Search Results (3,722)

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

Stomoxys calcitrans (Linnaeus, 1758) is a hematophagous fly species of veterinary importance, known for its negative effects on animal health and productivity. The stress caused by their painful bites results in losses in milk and meat production. Despite its impact, data on its ecology and activity in Algeria are lacking. Such knowledge is needed to evaluate its potential effects on livestock production and rural health, and to support surveillance, outbreak prediction, and control strategies. This study aimed to investigate the monthly and daily dynamics of S. calcitrans in livestock farms in the Batna region and evaluate the influence of climatic factors on its abundance. From July 2022 to July 2023, Vavoua traps were placed monthly from 7 a.m. to 6 p.m. on four farms in the Batna region, representing different livestock types. Captured flies were identified, sexed, and counted every two hours. Climatic data were collected both in situ and from NASA POWER datasets. Fly abundance was analyzed using non-parametric statistics, Spearman’s correlation, and multiple regression analysis. A total of 1244 S. calcitrans were captured, mainly from cattle farms. Activity occurred from August to December, with a peak in September. Males were more abundant and exhibited a bimodal activity in September. Fly abundance was positively correlated with temperature and precipitation and negatively correlated with wind speed and humidity. This study presents the first ecological data on S. calcitrans in northeastern Algeria, highlighting its seasonal dynamics and the climatic drivers that influence it. The results highlight the species’ preference for cattle and indicate that temperature and rainfall are key factors influencing its abundance. These findings lay the groundwork for targeted control strategies against this neglected pest in Algeria. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series forecasting models for rainfall prediction in Barranquilla, Colombia. To this end, five models were applied, namely, Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (ES), and multiplicative and additive Holt–Winters models, using 139 monthly precipitation records from the IDEAM database covering the period 2013–2025. Model accuracy was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE), and nonlinear optimization techniques were applied to estimate smoothing and weighting parameters for improved accuracy. The results showed that optimization significantly enhances model performance, particularly in the multiplicative Holt–Winters model, which achieved the lowest errors, with a minimum MAE of 75.33 mm and an MSE of 9647.07. The comparative analysis with previous studies demonstrated that even simple models can yield substantial improvements when properly optimized. Furthermore, forecasts optimized using MAE were more stable and consistent, whereas those optimized with MSE were more sensitive to extreme variations. Overall, the findings confirm that seasonal models with optimized parameters offer superior predictive capacity, making them valuable tools for hydrological risk management. Full article

Sulfate minerals (SMs), such as BaSO₄, SrSO₄, and CaSO₄, precipitate when incompatible solutions from the oil industry, such as seawater (SW) and high-salinity brine solutions (HSBSs), are mixed during the oil production process. To investigate the potentiality to extract SM by mixing three different brine solutions, such as HSBS-1, -2, and -3, with SW, at different temperatures and pressures, a practical simple model was used to predict the saturation index (SI), the quantity of precipitated minerals (Y), and the induction time (t_ind) required for precipitation. From the results, it was found that CaSO₄ hemihydrate and SrSO₄ yield lower amounts of precipitate. BaSO₄ precipitation ranges from 20 to 60 mg/L and 1500 mg/L of CaSO₄ anhydrous under ambient conditions. These findings suggest that recovering low-solubility minerals is technically feasible and environmentally preferable to direct disposal. Full article

The increasing frequency of extreme climate events (ECEs) is expected to significantly affect crop yields in the future, threatening regional and global food security. However, uncertainties in yield projections persist due to regional variability, model differences, and scenario assumptions. Leveraging historical agricultural disaster and meteorological data from China (1995–2014), this study employs the vulnerability curve assessment to determine the most appropriate models for assessing crop yields affected by different ECEs (drought, extreme precipitation, extreme low temperature, and extreme wind) across six regions. By integrating multi-model and multi-scenario (SSP1-2.6, SSP3-7.0, SSP5-8.5) future climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6), we conducted pooled prediction of the individual and combined impacts of different ECEs on crop yields for the near-term (2020–2040) and mid-term (2041–2060). The median of multi-model prediction of crop yield reductions in China was −16.0% (range: −32.5% to −2.6%), with more severe losses in Northeast, Northwest, and North China, particularly under higher radiative forcing scenarios. Drought is the most destructive of the four types of ECEs. These results will aid decision-makers in identifying high-risk zones for crop yields affected by ECEs and provide a scientific basis for the developing targeted adaptation strategies in various regions. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Camelina (Camelina sativa) is an oilseed crop of increasing importance, valued not only for its adaptability to diverse environmental conditions and potential for sustainable agriculture but also for its economic advantages, including low input requirements and suitability for biofuel production and niche markets. This study examines the relationship between camelina yield and climatic variables—specifically temperature and precipitation—based on a ten-year field experiment conducted in Poland. To capture the temporal dynamics of weather conditions, Functional Data Analysis (FDA) was applied to daily temperature and precipitation data. The analysis revealed that yield variability was strongly influenced by the length of the vegetative period and specific weather patterns in April and July. Higher yields were recorded in years characterized by moderate spring temperatures, elevated temperatures in July, and evenly distributed rainfall during the early generative growth stages. The Maximal Information Coefficient ($\mathrm{M}\mathrm{I}\mathrm{C}$) confirmed the relevance of these variables, with the duration of the vegetative phase showing the strongest correlation with yield. Cluster analysis further distinguished high- and low-yield years based on functional weather profiles. The FDA-based approach provided clear, interpretable insights into climate–yield interactions and demonstrated greater effectiveness than traditional regression models in capturing complex, time-dependent relationships. These findings enhance our understanding of camelina’s response to climatic variability and support the development of predictive tools for resilient, climate-smart crop management. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region. Full article

