Search Results (539)

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes. Full article

Stem radius growth (GRO), tree water deficit (TWD), and maximum daily shrinkage (MDS) were monitored throughout 2023 in a semi-arid Mediterranean forest stand in Burdur, Türkiye, where Pinus nigra subsp. pallasiana (Lamb.) Holmboe and Pinus brutia Ten. naturally co-occur. These indicators, derived from electronic band dendrometers, were analyzed in relation to key climatic variables. Results indicated that P. brutia had a longer growth period, while P. nigra exhibited a higher average daily increment under the environmental conditions of 2023 at the study site. Annual stem growth was nearly equal for both species. Based on dendrometer observations, P. brutia exhibited lower normalized TWD and higher normalized MDS values under varying vapor pressure deficit (VPD) and soil water potential (SWP) conditions. A linear mixed-effects model further confirmed that P. brutia consistently maintained lower TWD than P. nigra across a wide climatic range, suggesting a comparatively lower degree of drought-induced water stress. GRO was most influenced by air temperature and VPD, and negatively by SWP. TWD was strongly affected by both VPD and SWP, while MDS was primarily linked to minimum air temperature and VPD. Moreover, MDS in P. brutia appeared more sensitive to climate variability compared to P. nigra. Although drought limited stem growth in both species during the study year, the lower TWD and higher MDS observed in P. brutia may indicate distinct physiological strategies for coping with drought. These findings offer preliminary insights into interspecific differences in water regulation under the particular climatic conditions observed during the study year in this semi-arid Mediterranean ecosystem. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

The goal of this study was to look at changes in mean air temperatures, minimum air temperatures, maximum air temperatures, dew points, and precipitation over each of 1033 lakes in the coterminous United States over the summer months in the years 1981–2024. Near-surface water temperatures in the same lakes were calculated with equations using 8-day mean daily air temperatures, latitude, elevation, and the year of sampling. Over the past 43 years, there have been changes in air temperatures over many lakes of the United States with generally increasing trends for minimum air temperatures and mean air temperatures during the months of June through September. The greatest increases have been in daily minimum air temperatures followed by the mean daily air temperatures. Maximum daily air temperatures did not show a statistically significant increase for the summer season but did show a significant increase for the month of September. Along with the changes in the climate, the near-surface water temperatures of the lakes of the United States on average showed increases of 0.33 °C decade⁻¹ for the four summer months and increases for each of the summer months. Full article

Amid rapid urbanization and climate change, global urban water consumption, particularly household water use, has continuously increased in recent years. However, the impact of climate change on individual and household water use behavior remains insufficiently understood. In this study, we conducted tracking surveys in Beijing, China, to determine the correlation between climatic factors (e.g., temperature, precipitation, and wind) and household water use behaviors and consumption patterns. Furthermore, we proposed a genetic programming-based algorithm to identify and quantify key meteorological factors influencing household and personal water use. The results demonstrated that water use is mainly affected by temperature, particularly the daily maximum (TASMAX) and minimum (TASMIN) near-surface air temperature. In addition, showering and personal cleaning account for the largest proportion of water use and are most affected by meteorological factors. For every 10 °C increase in TASMAX, showering water use nonlinearly increases by 3.46 L/d/person and total water use nonmonotonically increases by 1.14 L/d/person. When TASMIN varies between −10 °C and 0 °C, a significant change in personal cleaning water use is observed. We further employed shared socioeconomic pathway scenarios of the Coupled Model Intercomparison Project 6 to forecast household water use. The results showed that residential water use in Beijing will increase by 21–33% by 2035 compared with 2020. This study offers a groundbreaking perspective and transferable methodology for understanding the effects of climate change on household water use behavior, providing empirical foundations for developing sustainable water resource management strategies. Full article

