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Climate warming is expected to increase global methane (CH4) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane‐producing sites] in wetland, upland, and lake sites of the FLUXNET‐CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981–2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4, with increases in FCH4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high‐CH4‐emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non‐growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions. [ABSTRACT FROM AUTHOR]

To understand patterns in CO2 partial pressure (PCO2) over time in wetlands' surface water and porewater, we examined the relationship between PCO2 and land–atmosphere flux of CO2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (Reco) and gross primary production (GPP), components of vertical CO2 flux, were compared to PCO2, a component of lateral CO2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, PCO2 was strongly positively correlated with air temperature (Tair) at most sites. Monthly average PCO2 tended to peak towards the middle of the year and was more strongly related to Reco than GPP. Our results suggest Reco may be related to biologically driven PCO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between Reco and temperature normalized PCO2. This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration. [ABSTRACT FROM AUTHOR]

The recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations. [ABSTRACT FROM AUTHOR]

Wetlands are the largest natural source of methane (CH4) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4, but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant‐mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes. [ABSTRACT FROM AUTHOR]

Quantifying the emissions of the three main biogenic greenhouse gases (GHGs), carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4), from agroecosystems is crucial. In this study continuous measurements of N2O, and CH4 emissions from potato and pea crops in southwest British Columbia, Canada were made using the eddy‐covariance (EC) technique. Flux footprint analysis, coupled with EC and manual nonsteady state chamber measurements, was used to address the spatial heterogeneity resulting from the field edge at the study site. Flux footprint corrections had a larger effect on N2O fluxes than CO2 fluxes because of a more pronounced difference in N2O fluxes between the crop and edge areas. After flux footprint corrections, the potato and pea crops were both weak CO2 sinks with annual net ecosystem exchange values of −57 ± 9 and −97 ± 16 g C m−2 yr−1, respectively. However, after taking carbon (C) export via crop harvest and C import via seeding into account, the potato crop shifted to being a moderate C source of 284 ± 55 g C m−2 yr−1, while the pea crop became near C neutral, sequestering only 30 ± 26 g C m−2 yr−1. Annual GHG balances, quantified by converting N2O and CH4 to CO2 equivalents as pulse emissions using respective global warming potentials on a 100‐year timescale, were 417 ± 88 and 152 ± 106 g CO2e m−2 yr−1 for the potato and pea crops, respectively, with N2O contributing the largest proportion to annual total GHG balances and outweighing the CO2 uptake from the atmosphere. Plain Language Summary: To better mitigate climate change, quantifying the emissions of the three main biogenic greenhouse gases (GHGs), carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4), from agroecosystems is critical. Therefore, in this study we made continuous half‐hourly measurements of CO2, N2O, and CH4 emissions from potato and pea crops in southwest British Columbia, Canada using micrometeorological instrumentation installed at the field edge. To correct for the effects of the field edge on the micrometeorological measurements, we used supplementary chamber measurements and a footprint model. This enabled us to estimate the actual GHG budgets of the study crop areas. The correction had a larger effect on N2O fluxes than CO2 fluxes because of a more pronounced difference in N2O fluxes between the crop and edge areas. Both crops sequestered CO2 on an annual basis. However, after taking carbon (C) export via crop harvest and C import via seeding into account, the potato crop shifted to being a moderate C source while the pea crop became near C neutral. Even though CH4 emissions were low, substantial N2O emissions outweighed the CO2 uptake from the atmosphere by both crops, resulting in both being GHG sources. Key Points: Combining eddy‐covariance and flux‐chamber data with flux analysis allowed estimation of actual greenhouse gas fluxes from crop fieldsIncluding export (harvesting) and import (seeding), peas were near carbon neutral while potatoes were a moderate carbon sourceFor both potato and pea crops, nitrous oxide contributed the largest proportion of the annual total greenhouse gas emissions [ABSTRACT FROM AUTHOR]

