Search Results (348)

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

Invasive plant species pose an increasing threat to mangroves globally. This study assessed the impact of Paspalum vaginatum invasion on carbon loss and early recovery following four years of restoration in a mangrove forest with Rhizophora racemosa in Benin. Organic carbon was quantified in the total biomass, including both aboveground and belowground components, as well as in the soil to a depth of −50 cm. In addition, soil gas fluxes of CO₂, CH₄, and N₂O were measured. Three sites were evaluated: a conserved mangrove, a site degraded by P. vaginatum, and the same site post-restoration via hydrological rehabilitation and reforestation. Invasion significantly reduced carbon storage, especially in soil, due to lower biomass, incorporation of low C/N ratio organic residues, and compaction. Restoration recovered 7.8% of the total biomass carbon compared to the conserved mangrove site, although soil organic carbon did not rise significantly in the short term. However, improvements in deep soil C/N ratios (15–30 and 30–50 cm) suggest enhanced soil organic matter recalcitrance linked to R. racemosa reforestation. Soil CO₂ emissions dropped by 60% at the restored site, underscoring restoration’s potential to mitigate early carbon loss. These results highlight the need to control invasive species and suggest that restoration can generate additional social benefits. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

As climate change intensifies the frequency and magnitude of extreme weather events, such as storm surges, understanding how extreme weather events alter mangrove carbon dynamics is critical for predicting the resilience of blue carbon ecosystems under climate change. Mangrove forests are generally recognized for their resilience to natural disturbances, a characteristic largely attributed to the evolutionary development of species-specific functional traits. However, limited research has explored the impacts of storm surges on carbon flux dynamics in both natural and restored mangrove ecosystems. In this study, we analyzed short-term responses of storm surges on carbon dioxide flux and methane flux in natural and restored mangroves. The results revealed that following the storm surge, CO₂ uptake decreased by 51% in natural mangrove forests and increased by 20% in restored mangroves, while CH₄ emissions increased by 14% in natural mangroves and decreased by 22% in restored mangroves. GPP is mainly driven by PPFD and negatively affected by VPD and RH, while Reco and CH₄ flux respond to a combination of temperature, humidity, and hydrological factors. NEE is primarily controlled by GPP and Reco, with environmental variables acting indirectly. These findings highlight the complex, site-specific pathways through which extreme events regulate carbon fluxes, underscoring the importance of incorporating ecological feedbacks into coastal carbon assessments under climate change. Full article

Land surface models (LSMs) play a crucial role in climate prediction and carbon cycle assessment. To ensure their reliability, it is crucial to evaluate their performance in simulating key processes, such as evapotranspiration (ET) and gross primary productivity (GPP), across various temporal scales and vegetation types. This study systematically evaluates the performance of the newly modernized Noah-MP LSM version 5.0 in simulating water and carbon fluxes, specifically ET and GPP, across temporal scales ranging from half-hourly (capturing diurnal cycles) to annual using observational data from 105 sites within the globally FLUXNET2015 dataset. The results reveal that Noah-MP effectively captured the overall variability of both ET and GPP, particularly at short temporal scales. The model successfully simulated the diurnal and seasonal cycles of both fluxes, though cumulative errors increased at the annual scale. Diurnally, the largest simulation biases typically occurred around noon; while, seasonally, biases were smallest in winter. Performance varied significantly across vegetation types. For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. Conversely, GPP simulations were most accurate for wetlands and closed shrublands, showing the largest deviation for evergreen broadleaf forests. Furthermore, an in-depth analysis stratified by the climate background revealed that ET simulations failed to capture inter-annual variability in the temperate and continental zones, while GPP was severely overestimated in arid and temperate climates. This study identifies the strengths and weaknesses of Noah-MP in simulating water and carbon fluxes, providing valuable insights for future model improvements. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

