Post-Disaster Loss Accounting Vs Disaster Forensic: Insights from the November 2013 ﬂood in the Umbria Region

2.2. | IL MARCHIO, IL LOGOTIPO: LE DECLINAZIONI

School of Civil, Environmental and Land Management Engineering MSc Civil Engineering for Risk Mitigation

Post-disaster loss accounting vs disaster forensic: insights from the November 2013 ﬂood in the Umbria region

Supervisors: Scira Menoni and Daniela Molinari

MSc graduation thesis by: Maria Camila Rodriguez Parra Matricola: 815983

December 2015 2 Acknowledgements

This thesis is dedicated to my dad that has unconditionally supported me on each of my dreams, making them a reality, even now from far away. Together with mi mom they made me the person I am today. IwouldliketothankmyfamilyandfriendsinColombiaandItalyforencour- aging me to keep going, specially in rough times. This work would not have been possible with out all the great people that sewed a net not to let me down. I would like to thank Jose for being my life companion through adventures and misadventures, but moreover, the person capable of holding me together when it seemed impossible to me. Many thanks to all the professors in Politecnico di Milano that shaped my learning process but specially to Prof. Scira Menoni and Prof. Daniela Moli- nari that as my thesis supervisors, guided this thesis through the path it took with valuable advises from their expertise. I am grateful to the people in Umbria region for all their help and in general to all the people that made this thesis possible.

3 4 Contents

Aknowledgements 3

Abstract 11

Abstract italiano 13

Introduction 15

1Framework 17 1.1 Flooddamage...... 17 1.2 Collection and recording of damage data ...... 19 1.2.1 Loss accounting ...... 20 1.2.2 Disaster forensics ...... 21 1.2.3 Risk modelling ...... 22 1.2.4 Compensation ...... 22 1.2.5 Overview of the applications ...... 23 1.3 Disaster forensics: the FORIN project ...... 26 1.3.1 Usesofforensicinvestigations ...... 27 1.3.2 Methods for forensic investigations ...... 28

2 Practices on damage data collection and recording 31 2.1 In Europe ...... 32 2.2 In Italy ...... 36 2.3 Challenges of these practices ...... 40

3 The November 2013 ﬂood in Umbria 43 3.1 TheUmbriaregionriskmanagement ...... 44 3.2 Transition tests ...... 47 3.3 Results 2013 event ...... 47 3.3.1 Physic scenario ...... 48 3.3.2 Data processing ...... 49 3.3.3 Results...... 62

5 4 Discussion of the results 81 4.1 Data quality ...... 81 4.2 Challenges for forensic use of data ...... 86

5 Conclusions 91 5.1 Lessons learned from this project ...... 91 5.2 Future recommendations ...... 93

A General maps 97

B Point maps 111

6 List of Figures

1.1 Working scale and scope for loss accounting. Adapted from [De Groeve et al., 2014] ...... 21 1.2 Working scale and scope for disaster forensics. Adapted from [De Groeve et al., 2014] ...... 22 1.3 Working scale and scope for risk modelling. Adapted from [De Groeve et al., 2014] ...... 23 1.4 Working scale and scope for compensation ...... 24 1.5 Role of loss data in national processes of risk management [De Groeve et al., 2014] ...... 26 1.6 Target levels of the questions in the FORIN framework [Inte- grated Research on Disaster Risk, 2011] ...... 29

3.1 Administrative limits in the Tiber basin. [Mondo del Gusto, 2009] 44 3.2 Scheme of the cyclic conceptualization Poli-RISPOSTA project . 46 3.3 Flooding levels reached during 2013 event. [Centro Funzionale RegioneUmbria,2013] ...... 49 3.4 A↵ected municipalities during the November 2013 - February 2014 event. [Protezione Civile Regione Umbria, 2014] ...... 50 3.5 Timeline of source tables and strikes of the flood event...... 52 3.6 Scheme of the correlation process between the three source tables 56 3.7 Change of the financial request over time ...... 64 3.8 Change of the financial request and prioritization over time . . . 66 3.9 Change of the financial request and responsible stakeholder over time ...... 66 3.10 Change of the financial request and sector over time ...... 67 3.11 Frequency analysis for emergency management sector: type of activity ...... 68 3.12 Frequency analysis for public area sector: type of area and type of direct damage ...... 69 3.13 Frequency analysis for public good sector: type of infrastructure and type of direct damage ...... 70

7 3.14 Frequency analysis for road sector: type of infrastructure and typeofdirectdamage...... 71 3.15 Frequency analysis for public hydrogeological sector: type of direct damage ...... 72 3.16 Frequency analysis for water/sewage system sector: type of infrastructure, cause of damage and type of direct damage . . . . 73 3.17 General map for road sector 4 months after the event ...... 75 3.18 General map for the road sector 1 year 3 months after the event 76 3.19 Point map for road sector and road network of Umbria . . . . . 78 3.20 Point map for road sector and blocked streets in the road net- workofUmbria ...... 79

4.1 Conectivity between types of uncertainty [De Groeve et al., 2014] 82 4.2 Example of measurement uncertainty obtaining coordinates of an intervention ...... 83

A.1 General map for emergency management sector 4 months after theevent...... 98 A.2 General map for public area sector 4 months after the event . . 99 A.3 General map for public area sector 1 year 3 months after the event...... 100 A.4 General map for public good sector 4 months after the event . . 101 A.5 General map for public good sector 1 year 3 months after the event...... 102 A.6 General map for road sector 4 months after the event ...... 103 A.7 General map for road sector 1 year 3 months after the event . . 104 A.8 General map for hydrogeological protective measures sector 4 months after the event ...... 105 A.9 General map for hydrogeological protective measures sector 1 year 3 months after the event ...... 106 A.10 General map for water/sewage system sector 4 months after the event...... 107 A.11 General map for water/sewage system sector 1 year 3 months after the event ...... 108 A.12 General map for electric sector 4 months after the event . . . . . 109 A.13 General map for electric sector 1 year 3 months after the event . 110

B.1 Pointmapforpubicareasector ...... 112 B.2 Pointmapforpublicgoodsector ...... 113 B.3 Point map for road sector and road network of Umbria . . . . . 114 B.4 Point map for road sector and blocked streets in the road net- workofUmbria ...... 115

8 List of Tables

1.1 Examples of types of damage. Adapted from [Messner et al., 2007] ...... 19 1.2 Application areas of disaster damage/loss data. Adapted from [De Groeve et al., 2014] ...... 25

2.1 Analysisofnationaldriversforlossdata ...... 33 2.2 Analysis methodology of loss data collection ...... 34 2.3 Analysis methodology of loss data recording ...... 35 2.4 AnalysisforAVIItaliandatabase ...... 37

3.1 List of responsible stakeholders for each sector. Adapter from [Politecnico di Milano & Umbria Region, 2015] ...... 51 3.2 Structure of source tables: Preliminary table, March 2014; First emergency table, July 2014; Final table, February 2015 . . . . . 53 3.3 Raw data resume of ﬁnancial resources required by stakeholders: Preliminary table, March 2014; First emergency table, July 2014;Finaltable,February2015...... 55 3.4 Summary of the source tables ...... 55 3.5 Required information for a disaster forensic analysis for emergency management sector ...... 58 3.6 Required information for a disaster forensic analysis for public areas and public goods sectors ...... 59 3.7 Required information for a disaster forensic analysis for roads and hydrogeological protective measures sectos ...... 60 3.8 Required information for a disaster forensic analysis for water/sewage system and electric sectors ...... 61 3.9 List of output of the disaster forensic analysis for all the sectors 63

4.1 Simpliﬁed example of an intervention request for Gubbio municipality from partial source table ...... 84 4.2 Simpliﬁed example of an intervention request for Perugia province for hydraulic protection from partial source table . . . 85

9 4.3 Simplified example of an intervention request for Gualdo Cat- taneo municipality from final source table ...... 85 4.4 Simplified example of an intervention request for Campello sul Clitunno municipality from partial and final source tables . . . . 86 4.5 Comparison between priority letters definition according to general Law and specific-event Order ...... 88

10 Abstract

With increasing disasters provoking increasing damage along the world, damage data collection has become a raising concern as the basis to determine the e↵ectivity of risk management strategies. However present methodologies for damage data collection and recording after a disaster, as well as the uses given to these data, di↵er along countries, creating inconsistencies. In such a context, this thesis focuses on the feasibility of using damage data obtained with current methodologies for collection and recording for a specific use that is disaster forensic investigation. Aliteraturereviewwasperformedtodenethedi↵erentrequirementsofdamage data for each of their four applications: loss accounting, risk modelling, disaster forensic analysis and compensation. Special attention was given to disaster forensic analysis given its utility to supply a post-event comprehensive overview of the disaster. Through literature review it was also possible to describe the current practices for damage data collection and recording in Europe and specifically in Italy. This let to the identification of key challenges for the use of damage data for a di↵erent application than its original purpose. The November 2013-February 2014 flood in the Umbria region was chosen as the case study to perform a disaster forensic analysis with data collected with current practices in Italy, which are compensation oriented. As a result, it was possible to identify overlapping characteristics of data for compensation and disaster forensics applications. Moreover, a second set of specific challenges for the use of data was set, like: dealing with uncertainty leading to low quality of data in a smarter way, improving standards, having a more coherent way to account for the change of data on time, decreasing the time needed for data elaboration. Future developments on this field are encouraged to address these challenges. This will also set a step forward towards compatible methods for the collection of loss data worldwide that eventually can lead to European or global structured databases.

11 12 Abstract Italiano

Con il continuo aumentare dei disastri naturali e dei danni conseguenti in tutto il pianeta, la raccolta dei dati di danno èdiventata una delle principali priorità, come base per determinare l’ecacia delle strategie di gestione del rischio. Ciononostante, le attuali metodologie di raccolta e archiviazione dei dati di danno, cos`ıcome gli usi che si danno a questi dati, divergono da paese a paese, creando incompatibilità. In questo contesto, questa tesi si concentra sulla possibilitàdi usare i dati di danno ottenuti attraverso le attuali metodologie di raccolta e archiviazione per il particolare uso di analisi dell’evento calamitoso di tipo forense. In primo luogo, èstata fatta un’analisi della letteratura per identificare i req- uisiti dei dati di danno per ognuno dei quattro usi possibili: contabilitàdelle perdite, modellazione del rischio, analisi forense e risarcimento. Particolare attenzione èstata rivolta alle analisi di tipo forense, data la loro utilitànel fornire un quadro comprensivo dell’evento. Attraverso l’analisi della letteratura èstato anche possibile descrivere le attuali pratiche per la raccolta e archiviazione dei danni in Europa, e specicatamente in Italia. Questo ha per- messo di identicare alcune criticitàper l’uso di dati di danno per uno scopo diverso da quello originale. L’alluvione del Novembre 2013-Febraio 2014 in Umbria èstata scelta come caso studio per un’analisi di tipo forense dell’evento con i dati raccolti con le pratiche attuali in Italia, finalizzate al risarcimento. Come risultato, èstato possibile identicare le caratteristiche comuni dei dati per gli scopi di risarcimento e analisi forense. Quindi, ulteriori criticitàper l’uso dei dati sono state identificate, tra cui: la gestione dell’incertezza legata alla qualitàdel dato, la gestione coerente dell’evoluzione temporale del dato, la riduzione dei tempi necessari all’elaborazione del dato. I futuri sforzi di ricerca dovrebbero con- centrarsi sul superamento di tali criticità.Questo permetterebbe fare un passo avanti verso metodi compatibili per la raccolta dei dati di danno a livello Eu- ropeo e globale, archiviati in database compatibili.

13 14 Introduction

The floods at the european level are the most frequent disasters and also the ones causing more losses with e52 billion between 1998 and 2009 [European Environmental Agency, 2011]. Moreover it is suspected that climate change will increase the occurrence of floods, making them also more severe (more frequent, more magnitude) [Messner et al., 2007]. New strategies leading to diminish the losses of floods have become an increasing concern worldwide; but the question is: what losses do these strategies want to diminish and how. Disaster forensic analysis is a tool for the reconstruction of a disastrous event and its causes; it can help understand what went wrong during the event, leaving room for improvements in future events. This thesis project focuses on the feasibility of using data obtained with the current methodology for collection and recording of loss data for a disaster forensic analysis. The damage data collection and recording after a disaster di↵ers along Europe, as well as the purposes for which the recorded data is used for. The November 2013-February 2014 flood event in Umbria region is the case study of the project, where a disaster forensic analysis will be performed with the data obtained from the current practices. This will permit to establish existing limitations and pro- pose challenges to be faced in the future with improvements on methodologies for damage data collection and recoding. The work starts with the definition of the framework in chapter 1. Basic key definitions regarding flood damage will be provided as well as the four applications that can be given to loss data. This chapter will also introduce the disaster forensic as one of the applications and will provide a wider description of the methods to perform it. Chapter 2 will give an overview of the current practices for damage data collection and recording in Europe and more specifically in Italy. This will provide the context on which the case study is set on. The final part of this chapter will outline the identified challenges of using these practices. The case study will be presented in chapter 3, starting form the description of

15 the research context on which it is developed. After that, the physic scenario of the event is described and the data processing steps done to the raw data to perform a disaster forensic investigation will be outline. The chapter ends with the presentation of the results obtained from the described processing steps. The discussion of the results of the case study will be presented in chapter 4. The data quality and the challenges of using the data collected from current practices for a disaster forensic investigation will be presented. The final chapter 5 will present the conclusion of the thesis through the presentation of the lessons learned from it. Moreover it will give some suggestions for future development of this field, basically based on the identified challenges to be faced. It can be seen as a the guiding path for future improvements of the practices for damage data collection and recording.

16 Chapter 1

Framework

This chapter is focuses on setting the reader into the framework of the project by establishing some key definitions and tools that will guide the whole understanding of the subsequent reading. First, a key definition of flood damage will be given, as well as concepts of types of damage and losses that will be used along the whole document. Second of all, the main reasons for collecting and recoding damage data are described based on recent guidelines set at the European level [De Groeve et al., 2013], giving the identified applications. Fi- nally in the last section, a expanded definition of disaster forensic is given as well as the various uses that can be given to this investigations and some of the suggested methods to perform it based on the results of the FORIN project [Integrated Research on Disaster Risk, 2011]. This final section is important because it will define the required characteristics to perform a disaster forensic investigation like the one that will be done for the case study in chapter 3.

1.1 Flood damage

Disaster according to UNISDR [UNISDR, 2009] is a disruption of a community or a society involving widespread human, material, economic or environment losses and impacts, which exceeds the ability of the a↵ected community or society to cope with using its own resources. From this, it is possible to classify a flood as a potential disaster, where a flood is a condition of inun- dation of a normally dry area [FEMA, 2014]; the causes of a flood can be: heavy rains causing overflow of a water source, snow melt, dam break among others. Floods, when in contact with a vulnerable and exposed community, can cause the disruption of the community or society. Moreover, floods are

17 often accompanied by other kinds of hazards that are also triggered by the same or similar causes: landslides, erosion, debris ﬂows; this group of hazards are called hydrogeological hazards and are the one to be treated in the case-study.

According to the Economic Commission for Latin America and the Caribbean [ECLAC, 2003], disaster damage is defined as the total or partial destruction of physical assets existing in the a↵ected area, measured in physical units, occurred after a disaster. And according to the Nuclear Regulatory Commis- sion [NRC, 1999] the disaster loss is the market-based negative economic impact caused by a disaster; in this project the disaster loss consists of the transformation of the disaster damage (both direct and indirect as it will be shown below) into the equivalent economic units representing the replacement or compensation cost; this process is also known as monetarization. For example, the destruction of a 100 m2 of floor in a house after a flood is the disaster damage; if every m2 of that floor costs e20 to be replaced, then the disaster loss would be e2000.

The process of monetarizing the disaster damage is not always straightforward, take for instance the example of transforming the human damage into an economic value. Two types of disaster damage can be identiﬁed, tangible damage when it can be monetarized and intangible damage when it cannot.

Another classification of disaster damage/loss distinguishes between direct damage and indirect damage. ECLAC [ECLAC, 2003] defined the direct damage of a disaster as the physical damage to capital assets due to direct physical contact with the hazard, in this case the flood water (or the landslide). While according to Benson & Clay [2000] the indirect damage is the damage to the flow to goods and services due to a dysfunctionality of services, infrastructure, businesses. Note also that when the direct or indirect damage can be monetarized (meaning it is tangible) it can be translated into direct loss or indirect loss respectively. Some examples on the types of damage are given in table 1.1.

