Search Results (3,173)

Kinmen Island, historically known as Quemoy, holds significant historical and geopolitical importance due to its strategic location in the Taiwan Strait, just a few kilometers from the Chinese mainland. This study presents the first comprehensive assessment of soil erosion and deposition on Kinmen, providing a scientific foundation for future land conservation and sustainable development initiatives. It also addresses the underrepresentation of small-island environments in soil erosion modeling by adapting the Revised Universal Soil Loss Equation (RUSLE) and Unit-Stream-Power-based Erosion Deposition (USPED) models for coarse-textured soils under limited rainfall conditions, offering insights into the reliability and limitations of these models in such contexts. The rainfall–runoff erosivity factor (${\mathrm{R}}_{\mathrm{m}}$) was derived from precipitation data at four stations, while soil samples from ten locations were analyzed for the Soil Erodibility Factor (${\mathrm{K}}_{\mathrm{m}}$). Topographic factors, including the Slope Length and Steepness ($\mathrm{LS}$) and the Topographic Sediment Transport (${\mathrm{LS}}_{\mathrm{T}}$) factors, were computed from a 20 m DEM (Digital Elevation Model), and the Cover-Management Factor ($\mathrm{C}$) was obtained from land use classification. The RUSLE estimated a mean soil erosion rate of 2.17 Mg ha⁻¹ year⁻¹, while the USPED results varied with parameterization, ranging from 0.87 to 3.79 Mg ha⁻¹ year⁻¹ for erosion and 1.39 to 6.51 Mg ha⁻¹ year⁻¹ for deposition. The results were compared with studies from the neighboring Fujian Province and contextualized through two field expeditions. This pioneering research advances the understanding of erosion and deposition processes in a strategically located island environment and supports evidence-based policies for land conservation, contributing to SDG 15 (Life on Land) and SDG 13 (Climate Action). Full article

The soil microbiome is an essential component of agroecosystems. However, managing it remains a challenge due to our limited knowledge of how various environmental factors interact and shape its spatial distribution. This study presents a hierarchical ecological model to explain the assembly of the microbiome in sloping agricultural landscapes. Through a comprehensive analysis of bacterial and fungal communities, as well as the examination of metabolic and phytopathogenic profiles across a topographic gradient, we have demonstrated that topography acts as the main filter, structuring bacterial communities. Land use, on the other hand, serves as a secondary filter, refining fungal functional guilds. Our results suggest that hydrological conditions in floodplains favor the growth of stress-tolerant bacterial communities with low diversity, dominated by Actinomycetota. Fungal communities, on the other hand, are directly influenced by land use. Long-term fallow periods lead to an enrichment of arbuscular mycorrhiza, while agroecosystems shift towards pathogenic and saprotrophic niches. Furthermore, we identify specific topographic positions that may be hotspots for phytopathogenic pressure. These hotspots are linked to certain taxa, such as Ustilaginaceae and Didymellaceae, which may pose a threat to plant health. The derived hierarchical model provides a scientific foundation for topography-aware precision agriculture. It promotes stratified management, prioritizing erosion control and soil restoration on slopes, customizing nutrient inputs in fertile floodplains, and implementing targeted phytosanitary monitoring in identified risk areas. Our research thus offers a practical framework for harnessing soil spatial variability to improve soil health and proactively manage disease risks in agricultural systems. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

Erosion caused by sediment-laden flow significantly affects the efficiency and durability of Francis turbines. In this study, the Euler–Lagrange multi-phase flow model was employed to simulate solid-liquid two-phase flow with different sediment particle sizes to analyze erosion characteristics in turbine components. The results show that the maximum erosion rate of the runner blades is positively correlated with particle impact velocity, confirming that impact velocity is the dominant factor influencing local material removal. The total erosion rate of the runner blades, guide vanes, and draft tube corresponds closely with vorticity, indicating that vortex-induced flow separation accelerates particle–wall collisions and intensifies erosion. Both vorticity and erosion exhibit a nonlinear variation with particle size, reaching a minimum at 0.05 mm. These findings establish clear qualitative and quantitative relationships between erosion and key flow parameters, providing theoretical guidance for understanding and mitigating sediment-induced wear in Francis turbines. Full article

