Establishment of a Flood Nomograph in Coastal Areas Considering Sea-Level Rise

Climate change caused by global warming is raising the average sea level. The rise in sea level leads to an increase in river water levels within the affected range, which increases the possibility of flooding in water due to erosion of outfall to the coast and rivers. Therefore, it is necessary to recognize in advance the risk of occurrence of domestic flooding, which is aggravated by the effect of rising sea levels, and to construct new boundary conditions for predicting urban flooding accordingly. In this study, Flood Nomograph for two research areas was selected in consideration of the regional characteristics of coastal areas and the scenario of sea level rise. As a result of the analysis, as the sea level rose, the amount of flood critical rainfall decreased numerically. It is believed that this study can be used as a necessary basis for improving flood forecast and warning data considering sea level rise in coastal cities in the future.

Hybrid Approach for Excess Stormwater Management: Combining Decentralized and Centralized Strategies for the Enhancement of Urban Flooding Resilience

Urban sprawl and soil sealing has gradually led to an impervious surface increase with consequences on the enhancement of flooding risk. During the last decades, a hybrid approach involving both traditional storm water detention tanks (SWDTs) and low-impact development (LID) has resulted in the best solution to manage urban flooding and to improve city resilience. This research aimed at a modeling comparison between drainage scenarios involving the mentioned hybrid approach (H-SM), with (de)centralized LID supporting SWDTs, and a scenario representative of the centralized approach only involving SWDTs (C-SM). Results highlighted that the implementation of H-SM approaches could be a great opportunity to reduce SWDTs volumes. However, the performances varied according to the typology of implemented LID, their parameterization with specific reference to the draining time, and the rainfall severity. Overall, with the increase of rainfall severity and the decrease of draining time, a decrease of retention performances can be observed with SWDTs volume reductions moving from 100% to 28%. In addition, without expecting to implement multicriteria techniques, a preliminary cost analysis pointed out that the larger investment effort of the (de)centralized LID could be, in specific cases, overtaken by the cost advantages resulting from the reduction of the SWDTs volumes.

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.

Urban Flooding Risk Assessment Based on Numerical Simulation of Sub-catchment Area: A Case Study From Chengdu, China

Urbanization and climate change usually result in frequent urban flooding. Since the floods cannot be avoided, the scenario simulation combined with risk analysis is an effective way to assess the disaster level and reduce direct damage loss when facing the emergency management problems. Different from the whole city dimension, the paper proposed a sub-catchment multi-index hesitant fuzzy evaluation model for the community planning level, and takes Jinjiang District of Chengdu city as the research object. Firstly, based on the PSR (Pressure-State-Response) model, the risk assessment system has been established in three aspects, including the current situation of urban drainage, the basic geographic information, and the social influence. Secondly, A total of 14 evaluation indexes were selected, among which the pressure index came from the calculation results of ArcGIS and EPASWMM5 model such as runoff coefficient, maximum water depth, etc. Thirdly, the expert hesitate fuzzy evaluation method was used to obtain the weight of 14 indexes of each sub-catchment. Finally, the 224 evaluation results were compared, and the urban flooding disaster risk map has been drawn. It is mainly concluded that 160 medium-higher risk areas were mainly concentrated in high built-up area in study area. Furthermore, the evaluation model is very useful as a decision-making tool for mitigation of the flood hazard and its associated risk.

Blue lines and blues infrastructures: Notes on water, race, and space

In Detroit, Michigan, the urban poor fear they are being displaced and replaced by water. As part of the city’s recent redevelopment efforts, planners have proposed creating green and blue infrastructure zones to manage urban flooding and mitigate the volume of overflow storm and sewer waters that pollute the Great Lakes each year. The areas slated for these water retention zones are the same marginal neighborhoods where Black residents face frequent foreclosures due to water debts and mass shutoffs from water and sewer services. This paper explores how water materializes and mediates uneven landscapes of livability, as well as new modes of living in common among those excluded from the urban commons. I introduce the concepts of “bluelining” and “blues infrastructures” in order to think through the contested assemblages of water, race, and space at the margins of urban life.