redd-plus after cancun: moving from negotiation to implementation
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REDD-plus after Cancun: Moving from Negotiation to Implementation
-Building REDD-plus Policy Capacity for Developing Country Negotiators and Land Managers-
at Hotel Nikko Hanoi, Hanoi, Vietnam, 18-20 May 2011
Developing Robust MRV Systems: Learning from Country Experience in Indonesia
Mitsuru Osaki*, Farhan Helmy**, Doddy Skadri**, and Kazuyo Hirose***
*Research Faculty of Agriculture, Hokkaido University, Japan**National Council on Climate Change (DNPI), Indonesia***Center of Sustainability Science (CENSUS), Hokkaido University, Japan
General Introduction
Net primary production decreased 1% (0.55 petagrams of carbon over 10 years) globally from 2000 to 2009
Maosheng Zhao, et al.: Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 Science 329, 940 (2010)
The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere.
Net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally during 1982 to 1999
Ramakrishna R. Nemani, et al : Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science 300, 1560 (2003);
We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation.
Project Introduction
Study Site from 1997• Central Kalimantan, Indonesia• Peatland• Mega Rice Project
Palangkaraya
Study Topics:・ Green House Gasses Flux (CO2, CH4, N2O)・ Fire Detection and Protection・ Water Table Monitoring and Management・ Peatland Ecology・ Integrated Farming
Sources:1) Forestry Statistics of Indonesia 2007, Ministry of Forestry, Jakarta 2008.2) Wetlands International - Indonesia Programme, Bogor July 2008.
Location Total
Forest area1) Mha (x 1,000 ha) 93,924.33 (100%)
Peatland area2) Mha (x 1,000 ha) 21,000 (100%)
Legend:
Kalimantan
28.200 (30.06%)
5.800 (27.50%)
Sumatera
14.700 (15.60%)
7.200 (34.30%)
Jawa/Madura
3.000 (3.30%)
na
Bali/Nusa Tenggara
2.700 (2.88%)
na
Papua/West Papua
32.400 (34.45%)
8.000 (38.10%)
Sulawesi
8.900 (9.45%)
na
Maluku/North Maluku
4.000 (4.28%)
na
Forest and Peatland Areas in Indonesia
7
What Factors Regulate Carbon in Tropical Peat?
Carbon Emission by Fire
Water
Carbon Emission by Microorganism
Degradation
Deforestation
・ Dryness of ground surface
・ Decrease water holding capacity
Drainage
・ Decrease water table
Carbon Loss through Water
Ecosystem Change
・ Farming/ Vegetation
Tree Growth/Mort
ality
Tree Growth/Mort
ality
PALSAR, AMSR-E (4), (5), (6), (7)
GOSAT (1)
Satellite
Airborne/UAV
Ground Tower(1)
Terra & Aqua MODIS (2)
LiDAR (4), (5), (7)UAV(1), (3)
Landsat, SPOT, Quickbird, TerraSAR, AVNIR-2, ASTER, Hisui,
(3), (8)
(5)Peat subsidence(5)Peat subsidence
(6)Water level, (6)Water level, Soil moistureSoil moisture
(4)Forest (4)Forest biomass biomass changechange
(3)Deforestation, Forest (3)Deforestation, Forest degradation, Species degradation, Species
mappingmapping
(1) CO(1) CO22 concentrationconcentration
FES-C (1)FES-C (1)
*FES-C : Fiber Etalon Solar measurement of CO2
Lateral COLateral CO22 Flux Flux
Vertical CO2 Flux
DGPS(5)DGPS(5)DGPS(5)
Chamber(1)
Water Gauge(6)(7)Peat dome detection(7)Peat dome detection
& Peat thickness& Peat thickness
Drilling(7)Drilling(7)
(2)Wildfire (2)Wildfire detection & detection &
HotspotHotspot
((8)Water8)Water soluble organic soluble organic carboncarbon
Red: InstrumentBlack: Target
Key Elements for Carbon Flux Estimation (Integrated MRV system proposed as Sapporo Initiative)
(1) Emissions by fires
Fire DetectionNew Generation Fire Detection
• Doubled S/N ratio (ASTER comparing to MOD14, and Algorism Improvement)– 80% more HS and & 10% less False Alarm– Smoldering, small fire or slush and burn– Geographical distribution is completely different– Suitable to decide firefighting strategy and confirm extinction
MOD14 Proposed
Toshihisa Honma, Hokkaido University, Japan
Example of Thermograph Imageof flight observation
RGB IR
UAV (Unmanned aerial vehicle) flight observation and Wireless Sensor Network are indispensable as well as ground observations.
Toshihisa Honma, Hokkaido University, Japan
Fire Expan. Simulation
• Simulation Result at 16:00, June25 (after 24 hours run).• The expansion for the very slow expansion mainly to southward is
overestimated. • The rapid expansion toward eastward is underestimated because of the limit
of time step.Toshihisa Honma, Hokkaido University, Japan
14
By Hidenori Takahashi, Japan
By Hidenori Takahashi, Japan
Peat Fire Index An indicator of peat fire damage (Carbon emission data is
offered by Dr. Erianto Indra Putra)
1Mha
MRP area in KaltengPFI
Carb
on e
mis
sion b
y p
eat
fire
(G
tC/M
ha)
By Hidenori Takahashi, Japan
GHGs Emission by Peat Fire
R. Hatano et al. (unpublished)
The organic matters eluted from burned soil
‣Amount of eluviation greatly increases at 220℃burn. ‣Most part of eluted organic matters from burned soil have
hydrophilic. (by Kuramitsu et al.)
