Search Results (1,653)

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

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

The sources of potentially toxic elements (PTEs) in the lower reaches of the Yellow River (LYR) remain poorly understood due to intensive human activities in this region. To elucidate the spatiotemporal distribution characteristics and sources of PTEs, water samples were collected from both mainstream and tributary sites during the dry season (DS) and flood season (FS). Concentrations of eight PTEs (Fe, Mn, Cu, Zn, Pb, As, Cr, and Hg) were determined. The single-factor pollution index, Nemerow comprehensive pollution index, statistical techniques, and the positive matrix factorization (PMF) receptor model were jointly employed to evaluate PTEs pollution levels and quantitatively apportion its sources. The results showed that PTEs concentrations in the mainstream were significantly higher than those in the tributaries, with Fe and Mn being the primary contaminants exceeding standards. During the DS, the mean concentrations of Fe and Mn were 1.33 mg/L and 0.34 mg/L, with exceedance rates of 100% and 84.2%, respectively. In contrast, both concentrations declined markedly in the FS (Fe: 0.27 mg/L; Mn: 0.112 mg/L). The PMF model identified three sources in the DS, with contribution rates of 42.1% (geogenic background and domestic sewage), 32.4% (industrial wastewater), and 25.5% (agricultural sources). In the FS, two sources were resolved, namely a mixture of non-point source pollution and domestic sewage (64.3%) and a mixture of geogenic background and industrial wastewater (35.7%). The pronounced increase in non-point source contribution during the FS highlights the role of rainfall runoff in driving pollutant input. This study provides a scientific basis for PTEs pollution control in the LYR. Full article

Longnan, located in Gansu Province, China, at the junction of Shaanxi, Gansu, and Sichuan provinces, represents one of the key corridors of the Northern Tea-Horse Ancient Road. This region preserves abundant traditional timber covered bridges with distinct local characteristics. This study employs ArcGIS spatial analysis and documentary research methods to explore the spatial distribution, spatiotemporal evolution, and influencing factors of these bridges. Spatial analyses (nearest neighbor index, kernel density, and standard deviational ellipse) are based on 71 bridges with traceable coordinates, while the temporal evolution analysis incorporates 80 bridges (64 with definite construction periods and 16 with unknown dates; the latter are handled through a sensitivity analysis as described later in this paper The results indicate that the timber covered bridges in Longnan exhibit a significantly clustered distribution, presenting a pattern of “dense in the southwest and sparse in the northeast”, with Wen County and Kang County as the core clustering areas. Temporally, they follow a unimodal evolution pattern: initiation in the Ming Dynasty, peak in the Qing Dynasty, decline in the Republic of China period, and near stagnation in modern times. The location and distribution of the covered bridges show a strong statistical association with natural conditions (e.g., topography, hydrology) and exhibit spatial coincidence with modern vegetation coverage—the latter treated solely as a contemporary context variable rather than a historical driver. Spatial coincidence with the ancient road is quantified (60.56% within a 2000 m buffer), while settlement proximity is only qualitatively noted as background. Socio-economic factors (e.g., population, transportation, and settlements) are examined qualitatively and display spatial coincidence rather than quantitatively measured influence; these factors cannot be directly compared with natural factors. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article

Large-scale urbanization in China has altered land surface characteristics, affected climate and hydrological cycles, and changed the spatial and temporal distribution of precipitation. The combined effects of global warming and the urban heat island effect have further intensified changes in urban rainfall patterns. Therefore, it is essential to clarify the spatiotemporal evolution of precipitation under China’s rapid urbanization process in order to reduce multiple disaster risks. To achieve this, historical precipitation data and multisource remote sensing imagery were integrated to construct a spatiotemporal coupling model for analyzing the relationship between urbanization patterns and precipitation distribution in China. In addition, combined with the background of global climate change, the spatiotemporal evolution characteristics of annual, monthly, and seasonal precipitation were investigated. The main conclusions are as follows: (1) China still has great potential for urbanization and economic development and is currently in a new stage of rapid growth; (2) During 1992–2020, the national area proportion receiving annual precipitation of (200, 400] mm decreased by approximately 0.12 percentage points per year, whereas the area proportion receiving (400, 800] mm increased by approximately 0.11 percentage points per year, indicating a measurable shift toward wetter precipitation conditions; (3) Heavy rainfall events in China are expected to increase in the future, mainly occurring from June to August, with a maximum monthly precipitation reaching 1137.9 mm; (4) Urbanization may be one of the important factors associated with precipitation changes in China, with 2008 identified as a key turning point, when the urbanization rate approached 50% and began to exhibit a preliminary scale effect. Full article

