Search Results (13,783)

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide),YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

Flooding is one of the most persistent and destructive natural hazards in West Virginia. However, community-scale assessments that connect social vulnerability with physical flood vulnerability are still limited. Existing floodplain management plans often focus on infrastructure and hydrology, overlooking how socioeconomic disparities shape resilience. This study assesses flood resilience in West Virginia communities by connecting socioeconomic vulnerability with physical flood vulnerability. Using data from the American Community Survey (ACS) and state floodplain maps, we developed a Socioeconomic Vulnerability Index (SEVI) and combined it with physical indicators, such as the percentage of residential buildings in the 100-year floodplain, the share of mobile homes in flood-prone areas, the presence of essential facilities and community assets within flood zones, and the proportion of roads submerged by at least one foot of water. Incorporated and unincorporated communities were analyzed separately to reflect differences in governance and service capacity. The results reveal that high flood vulnerability areas often coincide with high socioeconomic vulnerability, especially in the southern and southeastern counties, where long-term economic decline has increased risks. Communities like McDowell and Mingo face a combined challenge of social and physical vulnerability, adding pressure to populations already dealing with limited resources. These findings emphasize the importance of integrated resilience planning that combines physical protection with social support. Considering the increasing intensity of extreme precipitation events associated with climate change, these findings also highlight the importance of incorporating long-term climate considerations into flood resilience planning. Policy suggestions include expanding targeted flood insurance subsidies for low-income households, prioritizing the relocation or retrofitting of mobile homes and essential facilities, investing in green and open spaces, and encouraging community-based mitigation strategies. Together, these actions can lower long-term flood risks while addressing structural inequalities that make certain populations more vulnerable. Full article

Quantifying dynamic urban resilience is critical for climate adaptation. This study assesses the spatiotemporal resilience of 6838 flood-affected communities across 39 Chinese cities using high-resolution human activity data. By establishing a multi-phase framework, we extract six metrics characterizing resistance and recovery trajectories. Results reveal a distinct resilience paradox: coastal cities, despite suffering deeper instantaneous shocks from typhoons, exhibit superior adaptive capacity compared to inland cities, which face chronic recovery deficits under rainstorm stress. Unsupervised clustering identifies 12 distinct resilience phenotypes, ranging from brittle collapse to adaptive growth. Structural analysis confirms a Matthew Effect where functional diversity and economic vitality enable resource-rich communities to bounce forward, while peripheral areas remain trapped in vulnerability. These findings underscore the need for resilience-based regeneration policies that prioritize spatial justice and resource optimization over static engineering standards. Full article

In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building. Full article

The northeast monsoon prevailing over southeastern China in late seasons, generally from October to March, frequently generates water level anomalies upstream of the Taiwan Strait (TWS) that reach the coastal waters of Guangdong in South China, and, with compounding astronomical high tides, elevate coastal flood risk over the region. The risk of coastal flooding or sea inundation is further heightened when monsoon forcing co-occurs with storm surge brought by late-season tropical cyclones (TCs). This study integrates tide gauge observations from Hong Kong (HK) and its vicinity together with Delft3D Flexible Mesh simulations to diagnose a tide-modulated anomaly wave mechanism. Observations show that anomalies originating in or near TWS arrive in HK with station-dependent phasing. These water level anomalies exhibit a characteristic ~6 h periodicity west of the Taiwan Shoal, and display peaks that systematically align with the astronomical high tide. Time–frequency analysis reveals a wave period transformation from ~12 h north of Dongshandao over the coast of southeastern China to ~6 h west of the Taiwan Shoal. We test the hypothesis that wind-forced water anomalies generated in or near TWS undergo shoal-modulated nonlinear tide–wind interaction and tidal-current advection that transform their dominant period and phase-lock them to the tide, producing four anomaly peaks per day downstream and station-dependent phasing in HK. Hindcasts of the November 2024 monsoon episode reproduce the observed timing, periodicity, and spatial transition, while constituent experiments demonstrate that semi-diurnal forcing entering via the TWS is the primary driver of the ~6 h signal, with the Taiwan Shoal acting as the modulation locus. Accurate water level forecasts for the Guangdong coast, therefore, need to incorporate upstream wind forcing over the TWS and bathymetric controls around the Taiwan Shoal, with practical implications for compound flood risk during spring tides and co-occurring monsoon and/or TC events. Full article

