Search Results (195)

This study aims to deepen the understanding of the spatiotemporal evolution of the Extreme Precipitation Index in the YRD, evaluating the comprehensive flood disaster risk across the region. Existing studies have rarely incorporated the frequency, duration, and intensity of extreme precipitation events as indicators of the hazard of causative factors into comprehensive flood risk assessments. Using daily precipitation records from 107 national meteorological stations spanning 1960–2024, this study employs four extreme precipitation indices recommended by the ETCCDI (PREPTOT, CWD, R95p, and Rx1day) to examine the spatiotemporal characteristics of extreme precipitation in the YRD, one of China’s most densely populated and economically significant regions. Furthermore, a comprehensive flood risk assessment framework encompassing the hazard of causative factors, the sensitivity of disaster-forming environments, the vulnerability of disaster-affected entities, and disaster prevention capabilities is constructed. Based on this framework, an integrated Analytic Hierarchy Process–Entropy Weight method is adopted to evaluate Flood Risks in the YRD. The results showed that: (1) during the period 1960—2024, only the Extreme Precipitation Index (R95p) exhibited a significant upward trend, increasing at a rate of 1.7 mm/10a, whereas PREPTOT, CWD, and Rx1day showed slight declining trends. Nevertheless, all four indices displayed pronounced oscillatory characteristics, characterized by recurring “decrease—increase” cycles over time. (2) In terms of spatial distribution, PREPTOT and R95p exhibited a clear south-to-north gradient pattern, while CWD and Rx1day demonstrated a multicentric distribution. This pattern highlights the combined influence of typhoon landfall frequency and topographic conditions on extreme precipitation across the southern YRD. (3) The Flood Risks in the YRD exhibited a distinct spatial pattern characterized by higher risk levels in the east than in the west and in the south than in the north. Areas classified as moderate-to-high risk accounted for 48.6% of the total study area, with high-risk zones primarily concentrated in the Shanghai—Hangzhou—Ningbo corridor. These findings suggest that Flood Risks in the YRD are not driven by a single factor; rather, they result from the complex interactions among extreme precipitation, topographic and geomorphological conditions, levels of social exposure, and regional buffering capacities. Consequently, under the increasingly frequent occurrence of extreme precipitation events, flood management strategies that rely predominantly on static engineering measures are becoming insufficient. Greater emphasis should therefore be placed on enhancing the resilience of urban lifeline infrastructure, improving high-resolution forecasting and early-warning capabilities for extreme precipitation, and establishing dynamic warning-release mechanisms based on risk thresholds. Such measures are essential for effectively mitigating regional flood risks. Full article

Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. In this work, a physics-informed training framework is developed for CNN-LSTM models that predict urban flood depths at 15 min intervals over a $128\times 128$ spatial grid. Three differentiable penalty terms are embedded directly into the loss function: (i) a gravity loss that penalizes depth increases against the water-surface-elevation gradient, (ii) a continuity loss enforcing local mass conservation with rainfall-adaptive thresholds, and (iii) a topography-aware false-alarm penalty modulated by the topographic wetness index (TWI). The framework is evaluated on the Norfolk, Virginia, flood dataset spanning two major storm events (August 2017 and September 2022) comprising 300 samples, with all variants trained on identical splits and robustness assessed over repeated random splits and leave-one-storm-out tests. A road-proximal evaluation restricted to a TWI-derived street mask quantifies street-level skill. The physics-constrained model achieves near-zero gravity violations (∼10⁻⁶) and the highest street-channel recall (0.77 ± 0.09 versus 0.44 ± 0.10 for the unconstrained baseline), the capability most relevant to downstream traffic routing, and its recall advantage more than doubles on a held-out storm, while a uniform false-alarm variant attains 16% lower mean absolute error but suppresses street recall to 0.25. The proposed TWI-modulated penalty reconciles this trade-off: it improves upon the uniform variant on every metric measured, recovering 60% higher street recall at the lowest MAE among all constrained variants and the best street-level F1 score. These results expose a fundamental tension between aggregate pixel-level error metrics and application-specific physical plausibility, and demonstrate that terrain-aware loss modulation offers a principled resolution. Full article

