Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area?

Impact of vegetation composition and seasonality on sensitivity of modelled CO2 exchange in temperate raised bogs

Encroachment of vascular plants (VP) in temperate raised bogs, as a consequence of altered hydrological conditions and nutrient input, is widely observed. Effects of such vegetation shift on water and carbon cycles are, however, largely unknown and identification of responsible plant physiological traits is challenging. Process-based modelling offers the opportunity of gaining insights into ecosystem functioning beyond observations, and to infer decisive trait shifts of plant functional groups. We adapted the Soil-Vegetation-Atmosphere Transfer model pyAPES to a temperate raised bog site by calibration against measured peat temperature, water table and surface CO2 fluxes. We identified the most important traits determining CO2 fluxes by conducting Morris sensitivity analysis (MSA) under changing conditions throughout the year and simulated VP encroachment. We further investigated transferability of results to other sites by extending MSA to parameter ranges derived from literature review. We found highly variable intra-annual plant traits importance determining ecosystem CO2 fluxes, but only a partial shift of importance of photosynthetic processes from moss to VP during encroachment. Ecosystem respiration was dominated by peat respiration. Overall, carboxylation rate, base respiration rate and temperature sensitivity (Q10) were most important for determining bog CO2 balance and parameter ranking was robust even under the extended MSA.

Estimating intraseasonal intrinsic water-use efficiency from high-resolution tree-ring δ13 C data in boreal Scots pine forests

Intrinsic water-use efficiency (iWUE), a key index for carbon and water balance, has been widely estimated from tree-ring δ¹³ C at annual resolution, but rarely at high-resolution intraseasonal scale. We estimated high-resolution iWUE from laser-ablation δ¹³ C analysis of tree-rings (iWUE_iso ) and compared it with iWUE derived from gas exchange (iWUE_gas ) and eddy covariance (iWUE_EC ) data for two Pinus sylvestris forests from 2002 to 2019. By carefully timing iWUE_iso via modeled tree-ring growth, iWUE_iso aligned well with iWUE_gas and iWUE_EC at intraseasonal scale. However, year-to-year patterns of iWUE_gas , iWUE_iso , and iWUE_EC were different, possibly due to distinct environmental drivers on iWUE across leaf, tree, and ecosystem scales. We quantified the modification of iWUE_iso by postphotosynthetic δ¹³ C enrichment from leaf sucrose to tree rings and by nonexplicit inclusion of mesophyll and photorespiration terms in photosynthetic discrimination model, which resulted in overestimation of iWUE_iso by up to 11% and 14%, respectively. We thus extended the application of tree-ring δ¹³ C for iWUE estimates to high-resolution intraseasonal scale. The comparison of iWUE_gas , iWUE_iso , and iWUE_EC provides important insights into physiological acclimation of trees across leaf, tree, and ecosystem scales under climate change and improves the upscaling of ecological models.

Explicitly accounting for needle sugar pool size crucial for predicting intra-seasonal dynamics of needle carbohydrates δ18 O and δ13 C

We explore needle sugar isotopic compositions (δ¹⁸ O and δ¹³ C) in boreal Scots pine (Pinus sylvestris) over two growing seasons. A leaf-level dynamic model driven by environmental conditions and based on current understanding of isotope fractionation processes was built to predict δ¹⁸ O and δ¹³ C of two hierarchical needle carbohydrate pools, accounting for the needle sugar pool size and the presence of an invariant pinitol pool. Model results agreed well with observed needle water δ¹⁸ O, δ¹⁸ O and δ¹³ C of needle water-soluble carbohydrates (sugars + pinitol), and needle sugar δ¹³ C (R²  = 0.95, 0.84, 0.60, 0.73, respectively). Relative humidity (RH) and intercellular to ambient CO₂ concentration ratio (C_i /C_a ) were the dominant drivers of δ¹⁸ O and δ¹³ C variability, respectively. However, the variability of needle sugar δ¹⁸ O and δ¹³ C was reduced on diel and intra-seasonal timescales, compared to predictions based on instantaneous RH and C_i /C_a , due to the large needle sugar pool, which caused the signal formation period to vary seasonally from 2 d to more than 5 d. Furthermore, accounting for a temperature-sensitive biochemical ¹⁸ O-fractionation factor and mesophyll resistance in ¹³ C-discrimination were critical. Interpreting leaf-level isotopic signals requires understanding on time integration caused by mixing in the needle sugar pool.

Simple Models Outperform More Complex Big-Leaf Models of Daily Transpiration in Forested Biomes

Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE = 20%; R ² = 0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.

Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest?

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001-2017) of eddy-covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter-annual variability (IAV). The forest was a mean annual carbon sink (252 [ ± 42] gC m-2a-1 ), and NEP increased at rate +6.4-7.0 gC m-2a-1 (or ca. +2.5% a-1 ) during the period. This was attributed to the increasing gross-primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a-1 , and increase in GPP and NEP outside the main growing season contributed ca. one-third and one-fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long-term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi-layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30-40% of the observed ecosystem GPP increase could be attributed to CO2 . The increasing trend in leaf-area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid-rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone.

