Author’s Accepted Manuscript

Describing Xaver in disaster terms

Dorota Rucińska

www.elsevier.com/locate/ijdr

PII: S2212-4209(18)30572-7 DOI: https://doi.org/10.1016/j.ijdrr.2018.11.012 Reference: IJDRR1023 To appear in: International Journal of Disaster Risk Reduction Received date: 4 May 2018 Revised date: 6 November 2018 Accepted date: 12 November 2018 Cite this article as: Dorota Rucińska, Describing Storm Xaver in disaster terms, International Journal of Disaster Risk Reduction, https://doi.org/10.1016/j.ijdrr.2018.11.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Describing Storm Xaver in disaster terms

Dorota Rucińska*

University of Warsaw, Faculty of Geography and Regional Studies, ul. Krakowskie Przedmieście 30, 00-927 Warszawa

*Corresponding author: Dorota Rucińska, [email protected]

Abstract

The aim of this paper is to understand the relationship between the types of losses incurred in the context of Storm Xaver, and the use of the term ‘disaster’ for a winter storm that occurred in 2013. This understanding is important as regards disaster risk reduction policy.

This case study of the social-economic impact of Storm Xaver and the criteria that defined the 'disaster' in , , the UK and the allows us the opportunity to understand and assess whether such a term is justified.

This investigation reveals that affected populations are key when it comes to justifying the ‘disaster’ term.

This study looks into those hidden meanings within the description for those affected by such a disaster, with quoted figures provided for the numbers of individuals affected subject to correction. However, it soon became clear the importance of separating the impact of such an event over the short and long-term as regards the study of disaster risk reduction on these groups.

On the one hand, this case study reveals an imprecise use of the term ‘disaster’, and on the other misinformation in the numbers of those affected, which in turn leads to a misinterpretation in data and misleading optimism.

If the approach is focused on "affected people" and the consequential effects of living in the area, then this can be used as a tool to put together more responsible activities for

1 action towards Disaster Risk Reduction, e.g. an allocation of budget funds in the regions and locally.

Keywords: Disaster, term, criteria, affected, winter storm, Europe

1. Introduction

Natural disasters may include such natural event as floods, hurricanes, winter , and earthquakes where a serious disruption of the functioning of a community or a society. The term ‘disaster’ has been given as a definition by international organisations such as the Emergency Events Database (EM-DAT) [1] and the World Bank [2], although there is no regular criteria that allows detailed analysis of the term that would prove useful long-term. In fact, the lack of criteria is often the reason for the complications that occur when analysing differences as regards the impact upon various nation states. These complications can amount to the effect of misunderstanding a disaster, the limited available data to the public, or even being random or selected.

Generally there are differences when it comes to describing losses, and in turn, what can be defined as a disaster. Most of the previously mentioned criteria for disaster is used by the EM-DAT [1]. Other criteria is used for assessing the size and impact of disasters in countries by other institutions such as the World Bank [2], and the Re [3]. Considerations are continued in many contexts [4,5,6,7].

In general, damage from natural disasters in Europe in 2013 (16.9 billion EUR) was the fourth highest of the decade. The damage caused by the 2013 storm was also the fourth worst of the same period [8], with insured losses of €763 million and €1 billion in total damage [9].

Storm Xaver itself brought a significant to northern Europe, leaving at least 15 people dead and dozens of others injured. Damage was heaviest in , Germany, the Netherlands, , Scandinavia, and Poland, with more than 650,000 power outages occurring which also led to the suspensions of flight and rail services. Although insured losses were estimated at roughly €800 million, total economic losses were even higher [10]. Predictions show that in the next few decades the amount of strong winds in Central Europe will increase by about 20%, and that the wind speed could be 7-10% higher [11]. Confidence in any future changes as regards wind speeds is

2 relatively low, but it seems increasingly likely that there will be an increase in average and extreme wind speeds in northern Europe [12].

There is widespread acceptance that a wind speed of 17 metres a second (m/s) is considered strong (World Meteorological Organization, WMO) [13] and can cause damage. The winds are the reasons why we need to be able to understand the impact of winter storms better and use this understanding for DRR adaptation policies. In more extreme examples, Quimburga in 1972, in 1990, Cyclone Oratia in 2000 and in 2007 each caused significant losses in Europe.

The research problem is the use of (or not) criteria for defining what is or what is not a ""; that is, the description of a natural event based on wind speed, or tidal wave, and the height of the water level treated as an extreme event; but more often or not it is not on criteria based on the number of fatalities or affected people. Furthermore, any descriptions of losses as results are presented as accidents and not on the impact on relationships. Losses can be hidden in kinds of data; it is reason to identify gaps. The term ‘disaster’ is a very real subject because the safety of the population has to be taken into consideration, according to the Sendai Framework for Disaster Risk Reduction (SFDRR) (2015-2030) [14], along with risk management and the implementing of DRR in those affected nation states. The SFDRR aims are placed in the centre of interests and discussions [15, 16, 17, 18] which are a continuation of 'disaster' studies that are talked up as social topics [19, 20, 21, 22, 23, 24]. These authors emphasize the quality of data about natural disasters according to the SFDRR [25], for example: “Data and statistics are important in understanding the impacts and costs of disasters. Systematic disaster data collection and analysis can be used to inform policy decisions to help reduce disaster risks and build resilience.” "Access to information is critical to successful disaster risk management. You cannot manage what you cannot measure." - Margareta Wahlström, United Nations Special Representative of the Secretary-General for Disaster Risk Reduction [26]. “Global and national databases for monitoring losses from national hazards suffer from a number of limitations, which in turn lead to a misinterpretation in hazard loss data” [27]. Before the creation of the SFDRR there were comments as regards the differences and the lack of standardization as regards the term "disaster", as well as disaster

3 typology/classification and their primary and secondary effects which complicate any comparisons of data and decreased accuracy in reporting disaster related impact [28]. These sentences and citations show the direction for the article and focus on the data and the term ‘disaster’.

