Microsized enterprises vulnerability to flash fl

Authors: Konstantinos Karagiorgos Micha Heiser Thomas Thaler Johannes Hübl Sven Fuchs

Publish Date: 2016/07/25

Volume: 84, Issue: 2, Pages: 1091-1107

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Abstract

In the framework of risk assessment for flash floods vulnerability is a key concept to assess the susceptibility of elements at risk Vulnerability is defined as expected degree of loss for an element at risk due to a hazard impact of a defined magnitude and frequency Besides the increasing number of studies on flash floods available indepth information on vulnerability was missing so far In order to close this gap a vulnerability model was created for microsized enterprises exposed to flash floods in Greece This model was based on a nonlinear regression approach using data from four different events By means of bootstrapping different functions were fitted to the data and a modified Weibull distribution was found to represent the relationship between process magnitude and degree of loss best Moreover there is no need to distinguish between different business sectors when computing vulnerability for buildings exposed The model can be applied on a local scale and may serve as a basis for flash flood risk managementA significant increase in losses due to flooding was repeatedly claimed by several scholars including river flooding Barredo 2007 Kreibich et al 2014 Winsemius et al 2014 and flash floods Gaume et al 2009 Calianno et al 2013 Besides the ongoing discussion on climate change Keiler 2013 this increase is triggered by exposure dynamics of elements at risk Fuchs et al 2015 Flash flood is usually understand as a highintensity rainfall event intensive rainfall up to 12 h Gaume et al 2009 leading to high peak discharges IAHSUNESCOWMO 1974 where the size of the catchment area is in most of the cases less than 1000 km2 with rather low runoff coefficients Marchi et al 2010 Additionally the catchment shape includes a mean channel slope of less than 5–10  Rickenmann et al 2008 Scheidl and Rickenmann 2010 Heiser et al 2015 The timeliness of flood anticipation relationship between catchment size and flood response time is depending on the catchment size most of the time less than 6 h Creutin et al 2013 Nevertheless the literature shows no clear definition of flash flood hazards The National Weather Service Glossary NWS 2016 defines flash floods as “rapid and extreme flow of high water into a normally dry area or a rapid water level rise in a stream or creek above a predetermined flood level beginning within 6 h of the causative event eg intense rainfall dam failure ice jam However the actual time threshold may vary in different parts of the country Ongoing flooding can intensify to flash flooding in cases where intense rainfall results in a rapid surge of rising flood waters” On the other hand Borga et al 2014 p 194 described flash floods as a result of “extreme rainstorms in headwater catchments which may trigger liquid floods debris floods or debris flows The type of process triggered depends on several characteristics including the hydrologic geomorphometric and geotechnical features of the slopes the source materials and the availability of sediments and the frequencymagnitude characteristics of the precipitation event” Therefore flash flood events strongly depend on the interconnection between rainfall distribution as well as geomorphological and hydrological factors of the area Further important characteristics refer to the relationship between time and space of the rainfall distribution and the flash flood event usually both aspects occur at the same place Norbiato et al 2008 Rozalis et al 2010 The losses of such flood events highlight the increased importance of studies on flood hazard and risk not only on a global scale but in particular on a national and subnational level Adhikari et al 2010 Karagiorgos et al 2016a Apart from droughts flash floods are reported to be among the most severe hazards in Mediterranean countries Llasat et al 2010 The Mediterranean region is especially vulnerable because of its propensity for high intense rainfalls in certain areas Koutsoyiannis et al 2012 its relatively high population density Ganoulis 2003 and degree of development compared with some other semiarid regions Furthermore the long history of settlement and land use Papagiannaki et al 2015 resulting in urban sprawl has produced major soil erosion and associated environmental impacts Ganoulis 2003 Hooke 2016 which in turn support the generation of flash floodsAn analysis of flash flood events has shown a high amount of economic loss and fatalities resulting from the impact on the built environment Gaume et al 2009 Traditionally besides the increasing amount of studies available on flash floods eg Zorn et al 2006 Comiti et al 2008 Gaume et al 2009 Llasat et al 2010 most of the efforts are centred around physical process characteristics so far eg Gaume et al 2004 Delrieu et al 2005 Zorn et al 2006 Recently some studies were explicitly focusing on the effects of flash floods such as Gaume et al 2009 taking a European perspective Llasat et al 2010 for Mediterranean countries or Vinet 2008 Lasda et al 2010 and Karagiorgos et al 2016b funnelling down to individual countries or regions exposed In order to study the effects of flash floods in addition to meteorological triggering and hydrological or hydraulic process propagation information on elements at risk and their vulnerability is required Consequently a particular need for studies on vulnerability was repeatedly claimed in order to enhance risk management capabilities De Marchi and Scolobig 2012 Borga et al 2014 Following the axiom that risk is a function of hazard ie events with a given magnitude and probability times consequences ie economic loss the ability to quantitatively determine the vulnerability to flash floods is an essential need for reducing these consequences and planning for mitigation and adaptation Fuchs 2009 While the understanding of hazard and exposure has significantly improved over the last decades the analysis of vulnerability remains one of the challenges in the ongoing flood risk management discussion Koks et al 2015In overall vulnerability to natural hazards refers to the potential losses based on an impact event and exposed elements such as people or buildings Cutter et al 2003 Birkmann 2006 Thywissen 2006 Fuchs et al 2015 Within the