D 6.3 Report on Benefits of Critical Infrastructure

RAIN – Risk Analysis of Infrastructure Networks in Response to Extreme Weather

Project Reference: 608166

FP7-SEC-2013-1 Impact of extreme weather on critical infrastructure

Project Duration: 1 May 2014 – 30 April 2017

Security Sensitivity Committee Deliverable Evaluation

Deliverable Reference D 6.3 Deliverable Name Report on benefits of critical infrastructure protection Contributing Partners ROD, ISIG, HI, Dragados, UNIZA, AIA Date of Submission May 2017

The evaluation is: • The content is not related to general project management • The content is not related to general outcomes as dissemination and communication • The content is related to critical infrastructure vulnerability or sensitivity • The content is not publicly available or commonly known • The content does not add new information that might be misused by possible criminal offenders to exploit vulnerabilities • The content does not cause any harm to essential interests of EU or one or more member states • The content does not cause societal anxiety or social unrest • There are no uncertainties that might need to contact the National Security Authority • Diagram path 1, 2, 3, 4, 6, 7, 8, 9. Therefore, the evaluation is Public.

Decision of Evaluation Public Confidential Restricted

Evaluator Name P.L. Prak, MSSM Evaluator Signature Signed by the chairman of the SSC Date of Evaluation 2017-05-29

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 608166

RAIN – Risk Analysis of Infrastructure Networks in Response to Extreme Weather

Project Reference: 608166

FP7‐SEC‐2013‐1 Impact of extreme weather on critical infrastructure

Project Duration: 1 May 2014 – 30 April 2017

Deliverable 6.3‐Report on benefits of critical infrastructure protection

Authors Ciaran Carey* (Roughan & O’Donovan) Julie Clarke (Roughan & O’Donovan) Robert Corbally (Roughan & O’Donovan) Lorcan Connolly (Roughan & O’Donovan) Donya Hajializadeh (Roughan & O’Donovan) Mark Tucker (Roughan & O’Donovan) Chiara Bianchizza (ISIG) Olivia Ferrari (ISIG) Timo Hellenberg (Hellenberg) Carlos Barcena, (Dragados) Maria Luskova (University of Zilina) Xavier Clotet (Groupo AIA)

*Correspondence author: Arena House, Arena Road, Sandyford, Dublin 18,

[email protected], +35312940800 D6.3‐Report on benefits of critical infrastructure protection

DOCUMENT HISTORY

Index Date Author(s) Main modifications

E01 11/04/2016 DH, CC and MT First Draft

E02 12/05/2017 CC, JC, RC and LC, MT Second Draft

E03 18/05/2017 CC, RC Addressing Comments Form GDG And UNIZA reviews

Document Name: Report on benefits of critical infrastructure protection

Work Package: 6

Task: 6.5

Deliverable: 6.3

Deliverable scheduled date (35th Month) 31st March 2017

Responsible Partner: Roughan & O’Donovan

D6.3‐Report on benefits of critical infrastructure protection

Table of Contents

Table of Contents ...... 3 General Glossary ...... 6 1. Introduction ...... 9 1.1. Motivation ...... 9 1.2. Aims and Objectives ...... 9 1.3. Deliverable Structure...... 10 2. Risk‐Based Decision Making Framework ...... 12 2.1. Bayesian Network Modelling ...... 13 2.1.1. Multi‐Mode Risk Model ...... 13 2.1.2. Risk Models and Bayesian Network Nodes ...... 14 3. Case Study 1 ‐ Alpine Region, Italy ...... 16 3.1. Emergency response to Extreme Weather Event ...... 16 3.1.1. The framework for civil protection at national level ...... Error! Bookmark not defined. 3.1.2. The civil protection framework in the Friuli Venezia Giulia RegionError! Bookmark not defined. 3.1.3. Procedures in decision making ...... Error! Bookmark not defined. 3.1.4. Situational awareness and communications in civil protection at national level ... Error! Bookmark not defined. 3.1.5. Civil protection and flash flood of 29th August 2003 ...... Error! Bookmark not defined. 3.2. Application of Risk Analysis Framework ...... 27 3.2.1. Enumeration ...... 28 3.2.1.1 Extreme Weather Events: ...... 28 3.2.1.2 Hazards ...... 29 3.2.1.3 Network Vulnerability ...... 30 3.2.1.4 Consequences ...... 31 3.2.2. Quantification of Likelihoods ...... 32 1.1.1.1 Likelihood of EWEs ...... 32 3.2.2.1 Likelihood of Hazards ...... 33 3.2.3. Consequence Analysis ...... 41 3.2.3.1 Direct economic risks of Landslides ...... 42 3.2.3.2 Direct societal risks of Landslides ...... 43 3.2.3.3 Direct economic risks of Inundation ...... 44 3

D6.3‐Report on benefits of critical infrastructure protection

3.2.3.4 Direct societal risks of Inundation ...... 45 3.2.3.5 Direct economic risks of Bridge Scour ...... 46 3.2.3.6 Direct societal risks of Bridge Scour ...... 46 3.2.3.7 Direct security risks of Landslides, Inundation & Bridge Scour ...... 47 3.2.4. Bayesian Network Modelling for Economic and Societal Risks ...... 47 3.2.4.1 Debris Flows ...... 47 3.2.4.2 Flooding ...... 48 3.2.5. BN Modelling for Security Risks...... 50 3.2.5.1 Land‐Transport Cut‐Off ...... 50 3.2.5.2 Electricity Supply Cut‐Off ...... 53 3.2.6. Construct Outcome/Utility Probability Distributions ...... 54 3.2.6.1 Direct Economic Risks ...... 54 3.2.6.2 Direct Societal Risks ...... 55 3.2.6.3 Direct Security Risks ...... 56 3.2.7. Optimising Mitigation Strategies ...... 57 3.2.8. Conclusions ...... 60 4. Case Study 2 ‐ Uusimaa Storm Surge, Finland ...... 62 4.1. Introduction ...... 62 4.2. Context ...... 62 4.2.1. Storm Surge Event, January 2005 ...... 63 4.2.2. Impact of Storm Surge Event ...... 64 4.2.3. Emergency management cycle ...... 67 4.2.3.1 Introduction ...... 67 4.2.3.2 National and Government Level Response ...... 68 4.2.4. Consequence Management ...... 70 4.2.5. Material Damage ...... 73 4.2.6. Nuclear Power Plant ...... 77 4.2.7. Lessons Learned, Improvements and Guidelines ...... 79 4.3. Application of Risk Analysis Framework ...... 82 4.3.1. Enumeration ...... 82 4.3.2. Quantification of Likelihoods ...... 85 4.3.2.1 Likelihood of EWEs ...... 85 4.3.2.2 Likelihood of Hazards ...... 86

D6.3‐Report on benefits of critical infrastructure protection

4.3.3. Critical Infrastructure ...... 93 4.3.3.1 Land Transport Infrastructure ...... 94 4.3.3.2 Energy and Telecommunication infrastructure...... 95 4.3.4. Modelling Approach for Uusimaa Case Study Region ...... 97 4.3.5. Consequence Analysis ...... 107 4.3.5.1 Direct economic risks of inundation to roads ...... 108 4.3.5.2 Direct economic risk due to of inundation to rail ...... 110 4.3.5.3 Direct economic risks of high winds to power lines ...... 110 4.3.5.4 Indirect economic risks of high winds to power lines ...... 112 4.3.5.5 Results & Discussion ...... 114 5. Conclusions ...... 116 6. References ...... 117 Appendix A Methodology for provision of inputs for Risk Assessment framework ...... 125 A.1 Methodology ...... 125 A.2 BRIDGE EXAMPLES ...... 136 Appendix B Indirect consequences and Indirect mitigation ...... 144 B.1 Indirect consequences ...... 144 B.2 Indirect mitigation ...... 145

D6.3‐Report on benefits of critical infrastructure protection

Executive Summary

This report demonstrates the benefits of providing critical infrastructure protection from extreme weather through the application of the RAIN Risk‐Based Decision Making Framework in the context of two case studies. As well as this, the report also describes the emergency management of the case study countries, detailing the response to the extreme weather events upon which the case studies are built.

Case study 1 is based on a 2003 event in the alpine area of the Friuli Venezia Giulia Region located in North Eastern Italy in which extreme rainfall caused flooding and landslides which resulted in widespread damage to the critical infrastructure in the area. The civil protection in the region comprises of institution, civil society, volunteering associations and the government with focus at a local level. The local presence of the volunteers of the civil protection was key feature in the management of the 2003 event. In applying the Risk‐Based Decision Making Framework the direct consequences of damage to road segments and bridges, rail lines, and electrical lines caused by landslides and flooding induced by extreme rainfall is examined using Bayesian Network modelling. The optimal strategy for mitigating the risk to critical infrastructure, given a budgetary constraint, is chosen and the benefits of protecting critical infrastructure are shown.

The second case study focuses on the risk posed to the Uusimaa region for Southern Finland by storm surge events. A 2005 storm surge hit the region causing major disruption and damage. The Finnish emergency response community, who were alert due to other recent events and weather forecasting, began preparations in the days preceding the event. In a manner similar to case study 1 the responsibility of the emergency response falls firstly on the local and regional authorities. The Risk‐Based Decision Making Framework application analyses the risk to both the land transport and electricity networks posed by sea water flooding and high wind speeds.

D6.3‐Report on benefits of critical infrastructure protection

General Glossary

This section presents the terminology employed throughout the report.

Climate Change A change in the state of the climate that can be identified (e.g. by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings, or to persistent anthropogenic changes in the composition of the atmosphere or in land use (IPCC, 2012).

Critical Infrastructure An asset belonging to the surface transport network (road and/or rail) system or part thereof located in Member States that is essential for the maintenance of vital societal functions, transport, health, safety, security, economic or social well‐being of people, and the disruption or destruction of which would have a significant impact on a Member State as a result of the failure to maintain those functions (European Commission, 2015).

Direct Losses Costs or losses that are a direct consequence of the extreme weather event and refer to the physical impacts on land‐based transport, energy and telecommunications infrastructure.

Disaster Severe alterations in the normal functioning of a community or a society due to hazardous physical events interacting with vulnerable social conditions, leading to widespread adverse human, material, economic, or environmental effects that require immediate emergency response to satisfy critical human needs and that may require external support for recovery (IPCC, 2012).

Disaster Risk Management The process for designing, implementing, and evaluating strategies, policies, and measures to improve the understanding of disaster risk, foster disaster risk reduction and transfer, and promote continuous improvement in disaster preparedness, response, and recovery practices, with the explicit purpose of increasing human security, well‐being, quality of life, and sustainable development.

Economic Risk The probability of monetary losses associated with the occurrence of an extreme weather event. Notably, many of the impacts associated with disaster events cannot be quantified in monetary terms, e.g. loss of human lives, cultural heritage and ecosystems.

Exposure The presence of people, livelihoods, environmental services and resources, infrastructure, or economic, social, or cultural assets in places that could be adversely affected (IPCC, 2012).

Extreme Weather Event The occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of the variable (IPCC, 2012).

Hazard The possible future occurrence of natural or human‐induced physical events that may have adverse effects on vulnerable and exposed elements. D6.3‐Report on benefits of critical infrastructure protection

Indirect Losses Costs or losses that arise due to the disruption caused to land‐based transport, energy and telecommunications infrastructure due to an extreme weather event.

Mitigation The amelioration of disaster risk through the reduction of existing hazards, exposure, or vulnerability including the use of different disaster preparedness measures (IPCC, 2012).

Preparedness Measures that may include early warning and the development of contingency or emergency plans.

Resilience The ability of a system and its component parts to anticipate, absorb, accommodate, or recover from the effects of a hazardous events in a timely and efficient manner, including through ensuring the preservation, restoration, or improvement of its essential basic structures and functions (IPCC, 2012).

Risk UN‐ISDR: ‘Risk is the probability of harmful consequences, or expected losses from death, injuries, property, livelihoods, economic activity disrupted security or environment damaged resulting from interactions between (natural, human‐induced or man‐made) hazards and vulnerable conditions.’ (Hajializadeh et al. 2015)

Risk Assessment A methodology to determine the nature and extent of risk by analysing potential hazards and evaluating existing conditions of vulnerability that could pose a potential threat or harm to people, livelihoods and the environment on which they depend. (Hajializadeh et al. 2015)

Security Risk The probability of chronic threats to humans, such as hunger, disease, and repression, associated with the occurrence of an extreme weather event (IPCC, 2012).

Societal Risk The probability of injury of death of humans due to the impact of an extreme weather event on land‐based transport, energy and telecommunications infrastructure.

Susceptibility A physical predisposition of human beings, infrastructure and environment to be affected by a dangerous phenomenon due to lack of resistance and predisposition of society and ecosystems to suffer harm as a consequence of intrinsic and context conditions making it plausible that such systems once impacted will collapse or experience major harm and damage due to the influence of a hazard event (IPCC, 2012).

Vulnerability The degree to which a system is susceptible to and unable to cope with adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity (IPCC, 2012).

D6.3‐Report on benefits of critical infrastructure protection

1. Introduction

1.1. Motivation

Extreme weather events such as floods, droughts, windstorms, thunderstorms, snow storms, wildfires, as well as others, have the potential to cause significant disruption to critical infrastructure, resulting in considerable losses. Over the past number of decades, a significant increase in the number of extreme weather events and the associated losses has occurred due to changing climatic conditions, land‐use changes and increasing exposure.

RAIN WP2 Deliverable D2.2 has documented 21 past cases of extreme weather events, describing the meteorological conditions that gave rise to the weather and the impact on critical infrastructure. These events included the following: an extreme wind event in 2007 that resulted in 47 fatalities and billions of Euros of damage across Europe; an extreme rainfall event in 2010 that resulted in the death of 45 people and €1.5 billion worth of damage on the island of Madeira, Portugal; thunderstorms in Germany in 2004 that resulted in €880 million worth of damage and 6 fatalities; a snow storm in Finland in 2005 that resulted in 60 people being injured and 3 fatalities; coastal flooding in 2010 that resulted in 41 fatalities and contributed to €2.5 billion worth of damage.

Risk assessment for extreme weather events is a complex process that requires an understanding of weather phenomena affected by climate change, an ability to identify critical infrastructure elements that will be affected and, furthermore, requires an insight into the consequences of critical infrastructure failure. Reliable risk assessments for distributed infrastructure due to extreme weather events can assist in the protection of critical infrastructure, as well as the minimisation of losses.

1.2. Aims and Objectives

The objective of this deliverable is to demonstrate the systematic application of the Risk‐Based Decision Making Framework that has been outlined in RAIN Deliverables D5.1 (van Erp & van Gelder 2015) and D5.2 (van Erp et al. 2017), as a means for providing a quantifiable measure of resilient infrastructure. The proposed framework has been applied to two case studies and, in that application, methodologies and tools that have been presented in work packages 2, 3, 4, 5, 6 and 7 of the RAIN project are brought together. In this the various project outputs have been merged to provide an operational analysis framework that can be used to assist in decision‐making with regard to the protection of European infrastructure networks against extreme weather events.

1.3. Scope

Extreme weather affects critical infrastructure in two ways. Either extreme weather can damage components of the infrastructure, or impair its functioning without causing structural damage (Groenemeijer et al. 2015). The work herein, as outlined in Deliverables 6.1 (Hajializadeh et al. 2015) and 6.2 (Hajializadeh et al. 2016) considers the impacts of extreme weather events on critical infrastructure i.e. damage to the components, and the direct consequences that result.

D6.3‐Report on benefits of critical infrastructure protection

Similarly, the mitigation actions discussed are direct engineering interventions that are employed to reduce 1) the probability of hazard occurrence 2) the impact of the hazard on the critical infrastructure or 3) the consequence of the impact on the critical infrastructure. What measures to employ and the basis for how to judge the effectiveness of the measures are taken from RAIN Deliverable D7.2 (Gavin & Murphy 2016). The mitigation measures in this document fall into the second of Deliverable D2.3 (Groenemeijer et al. 2015) categories of measures employed by critical infrastructure operators: preventive measures and measures taken once a warning for extreme weather has been issued. Details on warning systems for extreme weather and infrastructure failure can be found in RAIN Deliverable D2.3 (Holzer et al. 2015) and Deliverable D7.5 (Murphy & Gavin, 2017) respectively. The infrastructure networks included in the RAIN project include land transport, electrical and telecommunications networks. The case studies in Sections 3 and 4 analyse the consequence of impacts of extreme weather on land transport and electrical networks present in the case study areas.

Appendix B describes the basis of methodologies to consider indirect impacts and indirect mitigation measures. Neither of which are used in the analysis carried out in Sections 3 and 4 as the methodologies were not fully developed in time for data collection and modelling. Similarly, there are aspects of the work described in RAIN Deliverable D5.2 (van Erp et al. 2017) that could not be included in the analysis carried out herein. The case study demonstration employs the probability sort algorithm discussed in Section 4 of that deliverable but the modelling of cascade effects and the time dependant consequences could not be incorporated.

As mentioned, the choice and effectiveness individual mitigation measure assessment is based on the work completed in RAIN Deliverable D7.2 (Gavin & Murphy 2016). For one of the case studies, in order to assess the benefits of a broader strategy of securing various components of the critical infrastructure networks a wider mitigation strategy is modelled in which combinations of various measures, constrained by a budget, are compared. The comparison is carried out by applying the WP5 criterion of choice methodology instead of a traditional cost benefit analysis.

1.4. Deliverable Structure

The two case studies help to demonstrate how various aspects of the work carried out in other RAIN work packages can be applied and demonstrate the benefits of providing critical infrastructure protection. Central in these is the RAIN Risk‐Based Decision Making Framework presented in WP5 Deliverable D5.1 (van Erp & van Gelder 2015). Section 2 discusses the Framework and briefly describes how the probability models developed in the case study analyses fit into the Framework.

Both case studies assess the risk associated with a specific extreme weather event. The two case studies are also quite different in terms of geographic scale and the density of the transport, electrical and societal networks within each case study area. These differences highlight the flexibility of the RAIN Risk‐Based Decision Making Framework.

A Section is given each of the two case studies undertaken in this deliverable. The first case study, discussed in Section 3, centres around a 2003 extreme rainfall event which took place within the Friuli Venezla Giulia Region of North Eastern Italy. The extreme rainfall caused landslides and river flooding and resulted in damage to critical infrastructure in the area. In Section 4, case study two is 10

D6.3‐Report on benefits of critical infrastructure protection

presented. The case study examines a part of the Usiimaa region in Finland and the Risk‐Based Decision Making Framework is used to evaluate the impact of an extreme storm event on infrastructure in this region. A storm surge event which occurred in 2005 acts as the basis for this case study.

Both case study Sections follow a similar structure – beginning with an overview of the emergency management of the respective country in the context of the historical events before elaborating on how the RAIN Risk‐Based Decision Making Framework (Figure 1) was applied to provide a measure on the benefit of critical infrastructure protection.

D6.3‐Report on benefits of critical infrastructure protection

2. Risk‐Based Decision Making Framework

A primary goal of Work Package (WP) 6 has become the demonstration of the implementation of the Risk‐Based Decision Making Framework developed in WP5 (van Erp and van Gelder, 2015) in the analysis of the impact of extreme weather events (EWE) on critical infrastructure. The Framework proposed by van Erp and van Gelder (2015) is presented in Figure 1. The Framework consists of two main phases: 1) the inference phase, and 2) the decision phase.

Figure 1 Risk‐Based Decision Making Framework (van Erp and van Gelder, 2015)

The case study analyses in the latter half of Sections 3 and 4 are discussed in the context of working through the steps of the Framework:

 Enumeration  Quantification of Likelihoods 12

D6.3‐Report on benefits of critical infrastructure protection

 Consequence Analysis  Construction of Outcome Distribution(s)  Application of Criterion of Choice (Case Study 1)

Detail on the Framework can be found in RAIN Deliverable D5.1 (van Erp and van Gelder, 2015).

2.1. Bayesian Network Modelling

To implement the inference phase of the Risk‐Based Decision Making Framework proposed by van Erp and van Gelder (2015), Bayesian Network (BN) modelling is employed for the case studies. BN modelling is a graphical method that employs Bayesian probability theory to graphically represent complex processes or networks (Aguilera, et al., 2011). BN modelling facilities the combination of information from various sources and is an appropriate method for dealing with multi‐disciplinary complex problems, such as the risk due to the impact of an EWE on infrastructure networks. Such an approach allows the interactions between elements to be clearly visualised, with values assigned to these interactions to allow the model to be numerically evaluated.

For the Alpine case study a BN is configured to determine the risk due to heavy rainfall while in the Finnish case study, BN modelling is used to determine the risk due to an extreme storm surge event in the Gulf of Finland.

The main steps involved in the development of a BN model are as follows:

 Define the nodes, i.e. random variables.  Construct the Directed Acyclic (DAG) graph, i.e. the BN model.

 Assign a Conditional Probability Distribution (CPD) to each node, i.e. the distribution P(Xi|Pai)

where Xi represents the node i and Pai are its parents.  Employ an inference algorithm to compute a set of hidden variables given a set of observed variables, i.e. the probable economic losses due to an extreme storm surge event.

The Bayesian Network software employed in the case study analysis is GeNIe1.

2.1.1. Multi‐Mode Risk Model

Sections 3 and 4 analyse the consequences of multi‐mode risks in the case study analysis carried out in the latter half of Sections 3 and 4. As described in RAIN Deliverable D6.1 (Hajializadeh et al. 2015), a single risk assessment is a risk of particular hazard occurring in a particular geographical area, while, the multi‐risk concept, discussed in RAIN Deliverable D6.2 (Hajializadeh et al. 2016), refers to various combinations of hazards and various combinations of vulnerabilities. Figure 2 illustrated the multi‐risk concept can be modelled using a Bayesian Network. The multi‐hazard concept may refer to:

a) Different hazards may threaten the same exposed elements. b) Hazardous event can be triggered (cascade effects).

1 GeNIe Modeler from BayesFusion, LLC ‐ available at http://www.bayesfusion.com/ 13

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The multi‐vulnerability perspective may refer to:

a) Various exposed sensitive element with possible different vulnerability to the various hazards. b) Time‐dependent vulnerabilities, in which the vulnerability of a specific class of exposed elements may change with time as a consequence of difference factors.

Figure 2 Multi‐mode risk

2.1.2. Risk Models and Bayesian Network Nodes

Each component in the above figure has a number of possible states – represented by three colours in the figure. In employing the Framework we must enumerate all possible states that might arise.

For instance, each element of indentified critical infrastructure (e.g. a bridge) can be in one of a number of damage states ranging from healthy to, say, destroyed. The probability that the infrastructure is in each state is calculated in the application of the Risk‐Based Decision Making Framework. The term risk model is used to refer to how the calculation for each component is carried out. The risk models employed to calculate the risk to elements from hazards, e.g. road from landslide, are described in the manner of a formula of the product of the probability times the consequence.

For example, the risk of damage to roads from a landslide of a certain magnitude is described as

, , where HL and PL relate to the probability of the landslide occurring, VL,R is a variable that relates the probability the landslide impacting the road (accounting for position of the road in relation to the slope and the runoff length of the landslide) and AL,R is the cost of the impact on the road element by a landslide of that magnitude.

D6.3‐Report on benefits of critical infrastructure protection

The risk models are generally presented in this manner (as a formula) while the calculation of the risk within the Bayesian Network is somewhat different: employing calculations of conditional probability i.e. the probability that the road is damaged equal to the probability that the extreme weather occurs (In this case rainfall) multiplied by the probability that the landslide occurs given that the rainfall occurs multiplied by the probability that the road is damaged given that the landslide occurs. This is implemented in the Bayesian Network software using three nodes and assigning the conditional probabilities to each:

EWE Landslide Road

Figure 3 Bayesian Network for the risk model employed for a landslide damaging a road

The Bayesian Network software outputs the probability of each state. These states are then imported into a mathematical programming software (MATLAB 8.6 R2015b) and the consequences of the extreme weather event is calculated.

D6.3‐Report on benefits of critical infrastructure protection

3. Case Study 1 ‐ Alpine Region Heavy Rain, Italy

3.1. Emergency response to Extreme Weather Event

The first case study is the 2003 flash flood and landslides of Malborghetto – Valbruna towns located in the Friuli Venezia Giulia Region (FVG) of Italy. The region of the Friuli Venezia Giulia, illustrated in Figure 4 consists of 4 provinces. The Alpine case study area is located in the largest of these, Udine, and, centred around the Fella river, includes parts of the Communes (municipalities) of Malborghetto Valbruna, Dogna and Pontebba. This area is also highlighted on Figure 4.

Alpine case study area

Figure 4‐ Geographical location of Alpine case study area

D6.3‐Report on benefits of critical infrastructure protection

As well as being the administrative sub‐division to which the case study belongs the Friuli Venezia Giulia (FVG) region has also been a pioneer in the shaping of the Italian civil protection system as it is structured today and has a long tradition in civil protection dateing back hundreds of years.

The history of the Italian Civil Protection is entwined with the disasters that have afflicted its territory. The Italian territory has been subject to natural disasters since ancient times. Earthquakes and floods date back as far as to the Roman Empire (Istituto Nazionale di geofisica e vulcanologia, n.d.). The Italian peninsula is subject to various risks of diverse origin and varying intensity: earthquakes, floods, landslides, volcanic eruptions and fires, worsened by human activities that alter natural conditions. The peninsula, with the exception of Sardinia, has been subject to earthquakes due to the fact that it is located above two tectonic plates. The same geological reason is at the origin of the volcanic risk. Volcanic eruptions are less frequent than earthquakes however they do pose a threat as they can involve densely populated areas. Earthquakes and volcanic eruptions give origin to the risk of tsunamis in the entire Mediterranean coastal zones and especially on the Southern isles. Human activities and urbanisation are the main causes of the hydro‐geological risks seen throughout the country and worsen the instability of the territory (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (t), n.d.). Human activity is also the source of the fires that are mainly caused by actions of fraud or incorrect human behaviour and have already disrupted 12% of the Italian forests in the last thirty years (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (u), n.d.).

The FGV regional civil protection is nowadays part of the national system of civil protection. The Italian State is organized into territorial subdivisions, i.e. autonomous bodies, which all have their own statutes: Regions, Metropolitan Cities, Provinces and Municipalities (art. 114 of the Italian Constitution). Italy counts 20 regions of which 5 hold a special status due to their geographical location and the presence of minorities. These 5 regions are Valle d’Aosta, Friuli Venezia Giulia, Sardegna, Sicilia, and Trentino Alto Adige. The special statutes of these Regions are acknowledged in the Italian legal framework with a constitutional law which give large autonomy to the administrative subdivision “region” (art. 116 of the Italian Constitution). Except for the laws listed in art.117 which are the exclusive domain of the State legislations, all other matters, including laws on civil protection, fall under “concurrent legislation” (legislazione concorrente) and are therefore power of the Regions. However, the special status entitles the five Regions listed above to a broader autonomy in terms of legislative, administrative and financial powers, mainly in the fields of school, health and public infrastructures, industry and commerce, tourism, transport, agriculture, hunting and fishing activities. It follows that Autonomous Regions are also in charge of more responsibilities with respect to ordinary sub‐divisions.

3.1.1. The framework for civil protection at national level

Civil protection can be defined as a fundamental public service to protect the lives and environment against the damaging effects of disasters. Currently, the concept of civil protection is a far‐reaching concept that encompasses not only the facing of an emergency, but also the monitoring and prevention of risks and damages (Protezione Civile – regione autonoma Friuli Venenzia Giulia, n.d.).

The civil protection is in charge of constantly monitoring the territory and the potential arising risks; it coordinates the activities of risk awareness and assessment, and intervenes in cases of emergency 17

D6.3‐Report on benefits of critical infrastructure protection

to protect people, assets and environment. Its role combines forecasting, urgent prevention and intervention in the aftermath of an emergency (Protezione Civile ‐ Regione Friuli venenzia Giulia (a), n.d.).

Although the civil protection, as a concept, has far older roots, the National Civil Protection System in Italy was established by law n° 225 in 1992 (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri, 2015). The Civil protection is informed according to the principle of subsidiarity, this means actions are taken at national and local level, i.e. Regions, Provinces, Municipalities and mountain communities (Comunità Montane ‐ art. 6 of the law n°225 of 24th February 1992 regarding Establishment of the National Civil Protection Service) in order to always guarantee that activities are carried out at the closest possible level to citizens. Thus the Italian civil protection is an integrated and structured system that could be described as series of Chinese boxes in which the national level coordinates the local subdivisions and intervenes in case of emergencies of very large proportions, but where the monitoring, risk assessment and emergency aid lies in the hands of the local Italian authorities: regions, provinces and municipalities (Protezione Civile ‐ regione autonoma Friuli Venenzia Giulia, n.d.).

The Civil Protection system as a whole, according to the law 225/1992 is in charge of (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (a), n.d.):

 Forecasting and risk prevention: monitoring and identifying possible risks in real time.  Bringing relief to the population affected: emergency aid.  Contrasting and overcoming the emergency: helping restoration to standard life conditions.  Risk mitigation: comprises all the means and activities that are implemented during routine times (i.e. when no emergency is occurring), such as planning, training, spreading of knowledge and information within the population and implementation of the technical provisions of the law. All these activities, carried out as routine duties, have the final aim of reducing possible damages in case of a disaster happening.

If no emergency is going on, the main activities of the Civil protection concern prevention and forecasting in close cooperation with the scientific community involved through the Functional Centres (Centri Funzionali) responsible for monitoring and alert mechanisms and through the Competence Centres (Centri di Competenza), manly acting as research centres.

When an emergency hits, the authority responsible for taking action is the Mayor, who coordinates emergency aid to the population according to the municipal emergency plans. In case the means of the municipality are insufficient, the intervention of the Province or the Region may be necessary. Only if the Government of the Region requests the intervention of the State, the national level of civil protection is activated. At this point the head of the operations is the Prime Minister together with the Department for Civil protection. Furthermore Regions, Provinces, and Municipalities periodically revise the Emergency Plans concerning operational measures to intervene in case of disasters.

The National Civil Protection System comprises the following operational structures (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (b), n.d.): The National Firefighting Service; the Armed Forces; the Police; the State Forestry Corps; the Italian Red Cross; the structures 18

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of the National Health Service; voluntary organizations; the National Mountain Rescue Corps; and the scientific community. The latter mainly refers to scientific centres that have signed specific agreements and are in charge of providing scientific and technical support for monitoring, prevention, and forecasting. The scientific bodies in charge of these activities are mainly three: INGV (National institute of Geophysics and Volcanology), the CNR (National research Council) and ENEA (Agency for New Technologies, Energy and the Environment). INGV is in charge of monitoring the volcanic and seismic activity through the entire national territory; CNR cooperates with the Network of Functional Centres in order to improve the monitoring and prevention system as well as conducts researches and develops new monitoring tools; finally, ENEA focuses on monitoring seismic risk throughout the Italian peninsula and realizes cutting‐edge technologies in order to protect private and public infrastructures from damages originated by earthquakes (Dipartimento di prrotezione Civile ‐ Presidenza del Consiglio dei Ministri (r), n.d.).