The residues of rice production could be used as a mulch to reduce the effects of rill erosion on long and steep hillslopes. However, there is a need to identify the most effective size of this residue to apply as a countermeasure of rill erosion, exploring its effect on hydraulic variables. Several investigations have focused on the anti-erosive effects of other crop residues, while experiences on rice straw applications to reduce rill erosion are still lacking. To fill this gap, this study has measured the variability in flow velocity, stream power and the resulting soil loss in a rill covered by rice straw. Flume experiments simulating rill erosion have been carried out comparing soil loss among treatments with rice straw (dose of 3 tonnes ha⁻¹ and lengths between 20 and 70, 80 and 130, or 140 and 190 mm) and a non-mulched control. Moreover, a multiple regression model that predicts soil loss for a rill cover with rice straw of a given length has been proposed. The application of rice straw reduced the soil loss by at least 20% compared to bare soils. The most suitable size of the applied straw was 90 to 130 mm, which reduces soil loss by 45%. Finer straw (20 to 70 mm) did not significantly improve the soil’s resistance to rill erosion. The beneficial effects of straw must be ascribed to the reduction in flow velocity due to the presence of straw, as shown by accurate power equations regressing the soil loss to this variable. In spite of some limitations (small experimental scale, local environmental conditions, and low incorporation level of the substrate), the results are useful for land managers and hydrologists for soil conservation in hillslopes subjected to intense rill erosion and with similar climatic and hydrological and geomorphological conditions as the case study. Full article

Forests play a critical role in regulating hydrological processes and reducing soil erosion and sediment load. However, climate change has increased the frequency and severity of wildfires, which can significantly impact these ecosystem services. A historical megafire burned in January of 2017 in Central Chile, affecting the Purapel in Sauzal experimental watershed (an area dominated by Pinus radiata plantations), providing a unique opportunity to study post-fire sediment load dynamics. We hypothesized that sediment load would significantly increase following the wildfire, especially in areas with exotic commercial plantations. To test this, we analyzed daily sediment load and streamflow data collected the Purapel River during the 1991–2018 period, as well as other variables. Descriptive statistics and a sediment rating curve model were used to assess temporal variations in sediment load. Contrary to expectations, results showed no significant increase in sediment concentration following the devastating 2017 wildfire event. In fact, the Mann–Kendall test revealed a significant decreasing trend in winter sediment production over the study period. These findings may be explained by a reduction in precipitation during the mega-drought of the 2010s and, importantly, a rapid and dense post-fire pine seedling regeneration. This study highlights the complex interactions between climate, vegetation, and geomorphic processes, as well as the need for further research on post-fire sediment dynamics in Mediterranean plantation forests. Full article

With increasing energy demands and environmental pressures, hydropower, as a clean and renewable energy source, has attracted widespread attention for its development and utilization. However, hydropower systems are highly sensitive to climate change, significantly impacting generation, management, and safety. This study addresses the stability of hydropower resources in the Vakhsh River Basin, Tajikistan, using digital analysis, snowmelt runoff simulation, and soil erosion assessment to estimate spatial distribution. Under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), hydropower trends were simulated, and soil erosion was quantified. Results show annual hydropower potentials: Garm (55.465 billion kWh/a), Rogun (112.737 billion kWh/a), Nurex (78.853 billion kWh/a). Across all scenarios, runoff and hydropower generation increase (162–328,108 kWh/a), with growth rates following RCP4.5 < RCP2.6 < RCP8.5. Soil erosion simulation results indicate that a one millimeter increase in precipitation could lead to sediment deposition of 1.57 × 10⁶ kWh/year in upstream reservoirs. These results demonstrate that climate change has a significant impact on hydropower development in the Vakhsh River Basin. The research provides technical support for hydropower development under climate change. Full article

