Flood Damage Survey and Assessment E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Geophysical Monograph Series
• Sprache: Englisch

Seitenzahl: 792

Veröffentlichungsjahr: 2017

Beschreibung

Floods can have a devastating impact on life, property and economic resources. However, the systematic collection of damage data in the aftermath of flood events can contribute to future risk mitigation. Such data can support a variety of actions including the identification of priorities for intervention during emergencies, the creation of complete event scenarios to tailor risk mitigation strategies, the definition of victim compensation schemes, and the validation of damage models to feed cost-benefit analysis of mitigation actions. Volume highlights include: * Compilation of real world case studies elaborating on the survey experiences and best practices associated with flood damage data collection, storage and analysis, that can help strategize flood risk mitigation in an efficient manner * Coverage of different flooding phenomena such as riverine and mountain floods, spatial analysis from local to global scales, and stakeholder perspectives, e.g. public decision makers, researchers, private companies * Contributions from leading experts in the field, researchers and practitioners, including civil protection actors working at different spatial and administrative level, insurers, and professionals working in the field of natural hazard risks mitigation Flood Damage Survey and Assessment: New Insights from Research and Practice will be a valuable resource for earth scientists, hydrologists, meteorologists, geologists, geographers, civil engineers, insurers, policy makers, and planners. Read an interview with the editors to find out more: https://eos.org/editors-vox/the-value-of-disaster-damage-data

Leseprobe

Table of Contents

COVER

TITLE PAGE

CONTRIBUTORS

PREFACE

REFERENCES

ACKNOWLEDGMENTS

Part I: Introduction

1 Overview of the United Nations Global Loss Data Collection Initiative

1.1. DISASTER RISK REDUCTION: A FRAMEWORK FOR ACTION

1.2. THE SENDAI AND OTHER FRAMEWORKS OF 2015

1.3. THE SENDAI FRAMEWORK AND LOSS DATA COLLECTION

1.4. WHERE WE ARE: BASIC PRINCIPLES OF THE UNITED NATIONS INITIATIVE

1.5. WHERE DO WE GO? EXPERIENCE FROM THE PAST INDICATES CHALLENGES FOR THE FUTURE

1.6. CONCLUSIONS

REFERENCES

2 Technical Recommendations for Standardizing Loss Data

2.1. INTRODUCTION

2.2. REQUIREMENTS FOR LOSS DATABASES

2.3. THE LOSS DATA MODEL

2.4. TECHNICAL CHALLENGES

2.5. CHALLENGES FOR REPORTING IN THE EUROPEAN UNION

2.6. RECOMMENDATIONS FOR BEST PRACTICES IN LOSS DATA RECORDING

2.7. CONCLUSIONS

REFERENCES

Part II: Data Storage

3 Overview of Loss Data Storage at Global Scale

3.1. INTRODUCTION

3.2. EUROPEAN UNION GUIDELINES USED FOR THE CONTEXTUAL ANALYSIS OF THE DATA SETS

3.3. OVERVIEW OF DATA SETS AT GLOBAL SCALE (EM‐DAT, NATCATSERVICE, SIGMA)

3.4. NATIONAL DATA SETS INCLUDING GOOD PRACTICES (SLOVENIA, MOLDOVA, UNITED STATES, COLOMBIA)

3.5. NATIONAL DATA SETS IN A REGIONAL CONTEXT AND GLOBAL CONTEXT (EUROPEAN UNION EFFORT, THE COMMONWEALTH OF INDEPENDENT STATES EFFORT, DESINVENTAR DATABASE)

