Search Results (1,580)

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

Direct flood loss estimation for industrial complexes is jointly sensitive to terrain representation, rainfall magnitude, infiltration assumptions, and depth–damage function selection, yet these uncertainties are rarely evaluated together. We quantify their combined effects for the Gumi National Industrial Complex (GNIC), South Korea, using five DEM resolutions (0.5–10 m), six rainfall return periods (10–200 years plus the observed July 2024 event), and three infiltration regimes (5, 10, 20 mm h⁻¹), yielding 90 hydrodynamic realisations from a GPU-accelerated 2D shallow-water model. Each was combined with a harmonised inventory of 16,463 buildings (replacement value 43.07 trillion KRW) and three vulnerability-function families (HAZUS-MH, JRC Huizinga, Korean MD-FDA), producing 270 loss estimates under a common dimensionless transformation. A three-way ANOVA on log-transformed damage confirmed highly significant main effects of resolution, rainfall, and infiltration across all functions, more than an order of magnitude larger than interactions, and robust to heteroscedasticity-consistent and permutation tests. Coarsening the DEM from 0.5 to 10 m reduced expected annual loss (EAL) by 55–57%, while inter-function depth–damage divergence exceeded four-fold at shallow inundation. Validation against the July 2024 event gave the best skill at 2 m resolution (critical success index 0.80, accuracy 0.86). Multi-family residential and heavy industry accounted for 83–89% of total EAL. These results show that terrain resolution and damage-function selection are first-order, statistically independent controls on industrial flood loss, and that omitting any sensitivity axis can bias EAL by more than two-fold. Full article

Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km² that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river’s hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management. Full article

In underwater navigation, MUVs risk damage from obstacles and equipment. Effective damage management supports timely decisions and maximizes functionality recovery. Existing approaches can be roughly categorized into rule-based reasoning, case-based reasoning and expert systems. However, the primary limitation of the existing approaches is their inability to adapt to dynamically changing scenarios. In this paper, an auxiliary decision-making system (ADMS) for manned underwater vehicle (MUV) damage management based on deep reinforcement learning (DRL) is proposed to address the problem of cabin flooding. This system is designed to provide auxiliary decision-making in emergency situations and help preserve MUV vitality. Furthermore, a comprehensive States–Actions cluster encompassing various damage management measures for real damage scenarios is constructed and digitized. Moreover, several novel reward functions are developed to ensure the DRL model obtains a safe strategy with ADMS operations. Finally, the MUV buoyancy and stability vitality evaluation criteria are defined and analyzed. The simulation results show that the auxiliary decision-making measures given by the ADMS in the damage state are effective and rational. The evaluation criterion for buoyancy vitality can exceed 38%, while the criterion for stability vitality can surpass 92%, with an optimal value exceeding 99%. Full article

Slush flows are rapid mass movements of water-saturated snow and debris that develop when the liquid water content of the snowpack exceeds the critical threshold for the fully funicular regime, resulting in viscoplastic flow behavior fundamentally distinct from that of dry snow avalanches. These phenomena have been extensively documented globally as responsible for fatalities and economic damage comparable to those of snow avalanches. In the northwestern Italian Alps, the situation is markedly different. Until 2025, rain-on-snow (ROS) events in this region had produced amplified effects on the ground in the form of increased landslide activity, debris flow mobilization, and flooding, as typical consequences of snowmelt superimposed on pre-saturated soils but had not generated widespread slush flow phenomena. The 2025 season marked a critical threshold: for the first time, diffuse and unambiguous slush flow events were documented in Italian northwestern Alpine sectors, signaling a qualitative shift in the hazard regime rather than a mere quantitative intensification of known processes. Documented emergence of slush flows in the Italian Alps must be interpreted not as an anomaly but as a measurable indicator of climate change and of its progressive effects on the Alpine environment. Full article

