Climate change leads to more frequent and intense heavy rainfall events, posing significant challenges for urban stormwater management, particularly in rapidly urbanizing cities of developing countries with constrained infrastructure. However, the quantitative assessment of urban stormwater, encompassing both its volume and quality, in these regions is impeded due to the scarcity of observational data and resulting limited understanding of drainage system dynamics. This study aims to elucidate the present and projected states of urban flooding, with a specific emphasis on fecal and organic contamination caused by combined sewer overflow (CSO). Leveraging a hydrological model incorporating physical and biochemical processes validated against invaluable observational data, we undertake simulations to estimate discharge, flood volume, and concentrations of suspended solids (SS), Escherichia coli (E. coli), and chemical oxygen demand (COD) within the drainage channel network of Phnom Penh City, Cambodia. Alterations in flood volumes, and pollutant concentrations and loads in overflow under two representative concentration pathways (RCPs 4.5 and 8.5) for extreme rainfall events are projected. Furthermore, we employ a multi-criteria decision analysis (MCDA) framework to evaluate flood risk, incorporating diverse indicators encompassing physical, social, and ecological dimensions. Our results demonstrate the exacerbating effects of climate change on flood volumes, expansion of flooded areas, prolonged durations of inundation, elevated vulnerability index, and heightened susceptibility to pollutant contamination under both scenarios, underscoring increased risks of flooding and fecal contamination. Spatial analysis identifies specific zones exhibiting heightened vulnerability to flooding and climate change, suggesting priority zones for investment in flood mitigation measures. These findings provide crucial insights for urban planning and stormwater management in regions with limited drainage infrastructure, offering essential guidance for decision-making in locales facing similar challenges.

Drainage modeling that accurately captures urban storm inundation serves as the foundation for flood warning and drainage scheduling. In this paper, we proposed a novel coupling ideology that, by integrating 2D-1D and 1D-2D unidirectional processes, overcomes the drawback of the conventional unidirectional coupling approach that fails to properly represent the rainfall surface catchment dynamics, and provides more coherent hydrological implications compared to the bidirectional coupling concept. This paper first referred to a laboratory experimental case from the literature, applied and analyzed the coupling scheme proposed in this paper and the bidirectional coupling scheme that has been widely studied in recent years, compared the two coupling solutions in terms of the resulting accuracy and applicability, and discussed their respective strengths and weaknesses to validate the reliability of the proposed method. The verified proposed coupling scheme was then applied to the modeling of a real drainage system in a region of Nanjing, China, and the results proved that the coupling mechanism proposed in this study is of practical application value.

Urban flooding is recognized as a nature-driven disaster shaped by inherent factors such as climate, morphology, and hydrology, affecting vulnerability and flood exposure. While these factors play a paramount role, significant psychosocial intricate drivers are acknowledged, though they are challenging for prediction and assessment. This study delves into these drivers in a specific context, aiming to draw conclusions that extend beyond. It undertakes a comprehensive approach, integrating cloud-based Radar flood detection, analysis of flood causation patterns, and geostatistical analysis of a social survey based on cross-synthesis, contingency analysis, and structural equation modeling. In particular, we characterize the case of the coastal city of Tetouan in Morocco, which is representative in its environmental and socioeconomic settings to most cities in North Africa. It unraveled the nuanced interplay of psychosocial, economic, and territorial dynamics influencing flood exposure. The findings reveal how watershed location molds unique environmental exposures, steering nuanced, emotional, and behavioral responses among residents. Gender and education differentials reveal diverse perceptions and awareness of flood risks. Psychosocial intricacies come to the forefront, portraying education, income, and awareness as crucial mediators influencing cognitive and affective responses. Elevated education, increased income, and heightened awareness correlate with heightened perception and coping strategies. Findings reveal that risk perception significantly and differently influences risk acceptance, coping, and aversion through an array of identified key factors influencing coping strategies, mediating elements in flood damage relationships, and underscoring the pivotal role of perception in shaping responses to risk. Moreover, it found that lower risk acceptance leads to higher coping and aversion, and the latter positively affects coping, indicating that acceptance reduces the motivation to avoid the risk and decreases the willingness to adopt coping strategies to reduce the exposure. The outcomes carry critical implications for comprehending individual and collective social behaviors, informing strategies, and mitigating flood risk that apply at a wider context. It accentuates the inadequacy of relying solely on structural engineering for risk management, citing spatial constraints, misinformation, and lapses in prior-risk memory as compounding exposure challenges. This recognition catalyzes action, advocating tailored awareness campaigns, educational initiatives, and capacity-building programs, spotlighting the need for heightened individual profiles to enhance social understanding, engagement, and resilience. We anticipate profound insights, fostering a richer comprehension of urban flooding complexities and informing adaptive strategies on a broader scale.

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In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Although previous studies have posited that the high-density development of urban buildings and infrastructure contributes to urban flooding, empirical analyses and in-depth investigations into the interaction factors have remained limited. This study aims to analyze the influence and moderating effect of high-density development on urban flooding. Thus far, various land-use and interaction factors related to urban development density have been explored. Subsequently, the urban watershed was selected, utilizing panel data 2002 to 2017, and employing the Tobit model for analysis. The analysis revealed that high-density development had an adverse effect on urban flooding and that the runoff characteristics of high-density development were not limited to those of impervious surfaces. The horizontal and vertical aspects of dense buildings and structures acted as sub-watersheds that increased the time to reach peak flow. Moreover, high-density development had a moderating effect in low-lying areas. The results of this study underscore the necessity of urban disaster prevention planning to consider the direct and indirect effects, as well as the runoff characteristics, of high-density development on urban flooding.

