Search Results (153)

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

The article is focused on the quantitative assessment of the thermal impact of polar mesospheric clouds (PMCs) on the mesopause caused by the emission of absorbed solar and terrestrial infrared (IR) radiation by cloud particles. For this purpose, a parameterization of mesopause heating by PMC crystals has been developed, the main feature of which is to incorporate the thermal properties of ice and the interaction of cloud particles with the environment. Parametrization is based on PMCs zero-dimensional (0-D) model and uses temperature, pressure, and water vapor data in the 80–90 km altitude range retrieved from Solar Occultation for Ice Experiment (SOFIE) measurements. The calculations are made for 14 PMC seasons in both hemispheres with the summer solstice as the central date. The obtained results show that PMCs can make a significant contribution to the heat balance of the upper atmosphere, comparable to the heating caused, for example, by the dissipation of atmospheric gravity waves (GWs). The interhemispheric differences in heating are manifested mainly in the altitude structure: in the Southern Hemisphere (SH), the area of maximum heating values is 1–2 km higher than in the Northern Hemisphere (NH), while quantitatively they are of the same order. The most intensive heating is observed at the lower boundary of the minimum temperature layer (below 150 K) and gradually weakens with altitude. The NH heating median value is 5.86 K/day, while in the SH it is 5.24 K/day. The lowest values of heating are located above the maximum of cloud ice concentration in both hemispheres. The calculated heating rates are also examined in the context of the various factors of temperature variation in the observed atmospheric layers. It is shown in particular that the thermal impact of PMC is commensurate with the influence of dissipating gravity waves at heights of the mesosphere and lower thermosphere (MLT), which parameterizations are included in all modern numerical models of atmospheric circulation. Hence, the developed parameterization can be used in global atmospheric circulation models for further study of the peculiarities of the thermodynamic regime of the MLT. Full article

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution. Full article

Differences in environmental conditions due to slope topography result in differences in evapotranspiration along slopes, but it is unclear how changes in environmental conditions affect the variations in evapotranspiration along slopes. Therefore, we monitored dry-day evapotranspiration (ET_d), solar radiation, vapor pressure deficit (VPD), and soil moisture downslope and upslope on a larch plantation hillslope from July to September 2023 to reveal the mechanisms driving ET_d variations. The results revealed that the difference in ET_d values between the downslope and upslope positions varied by month, with comparable ET_d values at both positions in July and higher ET_d values at the downslope position than at the upslope position in August and September. An ET_d model combining the effects of solar radiation, VPD, and soil water content was developed, which explained 68% of the variation in ET_d. The contributions of solar radiation, VPD, soil moisture, and their interactions to ET_d varied across slope positions, and ET_d was limited mainly by solar radiation downslope and by soil moisture upslope. Our study improves the understanding of the mechanisms governing the variations in evapotranspiration along slopes, and provides a new methodology for quantifying the effects of environmental differences between slope positions on evapotranspiration. Full article

Irrigation management in areas affected by climate change requires an accurate determination of transpiration losses in crops, such as grapevines. The existing literature has primarily focused on estimating transpiration losses based on two critical microclimate factors: vapor pressure deficit (VPD) and solar radiation intensity (R_s). However, most studies have been conducted under abundant soil water availability conditions, whereas research under limited water availability remains scarce. Thus, this study aims to develop models capable of accurately determining transpiration losses of grapevines under both full irrigation and limited soil water conditions. Sap flow sensors using the heat ratio method were employed to measure transpirational losses. These measurements were compared with the results from the models afterward. The results suggest that VPD was the dominant factor affecting canopy conductance, which decreased exponentially as VPD increased. Furthermore, a piecewise linear regression analysis revealed a threshold value for Rs during both study years. This finding suggests that Rs impacts transpiration losses in two distinct ways, highlighting the necessity to develop two separate models for determining transpiration losses each study year. The estimation capability of the models was verified using the k-fold cross-validation method, suggesting that reliable predictions can be made under both well-watered and rainfed conditions. Full article

Clouds play a central role in regulating incoming solar radiation and outgoing terrestrial emission; hence, they must be internally constrained to prognose Earth’s temperature. At the same time, planetary fluids are inherently turbulent, so the climate state would tend toward maximum entropy production—a generalized second law of thermodynamics. Incorporating these requirements, I have previously formulated an aquaplanet model to demonstrate that intrinsic water properties may strongly lower the climate sensitivity to solar irradiance, thereby resolving the faint young Sun paradox (FYSP). In this paper, I extend the model to include other external forcings and show that sensitivity to the reduced outgoing longwave radiation by the elevated pCO₂ can be several times greater, but the global temperature remains capped at ~40 °C by the exponential increase in saturated vapor pressure. I further show that planetary albedo augmented by a tropical supercontinent may cool the climate sufficiently to cause tropical glaciation. And since the glacial edge is marked by above-freezing temperature, it abuts an open, co-zonal ocean, thereby obviating the “Snowball Earth” hypothesis. Our theory thus provides a unified framework for interpreting Earth’s diverse climates, including the FYSP, the warm extremes of the Cambrian and Cretaceous, and the tropical glaciations of the Precambrian. Full article

