Search Results (316)

Vento Norte (VNOR; Portuguese for North Wind) is a downslope windstorm that develops over modest terrain in the central region of Rio Grande do Sul (RS), southern Brazil. The regional topography is characterized by an abrupt terrain transition with elevation differences of approximately 400–500 m. This atmospheric flow typically occurs during the cold season and is characterized by strong wind gusts, rapid warming, and drying of the planetary boundary layer (PBL). In this study, the performance of different PBL parameterization schemes in the Weather Research and Forecasting (WRF) model is assessed for simulating a VNOR event that occurred between 19 and 20 August 2021 in Santa Maria (SMA), RS. Five high-resolution numerical simulations were conducted using the Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN2.5), Quasi-Normal Scale Elimination (QNSE), and Three-Dimensional Turbulent Kinetic Energy (3DTKE) PBL schemes. Model results were evaluated against observations from a flux tower providing turbulence measurements, twice-daily radiosoundings, and hourly surface meteorological observations. Statistical metrics indicate that the MYNN2.5 scheme provided the most accurate representation of the nighttime stable boundary layer preceding the VNOR, as well as its onset and subsequent evolution. Although this study analyzes a single VNOR event and the results may be case-dependent, the overall performance of the MYNN2.5 scheme suggests that it is a promising option for the operational forecasting of VNOR events. These findings provide new insights into the ability of different PBL schemes to reproduce the mean boundary-layer structure and turbulence characteristics associated with downslope windstorms over modest terrain, contributing to the understanding of these events. Full article

The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R² values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m⁻² yr⁻¹, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China. Full article

The planning and design of Extra-High Voltage (EHV) overhead power lines require strict adherence to electromagnetic field exposure limits to ensure public safety. This paper presents a comprehensive analysis of the minimum ground clearance required for standard 400 kV transmission towers to comply with international safety guidelines. A review of legislative frameworks across 37 countries indicates a widespread consensus on limiting values of 5 kV/m for the electric field and 100 $\mathsf{\mu }$T for magnetic flux density. Using analytical methods, the electric and magnetic fields were calculated for four common tower geometries (Cat, Portal, Danube, and Barrel) under varying ground clearances and phase configurations. The results demonstrate that the magnetic flux density is not a limiting factor, as it remains well below safety thresholds even at standard technical clearances. Conversely, the electric field intensity proves to be the critical design constraint, often requiring clearances significantly higher than those dictated by insulation coordination. The study identifies that optimizing the phase sequence in double-circuit towers can reduce the required ground clearance by up to 28%, offering a cost-effective mitigation strategy. These findings provide power line designers with essential decision-making data for the preliminary design phase, enabling the optimization of tower geometry and phase arrangement without the need for computationally intensive simulations. Full article

Concentrated Solar Power (CSP) tower systems require receiver materials capable of operating above 1000 °C to meet the efficiency targets of third-generation technologies (25–30%). Hybrid solutions, combining ceramic coatings with metallic substrates, offer promising thermomechanical stability under severe thermal cycling. This study investigates the high-temperature behavior of silicon carbide (SiC) coatings deposited on Fe-C-Al-Mo alloys under concentrated solar flux. Substrates were pre-oxidized to form a continuous 1–2 µm α-Al₂O₃ interlayer, serving as a chemical and mechanical buffer. SiC coatings (10–24 µm thick) were deposited via High-Temperature Chemical Vapor Deposition (HT-CVD). Characterization using XRD, SEM, EDS, and optical spectrophotometry identified cubic 3C-SiC with a globular microstructure and high compressive residual stresses (−2000 to −2400 MPa), inducing microcracking. Stress relaxation was achieved by increasing coating thickness or post-deposition annealing. Controlled oxidation formed a thin silica layer, enhancing solar absorptivity to over 90%. Accelerated thermal cycling (up to ~900 kW/m², 1050–1200 °C) revealed that coating stability depends on SiC thickness, residual stress evolution, α-Al₂O₃ interlayer thickness, and cycling severity. Optimizing these parameters is essential for ensuring the long-term durability of hybrid CSP receivers. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article

