ABSTRACT Maintaining the robustness of emergency response networks (ERNs) between diverse stakeholders is critical to ensure effective emergency response during urban floods. However, most of the previous studies on ERNs have mainly concentrated on either the robustness evaluation of ideal ERNs in emergency plans prior to disasters or the modelling of actual ERNs during disasters. Less attention has been paid to improving robustness of actual ERNs during disasters. To overcome the robustness reduction of actual ERNs caused by the malfunction of stakeholders during urban floods, an enforced emergency collaboration strategy is proposed and validated in case study of ERNs during the 7·20 flood in Zhengzhou, Henan province of China. The robustness of the ERNs with different application time of the proposed strategy is investigated to verify the proposed strategy. Moreover, taking the ideal ERN in the urban flood emergency plan of Zhengzhou, Henan province of China as the blueprint, the proposed strategy is further validated by stochastic evolutionary simulation. The results show that the proposed strategy can effectively enhance robustness of ERNs. This study is helpful for achieving more effective and efficient emergency responses during urban floods.

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks.

ABSTRACT Physics‐based flood hydrodynamic models are widely used for predicting inundation in urban basins with complex building layouts. While the treatment of urban buildings in these models has been extensively discussed, over‐assumptions can introduce inaccuracies, uncertainties, and excessive computational effort, particularly under data‐scarce conditions. This study proposes a simple yet effective method, the Building Coverage Ratio (BCR) scheme, to account for building effects in city‐scale urban inundation modeling. The BCR scheme quantifies water abstraction to generate surface runoffs in densely built‐up areas by dynamically adjusting drainage and infiltration volumes based on the proportion of building footprint in each grid cell. This approach improves the accuracy of urban flood predictions when high‐resolution data is unavailable. Validated against a historical rainstorm event in Zhuhai, China, the BCR scheme demonstrated its capability to efficiently and accurately reproduce spatiotemporal inundation patterns. The method significantly improved street‐level flooding simulations, which are often underestimated in traditional approaches that neglect building effects. Results show that simulation accuracy increases from 33% without treatment to over 85% when the BCR scheme was applied to 30 m‐resolution Digital Elevation Model (DEM). As the method relies entirely on open‐source datasets, it offers a practical and transferable solution for urban flood prediction in data‐scarce regions.

Rainstorm floods constitute one of the major natural hazards threatening the safe and stable operation of power grid facilities. Constructing a rapid and accurate prediction model is of great significance in order to enhance the disaster prevention capacity of the power grid. This study proposes a rapid prediction model for urban rainstorm flood targeting power grid facilities based on deep learning. The model utilizes computational results of high-precision mechanism models as data-driven input and adopts a dual-branch prediction architecture of space and time: the spatial prediction module employs a multi-layer perceptron (MLP), and the temporal prediction module integrates convolutional neural network (CNN), long short-term memory network (LSTM), and attention mechanism (ATT). The constructed water dynamics model of the right bank of Liangshui River in Fengtai District of Beijing has been verified to be reliable in the simulation of the July 2023 (“23·7”) extreme rainstorm event in Beijing (the July 2023 event), which provides high-quality training and validation data for the deep learning-based surrogate model (SM model). Compared with traditional high-precision mechanism models, the SM model shows distinctive advantages: the R2 value of the overall inundation water depth prediction of the spatial prediction module reaches 0.9939, and the average absolute error of water depth is 0.013 m; the R2 values of temporal water depth processes prediction at all substations made by the temporal prediction module are all higher than 0.92. Only by inputting rainfall data can the water depth at power grid facilities be output within seconds, providing an effective tool for rapid assessment of flood risks to power grid facilities. In a word, the main contribution of this study lies in the proposal of the SM model driven by the high-precision mechanism model. This model, through a dual-branch module in both space and time, has achieved second-level high-precision prediction from rainfall input to water depth output in scenarios where the power grid is at risk of flooding for the first time, providing an expandable method for real-time simulation of complex physical processes.

Stress-testing the cascading economic impacts of urban flooding across 306 Chinese cities

Microplastics pollution in urban freshwater sediments: A descriptive assessment of land-use categories.

The study aimed to systematically assess community-level risk perceptions and informal resilience capacities concerning urban fluvial hazards within Peshawar, Pakistan. The research addresses the global acceleration of urban flood hazards, a phenomenon increased by unregulated urban expansion and anthropogenic climate change. Methodologically, the study adopted a qualitative inquiry and the study was framed in an Interpretive Phenomenological Approach, utilizing the Socio-Ecological Systems (SES) framework as its theoretical construct. The SES framework operationalizes the local context by integrating the core components of risk (hazard, vulnerability, and exposure) and resilience (defined by anticipatory, adaptive, and restorative capacities) within the paradigm of the human-environment relationship. Data collection employed a multi-modal strategy including nine Focus Group Discussions (FGDs), 15 Key Informant Interviews (KIIs), and five In-Depth Interviews (IDIs), all conducted via purposive sampling. The empirical data reveal that local conceptualizations of urban flooding are primarily attributed to shifts in precipitation regimes and a spectrum of anthropogenic interventions. These interventions include uncontrolled informal settlements (encroachment), faulty urbanization, elevated groundwater tables and deficiencies in critical infrastructure, with all factors being aggravated by pervasive governance deficits. The resultant vulnerability is characterized as multidimensional vulnerabilities extending across socio-economic, physical, environmental and motivational axes. Parallel to it, communities demonstrate emergent resilience mechanisms, specifically manifesting as self-organized early warning systems and adaptive structural modifications such as elevated building plinths. The study suggests that effective urban flood risk management necessitates a paradigm shift from the silos-based top-down governance model toward a holistic, risk-informed urban planning framework. Such transition requires support from institutional reforms and formalized community engagement to effectively use indigenous knowledge and local capacities, thereby adding the system’s inherent capacity to absorb, adapt and transform in response to hydrometeorological stressors.