The paper addresses the intersection between modern built heritage preservation and Climate-Aided Design (CADe) processes in fragile coastal Mediterranean contexts. The study focuses on the city of Beirut in Lebanon, part of the Eastern Mediterranean and Middle East (EMME) region and considered a climate change hotspot facing extreme challenges. Rapid urbanization and socio-political instability, especially during the twentieth century, have undermined the city’s ability to mitigate and adapt to future climate change scenarios. Moreover, Beirut’s modern built heritage faces a constant threat of demolition due to the absence of protective legislation, compounded by aggressive real-estate development ambitions. The hypothesis is that the integration of climatic data and regenerative design with modern cultural heritage classification frameworks can aid the preservation process, drive a more adaptive and inclusive approach to urban regeneration, and inform legislative integration of climate adaptation in conservation frameworks. To test this hypothesis, a multi-scalar case-study-based methodology is adopted using a combination of digital tools to assess and analyze the current and future impacts of climate change on two main case studies. First, the St. George Hotel & Bay, one of the first reinforced concrete recreational buildings in the city, was built during the French Mandate (1920–1946) and is vulnerable to sea-level rise, flooding, and demolition. Second, the Beirut City Center “The Egg”, a Brutalist structure built during Beirut’s modernist “golden era”, which is prone to structural deterioration and demolition. The main objective is to highlight 20th-century built heritage as part of Beirut’s spatial narrative worthy of conservation and rehabilitation by analyzing their capability to adapt to, mitigate, or benefit from future environmental risk. Ultimately, the study explores their potential to catalyze climate-resilient urban regeneration practices in the city. Results show that the integration of current and future forecast environmental analyses informed early preservation and intervention decision-making stages to position 20th-century modern built heritage as an asset to climate action in addition to being a socio-cultural and economic asset. Full article

This study addresses the dual challenges of seasonal water scarcity and urban flooding in the Garzan River basin, a region with a semi-arid climate. We propose and analyze an integrated water management system designed to mitigate these risks and promote both ecological and economic sustainability. Our methodology began with a comprehensive analysis of meteorological data from 2000 to 2024, which quantified the significant seasonal irregularity in the annual rainfall regime. The findings revealed that the bulk of the average 800 mm of rainfall occurs between January and May, while the summer months experience near-drought conditions. Based on this, we calculated the potential of various water conservation strategies. The system combines rainwater harvesting from a 1000 m² roof and a 500 m² parking lot, projected to collect 1020 m³ annually, with greywater reclamation and low-flow fixtures, which add a combined 400 m³ of annual savings. The total annual water savings of 1420 m³ were found to provide a gross annual economic benefit of $3550. Considering the installation and maintenance costs, the project’s payback period is estimated to be around 32 years. We also developed an annual precipitation prediction model providing a locally applicable early warning mechanism that forecasts total rainfall based on spring data. The use of proactive hydrometeorological data can improve the feasibility of long-term infrastructure projects to a certain extent. Finally, the proposed system’s design was confirmed to be eligible for multiple LEED certification credits, demonstrating its alignment with international sustainability standards. In conclusion, this research provides a comprehensive and viable solution that addresses local water issues and offers a valuable model for other regions facing similar challenges. Full article

