The risk data hub loss datasets : the risk data hub historical event catalogue.
Date: 2020-01-17
Institutions: Joint Research Centre
Subjects: catalogue, cloud computing, data collection, data processing, database, information technology, man-made disaster, natural disaster, research report, risk management, software

The Risk Data Hub loss datasets 

The Risk Data Hub Historical 
Event Catalogue 

 Faiella, A., Antofie, T., Luoni, S., Ríos Díaz, F., Marin Ferrer, M. 

2020 

EUR 30036 EN 

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a 
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of its frontiers or boundaries. 

Contact information  
Name Antofie Tiberiu-Eugen 
Address: Via E. Fermi, 2749, TP 267, I – 21027, Ispra (VA), Italy 
Email: Tiberiu-Eugen.ANTOFIE@ec.europa.eu 
Tel.: +39 0332 78 9363 

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https://ec.europa.eu/jrc 

JRC116366 

EUR 30036 EN 

PDF 

ISBN 978-92-76-14656-8 

ISSN 1831-9424 

doi:10.2760/488300 

Luxembourg: Publications Office of the European Union, 2020 

© European Union, 2020 

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How to cite this report: Faella, A., Antofie, T., Luoni, S., Rios Diaz, F., Marin Ferrer, M., The Risk Data Hub loss datasets - The Risk Data Hub 
Historical Event Catalogue, EUR 30036 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-14656-8, 
doi:10.2760/488300, PUBSY JRC116366.

Contents 

Abstract .................................................................................................................................................................. 4 

1 

Introduction ...................................................................................................................................................... 5 

2  Discussion on post-disaster loss data and current datasets .......................................................................... 7 

2.1  Global datasets ........................................................................................................................................ 7 

2.2  Regional and National Dataset ................................................................................................................ 9 

3  The RDH Historical event collection methodology ......................................................................................... 11 

4  The RDH Historical Event Catalogue Characteristics ..................................................................................... 12 

4.1  Hazard consideration ............................................................................................................................. 12 

4.2  Spatial coverage .................................................................................................................................... 12 

4.3  Temporal coverage ................................................................................................................................ 12 

4.4  Source of information ............................................................................................................................ 12 

HAZARD CATALOGUES .................................................................................................................................... 13 

DAMAGE AND LOSS SOURCES OF INFORMATION .......................................................................................... 13 

4.5  Criteria of selection for inclusion in the database ................................................................................ 14 

4.6  Challenges .............................................................................................................................................. 14 

Differences in the time of occurrence ........................................................................................................... 14 

Differences in classifying the type of disaster .............................................................................................. 14 

Differences due to multiple entry of a single disaster event ........................................................................ 15 

Differences in classifying the spatial extent and the exact location of an event......................................... 15 

Differences in the definitions of the indicators ............................................................................................. 15 

Differences in currencies and prices of economic losses .............................................................................. 15 

4.7  Conversion of economic damage values ............................................................................................... 15 

5  The RDH Historical Event Catalogue .............................................................................................................. 17 

5.1  Content overview ................................................................................................................................... 17 

5.2  Comprehensiveness, completeness and origin of the data .................................................................. 18 

6  Analysis of the collected data ........................................................................................................................ 20 

6.1  Most destructive events ......................................................................................................................... 21 

6.2  Most affected countries ......................................................................................................................... 22 

6.2.1 

Completeness and its influence on the analysis........................................................................ 27 

6.3 

Impact data processing for the past 18-years ..................................................................................... 28 

6.3.1 

Fatalities and economic losses .................................................................................................. 28 

6.3.2 

Affected and injured people ....................................................................................................... 29 

6.3.3 

Affected area .............................................................................................................................. 29 

6.3.4 

Limitations of the approach ....................................................................................................... 30 

i 

6.4  Discussion of trends .............................................................................................................................. 30 

7  Discussion of results ...................................................................................................................................... 32 

EARTHQUAKES ................................................................................................................................................ 32 

FLOODS .......................................................................................................................................................... 32 

7.1  Floods cause most fatalities ................................................................................................................. 32 

7.2  Signs of increased resilience and lower vulnerability ........................................................................... 32 

8  Conclusions and recommendations ............................................................................................................... 34 

References ............................................................................................................................................................. 1 

List of abbreviations and definitions ..................................................................................................................... 5 

List of figures ......................................................................................................................................................... 6 

List of tables .......................................................................................................................................................... 7 

Annexes .................................................................................................................................................................. 8 

Annex 1. Recorded information contained in the Historical Event Catalogue - Flood module ....................... 8 

Annex 2. Recorded information contained in the Historical Event Catalogue - Earthquake module ............. 9 

Annex 3. Recorded information contained in the Historical Event Catalogue – Forest Fire module ............ 10 

Annex 4. Recorded information contained in the Historical Event Catalogue - Landslides module ............. 10 

ii 

 
 
 
 
 
 
 
 
 
 
 
 
 
Authors 

Anna Faiella, traineeship program at European Commission, Joint Research Centre (JRC), Ispra, Italy/ Politecnico 
di Milano, Dipartimento Di Architettura E Studi Urbani (DAStu)  
Antofie Tiberiu-Eugen, European Commission, Joint Research Centre (JRC), Ispra, Italy 
Stefano Luoni, GFT Italia S.r.l external service provider of European Commission, Joint Research Centre (JRC), 
Ispra, Italy 
Rios Diaz Francisco, European Commission, Joint Research Centre (JRC), Ispra, Italy 
Montserrat Marin Ferrer, European Commission, Joint Research Centre (JRC), Ispra, Italy 

3 

 
 
Abstract 

In recent decades, the systematic collection of disaster related data has rapidly become a crucial concern; there 
is a growing recognition of the importance of post-disaster loss data, which are an essential mean to inform 
policy  decisions  oriented  to  reduce  disaster  risk  as  they  are  strictly  linked  to  a  variety  of  aspects:  from  the 
understanding of the impacts of occurred events to the calibration and validation of forecasting models till the 
evaluation of progresses in reducing disaster risk. Post-event damage and loss data have an intrinsic key role 
in all the phases of the DRM.  

Records of damage and losses occurred due to past disastrous events are not always available. Rarely countries 
have procedures and databases to collect and store post-event damage data; in many countries there are no 
organizations in charge of collecting data and open global datasets often have different quality of data (Petrucci 
at al., 2018). At European level loss and damage data are available through global multi-hazards databases 
(Wirtz, K., et al., 2014), but there is no authoritative loss database that can provide a trend at European level 
(De Groeve, 2015).  

The Disaster Risk Management Knowledge Centre (DRMKC) is currently developing a web-based geographical 
information system platform, the DRMKC Risk Data Hub (RDH), which aims at improving the access and share 
of curated EU-wide disaster risk information in order to support the implementation of international actions for 
DRM. Currently, Risk Data Hub structures the information into three modules that covers the Exposure Analysis, 
Historic Events – as an EU-wide loss and damage database and Risk Analysis module. 

The aim of this document is to report the methodology used to create the Historic Events module and the use 
that can be done of damage and loss data. 

The RDH Historical Event Catalogue consists in a collection of past events data occurred in EU created from a 
wide array of data published in several sources and datasets. This collection makes use of inventoried data; 
precisely different open access datasets have been interrogated collecting European-related records on past 
disastrous events. Both hazard and loss data have been systematically collected from various sources, checked, 
linked and homogenized to be provided in tabular and geospatial format in order to create the RDH Historical 
Event Catalogue.  

The work carried out consists in an effort done to improve the existing lack of homogeneous and comparable 
data on past events occurred across European Countries. This work represents an assemblage of sources that 
become complementary. Considering that each source focuses on different aspects of the impact events, the 
objective of the collection is to describe the phenomenon, gather data on loss and damage records and present 
the spatial extent of the damages. Finally, analyses intended to illustrate ways of examining global loss data 
and identifying possible trends in terms of peril or geographical prone areas within the European Countries are 
performed on the collected damage and loss data. 

4 

 
 
1 

Introduction 

In recent decades - and especially in the context of the adoption of the Sendai Framework for Action on Disaster 
Risk Reduction 2015-2030, the predecessor Hyogo Framework for Action 2005-2015, and the Paris Agreement 
- the systematic collection of disaster related data has rapidly become a crucial concern; loss data accounting 
is now in demand at all levels from national, to European and international. There is a growing recognition of 
the  importance  of  post-disaster  loss  data,  which  are  essential  not  only  for  understanding  the  impacts  of 
occurred events but they are fundamental also to support a variety of actions aimed at reducing disaster risk. 
They  are  fundamental  component  in  the  construction  of  trends,  observation  of  vulnerability  and  resilience 
fluctuation, calibration and validation of forecasting models and in the preparation of evidence-based National 
Risk Assessments (NRA) - Decision No.1313/2013/EU of the European Parliament and of the Council on a Union 
Civil Protection Mechanism (UCPM)- which requires a sound collection of disasters damage and loss data for a 
wide range of events of different nature (Poljanšek et al., 2018). Post-event damage and loss data have an 
intrinsic key role in all the phases of the DRM. 

Records of damage and losses occurred due to past disastrous events are not always available. Rarely countries 
have procedures and databases to collect and store post-event damage data; in many countries there are no 
organizations in charge of collecting data and open global datasets often have different quality of data (Petrucci 
at al., 2018).  

Global  datasets  provide  low  resolution  data  as  they  contain  aggregations  of  information;  therefore  many 
assumptions are performed on the data in order to have homogeneous information that can cover large areas. 
The  lack  of  consistency  of  data  collection,  which  is  the  result  of  different  methodologies  used  during  the 
collection, the different scope and the different spatial scale considered lead to data which have a questionable 
accuracy and that  can be  hardly comparable (UNISDR, 2015). Disaster loss  datasets are based on different 
methodologies, such as the definition or threshold for what qualifies a disaster, as well as in the procedures 
used to collect the data. Disaster loss and damage datasets do not provide a complete picture of the events 
and often they do not include records for "smaller" but often recurrent events.  

At European level the loss and damage data are available through global multi-hazards databases (Wirtz, K., et 
al., 2014), but there is no authoritative loss database that can provide a trend at European level (De Groeve, 
2015).  

In such a context, different open access datasets have been interrogated collecting European-related records 
on past disastrous events. Both hazard and loss data have been systematically collected from various sources, 
checked, linked and homogenized to be provided in tabular and geospatial format in order to create the RDH 
Historical Event Catalogue.  

The RDH Historical Event Catalogue is an integrating part of the RDH web-platform development. The Risk Data 
Hub proposes to become the reference point for data collection, developing a centralised pan European platform 
for collection of loss and damages data. 

The RDH Historical Event Catalogue consists in a collection of past events occurred in EU created from a wide 
array of data published in several sources and databases. The work carried out consists in an effort done to 
improve  the  existing  lack  of  homogeneous  and  comparable  data  on  past  events  occurred  across  European 
Countries. This work represents an assemblage of sources that become complementary. Considering that each 
source focuses on different aspects of the impact events, the aim of this work is to define the event, gather 
data on loss and damage records and present the spatial extent of the damages.  

