Search Results (226)

Internal waves play a critical role in material transport, vertical mixing, and energy dissipation within stratified aquatic systems. Their dynamics are strongly modulated by thermal stratification and surface meteorological forcing. This study examines the influence of interannual meteorological variability from 1980 to 2010 on internal wave behavior using a series of numerical simulations in Lake Biwa in Japan. In each simulation, air temperature, wind speed, or precipitation was perturbed by ±2 standard deviations relative to the climatological mean. Power spectral analysis of simulated velocity fields was conducted for the surface, thermocline, and bottom layers, focusing on super-inertial (6–16 h), near-inertial (~16–30 h), and sub-inertial (>30 h) frequency bands. The results show that higher air temperatures intensify stratification and enhance near-inertial internal waves, particularly within the thermocline, whereas cooler conditions favor sub-inertial wave dominance. Increased wind speeds amplify internal wave energy across all layers, with the strongest effect occurring in the high-frequency band due to intensified wind stress and vertical shear, while weaker winds suppress wave activity. Precipitation variability primarily affects surface stratification, exerting more localized and weaker impacts. These findings highlight the non-linear, depth-dependent responses of internal waves to atmospheric drivers and improve understanding of the coupling between climate variability and internal wave energetics. The insights gained provide a basis for more accurate predictions and sustainable management of stratified aquatic ecosystems under future climate scenarios. Full article

Adverse climate events have recently highlighted an increasing need to deploy sustainable energetic infrastructures. The existing electric conversion circuits for solar energy provide high efficiency; however, gaps in sustainability and robustness can be identified by considering their operation during intense perturbations, potentially occurring for interplanetary energy transfer. Additionally, charging characteristics for energy storage units influence differently the operation life of battery arrays, with increased stability providing favorable operating conditions. Therefore, the present study develops an alternative controller for managing solar energy as well as a prototype for tracking the maximum power point, both constrained by robustness and renewability studies. For the presented design, stability analyses and simulations validated the management of electric energy from solar panels and the developed configuration resulted in improving current peak integral transient characteristics by using an alternative control method, demonstrating stability for an indefinite number of energy storage units. Furthermore, the estimation for VLSI (Very-Large-Scale Integration) of this constrained design has been concluded to potentially provide a solution with adequate performance, comparable to state-of-the-art computational circuits. However, certain limitations could arise when substituting the main computation parts with analyzed solutions and proceeding with integration-based manufacturing. Full article

Soil moisture is a critical variable linking the land surface and atmosphere over the Tibetan Plateau. Identifying its spatial variability is essential for understanding the regional water cycle, particularly how landscape features shape soil moisture patterns. While previous studies emphasized climate, topography, and vegetation, the role of land-cover morphology has been largely overlooked. Here, we combined TerraClimate reanalysis and satellite data from 2018 to 2022 with morphological analysis and the GeoDetector method to examine 14 factors affecting soil moisture heterogeneity. Results show that precipitation and vegetation dominate soil moisture distribution, yet the influence of landscape morphology in forests and barren lands exceeds that of temperature. Forest cores retain extremely high soil moisture, while transitional zones such as edges, perforations, and islets play a critical role in grasslands and croplands. Interaction analysis indicates that forests and barren morphologies mainly respond to linear climatic drivers, whereas croplands, grasslands, urban areas, and water morphologies are shaped by nonlinear multi-factor effects. Perturbation experiments further reveal that warming weakens the buffering capacity of forests and enhances drying in grasslands and barren areas. These findings highlight the importance of landscape morphology for predicting soil moisture resilience and improving ecological management on the Tibetan Plateau. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article

The amount of energy consumed worldwide is raising at a startling rate. This has led to a global energy crisis and a hike in fuel prices and has caused environmental jeopardy. Renewable energy resources offer a promising solution to the above situation. Solar energy is examined to be the most liberal source of renewable energy. The efficiency of solar PV cells show nonlinear characteristics and deliver poor performance. Consequently, it is imperative to use the maximum power point tracking (MPPT) technique to extract the optimum amount of energy from photovoltaic (PV) cells. Perturb and Observe (P&O) and Incremental Conductance (INC) are examples of MPPT algorithms. The performance of MPPT schemes below varying climatic ambience should be predominantly considered. The workings of these schemes under various load conditions becomes critical to analyze. This work deals with this issue and compares the conventional P&O MPPT and INC MPPT schemes for various solar irradiation and load conditions and designing solar panels optimized for maximum power generation. The designed MPPT scheme is carried out in the control circuit of a boost converter, evaluating and designing a converter to convert solar panel DC power into grid-compatible AC power. By analyzing different methods for managing and tracking PV power, this method proves to be fast and gives better results under changes in solar insolation. Full article

