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DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS
Research and Development
CSG 15
Final Project Report
(Not to be used for LINK projects)
Two hard copies of this form should be returned to:
Research Policy and International Division, Final Reports Unit
DEFRA, Area 301
Cromwell House, Dean Stanley Street, London, SW1P 3JH.
An electronic version should be e-mailed to [email protected]
Project title
Modelling soil carbon fluxes and land use change for the National Carbon
Dioxide Inventory
DEFRA project code
CC0242
Contractor organisation
and location
Agriculture & Environment Division
Rothamsted Research
Harpenden, Herts AL5 2BE
Total DEFRA project costs
Project start date
£ 179,988
01/04/00
Project end date
30/09/03
Executive summary (maximum 2 sides A4)
As part of its climate change programme, DEFRA is funding a project led by the Centre for Ecology
and Hydrology (CEH) to quantify soil carbon fluxes associated with land management and land use
change (LUC). This feeds into the UK Greenhouse Gas Emissions Inventory (GHGEI) category on
Forests and LUC. Maintaining and regularly updating the inventory is a commitment under the United
Nations Framework Convention on Climate Change (UNFCCC) and the EU Monitoring Mechanism
for greenhouse gas emissions. The CEH researchers are using a step change in a soil-related
coefficient to estimate the impact of LUC on soil sinks and sources of carbon. This generates figures
required for DEFRA’s annual reports to the UNFCCC and the EU Monitoring Mechanism. Article 3 of
the Kyoto Protocol lays the foundations for inclusion of human-induced changes in fluxes of C from
agricultural soils and LUC and forestry in Annex I Parties’ commitments. However there is a great
deal of scientific uncertainty associated with C flux estimates, particularly on a regional basis.
Current methods for estimating changes in soil organic carbon (SOC) stocks for the UK
National GHGEI use a modification of the IPCC method in which a first order dynamic model is used
to determine changes in SOC, involving the use of a step change in a soil related coefficient (SRC).
We have recently developed approaches that can be used to estimate soil C fluxes which rely upon
dynamic simulation models (RothC) linked to spatially explicit datasets. The RothC model is a
monthly time-step dynamic, compartmental SOC decomposition model that accounts for the effects
of temperature, moisture, soil texture and land use on decomposition, and is one of the most widely
used and evaluated SOC models worldwide.
CSG 15 (Rev. 6/02)
1
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
We have adapted the RothC soil process model to calculate C fluxes over time due to LUC,
which will help reduce uncertainty associated with LUC and C fluxes, and provide more accurate
assessments of C balances in agriculture, thus helping to align soil work for the GHGEI with the
DEFRA programme on SOM and sustainability. The approach developed under this project is more
process based than the method currently used in the GHGEI and allows climate change and LUC to
be investigated separately or together and for interactions to be analysed, as well as estimating C
mitigation options spatially, accounting for soil and climatic differences across the UK.
RothC was compared to SRC for CO2 fluxes from hypothetical LUC and observed LUC 19902000 from the Countryside Survey. The original SRC method used tended to overestimate C losses
and gains, although RothC estimates of C flux following LUC were generally within the range of
fluxes predicted by the Monte Carlo/SRC method currently used. We have more confidence in RothC
since it accounts for the effects of local soil type and climate explicitly. Short-term C fluxes due to
LUC were affected by a) whether LUC was assumed to occur instantaneously or gradually as a
change from one land use to another, and b) the quantity and quality of C inputs to soil during LUC.
We examined the sensitivity of RothC and SRC to the depth to which LUC is applied at grid square
and national level, which showed that considering only the top 30cm to be affected by LUC could
have significant effects on predicted C flues, although there was no differential sensitivity between
the methods. Components of uncertainty were investigated since formal uncertainty analysis was not
possible due to data constraints. This showed that a) using dominant soils rather than component
soils data to represent 1km squares, b) only considering the top 30cm to be affected by LUC (see
above), and c) the timing and spatial distribution of LUC, could all have significant effects on
predicted C fluxes. We also investigated model outputs at national and regional scales, which
showed that currently more data would be needed on the error around SOC measurements at longterm experiments for model validation in order to reduce uncertainty in model predictions sufficiently
to meet Kyoto Protocol reporting requirements. A suggested method for modelling LUC with RothC
has been developed, involving the use of gradual land use change, dominant soils data to represent
1km squares, and a step change to the new land use at the mid-point between survey dates
Estimated C fluxes due to LUC 1990-2000 were compared between the spatially explicit 1km
RothCUK model and the SRC method using highly aggregated data. This showed a considerable
difference in predicted C fluxes, attributable to differences between models, assumptions, and input
data. A computer model for estimating C fluxes from LUC conditions representative of the UK was
developed and evaluated with new datasets. Further work is required for modelling deep soil layers,
organic soils and permanently waterlogged soils. A GIS-compatible version of RothC (RothCUK) was
developed and tested with Hungarian datasets, then attached to preliminary and final versions of the
UK soils, land use, LUC and climate databases. RothCUK was used to simulated SOC storage and
C inputs to UK soils, and further work with the system was completed under Tasks 3, 6, 7 and 8.
National figures on C fluxes due to LUC 1990-2000 were provided as outputs from the RothCUK
model at 1km level. These outputs were compared to the SRC method using highly aggregated data,
which showed GB to be a net source of CO2 due to LUC for this period, mainly due to conversion of
pasture to arable land. Projected C fluxes due to LUC and climate change were provided. It was only
possible to provide C fluxes due to LUC for 1990-2000 due to data limitations. Climate change was
shown to reduce the SOC stocks of arable soils from 2000-2099 due to increased decomposition
from greater rainfall and higher temperatures. The RothCUK system was used to quantify the C
mitigation potential of alternative land management practices, and their interaction with climate
change. The C mitigation options investigated showed considerable potential to contribute towards
Kyoto Protocol targets, although this potential could be significantly reduced due to the effects of
climate change.
CSG 15 (Rev. 6/02)
2
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Scientific report (maximum 20 sides A4)
Purpose
As part of its climate change programme, DEFRA is funding a project led by the Centre for Ecology
and Hydrology (CEH) to quantify soil carbon fluxes associated with land management and land use
change (LUC). This feeds into the UK Greenhouse Gas Emissions Inventory (GHGEI) category on
Forests and LUC. Maintaining and regularly updating the inventory is a commitment under the United
Nations Framework Convention on Climate Change (UNFCCC) and the EU Monitoring Mechanism
for greenhouse gas emissions. The CEH researchers are using a step change in a soil-related
coefficient to estimate the impact of LUC on soil sinks and sources of carbon. This generates figures
required for DEFRA’s annual reports to the UNFCCC and the EU Monitoring Mechanism. Article 3 of
the Kyoto Protocol lays the foundations for inclusion of human-induced changes in fluxes of C from
agricultural soils and LUC and forestry in Annex I Parties’ commitments. However there is a great
deal of scientific uncertainty associated with C flux estimates, particularly on a regional basis. This
project will help reduce the uncertainty in the UK estimates and projections of C flux from soils. The
outputs will also help inform the UK’s position in international negotiations on methodological issues
for reporting C fluxes from soils. This longer-term approach will involve adapting existing models of
soil processes to calculate the loss or accumulation of carbon by different soil types over time. It will
also provide a more accurate assessment of soil organic matter (SOM) balances in agriculture, and
ensure that soil work for the UK GHGEI is integrated with the large programme of research funded by
DEFRA on SOM and sustainability.
Scientific Context
Current methods for estimating changes in soil organic carbon (SOC) stocks for the UK National
GHGEI use a modification of the IPCC method (Paustian et al., 1997) in which a first order dynamic
model is used to determine changes in SOC. In this case SOC stocks (when decreasing) are
assumed to change from one equilibrium level to another after 25yr (Cannell et al., 1999) – this
involves the use of a step change in a soil related coefficient (SRC), and the current method is
referred to hereafter as SRC. We have recently developed approaches that can be used to estimate
soil C fluxes which rely upon dynamic simulation models linked to spatially explicit datasets (e.g.
