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The impact of intense rainfall on insurance losses in two Swedish cities
Journal of Flood Risk Management
Abstract
While a major part of previous research in the field of flood damage has focused on water depth as the most important causal factor, little attention has been paid to the role of rainfall intensity. As a test, this paper used correlation and regression analyses to investigate rainfall intensity as a factor affecting flood damage. For a time period of 15 years, the relationship between insurance losses caused by floods and rainfall intensity data from rain gauges were examined in two Swedish cities. Another objective was to find an approach for damage functions based on rainfall intensity as explanatory variable. Using linear regression, two approaches with considerable high degrees of explanation were found – one based on an exponential function and one on a power function. Using a lower limit for rainfall intensity, the approaches reached degrees of explanation between 30 and 78 %. From this study it was concluded that rainfall intensity during the summer months and the occurrence of insurance damages per day caused by floods were correlated and further that rainfall intensity has a great potential to explain urban flood damages. In the future, additional studies are needed to validate the proposed methods and integrate other flood damage affecting factors in the approach.
... Pluvial floods are, however, frequent and cause substantial losses in Sweden [6]. For the period 2001-2015, a large majority of the flood-caused insurance claims was caused by cloudbursts [7]. ...
... Hydraulic models are site-specific, whereas a damage model based on rain intensity may be less so. Several Swedish flash-flood studies [6,7,10,11] support the hypothesis that cloudburst intensity may be a primary variable to explain insurance claims, thus inviting further studies of the rainfall-intensity approach. ...
... The authors report that events with high rain intensity result in substantially higher number of claims than events with a lower intensity even if the daily rain amount is high. Blumenthal and Nyberg [7] investigate the relation between rain characteristics and insurance claims for 111 pluvial-flood events during a 15-year period in the cities of Gothenburg and Malmö. One weather station with high temporal rain-data resolution is used in each city, and the insurance data are aggregated daily at parish (sub-municipal) level for the parishes surrounding the weather station. ...
Cloudburst flash floods cause big casualties and economic losses. This study primarily investigated if a cloudburst catastrophe (cat) model could be constructed to meaningfully assess such a hazard, exposure and vulnerability in Swedish urban context. Rainfall intensity was used directly as hazard measure, bypassing hydraulic water-level modelling, to predict vulnerability. The Splash (Swedish pluvial modelling analysis and safety handling) cloudburst-disaster model was constructed using the Oasis Loss Modelling Framework, and was based on individual property values and building locations, property-level insurance-loss data, high-resolution geographical data, and rainfall data from a dense municipal gauge network in the city of Jönköping. One major cloudburst event was used to derive a vulnerability curve. The following two events were used for validation and supported the hypothesis that the vulnerability curve changed with time because of municipal flood-risk-reduction measures after the first event. A faulty rain gauge during the first event, replaced by a trustworthy private gauge, clarified the very high sensitivity to cloudburst input. Given the limited amount of loss data, our results were uncertain but they pointed towards possible ways to further this study with other loss data at other locations, possibly using more easily available aggregated loss data. We concluded that a cat model based only on rainfall intensity provided acceptable results, thus providing an opening for future, simplified cloudburst cat models applicable in most geographical contexts where reliable cloudburst data is available, especially in cities with limited topographic data and hydraulic-modelling capacity.
... Actual flood damage data is fundamental for ex-post approaches and crucial for building reliable ex-ante impact models [34,37]. Common sources for historical flood damage data are insurance data, surveys, and call centres such as emergency centres that are contacted by people affected by flooding [19,[37][38][39][40][41][42]. Flood damage can be categorised as direct or indirect and tangible or intangible [43]. ...
... 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]. ...
... 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]. ...
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.
... The scientific literature has paid increasing attention to the link between rainfall and insurance claims. Blumenthal and Nyberg (2019) demonstrate the strong correlation between rainfall intensity and insurance losses in two Swedish cities using Pearson correlation coefficients and linear regression models. Van Ootegem et al. (2018) find that the impact of rainfall accumulation on monetary damages from pluvial flooding depends on the source of the data. ...
... The novelty of this study lies in applying the state-of-theart bivariate POT approach to model the joint behavior of two extremes in the field of nonlife insurance. Most existing studies on the impact of rainfall extremes fall into the field of hydrology and urban flooding management (e.g., Blumenthal & Nyberg, 2019;Van Ootegem et al., 2018;Bengtsson & Rana, 2014). Also, analyses of insurance claims considering the effect of weather mostly use the entire range of various weather variables instead of the extreme part (e.g., Wahl et al., 2022;Spekkers et al., 2015;Scheel et al., 2013), which means that they look at general weather conditions as opposed to extreme weather events. ...
Climate change has increased the frequency and intensity of extreme weather events. For insurance companies, it is essential to identify and quantify extreme climate risk. They must set aside enough capital reserve to bear the costs of extreme events, otherwise, they can be put in a danger of facing bankruptcy. In this article, I employ the state‐of‐the‐art bivariate peak over threshold method to study the dependence between extreme rain events and extreme insurance claims. I utilize a novel insurance data set on home insurance claims related to rainfall‐induced damage in Norway and select two large Norwegian municipalities to investigate the impact of heavy rain on large claim numbers. Based on the model estimates and tail dependence measures, I find evidence that extremely high numbers of insurance claims have the strongest dependence with rainfall intensity and daily rain amounts. I also identify the region‐specific difference in rainfall variables as a key indicator of home insurance risk. The findings offer insights into the complex dynamics between extreme rainfall and extreme claim numbers in home insurance. Contributing to the long‐term sustainability of the insurance industry, the proposed method facilitates the development of tailor‐made pricing models and robust capital reserve management in the face of changing climate.
