Search Results (3,768)

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Accurate traffic flow forecasting is essential for the stable operation and efficient scheduling of intelligent transportation systems. The key lies in identifying the complex spatio-temporal dependencies within the road network structure. In the real world, traffic data are often noisy and incomplete due to sensor failures, communication interruptions, and other unexpected disturbances. To overcome these challenges, this paper proposes an adaptive spatio-temporal self-supervised traffic flow forecasting method based on contrastive learning (ASTSS-CL). At the graph level, structural perturbations are generated by combining node centrality with nonlinear probabilities, while a learnable temporal-periodic parameter matrix and an attention-based fusion mechanism are introduced to adaptively optimize adjacency relationships. At the temporal level, complementary augmentations are designed in both the time and frequency domains. Dynamic interpolation captures continuous traffic variations, while wavelet decomposition and node-adaptive frequency masking balance low-frequency trends and high-frequency details; random masking further improves robustness to missing observations and disturbances. In addition, spatial heterogeneity learning and contrastive consistency learning are jointly employed to enhance representation quality. Experiments on the PeMS04 and PeMS08 datasets show that ASTSS-CL achieves MAE, RMSE, and MAPE values of 17.95, 28.86, and 12.07% on PeMS04, and 13.78, 22.05, and 9.46% on PeMS08, respectively, outperforming the best-performing baseline. These results validate the effectiveness of the proposed method and demonstrate its potential to support traffic management and the operation of intelligent transportation systems. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

The yield of highland barley is not only related to the food security of Tibet but also to the social stability and development in the frontier region. This study revealed the spatiotemporal distribution of highland barley yield potential using the DSSAT model and GIS technology in the Yarlung Zangbo River and its two tributaries (YZTT) of Tibet from 1981 to 2020, and analyzed its response relationship to climate factors. The results show that the highland barley yield potential ranged from 4284.75 to 7341.15 kg/ha in the YZTT region during 1981 to 2020, with an average of 6719.87 kg/ha. Under the climate change, the highland barley yield potential was on a downward trend of −14.49 kg/ha·a over the past 40 years. In terms of the response of highland barley yield potential to climate change, the highland barley yield potential decreased by 2.90 kg/ha for every 1 MJ/m² decrease in solar radiation. For every 1 °C increase in the maximum temperature, the highland barley yield potential increased by 219.68 kg/ha. Meanwhile, for every 1 °C increase in the minimum temperature, the highland barley yield potential increased by 91.40 kg/ha. These findings aim to provide reference for decision-making in agricultural policy and spatial allocation of agricultural resources. Full article

Considering rising global uncertainties and intensifying resource and environmental pressures, it has become an inevitable trend to add more ecologically green factors to the traditional industrial chain resilience system and build a system of green industrial chain resilience (GICR). To address the inherent tension between security and green goals, this study develops a novel two-dimensional analytical framework encompassing fracture repair capacity and development regeneration capacity. This framework provides the theoretical foundation for constructing a pioneering city-level evaluation system for GICR. Employing this system and a suite of spatial econometric methods, we empirically analyze the spatiotemporal evolution, convergence, and driving mechanisms of GICR across 245 Chinese cities. The main findings are threefold. First, the proposed framework effectively captures the complexity of GICR, revealing an overall upward trend but significantly widening regional disparities, with a persistent core-periphery spatial pattern. Second, convergence analysis uncovers a club convergence dynamic nationwide, characterized by a notable “high-level equilibrium lock-in” in the advanced eastern region, in contrast to the catch-up convergence observed in central, western, and northeastern China. Third, geographical detector analysis identifies talent agglomeration as the paramount driver, with its interaction with other factors producing nonlinear enhancement effects. These findings underscore that enhancing GICR requires regionally differentiated strategies: policies must break the innovation lock-in in the east, embed resilience standards into industrial transfer in the central and western regions, and prioritize talent as the core lever for synergistic capacity building. Full article