To address the limited inversion accuracy caused by low-fidelity data in satellite gravimetry, this study proposes a data preprocessing framework based on iterative residual correction. Utilizing Level-1B observations from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite (January 2020), outliers were systematically detected and removed, while data gaps were compensated through spline interpolation. Experimental results demonstrate that the proposed method effectively mitigates data discontinuities and anomalous perturbations, achieving a significant improvement in data quality. Furthermore, a 60-order Earth gravity field model derived via the energy balance approach was validated against contemporaneous models published by the University of Texas Center for Space Research (CSR), German Research Centre for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL). The results reveal a two-order-of-magnitude enhancement in inversion precision, with model accuracy improving from 10⁻⁶–10⁻⁷ to 10⁻⁸–10⁻⁹. This method provides a robust solution for enhancing the reliability of gravity field recovery in satellite-based geodetic missions. Full article

In order to explore the correlation between meteorological factors and power load changes, as well as the role of these factors in load forecasting, a hybrid load forecasting modeling framework based on quantile Granger causality test and bidirectional long short-term memory (QGCT-BiLSTM) is proposed. The Augmented Dickey–Fuller test (ADF) is used to test the smoothness of the influencing factor series and the load series, and the variables that passed the smoothness test are subjected to QGCT for identification of the characteristic variables with significant causal associations. Furthermore, the BiLSTM model is then constructed using the selected factors to generate load forecasts. Using real data from Fujian, China, we demonstrate that QGCT-based feature screening reduces forecasting errors by an average of 34.96%, where the RMSE, MAE and MAPE are 29.19%, 30.06% and 45.63%, respectively, thereby validating the necessity of causal factor selection. Additionally, single-factor perturbation analysis at seasonal scales quantifies load sensitivity to environmental changes, while geographic–climatic coupling mechanisms explain observed load variation patterns. The results confirm that QGCT-BiLSTM effectively isolates critical meteorological drivers and significantly enhances prediction accuracy compared to conventional approaches, achieving 20.3% lower RMSE and 16.8% lower MAE than LSTM. Full article

The structure and density of plankton communities greatly influence carbon and nutrient cycling as well as the environmental status of lake ecosystems. This community can respond to a range of environmental drivers, including those influenced by human perturbations on local and regional scales, causing abrupt changes and imbalances. While the implications of climate and land-use changes are evident for a range of tropical lake conditions, their impacts on planktonic population dynamics are less understood. In this study, we aimed to investigate how distinctive levels of nutrients, allochthonous organic matter (OM), and sunlight availability change phytoplankton and zooplankton density and structure in a natural tropical lake. Using an in situ mesocosm facility, we manipulated the addition of nutrients and OM, in addition to sunlight availability and a combination of these treatments. We monitored limnological parameters, plankton count, and identification for 12 days. The mesocosms included eight different combinations in a 2 × 2 × 2 factorial design, each with two replicates. Inorganic nutrient addition reduced phytoplankton species richness, favoring the dominance of opportunistic species such as Chlorella sp. at much higher densities. Organic matter also increased light attenuation and caused the substitution of species and changes in dominance from Pseudanabaena catenata to Aphanocapsa elachista. On the other hand, physical shading had less influence on these communities, presenting densities similar to those found in the control mesocosms. Zooplankton presented a group dominance substitution in all mesocosms from copepod to rotifer species, and copepod growth seemed to be negatively affected by Chlorella sp. density increase. Furthermore, this community was associated with the light attenuation indices and bacterioplankton. These results indicate that tropical planktonic responses to environmental changes can effectively occur in just a few days, and the responses can be quite different depending on the nutritional source added. The punctual nutrient addition was sufficient to provide changes in this community, evidencing the strength of anthropic events associated with strong nutrient input. Understanding tropical plankton dynamics in response to environmental changes, such as those simulated in this work, is important for understanding the effects of climate and anthropogenic changes on tropical lake functioning. This knowledge can strengthen measures for the conservation of freshwater systems by allowing predictions of plankton community changes and the possible consequences for the aquatic food chain and water quality. Full article

