021 0.0006 0.0000 (2) November 1983 0.0120 0.0066 0.0022 0.0007 0.0000 (3) November 1989 0.00034 0.00019 0.00006 0.00002 0.00000 10
Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Hazard map Triggering scenario (1) 128 mm / 3 days (RP=8.5 years) ROTATIONAL SLIDES Percentage of predicted validation group landslides 100 90 80 70 60 50 40 30 20 10 0 I III II Prediction-Rate Curve IV 0 10 20 30 40 50 60 70 80 90 100 Percentage of study area predicted as susceptible using estimation group landslides V Class Probability by pixel I 0.0113 II 0.0062 III 0.0021 IV 0.0006 V 0.0000 Predictive value of susceptibility es Susceptibility % I 13 II 44 III 11 IV 32 V 0 Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Hazard map Triggering scenario (3) 217mm / 15 days (RP=4.5 years) Percentage of predicted validation group landslides TRANSLATIONAL SLIDES 100 90 80 70 60 50 40 30 20 10 0 I Prediction-Rate Curve III IV V II 0 10 20 30 40 50 60 70 80 90 100 Percentage of study area predicted as susceptible using estimation group landslides Class Probability by pixel I 0.00034 II 0.00019 III 0.00006 IV 0.00002 V 0.00000 Predictive value of susceptibility es Susceptibility % I 61 II 14 III 4 IV 21 V 0 House (500 m2) Shallow translational slides Fanhões-Trancão test site Joint Conditional Probability Function Hazard map WORKING ON A SCENARIO BASIS Scenario January 1996: Critical rainfall amount/duration - 495mm/40 days Return period - 20 years 20( = sizeof house) Taffected P' = 1 1.0.41( = predi ) ( TI ) 64,150 = Taffected = Total area to be affected by landslides in a scenario (x); TI = Total area of susceptibility I; PredI = prediction value of susceptibility 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% Affected area by landslides (m2) Rotational slides 48,127 Translational slides 6, 552 Shallow translational slides 1, 000 11
ROTATIONAL SLIDES TRANSLATIONAL SLIDES SHALLOW TRANSLATIONAL SLIDES susceptibility Area (# pixels) Predictive value of susceptibility Probability to each pixel to be affected by a landslide I - Top 1% 8122 0.1275 0.0302 II - 1-12% 88934 0.4448 0.0096 III - 12-17% 40334 0.1129 0.0054 IV - 17-70% 423765 0.3148 0.0014 V - 70-100% 236954 0.000 0.0000 susceptibility Area (# pixels) Predictive value of susceptibility Probability to each pixel to be affected by a landslide I - Top 9% 72518 0.6080 0.0022 II - 9-13% 32423 0.1418 0.0011 III - 13-17% 33071 0.0426 0.0003 IV - 17-75% 465020 0.2076 0.0001 V - 75-100% 195077 0.000 0.0000 susceptibility Area (# pixels) Predictive value of susceptibility Probability to each pixel to be affected by a landslide I - Top 8% 64150 0.4071 0.00025 II - 8-18% 82044 0.2885 0.00014 III - 18-35% 142342 0.1647 0.00005 IV - 35-84% 382114 0.1397 0.00001 V - 84-100% 127459 0.000 0.00000 TOWARDS LANDSLIDE RISK ASSESSMENT AND MANAGEMENT Temporal dimension Susceptibility Causes Effects Vulnerable elements Typology Value Hazard Intensity Vulnerability Specific Risk Potential loss TOTAL RISK RISK MANAGEMENT adapted from Canuti & Casagli (1994 ) Acceptable Risk 12