Search Results (412)

Understanding long-term snow cover dynamics is essential in mountain regions with limited meteorological or in situ observations. This study examines seasonal snow cover evolution across the Romanian Carpathians (2000–2025) using daily MODIS/Terra MOD10A1 Cloud-Gap-Filled data at 500 m resolution. Snow-covered pixels were identified using an NDSI ≥ 40 threshold, and snow cover duration (SCD), snow onset date (SOD), and snow end date (SED) were analyzed in relation to elevation and aspect from the FABDEM, complemented by snow-covered area (SCA) and snowline elevation (SLE) metrics. Across the entire range, the snow season shortens mainly due to later onset (+0.28 days/year) and earlier melt (−0.78 days/year), resulting in an SCD decrease of −1.14 days/year. High-elevation (>2000 m) areas show only small changes (SCD: −0.13 days/year; SOD: +0.46 days/year; SED: +0.32 days/year), while the strongest reductions occur at low and mid elevations, where snow persistence is most sensitive to warming; consistent declines in seasonal SCA and a pronounced monthly SLE cycle further document the spatial expression of this variability. Uncertainty was assessed by comparison with station-based snow cover duration (n = 230 station-years), indicating strong agreement (r = 0.95) with a modest negative bias (median: −8 days) and a mean absolute error (MAE) of 16.7 days. Climate correlations highlight air temperature as the dominant covariate of interannual snow-phenology variability, whereas precipitation associations are weaker. Overall, these shifts in snow phenology highlight increasing instability of the Carpathian snow regime and emphasize the value of long-term MODIS observations for tracking cryospheric change in a warming southeastern European mountain system. Full article

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

The multimodal North Sea–Baltic corridor, consisting of 6934 km of road, is an integral part of the EU’s trans-European transport network. However, an unsatisfied level of development of alternative fuels infrastructure for road transport is considered one of the obstacles to connecting northern Member States and North-East countries. A “what-if” scenario was employed to obtain useful insights into how a given situation might be handled, and a comparison of several paths forward to make better decisions was analysed. Environmental insights for transportation sector scenarios in 2030–2035 were explored and analysed using the COPERT v5.5.1 software program. In this study, the installation of natural gas infrastructures of various station sizes and with varying capacities and types of natural gas (LNG, CNG, bio-methane) dispensed was evaluated in detail. Replacement of the existing HDV fleet (heavy-duty vehicles) with LNG-powered trucks would result in the following investment to upgrade the existing network and build new stations to meet rising LNG demand: from €21.47 to €32.3 million (the scenario of 10% market share for HDVs running on LNG), €42.94 to €64.6 (20%), and €64.4 to €96.9 (30%). The dual-fuel 10–diesel fuel 90% scenario seems to be the safest option for a large-scale investment until 2035 which may lead to moderate emission savings of 84.6 kton CO₂ eq. compared to 2022 levels. Full article

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

Continuous missing data is a prevalent challenge in long-term air quality monitoring, undermining the reliability of public health protection and sustainable urban development. In this paper, we propose ConFill, a novel contrastive learning-based framework for reconstructing continuous missing data in air quality time series. By leveraging temporal continuity as a supervisory signal, our method constructs positive sample pairs from adjacent subsequences and negative pairs from distant and shuffled segments. Through contrastive learning, the model learns robust representations that preserve intrinsic temporal dynamics, and enable accurate imputation of continuous missing segments. A novel data augmentation strategy is proposed, to integrate noise injection, subsequence masking, and time warping to enhance the diversity and representativeness of training samples. Extensive experiments are conducted on a large scale real-world dataset comprising multi-pollutant observations from 209 monitoring stations across China over a three-year period. Results show that ConFill outperforms baseline imputation methods under various missing scenarios, especially in reconstructing long consecutive gaps. Ablation studies confirm the effectiveness of both the contrastive learning module and the proposed augmentation technique. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

