Search Results (5,444)

Soil structural instability in reservoir riparian zones, induced by water level fluctuations, threatens sustainable land use by accelerating land degradation. This study examined the impact of water-level variations on soil aggregate composition and stability based on key indicators, including water-stable aggregate content (WSAC), mean weight diameter (MWD), and geometric mean diameter (GMD). The Savinov dry sieving, Yoder wet sieving, and Le Bissonnais (LB) methods were employed for analysis. Results indicated that, with decreasing water levels and increasing soil layer, aggregates larger than 5 mm decreased, while aggregates smaller than 0.25 mm increased. Rising water levels and increasing soil layer corresponded to reductions in soil stability indicators (MWD, GMD, and WSAC), highlighting a trend toward soil structural instability. The LB method revealed the lowest aggregate stability under rapid wetting and the highest under slow wetting conditions. Correlation analysis showed that soil organic matter positively correlated with the relative mechanical breakdown index (RMI) (p < 0.05) and negatively correlated with the relative slaking index (RSI), whereas soil pH was negatively correlated with both RMI and RSI (p < 0.05). Comparative analysis of aggregate stability methods demonstrated that results from the dry sieving method closely resembled those from the SW treatment of the LB method, whereas the wet sieving method closely aligned with the FW (Fast Wetting) treatment of the LB method. The Le Bissonnais method not only reflected the outcomes of dry and wet sieving methods but also effectively distinguished the mechanisms of aggregate breakdown. The study concluded that prolonged flooding intensified aggregate dispersion, with mechanical breakdown influenced by water levels and soil layer. Dispersion and mechanical breakdown represent primary mechanisms of soil aggregate instability, further exacerbated by fluctuating water levels. By elucidating degradation mechanisms, this research provides actionable insights for preserving soil health, safeguarding water resources, and promoting sustainable agricultural in ecologically vulnerable reservoir regions of the Yangtze River Basin. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Radar is one of the primary means of monitoring maritime targets. Compared to electro-optical systems, radar offers the advantage of all-weather, day-and-night operation. However, existing radar target detection algorithms predominantly achieve binary detection (i.e., determining the presence or absence of a target) and are unable to accurately classify target types. This limitation is particularly significant for coastal-deployed maritime surveillance radars, which must contend with not only maritime vessels but also various land-based and island targets within their monitoring range. This paper aims to enhance the informational breadth of existing binary detection methods by proposing a land–sea classification method of radar targets based on dynamic dense connections. The core idea behind this method is to merge the interlayer output features of the network and to augment and weigh them through dynamic convolutional combinations to improve the feature extraction capability of the network. The experimental results demonstrate that the proposed attribute recognition method outperforms current deep network architectures. Full article

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

Healthy soils are an essential ingredient of land systems and ongoing global change. Urbanization as a global change process often works through the lens of urban planning, which involves urban agriculture, urban greening, and leveraging nature-based solutions to promote resilient cities. Yet, urbanization frequently leads to soil erosion. Despite recognition of this tension, the rate at which the urban growth boundary accelerates soil erosion above natural background levels has not yet been determined. Our goal here is to provide a first broad estimate of urbanization’s impact of soil erosion. By combining data on modern erosion levels with techniques for estimating long-term natural erosion rates through cosmogenic nuclide ¹⁰Be analysis, we modeled the impact of urbanization on erosion across a range of cities in different global climates, revealing an acceleration of soil erosion ~7–19x in environments with mean annual precipitation <1500 mm; growth in wetter urban centers accelerated soil erosion ~23–72x. We tested our statistical model by comparing natural erosion rates to decades of monitoring soil erosion on the margins of Phoenix, USA. A century-long expansion of Phoenix accelerated soil erosion by ~12x, an estimate that is roughly at the mid-point of model projections for drier global cities. In addition to urban planning implications of being able to establish a baseline target of natural rates of soil erosion, our findings support the urban cycle of soil erosion theory for the two USA National Science Foundation urban long-term ecological research areas of Baltimore and Phoenix. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Trachypteris picta (Pallas, 1773) is a significant pest that can cause serious damage to poplars and willows. To assess the impact of climate change on the suitable habitats of T. picta, this study conducted a comparative analysis of its global suitable habitats using climatic factors, global land use type, and global vegetation from different periods, in combination with the maximum entropy (MaxEnt) model. The results indicate that the annual mean temperature (Bio01), mean temperature of the coldest quarter (Bio11), precipitation of the coldest quarter (Bio19), and isothermality (Bio03) are the four most important climate variables determining the distribution of T. picta. Under the current climate conditions, the highly suitable areas are primarily located in southern Europe, covering an area of 2.22 × 10⁶ km². Under future climate scenarios, the suitable habitat for T. picta is expected to expand and shift towards higher latitudes. In the 2050s, the SSP5-8.5 scenario has the largest suitable area compared to other scenarios, while the SSP2-4.5 scenario has the largest suitable area in the 2090s. In addition, the centroids of the total suitable areas are expected to shift toward higher latitudes under future climate conditions. The results of this study provide valuable data for the monitoring, control, and management of this pest. Full article