... 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]. ...
... For example, due to a lack of data on actual economic losses, some studies targeting European cities have used alternative impact indicators, such as emergency calls to the local fire brigade [10], requests related to insurance claims received at meteorological services [17], or crowdsourced flooding reports [18]. 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]. ...
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.
... A major number of studies on flood loss estimation models focused on water depth (e.g. Thieken, Olschewski, Kreibich, Kobsch and Merz (2008) (2017)), which is the most important hydrological causal factor especially for riverine floods (Kreibich et al., 2009;Spekkers et al., 2014;Van Ootegem et al., 2018;Blumenthal and Nyberg, 2019). However, a few studies also focused on the relationship between the rainfall intensity and flood losses and found that a correlation between rainfall intensity and economic damages for pluvial urban floods (Torgersen et al., 2015;Van Ootegem et al., 2018;Blumenthal and Nyberg, 2019). ...
... Thieken, Olschewski, Kreibich, Kobsch and Merz (2008) (2017)), which is the most important hydrological causal factor especially for riverine floods (Kreibich et al., 2009;Spekkers et al., 2014;Van Ootegem et al., 2018;Blumenthal and Nyberg, 2019). However, a few studies also focused on the relationship between the rainfall intensity and flood losses and found that a correlation between rainfall intensity and economic damages for pluvial urban floods (Torgersen et al., 2015;Van Ootegem et al., 2018;Blumenthal and Nyberg, 2019). Furthermore, Torgersen et al. (2015) indicated that short-duration rainfall confirms that the most costly events occur during the most intensive rainfall for urban floods. ...
Over the past decades, natural hazards, many of which are aggravated by climate change and reveal an increasing trend in frequency and intensity, have caused significant human and economic losses and pose a considerable obstacle to sustainable development. Hence, dedicated action toward disaster risk reduction is needed to understand the underlying drivers and create efficient risk mitigation plans. Such action is requested by the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), a global agreement launched in 2015 that establishes stating priorities for action, e.g. an improved understanding of disaster risk. Turkey is one of the SFDRR contracting countries and has been severely affected by many natural hazards, in particular earthquakes and floods. However, disproportionately little is known about flood hazards and risks in Turkey. Therefore, this thesis aims to carry out a comprehensive analysis of flood hazards for the first time in Turkey from triggering drivers to impacts. It is intended to contribute to a better understanding of flood risks, improvements of flood risk mitigation and the facilitated monitoring of progress and achievements while implementing the SFDRR. In order to investigate the occurrence and severity of flooding in comparison to other natural hazards in Turkey and provide an overview of the temporal and spatial distribution of flood losses, the Turkey Disaster Database (TABB) was examined for the years 1960-2014. The TABB database was reviewed through comparison with the Emergency Events Database (EM-DAT), the Dartmouth Flood Observatory database, the scientific literature and news archives. In addition, data on the most severe flood events between 1960 and 2014 were retrieved. These served as a basis for analyzing triggering mechanisms (i.e. atmospheric circulation and precipitation amounts) and aggravating pathways (i.e. topographic features, catchment size, land use types and soil properties). For this, a new approach was developed and the events were classified using hierarchical cluster analyses to identify the main influencing factor per event and provide additional information about the dominant flood pathways for severe floods. The main idea of the study was to start with the event impacts based on a bottom-up approach and identify the causes that created damaging events, instead of applying a model chain with long-term series as input and searching for potentially impacting events as model outcomes. However, within the frequency analysis of the flood-triggering circulation pattern types, it was discovered that events in terms of heavy precipitation were not included in the list of most severe floods, i.e. their impacts were not recorded in national and international loss databases but were mentioned in news archives and reported by the Turkish State Meteorological Service. This finding challenges bottom-up modelling approaches and underlines the urgent need for consistent event and loss documentation. Therefore, as a next step, the aim was to enhance the flood loss documentation by calibrating, validating and applying the United Nations Office for Disaster Risk Reduction (UNDRR) loss estimation method for the recent severe flood events (2015-2020). This provided, a consistent flood loss estimation model for Turkey, allowing governments to estimate losses as quickly as possible after events, e.g. to better coordinate financial aid. This thesis reveals that, after earthquakes, floods have the second most destructive effects in Turkey in terms of human and economic impacts, with over 800 fatalities and US$ 885.7 million in economic losses between 1960 and 2020, and that more attention should be paid on the national scale. The clustering results of the dominant flood-producing mechanisms (e.g. circulation pattern types, extreme rainfall, sudden snowmelt) present crucial information regarding the source and pathway identification, which can be used as base information for hazard identification in the preliminary risk assessment process. The implementation of the UNDRR loss estimation model shows that the model with country-specific parameters, calibrated damage ratios and sufficient event documentation (i.e. physically damaged units) can be recommended in order to provide first estimates of the magnitude of direct economic losses, even shortly after events have occurred, since it performed well when estimates were compared to documented losses. The presented results can contribute to improving the national disaster loss database in Turkey and thus enable a better monitoring of the national progress and achievements with regard to the targets stated by the SFDRR. In addition, the outcomes can be used to better characterize and classify flood events. Information on the main underlying factors and aggravating flood pathways further supports the selection of suitable risk reduction policies. All input variables used in this thesis were obtained from publicly available data. The results are openly accessible and can be used for further research. As an overall conclusion, it can be stated that consistent loss data collection and better event documentation should gain more attention for a reliable monitoring of the implementation of the SFDRR. Better event documentation should be established according to a globally accepted standard for disaster classification and loss estimation in Turkey. Ultimately, this enables stakeholders to create better risk mitigation actions based on clear hazard definitions, flood event classification and consistent loss estimations.
