Search Results (118)

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. 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

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies. Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

Surface all-wave net radiation ($R_n$) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of $R_n$ from satellite data is gaining more and more attention. In order to obtain the daily $R_n$ ($R_{nd}$) from the instantaneous satellite observations, a parameter $C_d$, which is defined as the ratio between the $R_n$ at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for $C_d$ estimation, namely New Model, was proposed based on the comprehensive clear-sky $R_n$ measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate $C_d$ at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the $R_{nd}$ obtained by multiplying $C_d$ from New Model had the best accuracy, yielding an overall $R^2$ of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model $C_d$ estimate was applied with MODIS data to calculate $R_{nd}$. After validation, the $R_{nd}$ computed from $C_d$ was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with $R_{nd}$-validated $R^2$ of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new $C_d$ model only works for clear-sky, it has the strong potential to be used in estimating $R_{nd}$ from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (D_e) advanced markedly across the entire plateau (0.29 days yr⁻¹, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (D_o) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of D_e variability stems from increased net shortwave radiation (+0.38 Wm⁻² yr⁻¹) and rising temperatures (+0.06 °C yr⁻¹) during the melting season, with net shortwave radiation exerting stronger control (R² = 0.73) than temperature (R² = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article

Hallux valgus is a common foot deformity. Traditional diagnosis mainly relies on X-ray images, which present radiation risks and require professional equipment, limiting their use in daily screening. In addition, in large-scale community screenings and resource-limited regions, where rapid processing of numerous patients is required, access to radiographic equipment or specialists may be constrained. Therefore, this study improves the MobileNetV3 model to automatically determine the presence of hallux valgus from digital foot photographs. In this study, we used 2934 foot photos from different organizations, combined with the segment anything model (SAM) to extract foot regions and replace the photo backgrounds to simulate different shooting scenarios, and used data enhancement techniques such as rotations and noise to extend the training set to more than 10,000 images to improve the diversity of the data and the model’s generalization ability. We evaluated several classification models and achieved over 95% accuracy, precision, recall, and F1 score by training the improved MobileNetV3. Our model offers a cost-effective, radiation-free solution to reduce clinical workload and enhance early diagnosis rates in underserved areas. Full article

This study aimed to understand the difference in forest transpiration (T) between slope positions and to separate the contributions of main influencing factors to improve the accuracy of forest transpiration estimation at the slope scale by up-scaling the results measured at the plot scale, especially in semiarid regions with significant soil moisture differences along slope positions. Two plots of larch plantation were established, one at the lower position and another at the upper position of a northwest-facing slope in the semiarid area of the Liupan Mountains in northwest China. The sap flow velocity ($J_S$, mL·cm⁻²·min⁻¹) of sample trees, meteorological parameters in the open field, and soil water potential in the main root zone (0–60 cm) were monitored simultaneously in the growing season (from July to September) of 2015. However, only the transpiration data of 59 selected effective days were used, after excluding the days with rainfall and missing data. Based on the relative sap flow velocity (the ratio of instantaneous sap flow velocity to its daily peak value), the impacts of terrain shading and soil water potential on sap flow velocity at varying slope positions were quantitatively disentangled. The reduction in $J_S$ at the lower slope plot, attributed to terrain shading, exhibited a positive linear correlation with solar radiation intensity. Conversely, the $J_S$ reduction at the upper slope plot demonstrated a quadratic functional relationship with the differential in soil water potential between the two plots. Subsequently, employing the relationship whereby transpiration is equivalent to the product of sap flow velocity and sapwood area, we conducted a quantitative analysis of the contributions of soil water potential, sapwood area, terrain shading, and their interaction to the disparity in transpiration between the two slope positions. The total transpiration of the 59 effective days was 41.91 mm at the lower slope plot, slightly higher than that at the upper slope plot (37.38 mm), indicating a small difference (4.53 mm) due to the offsetting effects of multiple factors. When taking the upper slope plot as a reference, the plot difference in soil water potential increased the total transpiration for the 59 days at the lower slope plot by 16.40 mm, while the differences in sapwood area and terrain shading and the interaction of the three factors decreased the total transpiration at the lower slope plot by 6.61, 2.86, and 2.40 mm, respectively, making a net increase of 4.53 mm. Based on the pilot study under given conditions of location, soil, climate, and vegetation, the contributions of the influencing factors to the stand transpiration differences between the upper and lower slopes are as follows: soil moisture (soil water potential) > stand structure (sapwood area) > solar radiation (terrain shading) > interaction of all factors. All these impacts should be considered for the accurate prediction of forest transpiration at the slope scale through up-scaling from measurement at the plot scale, especially in semiarid regions. Full article

