144 research outputs found
Belowground plant allocation regulates rice methane emissions from degraded peat soils
Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO and NO emissions from peat. However, wet rice cultivation can release considerable methane (CH). Water table and soil management strategies may enhance rice yield and minimize CH emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH. Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH and BGB were positively related, with BGB explaining 60% of the variation in CH but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH emissions from wet rice cultivation in degraded peatlands
On the asymptotic giant branch star origin of peculiar spinel grain OC2
Microscopic presolar grains extracted from primitive meteorites have
extremely anomalous isotopic compositions revealing the stellar origin of these
grains. The composition of presolar spinel grain OC2 is different from that of
all other presolar spinel grains. Large excesses of the heavy Mg isotopes are
present and thus an origin from an intermediate-mass (IM) asymptotic giant
branch (AGB) star was previously proposed for this grain. We discuss the
isotopic compositions of presolar spinel grain OC2 and compare them to
theoretical predictions. We show that the isotopic composition of O, Mg and Al
in OC2 could be the signature of an AGB star of IM and metallicity close to
solar experiencing hot bottom burning, or of an AGB star of low mass (LM) and
low metallicity suffering very efficient cool bottom processing. Large
measurement uncertainty in the Fe isotopic composition prevents us from
discriminating which model better represents the parent star of OC2. However,
the Cr isotopic composition of the grain favors an origin in an IM-AGB star of
metallicity close to solar. Our IM-AGB models produce a self-consistent
solution to match the composition of OC2 within the uncertainties related to
reaction rates. Within this solution we predict that the 16O(p,g)17F and the
17O(p,a)14N reaction rates should be close to their lower and upper limits,
respectively. By finding more grains like OC2 and by precisely measuring their
Fe and Cr isotopic compositions, it may be possible in the future to derive
constraints on massive AGB models from the study of presolar grains.Comment: 10 pages, 8 figures, accepted for publication on Astronomy &
Astrophysic
Substantial hysteresis in emergent temperature sensitivity of global wetland CH<sub>4</sub> emissions
Wetland methane (CH4) emissions (FCH4
) are important in global carbon budgets and climate
change assessments. Currently, FCH4
projections rely on prescribed static temperature
sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent
FCH4
temperature dependence across spatial scales for use in models; however, sitelevel
studies demonstrate that FCH4
are often controlled by factors beyond temperature.
Here, we evaluate the relationship between FCH4
and temperature using observations from
the FLUXNET-CH4 database. Measurements collected across the globe show substantial
seasonal hysteresis between FCH4
and temperature, suggesting larger FCH4
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.s
Substantial hysteresis in emergent temperature sensitivity of global wetland CH emissions
Wetland methane (CH) emissions (F) are important in global carbon budgets and climate change assessments. Currently, F projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F are often controlled by factors beyond temperature. Here, we evaluate the relationship between F and temperature using observations from the FLUXNET-CH database. Measurements collected across the globe show substantial seasonal hysteresis between F and temperature, suggesting larger F 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 CH production are thus needed to improve global CH budget assessments
FLUXNET-CH<sub>4</sub> synthesis activity: objectives, observations, and future directions
We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements
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 half-hourly 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)
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