Falloon et al. 2002). This provides more process-based and spatially explicit estimates of soil C flux
which explicitly account for difference in climate, land-use, and soil type. This approach allows
climate change and LUC to be examined either separately or together and for interactions to be
predicted. Other recent work has focused upon agricultural carbon mitigation options (e.g. Smith et al
2000).. Using the simulation model approach described here, it will be possible to also examine
possible carbon mitigation options in UK agriculture in a spatially explicit way, taking account of soil
and climatic differences across the UK. All model outputs can be provided at both national and
regional level.
Objectives
1. To compare a dynamic simulation approach for estimating CO 2 fluxes from LUC with the soilrelated coefficient method currently used.
2. Assess the uncertainty associated with estimates of CO2 flux from LUC as a function of
assumptions about rates and geographical location of LUC.
3. To compare a spatially explicit approach for estimating CO 2 fluxes from LUC with the methods
currently used
4. To develop a computer-based model for estimating CO2 flux from soil type and LUC combinations
representative of UK conditions – prototype and final versions
5. To develop a GIS-compatible version of the model, attached to the National Soil Map at a suitable
scale - prototype and final versions
CSG 15 (Rev. 6/02)
3
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
6. Provide annual national and regional figures on CO2 flux from LUC, in a suitable format to be
included in the UK GHGEI, from 1990 to 2012 – full high-spatial-resolution estimates for all years
and interim estimates derived from more aggregated data annually.
7. Projected long term CO2 flux from LUC, according to a range of land use and climate change
scenarios to be developed within the research work, and estimated historic fluxes since 1960.
8. Quantified assessment of the potential for a range of soil management practices to be used as
CO2 emission mitigation
9. Co-ordinate the two DEFRA components of the project and liase with other relevant parties such
as the CEH, DEFRA and the project steering committee.
The RothC model
The RothC model (Coleman et al. 1996) is a monthly time-step dynamic soil organic carbon (SOC)
decomposition model that splits incoming plant residues into decomposable plant material (DPM) and
resistant plant material (RPM); these both decompose to form microbial biomass (BIO), humified
organic matter (HUM) and evolved CO2. The model also includes an inert pool of organic matter
(IOM). With the exception of IOM, each compartment decomposes by first order kinetics, and each
has an intrinsic maximum decomposition rate. The actual rate of decomposition is determined using
modifiers for soil moisture, temperature and plant cover, operating on the maximum rate. The clay
content of the soil affects the apportioning of SOC between the evolved CO2, the BIO and the HUM
pools. The main input data required to run the model are soil clay content, SOC, soil bulk density,
total monthly precipitation, mean monthly temperature, total monthly evaporation, residue quality, soil
cover, residue C inputs and manure C inputs. Plant inputs of C to soil for RothC can be set in one of
two ways. Firstly, RothC may be run using 'inverse modelling' of SOC (Coleman et al. 1997): by
adjusting the annual plant input of C to soil to obtain the best fit to modelled SOC values (‘fitted’).
Model best fit can then be assessed using the Root Mean Square Error (RMSE), a measure of the
mean error between simulated and measured data points irrespective of the direction of the error
(Smith et al. 1996b). Secondly, RothC can be run using plant inputs set independently of SOC data
(‘defaults’). Initial pool sizes for RothC may be estimated as described below. If both total SOC
measurements and soil 14C dates are available, an inverse modelling procedure can be used to
derive the IOM content of the soil for the RothC model. In the absence of soil 14C dates, the equation
derived by Falloon et al. (1998) may be used to estimate IOM from total SOC content.
Results
Task 1: To compare a dynamic simulation approach for estimating CO2 fluxes from LUC with
the soil-related coefficient method currently used
1.1 Compare current method with dynamic simulation model using data at highest levels of
aggregation
The SRC method is not applied directly to SOC contents, but to changes in SOC under LUC at
aggregated levels, and thus a direct or meaningful comparison of RothC and SRC at aggregated
levels was not possible. Because of this, it was decided to proceed directly to task 1.2 since this
enabled a more meaningful comparison of the two methods, using actual data.
1.2 Compare current method with dynamic simulation model using actual data from a number of
selected, representative grid-squares
Model simulations using RothC were compared to those from the SRC method for a number of
hypothetical land use transitions, corresponding to those used in the GHGEI. These simulations
were based on model runs for measured long-term SOC values under different land uses at
Rothamsted, UK including Park Grass (grassland), Geescroft Wilderness (natural woodland
regeneration), and Broadbalk and Hoosfield (arable), using soil and weather data from Rothamsted.
Comparisons were based on the original SRC method and rate constants (Cannell et al. 1999), not
CSG 15 (1/00)
4
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
Project
title
DEFRA
project code
CC0242
the later Monte Carlo approach currently used for the Inventory (e.g. Milne & Smith 2001). This work
showed that the SRC method tends to overestimate the loss of soil carbon in scenarios where soil C
is in decline, and overestimates the gain in soil carbon where soil C is increasing, particularly in the
short-term. Figure 1 shows predicted SOC changes for the two methods over a 300 year period.
The SRC/Monte Carlo method currently used for the GHGEI was also compared to RothC
using actual data from a number of selected grid squares (Countryside Survey) in the UK
representing different regions, dominant soil types (National Soil Map) and land-use changes
(Countryside Survey). Four representative squares were chosen, covering a range of LUCs from
1990-2000 including arable to natural, natural to pasture and pasture to natural; relevant soil data
were taken from the 1km soils database, and weather data from the CRU 1961-1990 long-term
averaged dataset – results are shown in Fig 2.
Figure1: Predicted changes in SOC to 30cm for LUCs from a) arable, b) woodland, c)
unimproved grassland and d) improved grassland to other land uses, comparing RothC and
the SRC method
b) Woodland
a) Arable Land
90
90
80
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70
60
SOC (t/ha)
SOC (t/ha)
60
50
Woodland - RothC
50
40
Woodland - SRC
40
Improved grassland - RothC
30
Improved grassland - SRC
30
Improved grassland - RothC
Unimproved grassland - RothC
Improved grassland - SRC
20
Unimproved grassland - SRC
Unimproved grassland - RothC
20
Unimproved grassland - SRC
10
Tilled arable - RothC
Tilled arable - SRC
10
0
0
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Year
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d) Improved Grassland
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SOC (t/ha)
SOC (t/ha)
c) Unimproved Grassland
50
40
30
50
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30
Tilled arable - RothC
Tilled arable - SRC
Improved Grassland - RothC
Improved Grassland - SRC
Woodland - RothC
Woodland - SRC
20
10
Tilled arable - RothC
Tilled arable - SRC
Unimproved Grassland - RothC
Unimproved Grassland - SRC
Woodland - RothC
Woodland - SRC
20
10
0
0
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50
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300
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Year
Year
Initial RothC runs in the four test squares assumed instantaneous LUC, although later work
concentrated on succession and intermediate LUCs. This is of importance when considering LUCs
which take a considerable time to occur, for example natural woodland regeneration on arable land
would not naturally occur as a direct transition from arable to woodland, but through arable to grassy
weeds, to scrubland and finally woodland succession. This work showed that the long-term trajectory
of C flux under LUC was not greatly affected by intermediate land uses, but in contrast, strong effects
were observed in the short term (up to 10 years or so), which would be of great importance in
inventory calculations and Kyoto Protocol reporting. Outputs of C input values from the CENTURY
model (which incorporates above ground vegetation C dynamics) for woodland vegetation
succession runs were also used to drive RothC runs, although the output C fluxes were similar to
those using step changes in intermediate land uses with RothC C input values. The quality and
quantity of C inputs during early LUC succession is important in determining the short-term trajectory
of C fluxes Comparisons were also made between soil C changes predicted for gradual and
instantaneous LUCs using the RothC model and the SRC method over 10 year timescales for the
test squares.
