Int. J. of Usuf. Mngt. 16 (2): 40-60 (2015)
ISSN 0972-3927
MODELING POTENTIAL CLIMATE CHANGE IMPACTS WITH SPECIAL REFERENCE TO INDIAN FORESTS M. Manjunatha1, Vikas kumar*2 and A. V. Santhoshkumar3
ABSTRACT Climate change projections for India under coupled inter-comparison model provide temperature and precipitation projection for India for the period 18602099. The mean temperature rise is predicted in the range 1.7-20C by 2030’s and 3.3-4.80C by the 2080’s relative to pre-industrial times and precipitation increase from 4 to 5 per cent by 2030’s and 6 per cent towards the end of the century compared to 1961-1990 baseline. Integrated Biosphere Simulator model (IBIS) indicated that 39 per cent of forest grids in India are likely to undergo vegetation type change under the A2 (740 ppm of CO2) scenario and 34 per cent under the B2 (575 ppm of CO2) scenario by the end of this century. In many forest dominant states such as Chhattisgarh, Karnataka and Andhra Pradesh up to 73 per cent, 67 per cent and 62 per cent of forested grids respectively, are projected to undergo change. The dynamic global vegetation economic model for Global Responses to Anthropogenic Changes in the Environment Focusing on India (GRACE-IN) estimates biomass changes in Indian forests the year 2085. The model indicates the biomass increase in all four zones except the Central zone. The variation is large; from more than 37 per cent increases in the North East zone to 9.3 per cent reduction in the Central zone. Models are gaining global significance for future projections. In India it is expected that high resolution multi-model regional climate change projections will be available in future which will be useful for assessing the vulnerability of the forest sector including species level assessment. Key words: Carbon sequestration, climate change, climate models, scenarios, pathways INTRODUCTION During their evolutionary and ecological histories, forest tree species have experienced numerous environmental changes. Changed environments may have lasted as long as 100,000 years (Bowen, 1979; Imbrie & Imbrie, 1980; Pisias & Moore, 1981) or they may have lasted only a decade (i.e. well within the lifetime of an individual tree). Environmental changes may have been gradual or sudden occurring over a relatively few years (Bryson et al., 1970). The role of land use system in 1,3Department
capturing atmospheric carbon dioxide (CO2) and storing the carbon in plant parts, soil (Vikas Kumar, 2015a) and the rapid climatic changes thought to have been brought on by recent human activities (Davis & Zabinski, 1992; Alig et al., 2002). Forests play a key role in managing climate change. The growth of biomass has a direct impact on the release of carbon to the atmosphere, biofuels is a substitute for fossil fuels, and afforestation prevents irrigation, which may be a way to adapt to climate change
of Tree Breeding and Tree Physiology, College of Forestry, Vellanikkara of Silviculture and Agroforestry, College of Forestry, Vellanikkara College of Forestry, Kerala Agricultural University, Thrissur, Kerala - 680656 (India) *Corresponding author email:
[email protected] 2Department
(Vikas Kumar, 2015b). A careful control of forested areas, harvest and composition of species may lead to synergies between mitigation and adaptation. However, Traditional resource management adaptations such as agroforestry systems may potentially provide options for improvement in reconsidering for carbon sequestration, livelihoods improvement, biodiversity conservation, soil fertility enhancement, and poverty reduction, livelihoods, through simultaneous production of food, fodder and firewood as well as mitigation of the impact of climate change. There is a need to build a bridge between adaptation and mitigation measures for creating environmental secure options of carbon sequestration with multifunctional benefits from agroforestry and other objectives than climate policies, such as supplying timber, sustaining biodiversity and tourism (Vikas Kumar, 2016).
phenomena, such as agriculture, forests, sea-level rise, extreme events etc. Some of the earliest studies of modeling potential climate change impacts have conducted in US (Cline, 1992; Fankhauser, 1995; Nordhaus, 1991; Nordhaus & Boyer, 2000; Tol, 2002). Assessments of potential climate change impacts on forests in India (Ravindranath et al., 1998 Ravindranath & Sukumar 1998; Ravindranath et al., 2006; Ravindranath et al., 2008; Aaheim et al., 2011) were based on BIOME model (versions 3 and 4)-which being an equilibrium model, does not capture the transient responses of vegetation to climate change. The recent study (Ravindranath et al., 2006) concludes that 77% and 68% of the forested grids in India are likely to experience shift in forest types for climate change under A2 and B2 scenarios, respectively. The main application of these models is to produce consistent future pathways for the different systems, and analyze consequences of climate policy strategies for emissions and impacts of climate change.