The paper addresses the term “disaster” within the context of the European winter storm Xaver in 2013. The aim of the study is to know whether the use of the term "disaster" is justified, in the case of Storm Xaver in those selected countries. What criteria determines that Xaver is a disaster? This question is important because such criteria indicates those serious effects that should be used in social education to reduce the risk of any losses. Therefore, studies were conducted in Germany, the Netherlands, the and Poland; those countries where Storm Xaver was an extreme event. This storm was considered a meteorological disaster because the highest ever ocean levels were recorded, along with very high wind speeds. Those highest wind speeds were restricted to the islands and coastline in the north of Germany, and Storm Xaver was deemed as a meteorological disaster. Although the resulting storm surge reached six metres above mean sea level, fortunately for the area, it was protected by an eight-metre high dyke. The water in the River Elbe in was at its second highest level since measurements were first recorded. The storm produced a storm surge over the that was 3.5 metres above mean high water. As a result of Storm Xaver, people in the north of the country were without power; public schools were closed, along with Christmas markets and many roads; several flights out of Hamburg were cancelled, and the high-speed rail line between Hamburg and Berlin was closed because of fallen debris on the tracks. There were significant costs to the public, private and economic sectors, with floods and hailstorms accounting for 77% of all disaster damage in 2013 [8].

Also in the Netherlands, Storm Xaver was considered a meteorological disaster, along with the UK. The Netherlands had never recorded winds of such intensity since 1910, with speeds of 38 m/s recorded in Stavoren on the coast. The estimated surface wind speed was similar to that experienced by the Netherlands during the floods of 1953. Fortunately there were no fatalities recorded in the country as a result of the storm, but estimated damage. In the south-west of the Netherlands (the Zeeland province), the sea

4 reached 3.99 metres above mean sea level [29]. Widespread transport disruption occurred throughout the country, and the windstorm caused major logistical disruption. At Amsterdam’s Schiphol airport, the Dutch carrier KLM cancelled 42 flights and train services were disrupted throughout the country. Numerous ferry services were cancelled across the and along the coast on October 28. There were three fatalities in the Netherlands due to falling trees or individuals being swept out to sea (Severe-Weather.eu). Schools were closed in three provinces [30]. It was during Storm Xaver that the highest ever extreme ocean levels were recorded in areas of both Germany and .

As a result of Storm Xaver, the east coast of the UK was hit by the largest surge since the 1953 floods. This storm was considered a meteorological disaster with two fatalities, and the total number of people affected reached 4,200 (EM-DAT). The tidal surge was the worst in 60 years along the east coast, with tens of thousands across the UK left without power [31]). Although there was minimal influence of the moon tidal surge along the coast of Poland, very strong winds resulted in extreme winter damage. The topic of winter storm losses is still current in the country, with two more winter storms in 2016 in the forms of Storm Barbara (Dec 2016) and (Jan 2017) [32].

From a general perspective, the damage caused by such a natural disaster is very real because of the economic and social aspects. In addition, the trend is increasing as regards the total economic costs of damage as a result of global natural disasters, which is measured at today’s current US$ value [33]. Gauging what are acceptable losses is one research topic being undertaken [34], but one of the problems of such an area is the group of people that would be termed “affected”.

Another perspective is the use of the ‘disaster’ term in the context of the long-term effects of the event. The subject of long-term effects is well known in the psychological approach to the issue as PTSD (Post-traumatic stress disorder) [35, 36, 37, 38]. Psychological research [39] has drawn attention for the need to estimate the effects of long-term natural disasters, such as the lack of, or the need for, job relocation as a result of the destruction of a business, the time required to rebuild a home or to relocate, and to provide assistance to families in finding new homes and new living conditions, along

5 with the lack of health care as a result of a disaster (as in direct costs of treatment and indirect costs of health changes). In the USA, there has been an increasing number of cases of Lyme Disease (transmitted to humans through the bite of infected ticks which is treated like an epidemic), an excellent example in the field of biotic disaster research.

Although Lyme Disease is not mentioned as an ailment with a high mortality rate in the country's population, the specificity of the disease and the problems with recognising its symptoms [40] resulted in extensive indirect effects. The indirect (non-medical) costs were much higher for one patient than the annual direct (medical) costs [41]. Taking into account the estimated corrections, the cost of treatment on a national scale is potentially, with 300,000 patients, about 2.6 billion EUR [42]. Due to the number of patients affected (not deaths) and indirect financial losses, this type of disease can be classified as a biotic and abiotic natural disaster.

So in this context it is important that we should adopt some kind of accurate wording and criteria when using the term ‘disaster’, and to better understand the significance of losses. Sometimes losses are hidden in the types of data.

Identifying research problems is a condition that needs to be improved upon, along with being given the information about potential losses to society and its education. Winter storms point to a need for meaningful understanding and deliberate investigation. But the problem is so challenging to solve because there is a lack of a universal definition of 'disaster' for several countries, as well as there being no model for a database for types of natural disaster and collection by states, and also making the information generally available. Eliminating this is difficult, but does exists in theory; but as there is no universal criteria of a 'disaster' term, it only appears as a chaotic form of data. But what is of importance is the extent – or rather, the lack of – social awareness as regards natural hazards and risk. Being able to identify these gaps presents opportunities to us for developing methods leading towards disaster risk reduction.