3.6. THE USE OF GLOBAL DATA SETS: A CHANGE IN PARADIGM

3.7. CONCLUSIONS: TOWARD A COMPREHENSIVE GLOBAL DATA SET

ACKNOWLEDGMENTS

REFERENCES

4 Direct and Insured Flood Damage in the United States

4.1. INTRODUCTION

4.2. FLOOD LOSS PATTERNS IN THE UNITED STATES

4.3. SOURCES OF FLOOD INFORMATION IN THE UNITED STATES

4.4. UNCERTAINTIES IN US FLOOD LOSS ACCOUNTING

4.5. FUTURE DATA NEEDS

4.6. CONCLUSION

ACKNOWLEDGMENTS AND DATA

REFERENCES

5 HOWAS21, the German Flood Damage Database

5.1. INTRODUCTION

5.2. OTHER FLOOD DAMAGE DATABASES

5.3. HOWAS21 DATABASE CONCEPT AND STRUCTURE

5.4. TECHNICAL DESIGN AND IMPLEMENTATION

5.5. DATA SOURCES: SURVEYS AND DATA ACQUISITION CAMPAIGNS

5.6. DATA QUALITY CONCEPT

5.7. EXEMPLARY DATA ANALYSES AND USE

5.8. CONCLUSIONS

REFERENCES

Part III: Data Collection

6 Best Practice of Data Collection at the Local Scale

6.1. INTRODUCTION: WHY AND WHERE TO APPLY RISPOSTA

6.2. THE LOGICAL STRUCTURE OF RISPOSTA: THE FOUR AXES

6.3. STATE OF IMPLEMENTATION OF THE RISPOSTA PROCEDURE

6.4. FLOODED AREAS, RESIDENTIAL BUILDINGS, AND INDUSTRIAL/COMMERCIAL PREMISES (DIRECT SURVEY CENTERED)

6.5. OTHER SECTORS (DATA GATHERING CENTERED)

6.6. DATA COORDINATION

6.7. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

7 Data Collection for a Better Understanding of What Causes Flood Damage–Experiences with Telephone Surveys

7.1. INTRODUCTION

7.2. SURVEY METHODOLOGIES AND SAMPLING STRATEGIES

7.3. LOSS DATA COLLECTION IN GERMANY AFTER SEVERE FLOOD EVENTS

7.4. CONCLUSIONS

REFERENCES

8 Utilizing Post‐Disaster Surveys to Understand the Social Context of Floods–Experiences from Northern Australia

8.1. THE MYTHOLOGIZING OF FLOODS AND NATURAL DISASTERS IN AUSTRALIAN CULTURE

8.2. NATURAL HAZARDS IN AUSTRALIA

8.3. RESEARCH IN THE POST‐DISASTER CONTEXT

8.4. CENTRE FOR DISASTER STUDIES RESEARCH

8.5. FLOOD TYPOLOGY AND IMPACTS

8.6. SPECIFIC ISSUES IDENTIFIED IN NORTHERN AUSTRALIA

8.7. IMPACT OF FLOODS

8.8. METHODOLOGY AND OBJECTIVES OF POST‐DISASTER SURVEYS

8.9. SUMMARY OF FINDINGS FROM FLOOD POST‐DISASTER SURVEYS

8.10. CONCLUSION

REFERENCES

9 Understanding Crowdsourcing and Volunteer Engagement

9.1. INTRODUCTION

9.2. UNDERSTANDING CROWDSOURCING

9.3. CASE STUDIES

9.4. HARNESSING THE POWER OF THE CROWD

9.5. SUMMARY

REFERENCES

Part IV: Data Analysis

10 After the Flood Is Before the Next Flood

10.1. INTRODUCTION: DISASTER RESILIENCE, DISASTER FORENSICS, AND POST‐EVENT REVIEW CAPABILITY

10.2. THE POST‐EVENT REVIEW CAPABILITY FRAMEWORK AND ANALYSIS

10.3. HOW A POST‐EVENT REVIEW CAPABILITY IS CONDUCTED

10.4. CONSOLIDATED FINDINGS FROM POST‐EVENT REVIEW CAPABILITY STUDIES CONDUCTED BETWEEN 2013 AND 2015

10.5. CONCLUSION

REFERENCES

11 Defining Complete Post‐Flood Scenarios to Support Risk Mitigation Strategies

11.1. INTRODUCTION

11.2. DEFINITION OF THE “COMPLETE EVENT SCENARIO” CONCEPT

11.3. KEY COMPONENTS OF A COMPLETE POST‐FLOOD EVENT SCENARIO IN THE PROPOSED METHODOLOGY

11.4. REPORTING THE COMPLETE DAMAGE SCENARIO AND APPLICATION TO THE CASE STUDY AREA

11.5. DISCUSSING THE UTILITY OF THIS WORK

11.6. CHALLENGES

11.7. CONCLUSION

REFERENCES

12 Rebuild and Improve Queensland

12.1. INTRODUCTION

12.2. AUSTRALIA AND QUEENSLAND NATURAL DISASTER SITUATIONS (2011–2015)

12.3. NATURAL DISASTER EVENTS IN QUEENSLAND

12.4. RESPONSE–DAMAGE ASSESSMENT IN QUEENSLAND

12.5. RECOVERY–THE QUEENSLAND EXPERIENCE

12.6. MITIGATION AND BETTERMENT PROGRAMS INCORPORATING DAMAGE ASSESSMENT METHODOLOGY

12.7. THE FUTURE

REFERENCES

13 Forensic Disaster Analysis of Flood Damage at Commercial and Industrial Firms

13.1. INTRODUCTION

13.2. FORENSIC ANALYSIS: QUANTITATIVE AND QUALITATIVE APPROACHES

13.3. EXAMPLE OF FORENSIC INVESTIGATION PROCEDURE

13.4. BUSINESS VULNERABILITY TO NATURAL DISASTERS

13.5. CONCLUSION: USING FORENSIC ANALYSIS FOR PLANNING BUSINESS RESILIENCE MEASURES

REFERENCES

Part V: Information and Communication Technology Tools

14 Response to Flood Events

14.1. INTRODUCTION

14.2. EMERGENCY MAPPING

14.3. COPERNICUS EMERGENCY MANAGEMENT SERVICE RAPID MAPPING

14.4. FLOOD IMPACT ASSESSMENT: OPERATIONAL APPROACH

14.5. COPERNICUS EMERGENCY MANAGEMENT SYSTEM CASE STUDY

14.6. CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

15 Data Collection and Analysis at Local Scale: The Experience within the Poli‐RISPOSTA Project

15.1. INTRODUCTION: ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN DISASTER MANAGEMENT

15.2. POLI‐RISPOSTA: A FLOOD DATA MANAGEMENT SYSTEM FOR THE LOCAL/REGIONAL SCALE

15.3. REMARKS ABOUT THE POLI‐RISPOSTA INFORMATION AND COMMUNICATION TECHNOLOGY SYSTEM

15.4. CONCLUSIONS

ACKNOWLEDGMENTS AND DATA

REFERENCES

Conclusions

1. THE HISTORY OF FLOOD DAMAGE DATA COLLECTION AND MANAGEMENT

2. CURRENT MOTIVATION AND RATIONALE FOR DAMAGE DATA COLLECTION AND ASSESSMENT

3. RECOMMENDATIONS FOR THE FUTURE FOR ENABLING TECHNOLOGIES AND INTEGRATING SECTORS AND STAKEHOLDERS IN A NEW GENERATION OF POST‐FLOOD DAMAGE INFORMATION SYSTEMS

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 01

Table 1.1 Set of Indicators Agreed Upon by the OEIWG in Geneva.

Chapter 03

Table 3.1 Global and Regional Disaster Loss Data Repositories.

Table 3.2 Good Practices of National Loss Data Sets.

Table 3.3 National Databases in a Regional and Global Context EU Databases, DesInventar and CIS Countries Database.

Table 3.4 Comparative Table on Disaster Losses Recording for EU Minimum Requirements, Sendai Requirements, EUFD/2007, Desinventar, CIS Regional Level.

Table 3.5 Complement Data Sets on Global and Regional Level with Risk and Assessment‐related Disaster Information.

Chapter 05

Table 5.1 Exemplary Overview of the Main Damage Information Tables for Private Households.

Table 5.2 Rules for the Assessment of Data Quality for Data Subsets Selected in HOWAS21.

Table 5.3 Comparison of Depth‐Damage Curve Performance Evaluated on Basis of 40 Damage Cases from the 2005 Flood in the Danube Catchment (MBE: Mean Bias Error, MAE: Mean Absolute Error, RMSE: Root Mean Square Error).

Chapter 06

Table 6.1 Main Activities Included in RISPOSTA, According to the Logical Axes of the Procedure (Time, Actors, Actions, and Exposed Sectors).

Table 6.2 Scheme of the RISPOSTA Procedure for Data Collection on the Physical Event: Activities to be Performed, Times of Actions, and Responsible Actors (Extract from Table 6.1).

Table 6.3 Scheme of the RISPOSTA Procedure for Data Collection on Damage to Residential Buildings and Industrial/Commercial Premises: Activities to be Performed, Times of Actions, and Responsible Actors (Extract from Table 6.1).

Table 6.4 Information Collected by Means of the forms for Damage to Residential Buildings [from

Molinari et al.,

2013].

Table 6.5 Information Collected by Means of the forms for Damage to Residential Buildings.

Table 6.6 Scheme of the RISPOSTA Procedure for Data Gathering: Activities to be Performed, Times of Actions, and Responsible Actors (Extract from Table 6.1).