domain of natural sciences vulnerability is usually considered as a function of a given process magnitude towards physical structures Mazzorana et al 2014 often referred as “technical” or “physical” vulnerability and is defined as the expected degree of loss for an element at risk as a consequence of a design event eg Fell et al 2008 Fuchs et al 2012a The assessment includes in many cases the analysis of a complex system with the evaluation of several different parameters and factors such as building materials and techniques Holub et al 2012 damage analysis Fuchs et al 2007 2011 2012b and process characteristics Mazzorana et al 2009 2012 Consequently vulnerability values range from 0 no damages to 1 complete destruction Varnes 1984In recent years several attempts have been made to address vulnerability to flooding focusing on tangible damages as outlined by Messner 2007 and Meyer et al 2013 as well as on different empirical or synthetic approaches of model development for use on different scales PapathomaKöhle et al 2011 The most common internationally accepted approach for the assessment of physical vulnerability for all hazards considered is the use of vulnerability functions Governmental agencies research institutions and insurance companies in many countries develop and use these functions to assess the potential damages and further apply these functions as a basis for prioritisation in flood risk management options PenningRowsell et al 2005 In almost all the models in use flood depth is treated as the determining parameter for expected damages Jongman et al 2012 because there is a particular lack of other factors defining magnitude such as eg flow velocity Fuchs et al 2007 Localscale analyses are used to evaluate losses on an object level individual buildings eg PapathomaKöhle et al 2015 in contrast to regional analyses which are based on aggregated data such as different landuse categories using vulnerability indicators eg Eidsvig et al 2014 Different vulnerability models were developed in the past based on different approaches for the estimation of losses These empirical models use observed data collected after an event by official authorities or insurance companies or they are based on surveys such as Fuchs et al 2007 Totschnig et al 2011 and PapathomaKöhle et al 2012 for torrential flooding in the European Alps Thieken et al 2008 and Kreibich et al 2010 for river flooding in Central Europe and Luino et al 2009 for flash floods in Southern Europe Most of the studies performed were aiming in vulnerability assessment for either residential buildings Totschnig et al 2011 PapathomaKöhle et al 2012 or commercial buildings Kreibich et al 2010 Seifert et al 2010 where some of the works were also targeted at hostels and hotels to mirror the importance of the tourism sector in individual case studies Totschnig and Fuchs 2013Focusing on the commercial sector exposed to river flooding Kreibich et al 2010 presented an empirical model based on three different flood events in Germany Losses were estimated using relative loss functions expressed as a ratio between the loss and the total value of elements at risk on local scale Loss was separately computed for the building envelope the building equipment and the goods and products for different enterprise sizes small and mediumsized businesses and larger companies The data were gained through interviews followed by the development of a vulnerability model and a sensitivity analysis was performed using results of competitive models and information gained from reconstruction grants PenningRowsell et al 2005 presented flood damage losses on a local scale expressed in absolute monetary terms by combining flood duration and water depth Information on vulnerability was provided for the building envelope the building equipment the mobile and immobile inventory as well as the stock of products and finally summed up in terms of cumulative vulnerability Further the US HAZUSMH model Scawthorn et al 2006a b is based on an assessment of relative loss and provides vulnerability information for the building envelope the building equipment and the goods and products for different enterprise sizes small and mediumsized businesses and larger companies Similarly the Australian RAM model is focusing on an assessment of vulnerability taking absolute figures for larger enterprise sizes NRE 2000It was repeatedly stated that an estimation of flood losses in the commercial sector is challenging because of the data generation and inhomogeneity due to the high range of loss for different types of companies and economic sectors affected Seifert et al 2010 or because of a general lack of information on losses Gissing and Blong 2004 Kreibich et al 2010 While for larger river floods in Europe these challenges have been recognised and increasingly acknowledged in the different modelling approaches see Kreibich et al 2010 for a discussion the gap still remains open for localscale flash flood hazards While it has been shown by Totschnig et al 2011 PapathomaKöhle et al 2012 and Totschnig and Fuchs 2013 that fundamental differences between vulnerability functions for river flooding and torrential flooding exist indepth studies on flash flood vulnerability are still outstanding However such localscale events repeatedly cause considerable damage in particular in Southern European countries as shown for the example of Greece by Diakakis et al 2012 and Karagiorgos et al 2016aHence the objective of this study is to contribute to this gap and to assess the vulnerability of buildings occupied by microsized enterprises using data from welldocumented flash flood events in Greece Focusing on the commercial sector in Greece 85  of private employment is concentrated in small and mediumsized enterprises SMEs and more than 50  in microsized enterprises Microsized enterprises are defined as businesses with less than ten employees and an annual turnover and/or annual balance not exceeding €2 million EU 2003 as such they often are family enterprises In terms of total numbers 967  of businesses belong to the category of microsized enterprises 923  for the EU27 employing 545  of workforce 289  for the EU27 and adding to the local economy a share of 346  of the added value 211  for the EU27 EU 2013 These figures indicate the high dependence of the Greek economy on this type of enterprises compared to other European countries

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