Search Results (1,478)

The use of the Visibility Graph Algorithm (VGA) has proven to be a valuable tool for analyzing both real and synthetic seismicity series. Specifically, VGA transforms time series into a network representation in which structural properties such as node connectivity, clustering, and community structure can be quantitatively measured, thereby revealing underlying correlations and dynamics that may remain hidden in traditional linear or spectral analyses. The time series transformation into complex networks with VGA provides a new approach to analyze seismic dynamics, allowing scientists to extract trends and behaviors that may not be possible by classical time-series analysis. On the other hand, many studies attempt to find viable trends in order to identify preparation mechanisms prior to a strong earthquake or to analyze the aftershocks. In this work, the seismic activity of Southern California Earthquake was analyzed focusing only on the significant earthquakes. For this purpose, seismic series preceding and following each earthquake were constructed using a windowing method with different overlaps and the slope of the connectivity (k) versus magnitude (M) graph (k-M slope) and the average degree were computed from the mapped complex networks. The results revealed a significant decrease in these parameters after the earthquake, due to the contribution of the aftershocks from the main event. Interestingly, the study was extended to synthetic seismicity series and the same behavior was observed for both k-M slope and average degree. This finding suggests that the spring-block model reproduces a relaxation mechanism following a large-magnitude event like those of real seismic aftershocks. However, this conclusion contrasts with conclusions drawn by other researchers. These results highlight the utility of VGA in studying events that precede and follow major earthquakes. This technique may be used to extract some useful trends in seismicity, which could eventually be employed for a deeper understanding and possible forecasting of seismic behavior. Full article

As an important drinking water source for Beijing, the capital of China, the water inflow of Miyun Reservoir has been decreasing year by year, which has affected the urban water supply security. To understand the variation trend of the inflow and analyze the main factors influencing the runoff change, this research focused on the watershed of Miyun Reservoir as the target. Based on the runoff data from 1984 to 2020 at the outlet of the basin, as well as the precipitation, potential evaporation intensity, NDVI (normalized difference vegetation index), population, and GDP (Gross Domestic Product) data, combined with correlation analysis methods, empirical statistical methods, the SCRCQ (Slope Change Ratio of Cumulative Quantity) method, and the GIS, the interannual variation characteristics of various elements in the basin were analyzed, the correlation between runoff and other factors was studied, and the influencing degrees of precipitation, water surface evaporation intensity, human activities, and other factors on the runoff change in the basin were quantitatively separated. The research results showed that the runoff exhibited a distinct decreasing trend, and there were two mutation points in the basin runoff from 1984 to 2020, which were 1995 and 2014, respectively. The runoff change was divided into three stages: 1984–1995 (upward trend in T1), 1995–2014 (downward trend in T2), and 2014–2020 (stable trend in T3). Runoff was significantly correlated with four indicators: the summer leaf area index of the Chaohe River and Baihe River, the regional GDP and population, among which the correlation of the summer leaf area index was the largest. Compared with the period T1, the contribution rates of climate change to the runoff reduction in T2 and T3 were 6.38% and 5.73%, and the contribution rates of human activities to the runoff reduction were 93.62% and 94.27%, respectively. Therefore, the change in annual runoff in the Miyun Reservoir watershed is mainly affected by human activities, and the contribution of climate change to the runoff attenuation is weak. This study is significant in the maintenance and enhancement of runoff in typical watershed. Full article

Rapid human development has altered land use types, significantly impacting carbon stock, and poor land use will lead to an increase in carbon emissions and exacerbate climate change. Understanding the relationship between land use changes and carbon storage is critical for developing sustainable land management strategies that support carbon sequestration and climate change mitigation. In this study, we analyzed and processed the land use transition changes from 1990 to 2020 and calculated the corresponding carbon storage. Based on the patterns of change and influencing factors (elevation, slope, soil type, GDP, population density, etc.), we predicted the future changes in land use and carbon storage in the Yiluo River Basin under different social development scenarios. It was found that due to the severe impact of natural factors, from 1990 to 2020, the area of cultivated land and grassland decreased by 1150.04 km² and 936.66 km², respectively, and the area of forested land and built-up area expanded by 1087.84 km² and 969.26 km², respectively. Carbon stocks in the region decreased between 1990 and 2010, followed by a modest recovery from 2010 to 2020, resulting in a total reduction of approximately 2.188 × 10⁶ t. Spatially, carbon stocks diminished in the eastern part but increased in the western part. To assess the long-term sustainability implications, the study simulated four future development scenarios for human society: natural development, urban development, ecological protection, and water conservation. The results showed that in the urban expansion scenario, the proportion of construction land increased significantly, while the ecological protection scenario led to a substantial expansion of forested areas. Notably, carbon stocks showed a significant increase only under the ecological protection scenario, whereas they exhibited a declining trend in all other scenarios. Full article

