Search Results (964)

Anthropogenic climate change has disrupted the global hydrological cycle, increasing compound extreme events like Dry–Wet Abrupt Alternations (DWAAs). Regarding the Mediterranean Basin, Greece is highly susceptible to these abrupt hydroclimatic shifts, which frequently overwhelm reactive disaster management. This study quantifies the spatiotemporal dynamics of DWAA events across Greece from 1990 to 2024. Using the 1-month Standardized Precipitation Evapotranspiration Index (SPEI-1) from ERA5 reanalysis, transitions were classified into dry-to-wet (DW) and wet-to-dry (WD) across moderate (±1.0), severe (±1.5), and extreme (±2.0) thresholds. Core physical metrics (duration, severity, and intensity) were evaluated using Anselin Local Moran’s I (LISA) and Mann–Kendall tests to identify spatial hotspots and temporal trends. Results revealed a spatially decoupled hazard regime dictated by topography and atmospheric mechanics. Severe DW transitions primarily manifest as intense autumn flash floods (62.7%) concentrated in western and southern districts. Conversely, severe WD transitions emerge as high-magnitude summer agricultural flash droughts (52.5%) clustered in central and northern continental plains. Crucially, while the magnitudes of these events demonstrate historical temporal stationarity, their decadal frequency doubled in the 2020s. This increase validates the idea that global warming accelerates systemic climate extremes, necessitating an urgent shift toward proactive, highly localized adaptation strategies. Full article

Accurate estimation of floodwater depth is vital for disaster management but traditionally relies on data-intensive hydrodynamic models or supervised deep learning restricted by labeled data requirements. To address these bottlenecks, this study proposes a fully unsupervised, training-free framework for rapid depth estimation of standing or slowly receding residual floodwater using post-event remote sensing imagery and DTMs. First, a binary flood extent map is automatically delineated by adapting an existing unsupervised color-based segmentation algorithm for UAV imagery. Second, leveraging the hydrostatic equilibrium principle, floodwater depth is computed by integrating the extracted flood footprint with the underlying DTM. This framework was evaluated using the Inundation2Depth dataset, encompassing twelve urban and peri-urban sites in the Southeastern US impacted by Hurricanes Matthew and Florence. Experimental results across all examined sites demonstrated the framework’s viability, with segmentation $F_1$-scores ranging from 63% to 96% and absolute flood depth RMSE ranged from 0.16 m in well-defined catchments to 1.69 m in highly obscured regions. Bypassing the need for manual annotations and task-specific training, the proposed framework offers a scalable, rapidly deployable solution for first-order flood mapping and depth estimation. Its computational efficiency enables execution on standard CPU hardware within seconds, making it ideal for time-critical, on-site emergency response. Full article

Improving the flood adaptability of urban waterfront spaces is an essential entry point for enhancing regional stormwater regulation capacity, scientifically preventing flood disasters, and stabilizing urban water security. Existing flood adaptability assessments mostly rely on single weighting methods and individual evaluation models, inevitably causing systematic bias and low result robustness. Against this limitation, this study integrates remote sensing intelligent interpretation, spatiotemporal landscape pattern analysis, and multi-criteria decision theory to construct a comprehensive flood adaptability evaluation system under the pressure–state–response (PSR) framework. Innovatively, a game-theoretic combined weighting scheme integrating the entropy weight method, CRITIC method, and standard deviation method is proposed, and three complementary models including TOPSIS, VIKOR, and EDAS are coupled for cross-verification evaluation, which effectively improves the objectivity and robustness of spatial flood adaptability quantification. Taking Anqing City as a typical case, this study adopts Sentinel-2 time-series remote sensing images from 2016 to 2023 and applies an optimized random forest algorithm to automatically classify land cover. Five underlying surface types, including water bodies, vegetation, farmland, built-up areas, and bare land, are accurately extracted with an overall classification accuracy of around 90% for most years. Core landscape metrics such as Shannon’s diversity index and patch density are selected to systematically analyze the spatiotemporal differentiation characteristics of waterfront landscape patterns during the study period. The results indicate the obvious spatial heterogeneity of flood adaptability in Anqing City. Yingjiang District and Yuexi County present high comprehensive flood adaptability, while Wangjiang County and Huaining County show relatively low performance. Urban areas gain strong flood resistance from complete disaster prevention infrastructures and economic resilience; mountainous areas possess natural advantages in flood retention and drainage due to high vegetation coverage and topographic relief; by contrast, plain districts are severely restricted by low-lying terrain and insufficient drainage systems, resulting in prominent flood vulnerability. The proposed method is helpful for providing reliable scientific support for waterfront landscape optimization, zoned flood disaster management, and resilient water space planning in riverine cities. Full article