The system of monitoring and prevention is structured into three levels, depending on the severity of the event (Renzulli, 2013):

 Type A events – this category comprises events that do not represent a sensitive threat and can be managed at municipal level as a routine duty;  Type B events – this category comprises events that still hold a routine nature, but due to their magnitude (i.e. outside the municipal boundaries) require coordination of a number of local and regional authorities;  Type C events – this category comprises concern events that for severity and extension require an intervention at national level.

According to this structure Regional programming is based on the guidelines developed at national level for Type B events. Regions in turn provide the basis for the implementation of the forecasting, monitoring and prevention activities at municipal level (Renzulli, 2013). Type B events are also tackled by Provinces both in the prevention and emergency management stages. Only Type C events require an intervention at national level.

3.1.2. The civil protection framework in the Friuli Venezia Giulia Region

The civil protection of Friuli Venezia Giulia was born in absence of a framework law at central level and was a one of a kind law in the matter of civil protection at that time, recognizing a core function of autonomous regions in protecting people from natural hazards and disasters (Di Bendetto, 1987).

Since the very beginning, the regional civil protection system was organised according to an integrated structure that comprised: institutions; civil society; and volunteering associations; in accordance with the central government, but structured at local level into associations and Municipal Groups of civil protection (Protezione Civile ‐ Regione Autonoma Friuli Venenzia Giulia, n.d.). Furthermore, the regional law issued the creation of an operational room for the coordination of national and regional levels for the management of emergencies in order to create a stable structure that eliminated the need to recreate, ex novo, a coordination body every time an emergency broke. Other relevant features of the regional law were the official recognition of the volunteering groups as part of the bodies that have role in coping with the emergency (Di Bendetto, 1987). 19

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Italy has a very complex structure for civil protection, thus ensuring its presence throughout the territory in order to tackle the numerous risks the Italian area is exposed to. The National Department of Civil Protection ensures (Dipartimento di protezione Civile ‐ Presidenza del Consiglio dei Ministri (f), n.d.):

 The smooth functioning of the whole civil protection system and of the communication processes between the different levels of the civil protection. In doing so, it is supported by the Joint Committee on State‐Regions‐Local Authorities, the National Commission for the prediction and prevention of major risks, Operations Committee of civil protection;  Prevention and forecasting of risks of diverse origin in close cooperation with the regional civil protection bodies;  Drafting of the guidelines for the elaboration of local emergency programmes and plans,  Ensuring cooperation and close coordination between the several bodies active in the field of monitoring and emergency assessment: the central Functional Centre, “Sistema” (a coordination body for emergencies), the Unified Air Operation Centre and the Operations Centre for the Maritime Emergencies;  Support to the volunteering service,  Coordination of the emergency measures in case of a Type C event, i.e. an extraordinary event of large proportions that requires non‐ordinary measures to be assessed properly.

As mentioned, the operational structures of the national service consists of: The National Firefighting Service; the Armed Forces; the Police; the State Forestry Corps; the Italian Red Cross; the structures of the National Health Service; voluntary organizations; the National Mountain Rescue Corps; and the scientific community. In ordinary times (i.e. when no emergency is occurring) they all cooperate with the National Department and Regional bodies in monitoring and detecting potential risk situations.

In case a national emergency hits the functions are as follows:

 Operational Committee: it is made up of representatives of the Civil Protection department, of the operational structures listed above and of other private institutions. The committee is summoned by the head of the Department and it is active during every emergency situation during which it collects and examines data on the disaster, defines strategies, and coordinates aid to the population (Dipartimento di Protezione Civile ‐ Presidenza del Consiglio dei Ministri (g), n.d.); the coordination and constant monitoring are carried out by “Sistema”, the national coordination centre for civil protection  The national department coordinates the rescue activities, local authorities act according to the national guidelines and emergency plans; at local level mayors provide instructions to the population and volunteers intervene to give emergency aid to the population together with international networks (OECD, 2009);  The national department and the government act in the reconstruction phase, the first holding a leading role in the recovery stage and the latter by providing financial aid and enacting emergency legislation; at local level the President of the Region establishes ad hoc commissions to assess damages and evaluate progress in reconstruction (OECD, 2009).

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Regions are in charge of preparing program of forecasting, prevention and emergency management following the national guidelines, moreover they are responsible for the fielding of urgent relief to the population in case of Type B events and for ensuring the restoration of normal living conditions as fast as possible and for organizing volunteering interventions (art. 108, Law decree 112/1998); together with the River Basin Authorities they are in charge of risk assessment as far as floods and other hydro‐geological risks are concerned. For all the monitoring and prevention activities the Region is supported by the Scientific and Technical Committee, the Regional Committee for the Emergencies supports the President of the Regional Government in managing the emergency (art. 13 – 15, law 64/1986);

Provinces enact the Provincial program of emergency and implement the activities stated in the regional program (art. 108, paragraph 1of the law‐decree n. 112/1998);

Municipalities have similar duties as those of the Provinces and are in charge of providing emergency aid and volunteering coordination “on the field” in the aftermath of a disaster (art. 108, paragraph 1of the law‐decree n. 112/1998) through the work of the Municipal Operational Centres (COC: the operative branch of the mayor in case of Type A events ‐ events that can be managed through ordinary measures), moreover they are in charge of bringing support to the population and of coordinating and directing the civil protection activities at local level and of managing the volunteering municipal groups; municipalities are also responsible for the drafting of municipal emergency plans (Renzulli, 2013);

Mayors, are the first level authority of civil protection and the closest to the citizens, they are in charge of providing information to the population in the aftermath of a disaster, The Prefect coordinates the management of the emergency together with the local structures of the civil protection and is in charge of the communication activities to the population; they represent the State on the local territory and is in charge of public security (i.e. coordination of public forces, such as the police); if so delegated by the law declaring the state of emergency, the Prefect represents the Prime Minister at local level and exercises the power order (potere d’ordinanza) (Renzulli, 2013).

3.1.3. Procedures in decision making

The decision making process is conducted by decision makers and scientists and it is influenced by subjects indirectly involved in the process: civilians, mass media, and judiciary system.

The decision making process follows the organization of the civil protection, thus it is structured in national and local levels. The national functional centres and the network of regional functional centres cooperate in order to monitor the territory. The system focuses on a number of risks: landslides, forest fires, floods and other hydro‐meteorological risks. Furthermore the national Department is supported by the Commission for Prediction and Prevention of Major Risks and by the Public Weather Forecast and Meteorological Service of the National Air Force, which, as it monitors a wide area, might not always be accurate in alerting the system. Support is also provided at local level by the monitoring relays on ARPAs (Agencies for Environmental protection) or similar private organization, however not all the southern Italian regions have established similar monitoring institutions (OECD, 2009).

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In case an emergency hits, decision makers are required to take decisions quickly, since in the aftermath of an emergency a slow decision‐making processes has high costs in terms of lives and damage to assets. Decisions are taken at municipal, provincial, regional or national levels depending on the severity of the risk/disaster. In the aftermath of a Type A event, which can be assessed through ordinary means, it is the Mayor who manages the intervention, being the first authority of civil protection at local level; if the emergency goes beyond the municipal duties and means of intervention (Type B events), the Provinces and eventually the President of the Region intervene, it is the Head of the Region, who decides wherever to alert the Government in order to obtain a declaration of the state of emergency which is issued by the Council of Ministers (Type C events).

Decision makers can be distinguished into: political and technical ones. The decision‐making process unfolds as follows (Dolce, 2014):

 Policy decision makers decide the “acceptable level of risk according to established policy”,  Scientists operate a quantitative assessment of the risk,  Technical decision makers contribute with finding solutions that can bring risk levels back into the acceptable parameters,  Scientists engage in a cost‐benefit evaluation of the actions that need to be implemented in order to stem the damages,  Technical decision makers decide which actions are the most efficient and implement them.

However, the subdivision of the different steps and roles often mingles especially if scenarios are extended and complex. In those cases, technical decision makers and scientists often cooperate throughout the emergency and political decision makers give their contribution in choosing the most effective actions to be taken in the aftermath of the disaster.

3.1.4. Situational awareness and communications in civil protection at national level

As highlighted in the history of the civil protection, this body was originally created to intervene after an emergency had occurred, however its role grew during the decades and it is now an institution that operates also in the prevention phase.

As far as the situational awareness is concerned the Civil Protection cooperates with research institutes in order to acquire knowledge on the state of the art of the territory at any given time and develop adequate strategies for prevention and risk mitigation and containment.

The forecasting system is organized as follows (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (g), n.d.):

1. National Warning System: the system is responsible for warning the civil protection structures and it is made up of a National Functional Centre (under the coordination of the Department for Civil Protection) and a Network of Regional Functional Centres. These centres operate through constant, real time, monitoring and surveillance of the territory and of the meteorological phenomena with the aim of detecting potential risk situations. Presidents of the Regions (or of the Autonomous Provinces) are in charge of alerting the civil protection at local level. The National Warning System can be defined as a set of procedures, tools and methods that, through a coded language, monitor and alert prevention and emergency structures; more specifically, 22

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according to the Directive of the President of the Council of Ministers (27th February 2004), the National Warning System has the following stages: 1.1. Forecasting phase: evaluation of the state of the art of the morphological, hydrographic, and meteorological most likely conditions and of their possible effects on the levels and integrity of people, environment and assets; 1.2. Monitoring phase: monitoring of the phenomena that could represent a risk; 1.3. The first two stages determine if the prevention phase and the managing of emergency are to be activated or not.

Moreover, the situational awareness is also ensured by:

2. The National commission for prediction and prevention of major risks, the Public weather forecast and Meteorological Service and the Regional Agencies for Environmental protection are all part of the monitoring and early warning system; 3. Zones of alert (hydro‐geological risks): Regions and Autonomous Provinces cooperate with the National Department in order to classify watersheds under their competence in zones of alert on the basis of the possible consequences due to adverse weather events; 4. Thresholds and levels of criticality: three thresholds of alert (ordinary, moderate, serious) are linked with the different levels of imminent risk as identified by the Regions and the Autonomous Provinces (e.g. continuous and abundant rain is known to be possible cause of landslides); 5. Alert levels: depending on the seriousness of the risk level the President of the Region is in charge of alerting the national Department; the Mayors are responsible for the enactment of the measures of the municipal emergency plans. 6. Information on an out‐breaking emergency can derive from three different sources, depending on the type of the event: 7. Monitoring systems that automatically send alarm signals, in case the event has a predictable origin, 8. Detection through tools and equipment, in case of events characterized by the unpredictability, 9. Direct reporting of the population, mainly through the Contact centres established in 2011 (see below for further details).

After the emergency is detected, and verified, the regional Operational Room evaluates the severity of the event, analyses its space‐time dimension and alerts the Regional Civil Protection System and Prefectures of the Region. If the emergency gets over the criticality levels (Type C events) the Region asks for the declaration of the State of emergency, issued by the Council of Ministers.

An essential part of the prevention and risk mitigation activities is the Communication, which is mainly a task of the Prefect as a part of his role as a national authority. The Prefecture gives information to the population together with the mayors who are in charge of delivering instructions to local citizens in case of emergency. Since the enactment of the law 100/2012 every municipality is required to draft an emergency plan following the guidelines issued by the National Department of the Civil Protection and the Regions. The emergency plan is both a tool for prevention and the framework in case of emergency, containing warning and evacuation procedures. Plans are flexible

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in order to adapt to a variety of situations and are frequently updated (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (h), n.d.).

The Municipal Operation Centres, established in every Italian municipality, define intervention strategies and host operation rooms. The Prefect and the mayors play a key role in providing information and instructions to the population. During emergencies a central role is played by volunteers, who act as the link between the formal bodies of Civil Protection and the local population taking advantage of their proximity to the territory and the people (Renzulli, 2013).

Communication is also part of the ordinary tasks of the civil protection and does not occur only in case of emergency; the Civil Protection and its local volunteers are in charge of the risk education of the population and, specifically, a program that schools can include in their curricula. Some communication activities are the result of cooperation with other NGOs working in the field (Bianchizza, et al., 2011).

Part of the ordinary Communication are information campaigns that train people on the emergency procedures and on how to facilitate the operations of the civil protection after a disaster. Information and prevention is also done through exercises and simulations (Dipartimento della Protezione Civile ‐ Presidenza del Consiglio dei Ministri (i), n.d.).

Furthermore the official website of the national Civil Protection is the communication channel used to deliver alerts and news to the population together with press releases and publications.

Citizens play a key role in facilitating emergency operation during a crisis and supporting the civil protection. Risk prevention and mitigation of damages are best achieved if the population is well informed and prepared to manage a crisis. Avoiding panic is the best solution to avert further damage and ensure that members of the civil protection can work easily and effectively. In order to meet these goals, citizens are trained and informed with the final aim of achieving a widespread culture of civil protection. In this sense, a central role should be played by the regional bodies of civil protection and by the educational system (Protezione Civile ‐ Regione autonoma Friuli Venenzia Giulia (b), n.d.).

Finally, citizens may active play a role also in monitoring the territory and warning the system, through the Contact Centres mentioned in the previous paragraphs people have the chance of reporting potential situations of risk or imminent emergencies.

3.1.5. Civil protection and flash flood of 29th August 2003

The flash flood of 29th August 2003 involved seven municipalities: Moggio Udinese, Resiutta, Chiusaforte, Dogna, Pontebba, Malborghetto – Valbruna and Tarvisio, causing two causalities, damages and hardships to the 8665 inhabitants of the area. The Municipality of Malborghetto‐ Valbruna was the most affected in terms of damage to private and public infrastructures, its hamlet “Cucco” was hit by debris flow and some houses were covered with gravel and evacuated (Berlasso, Presentazioni sull'alluvione della Valcanale Canal del Ferro del 2003 e sulle opere di ricostruzione, 2013). The flood took tragic proportions due to: the extreme dry soil after a long drought that preceded the flood and a storm that caused 335 mm of rain to fall in six hours (De Marchi et al., 2007). The flash flood was responsible for two casualties and material damages amounting to €190 24

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million only in the Malborghetto – Valbruna area. Basic services were severely damaged and hundreds of people were evacuated from their homes.

The regional civil protection had received warnings before the 29th August 2003, however the forecasts predicted less than half the rainfall that actually submerged the valley. The Malborghetto‐ Valbruna area was evacuated between 5 p.m. and 7 p.m. Hamlets of the municipality rapidly found themselves isolated and the alarm signals were difficult to hear in the distance because of the noise the rain was causing. The evacuation plans worked less effectively in Cucco which was the most isolated hamlet following the flood. It took one month to remove all the mud from the streets and buildings (De Marchi et al., 2007).

As far as the 2003 flood is concerned, according to the powers he was invested by the Presidential decree (n° 3309/2003), the Deputy Commissioner and Assessor of the Friuli Venezia Giulia Region, Mr. Moretton, cooperated with the local mayors in order to assess the severity of damages, bring aid to the citizens and take all the necessary and most urgent measures in order to guarantee safety for people and assets (art.1, paragraph 2 of the presidential decree). In order to fulfil these aims, the deputy Commissioner cooperated with the local bodies of the Public Administration and of the regional structures. The institutions responsible for the management of public services (i.e. electricity) were in charge of restoring them with their own economic means. Moreover the Deputy Commissioner or the mayors, in his place, assigned contributions for accommodation to all citizens who lost their homes or were evacuated (art.2, paragraph 2 of the presidential decree). Contributions were given to the local enterprises too, at the request of the interested parties (art.3 of the presidential decree).

A number of actions were taken immediately, others waited longer to come into practice due to lack of funds and favourable geographical conditions (Bianchizza et al., 2011). The Decrees 1580/DPCR/2003 and 1581/DPCR/2003 stated the need to implement the actions of utmost urgency and authorized their financing through the Regional fund of the civil protection. Among the actions taken immediately after the flood were: interventions for clearing and cleaning roads and buildings and re‐embedment of the overflowing rivers, restoration and reconstruction of roads and actions to ensure the safety of the territory. Thanks to the prompt interventions of the civil protection citizens in Cucco, one of the areas that were most severely hit by the flood, were able to re‐enter their homes by the end of 2003 (Berlasso, Presentazioni sull'alluvione della Valcanale Canal del Ferro del 2003 e sulle opere di ricostruzione, 2013).

Shortly after, regional and local authorities engaged in a discussion on the most effective protection measures, i.e. the construction of structural devices in order to stem the most dangerous rivers as the civil protection had begun to do following the flood. The building of protection works clashed against the opposition of residents concerned with safety issues for their assets and claiming stakeholders had no chance to take part into the decision making procedure (De Marchi et al., 2007).

According to the results of the workshops conducted in 2011 for the CapHaz‐Net project on the basis of a preliminary SWOT Analysis, one of the most relevant features in managing the crisis was the presence of volunteers of the civil protection at local level (Bianchizza, et al., 2011). 181 municipal civil protection teams and 15 associations of civil protection (i.e. 2687 volunteers) brought their help to towns hit by the flood. The regional volunteers of the civil protection helped all municipalities of 25

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the area under the coordination and direction of the Regional operational Room of Palmanova (Berlasso, Presentazioni sull'alluvione della Valcanale Canal del Ferro del 2003 e sulle opere di ricostruzione, 2013).

They positively influenced the management of the emergency by being well prepared and trained and also facilitated the communication and information activities in the long run. However, issues came up soon as the population had a different idea on what should be done and wanted part in the decision making process. Citizens’ claims were heard and assessed also thanks to the alignment of the civil protection to the alignment on the same positions of the citizens. People helped identify other sources of danger and created a Local Committee to ask for more protection by the local authorities. The mayor played a key role in the rebuilding phase, thus granting a seamless dialogue between Regional and local authorities and the population involved (Bianchizza, et al., 2011).

At national level, the State intervened by declaring the “state of emergency” and coordinating the relief activities to the population that were implemented at regional level. On 4th September 2003 the Prime Minister issued a decree to declare the State of emergency as a consequence of the flash flood of August 2003. The declaration of the State of emergency made funds for managing the disaster and the reconstruction process immediately available with all the advantages that followed. The powers of the Prime Minister derive from the law 286/2002 according to which the Prime Minister is invested with full powers in order to cope with a Type C event (Bianchizza, et al., 2011). Furthermore, the Prime Minister named the assessor to the Civil protection of the Autonomous Region of Friuli Venezia Giulia, Mr. Moretton Gianfranco, Deputy Commissioner for overcoming the emergency. Members of the National civil protection Department cooperated with the Deputy Commissioner as long as the emergency lasted in order to help the population recover from the disaster and fulfilling coordination tasks as the emergency, in the Italian civil protection system, is managed at regional level. Furthermore, the status of Autonomous Region of Friuli Venezia Giulia gives it the power to legislate in matters concerning civil protection with a broader extent compared with the other Italian regions that have and ordinary status (art. 12 law 225/1992). The sub‐ constitutional law l.c. 1/1963 approving the Statute of the Region states that Friuli Venezia Giulia maintains legislative powers in the field of fire‐fighting services and in the works of prevention and relief for natural disasters.

The management of the 2003 flood had both strengths and weaknesses in it. As far as the points of strength are concerned, several months after the flood every municipality of the Valcanale created a “Flood Office” under the coordination of the Region in order to follow the procedures for the compensation the citizens were entitled and to support other municipal departments on technical aspects.

First of all, it is impossible to question the value of volunteering aid and the efficient relationship established through the decades after the 1976 earthquake between citizens, municipalities and civil protection was highlighted as a very positive element during the discussion groups organized in 2011 for the CapHaz‐Net Project on the Malborghetto ‐ Valbruna case study, inverting the results of previous researches (Bianchizza, et al., 2011). Moreover, the intense and uninterrupted dialogue between the authorities and the population was mainly due to the central role the mayor was able

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to play in terms of making the most of the social capacities of the population and of the actors involved.

The opinion of citizens and local stakeholders was taken into account when drafting the proposals for mitigation works, furthermore the tragedy helped raise the awareness and the civil society engagement on the importance of prevention and the risks of floods.

The dialogue between citizens, local authorities and civil protection together with the work of the volunteers can be traced as the main strengths of the recovery strategy to tackle the 2003 flash flood.

On the other hand, the emergency and its management highlighted also some possible improvements. The discussions held with the stakeholders in 2011 pointed out the need of increasing the mitigation structures and the technologies used to monitor the territory (Bianchizza, et al., 2011), as risk mitigation and emergency avoidance are primarily built on a very effective monitoring system and on prompt preventive actions. Moreover, issues emerged, concerning compensation payments and different points of view of the citizens regarding the reconstruction process, thus highlighting the need for a more efficient coordination and governance structures in order to improve the management of the emergency and the reconstruction stages. In fact, the main obstacles in the post emergency period were related with the scarce involvement of citizens in the decision making process and their different perception of what were the most urgent measures to be taken in the aftermath of the emergency. As highlighted in the previous paragraph issues emerged also in the allocation of funds and contributions to the citizens. Difficulties were also reported in the on‐the‐field coordination between the various agencies and services active in managing the emergency.

As far as the hydro‐geological risk is involved, further progress can still be made in the implementation of the EU Directives especially in terms of citizens’ involvement and awareness. Community maps, displaying the social and geographical structure of the community, seem to be an effective tool in order to grant citizens’ participation, involvement in the decision‐making process and people’s awareness on the risk of hydro‐geological origin.

Furthermore, the absence of mandatory insurances to protect citizens against damages provoked by natural hazards is still a core issue of the national debate. Attention to the matter raises and decreases in the political arena according to the time lapse that passed since the last disaster hit, however the matter is of high importance as a public private partnership is more and more desirable (Fauttilli, et al., 2013).

3.2. Application of Risk‐Based Decision Framework

This section describes the application of the Risk‐Based Decision Making Framework Risk Analysis Framework developed in Deliverable 5.1 of the RAIN project will be applied to the Alpine case study. As seen in Figure 1 the stages involved in the application of the Framework are as follows::

 Enumeration  Quantification of likelihoods

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 Quantification of consequences  Construction of output/utility probability distributions  Apply criterion of choice (chose optimal mitigation action)

3.2.1. Enumeration

In this section, the various hazards, vulnerabilities and consequences present in the case study area and included in Risk‐Based Decision Making Framework are considered. The spatial boundaries of the system are illustrated in Figure 5. In order to ensure that reasonable actions are considered, the enumeration of actions stage will come after calculation of the hazards and associated infrastructure vulnerabilities.

Figure 5‐ Alpine case study boundary and key features

3.2.1.1 Extreme Weather Events:

RAIN Work Package 2 considered the following extreme weather events (EWEs) to various extents:

 Windstorms  Heavy rainfall  Thunderstorm gusts  Tornado  Hail

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 Lightning  Snow and snow storms  Freezing rain  Wildfire  Coastal Flood

Amongst the main outputs of WP2 are the Pan‐European gridded datasets of extreme weather probability showing trends of extreme weather throughout Europe. Amongst the data are projections both present and projected future weather across Europe, based on different climate change scenarios. This data can downloaded from the internet2 with a description of the data sets and methodologies used in the analyses provided in RAIN Deliverable D2.5 (Groenemeijer et al. 2016). The extreme weather investigated in this case study in heavy rainfall which caused the August 2003 event discussed in section.

3.2.1.2 Hazards

Similarly to the selection of heavy rainfall as only extreme weather event assessed in the case study, the hazards are chosen due to their presence in the August 2003 event: flooding and landslides. This multi hazard scenario is in line with Table 1 below, previously presented in RAIN Deliverable D6.2 (Hajializadeh et al. 2016), in which heavy rainfall (‘Storms’ in the Table) triggers, or increases the likelihood, of Landslides and Floods.

Table 1 Ability to characterize triggered and increased probability secondary hazards given information from the primary hazard. (Gill and Malamud, 2014). PRIMARY SECONDARY FORECASTING FACTORS OVERALL HAZARD HAZARD LOCATION TIME MAGNITUDE RATING Landslide N – L – M – H N – L – M – H N – L – M – H 6/9 Landslide Flood N – L – M – H N – L – M – H N – L – M – H 6/9 Landslide N – L – M – H N – L – M – H N – L – M – H 5/9 Snow Snow Avalanche N – L – M – H N – L – M – H N – L – M – H 5/9 Avalanche Flood N – L – M – H N – L – M – H N – L – M – H 5/9 Flood Landslide N – L – M – H N – L – M – H N – L – M – H 5/9 Drought Wildfire N – L – M – H N – L – M – H N – L – M – H 3/9 Landslide N – L – M – H N – L – M – H N – L – M – H 7/9 Snow Avalanche N – L – M – H N – L – M – H N – L – M – H 5/9 Storms Flood N – L – M – H N – L – M – H N – L – M – H 7/9 Tornado N – L – M – H N – L – M – H N – L – M – H 3/9 Lightning N – L – M – H N – L – M – H N – L – M – H 4/9 Tornadoes Lightning N – L – M – H N – L – M – H N – L – M – H 4/9 Landslide N – L – M – H N – L – M – H N – L – M – H 6/9 Hailstorm Snow Avalanche N – L – M – H N – L – M – H N – L – M – H 5/9 Flood N – L – M – H N – L – M – H N – L – M – H 7/9

2 http://data.4tu.nl/repository/collection:ab70dbf9‐ac4f‐40a7‐9859‐9552d38fdccd 29

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PRIMARY SECONDARY FORECASTING FACTORS OVERALL HAZARD HAZARD LOCATION TIME MAGNITUDE RATING Lightning N – L – M – H N – L – M – H N – L – M – H 4/9 Volcanic N – L – M – H N – L – M – H N – L – M – H 3/9 Eruption Landslide N – L – M – H N – L – M – H N – L – M – H 6/9

Snowstorm Snow Avalanche N – L – M – H N – L – M – H N – L – M – H 5/9 Flood N – L – M – H N – L – M – H N – L – M – H 7/9 Ground Collapse N – L – M – H N – L – M – H N – L – M – H 3/9 Ground Heave N – L – M – H N – L – M – H N – L – M – H 6/9 Lightning Wildfire N – L – M – H N – L – M – H N – L – M – H 6/9 Landslide N – L – M – H N – L – M – H N – L – M – H 4/9 Snow Avalanche N – L – M – H N – L – M – H N – L – M – H 4/9 Extreme Flood N – L – M – H N – L – M – H N – L – M – H 5/9 Temperature (Heat) Drought N – L – M – H N – L – M – H N – L – M – H 5/9 Storm N – L – M – H N – L – M – H N – L – M – H 2/9 Wildfire N – L – M – H N – L – M – H N – L – M – H 3/9 Drought N – L – M – H N – L – M – H N – L – M – H Extreme 5/9 Temperature Hailstorm N – L – M – H N – L – M – H N – L – M – H 6/9 (Cold) Snowstorm N – L – M – H N – L – M – H N – L – M – H 6/9 Landslide N – L – M – H N – L – M – H N – L – M – H 5/9 Flood N – L – M – H N – L – M – H N – L – M – H 5/9 Wildfires Wildfire N – L – M – H N – L – M – H N – L – M – H 6/9 Extreme Temp. N – L – M – H N – L – M – H N – L – M – H 6/9 (Heat)

3.2.1.3 Network Vulnerability

The principal infrastructure in the case study region consists of the SS13 (part of the Italian national network of state highways), the A23 Motorway (part of European route E55) and a section of the Baltic‐Adriatic TEN‐T railway line. As seen in Figure 5 the lines for these three pieces of Infrastructure follow the path of the river Fella.

RAIN Deliverable D3.1 (Dvorak & Luskova, 2015) presents a methodology developed to identify critical infrastructure components for land transport infrastructure. The methodology is based on Slovakian and European legislation. Each infrastructure component is assessed using sector and cross‐cutting criteria and by doing so potential critical infrastructure elements are identified. Similarly, in RAIN Deliverable D4.1, Marin & Halat (2015) identify the components of both the electrical and telecommunication infrastructure networks that are critical to operation of said networks.

The main infrastructure elements considered in the FVG region include roads, railway tracks, bridges, tunnels and electricity lines. These are considered for each hazard that result from the extreme rainfall. Telecommunication lines are not considered as they are not affected by the hazards 30

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investigated. A list of possible critical infrastructure components, taken from the WP3 and WP4 deliverables mentioned above (and previously seen in WP6 Deliverable D6.2; Hajializadeh et al. 2016) is given in Table 2 below.

Table 2 Critical Infrastructure Components Telecommunication Land Transport Infrastructure Energy Infrastructure Infrastructure • Roads; • Generators and their • Outside Plant equipment; • Intersections; auxiliary power systems; • The End Offices; • Stations of public transport; • Transmission lines (including • The Central Offices; • Bridges; HVDC links); • Aerial trunk lines; • Tunnels; • Transmission transformers • Underground trunk lines; • Intersection control systems; (including feeders to • RF link trunk lines. • Railway tracks; distribution); • Class 1, 2, and 3 centres; • Railway stations; • Switches and breakers; • Aerial backbone lines; • Railway bridges; • Protection relays; • Underground and submarine • Railway tunnels; • SCADA and associated backbone lines; • Terminals of intermodal Telecoms; • RF and Satellite Backbone transport; • Other Voltage‐management lines; • ETCS (European Train Control devices. • Base Stations (BS); System); • Base Station Controllers • Electronic signal boxes; (BSC); • Train control; • Mobile Switching Centre • Remote operation (MSC); management; • Gateway MSC; • Security systems of railway • Home Location Register crossings. (HLR); • Visitor Location Register (VLR).

3.2.1.4 Consequences

As discussed in RAIN Deliverable D6.2 (Hajializadeh et al. 2016), only direct consequences are considered in this work. Economic, Societal and Security risks will be included. The various consequences are listed in Table 3.