Area burned, number of fires, seasonal fire severity, and fire season length are all expected to increase in Canada, with largely unquantified ecosystem feedbacks. However, there are few observational studies measuring ecosystem-scale biogeochemical (e.g., carbon dioxide exchanges) and biophysical (e.g., energy partitioning) properties during smoke episodes and hence assessing responses of gross primary production (GPP) to changes in incoming diffuse photosynthetically active radiation (PAR). In this study, we leveraged two long-term eddy covariance measurement sites in forest and wetland ecosystems to study four smoke episodes, which happened at different times and differed in length, over 4 different years (2015, 2017, 2018, and 2020). We found that the highest decrease in shortwave irradiance due to smoke was about 50 % in July and August but increased to about 90 % when the smoke arrived in September. When the smoke arrived in the later stage of summer, impacts on sensible and latent heat fluxes were very different than the earlier ones. Smoke generally increased the diffuse fraction (DF) from ∼ 0.30 to ∼ 0.50 and turned both sites into stronger carbon dioxide (CO2) sinks with increased GPP up to ∼ 18 % and ∼ 7 % at the forest and wetland site, respectively. However, when DF exceeded 0.80 as a result of dense smoke, both ecosystems became net CO2 sources as total PAR dropped to low values. The results suggest that this kind of natural experiment is important for validating future predictions of smoke–productivity feedbacks. [ABSTRACT FROM AUTHOR]

Area burned, number of fires, seasonal fire severity, and fire season length are all expected to increase in Canada, with largely unquantified ecosystem feedbacks. However, there are few observational studies measuring the ecosystem-scale biogeochemical and biophysical properties during smoke episodes, and hence accessing productivity effects of changes in incident diffuse photosynthetically active radiation (PAR). In this study, we leverage two long-term eddy covariance measurement sites in forest and wetland to study four smoke episodes, which happened at different times and differed in length, over four different years. We found that the highest decrease of shortwave irradiance due to smoke was about 50% in July and August but increased to about 90% when the smoke arrived in September. When the smoke arrived in the later stage of summer, impacts on H and LE were also greatest. Smoke generally increased the diffuse fraction from ~0.30 to ~0.50 and turned both sites into stronger carbon-dioxide (CO2) sinks with increased productivity of ~18% and ~7% at the forest and wetland sites, respectively. However, when the diffuse fraction exceeded 0.80 as a result of dense smoke, both ecosystems became CO2 sources as total PAR dropped to low values. The results suggest that this kind of natural experiment is important for validating future predictions of smoke-productivity feedbacks. [ABSTRACT FROM AUTHOR]