The estimation of indirect damage is normally more complicated because it requires a more detailed and a long term evaluation, take of instance the case of e↵ects to the economic productivity of a business, where the e↵ects will only be evident some months after the event (and there are cases where they are evident some years after the event). The accounting of intangible damage also represents a challenge, take for example the di↵erent categories that can derive for a↵ected people: fatalities, injured, evacuated, isolated [De Groeve et al., 2014]; another example is the damage to cultural heritage sites or goods or the

18 Table 1.1: Examples of types of damage. Adapted from [Messner et al., 2007] Tangible Intangible Physical damage to buildings Illnesses Physical damage to stock Effects to the environment Direct Damage to agricultural products Damage to cultural heritage … … Emergency management cost Trauma Indirect Reduction of business production Loss confidence in authorities … … damage to memorabilia1.Thisiswhymostofthedamageassessmentsdonot include these e↵ects in the accounting and many databases only include the direct tangible losses and a small portion of the direct intangible damage (in terms of a↵ected people).

1.2 Collection and recording of damage data

The European Union (EU) represents an environment to discuss create and further individually apply the legislative basis and guides in various fields. The risk management is an important field frequently debated on this environment and a particular interest has been given to the need of an European level loss data recording system; this need stands, among others on the concern of EU on transboundary2 risk management, and to check the eciency of Disaster Risk Reduction (DRR)3 policies [De Groeve et al., 2013]. This is why nowadays many of the legislative tools in Europe, within the EU disaster prevention framework [Council of the European Union, 2009], require or addresses loss data recording, some of them are: the Flood Directive [European Parliament, 2007], the European union solidarity fund (EUSF 2014), the union civil protection legislation (2013), the ISPIRE directive (2007), the green paper on insurance of natural and manmade disasters (2012). However, all these tools lack in requiring a specific standard for the loss data

1Memorabilia is the losses of high a↵ective-value objects that cannot be replaced like family photographs or pets 2Transboundary refers to things that are not contained within administrative boundaries. Many things can be transboundary: e↵ects, management, resources, etc. 3Disaster Risk Reduction is the concept and practice of reducing disaster risks through systematic e↵orts to analyse and reduce the causal factors of disasters, further information can be found in [UNISDR, nd]

19 collection, recording, analysis and uses. This is why the European Commis- sion set up a speciﬁc working group on the topic; the results are collected in three series of reports on disaster loss data recording [De Groeve et al., 2013, De Groeve et al., 2014, Corbane et al., 2015] which constitute the latest e↵ort at European level of approaching the need of loss data recording and o↵er some guidelines and recommendations for standardization that permits the required compatibility for sharing purposes. These recommendations and guidelines are further intended to permit the future creation of an European level disaster loss database through the aggregation of compatible and consistent national databases.

According to the reports, the uses that can be given to loss and damage data are numerous; from checking the e↵ectiveness of a determined mitigation measure during a disaster, to understanding the global e↵ects of climate change. But all of them are, in some way, directed to risk mitigation strategies; this means that the loss data are the base to define the success of mitigation strategies after an event, as well as to design new mitigation strategies for future events. From the various uses that can be given this data, there are some clear applications that group these uses. Initially the first report of the JRC [De Groeve et al., 2013] identified three applications to give to damage data (loss accounting, risk modelling and disaster forensic), but the second report [De Groeve et al., 2014] included a fourth application that was identified form the study of the current practices in Europe (Compensation). In the following subsections the four applications are described, together with the required characteristics of the data for each application based on the review of the mentioned reports. Some of the basic requirements are the scale, meaning the detail of recording, and the scope referring to the geographic coverage respectively.

1.2.1 Loss accounting

Loss accounting is the principal motivation for recording data. This application uses the data to document the trends of the losses, to measure the eciency of DRR policies or for decision making on balancing prevention budget with loss compensation funds. Figure 1.1 shows in red the working area to perform a loss accounting in terms of scale (vertical axis)-scope (horizontal axis) of data. The process for the collection and recording basically intends the collection at small levels (small scale) and the aggregation of the data to reach larger levels (larger scope) where the data can be analyzed. Compatibility is required for the aggregation process.

20 Take for instance the documentation of a regional trend (scope) of disaster losses due to ﬂooding. This can be accomplished for example with options 1 or 2 illustrated in the ﬁgure 1.1: by collecting data (scale) at the regional level, or at the asset level that can later on be aggregated until the regional level is reached respectively.

ACCOUNTING

Global

Nation

Region 1 Scale

Munic

Asset 2

Asset Munic Region Nation Global Scope

Figure 1.1: Working scale and scope for loss accounting. Adapted from [De Groeve et al., 2014]

1.2.2 Disaster forensics

The objective of disaster forensics is to identify the loss causes during a disaster by understanding the unfolding of the event form the recorded damage data. The analysis measures the contribution of exposure, vulnerability, resilience, mitigation and response to later estimate what aspects need to be strength- ened. This kind of analysis permits to evaluate the e↵ectiveness of speciﬁc disaster prevention measures. Figure 1.2 shows in green the working area to perform a disaster forensic investigation in terms of scale-scope of data. More detailed information is required to perform disaster forensic investigations and this is why the maximum scale is the regional level. For a regional disaster ﬁgure 1.2 shows two ways to perform a disaster forensic investigation: with information gathered at the regional level (number 1), or with information gathered at asset level (number 2).

21 FORENSIC

Global

Nation

Region 1 Scale

Munic

Asset 2

Asset Munic Region Nation Global Scope

Figure 1.2: Working scale and scope for disaster forensics. Adapted from [De Groeve et al., 2014]

1.2.3 Risk modelling

Risk modelling is the last application identified in the first report of the JRC [De Groeve et al., 2013]. This application uses the loss data for the calibration and validation of risk models, on any of its components (hazard, vulnerability, exposure, damage). Correctly calibrated and validated models are used to estimate future disasters and are also useful for the decision making process for the preparedness of future events. Figure 1.3 shows in purple the working area to perform risk modelling in terms of scale-scope of data. A clear example is shown in figure 1.3 with number 1 and refers to the use of damage data at building leve and the height reached by the flood water to construct and validate depth-damage curves (also known as vulnerability curves). A second example is shown in the same figure with the number 2, referring to the construction of national hazard, vulnerability and exposure maps using regional data.

1.2.4 Compensation

The second JRC report [De Groeve et al., 2014] identiﬁed compensation as an additional application from the study of the current practices in Europe. The compensation process is part of the recovery and can occur at di↵erent levels: national, European or international. It is guided by solidarity mechanism

22 RISK MODELLING

Global

Nation

Region 2 Scale

Munic

Asset 1

Asset Munic Region Nation Global Scope

Figure 1.3: Working scale and scope for risk modelling. Adapted from [De Groeve et al., 2014] or insurance markets and requires a detail loss assessment. One example of compensation is the European Union Solidarity Fund, established in 2014. Figure 1.4 shows in blue the working area scale-scope to perform compensation. The number 1 in this ﬁgure represents the example of a regional event that collects data at the asset level and aggregates it until the regional level to request the compensation of losses.

1.2.5 Overview of the applications

Table 1.2 shows additional features for the applications, permitting a critical comparison to understand the overlapping aspects as well as the di↵erences between the applications. This table evaluates 6 features for each of the applications: Driver, referring to the reasons to record loss data in a certain application; the relevant legislation agreements that require loss data recording; the loss period evaluated for each of the applications; the interested stakeholders on recording loss data; required scale level to collect the data for each of the applications; and the required characteristics to be recorded. From this table it is possible to see that some of the features are overlapping for the applications, for example the direct monetary losses are recorded for all the applications; this overlapping can be a starting point towards compatible national loss databases towards the objectives of the JRC reports.

23 COMPENSATION

Global

Nation

Region Scale

Munic

Asset 1

Asset Munic Region Nation Global Scope

Figure 1.4: Working scale and scope for compensation

Such level of compatibility will required that the recorded data has to serve to each one of the applications the states are currently using them for (compensation, loss accounting, disaster forensics and risk modelling); it means that if the loss data of a Member State is currently being used for compensation, in the future a shift of its methodologies will be required so that the recorded data serves also for loss accounting, forensics and risk modelling, since other Member states are using the data for those purposes. Aconsistentstructureddatabasethatcanbecompatiblewiththedatabases of other Member States (or other extra-european states) can be then used for several purposes and permit a flow of information like the one illustrated in figure 1.5, where the database is both fed and used by three di↵erent actors during the processes of risk management: scientists, using the data for example for the assessment of hazard and risk, as well as feeding by supporting minimum requirements of the data model; Decision makers, using the data for instance to check the objectives and the eciency of investments; and practitioners, using the data for example for the definition of the plans and feeding it with the loss collection. Other actions performed by the three actors can be found on the mentioned figure. Since the ideal collection and recording of damage data has small scale but large scopes, it puts more pressure on smaller territorial levels to gather the information while the larger levels are benefitting the most form the use of this information. Therefore, one of the most important recommendations for the design of the databases is to engage the local actors to establish a loss database

24 Table 1.2: Application areas of disaster damage/loss data. Adapted from [De Groeve et al., 2014] Use Compensation Accounting Forensics Risk modelling Fair and efficient Avoiding sovereign Evaluate prevention Accurate risk assessment solidarity mechanism- insolvency measures and protection based on local loss insurance market exceedance curves

Driver Balance prevention and Improve prevention Develop economic loss compensation budgets policy models to estimate indirect losses National legislation on National legislation on EU Council conclusions EU Flood directive compensation of victims disaster prevention and on risk management and government aid risk assessment capability

Insurance policy HFA-2 Union Civil Protection HFA-2 Mechanism and agreements and

Relevant legislation legislation Relevant EU solidarity fund EU Council Conclusions on a Community framework on disaster prevention Event-based Cover future losses Event-based Use archive to estimate

Loss future losses period Monitor trends in losses MSs with public MSs with high annual MSs Emergency MSs potentially affected compensation scheme average losses-high Management authority by climate change maximum probable loss

European Union

Insurance industry Financial system Regional and local Scientific community emergency management United natons authorities Insurance industry Interested stakeholders Interested Civil society EU Member States and Institutions Asset-based National-regional Event-based Asset-based (sampling) aggregates scale Required Required Direct monetary losses Direct monetary losses Direct and indirect Direct and indirect monetary losses monetary losses

Human losses Human losses

Uncertainty Dynamics of impacts Specific asset-related (population movements, information (number of evacuations), response floors, water height, (decisions, actions) and level of damage, etc.) hazard (evolution) Narrative What needs to be recorded be to needs What Human losses Uncertainty HFA-2: Hyogo Framework for Action beyond 2015; MS: Member State

25 INTRODUCTION

What needs to Direct monetary Direct monetary Direct (and indirect) Direct and indirect be recorded losses losses monetary losses economic losses

Human losses Human losses Human losses

Uncertainty Dynamics of impacts Specific asset-related (population information (number movements, of floors, water height, evacuations), response local soil type, level of (decisions, actions) and damage, etc.) hazard (evolution) Narrative

Uncertainty

1.2 NATIONAL LEVEL: PROCESS VIEW

The applications of loss data are interwoven and stakeholders typically are involved in multiple strands at once. At national level and seen as a process, loss data recording can be described as in Figure 1.

Disaster loss data collection involves a number of stakeholders, such as decision makers, scientists and practitioners with each their responsibility and function.

Figure 1. Conceptual information flow for the implementation of crisis management plans and the role Figure 1.5: Role of lossof disaster data loss in data national within this process processes of risk management [De Groeve et al., 2014] Disaster risk awareness, unless made obvious by a disastrous event, is often brought to attention as an issue for the safety of societies by academia/and or practitioners (S1 in the figure 1). Policy thatmakers they may can act use on the on scientific their risk evidence management by establishing routines, a national andrisk assessment, that can and later there on be aggregatedfrom defining at disaster national risk reduction and international objectives through level the for allocation the robust of resou strategicrces and drafting purposes [Delegislation Groeve (P1). et al., It is then 2013]. taken up by the mandated bodies that draft the implementation plan (T1) and execute the plans (T2). The appropriateness of risk reduction and prevention measures is evaluated over time (P2, e.g. through peer review processes or internal review processes) and 1.3 Disaster forensics: the FORIN project 19

The Integrated Research on Disaster Risk IRDR within the FORIN project developed between 2010 and 2011 a series of disaster forensic investigation case studies. All the case studies are available to public in the webpage bank of the IRDR. As a result of these case studies, an approach of a methodology to perform disaster forensic investigations was developed; the methodology was written in 2011 in the report of the FORIN approach [Integrated Research on Disaster Risk, 2011]. This section is intended to provide a wider deﬁni- tion of disaster forensics, its uses and the possible methods to perform this investigations based on the mentioned report. Most of the post disaster investigations focus their attention on the physical aspect of the events (magnitude, frequency, distribution, causal mechanism), some of them also investigate the response of the emergency, but few of them deeply investigate the whole decision making process and the policy justifying it. These aspects are the link of the physical aspect of the event and the response given to the emergency [Integrated Research on Disaster Risk, 2011]. The decision making process, together with the socio cultural aspects of the community are the key aspects shaping the resilience and vulnerability of a

26 community; therefore it is important to pay more attention on such aspects during the aftermath of a disaster. This is the principal motivation to perform structured disaster forensic investigations.

1.3.1 Uses of forensic investigations

The uses of disaster forensic investigations are multiple and can vary from local scopes like determine the eciency of a specific mitigation measure during a disaster, to global scopes like the identification of the main weaknesses of policies driving the unequally distribution of disasters occurrence and losses between developed and developing countries. In the FORIN approach [Inte- grated Research on Disaster Risk, 2011], the identification of such uses required first the establishment of the research elements and the final identification of the five categories of objectives that summarize the uses that can be given to forensic investigations. The four key research elements for the FORIN project are the following [In- tegrated Research on Disaster Risk, 2011]: 1) to identify the circumstances, causes and consequences of loss, as well as the conditions that have prevent loss; 2)toidentifyandtestaseriesofhypothesisofdamagecasualty;3)to identify key factors in the expanding number of losses in disasters and how they enter into risk and disaster; 4)toinvestigatetheuseofexistingscientific knowledge in risk assessment and management. The identification of the elements lead to the establishment of the objectives of the FORIN project [Integrated Research on Disaster Risk, 2011], grouped into five categories: Policy objectives to experiment with multi-disciplinary and multi- stakeholder inputs; to encourage participation of decision makers; to guide policy as well as public and private investments on risk reduction. Management objectives to link the research findings with policy improvements through the establishment of a case-study bank for the e↵ective communication of causes of disasters. Scientific research objectives to advance methodological diversity; to test existing theories and concepts; to build interdisciplinary capacity of policy-oriented research. Development objectives to promote learning culture; to guide recovery and reconstruction e↵orts; to communicate key messages required for paradigm change; to advance understanding of causal factors of disasters

27 like: justify that solutions are locally e↵ective, that e↵ects of disasters are impediments to development and also that some development initiatives can be causal factors of disasters. DRR objectives to help and support DRR and Hyogo Framework for Action implementations with the case-studies, giving priority to the reduction of human losses and shifting paradigm of responsibilities (from nature to social sectors of the society) To sum up, the disaster forensic investigations permit to improve policies, to improve the management of information; to improve the scientiﬁc research methods; to understand and improve the dynamics of development; to improve the implementation of Disaster Risk Reduction within the Hyogo Framework. All these uses, as it was mentioned, vary form a local scope to the global scope, but all look towards the reduction of disaster losses.

1.3.2 Methods for forensic investigations

The FORIN approach [Integrated Research on Disaster Risk, 2011] identifies six levels of driving factors for disaster risk reduction. These levels constitute the framework of the FORIN approach and can be seen in figure 1.6: level 1, governance; level 2, risk assessment, including the causal agents, social systems and infrastructure; level 3, understanding & awareness; level 4, outcomes and impacts, including the distinction between sector, spatial and susceptive population; level 5, risk reduction; and level 6, resilience enhancement. All of them evaluated during the antecedent conditions, the emergency response and the long-term recovery. Forensic investigations should therefore understand the role of each of the levels on the disaster and it is also suggested to address all the levels during the investigation. Once the framework of the FORIN investigations has been understood, the report suggests four di↵erent methods to guide the path of a forensic investigation. The choice of a method depends on the context of the disaster, as well as the objectives of the investigation: Critical case analysis Seek to identify the root causes of the disaster events with a multi-disciplinary approach that integrates social, environmental and technical assessments. Meta-analysis Systematic reviews of the available literature carried out to identify and assess consistent findings across diverse studies for causal linkages as well as the e↵ectiveness of interventions.

28 5. The FORIN framework

A conceptual framework for the FORIN investigations is shown diagrammatically in Figure 1. This is derived in part from the Hyogo Framework for Action, which guides the work of governments and international organizations under the United Nations International Strategy for Disaster Reduction (UNISDR).

