RISK SCENARIOS. Juan Remondo. DCITIMAC, Universidad de Cantabria, Santander, Spain. Quantitative Landslide Risk Assessment and Risk Management

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1 Quantitative Landslide Risk Assessment and Risk Management Barcelona, 1-4 September 2008 RISK SCENARIOS Juan Remondo DCITIMAC, Universidad de Cantabria, Santander, Spain

2 Geomorphologic hazards and risks represent an increasing concern Medellín, Colombia, 1987

3 Vajont, 1963

4 Villages destroyed: Longarone, Pirago, Rivalta, Villanova, Faé Basso, Vajont Zona Malcom. Codissago, San Martino, Pineda Vajont, Italia, 1963

5 Concern due to subjective causes.. More information about disater ocurrence and greater awareness, particularly in industrialised nations. Cerro Casitas, Nicaragua, 1998

6 And to objective data, such as disaster damages

7

8 Specialists in this field are devoting considerable efforts to assess hazards and risks. This implies developing and applying tools and models to look into the future. Santander, 1986

9 Usual conceptual framework for making forecasts about geomorphic processes based on the uniformitarian principle Modeling time analysis present prediction R = f (H, E, V)

10 Increasing risks should be expected -other things being equal- due to growth of population + wealth (greater exposure). Translation of panel: Plot purchased by the municipality of Santa Cruz de Bezana for the construction of a new public school Soto de la Marina, Cantabria, Spain, 80 s

11 More exposure and similar hazard = Greater risk Easy to predict result Soto de la Marina, Cantabria, Spain, 90 s

12 Remedial measures: Expenditure to correct former mistakes far exceeds the cost of the element to be protected. Soto de la Marina, Cantabria, 2005

13 Is that type of action the main explanation of the trends shown? What do global figures suggest?

14 World population (billions) Population (million*1000) GEO 97 Year , approx. x 2.3

15 World energy consumption Energy consumption (exajoule) GEO 97 Year , approx. x4

16 World GDP 4,00E+07 3,50E+07 GDP (millions 1990G-K dollars) 3,00E+07 2,50E+07 2,00E+07 1,50E+07 1,00E+07 5,00E GEO 97 Year , approx. x7

17 Natural hazards events Munich Re, , approx. x9

18 Munich Re, 2004 World losses due to natural hazards , approx. x25

19 Figures presented indicate a clear improvement in the management of productive systems: - greater economic output per unit energy spent and per person.and considerable worsening in our management of natural risks: - catastrophic events grow clearly more than socioeconomic drivers - damages more than triple GDP growth To what extent can we consider valid the normal uniformitarian assumption?

20 Why risk scenarios? R = H x V x E Risk means prediction (future) The future H, V and E are uncertain Where?, when?, run-out, magnitude?, etc. Probability of loss Location of future elements?, future indirect costs?, etc. Therefore, scenarios are needed

21 Pérdidas estimadas para el conjunto de los riesgos en España (Ayala et al., 1987). 1) nulas; 2) de 0 a 100 millones de pesetas; 3) de 100 a millones de pesetas; 4) de a millones de pesetas; 5) más de millones de pesetas.

22 R = f (H, E, V) Modeling time analysis present prediction What we would like to predict: Where, what magnitude, how often? (hazard assessment) When next? (prediction of next event; alert and alarm systems) How much? (risk assessment; risk reduction) Can we answer satisfactorily? Do we know the reliability of our forecasts?

23 WHAT? Identification of Past Mass Movements Inventory of Landslides & Conditioning Factors Inventory of Exposed Elements Identification & Classification of Exposed Elements Magnitude & Run-out distance Scenarios WHAT IS AFFECTED? WHERE? Inventory of Damage in the Past Statistical Analysis Susceptibility Models Exposed Elements Value Maps HOW MUCH DAMAGE? HOW OFTEN? Vulnerability Models Direct & Indirect Losses Estimation Hazard Scenarios Hazard Models CONCEPTUAL METHOD FOR LANDSLIDE RISK ASSESSMENT INTEGRATION RISK? Specific Risk Models Validation Total Risk Models

24 Causal factors Occurr. Exposed elements Type movement Scenario Probabilistic modeling SDA Susceptibility Value assessment Vulnerability assessment Future frequency Scenario Landslide frequency analysis Spatial-temporal probability Magnitude Scenario Volume, velocity, run-out distance Hazard E x V x H = + Rs - INFRASTRUCTURES Rs -LAND Socio-economic analysis + Rs - BUILDINGS Indirect effects scenario Indirect Risk TOTAL RISK = Direct Risk