Understanding the spatiotemporal evolution patterns and drivers of cropping structures is crucial for adjusting cropping structure policies, ensuring the sustainability of land resources, and safeguarding food security. However, existing research lacks sub-pixel scale data on planting structure, where planted area data are mainly derived from manual counting results. In this study, remote sensing technology was combined with geostatistical methods to realize the spatiotemporal evolution of crop planting structure at sub-pixel scale. Firstly, the spatial distribution of the multiple cropping structure in Henan Province was extracted based on a mixed-pixel decomposition model, and spatiotemporal evolution of the crop planting structure was analyzed using a combination of Sen’s slope estimator and Mann–Kendall trend analysis, as well as centroid migration. Then, Pearson correlation coefficients were calculated to explore the contribution of driving factors. The results indicate the following: (1) from 2001 to 2022, the cropping structure in Henan Province shows a slightly obvious increase. (2) The centroid of different cropping structures migrates to the main production areas as a whole. (3) Among the driving factors, there was a positive correlation with the labor force and a negative correlation with the urbanization rate. This study provides new insights into the evolution of large-scale crop planting structures and offers significant theoretical and practical value for sustainable agricultural development and the optimization of agricultural planting structures. Full article

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R² of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region. Full article

Net primary productivity (NPP) is a core ecological indicator within terrestrial ecosystems, representing the potential of vegetation growth to offset anthropogenic carbon emissions. Thus, assessing NPP in a given region is crucial for promoting regional ecological restoration and sustainable development. This study utilized the CASA model and GEE to calculate the annual average NPP in Shandong Province (2001–2020). Through trend analysis, Moran’s Index, and PLS−SEM, the spatiotemporal evolution and driving factors of NPP were explored. The results show that: (1) From 2001 to 2020, NPP in Shandong showed an overall increasing trend, rising from 254.96 to 322.49 g C·m⁻²/year. This shift was accompanied by a gradual eastward movement of the NPP centroid, indicating significant spatial changes in vegetation productivity. (2) Regionally, 47.9% of Shandong experienced significant NPP improvement, 27.6% saw slight improvement, and 20.1% exhibited slight degradation, highlighting notable spatial heterogeneity. (3) Driver analysis showed that climatic factors positively influenced NPP across all four periods (2005, 2010, 2015, 2020), with the strongest impact in 2015 (coefficient = 0.643). Topographic factors such as elevation and slope also had positive effects, peaking at 0.304 in 2015. In contrast, human activities, especially GDP and nighttime light intensity, negatively impacted NPP, with the strongest negative effect in 2010 (coefficient = −0.567). These findings provide valuable scientific evidence for ecosystem management in Shandong Province and offer key insights for ecological restoration and sustainable development strategies at the national level. Full article

Climate change is profoundly impacting snow-dependent regions, altering hydrological cycles and threatening water security. This study examines the relationships between snow water equivalent (SWE), snow cover, temperature, and wind speed in Jammu and Kashmir, India, over five decades (1974–2024). Using ERA5 reanalysis and Indian Meteorological Department (IMD) datasets, we reveal significant declines in SWE and snow cover, particularly in high-altitude regions such as Kupwara and Bandipora. A Sen’s slope of 0.0016 °C per year for temperature highlights a steady warming trend that accelerates snowmelt, shortens snow cover duration, and reduces streamflow during critical agricultural periods. Strong negative correlations between SWE and temperature (r = −0.7 to −0.9) emphasize the dominant role of rising temperatures in SWE decline. Wind speed trends exhibit weaker correlations with SWE (r = −0.2 to −0.4), although localized effects on snow redistribution and evaporation are evident. Temporal snow cover analyses reveal declining winter peaks and diminished summer runoff contributions, exacerbating water scarcity. These findings highlight the cascading impacts of climate variability on snow hydrology, water availability, and regional ecosystems. Adaptive strategies, including real-time snow monitoring, sustainable water management, and climate-resilient agricultural practices, are imperative for mitigating these challenges in this sensitive Himalayan region. Full article

