Results

The spatiotemporal LU analyses presented in Fig. 4, highlights the loss of forest cover due to anthropogenic pressure. The region had 16.21% evergreen forest cover in 1985, which is reduced to 11.3% in 2018. The region has 17.92%, 37.53%, 4.88% under plantations, agriculture, mining and built-up respectively (Fig. 5). The increase in monoculture plantations such as Acacia, Eucalyptus, Teak, Rubber, developmental projects and agriculture expansions are the major drivers of LU changes. The region has lost 12% of interior (contiguous) forest cover during 1985 to 2018 with an increase of non-forest cover (11%). The interior forests (25% in 2018) are confined to major protected areas; edge forests are becoming more prominent due to sustained anthropogenic pressure (Fig. 6). The Goa has experienced loss of large tracts of interior forest cover due to the indiscriminate rampant mining activities. The simulated LU (of 2018) was compared with the actual LU’s (of 2018), which shows a consistent result evident from higher accuracies (92.6%) and overall kappa (K_(overall): 0.91, K_(histo): 0.95, K_(location): 0.95). The projected LU of 2031 highlights likely loss of evergreen forest with increases in agriculture cover (39%) and built-up area (5%). The large scale changes of agriculture and built-up cover are noticed as per Fig. 7, in the eastern Kerala, Tamilnadu, Maharashtra states of WG. The evergreen forest will cover only 10% of the WG, which would threaten the sustenance of water and other natural resources, affecting the food security and livelihood of people in the peninsular India.

Quantification of carbon sequestration

The carbon sequestration potential of WG has been quantified as discussed earlier in the method section, by using field data as well as the information from published literatures. The current study confirms that the forests of WG are incredible reservoirs of biomass and carbon stock, which highlights the decisive role of forests in lowering atmospheric carbon (emitted due to anthropogenic activities) and mitigating global warming. The above ground biomass in WG is about 1.62 MGg (Million Giga Grams or Tera metric tons or Tt) with the sequestered carbon of 0.81 MGg per year respectively, which are reflected spatially across WG in Fig. 8a and Fig. 8b respectively. The southern and central WG regions endowed with the rich native forests have biomass > 1200 Gg/Ha and carbon 600 Gg/Ha. The soils are rich in carbon (0.42 MGg) especially southern and central WG, evident from Fig. 8c. The total carbon captured by WG forests given in Fig. 8d, above ground biomass and soil is 1.23 MGg as shown. The annual incremental biomass of 62869.11Gg (Fig. 9a) with the carbon capture of 31434.55 Gg (Fig. 9b) with the higher carbon sequestration potential in southern WG.

Table 2. Soil carbon storage in different forest types

Sno	Forest Types	Mean soil carbon in top 30 cm (t/ha)
1	Tropical Wet Evergreen Forest	132.8
2	Tropical Semi Evergreen Forest	171.75
3	Tropical Moist Deciduous Forest	57.14
4	Littoral and Swamp Forest	34.9
5	Tropical Dry Deciduous Forest	58
6	Tropical Thorn Forest	44
7	Tropical Dry Evergreen Forest	33

Similar trend is noticed in the incremental carbon captured by soil 15120 Gg as shown in Fig. 9c, 9d and relatively higher carbon content increment per year is noticed in Karnataka and Central Kerala parts of WG. The productivity of biomass (17442.01 Gg) given in Fig. 10a-d reveal higher values for the states of Karnataka, Kerala, Tamil Nadu portions of WG (Fig. 10a-d). The total incremental carbon excluding carbon loss through productivity is accounted to be 37507.3 Gg. The likely changes in carbon sequestration potential in the WG is estimated considering simulated LU’s considering (a) conservation scenario and (ii), business as usual scenario. The business as usual scenario (with the current trend of decline of forest cover due to LU changes) depicts the above ground biomass of 1.3 MGg with stored carbon of 0.65 MGg and soil carbon of 0.34 MGg. The total carbon captured by WG forests in 2031 shown in Fig. 11a, will be about 1.0 MGg from both above ground biomass and soil. The large tracts of forest cover is likely to retreat due to LU changes with increases in the agriculture and built-up area, which will erode the carbon sequestration potential of forests by 0.23 MGg (2018-2031) as illustrated in Fig. 11b. The conservation scenario depicts an increase in carbon sequestration potential of WG forests with the protection. The total carbon sequestered would be 1.5 MGg by 2031 as shown in Fig. 11c due to higher protection with minimal disturbances.