Several types of models have been used previously to assess the potential effects of climate change on vegetation of the Indian’s forests. At the global and continental scale, models of the biogeography and biogeochemistry of ecosystems have been used, for example in the Vegetation/Ecosystem Modeling and dynamic global vegetation model (DGVM) (Chaturvedi et al., 2010). For example, models used to estimate the effects of climate change on forest production and composition do not match models used to predict forest yield. Sohngen et al. (2007) moreover point at the difficulties in matching economic and ecological models, which are developed with reference to widely different scales. The different models may refer to various studies of impacts of climate change on activities and
Climate Change: Trends and Impacts According to the latest 4th Assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2007) warming of the climate system is unequivocal, as is now evident from observations of increase in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level. In last 100 years, between 1906 and 2005, the average global temperature has risen by 0.740C. Rising sea level is consistent with warming. The average Northern Hemisphere temperature during the second half of the 20th century is higher
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than any other 50 years period in the last 500 years and possibly the highest in past 1,300 years. For over 420,000 years the CO2 concentration (over geological time scale) in the atmosphere has remained in the range of 180-280 ppm but assessments have recent projected this concentration to reach the level of more than 650ppmv (parts per million by volume) by the end of the century.
rangelands and affect livelihood of a large population. Modeling impacts of climate change on forest ecosystems The models surveyed in the preceding section show how important management is for the assessment of impacts of climate change on forests. Common to the studies by economic models integrated with ecological models is that decisions are made according to some predefined behaviour, usually traditional utility maximizing agents, who consider forests as a stock or property which may be transformed into an economic amount of wealth. There are no further evaluations of management options or choice among alternative management strategies. The models used to predict large-scale vegetation responses to future climate change can be categorized into deterministic and statistically based models. Statistically based models treat plant distributions as stochastic, and include spatial realizations of response surfaces, decision trees and bio-climatic envelopes. Statistical functions are used to generate expected distribution of species, which depend on the combined effects of a range of environmental variables. A brief description of a selection of models under each of the categories is given in the following sections, and the required inputs and outputs from each model are provided in Table 1.
The projected impacts of climate change on biodiversity and vulnerable ecosystems in Asia highlight varied nature. Some of the impacts are: the species in high-elevation ecosystems tend to shift higher; weedy/invasive species with a wide ecological tolerance will have an advantage over others; in temperate Asia, species are likely to shift pole wards and boreal forest species are projected to show large shifts; frequency and intensity of forest fires and pest outbreaks in the boreal forests are likely to increase; forest ecosystems in boreal Asia are expected to be affected by floods and increased volume of run-off as well as melting of permafrost; sea-level rise could cause recession/loss of flat coastal habitats; mangroves (e.g., those in the Sundarbans) and coral reefs are particularly vulnerable. With a 1m rise in sea level, the Sundarban might disappear, leading to colossal damage to wildlife and local human populations with the decrease in productivity (of 40– 90 %). Climate change is likely to represent an additional stress on
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Table 1 Main properties of selected models of forest ecosystems Model
BIOME– BGC
ROTHC
CENTUR Y
HYBRID
Features
Data needs
Terrestrial global Biogeochemistry process model: simulates the development of forest carbon and nitrogen pools over time
The following near-surface meteorological parameters are required by Biome-BGC: 1) Daily maximum temperature (°C) 2) Daily minimum temperature (°C) 3) Daylight average temperature (°C) 4) Daily total precipitation (cm) 5) Daylight average partial pressure of water vapour (Pa) 6) Daylight average shortwave radiant flux density (watt per square metre i.e W/m2) 7) Day length (s)
Terrestrial, ecosystem Biogeochemistry Model: Simulates the turnover of organic carbon in nonwaterlogged topsoils that allows for the effects of soil type, temperature, moisture content and plant cover on the turnover process Terrestrial, ecosystem Biogeochemistry process Model: The model simulates soil organic matter dynamics in response to changes in management and climate. The model uses monthly time steps for simulations of up to several thousand years to examine the flows of carbon, nitrogen and phosphorus
Numerical process based model, treats the daily cycling of C,N and H2O within the biosphere and between the biosphere and atmosphere
Weather data used to run the model 1. Rainfall, 2. Air temperature 3. Evaporation over water Soil data used to run the model 1) Clay content 2) Inert carbon composition 3) Sampling depth 4) Bulk density
Outputs
Fluxes and storages of energy, water, carbon and Nitrogen for vegetation and soil compartments of terrestrial ecosystem.
