1 JCM Feasibility Study 2015 Summary Report Degraded peatland management in Jambi Table of Contents 1. Project background……………………………………………………………………………………………………………………………..4 2. Objective of the FS……………………………………………………………………………………………………………………………….5 3. Project description: a. Project location……………………………………………………………………………………………………………………………..6 b. Indonesian partner(s) ….…………………………………………………………………………………………………………..……4 c. Description of the technology………………………………………………………………………………………………………..4 d. Project details…………………………………………………………………………………………………………………………………6 4. The result of the study a. Role of each participant………..………………………………………………………………………………………………………..7 b. Reference scenario setting………………………………………………………………………………………………………….….7 c. Monitoring methods……………………………………………………………………………………………………..……………….8 d. Quantification of GHG emissions and their reductions……………………………………………………………………9 e. MRV methodology…..………………………………………………………………………………………………………………..….10 f. Project site monitoring and emission reduction assessment………………………………………………………….16 g. Satellite based analysis………………………………………………………………………………………………………………….31 h. Scale of investment & financial viability……………………………………………………………………………………..…39 i. Contribution to Indonesian Sustainable Development……………………………………………………………..……39 j. Proposed implementation schedule…………………………………………………………………………………………..….40 k. Capacity building to the host country…………………………………………………………………………………………….40 5. Conclusion and Next Steps………………………………………………………………………………………………………………….40 References…………………………………………………………………………………………………………………………………………..41
Tropical peatland covers approximately 250,000 km2 of land in Southeast Asia including Indonesia, and around 68.0 billion tons of carbon is estimated stored (Page et al., 2011). Over 80% of this is situated in Indonesia, and it is estimated that approximately 0.8 billion tons of CO2 per year is being emitted as a result of peat fires and peat decomposition (DNPI, 2010). This is comparable to the amount of CO2 emitted in Japan and accounts for around 5% of the world CO2 emissions. Peat decomposition as well as peat fires occur mainly during the dry season due to the decline of groundwater levels in peatland caused by deforestation and associated drainage, and are especially severe in El Niño years (such as 1997, 2002 and 2006). To mitigate these problems, water levels in peatland need to be restored to rewet the peat, and corresponding MRV (Measurable, Reportable and Verifiable) methodology to quantify the effects of such countermeasure is necessary to be developed.
Project area is a rice farming coastal lowland in Berbak Delta region of Jambi province in Indonesia. Much of the area has peat soil, with a remaining thickness around 1m on average after decades of drainage and associated peat decomposition. The area was deforested in the 1970s, and water channels and gates were built in the 1980s for growing rice by trans‐immigrants. The water management facility has since been not well maintained, allowing drop of average water levels as low as ‐1m below ground level during the dry season, causing CO2 emissions due to aerobic peat decomposition. The low water table also cause oxidation of pyrite layer beneath the peat layer causing acidification of the soil and lower pH as low as 3‐4. During the rainy season on the other hand, flooding condition due to failure of the water table control prevents timely manner rice farming resulting in reduced production with often poor quality and quantity of rice. Because of these conditions, rice production in the area is very low at 1‐2 tons/ha compared with normal 6 tons/ha in tropical area. To curb the situation, peat soil oxidation needs to be suppressed by restoring water levels through proper water management including water facility upgrade.
Because of the lack of proper water management, the area has been suffered low rice productivity; almost no production during the dry season due to dryness and lower yield during the rainy season due to the flooding problem. Among various conditions to increase rice productivity, water management is the most fundamental key issue to be improved.
Because of the low rice yield, farmers living in the lowlands with peat soil, a part of main rice production area of Indonesia, have been in poor conditions. Also there are farmers converting their lands without enough knowledge from rice paddy to oil palm plantation to try to increase their income. Oil palm is not suitable to grow in lowlands as it requires low water table close to ‐1m below the ground level that enhance peat decomposition because of required low water table. This situation has been lowering rice production in Indonesia, making the country as the largest importer of some 2 million ton rice per year, causing food security problem of the country.
An integrted water and carbon management with consideration of landscape approach is necessary to mitigate the situation.
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2. Objective of the FS
The following items are being studied in the current FS to develop MRV methodology and project
formation.
(1) Methodology development
i) Monitoring tools development
ii) Site monitoring
iii) Methodology development and emission reduction evaluation
(2) Host country situation for climate change and REDD+ projects
i) Situation of central government
ii) Situation of local government
iii) Situation of peatland management
(3) Project partnership and finance scheme
i) Project partnership
ii) Project finance scheme
(4) Economical and other benefit of Project
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3. Project description:
a. Project location
The project site covers an area of approximately 20,000 ha in the East Tanjung Jabung district of Jambi, Sumatra as seen in Fig. 3.1. Site : Tanjung Jabung Timur Regency, Jambi Province
Area: About 20,000ha (= 200km2)
History of the site
Developed canals for rice field during 1970s.
Due to drainage, the lands became dry and rice yield is 1‐2 tons/ha.
Peat not been conserved and decomposition and resulting subsidence are advancing
Fig. 3.1 Project site (East Tanjung Jabung district of Jambi, Sumatra)
b. Indonesian partner(s)
The project counterparts are the Ministry of Public Works, the Jambi Provincial Government and Tanjung Jabung Timur Regency Government.
c. Description of the technology
Main scope of the project activity is to use best practice methods for water table control in the project area of degraded rice farming lands with peat soil by upgrading water canals and gates in 20,000ha of Berbak delta of Tanjung Jabung Timur in Jambi (Fig.4). Through the activity, it is expected to increase rice productivity (adaptation) and to mitigate peat CO2 emission due to its decomposition under JCM and NAMAs (Figs. 1 & 5).
Rice production increase in sustainable manner in degraded lowland is the goal of the proposed project activity through the proper water management, which prevents water acidification in the dry season and flooding in the rainy season. The activity will be linked governmental program LP2B, which aims to
5
sustain existing rice‐farming area under the pressure of conversion to palm plantation.
Through the activity, robust MRV (Measurebale, Reportable and Verifiable) methodology to evaluate GHG emission reductions as well as best practice guideline for water management will be developed.
Develop policy and institutional mechanism including water association of local communitythat enable to sustain rice farming in degraded peat lowlands. Also partnership between national and local governments, private sector and research institutions to promote large scale climate change adaptation and mitigation projects under NAMA and FVA (Framework for Various Approach) including JCM (Joint Crediting Mechanis) between Indonesia dn Japan.