Rewetting of disturbed peatlands is an important restoration strategy for climate change mitigation. Previous work primarily focuses on the biogeochemical processes altered by rewetting and few studies have investigated the biophysical impacts, which can diminish or amplify biogeochemical effects beyond the ecosystem scale. We used a paired flux tower approach in a restored peatland to collect year‐round eddy covariance data to assess the biophysical impacts of disturbance and management practices. The first site was actively rewetted and is characterized by Sphagnum and white beak‐rush with patches of open water. The second site represents a disturbed ecosystem, which underwent natural regeneration and is dominated by scrub pine, Sphagnum, and low shrubs. We found that the actively restored site had higher net radiation compared to the second site due to more surface water ponding; however, the higher aerodynamic conductance at the passively restored site contributed to enhanced daytime turbulent fluxes, and hence, both sites had similar aerodynamic temperatures during the daytime. The actively restored site experienced warmer nighttime and seasonal aerodynamic temperature as much of the excess radiation during the day was stored in the water column and released at night. To achieve restoration goals, higher water tables are now maintained throughout large sections of the bog. The study implies that water table manipulation has the potential to minimize greenhouse gas emissions from the bog, thereby allowing the biophysical impacts of peatland restoration to enhance the biogeochemical benefits. Therefore, it is important to consider both biophysical and biogeochemical changes in peatland restoration management. Plain Language Summary: Peatland restoration through rewetting influences the climate by altering land‐atmosphere greenhouse gas dynamics and energy and water exchanges. The energy and water exchanges altered by rewetting can influence surface and air temperature, and hence local and regional climate; however, they are understudied. This study analyzed year‐round water and energy exchange measurements at two sites in the Burns Bog peatland in British Columbia, Canada, which represent two different dominant ecosystem types that experienced different restoration strategies. We found that the rewetted site with more open water had warmer nighttime and seasonal surface temperature. The results indicate that water levels and vegetation type influenced by restoration strategies have a strong impact on energy partitioning, surface aerodynamic characteristics, and consequently surface and air temperatures. Peatland restoration management should recognize the implications of both greenhouse gas fluxes and energy and water exchanges to fully understand climatic impacts of restoration. Key Points: Significant surface temperature differences at night between the two restored peatland sites with different surface characteristicsThe warmer nighttime and seasonal temperatures at the actively rewetted site were due to energy stored in standing water during daytimeRestoration management has to consider biophysical impacts in addition to biogeochemical changes [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In October 2020, under COVID‐19 quarantine, AmeriFlux held its largest and one of its most successful annual meetings. Historically, ∼100 scientists attend; this meeting had over 400 registrants and participants. Participants expressed that this was among the best virtual meetings that they had ever attended, and 100% of post‐meeting survey respondents stated that they would attend again. Feedback revealed the meeting fostered a strong sense of connection to the AmeriFlux community, especially among early career and international scientists. How did a feeling of strong connection to the community arise from the seemingly cold and isolated structures of virtual meetings? The meeting emphasized Diversity, Equity, and Inclusion (DEI), which resulted in an unexpected enhancement of science communication and community connections. Additionally, the meeting experimented with an online virtual gaming‐like world (Gather.town), where users controlled video/audio‐enabled avatars in a conference center and poster hall environment to create spontaneous conversations and discussions. In lieu of a social‐bonding field trip, participants showed videos of their field sites accompanied by informal banter, which were watched in group settings in Gather. Social mixers were structured over Zoom breakout rooms that were limited in size to promote participation with accessible games. Science talks were selected based on appeal to a demographically diverse organizing committee, which enhanced appeal to a broad meeting audience. Finally, breakout reports were given not in the format of bullet point slides, but instead as creatively improvized fairytales, which dramatically enhanced engagement. Here, we describe some of the process that went into the AmeriFlux 2020 meeting. Keeping with the theme of experimenting and fairytales, we present this narrative in the form of a fairytale; and, without further ado... Key Points: Diversity, Equity, and Inclusion are keys to enhanced science communication and community connectionsLean‐in to experimenting with novel meeting tools and options for improved virtual meetingsStory telling as a means of effective science communication and engagement [ABSTRACT FROM AUTHOR]

Coastal wetlands are among the most productive habitats on Earth and sequester globally significant amounts of atmospheric carbon (C). Extreme rates of soil C accumulation are widely assumed to reflect efficient C storage. Yet the fraction of wetland C lost via hydrologic export has not been directly quantified, since comprehensive budgets including direct estimates of lateral C loss are lacking. We present a complete net ecosystem C budget (NECB), demonstrating that lateral losses of C are a major component of the NECB for the largest stable brackish tidal marsh on the U.S. Pacific coast. Mean annual net ecosystem exchange of CO2 with the atmosphere (NEE = −185 g C m2 year−1, negative NEE denoting ecosystem uptake) was compared to long‐term soil C burial (87–110 g C m2 year−1), suggesting only 47–59% of fixed atmospheric C accumulates in soils. Consistently, direct monitoring in 2017–2018 showed NEE of −255 g C m−2 year−1, and hydrologic export of 105 g C m−2 year−1 (59% of NEE remaining on site). Despite their high C sequestration capacity, lateral losses from coastal wetlands are typically a larger fraction of the NECB when compared to other terrestrial ecosystems. Loss of inorganic C (the least measured NECB term) was 91% of hydrologic export and may be the most important term limiting C sequestration. The high productivity of coastal wetlands thus serves a dual function of C burial and estuarine export, and the multiple fates of fixed C must be considered when evaluating wetland capacity for C sequestration. Key Points: We present a complete carbon budget for the largest brackish tidal marsh in the Pacific United StatesDirect and indirect measurement showed 47% to 59% of fixed carbon is stored on site and most loss is through inorganic carbon exportA compilation of global data sets showed lateral loss larger term in coastal wetland carbon budgets, relative to other terrestrial systems [ABSTRACT FROM AUTHOR]

