This study presents an enhanced high-resolution modeling system (WRF-NEMO-CAMx) designed to simulate airborne pollen concentrations for three key taxa for Mediterranean climates, Quercus, Pinus spp. and Olea europaea L., in the broader Thessaloniki area, Greece, at a spatial resolution of 2 km for the year 2019. Three emission scenarios (“Base,” “Max,” and “Min”) were implemented using literature-based pollen production values. The system was evaluated using ground-based meteorological observations and daily airborne pollen concentrations. The evaluation of simulated pollen concentrations confirmed the overall satisfactory performance of the modeling system. Among the three scenarios examined, the “Min” simulation yielded the best agreement with observations for Quercus (Index of Agreement, IOA = 0.81) and Pinus spp. (IOA = 0.76), while for Olea, the “Max” scenario demonstrated superior performance (IOA = 0.89). Analysis of subcategory contributions revealed that QRPR (Quercus Robur/Petraea) was the dominant contributor to Quercus pollen concentrations, while PMSC, representing Pinus species excluding Pinus pinaster and Pinus sylvestris, accounted for the largest share among the Pinus spp. categories. The spatial distribution of seasonal emissions indicated higher values in areas with dense vegetation cover for Quercus and Pinus spp., and in coastal and agricultural zones for Olea, reflecting the known patterns of species occurrence in the study region. These findings support the future integration of the modeling system into a high-resolution pollen health-warning framework, aimed at improving exposure forecasts.
Dead branches are an indicator of tree health, offering insights into water relations and stress, structural instability and disease. We hypothesize that dead branches exhibit hygroscopic movements driven by a moisture-sensitive reaction and normal wood bilayer. Thus, these movements can be detected through close-range remote sensing time series to identify dead branches for ecological analysis. We analysed hourly permanent laser scanning data from a boreal forest in Hyytiälä, Finland, monitoring 17 trees (16 coniferous, 1 deciduous) over 3.5 days with < 1 mm/h precipitation and < 3 m/s wind (933 total observations). To detect branch movement, we developed a dedicated method using non-rigid registration, quantifying movement as changes in angle between the branch orientation and the vertical. Linear mixed-effects models tested the relationship between branch angle and modelled wood equilibrium moisture content (EMC), considering time lags up to 8 h. Our results show that all studied branches followed a clear, systematic movement pattern: coniferous branches moved downward during the day and upward at night, with mean movement ranges of 23 ± 11 cm, lagging on average 3.5 ± 1.4 h behind EMC. The deciduous branch showed an inverted pattern, consistent with our hypotheses. Branch movement correlated strongly with EMC, confirming a hygroscopic mechanism. We concluded that systematic, daily dead branch movement is primarily hygroscopic, and can be reliably captured with hypertemporal terrestrial laser scanning under field conditions. Quantifying distinctive movement patterns of individual dead branches on living trees may support developing novel methods for early detection of tree vitality decline.
Accurate quantification of nitrous oxide (N2O) emissions from grasslands remains challenging owing to their episodic, spatially heterogeneous and diurnally variable nature. This study compared automated chamber (AC) and manual chamber (MC) methods to evaluate the influence of chamber methodology on N2O emissions following nitrogen (N) fertilisation. A field experiment was conducted on a perennial ryegrass (Lolium perenne) sward using 16 plots arranged in four replicate blocks, with paired AC and MC installed in each plot. Nitrogen fertilisers were applied in five equal splits of 40 kg N ha−1, giving a total of 200 kg N ha−1 yr−1, and fluxes were monitored for eight months.During the measurement period, N2O fluxes showed pronounced temporal variability, including sub-daily fluctuations and hot moments following fertilisation, particularly when N inputs coincided with increases in soil moisture or dry–rewetting transitions. Daily N2ON fluxes ranged from -0.52 to 191.47 g ha−1d−1 for AC and -1.64 to 95.21 g ha−1d−1 for MC. Although both methods captured similar seasonal dynamics, AC detected more hot moments and low-magnitude fluxes than MC. Cumulative N2ON emissions and partial emission factors (EFp) were on average 52 % and 74 % higher, respectively, for AC than MC. These discrepancies may partly reflect differences in headspace mixing, enclosure duration, analytical sensitivity and flux-calculation approaches.Diurnal variability and sampling time influenced daily N2ON estimates, but no fixed sampling hour represented the 24 h AC-derived daily mean, particularly during hot-moment days. Simulated MC sampling schemes revealed that sampling frequency influenced cumulative emissions more than sampling hour, with hybrid schemes combining routine background sampling and intensified event-period sampling providing the best overall balance between daily error and relative bias in cumulative N2O emissions. Overall, this study shows that method selection influences N2O estimates and that high-resolution measurements can inform MC sampling design, EF development and Tier 2 inventories.