CO2 emissions by peat decomposition
Dry peatdrainage
Before
Rice husk
BiomassRice yield increaseWater control ＝
ash
Drying facility
Emissions reductions by water table increase
Water gate Peat in GroundwaterWater table
increase
After
Fig. 3.2 Project scheme of carbon and water management for rice‐farming degraded peat lowland
Rice husk ahs and dry soil ration
Biomass dryer
After
Before
Fig. 3.3 Water control and rice‐husk utilization for energy and soil neutralization
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d. Project details
The project activity scenario includes water table control for rice‐farming in the peat soil of the lowlands with upgrade of water‐gates and canals and their improved operation to increase rice yield with reduced peat decomposition. Brief description of the project componets and expected outcomes are the followings.
‐ Water controrol: Raise water table in suitable hydrogeological plots of rice farming peatlands in Jambi. Through stakeholders meeting of the plots, functions to grow rice during the dry season with tertiary canal and gates installation. This stage project exhibit how to rewet peatland to reduce peat oxidation and its monitoring (groundwater level, subsidence etc), which become basis for future countrywide implementation. Through the water management, local farmers can produce rice even during the dry season and minimize damage to the rice growth due to pyrite oxidation as well as reduce flooding impact during the rainy season.
‐ An integrated approach: To establish functional mechanism of proper water‐soil management for the above stage project and its continued program, an integrated approach to synchronize organizations functions at the levels of farmers, local and central govrnments be taken ; i) support to re‐organize water association by farmers to operate water control facility, ii) appropriate information for farmers to enable timely manner rice‐farming, iii) proper water control facility maintenance, iv) continuous capacity building for rice‐farming improvement.
‐ Dissemination: Results of the demonstration project be compiled in a guideline and easy understanding video, which shall be effectively utilized for the area development plan of East Tanjung Jabung Regency, which has a proposed plan of raising productivity of existing 17,000ha paddy field (LP2B: Kondisi Pendataan Lahan Pertanian Pangan Berkelanjutan, Documenting Condition of Agricultural Land Sustainable Food).
‐ Continues monitoring for MRV (Measurable/Reportable/Verifyable) methodology: Monitoring of groundwater level, subsidence and weather conditions shall be continued in the Jambi site with intense monitoring in the demonstration plots. The monitoring parameters be measured at more than 100 locations in the Jambi site and satellite data covering the site be utilized. These long term dataset shall become the database for various purpose of sustainability of degraded peatlands. A hydrogeological methematical model be applied to calculate water table in the site using the dataset to quantify the difference of water table before and after the project and emission reductions.
‐ Capacity building & PPP: Concerned Indonesian organizations and their staff take part in multi‐phase activities in the Jambi site and the project with the integrated approach for effective water management and monitoring techniques as well as the MRV methodology. Through the activities, basic scheme for public private partnership for a large scale emission reduction project such as peatland mitigation and adaptation under JCM (Joint Crediting Mechanism) between Indonesia and Japan shall be established.
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4. The result of the study
a. Role of each participants The project participants and their partnership is shown in Fig. 3.4. Function of each organization is
summarized below.
Project consortium
- Private companies that invest to the project and receive corresponding carbon credit. The
consortium make project agreement with Min. of Public Works.
Government
- Min. of Public Works: Plan and check the project activity along with the government policy.
- Local governments (Jambi Province, East Tanjung Jabung Regency): Plan and implement the
project activity along with the local government policy such as LP2B.
Research Institutions
- Univ. of Jambi: Mainly involved in the peat characterization
- Univ. of Sriwijaya: Mainly involved in the farming activity
- Univ. of Tokyo, Japan: Mainly involved in satellite sensing monitoring
Public-Private Joint Project
Min of Public Works
Local Government– Jambi province– Tanjung Jabung Timur
Project Consortium
SupervisionCooperation
Fig. 4.1 Project partnership
b. Reference scenario setting
For CO2 emissions of peat oxidation, reference scenario is current condition that is no mitigation action has been taken in Indonesia. Therefore, there is no baseline project for farming peat lowland mitigation in Indonesia.
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c. Monitoring methods
Fig. 3.5 shows monitoring methods and base map made compiling the monitoring data. Frequency of the
monitoring is shown below.
On site monitoring
- Groundwater level (GWL): Hourly with data loggers or biweekly by hand measured.
- Peat depth: Once for initial mapping
- Canal/gate survey: Once for initial mapping. Monitored at verification timing.
- Stakeholders meeting: Once for initial mapping. Be monitored at least once a year.
Remote sensing:
- Weather conditions: Daily data collection for weather data.
- Topography and land cover/use: Monitored for initial and verification mappings.
Hydrology modeling:
- Reference GWL: Model be calibrated with at least one year groundwater data prior to the project
start.
- Project GWL: Model be calibrated with measured GWL after the project start in the monitoring
report.
Pipe installation
GWL data download
Remote sensing0
50
100
150
200
250
300
350
400
450
500
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Pre
cip
itat
ion
(m
m/d
)
GW
L (
GL
-m)
Rainfall(mm/d)
Box Model
Obs. (Average)
Stakeholders mtg
Hydrology modeling Topography map
Peat depth
Project area map
Peat map
GWL map
On Site Monitoring
Gate/canal survey
Fig. 4.2 Monitoring methods
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d. Quantification of GHG emissions and their reductions
Fig. 3.6 summarize flowchart to calculate emission reductions. Based on the flowchart, the followings are
the expected GHG reductions.
414,000 tCO2/y be reduced with water table control
69 tCO2/ha/m/y x 20,000ha x 0.3m = 414,000 tCO2/y
6 million tCO2/y can be reduced when applied nationwide
(assuming degraded peatland area of 280,000ha).
I. ON SITE MONITORING GWL Peat thickness Topography
II. REMOTE SENSING Topography (SRTM) Land Cover (Landsat) Rainfall (GSMaP)
III. HYDROLOGY MODELINGCalculate Ground Water Level with Lumped Model or 3D Model.
IV. GHG EMISSION CALCULATIONER = Σ Ai * EFPEAT * (RWL – PWL)
Ai : Plot areaEFPEAT : Emission factorRWL: Reference water levelPWL: Project water level
The flowchart shown in Fig. e‐1.1 summarize steps in calculation of GHG emissions for degraded peatland rewetting methodology. Detail of the methodology is then described in the following.