CSG 15 (1/00)
5
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Figure 2: Predicted SOC changes 0-30cm due to LUC for selected Countryside Survey test squares using RothC
and the SRC default method , a) Square 1 arable to natural, b) Square 9 arable to natural, c) Square 4 natural to
pasture and d) Square 4 pasture to natural. Heavy lines – RothC/RothC-(gradual change of land use); Dots,
whiskers and boxes show 5, 10, 25, 50, 90, 95% of results from Monte Carlo SRC. Limits = extreme output values
for the extremes of the input range. Input “time constant” ranges (to 99% change) – uniform: Gain: 100-300
years, Loss: 50-150 years. Y-axis is change in t carbon/ha from initial value. Final “target” carbon is change from
the soil carbon database and as used in RothC. X-axis is time since land use change.
a)
b)
10
10
SQ9 A to N
SQ1 A to N
8
Plot 1
RothC
Slow limit (300)
Fast limit (100)
8
Plot 1
RothC
Slow limit (300)
Fast limit (100)
RothC (gradual)
6
Y Data
Y Data
6
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4
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X Data
X Data
c)
d)
40
0
SQ4 P to N
Y Data
Y Data
Plot 1
RothC
Slow limit (300)
Fast limit (100)
RothC (Gradual)
30
-10
-20
20
SQ4 N to P
Plot 1
RothC
Slow limit (150)
Fast limit (50)
10
-30
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-40
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CSG 15 (1/00)
0
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X Data
X Data
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13
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
We have more confidence in the RothC method since it accounts for local climate, land use and soil
type, and thus can provide more specific estimates of local decomposition rate constants, and hence
predicted C fluxes. Where applicable, intermediate LUCs (not just initial and final land uses) should
be accounted for in further modelling, and future work would benefit from linkage with plant
production models.
1.3 Examine sensitivity of each approach to the assumed depth to which the LUC is applied
At the grid-square level and at the regional / national (highly-aggregated) levels, the effects of
assuming a change in SOC to 1m, versus an effect only in the topsoil (0-30cm) were examined. For
grid-square level comparisons, we used the same squares from the Countryside Survey as in Task
1.2, except for using soil data for the 0-30cm and 30-100cm layers instead of simply the 0-30cm
layer. As in task 1.2, we investigated using gradual and instant LUC simulations with the RothC
model. The outputs of these runs were compared with outputs for the same data for the SRC method.
The RothC and SRC runs showed that the effect of assuming a change in SOC to 1m rather
than only 30cm can have significant and very variable effects on the predicted SOC change (Figure
3). Since the model runs were executed by estimating C inputs required to maintain the measured
SOC for each land use in each square, and then using these values to predict fluxes following LUCs,
the results were highly dependent upon the different SOC values for each land use/soil layer in each
square, and the LUC being considered. Considering LUC to affect the 0-100cm layer rather than just
the 0-30cm layer influenced both the magnitude and direction of change in C flux in some cases.
For scenarios where either both SOC increases or decreases were predicted in the 0-30cm
layer and 30-100cm layer, there was an additive effect on SOC flux; whereas an increase in SOC in
the 0-30cm layer and a decrease in SOC in the 30-100cm layer (or vice-versa) would result in a
‘cancelling out’ effect on the predicted profile C flux. For example, in Square 1, the conversion of
pasture to natural vegetation was predicted to have a slight positive effect on SOC in the 0-30cm
layer of approximately 1.3 t C/ha, whilst the 30-100cm layer was predicted to lose approximately 2.5 t
C/ha, resulting in an overall loss in SOC from the 0-100cm of 1.2 t C/ha. Conversely conversion of
pasture to natural vegetation in Square 4 was predicted to increase SOC in the 0-30cm layer by 8 t
C/ha, and SOC in the 30-100cm layer by 4 t C/ha, resulting in an overall gain in 12 t C/ha for 0100cm. There was no evidence of differential sensitivity of the RothC and SRC methods to the soil
depth to which land LUC was assumed to affect.
The effect of assuming LUC to affect either only the 0-30cm layer or the whole 0-100cm layer
was also investigated at national level for Great Britain 1990-2000, using the RothCUK model and
1km soils, weather and LUC data described in Tasks 5-8 (Figure 4). This showed that assuming
LUC to affect only the 0-30cm layer would result in an underestimation of approximately 1736 kt C, or
27% of the predicted SOC flux had the entire 0-100cm layer been considered, over the period 19902000.
Since model results were highly dependent upon input data, i.e. available SOC measurements,
it is recommended that further measurement studies are made on SOC storage under different land
uses and under LUC for the same soil series, since many of the data for different land uses for each
soil series in the national database were derived using statistical relationships rather than actual
measurements under different land uses for the same soil series. Future progress in predicting SOC
change due to land use change should also focus on linking soil C models with plant production
models as this would enable independent estimation of C inputs to soil, and comparison with soil
database-derived estimates of C flux. The RothC runs in this exercise simply applied the standard
RothC model to the 0-30cm and 30-100cm layers, although the model was developed for topsoils
and has not been specifically calibrated for use with depths much greater than 30cm. There is
considerable evidence that C turnover rates at depth are different from those in the topsoil (e.g.
Falloon, 2001) and a number of processes in topsoil models may need altering to allow accurate
prediction of C fluxes. Factors which may need further development for subsoils could include a)
turnover rate of SOC, b) the proportion of inert organic matter at depth, c) water balance (the current
CSG 15 (1/00)
7
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Figure 3: RothC and SRC predicted C fluxes due to selected LUCs for Countryside Survey Test Squares a) no 9,
arable to natural, 0-30cm only, b) no 9, arable to natural, 0-100cm, c) no 4, natural to pasture, 0-30cm only, b) no
4, natural to pasture, 0-100cm. Heavy lines – RothC/RothC-(gradual change of land use); Dots, whiskers and
boxes show 5, 10, 25, 50, 90, 95% of results from Monte Carlo SRC. Limits = extreme output values for the
extremes of the input range. Input “time constant” ranges (to 99% change) – uniform: Gain: 100-300 years, Loss:
50-150 years. Y-axis is change in t carbon/ha from initial value. Final “target” carbon is change from the soil
carbon database and as used in RothC. X-axis is time since land use change.
a)
b)
c)
CSG 15 (1/00)
d)
8
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
Project
title
DEFRA
project code
CC0242
model uses the same data as the topsoil), d) temperature balance (the current model uses air
temperature), and e) C input quality, which may be different to that for the topsoil.
Figure 4: Sensitivity of RothC-predicted SOC fluxes from LUC to soil depth at National level
(using 1km data), considering only the 0-30cm or the whole 0-100cm layer to be affected.
200
0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Change in GB SOC stock (kt C/y)
-200
-400
-600
0-30cm
0-100cm
-800
-1000
-1200
-1400
-1600
Year
Task 2: To assess the uncertainty associated with estimates of CO2 flux from LUC as a
function of assumptions about rates and geographical location of LUC
2.1 Examine sensitivity of the modelling approach to different assumptions about the rate and
geographical location of LUC and assess the uncertainty in these estimates
Detailed uncertainty analysis was not possible using the data available for 1km scale modelling since
standard error or standard deviation data were not available from individual measurements in the soil,
land use and climate databases. Indeed, the only available information on uncertainty in the input
data suggest uncertainty in the size of the measured carbon pools of around 25% (Milne & Brown,
1997). However, it was possible to investigate various components of uncertainty in modelling C
fluxes due to LUC, including a) comparison of modelling C fluxes using only the dominant soil series
for each 1km square, or each component soil series, b) investigation of the effect of modelling LUC
on only the 0-30cm layer or the whole 0-100cm layer (see task 1.3 for more information), and c)
investigation of the influence of timing and spatial distribution of LUC on predicted C fluxes.
A sample soils dataset for an area of Derbyshire was provided by NSRI. Thirty 1km squares
were chosen from an area of North Derbyshire, with the dominant soil series covering from 19-80% of
each 1km square, and matching weather data were taken from the CRU 1961-1990 long-term
averaged database. Soil clay and carbon contents ranged from 0-53% clay and 81-372 t C/ha for 030cm and 0-36% clay and 31-918 t C/ha for 30-100cm. These data were used to investigate C inputs
to soil required to maintain measured SOC stocks for the 0-30cm, 30-100cm and 0-100cm layers,
CSG 15 (1/00)
9
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
Project
title
DEFRA
project code
CC0242
Figure 4: RothC-predicted SOC changes over a 20 year period following LUC of pasture to a)
arable land and b) woodland, using either dominant soils or component soils data to
represent a 1km square.
a)
10000
9000
8000
Dominant unit only
Change in SOC (t C)
7000
Component data
6000
5000
4000
3000
2000
1000
0
1
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8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Soil unit
b)
8000
7000
Dominant unit only
6000
Component data
Change in SOC (t C)
5000
4000
3000
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1000
0
1
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10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
-1000
Soil unit
CSG 15 (1/00)
10
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
predicted by RothC. This suggested that RothC may be inadequate for simulating SOC dynamics in
highly organic and/or waterlogged soils.