Biomass burning associated with Amazonia deforestation increases atmospheric aerosol loading, altering the partitioning of solar radiation and potentially affecting ecosystem carbon exchange. While diffuse radiation effects are documented, its influence on pasture remains uncertain. This study quantified aerosols and cloud effects on carbon uptake in a Brachiaria brizantha pasture in Southwestern Amazonia using eddy covariance and micrometeorological data (1999–2007) from the Fazenda Nossa Senhora site. Aerosol optical depth (AOD) increased from 0.10 (rainy) to 0.69 (dry season), showing a significant correlation with regional hotspots (R2 = 0.60, p < 0.05). Grassland ecosystems showed enhanced photosynthetic activity under diffuse radiation, as clouds and aerosols improved light use efficiency (LUE), promoting better energy utilization by the canopy. Variations in the diffuse fraction explained 34% of the LUE variability (R2 = 0.34, p < 0.05). Under cloudy conditions, Gross Primary Productivity (GPP) peaked at 0.6 gC m−2 s−1, while the optimal photosynthetically active radiation (PAR) level for maximum GPP was approximately 2000 µmol m−2 s−1. Increasing AOD shifted diffuse PAR from 50% to 90%, while total radiation decreased by 20% compared to clear-sky conditions. Atmospheric turbidity reduced canopy temperature by up to 10 °C and vapor pressure deficit from 2.5 kPa to 1.5 kPa, creating a cooler microclimate. Although GPP declined under extreme turbidity (AOD > 2.0), optimal carbon uptake occurred under intermediate conditions. These results demonstrate that moderate increases in diffuse radiation can enhance photosynthetic efficiency in Amazonian pastures.
Soil respiration (Rs) links canopy carbon assimilation with belowground carbon cycling; however, its drivers and predictability vary substantially across crop development. Most traditional, season-aggregated approaches implicitly treat the growing season as homogeneous, thereby obscuring phenology-dependent controls on soil CO2 efflux. To address this limitation, we applied stage-explicit modeling to examine how the predictability and dominant drivers of Rs shift across the life cycles of cotton (Gossypium hirsutum L.) and corn (Zea mays L.) in a no-till dryland system. Using this framework, we found that both Rs predictability and dominant controls were strongly stage-dependent. In particular, predictive skill was confined to specific phenological stages (R2 up to 0.52) and consistently collapsed during reproductive transitions, indicating reduced coupling between Rs and the measured surface predictors during these developmental periods. Within these stage-dependent regimes, vegetation indices (VIs) provided informative signals for Rs prediction during select phenological periods, whereas their predictive utility diminished during transitional phases. By contrast, cover crop (CC) residual effects primarily functioned as a magnitude filter, modulating the baseline intensity of Rs without fundamentally altering its sensitivity to environmental drivers (∆R2 ≤ 0.04). To further interpret these patterns, interpretable analyses (SHAP/PDP/ICE) revealed crop-specific nonlinearities in predictor-response relationships. Most notably, non-monotonic VI-Rs trajectories emerged during late-season stages, indicating that increases in canopy greenness or cover did not consistently correspond to higher soil respiration later in crop development. Taken together, these results demonstrate that the governing logic of Rs changes systematically with phenology. Consequently, stage-explicit modeling provides a framework for interpreting phenology-dependent shifts in soil respiration predictability and identifying developmental windows of enhanced or reduced flux detectability, informing phenology-aware data collection and model evaluation.