This study investigates the seasonal variability of water quality in the Yelek River Basin, Western Kazakhstan, using data from 2010 to 2025 that combine remote sensing, GIS, and hydrochemical monitoring data. This research addresses growing pressures on river systems from both natural and anthropogenic factors. Archival records from Kazhydromet and recent field measurements were analysed for dissolved oxygen, total suspended solids (TSSs), and total dissolved solids (TDSs), while satellite indices (NDWI, NDTI) provided spatiotemporal insights into turbidity. The results show clear seasonal contrasts: total suspended solids and turbidity rise sharply during spring floods due to snowmelt and erosion; water quality declines during summer–autumn low-flow periods under intensified human influence; and partial recovery occurs in winter when ice cover stabilises flow. Dissolved oxygen consistently indicates moderate pollution, while total dissolved solids (TDSs) remains within the “clean” range. Integration of satellite data with field observations enabled the development of a turbidity model and highlighted the lower river reaches as most vulnerable, where total suspended solids exceeded permissible limits. The findings confirm the value of combining remote sensing and GIS with traditional monitoring to capture long-term river water dynamics. This approach offers practical tools for sustainable water management, informs regional environmental policies, and provides transferable insights for semi-arid transboundary basins in Central Asia. Full article

Urban underground pipeline aging and leakage can result in soil erosion and ground collapse, constituting a major threat to urban public safety. To investigate this disaster mechanism, this present study established a two-dimensional numerical model based on the computational fluid dynamics–discrete element method (CFD–DEM) two-way fluid–solid coupling approach, simulating and reproducing the entire process from soil erosion, soil arch evolution to ground collapse caused by underground pipeline leakage in water-rich sand layers. The simulation shows that under the action of seepage pressures, soil particles are eroded and lost, forming a cavity above the pipeline defect. As soil continues to be lost, the disturbed zone expands toward the ground surface, causing ground settlement, and in water-rich sand layers, a funnel-shaped sinkhole is eventually formed. The ground collapse process is closely related to the groundwater level and the thickness of the overlying soil layer above the pipeline. Rising groundwater levels reduce the effective stress and shear strength of the soil, significantly exacerbating seepage erosion. Increasing the thickness of the overlying soil layer can enhance the confining pressure, improve soil compactness, and promote the formation of soil stress arch, thereby effectively slowing down the rate of ground collapse. This study reproduces the process of ground collapse numerically and reveals the mechanism of ground collapse induced by underground pipeline leakage in water-rich sand layers. Full article

IEA Wind Task 43 seeks to “unlock the full value of wind energy through digital transformation”. One mechanism to realize value is through enhanced data-driven decision-making and, while many areas in the wind sector can benefit from improved decision support, this case study focusses on a well-defined wind energy maintenance scenario involving blade inspection and repair. The solution concentrates on the specific damage category of blade leading edge erosion (LEE) and the optimum action to be taken for a given level of damage detected during periodic inspections. The key decision is whether to initiate repairs immediately or continue operating the turbine until the next inspection—and, if so, when that next inspection should take place. Even for such a specific damage type and decision option, the overall solution draws on multiple data types, ranging from damage classifications to cost drivers, and integrates a number of components including damage propagation, performance, and cost models. The core of the solution is a risk-based decision model using heuristic strategies, and Bayesian networks for optimized decision-making. This paper outlines the overall solution, expands on the data and modelling implementations, and discusses the results and conclusions arising from the investigation. Full article

Soil erosion affects agricultural and environmental sustainability and needs to be addressed. The Cagayan de Oro River Basin (CDORB), one of the major river basins in the Philippines, provides economic, social, and environmental services to the city and municipalities inside the basin. More than 70% of the area of the river basin is devoted to various forms of agricultural production. Land cover critically influences erosion dynamics as vegetation reduces rainfall impact, enhances infiltration, and limits sediment transport. This study employs the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) integrated with the Modified Universal Soil Loss Equation (MUSLE) to evaluate soil erosion under different rainfall return periods (5, 10, 25, 50, 100 years) and four land cover scenarios: No Reforestation Intervention (NI), Maximum Forest Cover (MF), Slope-Based Land Use (SB), and Reforestation on Public Domain (PD). Model results showed that soil loss increased with rainfall intensity, with NI yielding the highest average erosion of 1443 t ha⁻¹. Conservation scenarios reduced erosion by up to 53% compared to NI. Among the conservation scenarios, MF, SB, and PD yielded average erosion of 21, 716, and 1304 t ha⁻¹, respectively. While the MF scenario had the least soil loss, no space was assigned for economic production. On the other hand, the SB approach offered the best balance, halving erosion across all rainfall return periods, but at the same time has sufficient space available for economic production. These findings demonstrate the scientific value of integrating HEC-HMS and MUSLE for event-based erosion modeling and highlight how comparing multiple land-cover scenarios can inform data-driven land use planning and policy formulation for sustainable watershed management. Full article

