Search Results (433)

Accurate estimation of crop evapotranspiration (ET) is essential for achieving efficient agricultural water use in the North China Plain. Although machine learning techniques have demonstrated considerable potential for ET simulation, a systematic evaluation of model-architecture suitability and hyperparameter optimization strategies specifically for summer soybean ET estimation in this region is still lacking. To address this gap, we systematically compared several machine learning architectures and their hyperparameter optimization schemes to develop a high-accuracy daily ET model for summer soybean in the North China Plain. Synchronous observations from a large-scale weighing lysimeter and an automatic weather station were first used to characterize the day-to-day dynamics of soybean ET and to identify the key driving variables. Four algorithms—support vector regression (SVR), Random Forest (RF), extreme gradient boosting (XGBoost), and a stacking ensemble—were then trained for ET simulation, while Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Randomized Grid Search (RGS) were employed for hyperparameter tuning. Results show that solar radiation (R_S), maximum air temperature (T_max), and leaf area index (LAI) are the dominant drivers of ET. The Stacking-PSO-F3 combination, forced with Rs, T_max, LAI, maximum relative humidity (RH_max), and minimum relative humidity (RH_min), achieved the highest accuracy, yielding R² values of 0.948 on the test set and 0.900 in interannual validation, thereby demonstrating excellent precision, stability, and generalizability. The proposed model provides a robust technical tool for precision irrigation and regional water resource optimization. Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article

Pre-emergence control is one of the critical steps in the agricultural production of direct-seeding rice. To investigate the mechanism of pre-emergence flame control, a flame control test bench and a flame control and sowing integrated operation machine were designed and made. The experimental results demonstrate that tall fescue seeds achieved complete inactivation (100% rate) when exposed to a target temperature of 140 °C for 1 min. A temperature distribution analysis revealed that the 1 mm soil layer exhibited a lower temperature rise compared with the surface layer, while the 2 mm layer recorded the minimum temperature elevation. Among the tested nozzle–soil distances, 150 mm significantly improved the soil-heating efficacy over 200 mm, with 100 mm yielding the optimal performance. Statistical analysis confirmed that the nozzle–soil distance, seed burial depth, and operating speed exerted highly significant (p < 0.01) effects on the tall fescue seed inactivation rate. The seed burial depth emerged as the most influential factor, followed by the operating speed and nozzle–soil distance. Data from the field experiment further revealed a speed-dependent decline in the inactivation rates: 80.27% at 3 km·h⁻¹, 66.30% at 4 km·h⁻¹, and 46.10% at 5 km·h⁻¹, and SPSS analysis indicated that there were extremely significant differences between every pair of groups of data (p < 0.01). This study verified that pre-emergence flame control technology can effectively eliminate grass seeds on the soil surface and has a certain inhibitory effect on shallow-buried seeds, which contributes to the advancement of pre-emergence control technology. Full article

Accurate air temperature and precipitation data are fundamental for environmental and socioeconomic applications in Brazil. However, the observational network managed by the National Institute of Meteorology, suffers from spatial gaps, necessitating the use of gridded datasets. This study provides a rigorous comparative assessment of three prominent gridded products—the station-interpolated dataset of Brazilian Daily Weather Gridded Data (BR-DWGD), the satellite-gauge blended product MERGE, and the ERA5-Land Reanalysis dataset—against station data. We evaluate the performance of the institutionally supported MERGE and ERA5-Land products as viable alternatives to the interpolated dataset. Daily data for maximum temperature (Tmax), minimum temperature (Tmin), and total precipitation were selected from 1994 to 2024 and analyzed using statistical metrics. The interpolated product showed the highest fidelity to observations, especially for temperature. For precipitation, the MERGE product demonstrated the best performance, achieving higher correlation and lower error than both the interpolated dataset and the poorly performing ERA5-Land. For temperature, ERA5-Land proved to be an excellent alternative for minimum temperature, but exhibited significant regional biases for maximum temperature and a tendency to underestimate heat extremes. We conclude that MERGE is the most robust alternative for precipitation studies in Brazil. ERA5-Land is a highly reliable source for minimum temperature, but its direct use for maximum temperature requires caution. Full article