1) Total carbon 2) Biomass carbon 3) Carbon-di-oxide 4)The radiocarbon content of the biomass carbon
Applications Impact of Climate Change on Net Primary Productivity & Soil Organic Carbon dynamics
Impact of Climate Change on Soil Organic Carbon dynamics
1) Monthly average maximum and minimum air temperature, 2) Monthly precipitation, 3) Lignin content of plant material, 4) Plant N, P and S content 5) Soil texture, 6) Atmospheric and soil N inputs, 7) Initial soil C, N, P and S levels.
1) Total carbon in soil 2) Total Nitrogen 3) Total mineral nitrogen 4) Soil biomass carbon 5) Soil water dynamics 6) Soil temperature dynamics
Impact of Climate Change on Soil Organic Carbon dynamics
Climatic Parameters Fraction of wet days in each month, Mean amount of ppt per wet day, 3) Mean monthly 24 h. max temperature 4) Mean monthly 24 h. min temperature 5) Solar irradiance 6) Vapour pressure, 7) Past CO2 Concentrations. 8) Projected CO2 Concentrations. Climatic Parameters 1) Species level parameters (33 for each species or GPTs) 2) Plot level parameters (23) 3) Individual Level parameters (17) Plant Phenological parameters (2)
1) Carbon in vegetation (kgCm-2) 2) Carbon in soil (kgCm-2) 3) Annual evapotranspiration (accumulated solar radiation of the most irradiated month i.e MJm-2year-1) 4) Annual gross primary productivity (kgCm-2yr-1) 5) Annual maximum leaf area index (m2m-2) 6) Annual net primary productivity (kgCm-2yr-1) 7) Annual heterotrophic
Impact of Climate Change on Net Primary Productivity & Soil Organic Carbon dynamics
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('soil') respiration (kgCm-2yr-1
BIOME
It works using a ‘coupled carbon and water flux scheme, which determines the seasonal maximum leaf area index (LAI) that maximizes net primary production (NPP) for any given plants functional types (PFT), based on a daily time step simulation of soil water balance and monthly process- based calculations of canopy conductance, photosynthesis, respiration and phenological state.
MAPPS
MAPSS (Mapped Atmosphere Plant Soil System) is a predictive and deterministic point model used to examine the relationship between vegetation growth and distribution, and site water-balance.
1) Monthly precipitation, 2) Average temperature, 3) Humidity 4) Wind speed, and 5) Soil texture.
1) Vegetation classification 2) LAI of trees, shrubs, grasses 3) Monthly soil moisture in three layers, 4) Monthly surface and base flow for total runoff 5) Monthly stomatal conductance 6) Indices of productivity and water-use-efficiency.
IBIS
IBIS integrates a variety of terrestrial ecosystem phenomena within a single, physically consistent model that can be directly incorporated within AGCMs. IBIS model consists of the following four modules: 1) The land surface module 2) Vegetation phenology module 3) Carbon balance module 4) Vegetation dynamics module
1) Monthly mean cloudiness (%) 2) Minimum temp ever recorded at that location minus average temp of coldest month (C) 3) Monthly mean precipitation rate (mm/day) 4) Monthly mean relative humidity (%) 5) Percentage of sand (%) 6) Percentage of clay (%) 7) Monthly mean temperature (C) 8) Topography (m) 9) Monthly mean temperature range (C) 10) Initial vegetation types 11) Mean "wet" days per month days 12) Monthly mean wind speed at sig=0.995 m/s
(Annual outputs) 1) Total soil carbon 2) Average evapotranspiration 3) Fractional cover of canopies 4) Leaf area index 5) Average soil temperature 6) NPP 7) Total soil nitrogen 8) Average sensible heat flux 9) Height of vegetation canopies 10) Vegetation types—IBIS classification 11) Total carbon from exchange of CO2
Climate data needs: 1) Monthly mean temperature (degree C) 2) Monthly mean precipitation (mm) 3) Monthly sunshine hours (% of maximum) Non-climate data: 1) Water holding capacity of top 30 cm of soil 2) WHC of next 120 cm of soil Conductivity indices of water through these two columns
1. Statistically-based models
1) Area changes in forest types 2) NPP Changes
Impact of Climate Change on Net Primary Productivity
Impact of Climate Change on vegetation productivity
Impact of Climate Change on Net Primary Productivity & Soil Organic Carbon (The model is used to study how ecosystems respond to changes in land use and climate
responses to a changing climate in forests (Dixon & Wisniewski, 1995). Uncertainties in the input variables and parameters of forest growth models can be addressed by the use of Monte Carlotechniques (van der Voet & Mohren, 1994). For example Woodbury et al.