Compound flooding increasingly threatens developed coastal deltas. High risk pressure does not necessarily produce a resilience deficit where capacity is sufficient, whereas moderate-pressure areas may remain vulnerable when capacity is weak. Recent assessments increasingly integrate hazard, exposure, vulnerability, and adaptive capacity within unified risk frameworks such as the IPCC AR5 risk model. However, by collapsing these dimensions into a single composite risk score, such formulations cannot explicitly diagnose where—and by how much—compound flood risk pressure exceeds intrinsic resilience capacity, which is the information most directly needed for prioritizing resilience investment. This study diagnoses compound flood resilience deficits across Jiangsu Province, China, at county scale from 2000 to 2020. We introduce a diagnostic approach that separates risk pressure from intrinsic resilience capacity and quantifies their spatial mismatch. The risk pressure index is evaluated for consistency with observed disaster-loss indicators—direct economic loss and flood-affected area—over 2010–2020, and spatial statistics, time-series clustering, and explainable machine learning identify deficit patterns, pathways, and associated factors. Both the risk-pressure index and the derived deficit index are positively associated with observed losses, confirming that the framework captures major flood impacts. The resilience deficit index reveals persistent risk–resilience mismatch across Jiangsu. Three pathways emerge: capacity-buffered exposure, inland adaptive adjustment, and coastal resilience-deficit lock-in. Land-system conditions, communication access, transport connectivity, and economic recovery capacity are jointly associated with resilience deficits. The framework offers a transferable approach for prioritizing differentiated flood-risk management in coastal deltas. Full article

Urban pluvial flood resilience relies not only on engineering defenses but also on effective behavioral coordination among local governments, property management companies (PMCs), and residents. Yet even when risks are recognized, preparedness often fails because costs are immediate and certain, whereas benefits are delayed and probabilistic. This study addresses this dilemma by integrating prospect theory into a tripartite evolutionary game model. We position PMCs as pivotal intermediaries whose maintenance decisions critically influence whether physical infrastructure can be translated into operational resilience. Simulation results show that actors initially remain inactive under uncertainty. Stable collaboration emerges only when government regulation, PMC maintenance, and resident participation reinforce one another. Equal-magnitude policy comparisons reveal an asymmetric policy effect: sufficiently strong penalties induce tripartite cooperation, whereas equivalent subsidies primarily improve PMC maintenance without sustaining resident engagement. Excessive post-disaster relief further weakens ex ante incentives by creating moral hazard. These findings advance community flood governance toward psychologically informed, actor-specific policy mixes, emphasizing clear responsibility allocation, targeted incentives, and conditional relief, to strengthen community-level climate adaptation and adaptive resilience under uncertainty. Full article

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management. Full article

Natural floods are unusual extreme events resulting from significant or prolonged rainfall, which sometimes turn into disasters. Trends indicate that these phenomena may become more frequent in the future. Therefore, many solutions are being implemented in practice to deal with the problem. In the European Union, enhanced measures have been taken, including the assessment and mapping of flood risks, triggered by the adopted legislation. Despite these solutions, flood disasters continue to occur, even in areas included in Flood Risk Management Plans but outside the territories designated as vulnerable. To investigate the reason, a study was conducted accounting for flood genesis and the role of flood debris and anthropogenic waste as a factor that can worsen the harmful impact of these events. It was considered whether there is a gap in the application of current methods and legislation. A brief review was performed concerning the physical description of the phenomenon and the evaluation methods used, accounting for the role that debris and anthropogenic waste play in them. Some main practices and applicable tools were also discussed in brief. As an illustration of the impact, a case study of a recent significant flood in Bulgaria was also presented. This flood affected an area that is not identified as at risk under the Flood Risk Management Plans developed in accordance with the European and Bulgarian legislation and requirements. The information presented here confirms the significant role that debris and anthropogenic waste play in the process of turning a flood into a disaster and highlights the need for additional work concerning their proper incorporation in both the regulation and assessment processes. Full article

