Transcript INDO-UK PROGRAME

Climate change, land use and forests
in India: research and institutional
framework in the context of the
Indo-US flux programme
R. Sukumar & N.H.Ravindranath
Centre for Ecological Sciences
Indian Institute of Science
Bangalore
Obligations under UNFCCC
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Periodic report of greenhouse gas emissions
inventory from all sectors including land use
sectors such as forests, grassland, wetlands, etc
Assess the vulnerability of natural ecosystems
and socioeconomic systems to projected climate
change
Report the steps taken to address climate
change (mitigation, adaptation)
Forests & Climate Change
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Forests play a critical role in global carbon
cycle
Forests contribute about 20% of global CO2
emissions
Forest ecosystems are vulnerable to projected
climate change
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Likely to have adverse impacts on forest
biodiversity and biomass production
Thus need to assess impacts and develop
adaptation strategies
Forests provide mitigation opportunity to
stabilize GHG concentration in the
atmosphere, along with significant co-benefits
Mitigation through forest sector has been a
contentious issue in climate negotiations
GHG Emissions from forest
sector
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Global emissions of carbon = 7 GtC
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Emissions from LUCF = 1.6 to 1.7 GtC  1
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Tropical deforestation = 13 to 15 Mha
annually
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Land use change is the dominant factor
in tropical countries
ESTIMATES OF STOCKS AND FLUXES FROM
INDIAN FORESTS
12000
Estimates of fluxes from Indian Forests
10
8000
6000
4000
2000
Biomass carbon
2005
1994
1986
1986
1980
1880
0
Fluxes in Million tonnes
Carbon stock (MtC)
10000
5
0
-5
1986
1986
1990
1994
1994
-10
-15
-20
Soil carbon
Fig 1: Estimates of C-stock from Indian forests
Fig 2: C-flux estimates from Indian forests
(Sources:
(Sources:
1880: Richard and Flint, 1994;
1986-Ravindranath et al., 1997;
1980-Richard and Flint, 1994;
1986:Chhabra and Dadhwal, 2004;
1986:Ravindranath et al., 1997;
1990 – ALGAS (ADB)., 1999;
1986:Chhabra and Dadhwal, 2004;
1994:Haripriya, 2003;
1994:Haripriya, 2003; 2005:FAO, 2005)
1994: NATCOM, 2004)
GAPS IN C FLUX ESTIMATES
1.
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Estimation of CO2 emissions are based on
Different methods
Different sources of data
Different C –pools
Different years
Thus the estimates are not comparable
Uncertainties are high
Periodic spatial data, forest-type wise, lacking for
flux estimates
1.
2.
C - Inventory process requires information
pertaining to activity data (i.e. land area change
statistics) and impact of land use change on the
C stock dynamics.
C stock dynamics under different land use
change systems is poorly understood.
MITIGATION POTENTIAL OF LULUCF SECTOR
BOREAL
TEMPERATE
17.0%
60 – 87 Gt C
(cumulative)
1.09 – 1.58 Gt C (annual)
80.1%
TROPICAL
Projections for mitigation potential for the period 1995 to 2050
Brown et al. 1996, 1999; IPCC 2001
Climate change impact studies at IISc
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Evaluate and select models to assess climate
impacts on forests
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Regional Climate Model;
Vegetation Response Model;
Assess impacts of climate change on forest
ecosystems at national level
Assess impacts on biodiversity and socioeconomic systems through case studies
Analyze policy implications of climate impacts
Strategies for future
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Research; modeling and database
Adaptation strategies
Impact of Climate Change on
Forest Ecosystems
SELECTION OF VEGETATION MODEL
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Equilibrium models: BIOME 3
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Dynamic model: HYBRID 4.2
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BIOME3 used due to input data limitations
for the HYBRID Model
CLIMATE DATA FOR BIOMES
Model used: Hadley Centre Regional Model; Had
RM3
Mean monthly temp. & rainfall, cloud cover
Scale: 0.44 x 0.44 degree RCM grid
Scenarios: SRES; A2 and B2
Period: 2071-2100 mid period: 2085
Observed Climate data: CRU data set for 1901-1995
from East Anglia (0.5x0.5 degree grid)
Projections of seasonal surface air temperature for
the period 2041-60, based on the regional climate
model HadRM2.
Source:
IITM Pune
Natcom
Projections of seasonal precipitation for the
period 2041-60, based on the regional climate
model HadRM2.