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

Understanding alpine plants’ survival and reproduction is crucial for their conservation in climate change. Based on 423 valid distribution points, this study utilizes the MaxEnt model to predict the potential habitat and distribution dynamics of Leontopodium nanum under both current and future climate scenarios, while clarifying the key factors that influence its distribution. The primary ecological drivers of distribution are altitude (2886.08 m–5576.14 m) and the mean temperature of the driest quarter (−6.60–1.55 °C). Currently, the suitable habitat area is approximately 520.28 × 10⁴ km², covering about 3.5% of the global land area, concentrated mainly in the Tibetan Plateau, with smaller regions across East and South Asia. Under future climate scenarios, low-emission (SSP126), suitable areas are projected to expand during the 2050s and 2070s. High-emission (SSP585), suitable areas may decrease by 50%, with a 66.07% reduction in highly suitable areas by the 2070s. The greatest losses are expected in the south-eastern Tibetan Plateau. Regarding dynamic habitat changes, by the 2050s, newly suitable areas will account for 51.09% of the current habitat, while 68.26% of existing habitat will become unsuitable. By the 2070s, newly suitable areas will rise to 71.86% of the current total, but the loss of existing areas will exceed these gains, particularly under the high-emission scenario. The centroid of suitable habitats is expected to shift northward, with migration distances ranging from 23.94 km to 342.42 km. The most significant shift is anticipated under the SSP126 scenario by the 2070s. This study offers valuable insights into the distribution dynamics of L. nanum and other alpine species under the context of climate change. From a conservation perspective, it is recommended to prioritize the protection and restoration of vegetation in key habitat patches or potential migration corridors, restrict overgrazing and infrastructure development, and maintain genetic diversity and dispersal capacity through assisted migration and population genetic monitoring when necessary. These measures aim to provide a robust scientific foundation for the comprehensive conservation and sustainable management of the grassland ecosystem on the Qinghai–Tibet Plateau. Full article

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam. Full article

It is well established that combining exercise with medication may benefit functionality in individuals with PD (Parkinson’s disease). However, the long-term evolution of gait biomechanics under this combination remains poorly understood. Objectives: This study aims to analyze the evolution of spatiotemporal gait parameters, kinetics, and kinematics throughout a long-term exercise program conducted in water and on dry land. Methods: We have compared the trajectories of biomechanical variables across the treatment phases using statistical parametric mapping (SPM). A cohort of fourteen individuals with PD (mean age: 65.6 ± 12.1 years) participated in 24 sessions of aquatic exercises over three months, followed by a three-month retention phase, and then 24 additional sessions of land-based exercises. Three-dimensional gait data and spatiotemporal parameters were collected before and after each phase. Two-way ANOVA with repeated measures was used to compare spatiotemporal parameters. Results: The walking speed increased while the duration of the double support phase decreased. Additionally, the knee extensor moment consistently increased in the entire interval from midstance to midswing (20% to 70% of the stride period), approaching normal gait patterns. Regarding kinematics, significant increases were observed in both hip and knee flexion angles. Furthermore, the abnormal ankle dorsiflexion observed at the foot strike disappeared. Conclusions: These findings collectively suggest positive adaptations in gait biomechanics during the observation period. Full article

This paper examines the farming systems and operational structures in the Region of Western Macedonia (RWM), Greece and constructs a typology of farms based on structural, operational, and socio-economic characteristics. Agriculture remains a vital pillar of the regional economy, particularly in the context of RWM’s ongoing transition to a post-lignite development model. Using farm-level data from the 2018 Farm Accountancy Data Network (FADN), Principal Component Analysis (PCA) identified four latent dimensions of farm heterogeneity—income and productivity, asset base, land size, and labour structure. Hierarchical and K-means cluster analysis revealed three distinct farm types: (1) medium-sized, high-efficiency farms with moderate reliance on subsidies (30% of the sample); (2) small-scale, family farms with modest productivity and limited capitalisation (48%); and (3) large, asset-rich farms exhibiting structural inefficiencies and lower output per hectare (22%). These findings highlight structural vulnerabilities, particularly the predominance of undercapitalised smallholdings, and provide a data-driven foundation for Thdesigning differentiated policies that support farm resilience, generational renewal, and sustainable rural development. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article