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Occurrence rates and relative occurrence frequencies of failure mechanisms causing water-related claims (n = 3126). An asterisk next to a bar indicates a residual group: a group of claims for which exact failure mechanisms could not be derived from communication transcripts. Percentages are based on the number of claims in the non-residual groups.
Source publication
Rainstorm damage caused by the malfunction of urban drainage systems
and water intrusion due to defects in the building envelope can be
considerable. Little research on this topic focused on the
collection of damage data, the understanding of damage mechanisms
and the deepening of data analysis methods. In this paper, the
relative contribution of d...
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... • The pluvial flood increased learning on adaptation but did not trigger adaptive action. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited by Linköping University user the Netherlands, found that most insurance claims were caused by sewer backups, to a lesser degree by runoff entering building entrances, and occasionally from latent leaks which only could be observed during rainfall (Spekkers et al. 2015). Moreover, correlations between water depth and damage costs for buildings are widely researched, assuming water on the surface as the leading cause of flood damage (Van Ooetegem et al. 2015;Wing et al. 2020). ...
... Post-analyses of urban flood events can give real descriptions of the impacts of flooding, and thus present an opportunity for learning and developing applicable actions to mitigate future impacts (Laudan et al. 2017;Rosenzweig et al. 2018;Schanze 2018). Consequently, such analyses have been conducted both in Sweden (Grahn & Nyberg 2017;Mobini et al. 2021;Knös et al. 2022) and abroad (Spekkers et al. 2015;Van Ooetegem et al. 2015;Rosenzweig et al. 2018;Koç et al. 2021) often focusing on economic, health or infrastructure impacts. For example, an evaluation of pluvial flood events in six US cities found that impacts on roads, underpasses, cars, and housing were common, not only in low-lying areas, while personal injuries and fatalities were rare (Rosenzweig et al. 2018). ...
... It was decided to follow-up on the extent and origin of the damages using an open-ended survey, the second data source, to gauge: where water entered the flood-damaged buildings, the location and extent of water in the building, how the building was impacted by the flood, and if and how the welfare service operation was impacted. To categorise where water entered the damaged buildings, a distinction was made in the survey between: (1) surface openings: water entered the building through openings such as entrances, garage doors, windows, external access to basement or exterior basement stairs (Godfrey et al. 2015;Hjerpe et al. 2020;Sandink & Binns 2021), (2) sewer backup: water flowed up through floor drains or toilets (Spekkers et al. 2015;van Ootegem et al. 2015;Rözer et al. 2016), (3) other openings: water entered the building from latent openings such as ventilation shafts, sump pits, groundwater leaks or other ways (Godfrey et al. 2015;Spekkers et al. 2015;Sandink & Binns 2021), and (4) multiple causes of flooding, i.e. from combinations of the other three ways. Nine out of ten property managers responded to the survey, resulting in responses for 53 of the 60 damaged buildings. ...
Climate-related risks, vulnerabilities, and impacts are increasing in cities, illustrated by precipitation-driven pluvial floods. Post-event analyses can aid in reducing urban flood risks, but knowledge gaps exist regarding how welfare services and premises are impacted and can be adapted. This study analyses an extreme precipitation-driven event generating extensive flooding in Gävle, Sweden, in 2021. The objective is to increase knowledge about how municipal welfare services are vulnerable to pluvial floods, and of appropriate actions towards improving the response capacity and building more resilient welfare premises and operations. The study shows that the Swedish weather warning system generally worked well, but the analysed property companies lacked strategies and equipment to evade flooding in their properties. Flood damages in 60 analysed buildings were generated by different causes, demonstrating the importance of contemplating the vulnerability of welfare buildings when conducting flood risk assessments. Although the flood event did not generate deaths or serious personal injuries, the study identified impacts on welfare service operations in both the short and long terms. The event increased learning on climate adaptation but did not trigger adaptive action. Identified keys for adaptation include prioritizing premises to protect, knowledge of flood protection equipment, insurance company requirements, and updated emergency plans.