Drought Affected Ecosystem Water Use Efficiency of a Natural Oak Forest in Central China

Global climate models project more frequent drought events in Central China. However, the effect of seasonal drought on ecosystem water use efficiency (WUE) and water regulation strategy in Central China’s natural forests is poorly understood. This study investigated variations in WUE associated with drought in a natural oak (Quercus aliena) forest in Central China from 2017 to 2020 at several timescales based on continuous CO2 and water vapor flux measurements. Results showed that the 4-year mean gross ecosystem production (GEP), evapotranspiration (ET) and WUE of the natural oak forest was 1613.2 ± 116 g Cm−2, 637.8 ± 163.3 mm and 2.6 ± 0.68 g Ckg−1 H2O, with a coefficient of variation (CV) of 7.2%, 25.6% and 26.4%, respectively. The inter-annual variation in WUE was large, primarily due to the variation in ET caused by seasonal drought. Drought increased WUE distinctly in summer and decreased it slightly in autumn. During summer drought, surface conductance (gs) usually decreased with an increase in VPD, but the ratios of stomatal sensitivity (m) and reference conductance (gsref) were 0.21 and 0.3 molm−2s−1ln(kPa)−1 in the summer of 2019 and 2020. Strong drought can also affect ecosystem WUE and water regulation strategy in the next year. Decrease in precipitation in spring increased annual WUE. These results suggested that drought in different seasons had different effects on ecosystem WUE. Overall, our findings suggest that the natural oak forest did not reduce GEP by increasing WUE (i.e., reducing ET) under spring and summer drought, which could be due to its typical anisohydric characteristics, although it can also reduce stomatal opening during long-term drought.

Determinants of the ratio of actual to potential evapotranspiration

A widely used approach for estimating actual evapotranspiration (AET) in hydrological and earth system models is to constrain potential evapotranspiration (PET) with a single empirical stress factor (Ω = AET/PET). Ω represents the water availability and is fundamentally linked to canopy-atmosphere coupling. However, the mean and seasonal variability of Ω in the models have rarely been evaluated against observations, and the model performances for different climates and biomes remain unclear. In this study, we first derived the observed Ω from 28 FLUXNET sites over North America during 2000-2007, which was then used to evaluate Ω in six large-scale model-based datasets. Our results confirm the importance of incorporating canopy height in the formulation of aerodynamic conductance in the case of forests. Furthermore, leaf area index (LAI) is central to the prediction of Ω and can be quantitatively linked to the partitioning between transpiration and soil evaporation (R²  = 0.43). The substantial differences between observed and model-based Ω in forests (range: 0.2~0.9) are highly related to the way these models estimated PET and the way they represented the responses of Ω to the environmental drivers, especially wind speed and LAI. This is the first assessment of Ω in models based on in situ observations. Our findings demonstrate that the observed Ω is useful for evaluating, validating, and optimizing the modeling of AET and thus of water and energy balances.

Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

Hydrology of Drained Peatland Forest: Numerical Experiment on the Role of Tree Stand Heterogeneity and Management

A prerequisite for sustainable peatland forestry is sufficiently low water table (WT) level for profitable tree production. This requires better understanding on controls and feedbacks between tree stand and its evapotranspiration, drainage network condition, climate, and WT levels. This study explores the role of spatial tree stand distribution in the spatiotemporal distribution of WT levels and site water balance. A numerical experiment was conducted by a three-dimensional (3-D) hydrological model (FLUSH) applied to a 0.5 ha peatland forest assuming (1) spatially uniform interception and transpiration, (2) interception and transpiration scaled with spatial distributions of tree crown and root biomass, and (3) the combination of spatially scaled interception and uniform transpiration. Site water balance and WT levels were simulated for two meteorologically contrasting years. Spatial variations in transpiration were found to control WT levels even in a forest with relatively low stand stem volume (<100 m3/ha). Forest management scenarios demonstrated how stand thinning and reduced drainage efficiency raised WT levels and increased the area and duration of excessively wet conditions having potentially negative economic (reduced tree growth) and environmental (e.g., methane emissions, phosphorus mobilization) consequences. In practice, silvicultural treatment manipulating spatial stand structure should be optimized to avoid emergence of wet spots.

Towards physiologically meaningful water-use efficiency estimates from eddy covariance data

Intrinsic water-use efficiency (iWUE) characterizes the physiological control on the simultaneous exchange of water and carbon dioxide in terrestrial ecosystems. Knowledge of iWUE is commonly gained from leaf-level gas exchange measurements, which are inevitably restricted in their spatial and temporal coverage. Flux measurements based on the eddy covariance (EC) technique can overcome these limitations, as they provide continuous and long-term records of carbon and water fluxes at the ecosystem scale. However, vegetation gas exchange parameters derived from EC data are subject to scale-dependent and method-specific uncertainties that compromise their ecophysiological interpretation as well as their comparability among ecosystems and across spatial scales. Here, we use estimates of canopy conductance and gross primary productivity (GPP) derived from EC data to calculate a measure of iWUE (G₁ , "stomatal slope") at the ecosystem level at six sites comprising tropical, Mediterranean, temperate, and boreal forests. We assess the following six mechanisms potentially causing discrepancies between leaf and ecosystem-level estimates of G₁ : (i) non-transpirational water fluxes; (ii) aerodynamic conductance; (iii) meteorological deviations between measurement height and canopy surface; (iv) energy balance non-closure; (v) uncertainties in net ecosystem exchange partitioning; and (vi) physiological within-canopy gradients. Our results demonstrate that an unclosed energy balance caused the largest uncertainties, in particular if it was associated with erroneous latent heat flux estimates. The effect of aerodynamic conductance on G₁ was sufficiently captured with a simple representation. G₁ was found to be less sensitive to meteorological deviations between canopy surface and measurement height and, given that data are appropriately filtered, to non-transpirational water fluxes. Uncertainties in the derived GPP and physiological within-canopy gradients and their implications for parameter estimates at leaf and ecosystem level are discussed. Our results highlight the importance of adequately considering the sources of uncertainty outlined here when EC-derived water-use efficiency is interpreted in an ecophysiological context.