2. Material and methods

In Poland there has not been any publicly available information as regards recent storm damage. Data is fragmentary; most of this was estimated and was only published shortly after the event, and created by the Ministry (or as an answer to a question via email).

6 However, there had been information published as a result of real intervention made by not only the National Headquarters of the State Fire Service (KG PSP), but also by locally selected regions, Another option had been to use the international EM-DAT database [1], but despite critical remarks regarding this, the criteria used had necessarily raised the threshold so that it would capture the great socio-economic disruptions as well as copying capacity. As a result, secondary date sources were used for this study.

This paper focuses on the term 'disaster', along with the investigations conducted into the differing descriptions of a natural disaster, and connected with the various data that is available.

There is one main aim – to discuss which criteria would be of legitimate use by assessing the consequences of Storm Xaver in 2013 on individual countries when placed in a ‘disaster’ context. The questions asked were:

1. What has been the social and economic impact of Storm Xaver in those selected European countries?

2. What kinds of damage would be deemed adequate for usage as criteria for a disaster as regards Storm Xaver and by which countries – Poland, Germany, the United Kingdom, or the Netherlands?

Two main definitions of natural disaster were used for this analysis. This criteria includes all the disasters recorded from 1900 until the present day that conform to at least one of the following criteria: the deaths of ten or more people, a hundred or more people that were affected, the declaration of a and a call for international assistance [1]. In various literature there is other criteria being used to assess the significance of disasters in countries by institutions such as the World Bank and Munich Re: [3]: whether there were more than 100 casualties, whether the economic damage is in excess of 1% gross national product (GNP), or if more than 1% of an impacted country's population has been affected [5].

The main methods used are the result of a descriptive case study on the impact of Storm Xaver, along with a critical analysis of data. It was necessary to make use of the different kinds of sources in the analysis to describe the economic impact on these

7 countries. So this article is partially a theoretical paper, from which it could then be developed into a useful framework for DRR.

The aim of the study was to identify whether the use of the term "disaster" is justified in the case of Storm Xaver in selected countries. Which criteria determined that Xaver was a disaster? This was important because such criteria would indicate the serious effects that should be used in social education so to reduce the risk of loss, and therefore the study of those countries analysed in which Storm Xaver was an extreme event: Germany, the Netherlands, the United Kingdom and Poland.

Tables have been created that contain data on those resulting losses, calculating the values based on the number of affected individuals. Table 1 contains the data available from the EM-DAT database [1], after which it was supplemented with data from secondary sources and presented in table 2. Table 3 includes the conversion of losses by 1% of GDP, and table 4 includes 1% of the population compared to those numbers affected in other countries. For a more accurate estimation, the number of homes without electricity per number of people affected, and the number of evacuees, were calculated, respectively, by states. The study allowed us to more precisely specify the number of those affected which were previously presented using more general data.

3. Results

When comparing the definitions as presented above, we can observe three kinds of disaster that are being described by using different terms, actions and indexes:

(i) In the social aspect, when there were dead and/or affected casualties, or more than 1% of an impacted country's population were harmed; (ii) When declarations were made by officials such as a state of emergency and a call for international assistance; (iii) Having used an economic approach which described damages in numbers: with economic damage in excess of 1% gross national product (GNP).

Based on EM-DAT, the table below (Table 1) shows that Germany recorded the highest number of fatalities as a result of Storm Xaver, with the addition of figures for the UK and Poland. In addition, the UK recorded the highest number of affected people, and the Netherlands the highest recorded cost of damage.

8 Table 1. Losses for Storm Xaver, 4-7 December 2013

December 2013 States Total deaths Total affected Damages in million EUR

Poland 4 53 No data Germany 7 2 No data The UK 2 4,200 No data The Netherlands No data No data 8.2

Source: Based on EM-DAT [1].

Based on news sources such as internet information and research analysis of articles, we can describe the situation by country (Ramos Ribeiro, Rucińska 2017). These were the differences in the amount of damage per nation state after Storm Xaver (table 2).

This information reveals the differences in fatalities, those affected and the amount of losses by country.

Table 2. Collected data from fatalities, those affected and amount of losses after Xaver in 2013.

States Fatalities Affected Losses in EUR Insurance losses Poland 4 53 14,907,573 No data Germany 7 2 4,536,019,082 No data The UK 2 4,200 No data 1,900,000,000 The Netherlands 0 0 10,000,000 No data

Sources: Based on: [1, 30, 43, 8].

The table below (table 3) presents data other than that provided by EM-DAT. [1]. However, the calculation [44] shows losses by states which are lower than 1% value of GDP.

Table 3. Loss values of 1% of GDP.

GDP PPP $ Losses States in 2013 EUR 1% of GDP in EUR Poland 524,215,000,000 381,044,350,644.69 3,810,443,506 14,907,573

9 Germany 3,750,000,000,000 2,725,821,113,317.23 27,258,211,133 4,536,019,082 The UK 2,720,000,000,000 1,977,128,914,192.76 19,771,289,142 1,900,000,000 The Netherlands 866,680,000,000 629,977,237,997.27 6,299,772,380 10,000,000

Sources: Data of GDP [45].

When analysing the data describing the number of properties destroyed in Poland and the UK (table 4) based on news collated from the internet, there were some important points that needed consideration. This selection of data may conceal or mislead the public as regards the numbers of people that were affected during Storm Xaver. Based on the EM-DAT in Poland, there were 53 people affected (table 1 and 2). Based on local data, in total there were 94,000 consumers in the Zachodniopomorskie voivodeship that were without electricity (table 4) [46]. Another source stated that more than 400,000 houses overall were without electricity [47, 48] (table 4).

