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Cities across the globe are prioritizing resilience in the wake of increasing climate change-related disasters. About 44% of these disasters are floods and their manifestation in cities is more pronounced, threatening urban social, ecological, and economic systems. This study draws on community resilience and participatory GIS, to examine land use vulnerability to flooding and local coping and adaptive strategies to achieve resilience. Using Ghana as a case study, the results show that participatory mapping offers community resilience benefits by providing context to community resilience challenges and potentials, enabling a deeper understanding of socio-environmental coupling that contributes to flood vulnerability and builds on community adaptive strategies through harnessing local community knowledge. We identified that topography, poor drainage and road network, rainfall variability, residents' land use practices, and land use planning conundrum drive disparities in land use vulnerability to flooding. Participants underscored the necessity of critical urban infrastructure in facilitating community adaptability to floods. The findings indicate that socio-spatial inequities threaten urban community resilience, especially in increasingly cosmopolitan urban contexts, by putting the marginalized urban population in a more vulnerable position. We recommend the prioritization of recognitional equity in community resilience planning efforts to allow for the targeting of resilient interventions that reflect and respect social differentiation in the urban environment so that outcomes will not exacerbate or generate new urban socio-spatial inequalities.

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Urban flooding poses a significant challenge to the rapidly growing Indian cities. Low-impact development strategies such as green roofs have shown the potential to reduce urban flooding. However, their performance assessment significantly varies across different studies. Therefore, the study's primary objective is to evaluate green roofs in the Indian context. For this evaluation, the green roofs are assessed based on building-level implementation scenarios for a high-density urban area in India for 25%,50%, and 75% application rates and different rainfall intensities (2,3 and 4-h duration and 2,5,10 and 25-year frequencies). Secondly, to probe the variations in the green roof performance across studies, uncertainty contributions to the runoff reduction from different parameters are quantified. The results show that green roofs can reduce up to 62% of flood volume and 24% of runoff. However, they are reasonably effective only beyond 25% application rates. Further, rainfall intensity contributes the most to the uncertainty of runoff reduction from green roofs. This uncertainty assessment implies that localized evaluation of green roofs depending on local rainfall conditions is required for city-wide policy planning. The study has a significant contribution to building confidence in the ability of green roofs to reduce urban floods in the context of developing countries like India.

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Storm Water Management Model (SWMM) developed by the United States Environmental Protection Agency (EPA) has been widely applied throughout the world for analysis associated with stormwater runoff, combined sewers, and other drainage facilities. To appropriately manage the runoff in urban areas, an integrated system including the simulations of sewer flow, street flow, and regional channel flow, called the 1D/1D SWMM model, was advocated to be performed. Nevertheless, the execution efficiency of this integrated system still needs to be promoted to meet the demand for real-time forecasting of urban floods. The objective of this study is to seek an alternative for predicting water levels both in the sewer system and on the streets within an urban district during rainstorms by utilizing a dynamic neuron network model. To strengthen the physical structure of the artificial intelligence (AI) model and simultaneously make up for the lack of measured data, simulation results of the 1D/1D SWMM model are provided as labels for the training of the proposed model. The novelty of this research is to propose a new methodology to effectively train the AI model for predicting the spatial distributions of depths based on the hydrologic conditions, geomorphologic properties, as well as the network relation of the drainage system. A two-stage training procedure is proposed in this study to consider more possible inundation conditions in both sewer and street (open channel) drainage networks. The research findings show that the proposed methodology is capable of reaching satisfactory accuracy and assisting the numerical-based SWMM model for real-time warning of drainage systems in the urban district.

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Droughts and floods are weather-related hazards affecting cities in all climate zones and causing human deaths and material losses on all inhabited continents. The aim of this article is to review, analyse and discuss in detail the problems faced by urban ecosystems due to water surplus and scarcity, as well as the need of adaptation to climate change taking into account the legislation, current challenges and knowledge gaps. The literature review indicated that urban floods are much more recognised than urban droughts. Amongst floods, flash floods are currently the most challenging, which by their nature are difficult to monitor. Research and adaptation measures related to water-released hazards use cutting-edge technologies for risk assessment, decision support systems, or early warning systems, among others, but in all areas knowledge gaps for urban droughts are evident. Increasing urban retention and introducing Low Impact Development and Nature-based Solutions is a remedy for both droughts and floods in cities. There is the need to integrate flood and drought disaster risk reduction strategies and creating a holistic approach.

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Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels.

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This study introduces a new approach for the investigation of infections after an accidental ingestion of contaminated floodwater. The concept of Expected Annual Probability of Infection (EAPI) is introduced and implemented in an infection risk-model approach, by combining a Quantitative Microbial Risk Assessment (QMRA) with the four steps in flood risk assessment. Two groups and exposure paths are considered: adults wading in floodwater and small children swimming/playing in floodwater. The study area is located in Ghana, West Africa. Even though Ghana is one of the most urbanized countries in Africa it has significant problems with water resources management and public health. While cholera is classified as endemic in Accra, the natural and human-made characteristics of the capital makes it prone to flooding. The results of the EAPI approach show that on one hand the concentration of pathogens in floodwater, and thus the risk of infection, decreases with the increase of the flood magnitude. On the other hand, larger floods can spread the pathogens further from the point source, threatening populations previously not identified as at risk by small-scale floods. The concept of EAPI is demonstrated for cholera but it can be extended to other waterborne diseases and also different pathways of exposure, requiring minimal adaptations. For future applications, better estimation of EAPI key components and improvement points are discussed and recommendations given for all the assessment steps.