Against the backdrop of the global green development concept, scientifically assessing the level of urban green transformation (UGT) in China and revealing its spatiotemporal evolution are critical for promoting high-quality development in the country. We constructed an evaluation index system based on four dimensions: economic, social, resource, and environmental transformation. Using the entropy method, we determined the scores for a comprehensive green transformation development index for 285 prefecture-level-and-above cities in China from 2000 to 2023. We further employed kernel density estimation, standard deviation ellipses, the Dagum Gini coefficient, and convergence models to systematically examine the dynamic evolution, regional disparities, and convergence characteristics pertaining to UGT. The key findings are as follows: (1) There is a steady upward trend in the overall level of UGT in China, with intra-regional differences gradually converging. However, a “better–getting-better” differentiation pattern exists, while there is no observable multi-peak polarization. (2) Based on the UGT level, cities in China can be classified into four types: leading areas, potential areas, catching-up areas, and lagging areas. Spatially, a gradient pattern consisting of “high in coastal areas and low in inland areas” was identified. The overall centroid of green transformation has shifted southward, with a northeast–southwest distribution direction. The spatial agglomeration pattern exhibits a transition from dispersion to concentration. (3) There is a decreasing trend in overall disparity among the eight major economic regions, with inter-regional disparity being the primary source, while intra-regional disparity in coastal areas has increased. (4) Regarding convergence characteristics, σ-convergence can be observed in all economic regions except the Eastern Coastal, Southern Coastal, and Middle Yangtze River economic regions. Both absolute β-convergence and conditional β-convergence were found for China overall and its eight comprehensive economic regions, with the highest convergence speed in the Northeast region and the lowest in the Middle Yangtze River region. Furthermore, spatial absolute β-convergence and conditional β-convergence are also present, indicating strong spatial dependence among cities. This study provides empirical evidence and policy references for promoting UGT and optimizing regional development layouts in China. Full article

[Objective] This study aims to reveal the spatiotemporal evolution and transition patterns of green utilization efficiency of cultivated land (GUECL) across Chinese provinces and to identify multidimensional configurational pathways for improving efficiency. [Method] Carbon emissions and total carbon sinks were incorporated into the evaluation index system of GUECL. The super-efficiency SBM model was used to measure GUECL. A three-dimensional analytical framework of “driving forces–external foundations–internal conditions” was then constructed. Exploratory Spatio-Temporal Data Analysis and the fsQCA method were combined to examine the spatiotemporal evolution characteristics and multiple configurational pathways. [Results] (1) From 2013 to 2023, GUECL showed a fluctuating upward trend, with the mean value increasing from 0.550 to 0.835. Spatially, it presented a pattern of high efficiency in Northeast China and low efficiency in Southwest China. (2) The local spatial structure of GUECL was generally stable, although its spatiotemporal transition paths fluctuated to some extent. The cooperative effects in northeastern and western provinces were stronger than the competitive effects. The spatiotemporal evolution showed strong path dependence and lock-in effects, and the spatial association pattern was mainly positive, indicating a high degree of spatial integration. (3) Efficiency improvement was driven by the coupling of multiple factors. Four specific configurations were identified and further summarized into three typical pathways: a socially driven and economic-foundation-led pathway assisted by resource conditions; an economic- and technological-foundation-led pathway dominated by resource conditions and assisted by policy support; and a multi-factor synergistic pathway. [Conclusion] GUECL is driven by the combined and synergistic effects of driving forces, external foundations, and internal conditions. Therefore, differentiated regional strategies should be adopted to promote the precise matching and coordinated governance of multiple factors, thereby supporting the green and high-quality development of agriculture. Full article

