Search Results (1,621)

Ice and snow tourism (IST) is a significant global trend, offering Russia opportunities for tourism growth and seasonal diversification. This study investigates the potential of ice and snow art as a distinct subcategory of IST on Lake Baikal. Our research is based on an analysis of academic publications and official policy documents, field surveys conducted in winter 2025, and stakeholder consultations, with the “Olkhon Ice Fest” serving as a case study. The findings indicate a clear shift toward IST, with the number of winter tourists on Olkhon Island increasing by 70% between 2021 and 2024. The festival’s key features—its use of the natural ice landscape, a unique artistic technique, an explicit ecological focus, and strong entrepreneurial initiative—support the development of a conceptual model of IST on Lake Baikal grounded in ecotourism principles. Ensuring the long-term sustainable development of IST in the region requires improved governance, infrastructure, and transport systems, as well as support for green businesses and increased environmental awareness among tourists. This study contributes to the ongoing discourse on sustainable winter tourism by demonstrating the interconnections among environmental sustainability, socioeconomic benefits, and cultural innovation, thereby situating local IST practices within the broader framework of the United Nations Sustainable Development Goals (SDGs). Full article

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales. Focusing on the Qilihai Wetland in Tianjin, China, we combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset. Partial least squares regression (PLSR) was applied across a continuous spatial gradient (100–3000 m) to quantify scale-dependent statistical associations between landscape composition and configuration derived from satellite-mapped habitat mosaics on different functional groups. Waterbird diversity exhibited pronounced seasonal contrasts. During the breeding and post-fledging period, high-diversity assemblages were stably concentrated within core wetland areas, showing limited spatial variability. In contrast, during the wintering and stopover period, community distributions became increasingly dispersed, with elevated spatial heterogeneity and interannual variability associated with habitat reorganization. The scale of effect shifted systematically between seasons. In the breeding and post-fledging period, both waterfowl and waders responded predominantly to local-scale landscape factors (<800 m), consistent with nesting requirements and microhabitat conditions. During the wintering and stopover period, however, the characteristic response scale of waterfowl expanded to 1500–2000 m, suggesting stronger associations with broader landscape context, whereas waders remained closely linked to local-scale shallow-water and mudflat connectivity (~200 m). Functional traits played a key role in structuring these scale-dependent responses, with diving behavior and tarsus length being associated with strong constraints on habitat use. Overall, our results suggest that waterbird diversity patterns emerge from the interaction between seasonal habitat dynamics, landscape structure, and functional trait filtering, underscoring the need for phenology-informed, multi-scale conservation strategies that move beyond static spatial boundaries. Full article

The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability. Full article

The time-scale mismatch between rapid influent fluctuations and slow biochemical responses hinders the stability of wastewater treatment plants (WWTPs). Existing models often fail to capture shock signals due to noise interference (“signal pollution”). To address this, we propose the HD-MAED-LSTM model, which employs a “decompose-and-conquer” strategy. Targeting the dynamic characteristics of different components, this study innovatively designs heterogeneous attention mechanisms: utilizing Long-term Dependency Attention to capture the global evolution of the trend component, employing Multi-scale Periodic Attention to reinforce the cyclic patterns of the seasonal component, and using Gated Anomaly Attention to keenly capture sudden shocks in the residual component. In a case study, the effectiveness of the proposed model was validated based on one year of operational data from a large-scale industrial WWTP. HD-MAED-LSTM outperformed baseline models such as Transformer and LSTM in the medium-to-long-term (10-h) prediction of COD, TN, and TP, clearly demonstrating the positive role of differentiated modeling. Notably, in the core task of shock load early warning, the model achieved an F1-Score of 0.83 (superior to Transformer’s 0.77 and LSTM’s 0.67), and a Mean Directional Accuracy (MDA) as high as 0.93. Ablation studies confirm that the specialized attention mechanism is the key performance driver, reducing the Mean Absolute Error (MAE) by 56.7%. This framework provides precise support for shifting WWTPs from passive response to proactive control. Full article