Most of the sources considered, are internal JRC databases: European Forest Fire Information System (EFFIS), 
EFAS (European Flood Awareness System), Emergency Management Service Rapid Mapping (EMS Copernicus), 
European  Drought  Observatory  (EDO)  etc.  Moreover,  having  a  European-wide  coverage  and  with  a  reduced 
access to national records, the identified sources for information are various: online media (e.g. Europe Media 
Monitor), online encyclopaedia (Wikipedia), existing multi-hazards databases (e.g. Munich Re, Swiss Re, EM-DAT, 
GLC), EU services (e.g. EMS Copernicus, ERCC), EU financed projects (e.g. Share) or academic research. 

Finally, the RDH Historical Events Catalogue offers an overview of currently available collection of past events 
and related losses and damages. This collection makes use of inventoried data which eventually is spatially 
represented as maps of impacted areas or further structured into different types of analysis (e.g. Sendai targets 
or EU Solidarity Funds).      

This document presents firstly a damage and losses datasets review, then the description of the RDH Historical 
Event  Catalogue  construction  divided  in  different  phases  such  as:    the  data  collection  methodology;  the 

5 

 
procedure to construct a complete and efficient set of damage and losses data; and, finally an analysis of the 
records which leads to a better understanding of the quality/amount of collected data of past events and the 
possible information that can be retrieved from them with an effort to evaluate the level of compliance with 
some selected existing forecasting models. 

6 

 
 
2  Discussion on post-disaster loss data and current datasets 

Due to the growing recognition of the importance of post-disaster loss data many agencies and organizations 
started working on disaster data collection establishing priorities on its methodologies and quality of the results. 
At the European level, many initiative and activities have been and are still carried out in the Directorate General 
Joint Research Centre (JRC) and in the Academia. In 2014 an EU expert working group on disaster damage and 
loss data has been established (De Groeve et al., 2014) to identify the gaps and challenges for recording loss 
data in Europe with participants from Member States, United Nations Office for DRR (UNISDR) and academic 
and scientific institutions. The EU working group main aim was to establish a common framework for recording 
disaster damage and loss data in the European Union, but the situation is still characterized by gaps and high 
level of fragmentation that needs to be improved in order to have a more integrated interpretation of events’ 
impacts  with  a  view  to  support  a  variety  of  actions.  Available  post  event  damage  data  still  present  many 
weaknesses since they do not allow to perform robust statistics and analyses due to the lack of standardization 
in the collection methodologies and the inconsistent adopted taxonomy; and, their reliance is questionable since 
they are not based on evidence-based assessments: they can be either affected due to the lack of integration 
between different phases of the damage data collection (collection – storage -management) and the original 
information is usually transferred from one source to another through traditional flat files, or retrieved from 
unverified  organizations  and  media  reports  which  provide  only  preliminary  estimates  and  thus  enter  the 
international loss databases. In regards, since as underlined by a 2013 report (De Groeve et al., 2013) much 
information  and  useful  knowledge  can  be  drawn  from  damage  and  loss  data,  detailed  studies  have  been 
conducted  in  EU  founded  projects  such  as  IDEA  (see  http://www.ideaproject.polimi.it),  Educen  (see 
www.educenhandbook.eu section 6) and LODE (see https://www.lodeproject.polimi.it). 

Damage and loss data could support a variety of actions: 

—  Policy monitoring (construction of trends, observation of vulnerability and resilience fluctuation, forensic 

analysis) 

—  Policy actions (compensation, legislation, investments, cost benefit analysis) 

—  Prevention and preparedness (Early Warning Systems, response actions, reconstruction process with a view 

to build back better, response) 

—  Disasters’ impact evaluation (loss accounting, systemic and cascading effects) 

—  Risk modelling (deterministic and probabilistic) 

Therefore, the granularity of the data becomes fundamental. High level of detail would allow the aggregation 
of information according to geographical scale needed. Data should be collected at the asset level, for different 
societal sectors which consider both physical as well as functional and systemic damage, and the their temporal 
evolution. All the above features are key for their intrinsic multi-usability. Sharing a common goal with LODE 
project  (https://www.lodeproject.polimi.it/)  financed  by  DG  ECHO,  the  RDH  will  take  advantage  of  the 
technological development for the L&D data-recording interface.  The outcome of this collaboration is foreseen 
to bring technological development for RDH in agreement to local needs. 

However  as  mentioned,  available  data  derive  from  unverified  organizations  and  media  reports  which  enter 
international loss databases frequently interrogated for numerous researches and analyses. In the following 
chapters, a review of available datasets - global, regional, national and local - is presented.  

2.1  Global datasets 

In  the  following  sections  different  datasets  are  analysed  and  compared,  defining  the  main  peculiarities, 
strengths and weakness when possible.  

The three global multi-peril loss databases, EM-DAT, NatCat and Sigma, which provide a global coverage for a 
large  time  span,  are  firstly  discussed.  The  data  of  these  datasets  are  entered  in  the  system  according  to 
different  criteria  and  definitions,  below  the  description.  However  it  is  worth  to  mention  that  definitions  are 
delineated and adopted according to the aim for which every database has been created (Menoni and Margottini, 
2011). 

The Centre for Research on the Epidemiology of Disasters (CRED) at Louvain University in Belgium manages the 
Emergency Events Database (EM-DAT) which started operating in 1988 with the initial support of the World 
Health  Organization  (WHO)  and  the  Belgian  Government  is  one  of  the  most  known  global  datasets,  which 
focuses  mainly  on  health  aspects  with  information  that  dates  back  to  1900.  The  database  encompasses 

7 

 
humanitarian data such as fatalities, injured and evacuated. Moreover, economic loss data based on information 
provided by UN organizations and other institutions (Lloyds) and press agencies are present. Criteria for inclusion 
in the database are as follows: ≥ 10 people killed, and/or ≥ 100 people reported affected, and/or a declaration 
of a state of emergency, and/or a call for international assistance. The global database is publicly accessible 
previous registration, therefore is one of the most cited in literature. However, amongst disaster databases, EM-
DAT  provides  one  of  the  most  comprehensive  and  transparent  explanations  of  the  methodology  employed 
(Tschoegl et al., 2006; Wirtz et al., 2014). EMDAT defines a disaster as “a situation or event, which overwhelms 
local capacity, necessitating a request to national or international level for external assistance; an unforeseen 
and often sudden event that causes great damage, destruction and human suffering” (CRED, 2006).  

On the other hand, the database managed by SWISS RE of man-made and natural catastrophe losses (SIGMA 
EXPLORER)  stores  records  which  go  back  to  1970.  The  reinsurance  company  has  been  publishing  annually 
statistical analyses since that time. Information regarding the hazard such as the date and location of the event 
are  included,  complemented  by  the  value  of  the  losses,  victims,  injured  and  homeless.  The  sources  of  the 
database are mainly insurance and reinsurance periodicals, internal reports, Lloyd's list, online database and 
news. Raw data are not openly available, only statistical analyses are publically accessible.  

Finally, NATCATSERVICE from MUNICH RE was established in 1974, it contains only natural hazards related 
data.  World-wide  data  suitable  for  analytical  evaluation  are  available  starting  from  1980.  In  addition  to 
metadata, information about insured and economic losses is present. Records are completed from information 
gathered  through  internal  reports  primarily  and  through  external  organizations.  The  database  has  closed 
accessibility. Reviews with essential information are published yearly.  

Considering that, both Sigma and NatCat have been designed to meet insurance companies' needs, definitions 
and thresholds, therefore the meaning of the data, are far different from datasets created with other scopes. 
According to Sigma, "a natural catastrophe is a  harmful  event determined by natural forces. Such an event 
generally results in a large number of individual losses involving many insurance policies" (Swiss Re, 2018); 
while  NatCat  classifies  disasters  in  four  classes  according  to  the  economic  losses  caused  (marginal,  small, 
medium, large, and catastrophic). According to Munich Re Glossary (2018) a natural catastrophe is an "extreme 
natural forces causing human and/or economic loss of considerable scale and disruptions of societies. It can be 
of  local,  regional  or  global  scale".  In  quantitative  terms  a  catastrophic  event  is  a  "natural  loss  event  with 
fatalities  ≥  1000  or  normalised  overall  loss  ≥  US$  100m,  300m,  1bn,  or  3bn  depending  on  the  assigned 
Worldbank income group of the affected country".  

EM-DAT and NatCat contain individual entries for each country affected, while Sigma records the same event 
as the basis for each entry even if it affects multiple nations. Moreover, the dates of the events do not always 
coincide: the same event can be reported at different dates, EM-DAT which has humanitarian scope records the 
date in which it was declared the humanitarian emergency, while in the reinsurance datasets the date is related 
to the event occurrence itself (GFDRR, 2002). 

However, global databases include mostly catastrophic events. Marginal loss events are ignored in statistical 
analyses. Applying minimum thresholds limits the understanding of the occurrence of smaller events.  

Table 1. Global datasets and characteristics  

Access 

EM-DAT 

Public 

NatCat 

Sigma 

Partially public* 

Partially public* 

Period covered 

1900-present 

1979-present 

1970-present 

Disasters 
Considered 

Main Sources 

Natural (considering 
epidemics), technological, 
conflicts 

Un agencies, Lloyds, press 
agencies 

Natural 

Natural, man-made 

Lloyds, internal 
reports, reinsurance 
periodicals 

Lloyds, internal 
reports, reinsurance 
periodicals 

*raw data are not accessible, only statistical analyses are published 

At the global level, but hazard-based there are noteworthy initiatives: 

8 

 
  
  
 
—  The  Significant  Earthquake  Database  of  the  National  Geophysical  Data  Center  (NOAA)  which  contains 
information on destructive earthquakes around the  globe. Information concerning past events and their 
impact were collected in order to populate the RDH Historical Event Catalogue; 

—  The Global Landslide Catalog (GLC) accessible from the NASA's open data portal which stores information 
on mass movements triggered by rainfall around the world. Number of injured and fatalities and qualitative 
description of the event are the details collected from the open dataset.  

—  The “Global Active Archive of Large Flood Events” managed by the Dartmouth Flood Observatory (DFO), 
which documents flood events from 1985 to the present. The database provides tabular and georeferenced 
data of all flood events and includes information on location, rivers involved, beginning and end date where 
possible and duration, number of people killed/displaced, the cost and cause of damages, and information 
about the area affected, which is unfortunately roughly estimated, and qualitative magnitude of the flood. 
Government, academic institutions, news media provide information, along with satellite-based sources. 

2.2  Regional and National Dataset 

Countries do not always have procedures and databases to collect and store post-event damage data; in many 
countries there are no organizations in charge of collecting data and in the few countries where databases exist 
non-governmental institutions operate for the collection and management of the data (Wirtz et al., 2014). 

The  Network  for  Social  Studies  on  Disaster  Prevention  in  Latin  America  (LA  Red)  began  developing  the 
DesInventar methodology in 1994. Currently, the United Nations Office for Disaster Risk Reduction (UNISDR) 
is promoting a global initiative to create national disaster databases with a clear defined methodology, using 
for this purpose the DesInventar methodology and software. DesInventar maintains approximately 16 national 
level  natural  and  technological  disaster  databases  in  Latin  America.    The  databases  represent  over  44,000 
disaster event entries. DesInventar collects variables such as number killed, injured, and estimated economic 
losses, but also attempts to collect damage variables related to infrastructure in order to track social effects 
of disasters. As the databases created with the Desinventar methodology contain data collected at the national 
level, more comprehensive information are being stored such as small and medium scale disasters that are 
often  not  represented  in  larger  scale  databases  (Tschoegl  et  al.,  2006).  DesInventar  utilizes  government 
agencies, NGOs, and research institutes for source data; however it relies on news media as a priority source. 

