Search Results (332)

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (R_a) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal R_a value is 6. The coefficient of determination (R²) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R² (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation. Full article

Understanding and accurately predicting the outflow hydrograph from embankment dam breaches is essential for managing the associated flood hazard and improving emergency preparedness. This work simulates the breaching process using a high-resolution 3D computational fluid dynamics (CFD) model, a critical natural hazard for earth-fill dams under overtopping conditions. The model was validated against the experimental data, showing high accuracy in predicting breach development and failure timing. A parametric analysis was performed to assess the influence of the initial breach geometry on erosion dynamics. The results indicated a high sensitivity, while increasing the breach width by 5% led to an average 11% increase in the erosion rate, and decreasing the depth by 5% caused an average 16.5% rise. To enhance predictive capabilities for this hazard, a multilayer neural network (MLNN) was trained on the CFD-generated dataset. The network utilized breach geometry and time as inputs to forecast the peak outflow and erosion rate, achieving excellent accuracy (RMSE = 0.019, R² = 0.99). This integrated modeling strategy combines data-driven learning with physics-based simulation and demonstrates its effectiveness for laboratory-scale dam breach modeling. This approach is a step toward more efficient surrogate-based tools for flood risk analysis, though its extension to full-scale dams and varied material properties requires additional validation and scaling analyses beyond the scope of this work. Full article

Deep stormwater drainage tunnels are increasingly being used to mitigate urban flooding, but in-tunnel sediment deposition reduces their discharge capacity and complicates their maintenance. With direct field observation constrained, numerical simulation is essential, and river-based total sediment load formulas require reassessment for use in deep tunnels. The three-phase (air–water–sediment) CFD solver SedInterFoam is first validated against a benchmark open-channel suspended sediment experiment, and is then applied to a horseshoe tunnel under a fixed design discharge for multiple inlet sediment concentrations spanning urban stormwater conditions. Four classical formulas (Yang, Shen–Hung, Ackers–White, Engelund–Hansen) are evaluated at the CFD-resolved hydraulic state; Toffaleti is omitted because its zone-based formulation is incompatible with the partially filled horseshoe geometry. The CFD consistently shows persistent retention of a substantial fraction of the inlet sediment load, whereas the transport capacity-limited interpretation of the classical formulas predicts near-complete sediment throughput—indicating structural inadequacy for the dilute, supply-limited regime typical of urban stormwater. A Universal Soil Loss Equation (USLE)-style dimensionless deposition formula is therefore proposed, with inlet sediment loading as the explicit independent variable and a tunnel correction factor $K_{tunnel}$ absorbing the geometric, hydraulic, and sediment variations. Its regression yields an almost linear scaling and a nearly constant deposition ratio, while analysis of the internal flow and concentration fields shows that the retained sediment is strongly concentrated near the bed and that near-bed turbulent mixing weakens moderately with a rising inlet concentration. While calibrated for a single non-cohesive settleable sand fraction, the framework provides a transferable basis for inlet-loading-dependent deposition prediction in deep stormwater drainage tunnels, and subsequent extension of $K_{tunnel}$ to broader sediment conditions with field-based validation is expected to enable maintenance planning, dredging volume estimation, and sediment retention risk assessment. Full article