Carposina sasakii Matsumura is a significant lepidopteran pest in the Carposinidae family, inflicting substantial damage on stone and pome fruit trees such as jujube, peach, and apple. Using MaxEnt, we assessed the worldwide climatic suitability for C. sasakii and its key environmental drivers, evaluating how climate change impacts dispersal risks. Integrating global occurrence records with 37 environmental variables, the model (AUC = 0.982) quantitatively identifies July precipitation (prec7), minimum average temperatures in April and August (tmin4 and tmin8, respectively), and maximum average temperature in May (tmax5) as critical distribution determinants. Among these, prec7 exhibits the highest contribution (threshold approximately 370 mm). The current suitable habitat spans 10.39 × 10² km², concentrated predominantly in East Asia’s temperate monsoon zone (eastern China, the Korean Peninsula, and Japan) and southern North America. Under future climate scenarios, the high-emission pathway (SSP585) will reduce highly suitable areas, while moderately suitable zones expand coastward. In contrast, SSP370 projects a significant, albeit phased, habitat increase with a 19.61% growth rate. Precipitation regimes and extreme temperatures jointly regulate niche differentiation in C. sasakii, whose range shifts toward Southeast Asia and suboptimal regions in Europe and America, underscoring cascading climate change effects. These findings provide a scientific basis for transnational monitoring, early warning systems, and regional ecological governance. Full article

The advance and delay of the rainy season is among the most frequently cited effects of climate change in the central Ecuadorian Andes. However, its assessment is not feasible using the indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), designed to detect changes in intensity, frequency, or duration of intense events. This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in situ data are scarce. Daily in situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross-Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Krigging for regionalization. The year 2000 was used as a study point for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be fruitful despite of the missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts. Full article

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research) experts and 83 highly qualified (a minimum of a PhD degree in agriculture and allied fields was the prerequisite criterion for acting as a judge) judges in the construction of this scale. Further, we adopted factor analysis to draw valid conclusions. We proposed 138 items/statements related to 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emissions, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios. Finally, 102 items/statements were retained with six indicators/dimensions. The results indicate that the scale explains 83% of variance. The scale is highly consistent (Cronbach alpha = 0.985) and widely applicable to future research and policy decisions. Further, the scale was adopted (with 100 respondents) to assess consistency and validity. Finally, the tool (scale) for assessing farmers’ knowledge and perceptions of CC was prepared for further use and replication. The policy and research system may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC. The findings of this study may be helpful for policymakers, researchers, development workers, and extension functionaries. Full article

Detailed knowledge regarding the thermophysical properties and electrical conductivity of the combustion products derived from solid propellants is essential for the optimized design and operation of solid-fuel rocket engines employing magnetohydrodynamic drive technology. However, the high-temperature and high-pressure environment prevailing during rocket operation makes the experimental measurement of these characteristics extremely difficult, while the ionization reactions obtained by adding ionization seeds containing cesium to solid propellants for increasing the electrical conductivity of gaseous combustion products makes the theoretical calculation of these characteristics extremely problematic as well. The present work addresses these issues by constructing a minimum Gibbs free energy constraint function in conjunction with the Debye–Hückel correction under the condition of ionization to calculate the equilibrium components of combustion products. The obtained equilibrium components are then applied in conjunction with Lennard–Jones potential energy theory and the Champan–Enskog framework to approximately calculate the specific heat, viscosity coefficient, and thermal conductivity of propellant gases over a wide range of temperatures and pressures. The Kantrowitz model is proposed to solve the electrical conductivity of combustion products. Finally, the accuracy of the numerical calculations is validated through the Langmuir probe experiment. The discrepancy between calculated and measured electron density decreases with increasing temperature and remains within 5% when the combustion product temperature exceeds approximately 1800 K. The validity of the proposed framework is demonstrated by examining the effects of temperature, pressure, and ionization seed content on the thermophysical properties and electrical conductivity of the combustion products derived from tri-base solid propellant with cesium atoms employed as ionization seeds. Full article