Understanding the climate–weathering coupling mechanisms remains pivotal for interpreting global glacial–interglacial cycles, yet advancements have been constrained by the limited high-resolution sedimentary archives. The newly acquired BXZK2017-2 borehole (30.5 m core) from Bohai Bay provides an exceptional sedimentary sequence to investigate the Late Quaternary climate–weathering interactions. Through an integrated high-resolution chronostratigraphic framework (AMS ¹⁴C and OSL dating) coupled with multi-proxy sedimentological analyses (major element geochemistry and granulometric parameters), we reconstructed the chemical–weathering dynamics in the Bohai coastal region since the Late Pleistocene. Our findings revealed four distinct climate-weathering phases that correlate with the regional paleoenvironmental evolution and global climate perturbations: (1) enhanced weathering during mid-MIS3 to ~37.5 cal kyr BP (Chemical Index of Alteration (CIA): 55.9–62.2), corresponding to regional warming and strengthened summer monsoon circulation; (2) weathering minimum in late MIS3 through early–mid-MIS2 (37.5–14.8 cal kyr BP, CIA < 55), marking the peak aridity before the Last Glacial Maximum; (3) maximum weathering intensity from mid-MIS2 to early MIS1 (14.8–3.34 cal kyr BP, CIA: 65–68), documenting the postglacial humidification driven by the intensified East Asian Summer Monsoon; (4) renewed weathering decline during the Neoglacial (3.34 cal kyr BP-present, CIA: 59–63), coinciding with the late Holocene cooling events. Remarkably, this study identifies a striking synchronicity between the CIA in marine drill cores and δ¹⁸O records derived from Greenland ice cores. Our results indicate that chemical weathering proxies from marginal sea sediments can serve as robust recorders of post-Late Pleistocene climate variability, establishing a new proxy framework for global paleoclimate comparative research. Full article

Trees mediate critical biogeochemical cycles involving nutrients, pollutants, water, and energy at the interface between terrestrial biosphere and atmosphere. Forest ecosystems significantly influence the global cycling of mercury (Hg), serving as important sinks and potential sources of re-emission through various biotic and abiotic processes. Anthropogenic Hg emissions, predominantly from industrial activities, mining, and fossil fuel combustion, have substantially altered the natural Hg cycle, intensifying ecotoxicological concerns and establishing forests as primary routes for atmospheric Hg deposition into terrestrial reservoirs. This perturbation profoundly affects global atmospheric Hg concentrations, residence times, and spatial distribution patterns. While early investigations focused on forest stands near heavily polluted areas, contemporary research has expanded to diverse ecosystems, revealing that trees provide tissues that function as temporal archives for atmospheric-terrestrial Hg exchange. Leaves capture high-resolution records of contemporary Hg dynamics at sub-annual timescales, whereas annual growth rings preserve multi-decadal chronologies of historical atmospheric exposure. Incorporating this dual temporal perspective is crucial for analysing Hg deposition trends and assessing the efficacy of environmental policies designed to control and mitigate Hg pollution. This review critically evaluates recent developments concerning the ecophysiological determinants of Hg accumulation in trees, highlighting how combined foliar and dendrochemical analytical methods strengthen our mechanistic understanding of vegetation-atmosphere Hg exchange. To enhance biomonitoring approaches, we emphasised the need for methodological standardisation, deeper integration of ecophysiological variables, and consideration of climate change implications as priority research areas. Furthermore, integrating Hg measurements with functional markers (δ¹³C and δ¹⁸O) and Hg isotope analyses strengthens the capacity to differentiate between physiological and environmental influences on Hg accumulation, thereby refining the mechanistic framework underlying effective tree-based Hg biomonitoring. Full article

