Journal of Geophysical Research: Biogeosciences最新文献

We evaluate the use of clumped isotopes of methane (CH₄) to fingerprint local atmospheric sources of methane. We focus on a regenerative stormwater conveyance (RSC) stream wetland site running through the University of Maryland campus, which emits methane due to its engineering. Air samples in the RSC were collected at different heights above the surface and at different times of the day including both early in the morning, after methane accumulated below the nocturnal boundary layer, and late in the afternoon when convection mixed air to the cloud layer. Measured Δ¹²CH₂D₂ values of air samples record mixing between locally produced methane with low D/H and ambient air. The Δ¹²CH₂D₂ of the near surface air collected at the RSC during the early morning ranges from ∼+23‰ to ∼+35‰ which is lower than the ∼+50‰ values of tropospheric air. Mixing between background air (with Δ¹²CH₂D₂ ∼+50‰) and methane captured from chamber and bubble samples, as well as produced in incubation (all with negative Δ¹²CH₂D₂), explains the observed values of Δ¹²CH₂D₂ and Δ¹³CH₃D of near surface RSC air samples. The effect of mixing with biogenic sources on Δ¹³CH₃D is much smaller. The findings demonstrate how methane isotopologues can be used as a tool not only to fingerprint local contributions to these greenhouse gas emissions but also to identify sources of near-surface methane hot spots.

Exploring nitrogen dynamics in stream networks is critical for understanding how these systems attenuate nutrient pollution while maintaining ecological productivity. We investigated Oak Creek, a dryland watershed in central Arizona, USA, to elucidate the relationship between terrestrial nitrate (NO₃⁻) loading and stream NO₃⁻ uptake, highlighting the influence of land cover and hydrologic connectivity. We conducted four seasonal synoptic sampling campaigns along the 167-km network combined with stream NO₃⁻ uptake experiments (in 370–710-m reaches) and integrated the data in a mass-balance model to scale in-stream uptake and estimate NO₃⁻ loading from landscape to the stream network. Stream NO₃⁻ concentrations were low throughout the watershed (<5–236 μg N/L) and stream NO₃⁻ vertical uptake velocity was high (5.5–18.0 mm/min). During the summer dry (June), summer wet (September), and winter dry (November) seasons, the lower mainstem exhibited higher lateral NO₃⁻ loading (10–51 kg N km⁻² d⁻¹) than the headwaters and tributaries (<0.001–0.086 kg N km⁻² d⁻¹), likely owing to differences in irrigation infrastructure and near-stream land cover. In contrast, during the winter wet season (February) lateral NO₃⁻ loads were higher in the intermittent headwaters and tributaries (0.008–0.479 kg N km⁻² d⁻¹), which had flowing surface water only in this season. Despite high lateral NO₃⁻ loading in some locations, in-stream uptake removed >81% of NO₃⁻ before reaching the watershed outlet. Our findings highlight that high rates of in-stream uptake maintain low nitrogen export at the network scale, even with high fluxes from the landscape and seasonal variation in hydrologic connectivity.

Glaciers in the Svalbard Archipelago are retreating rapidly in response to climate change. This retreat leads to the alteration of the hydrological and thermal regimes of freshwater ecosystems. In this delicate context, existing anadromous Arctic charr (Salvelinus alpinus) populations are at severe risk and might disappear from the archipelago. However, the retreat of glaciers also promotes the formation of new lake systems that might be suitable for colonization by anadromous Arctic charr. These systems may provide a substantial opportunity for the establishment of new populations of anadromous charr, potentially buffering the decline in existing systems. To date, there is a lack of information on the number of recently deglaciated lake systems that have emerged since the end of the Little Ice Age (ca. 1920) that might be suitable for charr colonization. Therefore, the goal of this paper is to provide an initial assessment of the number of these lakes. To this end, and in accordance with previously published research, this study assesses whether a recently deglaciated lake system is potentially open to colonization based on gradient, river length, and lake surface area. Depending on the applied threshold (four in total), up to 24 lake systems are classified as potentially open to colonization by anadromous Arctic charr, with Spitsbergen emerging as a potential hotspot for colonization. The findings of this paper might serve as basis for new studies and for implementing proactive management and conservation strategies to protect anadromous charr populations.