25 Study area gbilbao gsan Sebastián VIZCAYA GUIPÚZCOA ÁLAVA g Vitoria

26 Detailed inventory of past landslides Shallow translational slides (debris slides) 8 temporal data sets ( ) Characteristics: Landslides Pre Mean landslide size = 510 m 2 Mean failure zone size = 160 m 2 Mean thickness = 1.2m 2 Mean landslide dimension in the sense of the movement = 29 m Mean landslide dimension perpendicular to the movement = 24 m Mean volume = 250 m 3 Velocity : rapid-extremely rapid 16 Thematic layers (conditioning factors according to the conceptual physical model) Terrain geometry, hydrologic conditions, land-use and lithology Susceptibility analysis (SPM) Validation mainly Favourability Functions

27 R = value (cost) x Prob{D L G} ; assuption: No. events (S) Prob{D L G} = Prob{(D L) G} x Prob{L G} = Prob{D L} x Prob{L G} R = value x Prob{D L} x Prob{L G} = E x H x V Prob{D L G} is the probability that the element will be damaged, if the location p is affected by the occurrence of a landslide and knowing the causal factors at p Prob{D L} is the probability that the element will be damaged, if the location p is affected by the occurrence of a landslide Prob{L G} is the probability of the occurrence of a future landslide knowing the causal factors at p

28 Favorability Functions A function g (.) is constructed in each pixel: g (Y=1 : m causal factors) Y=1: p is a landslide g (c 1,, c m ) represents a relative level of susceptibility in the pixel, given the m values (c 1,, c m ) If the m values (c 1,, c m ) in pixel i have conditions more prone to landsliding than those in pixel j, (d 1,, d m ), then g i (Y=1 c 1,, c m ) > g j (Y=1 d 1,, d m )

29 Three mathematical frameworks: g (Y=1 c 1,, c m ) 1. Theory of Probability Joint Conditional Probability Likelihood ratio CF WoE 2. Zadeh s Fuzzy Set Theory Membership Fuction 3. Evidential Dempster-Shafer Theory

30 g (Y=1 c1,, cm) : function understood as a likelihood ratio function M p : p of the area affected by landslides, M M p : p of the area not affected by landslides, M ƒ{c 1,, c m M p }: Empirical distribution function of the area affected by landslides ƒ{c 1,, c m M p }: Empirical distribution function of the area not affected by landslides Likelihood ratio highlights the differences between two functions ƒ{c 1,, c m M p } λ p = ƒ{c 1,, c m M p }

31 FF: ratio likelihood Empirical distribution functions Landslides No Landslides X X Landslide area No landslide area λp( slope ) = g{slope X} g{slope X }

32 Prediction models for several layers (continuous and thematic) Likelihood ratio function λ p (c 1,, c m ) = ƒ{c 1,, c m M p } ƒ{c 1,, c m M p } Assuming that (c 1,, c k ), (c k+1,, c m ) are independent λ p (c 1,, c m ) = λ p (c 1,, c k ) λ p (c k+1,, c m ) = ƒ{c 1,, c k M p } ƒ{c k+1,, c m M p } ƒ{c 1,, c k M p } ƒ{c k+1,, c m M p }

33 Landslides No Landslides SUSCEPTIBILITY

34 Different statistical techniques has been applied and evaluated. Different methods produce similar results.

35 Portion of predicted landslides 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 Model After 2 years prediction After 10 years prediction After 15 years prediction 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 Portion of the study area Validation rate curves of landslide susceptibility model elaborated on the basis of movements using slope gradient, elevation, aspect, lithology and vegetation as variables.

36 The quality or predictive value of the model remains the same for the different time spans considered. Uniformitarian assumption appears to be valid with respect to susceptibility (differences in terrain behaviour within the study area do not seem to change with time). Is that assumption also valid for hazard?

37 Transformation of susceptibility into hazard maps requires data on the temporal occurrence of landslides, so that relative spatial probability classes can be transformed into temporal probability for each point (pixel).

38 Climate data for the area does not explain the trend shown Total rainfall Total 1945 Total 1948 Total 1951 Total 1954 Total 1957 Total 1960 Total 1963 Total 1966 Total 1969 Total 1972 Total 1975 Total 1978 Total 1981 Total 1984 Total 1987 Total 1990 Total 1993 Total 1996 mm año Título del gráfico nº días >50 mm Nº tormentas No. storms No. days with rainfall > 50mm año año

39 Temporal frequency Direct: extrapolating from past occurrences Indirect: based on the frequency of the trigger Cronology based on geomorphological relationships, radiometric and archaeological dating, etc.

