Assessment of pluvial flood exposure and vulnerability of residential areas

Grahn, Tonje; Nyberg, Lars

doi:10.1016/j.ijdrr.2017.01.016

Cited by 72 publications

(78 citation statements)

References 33 publications

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“…Inherently, SWFs are not constrained to areas close to watercourses but can occur practically anywhere in the landscape (Kron, 2009). Consequently, such floods are difficult to document, study and forecast (e.g., Pitt, 2008;Steinbrich et al, 2016) and related data are scarce (e.g., Hankin et al, 2008;Douglas et al, 2010;Blanc et al, 2012;Grahn and Nyberg, 2017). Spekkers et al (2014) mention the lack of data and the impact on small spatial scales as possible explanations why relatively little scientific research has been dedicated to such SWF in comparison to fluvial floods.…”

Section: Introductionmentioning

confidence: 99%

“…Although this strategy is certainly heading in the right direction, we have to be conscious about the basis on which current and future decisions concerning SWFs are made. Undoubtedly, the lack of quantitative data and studies hampers our process understanding (Grahn and Nyberg, 2017). Therefore, the underlying crucial question is "how can we reduce losses from natural hazards when we do not know .…”

Section: Introductionmentioning

confidence: 99%

“…As there are no such data concerning SWFs (Steinbrich et al, 2016), we must exploit other data sources in order to quantify the relevance of this flood type in space and time. Possible data sources include, but are not limited to, insurance claim records (e.g., Spekkers et al, 2013;Zhou et al, 2013;Moncoulon et al, 2014;Bernet et al, 2016;Grahn and Nyberg, 2017), disaster databases (e.g., Gall et al, 2009;Kron et al, 2012), press reports (e.g., Hilker et al, 2009) and interviews with or reports from affected people (e.g., Thieken et al, 2007;Evrard et al, 2007;Gaitan et al, 2016). All data sources are probably subjected to a varying degree of a socalled "threshold bias", which refers to the bias introduced due to varying damage inclusion criteria (Gall et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Not surprisingly, they have been the base for several studies related to SWFs (e.g., Cheng et al, 2012;Spekkers et al, 2013Spekkers et al, , 2015Zhou et al, 2013;Moncoulon et al, 2014;Bernet et al, 2016;Grahn and Nyberg, 2017). Unfortunately, insurance claim data are generally difficult to collect, since most insurance companies do not publish or provide loss data due to confidentiality issues (Boardman, 2010;Grahn and Nyberg, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…As insurance companies usually do not assess and record detailed information for each damage claim, it is difficult to verify and differentiate the cause of each damage without at least knowing the explicit location of the damaged object. This is particularly important, as the corresponding data often cover different processes without explicit classification: for instance, Grahn and Nyberg (2017) had to exclude all damage records with dates that coincided with dates of known fluvial flood events to obtain a subset of SWF-related claims. Spekkers et al (2013) chose a more elaborate method of applying a statistical filter based on the assumption that rainfall-related damage is clustered around wet days, while other causes of damage occur on any day throughout the year.…”

Section: Introductionmentioning

confidence: 99%

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Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time

Bernet

Prasuhn

Weingartner

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Surface water floods (SWFs) have received increasing attention in the recent years. Nevertheless, we still know relatively little about where, when and why such floods occur and cause damage, largely due to a lack of data but to some degree also because of terminological ambiguities. Therefore, in a preparatory step, we summarize related terms and identify the need for unequivocal terminology across disciplines and international boundaries in order to bring the science together. Thereafter, we introduce a large (n = 63 117), long (10-33 years) and representative (48 % of all Swiss buildings covered) data set of spatially explicit Swiss insurance flood claims. Based on registered flood damage to buildings, the main aims of this study are twofold: First, we introduce a method to differentiate damage caused by SWFs and fluvial floods based on the geographical location of each damaged object in relation to flood hazard maps and the hydrological network. Second, we analyze the data with respect to their spatial and temporal distributions aimed at quantitatively answering the fundamental questions of how relevant SWF damage really is, as well as where and when it occurs in space and time.This study reveals that SWFs are responsible for at least 45 % of the flood damage to buildings and 23 % of the associated direct tangible losses, whereas lower losses per claim are responsible for the lower loss share. The Swiss lowlands are affected more heavily by SWFs than the alpine regions. At the same time, the results show that the damage claims and associated losses are not evenly distributed within each region either. Damage caused by SWFs occurs by far most frequently in summer in almost all regions. The normalized SWF damage of all regions shows no significant upward trend between 1993 and 2013. We conclude that SWFs are in fact a highly relevant process in Switzerland that should receive similar attention like fluvial flood hazards. Moreover, as SWF damage almost always coincides with fluvial flood damage, we suggest considering SWFs, like fluvial floods, as integrated processes of our catchments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time

Bernet

Prasuhn

Weingartner

2017

Nat. Hazards Earth Syst. Sci.

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Intersecting Near-Real Time Fluvial and Pluvial Inundation Estimates with Sociodemographic Vulnerability to Quantify a Household Flood Impact Index

Preisser

Passalacqua

Bixler

et al. 2022

Preprint

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Pluvial flood risk and opportunities for resilience

et al. 2018

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The risk presented by pluvial flooding has emerged as a critical issue in urban water management. Pluvial flooding occurs when precipitation intensity exceeds the capacity of natural and engineered drainage systems, and it is expected to increase in frequency, severity and impact through the 21st century due to the combined effects of climate change and urbanization. Although there have been recent advances in approaches to assess the risk presented pluvial flooding and to enhance the resilience of cities to its impacts, they have not been broadly implemented and there are many opportunities for additional research. We provide case studies of pluvial flooding in six cities in the continental United States, which serve as examples of the current vulnerability of cities that have not developed comprehensive pluvial flood management plans and the challenges in conducting pluvial flood research in light of existing data gaps. We also identify key research challenges that should be prioritized by the interdisciplinary water research community to better support urban resilience practice. This article is categorized under: Engineering Water > Sustainable Engineering of Water Science of Water > Water Quality Engineering Water > Planning Water

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Assessment of pluvial flood exposure and vulnerability of residential areas

Cited by 72 publications

References 33 publications

Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time

Surface water floods in Switzerland: what insurance claim records tell us about the damage in space and time

Intersecting Near-Real Time Fluvial and Pluvial Inundation Estimates with Sociodemographic Vulnerability to Quantify a Household Flood Impact Index

Pluvial flood risk and opportunities for resilience

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