We also used these data to investigate the effects of using the dominant soil type to represent
each 1km grid square versus using each component soil type, for the 0-30cm, 30-100cm and 0100cm layers for a) calculating SOC stocks for the region and b) predicting SOC changes with RothC
for a number of hypothetical LUC scenarios. Using only data on the dominant soil series to represent
a 1km square was shown to have considerable effects on predicted C fluxes following LUC (see Fig
4), for the 30 squares investigated, and using only dominant soils information could lead to significant
errors in estimated SOC stocks. However, this was strongly dependent on the nature of the soils
making up each 1km square and the variation in soil properties between them – with a large variation
in SOC between units (e.g. organic and mineral soils in the same 1km square) being particularly
important. For conversion of grassland to arable land, using dominant soils data to represent each
1km square resulted in an average difference in predicted C flux of +556 t C (range -1179 to +3477 t
C, standard deviation 1258 t C), equivalent to a mean difference of 10% (range -30 to +64%,
standard deviation 24%) , compared to using component soils data. The figures for conversion of
grassland to woodland were an average difference in predicted C flux of +183 t C (range -1572 to
+2796 t C, standard deviation 1085 t C), or a mean difference of 360% (range -78 to +7430%,
standard deviation 1366%).
The timing of the LUC potentially has significant impacts on the predicted stock. The RothC
model was used to investigate how timing and spatial allocation of LUC might affect C fluxes for
hypothetical grid squares, by examining the effect of a step change in land use on the whole 1 ha
area at yearly intervals over a 10yr period versus the effect of converting 0.1 ha of land to a new land
use every year over a period of ten years. The input data used were the same as those used in Task
1.1 for hypothetical LUCs, based on information from long-term experiments at Rothamsted, UK. For
conversion of unmanaged grassland to arable land, the maximum difference (resulting from
conversion of the whole plot in the first year) in the C stock at the end of year 10, was a loss of 14%
of the initial C stock. Similar figures were obtained for conversion of managed grassland to arable
land and unmanaged grassland to managed grassland - losses of 10% and 5% respectively, with a
gain of 30% of the initial C stock for arable-woodland conversion. Minimum changes in C stock
occurring due to assumed land-use conversion in year 10 for unmanaged grass-arable, managed
grass-arable, unmanaged grass-managed grass and arable-woodland were -2, -2, -1 and +5%
respectively. Using a dynamic simulation model to determine the effects of LUC on SOC over the
time span of a land-use census (i.e. 10 years) showed that assumed step changes for the whole land
block each year differed from gradual incremental changes of 10% of the area each year. In all of the
land use change scenarios a gradual change in increments of 0.1 ha yr-1 resulted in a SOC
concentration at the end of 10yr that were close to values assuming a step change in the whole 1 ha
at year 6.
These results demonstrate that there are large differences between maximum and minimum
predicted changes in C stock resulting from uncertainties in the timing of the LUC between land-use
census periods. Uncertainty surrounding the exact date of a land-use conversion within a grid-square
in a national inventory could potentially lead to large errors in the estimated change in C stock in that
square. Assuming a gradual change is unlikely to represent reality, but may offer a “best-estimate”
solution to this uncertainty. These differences are likely to be less pronounced than differences
resulting from the allocation of land-use change to different soil types within a land block.
Further investigation of uncertainty in model outputs at the site and regional scale was
conducted and published (Falloon & Smith 2003). This work showed that whilst SOC models are
often tested against data-sets of long-term changes in SOC, most data-sets have only mean SOC
values available at each sample date, with no estimates of error about the mean. When using datasets which do not include estimates of error about the mean, it was not possible to reduce the error
(root mean squared error; RMSE) between modelled and measured values below 6.8-8.5%, even
with site-specific model calibration. Equivalent errors for model runs using regional default input
CSG 15 (1/00)
11
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
values were 12-34%. Using error as an indicator of the certainty that can be attached to model
projections, a significant reduction in uncertainty would be needed for Kyoto accounting.
Uncertainties for modelling during the first Kyoto Commitment Period could be reduced by better
replication of soil measurements at benchmark sites. This would allow model error to be separated
from measurement error, which would allow more comprehensive model testing and ultimately, more
certainty to be attached to model predictions.
2.2 Derive methods for allocating the spatial location and timing of LUC to provide meaningful
regional and national estimates of impact on CO2 fluxes.
Work completed under tasks 1.2 and 2.1 showed that there were significant differences in predicted
C fluxes after LUC depending on the assumptions made regarding the timing, spatial allocation and
nature of the LUC. Timing and spatial allocation of LUC are complex and interdependent issues.
From the LUC data currently available for modelling at the 1km level (i.e. derived from the 1990 and
2000 Land Cover Maps), it is possible to deduce that a particular LUC has occurred between 1990
and 2000, but it is not possible to determine precisely when a particular LUC occurs. The data could
show, for example a particular grid square to be arable land in 1990, and grassland in 2000, although
this provides no information on the timing of the change – it could equally have been in 1990
following the mapping exercise, shortly before mapping in 2000, or anywhere in between. It is
important to determine when LUC occurred between these time points, since as shown in tasks 1.2
and 2.1, this has considerable influence on the predicted C flux following LUC. In the absence of
other information, for modelling LUC over the 1990-2000 period, it was decided to implement any
LUC that occurred at year 6 (i.e. 1995) – this also has the advantage that it coincides with practical
assumptions about spatial allocation of LUC within a grid square (see below).
Assuming gradual spatial change from the old land use to the new one across a grid square
may be the most realistic representation of change, although implementing this in the modelling
framework would increase computation time considerably since a series of separate runs would be
required for each sub-patch of land and LUC to be considered in each grid square. However, tasks
1.2 and 2.1 showed that numerically, the predicted C flux from LUC over a 10 year period was
practically identical whether assuming a) a step change for the whole grid square to the new land use
at year 6, or b) assuming a gradual change of conversion 10% of the grid square each year to the
new land use. It was therefore decided to represent spatial allocation and timing of LUC over a 10
year period by assuming a step change for the whole area affected by any particular LUC to the new
land use at the midpoint, in year 6.
Work completed under tasks 1.2 and 2.1 also showed that assumptions made about the nature
(or intensity) of LUC could have significant effects on predicted C fluxes. For certain LUCs, this is not
an important issue since LUC can be assumed to occur instantaneously – for example conversion of
arable land to newly seeded grassland, or ploughing up of old pasture and conversion to arable land.
However, lack of further information from available data makes it difficult to specify how land use
changes that could occur more gradually might have occurred, and how to model them. Examples of
gradual LUCs could be afforestation of arable land – if this was natural regeneration, this would
involve a period with grassy species, followed by a period dominated by scrub, and finally
establishment of trees, or if involving plantation of fast-growing conifer species, might be a more
instant LUC. A suggested strategy for modelling LUCs was developed, based on gradual LUC.
The use of dominant or component soils data in conjunction with the RothC model in
predicting SOC changes under hypothetical LUC scenarios for the region also suggests that using
dominant soil properties only could lead to significant errors in predicted SOC fluxes (Task 2.1).
However, there are considerable difficulties in combining component soils data with LUC matrix data
at 1km level, and for this reason the current modelling work has concentrated on modelling using only
the dominant soil series for each 1km square. The database and model structure allows for
modelling of component soil series to be implemented at a later date.
CSG 15 (1/00)
12
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
At the current time, we consider the following approach to be provide our best and most
practical estimate of C fluxes following LUC: using dominant soil series to represent 1km squares,
implementing LUC as a step change at the mid-point of the change period for the whole of an area to
which land LUC is applied (i.e. 1995 for 1990-2000), and assuming the LUC transitions modelled as
outlined in Table 1. However it is strongly recommended that further work will be needed to
manipulate currently available digital (and other) datasets, in order to obtain more information on the
actual timing and spatial distribution, and nature of LUC, in order to reduce the number of
assumptions made when modelling C fluxes following LUC. Further work is also needed to allow
allocation of LUC to component soil series. A working document on allocating the spatial location and
timing of LUC was written and circulated to collaborators.