... About 10-20% of these non-zero rainfall flood claims were associated with high tide levels. Another potential explanation for these claims is water-related damage such as roof, wall, or pipe leakage (Spekkers et al., 2015). It may also be possible that the date of loss documented in the flood claims was incorrect. ...
... Insurance companies have been reluctant to provide their databases to researchers, despite their importance, due to confidentiality restrictions (André et al., 2013;CNT, 2014). Recently, this has been changing (Gradeci et al., 2019), allowing insurance data to be used, for example, in urban flooding studies (Bernet et al., 2017;Cortès et al., 2018;Grahn and Nyberg, 2017;Leal et al., 2018Leal et al., , 2019Leal et al., , 2020Mobini et al., 2020Mobini et al., , 2021Moncoulon et al., 2014;Sörensen and Mobini, 2017;Spekkers et al., 2013Spekkers et al., , 2015Torgersen et al., 2015;Zhou et al., 2013). Urban flooding is an increasing problem worldwide, it is often neglected and not fully understood (Qi et al., 2020). ...
This study addresses the role of natural hazard insurance in two European countries with different insurance markets and socioeconomic conditions: Sweden and Portugal. The analyses were conducted at the national, regional (Southern Sweden and Lisbon Metropolitan Area – LMA), and local (Malmö and Lisbon cities) scales. Most damage caused by weather and climate-related (WCR) hazards during the 1980–2019 period was not covered by insurance companies in Sweden (71%) and Portugal (91%). An insurance affordability analysis was performed using income for the national and regional scales. Unaffordability is higher in Southern Sweden than in LMA, implying that better socioeconomic conditions do not necessarily mean a higher average capacity to pay for insurance.
At the local scale, urban flooding was analysed for Malmö (1996–2019) and Lisbon (2000–2011) using insurance databases, in which the most relevant 21st century rainfall events for each city are included (2014 and 2008, respectively). The influence of terrain features on flooding claims and payouts was determined using Geographic Information Systems (GIS) spatial analyses. The flat Malmö favours ponding and extensive flooding, while the distance to the drainage network and flow accumulation are key factors to promote flooding along valley bottoms in the hilly Lisbon. Flooding hotspots tend to result from a combination of higher depths/lower velocities (accumulation of floodwaters and ponding) and not from a pattern of lower depths/higher velocities (shallow overland flow).
More detailed data on insurance, flooding, and socioeconomic conditions, at regional and mainly local scales, is needed to improve affordability and urban flooding risk assessments.
... Two main distinct approaches exist to develop flood loss models (Merz et al., 2010b): empirical approaches which use loss data from flood events (e.g., HOWAS21 the German flood damage database; Kreibich et al., 2017c) and synthetic approaches which use loss estimates or functions collected by building experts (e.g., the Multi Coloured Manual for the UK; Penning-Rowsell et al., 2005;Penning-Rowsell et al., 2018). Loss models can be based on empirical loss data surveyed on the one hand by science (e.g., Schröter et al., 2014;Sairam et al., 2019a) and on the other hand by governmental agencies (e.g., Merz et al., 2004;Hasanzadeh Nafari et al., 2016b) and insurance companies (e.g., Spekkers et al., 2015;Cortès et al., 2018) in the frameworks of loss compensation. ...
