ALBERT

Description: Drained peatlands often act as carbon dioxide (CO2) hotspots. Raising the groundwater table is expected to reduce their CO2 contribution to the atmosphere and revitalize their function as carbon (C) sink in the long term. Without strict water management rewetting often results in partial flooding and the formation of spatially heterogeneous, nutrient-rich shallow lakes. Uncertainties remain as to when the intended effect of rewetting is achieved, as this specific ecosystem type has hardly been investigated in terms of greenhouse gas exchange (GHG) exchange. In most cases, methane (CH4) emissions increase under anoxic conditions due to a higher water table and in terms of global warming potential (GWP) outperform the shift towards CO2 uptake, at least in the short-term. Based on eddy covariance measurements we studied the ecosystem–atmosphere exchange of CH4 and CO2 (NEE) at a shallow lake situated on a former fen grassland in Northeast (NE) Germany. The lake evolved shortly after flooding, 9 years previous to our investigation period. The ecosystem consists of two main surface types: open water (inhabited by submerged and floating vegetation) and emergent vegetation (particularly including the eulittoral zone of the lake, dominated by Typha latifolia). To determine the individual contribution of the two main surface types to the net CO2 and CH4 exchange of the whole lake ecosystem, we combined footprint analysis with CH4 modelling and NEE partitioning. The CH4 and CO2 dynamics were strikingly different between open water and emergent vegetation. Net CH4 emissions from the open water area were around 4-fold higher than from emergent vegetation stands, accounting for 53 and 13 g CH4 m−2 a−1, respectively. In addition, both surface types were net CO2 sources with 158 and 750 g CO2 m−2 a−1, respectively. Unusual meteorological conditions in terms of a warm and dry summer and a mild winter might have facilitated high respiration rates. In sum, even after 9 years of rewetting the lake ecosystem exhibited a considerable C loss and global warming impact, the latter mainly driven by high CH4 emissions. We assume the eutrophic conditions in combination with permanent high inundation as major reasons for the unfavourable GHG balance.

Description: 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 30–40 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 30–40 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.

Description: While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

Description: 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).

Description: The Earth and its surface are tightly linked to the global climate system by turbulent exchange fluxes of energy and matter, for example greenhouse gases. This is true both for “deep Earth” geologically generated heat or gases reaching the surface and those of biogenic origin in the near-surface environment. For a better understanding of Earth-atmosphere interactions and in particular their feedbacks, the Helmholtz Young Investigators Group TEAM at GFZ studies the surface-atmosphere exchange of heat, water vapor, carbon dioxide (CO2) and methane (CH4) on a range of temporal and spatial scales – from hours to years and from 〈 1m² to more than 10 000 km². A regional focus is on degraded peatlands. While natural peatlands play a central role in the climate system as major carbon sink storing twice the amount of carbon that is contained in the Earth’s forests, a large fraction of the world’s peatlands has been drained for agriculture and subsequently lost that carbon storage capacity. Drained and degraded peatlands are significant and longterm carbon dioxide sources and thus contribute to further climate warming. In NE Germany, up to 20 % of the overall carbon dioxide emissions are from drained peat and a tool in reducing national greenhouse gas emission is therefore the re-wetting of peatlands to restore their natural carbon sink capacity. TEAM monitors the long-term greenhouse gas dynamics in such re-wetted sites and investigates the underlying processes and spatiotemporal drivers of the dynamics.