Methane emissions from soils: synthesis and analysis of a large <scp>UK</scp> data set

Levy, Peter E.; Burden, Annette; Cooper, Mark; Dinsmore, Kerry J.; Drewer, Julia; Evans, C. D.; Fowler, D.; Gaiawyn, Jenny; Gray, Alan; Jones, Stephanie; Jones, Timothy G. J.; McNamara, Niall P.; Mills, Robert T. E.; Ostle, Nicholas J.; Sheppard, Lucy J.; Skiba, U.; Sowerby, Alwyn; Ward, Susan E.; Zieliński, Piotr

doi:10.1111/j.1365-2486.2011.02616.x

Cited by 122 publications

(119 citation statements)

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“…The correlation between the depth of the water table and CH 4 fluxes accords with field experiments [Heikkinen et al, 2002;Nykänen et al, 1998], suggesting that an inverse relationship exists between water table position and CH 4 fluxes (deeper water tables lead to smaller emissions). In addition, the heterogeneity of soil properties is also an important control on the variations of CH 4 emissions [Levy et al, 2011].…”

Section: Discussionmentioning

confidence: 99%

“…[8] To begin, we collected direct CH 4 flux chamber measurements of wetland ecosystems in the northern high latitudes from peer-reviewed literature [e.g., Glagolev et al, 2011;Levy et al, 2011]. Our data contain CH 4 flux chamber measurements from 34 sites, covering a range of wetland types under various field conditions (Table 1 and Figure 1).…”

Section: Data Organizationmentioning

confidence: 99%

“…[4] Estimates of wetland CH 4 emissions are often obtained using "bottom-up" approaches, ranging from simple empirical or statistical models [e.g., Andronova and Karol, 1993;Granberg et al, 1997;Levy et al, 2011] to detailed process-based models [e.g., Cao et al, 1996;Walter et al, 2001;Zhuang et al, 2004]. Previous process-based model simulations presented a large uncertainty in the estimates of [5] The uncertainty in these estimates could result from many sources including model structures, assumptions, parameterization, and choice of forcing data.…”

Section: Introductionmentioning

confidence: 99%

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Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

Zhu

Zhuang

Qin

et al. 2013

Global Biogeochemical Cycles

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[1] Methane (CH 4 ) emissions from wetland ecosystems in nothern high latitudes provide a potentially positive feedback to global climate warming. Large uncertainties still remain in estimating wetland CH 4 emisions at regional scales. Here we develop a statistical model of CH 4 emissions using an artificial neural network (ANN) approach and field observations of CH 4 fluxes. Six explanatory variables (air temperature, precipitation, water table depth, soil organic carbon, soil total porosity, and soil pH) are included in the development of ANN models, which are then extrapolated to the northern high latitudes to estimate monthly CH 4 emissions from 1990 to 2009. We estimate that the annual wetland CH 4 source from the northern high latitudes (north of 45 N) is 48.7 Tg CH 4 yr À1 (1 Tg = 10 12 g) with an uncertainty range of 44.0~53.7 Tg CH 4 yr À1 . The estimated wetland CH 4 emissions show a large spatial variability over the northern high latitudes, due to variations in hydrology, climate, and soil conditions. Significant interannual and seasonal variations of wetland CH 4 emissions exist in the past 2 decades, and the emissions in this period are most sensitive to variations in water table position. To improve future assessment of wetland CH 4 dynamics in this region, research priorities should be directed to better characterizing hydrological processes of wetlands, including temporal dynamics of water table position and spatial dynamics of wetland areas.Citation: Zhu, X., Q. Zhuang, Z. Qin, M. Glagolev, and L. Song (2013), Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks, Global Biogeochem. Cycles, 27, 592-604,

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Section: Discussionmentioning

confidence: 99%

Section: Data Organizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

Zhu

Zhuang

Qin

et al. 2013

Global Biogeochemical Cycles

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“…Additionally, low water levels and oxygen availability are also key drivers of nitrous oxide (N 2 O) production in organic soils (Regina et al, 1996), which increases the relevance of organic soils for climate change mitigation policy. During anaerobic conditions when water levels are at or above the land surface, substantial methane (CH 4 ) emissions can occur (Levy et al, 2012).…”

Section: Bechtold Et Al: Large-scale Regionalization Of Water Tabmentioning

confidence: 99%

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

Bechtold¹,

Tiemeyer²,

Laggner³

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Fluxes of the three main greenhouse gases (GHG) CO 2 , CH 4 and N 2 O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WL t ). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WL t is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45 % of WL t variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WL t and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WL t variance cannot be explained by nationally available predictor variables and that predictors with stronger WL t indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

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“…Freibauer and Kaltschmitt (2003) reported that the fluxes of N to the atmosphere and to ground water by leaching (Hack-ten Broeke et al 1999) are greater from the intensively managed grasslands than from croplands due to higher nitrate input. Lee et al (2006) and Abdalla et al (2014) observed negative CH 4 fluxes from cropland; however, fluxes from organic soils, which are typically poorly drained in their natural state (not included in this study), could be high (Levy et al 2012). However, the estimate of an annual net GHG balance of 0.34 Mt CO 2 e y -1 assumes stability of existing land uses, and any recent historic land use change is a source of uncertainty in the result, e.g.…”

Section: Effects Of Climate Change On Ghg and Soc Fluxes And Net Primmentioning

confidence: 93%

Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate

et al. 2016

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The Welsh Government is committed to reduce greenhouse gas (GHG) emissions from agricultural systems and combat the effects of future climate change. In this study, the ECOSSE model was applied spatially to estimate GHG and soil organic carbon (SOC) fluxes from three major land uses (grass, arable and forest) in Wales. The aims of the simulations were: (1) to estimate the annual net GHG balance for Wales; (2) to investigate the efficiency of the reduced nitrogen (N) fertilizer goal of the sustainable land management scheme (Glastir), through which the Welsh Government offers financial support to farmers and land managers on GHG flux reduction; and (3) to investigate the effects of future climate change on the emissions of GHG and plant net primary production (NPP). Three climate scenarios were studied: baseline ) and low and high emission climate scenarios . Results reveal that grassland and cropland are the major nitrous oxide (N 2 O) emitters and consequently emit more GHG to the atmosphere than forests. The overall average simulated annual net GHG balance for Wales under baseline climate ) is equivalent to 0.2 t CO 2 e ha -1 y -1 which gives an estimate of total annual net flux for Wales of 0.34 Mt CO 2 e y -1 . Reducing N fertilizer by 20 and 40 % could reduce annual net GHG fluxes by 7 and 25 %, respectively. If the current N fertilizer application rate continues, predicted climate change by the year 2050 would not significantly affect GHG emissions or NPP from soils in Wales.

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Methane emissions from soils: synthesis and analysis of a large UK data set

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Cited by 122 publications

References 54 publications

Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate

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