The shared aim of the Hyogo Framework and the UNISDR and of the FORIN studies is to achieve 2009 UNISDR Global Assessment Report on Disaster Risk Reduction and the 2011 report, of the same title, both strongly emphasize governance as a critical element. A second essential element is risk assessment, made up of causal agents, social systems and infrastructure. Then come understanding and awareness of underlying causal processes and outcomes and impacts in terms of sectors, spatial distribution and susceptible populations. The results of the application of the framework and its concepts are intended to lead to the

Figure 1. A conceptual framework for key questions.

Priority /Governance*

Risk Assessment Causal Social Infrastructure agents systems

Understanding & Awareness

Outcomes & Impacts Susceptible Sector Spatial populations

Risk Reduction

6 Resilience Enhancement

Antecedent Emergency Long-term Condions Response Recovery

This frameworkFigure 1.6: is intended Target as levels a guide of to the the development questions in of theFORIN FORIN investigations framework and the [Integrated wider IRDR programme.Research on Disaster Risk, 2011]

Longitudinal analysis Repeated observations of comparable events, geo- graphically or in-situ Scenario disaster Science bases retrospective re-construction of speciﬁc conditions, causes and responses involved in particular destructive events selected on the basis of a known hazard that represents a realistic and possibly inevitable future event. AcompletedisasterforensicinvestigationusingtheFORINmethodmight require a long and exhaustive process. Therefore it is possible (and even suggested) to divide the investigation into manageable parts that constitute steps 15 for the complete investigation. Here it can be useful to support the investigation with narratives 4 as a plan of the investigation (before) or as summary conclusions of the investigation (after). The FORIN approach also included a set of guiding questions in the template for the investigations where each of them refer to one of the levels in ﬁgure 1.6. For this thesis project it is important to highlight the following two questions: 12. Provide a list of the impacts of the disaster in qualitative and quantitative

4Narratives: complementary small investigations that focuses on the four hypothesis of the FORIN project and help on the long process of disaster forensic investigations

29 terms specified in detail according to a breakdown such as: mortality with cause of death; morbidity with kinds and numbers of injuries; direct economic damages and losses by sector, property losses, business disruption and discon- tinuity; losses covered by insurance and not covered, and other losses. G2. What key factors a↵ected or cause the major damage(primary and secondary hazard, settlement, land use, build environment)? These questions all address the level 4 of the FORIN framework: the outcomes and impacts (see figure 1.6) which is the relevant level for the scope of the thesis. Therefore, in chapter 3 it will be attempt to answer these two questions for the case study, as far as the public sector is concerned. It is also important to remark that even though this project refers only to the level 4 of the mentioned framework, the other levels are also relevant for the complete forensic investigation and also a↵ect the other levels. For instance, the governance level (level 1) influences how the raw data is distributed among the stakeholders, and the processes and procedures to gather it.

30 Chapter 2

Practices on damage data collection and recording

After having defined the applications and uses of damage data and specifically the importance of disaster forensics, this chapter will be focus on the common current practices of damage data collection both in the European Union and in Italy; this will permit to understand which of the four identified applications, described in section 1.2, are the EU member states using their damage data for. First of all, the current status of practices for damage data collection and recording in Europe will be presented in section 2.1, based on the analysis performed in the second report form JRC [De Groeve et al., 2014] for the participating Member States. Second of all, in section 2.2 the specific case of Italy’s practices on damage data collection and recording will be studied. This section will also give a brief description of the Italian law regulating the financial aspect of emergency management, specifically for the expenditure in the recovery phase; this part is important because it outlines the governance level that shapes the whole risk management and specifically motivates the current practices for damage data collection. Special attention will be given to the public sector, being more complex and critical with respect to the private sector. All this will be done with the objective of defining the context on which the case-study is (see section 3).

After having defined the current practices on damage data collection and recording in Europe and in Italy and after having identified in the previous chapter the basic requirements to perform forensic investigations, the last section of this chapter will identify a first set of hypothetic challenges for the use of the loss data, as it is currently recorded, for forensic investigations. These hypothetic challenges will further be confirmed from the analysis of the

31 case-study in section 4.2.

2.1 In Europe

The second report of the JRC working group [De Groeve et al., 2014] has the purpose of establishing the current status and the best practices for disaster loss data recording at the european level. The development of this report counted with the collaboration of experts for the 15 participating EU Mem- ber States, the United Nations Agency for Disaster Risk Reduction (DRR) and the working group of the Integrated Research on Disaster Risk (IRDR) in three meetings of 2014. This report will be briefly studied in this section as a comprehensive overview of the current practices of damage data collection and recording in Europe based on the current situation of the participating Member States: Austria, Belgium, Bulgaria, France, Germany, Greece, Croa- tia, Italy, Netherlands, Portugal, Rumania, Slovenia, Spain, Sweden, United Kingdom. The analysis and subsequent comparison of the current practices was developed in the report based on five aspects: national drivers for loss data, methodology of collection, methodology of recording, model of disaster loss database, public communication. Considering that the last two aspects are related to the structure of the database to store collected damage data and sharing them, while this thesis project is focus on the feasibility of using data collected according to the current methodologyfor forensics, only the three first mentioned aspects are relevant for the scope of this project. In the following, the analysis of the these aspects will be presented. The national drivers for loss data is an important aspect because it represents the reason of a Member State for collecting and recording the loss data. Some of the identified drivers are: the application areas (see section 1.2), the legal basis requiring the collection, the scale-scope of collection, and the main users of this data. The table 2.1 was produced from the analysis of the data in [De Groeve et al., 2014] regarding the national drivers for loss data for 13 out of the 15 member states participating in the initiative. The first driver is the application area of the loss data, where the loss accounting is the main application given to the collected data, followed by disaster forensic, risk modelling and compensation respectively; it is also important to see that 3 of the participating member states do not have a clear application of the loss data and 5 of the member states apply the data to the three areas. The second driver is the existence of legal basis, the table shows that the legal basis are present just in 40% of the analyzed Member States, other 40% do not have

32 alegalbasisandtheremaining20%havenoinformation.Thethirddriver is the level of the main users, where almost half have as a main user a national level authority with less percentage for other levels, and it is relevant to highlight that almost 30% have other main users among which universities, mass media, consultancy, external organizations and insurance actors could be identiﬁed. The fourth driver is the scale-scope relationship, with the majority of the participating Member States using asset-national relationship, meaning the smallest collection detail (scale) and large use of this data after aggregation (scope); also more than 30% are using a municipality scale, Belgium has aregionalscopebutiscurrentlypassingtoanationalone.

Table 2.1: Analysis of national drivers for loss data Number Percentage Loss accounting 11 32% Italy Disaster forensic 8 24% Application Risk modelling 7 21% area Compensation scheme 5 15% N/A 3 9% Yes 6 40% Legal basis No 6 40% N/A 3 20% National 12 46% Regional 2 8% Main users Local 3 12% level Other 7 27% N/A 2 8% Asset 8 53% Scale Municipality 5 33% N/A 2 13% National 12 80% Scope Regional 1 7% N/A 2 13%

Number Percentage The methodology of loss data collection is also an important aspect because Mandated organization 11 55% it describesCollecting the collection Academic process. project The features to take3 into account15% are: the mandatedorganization organizationPrivate to perform the collection, the triggering3 15% mechanisms to collect loss data, theN/A data assessment technique, and3 the quality15% assurance. The table 2.2 was producedDesk research form the analysis of the data5 in [De20% Groeve et al., 2014] for the methodologySectorial of lossfield dataassessment collection, performed7 for 1328% out of the 15 Data participating MemberOfficial States. reporting The ﬁrstmechanism feature is the mandated5 20% organization assessment to perform the collection,Remote where sensing half of the surveyed Member3 12% States have a technique mandated organizationIntervention for this reports task, andof emergency other 2 units parts2 of 15%8% each have an N/A 3 12% Yes 12 80% Quality 33 No 1 7% assurance N/A 2 13% Common methodology 2 13% Collection Collection forms 4 27% method N/A 9 60% Number Percentage Loss accounting 11 32% Italy Disaster forensic 8 24% Application Risk modelling 7 21% area academic project, privatesCompensation to perform scheme the task, and the5 remaining15% 15% do not have related information.N/A The second evaluated feature3 is the9% assessment technique, where it isYes possible to see that, among the options,6 40% there is not a clear preferenceLegal basis of theNo Member States to use a certain technique;6 40% however, it is important also to noteN/A that the mentioned techniques have3 di↵20%erent precision (level of detail) and requireNational di↵erent times, a↵ecting therefore12 the46% outcome of Regional 2 8% the data. TheMain third users feature is the quality assurance, where most of the Member Local 3 12% States give indicationlevel of the quality of the collected data at di↵erent levels and Other 7 27% with di↵erent processes. In this table it was also included an indication of the N/A 2 8% number of Member States having a common methodology of collection; this Asset 8 53% indicator reached a very low level with 60% of thye surveyed Member States not Scale Municipality 5 33% having common methodologyN/A of data collection or standard2 collection13% forms; it means that 60% of theNational Member States change their collection12 80% methods with each event. Scope Regional 1 7% N/A 2 13% Table 2.2: Analysis methodology of loss data collection Number Percentage Mandated organization 11 55% Collecting Academic project 3 15% organization Private 3 15% N/A 3 15% Desk research 5 20% Sectorial field assessment 7 28% Data Official reporting mechanism 5 20% assessment Remote sensing 3 12% technique Intervention reports of emergency units 2 8% N/A 3 12% Yes 12 80% Quality No 1 7% assurance N/A 2 13% Common methodology 2 13% Collection Collection forms 4 27% method N/A 9 60%

Finally, the methodology of loss data recording after being collected also constitutes an important aspect to be evaluated because it represents how the data processing is performed. For this aspect the features to take into account are: the mandated organization to perform the recording, the external references for the processing of data, the uncertainty handling, the public access and the aggregation of collected data. The table 2.3 was produced from the analysis of the data in the report for the methodology of loss data collection, also performed for 13 out of the 15 participating Member States [De Groeve

34 et al., 2014] and includes 4 of the 5 mentioned features. The mandated organization to perform the recording is the ﬁrst feature evaluated, with almost 70% of the surveyed member states having a mandated organization to perform this task, and smaller percentages corresponding to academic projects and private organizations performing the task, as well as 13% of the member states with no related information about this methodology. The second evaluated feature is the use of external references for the processing of collected data, meaning to separate the collected information into a hazard database, an exposure database and a loss database; for this point it is relevant to see that 40% of the surveyed Member States do not perform the separation into external references, while the ones that perform this separation separate it in the ﬁrst place for loss data followed by exposure database and hazard database. The third evaluated methodology is the uncertainty handling, with more than half of the participating Member States taking some measures for the uncertainty handling, 33% not taking any measure and a remaining 13% with no information. The fourth evaluated feature concerns the public access with 60% of the evaluated member states not having public access; this might be relevant for the future scope of sharing data at the european level.

Table 2.3: Analysis methodology of loss data recording Number Percentage Mandated organization 11 69% Recording Academic project 2 13% organization Private 1 6% N/A 2 13% Processing of Hazard database 1 5% collected Exposure database 3 16% data: external Loss data 7 37% references N/A 8 42% Yes 8 53% Uncertainty No 5 33% handling N/A 2 13% Yes 4 27% Public access No 9 60% N/A 2 13%

It was possible to see that 12 of the studied member states have stablished and maintain a loss database at the national level, where the 3 member states that don’t have it are Croatia, UK and Netherlands [De Groeve et al., 2014]. These 3 countries however have some e↵orts on recording disaster data even if they are not consolidated in a formal national loss database. For the existing databases many di↵erences could be found, mainly in the following factors:

35 processes for loss data collection and recording, supporting IT systems, peril classiﬁcation and database structure. The di↵erences are essentially due to the lack of guides and standards and the di↵erent drivers for its recording. These factors make it impossible to share data at the european level because the data are not comparable. In order to improve the current situation, the report also includes guidelines and recommendations intended to make the databases compatible for sharing purposes. As it was explained in section 1.2.5, to accomplish this compatibility, it will be required that the recorded data has to serve to each one of the applications the states are currently using them for.

2.2 In Italy

Italy is a country with a wide variety of land characteristics, from the Mediter- ranean cost, to the northern alps; all within the convergence of the Eurasian and African tectonic plates. These characteristics also influence the large number of hazards the country is exposed to. The Hazard history (varying in each database) includes circa 442 volcanic eruptions, from 8480 BC including the famous Vesuvius eruption of 79 that buried two entire cities [Smithsonian Institution, 2015]; 1364 recorded strong earthquakes since 10 BC until 1901 [Baratta, 1901], and 30000 earthquakes of medium to large intensity in the last 2500 years [Protezione Civile, 2015]; 29000 landslides and 32000 flooding events in the short period from 1900 to 2002 which are the most frequent events a↵ecting the country [Guzzetti & Tonelli, 2004]. With all those hazards in the country and with the large number of disasters they have provoke, it is not estrange there is a concern regarding the loss data recording, as well as a vast development on the law for risk management of the country. However, these e↵orts do not always follow a coherent integrated plan. In Italy there are two collection and recoding paths: the AVI database (Aree Vulnerate Italiane), and the damage data collection for compensation. These two paths will be studied in the following paragraphs for a better understanding of the context for damage data collection and recording in Italy. The first path is the AVIdatabase used in Italy to record the losses due to flooding and landslides. This database was analyzed for the second report of the JRC [De Groeve et al., 2014], previously described in section 2.1, given that Italy was one of the member states that participated in the meetings of 2014. This database works as an event catalog for research purposes but it has also been used for institutional evaluations. AVI is temporarily the national flood database (following the Flood directive guides) but in the future there

36 will be two synchronized and compliant separated databases. In the table 2.4 a resume of the evaluation done in the second report of the JRC [De Groeve et al., 2014] for this Italian database is presented; as it was mentioned in the previous section, only the ﬁrst three evaluation aspects (national drivers, methodology of collection, methodology of recording) are relevant for this thesis project and therefore only these aspects are evaluated on the table. Several observations can be noted from this analysis. It is possible to see that the application areas for which the AVI database is currently being used do not include the disaster forensic. Another remark is that in spite of the existence of legal basis for the compensation and the risk management procedures (described in section 2.2), there are no speciﬁc legal basis for the collection and recoding of data. Italy has to work towards instituting a common methodology for data collection and standard collection forms (like the RISPOSTA procedure, described in section 3.1). Regarding the data assessment techniques, it is possible to see that the used techniques require quite long times.

Table 2.4: Analysis for AVI Italian database

Loss accounting Application Areas Risk modelling Legal basis No Municipal National Scale Asset (not all) drivers Scope National Institutions Main users Civil Protection Planning authorities Common methodology No Mandated organization Regions with the support of Civil Protection Methodology Desk research (media & government reports) Data assessment of collection Sectorial field assessment techniques Official reporting mechanisms Quality assurance Varies on each event Mandated organization Civil Protection External references No Methodology Uncertainty handling No of recording Aggregation units NUTS Public access yes (password protected)

On the other hand, the base of the second path is the damage data collection with the objective of reimbursing the damages and for the emergency management after a disaster. The Italian law shaping the practices on public expenditure for risk management will be brieﬂy described, followed by the

37 description of the practices for damage data collection and recording. The reasoning behind the compensating falls in the fact that the flooding disasters are caused by natural and human interaction. It is therefore impossible to track a guilty figure who should pay for the losses. Instead, the government compensates the flooding victims as if the whole collectivity is compensating them, for being part of the humanity and therefore, in part, responsible for the disaster [Cellerino, 2004]. Moreover it is the governments duty to ensure public safety [Torre, 2014], meaning that not only it should compensate the disasters losses but it should also, as much as possible, prevent their occurrence, as well as mitigate their e↵ects. The government has established more than 3000 authorities [Cellerino, 2004] in all the territorial levels to cope with this important task. All these authorities are supported and guided by an extensive number of laws to regulate the processes, and have been shaped through the long history on disasters of the country, exposed to wide variety of hazards as it was described in section 2.2. As it was mentioned before, the case study was develop with special attention to the public sector, therefore the processes for the public expenditure of this sector will be explained. Two di↵erent procedures had been stablished by the Italian law (L. 225 24 February1992) for the financial approval of the interventions in risk management of the public sector: the ordinary management and the extraordinary management. The ordinary management intends essentially to finance all the interventions intended to prevent, prepare for a disastrous event and mitigate its e↵ects in time ”of peace”; among which: hydraulic structures, planning, maintenance of water flows. This procedure requires to chose the most suitable contractor for the work through tender process. This process requires detailed planning and design of the work, all these intended to maintain a competitive market, search for better values or to warranty the transparency on the process. The extraordinary management on the other hand intends to finance the interventions after a disaster, in this case a hydrogeological disaster; among which: reconstruction, restoration, extraordinary maintenance and renewal. This procedure, in contrast with the ordinary one, is financed with faster and simpler tools to assure that the emergency is managed in the most e↵ective way, where time is one of the most important factors. This tools are: the quick interventions and the primary importance interventions (in italian ”pronti interventi” and ”somme urgenze” respectively) with the latest being the fastest. The use of this tools activate only after the declaration of the state of emergency according to the requirements set in the law L. 225 24 February 1992 and permit

38 the immediate execution of works as well as to skip the tender procedure. The extraordinary management is then a fast procedure to ﬁnance interventions in the aftermath of a disaster to assure a satisfactory emergency management [Cellerino, 2004].