Wildfires play a critical role in boreal forest ecosystems, yet their increasing frequency poses significant challenges for carbon emissions, ecosystem stability, and fire management. Accurate burned area detection is essential for assessing post-fire landscape recovery and fire-induced carbon fluxes. This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. The dataset was randomly split into training (70%), validation (15%), and testing (15%) sets for model development and assessment. Combined labels were used due to class imbalance, and the model performance was assessed using kappa and the F1-score. Among the ML techniques tested, deep learning (DL) with a Multi-Layer Perceptron (MLP) outperformed Support Vector Machines (SVMs) and Random Forest (RF) by demonstrating superior classification accuracy in detecting burned area. It achieved an F1-score of 0.89 for burned pixels, confirming its potential for improving the long-term wildfire monitoring and management in boreal forests. Despite the computational demands of processing large-scale remote sensing data at 250 m resolution, the MLP modeling approach that we used provides an efficient, effective, and scalable solution for long-term burned area detection. These findings underscore the importance of tuning both network architecture and regularization parameters to improve the classification of burned pixels, enhancing the model robustness and generalizability. Full article

Headwater streams serve as a crucial link between forest and downstream aquatic ecosystems and also act as crucial agents in carbon (C) and nutrient storage and flux. These aquatic systems play a pivotal role in regulating biogeochemical cycles. Plant litter is an important contributor of nutrients to headwater streams, having significant impacts on downstream ecosystems. However, current research predominantly focuses on the dynamics of plant litter C and nutrients such as nitrogen and phosphorus, and we know little about those of nutrients such as sodium (Na). In this study, we conducted a comprehensive evaluation of the annual dynamics of plant litter Na storage within a subtropical headwater stream. This study took place over a period of one year, from March 2021 to February 2022. Our results showed that (1) the average annual concentration and storage of litter Na was 538.6 mg/kg and 2957.6 mg/m², respectively, and litter Na storage exhibited a declining trend from stream source to mouth, while demonstrating significantly higher values during the rainy season compared to the dry season; (2) plant litter type had significant impacts on Na concentration and storage, with leaf, twig, and fine woody debris accounting for the majority of litter Na storage; and (3) hydrological (precipitation, discharge) and physicochemical (water temperature, flow velocity, pH, dissolved oxygen, alkalinity) factors jointly affected Na storage patterns. Overall, the results of this study clearly reveal the dynamic characteristics of Na storage in plant litter in a subtropical forest headwater stream, which contributes to a more comprehensive understanding of the role of headwater streams in nutrient cycling and the dynamic changes of nutrients along with hydrological processes. This research will enhance our predictive understanding of nutrient cycling at the watershed scale. Full article

The net ecosystem CO₂ exchange (NEE) of spruce forest ecosystems is poorly understood by the lack of measurements of CO₂ in the Qilian Mountain of Western China. Thus, we conducted consecutive measurements of CO₂ fluxes using tower-based the eddy covariance method from 2021 to 2022. These results indicated that daily NEE of spruce forest indicated a robust temporal pattern ranging from −28.43 to 29.62 g C m⁻² from 2021 to 2022. Remarkable carbon sink characteristics were presented from late May to late September. Month accumulative NEE fluxes ranged from −336.57 to 142.22 g C m⁻² in two years. Additionally, average carbon sink was 591.51 ± 37.41 g C m⁻² in Qilian Mountain. NEE was negatively driven by vapor pressure deficit (VPD) and average air temperature (p < 0.05), as determined using the structural equation model. However, the direct effect coefficient of precipitation on NEE was weak. VPD was positively driven by air temperature and negatively determined by precipitation. In conclusion, a future warming scenario would significantly decrease the carbon sink of the spruce forest in Qilian Mountain. Full article