The peat land fire accelerate the eluviation of Carbon.
Burned at 220℃
Burned at 350℃
Hydrophilic matters
Hydrophobic acids
5 min 5 min30 min 30 minUnburnedsoil
(2) Emission by oxidation of microorganisms
Eddy covariance technique
Within the boundary layer, vertical flux is almost constant.
If flux is measured at an appropriate height within the boundary layer, we can obtain flux averaged spatially over the fetch.
CO2 flux (Net ecosystem CO2 exchange) is calculated as the covariance of vertical wind speed and CO2 density.
By Takashi Hirano (Hokkaido Univ., Japan)
Undrained forest (UDF)
Drained forest (DF)
Burnt forest after drainage (BC)
By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)
Seasonal variation in NEE (net ecosystem CO2 exchange) in DF site
Dec. Jun. Dec. Jun. Dec. Jun. Dec.-2
0
2
4
6
Month
NEE
(gC
m-2
d-1
) 2002 2003 2004
NEE was positive or neutral throughout 3 years (CO2 source).
CO2 emission was the largest in the late dry season, partly due to the shading effect by smoke from farmland fires.
CO2 emission was the largest in 2002, an El Nino year, because of dense smoke from large-scale fires.
CO2 source
CO2 sink
J F M A M J J A S O N D20
25
30
35
40
45
Month
PP
FD
(m
ol m
-2 d
-1)
b) Daily PPFD
200220032004
By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)
Inter-site comparison of annual CO2 balance
May 2004 to May 2005, Unit: gC m-2 yr-1
Positive NEE (CO2 source strength): BC > DF > UDF
Site GPP RE NEE
UDF (undrained) 4000 4103 103
DF (drained) 3287 3724 437
BC (burnt & drained) 1075 1899 824
UDF also functioned as a CO2 source to the atmosphere.
→ -1.4 mm yr-1
→ -6.1 mm yr-1
→ -11.6 mm yr-1
Peat decomposition
Peat growth rate in Indonesia : 1–2 mm yr-1 (Sorensen 1993)Carbon accumulation rate in Palangkaraya: 56 gC m-2 yr-1 (0.8
mm y-1) (Page et al. 2004)
Results of peat sampling
By Takashi Hirano (Hokkaido Univ., Japan) (Unpublished)
Effects of water table (WL) on respiration in forest
-2.0 -1.5 -1.0 -0.5 0.0 0.50.8
1.0
1.2
1.4
1.6
1.8
WL (m)
RE
/ G
PP
DFUDF
RE/GPP vs. WL for UDF & DF
Soil respiration vs. WL for UDF by automated chamber systems
Hirano et al., Ecosystems 2008
GWP= CO2 flux + CH4 flux ×23 + N2O flux ×296
GWP in forest → influenced by CO2 GWP in cropland → influenced by N2O
: CO2 flux : CH4 flux ×23 : N2O flux ×296
Some results of greenhouse gases emission from tropical peat soil, Indonesia
Central Kalimantan, Indonesia; Arai et al., unpublished
(3) Carbon Loss through Waterborne Carbon
by I Tanaka et al., Unpublished
Seasonal Changes of DOC
Correlation between Water Table and DOC
Hyper sensor for carbon dissolved in waterHyper sensor for carbon dissolved in water
N2O
N2O
Colored Dissolved Organic Matter (CDOM) and Dissolved Organic Carbon (DOC) for Southern Finland and the Gulf of Finland by ALI image on14 July, 2002 (Kutser et al., 2005)
Hyper sensor
Monitoring target1)Dissolved Organic Carbon (DOC)2)Dissolved Inorganic Carbon (DIC)3)Particulate Organic Carbon (POC)4)Colored Dissolved Organic Matter (CDOM)
*Potential carbon release from peat.Indonesian rivers transfer around 10% DOC of the global riverine DOC oceanic input (Baum et al.,2007).
(Example)
Robust MRV Systems:Water Table is Key for Measuring!
Water Table is Key for Peatland Ecosystem!!
1) Oxidation
2) Fire Factors
3) Tree growth and Mortality
4) DOC
30
31By Wataru Takeuchi, University of Tokyo, Japan
Algorism
32By Wataru Takeuchi, University of Tokyo, Japan
By Wataru Takeuchi, University of Tokyo, Japan
Simulator: SimCycle-Visit for East Asia
Column averaged dry air mole fraction distribution of carbon dioxide for the month of September, 2009, obtained from IBUKI observation data (unvalidated) By JAXA
Satellite GOSAT “IBUKI” Senescing: CO2
Top-down• satellite• airplane• inverse model
Bottom-up• field survey• flux obs.• process model
Integrated, practical carbon
budget map
・ Carbon Emission by Fire・ Carbon Loss through Water
・ Carbon Emission by Microorganisms Degradation
・ Tree Growth/Mortality
Carbon-Water Simulator
Biomass Carbon
Soil Carbon
Wet Dry
Thank you for your attention
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