As a natural disaster, drought can endanger global ecology, socio-economic systems, and sustainable development. To address sudden droughts in the future, assess drought disasters, and propose mitigation measures, in-depth research on the spatiotemporal variations in and driving factors of meteorological drought is essential. To study drought in the Yellow River Basin, we calculated the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI), derived from monthly meteorological data recorded at weather stations from 1968 to 2019. We examined the features of drought and its driving factors using the trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Sen’s slope estimator, as well as a drought frequency analysis, center of gravity migration model, standard deviation ellipse model, and geographic detector. Our analysis shows that (1) from 1968 to 2019, the Yellow River Basin exhibited a shift from aridity to increased moisture on an annual basis, with the smallest SPEI of −1.47 in 2002 indicating a moderate drought; SPEI3 showed a growing tendency in all seasons, particularly in winter (0.00388/year), followed by spring (0.00214/year), summer (0.00232/year), and fall (0.00196/year). The SPEI3 exhibited higher fluctuations in frequency compared to the annual-scale SPEI12; (2) in terms of spatial variability, there was no significant change in drought conditions at any scale, with the probability of a drought event being greater in the eastern and northwestern portions of the watershed. The epicenter of the drought exhibited a tendency to migrate southwestward; (3) among the seven driving factors, land use and night lighting were the dominant factors affecting drought conditions, with driving force values of 0.75 and 0.63, respectively. Full article

Terrestrial ecosystems are vital carbon sinks that can effectively restrain the rise in CO₂ in the atmosphere. How ecosystem carbon storage (CS) in semi-arid watershed areas with slow urbanization is affected by comprehensive factors of the environment and land use, along with its temporal and spatial changes has still not been fully explored. Notably, there is a paucity of research on the temporal and spatial changes and development trends of CS in the rapid deformation belt of slopes from the eastern margin of the Qinghai–Tibet Plateau to the Loess Plateau. Taking Bailong River Basin (BRB) as an example, this study combined GeoSOS-FLUS, the InVEST model, and localized “social–economic–nature” scenario to simulate the long-term dynamic evolution of CS. The aim was to study how topographic factors and land use change, and their interactions impact carbon sinks and gradient effects in steep-slope areas, and then find out the relationship between carbon sinks and topographic factors to explore strategies to improve regional carbon sink capacity. The results showed that the following: (1) CS in BRB increased year by year, with a total increase of 558 tons (3.19%), and showed significant spatial heterogeneity, mainly due to the conversion of woodland and arable land; (2) except for land use type, the relationship between CS and topographic gradient is inverted U-shaped, showing a complex spatial response; and (3) it is estimated that by 2050, under the arable land protection and natural development scenarios, CS will decrease by 0.07% and 0.005%, respectively, encroachment on undeveloped mountain areas, while the ecological protection scenario gives priority to protecting the carbon sinks of woodland and grassland, and CS will increase by 0.37%. This study supports the implementation of targeted ecological protection measures through topographic gradient zoning, provides a reference for policy makers in similar topographic regions to effectively manage the spatial heterogeneity of CS, and helps further strengthen global and regional climate change mitigation efforts. Full article

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts. Full article

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicated that: (1) The overall accuracy of the abandoned land extraction was 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms. Full article

Understanding vegetation heterogeneity dynamics is crucial for assessing ecosystem resilience, biodiversity patterns, and the impacts of environmental changes on landscape functions. While previous studies primarily focused on NDVI pixel trends, shifts in landscape heterogeneity have often been overlooked. To address this gap, our study evaluated the effectiveness of continuous metrics in capturing vegetation dynamics over time, emphasizing their utility in short-term trend analysis. The study area, located in Iran, encompasses a mix of urban and agricultural landscapes dominated by farming-related vegetation. Using 11 Landsat 8 OLI images from 2013 to 2023, we calculated NDVI to analyze vegetation trends and heterogeneity dynamics. We applied three categories of continuous metrics: texture-based metrics (dissimilarity, entropy, and homogeneity), spatial autocorrelation indices (Getis and Moran), and surface metrics (Sa, Sku, and Ssk) to assess vegetation heterogeneity. By generating slope maps through linear regression, we identified significant trends in NDVI and correlated them with the slope maps of the continuous metrics to determine their effectiveness in capturing vegetation dynamics. Our findings revealed that Moran’s Index exhibited the highest positive correlation (0.63) with NDVI trends, followed by Getis (0.49), indicating strong spatial clustering in areas with increasing NDVI. Texture-based metrics, particularly dissimilarity (0.45) and entropy (0.28), also correlated positively with NDVI dynamics, reflecting increased variability and heterogeneity in vegetation composition. In contrast, negative correlations were observed with metrics such as homogeneity (−0.41), Sku (−0.12), and Ssk (−0.24), indicating that increasing NDVI trends were associated with reduced uniformity and surface dominance. Our analysis underscores the complementary roles of these metrics, with spatial autocorrelation metrics excelling in capturing clustering patterns and texture-based metrics highlighting value variability within clusters. By demonstrating the utility of spatial autocorrelation and texture-based metrics in capturing heterogeneity trends, our findings offer valuable tools for land management and conservation planning. Full article