Flood-induced scour and earthquake loading jointly govern the seismic performance of river-crossing bridges. Existing conditional fragility assessment frameworks based on static dependence structures do not fully capture the evolving correlations between component failure modes under cumulative hydraulic degradation. This study develops a probabilistic conditional fragility assessment framework for continuous bridges and quantifies the scour-dependent fragility at both the bearing and pier levels, along with the resulting system fragility under series and parallel idealisations. A three-dimensional nonlinear finite element model with scour-dependent soil–structure interaction is constructed in OpenSees, and incremental dynamic analysis is conducted using spectrally compatible ground motions. The results indicate that scour primarily affects the bearing fragility in the moderate to complete regimes, whereas it has a negligible influence on the bearing under minor damage conditions. Unlike bearings, the fragility of piers decreases systematically toward lower PGA values with increasing scour depth, accompanied by a distinct threshold-like sensitivity shift within a specific scour depth range. At the system level, the series model is influenced by the early exceedance probability of the bearings at low PGA, whereas the parallel model is primarily governed by the exceedance probability of the piers at high PGA. Overall, the results demonstrate that scour affects system reliability not only by altering the PGA of the structural components but also by modifying the exceedance probability gap between the bearing and pier. These findings suggest that linear degradation-based management approaches can lead to biases in risk assessment and provide a practical extension and scientific basis for developing bridge system assessments under multi-hazard conditions. Full article

A critical challenge in coalbed methane (CBM) extraction is the severe formation damage induced by conventional foam fracturing fluids, primarily through polymer retention and hydrogen bond disruption within the microporous matrix. This study presents a molecularly engineered, low-damage foam fracturing fluid that leverages synergistic nanoparticle–surfactant interactions to construct a robust interfacial pseudo-gel network, thereby decoupling effective fracture stimulation from adverse geochemical damage. The primary novelties of this work are threefold: (i) establishing a direct, quantitative cause-and-effect relationship between molecular interfacial architecture and reservoir protection, (ii) proposing a comprehensive “interfacial control” design paradigm that engineers viscoelasticity at the gas–liquid interface rather than through bulk polymer gelation, and (iii) demonstrating the complete decoupling of foam stability from formation damage in a polymer-free system. A systematic optimization methodology was employed: initial foaming agents were screened via the Waring Blender method, evaluating foam volume, half-life, and a derived comprehensive index; subsequently, synergistic binary surfactant mixtures and foam stabilizers were assessed to formulate the final systems. An optimized formulation, designated Foam System I (0.5 wt.% fluorosurfactant FK + 0.5 wt.% nano-silica RX + 2.0 wt.% KCl), demonstrated exceptional foam quality (Γ = 77.1 ± 1.5%) and kinetic stability (T₁/₂ > 350 s). Rheological characterization confirmed shear-thinning behavior conforming to the Herschel–Bulkley model (n = 0.38–0.42, R² > 0.98) and a structural recovery of 92.5 ± 2.1%—comparable to crosslinked polymer gels but achieved without any bulk viscosifier. Core flood analyses revealed that Foam System I induced a permeability damage of only 12.75 ± 1.8%, representing a 55–75% reduction compared to polyethylene glycol (PEG)-stabilized reference fluids (28.36–51.91%). X-ray photoelectron spectroscopy (XPS) correlated this enhanced reservoir compatibility with an 18.0 ± 2.0% suppression of oxygen-containing functional group adsorption, attributed to the steric hindrance conferred by the fluorinated hydrophobic moieties. This work establishes an “interfacial control” paradigm wherein gel-like stabilization for proppant transport is achieved via interfacial viscoelasticity rather than bulk polymer gelation, thereby directly addressing the critical imperative to harmonize fracture conductivity with reservoir protection in unconventional energy development. The findings are validated for shallow CBM reservoir conditions (25–35 °C), with extension to higher-temperature formations identified as a priority for future investigation. Full article

Multisource remote sensing data is utilised for the purpose of monitoring annual and interannual changes associated with climate change in the water bodies of the Chotts of Merouane and Melrhir, which are located in the Zone of Chotts in North Africa. These endorheic depressions are distinguished by recurrent flooding events of varying magnitude and frequency, which are contingent on fluctuations in climate parameters. It has been determined that certain cities located within the surrounding watersheds, such as Biskra, are subject to the intermittent threat of severe flooding. This has been shown to result in land degradation and soil salinisation during the drying-up process. A detailed examination of chronological data from the 1960s onwards reveals a decline in the frequency of flooding in Chott Melrhir in recent years. It is noteworthy that the region has not experienced any substantial flooding since 2020. This phenomenon is concomitant with the marked decline in precipitation levels observed in the region. Since 1980, there have been at least ten significant floods, resulting in varying degrees of damage and disruption. In contrast, Chott Merouane exhibits a more consistent hydrological pattern, with water flowing almost year-round due to wastewater and the drainage of the palm groves by the Oued Righ. Until the 1970s, the occurrence of flooding in the region was exclusively attributable to the direct overflow of the Biskra River and its tributaries. However, from the 1980s onwards, a new type of flooding emerged, linked to insufficient infiltration and drainage capacity in the soil and sewage systems during rainfall that was sometimes considered normal. The hydrological regime in the area has severe ramifications for the water supply and the state of the oases, which are vulnerable to salinisation. Full article