Hochwasser stellt ein großes Risiko für Wohngebäude in Europa dar, und es wird erwartet, dass das Risiko in der Zukunft aufgrund klimatischer und sozioökonomischer Veränderungen zunehmen wird. Aktuelle Hochwasserrisikomodelle basieren meist auf einfachen Wasserstands-Schadenskurven. Diese Ansätze vereinfachen die Hochwasserschadensprozesse stark, können ungenau sein und bergen große Unsicherheiten, die oft nicht quantifiziert. Die Doktorarbeit stellt die Integration neuer Daten in probabilistische, multivariable Schadensmodelle zur Verbesserung ihrer Übertragbarkeit vor. Diese neuen Datenquellen und Modellierungsansätze werden verwendet, um zukünftige Veränderung des Hochwasserrisikos für Wohngebäude in Europa abzuschätzen und Risikokomponenten zu analysieren. Die Arbeit zeigt, OpenStreetMap (OSM) Daten liefern nützliche Informationen für die Modellierung von Hochwasserschäden und ermöglichen Modelltransfers. Die Integration von aus OSM abgeleiteten Gebäudeeigenschaften und Hochwassererfahrung aus Ereignisdatenbanken in das Bayes’sche Netzwerk basierte Hochwasserschadensmodelle für den privaten Sektor (BN-FLEMOps) ermöglichte die Implementierung auf der Mesoskala. Durch Vergleiche von Schadensschätzungen mit beobachteten Schäden in mehreren Fallstudien in Europa wurde das Modell validiert und detailliert mit einem Ensemble aus 20 Schadensmodellen verglichen. In einer abschließenden Studie werden die zukünftigen Veränderungen des Risikos für Wohngebäude in Europa modelliert. Die erwarteten jährlichen Schäden bis zum Ende des 21. Jahrhunderts werden um das 10-fache ansteigen. Die Britischen Inseln und der größte Teil von Zentral-Europa müssen mit einer starken Risikozunahme rechnen. Teile Skandinaviens und des Mittelmeerraums werden dagegen ein stagnierendes oder abnehmendes Hochwasserrisiko verzeichnen. Eine Verbesserung der privaten Vorsorgemaßnahmen könnte das Hochwasserrisiko im Mittel um 15 % und in einigen europäischen Regionen um bis zu 20 % verringern.
... Studies show that particularly urban areas are vulnerable to flash floods that even less important rainfall amounts can cause [9][10][11]. Despite the progress made towards protective infrastructure, risk communication, and understanding a wide pallet of vulnerability features, rainfall events cause repetitive and eventually severe financial losses for citizens, the state, and insurance companies [8,[12][13][14]. Part of the exposure of elements to the rainfall hazard can be addressed by appropriate and timely reactions, starting with identifying the potential risk occurrence related to an upcoming hazardous event [15]. ...
... Financial loss data, such as insurance claims, are scarce, but when available, they can be a reliable indicator of flood and storm damage [13,[19][20][21]. Scientists have used insurance data to examine the role of socio-environmental or infrastructureinduced vulnerability to rainfall hazards and urban flooding [14,22], to develop damage functions for coastal flooding and storms [23,24], and to model the financial exposure to floods for the insurance market [25]. At the same time, methodological difficulties associated with collecting and processing primary insurance data have been of particular concern among scholars [12,14,26], making the elaboration of such data a methodological achievement. ...
... Scientists have used insurance data to examine the role of socio-environmental or infrastructureinduced vulnerability to rainfall hazards and urban flooding [14,22], to develop damage functions for coastal flooding and storms [23,24], and to model the financial exposure to floods for the insurance market [25]. At the same time, methodological difficulties associated with collecting and processing primary insurance data have been of particular concern among scholars [12,14,26], making the elaboration of such data a methodological achievement. Overall, insurance datasets can be a promising source for weather-related damage assessment. ...
Flood-producing rainfall amounts have a significant cumulative economic impact. Despite the advance in flood risk mitigation measures, the cost of rehabilitation and compensation of citizens by the state and insurance companies is increasing worldwide. A continuing challenge is the flood risk assessment based on reliable hazard and impact measures. The present study addresses this challenge by identifying rainfall thresholds likely to trigger economic losses due to flood damages to properties across the Athens Metropolitan Area of Greece. The analysis uses eight-year rainfall observations from 66 meteorological stations and high spatial resolution insurance claims on the postal code segmentation. Threshold selection techniques were applied based on the ROC curves widely used to assess the performance of binary response models. The model evaluates the probability of flood damages in terms of insurance claims in this case. Thresholds of 24-h rainfall were identified at the municipal level, as municipalities are the first administration level where decision making to address the local risks for the citizens is needed. The rainfall thresholds were further classified to estimate and map the local risk of flood damages. Practical implications regarding the applicability of the detected thresholds in early-warning systems are also discussed.
... Physically it is logical as the doors are designed to start from the floor level to a certain level and the fences of houses are usually weaker than walls. As a result, damage to buildings can be realized by walls or fences having defects (Spekkers et al. 2015;Schroeder et al. 2016). Similarly, the damage to household contents such as furniture and carpets and other tangible elements seem to have a significant level of impact by flash floods (Molinari et al. 2014;ten Veldhuis 2011). ...
