Search Results (422)

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

The Ichubamba Yasepan wetlands, in the Andean páramos of Ecuador, suffer heavy metal contamination due to anthropogenic activities and volcanic ash from Sangay, impacting biodiversity and ecosystem services. This quasi-experimental study evaluated the bioaccumulation and tolerance of metals in high Andean species through stratified random sampling and linear transects in two altitudinal ranges. Concentrations of Cr, Pb, Hg, As, and Fe in water and the tissues of eight dominant plant species were analyzed using atomic absorption spectrophotometry, calculating bioaccumulation indices (BAIs) and applying principal component analysis (PCA), clustering, and linear discriminant analysis (LDA). Twenty-five species from 14 families were identified, predominantly Poaceae and Cyperaceae, with Calamagrostis intermedia as the most relevant (IVI = 12.74). The water exceeded regulatory limits for As, Cr, Fe, and Pb, indicating severe contamination. Carex bonplandii showed a high BAI for Cr (47.8), Taraxacum officinale and Plantago australis for Pb, and Lachemilla orbiculata for Hg, while Fe was widely accumulated. The LDA highlighted differences based on As and Pb, suggesting physiological adaptations. Pollution threatens biodiversity and human health, but C. bonplandii and L. orbiculata have phytoremediation potential. Full article

Gas–water two-phase flow in horizontal and inclined wells is significantly influenced by gravitational forces and spatial asymmetry around the wellbore, resulting in complex and variable flow patterns. Accurate measurement of water holdup is essential for analyzing phase distribution and understanding multiphase flow behavior. Water holdup imaging provides a valuable means for visualizing the spatial distribution and proportion of gas and water phases within the wellbore. In this study, air and tap water were used to simulate downhole gas and formation water, respectively. An array capacitance arraay tool (CAT) was employed to measure water holdup under varying total flow rates and water cuts in a horizontal well experimental setup. A total of 228 datasets were collected, and the measurements were processed in MATLAB (2020 version) using three interpolation algorithms: simple linear interpolation, inverse distance interpolation, and Lagrangian nonlinear interpolation. Water holdup across the wellbore cross-section was also calculated using arithmetic averaging and integration methods. The results obtained from the three imaging algorithms were compared with these reference values to evaluate accuracy and visualize imaging performance. The CAT demonstrated reliable measurement capabilities under low- to medium-flow conditions, accurately capturing fluid distribution. For stratified flow regimes, the linear interpolation algorithm provided the clearest depiction of the gas–water interface. Under low- to medium-flow rates with high water content, both inverse distance and Lagrangian methods produced more refined images of phase distribution. In dispersed flow conditions, the Lagrangian nonlinear interpolation algorithm delivered the highest accuracy, effectively capturing subtle variations within the complex flow field. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

As a critical link connecting groundwater and vegetation, the vadose zone’s lithological structural heterogeneity directly influences soil water distribution and vegetation growth. A comprehensive understanding of the ecological effects of the vadose zone can provide scientific evidence for groundwater ecological protection and natural vegetation conservation in arid regions. This study, taking the Minqin Basin in the lower reaches of China’s Shiyang River as a case, reveals the constraining effects of vadose zone lithological structures on vegetation water supply, root development, and water use strategies through integrated analysis, field investigations, and numerical simulations. The findings highlight the critical ecological role of the vadose zone. This role primarily manifests through two mechanisms: regulating capillary water rise and controlling water-holding capacity. They directly impact soil water supply efficiency, alter the spatiotemporal distribution of water deficit in the root zone, and drive vegetation to develop adaptive root growth patterns and stratified water use strategies, ultimately leading to different growth statuses of natural vegetation. During groundwater level fluctuations, fine-grained lithologies in the vadose zone exhibit stronger capillary water response rates, while multi-layered lithological structures (e.g., “fine-over-coarse” configurations) demonstrate pronounced delayed water release effects. Their effective water-holding capacities continue to exert ecological effects, significantly enhancing vegetation drought resilience. Full article

