CHAPTER 19 - Greenhouse Gas Assessment at Barksdale Air Force Base—A Case Study

Abstract. Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO2 uptake, it also revealed small but widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale. Average N2O fluxes were negligible. CO2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH4 fluxes by soil moisture (increases net emissions) and vegetation type, and N2O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO2 fluxes across the tundra landscape but suggest that CH4 uptake in dry upland soils might play a significant role in the regional GHG budget.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO₂ uptake, it also revealed small but widespread CH₄ uptake in upland vegetation types, shifting this region to an average net CH₄sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N₂O fluxes were negligible. CO₂ fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH₄ fluxes by soil moisture (increases net emissions) and vegetation type, and N₂O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO₂ fluxes across the tundra landscape, but suggest that CH₄ uptake might play a significant role in the regional GHG budget.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO₂ uptake, it also revealed small but widespread CH₄ uptake in upland vegetation types, shifting this region to an average net CH₄sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N₂O fluxes were negligible. CO₂ fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH₄ fluxes by soil moisture (increases net emissions) and vegetation type, and N₂O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO₂ fluxes across the tundra landscape, but suggest that CH₄ uptake might play a significant role in the regional GHG budget.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO₂ uptake, it also revealed small but widespread CH₄ uptake in upland vegetation types, shifting this region to an average net CH₄sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N₂O fluxes were negligible. CO₂ fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH₄ fluxes by soil moisture (increases net emissions) and vegetation type, and N₂O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO₂ fluxes across the tundra landscape, but suggest that CH₄ uptake might play a significant role in the regional GHG budget.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km²). We used these field data together with high-resolution remote sensing data to develop machine learning models to predict (i.e., upscale) daytime GHG fluxes across the landscape at 2-m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO₂ uptake, it also revealed small but widespread CH₄ uptake in upland vegetation types, shifting this region to an average net CH₄sink at the landscape scale during growing season, despite the presence of high-emitting wetlands. Average N₂O fluxes were negligible. CO₂ fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH₄ fluxes by soil moisture (increases net emissions) and vegetation type, and N₂O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modelling of GHG fluxes in the Arctic. They also reveal the dominant role of CO₂ fluxes across the tundra landscape, but suggest that CH₄ uptake might play a significant role in the regional GHG budget.

There is a lack of information regarding carbon dioxide (CO), methane (CH), and nitrous oxide (NO) emissions from pasture soils and the effects of grazing. The objective of this study was to quantify greenhouse gas (GHG) fluxes from pasture soils grazed with cow-calf pairs managed with different stocking rates and densities. The central hypothesis was that irrigated low-density stocking systems (SysB) would result in greater GHG emissions from pasture soils than nonirrigated high-density stocking systems (SysA) and grazing-exclusion (GRE) pasture sites. The nonirrigated high-density stocking systems consisted of 120 cow-calf pairs rotating on a total of 120 ha (stocking rate 1 cow/ha, stocking density 112,000 kg BW/ha, rest period of 60 to 90 d). The irrigated low-density stocking systems consisted of 64 cow-calf pairs rotating on a total of 26 ha of pasture (stocking rate 2.5 cows/ha, stocking density 32,700 kg BW/ha, rest period of 18 to 30 d). Both systems consisted of mixed cool-season grass-legume pastures. Static chambers were randomly placed for collection of CO, CH, and NO samples. Soil temperature (ST), ambient temperature (temperature inside the chamber; AT), and soil water content (WC) were monitored and considered explanatory variables for GHG emissions. GHG fluxes were monitored for 3 yr (2011 to 2013) at the beginning (P1) and at the end (P2) of the grazing season, always postgrazing. Paddock was the experimental unit (3 pseudoreplicates per treatment), and chambers (30 chambers per paddock) were considered multiple measurements of each experimental unit. A completely randomized design considered the term year × period as a repeated measure and chamber nested within paddock and treatment as the random term. Generally, SysB had greater CO emissions than SysA and GRE pasture sites across years and periods ( < 0.01). Soil temperature, AT, and WC had effects on CO emissions. Methane and NO emissions were observed from pasture sites of the 3 systems, but the effect of grazing was not constantly significant for CH and NO emissions. In addition, ST, AT, and WC did not conclusively explain CH and NO emissions. No clear trade-offs between GHG were observed; generally, GHG emissions increased from 2011 to 2013, which was likely associated with weather conditions, such as higher daily temperature and precipitation events. The central hypothesis that SysB would result in greater GHG emissions from pasture soils than SysA and GRE was not confirmed.