現地計測 水理モデリング
事業実施前（リファレンスケース）
事業実施後（プロジェクトケース）
既存の計測値・資料収集
（地形、地質、被覆、水利用他）
水理モデル構築
（集中型・分布型モデル）
プロジェクトバウンダリの設定
現況再現計算
計測値.vs.計算値
GHG 排出量算定(Ex-Ante)
OK
水面安定化の事後評価
新規の現地計測
（雨量、気温、地形、地質、土地利用・被覆、
水利用、水位、水質、水路流量他）
リファレンス水位の確定
GHG 排出量算定(Ex-Post)
計測値.vs.計算値
NG 水位
水位観測の継続
（水位他）
NG
衛星データの収集
（雨量、気温、地形、土地利用・被覆他）
OK
水位
Fig. e‐1.1 Steps in calculation of GHG emissions for degraded peatland rewetting methodology
Hydrologic model Field measurements
Bef
ore
proj
ect (
refe
renc
e ca
se)
Aft
er p
roje
ct (
proj
ect c
ase)
Determination of project boundary
Compilation of satellite data(precipitation, temperature, topology, land
use, covering, etc.)
Continuation of water level monitoring
(water level, etc.)
Calculations to reproduce present situation
Development of hydrologic model
(box/dispersed models)
Compilation of existing measurements and data
(topography, geological features, covering, water use, etc.)
New field measurements
(precipitation, temperature, topography, geological features, land use, covering,
water use, water level, water quality, canal flow rate, etc.)
Water level
Ex-post assessment of water surface stabilization
Calculation of GHG emissions (ex ante)
Determination of reference water level
Calculation of GHG emissions (ex post)
Measured values vs.calculated values
Measured values vs.calculated values
Water level
11
1. Title of the Methodology
“Mitigation of Peat Decomposition through Water Table Management for Drained Peatlands in the Republic of Indonesia”
2. Summary of the Methodology
The methodology can be applied to project activities of water table control to reduce aerobic decomposition of peat in drained tropical peatlands through rewetting using technical methods, such as installation of water gates in drainage channels in the Republic of Indonesia.
Related methodology: VCS Methodology, REWETTING OF DRAINED TROPICAL PEATLANDS IN SOUTHEAST ASIA.
3. Applicability Conditions
This methodology is applicable to projects that satisfy all of the following conditions. Condi -tion
Check
1 This project controls groundwater level for rewetting of peatlands by technical methods such as installation of water-gates in drainage channels in tropical peatlands where manmade drainage was implemented prior to January 1, 2014.
2 The project site is tropical peatlands located at altitude lower than 100 m in the Republic of Indonesia, where thickness of peat should be more than 0.5 m in average*.
3 The project area includes singular or multiple complete watersheds. It is clear that the project area has no hydrological relation to peatlands located outside of the project boundary, or if a relationship does exist it exerts no adverse impact on the environment or local citizens.
4 It can be demonstrated that the peatlands inside the project area are influenced by drainage, e.g. there is data indicating groundwater level lowering and/or peat subsidence.
5 Following project implementation, it is possible to evaluate the mean groundwater level by measurements or a hydraulic model confirmed with measurements. During project implementation, the reference groundwater level should be able to be calculated with the hydraulic model.
6 The project implementation shall not cause additional nature destruction. *１ Policy Memo: Peatland Definition Form Uncertainty to Certainty, 2012.08, Indonesia Climate Change Center
4 Necessary Data for Calculation
The data that needs to be set in advance in the project registration stage or data that requires monitoring after project implementation are as indicated below.
The calculation tool is attached to the methodology, so it is possible to calculate the emission reductions by inputting the following data.
12
Explanation of data Symbol Value Unit
Area of plot Ai m2
Peat depth DPi mReference mean annual water level RWTi mMean annual water level when project is implemented
PWTi m
Emission factor for CO2 from peat decomposition EFPEAT-CO2 tCO2/ha/m/yEmission factor for N2O from rice cultivation EFPEAT-N2O tCO2/ha/m/yEmission factor for CH4 from water level raising EFPEAT-CH4 tCO2/ha/m/ySubscript i corresponds to a hydrogeological unit area, which is sub-region of the project area.
6 Terms and Definitions
Term DefinitionTropical peat Peatland is an area with an accumulation of partly decomposed organic
matter, with ash content equal to or less than 35%, peat depth equal to or deeper than 50 cm, and organic carbon content (by weight) of at least 12% (Policy Memo: Peatland Definition Form Uncertainty to Certainty, 2012.08, Indonesia Climate Change Center)
Peat decomposition control
In cases where peat is dried as a result of drainage by human activities, aerobic microbial decomposition of peat takes place. The decomposition rate can be reduced through restoring the groundwater level and thereby rewetting the dried peat. The reference mean annual water level (RWL) is the mean annual water level in the case where groundwater level management isn’t carried out. The mean annual water level when project is implemented is the mean annual water level following restoration of the groundwater level (PWL).
Plot This is the unit of hydraulic terrain at which the mean water level and peat characteristics are deemed to be uniform.
Emission factor of peat decomposition EFPEAT
This expresses the CO2 emissions per unit area of peat and at each annual groundwater level.
7 Project Boundaries
The project boundary shall include the following GHG emission sources and GHG emissions.
The project boundary is as described below.
Geographical Boundary
The geographical boundary of the project is one or more independent watershed, and each watershed should be hydrologically independent of peatlands in other watersheds. The watershed boundaries are set based on topographical characteristics, etc. and are clarified using electronic topographical information, etc.
Moreover, the project participants need to demonstrate the relationship between land inside the project boundary and position of peatland by using measured values and/or satellite images, etc.
Target GHGs
The following tables indicate the targeted GHG carbon pools and GHGs. Carbon Pool Included? Justification/Explanation
Aboveground tree biomass No It is conservative to omit. Aboveground non-tree biomass No It is conservative to omit. Underground (roots, etc.) biomass No It is conservative to omit.
13
Litter No It is conservative to omit. Dead trees No It is conservative to omit. Soil Yes Main pool addressed by project activities. Wood products No It is conservative to omit.
Source Gas Included? Reason/Explanation
Reference scenario
Aerobic decomposition in peatland used as paddy field
CO2 Yes Main source and gas to be addressed by the project activities.
N2O No N2O emissions are conservatively not accounted for in the reference scenario by this methodology
Anaerobic decomposition CH4 No
Considered negligible in drained peatlands. CH4 emissions can be generated in drainage channels, but these are conservatively not accounted for in the reference scenario by this methodology.
After project implementation
Aerobic decomposition in peatland used as paddy field
CO2 Yes Main source and gas to be addressed by the project activities.
N2O Yes
When rice production exceeds national policy target 3 ton/ha in 2004-2014, 4 ton/ha in 2015-2019, 5-6 ton/ha after 2020), N2O emission shall be evaluated.
Anaerobic decomposition in peatland used as paddy field
CH4 Yes
When rice production exceeds national policy target (3 ton/ha in 2004-2014, 4 ton/ha in 2015-2019, 5-6 ton/ha after 2020), CH4 emission shall be evaluated.