Table 4. Estimates of people affected.

States 1% of Population population Affected Kind of affected Poland 38,000,000 380,000 94,000 Blackout Blackout in 400,000 1,110,400 houses

Germany 80,600,000 806,000 2 Evacuated

The UK 64,100,000 641,000 10,000 3,360 1,400 houses flooded

The 0 Netherlands 16,800,000 168,000 0

Source: Based on: [49, 30, 50, 45, 51].

This study shows the use of the term ‘disaster’ even when there are no losses described in the definition. This study of data as regards damage also reveals a lack of precision between reports, but are unified in their descriptions of losses and differences in the situation by country. They also impact upon us the quality of the information about the damage upon society and reception of the information, as well as their potential activity

10 for DRR preparation towards hazards. But generally all of the countries analysed were commonly using the term ‘disaster’ when describing winter storm Xaver in 2013 as a result of the intense wind speeds.

When comparing the number of fatalities as a result of Storm Xaver, the figures reveal that, when defining a disaster, none of the countries were using EM-DAT criteria with the exception of the UK. Figures showed that among the 4,200 that were affected, there were two fatalities (tables 1 and 2).

However, when comparing different sources of data, and using a calculation based on the number of people in Poland without electricity (table 4) as reported in the media (400,000 households), then the average number of people (2.76 for each household by GUS data) [51] gives us a result telling us that 1.1 million people were affected; this would then be justification for describing Storm Xaver as a disaster. So only the UK and Poland could be classified as a natural disaster (table 5) using criteria based on more than 100 people affected [1] or the number of casualties [2].

Using the calculations based on the 1.1 million affected in Poland, or data based on around a hundred thousand households without electricity (table 4), then this storm was classed as a disaster. The same information shows 3,360 were affected, using a calculation based on the 1,400 houses flooded during Storm Xaver (In 2013, the average number of people per household was 2.4) (table 2). The other criteria provided by WB did not class Xaver as a disaster in those countries analysed; no more than 1% of the impacted country's population was affected, and the economic damage was not in excess of 1% of their gross domestic product (GDP) (table 3).

While the strength of the wind was especially strong in the north of Poland, it was also specific to the mountains in the south of the country; any fatalities were the result of fallen trees. In percentages of GDP, economic losses in these four countries were Germany (0.17%) and the UK (0.1%); the storm having had a stronger economic impact than in Poland (0.004%) and the Netherlands (0.002%).

Now it is when we focus on the theoretical that we observe ‘differences’ in what is termed as a disaster (above) and ‘affected’.

11 When conducting a deeper analysis of terms, there is not a homogenous description as regards two key words: (those) affected, and casualties, particularly as ‘affected’ has been used more widespread than ‘casualties’. There are two definitions when we talk about people affected: 1. more general [52], 2. and the more detailed [53]:

1) The definition of a group of casualties as injured are individuals suffering from physical injuries, trauma or illnesses that require medical treatment [52].

2.1) People who are adversely affected by a crisis or disaster and may require assistance, if they are unable to cope with the effects of the situation on their own [52];

2.2) People who are affected, either (i) directly (injury, illness or other health effects; individuals who were evacuated, displaced, relocated or have suffered direct damage to their livelihoods, economic, physical, social, cultural and environmental assets; and people who are missing or dead may be considered as directly affected) or (ii) indirectly (those who have suffered consequences other than, or in addition to, direct effects over time due to disruption or changes in the economy, critical infrastructure, basic services, commerce or work, or social, health and psychological consequences), by a hazardous event. This definition underlines the short-term or long-term consequences to lives of affected individuals, their livelihoods or health, and to their economic, physical, social, cultural and environmental assets [53].

As regards the definition of 'affected people' by a natural hazard, these are individuals who have been evacuated or have suffered direct damage to their livelihoods; these can be economic, physical, social (either directly or indirectly), or have suffered consequences due to disruption, changes in basic services, critical infrastructure, commerce or work, social consequences or the economy (indirectly) [53]. In the context of the definition above, power blackouts – as well as any evacuation – can have short or long-term consequences to their lives after the cascading effects caused by widespread power failures. The topic of a cascading effect in the context of a blackout has been recently looked into [54]; it is a specific situation in which large groups of people in neighbourhoods that are closely located together are caught up at the same moment and in the same situation, and are unable to give assistance to themselves, being dependent on the situational awareness of decision makers and emergency operators. The length of a blackout is key when it comes to its impact on society, along with its short or long-

12 term effects. It is reasonable to include this group of people as those affected during a natural disaster. Results can range from mild to a strong impact, including the time of day or night, as well as the time of year, especially in temperate zone of climate.

The winter storm is an example of an extremely difficult situation for people. In this study, using the term "affected people" should be used when we include the number of individuals who faced a blackout during Storm Xaver (1.1 million people were affected in Poland and 3,360 during the flood in the UK) (table 4).

Any low numbers of fatalities would indeed show that there is good monitoring and sufficient warning systems in any country, but the problem is that when we look closer at the numbers of those people affected and the cost of damage, we can see a vast social-economic problem; the fact that they are merely statistics which describe a disaster in European countries in 2013.

When taking a locally-based approach on Poland as an example [32], people were affected indirectly (more details: Ad. 2.2) because of the cultural-economic damage such as the Pomeranian Dukes' Castle (local economic losses); damage in the forest (the social-cultural aspect as regards local community use of the forest, along with the environmental-ecological implications, and not only the value per cubic metre); and because there was no available data as regards losses in business. A lack of data about trauma after a natural disaster, as well as the short and long-term impact, can be especially problematic when collecting the information and assessing the impact.

