Geostatistical inverse modeling with very large datasets: an example from the 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 CO2 observations from 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 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 approximation are typically an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

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

Geoscientific Model Development ◽

10.5194/gmd-13-1771-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1771-1785

Author(s):

Scot M. Miller ◽

Arvind K. Saibaba ◽

Michael E. Trudeau ◽

Marikate E. Mountain ◽

Arlyn E. Andrews

Keyword(s):

Greenhouse Gas ◽

Inverse Modeling ◽

Initial Guess ◽

In Situ Monitoring ◽

Co2 Fluxes ◽

Gas Fluxes ◽

Wide Range ◽

Greenhouse Gas Fluxes

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.

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Estimating the greenhouse gas fluxes of European grasslands with a process-based model: 1. Model evaluation from in situ measurements

Global Biogeochemical Cycles ◽

10.1029/2005gb002611 ◽

2007 ◽

Vol 21 (1) ◽

Cited By ~ 26

Author(s):

Nicolas Vuichard ◽

Jean-François Soussana ◽

Philippe Ciais ◽

Nicolas Viovy ◽

Christof Ammann ◽

...

Keyword(s):

Greenhouse Gas ◽

Model Evaluation ◽

In Situ Measurements ◽

Gas Fluxes ◽

Greenhouse Gas Fluxes

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Land use change and the impact on greenhouse gas exchange in north Australian savanna soils

Biogeosciences Discussions ◽

10.5194/bgd-8-9087-2011 ◽

2011 ◽

Vol 8 (5) ◽

pp. 9087-9123 ◽

Cited By ~ 1

Author(s):

S. P. P. Grover ◽

S. J. Livesley ◽

L. B. Hutley ◽

H. Jamali ◽

B. Fest ◽

...

Keyword(s):

Land Use ◽

Nitrous Oxide ◽

Land Use Change ◽

Greenhouse Gas ◽

Water Content ◽

Soil Water Content ◽

Co2 Fluxes ◽

Soil Co2 ◽

Gas Fluxes ◽

Greenhouse Gas Fluxes

Abstract. Savanna ecosystems are subject to accelerating land use change as human demand for food and forest products increases. Land use change has been shown to both increase and decrease greenhouse gas fluxes from savannas and considerable uncertainty exists about the non-CO2 fluxes from the soil. We measured methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) over a complete wet-dry seasonal cycle at three replicated sites of each of three land uses: savanna, young pasture and old pasture (converted from savanna 5–7 and 25–30 yr ago, respectively) in the Douglas Daly region of northern Australia. The effect of break of season rains at the end of the dry season was investigated with two irrigation experiments. Land use change from savanna to pasture increased net greenhouse gas fluxes from the soil. Pasture sites were a weaker sink for CH4 than savanna sites and, under wet conditions, old pastures turned from being sinks to a significant source of CH4. Nitrous oxide emissions were generally very low, in the range of 0 to 5 μg N2O-N m−2 h−1, and under dry conditions soil uptake of N2O was apparent. Break of season rains produced a small, short lived pulse of N2O up to 20 μg N2O-N m−2 h−1, most evident in pasture soil. Annual cumulative soil CO2 fluxes increased after clearing, with savanna (14.6 t CO2-C ha−1 yr−1) having the lowest fluxes compared to old pasture (18.5 t CO2-C ha−1 yr−1) and young pasture (20.0 t CO2-C ha−1 yr−1). Clearing savanna increased soil-based greenhouse gas emissions from 53 to ~70 t CO2-equivalents, a 30% increase dominated by an increase in soil CO2 emissions and shift from soil CH4 sink to source. Seasonal variation was clearly driven by soil water content, supporting the emerging view that soil water content is a more important driver of soil gas fluxes than soil temperature in tropical ecosystems where temperature varies little among seasons.

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Land use change and the impact on greenhouse gas exchange in north Australian savanna soils

Biogeosciences ◽

10.5194/bg-9-423-2012 ◽

2012 ◽

Vol 9 (1) ◽

pp. 423-437 ◽

Cited By ~ 36

Author(s):

S. P. P. Grover ◽

S. J. Livesley ◽

L. B. Hutley ◽

H. Jamali ◽

B. Fest ◽

...

Keyword(s):

Land Use ◽

Nitrous Oxide ◽

Land Use Change ◽

Greenhouse Gas ◽

Water Content ◽

Soil Water Content ◽

Co2 Fluxes ◽

Soil Co2 ◽

Gas Fluxes ◽

Greenhouse Gas Fluxes

Abstract. Savanna ecosystems are subjected to accelerating land use change as human demand for food and forest products increases. Land use change has been shown to both increase and decrease greenhouse gas fluxes from savannas and considerable uncertainty exists about the non-CO2 fluxes from the soil. We measured methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) over a complete wet-dry seasonal cycle at three replicate sites of each of three land uses: savanna, young pasture and old pasture (converted from savanna 5–7 and 25–30 yr ago, respectively) in the Douglas Daly region of Northern Australia. The effect of break of season rains at the end of the dry season was investigated with two irrigation experiments. Land use change from savanna to pasture increased net greenhouse gas fluxes from the soil. Pasture sites were a weaker sink for CH4 than savanna sites and, under wet conditions, old pastures turned from being sinks to a significant source of CH4. Nitrous oxide emissions were generally very low, in the range of 0 to 5 μg N2O-N m−2 h−1, and under dry conditions soil uptake of N2O was apparent. Break of season rains produced a small, short lived pulse of N2O up to 20 μg N2O-N m−2 h−1, most evident in pasture soil. Annual cumulative soil CO2 fluxes increased after clearing, with savanna (14.6 t CO2-C ha−1 yr−1) having the lowest fluxes compared to old pasture (18.5 t CO2-C ha−1 yr−1) and young pasture (20.0 t CO2-C ha−1 yr−1). Clearing savanna increased soil-based greenhouse gas emissions from 53 to ∼ 70 t CO2-equivalents, a 30% increase dominated by an increase in soil CO2 emissions and shift from soil CH4 sink to source. Seasonal variation was clearly driven by soil water content, supporting the emerging view that soil water content is a more important driver of soil gas fluxes than soil temperature in tropical ecosystems where temperature varies little among seasons.

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Direct and Indirect Effects of Soil Fauna, Fungi and Plants on Greenhouse Gas Fluxes

Measuring Emission of Agricultural Greenhouse Gases and Developing Mitigation Options using Nuclear and Related Techniques ◽

10.1007/978-3-030-55396-8_5 ◽

2021 ◽

pp. 151-176

Author(s):

M. Zaman ◽

K. Kleineidam ◽

L. Bakken ◽

J. Berendt ◽

C. Bracken ◽

...

Keyword(s):

Greenhouse Gas ◽

Indirect Effects ◽

Biological Properties ◽

Plant Residues ◽

Direct And Indirect Effects ◽

Gas Fluxes ◽

Wide Range ◽

Greenhouse Gas Fluxes ◽

Metabolic Activities ◽

Ghg Fluxes

AbstractSoils harbour diverse soil faunaand a wide range of soil microorganisms. These fauna and microorganisms directly contribute to soil greenhouse gas (GHG) fluxes via their respiratory and metabolic activities and indirectly by changing the physical, chemical and biological properties of soils through bioturbation, fragmentation and redistribution of plant residues, defecation, soil aggregate formation, herbivory, and grazing on microorganisms and fungi. Based on recent results, the methods and results found in relation to fauna as well as from fungi and plants are presented. The approaches are outlined, and the significance of these hitherto ignored fluxes is discussed.

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