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Table 3 System investigated for FVG region EWE Rainfall

Hazard Landslides Flooding

1. Damage to Roads 1. Road Inundation Vulnerability 2. Damage to Railways 2. Railway Inundation 3. Damage to Electricity Lines 3. Bridge Scour

Consequences:

1. Repair of Roads/Railways 1. Repair of Roads/Railways Economic 2. Loss of Vehicles 2. Repair of Bridges 3. Repair of E & TC Networks 3. Loss of Vehicles

Societal Loss of Life of Road/Rail users Loss of Life of Road/Rail users

1. Loss of Food Supply Security 1. Loss of Food & Water Supply 2. Loss of Electricity Supply

3.2.2. Quantification of Likelihoods

3.1.1.1 Likelihood of EWEs

Before assessing the likelihood of hazard occurrence, the likelihood of the EWE generating the risk is quantified. In the context of the RAIN project, the aim is to define P , where is the rainfall level and are rainfall thresholds for “Low”, “Medium” and “High” levels of extreme rainfall. Data is taken from the rainfall station at Pontebba. 8 years of hourly rainfall levels (mm) are available for the analysis. 24 hour rainfall events are extracted and a Generalized Pareto (GP) distribution is fitted to the data, using the 95th percentile of the data as a threshold. The location, scale and shape parameters of the distribution are calculated as 113.62mm, 41.40mm and 0.26, respectively. The CDF of the rainfall intensity is plotted in Figure 6. Note that the parameters listed above are given as the rainfall levels (i.e. intensity times 24 hours). “Low”, “Medium” and “High” levels of extreme rainfalls are defined as follows:

 , 10/ 0.8942

 , 10/ 20/ 0.0956

 , 20/ 0.0956

These correspond to rainfall levels of 240mm, 480mm and 720mm, respectively.

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Probability

Figure 6‐ Generalised Pareto distribution for 24 hour rainfall data and rainfall levels investigated

3.2.2.1 Likelihood of Hazards

Landslide Hazard

The methodology is tested using a number of sample slopes along the A23 and SS13 road sections. The railway line in the region is mostly within a tunnel, and as such there is no landslide related risk considered to railways. The slopes are identified by investigation using Google Earth. In general, “Channel” section slopes are selected, as this is usually where debris flows occur (Marchi et al., 2002). Table 4 lists the coordinates of each slope assessed. Only two slopes are identified as potentially being at risk on the A23, possibly due to the risk being intentionally “designed out” on these roads, where possible. Many slopes along the SS13 are “engineered” channel sections, meaning that they are built with some mitigation measure/drainage in place to reduce the risk of debris flows. An example is illustrated in Figure 7. These are all classified as “medium” level mitigation measures as explained in section 3.2.4.1. Engineered channel sections which had “high” level mitigation measures in place are not considered. Table 4 also indicates whether each slope/channel is natural or engineered. The final column of Table 4 indicates the relative spatial probability of landslide initiation, as discussed later in this section.

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Table 4 Locations of debris flows investigated No. Coordinates Road Slope Type 1 46.502420, 13.496904 SS13 Natural 0.5 2 46.505897, 13.493425 SS13 Natural 0.3 3 46.509031, 13.481780 SS13 Engineered 0.1 4 46.509495, 13.480084 SS13 Engineered 0.3 5 46.509854, 13.478751 SS13 Engineered 0.1 6 46.502931, 13.405054 SS13 Engineered 0.3 7 46.501052, 13.368655 A23 Natural 0.1 8 46.512268, 13.342698 A23 Natural 0.3 9 46.489463, 13.298685 SS13 Engineered 0.4 10 46.488125, 13.299355 SS13 Engineered 0.5 11 46.465617, 13.293945 SS13 Natural 0.5 12 46.454406, 13.308408 SS13 Natural 0.2

(a) (b)

Figure 7 Engineered slope on the SS13 (a) and natural slope on the A23 (b)

A model similar to that of Jaiswal et al. (2010(a)), shown in equation 1, is employed to calculate the landslide hazard ():

∙ ∙ Eq. 1.

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where is the temporal probability, indicating the annual probability of occurrence of triggering events that generate landslides, is the spatial probability, indicating the relative spatial probability of occurrence of landslides of a given type and is the probability of a landslide intensity.

The temporal probability of rainfall‐triggered debris flows may be quantified by use of existing rainfall‐landslide threshold curves for the region, along with consideration rainfall data from the Pontebba region. Various authors have developed such rainfall‐landslide threshold curves for the region (Calligaris et al., 2012; Marchi et al., 2002 and Paronuzzi et al., 1998). The most critical curve derived by Paronuzzi et al. (1998) is used for the current study, as this is derived from the most extensive data set. The curve is defined by the relationship outlined in Equation 2, where is the rainfall intensity and is the duration (hours). The relationship is illustrated in Figure 8.

47.742. Eq. 2. (mm/hr)

Intensity

Figure 8 Rainfall threshold curve for triggering of landslides (Paronuzzi et al., 1998)

The yearly probability of a rainfall‐triggered landslide is quantified by Jaiswal et al. (2010(a)) as:

∙| Eq. 3.

Where is the probability of exceeding the rainfall thresholds defined in Section 1.1.1.1 and | is the probability of landslide occurrence, given rainfall threshold exceedance. The value of | calculated by Jaiswal et al. (2010(a)) of 0.73 is adopted here. In modelling the interaction between the Primary (triggering) hazard, rainfall, and the secondary hazard of landslides in this manner a ‘Threshold Alone’ relationship from Figure 9 below, presented previously in RAIN Deliverable D6.2 (Hajializadeh et al. 2016), is being modelled i.e. there is no chance of a landslide occurring unless the rainfall has increased beyond a certain point and the intensity of the landslide is not directly related to the intensity of the rainfall.

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Figure 9 Possible triggering intensity relationships (Gill and Malamud, 2014)

The relative spatial probability of occurrence of debris flows is a function of many topographical properties. A debris flow initiation susceptibility map was developed for the region as part of the INCREO Project (Chen et al. 2016). The map is shown in Figure 10 along with the landslide locations investigated. This was used in the current analysis in order to define the spatial probability of failure. The susceptibility map is divided into five levels of susceptibility, numbered 1‐5, with 5 being the highest susceptibility. The five levels are assigned spatial probabilities (P) of 0.1, 0.2, 0.3, 0.4 and 0.5. The vales assigned to each slope location are listed in Table 3. The debris flow locations investigated are labelled consecutively from right to left.

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Figure 10‐ Susceptibility of debris flow initiation (INCREO project)

The frequency distribution of landslide size is unclear in the literature and can vary greatly depending on location and topology. Hence, in this study landslide intensities similar to those of Jaiswal et al. (2010(a)) are investigated. Landslide volumes (V) of 1000m3 and 10,000 m3 are considered, with associated probabilities of exceedance of 0.07 and 0.01, respectively. Only two landslide categories are selected here as we are interested in extreme events and landslides less than 1000m3 typically have no societal risk and negligible economic risks (Jaiswal et al., 2010(a)). These landslide levels are defined as “No Landslide” (V<1000m3), “Level 1” (1000m3< V <10,000m3) and “Level 2” (V >10,000m3).

A similar method was used to model the effect of landslides on the electricity network. Both stations and towers are considered, with the probability of a landslide affecting each being quantified. A total of 354 towers, 335 poles and 37 stations are considered. Only a landslide magnitude of 10,000m3 is considered for the electricity network, as this is considered sufficient to cause failure of an electricity pylon, and take account of the relative distance of the pylon to the source of the event.

Flooding Hazard

In order to assess the hazards related to flooding, 10,000 flow values are calculated by the RAIN consortium at each of 235 locations, based on 10,000 samples of extreme rainfall sampled from the rainfall CDF illustrated in Figure 6. For 26 of these locations located along the main transport route, 10,000 values of flood heights are also calculated. A total of 18 points are then selected to perform an analysis of the risks of bridge scour, road inundation and rail inundation. For scour, only bridges

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with a support in a river for which there is a river flow node within 500m are selected. Few cases of either scour or inundation are considered on the A23 as most of the motorway is built on bridges with high level scour protection in place. An example is given in Figure 11. For inundation of roads, railway lines and tunnel entrances, only sections for which there is a node with flood depths available within 150m are selected. Bridges and tunnel entrances at high levels above the flow nodes are not investigated for inundation.

Table 5 Discharge locations and Infrastructure/hazards assessed No. Coordinates Infrastructure Hazard 1 46.503147, 13.405501 SS13 Tunnel Inundation 2 46.501747, 13.402645 SS13 Bridge Scour 3 46.500847, 13.396581 SS13 Inundation 4 46.502842, 13.381129 SS13 Inundation 5 46.503067, 13.373978 SS13 Inundation 6 46.508656, 13.354574 SS13 Inundation 7 46.510491, 13.350585 A23 Bridge Scour 8 46.512298, 13.331843 Rail Tunnel Inundation 9 46.509416, 13.326122 SS13 Bridge Scour 10 46.508176, 13.314588 SS13 Inundation 11 46.508735, 13.314434 Rail Inundation 12 46.506565, 13.305141 Rail Bridge Scour 13 46.502441, 13.302042 Rail Inundation 14 46.497904, 13.299060 Rail Tunnel Inundation 15 46.494471, 13.301113 SS13 Bridge Scour 16 46.485543, 13.299929 SS13 Inundation 17 46.476168, 13.296192 A23 Bridge Scour 18 46.460396, 13.297073 SS13 Inundation

The rainfall is modelled in the same way as for the landslide risk assessment. The thresholds for classification of “Low”, “Medium” and “High” extreme rainfall events are again assumed to be equal to 0‐10mm/hr, 10‐20mm/hr and 20mm/hr or greater, in a 24‐hour period, corresponding to rainfall levels of 0mm, 240mm and 480mm.

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A23

Column encased in concrete for scour protection

River Channel

Figure 11‐ High level scour protection on the A23

The hazard relating to inundation is defined for “no hazard”, “level 1 and “level 2” hazards. The depth of water relating to each hazard level is dependent on the infrastructure type. For example, for roads, a water depth of 0mm (i.e. water level has not reached the road surface) is defined as “no hazard”. A water depth of 0‐250mm is defined as “level 1” hazard and 250mm or greater is defined as “level 2” hazard. The respective damage levels for railways relating to “no hazard”, “level 1” hazard and “level 2” hazard are defined as less than 0mm, 0‐150mm and greater than 150mm. The reason for the difference between railways and roads is that the railway flooding levels are based on track inundation below the top of the sleeper, between the top of the sleeper and the top of the rail, and above the top of the rail. These thresholds are used to directly relate consequences to associated effects on railways (e.g. washing away of ballast, incapacity of operation on lines etc). On the other hand the road thresholds are based on potential to cause damage to cars which drive through floods. In order to calculate the probability of being at a particular flood hazard level, given a particular rainfall event, the 10,000 samples of extreme rainfall and associated water levels are employed. As an example, in order to investigate the likelihood of a “high” level road inundation event for a “Medium” level rainfall event, the procedure is as follows:

1. Extract all water depths relating to “Medium” level rainfall; 2. Count the number of these events which result in road inundation greater than 250mm; 3. Divide the number of these events by the total number of “Medium” rainfall events.

By modelling the interaction between the rainfall (primary hazard) and the flood (secondary hazard) in this manner a “Continuous alone” hazard interaction as presented by Gill and Malamud (2016) is being modelled (see Figure 9)

In order to perform this analysis, a detailed Digital Elevation Model (DEM) is obtained from Tarquini et al. (2012). The resolution of the DEM is 10m x 10m. This allowed calculation of the levels both at

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the flow nodes and at the nearest road/rail surface. After investigating the probabilities of inundation for the various EWEs considered with the DEM, it is found that only 4 of the 12 inundation nodes investigated are susceptible to flooding, due to the levels of the roads/railways being higher than expected above the river. These are nodes 3, 8, 10 and 11 in Table 5. The probabilities of inundation for these nodes are listed in Table 6.

Table 6 Probabilities of inundation for nodes susceptible to flooding EWE Inundation No hazard 0.0 0.0 0.9067 0.5065 Low Level 1 0.0 0.0 0.0933 0.2450 Level 2 1.0 1.0 0.0 0.2485 No hazard 0.0 0.0 0.0 0.0 Medium Level 1 0.0 0.0 0.6642 0.0 Level 2 1.0 1.0 0.3358 1.0 No hazard 0.0 0.0 0.0 0.0 High Level 1 0.0 0.0 0.0095 0.0 Level 2 1.0 1.0 0.9905 1.0

In order to assess the hazard of flooding resulting in bridge scour, a similar methodology is employed to that of inundation. For the purpose of scour, the flow rates (m3/s) are used to evaluate scour likelihood. The flow rates relating to “No Flow”, “Low”, “Medium” and “High” level flow are assigned as 0‐280m3/s, 280‐380m3/s, 380‐480m3/s, and greater than 480m3/s, respectively. The threshold of 280m3/s is the 99th percentile of all flow data. The values are based on a combination of selecting extreme events in the data, a review of typical flow rates corresponding to bridge failures (Coleman and Melville, 2001) and the relative probabilities of bridge scour within the clusters from the RAIN methodology for inputs (Appendix A). After investigating the probabilities of these flow rates at each of the nodes relating to bridge scour in Table 5, it is found that only 4 of the 6 bridges identified are susceptible to bridge scour (nodes 7, 9, 15 and 17) for the analysis performed. The probabilities of “None”, “Low”, “Medium” and “High” level flows for these nodes are listed in Table 7.

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Table 7 Probabilities of flow rates for bridge nodes susceptible to scour EWE Flow Rate 0‐280m3/s 1.0 1.0 0.9087 0.8519 280‐380m3/s 0.0 0.0 0.0913 0.1480 Low 380‐480m3/s 0.0 0.0 0.0 0.0001 > 480m3/s 0.0 0.0 0.0 0.0 0‐280m3/s 1.0 0.9349 0.0 0.0 280‐380m3/s 0.0 0.0651 0.8353 0.6842 Medium 380‐480m3/s 0.0 0.0 0.1647 0.3158 > 480m3/s 0.0 0.0 0.0 0.0 0‐280m3/s 0.9714 0.4381 0.0 0.0 280‐380m3/s 0.0286 0.5619 0.0952 0.0 High 380‐480m3/s 0.0 0.0 0.8952 0.9810 > 480m3/s 0.0 0.0 0.0095 0.0190

Once the relative probabilities of achieving a flow rate are obtained, it is then necessary to quantify the likelihood that scour of certain levels occur on the bridges investigated, given the occurrence of a certain level of flow rate. For this purpose, the land transport fragility spreadsheets developed as part of the RAIN project are employed. These spreadsheets split infrastructure into various clusters based on input information. These are populated for the bridges investigated, including information on construction and materials for piers, abutments, deck and the river itself. The relative probabilities of obtaining “No Failure” (NF), “Operational Failure” (OF), “Partial Failure” (PF) and “Full Failure” (FF) are given in Table 8 for each flow rate and cluster.

Table 8 Probabilities of various levels of bridge scour for bridges in specific clusters (see Appendix A) Flow Rate Cluster 280‐380m3/s 380‐480m3/s > 480m3/s NF OF PF FF NF OF PF FF NF OF PF FF 1 0.9 0.05 0.04 0.01 0.4 0.3 0.25 0.05 0.2 0.4 0.35 0.05 2 0.79 0.08 0.1 0.03 0.25 0.25 0.35 0.15 0.15 0.35 0.35 0.15 3 0.68 0.1 0.15 0.07 0.2 0.2 0.4 0.2 0.1 0.1 0.45 0.35 4 0.55 0.15 0.2 0.1 0.14 0.18 0.38 0.3 0.05 0.1 0.35 0.5 5 0.4 0.2 0.25 0.15 0.09 0.16 0.35 0.4 0.03 0.07 0.2 0.7 6 0.25 0.25 0.3 0.2 0.05 0.15 0.3 0.5 0.01 0.04 0.05 0.9

3.2.3. Consequence Analysis

In this section, the consequences of the various direct economic, societal and security risks are quantified. It should be noted that only direct risks to critical infrastructure is considered. It should also be noted that consideration of road/rail users relates to moving vehicles only, as static/parked vehicles are not statistically relevant at the locations investigated.

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3.2.3.1 Direct economic risks of Landslides

Damage to road elements

The risk model employed by Jaiswal et al. (2011) is used to quantify direct economic risk (€) for road elements in the run‐out path of debris flows, ,:

Eq. 4. 3 , , ,

Where is the hazard or the probability of occurrence of a landslide of size “”, as calculated above, is the probability of a landslide of magnitude “” reaching the element at risk from the upslope areas (0–1), , is the vulnerability of the element at risk due to a landslide run‐out caused by a landslide of size “” (0–1), and , is the quantification (monetary value) of the element at risk (€). Jaiswal (2011) calculated an empirical relationship between landslide magnitude and run‐out length by regression analysis of data relating to 55 debris slides that occurred between 1978 and

2004. The run‐out () is found to be related to the debris flow volume () by:

. Eq. 5.

From equation 5 it can be seen that the minimum landslide volume investigated in this study (1,000m2) equates to a minimum run‐off of 44.7m. As the distance between the foot of the landslide channel and the road edge is less than this for all locations in Table 4, the value of is equal to 1.0 for all locations and landslide magnitudes. Jaiswal (2011) calculated the value of , to be equal to 0.4 for landslides with a magnitude of 1,000m3 and 0.8 for landslides with a magnitude of 10,000m3.

The value of , may be quantified as a fraction of the construction cost of the element (Jaiswal et al. 2011). Based on expert opinion within the RAIN consortium, a construction cost of €10,000,000 per km is assigned to the A23 motorway, and €5,000,000 per km to the SS13 national road. Of course, these values vary depending on location, topography etc, and can be set by the road owner. It is assumed that approximately 100m of road is damaged by the landslides investigated.

Damage to moving vehicles

Jaiswal et al. (2010(b)) quantified the risk of a moving vehicle being hit by a landslide:

4 , , , Eq. 6.

Where and are defined as per the road element model. is the probable number of vehicles hit by the landslide, given by:

3 The way to interpret this variable name, as with many of the variables in the analyses is Risk of Damage from Landslides to Roads 4 Risk of Damage from Landslides to Vehicles 42

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/24 1000 Eq. 7.

Where ADT is the average daily traffic, L is the vehicle length (m) and S is the vehicle speed (km/hr). The value of PV depends on vehicle type and road type. The calculation is shown for each vehicle/road type considered in Table 9. The ADT values are taken from the Eurocode totals (National Annex to BS EN 1991‐1‐7 (2003)). The percentage breakdown and length /speed values for each type of road and class are taken from an Irish traffic monitoring database on similar roads. It is noted that there are slight dissimilarities between the data in Table 9 and that which is used by Jaiswal et al. (2010b), as expected, due to the dissimilarity in traffic between India and Europe. Table

9 also lists the values of the vulnerability of each vehicle type, ,, as given by Jaiswal et al. (2010b), for either landslide magnitude assessed in this study. The values of , may be taken to be equal to the economic value of the damaged vehicle.

Table 9 Calculation of and vulnerabilities for each vehicle type considered for landslide hazard Vehicle ADT Length Speed (km/hr) V Type A23 SS13 (m) A23 SS13 A23 SS13 , Bus 177 69 12.8 85 73.2 0.00111 0.00050 0.8 Lorry 3,690 2,104 9.3 75 80.6 0.01907 0.01012 0.8 Car 25,938 11,733 4.3 95 84.1 0.04892 0.02500 1.0 Motorbike 195 94 1.4 95 101.3 0.00012 0.00005 1.0

Damage to the electricity network

The risk model employed to quantify direct economic risk (€) for electricity network components in the run‐out path of debris flows, ,, is as follows:

Eq. 8. 5 , , ,

Where and are as per the road elements, , is the vulnerability of the element at risk due to a landslide run‐out caused by a landslide of size “” (0–1), and , is the quantification (monetary value) of the element at risk (€). The vulnerability to a landslide of magnitude greater than 10,000m3 is quantified by expert opinion within the RAIN consortium. Only landslides of magnitude greater than 10,000m3 are considered as the damage due to a landslide of magnitude 1,000m3 is deemed to be negligible. Values of 0.5, 0.3 and 0.5 are adopted for poles, towers and stations respectively.

Monetary (,) values of €45,000, €100,000 and €1,000,000 are assumed for poles, towers and stations, respectively.

3.2.3.2 Direct societal risks of Landslides

Direct societal risks are quantified for transport infrastructure by quantifying the probability of loss of life of a road user. A model similar to Jaiswal et al. (2010b) is used:

5 Risk of Damage from Landslides to Electricity Network 43

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6 , , Eq. 9.

All terms within equation 9 are defined as per equations 8 and 7, with the exception of ,, the vulnerability of the individual (probability of death) given that a landslide hits the vehicle. Jaiswal et al. (2010b) quantified the vulnerabilities of the individual due to a landslide magnitude of greater than 1,000m3 to be equal to the vulnerability of the vehicles, as listed in Table 9 for all vehicle types. For this reason, the probability of loss of life in this study is equal to the probability of impact on the vehicle, for either landslide magnitude assessed.

3.2.3.3 Direct economic risks of Inundation

Damage to road/rail elements

The risk model employed for direct economic costs due to landslides is also applied to quantify direct economic risk (€) for inundated road/rail elements, ,:

Eq. 10. 7 , , ,

Where is the inundation hazard, is the probability of the hazard reaching the infrastructure element, , is the vulnerability of the element at risk due to inundation of level “” (0–1), and , is the quantification (monetary value) of the element at risk (€). The product is the output of the flooding hazard model described in 3.2.2.1. The values of , are taken to be the probability that damage states 1 and 2 occur due to flood hazard levels 1 and 2. For both roads and rails, damage state 1 is taken to be the case where no structural damage occurs, but the inundation must be inspected, at a cost of €200. For roads, damage state 2 is considered where flooding‐ induced cracking occurs, with a €2,000 cost of repair. For railways, damage state 2 is considered where ballast washout occurs, with a €2,000 cost of repair. The vulnerabilities to these damage states are given in Table 10 for roads and railways

Table 10 vulnerability of Infrastructure (,) to Inundation Damage Infrastructure , , State Inundation State 1 Inundation State 2 1 1.0 0.95 Roads 2 0.0 0.05 1 1.0 0.9 Railways 2 0.0 0.1

Damage to moving vehicles

6 Risk of Damage from Landslides to People 7 Risk of Damage from Inundation to Roads 44

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The vulnerability of moving vehicles to inundation on roads is an area seldom studied for road networks, with the majority of the research being in the area of flooding of buildings and damage to parked cars in urban areas (e.g. Xia et al., 2011). Nevertheless, an effort is made here to quantify the risk to moving vehicles. The risk model employed to quantify direct economic consequences for moving vehicles is as follows:

Eq. 11. 8 , , ,

is defined here as the probability that a person drives through flood water. The values of depend on a number of factors such as age, flood warning attitudes, flood danger knowledge and flood experience (Drobot et al., 2007). Drobot et al. (2007) fitted a logistic regression model to survey data to develop a probabilistic model for the likelihood of a person driving through a flood. In the current analysis, a value of 0.4 is adopted for , based on the percentage of people in a survey from Denver, USA, which responded that they would drive through flood water. This figure is independent of vehicle type. In addition, although this is in the upper region of the values calculated buy Drobot et al. (2007), it considered appropriate to use here as there is an additional chance that motorists drive through the flood unaware of it. Direct economic consequences are not considered for trains as the inundation investigated does not have any implications for derailment of trains. The values of ,, the vulnerability of vehicles to flood levels are given in Table 11. Again, these are considered to be independent of vehicle type. Although it may be argued that lorries, for example, are less likely to be affected by inundation than cars, the relative economic value of lorry’s are higher, which is expected to balance the risk.

Table 11 vulnerability of vehicles (,) and people (,) to Inundation of roads Inundation Inundation

Level 1 Level 2

, 0.1 0.5

, 0.0 0.01

3.2.3.4 Direct societal risks of Inundation

Direct societal risks due to inundation are again quantified for transport infrastructure by quantifying the probability of loss of life of a road user:

Eq. 12. , ,

Most of the terms within equation 12 are defined as per equation 11. The values of ,, the vulnerability of the individual (probability of death) given that the vehicle drives through a flood and is damaged are listed in Table 11.

8 Risk of Damage from Inundation to People 45

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3.2.3.5 Direct economic risks of Bridge Scour

Damage to bridge elements

The direct economic consequences of bridge scour are modelled in a similar way to the landslide/inundation models for roads and railways. The direct costs associated with “No Failure are taken to be equal to zero. Based on expert opinion, the respective costs attributed to “Operational Failure”, “Partial Failure” and “Full Failure” are taken to be equal to €50,000, €200,000 and €3,000,000.

Damage to moving vehicles

The consequence modelling of the risk to moving vehicles is performed in a similar manner to that of landslides, with the notable exception that the length this time relates to the total span length supported by a failed pier, plus the total stopping distance (taken to be 50 metres). The ADT is taken as the total for all vehicles in this case and the speed is taken as the average for all vehicles. This corresponds to a speed of 87.5 km/hr on the A23 and 84.8 km/hr on the SS13. The vulnerability is taken to be equal to 1.0 for all vehicles, although it is assumed that vehicles are only damaged in the “Full Failure” Scenario. The results are shown in Table 12.

Table 12 Calculation of for each bridge scour node, for all traffic Bridge Length Speed ADT node (m) (km/hr) 7 30,000 130 87.5 1.86 9 14,000 110 84.8 0.76 15 14,000 130 84.8 0.89 17 30,000 110 87.5 1.57

The number of each type of vehicle damaged at each location is then equal to the percentage of that vehicle attributed to the ADT.

3.2.3.6 Direct societal risks of Bridge Scour

Direct societal risks due to scour are quantified by considering the number of lives lost per event. A model similar to that of equation 12 is used, with the vulnerability again taken to be equal to 1.0 for all vehicle types. However, in the case of bridge scour, the number of lives lost is equal to the number of each vehicle type damaged times the expected number of people in a vehicle. The expected number of people in each vehicle is equal to 1.0, with the exception of buses, for which the value is taken to be equal to 10. The number of deaths due to full scour failure at nodes 7, 9, 15 and 17 are calculated to be equal to 1.96, 0.79, 0.93 and 1.65, respectively. The associated probability is then equal to the probability of full scour failure at each node.

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3.2.3.7 Direct security risks of Landslides, Inundation & Bridge Scour

The direct security risks are quantified in a multimodal system whereby the probability of a town being cut‐off is considered. This is performed both by considering the probability of cut‐off of land transport and the probability of electricity blackout.

3.2.4. Bayesian Network Modelling for Economic and Societal Risks

In this section, the BN model used to carry out the assessment of direct economic and societal risks is described. The model is generated using the GeNIe software programme (BayesFusion LLC). The model is described initially for debris flow and, subsequently, for inundation and scour. The direct economic risk to electricity network components due to landslides is performed using MATLAB software (MATLAB 8.6 R2015b) due to the large number of electricity network components analysed.

3.2.4.1 Debris Flows

The BN model developed to assess landslide risk is illustrated in Figure 12. The “EWE” node quantifies the probabilities of the extreme weather events as defined in section 1.1.1.1. The “LS” nodes represent the probability of landslides at locations 1‐12 in Table 4.

Figure 12 Bayesian Network for Landslide Risk

The orange “RE” nodes provide the probabilities of damage to each landslide element, given a certain landslide magnitude. The blue “Mit” nodes specify mitigation measures in place at the slope, and are divided into three possible “states”:

 State 1: No mitigation in place.  State 2: Level 1 mitigation measures in place.  State 3: Level 2 mitigation measures in place.

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The probabilities of damage at each node on both the probabilities of landslides of various magnitude and the mitigation measures in place, as shown in Table 13. When no mitigation measures are in place, the probability of damage when no landslide occurs is zero, the probability of damage when medium and high intensity landslides occur is 0.4 and 0.8, respectively. This is from the vulnerability modelling described in section 3.2.3.1. When Level 1 mitigation is in place, the probability of damage when medium and high intensity landslides occur changes to 0.0 and 0.4, respectively (i.e. the risk due to medium intensity landslides is removed and the risk due to high intensity landslides is reduced to that of a medium intensity landslide). Finally, when Level 2 mitigation is in place, the probability of damage is 0.0, regardless of landslide intensity.

Table 13 Nodal probabilities of RE node for natural slope and engineered slope (bold outline) Mit1 State 1 State 2 State 3

LS1 Intensity None Int 1 Int 2 None Int 1 Int 2 None Int 1 Int 2

No Damage 1.0 0.6 0.2 1.0 1.0 0.6 1.0 1.0 1.0

Damage 0.0 0.4 0.8 0.0 0.0 0.4 0.0 0.0 0.0

An example of Level 1 mitigation is shown in Figure 7 (a). All of the “engineered” slopes selected on the network are considered to already have Level 1 mitigation measures in place. Because of this, the mitigation nodes feeding into these locations only have 2 states:

 State 1: Level 1 mitigation measures in place.  State 2: Level 2 mitigation measures in place.

Therefore, the nodes relating to engineered slopes look like that which is inside the bold outlined area on the right of Table 13. An example of Level 2 mitigation would include rebuilding of road sections or re‐directing of flows to ensure no damage can be sustained. The costs associated with mitigation levels 1 and 2 are €20,000 and €50,000, respectively. The mitigation system applied for landslide risk to road elements is also used for electricity networks.

The magenta coloured “Veh” nodes specify the probability of each vehicle type listed in Table 9 being struck by a landslide at each location and the yellow coloured “D” nodes specify the probability of death, given that a vehicle is struck by a landslide.

3.2.4.2 Flooding

The BN model developed to assess flood risk is illustrated in Figure 13. It should be noted that all redundant inundation and flow nodes have been removed from the model. The red nodes quantify probabilities of inundation levels 1 and 2 at each location due to each weather event as described in section 3.2.2.1. The first green nodes (“Q nodes”) beneath the EWE node describe the probabilities of obtaining each flow level as given in Table 7.

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Figure 13 Bayesian Network for Flooding Risk with Redundant Nodes Removed The second green nodes beneath the Q nodes define the probability of scour of various levels, given a certain flow level as described in section 3.2.2.1. The orange nodes describe the probabilities of road/railways sustaining damage levels 1 and 2, given a certain level of inundation as described in section 3.2.3.3. The blue “Mit” nodes define mitigation strategies for roads, railways and bridges. A description and cost for each mitigation level is given in Table 14.

Table 14 Description and costs of mitigation strategies for flooding Infrastructure State Description Cost Effect / Hazard Cleaning and repair of Probabilities of damage states 1 €500 Road / drainage systems 1 and 2 are halved Flooding Construction of superior Probabilities of damage states 2 €5,000 drainage systems 1 and 2 move to zero 1 N/A N/A N/A Rebuilding of track to Rail / Flooding €3,500,000 Probability of damage state 2 2 “slab‐track” to prevent per km changes to zero ballast wash out Bridge cluster state (Table 8) Low‐level erosion counter 1 €10,000 reduced for two of four measure such as “rip‐rap” bridges Bridge / Scour High‐level scour Probability of scour damage 2 protection as per Figure €100,000 moves to zero 11

As per the landslide model, dark blue “Mit” nodes correspond to areas where only Level 2 mitigation measures are of benefit. For example:

 Where road elements are only affected by Level 2 Inundation.  For all railway sections, where there are no Level 1 interventions conceivable. 49

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 For bridges which do not move to a lower risk cluster as a result of erosion counter measures.

