Journal of Geophysical Research: Biogeosciences最新文献

Eurasian permafrost soils contain large amounts of organic carbon (OC) and mercury (Hg), sequestered by vegetation during past and present interglacial periods. Lake sediment archives may help understand past OC and Hg dynamics and how they interact with climate-related variables. We investigated Hg accumulation, OC dynamics, and Hg and OC stable isotopes in a 14,000-year sediment record from Lake Malaya Chabyda (Central Yakutia, Russia). Sediment Hg was correlated to OC (p value < 0.01), with lower OC and Hg accumulation rates (OCAR, HgAR) during the cold Younger Dryas (YD, 12,900–11,700 cal BP), when the lake level was low. Elevated sediment Δ²⁰⁰Hg (0.05‰ ± 0.11‰), representing dominant Hg^II deposition, and low δ¹³C, indicates low lake primary productivity during the YD. During the early Holocene, Δ²⁰⁰Hg and Δ¹⁹⁹Hg decreased, while δ¹³C, δ²⁰²Hg, OCAR, and HgAR increased, suggesting enhanced algal primary productivity in deeper, more turbid waters. From 4,100 cal BP to present, Hg/OC ratios and HgAR increased at constant OCAR, indicating an additional Hg source to the lake. Analysis of Hg isotopes suggests direct Hg⁰ uptake into lake waters, potentially driven by primary production and efficient Hg burial. Our observations suggest that the gradual climate warming since the Last Glacial Termination and into the early Holocene led to enhanced OC and Hg burial in northern lakes and watersheds. Late Holocene enhanced Hg burial, but not OC, is possibly related to a renewed increase in lake primary productivity. Continued global warming may lead to further Hg sequestration in northern aquatic ecosystems.

Water column removal in streams is a nitrogen (N) cycling pathway that has been historically overlooked. Studies filling this knowledge gap have focused on the role of water column N removal in mid-to-large-order rivers with consistently high suspended sediment concentrations. However, smaller streams may provide comparable suspended sediment concentrations during and after storm events, creating favorable conditions for water column N removal. To assess the presence, magnitude, and control of water column N removal during storms in low-order watersheds, we measured water column denitrification and heterotrophic assimilatory N uptake rates at three locations in a Mid-Atlantic watershed during five storm events of different magnitude, sediment loads, and nutrient availability. We found large variations in water column denitrification (0–5.56 mg N g⁻¹ d⁻¹) and assimilatory uptake (0.003–1.67 mg N g⁻¹ d⁻¹). Higher rates of N removal occurred during flow recession, with a correlation between suspended sediment organic matter content and denitrification. On average, denitrification rates in the water column were higher when flashy responses to storm events occurred. In contrast to denitrification, water column N removal rates (as both denitrification and heterotrophic assimilation) during storm events were comparable to those measured at baseflow in larger rivers. However, water column denitrification could only account for less than 10% of potential reach-scale N removal during most of the storm events. Our findings provide insight into the ecological relevance of small stream water columns and suggest that more research is needed to understand the magnitude of stream water column processing on watershed-scale N removal.

Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO₂) and methane (CH₄) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high-frequency, ecosystem-scale CO₂ and CH₄ flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT-Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO₂ and CH₄ emissions. Using a model-data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R_eco) modules explained, on average, 73% of the variation in CO₂ exchange with low model error (normalized root mean square error (nRMSE) <1). The CH₄ module also explained the majority of variance in CH₄ emissions in validation sites (R² = 0.54; nRMSE = 1.15). The PEPRMT-Tidal-CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R_eco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH₄ emissions, and (d) CH₄ responses to non-periodic changes in salinity.

Ecosystem respiration (R_eco) arises from interacting autotrophic and heterotrophic processes constrained by distinct drivers. Here, we evaluated up-scaling of observed components of R_eco in a mature eucalypt forest in southeast Australia and assessed whether a land surface model adequately represented all the fluxes and their seasonal temperature responses. We measured respiration from soil (R_soil), heterotrophic soil microbes (R_h), roots (R_root), and stems (R_stem) in 2018–2019. R_eco and its components were simulated using the CABLE–POP (Community Atmosphere-Biosphere Land Exchange–Population Orders Physiology) land surface model, constrained by eddy covariance and chamber measurements and enabled with a newly implemented Dual Arrhenius and Michaelis-Menten (DAMM) module for soil organic matter decomposition. Eddy-covariance based R_eco (R_eco.eddy, 1,439 g C m⁻² y⁻¹) was slightly higher than the sum of the respiration components (R_eco.sum, 1,295 g C m⁻² y⁻¹) and simulated R_eco (1,297 g C m⁻² y⁻¹). The largest mean contribution to R_eco was from R_soil (64%) across seasons. The measured contributions of R_h (49%), R_root (15%), and R_stem (22%) to R_eco.sum were very close to model outputs of 46%, 11%, and 22%, respectively. The modeled R_h was highly correlated with measured R_h (R² = 0.66, RMSE = 0.61), empirically validating the DAMM module. The apparent temperature sensitivities (Q₁₀) of R_eco were 2.22 for R_eco.sum, 2.15 for R_eco.eddy, and 1.57 for CABLE-POP. This research demonstrated that bottom-up respiration component measurements can be successfully scaled to eddy covariance-based R_eco and help to better constrain the magnitude of individual respiration components as well as their temperature sensitivities in land surface models.