Climate change is impacting atmospheric patterns and therefore wave conditions, with ports being among the most affected infrastructures, making it crucial to ensure their operability under changing climatic conditions. Most scientific studies on climate change focus on coastal erosion and flooding, whereas research on its impact on port operability remains relatively scarce. This challenge could be tackled with the emergence of Artificial Intelligence (AI), where alternative modeling approaches can be developed. Thus, a novel AI-based model specifically designed for studying port agitation is introduced herein. By integrating a hybrid deep learning approach, combining Feedforward Neural Networks (FFNNs) to model wave climate and Convolutional Neural Networks (CNNs) for port image analysis, port agitation has been successfully predicted compared to linear wave propagation models. This marks the first instance of utilizing image processing tools to analyze port agitation, resulting in a model with a remarkably low error rate, while offering a significant reduction in computational time compared to traditional wave propagation models, reducing computational time by a factor of four to ten. The accuracy of the proposed model has been investigated and validated for the Port of Valencia, located in the Spanish section of the Mediterranean Sea. Full article

An effective understanding of sediment deposition and erosion in river basins, particularly floodplains, is critical for modeling geomorphic evolution, managing flood risks, and maintaining ecological integrity. However, most related studies have been limited to hydraulic or hydrodynamic modeling approaches. Therefore, this study integrated Sentinel-1 differential interferometric synthetic aperture radar (DInSAR) coherence, Sentinel-2 normalized difference vegetation index, and soil surface moisture index data with one-dimensional hydraulic modeling to assess flood-induced sediment deposition and erosion in the Gamcheon River basin under non-flood, short flood, and long flood scenarios. The DInSAR deformation analysis revealed a clear pattern of upstream erosion and downstream deposition during flood events, indicating a total depositional uplift of 0.33 m during the long flood scenario but dominant erosion with a total measured surface lowering of −2.03 m during the non-flood scenario. These results were highly consistent with the predictions from the hydraulic model and supported by the hysteresis curves for in situ suspended sediment concentration. The findings of this study demonstrate the effectiveness of the proposed integrated approach for quantifying floodplain sediment dynamics, offering particular application value in data-scarce or inaccessible floodplains. Furthermore, the proposed approach provides practical insights into sediment management, flood risk assessment, and ecosystem restoration efforts. Full article

Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care. Full article

Climate change has intensified torrential rainfall and floods, causing frequent floodplain inundation with erosion and deposition. Large-scale waterfront facilities such as park golf courses are highly vulnerable, requiring systematic hydraulic safety evaluation. We simulated a recent flood in the Musim Stream using a two-dimensional FaSTMECH model to assess floodplain safety. The model showed excellent reproducibility (RMSE = 0.0176 m, NSE = 0.95 for depth; RMSE = 0.016 m/s, NSE = 0.87 for velocity). Flood risk indices—flood intensity (FI) and flood hazard rating (FHR)—and erosion–deposition indices—transient erosion and deposition index (TEDI) and steady erosion and deposition index (SEDI)—were applied. FI values were in the range of 0.3–6.4 (median 2.8) and FHR was in the range 0.7–10.2 (median 3.0), indicating that most floodplain areas exceeded the “high” to “extreme” risk range. TEDI was in the range of 0.004–4.15 (mean = 0.60), while SEDI was in the range of 0.001–5.59 (mean = 2.12). High TEDI values (0.6–0.9) occurred in curved and contracted sections, corresponding to observed erosion zones, whereas high SEDI values (0.8–1.0) were concentrated in the main channel. These results demonstrate that the indices effectively quantify and visualize floodplain risk, providing a practical basis for the design, placement, and maintenance of floodplain facilities. Full article