Methane flux (FCH4) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap‐filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap‐filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4. However, there is still no consensus regarding FCH4 gap‐filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA‐derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap‐filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem‐scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes. [ABSTRACT FROM AUTHOR]

Reflooding formerly drained peatlands has been proposed as a means to reduce losses of organic matter and sequester soil carbon for climate change mitigation, but a renewal of high methane emissions has been reported for these ecosystems, offsetting mitigation potential. Our ability to interpret observed methane fluxes in reflooded peatlands and make predictions about future flux trends is limited due to a lack of detailed studies of methanogenic processes. In this study we investigate methanogenesis in a reflooded agricultural peatland in the Sacramento Delta, California. We use the stable‐and radio‐carbon isotopic signatures of wetland sediment methane, ecosystem‐scale eddy covariance flux observations, and laboratory incubation experiments, to identify which carbon sources and methanogenic production pathways fuel methanogenesis and how these processes are affected by vegetation and seasonality. We found that the old peat contribution to annual methane emissions was large (~30%) compared to intact wetlands, indicating a biogeochemical legacy of drainage. However, fresh carbon and the acetoclastic pathway still accounted for the majority of methanogenesis throughout the year. Although temperature sensitivities for bulk peat methanogenesis were similar between open‐water (Q10 = 2.1) and vegetated (Q10 = 2.3) soils, methane production from both fresh and old carbon sources showed pronounced seasonality in vegetated zones. We conclude that high methane emissions in restored wetlands constitute a biogeochemical trade‐off with contemporary carbon uptake, given that methane efflux is fueled primarily by fresh carbon inputs. [ABSTRACT FROM AUTHOR]

Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process ("bottom-up") or inversion ("top-down") models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45 ∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr -1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at 10.5281/zenodo.2560163 (Peltola et al., 2019). [ABSTRACT FROM AUTHOR]

Abstract: Land use change and management affect climate by altering both the biogeochemical and biophysical interactions between the land and atmosphere. Whereas climate policy often emphasizes the biogeochemical impact of land use change, biophysical impacts, including changes in reflectance, energy partitioning among sensible and latent heat exchange, and surface roughness, can attenuate or enhance biogeochemical effects at local to regional scales. This study analyzes 3 years (2015–2017) of turbulent flux and meteorological data across three contrasting wetland restoration sites and one agricultural site, colocated in the Sacramento‐San Joaquin Delta, California, USA, to understand if the biophysical impacts of freshwater wetland restoration can be expected to attenuate or enhance the potential biogeochemical benefits. We show that despite absorbing more net radiation, restored wetlands have the potential to cool daytime surface temperature by up to 5.1  °C, as compared to a dominant drained agricultural land use. Wetland canopy structure largely determines the magnitude of surface temperature cooling, with wetlands that contain areas of open water leading to enhanced nighttime latent heat flux and reduced diurnal temperate range. Daytime surface cooling could be important in ameliorating physiological stress associated with hotter and drier conditions and could also promote boundary layer feedbacks at the local to regional scale. With a renewed focus on the mitigation and adaptation potential of natural and working lands, we must better understand the role of biophysical changes, especially in novel land use transitions like wetland restoration. [ABSTRACT FROM AUTHOR]