There are large uncertainties about how climate change impacts the growth of forests. The uncertainties are related both to the magnitude and character of climate change, and to the various
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(1998) apply a modeling approach to analyze the potential effects of climate change on the growth of loblolly pine using available research and regional
monitoring data. Model inputs and functional relationships within the model are defined by statistical distributions (Figure 1).
Figure1 : Model approach to Statistical Based Model 2. Deterministic models
organic matter span millennia (Bugmann, 1994). Accordingly the deterministic forest models are classified with respect to a variety of criteria. Broadly they can be seen as the models which follow temporal scale or those which follow spatial scales (Figure 2).
The term “forest dynamics” spans huge ranges both in time and space. The enzymatic reactions of photosynthesis operate within fractions of a second; foliage development takes a few weeks, while tree growth lasts decades to centuries, and the dynamics of soil
Figure 2: Model approach to Deterministic Model
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a. Temporal scale models
predictions at the regional scale or for ecosystems, but have not yet been applied at the global scale (Smith et al., 1994; Shugart & Smith, 1996).
Temporal scale models can be distinguished by the modeling of the vegetation response to a changing climate. The static models determine shifts of equilbria while the dynamic models determine the process of change resulting from a perturbation of the initial state.
b. Spatial scale models The spatial scale models can be divided into regional and global models. At the regional level, ecosystems are analyzed at a sub-process level, which are difficult to capture and manage at the global level. Within these spatial models, there are generally two classes of global vegetation models, biogeography models and biogeochemistry models.
I) Static biogeographical models assume equilibrium conditions in both climate and terrestrial vegetation in order to predict the distribution of potential vegetation by relating the geographic distribution of climatic parameters to the vegetation. The equilibrium approach applies only to large scales in nature as it ignores dynamic processes. In general, it requires much less information than dynamic models, and provides estimates of potential magnitude of the vegetation response at regional to global scales. These equilibrium models are restricted to the estimation of the steady-state conditions. Prominent examples of static models include BIOME (Kaplan et al., 2003), and MAPPS.
I) Biogeochemistry models project changes in basic ecosystem processes such as the cycling of carbon, nutrients, and water. These models are designed to predict changes in nutrient cycling and primary productivity. They are also known as "gap models" and simulate all the dynamic relationships for small representative areas. They simulate the cycles of carbon, nutrients and water in terrestrial ecosystems. The inputs to these models are temperature, precipitation, solar radiation, soil texture, and atmospheric CO2 concentration. The plant and soil processes simulated are photosynthesis, decomposition, soil nitrogen transformations mediated by microorganisms, evaporation and transpiration. Common outputs from biogeochemistry models are estimates of net primary productivity, net nitrogen mineralization, evapotranspiration fluxes and the storage of carbon and nitrogen in vegetation and soil. Some of the popular models include BIOME-BGC (Running & Hunt, 1993), CENTURY
II) The dynamic biogeographical models capture the transient response of vegetation or simple biomes to a changing environment using explicit representation of key ecological processes such as establishment, tree growth, competition, death, nutrient cycling (Shugart & West, 1980; Shugart 1984; Botkin, 1993). Dynamic models also require much more information on the characteristics of species than is easily available or even known for some areas of the globe (Solomon, 1986). These models are used in
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(Parton et al., 1993), and the Terrestrial Ecosystem Model (McGuire et al., 1992).
The Representative Pathways
Concentration
The need for new scenarios prompted the IPCC to request the scientific communities to develop a new set of scenarios to facilitate future assessment of climate change (IPCC, 2007). The IPCC also decided such scenarios would not be developed as part of the IPCC process, leaving new scenario development to the research community. The community subsequently designed a process of three phases (Moss et al., 2010).