Small-scale flash flood events are climate-related disasters which can put multiple aspects of the system at risk. The consequences of flash floods in densely populated cities are increasingly becoming problematic around the globe. However, they are largely ignored in disaster impact assessment studies, especially in assessing socioeconomic loss and damage, which can provide a significant insight for disaster risk reduction measures. Using a structured questionnaire survey, this study applied a statistical approach and developed a structural equation model (SEM) for assessing several socioeconomic dimensions including physical impacts, mobility disruption, lifeline facilities, health and income-related impacts. The study reveals that respondents have experienced a stronger impact on direct tangible elements such as household contents and buildings as well as direct intangible elements with β coefficients 0.703, 0.576 and 0.635, respectively, at p < 0:001 level. The direct intangible impacts affect mobility disruption with β coefficients equal to 0.701 at p < 0:001 level which then further cause adversity to income-generating activities with β 0.316 at significant p < 0:001 as well. The overall model fit indices show highly acceptable scores of SRMR 0.068, RMSEA 0.055 and PClose 0.092. Thus, the SEM has successfully incorporated the socioeconomic dimensions of disaster impact and explained the impact phenomena reliably. This modeling approach will allow inclusion of various variables from different disciplines to assess hazard impact, vulnerability and resilience.
... Europe has experienced many extreme cloudburst events that caused pluvial flooding in recent years. For instance, the 2008 cloudburst in Dortmund (Germany) resulted in flood damage amounting to 17.2 million Euro [17]; the 2011 cloudburst in Copenhagen (Denmark) resulted in more than 800 million Euro in insurance payouts [6,[18][19][20], and the 2014 cloudburst in Malmö (Sweden) resulted in more than 600 million SEK of damage. Such sensational events call into question the conventional notion that fluvial flooding is more devastating than pluvial flooding. ...
... Many valuable studies have focused on the hazard aspect of urban pluvial flood damage apply different 1D-2D hydraulic models and damage modelling [10,27,[53][54][55] that include factors like rainfall (intensity, duration), floodwater level, contamination, flood warning, and buildings (construction, age, material) [36,38,48,[56][57][58]. Hazard assessment is an essential part of the flood risk assessment, and rainfall is often suggested as a crucial factor in both ex-ante and ex-post approaches [10,19,20,38,39,59,60]. For instance, both Blumenthal and Nyberg [38] and Sörensen and Mobini [19] found that rainfall was an essential driver of flood damage in the 2014 cloudburst in Malmö (Sweden) and that the relationship between flood damage and rainfall intensity is nonlinear [19,38]. ...
... While hydrological and hydraulic factors have attracted substantial attention, a need for more research into other potentially important drivers of urban pluvial flood damage has been suggested [20]. For instance, it has been pointed out that the importance of socio-economic factors and building characteristics cannot be neglected [38][39][40]. ...
Pluvial flood damage to residential buildings causes a significant part of direct tangible flood losses. In this study, we investigate the non-hazard variables and sewer system types in relation to damage costs in the city of Malmö, Sweden. A comprehensive data set of around 1000 records of direct damage to residential buildings from a cloudburst event on 31 August 2014 in Malmö, Sweden has been analysed at property scale with no lumping together of data. The results show that properties connected to combined sewer systems are much more exposed to pluvial flood damage than properties connected to separated sewer systems, with the ratio of the number of claims being close to three. The analysis of building-specific variables shows no clear statistical relationships to the damage costs. To further the understanding of damage costs caused by urban pluvial flooding, it is necessary to extend the group of explanatory variables to include information about the socio-economic background of households, the actual value of assets in basements and the precautionary measures taken by house owners.
