1. Climate Change Research Programme
EPA Greenhouse Gas Modelling Workshop held on 24 November 2010 in the Gresham Hotel, Dublin

2. Reporting of total anthropogenic emissions &
removals of GHG to EU & UNFCCC
Six sectors:
1. Energy
2. Industrial processes
3. Solvents and other products
4. Agriculture
5. Land Use, Land Use Change and Forestry
6. Waste

3. Methodological Development
Tier 1: Simple approach, relies on default emission factor (EF)
drawn from previous studies and even somewhat on Activity
Data (AD)
Tier 2: Complex approach, requires detailed country-specific data
derived from enhanced characterisation- disaggregated.
Tier 3: Models (ecosystem/process-based), taking into account the
country-specific measured data as well as soil and
environmental conditions.
Moving from Tier 1 to Tier 2 and 3 depending on robust
data available under Irish conditions

4. Carbon and Nitrogen Accounting
• Tier 2 approach over Tier 1 would provide better estimates, depending on
the variability of soil organic carbon (SOC) and N dynamics.
• Tier 3 approach reflects robust emission accounting and identify mitigation
options but needs to include more variables regulating GHG emissions.
• Tier 3 also provide a flexible and a robust way to assess how different
scenarios and measures for land use management (LUM) and change
(LUC) can affect soil C and N dynamics.
• Even the best models require measurement-based validation at field scale
and therefore benchmark sites are required.
• Combining modelling and geostatistical techniques may be a better option to
assess and project soil C and N stocks/emissions.

5. Preliminary Concepts: SOIL CARBON
MONITORING, ACCOUNITNG & REPORTING
Step 6: Develop SOC Map (Arc-
GIS) and update/improve data
Step 1: Data acquisition
(National Database & Others
Step 5: Total C stock by • Identify locations, relevant)
integration, meta-analysis missing land parcels
& land transition factors & new soil C data.
• Predict coefficients
of change for major
land use categories.
• Update LULUCF.
• Prioritise research
gaps. Step 2: Data Compilation
(Depth Distribution: LU, Soil
Step 4: Develop 3D SOC type, Climate, etc.)
model by substitution of
empirical models
Step 3: Synthesize/develop
empirical models using
pedotransfer functions

6. Step 1: Data Acquisition
CORINE Land Cover (LC), National Soil Database (NSDB), Kiely et al. (2009),
Land Parcel Information System (LPIS), Soil Maps and Others
• 1 km Buffer on Irish
National Grid: SOC
under a LC contains a
Great Soil Group
(GSG) >50% area

7. Number of Sites/Land Cover and Great Soil
Group (GSG) represented
Grassland Rough Arable Others
Gleys 83 10 7
Podzols 15 3 NA
Brown Podzolics 50 1 12
 Soil depth: 0-10 cm 111 SOC (confidence 75%), no Bulk
Grey Br Podzolics for 9 16
density
Brown Earth 66 NA 5
 Some anomalies in representing5major soil group
Lithosols 3 NA
 Specific LU absent 4
Rendzinas 2 NA
Peats 18 21 6 (?)
Regosol/Sand 0 0 0
Total 350 51 46 581
NA = Not available

8. Number of sites and GSG represented
Kiely et al. (2009) database
Grassland Arable Rough Forest Peat
29 (7) 12 (4) 10 (4) 9 (5) 11 (3)
 Soil depth: 0-50 cm, no matching SOC with bulk density (BD)
 Representation of all GSGs under a LC is not available
 Specific LU information, as of NSDB, are absent
 SOC contents are highly variable with NSDB.

9. Step 2: Data Compilation
(Depth Distribution: LU/LC, Soil type, Climate, etc.)
• In addition to 50 cm depth, SOC for arable and
grassland measured at 100 cm depth are also
included.
• Non-linear relationship between soil depth, SOC and
bulk density (BD) are adopted.
• Empirical equations are developed to estimate SOC
and BD (to calculate soil mass) down to 100 cm
except Rendzinas to 50 cm.

10. Step 3: Synthesize/develop empirical models
using pedotransfer functions
 Data for SOC in the NSDB are up to10 cm depth and that
original data are taken to calculate its stocks as:
SOC (Z 10cm) = SOCz10
 SOC for depths (Z) >10 cm are calculated using empirical
models developed from the measured/interpolated SOC
ratio functions with depth as:
SOC (Z >10cm) = a e(b*z)*SOCz10
 Due to lack of BD information in the NSDB, empirical
models are also developed from measured/interpolated
data to calculate it, as:
BD (Z=10-100 cm) = a e(b*SOCz)