Task 3: To compare a high-resolution spatially explicit approach for estimating CO2 fluxes
from LUC, with methods using highly aggregated data on soils and LUC
Having derived the most appropriate method for allocating LUC within a grid square under Task 2.2,
the RothCUK model was applied at national/regional levels. This used the British Isles Climatology
and SRES climate change data, the 1km soils and land use dataset from the National Soils
Inventory/National Land Use Map, and the 1km LUC matrix data for 1990-2000 provided by CEH.
The RothCUK model approach considered up to 36 LUCs for each of the 234,951 one km squares in
the soil and land use database, using the land use transitions outlined in Table 1, and C inputs fitted
to the SOC data for each soil layer and land use in each 1km square. The total predicted C fluxes
using the high resolution spatial RothCUK approach were compared to results from the SRC stepchange method using highly aggregated data (Figure 5). This showed a significant difference in
predicted C fluxes due to LUC between the approach – the total C fluxes over the period 1990-2000
from RothC (1km data) and SRC (highly aggregated data – mean of Monte Carlo outputs) were 6488
and 29750 kt C, respectively.
Figure 5: Comparison of SOC fluxes due to LUC for GB 1990-2000 predicted by SRC using
highly aggregated data (Monte Carlo), and RothC using 1km data
2500
1500
Change in GB SOC stock (kT C/y)
500
-5001990
1991
1992
1993
1994
1995
1996
1997
1998
1999
-1500
-2500
RothC
-3500
SRC (minimum)
SRC (mean)
-4500
SRC (maximum)
-5500
-6500
-7500
Year
CSG 15 (1/00)
13
2000
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Possible reasons for this difference could include a) differences in input data and b) differences
between the models. Differences in input data could include: resolution of LUC data; assumptions
about timing of LUC (for RothC, LUC was modelled as outlined in Task 2.2, with LUC not occurring
until 1995); some 1km squares were not modelled with RothC due to missing data; the RothC model
only used data for the dominant soils to represent 1km squares; and since the RothC approach
assumed gradual LUC transitions, smaller predicted C fluxes would be expected. There are a number
of differences between the SRC and RothC models including SOC turnover rates, and the influence
of soil type and climate on SOC turnover – these factors have been discussed in greater detail by
Ashman et al. (2000).
Task 4: To develop a computer-based model for estimating CO2 flux from soil type and LUC
combinations representative of UK conditions
A prototype computer-based carbon model for use with LUC scenarios was developed, based on the
RothC model and retaining its simplicity and ease of use. A Windows interface is available for use
with the standard, field scale version of RothC, although it was not felt appropriate to develop a
windows interface for 1km modelling purposes since the model requires very long running times.
Since the RothC model is not applicable to organic soils, a review of soil carbon cycling in peat soils
was conducted, and possible developments of the RothC model, or other alternative models for
simulating C cycling in peat soils were investigated. Indeed, work under Task 2.1/2.2 showed RothC
to predict much greater than expected C inputs to soil for organic soils. A recently-funded SEERAD
project is aiming to develop a compatible model for C cycling in organic soils. Previous work under
the main DEFRA inventory contract showed concern over the prediction of SOC fluxes following the
conversion of grassland to arable land, although the available datasets were used in the original
model development.
Figure 6: Measured and RothC-simulated SOC changes at Morrow Plots, USA (RothC – lines;
measured values – points)
80
Bluegrass
Border
70
SOC (t/ha to 15cm)
60
50
Corn-oats
-clover
40
30
Continuous
Corn
20
10
0
1860
CSG 15 (1/00)
1880
1900
1920
1940
Year
1960
14
1980
2000
2020
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Testing of RothC predictions was therefore completed for a number of independent sites where
grassland has been converted to arable land use, and where RothC had not formerly been applied
(Morrow Plots, Illinois; SATWAGL, Australia; Waite Rotation, Australia), and shown a good
reproduction of observed results in general (Figure 6). RothC was also further evaluated for different
land management, climate and soil type combinations at a number of long term experiments in
Europe covering a range of land use/management, soil types and climates (Martonvasar, Hungary;
Geescroft Wilderness, UK; Park Grass, UK; Nagyhorcsok, Hungary; Woburn ley arable, UK; Ultuna,
Sweden -see publications list; Falloon & Smith 2002). The RothC model was also compared to the
widely used CENTURY SOC model at these European sites. Issues related to modelling C turnover
in subsoils have been discussed under Task 1.3.
Task 5: To develop a GIS-compatible version of the model, attached to the National Soil Map
at a suitable scale
A Geographic information system (GIS)-compatible version of the model was developed. A prototype
interface for running the RothC model with spatial databases was created, allowing it to access soil,
meteorological and land use information output from GIS and to output results in a format compatible
with GIS systems. The mode of operation of this version of the model involves it accessing georeferenced text files exported from GIS package such as ArcInfo/ArcView. The model then writes
output results to geo-referenced text files which can then be visualised (e.g. for map production) in
ArcView. Output files are also compatible with relational database packages (e.g. ACCESS) and
Spreadsheet packages (e.g. Excel) for numerical and statistical processing. The prototype GIS-model
interface was also tested before UK datasets were available, by applying it to Hungarian datasets for
a number of carbon sequestration scenarios, and results from these studies have been published
(Falloon et al 2002).
Missing data checks were completed on the preliminary 1km soils and land use datasets
collated and provided by NSRI. A report on this work was written and circulated to collaborators, and
placed on the project website. British Isles Climatology and SRES climate change scenario data
were obtained for modelling purposes from CRU-UEA. Test GIS were formatted for prototype RothC
model application at 1km scale. The prototype RothCUK interface program was created to utilise
these datasets, was based on the plot/field scale RothC model, and can provide predicted fluxes of C
from SOC or CO2 emissions.
Programs were also created to process and format climate files for use with the RothCUK
interface. In the first instance, the prototype RothCUK system was tested with a subset of the
preliminary 1km soils and land use datasets to investigate 1) fitting C inputs to measured SOC data,
and 2) using default C input values to predict equilibrium SOC stocks, using the dominant soil type
and component land use data for the 0-30cm and 30-100cm layers of each 1km square. These runs
concentrated on arable, grassland and semi-natural land uses since woodland soils data were not
available for the test area in the preliminary datasets. RothC output data from these runs were
analysed, which 1) allowed calculation of average C input values for different land uses in the 0-30cm
and 30-100cm layers, 2) showed that using default C input values for each land use and soil layer
does not allow accurate prediction of measured SOC stocks, and 3) showed that the RothC model
may be inappropriate for certain soil types (notably organic soils). C inputs fitted to measured SOC
values were well correlated with mean annual rainfall for grassland soils, but less so for arable soils.
Since net primary production is strongly related to mean annual rainfall, better prediction of C input
values for the RothC model may be achieved by linking RothC to a plant production model, which
would also allow prediction of management effects on above and below ground C cycling.
Version two of the 1km soils and land use database was received from NSRI, and missing data
checks on these datasets were completed. The new datasets were then formatted for use with the
RothCUK interface at 1km level. An updated, final version of the RothCUK interface was created,
with several improvements including enabling the model to run for Northern Ireland, and for woodland
CSG 15 (1/00)
15
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
soils, as well as enabling the model to run with the 1km LUC matrix data for 1990-2000 provided by
CEH (see tasks 3,6,7 and 8).
The updated (final) RothCUK system was used with the version two 1km soil and land use
datasets to investigate C inputs to soil required to maintain measured SOC values from the database
across all land uses for Great Britain and Northern Ireland (Table 1). This work showed that a)
greater C input values were predicted for Northern Ireland than for Great Britain, b) generally slightly
greater C input values were required than would be expected from former RothC modelling
experience modelling long-term experimental data, c) there was a considerable range of C inputs for
all land uses, d) C inputs to subsoils were generally less than those for topsoil layers, and e) C inputs
differed between land uses. As with the work completed under task 2.1 and with the prototype
RothCUK, some very large and unreasonable C input values were obtained for organic soils,
indicating that the current model has limited applicability under these conditions.