Table 6.7 Scheme of the RISPOSTA Procedure for Data Coordination: Activities to be Performed, Times of Actions, and Responsible Actors (Extract from Table 6.1).

Chapter 07

Table 7.1 Chronological Overview of Flood Events and Related Household Surveys in Germany. In the Column “Timing,” the Number of Months Between the Damaging Event and the Data Collection is Given.

Table 7.2 Chronological Overview of Flood Events and Related Surveys Among Flood‐Affected Companies in Germany. In the Column “Timing,” the Number of Months Between the Damaging Event and the Data Collection is Given.

Table 7.3 Sub‐Sample Sizes, Item Non‐Response Rates, and Average Values for Flood Water Levels as well as Damage Costs for Buildings and Household Contents in the Household Surveys after River Floods in Germany. (See also Table 7.1).

Chapter 08

Table 8.1 Summary of Deaths in Natural Hazards in Australia: 1788–2014.

Table 8.2 Insurance Losses by Natural Hazard in Australia 1970–2013 (Millions of Dollars [2011 Dollars]).

Table 8.3 Post‐Disaster Studies Following Floods, Australia.

Table 8.4 Research Approaches to Post‐Disaster Studies.

Chapter 09

Table 9.1 Case Studies.

Chapter 10

Table 10.1 The Five Cs (capitals) that Make up a Set of Measurable Indicators.

Table 10.2 The Four Properties of a Resilient System.

Table 10.3 Disaster Forensic Methodologies Summary.

Table 10.4 PERC Studies Conducted so far.

Table 10.5 PERC Analysis Meta‐Structure.

Chapter 11

Table 11.1 Standardized Index for Post‐Event Damage Reporting.

Table 11.2 Affected Lifelines in the 2012 Flood.

Table 11.3 Affected Lifelines in the 2013 Flood.

Table 11.4 Commercial Activities Eligible Losses by Sub‐Sector, 2012 Flood.

Table 11.5 Commercial Activities, Self‐Declared Losses by Sub‐Sector, 2013 Flood.

Table 11.6 Comparison Between the Pre‐Event Damage Assessment in the Three Most Affected.

Chapter 12

Table 12.1 Summary of Reforms (Extract).

Chapter 13

Table 13.1 Forensic Analysis Listing Contributing Factors Assigned to Vulnerability Class.

Table 13.2 Assigning Impact to Affected Sectors.

Table 13.3 Assigning of Weight to Impact on Affected Sectors. Total is Calculated by Summing Weights Across all Sectors.

Table 13.4 Total Weight of Factor is Substituted Back into Vulnerabilities Columns to Calculate total Significance for Each Vulnerability.

Table 13.5 Total Weight of Each Vulnerability Calculated by Summing Accumulated Impact.

Chapter 14

Table 14.1 Imagery Used to Produce Post‐Event Maps for Flood Events (as of 31 October 2015); Number of Scenes Per Sensor Type for Delineation and Grading Maps.

Table 14.2 Timeline for the First Flood Extent Map Released in EMSR120.

List of Illustrations

Chapter 01

Figure 1.1 Consolidated extreme precipitation related disasters in South America (1970–2013).

Chapter 02

Figure 2.1 The requirements determine the data model that in turn determines the data to be collected [Re‐drawn from

De Groeve et al.,

2014].

Figure 2.2 The three application areas that generate risk knowledge, loss accounting, forensic analysis, and modeling, rely on loss, exposure, vulnerability and hazard data sets that ideally would be structured in a common spatially referenced database.

Figure 2.3 Conceptual loss data model that could be used to share loss data.

Figure 2.4 Comparison of hazard reporting and main loss indicators for 14 loss data sets originating from European Union countries.

Figure 2.5 The indicators that are considered for developing composite indicators used in Sendai recording and the corresponding data available from European loss databases.

Chapter 04

Figure 4.1 Direct and insured flood losses from three different data sources: (1) insured flood losses/claims paid through the National Flood Insurance Program as reported by FEMA, (2) direct flood losses as reported by the National Weather Service (NWS), and (3) direct flood losses as reported by the Spatial Hazard Events and Losses Database for the United States (SHELDUS). Please note that NWS losses are reported by water year (October through September) and not by calendar year as done by FEMA and SHELDUS. In addition, SHELDUS distinguishes between tropical and non‐tropical flooding. Here, only non‐tropical flooding is reported. As a result, storm surge damage is excluded, which explains the low loss estimates for 2005 (Hurricane Katrina). All trend lines show an upward trend with direct losses according to the NWS being highest, followed by direct losses as reported by SHELDUS and then insured losses.

Figure 4.2 Spatial distribution of direct, non‐tropical flood losses at the county level. The four loss categories range from light ($551 to $5 million), medium ($5 million to $50 million), and dark gray ($50 million to $500 million) to black ($500 million to $10 billion). Direct losses include property and crop losses. Losses are adjusted to 2014 dollars. Period of record is from 1960 to 2014.

Figure 4.3 Spatial distribution of direct, non‐tropical flood losses per capita at the county level. The four loss categories range from light (up to $1,000 per capita), medium ($1,000 to $10,000 per capita), and dark gray ($10,000 to $50,000 per capita) to black ($50,000 to $100,000 per capita). Losses are adjusted to 2014 dollars, and the county population counts at the time of the flood event. Period of record is from 1960 to 2014.

Figure 4.4 Map of the U.S. National Streamflow Information Program and its about 7,500 stream gauges.

Chapter 05

Figure 5.1 HOWAS21 database and web application.

Figure 5.2 HOWAS21 web application.

Figure 5.3 Locations of available flood damage data in HOWAS21.

Figure 5.4 Kite diagram for a data subset of HOWAS21 showing data quality dimensions assessed on the basis of the rules listed in Table 5.2.

Figure 5.5 Examples of absolute depth‐damage curves for residential building loss, i.e., square‐root (blue), linear (red), and polynomial (green) curves calculated on the basis of two different samples of empirical damage data (top and bottom).

Chapter 06

Figure 6.1 Typical survey context for water elevation measurement.

Chapter 07

Figure 7.1 Underlying theoretical model used to operationalize the contents of the flood loss questionnaire in Germany

Figure 7.2 Mean flood damage at residential buildings in the German federal states Bavaria and Saxony as well as in the Austrian province Tyrol for some flood events. (All prices are indexed to the reference year 2005. HOWAS data for the floods in 1985 and 1988 were taken from

Buck and Merkel

[ 1999]. SAB data for the flood 2002 were provided by the Saxon Relief Bank (Sächsische Aufbaubank), and data for the 2005 flood in Tyrol, Austria, were provided by the alpS GmbH, Innsbruck. CATI interviews as listed in Table 7.1.)

