Assessing urban potential flooding risk and identifying effective risk-reduction measures

Cherqui, Frédéric; Belmeziti, Ali; Granger, D.; Sourdril, A.; Gauffre, P. Le

doi:10.1016/j.scitotenv.2015.02.027

Cited by 96 publications

(44 citation statements)

References 27 publications

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“…However, the actual impact can vary, as shown by this study, for two major reasons. First, fallen leaves and soil erosion from green lands can act to block gully pots and sewer inlets, resulting in increased pluvial inundations (ten Veldhuis et al 2011;Cherqui et al 2015). Secondly, the green lands themselves may be improperly designed from a perspective of flood mitigation.…”

Section: Discussionmentioning

confidence: 99%

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Wang

Wen

et al. 2016

Stoch Environ Res Risk Assess

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In the context of climate change and rapid urbanization, urban pluvial floods pose an increasing threat to human wellbeing and security in the cities of China. A valuable aid to managing this problem lies in understanding the roles of environmental factors in influencing the occurrence of pluvial floods. This study presents a spatial analysis of records of inundated streets in the inner city of Shanghai during 1997-2013. A geographically weighted regression (GWR) is employed to examine the spatially explicit relationships between inundation frequency and spatial explanatory factors, and an ordinary least squares regression (OLS) is used to validate the GWR results.Results from the GWR model show that the inundation frequency is negatively related to elevation, pipeline density, and river density, and is positively related to road/ square ratio and shantytown ratio. The green ratio is another significant explanatory factor for inundation frequency, and its coefficients range from -1.11 to 0.81. In comparison with the OLS model, the GWR model has better performance as it has higher R 2 , and lower corrected Akaike information criterion and mean square error values, as well as insignificant spatial autocorrelation of the model residuals. Additionally, the GWR model reveals detailed site-specific roles of the related factors in influencing street inundation. These findings demonstrate that the GWR model is a useful tool for investigating spatially explicit causes of disasters. The results also provide guidance for policy makers aiming to mitigate urban pluvial flood risks.

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Section: Discussionmentioning

confidence: 99%

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Wang

Wen

et al. 2016

Stoch Environ Res Risk Assess

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“…gully pot or sewer inspections. Report data allows system operators to identify dominant failure 15 mechanisms, flood-prone areas or urban flood risks (Arthur et al, 2008;Caradot et al, 2011;Cherqui et al, 2015;Gaitan et al, 2015Gaitan et al, , 2016Spekkers et al, 2015;ten Veldhuis et al, 2011;Verstraeten and Poesen, 1999). Therefore, this approach is well suited to capture small-scale in-sewer defects.…”

Section: Approaches To Quantifying Urban Pluvial Flooding Occurrence mentioning

confidence: 99%

“…This has resulted in the identification of important failure mechanisms and a rainfall threshold value around 10 mm/60min, 20 above which damage to private property by street flooding increases significantly (Spekkers et al, 2015). Also, sewer system maintenance records have been used to identify flood-prone locations and dominant failure mechanisms in the French city of Bordeaux (Cherqui et al, 2015). A final example is given by Municipal call center data (crowdsourced flooding reports), which have been used to determine dominant failure mechanisms and to quantify urban flood risk probabilities in the cities of Prinsenbeek and Haarlem (ten Veldhuis et al, 2011).…”

Section: Approaches To Quantifying Urban Pluvial Flooding Occurrence mentioning

confidence: 99%

Towards identification of critical rainfall thresholds for urban pluvial flooding prediction based on crowdsourced flood observations

Bouwens

Veldhuis

Schleiss

et al. 2018

Preprint

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Urban drainage systems are challenged by both urbanization and climate change, intensifying urban pluvial flooding impacts. Urban pluvial flooding impacts can be reduced by improving infrastructure and operational urban flood management strategies. This study investigated the relation between heavy rainfall and urban pluvial flooding in Rotterdam with the aim to identify parameters and thresholds that can be used as predictors of urban pluvial 5flooding. The focus of the investigation was on an area of 16 km 2 . Datasets for this research included historical crowdsourced flooding reports, overflow pumping volumes, open spatial data and rainfall data at different temporal and spatial resolutions. Threshold values, (which can be used as part of early warning systems,) were derived from historical flooding data and rainfall depths over sub daily durations. Threshold values of rainfall depth were found to be 6 mm (±3 mm) in 15 min and 11 mm (±6 mm) in 60 min. Surprisingly, the derived thresholds are only 10 approximately half of the theoretical drainage system design capacity. Furthermore, a threshold value of 70% (± 4%) imperviousness was found above which flooding incidents significantly increase. Results also suggested a strong dependence on spatial aggregation scale, as it highly influences correlation coefficients and parameter density values. Elevation differences did not appear to contribute to urban pluvial flooding, based on a flow path analysis for the study area. Finally, we showed that antecedent rainfall does not explain additional variance in 15 reports, meaning it is not an influential urban pluvial flooding predictor, at least not on a daily temporal resolution.

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“…This is expressed by the low correlation coefficient (r = 0.51) between the number of APS claims per event in Lisbon and the daily rainfall values recorded in the Lisboa/Geofísico rain gauge. Several factors can explain this: (a) the quality and availability of rainfall and insurance data [33,34]; (b) the spatial and temporal distribution of the triggering rainfall; (c) the geomorphological, lithological and hydrological features; (d) the extent of built-up areas; (e) the amount of exposed (and insured) dwellings; (f) the capacity of the stormwater drainage systems to cope with heavy rainfall events; (g) the obstruction by leaves or debris of the grated inlets [35]; (h) the urban grid features; and (i) the tidal and/or storm surge influence.…”

Section: The Aps Database Global Results Formentioning

confidence: 99%

The potential of two types of urban flooding to cause material damages in Lisbon, Portugal

Leal¹,

Ramos²

2017

Int. J. SAFE

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The incapacity to cope with heavy rainfall events leads to flooding in urban areas. Urban flooding is usually divided into pluvial flooding, sewer flooding, among others. Determining which of these types was responsible to cause a flooding occurrence is, in most cases, an error-prone task, because it can be triggered by more than one of them and there is frequently an overlap in their extent.Given these problems, a classification based on the hydro-geomorphological features was applied to Lisbon, in which the relief and the ancient floodplains are still crucial factors in the city's current overland flow behavior. Two types of flooding are proposed: flooding related to natural drainage network (FREN) and flooding unrelated to natural drainage network (FUNN). In order to determine the material damages associated to FREN and FUNN and their spatial distribution, an insurance flooding database (2000-2011) was used. Through the accurate location of the APS (the Portuguese Association of Insurers) database flooding records triggered by rainfall and the reconstruction of the Lisbon's natural drainage network, it was possible to define which type of flooding caused each reported claim.There are different spatial patterns in Lisbon concerning FREN and FUNN. FREN occurs along the valley bottoms, while FUNN has a more scattered spatial pattern, meaning that FUNN can occur where there are overland flow difficulties. 53% of the claims were caused by FUNN; however, higher payouts are associated with FREN (58%). Resorting only to the claims recorded during an extreme rainfall event (in 18 February 2008), the weight of FREN is even more pronounced with 58% of the claims and 71% of the payouts. This proves the higher FREN's potential to cause material damages when compared to FUNN's. This knowledge can be applied to the flooding mitigation or adaptation measures to be included in urban planning.

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Assessing urban potential flooding risk and identifying effective risk-reduction measures

Cited by 96 publications

References 27 publications

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Towards identification of critical rainfall thresholds for urban pluvial flooding prediction based on crowdsourced flood observations

The potential of two types of urban flooding to cause material damages in Lisbon, Portugal

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