Global climate change and rapid urbanization have intensified extreme precipitation events, making urban pluvial flood risk management particularly urgent. However, existing research exhibits theoretical and methodological limitations in reconciling pluvial flood resilience within high-density urban development, especially lacking a quantitative framework for human mobility resilience based on natural hydrological units. To address this, this study takes 48 natural catchment areas in Shenzhen as research units. By integrating mobile phone signaling data, remote sensing imagery, and multi-dimensional spatial form indicators, it constructs a post-disaster recovery curve based on population dynamics to quantify human mobility resilience. Using Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and the K-means clustering method, this research analyzes the mechanisms through which green infrastructure, road network structure, and three-dimensional building morphology influence resilience. The results show that three-dimensional building morphology is the core driver of post-disaster recovery capacity, with Floor Area Ratio (FAR), Standard Deviation of Building Height (SDBH), and Building Coverage Ratio (BCR) significantly promoting recovery, while Building Shape Coefficient (BSC) exerts an inhibitory effect. A key comparative analysis reveals that the model based on natural catchment areas has significantly better explanatory power than the model using stormwater management units, and the dominant factors differ: building morphology factors are more prominent in natural hydrological units, whereas road network structure factors are more significant in stormwater management units. This study confirms that natural geographic boundaries can more authentically reveal the intrinsic “morphology–resilience” relationship, providing an important theoretical and empirical basis for optimizing the planning and management units of sponge cities in high-density urban areas. Full article

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

Over the past three decades, the United States has experienced a notable increase in weather-related disasters, including hurricanes, floods, tornadoes, wildfires, and severe storms, posing growing challenges to healthcare preparedness and public health systems. This study analyzes Federal Emergency Management Agency (FEMA) disaster declarations from 1989 to 2019 to characterize temporal and geographic trends in weather-related events. Data after 2019 were excluded to avoid confounding effects associated with the COVID-19 pandemic, which disrupted disaster declarations, resource allocation, and healthcare system demands. Using descriptive statistics, generalized linear mixed models, and spatial clustering techniques, we identified substantial increases and nonlinear patterns in disaster declarations, with variation across hazard types and regions. These trends reflect evolving hazard exposure, regional differences, and policy-driven declaration practices. Although this study does not directly measure health outcomes or social vulnerability, the observed patterns have important implications for healthcare system capacity, workforce preparedness, and populations known to be disproportionately affected by disasters. The findings highlight the need for climate-informed training, data-driven preparedness planning, and integration of disaster trend analysis into nursing education and public health practice. Strengthening the ability of healthcare systems to anticipate and respond to evolving disaster patterns is critical for advancing resilience and promoting equitable health outcomes in the context of climate change. Full article

Accurate prediction of extreme runoff events is crucial for flood control and disaster mitigation and water resource risk management. In recent years, deep learning has emerged as a key method for runoff simulation. However, its training strategy applies uniform optimization across all time steps, which encourages the model to fit normal hydrological processes while reducing its sensitivity to extreme runoff events. To address this issue, this study introduces Selective Learning and proposes a runoff prediction model with a flood peak protection mechanism, namely PPSL-DI-LSTM. In this framework, sample importance is dynamically regulated through uncertainty filtering, anomaly filtering, and flood peak protection, thereby reducing the influence of unstable or non-generalizable samples while enhancing the learning of high-flow processes. Based on this strategy, a dynamic weighted loss function, termed Selective Weighted RMSE (SW-RMSE), is designed to optimize the training process. Experimental results on the CAMELS dataset show that, compared to the baseline DI-LSTM model, the proposed model achieves improvements of approximately 4% and 6% in median Nash-Sutcliffe Efficiency (NSE) and median Kring-Gupta Efficiency (KGE), respectively. Simultaneously, the Flow Duration Curve High Flow Variability (FHV) was significantly reduced by approximately 84% and the overall percent bias (PBIAS) improved from −3.80% to 1.15%. Additionally, when integrated into a standard LSTM, PPSL maintains comparable median NSE while improving median KGE (from 0.751 to 0.778) and substantially reducing FHV (from −12.86% to −3.34%). Our method overcomes the limitations of uniformly treating all training samples in complex hydrological simulations, and has strong potential to substantially improve the accuracy and reliability of future runoff modeling studies. Full article

Participatory citizenship plays a critical role in strengthening climate change resilience, particularly in the context of natural disasters such as floods and other hydrological extremes. Citizen-generated data shared through social media platforms offer valuable real-time insights that can complement traditional environmental monitoring systems. This study proposes a machine learning-based framework to analyze multilingual news data and global X (formerly known as Twitter) data that can complement street level sensor data for improved detection and understanding of extreme hydrological events: floods and landslides. The approach identifies and filters tweets related to hazards such as floods and contextualizes them with information extracted from news reports to enhance event characterization. In addition, sentiment and emotion analysis are applied to assess public reactions and perceived event intensity. By integrating physical event signals with societal responses, the method provides a broader perspective on disaster impacts and the effectiveness of emergency responses. The results highlight the potential of combining social media analytics and machine learning to support hydrological monitoring, enhance situational awareness, and contribute to more responsive disaster management strategies in the face of increasing climate-related risks. Full article

