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Different stormwater management practices have been used to mitigate the impacts of urbanization and attenuate urban flooding risk. However, there is a lack of studies on the comparison between conventional stormwater control measures such as detention basins, their retrofits, and low impact development (LID) practices, especially from the perspective of their impacts on erosive flow attenuation and flood control. The aim of this study is to compare the effects of two detention basins and their outlet retrofits with LID, in an urbanized catchment under designed storm events with different return periods, using a Storm Water Management Model (SWMM) modeling approach. The results show that detention basins are effective in decreasing peak flow and delaying peak time. Detention basin retrofits could significantly reduce the frequency and duration of erosive flows in Sub A detention basin compared with LID, especially in smaller rainfall events. LID outperforms the detention basin retrofits in reducing peak flow from larger storms. [ABSTRACT FROM AUTHOR]

The evolution of surface conditions caused by urbanization has a great impact on urban flooding, whose increasing frequency of occurrence has severely affected urban safety. Most previous studies have focused on land use/cover change (LUCC) but have not specifically explored the effects of LUCC on the spatial distribution of flood points and the ponded depth. We combined the storm water management model (SWMM) with multiyear remote sensing data to simulate the flood points and ponded depth in different rainstorm types. Results showed that the ratios of flood points with ponded depths >0.7 m increased from 2.58% to 17.12% with the evolution of the urban surface conditions in 1988–2021. Also, the flood points with ponded depths >0.7 m were mainly located in the southern and northwestern parts of Bantian Street. These results can provide better-informed decision making for storm water management and early warning of flooding disasters in Shenzhen. [ABSTRACT FROM AUTHOR]

In recent years, urban flooding has emerged as a major challenge, with land cover change identified as a key contributing factor. This study investigates the sensitivity of urban flooding risk to land cover changes. Seven urban land cover maps from different years and five different rainfall events, were selected as the examples. Based on hydrodynamic model simulations, this study analyzed the relationship between the total area of urban flooding and the proportion of ponding depths across various depth intervals and the land cover change. The study region was divided into 41 sub-areas based on road classifications and building clusters. The urban flood risk considering the aggregation of urban flooding, maximum ponding depth, the extent of the ponded area, and the average ponding depth was quantified within these sub-regions. Additionally, ten characteristic points were extracted from two sub-area with significant risk changes to analyze the logic of urban flooding risk evolution under land use change. The results indicate that: (1) There is a positive correlation between the total area of urban flooding and the proportion of high ponding depths and increasing impervious surfaces. (2) Urbanization significantly increases urban flooding risk, with 28 out of 41 areas experiencing heightened risk, including 6 sub-areas with risk increases exceeding 100%. (3) When the rainfall event changes from a 20-year to a 100-year return period, the maximum ponding depth in cropland stabilizes compared to impervious surfaces. Conversion of cropland to impervious surfaces accelerates increases in ponding depth and can lead to higher maximum ponding depths. [ABSTRACT FROM AUTHOR]

Urbanization is known to increase the volume of stormwater runoff and peak flow rates, which leads to changes in the natural flow regime and increases the likelihood of flooding. Low-impact development (LID) practices seek to reduce runoff volume and peak flow and are generally considered to be a more sustainable solution for urban stormwater management. In this study, we present a systematic approach to address nuisance flooding issues in small cities and communities. As an application, the effectiveness of two LID practices, rain barrels and permeable pavements, were explored in mitigating the urban flooding problem of a highly urbanized small coastal watershed in Alabama, USA. The EPA Stormwater Management Model (SWMM) was first calibrated for water depth using data collected at multiple sites within the watershed during the 2014–2015 period. The calibrated model was then used to first identify the areas prone to flooding using design storms with 1, 2, 5-, 10-, 50-, and 100-year return periods. Floodplain maps were generated for those design storms with HEC-RAS. Next, LID options upstream of those flood-prone areas were assessed to potentially minimize the flooding risks. The results indicate that LID controls can have considerable benefits for stormwater management by reducing runoff volume (1–24%), peak flow rates (18–25%), and water depth (5–15%), potentially returning watersheds to their natural flow regimes, thereby minimizing the flooding risk in urbanized areas. However, the effectiveness of LIDs, especially for the runoff volume, quickly diminishes as the return periods of the storms increase. Rain barrels were identified as the most economical and effective LID within the drainage system. [ABSTRACT FROM AUTHOR]