Reliable forecasting of seismic activity trends is essential for regional seismic hazard analysis. Based on earthquake catalogs from 1500 to 2026, this study investigates the spatiotemporal evolution of seismic activity in the North-South Seismic Belt using a hybrid framework that integrates Wiener filtering and artificial neural networks. Seismic activity is modeled as a discrete-time stochastic process, and a time series of earthquakes with magnitudes ≥ 6.0 is constructed. Wiener filtering is applied to establish an optimal linear relationship between input and output under the minimum mean square error criterion, and multi-origin extrapolation is employed to predict earthquakes with magnitudes ≥ 7.0 over the next century. The results reveal several stable peaks or peak clusters that agree well with historical strong earthquakes, with prediction errors generally within approximately three years. Sensitivity analyses indicate that longer time series (∼500 years) and higher threshold magnitudes (≥6.0) enhance prediction stability, although the method shows limitations in spatial prediction. To address this issue, a 16–8–4 artificial neural network model is developed, and seismic sequence features are extracted using a sliding time window approach to perform both temporal and spatial forecasting. The artificial neural network achieves high accuracy in temporal prediction (maximum error ≈ 0.5) and outperforms Wiener filtering in spatial prediction, capturing the migration characteristics of seismic activity. The results further suggest that earthquakes with magnitudes ≥ 7.0 are more likely to occur within the latitude range of 30.5–33.0° N in the near future. Full article

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R² = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m³ (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges. Full article

Based on ERA5 reanalysis data, best-track data of tropical cyclones, and satellite nighttime light data from 1995 to 2024, this study employs a statistical composite method to analyse spatiotemporal evolution characteristics and impact mechanisms of gale events in the South China Sea. The results indicate: ① The gale days exhibit a pattern of ‘high in the northeast and southwest, low in the middle’ with three high-value regions located in the Taiwan Strait, the Bashi Strait, and the offshore region southeast of Vietnam, where the average wind speed at the centres reaches 8 m/s. Maximum wind speeds show a ‘high in the north, low in the south’ pattern, with the dividing line near 10° N. The number of gale days peaks in winter, while maximum wind speeds are higher in summer and autumn than in winter and spring. ② The spatial distribution of gales is primarily influenced by the combined effects of land–sea topography and weather systems. Cold air masses in winter and spring are the dominant cause of gales in the South China Sea. Although typhoons in summer and autumn occur less frequently, they are more likely to trigger extreme gales. ③ Most regions of the South China Sea show an increasing trend in the gale days, while a few areas in the south and near Guangdong exhibit a decrease. The overall increase is primarily attributed to the intensification of the subtropical high, whereas the reduction near Guangdong is mainly due to increased surface roughness caused by urbanisation, which enhances friction and suppresses wind speeds. Full article

Poyang Lake represents China’s largest freshwater wetland. The wetland landscape has undergone substantial changes driven by climate change and intensive human activities. Nevertheless, long-term classified analyses of wetland evolution and quantitative assessments of its driving factors remain scarce in the region. Based on 21 Landsat images from 2000 to 2020, this study systematically examined the spatiotemporal dynamics of the wetland landscape. Analyses incorporated land-use dynamic degree, landscape metrics, transfer matrices, and standard deviational ellipses, with key driving forces identified via Pearson correlation and structural equation modeling. Results indicate a 3029.63 km² reduction in wetland area, exhibiting contrasting trends between natural and artificial wetlands. The wetland centroid shifted 7.4 km southwestward. Connectivity of lake increased and fragmentation declined, whereas paddy field fragmentation intensified. Wetland evolution was predominantly driven by socioeconomic factors, whereas climate primarily influenced natural wetlands. The study elucidates the coupled effects of anthropogenic and natural factors, offering insights into wetland restoration and ecological security in the middle and lower Yangtze River. The findings suggest prioritizing natural wetland connectivity, controlling wetland-to-non-wetland conversion, and incorporating long-term remote-sensing monitoring into regional wetland restoration planning. Full article

