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

WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes

The analysis of gross primary production (GPP) is crucial to better understand CO2 exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the ecosystem. Understanding these dynamics is essential to better comprehend the responses of environments to ongoing climatic changes. The objective of the present study was to analyze, through AMERIFLUX and LBA network measurements, the variability of GPP and WUE in four distinct tropical biomes in Brazil: Pantanal, Amazonia, Caatinga and Cerrado (savanna). Furthermore, data measured by eddy covariance systems were used to assess remotely sensed GPP products (MOD17). We found a distinct seasonality of meteorological variables and energy fluxes with different latent heat controls regarding available energy in each site. Remotely sensed GPP was satisfactorily related with observed data, despite weak correlations in interannual estimates and consistent overestimations and underestimations during certain months. WUE was strongly dependent on water availability, with values of 0.95 gC kg−1 H2O (5.79 gC kg−1 H2O) in the wetter (drier) sites. These values reveal new thresholds that had not been previously reported in the literature. Our findings have crucial implications for ecosystem management and the design of climate policies regarding the conservation of tropical biomes, since WUE is expected to change in the ongoing climate change scenario that indicates an increase in frequency and severity of dry periods.

Long-term dynamics of soil, tree stem and ecosystem methane fluxes in a riparian forest

The carbon (C) budgets of riparian forests are sensitive to climatic variability. Therefore, riparian forests are hot spots of C cycling in landscapes. Only a limited number of studies on continuous measurements of methane (CH₄) fluxes from riparian forests is available. Here, we report continuous high-frequency soil and ecosystem (eddy-covariance; EC) measurements of CH₄ fluxes with a quantum cascade laser absorption spectrometer for a 2.5-year period and measurements of CH₄ fluxes from tree stems using manual chambers for a 1.5 year period from a temperate riparian Alnus incana forest. The results demonstrate that the riparian forest is a minor net annual sink of CH₄ consuming 0.24 kg CH₄-C ha^-1 y^-1. Soil water content is the most important determinant of soil, stem, and EC fluxes, followed by soil temperature. There were significant differences in CH₄ fluxes between the wet and dry periods. During the wet period, 83% of CH₄ was emitted from the tree stems while the ecosystem-level emission was equal to the sum of soil and stem emissions. During the dry period, CH₄ was substantially consumed in the soil whereas stem emissions were very low. A significant difference between the EC fluxes and the sum of soil and stem fluxes during the dry period is most likely caused by emission from the canopy whereas at the ecosystem level the forest was a clear CH₄ sink. Our results together with past measurements of CH₄ fluxes in other riparian forests suggest that temperate riparian forests can be long-term CH₄ sinks.

Quantification of Ecosystem-Scale Methane Sinks Observed in a Tropical Rainforest in Hainan, China

Tropical rainforest ecosystems are important when considering the global methane (CH4) budget and in climate change mitigation. However, there is a lack of direct and year-round observations of ecosystem-scale CH4 fluxes from tropical rainforest ecosystems. In this study, we examined the temporal variations in CH4 flux at the ecosystem scale and its annual budget and environmental controlling factors in a tropical rainforest of Hainan Island, China, using 3 years of continuous eddy covariance measurements from 2016 to 2018. Our results show that CH4 uptake generally occurred in this tropical rainforest, where strong CH4 uptake occurred in the daytime, and a weak CH4 uptake occurred at night with a mean daily CH4 flux of −4.5 nmol m−2 s−1. In this rainforest, the mean annual budget of CH4 for the 3 years was −1260 mg CH4 m−2 year−1. Furthermore, the daily averaged CH4 flux was not distinctly different between the dry season and wet season. Sixty-nine percent of the total variance in the daily CH4 flux could be explained by the artificial neural network (ANN) model, with a combination of air temperature (Tair), latent heat flux (LE), soil volumetric water content (VWC), atmospheric pressure (Pa), and soil temperature at −10 cm (Tsoil), although the linear correlation between the daily CH4 flux and any of these individual variables was relatively low. This indicates that CH4 uptake in tropical rainforests is controlled by multiple environmental factors and that their relationships are nonlinear. Our findings also suggest that tropical rainforests in China acted as a CH4 sink during 2016–2018, helping to counteract global warming.

The impacts of climate change on bank performance: What’s the mediating role of natural disasters?

This research estimates the effect of climate change on bank performance by considering the mediating role of natural disasters via unbalanced panel data from 2005 to 2018 in 127 countries. Moreover, this paper uses forest land, carbon dioxide emissions, and temperature change to measure climate change, takes economic loss and the number of deaths as costs of natural disasters, and utilizes the non-performing loan ratio and the ratio of bank capital to assets as bank performance. The following conclusions are reached according to our empirical evidence. First, forest land has a strong negative effect on the non-performing loan ratio. Second, forest land decreases the economic losses and deaths due to natural disasters, and natural disasters also decrease banks’ non-performing loan ratio. We also find that forest land increases the ratio of bank capital to assets through decreasing natural disasters. Third, there is a significant mediating effect of climate change on bank performance via natural disasters in high-income countries, but not in low-income countries. Finally, the decline of forest land and the rise of carbon dioxide emissions both have significantly positive effects on man-made disasters, which increase banks’ non-performing loan ratio and decrease the ratio of bank capital to assets. One policy implication is that improving climate change, preventing natural disasters, and promoting financial institutions can help decrease the economic losses of natural disasters and banks.

Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America.

Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO₂ ) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH₄ ) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH₄ flux (F_CH4 ) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale F_CH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F_CH4 reached a peak of over 0.1 g CH₄ -C m^-2  day^-1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime F_CH4 was negligible. In this mangrove, the mean annual CH₄ emission was 11.7 ± 0.4 g CH₄ -C m^-2  year^-1 while the annual net ecosystem CO₂ exchange ranged between -891 and -690 g CO₂ -C m^-2  year^-1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F_CH4 could offset the negative radiative forcing caused by CO₂ uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F_CH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale F_CH4 in a mangrove wetland with long-term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH₄ emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.

Aboveground Carbon Storage and Cycling of Flooded and Upland Forests of the Brazilian Pantanal

Tropical forests and savanna (cerrado) are important carbon (C) sinks; however, few data exist for seasonally flooded forests. We quantified the annual rates of aboveground net primary production (ANPP) over a five-year period for two forests, an upland mixed forest and a seasonally flooded cerrado forest, located in the northern Pantanal region of Brazil. We hypothesized that rates of ANPP would be higher for the mixed forest than the cerrado forest because seasonal flooding can limit rates of tree growth. ANPP was calculated as the sum of the annual litter production measured from litterfall traps and the stem growth increment measured from dendrometers and tree density. ANPP varied between 3.8–5.5 MgC ha−1 y−1 for the flooded forest and 1.6–4.6 MgC ha−1 y−1 for the upland forest. Litter production accounted for 57% of the ANPP, and the difference in ANPP between the upland and flooded forests was due to consistently higher litter production in the flooded forest. Annual variations in ANPP were not correlated with annual precipitation, presumably because the hydrology of these sites is driven more by the flood stage of the Cuiaba River than by local precipitation. However, consistent declines in forest floor litter mass occurred at both sites, suggesting that C storage may be responding to some unknown disturbance that occurred prior to our sampling campaign. Seasonal variation in rainfall exerted an important control on litter production dynamics, with leaf litter production increasing during the dry season and stem and reproductive litter production increasing during the wet season. While there are few studies of seasonally flooded tropical forests, our data suggest that the seasonally flooded and upland forests of the northern Pantanal can act as appreciable aboveground C sinks.

Impact of forest plantation on methane emissions from tropical peatland

Tropical peatlands are a known source of methane (CH4 ) to the atmosphere, but their contribution to atmospheric CH4 is poorly constrained. Since the 1980s, extensive areas of the peatlands in Southeast Asia have experienced land-cover change to smallholder agriculture and forest plantations. This land-cover change generally involves lowering of groundwater level (GWL), as well as modification of vegetation type, both of which potentially influence CH4 emissions. We measured CH4 exchanges at the landscape scale using eddy covariance towers over two land-cover types in tropical peatland in Sumatra, Indonesia: (a) a natural forest and (b) an Acacia crassicarpa plantation. Annual CH4 exchanges over the natural forest (9.1 ± 0.9 g CH4  m-2  year-1 ) were around twice as high as those of the Acacia plantation (4.7 ± 1.5 g CH4  m-2  year-1 ). Results highlight that tropical peatlands are significant CH4 sources, and probably have a greater impact on global atmospheric CH4 concentrations than previously thought. Observations showed a clear diurnal variation in CH4 exchange over the natural forest where the GWL was higher than 40 cm below the ground surface. The diurnal variation in CH4 exchanges was strongly correlated with associated changes in the canopy conductance to water vapor, photosynthetic photon flux density, vapor pressure deficit, and air temperature. The absence of a comparable diurnal pattern in CH4 exchange over the Acacia plantation may be the result of the GWL being consistently below the root zone. Our results, which are among the first eddy covariance CH4 exchange data reported for any tropical peatland, should help to reduce the uncertainty in the estimation of CH4 emissions from a globally important ecosystem, provide a more complete estimate of the impact of land-cover change on tropical peat, and develop science-based peatland management practices that help to minimize greenhouse gas 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.

Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest

Several studies have explored the linkages between phenology and ecosystem productivity across the Amazon basin. However, few studies have focused on flooded forests, which correspond to c.a. 14% of the basin. In this study, we assessed the seasonality of ecosystem productivity (gross primary productivity, GPP) from eddy covariance measurements, environmental drivers and phenological patterns obtained from the field (leaf litter mass) and satellite measurements (enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer/multi-angle implementation correction (MODIS/MAIAC)) in an Amazonian floodplain forest. We found that ecosystem productivity is limited by soil moisture in two different ways. During the flooded period, the excess of water limits GPP (Spearman’s correlation; rho = −0.22), while during non-flooded months, GPP is positively associated with soil moisture (rho = 0.34). However, GPP is maximized when cumulative water deficit (CWD) increases (rho = 0.81), indicating that GPP is dependent on the amount of water available. EVI was positively associated with leaf litter mass (Pearson’s correlation; r = 0.55) and with GPP (r = 0.50), suggesting a coupling between new leaf production and the phenology of photosynthetic capacity, decreasing both at the peak of the flooded period and at the end of the dry season. EVI was able to describe the inter-annual variations on forest responses to environmental drivers, which have changed during an observed El Niño-Southern Oscillation (ENSO) year (2015/2016).