... Liu et al. (2020) analyzed the relationship between extreme rainfall days and economic loss rates based on the spatial differentiation of disaster-causing precipitation threshold. Blumenthal and Nyberg (2019) used both exponential function and power function to construct rainfall-damage relationships and found that rainfall intensity could explain 30% and 78% of the variation of urban flood damages in two Swedish cities. ...
Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.
... Some examples are the studies that Grahn et al. carried out [14] that examined 2140 individual observations of insurance payouts for residential buildings caused by 49 different rainfall events in Sweden and another study which used insurance claims between 1987 till 2013 with 304 data observations [35]. Blumenthal et al. research on the impact of intense rainfall on insurance losses in two Swedish cities (Malmö and Göteborg) [36]. ...
Urban flood damage leads to major costs for private house owners, insurance companies, and water and wastewater utilities. Analysis of flood damage claims can be used to improve the understanding of the details of flood damage characteristics and reasons for drainage system failures. However, few studies have used data of this kind to investigate urban flood characteristics. We examined 3113 damage claim cases over 30 years (1992–2019) for the city of Malmö, Sweden. We quantified the distribution and frequency of both major and minor flood damages over this period. Our analysis showed that most floods occur in August, but we could not find any significant trend in the count of damage claims between 1992 till 2019. The main drivers of flood damages were rainfall and failure mechanisms in the drainage system. In total, 24.5% of properties suffered from repeated flood damages, 44% originating from combined sewer system connection and 17% from the separated sewer system. This highlights the importance of sewer system types in flood damage claims. In addition, there was an uneven claim count between insurance companies and owners per event. About 42% of insurance company claims were not accompanied by the owner's deductible claims. Our results highlight the need to further investigate the reason behind this difference and ways for better future planning to minimize the damage from flood events.
... Intense rainfall events are common phenomena in Sweden during the summer months 24 ( Gustafsson et al., 2010;Devasthale and Norin, 2014) and have caused considerable amounts 25 of economic damage as a result of flooding and disruptions of infrastructures (MSB, 26 2018; Johansson and Blumenthal, 2009). Blumenthal and Nyberg (2018) concluded that 27 rainfall intensity during the summer months in Sweden and the occurrence of insurance 28 damages per day caused by floods were highly correlated and that damage is non-linear rising 29 with increasing rainfall intensity. The conditions may become worse as frequency and 30 intensity of extreme rainfalls during the summer months are expected to increase in 31 Scandinavia as a consequence of climate change (Nikulin et al., 2011;Olsson and Foster, 32 2014). ...
This paper investigates causal factors leading to pluvial flood damages, beside rainfall amount and intensity, in two Swedish cities. Observed flood damage data from a Swedish insurance database, collected under 13 years, and a set of spatial data, describing topography, demography, land cover and building type were analyzed through principal component analysis (PCA). The topographic wetness index (TWI) is the only investigated variable that indicates a significant relationship with to the number and amount of insurance damage. The Pearson correlation coefficient is 0.68 for the number of insurance damages and 0.63 for amount of insurance damages. With a linear regression model TWI explained 41% of the variance of the number of insurance flood damages and 34% of variance of amount of insurance flood damage.
Future studies on this topic should consider implementing TWI as a potential measure in urban flood risk analyses.
Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A "sweet spot" between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this "sweet spot." For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments.
Urban flooding is a growing concern in Northern Europe. While all countries in this region invest substantial resources into urban flood risk management, all property owners have unequal opportunity to have their flood risk managed. This paper presents the notion of equity in the urban flood risk management, focusing on urban flood exposure and compensation after sewage surcharge on the 31 August 2014 cloudburst over Malmö in Sweden. All damage claims and the procedures assessing them were analysed based on the type of sewer system (combined or separated) that affected the properties. The results demonstrated considerable systemic inequity in both flood exposure and damage compensation. The owners of properties connected to combined systems were four times more likely to make a damage claim, while having their damage claims approved just over half as often, compared with owners of properties connected to separated sewage systems. Considering the multifaceted nature of both inequities and their possible resolution, current praxis is not sufficient for the management of future urban drainage systems; not only concerning changing climatic conditions but also concerning social conditions.