4. Discussion and conclusion

Referring to definition the ‘disaster’ the UNISDR presents descriptive definition of the term. Disaster it is a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more kind of impacts. The effect of the disaster can be immediate and localized or widespread and could last for a long period of time. The effect may exceed the capacity of a community or society, and therefore may require assistance from external sources. Disaster damage occurs during and immediately after the disaster. This is usually measured in physical units, and describes the total or partial destruction of physical assets, the disruption of basic services and damages to sources of livelihood in the affected area. Such as Earthquake, volcanic eruption, , the

13 are sudden-onset disasters emerges quickly or unexpectedly. Disaster impact includes economic, human and environmental impacts, and may include death, injuries, disease and other negative effects on human physical, mental and social well-being. The impact of frequent disasters could be cumulative, or become chronic for a community or a society. For the Sendai Framework for Disaster Risk Reduction 2015-2030 are described two scale of disaster: Large-scale disaster: a type of disaster affecting a society which requires national or international assistance and small-scale disaster: a type of disaster only affecting local communities which require assistance beyond the affected community [55]. In this context winter storm Xaver in Poland was small-scale disaster because delimited to coast regions in affected people. The impact of strong wind was observed also in mountains in the South of Poland. Fatalities were in region Lembork in the North as well as in Wrocław, in the West-South. In Poland there were cancelled flights, too. Although an event is not considered a disaster in national terms, it may very well be so in a local context.

There are many definitions of "disaster". A descriptive definition such as by the UNISDR does not go into the details of the differences in direct and indirect losses. There are also those definitions that take into account the criteria for socio-economic losses (EM-DAT, WB) or insurers losses that were not analysed in this study. While the criterion relating to response (the declaration of a state of emergency and a call for international assistance) is not questionable, considering the numerical criteria, it can be concluded that they do not have a substantive justification for criterion by EM-DAT. Some of them are supplemented only by the definitions of other terms, such as the type of losses (direct and indirect socio-economic losses) as well as affected people. In the context of supplementing the descriptive definition of UNISDR through other definitions, we can conclude that there is a gap, especially in terms of estimating the affected people.

The results of this study do not entitled to too general conclusions, but it revealed significant differences in the description of the number of affected people. This is important for several reasons:

1. Estimating losses in the context of affected people who are in the group of indirect losses.

14 2. The use of specific data to describe the consequences brings us closer or move away from the definition of social vulnerability.

3. Directing appropriate help to this group, the scope of which is not adequately identified through other material data (such as destroyed buildings).

4. Creation of social awareness allowing increasing resilience and resistance for winter storm and finally for multi-hazards

Continuing, it is debatable to assess the effects by recognizing% GDP or% affected people. Although they are a comparable measure on a global scale, they do not capture the problem of economic and political stability, abyss in terms of social wealth in developing and developed countries, and individual resilience and coping ability that are different than in the world.

This paper refers to the term 'disaster' and the different descriptions of a natural disaster, which is connected with the kinds of data which describe the phenomenon.

The widespread use of the term ‘natural disaster’ in colloquial speech makes it difficult to use the term according to existing criteria. Despite the fact that in many countries the Xaver storm was a meteorological disaster due to the fact that physical norms were exceeded in nature, in some cases this event could also be called a natural disaster because of the socio-economic effects, as the study indicates.

Unfortunately there appears to be specific use of different types of available data to match the event in a given country. This is a significant problem in Poland. The lack of agreed data slows down the research process, forcing the use of differing data sources which in turn was also a limitation for this study; global and national databases for monitoring losses from national hazards gave us differing interpretations as regards hazard losses. All this can lead to a misinterpretation in data, resulting in spinning a more positive slant and the impression of reality.

Gathering data, statistics and access to information is critical when trying to understand the impact and the economic costs of disasters, and should be used to inform policy decisions and management to help reduce disaster risk and build social resilience.

15 Undertaking this study allows Poland to be included in a group of countries where previously earlier attention was paid to the serious effects of winter storms in Europe.

This study makes it possible to explain three issues:

 Presenting the effects on Poland against other European countries have revealed that these effects were indeed serious. When using deaths in Poland as the criteria, this makes the country more vulnerable than the United Kingdom and the Netherlands. And then, when using people affected as the criteria, this makes the country more vulnerable than the Netherlands and Germany. But comparing the economic effects in the four countries is difficult because of their different localisations and those particular areas that were directly affected by the storm.  Based on this case study, determining which criteria were met for defining the disaster that was Storm Xaver in the context of socio-economic damage – and when comparing those differing sources of data, using a calculation based on the number of people in Poland without electricity (as affected people) (table 4.), and as reported on the news (400,000 households) – gives us a result that tells us that 1.1 million people were affected, and so justifies describing Storm Xaver as a disaster. In this context only the UK and Poland could be classified as having experienced a natural disaster, using criteria based on more than 100 which were affected or were casualties of the storm. Presented calculation of affected people is a kind of example of using different data.

 This study reveals the misuse of the term ‘disaster’ on the one hand, and misinformation in the numbers of affected people on the other. The fact that this information is incomplete and misleading cannot be said to inform, educate and make society aware of the subject of natural hazards as a winter storm as well as DRR. The misinformation as regards the lower numbers of affected people during a winter storm leads many to believe that such a natural event is not so dangerous, and as a consequence, only a small group of people would need to be informed as regards insurance and to take action as regards protection.