For the description of the current practices for damage data collection and recording in this path, a distinction has to be made between the two main sectors that can be a↵ected during a disaster: the private sector, and the public sector. The private sector refers to the citizens or companies and their goods, where the government will compensate the losses if sucient funds are available. The public sector refers to the goods of the state that are meant for public use like parks, schools or for use of the government in the development of its duties like municipal oces.

In the private sector, the damage data is collected by forms by means of which the individual citizens or industry owners present a request to the respective province or municipal oce to access to reimbursing (pg 84 [Cellerino, 2004]). These requests are a subjective self-declaration of the loss caused by the event and are voluntary based. The described modality has several problems: it is not guaranty to be complete, it is subjective and commonly the costs are overestimated, it generally does not include indirect and intangible damages, not all the damages are reported (maybe because people do not know the reimbursement procedures or they do not want to go through it), they are useful only for two applications: loss accounting and damage compensation. It is also important to note that the collection forms are not standard and change for each event.

In the public sector, the damage data is collected by each municipality or stakeholder responsible for the damaged good. They send a request with the information of the costs of interventions to the regional authority to access to the ﬁnancial resources they need from the extraordinary fund through the fast tools the extraordinary management has deﬁned to deal with disaster consequences (see section 2.2). This information however is in unstandardized formats and has di↵erent contents. The regional authority then compile them into the required formats (that, as for the private sector, change for each event) to the funding authority for the corresponding request and assign an estimated prioritization based on the article 5, coma 2, of the law 225/92.

The prioritization defined in the article 5, coma 2 of law 225/92 states five levels for the classification of interventions defined in each letter of this comma: letter a)fortheorganizationandperformingoftherescueservicesandas- sistance to the a↵ected population; letter b)forthefunctionalityrestoration of the public services and strategic infrastructure network; letter c)forthe

39 interventions for the reduction of the residual risk related to the event, and prioritize according to the public and private safety protection; letter d)tothe recognition of the need of restoration of the public or private infrastructure and structures, as well as the immediate damages to the businesses and the cultural heritage; and letter e)forthefirstinterventionmeasuresidentifiedin letter d. This definitions however are not always clear enough, the interventions requests fall into more than one category or cannot be categorized given the incomplete or not detailed information. Moreover, the definitions of the letters for the prioritization are modified for each emergency state according to the order for the establishment of the procedures for the preparation of the interventions plan to face each event. The described procedure has most of the problems that the private sector has: incomplete, subjective, overestimated, lacks of indirect and intangible damages, not reported because the responsible authority does not need the resources, and diculties to prioritize.

2.3 Challenges of these practices

The economic value from the financial request (for both the private and the public sector) is a very important information for the loss accounting as an indication of the economical dimension of the disaster [Cellerino, 2004]. How- ever it is a subjective assessment, especially in the first months after the event, that should be consider as an estimate of the physical monetarized damage. This request is normally collected in forms that change with each event, as well as the prioritization denitions so the data can not be compared. Other relevant problem is the quality of the collected data. Anewperspectiveshouldbeadoptedforthecurrentandfutureeventsbecause due to the economic crisis, the government can not support in the same way all the financial requirements of the practices described above. The declaration of the state of emergency allow fast procedures to face the emergency. However, the state of emergency also permits the compensation of victims based on a rough damage estimation in the private sector; and the inclusion of as many intervention requests as the law permits in the extraordinary management, due to the facilities it gives, with no distinction between the interventions for the recovery (like compensation) and the interventions for amelioration purposes with respect to the pre-event state (like structural mitigation measures). These shows the need of e↵ective methods with accurate tools to estimate damages to cope with the new requirements of the financial situation; more con-

40 crete strategies for projects in mitigation, prevention and preparedness within a defined robust context towards clear objectives instead of spear and poorly defined projects; the establishment of more clear prioritization of intervention methodologies as well as a clear distinction between recovery and amelioration interventions. It is necessary to emphasize the last mentioned problem regarding the diculty to distinguish between the interventions intended for recovery of the pre-event characteristics and the ones intended to ameliorate the pre-event characteristics. Clearly the amelioration interventions constitute an important part of the risk management, but they play a role on the prevention, preparation and mitigation measures for the up-coming events and not a damage caused by the current event. The procedure briefly presented in section 3.1 (complete in the report [Politec- nico di Milano & Umbria Region, 2015]) represents a complete methodology for the data collection and recording that serves to compensation purposes and for disaster forensics (as well as the other aplications) following as much as possible the guidelines given by the JRC reports.

41 42 Chapter 3

The November 2013 ﬂood in Umbria

The analysis of the feasibility of using data collected with the current methodology for a disaster forensic investigation through a case study is the main scope of this thesis. The Umbria Region in Italy, being regularly a↵ected by ﬂoods, represents the experimental background where new policies and methods towards improved risk management are being applied; one of them is the Poli-RISPOSTA project developed with Politecnico di Milano. It is in this context that the analysis of available damage data for the November 2013 ﬂood was implemented as a case study.

The presentation of the case study will begin with the introduction of current practices for risk management in Umbria region; followed by the presentation of the mentioned Poli-RISPOSTA project in section 3.1. Second section 3.2 will describe the two events occurred during the development of the Poli- RISPOSTA project seen as transition tests from the current practices for data collection and recording, to the practices suggested in the research project. After that, section 3.3 will introduce the case study starting with the presentation of the physic scenario that a↵ected the Umbria region in November 2013 ﬂood; subsequently, the loss data to the public sector obtained from the current practices for compensation purposes and the ﬁve needed processing steps for a forensic analysis are described; Finally the results of the performed forensic analysis to the public sector will be exposed. The next chapter will discuss the results obtained in this chapter.

43 3.1 The Umbria region risk management

The Umbria region, located in central Italy, is exposed to two of the main hazards in Italy: Earthquake and hydrogeological. For earthquake there are 75% of its municipalities in zones with medium to high seismicity [Regione Um- bria, 2003] and it is possible to recall the Umbria-Marche earthquake in 1997 that left 11 victims and irrecoverable damages to cultural heritage [Protezione Civile, 2015]. The second kind of hazard is the hydrogeological hazard, with the Tiber basin, one of the mains in Italy, covering 96% of the regions territory (see ﬁgure 3.1) and with a predominant hill and mountain topography, it is not a surprise that Umbria is exposed to ﬂooding, erosion and landslides phenomena during the frequent rainy seasons.

Figure 3.1: Administrative limits in the Tiber basin. [Mondo del Gusto, 2009]

According to the law 193/1990, the Tiber River Basin Authority is the responsible authority for hydrogeological risk management. For this objective, this authority wrote in 2002 the hydrogeological risk management plan PAI (after the italian Piano di Assetto Idrogeologico [Autorit`adi Bacino del Fiume Tevere, 2002]), dealing with both geomorphologic and hydraulic hazards. In a nutshell for both, the PAI delimited the zones of the territory exposed to

44 the hazard and then evaluated the risk for the elements inside this areas. The trend of the hydrogeological and anthropic development was analyzed to set priorities of intervention. The defined priorities were adopted as norms and management plans. This plan will be substituted at the end of 2015 with the Flood Risk Management plan mandated by the Floods Directive. At the regional level, the Umbria region has done several e↵orts on the application of the regulations set by the guiding plans (like the PAI) to forecast, prevent and mitigate the risks it is exposed to, requiring therefore a multi- risk perspective. One of this e↵orts is the coordinated regional multi-risk plan of 2010 (italian Piano Regionale Coordinato Multirischio) that takes into account also risks of non-natural sources like technological risk. Another e↵ort is the development of a research program on the di↵erent aspects of flood risk management in association with Politecnico di Milano; within this context the Poli-RISPOSTA project was developed between 2013 and 2015. It intendeded to give the civil protection a procedure, tools (ICT and GIS), and technological advanced solutions for the data collection, elaboration and mapping in the post event damage assessment to facilitate the emergency and recovery phases [Politecnico di Milano & Umbria Region, 2015]. PoliRISPOSTA produced an operative procedure [Politecnico di Milano & Um- bria Region, 2015] to perform the three identified stages: data collection, structured recording and analysis of the data. The procedure is organized according to exposed sectors: population, residential, hydrogeological interventions, infrastructure, public areas and public goods; for each of them specific procedures and tools were developed. The final outcome of the procedure are two versions of a complete post event scenario, one intermediate (1-6 moths after the event) and one final (6 months-1 year after the event). The intermediate scenario is intended to support and guide the reconstruction and recovery phase by giving key information regarding the damages, the priorities of intervention to avoid future damage, and the used and needed resources to face the emergency and recovery; instead, the final scenario gives a complete overview of the damages caused by the event and aims to analyze the lessons learned from the event towards the guide and restructure of the risk mitigation and management strategies for the planning of the coming events. For the final scenario the complete analysis of direct and indirect damages should be presented (see section 1.1) since by this time they should already be evident. RISPOSTA follows at least two of the suggestions reported in section 1.2.5 for the compatibility of damage data bases at european level. First to engage local authorities for the collection and recording of damage data by allowing this data to be used first in their emergency management routine and, after that, to be aggregated for more robust strategic purposes; this system can incentive

45 the desired small scale and big scope. Second, data recorded according to the procedure have the necessary characteristics for all the identiﬁed applications: loss accounting, disaster forensic, risk modelling, as well as damage compensation; this makes the RISPOSTA procedure an attempt to collect damage data for di↵erent applications into one unique procedure.

To warranty the success of the procedure it should be related to the real context; an important way to do this is to use real case studies as transition tests between the current practices for damage data collection and recording, and the complete application of the RISPOSTA procedure. Two events have have been analyzed during the project permitting the testing of the procedure: the 2012 event and the 2013 event, making the whole project a cyclic and adaptive process as it can be seen in ﬁgure 3.2. The data used in this thesis project constitutes a part of the data collected after the 2013 event in Umbria; such data regard only damage to public items including: roads, electric system, water/sewage system, public goods, public areas, emergency management. The division of the whole study into manageable coherent parts also constitutes one of the suggestions given in section 1.3 for the guide of complete disaster forensic studies. A brief description of the transition tests will be given in the following section.

Event

Upda8ng Data analysis

Requirements Applica8on analysis

Design and development

Figure 3.2: Scheme of the cyclic conceptualization Poli-RISPOSTA project

46 3.2 Transition tests

The November 2012 and the November 2013-February 2014 events constituted great opportunities to perform transition tests between the current practices for damage data collection and recording, and the complete application of the PoliRISPOSTA procedure. The current practices for damage data collection and recording in the private and the public sector are explained in section 2.2 and a first set of assumed challenges of the use of this practices in disaster forensics are defined in section 2.3. The new practices suggested by the poli- RISPOSTA procedure for the private sector consist of specific survey teams, supported with technological tools, in the a↵ected places that will estimate the damage a↵ected asset. For the public sector, the suggested procedure consists of coordinated stakeholders in all the territorial levels filling standard information in formats that allow: the comprehension of the event, the assessment of the damages caused by the event (direct and indirect), the estimation of suggested interventions and the access to the financial resources. Collected data will then be reported to the regional authority that will prioritize them with standard procedures. It is important to clarify that data collection and analysis for the two events was not performed with the complete implementation of the PoliRISPOSTA procedure; instead, they are transitions that take some elements of the current practices (see section 2.2) and the new suggested procedure (see section 3.1). The analysis of the 2013 event in Umbria region has, among others, the objective of understanding how to shift from the current collection procedure to a more complete procedure that permits also a disaster forensic investigation. The next section will describe in detail the 2013 event as far as its main distinctive features are concerned and available data for the transition test.

3.3 Results 2013 event

The November 2013-February 2014 event constitutes the case study of this thesis project. First, in section 3.3.1 the physic scenario of the event will be described based on the report performed after the event by the regional civil protection authority [Centro Funzionale Regione Umbria, 2013]. Second, section 3.3.2 presents the description of the gathered raw data for losses to the public sector for compensation purposes, together with the description of the performed procedures for its processing and subsequent analysis for the disaster forensic investigation. Finally, the results obtained from the processing

47 procedures of the data for the forensic analysis is presented in the last section of this chapter. The next chapter will discuss the results obtained in this chapter, as well as the challenges of using data for compensation in a disaster forensic analysis. This will permit the understanding of which data must be included for the multi-usability of collected data.

3.3.1 Physic scenario

In Umbria, between the 10th-12th of November of 2013 the north-east territory of the region registered exceptional pluviometric levels in its monitoring network. In the event report build by the regional civil protection authority (Italian Centro Funzionale Decentrato Regione Umbria), the physical characteristics of the event are described, together with some of the e↵ects and the general emergency management[Centro Funzionale Regione Umbria, 2013]. In brief, the instruments registered a cumulated precipitation for 72 hours of 439 mm in Castelluccio di Norcia and 329 mm in Gualdo Tadino. This maximum levels are equivalent for an event with a return period of 200 years; the levels in other pluviometric stations suggest lower return periods, from 100 years to ordinary rainfalls for registered levels of 16 mm in the south-west of the region. Three main rivers were a↵ected (Tiber, Chiascio and Topino) with other minor rivers and water flows. The analysis of the trend evolution through modelling showed that the pre-alarm, alarm and flooding levels of the hydrometers of the monitored rivers were reached in almost all region and correspond to an event of 75 years return period. For the emergency management, the control centers at di↵erent territorial levels (municipal, provincial, regional) were activated for 24 hours a day, even before the emergency for monitoring and forecasting purposes and following the emergency plans in each municipality. 40 out of the 200 available volunteer organizations were activated. A close work with the public services stakeholders was done to keep monitor of the services interruption, especially the electric and the water-sewage services with ENEL and Umbra Acque. The emergency also a↵ected the railway services due to landslides and fallen trees on the rails, causing some delays on the services. After the November 2013 event, the management of the recovery phase was quite long and it kept raining for the following months. With some of the damages still unfixed (leading to increased vulnerability) and with a new strong rainy season in February 2014, new damages occurred. For this reason on 30th June 2014 was declared the state of emergency that activates the extraordinary

48 Figure 3.3: Flooding levels reached during 2013 event. [Centro Funzionale Regione Umbria, 2013] management procedures described in section 2.2. A prorogue of the state of emergency was declared on 24th December 2014 and expired on 24th June 2015. Because of this, the data that will be presented constitutes the data of two events, or a series of sparse events. From the collected data, just few of the interventions requests had reported the distinction between the date of occurrence, making it hard to consider as individual events. A general map of the a↵ected municipalities during the two strikes of the event is presented in ﬁgure 3.4.

3.3.2 Data processing

As it was explained before, this event permitted a transition test to the complete application of the Poli-RISPOSTA procedure. This means that the data gathered for the public sector, used in this project, still follows the same collection and recording practices as the ones explained in section 2.2. This raw data are then transformed according to the standard formats (separated by sector) that the procedure suggests; this is what is called the data processing, consisting of a restructure of the original data and an analysis where a deeper investigation of the situation is performed. This is done to understand how to change and integrate the procedure towards the multi-usability of data, speciﬁcally for disaster forensic and compensation.