The Jinsha River Basin constitutes the largest hydropower base in China. However, its complex terrain results in insufficient accurate data support for numerical forecasts, leading to low accuracy in precipitation predictions. To investigate the spatiotemporal distribution characteristics of precipitation in this basin with high precision, we evaluated the applicability of several mainstream precipitation products—GSMAP (Global Satellite Mapping of Precipitation), GPM-IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), CMORPH (Climate Prediction Center Morphing technique), and ERA5 (European Center for Medium-Range Weather Forecasts Reanalysis 5)—in the Jinsha River Basin. Based on the XGBoost algorithm, we developed a merging model that integrates satellite and reanalysis data with station observations for daily-scale applications. The results indicate that the GSMAP-Gauge precipitation product exhibits strong performance in both quantitative accuracy and precipitation event detection, with a better correlation coefficient (CC = 0.66), the lowest root mean square error (RMSE = 4.45), and higher probability of detection (POD = 0.88) and critical success index (CSI = 0.59). The ERA5 and GSMAP-Gauge products performed well in detecting light rain events (daily precipitation < 10 mm), with hit rates of 0.92 and 0.90, respectively. Meanwhile, the GPM-IMERG and CMORPH-BLD products showed higher hit rates for heavy rain events (daily precipitation > 25 mm) compared to the other two products. Specifically, the POD indices for GPM-IMERG and CMORPH-BLD were 0.45 and 0.60, respectively, while those for ERA5 and GSMAP-Gauge were below 0.4. Following the precipitation merging experiment, the multi-source precipitation merged product (MSP) substantially enhanced the accuracy of precipitation estimates, and the spatiotemporal distribution characteristics of the merged data aligned more closely with the station observations. This study analyzes the strengths and limitations of various precipitation products in the Jinsha River Basin and provides a feasible multi-source precipitation data merging scheme, offering a novel approach to constructing high-precision daily precipitation datasets in complex terrain regions. Full article

Intensifying heatwaves threaten the reliability of electric distribution systems, yet the quantitative relationship between heatwave characteristics and observed power outage behavior remains poorly understood at multi-year, statewide scales. This study develops an event-based, spatiotemporal framework to quantify heatwave-induced outage risk across 254 Texas counties from 2014–2021 by integrating county-level EAGLE-I outage records with reanalysis-derived heat index measurements. An adaptive percentile-based threshold identifies 3048 heatwave events; logistic regression quantifies the probabilistic relationship between heat intensity and major-outage occurrence under three severity definitions. Across 3048 identified heatwave events, 51% involved at least one outage, a rate significantly above the non-heatwave warm-season baseline and revealing widespread heat-related reliability challenges. Outage severity and duration exhibit heavy-tailed distributions, with a small number of extreme events disproportionately affecting customers. Logistic regression models under three severity definitions (P90, P95, and ≥500 customers) demonstrate that heat intensity is a statistically robust probabilistic predictor of major outages, with each +1 °F increase in mean event heat index raising the odds by approximately 43–52%. The predicted probability of a P90-severity major outage approximately doubles across the interquartile range of event heat intensity (~7% to ~14%), providing actionable guidance for utility pre-staging decisions during forecast heatwave episodes. These findings offer a scalable methodology for climate-related reliability assessment, supporting grid hardening, resource planning, and public health preparedness. Full article

To investigate the spatiotemporal distribution of phytoplankton communities and their major environmental influences in the mainstream of the Yellow River (Shandong section), 29 sampling sites were surveyed in the summer and autumn of 2022 and the spring of 2023. Among the 206 phytoplankton species belonging to 8 phyla and 99 genera, Bacillariophyta, Chlorophyta, and Cyanobacteria were the main dominant groups, and Aulacoseira granulata var. angustissima (O.Müller) Simonsen, Fragilaria capucina Desmazières, and Ulnaria acus (Kützing) Aboal were the most important dominant species. The number of phytoplankton species and cell densities exhibited significant seasonal changes in the sequence of summer > autumn > spring. The phytoplankton community underwent a succession from Bacillariophyta–Chlorophyta dominance in spring and summer to Bacillariophyta–Cyanobacteria dominance in autumn. Water temperature, dissolved oxygen, and transparency were the most important factors affecting the growth of the dominant Bacillariophyta species. The spatiotemporal distribution of phytoplankton is jointly regulated by multiple environmental factors, providing a scientific basis for the evaluation and management of regional aquatic ecosystems. Full article

