Search Results (6,609)

In managed pressure drilling (MPD), wellbore flow states cannot be obtained in real time, so kick intervention decisions rely on the empirical judgment of engineers, which introduces a significant lag. The central hypothesis of this study is that fusing a physics-constrained transient two-phase flow model with real-time surface measurements through data assimilation can reconstruct the unobservable downhole flow state and, on this basis, enable quantitative and earlier prediction of the safe intervention timing than empirical judgment alone. To this end, this paper proposes a method for real-time inversion of wellbore flow states and safety intervention timing prediction based on the Ensemble Kalman Filter (EnKF). Using a transient wellbore gas–liquid two-phase flow model as the EnKF model operator, the method continuously assimilates real-time casing pressure, standpipe pressure (SPP), and pit gain data. This process dynamically corrects model prediction bias while maintaining multiphase flow physical constraints. Thus, the method achieves high-precision dynamic inversion of wellbore pressure profiles and gas holdup distributions. On this basis, the authors use the inverted states as initial conditions to calculate safety casing pressure with the multiphase flow model. The method then predicts intervention timing by combining three trigger conditions: safety casing pressure, pit gain, and the density difference between the inlet and outlet. The authors validated the method using kick scenarios from Well L and Well Z in the Shunbei block. The results showed that the mean absolute errors (MAEs) for casing pressure inversion were 0.113 MPa and 0.135 MPa, respectively. The MAEs for SPP were 1.324 MPa and 0.954 MPa. The MAEs for pit gain were 0.174 m³ and 0.114 m³. The inverted spatiotemporal distribution of gas holdup reflected the entire process of gas migration and expansion in the wellbore. Prediction results for intervention timing showed that the method issued early warning signals approximately 53 min and 29 min earlier than actual field operations. This method provides a quantitative decision-making basis with safety redundancy for MPD field operations. Full article

Layered cloud cover (LCC) describes the vertical distribution of cloud occurrence and is a key variable for assessing the radiation budget of the Earth-atmosphere system. However, ground-based radars have limited spatial coverage, while existing satellite cloud-cover products rarely provide both spatiotemporal continuity and high accuracy. Because nighttime satellite observations lack visible-channel information, conventional passive satellite remote sensing remains limited in providing day-night continuous LCC retrievals. In this study, we propose an infrared-based framework for retrieving large-scale day-night LCC from geostationary satellite observations. The framework first resolves cloud vertical structure using a hybrid machine learning and physical algorithm for day-night cloud-base height (CBH) retrieval, and then derives cloud cover in different vertical layers. Validation against active measurements from spaceborne and ground-based cloud radar demonstrates that the satellite-retrieved LCC captures cloud vertical distributions and their diurnal variations. The cloud-layer identification accuracies reach 76.3% and 77.9% for daytime and nighttime, respectively, with corresponding Cohen’s kappa coefficients of 0.66 and 0.68. The primary source of algorithmic uncertainty is the low precision of low-cloud identification, which is constrained by objective factors and physical characteristics. The retrieved annual mean LCC fields reproduce major climatological features, including enhanced high and deep convective clouds over the tropical western Pacific and dominant low-cloud occurrence over the mid-latitude oceans. A case study of Typhoon Doksuri further shows that the 10 min LCC retrievals capture the vertical evolution of the typhoon cloud system during intensification, eyewall structural adjustment, landfall, and post-landfall decay. These results indicate that the proposed infrared-based retrieval framework provides a promising basis for constructing large-scale day-night LCC datasets and can support cloud-radiation studies, climate-model evaluation, and weather monitoring. Full article

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

Karst desertification is a major ecological and environmental issue that threatens regional ecological security and sustainable development; its dynamic monitoring is of great significance for evaluating the effectiveness of ecological restoration and promoting regional sustainable development. Based on the Web of Science database, this paper offers a bibliometric-informed narrative review of the evolution of remote sensing monitoring and information extraction technologies for karst desertification from 1987 to 2025. It focuses on analyzing research progress in methods such as multi-source remote sensing data fusion, deep learning models, and integrated GIS analysis, with regard to improving the accuracy of information extraction and the ability to identify spatiotemporal dynamics of karst desertification. This paper also compares the advantages and limitations of different technologies in terms of high-resolution identification and long-term dynamic monitoring. Building on this foundation and drawing on relevant domestic and international research findings, this study examines the development trends and major challenges facing karst desertification monitoring technologies. It further outlines the direction for establishing a long-term, standardized dynamic monitoring system, with the aim of providing a scientific basis for ecological governance and sustainable development in the karst regions of Southwest China. Full article