Forests are aerodynamically rough surfaces above which turbulent exchange is enhanced, generating smaller vertical gradients in windspeed and air temperature than those predicted by the Monin-Obukhov Similarity Theory (MOST). Roughness sublayer (RSL) corrections, based on canopy structure, have been proposed to account for this enhancement in turbulent exchange, but evaluation of these RSL corrections above a range of forest types is still lacking. In this study, we mobilised multiyear datasets of canopy structure, turbulent fluxes and microclimate gradients from the ICOS European network to evaluate two widely-used RSL corrections in a range of climates, forest structures and atmospheric conditions. As expected, observed windspeed gradients above these forest sites were smaller than MOST predictions. The two RSL corrections improved the windspeed gradient predictions with similar accuracy, irrespective of atmospheric stability conditions. The need of RSL corrections for predicting air temperature gradients above forests was more contrasted than for windspeed, and depended on the site and the atmospheric stability conditions. Overall, the RSL corrections at canopy height remained relatively small, around 0.5°C for air temperature and 0.5 m s-1 for windspeed on average, and were most pronounced under stable atmospheric conditions. Based on the evaluation of the simplifying assumptions behind each RSL correction, we built recommendations to implement those corrections in models. We also discussed the potential impact of wind sensor positions to estimate aerodynamic parameters needed to apply MOST and RSL corrections and provide recommendations for improvement of the ICOS network. Our results highlight the difficulty to estimate displacement height and relate it to canopy structure, and question the idea that the drag coefficient does not change with leaf area or clumping.
In summer 2021, Siberia experienced its most extreme wildfire season on record. Yet the influence of soil moisture (SM)–atmosphere coupling on this event remains unclear. Using multisource datasets, we show that fire radiative power in the Siberian hotspot region during 2021 was nearly 9 times the 2004–2024 average (excluding 2021), representing an exceptional anomaly. Wildfire intensity scales exponentially with temperature, vapor pressure deficit, precipitation, SM, and climatic water deficit, underscoring the strong climate sensitivity of fire activity under the influence of anthropogenic climate change. Applying the flow analogue method, we found that negative SM anomalies explained more than 60% of extreme daily temperatures during peak fire days, pointing to the dominant role of surface drying in amplifying anomalous warming. During 2004–2024, SM–atmosphere coupling has strengthened and remained persistently synchronized with key fire-weather variables, driving the system toward hotter and drier states. Through SM–atmosphere coupling, the impacts of temperature and SM changes are further reinforced, potentially pushing the system beyond critical thresholds, thereby increasing the likelihood of extreme wildfires. These findings highlight the pivotal role of SM–atmosphere coupling in wildfire dynamics and its growing importance for understanding risks to high-latitude ecosystems under climate change.
The transpiration-to-evapotranspiration ratio (T/ET) is a key biophysical indicator of land-atmosphere coupling, reflecting how climate and vegetation jointly regulate water partitioning and surface energy exchange. However, the substantial diversity and inconsistency among existing T/ET estimates undermine the reliability of large scale hydrological assessments. Therefore, we employed the extended double instrumental variable algorithm (EIVD) method to independently fuse three representative and widely used transpiration and evapotranspiration datasets (PT-JPL-derived, GLEAM, and SiTHv2-derived) over China for the period 1982-2020, producing a high accuracy T/ET product. Validation against 54 flux tower observations demonstrated strong agreement (R² = 0.73, RMSE = 0.067), with the fused EIVD product outperforming all individual products. Nationally, the mean annual T/ET was 0.51, with a significant upward trend (p < 0.01), and spatially showed a southeast-to-northwest decreasing gradient. Change point analysis identified 2003 as a critical transition year, after which the rate of increase in T/ET accelerated 2.7 times (0.0016 yr⁻¹ vs. 0.0006 yr⁻¹; p < 0.01). Vapor pressure deficit and normalized difference vegetation index were the most dominant factors to T/ET, accounting for 21.61% and 21.41% of the land area, respectively. Meanwhile, structural equation modeling revealed a shift in dominant controls on T/ET—from climatic drivers during 1982-2002 to vegetation regulation during 2003-2020. This finding underscores the increasingly pivotal role of vegetation in regulating ecosystem water fluxes, and suggests that recent shifts in T/ET dynamics may be associated with broader vegetation recovery, offering valuable insights into ecohydrological adaptation under long term environmental transformation.