Multi-stage hydraulic fracturing is a key technology adopted in the energy industry to make shale gas and shale oil fields profitable. Post-frac fluid soaking before putting wells into production has been found essential for enhancing well productivity. Finding the optimum time to terminate the fluid-soaking process is an open problem to solve. Post-frac shut-in pressure data from six wells in two shale gas fields were investigated in this study based on pressure transient analysis (PTA) to reveal fluid-soaking performance. It was found that pressure-derivative data become scattering after 1 day of well shut in. The overall trend of pressure-derivative data after the first day of well shut in should reflect the effectiveness of fluid soaking. Two wells exhibited flat (zero-slope) pressure derivatives within one week of fluid soaking, indicating adequate time of fluid soaking. Four wells exhibited increasing pressure derivatives within one week of fluid soaking, indicating inadequate time of fluid soaking. This observation is consistent with the reported well’s Estimated Ultimate Recovery (EUR). This study presents a new approach to the assessment of post-frac fluid-soaking performance with real-time shut-in pressure data. Full article

In the context of seismic hazard assessment and engineering design, a comprehensive understanding of local geological and geophysical factors is essential. However, previous studies have lacked crucial components such as local soil condition, ground response analysis, topographic influences, active fault characteristics, slip rates, groundwater behaviour, and slope considerations. To ensure the accuracy of the seismic hazard map of a country for the safe and cost-effective design of engineering structures in urban areas, a detailed analysis of these factors is imperative. Moreover, multidisciplinary investigations, such as logic-tree considerations, are needed to enhance seismic hazard maps. As a result, adopting a performance-based approach in structural design has become an essential priority. A performance-based approach allows engineers to design buildings to specified performance levels (IO, LS, CP) even without a reliable seismic hazard map. This approach is akin to a miracle for countries that do not have a reliable seismic hazard map. This study presents a systematic and comprehensive bibliometric analysis of the academic literature pertaining to performance-based design (PBD). By fostering collaborative efforts and expanding research networks, we aim to facilitate the development of coordinated initiatives within the field. Preferred journals, leading countries, leading organisations, and international institutions were identified utilizing the Scopus database. This study examined 3456 PBD-related publications spanning from 1969 to 2023 using VOSviewer version 1.6.19, a bibliometric mapping and visualization software tool. The analysis of co-citations revealed that performance-based design serves as the primary theoretical foundation for structural design and analysis. Furthermore, through a co-word analysis, we tracked the evolution of research topics within the PBD domain over time. This investigation uncovered noteworthy trends, including the steady growth of research output, the increasing prominence of the term “PBD”, and a focus on various types of performance-based analyses. Full article

The impacts of climate change on snowfall have received great interest in cold regions for water resource and flood risk management. This study investigated the effects of rises in air and sea surface temperatures and sea ice on snowfall in Hokkaido, northern Japan, over the period from 1961 to 2020 (60 years). Climate data observed at the 22 weather stations operated by the Japan Meteorological Agency (JMA) were analyzed. Statistics describing the effects of climate change on snowfall were computed. The trend in these quantities was obtained using Sen’s slope estimator, and their statistical significance was evaluated by the Mann–Kendall test. The warming trends obtained at these stations were all positive and statistically significant. Annual snowfall increased at seven stations but decreased at two stations. The snowfall period decreased mainly on the southern coast. This is attributed to the fact that these sites are on the leeward side of the Eurasian monsoon, and that air temperatures on the coast and the surface temperature of the sea off Kushiro have risen sufficiently. The results suggest that the flood risk may increase in response to the acceleration of the increase in the level of a river due to early melting snow in spring (March and April). Although the weather stations on the east coast are also on the leeward side, the snowfall period has not shortened. The warming trends in April are very weak on the east coast. The correlation between the air temperature in March and April and the period of sea ice accumulation suggests that melting sea ice in spring plays an important role in preventing the winter period from shortening. Decrease in sea ice due to a rise in both air and sea surface temperatures may increase flood risk in early spring, and thus, some measures may need to be taken in the future. Full article