Traditionally, urban stormwater infrastructure systems consist of passive infrastructure that is not actively controlled in response to rainfall events. Recently, real-time control (RTC) has been considered as a means to significantly increase the capacity and lifespan of these systems. This paper introduces the target flow control systems (TFCS) approach, which can use real-time control of systems of storages to achieve the desired flow conditions at the locations of interest. The first distinctive feature of this approach is that it does not require calibration to catchment-specific data, unlike existing approaches. This means that the TFCS approach is generally applicable to different catchments and is able to respond to future changes in runoff due to land use and/or climate change. The second distinctive feature is that the approach only requires storage-level information measured in real time with the aid of low-cost pressure sensors. This means that the approach is practical and relatively easy to implement. In addition to the introduction of the novel TFCS approach, a key innovation of this study is that the approach is tested on three case studies, each with different physical configurations and stormwater management objectives. Another key innovation is that the TFCS approach is compared to five RTC approaches, including three of the best-performing advanced approaches from the literature. Comparisons of multiple RTC approaches that consider both performance and practicality across multiple case studies are rare. Results show that the TFCS approach is the only one of the five control approaches analysed that has both the best overall performance and the highest level of practicality. The outcomes highlight the potential of the TFCS approach as a practical RTC approach that is applicable to a wide range of catchments with different stormwater management objectives. By maximizing the performance of existing stormwater storages, the TFCS approach can potentially extend the lifespan of existing infrastructure and avoid costly upgrades due to increased runoff caused by land use and climate change. [ABSTRACT FROM AUTHOR]

Flood forecasting is crucial for flood prevention and mitigation in both watersheds and urban areas. In watersheds, the development of flood forecasting methods is relatively mature, while their application in urban areas is still in the research and exploration stage. In practice, high-precision hydrological and hydrodynamic modeling during large floods is constrained to varying degrees by power, timeliness, and transmission conditions, whereas the unit hydrograph method is simple, efficient, has fewer constraints, and plays an important role. To address the current situation, this study introduces an improved synthetic unit hydrograph method to evaluate its practical effectiveness in watershed and urban flood forecasting. Firstly, based on the Snyder's synthetic unit hydrograph method, the flooding process in Muyangxi watershed in Fujian Province was calculated and compared with the results of the Xin'anjiang model and the measured data; secondly, based on the Snyder's synthetic unit hydrograph method, the urban synthetic unit hydrograph was established by taking into account the impermeability of the sub-surface and the slope, which was then applied to the calculation of the flooding process line in the urban area of Zhengzhou, and the differences of the simulation results between this method and the mainstream SWMM model were compared. The differences between this method and the mainstream SWMM model simulation results are compared. The results show that the synthetic unit hydrograph method is close to the measured and hydrological model results in the selected watersheds, and the forecast pass rate is 91.7%, with no peak-present time difference; compared with the SWMM model results, the relative error of the peak flow of the most floods is within 10%, and the coefficient of certainty of the floods in 72.7% is above 0.9. The example application shows that compared with the hydrological and hydrodynamic modeling methods, the partial rise and fall process of the flood calculated by the synthetic unit hydrograph method is better than the model results, which are more in line with the actual process, and its calculation is concise. The synthetic unit hydrograph method requires fewer parameters, and has strong implementation and popularity. The research further verifies the superiority of the synthetic unit hydrograph method in flood forecasting and provides an effective method for flood process calculation. [ABSTRACT FROM AUTHOR]

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development. [ABSTRACT FROM AUTHOR]

The concept of sponge cities is widely recognized, but there is still no research on what a new drainage system for sponge cities should look like. This study proposes a new drainage system for sponge cities, a sponge-type comprehensive pipe corridor rainwater chamber (SCRC) system, which combines a comprehensive pipe corridor with low-impact development measures (LIDs) into one system. The SCRC system is predicted by using a long- and short-term neural network to verify whether the neural network can be applied to the prediction of flooding in sponge cities. The results show that the SCRC system can effectively control sponge city flooding, where the surface runoff coefficient under different rainfall intensities (P = 1–10 yr) is between 0.273 and 0.44, the pipe overload time is between 0.11 and 3.929 h, and the node overflow volume is between 0 and 23.89 Mltr. The neural network has a high reliability in sponge city flood prediction, and the coefficients of determination R2 of the test set of PSO–LSTM prediction models are all above 0.95. This study may provide an idea for predicting flooding in sponge cities. [ABSTRACT FROM AUTHOR]