Deterministic empirical formulas often fail to capture the epistemic uncertainties of dam failure mechanisms, leading to biased risk estimations. To address this, we propose the Probabilistic Hydro-Risk Forecasting System (PHRFS), a stochastic framework integrating Latin Hypercube Sampling (LHS) with HEC-RAS 2D hydrodynamic modeling. Applied to the Honghu-Erji Reservoir (Inner Mongolia), an ensemble of 60 stratified scenarios was generated based on a multi-model envelope of breach parameters and simulated over a $12.5\mathrm{m}$ ALOS PALSAR DEM using the Full Momentum Shallow Water Equations with the Eulerian–Lagrangian method (SWE-ELM). The SWE-ELM simulations reveal a heavy-tailed distribution of peak discharge (mean $2.65\times {10}^4{\mathrm{m}}^3/\mathrm{s}$; max $5.85\times {10}^4{\mathrm{m}}^3/\mathrm{s}$), significantly exceeding deterministic estimates. Sensitivity analysis identifies a physical dichotomy: breach depth ($D_b$) primarily controls flood magnitude ($r\approx 0.7$), whereas formation time ($t_f$) governs the arrival timeline. This temporal uncertainty propagates downstream, creating a distinct longitudinal gradient in SWE-ELM-derived warning time—ranging from rapid onset ($33.0\pm 19.3\min$) in proximal zones to a substantial lead time ($33.1\pm 4.2\mathrm{h}$) in the distal Hailar District. Consequently, a moderate coupling ($r=0.65$) emerges between economic loss and loss of life, while their spatial patterns remain strongly differentiated by warning time and exposure. Upstream settlements face high mortality risks due to insufficient evacuation windows, whereas the downstream urban center faces high economic exposure (∼1.57 billion CNY) but limited life loss (∼12.15 persons). These findings provide a scientific basis for differentiating emergency strategies, shifting from immediate life-saving in upstream reaches to asset protection in downstream areas. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Quantifying the impact of land use changes on the threat of flash-floods is a critical consideration in flood hazard planning and risk reduction, and is an area of active research. Here, a coupled Weather Research and Forecasting model hydrological extension package (i.e., WRF-Hydro) modeling approach is applied to simulate flash-flooding processes for short-duration, localized, intense precipitation events. To better understand the effect of urbanization on flash floods, a series of numerical experiments is performed surrounding Ellicott City, Maryland, a location which has experienced both significant heavy rainfall events and suburban development over the past several decades. Two intense rainfall events occurring on 30 July 2016 and 27 May 2018 are investigated, respectively, to first calibrate the hydrologic model performance and then quantify the sensitivity of flash flooding to varying degrees of urbanization. Performing the same experiments using observed historical land use states is of more limited insight, as the thrust of suburban development in the Ellicott City region significantly predates satellite-derived land use datasets. Results confirm that urbanization produces larger river streamflow, higher water stages, faster hydrologic responses to achieve peak flow discharge, and shorter recession limbs, even for very intense, short-duration events. The collective findings suggest that WRF-Hydro is applicable for both watershed flash flood prediction and hypothesis testing, and demonstrates potential utility to urban development decision-makers in locations such as Ellicott City, which could face future increases in catastrophic flooding. Full article

Extreme weather events exacerbated by global warming pose severe threats to urban safety, underscoring the urgent need for highly accurate precipitation nowcasting. Short-term local heavy precipitation remains a particular challenge for traditional forecasting due to its suddenness and high disaster potential. To address this, we propose a multi-modal fusion framework that integrates ground-based GNSS-derived Precipitable Water Vapor (GNSS-PWV) and ground-based Radar Composite Reflectivity (CR). While GNSS-PWV keenly captures pre-convective atmospheric water vapor accumulation, radar CR details the morphological distribution of hydrometeors. Specifically, we developed the Spatio-Temporal Enhanced Attention Swin U-Net (STEA-Swin) model to synergize these heterogeneous datasets over the Beijing–Tianjin–Hebei region. High-precision PWV was retrieved from 250 Continuously Operating Reference Stations (CORS) using the dual-frequency ionosphere-free Precise Point Positioning (PPP) method, achieving a strong correlation (>0.97) with ERA5 reanalysis data. Validated against measured data from the 2025 flood season, the STEA-Swin model achieved a Probability of Detection (POD) of 0.68 for torrential rain events at a +1 h forecast lead time. Notably, compared to single-source models, the Critical Success Index (CSI) and POD for torrential rain improved by 18.5% and 21.5%, respectively. These findings demonstrate that coupling deep learning with ground-based GNSS-derived atmospheric thermodynamic information can significantly enhance early warning capabilities, providing a promising technical approach for regional disaster prevention and climate resilience. Full article