Building energy management is crucial in reducing energy consumption and maintaining occupant comfort, especially in heating systems. However, achieving optimal space heating efficiency while maintaining consistent comfort presents significant challenges. Traditional methods often fail to balance energy consumption with thermal comfort, especially across multiple zones in buildings with varying operational demands. This study investigates the role of deep learning models in optimizing space heating while maintaining thermal comfort across multiple building zones. It aims to enhance heating efficiency by developing predictive models for building temperature and heating consumption, evaluating the effectiveness of different deep learning architectures, and analyzing the impact of model-driven heating optimization on energy savings and occupant comfort. To address this challenge, this study employs Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to forecast area temperatures and predict space heating consumption. The proposed methodology leverages historical building temperature data, weather station measurements such as atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation, along with other weather parameters, to develop accurate and reliable predictions. A two-stage deep learning process is utilized: first, temperature predictions are generated for different building zones, and second, these predictions are used to estimate global heating consumption. This study also employs grid search and cross-validation to optimize the model configurations and custom loss functions to ensure energy efficiency and occupant comfort. Results demonstrate that the Long Short-Term Memory and Transformer models outperform the Gated Recurrent Unit regarding heating reduction, with a 20.95% and 20.69% decrease, respectively, compared to actual consumption. This study contributes significantly to energy management by providing a deep learning-driven framework that enhances energy efficiency while maintaining thermal comfort across different building areas, thereby supporting sustainable and intelligent building operations. Full article

The solar radiation pressure (SRP) model, as a key factor affecting the precise orbit determination (POD) accuracy of navigation satellites, is related to the state and optical properties of the satellite surface. This study establishes a high-precision SRP model for BDS-3 medium earth orbit (MEO) satellites manufactured by the China Academy of Space Technology based on the satellite engineering parameters, which comprises the satellites’ size and optical properties measured before launch. Then, the physical-based SRP model is re-constructed into the body-fixed coordinate as the function of the Sun elongation angle. The use of the hybrid SRP model, combining the reconstructed SRP model and the 5-parameter ECOM, results in a better POD performance. The orbit results, validated using satellite laser ranging (SLR) observations, show that the radial precision of approximately 3–4 cm can be achieved, with a reduction of the bias by up to 38% and a removal of the systematic error related to the Sun elongation angle in SLR residuals. Considering the possible degradation of the reconstructed SRP model with the engineering parameters, the evolution of SRP accelerations along with orbit quality based on a time series from over 5 years was studied. The results indicate that a variation of the total SRP acceleration for the BDS-3 satellites is minor and there is no apparent degradation in validations of 2019–2023, which proved the reliability and usability of the proposed SRP model for the BDS-3 MEO satellites. Full article

The Leaf Area Index (LAI) is a key physiological metric in viticulture, associated with vine health, yield, and responsiveness to environmental and management factors. This study, conducted in a Mediterranean Sauvignon Blanc vineyard (2017–2023), examines how irrigation and environmental variables affect LAI across phenological stages, and their impact on yield (clusters per vine, cluster weight, total yield) and pruning parameters (cane weight, pruning weight). Results show that irrigation is the primary driver of LAI, with increased water availability promoting leaf area expansion. Environmental factors, including temperature, vapor pressure deficits, and solar radiation, influence LAI dynamics, with chilling hours playing a crucial role post-veraison. Excessive LAI (>1.6–1.7) reduces yield due to competition between vegetative and reproductive sinks. Early-season LAI correlates more strongly with yield, while late-season LAI predicts pruning weight and cane growth. Machine learning models reveal that excessive pre-veraison LAI in one season reduces cluster numbers in the next. This study highlights LAI as a critical tool for vineyard management. While irrigation promotes vegetative growth, excessive LAI can hinder fruit set and yield, emphasizing the need for strategic irrigation timing, canopy management, and climate adaptation to sustain long-term vineyard productivity. Full article