Prediction of the Electric Vehicle (EV) charging demand is of great importance to charging stations, especially for newly established charging stations whose demand is difficult to predict due to the absence of past time-series transaction data. This paper develops a deep learning method to fill the literature gap to predict charging demands for the new EV charging stations in the next few days, using a transaction dataset containing over 270 charging stations in Nanjing, eastern China. Specifically, our study introduces the average transactions of neighboring stations as new time-series variables and constructs a Convolutional Neural Network (CNN) model, which is a novel deep learning method. The R-squares of the CNN model achieve an average value of 0.90, which outperforms four time-series prediction models, e.g., the Long Short-Term Memory Network (LSTM) and the Extreme Gradient Boosting (XGBoost). In addition, we visualize the areas with high predicted demand for new charging stations using the trained CNN model and achieve a recommendation accuracy rate of 0.70, providing a reference for EV charging operation companies to find the optimal location of new charging stations. Accurate prediction for new charging stations in this study can provide actionable insights to charging station operators in location selection and create a more favorable EV ecosystem. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

The North China Plain, a major grain production base in China, is facing the chronic threat of climate-change-induced delays in winter wheat sowing, with late sowing constraining yields by shortening the pre-winter growth period, and soil moisture at sowing potentially serving as a key factor to alleviate late-sowing losses. However, previous studies have mostly independently analyzed the effects of sowing time or water stress, and there is still a lack of systematic quantitative evaluation on how the interaction effects between the two determine long-term yield potential and risk. To fill this gap, this study aims to quantify, in the context of long-term climate change, the independent and interactive effects of different sowing dates and baseline soil moisture on the growth, yield, and production risk of winter wheat in the North China Plain, and to propose regionally adaptive management strategies. We selected three representative stations—Beijing (BJ), Wuqiao (WQ), and Zhengzhou (ZZ)—and, using long-term meteorological data (1981–2025) and field trial data, undertook local calibration and validation of the APSIM-Wheat model. Based on the validated model, we simulated 20 management scenarios comprising four sowing dates and five baseline soil moisture levels to examine the responses of phenology, aboveground dry matter, and yield, and further defined yield-reduction risk probability and expected yield loss indicators to assess long-term production risk. The results show that the APSIM-Wheat model can reliably simulate the winter wheat growing period (RMSE 4.6 days), yield (RMSE 727.1 kg ha⁻¹), and soil moisture dynamics for the North China Plain. Long-term trend analysis indicates that cumulative rainfall and the number of rainy days within the conventional sowing window have risen at all three sites. Delayed sowing leads to substantial yield reductions; specifically, compared with S1, the S4 treatment yields about 6.9%, 16.2%, and 16.0% less at BJ, WQ, and ZZ, respectively. Moreover, increasing the baseline soil moisture can effectively compensate for the losses caused by late sowing, although the effect is regionally heterogeneous. In BJ and WQ, raising the baseline moisture to a high level (P85) continues to promote biomass accumulation, whereas in ZZ this promotion diminishes as growth progresses. The risk assessment indicates that increasing baseline moisture can notably reduce the probability of yield loss; for example, in BJ under S4, elevating the baseline moisture from P45 to P85 can reduce risk from 93.2% to 0%. However, in ZZ, even the optimal management (S1P85) still carries a 22.7% risk of yield reduction, and under late sowing (S4P85) the risk reaches 68.2%, suggesting that moisture management alone cannot fully overcome late-sowing constraints in this region. Optimizing baseline soil moisture management is an effective adaptive strategy to mitigate late-sowing losses in winter wheat across the North China Plain, but the optimal approach must be region-specific: for BJ and WQ, irrigation should raise baseline moisture to high levels (P75-P85); for ZZ, the key lies in ensuring baseline moisture crosses a critical threshold (P65) and should be coupled with cultivar selection and fertilizer management to stabilize yields. The study thus provides a scientific basis for regionally differentiated adaptation of winter wheat in the North China Plain to address climate change and achieve stable production gains. Full article