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved. Full article

This study focuses on the quantitative simulation of the spatiotemporal distribution characteristics of permafrost area, providing scientific value for Mongolian Plateau permafrost dynamics. Understanding the permafrost area of the Mongolian Plateau and accurately predicting future changes in permafrost area are crucial for sustainable environmental development. In this study, ERA5-Land surface temperature (LST) combined with the temperature at the top of permafrost (TTOP) model are used to calculate the annual permafrost area from 1980 to 2024. In addition, this study used the long short-term memory (LSTM) model to predict permafrost area on the Mongolian Plateau from 2025 to 2100. In this study, it is concluded that (1) the study area is not uniformly covered with permafrost, and its distribution is mainly limited to the northern part of the Mongolian Plateau, with a permafrost area of 53.20 × 10⁴ km²; (2) the permafrost area is estimated with an accuracy and precision of 0.94 when compared to the baseline value derived from borehole permafrost data; (3) under the CMIP6 three different shared socioeconomic pathway (SSP) 1-2.6, 2-4.5, and 5-8.5 future scenarios, the distribution of permafrost area shows a downward trend. This study provides a theoretical reference for distribution permafrost area in geographical space, which can help achieve the sustainable development of ice and snow resources. Full article

Natural climate variations and human activities have significantly altered the river–lake hydrological regimes in the middle and lower reaches of the Yangtze River, leading to substantial changes in the inundation patterns of the Dongting Lake wetland, which in turn profoundly affect the spatial distribution and landscape patterns of wetland vegetation. Determining the response mechanisms and appropriate thresholds of wetland landscape patterns to hydrological rhythm changes is of great importance for maintaining the health of wetland ecosystems and optimizing the ecological operation of water conservancy projects. Based on long-term measured water level data (1992–2023) and multi-temporal Landsat remote sensing images (1997–2022), combined with a digital elevation model (DEM), this study systematically analyzed the spatiotemporal evolution characteristics of the inundation processes in Dongting Lake before and after the operation of the Three Gorges Project (TGP) and their driving mechanisms on the plant landscape patterns of the floodplain wetland. The results show that after the TGP operation, the inundation pattern of Dongting Lake exhibited a drying trend, with a significant decline in annual mean water level (the largest drop of approximately 0.7 m in East Dongting Lake) and a marked reduction in the lake-wide average inundation duration (T) and inundation frequency (F). From 1997 to 2022, the total area of wetland vegetation in Dongting Lake showed a significant expansion trend, and the succession of the landscape pattern experienced a nonlinear process of stability, fragmentation, and recovery. The stepwise regression model revealed that the three elements of the inundation process explained more than 80% of the landscape pattern variation, among which inundation frequency (F) and inundation duration (T) were the core driving factors. Specifically, inundation frequency primarily regulated landscape diversity (SHDI) and contagion (CONTAG) through an environmental filtering effect, while maximum inundation depth (H) mainly maintained the physical connectivity (COHESION) of the landscape. Furthermore, the study quantified the stable hydrological range of the Dongting Lake wetland ecosystem: when the inundation frequency is maintained at 0.40–0.50 and the annual inundation duration is controlled at 4–5 months, the wetland landscape is in an optimal structural state. Once the warning thresholds are breached (e.g., F < 0.35 or T < 90 days), it may trigger the rapid expansion of cultivated poplar forests under combined hydrological and anthropogenic influences, leading to severe habitat fragmentation. These findings deepen the understanding of the response mechanisms of vegetation landscape patterns in large lake wetlands under altered hydrological rhythms. Full article

Holotrichia parallela is a globally distributed soil-dwelling pest that poses a major threat to peanut cultivation in China. Neuropeptides, as critical signaling molecules, regulate multiple physiological and behavioral processes in insects and represent highly promising targets for pest management. To date, the functional characteristics of neuropeptides in H. parallela remain unreported. In this study, we isolated and cloned one NPF and one NPFR gene, respectively. Bioinformatics analysis revealed that alternative splicing of the NPF gene produces two transcript variants, NPFa (255 bp) and NPFb (369 bp). The NPFR gene spans a length of 1188 bp, encoding 395 amino acids that contain seven α-helical transmembrane domains, indicating that it belongs to the family A G protein-coupled receptor (GPCR) family. Spatiotemporal expression profiles demonstrated that NPF was most abundant in the adult brain, whereas NPFR was highly enriched in the brain and antennae. NPF expression peaked in second-to-third-instar larvae, while NPFR was highly expressed in eggs. Starvation stress significantly upregulated the expression of both genes. RNA interference (RNAi)-mediated silencing of NPF and NPFR significantly reduced food intake, female fecundity, and glycogen content in adults. These findings enhance our understanding of insect neuropeptides signaling networks and support the development of behavior-based pest control strategies. Full article