49 Figure 3.4: A↵ected municipalities during the November 2013 - February 2014 event. [Protezione Civile Regione Umbria, 2014]

As it was explained in section 2.2 the current practices for data collection for the public sector foresees that each stakeholder responsible of a public good, assess the damage, the necessary interventions, and then prioritizes the most important ones; a list of the responsible stakeholders for each sector is presented in table 3.1. Each stakeholder stores the information of the required interventions, including a cost estimation; however these data are not yet stan- dardize and varies with each stakeholder. After that, if it is needed to access to the ﬁnancial resources for the intervention, the stakeholders send their respective intervention request to the regional authority. At this point the regional authority will have plenty of unstandardize and unclear information, with different prioritization standards. It is now the job of the regional authority to organize and set the prioritization of the interventions request; this due to the fact that, as it was explained in section 2.2, for current and future emergencies and due to the crisis it is not possible to ﬁnance the totality of the interventions within the extraordinary management, and it is the regional authority who should decide, based on the

50 Table 3.1: List of responsible stakeholders for each sector. Adapter from [Politecnico di Milano & Umbria Region, 2015]

Sector Responsible Stakeholder

Population SOUR, Municipalities Umbria Region - Direzione Agricoltura e Foreste, Aree Protette, Valorizzazione dei Sistemi naturalistici e paesaggistici, Beni e attività Agriculture culturali, Sport e spettacolo - Servizio "Aiuti alle imprese e alle filiere del sistema produttivo agroindustriale” Hydrogeological protective Regional services “Risorse Idriche e Rischio measures Idraulico” e “Geologico e Sismico” Electric ENEL SpA, Terna SpA, ASM Terni Road ANAS, Provinces, Municipalities Railway network RFI, Umbria Mobilità Water/sewage system Umbria Acque, SII, Valle Umbra Services Local transportation Umbria Mobilità 2i Rete Gas, ASI Multiservices, Estra Energie, Gas system Italgas, Sienergas, Tecniconsult Costruzioni e Gestioni, Valle Umbra Servizi, Vivigas Telecomunication network Telecom Public area and public good Uffici Regione Umbria competenti, Municipalities Environment Uffici Regione Umbria competenti, Municipalities Cultural heritage Uffici Regione Umbria competenti, Municipalities Emergency management SOUR assigned resources by the nation, which interventions will be financed first. From this process, the two first source data are obtained; they consist of two tables including the requests made by the public stakeholders relaborated by the regional authority and they have been named for this project: first emergency table and preliminary table, where the first emergency table contains the intervention requests that were financed immediately with the availability of e3.5 million approved in the 30 June of 2014 council of ministers for the declaration of the emergency state, and the preliminary table includes the complete set of intervention request, build on March 2014 to ask for the emergency state. It is now important to consider the timing because the more time passes, the di↵erent data is available: damage data have more accuracy, new interventions requests arrive and some ”disappear” (in section 4.1, it will be explained why this happens) so that a new source data was available, we will call it the final table; it includes the complete set of interventions requests 1 year and 3 months after the event (February 2015). This is a clear sign of how our damage and

51 loss accounting has a dynamic factor that should be taken into account; a more complete analysis will be done in section 4.1. To sum up, the source data for this project are: Preliminary table build in March 2014, 4 months after the first strike of the flood event. Includes all the interventions requests reported within the creation date. First emergency table build in July 2014, 8 months after the first strike of the flood event. Contains the priority interventions requests that can be financed with the available e3.5 million approved by the council of ministers for the management of the emergency on 30th June 2014. Final table build in February 2015, 1 year and 3 months after the first strike of the flood event. Includes the interventions requests reported within the creation date except the finalized interventions already financed (with the first emergency resources or with other resources). Figure 3.5 shows a timeline with the two strikes of the flood events and the construction of the three source tables. It is also important to remark that the preliminary table was our first source of information, and during the whole project and when it was possible all the requests were referred to this table. Also table 3.2 shows the structure of the three source tables, and the highlighted fields in blue show the common fields among them.

Figure 3.5: Timeline of source tables and strikes of the ﬂood event.

5processingstepswereidentiﬁedtoverifythepossibilityofusingthedataon these source tables, for a disaster forensic analysis: 1. Validation of the requests in the source tables: Validate each of the interventions requests in the source tables to identify and delete those that were added mistakenly. 2. Correlation between requests in the source tables: Correlate the intervention requests in each of the source tables; this means to ﬁnd the

52 Table 3.2: Structure of source tables: Preliminary table, March 2014; First emergency table, July 2014; Final table, February 2015 First Emergency Table Stakeholder Final table Finished State of Event date Preliminary table Ongoing Municipality/stakeholder intervention Stakeholder Planned Province Damage description, location, Localization District type of intervention Designation risk Address Type of damage infrastructure/situation Use Number of evacuated families Cathegory Landslide Declarator Actuator Landslide classificatio Road infrastructure Project Cathegory n Structure/Infrastructure Intervention title Roads classification State intervention Infrastructure Brief description of intervention a) Brief description of Priority b) a) Type c) intervention b) Priority letter a) intervention Estimated cost c) Financing Co-financing Cost of Priority letter b) Duration Funds intervention Priority letter c) Cost of Overall estimate Not compensable Priority letter d) inervention Already incured Insurance Compensable General total General total Insurance premium payment

8 months 1y 3 months 4 months equivalent intervention request in other source tables 3. Classification of the requests into predefined sectors: Classify each of the requests in the source tables into the sectors identified by RISPOSTA 4. Restructuring available information according to required data for forensic analysis: Fill with the available information in the source tables the required data for a disaster forensic investigation; the required data changes for each of the evaluated sectors. 5. Search for missing required data with complementary tools: Search the missing required data for a disaster forensic investigation in each of the sectors with complementary tools among which google maps, other source tables and detailed documents of the intervention request. It is also important to remark that the performance of these steps is not con- secutive and requires sometimes a parallel or serial organization according to the development of the conditions. The following paragraphs will describe in detail the processing steps: 1.Validation of requests in the source tables Table 3.3 contains a resume by stakeholder of the financial requirements for interventions contained in each of the three source tables. As it was explained before, these

53 tables represent the raw data as it was gathered by the region, therefore some remarks should be done for each of the tables. First, the preliminary table shows required resources that sum up to e94,301,569.57 but from the analysis of each request it was possible to spot some requests that corresponded to compensations to privates and were mistakenly included in this table; these requests were not consider in the study, ending up with a cost of e80,726,119.57. Second, for the first emergency table the reported resources were of e3,500,000.00 from selected priority intervention requests of all the required interventions at that time; after a careful study of each intervention request in this table it was possible to see that all of them were accurately selected and no mistakes could be identified, therefore it was not necessary to ignore any request. Third, for the final table the reported required resources were e87,679,376.44 but after the individual analysis of the requests, there was one which was already finalized and had already assigned resources from the first emergency table; therefore this request was not consider in the table, ending up with a cost of e87,648,875.58. A resume of the results obtained after this first processing of the raw data in the source tables can be seen in the table 3.4. 2. Correlation between requests in the source tables The second processing step performed was the correlation between the single requests items in each of the source tables. Figure 3.6 shows a simplified scheme of how the correlation process was performed with some fake example requests in each of the three tables. From this example several remarks can be set: The ID of the requests is not the same in the source tables, making • this process very hard because an exhaustive analysis of the single intervention requests has to be done. It is easy to see that there is no one-to-one correlation between the • tables. it is also easy to see that a single request does not appear in all of • the three source tables. It is also possible to see that some descriptions refer to the a↵ected • item (80 final table), others to the damage (10 preliminary table) and others to the intervention needed to fix the damage (c first emergency) making it hard to find the equivalent requests in other tables. Note that the requests in the preliminary table with ID 1 and 5 • 54 Table 3.3: Raw data resume of financial resources required by stakeholders: Preliminary table, March 2014; First emergency table, July 2014; Final table, February 2015

4 months Stakeholder Total Acquasparta € 600.000,00 Alviano € 150.000,00 8 months Amelia € 415.000,00 Stakeholder Total 1 year 3 months Arrone € 399.200,00 Arrone € 129.200,00 Stakeholder Total Bastia Umbra € 150.000,00 Citta' Di Castello € 208.262,38 Acquasparta € 550.000,00 Bettona € 200.000,00 Costacciaro € 30.576,07 Alviano € 1.155.000,00 Calvi Dell'Umbria € 370.000,00 Deruta € 100.000,00 Amelia € 1.400.000,00 Campello Sul Clitunno € 225.000,00 Foligno € 117.000,00 Arrone € 80.000,00 Cannara € 150.000,00 Bettona € 250.000,00 Castel Ritaldi € 220.000,00 Gualdo Cattaneo € 560.270,72 Calvi dell'Umbria € 70.000,00 Cerreto Di Spoleto € 50.000,00 Gualdo Tadino € 46.044,06 Campello sul Clitunno € 650.000,00 Citta' Di Castello € 5.976.471,18 Gubbio € 3.660,00 Magione € 166.536,03 Cannara € 820.000,00

… Castel Ritaldi € 950.000,00 … Montecastrilli € 189.000,00 Cerreto di Spoleto € 705.000,00 Valfabbrica € 252.000,00 Montegabbione € 4.000,00 Città di Castello € 6.264.000,00 Valtopina € 156.100,00 Orvieto € 189.083,75 Azienda Ospedaliera Santa Perugia € 215.200,00 … € 275.000,00 … Maria Di Terni Pietralunga € 90.000,00 Tuoro sul Trasimeno € 620.000,00 Comunita' Montane Alta Scheggia E Pascelupo € 234.362,33 € 6.030.000,00 Umbertide € 8.140.000,00 Umbria - Ambito Alto Tevere Sellano € 7.000,00 Valfabbrica € 580.000,00 Comunita' Montane Alta Sigillo € 23.553,74 € 2.560.000,00 Valtopina € 375.000,00 Umbria - Ambito Alto Chiascio Valfabbrica € 9.000,00 Comunita' Montane Alta Comunità Montana Alta € 1.545.000,00 Valtopina € 29.890,00 € 1.545.000,00 Umbria Umbria Consorzio Bonificazione Consorzio Bonificazione € 85.679,23 Consorzio Bonificazione € 24.960.000,00 Umbra € 13.101.000,00 Umbra Umbra Provincia Di Perugia Area Provincia Di Perugia Viabilita' € 11.310.343,33 € 930.723,71 Provincia di Perugia Viabilità € 9.228.081,86 Provincia Di Terni-Viabilita' € 150.000,00 Viabilita' Ente Acque Umbre Toscane € 507.000,00 Provincia Di Perugia Area Provincia di Perugia Idraulica € 4.753.000,00 Ente Acque Umbre Toscane € 333.000,00 € 32.727,89 Provincia Di Perugia Difesa Idraulica Servizio Idrico Integrato € 986.671,34 € 4.655.756,82 Idraulica Ente Acque Umbre Toscane € 20.000,00 Provincia di Perugia Edilizia € 1.950.000,00 Servizio Idrico Integrato € 200.000,00 Servizio Idrico Integrato € 78.230,09 Scolastica € 94.301.569,57 € 3.500.000,00 € 87.679.376,44

Table 3.4: Summary of the source tables Cost after Source table Date Timing Reported cost processing step 1 Preliminary March 2014 4 months € 94,301,569.57 € 80,726,119.57 First emergency July 2014 8 months € 3,500,000.00 € 3,500,000.00 Final February 2015 1 year 3 months € 87,679,376.44 € 87,648,875.58

correspond to compensation in the private sector and therefore they were not considered in the analysis as it was already explained. Also note that since the preliminary table was performed in very • short time after the event, many descriptions are general and do not correspond to a unique request in the other tables (12 preliminary table with 70 and 90 ﬁnal table) The given description can change in each of the tables even for the • same request, as well as the cost of the intervention (see 9 preliminary table with 130 ﬁnal table represented with a dashed line). This

55 require to assume and to further check in additional documentation (processing step 5), but it was not possible to perform this to all the requests because it would require a lot of time; this leads to a degree of subjectivity and uncertainty that will be treated later. Finally it is possible to see that some requests do not have any • correlation with other tables

Preliminary table First emergency table Final table ID Description Cost ID Description Cost ID Description Cost Cost of clean Trafic Repair sewage tube 1 house Mr Williams € 5,00 a management in x € 6,00 10 in O € 20,00 Trafic Reopening of Roof damage in 2 management in x € 7,00 b road x € 3,00 20 school Taylors € 4,00 Clean sediments of Reconstruction 3 Flinders river € 15,00 c bridge High € 45,00 30 Landslide in road z € 24,00 Stibilize slope in Repair sewage 4 city R € 60,00 d tube in O € 20,00 40 Collapsed bridge U € 10,00 Flooded stock Stabilize historic Blocked road to go 5 Industry T € 3,00 e wall in city R € 60,00 50 to school P € 8,00 Repair roof in Remove fallen 6 church St Pauls € 6,00 60 vegetation river Y € 30,00 Remove fallen Repair main road of 7 vegetation river Y € 30,00 70 city B € 20,00

8 Damage of road a € 20,00 80 Damage road a € 20,00 Flood september Stabilize a slope in 9 square € 30,00 90 Erosioncity B in the € 30,00 Fallen roof in public gym of city 10 public library € 17,00 100 Q € 3,00 Landslide of road Damage in school 11 V € 21,00 110 K € 14,00 Urgent repairs in Road between city 12 city B € 90,00 120 N and city M € 18,00 Stabilize September square 13 cementary walls € 35,00 130 was damaged € 50,00

Figure 3.6: Scheme of the correlation process between the three source tables

3. Classification of the requests into the predefined sectors This processing step requires the classification of each request into the following a↵ected sectors after the suggestions given in the PoliRISPOSTA procedure [Politecnico di Milano & Umbria Region, 2015], intended to warranty the usability through specific standard data collection for each sector. Emergency management • 56 Public area • Public good • Road • Hydrogeological protective measures • Water/sewage system • Electric • Even though this sector separation was constructed specifically for the context of Umbria region, when applied to the real interventions, the process is not straight forward and requires a careful analysis; this due to the fact that the reported requests do not always refereed to a unique a↵ected sector. For example, a typical required intervention is the cleaning of a street that was obstructed after the flood of the river parallel to it; this intervention refers to the road sector as well as to the hydrogeological sector. For such cases, the request was included in both sectors but the cost was included only in the hydrogeological sector, so the cost sum of both requests will not a↵ect the outcome but the road block will be taken into account. 4. Restructuring available information according to required data for forensic analysis As it was exposed in section 1.3.2, for a disaster forensic analysis, the following information is required: a↵ected item, including specific location and vulnerability characteristics; type of damage, taking also into account direct indirect damages; and the required intervention, including the description, the cost and the priority. However, the attributes to obtain this information vary for each of the mentioned sectors. Tables 3.5, 3.6, 3.7, 3.8 show the identified required information for a disaster forensic analysis for each of the sectors according to proposed development of the RISPOSTA procedure. It is possible to see from table 3.5 that the emergency management sector has a di↵erent structure and requires di↵erent fields of information than the other sectors; this due to the fact that this sector can provide information on the whole process of the emergency management and not just to a specific damaged ob- ject; it can then help to understand how much did it cost to manage the emergency and what were the results. Then the objective is to fill the required information for each of the

57 Table 3.5: Required information for a disaster forensic analysis for emergency management sector

Global ID Preliminary table ID Final table ID Date Province Localization Municipality District Activation of the control centre Activation operational centre (Number) Activation of Number of volunteers volunteers Association Type Number of evacuated families intervention Evacuation Number of evacuated persons Number Orders Type Activation of shelters Number Rescue affected population Evacuation Type Traffic control activity Hydraulic watch Other (specify) Number Heavy vehicles Specify Tools Used Pumps (Number) resources Sand bulks (Number) Type Field equipment Number Other (specify)

Emergency management sector management Emergency Extra time cost First emergency table Heavy vehicles Preliminary table First emergency table Tools Preliminary table First emergency table Pumps Cost Preliminary table First emergency table Sand bulks Preliminary table Rescue affected First emergency table population Preliminary table First emergency table Volunteers cost Preliminary table

sectors with the available information of the source tables described in table 3.2; tables 3.5, 3.6, 3.7, 3.8 highlight in blue the ﬁelds that can be ﬁll with the available common information in the source tables. It is evident that this task is not easy and to complete the missing information the processing step number 5 is required.