Table 1: C inputs fitted to measured SOC values for 1km squares of Great Britain and
Northern Ireland
Arable 0-30cm
Arable 30-100cm
Grass 0-30cm
Grass 30-100cm
Natural 0-30cm
Natural 30-100cm
Wood 0-30cm
Wood 30-100cm
Mean
(GB)
3.674
1.889
4.183
2.016
6.715
2.929
4.863
2.361
Mean
(NI)
5.859
2.306
5.607
2.403
8.089
3.528
9.251
3.977
Minimum
(GB)
1.406
0.054
1.224
0.039
0.664
0.034
0.756
0.046
Minimum
(NI)
2.544
0.172
2.154
0.144
2.555
0.175
5.273
1.87
Maximum
(GB)
22.308
29.048
20.547
21.748
28.244
17.018
26.346
15.762
Maximum
(NI)
19.917
20.232
17.524
19.342
17.015
17.829
12.687
6.689
Standard
Deviation
(GB)
1.706
1.327
2.162
1.313
3.647
2.003
3.186
1.902
Standard
Deviation
(NI)
2.016
1.633
1.865
1.329
3.046
2.57
2.627
1.481
Possible reasons for errors in predicted C inputs could include a) measured SOC not being at
equilibrium (as assumed in the model runs), b) management practices not considered in modelling
runs, or c) soil/climatic conditions outside range of model applicability. It is possible that a number of
the soils from the 1km database were not at equilibrium – and indeed without time-series data on
SOC it would be difficult to conclude that SOC was at equilibrium at the time of sampling. If SOC was
actually increasing at sampling time (e.g. recently converted pasture from arable land), this would
result in an over prediction of C input, conversely if SOC was decreasing (e.g. conversion of pasture
to arable land), this would result in an under prediction of C input values. General land management
practices were assumed for the model runs presented here – for example, additions of organic
amendments and cereal straw to arable land were not specified explicitly. If the model was being
used to estimate C inputs for a soil receiving additional organic inputs which were not modelled, this
would result in an underestimate of C input. Finally, the RothC model was not developed for
permanently waterlogged or organic soils, and application of the model to these conditions would be
expected to lead to erroneous values of C input – for example very large C inputs for organic soils as
a consequence of over-estimated decomposition rates. Example outputs from the GIS compatible
version of RothC are shown in Figs 7 and 8.
Collaboration with the Hadley Centre was developed for prediction of climate change effects
on SOC stocks. The aim of this work is to include the RothC model in the HADCM3 climate model,
which currently has a very simple soil C model, to enable more accurate prediction of changes in
ecosystem C stocks and climate feedbacks under climatic and land use change. Progress to date
has shown that increases in temperature under future climate could lead to SOC losses globally, thus
creating an additional climate feedback. The two models showed similar patterns of change but
different total fluxes. RothC should help improve prediction of climate impacts, but it needs to be fully
incorporated into the climate model for full assessment of feedbacks.
CSG 15 (1/00)
16
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Task 6: Provide annual national and regional figures on CO2 flux from LUC, in a suitable
format to be included in the UK GHGEI, from 1990 to 2012
The GIS version of the model (RothCUK) was applied, using high-resolution spatially explicit data, to
provide national/regional figures for C fluxes due to LUC. This used the same input data as described
in Task 3, namely 1km soils and land use database (using only data on the dominant soil type for
each 1km square), long-term averaged 1961-1990 CRU climate database, and 1km LUC matrices
1990-2000 provided by CEH. It was only possible to provide figures on LUC from 1990-2000 since
1km data were only available for this period. Results from these runs are presented under Task 3,
and shown in Figures 5 and 7. LUC was found to both increase and decrease SOC stocks over the
period investigated, although overall GB was found to be a source of CO 2 due to LUC, in agreement
with the SRC method results. However as discussed under Task 3, there were differences in the
predicted SOC fluxes between the RothC and SRC methods – possible reasons for these differences
have been discussed under Task 3. The largest contributor to C fluxes due to LUC was found to be
conversion of pasture to arable land – the areas affected by this LUC, and resulting SOC changes
are shown in Figure 7. This shows that although differences in the SOC change per unit area are
important in determining the total SOC flux from a grid square (Fig 7b) the dominant factor is the
amount of land changing to a different land use in each grid square (Fig 7a and c). Figure 7d shows
the total C flux due to LUC from each 1km square over the 1990-2000 period, which also indicates
the areas of greatest SOC loss are correlated with areas where considerable conversion of arable
land to pasture was observed.
Further work is needed to use RothC for inventory purposes, particularly regarding data on the
timing and nature of LUC, modelling component soils for 1km squares, and missing data filling, as
well as extending the modelling approach to subsoils and organic soils.
Task 7: Projected long term CO2 flux from LUC, according to a range of land use and climate
change scenarios to be developed within the research work, and estimated historic fluxes
since 1960
Digital LUC data, or future LUC scenario data in 1km format were not available for the UK other than
for 1990-2000 and hence it was not possible to provide predicted fluxes due to LUC previous to 1960,
or post 2000. However, potential C fluxes due to future soil management practices were quantified
under task 8, and predicted SOC fluxes due to LUC are reported under Tasks 3 and 7.
The preliminary 1km soils and land use datasets and the prototype RothCUK system were
also used to make preliminary estimates of changes in CO2 emissions from arable topsoils (0-30cm)
under two SRES climate change scenarios, a1f1 and B1a (representing ‘high’ and ‘low’ climate
changes), for the period 1961-2099, relative to the 1961-1990 baseline climate. These initial
scenarios showed that increased CO2 emissions would be expected from most arable soils after
about 2050, with the greatest losses occurring from soils containing more organic matter. Simulated
C fluxes were strongly dependent on modelling assumptions, particularly how RothC was initialised
with C input data.
The second version of the 1km soils and land use database, and long-term averaged 1961-1990
climate data and SRES climate change scenario data for GB and NI were used with the final version
of the RothCUK system to predict the impact of climate change on arable SOC stocks (0-30cm) in
GB and NI, also under two scenarios, a1f1 and b1a. This work also showed a considerable loss of
SOC would be expected from most arable soils, with a greater SOC loss predicted under the more
extreme (a1f1) scenario (Figure 8). The main reason for SOC loss is the predicted increases in
temperature and rainfall under both climate scenarios, which will enhance decomposition rates.
Greater SOC losses were predicted in Western regions of GB generally. Further simulations are
needed to make concrete conclusions, and linkage with plant C cycling models will be necessary for
predicting the interaction of plant and soil C cycling under climate change.
CSG 15 (1/00)
17
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Figure 7: 1km LUC model outputs a) % area of 1km squares converted from pasture to arable
land use 1990-2000, b) C fluxes from conversion of pasture to arable land 1990-2000 (t/ha), c)
C fluxes from of conversion of pasture to arable land 1990-2000 (tonnes/1km sq.), d) Overall
soil C fluxes from LUC in 1km squares 1990-2000
a)
b)
c)
CSG 15 (1/00)
d)
18
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Figure 8: Change in SOC 0-30cm for arable 1km squares 2000-2099 resulting from two climate
change scenarios, a) b1a and b) a1f.
a)
b)
Task 8: Quantified assessment of the potential for a range of soil management practices to be
used as CO2 emission mitigation options
A range of model experiments were run to determine the potential for practices of alternative landuse and changed land-management practice, and their interaction with climate change, to mitigate
CO2 emissions. These runs used the final version of the RothCUK model in combination with the
second version 1km soils and land use database and 1961-1990 long-term averaged, and SRES
a1f1 and b1a climate scenario data. Initial practices were based upon previous work (Smith et al
2000, Falloon et al 2002). The scenarios investigated were addition of 10 t FYM/ha/y or 5 t cereal
straw/ha/y to all arable soils, with or without climate change from the SRES scenarios a1f1 and b1a,
for GB and NI, over the period 2000-2099. These runs showed that a) there is considerable potential
for single land management options to contribute towards Kyoto Protocol emissions reductions
targets (up to 0.8% of 1990 UK CO2 emissions, or 6.4% of the UK target), b) estimates were similar
to those of Smith et al (2000), c) climate change could reduce C sequestration potential of alternative
land management options and d) the combined effects of climate change and C sequestration are not
simply additive (see Fig. 9). The RothCUK approach also enables geographic areas with the
greatest C mitigation potential to be identified, although the current approach has not considered
resource accessibility or availability issues. It was not possible to investigate the conservation of
particular semi-natural or natural ecosystems due to the lack of data on location and classification at
1km level.
CSG 15 (1/00)
19
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
Project
title
DEFRA
project code
CC0242
Figure 9: C mitigation potential of land management options with/without climate change
interactions for the UK, predicted by the RothC model from 1km data.
