Search Results (232)

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

Nodal tides, which follow an 18.6-year cycle, influence tidal variations at any given location in the ocean. Conventional nodal tide theory neglects land effects and topological change. Due to the complex seabed topography around Taiwan waters, the purpose of this paper is to use the long-term tidal data of six stations to discuss the effects of perigean and nodal tides on 20 constituents and to compare the results with previous theories. A modulation method is employed to fit the annual amplitude estimated by harmonic analysis (HA). The top four constituents of the fitted and theoretical values of nodal amplitude factor (AF) and phase factor (PF) are O₁, K₁, K₂, and Q₁. We find that perigean tides or second-order nodal tides considered in the fitting contribute to almost identical performance. The linear time change considered in the AF fitting has better fitting than the mean water level involved. Full article

Global mean sea level has been rising steadily since the early 1990s, yet regional sea level changes exhibit complex spatial variability that frequently contrasts with global trends. Investigating sea level variations in semi-enclosed basins such as the Black Sea is crucial for elucidating regional responses to climate change and characterizing its unique spatiotemporal evolution patterns. In this study, we employ satellite altimetry (SA) data to study sea level changes, spatial variability, and seasonal patterns in the Black Sea over eight distinct time periods with temporally correlated noise, and our results show good consistency with existing studies. The results show that sea level changes are non-linear over time and exhibit spatial variability in the Black Sea. The estimated sea level trend fluctuates over brief intervals, but extended time series provide reduced uncertainty in the trend and more precise estimation over a 28-year time series. The annual amplitude and phase derived from virtual altimetry data (1993–2020) exhibit a distinct seasonal pattern, with peak sea levels typically occurring between November and February. Furthermore, to reduce the uncertainty induced by noise in the sea surface height (SSH) time series, principal component analysis (PCA) was utilized to denoise the SSH data from 1993 to 2020, yielding a sea level trend of 1.76 ± 0.56 mm/yr. Denoising reduced the trend uncertainty by 57%, decreased the root mean square error of the SSH series by 5.06 mm, and decreased the annual amplitude by 23.35%. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

Prefabricated components inevitably generate numerous assembly joints during installation, with each 1 mm increase in joint width correlating to a 15–20% elevation in the annual occurrence frequency of the frost formation risk. In the Inner Mongolia Region, the water migration at wall connection interfaces during winter significantly exacerbates freeze–thaw damage due to persistent thermal gradients. A coupled heat–moisture transfer model incorporating gas–liquid–solid phase transitions was developed, with the liquid moisture content and temperature gradient as dual driving forces. A validation against internationally recognized BS EN 15026:2007 benchmark cases confirmed the model robustness. The prefabricated sandwich insulation walls reconstructed with region-specific volcanic ash materials underwent a comparative evaluation of temperature and relative humidity distributions under varied winter conditions. Furthermore, we analyze and assess the potential for freezing at connection points and identify the specific areas at risk. Synergistic effects between assembly gaps and indoor–outdoor environmental interactions on wall performance degradation were systematically assessed. The results indicated that, across all working conditions, both the temperature and relative humidity at each wall measurement point underwent periodic variations influenced by the outdoor environment. These fluctuations decreased in amplitude from the exterior to the interior, accompanied by a noticeable delay effect. Specifically, at Section 2, the wall temperatures at points B2–B8 were higher compared to those at A2–A8 of Section 1. The relative humidity gradient remained relatively stable at each measurement point, while the temperature fluctuation amplitude was smaller by 2.58 ± 0.3 °C compared to Section 1. Under subfreezing conditions, Section 1 demonstrates a marked reduction in relative humidity (Cases 1-3 and 2-3) compared to reference cases, which is indicative of internal ice crystallization. Conversely, Section 2 maintains higher relative humidity values under identical therma. These findings suggest that prefabricated building joints significantly impact indoor and outdoor wall temperatures, potentially increasing the indoor heat loss and accelerating temperature transfer during winter. Full article

This study focuses on optimizing two tandem columns to separate ethylbenzene and styrene. A steady-state model is developed to minimize total energy consumption (TEC) and total annualized cost (TAC) by optimizing the reflux flow rates. An integrated dynamic model is created using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. This model is designed to account for transitions in operating conditions and to identify optimal dynamic strategies for adjusting operations to maintain optimal performance. The optimization considers factors such as fluctuation amplitude, the number of fluctuations, and fluctuation duration. The aim is to reduce fluctuation amplitudes while ensuring higher energy efficiency and stable operation. The results reveal that the optimal reflux flow rates are 41,152.2 kg/h and 1012.7 kg/h, leading to reductions in TEC and TAC by 16.7% and 17.4%, respectively. Compared with the industry standard level, the energy consumption has decreased by 11.25%. Against the backdrop of increasingly strict global carbon emission control, the market competitiveness of ethylbenzene/styrene production has been significantly enhanced. The variable-step adjustment method requires less time to reach a stable state, while the equal-step fluctuation method provides more stability. The Pareto solution set derived from the two optimization techniques can be used to select the most suitable adjustment strategy, ensuring a fast and smooth transition. Full article

