terrestrial NEE during the Indian summer monsoon. These
authors showed that while the terrestrial ecosystems act as a
net source of CO 2 during June and July, they transform into
the net CO 2 sink during August and September. However,
due to spatial variability in GHGs distribution and dynamics,
this characteristic feature may differ in specific regions. For
example, the eddy covariance-based results show that the
deciduous forest (Kaziranga in Assam) in the Northeast
India acted as a strong sink of carbon during the
pre-monsoon period (May–June; Sarma et al. 2018). On the
other hand, as mentioned earlier, most of the forests in
mainland India sequester significant carbon in the monsoon
but maximum carbon during the post-monsoon to early
winter. Hence, the study by Valsala et al. (2013) points out
the limitations of satellite measurements (GHG column
concentrations) in illustrating the GHGs dynamics in the
Indian landmass. However during monsoon season and due
to cloud cover, GHG absorption bands are obscured; hence,
ground-based direct measurements of GHG are also necessary to complement the satellite measurements.
Figure 4.5 schematically shows the carbon sequestration
by some Indian forests measured by means of the eddy
covariance technique. The blue bar represents the maximum
value of the net ecosystem exchange on the diurnal timescale
for a particular month as indicated. The height is proportional to the amount of carbon uptake by the vegetation
having a unit of gC m
−2 d
−1 . The gross primary productivity
(expressed in gC m
2 yr
−1 ), available only for a few
ecosystems, is shown as yellow bar.
4.3.6 Nitrous Oxide Fluxes
Another potent GHG is N 2 O, which is produced in the
natural biological processes occurring in soil, ocean and
inland water by the microbes (Thomson et al. 2012;
Davidson and Kanter 2014). Various anthropogenic activities such as agriculture, energy production, heavy industries
and waste management also contribute to the rising N 2 O flux
into the atmosphere (Wassman et al. 2004; Malla et al. 2005;
Datta and Adhya 2014). Prasad et al. (2003) studied N 2 O
emissions from India’s agricultural sector between 1961 and
2000 and suggested that the total N 2 O emission had
increased approximately 6 times over 40 years. Presently,
agricultural activities alone account for more than 90% of
the total anthropogenic N 2 O emissions in India, with 65% of
the total N 2 O emission being attributable to the chemical
fertilizers. In a study done by Ghosh et al. (2003) over an
upland rice ecosystem grown during the summer monsoon
months in New Delhi, N 2 O fluxes were found to vary within
4.32–2400 µg m
−2 d
−1 , whereas seasonal N 2 O loss by the
ecosystem varied between 0.037 and 0.186 kg ha
−1 . An
agricultural denitrification and decomposition model was
calibrated for predicting the crop yield and GHG emissions
and validated for Indian conditions by Pathak et al. (2005).
According to their study, continuous flooding of rice fields
results in annual net emissions as follows: 21.16–60.96 TgC
in the form of CO 2 , 1.07–1.10 TgC in the form of CH 4 and
0.04–0.05 TgN in the form of N 2 O, whereas intermittent
flooding changes the emissions to 16.66–48.80 TgC in the
form of CO 2 , 0.12–0.13 TgC in the form of CH 4 and 0.05–
0.06 TgN in the form of N 2 O. Noticeably, the agricultural
practice of intermittent flooding of the rice paddy field had
opposite effects on carbon and nitrogen emissions. An
analysis using atmospheric observations and models suggests acceleration in N 2 O emission per unit of nitrogen
fertilizer use, thus a global emission factor of 2.3 ± 0.6%,
which is significantly larger than the IPCC default for
combined direct and indirect emissions of 1.375%
(Thompson et al. 2019).
4.4 Model Simulation of GHGs
4.4.1 Biogeochemical Model Study
Biogeochemical models are widely used to simulate the life
cycles of GHGs. These models adopt a bottom-up approach
to simulate the concentrations and fluxes of GHGs within
and between various reservoirs such as the atmosphere,
biosphere, pedosphere, geosphere and hydrosphere using
mathematical representations of various biogeochemical, as
well as biogeophysical, processes that affect the storage and
transport of GHGs. Hence, they are able to simulate composite fluxes such as NEP and NBP that include soil
dynamics as well as disturbances such as fire and land
use/land cover change (Watson et al. 2000). Hence, these are
more appropriate measures of the carbon source/sink status
of a reservoir than GPP and NPP obtained using typical
empirical methods.
Studies with biogeochemical models are very limited in
the Indian context and confined to the carbon cycle in terrestrial ecosystems. Some studies have conducted simulations over India (Banger et al. 2015; Gahlot et al. 2017),
while others have extracted India-specific information from
global-scale biogeochemical simulations (Cervarich et al.
2016; Gahlot et al. 2017; Rao et al. 2019). Banger et al.