Because of its low‐lying location, urbanization, and inadequate infrastructure, Jakarta (Indonesia) has experienced an increase in annual flooding events, rising from an average of five significant floods per year in the 1990s to over 20 annually (2010–2020). With climate change exacerbating extreme weather events, Jakarta encounters escalating risks of flooding. Although the recurrent flooding is exacerbated by non‐point source (NPS) of pollution such as urban runoff and agricultural discharge that contribute to 40% of total pollutants leading to flood‐related issues in Jakarta, none has investigated this research gap. To reflect its novelty, this work explores the implications of climate change on the annual flooding in Jakarta by focusing on NPS and analyzes their impacts from social perspectives. This work also underscores the implications of flooding on livelihoods, health, and social cohesion in Jakarta. Focus group discussion with affected residents was used to shed light on the coping strategies employed in response to recurrent floods, ranging from community‐based initiatives to reliance on informal networks. The empirical findings show that the implications of flooding extend beyond physical damages. Displacement of communities, loss of livelihoods, disruption of essential services, and increased health risks are among the social impacts experienced by local residents. Vulnerable populations, including low‐income communities residing in informal settlements, bear their consequences. Economic losses from flooding amount to USD 500 million annually, impacting over 1 million residents. However, recent interventions have led to a 15% reduction in peak flood levels and a 20% reduction in flood duration in affected areas. Community resilience has also improved, with a 25% increase in flood insurance coverage and a 20% rise in community response initiatives. Overall, this study highlights that climate change exacerbates annual flooding in Jakarta, significantly impacting vulnerable communities through NPS pollution. Addressing the challenges requires integrated approaches combining effective pollution control, resilient infrastructure, and community engagement to mitigate social and long‐term environmental impacts. Practitioner Points: Climate‐induced flooding disproportionately affects vulnerable communities in Jakarta.Non‐point source pollution from urban runoff contributes to the severity of flooding in Jakarta.Waterborne diseases, disruption of livelihoods, and reduced access to clean water are major concerns identified in the study.The study highlights the importance of community‐based adaptation strategies to mitigate the impact of flooding and pollution. [ABSTRACT FROM AUTHOR]

The changing climate and rapid urbanization have resulted in an increase in urban floods. It is crucial to develop and improve flood management measures in urban areas to minimize losses. The sponge city strategy, which is based on the concept of Low Impact Development (LID), has proven effective in mitigating urban surface water floods and improving water management. However, there is still a need to determine the scope of flood improvement based on flood risk assessment and consider the cost-effectiveness of improvement measures. This study addresses this gap by developing a rapid cost–benefit assessment method for LID measures using cellular automata. The study area was Waterloo, and potential LID measures such as green roofs, permeable pavements, and bio-retention facilities were chosen. LID mitigation scenarios were generated based on the risk distribution of buildings. The CADDIES model, which is based on cellular automata, was used to simulate urban surface floods. The study found that the submerged area of flood water depth greater than 0.3 m increased logarithmically with the rainfall return period, while areas with depths greater than 0.6 m and 0.9 m increased linearly. Implementation of all three LID scenarios had the best effect on flood control, with scenario S3 showing the highest economic benefit and scenario S12 demonstrating the best investment return. When only the green roofs measure was implemented, the recommended renovation area accounted for 30%-50% of the total building area. If the green roofs measure was combined with the other two measures, the renovation area should be as small as possible. The study has shown that this method efficiently selects an investment strategy with favorable cost–benefit outcomes, thereby assisting decision-making in the planning of sponge city construction. This practical and effective approach offers scientific support for the sustainable management of urban floods. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent. [ABSTRACT FROM AUTHOR]

With urban expansion, traditional drainage systems in densely populated cities face significant challenges, leading to frequent flooding and pollution issues. Deep tunnel drainage systems emerge as an innovative approach, offering underground storage for excess precipitation and alleviating urban inundation. This research investigates the deployment of a deep tunnel system in Guangzhou's densely populated urban core. By integrating with existing networks, this system aims to curtail over-flow contamination and boost sewage-handling capacity. Successful implementation hinges on the thorough evaluation and synchronization with broader urban development objectives. In Guangzhou, where traditional methods fall short, deep tunnels present a viable option. This study explores techniques for identifying drainage deficiencies, devising enhancements, and refining citywide strategies. Economic analysis indicates that deep tunnels are more cost-effective than conventional drainage upgrades, offering long-term benefits for land conservation and drainage efficiency. Following implementation, these systems markedly enhance sewage management, diminish overflow incidents, and improve pollution mitigation. Although initial investments are substantial, the enduring advantages in land preservation and drainage efficiency are significant. Thus, deep tunnel systems emerge as a practical flood control solution for high-density urban areas like Guangzhou, fostering sustainable metropolitan growth. [ABSTRACT FROM AUTHOR]