In order to improve the assessment of the dynamic risk of urban waterlogging, this study addresses the limitations of existing methods in capturing the responses of surface roads and subway systems to inundation, as well as the resulting spatiotemporal risks. Using the Hanyang District in Wuhan as a case study, the research proposes a framework for assessing urban waterlogging risks based on the dynamic inundation responses of surface and underground transport systems under various rainfall scenarios. The waterlogging process is simulated using seven representative rainfall scenarios with a hydrodynamic model that integrates a one-dimensional pipe network, a two-dimensional surface overland flow model, and a generalized underground space model. A coupled road–subway transportation network is developed to analyze traffic capacity degradation, path redistribution, and cascading failures caused by waterlogging disturbances. Quantified dynamic response indicators are integrated into the H-E-V-C framework to assess dynamic urban waterlogging risk. The results indicate that direct failure caused by water accumulation is typically the primary catalyst for extensive degradation of the transportation network, while the expansion of congestion and localized overload failures further exacerbate cascading effects. Different rainfall patterns influence not only peak risk but also the duration and spatial development of high-risk areas. Incorporating the dynamic response of the transport system enables a more accurate assessment of the degradation of emergency accessibility and the ongoing accumulation of localized high-risk areas. These findings highlight the importance of dynamic risk assessment in identifying time-varying urban vulnerabilities and supporting the planning of sustainable urban drainage, traffic management, and phased early warning systems. Full article

Fujian sea areas suffer frequent disastrous wave events in southeast China. Research on wave characteristics are crucial for marine engineering and coastal disaster risk reduction. Based on TOMAWAC hindcast wave data, this study analyzes the spatiotemporal variations in wave parameters in the Fujian sea areas during 1980–2023. Six typical feature points are selected for comparative analysis to clarify wave climate features across different water depths. Results indicate that the maximum significant wave height (SWH) in the Fujian sea area declines from offshore to inshore and from north to south, with a peak of 15 m off Ningde. Seasonally, maximum SWH is induced by tropical cyclones in summer and autumn, generally exceeding 10 m. Under the influence of the East Asian monsoon, the mean SWH reaches its annual maximum of 2.5 m during the winter season. Severe waves show a stepped increasing from inshore to offshore seas, with the longest duration in autumn. The Taiwan Strait is characterized by a widespread high SWH region, where severe wave events persist for more than 15 h. Fujian sea wave variations are governed by water depth-topography effects and seasonal wind-swell regimes. Full article

Changing environments have intensified drought impacts on terrestrial ecosystems. By integrating meteorological and multi-platform remote sensing observations spanning 2000–2020, this investigation employed spatiotemporal analysis and correlation analysis to investigate multi-dimensional drought characteristics and their impacts on ecosystem patterns and net primary productivity (NPP) across the Yellow River Basin. The meteorological drought center was predominantly located in the midstream region, with the most intense drought persisting for 32 months (January 2000–May 2002) and shifting 419.18 km in 2003. Human activities dominated land use transformation: cropland decreased by 8523.48 km², built-up land increased by 4550.79 km², and severely ecologically degraded areas rose to 28.61%. Forestland and cropland showed continuous upward trends in EQI and LUE, while grassland and unused land exhibited severe interannual fluctuations and slow improvement. Land use type determined coupling differences between climatic and ecological indicators. Under human intervention, cropland formed unique correlations: −0.9 between SPEI-12 and CWSI, −0.8 between SPEI-12 and TVDI, and 0.9 between CWSI and TVDI. These findings indicate that human activities critically regulate drought–ecosystem feedbacks, highlighting the need for land use-tailored management approaches. Full article