Chapter 08

Figure 8.1 Flooding hazards and CDS post‐disaster research study areas in Queensland

Map generated from Global Risk Data Platform 2015

.

Chapter 09

Figure 9.1 Technological and Organization Environment.

Chapter 11

Figure 11.1 Connection between pre‐event and post‐event scenarios.

Figure 11.2 Methodological framework to build the complete event scenario.

Figure 11.3 The Umbria region in the context of central Italy.

Figure 11.4 Affected municipalities in the 2012 and 2013 floods.

Figure 11.5 Municipalities that reported damage to industrial and commercial activities.

Figure 11.6 Distribution of damage across sectors in the 2012 and 2013 floods.

Chapter 12

Figure 12.1 Natural disaster events in Queensland 2006–2015 and expenditures under NDRRA.

Figure 12.2 Number of Local Government Authorities activated under NDRRA in Queensland 2011–2015.

Figure 12.3 Queensland cyclone activity 2010–2015.

Figure 12.4 Typical questions used in DARMSys™.

Figure 12.5 Example of Rapid Damage Assessments – Chinchilla, Queensland, October 2015. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.6 DARMSys™ case study – Cardwell, Queensland.

Figure 12.7 Data collection showing damage severity and progress of reconstruction. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.8 Example DARMSys™ data in Yeppoon following Severe Tropical Cyclone Marcia 2015. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.9 Infrastructure damage capture – feature breakdown screens.

Figure 12.10 IDARM Streamlined NDRRA submission workflow.

Figure 12.11 Reconstruction program phases 2011–2015.

Figure 12.12 Business process management life cycle.

Figure 12.13 GMRS integration.

Figure 12.14 Indicative flood mapping – Tablelands Regional Council. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.15 Betterment project performance post Severe Tropical Cyclone Marcia.

Figure 12.16 Betterment Project financial benefit forward projections.

Figure 12.17 Betterment example – Gayndah‐Mundubbera Road, North Burnett Regional Council. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.18 Betterment example – Gayndah Water Supply Intake, North Burnett Regional Council. Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.

Figure 12.19 The Master Plan for Grantham involved the relocation of the community to higher ground.

Chapter 13

Figure 13.1 Process of critical cause analysis.

Figure 13.2 Data required in relation to businesses and flooding.

Figure 13.3 Data sources and categories (sectors) for 2007 Gloucestershire flood.

Figure 13.4 Interrelationships of infrastructure system and civil society.

Figure 13.5 Significance of indirect vulnerabilities to business.

Figure 13.6 Significance of direct vulnerabilities to business.

Figure 13.7 Damage categories according to commercial activities.

Chapter 14

Figure 14.1 Share of occurrence of natural disasters by disaster type (1994–2013). [CRED, UNSIDR, 2015].

Figure 14.2 Copernicus Emergency Management Service–overview of the two main components, Early Warning and Mapping, with the two mapping modules, and indication of which phases of the disaster management cycle each is supporting.

Figure 14.3 Flooded areas derived from the water bodies extracted from the post‐event imagery and the pre‐event reference hydrography.

Figure 14.4 Example of erosion phenomena related to a flood event and impact on the road network. (a) Pre‐event situation (Source: ESRI World Imagery © Digital Globe); (b) Post‐event situation (aerial orthoimagery © European Union) with road damage assessment (completely destroyed in red and partially affected in yellow.

Figure 14.5 Example (detail) of a grading map showing damages on the road network, industrial utilities, and areas affected by floodwater: (a) Pre‐event imagery; (b) Post‐event imagery; (c) Post‐event imagery overlaid with crisis information: road blocks are identified by the crossed orange square; highly affected roads in orange; highly and moderately flooded areas in dark and light blue, respectively; moderately affected quarry in light orange.

Figure 14.6 Example of a flood delineation monitoring map, where the flood extent at two different dates is displayed with different transparent colors (previous flood extent in light blue, most recent flood extent in dark blue).

Figure 14.7 Copernicus EMS‐RM workflow (simplified).

Figure 14.8 Average duration of the four main steps controlled by the EMS‐RM service provider (April 2012–September 2015). Note that the value for “map production and delivery time” is the average for version 1.

Figure 14.9 Areas of interest and satellite image footprints of Copernicus EMS‐RM activation for floods in Spain (EMSR120).

Figure 14.10 One of the eight delineation maps produced in the Copernicus EMS‐RM activation for floods in Spain early 2015 (EMSR120). The map shows the flood extent in the area of Luceni on 28 February (light blue) and 2 March 2015 (darker blue). The zoom, which is not a feature of the standard product, gives an impression of the crisis information and the various reference layers.

Chapter 15

Figure 15.1 Poli‐RISPOSTA general architecture. Its tree main features Data Model, Web Visualization Client, and Mobile Data Collection, which are explained in detail in sections 15.2.2, 15.2.3, and 15.2.4, respectively.

Figure 15.2 Database Entity Relationship diagram.

Figure 15.3 Web Portal Functionalities. Eight basic functionalities were defined for the Poli‐RISPOSTA project: layer and documents upload, flooded area definition, damage report, direct damage survey, layers, maps, creation of graphs and tables, and archive.

Figure 15.4 Maps functionality.

Figure 15.5 Graph and Tables functionality.

Figure 15.6 Graph and Tables functionality.

Figure 15.7 Mobile map functionality.

Figure 15.8 Forms functionality.

Guide

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Table of Contents

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Climates, Landscapes, and Civilizations

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Dynamics of the Earth’s Radiation Belts and Inner Magnetosphere

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Lagrangian Modeling of the Atmosphere

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Modeling the Ionosphere‐Thermosphere

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The Mediterranean Sea: Temporal Variability and Spatial Patterns

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Future Earth ‐ Advancing Civic Understanding of the Anthropocene

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The Galápagos: A Natural Laboratory for the Earth Sciences

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Modeling Atmospheric and Oceanic Flows: Insightsfrom Laboratory Experiments and Numerical Simulations

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Remote Sensing of the Terrestrial Water Cycle

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Magnetotails in the Solar System

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Hawaiian Volcanoes: From Source to Surface

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Fluid Dynamics in Complex Fractured‐Porous Systems

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Subduction Dynamics: From Mantle Flow to Mega Disasters

Gabriele Morra, David A. Yuen, Scott King, Sang Mook Lee, and Seth Stein (Eds.)