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10
+
a/
y
-1
YM
/h
tF
YM
/h
a/
y
fs
+
ce
a1
fs
na
rio
ce
10
tF
na
rio
-0.5
b1
a
YM
/h
a/
(s
im
ul
y
on
l
at
ed
)
y
0
% offset of 1990 UK CO2-C emissions
Maximum yearly C mitigation potential (Tg C/y)
1.5
Task 9: Co-ordinate the MAFF and DETR components of the project and liase with other
relevant parties such as the ITE, DETR and the project steering committee.
Rothamsted Research co-ordinated the different DEFRA components of the project and liased with
other relevant parties such as CEH, UKCIP and the project steering committee. Regular project
meetings were held (see below), with frequent contact between NSRI and CEH (responsible for data
collation) and Rothamsted Research (responsible for modelling). Project reports and working
documents were also produced and distributed to collaborators as and when appropriate (see below).
A project website was also developed with information on the project, and downloadable reports and
documents (http://www.rothamsted.bbsrc.ac.uk/aen/ukcarbon/). We have built a collaboration with
the Climate Research Unit at the University of East Anglia (Dr David Viner), who provided formatted
relevant current/historic and future (scenario) climatic data. Collaboration with a GEF-funded project
‘Assessment of soil carbon stocks at national scale’ was also been initiated, which will promote work
on inventory method development with the NREL group at Colorado State University (using the
CENTURY model) in case study countries, and a BBSRC ISIS exchange grant has been obtained to
harmonize approaches between Rothamsted Research and NREL, who have a great deal of
experience in utilising soil C models for inventory purposes. Collaboration with the Hadley Centre on
incorporating the RothC model into the HADCM3 climate model is expected to produce a peerreviewed publication in the near future.
Project reports:
‘Data requirements working paper 2000’, ‘Project report for MAFF GHG Programme Review
September 2001’, ‘Report on pilot study using NSRI data 2001’, ‘Working document on allocating the
spatial location and timing of land use change 2001’, ‘Annual report to DEFRA 2000/2001’, ‘Notes on
CSG 15 (1/00)
20
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
project meeting 10/01/02’, 'Minutes of project meeting 27-7-02' , 'Summary of soil and land use data
report September 2002' , 'Minutes of a project meeting 17-10-02', ‘Annual report to DEFRA
2001/2002’, 'Preliminary 1km Soil C modelling outputs for DEFRA CCO2421 January 2003' , ‘Annual
report to DEFRA 2002/2003’.
Project meetings: 29th April 2003 CEH Bush - DEFRA LULUCF meeting, 27th May 2002 CEH Bush DEFRA LULUCF meeting, 6th December 2002 CEH Bush - DEFRA LULUCF meeting, 17th October
2002 CEH Bush - Project data meeting, January 10th 2002 University of Aberdeen - Discussion of
spatial location and timing of land use change and combination of land use change with soils data,
11th December 2001 NSRI - Discussion of soils data for the project, 26th November 2001 CEH Bush
- DEFRA Inventory project meeting, 26th June 2001 Rothamsted Research - Discussion of soils and
land use data, 21st May 2001 Rothamsted Research - Discussion of soils data, 18th May 2001 CEH
Bush - DEFRA Inventory project meeting, February 2001 NSRI – data requirements workshop.
Conclusions
We have adapted the RothC soil process model to calculate C fluxes over time due to LUC, which
will help reduce uncertainty associated with LUC and C fluxes, and provide more accurate
assessments of C balances in agriculture, thus helping to align soil work for the GHGEI with the
DEFRA programme on SOM and sustainability. The approach developed under this project is more
process based than the method currently used in the GHGEI and allows climate change and LUC to
be investigated separately or together and for interactions to be analysed, as well as estimating C
mitigation options spatially, accounting for soil and climatic differences across the UK.
Under Task 1, RothC was compared to SRC for CO2 fluxes from hypothetical LUC and
observed LUC 1990-2000 from the Countryside Survey. The original SRC method used tended to
overestimate C losses and gains, although RothC estimates of C flux following LUC were generally
within the range of fluxes predicted by the Monte Carlo/SRC method currently used. We have more
confidence in RothC since it accounts for the effects of local soil type and climate explicitly. Shortterm C fluxes due to LUC were affected by a) whether LUC was assumed to occur instantaneously or
gradually as a change from one land use to another, and b) the quantity and quality of C inputs to soil
during LUC. We examined the sensitivity of RothC and SRC to the depth to which LUC is applied at
grid square and national level, which showed that considering only the top 30cm to be affected by
LUC could have significant effects on predicted C flues, although there was no differential sensitivity
between the methods.
Under Task 2, components of uncertainty were investigated since formal uncertainty analysis
was not possible due to data constraints. This showed that a) using dominant soils rather than
component soils data to represent 1km squares, b) only considering the top 30cm to be affected by
LUC (see above), and c) the timing and spatial distribution of LUC, could all have significant effects
on predicted C fluxes. We also investigated model outputs at national and regional scales, which
showed that currently more data would be needed on the error around SOC measurements at longterm experiments for model validation in order to reduce uncertainty in model predictions sufficiently
to meet Kyoto Protocol reporting requirements. A suggested method for modelling LUC with RothC
has been developed, involving the use of gradual land use change, dominant soils data to represent
1km squares, and a step change to the new land use at the mid-point between survey dates
Under Task 3, estimated C fluxes due to LUC 1990-2000 were compared between the spatially
explicit 1km RothCUK model and the SRC method using highly aggregated data. This showed a
considerable difference in predicted C fluxes, attributable to differences between models,
assumptions, and input data.
Under Task 4, a computer model for estimating C fluxes from LUC conditions representative of
the UK was developed and evaluated with new datasets. Further work is required for modelling deep
soil layers, organic soils and permanently waterlogged soils.
CSG 15 (1/00)
21
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Under Task 5, a GIS-compatible version of RothC (RothCUK) was developed and tested with
Hungarian datasets, then attached to preliminary and final versions of the UK soils, land use, LUC
and climate databases. RothCUK was used to simulated SOC storage and C inputs to UK soils, and
further work with the system was completed under Tasks 3, 6, 7 and 8.
Under Task 6, national figures on C fluxes due to LUC 1990-2000 were provided as outputs
from the RothCUK model at 1km level. These outputs were compared to the SRC method using
highly aggregated data, which showed GB to be a net source of CO 2 due to LUC for this period,
mainly due to conversion of pasture to arable land.
Under Task 7, projected C fluxes due to LUC and climate change were provided. It was only
possible to provide C fluxes due to LUC for 1990-2000 due to data limitations. Climate change was
shown to reduce the SOC stocks of arable soils from 2000-2099 due to increased decomposition
from greater rainfall and higher temperatures.
Under Task 8, the RothCUK system was used to quantify the C mitigation potential of alternative
land management practices, and their interaction with climate change. The C mitigation options
investigated showed considerable potential to contribute towards Kyoto Protocol targets, although
this potential could be significantly reduced due to the effects of climate change.
Future work
1. Further work is needed to determine when, and how LUC occurs between survey dates from
available data, and identifying the spatial allocation, timing and nature of LUC
2. Further work is needed to develop a) management practice data, b) resource availability and
accessibility and c) location of semi-natural and natural ecosystems that could be used at 1m
level, and to d) develop future scenarios for land use change at 1km level.
3. Further work is required to consider methods for filling missing soils and land use data gaps
4. Predicted SOC changes are highly dependent on SOC data for different land uses and soil
depths, and more measurements of SOC under different land uses but for the same soil series
are recommended.
5. More data is required on replicate SOC measurements for the long-term experiments that are
used to test and validate SOC models, which would aid the significant reduction in uncertainty
that would be needed to use SOC models with confidence for Kyoto Protocol accounting.
6. Further work is required to determine whether UK soils are currently at an equilibrium state for
SOC, since this affects predicted C fluxes.
7. The RothC model requires further development for a) C turnover at depth b) C turnover in
organic soils, and c) turnover in waterlogged soils.
8. More data are required to improve estimates of uncertainty for model outputs.
9. Further work is needed to extend the RothCUK approach to modelling component soils for
1km squares and to allocate LUC to component soil types.
10. Further work is needed to assess how aggregating true soil profile layers into the 0-30cm and
30-100cm layers used in the current work could affect output results.