Fig. 4 LU analyses of WG from 1985-2018

Fig. 5 5 Spatio temporal LU and fragmentation details

Fig. 6 Forest fragmentation from 1985-2018

Fig. 7 Likely LU and fragmentation of WG for year 2031

Fig. 8 Above ground biomass (standing biomass), carbon, soil carbon content and total carbon stock of WG

Fig. 9 Incremental carbon of WG from above ground biomass, below ground biomass

Fig. 10 Productivity of forests across WG

Fig. 11 Simulated carbon stock across various scenarios

CO₂ emissions in India is about 3.1 MGg (2017), with the per capita CO₂ emissions of 2.56 metric tonnes (WRI 2014; Garg et al. 2017; Le Quéré et al. 2018). Carbon footprint is contributed by emissions from the energy sector (68%), agriculture (19.6%), industrial processes (6%), LU change (3.8%) and forestry (1.9%) respectively.

(Source: Swamy, 1992; Ravindranath et al. 1997; Ravindranath et al. 2007; Ramachandra and Bharath, 2019)

Table 3. Biomass and sequestered carbon estimations based on forest types

Index	Equation	Significance	Forest type
Biomass (T/Ha)		Above ground biomass content	Evergreen
			Deciduous
			Scrub
Carbon stored (T/Ha)		Sequestered carbon content	All
Annual Increment in Biomass (T/Ha)		Incremental growth in biomass (Ramachandra et al. 2000b; Pandey et al. 2011; Do et al. 2018)	Evergreen
			Deciduous
			Scrub
Annual increment in Carbon (T/Ha)		Incremental growth in carbon storage	All
Net annual Biomass productivity (T/Ha)		Used to compute the annual availability of woody biomass in the region. (Ramachandra et al. 2000b)	Evergreen
			Deciduous
			Scrub
Carbon sequestration of forest soil (T/Ha)		Carbon stored in soil (Ravindranath et al. 1997)	Evergreen
			Deciduous
			Scrub
Annual Increment of soil carbon		Annual increment of carbon stored in the soil	All

Daily Rainfall and temperature data (of 0.50 º resolution) for the period 1901 and 2017 was collated from Princeton University Database (Princeton data- THRG, 2014), NCAR climate data guide (NCAR, 2019), Indian Meterological Department (IMD, 2018) and Local climate data (Karnataka State Natural Resource Disaster Monitoring Centre (KSNDMC, 2018)). Princeton data and NCAR data were validated at latitude levels by comparing with the surface measurements of IMD and Local Climate data. The mean and variance were computed and compared, which illustrates the global data are comparable to surface measurements (with deviation ≤ 4.3%) These climatic data at latitude level were further analysed for variability and trend. Climate (rainfall and temperature) data were compared with LU changes to understand the role of LU’s in the regional climate variability.