Dryland coastal–mountain systems stand at the frontline of climate change, where steep topographic gradients amplify the balance between resilience and collapse. Mount Elba—Egypt’s hyper-arid coastal–mountain reserve—embodies this fragile equilibrium, preserving a seventy-year climatic record across a landscape poised between sea and desert. Here, we present the first multi-decadal, spatio-temporal assessment (1950–2021) integrating the Standardized Precipitation–Evapotranspiration Index (SPEI-6) with satellite-derived vegetation responses (NDVI) along a ten-grid coastal–highland transect. Results reveal a pervasive drying trajectory of −0.42 SPEI units per decade, with vegetation–climate coherence (r ≈ 0.3, p < 0.05) intensifying inland, where orographic uplift magnifies hydroclimatic stress. The southern highlands emerge as an “internal drought belt,” while maritime humidity grants the coast partial refuge. These trends are not mere numerical abstractions; they trace the slow desiccation of ecosystems that once anchored biodiversity and pastoral livelihoods. A post-1990 regime shift marks the breakdown of wet-season recovery and the rise in persistent droughts, modulated by ENSO teleconnections—the first quantitative attribution of Pacific climate signals to Egypt’s coastal mountains. By coupling climatic diagnostics with ecological response, this study reframes drought as a living ecological process rather than a statistical anomaly, positioning Mount Elba as a sentinel landscape for resilience and adaptation in northeast Africa’s rapidly warming drylands. Full article

Understanding seasonal water acquisition strategies of desert plants is critical for predicting vegetation resilience under increasing hydrological stress in arid inland river basins. In hyper-arid oases, strong evaporative demand and declining groundwater levels impose tightly coupled constraints on plant water uptake across soil–plant–atmosphere continua. In this study, we combined hydrogen and oxygen stable isotopes, Bayesian mixing models, soil moisture measurements and groundwater monitoring, and leaf δ¹³C analysis to quantify monthly water-source contributions and long-term water-use efficiency of three dominant species (Reaumuria soongarica, Tamarix ramosissima, and Populus euphratica) in the Ejina Oasis. Clear ecohydrological niche differentiation was evident among the three species. R. soongarica exhibited moderate temporal flexibility by integrating shallow and deep soil water with episodic groundwater use, whereas T. ramosissima adopted a vertically integrated and hydraulically plastic strategy combining precipitation, multi-depth soil water, and groundwater. In contrast, P. euphratica followed a conservative strategy, relying predominantly on deep soil water with only minor and transient inputs from precipitation and groundwater. Across species and seasons, deep vadose-zone soil water (120–200 cm) consistently acted as the most stable and influential reservoir, buffering seasonal drought and sustaining transpiration. T. ramosissima maintained the highest intrinsic water-use efficiency, and P. euphratica exhibited consistently lower efficiency associated with sustained access to stable deep soil water. These contrasting strategies reveal multiple pathways of hydraulic stability and plasticity that underpin vegetation persistence under progressive groundwater depletion. By linking water-source partitioning with physiological regulation, this study provides a mechanistic basis for understanding plant water-use strategies and informs ecological water management and species-specific restoration in hyper-arid inland oases. Full article

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article

This study evaluates the effectiveness of integrating a Residual Shifting (ResShift)-based deep learning super-resolution (SR) technique with the Robust Dense Feature Matching (RoMa) algorithm for high-precision inter-satellite image registration. The key findings of this research are as follows: (1) Enhancement of Structural Details: Quadrupling image resolution via the ResShift SR model significantly improved the distinctness of edges and corners, leading to superior feature matching performance compared to original resolution data. (2) Superiority of Dense Matching: The RoMa model consistently delivered overwhelming results, maintaining a minimum of 2300 correct matches (NCM) across all datasets, which substantially outperformed existing sparse matching models such as SuperPoint + LightGlue (SPLG) (minimum 177 NCM) and SuperPoint + SuperGlue (SPSG). (3) Seasonal Robustness: The proposed framework demonstrated exceptional stability, maintaining registration errors below 0.5 pixels even in challenging summer–winter image pairs affected by cloud cover and spectral variations. (4) Geospatial Reliability: Integration of SR-derived homography with RoMa achieved a significant reduction in geographic distance errors, confirming the robustness of the dense matching paradigm for multi-sensor and multi-temporal satellite data fusion. These findings validate that the synergy between diffusion-based SR and dense feature matching provides a robust technological foundation for autonomous, high-precision satellite image registration. Full article