II) Biogeography models simulate shifts in the geographical distribution of major plant species and communities i.e. they analyze the essential environmental conditions over entire continents to estimate the type of vegetation that is most likely to cover a given area. These types of models are best suited for assessing broad-scale changes in vegetation. They project the local dominance of various terrestrial vegetation forms. The models determine the broad distribution of major categories of woody plants, and include response limitations with reference to ecophysiological constraints. Specific aspects of community composition are determined, such as the competitive balance of trees and grasses.
A. Development of a scenario set containing emission, concentration and land-use trajectories-referred to as “representative concentration pathways” (RCPs). B. A parallel development phase with climate model runs and development of new socio-economic scenarios. C. A final integration dissemination phase.
The biogeography models predict the dominance of different plant species under different climatic and environmental scenarios. The several biogeography models used for such assessment include the Mapped Atmosphere Plant Soil System [(MAPSS), Neilson, 1995], BIOME3 (Haxeltine & Prentice, 1996), and MC1 (Bachelet et al., 2004). Input datasets for biogeography models mainly include latitude, mean monthly temperature, wind speed, solar radiation, and soil properties such as texture and depth. All of these models project vegetation responses to changes in the concentrations of CO2, but through different mechanisms.
and
The main purpose of the first phase (development of the RCPs) is to provide information on possible development trajectories for the main forcing agents of climate change, consistent with current scenario literature allowing subsequent analysis by both Climate models (CMs) and Integrated Assessment Models (IAMs). Numbers refer to the different steps mentioned in overall development method included 7 sequential steps, most of which are directly related to the design criteria discussed above. These steps are all discussed in more detail in the subsequent sections. I. Four existing scenarios were selected from the literature.
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II. The four scenarios were updated to reflect advances in integrated assessment modeling and to use common base year emissions and land-use data, where possible. Preliminary releases by individual teams were subjected to internal review by the RCP research groups. This process resulted in several rounds of revision of the scenarios.
Overview of Representative Concentration Pathways The RCP 8.5 was developed using the MESSAGE model and the IIASA Integrated Assessment Framework by the International Institute for Applied Systems Analysis (IIASA), Austria. This RCP is characterized by increasing greenhouse gas emissions over time, representative of scenarios in the literature that lead to high greenhouse gas concentration levels (Riahi et al., 2007).
III. The land-use data of the RCPs were harmonized (i.e. made consistent with a selected set of base year data; see also the next sections) and downscaled (data were provided at a 0.5×0.5 grid).
The RCP6 was developed by the AIM modeling team at the National Institute for Environmental Studies (NIES) in Japan. It is a stabilization scenario in which total radioactive forcing is stabilized shortly after 2100, without overshoot, by the application of a range of technologies and strategies for reducing greenhouse gas emissions (Fujino et al., 2006; Hijioka et al., 2008).
IV. The emission data on the RCPs were harmonized and downscaled (to a 0.5×0.5 grids) for air pollutants, i.e. aerosols and tropospheric ozone precursors. V. The emission data were converted to concentration data, using a selected simple carboncycle climate model for wellmixed greenhouse gases and an atmospheric chemistry model for reactive short-lived substances.
The RCP 4.5 was developed by the GCAM modeling team at the Pacific Northwest National Laboratory’s Joint Global Change Research Institute (JGCRI) in the United States. It is a stabilization scenario in which total radioactive forcing is stabilized shortly after 2100, without overshooting the long-run radioactive forcing target level (Clarke et al., 2007; Smith & Wigley, 2006; Wise et al., 2009; Clarke et al., 2010).
VI. Simple extensions of the RCPs for the 2100–2300 periods were developed. VII. All relevant information has been made available for downloading, by using a central repository. This repository allows the user to preview and download data on emissions, concentrations, radioactive forcing and land use-both at the level of aggregated regions and in gridded form.
The RCP2.6 was developed by the IMAGE modeling team of the PBL Netherlands Environmental Assessment Agency. The emission pathway is representative of scenarios in the literature that lead to very low greenhouse gas concentration levels. It is a “peak-and-decline” scenario; its radioactive forcing level first reaches a
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value of around 3.1 W/m2 by midcentury, and returns to 2.6 W/m2 by 2100. In order to reach such radioative forcing levels, greenhouse gas emissions (and indirectly emissions of air pollutants) are reduced substantially, over time (Van Vuuren et al., 2007a). Based on these representative concentration pathways Chaturvedi et al. (2012) has been estimated the multimodel climate change projections for India by using coupled Model intercomparison project 5(CMIP 5).
observed annual temperature over India for the same period (Figure 3).
