Search Results (391)

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. 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

The in situ emulsification synergistic self-profile control system has wide application prospects for efficient development on offshore oil reservoirs. During water flooding in Bohai heavy oil reservoirs, random emulsification occurs with superimposed Jamin effects. Effectively utilizing this phenomenon can enhance the efficient development of offshore oilfields. This study addresses the challenges hindering water flooding development in offshore oilfields by investigating the emulsification mechanism and key influencing factors based on oil–water emulsion characteristics, thereby proposing a novel in situ emulsification flooding method. Based on a fundamental analysis of oil–water properties, key factors affecting emulsion stability were examined. Core flooding experiments clarified the impact of spontaneous oil–water emulsification on water flooding recovery. Two-dimensional T1–T2 NMR spectroscopy was employed to detect pure fluid components, innovating the method for distinguishing oil–water distribution during flooding and revealing the characteristics of in situ emulsification interactions. The results indicate that emulsions formed between crude oil and formation water under varying rheometer rotational speeds (500–2500 r/min), water cuts (30–80%), and emulsification temperatures (40–85 °C) are all water-in-oil (W/O) type. Emulsion viscosity exhibits a positive correlation with shear rate, with droplet sizes primarily ranging between 2 and 7 μm and a viscosity amplification factor up to 25.8. Emulsion stability deteriorates with increasing water cut and temperature. Prolonged shearing initially increases viscosity until stabilization. In low-permeability cores, spontaneous oil–water emulsification occurs, yielding a recovery factor of only 30%. For medium- and high-permeability cores (water cuts of 80% and 50%, respectively), recovery factors increased by 9.7% and 12%. The in situ generation of micron-scale emulsions in porous media achieved a recovery factor of approximately 50%, demonstrating significantly enhanced oil recovery (EOR) potential. During emulsification flooding, the system emulsifies oil at pore walls, intensifying water–wall interactions and stripping wall-adhered oil, leading to increased T2 signal intensity and reduced relaxation time. Oil–wall interactions and collision frequencies are lower than those of water, which appears in high-relaxation regions (T1/T2 > 5). The two-dimensional NMR spectrum clearly distinguishes oil and water distributions. Full article

As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes a novel XAJ-LSTM-TFM hybrid model that accounts for time-lagged hydrological responses and enhances the regional applicability of the Xinanjiang model. The model innovatively integrates the physical mechanisms of the Xinanjiang model with the temporal learning capacity of LSTM networks. By incorporating intermediate hydrological variables (including interflow and groundwater flow) along with 1–3 day lagged meteorological features, the model achieves an average 15.3% improvement in Nash–Sutcliffe Efficiency (NSE) across five sub-basins, with the Ganjiang Basin attaining an NSE of 0.812 and a 25.7% reduction in flood peak errors. The results demonstrate superior runoff simulation performance and reliable generalization capability under intensive anthropogenic activities. Full article

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article

Climate change increasingly threatens heritage-rich river basins, yet the integration of traditional ecological knowledge into formal environmental governance remains underexplored. This study investigates how historically embedded water management practices in Tuscany’s Arno River and New York’s Hudson River can inform adaptive strategies under conditions of climate uncertainty. Employing a Triangulated mixed-methods approach—including a systematic narrative literature review, variable coding (hydrological dynamics, cultural heritage, governance structures, economic livelihoods, and adaptive knowledge), and effect size analysis—we conducted a comparative assessment to uncover regional challenges, capacities, and implementation dynamics. The findings reveal that while both basins contend with hydrological volatility and fragmented governance, the Arno benefits from legally embedded heritage practices that continue to shape canal-based agriculture and flood mitigation. In contrast, the Hudson showcases strong multi-level stakeholder engagement and ecological restoration, though with less institutional reliance on traditional land stewardship. By integrating codified traditional practices with participatory governance and applying a weighted implementation structure, this study illustrates how resilience planning can be more context-sensitive, operationally feasible, and socially inclusive. Ultimately, this research positions cultural landscapes as active infrastructure for climate adaptation—provided they are institutionally supported and community-endorsed—offering a transferable model for policy innovation in similarly vulnerable riverine systems. Full article