Abstract: Peatland drainage is an important driver of global soil carbon loss and carbon dioxide (CO2) emissions. Restoration of peatlands by reflooding reverses CO2 losses at the cost of increased methane (CH4) emissions, presenting a biogeochemical compromise. While restoring peatlands is a potentially effective method for sequestering carbon, the terms of this compromise are not well constrained. Here we present 14 site years of continuous CH4 and CO2 ecosystem‐scale gas exchange over a network of restored freshwater wetlands in California, where long growing seasons, warm weather, and managed water tables result in some of the largest wetland ecosystem CH4 emissions recorded. These large CH4 emissions cause the wetlands to be strong greenhouse gas sources while sequestering carbon and building peat soil. The terms of this biogeochemical compromise, dictated by the ratio between carbon sequestration and CH4 emission, vary considerably across small spatial scales, despite nearly identical wetland climate, hydrology, and plant community compositions. [ABSTRACT FROM AUTHOR]

The objectives of this study were to measure and evaluate the energy balance of a snowpack in a northern peatland, with a particular emphasis on the ground heat flux ( G), and to evaluate the performance of a point energy and mass balance snowmelt model (SNOBAL) in peatland ecosystems. G is typically considered a small component of the snowpack energy balance (EB) when compared with radiative and turbulent fluxes. However, in environments where the soil temperature remains above freezing throughout the winter, G may be an important energy input to the snowpack. For direct assessment of the role of G in the snow energy budget of such an environment, the EB components of the snowpack at the Mer Bleue bog, a northern peatland, were directly measured and modelled using SNOBAL during the 2009-2010 winter. When integrated over the pre-melt period, simulated and measured G proved to be a large contributor to the EB (25%). Net radiation and G were somewhat under-predicted by SNOBAL, whereas turbulent fluxes (especially latent heat fluxes LE) were considerably over-predicted. G calculated by SNOBAL was found to be sensitive to the temperature gradient between the soil and the lower layer of the snowpack, whereas simulated turbulent fluxes were sensitive to the parameterization chosen to estimate roughness lengths for heat and water vapour. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Wetlands can influence global climate via greenhouse gas (GHG) exchange of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O). Few studies have quantified the full GHG budget of wetlands due to the high spatial and temporal variability of fluxes. We report annual open-water diffusion and ebullition fluxes of CO 2 , CH 4 , and N 2 O from a restored emergent marsh ecosystem. We combined these data with concurrent eddy-covariance measurements of whole-ecosystem CO 2 and CH 4 exchange to estimate GHG fluxes and associated radiative forcing effects for the whole wetland, and separately for open-water and vegetated cover types. Annual open-water CO 2 , CH 4 , and N 2 O emissions were 915 ± 95 g C-CO 2  m -2  yr -1 , 2.9 ± 0.5 g C-CH 4  m -2  yr -1 , and 62 ± 17 mg N-N 2 O m -2  yr -1 , respectively. Diffusion dominated open-water GHG transport, accounting for >99% of CO 2 and N 2 O emissions, and ~71% of CH 4 emissions. Seasonality was minor for CO 2 emissions, whereas CH 4 and N 2 O fluxes displayed strong and asynchronous seasonal dynamics. Notably, the overall radiative forcing of open-water fluxes (3.5 ± 0.3 kg CO 2 -eq m -2  yr -1 ) exceeded that of vegetated zones (1.4 ± 0.4 kg CO 2 -eq m -2  yr -1 ) due to high ecosystem respiration. After scaling results to the entire wetland using object-based cover classification of remote sensing imagery, net uptake of CO 2 (-1.4 ± 0.6 kt CO 2 -eq yr -1 ) did not offset CH 4 emission (3.7 ± 0.03 kt CO 2 -eq yr -1 ), producing an overall positive radiative forcing effect of 2.4 ± 0.3 kt CO 2 -eq yr -1 . These results demonstrate clear effects of seasonality, spatial structure, and transport pathway on the magnitude and composition of wetland GHG emissions, and the efficacy of multiscale flux measurement to overcome challenges of wetland heterogeneity., (© 2017 John Wiley & Sons Ltd.)