Search Results (58)

Weather forecasts are crucial in agricultural decision-making. The first objective was to assess the performance of eight weather forecast models for predicting five key meteorological variables in agriculture: air temperature, humidity, wind speed, global radiation, and precipitation. The tested models were ICON-D2, AROME, HARMONIE, MAR, GFS, and three models from the ECMWF: the deterministic model (HRES), the ensemble model (ENS), and the deep learning-based model (AIFS). The forecasts were compared over a six-month period with observations from 22 weather stations located in Wallonia (Belgium). AIFS achieved the lowest RMSE for predicting global radiation. For lead times up to 48 h, ICON-D2 had the lowest RMSE for wind speed; ICON-D2 and AIFS showed the lowest RMSE for air temperature and humidity, and AIFS and HRES performed best at predicting rainy versus non-rainy days. For lead times beyond 48 h, AIFS had the lowest RMSE for predicting air temperature and humidity, and ENS had the lowest RMSE for predicting wind speed. The second objective was to evaluate the suitability of these forecasts to feed a simple agricultural decision-support tool, helping farmers identify the optimal time windows for spraying plant protection products. ICON-D2 and AIFS yielded the most reliable recommendations. Full article

To address the low accuracy and poor reliability of short-term photovoltaic (PV) power forecasting under complex weather conditions, this study proposes a multi-strategy synergistic optimization framework for point-interval prediction. The methodology integrates similar day classification (SDC) via RDP-DTW-DBA-K-means, a hybrid BiTCN-MAOBiGRU-AM model with a mutation-aware mechanism, and Dream Optimization Algorithm (DOA) for global hyperparameter tuning of six key parameters. For interval prediction, an adaptive bandwidth kernel density estimation (ABKDE) dynamically adjusts bandwidth based on local error density and weather scenarios. Experiments using data from a Guangxi PV station demonstrate that the synergistic model reduces RMSE by 29.58% (cloudy) and 32.37% (overcast/rainy) versus the baseline, and cuts RMSE by 16.2–19.0% under abrupt weather events and 22.4–40.2% under non-ideal input data. At the 95% confidence level, ABKDE improves prediction interval coverage probability by 3.9–5.4 percentage points and reduces normalized average width by 20.8–23.6% compared to conventional KDE. The proposed framework significantly enhances prediction accuracy, robustness, and generalization, offering a reliable solution for PV power forecasting in highly variable meteorological scenarios. Full article

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU. Full article

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

This study aimed to determine the actual crop evapotranspiration (ET_{c act}) and the crop coefficient (K_c) of economically important fruit crops in the Amazon, under both irrigated and non-irrigated conditions. The ET_{c act} was determined using the soil water balance method, while K_c was determined using the ratio of ET_{c act} to reference evapotranspiration (ET_o). The treatments were evaluated during the rainy period (RP) and the less rainy period (LRP). During the RP, ET_{c act} showed no significant differences between treatments, ranging from 2.26 to 3.03 mm day⁻¹. During the LRP, the irrigated treatment (2.91 to 4.02 mm day⁻¹) showed higher ET_{c act} compared to the non-irrigated treatment (1.53 to 2.87 mm day⁻¹). For the non-irrigated treatment, only the dwarf green coconut and the acid lime had a higher ET_{c act} in the LRP than the RP, while the açaí palm and the cocoa showed lower values during the LRP. In general, ET_{c act} remained below ET_o, with K_c values ranging from 0.81 to 0.85 during the RP and increasing to 0.89–0.93 during the LRP. Irrigation provided water support to the studied fruit crops during periods of lower rainfall, meeting the higher atmospheric demand during the less rainy period. Full article

This study investigates the scaling properties of rainfall in Tunis over temporal scales ranging from 5 min to 2.5 years using high-resolution rain gauge data from three recording stations. We employ the Universal Multifractal (UM) framework to characterize scaling properties across multiple temporal regimes. The UM model was selected over alternative multifractal approaches because of its parsimonious three-parameter formulation (C₁, α, H). It explicitly accounts for non-conservative processes through the Fractionally Integrated Flux (FIF) extension and includes established bias correction methods for highly intermittent signals. This framework has demonstrated universality across diverse climatic conditions and enables direct comparison with existing rainfall studies in Mediterranean environments. Spectral analysis reveals three distinct scaling regimes: micro-scale (5 min–2 h 40 min), meso-scale (2 h 40 min–7 days), and synoptic scale (>7 days). The non-conservative nature of the micro-scale regime is addressed through a multifractal fractionally integrated flux model. A key challenge in applying UM analysis to rainfall data is the prevalence of low and zero rain rates (>98% zeros in our dataset). This extreme intermittency introduces significant bias in parameter estimation. Existing correction methods require either continuous rain sequences—scarce in semi-arid climates—or are limited to moderate intermittency levels. We propose an empirical correction method that extends the existing semi-empirical approach by explicitly linking the percentage of zero values to biased UM parameters through empirical relationships applicable to sequences with as few as 50% rainy observations. This advancement enables reliable parameter estimation from highly intermittent datasets. In such conditions, traditional event-by-event analysis yields insufficient samples (only five continuous events longer than 2 h 40 min over 2.5 years in Tunis). The corrected estimates (α = 1.63, C₁ = 0.16 for micro-scales) demonstrate strong consistency with continuous rainfall events and align well with high-resolution studies, validating our approach for extreme intermittency conditions characteristic of Mediterranean semi-arid climates. Full article