The amount of irrigation needed can be determined using reference evapotranspiration (ETo), the crop coefficient (Kc), and the water deficit index. Reference evapotranspiration is typically calculated utilizing the Penman–Monteith (PM) model, which necessitates various meteorological parameters, including temperature, humidity, net radiation, and wind speed. In regions where meteorological stations are absent, alternative methods must be employed to estimate these parameters. This study employs a combination of estimated meteorological parameters derived from different methodologies to calculate both reference evapotranspiration and irrigation rates, subsequently evaluating the results through wheat irrigation experiments. The daily irrigation rate for the T1 treatment was computed using real-time meteorological data, resulting in the highest grain yield of 561.73 g/m² and an irrigation water use efficiency of 7.61 kg/m³. The irrigation rate for the T2 treatment was determined based on real-time net radiation alongside monthly average values of temperature, humidity, and wind speed. In comparison to T1, the irrigation amount, yield, and irrigation water use efficiency for T2 decreased by 1.59%, 2.96%, and 1.42%, respectively. For the T3 treatment, the irrigation amount was calculated using monthly average values of temperature, humidity, and wind speed, with net radiation derived from daily light duration. The yield for T3 decreased by 19.4% relative to T1, the irrigation amount decreased by 12.95% relative to T1, and the irrigation water use efficiency decreased by 7.45% relative to T1. In the case of the T4 treatment, monthly average values of temperature, humidity, and wind speed were utilized, while net radiation was calculated using the Hargreaves–Samani (HS) model in conjunction with real-time temperature data. The yield for T4 decreased by 8.75% relative to T1, the irrigation amount decreased by 5.58% relative to T1, and the irrigation water use efficiency decreased by 3.39% relative to T1. For the T5 treatment, similar monthly average values were employed, and net radiation was calculated using HS methodology combined with monthly average temperature data. The yield for T5 decreased by 11.96% relative to T1, the irrigation amount decreased by 6.07% relative to T1, and the irrigation water use efficiency decreased by 6.3% relative to T1. Furthermore, the yield for the CK treatment under conventional irrigation decreased by 20.89% compared to T1, while the irrigation amount increased by 1.57% compared to T1 and the irrigation water use coefficient decreased by 22.14% compared to T1. Above all, this article posits that in areas lacking meteorological stations, monthly mean meteorological data should be utilized for parameters such as temperature, humidity, and wind speed, while the HS model is recommended for calculating net radiation. Full article

Alpine meadows are vital ecosystems on the Qinghai–Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (R_n), air temperature, vapor pressure deficit (VPD), wind speed (U), and soil water content (SWC) influence sensible heat flux (H) and latent heat flux (LE). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023. The annual average daily R_n was 85.29 W m⁻², with H, LE, and G accounting for 0.56, 0.71, and −0.32 of R_n, respectively. Results show that R_n is the main driver of both H and LE, highlighting its crucial role in turbulent flux variations. Additionally, a negative correlation was found between air temperature and H, suggesting that high temperatures may suppress H. A significant positive correlation was observed between soil moisture and LE, further indicating that moist soil conditions enhance LE. In conclusion, this study demonstrates the impact of climate change on energy distribution in alpine meadows and calls for further research on the ecosystem’s dynamic responses to changing climate conditions. Full article