In this study of constructing gross primary production (GPP) based on solar-induced chlorophyll fluorescence (SIF) and analyzing its spatial–temporal characteristics and influencing factors, numerous challenges are encountered, especially in arid regions with fragile ecologies. Coupling SIF with other factors to construct the GPP and elucidating the influencing mechanisms of environmental factors could offer a novel theoretical method for the comprehensive analysis of GPP in arid regions. Therefore, we used the GPP station data from three different ecosystems (grasslands, farmlands, and desert vegetation) as well as the station and satellite data of environmental factors (including photosynthetically active radiation (PAR), a vapor pressure deficit (VPD), the air temperature (Tair), soil temperature (Tsoil), and soil moisture content (SWC)), and combined these with the TROPOMI SIF (RTSIF, generated through the reconstruction of SIF from the Sentinel-5P sensor), whose spatiotemporal precision was improved, the mechanistic light reaction model (MLR model), and different weather conditions. Then, we explored the spatiotemporal characteristics of GPP and its driving factors in local areas of Xinjiang. The results indicated that the intra-annual variation of GPP showed an inverted “U” shape, with the peak from June to July. The spatial attributes were positively correlated with vegetation coverage and sun radiation. Moreover, inverting GPP referred to the process of estimating the GPP of an ecosystem through models and remote sensing data. Based on the MLR model and RTSIF, the inverted GPP could capture more than 80% of the GPP changes in the three ecosystems. Furthermore, in farmland areas, PAR, VPD, Tair, and Tsoil jointly dominate GPP under sunny, cloudy, and overcast conditions. In grassland areas, PAR was the main influencing factor of GPP under all weather conditions. In desert vegetation areas, the dominant influencing factor of GPP was PAR on sunny days, VPD and Tair on cloudy days, and Tair on overcast days. Regarding the spatial correlation, the high spatial correlation between PAR, VPD, Tair, Tsoil, and GPP was observed in regions with dense vegetation coverage and low radiation. Similarly, the strong spatial correlation between SWC and GPP was found in irrigated farmland areas. The characteristics of a low spatial correlation between GPP and environmental factors were the opposite. In addition, it was worth noting that the impact of various environmental factors on GPP in farmland areas was comprehensively expressed based on a linear pattern. However, in grassland and desert vegetation areas, the impact of VPD on GPP was expressed based on a linear pattern, while the impact of other factors was more accurately represented through a non-linear pattern. This study demonstrated that SIF data combined with the MLR model effectively estimated GPP and revealed its spatial patterns and driving factors. These findings may serve as a foundation for developing targeted carbon reduction strategies in arid regions, contributing to improved regional carbon management. Full article

Introduction: The Earth’s growing population is increasing resource consumption, heavily pressuring agriculture, which, currently, uses 70% of the world’s freshwater from rivers and lakes, which, themselves, comprise only 1% of the Earth’s water reserves. Combined with climate change, the situation is alarming. These challenges drive Agriculture 4.0, which is focused on sustainable agricultural processes to optimise water use. Objective: Given this context, this study proposes a model, based on Artificial Intelligence (AI) techniques to predict topsoil moisture in a study area located in the south of the Iberian Peninsula, primarily an agricultural region facing recurrent droughts and water scarcity. Methods: To develop the model, a comparison between Artificial Neural Networks (ANNs) and Gradient Booster Regressors (GBRs) was conducted, and topsoil moisture data from seven probes distributed over the study area were used, in addition to several variables (temperature, relative humidity, solar radiation, wind speed, precipitation and evapotranspiration) from a selection of weather stations and ensemble forecasts from meteorological models. Results: The final GBR model, with a 0.01 learning rate, 5 max depth, and 350 estimators, predicted topsoil moisture with an average mean squared error (MSE) of 0.027 and a maximum difference between observed and predicted data of 20.09% in a two-year series (May 2022–June 2024). Full article

This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance the prediction of coastal dynamics along the Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data from the Copernicus Marine Service. TFTs are employed to refine predictions of shallow water dynamics by considering atmospheric influences, with a particular focus on wave-wind correlations in coastal regions. Atmospheric pressure and temperature are treated as latitude-dependent constants, with specific investigations into extreme events like freezing and solar radiation-induced turbulence. Explainable AI (XAI) is exploited to ensure transparent model interpretations and identify key influential input variables. Data attribution strategies address missing data concerns, while ensemble modelling enhances overall prediction robustness. The models demonstrate a significant improvement in prediction accuracy compared to traditional methods. This research provides a deeper understanding of atmosphere-marine interactions and demonstrates the efficacy of Artificial intelligence (AI)/Machine Learning (ML) in bridging observational and modelled data gaps for informed coastal zone management decisions, essential for maritime safety and coastal management along the Western Black Sea coast. Full article

The present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which were divided into clean and dirty groups. The following variables were monitored and recorded for a period of six months: radiation, panel temperature, air temperature, wind speed, humidity, pressure, and ultraviolet (UV) radiation. Additionally, current, voltage, and power were recorded. These measurements were taken during the production of energy by PV panels. Monocrystalline PV panels exhibited an 8.6% increase in energy efficiency, while polycrystalline PV panels demonstrated a 6.2% increase, following the collection and cleaning of data in accordance with the IEC 61724 standard. Six distinct machine learning regression analyses were conducted on the dataset. The results were compared using the Root Mean Square Error (RMSE) and the coefficient of determination (R²). The Random Forest model demonstrated the greatest predictive success, while the Support Vector Regression (SVR) model exhibited the lowest performance. Full article