40 Deslizamientos superficiales 1950 Deslizamientos superficiales 1970 Deslizamientos superficiales 1983 Deslizamientos superficiales N N N N m m m m Pre Deslizamientos superficiales 1991 Deslizamientos superficiales 1993 Deslizamientos superficiales N N N Dating from aerial photos m m m

41 Monitoring and prediction

42 It rather appears to be due to human factors (increasing human intervention derived from growing GDP) ,3 PIB (millones de pesetas constantes) PIB Tasa GDP Landslide rate 0,25 0,2 0,15 0,1 0,05 Tasa de movilización (mm/año) año PIB (millones de pesetas GDP y = Ln(x) + 1E+06 R 2 = 0,7331 Landslide rate 0 0,05 0,1 0,15 0,2 0,25 0,3 Tasa de movilización (mm/año)

43 (EM-DAT, 2005) Number of landslides in Bajo Deva area a) (Remondo et al., 2005) Year Number of andslides events in Italy (Guzzetti & Tonelli, 2004) Year

44 Landslide frequency can be established for the different susceptibility classes. But what frequency should we use for our forecasts? POSSIBLE SCENARIOS: A) Next 50 years similar to last 50 years; B) Next 50 years similar to most recent period; C) Future frequency will increase according to trend shown

45 HAZARD ASSESSMENT Time interval Number/year Pre /? years Landslide frequency in the Deva area. Three scenarios: A) No. landslides next 50 years = past 50 years; B) No. landslides year = last 5 years; C) No. landslides increasing yearly according to past trend. No. landslides year Prob. C Prob. B Prob. A year Area that will be affected by landslides Extrapolations of landslide spatial-temporal probability for scenario A. Suscept. Class % lands. in class Corrected % (fit) Prob. A (50 years)

46 SCENARIO A SCENARIO B 7,6 21,5 2,0 2,0 SCENARIO C 50 years prediction 44,3 2,0

47

48 If, as suggested by data presented, landslide frequency is increasing as a consequence of growing human intervention, driven by population+technology+wealth, the uniformitarian assumption would not be valid with respect to hazard. The process would be much more active in the future, as a consequence of both direct (landslides triggered by human action) and indirect (decreasing terrain resilience due to widespread changes in the surface layer) human influence. If that is correct, the pessimistic (extrapolated trend) rather than the uniformitarian (frequency as in the past) scenario would be more likely. Much greater hazard in the future than in the past?

49 Frequency of future landslides can also be established on the basis of relationships with the frequency of triggering rainfall events. Climate change trends can be incorporated into forecasts. If the type of relationship suggested by data presented is correct, projections on the basis of future rainfall would also considerably underestimate future hazard, unless the likely reduction of surface layer resilience were incorporated.

50 Uncertainties are even greater for risk assessment. R = f (H, E, V) To assess risk we need data on exposure (value of material elements and economic activities potentially affected) and vulnerability (degree of destruction of that value in case of a hazardous event). How good are data/estimates about those? Again, is the uniformitarian assumption reasonable?

51 buildings VULNERABILITY ASSESSMENT land

52 VULNERABILITY ASSESSMENT Hazard analysis Scenarios type movement/magnitude/element Past Ocurrences Analysis Process Damage Infrastruc.Buildings Specific losses ( /pixel) Land People Activities expert s knowledge Inventory Infrastruc. Buildings = = Vulnerability (0-1) Vulnerable Elements Land = People No Activities alternative Value assessment ( /pixel)

53

54 Infrastructure Buildings Land use

55 Type Value ( /m) Losses ( /m) Vulnerability (0-1) Railway track Local road Regional road-b Regional road-a National road Motorway Removal and transportation of land Retaining wall 1176 Resurfacing works 173 Labour 190 Totalcosts per meter 92.5 Building Id Market value ( /m 2 ) Losses ( /m 2 ) Vulnerability (0-1) Removal and transportation of land Labour Total costs by landslide 1, Land Market value ( /m 2 ) Losses ( /m 2 ) Vulnerability (0-1) Built-up area (unproductive) Water (unproductive) Rock (unproductive) Grasslands Pasturelands Scrubland Hawthorn land New reforestations, soil Coniferous reforested degradation, loss of Cultivation: fruit tree agricultural production, etc.

56 Vulnerability assessment empirical, with high uncertainty. Diificult to make realistic assessments of degree of damage for every element due to a future, unknown event. Many future vulnerable elements (structures and activities) do not exist now. Not only hazard but exposure is likely to increase, but we do not know how much. Population and GDP trends indicate that exposure will grow significantly in the future, but extrapolations have a limited value, especially at detailed levels (pixel). Does this mean that risk assessment is pointless?