A well-organized country-level hazard database is SHELDUS for United States. It covers the period from 1960 
to 2016 for several perils such as floods, wildfires, hurricanes, tornados etc. The database contains information 
on the date of an event, affected location (county and state) and the direct losses caused by the event and 
insured crop losses. Correspondingly, in Canada there is an ongoing effort to manage and record data in the 
Canadian  Disaster  Database  (CDD).  Data  regarding  natural,  technological  and  conflict-related  disasters  and 
their  impacts  are  collected.  Government  is  one  of  the  sources  of  information  along  with  emergency 
management and insurance organizations (Tschoegl et al., 2006). 

Whereas, considering European Countries and regions several are the different are the datasets or the initiative 
launched to collect damage and losses data. However, they generally focus on small areas or on few hazards 
if not even only one. 

Examples are the Swiss Flood and Landslide Damage Database managed by the Swiss Federal Research 
Institute WSL which has been systematically collecting information on flood and mass movement damage in a 
database since 1972, being  Switzerland recurrently affected by  hydrogeological events (Hilker et al., 2009); 
and, the DISASTER database for Portugal (Zezere et al., 2014). The DISASTER database contains georeferenced 
information about floods and landslides and their impacts in Portugal in the period 1865-2010. 

Another regional case is the database of Mediterranean Flood Fatalities (MEFF). MEFF is the result of a study 
about flood mortality in the Mediterranean area covering a period from 1980 to 2015. Information on fatal 
accidents  was  disaggregated;  the  database  is  characterized  by  fields  describing  victim's  profile  and  the 
circumstances of the accidents for five specific areas (Catalonia and Spanish Balearic Islands, South France, 
Greece, Calabria). It is interesting to mention that, in literature, often the number of fatalities caused by natural 
hazards as floods, storms, and landslides are aggregated, making it impossible to analyse them separately. 
While the research in consideration focuses on flood fatalities only with a special regard to the understanding 
of the features leading to fatal accidents in a specific geographical framework. The construction methodology 
of this database represents a good step towards the challenge posed from the Sendai Framework guidelines to 
collect damage and loss data to construct the population's indicators. 

9 

 
A  significant  collection  of  damage  and  loss  data  is  the  ‘Historical  Analysis  of  Natural  Hazards  in  Europe’ 
database (HANZE) of the Delft University of Technology which provides a compilation of past damaging floods 
for 37 European countries. Comprehensive and accurate information on date, location, extent and economic 
losses of past damaging floods from 1870 to 2016 is provided. Considering that historic data on floods losses 
and casualties are neither comprehensive nor standardised for European Countries (Mitchell 2003), the study 
represents a significant effort done to alleviate the situation. Records of past events were obtained from a large 
variety of sources (more than 300), including international and national databases, scientific publications, and 
news reports. However, as events which affected only a small part of one region, with no fatalities and less 
than 200 persons affected, were not included, the database excludes event which can be significant for specific 
geographic and social context of Europe. 

However, in addition to all the previously analysed datasets, other initiatives worth of attention are described 
below. Several are the local and single-hazard datasets that generally focus on small areas, so that the data 
have  a  high  resolution  and  contain  information  which  can  be  used  to  answer  to  the  Sendai  Framework  for 
Disaster Risk Reduction 2015–2030 (UNSIDR, 2015) call, such as the identification of possible vulnerable groups 
in terms of gender and age, and the recognition of the circumstance in which people lost their lives in order to 
reduce losses in terms of lives and affected people and to increase community resilience to geo-hydrological 
hazards. These initiatives are: studies regarding the impact of catastrophic events for 141 countries over the 
period 1981-2002 and its interaction with the genders (Neumayer and Plumper, 2007), the study of  Salvati et 
al., 2018 which specifically considers the gender and the age of fatalities occurred due to flood events; and 
other  works  such  as  LAND-deFeND  (Napolitano  et  al.,  2018),  DamaGIS  flood  geodatabase  for  France  (San-
Martin et al., 2018), Inungama, a flood geodatabase for Catalonia (Barnolas et al., 2007); Global Volcanism 
Program (GVP) of Washington D.C. which documents current and past activity for all volcanoes on the planet 
active  during  the  last  10,000  years  (GVP,  2013);  related  to  floods  at  national  level  existing  databases  are:  
HOWAS21 database (Kreibich et al., 2017) in Germany, Swiss Flood and Landslide Damage Database (Kron et  
al.,  2012) in Switzerland; FloodCat  database  (Molinari  et al.,  2013) in Italy; AZORIS, GIS database for Azores 
which  contains  data  for  different  hazards  (Gaspar  et  al.,  2004);  the  National  Landslide  Databases  of  Great 
Britain and Slovenia (Foster et al., 2012; Komac et al., 2015). Moreover, specific technological disaster datasets 
that should be mentioned are MARS (Major Accident Reporting system) managed by the JRC of the European 
Commission which records industrial accidents that have occurred since 1980, with information on the date of 
incident, type of industry, accident type, substance type, immediate effects such as fatalities, injured, material 
loss,  emergency  measures  taken  and  lesson  learned;    and  MHIDAS  (Major  Hazard  Incident  Data  Service) 
maintained by AEA Technology on behalf of the UK Health and Safety Executive which records disaster involving 
hazardous materials with a worldwide coverage, but focusing mainly on events occurred in the UK and the US 
(Menoni and Margottini, 2011). 

10 

 
3  The RDH Historical event collection methodology 

Considering the circumstances related to the weaknesses and gaps of the methodologies for the collection of 
damage and loss data and therefore their resulting quality and resolution, an effort has been done to improve 
the  fragmented  situation  of  European-wide  damage  and  loss  available  data  through  the  creation  and 
compilation of the RDH Historical Event Catalogue. 

The methodological approach used for the construction of the first RDH Historical Event Catalogue model is 
based  on  the  systematic  collection  of  damage  and  loss  data,  preliminary  from  various  publicly  accessible 
sources to start shaping the RDH archive. A large number of sources have been consulted encountering many 
challenges and restrictions.  

Despite the uncertainty that can affect historical data, the collected available data represent the only tool for 
the construction of databases of hazardous events and their impacts over a large area as the one used for the 
RDH Historical Event Catalogue.  

Different open access datasets have been interrogated collecting European-related records on past disastrous 
events. Both hazard and loss data have been systematically collected from various sources, crosschecked, linked 
and homogenized to be provided in tabular and geospatial format. Every available record has been crosschecked 
with the previous data, and the catalogue has been updated, or modified accordingly. As a result, the original 
information contained in the different sources consulted has been improved by compiling and linking it with 
information from other sources to construct the Catalogue.  

This  collection,  obtained  from  available  existing  historical  data,  presents  incompleteness  due  to  the 
aforementioned gaps of the current situation. As consolidated in the literature, it is impossible to validate most 
of the data coming from open dataset, because independent additional information is not available (Petrucci et 
al.,  2018).  Actually  the  common  situation  is  that  available  information  is  barely  sufficient  to  compile 
comprehensive and complete catalogues and datasets. The level of reliability of the collected disaster damage 
and loss data cannot be really delineated. According to the study conducted in De Groeve et al. (2014) the level 
of  reliability,  which  is  generally  associated  to  quality,  depends  on  the  type  of  procedure  used  for  the 
quantification of a given loss component and the availability of adequate and sufficient data to perform such 
quantification. But available data is frequently insufficient, such as in the current case, consequently it is not 
possible to perform adequate statistical analysis to define the quality level. The Pedigree method (De Groeve 
et al., 2014) could be an appropriate mean to analyze the overall quality of the process that lead to the data 
under  analysis,  but  it  requires  information  regarding  both  collection  and  processing  of  the  data,  which  are 
currently unavailable for the sources and datasets interrogated for the present work. For instance, the Pedigree 
method requires information regarding accuracy during the collection phase – method, human error analysis – 
comparison  with  independent  measurements,  reliability  of  the  source  –  data  based  on  measurements  or 
assumptions, completeness etc. Given the available data, it is not even possible to express a level of agreement 
or  disagreement  between  different  sources  since  rarely  the  same  record  has  been  found  more  than  once. 
Therefore, defining qualitative expressions of uncertainty is the only option. 

Moreover, the collected data are partially compliant with the Sendai Framework Indicators. Information about 
mortality can be retrieved (Target B), but reliable information about affected people (Target A) becomes already 
an  issue  since  datasets  have  different  or  not  clear  definitions  of  “affected  people”.  No  evidence  is  present 
regarding critical infrastructures and basic services; available data consist in the total “Economic Loss” and no 
indication  is  available  regarding  the  impact  on  different  societal  sectors  (Target  D)  neither  regarding  the 
estimation method. 

However, the work carried out is not focused on testing the quality of existing records, but rather on collecting, 
crosschecking and linking information deriving from heterogeneous sources to provide a comprehensive set of 
damage and loss data occurred in European Countries due to hazardous events according to the available data. 

The  present  work,  which  originally  aims  at  making  the  best  use  of  available  data  to  start  shaping  the  RDH 
Historical  Catalogue,  on  the  one  hand  represents  the  first  step  towards  the  objective  of  improving  the 
fragmented situation where most of the studies carried out and the information remain spread without having 
the possibility to be exploited according to their potential. On the other hand, highlights the scarcity of quality 
damage and loss data which are essential for different purposes in all the phases of the DRM cycle creating 
further  awareness  regarding  gaps  in  the  current  situation  which  should  be  filled  with  better  developments, 
improvements and researches. 

11 

 
 
4  The RDH Historical Event Catalogue Characteristics 

4.1  Hazard consideration 

Four modules compose the RDH Historical Event Catalogue. Each module corresponds to the hazards considered 
for the collection of damage and loss data and differs in terms of temporal coverage and variables considered. 
Specifically, the hazards considered in the present work are: 

—  FLOOD precisely: 

●  RIVER FLOOD 

●  FLASH FLOOD 

●  COASTAL FLOOD 

—  EARTHQUAKE 

—  FOREST FIRE 

—  LANDSLIDE 

4.2  Spatial coverage 

The RDH Historical Event Catalogue covers almost all the European area. The records collected refer to all 28 
European Union member states, all European Free Trade Agreement members, the four microstates located in 
Western  Europe  (Andorra,  Monaco,  San  Marino  and  the  Vatican)  and  one  Crown  Dependency  of  the  United 
Kingdom (Isle of Man). 

4.3  Temporal coverage 

The  RDH  Historical  Event  Catalogue  is  characterized  by  an  assorted  temporal  coverage  for  each  hazard 
considered. As data have been retrieved from different sources, the catalogue has been shaped in accordance 
with the available information. The time coverage details are listed in Table 2 according to each module of the 
catalogue. 

Table 2. RDH Historical Event Catalogue's time coverage by hazard considered 

HAZARD 

Time Coverage 

RIVER FLOODS 

FLOODS 

FLASH FLOODS 

1871-2018 

COASTAL FLOODS 

EARTHQUAKES 

WILDFIRES 

LANDSLIDES 

1992-2018 

2000-2018 

1993-2018 

4.4  Source of information  

The  sources  of  information  consulted  during  the  research  to  collect  information  in  order to  define  occurred 
events, and their impact are listed below: 

—  Historical Analysis of Natural Hazards in Europe’ database of the Delft University of Technology (HANZE) 
which provides a compilation of past damaging floods from 1870 to 2016 for 37 European countries. 

