Physical exposure identification and mapping ... - IMPROVER Project

Physical exposure identification and mapping methodologies Pierrick Mindykowski1 Dániel Honfi1 David Lange1 Johan Sjöström1 Gonçalo Rodrigues Cadete2 Elisabete Carreira2 Christian Bouffier3 Laurent Cauvin3 Auxane Cherkaoui3 Matthieu Landes4 Laura Petersen4 1. SP Technical Research institute of Sweden 2. INOV 3. INERIS 4. EMSC

Deliverable Number:

D3.1

Date of delivery:

August 31, 2016

Month of delivery:

M15

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 653390 Coordinator:

David Lange at SP Sveriges Tekniska Forskningsinstitut (SP Technical Research Institute of Sweden)

Physical exposure identification and mapping methodologies

Table of Contents 1

Executive Summary

3

2

Nomenclature

4

3

Introduction

6

3.1 3.1.1 3.1.2 3.1.3 3.1.4

Existing legislation National risk assessments Water framework directive EU Flooding directive SEVESO II Directive

8 8 8 8 8

4

Hazard mapping

10

4.1

General methodologies

10

5

Natural hazards

13

5.1 5.1.1 5.1.2 5.2 5.2.1 5.2.2 5.3 5.3.1 5.3.2 5.4 5.4.1 5.4.2 5.5 5.5.1 5.5.2 5.6 5.6.1 5.6.2 5.7 5.7.1 5.7.2 5.8 5.8.1 5.8.2

Heat wave, cold wave and snow storm Definition of the hazard Examples of heat wave mapping Drought Definition of the hazard Examples of drought mapping Storm surge / coastal flooding and coastal erosion Definition of the hazard Examples of storm surge / coastal flood mapping Earthquake Definition of the hazard Examples of earthquake mapping Landslide Definition of the hazard Examples of landslide mapping Extreme winds Definition of the hazard Examples of extreme wind mapping Lightning Definition of the hazard Examples of lightning hazard maps Heavy rain and flooding Definition of the hazard Examples of flood hazard mapping

13 13 14 15 15 16 17 17 17 19 19 20 21 21 21 21 21 22 23 23 23 24 24 24

6

Technological hazards

29

6.1 6.2 6.3

Risk mapping of technological hazards Technological risk maps Wildfire

29 30 32

7

Summary of indicators

34

8

Linking hazard mapping with critical infrastructure

35

8.1 8.2 8.3

Risk assessment of critical infrastructure Response of CI to specific hazard levels Link between hazard maps and CI

35 37 42

9

Conclusions

45

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Deliverable number: D3.1

Executive Summary

IMPROVER is a Horizon 2020 project focusing on how to improve European critical infrastructure resilience to crises and disasters through the implementation of resilience concepts to real life examples of pan-European significance, including cross-border examples. The project is developing methodologies for the implementation of societal, organisational and technological resilience concepts to critical infrastructure. This requires that relevant hazards should be identified and linked to different types of critical infrastructure in a consistent and appropriate manner. This report outlines the fundamentals of hazard mapping, beginning with the 4 steps needed for hazard mapping: 1) the identification of the hazard in question and the region; 2) the specification and temporal description of the hazard; 3) the definition of the probability of occurrence of the hazard; and finally 4) the definition of the hazards’ intensity. Examples of this applied to various hazards are given. The output of these stages results in an intensity measure, expressed in one or many forms depending on the hazard. The report introduces intensity measures for a number of different natural and technical hazards, of interest for the IMPROVER living labs. The link between hazard maps and critical infrastructure is proposed to be, e.g. some of the indicators of resilience identified elsewhere in the project, some of these are level 2 indicators in the IMPROVER CIRI framework. Although this identification is specific to the project in question, they are generic indicators which are used in many methods for determining the resilience of critical infrastructure. The point being that the calculation or quantification of these indicators depends upon the outcome of the risk assessment and the resulting hazard assessment being undertaken.

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Nomenclature

CIMS

Critical Infrastructure Modelling Simulation

CIPDSS

Critical Infrastructure Protection Decision Support System

CIPMA

Critical Infrastructure Protection Modelling and Analysis

CMI

Crop Moisture Index

CRAF

Coastal Risk Assessment Framework

DEM

Digital Elevation Model

DPM

Damage Probability Matrix

EC

European Commission

ECMWF

European Centre for Medium-Range Weather Forecasts

EFEHR

European Facility For Earthquake Hazard And Risk

EFFIS

European Forest Fire Information System

ESHM13

The 2013 European Seismic Hazard Model

EU

European Union

FAIT

Fast Analysis Infrastructure Tool

FWI

Fire Weather Index

GIS

Geographic Information System

k

population density and the socio-economic significance of the function of the building

LST

Land Surface Temperature

MIN

Multilayer Infrastructure Network

N-ABLE

Next-generation Agent-Based Economic Laboratory

NEMO

Net-centric Effects-based operations MOdel

PDSI

Palmer Drought Severity Index

PGA

Peak Ground Acceleration

RISC-KIT

Resilience-Increasing Strategies for Coasts – Tool kit

RVA

Risk and Vulnerability Analysis

SPI

Standardised Precipitation Index

SWSI

Surface Water Supply Index

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WFD

Water Framework Directive

WHO

World Health Organization

Danger: a qualitative description of the combination of the intensity of an event and the probability of the event occurring. Hazard: An accidental or naturally occurring phenomenon with the potential to cause physical or psychological harm to humans including loss of life, damage or losses of property, and/or disruption to the environment or to structures (economic social, political) upon which a community's way of life depends. Reanalysis: the use of forecast models and data assimilation systems to 'reanalyse' archived observations, creating global data sets describing the recent history of the atmosphere, land surface, and oceans. Reanalysis data are used for monitoring climate change, for research and education, and for commercial applications.

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Physical exposure identification and mapping methodologies

Introduction

3

The current report is prepared within the IMPROVER (Improved risk evaluation and implementation of resilience concepts to Critical Infrastructure) project as Deliverable 3.1 and is an outcome of Task 3.1. The objective of this task is to create a link between critical infrastructure and different hazard maps according to different scenarios (e.g. natural hazards: floods, earthquakes, landslides, forest fires, meteorological extreme events due to climate change; man-made hazards: terrorist attacks, industrial accidents etc.). The maps may later be used to indicate the intensity of a potential incident as a function of a return period as part of a probabilistic hazard analysis to be implemented within the overall IMPROVER approach to operationalising resilience concepts to critical infrastructure. This work is part of task 5.1 which aims to develop a risk based approach for implementation of resilience concepts to critical infrastructure. With this methodology the potential consequences associated to different scenarios could be mapped. The work described in this report draws heavily on previous work, in particular as a basis the Risk Assessment and Mapping Guidelines for Disaster Management1, as well as other Horizon 2020 and FP7 European projects. The methodologies1 are based on risk assessment incorporating three steps: risk identification, risk analysis and risk evaluation. The actions that are involved are described in the EC guidelines1. In Figure 3.1, the three steps of risk assessment are shown:   

Risk identification; the process of finding, recognizing and describing risks2 Risk analysis; the process of comprehending the nature of risk and to determine the level of risk3 Risk evaluation; the process of comparing the results of risk analysis with risk criteria to determine whether the risk and/or its magnitude is acceptable or tolerable4

1

Risk Assessment and Mapping Guidelines for Disaster Management, SEC, 2010. ISO.IEC 31010:2009 – Risk management – Risk assessment techniques 3 Idem. 4 Idem. 2

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Figure 3.1 The risk assessment process according to the EC guidelines5. The risk assessment process relies heavily on the idea of hazards. According to the CIPRNet projects CIPedia6, a hazard may be defined as “an accidental or naturally occurring phenomenon with the potential to cause physical or psychological harm to humans including loss of life, damage or losses of property, and/or disruption to the environment or to structures (economic social, political) upon which a community's way of life depends.” At the beginning of the risk assessment process, three preliminary steps must be made as part of the “establishing the context” step:   

Selecting the same target area for the studied hazard; Selecting the same time window for the studied hazard; Defining the same metric for the risk (impact measures).