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research. Full article

Terrestrial evapotranspiration (ET) plays a vital role in the water cycle, comprising components such as transpiration, interception loss, bare-soil and open-water evaporation, etc. This study has validated the GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) product with eddy covariance ET data. The spatiotemporal variations in total ET and its components in China during 1980–2024, derived from the GLEAM model, and their relations with air temperature, precipitation and solar radiation in the context of climate change have been studied. During the study period, a significant increase in total ET was found over the southeast of China, especially in spring and summer. The different ET components showed somewhat different trends. While transpiration and interception losses increased significantly in humid and transitional zones, bare-soil evaporation declined markedly in humid regions but remained stable or increased slightly in the northwest and the Tibetan Plateau. Precipitation accounts for the largest share of total ET variability in arid regions, whereas transpiration in humid regions shows the strongest association with available energy. In transitional zones and the Tibetan Plateau, total ET reflects the synergistic regulation of both water and energy availability. Recent enhancements in total ET are primarily associated with rising precipitation in the Tibetan plateau and increasing air temperature in transitional zones. Full article

This study explored a spatio-temporal deep learning approach for optical soft sensing of combustion emissions in a coal–biomass co-firing scenario. High-speed RGB flame sequences from a 0.5 MW test rig co-firing hard coal with 10% straw were synchronized with extractive measurements of O₂, CO₂, and NO. These sequences were used to train three shallow 3D CNNs and three 3D ResNet-50 architectures with squeeze-and-excitation attention. The proposed 3D CNN/ResNet models performed simultaneous regression of all three gas species from flame image volumes. The best configuration achieves R² values of 0.975, 0.987, and 0.980, accompanied by mean absolute errors of 0.23% by volume, 13.15 mg/m³, and 0.19% by volume for O₂, NO, and CO₂, respectively, at a resolution of 128 × 96 × 96 pixels. Within the scope of the available dataset, comprising a single measurement run and a single fuel mixture, the results indicate that a comprehensive spatio-temporal analysis of flame images can yield accurate estimates of multiple gas concentrations, thereby providing a promising foundation for the future development of soft optical sensors. At the same time, the study is limited to a single combustion experiment, a single biomass fraction, and a single borescope orientation, and the inference delay and hardware requirements were not quantified; therefore, issues regarding the generalizability of the proposed approach to different conditions and its implementation remain open for further work. Full article

Negative air ions (NAIs) are key ecological indicators of atmospheric cleanliness and forest ecosystem service functions, particularly in the context of forest wellness and ecotourism. However, long-term, high-frequency observations of NAIs across broad spatial scales remain scarce, limiting our understanding of its regional spatiotemporal dynamics and environmental controls. Here, we present a six-year (2018–2023) continuous, hourly monitoring dataset of NAI concentrations from 60 fixed forest sites across Zhejiang Province, a typical subtropical humid region in southeastern China. The provincial mean NAI concentration over the study period was 1672 ions·cm⁻³, with a pronounced “high around the periphery, low in the center” spatial pattern, with the mountainous southwestern areas consistently showing the highest concentrations and the central Jinqu Basin the lowest. On diurnal scales, NAIs exhibited a bimodal pattern with primary peaks at 7:00 and secondary peaks at 16:00, rather than a simple daytime–nighttime dichotomy. Seasonal dynamics showed significantly higher NAI in summer than in autumn and winter; however, the summer–winter difference was only ~25%, much smaller than the ratios reported for temperate regions. Interannually, NAI concentrations increased from 2018 to 2023 (average annual increase of 158 ions·cm⁻³), peaking during the 2020–2022 period, when anthropogenic emissions were substantially reduced. Using linear mixed-effects models, we identified relative humidity as the dominant positive driver of NAI variability, followed by wind speed as a negative modulator, and precipitation playing a minor role. These findings reveal the multi-scale spatiotemporal dynamics of NAIs in subtropical forests and underscore the overriding control of humidity over ion persistence. Our study provides a robust regional benchmark for background NAI levels in humid subtropical climates and offers direct scientific support for forest-based health resource planning and air quality assessment. Full article