Quantitative characterization of permeability in porous media represents a central problem in filtration theory, geosciences, and materials engineering. Standard numerical approaches, including finite element methods and Lattice Boltzmann simulations, typically require extensive domain-specific expertise together with specialized computational software. This motivates the development of computationally simpler and more accessible geometric approaches applicable directly to binary volumetric data. We introduce a novel algorithmic framework for the analysis of porous structures that reformulates permeability-related characterization in terms of discrete geometry and graph-based computation. The method combines parallel raster-grid and graph representations of a binarized three-dimensional CT image. The principal transport-limiting feature of the pore network, interpreted as the minimal constriction governing connectivity, is identified through iterative morphological dilation coupled with a three-dimensional scanline seed-fill procedure. In addition, a dichotomous bisection strategy is proposed to accelerate the determination of the critical bottleneck scale. The proposed methodology was evaluated on five volumetric datasets of size 100 × 100 × 100 voxels obtained from CT-derived porous structures. Experimental results demonstrate that dilation- and erosion-based formulations yield equivalent estimates of the bottleneck parameter in four of the five investigated samples. Furthermore, incorporation of the bisection optimization reduces computational time in three-dimensional experiments by approximately 50% relative to sequential iteration. The presented approach provides a computationally efficient and fully open-source alternative to conventional physics-based permeability solvers for binary porous media. The resulting bottleneck parameter b should be interpreted as a discrete geometric invariant characterizing the pore-network connectivity and minimal transport cross-section. It is not intended to replace the absolute permeability coefficient K appearing in Darcy’s law, but rather to serve as an independent structural descriptor suitable for comparative and topological analysis of porous systems. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Due to complex terrain, Earth surface curvature, and limited distribution of radars, there are often serious data gaps in base radar data or in 3D radar reflectivity mosaics of a radar network. These gaps greatly limit the application of radar data in short-term severe convection forecasting and quantitative precipitation estimation for flood events. This paper develops a generative adversarial network (GAN)-based radar data gap-filling model, named RadGF-GAN, for completing gaps in 3D radar reflectivity mosaic data. The 2020–2025 high-resolution (at 1 km grid spacing) outputs of a Weather Research and Forecasting and four-dimensional data assimilation model (WRF-FDDA) in an eastern China region are used to generate the data to train and test RadGF-GAN. Observations of the geostationary satellite FY-4A 15-channel AGRI (Advanced Geostationary Radiation Imager) are simulated with the radiative transfer for TOVS (RTTOV), and the radar reflectivity data are simulated with an empirical diagnostic model. By testing on 1705 test samples for satellite-only, radar-only, and radar–satellite fused inputs, it is demonstrated that the proposed RadGF-GAN gap-filling model significantly outperforms the existing interpolation methods in restoring the spatial distribution and structural textures of the radar reflectivity in the 3D gaps. Furthermore, satellite imager measurements play a great role in reconstructing the overall rainband structures in large 3D gaps, and by jointly inputting radar and satellite data, RadGF-GAN greatly outperforms the model with either radar data or satellite data alone. Full article

The assessment of the hydromorphological state of a river is fundamental for both correctly evaluating its ecological conditions and planning its restoration. Despite this, there is a critical gap in studies on this topic in Southern Italy, although they are recommended by several EU Framework Directives. This research provides a contribution to filling this gap by assessing the hydromorphological quality of the Middle and Lower Sabato River (Southern Italy), by using the method officially adopted by the Italian Institute for Environmental Protection and Research (ISPRA), named IDRAIM. The method presents the advantage of considering the specific Italian context in terms of channel adjustments and anthropogenic impacts. However, it also considers pre-existing geomorphological approaches developed in other countries that make the method applicable at least in the entire Mediterranean area. To apply the method, in this study, we used data obtained by GIS analysis, remotely sensed data, and field-surveyed data. The study has highlighted that, in the Middle and Lower Sabato R., eight river reaches out-of-fifteen have displayed a “moderate or sufficient” morphological quality, five reaches a “good” morphological quality, while the remaining two reaches have been characterized by a “poor” morphological quality. Functional alterations have seemed to prevail over artificiality and intensity of short-term channel adjustments in conditioning hydromorphological quality. These results will be a key starting point for already planned studies dealing with both the restoration of the Sabato R. and flood hazard and risk assessment. Full article

The chemical agent injection modes and displacement characteristics of chemical compound flooding, consisting of a plugging agent, an oil displacement agent, and a viscosity reducer, were investigated by laboratory experiments for target heavy oil reservoirs after multiple cycles of huff-and-puff. The performances of the oil displacement agent, viscosity reducer and plugging agent were evaluated, and the formulation and concentration were optimized. The oil displacement effects and displacement characteristics of different injection modes were studied by sand-filled two-pipe models. The experiment results showed that alternating injections of the oil displacement agent and viscosity reducer yielded better results than their mixed injection, and small segments alternating injections achieved the highest recovery. The larger the dosage of the oil displacement agent, the larger the maximum liquid production ratio between the high- and low-permeability layers, but with the smaller the liquid production reverse duration. The larger the dosage of the viscosity reducer, the greater the water cut decrease but the smaller the maximum liquid production ratio. For chemical compound flooding in the Zhong’er block in the Gudao oilfield, the recommended injection mode was 0.1 PV plugging agent + 2000 mg/L of oil displacement agent + 0.5% viscosity reducer, with small segments of the oil displacement agent being followed by a viscosity reducer at an injection slug ratio of 6:4. However, the injection mode depends on the prices of oil and the chemical agent. When prices fluctuate, the chemical agent concentration should be adjusted accordingly. Full article