In temperate freshwater ecosystems, zooplankton play a crucial role in the pelagic food web and act as sensitive indicators of environmental change. They respond to shifts in water temperature, hydrodynamic mixing, and short-term meteorological events. This study investigated the epilimnetic zooplankton fauna of Lake Karamurat (Bolu, Türkiye), a small tectonic temperate lake, with a specific focus on the influence of rainfall events and wind speed on community structure. The samples were taken seasonally and horizontally using a plankton net (55 µm mesh size) and were analyzed alongside in situ physico-chemical measurements and meteorological data. In total, 74 zooplankton taxa were identified, comprising 54 rotifer species and 20 crustacean species (16 Cladocera and 4 Copepoda). Testudinella greeni was recorded for the first time in Türkiye, representing a new addition to the Turkish Rotifera fauna. Multivariate analyses revealed that electrical conductivity, water temperature, dissolved oxygen, precipitation, and wind speed were key drivers shaping community composition. The findings suggest that wind-driven surface mixing and episodic rainfall events enhanced vertical redistribution, leading to dominance of rotifers and small-bodied cladocerans in the epilimnion. These findings underscore the critical role of sampling strategy in shallow lakes under dynamic conditions and provide new faunistic insights into the zooplankton diversity of Anatolian lakes. Full article

Accurate and stable mapping of crop types is fundamental to agricultural monitoring and food security. However, inter-annual phenological shifts driven by variations in air temperature, precipitation, and sowing dates introduce systematic changes in the spectral distributions associated with the same day of year (DOY). As a result, the “date–spectrum–class” mapping learned during training can become misaligned when applied to a new year, leading to increased misclassification and unstable performance. To tackle this problem, we develop TABS-Net (Temporal–Spectral Attentive Block with Space–Time Fusion Network). The core contributions of this study are summarized as follows: (1) we propose an end-to-end 3D CNN framework to jointly model spatial, temporal, and spectral information; (2) we design and embed CBAM3D modules into the backbone to emphasize informative bands and key time windows; and (3) we introduce DOY positional encoding and temporal jitter during training to explicitly align seasonal timing and simulate phenological shifts, thereby enhancing cross-year robustness. We conduct a comprehensive evaluation on a Cropland Data Layer (CDL) subset. Within a single year, TABS-Net delivers higher and more balanced overall accuracy, Macro-F1, and mIoU than strong baselines, including 2D stacking, 1D temporal convolution/LSTM, and transformer models. In cross-year experiments, we quantify temporal stability using inter-annual robustness (IAR); with both DOY encoding and temporal jitter enabled, the model attains IAR values close to one for major crop classes, effectively compensating for phenological misalignment and inter-annual variability. Ablation studies show that DOY encoding and temporal jitter are the primary contributors to improved inter-annual consistency, while CBAM3D reduces crop–crop and crop–background confusion by focusing on discriminative spectral regions such as the red-edge and near-infrared bands and on key growth stages. Overall, TABS-Net combines higher accuracy with stronger robustness across multiple years, offering a scalable and transferable solution for large-area, multi-year remote sensing crop mapping. Full article