Grass pollen is considered a primary allergenic source worldwide and constitutes an important global public health concern. This is due to several characteristics, including massive pollen production for wind pollination and the abundance of grass-dominated vegetation near densely populated areas. Temporal changes in factors affecting any of these characteristics could considerably impact the allergenic rates of grasses in the long term. The main aim of this study was to analyse grass pollen trends for stations in the Madrid Autonomous Region (central Spain) during the 1996–2025 period and to identify the factors influencing temporal changes in airborne grass pollen. Three main factors were analysed, namely, the abundance of sources identified as major contributors to airborne grass pollen, seasonal meteorological influence, and remotely-sensed vegetation productivity. The distribution and abundance of natural and semi-natural grasslands were significantly related to airborne grass pollen amounts. The results did not reveal any generalisable trends in grass pollen incidence, although specific stations in the north and south showed an increased pattern. While global warming is evident, most relevant meteorological variables influencing pollen incidence did not exhibit any consistent long-term climate trend throughout the entire study area. Consequently, climate change does not appear to considerably affect airborne grass pollen amounts. Furthermore, a compensation effect could be occurring in the short term where anthropogenic land-use transformations have reduced the abundance of pollen sources. On the other hand, the vegetation productivity of the pollen sources was also related to airborne pollen production, which confirms an important link between vegetative and reproductive productive rates. Years with the highest pollen incidence coincided with maximum vegetation productivity rates and favourable meteorological conditions for pollen production and dispersal. Beyond general trends, extreme pollen episodes must be considered as atypical environmental conditions that may cause unexpected high-exposure events that deviate from the average.
The rising demand for animal products globally underscores the need to diversify feed grain inclusion in beef finishing systems while reducing the environmental footprint of animal agriculture. The primary objective of this study was to quantify and compare the effects of grain sorghum- versus corn-based finishing diets on methane (CH4) and ammonia (NH3) emissions from different groups of cattle in a feedlot. The study was conducted at the Kansas State University Southeast Research-Extension Center in Mound Valley, Kansas, USA for approximately six months. The cattle in this study consisted of black-hided, crossbred yearling steers. We simultaneously monitored CH4 and NH3 emissions using open-path dual-comb spectroscopy (DCS) from five paired cohorts of steers, with one fed dry-rolled corn diet and the other fed a ground sorghum diet. Line-averaged gas concentrations, obtained using DCS along with wind statistics and atmosphere stability parameters, were used in an inverse dispersion model to determine CH4 and NH3 fluxes. The use of a single DCS system eliminates potential biases occurring when using multiple instruments to measure concentration differences. The prevailing wind direction at the site and the east-west layout of the pens maximized data retention at the site. However, our touchdown analysis demonstrated that screening flux data using solely wind direction is insufficient to prevent contamination from other neighboring source areas in the feedlot. The median hourly NH3 fluxes in this study ranged from 14.4 to 212.3 µg m–2 day–1 for NH3 and from 144.9 to 513.4 g CH4 head–1 day–1 for CH4, respectively. NH3 fluxes showed a pronounced diurnal variation relative to CH4 fluxes. Overall, the sorghum diet resulted in a 12% reduction (95 CI: 1% to 21%) in CH4 fluxes relative to the corn diet. Median hourly NH₃ fluxes were typically higher under the sorghum diet, but the statistical analysis did not confirm NH3 emission differences between diets.
Understanding how large-scale ocean–atmosphere interactions influence tropical tree growth is essential for predicting the impacts of climate change on Amazonian forests. Here, we investigated the climatic sensitivity and annual rings formation of Pentaclethra macroloba in the Amazon estuary, a poorly studied region in tropical dendroclimatology. Tree-ring chronologies were developed from floodplain forests located in the eastern Amazon estuary and correlated with local climate variables and Sea Surface Temperature (SST) anomalies from the Equatorial Pacific and Tropical Atlantic Oceans. Ring periodicity was validated using the Mariaux window method, confirming annual growth-ring formation in the species. Correlation and spatial analyses revealed significant associations between radial growth and SST anomalies, particularly in the Niño 3.4 region and the North Tropical Atlantic. Structural equation modeling suggested that the influence of large-scale oceanic climate variability on tree growth was primarily mediated through local climatic conditions. Dry-season maximum temperature was the main positive predictor of radial growth, whereas Equatorial Pacific warming was associated with reduced rainfall during the wet season. The results indicate that P. macroloba is sensitive to interannual climatic variability and exhibits growth patterns associated with ocean–atmosphere teleconnections. These findings expand the dendroclimatological potential of estuarine Amazonian forests and highlight the importance of tropical floodplain ecosystems as natural archives of climate variability and environmental change.