11. Further analysis of results is required to delineate areas where the RothC model is not
applicable.
12. More detailed analysis of the combined effects of climate and LUC on SOC stocks requires
integration of the RothC model approach with plant models and climate prediction models in
order to fully capture feedback mechanisms.
References
Ashman M, Falloon P, Coleman K & Smith P. 2000. Size and rate of change of carbon density due to
changes in agricultural activity. In: Carbon sequestration in vegetation and soils. Annual report
for DETR Contract EPG1/1/39 (Ed. by R. Milne).
CSG 15 (1/00)
22
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Cannell, M.G.R., Milne R, Hargreaves K J, Brown T A W, Cruickshank M M, Bradley R I, Spencer T,
Hope D, Billett M F, Adger W N and Subak S 1999. National Inventories of terrestrial carbon
sources and sinks: the UK experience. Climatic Change 42: 505-530.
Coleman K & Jenkinson DS 1996. RothC-26.3- A Model for the turnover of carbon in soil. In:
Evaluation of Soil Organic Matter Models Using Existing, Long-Term Datasets, eds. DS
Powlson P Smith & JU Smith, NATO ASI Series I, Vol.38, Springer-Verlag, Heidelberg pp.
237-246.
Coleman K Jenkinson DS Crocker GJ Grace PR Klir J Körschens M Poulton PR & Richter DD 1997.
Simulating trends in soil organic carbon in long-term experiments using RothC-26.3. Geoderma
81, 29-44.
Falloon PD Smith P Coleman K & Marshall S 1998. Estimating the size of the inert organic matter
pool for use in the Rothamsted carbon model. Soil Biology and Biochemistry 30, 1207-1211.
Falloon P 2001. Large scale spatial modelling of soil organic matter dynamics. PhD Thesis, School
of Life and Environmental Sciences, University of Nottingham, UK.
Falloon P & Smith P 2002. Simulating SOC dynamics in long-term experiments with RothC and
CENTURY: model evaluation for a regional scale application. Soil Use and Management 18,
101-111.
Falloon P, Smith P, Szabó J & Pásztor L 2002. Comparison of approaches for estimating carbon
sequestration at the regional scale. Soil Use and Management 18, 164-174.
Falloon P and Smith P 2003. Accounting for changes in soil carbon under the Kyoto Protocol: need
for improved long-term data sets to reduce uncertainty in model projections. Soil Use and
Management 19, 265-269.
Milne R & Brown TA 1997. Carbon in the vegetation and soils of Great Britain. Journal of
Environmental Management, 49, 9-15.
Milne R. & Smith P. 2001. Country specific data for the UK relating to Articles 3.3 and 3.4 of the
Kyoto protocol: An Update. In: UK Emissions by Sources and Removals by Sinks due to Land
Use, Land Use Change and Forestry Activities. Annual report for DETR Contract EPG1/1/160
(Ed. by R. Milne). pp 61 – 70.
Paustian, K., Mosier, A., Janzen, H., Tiessen, H., van Noordwijk, M., Andren, O., Davidson, E.,
Eswaran, H., Fernandes, E., Grace, P., Houghton, R., Kimble, J., Kolchingna, T., Lal, R.,
Scholes, M., Smith, P., Tian, G., Woomer, P. & Zhong, L. 1997. Chapter 5. Land Use Change
and Forestry: Emissions and Uptake of CO2 from Soils. In: Revised 1996 IPCC Guidelines for
National Greenhouse Gas Inventories, WMO, Bracknell, Berks, UK: 5.23-5.38.
Smith P, Powlson DS, Smith JU, Falloon PD, and Coleman K (2000) Meeting the UK's Climate
Change Commitments: Options for carbon mitigation on agricultural land. Soil Use and
Management 16, 1-11.
Technology Transfer
Papers published
Falloon P & Smith P 2002. Simulating SOC dynamics in long-term experiments with RothC and
CENTURY: model evaluation for a regional scale application. Soil Use and Management 18,
101-111.
Falloon P, Smith P, Szabó J & Pásztor L 2002. Comparison of approaches for estimating carbon
sequestration at the regional scale. Soil Use and Management 18, 164-174.
Falloon P and Smith P 2003. Accounting for changes in soil carbon under the Kyoto Protocol: need
for improved long-term data sets to reduce uncertainty in model projections. Soil Use and
Management 19, 265-269.
Falloon PD Smith P Szabo J Pasztor L Smith JU Coleman K & Marshall S 2002. Comparing
estimates of regional C sequestration potential using GIS, dynamic SOM models, and simple
relationships. Chapter 13 In: Kimble JM, Lal R & Follett RF (eds). Agricultural Practices and
Policies for Carbon Sequestration in Soil, Lewis Publishers, Boca Raton. pp141-154.
CSG 15 (1/00)
23
Project
title
Modelling soil carbon fluxes and land use change for the
National Carbon Dioxide Inventory
DEFRA
project code
CC0242
Smith, P. & Falloon, P. 2003. Agricultural practice, soil carbon storage and CO2 emissions.
Comparative Biochemistry and Physiology Part A 134: S180.
Smith P, Powlson DS, Smith JU, Falloon P, Coleman K & Goulding K. (2000) Agricultural carbon
mitigation options in Europe: Improved estimates and the Global perspective.
Acta
Agronomica Hungarica 48, 209-216.
Smith P, Goulding KW, Smith KA, Powlson DS, Smith JU, Falloon PD, and Coleman KC (2001)
Enhancing the carbon sink in agricultural soils: including trace gas fluxes in estimates of
carbon mitigation potential. Nutrient Cycling in Agroecosystems 60, 237-252 2001
Powlson DS, Christian DG, Falloon P & Smith P (2001) Biofuel crops: their potential contribution to
decreased fossil carbon emissions and additional environmental benefits. Aspects of Applied
Biology 65, 289-294.
Talks
Falloon P, Smith P, Ashman MR, Smith JU, Szabo J, Pasztor L, Coleman K (2000) Modelling C
fluxes due to land use change for the UK national CO 2 inventory. BSSS Eurosoil 2000
Conference, University of Reading, 4-6 September 2000. Abstracts.
Falloon P and Smith P (2001) Modelling SOM dynamics in arable and grassland soils. COST 627:
Carbon storage in European grasslands. Meeting of WG3, WG4 and MC on 28-29 September
2001, Abstracts. Danish Institute of Agricultural Sciences, Research Centre Foulum,
Denmark. p11.
Gaunt J, Powlson D, Sohi S & Falloon P. Soil organic matter and how to change it. Abstracts of
Meeting "The draft soil protection strategy: implications for the agricultural and environmental
sectors". 12 March 2002, SCI, London.
Falloon P, Smith P, Powlson D. 2002. Carbon cycling and sequestration opportunities in temperate
arable agriculture. Abstracts of BSSS Meeting, 'Soils as carbon sinks: opportunities and
limitations', Friday 28 June 2002, London. BSSS Edinburgh.
Smith P & Falloon P. Agricultural practice, soil carbon storage and CO2 emissions. The Carbon
Balance of Forest Biomes -A symposium of the Society of Experimental Biology and the
Physiological Ecology Group of the British Ecological Society (BES) at the University of
Southampton 1st - 4th of April 2003, Abstracts.
Posters
Falloon P, Ashman MR, Smith P and Coleman K. (2000) Model estimates of soil carbon changes
after land use changes in the UK: sensitivity to the size and rate of land use change. BSSS
Eurosoil 2000 Conference, University of Reading, 4-6 September 2000. Abstracts.
Falloon P, Smith P (2000) Modelling the un-measurable - the importance of inert organic matter for
modelling soil carbon sequestration. BSSS Eurosoil 2000 Conference, University of Reading,
4-6 September 2000. Abstracts.
Falloon P & Smith P. Modelling soil carbon fluxes due to land use change for the national CO 2
inventory: challenges and opportunities. Abstracts of Meeting "Trees and peat: sources or
sinks of carbon?” 5th November 2002, ECCM, Edinburgh.
Falloon P & Smith P. Modelling soil carbon fluxes due to land use change for the national CO 2
inventory: challenges and opportunities. Abstracts of Meeting "The draft soil protection
strategy: implications for the agricultural and environmental sectors". 12 March 2002, SCI,
London.
CSG 15 (1/00)
24