Catastrophe risk models used by the insurance industry are likely subject to
significant uncertainty, but due to their proprietary nature and strict
licensing conditions they are not available for experimentation. In
addition, even if such experiments were conducted, these would not be
repeatable by other researchers because commercial confidentiality issues
prevent the details of proprietary catastrophe model structures from being
described in public domain documents. However, such experimentation is
urgently required to improve decision making in both insurance and
reinsurance markets. In this paper we therefore construct our own
catastrophe risk model for flooding in Dublin, Ireland, in order to assess
the impact of typical precipitation data uncertainty on loss predictions. As
we consider only a city region rather than a whole territory and have access
to detailed data and computing resources typically unavailable to industry
modellers, our model is significantly more detailed than most commercial
products. The model consists of four components, a stochastic rainfall
module, a hydrological and hydraulic flood hazard module, a vulnerability
module, and a financial loss module. Using these we undertake a series of
simulations to test the impact of driving the stochastic event generator
with four different rainfall data sets: ground gauge data, gauge-corrected
rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a
satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because
they use the upper three components of the modelling chain to generate a
large synthetic database of unobserved and severe loss-driving events for
which estimated losses are calculated. We find the loss estimates to be more
sensitive to uncertainties propagated from the driving precipitation
data sets than to other uncertainties in the hazard and vulnerability
modules, suggesting that the range of uncertainty within catastrophe model
structures may be greater than commonly believed.
In this paper, a database of water-related insurance damage claims related to private properties and content was analysed. The aim was to investigate whether the probability of occurrence of rainfall-related damage was associated with the intensity of rainfall. Rainfall data were used for the period of 2003–2009 in the Netherlands based on a network of 33 automatic rain gauges operated by the Royal Netherlands Meteorological Institute. Insurance damage data were aggregated to areas within 10-km range of the rain gauges. Through a logistic regression model, high claim numbers were linked to maximum rainfall intensities, with rainfall intensity based on 10-min to 4-h time windows. Rainfall intensity proved to be a significant damage predictor; however, the explained variance, approximated by a pseudo-R2 statistic, was at most 34% for property damage and at most 30% for content damage. When directly comparing predicted and observed values, the model was able to predict 5–17% more cases correctly compared to a random prediction. No important differences were found between relations with property and content damage data. A considerable fraction of the variance is left unexplained, which emphasizes the need to study damage generating mechanisms and additional explanatory variables.
Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss–or flood vulnerability–relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.
During the last decades an increase in extreme rainfall has led to more urban flooding. This study is based on insurance claims of damages caused by heavy rain during 2006−2012 in Fredrikstad, Norway. Data are analysed using Principal Component Analysis. The purpose has been to find characteristics of extreme rainfall and its influence on the extent of urban flooding. The number of claims seems to be peaked in the late summer period. Furthermore, the precipitation depth the week before an extreme rainfall seems to have significantly influence for the pay out from insurers, and thus the changing in runoff factor due to soil wetness is of importance. Compared to 25-year frequency rainfall with 30 min duration, relatively less intensive, but more stable and long-lasting rain seems to lead to more claims. Experiences from previous events may help to determine the level of flood risk when extreme rainfall is forecasted.
Very extreme events occur randomly in space and in time. In this paper daily precipitation from more than 200 stations within a 10 000 km 2 region in the very south of Sweden is analysed. While there is a relation between moderate daily precipitation and annual precipitation, the most extreme events are found to be independent of annual precipitation and also of altitude and distance to the sea. The most extreme events are distributed evenly over the region. Variations of the climate are considered. For three cities daily storms are analysed for a period of 89 years. No trend over time is found of the daily high precipitation. Hourly storms are analysed for four stations within the city of Malmö. The probability of concurrent events is rather high, but there is little correlation between the rain intensities at the different stations during the most intense events.
Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008-10. Each observational data source has limitations; a major asset of the unified flash flood database is its collation of relevant information from a variety of sources that is now readily available to the community in common formats. It is anticipated that this database will be used for many diverse purposes, such as evaluating tools to predict flash flooding, characterizing seasonal and regional trends, and improving understanding of dominant flood-producing processes. We envision the initiation of this community database effort will attract and encompass future datasets.
Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities.
The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998–2011. The databases include claims of water-related damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households.
Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22–26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11–18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.
Several different factors external to the natural hazard of flash flooding can contribute to the type and magnitude of their resulting damages. Human exposure, vulnerability, fatality and injury rates can be minimized by identifying and then mitigating the causative factors for human impacts. A database of flash flooding was used for statistical analysis of human impacts across the U.S. 21,549 flash flood events were analyzed during a 6-year period from October 2006 to 2012. Based on the information available in the database, physical parameters were introduced and then correlated to the reported human impacts. Probability density functions of the frequency of flash flood events and the PDF of occurrences weighted by the number of injuries and fatalities were used to describe the influence of each parameter.
The factors that emerged as the most influential on human impacts are short flood durations, small catchment sizes in rural areas, vehicles, and nocturnal events with low visibility. Analyzing and correlating a diverse range of parameters to human impacts give us important insights into what contributes to fatalities and injuries and further raises questions on how to manage them.
Using measurements from the national network of 12 weather radar stations for the 11-year period 2000–2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate- to high-intensity events (precipitation > 0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation > 1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.
An appropriate distribution of flood losses over time span may dampen the degree of disruption. Flood insurance is one of the effective ways in order to cope with the aftermaths of flood events. In the absence of an acceptable risk-based assessment framework, flood insurance and its extensive benefits are not fully achieved yet. Consequently, flood insurance is trivially practiced worldwide. To ensure the integration of flood insurance into regular flood management practices, its acceptability by floodplain inhabitants is a prerequisite. Therefore, an admissible insurance rate is a vital factor for the acceptability of insurance policy. This article introduces a risk-based methodology to calculate insurance rate. Normally, insurance rates are based on historic flood events or on a specific design flood. Expected annual damages distribution map (EADDM) is developed to assess spatial distribution of risk. Distribution of flood losses against a specific flood is compared with EADDM. The impacts of both approaches on insurance provider and insured are studied. Single flood approach ignores the impacts because of other floods. Therefore, insurance rates based on single flood are not justifiable. The proposed framework not only facilitates risk-based insurance rate calculation but also endorse flood insurance acceptability.