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Timothy Fuller‐Rowell, Endawoke Yizengaw, Patricia H. Doherty, and Sunanda Basu (Eds.)

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Qiuhong Tang and Taikan Oki (Eds.)

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Geophysical Monograph 228

Flood Damage Survey and Assessment

New Insights from Research and Practice

Daniela MolinariScira MenoniFrancesco BallioEditors

This Work is a co‐publication of the American Geophysical Union and John Wiley and Sons, Inc.

This Work is a co‐publication between the American Geophysical Union and John Wiley & Sons, Inc.

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Cover image: Flash flood that occurred in the Umbria Region in the 2013 flood in the municipality of Scheggia e Pascelupo (GRID‐Politecnico di Milano, 2013). Insert 1: Wet documents in a firm flooded in the municipality of Marsciano, in Umbria in the 2012 flood (GRID‐Politecnico di Milano, 2012). Insert 2: Debris flow that occurred on November 2013 in Umbria in the municipality of Gualdo Tadino (GRID‐Politecnico di Milano, 2013).Cover design by Wiley

CONTRIBUTORS

Andrea AjmarGeodatabase AdministratorInformation Technology for Humanitarian Assistance, Cooperation and Action (ITHACA)Turin, Italy

Shahrzad AmouzadPhD StudentSchool of ArchitectureOxford Brookes UniversityOxford, UK

Danilo ArdagnaAssociate ProfessorDepartment of Electronics, Information and BioengineeringPolitecnico di MilanoMilan, Italy

Funda AtunResearch FellowDepartment of Architecture and Urban StudiesPolitecnico di MilanoMilan, Italy

Francesco BallioFull ProfessorDepartment of Civil and Environmental Engineering Politecnico di MilanoMilan, Italy

Tatiana BedrinaResearcherCIMA Research FoundationSavona, Italy

Nicola BerniHead of Functional CentreUmbria Region Civil Protection AuthorityFoligno (PG), Italy

Piero BoccardoAssociate ProfessorInteruniversity Department of Science, Design and Land PoliciesPolitecnico di TorinoTurin, Italy

Marco BrogliaScientific OfficerEuropean Commission Joint Research CentreIspra, Italy

Maria BrovelliFull ProfessorGeomatics LaboratoryDepartment of Civil and Environmental EngineeringPolitecnico di MilanoMilan, Italy

Christina CorbaneScientific and Technical Project OfficerEuropean Commission Joint Research CentreIspra, Italy

Tom De GroeveActing Head of the Global Security and Crisis Management UnitEuropean Commission Joint Research CentreIspra, Italy

Martin DolanResearch FellowSchool of ArchitectureOxford Brookes UniversityOxford, UK

Tiernan DoyleProject CoordinatorVOAD and Resilience NetworksBoCo StrongBoulder, Colorado, USA

Daniele EhrlichSenior Staff MemberEuropean Commission Joint Research CentreIspra, Italy

Melanie GallResearch Assistant ProfessorDepartment of GeographyUniversity of South CarolinaColumbia, South Carolina, USA

Fabio Giulio‐TonoloGeomatics Expert and Remote Sensing SpecialistInformation Technology for Humanitarian Assistance, Cooperation and Action (ITHACA)Turin, Italy

Yetta GurtnerResearcherCentre for Disaster StudiesJames Cook UniversityTownsville, Queensland, Australia

Sören‐Nils HaubrockDirectorBeyond Concepts GmbHOsnabrück, Germany

Adriana KeatingResearch ScholarInternational Institute for Applied Systems AnalysisLaxenburg, Austria

David KingAssociate Professor and DirectorCentre for Disaster StudiesCentre for Tropical Urban and Regional PlanningSchool of Earth and Environmental SciencesJames Cook UniversityTownsville, Queensland, Australia

Heidi KreibichSenior Scientist and Head of WG Flood Risk and Climate AdaptionSection HydrologyGFZ German Research Centre for GeosciencesPotsdam, Germany

Jan KuceraScientific OfficerEuropean Commission Joint Research CentreIspra, Italy

Jessica LamondAssociate ProfessorArchitecture and the Built EnvironmentUniversity of the West of EnglandBristol, UK

Karen MacCluneSenior Staff ScientistInstitute for Social and Environmental Transition‐InternationalBoulder, Colorado, USA

Marco MassabòProject LeaderCIMA Research FoundationSavona, Italy

Mirjana MazuranResearch AssistantDepartment of Electronics, Information and BioengineeringPolitecnico di MilanoMilan, Italy

Scira MenoniFull ProfessorDepartment of Architecture and Urban StudiesPolitecnico di MilanoMilano, Italy

Guido MinucciResearch FellowDepartment of Architecture and Urban StudiesPolitecnico di MilanoMilan, Italy

Daniela MolinariResearcherDepartment of Civil and Environmental EngineeringPolitecnico di MilanoMilan, Italy

Brendan MoonChief Executive OfficerQueensland Reconstruction AuthorityBrisbane, Australia

Meike MüllerGeoecologistDeutsche Rückversicherung NatCat‐CenterDüsseldorf, Germany

Carolina Arias MunozPhD StudentDepartment of Civil and Environmental EngineeringPolitecnico di MilanoMilan, Italy

Ray OgdenProfessor and Associate DeanSchool of ArchitectureOxford Brookes UniversityOxford, UK

Shadrock RobertsDirectorUshahidiResilience Network InitiativeAthens, Georgia, USA & Nairobi, Kenya

Roberto RudariResearch DirectorCIMA Research FoundationSavona, Italy

Kai SchröterResearcher and Project ManagerSection HydrologyGFZ German Research Centre for GeosciencesPotsdam, Germany

Julio SerjeProgram Manager/Senior Software EngineerUnited Nations Office for Disaster Risk ReductionGeneva, Switzerland

Michael SzoenyiFlood Resilience Program LeadZurich Insurance GroupZürich, Switzerland

Annegret ThiekenProfessorInstitute of Earth and Environmental ScienceUniversity of PotsdamPotsdam, Germany

Kanmani VenkateswaranResearch AssociateInstitute for Social and Environmental Transition‐InternationalBoulder, Colorado, USA

Nicholas WallimanSenior LecturerSchool of ArchitectureOxford Brookes UniversityOxford, UK

Annett WaniaScientific OfficerEuropean Commission Joint Research CentreIspra, Italy

PREFACE

In this book, state of the art methods and procedures for post‐flood damage data collection and analysis are discussed, suggesting also best practices that may guide the reader toward the improvement of the quality and comparability of data and analyses across time and geographic areas.