When these three steps have been carried out, the risk identification can be started. Risk identification can be seen as a screening exercise, serving as a preliminary step for the subsequent risk analysis stage. This report focusses on the risk identification and risk analysis aspects of the risk assessment process – the latter stage of risk evaluation will be discussed in WP5 of the IMPROVER project. Risk identification is based on quantitative data. The main outcome of this process is risk scenarios, which are “representations of one single-risk or multi-risk situation leading to significant impacts, selected for the purpose of assessing in more detail a particular type of risk for which it is representative, or constitutes an informative example or illustration”1. The risk scenarios may take into account quantitative data as well as qualitative, and are used in the risk analysis stage.

5

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COMMISSION STAFF WORKING PAPER Risk Assessment and Mapping Guidelines for Disaster Management Brussels, 21.12.2010 SEC(2010) 1626 final . CIPedia; https://publicwiki-01.fraunhofer.de/CIPedia/index.php/CIPedia%C2%A9_Main_Page; accessed 5th October 2016

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Physical exposure identification and mapping methodologies Hazards are often characterised by some measure of their intensity, referred to as an intensity measure. This may be some qualitative or quantitative measure of the degree of magnitude of a hazard event, examples of which are discussed in the sections below. Hazard maps therefore illustrate, for some intensity measure, the geographic distribution of the magnitude of the hazard. Often, as will be discussed below, this is done for a specific return period which can be varied to represent events of varying likelihoods (an intensity of a hazard with a short return period of exceedance has a higher likelihood of occurring than an intensity of a hazard with a long return period of exceedance).

3.1

Existing legislation

3.1.1

National risk assessments

In 2010, the European Commission prepared guidelines on risk assessment based on research and good practice examples. EU Member States agreed to prepare or update their own national risk assessments and to share the results of those 28 EU Member States as well as 4 third countries participating in the Civil Protection Mechanism (Norway, Iceland, Liechtenstein and the Former Yugoslav Republic of Macedonia). 3.1.2 Water framework directive Drought hazard is a part of the River Basin Management Plans under the EU Water Framework Directive (WFD)7. 3.1.3

EU Flooding directive

The EU Flooding Directive8 specifies that flood hazard maps will identify areas with a medium likelihood of flooding (at least 1 in 100 years event) and extremes or low likelihood events, in order to build the hazard extent maps. For example, in Catalonia, Spain9 the coastal inundation risk mapping has been done for 2 return periods, of 100 (medium likelihood) and 500 (low likelihood) years. 3.1.4

SEVESO II Directive

The SEVESO II Directive10 is of special importance in the context of industrial accidents (which fall under the definition of technological hazards and are described in more detail in section 6 later). The Directive deals with the presence of dangerous substances in establishments, covering industrial activities as well as the storage of dangerous chemicals. Its name is a reminder of the catastrophic accident in the Italian town of Seveso, in 1976. The Directive is integrated with other EU policies, namely the related policy areas11:   

Classification, labelling and packaging of chemicals; The Union's Civil Protection Mechanism; Protection of critical infrastructure;

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Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy, Official Journal of the European. 8 EC: Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks. Official Journal L 288, 06/11/2007, 27-34 9 ACA (Water Agency of Catalonia) Mapes de perillositat i risc d'inundació del districte de conca fluvial de Catalunya. Memòria. Generalitat de Catalunya, Barcelona, 2014. 10 See http://ec.europa.eu/environment/seveso/ . 11 Idem.

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Policy on environmental liability and on the protection of the environment through criminal law; Safety of offshore oil and gas operations.

In addition to operators, public authorities must also comply with certain obligations to inform the public. Also, land-use planning policies shall ensure that “appropriate distances between hazardous establishments and residential areas are maintained”12. Note that the Barreiro living lab includes significant SEVESO infrastructure areas and is therefore a good example to illustrate opportunities for mapping technological hazards. This will be discussed in more detail later on.

12

Idem.

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Physical exposure identification and mapping methodologies

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Hazard mapping

4.1

General methodologies

For the production of hazard maps, four steps should be followed. Step 1: Identification of the hazard and geographical analysis (location, extent) The type of the hazard to be studied needs to be identified and characterized by specific traits. Its identification should be justified (for instance: dominant hazard in the studied area). Care should be taken with the identification of the geographical location of the hazard - the geographical location should always be conservative when considering the target of the risk assessment process. Placing this step in the context of the rest of the IMPROVER project, this first step is dependent upon the context of the critical infrastructure analysis, as discussed in deliverable 2.2; as well as the agreed upon hazards of interest defined according to, e.g. national risk assessments, traditional hazard identification methodologies or the expert elicitation process proposed in deliverable 2.1. Step 2: Specification and description of the hazard by temporal analysis (frequency, duration) Here, the hazard is assessed by looking at how often it has occurred historically, if such data exists. This gives data about the frequency, likelihood or return period of a hazard (likelihood can mean either quantitatively measured probability or qualitatively judged plausibility of an event occurring). An analysis of past accidents can also provide information about the variation of the intensity of the hazard with the likelihood. Step 3: Definition of the probability of occurrence of the hazard There are three different approaches for estimating the likelihood of an event:   

Reanalysis of historic data, Probability forecasts, Expert opinion.

The choice of which of these three approaches should be used depends mainly on the quality of the available historical data and the considered hazard. For instance, it is difficult to have a quantitative probability of occurrence for the hazard of landslide hazards, as will be discussed below5. Table 4.1 provides some examples of how the probability of occurrence can be expressed – either as quantitative descriptions of the likelihood, or frequency; or as ranges of recurrence or annual probability of exceedance.

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Table 4.1 Different expressions of frequency/probability of occurrence adapted from AEMC13 Average Annual Exceedance Frequency Likelihood Level Recurrence Interval Probability Almost certain Once or more per year < 3 years > 0.33 Likely Once per ten years 3 - 30 years 0.033 – 0.33 Possible Once per hundred years 31 – 300 years 0.0033 – 0.033 Unlikely Once per thousand years 301 – 3 000 years 0.00033 – 0.0033 Once per ten thousand Rare 3 001 – 30 000 years 0.000033 – 0.00033 years Once per hundred 30 001 – 300 000 Very Rare 0.0000033 – 0.000033 thousand years years Less than once per Almost incredible > 300 000 years < 0.0000033 million years

Step 4: Definition of the intensity of the hazard As discussed in the introduction, the intensity of a hazard describes the magnitude of a credible scenario. It is usually determined based on extrapolation of historical data or the results of some other analysis. From these four steps, maps can be used to represent hazards in four different ways:  

 

The basic hazard map showing unique data points of hazard intensity at a given time. The extent map, which is the most common type of hazard map. The difference between the extent map and the basic hazard map is that data are not presented as unique data points but as a continuous area in the extent map. Both the basic hazard map and the extent map can be based on a historical event, but also hypothetical events with specific return periods. When the extent map is calculated for specific return periods, intensity of the hazard can also be calculated which gives the intensity map. In order to have an impression of the overall hazard, parameters (as for instance, probability and intensity in Figure 4.1) are aggregated into qualitative classes in a danger matrix, in order to represent the danger map.

13

AEMC 2010. National Emergency Risk Assessment Guidelines. Australian Emergency Management Committee, Emergency Tasmanian State Emergency Service, Hobart.

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Figure 4.1 Danger matrix. The overall view of the hazard mapping process is summarised in Figure 4.2.

