Search Results (872)

Delayed chilling stress is a frequent meteorological disaster in the spring maize-growing region of Northern China. Understanding the physiological responses and key characteristics of cold-tolerant maize varieties under such stress is crucial for their selection and utilization. This study compared the physiological and biochemical responses of a cold-tolerant variety (XY335) and a conventional variety (KH8) to simulated delayed chilling stress induced by early field sowing. Results showed that the emergence percentage and emergence uniformity of the cold-tolerant variety were 9.6% and 2.8% higher than those of the conventional variety, respectively. Under chilling stress, the root diameter of the cold-tolerant variety remained stable, while root length decreased by 24.5%. In contrast, the conventional variety exhibited the opposite response. Growth of the cold-tolerant variety slowed during stress but accelerated significantly after temperature recovery, achieving 1–2 more leaf ages than the conventional variety. The SPAD value (chlorophyll content) of the cold-tolerant variety was less affected, remaining 14.3% higher than the conventional variety, thereby maintaining higher photosynthetic efficiency. The enhanced stress tolerance of XY335 correlated with a robust antioxidant system: leaf peroxidase (POD) activity was 60.7% higher, and malondialdehyde (MDA) content was 42.4% lower compared to KH8. In summary, under delayed chilling stress, the cold-tolerant variety ensured higher emergence and seedling uniformity by reducing coleoptile length, maintained root diameter and absorption capacity by shortening root length, preserved chlorophyll synthesis and photosynthetic performance under the protection of a POD-dominated enzyme system, and employed a “standby mode” with compensatory leaf growth to ensure adequate dry matter accumulation and yield formation. Full article

Straw return plays a pivotal role in sustaining soil fertility and crop production, but the interaction between straw return and consecutive fertilizer applications on yield sustainability and soil quality under climate change are unclear. Therefore, a long-term field experiment (2005–2022) was conducted to examine how straw return and fertilizer application improve soil properties, increase crop production, enhance the ability to resist climatic changes, and thus improve yield sustainability in a rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping system. This study established five treatments, including the control, NPK treatment, S treatment, NPK + 1/2S treatment, and NPK + S treatment. Compared with the control, the treatments involving chemical fertilization combined with straw return increased on average rice and wheat yield by 52.9% and 95.4%, respectively, with higher values of the sustainable yield index (SYI) and lower values of the coefficient of variance (CV) for the two crops. Moreover, the treatments that combined chemical fertilization with straw return improved soil quality by increasing soil organic matter (SOM), total N, total P, and available K contents and presented a higher soil quality index (SQI) value compared to the other three treatments. The crop yield, SYI, and apparent nutrient balance increased with increasing SQI. The SOM and AP were identified as the most crucial soil fertility indices, exerting a significant impact on crop yields. Meanwhile, precipitation emerged as the key meteorological factor restricting the yield of winter wheat. The PLS-SEM suggested that fertilizer application, climatic conditions, and soil properties strongly influenced crop yield, and the magnitude of this influence varies between rice and wheat. In conclusion, the long-term fertilization combined with straw return represents an effective strategy to safeguard the sustainability of crop yields under climate change. Full article

Timely and accurate estimation of regional winter wheat yield is of great significance for safeguarding food security and promoting sustainable agricultural development. In recent years, deep learning has been widely applied in crop yield estimation due to its powerful capability in mining complex relationships. However, the irregular shapes of administrative regions pose challenges for integrating spatial data such as remote sensing into deep learning models. To address this issue, this study employed mean-based aggregation and histogram-based dimensionality reduction techniques to preprocess spatial data, including remote sensing and meteorological data, thereby generating sample sets suitable for deep learning models. This study identified the phenomenon of feature conflict when processing heterogeneous features in conventional Long Short-Term Memory (LSTM) models and proposed a TB-LSTM (Two-Branch LSTM) model to mitigate such conflicts. The impact of different input feature combinations on estimation accuracy was analyzed, and the model’s capability for early yield prediction was further evaluated. The results show that: (1) The proposed TB-LSTM model achieved superior performance (R²: 0.853, RMSE: 516.619 kg/ha) compared to the baseline LSTM (R²: 0.353–0.732; RMSE: 735.378–1126.062 kg/ha), confirming its efficiency in resolving feature conflict and better exploiting the yield estimation potential of remote sensing and meteorological data. (2) The integration of meteorological data, spectral reflectance, and vegetation indices proved essential for achieving optimal yield estimation accuracy. Meteorological data provided the most significant contribution, while spectral reflectance and vegetation indices offered complementary information that improved model robustness. When all three data types were utilized simultaneously, the TB-LSTM model achieved peak estimation accuracy (R²: 0.853; RMSE: 514.013 kg/ha; MAE: 380.563 kg/ha). (3) The TB-LSTM model demonstrated robust early prediction capability. Using data from the first 27 time phases (covering growth stages up to heading), it successfully predicted winter wheat yields 48 days before harvest with optimal precision (R²: 0.868; RMSE: 487.327 kg/ha; MAE: 361.353 kg/ha). This capability supports proactive decision-making and resource allocation in agricultural management. Full article