—  The  Dartmouth  Flood  Observatory  (DFO)  at  the  University  of  Dartmouth  in  USA  which  is  a  global  data 

depository for spatially referenced floods, which covers the period from 1985 to the present.  

—  The SHARE European Earthquake Catalogue (SHEEC): a set of seismic events occurred in Europe, with the 
exception of Greece and surrounding areas, from 1900 to 2006. Mainly the information contained in the 
catalogue regards: date, time, magnitude, latitude and longitude. 

12 

 
—  The Greek seismological catalogue of the University of Athens containing hazard related information. 

—  The Significant Earthquake Database of the National Geophysical Data Center (NOAA). 

—  Information  concerning  the  date,  location  and  geographical  extent  of  forest  fires  was  provided  by  the 
European Forest Fire Information System (EFFIS). The time coverage of the forest fires ranges from 2000-
onwards. Moreover, information on fatalities and injured was retrieved from annual reports of EFFIS, though 
the information is given for fire-season and not per event. 

—  The Global Landslide Catalog (GLC) accessible from the NASA's open data portal contains information on 
mass movements triggered by rainfall around the world and their impact. Number of injured and fatalities 
and qualitative description of the event are the details collected from the open dataset.  

—  The  Emergency  Events  Database  (EM-DAT)  of  the  Centre  of  Research  on  Epidemiology  of  Disasters  in 

Brussels and United States Office for Foreign Disaster Assistance. 

—  Copernicus  Emergency  Management  Service  (Copernicus  EMS)  provides  information  for  emergency 
response in relation to different types of disasters. Satellite imagery is the main data source; the produced 
maps from the list of activations of rapid mapping were connected to the RDH Historical Event records. 

—  The free online encyclopaedia Wikipedia was also used as a source of information to delineate the impact 
of  past  events  especially  quantitative  information  such  as  fatalities,  injured  and  economic  losses  was 
collected. 

—  The Emergency Response Coordination Centre (ERCC) Daily Maps service of the European Commission has 
been consulted. The ERCC publishes maps, available to the public, regarding the most  important events 
which contain concise information regarding the impact and the extension of events. 

—  FloodList,  founded  by  Copernicus,  which  brings  news  and  information  on  floods,  occurred  worldwide,  is 

another source consulted. 

—  Europe  Media  Monitor  (EMM)  natural  hazards  dataset  is  a  source  of  information  considered  for  RDH 
Historical Event Catalogue.  Data from this source are not yet implemented;  however in the near-future 
EMM data will be included in the catalogue. 

However, sources serve for different purpose: some identify the event; others offer records about the event’s 
losses and damages while others provide the location and the extension of the disastrous event. This work 
provides a methodological example in bringing together all these information. 

HAZARD CATALOGUES 

Mainly information such as date of occurrence, geographical extent, location and intensity of the events has 
been collected from Hazard Catalogues and where necessary this information has been georeferenced through 
GIS software.  

The different sources analysed are reported below for each hazard: 

—  FLOODS : Hanze, DFO 

—  EARTUQUAKES: SHEEC, University of Athens Catalogue, NOAA 

—  FOREST FIRES: EFFIS 

—  LANDLIDES: GLC 

DAMAGE AND LOSS SOURCES OF INFORMATION 

In order to have a complete catalogue, additional source of information have been analysed to gather historical 
impacts of disastrous events. 

The different sources analysed are reported below for each hazard: 

—  FLOODS : Hanze, DFO, EM-DAT, COPERNICUS, WIKIPEDIA 

—  EARTUQUAKES: NOAA, EM-DAT, WIKIPEDIA 

—  FOREST FIRES: EFFIS, WIKIPEDIA 

—  LANDLIDES: GLC 

13 

 
4.5  Criteria of selection for inclusion in the database 

Within  RDH  Historical  Event  Catalogue  an  historical  event  is  defined  by  a  temporal  and  spatial  extension, 
magnitude of impact, hazard type, cause, damage type and reference of the event (source or original ID).  

Criteria of selection for inclusion in the catalogue were not specifically severe. Since the present work consist 
in an effort done to bridge the gaps between the information generated from different sources, the primary 
and most essential inclusion criteria is: at least one of the record's attributes related to the impact (see Annexes 
1,  2,  3  and  4)  had  to  be  available  for  an  event.  It  is  important  to  be  noticed  that,  since  the  catalogue  is  a 
collection of damage and loss data, the records are entered by impact accompanied by adequate information 
to identify the event or the phenomena (see first technical report JRC114712 on DRMKC RDH). However, it is 
common (and depending on the source and of the hazard) that the location of the impact is referred to the 
spatial occurrence of the hazard (i.e. EM-DAT for an earthquake the location consist in the epicentre and not in 
the  place  of  occurrence  of  the  damage).  The  Landslide  Module  represents  another  example,  where  data 
retrieved (in this case from the GLC Catalogue) covers incomplete impact record types (and this is the case of 
all the datasets used in the RDH). The impacts recorded refers to injured people and fatalities but not to other 
impacts (e.g. affected people or economic losses). Therefore, for the some impact types, the event is reported 
in the catalogue even if no impact was reported.  

Hazard event identification provides data to characterize the event both in terms of spatial and temporal scale, 
however datasets do not provide detailed information. According to De Groeve et al (2013) the loss database 
should  contain  enough  data  on  the  hazard  to  uniquely  identify  it  and  to  provide  useful  search  and  filtering 
functions for studies and analyses. 

4.6  Challenges 

As different  sources were analysed, data collected  with  different approaches and for different scopes were 
stored, therefore many difficulties, in terms of creating a catalogue with homogeneous data, were encountered. 
Main challenges in creating a coherent catalogue were due to: 

—  Differences in the time of occurrence  

—  Differences in the definitions of the attributes 

—  Differences in classifying the type of disaster 

—  Differences due to more than one entry of a single event 

—  Differences in classifying the spatial extent and the exact location of an event 

—  Differences in the definitions of the indicators 

—  Differences in currencies and prices of economic losses 

Differences in the time of occurrence  

An event could be reported at different dates, especially disasters like floods which end also after couple of 
months.  In  this  case,  the  events  had  to  be  verified  and  crosschecked  with  the  location  primary  and  other 
attributes to know whether or not it was the same event coming from other sources. In addition, according to 
the literature EM-DAT records the day it was declared as humanitarian emergency (GFDRR, 2002), while other 
sources usually record a period for the disaster itself (start/end) and not always the criterion by which the end 
date is set by some datasets is known. 

Differences in classifying the type of disaster  

Disasters are not always classified in the same way by different databases. This becomes an obstacle especially 
in the case of associated disasters or secondary disasters, for example a flood which caused landslide or an 
earthquake which triggered an avalanche, may be recorded as one or the other (i.e. the event occurred in South 
Italy, specifically in Abruzzo region on 18th January 2017, where an avalanche has been triggered by different 
shakes causing 29 fatalities). Verification that two different disaster types occurring in the same country on the 
same  day  should  be  done.  This  is  not  always  possible  especially  if  location  is  not  properly  assigned  in  the 
different databases. 

14 

 
Differences due to multiple entry of a single disaster event  

A disaster could be registered as multiple events, if they occurred in different regions of a country in consecutive 
time and the same event could be recorded in another data set as a specific single event with the total dead 
and affected for all the affected areas. This situation came across the linking phase between different sources 
such as COPERNICUS rapid activation mapping for flooding events and HANZE dataset or Daily Maps from DG 
ECHO  for  forest  fires  and  EFFIS  burned  area  polygons.  A  clear  example  could  be  the  heavy  rainfall  events 
happened in United Kingdom during the winter of late 2015 and beginning of 2016 which led to flooding in a 
vast area. HANZE database reports a single event with start date on 05/12/2015 and end date on 26/01/2016 
with flood source the Ouse River (England) while COPERNICUS makes available two activation maps with two 
specific unique identifier codes one with activation on 05/12/2015 for Cumbria region and one on 27/12/2015 
for England. Moreover, this specific case remains not easy to handle as in the same period a flood occurred 
also in Northern Ireland, so Wikipedia consider all the events under the name "2015-16 Great Britain and Ireland 
floods"  and  COPERNICUS  makes  available  another  map  produced  on  11/01/2016  for  Northern  Ireland. 
Therefore, it becomes complicated to link the events under a clear and specific record.  

Differences in classifying the spatial extent and the exact location of an event 

A disaster could be reported two times in the same database, under two different ID or the same according to 
the entry criteria.  This case was encountered specifically in the cases of cross-border events which are generally 
doubled  and  not  always  information  about  the  proportion  of  the  impact  on  each  country/region  involved  is 
available.  

Differences in the definitions of the indicators 

Generally definitions are given for each dataset; however, there is an ambiguity of terminology that makes 
difficult to delineate what some indicators precisely represent (i.e. affected, displaced and/or evacuated people). 
This limits comparisons between data retrieved from different sources.  A concrete example is the definitions 
given by DFO e HANZE database: 

—  AFFECTED PEOPLE (HANZE): "Number of people whose houses were flooded. The reported numbers often 
only show the number of evacuees or homeless persons. If only the number of houses flooded was reported, 
the number persons affected was estimated by multiplying the number of houses by 4". 

—  DISPLACED PEOPLE (DFO): "This number is sometimes the total number of people left homeless after the 
incident, and sometimes it is the number evacuated during the flood. News reports will often mention a 
number of people that are 'affected', but we do not use this. If the only information is the number of houses 
destroyed or damaged, then DFO assumes that 4 people live in each house. If the news report only mentions 
that "thousands were evacuated", the number is estimated at 3000. If the news reports mention that "more 
than 10,000" were displaced then the DFO number is 11,000 (number plus 10%). If the only information is 
the number of families left homeless, then DFO assumes that there are 4 people in each family". 

Accordingly, it is noticeable that definitions are not clear, moreover during the work it has been noticed that for 
some events the two values were exactly the same and for others they showed complete different order of 
magnitude.  This  led  to  confusion  making  the  linkage  process  fairly  complicated.  In  order  not  to  miss  the 
information from DFO a different class of attributes has been created to store the "Number of displaced" so 
that the user can evaluate which specific value is preferable for his case according to the definitions.  

Differences in currencies and prices of economic losses 

The different sources of information analysed during the study, reported different currencies of the economic 
losses. For instance, EM-DAT reports economic losses in American dollars, while HANZE reports economic losses 
in the currency used at the time of the event and then the same amount converted in euro in 2011 prices. 
Therefore  in  order  to  make  a  homogeneous  and  standardized  collection  the  economic  losses  needed  to  be 
converted.  

4.7  Conversion of economic damage values 

Economic losses collected during the study needed to be adjusted in order to have comparable values. Principally 
economic losses occurred due to floods and earthquakes were collected. The main sources of information were: 
HANZE, EM-DAT, and Wikipedia. 

HANZE database contains economic losses both in the original currency of the time of the event (nominal values) 
and in Euro deflated to 2011 prices. Though, in EM-DAT, the value of estimated damage in monetary terms is 

15 

 
given in Dollars (US $). For each disaster the amount of damage corresponds to the damage value at the time 
of the event. Finally Wikipedia either reports the damage in dollars or in Euro.  

