scholarly journals Seasonal and spatial variability of methane emissions from a subtropical reservoir in Eastern China

2018 ◽  
Author(s):  
Le Yang ◽  
Hepeng Li ◽  
Chunlei Yue ◽  
Jun Wang
Keyword(s):  
Spatial Variability ◽  
Time Scales ◽  
Seasonal Variability ◽  
Eastern China ◽  
Methane Emissions ◽  
Spatiotemporal Variability ◽  
Ch4 Emission ◽  
Atmospheric Methane ◽  
Ch4 Flux ◽  
Minimum Value

Abstract. Subtropical reservoirs are important source of atmospheric methane (CH4). This study aims to investigate the spatiotemporal variability of CH4 emission, using the methods of static floating chambers and bubble traps, from the water surfaces of Xin'anjiang Reservoir. Seasonal variability showed that CH4 emission from the main reservoir body was high in autumn and low in spring, with medium values in summer and winter. The dynamics of CH4 emission was flat from February to June, but fluctuated dramatically from July to January in the upstream river, which was interrupted by the bubbles in the second half year. However, CH4 emission was largely influenced by the streamflow in the downstream river, with a minimum value in February due to an extreme low streamflow (275 m3 s−1). Spatial variability showed the upstream river had the highest CH4 flux (3.90 ± 7.80 mg CH4 m−2 h−1), followed by the downstream river (0.50 ± 0.41 mg CH4 m−2 h−1), and the main reservoir body stood the last place (0.01 ± 0.07 mg CH4 m−2 h−1). Therefore, it was necessary to capture the variation of CH4 emission from reservoirs in the space and time scales to avoid the error of estimating the CH4 emission incorrectly.

scholarly journals A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

2014 ◽  
Vol 7 (5) ◽  
pp. 1901-1918 ◽  
Author(s):  
J. Ray ◽  
V. Yadav ◽  
A. M. Michalak ◽  
B. van Bloemen Waanders ◽  
S. A. McKenna
Keyword(s):  
Spatial Variability ◽  
Carbon Dioxide Emissions ◽  
Fossil Fuel ◽  
Matching Pursuit ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Spatiotemporal Variability ◽  
Realistic Representation ◽  
Non Gaussian ◽  
Low Dimensional

Abstract. The characterization of fossil-fuel CO2 (ffCO2) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based) spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a~parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

scholarly journals Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

2012 ◽  
Vol 9 (7) ◽  
pp. 2793-2819 ◽  
Author(s):  
L. Meng ◽  
P. G. M. Hess ◽  
N. M. Mahowald ◽  
J. B. Yavitt ◽  
W. J. Riley ◽  
...  
Keyword(s):  
Soil Ph ◽  
High Latitude ◽  
Large Range ◽  
Rice Paddy ◽  
Temporal Variations ◽  
Methane Emissions ◽  
Biogeochemical Model ◽  
Atmospheric Methane ◽  
Rice Paddies ◽  
Community Land Model

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources are still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model, because there are large differences between simulated fractional inundation and satellite observations, and thus we do not use CLM4-simulated hydrology to predict inundated areas. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid-cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1 (including the soil sink) and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78% of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. However, sensitivity studies show a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions (excluding emissions from rice paddies). The large range is sensitive to (1) the amount of methane transported through aerenchyma, (2) soil pH (±100 Tg CH4 yr−1), and (3) redox inhibition (±45 Tg CH4 yr−1). Results are sensitive to biases in the CLMCN and to errors in the satellite inundation fraction. In particular, the high latitude methane emission estimate may be biased low due to both underestimates in the high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4.

scholarly journals Methane emissions by alpine plant communities in the Qinghai–Tibet Plateau

2008 ◽  
Vol 4 (6) ◽  
pp. 681-684 ◽  
Author(s):  
Guangmin Cao ◽  
Xingliang Xu ◽  
Ruijun Long ◽  
Qilan Wang ◽  
Changting Wang ◽  
...  
Keyword(s):  
Plant Communities ◽  
Methane Emissions ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Atmospheric Methane ◽  
Closed Chamber ◽  
Methane Budget ◽  
Alpine Plant Communities ◽  
Regional Basis ◽  
Qinghai Tibet Plateau

For the first time to our knowledge, we report here methane emissions by plant communities in alpine ecosystems in the Qinghai–Tibet Plateau. This has been achieved through long-term field observations from June 2003 to July 2006 using a closed chamber technique. Strong methane emission at the rate of 26.2±1.2 and 7.8±1.1 μg CH 4 m −2  h −1 was observed for a grass community in a Kobresia humilis meadow and a Potentilla fruticosa meadow, respectively. A shrub community in the Potentilla meadow consumed atmospheric methane at the rate of 5.8±1.3 μg CH 4 m −2  h −1 on a regional basis; plants from alpine meadows contribute at least 0.13 Tg CH 4 yr −1 in the Tibetan Plateau. This finding has important implications with regard to the regional methane budget and species-level difference should be considered when assessing methane emissions by plants.

scholarly journals SOIL CONTROLLING FACTORS OF METHANE GAS PRODUCTION FROM FLOODED RICE FIELDS IN PATI DISTRICT, CENTRAL JAVA

2016 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
P. Setyanto ◽  
Rosenani A.B. ◽  
A.K. Makarim ◽  
Che Fauziah I. ◽  
A. Bidin ◽  
...  
Keyword(s):  
Great Influence ◽  
Gas Production ◽  
Rice Fields ◽  
Limiting Factors ◽  
Stepwise Multiple Regression ◽  
Ch4 Emission ◽  
Atmospheric Methane ◽  
Production Potential ◽  
Central Java ◽  
Ch4 Production