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day⁻¹ in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment. Full article

Single-axis solar tracking maximizes photovoltaic energy production under clear-sky conditions; however, its effectiveness decreases under cloudy and overcast skies, where diffuse irradiance dominates and the optimal module orientation changes. Conventional tracking algorithms either neglect sky conditions or rely on simplified diffuse-response strategies that may trigger frequent tracker repositioning under variable cloud cover, leading to increased mechanical wear with marginal energy gains. This work proposes an enhanced diffuse-response tracking algorithm that explicitly accounts for both the intensity and temporal persistence of cloudiness. By requiring overcast conditions to persist for a minimum duration before reorienting the tracker to a diffuse-stow position, the proposed approach reduces unnecessary movements while preserving the benefits of diffuse-response operation. The algorithm is evaluated through numerical simulations based on historical meteorological data and validated using field measurements on monofacial and bifacial photovoltaic strings. The results show that the proposed strategy reduces excess tracker movement from 114% to 0.16% while maintaining nearly the same energy yield. Compared to a conventional diffuse-response algorithm, the associated energy reduction is minimal (≈0.17%) relative to the ≈0.37% yield gain observed at the studied location. These findings demonstrate that incorporating cloudiness duration enables a practical compromise between energy performance and tracker durability, particularly for monofacial photovoltaic systems. Full article

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R² = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R² = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale. Full article

Crops grown in ecologically vulnerable oases are increasingly vulnerable to climate change, a trend that poses a severe threat to the sustainability of agricultural production in arid zones. Clarifying the relative contributions of climate change and crop management to crop phenology is critical for designing climate-resilient agricultural practices—yet this remains underexplored for wheat in Xinjiang’s oases, a major arid-region agricultural hub. Using 1981–2021 phenological and meteorological data from 26 agrometeorological stations, we integrated a first-difference multiple regression model, Pearson’s correlation, multiple linear regression, and path analysis to quantify spatiotemporal phenological dynamics; disentangle the distinct impacts of climate and management factors; and identify dominant climatic drivers regulating wheat growth. Temperature was confirmed as the dominant climatic factor regulating wheat growth in arid oasis regions. Results showed that the annual change rates of sowing, emergence, booting, flowering, and maturity dates were 0.261 (−0.027), 0.265 (−0.103), −0.272 (−0.161), −0.269 (−0.226), and −0.216 (−0.127) days/year for winter (spring) wheat, respectively. For phenological durations, the annual change rates of sowing-to-emergence, emergence-to-anthesis, anthesis-to-maturity, vegetative growth period, reproductive growth period, and whole growth period were 0.007 (−0.076), −0.537 (−0.068), 0.096 (0.099), −0.502 (−0.134), 0.068 (0.034), and −0.434 (−0.100) days/year for winter (spring) wheat, respectively. Regarding climatic effects, maximum, minimum, and mean temperatures generally exerted positive impacts on wheat phenological durations; increased precipitation prolonged growth periods; and higher sunshine hours shortened winter wheat growth periods while extending those of spring wheat. Multiple regression and path analysis were employed to clarify the relative importance of climatic and management factors, as well as their direct and indirect effects on wheat phenology and yield. Furthermore, climate change had a substantially weaker impact on wheat phenology and yield compared to crop management, with climatic driver intensity following the order of mean temperature > precipitation > sunshine hours—confirming mean temperature as the key climate-induced driver. Correlation analysis revealed a positive relationship between yield and growth period length. This study provides novel insights into region-specific climate adaptation for wheat production in arid oases, highlighting that planting longer-growth-period varieties is an effective, eco-friendly strategy to enhance climate resilience and ensure sustainable agricultural development in fragile ecosystems. Full article