In arid regions, the challenges posed by rainfall data availability, missing data, and limited historical records significantly affect hydrological modeling studies and climate change assessments. For various hydrology applications, it is essential to implement advanced techniques in order to obtain a complete dataset series. This study explores the implementation of multiple machine learning techniques to address the complexity of filling daily rainfall data for 88 rainfall stations in the Al-Batinah region of Oman, covering the period from 1993 to 2024. The machine learning models applied in this study include Multiple Linear Regression (MLR), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Gradient-Boosting Trees (GBT). A non-clustering approach is used as well as a clustering approach as part of the methodology. In the first method, rainfall stations are not clustered, while in the second method, optimal cluster numbers are calculated using K-means clustering. The target station utilizes the nearby rainfall station data located within a 50 km radius with the highest correlation coefficients. A novel Ensemble Fusion Model has been applied to improve the efficacy of multiple predictive models, including the RF Fusion Model (RF) and Multi-Model Super Ensemble Fusion Model (MMSE). The estimation approaches are further enhanced and evaluated by Bayesian optimization of hyperparameters, dataset imputation utilizing Multiple Imputation by Chained Equations (MICE), and Leave-One-Year-Out (LOYO) cross-validation. Based on the results, it can be concluded that the GBT model performs the best in both cluster and non-cluster approaches. A further benefit of applying Ensemble Fusion Models to rainfall gap-filling methods is that the coefficient of determination (R²) for clustering and non-clustering approaches increases to 22.5% and 22.2%, respectively. Full article

Antarctica’s pristine environment and geographical isolation make it an ideal location for conducting research on the global transport and fate of pollutants. Given its unique environmental characteristics, research on this continent is essential for identifying and characterizing the various types of pollution and understanding their transport dynamics. This study employs a comprehensive analytical approach to examine the physico-chemical and chemical characteristics of water samples collected from catchments at the Lions Rump headland, including assessments of pH, specific electrical conductivity, total organic carbon, inorganic analytes (anions, cations, metals and metalloids), and polycyclic aromatic hydrocarbons (PAHs). The results showed that stream waters exhibited neutral to slightly alkaline conditions (pH 7.0–8.1) and relatively high conductivity, indicating a significant contribution of volcanic and marine inputs. TOC concentrations remained low (<2 mg L⁻¹), while elevated levels of Cl⁻ and SO₄²⁻ reflected the strong imprint of halogen deposition. PAHs were detected at low concentrations (41.5–67.4 ng/L), with their distribution pointing to long-range atmospheric transport as the dominant source and additional evidence of re-emission from sediments. The obtained results fill gaps in knowledge about the chemical composition of water, including the levels of potentially toxic substances in areas of Antarctica that are not directly influenced by research stations. Full article

Pumped storage power stations provide essential benefits to power grids by cutting peak loads, filling valleys, and boosting renewable energy integration rates. They serve as the foundation for developing a new power system based on renewable energy. Pump turbines are currently evolving to provide higher heads, larger capacities, and higher rotating speeds. The performance and dependability of its basic components have a direct impact on the safety and stability of unit operation. Based on this, this research looks into the modal characteristics and structural aspects of essential stationary components, such as the pump-turbine head cover. By comparing the mechanical performance of two different structural designs (Design A and Design B), Design B features an overall thickness 1.5 times that of Design A and incorporates an upper flange structure. Its design aims to enhance the component’s resistance to bending and deformation, optimize stress distribution while reducing peak stress values, and improve modal characteristics. This approach elevates the overall structural performance of the fixed components to accommodate the complex operating conditions of ultra-high-head pumped storage units. It was discovered that Design B had greater bending and deformation resistance than Design A, as well as better stress distribution and lower maximum stress values. This study further indicates that variations in structural design lead to significant differences in modal characteristics and overall structural performance. In particular, the thicknesses of the head cover’s main body and stiffening ribs are critical parameters that govern the modal behavior and structural properties of stationary components. These insights provide critical technical guidance for optimizing the design of stationary parts, such as the head cover, in pumped storage power plant units. Full article

This article presents an innovative procedure that combines advanced quality control (QC) methods with machine learning (ML) techniques to produce reliable, continuous, high-resolution meteorological data. The approach was applied to hourly air temperature records from six automatic weather stations located around Lake Titicaca in the Altiplano region of South America. The raw dataset contained time gaps, inconsistencies, and outliers. To address these, the QC stage employed Interquartile Range, Biweight, and Local Outlier Factor (LOF) statistics, resulting in a clean dataset. Two gap-filling methods were implemented: a spatial approach using time series from nearby stations and a temporal approach based on each station’s time series and selected variables from the ERA5-Land reanalysis. Several ML models were also employed in this process: Random Forest (RF), Support Vector Machine (SVM), Stacking (STACK), and AdaBoost (ADA). Model performance was evaluated on a validation subset (30% of station data). The RF model achieved the best results, with R² values up to 0.9 and Root Mean Square Error (RMSE) below 1.5 °C. The spatial approach performed best when stations were strongly correlated, while the temporal approach was more suitable for locations with low inter-station correlation and high local variability. Overall, the procedure substantially improved data reliability and completeness, and it can be extended to other meteorological variables. Full article

