Tropical peat forests are a globally important reservoir of carbon, but little is known about CO2 exchange on an annual basis. We measured CO2 exchange between the atmosphere and tropical peat swamp forest in Sarawak, Malaysia using the eddy covariance technique over 4 years from 2011 to 2014. The CO2 fluxes varied between seasons and years. A small carbon uptake took place during the rainy season at the beginning of 2011, while a substantial net efflux of >600 g C/m2 occurred over a 2 month period in the middle of the dry season. Conversely, the peat ecosystem was a source of carbon during both the dry and rainy seasons in subsequent years and more carbon was lost during the rainy season relative to the dry season. Our results demonstrate that the forest was a net source of CO2 to the atmosphere during every year of measurement with annual efflux ranging from 183 to 632 g C m-2 year-1 , noting that annual flux values were sensitive to gap filling methodology. This is in contrast to the typical view of tropical peat forests which must have acted as net C sinks over time scales of centuries to millennia to create the peat deposits. Path analyses revealed that the gross primary productivity (GPP) and ecosystem respiration (RE) were primarily affected by vapour pressure deficit (VPD). Results suggest that future increases in VPD could further reduce the C sink strength and result in additional net CO2 losses from this tropical peat swamp forest in the absence of plant acclimation to such changes in atmospheric dryness.
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.
Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.