REDD-plus after Cancun: Moving from Negotiation to Implementation

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


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)


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


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


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

Documents

REDD-plus after Cancun: Moving from Negotiation to Implementation