The present paper shows an in-depth analysis of the evolution of floods and precipitation in Catalonia for the period 1981-2010. In order to have homogeneous information, and having in mind that not gauge data was available for all the events, neither for all the rivers and stream flows, daily press from a specific newspaper has been systematically analysed for this period. Furthermore a comparison with a longer period starting in 1900 has been done. 219 flood events (mainly flash flood events) have been identified for the period of 30years (375 starting in 1900), 79 of them were ordinary, 117 of them were extraordinary and 23 of them were catastrophic, being autumn and summer the seasons with the maxima values. 19% of the events caused a total of 110 casualties. 60% of them died when they tried to cross the street or the stream. Factors like the evolution of precipitation, population density and other socio-economical aspects have been considered. The trend analysis shows an increase of 1 flood/decade that probably has been mainly due to inter-annual and intra-annual changes in population density and in land-use and land-cover.
Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities.
The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998?2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households.
Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22?26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11?18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.
Using measurements from the national network of 12 weather radar
stations for the last decade (2000-2010), we investigate the large-scale
spatio-temporal variability of precipitation over Sweden. These
statistics provide useful information to evaluate regional climate
models as well as for hydrology and energy applications. A strict
quality control is applied to filter out noise and artifacts from the
radar data. We focus on investigating four distinct aspects namely, the
diurnal cycle of precipitation and its seasonality, the dominant time
scale (diurnal vs. seasonal) of variability, precipitation response to
different wind directions, and the correlation of precipitation events
with the North Atlantic Oscillation (NAO) and the Arctic Oscillation
(AO). When classified based on their intensity, moderate to high
intensity events (precipitation > 0.34 mm (3 h)-1) peak
distinctly during late afternoon over the majority of radar stations in
summer and during late night or early morning in winter. Precipitation
variability is highest over the southwestern parts of Sweden. It is
shown that the high intensity events (precipitation > 1.7mm (3
h)-1) are positively correlated with NAO and AO (esp. over
northern Sweden), while the low intensity events are negatively
correlated (esp. over southeastern parts). It is further observed that
southeasterly winds often lead to intense precipitation events over
central and northern Sweden, while southwesterly winds contribute most
to the total accumulated precipitation for all radar stations. Apart
from its operational applications, the present study demonstrates the
potential of the weather radar data set for studying climatic features
of precipitation over Sweden.
There is a wide variety of flood damage models in use internationally, differing substantially in their approaches and economic estimates. Since these models are being used more and more as a basis for investment and planning deci-sions on an increasingly large scale, there is a need to reduce the uncertainties involved and develop a harmonised Euro-pean approach, in particular with respect to the EU Flood Risks Directive. In this paper we present a qualitative and quantitative assessment of seven flood damage models, us-ing two case studies of past flood events in Germany and the United Kingdom. The qualitative analysis shows that mod-elling approaches vary strongly, and that current methodolo-gies for estimating infrastructural damage are not as well de-veloped as methodologies for the estimation of damage to buildings. The quantitative results show that the model out-comes are very sensitive to uncertainty in both vulnerability (i.e. depth–damage functions) and exposure (i.e. asset val-ues), whereby the first has a larger effect than the latter. We conclude that care needs to be taken when using aggregated land use data for flood risk assessment, and that it is essen-tial to adjust asset values to the regional economic situation and property characteristics. We call for the development of a flexible but consistent European framework that applies best practice from existing models while providing room for in-cluding necessary regional adjustments.
In recent years, there has been an increase in flash flood impacts, even as our ability to forecast events and warn areas at risk increases. This increase results from a combination of extreme events and the exposure of vulnerable populations. The issues of exposure and vulnerability to flash floods are not trivial because environmental circumstances in such events are specific and complex enough to challenge the general understanding of natural risks. Therefore, it seems essential to consider physical processes of flash floods concurrently with the impacts they trigger. This paper takes a first step in addressing this need by creating and testing the coherence of an impact-focused database based on two pre-existing public and expert-based survey datasets: the Severe Hazards Analysis and Verification Experiment (SHAVE) and the US National Weather Service (NWS) Storm Data. The SHAVE initiative proposes a new method for collecting near-real-time high-resolution observations on both environmental circumstances and their disastrous consequences (material and human losses) to evaluate radar-based forecasting tools. Forecast verification tools and methods are needed to pursue improving the spatial and temporal accuracy of forecasts. Nevertheless by enhancing SHAVE and NWS datasets with socially and spatially relevant information, we aim at improving future forecast ability to predict the amount and types of impacts.