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. This study focuses on examining how annual, seasonal and daily temperatures have changed over recent decades in 12 historical sites spread across the island for the 1970–2024 period and how it relates to climate change. The Mann–Kendall tests for trends were employed for the annual and seasonal series to identify areas of the island where warming has been found to be statistically significant. The 90th, 95th, and 99th percentiles of daily temperature series were also analyzed. This study found that Puerto Rico has experienced significant warming from 1970 to 2024, with the most consistent increases in minimum temperatures, especially during the summer and nighttime hours. The frequency of extreme heat events has increased across nearly all stations in different areas of the island. Stepwise regression models identified surface air temperature (SAT), sea surface temperature (SST), and total precipitable water (TPW) as the most influential regional climate predictors driving mean temperature trends and the occurrence of extreme heat events. Full article

Leonardite (LEO), a microbial derived product rich in humic and fulvic acids, has been tested, due to its beneficial properties for health and well-being, as a feed additive, mainly in non-ruminant species. Although there are some reports of LEO supplementation in ruminants fed with high-to medium-forage based diets, there is no information available of the potential effects of LEO in ruminants fed, under sub-tropical climate conditions, with high-energy diets during long-term fattening. For this reason, the objective of the present experiment was to evaluate the effects of LEO levels inclusion in diets for feedlot lambs finished over a long-term period. For this reason, 48 Pelibuey × Katahdin lambs (initial weight = 20.09 ± 3.55 kg) were fed with a high-energy diet (88:12 concentrate to forage ratio) supplemented with LEO (with a minimum of 75% total humic acids) for 130 days as follows: (1) diet without LEO, (2) diet supplemented with 0.20% LEO, (3) diet supplemented with 0.40% LEO, and (4) diet supplemented with 0.60% LEO. For each treatment, Leonardite was incorporated with the mineral premix. Lambs were blocked by weight and housed in 24 pens (2 lambs/pen). Treatment effects were contrasted by orthogonal polynomials. The average climatic conditions that occurred during the experimental period were 31.6 ± 2.4 °C ambient temperature and 42.2 ± 8.1% relative humidity (RH). Those values of ambient temperature and RH represent a temperature humidity index (THI) of 79.07; thus, lambs were finished under high heat load conditions. The inclusion of LEO in diet did not affect dry matter intake (p ≥ 0.25) and average daily gain (p ≥ 0.21); therefore, feed to gain ratio was not affected (p ≥ 0.18). The observed to expected dietary net energy averaged 0.96 and was not affected by LEO inclusion (p ≥ 0.26). The lower efficiency (−4%) of dietary energy utilization is an expected response given the climatic conditions of high ambient heat load presented during fattening. Lambs that were slaughtered at an average weight of 49.15 ± 6.00 kg did not show differences on the variables measured for carcass traits (p ≥ 0.16), shoulder tissue composition (p ≥ 0.59), nor in visceral mass (p ≥ 0.46) by inclusion of LEO. Under the climatic conditions in which this experiment was carried out, LEO supplementation up to 0.60% in diet (equivalent to 0.45% of humic substances) did not did not help to alleviate the extra-energy expenditure used to dissipate the excessive heat and did not change the gained tissue composition of the lambs that were fed with high-energy diets during long-term period under sub-tropical climate conditions. Full article

