Estimate Greenhouse Gas Fluxes Research Articles

Dynamics of Picea abies mortality and CO2 and CH4 fluxes from spruce trees decomposition in the southwest of the Valdai Upland, Russia

A mass decline of Picea abies (hereinafter – spruce), often associated with outbreaks of Ips typographus, is one of the main reasons for the reduction of spruce forests. In turn, dry and fallen trees can be both stock and source of greenhouse gases at various stages of decomposition. In our study, using an unmanned aerial vehicle, we evaluated the dynamics of spruce decline in two forest types in the southwest of the Valdai Upland (Central Forest State Nature Reserve, Russia), namely Sphagnum-bilberry forests and nemoral spruce forests. It was found that the rate of decline in Sphagnum-bilberry spruce forest was much higher than in nemoral spruce forest. By the fourth year after a windfall on 0.13 km2, 913 spruce individuals had withered in Sphagnum-bilberry forest and 66 ones in the nemoral spruce forest. Based on direct measurements of greenhouse gas fluxes by chamber method on dead trunks and coarse woody debris, it was found that in relative values the highest amount of CO2 is emitted by coarse woody debris of the decay classes 3–4 (800–1800 mg CO2 × m-2 × h-1). Deadwood and coarse woody debris from the first decay classes are assumed to be a source of CH4 (0.0008–0.0070 mg CO2 × m-2 × h-1), and from classes 3–5 they are a stock (from -0.0070 mg CO2 × m-2 × h-1 to -0.0009 mg CO2 × m-2 × h-1). When converted to the total surface areas of deadwood and coarse woody debris of the study sites, it was found that coarse woody debris of the decay classes 3–5 (2.3–13.6 kg CO2 × h-1) made the highest contribution to the integral CO2 emission, and deadwood (67 mg CH4 × h-1) made the highest contribution to the CH4 emission. Significant differences in greenhouse gas fluxes were found both between deadwood and decay classes of coarse woody debris, and between fluxes from deadwood and coarse woody debris of individual decay classes in various forest types. The results have shown the importance of considering deadwood and all available decay classes of coarse woody debris when estimating greenhouse gas fluxes from dead timber and the contribution of debris to the carbon cycle in forest ecosystems.

Computationally efficient methods for large-scale atmospheric inverse modeling

Abstract. Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix–vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.

Numerical experiments with real data for estimating greenhouse gas fluxes in a region

This work is devoted to the problem of obtaining an estimate of methane emissions using satellite data and the results of mathematical modeling. To implement the algorithm, a variant of the local Kalman ensemble filter (LETKF) is used, which represents an optimal estimate of the desired parameter based on observational data and a forecast based on a metematic model in a given time interval. This algorithm has properties that allow it to be used locally, i.e., to assimilate data by subdomains. The paper presents the implementation of the algorithm for real observational data and the results of mathematical modeling (calculation of the forecast of the state of the system). The results of the three-dimensional model of transport and diffusion (MOZART-4) are taken as the results of mathematical modeling, and satellite observations (AIRS data) are used as observational data. As a result of the algorithm, an average estimate of methane fluxes in the subdomain was obtained at specified time intervals.

Unraveling the Contribution of Turbulence and Bubbles to Air-Water Gas Exchange in Running Waters.

Quantifying air‐water gas exchange is critical for estimating greenhouse gas fluxes and metabolism in aquatic ecosystems. In high‐energy streams, the gas exchange rate k is poorly constrained, due to an incomplete understanding of turbulence and bubble contributions to k. We performed a flume experiment with air bubble additions to evaluate the combined effects of turbulence and bubbles on k for helium, argon, xenon, and methane. We created contrasting hydraulic conditions by varying channel slope, bed roughness, water discharge, and bubble flux. We found that k increased from 1–4 to 17–66 m d−1 with increases in turbulence and bubble flux metrics. Mechanistic models that explicitly account for these metrics, as well as gas diffusivity and solubility, agreed well with the data and indicated that bubble‐mediated gas exchange accounted for 64–93% of k. Bubble contributions increased with bubble flux but were independent of gas type, as bubbles did not equilibrate with the water. This was evident through modeled bubble life and equilibration times inferred from bubble size distributions obtained from underwater sound spectra. Sound spectral properties correlated well with turbulence and bubble flux metrics. Our results demonstrate that (a) mechanistic models can be applied to separate free surface‐ and bubble‐mediated gas exchange in running waters, (b) bubble life and equilibration times are critical for accurate scaling of k between different gases, and (c) ambient sound spectra can be used to approximate contributions of turbulence and bubbles.