Methane (CH₄) is a potent greenhouse gas with a significant impact on short- and medium-term climate forcing, and its atmospheric concentration has been increasing rapidly in recent decades. This study aims to analyze spatio-temporal patterns of atmospheric methane concentrations between 2019 and 2025, focusing on comparisons between regions characterized by high and low emission intensities. Level-3 XCH₄ data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite were used, which were aggregated into seasonal and annual composites. High-emission regions, such as the Mekong Delta, Nile Delta, Eastern Uttar Pradesh and Bihar, Central Thailand, Lake Victoria Basin, and Eastern Arkansas, were contrasted with low-emission areas including Patagonia, the Mongolian Steppe, Northern Scandinavia, the Australian Outback, the Sahara Desert, and the Canadian Shield. The results show that high-emission regions exhibit substantially higher seasonal amplitude in XCH₄ concentrations, with an average seasonal variation of approximately 30.00 ppb, compared to 17.39 ppb in low-emission regions. Methane concentrations generally peaked at the end of the year (Q4) and reached their lowest levels during the first half of the year (Q1 or Q2), particularly in agriculturally dominated regions. Principal component and cluster analyses further confirmed a strong spatial differentiation between high- and low-emission regions based on both temporal trends and seasonal behavior. These findings demonstrate the potential of satellite remote sensing to monitor regional methane dynamics and highlight the need for targeted mitigation strategies in major agricultural and wetland zones. Full article

This study analyzes sea-level changes in the Yellow and Bohai Seas from 1993 to 2023 based on satellite altimetry data. After reconstructing the gridded sea-level data using local mean decomposition (LMD), the annual mean sea level was estimated at 28.86 mm, with an average rise rate of 2.21 mm per year (mm/a). Temporal and spatial variations were examined through nonlinear least squares fitting to capture interannual variability and decadal amplitude distributions. Empirical orthogonal function (EOF) analysis identified the first three modes, explaining 90.40%, 2.78%, and 1.47% of the total variance, respectively, and their spatial patterns and temporal coefficients were derived. The first mode was strongly correlated with sea surface temperature (SST) and precipitation, showing distinct spatial structures. Temperature and salinity profiles revealed a decadal-scale trend of increasing temperature and decreasing salinity with depth. Seasonal variations of sea-level anomaly (SLA) were evident, with mean values and trends of −11.47 mm (2.19 mm/a) in spring, 57.12 mm (2.29 mm/a) in summer, 75.68 mm (2.24 mm/a) in autumn, and −13.90 mm (2.11 mm/a) in winter. Seasonal correlations among SLA, SST, salinity, and precipitation were assessed, highlighting interannual amplitude variations. This integrated analysis provides a comprehensive understanding of the dynamics and drivers of sea-level fluctuations, offering insights for future research. Full article

Semi-annual climate oscillations in the Western Hemisphere (20 S–35 N, 150 W–20 E) were studied via empirical orthogonal function (EOF) eigenvector loading patterns and principal component time scores from 1980 to 2023. The spatial loading maximum for 850 hPa zonal wind extended from the north Atlantic to the east Pacific; channeling was evident over the southwestern Caribbean. The eigenvector loading maximum for precipitation reflected an equatorial trough, while the semi-annual SST formed a dipole with loading maxima in upwelling zones off Angola (10 E) and Peru (80 W). Weakened Caribbean trade winds and strengthened tropical convection correlated with a warm Atlantic/cool Pacific pattern (R = 0.46). Wavelet spectral analysis of principal component time scores found a persistent 6-month rhythm disrupted only by major El Nino Southern Oscillation events and anomalous mid-latitude conditions associated with negative-phase Arctic Oscillation. Historical climatologies revealed that 6-month cycles of wind, precipitation, and sea temperature were tightly coupled in the Western Hemisphere by heat surplus in the equatorial ocean diffused by meridional overturning Hadley cells. External forcing emerged in early 2010 when warm anomalies over Canada diverted the subtropical jet, suppressing subtropical trade winds and evaporative cooling and intensifying the equatorial trough across the Western Hemisphere. Climatic trends of increased jet-stream instability suggest that the semi-annual amplitude may grow over time. Full article

