Search Results (1,507)

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article

Recent years have seen frequent heavy rainfall events in Japan, often linked to Baiu fronts and typhoons. These events are exacerbated by global warming, leading to an increased frequency and intensity. As floods represent a serious threat to sustainable urban development and community resilience, this study contributes to sustainability-focused risk reduction through integrated analysis. This study focuses on the 2 June 2023 heavy rain disaster in Toyohashi City, Japan, which caused extensive damage due to flooding from the Yagyu and Umeda Rivers. Using numerical models, this study accurately reproduces flooding patterns, revealing that high tides amplified the inundation area by 1.5 times at the Yagyu River. A resident questionnaire conducted in collaboration with Toyohashi City identifies key trends in evacuation behavior and disaster information usage. Traditional media such as TV remain dominant, but younger generations leverage electronic devices for disaster updates. These insights emphasize the need for targeted information dissemination and enhanced disaster preparedness strategies, including online materials and flexible training programs. The methods and findings presented in this study can inform local and regional governments in building adaptive disaster management policies, which contribute to a more sustainable society. Full article

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Ashville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Ashville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United Stares from Helene. This is of extreme concern and should be investigated in detail as the public expect the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, run off from such rainfall events increases, which causes greater devastation. Full article

Floods are natural hazards that have the greatest socioeconomic impact worldwide, given that 23% of the global population live in urban areas at risk of flooding. In this field of research, the analysis of flood risk has traditionally been studied based mainly on approaches specific to civil engineering such as hydraulics and hydrology. However, these patterns of approaching the problem in research seem to be changing in recent years. During the last few years, a growing trend has been observed towards the use of methodology-based approaches oriented towards urban planning and land use management. In this context, this study analyzes the evolution of these research patterns in the field by developing a bibliometric meta-analysis of 2694 scientific publications on this topic published in recent decades. Evaluating keyword co-occurrence using VOSviewer software version 1.6.20, we analyzed how phenomena such as climate change have modified the way of addressing the study of this problem, giving growing weight to the use of integrated approaches improving territorial planning or implementing adaptive strategies, as opposed to the more traditional vision of previous decades, which only focused on the construction of hydraulic infrastructures for flood control. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km²) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article

With global warming and accelerated urbanization, urban flooding became one of the top ten international natural disasters in 2024. In order to accurately and efficiently simulate the impact of upstream river water transport on downstream river inundation under heavy rainfall scenarios, this study proposes a river inundation water depth calculation model based on a deep ensemble learning approach. The model integrates flood inundation data from hydrodynamic models with machine learning techniques, introducing a matrix-based deep ensemble learning method. The results demonstrate superior prediction accuracy, with an RMSE of 0.04 and R² of 0.95. Validation using typical rainfall data from 6 July 2022 shows that the model achieves a prediction error of less than 0.15 m across 99.8% of the domain, outperforming standalone models. These findings confirm that the deep ensemble model effectively captures the complex relationships between rainfall, terrain, and flow dynamics, providing reliable water depth predictions in data-scarce regions through multi-coupling modeling based on river characteristics. Full article

This study projects future Residential Water Use (RWU) under climate change scenarios using a Bayesian Neural Network (BNN) model that quantifies the relationship between observed temperatures and RWU. Eighteen Global Climate Models (GCMs) under the Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) scenario were used to assess the uncertainties across these models. The findings indicate that RWU in Republic of Korea (ROK) is closely linked to temperature changes, with significant increases projected in the distant future (F3), especially during summer. Under the SSP5–8.5 scenario, RWU is expected to increase by up to 10.3% by the late 21st century (2081–2100) compared to the historical baseline. The model achieved a root mean square error (RMSE) of 11,400 m³/month, demonstrating reliable predictive performance. Unlike conventional deep learning models, the BNN provides probabilistic forecasts with uncertainty bounds, enhancing its suitability for climate-sensitive resource planning. This study also projects inflows to the Paldang Dam, revealing an overall increase in future water availability. However, winter water security may decline due to decreased inflow and minimal changes in RWU. This study suggests enhancing summer precipitation storage while considering downstream flood risks. Demand management strategies are recommended for addressing future winter water security challenges. This research highlights the importance of projecting RWU under climate change scenarios and emphasizes the need for strategic water resource management in ROK. Full article