Non-point source pollution poses a severe threat to the water quality of the Li River. This study conducted field monitoring of pollution loads from different land-use types on Maozhou Island in the Li River during the 2023 rainy season. Runoff water quality from vegetable plots, orchards, and bamboo forests consistently exceeded standards, with vegetable plots being the primary source of pollution. Their total phosphorus (TP) concentration exceeded standards by nearly 25 times, contributing the highest annual load. The transport of pollutants (TP, total nitrogen(TN), chemical oxygen demand(COD_Cr)) was closely correlated with suspended solids (SS), with the finest particles (<5 μm) identified as the primary carrier exhibiting the strongest pollutant enrichment capacity (e.g., in vegetable fields, the correlation coefficient r between < 5 μm particles and TP was >0.85, p < 0.01). Rainfall patterns significantly influenced pollutant concentrations; TN and TP levels increased with preceding dry days, while phosphorus output from vegetable plots decreased with rising average rainfall temperature. Compared to bamboo forests, vegetable plots and orchards exhibited lower soil adsorption capacity. This study recommends a connectivity-based strategy prioritizing the interception of heavily enriched fine particulate matter (<5 μm) through runoff control and enhanced wetland retention functions. These findings underscore the importance of controlling fine particulate matter for reducing non-point source pollution and maintaining ecological health in the Lijiang River basin. Full article

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

The objective of this study to evaluate the effects of two levels of concentrate supplementation (1 g/kg or 2 g/kg of body weight, BW) on the intake, apparent digestibility and performance of Girolando steers on tropical pastures during the post-weaning phase in the dry to rainy season transition. We used 20 Girolando steers (half Holstein x half Zebu), with an average initial BW of 151.15 ± 50 kg and 12 months of age. The steers grazed on Urochloa brizantha cv. Marandu pasture. The animals were randomly assigned supplementation with 1 g/kg of BW (SC1) or supplementation with 2 g/kg of BW (SC2) of a concentrate supplement. The forage dry matter intake (%BW), neutral detergent fiber intake (NDF) and NDF digestibility were higher (p < 0.05) for steers supplemented with a level of 1 g/kg of BW. Supplement intake (kg/day), non-fibrous carbohydrate (NFC) intake and NFC digestibility were higher (p < 0.05) for steers consuming 2 g/kg BW of the concentrated supplement. The body weight at slaughter (297 kg) and average daily gain (0.57 kg/day) were not influenced by the level of supplementation. The use of 1 g/kg BW of a concentrated supplement is recommended for post-weaning steers on tropical pastures during the dry to rainy season transition. Full article

In spaced crop systems, understanding the interactions between different types of vegetation in the agroecosystem and solar radiation is essential for understanding surface radiation dynamics. This study aimed to both seasonally and spatially quantify and characterize the components of the solar radiation balance in the cultivation of green dwarf coconut. The experiment was conducted in Santa Izabel do Pará, Brazil, and monitored the following meteorological parameters: rainfall, incident global radiation (R_g), and net radiation (R_n). Landsat 8 satellite images were obtained between 2021 and 2023, and the estimates for global and net radiation were subsequently calculated. The resulting data were subjected to mean tests and performance index analysis. The dry season showed higher values of R_g and R_n due to reduced cloud cover. In contrast, the rainy season exhibited lower R_g and R_n totals, with reductions of 21% and 23%, respectively. In the irrigated area, a higher R_n/R_g fraction was observed compared to the non-irrigated area, with no significant differences between the row and inter-row zones. In the non-irrigated system, there were no seasonal differences, but a spatial difference between row and inter-row was noted, with the row having higher net radiation (9.95 MJ m⁻² day⁻¹) than the inter-row (8.36 MJ m⁻² day⁻¹), which could result in distinct energy balances at a micrometeorological scale. Spatially, the eastern portion of the study area showed higher global radiation totals, with the radiation balance predominantly ranging between 400 and 700 W m⁻². Based on the performance indices obtained, satellite-based estimates proved to be a viable alternative for characterizing the components of the radiation balance in the region, provided that the images have low cloud cover. Full article