The fact that better data are needed is a common plea put forward by researchers in many areas of investigation, including risk analysis. The call for better data on natural hazards impacts is certainly not new and has been on the agenda for a long time. So why bother now? Today the novelty stands at multiple levels to justify the proposal of such a thorough reflection proposed to the reader.

First, not only scientists are concerned about lack of data. It also has become a strategic issue for a variety of stakeholders, pertaining both to private and public sectors, who hold responsibility in different ways for disaster risk management. This explains why in the book contributions from a variety of actors can be found, ranging from institutions working at different spatial scales, to reinsurers, to practitioners. The reasons are varied and reflect specific interests and the mission of each actor. For governments, public administrations, and national and international organizations, the need to be able to compare events across time and space has become a prominent factor as the number and the extent of disasters have been constantly increasing over the last years putting at risk lives, public investments, and economic development. To fully appreciate the root causes of such an increase, there is the need first to be able to rely on the data related to the most obvious indicators, such as the number of victims, lost assets, and damages to items and systems.

Different studies suggest that such an analysis of trends over time and across geographic areas is not really possible given the low quality of available databases and the lack of agreed upon standards that are used to collect data when a disaster strikes and afterward. In front of the evidence of increased impacts and associated costs of repair and lost revenue, particularly in times of financial crisis, the need of programming investments in mitigation becomes key, in order to achieve the best results in terms of avoided damage at sustainable costs. However, such appreciation clearly requires that the background information on which such evaluations of potential investments is done be reliable at least at a minimal level, which apparently is not the case as for now.

It would be a mistake, however, to think that such concerns take into account only public bodies. Private organizations at large would greatly benefit from an enhanced capacity to estimate and prepare for damage before an event strikes. Insurance companies have relied until now on the large amount of data that is available in their databases. However, such data are very partial, of varied quality, depending significantly on the skills and time devoted to surveys by experts appointed to set the claims after an event. Such data are useful for identifying key variables benefitting from a very large number of surveyed values, but the data cannot account for extraordinary situations (linked for example to catastrophic events or whenever cascading effects are implied) or to appreciate the interaction of factors in very complex environments. As urbanized areas have grown exponentially over the last few decades so has the complexity of disasters. A variety of interdependent and tightly interconnected systems (including social, economic, built up, natural) have created the starting point for unanticipated damage that can be very costly. Gaining a finer understanding of how a variety of initial conditions in different environments produce larger and more complex ways to solve problems is becoming an issue also for insurance and reinsurance companies. In the meanwhile, studies [Rose and Huyck, 2016] have shown that the cost of collecting new and more data is fairly repaid by the possibility of better appraising how the emergency context affects businesses and what the factors are that provoke the highest impact on businesses’ capacity to recover quickly.

The reasons for a growing interest by a variety of actors for enhanced disaster damage data that we have just discussed explain why now different initiatives at the national and international levels, such as the Working Group established by the European Union (EU) Commission, or the Sendai Framework for Action, have raised interest on the topic. At the heart of the reasons for such interest is certainly the recognition that data and information are the bricks of knowledge. It is not just a matter of accounting to better program resources to be allocated for disaster management or to evaluate trends of losses to identify the potential impact of climate or social changes leading to different patterns in the natural and the built environments. It is also an issue of identifying and selecting the most effective mitigation measures while gaining a better perspective on what the factors are of the risk function, hazard, exposure, and vulnerability, that have contributed most to the final outcome in terms of losses.

On the other hand, enhanced knowledge of natural hazards accomplished in the last decades is key for identifying what the most useful data are to collect. In addition, identifying the crucially missing information is key. Without both, a better understanding of how risk factors play in each context and better modeling capacity for forecasting damage before the event occurs will not be achieved.

The book is organized in five parts. Part I comprises two chapters that lead the reader into the international debate on loss data needs, discussing loss data requirements defined by the Sendai Framework and the main initiatives to meet such requirements.

Part II starts with a comprehensive overview of loss data storage at the global level, highlighting limits, strengths, and needs of available databases in order to accomplish the Sendai Framework requirements (Chapter 3). Then, the focus shifts to the national level with a critical discussion of flood loss databases in the United States of America (Chapter 4) and the German HOWAS21 database (Chapter 5), presented as a best practice of loss databases tailored to risk modeling needs.

Part III focuses on best practices of damage data collection, at both the meso and the local scale. As for the former, the experience gained in Germany after the Elbe flood in 2002 is analyzed (Chapter 7). In this instance, computer‐aided telephone interviews were carried out to “survey” observed damage at residential buildings and firms. Such practice is now a standard in Germany after every flood event and could be considered for replication in other countries. As for the local scale, the survey experience gained in the Umbria region (Central Italy), after the 2012 flood, is discussed in Chapter 6. Such experience brought the development of a procedure for damage data collection, at the individual affected item scale, to be implemented every time a flood occurs in the region. The procedure has been designed to meet several user needs (i.e., emergency management, damage compensation, disaster forensic, and risk modeling) and includes specific forms for damage surveys.

Chapter 8 presents a comprehensive overview of the surveys carried out at the Centre For Disaster Studies Research (at James Cook University) on the occasion of 13 floods in Australia. Such an experience can be seen as a best practice situation to address issues that contribute to mitigation as well as to understand community experience in a disaster. The main results from the study are described in terms of communities’ vulnerability and resilience. In Chapter 9, that closes the third section, the main advantages and limits of crowdsourcing as a reliable and complementary source of loss data are discussed. In detail, the authors, who are practitioners working within humanitarian organizations and community‐based flood relief organizations, describe their own experience by presenting several case studies. The latter constitute the basis for illustrating the value of crowdsourcing but reflect also on how to ensure its effective integration into disaster response.

Part IV supplies examples of data analysis, of how collected and stored data can be used to support multiple objectives for which data are collected. Following De Groeve et al. [2013], objectives can be synthetically indicated as accounting, forensic analysis, needs assessment, and improved risk modeling capacity.

The first contribution to this section deals with the Post‐Event Review Capability (PERC) methodology (Chapter 10). The methodology has been designed as part of the Zurich Insurance’s resilience alliance as a process to evaluate what happened before, during, and after a disaster, to identify the critical gaps and successes in the overall disaster risk management system, and to present actionable recommendations. Then, the use of damage data to develop complete event scenarios after flood events is discussed, providing an application to an Italian case study (Chapter 11). Chapter 12 presents the experience gained by the Queensland Reconstruction Authority in Australia after the 2010–2011 floods. The chapter highlights how the knowledge of observed impacts allowed the definition of the most suitable strategies to build a more resilient Queensland. The final contribution to this section (Chapter 13) supplies insights on the use of collected and stored data to carry out a forensic investigation of flood damage at the industrial sector. In particular, the chapter discusses how disaster forensics can be used to understand damage cause and mechanisms and then to define proper risk mitigation measures.