An integrated crop-livestock system (ICLS), when managed properly, can help in mitigating soil surface greenhouse gas (GHG) fluxes, especially carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). However, the impacts of an ICLS on GHG fluxes are poorly understood. The present study was conducted at two sites (northern Brookings [Brookings-N] and northwestern Brookings [Brookings-NW]) established in 2016 and 2017, respectively, under loamy soils in South Dakota. The specific objective was to evaluate the impact of cover crops (CCs) and grazed CCs under oat (Avena sativa L.)-CCs-maize (Zea mays L.) rotation on GHG fluxes. Study treatments included the following: (a) a legume-dominated CC (LdC), (b) a cattle-grazed LdC (LdC+G), (c) a grass-dominated CC (GdC), (d) a cattle-grazed GdC (GdC+G), and (e) one without CC or grazing (NC). Greenhouse gas monitoring occurred weekly during the growing crop seasons in 2016 and 2017 for Brookings-N and in 2017 and 2018 for Brookings-NW. Data showed that cumulative CO2 and N2 O fluxes at Brookings-N were lower for GdC+G (4042 kg C ha-1 for CO2 and 1499 g N ha-1 for N2 O) than for LdC+G (4819 kg C ha-1 for CO2 and 2017 g N ha-1 for N2 O), indicating the superiority of GdC+G over LdC+G in reducing GHG fluxes. However, no effect from grazed CC on cumulative CO2 and N2 O fluxes were observed at the Brookings-NW site. Cumulative CH4 flux was not affected by an ICLS at either site. This short-term investigation showed that, in general, CCs and grazing of CCs and maize residue did not impact GHG fluxes.

Hydro-geomorphic controls of greenhouse gas fluxes in riparian buffers of the White River watershed, IN (USA)

Freshwater wetlands can contribute significantly to the global carbon budget as a net source or sink of the major greenhouse gas (GHG) fluxes such as carbon dioxide (FCO2) and methane (FCH4). The amount of GHG fluxes in the freshwater wetlands is highly variable and depends on a range of environmental drivers. These wetlands are commonly hypothesized to be net sinks (i.e., burial) of FCO2 and net sources (emission) of FCH4 at the monthly to annual scales. Understanding the environmental controls on the wetland GHG fluxes is essential for an accurate estimation of the global GHG budget, which is often used as a pivotal measure to reduce GHG emissions and enhance carbon sequestration. In this study, we analyzed FLUXNET data from 38 freshwater wetlands located across the globe to investigate the relationships of monthly-scale GHG fluxes with various climatic and ecohydrological drivers. Data analytics with multivariate pattern recognition techniques—including principal component analysis, factor analysis, and partial least squares regression— were performed to identify and quantify the dominant controls of wetland FCO2 and FCH4 fluxes. The environmental controls on the GHG fluxes in freshwater wetlands were found to highly vary based on the climatic zones. In the tropical (i.e., mega thermal) zone, the GHG fluxes were overall primarily controlled by photosynthetically active radiation (PAR), soil temperature (TS), wind speed (WS), friction velocity (USTAR), and vapor pressure deficit (VPD). However, the latent heat flux (LE) and VPD, alongside PAR, TS, and USTAR, exhibited the dominant controls on the GHG fluxes in the dry (or arid) zone wetlands. Both GHG fluxes in wetlands of the temperate (or mesothermal) zone were mainly controlled by water table depth (WL), TS, and LE. Surprisingly, PAR did not appear to be a strong driver of the monthly averaged fluxes in the temperate wetlands. In contrast, PAR, LE, TS, WS, and USTAR were the primary controlling factors of the GHG fluxes in wetlands representing continental (or microthermal) climates. However, in wetlands of the polar (alpine) region, sensible heat flux (H) had a strong linkage with the GHG fluxes, alongside the controls of PAR, TS, WS, VPD and USTAR. These findings and new knowledge can help inform wetland management and conservation strategies, particularly in the context of climate and land cover changes. Effective management and conservation of wetlands can help reduce GHG emissions, thereby contributing to the mitigation efforts on global warming.