Accurate reservoir outflow simulation is crucial for water resource management. However, traditional machine learning-based simulation methods have not sufficiently considered the physical constraints of reservoir operation, which may lead to unrealistic issues such as negative outflows or water levels exceeding the reservoir’s own limitations. This study integrates physical constraints into the random forest (RF) model using the Sigmoid function, constructing a physics-constrained random forest model (PC-RF) for cascade reservoir outflow simulation. A stratified sampling strategy based on hydrological year types is used to create the training and validation datasets. The coefficient of determination (R²) and root mean square error (RMSE) are used to evaluate the model’s performance for medium- to long-term predictions of reservoir outflows on a 10-day time scale. Additionally, the mean decrease in impurity method is used to assess the importance of input features, thereby enhancing the model’s interpretability. The application the Yalong River cascade reservoir indicates that (1) compared to traditional RF, the PC-RF achieved significant breakthroughs, with an increase of 37.13% in the R² and a decrease of 60.04% in the RMSE when simulating outflows from the Lianghekou Reservoir, with all reservoirs maintaining an R² above 0.95, with no instances of unrealistic outcomes; (2) PC-RF effectively integrated historical operational patterns with top three features being previous period outflow, current inflow, and previous period inflow, providing interpretable insights for operational decision-making. The PC-RF model demonstrates high accuracy and practical potential in cascade reservoir outflow simulation, providing valuable applications for cascade reservoir management and water resource optimization. Full article

Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in reducing this requirement while keeping estimation accuracy at acceptable levels by adapting a deep learning model pretrained on data from Puck Lagoon (Poland) to a new coastal site in Agia Napa (Cyprus). Leveraging the open MagicBathyNet benchmark dataset and a lightweight U-Net architecture, three scenarios were studied and compared: direct inference to Cyprus, site-specific training in Cyprus, and fine-tuning from Poland to Cyprus with incrementally larger subsets of training data. Results demonstrate that fine-tuning with 15 samples reduces RMSE by over 50% relative to the direct inference baseline. In addition, the domain adaptation approach using 15 samples shows comparable performance to the site-specific model trained on all available data in Cyprus. Depth-stratified error analysis and paired statistical tests confirm that around 15 samples represent a practical lower bound for stable SDB, according to the MagicBathyNet benchmark. The findings of this work provide quantitative evidence on the effectiveness of deploying data-efficient SDB pipelines in settings of limited in situ surveys, as well as a practical lower bound for clear and shallow coastal waters. Full article

This study assessed the applicability of visible–near-infrared (vis-NIR) spectroscopy to predicting the water retention characteristics of forest soils in Japan, which vary widely owing to the presence of volcanic ash. Soil samples were collected from 34 sites, and the volumetric water content was measured at eight levels of matric suction. Spectral data were processed by using the second derivative of the absorbance, and regression models were developed by using explainable boosting machine (EBM), which is an interpretable machine learning method. Although the prediction accuracy was limited owing to the small sample size and soil heterogeneity, EBM performed better under saturated conditions (R² = 0.30), which suggests that vis-NIR spectroscopy can capture water-related features, especially under wet conditions. Importance analysis consistently selected wavelengths that were associated with organic matter and hydrated clay minerals. The important wavelengths clearly shifted from free-water bands in wet soils to mineral-related absorption bands in dry soils. These findings highlight the potential of coupling vis-NIR spectroscopy with interpretable models like EBM for estimating the hydraulic properties of forest soils. Improved accuracy is expected with larger datasets and stratified models by soil type, which can facilitate more efficient soil monitoring in forests. Full article

This study focused on the carbonate system dynamics and air–sea CO₂ fluxes in the open-ocean waters of the Madeira–Tore Seamount Complex during August 2024. Surface water properties revealed pronounced latitudinal gradients in sea surface temperature (21.9–23.1 °C), salinity (36.2–36.7), and dissolved oxygen (228–251 µmol Kg⁻¹), influenced by mesoscale eddies and topographically driven upwelling. Despite oligotrophic conditions, distinct phytoplankton assemblages were observed, with coccolithophores dominating southern seamounts and open-ocean stations, and green algae and diatoms indicating episodic nutrient input. Surface total alkalinity (TA: 2236–2467 µmol Kg⁻¹), dissolved inorganic carbon (DIC: 2006–2183 µmol Kg⁻¹), and pCO₂ (467–515 µatm) showed spatial variability aligned with water mass characteristics and biological activity. All stations exhibited positive air–sea CO₂ fluxes (2.8–11.5 mmol m⁻² d⁻¹), indicating the region is a CO₂ source during summer. Calcite and aragonite saturation states were highest in stratified, warmer waters. Principal Component Analysis highlighted the role of physical mixing, carbonate chemistry, and biological uptake in structuring regional variability. Our findings emphasize and contribute to the complex interplay of physical and biogeochemical drivers in modulating carbon cycling and ecosystem structure across Atlantic seamounts. Full article

