Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales.

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.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

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.

Radiative forcing of methane fluxes offsets net carbon dioxide uptake for a tropical flooded forest.

Wetlands are important sources of methane (CH4 ) and sinks of carbon dioxide (CO2 ). However, little is known about CH4 and CO2 fluxes and dynamics of seasonally flooded tropical forests of South America in relation to local carbon (C) balances and atmospheric exchange. We measured net ecosystem fluxes of CH4 and CO2 in the Pantanal over 2014-2017 using tower-based eddy covariance along with C measurements in soil, biomass and water. Our data indicate that seasonally flooded tropical forests are potentially large sinks for CO2 but strong sources of CH4 , particularly during inundation when reducing conditions in soils increase CH4 production and limit CO2 release. During inundation when soils were anaerobic, the flooded forest emitted 0.11 ± 0.002 g CH4 -C m-2  d-1 and absorbed 1.6 ± 0.2 g CO2 -C m-2  d-1 (mean ± 95% confidence interval for the entire study period). Following the recession of floodwaters, soils rapidly became aerobic and CH4 emissions decreased significantly (0.002 ± 0.001 g CH4 -C m-2  d-1 ) but remained a net source, while the net CO2 flux flipped from being a net sink during anaerobic periods to acting as a source during aerobic periods. CH4 fluxes were 50 times higher in the wet season; DOC was a minor component in the net ecosystem carbon balance. Daily fluxes of CO2 and CH4 were similar in all years for each season, but annual net fluxes varied primarily in relation to flood duration. While the ecosystem was a net C sink on an annual basis (absorbing 218 g C m-2 (as CH4 -C + CO2 -C) in anaerobic phases and emitting 76 g C m-2 in aerobic phases), high CH4 effluxes during the anaerobic flooded phase and modest CH4 effluxes during the aerobic phase indicate that seasonally flooded tropical forests can be a net source of radiative forcings on an annual basis, thus acting as an amplifying feedback on global warming.

Biochar feedstock and pyrolysis temperature effects on leachate: DOC characteristics and nitrate losses from a Brazilian Cerrado Arenosol mixed with agricultural waste biochars.

Dissolved organic carbon (DOC) leached from Brazilian Cerrado Arenosols can lead to carbon (C) losses and lower soil fertility, while excessive nutrient, e.g. nitrate (NO3-), leaching can potentially cause water contamination. As biochar has been shown to stabilize C and retain soil nutrients, a greenhouse experiment was conducted to test different biochars' contributions to DOC and NO3- leaching from a sandy soil. Biochars were made from four local agricultural waste feedstocks (cotton residue, swine manure, eucalyptus sawmill residue, sugarcane filtercake) pyrolysed at 400, 500 and 600 °C. Biochar was mixed with soil at 5% weight in pots and maize seeds planted. Leachate was collected weekly for six weeks and analyzed for DOC and NO3- concentrations, while fluorescence spectroscopy with parallel factor analysis (PARAFAC) was used to interpret DOC characteristics. Cotton and swine manure biochar treatments had higher DOC and NO3- losses than eucalyptus biochar, filtercake biochar, and control treatments. Cotton and swine manure biochar treatments at high temperatures lost mostly terrestrial, humified DOC, while swine manure, filtercake, and eucalyptus biochars at low temperatures lost mostly labile, microbially-derived DOC. Through the practical use of fluorescence spectroscopy, our study identified filtercake and eucalyptus biochars as most promising for retaining DOC and NO3- in a Cerrado Arenosol, potentially reducing stable C and nutrient losses.