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Over the last two decades, urban floods and their impacts have been on the rise worldwide, owing to both climatic changes and human activities. The present study examines different at-risk elements, such as residential, commercial, and critical facilities, to evaluate their multi-components of vulnerability to urban floods in Peshawar, Pakistan. Based on the impacts of urban floods, the weightage of each component of the vulnerability for the selected elements at risk is defined. This study presents and uses the modified Fisher's ideal quantity index to combine the different vulnerability components into a single value. Additionally, the Patnaik and Narayan vulnerability index is employed to generalize sector-wise vulnerabilities across the study area. The results show that the old physical infrastructure of commercial and manufacturing units in the Kohati Gate area is highly vulnerable to urban floods, while the residential units are the least susceptible due to their distanced location from the drainage system. In Hayatabad, encroachments along the torrent's sides, affecting housing and educational institutions, contributed to increased vulnerability to urban floods, despite their relatively lower physical vulnerability. The study provides a new platform for understanding the multi-components of vulnerability to urban floods and tackling the challenges posed by urban floods effectively.

Abstract Urbanization has led to extensive pavement construction, which, coupled with improper drainage infrastructure, exacerbates urban flooding. Conventional bituminous pavements exacerbate flood risks due to their impermeability, leading to safety hazards and infrastructure damage. To address these challenges, this study investigates the efficacy of Porous Asphalt Concrete (PAC) as a sustainable solution for flood mitigation and pavement durability enhancement. Through laboratory evaluations and flood simulation analyses, the study compares PAC with conventional bituminous mixes in terms of structural, functional, and permeability characteristics. Results indicate that PAC exhibits superior permeability, facilitating effective stormwater management and reducing surface runoff by up to 40%. Furthermore, PAC demonstrates comparable or enhanced performance in Marshall Properties, including stability, flow, and air voids, when compared to conventional mixes. The incorporation of Induction Sensitive Fibers (ISF) enhances pavement durability and moisture resistance, leading to improved tensile strength and reduced abrasion. Binder performance analysis reveals that CRMB-55 exhibits superior stability and durability, making it a promising option for sustainable pavement construction. Flood simulation analyses using QSWAT software demonstrate the effectiveness of PAC in mitigating urban flooding, with notable reductions in surface runoff and discharge. Overall, this research underscores the potential of PAC as a viable solution for sustainable pavement construction, offering both flood mitigation benefits and enhanced pavement durability in urban environments. Graphical Abstract

Abstract Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1–10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments. HIGHLIGHTS The future climate change impact has been simulated using Markov chain Precipitation Generator.; The future land use land cover change has been generated using Markov chain-CA model.; The combined impact of climate and LULC change on urban hydrology using PCSWMM model for XII, IV&V stormwater zones of Greater Hyderabad Municipal Corporation was analyzed.;

This research seeks to analyze and compare the performance of impervious surface as control (O), sandy loam substrate, gravel, gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RGE), turf (T), and turf with geocell layer (TGE) in the reduction of rainfall-runoff volume, time to start runoff (TSR), runoff coefficient (C), time to end runoff (TER), peak flow (PF) rate, time to peak (TP), and time base (TB) under three conditions of rainfall, horizontal runoff, and the combination of rainfall and runoff in a slope of 5% using a rainfall simulator. Regarding the factorial results of the effective parameters of the test mode and the test sample on TSR, TER, TP, TB, C, and PF, there was a significant difference at the 5% level between the data in all cases. In all cases, GGE treatment performed better compared to the rest of the test groups in reducing runoff and cumulative volume. On the other hand, treatments O and GGE experienced the highest and lowest flow rate, respectively. For a given test condition, the value of C is the lowest for the GGE treatment. HIGHLIGHTS The simultaneous laboratory investigation of rainfall and horizontal runoff can be effective in estimating the parameters affecting the flood.; Implementation of geocell to cover gravel, rosemary, and turf can be effective in reducing floods.; GGE had the best performance in reducing floods and improving parameters affecting it.;