Forest–atmosphere interactions through mass and energy fluxes significantly influence climate processes. However, due to anthropogenic actions, native Araucaria forests in southern Brazil, part of the Atlantic Forest biome, have been drastically reduced. This study quantifies CO₂ and energy flux contributions from each forest stratum to improve understanding of surface–atmosphere interactions. Eddy covariance data from November 2009 to April 2012 were used to assess fluxes in an Araucaria forest in Paraná, Brazil, across the ecosystem, understory, and overstory strata. On average, the ecosystem acts as a carbon sink of −298.96 g C m⁻² yr⁻¹, with absorption doubling in spring–summer compared to autumn–winter. The understory primarily acts as a source, while the overstory functions as a CO₂ sink, driving carbon absorption. The overstory contributes 63% of the gross primary production (GPP) and 75% of the latent heat flux, while the understory accounts for 94% of the ecosystem respiration (RE). The energy fluxes exhibited marked seasonality, with higher latent and sensible heat fluxes in summer, with sensible heat predominantly originating from the overstory. Annual ecosystem evapotranspiration reaches 1010 mm yr⁻¹: 60% of annual precipitation. Water-use efficiency is 2.85 g C kgH₂O⁻¹, with higher values in autumn–winter and in the understory. The influence of meteorological variables on the fluxes was analyzed across different scales and forest strata, showing that solar radiation is the main driver of daily fluxes, while air temperature and vapor pressure deficit are more relevant at monthly scales. This study highlights the overstory’s dominant role in carbon absorption and energy fluxes, reinforcing the need to preserve these ecosystems for their crucial contributions to climate regulation and water-use efficiency. Full article

Global warming is driven by the increasing atmospheric emissions of greenhouse gases. Soils are highly sensitive to climate change and can shift from being carbon reservoirs to carbon sources under warmer and wetter conditions. This study is the first to simultaneously measure trace gas fluxes in Euterpe oleracea (açaí) plantations in upland areas, contrasting them with floodplain areas managed for açaí production in the eastern Amazon. Flux measurements were conducted during both the rainy and dry seasons using the closed dynamic chamber technique. In upland areas, CO₂ fluxes exhibited spatial (plateau vs. lowland) and temporal (hourly, daily, and seasonal) variations. During both the rainy and dry months, CH₄ uptake in upland soils was higher in lowland areas compared to the plateau. When comparing the two ecosystems, upland areas emitted more CO₂ during the rainy season, while floodplain areas released more CH₄ into the atmosphere. Unexpectedly, during the dry season, floodplain soils produced more CO₂ and captured more CH₄ from the atmosphere compared to upland soils. In upland areas, CO₂-equivalent production reached 59.1 Mg CO₂-eq ha⁻¹ yr⁻¹, while in floodplain areas, it reached 49.3 Mg CO₂-eq ha⁻¹ yr⁻¹. Soil organic matter plays a vital role in preserving water and microorganisms, enhancing ecosystem productivity in uniform açaí plantations and intensifying the transfer of CH₄ from the atmosphere to the soil. However, excessive soil moisture can create anoxic conditions, block gas diffusion, reduce soil respiration, and potentially turn the soil from a sink into a source of CH₄. Full article

Soil respiration (Rs) is a significant contributor to the global carbon cycle, with its two main sources—microbial (heterotrophic, Rh) and plant root (autotrophic, Ra) respiration—being sensitive to various environmental factors. This study investigates the impact of ecosystem disturbances (Ds), including fire, biogenic (insects and pathogens), and harvesting, on soil respiration in Russia’s forest ecosystems. We introduced response factors to account for the effects of these disturbances on Rh over three distinct stages of ecosystem recovery. Our analysis, based on data from case studies, remote sensing data, and the national forest inventory, revealed that Ds increase Rh by an average of 2.1 ± 3.2% during the restoration period. Biogenic disturbances showed the highest impacts, with average increases of 16.5 ± 3.2%, while the contributions of clearcuts and wildfires were, on average, less pronounced—2.0 ± 3.1% and 0.8 ± 3.3%, respectively. These disturbances modify forest soil dynamics by affecting soil temperature, moisture, and nutrient availability, influencing carbon fluxes over varying timescales. This research underscores the role of ecosystem disturbances in altering soil carbon dynamics and highlights the need for improved data and monitoring of forest disturbances to reduce uncertainty in soil carbon flux estimates. Full article