Biogenic volatile organic compound (BVOC) emissions estimation models are driven by various physical factors. Many studies use weather forecasting models coupled with simple BVOC emission algorithms, where the physical factors driving variations in emissions are largely oversimplified. This study employs the land surface scheme CLM4 (Community Land Model version 4) coupled in the advanced Weather Research and Forecasting model (WRF), and the MEGAN (Model of Emissions of Gases and Aerosols from Nature) algorithms embedded within CLM4, to quantify the effects of three simplified parameters on BVOC emission estimates in China. Our sensitivity analysis results show that the annual BVOC emissions estimated using 2-m air temperature are about 48% lower than those estimated using leaf temperature in our study. Neglecting the shaded fraction of the canopy leads to a 1.7 times increase in total annual BVOC emissions compared to the separate treatment of sunlit and shaded leaves. Employing fixed values in the default WRF-CLM4-MEGAN results in a 51% reduction in total BVOC emissions in July compared to using dynamic weather history for the past few days. Each scenario is evaluated against field measurements, revealing that enhancing a single parameterization does not necessarily lead to improved model performance. Uncertainties from specific simplified parameters can be partially masked by other factors, and vice versa, which therefore pose limitations on overall model performance. Our findings highlight the non-negligible impact of the three oversimplified parameters and their underlying physical processes on BVOC emission estimates, while also deepening the understanding of uncertainties in BVOC emission modeling.

Seagrass meadows are effective sinks of atmospheric carbon dioxide (CO₂). However, there is little insight on how methane (CH₄) emissions may potentially offset carbon sequestration in seagrass meadows. Here, we resolve diel and seasonal dynamics of CH₄ and CO₂ water-air fluxes over a cold-temperate Zostera marina seagrass meadow using high-resolution timeseries observations in seawater. CH₄ was emitted from the seagrass-dominated coastal bay year-round to atmosphere with CH₄ fluxes ranging from 0.2 to 2.6 μmol m⁻² d⁻¹. These fluxes are at the lower end of earlier estimates based mostly on short-term (i.e., 1 day) observations. The 13-fold seasonal fluctuations in CH₄ emissions were greater than the 6-fold diel fluctuation. Radon observations imply that dissolved CH₄ was primarily originated from sediment porewater. The main fate of CH₄ in the water was outgassing to the atmosphere via wind forcing. Oxygen and temperature partially controlled dissolved CH₄ seasonal dynamics. There was an annual average uptake of CO₂ from the atmosphere (−0.9 ± 1.5 mmol m⁻² d⁻¹) driven by enhanced photosynthesis in the spring and summer. The CO₂-equivalent CH₄ outgassing (0.5 ± 0.6 g CO₂ eq m⁻² yr⁻¹) offsets only 0.8% of the sediment carbon accumulation in this cold-temperate Z. marina meadows over a 20-year time horizon. The CO₂-equivalent CH₄ flux was 6% of the average annual CO₂ uptake. Hence, CH₄ emissions from this cold-temperate seagrass meadow acted as a minor offset to carbon sequestration.

Monitoring gross primary productivity (GPP), the rate at which terrestrial ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near-infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES-R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR-enhanced Near-InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux and NEON research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half-hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fractional photosynthetically active radiation, a proxy for GPP, especially for ecosystems with low leaf area index. Results highlight that sNIRv-based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.

Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.