Figure 4.2 Hazard mapping framework

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Natural hazards

5

In the IMPROVER project, four regions have been identified and are the focus of the projects’ research, Table 5.1. A preliminary study has been performed in order to identify the main hazards for each of these living labs, summarised in Table 5.2. Table 5.1 List of the living labs Living lab 1 2 3 4

Sjursøya region in the port of Oslo, Norway Infrastructure of the Öresund region, Sweden A31 Highway in France Water Supply System in Barreiro, Portugal

Table 5.2 Main natural hazards for the four living labs Water Supply Öresund Natural Sjursøya A31 Highway System in region hazards Barreiro Snow storm X X X Heat wave X X X Cold wave X X X Drought X Heavy rain and X X X X flooding Storm surge/Costal X X X X flooding Earthquake X X X X Tsunami X Landslide Costal erosion Extreme winds Lightning

X X

X X

X X

X

For each of the natural hazards involved in at least one living lab, a succinct description of the hazard and some examples of hazard mapping is given below. A deeper review is done for the case of heavy rain and flooding.

5.1

Heat wave, cold wave and snow storm

5.1.1

Definition of the hazard

According to the European Commission14, a heat wave is defined as a lengthy period of extraordinarily hot and/or humid weather patterns. Cold waves are often associated with extreme winter conditions defined as 'damage caused by snow and ice' 15. A more exact definition proposed by the World Health Organization for a heat wave or a cold wave is a period when the maximum or minimum apparent

14

Overview of natural and man-made disaster risks in the EU, SWD (2014) 134.

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Physical exposure identification and mapping methodologies temperature is over or under the 90th percentile of the monthly distribution for at least two days15. Following this definition, the way to map the heat wave and cold wave is the same. Weather stations are often located in sparsely inhabited areas and are therefore not necessarily representative of the local heat or cold experienced in residential settings. This is relevant in case the target of the risk assessment is a society or the populace of a region. Because of this, remote sensing satellites16 are increasingly used to assess the thermal exposure during either a heatwave or a coldwave17, 18, 19. 5.1.2

Examples of heat wave mapping

In the following, two examples of heat wave hazard maps are presented: the first one (Figure 5.1) is an extent hazard map of historical data, showing land surface temperatures under heatwave conditions at the pixel level on June 22, 2001, at 10:43 AM UTC from a Landsat ETM + image of the city of Rennes, in France. In this example temperature is used as the measure of intensity. Figure 5.2 shows the same data with qualitative temperature banding as intensity measure under heatwave conditions at the IRIS level from the same date and location.

Figure 5.1 Heatwave extent map with land surface temperature as intensity measure20, reproduced with permission

15

Annual report 2010 and Environmental statement 2011, European Environment Agency. U.S. National Oceanic and Atmospheric Administration satellite, the land surface temperature (LST) was estimated from Landsat Enhanced Thematic Mapper. 17 Stathopoulou M, Cartalis C: Daytime urban heat islands from Landsat ETM + and Corine land cover data: an application to major cities in Greece. Sol Energy. 2007, 81: 358-368. 18 Dousset B, Gourmelon F: Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS J Photo Remote Sens. 2003, 58: 43-54. 19 Gallo K, Tarpley D, McNab A, Karl T: Assessment of urban heat islands: a satellite perspective. Atmos Res. 1995, 37: 37-43. 20 Buscail C., Upegui E., Viel J.F.: Mapping heatwave health risk at the community level for public health action, International Journal of Health Geographics 2012, 11:38. 16

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Figure 5.2 Extent hazard map of qualitative temperature banding as intensity measure20, reproduced with permission

5.2

Drought

5.2.1

Definition of the hazard

Drought can be defined as a condition of abnormal dry weather resulting in a serious hydrological imbalance, with consequences such as losses of standing crops and shortage of water needed by people and livestock21. One of the European Commission’s FP7 programmes is related to droughts: DROUGHT-R&SPI (Fostering European drought research and science – Policy interfacing)22. In the case of drought, hazard mapping may be done by either an extent or a basic map based on historical data. Alternatively, drought intensity may be represented by one of the drought indices shown in Table 5.3Error! Reference source not found.. However the choice of indices for drought intensity varies from country to country. Being based on historical data, none of these indices have any forecasting built in to their definition and any forecasting would require reanalysis of historical data. Table 5.3 Drought indices23. Description

Drought Index Standardised Precipitation Index (SPI) Palmer Drought Severity Index (PDSI)

Crop Moisture Index (CMI)

Surface Water Supply Index (SWSI)

Calculated from the long-term record of precipitation in each location (at least 30 years) Calculated from precipitation, temperature and soil moisture data CMI is a derivative of PDSI which was developed from moisture accounting procedures as the function of the evapotranspiration anomaly and the moisture excesses in the soil Used for frequency analysis to normalize long term data such as precipitation, snow pack, stream flow and reservoir level

21

Alexander, D. : Natural hazards. In: EDITOR(S), H. T. (ed.) Encyclopedia of Life Support Systems (EOLSS). Oxford, UK: Eolss Publishers, 2003. 22 DROUGHT-R&SPI, http://www.eu-drought.org/ 23 Belal, A, El-Ramady, HR, Mohamed, ES, SALEH, A: Drought risk assessment using remote sensing and GIS techniques. Arab J GeosciArab, 2012, 35:53.

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Examples of drought mapping

An example of a basic map based on historical data, in terms of number of droughts over a given period is given in Figure 5.3. An example based on precipitation deficit is shown in Figure 5.4. An example of a map showing one of the drought indices is shown in Figure 5.5.

Figure 5.3 Global drought showing number of droughts as intensity measure24, from data in24

Figure 5.4 Map of precipitation deficit as intensity measure of drought25, reproduced with permission

24

D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium 25 ESPON database, http://database.espon.eu/db2/.

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Figure 5.5 3-month SPI value as an intensity measure of droughts in Romania26. reproduced from26 The maps shown in these examples are generally of too coarse a scale to be useful for determining the hazard intensity to which infrastructure may be exposed, however they are still useful to illustrate the concept. More suitably finer grained maps are available for specific regions which are more susceptible to drought.

5.3

Storm surge / coastal flooding and coastal erosion

5.3.1

Definition of the hazard

Coastal floods occur as a result of a force that pushes or pulls water from the ocean onshore. Thus coastal floods can be classified in two categories, depending on force source. The forces can be a sudden displacement of ocean water (e.g. tsunami, this hazard mapping is studied in another section) or can be wind (from cyclones or hurricane). Coastal flooding can also be linked to another hazard as one driver of coastal erosion, as has been highlighted by the European project: Resilience-Increasing Strategies for Coasts – Tool kit (RISC-KIT)27. The project proposes guidance to Coastal Risk Assessment Framework (CRAF) users, especially for identifying potential hotspots and their coastalindex approach considering hazard intensities, utilizing simple hazard models. This coastal-index is based on the potential extension of the inundation as the indicator of the hazard. 5.3.2

Examples of storm surge / coastal flood mapping

An example of a flood extent map of Europe in terms of probability is presented in Figure 5.6. This figure highlights the coastal areas for which there is a medium likelihood that a storm surge will reach them.

26

Developing methodology for drought hazard mapping with the use of measures for drought susceptibility assessment, Tokarczyk et al., Integrated Drought Management Programme, Global Water Partnership Central and Eastern Europe. 27 RISC-KIT, http://www.risckit.eu/

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Figure 5.6 Flood (caused by storm surge) extent map, according to the EU Floods Directive (at least 1 in 100 years event)28, reproduced with permission As an example of an intensity map for storm surge, RISC-KIT proposes for the surge hazard, the bathtub approach giving a flood depth, and for coastal erosion, the Kriebel and Dean Method29 (giving eroded volume and shoreline retreat). An intensity map can also be built from extrapolated historical data as shown in Figure 5.7.