Therefore in order to make a homogeneous and standardized collection the damage data in monetary terms 
needed to be converted. The conversion has been conducted following the same approach of HANZE database. 
Sendai  Loss  Data  guidance  invites  States  to  express  losses  into  USD,  to  enable  global  summation  and 
recommends to use official exchange rate, in the RDH catalogue losses are adjusted but converted in Euro since 
comparison is done between European countries. However nominal loss and GDP deflator are at the base of the 
computation, the reference year is selected to homogenize all the values and from the reported adjusted losses 
using the correct methodology it is possible to further convert in USD deflated to the preferred year of reference. 

HANZE  database  consists  in  a  comprehensive  set  of  data  and  information,  it  provides  a  set  of  currency 
information, GDP data and deflator indexes for the area and time coverage considered in the study establishing 
the 2011 as the reference year. Thereafter, in order to make all the records homogeneous and comparable, 
HANZE  methodology  has  been  adopted  for  economic  losses  deriving  from  other  sources.  However,  as 
aforementioned in EM-DAT and Wikipedia the estimated damage in monetary terms is given in dollars (US $) 
but HANZE does not provide a methodology for the exchange rates. Conversions have been carried out through 
a methodology accessible from the Portal for Historical Statistics of Stockholm University (Historical currency 
converter edited by Rodney Edvinsson). 

The economic losses measured in dollars at the time of the event have been converted in the currency of the 
country at the time of the event (nominal values). After that, the currencies have been converted and normalized 
in  Euro-  2011  prices-  using  the  conversions  factors  between  new  and  old  currencies  and  the  GDP  deflator 
indexes of HANZE database. 

An example of conversion and normalization to 2011 prices is shown below. 

The sample of the explanation regards the earthquake of magnitude 7.3 happened in Romania in 10/11/1940. 
According to the RDH Historical Event catalogue the occurrence of this event caused a total of 1000 fatalities 
and 4000 injured. Recorded economic losses are equal to 10 million dollars (US$) at the time of the event. 

Following the aforementioned procedure the conversion has been carried out as follow: 

Through the Historical Converter the equivalent of 10 million Dollars (US$) in year 1940 has been transformed 
in ROS in 1940 prices equal to 1986 million Romanian Silver Leu: 

10 mln US$ (1940) = 1986 mln ROS (1940) 

Once the nominal value has been obtained, it has been converted to Romanian Socialist Leu, Old Leu and finally 
to the current Leu: 

1986 mln ROS /20000/20/10000= 0.4965 RON 

The obtained value of current Leu has been divided by 4.2391 to obtain the value in Euro and finally adjusted 
with the Deflator index (2011 prices): 

10 mln US$ (1940) = 39902 mln EUR (2011) 

16 

 
5  The RDH Historical Event Catalogue 

The RDH Historical Event Catalogue is a collection of past events occurred in EU created from a wide array of 
data  published  in  several  sources  and  databases.  It  offers  an  overview  of  currently  available  collection  of 
extreme events and related losses and damages. Therefore the data collected is not an aggregation of official 
national  datasets,  but  rather  a  collection  of  sources that  become  complementary  in  a  collection  of  existing 
practises, which use methodologies and practices widely used and recognized at scientific and policy level. 

5.1  Content overview 

As a result of the research, a catalogue composed from four modules, corresponding to the hazards considered 
has been created. The RDH Historical Event Catalogue presents a total of 18960 records, both in tabular and 
geospatial format.    

Each event-module differs in terms of temporal coverage and variables considered.  

The  module  containing  the  Flood's  records  covers  a  period  from  1870  to  2018.  It  is  composed  from  three 
subclasses with data regarding: 

—  River floods: 827 events 

—  Flash Floods: 879 events 

—  Coastal floods: 56 events 

In the Flood's module fatalities, affected, flooded area and economic losses (mln euro in 2011 prices) are 
reported.  

The Earthquake's module contains a total of 211 events occurred in in the period from 1901 onwards; each 
event is characterized  by the magnitude and the exact time of occurrence.  Moreover,  information regarding 
fatalities, injured, affected and economic losses (mln euro in 2011 prices) is presented.  

The Landslide's module covers a period from 1993 to 2018 comprising a total of 580 events across Europe. 
Each event is qualitatively described in terms of spatial extent and information about injured and fatalities is 
reported. 

Finally the Wildfire's module is characterized from a relatively minor temporal coverage in comparison with 
the aforementioned modules, though it embraces a comparatively big amount of hazard-related records. The 
wildfire's records have not been classified as events; the catalogue includes a total of 16407 burned areas 
across European countries called “Phenomena” according to the architecture of the RDH (see Antofie, T. et al., 
2019). As these records are most of the time seasonal or linked with climatology (heat waves, drought) the 
inventory of burned areas is aggregated according to the season or time period the records refers to. Therefore, 
finally the module contains numbers of fatalities and injured per fire-seasons and total area burned as well. 
Tables 3 and 4 present detailed information regarding the collected records. 

Table 3. Total sum of fatalities, injured and affected people per hazard 

HAZARD 

Time 
Coverage 

#EVENTS 

#FATALITIES  #INJURED  #AFFECTED 

RIVER FLOODS 

FLOODS 

FLASH FLOODS 

1870-2018 

COASTAL 
FLOODS 

827  

879 

56 

5769 

10510 

2225 

- 

- 

- 

6037963 

1118692 

352471 

EARTHQUAKES 

1992-2018 

211 

125399 

54428 

2526894 

WILDFIRES 

2000-2018 

- 

LANDSLIDES 

1993-2018 

580 

662 

231 

3359 

103 

- 

- 

17 

 
Table 4. Total sum of economic losses and affected area per hazard 

HAZARD 

Time 
Coverage 

#EVENTS 

ECONOMIC  LOSSES 
(Mln EUR 2011) 

AFFECTED  AREA 
(km^2) 

RIVER FLOODS 

 827 

144953 

116513 

FLOODS 

FLASH FLOODS 

1870-2018 

879 

69371 

COASTAL FLOODS 

56 

13483 

EARTHQUAKES 

1992-2018 

211 

170915 

WILDFIRES 

2000-2018 

- 

LANDSLIDES 

1993-2018 

580 

- 

- 

2904 

3791 

- 

66256 

- 

*Wildfires are not grouped in events because of coherency with definitions; however the collection presents a total of 16407 burned 
area polygons 

5.2  Comprehensiveness, completeness and origin of the data 

Given the differences between the origins of the data, some data processing has been performed. 

Not every record entered in the RDH Historical Events Catalogue presents complete set of values for the damage 
and losses attributes (see Annexes 1, 2 3 and 4); but, more significantly, it is important to highlight that often, 
if not even always, it is not specified if the value of some attributes was not recorded or it was null. A detail of 
great  importance,  which  would  have  been  beneficial  to  know  for  indicators  such  as  fatalities,  injured  and 
affected. Not having available and clear indicators makes comparisons less reliable and statistics inconsistent 
and  highlights  the  need  for  a  more  systematic  and  comprehensive  damage  and  loss  data  collection.  A 
comparison of the events and records is presented in Table 5. 

Table 5. Comparison between events and total records present in the catalogue 

HAZARD 

Time 
Coverage 

#EVENTS  FATALITIES 

INJURED 

AFFECTED 

ECONOMI
C  LOSSES 
(Mln  EUR 
2011) 

AFFECT
ED 
AREA 
(km^2) 

RIVER 
FLOODS 

827 

505 

FLOODS 

FLASH 
FLOODS 

1870-
2018 

879 

720 

COASTAL 
FLOODS 

56 

34 

- 

- 

- 

358 

38 

122 

306 

236 

21 

EARTHQUAKES 

WILDFIRES 

LANDSLIDES 

1992-
2018 

2000-
2018 

1993-
2018 

211 

153 

106 

82 

- 

- 

580 

55 

- 

30 

- 

- 

18 

23 

14 

- 

ALL 

- 

10 

57 

- 

- 

 
 
 
Additionally, a quantitative analysis of the origin of the data, by hazard type, has been carried out.  

For the Flood's module most of the data were implemented from HANZE database. Nevertheless many of the 
events reported in HANZE have been found in the DFO database and connected. Some Copernicus activations 
have been correspondingly linked. For the river flood a total number of 827 records is present, of this amount 
a total of 629 has been directly implemented from the HANZE-Event database. However, 119 events have been 
found  in  DFO,  therefore  the  ID  code  used  in  the  database  has  been  inserted  in  the  "Source"  category  of 
attributes. Finally, to 11 of those an ID code of the Copernicus rapid map activation has been linked. Though, 
the remaining 198 records have been implemented directly from the DFO database, from those events 13 have 
been linked to the ID code of the Copernicus rapid map activation.  

While for the flash floods over a total of 879 records coming from HANZE database a link has been done with 
DFO for 77 events and 8 with Copernicus.  

Regarding Coastal floods only 2 events have been found in the DFO. 

Concerning Earthquakes, SCHEEC and Athens's University Seismological Catalogue were the main fonts for the 
hazard-related information (latitude, longitude, time of occurrence, magnitude). While for damage and loss data 
most of the information was retrieved from EM-DAT, NOAA and Wikipedia. A number of 61 events has been 
collected from SHEEC and 29 from Athens's University Seismological Catalogue. While over the total number 
of collected events, 96 has been found in EM-DAT, 90 in NOAA and 118 in Wikipedia. 

Although, temporarily for the Landslides module no double-check and linkage with different sources has been 
carried out since no open-source dataset or available information have been collected yet.  

Concerning the Wildfire module, no number of events is present, however the RDH Historical Event Catalogue 
stores a total of 16407 records of burned areas across European countries reporting georeferenced location, 
date and hectares impacted. Yearly number of fatalities and injuries has been collected for the period 2000-
2018 for the most affected European countries.  

19 

 
6  Analysis of the collected data 

The following section offers an overview of the collected data through analyses and comparisons in order to 
have a more quantitative understanding of the damage and losses caused by natural hazards across Europe. 

A general overview is presented in order to understand the amount of data collected and their respective values. 
However, an indicative exploration of the records is firstly performed considering that the different modules 
present very heterogeneous time coverage; therefore, comparisons need to be performed carefully.  It is worth 
to specify that the wildfire's number of events has been reported intentionally equal to zero since this study 
doesn’t focus on the definition of “Events” and for this hazard a total amount of 16407 burned areas across 
European countries is collected and these records are generally referred to seasons or linked to climatology 
(heat waves, drought), hence the inventory of burned areas is aggregated according to the season or time period 
the records refers to (Fig. 1).  

Figure 1. Number of events in relation with the time coverage of the module 

1762

148

FLOODS

211

117

0

18

EARTQUAKES

WILDFIRES

580

25

LANDSLIDES

Time Coverage
(YEARS)
Number of Events

As illustrated in Figure 1 the RDH Historical Event Catalogue presents higher number of records for floods. This 
for two reasons: (1) most of the data have been retrieved from a solid and robust dataset which focuses only 
on flood events (HANZE Dataset); (2) the records go back to 1870; therefore, events occurred in 148 years are 
collected. Those past events have been classified in river, flash and coastal floods (Fig. 2). 

Figure 2. Number of Floods by type 

827

879

RIVER FLOODS

FLASH FLOODS

56

COASTAL FLOODS

Complementary to this, the RDH Historical Event Catalogue contains earthquake's records happened in the last 
117 years across Europe, wildfire's records for a time span of 18 years; finally, landslide's records for a period 
of 25 years.  