This paper introduces the development of a database of high-impact weather events that occurred in Greece since 2001. The selected events are related to the occurrence of floods, flash floods, hail, snow/frost, torna-dos, windstorms, heat waves and lightning with adverse consequences (excluding those related to agriculture). The database includes, among others, the geographical distribu-tion of the recorded events, relevant meteorological data, a brief description of the induced impacts and references in the press. This paper further offers an extensive analysis of the temporal and spatial distribution of high-impact weather events for the period 2001–2011, taking into account the in-tensity of weather conditions and the consequent impact on the society. Analysis of the monthly distribution of high-impact weather events showed that they are more frequent during October and November. More than 80 people lost their lives, half of which due to flash floods. In what con-cerns the spatial distribution of high-impact weather events, among the 51 prefectures of the country, Attica, Thessa-loniki, Elia and Halkidiki were the most frequently affected areas, mainly by flash floods. Significant was also the share of tornados in Elia, of windstorms in Attica, of lightning and hail events in Halkidiki and of snow/frost events in Thessa-loniki.
The NW Mediterranean region experiences every year heavy rainfall and
flash floods that occasionally produce catastrophic damages. Less
frequent are floods that affect large regions. Although a large number
of databases devoted exclusively to floods or considering all kind of
natural hazards do exist, usually they only record catastrophic flood
events. This paper deals with the new flood database that is being
developed within the framework of HYMEX project. Results are focused on
four regions representative of the NW sector of Mediterranean Europe:
Catalonia, Spain; the Balearic Islands, Spain; Calabria, Italy; and
Languedoc-Roussillon, Midi-Pyrénées and PACA, France. The
common available 30-yr period starts in 1981 and ends in 2010. The paper
shows the database structure and criteria, the comparison with other
flood databases, some statistics on spatial and temporal distribution,
and an identification of the most important events. The paper also
provides a table that includes the date and affected region of all the
catastrophic events identified in the regions of study, in order to make
this information available for all audiences.
The assessment of coastal flood risks in a particular region requires the estimation of typical damages caused by storm surges of certain characteristics and annualities. Although the damage depends on a multitude of factors, including flow velocity, duration of flood, precaution, etc., the relationship between flood events and the corresponding average damages is usually described by a stage-damage function, which considers the maximum water level as the only damage influencing factor. Starting with different (microscale) building damage functions we elaborate a macroscopic damage function for the entire case study area Kalundborg (Denmark) on the basis of multiple coarse-graining methods and assumptions of the hydrological connectivity. We find that for small events, the macroscopic damage function mostly depends on the properties of the elevation model, while for large events it strongly depends on the assumed building damage function. In general, the damage in the case study increases exponentially up to a certain level and then less steep.
Damage assessments of natural hazards supply crucial information to decision support and policy development in the fields of natural hazard management and adaptation planning to climate change. Specifically, the estimation of economic flood damage is gaining greater importance as flood risk management is becoming the dominant approach of flood control policies throughout Europe. This paper reviews the state-of-the-art and identifies research directions of economic flood damage assessment. Despite the fact that considerable research effort has been spent and progress has been made on damage data collection, data analysis and model development in recent years, there still seems to be a mismatch between the relevance of damage assessments and the quality of the available models and datasets. Often, simple approaches are used, mainly due to limitations in available data and knowledge on damage mechanisms. The results of damage assessments depend on many assumptions, e.g. the selection of spatial and temporal boundaries, and there are many pitfalls in economic evaluation, e.g. the choice between replacement costs or depreciated values. Much larger efforts are required for empirical and synthetic data collection and for providing consistent, reliable data to scientists and practitioners. A major shortcoming of damage modelling is that model validation is scarcely performed. Uncertainty analyses and thorough scrutiny of model inputs and assumptions should be mandatory for each damage model development and application, respectively. In our view, flood risk assessments are often not well balanced. Much more attention is given to the hazard assessment part, whereas damage assessment is treated as some kind of appendix within the risk analysis. Advances in flood damage assessment could trigger subsequent methodological improvements in other natural hazard areas with comparable time-space properties.
Pluvial flooding is a problem in many cities and for city planning purpose the mechanisms behind pluvial flooding are of interest. Previous studies seldom use insurance claim data to analyse city scale characteristics that lead to flooding. In the present study, two long time series (∼20 years) of flood claims from property owners have been collected and analysed in detail to investigate the mechanisms and characteristics leading to urban flooding. The flood claim data come from the municipal water utility company and property owners with insurance that covers property loss from overland flooding, groundwater intrusion through basement walls and flooding from the drainage system. These data are used as a proxy for flood severity for several events in the Swedish city of Malmö. It is discussed which rainfall characteristics give most flooding and why some rainfall events do not lead to severe flooding, how city scale topography and sewerage system type influence spatial distribution of flood claims, and which impact high sea level has on flooding in Malmö. Three severe flood events are described in detail and compared with a number of smaller flood events. It was found that the main mechanisms and characteristics of flood extent and its spatial distribution in Malmö are intensity and spatial distribution of rainfall, distance to the main sewer system as well as overland flow paths, and type of drainage system, while high sea level has little impact on the flood extent. Finally, measures that could be taken to lower the flood risk in Malmö, and other cities with similar characteristics, are discussed.
Floods are a large problem around the world but the understanding of flood risks is hampered by a lack of data and knowledge about flood losses at different scales. The objective of this study was two-fold 1) to assess available temporally and spatially distributed data of rain events and flood damages during those events, regarding the usefulness of these data to quantify precipitation-related hazards and consequences, and 2) to assess the potential for deriving reliable damage functions based on the information compiled under objective 1. The study examined 2140 individual observations of insurance payouts for residential buildings caused by 49 different rainfall events in Sweden. Radar data were used to extract daily precipitation amounts and to capture the spatial and temporal distribution of the rainfalls. This study demonstrates that including the duration of a rainfall, as opposed to only the aggregated amount of daily precipitation, is highly important in estimating the extent of damage. Furthermore, higher rainfall intensities increased the number of damaged properties but had less influence on the mean damage cost per property. In order to draw conclusions from damages at the micro level, both availability and detail level of data must be improved.