India emitting 7% of total GHG emissions across the globe (336.6 MGg) is in the 4^th place after major carbon emitters - China (27 %), United States (15%), European Union (10 %). Carbon emissions from major metropolitan cities of India is about 1.3 MGg contributed by major cities such as Delhi (38633.20 Gg) Greater Mumbai (22783.08 Gg), Chennai (22090.55 Gg), Bengaluru (19796.6 Gg), Kolkata (14812.1 Gg) Hyderabad (13734.59 Gg) and Ahmedabad (6580.4 Gg) from energy, transportation, industrial sector, agriculture, livestock management and waste sectors per year (Ramachandra et al. 2015). The current study illustrates the pivotal role of sequestering carbon by an ecologically fragile WG. The Western Ghats has the potential to sequester carbon emission of all southern Indian cities and 1.62% of the total CO₂ emissions from India. The total emissions from WG states accounted to be 352922.3 Gg (Table 4) and forests of WG have the ability to sequester 11% of the emissions, which highlights vital carbon mitigation role and moderating climate. India has committed to reducing its the emissions by 33-35% by 2030 during the Paris Climate Change Agreement, which is challenging task considering the likely economic growth momentum to sustain 1.25 billion people consumptions (Garg et al. 2017). This necessitates immediate implementation of carbon capture (with afforestation of degraded landscapes with native species, regulations of LULC changes) and de-carbonisation (through large scale implementation of renewable and sustainable energy alternatives) through stringent norms towards (i) protection of ecologically fragile regions, (ii) dis-incentives for continued higher emissions based on ‘polluter pays’ principle, (iv) adoption of cluster based decentralized developmental approaches and (iii) incentives for reduced emission. The carbon trading has demonstrated the potential in monetary values across the globe of Indian forests in capturing carbon (Atkinson and Haripriya 2006; Guthrie and Kumareswaran 2009; Damandeep 2017). The monetary values of sequestered carbon vary from $10 to $1000 based on specific assumptions (Ricke et al. 2018). Based on this, the WG forests are worth INR 100 billion ($1.4 billion) at $30 per tonne. Carbon credit payments offers an effective means of increasing carbon sequestration and will proved to be a viable conservation approach via business plan. The push of carbon credit payments with streamlining through stakeholder’s active participations, would dramatically reduce the abuse of forests. This would also encourage farmers to grow trees and converting the land to its next best use.

Climate trend with LU relationship

Global databases (NCAR and Princeton University) were validated through comparison with the surface measurements of ground based monitoring stations in the regional climate datasets (IMD, KSNDMC), which shows 90% similarities. Spatial variability of precipitation, rainy days and temperature are presented in Fig. 12, which illustrates that Central and Northern WG have annual average temperatures less than 25.5^oC, while at the Southern WG show temperatures of 25.5^oC to over 26.5^oC. Rainfall analysis shows that the central WG receive rainfall of over 2500 mm and tends to decline to less than 1500 mm from south WG to the northern WG. Spatial distribution of rainy days illustrates that southern WG have significantly higher rainy days receiving precipitation for more than 180 days, compared to the northern parts.

Fig. 12 Spatial variations of climatic parameters

Long term trend analysis of climatic variable is depicted in Fig. 13. The southern Kerala (latitudes of 8-9⁰) shows an increase in temperature from 0.5^oC to greater than 1 ^oC during the past 100 years, while the rainfall has declined by 250 mm and also the decline of rainy days by 2 to 4 days. Latitude 10-12 shows that the temperatures have increased between 0.25 ^oC to 0.5 ^oC in a century while rainfall has declined between 100 to 250 mm and decline of rainy days by 2 days. The analysis indicated that the regions in the southern WG of Kerala, part of Karnataka (8-13⁰) have witnessed large scale climate changes. The Central WG portions (Karnataka) shows a very meager change in temperature i.e., less than 0.05 ^oC increase, while the rainfall shows increasing trends close to 100 mm and increase of rainy days up to 2 days. The Northern portion of the WG latitudes (16-21⁰) shows increasing temperature of 0.5 degrees, 4 days increase in the number of rainy days and the rainfall has increased between 100 mm and just over 250 mm. This analyses demonstrates that LU has played a major role in moderating microclimatic conditions in WG over a temporal scale. The reduction of rainfall and an increase in climate can affect carbon stock in the region. The fixed major soil carbon can release to the atmosphere due to LU change and increase in temperature. The instances of vegetation die-off can occur with microclimate alterations, which further contributes to the increase in the carbon content of the atmosphere. Forests are also known as water towers and are responsible for capturing water from the atmosphere through rainfall and also aid in condensation, influencing land surface properties such as evapotranspiration, temperature and humidity (Bonan, 2008; Syktus and McAlpine, 2016). Reduction in the natural forests have reduced the surface roughness and aerodynamics, due to which the rain bearing clouds move along the winds causing rainfall in the windward direction. This could be observed with increasing rainfall and rainy days to the Northern WG and decreasing rainfall and rainy days in the Southern WG.