11. SOC distribution ratio with soil depth: Grassland
Great Soil LC Specific LCS (All)
Group Soil Type Specific (STS, Mean) (LCS, Mean)
Gleys 1.3397*e(-0.034*z)*SOCz10; (R2 = 0.998) 1.3620 1.3071
* e(-0.035*z) *e(-0.034*z)
Podzols 1.4432*e(-0.040*z)*SOCz10; (R2 = 0.953) *SOCz10 *SOCz10
Brown Podzolics 1.4275*e(-0.035*z)*SOCz10; (R2 = 0.999)
(R2 = 0.999) (R2 = 0.894)
Grey B. Podzols 1.2800*e(-0.034*z)*SOCz10; (R2 = 0.995)
Brown Earth 1.4356*e(-0.034*z)*SOCz10; (R2 = 0.999)
Lithosols a 1.0611*e(-0.057*z)*SOCz10; (R2 = 0.974)
Rendzinas b 1.9042*e(-0.040*z)*SOCz10; (R2 = 0.968)
Peats c 0.9206*e(-0.037*z)*SOCz10; (R2 = 0.918)
Sand d 0.8167*e(-0.019*z)*SOCz10; (R2 = 0.890)
a= df rough; b= df IFS 12, 22 &31, rep BE & peat mineral; c= df from both grass * peat; d= Original

12. BD from pedotransfer function (SOC): Grassland
Great Soil STS (Mean) LCS (Mean) LCS (All)
Group
Gleys 1.4725*e(-0.085*SOCz); (R2 = 0.998) 1.3582 1.3949
*e(-0.074*SOCz); *e(-0.084*SOCz);
Podzols 1.7859*e(-0.104*SOCz); (R2 = 0.918)
(R2 = 0.990) (R2 = 0.643)
Brown Podzolics 1.1509*e(-0.044*SOCz); (R2 = 0.964)
Grey Br. Podzols 1.4306*e(-0.089*SOCz); (R2 = 0.998)
Brown Earth 1.2400*e(-0.047*SOCz); (R2 = 0.988)
Lithosols a 0.8593*e(-0.033*SOCz); (R2 = 0.908)
Rendzinas b 1.1730*e(-0.050*SOCz); (R2 = 0.936)
Peats c 1.1078*e(-0.003*SOCz); (R2 = 0.830)
Sand d 1.1858*e(-0.0025*SOCz); (R2 = 0.956)
a= df rough; b= df IFS 12, 22 &31, rep BE & peat mineral; c= df from both grass * peat; d= Original

13. Rough and Arable
SOC distribution ratio with soil depth BD from pedotransfer function (SOC)

14. Step 4/5: Depth distribution of SOC stocks for
each GSG
STS equations
better represent
SOC stocks with
depth for a
particular soil.
LCS would provide
similar estimate of
SOC stocks in a LC
but either over- or
under-estimate for
a soil type

15. Depth distribution of SOC stocks for major LC
± peat
Large variability in SOC stocks
under a LC can be reduced by
separating peats from other
soil types
STS could best estimate
of SOC density.
For 0-30 cm:
Grass = 1
Rough = 1.57 (+67 t)
Arable = 0.74 (-30 t)
Representative samplings for
peats could better estimate
SOC under a LC.

16. LU areas covering IS and GSG derived
from overlaying LPIS, GSM and ISM
ISM/GSM
LPIS
Map

17. OC stocks (STS) in Indicative soils (IS) & GSG
SOC stocks are calculated using the equations developed
but covering soils of ISM and GSM
• Giving higher level of disaggregation for SOC across soil depth

18. OC stocks (STS) in IS & GSG
Showing higher SOC stocks than grassland in all soil types and depths

19. SC stocks (STS) in IS & GSG
Demonstrating lower SOC stocks than grassland and rough.
Peats under arable are misplacement/anomalies

20. Disaggregated total SOC stocks (STS)
under grassland (LPIS 2004)
Calculation: LC LU GSG ISM
Area (ha)
Pasture = 4,328,569
Rough = 3,185
Hay = 81
Silage = 1,173
Total = 4,333,009
Disaggregation of grassland using LPIS is non-realistic due to
identification problems of LU by farmers but CSO

21. Disaggregated total SOC stocks (STS)
under arable crops (LPIS 2004)
CSO reported area = 424,000 ha: This underestimation is related to areas
misplaced /identification error in the LPIS but exist, requiring re-synthesis

22. National SOC stocks: Other stocks derived
from Eaton et al. (2008)

23. Conclusions and further studies
 The empirical approaches provide robust estimate of SOC stocks for the
development of Tier 2 through 3 and thereby for LUC.
 It can further be improved through elimination of following anomalies:
* Missing/misplaced LU area in the LPIS
* Missing SOC data for soil types under various LU
* Inclusion of LUM and Input categories in the LPIS, advantageous
 Update/improve data for LPIS and refine SOC & develop Maps (Step 5 & 6).
 Accounting N2O emission for Irish agriculture using same data sources.
 LULUCF: Land transition factors (LU, LUM & Input) through Meta-analysis,
leading to Tier 2 development.
 Develop/validate models for GHG accounting through geo-regression
using LU, soil & environmental variables .
 Identify research gaps

24. Acknowledgements
 Christoph Müller and Tom Bolger, UCD
 Phillip O’Brien and Frank McGovern, EPA
 Ger Kiely, UCC
 Gary Lanigan and Karl Richards, Teagasc
 Researchers from UCD, TCD, UL, UCC...
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