Again, the magenta “Veh” nodes specify the probability of each vehicle type listed in Table 9 being struck by a landslide at each location and the yellow “D” nodes specify the probability of death, given that a vehicle is struck by a landslide. There are no “Veh” or “D” nodes coming from the railway inundation nodes as it is assumed that trains are not damaged due to the flooding investigated. There are also no “Veh” or “D” nodes coming from the green bridge failure nodes as the probabilities of death is quantified separately for these scenarios as discussed in section 3.2.3.6.

3.2.5. BN Modelling for Security Risks

In this section, the BN model which used to carry out the assessment of security risks is described. This model is also generated using GeNIe software. Separate models are built to quantify the risk of cut‐off of land transport and electricity supply.

3.2.5.1 Land‐Transport Cut‐Off

The landslide, flooding and bridge failure hazards to be considered for cut‐off of the land transport network are shown in Figure 14. The main towns of the region include Pontebba, Malborghetto, Valbruna and Camporosso. It is clear that due to the low number of hazards to the right of the Figure, and considering the locations of junctions off the road network, Pontebba is the only town which is at risk from cut‐off. Therefore, only Pontebba is considered.

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Figure 14 Hazard Locations for Consideration of Land‐Transport Cut‐Off

The risk of cut‐off of the railway system is not included in this model as only two rail locations are identified as being at risk, and trains are always able to access either of the two stations in the region from one side. A logic gate representation of the model for land‐transport cut‐off is shown in Figure 15. Red nodes represent hazards on the SS13, while blue nodes represent hazards on the A23. In order for cut‐off to occur, both the west and east approach to the town must be cut off. The west can only be approached via the SS13. Therefore, if any of the hazards on this route (constituting RE9, RE10, RE11 and RE12) occur, the west approach is cut‐off. The dashed line in Figure 15 from the AS23BS2 node to the west approach takes account of the fact that failure of this bridge on the A23 may result in failure/blockage of the SS13, as shown in Figure 16. The probability of occurrence of this is taken to be 0.25. Considering the east approach, if either of the two hazards on the SS13 after the Pontebba junction (SS13BS2 and REF6) occur, Pontebba is cut‐off. In addition, if both the A23 and the SS13 are cut‐off before the Pontebba junction, Pontebba is cut‐off. It should be noted that the Pontebba junction can be approached via the A23 either from the east (consisting of hazards AS23BS1, RE8 and RE9) or west (AS23BS2), and both must be cut‐off for the junction to be inaccessible. The hazard nodes along the SS13 before the Pontebba junction are RE4, RE5, RE6 and RE7. All nodes are named as per Figure 12 and Figure 13.

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Figure 15 Logic Gate Representation of Risk of Land‐Transport Cut‐Off

AS23BS1 – potential to block SS13 SS13 if failure occurs

Figure 16 Bayesian Network for Risk of Land‐Transport Cut‐Off

The BNM model is illustrated in Figure 17. Red arrows represent ‘AND’ operators in the BN model.

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Figure 17 Bayesian Network for Risk of Land‐Transport Cut‐Off

3.2.5.2 Electricity Supply Cut‐Off

The BN Model used to quantify the probability of electricity supply cut‐off is shown in Figure 18. The probability of cut‐off is considered for the three main towns of Pontebba, Valbruna and Camporosso.

Figure 18 Bayesian Network for Risk of Electricity Supply Cut‐Off

Notably, some elements in an electric grid are redundant (like parallel line segments) and, therefore, all of them have to fail to get a failure of that element. In other cases the elements are arranged in series (like towers in a line segment), there if one of the elements fails, all the segment fails. In the context of the current case study:

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1. Blackout (represented by the purple node in Figure 18) may be considered for individual regions or for combinations of regions. 2. The three dark blue nodes represent the three regions (Pontebba, Valbruna and Camporosso) investigated. 3. An electrical failure at these points may arise from a failure of the electrical station at the consumption point itself (red nodes) OR due to a problem in energizing the station through the electrical paths available (also represented by red nodes). 4. A number of paths to energize the station are possible, and they ALL need to fail in order to cut the power supply to the consumption point (i.e. for the Paths node to fail). These paths are represented by the black nodes 5. Each path is split into the stations (orange nodes) and lines (green nodes) that compose it. A failure happens when either stations OR lines fail. 6. All electrical stations involved in a path are grouped (yellow nodes) and system failure occurs if ANY of them fail. 7. All electrical lines involved in a path are grouped (yellow nodes) and system failure occurs if ANY of them fail. 8. Each line is composed of a number of independent segments (light blue nodes). ALL segments must fail in order to cause damage. 9. Each segment is composed of a number of towers (not shown in Figure 18) and segment failure may occur if any tower fails in that segment.

Red arrows represent AND operators in the BNM.

3.2.6. Construct Outcome/Utility Probability Distributions

This section describes the methodology used to generate outcome probability distributions by mapping numerical consequences to the states of probability. It should be noted that in the context of RAIN Deliverable D5.1, these outcome probability distributions are used as the utility distributions, as the wealth constraints are not known for the region for the various risks considered. In addition, since the wealth constraints are the same across the region, and the costs are deterministic, the consideration of wealth constraints will not affect he results.

3.2.6.1 Direct Economic Risks

The outcome probability distribution for direct economic costs is considered for the transport and electricity network together. There are 709 elements in total on this network. Therefore, since there are up to 4 damage states are considered per element, there are up to 4709 potential outcomes to be considered, each outcome being a vector with the states of the 709 elements. It is not feasible to consider so many outcomes so the RAIN WP5 probability sort algorithm is used to solve the problem. The algorithm, discussed in RAIN Deliverable D5.2 (van Erp et al. 2017) finds the more likely outcomes; which are those with a probability above a set ‘critical’ level of probability.

Setting the critical level of probability to 1x10‐6 results in 43,310 outcomes and a probability coverage of 0.77 (if all 4709 outcomes were assessed then the probability coverage would be 1.0). The probability sort calculates the most likely outcomes, saving the probability of each and the state of

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every element for each outcome. It is considered appropriate to set the critical level of probability at 1x10‐6, as outcomes with lower probability are considered to be too unlikely to be of interest.

The total cost for each outcome is calculated using the costs and the element states of each outcome. The outcome costs are the sum of the cost of each element state. As the same cost is assigned to many elements, such as electricity poles, the costs of many outcomes are the same. For the 43,310 outcomes there are only 47 unique outcome costs. These costs and their associated probability can been seen in Figure 19. The outcome probability distribution is irregular, due to the large number of independent cost/probability state combinations involved. This does not affect the calculation of the “position” of the distribution

Figure 19 Probabilities for Each Unique Outcome Economic Cost

3.2.6.2 Direct Societal Risks

For the calculation of the direct societal risk, two states are considered: 1) occurrence of human fatality (death) and 2) no death. There are only 18 elements with which risk of death is considered (the likelihood of death due to failure of the electricity network is not considered). Therefore, there are 218 possible scenarios and the probability of each scenario is quantified. The resulting outcome probability distribution is shown in Figure 20. It is clear that there is potential for up to 20 deaths to occur, due to the extreme weather event investigated. However, it is considered inappropriate to use this outcome probability distribution, as it considers probabilities below 1x10‐6. For this reason, the probability sort algorithm is again applied with a cut‐off of 1x10‐6. The result is shown in Figure 21. Again the distribution is quite irregular.

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Figure 20 Probabilities of Death for Each Unique Outcome

Figure 21 Probabilities of Death for Each Unique Outcome with Probability > 1x10‐6

3.2.6.3 Direct Security Risks

In relation to security of food supply, considered by the probability that the land transport to an area is cut‐off, an outcome probability distribution is not constructed, as only the town of Pontebba is vulnerable. The probability of cut‐off of Pontebba, given that an extreme weather event occurs, is found to be equal to 0.0071.

An outcome probability distribution may be constructed for the risk of electricity supply cut‐off. In this case, the cost is considered in terms of the number of people affected by cut‐off at Pontebba, Valbruna and Camporosso, and the various combinations of these events. The maximum populations at each of these respective towns are 1215, 763 and 4253. The outcome probability distribution is illustrated in Figure 22. 56

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Figure 22 Probabilities of Electricity Cut‐Off vs Population Affected

3.2.7. Optimising Mitigation Strategies

In this section, the final step of the RAIN Deliverable D5.1 (van Erp & van Gelder 2015) Risk‐Based Decision Making Framework is applied. Namely, choosing that action that optimises the position of the Utility Probability Distributions. The actions are what mitigation procedure to put into place. In the context of risk, the optimal action is the one that minimises the risk utility distribution. Based on the methodology described in RAIN Deliverable D5.1 (van Erp & van Gelder 2015), the sum of confidence bounds (upper bound, UB; lower bound LB) and the expectation value (E) is used as a position measure. The optimum strategy i.e. the strategy which minimises across each risk (direct economic, direct societal and direct security risks) is chosen:

Eq. 13. 3

Where, for a range of outcome probabilities, and a range of corresponding consequences, :

Eq. 14.

min ,…, Eq. 15. ∙

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max ,…, Eq. 16. ∙

Where

Eq. 17.

The ‐value is the sigma‐level of the bounds. A ‐value of 2.0 is used for this study. The values of , and are presented in Table 15 for each risk type for the “do‐nothing” scenario.

Table 15 calculation of for “do‐nothing” scenario for direct economic, societal and security risks

Risk

Direct Economic 0 181,610 135,1615 511,075 (€) Direct Societal 0.0 0.078 0.73 0.271 (No. Deaths) Direct Security 0 19 348 122 (No. People cut‐off from electricity)

The methodology is exemplified for the case of direct societal risks, as the state space for this risk type is sufficiently small to produce results which can be reported, due to the fact that the extensive electricity network is not involved is that calculation. Considering the case of direct economic risks and direct security risks, the number of possible mitigation strategies approaches infinity, and so the results are not assessed in this report.

An algorithm, developed in WP 6, to generate all possible combinations of mitigation which can be applied for a given budget is implemented. Only scenarios where the full use of the budget is made are considered. For example, the scenario of only applying mitigation level 1 to a single landslide risk node is ignored. A budget of €500,000 is considered which resulted in 44 possible unique mitigation strategies. Although this budget is quite close to the value for economic costs in Table 15, it considered appropriate as this effects the economic and societal costs. These mitigation strategies are listed in Table 16, along with the associated value of . It should be noted that these values do not take account of the input cost of the mitigation, as this would require consideration of the economic value of a human life. Table 16 gives the number of each mitigation type which should be applied for each scenario. The individual elements (bridges, roads etc.) on which to apply each mitigation type is decided upon based on the expected values of the individual failure outcomes. For example, for mitigation strategy number 22, two no. Full scour protections are required. These are applied to the two bridges which had the two highest values of (probability of failure)×(Number of Deaths).

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Table 16 All unique mitigation strategies for direct societal risk Flood Flood Landslide Landslide Scour Scour No. Level 1 Level 2 Level 1 Level 2 Level 1 Level 2 (€500) (€5000) (€20,000) (€50,000) (€10,000) (€100,000) 1 1 1 4 8 1 0 0.266 2 0 2 4 8 1 0 0.266 3 0 0 4 8 2 0 0.270 4 1 1 1 9 2 0 0.266 5 0 2 1 9 2 0 0.266 6 1 1 2 9 0 0 0.267 7 0 2 2 9 0 0 0.267 8 0 0 2 9 1 0 0.270 9 0 0 0 10 0 0 0.270 10 1 1 6 5 2 1 0.111 11 0 2 6 5 2 1 0.111 12 1 1 4 6 1 1 0.111 13 0 2 4 6 1 1 0.111 14 0 0 4 6 2 1 0.117 15 0 0 5 6 0 1 0.118 16 1 1 1 7 2 1 0.111 17 0 2 1 7 2 1 0.111 18 1 1 2 7 0 1 0.112 19 0 2 2 7 0 1 0.112 20 0 0 2 7 1 1 0.117 21 0 0 0 8 0 1 0.118 22 1 1 6 3 2 2 0.018 23 0 2 6 3 2 2 0.017 24 1 1 4 4 1 2 0.018 25 0 2 4 4 1 2 0.018 26 0 0 4 4 2 2 0.037 27 0 0 5 4 0 2 0.038 28 1 1 1 5 2 2 0.018 29 0 2 1 5 2 2 0.017 30 1 1 2 5 0 2 0.021 31 0 2 2 5 0 2 0.020 32 0 0 2 5 1 2 0.037 33 0 0 0 6 0 2 0.038 34 1 1 4 2 1 3 0.017 35 0 2 4 2 1 3 0.016 36 0 0 5 2 0 3 0.033 37 1 1 2 3 0 3 0.010 38 0 2 2 3 0 3 0.008 39 0 0 2 3 1 3 0.036 59

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Flood Flood Landslide Landslide Scour Scour No. Level 1 Level 2 Level 1 Level 2 Level 1 Level 2 (€500) (€5000) (€20,000) (€50,000) (€10,000) (€100,000) 40 0 0 0 4 0 3 0.033 41 0 0 5 0 0 4 0.032 42 1 1 2 1 0 4 0.008 43 0 2 2 1 0 4 0.007 44 0 0 0 2 0 4 0.033

The optimal mitigation strategy identified to minimise the probability of death is highlighted in bold in Table 16. It involves providing full scour protection to each of the four bridges investigated, “Level 2” mitigation to landslide node 8, “Level 1” mitigation to landslide nodes 1 and 11 and “Level 2” flood protection to flood nodes 3 and 10. The strategy identified also reduced the expected value of the number of deaths given an extreme weather event from 0.078 to 9.4×10‐5. It is noted that this strategy is also found to be the optimum when the expected value is used as a position indicator. In addition, this strategy is found to be the optimum for a range of ‐values.

This mitigation strategy is also applied to the economic risk model in order to assess the concurrent economic benefits. It is found that the for economic benefit changed from €511,075 to €157,689, without taking account of the cost of mitigation. The ‐value with the mitigation cost taken into account is equal to this value plus €500,000, or €657,689. While it may appear that the overall is greater than the “do‐nothing strategy, one also needs to consider the concurrent benefits associated with societal risks. The of the electricity network did not change for the strategy identified.

It is recommended when applying this methodology that an investigation be performed for various budgets to identify the optimum spend for risk mitigation. For example, repeating the procedure with a mitigation budget of €200,000 yields 22 unique mitigation scenarios. The optimum scenario is to provide full scour protection to the two bridges highest expected loss (sum of the probability of failure for each state time the outcome of that state). The result is that the for direct societal risks reduces to 0.012 and the corresponding for economic risks reduces to €366,369, including the mitigation costs. It is clear that this strategy may be preferable to the €500,000 mitigation strategy, depending on available budget and risk perceptions and the cost assigned to a human life.

3.2.8. Conclusions

The benefits of the Risk‐Based Decision Making Framework are evident from the results presented above where POS for number of lives lost drops from 0.271 to 0.007. The has been used as an indicator for the relative position of the risk state space and it is clear that the procedure has yielded the optimum scenario for mitigation of societal risks. This procedure could also be followed to minimise economic or security risks, and the concurrent benefits to other risk types could also be quantified. The should also be optimised for various budgets, to identify the optimum spend for risk mitigation. Nevertheless, this section has fully demonstrated how the RAIN Risk‐Based Decision Making Framework can be applied to a real world example.

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It should be noted that only the direct benefits of the associated mitigation measures have been quantified. The measures put in place will have added benefits to the indirect effects of extreme weather events on the infrastructure, which have not been quantified.

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4. Case Study 2 ‐ Uusimaa Storm Surge, Finland

4.1. Introduction

The second case study analysed the risk to the Uusimaa region due to an extreme storm surge event. This case study region is chosen for the analysis due to the fact that in 2005 an extreme storm surge event hit the Uusimaa region, causing major disruption and damage. The description of this case study commences with an overview of the previous event, describing the impacts of the event in the Uusimaa region and also in surrounding areas. The emergency response procedures are also described in detail, providing evidence that there were a number of lessons learned and that there is room for improvement in preparing for and responding to an extreme weather event of this nature. In Section 4.3, the application of the RAIN Risk‐Based Decision Making Framework to the Uusimaa case study region is presented, and the various approaches used to carry out the analysis are described in detail before presenting the outputs of the risk assessment.

4.2. Context

The main natural hazards that pose a threat to Finland are caused by extreme weather conditions, especially in winter due to low temperatures and storm events. Flooding of coastal regions and along riverbanks in northern regions is also a challenge for the municipal and regional civil security authorities. The extensive coastline along the Baltic Sea means that maritime safety issues are essential for civil security measures.

The Uusimaa region, Figure 23, is a region in Southern Finland made up of 26 municipalities of which 13 have city status. Finland’s capital Helsinki (its largest city), its second largest city, Espoo, and its fourth largest city Vantaa are all located centrally in Uusimaa, making it by far the most populous region, with a population of approximately 1.6 million, 30% of the total population.

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Figure 23 Uusimaa Region

4.2.1. Storm Surge Event, January 2005

On Friday, January 7th 2005, the Finnish Institute for Marine Research (FIMR) received weather forecasts that showed a strong winter storm approaching southern Finland. Storm winds of up to 25 m/s were forecast over the weekend for sea areas in the south and south‐west regions. In addition, high wind speeds were forecasted for land areas. By the evening of January 8th, wave heights were up to 8m and there was a significant sea level rise in the Gulf of Finland. Consequently, a severe weather outlook was issued to emergency authorities. Subsequently, for the first time in the operational history of FIMR, an early warning signal was issued to the Interior Ministry’s Rescue Department. The warning message stated that severe flooding would occur in the Gulf of Finland on January 9th. As a result of the warning, local rescue services began to prepare for the flood and the Finnish emergency management agencies began to develop their situational awareness based on sea level predictions carried out by FIMR.

These predictions were based on a sea level forecasting system which employed several models using various data sources, which were automatically run four times a day. The model results were compared with results from other models within the Baltic Operational Oceanographic System BOOS.

The warning signal issued by FIMR incorporated the following pertinent information;

 A storm intensity in excess of the 2004 Rafael storm was expected.  An Intense low pressure area moving eastwards across central parts of Finland during the night of January 8th.  Wind speeds of approximately 25m/s along the North Baltic Sea and the Gulf of Finland, as well as wind gusts of up to 30 m/s.  Very high wind speeds in southern parts of the country.  Rainfall in excess of 20mm in places during the night of January 8th. 63

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 Snow and sleet in central Finland, leading to severe road conditions.  Flood duration of up to several hours with two peaks.

4.2.2. Impact of Storm Surge Event

In the Helsinki area, the maximum rise in sea level was forecasted to be 120 cm greater than normal levels (denoted +120), with a possibility of reaching +150cm, however, on January 8th, it was estimated that a sea level rise of +130cm in Helsinki was more likely. Equally, the FIMR forecasted that a new record of 8m high waves would be reached, with individual waves reading 14m‐15m in some cases.

On January 9th, the sea level in the Gulf of Finland had risen to record heights. The record heights are shown in Figure 24. The wave height was almost a record on the North Baltic Sea, where the significant wave height was 7.2m at its maximum. The record is still 7.7m, measured during the Rafael storm in late 2004. No new wave height records were made on the FIMR’s North Baltic Sea wave buoy, because the storm centre passed the Baltic Sea a little farther south than was predicted. On all mareographies of the Gulf of Finland the sea level rose to a record height. Locally, the sea level in Turku was +130cm (record before +127cm); in Hanko +132cm (+123cm); in Helsinki +151cm (+136cm); in Hamina +197cm (166cm) and in St Petersburg, Russia, the sea level height was +239cm, according to NWAHEM.

All such records were broken in the Gulf of Finland, observed senior researcher Kimmo Kahma. Kahma concluded that the cause of the storm as being due to increasing air pressure differences on the North Atlantic Ocean and alternation of the western currents. This phenomenon is called North Atlantic Oscillation (NAO). The air pressure difference between different parts of Atlantic Ocean has increased over the last 30 years. In addition, the wind blowing from west to east has gained in strength. According to Kahma, it is not certain if this phenomenon is related to the greenhouse effect.

It is worth noting that on the 10th January (four days after the first early warning signals) the Finnish Meteorological Institute (FMI) reported that the storm was not exceptional in terms of wind speed, with gusts around 30 m/s in Tulliniemi, Hanko. However, what did make the storm exceptional was the record rise in sea level. Of all the storms that Finland experienced in January from 1990‐2004, 28 per cent were strong ones. The storm of the 8th & 9th of January was not exceptionally fierce. The dominant air flows had been from the Atlantic that winter and they brought with them above average temperatures and rainfall.

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Figure 24 Sea level rises recorded during the 2005 storm surge event

Although large‐scale flooding and storm damage was avoided, the storm surge event resulted in partial road closures due to flooding, causing traffic disruption in Helsinki. Roads that were partially closed due to flooding included the Kehä I circle road in Otaniemi as well as the Kehä III at Itäväylä, as shown in Figure 25. Furthermore, the flooding caused road closures in the coastal region resulting in traffic being cut‐off in Pohjoisranta and Pohjois‐Esplanad, as shown in Figure 26.

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Figure 25 Location of roads that were partially closed due to flooding during the 2005 storm surge event

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Figure 26 Location of cut‐off traffic due to flooding of coastal roads during the 2005 storm surge event

4.2.3. Emergency management cycle

4.2.3.1 Introduction

The 2005 extreme storm surge event was of serious concern to the research institutes who were responsible for delivering the situational awareness information. Since the operational situational awareness was gathered at both the central and regional levels of the Finnish rescue and emergency management services, a deeper and comprehensive understanding of the duration and severity of the incident and its larger implications, started to emerge.

Consequently, for the first time in their history, during the evening of the 8th January, FIMR issued an exceptional sea level rise and rough seas warning. According to FIMR, for the North Baltic Sea, a typical wave height of 8.0m, with individual waves of 14‐15m was forecast, with the highest waves estimated to go travel further south than originally predicted. FIMR referred to the forecasted waves as the ''breakers of the century'', since nothing on this magnitude had been measured in the previous 45 years. The height of the waves was affected not only by the speed of the wind but also the direction of the wind and the area of water being moved. In this particular case the winds blew from the south and so raised the breakers. Winds from the North caused more harm on land areas in Finland, but did not significantly affect the wave height on coastal areas.

The record sea level rise observed was due to a ‘bathtub effect’ associated with the storm in the Baltic Sea, whereby water moves from one side of the sea to the other. FIMR did note however that

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the sea level height that would occur in the vicinity of Helsinki, would more than likely be in the region of +130cm.

4.2.3.2 National and Government Level Response

The national flood response system generated its operational preparedness after the Rescue Service Unit (RSU) of the Ministry of the Interior received the early warning about the rising sea level and impending storm from the FIMR and the FMI on the afternoon of the 7th January. The FIMR also warned that a severe flood with long duration in the Gulf of Finland would commence on the morning of Sunday, the 9th January, probably lasting until the afternoon.

According to the Director‐General of Department for Rescue Services, Pentti Partanen, the Ministry of Interior immediately ensured that the warning and flow of information would proceed to all relevant actors as efficiently and effectively as possible. The information was sent to the rescue departments at coastal regions, to the Coast Guard and the Finnish Maritime Administration.

Since the regionalization of the rescue services in Finland, the responsibility of the operations falls mostly on local and regional authorities. The RSU at The Ministry of Interior held a specific file that included the necessary contact information for the all the key authorities that might be needed. In such a situation the first step was to contact the rescue departments, advise them of the developing situation and confirm that they have received all the necessary information about the event. As the flood warning affected several rescue departments, the information was first passed to the State Province Offices of Western and Southern Finland who subsequently contacted the rescue departments responsible for the affected areas. Subsequently, the information was passed to the Finnish Environment Institute (SYKE) and Ministry of Agriculture and Forestry as well as to the Government Situation centre, the latter of whom informed ministers about the events. In this way those responsible at government level for emergency preparedness were fully aware of the situation unfolding and were in a position to respond accordingly.

On Saturday 8 January the RSU of the Ministry of Interior decided that a meeting of the emergency preparedness leaders would be held at 9pm. The purpose of the meeting was to ensure that the appropriate procedures were in place to deal with the event and that the different actors were aware of their responsibilities. In addition to the attendance of the preparedness leaders from the essential governmental agencies, the meeting was supplemented with experts from the FMI and the FIMR to give the most accurate forecasts of the storm. Representatives of the Coast Guard and the Police on call were also present. The State Province Offices of Western and Southern Finland were asked to give their reports on the preparedness of the coastal areas in the meeting.

At the meeting, the state of readiness was officially noted and that at this point no special actions were expected from the side of the Finnish government. It was also decided that a more detailed communication, based on the most recent forecasts, would be given to the public. Equally, different branches of administration would be advised to prepare themselves with adequate resources. One could assume that the shortcomings of the national crisis communications during the tsunami disaster, which occurred roughly one week before the storm surge event, had revived the readiness of the government authorities to activate and inform the public about the situation. This could be

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described as widened situational knowledge or as the mobilization of the first responders, the citizens themselves.

Following the meeting, the Ministry of Interior advised citizens to take the necessary precautions and, in addition, the rescue departments of the City of Helsinki and Southwest Finland advised citizens to stay indoors. This was also the first time the ministry used a direct radio broadcast to issue a warning to the public.

The meeting also paved the way for several other semi‐governmental agencies to issue their own early warning signals and notify the public of their responsibilities. For instance, SYKE, which operates with the support of the Ministry of Environment and is responsible for the response to a wide range of natural and man‐made risks, announced that the storm would bring rains that could cause flooding in the rivers on the south coast and in southwest Finland. It was reported that water levels in rivers might rise by one meter with lake levels potentially rising by around 20cm. The Finnish Maritime Administration estimated that the storm would be strongest storm to hit Finland in over 100 years. The Finnish Maritime Administration urged mariners to remain in the harbour or not to return from open sea until the storm was over. The Finnish Maritime Administration also increased its preparedness for the sea traffic control and monitoring. The Finnish Road Administration warned of very poor road conditions in the south, and south western coastal areas and central Finland.

The storm also affected neighbouring countries. In Sweden, Storm ‘Gudrun’, as it was called, caused severe damage in Southern and Western Sweden. Swedish public TV, SVT, described the night when Gudrun hit Sweden as a night ‘that changed everything’. According to SVT, winds speeds were hurricane‐strong, with gusts of over 40 m/s on the coast. Electricity lines and mobile network failed, roads were closed and railway traffic ceased to operate. In addition, a total of 75 million cubic meters of timber was damaged/fell, equating to a year's worth of tree growth or 200 million pieces. In some places, 50% to 60% of wooded areas were damaged. According to newspaper Dagens Nyheter, 17 people were killed as a as a result of the storm, seven of whom dies as a direct consequence (e.g. crushed by fallen trees). However, there were also indirectly related deaths, one of whom committed suicide due to the damage that occurred in his forest. SVT subsequently published a series of stories 10 years after the storm, in January 2015.

In Estonia, the public broadcasting service (ERR) published various stories before, during and after the storm. ERR told about a approaching storm ‘that is something that has seen never before’. In Pärnu, a sea level rise of at least 2.4m above the normal level was expected along with a rise in sea level of at least 1.5m above the normal level along coastal areas. Sea level was also predicted to rise on the coast of the City of Haapsalu. It should be noted that this information from Finland and Estonia was added by the Government Council to the overall situation awareness, and as such served for mobilising the wider society to deal with sudden flash flooding.

In Pärnu, the actual sea level rose to 2.72m above regular sea level. In Haapsalu, there 103 people were evacuated. On the 9th January, the emergency commission of West‐Estonian Islands (Saaremaa, other islands around as well as Ruhnu on Pärnu Bay) described the situation to be critical. Ferries between the main land and the islands were not operational because of the storm, resulting in the islanders being isolated. Roads in the main city Kuressaare in Saaremaa Island were flooded.

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On the 10th January, a new flood warning was issued on the coastal area of Estonia. More than 400 people had been evacuated from Pärnu over the weekend and although the water level was dropping on the streets of Pärnu, the Estonian Rescue Centre advised residents to expect a possible new storm before returning home. The majority of the Finnish tourists that had been evacuated from spa hotels returned to Finland on Monday. The spas in the city of Haapsalu were spared evacuation. In Kuresaari there were no evacuations from the spas, and new tourists were arriving from Finland on the 10th.

Estonia’s government held a crisis meeting on the 11th January, where they estimated the storm damage as well as the success of the rescue work throughout the country. A third of Estonians were suffering electricity blackouts, and sea water flooded into hundreds of dwellings in coastal areas. The storm damaged roofs and felled trees throughout the country. At least 15 people were injured. In Pärnu, Saarenmaa and Jõgevassa most of the schools were closed on the 10th January. In Pärnu, the storm destroyed about 40,000 hectares of forest, and killed sheep, cows, and other livestock. The damage in the province of Pärnu alone was estimated to be about 200‐300 million kronas. In Pärnu the flood levels were a record 2.95 m high and sea level was 2 kilometres from the shore to the inland. The Prime Minister of Estonia, Juhan Parts, pledged that the state would cover the costs for part of the damage. Meanwhile, the City of Pärnu had already begun to plan new preparedness actions for the next time a flood of such magnitude occurs.

On Monday 10th January, the Estonian Meteorological Institute warned of a new storm and further flooding during the night of the 10th and 11th of January. It forecasted a rise in sea level of 190cm in Pärnu and around 150cm in other coastal areas of Estonia.

After the storm on the 9th, clear failings in the preparedness of the authorities had been apparent. The Estonian Meteorological Institute did not take the storm forecasts of the 7th seriously resulting in the Estonian rescue department only responding to the warnings after a 26‐hour delay. Consequently, a crisis commission of government initiated an investigation, if the storm was forecasted well enough, ERR told on 14th January. Storm was described to be the worst in a decade and commission suggested that people should be better informed, if an extreme weather event was approaching Estonia.

4.2.4. Consequence Management

Finland was also hit by the Rafael storm a couple of weeks prior to the January 2005 storm. This may have prepared the authorities for this storm as the responders were already on high alert following the Rafael storm. The first responders were the few governmental agencies that had already commenced their response operations on the 7th January after the first signs of severe weather conditions. Equally, the West Finland Coast Guard and the Gulf of Finland Coast Guard were preparing for the storm by raising the alert. According to Lieutenant Jim Johansson from the Gulf of Finland Coast Guard, helicopters were on standby at Turku and Malmi airports.

Equally, the Finnish Road Administration was also suitably prepared for the storm. The Chief of the Traffic Management Centre, Veli‐Pekka Pelttari, estimated that trees and pylons would be felled onto roads and that the roads could be covered by rising water. Within the private sector, which owns most of the critical infrastructures, the situation was taken seriously and preparatory measures 70

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were in place to deal with the consequences. The exceptions were the railway company VR, which was not prepared specifically for the storm, although there could have been problems if trees were to fall across rail tracks. The national airline carrier Finnair reported that the storm would not cause any disruptions to its operations.