Gross primary production (GPP) models are widely used to estimate carbon fluxes at local and global scales, and play a crucial role in understanding the dynamics of terrestrial carbon cycling. While numerous studies have compared the performance of various GPP models, most evaluations rely on in situ GPP derived from eddy covariance flux towers, which may be constrained by estimation uncertainties and limited spatial representativeness. In this study, we employed a three-dimensional (3D) simulation framework characterized by high accuracy and strong environmental controllability to evaluate the performance of GPP models of varying complexity (FvCB, MOD17, VPM, and MVPM) under different leaf area index (LAI) levels and environmental stress conditions. The results revealed significant differences among the models at both instantaneous and daily scales. Under high-temperature stress, the performance of VPM was most comparable to that of MOD17. FvCB and MOD17 exhibited strong consistency in their sensitivity to environmental variations, whereas MVPM generally produced lower GPP estimates but showed the highest responsiveness to environmental changes. The process-based FvCB model was the most sensitive to canopy structure and light distribution, and its resilience to environmental stress increased with LAI. These findings provide a novel methodological perspective for evaluating GPP models and offer important insights into the structural and mechanistic factors driving performance differences among the models. Full article

Urban wetlands on the Qinghai–Tibetan Plateau are increasingly recognized as potentially important components of city-scale carbon budgets; however, their CO₂ flux dynamics and associated environmental drivers remain insufficiently quantified, particularly under high-altitude urban conditions. In this study, we addressed this knowledge gap by conducting continuous eddy covariance observations at Haihu Wetland Park in Xining City, China. Carbon fluxes were monitored throughout 2023 using the Huangshui Park Station flux tower. We quantified the temporal dynamics of gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem exchange (NEE), and systematically assessed their responses to key environmental drivers across multiple temporal scales. GPP and Re exhibited unimodal seasonal patterns, with substantially higher values during the growing season. NEE showed pronounced diel cycling, with nighttime CO₂ release and daytime uptake, and shifted seasonally between net source and net sink states. At the daily scale (n = 365), Pearson correlations showed that air temperature (T_a), 5 cm soil temperature (T_s5) and volumetric soil water content (SWC) exhibited the strongest associations with the flux components, whereas photosynthetic photon flux density (PPFD) showed moderate associations and precipitation was weak. At the monthly scale (n = 12), Mantel tests further highlighted a dominant thermal control on GPP and Re (T_a and T_s5), whereas precipitation showed additional associations with Re and NEE. Overall, the ecosystem acted as a net CO₂ sink in 2023 (annual NEE = −292.25 g C m⁻² yr⁻¹ under our sign convention), with uptake concentrated in the first eight months of the year. Under the combined effects of multiple environmental factors, plateau urban wetlands functioned as a strong carbon sink, and the results of this study provide a data basis for improving the accuracy of carbon budget estimates for this type of ecosystem. Full article

Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. In this study, we first improved the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model by replacing the relative humidity-based soil moisture constraint with the land surface water index (LSWI), aiming to enhance model performance in water-limited environments. Second, we developed a Crop Unmixing and Weight Fusion Model for ET (CUWFM) to generate daily ET products at a 30 m spatial resolution by integrating high-resolution but infrequent PT-JPL-ET data with coarse-resolution but frequent PML-V2-ET data. The CUWFM employs a hybrid approach combining sub-pixel crop fraction decomposition with similarity-weighted regression, allowing for more accurate ET estimation over heterogeneous agricultural landscapes. The proposed methods were evaluated in the Changji region of Xinjiang, China, using field-measured ET data from two-flux-tower sites. The results show that the improved PT-JPL model increased ET estimation accuracy compared with the original version, with higher R² and Nash–Sutcliffe efficiency (NSE), and lower root mean square error (RMSE). The CUWFM outperformed benchmark spatiotemporal fusion methods, including STARFM, ESTARFM, and Fit-FC, in both pixel- and field-scale assessments, achieving the highest overall performance scores based on the All-round Performance Assessment (APA) framework. This study demonstrates the potential of integrating vegetation indices and crop-specific spatial decomposition into ET modeling, providing a feasible pathway for producing high spatiotemporal resolution ET datasets to support precision agriculture in arid and semi-arid regions. Full article