58 Table 3.6: Required information for a disaster forensic analysis for public areas and public goods sectors

Global ID Preliminary table ID First emergency table ID Global ID Final table ID Preliminary table ID Date First emergency table ID Province Final table ID Municipality District Date Localization Road Province GPS N Municipality coordinates E District Localization School Road Municipality office GPS N Theatre/cinema Type of coordinates E Gym infrastructure Square/parking lot Hospital Green area/park Barrack Type of area Cemetery Other (specify) Sport area Direct Flood Other (specify) damage Other (specify) Flood Inaccessibility Direct Sediment accumulation Disruption electric system damage Disruption water/sewage system Type of Other Type of damage damage Inaccessibility Indirect Disruption gas system Indirect damage Inaccessibility Total Disruption of Days damage order Partial service Affected municipality Disruption Days Municipality provided services Affected municipality Province Responsible Municipality Region stakeholder Province

Public goods sector Public Responsible State Region stakeholder Other (specify) State Public areas sector areas Public Description Other (specify) Finished Description State Ongoing Finished Intervention Planned State Ongoing Duration Intervention Planned Real cost Duration Assumed cost Real cost Preliminary table Assumed cost a Fist emergency table Preliminary table a Fist emergency table Final table Final table Preliminary table Preliminary table Cost of b Fist emergency table Priority Cost of b Fist emergency table Priority intervention Final table intervention Final table Preliminary table Preliminary table c Fist emergency table c Fist emergency table Final table Final table d Preliminary table d Preliminary table

59 Table 3.7: Required information for a disaster forensic analysis for roads and hydrogeological protective measures sectos

Global ID Preliminary table ID First emergency table ID Final ID Date Province Municipality District Global ID Localization Road Preliminary table ID GPS N First emergency table ID coordinates E Final table ID Name Date Secondary Province Municipal Municipality Type Provincial District Localization affected Regional Road Type item National GPS N Highway coordinates E Bridge Name of affected network Underpass Bank erosion Pavement Levees damage debris Direct Type of Silting Retaining wall Type damage damage Sediment transport Drainage system damage Transverse work (weird) Landslide Block/damage of street Indirect Days of disruption Other (specify) damage Affected municipalities Municipality Municipality Roads sector Province Province Responsible Responsible Consorzio di bonifica Region stakeholder stakeholder Comunità Montana ANAS Other (specify) Other (specify) Description Description Finished Finished State Ongoing State Ongoing Intervention Planned Intervention Planned Duration Duration Real cost Real cost Assumed cost Assumed cost Preliminary table Preliminary table a Fist emergency table sector measures protective Hydrogeological a Fist emergency table Final table Final table Preliminary table Preliminary table Cost of b Fist emergency table Cost of b Fist emergency table Priority Priority intervention Final table intervention Final table Preliminary table Preliminary table c Fist emergency table c Fist emergency table Final table Final table d Preliminary table d Preliminary table

60 Table 3.8: Required information for a disaster forensic analysis for water/sewage system and electric sectors

Global ID Preliminary table ID First emergency table ID Final table ID Global ID Date Preliminary table ID Province First emergency table ID Municipality Final table ID District Localization Date Road Province GPS N Municipality coordinates E District Localization Aqueduct collector Road Sewage collector GPS N Waterwork Type of coordinates E sewage disposal infrastructure Low tension line Treatment plant Sinks Type of Mid tension line Other (specify) infrastructure High tension line Name of infrastructure Cabins Umbra Acque Managing ENEL Managing Servizio idrico SII stakeholder Other stakeholder Other (specify) Flood Cause of Flood Erosion Cause of damage Erosion damage Landslide Landslide Days Indirect Break Affected municipalities damage: Direct Infiltration/contamination Residential damage Disruption of Affected Exposition Public Other service Users Industrial/commercial Type of Days Internal damage Affected municipalities persons Disruption Used External Indirect Residential sector Electric of service Affected damage Public resources Installed Number Users Days Water/sewage system sector system Water/sewage Industrial/commercial generators Other (like microorganism dead) Description Description Finished Finished State Ongoing State Ongoing Intervention Planned Intervention Planned Duration Duration Real cost Real cost Assumed cost Assumed cost Preliminary table Preliminary table a Fist emergency table a Fist emergency table Final table Final table Preliminary table Preliminary table Cost of b Fist emergency table Cost of b Fist emergency table Priority Priority intervention Final table intervention Final table Preliminary table Preliminary table c Fist emergency table c Fist emergency table Final table Final table d Preliminary table d Preliminary table

61 5. Search for missing required data with complementary tools As we saw from the previous processing step, the filling of the needed information is not a straightforward process, and requires lots of patience. This step wants to find the required information that is not available from the source table by looking for it in other ways. First of all by using the correlation table, produced during processing step 2, it is possible to look for the same intervention request in all the source tables; this because sometimes the information regarding the same request can change among the tables (for example more precise in the final table than in the preliminary table) ; in this way it is possible to complement the information of each request with all the source tables on which it appears. A second approach used specially for the filling of the information regarding N and E coordinates is to look for the address or specified location in google maps and obtain from it the coordinates. This approach is however not precise and for some descriptions it is not possible to estimate the exact location of the intervention; take for instance the damage of a road, even if the road can be easily located with google maps, it is not possible to identify at what exact point of the road was the damage located (and consider that there are roads practically as long as the region). A third and final approach is to look for the specific documents of each of the interventions, this was done just in few cases even if it would have been better to do it for all the problematic cases but due to the great number of requests this would have required a lot of time. To make assumptions can be seen as another way of filling the missing information, some assumptions are easy to make others are more risky; the implications of making assumptions also vary in terms of how important it is. To have track of the source of the information, a color code was used to identify the assumed information from the information obtained with the di↵erent tools above explained. Unfortunately, even with the use of all these tools, it was not possible to fill all the required information and many of the required fields remained empty. This because the required data is currently not being collected.

3.3.3 Results

After having performed the 5 processing steps to the raw data described in the previous section, it is now time to perform the analysis of the processed data for a disaster forensic analysis. This can help to understand the causes

62 of damage among the elements of risk (hazard, vulnerability, exposure); the exercise however does not include the hazard analysis because it goes beyond the scope of this thesis but for future exercises it is advised to include it. Two di↵erent output types were selected to show the result of this analysis: graphs and maps. Graphs were done with data aggregation taking also into account the empty fields as N/A. Two kinds of frequency analysis were performed: damage evolution on time, from the analysis of the financial request on each of the source tables; and the sectorial frequency analysis that studies the fields of the required information for each of the sectors, mainly the type of item damaged and type of direct damage. Two kinds of maps were also produced: general maps for each sector aggregating the cost of interventions by municipality; and point maps containing the location of interventions when it was possible to find its reliable georeferencing coordinates. As it was mentioned before, the coordinates of the intervention are not available in the source tables and the processing step 5 was used to obtain this information; therefore, just 3 of the sectors have enough georeferenced interventions that permitted the creation of partial point maps (public area, public good and road). Partial point maps refer to maps that do not contain all the interventions listed for the specific sector, but only the interventions for which it was possible to obtain their coordinates. Table 3.9 shows a list of the outputs in each of the described modalities. Table 3.9: List of output of the disaster forensic analysis for all the sectors Graphs Source tables Damage Priority of source tables evolution Priority of source tables (old and new) on time Responsible stakeholder of source tables (old and new) Sectors of source tables (old and new) Emergency management Type of activity Maps Type of area Public area Emergency management Type of direct damage Public area Type of infrastructure Public good Public good General Frequency Type of damage Road analysis by Type of infrastructure Road Hydrogeological Sector Type of damage Water/sewage Hydrogeologic Type of direct damage Public area Type of infrastructure Public good Point Water/sewage Cause of damage Road Type of direct damage Blocked roads

In the following paragraphs, a brief presentation of each of the outputs grouped

63 by the mentioned types will be presented, as well as some remarks and com- ments on each of them will be presented. The complete analysis of these results will be done in chapter 4 as well as the critical analysis of the use of this data for a disaster forensic analysis. Graphs of damage evolution on time The figure 3.7 shows the financial request in the tree source tables. It is however important to remark that the first emergency table, build 8 months after the event, does not represent the request at that specific moment of time but the priority interventions that were selected to be financed first with the approved e3,5 million by the council of ministers of in the 30 June of 2014 for the declaration of the emergency state. From this graph it is possible to see that the final table, build 1 year and 3 months after the event, requests more resources than the preliminary table, build 4 months after the event. This can be due to the fact that a more precise and detail assessment of the damages could be done for the final table than the one done for the preliminary table and new interventions were estimated.

Financial request on time

100 €

90 €

80 €

70 €

60 € 4 months

50 € Million 40 € 8 months

30 €

20 € 1 year 3 10 € months

- €

Figure 3.7: Change of the ﬁnancial request over time

Figure 3.8 shows the ﬁnancial request in the source tables and the prioritization of interventions. The prioritization of interventions is assigned as letters according to the article 5, coma 2, of the Law 225/92 as follows: letter a)for the organization and performing of the rescue services and assistance to the a↵ected population; letter b)forthefunctionalityrestorationofthepublicser- vices and strategic infrastructure network; letter c)fortheinterventionsfor the reduction of the residual risk related to the event, and prioritize according to the public and private safety protection; letter d)totherecognitionofthe need of restoration of the public or private infrastructure and structures, as

64 well as the immediate damages to the businesses and the cultural heritage; letter e) for the first intervention measures identified in letter d. . However, this prioritization definitions were used only for the preliminary source table, when performing the request for the state of emergency; the Order 180 of 11 July 2014 for the establishment of the procedures for the preparation of the interventions plan after the declaration of the emergency state, specified new definitions of the prioritization as follows: letter a)forfirsturgentinterven- tions; letter b)forrecoveryinterventions;letter c)forstructuralinterventions aimed at reduction of residual risk These new definitions were used for the prioritization of the first emergency table and the final table. It is not clear the reason of changing the priority definitions for each emergency state; moreover it leads to misunderstanding, confusion and the need to perform the prioritization twice (one for the request of the emergency state and one for the request of the resources). This change might indicate that the definitions are not clear and its standard improvement might be needed. This will clearly improve the standards that can lead to easier accounting of damages as it will be mentioned in section 4.2. It is important to remember that the first emergency source table (from which the 8 months information is being gathered) does not represents the total required financial resources at this time but the available resources to finance the priority interventions. This is why in the next outputs it will not be included anymore. For the distribution among stakeholders in each of the territorial levels, figure 3.9 was produced, taking also into account the di↵erence between the source tables representing the time evolution. In this figure, it is interesting to see that the municipal stakeholders increase their request over time while regional and provincial stakeholders decrease it. This can be explained because the municipalities have less resources and therefore require time to increase the accuracy of the damage assessment and loss estimation; on the other hand, Regional and provincial stakeholders have more resources and are responsible of more critical infrastructure, leading to a fast damage assessment as well as a faster repair of the damages in these levels. More detail analysis will be given in section 4.2. The figure 3.10 shows the financial request for each sector in the source tables. In this figure it is possible to see that the sectors with largest financial request are the roads and the hydrogeological sectors. For the electric sector, it was not possible to acquire the data of all the interventions from ENEL, the responsible stakeholder, only few intervention requests managed by public stakeholders were included, and therefore its magnitude is not representative. Another remark is that the emergency management sector does not request resources

65 Figure 3.8: Change of the ﬁnancial request and prioritization over time

Figure 3.9: Change of the ﬁnancial request and responsible stakeholder over time for the ﬁnal table (1 year 3 months after the emergency), this because all the emergency management requests, due to their clear priority, were already been

66 ﬁnanced either with the e3,5 million distributed on the ﬁrst emergency table, or with other resources from the responsible stakeholder.

Financial request on time

45 €

40 €

35 €

30 €

25 €

Million 4 months 20 €

15 € 1 year 3 months 10 €

5 €

- € Emergency Public area Public good Road Hydrogeolog. Electric Water/ management sewage Sector

Figure 3.10: Change of the ﬁnancial request and sector over time

67 Graphs frequency analysis by sector The following paragraphs will present the description of the graphs of the frequency analysis by sector. In most of the sectors a graph for the type of direct damage was build, others present the analysis of the type of a↵ected item or other important details that could be obtained from the gathered data. As it was mentioned before, the electric sector will not be presented because the necessary data to performed this analysis could not be gathered. The number of requests in the N/A field were analyzed for each of the graphs because they might indicate the eciency of the classificatory fields according to the available information. Figure 3.11 shows the frequency of type of activities in the emergency management sector. In this graph it is possible to appreciate that the evacuation is the most frequent activity. Another important remark is that there is a large number of requests in the N/A field, meaning that some of them could not be classified in any of the fields. This sector consists of a small number of intervention requests (only 14) and therefore the analysis is not very conclusive.

Emergency management-Type ac7vity 6

Absolut frequency 2

0 Rescue affected Evacua5on Check hydraulic Evacua5on & traffic Rescue affected N/A popula5on condi5ons control popula5on & evacua5on

Figure 3.11: Frequency analysis for emergency management sector: type of activity

Public area is the next sector to be analyzed with figure 3.12 with two graphs, one for the type of area a↵ected and for the type of indirect damage. City walls were the type of area that were a↵ected the most by the event; it is also possible too see that the N/A field has a considerable amount of intervention requests. From the second graph, showing the type of direct damage, it is possible to see that the landslide were the principal source of direct damage to the public areas; this graph also shows that the N/A field has the majority of intervention requests, meaning that most of the interventions could not be

68 classify into the direct damage types. The number of intervention requests analyzed in this sector is 36 and represents also a small number of requests to have concrete conclusions

Public area-Type of area

6 Absolut frequency

0 Sport area Cemetery City wall Green area/ park Square/ parking lot N/A

Figure 3.12: Frequency analysis for public area sector: type of area and type of direct damage

The figure 3.13 shows the analysis of the public good sector with one graph for the type of a↵ected good, and one for the type of direct damage. From the first graph it is possible to appreciate that the schools were for far the most frequently a↵ected public goods. The second graph shows that the roof was the most frequent direct damage to public goods, which agrees with the large number of schools and gyms because these buildings are frequently having damages to their roofs. The amount of N/A fields is less representative than for other sectors but it is still an important amount to be taken into account. This improvement might be explained due to a larger number of intervention requests in this sector (64).

69 Public good-Type of infrastructure

15 Absolut frequency 10

0 Church Museum Hospital Gym Public School School & AVIS Municipal Theatre N/A residence municipal office office

Figure 3.13: Frequency analysis for public good sector: type of infrastructure and type of direct damage

The figure 3.14 shows two graphs for the frequency analysis of the road sector. The first graph represents the frequency of the type of infrastructure a↵ected, where it is possible to appreciate that the municipal roads are the most affected types of roads. The second graph shows that the most frequent types of direct damage to the roads were the damage to pavement, the damage due to landslides and debris accumulation. Finally, it is remarkable that the number of intervention requests in the N/A fields is less not representative and can be seen as a more normal behavior; this can be due to 421 intervention requests classified in the road field, a the large sample that permits a better analysis.

For the hydrogeological ﬁeld, a unique graph representing the type of direct damage was build and is presented in ﬁgure 3.15. This graph shows that the most frequent types of direct damage were the bank erosion and the silting.

70 Figure 3.14: Frequency analysis for road sector: type of infrastructure and type of direct damage

This graph shows, as in the previous sector, that the number of intervention requests in the N/A field is not representative, and the same attribution to a good number of sample (232) can be done. The last figure 3.16 shows in three graphs the frequency analysis for the water sewage system sector. The first graph shows the type of a↵ected infrastructure, with the water sewage collector being the most a↵ected one. The second graph shows the cause of damage where the landslide is the most frequent cause of damage in this sector. The third graph shows that the break was the only identified type of direct damage. Finally the N/A fields in all the graphs have a great importance to the point that in the second and third graphs it is the most frequent field; this can be due to the small sample for this sector (21).

71 Hydrogeological-Type of direct damage

140

120

100

0 Bank erosion Levees Silting Sediment Transverse Block/ Flood Vegetation N/A damage transport work damage accumulation street

Figure 3.15: Frequency analysis for public hydrogeological sector: type of direct damage

72 Water/sewage system-Type of direct damage

6 Absolut frequency

0 Break Infiltration/ contamination Exposition N/A

Figure 3.16: Frequency analysis for water/sewage system sector: type of infrastructure, cause of damage and type of direct damage

73 General maps The general maps were produced as municipality-aggregation of the requested resources for interventions for the source tables. the ﬁgures A.6 and A.7 are an example of the produced maps for the road sector from the preliminary source table and ﬁnal source table respectively. The general maps for all the other sectors can be found in appendix A. All the sectors show the time variability of the required resources from the source tables.