The energy company Fortum Oyj faced electricity distribution problems as the Rafael storm damaged power transmission lines, which were still undergoing repair in southwest Finland and in west Uusimaa. This case looked somewhat more alarming for Fortum’s business strategy. The company had elevated its storm preparedness in southwest Finland and in west Uusimaa. A large amount of service personnel were ready to be mobilized if needed and 200 employees were on standby to deploy on the 9th January in case of widespread damage.

Large passenger ships operating in the Gulf of Finland were also prepared for schedule disruptions as a result of the storm and had advised passengers to expect cancellations. High‐speed ships between Helsinki and Tallinn would not operate if wave heights were more than 3 meters. According to Viking Line, one of the two major ferry operators, storms were not expected to affect the operation of the large ships, but smaller ships might be effected. According to the technical director of Viking Line, Kaj Jansson, the situation was not as bad as was predicted. TallinkSilja, the Estonian ferry operator, had also prepared for schedule changes.

In general, the storm situation was followed carefully by maritime operators and ships were able stay in port to wait for the storm to subside. In large passenger ships trucks were strapped down on the car decks, whereas normally only their detachable rears would be secured. Mobile items such as bottles and dishes were also secured. The ships to Stockholm left normally on the 8th January, but TallinkSilja cancelled its operations from the Estonian harbours. The last high‐speed ships running between Tallinn and Helsinki were in Helsinki before the storm was due to hit.

The City of Helsinki Rescue Department prepared for the storm and the flooding of Kauppatori by adopting a new approach ‐ to dam up Kauppatori using massive paper bales a meter high which are quicker to build and move than the traditionally more cumbersome sand bags. This was based on a similar approach adopted in Britain which had proved successful in the past. In Helsinki the flood barrier was built first in Kauppatori, as experience had shown that the sea level always rose there first. The flood barrier in Kauppatori was 300‐400m long. Other such places protected were the bridges of Lehtisaari and Kuusisaari, which could be closed by police if necessary. The Rescue departments would receive technical assistance and know‐how from the Finnish Defence Forces conscripts, preparedness groups of the civil service departments of the city of Helsinki as well as from volunteer groups when necessary. The City of Helsinki Rescue Department has group of 80 men ready on three‐minute standby during weekends.

On Saturday afternoon of 8th January, the Helsinki’s volunteer fire brigade built flood barriers in the city centre. It was predicted that the water would rise to Kauppatori, but the City Hall and the Presidential Palace were expected to be safe since they were out of reach of the predicted water levels. Equally, since the 7th January, units of the rescue department had been monitoring the sea level at nine different locations in Helsinki, such as Kauppatori, Vattunokka, Iso and Pikku‐ Huopalahti, Töölönranta 14. and Kyläranta. Fire fighters were on call throughout the night between the 8th and the 9th January. In addition to the rescue department, the Police, Helsinki Water, the Port 71

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of Helsinki, the Public Works Department, the Environment Centre of the City of Helsinki, the volunteer fire brigades and the Finnish Defence Forces were on standby to minimise flood damage. For instance, Helsinki Water offered sand bags to members of the public and first responders to tackle flash floods. It also started to seal off sewer overflows so that sea water could not enter buildings through drainage systems.

The storm that raged during the night of the 8th January was expected to subside on the night of January 9th. According to the Finnish Meteorological Institute (FMI), wind could still be dangerous on land areas in the southwest Finland and in the west Uusimaa region, especially on the 9th January. The FMI estimated that the worst gales passed Finland from the south. The storm was at its worst in Great Britain, Denmark, southern Sweden and the Baltic countries though Finland was also affected. The storm warning was in force until the evening of the 9th, but according to the FMI, wind was gradually decreasing during the night of the 10th.

Following the storm of the 7th‐10th January, a new storm approached Sweden and Finland during the night between the 13th and 14th January. According to the Finnish Meteorological Institute, the storm would not be as intense as the previous weekend. The risk of severe floods would decrease in the rivers and the lakes of south and southwest coast. Water levels would cease to rise if the rain were to cease during the coming weekend, as was predicted. The level of the inland waters of the region was above average. According to senior inspector Leena Villa of the Environment Centre of Uusimaa, water levels did not pose as significant threat to dwellings as they had previously. In Vantaajoki the water was 1.8m higher than average. The Finnish Institute of Marine Research (FIMR) warned that sea level could rise by 1.2m in Helsinki and even 1.5m in Kemi during Thursday night. On Wednesday morning, the sea level was 85cm above average. The City of Helsinki Rescue Department was again preparing for the sea level to rise along coastal areas. The rescue department had an ongoing operation in the Marjaniemi seaside residential area, where around 20 houses had suffered water damage. The Rescue Department, the Public Works Department of Helsinki and the Waterworks, together with three volunteer fire brigades, protected the bay area with sand bags. According to the fire chief on call, Juha‐Pekka Lassila, the sea water could cause damage if the sea level rises by more than 120cm above average. At around noon the sea level was 91cm above average on the Helsinki coast. The decision to build flood barriers was made in the morning, when it still seemed that the sea level would rise by more than 120cm above average and would cause damage. The barrier was ready in place in the afternoon, when the sea level was 94cm above average.

According to the forecast of the FIMR on the night of the 13th January, the situation was predicted to remain calm, because the sea level had started to fall. At 20.00 the sea level was 88.5cm and falling. The highest point the sea level reached was 95cm. In Turku the sea rose again to the area of the port of Turku and surrounded the hotel Seaport and the passenger terminals of the large passenger ships. The sea level was about a metre above average.

SYKE warned that rain and melting snow caused by the storm had raised the levels of rivers and lakes in southwest Finland and the south coast to flood heights. The water levels in lakes were estimated to continue to rise and new rains and warm weather had been forecasted after a short cooling down. The water flow in rivers was considerably heavy for this time of year in southwest and south Finland.

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This kind of flow is typical for spring time. High sea level also raised water levels in river deltas, and flood problems emerged in the Kokemäenjoki and Loimijoki rivers. The weather forecasts predicted that the level of water in all southwest and south Finland’s lakes would rise again to unseasonable highs during the following week. At this stage the rest of the emergency authorities realized that the situation might not be over. During the night of the 9th – 10th January there were no new emergency assignments for the rescue departments as most of the roads that were cut off by the flood had been reopened to traffic and the water had been pumped from the cellars of private houses. However, the flood still meant work for the Helsinki rescue departments on Monday. The pumping of the water from the tunnels continued, but the situation was seemingly under control in parts of these underground critical infrastructures, where district heating, electricity, phone, water and sewage lines lie in tunnels dozens of kilometres long. Water was also pumped out of the metro transformer booths. The massive paper bales and the sand bags were removed from Kauppatori, central market square by the sea, on the morning of the 10th January.

The pumping of the water out of the many private houses was mostly done by midday on the 10th. In many places dryers replaced pumps (drying work can last for weeks). In Helsinki the worst situation was still in Marjaniemi, but also houses in Tammisalo, Laajasalo and Kulosaari were flooded, according to Helsinki Water’s production manager Petteri Niemi. Paradoxically, these areas are traditionally regarded as the most secure and valued residential areas in Helsinki. In other places in Helsinki flooding was due to sea water blocking up sewers. As a result of the overload of the sewer network, 63,000 cubic meters of untreated waste water was dumped into the sea. About 100 buildings in the Helsinki region were in need of rescue. In Hamina, where the sea level rose 198cm, more than 200 places needed help from the Rescue Department.

According to Rescue Commander Kari Lehtokangas, the prevention of the flood damage in Helsinki was successful: “Action was taken in time, when the weather forecasts were given.” Private home owners called the Rescue Department and asked what they should do, and the rescue department’s crew went to advise them on the spot. Of course there was still some bad damage. The City of Helsinki Rescue Department will consider updating its preparedness plans. According to Lehtokangas, it is a good to question whether one should build on the waters’ edge with the risk that the building’s ground floor could be flooded once every 20‐30 years. In Finland, the responsibility of the safety of the building sites belongs to the municipalities’ city planning and construction licence policy.

According to the Finnish Road Administration’s Traffic Management Centre, traffic was running smoothly already on the morning of the 10th January. The morning traffic was not exceptional, contrary to what was expected on the Sunday.

4.2.5. Material Damage

Passenger ferries en route Helsinki and Stockholm were operating on time, but operations between Helsinki and Tallinn were cancelled during the storm, resulting in thousands of passengers having to remain in Tallinn overnight.

The Turku City harbour also experienced flooding resulting in damage to parked cars. The basement of Hotel Seaport, located in the harbour, was flooded. Regional reports noted that roughly ten hotel 73

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customers were relocated from the first floor to the second floor and Conference rooms located in the basement had around 20 cm of water on the floor. Terminal infrastructure such as ramps experienced operational malfunctions and passengers had difficulties boarding ferries. As a result, temporary ramps were constructed to enable passengers to board.

On the 12th January, Helsingin Sanomat newspaper reported that flooding is ‘not threatening Helsinki’. City planners had recognised the flooding potential scenario. Housing regulations required that houses are built 2.5–3.0 m above sea level. If the sea level rises dramatically, they envisaged the possibility of building a wall to protect the city. However, it was recognised that there are areas in the city that are prone to flooding, like the area of Marjaniemias a number of detached houses in that area are only 1.2 m above sea level. Equally central areas and Töölö Bay have relatively low elevation. Following the events of early January, a representative of the Helsinki City Geotechnical department hoped that a potential flood scenario will be taken more seriously in the future. Those residents subjected to flooding were displaced from their homes for months. One resident of Virolahti noted that they had had never witnessed flooding as bad as in January 2005.

However, Large‐scale flood and storm damage was avoided, but water cut off roads and traffic was cut off in many places in the Helsinki region, particularly along coastal roads. Routes that were partly cut off included Kehä I in Otaniemi as well as the intersection of Kehä III and Itäväylä and traffic in Pohjoisranta and Pohjois‐Esplanad was also disrupted.

In the centre of Helsinki the sea rose to Kauppatori and in eastern Helsinki into the cellars and garages of private houses and flood waters forced their way into dozens of houses in Helsinki. In Kauppatori, water was pumped from the sewer network to the sea to prevent it from flooding cellars. In the port of Sörnäinen in Helsinki, about 400‐500 imported new cars were damaged by floodwater when the sand wall protecting them disintegrated during the night. The wharf of the ferry to Suomenlinna was submerged as was Kompassitori in Kaivopuisto. Consequently, Suomenlinna Island was isolated because the ferry could not carry passengers from the submerged wharf. Equally the maintenance tunnel to Suomenlinna had been barricaded with sandbags from the side of the mainland on the 8th to prevent seawater from flooding it.

According to the City of Helsinki Rescue Department the massive paper bales proved successful in preventing the peak flood levels, though they did not totally prevent the sea from rising to Kauppatori. According to fire chief Markku Lehmuskallio from the Helsinki Rescue Department, the paper bales made from newspapers were best in absorbing the water. Bales made from milk cartons or other such materials were not so effective. The Rescue department procured the massive paper bales from Paperinkeräys Oy in Helsinki and it was concluded that it would be good to have a stock of paper bales in the event of future floods.

During the night the rescue department had 80 rescue and flood prevention assignments in Helsinki. The rescue department was still pumping the rainwater drains on the afternoon of the 9th near Kauppatori. The worst situation was in Marjaniemi, where the floodwater entered cellars and garages and surrounded many of the private houses by the morning. Equally, the rising sea level melted snow which caused further flooding in ditches.

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The police estimated that traffic in the centre of Helsinki would revert to normal by the morning of the 10th and business operations in Kauppatori were able to continue as normal. Helsinki Water advised members of the public to take precautions in basement car parks and buildings where there are floors below ground level. Pumping of floodwater from the sewers took place over a number of days. It was also possible but not likely that water would get into the metro tunnels.

In Virolahti, on the coast of the Gulf of Finland, elderly people were evacuated from two terraces of houses and there were other evacuations in Pyhtää. In total approximately twenty people were evacuated. In Porvoo the water rose to the lower parts of the buildings along the shore. Residents from the houses near the railway station were carried by boat to safety. A confirmation camp was evacuated from the Pelling island of Porvoo. Also, in Tammisaari, Loviisa and other east coast cities of the Gulf of Finland, the elevated sea levels flooded buildings along the shore. The rescue department pumped water from buildings in different parts of the Uusimaa region until late afternoon on the 9th. Some streets were closed off in Espoo, Kotka, Kirkkonummi, Sipoo, Raisio and Porvoo.

On a wider scale, in St Petersburg, Russia, the Emergency Ministry EMERCOM did not expect the storm to accelerate, but warned of strong winds. The water level in the river Neva was expected to rise 1.6 – 1.8 m above average during the morning of the 9th and rescue services were on full evacuation alert and would be mobilised if the flood levels rose to 2 m above the normal water level. In Kaliningrad there was warning of the wind gusts of 28 m/s and small ships were urged to come ashore. The Road E18 was closed to traffic during Sunday afternoon in Viipuri, and the Vaalimaa frontier station was closed as water rose over the E18 from Vaalimaa to Viipuri. In St Petersburg, the river Neva was 2.4m above average. However, according to the authorities, large‐scale damage was avoided. Six metro stations were closed as a result of the flood risk. In Kaliningrad the storm wind caused much damage to roofs and power lines.

In Estonia, the worst flood situation was in Pärnu. The sea surrounded the area on the shore where most of the spa hotels were located. More than 200 tourists were evacuated in Pärnu region. Many of the tourists were Finnish retirees. Also an elderly people’s home, school, kindergarten and psychiatric hospital were surrounded by water. The sea level was 3m above average at its highest in Pärnu. By the evening of the 9th, in the Pärnu region there were 11 Estonians who had been taken to hospital because of hypothermia. The City of Pärnu estimated that the city would run out of drinking water before 9 pm that night. The sea level also rose in the city of Haapsalu. During Sunday around 60 people were evacuated from areas within the city by the Fire Departments and Estonian army. The defence forces provided vehicles and boats for evacuation. On Sunday night about 15% of Estonian houses were without electricity. The ferry connection from the mainland to Saarenmaa Island was cut off because the wharfs at both ends were underwater.

There were also electricity blackouts. According to estimates, it took at least three weeks to repair the flood damage to Saarenmaa´s hotels. The Ministry of Foreign Affairs informed the Rescue Service Unit about the situation in Pärnu, Estonia as well as in St Petersburg, Russia. People had been evacuated and one helicopter was in use in Pärnu, however the situation seemed to be under control. Still the RSU had the Finnish Border Guard on notice to loan one of its Agusta Bell 412 helicopters to Estonia, if requested.

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A day after the storm newspapers told of a ‘sudden flood’ in the Gulf of Finland. In Denmark and in Sweden there were over 10 causalities. Members of the public were surprised that not all insurance policies covered flood damage and it was reported in the media that many families were facing an ‘economical disaster’ as their insurance would not cover the repair costs or other expenses. On the 11th January representative of Ministry of the Interior estimated that flooding would have caused damage worth ‘millions’ of Euros. According to the estimates, the storm resulted in a cost of €20 million to the insurance company Sampo alone. Sampo estimates that in Finland its customers suffered damage worth €7million. Bigger loses were experienced in Sweden, where the loss was about €11‐17 million.

As often in a large scale disaster, the private and public sector had different policies for the compensation of the flood damage. Some of the insurance companies were not willing to cover the flood damage at all, and some of them considered the flooding caused by the storm an exception that they would cover. In some cases, insurance companies covered flood damage to homes but not to other forms of building damage. In Finland, flood damage is usually covered by the state, but in case of a flood caused by a storm, home insurance policies are expected to cover most of the costs of the damage. Damage is covered by insurance if storm is mentioned in the insurance conditions. The Counsellor for Water Administration at the Ministry of Agriculture and Forestry, Jaakko Sierla, pointed out that in this case it was the storm that caused the sea level rise and which led to the flooding. Prime Minister Matti Vanhanen made promised on January 10th that the state would cover the damage that the insurance companies would not, while his Estonian counterpart, Juhan Parts, promised that the state would only cover some of the damage.

The prevailing practise is that the insurance companies do not cover any flood damage. According to Jaakko Sierla, this is logical since large floods are rare and usually localised. If the insurance was sold to large groups, it would be difficult to consider also the special needs of some particular flood risk area. Since the 1980s about €800,000‐900,000 annually had been sufficient for covering flood damage by the state’s. The only exception was in 2004, when there were large floods in Ostrobothnia. The insurance companies have stated that they are in the process of developing better insurance policies to cover natural disasters.

The flood damaged not only private property but also sewerage systems and harbor equipment. Some road foundations also collapsed. According to Preparedness Director Janne Koivukoski at the Ministry of Interior, it is still hard to estimate the total costs. As far as it is known the damage was predominately only material. In neighbouring countries, the floods and storm claimed livesbut in Finland no one died.

According to Senior Officer Taito Vainio from the Ministry of Interior no individual case had been identified damage costs would be more than €200,000. This is the limit after which the rescue department is supposed to inform the Ministry of Interior directly. It is still unclear how the state will cover the flood damage, since damage caused by a storm or flooding by sea water are not considered in the laws that regulates the state’s compensation for flood damage. It is also unclear which ministry department would take responsibility for the compensation if the government were to decide that the state would cover part of the damage. The Ministry of Agriculture and Forestry

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was responsible for compensation for the summer of 2004 floods, as it was a result of an inland water flood. Damage caused by sea water is not included in its remit.

If the state decides to cover the damage caused by the storm on the same basis as the flood damage of the summer of 2004 then compensation would be paid to only those that would not get money from other actors, such as an insurance company. Compensation can be given for damage caused to buildings, private roads, gathered harvest or livestock. According to the law, the maximum compensation is 80% of the damage costs, but in practice this can be different.

To date, the Finnish government has allocated €841,000 to its annual budget for the compensation as a result of the flood damage. This amount will not be enough to repair all the damaged houses. The final compensation sums depend on the extent of the damage reported and the possible assets allocated in a supplementary budget, according to Senior Inspector Mirja Eerola at the Ministry of Agriculture and Forestry. One complicating factor is that the applications for compensation for the floods that took place have been progressing very slowly. There are altogether 886 declared flood damage cases worth in total 7.2 million euros. Only about 1 million has been paid to date. The rest of the compensations will be paid at a later date. The handling of the cases has been so slow partly because the regional environment centres have to make a separate statement on each case. According to engineer Kari Rantakokko at the Environment Centre of Uusimaa, one particular factor is that in practice only one person prepares such statements at the Centre.

In Estonia, the government decided to give 650,000 kronas of crisis aid to the storm‐damaged homes so that they could be repaired as soon as possible. According to preliminary estimates, the storm caused damage worth tens of millions of Euros. The worst damage was caused by flooding, followed by broken roofs and fallen trees. The Estonian media heavily criticized the storm damage prevention protocols and proceedures. While early warnings about the storm of the century were spreading in Estonia on the 8th January, before the storm hit but no concrete instructions were in place. The government press officer, Erki Peegel, has admitted that there is a need for improved communications. People could have been informed what kind of consequences there would as a result of a two‐metre water rise on certain streets. The flood caused one loss of life in Pärnu.

4.2.6. Nuclear Power Plant

The major cascading effect caused by January 2005 flooding at the Gulf of Finland was somewhat comparable to the Japanese Fukushima disaster in 2011. The complex, sudden, multidimensional and unpredictable situation was caused by the rising sea water level. In both cases the effects were most destructive on critical infrastructures.

Loviisa power plant is situated on an island of Hästholm, 90 kilometers East from capital Helsinki. The plant announced to the Radiation and Nuclear Safety Authority (STUK) that it was on alert because of the sea level rise. At 09.00 on 8th January it gave the same announcement to the Rescue Service Unit of the Ministry of Interior. In the morning the sea level was 171cm above normal levels and the Energy Company Fortum was preparing to close down the power plant if the sea level continued to rise. A 200cm rise is the critical point after which the power plant needs to be secured and shut down. Fortunately, the sea level started to drop on the afternoon of Sunday 9th January afternoon. The situation was monitored closely by STUK and the staff of the nuclear power plant. 77

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Closing down the nuclear power plant would have affected the distribution of electricity. There was a representative of STUK based in Loviisa so the STUK was prepared to establish a command centre at the plant. STUK was responsible for communications, as is stated in the communications plan. The Rescue Service Unit of the Ministry of Interior informed the State Province Office of Southern Finland about the situation. The Minister of the Interior, Kari Rajamäki, State Secretary Risto Volanen and other key officials were briefed. However, the alert in Loviisa was subsequently cancelled. Shutting down a nuclear plant takes a few hours, depending on whether only the power is wound out or if the reactor itself has to be shut down. The sea level had previously never been so high in Loviisa was and it was also odd only the sea level was rising with only a few high waves near Loviisa. The previous record height was 1.6m. At the other nuclear power plant site in Olkiluoto there was no need for special measures. The sea level rose only 0.8m and the danger limit is 2.3m, according to Reijo Sundell, production manager of Olkiluoto nuclear power plant.

Helsingin Sanomat reported a day after the storm that there had been an emergency situation in Loviisa nuclear power plant due to the rise in sea level.

Loviisa nuclear power plant was monitoring the rise of sea level and was close to preparing to shut down the reactor. According to the security code of conduct, preparation for shut down is started if sea level rises over 1.75m. At the worst, it was at 1.73m – only 2cm away. Loviisa nuclear power plant informed the Finnish Radiation and Nuclear Authority STUK first time in the morning of 9th December. At 4.35 o'clock the sea level was +1,4 meters, and this was considered to be an ”unusual situation” (erikoistilanne). At 7.39 o'clock sea level was at +1,6 meters. Consequently, the Nuclear facility started to prepare for preparations to run down the reactor (varautumistilaan siirtyminen).

Later, representative of STUK said in an interview with Taloussanomat, that + 3 meters is a level, when actual damage could happen, but +1,75 meters is considered to be a hazard scenario and results only in running down the nuclear reactor.

Based on the law on civil protection, the Nuclear Power plants will be testing their capacities every third year. The latest exercise was organized on 14 March 2013. The exercise was conducted jointly among key national and local civil protection actors and led by the Itä‐Uusimaa Rescue Service.

The exercise focused on preparatory measures and inter‐agency prevention. Special attention was paid to start‐up measures, building up coherent situational picture and maintenance, assessment methods, inter‐agency and private‐public communications and juridical and administrative challenges of crisis management.

The exercise was conducted of the table‐top part and live exercise. The tabletop phase tested the rescue plan produced by Itä‐Uusimaa Rescue Service to respond to a serious radiology threat caused by plant failure. The live exercise focused on operational interaction between service provider Fortum Corporation, Itä‐Uusimaa Rescue Service, Kymenlaakso Rescue Service, hospitals, Border Guard and Defence Forces. Special emphasis was laid on evaluation and on‐site monitoring.

In general, as in “Loviisa13” exercise the overall evaluation is positive and only a few obstacles and areas which need further testing and development were noted. All too often these types of exercises are designed, conducted and even evaluated by those who are practicing them. There is no space for

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real life challenges and “external expertise” which would test the system to its limits and by doing so, identify shortcomings and gaps.

One could also argue that having multiple overlapping situational surveillance processes in place does not always serve the stakeholders and key agencies. For instance, the Finnish Radiology and Nuclear Safety Authority (STUK) have a new situation and threat system called TIUKU where the civil protection authorities use a PEL‐JOKE system combined with the LUOVA system from the Finnish Meteorological Institute (FMI) which raises awareness of the natural hazards and disasters.

On a wider sacale, during Sunday 9th January STUK was in contact on several occasions with the nuclear power plant in Sosnovyi Bor, in Russia. The nuclear power plant also increased its preparedness in teh event that the sea level rose to dangerously high levels. According to information received from the Russian authorities, the sea level rise was estimated to be 240cm above the average, still under the risk limit of 325cm.Since then, safety cooperation between the STUK and the Sosnovyi Bor has focused on building an estimation system for water level in Sosnovyi Bor that would be linked to the Finnish forecasting network.

4.2.7. Lessons Learned, Improvements and Guidelines

According to different estimates, the sea level of the Baltic Sea is predicted to rise by about 20‐40cm during the next 100 years due to climate change. This would cause big problems to the areas such as Marjaniemi, the hardest hit residential area in Helsinki. Even if the sea level were to rise appreciably, there would not be need to evacuate Helsinki, since it is relatively easy to build a flood barrier from soil. However, to date no barrier of this kind has been officially planned.

According to the Finnish Environment Institute, it is likely that autumn and winter storms have improved the oxygen content in the deepest parts of the Gulf of Finland, thus improving water quality. On the other hand, during the flooding, the quality of the water deteriorated in Helsinki due to the fact that only partly purified waste water was released to the Vanhankaupunginlahti. Due to the high sea level, some waste water also ended up in the ponds of the conservation area of Vanhankaupunginlahti. Also other sea areas were contaminated by dirty water from the overflows.

A rise in the sea level of the Baltic Sea is not seen as a threat to the city. Still, the experiences that the flood caused will most probably affect future planning and building. While there is no expectation of extreme flooding scenarios low lying areas like Marjaniemi will face even more problems due to the rise in sea level. For new buildings the minimum building height is now 3 meters above ground level. In the old residential areas, the building height is much lower. Other low lying areas are in Kyläsaari and Vartiokylälahti. In the centre of Helsinki there are low lying areas in Töölönlahti and Rautatietori. Theoretically, these areas could end up being inundated by flood water.

Information flow between the different authorities and the Rescue Service Unit of the Finnish Ministry of Interior worked along existing guidelines and there were no disruptions recorded.

The Finnish Institute of Marine Research was able to predict the rising sea level during working hours on the 7th January. The FIMR does not have 24/7 services, and if the forecast had been available later during the weekend, the warning could not have been made in time. This kind of 24/7 warning system still does not exist. 79

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The timing was crucial factor for successful early warning, since the information came to the Ministry of Interior just before 16.00 on the afternoon of Friday 7th, just before the end of office hours. While there was information about the coming storm and bad weather, there was no information available about the rising sea level which had already occurred before the call from the FIMR.

It was beneficial that the representatives of the FIMR and the FMI were present in the preparedness leaders meeting on the evening of the 8th January. The Finnish authorities did not have the information about the situation in Estonia and they were not aware that the Estonian authorities did not have the same kind of forecast information in use before the situation manifested itself. A week later, the situation authorities from Finland and Estonia started to discuss means to ensure improvedinformation sharing between the countries in these kind of situations. Information sharing is essential and can minimise the impacts. Also, Geographical Information Systems can be used to make it easier to visualize information.

The early warning signals were taken seriously. It is rare that the rescue personnel will have information about impending hazardous events 1.5 days in advance, so there was enough time for to make the necessary prepartions.

Legislation in Finland regarding early warning and response is regarded as being clear enough by the authorities. The action is taken in the field and the authorities are well aware of their responsibilities.

The responsibilities between different agencies depends on the flood situations whether it is an inland water flood or a sea flood. In all cases the FMI should be responsible for the meteorological forecasts and when the incident is caused by the inland water flood , the Finnish Environment Institute will be central actor. The Ministry of Agriculture and Forestry has responsibility for dams. With sea flooding the FIMR plays an important role in forecasting sea level rises. In the field,the rescue departments and municipalities have their roles. In a large scale flooding assistance from the Finnish Defence Forces and the Border Guard can be requested.

The record shows that often this kind of cases are taken to the Ministries decision making level and even to the Government Council. Usually, there is no need for decision making at the government level during cases such as this. Government will possibly take some decisions later about such things as compensation, but there is no need for the government to head the operation during the situation.

In Finland, the responsibility for the safety of the building sites is the remit of the municipalities’ city planning and construction licence policy. The possibility of flood and rising sea level should be taken into account even better in the municipalities.

Sea level rises should be addressed using an automatic system that would directly inform the environment and the rescue authorities about certain sea level rise values through the Emergency Response Centres. This already partly happens through the Weather Warning Service of the FMI. These early warning outlooks for severe weather go to all rescue authorities as well as other authorities. There are 150 recipients on the list.

The FMI has started to build an early warning system for natural hazards, but there is no funding for it yet. At present, it is already possible to foresee a bad weather scenario developing two weeks 80

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beforehand. The FMI gathers the information about the potential risks and sends it to the authorities (early warning outlooks for severe weather system, rescue authorities are using the same email listing of 150 different actors that include also private actors like electricity companies).

The present sea level forecast system in the FIMR is such that an operational system is up and running. Several models with different input data automatically run four times a day. Observations and model results are saved into a data base. Automatic graphs of data and model results for each sea level station are output separately four times a day. Outputs from other countries can easily be used for comparisons within the BOOS (Baltic Operational Oceanographic System). Contacts to rescue authorities and services are good, but there are not enough human resources to make daily forecasts. The FIMR is not able to give daily forecasts to the public about sea level changes. This kind of service was working during the two years after the January 2005 flood case, but lack of funding has effectively resulted in this service ceasing. 150,000 euros would be needed during one year to cover the overtime work in the cases if something happens.

Because of the tsunami disaster which occurred roughly a one week before this event, the preparedness of authorities was at a higher than normal level and co‐operation with other authorities had been practised. This might have been one of the reasons why the emergency response and the early warning system worked well.

The official viewpoint at the Ministry of Interior is that there is not much to do when preparing for the flood. It is of course possible to build flood barriers and evacuate people, depending on how much time there is available before the water rises. On the other hand, construction is well regulated in Finland so that there are fewer residents living in flood‐prone areas.

Flood prevention action should always be based on pre‐planning, before the risks materialize. If there are areas that are known to be flooded from time to time, there should be plans about how to work on these areas when the flood warning comes (building barriers, planning of evacuations etc).

There is no 24/7 service at the FIMR and no organizational arrangements for one. During this flood people worked night and day and during the weekend but without proper organization.

The chronology shows that sea level rise and sea flooding are probable on the coast of Finland during winter, not during summer so a 24/7 organization of the FIMR would be more beneficial during winter time. Theoretically, these kinds of floods only happen once in 200‐240 years.

The effects of winter storms to the sea are variable. Multiple natural conditions acting simultaneously in parallel are needed to generate a big flood. Human interpretation of the numerical sea level forecasts can give essential added value. Model results should be looked at routinely, but there are limited resources for that, especially at weekends.

Flood risk must be taken into account in the community planning.

The Estonian experience and problems also became evident during this case study. ‐ Preparing for the flood was not sufficient since it was too hard to imagine that such a situation would happen. It is not common to have these kinds of floods in Estonia. The last was in 1964. This made the forecasting of the flooding level also difficult and people were not prepared for the level of flooding that 81

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occurred. One problem was that the flood began during night time so people could not be warned accurately. Also, there was a belief that the flood would not be as large as it turned out There were problems with the evacuation of the people, since there was no one to take care of evacuated people; communication was difficult because of the power cuts and people were also present in dangerous areas because they wanted to see the extraordinary flood. Securing drinking water to the inhabitants was challenging because of the salty water that flooded the city and its wells, especially in Pärnu.