Forest fire susceptibility prediction is essential for effective management, yet considerable uncertainty persists under future climate change, especially in climate-sensitive plateau regions. This study integrated MODIS fire data with climatic, topographic, vegetation, and anthropogenic variables to construct an Extreme Gradient Boosting (XGBoost) model for the Yunnan Plateau, a region highly prone to forest fires. Compared with Support Vector Machine and Random Forest models, XGBoost showed superior ability to capture nonlinear relationships and delivered the best performance, achieving an AUC of 0.907 and an overall accuracy of 0.831. The trained model was applied to climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 to assess future fire susceptibility. Results indicated that high-susceptibility periods primarily occur in winter and spring, driven by minimum temperature, average temperature, and precipitation. High-susceptibility areas are concentrated in dry-hot valleys and mountain basins with elevated temperatures and dense human activity. Under future climate scenarios, both the probability and spatial extent of forest fires are projected to increase, with a marked expansion after 2050, especially under SSP5-8.5. Although the XGBoost model demonstrates strong generalizability for plateau regions, uncertainties remain due to static vegetation, coarse anthropogenic data, and differences among climate models. Full article

This study analyzes the relationship between fires and climate extremes in the Caatinga biome from 2012 to 2023 by integrating Fire Radiative Power (FRP) from VIIRS (S-NPP and NOAA-20), Vapor Pressure Deficit (VPD) and air temperature from ERA5, drought indices (SPI-1 and SPI-6), and heatwave events from the Xavier database. Daily percentiles of maximum (CTX90pct) and minimum (CTN90pct) temperatures were used to characterize heatwaves. Spatial and temporal dynamics of fire patterns were identified using the HDBSCAN algorithm, an unsupervised Machine Learning clustering method applied in three-dimensional space (latitude, longitude, and time). A marked seasonality was observed, with fire activity peaking from August to November, especially in October, when FRP reached ~1000 MW/h. The years 2015, 2019, 2021, and 2023 exhibited the highest fire intensities. A statistically significant upward trend in cluster frequency was detected (+1094.96 events/year; p < 0.001). Cross-correlations revealed that precipitation deficits (SPI) preceded FRP peaks by about four months, while VPD and air temperature exerted immediate positive effects. FRP correlated positively with heatwave frequency (r = 0.62) and negatively with SPI (r = −0.69). These findings highlight the high vulnerability of the Caatinga to compound drought and heat events, indicating that fire management strategies should account for both antecedent drought conditions, monitored through SPI, and real-time atmospheric dryness, measured by VPD, to effectively mitigate fire risks. Full article

The water sustainability in Chihuahua City is challenged by rapid urbanization, population growth, industrial expansion, and climate variability. This study examines how these factors impact water demand by analyzing six decades of local precipitation, extreme temperature, demographic, and water consumption data. Statistical methods (time series and gamma distribution with R-package) and spatial analysis using Landsat and Spot satellite imagery were employed. Chihuahua’s urban area grew at an average annual rate of 7.4% from 1992 to 2020. Minimum and maximum temperatures have increased by 0.07 °C and 0.05 °C per year, respectively, leading to more frequent heatwaves over the past 30 years. Since the 1990s, there has been a noticeable trend towards more frequent extreme precipitation events coinciding with a sustained rise in extreme temperatures. Urban expansion and rising temperatures have increased water consumption by approximately 40% per °C over the past 30 years, accelerating the depletion of groundwater reserves in the city’s three main aquifers. These trends highlight the urgent need for integrated urban planning and climate-adaptation measures to reduce vulnerability and ensure long-term water security for Chihuahua. Full article