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

The urban heat island (UHI) effect has become a prominent ecological and energy challenge amid rapid urbanization. This study comprehensively examined the spatiotemporal dynamics of UHI intensity in built-up areas across 216 Chinese cities spanning five climatic zones from 2000 to 2020 and quantified UHI-triggered energy consumption, as well as revealing its driving mechanisms. The results showed a significant increasing trend in UHI intensity across China’s urban built-up areas during summer days, summer nights, and winter nights from 2000 to 2020, with corresponding annual growth rates of 10.23, 5.61, and 5.08 km²·°C·a⁻¹, respectively. However, winter daytime UHI intensity declined dramatically from 4.72 °C in 2000 to −10.21 °C in 2020, which can be attributed to the reduction in socioeconomic activities during the COVID-19 period. UHI intensity intensified significantly across all climate zones, with the largest increases observed in the middle temperate zone and warm temperate zone, reaching 127.23 km²·°C and 116.04 km²·°C, respectively. Spatially, 39.8% of the 216 cities exhibited a significant increasing trend in UHI intensity, while only 2.8% showed a decreasing trend. After 2005, the contribution of large cities to UHI intensity continued to rise, reaching 54% in 2020. This study estimated UHI-induced energy consumption in terms of standard coal equivalent, with the northern and middle subtropical zones jointly accounting for over 61.9% of the annual average consumption. Regression results confirmed that impervious surface expansion served as the dominant positive driver of UHI, while vegetation coverage exerted a strong cooling effect. These findings can facilitate the formulation of region-specific UHI mitigation and energy conservation policies for cities under different climatic conditions and at diverse development scales. Mechanistic analysis further revealed that variations in impervious surface area dominated the rise in UHI intensity, whereas changes in the normalized difference vegetation index exerted a significant mitigating effect. These findings provide a solid scientific basis for targeted UHI mitigation and energy-saving management strategies for cities across different climate zones and urban scales. Full article

Spatial differentiation of urban natural basement conditions leads to significant differences in urbanization development patterns and land evolution patterns in different regions. Taking Fuzhou, a typical coastal basin city located in the Minjiang River Estuary, as the study area, this paper analyzes the spatiotemporal evolution characteristics of land use/cover change (LUCC) and quantifies its driving mechanism from 1990 to 2020, by using the land use transition matrix (LUTM), the center-of-gravity model (CGM), the standard deviation ellipse (SDE), and the optimal parameters-based geographical detector (OPGD). The results show that (1) the land use structure has undergone drastic restructuring, the built-up land has increased significantly, the grassland has decreased significantly, and the cropland and forest land have shown phased evolution characteristics: a light increase from 1990 to 2000 and a continuous decline from 2000 to 2020. Water exhibited a fluctuating pattern: shrinking from 1990 to 2000, expanding from 2000 to 2010, and shrinking again from 2010 to 2020. (2) Constrained by the terrain of the Minjiang Estuary Basin, the gravity centers of cropland and grassland shifted northwestward, forest land moved southeastward, water shifted northeastward, and built-up land expanded northward. (3) Driving factors exhibited stagewise differences: socioeconomic factors played a dominant role from 1990 to 2000, with population density ($q$ = 0.4029) and nighttime light ($q$ = 0.3639) being significantly higher than other factors. From 2000 to 2010, the terrain constraint effect continued to intensify, with GDP ($q$ = 0.4470), nighttime light ($q$ = 0.3658) and DEM ($q$ = 0.3638) as the dominant factors. From 2010 to 2020, urban land pattern evolution was jointly driven by multiple factors. This study clarifies the land use evolution mechanism of coastal basin cities during urbanization, providing a scientific reference for the sustainable development of similar coastal basin cities. Full article

The invasion of the marine alien gastropod Rapana venosa (Valenciennes, 1846) across different basins is investigated through a spatiotemporal analysis of distribution patterns, aggregation processes, and spatial structure. Occurrence data from scientific literature and citizen science were integrated with GIS-based spatial statistics to compare invasion dynamics in the Black Sea, the Mediterranean Sea, and Northwest Europe. The Black Sea represents the most advanced invasion stage, characterized by extensive distribution, multiple aggregation zones, and strong associations with brackish, nutrient-rich areas influenced by major river outflows. In the Mediterranean, the invasion has progressed from a prolonged establishment phase to a recent acceleration, with the Adriatic Sea acting as the historical core of expansion. Here, persistent populations are concentrated near the Po River delta and lagoon systems, where reduced salinity and high nutrient loads favor both settlement and long-term persistence. In Northwest Europe, R. venosa remains in the establishment phase, forming a compact and localized nucleus along the French Atlantic coast without evidence of broad spatial expansion. Our analyses suggest that environmental factors, particularly salinity gradients and riverine inputs, are possibly related to the observed invasion patterns. Transitional coastal environments emerge as important areas for establishment and subsequent spread, suggesting that monitoring efforts should prioritize these environments. Full article