Scale deposition, particularly calcium sulfate, poses a major challenge in carbonate reservoirs, leading to permeability reduction and operational inefficiencies. In this study, the performance of a polymeric scale inhibitor, polyphosphinocarboxylic acid (PPCA), was systematically investigated through dynamic core flooding experiments combined with statistical modeling. To address scale inhibition performance and minimum inhibitor requirements, additional static jar tests and dynamic tube blocking experiments were conducted. The results confirmed a minimum inhibitory concentration (MIC) of 40 ppm PPCA, where inhibition efficiency exceeded 90% at elevated temperatures. Moreover, the desorption behavior of PPCA was evaluated under a wide range of operational conditions, including pore volume (0–40 PV), temperature (50–100 °C), injection rate (2–6 mL/min), and pH (6–8). Effluent concentrations were quantified using a spectrophotometric method and expressed as the C_f/C_i ratio (effluent concentration to injected concentration) to characterize inhibitor return behavior. A comprehensive dataset comprising 224 experimental runs was analyzed using Response Surface Methodology (RSM), leading to the development of two predictive models for low (0–10 PV) and high (10–40 PV) pore volume ranges. The models demonstrated excellent predictive capability, with R² values of 0.9934 and 0.9979, respectively. In addition, statistical analysis confirmed that pore volume and injection rate were the most influential parameters, while pH exhibited a comparatively minor effect. Results showed that increasing PV, temperature, injection rate, and pH led to a decrease in C_f/C_i, indicating enhanced desorption. For instance, C_f/C_i decreased from approximately 0.12 at 10 PV to 0.06 at 40 PV under reference conditions. Furthermore, optimization results revealed that maintaining an effective inhibitor concentration (C_f/C_i more than 0.05) is strongly dependent on operating conditions. At 60 °C, a wide operational window was observed, whereas at 100 °C, the effective region significantly narrowed due to accelerated desorption. Furthermore, permeability reduction analysis (K_d/K_i) demonstrated significant suppression of scale-induced formation damage in the presence of PPCA, while blank tests showed severe permeability decline. The integrated results validate the dual role of PPCA in both scale inhibition efficiency and formation protection under dynamic conditions. The novelty of this work lies in integrating polymer-specific behavior with dynamic core flooding and multivariable statistical modeling, providing a robust predictive framework for optimizing squeeze treatment design in carbonate reservoirs. Full article

Urban forests and green spaces provide important ecosystem services that support climate adaptation, public health, and urban sustainability. Despite growing evidence from individual Portuguese cities, nationwide data on how citizens perceive, use, and support the governance of urban green spaces remain limited. This study addresses that gap through a nationwide online survey conducted in Portugal in 2024, gathering 927 valid responses from Portuguese adults across metropolitan, intermediate-density, and low-density municipalities, to investigate public perceptions of ecosystem services, patterns of green space use, management challenges, and attitudes toward urban forestry governance policies. Results revealed strongly positive perceptions of urban trees and green spaces across all sociodemographic groups, with over 95% of respondents acknowledging that urban green spaces positively influence physical and mental health. Regulating services, including air quality improvement, urban noise reduction, climate change mitigation, and flood mitigation, received the highest levels of agreement, while cultural ecosystem services were positively perceived but with comparatively lower agreement. Accessibility emerged as a critical determinant of visitation frequency: 85% of respondents could reach a green space within 15 min, and 82% of daily users lived within 300 m of one, broadly consistent with the 3 + 30 + 300 principle. Frequent visitation was primarily associated with relaxation, physical activity, and social interaction. Conversely, only 6% considered that trees cause more damage than benefits, with pavement damage and superficial roots cited as the more significant management challenges. Support for public investment was broad, with over 90% of respondents favouring allocating municipal tax revenues to urban tree management. However, 68% remained unfamiliar with Law No. 59/2021, revealing a gap between public support and policy awareness. These findings establish a national baseline to support municipalities in developing more resilient, inclusive, and health-promoting urban environments in the face of climate change, as they align urban forestry practices with citizens’ expectations. Full article