The last section (Part V) includes best practices on the use of Information and Communication Technology (ICT) supporting data collection, storage, and analysis. Chapter 14 focuses on the use of satellite data to survey and assess damage at the global scale. In particular, the Copernicus Emergency Management Service (EMS) is described making reference to some case studies. Chapter 15 describes tools developed within the Italian project Poli‐RISPOSTA for data collection and analysis at the local scale. Such tools consist of mobile applications for data survey, spatial databases for the storage of data, and a web‐GIS application for data analysis and representation.

Conclusions close the volume and include recommendations, guidelines, and best practices starting from the experiences described in the book.

REFERENCES

De Groeve, T., K. Poljansek, and D. Ehrlich (2013), Recording Disasters Losses: Recommendation for a European Approach, JRC Scientific and Policy Report.

Rose, A., and C. Huyck (2016), Improving catastrophe modeling for business interruption and insurance needs, Risk Analysis, doi:10.1111/risa.12550.

Daniela MolinariDepartment of Civil and Environmental EngineeringPolitecnico di Milano, Milan, Italy

Scira MenoniDepartment of Architecture and Urban StudiesPolitecnico di Milano, Milan, Italy

Francesco BallioDepartment of Civil and Environmental EngineeringPolitecnico di Milano, Milan, Italy

ACKNOWLEDGMENTS

The editors would like to thank all the authors of the chapters for their valuable contributions to the book, as well as the reviewers of the various chapters for their critiques and suggestions that surely contributed to the improvement of the book, for this final version. The editors would also like to thank AGU‐Wiley for fostering and supporting the realization of the book and, in particular, Dr. Rituparna Bose, Mary Grace Hammond, Vishnu Narayanan, Peggy Hazelwood and Shiji Sreejish for their help in the entire production phase. Finally, the editors acknowledge that the main ideas behind the design of this book come from activities carried out within the EU expert working group on disaster damage and loss data at the Joint Research Centre (JRC) and the research projects Poli‐RISPOSTA (stRumentI per la protezione civile a Supporto delle POpolazioni nel poST Alluvione), which was funded by the Poli‐SOCIAL funding scheme of Politecnico di Milano, IDEA (Improving Damage assessments to Enhance cost–benefit Analyses), a EU prevention and preparedness project in civil protection and marine pollution, funded by DG‐ECHO, G.A.N. ECHO/SUB/2014/694469 and EDUCEN ‐ European Disasters in Urban centres: a Culture Expert Network (3C [Cities, Cultures, Catastrophes]) funded by EU Horizon 2020, C.N. 653874, in which the editors have been actively involved.

Part IIntroduction

1Overview of the United Nations Global Loss Data Collection Initiative

Julio Serje

United Nations Office for Disaster, Risk Reduction, Geneva, Switzerland

ABSTRACT

The Year 2015 was marked by the emergence of three international agreements: The Sendai Framework for Disaster Risk Reduction, the 2030 Agenda for Sustainable Development, and in the Intergovernmental Panel on Climate Change (IPCC) Conference of the Parties (COP) 2015, a global legally binding agreement on Climate Change now known as the Paris Agreement.

All of these frameworks explicitly recognize the importance and usefulness of collecting and analyzing loss data in their corresponding implementations. The Sendai Framework, in particular, calls for the collection of data about disaster of all scales. It also calls for the collection of data about man‐made, technological, environmental, and other hazards, with an emphasis on climate‐related risks.

Most importantly, the Sendai Framework sets out seven targets, of which four relate to losses: mortality, people affected, economic loss, and damages to infrastructure. This implies that the coverage of national disaster loss data sets will have to be expanded to be global so that countries can report on these targets. This development represents a unique opportunity to build a bottom‐up constructed global disaster loss database.

Many actors have collected national loss data for many years. For over a decade, the United Nations (UN) system has supported and promoted the construction of national disaster databases based on the Disaster Information Management System (DesInventar) methodology and software tools. Additionally, a number of countries have been collecting data with proprietary specifications and different levels of resolution. These include several countries that collect data at a localized level, for example, European countries where data are associated with compensation mechanisms.

DesInventar‐based national data sets also cover small disasters, breaking down event data by municipality aggregates and using a rich set of indicators, which contain those that will be required to report against the Sendai Framework. The number of indicators implies bigger efforts may be required to build or retrofit and sustain these databases, which in addition can provide a clearer picture of damage trends and patterns at sub‐national scales and contribute to a better understanding of risk.

There are, however, methodological, conceptual, and practical challenges associated with a relatively localized data collection. These challenges may range from discrepancies in the perception of what an “event” is, to difficulties in the integration of multiple data sources, to the additional effort required to disaggregate information collected otherwise and the challenge of the economic valuation of the damage aggregates using a consistent and homogeneous methodology.

Despite these challenges, the 2015 edition of the Global Assessment Report on Disaster Risk Reduction (GAR) by the UN features analyses using a consolidated, homogenized, and standardized data set covering 82 countries and several states in India, which includes a uniform economic valuation of damage. The United Nations Office for Disaster Risk Reduction (United Nations International Strategy for Disaster Reduction [UNISDR]) has been using this data set as a proof of concept of what a global database could look like. The UN Initiative, which started in 2005 when only 15 countries had these data sets, has continued to approach 100 countries in 2015. It will continue with renewed enthusiasm in the next few years, with the target of global coverage by 2020, as stated by the Sendai Framework.

1.1. DISASTER RISK REDUCTION: A FRAMEWORK FOR ACTION

The concept and practice of reducing disaster losses and risk through systematic efforts to analyze and reduce the causal factors of disasters and therefore reduce its impacts is known today as Disaster Risk Reduction (DRR). Reducing exposure to hazards, lessening vulnerability of people and property, wise management of land and the environment, and improving preparedness and early warning for adverse events are all examples of disaster risk reduction [UNISDR, 2009a].

Progress in reducing risk has been undeniable over the past decades. However, global models suggest that the risk of economic losses is rising as a result of a series of factors, including increases in exposure and vulnerability, exacerbation of hazards because of climate change, and the rapidly increasing value of the assets that are exposed to major hazards [UNISDR, 2015a]. In addition, a large proportion of losses continue to be associated with small and recurring disaster events that severely damage critical public infrastructure, housing, and production, which are key pillars of growth and development in low‐ and middle‐income countries.