Urban flooding is becoming more frequent and severe due to the impact of climate change, underscoring the urgent need for effective flood risk management. This study investigates the dynamics of flood risk through two decades, from 2000 to 2020, in New Orleans, United States—a city historically marked by catastrophic flooding events. This research also explores the spatial patterns of socially vulnerable neighborhoods at the census tract level and patterns that have changed over the two decades. The Modified Normalized Difference Water Index (MNDWI) was used to indirectly evaluate flood risks over time utilizing Landsat 5 and Landsat 8 satellite imagery and geospatial analyses. Thematic mapping and geospatial analysis were used to generate maps revealing neighborhoods at the intersection of high flood risk and social vulnerability in New Orleans. Integrating flood maps derived from satellite observations with Social Vulnerability Index (SVI) calculations provides a comprehensive view of flood dynamics in the context of social vulnerability in an urban setting. The final composite products provide insight into zones where past resilience-building and risk-reduction efforts have reduced vulnerability in New Orleans and identify zones requiring intervention. The findings demonstrate how integrated data-driven analysis can inform urban infrastructure and policy development, thereby promoting discussions on urban resilience and the nuanced understanding of interactions between urban settings and flood risks, potentially aiding in implementing adaptive strategies to build resilience in New Orleans. [ABSTRACT FROM AUTHOR]

Digital Elevation Models (DEMs) play a crucial role in flood management. This study aims to assess the effect of various global DEMs (GDEMs), including ALOS-12.5 m, ALOS-30 m, SRTM-30 m, SRTM-90 m, and NASADEM-30 m, on flood risk modeling in a densely urban area. The 1D-2D MIKE FLOOD hydraulic model was employed for the flood modeling. The process involved using a high-resolution DEM (Pleiades-1A 1 m) as the reference map (RM1), along with other GDEMs, to simulate a 50-year return period flood. The performance of GDEMs was then assessed in terms of flood inundation extent, flood hazard, and flood damage estimation, assessing their accuracy against the RM1. The study also explored the trade-offs between accuracy and efficiency by examining the effects of substituting the high-resolution map with a 5-m resolution map (Res_5 m) created through resampling. Results revealed that GDEMs tend to overestimate flood extent and underestimate depth, leading to inaccurate flood risk assessments. Among the GDEMs, NASADEM-30 and SRTM-30 outperformed others in simulating flood inundation extent but resulted in a more uniform flood depth distribution; approximately 70% of the flood extent was categorized with depths of less than 0.3 m, nearly double that of the RM1. This discrepancy led to an underestimation and overestimation of higher (H3-H6) and lower (H1) hazard levels by approximately 50%, respectively. Furthermore, GDEMs significantly overestimated flood damages, with NASADEM-30 showing a 161% overestimation compared to the RM1. Ultimately, the Res_5 m was a viable alternative for urban flood simulations as it led only to a modest 6% decrease in the flood zone area. [ABSTRACT FROM AUTHOR]

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model. [ABSTRACT FROM AUTHOR]

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Urban flooding is becoming more frequent and severe due to the impact of climate change, underscoring the urgent need for effective flood risk management. This study investigates the dynamics of flood risk through two decades, from 2000 to 2020, in New Orleans, United States—a city historically marked by catastrophic flooding events. This research also explores the spatial patterns of socially vulnerable neighborhoods at the census tract level and patterns that have changed over the two decades. The Modified Normalized Difference Water Index (MNDWI) was used to indirectly evaluate flood risks over time utilizing Landsat 5 and Landsat 8 satellite imagery and geospatial analyses. Thematic mapping and geospatial analysis were used to generate maps revealing neighborhoods at the intersection of high flood risk and social vulnerability in New Orleans. Integrating flood maps derived from satellite observations with Social Vulnerability Index (SVI) calculations provides a comprehensive view of flood dynamics in the context of social vulnerability in an urban setting. The final composite products provide insight into zones where past resilience-building and risk-reduction efforts have reduced vulnerability in New Orleans and identify zones requiring intervention. The findings demonstrate how integrated data-driven analysis can inform urban infrastructure and policy development, thereby promoting discussions on urban resilience and the nuanced understanding of interactions between urban settings and flood risks, potentially aiding in implementing adaptive strategies to build resilience in New Orleans.

Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency. HIGHLIGHTS The incorporation of spatial rainfall distribution into urban flood forecasting models is performed in this study.; Integration of attention mechanisms enhanced the identification of high-risk flood areas.; The developed model can predict rapid floods within seconds.;

The incidence of flooding is set to rise due to climate impacts in the coming years. Nigeria is one of the countries increasingly experiencing flooding. Its urban areas are expected to suffer more from the impacts of flooding due to the concentration of economic activities therein and projected population growth. To sustainably manage flood risks, there is a growing call to incorporate indigenous knowledge and practices in contemporary flood risk management. This work engaged with experts working in the field of flood risk management in public institutions to understand if indigenous knowledge and methods could positively inform modern flood risk management in Port Harcourt, a major flood prone Nigerian city. It finds that the applicability of indigenous knowledge in contemporary flood management in the research location is limited given the poorly managed transformation, growth, and evolution the city has experienced over time. However, some practice rooted in Indigenous knowledge and practices like planting of certain trees and mangrove species still have utility today. The government is encouraged to halt reclamations and conversions of wetlands and instead, seek ways of restoring and bringing back these important ecosystems given their natural role in flood mitigation and control. Developing urban forests can also play an integral role in managing rainwater runoff while improving the overall environmental quality.

Informal urban settlements represent a structural problem in Paraguay. They house about 20% of Asuncion’s population and are constantly threatened by urban flooding caused by the river regime and precipitations. The following article is a summary of the authors’ thesis work on the case of Santa Ana, a neighborhood located on a flood area of Asuncion. Paraguay’s Public Works Department offered two possible solutions to tackle floods, namely polder and hydraulic filling), which resulted incomprehensible to the residents of the area. This research work is intended to provide an analysis of both proposed solutions to flood risks as a contribution to decision making for those who live in informal urban settlements, spreading the advantages and disadvantages of these proposals. Providing a design solution is beyond the scope of this work. At the time of this research (2023), the population was about 7,344 inhabitants. The overall situation of the neighborhood was analyzed to determine the possible impacts of both solutions. Pros and cons were systematized. An overview of the impact and risks involved in both techniques was obtained, highlighting the importance of disclosing that information to the population for allowing conscious decision making.

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. HIGHLIGHTS A novel coupling approach by combining two unidirectional processes.; Comparison with bidirectional tight coupling scheme in terms of simulation accuracy and applicability.; The proposed coupling idea provides reliable outcomes with enhanced applicability.;

Suzhou City, located in the Yangtze River Delta in China, is prone to flooding due to a complex combination of natural factors, including its monsoon climate, low elevation, and tidally influenced position, as well as intensive human activities. The Large Encirclement Flood Control Project (LEFCP) was launched to cope with serious floods in the urban area. This project changed the spatiotemporal pattern of flood processes and caused spatial diversion of floods from the urban area to the outskirts of the city. Therefore, this study developed a distributed flood simulation model in order to understand this transition of flood processes. The results revealed that the LEFCP effectively protected the urban areas from floods, but the present scheduling schemes resulted in the spatial diversion of floods to the outskirts of the city. With rainstorm frequencies of 10.0% to 0.5%, the water level differences between two representative water level stations (Miduqiao (MDQ) and Fengqiao (FQ)) located inside and outside the LEFCP area, ranged from 0.75 m to 0.24 m and from 1.80 m to 1.58 m, respectively. In addition, the flood safety margin at MDQ and the duration with the water level exceeding the warning water level at FQ ranged from 0.95 m to 0.43 m and from 4 h to 22 h, respectively. Rational scheduling schemes for the hydraulic facilities of the LEFCP in extreme precipitation cases were developed according to flood simulations under seven scheduling scenarios. This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

The main objectives of this research are to identify (i) how women in Can Tho City experience the impacts of flooding on their health, income, household and personal finances; and (ii) limitations to women's participation in flood‐related planning activities. Qualitative data collection included a household survey, followed by field observation and in‐depth interviews of the affected women and other actors. Following a feminist framing, the research not only offers insight into the unique harms that women experience from floods, but also demonstrates that women have important knowledge and insights into flood‐related planning. The research found that women took on additional flood management roles in the home. Carrying out this work in polluted flood waters placed a physical burden on their bodies, and led to associated financial burdens related to health treatment and protective equipment. Moreover, the time dedicated to this labour resulted in a reduction in women's incomes. Their houses were also damaged by the floods, resulting in repair costs. Women in higher income of full‐time employment are dealing with the losses of income activity and housing, women in lower income or unemployment are dealing with the losses of personal expense and housing. Though women have a unique understanding of and relationship to flooding, government officials exclude them from local planning and training workshops about flooding risks. As a result, they have limited opportunities to use their knowledge to shape flood mitigation efforts. This research concludes that addressing vulnerability to future flooding must draw on Vietnamese women's knowledge and experiences. [ABSTRACT FROM AUTHOR]

