Research (EDGAR) during the same period (Fig. 4.4) and
consistent with the emissions reported by India to the
UNFCCC (NATCOM BUR-1 and BUR-2). The data used in
Ganesan et al. (2013) are XCH 4 based on the CO 2 proxy
method from the GOSAT satellite; flask-based measurements of dry-air CH 4 mole fraction from Sinhagad (73.75°
E, 18.35° N, 1600 m ASL), Cape Rama, India (73.83° E,
15.08° N, 60 m ASL), in situ measurements from Darjeeling, India (88.25° E, 27.03° N, 2200 m ASL); and
upper-atmospheric in situ measurements from the CARIBIC
aircraft (Fig. 4.4a). Further, the Lagrangian particle dispersion model, Numerical Atmospheric dispersion Modeling
Environment (NAME), was used to provide a quantitative
relationship between atmospheric mole fractions and emissions. However, the observation of Ganesan et al. (2017) is
subjected to further verification for certain reasons. It is well
known that the inverse modeling results depend strongly on
the selection of chemistry transport model and treatment of
atmospheric measurements. In particular, the regional
transport and inverse models for long-lived gas simulation
suffer from the use of boundary conditions; e.g., a recent
study shows the emission estimates based on observations
over Siberia are greatly affected by the trends in emissions of
CH 4 over Europe and Asia (Sasakawa et al. 2017). Accurate
estimation of emission trends requires uninterrupted long
term atmospheric observations from the region. Although
Ganesan et al. (2017) used multiple streams of in situ data
over India, none of the measurement locations continuously
measured CH 4 during the period of their analysis (2010–
2015). The remote-sensing measurements from GOSAT do
not retrieve XCH 4 for the regions covered by clouds at any
thickness, leading to a seasonal bias in inverse model calculation. To overcome these limitations, a well-structured
GHGs long-term observational network is to be set up across
India (Nalini et al. 2019).
Apart from the above activities, a few aircraft-based
GHGs measurements have also been carried out over the
Indian subcontinent for the vertical profiling of CO 2 over
Bhubaneswar, Varanasi and Jodhpur in order to study the
spatiotemporal distribution of CO 2 mixing ratio and
inter-comparison with the satellite-derived products
(Sreenivas et al. 2019). Out of these three places, Varanasi
showed a strong gradient in CO 2 mixing ratio (ca.
3.12 ppm/km), while in the other two cities the gradient was
much smaller (<2 ppm/km). Varanasi region being in the
Indo-Gangetic Plain is characterized by strong anthropogenic loading, which has resulted in a relatively steep
vertical gradient. The Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) Project of
the Indian Institute of Tropical Meteorology also carried out
similar observations during the Indian summer monsoon
months of 2014, 2015 and 2018, 2019 over India and
adjacent oceanic regions. The vertical profiling of methane
mixing ratio revealed a strong peak at about 4.5 km. It is
believed that this kind of mid-tropospheric peak occurs due
to strong convective activities (Chandra et al. 2017). Among
other work, uptake of winter time carbon by the agricultural
practices around Delhi has been discussed in Umezawa et al.
(2016).
4.3 Greenhouse Gas Flux Measurements
in Natural Ecosystems
GHGs fluxes are monitored over India primarily by two
major measurement networks that corroborate efforts from
multiple research institutes, universities and government
organizations. Two important GHG components, viz., CO 2
and CH 4 , are being monitored by the chamber-based or eddy
covariance (EC) systems along with the other scalar fluxes
including water vapor and energy.
Estimated CO 2 flux from these measurements is subsequently used to calculate net ecosystem exchange (NEE),
gross primary productivity (GPP) and total ecosystem respiration (TER) using various process-based biogeoscientific
models. These parameters are the different components of
the ecosystem carbon cycle. Some definitions are as follows:
NEE: The net amount of carbon exchanged (in the form of
CO 2 ) between the land biosphere and the atmosphere at a
given location over a particular period of time; negative and
positive values of NEE denote uptake and release of carbon
by the land biosphere, respectively.
NEP: Net Ecosystem Productivity; opposite of NEE i.e.
positive and negative values of NEP denote carbon uptake
and loss by the biosphere, respectively.
NBP: Net biospheric productivity. NEP–disturbance fluxes
(fire, etc.).
GPP: The total amount of carbon exchanged between the
land biosphere and the atmosphere, through the process of
photosynthesis.
NPP: Net primary productivity; GPP - autotrophic
respiration.
TER: A part of the gross carbon uptake is lost through
autotrophic, heterotrophic, microbial and soil respiration,
often clubbed together as TER.
The larger value of GPP signifies greater carbon assimilation by the ecosystem; however, the net carbon uptake is
defined by the NEE. In general, the carbon sequestration
potential of an ecosystem depends on climatological conditions, soil moisture, texture and nutrient content, and vegetation type. Hence, for a regional or country-scale estimation
of carbon budget, GHGs fluxes must be measured over
different ecosystems scattered across the length and breadth
of the region or the country.