Considering the differences of the temporal coverage for the hazards considered in the RDH Event Catalogue, 
Figure 3 illustrates the average of causalities by year displaying a greater percentage (86%) for earthquakes 
fatalities which reach a total of 125399 fatalities during 117-years, followed by floods fatalities (10%) equal 
to 19107 occurred during the last 148-years. Minor percentages refer to wildfires (3%) and landslides (1%) 
fatalities;  662  fatalities  occurred  in  the  last  18-years  due  to  wildfires  and  231  fatalities  occurred  due  to 
landslides in the last 25-years are recorded in the RDH Catalogue. 

20 

 
 
 
 
 
Figure 3. Share of average number of fatalities by year 

3% 1%

10%

86%

FLOODS

EARTHQUAKES

WILDFIRES

LANDSLIDES

The  RDH  Historical  Event  Catalogue  contains  damages  in  monetary  terms  for  floods  and  earthquakes. 
Specifically, within the 148 years considered, floods caused a total of 230 billion Euros worth of damage. On 
the  other  hand,  over  a  117-year  period  considered,  earthquake  caused  170  billion  Euros  of  damage.  The 
proportion of the economic losses is shown in Figure 4. 

Figure 4. Share of average economic losses by year 

49%

51%

FLOODS
EARTHQUAKES

6.1  Most destructive events 

From the analysis of the collected data, a list of the deadliest and costliest events can be defined. Examining 
the data, regardless the temporal coverage of each module, the values listed in Table 6 have been retrieved. 

Table 6. Deadliest and costliest events 

THE DEADLIEST 

THE COSTLIEST 

COUNTRY 

YEAR 

FATALITIES 

COUNTRY 

YEAR  mln € (2011) 

FLOODS 

RIVER FLOODS 

France 

1875  500 

FLASH FLOODS 

Spain 

1962  805 

Italy 

Spain 

1966  11863 

1983  11521 

COASTAL 

FLOODS 

Netherlands 

1953  1835 

Germany 

1962  4932 

EARTHQUAKES 

Italy 

1908  75000 

Italy 

1980  44000 

WILDFIRES 

LANDSLIDES 

Greece 

2018  100 

Portugal  

2010  42 

- 

- 

- 

- 

- 

- 

The deadliest flood, between the different types, was the North Sea flood occurred in 1953. The flood caused 
by  a  heavy  storm  struck  in  the  Netherlands,  Belgium  and  United  Kingdom  killed  more  than  2000  people 
(Gerritsen, 2005). The highest number of deaths, equal to 1835,  was  reported in Netherlands.  On the other 
hand,  the  costliest  one  was  a  river  flood  occurred  in  Italy  in  1966  causing  about  12  billion  Euros  worth  of 

21 

 
 
 
  
  
  
damage. However, according to the data collected the deadliest river flood occurred in France in 1875 causing 
500 deaths, while both the deadliest and costliest flash floods occurred in Spain. Finally, the costliest coastal 
flood occurred in Germany in 1962 causing about 5 billion Euros worth of damage. 

No economic losses data are available for wildfires and landslides, however the deadliest landslide (42 deaths) 
refers to the event occurred on 20th of February 2010 in Madeira Island, Portugal. It is worth to notice that this 
record is a clear example of the challenges encountered during the research; since the landslide event under 
consideration was the result of an extreme weather event which caused an extensive flood and considering 
that the number of fatalities caused by natural hazards such as floods which triggered landslides is often an 
aggregated  value  (Petrucci  et  al.,  2018),  it  becomes  impossible  to  retrieve  separately  the  exact  number  of 
deaths caused by the flood and/or the mudslide.  

Through the last eighteen years, the deadliest wildfire season began in Greece in the Attica area in July 2018 
during the 2018 European heat wave. A series of wildfires struck in the region causing 100 fatalities and 172 
injuries. This incident is classified as the second-deadliest wildfire event in the 21st century, after the  Black 
Saturday bushfires occurred in 2009 in Australia that caused 180 deaths (Wikipedia, 2018). 

In conclusion, it arises that earthquakes are the most catastrophic natural hazard, in terms of both fatalities 
and overall economic losses caused per event. The most catastrophic earthquake between 1901 and 2018 in 
terms of fatalities was the Messina Earthquake occurred on 28th of December 1908 between Sicily and Calabria 
in southern Italy with a magnitude of 7.1 which caused 75000 deaths. On the other hand, the costliest event, 
between  the  most  catastrophic  events  recorded  in  the  catalogue,  was  the  Irpinia  Earthquake  occurred  in 
southern Italy on November 1980 with a magnitude of 7.2 which caused a total of 44 billion Euros of damages. 
However, as stated by Wirtz et al. (2014) the highest losses in economic terms are often caused by earthquakes. 

6.2  Most affected countries 

Data collected during the research can be analysed by country. Regardless the time coverage of each module, 
a ranking of the most affected counties can be performed.  

Damage and loss  data collected and stored in the RDH  Historical Events Catalogue  can be used to perform 
general analysis for European countries. According to the analysed values, the most affected countries in terms 
of fatalities are illustrated in Figure 5 for each time coverage and hazard considered. Spain and Italy are the 
most affected countries in terms of flood fatalities for the past 148-years followed by Netherlands. Italy stands 
between the most affected countries also for fatalities occurred due to earthquakes and landslides; while, for 
fatalities occurred due to wildfires, Greece is the most affected country, followed by Portugal. Detailed values 
are listed in Table 7. 

Table 7. Top three most affected countries – FATALITIES AGGREGATED VALUE 

Time Coverage 

MOST  AFFECTED 

COUNTRIES 

FATALITIES 

FLOODS 

1870-2018 

Spain 

Italy 

Netherlands 

4514 

4491 

1907 

Italy 

119353 

EARTHQUAKES  1901-2018 

Romania 

WILDFIRES 

2000-2018 

Portugal 

Greece 

Greece 

Italy 

Italy 

LANDSLIDES 

1993-2018 

Portugal 

Switzerland 

3159 

2056 

282 

201 

57 

94 

46 

27 

22 

 
 
Figure 5. Fatalities Maps by hazard for specific time coverage 

23 

 
 
 
 
Losses in terms of economic impact are reported only for floods and earthquakes. Italy is the most affected 
country for both hazards (Fig. 6). Following, Germany and Spain are the second and third most affected countries 
concerning  flood  impacts;  while  concerning  earthquakes'  economic  impact  Romania  and  Greece  are  listed 
between the top three affected countries.  

Table 8. Top three most affected countries – ECONOMIC LOSSES AGGREGATED VALUE 

MOST 

ECONOMIC 

Time Coverage 

AFFECTED 

LOSSES  (mln  EUR 

COUNTRIES 

2011) 

FLOODS 

1870-2018 

Germany 

Italy 

EARTHQUAKES  1901-2018 

Spain 

Italy 

Romania 

Greece 

64683 

33473 

28144 

93693 

55422 

20746 

Figure 6. Flood and Earthquake Economic Losses for specific time coverage 

Moreover,  an  aggregated  sum  of  affected  people  for  the  same  two  hazards  has  been  performed  (Fig.  7). 
Detailed values for the top three affected countries in terms of affected people are listed in Table 9. 

24 

 
  
 
 
 
 
Table 9. Top three most affected countries – AFFECTED PEOPLE AGGREGATED VALUE 

Time Coverage 

FLOODS 

1870-2018 

EARTHQUAKES 

1901-2018 

MOST  AFFECTED 

AFFECTED 

COUNTRIES 

Poland  

Germany 

PEOPLE 

1143034 

922050 

United Kingdom 

920235 

Italy 

Greece 

Romania 

1062664 

1037463 

392850 

Figure 7. Flood and Earthquake Affected people for specific time coverage 

According to the collected data, a comparison between the most affected countries in terms of flooded and 
burnt area can be performed (Fig. 8). The most affected countries in terms of flooded area, for the past 148-
years, are: Poland (55740 km2 of total flooded area, where the worse episodes have been in 1980 due to river 
flood - San, Wisłok, Odra, Wisła, Noteć, Wieprz, Bóbr - affecting a total of 17450 km2 and in 1970 with an area 
flooded of 10000 km2), Hungary (18366 km2 of flooded area) and Romania (4783 km2 of flooded area). While, 
concerning burnt areas, the most affected countries, for the past 18-years, are: Portugal (2304275 km2 of burnt 
area), Spain (1281828 km2 of burnt area) and Italy (754666 km2 of burnt area). 

25 

 
 
 
 
 
 
 
Figure 8. Flooded and Burnt area for specific time coverage 

The stored data show Italy at the top for number of injured due to earthquake followed by Romania and Greece. 
On the other hand, Portugal counts the highest number of injuries for wildfires, followed by Italy and Greece. 
Finally,  France,  Switzerland  and  Italy  are  the  most  affected  counties  in  terms  of  injuries  occurred  due  to 
landslides.  

Table 10. Top three most affected countries – AFFECTED PEOPLE AGGREGATED VALUE 

Time Coverage  COUNTRIES 

INJURED 

EARTHQUAKES 

1901-2018 

Romania 

Italy 

Greece 

Portugal 

WILDFIRES 

2000-2018 

Italy 

Greece 

France 

LANDSLIDES 

1993-2018 

Switzerland 

United Kingdom 

27944 

16171 

7734 

2250 

420 

277 

35 

16 

12 

26 

 
 
 
  
 
 
 
 
Figure 9. People Injured Maps by hazard for specific time coverage 

6.2.1  Completeness and its influence on the analysis  

Comprehensiveness and completeness of the data has been already discussed in Chapter 5.3 and quantitative 
information is reported in Table 5 which clearly shows the scarce amount of damage and losses attributes. 
Some of the performed analysis demonstrate the excessive insufficiency of data to make reliable comparisons 
and consistent statistics, thus emphasises the deficiency and fragmentation of the current damage and loss 
data availability and drives attention towards the need for a more systematic and comprehensive damage and 
loss data collection for more reliable information.  

27 

 
 
 
 
6.3 

Impact data processing for the past 18-years  

6.3.1  Fatalities and economic losses 

In order to have a better perspective of the risks that European Countries face, some analyses have been carried 
out on selected records covering the same temporal range which goes from 2000 to 2018. Figure 10 shows 
the  number  of  events  by  occurred  in  the  past  18-years.  Wildfires  are  not  grouped  in  events  because  of 
coherency with definitions; the collection presents a total of 16407 burned area polygons for the period 2000-
2018  called  “Phenomena”  according  to  the  architecture  of  the  RDH  (see  Antofie, T.  et  al.,  2019),  hence  the 
inventory of burned areas is aggregated according to the fire-season. 

Figure 10. Number of events by Hazard (2000-2018) 

532

577

49

0

FLOODS

EARTHQUAKES

WILDFIRES

LANDSLIDES

The discrepancy between the values obtained from the two different analyses is remarkable. The difference of 
the results is not only due to the homogenization of the time considered but is also due to the characteristics 
of the  records itself. The Landslide's catalogue  covers a  period from 1993 onwards, but as matter of facts 
between a total of 580 events only 3 are prior the year 2000. While floods from a total of 1753 events along 
the whole time coverage get reduced to 532 events for the last eighteen years.  

However, according to the temporal coverage normalization, floods caused both the highest number of fatalities 
and amount of economic losses during the past eighteen years as shown in Figure 11.  

