Ejemplos de evaluación de riesgo

Transcription

1 Ejemplos de evaluación de riesgo RISK ASSESSMENT RISK = HAZARD * VULNERABILITY * AMOUNT Hazard= PROBABILITY of event with a certain magnitude Vulnerability = Degree of damage. Function of: magnitude of event, and type of elements at risk Amount = Quantification of the elements at risk e.g. Replacement costs of buildings, infrastructure etc. Loss of function or economic activities Number of people

2 Firework explosion Enschede 13 May tons Explosive Size of the disaster area Number of inhabitants in most affected zone Number of completely destroyed houses Number of completely damaged business and industrial buildings Number of houses declared inhabitable Number of damaged houses outside mostly affected zone Number of persons killed Number of persons injured Number of homeless persons Number of persons that had to be evacuated Total mate rial damage 40 ha ± ca ± billion guilders Pre-disaster airphoto 1998 Outer-ring Inner-ring Firework storage

3 Grolsch brewery S.E.F. RISK ASSESSMENT RISK = HAZARD * VULNERABILITY * AMOUNT Hazard= PROBABILITY of event with a certain magnitude Vulnerability = Degree of damage. Function of: magnitude of event, and type of elements at risk Amount = Quantification of the elements at risk e.g. Replacement costs of buildings, infrastructure etc. Loss of function or economic activities Number of people

4 Vulnerability Degree of loss to a given type of elements at risk resulting from the occurrence of a damaging phenomena. Normally expressed on a scale between 0 (no damage) and 1 (complete damage) Determined: Using existing damage reports Using analytical methods What do we normally use: Data from literature Educated guesses Foreign damage datasets: handle with care!

5

6

7 POPULATION VULNERABILITY At home 20:30 08:30 14:30-17:30 At work 9:00 14:00 17:00 20:00 Commuting 8:30 9:00 14:00 14:30 16:30 17:00 20:00 20:30 DENSITY CHANGES (average ITC staff) At home 18:00-8:30 am At work 9:00-17:30 Commuting 8:30-9:00 17:30-18:00 Distribution of People in Census Tract Basic Group 2:00 a.m. 2:00 p.m. 5:00 p.m. Residential 0.99(NRES) 0.80(DRES) 0.95(DRES) Commercial 0.02(COMW) 0.98(COMW) (COMW) 0.15(DRES) (AGE_16) Industrial 0.10(INDW) 0.80(INDW) 0.50(INDW) Commuting 0.01(POP) 0.05(POP) 0.05(DRES) + 1.0(COMM) where: POP is the census tract population taken from census data DRES is the daytime residential population inferred from census data NRES is the nighttime residential population inferred from census data COMM is the number of people commuting inferred from census data COMW is the number of people employed in the commercial sector INDW is the number of people employed in the industrial sector. UNESCO AGE_16 RAPCA is the number of people 16 years of age and under inferred from

8 POPULATION VULNERABILITY POPULATION DISTRIBUTION Table 13.2 INVENTORY VULNERABILITY CASUALTY Table 13.4 Table 13.3 Table 13.5 CASUALTY Table 13.6 Table 13.7 Level 1 Level 1 Residential Population Commercial Population Industrial Population B ldg. Type 1 Damage State 1 Damage State 2 Damage State 3 Damage State 4 Level 2 Level 3 Level 4 No Collapse With Collapse Level 2 Level 3 Level 4 CASUALTY Level 1 Commuting Population Bldg. Type 36 Level 2 Level 3 Bridge 1 Damage State 4 CASUALTY Level 4 Level 1 Bridge 4 Damage State 4 Level 2 Level 3 Level 4 Cost estimation

9 Which costs? Real-estate agencies Market price real Cadastres in most developing countries Ratable price fictitious Engineering societies Construction price replacement With renovation/good maintenance Price m2 Depreciation factor Inflation without renovation/good maintenance Age Source: NYCEM

10 Source: NYCEM Deterministic (Fixed Location) Casualties 2pm earthquake Casualties 4 (Instant Death) # of People CAS 4 or CAS 3 5.0M 6.0M 7.0M At least 24 District Key CAS 4 District Instant # Death 2pm CAS 4 District Instant Death # 2pm % % % % % % % % % < 1% % % % CAS 4 District Instant Death # 2pm % % % % % % % % % % % % % All - All % All %

11 Deterministic (Fixed Location) medical facility functionality people in need of at day 0 (%) hospitalization 5.0M 6.0M 7.0M Essential Facilities Medical Casualties 2 (Hospitalization Required) + Casualties 3 (Immediate Medical Attention) distance to nearest maj or medical facility (meters) 300 1, each dot is 5 five people , Above 4,000 District Key beds av ailable 9,387 Average Functionality 96% Need District Hospital # 2pm All - beds av ailable 6,130 Average Functionality 63% District Need Hospital # 2pm % % % % % % % % % < 1% % % % All % beds av ailable 2,627 Average Functionality 26% District Need Hospital # 2pm % % % % % % % % % % % % % All % Source: NYCEM