Climate change causes erratic rainfall and often results in flooding of urban areas. Floods are hydrometeorological disasters that occur in various regions of Indonesia. Flood vulnerability in urban areas has increased over the past 30 years. Kauman Village is included in the Asri water catchment area, which has an Asri primary channel downstream of the urban area of Nganjuk District. However, from 2019 to 2022, the urban area of Nganjuk District, including Kauman Village, was affected by flooding. Urban floods inundated office areas, schools, housing, and public facilities such as the Nganjuk District General Hospital. The factor indicated as the cause of flooding is the clogging of the drainage channel with rubbish. Therefore, field research and mathematical calculations were conducted to evaluate the discharge capacity of drainage channels in the village. Based on the research, it was found that the existing drainage channel discharge in the research area could not accommodate the planned discharge for the 10-year return period. In addition, there are 33 channels that are unable to accommodate the planned discharge because the channel dimensions are too small, some channels are slightly damaged, sedimentation occurs, and they are blocked by rubbish. Drainage channels that do not function optimally affect urban flooding. Therefore, several efforts have been made to reduce the risk of flooding by changing the dimensions of drainage channels, normalizing drainage channels, and getting used to maintain drainage channels and not throwing rubbish in drainage channels. [ABSTRACT FROM AUTHOR]

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national‐scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo‐Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo‐Drain estimates of population and property value exposed to 1%‐ and 5%‐annual‐chance hazards diverge at finer scales of governance, for example, by 4‐ to 18‐fold at the municipal scale. FSF and PRIMo‐Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national‐scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models. Plain Language Summary: Flooding presents a significant risk to human activities and development, and its impacts have been rapidly increasing over recent decades. However, government flood mapping in the U.S. has not kept pace with adaptation needs, and communities have now turned to other sources of information to inform planning and design decisions. This study examines the uncertainties of flood hazard data available from the First Street Foundation across Los Angeles, California, the second largest city in the U.S. With a comparision to two different models that more fully capture processes known to affect urban flooding, we show concerning levels of uncertainty in the First Street Foundation data at scales of municipalities and properties. These results highlight the need for more robust validation of urban flood hazard models, and caution against overliance of First Street Foundation data for urban flood management. Key Points: Flood risks are concentrated in urban areas, where national‐scale hazard models are less accurateFlood exposure estimates become increasingly uncertain at finer scales and may misrepresent the social distribution of riskRefined and validated urban flood models are needed to effectively and equitably manage increasingly severe flood risks [ABSTRACT FROM AUTHOR]

The 'vulnerability' approach highlights institutional factors as the primary drivers of flooding risk. However, the intricate connections between institutional vulnerability and urban flood disaster risk have not been extensively explored until now. This study aims to address this gap by examining the case of the 7·20 heavy rainfall disaster in Zhengzhou. Employing a hybrid analysis methodology that integrates text coding, fault tree analysis and analytic hierarchy process methods, the study seeks to identify and assess the distinct contributions of various institutional factors and their interplay, culminating in ineffective urban flood risk management. In contrast to previous research findings that emphasize the determinant role of policy‐making and organizational coordination, this study demonstrates that inadequate legislative compliance constitutes the root cause determining the ineffectiveness of urban flood risk management. It is further exacerbated by insufficient policy attention, resulting in inadequate allocation of resources that ensure the legislative quality, risk coping skills and knowledge and stakeholders' coordination and collaboration. By emphasizing the significance of legislative compliance and policy attention, the study offers a fresh conceptual perspective to understand the factors influencing the efficiency of urban flood risk management. It provides valuable insights to develop targeted countermeasures for mitigating similar urban flood risks in the future. [ABSTRACT FROM AUTHOR]

Motivation: Recurring urban flooding in Bengaluru, India, has brought multiple intersecting development challenges to the forefront. While climate change is a catalyst for flooding, rapid urbanisation has aggravated the problem by neglecting its ecological history. Repeated floods have particularly affected migrants living in the slums, further worsening their already vulnerable conditions. Currently, only about 40% of slums are formally recognized by city authorities, leaving most slum dwellers with limited access to public benefits and basic infrastructure. Although the city offers piecemeal solutions, it currently lacks foresight for long‐term planning that includes marginalized voices. Purpose: We explore the multiple and intertwined development challenges faced by Bengaluru city, attempting to frame them from the perspective of migrant slum dwellers experiencing flooding. We try to bring to the forefront the everyday risks and vulnerabilities of the marginalized populations in Indian cities, which have received limited attention both in research and policy. The results of this exercise are intended to create sustainable collaborative processes to inform future decisions, particularly addressing the problem of urban flooding. Methods and approach: Our proposed methodology integrates climate risk assessment—urban flood modelling and exposure mapping of slums across the city—with vulnerability assessments at the household level including analysis of life histories to capture the relative vulnerabilities of slum dwellers and the slums in which they live. Findings: We deconstruct urban flooding, particularly from the perspective of migrant slum dwellers to identify some critical challenges, especially that of recognition, to foresight thinking. By incorporating marginalized voices, our methods aim to be inclusive and contextually relevant, while considering intersectional variations among those marginalized. A mixed‐methods approach allows climate risk assessment to be augmented by life histories of vulnerable slum populations to collaboratively reimagine a more inclusive future. Policy implications: To make policy more inclusive, more participatory processes are needed. The proposed methods will contextualize everyday vulnerabilities and risks of migrant slum dwellers to bring these perspectives into conventional climate risk assessment. Thus, a more inclusive future with lower impacts from urban floods can be envisaged. [ABSTRACT FROM AUTHOR]