River flooding and urban inundation require forecasting systems that can anticipate future risk, rather than systems that only estimate the current water state. However, real-world closed-circuit television (CCTV)-based flood datasets often contain imbalanced or temporally inconsistent risk labels. In addition, most image-based approaches remain limited to static scene understanding. This study proposes a dataset reformulation and temporal multi-task forecasting framework for CCTV-based flood-risk prediction. First, we introduce a site-relative relabeling strategy that converts noisy frame-level danger annotations into four risk levels using visual flood indicators and lightweight environmental cues. Second, we transform the original frame-based dataset into site-hour sequences for multi-horizon forecasting at 1 h, 3 h, and 6 h. Third, we evaluate image-only, weather-only, and naive multimodal configurations to examine the role and limitations of heterogeneous sensor fusion. On the reformulated dataset, the image-only temporal model achieved the best overall performance, with a mean Intersection over Union (mIoU) of 0.892, Dice score of 0.940, macro-averaged F1 score (Macro-F1) of 0.532, and high-risk recall of 0.642. In contrast, naive multimodal fusion reduced Macro-F1 to 0.267 and high-risk recall to 0.070. This result indicates that additional weather inputs do not automatically improve prediction when cross-modal signals are noisy, weakly correlated, or temporally misaligned. The ablation results further showed that removing temporal modeling decreased Macro-F1 to 0.227 and high-risk recall to 0.000. These findings demonstrate that dataset reformulation and temporal modeling are essential for extending CCTV-based flood analysis from static estimation to future risk forecasting. They also suggest that robust cross-modal alignment is required before multimodal sensing can provide reliable performance gains. Full article

Accurate short-term prediction of urban waterlogging depth is essential for real-time flood risk management in rapidly urbanizing areas under climate variability. Departures from quasi-stationary operating conditions, caused by changes in drainage efficiency, inflow patterns, or measurement quality, weaken historical rainfall–water depth relationships, making purely data-driven models prone to error accumulation. In this study, a GeoAI-based, physics-enhanced machine learning framework is proposed, which translates the water balance principle into Physical Violation Scores (PVSs) and incorporates them as additional input features. PVSs remain zero under expected rainfall–water depth behavior and become positive only under departure scenarios, providing sparse and lightweight diagnostic signals without modifying model structures or loss functions. The framework is implemented on five algorithms (Support Vector Machine, Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and XGBoost) to construct physics-enhanced models (PEMs). These are evaluated against original feature models (OFMs) across 1 h and 2 h forecasting horizons. Results show that most PEMs improve prediction performance compared with their corresponding OFMs, with more pronounced gains at the 2 h horizon. Bootstrap analysis and RMSE-based error amplification factor further indicate comparable or lower R² variability and reduced recursive error amplification for most PEMs. Interpretability analyses show that rainfall forcing and water-depth persistence remain dominant predictors, whereas PVSs act as auxiliary diagnostic signals. Overall, the proposed framework provides a lightweight, reliable, interpretable, and scalable GeoAI approach for incorporating water balance knowledge into short-term urban waterlogging prediction, supporting climate resilience and smart urban water management. Full article

Short-term precipitation forecasting is an important research direction in meteorological studies, holding significant implications for disaster prevention and mitigation, urban flood drainage, and agricultural meteorological management. Existing deep learning models have achieved favourable results in modeling local features, yet they generally suffer from insufficient sensitivity to heavy precipitation areas, limitations in modeling temporal dependencies, and gradient instability issues. To address these limitations, we propose a novel spatiotemporal dual-branch neural network (ST-DualNet) for short-term precipitation forecasting based on radar echo maps. The network comprises a temporal branch (based on an enhanced ST-DConvLSTM) and a spatial branch (based on dilated convolutions and Transformer), respectively capturing the dynamic evolution and spatial structural features of precipitation. The two branches are integrated through the CBAM attention module and 3D convolution layer to achieve cross-branch feature fusion and prediction output. Experimental results demonstrate that ST-DualNet outperforms multiple mainstream models on the KNMI radar precipitation dataset, especially in heavy precipitation forecasting, providing an effective new framework for short-term precipitation forecasting. Full article