Figure 11. Fatalities and economic losses for common time coverage (2000-2018) 

FATALITIES

OVERALL ECONOMIC LOSSES 

7%

22%

47%

24%

1431 FATALITIES
(FLOODS)

747 FATALITIES
(EARTHQUAKES)
662 FATALITIES
(WILDFIRES)

231 FATALITIES
(LANDSLIDES)

22%

78%

FLOODS

EARTHQUAKES

Both charts show a net difference between the previous Figures 3 and 4, where losses and fatalities are taken 
into account as average by year and a greater percentage for earthquakes fatalities and losses is shown. 

This result appears to be in contrast if compared with other studies such as Corbane et al. (2017) which states 
that earthquakes are the second deadliest natural events in Europe after extreme temperatures for the period 
1980-2014 according to EM-DAT  records. Even though the total time span  considered is different a certain 
correspondence could be expected; the meaning of such difference lies in the fact that in 1980 Italy experienced 

28 

 
 
 
one of the deadliest earthquake in Irpinia (4689 deaths – EM-DAT) which is not considered in figure 11 give the 
time frame, and explanations could be found in the fact that in recent decades there has been an increased 
sprawl in areas prone to floods which created an increased exposure of human lives (Jongman et al., 2012; 
Brown et al., 2014). Moreover the data collected in the RDH Catalogue represent a step forward since damage 
and loss data retrieved from several complementary sources.  

6.3.2  Affected and injured people 

The  RDH  Historical  Event  Catalogue  contains  other  specific  people-related  attributes,  such  as  number  of 
affected people for floods and earthquakes and number of injured for earthquakes, wildfires and landslides. 
Table 11 lists a sum of the  collected  values for  both injured and affected  people for the  different  hazards 
considered. 

Table 11. Affected and injured people, totals per hazard 

HAZARD 

Time Coverage 

#EVENTS  #INJURED 

#AFFECTED 

RIVER FLOODS 

FLOODS 

FLASH FLOODS 

2000-2018 

COASTAL FLOODS 

EARTHQUAKES 

WILDFIRES 

LANDSLIDES 

2000-2018 

2000-2018 

2000-2018 

332 

189 

11 

49 

- 

577 

- 

- 

- 

3721 

3359 

103 

12890 

199232 

24950 

226489 

- 

- 

*Wildfires  are  not  grouped  in events  because  of  coherency  with  definitions; however  the  collection  presents  a  total  of  16407 
burned area polygons 

Figure 12. Percentage of affected and injured perople from all-hazard totals 

INJURED

1%

AFFECTED

47%

52%

EARTHQUAKES

WILDFIRES

LANDSLIDES

49%

51%

FLOODS

EARTHQUAKES

6.3.3  Affected area 

The occurrence of disasters leaves vast areas under the effect of their impacts; in the RDH 

Historical Event Catalogue hectares of burned areas and square kilometres of flooded areas 

are collected. Their values are summed and listed by hazard in Table 12. 

Table 12. Total affected area per hazard 

HAZARD 

Time Coverage 

#EVENTS 

(km^2) 

RIVER FLOODS 

2000-2018 

332 

116513 

AFFECTED 

AREA 

29 

 
 
 
 
FLOODS 

FLASH FLOODS 

COASTAL FLOODS 

WILDFIRES 

2000-2018 

189 

11 

- 

2904 

3791 

66257 

*Wildfires are not grouped in events because of coherency with definitions; however the collection presents a 
total of 16407 burned area polygons 

6.3.4  Limitations of the approach 

The decision of analysing data concerning events occurred in the period 2000-2018 has been determined by 
the need to have homogenous time coverage for all hazards. In such a perspective, the impact of wildfires is 
clearer. The RDH Event Catalogue records a total of 662 deaths, 3359 injuries and 66257 square kilometres of 
burned areas.  

Forest fires are complex phenomena caused by a combination of different factors such as land management, 
human  activities,  and  especially  climate  and  weather  conditions.  Climate  change  affects  the  occurrence  of 
forest fires due to both weather conditions and effects on vegetation. Hazardous conditions are expected to 
evolve increasing the fire danger due to climate change in Europe and especially around the Mediterranean 
(Jesús San-Miguel-Ayanz et al., 2018; Ciscar et al., 2018).  

In conclusion, it is fundamental to highlight that these statistic are performed on existing data which have been 
merged together from various sources which from the origin lack in terms of consistency and resolution.  

However, the analyses of damage and loss data shown are intended to illustrate ways of examining global loss 
data and identifying possible trends in terms of peril or geographical prone areas within the European Countries. 

6.4  Discussion of trends  

The  data  stored  in  the  RDH  Event  Catalogue  show  several  peculiarities  in  terms  of  temporal  coverage  and 
hazard-related characteristics; however considering that flood and earthquake records cover the longest time 
span a trend analysis has been performed for the past 50 years. Results are shown in the Figures 13 and 14. 

Figure 13. Logarithmic trend of average fatalities from earthquake per event in the last 50 years 

10000

1000

100

10

1

FATAL EVENTS

AVERAGE
FATALITIES/EVENTS

FATALITIES/EVENTS
TREND

30 

 
 
 
 
 
 
 
 
 
Figure 14. Linear trend of average flood fatal events in the last 50 years 

Both  charts  show  interesting  and  peculiar  results.  The  number  of  earthquake  fatalities across  Europe  show 
oscillations over the last 50 years but no clear increasing trend has been recorded throughout the study period.  

For the  same  study  period, the  number of  fatalities per flood  event  show  a  decline  despite  the  noteworthy 
increase of fatal flood events with fewer deaths per event in the same period.  

31 

 
 
 
 
7  Discussion of results 

EARTHQUAKES 

Over the last 50 years, earthquakes in Europe have resulted in thousands of victims; nonetheless trends show 
a  general  decrease  over  time  in  the  number  of  victims  due  to  earthquakes.  The  observed  decrease  can  be 
explained  by  a  general  increase  and  improvement  of  knowledge  and  earthquake  coping-  and  adapting-
capacities, a tangible example could be represented by the actions undertaken at national level in a seismic 
country such as Italy (Dolce, 2012). This remarkable  result could be an evidence of the improved collective 
actions performed. However, it is known that the consequences of earthquakes differ in function of the region 
in  which  they  strike  according  to  its  socio-economic  development  (The  Guha-Sapir,  2011).  Specifically,  the 
impact of earthquakes is directly related to the degree of disaster mitigation and preparedness measures taken 
in  the  prone  areas  (Noji,  1997).  Compliance  to  building  codes,  zoning  ordinances,  strategic  urbanization 
development  and,  retrofitting  of  structures  can  significantly  reduce  the  devastation  of  major  earthquakes 
saving many lives and prevent buildings collapse (Anbarci et al., 2005; Spence et al., 2004); however structural 
measures to reduce buildings’ physical vulnerability have been strongly promoted for seismic risk (Menoni and 
Margottini, 2011). Moreover, the influence of the adoption of Eurocodes and parallel national standards and 
improvements in civil protection’s capability to manage emergencies and post-event actions should be better 
analyzed.  

FLOODS 

According  to  the  results  of  this  study,  the  number  of  fatalities  per  flood  event  show  a  decline  despite  the 
noteworthy increase of fatal flood events with fewer deaths per event in the same period. Floods are still a 
significant threat to people and to societal sectors, causing loss of human life  and extended economic damages, 
however trends indicate that flood disasters have been progressively less lethal over time. Already Diakakis in 
a work carried out in 2016 has highlighted how, despite the increase in recorded flood events in Greece in the 
last decades, related mortality does not show a corresponding rise. Also in Portugal, according to Pereira et al. 
(2016) analysis indicate a significant decrease of the annual flood mortality in the period from 1970–2010, 
this result is explained by the adoption of structural and non-structural mitigation measures, i.e. hydropower 
dams used to regulate peak flows of the main rivers and improved early warning system for floods and the 
evacuation  of  people  living  in  floodplain  areas.  If  few  studies  have  been  carried  out  to  examine  qualitative 
aspects of mortality(Diakakis, 2016), the increased value observed in the recurrence of fatal events could be 
explained  by:  (i)  the  increased  reporting  of  small  impact  events  in  later  years  due  to  improved 
telecommunication  (Kundzewicz  et  al., 2013);  (ii)  the  projected  increase  of  flood  risk  not  only  due  to  socio-
economic developments but also due to climate change factors (Alfieri et al., 2018; Alfieri et al., 2015; Forzieri 
et al., 2016; Kreibich et al. 2014;). 

7.1  Floods cause most fatalities 

Despite the decreasing trends in flood mortality, they are still responsible for most human loss and economic 
damages to societal sectors. Figure 11 shows that for the period 2000-2018 floods cause most fatalities.  

Many  initiatives  such  as  the  Global  Human  Settlement  Layer  (see  https://ghsl.jrc.ec.europa.eu),  the  Atlas  of 
Urban  Expansion  (see  http://www.atlasofurbanexpansion.org)  provide  clear  dimensions  of  the  massive 
urbanization occurred in the last century with a rapid expansion and sprawling growth of cities and metropolitan 
regions. It has been repeatedly claimed with respect to ﬂood hazards in Europe that the main driver of increases 
in  observed  losses  over  the  past  decades  is  increased  physical  and  economic  exposure  (Bouwer,  2013; 
Hallegatte et al., 2013; Jongman et al., 2014, Barthel et al., 2012; Forzieri et al., 2017; UNISDR, 2015). 

Those hazard prone areas (coastlines, flood plains etc.) attracted non only economic and urban development, 
offering significant economic benefits (Jongman et al., 2012; Brown et al., 2014); but also coastal and mountain 
tourism activities (Kellens et al., 2012; Ruin et al., 2008).  

As  more  people  and  assets  are  exposed,  risk  in  these  areas  becomes  more  intense.  Moreover,  urban 
development affects the occurrence of floods increasing runoff due to reduced vegetation and draining soil 
surface. This leads to an increase of volumes and frequencies of floods, therefore this conveys to a greater 
exposure of communities to increasing flood hazards (Konrad, 2003; Wamsler, 2013; Hallegatte et al., 2013).  

7.2  Signs of increased resilience and lower vulnerability 

Are those trends the reflection of increased resilience and lower vulnerability? 

32 

 
There is a very limited discussion and examination of qualitative aspects of mortality (Diakakis, 2016) and on 
possible improvements and advances of civil protection’s capability to manage emergencies, and no information 
on  the  improvements  of  policies  and  on  their  implementation  in  the  context  of  prevention  and  adaptation 
(Menoni and Faiella, 2019). 

FLOODS 

Do the Flood Directive (2007/60/EC) which addresses flood risk mitigation through  structural measures and 
vulnerability reduction, preceded by the Water Directive (2000/60/EC),  influence the seen change in mortality 
trends? 

To answer this question, detailed studies should be conducted, but in recent years EU has taken an active role 
towards disaster prevention and mitigation; initiatives such as European Flood Alert System (EFAS) which from 
2003  provides  probabilistic  flood  forecasting  to  local  authorities  (Thielen  et  al.,  2009)  and  Meteoalarm, 
developed  by  the  European  Meteorological  Services  Network,  which  provides  warnings  for  extreme  weather 
events across  European  countries (Alfieri  et  al.,  2012);  investments  in  technology  and  green  infrastructures 
(European Court of  Auditors, 2018), the promotion of sustainable land use practices, improvement of  water 
retention and the controlled flood, represent a concrete shift towards a general improvement of the existing 
conditions. Awareness campaign conducted among citizens to raise flood awareness, such as in Italy the “Io 
non  rischio”  campaign  conducted  by  the  Civil  Protection,  show  a  renewed  and  intensified  attention  towards 
prevention, protection and preparedness aspects. 