Urbanization and climate change pose a critical challenge to stormwater management, particularly in rapidly developing cities. These cities experience increasingly impervious surfaces and more intense rainfall events. This study investigates the effectiveness of the existing drainage system in Lahore, Pakistan, a megacity challenged by rapid urbanization and the impacts of climate change. To address the lack of predefined storm patterns and limited historical rainfall records, we employed a well-established yet adaptable methodology. This methodology utilizes the log-Pearson type III (LPT-III) distribution and alternating block method (ABM) to create design hyetographs for various return periods. This study applied the stormwater management model (SWMM) to a representative community of 2.71 km2 to assess its drainage system capacity. Additionally, geographic information systems (GISs) were used for spatial analysis of flood risk mapping to identify flood-prone zones. The results indicate that the current drainage system, designed for a 2-year return period, is inadequate. For example, a 2-year storm produced a total flood volume of 0.07 million gallons, inundating approximately 60% of the study area. This study identified flood risk zones and highlighted the limitations of the system in handling future, more intense rainfall events. This study emphasizes the urgent need for infrastructure improvements to handle increased runoff volumes such as the integration of low-impact development practices. These nature-based solutions enhance infiltration, reduce runoff, and improve water quality, offering a sustainable approach to mitigating flood risks. Importantly, this study demonstrates that integrating LPT-III and ABM provides a robust and adaptable methodology for flood risk assessment. This approach is particularly effective in developing countries where data scarcity and diverse rainfall patterns may hinder traditional storm modeling techniques. Our findings reveal that the current drainage system is overwhelmed, with a 2-year storm exceeding its capacity resulting in extensive flooding, affecting over half of the area. The application of LPT-III and ABM improved the flood risk assessment by enabling the creation of more realistic design hyetographs for data-scarce regions, leading to more accurate identification of flood-prone areas. [ABSTRACT FROM AUTHOR]

Abstract Urban flooding is one of the significant issues that many cities are dealing with to ensure sustainable development. Upgrading the drainage systems is a standard measure that engineers have often used to reduce the risk of flooding. Because of climate change and urbanization, stormwater management systems may be inadequate to convey generated overflow in their catchment, which results in severe flooding in many cities worldwide. This research investigates climate change and urbanization's influence on urban flooding by simulating the EPA Stormwater Management Model for drainage systems in an urban catchment in An Ha, Tam Ky, Quang Nam. The results showed that urbanization shortens the initial abstraction ability while climate change increases extreme rainfall and water levels of receiving sources. Additionally, the impacts are even more substantial when the high urbanization rate increases by more than 70%, together with climate change.

Green computing is the current research hotspot; adapting the professional computing model to the low-power ARM architecture processor is a research trend. This article constructs a hydrological–hydrodynamic model by choosing SWMM and TELEMAC-2D as submodules, taking stormwater grates and inspection wells as water flow exchange nodes. The container technology was selected to couple the model on the ARM architecture processor, and the adapted coupling model is named the ARM hydrological–hydrodynamic model (AHM). To verify the reasonableness and accuracy of the model, taking the inner harbor of Macao Peninsula as the study area, a numerical simulation study on the evolution of waterlogging was carried out in various scenarios. The analysis showed that the maximum flow velocity was concentrated in the streets with low topography in the city center, and the areas of standing water were distributed in a point-like manner. Risk distribution maps during different storm recurrence periods were also constructed. Finally, the direct economic losses caused by Typhoon Mangkhut were calculated based on the model results and compared with the statistical values, with an error of only 2.3%, and the direct flooding losses in the inner harbor area under different rainfall scenarios were derived accordingly. HIGHLIGHTS A coupled urban hydrology and hydrodynamics model was constructed and adapted to a low-power ARM processor.; Numerical simulation studies on the evolution of waterlogging in Macao were carried out under various scenarios.; Risk distribution maps for the inner harbor area under different rainfall return periods were prepared, and flood-prone points were noted.; Predicting and projecting the direct loss of waterlogging in the inner harbor area under different rainfall scenarios.;