4.3. Application of Risk‐Based Decision Making Framework

To demonstrate the Risk‐Based Decision Making Framework proposed in the RAIN project (van Erp and van Gelder, 2015), BN modelling was employed to determine the impact of extreme storm surge events in the Gulf of Finland on critical land based transport and electrical infrastructure in the case study region shown in Figure 27. This region, hereafter referred to as the Uusimaa case study region considered 13 municipalities located within the Uusimaa region. It is noted that for the purposes of the analysis an extreme storm surge event refers to a storm surge magnitude with a return period of 100 years or more.

Figure 27 Selected region for Finnish case study (13 municipalities in the region of Uusimaa)

4.3.1. Enumeration

To assist in the development of a BN model for the Uusimaa case study region due to an extreme storm surge event in the Gulf of Finland, a stakeholder workshop was hosted by the Finnish Meteorological Institute. The objective of the workshop was to gather key stakeholders and experts relating to extreme weather events and to identify the potential impacts on critical transport, energy and telecommunications infrastructure. The intention was to gather input to enable the nodes (i.e.

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random variables) of the BN model to be specified and to define the relationships between the variables.

A working session was held during the afternoon session of the workshop, whereby participants were split into three groups: 1. Hazard assessment and emergency response, 2. Land‐based transport infrastructure, 3. Energy and telecommunications infrastructure. Each group was presented with questions relating to their relevant area of expertise and a brainstorming session was held whereby participants were provided with post‐its and asked to respond to the questions.

Table 17 provides a summary of the information gathered during the stakeholder workshop, which have been broadly grouped into the following categories: hazards, vulnerable infrastructure, consequences, risks, mitigation measures and emergency response procedures. This information was subsequently used to generate a BN model for the Finnish case study to quantify the risks due to the impact of an extreme storm surge event on critical infrastructure in the Ussimaa region, as will be described in Sections 4.3.2 ‐ 4.3.5.

Table 17 Information gathered during stakeholder workshop Hazards (due to an extreme storm surge event) High Sea Levels Coastal Flooding High Wind Speeds Vulnerable Infrastructure (Land‐based transport, energy & telecommunications) Road Rail Energy Telecommunications  inundation  undermining of  flooding of  flooding of  blockage due to foundations due to underground underground fallen trees flooding facilities facilities  undermining of  malfunctioning of  flooding of nuclear  damage to masts foundations due to rail control systems power plant / due to high wind flooding  flooding of rail shutdown due to speeds  damage to bridges tunnels storm surge due to high wind  damage to bridges heights speeds. due to high wind  damage to  disruption to traffic speeds pylons/power lines lights due to power  disruption to due to high wind failure operation due to speeds power failure Consequences Road Rail Energy Telecommunications  traffic disruption  rail service  power loss  disruption to disruption landline phone services  disruption to mobile phone services  disruption to internet services Risks (societal, security and economic)  Illness / fatalities due to cold (e.g. hypothermia)  Disruption to life‐support services  Repair costs  Disruption to food / water supply 83

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 Disruption to rescue services  Disruption to domestic heating supply  Disruption to domestic power supply  Disruption to freezing capabilities (e.g. food storage, chemical plants)  Disruption to banking services  Disruption to fuel supply Mitigation measures to reduce infrastructure vulnerability Road Rail Energy Telecommunications  Rail traffic  OBS power lines limitations  OBS power lines Emergency response procedures to reduce risks Preparedness:  Early weather warning system  Legislation  Presence of emergency response plan  Presence of bilateral or multilateral agreements  Presence of HNS guidelines  Situational awareness, e.g. training activities, dissemination of information, management of expectations  Contingency planning  Routine maintenance  Obligation for operators to provide mobile base stations when existing stations are damaged

Prior to known event  Introduction of curfew  Coordinated transport of vulnerable people  Implementation of traffic limitations  Distribution of Sat‐phones with local energy supplies  Presence of TETRA‐VERVE communication network for the authorities

During / Following Emergency Event:  Availability of technical equipment, e.g. forwarders, high capacity pumps, hydro copters, tents with heaters, beds, and foldable water containers.  Availability of rescue personnel (and familiarity with locality)  Capability of rescue services to conduct repairs  Availability of volunteers  Availability of reserves, e.g. emergency generators, technological solutions  Communications between weather forecasting authorities and emergency response teams

Other influencing factors:  Lighting conditions (i.e. night/day, time of year)  Accessibility of affected areas  Population density  Interaction between authorities  Number of vulnerable people (e.g. elderly and children)  Density of road / rail networks 84

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 Level of road / rail traffic demand

Moreover, an important concept that emerged from the workshop was the need for a decision‐ making methodology that combines a ‘rule‐based’ methodology, i.e. based on scientific evidence, with a rule‐based‐methodology, i.e. based on the first‐hand experience and behaviour of emergency responders when faced with the impacts of extreme weather events.

4.3.2. Quantification of Likelihoods

4.3.2.1 Likelihood of EWEs

To quantify the likelihood of an extreme storm surge event in the Gulf of Finland, the pan‐European hazard maps developed in the RAIN project were employed (see http://data.4tu.nl/repository/uuid:e06ca666‐90e2‐4a2c‐a1d0‐4c39f815b04d). For storm surge events, the hazard maps indicate the probability of occurrence of storm surge heights and the associated coastal flooding under present and future climate scenarios.

Since the historical data that was used calibrate and validate the hydrodynamic model (see RAIN Deliverable D2.5; Groenemeijer et al. 2016) did not include any observations from the Gulf of Finland, the availability of additional data in the form of historical values of storm surge heights (for the period 1971 – 2000) that were recorded at 6‐hourly intervals at the locations shown in Figure 28, allowed the results from the model to be corrected in the region of the study area.

Figure 28 Stations located in Finland used for calibration of pan‐European hazard maps

Extreme value statistics are subsequently employed to determine the values of storm surge height at these locations that corresponded to given return periods, and these values are compared to the storm surge height values specified according to the pan‐European hazard maps (i.e. the storm surge model described in RAIN Deliverable D2.5; Groenemeijer et al. 2016). As shown in Table 18, the storm surge model that was used to generate the pan‐European hazard maps resulted in an over prediction of surge heights due to the following reasons: 85

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 The gradient of extreme surge levels is very steep in the Gulf of Finland, making it difficult to simulate accurately.  Finland has a complex coastline that consists of many small islands (skerries), which are not represented in the storm surge model due to the associated resolution. In reality, the presence of these islands results in a dampening of surge levels.

Consequently, the percentage error in the model ranged from approximately 20 – 25% in the regions of Hanko and Helsinki, but reduced to approximately 11% at Hamina, where the coastline is less complex.

Table 18 Comparison of extrapolated storm surge height values based on observed and modelled data Storm Surge Height Return Period (years) (m) Based on Observed 1000 500 300 200 100 50 30 20 10 2 Data Hamina 2.44 2.30 2.19 2.11 1.97 1.83 1.73 1.65 1.51 1.13 Helsinki 1.68 1.59 1.51 1.45 1.35 1.25 1.18 1.12 1.02 0.78 Hanko 1.96 1.85 1.76 1.70 1.58 1.47 1.39 1.32 1.20 0.89 Predicted Values 1000 500 300 200 100 50 30 20 10 2 Hamina 2.73 2.56 2.44 2.35 2.18 2.02 1.89 1.79 1.62 1.17 Helsinki 2.10 1.95 1.85 1.77 1.62 1.48 1.37 1.29 1.14 0.76 Hanko 2.40 2.25 2.14 2.05 1.89 1.74 1.62 1.53 1.37 0.95 % Diff 1000 500 300 200 100 50 30 20 10 2 Hamina 11.9 11.3 11.4 11.4 10.7 10.4 9.2 8.5 7.3 3.5 Helsinki 25.0 22.6 22.5 22.1 20.0 18.4 16.1 15.2 11.8 ‐2.6 Hanko 22.4 21.6 21.6 20.6 19.6 18.4 16.5 15.9 14.2 6.7

To re‐calibrate the pan‐European hazard maps, using the observations from the historical data, the predicted values of storm surge heights are reduced by 14%. The Extreme Water Levels (EWL) associated with each return period are subsequently re‐calculated based on Equation 18, as outlined in RAIN Deliverable D2.5 (Groenemeijer et al. 2016), where p is the probability of occurrence, T is the time period, S is the climate model scenario, SURGEp,T,S, calibrated is the calibrated storm surge height for a given value of p, T and S, TIDE is the mean high tide, MSL is the baseline mean sea level, SLRT,S is the difference between mean sea level in time period T and scenario S compared to baseline MSL, and GIAT is the accumulated effect of glacial isostatic adjustment between time period T and the year 2000.

,, ,,, , Eq. 18.

4.3.2.2 Likelihood of Hazards

Flood Depths

The flood depths specified by the pan‐European hazard maps are also re‐calibrated by subtracting the storm surge height error (14%) from the flood water values. Figure 29 illustrates the re‐

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calibrated water depths for portion of the selected study area based on the 1971‐2000 historical scenario for a 1000 year return period.

Figure 29 Coastal flood depths based on 1971‐2000 historical scenario for 1000 year return period

There are several sources of uncertainty associated with the coastal flooding hazard maps. For example, there may be significant inaccuracies in the Digital Elevation (DEM) that was used to generate the flood maps (see RAIN Deliverable D2.5; Groenemeijer et al. 2016). The maps developed within RAIN Deliverable D2.5 (Groenemeijer et al. 2016) represent the results of modelling at a European scale, without capturing the specifics of localised or regional effects. While the flood depths shown in Figure 29 have been corrected using observed data from the Gulf of Finland, more regional or localised modelling approaches would provide more accurate results for a localised analysis. As such, it should be noted that for the purposes of demonstrating the application of the Risk‐Based Decision Making Framework, these maps serve to provide indicative values to facilitate the analysis presented in this report. Depending on the scope or requirements of analysis, more accurate region‐specific maps and data could be used to fine‐tune the inputs to the Risk‐Based Decision Making Framework.

Wind Speed

High winds are often associated with storm surge events and as such, when analysing the potential impact of storm surge events on the transport and E&TC networks in the region of Helsinki, the impact of wind speed on E&TC infrastructure is also considered. In order to include the effect of wind speed it is necessary to identify the appropriate wind speeds considered to occur in

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conjunction with any given storm surge event. This section outlines the approach adopted to identify the probability of experiencing various wind speeds associated with the occurrence of a given storm surge.

To identify the wind speeds associated with a given storm surge event it is necessary to examine the relationship between skew surge height and wind speed. EURO‐CORDEX hindcast of onshore wind speed and coastal storm surge heights for 36 years, taken at 6‐hourly intervals between 1970 and 2005, are used to examine this relationship and develop an approach which could be used to calculate the wind speeds associated with any surge event of interest. The values of surge height and the wind speed measurements are available for each of the grid cells shown in Figure 30.

Figure 30 Skew surge height and wind speed grid cells

To simplify the analysis, the maximum surge heights measured at any location along the coast (i.e. in any of the surge height grid cells) are compared to the maximum wind speeds measured within any of the wind speed grid cells. The relationship between the maximum surge heights and associated maximum wind speeds at any location within the region could then be established. In order to verify this assumption, the variation in (i) skew surge heights and (ii) wind speed measurements across the different grid cells is examined. At the time of each measurement the difference between the maximum recorded value and the minimum recorded value in any of the grid cells is calculated and expressed as a percentage of the maximum recorded value across all of the cells. Due to the variable nature of wind speed measurements, comparisons between different cells are only made for measurements where the values recorded exceeded 10km/h. This is done to ensure that variations recorded during low wind speed periods would not be over‐emphasised. Figure 31 and Figure 32 show this variation for skew surge heights and wind speeds respectively. As expected, it can be seen that there is some level of variation between measurements recorded in different grid cells, however in both cases the observed variations tend to below 60% and it is deemed reasonable to assume that the variations across the grid cells in the region could therefore be neglected in order to simplify the analysis. 88

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1400

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0 0 20 40 60 80 100 120 140 160 180 200

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Figure 31 Variation of skew surge height between grid cells

1400

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Figure 32 Variation of wind speed between grid cells

To allow the wind speeds associated with a given storm surge event to be quantified, the relationship between storm surge height and wind speed is examined using the 36 years of historical

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data. Initially, the direct relationship between storm surge height and wind speed is examined, as shown in Figure 33. No strong correlation between the two variables is evident. Figure 33 also includes a linear fit to the data; however it is clear that there is no obvious linear relationship between the two variables. As previously described, this relationship is examined using the average value of wind speed and surge height across all of the grid cells when comparing the values measured at any point in time.

Figure 33 Skew surge height vs. wind speed

Further examination of the historical data showed that a stronger relationship could be seen when comparing the maximum surge heights recorded each year to the wind speeds measured in the days surrounding the extreme surge event. More specifically, the strongest correlation is seen when comparing the maximum storm surge height in any given year to the averaged value of the maximum wind speeds recorded in the month surrounding the storm surge event (i.e. 2 weeks before and 2 weeks after). Figure 34 shows a plot of the 36 annual maximum skew surge values (for each of the 36 years of measured data) and the associated average maximum wind speeds recorded in the two weeks before and after the surge event. The associated average maximum wind speeds are calculated by extracting all of the 6‐hourly measurements, across all of the grid cells, occurring 14 days either side of the maximum surge event. At each 6‐hourly measurement, the maximum wind speed occurring in any of the grid cells is then extracted. Finally, an average value of all of these maximum values is then taken as this is shown to provide the strongest correlation with the surge event, exhibiting a correlation coefficient of 0.67. Figure 34 also shows a linear relationship which is fitted to the data which is subsequently used to calculate the mean value of the maximum wind speed associated with storm surges for different return periods.

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Wind Speed (m/s)

Figure 34 Maximum annual surge height vs. averaged maximum wind speeds

The relationship shown in Figure 34 allows a single (mean) value of the maximum wind speed associated with any given surge height to be calculated. In order to facilitate the BN model, as described in Section 2.1, it is more useful to determine the probability of experiencing various levels of wind speed (note that Figure 46 & Figure 47 show the BN models used in the analysis). Therefore, the approach used above is extended to also consider the uncertainty in the mean value of the maximum wind speed. This involved examining the variation of the maximum wind speed measurements recorded at 6‐hourly intervals in the two weeks before and after each of the annual maximum surge events. It is shown that there is a large amount of variation in the wind speed associated with each of these surge events and the mean value of the coefficient of variation is calculated as 43.4%. In order to consider uncertainty in the wind speed, this coefficient of variation is adopted, along with the mean value of the maximum wind speed, to assign a normal distribution to the wind speed.

Using this approach, a probabilistic distribution is subsequently generated to represent the maximum wind speed associated with any storm surge event. Figure 35 shows some sample distributions used to represent the wind speeds associated with storm surges of 100, 300 and 1000 year return periods. These distributions are represented as normal distributions with mean values derived using the relationship shown in Figure 34 and a coefficient of variation of 43.4%.

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0.12 100 Year Storm Surge 300 Year Storm Surge 0.1 1000 Year Storm Surge

0.08

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0 -10-50 5 10152025 Wind Speed (m/s)

Figure 35 Distributions for maximum wind speed associated with different storm surges

The final step in calculating the wind speed values to be used in the BN model is to define low, medium and high wind speed ranges and calculate the likelihood of experiencing wind within each of these ranges for a particular storm surge event. These wind speed ranges, roughly aligned with the warning levels used for sea wind speeds in Finland, are defined as follows:

 Low (L): wind speeds < 14m/s  Medium (M): wind speeds between 14m/s – 21m/s;  High (H): wind speeds >21m/s

As shown in Figure 35, the wind speeds associated with storm surge events are at the lower end of extremity (i.e. typically falling below 14 m/s), perhaps explained by the fact that wind is only one of the factors contributing to the occurrence of a storm surge event (see RAIN Deliverable D2.5 (Groenemeijer et al. 2016) for more information on storm surge hazard). Using these ranges along with the wind speed distributions, the probability of experiencing wind speeds within each of these ranges could be calculated, as illustrated in Figure 36. In Figure 36 the probability of a wind speed falling within any of these ranges is represented by the area under the probability density function between the wind speed values outlined above.

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Figure 36 Calculation of wind speed probabilities for Bayesian network model

This approach is adopted to allow the effects of wind speed to be included within the BN model used in the Finnish case study. As outlined previously, the assumption is made that the variation in wind speed between the different cells low enough that it could be neglected for the purposes of simplification in the analysis. Therefore, using the approach above, probabilities are calculated for experiencing low, medium or high wind speeds for each storm surge return period of interest. These probabilities are deemed appropriate to represent the wind speed at any location in the region and are used to assess the likelihood of damage to the E&TC infrastructure.

4.3.3. Critical Infrastructure

In order to examine the impact of storm surge events in the Uusimaa case study region, the analysis included some of the infrastructure which is considered critical to the region and which would potentially be affected given the occurrence of a storm surge event. On the basis of the 93

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recommendations from the workshop, along with the observations from the 2005 storm surge event, it is considered appropriate to examine the impacts on land transport infrastructure along with the electricity infrastructure in the region. It is noted that the demonstration of the methodology presented in this report could also be applied or extended to consider other critical infrastructure, for example the telecommunications network could be included in a similar manner to the electricity network. This section describes the critical infrastructure and the approach adopted to model the infrastructure vulnerability in the BN model for the Uusimaa case study region. The list below outlines the hazards associated with an extreme storm surge event which are considered to affect each infrastructure type:

 Road infrastructure: inundation due to coastal flooding;  Rail infrastructure: inundation due to coastal flooding;  Electricity infrastructure: damage to power lines due to high winds.

4.3.3.1 Land Transport Infrastructure

Road Network

The road network in the Uumisaa region is made up of motorway, trunk, primary, secondary and tertiary roads, with a total length of approximately 2,200 km. Open Street Map GIS datasets (Open Street Map, 2017) of the road network are employed for the analysis, as shown in Figure 37.

Figure 37 Road Network in the Uusimaa Case Study Region

For the purposes of the analysis only the impacts to motorways and trunk roads is examined as these are considered to be of most importance to the region. The motorways comprised approximately 230 km of carriageway, with trunk roads comprising approximately 170 km.

Rail Network

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Figure 38 shows the rail Network in the Uusimaa case study region, which consists of almost 500 km of railway track. The rail lines shown include all of the major heavy rail lines in the region and are included in the analysis. In the city of Helsinki there are also subway and tram lines, however these are not considered within the Bayesian Network model and hence are not displayed in Figure 38.

Figure 38 Rail Network in the Uusimaa Case Study Region

4.3.3.2 Energy and Telecommunication infrastructure

The everyday functioning of any developed region is heavily dependent on the energy network. Furthermore, the energy network is closely interlinked with many other critical infrastructure networks and failures in the energy network can directly impact the operation of land based transport and other networks. Figure 39 shows the elements of the energy network in the Uusimaa case study region.

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Figure 39 E&TC Network in the Uusimaa Case Study Region For the purposes of assessing the damage to the electrical network as part of the case study analysis it is decided that the most suitable approach is to consider the high voltage lines connected to the Loviisa nuclear power plant as these lines provide the primary power supply to the Uusimaa case study region. In order to facilitate the analysis three lines are considered separately, labelled as lines A, B and C as shown in Figure 40.

Figure 40 High Voltage Power Lines Connected to Loviisa Power Plant

Line A, which is directly connected to the Loviisa power plant, is a 400 kV line, Line B is a 110 kV line and Line C is a 400 kV line. In order to describe the vulnerability of these power lines to high wind speeds, they are divided into 5 km segments and fragility functions are developed to quantify the probability of failure of the lines under high wind speeds as described in the following section. This 96

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allowed the probability of failure, given low, medium or high wind speeds to be defined within the Bayesian Network model.

4.3.4. Modelling Approach for Uusimaa Case Study Region

Due to the scale of the Uusimaa case study region, which covers approximately 4,500 km2, and the associated infrastructure, it is necessary to develop a suitable approach to allow the overall risk to the region to be assessed within the Bayesian Network model. In order to facilitate such an approach the analysis considered the case study region as being sub‐divided into the 13 municipalities located within the case study region (as shown in Figure 27), with each municipality represented by a node in the Bayesian Network.

The properties assigned to each node in the Bayesian Network are calculated based on an examination of the characteristics of each of the municipalities. The following characteristics are of interest for each municipality when considering the requirements of the risk assessment for the effects of a given storm surge:

 The likelihood of experiencing different levels of flooding;  The likelihood of experiencing low/medium/high wind speeds;  The vulnerability of road infrastructure to flooding;  The vulnerability of rail infrastructure to flooding;  The vulnerability of the power lines to high wind speeds.

Likelihood of Flooding

In order to quantify the likelihood of flooding in each municipality the flood hazard maps described in Section 4.3.2.2 are employed and the probability of experiencing low, medium or high flood levels (as described in Table 19) are estimated for each municipality.

Table 19 Quantification of Flooding for Inclusion on Bayesian Network Coastal Flooding Description None No flooding Limited potential for flooding (i.e. limited areas with water depth between Low 0 m – 0.5 m). Medium Several areas with water depths between 0.5 m – 1.0 m High Areas with water depth > 1.0 m

Using this approach, each municipality is examined individually and the probability of flooding is specified for 100, 300 and 1000 year return periods. For each of these storm surge events the probability of experiencing low, medium or high flooding along with the probability of no flooding in the municipality are estimated and used to populate the nodes representing the flooding in each municipality in the BN model.

Likelihood of Wind

Section 4.3.2.2 describes the approach used to calculate the probabilities of experiencing low medium or high wind speeds. Based on an analysis of historical wind data it is considered reasonable 97

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to assume the same maximum wind speeds might be experienced in all of the municipalities examined given the occurrence of a storm surge event. As such, the probabilities of experiencing low, medium or high wind speeds are considered to be uniform across the Uusimaa case study region and the same values are assigned to each municipality, as shown in Table 20.

Table 20 Probabilities of Various Wind Speeds for Different Storm Surge Intensities Probability Storm Surge Low Wind Medium Wind High Wind (< 14 m/s) (14 – 21 m/s) (>21 m/s) 100 Year 0.9805 0.0195 0 300 Year 0.9664 0.0336 0 1000 Year 0.9442 0.0556 0.0002

Based on the analysis of the historical storm surge data and the corresponding wind data, Table 20 demonstrates that the wind speeds associated storm surge events of 100, 300 and 1000 year return periods are not very extreme (based on the relationship described in Section 4.3.2.2 to calculate the wind speed associated with a storm surge event). The probability distributions used to model the wind speeds, shown in Figure 35, infer relatively low wind speeds, and hence the associated probabilities assigned to the BN model indicate that it is most likely that low (but sustained) wind‐ speeds will be associated with these extreme storm surge events. Due to the complexity of storm surge events, and the many factors which influence the intensity of a storm surge event, it is noted that the approach used in this analysis uses a statistical correlation between storm surge height and wind speed based on historical data. While this approach provides a suitable method for facilitating the analysis presented in this report, it does not necessarily encompass the complex nature of the processes driving the storm surge, and the extent to which the wind speeds associated with a given storm surge event can be accurately predicted.

Vulnerability of Road Infrastructure

In order to define the vulnerability of the road infrastructure within the Uusimaa case study region to flooding the same damage states and associated cost of repair as used in the Italian case study are adopted. Again, three damage states are considered for 1 km road segments as outlined in Table 21.

Table 21 Damage States and Associated Costs for Road Segments Damage Cost of Repair Description State (€/km) DS0 0 No damage. Inspection of road segment required but DS1 200 no structural damage. Flooding induced cracking occurs, DS2 2000 repairs/resurfacing required.

The vulnerability of the roads in the Uusimaa case study region is defined based on expert judgement, in terms of the probability of reaching each of these damage states given low, medium or high flooding. The values presented in Table 22 describe the vulnerability of typical road sections which may be subjected to different flood levels (as per Table 19).

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Table 22 Vulnerability of Roads to Flooding Probability Flood Level DS0 DS1 DS2 None 1.00 0.00 0.00 Low 0.10 0.60 0.30 Medium 0.05 0.50 0.45 High 0.00 0.30 0.70

While the values presented in Table 22 describe the likelihood of road segments experiencing various levels of damage depending on the inundation level, the approach adopted in the analysis of the Uusimaa case study region requires these characteristics to be defined per municipality and as such the characteristics of the road network in each municipality are examined to allow a suitable vulnerability level to be adopted for each municipality. This involved calculating the length of roads in each municipality and also defining the ‘exposure level’ of the roads in terms of the proportion of the road network which is located within the areas which the flood maps identified as being prone to flooding. This approach is necessary as some municipalities may be considered prone to flooding, however the roads may not be located anywhere near the coastal regions which are exposed to flooding and hence the vulnerability defined using the values in Table 22 may not necessarily represent the vulnerability of the roads in that particular municipality. In order to account for this and for the fact that the different municipalities have varying densities of road network, the total length of road in each municipality is calculated, as presented in Table 23. Table 23 also presents the exposure level assigned to each municipality, as defined in the subsequent Table 24.

Table 23 Municipality Characteristics for Roads Municipality Municipality Name Length of Exposure No. Road (km) Level 1 Lapinjärvi 20.8 E0 2 Liljendal 8.2 E0 3 Loviisa 9.0 E2 4 Pernaja 35.6 E1 5 Ruotsinpyhtää 12.0 E1 6 Askola 0 E0 7 Borgaa lnadskommun 50.0 E1 (Porvoon maalais) 8 Myrskylä 0 E0 9 Porvoo 5.0 E0 10 Pukkila 0 E0 11 Sipoo 45.0 E1 12 Helsinki 99.5 E1 13 Vantaa 112.7 E0

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Table 24 Exposure Level for Roads & Rail Exposure Description Level E0 Municipalities not prone to flooding. Municipalities prone to flooding where < 50% of infrastructure is E1 within/adjacent to flood prone* areas. Municipalities prone to flooding where > 50% of infrastructure is E2 within/adjacent to flood prone* areas. *Flood prone areas refer to the locations within the municipalities which the flood maps have shown to be susceptible to flooding.

Using these exposure levels the vulnerabilities, as per Table 22, are adjusted to more realistically represent the vulnerability of each individual municipality. In the cases where the exposure level is E0 the standard vulnerability values are adopted as the probability of flooding is very low so the risk will not be overestimated in these regions. In the cases where the exposure level is either E1 or E2 the standard vulnerability values are adjusted using engineering judgement to suitably represent the likelihood of reaching each of the damage states.

Vulnerability of Rail Infrastructure

A similar approach is adopted to represent the vulnerability of the rail infrastructure in each municipality. The damage states and associated repair costs considered for railway lines are presented in Table 25.

Table 25 Damage States and Associated Costs for Rail Segments Damage Cost of Repair Description State (€/km) DS0 0 No damage. Inspection of road segment required but DS1 200 no structural damage. Flooding induced ballast washout DS2 2000 requiring repairs.

In a similar fashion to that used for roads, the vulnerability of rails to inundation, based on expert judgement is presented in Table 26 with the length of railway per municipality, along with the exposure level of the rail infrastructure in each municipality being presented in Table 27.

Table 26 Vulnerability of Rails to Flooding Probability Flood Level DS0 DS1 DS2 None 1.00 0.00 0.00 Low 0.10 0.60 0.30 Medium 0.05 0.50 0.45 High 0.00 0.30 0.70

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Table 27 Municipality Characteristics for Rail Municipality Municipality Name Length of Exposure No. Rail (km) Level 1 Lapinjärvi 14.25 E0 2 Liljendal 11.61 E0 3 Loviisa 7.59 E2 4 Pernaja 8.60 E1 5 Ruotsinpyhtää 0 E0 6 Askola 0 E0 7 Borgaa lnadskommun 80.35 E1 (Porvoon maalais) 8 Myrskylä 5.47 E0 9 Porvoo 3.98 E1 10 Pukkila 0 E0 11 Sipoo 19.85 E1 12 Helsinki 235.04 E1 13 Vantaa 111.39 E1

Vulnerability of Power Lines

In order to assess the likely damage to the electricity network it is necessary to examine the vulnerability of the various components of the network to wind. This allowed the probability of failure of the various components on the network to be quantified when subjected to low, medium or high wind speeds. Due to the scale of the case study region it is not feasible to assess the individual components to establish their likelihood of failure when subjected to varying wind speed. As such, an approach suitable for the scale of this region is adopted.

Fragility Functions for Electrical Network

In order to quantify the failure probabilities for the power network, fragility curves are developed to represent the likelihood of failure of the power lines depending on the wind speeds to which they are exposed during the storm surge event. Fragility curves (as presented in Figure 41) provide a representation of the probability of failure at different wind speeds. In order to facilitate a network level analysis, the vulnerability of 5 km segments is considered. The towers and power lines within each 5 km section are modelled when calculating the fragility curves (it is noted that there are typically 2.5 ‐3.5 towers per 5 km segment). The vulnerability of these segments of the electrical network depends on both the quality of maintenance and the properties of the forest adjacent to the line. The categories outlined in Table 28 are used to define the maintenance and forest properties:

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Table 28 Maintenance and Forest Categories for Power line Segments Forest Maintenance Category Description Category Description M1 Good F0 No Forest M2 Regular F1 Good (well maintained forest with clearance > 46 or 60 m for 110kV/400kV lines respectively) M3 Poor F2 Poor (poorly maintained forest or clearance > 46 or 60 m for 110kV/400kV lines respectively)

The fragility curves are developed using sigmoidal functions as per Eq.19, in which and are parameters which depend on the maintenance and forest categories respectively.

1 Eq. 19. 1

Fragility curves are developed considering all potential combinations of maintenance and forest categories and are presented in Figure 41.

Figure 41 Fragility functions for power line sections as a function of maintenance and forest status

Table 29 outlines the parameters for each of the fragility curves show in Figure 41.

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Table 29 Fragility curve parameters Maintenance Forest Category Category (km/h) (km/h) M1 F0 190.0 9.0 M2 F0 178.0 9.0 M3 F0 154.0 9.0 M1 F1 165.0 7.5 M2 F1 155.0 7.5 M3 F1 135.0 7.5 M1 F2 140.0 6.0 M2 F2 132.0 6.0 M3 F2 116.0 6.0

For the purposes of assigning the appropriate fragility functions to different segments of the power network, each of the lines A, B and C (as per Figure 40) are visually examined using Google Maps to establish the forest category and maintenance category which is most appropriate. Using this approach, it is difficult to draw any significant conclusions in relation to the maintenance practices for the power lines and as such it is considered appropriate to conservatively consider all segments to fall within the M3, poor, maintenance category. In relation to the status of the forestry adjacent to the power lines it is possible to use Google Maps to categorise the various segments of the power line. A summary of the findings of this visual analysis is provided below.