Wetland methane (CH₄) fluxes are highly variable over spatial and temporal scales due to variations in CH₄ production, oxidation, and transport. While some aspects of temporal variability in CH₄ fluxes are well documented, diel variability is poorly constrained, and studies report conflicting findings, making it difficult to generalize. Topographic, geochemical, hydroclimatic, and vegetative variability can result in characteristically different “patches” that likely influence differences in diel patterns. We investigated diel patterns of CH₄ fluxes from a large seasonal-mineral soil wetland in Maryland (USA) across three functionally unique patches: two with vegetation (emergent and submerged aquatic vegetation) and one without (open water) during the summer of 2021. To explore the relationships between vegetation, environmental conditions, and flux patterns, we also measured physiochemical variables (air and water temperature, pH, relative humidity, PAR, dissolved oxygen, and water depth). To our knowledge, this is the first study comparing diel variability using chambers across such distinct vegetation patch types. We found that diel patterns were strongly linked to patch types: CH₄ fluxes from the emergent vegetation did not display a consistent diel pattern, while fluxes from the submerged vegetation and no vegetation patches frequently peaked at 13:00 and 05:00, respectively. These differences could be a direct result of vegetation impact on production, oxidation, and/or transport of CH₄ or on conditions covarying with patch type. This study contributes to the growing understanding of how CH₄ fluxes vary spatially over diel cycles and emphasizes the importance of considering spatially varying diel patterns when estimating fluxes.

Carbon evasion from urban river networks becomes increasingly significant as urbanization accelerates. However, there remains a limited understanding of the overall carbon emission impact integrating CO₂ and CH₄ dynamics, particularly in response to ecological restoration efforts. In this study, we investigated patterns of fluvial CO₂ and CH₄ diffusive fluxes across an urban river network in Wuxi, China. Our results reveal that water quality variables, especially dissolved oxygen (DO) and phosphorus content, predominantly influence the variability of carbon emissions. These factors exhibit a stronger correlation with CO₂ emissions compared to CH₄, indicating a net increase in carbon emissions as water quality deteriorates. Seasonally, higher water temperatures, phosphate levels, and lower DO concentrations lead to increased carbon emissions during summer months. Spatially, areas with lower carbon emissions (averaged 86 mmol m⁻² d⁻¹ CO₂ and 0.13 mmol m⁻² d⁻¹ CH₄) are primarily situated near the lake and in river sections where significant water quality improvements have been achieved through ecological restoration efforts. Cluster analysis shows that over 60% of high-carbon emission (averaged 162 mmol m⁻² d⁻¹ CO₂ and 1.21 mmol m⁻² d⁻¹ CH₄) sites in the study area have undergone ecological restoration, suggesting potential for further carbon emission reduction through enhanced restoration practices. Our findings underscore the importance of implementing carbon reduction strategies such as nutrient removal and aeration for oxygenation within water ecological restoration initiatives. Effective matching of restoration strategies holds further potential for mitigating carbon emissions from urban river networks.

Elucidating the climate feedback due to forest cover loss is critical for a comprehensive understanding of the role of forests in mitigating climate change. Current research studies predominantly focus on the impacts of permanent forest conversion, often overlooking the effects of recurrent disturbances such as fire and harvest. This study addresses this gap by examining the impact of forest cover loss caused by two distinct drivers in China over the period 2003–2020. Our analysis revealed that fire-induced forest cover loss accounted for approximately 10% of total forest cover loss in China. The immediate (i.e., 1 year after disturbance) changes in land surface temperature (ΔLST) due to fire were higher (ΔLST = 0.11°C, interquartile range (IQR): [−0.02°C–0.23°C]) compared to those caused by harvest (ΔLST = 0.04°C, IQR: [−0.01°C–0.09°C]). This finding highlights the immediate warming effect of fire-induced forest cover loss, was about triple as large as that caused by harvest. Our analysis also found that the warming effect post-fire gradually lessened but still maintained approximately 0.02°C 5 years later. Change in evapotranspiration is a primary factor influencing surface temperature changes following forest disturbances. Our study provides comprehensive insights into the differential and persistent effects of LST responses to fire and harvest, underscoring the importance of understanding the climate feedback from forest dynamics from different drivers.