In bulk liquid or on solid surfaces, nanobubbles (NBs) are gaseous domains at the nanoscale. They stand out due to their extended (meta)stability and great potential for use in practical settings. However, due to the high energy cost of bubble generation, maintenance issues, membrane bio-fouling, and the small actual population of NBs, significant advancements in nanobubble engineering through traditional mechanical generation approaches have been impeded thus far. With the introduction of the electric field approach to NB creation, which is based on electrostrictive NB generation from an incoming population of “electro-fragmented” meso-to micro bubbles (i.e., with bubble size broken down by the applied electric field), when properly engineered with a convective-flow turbulence profile, there have been noticeable improvements in solid-state operation and energy efficiency, even allowing for solar-powered deployment. Here, these innovative methods were applied to a selection of upstream and downstream activities in the oil–water–fuels nexus: advancing core flood tests, oil–water separation, boosting the performance of produced-water treatment, and improving the thermodynamic cycle efficiency and carbon footprint of internal combustion engines. It was found that the application of electric field NBs results in a superior performance in these disparate operations from a variety of perspectives; for instance, ~20 and 7% drops in surface tension for CO₂- and air-NBs, respectively, a ~45% increase in core-flood yield for CO₂-NBs and 55% for oil–water separation efficiency for air-NBs, a rough doubling of magnesium- and calcium-carbonate formation in produced-water treatment via CO₂-NB addition, and air-NBs boosting diesel combustion efficiency by ~16%. This augurs well for NBs being a potent agent for sustainability in the oil and fuels sector (whether up-, mid-, or downstream), not least in terms of energy efficiency and environmental sustainability. Full article

Floods and droughts are natural disasters that disrupt livelihoods and destroy the environment, with floods constituting up to 40% of all natural disasters globally. South Sudan has experienced severe, recurrent flooding for decades, with two-thirds of the country affected. An integrated flood management system is urgently needed to mitigate impacts and improve community resilience. This requires understanding the inundation process and analyzing flood causes and characteristics. This research leverages data from the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS v2.0) to examine rainfall patterns and analyze trends in annual total precipitation (PRCPTOT), days with precipitation ≥ 20 mm (R20 mm), and simple precipitation intensity (SDII) at the basin scale. It also incorporates Nile River flow data from the Mangala station and Lake Victoria water levels from satellite altimetry. Findings indicate decreasing trends in PRCPTOT, R20 mm, and SDII in Jonglei and Unity States, but increasing trends in river flows and Lake Victoria levels. The Global Surface Water dataset reveals increased water surface areas in these states. These findings suggest that river flow trends oppose rainfall patterns, indicating that local rainfall is not the primary contributor to the recurrent flooding in the area. Full article

Global warming and intensified human activities increase flood disasters, causing annual casualties and economic losses. Mountain shadows are a major source of interference in floodwater extraction from SAR imagery, severely impacting the accuracy of water body detection. This study proposes an innovative approach based on the Inverted Exponential Shadow Removal Model (IESRM). This model can adaptively and dynamically adjust the slope threshold according to the terrain characteristics. It is easy to use, eliminating the need for manual parameter setting. The experimental results demonstrate the following: (1) Water body detection tests across diverse terrains (mountains, plains, and foothill plains) show robust results even in complex foothill regions, with an overall accuracy of 94.51% and a Kappa coefficient of 0.86. (2) A comparative analysis with the shadow formation mechanism method and the HAND (Height Above Nearest Drainage) method revealed that the inverted exponential function model achieved the highest accuracy, with an overall accuracy of 96.46% and a Kappa coefficient of 0.89. The IESRM provides an innovative solution for removing mountain shadows, enhancing SAR imagery-based flood monitoring in complex terrains. It offers timely and accurate data support for flood disaster management agencies. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

To address the challenges of low displacement efficiency and gas channeling in the Lukqin thick oil reservoir, characterized by high viscosity (286 mPa·s) and strong heterogeneity (permeability contrast 5–10), this study systematically investigated water flooding and foam flooding mechanisms using a large-scale 2D sandpack model (5 m × 1 m × 0.04 m). Experimental results indicate that water flooding achieves only 30% oil recovery due to a mobility ratio imbalance (M = 128) and preferential channeling. In contrast, foam flooding enhances recovery by 15–20% (final recovery: 45%) through synergistic mechanisms of dynamic high-permeability channel plugging and mobility ratio optimization. By innovatively integrating electrical resistivity tomography with HSV color mapping, this work achieves the first visualization of foam migration pathways in meter-scale heterogeneous reservoirs at a spatial resolution of ≤0.5 cm, reducing monitoring costs by approximately 30% compared to conventional CT techniques. Key controlling factors for gas channeling (injection rate, foam quality, permeability contrast) are identified, and a nonlinear predictive model for plugging strength ((S = 0.70C^0.6 k_r^−0.28) (R² = 0.91)) is established. A composite optimization strategy—combining high-concentration slugs (0.7% AOS), salt-resistant polymer-enhanced foaming, and multi-round profile control—achieves a 67% reduction in gas channeling. This study elucidates the dynamic plugging mechanisms of foam flooding in heterogeneous thick oil reservoirs through large-scale physical simulations and data fusion, offering direct technical guidance for optimizing foam flooding operations in the Lukqin Oilfield and analogous reservoirs. Full article