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The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.

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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.

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This paper presents the impact of the choice of building representation techniques and hydrodynamic models on urban flood simulations using HEC-RAS 2-D for the Toce River physical model. To this end, eight numerical models based on previous laboratory experiments were prepared to simulate unsteady urban flooding on each side of building units. Two simplified building layouts (aligned and staggered) were examined, where models were prepared for two different building representation techniques: Building Block (BB) and Building Resistance (BR). Water depth variation computations using the BR and BB techniques were compared to the laboratory measurements and previous studies in the literature. A statistical analysis was performed using both the Root Mean Square Error (RMSE) and the Pearson Product-Moment Correlation Coefficient (PPMCC) in order to evaluate the performance of the models. A sensitivity analysis showed that the proper mesh resolution and model parameter values were obtained. As far as the BR technique is concerned, it is well-suited for representing building units in numerical simulations using high Manning coefficients. Furthermore, this study confirms the importance of the BR technique, which should help researchers in using low-resolution Digital Elevation Models (DEMs) along with open-source programs. Moreover, the study aims to produce a deeper comprehension of numerical modeling and urban flooding.

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As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.

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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.

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A better understanding of the effects of different urban and recreational surfaces on the die-off of water-borne pathogens that can cause infections after urban floods if released from surcharged combined sewers and other sources of fecal contamination is needed. The die-off of fecal indicator Escherichia coli was studied under controlled exposure to simulated sunlight on a range of different surfaces found in urban environments: gravel, sand, asphalt, pavement blocks, concrete, playground rubber tiles and grass, using glass as control. The surfaces were inoculated with artificial flooding water containing 105 colony forming units (CFU) of E. coli per mL and sampled periodically using the sterile cotton swab technique, after lowering the water level. The results show that dark inactivation was not statistically significant for any surface, suggesting that chemical composition and pH (varying between 6.5 ±â€¯0.8 and 9.2 ±â€¯0.4) did not affect the die-off rates. The highest light-induced die-off rates for E. coli after the floodwater recession, observed on rubber (>3.46 h-1) and asphalt (2.7 h-1), were attributed to temperature stress and loss of surface moisture.

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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 = k D+ 0.03·I and k = k D+ 0.65·T, respectively) and on the concentration of TSS (mg L-1), in an inversely proportional exponential way (k = k D+ 14.57·e-0.02·[TSS] ). The first-order inactivation rate coefficient in dark conditions, k D= 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.

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The objective of this paper is to investigate the respective influence of various urban pattern characteristics on inundation flow. A set of 2000 synthetic urban patterns were generated using an urban procedural model providing locations and shapes of streets and buildings over a square domain of 1×1km2. Steady two-dimensional hydraulic computations were performed over the 2000 urban patterns with identical hydraulic boundary conditions. To run such a large amount of simulations, the computational efficiency of the hydraulic model was improved by using an anisotropic porosity model. This model computes on relatively coarse computational cells, but preserves information from the detailed topographic data through porosity parameters. Relationships between urban characteristics and the computed inundation water depths have been based on multiple linear regressions. Finally, a simple mechanistic model based on two district-scale porosity parameters, combining several urban characteristics, is shown to capture satisfactorily the influence of urban characteristics on inundation water depths. The findings of this study give guidelines for more flood-resilient urban planning.

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RESUMO: As inundações urbanas estão se tornando cada vez mais frequentes, de tal forma que a União Europeia publicou a Diretiva 2007/60/CE no sentido de mitigar as consequências relacionadas com este fenômeno e de impor limites à concentração de poluentes nas águas pluviais. O objetivo deste artigo foi apresentar uma modelação dos sistemas de drenagem em situação de inundações, baseada no software da United States Environmental Protection Agency (EPA), Storm Water Management Model (SWMM), em que o estudo de caso é analisar um sistema de drenagem urbana unitário, inserido na Zona Central de Coimbra, em Portugal. Recorrendo à metodologia Automatic Overland Flow Delineation (AOFD) para a geração de uma rede de drenagem superficial e implementando-a no SWMM, obteve-se um modelo de drenagem dual, que permite a análise do escoamento em situação de inundações, incluindo o controle de escoamento entre a superfície e a rede de coletores e a modelação da qualidade da água à superfície. Este modelo permite quantificar a carga poluente à superfície, relativamente ao parâmetro de sólidos suspensos totais, para um evento de precipitação extrema.

ABSTRACT: Urban floods are becoming more frequent. Thus, the European Union published the Directive 2007/60/CE to mitigate costs related with this phenomenon and to impose limits on the concentration of pollutants in pluvial water. The purpose of this study was to present the modelling of drainage systems in flood situations, based on the software of the United States Environmental Protection Agency (EPA), the Storm Water Management Model (SWMM), in which the study case is to analyze a unitary urban drainage system of Zona Central catchment in Coimbra, Portugal. By using the Automatic Overland Flow Delineation (AOFD) methodology for the generation of a superficial drainage network and implementing it in the SWMM, it was obtained a dual drainage model that allows the flow analysis in flood conditions, including flow control between the surface and collectors network and also modeling of water quality at the surface. This model allows quantifying the pollution load at the surface, relatively to the parameter of total suspended solids, for any extreme rainfall event.