Ground-mounted photovoltaic (GMPV) systems are a crucial component of photovoltaic (PV) applications, and their environmental impacts during large-scale development require thorough attention. This study conducted continuous observations at a GMPV plant in an arid region, employing a three-site comparative monitoring system to assess the environmental impact of both shaded and non-shaded areas within GMPV systems. The parameters measured included atmospheric temperature (AT), relative humidity (RH), soil temperature (ST), soil water content (SWC), and wind speed. The results revealed significant diurnal and seasonal variations in AT, with daytime warming and nighttime cooling ranging from 0.1 to 0.7 °C, with particularly large variations observed during high-temperature seasons. Shaded areas under the PV panels exhibited increased RH at night and decreased RH during the day, along with a cooling effect on ST, with a maximum reduction of 7 °C. SWC was higher in shaded areas during dry seasons but exhibited complex redistribution patterns during rainy seasons. Wind speed and direction were notably altered, demonstrating a corridor effect. These findings contrast with previous studies that only focused on the environmental assessment of non-shaded areas within PV systems and external areas using two-site monitoring. This study highlights the critical role of shaded areas in understanding the local environmental impacts of PV systems. This comprehensive approach offers deeper insights into how PV systems influence local meteorological and environmental conditions, suggesting that optimized design and placement of PV systems can enhance their ecological benefits and mitigate adverse environmental impacts in arid regions. Full article

In ruminants, diet composition has a positive correlation with heat production, which can influence thermoregulation, energy expenditure and, consequently, animal performance. The objective of this work was to evaluate the effects of climatic conditions and supplementation based on palm kernel cake, on the thermoregulation of crossbred buffaloes in the eastern Amazon. The research was carried out at Embrapa Amazônia Oriental (01°26′ S and 48°24′ W), Belém, Pará, and lasted 12 months (representing the entire year). Twenty-four buffaloes, females, with initial age and an average weight of 54 ± 7 months and 503.1 ± 23 kg, respectively, non-pregnant, non-lactating and clinically healthy were used, divided into four treatments based on the supplementation content of the palm cake (%DM) in relation to their body weight (%): 0, 0.25, 0.50 and 1.0. The animals were kept in paddocks with Brachiaria brizantha (cv. Marandu), in a rotating system, with water to drink and mineral salt ad libitum. Equipment was installed to record environmental data (temperature and relative humidity, dew point temperature, wet bulb and black globe) and physiological data: rectal temperature (RT); respiratory rate (RR); and body surface temperature (BST), recorded twice a day, always in the morning (6:00 a.m. to 7:00 a.m.) and afternoon (12:00 p.m. to 1:00 p.m.) shifts, and were used to calculate the Globe Temperature and Humidity Index (GTHI). Supplementation did not influence the physiological variables of thermoregulation (p > 0.05). However, there were differences in the GTHI between the shifts, with higher means in the afternoon shift, especially in the less rainy period of the year, where the GTHI reached 92.06 ± 2.74 (p < 0.05). In all periods of the year, the mean values of RT, RR and BST were higher in the afternoon shift (p > 0.05). The respiratory rate (RR) is associated with the annual seasonality of the thermal waters, with higher averages in the afternoons of the rainy season. The positive correlation for rectal temperature, respiratory rate and body surface temperature indicated that buffaloes respond to thermal elevations in the atmosphere (afternoon period) and, consequently, reflect on the GTHI. Supplementation does not influence thermoregulation; the changes observed occurred in response to the region’s thermal and rainfall conditions (mainly in the afternoon shift), with higher GTHI values. Full article

The main goal of the present study is to identify the prevailing atmospheric circulation patterns (circulation types) that are associated with the occurrence of precipitation (both mean and extreme) over southern Romania. A daily circulation type calendar derived from an automatic and objective classification scheme is used in synergy with the daily precipitation time series from five weather stations in the study area for a sixty-year period (1961–2020). Both mean and extreme precipitation do not show statistically significant trends, except for the annual precipitation at Constanța, for the value with daily precipitation totals greater than the 95th percentile at Craiova and the number of days exceeding the 99th percentile at Buzău and Râmnicu -Vâlcea, where significant negative trends were noticed. Moreover, the precipitation trends were analyzed in relation to the atmospheric circulation types. Non-significant positive trends were observed for the precipitation amounts (annually, winter, spring, and autumn) corresponding to very rainy circulation types (C, Cwsw), while for summer, the equivalent trends were negative. Moreover, it became evident that during extreme precipitation events, the predominant circulation types (C, Cwsw) are associated with western or almost western atmospheric circulation and Mediterranean- or Atlantic-originated depressions. Full article

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of lake water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations were used for the two lakes: Lake Koka and Lake Ziway, Ethiopia. Sampling stations were selected from sites infested with water hyacinth (low, medium, and high) and non-water hyacinth aquatic plants during wet or rainy and dry or non-rainy seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties, namely, phosphate, nitrate, pH, electrical conductivity (EC), the five-day biological oxygen demand (BOD₅), water temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu)). These water quality variables were compared by means of ANOVA. Despite the chemical oxygen demand (COD) of Lake Ziway, this study found no significant (p > 0.05) variation in the concentrations of Cu, EC, pH and water temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD₅, COD, nitrate, phosphate) between low, medium, and high levels of water hyacinth were significant in both lakes (p < 0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites. These findings suggest water hyacinth plant as a promising nature-based solution for removing undesirable chemicals from contaminated water bodies. Full article