Flooding is a destructive natural hazard intensified by climate change, posing challenges to sustainable disaster risk management. This study developed and evaluated machine learning models for flood severity prediction within a hexagonal grid system (H3, resolution 8) under rainy season conditions in Maha Sarakham, Thailand. Four models Random Forest (RF), XGBoost, Gradient Boosting (GB), and Support Vector Machine (SVM) were trained using 11 environmental variables across historical years (2018, 2021, 2022) and tested on a projected year (2025) under SSP scenarios. XGBoost demonstrated the most stable performance (accuracy > 0.95 across all years), while SVM achieved high historical accuracy (0.970 average) but failed to detect positive flood cases in 2025 (recall = 0), highlighting the importance of temporal validation. Topographic variables were the most consistent predictors, but NSMI (soil moisture) emerged as the top SHAP predictor in 2025 (r = 0.52), suggesting a shift in flood-generating mechanisms under climate change. A polarization pattern was observed: flood-affected area declined to 6.8% in 2025 (79% reduction from 2022), yet maximum flood point counts remained high at 14.0, indicating more concentrated but intense flooding. Under SSP projections, using the historical baseline (27.5%), SSP1-2.6 (45.2%) and SSP2-4.5 (45.0%) indicate increased flood risk relative to the historical baseline through 2040. The SSP5-8.5 projection (3.4%) is identified as a model extrapolation artifact through formal out-of-distribution assessment (Mahalanobis distance = 8.72, p < 0.001) and is therefore excluded from policy recommendations. Although GRU and LSTM achieved marginally higher AUC values in retrospective validation, we recommend XGBoost for operational forecasting due to its temporal stability, computational efficiency, and interpretability. We further recommend integrating real-time soil moisture monitoring into early warning systems and shifting to hotspot-targeted adaptation strategies. Full article

The Guanting Gorge section of the Yongding River features complex terrain and densely distributed hydraulic projects. Under extreme rainstorm conditions, flood disasters are highly likely to occur, triggering secondary disasters such as landslides, barrier lakes, and dam breaks, resulting in prominent chain disaster risks and difficult prevention and control. To accurately evaluate the potential flood disaster chain risks in this region, 12 representative evaluation indicators were selected to establish a flood disaster chain risk evaluation system. The analytic hierarchy process (AHP) and entropy weight method (EW) were adopted to calculate subjective and objective indicator weights, respectively. Game theory was applied to achieve the optimal weight fusion and determine the final indicator weights. On this basis, a variable fuzzy model was constructed to identify flood disaster chain risks, and the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) was used to verify the accuracy and reliability of the model. The results show that cumulative precipitation, terrain slope, and annual maximum precipitation are the core driving factors inducing regional flood disaster chains, with corresponding weights of 0.2058, 0.1293, and 0.0980, respectively. High- and extremely high-risk areas are mainly concentrated in the western and southern river valleys, among which the Zhaitang–Luopoling river reach and the river section from Luopoling Reservoir to Sanjiadian Hub present the most prominent risks. The spatial distribution of risk zones is highly consistent with the actual disaster sites of the July 2023 Haihe River extreme rainstorm event, and the model achieves an AUC value of 0.901, indicating high accuracy and reliable simulation results. This study accurately identifies the core inducing factors and high-risk sections of flood disaster chains in the Guanting Gorge section of the Yongding River, which can provide scientific references and technical support for regional flood disaster chain prevention and control, hydraulic project operation and management, and disaster prevention and mitigation planning. Full article

Accurate streamflow prediction is an important part of disaster management, as forecasting flow rates is an indispensable element of early warning systems, yet it remains challenging due to the complexity and nonlinearity of climatic inputs. This study evaluated the performances of Long Short-Term Memory (LSTM) and random forest (RF) machine learning algorithms for daily streamflow prediction across sixteen hydroclimatically diverse basins in the contiguous United States using the CAMELS dataset. The models were trained on five climatic features augmented with antecedent streamflow lag features at 7-, 14-, and 30-day intervals. The random forest algorithm demonstrated a better accuracy, achieving an average Nash–Sutcliffe efficiency (NSE) of 0.755 compared to 0.632 for the LSTM model. Both models performed well in the snowmelt-dominated basins and were least effective in flashy humid regimes. Additionally, both models exhibited high precision for flood detection, with accuracy rates exceeding 88% for distinguishing flood events and F1 scores of 0.734 and 0.797 for LSTM and RF, respectively. These results recommend RF for operational streamflow forecasting across hydroclimatically diverse settings and LSTM for perennial snowmelt- and groundwater-influenced catchments where long-range temporal dependencies govern runoff generation. Full article