28

ESPON database, http://database.espon.eu/db2/ Kriebel, D, Dean, RG: Convolution model for time-dependent beach-profile response. Journal of Waterway, Port, Coastal and Ocean Engineering, 1993, 119:204-226. 29

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Figure 5.7 Crude intensity map of the surge depth from typhoon Yolanda in the Philippines30 5.4

Earthquake

5.4.1

Definition of the hazard

When an earthquake occurs seismic waves are emitted. They propagate through the Earth and generate ground shaking. To quantify the intensity of the seismic hazard, seismologists often refer to the peak ground acceleration (PGA), although alternative intensity measures include horizontal displacement, vertical displacement, and the degree of liquefaction. PGA is often used on seismic hazard maps and in earthquake engineering. Seismic hazard maps usually illustrate the probability of exceeding a given intensity of earthquake at a certain place within a given return period. The seismic hazard can be evaluated by deterministic and probabilistic methods. Deterministic methods are based on the maximum PGA calculated for past earthquakes. Nowadays seismic hazard is usually estimated with probabilistic approaches and depends on many factors: the historical distribution of earthquakes, the energy and the geometry of the source (called magnitude and focal mechanism), the nature of the propagating medium and site effects characterizing the shallow surface under the site that increases or decreases the ground motion.

30

Torio, D., Knowing storm surge risks, 2013, www.rappler.com/science-nature/44475-mapping-storm-surgerisk

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5.4.2

Examples of earthquake mapping

The probabilistic representation of the seismic hazard estimated by seismologists is complex and is often simplified with maps to fit engineering or regulatory needs. Figure 5.8 illustrates the PGA throughout Europe which has a 10% probability of being exceeded in a 50 year period. The data is shown as contours of peak ground acceleration. A dark red colour indicates a strong shaking and light blue a weak shaking. For instance, the regions in red have a 10% probability to have a PGA greater than 30% of g in 50 years (g is the standard acceleration due to gravity equal to 9,81 m/s²). From an engineering point of view, values on this map are used to design structures that can resist (i.e. fulfil specific requirements on damage extent) a given level of shaking. In Europe, regulated seismic hazard maps are estimated at national levels and, as far as the authors are aware, there is no regulation at European level. As such hazard maps are developed according to legislation in individual Member States. However, the European Facility for Earthquake Hazard and Risk (EFEHR31) platform intends to homogenise seismic hazard estimations and provides access to data, models and tools for the assessment of seismic hazard and risk in Europe. Moreover seismic hazard maps in the EuroMediterranean region are proposed by the EU-FP7 SHARE project “Seismic Hazard Harmonization in Europe32”.

Figure 5.8 Map of the 2013 European Seismic Hazard Model (ESHM13)32, reproduced with permission

31 32

www.efehr.org http://www.share-eu.org/node/90, visited 2016-10-27.

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5.5

Landslide

5.5.1

Definition of the hazard

Deliverable number: D3.1

Due to the diversity and large volumes of data needed for landslide analysis, and the complexity in the analysis procedures, quantitative landslide risk assessment has only become common in the last decade or so, due to developments in the field of Geo-Information Systems (GIS). When using GIS, the following components of a landslide risk project can be differentiated: data collection, data entry, data management, and data modelling, the same steps as for other hazard risk mapping. Landslides are usually slow movements (a few millimetres per year) but they can also be faster (a few meters per day) on a slope, along a surface breaking flat, curved or complex. This is a coherent land mass, volume and variable thickness. The depth of the surface of rupture (or slip) is variable, from a few meters to several tens of meters, or even hundreds of meters for some slope landslides. As with many other hazards, the danger from landslides is difficult to assess, due to the absence of a clear historical magnitude-frequency relation at any particular location, although such relations can be made over larger areas. Their value for specific assets may be limited because of such a coarse granularity. In addition, the estimation of both magnitude (intensity) and probability of a landslide to determine the danger requires a large amount of information on the following aspects:      

Surface topography, Subsurface stratigraphy, Subsurface water levels, and their variation in time, Shear strength of materials through which the failure surface may pass, Unit weight of the materials overlying potential failure planes, The intensity and probability of additional triggering factors, such as rainfall and earthquakes.

All of these factors, required to calculate the stability of individual slopes, have a large spatial variation, and are only partly known, at best. If all these factors would be known in detail it would be possible to determine which slopes would generate landslides of specific volumes and with specific runout zones for a given period of time. 5.5.2

Examples of landslide mapping

Due to the difficulties in creating a hazard map for landslides, as described in the above section, a suitable example of landslide mapping was not found.

5.6

Extreme winds

5.6.1

Definition of the hazard

In the case of extreme wind hazard, the return period of a storm depends inevitably on how different characteristics are combined into a scalar measure, because they are strongly dependent on the scale of the study33. The assessment of extreme wind speeds with high spatial resolution requires the use of 2

33

Della-Marta, P, Mathis, H, Frei, C, Liniger, M, Kleinn, J, Appenzeller, C: The return period of wind storms in Europe, Int. J. Climatol., 2009, 29: 437-459.

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Physical exposure identification and mapping methodologies different data sets: (1) historical measurement data and (2) reanalysis data (usually ERA-40 reanalysis of the European Centre for Medium Range Weather Forecasting34). 5.6.2

Examples of extreme wind mapping

For the case of extreme wind, the hazard mapping goes from a simple hazard map (Historical map: Figure 5.9 showing mean wind speed for a historical storm at European scale) to a more complex map (Intensity map: Figure 5.10 showing wind speeds with a return period of 2 and 20 years).

Figure 5.9 Data from re-analysis EAR-40 in the case of the Storm Coranna on 11 November 1992 35, with permission.

Figure 5.10 Maximum wind speed in Germany on a 1×1km grid, with an exceed probability of p=0.5 (return period of 2 years; left) and p=0.05 (return period of 20 years; right)35, reproduced with permission

34

Uppala, SM, Kållberg, PW, Simmons, AJ, Andrae, U, Da Costa Bechtold, V, Fiorino, M, Gibson, JK, Haseler, J, Hernandez ,A, Kelly, GA, Li, X, Onogi, K, Saarinen, S, Sokka, N, Allan, RP, Andersson, E, Arpe, K, Balmaseda, MA, Beljaars, ACM, Van De Berg, L, Bidlot, J, Bormann, N, Caires, S, Chevallier, F, Dethof, A, Dragosavac, M, Fisher, M, Fuentes, M, Hagemann, S, Hólm, E, Hoskins, BJ, Isaksen, L, Janssen, PAEM, Jenne, R, McNally, AP, Mahfouf, JF, Morcrette, J-J, Rayner, NA, Saunders, RW, Simon, P, Sterl, A, Trenberth, KE, Untch, A,Vasiljevic, D, Viterbo, P, Woollen, J: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 2005, 131: 2961–3012. 35 Hofherr, T, Kunz, M: Extreme wind climatology of winter storms in Germany, Clim Res, 2010, 41:105-123.

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5.7

Lightning

5.7.1

Definition of the hazard

Deliverable number: D3.1

Under favourable conditions, electrical discharges occur from a charge centre in a cloud either to the induced charge on the earth, or to charge centres of another cloud or to a charge centre of the same cloud. Therefore, lightning may be categorized into two types: Cloud flash and ground flash. This second definition is the more important definition for hazard mapping. An existing European cooperation called EUCLID36 aims to identify and detect lightning all over the European area. 5.7.2

Examples of lightning hazard maps

The usual lightning hazard maps are historical maps as detected lightning flash density for a certain period in a certain area (annual data for Europe: Figure 5.11 or monthly data for UK: Figure 5.12).