Urban canals in rapidly growing cities often experience water quality deterioration from wastewater inputs and stormwater runoff, with impacts that vary across space and time. This study aimed to quantify five-year spatiotemporal patterns of key water quality indicators in the Mae Kha Canal, Chiang Mai, Thailand, and to identify persistent degradation hotspots to support management. Monthly longitudinal data (2020–2024) for dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, and water temperature (WT) were collected at 18 monitoring stations and analyzed using locally estimated scatterplot smoothing (LOESS) for trend exploration, repeated-measures correlation for association between parameters, and Geographic Information Systems-based spatiotemporal mapping using inverse-distance-weighted interpolation. Results showed that DO remains very low across much of the canal, while BOD was persistently high; pH was relatively stable near neutral and WT exhibited clear seasonal variability. Spatial mapping indicated that upstream sections generally had better quality, whereas the urban middle reaches repeatedly exhibited hotspots of low DO and high BOD. BOD and DO levels positively correlate with pH level (p < 0.001). In conclusion, the Mae Kha Canal has sustained impairment over 2020–2024, highlighting the need for strengthened wastewater control, stormwater management, and targeted remediation guided by hotspot-based monitoring. Full article

Drought–flood abrupt alternation (DFAA) has attracted increasing attention because of its severe compound impacts. This study used a daily SWAP index calculated by the precipitation data from 17 meteorological stations in Henan Province from June to September during the period of 1951–2020 to identify and analyze the spatiotemporal evolution of DFAA events. The results show that a drought duration of 10 d, together with a transition interval and a flood duration of 7 d, has a relatively good applicability for identifying DFAA events in Henan Province. The identified DFAA events were generally consistent with historical disaster records. DFAA events were characterized by slight decreasing trends in frequency and duration, with no obvious trend in intensity. The mean annual frequency, mean intensity, and mean duration of drought-to-flood (DTF) events were 2.19 events, 1.09, and 66.33 d, respectively, whereas those of flood-to-drought (FTD) events were 1.36 events, 0.36, and 73.82 d, respectively. Spatially, the distributions of DTF and FTD events exhibit distinct differences in their characteristics of frequency, intensity, and duration. Although the identification results obtained are based on precipitation as a single meteorological factor, the findings may provide a scientific basis for improving the understanding of DFAA evolution in the short term and enhancing regional disaster risk management in Henan Province, China. Full article

Understanding the spatiotemporal evolution and driving mechanisms of soil salinization in the Yellow River Delta is a key research focus in the comprehensive utilization of saline–alkali land. Taking Zhanhua District as the study area, this study extracted soil salinization information using four remote sensing salinity index models (SDI1, SDI2, SDI3, SDI4). Model accuracy was evaluated, and the optimal model (SDI1, with an overall accuracy of 86.21%) was selected to analyze the spatiotemporal dynamics of soil salinization from 1993 to 2023. The XGBoost-SHAP framework was then applied to identify and interpret the driving factors of salinization. Furthermore, future soil salinization trends under climate change were projected based on four scenarios from the Sixth Coupled Model Intercomparison Project (CMIP6), including SSP1-2.6 (low forcing), SSP2-4.5 (medium forcing), SSP3-7.0 (medium-to-high-forcing), and SSP5-8.5 (high forcing). The results show the following: (1) Spatially, soil salinization in Zhanhua District exhibits a pattern of being “lighter in the south and heavier in the north.” Over the past 30 years, salinization has undergone a phased evolution characterized by a transition from “severe in the north and mild in the south” to “overall expansion” and finally to “improvement in the north and optimization in the south,” while the proportional structure of salinization severity levels has remained relatively stable. (2) Among the driving factors, evaporation is the dominant contributor (SHAP value = 0.3357), followed by precipitation (0.1732) and population density (0.1518). Soil moisture, land use, and temperature exert moderate influences, while nighttime light intensity, slope, and elevation contribute relatively less. Overall, soil salinization is jointly controlled by climatic factors and human–nature interactions. (3) Among the future climate scenarios, the SSP1-2.6 low-emission scenario exhibits the most pronounced mitigation trend, with a further reduction in salinization intensity projected by 2100. This study provides a scientific basis and data support for formulating soil salinization control and saline–alkali land management strategies in Zhanhua District and the Yellow River Delta. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article