Currently, the development of oil reservoirs with high water cut faces numerous challenges, including poor economic efficiency, difficulties in residual oil recovery, and a lack of effective development technologies. In light of these issues, this paper conducts research on gas drive development during the high water cut stage in middle–high permeability reservoirs and introduces an innovative technical approach for air thermal miscible flooding. In this study, the Enhanced Oil Recovery (EOR) mechanism and the dynamic characteristics of thermal miscible flooding were investigated through laboratory experiments and numerical simulations. The N₂ and CO₂ flooding experiments indicate that gas channeling is likely to occur when miscible flooding cannot be achieved, due to the smaller gas–water mobility ratio compared to the gas–oil mobility ratio during the high water cut stage. Consequently, the enhanced recovery efficiency of N₂ and CO₂ flooding is limited. The experiment on air thermal miscible flooding demonstrates that under conditions of high water content, this method can form a stable high-temperature thermal oxidation front. The high temperature, generated by the thermal oxidation front, promotes the miscibility of flue gas and crude oil, effectively inhibiting gas flow, preventing gas channeling, and significantly enhancing oil recovery. Numerical simulations indicate that the production stage of air hot miscible flooding in reservoirs with middle–high permeability and high water cut can be divided into three phases: pressurization and drainage response, high efficiency and stable production with a low air–oil ratio, and low efficiency production with a high air–oil ratio. These phases can enable efficient development during the high water cut stage in medium to high permeability reservoirs, with the theoretical EOR range expected to exceed 30%. Full article

Climate change significantly impacts agricultural infrastructure, particularly in communal land farming systems, where socio-economic vulnerabilities intersect with environmental stressors. This study examined the effects of extreme weather events (floods, droughts, strong winds, frost, and hail) on various agricultural infrastructures—including bridges, arable land, soil erosion control structures, dipping tanks, roads, and fences—using an ordered probit model. A survey was conducted using structured questionnaires between August and September 2023, collecting data from communal farmers (n = 60) in oKhahlamba Municipality, Bergville. Key results from respondents showed that roads (87%), bridges (85%), and both arable land and erosion structures were reported as highly affected by extreme weather events, especially flooding and frost. Gender, the type of farmer, access to climate information, and exposure to extreme weather significantly influenced perceived impact severity. The ordered probit regression model results reveal that drought (p = 0.05), floods (p = 0.1), strong winds (p = 0.05), and frost (p = 0.1) significantly influence the perceived impacts on infrastructure. Extreme weather events, including flooding (p = 0.012) and frost (p = 0.018), are critical drivers of infrastructure damage, particularly for smallholder farmers. Cumulative impacts—such as repeated infrastructure failure, access disruptions, and increased repair burdens—compound over time, further weakening resilience. The results underscore the urgent need for investments in flood-resilient roads and bridges, improved erosion control systems, and livestock water infrastructure. Support should also include gender-sensitive adaptation strategies, education on climate risk, and dedicated financial mechanisms for smallholder farmers. These findings contribute to global policy discourses on climate adaptation, aligning with SDGs 2 (Zero Hunger), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action), and offer actionable insights for building infrastructure resilience in vulnerable rural contexts. Full article

Abandoned mines represent an innovative and under-exploited resource to meet current energy challenges, particularly because of their geothermal potential. Flooded open-pits, such as those located in the Thetford Mines region (Eastern Canada), provide large, thermally stable water reservoirs, ideal for the use of geothermal cooling systems. Thermal short-circuiting that can impact the system performance affected by both free and forced convective heat transfer is hard to evaluate in these large water reservoirs subject to various heat sink and sources. Thus, this study’s objective was to evaluate the impact of natural heat transfer mechanisms on the performance of an open-loop geothermal system that could be installed in a flooded open-pit mine. Energy needs of an industrial plant using water from the flooded Carey Canadian mine were considered to develop a 3D numerical finite element model to evaluate the thermal impact associated with the operation of the system considering free and forced convection in the flooded open-pit, the natural flow of water into the pit, climatic variations at the surface and the terrestrial heat flux. The results indicate that the configuration of the proposed system meets the plant cooling needs over a period of 50 years and can provide a cooling power of approximately 2.3 MW. The simulations also demonstrated the importance of understanding the hydrological and hydrogeological systems impacting the performance of the geothermal operations expected in a flooded open-pit mine. Full article