In Europe, floods are among the natural catastrophes that cause the largest economic damage. This paper explores the potential of two distinct types of multivariate flood damage models: “depth-damage” models and “rainfall-damage” models. We use survey data of 346 Flemish households that were victim of pluvial floods complemented with rainfall data from both rain gauges and weather radars. In the econometrical analysis, a Tobit estimation technique is used to deal with the issue of zero damage observations. The results show that in the “depth-damage” models flood depth has a significant impact on the damage. In the “rainfall-damage” models there is a significant impact of rainfall accumulation on the damage when using the gauge rainfall data as predictor, but not when using the radar rainfall data. Finally, non-hazard indicators are found to be important for explaining pluvial flood damage in both “depth-damage” and “rainfall-damage” models.
We analyse chronologies of historical flash floods derived from searches of newspaper archives and other sources commencing before 1800 and recent gauged rainfall and streamflow data. Five key examples are chosen to illustrate specific features of flash floods. Pluvial flash floods arise from rainfall before it reaches a watercourse and may cause severe flooding of land and properties far from rivers. River flash floods, like pluvial floods, have the characteristic of rapid speed of response, a principal source of risk to life. Intense rainfall can generate ‘walls of water’ in river courses which can propagate long distances downstream and steepen, without upstream structural failure. Steeply rising wave fronts more commonly occur on steep upland catchments but, where intensities of extreme short period rainfall are sufficient, such wave fronts can also occur on lowland catchments. A definition of flash floods from intense rainfall, relevant to British landscape and climate, is proposed.
Hydrology requires accurate and reliable rainfall input. Because of the strong spatial and temporal variability of precipitation, estimation of spatially distributed rain rates is challenging. Despite the fact that weather radars provide high-resolution (but indirect) observations of precipitation, they are not used in hydrological applications as extensively as one could expect. The goal of the present review paper is to investigate this question and to provide a clear view of the opportunities (e.g., for flash floods, urban hydrology, rainfall spatial extremes) the limitations (e.g., complicated error structure, need for adjustment) and the challenges for the use of weather radar in hydrology (i.e., validation studies, precipitation forecasting, mountainous precipitation, error propagation in hydrological models).
Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008–10. Each observational data source has limitations; a major asset of the unified flash flood database is its collation of relevant information from a variety of sources that is now readily available to the community in common formats. It is anticipated that this database will be used for many diverse purposes, such as evaluating tools to predict flash flooding, characterizing seasonal and regional trends, and improving understanding of dominant flood-producing processes. We envision the initiation of this community database effort will attract and encompass future datasets.
Societal interest to evaluate and learn from disasters is scale dependent. Low frequent hazards with small impacts are often invisible at national level from an evaluation point of view and limited possibilities exist to compile publicly available data on losses and management in the aftermath. This study presents an inventory of possible data sources for 14 extreme rainfall events in Sweden 2000–2012. The sources, such as official sectorial institutions and media, and their content are analyzed in relation to reliability and verification opportunities. The use of free-text fields in official reporting systems and questionnaires, primarily designed for basic data capture from daily occurring accidents, is highlighted as important to achieve enhanced data that can be used to verify information from other sources, especially media archives.
Climate change is expected to generate higher short-term precipitation intensities, which may have negative consequences in terms of, for example, increased risk of flooding and sewer overflow. In this study, extreme precipitation for durations between 30 min and 1 day in simulations with the RCA3 regional climate model (RCM) for Sweden are analysed. As compared with daily observations in the period 1961–2010, the simulated extremes are found to be overall realistic with respect to magnitude, spatial homogeneity and temporal variability. In the ensemble of future projections, from 1981 to 2010 the 10-year 30-min precipitation will increase by 6% until 2011–2040, 15% until 2041–2070 and 23% until 2071–2100. The increase decreases with increasing duration and at the daily scale the percentage values are approximately halved. The values are largely consistent with earlier estimates. Assessment of the impacts on the results of the spatial resolution and the specific RCM used indicated possibilities of both smaller and larger future increases.
This paper presents the results of an analysis using insurance data for damage description and risk model verification, based on data from a Danish case. The results show that simple, local statistics of rainfall are not able to describe the variation in individual cost per claim, but are, however, feasible for modelling the overall cost per day. The study also shows that in combining the insurance and regional data it is possible to establish clear relationships between occurrences of claims and hazard maps. In particular, the results indicate that with improvements to data collection and analysis, improved prediction of damage costs will be possible, for example based also on socioeconomic variables. Furthermore, the paper concludes that more collaboration between scientific research and insurance agencies is needed to improve inundation modelling and economic assessments for urban drainage designs.
s u m m a r y Flash floods are one of the most significant natural hazards in Europe, causing serious risk to life and destruction of buildings and infrastructure. This type of flood, often affecting ungauged watersheds, remains nevertheless a poorly documented phenomenon. To address the gap in available information, and particularly to assess the possible ranges for peak discharges on watersheds with area smaller than 500 km 2 and to describe the geography of the hazard across Europe, an intensive data compilation has been carried out for seven European hydrometeorological regions. This inventory is the first step towards an atlas of extreme flash floods in Europe. It contains over 550 documented events. This paper aims at presenting the data compilation strategy, the content of the elaborated data base and some preliminary data analysis results. The initial observations show that the most extreme flash floods are greater in mag-nitude in the Mediterranean countries than in the inner continental countries and that there is a strong seasonality to flash flood occurrence revealing different climatic forcing mechanisms in each region.