Figure 5.11 Annual detected lightning density (2008-2012)37, reproduced with permission

36

http://www.euclid.org/ Anderson, G, Klugmann, D: A European lightning density analysis using 5 years of ATDnet data, Nat. Hazards Earth Syst. Sci., 2014, 14:815–829. 37

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Figure 5.12 Detected lightning density for June in 201237, reproduced with permission

5.8

Heavy rain and flooding

5.8.1

Definition of the hazard

Heavy rain/Flood is defined by the EU Flooding directive/Water framework directive as XXXXXXX. Flood maps may be split in two groups: flood risk and flood hazard map. The last group contains information about the probability and/or magnitude of an event. The use of historical flood maps is restricted since it is impossible to compare them as return periods are not equal and boundary conditions (streambed, land cover, etc.) may have changed significantly over time. To solve this issue, statistical and modelling tools can be used to generate the hazard intensity of hypothetical floods. Various parameters can be used to represent the intensity of this hazard, such as flood extent, water depth, flow velocity, duration, propagation of water front, and the rate at which the water rises. From all of these parameters, water depth is the main factor. It should be noted that the hazard mapping is linked to the outcome of the hazard risk study, indeed for the example of a flood in the polder area of the Netherlands, the duration of inundation is an important factor for the resulting damage, or in other examples, information on the propagation of the flood wave and the rate at which the water rises is critical for emergency planners38. 5.8.2

Examples of flood hazard mapping

The conceptual framework behind flood mapping is quite general (Figure 5.13) and has three steps. Step 1: Estimation of discharges for specific return periods.

38

Jonkman, S.N: Loss of life estimation in flood risk assessment: theory and applications, PhD thesis, Delft University of Technology, 2007.

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Deliverable number: D3.1

This can be done with frequency analysis on discharge records, fitting the data to an extreme value distribution39.If there is no discharge data available, in order to deduce discharge data, runoff coefficients can be used40. In case of presence of measurement gauges, only a fraction of an area is gauged.In order to overcome this fact, gauged data can be extrapolated by using regionalisation techniques41. But in the most of cases, hydrological models are used to calculate discharges. These models require spatially explicit meteorological (e.g. temperature, precipitation, evaporation, radiation), soil, and land cover data as input. Those can be acquired from datasets of interpolated observed data, from re-analysis datasets (e.g. the ECMWF ERA datasets), or from climate models (e.g. the Hadley and ECHAM models). Spatial hydrological models solve the water balance for each geographical unit (e.g. grid-cell) for each time step and route the runoff downstream, yielding discharges throughout the entire catchment. Step 2: Estimation of the water level. The water level is derived from discharges and specific return periods using rating (stage-discharge) curves or hydrodynamic models. Step 3: Determination of the flooded area and flood depth. By combining water level with a digital elevation model (DEM), a flood map showing the extent or depth can be created.

Figure 5.13 Conceptual framework for flood hazard and risk calculationsError! Bookmark not defined., reproduced with permission In conclusion, the flood hazard map types are presented in Figure 5.14Error! Reference source not found. and explained in the following paragraph (map type E and F will be not discussed here, as they represent risk):

39

Linde, A H, Aerts, JCJH, van den Hurk, B: Effects of flood control measures and climate change in the Rhine basin, in: Proceedings of the 4th International Symposium on Flood Défense, Toronto, Canada, 2008, 118:1-9. 40 Merz, R, Blöschl, G, Humer, G: National flood discharge mapping in Austria, Natural Hazards, 2008, 46:53– 72. 41 Merz, R, Bloschl, G: Flood frequency regionalisation-spatial proximity vs. catchment attributes, J. Hydrol., 2008, 302:283–306.

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Physical exposure identification and mapping methodologies A) Historical map (Figure 5.14A) Historical maps are not really useful for hazard assessment as they are restricted to historical data and do not necessarily capture changes to the topology, as discussed above. B) Flood extent maps (Figure 5.14Error! Reference source not found.B) Flood extent maps are the most common flood hazard maps. They represent the inundated areas of a specific event (historical or hypothetical with a specific return period – once every 100 years for example). Additional data such as depth or velocity at key points might also be added. C) Flood depth maps and (maps displaying other flood parameters) (Figure 5.14C) As explained above, when flood extents are calculated for specific return periods, flood depth can be calculated and represented on flood depth maps. It should be noticed here that the methodology is a bit different in case of flooding based on of failing structures (in polder area for example). Indeed, in this case it’s not possible to calculate general flood extents and depth for a specific return period. Even if flood extents and depth are considered as the most important parameters, some other parameters (velocity, duration, propagation, rate of rising) can be very important, depending on the situation and the purpose of the map. One or several of those parameters can be added on flood depth maps as for example, in Flanders maps showing the rate of water rising, velocity in Austria and Luxembourg (Table 5.4). D) Flood danger maps (Figure 5.14D) As explained previously, flood maps show only one of several possible flood intensity measures (even if in Austria, flood depth information of a specific return period is added to a flood extent map). In order to get an impression of more than intensity measure, those can be aggregated onto qualitative classes, to give a flood danger map. Solution of this aggregation is the use of a matrix (Figure 5.13) or formula. For the case of the matrix, two axes are used to relate flood parameters (depth, velocity, return period) or even sometimes a grouped parameter is used (for example, intensity as a combination of water depth and flow velocity42.

42

van Alphen, J, Passchier, R: Atlas of Flood Maps, examples from 19 European countries, USA and Japan, Ministry of Transport, Public Works and Water Management, The Hague, Netherlands, prepared for EXCIMAP, available at: http://ec.europa.eu/ environment/water/flood risk/flood atlas/index.htm, 2007.

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Deliverable number: D3.1

Figure 5.14 Different flood map types. (A) basic flood hazard map; (B) flood extent map (including exceedance probabilities); (C) flood intensity map; (D) flood danger map: (E) qualitative risk map: (F) quantitative risk (damage) map. The displayed maps are purely illustrative and based on a hypothetical caseError! Bookmark not defined., reproduced with permission

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Physical exposure identification and mapping methodologies Table 5.4 Overview of the availability and use of flood maps in European countries. As Cyprus and Bulgaria do not have national flood maps, they have been omitted from the table. Belgium has been split in Flanders and Wallonia. (partly reproduced fromError! Bookmark not defined.) Flood map type Country

Coverage

Historical

Flood depth

Flanders France Switzerland

Entire territory Entire territory Entire territory

Netherlands

Entire territory

Great Britain Romania Slovakia Wallonia Hungary Ireland Lithuania Czech Rep. Slovenia Estonia Greece Germany Spain Italy Finland

Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Entire territory Some region Some region Some region Some region

Austria

Some region

X

Luxembourg

Some region

X

X

Poland Norway Portugal Sweden Croatia Denmark Latvia

Some region Some region Some region Some region Limited areas Limited areas Limited areas

X X X X X X X

X

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X X

Flood extent X X X

X

Other intensity measures Rate or rise

1 1

X X

Depends on region

X X X

X X X

Class of flood extent map

2

X X X X X

Propagation

2 3 3

X X X

X X X

X

X

1-4 3 3

X Depends on region Depends on region

3 4 2-8 1 2 1 1 1

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Technological hazards

6

The European risk assessment and mapping guidelines use a United Nations’ definition for the term “technological hazards”: A hazard originating from technological or industrial conditions, including accidents, dangerous procedures, infrastructure failures or specific human activities, that may cause loss of life, injury, illness or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage. (UNISDR, 2009) In this report we define technological hazards, as the class of hazards that originate from technological or industrial conditions, and present the main informational entities that are relevant for mapping purposes.

6.1

Risk mapping of technological hazards

In order to provide an overview of how technological hazards may be taken into account in real world risk assessment and risk mapping activities, we present some of the main informational entities present in the 2014 Portuguese National Assessment report43. This report is especially relevant since it’s aimed at compliance with the European risk assessment and mapping guidelines. The report presents a list of 25 risks, divided into the risk classes, Table 6.1:   

Natural risk (11 instances); Technological risks (13 instances); Mixed risks (1 instance).

