23 research outputs found
Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest
Droughts affect terrestrial ecosystems directly and concurrently and can additionally induce lagged effects in subsequent seasons and years. Such legacy effects of drought on vegetation growth and state have been widely studied in tree ring records and satellite-based vegetation greenness, while legacies on ecosystem carbon fluxes are still poorly quantified and understood. Here, we focus on two ecosystem monitoring sites in central Germany with a similar climate but characterised by different species and age structures. Using eddy covariance measurements, we detect legacies on gross primary productivity (GPP) by calculating the difference between random forest model estimates of potential GPP and observed GPP. Our results showed that, at both sites, droughts caused significant legacy effects on GPP at seasonal and annual timescales, which were partly explained by reduced leaf development. The GPP reduction due to drought legacy effects is of comparable magnitude to the concurrent drought effects but differed between two neighbouring forests with divergent species and age structures. The methodology proposed here allows the quantification of the temporal dynamics of legacy effects at the sub-seasonal scale and the separation of legacy effects from model uncertainties. The application of the methodology at a larger range of sites will help us to quantify whether the identified lag effects are general and on which factors they may depend.ISSN:1726-4170ISSN:1726-417
Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland
In natural coastal wetlands, high supplies of marine
sulfate suppress methanogenesis. Coastal wetlands are, however, often
subject to disturbance by diking and drainage for agricultural use and can
turn to potent methane sources when rewetted for remediation. This suggests
that preceding land use measures can suspend the sulfate-related methane
suppressing mechanisms. Here, we unravel the hydrological relocation and
biogeochemical S and C transformation processes that induced high methane
emissions in a disturbed and rewetted peatland despite former brackish
impact. The underlying processes were investigated along a transect of
increasing distance to the coastline using a combination of concentration
patterns, stable isotope partitioning, and analysis of the microbial
community structure. We found that diking and freshwater rewetting caused a
distinct freshening and an efficient depletion of the brackish sulfate
reservoir by dissimilatory sulfate reduction (DSR). Despite some legacy
effects of brackish impact expressed as high amounts of sedimentary S and
elevated electrical conductivities, contemporary metabolic processes
operated mainly under sulfate-limited conditions. This opened up favorable
conditions for the establishment of a prospering methanogenic community in
the top 30â40 cm of peat, the structure and physiology of which resemble
those of terrestrial organic-rich environments. Locally, high amounts of
sulfate persisted in deeper peat layers through the inhibition of DSR,
probably by competitive electron acceptors of terrestrial origin, for
example Fe(III). However, as sulfate occurred only in peat layers below
30â40 cm, it did not interfere with high methane emissions on an ecosystem
scale. Our results indicate that the climate effect of disturbed and
remediated coastal wetlands cannot simply be derived by analogy with their
natural counterparts. From a greenhouse gas perspective, the re-exposure of
diked wetlands to natural coastal dynamics would literally open up the
floodgates for a replenishment of the marine sulfate pool and therefore
constitute an efficient measure to reduce methane emissions.</p
Active afforestation of drained peatlands is not a viable option under the EU Nature Restoration Law
The EU Nature Restoration Law (NRL) is critical in restoring degraded ecosystems. However, active afforestation of degraded peatlands has been suggested by some as a restoration measure under the NRL. Here, we discuss the current state of scientific evidence on the climate mitigation effects of peatlands under forestry and its limitations, uncertainties and evidence gaps. Based on this discussion we conclude:
Afforestation of drained peatlands, while maintaining their drained state, is not equivalent to ecosystem restoration. This approach will not restore the peatland ecosystem's flora, fauna, and functions.
There is insufficient evidence to support the long-term climate change mitigation benefits of active afforestation of drained peatlands.
Most studies only focus on the short-term gains in standing biomass and rarely explore the full life cycle emissions associated with afforestation of drained peatlands. Thus, it is unclear whether the CO2 sequestration of a forest on drained peatland can offset the carbon loss from the peat over the long term.
In some ecosystems, such as abandoned or certain cutaway peatlands, afforestation may provide short-term benefits for climate change mitigation compared to taking no action. However, this approach violates the concept of sustainability by sacrificing the most space-effective carbon store of the terrestrial biosphere, the long-term peat store, for a shorter-term, less space-effective, and more vulnerable carbon store, namely tree biomass.
Consequently, active afforestation of drained peatlands is not a viable option for climate mitigation under the EU Nature Restoration Law and might even impede future rewetting/restoration efforts.
To restore degraded peatlands, hydrological conditions must first be improved, primarily through rewetting
Predominance of methanogens over methanotrophs in rewetted fens characterized by high methane emissions
The rewetting of drained peatlands alters peat geochemistry and often leads
to sustained elevated methane emission. Although this methane is produced
entirely by microbial activity, the distribution and abundance of
methane-cycling microbes in rewetted peatlands, especially in fens, is rarely
described. In this study, we compare the community composition and abundance
of methane-cycling microbes in relation to peat porewater geochemistry in two
rewetted fens in northeastern Germany, a coastal brackish fen and a
freshwater riparian fen, with known high methane fluxes. We utilized 16S rRNA
high-throughput sequencing and quantitative polymerase chain reaction (qPCR) on 16S
rRNA, mcrA, and pmoA genes to determine microbial community
composition and the abundance of total bacteria, methanogens, and
methanotrophs. Electrical conductivity (EC) was more than 3Â times higher in
the coastal fen than in the riparian fen, averaging 5.3 and 1.5 mSâcmâ1,
respectively. Porewater concentrations of terminal electron acceptors (TEAs) varied
within and among the fens. This was also reflected in similarly high intra-
and inter-site variations of microbial community composition. Despite these
differences in environmental conditions and electron acceptor availability,
we found a low abundance of methanotrophs and a high abundance of
methanogens, represented in particular by Methanosaetaceae, in both
fens. This suggests that rapid (re)establishment of methanogens and slow
(re)establishment of methanotrophs contributes to prolonged increased methane
emissions following rewetting.</p
Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)
Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe
Upscaling wetland methane emissions from the FLUXNETâCH4 eddy covariance network (UpCH4 v1.0): model development, network assessment, and budget comparison
Wetlands are responsible for 20%â31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency âŒ0.52â0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 yâ1 for 2001â2018 which agrees closely with current bottom-up land surface models (102â181 TgCH4 yâ1) and overlaps with top-down atmospheric inversion models (155â200 TgCH4 yâ1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253)