Remote sensing of forest gas exchange: Considerations derived from a tomographic perspective.

The global exchange of gas (CO2 , H2 O) and energy (sensible and latent heat) between forest ecosystems and the atmosphere is often assessed using remote sensing (RS) products. Although these products are essential in quantifying the spatial variability of forest-atmosphere exchanges, large uncertainties remain from a measurement bias towards top of canopy fluxes since optical RS data are not sensitive for the vertically integrated forest canopy. We hypothesize that a tomographic perspective opens new pathways to advance upscaling gas exchange processes from leaf to forest stands and larger scales. We suggest a 3D modelling environment comprising principles of ecohydrology and radiative transfer modelling with measurements of micrometeorological variables, leaf optical properties and forest structure, and assess 3D fields of net CO2 assimilation (An ) and transpiration (T) in a Swiss temperate forest canopy. 3D simulations were used to quantify uncertainties in gas exchange estimates inherent to RS approaches and model assumptions (i.e. a big-leaf approximation in modelling approaches). Our results reveal substantial 3D heterogeneity of forest gas exchange with top of canopy An and T being reduced by up to 98% at the bottom of the canopy. We show that a simplified use of RS causes uncertainties in estimated vertical gas exchange of up to 300% and that the spatial variation of gas exchange in the footprint of flux towers can exceed diurnal dynamics. We also demonstrate that big-leaf assumptions can cause uncertainties up to a factor of 10 for estimates of An and T. Concluding, we acknowledge the large potential of 3D assessments of gas exchange to unravelling the role of vertical variability and canopy structure in regulating forest-atmosphere gas and energy exchange. Such information allows to systematically link canopy with global scale controls on forest functioning and eventually enables advanced understanding of forest responses to environmental change.

Ecosystem transpiration and evaporation: Insights from three water flux partitioning methods across FLUXNET sites.

We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between .89 and .94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC-based T estimates show higher correlation to sap flow-based T than EC-based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high-quality estimates for at least two continuous years shows that T/ET variability was 1.6 times higher across sites than across years. Spatial variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall, T and T/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understanding T globally, while the magnitudes remain uncertain. Our results represent the first extensive EC data-based estimates of ecosystem T permitting a data-driven perspective on the role of plants' water use for global water and carbon cycling in a changing climate.

Contrasting impact of extreme soil and atmospheric dryness on the functioning of trees and forests.

Recent studies indicate an increase in the frequency of extreme compound dryness days (days with both extreme soil AND air dryness) across central Europe in the future, with little information on their impact on the functioning of trees and forests. This study aims to quantify and assess the impact of extreme soil dryness, extreme air dryness, and extreme compound dryness on the functioning of trees and forests. For this, >15 years of ecosystem-level (carbon dioxide and water vapor fluxes) and 6-10 years of tree-level measurements (transpiration and growth) each from a montane mixed deciduous forest (CH-Lae) and a subalpine evergreen coniferous forest (CH-Dav) in Switzerland, is used. The results showed extreme air dryness limitation on CO2 fluxes and extreme soil dryness limitations on water vapor fluxes. Additionally, CH-Dav was mainly affected by extreme air dryness whereas CH-Lae was affected by both extreme soil dryness and extreme air dryness. The impact of extreme compound dryness on net CO2 uptake (about 75 % decrease) was more due to higher increased ecosystem respiration (40 % and 70 % increase at CH-Dav and CH-Lae, respectively) than decreased gross primary productivity (10 % and 40 % decrease at CH-Dav and CH-Lae, respectively). A significant negative impact on evapotranspiration and transpiration was only observed at CH-Lae during extreme soil and compound dryness (about 25 % decrease). Furthermore, with some differences, the tree-level impact on tree water deficit, transpiration, and growth were consistent with the ecosystem-level impact on carbon uptake and evapotranspiration. Finally, the impact of extreme dryness showed no significant relationship with tree allometry (diameter and height) but across different tree species. The projected future is likely to expose these forest areas to more extreme and frequent dryness conditions, thus compromising the functioning of trees and forests, thereby calling for management interventions to increase the adaptive capacity and resistance of these forests.

Physiological response of Swiss ecosystems to 2018 drought across plant types and elevation.

Using five eddy covariance flux sites (two forests and three grasslands), we investigated ecosystem physiological responses to the 2018 drought across elevational gradients in Switzerland. Flux measurements showed that at lower elevation sites (below 1000 m.a.s.l.; grassland and mixed forest) annual ecosystem productivity (GPP) declined by approximately 20% compared to the previous 2 years (2016 and 2017), which led to a reduced annual net ecosystem productivity (NEP). At the high elevation sites, however, GPP increased by approximately 14% and as a result NEP increased in the alpine and montane grasslands, but not in the subalpine coniferous forest. There, increased ecosystem respiration led to a reduced annual NEP, despite increased GPP and lengthening of the growing period. Among all ecosystems, the coniferous forest showed the most pronounced negative stomatal response to atmospheric dryness (i.e. vapour pressure deficit, VPD) that resulted in a decline in surface conductance and an increased water-use efficiency during drought. While increased temperature enhanced the water-use efficiency of both forests, de-coupling of GPP from evapotranspiration at the low-elevation grassland site negatively affected water-use efficiency due to non-stomatal reductions in photosynthesis. Our results show that hot droughts (such as in 2018) lead to different responses across plants types, and thus ecosystems. Particularly grasslands at lower elevations are the most vulnerable ecosystems to negative impacts of future drought in Switzerland. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Quantifying deforestation and forest degradation with thermal response.

Deforestation and forest degradation cause the deterioration of resources and ecosystem services. However, there are still no operational indicators to measure forest status, especially for forest degradation. In the present study, we analysed the thermal response number (TRN, calculated by daily total net radiation divided by daily temperature range) of 163 sites including mature forest, disturbed forest, planted forest, shrubland, grassland, savanna vegetation and cropland. TRN generally increased with latitude, however the regression of TRN against latitude differed among vegetation types. Mature forests are superior as thermal buffers, and had significantly higher TRN than disturbed and planted forests. There was a clear boundary between TRN of forest and non-forest vegetation (i.e. grassland and savanna) with the exception of shrubland, whose TRN overlapped with that of forest vegetation. We propose to use the TRN of local mature forest as the optimal TRN (TRNopt). A forest with lower than 75% of TRNopt was identified as subjected to significant disturbance, and forests with 66% of TRNopt was the threshold for deforestation within the absolute latitude from 30° to 55°. Our results emphasized the irreplaceable thermal buffer capacity of mature forest. TRN can be used for early warning of deforestation and degradation risk. It is therefore a valuable tool in the effort to protect forests and prevent deforestation.