This study aims to characterize rainfall-triggered phenomena, including floods, landslides, and urban flooding, defined as damaging hydrogeological events (DHEs), through the integration of the scientific literature and historical documentary sources, and to analyze their rainfall-triggering conditions. The analysis focuses on a sector of the Ionian coast of Calabria (southern Italy) in the period 1925–2025. The identified DHEs were organized into 463 damage records (DRs), enabling a municipal-scale analysis at monthly temporal resolutions. To characterize the rainfall conditions associated with DHEs, we identified a rainfall indicator (R), defined as the ratio between the monthly rainfall observed during a DHE and the corresponding long-term climatological average rainfall. Results show that DHEs occur more frequently during autumn (46%) and winter (41%) and are mainly associated with moderate (1< R < 2) to strong rainfall anomalies (R > 3). Summer events, although limited in number, are often (43%) associated with very strong rainfall anomalies (R > 3). Spatial analysis highlights a heterogeneous distribution of DHEs in the study area, with some municipalities showing a greater occurrence of multiple phenomena. Landslides are the most frequent phenomenon, occurring in 29% of cases in combination with other processes and across a wide range of precipitation conditions. Floods are most often (over 60%) associated with moderate to strong anomalies, while urban flooding exhibits intermediate behavior. Stronger-rainfall-anomaly conditions are generally associated with DHE impacts with wider spatial extents. The study suggests that the proposed indicator may provide a useful framework for the first-order characterization of rainfall conditions associated with DHEs in contexts characterized by the limited availability of long-term data or in similar climatic areas. Full article

Floods are among the most catastrophic natural disasters globally, causing significant damage to both life and infrastructure. Consequently, immediate and accurate assessment of inundated areas is critical for effective emergency response. While optical remote sensing is typically used for flood assessment, it is often ineffective during active flood events due to persistent cloud cover and precipitation. To address this, this research develops a deep learning method utilizing Synthetic Aperture Radar (SAR), which offers all-weather, 24 h imaging capabilities. Specifically, an attention-based differential Siamese U-Net was developed to detect temporal changes in bi-temporal SAR imagery (e.g., Sentinel-1) acquired before and after flood events. The method was evaluated on the S1GFloods dataset, comprising 5360 bi-temporal Sentinel-1 SAR image pairs across 46 flood incidents on six continents. Experimental results demonstrate a flood Intersection over Union (IoU) of 92.43%, an F1 score of 96.07%, and a recall of 97.64%. These metrics rank the proposed approach third overall among top-performing methods on this dataset. Notably, the high recall rate indicates the model is particularly beneficial for emergency response, as it minimizes the number of undetected flooded areas. Despite utilizing a CNN-based architecture that is less complex than Vision Transformer models, this method achieves results comparable to the state-of-the-art DAM-Net, with a performance difference of only 0.77%. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. Full article

Empirical evidence shows that the El Niño-Southern Oscillation (ENSO) influences the frequency–damage relationship for floods. However, ENSO is generally not incorporated into indemnity-trigger modeling of Flood Catastrophe Bonds (FCBs), resulting in an incomplete representation of claim events. Therefore, this study aims to develop an FCB pricing model that incorporates ENSO as an external systematic risk factor affecting the indemnity trigger. The trigger is formulated as a doubly stochastic compound Poisson process, with its intensity modeled as an autoregressive integrated moving-average with exogenous variables. Bond prices are then derived by integrating the trigger process with the Cox-Ingersoll-Ross model under an arbitrage-free risk-neutral framework. To obtain stable numerical solutions, a Monte Carlo-based algorithm is also developed. Numerical simulations using data from Bandung Regency, Indonesia, show stable estimates under the relative Monte Carlo standard error measure. Then, incorporating ENSO empirically improves flood-intensity forecasting accuracy, as indicated by lower MAPE, MAE, RMSE, and Theil’s U. It also produces statistically significant price differences across all common maturities. This study advances the theoretical and practical pricing of FCBs by directly linking climate-driven flood intensity to indemnity triggers, equipping practitioners to quantify risk better and to set sustainable disaster risk financing, particularly in ENSO-affected regions. Full article