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In this study, investigation of the seasonal cycle of the tropospheric CO2 concentration over India was carried out using the GEOS-Chem atmospheric transport model, Greenhouse gas Observation SATellite (GOSAT) retrievals, and in-situ measurements. The model simulation is highly coherent with the satellite and in-situ datasets, and it shows a distinct seasonal cycle of the tropospheric CO2 tendency over India with a negative phase (decreasing concentration) during April–August and a positive phase (increasing concentration) during September–March. The model diagnostics were analyzed to estimate budgets of the surface layer CO2, up to 650 hPa pressure level, for the two-phases of the seasonal cycle. A mean tendency, equivalent to −0.70 ppmv month−1, observed during April–August, which results from the loss of CO2 content in the surface layer through horizontal advection (−2.25 ppmv month−1) and vertical diffusion (−0.20 ppmv month−1), that dominates the gain from vertical advection (1.53 ppmv month−1). The negative contribution of horizontal advection in this period comes from the transport of CO2 depleted air-parcels over the oceanic region to India by the southwest monsoon winds and the positive contributions of vertical advection comes from upwelling of CO2 enriched air-parcels. The mean tendency, equivalent to 1.01 ppmv month−1, during September–March results from the gain through vertical advection (0.78 ppmv month−1) and horizontal advection (0.37 ppmv month−1) and a small contribution of vertical diffusion (−0.15 ppmv month−1). In this period, positive contribution of horizontal advection is due to the transport of CO2 enriched air-parcels from the southeast Asian region to India by north-east monsoon winds. At the annual scale, CO2 content of the surface layer over India has a net gain of 0.75 GtC that comes from 14.31 GtC through vertical advection that exceeds the loss due to horizontal advection (−11.10 GtC) and vertical diffusion processes (−2.46 GtC). This net gain is almost 85% higher than the input of 0.4 GtC through surface fluxes, which composed of 0.61 GtC anthropogenic emission and −0.21 GtC net terrestrial ecosystem exchanges. Additional sensitivity experiment was carried out to elucidate the semi-annual features of the seasonal cycle of CO2 for north India, in contrast to the annual characteristics of the seasonal cycle for south India in relation to the GOSAT observation. Highlights: Greenhouse gas Observation SATellite (GOSAT) L3B and L4B retrievals and in situ flux tower measurements were analysed to describe seasonal cycle of tropospheric CO2 over India; and GEOS-Chem atmospheric transport model diagnostics were used to examine the causes of the variability. The seasonal cycle over north India is composed of mixed signature of annual and semi-annual frequencies while south India experiences dominance of annual oscillation. However, the surface layer CO2 seasonal tendency has a major negative phase during April–August and a positive phase during September–March. The net negative tendency during April–August results from the loss of CO2 from the surface layer through horizontal advection and vertical diffusion processes that dominates the gain from vertical advection; while the net positive tendency during September–March results from the gain through vertical advection and horizontal advection and a small negative contribution from vertical diffusion. At annual scale, the surface layer over India experiences net positive gain of CO2 concentration, which is 85% more than the net input from the surface fluxes, and is mostly contributed by large-scale transport processes. Sensitivity experiments were carried out to elucidate the semi-annual features of the seasonal cycle of CO2 over north India in relation to the GOSAT observation. It turns out that the secondary trough during October–December on the background of net positive tendency during September–March results from the drawdown of CO2 by the terrestrial ecosystem uptake. [ABSTRACT FROM AUTHOR]

Using satellite observations of Normalized Difference Vegetation Index together with climate data from other sources in a terrestrial biosphere model, inter-annual variability of Net Primary Productivity (NPP) over India during 1981-2006 was studied. It is revealed that the variability is large over mixed shrub and grassland (MGL), moderate over cropland and small over the forest regions. Inter-annual variability of NPP exhibits strong positive coherence with the variability of precipitation, and weak coherence with the variability of temperature and solar radiation. Estimated linear growth rate of annual NPP is 0.005 Pg C Yr−2 which is equivalent to 8.5% over the country during past 25 years. This increase is primarily due to the enhancement of productivity over agricultural lands in the country. NPP has increased over most parts of the country during the early 15-year period (1981-1995) resulting in a 10% growth rate of national NPP budget. On the other hand, the NPP growth rate has been reduced to 2.5% during later 15 years period (1991-2005) owing to large decline of NPP over the Indo-Gangetic plains. Climate had a strong control on NPP growth rate during both the periods. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract A spatial analysis of phytomass carbon (C) in Indian forests for the period (1988–94) at the district-level is reported. District-level forest phytomass C (1988, 1994) was computed by combining remote sensing-based forest area inventories on 1:250 000 scale, field inventories of growing stock volume by the Forest Survey of India (FSI), and crown density-based biomass expansion factors. The estimates of forest area inventory, forest phytomass C density, and forest phytomass C pools (1988–94) were linked to the district boundary coverage of India in ARC/INFO Geographic Information System (GIS). Of the total 386 districts examined, only 17 districts had more than 75% forest cover (as percent of their geographic area) in 1988. Estimated district-level forest phytomass C densities ranged from 4.3 to 206.8 MgC ha-1 *. At the national level, forest phytomass C pool was estimated to be 3871.2 and 3874.3 TgC# in 1988 and 1994, respectively. Although the aggregated national estimates were similar, the district-level change analysis indicated a decrease of 77.8 TgC and an increase of 81 TgC (1988–94). The highest decrease of 10.4 TgC was observed in Vishakapatnam district (Andhra Pradesh) while largest increase of 9.8 TgC in Bastar district (Madhya Pradesh) resulting from deforestation and afforestation activities, respectively. At the national level, the total forest cover decreased by ∼0.6 Mha, however, the district-level spatial analysis indicated an increase of 1.07 Mha, and decrease of 1.65 Mha forest cover during 1988–94 period. Although, this is the first district level phytomass C analysis of Indian forests, the aggregated results at state-level were close to the earlier estimates. The spatial analysis identified the districts that have undergone significant changes in their forest phytomass C during the study period. This improved understanding of forest phytomass C pools is important to sustainable... [ABSTRACT FROM AUTHOR]

This study provides application of Resourcesat-2 AWiFS satellite imagery for forest burnt area assessment in India. AWiFS datasets covering peak forest fire months of 2014 have been analysed. The total burnt area under vegetation cover (forest, scrub and grasslands) of India was estimated as 57,127.75 sq. km. In 2014, 7% of forest cover of India was affected by fires. Of the major forest types, dry deciduous forests are affected by the highest burnt area, followed by moist deciduous forests. Among the biogeographic zones, the highest forest burnt area was recorded in Deccan followed by North East and Western Ghats. The highest burnt area was recorded in Odisha followed by Andhra Pradesh, Maharashtra, Chhattisgarh, Tamil Nadu, Madhya Pradesh, Telangana, Jharkhand, Manipur and Karnataka. Spatial analysis shows that 232 grid cells in India have a burnt area greater than 20 sq. km. The database generated would be useful in ecological damage assessment, fire risk modelling, carbon emissions accounting and biodiversity conservation. [ABSTRACT FROM AUTHOR]