Source:
IITM Pune
Natcom
Potential impact on forest biomes
(B-2 scenario)
0
A2
B2
Depterocarpus (Gurjan) Hollong
Khasi pine
Bamboo Forest
Fir-Spruce
Sal
Teak
Chir-pine
Up-land Hardwoods
Miscellaneous forest
SCRUB
Fir
Blue-Pine(Kail)
Mixed conifer
Salai forest
Deciduous forest
Hardwoods Conifers mix
Deodar
Khair forest
Spruce
Mangrove
Western Ghat evergreen forest
Western Ghat semi-evergreen
% grids undergoing change
Percentage of grids under different forest types
undergoing change in A2 and B2 GHG scenarios
120
100
80
60
40
20
Climate impacts on NPP; % Forest biome-RCM grids subjected to change in
NPP under GHG scenario over the current scenario under B2 Scenario
oo dlan d
Temperate sclero ph yll w
blan d
Trop ical xero ph ytic sh ru
Trop ical savann a
fo rest
Decid uo us taig a/mon tane
fo rest
Ev eg reen taig a/mon tane
Co ld mixed fo rest
Co ol co nifer fo rest
Co ol mixed fo rest
W arm mixed fo rest
Temperate co nifer fo rest
rest
Temperate decidu ou s fo
st/woo dlan d
Trop ical decidu ou s fo re
fo rest
Trop ical semi-d ecid uo us
Trop ical ev ergreen fo rest
0
NPP Present
500
1000
NPP A2GHG
1500
2000
2500
NPP B2GHG
3000
SUMMARY OF IMPACTS
Had RM3 Model outputs using SRES: A2 and B2
scenarios & BIOME3 show;
1.
Over 85% of forest grids will undergo
changes in forest type (similar trend using
Had RM2)
2.
Regional assessment shows;
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Higher impact on Savanna biomes, Teak and Sal
forests of central and east, temperate biomes of
Himalayas
Lower impact on Western ghats and North-east;
Evergreen biomes
Large (potential) increase in Net primary
productivity
3.
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70% (B2) to 100% (A2)
GAPS IN UNDERSTANDING
CURRENT STATUS
 Large uncertainty in climate and
vegetation response models;
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regional climate level
equilibrium vegetation model
Inadequate or lack of data for the models
Adaptation not incorporated in impact
models
Location of the Mudumalai 50 ha Forest Dynamics Plot
Location of Mudumalai WLS
Detailed studies on the forest community
Over 50000 individuals from 250+ species monitored
Topography of the Mudumalai plot
Recruitment and Mortality in the 50 ha plot
18
16
% Mortality
% Recruitment
14
10
8
6
4
2
Year
20
04
19
90
19
91
*
19
92
*
19
93
19
94
*
19
95
19
96
*
19
97
19
98
19
99
20
00
20
01
20
02
*
20
03
*
0
19
89
*
% Individuals
12
Dry season fire
19
89
*
19
90
19
91
*
19
92
*
19
93
19
94
*
19
95
19
96
*
19
97
19
98
19
99
20
00
20
01
20
02
*
20
03
*
20
04
Mortality rate (%)
Mortality due to various causes
18
16
MortFire
14
MortEle
MortOthers
12
10
8
6
4
2
0
Year
Canopy trees: Average growth rates per size
class during 3 intervals
Canopy trees: Average growth rates per size class during 3 intervals
8
6
4
2
0
-2
Average growth(mm/yr)
10
1988-1992
1992-1996
1996-2000
0
20
40
60
Dbh (cm)
80
100
120
Basal area
changes (m2 /ha)
1988 = 24.4
1992 = 24.8
1996 = 24.7
2000 = 25.9
2004 = 25.5
Carbon stocks probably
increased to a greater degree because
of shift from lower wood density to
higher wood density species
SCIENTIFIC DATA NEEDS FOR CLIMATE CHANGE
AND LANDUSE AND LANDUSE CHANGE RESEARCH
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Flux programme should ideally complement “on the
ground” studies on soils and vegetation
Spatial data on land use, landuse changes & forests
(partly available)
Data on carbon stocks and fluxes under different land
use and landuse change systems (lacking)
Spatial data on soil, water and plant physiological
functions (limited availability)
Flux programme should thus network with institutions
in order to extract maximum scientific understanding
of C dynamics from the soil, through vegetation to
the atmosphere
Networking on Institutions
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Land use systems – NRSA, IRS, ISRO, SAC
& FSI
Vegetation carbon flux - IISc, KFRI,
ICFRE, NHU, BHU, etc
Soil carbon flux – NBSSLUP, ICAR
institutes, Agric. Univ
Climate data – IITM, IISc, IMD
Modeling of fluxes – IISc, IITM, IIT,
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National Coordination
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DST
Dedicated institution??
Regional lead institutions – Research area
Networking of all institutions
Funding
DST, MoEF, ICAR, ICFRE
 External funding
Linking with endusers such as – MoEF, ICAR, research
institutions
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