Journal of Geophysical Research: Biogeosciences最新文献

Dissolved organic matter (DOM) concentrations and composition within wet deposition are rarely monitored despite contributing a large input of bioavailable dissolved organic carbon (DOC) and nitrogen (DON) to the Earth's surface. Lacking from the literature are spatially comprehensive assessments of simultaneous measurements of wet deposition DOC and DON chemistry and their dependencies on metrics of climate and environmental factors. Here, we use archived precipitation samples from the US National Atmospheric Deposition Program collected in 2017 to 2018 from 17 sites across six ecoregions to investigate variability in the concentration and composition of depositional DOM. We hypothesize metrics of DOM chemistry vary with ecoregion, season, large-scale climate drivers, and precipitation geographic source. Findings indicate differences in DOC and DON concentrations and loads among ecoregions. The highest wet deposition concentrations are from sites in the Northern Forests and lowest concentrations from sites in Marine West Coast Forests. Summer and autumn samples contained the highest DOC concentrations and DON concentrations that were consistently above detection limit, corresponding with seasonality of peak air temperatures and the phenology of the growing season in the northern hemisphere. Compositional trends suggest lighter DOM molecules in autumn and winter and heavier molecules in spring and summer. Climate drivers explain 51% of variation in DOM chemistry, revealing differing drivers on the concentrations and loads of DOC versus DON in wet deposition. This study highlights the necessity of incorporating DOC and DON measurements into national deposition monitoring networks to understand spatial and temporal feedbacks between climate change, atmospheric chemistry and landscape biogeochemistry.

Estimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi-sensor optical satellite data sets (e.g., Landsat-8, 9; Sentinel-2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor-specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m² of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near-infrared and shortwave infrared-1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non-Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation-associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m²/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes.

Numerous efforts to measure land surface fluxes, from leaf to canopy scales, have significantly advanced the field of biogeoscience. However, upscaling these estimates to larger spatial and temporal scales remains a challenge. Recent advancements in remote sensing provide new opportunities to bridge these gaps in upscaling efforts. In this review, I propose that emerging satellite data can support the robust upscaling of land surface fluxes in terms of space through constellations of low Earth orbit satellites, in time through geostationary satellites, and in spectrum via optical, thermal, and microwave satellites. Lastly, I recommend the development of a long-term network integrating tower-based hyperspectral, thermal, and microwave instruments to rigorously evaluate the upscaling process of land surface fluxes.

Mediterranean areas are projected to face increased water scarcity due to global changes. Because a relatively large fraction of the precipitation in Mediterranean areas originates locally, changes at the land surface may further dampen local precipitation. Here, we study the contribution of evaporation to local precipitation for the first time on a scale of approximately 50 km using local evaporation recycling (ELMR) and local precipitation recycling (PLMR), and make a comparison among five Mediterranean climate regions: South West Australia, South West US, central Chile, the Mediterranean Basin, and the Cape region of South Africa. Specifically, this study aims to understand the effects of ecohydrological (dependent on vegetation or the hydrological cycle) and non-ecohydrological variables on ELMR and PLMR. We find that (a) on average, ecohydrological variables correlate more frequently and more strongly to ELMR and PLMR than non-ecohydrological variables; (b) ELMR is large over wet areas and PLMR is large over dry areas; and (c) there are differences in underlying factors of ELMR and PLMR among the regions due to differences in wetness, topography, and land cover. The results suggest that in Mediterranean regions, changes in vegetation cover or the hydrological cycle may strengthen the local water cycle through enhancing ELMR. Finally, ELMR and PLMR help to identify where in Mediterranean regions we might enhance the local water cycle through land cover changes.

Globally, the emission of biogenic volatile organic compounds (BVOC) by plants represents the dominant source of volatile organic compounds emitted to the atmosphere. Monoterpenes, as the major BVOC group, can contribute to forming secondary organic aerosols and influence cloud properties. In this study, we developed a process-based monoterpene module in the Vegetation Integrative SImulator for Trace gases (VISIT) model by considering the production, storage, and emission of monoterpene as three main processes. We further evaluated the modeled monoterpene emissions against the ecosystem-level observation data at a half-hour scale at a Japanese larch (Larix kaempferi) forest site on Mt. Fuji, Japan. The VISIT model performed with relatively higher accuracy with a Willmott's index of agreement at 0.61, a mean bias error (MBE) at 0.29, and a root mean squared error (RMSE) at 0.43, comparable to that of Model of Emissions of Gases and Aerosols from Nature model with a Willmott's index of agreement at 0.63, a MBE at 0.40, and a RMSE at 0.54. In a long-term simulation under high CO₂ emission scenarios, the ratio between monoterpene emission and gross primary production exhibited a stronger correlation with CO₂ concentration than temperature. Our study provides a process-based modeling approach for more accurately simulating monoterpene emissions from Japanese larch.

Land surface phenology (LSP) is useful to understand patterns of terrestrial ecosystems. Detecting LSP in drylands is more challenging when compared to agricultural and mesic environments due to vegetation heterogeneity, the presence of evergreen and seasonal species, and the dominant role of water (which is often received episodically with variable timing) in determining vegetation growth. In this study, LiDAR-derived vegetation classes are defined to guide and improve the interpretation of LSP metrics extracted using temporally decomposed Landsat fPAR time series. This method was applied to waterholes within the Cooper Creek floodplain, in dryland Australia, which are important for ecological conservation. Results showed that phenology is mostly associated with the recurrent vegetation (approximately 80% of all identified phenological events) in all waterholes. However, during high streamflow periods, the number of phenological events associated with the persistent vegetation greatly increased (up to 40% of the identified events). Non-annual phenology was also identified, with more than one phenological event occurring across a water year during high streamflow periods. The duration of the phenological events of the persistent vegetation exceeded one water year during periods of high streamflow. Phenological differences of the LiDAR-derived vegetation classes occupying the riparian zone of the waterholes were also identified. Streamflow movement across the floodplain exerts an important influence on the vegetation phenology, as suggested by a lag in the phenology when comparing southern and northern waterholes. The method developed herein can be applied to other highly spatially heterogeneous ecosystems where vegetation species simultaneously present permanent and seasonal patterns.

Accurate simulation of plant phenology is important in Earth system models as phenology modulates land-atmosphere coupling and the carbon cycle. Evaluations based on grid cell average leaf area index (LAI) can be misleading because multiple plant functional types (PFTs) may be present in one model grid cell and PFTs with different phenology schemes have different LAI seasonal cycles. Here we examined PFT-specific LAI magnitudes and seasonal cycles in the Community Land Model versions 5.0 and 4.5 (CLM5.0 and CLM4.5) and their relationship with the onset of growing season triggers in the Northern Hemisphere. LAI seasonal cycle and spring onset in CLM show the best agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) for temperature-dominated deciduous PFTs. Although the agreement in LAI magnitude between CLM5.0 and MODIS is better than CLM4.5, the agreement in seasonal cycles is worse in CLM5.0. Agreements between CLM and MODIS leaf phenology are primarily determined by the PFT and phenology scheme. While productivity depends on the environmental factors to which the plant is exposed during any given growing season, differences in phenology sensitivity to its environment necessitate a decoupling between the seasonality of LAI and GPP, which in turn could lead to biases in the carbon cycle as well as surface energy balance and hence land-atmosphere interactions. Because the discrepancy not only depends on parameterizing phenology but phenology-environment relationship, future improvements to other model components (e.g., soil moisture) could better align the seasonal cycle of LAI and GPP.