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

Land surface models (LSMs) play a crucial role in climate prediction and carbon cycle assessment. To ensure their reliability, it is crucial to evaluate their performance in simulating key processes, such as evapotranspiration (ET) and gross primary productivity (GPP), across various temporal scales and vegetation types. This study systematically evaluates the performance of the newly modernized Noah-MP LSM version 5.0 in simulating water and carbon fluxes, specifically ET and GPP, across temporal scales ranging from half-hourly (capturing diurnal cycles) to annual using observational data from 105 sites within the globally FLUXNET2015 dataset. The results reveal that Noah-MP effectively captured the overall variability of both ET and GPP, particularly at short temporal scales. The model successfully simulated the diurnal and seasonal cycles of both fluxes, though cumulative errors increased at the annual scale. Diurnally, the largest simulation biases typically occurred around noon; while, seasonally, biases were smallest in winter. Performance varied significantly across vegetation types. For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. Conversely, GPP simulations were most accurate for wetlands and closed shrublands, showing the largest deviation for evergreen broadleaf forests. Furthermore, an in-depth analysis stratified by the climate background revealed that ET simulations failed to capture inter-annual variability in the temperate and continental zones, while GPP was severely overestimated in arid and temperate climates. This study identifies the strengths and weaknesses of Noah-MP in simulating water and carbon fluxes, providing valuable insights for future model improvements. Full article

Background/Objectives: Non-communicable diseases (NCDs) are strongly linked to beverage consumption. This study aimed to assess the average daily beverage intake of Saudi residents, energy intake from beverages, and the influence of socio-demographic factors, health behaviors, and health status on beverage intake. Methods: A nationally representative, cross-sectional study utilized stratified quota sampling to survey adults (≥18 years) across all 13 administrative regions of Saudi Arabia. Data were collected from April 2022 to December 2023 using the validated Arabic Beverage Frequency Questionnaire (ABFQ), assessing consumption patterns of 28 beverage types. Results: The study included 4385 participants (mean age: 36.1 ± 11.14 years, 65% male). Sweetened tea (28 mL/day), regular soft drinks (22.1 mL/day), and Saudi coffee (18 mL/day) were the most frequent beverages after water. Sweetened tea contributed to the highest average energy intake (33.2 ± 46.4 kcal/day). Consumption of sugar-sweetened beverages (SSBs) was higher among younger individuals (18–29 years: OR: 4.0, 95% CI [2.6–6.3]; 30–44 years: OR: 2.8, 95% CI [1.8–4.3]), males (OR:1.6, 95% CI [1.4–1.8]), and residents of specific regions [Al-Jawf (OR: 1.9, 95% CI [1.2–3.2]) and Jazan (OR: 3.2, 95% CI [2.2–4.7])]. Higher water intakes were associated with males (OR: 1.5, 95% CI [1.3–1.7]), higher education levels (OR: 1.4, 95% CI [1.2–1.8]), physically active (OR: 1.5, 95% CI [1.3–1.8]), and those overweight (OR: 1.6, 95% CI [1.2–2.3]) or obese (OR: 2, 95% CI [1.4–2.8]). Conclusions: This study provides a valuable insight into beverage consumption patterns among Saudi residents. The findings highlight the need for targeted public health interventions to promote healthier beverage choices, particularly among younger populations and those with lower socioeconomic status. Full article