Short-term load forecasting (STLF) underpins the efficient and secure operation of power systems. This study develops and evaluates a hybrid architecture that couples deep neural networks (DNNs) with support vector regression (SVR) for national-scale day-ahead STLF using Croatian load data from 2006 to 2022. The approach employs an hour-specific framework of 24 hybrid models: each DNN learns a compact nonlinear representation for a given hour, while an SVR trained on the penultimate layer activations performs the final regression. Gradient-boosting-based feature selection yields compact, informative inputs shared across all model variants. To overcome limitations of historical local measurements, the framework integrates global numerical weather prediction data from the TIGGE archive with load and local meteorological observations in an operationally realistic setup. In the held-out test year 2022, the proposed hybrid consistently reduced forecasting error relative to standalone DNN-, LSTM- and Transformer-based baselines, while preserving a reproducible pipeline. Beyond using SVR as an alternative output layer, the contributions are as follows: addressing a 17-year STLF task, proposing an hour-specific hybrid DNN–SVR framework, providing a systematic comparison with deep learning baselines under a unified protocol, and integrating global weather forecasts into a practical day-ahead STLF solution for a real power system. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

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

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned. Full article

Accurate forecasting of photovoltaic (PV) power generation is essential for mitigating weather-induced variability and maintaining power-system stability. This study aims to improve PV power forecasting accuracy by enhancing the quality of numerical weather prediction (NWP) inputs rather than modifying forecasting model structures. Specifically, systematic errors in temperature, wind speed, and solar radiation data produced by the Unified Model–Local Data Assimilation and Prediction System (UM-LDAPS) are corrected using a Model Output Statistics (MOS) approach. A case study was conducted for a 20 kW rooftop PV system in Buan, South Korea, comparing forecasting performance before and after MOS application using a random forest-based PV forecasting model. The results show that MOS significantly improves meteorological input accuracy, reducing the root mean square error (RMSE) of temperature, wind speed, and solar radiation by 38.1–62.3%. Consequently, PV power forecasting errors were reduced by 70.0–78.7% across lead times of 1–6 h, 7–12 h, and 19–24 h. After MOS correction, the normalized mean absolute percentage error (nMAPE) remained consistently low at approximately 7–8%, indicating improved forecasting robustness across the evaluated lead-time ranges. In addition, an economic evaluation based on the Korean renewable energy forecast-settlement mechanism estimated an annual benefit of approximately 854 USD for the analyzed 20 kW PV system. A complementary valuation using an NREL-based framework yielded an annual benefit of approximately 296 USD. These results demonstrate that improving meteorological data quality through MOS enhances PV forecasting performance and provide measurable economic value. Full article

With the rapid advancement of clean energy, wind farm planning and construction are expanding worldwide, increasing the demand for accurate resource assessments. This study investigates the influence of vertical wind characteristics on wind farm siting and energy production, using measured meteorological data from the Hangjinqi wind farm. Results show that both mean wind speed increase substantially with altitude, indicating that upper layers provide richer and more stable wind resources. The estimated annual energy production of the site reaches 23,500 MWh, with capacity factors ranging from 35% to 42%, well above the national average. Seasonal and diurnal variations are evident: wind speeds strengthen during winter and spring, particularly at night, while turbulence intensity peaks in the daytime and decreases with height. Carbon dioxide (CO₂) reduction also increases with hub height, with the most pronounced seasonal reductions in spring (3367.6–5041.1 tCO₂, +49.7%) and winter (3215.7–5380.0 tCO₂, +67.4%), equivalent to several thousand tons of standard coal per turbine annually. Optimal performance is observed at 100–140 m, demonstrating efficient utilization of mid- to high-altitude resources. Nevertheless, discrepancies in turbine performance at different hub heights suggest untapped potential at higher elevations. These findings highlight the importance of incorporating vertical wind characteristics into wind farm siting decisions, and support the deployment of turbines with tower heights ≥140 m alongside intelligent scheduling and forecasting strategies to maximize energy yield and economic benefits. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article