Abstract. Urban areas are increasingly experiencing more frequent and intense pluvial flooding due to the combined effects of climate change and rapid urbanization – a trend expected to continue in the coming decades. This highlights the growing need for effective flood forecasting and disaster management systems. While recent advances in GPU computing have made high-resolution hydrodynamic modeling feasible at the urban scale, operational use remains limited, particularly for large domains where single-GPU processing falls short in terms of memory and performance. This study demonstrates the capabilities of the hydrodynamic model RIM2D (Rapid Inundation Model 2D), enhanced with multi-GPU processing, to perform high-resolution pluvial flood simulations across large urban domains such as the whole state of Berlin (891.8 km2) within operationally relevant timeframes. We evaluate RIM2D’s performance across spatial resolutions of 2, 5, and 10 m using GPU configurations ranging from 1 to 8 units. Two flood scenarios are analyzed: the real-world pluvial flood of June 2017 and a standardized 100-year return period (HQ100) event used for official hazard mapping. Results show that RIM2D can deliver detailed flood extents, flow characteristics, and impact estimates for the 48 h 2017 event in 8 min at 10 m resolution, 34 min at 5 m, and approximately 5.5 h at 2 m using 8 A100 GPUs – fast enough to be integrated into real-time early warning systems. Multi-GPU processing proves essential not only for enabling high-resolution simulations (e.g., dx= 2 m or finer), but also for making simulations at resolutions finer than 5 m computationally feasible for flood forecasting and early warning applications. Additionally, we find that beyond 4 GPUs, runtime improvements become marginal for 5 and 10 m resolutions, and similarly, more than 6 GPUs offer limited benefit at dx= 2 m resolution, illustrating the balance between computational nodes of the used GPUs and number of raster cells of the model. Moreover, simulations at a finer dx= 1 m resolution demand more than 8 GPUs to be run. Overall, this work demonstrates that large-scale, high-resolution flood simulations can now be executed rapidly enough to support operational early warning and impact-based forecasting. With models like RIM2D and the continued advancement of GPU hardware, the integration of detailed, real-time flood forecasting into urban flood risk management is both technically feasible and urgently needed.

This study aims to develop an Intensity–Duration–Frequency (IDF) curve for Pontianak City using short-duration rainfall data recorded at the BRIN automatic weather station from 2014 to 2022. The RAPS consistency test confirmed that the data were statistically valid and suitable for hydrological analysis. Frequency analysis was conducted using several probability distributions, including Normal, Gumbel Type I, Log Pearson Type III, and Log-Normal (2- and 3-parameter), with the Normal distribution selected as the best fit based on goodness-of-fit and chi-square tests. Rainfall intensities were then calculated using the Mononobe method, with a locally calibrated sensitivity parameter m = 0.846. The resulting intensity equations for return periods of 2, 5, 10, 20, 50, and 100 years follow the exponential form I = a⋅t−0.846, where I is the rainfall intensity (mm/hour), t is the duration (minutes), and a is a return period–specific coefficient. The IDF curves demonstrate an inverse relationship between intensity and duration, confirming expected tropical rainfall behavior. These results provide a reliable tool for urban drainage design and flood risk assessment in Pontianak City

Floods are one of the most common natural disasters and can occur anywhere in the world. In China, with climate change and urbanization leading to an incerase in the frequency and intensity of extreme rainfall, the damage caused by urban flooding is becoming more severe. Flood risk assessment is an important tool for flood prevention and mitigation, and has important practical applications in urban flood risk management and response. In this paper, a new multi-criteria decision analysis (MCDA) model for urban flood risk assessment was proposed, which integrates the game theoretic (GT) combination of AHP-CRITIC subjective and objective assignment methods to determine the weights of the indicators. Based on the hazard-vulnerability as the flood risk assessment system, a total of 17 representative indicator factors were selected to explore the distribution of flood risk in the main urban area of Jinan City. The results show that the very high risk zones were mainly distributed in the border area of four districts: Lixia District, Tianqiao District, Huaiyin District and Shizhong District. The collection of historical flood-prone point data to verify the assessment results shows that the GT method assigns weight values with comprehensive consideration of the impact of subjective and objective assignments, which verifies the reliability of the method, and thus the flood risk zoning maps obtained are more reasonable and reliable. The game-theoretic combination assignment method can reduce the subjectivity of a single assignment method, improve the accuracy of flood risk assessment, and provide a basis for urban flood risk management.

Hybrid adaptation to urban riverine floods: a cost-benefit analysis in Vilanova i la Geltrú (Spain)

A multi-objective optimization framework for urban flood mitigation using machine learning and optimization algorithms.

Field-theory inspired physics-informed graph neural network for reliable traffic flow prediction under urban flooding

Emerging Insights into Low Impact Development (LID) Strategy for Urban Flood Resilience under Climate Change

Evaluating blue–green Infrastructure for urban flood mitigation and sustainable development: a case study in the Acari River Watershed, Rio de Janeiro

A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems.

Efficient urban flood surface reconstruction: integrating deep learning with hydraulic principles for sparse observations

Modeling urban flood susceptibility and identifying key flood-inducing factor chains using Bayesian network

Nuisance flood risk: Defining a new horizon in urban flood risk management through hydrodynamic flood hazard modelling and indicator-based vulnerability assessment

Neighborhood-scale assessment of urban flood impacts on transportation network resilience: A case study of Mavişehir, İzmir