Validation of CMIP5 models over India
The spatial pattern of annual precipitation as simulated by the CMIP5 model ensemble is plotted along with the CRU-based observed annual precipitation over India for the period 1971–2000 (Figure 1). The CMIP5 ensemble is able to simulate the broad spatial patterns of precipitation distribution in India reasonably well. For example, rainfall maxima are simulated in the Western Ghats and North East India, and rainfall minima are simulated in western India.
CMIP5-based model ensemble simulates an all-India annual mean temperature of 22.9°C for the period 1971–2000, which is close to the observed annual mean temperature of 23.3°C for the same period. Apart from reasonably projecting the all India mean annual temperature, CMIP5 ensemble is also able to broadly capture the observed spatial distribution patterns of temperature over India (Figure 3).
Model-simulated baseline climatologists (individually as well as the model ensemble) are compared with the CRU observed climatology’s (Mitchell & Jones, 2012) over the period 1971–2000 for both temperature and precipitation. The spatial pattern of annual temperature over the period 1971–2000, as simulated by the model ensemble is plotted along with the CRU-based
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Figure 3: Comparison of ensemble mean temperature (°C) and precipitation (mm) as simulated by CMIP5 models for 1971–2000 (1980s) with observed temperature and precipitation distribution (Climate Research Unit; CRU) for the same period.
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Figure 4: CIPMP5 model ensemble mean temperature change (°C) projected for 2030s (2021–2050), 2060s (2046–2075) and 2080s (2070–2099) relative to the pre-industrial period (1880s, i.e. over 1861–1900)
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Figure 4 shows the CMIP5 model ensemble-based annual temperature change (°C) projected for 2030s, 2060s and 2080s relative to the pre-industrial baseline (1880s) for the four RCP scenarios. All India annual mean temperature increases by 1.7°C–2.02°C by 2030s under different RCP scenarios and by about 2°C–4.8°C by 2080s, relative to the pre-industrial base. Figure 4 projects a consistent warming trend over the country in short-, mid- as well as long-term scenarios. As expected in each of the three time slices RCP2.6 generally experiences the least warming, while RCP8.5 is associated with the highest warming, with RCP4.5 and RCP6.0 representing the moderate warming scenarios. Generally northern part of the country is projected to experience higher warming compared to the southern counterpart. Areas in the Himalayas and Kashmir are particularly subject to large warming to the tune of 8°C in RCP8.5 by 2099.
four RCP scenarios. All-India annual precipitation increases by 1.2–2.4% by 2030s under different RCP scenarios and by 3.5–11.3% by 2080s, relative to the pre-industrial base. Precipitation is projected to increase almost all over India except for a few regions in shortterm projections (2030s). As noted in the temperature trends in each of the three time slices, RCP2.6 experiences the least increase in precipitation, while RCP8.5 experiences the highest precipitation increase, and the precipitation changes are larger for each subsequent period (i.e. short, mid and long term). Figure 6 suggests that by 2080s under RCP8.5 on an average at least 14 models (more than 80% of GCMs) agree in the sign of precipitation change over India and in many of the regions, especially along central and eastern India, almost all the models agree in the sign of precipitation change. These results from CMIP5 climatology are significant as CMIP3-based climate projections suggested that less than 66% of the GCMs agree in the direction of the precipitation change over the Indian region (IPCC, 2007).
Figure 5 shows the CMIP5 model ensemble-based annual precipitation change (%) projected by 2030s, 2060s and 2080s respectively, compared to the pre-industrial baseline (1880s) for the
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Figure 5: CIPMP5 model ensemble mean precipitation change (%) projected for 2030s (2021–2050), 2060s (2046–2075) and 2080s (2070–2099) relative to the pre-industrial period (1880s, i.e. over 1861–1900)
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Figure 6: Number of models agreeing in the sign of precipitation change projections for each grid for short (2021–2050), medium (2046–2075) and long (2070–2099) term periods relative to pre-industrial period (1880s, i.e. over 1861–1900)
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Implications assessment
on
climate
impact
likely to witness a high increase in temperature and either decline or marginal increase in rainfall. Forests are likely to benefit to a large extent (in terms of NPP) in the northern parts of Western Ghats and the eastern parts of India, while they are relatively adversely affected in western and central India. This means that afforestation, reforestation and forest management in northern Western Ghats and eastern India may experience carbon sequestration benefits. Hence, in these regions, a species-mix that maximizes carbon sequestration should be planted. On the other hand, in the forests of western and central India, hardy species which are resilient to increased temperature and drought risk should be planted and care should be taken to further increase forest resilience. This may be achieved by planting mixed species, linking up forest fragmentations, devising effective pest and fire management strategies and carrying out anticipatory plantation activities.
