The definition of urban stormwater tolerance threshold and its conceptual estimation: an example from Taiwan

Chen, Chi-Feng; Liu, Chung-Ming

doi:10.1007/s11069-013-0645-7

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…This is partly explained by the fact of being the most built-up municipality (73%). Impervious surfaces lead to increases in volume and speed of overland flow [27][28][29], and, consequently, to increases in flooding frequency and magnitude [17,[30][31][32]. Lisbon has also the highest number of insurance policies (22%), dwellings (22%) and inhabitants (19%), which means a higher flooding exposure.…”

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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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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“…A heavy rainfall event occurred in Gongguan between 13:00 and 17:00 on 14 June 2015. This event had high-intensity rainfall over a short duration (≤131.5 mm/h) and precipitation exceeding the design drainage capacity of 78.8 mm/h [62]. This was a severe flood such as rarely occurs on this important road near National Taiwan University, so no water level monitoring equipment had been installed in the neighborhood.…”

Section: Case Studymentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

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Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons

Yang¹,

Yang

et al. 2015

Journal of Hydrology

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The definition of urban stormwater tolerance threshold and its conceptual estimation: an example from Taiwan

Cited by 7 publications

References 28 publications

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

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

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons

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