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration ($ET$) being a key ecohydrological process. However, monitoring $ET$ over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of $ET$ remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based $ET$ models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on $ET$ patterns. Our results showed a good agreement between modeled and observed $ET$ using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on $ET$, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor $ET$ in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions. Full article

Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R² up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R² ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article

Quantifying the time and space scale variability in air–sea fluxes is challenging. This study adopts tower-based in situ observations in the northern South China Sea (SCS) to evaluate widely used reanalysis and CO₂ flux products. For heat and momentum fluxes, three reanalysis products were considered: the fifth-generation European Centre for Medium-Range Weather Forecast reanalysis (ERA5), the NCEP Climate Forecast System Version 2 reanalysis (CFSv2), and third-generation Japanese Meteorological Agency reanalysis (JRA55). Comparisons of surface state variables show that these three reanalysis products generally agree well with observations on both the daily and monthly scales. On the daily scale, the correlation coefficients between observations and ERA5 exceed 0.93 for wind, air temperature, relative humidity, and longwave radiation. On the monthly scale, seasonal variations in wind, air temperature, and relative humidity are well captured. Nevertheless, the three reanalysis products all overestimate (underestimate) the latent (sensible) heat flux, with a root mean square error above 90.50 (33.35) ${\mathrm{W}/\mathrm{m}}^2$. For momentum fluxes, the three reanalysis datasets tend to underestimate 0.07∼0.08 ${\mathrm{N}/\mathrm{m}}^2$ with a high correlation coefficient above 0.71. In terms of CO₂ fluxes, the Multi-observation Carbon Assimilation System (MCAS), Surface Ocean CO₂ Atlas (SOCAT), and Global ObservatioN-based system for monitoring Greenhouse GAs (GONGGA) inversion CO₂ flux datasets were evaluated. SOCAT performs best with a correlation coefficient of 0.75, and GONGGA follows with 0.64, while MCAS demonstrates the lowest performance with a value of 0.36. In addition, the spatial patterns of the monthly mean surface CO₂ flux in the northern SCS illustrate significant discrepancies between MCAS, SOCAT, and GONGGA. These results can provide valuable insights for reducing uncertainties in air–sea flux products over coastal areas in the future. Full article

Evapotranspiration (ET) plays a vital role in understanding water and energy cycles in forest ecosystems, particularly in tropical regions where rubber plantations are widespread. In this study, a rubber plantation system was used. By combining meteorological data from flux towers and 30 periods of Landsat-8 image data, we estimated the daily ET of a rubber plantation from 2022 to 2024 using the Surface Energy Balance System (SEBS) model. Additionally, the study employed the eddy covariance method to validate the accuracy of the daily average ET estimated by the SEBS model in different source areas, in order to explore the model’s applicability. Simultaneously, we examined the key drivers influencing ET in rubber plantations by analyzing meteorological factors and physiological growth indicators. The results indicated that the SEBS model exhibited the highest estimation accuracy (R² = 0.90, RMSE = 0.43 mm, RE = 15.23%) for the rubber plantation ET in the region 1.5 km away from the flux tower, and the retrieval accuracy of 30 periods of ET was higher (RMSE ≤ 1 mm, RE ≤ 46.84%), indicating that the SEBS model was well-suited for estimating ET in rubber plantations. From 2022 to 2024, the daily average and monthly cumulative ET showed a unimodal distribution, with high summer and low winter values; the average monthly accumulated ET during the wet season (102.75 mm) was found to be significantly greater than that during the dry season (50.61 mm). On the daily and monthly scales, the correlation between atmospheric pressure, temperature, and ET was the most significant. These findings enhance our understanding of rubber plantation water use patterns and support the application of remote sensing models for regional water resource management, offering valuable insights for optimizing irrigation strategies and ensuring sustainable rubber production in tropical regions. Full article