74 SAN GIUSTINO Cost road Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 900,000 SCHEGGIA E PASCELUPO

MONTONE 900,001 - 3,000,000 COSTACCIARO

GUBBIO SIGILLO 3,000,001 - 4,800,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ASSISI BASTIA VALTOPINA PANICALE TORGIANO PACIANO BETTONA CANNARA SPELLO DERUTA CITTA' DELLA PIEVEPIEGARO MARSCIANO FOLIGNO BEVAGNA MONTEGABBIONE COLLAZZONE MONTELEONE D'ORVIETO GUALDO CATTANEO SELLANO FRATTA TODINA MONTEFALCO TREVI FABRO PARRANO CAMPELLO SUL CLITUNNO PRECI SAN VENANZO GIANO DELL'UMBRIA FICULLE ALLERONA MONTE CASTELLO DI VIBIO CASTEL RITALDI CERRETO DI SPOLETO TODI CASTEL VISCARDO MASSA MARTANA VALLO DI NERA NORCIA ORVIETO SPOLETO CASTEL GIORGIO POGGIODOMO BASCHI SANT'ANATOLIA DI NARCO PORANO CASCIA MONTECCHIO ACQUASPARTA SCHEGGINO AVIGLIANO UMBRO MONTECASTRILLI MONTELEONE DI SPOLETO GUARDEA FERENTILLO SAN GEMINI ALVIANO MONTEFRANCO LUGNANO IN TEVERINA TERNI POLINO AMELIA ARRONE

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure 3.17: General map for road sector 4 months after the event

75 SAN GIUSTINO Cost road Final (€) CITERNA 0 - 300,000 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 300,001 - 1,000,000 SCHEGGIA E PASCELUPO

MONTONE 1,000,001 - 2,500,000 COSTACCIARO

GUBBIO SIGILLO 2,500,001 - 5,200,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure 3.18: General map for the road sector 1 year 3 months after the event

76 Point maps Another approach to show the results is to show the location of the interventions when their coordinates were possible to obtain from the described processing steps in section 3.3.2. Figures B.3 and B.4 show the maps with the location of some of the interventions for the road sector. The maps for the other sectors that were produced can be found in the appendix B. It is important to remember that not all the reported interventions are shown in the maps because it was not possible to obtain all the coordinates and therefore the maps are partial. The map in ﬁgure B.3 shows the location of the reported interventions and the road network of Umbria. After performing the road block of the interventions that fell in a street, the map in ﬁgure B.4 was obtained. These maps constitute an important analysis of the disaster forensic because the road network is part of the critical infrastructure. The block of an important road in the network can lead to several problems in the emergency management.

77 # ### Road ## # # # Comunal # # # ## # # ## # # # # ## Provincial ### # # ####### # # ###### ## # National # # # ### ### # ###### # # # # # # Provincia di Perugia ## ## ## ### ##### # # # # #### # Road works # # ## # ## # ## # ### # ## # # # # ####### # ####### # # # ### # ### ### ## # # # # ## # # # #### ### # ######## # ### # # ### # # # ## # ## # # # # # ## # # #### ## ## # ## ## ## ## # # ## # # # # # # # # # # # # ## ## # # # # # ## ## # ## #### # # # # ## # # # ##

Figure 3.19: Point map for road sector and road network of Umbria

78 Blocked road Comunal Provincial National Provincia di Perugia Road Comunal Provincial National Provincia di Perugia

Figure 3.20: Point map for road sector and blocked streets in the road network of Umbria

Discussion of the results

Through the case study described in the previous chapter it was possible to perform a disaster forensic analysis with damage data collected from current practices for compensation purposes. This chapter wants to perform a critical analysis of the exercise performed, providing in this way key information for future development in this field; for this, the next chapter will provide some suggestions for this future development. An analysis of the characteristics of the data obtained from current practices, in terms of its quality, will be presented in section 4.1. Subsequently, section 4.2 will identify the challenges of using data collected for compensation purposes for disaster forensic investigations, based on the specific challenges that were identified from the case study. Finally, section 5.1 will sum up the lessons learned from the development of this project.

4.1 Data quality

According to the JRC [De Groeve et al., 2014], there are no specific criteria that data must possess to have quality. Instead, quality is measured according to the ability of that data to fulfil a certain need or objective; in this order of ideas the data can have high quality when it is used for an objective and at the same time low quality when the same data is used for a di↵erent objective. Then the analysis done concerns data that was collected with the objective of compensation, therefore having high quality of data for this objective, and how the quality is a↵ected when the same data is used for the objective of performing a disaster forensic investigation. The lack of availability of data to fulfill a certain objective is hindered by uncertainty of data; therefore the

81 ANNEX 2 : STUDY OF UNCERTAINTY FOR QUALITY ASSESSMENT OF LOSS DATA

each one is associated to a more advanced state of data processing. The three stages can be generally defined as:

Stage 1 - Gathering and collecting data Stage 2 - Sorting and manipulating data Stage 3 - Transforming data to reach the objectives of the process

According to this framework, each stage is associated to one of the five types of uncertainty. Stage 1 is associated to Measurement, Stage 2 is associated to Completeness, and Stage 3 is associated uncertaintyto Inference. The of dataremaining will two be analyzedtypes of uncertainty in detail (Disagreement in the following and Credibility) paragraphs. are said to Thespan across existing all three uncertainty stages. In inaddition, the data it is also leads found to inaccuracy,that Disagreement errors sometimes and subjectiv- increases ity.the Credibility According uncertainty to De [43] Grove. After et a detailed al., [2014] analysis there of this are classification, three stages it is possible for the to detect data recording:its inability to stageaccount 1 fo refersr certain to mechanisms the gathering related and to human collection error. Therefore, and in the the classification case study isframework performed adopted by herein the responsible includes a sixth stakeholders type of uncertainty of the termed damaged Human Error goods; that stageis added 2 refersto the original to the framework sorting and proposed manipulating in [43]. As Disagreement and in the and case Credibility, study is Human performed Error also by thespans regional across all authoritythree previously when referred it compiles stages. Furthermore, the data in in the some required occasions, formats; Human Error and stagealso leads 3 refers to an to increase transforming of Disagreement the data and/or to Credibilityreach the uncertainties. desired objectives, The hierarchy for andthe caseconnectivity study between was performed the types byof uncertainty this thesis covered project by with the framework the described adopted processing herein are stepsillustrated identified in Figure 22 in. 3.3.2.To understand Parallel more stages clearly canthe role be of consider each component for the of casethis framework study if thein defining processing the global step uncertainty 5 in 3.3.2 of a is process, taken ainto detailed account, description due of to each the type fact of thatuncertainty data is presented in the following. comes from di↵erent sources (google maps for example). Before detailing the different types of uncertainty involved in the proposed uncertainty During these stages, six types of uncertainty can be found: measurement, classification framework, it should be noted that such framework assumes that, in a given process, completeness, inference, human error, disagreement, credibility. These types data will need to go through the three stages before being suitable to meet a certain objective of uncertainty are not always independent and some connections among them (e.g. a subsequent decision-making procedure). However, certain processes may only require can occur as it is illustrated in figure 4.1. The following paragraphs briefly Stage 1 (i.e. the collected data is the exact data required for decision-making), or only Stage 1 and explain each type of uncertainty and their connections based on [De Groeve Stage 2 (i.e. the collected data needs some manipulation after which it is suitable for decision- etmaking). al., 2014], also it will be explained how they were relevant for the case study in a qualitative way providing examples when possible.

Inference Stage 3

Completeness Stage 2 Error

Measurement Credibility Disagreement Human Stage 1

Figure 22. Hierarchy and connectivity between types of uncertainty. Figure 4.1: Conectivity between types of uncertainty [De Groeve et al., 2014]

Measurement uncertainty occurs during the stage 1, it is related to quantitative data and relates two components: the accuracy and the precision. The accuracy refers to the value of a measurement comparing to the real value, while the precision refers to the ability of obtaining the same result when the measure is repeated. This type of uncertainty could be found in the case study125 for the measurement of the coordinates of the interventions with Google maps, it is not possible to warranty neither that the coordinates correspond to the exact coordinates of the intervention, nor that repeated measurements of the coordinates will give the same result. This could be clearly seen when produc-

82 ing the maps for the road sector because the interventions do not fall exactly on the streets. Figure 4.2 shows an example of two measurements performed under the same conditions and obtaining di↵erent result of the coordinates for aroadintervention.

Figure 4.2: Example of measurement uncertainty obtaining coordinates of an intervention

Completeness uncertainty occurs during the stage 2 and has three components: sampling, missing values, aggregation. Sampling refers to the aleatoric uncertainty of selecting a sample to estimate characteristics of a population; this uncertainty is not relevant because no sampling process was done for the case study. Missing values refers to the values that are intended to be included but are not present in the data. For this component it is relevant to see that for the objective of compensation, which was the reason of the data collection, there are no missing values in the source table and they are enough to present the financial request to compensate the costs; however for the objective of disaster forensic there are many missing values and new sources of information have to be found to fill this missing values; take for example again the coordinates of interventions which are not included in the first source tables because they are not needed for compensation but are consider as missing values for disaster forensic. To have an idea of the amount of missing values for the case study it is suggested to recall all the not-highlighted values in tables 3.5, 3.6, 3.7, 3.8 that represent the missing values out of the required values as it was explained in processing step 4 of section 3.3.2. Another evidence of the amount of missing values is the large number of values falling in N/A fields, corresponding, in part, to the intervention requests that could not answer to each of the elements to investigate in section 3.3.3 (figures 3.11, 3.12, 3.13, 3.14, 3.15, 3.16). Aggregation refers to the uncertainty of loosing information through the per-

83 formance of the irreversible procedure of aggregating data; this component was not so relevant for the case study but it was present; table 4.1 shows one line of an intervention request from the preliminary source table, and it is possible to see that it refers to several interventions in di↵erent sectors (private, emergency management, hydrogeological, road), due to aggregation it is not possible to distinguish between the cost of each of the interventions and therefore that information is lost. Another example of aggregation uncertainty that arise from the particular case study refers to the aggregation of the two strikes of the flood event (November 2013 and February 2014) into the same table; although there is a field in the final source table to specify the date, several mistakes were found in the use of this field, (not using this field, putting the date of compilation instead of dateoneri per pronti ofthe interventi event, già putting both dates); this is interventi di ripristino OOPP, realizzati per la messa in reticolo idrografico e messa in € 41.850,00 why in the end it was not possiblesicurezza to di analyzeinfrastrutture separately the two strikes of the sicurezza edifici 173 pubbliche e private event as it wouldcomune Umbertide have - beenponte S.P. desired. sul rimozione vegetazione € 13.800,00 597 fiume Tevere b Table 4.1: Simplified example of an intervention request for Gubbio municipality from partial source table

ID Stakeholder Damage descrip., location, Type of damage Description of Cost of Priority type of intervention intervention intervention letter Restoring interventions of Expenses of first emergency Gubbio public infrastructure, interventions already carried 173 € 41.850,00 b municipality hydrografic network and out for the safety of public securing buildings. and private infrastructure

Interference uncertainty occurs during the stage 3, it refers to the action of fitting the data into a model and has three components: modelling, prediction, extrapolation into the past. Modelling component of this uncertainty occurs when the model used is no representative of the data properties. The prediction component refers to the assumption of future data based on causal relationship. Extrapolation into the past refers instead to the assumption of past data based on causal relationships. For the case study several uncertainties of prediction and extrapolation were identified due to the fact that, as it was explained in processing step 2 of section 3.3.2, the description provided in the source tables can refer to the a↵ected item, to the damage to that item, or to the needed intervention to fix that damage; since the three descriptions are needed then extrapolation on time (to the past or to the future) had to be performed to fill the other data assuming the three fields (a↵ected item, damage to the item, needed intervention) as a time sequence. For example table 4.2 shows an intervention request where the a↵ected item and the intervention are provided but the damage description is not, from time extrapolation it can be inferred that the damage was: vegetation accumulation on the bridge. Human error uncertainty occurs in all the stages, and is inherent to any process

84 oneri per pronti interventi già interventi di ripristino OOPP, realizzati per la messa in reticolo idrografico e messa in € 41.850,00 sicurezza di infrastrutture sicurezza edifici 173 pubbliche e private comune Umbertide - ponte S.P. sul rimozione vegetazione € 13.800,00 597 fiume Tevere b Table 4.2: Simpliﬁed example of an intervention request for Perugia province for hydraulic protection from partial source table ID Stakeholder Damage descrip., location, Type of damage Description of Cost of Priority type of intervention intervention intervention letter Perugia province, Bridge on Tiber river. 597 Vegetation removal € 13.800,00 b hydraulic Municipality of Umbertide protection

performed by humans. It is dicult to quantify and to identify; therefore it is suggested to describe what kind of human errors can occur in detail with some suggested categorized approach that can be found in De Grove et al., [2014]. Even though the description of human errors was not performed according to this suggestion for the case study, it was possible to identify some uncertainties of this type. An example is presented in the intervention request for the municipality of Gualdo Cattaneo in table 4.3, where it is possible to see that under address it is reported the address of the reporting oce (municipality of Gualdo Cattaneo) instead of the address of the a↵ected item (Colle Ti↵o road). Table 4.3: Simpliﬁed example of an intervention request for Gualdo Cattaneo municipality from ﬁnal source table

ID Stakeholder Province District Address Use Intervention tittle Priority Cost letter

Gualdo P.za Umberto 1°, n. Deterioration 174 Cattaneo Perugia Urban area 3 – 06035 Gualdo Public municipal road Colle b € 40.000,00 municipality Cattaneo (PG) Tifo

Disagreement uncertainty occurs also in all stages and it can occur due to human errors. During stage 1 it can happen because of multiple measures or from di↵erent sources of the same phenomena. During stage 2 it can happen when several partially overlapping sources are available for the same phenomena. In the case study this disagreement was very frequent, specially due to the multiple sources of information to fill the data; table 4.4 shows an example of the same intervention request reported in the preliminary table and in the final table, disagreement can be seen in the many fields: the district, the type of damage, the cost of the intervention and the priority letter; it is important to remark that the disagreement for the priority letter is presented because it refers to di↵erent priority definitions for the creation date of source tables according to Law 225/92 and Order 180/2014 respectively. During the stage 3 this uncertainty can occur when two or more conclusions can be done for the

85 same data; this kind of uncertainty was not so frequent for the case study because the processing of data was done only once by the same person, however oneri per pronti interventi già it is probableinterventi that di ripristino if the OOPP, processing is done again by a di↵erent person this realizzati per la messa in reticolo idrografico e messa in € 41.850,00 sicurezza di infrastrutture uncertainty cansicurezza arise. edifici 173 pubbliche e private comune Umbertide - ponte S.P. sul rimozione vegetazione € 13.800,00 Table597 4.4: Simpliﬁedfiume Tevere example of an intervention request for Campellob sul Clitunno municipality from partial and ﬁnal source tables

ID Stakeholder Damage descrip., location, Type of damage Description of Cost of Priority type of intervention intervention intervention letter Localized disruption due to Campello sul the landslide of the slope of 37 Cozze district € 75.000,00 d Clitunno the mountain road, with partial obstruction of the road

ID Stakeholder Province District Address Use Intervention tittle Priority Cost letter

Hydraulic and Campello sul Campello Municipal hydrogeological work 24 Perugia 0+350 b € 300.000,00 Clitunno Alto road of the municipal road of Cozze

Finally the credibility uncertainty occurs in all stages and is referred to the lack of credibility of data due to the past incidents with the source of data; this uncertainty is not expressible mathematically and can be product of human errors or disagreement. Since the case study is a pioneer exercise in its type, and no previous knowledge of the sources was available, this uncertainty was not relevant for the case study, however it might be relevant as further studies are done.

4.2 Challenges for forensic use of data

Clearly the ﬁrst challenge that can be identiﬁed after the previous section is the challenge of dealing with uncertainty leading to low quality of data in a smarter way. As it was mentioned before, a color coding was used to account for the source of data and the reliability of the data (for instance when it was assumed), however this was performed only in the last stage of the processing and no similar estimation was done for the other stages. Moreover, there are more coherent ways to perform this accounting for damage data like NUSAP (Numerical Unit Spread Assessment and Pedegree) method suggested in [De Groeve et al., 2014] over traditional statistical methods; this due to the

86 fact that damage data are not likely to fulfil the requirements of statistical methods which were build for mathematical treatment. The NUSAP method instead is able to account for both quantitative and qualitative dimensions of uncertainty and to represent them in a standardized and self-explanatory way. The NUSAP method analyses five parameters from which three are quantitative and two are qualitative. number parameter refers tot he quantitative value of the field. Unit refers to the unit of measure on which number is given and it can also include the date of evaluation, which results very useful to track the dynamism of the data. Spread quantifies the uncertainty of data with traditional statistical methods when possible or with expert assessment. Assessment refers to a qualitative scale of the goodness of data from an expert assessment. Pedigree parameter is a matrix that can be used to quantify the qualitative assessment of the associated uncertainty components. An example of s pedigree matrix for disaster damage data can be found on the second report of the JRC.

Another way to manage the uncertainty is to manage the specific factors leading to uncertainty, one of them is the large number of intervention requests falling into N/A fields that indicates two things. First the identified fields for required information in each sector needs to be restructured; this due to the fact that even though during the performance of the exercise the fields definition were shaped and modified according to the conditions found, it was not possible to include all the conditions or satisfactory conditions of the available data. Second, this fact is an indication that the needed data to perform a disaster forensic investigation is not being collected by the current practices. It is suggested that these two factors are deeply studied in the future to avoid the production of inconclusive results as the one presented in the third graph of figure 3.16 where there are more N/A intervention requests than the provided information; from this figure it is also possible to see that the definition of the fields is not proper for the context of the case study.