Forecasting short-term heavy precipitation is crucial for the early warning of disasters such as flash floods, landslides, and urban flooding. However, under complex topographic conditions, traditional numerical forecasts still fall short in capturing high-resolution heavy precipitation events, and conventional radar extrapolation methods struggle to accurately characterize the nonlinear evolution of weather systems during advection, deformation, and intensity adjustment processes. To address the challenge of short-term heavy rainfall forecasting in high-altitude, complex terrain, this paper proposes Nowcast with Flow-Net (Nwf-Net), a short-term precipitation forecasting framework that integrates deep learning with multi-source meteorological data. This framework consists of a Morphological Evolution Track Module (MET) and a Rainfall Intensity Correction Module (RIC) connected in series: the former combines upper-air wind fields with traditional optical flow algorithms to jointly characterize the displacement of and morphological changes in radar echoes; the latter utilizes a deep recurrent neural network to correct the intensity of forecast results, thereby enhancing the model’s ability to characterize the evolution of strong convective echoes. Experiments in the Alpine region demonstrate that Nwf-Net achieves CSI, HSS, and F1 scores of 0.392, 0.506, and 0.546, respectively, at 32 dBz. These results outperform those of traditional numerical models and some mainstream models, indicating that Nwf-Net can accurately capture multiscale severe convective information and consistently generate precise forecasts. Full article

This study presents a two-dimensional hydraulic modelling framework for the simulation of flash and compound flooding in coastal urban areas with non-perennial river systems. The model employs a rain-on-grid approach within HEC-RAS v6.7 beta5 (2D solver) to simulate rainfall-driven runoff and explicitly incorporates coastal water-level forcing to represent storm tides. The framework is applied to an ungauged coastal basin in northern Greece using a 50-year return period design storm. Model results show good agreement with official Flood Risk Management Plan maps while identifying additional inundated areas linked to lower-order streams. Compound flooding simulations indicate a 21% increase in flooded areas, highlighting the importance of integrated modelling for operational flood forecasting. Full article

Effective mitigation of urban flash floods under extreme rainfalls requires integrated hydrologic monitoring and rapid response mechanisms. The study presents an adaptive flood response framework. It combines real-time rainfall forecasting, CCTV-based flood image classification, drainage network water level monitoring, pumping machinery operations, and automated response controls. The adaptive strategy is structured into three phases to support real-time decision-making: (1) atmospheric sensing and pre-alert actions, (2) subsurface drainage system monitoring and alert activation, and (3) surface run-off detection and response. Over three years of implementation in New Taipei City, the adapted strategy achieved an over 80% success rate in preventing street inundation during intense rainfall events (>25 mm per 10 min). By integrating ensemble modeling, remote sensing, and decision-support tools, the platform transforms climate-induced flood risks into opportunities for resilience. Full article

Urban pluvial flooding in highly developed basins is challenging to forecast in real time because detailed 1D–2D hydraulic models are computationally expensive, while purely data-driven approaches often lack physical consistency. This study aims to enable operational urban flood nowcasting by proposing a model-informed AI framework for short-term stream-stage and urban inundation prediction in the Bisan-dong district of Anyang, South Korea, where the Anyang and Hagui Streams frequently overflow. A gated recurrent unit (GRU) network was trained on 10 min rainfall and stream-stage observations from 2011 to 2018 and independently validated on 2019–2022 data at four gauges to forecast stream stage at lead times of 10–60 min. In parallel, an ANN–CNN inundation surrogate was trained on 864 XP-SWMM 1D–2D simulation scenarios, forced by design storms and downstream water-level boundary conditions, to produce 256 × 256 maps of maximum inundation depth. The GRU model achieved ${\mathrm{R}}^2$ and Nash–Sutcliffe efficiency values generally above 0.95, with a mean absolute percentage error (MAPE) below approximately 5% for 10–30-min lead times; performance decreased but remained useful at 60 min. The inundation surrogate reproduced XP-SWMM results with an MAPE of 8.89% for inundation area and 19.49% for grid-based depth. Together, the ANN–CNN system enables rapid generation of high-resolution flood maps and provides a practical basis for AI-assisted urban flood nowcasting and risk management. Full article