12 Source: NYCEM Source: NYCEM

13

14 14

15 Case study Kathmandu Building Damage Ratio in 1934

16 Actual Damage in 1934 From Images of Century Building Damage in 1934

17 Death Toll in 1934 Comparison of Results Damaged Houses Casualties (Death) Actual Calculated Actual Calculated 38,055 35,592 4,296 3,814

18 Building Damage (Number) 1934 EQ (in present) 1934 EQ (actual) Death Toll 1934 EQ (in present) 1934 EQ (actual) 18

19 Comparison of Results Damaged Houses Casualties (Death) Actual in 1934 Calculated in present Actual in 1934 Calculated in present 38, ,474 4,296 19,523 Vulnerability increased! Basic Unit for Analysis

20 Seismic Intensity Map I. Mid Nepal Earthquake Seismic Intensity Map II.North Bagmati Earthquake

21 Seismic Intensity Map III. KV Local Earthquake Liquefaction Potential I. Mid Nepal Earthquake

22 Fragility Curve for this Study Damage(%) PGA ~Intensity Damage is different at each building type!! A++ B B++ K5 K3 A++: ST, AD B: BM B++: BMW, BC K5: (RC5) K3: (RC3) Building Damage Ratio I. Mid Nepal Earthquake

23 Case study Tegucigalpa Digital Elevation Models

24 Digital Elevation Models LIght Detection And Ranging Position of the aircraft + Attitude of the aircraft + Distance between the aircraft and the ground + Angle under which the distance has been measured Digital Elevation Models

25 Lidar can be used to measure building height Lidar and Geometric corrections

26 View 3-D using analgyph image Mapping buildings

27 Mapping damage

28 Case study Turrialba Within: UNESCO programme on Capacity Building for Natural Disaster Reduction, regional action programme Central America. Test site in Turrialba, Costa Rica Objective: provide municipality information on expected losses due to natural disaster, as a basis for risk mitigation Relatively small municipality with limited resources Solution: use of low cost, easy to use system No digital urban data available Solution: use of orthophoto and extensive field campaign using graduate students No detailed hazard information available Solution: use of historical information on events and intensities, through questionnaires Data Input Orthophoto with segments of parcels Point map Seismic events soil map scarps map Field Survey and polygon conversion create table with vulnerability data per building type" Cadastral data join table Link to table with information on each parcel using field observations Attenuation relation relation between distance from epicentrum,magnitude and PGA value PGA values for return period 25,50,100 and 200 years PGA map with soil and topographic amplification Convert to mmi raster maps Soil amplification factors Cadastral map Topographic amplification factors Flowchart Seismic risk Assessment Add information on construction cost for different building types Calculate population densityboth during daytime and nighttime Add information on: minor injuries majo rinjuriesand casualties estimation of market price Apply age depretion factor join table calculate replacement cost by multipliying building area * constructuion cost * damage ratel Specific risk cross map, produce table: mmicomplete (every return period) Esitmation off: * Damage rate * Minor injuriesl * Major injuries * Casualtiesl Legend

29 Stored data Field questionnaires on flood depth Cadastral map Geomorphological map table with building cost informationt Survey on contents costs for buildings Segment map of main river interpolate Flood depth maps for 25, 50 and 75 years return period Link with vulnerability table maximum flood map content cost map Building cost recognize residential content value rasterize distance map Assign classes with probability Flowchart Flood risk Assessment Vulnerability map for each return period maximum flood damage map Lateral damage map damage maps 25,50,75 return period annual exceedance probability legend input data Process Risk analysis * 25,50,75 return period * lateral erosion * maximum flood Elements at risk database

30 Flood vulnerability assessment Flood vulnerability maps for different return periods Flood depth map = flood scenarios of different return periods Map of elements at risk: attribute landuse Degree of loss Vulnerability functions for each landuse Floodwater depths (m) Cost calculation Cost for contents of buildings Size of building Landuse class Social class Number of floors Cost for structure of buildings material type Size Percentage built-up area per plot

31 Cost maps Risk assessment: Probability*vulnerability*cost Risk = probability * vulnerability * cost Generation of risk curves for each hazard type Combine risk curves Information can be derived for: entire city Building block landuse types Public or private losses

32 Case study: San Sebastian, Retalhuleu, Guatemala

33 Growth of San Sebastian City block mapping 33

34 Data collection Damage assessment

35 Stage damage curves Vulnerability map

36 Risk map Vulnerability assessment Loss-Probability Curve Annual Risk Analysis Curve Ris Event k (Annual damages( loss In in million million US$) US$) Exceedence Probability Exceedence Probability Landuse landuse Infra Infrastructure structure Total Total