8 Reference Scenario
During the set project period, the rewetting of peat land is not carried out either as a policy or obligatory activity in the project area in the Republic of Indonesia.
9 Reference Emissions and Calculation
RE y = REPEAT, y
RE y CO2 emissions in the reference scenario [tCO2/y]
REPEAT, y Reference CO2 emissions due to peat decomposition [tCO2/y]
REPEAT, y = Σ Ai * min(RWTi,y, DPi,y ) * EFPEAT, y
Ai Unit of hydrogeological area (plot) [ha] at which the mean water level and peat depth are deemed to be uniform; the number of plots on the project site is N.
DPi, y Mean annual peat depth of unit i
RWTi, y Reference mean water level is the annual mean water level [m] in the case where groundwater management isn’t carried out.
i = 1
N
14
EFPEAT, y CO2 emission factor [tCO2/ha/y/m] of peat decomposition. The default value set by the Government of Indonesia shall be used. If the default value is not available, the project participant can set an emission factor based on latest IPCC Guidelines or latest peer reviewed paper.
10 Project Emissions and Calculation
PE y = PEPEAT, y
PE y CO2 emissions arising from the project [tCO2/y]
PEPEAT, y CO2 emissions due to peat decomposition when the project is implemented [tCO2/y]
PEPEAT, y = Σ Ai * max (PWTi,y, DPi, y ) * EFPEAT-CO2 + EFPEAT-N2O + EFPEAT-CH4)
Ai Unit of hydrogeological area (plot) [ha] at which the mean water level and peat characteristics are deemed to be uniform; the number of plots on the project site is N.
PWTi, y Mean annual water level [m] when the project is implemented. This is calculated using the hydraulic model using satellite climate data (precipitation, air-temperature) and should be verified with continuously monitored in-situ water levels.
EFPEAT-N2O This is N2O emission factor associated with rice production. N2O emission should be evaluated using default number set by Indonesian government. If the default value is not available, the project participant can set an emission factor based on latest IPCC Guidelines or latest peer reviewed paper.
EFPEAT-CH4 This is CH4 emission factor associated with rice production. CH4 emission should be evaluated using default number set by Indonesian government. If the default value is not available, the project participant can set an emission factor based on latest IPCC Guidelines or latest peer reviewed paper.
11 Leakage emissions and Calculation
It is assumed there will be no leakage arising as a result of project implementation.
12 Calculation of Emission Reduction
Emission reductions are calculated from specific reference emissions and project emissions.
ERy = REy - PEy
ERy Emission reductions in year y [tCO2/y]
REy Reference emissions in year y [tCO2/y]
PEy Project emissions in year y [tCO2/y]
13 Monitoring
The project developers must monitor the parameters described in the table below based on the calculation method of the selected GHG emission reductions.
Parameter Description Measurement Method A Area of plot, where
hydrology and peat conditions can be assumed identical in each plot.
The plot area shall be determined in PDD before project start, and updated every year based on satellite data and/or land survey.
RWTi Reference mean water level is the annual
This is calculated using the hydraulic model
i = 1
N
15
mean water level [m] in the case where groundwater management isn’t carried out.
using in-situ or satellite measured climate data (precipitation, air-temperature) and should be verified with monitored in-situ water levels at least for one year when the water level lower more than 50cm below the ground level in prior to the project implementation.
After the project implementation, this should be calculated using in-situ or satellite measured climate data (precipitation, air-temperature).
PWT Mean annual water level when the project is implemented
Calculated using a hydraulic model* using satellite climate data (precipitation, air-temperature) and confirmed with groundwater levels measured at specified points in PDD.
DP Mean annual peat depth
Peat depth of each plot shall be measured before project implementation. During project activity, peat depth shall be measured before each Verification, and DP can be determined assuming its annual change rate to be constant.
T limiti Maximum period (year) of possible credit claiming in each plot
Existing carbon stock in terms of CO2 shall be calculated using 1) peat depth, 2) peat bulk density and 3) carbon amount of the peat in each plot. The total amount of CO2 emission during the project period shall not exceed the existing carbon stock in terms of CO2. T limit can be determined by comparison of the two numbers of calculated CO2.
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f. Project site monitoring and emission reduction assessment
f‐1. Area covered
The area of coastal peatland in Jambi, Indonesia, shown in Figure f‐1.1 was monitored using the MRV
method described in Chapter e.
Utilizing the monitoring data set obtained, water table depth (WTD) was experimentally restored through
water level management of plots by means of improvement of water gates and excavation of canals. The
reduction in CO2 emissions achieved by controlling aerobic decomposition of peat due to drying caused
by artificial drainage was assessed.
Figure f‐1.1 Project site (agricultural land in East Tanjung Jabung region of Jambi Province, Sumatra)
f‐2 Monitoring
f‐2.1 Hydro‐topography units
As explained under the monitoring items below, the peat layer in the project area varies in thickness
according to location, and it is thinly and unevenly distributed in thicknesses of up to approximately 3 m.
WTD is generally shallower than GL‐1 m. WTD in peat layer and water level in canals change in response
to tide level across almost the entire area. Some areas become poorly drained and are submerged during
the rainy season. As regards vegetation in the area concerned, cleared low‐lying land covers the area
alongside the southern reaches of the Berbak River, and plantation farming is distributed in a mosaic
pattern inland.
The project area thus exhibits a complex spatial distribution characterized by diverse heterogeneity. It is
Rantau Rasau
Berbak river
Canals
Simpang Puding
▲Kampung Simpang
17
therefore difficult to define hydro‐topographical units simply by identifying watersheds from the
topography. Watersheds are, moreover, unlikely to always remain the same due to the effects of
subsidence of the peat layer, operation of water gates, tidal canals, and other factors. However, the site
also exhibits certain common characteristics. For example, almost the entire site forms a canal‐mediated
tidal zone, differences in thickness of the peat layer due to location are minor (not more than
approximately 3 m), and the topography is largely flat. While these commonalities suggest that the entire
project site could be treated as a single hydro‐topographical unit, the representativeness of the
monitoring data is not evident.
There exist no guidelines on how to determine reasonable hydro‐topographical units or go about
planning surveys of coastal peatlands of this kind, and there have also been no past studies that might
provide useful points of reference.
For the present survey, therefore, an approximately 100 ha area surrounded by primary and secondary
canals was first identified and plots selected for intensive surveying. These plots were established at
multiple locations in the project area (Figure f‐2.1). Numerous survey points were also established at
fixed points in the project area beyond these plots.
The monitoring data obtained from multiple points in the same plot were used to analyze spatial
distribution and dispersion within that plot. Monitoring data from among different plots were in addition
used to analyze spatial distribution and dispersion across the project area as a whole. Based on the
results of these analyses, the representativeness of measurements from coastal peatland was examined
and a study performed to identify hydro‐topographical units.