Synthetic Aperture Radar (SAR) images offer significant advantages for monitoring the dynamics of water bodies in tropical regions, mainly due to their ability to acquire data under adverse weather conditions, which frequently limit optical sensors. However, the automated classification of water bodies using SAR data still faces methodological challenges, particularly regarding the selection of the most suitable parameters and polarizations. This study proposes a multitemporal classification methodology using Sentinel-1 data to map the flood regimes of lagoon complexes in the State of Rio de Janeiro (Brazil). The approach integrates SAR image time series with the Random Forest machine learning algorithm, evaluating the performance of different polarization configurations (VV, VH, and VV–VH). The results show that the combined use of single and cross polarizations (VV–VH) achieved excellent performance, with a Kappa index of 0.83, F-score of 0.90, and overall accuracy of 0.96, demonstrating methodological robustness. The multitemporal analysis identified approximately 294 km² of permanently flooded areas, while seasonally flooded areas, associated with the seasonal variation in coastal lagoons, exhibited variations exceeding 30 km² over the time series. Full article

This study explores how demystifying Earth Observation (EO) through co-creation pathways and local language can enhance flood resilience and environmental governance in African informal cities. Using case studies from Maiduguri and Hadejia, Nigeria, the research employed a transdisciplinary mixed-methods design combining rapid evidence assessment, surveys, participatory workshops (n = 50 stakeholders) integrating simplified Sentinel-1/2 demonstrations, indigenous knowledge mapping, and pre-/post-engagement surveys on EO familiarity. Non-expert participants were trained to interpret satellite data using local language, linking distant teleconnections with local flood experiences. The findings revealed significant gains in EO literacy and improvements in interpretive confidence, gender-inclusive participation, and policy engagement. Localizing the curriculum enabled participants to translate technical EO concepts into locally meaningful narratives, fostering cognitive empowerment and practical application in flood preparedness and advocacy. The study demonstrates that data democratization is not only a matter of open access but also of open understanding. It advances a conceptual model linking Demystification, Literacy, Empowerment, Co-Production and Resilience, positioning EO as a social technology that bridges scientific and indigenous knowledge systems. The findings contribute to debates on decolonizing environmental science and propose a potential participatory framework for integrating EO into community-based adaptation, legal accountability, and policy reform across Africa’s rapidly urbanizing landscapes. Full article

Many advances in enhanced oil recovery (EOR) take advantage of the unique properties of nanomaterials to improve characterization of formation properties, achieve conformance control during flood operations, and extend the controlled release time of polymers. Magnetite nanoparticles (nMag) have been employed in these processes due to their low cost, low toxicity, and ability to be engineered to meet desired needs, especially with the application of a magnetic field. Similarly, silica dioxide (SiO₂) and aluminum oxide (Al₂O₃) nanoparticles have been evaluated for the delivery of scale and asphaltene inhibitors. However, the injection of nanoparticles into porous media comes with the risk of formation damage due to particle deposition, which can lead to increased injection pressures and reductions in permeability. The goal of this study was to develop a method to evaluate and assess nanoparticle formulations for their potential to cause formation damage. A screening apparatus was constructed to hold small sandstone discs (~2 mm) or cores (~2.5 cm) for rapid testing with minimal material use and the capability to be used with either aqueous brine solutions or non-polar solvents as the mobile phase. Image analysis of the disc and pressure measurements demonstrated increasing deposition of nMag and face-caking when the salinity was increased from 500 mg/L NaCl (8.56 mM) to API brine (2.0 M). Similarly, when the injected concentration of silica nanoparticles in 500 mg/L NaCl was increased from 1 to 10 wt%, the back pressure increased by 55 psi, and face-caking was observed. The screening test results were consistent with traditional core-flood tests and was able to be modified to accommodate organic liquid mobile phases. The screening test results closely matched nanoparticle transport and retention measured in sandstone cores, confirming the ability of the system to rapidly screen nanoparticle formulations for potential formation damage. 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 and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals. Full article

While various methods exist for assessing urban rainwater and flood resources, there is a lack of targeted evaluation for the rainwater harvesting potential of areas equipped with sponge city facilities. This study employs the Yield Before Spillage (YBS) principle to design rainwater collection tanks for sponge facilities under different design return periods, conducting a specialized assessment of the rainwater resource potential in built-up sponge facility areas within the “Dongsheng–Kangbashi–Ejin Horo Banner” urban cluster. The results indicate that the collection potential follows the patterns of “wet year > normal year > dry year” and “Ejin Horo Banner > Kangbashi District > Dongsheng District.” A rainwater collection tank designed for a 5-year return period (p = 5a) is more applicable to the study area. The sponge facilities in the study area achieve an annual runoff volume control rate exceeding 85%, effectively alleviating drainage pressure. The conclusions demonstrate that the YBS method can effectively assess the rainwater and flood resources of sponge facilities in arid regions. Tanks designed for the three different return periods all meet the rainwater retention requirements of sponge cities across various hydrological years. In arid areas, tanks designed for lower return periods are sufficient for harnessing rainwater collection potential, offering lower costs. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article