Under rainfed Mediterranean-style conditions, crop growth and yield are largely determined by the availability of water. We investigated the role of residue orientation (standing or horizontal) and quantity on temperature, soil water, and wheat growth in two experiments with annual (winter) cropping. In the first trial at Shenton Park, tall (0.3 m) standing residues combined with thick (4 t ha⁻¹) horizontal residues increased the soil water at sowing by more than 100 mm compared with the bare soil control, increasing the wheat yield by about 2 t ha⁻¹. The average soil water storage was linearly related to the total residue quantity (r² = 0.86). Both standing and horizontal residues reduced the daily soil temperature fluctuations, but increased the air temperature fluctuations. Tall-cut residues had higher maximum and lower minimum air temperatures 0.05 m above the ground than short-cut residues with more horizontal material. Under field conditions, more soil water was stored in the growing season with the residues cut relatively tall with less on the ground compared with an equivalent residue amount consisting of shorter residues with more on the ground, although the differences were not great. Tall stubble was also associated with greater green leaf area and PAR interception. At the Cunderdin trial, the residue was greater between the harvester wheel tracks than at the outer edge of the cutting front. Under the very dry seasonal conditions experienced during the trial, greater residue resulted in increased soil water storage, particularly in the top 0.5 m of soil (up to 29 mm), greater green leaf area index, and higher crop yields (up to 300 kg ha⁻¹) behind the harvester, associated with greater spike m⁻², greater spikelets spike⁻¹, and lower root:shoot ratio. These results demonstrate the importance of considering residue orientation to maximise crop water use efficiency and yield. Full article

Urban green areas, while providing numerous benefits, can also produce negative impacts, often referred to as “ecosystem disservices”. While fallen fruits, leaves, and branches may pose tripping hazards, falling trees present a more significant threat to the safety of citizens and buildings. A study was conducted to identify the factors that most influence tree falls, aiming to enhance monitoring and maintenance in high-risk areas and develop preventive felling plans. The analysis was carried out in the city of Venice (Italy) using data from 2019 to 2022. Key variables included daily rainfall and cumulative rainfall over the four days preceding tree falls, minimum temperature, average wind speed and direction, and maximum gust speed on the day of the event and two days prior, as well as detailed information on the affected trees from the municipal GreenSpaces application database (R3GIS). The distribution of fallen trees was assessed in relation to these parameters, and a spatial autocorrelation analysis was performed. The results revealed that tree falls were more frequent during the summer season, coinciding with more intense weather events, especially those characterized by gusts of strong wind (>15 m/s). Street trees and trees in groups, particularly those in parks and densely populated urban areas, were most affected. Tree falls during a single event often occurred in clusters within a radius of approximately 1.5 km. Species analysis indicated that maintaining a diverse mix of tree species could reduce the number of fallen trees, as different species exhibit varying levels of resistance to wind pressure and adaptability to urban conditions. Addressing these findings can help to create more sustainable and livable urban environments, maximizing the benefits of green spaces while mitigating their ecosystem disservices. Full article

Thermal comfort in public buildings is crucial for occupant well-being and energy efficiency. This study employs TRNSYS software to simulate the effects of thermal mass, window performance, and window–wall ratio (WWR) on summer thermal comfort. The results indicate that without energy-saving measures, increased thermal mass raises daily average maximum and minimum temperatures by 0.33–0.96 °C and 0.14–0.94 °C, respectively. Enhanced WWRs lead to higher daily average maximum and minimum temperatures for double-glazed windows (0.18–0.61 °C and 0.07–0.62 °C, respectively), while single-glazed windows show increased maximum temperatures (0.18–1.86 °C) but decreased minimum temperatures (−0.01 to −0.72 °C). Thermal mass has a modest effect on indoor overheating during high outdoor temperatures. Double-glazed windows and lower WWRs effectively reduce indoor overheating, decreasing the attenuation coefficient by 2.13–28.94%. Conversely, single-glazed windows and higher WWRs enhance heat dissipation, increasing daily average temperature fluctuations by 2.33–44.18%. Notably, single-glazed windows with WWRs ≥ 50% improve thermal comfort by reducing extreme superheat temperature occurrence in heavy-thermal-mass buildings by 0.81 to 14.63%. Despite lower cooling loads with heavy thermal mass, double-glazed windows, and low WWRs, the study suggests that single-glazed windows and high WWRs can enhance summer thermal comfort. Therefore, reasonable shading measures and lighter thermal mass are recommended for such buildings. Full article

Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (T_a) and daily minimum temperatures (T_min) can provide spatially continuous monitoring. The MOD07 Level-2 and MYD07 Level-2 atmospheric profile product provides air temperature at various altitude levels, facilitating a more direct estimation of T_a and T_min. However, previous validations mainly focused on daytime, with a lack of validation for nighttime. Therefore, this study comprehensively evaluated the MOD07 Level-2 and MYD07 Level-2 derived T_a by 2168 hourly meteorological measurements over 5000 m altitude spanning in China. Furthermore, a detailed evaluation of their capability to estimate T_min was also compared with MOD11 Level-2 and MYD11 Level-2 land surface temperature. Our results show that the highest available pressure method (HAP) estimated that, in instantaneous nighttime T_a, there was severe underestimation especially in high-altitude areas for both MOD07 (r = 0.95, Bias = −0.27 °C, and RMSE = 4.53 °C) and MYD07 data (r = 0.96, Bias = −0.17 °C, and RMSE = 3.73 °C). The adiabatic lapse rate (ALR) correction effectively reduced these errors, achieving optimal accuracy with MYD07 data (r = 0.97, Bias = −0.05 °C, and RMSE = 3.29 °C). However, the underestimation by the HAP method was still insufficient compared to T_min estimation by land surface temperature (LST). The LST method offers improved accuracy (r = 0.98, Bias = −0.16 °C, RMSE = 2.89 °C). In general, MYD-based estimations consistently outperformed MOD-based estimations. However, seasonal and elevational variability was observed in all methods, with errors increasing notably in mountainous areas (RMSE rapidly increases to 5 °C and above when the altitude exceeds 2000 m). These findings can provide practical guidance for selecting appropriate inversion methods according to terrain and season and support the development of more accurate air temperature products for a range of climate- and environmental-related applications. Full article

The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R² (0.87–0.95), outperforming the TCN (R²: 0.79–0.88), GRU (R²: 0.75–0.84), SVR (R²: 0.68–0.80), and RF (R²: 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region. Full article

The aim of this study was to examine variations in the hydrological regime of the Krapina River from 1964 to 2023. The river basin spans 1263 km² and is characterized by a temperate, humid continental climate with warm summers. Hydrological data from the Kupljenovo gauging station, which monitors 91.1% of the basin (1150 km²), indicate an average annual discharge of 11.2 m³/s, ranging from 3.25 m³/s to 18.3 m³/s. Over the 60-year study period, the minimum mean daily discharges show a statistically insignificant increasing trend, while the mean annual and maximum annual mean daily discharges exhibit statistically insignificant declines. Annual precipitation averages 1037 mm, varying between 606 mm and 1459 mm, with a non-significant decreasing trend. In contrast, the mean annual air temperatures demonstrate a statistically significant increasing trend, with a pronounced intensification beginning in 1986. The annual runoff coefficients series exhibits a statistically insignificant downward trend, with an average value of 0.293 (range: 0.145–0.399). Application of the New Drought Index (NDI) revealed a marked increase in the frequency of strong and extreme droughts since 2000. Full article

In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields. Full article

It is necessary to evaluate the response of extreme events to global warming across different climates and geographical regions. This study aims to examine the trend changes of 13 temperature extreme indices over a 30-year statistical period from 1990 to 2020, using daily maximum and minimum temperature data from 37 synoptic stations in Iran. The Mann–Kendall trend test was employed to analyze the data trends. The results indicate that, except for the two indices, frost days (FDs) and ice days (IDs), the temperature extreme indices show an increasing trend across the country. The forward and backward graphs of the Mann–Kendall test reveal that the trend of the indices is significant at the 0.05 significance level, with the two indices TNn and TXn intersecting in 2009. This indicates that a mutation occurred in that year, where the increasing slope of the trend after the mutation is greater than the slope of the trend before the mutation. Moreover, the decadal changes of the indices in the three decades 1990–2000, 2000–2010, and 2010–2020 demonstrate that the highest increasing trend in temperature occurred in the second and third decades. Full article