Project area (approx. 10,000 ha) Plots (approx. 100 ha)
A, B, B’, and C plots
Figure f‐2.1 Project area and hydro‐topographical units (plots)
B
B'
C
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f‐2.2 Precipitation
GSMaP satellite data were obtained to monitor precipitation in the project area. Data were obtainable on
a 0.1o mesh, and the data for four images (AO212, AO213, AP212, AP213) corresponded to the Berbak
Delta area being surveyed. Figure f‐2.2 shows the hourly rainfall data from March 2014 (AO212 in upper
right of figure). A comparison of cumulative rainfall during this period to confirm the differences from the
other images (bottom right of figure) reveals that cumulative rainfall during an approximately 10‐month
period was 1,800 mm, and no difference between scenes was observed.
Figure f‐2.2 GSMaP precipitation data
f‐2.3 Air temperature
In line with the methodology, daily air temperature data were obtained from the National Oceanic and
* 1973 Berbak Delta survey and satellite data ** Landsat TM analysis and information from farmers *** Landsat TM analysis, information from farmers, and in‐situ survey
An in‐situ survey was conducted to confirm the locations, state of use, and basic specifications of existing
water gates in the project area.
Table f‐2.1 shows excerpts from the results of this survey. Basic data including information on type (“gate
control type”), state of use (“status”), width (“W”) and height (“H”), water depth (“D”), and availability
for use (“available of gate operation”) were obtained for approximately 200 water gates. Photographs
were also taken of gates in their entirety and the surroundings to confirm the state of flow in canals and
state of surrounding vegetation.
The survey confirmed that most of the water gates are open and many cannot be completely closed due
mainly to scouring of channel beds by the flow of water or deformation of their embankments. Some
rural units know about tidal variations in flow from tide tables, and open and close gates accordingly.
However, gates are not systematically operated in collaboration with neighboring areas that benefit from
their use, and instead use appears more customary in scope.
Table f‐2.1 Results of survey of basic specifications and operational status of water gates (excerpt)
X Y W H OPEN CLOSE1 24-Nov-11 sk 20 -1.14459 104.09723 11 Wheel manual OPEN 100 288 95 125 OPEN CLOSE2 21-Nov-11 simpang puding -1.22234 104.07850 18 Wheel manual OPEN 100 559 260 222 OPEN CLOSE3 21-Nov-11 simpang puding -1.22234 104.07850 18 Wheel manual OPEN 100 559 110 222 OPEN CLOSE4 21-Nov-11 simpang alahan -1.22318 104.07882 20 Wheel manual OPEN 100 445 140 200 OPEN N.G5 21-Nov-11 simpang alahan -1.22318 104.07882 20 Wheel manual OPEN 100 445 150 200 OPEN N.G6 21-Nov-11 sk 3 -1.22678 104.08069 15 Wheel manual OPEN 100 287 88 123 OPEN CLOSE7 21-Nov-11 sk 4 -1.23836 104.08252 18 Wheel manual OPEN 100 305 98 175 OPEN CLOSE8 21-Nov-11 sk 5 -1.24475 104.08230 17 Wheel manual OPEN 100 306 80 137 OPEN CLOSE9 21-Nov-11 sk 6 -1.24956 104.08491 17 Wheel manual OPEN 100 287 80 95 OPEN CLOSE
10 21-Nov-11 sk 7 alahan -1.25243 104.08568 17 Wheel manual OPEN 100 603 313 293 OPEN N.G11 21-Nov-11 sk 7 alahan -1.25243 104.08568 17 Wheel manual OPEN 100 603 151 293 OPEN N.G12 21-Nov-11 sk 6 -1.23781 104.09840 14 Wheel manual OPEN 120 353 70 126 OPEN CLOSE13 21-Nov-11 sk 6 -1.23702 104.09944 17 Wheel manual OPEN 100 287 8 101 OPEN CLOSE14 21-Nov-11 sk 5 -1.23413 104.09570 16 Wheel manual OPEN 100 287 85 130 OPEN CLOSE15 21-Nov-11 sk 5 -1.23336 104.09661 19 Wheel manual OPEN 100 287 115 115 OPEN CLOSE16 22-Nov-11 sk 4 -1.23008 104.09245 14 Wheel manual OPEN 100 301 120 161 OPEN CLOSE17 22-Nov-11 sk 4 -1.22939 104.09324 13 Wheel manual CLOSE 100 287 0 119 OPEN CLOSE18 25-Nov-11 sk 3 -1.22604 104.08835 14 Wheel manual OPEN 100 287 124 122 OPEN N.G19 22-Nov-11 sk 3 -1.22504 104.08948 14 Wheel manual OPEN 100 287 112 164 OPEN CLOSE20 22-Nov-11 sk 2 -1.22181 104.08673 15 Wheel manual OPEN 100 284 91 158 OPEN CLOSE21 22-Nov-11 sk 1 -1.21928 104.08412 12 Wheel manual OPEN 100 548 270 270 OPEN CLOSE22 22-Nov-11 sk 1 -1.21928 104.08412 12 Wheel manual CLOSE 100 548 0 270 N.G N.G23 22-Nov-11 sk 1 -1.21928 104.08412 12 Wheel manual CLOSE 100 548 0 270 N.G N.G
COORDINATE SIZENO DATE CODE ELEVATION STATUS
GATE CONTROLTYPE
HEIGHT OFOVERFLOW
WATERDEPTH
AVAILABLE OFGATE
f‐2.7 Water table depth
(1) In‐situ monitoring results
WTD observation wells were installed at multiple locations inside and outside the plots in the
project area. Their locations are as shown in Figure f‐2.7.
To ascertain the dispersion and representativeness of measurements, several observation wells
were installed in each plot. Observation wells were also installed in several locations outside the
plots in order to confirm the dispersion and representativeness of measurements across the project
area as a whole. The monitoring period commenced in November 2011 and is ongoing, and WTD
23
measurements are taken once every two weeks.
Figures f‐2.8 shows the monitoring results. Each shows the distance from ground surface to water
level, and is classified by plot (Plot A, Plot B, and Plot C) and non‐plot (transections). Positive values
indicate the water level is above the ground surface, and negative ones indicate that it is below the
ground surface. The thick red line in each plot indicates the average for monitoring data obtained at
the same time in the same plot.
These monitoring results clearly reveal different patterns of water level fluctuation in the dry season
and the rainy season, and confirm that water level declines during the dry season from September
of each year. During the 2013 dry season, the decline in water level was very slight due to higher
precipitation relative to neighboring years. The average water level in each plot (indicated by the
thick red line) fluctuates according to almost the same pattern as the monitoring data for each plot,
and hydrological heterogeneity within them is not pronounced. This trend holds for all plots,
irrespective of plot.