Urban flood peak mitigation by green infrastructure (GI) is fundamentally a timing problem. Because GI storage is finite, interception occurs only within a brief active window; whether it reduces the outlet peak depends on GI placement in the network, routing lags, and rainfall timing. Here, we develop a timescale-based framework that links outlet peak reduction to the alignment among within-storm temporal structure, network response, and GI filling dynamics, providing a compact way to interpret when different network positions become most effective under a fixed GI design. Starting from a general convolution representation of runoff generation, interception, and routing, we show that peak reduction efficiency and location ranking can be organized by two nondimensional ratios—comparing storm duration and network response time to a characteristic GI filling time—plus simple descriptors of within-storm temporal structure. Under uniform rainfall, these ratios yield an interpretable regime diagram with analytical transition curves between downstream-, mid-network-, and upstream-optimal placement for a generic dispersive routing representation. Relaxing the uniform-rainfall assumption shows that within-storm variability can substantially reorganize these regimes because storm timing controls both how long GI storage remains available before it fills and which routed contributions overlap to form the outlet peak. Highly concentrated storms and storms with early internal peaks are especially likely to reorder the ranking of candidate locations relative to the uniform-rainfall baseline. Using 2351 observed hourly storm events evaluated across virtual catchments spanning fast to slow network responses, we quantify how often realistic event structure alters the optimal location and the regret associated with adopting a uniform design storm. The results motivate robustness-oriented placement strategies based on ensembles of plausible storm temporal structures, organized within the proposed timescale diagram rather than reliance on a single design hyetograph. Full article

Climate change affects flood frequency and intensity throughout the world, leading to a research gap in the traditional management framework. Furthermore, traditional frameworks often rely on complex hydrological patterns and one-way communication, demonstrating urgent needs for adaptive and two-way communication approaches. For this purpose, the current systematic literature review (SLR) fills this gap by analyzing the widely reported literature on the role of an artificial intelligence (AI)-based framework. This SLR provides conceptual and theoretical insight into the potential role of generative AI and an OpenAI-based theoretical framework for effective flood management. Therefore, 77 peer-reviewed articles published between 2010 and 2025 in reputed sources such as ScienceDirect, Springer Nature, MDPI, Wiley, and others were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. According to the results of this paper, four hypothetical applications of generative AI are described, namely: (i) a knowledge translator to provide simplified hydrological information, (ii) a decision-support assistant that aids real-time strategic analysis, (iii) a community engagement tool to increase the participation and understanding of people, and (iv) an interface to harmonize and synthesize various sources of information. The discussion indicates that there is a lot of potential in terms of generative AI improving the inclusiveness, real-time sensitivity, and cost-effectiveness of flood risk management practice. Nevertheless, the research also presents significant issues that are connected to data integrity, algorithm bias, digital equity, and ethical governance. The results indicate that generative AI has a significant potential of developing robust, more accessible, and more communicative flood risk management systems, and that additional studies on the responsible and ethical use of the technology are necessary. Full article

Conventional centralized drainage systems exacerbate urban flooding, pollution, and water stress. Low-impact development (LID) is a decentralized alternative; however, its multifunctional benefits, which go beyond the control of stormwater, are often undervalued in planning. This study fills this gap by developing an integrated benefit valuation framework to systematically quantify and estimate the economic value of the co-benefits of five widely implemented LID facilities (vegetated swale, green roof, in-filtration ditch, infiltration trench, and permeable pavement) in Seoul, South Korea. The framework combines annual benefits in four key sectors: water management (runoff reduction), energy savings (building cooling/heating demands), air quality (pollutant deposition and avoided emissions) and climate change (carbon sequestration and mitigation). Applying a transparent, localized spreadsheet model, the results indicate significant multifunctional value for LID systems. While water management provides the primary benefit, there is substantial added value in energy, air quality, and climate co-benefits. In the case of green roofs, such ancillary benefits can exceed hydrological values. The analysis further reveals a consistent scale-benefit relationship and a clear trade-off between the magnitude of benefits and the cost of implementation. This provides evidence of the need for context-sensitive, portfolio-based LID planning. The proposed framework is a practical decision support tool for urban planners and policymakers to consider LID not only as a stormwater solution but also as multifunctional green infrastructure that simultaneously promotes urban water security, energy efficiency, environmental quality, and climate resilience. Full article