Greenhouse gas budgets of severely polluted urban lakes in India

Inland waters are important sources of greenhouse gases and emissions from polluted subtropical systems may be contributing to the observed global increase in atmospheric methane concentrations. Here we detail a scoping study where dissolved concentrations of greenhouse gases methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) were measured in two contrasting urban lakes in Bangalore (Bengaluru), Karnataka, India, from June 2018 to February 2020. Bellandur Lake is a severely polluted system whilst Jakkur Lake has been subject to partial restoration via treatment of organic matter inputs. Methane concentrations in Bellandur Lake were three orders of magnitude higher than in Jakkur Lake, with a mean concentration of 3.02 ± 1.57 mg CH4-C L−1 compared to 1.72 ± 1.22 μg CH4-C L−1. At Bellandur Lake, dissolved CO2 concentrations were of the same order of magnitude as for CH4, whereas at Jakkur Lake dissolved CO2 concentrations were two orders of magnitude greater than for CH4. Concentrations of N2O were negligible in both lakes. Extrapolating our data to estimate greenhouse gas fluxes, mean daily methane fluxes from Bellandur Lake were consistently in excess of 1000 mg CH4 m2 d−1, rendering the lake a source of GHGs to the order of 148,350 ± 21,790 ton yr−1 CO2-e yr−1, compared to 100 ± 37 ton CO2-e yr−1 from Jakkur Lake, with CH4 contributing primarily to this difference. We propose that the contribution of severely polluted urban lakes to global CH4 production warrants further investigation, particularly as our evidence suggests that standard secondary wastewater treatment to support restoration of these systems has the potential to significantly reduce CH4 emissions.

Water Hyacinth’s Effect on Greenhouse Gas Fluxes: A Field Study in a Wide Variety of Tropical Water Bodies

Water hyacinth is able to sequester large amounts of carbon dioxide (CO2) in wetlands. At the same time, the high production of organic matter combined with the plant’s capacity to limit the diffusion of oxygen from the atmosphere into the water creates favorable conditions for the production of methane (CH4). The combination of these mechanisms challenges the prediction of water hyacinth’s net effects on greenhouse gas (GHG) emissions. To unravel the impact of water hyacinth on GHG fluxes, we performed an extensive fieldwork study encompassing 22 sites dominated by water hyacinth in the Pantanal and Amazon during two different seasons. The highest CH4 emissions from water hyacinth beds occurred in shallow systems where sediment rooting enabled plant-mediated CH4 transport (307 ± 407 mg CH4 m−2 day−1 in waters shallower than 1 m, as opposed to 6.1 ± 10.6 mg CH4 m−2 day−1 in deeper waters). When CO2 uptake rates are added to the GHG budget (in terms of global warming potential), the water bodies were usually a GHG sink (− 5.2 ± 10 gCO2 eq m−2 day−1). The strength of the sink is highest in deeper systems where even a low water hyacinth coverage may already offset open water emissions. This dual effect of strong CO2 uptake—and at least temporal carbon storage in biomass—in combination with a high CO2–to-biomass-to-CH4 (and possibly back to CO2) conversion highlights the necessity to include vegetation characteristics in relation to depth when estimating GHG fluxes for tropical wetlands.

Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite

Abstract. Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a single emissions inventory or a bottom–up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using a synthetic CO2 case study based upon NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4×106 unknown fluxes using 9.9×104 synthetic observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximations are, typically, an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Wetlands and carbon revisited