The implementation of the Floods Directive with its requirements (Preliminary flood risk assessments – 2011, 
Flood hazard and risk maps, - 2013, Flood risk management plans – 2015) increased Member States awareness 
about the risk they face shifting attention from protection against floods to management of flooding risks, this 
leads to increased resilience and lower vulnerability.  

33 

 
 
 
8  Conclusions and recommendations  

This chapter is intended to summarize the main findings and recommendations resulting 

from  the  work  carried  out  for  the  construction  of  the  Risk  Data  Hub  Historical  Events 

Catalogue. It may be appropriate to re-call the main concepts related to the work: 

—  Collection of existing damage and loss data from different sources; 

—  Harmonization of the collected data; 

—  Analysis of the records to provide an overview of the potential of damage and loss data; 

—  Creation of an open-access dataset.  

Damage data collection is the result of a systematic process to collect human, physical and economic losses as 
well social and environmental impacts caused by a hazardous event. Knowledge of damage and losses and 
their patterns, extent and root-causes is the key factor to improve both coping- and adaptive-capacities in order 
to avoid or reduce potential future damage and losses not only where the impact occurred, but also in areas 
similar to the ones that have been affected (Menoni et al., 2016; De Groeve at al., 2015). 

In a context where reliability of past damage and loss data remains an open question (Zêzere et al., 2014), 
weakness and gaps in the current state of the art have been concretely identified and observed, with the aim 
to handle them in order to produce a solid ground for a comprehensive damage and loss catalogue for European 
Countries. This is an open-ended work which represents a basis for future improvement in terms of: 

● 

Improvement of past-event damage assessment  

The work clearly highlighted all the gaps in the present damage and losses collection methodology through all 
the challenges encountered when existing data need to be analysed and/or compared (see Paragraph 4.6)  

● 

Integration of additional source of information  

The catalogue makes use of inventoried data, collecting, crosschecking and linking information deriving from 
heterogeneous sources; nonetheless, considering that a vast number of  initiatives which report damage and 
loss data for specific areas or hazards has been found, additional source of information should be integrated 
to provide a more comprehensive set of damage and loss data, not only trying to populate the existing modules, 
but also considering other types of hazards such as droughts and man –made accidents. 

In conclusion, considering that the work carried out is not focused on testing the quality of existing records, but 
rather on collecting, crosschecking and linking information deriving from heterogeneous sources to provide a 
comprehensive set of damage and loss data occurred in European Countries due to hazardous events; it can be 
stated that RDH Historical Event Catalogue represents a substantial contribution to the fragmented situation 
where most of the studies carried out and the information remain spread without having the possibility to be 
exploited  according  to  their  potential  and  a  significant  incentive  to  improve  the  damage  and  losses  data 
collection process through the identification of clear and practical existing weakness. 

Finally,  considering  that  the  Disaster  Risk  Management  is  complex  field  in  continuous  development,  as  an 
outcome of the work some recommendations can be mentioned:  

—  A natural disaster can impact different sectors; damage and loss data should be collected for each sector 
so  that  the  overall  damage  that  a  community  as  suffered  can  be  correctly  estimated  and  correct 
information can be retrieved. 

—  Damage and loss data are generally collected for compensation purpose; therefore, their quality reflects 
their scope. Specific conditions and directions shared across all European Countries should be delineated in 
order to analyse the damage drivers and causes. 

—  Terminology and taxonomy should be clearer and mutual between all the stakeholders involved if the real 
impact  of  the  events  has to  be  understood  and  compared  with  other occurrences or  between  different 
geographical areas. 

34 

 
 
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List of abbreviations and definitions 

DRM 

Disaster Risk Management 

DRMKC  Disaster Risk Management Knowledge Centre 

EDO 

EFAS 

EFFIS 

EMM 

EU 

GFDRR 

GHSL 

RDH 

European Drought Observatory 

European Flood Awareness System 

European Forest Fire Information System 

European Media Monitor 

European Union 

Global Facility for Disaster Risk Reduction 

Global Human Settlement Layer 

Risk Data Hub 

 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1. Number of events in relation with the time coverage of the module .................................20 

Figure 2. Number of Floods by type ................................................................................20 

Figure 3. Share of average number of fatalities by year ..........................................................20 

Figure 4. Share of average economic losses by year ..............................................................21 

Figure 5. Fatalities Maps by hazard for specific time coverage ..................................................23 

Figure 7. Flood and Earthquake Affected people for specific time coverage ....................................25 

Figure 8. Flooded and Burnt area for specific time coverage .....................................................26 

Figure 9. People Injured Maps by hazard for specific time coverage .............................................27 

Figure 10. Number of events by Hazard (2000-2018) ............................................................28 

Figure 11. Fatalities and economic losses for common time coverage (2000-2018) ..........................28 

Figure 12. Percentage of affected and injured perople from all-hazard totals .................................29 

Figure 13. Logarithmic trend of average fatalities from earthquake per event in the last 50 years ..........30 

Figure 14. Linear trend of average flood fatal events in the last 50 years .....................................31 

 
 
 
List of tables 

Table 1. Global datasets and characteristics ........................................................................ 8 

Table 2. RDH Historical Event Catalogue's time coverage by hazard considered ................................12 

Table 3. Total sum of fatalities, injured and affected people per hazard ........................................17 

Table 4. Total sum of economic losses and affected area per hazard ...........................................18 

Table 5. Comparison between events and total records present in the catalogue ..............................18 

Table 6. Deadliest and costliest events .............................................................................21 

Table 7. Top three most affected countries – FATALITIES AGGREGATED VALUE ................................22 

Table 9. Top three most affected countries – AFFECTED PEOPLE AGGREGATED VALUE ........................25 

Table 10. Top three most affected countries – AFFECTED PEOPLE AGGREGATED VALUE ......................26 

Table 11. Affected and injured people, totals per hazard .........................................................29 

Table 12. Total affected area per hazard ..........................................................................29 

 
 
 
Annexes 

In order to have a better understanding of the RDH Historical Event Catalogue contents, the following tables 
delineate loss and damage datasets characteristics structured by the type of event. 

Annex 1. Recorded information contained in the Historical Event Catalogue - Flood module 

FLOODS 

ATTRIBUTE 

DESCRIPTION 

ID Code 

Unique identifier code automatically generated by the system 

Hazard type code 

Descriptive code for each type of hazard (FL - river flood; FLSH 

- flash flood; CF - coastal flood) 

Country code 

Iso2 country code 

Region code 

Affected regions listed through the Nuts3 code 

Year 

Year of the event 

Country name 

Country in which the event occurred 

Start date 

Date  on  which  the  event  started  (or  ended  if  further 

information are not given) 

End date 

Date on which the event ended 

Type of flood event, which can be River, Coastal or Flash. The 

Type 

events  were  implemented  according  to  the  HANZE  database 

delineations. 

Flood source 

Name of the river, lake or sea from which the flood originated 

(qualitative and non-complete list of attributes) 

Area flooded 

Inundated area in km^2  

Fatalities 

Number of deaths due to the flood, including missing persons 

Number  of  people  whose  houses  were  flooded.  However, 

according to HANZE database the reported numbers of persons 

affected often only show the number of evacuees or persons 

Person affected 

rendered  homeless  by  the  event.  If  no  other  number  was 

available, those ones were used. If only the number of houses 

flooded  was  reported,  the  number  persons  affected  was 

estimated considering 4 people in each house. 

Losses 

(nominal 

Economic damage in the currency and price of the time of the 

value) 

event 

Losses 

(mln  EUR, 

2011) 

Economic damage in euro adjusted by inflation indexes 

 
 
 
Cause  

Notes 

Sources  

Descriptive attribute containing the meteorological causes of 

the event  

Descriptive  attribute 

containing 

relevant 

information 

concerning the event (i.e. triggering factors etc.) 

List of datasets, publication and other forms of sources from 

which the information was retrieved 

Annex 2. Recorded information contained in the Historical Event Catalogue - Earthquake module 

EARTHQUAKES 

ATTRIBUTE 

DESCRIPTION 

ID Code 

Unique  identifier  code  automatically  generated  by  the 

system 

Hazard type code 

Descriptive code of the hazard (EQ - earthquake) 

Country code 

Iso2 country code 

Region code 

Affected regions listed through the Nuts3 code 

Year 

Year of the event 

Country name 

Country in which the event occurred 

Start date 

Date  on  which  the  event  started  (or  ended  if  further 

information are not given) 

End date 

Date on which the event ended 

Epicentre 

Latitude and longitude  

Magnitude 

Measure of the seismic energy  

Time of occurrence   Exact time of the occurrence of the event  

Fatalities 

Number of the deaths  

Injured 

Number of injured people  

Person affected 

Number  of  affected  people  (umber  of  evacuees  or 

persons rendered homeless by the event) 

Losses 

(nominal 

Economic damage in the currency and price of the time of 

value) 

the event 

Losses 

(mln  EUR, 

2011) 

Notes 

Economic damage in euro adjusted by inflation indexes 

Descriptive  attribute  containing  relevant  information 

concerning the event (i.e. inconsistency between sources ) 

 
 
 
 
Sources 

List of  datasets,  publication and other forms of sources 

from which the information was retrieved 

Annex 3. Recorded information contained in the Historical Event Catalogue – Forest Fire module 

FOREST FIRE 

ATTRIBUTE 

ID Code 

Hazard type code 

DESCRIPTION 

Unique 

identifier 

code 

automatically 

generated by the system 

Descriptive  code  of  the  hazard  (FF  -  forest 

fire) 

Country code 

Iso2 country code 

Region code 

Affected  regions  listed  through  the  Nuts3 

code 

Year 

Year of the event 

Country name 

Country in which the event occurred 

Start date 

Date on which the event started (or ended if 

further information are not given) 

End date 

Date on which the event ended 

Area burned 

Area burned by the fire given in hectares 

Fatalities 

Injured 

Notes 

Number of fatalities per fire-season 

Number of injured per fire-season 

Descriptive  attribute  containing 

relevant 

information concerning the event  

List of datasets, publication and other forms 

Sources 

of  sources  from  which  the  information  was 

retrieved 

Annex 4. Recorded information contained in the Historical Event Catalogue - Landslides module 

LANDSLIDES 

ATTRIBUTE 

ID Code 

DESCRIPTION 

Unique 

identifier 

code 

automatically 

generated by the system 

Descriptive code for each type of hazard (FL 

Hazard type code 

- river flood; FLSH - flash flood; CF - coastal

flood) 

Country code 

Iso2 country code 

Region code 

Affected  regions  listed  through  the  Nuts3 

code 

Year 

Year of the event 

Country name 

Country in which the event occurred 

Start date 

Date on which the event started (or ended if 

further information are not given) 

End date 

Date on which the event ended 

Fatalities 

Number of fatalities  

Injured 

Cause  

Notes 

Number of injured 

Descriptive attribute, cause of the event (i.e. 

rain, earthquake etc.) 

Descriptive  attribute  containing 

relevant 

information  concerning 

the  event 

(i.e. 

qualitative description of the extension of the 

phenomenon) 

List of datasets, publication and other forms 

Sources  

of  sources  from  which  the  information  was 

retrieved 

 
 
 
 
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