80
S. Chakraborty et al.
consistent with the emissions reported by India to the
UNFCCC (NATCOM BUR-1 and BUR-2). The data used in
Ganesan et al. (2013) are XCH 4 based on the CO 2 proxy
method from the GOSAT satellite; flask-based measurements of dry-air CH 4 mole fraction from Sinhagad (73.75°
E, 18.35° N, 1600 m ASL), Cape Rama, India (73.83° E,
15.08° N, 60 m ASL), in situ measurements from Darjeeling, India (88.25° E, 27.03° N, 2200 m ASL); and
upper-atmospheric in situ measurements from the CARIBIC
aircraft (Fig. 4.4a). Further, the Lagrangian particle dispersion model, Numerical Atmospheric dispersion Modeling
Environment (NAME), was used to provide a quantitative
relationship between atmospheric mole fractions and emissions. However, the observation of Ganesan et al. (2017) is
subjected to further verification for certain reasons. It is well
known that the inverse modeling results depend strongly on
the selection of chemistry transport model and treatment of
atmospheric measurements. In particular, the regional
transport and inverse models for long-lived gas simulation
suffer from the use of boundary conditions; e.g., a recent
study shows the emission estimates based on observations
over Siberia are greatly affected by the trends in emissions of
CH 4 over Europe and Asia (Sasakawa et al. 2017). Accurate
estimation of emission trends requires uninterrupted long
term atmospheric observations from the region. Although
Ganesan et al. (2017) used multiple streams of in situ data
over India, none of the measurement locations continuously
measured CH 4 during the period of their analysis (2010–
2015). The remote-sensing measurements from GOSAT do
not retrieve XCH 4 for the regions covered by clouds at any
thickness, leading to a seasonal bias in inverse model calculation. To overcome these limitations, a well-structured
GHGs long-term observational network is to be set up across
India (Nalini et al. 2019).
Apart from the above activities, a few aircraft-based
GHGs measurements have also been carried out over the
Indian subcontinent for the vertical profiling of CO 2 over
Bhubaneswar, Varanasi and Jodhpur in order to study the
spatiotemporal distribution of CO 2 mixing ratio and
inter-comparison with the satellite-derived products
(Sreenivas et al. 2019). Out of these three places, Varanasi
showed a strong gradient in CO 2 mixing ratio (ca.
3.12 ppm/km), while in the other two cities the gradient was
much smaller (<2 ppm/km). Varanasi region being in the
Indo-Gangetic Plain is characterized by strong anthropogenic loading, which has resulted in a relatively steep
vertical gradient. The Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) Project of
the Indian Institute of Tropical Meteorology also carried out
similar observations during the Indian summer monsoon
months of 2014, 2015 and 2018, 2019 over India and
adjacent oceanic regions. The vertical profiling of methane
mixing ratio revealed a strong peak at about 4.5 km. It is
believed that this kind of mid-tropospheric peak occurs due
to strong convective activities (Chandra et al. 2017). Among
other work, uptake of winter time carbon by the agricultural
practices around Delhi has been discussed in Umezawa et al.
(2016).
4.3 Greenhouse Gas Flux Measurements
in Natural Ecosystems
GHGs fluxes are monitored over India primarily by two
major measurement networks that corroborate efforts from
multiple research institutes, universities and government
organizations. Two important GHG components, viz., CO 2
and CH 4 , are being monitored by the chamber-based or eddy
covariance (EC) systems along with the other scalar fluxes
including water vapor and energy.
Estimated CO 2 flux from these measurements is subsequently used to calculate net ecosystem exchange (NEE),
gross primary productivity (GPP) and total ecosystem respiration (TER) using various process-based biogeoscientific
models. These parameters are the different components of
the ecosystem carbon cycle. Some definitions are as follows:
NEE: The net amount of carbon exchanged (in the form of
CO 2 ) between the land biosphere and the atmosphere at a
given location over a particular period of time; negative and
positive values of NEE denote uptake and release of carbon
by the land biosphere, respectively.
NEP: Net Ecosystem Productivity; opposite of NEE i.e.
positive and negative values of NEP denote carbon uptake
and loss by the biosphere, respectively.
NBP: Net biospheric productivity. NEP–disturbance fluxes
(fire, etc.).
GPP: The total amount of carbon exchanged between the
land biosphere and the atmosphere, through the process of
photosynthesis.
NPP: Net primary productivity; GPP - autotrophic
respiration.
TER: A part of the gross carbon uptake is lost through
autotrophic, heterotrophic, microbial and soil respiration,
often clubbed together as TER.
The larger value of GPP signifies greater carbon assimilation by the ecosystem; however, the net carbon uptake is
defined by the NEE. In general, the carbon sequestration
potential of an ecosystem depends on climatological conditions, soil moisture, texture and nutrient content, and vegetation type. Hence, for a regional or country-scale estimation
of carbon budget, GHGs fluxes must be measured over
different ecosystems scattered across the length and breadth
of the region or the country.
80
S. Chakraborty et al.