Line A

Power Line A is approximately 12 km in length and the forest status should be considered ‘good’ (F1) for the whole length of the line, with the cleared path approximately 75 m wide for the whole length. Figure 42 shows a typical example of the forest status on Line A.

(a) (b) Figure 42 Forestry along Line A (a) view from ground level; (b) plan view of clear path Line B

Line B is approximately 55 km long, with approximately 13.8 km (25%) of the line being completely exposed without any forest on either side, falling into the F0, no forest, category. It is noted that this 13.8 km is not continuous, but constitutes the combination of all of the short (typically 300 m – 1000 m) segments which are not enclosed by forestry.

In relation to the sections of the line that are enclosed by forestry the clearance width is typically 30 – 40 m and hence falls into the poor (F2) category due to the fact that the clearance width is below 103

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the recommended 46 m for a 110 kV line. Figure 43 shows a plan view of the forestry surrounding the 110 kV line. This view is typical of the forestry clearance for the majority of the line.

35m

Figure 43 Forestry along Line B – plan view showing typical clearance width Line C

The length of the 400 kV Line C is approximately 55 km and in a similar fashion to Line A, the majority of the line is surrounded by reasonably dense forestry which has a clearance width of approximately 75 m. Based on this clearance width, which exceeds the recommended 60 m width for a 400 kV line, the forestry could be considered to fall within the ‘good’ category. Despite the fact that there is a sufficient clearance width throughout, it is less clear how well maintained the forestry is, so it may be conservative to assume a ‘poor’ (F2) condition status along this section.

It is also noted that along Line C, approximately 6.5 km (12%) of the line has no forest and falls into the F0 category. As with Line B, this 6.5 km is not continuous, but consists of a combination of short segments along the length of the line which are not surrounded by forestry. Figure 44 shows a typical segment which is not surrounded by forestry.

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Figure 44 Forestry along Line C – plan view showing typical exposed section of the power line

Calculation of Failure Probabilities

For the purposes of the BN model it is necessary to define the failure probabilities for each of the lines when subjected to low, medium or high wind speeds (as per Section 4.3.2.2) using the fragility curves discussed in the previous section. In order to calculate these failure probabilities, the area under the fragility curve in any of the low, medium or high ranges is considered to represent the likelihood of failure across all of the wind speeds within that range. The area above the curve (and bounded by the upper limit of 1.0) is considered to represent the likelihood of no failure across all of the wind speeds within that range. The failure probability is thus defined as the proportion of the area falling beneath the curve, as illustrated in Figure 45.

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Figure 45 Failure probability calculation using fragility curve

In Figure 45, AL(F), AM(F) and AH(F) denote the area related to the failure probability (below the curve) for the low, medium and high wind speed ranges respectively whereas AL(NF), AM(NF) and AH(NF) represent the areas associated with no failure (above the curve). Denoting the failure probabilities for the low, medium and high wind speed ranges as PFL, PFM and PFH respectively, these failure probabilities are calculated as per Equation 14 where x refers to any of the three wind speed ranges (L/M/H):

Eq. 20.

Applying this approach to the fragility curves developed for the power lines the failure probabilities for power lines in each maintenance/forest category are calculated and are outlined in

Table 30.

Table 30 Failure probabilities for electricity power lines subjected to wind Failure Probabilities Maintenance Forest Low Medium High Category Category < 14 m/s 14 – 21 m/s > 21 m/s M1 F0 0.00 0.00 0.49 M2 F0 0.00 0.00 0.54 M3 F0 0.00 0.00 0.65

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M1 F1 0.00 0.00 0.60 M2 F1 0.00 0.00 0.65 M3 F1 0.00 0.00 0.74 M1 F2 0.00 0.00 0.71 M2 F2 0.00 0.00 0.75 M3 F2 0.00 0.00 0.82 From

Table 30 it can be seen that the probabilities of failure for the power lines in any category are negligible unless high (>21 m/s) wind speeds occur. In order to facilitate the Bayesian Network analysis it is necessary to characterise the power lines within each municipality using a similar approach to that adopted for road and rail. Using the visual inspection approach discussed previously, categories are assigned to the power lines in each municipality and the relevant failure probabilities assigned. Table 31 presents the characteristics for each of the municipalities and the relevant failure probabilities (note that DS0 refers to the 'no‐failure' damage state and DS1 refers to failure). Note that for the municipalities which are not traversed by these power lines the maintenance and forest categories are marked as N/A and the probability of failure is set to zero.

Table 31 Municipality Characteristics and Failure Probabilities for Power Lines No Municipality Name Length of Category Low Wind Medium High Wind Power Wind Lines Maint Forest DS0 DS1 DS0 DS1 DS0 DS1 (km) 1 Lapinjärvi 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 2 Liljendal 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 3 Loviisa 17.87 M3 F2 1.00 0.00 1.00 0.00 0.18 0.82 4 Pernaja 36.72 M3 F2 1.00 0.00 1.00 0.00 0.18 0.82 5 Ruotsinpyhtää 3.58 M3 F1 1.00 0.00 1.00 0.00 0.26 0.74 6 Askola 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 7 Borgaa lnadskommun 55.70 M3 F2 1.00 0.00 1.00 0.00 0.18 0.82 (Porvoon maalais) 8 Myrskylä 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 9 Porvoo 4.91 M3 F2 1.00 0.00 1.00 0.00 0.18 0.82 10 Pukkila 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 11 Sipoo 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 12 Helsinki 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00 13 Vantaa 0 N/A N/A 1.00 0.00 1.00 0.00 1.00 0.00

4.3.5. Consequence Analysis

The assessment of risk can take many forms depending on the aspects which are of most concern when carrying out the assessment. In this case the economic costs of the damage arising from the flooding and winds associated with storm surges are assessed. Direct economic impacts are very often the most prevalent risk associated with extreme weather events, however, as demonstrated in the Italian case study and in the RAIN project deliverables in general, societal and security risk, along

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with some of the indirect effects associated with extreme weather events often require consideration when assessing the overall impact of the effects of extreme weather.

4.3.5.1 Direct economic risks of inundation to roads

As discussed in Section 4.3.4, the assessment of the economic risk associated with the inundation of roads involved using a Bayesian network model to assess the implications for the Uusimaa case study region based on a separate examination of each the 13 relevant municipalities. Combining the repair costs for road segments due to inundation, shown in Table 21, with the length of road in each municipality, the total repair cost associated with each municipality could be calculated. The BN model shown in Figure 46 is used to assess the probability of obtaining different levels of damage to roads (and railways) in each municipality given the occurrence of an extreme storm surge event. Typical outputs from the BN model are presented in Table 32, whereby the probability associated with different damage levels to the roads in each municipality are presented due to inundation arising from an extreme storm surge event. In order to assess the overall risk to the Uusimaa case study region, these probabilities are exported to MATLAB software (MATLAB 8.6 R2015b), where the probability sort algorithm (see RAIN Deliverable D5.2; van Erp et al. 2017) is applied to evaluate the economic risk. The probability sort algorithm is used to reduce the computational intensity associated with calculating the risk associated with all of the many potential combinations of damage.

Table 32 Typical BN Model Outputs – Probabilities of Experiencing Different Damage Levels to Roads in Each Municipality Municipality Municipality Name DS0 DS1 DS2 No. 1 Lapinjärvi 0.9933 0.0045 0.0023 2 Liljendal 0.9933 0.0045 0.0023 3 Loviisa 0.1693 0.3078 0.5230 4 Pernaja 0.6435 0.2283 0.1283 5 Ruotsinpyhtää 0.6435 0.2283 0.1283 6 Askola 1.0000 0.0000 0.0000 7 Borgaa lnadskommun 0.6597 0.2201 0.1201 (Porvoon maalais) 8 Myrskylä 1.0000 0.0000 0.0000 9 Porvoo 0.9348 0.0435 0.0218 10 Pukkila 1.0000 0.0000 0.0000 11 Sipoo 0.6753 0.2124 0.1124 12 Helsinki 0.6725 0.2138 0.1138 13 Vantaa 0.9348 0.0435 0.0218

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Figure 46 Bayesian Model for Risk due to Inundation D6.3‐Report on benefits of critical infrastructure protection

4.3.5.2 Direct economic risk due to of inundation to rail

In a similar fashion to roads, the economic risk associated with inundation of rail is also assessed by firstly combining the costs associating with repairing the different damage states (Table 25) with the length of railway line in each municipality to calculate the total potential repair costs associated with each municipality. Subsequently, the Bayesian Network model is utilised to assess the likelihood of experiencing different damage levels within each of the municipalities. Again, these probabilities are exported to MATLAB software (MATLAB 8.6 R2015b), in which the probability sort algorithm is utilised to calculate the overall economic risk to the region by examining the likelihood of all potential combinations of damage across the region, along with their associated repair costs.

4.3.5.3 Direct economic risks of high winds to power lines

In order to examine the economic risk associated with damage to power lines, the Bayesian Network model shown in Figure 47 is used to calculate the probabilities of failure associated with the power lines in each municipality. It is noted that this model is used separately to the one used to calculate the inundation risk to road and rail (Figure 46) as the damage to land transport due to inundation and the damage caused by wind to the power network are considered to be independent of each other and could therefore be separated out. This is merely carried out for convenience when setting up the Bayesian Network models, however it is noted that the analysis carried out represents the combined risk of potential (i) damage to roads due to inundation, (ii) damage to rails due to inundation and (iii) damage to power lines due to wind (where flooding and high wind are all considered to occur as part of an extreme storm surge event). D6.3‐Report on benefits of critical infrastructure protection

Figure 47 Bayesian Network Model for Risk to Power Lines due to Wind D6.3‐Report on benefits of critical infrastructure protection

The associated repair costs per km of power line, as per Table 33, are combined with the length of power lines per municipality to calculate the total potential damage costs for each municipality. Again, MATLAB software (MATLAB 8.6 R2015b) is used to assess the economic risk associated with damage to power lines.

Table 33 Damage States and Associated Costs for Power Lines Damage Cost of Repair Description State (€/km) DS0 0 No damage. DS2 285000 Structural Failure of Power Line & Pylons

4.3.5.4 Indirect economic risks of high winds to power lines

Failure of the power network has the potential to cause major indirect economic risks through loss of power supply. The probability of a blackout can be difficult to assess due to the fact that it depends on a number of factors including the failure probabilities of all of the components of the electrical network, the total electrical load of the grid and the distribution of this load throughout the network at the moment when the power lines become damaged amongst other important considerations. To accurately assess the impacts of power loss a simplified approach is adopted whereby, based on expert judgement, the potential impacts of failure along Line A, B or C (Figure 40) are considered as follows:

 Failure of Line A would cause a destabilisation of the electrical grid at national level and could result in blackouts in some regions of the country including the Helsinki area (it is estimated that 50% of the customers in that area would be affected).  Failure of Line B would cause smaller, more localised problems. It is estimated that power supply disruption would occur in the surrounding area, potentially also leading to some blackouts further away, including the Helsinki area (with an estimate of 20% of customers being affected).  Failure of Line C, similar to Line A, would cause a destabilisation of the electrical grid at national level and could result in blackouts in some regions of the country including the Helsinki area (it is estimated that 50% of the customers in that area would be affected).

The impact of a blackout on customers could then be converted into economic risk by examining the types of customers affected (i.e. industrial, services or residential). Table 34 provides economic costs (Value of Lost Load, VoLL) associated with the loss of power supply (see RAIN Deliverable D4.4) to different customers. D6.3‐Report on benefits of critical infrastructure protection

Table 34 Value of Lost Load (VoLL) of energy not provided during the first minute, between the first and the 20th, the 20th to the first hour etc. VoLL in €/kWmin Consumer Group Duration 1 20 60 240 480 Residential 0 0,0010 0,0019 0,0053 0,0058 Industrial 1,31 0,16 0,14 0,13 0,13 Services 2,29 0,43 0,35 0,37 0,34

Initially, the energy (in kW), per sector, not supplied as a result of a blackout is estimated. This can be carried out based on the total electrical consumption in the region which can be divided up into different sectors using typical proportions obtained from the European Environment Agency (industrial 36%, services 30%, residential 30%, other 4%). The next step required the duration of the power outage to be calculated. In order to do this, empirical results as per Figure 48 (see RAIN Deliverable D4.4 for more detail) are utilised to show that:

1. The number of affected customers decreases exponentially over time with a characteristic time constant; 2. The characteristic time (in days) is given by:

∝ ./ Eq. 21.

Where Max.Num is the number of customers initially affected by the blackout (the maximum number of customers affected) and A is a rescaling factor in units of people, found to be 160000. This characteristic time corresponds to revering 63.2% of the energy supply (in days).

Therefore, the actual duration of the outage depends on number of affected customers at which the outage is considered over. As an approximation, the outage duration can be considered to be the characteristic time obtained using the formula (although this time refers to recovering 63.2 % of the customers only) or 3 times this characteristic time (which corresponds to recovering 95 % of the service).

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(a) (b) Figure 48 (a) Number of affected people as a function of the time after the Gudrum/Erwin windstorm, 2005. Inset: same data in double logarithmic scale. (b) semi‐logarithmic plot of the characteristic recovery times as a function of the maximum number of affected people in the same incident

Using the duration of the outage and the level of lost load, the energy which is not supplied can be calculated and the associated economic loss can be calculated.

4.3.5.5 Results & Discussion

Using the quantitative approach outlined in the previous sections the economic risk to the Uusimaa case study region resulting from extreme storm surge events is assessed. The potential cost incurred through damage to the infrastructure is assessed using the Bayesian Network analysis approach and the probability sort algorithm to evaluate the risk associated with all events with a probability of occurrence greater that 1x10‐6. Figure 49 presents the outcome of the analysis as a histogram with cost on the x‐axis and probability of occurrence on the y‐axis.

0.12

0.1

0.08

0.06

0.04

0.02

0 0 200 400 600 800 1000 1200 Cost (thousand €)

Figure 49 Economic Risk to Uusimaa Case Study Region

It can be seen that the most likely damage scenario associated with an extreme storm surge event results in a cost of approximately €60,000, with another obvious peak in the histogram at €515,000. Given that this presents the economic risk for the occurrence of an extreme storm surge event, the 114

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costs shown in Figure 49, ranging from €0 ‐ €1 million are not extremely high. It is noted that the results showed an extremely low probability of failure of the power network due to wind. This is evident when examining the wind speeds that are calculated to occur alongside extreme storm surge events, which are shown to have an extremely high probability of being in the ‘low’ range of 0 – 14 m/s. As such the probability of damage to the power network is essentially zero and the costs shown in Figure 49 are not influenced by the repair costs for power lines or the economic losses associated with a blackout.

It is worth noting that the results of the analysis show costs which are significantly lower than those associated with the 2005 storm surge event in the Uusimaa region. This is likely as a result of the fact that many indirect costs have been neglected in this analysis but primarily due to the fact that only flood damage to road and rail has been considered, whereas during the 2005 event, a significant level of the damage is attributed to flooding of residential and commercial buildings. The simplified approaches used to analyse the region at a large scale may also not accurately capture the specific damages that could occur and may also play a part in the reasonably low costs predicted by the analysis.

However, that approach demonstrated here provides infrastructure owners with a quantifiable measure of risk (economic risk in this case) to their network given a particular extreme weather hazard. This approach can be used to compare various options for improving the security or resilience of infrastructure and hence assessing the associated economic benefits, by examining the change in economic risk depending on the measures adopted to protect critical infrastructure (as demonstrated in the Italian case study).

As discussed, there may be other risks of interest to infrastructure owners, which are likely to warrant consideration depending on the objective of the analysis. It is noted that while the analysis presented in this report for the Uusimaa case study region assesses the economic risks associated with failure of the road, rail and electricity networks, many additional risks could also be considered, including indirect economic impacts which may influence the decision making process for infrastructure owners. It is important to note that the suitability of the analysis, and the reliability of the outputs, rely heavily on the availability and accuracy of the required input data. As such, while the results presented in this report serve to demonstrate the application of the RAIN risk assessment methodology, the approach can be adapted to be suitable for the assessment of different critical infrastructure networks at different scales, depending on the requirements of the infrastructure owners.

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5. Conclusions

This report has demonstrated the application of the RAIN Risk‐Based Decision Making Framework to two case studies in order to consider multi‐risk scenarios posed by extreme weather to critical infrastructure and assess the benefits of critical infrastructure protection. It has been shown that the Framework can be applied to choose the optimal critical infrastructure protective actions to minimise economic, social and security consequences.

The case studies utilise the output and models from all technical RAIN work packages: The modelling of the extreme weather is accounted for using the models and outputs form WP 2 and the partners therein; WP 3 and 4 have provided the types of crucial infrastructure that these weather events impact upon and also provided input and guidance on how to assess the impact. The Framework developed within WP 5 is the basis on which the assessment is carried out and the near infinite number of consequences that arise from the analysis could not be handled without the probability sort algorithm described in RAIN Deliverable D5.2 (van Erp et al. 2017); finally the deliverable reports and methodologies of WP 7 inform mitigation measures implemented in case study 1.

The case studies examined take place at two very different geographic scales this highlights the flexibility of the Risk‐Based Decision Making Framework and demonstrates the variety of approaches that can be adopted within the Framework. Case study 1 took a component level approach, for instance examining individual road segments while the Finnish Case study inspected the risk to the roads within a geographic area (municipality).

One way in which the analyses carried out in this report could have been improved is through a streamlining of access to relevant data. Unavailability of data and the path to accessing available data are two road blocks in the development of accurate risk models. The probability models chosen in the case study analyses to assess the risk to individual components are from literature with input from stakeholders and experts but are the analysis to be carried out by infrastructure operators with intimate detail of their own network then the accuracy of the results would be improved.

It is understandable that stakeholders wish to keep private the details of their networks. That said if there is to exist a common focal point for easy access to available information concerning risk assessments the research could be enhanced. Improved communication, better sharing of resources such as maps etc. would result in no data being collected more than once and a more efficient progression of robust methodologies.

The work presented is a sample application of the RAIN Risk‐Based Decision Making Framework. The methodologies adopted within the Framework can be adopted to include indirect consequences and actions (Appendix B) which would help to further underline the need for critical infrastructure protection and highlight the role that wider governmental policies can take in combating negative consequences. It can been seen form this report that even if focusing on direct consequences, protecting critical infrastructure can save disruption to services, economic losses and human lives.

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Appendix A Methodology for provision of inputs for Risk Assessment framework

A.1 METHODOLOGY

Introduction

The methodology described below is borne from the need to assign a probability of damage state to a piece of infrastructure given that a hazard has impacted the infrastructure.

In order to be able to group the different infrastructures into clusters that can be later given a failure probability when impacted by an extreme weather event, an Excel sheet has been developed. This spreadsheet presents a survey made by a multilevel list compounded by a total of 3 levels. These levels, and their weighting process are going to be defined in the present document. The weighting process is explained with general examples in order to simplify the method, and after the explanation, an overview over the excel sheet will be carried out. In addition, on the final section of the present document, two specific examples, used for illustration purposes of the method developed, are described.

Excel sheet organisation

A specific sheet has to be developed for each infrastructure type. Although a fair part of these will be common for all infrastructures, each type will have specificities.

Eventually, we should come up with 4 spreadsheets: Bridges, Railways, junctions and tunnels

The sheet is divided into hierarchic levels. The first level corresponds to the "fields". For example, in the case of bridge infrastructure, it has been classified in the following “fields”:

 General  Piers  Buttress  Deck  Joints  River

The second level, corresponds to the "questions" for each Section. For example, in the first level, "General", we find the following “questions”:

 Active life  Bridge materials  Time from last inspection  Bridge defects (in piers, buttress, deck, etc.)  Design Lifetime  Bridge typology D6.3‐Report on benefits of critical infrastructure protection

 Relation between free height of the bridge (H) and the stream depth (Y)  Minimum bridge span length inside the stream  Aggressive environment

Finally, the third level, contains the "answers". This level is where the survey respondent must select an “answer” between all the options provided for each “question”. For example, in the case of "Bridge materials", the following possible answers can be found and the users have to select the answer that fit better within their own CI.

 Concrete  Masonry  Steel  Mix Steel‐Concrete

Weighting criteria/rules

Each case studied will need a tailoring process of the spreadsheet depending on the two main variables: type of infrastructure and the weather event. For this, once the case is clear and the appropriate spreadsheet has been chosen, the different levels described above have to be weighted by means of an expert panel which consists of several members that should provide their weighting values.

The first level is weighted with values between 1 and 10. A weight of 1 point represents that this particular field does not have importance considering the selected extreme weather event. Therefore, a value of 10 points indicates that this particular field presents a significant importance or relevance considering the selected EWE.

The “questions" in the second level also have a range of values between 1 and 10. However, in this case the values are associated with the importance of each "question" regarding to a combination of both, structure and event. The higher the value is; the higher importance has that particular element when it is affected by the studied EWE.

Finally, the third level corresponds with the "answers" to the "questions". In this case, the way to develop the weighting process is different, due to this, in the following lines are defined the required steps, in order to develop this weighting process.

The first step is ranking the answers of each "question" among themselves. This ranking procedure can be linear or not, for example {A}, if a “question” has a total number of 6 possible "answers", the minimum ranking value will be 1 and the maximum, 6. The rest of the “answers” will be given any rank within the range [1 – 6].

The following table contains several general possibilities of ranking procedure for the example {A} mentioned above. In all the different possibilities, the ranking values are located between the defined range, which in this example {A} is [1 – 6].

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Ranking

Weighting QUESTION 1 Weighting Weighting (not lineal, (lineal and in (lineal and not Weighting maximum not order) in order) (not lineal) included)

ANSWER 1 1 1 1 1

ANSWER 2 2 3 1 1.2

ANSWER 3 3 2 2 2.5

ANSWER 4 4 5 4 4

ANSWER 5 5 6 6 5.5

ANSWER 6 6 4 4 5

However, if the “question” has a total number of 4 “answers” the minimum ranking value will be 1 and the maximum, 4. The rest of the answers will have any value within the range [1 – 4].

Once the ranking procedure is developed, the second step is to standardise the values in order to transform the ranking values into weighting values (from 1 to 10). These weighting values will have the same range values for all the “answers” so that, a comparison between the “answers” of different “questions” will be feasible. In the provided excel sheet, all the ranking values have been transformed into a weighting range of [1 – 10].

Regarding to the example {A}, where we had 6 answers, the minimum ranking value was 1 and the maximum was 6. Now, after the standardisation process, the minimum still being 1, however, the maximum has been transformed into 10. In the following table are represented the final weighting values for the first step table of the example {A}.

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Ranking

Weighting QUESTION 1 Weighting Weighting (not lineal, (lineal and in (lineal and not Weighting maximum not order) in order) (not lineal) included)

ANSWER 1 1 1 1 1

ANSWER 2 2.8 4.6 1 1.36

ANSWER 3 4.6 2.8 2.8 3.7

ANSWER 4 6.4 8.2 6.4 6.4

ANSWER 5 8.2 10 10 9.1

ANSWER 6 10 6.4 6.4 8.2

Finally, in the following table are going to be briefly described the criteria adopted in the weighting procedure for each “Question” of the Excel sheet.

QUESTION ANSWER CRITERIA An old bridge incurs on more maintenance. Furthermore, the bridge will not be in the same Active life condition as in the year of the construction. If it is correctly constructed, concrete bridges are better than masonry bridges. However Bridge materials steel bridges are more corrosive than concrete bridges. Time from last It is better to have newest inspections inspection Bridge defects (in piers, All bridges will have some defects, however, an structural defect is worse than functional buttress, deck, defects. etc.) Design Lifetime Is better a bridge with a higher design lifetime. Regarding to the exposed area, the beam bridges are worse than the slab bridges. Furthermore, arch bridges are usually made with concrete and thus, are more durable while Bridge typology suspension cable bridges and truss bridges are usually metallic and therefore, more corrosive. Besides cable bridges usually requires more maintenance and suspended bridges depends only on the cables. Relation between free height of the If the relation is smaller, an increasing of water flow will affect to the bridge, easily. bridge (H) and the stream depth (Y) Bridge length The shorter is the bridge, the less possible failure areas will have.

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QUESTION ANSWER CRITERIA Aggressive According with the EHE08, the aggressive environment I is not aggressive and IV is the most environment aggressive. The range for the “answers” is based on a web source 9 Single rounded piers are better than the rest. If there are spaces between piers in the same Pier shape foundation, encourages debris accumulation. Pier placement / Debris location in bend streams is on the outside, however, in a straight stream, debris Straight or bend accumulation is in the middle of the stream. stream Minimum bridge If span length is small, the distance between piers is also short and helps debris span length accumulation. inside the stream Foundation Deep foundations are better than shallow foundations. typology Pier material Steel material is corrosive. Number of piers If a bridge has a lot of piers, the bridge will be more prone to fail. Number of piers If a bridge has a lot of piers inside the stream, the bridge will be more prone to fail. inside the stream Pier and foundation countermeasure If a bridge has countermeasures, the bridge will be less prone to fail. s (debris and erosion) Buttress foundation Deep foundations are better than shallow foundations typology Buttress inside If a bridge has the buttress inside the stream, the bridge will be more prone to fail. the stream Past event: flooding of Is better not to have past flooding events on the buttress Buttress Buttress Masonry buttresses are usually more affected due to flooding because foundation typology materials is in general shallow. Higher Deck slimness relation The more slimness, the more prone to fail. (edge/span) Past event: Is better not to have past flooding events on deck. flooding of deck Joints opened or the defects in joints could incur on structural defects. closed If the stream is too width, there will be a huge water flow and more possibilities of erosion, Stream width defects, etc. River erosion countermeasure If the stream has countermeasures, the bridge will be less prone to fail. s River prone to debris If the stream is prone to debris accumulation, the bridge will be more likely to fail. generation Erodible materials close Erodible materials produce debris accumulation. to the river

9 http://www.fomento.gob.es/mfom.cea.web/pg_info_eambiental.aspx?idClasesExposicion=G&lang=es‐ES 129

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QUESTION ANSWER CRITERIA Straight or bend Bend streams have worse solutions according to erosion. stream A big basin area produces higher water flow. The range for the “answers” is based on a web Basin area source 10 Vegetation close Vegetation is a natural countermeasure to the erosion. The range for the “answers” is to the stream based on a web source 11

Weighting procedure

The proposed idea is to develop an expert panel where the different members have to provide weighting values to all the levels. Following, a weighted average among all the members of the expert panel will be developed. The ranks given by each member of the expert panel will have more or less importance in the weighted average depending on their expertise in the studied area. (An independent person should decide these experts “weights”.) The higher the number of experts is, the more accurate the final weights for each chapter, questions and answers will be.

Results generation

On the one hand, in the following lines the process adopted in order to obtain the results is explained. First of all, in the next example {B} 3 different “chapters” (level 1) are selected. The first “chapter” contains 3 “questions”, the second “chapter”, 2, and the third “chapter”, 4. Furthermore the first “question” of the first “chapter” has 6 possible “answers” (level 3), the second “question” of the first “chapter” contains 4 “answers” and so on.

The following table presents an example {B} of weighting values for each level. Answers for the third level are already standardised from 1 to 10). The answers selected by the survey respondent are highlighted in dark green.

10 http://irrigacion.chapingo.mx/planest/documentos/apuntes/hidrologia_sup/CUENCAS.pdf 11 http://www.fomento.gob.es/NR/rdonlyres/2482CE5B‐4577‐4E8D‐81CF‐C5E18DA53679/136083/ORDENFOM_298_2016.pdf 130

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Level Level Level 3 [1‐10] Standardised weighting value 1 [1‐ 2 [1‐ 10] 10] Answer1 Answer2 Answer3 Answer4 Answer5 Answer6 Results

5 1 2 5 6 4 10 6x5x5=150

4 5 1 7 10 6x4x1=24

6 1 3 1 6 6x1x6=36

3 1 5 6 9 10 8x3x6=144

8 7 10 3 4 8 8x7x10=560

2 8 6 1 10x2x6=120

8 7 6 6 1 10x8x6=480

10 6 1 10x10x1=100

10 5 4 1 10x5x4=200

Weighting summation 1814

Finally, the partial results are obtained by multiplying the weight of the “chapter”, with the weight of the “question” and finally, with the weight of the selected “answer” for our critical infrastructure. Therefore, the final result is the summation of all the partial results.

Results interpretation

As it can be identified in the table above, the minimum value that can be obtained is the best possible value (when all the chosen “answers" are those whose value is 1. The worst possible value, is obtained when the selected answers are all 10.

Due to this, in the defined example {B}, the minimum value will reach 390 while the maximum value will reach 3900. Therefore, the final result will be always located within the range of [390‐3900] (in the case of this specific example {B}).

Failure probability

Clustering procedure

It is known that not all the infrastructures within a same typology have the same response facing an EWE hazard. Therefore, a clustering for the infrastructures which could have the same response 131

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facing an EWE, is required. Creating clusters reduces dramatically the number of final scores to which a failure probability would have to be provided.

So 6 different clusters, has been created. Cluster 1 represents the best infrastructures facing the EWE, and thus, cluster 6 includes the infrastructures that would have the worst response facing the same EWE.

The adopted criteria for clustering an infrastructure in a group are defined in the following table. This can vary depending on the case.

% Range

Clustering min max

Cluster 1 0 10

Cluster 2 10 30

Cluster 3 30 40

Cluster 4 40 70

Cluster 5 70 90

Cluster 6 90 100

Regarding the example {B}, where the minimum final result was 390 and the maximum was 3900, the clustering ranges should be identified as following.

Range value

Clustering min max

Cluster 1 390 741

Cluster 2 741 1443

Cluster 3 1443 1794

Cluster 4 1794 2847

Cluster 5 2847 3549

Cluster 6 3549 3900

Due to this, in the same example {B} the final result reaches a score of 1814, therefore, the infrastructure should be placed on cluster 4 which means that the CI has a fairly poor behaviour against the particular EWE 132

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Failure probability assignation

Once the infrastructure has been clustered, each cluster will have different probabilities of failure relating to the EWE intensity. Therefore, in order to identify the probabilities of failure, the following scenarios had been proposed.

 Low EWE intensity - No Failure - Operational Failure - Partial failure - Full failure  Medium EWE intensity - No Failure - Operational Failure - Partial failure - Full failure  High EWE intensity - No Failure - Operational Failure - Partial failure - Full failure

These different scenarios will provide a different probability of failure for each cluster as is presented in the following table. (table considered in the Excel sheet). Again, a panels of experts should provide these probability values.

EWE

Low Intensity Medium Intensity High Intensity

Clustering No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F.