... Vulnerability curves or stage-damage curves look at vulnerability as a degree of loss, being the most widely used methodology to assess vulnerability to floods at a local scale (Büchele et al., 2006;Hammond, Chen, Djordjevi c, Butler, & Mark, 2015;Papathoma-Köhle et al., 2011;Penning-Rowsell et al., 2005;Smith, 1994;Spekkers, Clemens, & Ten Veldhuis, 2015). However, they have some limitations when applied to flash floods (Freni, La Loggia, & Notaro, 2010;Merz, Kreibich, Thieken, & Schmidtke, 2004): (a) common use of water depth as the only impact parameter, not contemplating other factors, such as velocity or solid charge (Middelmann-Fernandes, 2010); (b) construction materials are often the only building property used, not considering other intrinsic elements (Kappes et al., 2012); and (c) those can only be adapted to a specific location and can hardly be applied to other regions (Papathoma-Köhle et al., 2017;Papathoma-Köhle, Cristofari, Wenk, & Fuchs, 2019). ...
This study focuses on the physical vulnerability of buildings to flash floods using an indicator-based methodology. A physical vulnerability index (PhVI) that combines intrinsic vulnerability (IV) of buildings and flash flood intensity (FFI) is proposed. IV evaluates the propensity to suffer damage, resulting from indicators related to building properties. FFI estimates the potential to cause damage, resulting from indicators related to flow parameters. PhVI was applied to a critical section of a small drainage basin in Portugal where flash floods are frequent. Evaluating IV and the intensity of natural hazards is essential in physical vulnerability assessments. This study addresses two problems found in the literature: the lack of flash flood-dedicated physical vulnerability assessments and the difficulties in assembling building properties and the intensity of natural hazards in a vulnerability index defined from indicator-based methodologies. PhVI is a useful tool where damage records are rare or non-existent, allowing the prioritisation of resources and application of local protection measures. This index can be adapted to other study areas and natural hazards, although more research is needed to improve the knowledge on the indicators and weights of IV and FFI.
... The recent collaborations with the scientific community have allowed the authors to explore the valuable insurance databases to estimate material damage resulting from natural hazards [32][33][34][35][36][37][38][39][40][41][42]. Four main categories of explaining variables were identified by Ref. [31] when they analyzed the scientific studies produced during the last decade on material damage caused by urban flooding based on insurance data: meteorological, geographical, demographic and property/building related. ...
This research quantifies and compares the material damage caused by flooding resulting from the two most extreme rainfall events of the 21st century in two Portuguese regions, using insurance data: the Lisbon Metropolitan Area (LMA) and Madeira Island. During the 2000-2011 period, 24% of the claims and 40% of the payouts in the LMA were caused by the February 2008 event. In Madeira, the February 2010 event represented 50% of the claims and 88% of the payouts. The maximum rainfall values during the 2010 event more than doubling those recorded during the 2008 event. The steep slopes of the Madeira’s drainage basins exacerbate the potential for flooding to cause damage. Peak discharges are higher and occur earlier than in the LMA’s basins. Flash floods are the most important type of flooding in Madeira, unlike what occurs in the LMA, where urban flooding is most prevailent. The greater destructive capacity of flash floods, the higher magnitude of the triggering rainfall during the 2010 event and the higher flooding susceptibility of the Madeira’s drainage basins justify the greatest material damage. The lower education levels and economic power of the Madeira’s population may affect the capacity to purchase insurance and can explain the lower number of insurance policies, contributing for a higher vulnerability to flooding. The obtained results represent an important knowledge regarding flooding in Portugal for spatial planning, risk management and insurance companies.
... Two main distinct approaches exist to develop flood loss models : empirical approaches which use loss data from flood events (e.g., HOWAS21 the German flood damage database; Kreibich, Thieken, et al., 2017) and synthetic approaches which use loss estimates or functions collected by building experts (e.g., the Multi Coloured Manual for the UK; Penning-Rowsell et al., 2005, andupdated for 2010). Loss models can be based on empirical loss data surveyed on the one hand by science (e.g., Sairam et al., 2019;Schröter et al., 2014) and on the other hand by governmental agencies (e.g., Hasanzadeh Nafari et al., 2016;Merz et al., 2004) and insurance companies (e.g., Cortès et al., 2018;Spekkers et al., 2015) in the frameworks of loss compensation. ...
In view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth‐damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN‐FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN‐FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended.