In addition, the study revealed significant differences in the description of the disaster if other types of data were used.

16 An example is the number of fatalities on the one hand (direct number, e.g. 4 people in Poland in 2013), and the number of affected people on the other, that is, those hidden in material descriptions of damaged property or the evacuation from the number of houses; these distort the figures in that they are not the actual number of affected people. According to the author, this is important for social communication as it is needed to increase the efforts for any preparation (that is, to adapt to any natural hazard). It should be emphasized that due to the differing political situations in countries around the world, correctly transferred data can be used, for example, to undercut the scale of the effects of the disaster. This study makes it possible to understand the need for the use of relevant data within appropriate groups of criteria. Furthermore, if an event is not considered a disaster in national terms, it may very well be so in a local context.

Based on this case study for four countries affected by the winter storm of 2013, the problem was identified in what was camouflaged data – that is, information that is not obvious and comprehensible for all citizens. We can continue the study and finally come up with, and provide, much better information for society and its institutions. It suggests that DRR should be more active, particularly in the context of winter storms. Information about the numbers of affected individuals caused by power blackouts should be included when it comes to describing any impact as indirect. In Poland, the numbers of affected people (in the whole country by EM-DAT) can not be lower finally than local number affected people cause locally impact is included in this country impact (table 1 and 4). Presenting an international model of data impact can be an important impulse to creating a universal standard of data by country.

Important suggestions for improving and disseminating data for DRR are listed below:

 Winter storms in Europe are not the most dangerous phenomena, but in the context of SFDRR – and the need to reduce the effects of multiple hazards – winter storms also have to be considered at all stages of DRR, and in Poland, which has, so far, been overlooked in these analyses.  The use of a loss rate of more than 1% of the country's GDP or those affected above 1% of the population is important in the world, but rigid adherence to these criteria at a national level leads to a general perception of damage as being minor or insignificant. However, despite not exceeding the 1% threshold, this

17 requires thorough studies and activities at a national, regional and local level for DRR purposes.  Similarly, incomplete information about the number of victims cannot provide sound education and conditions to create public awareness of natural threats such as a winter storm, as well as DRR. Information as it stands could suggest that such a natural event is not particularly dangerous and that only a minor proportion of people would have to learn, insure and create protection for themselves. An example is the use of 1,400 flooded homes or 3,360 affected people in the UK; in the case of Poland, 400,000 homes were without electricity and 1.1 million people were affected. These numbers could lead to the creation of a different damage description, and the type of numerical data available can lead to the creation of a different description of damages and perceptions of the hazard. The information about the lower numbers of affected people during a winter storm can be seriously misleading, leading to the belief that any significant natural event is not so dangerous and that only a small number of people would need to learn about insurance and create protection. This incomplete information cannot offer informative education and awareness for society when it comes to the subject of natural hazards such as winter storms as well as DRR.  In addition, this study draws attention to the importance of long-term effects – including abiotic natural disasters – causing secondary cascading effects of a health and economic nature, which are not normally estimated and made public. While the fact of long-term effects is already known in the case of biotic disasters (as in the example of research in the US), it is not recognized in the case of abiotic disasters. For example, there is no such research and available information in Poland.

 This study draws attention to the importance of the long-term effects of natural disasters – that cause those cascade effect hazards of a health and economic nature, which data are not estimated and made public. There is a need to collect data, and for this, create a scale that describes the length of time of the long or indirect impact during widespread power failures (as in the number of hours: 3, 6, 9 hours, etc., one day, more than the one day) so as not to overestimate the effects.

18  In Poland, the data published is general. It does not describe particular kinds of data such as the impact on a governmental budget only, or with individual people representing society. Most of the details we have obtained is from news media and from local services but most of this is chaotic, and there is no separate information about the impact to the governmental budget and the private sector in financial terms, when describing the effects of the disaster. Information has to be obtained separately from insurers. There is no data about the long-term impact of natural hazards.

Summarising, and based on the criteria given, Storm Xaver was classed as a natural disaster only in the UK. In Poland, the term was used to describe the situation only in particular regions rather than nationally.

However, it should be emphasized that the data on the numbers of affected people in Poland given in the EM-DAT database (53 people) do not reflect the numbers in the context of power blackouts (1.1 million people) in terms of individual losses. However, it should be emphasized that a response to the question of ‘what is the criteria that defines a disaster’ is not possible at this stage of the study. Put into context, terms such as "casualties" and "affected people" were used by UNISDR, as well being the criteria for any long-term impact. We can confidently say that the real impact was far bigger and far more complicated than this.

References

[1] Emergency Disasters Database http://www.emdat.be , 2015 (accessed 20 June 2015)

[2] International Bank for Reconstruction and Development / The World Bank, 2013 https://openknowledge.worldbank.org/bitstream/handle/10986/13108/758470PUB0EPI 0001300PUBDATE02028013.pdf (accessed 20 June 2015)

[3] Munich Re, www.munichre.com

[4] J.B. Smith, Standardized Estimates of Climate Change Damages for the United States. Climatic Change, 32(3) (1996) 313–26.

19 [5] M. Reinhard, Natural Disaster Risk Management and Financing Disaster Losses in Developing Countries, (2004) Karlsruhe: VVW

[6] D. Rucińska, Ekstremalne zjawiska przyrodnicze a świadomość społeczna, Wydawnictwa Wydziału Geografii i Studiów Regionalnych Uniwersytet Warszawski, Warszawa, 2012.

[7] R.R. Ramos Ribeiro, D. Rucińska. Analysis of physical factors of the windstorm Xaver in Poland: post‐ hazard review, Weather 72 (12) (2017) 378-382.