Epiphytic lichens are integral to boreal forest ecosystems, yet their allelopathic interactions with host trees, particularly in cryolithozone regions, remain poorly understood. This study elucidates the physiological and biochemical impacts of the epiphytic lichen Physcia alnophila on Larix gmelinii (Gmelin larch), a keystone species in Siberian permafrost forests. By combining dendrochronology, GC–MS metabolomic analysis, and HPLC–ESI–MS/MS analysis, we demonstrate that the lichen’s primary metabolite, atranorin (ATR), systemically migrates from thalli into the host’s cambium, roots, and needles, with root accumulation reaching 36.3 µg g⁻¹ DW. Lichen-colonized trees exhibited severe radial growth inhibition (27%–51% reduction over five years) and suppressed apical growth, despite comparable heights to controls, indicating chronic phytotoxicity. Metabolomic profiling revealed lichen-specific polyols (e.g., arabitol, mannitol) in larch tissues, alongside elevated stress biomarkers (terpenes, sterols, phenolic acids), and significant disruptions to the tricarboxylic acid cycle and oxidative phosphorylation. These metabolic perturbations correlate with reduced monosaccharide availability and impaired energy production, directly linking ATR translocation to growth suppression. L. gmelinii exhibited compensatory responses, including increased fatty acids and arabinogalactan synthesis, suggesting adaptive mechanisms to mitigate lichen-induced stress. Our findings suggest P. alnophila as a biotic stressor that affects tree physiology in extreme climates, with implications for boreal forest resilience. This work provides an insight to the rarely pointed out species interactions, which, when combined with climate change, may alter carbon cycling and forest dynamics in permafrost ecosystems. Full article

Soil health is essential for maintaining ecosystem balance, food security, and human well-being. Anthropogenic activities, such as climate change and excessive agrochemical use, have led to the degradation of soil ecosystems worldwide. Microbiome transplantation has emerged as a promising approach for restoring perturbed soils; however, direct soil transfer presents practical limitations for large-scale applications. An alternative strategy involves extracting microbial communities through soil washing processes, but its success highly depends on proper microbiota characterization and efficient extraction methods. This study evaluated a soil wash method using four different dispersant solutions (Tween-80, NaCl, sodium citrate, and sodium pyrophosphate) for their ability to extract the majority of microbial cells from Antarctic and Crop soils. The extracted microbiomes were analyzed using 16S rRNA gene metataxonomics to assess their diversity and abundance. We found that some treatments extracted a greater proportion of specific taxa, and, on the other hand, some extracted a lower proportion than the control treatment. In addition, these dispersant solutions showed the extraction of the relevant microbial community profile in soil samples, composed of multiple taxa, including beneficial bacteria for soil health. Our study aims to optimize DNA extraction methods for microbiome analyses and to explore the use of this technique in various biotechnological applications. The results provide insights into the effect of dispersant solutions on microbiome extractions. In this regard, sodium chloride could be optimal for Antarctic soils, while sodium citrate is suggested for the Crop soils. Full article

The long instrumental series of precipitation in Pisa, the earliest one in Italy, has been reconstructed after the careful recovery and critical analysis of its history, data, and metadata. Precipitation amounts have been recovered from May 1707 to December 2024, but there are gaps due to lost data. The recovered dataset includes 47.4% of the total daily, 65.0% of monthly, and 77.4% of yearly values. Original observation registers and metadata are scarce or even missing, so a thorough investigation of contemporary sources has been performed to recover as much information as possible concerning observers, instruments, locations, exposures, measuring protocols, and ancient local units. The main features of the precipitation regime in Pisa have been investigated, and the variability in the amount and frequency at different time scales, as well as extreme events, have been analysed. Pisa is characterized by intense precipitation in autumn due to the penetration of Atlantic perturbations, and the most extreme daily events occur mainly in the transition period between the end of summer and the onset of autumn. A small decreasing trend has been found in the anomaly of the yearly amount in the 1867–2024 unbroken period, with the most remarkable month anomalies in summer. The time series of the Standard Precipitation Index indicates that the period around 1945 was particularly dry, and also indicates a slight increase in arid conditions over time, mainly in spring. The most extreme yearly amounts were found in the 18th century, and the series of the daily 90th and 95th percentiles show a small decreasing trend in the 1884–2004 period. The comparison with other contemporary Italian series made it possible to identify the peculiarity of the precipitation regime in Pisa, adding an important piece to the historical research on the climate of the Italian peninsula from a long-term perspective. Full article