With the frequent occurrence of global floods, the demand for emergency rescue equipment has grown rapidly. The development and technological innovation of digital hydraulic drive systems (DHDSs) for emergency drainage pumps (EDPs) have become key to improving rescue efficiency. However, EDPs are prone to being affected by random and uncertain loads during operation. To achieve intelligent and efficient rescue operations, a DHDS suitable for EDPs was proposed. Firstly, the configuration and operation mode of the DHDS for EDPs were analyzed. Based on this, a multi-field coupling dynamic simulation platform for the DHDS was constructed. Secondly, the output characteristics of the system under alternating loads were simulated and analyzed. Finally, a test platform for the EDP DHDS was established, and the dynamic characteristics of the system under alternating loads were explored. The results show that as the load torque of the alternating loads increases, the amplitude of the pressure of the motor also increases, the output flow of the hydraulic-controlled proportional reversing valve (HCPRV) changes slightly, and the fluctuation range of the rotational speed of the motor increases. The fluctuation range of the pressure and the rotational speed of the motor are basically not affected by the frequency of alternating loads, but the fluctuation amplitude of the output flow of the HCPRV reduces with the increase in the frequency of alternating loads. This system can respond to changes in load relatively quickly under alternating loads and can return to a stable state in a short time. It has laudable anti-interference ability and output stability. Full article

Managed aquifer recharge (MAR) is a complex and hidden process of storing surplus water under the ground surface and extracting it as, when and where needed. Evaluation of the success of any MAR project is challenging due to uncertainty in estimating the hydrogeological characteristics of the subsurface media. This paper demonstrates the use of a groundwater model (MODFLOW) to evaluate a new, large-scale regional MAR project in the agricultural heartland in Punjab, Pakistan. In this MAR project, flood waters have been diverted to the bed of an abandoned canal, where 144 recharge wells (the wells for accelerating the recharge into the aquifer) have been constructed to accelerate the recharge to the aquifer. The model was calibrated for a period of five years from October 2015 to June 2020 on a monthly stress period and the resulting water levels were simulated till 2035. The water balance components and future response of the aquifer to different scenarios up to 2035 including with and without MAR situations are presented. The model simulations showed that MAR can contribute to the replenishment of the aquifer and its potential for the case study site to contribute significantly to the management of groundwater and to enhance supplies for intensive agriculture. It was further established that MODFLOW can help in the evaluation of effectiveness of a MAR scheme. This study is unique as it evaluates a significantly large MAR project in an area where this practice has not been developed for improving groundwater access for large scale irrigation. The model provides guidelines for decision makers in the region as well as for the global community and livelihood benefits for rural communities. Full article

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article

This paper examines Amitav Ghosh’s Gun Island through a Hindu eco-spiritual framework to explore how ancient cosmological concepts illuminate contemporary environmental crises. Building upon the legend of Bonduki Sadagar and Manasa Devi, Ghosh narrates the rupture of sacred human–nature relationships in both colonial and postcolonial contexts. This study employs a tripartite conceptual lens of karma, dharma, and ecosystem drawn from Hindu philosophy to analyze how the novel frames environmental degradation, human moral failure, and ecological interconnectedness. Karma, as the law of cause and effect, is used to depict the consequences of human exploitation through natural disasters, climate migration, and the collapse of ecosystems. Dharma emerges as a principle advocating ecological responsibility and symbiosis between humans and nonhuman life. This paper argues that Ghosh tactfully intertwines Hindu metaphysics with contemporary ecological science to critique capitalist modernity’s environmental violence. The novel’s depiction of floods, the sinking of Venice, and the global refugee crisis dramatizes karmic consequences, while its evocation of myth–science convergence offers a vision of sacred interdependence. Ultimately, this paper concludes that Gun Island provides an urgent eco-spiritual model for reimagining planetary ethics and responding to the Anthropocene through humility, relationality, and spiritual responsibility. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article