Upload
hope-roberts
View
224
Download
4
Embed Size (px)
Citation preview
European Carbon Sinks
Modeling Status, Data, Analytical Gaps, EUFASOM
Uwe A. Schneider
Research Unit Sustainability and Global Change
Hamburg University
Sink Modeling Status
• EU Commission 2002: Potential of European sinks from both agriculture and forestry unclear
• Fast analysis needed for– International negotiation of Kyoto Protocol
(define own position and understand others)– EU emission trading system
EU Emission Trading - Sinks
• No initial allowance to use credits from carbon sinks projects such as forestry to meet emission targets
• Review of the emissions trading directive in 2006: if reporting and accounting uncertainties surrounding sinks can be lifted, it leaves open the possibility of using the credits from 2008.
Integrated Sink Enhancement Assessment (INSEA) Project
• Funded by European Commission to address analytical gap of carbon sinks in European Agricultural and Forestry
• January 2004 – July 2006
INSEA Model Structure
Common Data
• Soil
• Forests
• Climate
• Technologies
• Markets
• Model Results
Biophysical Models• EPIC• PICUS
Economic Models• Hohenheim • AROPAJ• EFI• EU-FASOM• AGRIPOL
Geographical Analysis
Available Data
• Soils (MOSES, JRC)• Climate (MARS)• Forest Inventories (EFI)• Conventional Management (FADN,
EUROCARE, EUROSTAT, IIASA)
Problems: Confidentiality restrictions, Data quality, Property rights
Soil DataSource: Luca Montanarella, Joint Research Center, Ispra, Italy
Analytical and Data Gaps
• Farm level impacts of alternative agricultural and forest management– Costs – Inputs– Outputs– Environmental Impacts
Addressing the Gaps
• Engineering Analysis• Link to other (European) projects
– GREENGRASS - Sources and Sinks of Greenhouse Gases from managed European Grasslands and Mitigation Strategies
– CARBOINVENT - Multi-Source Inventory Methods For Quantifying Carbon Stocks And Stock Changes In European Forests
– MIDAIR - Greenhouse Gas Mitigation for Organic and Conventional Dairy Production
– CARBO-AGE - Age-related dynamics of carbon exchange in European forests
European Non-Food Agriculture (ENFA) Project
• Starting in 2005
• Includes detailed biofuel analysis
• Environmental impact analysis consistent with food options
• Integration in EUFASOM
• Analysis of fuel directives
Land use option Non-food product options
Miscanthus, Switchgrass Bioethanol, Pellets, Electricity, Heat, Biomaterial
Red Canary Grass Pellets and briquettes, Hot water energy
Willow, Poplar, Eucalyptus, Arundo
Energy
Hemp, Flax, Kenaf Fibre products
Maize, Sugar beet, Potatoes Bioethanol
Rape, Sunflower Biodiesel
Forest Activities Pulp, Paper, Timber, Fuel
Benefits for North American Sink Analysis
• Refinement of European Data in global models
• Parallel links, i.e. USFASOM and EUFASOM
• Extrapolation of European Strategies currently not modeled in US
European Forest and Agricultural Sector Model (EU-FASOM)
• Model built from scratch
• Uses conceptual approach of (US)-FASOM
• Mathematical programming based optimization model
• Partial equilibrium
Forest Inventory and Management Alternatives
Traditional Agricultural Technologies
Soil Data
Climate Data
Management Data
Simulation of Environmental Field Impacts with EPIC
Non-Food Technologies /
Engineering Models
Microeconomic, Community, and Environmental Analysis
Existing and Potential
Agricultural or Other Policies
Indu
stry
D
eman
ds
Res
ourc
e E
ndow
men
ts
Fully Integrated European Non-Food Agriculture and Forest Model
Prod
uctio
n fa
ctor
s
• Texture based land quality classifications
• Rotations vs. individual crops
• Dynamic soil carbon rates
• Validation
EU-FASOM - Deviations from USFASOM
Dynamic Soil Carbon Coefficients
• Soil-climate-regime and soil management history determines soil carbon coefficients
• Various strategies can be a source or sink depending on the carbon level of the associated land unit
Why changing coefficients?
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Soil o
rganic
matt
er
Time [Years]
Conventional TillageZero Tillage
Problem of Dimensionality
• Consider a forward looking decision model with 20 alternative soil management practices and 30 time periods
• The number of possible management sequences equals 2030 ~ 1E+39
• Many models yield more combinations (regions, crops, …)
Technical Implementation
• Details available in paper available from author• X = land use variable• S = Soil carbon variable• t = time index• r = region index• i = soil type index• u = land use index • o = soil carbon class index• s = sequestration coefficient• c = carbon content coefficient• = soil carbon class transistion probability
Soil Carbon Class Distribution
t ,r,i,u,o r,i,u,o,o t 1,r,i,u,ou u,o
X X
t ,r,i t 1,r,i t ,r,iS S S
Soil Carbon Levels
Calculation of probabilities is not shown but available in the paper
Soil Carbon Change
r,i,u,o t ,r,i,u,ou,o
s X
r,i,u,o t ,r,i,u,o r,i,u,o t 1,r,i,u,ou,o u,o
c X c X
irtS ,,
a)
b)
0
1
2
3
4
5
6
7
8
0 100 200 300 400 500 600 700 800 900 1000 1100
Su
m o
f S
qu
ared
Dev
iati
on
s
Number of Soil Carbon Status Classes
Average Deviation between Carbon Measures
Low Initial Carbon StatusMiddle Initial Carbon Status
High Initial Carbon Status