Investigating the carbon flux in orchard ecosystems is crucial for assessing agroecosystem productivity and optimizing management practices. We measured and estimated carbon fluxes (gross primary productivity, GPP; ecosystem respiration, R_e; and net ecosystem exchange, NEE) and environmental variables in a seven-year-old kiwifruit orchard over two years. Our results showed that diurnal carbon fluxes exhibited bell-shaped patterns, peaking between 12:30 and 15:30. Daily carbon fluxes exhibited a seasonal trend, characterized by an increase followed by a decrease. The average daily GPP, R_e, and NEE values were 6.77, 4.99, and −1.79 g C m⁻² d⁻¹ in 2018, and 5.88, 4.78, and −1.10 g C m⁻² d⁻¹ in 2019, respectively. The orchard sequestered −444.25 g C m⁻² in 2018 and −285.77 g C m⁻² in 2019, which accounted for 26.4% and 18.6% of GPP, respectively. Diurnal GPP and NEE were significantly influenced by photosynthetically active radiation (PAR), with direct path coefficients of 0.75 and 0.88 (p < 0.01), while air temperature (T_a) significantly affected GPP and NEE through PAR, with an indirect path coefficient of 1.12 for both. PAR had a similar effect on daily GPP and NEE, while both were indirectly influenced by soil temperature (T_s) at a 5 cm depth and vapor pressure deficit (VPD). R_e was primarily impacted by VPD, with a direct path coefficient of 0.64 (p < 0.01), while T_a and the concentration of air carbon dioxide (CCO₂) significantly affected GPP through VPD, with indirect path coefficients of 0.82 and −0.80. The leaf area index (LAI) and soil water content (SWC) at a 20 cm depth exhibited a significant correlation with carbon fluxes during the vigorous growing period. Full article

Forest carbon exchange is affected by various environmental variables, among which photosynthetically active radiation, temperature, saturated water vapor pressure deficit, and soil moisture content dominate. The global atmospheric temperature has risen significantly in recent decades, and the saturated water vapor pressure deficit has also increased, which has had a widespread and lasting impact on terrestrial carbon sinks. Here, using flux data from Mazongling in Jinzhai County from July 2020 to June 2023, the relationship between saturated water vapor pressure deficit and forest carbon flux was investigated on the basis of carbon flux changes in the forest ecosystem in response to environmental factors. Results revealed that vapor pressure deficit (VPD) and net ecosystem productivity (NEP) exhibited a quadratic relationship at the daily and monthly scales. When the VPD was greater than 1.2 kPa at the monthly scale, the NEP of the fir forest ecosystem decreased with increasing VPD. At the daily scale, the impact of the VPD on NEP was studied by month and season. The results revealed that the threshold value at which the VPD affected NEP differed across different months and seasons. Therefore, the VPD is an important factor in forest ecosystems and should be considered in the assessment of ecosystem carbon sinks. It also has far-reaching significance in the carbon cycle and ecological sustainable development. Full article

Urban areas present different climates from those of their rural surroundings. Energy balance and especially energy storage play important roles in the resulting climate since more than 50% of the net radiation is absorbed in densely urbanized areas. A simple model of energy storage in the urban fabric is presented, fed with the air temperature of a given area and its thermal properties; it can estimate heat storage (Q_S) with good results. The proposed Q_ST energy storage model was compared with Q_S results obtained from two sites in Mexico City, Escandón (objective hysteresis model, Q_SOHM) and ENP7 (residual, Q_SRES), and one site in Marseille, France (thermal mass scheme, Q_STMS). The comparison between Q_ST and Q_SOHM showed that r² = 0.71, while in ENP7 (Q_N > 0), Q_SRES and Q_ST were 116.5 Wm⁻² and 142 Wm⁻², corresponding to 49% and 60% of the Q_N, respectively. The results obtained using the Q_STMS model in Marseille and Q_ST in Escandón showed similar behavior, with RMSEs of 109 and 101 Wm⁻², respectively, in a daily period. Although it is difficult to discern which method is better, it is possible to affirm that the presented method is quite accurate, easier to use, and inexpensive. Full article