Figure f‐2.9 shows the correlations between the aggregated average WTD for each plot and the
average WTD over a wide area beyond the plots. The correlation with average WTD beyond the
plots is high, with all plots having a coefficient of determination of between 0.7 and 0.8. WTD thus
exhibits almost the same pattern of fluctuation throughout the project area, and there is no marked
difference according to location. This suggests that the spatial distribution of the hydrological
characteristics of coastal peatlands is almost uniform.
24
Figure f‐2.7 Locations of WTD monitoring points at project site
(measured once per two weeks, November 2011 to December 2014)
Figure f‐2.9 Correlations between WTD for each plot (x‐axis) and wider WTD beyond plots (y‐axis)
26
(2) Results of analysis using the hydrological model
In line with the methodology, WTD fluctuations were analyzed using the lumped hydrological model
to calculate WTD before and after the project (reference water level and project water level).
The averages of all WTD monitoring data for the entire project area (2012‐2014) were obtained and
compared with the results of the lumped hydrological model (Figure f‐2.10).
Further to calibration of the hydrological model using average inflow/output R, the R best
reproducing the average observed water level was found to be 0.025 mm/d (where under the
ground) and 5 mm/d (where above the ground). RMSE at this time was 0.156 m. The accuracy
required by the methodology is 0.1 m or less. Here, average outflow was expressed by the
precipitation dependent function R = A x PrB and recalibrated. This improved accuracy and resulted
in RMSE = 0.142 m. Similarly representing average outflow by the observed water level dependent
function R = A x RWTB resulted in RMSE = 0.141 m. However, accuracy still has to be improved by
approximately 0.04 m to meet the accuracy required by the methodology. Improvements in
techniques such as calibration against monitoring data for each rainy season and dry season in
multiple years (average precipitation and average WTD in multiple years) are needed.
For WTD monitoring data after the project, the results obtained by trial water level management in
Plot A were used. An analysis was similarly performed using the lumped hydrological model, and the
results compared to monitoring data (Figure f‐2.11). The R best reproducing the average observed
water level was ‐0.4 mm/d, which is less than the pre‐project figure. This points to the effectiveness
of restricting drainage from cultivated land to canals by means of water level management.
Figure f‐2.10 Results of calculation of reference water level by lumped hydrological model (pre‐project)
27
Figure f‐2.11 Results of calculation of project water level by lumped hydrological model (post‐project)
f‐2.8 Peat depth
A sampling survey was performed at approximately 200 points in the project area to determine the peat
distribution. This is the number of survey points required by the methodology (at least one point per
hydro‐topographical unit, i.e., plot). Figure f‐2.12 shows the sampling locations and the thickness of the
peat layer (categorized as 0‐1 m, 1‐2 m, or 2‐4 m) in each location. The distribution of the peat layer in
the project area was estimated by interpolating from thickness at these points (Figure f‐2.13). This
showed peat to be distributed in a non‐uniform mosaic pattern, with thickness in most areas being less
than 2 m. Although the peat layer is over 2 m thick in some areas, these represent isolated spots rather
than continuous expanses.
28
Figure f‐2.12 Sampling points for surveying peat thickness
Figure f‐2.13 Results of estimation of distribution of peat thickness
f‐2.9 Emission factor
Hooijer et al.’s (2012) emission factor EFPEAT‐CO2=69 tCO2/ha/y/m determined based on site measurement
Peat thickness (cm)
Peat thickness (m)
29
in Indonesia is used in various studies. It should be noted that the site consists of coastal peatland, and
the applicability of existing emission factors for other regions is unknown. The subsidence measured in
peatlands includes volumetric changes other than due to decomposition, including drainage and
absorption due to the effects of tides, and compaction underground surface load due to farming. It is not
easy to quantify solely long‐term trends in subsidence due to drainage from cultivated land. For the
present survey, therefore, the local emission factor at present was determined by measuring subsidence
in conjunction with the above WTD monitoring. Figure f‐2.14 shows the relationship between CO2
emissions and average WTD obtained by the subsidence measuring method. Despite somewhat
considerable dispersion, the emission factor was found to be 76.7 tCO2/ha/y/m, which is close to the
above mentioned Hooijer et al.’s (2012) value.
Figure f‐2.14 Relationship between CO2 emissions and average WTD determined by the subsidence
measuring method
As noted above, not all measurements of the amount of subsidence are attributable to decomposition.
CO2 emissions are arranged in Figure f‐2.15 after excluding the data from points where there is practically
no peat and points where no tendency to subside is observed (likely due mainly to expansion in volume
resulting from absorption of water), and using annual average WTD as the drainage depth. Here, 40% of
the measured values were assumed to represent subsidence caused by decomposition. The lower graph
shows Joosten et al.’s (2009) data. From this it can be seen that, despite differences in measurement
period and number of samples, CO2 emissions are currently approximately 75 tCO2/ha/y/m, which is
largely consistent with Joosten et al.’s data.
30
Figure f‐2.15 Relationships between CO2 emissions, subsidence, and drainage depth determined by the
subsidence measuring method
Joosten et al.,2009
Period: Nov.2011 to Oct. 2013Volumetric carbon content: 0.068 gC/cm3
40% oxidative component to total subsidence
This study
31
g. Satellite based analysis g‐1 Provision of satellite data
Satellite data (on precipitation, air temperature, topography, vegetation, etc.) will be provided for the Berbak Delta in Jambi, Indonesia. A satellite data set covering a rectangular area extending from latitude 6o North to 6o South, and from longitude 97o to 105o East, was developed with the ultimate aim of developing a tool applicable to the whole of the Sumatra region.