Hydrogen, a promising alternative to conventional fuels, presents significant combustion hazards due to its low minimum ignition energy (MIE) and wide flammability range (4–75 vol.%). The risks are amplified with liquid hydrogen (LH₂), which has an extremely low boiling point (20.3 K) and high diffusivity. Once released, LH₂ vaporizes rapidly and mixes with ambient air. This process forms a cryogenic and highly flammable cloud, which significantly increases ignition and explosion hazards. Therefore, a comprehensive understanding of the MIE of cryogenic hydrogen–air mixtures is crucial for quantitative risk assessment. This work develops and validates a numerical algorithm for predicting the MIE of hydrogen–air mixtures at cryogenic temperatures (down to 93 K) across a wide range of hydrogen concentrations (10~50 vol.%) and oxygen concentration ratios [O₂/(O₂ + N₂) = 21~52%]. By coupling a detailed H₂/O₂ reaction mechanism with a large eddy simulation (LES) turbulence model, this algorithm demonstrates high reliability and accuracy. The results indicate (1) an exponential increase in MIE with decreasing initial temperature; (2) a U-shaped dependence of MIE on hydrogen concentration, with the minimum occurring near 25% hydrogen concentration; (3) an asymptotic dependence of MIE on oxygen concentration ratio, particularly at 40% hydrogen concentration. The initial temperature has the greatest influence on MIE; hydrogen concentration is the second; and the oxygen concentration ratio has the weakest influence. This study provides a theoretical framework and a practical computational tool for assessing and mitigating cryogenic ignition associated with LH₂ leakage, thereby enabling safer application of liquid hydrogen technologies. Full article

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Biosurfactants (BSs) are natural, biodegradable compounds crucial for replacing synthetic emulsifiers in the food industry, provided their production costs can be reduced through the use of sustainable and low-cost substrates. This study evaluated the viability of licuri oil as a carbon source for BS production by Candida utilis and assessed the product’s functional stability in food formulations. Production kinetics confirmed the yeast’s efficiency, reducing the water surface tension to a minimum of 31.55 mN·m⁻¹ at 120 h. Factorial screening identified a high carbon-to-nitrogen ratio as the key factor influencing ST reduction. The isolated BS demonstrated high surface activity, with a Critical Micelle Concentration of 0.9 g·L⁻¹. Furthermore, the cell-free broth maintained excellent emulsifying activity (E₂₄ > 70%) against canola and motor oils across extreme pH, temperature, and salinity conditions. Twelve mayonnaise-type dressings were formulated, utilizing licuri oil, and tested for long-term physical stability. Six formulations, featuring the BS in combination with lecithin and/or egg yolk, remained stable without phase segregation after 240 days of refrigeration, maintaining a stable pH and suitable microbiological conditions for human consumption. The findings confirm that the valorization of licuri oil provides a route to produce a highly efficient and robust BS, positioning it as a promising co-stabilizer for enhancing the shelf-life and natural appeal of complex food emulsions. Full article

In the context of the rapid development of renewable energy and frequent extreme weather, accurate evaluation of the backup operation flexibility of multiple distributed resources is a prerequisite for improving the resilience of power systems. However, it is difficult to consider the detailed model of each distributed resource and evaluate its regulation ability in the operation of power systems because of the small number of distributed resources. Therefore, this paper first quantifies the capacity boundaries of distributed reserve resources on the power generation, load, and energy storage sides under different meteorological conditions through economic self-dispatching optimization and Minkowski aggregation methods. Subsequently, the maximum correlation–minimum redundancy (mRMR) principle and Granger causality test are combined to reduce the dimensionality of high-dimensional meteorological features. Finally, the stacking ensemble learning method is introduced to build an end-to-end modelling framework from multi-source weather input to reserve capability prediction. The results show that (1) the reserve capacity of multivariate distributed resources has significant intra-day and intra-day periodicity and seasonal differences; (2) the mRMR algorithm considering the Granger causality test can capture the correlation and causality between high-dimensional meteorological features and reserve capabilities, and the obtained features are more explanatory; (3) the average R² of the stacking model in both upper-reserve and lower-reserve predictions reaches 0.994. In terms of computational efficiency, the training time of the proposed model is 130.85 s for upper-reserve prediction and 133.71 s for lower-reserve prediction, which is significantly lower than that of conventional hybrid models while maintaining stable performance under extreme meteorological conditions such as high temperatures and strong winds; (4) compared with integration methods such as simple averaging and error weighting, the stacking integration strategy proposed in this paper remains stable in the mean and variance of prediction results, verifying its comprehensive advantages in structural design and performance integration. Full article