The risk matrix used in the report is presented in Table 6.2. The risk levels (low, moderate, high, and extreme) depend on likelihood and impact. Likelihood is defined in terms of a return period and impact depends on human life, environmental, and socio-economic losses.

43

http://www.prociv.pt/bk/RISCOSPREV/AVALIACAONACIONALRISCO/Documents/2016_Avaliacao_Nacio nal_Riscos.pdf .

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Physical exposure identification and mapping methodologies Table 6.1 Risk categories (not including natural hazards), taken from the 2014 Portuguese National Assessment report Risk class

Risk sub-class

Risk instance Roads Railways Serious transportation Maritime / rivers accidents Air Land transportation of dangerous substances Fixed infrastructures for transportation of dangerous substances Technological Urban fires risk Infrastructures Historical areas (urban centres) Collapse of tunnels, bridges, and other infrastructures Collapse of dams Industrial and Dangerous substances (industrial accidents) commercial Collapse of buildings in areas with high population concentration activities Radiological emergencies AtmosphereMixed risk Forest fires* related * Several factors may concur for defining this category, namely that severe forest fires occur every year (during Summer time) causing a high socio-economic and environmental impact, and that a significant part of these fires have human origin44. On the other hand, note that “urban fires” are classified under technological risks.

Table 6.2 Risk matrix, taken from the 2014 Portuguese National Assessment report

Likelihood

Impact

6.2

High ≤5 years Mediumhigh [5-20 yr.] Medium [20-50 yr.] Mediumlow [50-200 yr.] Low >200 years

Residual

Reduced

Moderate

High

Critical

Low risk

Moderate risk

High risk

Extreme risk

Extreme risk

Low risk

Moderate risk

High risk

High risk

Extreme risk

Low risk

Moderate risk

Moderate risk

High risk

Extreme risk

Low risk

Low risk

Moderate risk

High risk

Extreme risk

Low risk

Low risk

Moderate risk

Moderate risk

High risk

Technological risk maps

In the Barreiro living lab, the operator currently uses several hazard maps for the purpose of risk management. In this section we’ll present briefly the main features of two such maps, related to technological hazards.

44

See e.g. in the news “Civil Protection: 90% of fires have human origin”, Journal “i”, available at http://ionline.sapo.pt/406636 .

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The Barreiro’s “Technological Risks” map (see Figure 6.1) represents a “critical hazard zone”, that includes the chemicals industry area. Some critical assets are identified, according to SEVESO and non-SEVESO criteria.

Figure 6.1 Barreiro living lab: technological hazards, including SEVESO and non-SEVESO related hazards, as well as identifying a high risk zone ("Zona de Risco"), reproduced with permission The map also identifies critical infrastructure assets, spanning several sectors, namely:   

Transport sector: a railway segment (black and white traces), a highway segment (“IC21”). Health: hospital (“Hospital Distrital Barreiro/Montijo”) and clinic (“Clínica Hemodiálise”); Water: 3 water supply assets (water reservoirs).

These map features are especially important for discussing cascading effects. The Barreiro’s “Aquifer contamination” map (see Figure 6.2) identifies critical water supply assets, whose contamination would affect directly the water supply system. Note that this map gives a more thorough account of the critical assets than the previous map, namely identifying water storage and processing (chlorination) facilities.

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Figure 6.2 Barreiro living lab: map identifying the critical assets, whose contamination would affect directly the water supply system, reproduced with permission

6.3

Wildfire

The hazard from wildfires can be defined as: “A fuel complex, defined by volume, type, condition, arrangement, and location that determines the degree of ease of ignition and the resistance to control. Fire hazard expresses the potential fire behaviour for a fuel type, regardless of fuel type’s weatherinfluenced fuel moisture content45.” (In Europe, a devoted system to the wildfire hazard, called EFFIS (European Forest Fire Information System46) has been established and its purpose is to provide information for the protection of forests against fire in Europe addressing both pre-fire and post-fire conditions. The wildfire hazard is a consequence of a combination of other hazards (such as drought, extreme wind, lightning …), it can therefore be viewed as a multi-hazard disaster. Therefore the intensity (used by EFFIS) for the hazard mapping throughout Europe and neighbouring countries is based on the Canadian Fire Weather Index (FWI) System. It should be noted that EFFIS provides historical maps of each forest fire in Europe (in term of total burnt area, since 2000).

45

Hardy, C: Wildland fire hazard and risk: Problems, definitions, and context. Forest Ecology and Management, 2005, 211:73-82. 46 http://forest.jrc.ec.europa.eu/effis/

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Deliverable number: D3.1

The FWI system is based on different aspects of the fuel, including, e.g. its configuration, moisture content, and the impact of drought and wind (Figure 6.3).

Figure 6.3 Flowchart for the determination of the FWI From all data coming from the determination of the FWI, it’s possible to build six different hazard maps, selectable on the website of EFFIS47.

47

http://forest.jrc.ec.europa.eu/effis/applications/current-situation/

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7

Summary of indicators

As described in the previous two sections, there are numerous intensity measures for different hazards, and there are benefits and drawbacks in each of these and how they can represent hazards. Table 7.1 summarises the different intensity measures from the previous sections according to the different hazards. Hazard Heat wave

Table 7.1 Summary of intensity measures identified for different hazards Intensity measure Temperature (absolute) Temperature (range) Temperature (qualitative description)

Extreme cold

Temperature (absolute) Temperature (range) Temperature (qualitative description)

Drought

Standardised Precipitation Index (SPI) Palmer Drought Severity Index (PDSI) Crop Moisture Index (CMI) Surface Water Supply Index (SWSI) Number of droughts Precipitation deficit Extension of the inundation Surge depth Eroded volume Shoreline retreat Peak ground acceleration (PGA) Horizontal displacement Vertical displacement Degree of liquefaction Velocity of movement Mass Thickness Volume Depth of surface of rupture (slip) Size of the runout zone Number per square kilometres Velocity Quantity of lightning strikes Fire Weather Index (FWI) Initial Spread Index (ISI) Buildup index (BUI) Fine fuel moisture code (FFMC) Drought code (DC) Duff moisture code (DMC)

Storm surge / coastal flooding / coastal erosion

Earthquake

Landslide

Extreme winds Lightning Wildfire

Flooding

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Flood extent, Water depth Flow velocity Duration Propagation of water front Rate of water rising

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8

Linking hazard mapping with critical infrastructure

8.1

Risk assessment of critical infrastructure

Interruption or destruction of critical infrastructures has major effects on societies. Aside from the very nature of CI and the aspect of providing a vital service to society, the negative effects are further increased as a result of the complexity and interdependencies between infrastructure systems. This results in a difficulty in identifying and quantifying causal links between a multitude of potential causal agents and specific observed effects48. The resulting cascading effects are a part of the risk assessment of critical infrastructure. Several methodologies for this exist. The main methodologies are based on their citation records and their recognition in the scientific community. Giannopoulos et al49 evaluate these methodologies according to the following criteria:      

Scope of the methodology: which sector is addressed, to whom it is addressed (Policy makers, researchers, operators) Objectives of the methodology. Applied techniques and standards. Interdependencies coverage. Is resilience addressed? If cross-sectoral methodology, how are risks compared across sectors?

Table 8.1 is directly extracted from this document. This table presents all methodologies selected by the authors, and shows the targets of the methodologies and their hazards, to which the methodology is aimed, objectives, and if they take into account the interdependencies, cross-sectorial risk and if resilience is considered.

48

International Risk Governance Council, 2006, White Paper on Managing and Reducing Social Vulnerabilities from Coupled Critical Infrastructures 49 G. Giannopoulos, R. Filippini, M. Schimmer, Risk assessment methodologies for Critical Infrastructure Protection: Part I: A state of the art, JRC Technical Notes, EUR 25286 EN – 2012.