To clarify how nitrogen (N) and water regulate the microbe mediated carbon (C) cycle in farmland, a 3-year experiment was conducted in a wheat–maize rotation at Jiaozhou Station, North China. Twelve treatments combined four drip irrigation regimes (T1: no irrigation; T2: 40 mm irrigation at flowering; T3: 40 mm irrigation at the grain filling stage; T4: both, 40 mm each) and three N levels (N0: 0 kgN·hm⁻²; N1: 92 kgN·hm⁻²; N2: 184 kgN·hm⁻²). In this study, we measured wheat yield and biomass, soil organic carbon and nitrogen content, soil respiration, soil microbial community, and C-metabolic genes. The results showed that wheat yield increased with N, peaking at 8949.81 kg·hm⁻² in the N2T3 treatment, while irrigation had no significant independent effect on yield but interacted with nitrogen fertilization: under identical nitrogen levels (N1, N2), yields in the T1 and T2 treatments were significantly lower than those in the T3/T4 treatments. The soil organic carbon content in N2 was significantly higher; the soil C/N ratio was highest in N2, and T3 resulted in a significantly higher C/N ratio than T1 under the same N level; total soil respiration in N0 was significant lower, and T4 had higher respiration than T2 under the same N level. N addition increased Actinobacteriota, Chloroflexi, Gemmatimonadetes, and Ascomycota, while decreaing Proteobacteria and Acidobacteriota. No reduction in fungal phylum was observed with nitrogen addition. N application significantly upregulated key enzymes in the pentose phosphate pathway (e.g., transketolase K00615, transaldolase K00616), while irrigation increased phosphoserine aminotransferase (K00831) abundance and decreased methylmalonyl-CoA mutase (K01848) abundance. N2T3 maintains high SOC content while achieving maximum yield, promoting soil fertility retention. Compared to T4, N2T3 also enhances water use efficiency. The N2T3 treatment (high N and grain filling stage irrigation) achieved the optimal balance between high wheat yield and SOC sequestration. Full article

Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution is challenging due to the imbalance between rain and no-rain periods. In this study, we developed and tested two approaches for the imputation of missing 10-min rainfall data by means of machine learning (Multilayer Perceptron and Random Forest) and interpolation methods (Inverse Distance Weighting and Ordinary Kriging). The (a) direct approach operates on raw data to directly feed the imputation models, while the (b) two-step approach first classifies time steps as rain or no-rain with a Random Forest classifier and subsequently applies an imputation model to predicted rainfall depth instances classified as rain. Each approach was tested under three spatial scenarios: using all nearby stations, using stations within the same cluster, and using the three most highly correlated stations. An additional test involved the comparison of the results obtained using data from the imputed time interval only and data from a time window containing several time intervals before and after the imputed time interval. The methods were evaluated with reference to two different environments, mountainous and coastal, in Campania region (Southern Italy), under data-scarce conditions where rainfall depth is the only available variable. With reference to the application of the two-step approach, the Random Forest classifier shows a good performance both in the mountainous and in the coastal area, with an average weighted F1 score of 0.961 and 0.957, and an average Accuracy of 0.928 and 0.946, respectively. The highest performance in the regression step is obtained by the Random Forest in the mountainous area with an R² of 0.541 and an RMSE of 0.109 mm, considering a spatial configuration including all stations. The comparison with the direct approach results shows that the two-step approach consistently improves accuracy across all scenarios, highlighting the benefits gained from breaking the data imputation process in stages where different physical conditions (in this case, rain and no-rain) are separately managed. Another important finding is that the use of time windows containing data lagged with respect to the imputed time interval allows capturing the atmospheric dynamics by connecting rainfall instances at different time levels and distant stations. Finally, the study confirms that machine learning models outperform spatial interpolation methods, thanks to their ability to manage data with complicated internal structure. Full article