The long road of international agreements that started with the declaration of 1990–1999 as the International Decade for Natural Disaster Reduction (IDNDR) [UNISDR, 1999a], and which produced the Yokohama Strategy and Plan of Action, and the subsequent Hyogo Framework for Action, has shown the international continuous concern about the growing impacts of disasters.

1.2. THE SENDAI AND OTHER FRAMEWORKS OF 2015

On 18 March 2015, representatives from 187 United Nations Member States gathered in Sendai, Japan for the Third World Conference on Disaster Risk Reduction and adopted the Sendai Framework for Disaster Risk Reduction (SFDRR) (UNISDR, 2015). Later in the same year, the 2030 Agenda for Sustainable Development was also adopted, and to finalize a golden year in international agreements, countries participating in the Paris COP 21 reached for the first time a global legally binding agreement on climate change, now known as the Paris Agreement.

The international community made a big effort to align these three processes as much as possible. In its first page, the Paris Agreement welcomes “the adoption of United Nations General Assembly resolution A/RES/70/1, ‘Transforming our world: the 2030 Agenda for Sustainable Development,’ in particular its goal 13, the adoption of the Addis Ababa Action Agenda of the third International Conference on Financing for Development and the adoption of the Sendai Framework for Disaster Risk Reduction” [United Nations Framework Convention on Climate Change (UNFCCC), 2015].

The Sendai Framework, the first of these to be adopted, sets “the substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries” as its main outcome. It also sets as its only goal to “prevent the creation of new risks and to reduce existing ones through different measures and thus strengthen resilience.”

The 2030 Agenda for Sustainable Development embeds within its goals and targets all of the targets set by the Sendai Framework. Goal 11 Target 5 in particular comprises three of the seven targets of the Sendai Framework, all of them aiming at the reduction of human and economic losses [UN, 2015]. Targets in other goals, such as Goal 13 addressing climate change, also address similar challenges as those identified by SFDRR.

The Paris Agreement, in its Article 7 on adaptation, sets a global goal to increase adaptive capacity, strengthen resilience, and reduce vulnerability. This is the first time there is a formal agreement on a global adaptation goal. Article 8 on loss and damage (one of the problematic issues that delayed negotiations) includes reducing risk of losses and damages, early warning systems, emergency preparedness, and comprehensive risk assessment and management, all of which are aligned with the Sendai Framework Priorities for Action and Targets [UNFCCC, 2015].

1.3. THE SENDAI FRAMEWORK AND LOSS DATA COLLECTION

The Sendai Framework is structured around one main outcome and one goal, four priorities for action, seven targets and has a much wider scope than its predecessor, the Hyogo Framework for Action.

Priority 1. “Understanding disaster risk” states that disaster risk management should be based on a thorough understanding of disaster risk and losses in all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics, and the environment. Such knowledge can be used for risk assessment, prevention, mitigation, preparedness, and response.

Priority 2, “Strengthening disaster risk governance to manage disaster risk” recommends clear vision, plans, competence, guidance, and coordination within and across sectors, as well as participation of relevant stakeholders and fostering collaboration and partnership across mechanisms and institutions for the implementation of instruments relevant to disaster risk reduction and sustainable development.

Priority 3, “Investing in disaster risk reduction for resilience” suggests public and private investment in disaster risk prevention and reduction through structural and non‐structural measures, which are essential to enhance the economic, social, health, and cultural resilience of persons, communities, countries, and their assets, as well as the environment.

Priority 4, “Enhancing disaster preparedness for effective response and to ‘Build Back Better’ in recovery, rehabilitation, and reconstruction” recognizes there is a need to strengthen disaster preparedness and ensure capacities are in place for effective response and recovery at all levels. The recovery, rehabilitation, and reconstruction phases are critical opportunities to build back better than before and opportunities to integrate disaster risk reduction into development.

Both the Sendai Framework for reducing disaster risk and its predecessor, the Hyogo Framework for Action, explicitly recognize the importance and usefulness of collecting loss data as one of the actions that will help countries to increase the knowledge about the risks they face. In particular, the Sendai Framework Priority 1, “Understanding disaster risk,” suggests among other activities the following:

“(d) Systematically evaluate, record, share and publicly account for disaster losses and understand the economic, social, health, education, environmental and cultural heritage impacts, as appropriate, in the context of event‐specific hazard‐exposure and vulnerability information;

(e) Make non‐sensitive hazard exposure, vulnerability, risk, disaster and loss‐disaggregated information freely available and accessible, as appropriate”;

The text of the Framework calls for its application to disasters of all scales and, as opposed to the Hyogo framework, it requests countries to address and therefore collect data about hazards that are not only considered of “natural” origin:

“15. This Framework will apply to the risk of small‐scale and large‐scale, frequent and infrequent, sudden and slow‐onset disasters caused by natural or man‐made hazards, as well as related environmental, technological and biological hazards and risks”.

To support the assessment of global progress in achieving the outcome and goal of the framework, seven global targets were agreed upon. Most importantly, out of these seven targets, four are related to losses and impacts.

These targets will be measured at the global level and will be complemented by work of the Open Ended Intergovernmental Working Group (OEIWG), tasked with the responsibility of developing appropriate indicators, with all the details and precise definitions that will be required, and defining the rules regarding how those indicators will be used to compute the targets [UNISDR, 2015]. The seven global targets, in summary form, follow:

Substantially reduce relative (per capita) global disaster mortality.

Substantially reduce the relative number of affected people globally.

Reduce direct disaster economic loss in relation to global gross domestic product (GDP).

Substantially reduce disaster damage to critical infrastructure and disruption of basic services, among them health and educational facilities.

Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020.

Substantially enhance international cooperation to developing countries.

Substantially increase the availability of and access to multi‐hazard early warning systems and disaster risk information and assessments.

There are several consequences to the wider scope of the framework, the explicit recommendations of Priority Action 1 on loss data collection and, in particular, to the fact that Targets (a) to (d) are based on loss indicators. One is that countries are strongly encouraged to systematically account for disaster losses and impacts for a wide spectrum of disaster scales and a large set of hazards. This accounting must take into account an expectedly large number of loss indicators defined by the OEIWG, including human, infrastructure, and economic indicators. This set of indicators will allow, on one hand, the monitoring of the outcomes of the framework, reduction of losses, and the progress in achieving the targets, and on the other hand, it will allow improvement of the understanding of risk and the impacts of disasters in member states.

The work of the OEIWG has defined a relatively manageable but still numerous and complex set of indicators to measure these targets [UNISDR,