Climate fluctuations are expected to drive a decline in the growth of many conifer and broadleaf species, especially in the Mediterranean region, where these species grow at or very near the southern limits of their distribution. Such trends have important implications not only for forest productivity but also for plant diversity, as shifts in species performance may alter competitive interactions and long-term community composition. Using tree-ring data sourced from two Abies cephalonica stands with different elevation in Mount Parnassus in Central Greece, we evaluate the growth responses of the species to climatic variability employing a dendroecological approach. We hypothesize that radial growth at higher elevations is more strongly influenced by climate variability than at lower elevations. Despite the moderate to relatively good common signal indicated by the expressed population signal (EPS: 0.645 for the high-altitude stand and 0.782 for the low-altitude stand), the chronologies for both sites preserve crucial stand-level growth patterns, providing an important basis for ecological insights. The calculation of the Average Tree-Ring Width Index (ARWI) for both sites revealed that fir in both altitudes exhibited a decline in growth rates from the late 1980s to the early 1990s, followed by a general recovery and increase throughout the late 1990s. They also both experienced a significant decline in growth between approximately 2018 and 2022. The best-fit model for annual ring-width variation at lower elevations was a simple autoregressive model of order one (AR1), where growth was driven exclusively by the previous year’s growth (p < 0.001). At the higher elevation, a more complex model emerged: while previous year’s growth remained significant (p < 0.001), other variables such as maximum growing season temperature (p = 0.041), annual temperature (inverse effect, p = 0.039), annual precipitation (p = 0.017), and evapotranspiration (p = 0.039) also had a statistically significant impact on tree growth. Our results emphasize the prominent role of carry-over effects in shaping their annual growth patterns. Full article

This study investigates indoor CO₂ levels in Croatian schools to identify environmental and temporal factors influencing classroom air quality. Using data from hundreds of low-cost sensors installed in 243 schools, we analyze seasonal patterns and differences in CO₂ concentrations between schools. In two-shift schools, the longer occupied period was associated with CO₂ remaining elevated later in the day. Time-series forecasting with the Prophet model accounts for seasonal variations, while statistical analyses quantify variability and identify key factors driving concentration differences. Additionally, Land Use Regression (LUR) models are developed and compared with direct sensor measurements at the school level to assess their association with CO₂ levels across different counties in the country. The results reveal consistent seasonal trends and notable local differences between schools, emphasizing the importance of detailed monitoring in environments with vulnerable populations. This research offers insights into the strengths and limitations of statistical and modeling methods for school-based air quality assessment and provides recommendations for enhancing monitoring strategies in similar large-scale networks. Full article

The introduction of Super-High-Density (SHD) olive orchards represents a crucial innovation in modern olive growing, enhancing sustainability. However, the long-term success of these planting systems depends strongly on cultivar selection, combining suitable vegetative and reproductive traits. This three-year field study investigated key floral biology parameters—flowering phenograms, gynosterility, and self-compatibility—of ten olive cultivars grown under irrigated conditions in southern Italy: ‘Arbequina’, ‘Arbosana’, ‘Cima di Bitonto’, ‘Coratina’, ‘Don Carlo’, ‘Frantoio’, ‘Favolosa’ (=‘Fs-17’), ‘I-77’, ‘Koroneiki’, and ‘Urano’ (=‘Tosca’). Flowering phenograms varied significantly across years and cultivars, showing temporal shifts related to chilling accumulation and yield of the previous year. Early blooming cultivars (‘Arbequina’, ‘Arbosana’, and ‘Coratina’) exhibited partial flowering overlap with mid-season ones, enhancing cross-pollination opportunities. Quantitative analysis of flowering overlap revealed that most cultivar combinations exceeded the 70% threshold required for effective pollination, although specific genotypes (‘Coratina’, ‘Fs-17’, and especially ‘I-77’) showed critical mismatches, while ‘Frantoio’ and ‘Arbequina’ emerged as the most reliable pollinizers. Gynosterility exhibited statistical differences among cultivars and canopy positions: ‘I-77’ showed the highest values (71.4%), while ‘Coratina’ and ‘Cima di Bitonto’ showed the lowest ones (7.3 and 8.4%, respectively). The median portions of the canopies generally displayed a greater number of sterile flowers (29.4%), reflecting the combined effect of genetic and environmental factors such as light exposure. In the inflorescence, the majority of gynosterile flowers were concentrated in the lower part, for all canopy portions (modal value). Self-compatibility tests were performed considering a fruit set of 1% as a threshold to discriminate. For open pollination, the fruit set was highly variable among cultivars, ranging from 0.5% in ‘I-77’ to 4.7% in ‘Arbosana’. Apart from ‘I77’, all varieties achieved a fruit set greater than 1%. Instead, for the self-pollination, only ‘Arbequina’, ‘Koroneiki’, ‘Frantoio’, and ‘Cima di Bitonto’ could be identified as pseudo-self-compatible, whereas ‘Coratina’, ‘Fs-17’, and the others were clearly self-incompatible and therefore unsuitable for monovarietal orchards in areas with limited availability of pollen. By integrating self-compatibility and gynosterility data, the cultivars were ranked according to reproductive aptitude, identifying ‘Cima di Bitonto’ and ‘Frantoio’ as the most fertile genotypes, whereas ‘Don Carlo’ and particularly ‘I-77’ showed severe genetic sterility constraints. The findings underline the critical role of floral biology in defining reproductive efficiency and varietal adaptability in SHD systems. This research provides valuable insights for optimizing cultivar selection, orchard design, and management practices, contributing to the development of sustainable, climate-resilient olive production models for Mediterranean environments. Full article

