Temporal and spatial differences of methane flux at arctic tundra in Alaska

High latitude ecosystems were thought to enhance CH_4 emission in relation to the current arctic warming. However, we have little information about this potential feedback mechanisms on climate change, thus, model parameterization is insufficient and the observational data are required. We observed CH_4 flux at several types of tundra in Alaska over the growing seasons since 1995. From these observed data, we examined current CH_4 emission and its controlling factors on Alaskan tundra. Then we discussed about spatial and temporal differences in CH_4 flux. Daily trend of half hourly CH_4 flux had little relation with soil temperature, but the seasonal trend of daily flux changed with soil or water temperature. Cumulative CH_4 fluxes during the growing seasons were 8.1gCH_4m^(-2) on wet sedge tundra at Happy Valley in 1995, 3.3gCH_4m^(-2) on non-acidic moist tundra in 1996, and 3.58-8.24gCH_4m^(-2) on wet sedge tundra at Barrow between 1999-2003. Non-acidic tundra had low CH_4 emission with low CO_2 accumulation. There was large spatial difference in CH_4 flux caused by tundra type, and the large temporal difference at the wet sedge tundra reflected yearly weather variability

Ecological research in the Large Scale Biosphere Atmosphere Experiment in Amazonia: A discussion of early results

The Large-scale Biosphere–Atmosphere Experiment in Amazonia (LBA) is a multinational, interdisciplinary research program led by Brazil. Ecological studies in LBA focus on how tropical forest conversion, regrowth, and selective logging influence carbon storage, nutrient dynamics, trace gas fluxes, and the prospect for sustainable land use in the Amazon region. Early results from ecological studies within LBA emphasize the variability within the vast Amazon region and the profound effects that land-use and land-cover changes are having on that landscape. The predominant land cover of the Amazon region is evergreen forest; nonetheless, LBA studies have observed strong seasonal patterns in gross primary production, ecosystem respiration, and net ecosystem exchange, as well as phenology and tree growth. The seasonal patterns vary spatially and interannually and evidence suggests that these patterns are driven not only by variations in weather but also by innate biological rhythms of the forest species. Rapid rates of deforestation have marked the forests of the Amazon region over the past three decades. Evidence from ground-based surveys and remote sensing show that substantial areas of forest are being degraded by logging activities and through the collapse of forest edges. Because forest edges and logged forests are susceptible to fire, positive feedback cycles of forest degradation may be initiated by land-use-change events. LBA studies indicate that cleared lands in the Amazon, once released from cultivation or pasture usage, regenerate biomass rapidly. However, the pace of biomass accumulation is dependent upon past land use and the depletion of nutrients by unsustainable land-management practices. The challenge for ongoing research within LBA is to integrate the recognition of diverse patterns and processes into general models for prediction of regional ecosystem function

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 similar to 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.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

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 halfhourly 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).Peer reviewe

Soil and Community Characteristics Associated with Hazardia orcuttii (Asteraceae)

Volume: 56Start Page: 229End Page: 23

Dry Season Evapotranspiration Dynamics over Human-Impacted Landscapes in the Southern Amazon Using the Landsat-Based METRIC Model

Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land cover types over highly fragmented landscapes in the southern Amazon and characterize the ET dynamics during the dry season using the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC, a Landsat-based ET model, that generates spatially continuous ET estimates at a 30 m spatial resolution widely used for agricultural applications, was adapted to the southern Amazon by using the NDVI indexed reference ET fraction (ETrF) approach. Compared to flux tower-based ET rates, this approach showed an improved performance on the forest ET estimation over the standard METRIC approach, with R2 = 0.73 from R2 = 0.70 and RMSE reduced from 0.77 mm/day to 0.35 mm/day. We used this approach integrated into the METRIC procedure to estimate ET rates from primary, regenerated, and degraded forests and pasture in Acre, Rondônia, and Mato Grosso, all located in the southern Amazon, during the dry season in 2009. The lowest ET rates occurred in Mato Grosso, the driest region. Acre and Rondônia, both located in the southwestern Amazon, had similar ET rates for all land cover types. Dry season ET rates between primary forest and regenerated forest were similar (p > 0.05) in all sites, ranging between 2.5 and 3.4 mm/day for both forest cover types in the three sites. ET rates from degraded forest in Mato Grosso were significantly lower (p < 0.05) compared to the other forest cover types, with a value of 2.03 mm/day on average. Pasture showed the lowest ET rates during the dry season at all study sites, with the dry season average ET varying from 1.7 mm/day in Mato Grosso to 2.8 mm/day in Acre

Soil N, P, and C dynamics of upland and seasonally flooded forests of the Brazilian Pantanal

Seasonal variations in cerrado nutrient availability and mineralization are poorly understood, especially for “hyperseasonal” cerrado, which experiences both flooding and drought over an annual cycle. Here we quantified seasonal variations in soil ammonium (NH4+), phosphorus (P), and organic C (SOC) concentration and net mineralization in upland and seasonally flooded cerrado forests of the Brazilian Pantanal, and hypothesized that NH4+, P, and SOC concentrations and net mineralization would decline under flooding and increase during the dry season as soil becomes unsaturated. We found that C and nutrient concentrations and mineralization were significantly affected by seasonal variations in hydrology; however, differences between flooded and upland forests varied over time and were not always related to flooding. Soil extractable P, but not net mineralization, was approximately 10-times higher in the upland forest, while the flooded forest had higher extractable NH4+ concentration than the upland forest under both flooded and drained conditions. Soil C concentration was significantly higher in the upland forest even though C mineralization was similar for both forests. Thus, despite the large seasonal and spatial variations in hydrology, the effects of flooding depended on the particular response variable studied and the season. While a limited survey of the literature indicates that forests exposed to intermittent flooding had on average higher concentrations of extractable NH4+ and P, the upland and hyperseasonal forests studied here were richer in extractable NH4+ (upland and flooded) and P (upland) compared to other upland and hyperseasonal forests and woodlands. Given that the forests studied here shared nearly a third of the total tree species and had similar physiognomy, these results suggest that nutrient controls on cerrado structural diversity may be similar in upland and hyperseasonal cerrado

Maximum leaf photosynthetic light response for three species in a transitional tropical forest in Southern Amazonia

Measurements of CO2 and water vapor flux using eddy covariance are being made from a 40 m tower located in a transitional tropical forest near Sinop Mato Grosso. As complementary information to this study, the photosynthetic light response curves of three species located near the tower were measured at different heights in the forest canopy and light conditions with the objective of understanding seasonal and spatial (height in the forest canopy and gap or shade located plants) trends in the photosynthetic light response. The measurements were made in a canopy emergent tree (30 m tall) identified as Brosimum lactescens, and in two relatively short plants (0.6 to 1.6 m height) identified as Quiina pteridophylla and Diniszia excelsa located in different light condition. Measurements were made from the end of 2000 dry season to January 2002. These data suggest that species response to seasonal variations in rainfall are variable. In addition, shade plants have a higher quantum yield (A) and a lower estimated gross photosynthesis at saturating (photosynthetic active radiation - PAR) (Amax) than gap plants, presumably because shade plants are adapted to lower average light levels