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Urban flood mostly is pluvial flood, caused by high rainfall intensities combined with the unsuitable drainage system and land cover. Because of the heterogeneous drainage system and the storm distribution dynamics, urban floods are rapid and spontaneous in space and time. Flood risk analysis was created to understand and assess the flood behavior, manage and mitigate the flood damage. However, flood risk assessments recently only have focused on the spatial distribution of the flood while temporal flood evolution in urban area is still an open question. This research aims to provide a spatio-temporal analysis of the urban flood by implementing the simplified model-based representation of flood evolution/development in space and time (spatiotemporal patterns) including time to flood the manhole, the location, spatial and temporal sequences of flood.To specify, 3 precipitation distribution patterns were collected from rainfall incidents in 2008, 2019, 2020, then extrapolated to create 21 scenarios following the return periods (i.e. 1.25-year, 2-year, 2.5-year, 5-year, 10-year, 20-year, 50-year). In each scenario, the dynamics of flood was estimated using the urban drainage system by SWMM5. Case study (Do Lo, Yen Nghia, Ha Dong, Ha Noi, Viet Nam) was divided into 115 sub-catchtments based on the Digital Elevation Model (DEM) and the drainage system map of this area. Land cover was created based on the LANDSAT images. Domestic waste water distribution was included in the model. The model is validated with the extreme events in 2020 and 2022.A spatio-temporal risk map was generated to show the flooding spatio-temporal sequences and non-flood region. Flood evolution on the time scale was shown in this map. The rate of flood change diagrams shows the flood responses from urban areas which vary from 1 to 107 mins in different scenarios.Rain gauge distribution sensitivity is examined under ranges of rain gauge distribution combinations in term of space and time.

Typhoon-induced intense rainfall and urban flooding have endangered the city of Zhoushan every year, urging efficient and accurate flooding prediction. Here, two models (the classical shallow water model that approximates complex buildings by locally refined meshes, and the porous shallow water model that adopts the concept of porosity) are developed and compared for the city of Zhoushan. Specifically, in the porous shallow water model, the building effects on flow storage and conveyance are modeled by the volumetric and edge porosities for each grid, and those on flow resistance are considered by adding extra drag in the flow momentum. Both models are developed under the framework of finite volume method using unstructured triangular grids, along with the Harten-Lax-van Leer-Contact (HLLC) approximate Riemann solver for flux computation and a flexible dry-wet treatment that guarantee model accuracy in dealing with complex flow regimes and topography. The pluvial flooding is simulated during the Super Typhoon Lekima in a 46 km2 mountain-bounded urban area, where efficient and accurate flooding prediction is challenged by local complex building geometry and mountainous topography. It is shown that the computed water depth and flow velocity of the two models agree with each other quite well. For a 2.8-day prediction, the computational cost is 120 min for the porous model using 12 cores of the Intel(R) Xeon(R) Platinum 8173M CPU @ 2.00 GHz processor, whereas it is as high as 17,154 min for the classical shallow water model. It indicates a speed-up of 143 times and sufficient pre-warning time by using the porous shallow water model, without appreciable loss in the quantitative accuracy.

Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented ‘720’ Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, ‘reverse bathtub’, ‘ever-increasing’ and ‘ever-decreasing’ patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people’s gender or age. In view of the common association between travel behaviors and travelers’ socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.

An increasing trend of urban floods in India from past several years causes major damages on Indian cities. By 2050, more than half of the population in the developing countries like India are expected to migrate to urban regions. Urbanization is triggered in developing countries as people migrate to cities in search of employment opportunities resulting in formation of new slums. With high density of population concentration in cities, urban floods are triggered leading to a significant impact of human life and economy of the country. The review focuses on addressing the urban flood occurrence in India and its relationship with population growth climate change. The study also describes the impact of urban floods to the environment and integrated methodologies adopted over decades for the prediction and effective mitigation and management during a disaster event.

Urbanization and industrialization cause an increase in greenhouse gas emissions, which in turn causes changes in the atmosphere. Climate change is causing extreme rainfalls and these rainfalls are getting stronger day after day. Floods are threatening urban areas, and short-duration rainfall and outdated drainages are responsible for urban floods. Intensity–Duration–Frequency (IDF) curves are crucial for both drainage system design and assessment of flood risk. Once IDF curves are determined from historical data, they are assumed to be stationary. However, IDF curves must be non-stationary and time varying based on preparation for extreme events. This study generates future IDF curves with short-duration rainfalls under climate change. To represent future rainfall, an ensemble of four Global Climate Models generated under Representative Concentration Pathways (RCP) 4.5 and 8.5 were used in this study. A new approach to the HYETOS disaggregation model was applied to disaggregate daily future rainfall into sub-hourly using disaggregation parameters of hourly measured rainfalls. Hence, sub-hourly future rainfalls will be obtained capturing historical rainfall patterns instead of random rainfall characteristics. Finally, historical and future IDF curves were compared. The study concludes that increases in short-duration rainfalls will be highly intensified in both the near and distant futures with a high probability.