[8] D. Guha-Sapir, H. Philippe Hoyois, R. Below, Annual Disaster Statistical Review 2013. The numbers and trends. Brussels: CRED, (2014) 50.

[9] PERILS, 2014, Zurich - 05 December 2014, https://www.perils.org/web/news.html) (accessed 8 November 2015).

[10] GCR 2013, Global Catastrophe Recap, Dec 2013, AON Benfield.

[11] Leckebusch GC, Ulbrich U. On the relationship between and extreme windstorm events over Europe under Climate change. Global and Planetary Change, (44)1-4 (2004)181-193.

[12] Ec.europa.edu http://ec.europa.eu/regional_policy/sources/docoffic/working/regions2020/pdf/regions2 020_climat.pdf (accesed 1 January 2018).

[13] WMO https://www.wmo.int/pages/index_en.html

[14] Sendai Framework for Disaster Risk Reduction (UNISDR) https://www.unisdr.org/we/coordinate/sendai-framework (accessed 25 June 2015)

[15] L.M. Stough , D. Kang, The Sendai Framework for Disaster Risk Reduction and Persons with Disabilities. Vol. 6, Issue 2 (2015) 140–149.

[16] D. Rucińska, 2nd Disaster Risk Reduction Conference. Redukcja Ryzyka Klęsk Żywiołowych - II Konferencja Międzynarodowa (Warszawa, 15-16 X 2015). Kronika. Przegląd Geofizyczny, z. 3-4 (2015) 246-251.

[17] L. Guadagno, Human Mobility in the Sendai Framework for Disaster Risk Reduction, Int J Disaster Risk Sci., Vol. 7 (2016) pp. 30-40. DOI 10.1007/s13753-016- 0077-6

20 [18] M. Johansson, Experience of data collection in support of the assessment of global progress in the Sendai Framework for Disaster Risk Reduction 2015–2030 – A Swedish pilot study. International Journal of Disaster Risk Reduction, Vol. 24 (2017) 144-150 https://doi.org/10.1016/j.ijdrr.2017.06.008

[19] S. Prince, Catastropheand Social Change. NewYork: Columbia University, 1920.

[20] C.E. Fritz, Disasters. Contemporary Social Problems. Boston, 1961.

[21] K.N. Wetgate, P O'Keefe, Same Definitions of Disaster. Bradford, England: Dlsaster Research Unit, Bradford University, 1976.

[22] T. Cannon, Vulnerability Analysis and the Explanation of “Natural” Disaster, in: A. Varley (Ed.) Disasters, Development and Environment, John Wiley, Chichester, 1994, pp. 13-30.

[23] D. Alexander, The study of natural disasters, 1977–97: Some reflections on a changing field of knowledge. Disasters 21-4. 1997, Oxford. DOI: 10.1111/1467- 7717.00064

[24] J. Birkmann, Measuring vulnerability to natural hazards. Towards disaster resilient societies, UNU-EHS, Tokyo-New York-Paris, 2006.

[25] SFDRR, Sendai Framework for Disaster Risk Reduction https://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf (accessed 15 September 2015)

[26] UNISDR. United Nations International Strategy for Disaster Reduction. Margareta Wahlström, United Nations Special Representative of the Secretary-General for Disaster Risk Reduction https://www.unisdr.org/we/inform/disaster-statistics (accessed 19 December 2017).

[27] Gall M., Borden K.A., and Cutter S.I, 2009, When do losses count? Six Fallacies of Natural Hazards Loss Data. American Meterological Sciety, BAMS DOI:10.1175/2008BAMS2721.1

[28] R. Below, A.D. Writz,D. Guha-Sapir , Working paper. Disaster Category. Classification and peril Terminology for Operational Purposes, 2009, Munich Re, CRED, Université catholique de Louvain.

21 [29] J. Wolf, R.A. Flather, 2005 Modelling waves and surges during the 1953 storm. Phil. Trans. R. Soc. A. Vol. 363 (2006) 1359–1375, doi:10.1098/rsta.2005.157

[30] VOLKSKRANT "Texel is een stuk kleiner geworden door Sinterklaasstorm (6 December 2013) Geraadpleegd op 14 december 2013, http://www.volkskrant.nl/vk/nl/2726/Binnenland/video/detail/3557690/Texel-is-een- stuk-kleiner-geworden-door-Sinterklaasstorm.dhtml (accessed 21 June 2015).

[31] I. Dunbar, S. Gilbert, N. Phipps, D. Swaden, M. Szönyi, Raport: Risk Nexus, After the storm: how the UK’s flood defences performed during the surge following Xaver Flood resilience review 09.14, Zurich, 2014.

[32] M. Rudnicki. 2013, Bilans zniszczeń po huraganie. Szkody na 300 tys. Złotych.

11 grudnia 2013, http://www.gs24.pl/wiadomosci/region/art/5542326,bilans-zniszczen-po-huraganie- szkody-na-300-tys-zlotych-wideo,id,t.html (accessed 8 November 2015).

[33] Ourworldindata.org https://ourworldindata.org/grapher/damage-costs-from- natural-disasters?overlay=download (accessed 12 January 2018).

[34] K.D. Ash, S.L. Cutter,C.T. Emrich, Acceptable losses? The relative impacts of natural hazards in the United States, 1980–2009. International Journal of Disaster Risk Reduction, Vol. 5 (2013) 61-72.