The study of greenhouse gas (GHG) fluxes in terrestrial ecosystems is becoming increasingly important as the observed rise in global temperature and increased frequency of extreme weather events are attributed by the majority of climate experts to increased atmospheric GHG concentrations. Adequate and comprehensive knowledge of surface GHG fluxes is important for obtaining reliable information on CO2 and other GHG fluxes at regional and global scales, as well as for preparing reports on national GHG emissions and removals. The need to obtain accurate estimates of GHG fluxes at regional and global scales has led to the development of innovative mathematical models of varying complexity. These models can be divided into forward and inverse models. Forward algorithms provide the ability to estimate GHG fluxes when sufficient information on the structure of GHG sources and sinks is available. Inverse algorithms allow the retrieval of surface fluxes using remote sensing data. The most promising way to study high resolution fluxes over areas with complex topography and mosaic vegetation patterns is the use of unmanned aerial vehicles (UAVs).In our study, we proposed and tested a forward and inverse model for estimating GHG fluxes over an inhomogeneous underlying surface. The forward model is based on the RANS hydrodynamic model to calculate the wind velocity and turbulence coefficient, and the solution of the advection-diffusion equation to find a three-dimensional distribution of GHG concentrations. The GHG fluxes at the specified height above the ground surface are then calculated using the obtained concentration distribution and turbulence coefficient. The inverse algorithm is based on minimizing a cost functional, defined as the root mean square deviation of the modeled concentration field from the measured data. Concentration measurements at multiple (at least two) levels can be performed using UAV-based gas analyzers.Three experimental sites selected for our modeling study differ in geographic location, topography, and vegetation heterogeneity. These sites are: i) swampy and forested areas of the "Mukhrino" carbon supersite (Khanty-Mansiysk Autonomous Okrug, Russia, 60°53'20" N, 68°42'10" E), ii) the Roshni-Chu mountain forest site, which is part of the "Way Carbon" supersite (Chechen Republic, Russia, 43°2'59" N, 45°25'32" E), iii) the mixed forest experimental site "Lyali" (Komi Republic, Russia, 62°16'28" N, 50°39'54" E). For our numerical experiments we used measured data on surface topography, LAI, soil respiration, air temperature, prevailing wind direction, vertical canopy CO2 concentration profile and CO2 fluxes measured by eddy covariance technique.The model results show a rather good agreement with the measured data and could help to interpret the experimentally observed dependence of CO2 fluxes on wind direction in areas with an inhomogeneous underlying surface.

Evaluating temporal controls on greenhouse gas (GHG) fluxes in an Arctic tundra environment: An entropy-based approach

International News - August 2016

U.S. 4,639, 486 Fire retardant elastomers

Fluxes of carbon dioxide, methane and nitrous oxide in two contrastive fringing zones of coastal lagoon, Lake Nakaumi, Japan

Global warming will increase the greenhouse gas (GHG) fluxes of permafrost regions. However, little is known about the difference in GHG fluxes among different types of permafrost regions. In this study, we used the static opaque chamber and gas chromatography techniques to determine the fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in predominantly continuous permafrost (PCP), predominantly continuous and island permafrost (PCIP), and sparsely island permafrost (SIP) regions during the growing season. The main factors causing differences in GHG fluxes among three types of permafrost regions were also analyzed. The results showed mean CO2 fluxes in SIP were significantly higher than that in PCP and PCIP, which were 342.10 ± 11.46, 105.50 ± 10.65, and 127.15 ± 14.27 mg m-2 h-1, respectively. This difference was determined by soil temperature, soil moisture, total organic carbon (TOC), nitrate nitrogen (NO3--N), and ammonium nitrogen (NH4+-N) content. Mean CH4 fluxes were -26.47 ± 48.83 (PCP), 118.35 ± 46.93 (PCIP), and 95.52 ± 32.86 μg m-2 h-1 (SIP). Soil temperature, soil moisture, and TOC content were the key factors to determine whether permafrost regions were CH4 sources or sinks. Similarly, PCP behaved as the sink of N2O, PCIP and SIP behaved as the source of N2O. Mean N2O fluxes were -3.90 ± 1.71, 0.78 ± 1.55, and 3.78 ± 1.59 μg m-2 h-1, respectively. Soil moisture and TOC content were the main factors influencing the differences in N2O fluxes among the three permafrost regions. This study clarified and explained the differences in GHG fluxes among three types of permafrost regions, providing a data basis for such studies.