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

It is difficult to imagine the city of Cartagena de Indias, Colombia, the “Pearl of the Caribbean”, without water, as sandy beaches, lush mangrove forests, swampy landscapes and a maze of canals both surround the emblematic historic city centre and shape the expanding neighbourhoods in the so-called urban periphery. Water and the city are so closely intertwined that we introduce Cartagena as an Archipelago City to explore how urban residents navigate the city during seasonal urban rain and flooding. As climate change and rising sea level have become a pressing environmental challenge for coastal cities worldwide, we nuance mainstream discourses on governing and planning environmental futures by applying an actor-centred approach. We use navigation as an analytical lens, to provide insights into the way urbanites experience, anticipate and improvise around urban floods and imagine their city. Drawing on 13 months of ethnographic fieldwork in two distinct neighbourhoods, we dialectically explore how urban flooding slows down, disrupts and also speeds up Cartagena’s everyday life while its residents navigate their way through the Archipelago City. By arguing that these everyday experiences and imaginaries reflect the historical and social legacies of profound inequalities in the city, we are offering a window into the complex constituents of everyday social worlds that prevail in many Latin American coastal cities.

Establishing appropriate boundary conditions is essential for developing high-accuracy hydrodynamic models. However, this task is particularly challenging in topographically varying urban domains without monotonous slopes due to insufficient boundary information. This study investigates five different boundary conditions and establishes modeling practices of boundary conditions in pluvial urban flood modeling. A numerical test model within the city of Berlin is used, employing the 2D hydrodynamic finite element module of the open-source TELEMAC system. It performs unsteady simulations with nodal rainfall inputs for various precipitation scenarios, excluding infiltration. The results demonstrate that the suitability of boundary conditions is critically dependent on the surrounding topography. For boundary segments with a positive slope, a stage–discharge curve is found to outperform the other boundary conditions investigated in this study. Conversely, for segments with a negative slope, a closed wall boundary condition appears clearly preferable. Additionally, a drainage reservoir boundary condition performs effectively for more complex boundary segments but necessitates extensive preprocessing. Based on these insights, simulations were repeated with segment-tailored boundary conditions. The results indicate that this combined model outperforms the global application of each individual model.

Urbanization and climate change increasingly challenge urban water management. In this context, the design of stormwater drainage systems, which traditionally relies on historical rainfall records, is being questioned. Although significant efforts have been dedicated to optimizing drainage networks, the upgrading of existing systems remains understudied. This research devised a set of viable stormwater drainage networks, referencing the road network of the Sino-Singapore Tianjin Eco-City (data from Google Maps). On this basis, utilizing design rainfall data (sourced from the local meteorological center), an extensive array of scenario analyses was conducted. The investigation assessed the performance of implementing two redundancy-based interventions—introducing loops and enlarging pipe diameters—as well as the patterns of flood risk response, and by integrating a multi-objective optimization algorithm, this study proposes a framework for the optimization of grey infrastructure upgrades based on component replacement. The findings suggest that a precise deployment strategy for grey infrastructure is essential. The former improves the effective flow distribution of the drainage system, while the latter enhances its flow capacity, making each intervention suitable for drainage systems with a different degree of centralization. Further research shows that an integrated hybrid scheme brings significant flood risk improvement with strong applicability for most urban drainage systems. The upgrade model proposed in this study could be a valuable initiative, offering theoretical insights for the construction and development of resilient cities.