Geodetic data, especially from the Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO), are extensively employed in hydrological drought monitoring across various spatial scales due to their unique spatial resolution. In recent years, Brazil has experienced some of the most severe drought events in decades. This study focuses on Brazil and its northeastern Amazon Plain, investigates the spatiotemporal characteristics of terrestrial water storage (TWS) changes, and calculates the hydrological drought severity index (DSI) and meteorological drought index for comprehensive analysis of drought conditions. The results indicate that the time series of TWS changes derived from different data sources are highly correlated, with correlation coefficients exceeding 0.85, and are consistent with the trend of precipitation variation, reflecting notable seasonal fluctuations, i.e., an increase in precipitation during the spring and summer seasons leads to a rise in TWS, while a decrease in precipitation during the autumn and winter seasons triggers a reduction in TWS. In terms of spatial distribution, the annual amplitude of TWS variation is most pronounced in the northeastern Amazon Plain. The highest amplitude, approximately 800 mm, is observed near the Amazon River Basin, and this amplitude gradually weakens from northeast to southwest. GNSS and GRACE/GFO data reveal four hydrological drought events in Brazil from 2013 to 2024, with two of these events detected using GRACE/GFO data. The most severe droughts occurred between 2023 and 2024, primarily driven by prolonged precipitation deficits and the El Niño phenomenon, lasting up to nine months. Additionally, three distinct drought events were identified in the Amazon Plain, suggesting that its hydrological dynamics significantly influenced Brazil’s drought conditions. These results demonstrate the capability of geodetic data to effectively monitor water deficit and drought duration on both small spatial scales and short timeframes, thereby providing crucial support for timely responses to and the management of hydrological drought events. Full article

Human populations are scattered worldwide and live under widely different climates. Like other mammals, humans respond to climatic influences through various processes involving behavior, physiology, and various forms of adaptation. Human populations can be explored in investigating patterns of adaptation because many of their biological attributes have been monitored for over a century. Here, we evaluated the association between several thermal indices and human birth weight (BW) and offered some initial observations on the temporal integration of thermal cues associated with pregnancy outcomes. We compiled three datasets: (1) a dataset with global coverage of recent BWs; (2) an extended time series for seven European countries; and (3) a time series for four countries in equatorial Africa. Each dataset was analyzed for associations between BW and mean annual temperature, as well as seasonal and daily amplitudes. Mean annual temperatures, as well as seasonal and daily amplitudes, delivered consistent and comparable impacts in our analyses. The thermal indices can explain approx. 80% of the global variation in BW and 25–50% of the BW variation in time series covering the last 70 to 120 years. Mean BW in larger aggregates of humans (i.e., millions) is associated with several thermal indices, likely associated with systematic differences in proximate factors (e.g., maternal height, weight, food intake) between populations. This study underlines the diverse impact of the thermal environment on human reproduction, but it also underscores that this impact is less pronounced for differences in mean BW with respect to different communities, and it is possibly undetectable and/or irrelevant with respect to differences between individuals. Full article

The North American Monsoon (NAM) in southern Arizona continues to be a topic of interest to many ecologists studying the triggers and characteristics of plant growth and reproduction in relation to the onset of the monsoon. The purpose of this article is to report interannual variation in the timing of NAM onset found while researching the phenology of Saguaro cactus (Carnegiea gigantea). Using a daily rainfall dataset from 33 stations located in Pima and Pinal Counties, Arizona, from 1990–2022, we analyzed monsoon onset, monsoon precipitation, annual precipitation, and the proportion of annual station precipitation received during the monsoon season. Onset was measured by the first day from 1 June to 30 September with precipitation ≥ 10 mm counted from the day of the vernal equinox of the year. Generalized Additive Models (GAMs) identified sinusoidal waves with a period of 8.6 years and amplitudes of 14–29 days, providing frequency and amplitude estimates for Sinusoidal Regression Models (SRMs). Sinusoidal wave patterns found in the monsoon onset dataset are suggested in monsoon, annual, and proportion of monsoon in station-averaged annual precipitation although in and approximately mirror-image. These unexpected findings may have important implications for forecasters as well as ecologists interested in plant phenology. Full article

Accurate mapping of rice planting areas is of great significance in terms of food security and market stability. However, the existing research into high-resolution rice mapping has relied heavily on fine-scale temporal remote sensing image data. Due to cloud occlusion and banding problems, data extraction from Landsat series remote sensing images with medium spatial resolution is not optimal. Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. The proposed LR method initially generated rice distribution maps based on phenology, and 300 sample points were selected for meta-identification of rice images via an enhanced pixel-based phenological feature composite method (Eppf-CM) utilizing high-resolution imagery. Subsequently, the inundation frequency (F) and an improved sample point statistical feature, i.e., the ratio of change amplitude of LSWI to NDVI (RCLN), were introduced to combine Eppf-CM with combined consideration of vegetation phenology and surface water variation (CCVS) methods, to automate the generation of training data with the aid of phenology. The sample data were optimized by an alternate iterative method involving extraction of neighborhood information. Finally, a random forest (RF) probabilistic model trained by integrating data from different phenological periods was used for rice mapping. To test its performance, we mapped rice distribution at 30 m resolution (“LR_Rice”) across Heilongjiang Province, China from 2010 to 2022, with annual overall accuracy (OA) and Kappa coefficients greater than 0.97 and 0.95, respectively, and compared them with four existing rice mapping products. The spatial distribution characteristics of rice cultivation extracted by the LR algorithm were accurate and the performance was optimal. In addition, the extracted area of LR_Rice was highly consistent with the agricultural statistical area; the coefficient of determination R² was 0.9915, and the RMSE was 22.5 kha. The results show that this method can accurately obtain large-scale rice planting information, which is of great significance for food security, water resource management, and environmentally sustainable development. Full article