It is note that vulnerable forested grid points are spread across India. However, their concentration is higher in the upper Himalayan stretches, parts of central India, northern Western Ghats and Eastern Ghats. In contrast, northeastern forests, southern Western Ghats and the forested regions of eastern India are estimated to be least vulnerable. 1. Implications for afforestation and reforestation (A&R) Currently, within the forested area of 69 million hectare (mha) only 8.35 mha is categorized as very dense forest. More than 20 mha of forest is monoculture and more than 28.8 mha of forests are fragmented (open forest) and have low tree density (FSI 2009). Low tree density, low bio-diversity status as well as higher levels of fragmentation contribute to the vulnerability of these forests. The Government of India under NAPCC (National Action Plan on Climate Change), has timely brought a proposal to afforest more than 6 mha of degraded forested lands. We recommend that care should be taken to plant mixed species and planting should also be executed in such a way as to link the existing fragmented forests. Efforts should also be made to convert open forests to dense forests. Our analysis suggests that Western Ghats, though a bio-diversity hotspot, has fragmented forests in its northern parts. This makes these forests additionally vulnerable to climate change as well as to increased risk of fire and pest attack. Similarly, forests in parts of western as well as central India are fragmented and are having low biodiversity. At the same time these are the regions which are
2. Implication for forest conservation and REDD+ Northeastern forests, southern Western Ghats and Forests of eastern India are estimated to be least vulnerable. This is on account of their high biodiversity, low fragmentation, high tree density as well as low rates of vegetation change (as these regions experience lower levels of temperature increase and gain substantially in terms of precipitation). The resulting low vegetation vulnerability makes these regions especially suitable for reduced deforestation and forest conservation projects such as REDD+ (UNFCCC, 2009). For example, northeastern India which has more than 80% of land area
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classified as forests is currently under severe pressure of deforestation. This region witnesses the highest rate of deforestation in India (65% of total deforestation in India over the period of 2005–2007), mainly due to encroachment and shifting cultivation (FSI, 2009). Over the period 2005–2007, according to the latest FSI (2009), 201 km2 in Nagaland, 119 km2 in Arunachal Pradesh, 100 km2 in Tripura and 66 km2 in Assam were deforested. Given that the ecosystem in this region appears robust in the face of climate change, it is desirable to create REDD+ projects in this area to combat deforestation and resulting loss of flora.
of the general models, although to various degrees. Impacts on forests are often considered part of an aggregated impact of climate change, or forests may be considered a part of a stock of natural resources. Some models include forests indirectly by a land-use module. There are only a few examples of further linking land-use module to ecological models, which enable a more direct consideration of forest issues. The studies that have been carried out indicate, however, a large potential of addressing new and important issues. Some recent studies indicate the advantages of further integration of ecologic and economic models, pointing out potentially significant benefits of multipurpose management regimes. On the other hand, an ecological response also needs to be represented more comprehensively, for example in order to address combined ecological effects of management on ozone- and CO2concentrations. Regional, national and global economic models are fed by data delineated by institutional criteria, such as country borders, and cannot easily be broken down to the spatial resolution of the ecological models. It is expected that high-resolution multi-model regional climate change projections will be available for India in the near future through globally coordinated regional downscaling experiment (CORDEX). We expect that CORDEX-based regional climate projections will certainly bring more confidence to future climate projections for India.
CONCLUSIONS Forests represent a huge potential for mitigation, while at the same time being affected significantly by climate change itself. This explains why integrated studies of climate policy and forest management have been subject to a large amount of studies, which cover a broad range of issues. So far, most of these studies are somehow related to the mitigation of climate change. Studies of impacts and adaptation are less frequent. There is, however, a growing interest for approaching mitigation as an aspect of forest management, which includes adaptation to climate change. The models used for studies of economic impacts of climate change where forests and forestry are integrated fall into two categories: one is founded on general macroeconomic models, including general equilibrium models, which also describes activities within forestry. The other category is regional and partial models, which allows for closer studies of forest management. Forests and forestry are poorly represented in most
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