Background/Objectives: The phase angle (PhA) is an emerging biomarker reflecting the cellular integrity and nutritional status. This study aimed to explore potential associations between the PhA, clinical severity scores, and 60-day survival outcomes following an admission to the Intensive Care Unit (ICU). Methods: This prospective, single-center study included 43 critically ill patients admitted to the ICU at Evangelismos General Hospital between May and November 2024. Patients were stratified by their PhA (≤5.4° vs. >5.4°). The PhA was measured at admission and subsequently on days 5–7, 10–11, 13–14, and until discharge. Severity scores (SOFA and APACHE II) were recorded. Between-group differences were assessed using independent samples t-tests and Mann–Whitney U tests, as appropriate. Survival was analyzed using Kaplan–Meier curves and Cox proportional hazards models. Results: The mean age was 54.6 ± 17 years; 63.6% were male. At ICU admission, patients with a PhA > 5.4° were significantly younger (p < 0.001) and had a higher fat-free mass (p < 0.001), greater calf circumference (p < 0.001), higher extracellular water (p < 0.001), larger mid-upper arm circumference (p = 0.009), and higher resting energy expenditure per kilogram (27.4 vs. 23.1 kcal/kg, p = 0.002). The PhA declined significantly during the ICU stay (p < 0.001). The Kaplan–Meier analysis showed a significantly shorter survival in patients with a PhA ≤ 5.4° (HR: 6.32, p = 0.019), which remained significant after adjusting for sepsis (p = 0.017). In a multivariable Cox regression, both PhA and APACHE II scores independently predicted mortality. Conclusions: While limited by a small sample size and single-center design, these findings support the further exploration of the PhA as a monitoring tool in critical care. Full article

This study numerically investigates unsteady natural convection (NC) heat transfer (HT) and entropy generation (E_gen) in trapezoidal cavities filled with two thermally stratified fluids. Both air-filled and water-filled configurations are analyzed to evaluate and compare their thermal performance under varying conditions. The cavities are characterized by a heated base, thermally stratified sloped walls, and a cooled top wall. The governing equations are numerically solved using the finite volume (FV) approach. The study considers a Prandtl number (Pr) of 0.71 for air and 7.01 for water, Rayleigh numbers (Ra) ranging from 10³ to 5 × 10⁷, and an aspect ratio (AR) of 0.5. Flow behavior is examined through various parameters, including temperature time series (TTS), average Nusselt number (Nu), average entropy generation (E_avg), average Bejan number (Be_avg), and ecological coefficient of performance (ECOP). Three bifurcations are identified during the transition from steady to chaotic flow for both fluids. The first is a pitchfork bifurcation, occurring between Ra = 10⁵ and 2 × 10⁵ for air, and between Ra = 9 × 10⁴ and 10⁵ for water. The second, a Hopf bifurcation, is observed between Ra = 4.7 × 10⁵ and 4.8 × 10⁵ for air, and between Ra = 10⁵ and 2 × 10⁵ for water. The third bifurcation marks the onset of chaotic flow, occurring between Ra = 3 × 10⁷ and 4 × 10⁷ for air, and between Ra = 4 × 10⁵ and 5 × 10⁵ for water. At Ra = 10⁶, the average HT in the air-filled cavity is 85.35% higher than in the water-filled cavity, while E_avg is 94.54% greater in the air-filled cavity compared to water-filled cavity. At Ra = 10⁶, the thermal performance of the cavity filled with water is 4.96% better than that of the air-filled cavity. These findings provide valuable insights for optimizing thermal systems using trapezoidal cavities and varying working fluids. Full article

Climate change and extreme events such as droughts, heavy rainfall and flooding can influence the water column stratification in reservoir dams, decrease storage capacity, increase sediment and pollutant loads and, as a result, affect water quality. The seasonal variation in the water column stratification of reservoirs is an important parameter for the study of dam life cycle as well as water management and use. In the present study a detailed bathymetric survey was carried out, and a digital elevation model (DEM) of the reservoir was constructed. Seasonal physicochemical monitoring data such as temperature, dissolved oxygen, pH and conductivity are presented. The seasonal thermal stratification was recorded, resulting in an isolated hypolimnion where anoxic layers formed below 17 m in summer and autumn. Manganese and iron concentrations exhibited values higher than 150 mg/L in the anoxic hypolimnion during summer and autumn, indicating solubilization from the sediment. The observed seasonal and depth-dependent variations in physicochemical parameters underline the reservoir’s susceptibility to eutrophication and metal mobilization, particularly during stratified periods. These findings are critical for designing management strategies to mitigate potential water quality issues. Full article