The burgeoning significance of urban floods in the context of evolving climate dynamics and shifting rainfall patterns underscores the exigency for comprehensive investigation and mitigation strategies. The study employs a multi-criteria assessment (MCE) approach and the analytical hierarchy process (AHP) to evaluate flood-vulnerable zones, wards, and sub-category-wise flood locations in Greater Mumbai. The AHP technique is used to evaluate flood-vulnerable impacting parameters such as rainfall (29.42%), slope (20.96%), land use/land cover (17.52%), vicinity to sewers and storm-water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). The study area is classified under different vulnerable categories as severe vulnerable (46.72%), high to very high (18.74%), and slight to moderate (34.54%). Researchers analysed 234 waterlogged locations, revealing that 85.46% (200 locations) were in the severe to very high vulnerability category, and only 14.52% (34 locations) were in the other three categories. Flood locations are more affected by slope (under the categories of < 5 m and 5.01-10 m), built-up land, sewers and storm water drainage (< 125 m), natural drainage (< 250 m), rainfall (< 2000 to 2200 mm), lowest dense vegetation, and coastal alluvium in soils. These model-based flood vulnerability maps are crucial for planning flood conservation and mitigation measures.

Due to climate change and rapid urbanisation, many Norwegian cities and urban areas suffer from pluvial flooding caused by intense rainfall exceeding the capacity of the stormwater management system. This results in increased runoff rates, volumes and peak flows in the drainage network. In response to these challenges, the authors explore the potential of utilising the urban surface\'s ability to transport floodwater as an integral component of the stormwater infrastructure. When the capacity of the stormwater drainage system is exceeded, the overland flow paths transporting floodwater are considered a part of the stormwater management system, as floodways. The study proposes a spatial GIS method to map existing drainage lines and identify existing surface areas that function as floodways, combined with an automated process to identify which drainage lines could be implemented as stormwater management measures. Critical points are introduced to assess the floodways\' potential hazards, combined with a classification method to evaluate and sort floodways. A case study from Trondheim, Norway, was used to demonstrate how drainage lines can be identified as floodways using the proposed method. The case study is also used to illustrate how a GIS-based analysis can be extended from identifying to evaluating floodways and whether GIS is sufficient for floodway evaluation. The method enables urban planners and municipalities to identify which areas of the urban surface already function as floodways during extreme events, and to prioritise measures to secure such areas and increase the city\'s flood resilience. The results highlight the need to assess existing areas that function as floodways, and to implement and design needed areas as floodways. GIS-based methods combined with an evaluation scheme can be an adequate tool to map and evaluate floodways in urban areas. When using GIS-based methods, however, the corresponding hazard potential, and also the uncertainty of the floodway\'s spatial placement, should be considered.

In recent years, urban flooding has been a frequent occurrence, and seriously threatens the safety of lives and properties. Rational placement of distributed storage tanks is one of the effective ways to solve urban flooding, addressing stormwater management and rainwater reuse. However, existing optimization methods (such as genetic algorithm (GA) and other evolutionary algorithms) for determining the placement of storage tanks typically have a high computational burden; as such, they can be very time-consuming, and are not conducive to energy saving, carbon reduction and work efficiency improvements. In this study, a new approach and framework based on a resilience characteristic metric (RCM) and reduced modelling requirements are proposed. In this framework, the resilience characteristic metric, which is based on the linear superposition principle of system resilience metadata, is introduced, and a small number of simulations based on a coupling of MATLAB with SWMM are used to obtain the final placement scheme of storage tanks. The framework is demonstrated and verified with two cases in Beijing and Chizhou, China, and compared with a GA. The GA requires 2000 simulations for two cases (considering the placement of 2 and 6 tanks respectively), while the proposed method needs 44 simulations for the Beijing case and 89 simulations for the Chizhou case. The results show that the proposed approach is feasible and effective, and cannot only obtain a relative better placement scheme, but also considerably reduce computational time and energy consumption. It significantly improves the efficiency of determining the placement scheme of storage tanks. This method provides a new approach for the determining better storage tank placement schemes, and is useful for informing device placement in sustainable drainage systems.

Design rainfall is the basis for deriving design floods in areas where rainfall data are lacking and has a significant impact on the construction of water engineering facilities and municipal engineering designs. The Chicago rainfall pattern method has great applicability for urban short-duration design rainfall. In order to analyze the influence of design storm rainfall patterns on urban flooding, numerical models of hydrological and hydrodynamic processes were applied to simulate design rainfall with different recurrence periods and different rain peaks and were also used to compare and analyze the total amount of water accumulation and inundation extent by taking the central city of Zhoukou as an example. The results show that when the design rainfall recurrence period is less than 20 years, the total volume and inundation extent of waterlogging in design rainfall with a smaller peak ratio is larger. When the return period is greater than 20 years, the pattern is reversed. However, as the return period grows, the difference in peak inundation volume due to different peak rainfall amounts decreases. This study has certain guiding significance for urban flood forecasting and early warning efforts.