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Physical exposure identification and mapping methodologies Table 8.1 Risk assessment methodologies for Critical Infrastructures (reproduced from Giannopoulos49) Sector/ Hazards

Users

Objectives

Resilience

All sectors/All hazards

Operators, asset managers, policy makers

Vulnerabilities assessment, risk reporting

Yes

BMI51

All sectors/All hazards

Private companies, CI operators, Policy makers

CARVER 252

All sectors/All hazards

Policy makers

All sectors/All hazards

Policy and decision makers

All sectors/All hazards

Policy makers

Risk informed design

No

Policy makers, industry

Prevention, preparedness

Yes, implicitly

Policy makers

Impact assessment of ICT disruption

No

Transport, Energy/ Terrorist threats

Operators, asset managers

Risk reporting, protection measures effectiveness evaluation

No

DECRIS58

All sectors/ All hazards

Policy Makers, operators

EURACOM59

All sectors/ All hazards

Policy and Decision makers

Methodology Better Infrastructure Risk and Resilience50

CIMS (Critical Infrastructure Modeling Simulation)53 CIPDSS (Critical Infrastructure Protection Decision Support System)54 CIPMA (Critical Infrastructure Protection Modeling and Analysis)55 COMM-ASPEN56

Energy, Communications, banking and finance/All hazards Telecommunications , electricity, finance

Vulnerabilities and risk assessment, Foster collaboration between policy makers and private sector Risk evaluation, evaluation of alternatives, allocation of protective measures Rapid decision making, prioritization of emergency operations

No

Yes, partially

Yes, implicitly

57

Counteract (Generic Guidelines for Conducting Risk Assessment in Public Transport Networks)

Risk and vulnerabilities assessment, prioritization of scenarios Holistic, crosssectorial risk assessment

Yes, partially

No

50

www.dis.anl.gov/projects/ri.html http://www.bmi.bund.de 52 http://www.ni2cie.org/CARVER2.asp 51

53

https://inlportal.inl.gov/portal/server.pt/community/national_and_homeland_security/273/modeling_and_simulat ion/1707 54 http://www.lanl.gov/programs/nisac/cipdss.shtml 55 http://www.csiro.au/Organisation-Structure/Divisions/Mathematics-Informatics-and-Statistics/CIPMA.aspx 56 https://cfwebprod.Sandia NationalLaboratory.gov/cfdocs/CCIM/docs/040101_Simulating_Economic_Effects_of_Disruption.pdf 57 http://www.uitp.org/knowledge/projects-details.cfm?id=433 58

http://www.sintef.no/project/SAMRISK/DECRIS/Documents/DECRIS_paper_SAMRISK_final%20080808.pdf 59 http://www.eos-eu.com

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Deliverable number: D3.1 Sector/ Hazards

Users

Objectives

Resilience

Policy and decision makers CI operators and decision makers CI operators and decision makers

Interdependencies assessment and disruption impact

No

Optimal allocation of resources for CIP

No

Failure propagation effects by simulation

No

All sectors/technical, economic hazard

CI analyst, researcher failure

Propagation effects in term of economy losses by simulation

No

All sectors/technical hazards

CI operators and decision makers

What if analysis under malicious attacks

Yes

All sectors/ Technical hazards

CI operators and decision makers

What if analysis under malicious attacks

Yes

RAMCAP Plus66

All sectors/ Technical hazards

CI operators and decision makers

Risk assessment and mitigation, multi-level, cross-sector

Yes

RVA67 (risk and vulnerability analysis) Sandia Risk Assessment Methodology

All sectors/ Technical, sociotechnical hazards

CI Decision makers

Risk assessment, qualitative

No

All Sectors/Terrorism, man-made

CI policy makers

Risk Assessment

Yes, implicitly

Methodology FAIT60 (Fast Analysis Infrastructure Tool) MIN61 (Multilayer Infrastructure Network) Modular Dynamic Model62 N-ABLE63 (Nextgeneration agentbased economic laboratory) NEMO64 (NetCentric Effectsbased operations MOdel) NSRAM65 (Network Security Risk Assessment Modeling)

8.2

All sectors/ All hazards Transport/ Technical hazards All sectors/technical hazards

Response of CI to specific hazard levels

In the search to understand the application of hazard maps to critical infrastructure, it is important to consider the notions of hazard, vulnerability, and exposure. These terms are defined in the IMPROVER lexicon, D1.2 and D1.3. In the technical/engineering literature for natural hazards, vulnerability is defined on a scale ranging from 0 (no loss/damage) to 1 (total loss/damage). It represents the degree of loss/potential damage/fragility of a particular element or set of elements at risk, within the area affected by a hazardous event characterized by a given intensity or level68. The expressions of vulnerability might be through four different ways.

60

http://www.Sandia NationalLaboratory.gov/nisac/fait.html http://www.ivt.ethz.ch/news/archive/20030810_IATBR/peeta.pdf 62 http://www.Sandia.gov/nisac/docs/CRIS_paper_final.doc 63 http://www.sandia.gov/nisac/docs/ieee-ehlen-scholand.pdf 64 http://www.dodccrp.org/events/10th_ICCRTS/CD/papers/128.pdf 65 http://www.jmu.edu/iiia/wm_library/NSRAM_Application_to_Municipal_Electric.pdf 66 http://www.asmeiti.org/RAMCAP/RAMCAP_Plus_2.cfm 67 http://brs.dk/eng/inspection/contingency_planning/rva_model/Pages/rva_model.aspx 68 ISSMGE-TC32 (2004), Glossary of Risk Assessment Terms, International Society of Soil Mechanics and Geotechnical Engineering/Technical Committee (TC32) on Risk Assessment and Management (www.engmath.dal.ca/tc32/index.html). 61

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Physical exposure identification and mapping methodologies 1. The first one is the vulnerability indices, based on indicators of vulnerability but mostly not directly linked with the different hazard intensities. These vulnerability indices are mostly used for expressing social, economic and environmental vulnerability. 2. The second way is the vulnerability tables that link the hazard intensity to the degree of damage. 3. Those tables are a light version of the third expression of the vulnerability: vulnerability curves (Figure 8.1). They are constructed on the basis of the relation between hazard intensities and damage data. Two types of curves can be distinguished; relative curves that show the percentage of property value as the damaged share of the total value to hazard intensity; absolute curves that show the absolute amount of damage depending on the hazard intensity (the value of the asset is already integrated in the damage function). 4. And finally, the last expression of the vulnerability is the fragility curves that provide the probability for a particular group of elements at risk to be in or exceeding a certain damage state under a given hazard intensity (Figure 8.2).

Figure 8.1 Example of vulnerability curves69

Figure 8.2 Example of fragility curves69 Fragility can also be expressed in a qualitative way as in Figure 8.2, where fragility is expressed in terms of, e.g. slight, moderate, extensive damage or complete failure. On other hand, Figure 8.1 shows vulnerability expressed in quantitative term. The methods of measuring physical vulnerability can be split in two groups, empirical and analytical models. Each group has different methods that are explained in Table 8.2

69

Cees van Westen, Vulnerability assessment, International institute for geo-information science and earth observation.

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Deliverable number: D3.1 Table 8.2

Methods of measuring physical vulnerability69.

Empirical models Analysis of observed damage

Analytical models Simple analytical model

Expert opinion

Detailed analytical methods

A brief explanation of these methods follows here: 



Analysis of observed damage: This refers to assessments based on statistics of past hazard damage. This can be used if it is possible to collect information on the degree of physical damage to buildings or infrastructure after (a given) event(s). It is particularly suited for flooding or earthquake. It permits the generation of large samples in order to correlate the intensity (magnitude) of the hazard with the damage. This analysis of observed damage can be plotted in a vulnerability curve (e.g. Figure 8.3) or in a damage probability matrix (for earthquake events in Table 8.3).