Also, a challenge to improve uncertainty handling is improving general standards but specifically for the prioritization.Thecurrentpractices for damage data collection suggest a di↵erent format for the financial request of each event, leading to a low standardization of the information that cannot be compared; if there are standard comparable sources, then less disagreement uncertainty will occur. The prioritization is governed by the letters in article 5, coma 2 of Law 225/92, however new definitions are provided by the specific state of emergency Orders for the establishment of the procedures for the preparation of the interventions plan. The table 4.5 shows a comparison of the definitions for the priority letters according to Law 225/92 and Order 180/2014 for the emergency state of November 2013-February 2014 in Umbria, where it

87 is possible to see that the definitions of the Order 180/2014 are more general leaving room for more ambiguity of the classification. It is not clear the reason of changing the priority definitions for each emergency state; moreover it leads to disagreement uncertainty and double work from the need performing prioritization two times for each event. This is the main reason of the drastic change in the distribution of the priority letters among the three source tables that was spotted in figure 3.7.

Table 4.5: Comparison between priority letters deﬁnition according to general Law and speciﬁc-event Order

L. 225/92 O. 180/2014 For the organization and performing of the rescue For first urgent Letter a services and assistance to the interventions affected population For the functionality restoration of the public Letter b For recovery interventions services and strategic infrastructure network For the interventions for the reduction of the residual risk For structural related to the event, and Letter c interventions for reduction prioritize according to the of residual risk public and private safety protection To the recognition of the need of restoration of the public or private infrastructure and Letter d structures, as well as the immediate damages to the businesses and the cultural heritage For the first intervention Letter e measures identified in letter d

Regarding the di↵erence between the source tables, that correspond basically to the data on time, an important challenge is having a more coherent way to account for the change of data on time. The accomplishment of this challenge will permit a faster processing as well as a wider comprehension of the unfolding of the event on time. Also related to time is the challenge of decreasing the times needed to collect, process and record data so the data can be used earlier and for other applications. The improvement of this timings will eventually come with more frequent performance of this exercises

88 for damage data collection and recording, as well as the improvement of the practices. Achallengethatwasalreadysuggestedinsection2.3isestablishing a clear distinction between recovery and amelioration interventions and has increasing importance for disaster forensic investigations. This importance stands on the fact that disaster forensic investigations are essentially a critical view of the current event in order to determine its causes by reconstructing all the event’s causes and e↵ects (as explained in section 1.3), including the physical aspect as well as the decision-making in the whole emergency management. If the analyzed data for disaster forensic includes also amelioration interventions, then there will be errors on the estimations. A related challenge is Encouraging the recording of interventions financed with other tools because since the data is collected for compensation, it is presumed that the interventions that do not require fundings are not being reported; as a matter of fact this is the reason of some interventions ”disappearing on time”, meaning that they are reported in the preliminary table but not in any of the subsequent tables, because it is presumed that they were financed with di↵erent funds. Improving the understanding of the meaning of local terminology is another challenge that arise during the case study, this challenge can be accomplish through the involvement of local authorities familiar with such terminology, as well as training and experience. Another minor identified challenge was Managing transboundary interventions into correct formats and raised due to the existence of some interventions in two or more municipalities that were dicult to account during the processing. Another identified challenge is dealing with the classification of the interventions into each of the sectors, due to the fact that it is not always easy. Some intervention requests refer to more than one sector and it is dicult to perform the separation; this situation can be improved if the classification is suggested from an early stage of the procedure and not in the last processing stage. The last identified challenge is establishing ways to deal with bureaucratic processes to obtain the data.Thischallengereferstothedicul- ties to coordinate all the involved stakeholders to provide the data; as a mater of fact, it was not possible to gather the complete set of data for the electric sector with the responsible stakeholder ENEL. Overcoming this challenge will require time and experience as new exercises of this type develop that helps the institution of faster bureaucratic tools into a trustful environment.

89 90 Chapter 5

Conclusions

This chapter concludes the thesis from the presentation of the lessons learned but moreover, it is intended to provide some recommendations for future development in the field of damage data collection and recording after an event, focusing on the use of this data for disaster forensic investigations. This chapter is mainly focused on addressing the challenges identified in the previous chapter (section 4.2) regarding using data collected for compensation purposes for disaster forensic investigations; also some broader challenges regarding the use of current practices for damage data collection and recording for the use of the four applications identified in section 2.3 will be considered. First the presentation of the lessons learned from the development of this project are presented in section 5.1. After that, section 5.2 refers to the future recommendations from three di↵erent but overlapping points of view: research need, the shift of practices and the need of practical tests.

5.1 Lessons learned from this project

The research embedded in this thesis started from the deﬁnition of the four applications for damage data collection and recording (loss accounting, disaster forensic, risk modelling and compensation) in chapter 1, as key activities for improving mitigation strategies. A special attention was then given to the disaster forensic investigation application due to its importance for the objective of this thesis. Nonetheless current practices for damage data collection and recording in Eu- rope and in Italy presented in chapter 2 stated the current increasing concern

91 for improved practices on damage data collection and recording towards common compatible databases that can be aggregated at European and even at global levels. Some primary challenges of the use of these practices were iden- tiﬁed at the end of the chapter.

The presentation and discussion of the case study in chapters 3 and 4 of a disaster forensic investigation performed with data collected for compensation purposes, permitted the identification of some of the problems that can occur when data is used for a di↵erent purpose of its collection one. These problems were presented as specific challenges in section 4.2 that should be addressed with future research that leads to an improved development on the field of damage data collection and recording.

It is important to remark that this project is pioneer and therefore all its achievements can be counted as gainings that will in the future be improved through better practices. From this premise, lessons learned from the thesis are presented in the following paragraphs.

As described above this project lead first to the identification of problems that can occur when using damage data for a di↵erent application than its collection purpose (collected for compensation and used for disaster forensic). This will help to identify the overlapping and di↵erencing elements among those two applications. Also it can encourage the development of similar exercises to have deeper studies for the definition of other applications’ characteristics. Second, it identified the required data to perform a disaster forensic analysis; this will help to design the new practices for damage data collection and recording intended to perform both loss accounting and disaster forensic investigations.

With specific reference to this aspect, the large number of requests falling into N/A fields is an indication of: not proper definition of the fields’ structure for the required data for disaster forensic; and the lack of collection of required data for a disaster forensic investigation. Both indications are suggested to be deeper studied.

It is worth mentioning that since this exercise constitutes a pioneer trial that had never been performed, it is normal to have all the problems and challenges coming form changing the paradigm towards new improved practices. The importance of a future trustful environment product of more frequent exercises of this type on which it is possible to reach high coordination among the acting authorities is imminent. After the development of this environment, many of the challenges mentioned in the previous section can be address and improved in an easier way.

92 Alessonlearnedrespondstotheneededresourcestocollectandrecorddamage which are found to be very demanding, in terms of time, funds, and human resources. However collected data provides crucial information on the disaster that can lead to improved practices for risk management. As an example from the particular point of view of the case study, the results obtained through the development of this project will be used by the planning authorities in Umbria to evaluate the e↵ectiveness of mitigation measures, to direct new mitigation projects to the most vulnerable areas (for example the most a↵ected sectors), and for an improved risk management that eventually leads to a decrease of damages and e↵ects caused by future events. This project will help to increase the concern of improving the current practices of damage data collection and recording towards a common standard procedure that can be used for the four applications; moreover it will lead to better coordination among the involved stakeholders. All these improvements will eventually permit to decrease the needed resources until an equilibrium of the best cost/beneﬁt relation is reached.

5.2 Future recommendations

As it was stated in 2.1, there is a current increasing concern for improved practices on damage data collection and recording towards common compatible databases that can be aggregated at European and even at global levels. The research need stands for an improved understanding of the requirements of damage data to be collected and recorded depending on the applications for which they will be used. One of the next research steps that can be suggested is to analyze the feasibility of using data collected with current methodologies for the other applications; this means to perform a similar project to the one done in this thesis but for the performance of risk modelling and loss accounting applications. Through this kind of studies it will be possible to further individuate the specific requirements for each application. Data requirements should be translated into new practices for damage data collection and recording so that the data can be used for all the four applications. This will permit that the maximum benefit is obtained from the data whose collection implied a considerable amount of resources. Refering to what was stated in section 5.1 the shift to new methodologies and the experience from repeated practical tests will decrease the needed resources. The increase of the benefits (more applications for which data is used) and the decrease of the cost (less needed resources from better practices and experience) will eventually lead to an equilibrium in the cost/benefit analysis that can incentive

93 and justify the shift of practices in other places. Moreover new methodologies will also help to reach the objective of compatible databases that can be aggregated at european and global level for more robust measures towards improved risk management. This because the new methodologies suggest that the recorded data serves for the four applications, and therefore it will be collected with common and compatible characteristics; however further research regarding the compatible design of the database structure should be developed following the guidelines suggested by the JRC. One of these guidelines is the accounting of the uncertainty of the collected data with a coherent methodology like NUSAP. This for example addresses one of the challenges proposed in the previous chapter. As a prototype example of practices, section 3.1 presented the PoliRISPOSTA project and the developed standard but flexible procedure to collect and record damages after an event [Politecnico di Milano & Umbria Region, 2015] that can be use for the four applications. This procedure is very detailed in the collection of damage data for the private sector but requires further development on the standards that can be applied for the public sector. This thesis project, being a transition test, will in the future improve the procedure standards for the public sector. It is important to remark the role of the procedure on addressing the stated challenges as well as other challenges that can be found from the suggested future studies to explore deeper the characteristics and requirements of the other applications. For example how it should be standard to improve the prioritization process and decrease the required time; but also flexible to deal with the dynamism of the data and the unexpected particularities of each event, like the transboundary e↵ects found in the case study; it should perform the sectorial classification of interventions in early stages of the collection. Other examples of the challenges that have not yet been addressed by the procedure regard the distinction between amelioration and recovery interventions and complete recording of all the interventions instead of just the interventions requiring public emergency funding. All of these challenges can be consider as guiding steps to be addressed by the RISPOSTA procedure and similar procedures in the future inside the cyclic adaptive development. However as it was mentioned before, also the test of these procedures constitutes an important step for their validation as well as for adaptation due to new challenges found during each test. This is where the need of practical tests starts playing an important role. The RISPOSTA procedure has been tested with two transition tests in Umbria region, yet these tests did not apply the complete procedure but just part of

94 it. Therefore, new tests with the complete application of the procedure are required for its validation and adaptation. The two transition tests were done from flood events occurred in Umbria region (2012 and 2013-2014). This fact has the advantage of making the procedure locally valid and covers more specific characteristics of the place where it has been tried, for example the challenge regarding local terminology will only require the application of the procedure to more events and to involve people form Umbria. However it would also be useful to make these trials in other places to account for the spatial changing challenges; this will also permit the estimation of how standard against how flexible should a procedure be in order to apply it in di↵erent places. Also this can determine the eventual need of locally valid procedures or adapted versions of the same procedure. All these shows the need of performing many transition tests under di↵erent circumstances that improve the practices for damage data collection and recording either with the proposed procedure or with similarly developed procedures locally valid. It is therefore in the practice that the procedures and researches can be tested and validated for the future regular use during flooding events that permits improvements in risk management practices and eventually decrease the damages caused by these disasters.

95 96 Appendix A

General maps

97 SAN GIUSTINO Cost emergency m. Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 4200 SCHEGGIA E PASCELUPO

MONTONE 4201 - 54000 COSTACCIARO

GUBBIO SIGILLO 54001 - 438850

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.1: General map for emergency management sector 4 months after the event

98 Cost public area SAN GIUSTINO Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 500,000 SCHEGGIA E PASCELUPO

MONTONE 500,001 - 800,000 COSTACCIARO

GUBBIO SIGILLO 800,001 - 2,000,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.2: General map for public area sector 4 months after the event

99 SAN GIUSTINO Cost public area Final (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 350,000 SCHEGGIA E PASCELUPO

MONTONE 350,001 - 1,180,000 COSTACCIARO

GUBBIO SIGILLO 1,180,001 - 2,600,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.3: General map for public area sector 1 year 3 months after the event

100 SAN GIUSTINO Cost public good Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 270,000 SCHEGGIA E PASCELUPO

MONTONE 270,001 - 550,000 COSTACCIARO

GUBBIO SIGILLO 550,001 - 810,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.4: General map for public good sector 4 months after the event

101 SAN GIUSTINO Cost public good Final (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 200,000 SCHEGGIA E PASCELUPO

MONTONE 200,001 - 470,000 COSTACCIARO

GUBBIO SIGILLO 470,001 - 720,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.5: General map for public good sector 1 year 3 months after the event

102 SAN GIUSTINO Cost road Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 900,000 SCHEGGIA E PASCELUPO

MONTONE 900,001 - 3,000,000 COSTACCIARO

GUBBIO SIGILLO 3,000,001 - 4,800,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.6: General map for road sector 4 months after the event

103 SAN GIUSTINO Cost road Final (€) CITERNA 0 - 300,000 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 300,001 - 1,000,000 SCHEGGIA E PASCELUPO

MONTONE 1,000,001 - 2,500,000 COSTACCIARO

GUBBIO SIGILLO 2,500,001 - 5,200,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.7: General map for road sector 1 year 3 months after the event

104 Cost hydrogeological SAN GIUSTINO Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 750,000 SCHEGGIA E PASCELUPO

MONTONE 750,001 - 1,600,000 COSTACCIARO

GUBBIO SIGILLO 1,600,001 - 2,900,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.8: General map for hydrogeological protective measures sector 4 months after the event

105 Cost hydrogeological SAN GIUSTINO Final (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 850,000 SCHEGGIA E PASCELUPO

MONTONE 850,001 - 2,400,000 COSTACCIARO

GUBBIO SIGILLO 2,400,001 - 6,900,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.9: General map for hydrogeological protective measures sector 1 year 3 months after the event

106 SAN GIUSTINO Cost water/sewage s. Preliminary (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 150,000 SCHEGGIA E PASCELUPO

MONTONE 150,001 - 450,000 COSTACCIARO

GUBBIO SIGILLO 450,001 - 800,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.10: General map for water/sewage system sector 4 months after the event

107 SAN GIUSTINO Cost water/sewage s. Final (€) CITERNA 0 PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA 1 - 200,000 SCHEGGIA E PASCELUPO

MONTONE 200,001 - 500,000 COSTACCIARO

GUBBIO SIGILLO 500,001 - 800,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

CALVI DELL'UMBRIA

Figure A.11: General map for water/sewage system sector 1 year 3 months after the event

108 SAN GIUSTINO

CITERNA Cost electric PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA Preliminary (€) SCHEGGIA E PASCELUPO

MONTONE 0 COSTACCIARO

GUBBIO SIGILLO 5,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

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Figure A.12: General map for electric sector 4 months after the event

109 SAN GIUSTINO

CITERNA Costi int. elettrico PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA Final (€) SCHEGGIA E PASCELUPO

MONTONE 0 COSTACCIARO

GUBBIO SIGILLO 47,000

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

OTRICOLI

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Figure A.13: General map for electric sector 1 year 3 months after the event

110 Appendix B

Point maps

111 SAN GIUSTINO

CITERNA

PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA SCHEGGIA E PASCELUPO

MONTONE COSTACCIARO

GUBBIO SIGILLO ôóõ Public area works

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

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CALVI DELL'UMBRIA

Figure B.1: Point map for pubic area sector

112 SAN GIUSTINO

CITERNA

PIETRALUNGA CITTA' DI CASTELLO MONTE SANTA MARIA TIBERINA SCHEGGIA E PASCELUPO

MONTONE COSTACCIARO

GUBBIO SIGILLO ñ Public good works

FOSSATO DI VICO UMBERTIDE LISCIANO NICCONE GUALDO TADINO TUORO SUL TRASIMENO PASSIGNANO SUL TRASIMENO VALFABBRICA

MAGIONECORCIANO PERUGIA CASTIGLIONE DEL LAGO NOCERA UMBRA

ATTIGLIANO GIOVE NARNI PENNA IN TEVERINA STRONCONE

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Figure B.2: Point map for public good sector

113 # ### Road ## # # # Comunal # # # ## # # ## # # # # ## Provincial ### # # ####### # # ###### ## # National # # # ### ### # ###### # # # # # # Provincia di Perugia ## ## ## ### ##### # # # # #### # Road works # # ## # ## # ## # ### # ## # # # # ####### # ####### # # # ### # ### ### ## # # # # ## # # # #### ### # ######## # ### # # ### # # # ## # ## # # # # # ## # # #### ## ## # ## ## ## ## # # ## # # # # # # # # # # # # ## ## # # # # # ## ## # ## #### # # # # ## # # # ##

Figure B.3: Point map for road sector and road network of Umbria

114 Blocked road Comunal Provincial National Provincia di Perugia Road Comunal Provincial National Provincia di Perugia

Figure B.4: Point map for road sector and blocked streets in the road network of Umbria

Primary

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