Conogethes punctiferalis (Guenée, 1854) is a polyphagous pest with a wide host range and strong reproductive ability, and its potential threat to agricultural production cannot be ignored. Based on the optimized maximum entropy niche model, this study evaluated potential suitable habitats for C. punctiferalis in China and their dynamic changes under current conditions (Model 1: bioclimatic factors + elevation; Model 2: bioclimatic factors + elevation + human activity) and four different future climate scenarios (Model 3: bioclimatic factors + elevation + human activity). The results suggest that the potential suitable habitats for C. punctiferalis are mainly driven by a combination of temperature, precipitation, elevation, and human activity. Under current conditions, suitable habitats are mainly concentrated in southern Northeast China, North China, the Yangtze River Basin, and its south regions; highly suitable areas are primarily located in the main maize-producing regions of the Huang-Huai-Hai Plain. The area of suitable habitats predicted by Model 2 is smaller than that predicted by Model 1. Under future climate scenarios, the potential distribution range of C. punctiferalis will show an expanding trend, with the expanded area larger than the contracted area. Compared with Model 2, the suitable areas are expected to increase under Model 3 by approximately 91,799 km² to 723,711 km². This study provides an important basis for assessing the potential hazard risk of C. punctiferalis and is of major significance in guiding the formulation of targeted integrated pest management strategies and protecting the safety of agricultural production. Full article

This paper presents a novel methodology for predicting the tire–road friction coefficient in real-time under challenging climatic conditions based on a fuzzy logic inference system. The core innovation of the proposed approach lies in the integration and probabilistic weighting of a diverse set of input data, which includes signals from ambient temperature and precipitation intensity sensors, activation events of the anti-lock braking system (ABS) and electronic stability control (ESP), windshield wiper operation modes, and road marking recognition via a front-facing camera. This multi-sensor data fusion strategy significantly enhances prediction accuracy compared to traditional methods that rely on limited data sources (e.g., temperature and precipitation alone), especially in transient or non-uniform road conditions such as compacted snow or shortly after rainfall. The reliability of the fuzzy-logic-based predictor was experimentally validated through extensive road tests on dry asphalt, wet asphalt, and wet basalt (simulating packed snow). The results demonstrate a high degree of convergence between predicted and actual values, with a maximum modeling error of less than 10% across all tested scenarios. The developed methodology provides a robust and adaptive solution for enhancing the performance of Advanced Driver Assistance Systems (ADASs), particularly Automatic Emergency Braking (AEB), by enabling more accurate braking distance calculations. Full article

The aim of our study is to achieve a comprehensive understanding of the global distribution of suitable habitats for Sophora alopecuroides L., as well as how these habitats might change in response to climate change. We employed the MaxEnt niche model to integrate distribution data from the Global Biodiversity Information Platform, incorporating 19 bioclimatic factors. This approach enabled us to predict the potential geographic distribution of S. alopecuroides L. worldwide under both current climatic conditions and future greenhouse gas emission scenarios. The results were visualized via ArcGIS 10.8 software. The findings indicate that currently, the suitable habitat for S. alopecuroides L. spans 12,897,100 km², with the majority situated in the arid regions of Central and Eastern Asia. The key environmental variables influencing its distribution are annual mean temperature, maximum temperature of the warmest month, precipitation of the warmest season, and mean temperature of the coldest season. For future climate projections, suitable habitats generally exhibit a shrinking trend. The most pronounced decrease is anticipated under the moderate greenhouse gas emission scenario (SSP245). However, under the high greenhouse gas emission scenario (SSP585), the suitable habitat area is projected to increase marginally by 2060. This dynamic change warning suggests that it is necessary to optimize climate adaptation strategies, strengthen ecological protection and restoration in suitable areas, so as to maintain the ecological service functions of S. alopecuroides L. in arid and semi-arid ecosystems, such as sand fixation and soil conservation, and maintain biodiversity, and provide basic guarantee for the sustainable development and utilization of its medicinal and forage resources. This study reveals the dynamic impact of climate change on the distribution of S. alopecuroides L. suitable areas, which not only provides a scientific basis for ecological restoration and S. alopecuroides L. resource protection in arid and semi-arid areas, but also has important practical significance for promoting the regional practice of the concept of sustainable development of “harmonious coexistence between man and nature.” Full article

Structural adaptations of wood to environmental conditions play a crucial role in shaping its mechanical and hydraulic properties, which are vital for the performance and survival of fir and beech. In this study, we investigated how site-specific climatic conditions influence tree-ring widths and wood-anatomical traits of fir and beech in the Carpathians. Increment cores were collected from three forest stands across the Carpathians, each characterized by distinct climate regimes. We developed chronologies for mean tree-ring width (MRW), mean lumen area of vessels/tracheids (MLA), cell density (CD), relative conductive tissue area (RCTA), and, for fir, mean tangential cell wall thickness (CWTTAN), covering the period from 1980 to 2016. By comparing MRW and wood-anatomical traits with climatic variables—daily minimum and maximum temperatures and daily precipitation sums from E-OBS climate data—we identified clear differences among the three sites. The relationships between tree-ring widths and wood-anatomical traits varied between fir and beech, reflecting species-specific responses to local climate conditions. Notably, beech appeared more sensitive to warm summer temperatures, while fir was comparatively less affected. Evaluating the variability in radial growth and wood anatomy is essential for understanding the plasticity of fir and beech under diverse environmental conditions, and represents a first step toward predicting their responses to future climate scenarios. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article