Through-plane electronic transport in porous membrane electrode assembly (MEA) electrodes is governed by the three-dimensional (3D) connectivity of the conducting phase. Here, we quantify the role of the spanning-cluster fraction $P_{\infty }$, defined as the fraction of conducting-phase voxels that belong to the $z$-spanning connected component in a finite reconstructed volume, on effective conductivity using scanning electron microscopy (SEM)-informed 3D reconstructions of four archetypal morphologies: a granular catalyst layer (CL), labeled CL1; a fibrous gas diffusion layer (GDL), labeled GDL1; an open-cell foam (OCF); and a micro-fibrous non-woven (MFM), labeled MFM1. Each morphology is reconstructed on a $150\times 150\times 150$ voxel grid, and $z$-spanning connectivity is identified with a 26-neighbor flood-fill algorithm. Steady-state conduction is solved by a finite-volume method (FVM) with an imposed potential difference between the z-faces and no-flux lateral boundaries. Although all samples exhibit through-thickness connectivity, the normalized conductivity ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}/{\sigma }_{\mathrm{b}\mathrm{u}\mathrm{l}\mathrm{k}}$ varies widely, from $\approx 0.134$ (MFM1) to $\approx 0.706$ (OCF). The corresponding ${(P}_{\infty },{\sigma }_{eff}/{\sigma }_{bulk})$ pairs are $\left(0.996,\approx 0.306\right)$ for CL1, $\left(0.999,\approx 0.303\right)$ for GDL1, $\left(0.997,\approx 0.706\right)$ for OCF, and $\left(0.901,\approx 0.134\right)$ for MFM1. OCF exhibits the highest response due to vertically coherent channels, whereas MFM1 underperforms due to laminated constrictions; CL1 and GDL1 lie in an intermediate regime with nearly isotropic skeletons. Overall, the results show that while a $z$-spanning connected component is required for measurable conduction, the magnitude of ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ is dictated by percolating-skeleton quality (bottlenecks, cross-sectional constrictions, and pathway alignment) rather than phase amount alone. The proposed descriptors therefore enable percolation-aware screening metrics for designing and comparing MEA-relevant GDL and CL microstructures. Full article

Purple rice contains beneficial bioactive compounds, but the concentrations can be influenced by the growing conditions. This study investigated the interactive effects of water regime, biochar amendment, and nitrogen (N) sources on the yield and grain quality of purple rice. Purple rice grown under flooded conditions combined with biochar and urea or ammonium demonstrated significant increases in grain yield and yield components such as plant height, number of spikelets per panicle, and the percentage of filled grains compared to non-flooded conditions. Nitrate consistently resulted in the lowest yields and grain quality, especially under non-flooded conditions and with no added biochar. Grain anthocyanin concentration was highest under flooded conditions, with the maximum observed with biochar and nitrate application and with ammonium application without biochar. In contrast, the grain phenol content and antioxidant capacity were maximized by the biochar and water applications. The findings indicate that rice husk biochar can improve productivity without altering the color shade of purple rice. Combining flooding, biochar, and ammonium or urea improves the agronomic performance of purple rice, though the impact on nutritional qualities is more complex. Full article

Sea-level rise (SLR), a climate hazard driven by global warming, poses a severe threat to low-lying coastal regions when combined with strong typhoons and storm surges, endangering human lives and socio-economic development. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a core strategic zone for China’s economic development and is increasingly affected by such compound hazards, exacerbating its storm-related disasters amid climate change. Here, we analyze long-term observational data from the GBA using mathematical statistics and simulation methods to address these climate-related challenges. This study predicts future scenarios of extreme water levels in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), aiming to assess the hazard posed by storm surge disasters under varied sea-level rise (SLR) scenarios. The findings indicate that, under future climate projections, both the extreme water levels in the GBA and the hazard of storm surge disasters in its floodplain areas will exhibit a significant upward trend—with the degree of hazard amplification positively correlated with the magnitude of SLR. This study provides a scientific basis to improve the accuracy of extreme water-level prediction, supporting more reliable short-term early flood warnings. It also offers guidance for optimizing SLR-adapted coastal zone spatial planning, guiding the layout of storm surge control projects and land use in high-hazard areas. Additionally, our results fill a gap in the literature on the SLR’s impact in the GBA and support decision-makers in the GBA in building climate resilience and mitigating disaster hazards. Full article

In recent decades, the global fashion and textile market has been facing an unprecedented sector-wide crisis. The growing demand for clothing, combined with continuously decreasing prices and driven by the constant availability of new quantities and styles, has allowed fast fashion and super-fast fashion business models to flood the market with low-quality, short-lived, and high environmental impact products. Starting from 1 January 2025, the separate collection of textile waste came into force in the European Union. However, under current conditions, this regulatory change has generated an imbalance between collection capacity and the availability of effective sorting and recycling channels. Furthermore, due to the low market demand for recycled fibers, warehouses and landfills are increasingly filling with post-consumer textile waste, materials that could potentially serve as secondary raw materials but currently remain unsold. Moreover, the fast fashion business model promotes the use of short fibers and complex fiber blends that are resource-intensive and generate large volumes of low-quality waste. This material profile further limits reuse and recycling options, exacerbating inefficiencies within existing waste management systems. This review aims to identify and discuss available opportunities to address textile waste containing low-quality fibers through the examination of scientific literature, technical publications, and market-ready products that utilize regenerated textile materials. The results highlight open-loop applications and processes, such as those in the automotive, building, and design sectors, thereby opening to new end-of-life scenarios for waste textiles. Full article