Copepods are key components of marine food webs, linking primary producers such as microalgae to higher trophic levels, including many fish species. This study investigates long-term changes in the composition, density, and biomass of copepod communities along the Romanian coast of the Black Sea over six decades (1956–2015), based on historical records and recent monitoring from 18 sampling stations. Mean copepod density declined markedly over the study period, particularly during the cold season, decreasing from values exceeding 1000 ind/m³ in the 1960s to <300 ind/m³ after 2000, while biomass showed weaker but comparable long-term fluctuations. Seasonal variability was pronounced, with significantly higher densities and biomass during the warm season. Generalised Additive Models (GAMs) explained up to 40–55% of the variance in copepod density and biomass, depending on the season. During the warm season, phosphate exerted a positive effect on copepod abundance, consistent with bottom-up control via phytoplankton productivity, whereas during the cold season, temperature showed a positive effect and salinity a negative effect, indicating stronger physical control of copepod persistence. Species composition shifted over time, with a reduction in constant species and an increase in rare or accidental taxa in later decades. These results indicate that climate variability and anthropogenic pressures have reshaped copepod communities, with potential consequences for food-web efficiency and ecosystem resilience in the Black Sea. Full article

This study provides a systematic analysis of the spatiotemporal distribution and trends in the frequency of significant wave height (SWH) exceeding level 5 (SWH > 2.5 m) and level 7 (SWH > 6 m) in the Northwest Pacific (NWP) for 1993–2024, which are defined as f₅ and f₇, respectively, as well as their correlations with major climate indexes. Our results indicate that (1) the high-value zones for the annual mean f₅ and f₇ are both located in the south waters of the Aleutian Islands, with maximum values of 58.0% and 6.4%, respectively. Winter’s contribution is greatest (maximum values of 96.9% and 16.8% per year), while summer’s is the smallest. (2) f₅ exhibits a significant decline trend across the entire NWP basin (of −0.15 to −0.30%/yr), with the steepest decline occurring in autumn (−0.69%/yr) and the shallowest in summer. f₇ exhibits a significant linear decrease in the open ocean east of Japan (−0.08%/yr) while showing a significant linear increase in the waters east of the Kamchatka Peninsula (0.08%/yr). Both variations peak in winter (maximum values of −0.27% and 0.30% per year) and are smallest in summer. (3) Seasonal and regional variations in climate index–f₅ and f₇ relationships reflect large-scale atmospheric modulation of waves. For example, the Oceanic Niño Index shows a predominantly negative correlation with f₅ in winter (maximum correlation coefficient r_m = −0.70) around the Luzon Strait, shifting to a significant positive correlation in summer (r_m = 0.70) across the extensive region east of Taiwan Island and the Philippines. The Pacific Decadal Oscillation index shows a significant positive correlation with f₇ in summer and autumn (r_m = 0.69) east of Taiwan Island and a strong negative correlation in winter (r_m = −0.77) to the east of Kamchatka Peninsula. Full article