The high frequency and intensity of urban floods caused by climate change, urbanisation and infrastructure failures increase public health risks when the flood water contaminated from combined sewer overflows (CSOs) or other sources of faecal contamination remains on urban surfaces. This study contributes to a better understanding of the effects of urban and recreational surfaces on the occurrence of waterborne pathogens. The inactivation of selected indicator organisms was studied under controlled exposure to artificial sunlight for 6 h followed by 18 h in dark conditions. Concrete, asphalt, pavement blocks and glass as control were inoculated with artificial floodwater containing, as indicator organisms, Escherichia coli bacteria, which are common faecal indicator bacteria (FIB) for water quality assessment, Bacillus subtilis spores chosen as surrogates for Cryptosporidium parvum oocysts and Giardia cysts, and bacteriophages MS2 as indicators for viral contamination. On practically all the surfaces in this study, E. coli had the highest inactivation under light conditions followed by MS2 and B. subtilis, except asphalt where MS2 was inactivated faster. The highest inactivation under light conditions was seen with E. coli on a concrete surface (pH 9.6) with an inactivation rate of 1.85 h−1. However, the pH of the surfaces (varying between 7.0 and 9.6) did not have any influence on inactivation rates under dark conditions. MS2 bacteriophage had the highest inactivation under light conditions on asphalt with a rate of 1.29 h−1. No die-off of B. subtilis spores was observed on any of the surfaces during the experiment, neither in light nor in dark conditions. This study underpins the need to use different indicator organisms to test their inactivation after flooding. It also suggests that given the sunlight conditions, concentration of indicator organisms and type of surface, the fate of waterborne pathogens after a flood could be estimated.

Geotechnologies have significant potential for application in socio-environmental analysis coupled to disaster risk reduction. Equipment and applications are available that are supported by scientific computing, promoting advances in the acquisition and processing of remote sensing data. Among these are two types: (i) the associated equipment to technology LiDAR (light detection and ranging) and (ii) remotely piloted aircraft systems (RPAS) with platforms of remote sensors. Recently, an growing number of studies has been observed that have the potential for applications in the sensors equipped in RPAS for environmental studies, especially those that evaluate the impacts of natural disasters. In this context, the aim of this research is to demonstrate the possibilities of RPAS applications in the collection of data of interest in the management of natural disasters. Also associated with this task is the prospect of implementing some techniques of scientific computing necessary for the implementation of applications. With these activities, we seek to contribute to the advancement of the employment of RPAS in managing and preventing the risk of natural disasters.

In this article, we examine the coproduction of hazardous urban space and new formations of clientelist state governance. Work on hazards and vulnerability frequently demonstrates how hazardous urban spaces are produced, but a critical understanding of state power is often left untouched. Correspondingly, scholars analyzing clientelism and state formation habitually discuss the configuration of new forms of governance and the consolidation of state power without intersecting these processes with the production of vulnerabilities and "hazardous nature." Drawing on ethnographic research in urban areas susceptible to serious floods and landslides in Brazil and Mexico, we argue that clientelist governance and state making, including complex forms of political favoritism, create urban hazardscapes, as much as the management of urban disasters acts to reconfigure patron-client relations within "hazardstates." The article contributes to an emerging body of literature analyzing linkages between urban environmental governance, state authority, and the reproduction of vulnerability.

Community resilience is a key index for describing the response of human habitat systems to hazards. Evaluating and enhancing the community resilience requires indicators, identification, and quantitative measurements, especially for urban flooding management. In this study, an advanced index framework for measuring community resilience to urban flooding is proposed, integrating the fuzzy Delphi method (FDM) and the analytic network process (ANP). Seven indicators (public facilities, spatial structure of land use, flood management organizations, rescue capability, accuracy of weather forecasts, vulnerable population, and individual capability) of community resilience are identified using the fuzzy Delphi method. The indicators are classified into four dimensions, and the weights are determined by the analytic network process. This approach is applied to three different types of communities, namely, a newly built neighborhood, an ancient college, and a flood-prone village in the city of Nanning, China, using data collected from questionnaires, interviews, and field investigations. The neighborhood (with a total averaged score of 2.13) has the largest community resilience to urban flooding, followed by the college (1.8), and finally the village (0.91). Flooding management organizations play a leading role in the urban flooding resilience of the neighborhood and college, while the vulnerable population has a great impact on the community resilience of the village. Results of the strategy analysis suggest that science and technology improvement (0.543) is more important than social&ndash, economic status improvement (0.325) and built-environment improvement (0.132) for mitigating urban hazards in Nanning. The proposed framework in this study contributes to the interdisciplinary understanding of community resilience for urban flooding and is expected to be applied to sustain urban planning and flood evacuations.