This study aims to help the management of the Stormwater Drainage System (SDS) of Bejaia City to manage urban flood problems, i.e., to provide them with tools for a better organization of information on SDS combined with a better optimization of its interventions on the network. Our study is based on a multicriteria analysis of the “SDS–inundation–Impact” system. We used a multicriteria approach and classified the overflow points called Black Points (BPs) using two methods: Analytic Hierarchy Process (AHP) and Failure Mode, Effect and criticality Analysis (FMEA). The criteria and the evaluation scale were defined on the basis of past observations, expert opinions, and feedback experience. The map of the past flooded areas was made and used to calibrate the two models. We mapped the BPs according to intervention priorities (one to four). The outcomes from both models are greatly comparable to the results of the impact assessment of past floods. The proposed approach can also reduce flood risks by integrating some of influencing factors (causing floodings) and the application can be adapted and implemented in other cities too. Both methods are reliable, particularly the AHP for the most overflowing BPs. They could be advantageously combined to improve decision-making.

The increase in extreme rainfall events due to climate change, combined with urbanisation, leads to increased risks to urban infrastructure and human life. Physically based urban flood models capable of producing water depth maps with sufficient spatial and temporal resolution are generally too slow for decision makers to react in time during an extreme event. We present a surrogate model with high temporal and spatial resolution for real-time prediction of water levels during a pluvial urban flood. We used machine learning techniques to achieve short computation times. The recursive approach used in this work combines convolutional and fully coupled multilayer architectures. The database for the machine learning was pre-simulated results from a physically based urban flood model. The forcing input of the prediction is precipitation and the output is water level maps with a temporal resolution of 5 min and a spatial resolution of 6 x 6 meters. The prediction performance can be considered promising for testing the model in real operational applications.

The study aims to analyze the vulnerability of floods and their impact in the Giulești-Sârbi area, the marginal northwestern neighborhood of Bucharest. The location of the accumulation lake has hydrological importance for the studied area. The anthropogenic dam is one of the susceptible sources of floods, but the most inefficiency of the sewage system is the actual principal cause, which frequently flows back when there are significant increases in rainfall. The motivation for choosing the study is represented by the vulnerability of the Giulești-Sârbi area and by the lack of a study on flood vulnerability and frequency in the area. A questionnaire was also applied locally, some of the results being presented in this paper. The main objectives of the study refer to the spatialization at the street level of the vulnerability of floods and their following impact. It also compares the vulnerability of floods after two key periods: before and after the construction of the dam, respectively 1980 and 2020 – for the comparison and evolution of terrain changes. The problem was also analyzed by the vulnerability related to the slope, land use and geological characteristics. The results show that the Giulești-Sârbi neighborhood is still susceptible to floods. The most affected streets are those from the southeastern part of the neighborhood and punctually to the NW. On the other hand, the application of the questionnaire shows that the southernmost street (Răsadniței Street) did not register problems as often as the current hazard maps from the rowater.ro source for which the highest vulnerability was expected to show. The areas indicating problems according to the questionnaires and Romanian Waters National Administration maps largely coincide with the results of the vulnerability maps resulting in this paper. The main solution that can reduce the effect of flood vulnerability is the modernization of the sewage system.

The occurrence of floods has always represented a challenge to cities, with long-term impacts that are usually neglected, such as the degradation of the urban environment and the impoverishment of the population due to recurrent events. Besides the losses usually computed in flood damage assessment, there are also secondary effects, but not less important, resulting from failures of critical infrastructures, such as power supply, which generate diseconomies to the system. Thus, power outage can aggravate the effects of floods, reducing income generation, and consequently the population’s recovery capacity. Going beyond the “domino effect” metaphor, in which the disruption of a system can cause a chain of similar events disrupting other systems, this paper aims to expand the concept of cascading effects by considering not only the spatial approach that affects other urban services, but also incorporating temporal analysis. The main purpose, in this case, is to identify regions in which flooding is a catalyst for social impoverishment. Through hydrological and hydraulic modeling, the cascading effects can be interpreted as a series of interconnected events that are triggered by flood events and aggravated by their recurrence, what make it difficult or even impossible to recover from losses. The main innovation of the approach is the proposition of a global index to assess the watershed recovery deficit considering a sequence of flood events. Moreover, the results highlight that although the direct impacts of floods are more significant, the impact of power shortage worsens the societal situation.