(2015) used the Dynamic Land Ecosystem Model (DLEM)
to estimate NPP patterns for the 1901–2000 period. They
found that it has increased from 1.2 to 1.7 PgC yr
−1 during
this period. Using an ensemble average of nine different
dynamical vegetation models, Cervarich et al. (2016) found
NEP and NBP values for India to be in the
200.6 ± 137.7 TgC yr
−1
and 185.9 ± 145.6 TgC yr
−1
range, respectively, for the 2000–2013 period. These values
are much larger than other estimates. Gahlot et al. (2017)
84
S. Chakraborty et al.
authors showed that while the terrestrial ecosystems act as a
net source of CO 2 during June and July, they transform into
the net CO 2 sink during August and September. However,
due to spatial variability in GHGs distribution and dynamics,
this characteristic feature may differ in specific regions. For
example, the eddy covariance-based results show that the
deciduous forest (Kaziranga in Assam) in the Northeast
India acted as a strong sink of carbon during the
pre-monsoon period (May–June; Sarma et al. 2018). On the
other hand, as mentioned earlier, most of the forests in
mainland India sequester significant carbon in the monsoon
but maximum carbon during the post-monsoon to early
winter. Hence, the study by Valsala et al. (2013) points out
the limitations of satellite measurements (GHG column
concentrations) in illustrating the GHGs dynamics in the
Indian landmass. However during monsoon season and due
to cloud cover, GHG absorption bands are obscured; hence,
ground-based direct measurements of GHG are also necessary to complement the satellite measurements.
Figure 4.5 schematically shows the carbon sequestration
by some Indian forests measured by means of the eddy
covariance technique. The blue bar represents the maximum
value of the net ecosystem exchange on the diurnal timescale
for a particular month as indicated. The height is proportional to the amount of carbon uptake by the vegetation
having a unit of gC m
−2 d
−1 . The gross primary productivity
(expressed in gC m
2 yr
−1 ), available only for a few
ecosystems, is shown as yellow bar.
4.3.6 Nitrous Oxide Fluxes
Another potent GHG is N 2 O, which is produced in the
natural biological processes occurring in soil, ocean and
inland water by the microbes (Thomson et al. 2012;
Davidson and Kanter 2014). Various anthropogenic activities such as agriculture, energy production, heavy industries
and waste management also contribute to the rising N 2 O flux
into the atmosphere (Wassman et al. 2004; Malla et al. 2005;
Datta and Adhya 2014). Prasad et al. (2003) studied N 2 O
emissions from India’s agricultural sector between 1961 and
2000 and suggested that the total N 2 O emission had
increased approximately 6 times over 40 years. Presently,
agricultural activities alone account for more than 90% of
the total anthropogenic N 2 O emissions in India, with 65% of
the total N 2 O emission being attributable to the chemical
fertilizers. In a study done by Ghosh et al. (2003) over an
upland rice ecosystem grown during the summer monsoon
months in New Delhi, N 2 O fluxes were found to vary within
4.32–2400 µg m
−2 d
−1 , whereas seasonal N 2 O loss by the
ecosystem varied between 0.037 and 0.186 kg ha
−1 . An
agricultural denitrification and decomposition model was
calibrated for predicting the crop yield and GHG emissions
and validated for Indian conditions by Pathak et al. (2005).
According to their study, continuous flooding of rice fields
results in annual net emissions as follows: 21.16–60.96 TgC
in the form of CO 2 , 1.07–1.10 TgC in the form of CH 4 and
0.04–0.05 TgN in the form of N 2 O, whereas intermittent
flooding changes the emissions to 16.66–48.80 TgC in the
form of CO 2 , 0.12–0.13 TgC in the form of CH 4 and 0.05–
0.06 TgN in the form of N 2 O. Noticeably, the agricultural
practice of intermittent flooding of the rice paddy field had
opposite effects on carbon and nitrogen emissions. An
analysis using atmospheric observations and models suggests acceleration in N 2 O emission per unit of nitrogen
fertilizer use, thus a global emission factor of 2.3 ± 0.6%,
which is significantly larger than the IPCC default for
combined direct and indirect emissions of 1.375%
(Thompson et al. 2019).
4.4 Model Simulation of GHGs
4.4.1 Biogeochemical Model Study
Biogeochemical models are widely used to simulate the life
cycles of GHGs. These models adopt a bottom-up approach
to simulate the concentrations and fluxes of GHGs within
and between various reservoirs such as the atmosphere,
biosphere, pedosphere, geosphere and hydrosphere using
mathematical representations of various biogeochemical, as
well as biogeophysical, processes that affect the storage and
transport of GHGs. Hence, they are able to simulate composite fluxes such as NEP and NBP that include soil
dynamics as well as disturbances such as fire and land
use/land cover change (Watson et al. 2000). Hence, these are
more appropriate measures of the carbon source/sink status
of a reservoir than GPP and NPP obtained using typical
empirical methods.
Studies with biogeochemical models are very limited in
the Indian context and confined to the carbon cycle in terrestrial ecosystems. Some studies have conducted simulations over India (Banger et al. 2015; Gahlot et al. 2017),
while others have extracted India-specific information from
global-scale biogeochemical simulations (Cervarich et al.
2016; Gahlot et al. 2017; Rao et al. 2019). Banger et al.
(2015) used the Dynamic Land Ecosystem Model (DLEM)
to estimate NPP patterns for the 1901–2000 period. They
found that it has increased from 1.2 to 1.7 PgC yr
−1 during
this period. Using an ensemble average of nine different
dynamical vegetation models, Cervarich et al. (2016) found
NEP and NBP values for India to be in the
200.6 ± 137.7 TgC yr
−1
and 185.9 ± 145.6 TgC yr
−1
range, respectively, for the 2000–2013 period. These values
are much larger than other estimates. Gahlot et al. (2017)
84
S. Chakraborty et al.