[35] J. Strelau, M. Kaczmarek, B. Zawadzki, Temperament as predictor of maladaptive behavior under extreme stress. The Polish studies of natural disasters, in: Q. Jing, M.R. Rosenzweig, G. d’Ydewalle, H. Zhang, H.-C. Che, K. Zhang (Eds.) Progress in psychological Science around the World, Vol. 2, Social and Applied Issues, Hove and New York: Psychology Press, 2006, 139-158.

[36] J. Strelau, B. Zawadzki, Trauma and temperament as predictors of posttraumatic stress disorder and its dimensions 3, 15 months and two years after experiencing flood, Polish Psychological Bulletin, 35 (2004) 5-13.

[37] J. Strelau, B. Zawadzki, Individual differences as moderators of posttraumatic stress symptoms experienced after flood: The role of temperament and coping styles, in: J. Strelau, T. Klonowicz (Eds.), People under extreme stress, New York: Nova Science Publishers, 2006, pp. 67-82.

22 [38] X. Mao, O. Wai, M. Fung, X. Hu., A.Y. Loke, 2018, Psychological impacts of disaster on rescue workers: A review of the literature. International Journal of Disaster Risk Reduction, Vol. 27 (2018) 602-617. https://www.sciencedirect.com/science/article/pii/S2212420917303187 (accessed 19 December 2017).

[39] A. Popiel, Symptoms of posttraumatics disorders and their dynamics in time: The role of organizational and psychological support. SWPS University of Social Sciences and Humanities, Warsaw; in: D. Rucińska, M. Porczek, S. Moran (Eds.) 3rd Disaster Risk Reduction Conference in Warsaw. Abstract and Programme Book. Warsaw, 12th- 13th October, 2017. Warsaw, 2017.

[40] K.J. Kugeler, K.S. Griffith, L.H. Gould, K. Kochanek, M.J. Delorey, B.J. Biggerstaff, P.S. Mead, A Review of Death Certificates Listing Lyme Disease as a Cause of Death in the United States , Clinical Infectious Diseases, 52, Issue 3 (2011) 364-367. http://cid.oxfordjournals.org/content/52/3/364.full (accessed 8 November 2015).

[41] X. Zang, M.I. Meltze, C.A. Peña., A.B. Hopkins, L Wroth, A.D. Fix, Economic Impact of Lyme Disease, Vol.12, Number 4 (2006), Emerging Infectious Diseases

[42] US BIOLOGIC, Delivers Disease Prevention. http://usbiologic.com/lyme-disease/ (accessed 8 November 2015)

[43] KNF Reasekuracja i szkody katastroficzne w ubezpieczeniach majątkowych w 2013 r. data aktualizacji 14 października 2014. https://www.knf.gov.pl/?articleId=53071&p_id=18 (accessed 8 July 2015)

[44] Money.pl Calculation of money value for December 2013. https://www.money.pl/pieniadze/kalkulator/ (accessed 10 September 2017)

[45] World Bank. The GDP Data. https://data.worldbank.org/country/netherlands?view=chart (accessed 10 September 2017)

[46] Raport 2013, Raport z regionu po orkanie. Spokojnie, ale w wielu miejscach wciąż bez prądu, 7 grudnia 2013 http://www.gs24.pl/wiadomosci/region/art/5541842,raport-z-regionu-po-orkanie- spokojnie-ale-w-wielu-miejscach-wciaz-bez-pradu,id,t.html (accessed 20 July 2017).

23 [47] Polskieradio.pl, 2013, Orkan Ksawery w Polsce. 400 tys. domów bez prądu, ponad 1800 interwencji. 06.12.2013 https://www.polskieradio.pl/5/3/Artykul/996327,Orkan- Ksawery-w-Polsce-400-tys-domow-bez-pradu-ponad-1800-interwencji (accessed 6 July 2015).

[48] Fakt.pl 2013, Są zabici i ranni. Piąta ofiara orkanu Ksawery w Polsce! 6 grudnia 2013 http://www.fakt.pl/wydarzenia/polska/orkan-ksawery-uderzyl-w-polske-piec- osob-zginely-cztery-sa-ranne/ppw016v (accessed 6 July 2015).

[49] BBC 2013, Deadly storm and tidal surge batter northern Europe 6 December 2013. http://www.bbc.com/news/world-europe-25243460 (accessed 9 September 2017).

[50] ONS. Office for National Statistics. Statistical bulletin: Families and Households: 2013. The UK. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/fami lies/bulletins/familiesandhouseholds/2013-10-31#household-size (accessed 10 September 2017).

[51] GUS. Główny Urząd Statystyczny. Average number of persons in household in 2013: Polska w liczbach, Warszawa 2015/ Poland in figures, Warsaw 2015 https://stat.gov.pl/en/topics/other-studies/other-aggregated-studies/poland-in-figures- 2015,9,6.html (accessed 10 September 2017)

[52] ACAPS, Glossary https://www.acaps.org/glossary (accessed 7 September 2017).

[53] UNISDR, 2009 UNISDR Terminology on Disaster Risk Reduction. 2009, ISDR 2009 (accessed 9 September 2017)

[54] Pescaroli, G., S. Turner, T. Gould, D. Alexander and R. Wicks 2017. Cascading Impacts and Escalations in Wide-Area Power Failures. UCL IRDR and London Resilience Special Report 2017-01, Institute for Risk and Disaster Reduction, University College London.

[55] UNISDR. Terminology. https://www.unisdr.org/we/inform/terminology.

24 Highlights

 This study shows the misuse of the term ‘disaster’.  There is misinformation as regards the numbers of affected people.  Important is of separating the impact of an event on short-term and long-term.  ‘Affected people’ data can be used as a tool to education for the Disaster Risk Reduction.

25