Every year floods cause enormous damage all over the world. This study investigates loss of human life statistics for different types of floods and different regions on a global scale. The OFDA/CRED Database contains data on international disasters and is maintained by the Centre for Research on the Epidemiology of Disasters in Brussels (CRED) in cooperation with United States Office for Foreign Disaster Assistance (OFDA). Information from this source on a large number of flood events, which occurred between January 1975 and June 2002, is evaluated with respect to flood location and flood type. Due to the limited availability of information on coastal flood events, the scope of this study is limited to three types of freshwater flooding: river floods, flash floods and drainage problems. First, the development of loss of life statistics over time is discussed. Second, the dataset is analysed by region, by flood type and by the combination of type and region. The study shows that flash floods result in the highest average mortality per event (the number of fatalities divided by the number of affected persons). A cross analysis by flood type and location shows that average mortality is relatively constant for the different types over various continents, while the magnitude of the impacts (numbers of killed) and affected for a certain type varies between the different continents. On a worldwide scale Asian river floods are most significant in terms of number of persons killed and affected. Finally, a comparison with figures for other types of natural disasters shows that floods are the most significant disaster type in terms of the number of persons affected.
The southern part of France near the Mediterranean Sea is subject to flash floods generated by heavy rainfalls typical of the Mediterranean climate. In November 1999 (the 12th and 13th) and in September 2002 (the 8th and 9th), 5000 km2 were touched by rainfalls superior to 200 mm in the departments of Aude and Gard. In both cases, maximum precipitation exceeded 500 mm within 24 h. The damage amounted in the hundreds of millions of euros, and there were numerous fatalities: 35 in 1999, and 23 in 2002. Following a survey of available data, this article details the cost of the damage for both flash flood events. The distribution of the damage is quantified by sector of activity (e.g., industry, agriculture). The average ratio “euros of loss per inhabitant” is quite similar in both cases, but this average hides some geographical discrepancies. Losses in industry can locally worsen the overall toll. The mapping of damage on a local scale and the amount of losses per inhabitant demonstrate that rural areas underwent heavy losses. This was mostly due to the destruction of the public infrastructures (e.g., roads, bridges) that represented more than half of the overall loss. In some rural areas, the cost of flash floods can exceed 15,000 euros per inhabitant. Such flood prevention issues as flood warning systems and land use planning must not focus only on the cities. Death, injury and heavy material losses also disadvantage the rural and mountainous areas where populations and activities are concentrated near rivers.
The aim of this paper is to investigate the detailed hydrometeorological circumstances that lead to accidental casualties, and to better understand the prominent physical factors of risk. Based on an event that affected the Gard region (Southern France) in September 2002, it is a first attempt to combine analysis of the physical and human response to Mediterranean storms.After details concerning the methodology (for meteorological, hydrological and casualty analysis), the local context and the event, the authors examine two points: the dynamics of the event (flash-flood and riverine-flood response to the storm) together with human exposure on the one hand, and scale as a critical problem affecting flood risk on the other.This investigation stresses the specificity of small catchments, which are more dangerous both in hydrological and human terms. Moreover, this contribution linking social sciences and geophysics constitutes an important step in what [Morss, R.E., Wilhelmi, O.V., Downton, M.W., Gruntfest, E., 2005. Flood risk, uncertainty, and scientific information for decision making. Bull. Am. Meteor. Soc. 86 (11), 1593–1601] call the “End to end to end” process
Motor vehicle-related deaths account for more than half of all flood fatalities in the United States, but to date, very little is known about the risk factors associated with why people drive into flooded roads. Using data from survey questionnaires administered in Denver, CO, and Austin, TX, this paper suggests that people who do not take warnings seriously are more likely to drive through flooded roads, as are people aged 18–35, and those that do not know that motor vehicles are involved in more than half of all flood fatalities. In Denver, people who have not experienced a flood previously and those who do not know they live in flood-prone areas are also more likely to drive into flooded roads.
The objective of this paper is to investigate and to improve understanding of the causes and circumstances of flood disaster deaths. A standardised method of classifying flood deaths is proposed and the difficulties associated with comparing and assessing existing information on flood deaths are discussed. Thirteen flood cases from Europe and the United States, resulting in 247 flood disaster fatalities, were analysed and taken as indicative of flood disaster deaths. Approximately two-thirds of the deaths occurred through drowning. Thus, a substantial number of flood disaster fatalities are not related to drowning. Furthermore, males are highly vulnerable to dying in floods and unnecessary risk-taking behaviour contributes significantly to flood disaster deaths. Based on these results, recommendations are made to prevent loss of life in floods. To provide a more solid basis for the formulation of prevention strategies, better systematic recording of flood fatalities is suggested, especially those caused by different types of floods in all countries.
Extrem nederbörd i Sverige under 1 till 30 dygn
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Swedish natural hazards information system
- Msb
MSB. 2017. Swedish natural hazards information system. Swedish Civil Contingencies Agency, Karlstad, Sweden.