Figure g‐1.1 Geographical extent of satellite data set provided (rectangular area from lat. N. 6o to S 6o and long. E. 97o to 105o)
32
g‐1.1 Precipitation
GSMap1 provided by the Japan Aerospace Exploration Agency (JAXA) was used for the precipitation data. Hourly data were collected for the period from January 1, 2007, to December 31, 2014, and then geometrically corrected and segmented. Spatial resolution is 10 km and time resolution is 1 hour.
g‐1.2 Air temperature
Air temperature data were obtained using thermal infrared images from the Himawari meteorological satellite received and processed by the University of Tokyo’s Institute of Industrial Science. Ground surface temperature was calculated from hourly data for the period from January 1, 2007, to December 31, 2014, using an existing estimation algorithm (Oyoshi et al., 2010). To eliminate the impact of cloud, the peaks of the 24 observations for each day were selected as representative daily values for each pixel in order to create cloudless images. These were geometrically corrected and segmented. Spatial resolution is 4 km.
g‐1.3 Topography
Topographical data analysis was performed using PALSAR on Japan’s Advanced Land Observation Satellite (ALOS). From among the images taken of an area including the site area in Jambi between December 2007 and November 2010, six deemed to meet sufficient of the orbital conditions were selected. These were subjected to InSAR analysis using imagery for March 2009 as the master image (Tsunoda et al., 2014). This resulted in a distribution map of subsidence of the peat layer caused by submergence in the rainy season and decomposition of peat in the dry season. Figure 2.2 shows an image of Jambi and the surrounding area used for InSAR analysis. The pin near the middle of the figure marks the location of a landing place (a fixed point).
g‐1.4 Vegetation
Vegetation data were obtained from MODIS polar operating satellite data furnished by the National Aeronautics and Space Administration (NASA). Normalized difference vegetation index (NDVI) values were calculated from eight‐day composite images (MOD13Q1) for January 1, 2007, to December 31, 2014, and then geometrically corrected and segmented. Spatial resolution is 250 m. An example of a visible MODIS image is shown in Figure g‐1.2.
Figure g‐1.2 Example of visible MODIS image of Sumatra
g‐1.5 WTD analysis
Water table depth (WTD) was calculated for analysis by first creating a drought index—the Keetch‐Byram Drought Index (KBDI)—from information on precipitation, air temperature, vegetation index values, and land coverage, and then comparing the results with WTD readings taken at the site (Takeuchi et al., 2010). This approach employs an algorithm developed for analysis of peatland in central Kalimantan, which is also located in Indonesia on the island of Borneo, and was initially applied without modification. Figure g‐1.3 shows a comparison of the WTD estimates obtained based on satellite data for the Berbak Delta in Jambi and site readings. Between February and May, which corresponds to the rainy season, the ground appears to be almost submerged. Although the satellite‐based estimates tend to overestimate WTD on the dry side, the match is generally good over the course of a year. Parameters obtained in Palangkaraya were applied unmodified. These produced a good match when compared with the estimates for the Jambi site, which suggests that WTD behavior at the approximately 2 m depth that is estimable from satellite data may be regarded as equivalent.
Figure g‐1.4 shows the results of estimation of WTD from satellite data for the Berbak Delta (latitude 1.2o South, longitude 104.1o East). The solid black lines represent WTD, and the straight vertical lines represent precipitation. It is evident from this that there occurs little rainfall each year in August to September, which corresponds to the dry season, and WTD declines considerably. When there is rainfall, WTD recovers relatively quickly, but the drop in WTD due to drying is modest in comparison. There was extremely little rainfall during the dry season in 2011‐2012, which is considered to have been an El Niño year, and this is quantitatively corroborated by the fact that WTD is estimated to have declined almost 90 cm in mid‐September. As there is reportedly greater vulnerability to fire when WTD drops more than 60 cm, this approach provides a potential means of observation for determining the risk of fire (Takeuchi et al., 2011).
34
Figure g‐1.3 Comparison of WTD estimates based on satellite data for Berbak Delta
and site readings
35
Figure g‐1.4 WTD in the Berbak Delta (lat. S 1.2o, long. E 104.1o) estimated from satellite data (solid black lines represent WTD and straight vertical lines represent precipitation)
g‐1.6 Rice yield analysis
Rice yield was determined by regression analysis of MODIS NDVI vegetation data against rice yield data at the level of cultivated land. Figure g‐1.5 shows rice yield data at the level of cultivated land in Jambi and MODIS NDVI values. From this, it can be seen that single cropping in the rainy season is practiced on cultivated land in the Jambi region, yields vary widely between 1 and 4 tons per 1 ha, and rice productivity is generally poor. Wide variation is observable from year to year, with the harvest good in in 2012‐2013 and poor in 2013‐2014. It was confirmed that yields in poor years sank to around 40% of the level in good years. Figure g‐1.6 shows the MODIS NDVI values from 2010 to 2014 for three of the sample plots shown in Figure 2.6—namely, 2, 4, and 11—which were selected as representative points. It can be seen from this that, in all three cases, the NDVI values for November to February (which corresponds to the rainy season) rise, peak, and decline. As a decline in the NDVI due to cloud was observed, the averages of these values were used for analysis against yields. Figure g‐1.7 shows the results of a regression analysis of MODIS NDVI against rice yield data at the level of cultivated land. The MODIS NDVI represents the averages for November to February, which corresponds to the rainy‐season harvest. A regression analysis using an exponential function revealed a strong correlation, with the coefficient of determination (R2) being 0.728.
Figure g‐1.5 Rice yield data at the level of cultivated land in Jambi (example)
36
Figure g-1.6 MODIS NDVI values at plot 2，4，11 during 2010 and 2014
37
Figure g‐1.7 Results of regression analysis of MODIS NDVI against rice yield data at the
level of cultivated land
38
g‐1.7 MRV methodology tool
A portal site was created to enable the above WTD/rice yield analysis model to be used as an MRV methodology tool. Figure g‐1.8 shows the MRV methodology tool.
The top page provides up‐to‐date data information on the distribution of WTD in the whole of Sumatra (Figure g‐1.8(A)). Historical data can be traced by accessing a menu classified by year, month, and date from Figure g‐1.8(B). For WTD in the Berbak Delta in Jambi, it is possible to access time‐series graphs and original data for 2007 up to the latest year from Figure g‐1.8(C).
2. Opex: 0.2 million USD Gate/canal operation/maintenanceMonitoring equipmentMonitoring & data process Verification
Phased Project Cost (10,000ha)
Figure h‐1.1 Project cost breakdown
i. Contribution to Indonesian Sustainable Development
‐ Adaptation and mitigation: By implementing proposed project activities to improve water and carbon management for rice‐farming peatland, an effective method for adaptation and mitigation of climate change under JCM be developed;
‐ CO2 emission reductions: Water management of the project activity can achieve CO2 emission reductions, about 400,000 ton‐CO2/y in Berbak Delta and 6 million ton‐CO2/y when applied nationwide;
‐ Improved food security: Rice productivity can be doubled to 4 ton/ha compared to current 1‐2 ton/ha;
‐ Poverty combat: Through the improved productivity of rice, poor life conditions of farmers should be improved largely:
‐ Subsidence & Fire Prevention: In water managed project areas, water table being kept shallower than 40cm can prevent peat fires. For future project areas, satellite based water level analysis system can detect possible peat fires and send pre‐warning to local governments and communities. Based on these activities, land subsidence of the coastal lowlands currently 2‐4 cm/y can be mitigated, and
‐ Best practice guideline: There will be best practice guideline including robust MRV methodology developed through the project activity, which upon nationwide dissemination it is expected to increase 560,000 ton‐rice/y and reduce emission of 6 million ton‐CO2/y. Satellite based nation scale water table analysis and rice yeild prediction strongly support government decision making and local community operations to minimize peatland fires and weather caused damage to rice production.