Over the past few years, numerous urban areas have been identified in floodplains and coastal regions. These areas should be repurposed as water storage zones to enhance surface water infiltration. The escalating demand for land in flat areas adds complexity to the susceptibility of urban areas to flood hazards. The observation focuses on understanding how land use change influences urban flood susceptibility assessment. Several aspects assumed to have a significant relationship with the flood phenomenon include the impact of land use change, environmental health impact, modification of land typology, explanation of urban flooding, appropriate model for flood-prone assessment, current state of research, appropriate steps in decision-making in susceptibility areas, and challenges of the scenario-based flood-prone mapping model in the future. Additionally, the assessment aspect should consider the impact of land degradation resulting from land use change. Integrated measures are necessary to guide future studies aimed at improving ecological quality and restoring environmental health. The availability of free and open-source datasets facilitates conducting studies to support decision-making both locally and regionally.

Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding under changing climates, this study coupled the downscaled general circulation model (GCM) projection with hydrologic-hydraulic modeling, with consideration of both the low-greenhouse gas (GHG) emission scenario of SSP1–2.6 and the high-GHG emission scenario of SSP5–8.5. Results demonstrated that the original GCM projection effectively captured the changing trend of rainfall patterns in the given area, with an overestimation of rainfall peaks. The GCM downscaling through the k-nearest neighbors (kNN)-based analog method significantly improved the model accuracy. Scenario analysis indicated that climate change significantly affected the regional hydrology, with the precipitation, surface runoff, and floods increasing by a maximum value of 17.10%, 12.66%, and 63.26%, respectively. The interannual comparison demonstrated that the temporal variability in precipitation and flood intensified with the increase in GHG emissions during 2025–2100, suggesting the uncertainty of long-term climate forecasts. According to flood risk analysis, long-term and short-term floods exhibit varied changing trends across climate scenarios, despite the strong correlations between precipitation and runoff, implying the complexity of flood generation mechanisms. The methods and findings herein provide valuable insights for policymakers and practitioners, to cope with the increasing urban flood risk within the context of evolving environment.

A comprehensive understanding of the processes of storm surge and flooding induced by typhoons is crucial for the development of effective emergency plans and risk management strategies. This study employed the three-dimensional coastal ocean circulation model FVCOM to simulate the storm surge in the Pearl River Estuary (PRE) during Typhoon Hato in 2017. In addition, the storm surge predictions are integrated with the InfoWorks 2D-1D hydrologic network drainage model to study urban flooding in the Inner Harbor district in Macao for the first time. Typhoon's wind and pressure fields were reconstructed based on parametric models, and numerical experiments were conducted to investigate the effects of tide-surge interaction. A thorough comparison between observed and simulated wind and water levels demonstrated a high degree of agreement. The results reveal that nonlinear tide-surge interactions are not uniformly distributed across the PRE. In the Lingdingyang waters, the impact of tide-surge interaction on the total water level ranges from 1% offshore to 30% upstream. In the coastal waters near Macao, this impact varies from 5% to 25%, with a peak impact of 25% observed near Hac Sa Bay in Macao, indicating a significant influence of tide-surge interaction in this area. Predicted city flood levels during HATO are well-validated against field data. The Inner Harbor of Macao is identified as the zone most severely affected by flooding induced by storm surge, with the highest flood depths recorded at its northern and southern ends.

In this study, we developed a novel approach for designing flash flood control measures in a contact zone between agricultural land and a built-up area using the hydrological component of the SIMWE model implemented in GRASS GIS as r.sim.water module. The inputs for the Monte Carlo simulation of water flow in this module included a high-resolution digital surface model and land cover data derived from airborne laser scanning data and aerial orthophotographs. The preferential flow feature of the module helped to better represent concentrated flows such as streams. The flash flood control measures were represented by a modified digital surface model and the probabilistic permeability parameter. The proposed flash flood control measures reduced water depths in critical zones by over 50% (10–40 cm), thereby lowering the risk of house basement flooding and enabling the sewerage system to manage locally generated water flow. Time series of high-resolution water depth rasters elucidate the dynamics of the phenomena and efficiency of the flood control measures. The proposed approach has the advantage of providing high spatial resolution coupled with the flexibility of GIS to evaluate various scenarios and flash flood control measures thus improving the urban planning process.

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events. [ABSTRACT FROM AUTHOR]