Figure 8.3 Correlation between intensity and building damage

Table 8.3 Format for Damage Probability Matrix (DPM)70

70

Whitman R.V., Reed J.W., Hong S.-T.: “Earthquake damage probability matrices”. Proceedings of the fifth World Conference on Earthquake Engineering, pp. 2531, Rome, 1974.

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Damage state 0 1 2 3 4 5 6 7 8 



NonStructural Structural damage damage None None None Minor None Localized Not Widespread Noticeable Minor Substantial Substantial Extensive Major Nearly total Building condemned Collapse

Damage ratio (%) 0–0.05 0.0 –0.3 0.3-1.25

Intensity of Earthquake V

VI

VII

VIII

IX

1.25-3.5 3.5-4.5 7.5-20 20-65 100 100

Expert judgement: In case of no prior damage information, or no building classification, expert opinion is the most feasible option for obtaining vulnerability information. One of the first systematic attempts to codify the seismic vulnerability of buildings in case of earthquake came from the Applied Technology Council71, and is based on the estimation of 58 experts (as structural engineers, builders etc.). In order to come to a good assessment of the vulnerability, many experts have to be asked and this method is time consuming and subjective in general. Simple or analytical models: Analytical methods study the behaviour of buildings and structures based on engineering design criteria, analysing, using computer based methods. These methods can for example use shake tables, or wind tunnel, as well as computer simulations. In case of analytical model, the information about the hazards should be detailed. For example, in case of earthquake, it requires the peak acceleration coefficient, or in case of flooding, the water velocity and the water depth for the area of the studied buildings. This model can be seen as the limit between models studying building stocks and individual buildings. Indeed, as Lang72 explained in Table 8.4, the more precision is required, the heavier the computational effort. This same principal may be applicable to other infrastructure assets.

Table 8.4 Methods for the assessment of the vulnerability of buildings (taken from Lang72) increasing computation effort Expendidure Building stock

Application Methods 

Observed damage

Expert judgement

Individual building Simple analytical models

Detailed analytical methods

Detailed or numerical methods: As said previously, these methods are not suitable for scenarios where a large number of buildings have to be evaluated. It should be used as a further step after the rapid screening of potential hazardous buildings in a multi-phase procedure. Analytical methods can be divided into linear procedures (linear static and linear dynamic) and nonlinear procedures (nonlinear static and nonlinear dynamic)72.

71

Applied Technology Council “Earthquake damage evaluation data for California”. ATC-13, Redwood City, California, 1985. 72 K. Lang, Seismic vulnerability of existing buildings, Institute of Structural Engineering Swiss Federal Institute of Technology, 2002.

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Figure 8.4 Example of a numerical simulation of a masonry building under an earthquake, comparable to the Bam earthquake73 (taken from van Westen69) In the case of, e.g. seismic vulnerability of building, ENSURE Project illustrates the methods for the assessment of the vulnerability of buildings in a single flowchart (Figure 8.5).

73

Furukawa, A. and Ohta, Y, Failure process of masonry buildings during earthquake and associated casualty risk evaluation, Nat. Hazards, 49, 25–51, 2009.

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Figure 8.5 Schematic representation of the classical models for seismic vulnerability and risk assessment74, reproduced with permission

8.3

Link between hazard maps and CI

The link between hazard maps and the assessment of critical infrastructure resilience is based on the relation between intensity measures and fragilities/vulnerabilities. As discussed in Deliverables 2.2 and 3.2 of the IMPROVER project75,76. The hazard identification and the intensity measure have a direct impact on a number of the indicators of critical infrastructure resilience. Considering the CIRI level 1 and level 2 indicators, for example, as reproduced in Table 8.5; a direct link can be drawn between the hazard intensity and the performance of infrastructure in those indicators highlighted. With regards to monitoring, the nature of the hazard in question dictates in some ways the monitoring to be applied to an infrastructure system. A monitoring system utilised for increased resilience could generate an alarm signal if the output signal exceeds some pre-specified level, initiating a response

74

ENSURE Project, WP1: State-of-the art on vulnerability types, Del. 1.1.: Methodologies to assess vulnerability of structural systems. 75 Pursiainen et. al; Report of criteria for evaluating resilience; IMPROVER deliverable 2.2, 2016; available from www.improverproject.eu 76 Technological resilience concepts applied to critical infrastructure; IMPROVER deliverable 3.2, under preparation at time of writing

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Deliverable number: D3.1

action such as mobilizing resources for repair, evacuation or increasing capacity at alternative assets, reducing the consequences of performance loss associated with a damaged asset. Table 8.5 CIRI level 1 and level 2 indicators partially populated Risk assessment Failure data gathering Knowledge of the context Risk assessment procedure

Prevention

Preparedness

Warning

Response

Recovery

Learning

Safety and security culture

Preparedness plan and crisis organisation Redundancy plan

Audits

Situation awareness

Downtime

Evaluation

Monitoring

Decision making

Reduced service level

Institutional learning

Cooperation agreements (external resources) Capability building

Early warning and alarm

Coordination (internal and external)

Costs

Implementation of lessons

Communication (internal and external) Resource deployment Absorption and damage limitation Externalised redundancy

Unplanned maintenance

Technological upgradability

Physical and cyber entrance control Risk treatment plan

Monitoring and review

Risk communication

Testing and simulation

Resilience plan Resilient design Planned maintenance Information sharing

Capacity building Technical supportability Interoperability (internal and external) Stakeholder management

Restart Autonomy

Insurance

With regards to the absorption and damage limitation, the fragility, vulnerability or robustness of an asset exposed to different hazard levels, characterised by some measure of the intensity results in an indication of the actual damage experienced by the infrastructure. For example peak ground acceleration in the event of an earthquake. Based on a risk assessment carried out during the design phase, detailed information may be available about the performance of a system given hazards of different intensities. Similarly, the damage and reduced service level both depend on the level of damage, as indicated by the fragility curves of an asset, as shown in Figure 8.2. This relationship is shown schematically in Figure 8.6. The analysis is shown in 2 domains, that of the hazard and that of the critical infrastructure. Knowledge of the facility and the context in terms of the location and the design provides input to the hazard analysis; which comprises a hazard model and/or hazard maps which help to define the site hazard. A resulting hazard scenario is then implemented in the critical infrastructure domain; where an analysis of the indicators takes place accounting for the hazard analysis as appropriate. At this stage, the analysis of the indicators in the critical infrastructure domain may also account for any indicators calculated for interdependent infrastructure.

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Physical exposure identification and mapping methodologies

Figure 8.6 Relationship between the hazard domain and the critical infrastructure domain

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Deliverable number: D3.1

Conclusions

The current document describes the link between critical infrastructure and different hazard maps by reviewing hazard identification and mapping methodologies for both natural and man-made hazards with the purpose to provide a basis for a risk based approach for implementation of resilience concepts to critical infrastructure. The hazard mapping methodologies in general include 4 distinct steps: 1) Identification of the hazard by location and extent; 2) Specification and description of the hazard frequency and duration; 3) Definition of the probability of occurrence of the hazard; and 4) Definition of the intensity of the hazard. These steps and related metric have been reviewed in detail for the most hazards relevant for the living labs in the IMPROVER project A major issue with hazard mapping for critical infrastructure is that general (natural) hazard maps (prepared by e.g. seismologists, hydrologists, climatologist, meteorologist, urban planners) might use different intensity measures and scales that are needed for engineers designing the infrastructure facilities. For linking hazard mapping with critical infrastructure a number of indicators are identified (in deliverable 2.2 and 3.2) which depend upon the results of a hazard assessment. Concepts of fragility and vulnerability are briefly reviewed for the purpose of critical infrastructure risk assessment.

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