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j. Proposed implementation schedule
It may take a few years more for conditions to be fulfilled to implement this project in terms of the
financing scheme including credit valuing as well as feasible public‐private joint project scheme being
authorized.
k. Capacity building to the host country
Concerned Indonesian organizations and their staff take part in multi‐phase activities in the Jambi
site with the integrated approach for effective water management and monitoring techniques as
well as the MRV methodology toward the goal as described in terms listed in (m).
5. Conclusion and Next Steps
This project is designed to address the above mentioned contributions. By achieving the acitivities and disseminate them nationwide, the existing already developed peatlands for agriculture becomes more sustainable with reduced CO2 emissions. Furthermore, the increased productivity of rice in the existing area will require much less new land development, thus securing global environmental benefits.
The above mentioned environment constrains this kind of project to be implemented within a short time
range, and the followings are the proposed next steps to further nourish the feasibility of the project.
Integarated demonstration study: As seen in Fig. 4.6, water management and rice yield increase with GHG emission reductions at multiple plots in Jambi (link with Tanjung Jabung Timur Regency plan LP2B (Kondisi Pendataan Lahan Pertanian Pangan Berkelanjutan) for best practices and dissemination.
Continue monitoring for Methodology Development: Continues monitoring of groundwater level, peatland subsidence etc. in the Jambi site at 110 points. Together with satellite system, robust MRV methodology for GHG emission reduction and rice production quantification for degraded tropical peatlands be further established.
Capacity building: Through the above work, concerned staff should be able to learn the integrated water management and monitoring methods. Also scheme for public‐private joint for large scale GHG emission reduction projects under JCM and NAMAs can be developed.
Remote sensing On site monitoring Hydrology modeling
GWT & Subsidence1km-mesh GWT map
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Calibrate
GWT simulation
Lumped & physics-based model
MRV
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Figure 5.1 MRV methodology and applied technologies
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REFERENCES
S. Asmadi, S. Supiandi, S. Atang, S. Basuki, A. dan M., 2010, Land Use Change on Tidal Swamp Area After Reclamation in Berbak Delta, Jambi, J.Hidrolitan, Vol.1,3,37‐46. (Indonesian)
CRISP/NUS (Centre for Remote Imaging, Sensing and Processing, National University of Singapore) , : http://www.crisp.nus.edu.sg/
GSMaP (Global Satellite Mapping of Precipitation), : http://sharaku.eorc.jaxa.jp/GSMaP_crest/index_j.html
Hooijer, A.,2003. Quantifying wetland hydrological functions: some exampls of innovative methods using water table information. Summarized paper of the presentation at the 1st WERHYDRO workshop (12‐14 June 2003, Gonadz, Poland)
A. Hooijer, 2005, Hydrology of tropical wetland forests: recent research results from Sarawak peatswamps, Forests‐Water and people in the Humid Tropics, Cambridge University Press, 17, 447‐461.
Hooijer, A., Page, S., Jauhiainen, J., Lee, W.A., Lu, X.X., Idris, A. & Anshari, G. (2012) Subsidence and carbon loss in drained tropical peatlands. Biogeosciences, 9, 1053–1071.
H. Joosten, J. Couwenberg, 2009, Are emission reductions from peatlands MRV‐able?, Greifswald University commissioned by Wetlands International, Ede.
J. Lu, G. Sun, S. G. McNulty, D. M. Amatya, 2005. A comparison of six potential evapotranspiration methods for regional use in the southern United States, Journal of the American Water Resources Association, 621‐633.
J.Julia, 2010, 3D modelling and monitoring of Indonesian peatlands aiming at global climate change mitigation. Ludwig‐Maximilians‐Universität München.
H. Tosaka, K. Itho, T. Furuno, 2000, Fully coupled formulation of surface flow with 2‐phase subsurface flow for hydrological simulation, Hydrological Process, 14, 449‐464.
H. Tosaka, K. Mori, K. Tada, Y. Tawara, K. Yamashita, 2010, A General‐purpose Terrestrial Fluids/Heat Flow Simulator for Watershed System Management, IAHR International Groundwater Symposium, Valencia.
VCS Methodlogy, REWETTING OF DRAINED TROPICAL PEATLANDS IN SOUTHEAST ASIA, 2012, Version 08 Date of Issue 16‐November 2012, WWF Germany
P.E.V. van Walsum, A.A. Veldhuizen, P. Groenendijk,. 2011. SIMGRO 7.2.0, Theory and model implementation. Wageningen, Alterra. Alterra‐Report 913.1. 93 pp.
R. R. E. Vernimmen, A. Hooijer, Mamenun, E. Aldrian, and A. I. J. M. van Dijk, 2012: Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia, Hydrol. Earth Syst. Sci., 16, 133–146, 2012
Wataru Takeuchi, Takashi Hirano, Nanin Anggraini and Orbita Roswintiarti, 2010. Estimation of ground water table at forested peatland in Kalimantan using drought index towards wildfire control. 31st Asian conference on remote sensing (ACRS): Hanoi, Vietnam, 2010 Nov. 2.
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Al Hooijer, Sue Page, et al., 2014: SCIENTIFIC PAPER Carbon Emissions from Drained and Degraded Peatland in Indonesia and Emission Factors for Measurement, Reporting and Verification (MRV) of Peatland Greenhouse Gas Emissions. A summary of KFCP research results for practitioners, Kalimantan Forests and Climate Partnership, May 2014 Sho Tsunoda and Wataru Takeuchi, 2014. Assessment of peat‐ land subsidence in Jambi, Indonesia by using InSAR with ALOS/PALSAR. In‐ ternational seminar on land reclamation technology for sustainable land use (IS‐ LRT4LU): Jambi, Indonesia, Nov. 6, 2014. Wataru Takeuchi, Takashi Hirano, Nanin Anggraini and Orbita Roswintiarti, 2010. Estimation of ground water table at forested peatland in Kali‐mantan using drought index towards wildfire control. 31st Asian conference on remote sensing (ACRS): Hanoi, Vietnam, 2010 Nov. 2. Wataru Takeuchi, Takashi Hirano and Orbita Roswintiarti,2011. Relationship between ground water table and fires occurrence at forested peatland in Central Kalimantan. 50th conference of remote sensing society of Japan (RSSJ): Tokyo, Japan, May 26, 2011.