Urban floods are paralyzing surface transportation and inflicting heavy economic losses. Climate-induced increase in frequency and intensity of rainfalls and excessive urbanization makes urban centers even more vulnerable to floods. It is necessary to quantify all dimensions of losses caused to road connectivity to improve flood mitigation policy. There is a need to consolidate the existing body of peer-reviewed contemporary literature on flood inundation modeling and its impacts on road connectivity. This will improve the awareness of policymakers and researchers and help in science-based decision making. Articles archived in the Web of Science database having the keywords floods and road in their title published between 1977 and 2020 were analyzed using the blibliometrix library of R. Analysis shows that the flood inundation and flood extent modeling has evolved from the conventional hydrological models to the near real-time crowd-sourced modeling methods. Applications of geographical information systems and advanced remote sensing methods have been growing in identifying road network vulnerabilities. We observed a gap in harmonized data availability, due to the unstructured data formats at several scales, which hinders a generalized approach for flood risk modeling studies for urban planning. Concentrated efforts have to be made to fill the gaps in data availability and research methodologies, especially using crowd-sourced data. Further, efforts have to be made to increase awareness, early warning systems, and alternate transport networks, to make the cities less vulnerable to floods.

In the last decade, flooding has caused the death of over 60,000 people and affected over 900 million people globally. This is expected to increase as a result of climate change, increased populations and urbanisation. Floods can cause infections due to the release of water-borne pathogenic microorganisms from surcharged combined sewers and other sources of fecal contamination. This research contributes to a better understanding of how the occurrence of water-borne pathogens in contaminated shallow water bodies is affected by different environmental conditions. The inactivation of fecal indicator bacteria Escherichia coli was studied in an open stirred reactor, under controlled exposure to simulated sunlight, mimicking the effect of different latitudes and seasons, and different concentrations of total suspended solids (TSS) corresponding to different levels of dilution and runoff. While attachment of bacteria on the solid particles did not take place, the decay rate coefficient, k (d−1), was found to depend on light intensity, I (W m−2), and duration of exposure to sunlight, T (h d−1), in a linear way (k = kD+ 0.03·I and k = kD+ 0.65·T, respectively) and on the concentration of TSS (mg L−1), in an inversely proportional exponential way (k = kD+ 14.57·e−0.02·[TSS]). The first-order inactivation rate coefficient in dark conditions, kD= 0.37 d−1, represents the effect of stresses other than light. This study suggests that given the sunlight conditions during an urban flood, and the concentration of indicator organisms and TSS, the above equations can give an estimate of the fate of selected pathogens, allowing rapid implementation of appropriate measures to mitigate public health risks. Electronic supplementary material The online version of this article (10.1007/s41742-018-0160-5) contains supplementary material, which is available to authorized users., Article Highlights It was demonstrated under controlled conditions that the inactivation of fecal indicator bacteria E. coli is higher under higher solar irradiance, longer duration of daylight and low TSS concentrations.The results indicate that under high TSS concentrations the bacteria, even if not attached on particles, are protected from photo-inactivation for a period of a few days, as the decay rate decreases exponentially with an increase in TSS concentration. Electronic supplementary material The online version of this article (10.1007/s41742-018-0160-5) contains supplementary material, which is available to authorized users.

Many coastal cities in developing countries are at the risk of flooding due to a progressive increase in the built-up areas and poor management of stormwater. The flooding situation in coastal cities gets accentuated further due to climate induced natural disasters such as cyclones and climate change induced sea-level rise that adversely impact the city’s natural drainage potential. This study developed a composite urban flood risk index consisting of three sub-indices and 20 key natural, physical, social, and economic influencing variables for a coastal city (i.e. Cuttack) in eastern India, the intensity of storm runoff being one among the many. The intensity-duration-frequency curve developed shows that the city can experience floods with a peak discharge of 1,320 cubic metre per second every alternate year for a rainfall intensity of 2-hour duration. The urban flood risk index computed for all the city wards shows that out of the 59 wards, only one ward has low flood risk (index value

Urban flooding is getting attention due to its adverse impact on urban lives in mega cities of the developing world particularly Pakistan. This study aims at finding a suitable methodology for mapping urban flooded areas to estimate urban flooding vulnerability risks in the cities of developing countries particularly Lahore, Pakistan. To detect the urban flooded vulnerability and risk areas due to natural disaster, GIS-based integrated Analytical Hierarchy Process (AHP) is applied for the case of Lahore, which is the second most populous city and capital of the Punjab, Pakistan. For the present research, the flood risk mapping is prepared by considering these significant physical factors like elevation, slope, and distribution of rainfall, land use, density of the drainage network, and soil type. Results show that the land use factor is the most significant to detect vulnerable areas near roads and commercial areas. For instance, this method of detection is 88%, 80% and 70% accurate for roads, commercial and residential areas. The methodology implemented in the present research can provide a practical tool and techniques to relevant policy and decision-makers authorities to prioritize and actions to mitigate flood risk and vulnerabilities and identify certain vulnerable urban areas, while formulating a methodology for future urban flood risk and vulnerability mitigation through an objectively simple and organizationally secure approach.