The Tibetan Plateau, with its great hydrothermal gradients and diverse ecosystems, is considered vulnerable to climate change. Extreme drought can have detrimental effects on carbon sequestration in terrestrial ecosystems by disrupting plant eco-hydrological processes. Such effects are presumed to vary and depend on the vegetation types, environmental factors and drought properties. The drought timing has been widely highlighted in drought studies at both regional and site scales. However, the systematic insight into the impact of drought timing on the ecosystem functioning over the Tibetan Plateau remains unclear. In this study, we investigated the responses of vegetation greenness to meteorological drought and attributed them to the drought properties, climatic and edaphic factors. We found that the timing of drought plays a predominant role in regulating vegetation drought responses on the Tibetan Plateau. Notably, we observed significant differences in vegetation responses between late growing season drought and non-late growing season drought. In addition to drought timing, soil moisture and long-term hydrothermal conditions also played a significant role. Furthermore, our study revealed that alpine grassland was more sensitive to the drought timing, soil moisture and sand content than woody plants. We discovered a significant interplay between rainfall at hottest quarter and drought timing, with the role of drought timing weakening as the rainfall at hottest quarter increases. These findings underscore the crucial role of drought timing in shaping ecosystem functioning in response to the changing climate regime over the Tibetan Plateau and provide crucial insights into the improvement of land surface models.

Light use efficiency (LUE) models, along with satellite-based vegetation maps and climatic reanalysis products as drivers, are effective tools for estimating large-scale gross primary productivity (GPP). However, the climate-induced uncertainty in LUE-based GPP remains poorly understood, particularly the temporal scaling uncertainty from ignored climatic fluctuations. Here, two 1-hourly reanalysis products, along with site-based observations, were employed to drive a two-leaf LUE model at 194 eddy-covariance (EC) sites. The observation-driven and reanalysis-driven GPP at the 1-hourly resolution were evaluated against EC GPP to illustrate the uncertainty from reanalysis products, with mean absolute deviation (MAD) and Nash-Sutcliffe efficiency (NSE) as criterion. Moreover, the climate-induced temporal scaling uncertainty was characterized by comparing distributed GPP (modeled with 1-hourly resolution climatic drivers) and lumped GPP (modeled with 6-hourly resolution climatic drivers). At the 1-hourly resolution, results demonstrated that the reanalysis-driven GPP showed a weaker relationship with EC GPP (MAD = 0.14 gC m⁻²h⁻¹, NSE = 0.48) than the observation-driven GPP (MAD = 0.12 gC m⁻²h⁻¹, NSE = 0.60), confirming the nonnegligible climate-induced uncertainty from reanalysis products. Additionally, the climate-induced uncertainty arising from gridded radiation was found to be significantly larger than that associated with temperature and vapor pressure deficit (VPD). At the 6-hourly resolution, both the observation-driven and reanalysis-driven lumped GPP exhibited a lower relationship with EC GPP (MAD = 0.63 gC m⁻²6h⁻¹, NSE = 0.54) than the corresponding distributed GPP (MAD = 0.57 gC m⁻²6h⁻¹, NSE = 0.59), demonstrating that the climate-induced temporal scaling uncertainty in 6-hourly GPP estimates was significantly apparent. This study emphasizes the imperative to refine reanalysis products for more precise capture of short-term fluctuations and to reduce scaling uncertainties in coarse temporal resolution GPP estimates.

Climate change has significantly altered crop phenology, which has further impacted crop growth and yield. Accurate monitoring of crop phenology is essential for managing agricultural production in response. However, regional monitoring requires high spatial resolution distribution data, as medium resolution data suffers from mixed pixel issues. This study based on a long-term high spatiotemporal resolution fusion data set of Normalized Difference Vegetation Index and an annually updated maize distribution data set, used the relative threshold method to identify the maize phenology in 22 provinces of China from 2001 to 2020. We further analyzed the trend of maize phenology and assessed its responses to climate change. The results reveal large inter-annual fluctuations and spatial variability in maize phenology from 2001 to 2020. The length of the growth season (LOS) of spring maize has prolonged by 4.28 days in the northern maize zone and has shortened by 4.90 days in the southern maize zone. Additionally, the LOS of summer maize in the Huang-Huai-Hai region has shortened by 2.24 days. We also found a positive correlation between the length of the vegetative growth stage and the mean temperature and a negative correlation between the length of the reproductive growth stage and accumulated precipitation. This study utilized large-scale, high-resolution maize phenology data to analyze the trend of maize phenology and its response to climate change. These findings are expected to provide valuable support for assessing maize growth status and developing agricultural adaptive practices.

The fluxes of dissolved organic carbon (DOC) through tidal marsh-influenced estuaries remain poorly quantified and have been identified as a missing component in carbon-cycle models. The extreme variability inherent to these ecosystems of the land-ocean interface challenge our ability to capture DOC-concentration dynamics and to calculate accurate DOC fluxes. In situ discrete and continuous measurements provide high-quality estimates of DOC concentration, but these strategies are constrained spatially and temporally and can be costly to operate. Here, field measurements and high-spatial-resolution remote sensing were used to train and validate a predictive model of DOC-concentration distributions in the Plum Island Estuary (PIE), a mesotidal saltmarsh-influenced estuary in Massachusetts. A large set of field measurements collected between 2017 and 2023 was used to develop and validate an empirical algorithm to retrieve DOC concentration with a ±15% uncertainty from Sentinel-2 imagery. Implementation on 141 useable images produced a 6-year time series (2017–2023) of DOC distributions along the thalweg. Analysis of the time series helped identify river discharge, tidal water level (WL), and a marsh enhanced vegetation index 2 as predictors of DOC distribution in the estuary, and facilitated the training and validation of a simple model estimating the distribution. This simple model was able to predict DOC along the PIE thalweg within ±16% of the in situ measurements. Implementation for three years (2020–2022) illustrated how this type of remote-sensing-informed models can be coupled with the outputs hydrodynamic models to calculate DOC fluxes in tidal marsh-influenced estuaries and estimate DOC export to the coastal ocean.