With advancements in solar-induced fluorescence (SIF) observation technology and the evolution of vegetation radiative transfer models, SIF signals can now be more effectively interpreted and leveraged from a mechanistic perspective. This, in turn, facilitates a deeper understanding of the mechanistic link between SIF and photosynthesis. Considering the impact of water stress on terrestrial ecosystems, this paper simulated SIF and gross primary productivity (GPP) values using the STEMMUS-SCOPE model at half-hour scales from 2017 to 2023 at the Daman site. The simulation results were compared and validated against flux tower observations and SCOPE model outputs. Taking advantage of irrigation events in the semi-arid irrigated farmland, we assessed the accuracy of STEMMUS-SCOPE in simulating SIF and GPP under drought stress, as well as its capability to quantitatively analyze the impacts of water stress on SIF and GPP. The results show that the accuracy of the SIF and GPP values simulated by the STEMMUS-SCOPE model is higher than that of the SCOPE model. The averaged R² and RMSE between the SIF simulated by STEMMUS-SCOPE model and the observed SIF values are 0.66 and 0.29 mW m⁻² nm⁻¹, and the averaged R² and RMSE between the GPP simulated by the STEMMUS-SCOPE model and the observed GPP values from 2017 to 2023 are 0.88 and 4.93 µmol CO₂ m⁻² s⁻¹, respectively. Especially under relatively drought conditions, the R² between the SIF simulated values and observed values is 0.84, and the R² between the GPP simulated values and observed values is 0.96. By further combining soil moisture content (SMC) and canopy conductance (Gs) analyses, we found that the response of the STEMMUS-SCOPE simulations under water stress was consistent with previous findings on the impacts of water deficits, thereby confirming the model’s reliability for drought conditions. Under drought stress, the decline in fluorescence emission efficiency (ΦF) with decreasing Gs and SMC was smaller than that of the light use efficiency (LUE). Therefore, the STEMMUS-SCOPE model is promising for investigating the SIF–GPP relationship under drought stress. Full article

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software. Full article

Accurate quantification of regional ET is essential for agricultural water management. Upscaling methods based on flux tower observations have been widely applied in large-scale ET estimation. However, the coarse spatial resolution of existing upscaling approaches limits their utility in field-scale management. Therefore, this study proposes an integrated upscaling framework that combines data fusion and machine learning, enabling spatiotemporally continuous ET estimation at the field scale (30 m × 30 m). First, daily 30 m resolution land surface temperature (LST) and vegetation indices were generated by fusing MODIS, Landsat, and China Land Data Assimilation System (CLDAS) datasets. These variables, along with meteorological data and the footprint model, were used as inputs for machine learning. The upscaled ET was evaluated under varying surface heterogeneity using optical-microwave scintillometers (OMS). The results show that a one-dimensional convolutional neural network (1D CNN) using both remote sensing and meteorological data performed best in relatively homogeneous croplands, achieving a correlation coefficient (R) of 0.90, a bias of −0.14 mm/d, a mean absolute error (MAE) of 0.46 mm/d, and a root mean square error (RMSE) of 0.66 mm/d. In contrast, for heterogeneous urban-agricultural landscapes, the 1D CNN using only remote sensing data outperformed other models, with R, bias, MAE, and RMSE of 0.93, −0.14 mm/d, 0.66 mm/d, and 0.88 mm/d, respectively. Furthermore, SHapley Additive exPlanations (SHAP) revealed that LST and the two-band enhanced vegetation index (EVI2) were the most influential drivers in the models. The framework successfully enables ET modeling and spatial extrapolation in heterogeneous regions, providing a foundation for precision water resource management. Full article