Cluster 1 0.9 0.05 0.04 0.01 0.4 0.3 0.25 0.05 0.2 0.4 0.35 0.05

Cluster 2 0.79 0.08 0.1 0.03 0.25 0.25 0.35 0.15 0.15 0.35 0.35 0.15

Cluster 3 0.68 0.1 0.15 0.07 0.2 0.2 0.4 0.2 0.1 0.1 0.45 0.35

Cluster 4 0.55 0.15 0.2 0.1 0.14 0.18 0.38 0.3 0.05 0.1 0.35 0.5

Cluster 5 0.4 0.2 0.25 0.15 0.09 0.16 0.35 0.4 0.03 0.07 0.2 0.7

Cluster 6 0.25 0.25 0.3 0.2 0.05 0.15 0.3 0.5 0.01 0.04 0.05 0.9

The Excel sheet

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According with the present document introduction, in this section, a brief description of the Excel sheet is provided.

On the one hand, in the following image are pointed out where are placed both, the three different survey levels (“Chapters”, “questions” and “answers”), and the weighting values provided for them.

Once we have all the weights and answers’ values, we can determine the range of values that can be obtained. That range is the difference between the maximum and minimum sum, which will be used in order to define the clusters.

Then, once the answers are selected, the final value for a specific critical infrastructure can be obtained. This value will be within the range mentioned in the previous paragraph and thus, will be the way to cluster our CI into a group.

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The next step is to develop the clusters and. therefore, determine the probabilities of failure for each group and EWE intensity.

EWE

Low Intensity Medium Intensity High Intensity

Clustering No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F.

Cluster 1 0.9 0.05 0.04 0.01 0.4 0.3 0.25 0.05 0.2 0.4 0.35 0.05

Cluster 2 0.79 0.08 0.1 0.03 0.25 0.25 0.35 0.15 0.15 0.35 0.35 0.15

Cluster 3 0.68 0.1 0.15 0.07 0.2 0.2 0.4 0.2 0.1 0.1 0.45 0.35

Cluster 4 0.55 0.15 0.2 0.1 0.14 0.18 0.38 0.3 0.05 0.1 0.35 0.5

Cluster 5 0.4 0.2 0.25 0.15 0.09 0.16 0.35 0.4 0.03 0.07 0.2 0.7

Cluster 6 0.25 0.25 0.3 0.2 0.05 0.15 0.3 0.5 0.01 0.04 0.05 0.9

Finally, according with the introduction of the present document, in the following section are going to be described the two bridge examples that will be utilised in order to test the excel sheet.

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A.2 Bridge Examples The scope of this section is to analyse two different bridges in order to obtain a weighting result in the excel sheet, and thus, classify them in one of the clusters defined above. Concluded this step, the probabilities for each EWE intensity and type of failure can be defined.

Examples of two bridges can be seen below.

“Las llamas” Bridge

The bridge is located in Santander and connects the city west access with the University campus. In the following paragraphs the main characteristics of this bridge are going to be highlighted.

The bridge in question is a concrete arch bridge 102 m in total length and 81.6 m for the central span length. Furthermore, it has a width of 23.6 m and a height of approximately 7.5 m. It also has an edge of 2.25 m.

The arch is located along the central part of the bridge and continues under the deck, working as the piers. Due to this the piers are not vertical and besides, the shape is not rounded. The foundation typology is a direct foundation to the rock and is located at approximately 8 m under the natural ground.

In the past, some defects in the bridge were detected, most of them were related with concrete cracking, therefore, the bridge had to be repaired.

Finally, is important to know that this bridge does not have a river under it. However, is located in a stream bed and due to this is important to consider this bridge for the testing process.

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Bridge over the Bejar creek

The bridge is a 30‐year‐old infrastructure that was part of the national road N‐340 before the A7 motorway was built. When the A‐7 was built, the bridge was integrated in the Murcia‐Almeria lanes. A parallel bridge was designed and built to become the other direction of the motorway (Almería‐ Murcia). The “Rambla de Béjar”, is located between the towns of Lorca and Puerto Lumbreras.

The new bridge was built with a deep foundation, while the old infrastructure remained with its initial shallow foundation. Therefore, the bridge considered for this study is the old one, which collapsed due to the effect of heavy rains in September 2012. As a consequence of the scour effect, part of the ground material of the embankments which facilitated the access to the structure was also removed, affecting the transition slab of one of the abutments. The piers were also affected.

The studied bridge is a common concrete beam bridge with shallow foundation in piles and buttresses. It presents 8 foundations with 3 round piers on each one. Furthermore, the length reach 134 m and presents 9 spans.

Finally, another important aspect is that in flooding events, erosion and debris generation is one of the most common incidents within this stream. In addition, the basin presents erodible materials and the stream is quite wide, which incurs on high water flow. Therefore a foundation scouring due to water is the most probable threat for this potential CI element.

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WEIGHT VALUES

Infrastructure

Bridge

8.25 1.‐ General a) b) c) d) e) f) g)

1.1 7.25 Active life 0 to 10 1 10 to 20 1.375 20 to 30 2.125 30 to 40 3.5 40 to 50 4.5 >50 6 1.2 5 Bridge materials Concrete 1 Masonry 4 steel 2.625 Mix Steel‐Concrete 1.75 1.3 4.75 Time from last inspection 0 to 21 2 to 424 to 63 >64 1.4 9.25 Bridge defects (in piers, buttress, deck, etc.) Structural defects (low) 1.375 Structural defects (High) 3 functional effects 1 1.5 5 Design Lifetime 25 3 50 1.75 100 1 1.6 4.5 Bridge typology Slab 2.5 Beam 2.75 Arch 1.25 Truss 4.5 Suspension 4.75 Cable stay 4 1.7 7.5 Relation between free height of the bridge (H) and the stream depth (Y) H / Y < 13 1 < H / Y < 31.5H / Y > 31 1.8 4 Bridge length < 50 m1 50 m ‐ 100 m1.75> 100 m2.75 1.9 5 Aggressive environment I 1 Iia 2 Iib 2.75 IIIa 4.25 IIIb 5.25 IIIc 6 IV 7

9.5 2.‐ Piers a) b) c) d) e) f) g)

2.1 7.5 Pier shape Rounded 1 Not rounded 2.75 Open piles 3.25 Exposed piles caps 3.75 2.2 5.75 Pier placement / Straight or bend stream Centre / Straight 2.25 Between centre and outside bank / Bend 3 Other 1 2.3 6 Minimum bridge span length inside the stream < 10 m3 10 m ‐ 30 m2> 30 m1 2.4 10 Foundation typology Shallow 2 Deep 1 2.5 5 Pier material Concrete 1 Masonry 2.5 steel 2.5 wood 4 2.6 4.25 Number of piers < 3 piers 1 > 3 piers 1.775 2.7 4.25 Number of piers inside the stream < 30 %1 30 % ‐ 60 %2> 60 %3 2.8 8 Pier and foundation countermeasures (debris and erosion) Yes 1 No 2

6.25 3.‐ Buttress a) b) c) d) e) f) g)

3.1 10 Buttress foundation typology Shallow 2 Deep 1 3.2 6.5 Buttress inside the stream Yes 2 No 1 3.3 6.5 Past event: flooding of Buttress Yes 2 No 1 3.4 5 Buttress materials Concrete 1.25 Masonry 2.5 steel 1.25

4.25 4.‐ Deck a) b) c) d) e) f) g)

4.1 6.25 Higher Deck slimness relation (edge/span) < 0,04 1 0,04 ‐ 0,06 1.875 > 0,06 3 4.2 6.75 Past event: flooding of deck Yes 2 No 1

1.25 5.‐ Joints a) b) c) d) e) f) g)

5.1 3.75 Joints opened or closed Yes 2 No 1

9.25 6.‐ River a) b) c) d) e) f) g)

6.1 6.5 Stream width < 20 m1.520 m ‐ 80 m 1.875 > 80 m2.5 6.2 8.5 River erosion countermeasures Yes 1.25 No 1.75 6.3 7.75 River prone to debris generation Yes 2 No 1 6.4 6.75 Erodible materials close to the river Yes 2 No 1 6.5 6.5 Straight or bend stream Straight 1 Bend 2 6.6 6.25 Basin area < 250 Km2 1 250 km2 ‐ 2500 Km2 1.4 > 2500 Km2 2.5 6.7 5.25 Vegetation close to the stream Low forest density 2 High forest density 1

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WEIGHTING PROCESS

(Chapter x Question) value Answer not standardised Answer standardised (1‐10) Max Value Min Value Range Number of possible answers a b c d e f g a b c d e f g 15106.57 1951.55 13155.02

6 59.8125 1 1.375 2.125 3.5 4.5 6 0 1 1.675 3.025 5.5 7.3 10 598.13 59.81 4 41.25 1 4 2.625 1.75 0 0 0 1 10 5.875 3.25 412.50 41.25 4 39.1875 1 2 3 4 0 0 0 1 4 7 10 391.88 39.19 3 76.3125 1.375 3 1 0 0 0 0 2.6875 10 1 763.13 76.31 3 41.25 3 1.75 1 0 0 0 0 10 4.375 1 412.50 41.25 6 37.125 2.5 2.75 1.25 4.5 4.75 4 0 3.7 4.15 1.45 7.3 7.75 6.4 287.72 53.83 3 61.875 3 1.5 1 0 0 0 0 10 3.25 1 618.75 61.88 3 33 1 1.75 2.75 0 0 0 0 1 4.375 8.875 292.88 33.00 7 41.25 1 2 2.75 4.25 5.25 6 7 1 2.5 3.625 5.875 7.375 8.5 10 412.50 41.25

4 71.25 1 2.75 3.25 3.75 0 0 0 1 6.25 7.75 9.25 659.06 71.25 3 54.625 2.25 3 1 0 0 0 0 6.625 10 1 546.25 54.63 3 57 3 2 1 0 0 0 0 10 5.5 1 570.00 57.00 2 95 2 1 0 0 0 0 0 10 1 950.00 95.00 4 47.5 1 2.5 2.5 4 0 0 0 1 5.5 5.5 10 475.00 47.50 2 40.375 1 1.775 0 0 0 0 0 1 7.975 321.99 40.38 3 40.375 1 2 3 0 0 0 0 1 5.5 10 403.75 40.38 2 76 1 2 0 0 0 0 0 1 10 760.00 76.00

2 62.5 2 1 0 0 0 0 0 10 1 625.00 62.50 2 40.625 2 1 0 0 0 0 0 10 1 406.25 40.63 2 40.625 2 1 0 0 0 0 0 10 1 406.25 40.63 3 31.25 1.25 2.5 1.25 0 0 0 0 2.125 7.75 2.125 242.19 66.41

3 26.5625 1 1.875 3 0 0 0 0 1 4.9375 10 265.63 26.56 2 28.6875 2 1 0 0 0 0 0 10 1 286.88 28.69

2 4.6875 2 1 0 0 0 0 0 10 1 46.88 4.69

3 60.125 1.5 1.875 2.5 0 0 0 0 3.25 4.9375 7.75 465.97 195.41 2 78.625 1.25 1.75 0 0 0 0 0 3.25 7.75 609.34 255.53 2 71.6875 2 1 0 0 0 0 0 10 1 716.88 71.69 2 62.4375 2 1 0 0 0 0 0 10 1 624.38 62.44 2 60.125 1 2 0 0 0 0 0 1 10 601.25 60.13 3 57.8125 1 1.4 2.5 0 0 0 0 1 2.8 7.75 448.05 57.81 2 48.5625 2 1 0 0 0 0 0 10 1 485.63 48.56 139

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CLUSTERING PROCESS

EWE: Fluvial Flooding %Range Value Range Low Intensity Medium Intensity High Intensity Bridge Clustering min max min max No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. Cluster 1 0 10 1951.55 3267.05 0.999 0.0008 0.00019 0.00001 0.995 0.004 0.00098 0.00002 0.99 0.0094 0.0004 0.0002 Cluster 2 10 30 3267.05 5898.06 0.997 0.002 0.00098 0.00002 0.99 0.008 0.00196 0.00004 0.98 0.018 0.0015 0.0005 Cluster 3 30 40 5898.06 7213.56 0.995 0.004 0.00096 0.00004 0.98 0.015 0.00492 0.00008 0.96 0.035 0.004 0.001 Cluster 4 40 70 7213.56 11160.06 0.993 0.006 0.00094 0.00006 0.97 0.02 0.00985 0.00015 0.94 0.05 0.007 0.003 Cluster 5 70 90 11160.06 13791.07 0.991 0.008 0.00092 0.00008 0.96 0.025 0.0148 0.0002 0.92 0.06 0.015 0.005 Cluster 6 90 100 13791.07 15106.57 0.99 0.009 0.0009 0.0001 0.95 0.03 0.01975 0.00025 0.9 0.07 0.02 0.01

BRIDGE EXAMPLES

Puente de las llamas Puente sobre la rambla de bejar 1.‐ General Option Value Option Value

1.1 a 59.81 d 328.97 1.2 d 134.06 a 41.25 1.3 a 39.19 c 274.31 1.4 a 205.09 a 205.09 1.5 b 180.47 b 180.47 1.6 c 53.83 b 154.07 1.7 c 61.88 c 61.88 1.8 a 33.00 b 144.38 1.9 a 41.25 a 41.25

2.‐ Piers

2.1 a 71.25 c 552.19 2.2 c 54.63 a 361.89 2.3 b 313.50 b 313.50 2.4 b 95.00 a 950.00 2.5 a 47.50 a 47.50 2.6 a 40.38 b 321.99 2.7 a 40.38 c 403.75 2.8 b 760.00 b 760.00

3.‐ Buttress

3.1 b 62.50 a 625.00 3.2 b 40.63 b 40.63 3.3 b 40.63 b 40.63 3.4 a 66.41 a 66.41

4.‐ Deck

4.1 b 131.15 a 26.56 4.2 b 28.69 b 28.69

5.‐ Joints

5.1 b 4.69 b 4.69

6.‐ River 140

6.1 a 195.41 c 465.97 6.2 b 609.34 a 255.53 6.3 b 71.69 a 716.88 6.4 b 62.44 a 624.38 6.5 a 60.13 a 60.13 D6.3‐Report on benefits of critical infrastructure protection

WEIGHT VALUES (Tunnels)

Infrastructure

Tunnel

6.75 1.‐ General Tunnel Criteria a) b) c) d) e) f) g)

1.1 8.25 Tunnel typology False tunnel 1.25 Rock tunnel 1.55 Ground tunnel 2.75 Submerged tunnel 4 1.2 6 Active life (years) 0 to 10 1 10 to 20 1.35 20 to 30 2 30 to 40 3 40 to 50 4.625 >50 6 1.3 6.75 Time from last inspection (years) 0 to 21 2 to 41.754 to 62.5> 63.75 1.4 9.5 Tunnel defects Structural defects (low) 1.05 Structural defects (High) 2 1.5 5.75 Design Lifetime 2535021001 1.6 6.75 Coating typology Active 2 Passive (Segments) 1.5 Passive (RC) 2.25 Other 3 1.7 6 Tunnel diameter < 4m 1 4 to 10 m 1.825 > 10m 3 1.8 7 Tunnel length < 1Km 1 1 to 3 Km 1.75 3 to 5 Km 2.575 5 to 10 Km 3.75 > 10 Km 5 1.9 5.75 Aggressive environment I 1 IIa 1.75 IIb 2.25 IIIa 3.75 IIIb 5 IIIc 6 IV 7

9.88 2.‐ Drainage a) b) c) d) e) f) g)

2.1 9.813 Status of the drainage system No defects 1 Low defects 1.525 Strong defects 3 2.2 9 Status of the waterproofing system No defects 1 Low defects 1.525 Strong defects 2.875 2.3 5.5 Asphalt concrete typology Porous 1 Non porous 2

7.5 3.‐ General Tunnel (Portal) a) b) c) d) e) f) g)

3.1 7.5 Countermeasures for tunnel portal protection Flooding doors 1 Slope reinforcement 1.2 No 3 3.2 8.25 Material from surroundings prone to be unstable Yes 2 No 1 3.3 6.5 Past events: Land / rock slides Yes 2 No 1 3.4 8.25 Defects detected at the portal Structural defects (low) 1.05 Structural defects (High) 2

9.5 4.‐ Environment a) b) c) d) e) f) g)

4.1 7.75 Tunnel location Coastal zone 2 River zone 2.75 Other 1 4.2 8 Number of falls (along the length of the tunnel) High 4 Medium 3 Low 1.8 no faults 1 4.3 9.5 Stability of the falls Stable 1 Unstable 2 4.4 7.5 Height above sea level < 3m 3 10 to 40m 1.875 > 40m 1 4.5 8.25 Position relative to the river Above 1.35 Level zero 2.5 Below 4 no river 1 4.6 6.75 Past event: flooding Yes 2 No 1 4.7 6.25 Past event: filtering Yes 2 No 1 4.8 8.25 Seismic area Yes 2 No 1 4.9 7.25 Position relative to the phreatic level Above 1 Level zero 1.875 Below 3

7.75 5.‐ Ground properties a) b) c) d) e) f) g)

5.1 7.75 RMR index I 1 II 1.5 III 2.5 IV 3.875 V 5 Soil 6 5.2 8.25 Exhaustive geotechnical study Yes 1 No 2 5.3 7.75 Exhaustive geological study Yes 1 No 2 5.4 6.75 Runoff ratio 0 to 0,3 1 0,3 to 0,6 2 0,6 to 1,0 3 5.5 7.25 Past event: Uplift pressure Yes 2 No 1

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WEIGHTING PROCESS (Tunnels)

(Chapter x Question) value Answer not standardised Answer standardised (1‐10) Max Value Min Value Range Number of possible answers a b c d e f g a b c d e f g 18080.93 2005.27 16075.66

4 55.6875 1.25 1.55 2.75 4 0 0 0 1.75 2.65 6.25 10 556.88 97.45 6 40.5 1 1.35 2 3 4.625 6 0 1 1.63 2.8 4.6 7.525 10 405.00 40.50 4 45.5625 1 1.75 2.5 3.75 0 0 0 1 3.25 5.5 9.25 421.45 45.56 2 64.125 1.05 2 0 0 0 0 0 1.45 10 641.25 92.98 3 38.8125 3 2 1 0 0 0 0 10 5.5 1 388.13 38.81 4 45.5625 2 1.5 2.25 3 0 0 0 4 2.5 4.75 7 318.94 113.91 3 40.5 1 1.825 3 0 0 0 0 1 4.7125 10 405.00 40.50 5 47.25 1 1.75 2.575 3.75 5 0 0 1 2.6875 4.5438 7.1875 10 472.50 47.25 7 38.8125 1 1.75 2.25 3.75 5 6 7 1 2.125 2.875 5.125 7 8.5 10 388.13 38.81

3 96.8984375 1 1.525 3 0 0 0 0 1 3.3625 10 968.98 96.90 3 88.875 1 1.525 2.875 0 0 0 0 1 3.3625 9.4375 838.76 88.88 2 54.3125 1 2 0 0 0 0 0 1 10 543.13 54.31

3 56.25 1 1.2 3 0 0 0 0 1 1.9 10 562.50 56.25 2 61.875 2 1 0 0 0 0 0 10 1 618.75 61.88 2 48.75 2 1 0 0 0 0 0 10 1 487.50 48.75 2 61.875 1.05 2 0 0 0 0 0 1.45 10 618.75 89.72

3 73.625 2 2.75 1 0 0 0 0 5.5 8.875 1 653.42 73.63 4 76 4 3 1.8 1 0 0 0 10 7 3.4 1 760.00 76.00 2 90.25 1 2 0 0 0 0 0 1 10 902.50 90.25 3 71.25 3 1.875 1 0 0 0 0 10 4.9375 1 712.50 71.25 4 78.375 1.35 2.5 4 1 0 0 0 2.05 5.5 10 1 783.75 78.38 2 64.125 2 1 0 0 0 0 0 10 1 641.25 64.13 2 59.375 2 1 0 0 0 0 0 10 1 593.75 59.38 2 78.375 2 1 0 0 0 0 0 10 1 783.75 78.38 3 68.875 1 1.875 3 0 0 0 0 1 4.9375 10 688.75 68.88

6 60.0625 1 1.5 2.5 3.875 5 6 0 1 1.9 3.7 6.175 8.2 10 600.63 60.06 2 63.9375 1 2 0 0 0 0 0 1 10 639.38 63.94 2 60.0625 1 2 0 0 0 0 0 1 10 600.63 60.06 3 52.3125 1 2 3 0 0 0 0 1 5.5 10 523.13 52.31 2 56.1875 2 1 0 0 0 0 0 10 1 561.88 56.19 142

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CLUSTERING PROCESS (Tunnels)

EWE: Fluvial Flooding %Range Value Range Low Intensity Medium Intensity High Intensity Tunnel Clustering min max min max No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. No failure Operational F. Partial F. Full F. Cluster 1 0 10 2005.27 3612.84 0.999 0.0008 0.00019 0.00001 0.995 0.004 0.00098 0.00002 0.99 0.0094 0.0004 0.0002 Cluster 2 10 20 3612.84 5220.40 0.997 0.002 0.00098 0.00002 0.99 0.008 0.00196 0.00004 0.98 0.018 0.0015 0.0005 Cluster 3 20 40 5220.40 8435.53 0.995 0.004 0.00096 0.00004 0.98 0.015 0.00492 0.00008 0.96 0.035 0.004 0.001 Cluster 4 40 70 8435.53 13258.23 0.993 0.006 0.00094 0.00006 0.97 0.02 0.00985 0.00015 0.94 0.05 0.007 0.003 Cluster 5 70 90 13258.23 16473.36 0.991 0.008 0.00092 0.00008 0.96 0.025 0.0148 0.0002 0.92 0.06 0.015 0.005 Cluster 6 90 100 16473.36 18080.93 0.99 0.009 0.0009 0.0001 0.95 0.03 0.01975 0.00025 0.9 0.07 0.02 0.01

TUNNEL EXAMPLES

Laedral Tunnel Viella Tunnel 1.‐ General Tunnel Option Value Option Value

1.1 b 147.57 b 147.57 1.2 b 66.02 a 40.50 1.3 a 45.56 a 45.56 1.4 a 92.98 a 92.98 1.5 c 38.81 c 38.81 1.6 b 113.91 b 113.91 1.7 b 190.86 b 190.86 1.8 e 472.50 d 339.61 1.9 b 82.48 a 38.81

2.‐ Drainage

2.1 a 96.90 a 96.90 2.2 a 88.88 a 88.88 2.3 a 54.31 a 54.31

3.‐ General Tunnel (Portal)

3.1 a 56.25 a 56.25 3.2 b 61.88 a 618.75 3.3 a 487.50 b 48.75 3.4 a 89.72 a 89.72

4.‐ Environment

4.1 a 404.94 c 73.63 4.2 b 532.00 a 760.00 4.3 a 90.25 b 902.50 4.4 c 71.25 c 71.25 4.5 a 160.67 a 160.67 4.6 b 64.13 b 64.13 4.7 b 59.38 b 59.38 4.8 b 78.38 a 783.75 4.9 a 68.88 a 68.88

5.‐ Ground properties

5.1 a 60.06 a 60.06 143 5.2 a 63.94 a 63.94 5.3 a 60.06 a 60.06 5.4 b 287.72 b 287.72 5.5 a 561.88 a 561.88 D6.3‐Report on benefits of critical infrastructure protection

Appendix B Indirect consequences and Indirect mitigation

B.1 INDIRECT CONSEQUENCES As well as direct extreme weather impacts on transport infrastructure, there are generally indirect negative economic and societal impacts also affecting the population which are caused by the transport infrastructure dysfunctionality e.g. the economic loss due to detour, indirect societal impact expressed in the form of the number of affected population, economic loss due to non‐ loading/non‐unloading of commercial vehicles, etc. These consequences would vary depending on particular type of element (e.g. bridge or cargo station, etc.)

The economic loss/year due to necessary detour within the road infrastructure is (Transport Research Institute, 2014):

 EL ‐ economic loss, in €/year;  AADIT ‐ annual average of daily intensities of trucks on specific element, in vehicle/day;  DL ‐ detour length which is necessary to pass in order to provide the service (delivery of goods), in km;  N ‐ price per 1 km of freight and commercial passenger transport, in €/vehicle km.

The number of affected population per year due to failure of transport infrastructure is calculated (Transport Research Institute, 2014):

 AP ‐ number of population affected per year;  AADIP ‐ annual average of daily intensities of passenger cars on specific element, in vehicle/day;  AOP ‐ average occupancy of passenger car;  AOT ‐ average occupancy of truck. In the case of rail transportation the economic losses are based on the value of the effects caused by failure /destruction of the element (railway bridge, tunnel) calculated based on the capacity to carry goods over the track section and the value of the average price €/tkm. In railway transport in quantifying the economic losses the worst scenario, when destruction of the object caused termination of provided services (goods) on said track, is considered. Therefore, no economic loss due to detour was calculated. The economic loss is calculated (Transport Research Institute, 2014):

∗  EL ‐ economic loss, in €;  TC ‐ transport capacity over the track section for a period of time of 365 days, in tkm (tonne‐ kilometre);  AvP ‐ average price, in €/tkm. The indirect consequences of the destruction of cargo rail stations, resulting in no transportation to the destination, are determined on the basis of economic losses due to non‐loading/non‐unloading D6.3‐Report on benefits of critical infrastructure protection

of the commercial vehicles. Determination of economic losses is based on the assumption that if there is no transportation, there is no goods loaded, resulting in a decrease in revenues of carriers. The economic loss can be calculated using (Transport Research Institute, 2014):

 Q ‐ amount of manipulated material for period of 365 days, in tkm;  AvP ‐ average price, in €/t. Economic losses due to a bridge failure can be quantified as per the HYRISK project in which bridge failures due to scour in the USA were assessed. There are four components to the losses according to the HYRISK model: rebuilding costs; vehicle running costs; time loss costs; and the cost of a lost life (Khelifa et al 2013):

1 1 100 100 100 100

Rebuilding costs:

 C0 ‐ demolition cost, in €/m2;  C1 ‐ rebuilding cost, in €/m2;  e ‐ cost multiplier for early replacement estimated from average daily traffic;  W ‐ bridge width, in m;  L ‐ bridge length, in m; Vehicle running cost [24]:

 C2 ‐ cost of running vehicle;  T ‐ average daily truck traffic;  D ‐ detour length, in km;  A ‐ average daily traffic;  d ‐ duration of detour, in days; Time loss costs [24]:

 C4 ‐ value of time per adult;  ‐ occupancy rate;

 C5 ‐ value of time for truck;  S ‐ average detour speed, in km/h; Cost of lost life [23]:

 C6 ‐ cost for each life lost (HYRISK assumed 6 million USD);  X ‐ number of deaths.

B.2 INDIRECT MITIGATION Based on the discussions at a RAIN consortium meeting and workshop (Delft University of Technology, 4th April 2016) an analysis of the ‘’soft” mitigation measures which could address consequences of disasters within a case study was carried out.

In this instance “soft” means that non engineering measures are used to try and mitigate against the hazard occurrence, infrastructure failure, and the consequences of critical infrastructure damage e.g.

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land use planning restricting the construction of infrastructure in a flood prone region; improved education reducing effects on the population.

Both the costs and effectiveness of the measures are judged in terms of monetary valuation:

Cost of measures Categories (more than 1 mil. 0 Very Expensive euro) 1 Expensive (max 1 mil. euro)

2 Expensive to Medium price (max 100 000 euro)

3 Medium price (max 10 000 euro)

4 Cheap (max 1 000 euro)

5 Very Cheap (max 100 euro)

Effectiveness (max 1 000 euro) Low (max 10 000 Medium euro) (max 100 000 Medium to high euro) (max 1 000 000 High euro) (more than 1 mil. Very High euro)

The methodology is applied in the context of a case study of an area known to the authors (See table below). The Vratna valley is a system with some types soft measures applied before EWE occurred. The effect of these restrictions on the effects of flash flooding in combination with Landslides is assessed.

Firstly a value must be assigned to impact on the unmitigated system. In the case below, for economic loss, €1,000,000 is taken as the cost of repair & replacement of infrastructure in the area. The effectiveness of the mitigation is then taken as the reduction of that value minus the cost of the mitigation measure related to the tables above. This reduction is calculated using the % effect of the measure in question.

For instance, paying €100,000 for a measure that has a 50% reduction on the impact on the economic value of the infrastructure (cost of repair and replace = €1,000,000) results in a reduction of €500,000 minus the cost of €100,000 which gives an effectiveness of €400,000 which is given a rating of ‘High’.

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To calculate on the effectiveness of reduction of the societal and security impacts, monetary value must be assigned to the elements at risk i.e. if the outcome of the unmitigated scenario is 2 deaths and 10 injuries are both reduced by 50% then the effectiveness is:

1 x the assigned cost of 1 death + 5 x the assigned cost of 1 injury

The same is applicable to security impacts.

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HEAVY RAINFALL/FLASH FLOOD IN COMBINATION WITH LANDSLIDE in VRATNA VALLEY Reduction of possible consequences of damage of the ‘unmitigated’ system in % (moneraty value of the probability e.g. 10% means that the consequences would be about 10% higher Reduction of Vratna Valley Reduction of hazard change) infrastructure occuring Direct societal Direct security being damaged Direct economic impacts Cost of impacts impacts Effectiveness Explanation of effectiveness reduction measures reduction reduction Using insurance system and Reserves of the state budget have been 1 financial pooling instruments Establish funds for post disaster recovery 0% 0% 0% 0% 0% Low used after the EWE occured; only for (2 560 000 €) against risks ‐ reserves of state budget have been used reconstruction and recovery funding. Enhanced rescue services capacities 10% from 1 000 000 (cost of repair or ‐ strengthen technical capacities of sector professionals in 3 Medium to replacement) is 100 000, costs are about 10 0% 0% 0‐10% 20‐30% 0% management of major risks (max 10 000 €) high 000: positive impact about 90 000 ‐ establishing and using of state material reserves Enhancing capacities and Enhanced reconstruction and recovery capacities 10% from 1 000 000 (cost of repair or resources ‐ reallocate the resources to uses with more added value replacement) is 100 000, costs are about 10 3 Medium to (allocation of capacities ‐ heavy machinery, etc.) 0% 0% 0‐10% 0‐10% 0% 000: positive impact about 90 000 (max 10 000 €) high ‐ establishing and using of state material reserves

Restriction of construction save almost 100% of losses Land‐use planning 0% 100% 90‐100% 90‐100% 90‐100% 4 (max 1 000 €) Very high ‐ construction in hazard areas have been restricted Prevention Education scheme and raise of awareness 0% 0% 0% 20‐30% 20‐30% 4(max 1 000 €) Medium Positive impacts is difficult to determine Probability of Probability of an infrastructure EWE occuring is Probability of specific consequences is reduced being damaged is reduced reduced

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