The temperate evergreen needleleaf forest (ENF), primarily composed of Mongolian Scots pine (Pinus sylvestris var. mongolica), plays a pivotal role in the “The Great Green Wall” Shelterbelt Project in northern China as a major species for windbreak and sand fixation. Solar-induced chlorophyll fluorescence (SIF) has emerged as a revolutionary remote sensing signal for quantifying photosynthetic activity and gross primary production (GPP) at the ecosystem scale. Meanwhile, eddy covariance (EC) technology has been widely employed to obtain in situ GPP estimates. Although a linear relationship between SIF and GPP has been reported in various ecosystems, it is mainly derived from satellite SIF products and flux-tower GPP observations, which are often difficult to align due to mismatches in spatial and temporal resolution. In this study, we analyzed synchronous high-frequency SIF and EC-derived GPP measurements from a Mongolian Scots pine plantation during the seasonal transition (August–December). The results revealed the following. (1) The ENF acted as a net carbon sink during the observation period, with a total carbon uptake of 100.875 gC·m⁻². The diurnal dynamics of net ecosystem exchange (NEE) exhibited a “U”-shaped pattern, with peak carbon uptake occurring around midday. As the growing season progressed toward dormancy, the timing of CO₂ uptake and release gradually shifted. (2) Both GPP and SIF peaked in September and declined thereafter. A strong linear relationship between SIF and GPP (R² = 0.678) was observed, consistent across both diurnal and sub-daily scales. SIF demonstrated higher sensitivity to light and environmental changes, particularly during the autumn–winter transition. Cloudy and rainy conditions significantly affect the relationship between SIF and GPP. These findings highlight the potential of canopy SIF observations to capture seasonal photosynthesis dynamics accurately and provide a methodological foundation for regional GPP estimation using remote sensing. This work also contributes scientific insights toward achieving China’s carbon neutrality goals. Full article

Integrating green spaces into urban designs and planning for ecosystem services has become vital; however, in creating these spaces, the growth phase is often overlooked. This study provides insight into the changing energy and carbon dioxide (CO₂) fluxes in a developing forest, “The Forestias” project in Thailand. The eddy covariance technique was applied to determine real-time surface energies and CO₂ fluxes from December 2021 to September 2023. The results suggest that under fast growing conditions of the green areas, the diurnal latent energy flux corresponded with the area gained. This effect was supported by increasing evapotranspiration through the byproduct of canopy gas exchange. Consequently, the influence of green areas on lowering the average ambient temperature compared with the urban non-green surroundings was observed. In terms of CO₂ flux dynamics, the increasing efficacy of photosynthesis was parallel with the growing forest canopy. Changes in flux dynamics due to urban green areas show their potential as a mitigation tool for moderating ambient air temperatures. Moreover, they can serve as a carbon sink within tropical cities and provide a pivotal contribution in reaching carbon neutrality. Full article

Anthropogenic activities (represented by dams and land use change) and climate change have disrupted the delicate balance between natural and anthropogenic factors affecting riverine material transport, yet their effects across different river basins remain underexplored. This study investigated multi-decade (1980–2023) variations in sediment and particulate carbon (C), nitrogen (N), and phosphorus (P) exports from the Jinsha (JSR) and Jialing River (JLR) basins, two cascading developmental river systems in Southwestern China, and evaluated the cumulative impacts of land use change and dam construction. The results revealed significant decreases in particulate fluxes from both basins, despite stable water discharge. Particulate material fluxes declined by 90.9–99.6% in the JSR (last decade vs. 1980–1989, with an abrupt change occurring during 2002–2003) and by 54.0–79.3% in the JLR (with an abrupt change occurring in 1994). Over time, the influence of precipitation and water discharge on material transport has diminished, whereas land use change and dams have become increasingly dominant. Key drivers include forest expansion, increased impervious surfaces, reservoir construction, and reductions in grassland and farmland; however, there are spatial differences in the relative importance of these drivers. This study provides crucial insights for decision making on regional ecological conservation and cascading development. Full article