This paper summarizes 19 papers published in a special issue of Ecological Engineering under the general banner of wetlands and carbon. Many of the papers were presented at a special session at EcoSummit 2016 in Montpellier, France in August-September 2016. The papers are in four general categories: estimating greenhouse gas fluxes with eddy covariance; methane and other greenhouse gas emissions from wetlands; carbon sequestration by wetlands; and organic carbon decomposition in wetlands. Overall, we found that further development and wider use of eddy covariance measuring stations could help clarify long-term annual budgets of CO2 and CH4 in wetlands and, while CH4 fluxes in some coastal wetlands such as mangroves can be negligible, some inland wetlands can be significant sources of CH4 where hydroperiods are the major determinants of the emission rates. The papers here demonstrated a variety of parameters determining dynamics of methane fluxes and carbon sequestration in different wetlands and lead us to suggest that more attention should be paid to detailed analysis of the impact of environmental and management factors on carbon budgets in wetlands. Carbon sequestration is almost always estimated by soil dating methods such as with 137Cs and 210Pb isotopes and results continue to show order of magnitude differences among systems but sometimes among laboratories. This is due to the inexact nature of estimating soil dates over relatively short (∼50 year) time horizons. The idea of capping landfills, which are enormous methane sources, with wetlands which could then turn them into carbon sinks is worth investigating further but the design of these systems is still in its infancy. Organic carbon decomposition in wetlands has been investigated extensively for decades and more recently on the biogeochemical processes. ‘Enzymic latches” such as phenol oxidase, which can break down phenolics and therefore speed up decomposition, may be key controllers of organic matter decomposition in waterlogged and submerged soils in wetlands. The best approach for balancing greenhouse gases and carbon sequestration remains an enigma, but the papers in this special issue take us one step closer to providing clarity.

Effects of seasonality, transport pathway, and spatial structure on greenhouse gas fluxes in a restored wetland.

Wetlands can influence global climate via greenhouse gas (GHG) exchange of carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). Few studies have quantified the full GHG budget of wetlands due to the high spatial and temporal variability of fluxes. We report annual open-water diffusion and ebullition fluxes of CO2 , CH4 , and N2 O from a restored emergent marsh ecosystem. We combined these data with concurrent eddy-covariance measurements of whole-ecosystem CO2 and CH4 exchange to estimate GHG fluxes and associated radiative forcing effects for the whole wetland, and separately for open-water and vegetated cover types. Annual open-water CO2 , CH4 , and N2 O emissions were 915±95g C-CO2 m-2 yr-1 , 2.9±0.5g C-CH4 m-2 yr-1 , and 62±17mgN-N2 Om-2 yr-1 , respectively. Diffusion dominated open-water GHG transport, accounting for >99% of CO2 and N2 O emissions, and ~71% of CH4 emissions. Seasonality was minor for CO2 emissions, whereas CH4 and N2 O fluxes displayed strong and asynchronous seasonal dynamics. Notably, the overall radiative forcing of open-water fluxes (3.5±0.3kgCO2 -eqm-2 yr-1 ) exceeded that of vegetated zones (1.4±0.4kgCO2 -eqm-2 yr-1 ) due to high ecosystem respiration. After scaling results to the entire wetland using object-based cover classification of remote sensing imagery, net uptake of CO2 (-1.4±0.6ktCO2 -eqyr-1 ) did not offset CH4 emission (3.7±0.03ktCO2 -eqyr-1 ), producing an overall positive radiative forcing effect of 2.4±0.3ktCO2 -eqyr-1 . These results demonstrate clear effects of seasonality, spatial structure, and transport pathway on the magnitude and composition of wetland GHG emissions, and the efficacy of multiscale flux measurement to overcome challenges of wetland heterogeneity.

Measurement of greenhouse gas emissions from Australian feedlot beef production using open-path spectroscopy and atmospheric dispersion modelling

Feedlot production of beef cattle results in concentrated sources of gas emissions to the atmosphere. Reported here are the preliminary results of a micrometeorological study using open-path concentration measurements to determine whole-of-feedlot emissions of methane (CH4) and ammonia (NH3). Tunable near-infrared diode lasers were used to measure line-averaged (150–400 m) open-path concentrations of CH4 and NH3. A backward Lagrangian stochastic model of atmospheric dispersion and the software package WindTrax were used to estimate greenhouse gas fluxes from the measured concentrations. We studied typical Australian beef feedlots in the north (Queensland) and south (Victoria) of the continent. The data from a campaign during summer show a range of CH4 emissions from 146 g/animal.day in Victoria to 166 g/animal.day in Queensland and NH3 emissions from 125 g/animal.day in Victoria to 253 g/animal.day Queensland.