Atmospheric methane (CH4) is recognized as one of the most important greenhouse gases. Methane, with some 15-30 times greater infrared-absorbing capability than CO2 on a mass basis, may account for 20% of anticipated global warming. Soils are one of the key factors, which play an important role in CH4 production and emission. However, data on CH4 emission from different soil types and the characteristics affecting CH4 production are lacking when compared to data on agronomic practices. This study was conducted to investigate the potential of CH4 production of selected soils in Java, and determine the limiting factors of CH4 production. The results showed that addition of 1% glucose to the soils led to an increase in CH4 production by more than twelve fold compared to no glucose addition. The CH4 production potential ranged between 3.21 and 112.30 mg CH4 kg-1 soil. The lowest CH4 production potential occurred in brown-grayish Grumosol, while the highest was in dark-gray Grumosol. Chemical and physical properties of the soils have great influence on CH4 production. Stepwise multiple regression analysis of CH4 production and soil characteristics showed that pH and the contents of Fe2O3, MnO2, SO4, and silt in the soil strongly influenced CH4 production. Results of this study can be used for further development of a model on CH4 emission from rice fields.

scholarly journals SOIL CONTROLLING FACTORS OF METHANE GAS PRODUCTION FROM FLOODED RICE FIELDS IN PATI DISTRICT, CENTRAL JAVA

2016 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
P. Setyanto ◽  
Rosenani A.B. ◽  
A.K. Makarim ◽  
Che Fauziah I. ◽  
A. Bidin ◽  
...  
Keyword(s):  
Great Influence ◽  
Gas Production ◽  
Rice Fields ◽  
Limiting Factors ◽  
Stepwise Multiple Regression ◽  
Ch4 Emission ◽  
Atmospheric Methane ◽  
Production Potential ◽  
Central Java ◽  
Ch4 Production

Atmospheric methane (CH4) is recognized as one of the most important greenhouse gases. Methane, with some 15-30 times greater infrared-absorbing capability than CO2 on a mass basis, may account for 20% of anticipated global warming. Soils are one of the key factors, which play an important role in CH4 production and emission. However, data on CH4 emission from different soil types and the characteristics affecting CH4 production are lacking when compared to data on agronomic practices. This study was conducted to investigate the potential of CH4 production of selected soils in Java, and determine the limiting factors of CH4 production. The results showed that addition of 1% glucose to the soils led to an increase in CH4 production by more than twelve fold compared to no glucose addition. The CH4 production potential ranged between 3.21 and 112.30 mg CH4 kg-1 soil. The lowest CH4 production potential occurred in brown-grayish Grumosol, while the highest was in dark-gray Grumosol. Chemical and physical properties of the soils have great influence on CH4 production. Stepwise multiple regression analysis of CH4 production and soil characteristics showed that pH and the contents of Fe2O3, MnO2, SO4, and silt in the soil strongly influenced CH4 production. Results of this study can be used for further development of a model on CH4 emission from rice fields.

scholarly journals Carbonaceous aerosols in China: top-down constraints on primary sources and estimation of secondary contribution

2011 ◽  
Vol 11 (10) ◽  
pp. 28219-28272 ◽  
Author(s):  
T.-M. Fu ◽  
J. J. Cao ◽  
X. Y. Zhang ◽  
S. C. Lee ◽  
Q. Zhang ◽  
...  
Keyword(s):  
Eastern China ◽  
Western China ◽  
Spatiotemporal Variability ◽  
Anthropogenic Source ◽  
Carbonaceous Aerosols ◽  
Top Down ◽  
Bottom Up ◽  
Secondary Sources ◽  
Secondary Formation ◽  
Emission Estimate

Abstract. We simulate elemental carbon (EC) and organic carbon (OC) aerosols in China and compare model results to surface measurements at Chinese rural and background sites, with the goal of deriving "top-down" emission estimates of EC and OC, as well as better quantifying the secondary sources of OC. We include in the model state-of-the-science Chinese "bottom-up" emission inventories for EC (1.92 Tg C yr−1) and OC (3.95 Tg C yr−1), as well as updated secondary OC formation pathways. The average simulated annual mean EC concentration at rural and background site is 1.1 μg C m−3, 56% lower than the observed 2.5 μg C m−3. The average simulated annual mean OC concentration at rural and background sites is 3.4 μg C m−3, 76% lower than the observed 14 μg C m−3. Multiple regression to fit surface monthly mean EC observations at rural and background sites yields best estimate of Chinese EC source of 3.05 ± 0.78 Tg C yr−1. Based on the top-down EC emission estimate and observed seasonal primary OC/EC ratios, we estimate Chinese OC total emissions to be 6.67 ± 1.30 Tg C yr−1. Using these top-down estimates, the simulated average annual mean EC concentration at rural and background sites significantly improved to 1.9 μg C m−3. However, the model still significantly underestimates observed OC in all seasons (simulated average annual mean OC at rural and background sites is 5.4 μg C m−3), with little skill in capturing the spatiotemporal variability. Secondary formation accounts for 21% of Chinese annual mean surface OC in the model, with isoprene being the most important precursor. In summer, as high as 62% of the observed surface OC may be due to secondary formation in eastern China. Our analysis points to three shortcomings in the current bottom-up inventories of Chinese carbonaceous aerosols: (1) the anthropogenic source is severely underestimated, particularly for OC; (2) there is a missing source in western China, likely associated with the use of biofuels or other low-quality fuels for heating; and (3) sources in fall are not well represented, either because the seasonal shifting of emissions and/or secondary formation are poorly captured or because specific fall emission events are missing. More regional measurements with better spatiotemporal coverage are needed to resolve these shortcomings.

scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

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