57 RISK ASSESSMENT Risk ( /pixel/50 y) = Hazard (0-1/50 y) x Element cost ( /pixel) xv (0-1) (Losses ( /pixel) /Cost ( /pixel) ) H E V R Risk = E (1-(1-H 1 V 1 ) (1-H 2 V 2 ) (1-H n V n ))

58 DIRECT RISK = Rs infra + Rs buildings + Rs landuse + + = Specific risk: Infrastructures Specific risk: Buildings Specific risk: Land =

59 Risk model ( / m2 / 50 years). Pixel = 1 m2

60 INDIRECT RISK The interruption of infrastructures is the main effect of landsliding on economic activities in the study area: Losses due to loss of working time ( t) Losses due to distance increase ( d) t Type of infrastructure Interruption time (hour) Increase of distance (km) d + t sector b Railway Track Local roads sector a Regional roads -B Regional roads -A No. workers = No. vehicles* 1.55 persons/vehicle*rate of employment (45.5%) National road Motorway Worker cost = 10 /hour Potential Losses by MOTORWAY sector = [No. workers*worker cost*interruption time] Potential Losses by NATIONAL ROAD sector = [No. workers*worker cost*interruption time] + [additional length of the alternative road*no. of vehicles*cost /km] Potential Losses in RAILWAY = {[No. workers*worker cost*time delayed]} + cost of alternative transportation in buses INDIRECT RISK = HAZARD * INDIRECT POTENTIAL LOSSES

61 Indirect Risk Direct Risk

62 TOTAL RISK = DIRECT RISK + INDIRECT RISK Specific risk, direct risk, indirect risk and total risk for the different scenarios considered ( /50years). Risk Risk ( ) Scenario A Risk ( ) Scenario B Risk ( ) Scenario C Infrastructure 10,864,172 27,042,571 68,380,558 Land 569,439 1,399,855 3,417,604 Buildings 15,269 38,296 98,606 Direct 11,448,880 28,480,722 71,896,768 Indirect 2,174,220 4,407,944 12,329,452 TOTAL RISK 13,623,100 32,888,666 84,226,220

63 Type of infrastructure Risk ( ) Scenario A Risk ( ) Scenario B Risk ( ) Scenario C Motorway 81, , ,626 National road 519,210 1,267,703 3,034,251 Regional roads Local road Railway track Total 478,744 9,527, ,434 10,864,17 2 1,217,126 23,725, ,956 27,042,571 3,262,213 60,057,34 2 1,438,126 68,380,55 8 R = E x H x V Type of infrastructure Motorway National road Regional Regional road-1 Railway road-2 track Local road Minimum Vulnerabilit 0.00 y Maximum Vulnerabilit 0.04 y Medium Vulnerabilit 0.02 y

64 H is uncertain and seems to be increasing V is uncertain but might be reduced Future E are unknown to a great extend useful for planning Mitigation strategies are facilitated if prediction on the future distribution of landslides and damages are made in quantitative terms (understandable for comparison) communication strategies uncertainty must be clearly incorporated (evaluation)

65 Número de eventos No. events Año (EM-DAT, 2005) Number of andslides events in Italy Number of landslides in Bajo Deva area a) Year (Guzzetti & Tonelli,, 2004) Year (Remondo et al., 2005)

66 IS THIS THE CHAIN OF INCREASING EFFECTS? Population + Wealth + Technology Greater intervention on geomorphic systems Changes in process behaviour, system s resilience, exposure and vulnerability of elements Frequency and/or intensity of hazardous events and damages per event

67 Is perhaps that the explanation for the increasing number of geomorphic disasters? Is there a coupling between economic development and geomorphic hazards, as in the case of global warming? If so, what should we do to produce a decoupling between both? A kind of Kyoto Protocol to reduce the geomorphic dimension of global change? Venezuela, 1999

68 CRED, 2005

69 CRED, No. of Landslides Year

70 If that coupling is true: Observed trends in number of geomorphic disasters might be the result of a combination of global climate AND geomorphic change, both driven by population and economic growth. The assumption that behaviour of geomorphic processes (hazard) in the future can be anticipated on the basis of past/present behaviour - and even more so risk - may not be valid. We should better understand relationships between economic and geomorphic processes and find procedures to incorporate such coupling into models to improve forecasts.

71 SOME FINAL REMARKS We have reasonable methods to assess geomorphic hazards. We can quantitatively determine the reliability of our models and validity of our predictions. Uniformitarian assumption seems to be valid concerning susceptibility, but not hazard quantitatively expressed. Risk assessment includes considerable uncertainties and the uniformitarian assumption is even more doubtful. The possibility of coupling between economic development and geomorphic processes should be explored and incorporated into models to improve our predictions. It appears that measures should be taken to curb the effects of the global geomorphic change scenario.