37 Case study: flood risk assessment objectives Assessing the risk of different flood scenarios for a polder area in the Netherlands due to slow rising due to rainfall or snowmelt (not flash floods). Comparison of the expected losses regarding the land use type and flood magnitude Death by drowning Direct property losses (crops, buildings, main roads/railroads) Determination of the overall annual risk STUDY AREA: Area subject to riverine floods (not coastal floods). Population of approximately 106,000 Amsterdam Population density of 296 /km2. Grassland: 60%, orchards 9%, forest 4%, agriculture 8%, urban areas 5% of the area. BELGIUM 0 100km GERMANY

38 AVAILABLE SPATIAL DATA Digital Elevation Model (DEM), based on the Topographic Map 1: updated using terrestrial measurements Land-use, based on a classification of a Landsat-TM image (1992) and updated for urban land-use Municipality boundaries and population data METHODOLOGY Based on flood depth maps (several scenarios), vulnerability functions, a landuse map, the losses per scenario will be developed Each scenario will be transformed into annual losses (through probability data) in order to produce a final map depicting the total annual losses due to the flood hazard. Annual losses per hazard allow to compare all the hazards and decide which one should be given priority.

39 OVERVIEW OF METHODOLOGY Flood Extent E Hx Landuse consists of Landuse Classes Ly Flood level Z Hx Neighbourhood Funtion Subtraction Flood Depth D Hx Calculation Vulnerability functions F Ly(D) Hazards Hx (x=returnperiod) DEM Vulnerability V Hx Additional Data Exceed. probability P Hx Multiplication Multiplication Total Loss L Hx Loss L Hx Value of Elements at Risk A Annual Risk = * * R Hx Hx Hx Hx FLOOD SCENARIOS G A MAX. DEPTH (cm) 175 RETUR N PERIOD 5 PROBABILITY 0.2 A F B C B E D E C D F G

40 VULNERABILITY FUNCTIONS Scale between 0 (no destruction) and 1(complete destruction) according to the depth of the water. Other flood characteristics, such as duration of the flood, its velocity and sediment load of the water are not considered in this study. Vulnerability Houses Source: S tandar d M ethod (Vrisou van Eck et. al. 1999) Water depth (m) AGRICULTURE AND RECREATION Steeply rising up to 50 cms, with max. at around 4ms Vulnerability Water depth (m) IFF (D<350 MIN (D/ *D/

41 BEETS AND POTATOES Reaches 1 at water depth of 40cm. At 20cm, half of the crop is destroyed Vulnerability Water depth (m) GREENHOUSES Damage increases constantly up to complete destruction at aprox. 1.8m Vulnerability Water depth (m)

42 FOREST Low waters do not have a strong impact on trees. Maximum damage is reached at 3m Vulnerability Water depth (m) ROADS Relatively steep gradient up to 1m, with maximum damage at 5m Vulnerability Water depth (m)

43 HOUSES Little damage until 2 meters, steep raise until around 5 m Vulnerability Water depth (m) HOUSEHOLD GOODS High damage at low depths. Little extra damage between 1 and 2 m there is hardly any increase in vulnerability. From 2 mts onwards, increase in damage Vulnerability Water depth (m)

44 DEATH BY DROWNING Moderate damage up to 1m, high damage from 2 to 6.5m Drown factor Water depth (m) COMBINING VULNERABILTY MAPS landuse map landuse class function In two steps: VulXXXL2 = IFF ((landvul)="va", vulagric(fldxxx), IFF ((landvul)="vg", vulglass(fldxxx), IFF ((landvul)="vf", vulfor(fldxxx), IFF ((landvul)="vr", vulroads(fldxxx), 0)))) Where: XXX refers to the flood level of a flooding scenario. flood depth map The map landvul (based on the land use map) indicates which vulnerability function has to be applied on a land use class. vulagric, vulglass and vulfor are abbreviations for the vulnerability functions. fldxxx is the flood depth layer in each flood scenario. The command finds out for every raster cell in the map landvul the appropriate function to use, applies this function on the flood depth map and the writes the calculated value into the output file VulXXXL2. Values range from 0 to 1.

45 COSTS OF ELEMENTS AT RISK The cost data has to be converted from values per hectare to values (pixel of 936,4m 2 ). Roads: 500,000 Nlg per kilometer. This value is divided by the length of the pixel cell (30.6) leading to 15,300 per cell It will be assumed that the villages are 100% residential. OVERALL ANNUAL RISK The resulting maps are divided by the return period to create the annual losses per scenario. All the annual loss scenarios are added together to obtain the overall annual risk for flooding Event damgages (Mio. NLG) Total damage Urban damage Road damage Agricultural damage 0 0,05 0,1 0,15 0,2 Exceedence probability [1/r]