Fig. 13 Spatiotemporal Trend of Climatic parameters

Discussion

Forests sequester atmospheric carbon and helps in lowering GHG footprint apart from providing diverse goods and services to the humankind. The anthropogenic pressures resulting in large scale LULC changes is altering the landscape structure which has affected ecological functions namely hydrologic regime, biogeochemical functioning and nutrient cycling. Implication of the implementation of unplanned developmental activities are evident from barren hill tops, decline in diversity, spread of invasive species, alteration in hydrologic regime (conversion of perennial streams and rivers to the seasonal one), increase in temperature, higher instance of flooding and droughts. The increase in greenhouse gas (GHG) footprint due to deforestation with LULC changes has interrupted bidirectional interactions of surface vegetation cover, climate (Canziani and Carbajal Benitez, 2012) and will further modify carbon budget (Schulp et al. 2008). Nogueira et al. (2015) assessed the carbon stock loss from deforestation in Brazil's ‘Legal Amazonia’ and ‘Amazonia biome’ regions (documented in 41 published studies, through field investigations in 2317 one-ha plots) reported a gross reduction of 18.3% in Legal Amazonia (13.1 Pg C) and 16.7% in the Amazonia biome (11.2 Pg C). Emissions per unit area from forest clearing would lower the mean biomass of remaining vegetation due to various effects such as edges, disturbances and loss of microclimate. Deforestation will reduce the latent heat flux at a local scale that results in the increased warming, affecting the cloud formation process. The surface warming as a result of deforestation will increase drying of the boundary layer could lead to reduced clouds, increase the drier period, in turn, allow more downward solar radiation at the surface and hence warming (Bala et al. 2007).

LULC changes have directly modified the local climate, surface temperatures and rainfall regime in the WG, contributing to regional climate changes with water scarcity, increases in the vulnerability to fire and vegetation dieback. Few global studies demonstrated LULC changes and their interactions in climate and global terrestrial carbon cycle (Levy et al. 2004; Zaehle et al. 2007; Sitch et al. 2015; Zhu et al. 2018). Estimates of original biomass and likely changes in biomass are essential for estimating losses to forest degradation. In this context, the current endeavor estimates biomass, carbon stocks and also attempts to present the likely changes through modeling and simulation. The modeling of likely changes with insights of agent’s behavior helped in enhancing the accuracy, which will help in projecting d carbon sequestration (Zaehle et al. 2007; Schulp et al. 2008; Nogueira et al. 2018). Insights form the analyses of LULC Simulated changes would help in evolving policies for prudent ecosystem management to mitigate carbon footprint by reducing the deforestation process at a regional scale.

Table 4. Carbon emission across the states of WG

State/UT	Emission (Gg) per year			Total (Gg)	Carbon storage in WG (Gg) per year	% Removal
	CH4 (CO2 equivalent)	CO (CO2 equivalent)	CO2
Goa	233	337	3881	4451	872	20
Gujarat	15546	14498	79138	109182	1947	2
Karnataka	15662	15239	54337	85237	10401	12
Kerala	3167	6108	26047	35321	7617	22
Maharashtra	23129	26497	105260	154886	11020	7
Tamil Nadu	15761	19190	71107	106058	5375	5
Dadra and Nagar Haveli	46	63	1458	1567	601	38
Total Emission (Gg)				496703	37833	8