DEFRA PROJECT SP0567: ASSEMBLING UKWIDE DATA ON SOIL CARBON (AND
GREENHOUSE GAS FLUXES) IN THE CONTEXT
OF LAND MANAGEMENT
FINAL REPORT TO DEFRA FROM WCA
ENVIRONMENT LIMITED
wca environment limited
Brunel House
Volunteer Way
Faringdon
Oxfordshire
SN7 7YR
UK
Email: [email protected]
Web: www.wca-environment.com
EXECUTIVE SUMMARY
This project builds on the outputs and findings of a previous Defra Project - SP0562 which was initiated from the outcomes of a Defra Expert Group Workshop in 2006.
Project SP0562 concluded that data on stocks and fluxes of soil organic carbon in the UK
are available in sufficient quantity to assess the status of UK soil carbon without
recourse to significant additional research. However, while the data are available, they
are highly variable in terms of content, coverage, date range, and ownership;
consequently, what could be a valuable and extensive data set on soil organic carbon is
fragmented and unusable and, until now, the prediction of soil carbon behaviour and
fate has been subject to a range of uncertainties. A strategy and work programme to
integrate and combine the available data on soil carbon stocks and fluxes in the UK was
developed, which formed the basis for this current project.
This project integrates, for the first time, available soil carbon data and uses these to
provide evidence and predictions on the behaviour and fate of soil organic carbon in the
UK. These aims were achieved through a series of tasks, four of which provided data for
a decision tool to quantify soil carbon fluxes under different land use change scenarios.
These scenarios were:
•
•
•
Historical best case (the best it has ever been);
Recent best case (the best it could now feasibly be);
Historical worst case scenarios (these were set at 5, 10 and 20 percent plough out).
This decision tool was used to produce UK-wide estimates of soil carbon flux for several
established land use and management scenarios, within specified levels of confidence.
Land use change within the agricultural sector may not be a feasible large-scale option
for climate mitigation, as land use is largely determined by market conditions. Therefore,
much of the potential for mitigation will be by changing management on land that
remains in agricultural use. Consequently the results of the work have examined the
effect on greenhouse gas (GHG) emissions as a result of land use change through other
pressures.
The Historical Best Case scenario presents land use as it has been in the past (1930),
but agricultural land use is not considered likely to return to conditions similar to these
in the near future. However, the historical best case for Great Britain as a whole is not a
“best case” for Scotland or Wales. The Recent Best Case scenario is probably closer to
what could feasibly be achieved in the near term, but it only has a significant impact in
England, with no significant impact in Wales or Scotland. Even for England, the impact is
an order of magnitude lower than that of the Historical Best Case scenario. The Worst
Case scenarios, with 5%, 10% or 20% plough out of permanent grassland, represent
potential futures should cropland products (largely cereals in the UK) increase in market
i
value to favour more crop production at the expense of grasslands, leading to a tradeoff with grazed livestock production.
The Historical Best Case scenario delivers a Great Britain emission reduction of ~220 Mt
CO2-eq. over 20 years, or an annual GHG emission reduction of ~11 Mt CO2-eq. yr-1
relative to the 2004 baseline, due to an increased permanent grassland area. In
contrast, cultivation of 20% of the current (2004) permanent grassland area for arable
production could result in emissions of a similar magnitude (280 Mt CO2-eq. over 20
years; 14 Mt CO2-eq. yr-1). In the context of overall UK GHG emissions (695 Mt CO2-eq.
yr-1 in 2006) the yearly reductions and increases examined here are small, accounting
for <2% of yearly annual GHG emissions, even for the most extreme scenario (Worst
Case 20%). However, in the context of current UK Land Use, Land-Use Change and
Forestry (LULUCF) emissions, the changes in GHG emissions examined here are
considerable. The Recent Best Case scenario would deliver further emission reductions (1.4 Mt CO2-eq. yr-1), whereas even limited grassland plough out would result in an
increase of emissions of around 3.5 Mt CO2-eq. yr-1.
ii
CONTENTS
EXECUTIVE SUMMARY ................................................................................................ i CONTENTS
........................................................................................................... iii TABLES
............................................................................................................ v FIGURES
.......................................................................................................... vii ACKNOWLEDGMENTS ............................................................................................... ix 1 INTRODUCTION .................................................................................... 1 1.1 Scientific aims and objectives ........................................................................ 1 1.2 Project Methodology ..................................................................................... 2 2 WORK PACKAGE 1. RE-EXAMINATION AND INTERPRETATION OF
EXISTING SOILS DATABASES AND DATA SOURCES ..................................................... 7 2.1 Data processing ........................................................................................... 9 2.2 The Data Tool ............................................................................................ 15 2.3 Using the Data Tool .................................................................................... 15 2.4 Summary ................................................................................................... 20 3 WORK PACKAGE 2. INVESTIGATION OF THE LINKAGE BETWEEN
DISSOLVED ORGANIC CARBON FLUX VIA FLUVIAL PATHWAYS AND SOIL CARBON LOSS
.......................................................................................................... 21 3.1 Statistical treatment of the data .................................................................. 22 3.2 Results ...................................................................................................... 24 3.3 Discussion.................................................................................................. 30 3.4 Summary ................................................................................................... 32 4 WORK PACKAGE 3. ACCOUNTING FOR ALL THE CARBON ...................... 35 4.1 Organic-mineral soils .................................................................................. 35 4.2 Methods for organic-mineral soils ................................................................ 36 4.3 The significance of carbon stocks in organic-mineral soils ............................. 39 4.4 Influence of soil parameters on the carbon stocks of organic mineral soils ..... 40 4.4.1 Bulk density ........................................................................................ 40 4.4.2 Soil carbon content .............................................................................. 45 4.5 Summary of findings on organic mineral soils ............................................... 45 4.6 Salt marshes and soil organic carbon ........................................................... 47 4.7 Methods of calculation for salt marshes ....................................................... 49 4.7.1 Area of Salt Marsh ............................................................................... 51 4.7.2 Regional soil carbon stocks in salt marshes ........................................... 52 4.8 Influence of soil type on soil carbon stocks .................................................. 53 iii
4.8.1 Estimates for soil carbon stocks in UK salt marshes................................ 54 4.8.2 Sequestration of carbon in salt marsh soils ............................................ 54 4.9 Summary of findings on salt marshes .......................................................... 55 5 WORK PACKAGE 4. THE DEVELOPMENT OF LAND USE AND MANAGEMENT
SCENARIOS .......................................................................................................... 57 5.1 Lowland scenarios ...................................................................................... 57 5.2 Upland scenarios ........................................................................................ 58 5.3 Results for lowlands ................................................................................... 61 5.4 Results for uplands ..................................................................................... 62 5.5 Summary ................................................................................................... 63 5.5.1 Lowlands ............................................................................................. 63 5.5.2 Uplands............................................................................................... 63 6 WORK PACKAGE 5. THE DEVELOPMENT OF SOIL CARBON STOCKS AND
FLUXES DECISION TOOL.......................................................................................... 64 6.1 Summary ................................................................................................... 67 7 TIER 2: UK-WIDE PREDICTIONS OF SOIL CARBON STOCKS AND FLUXES
IN THE CONTEXT OF LAND USE ............................................................................... 69 7.1 Summary ................................................................................................... 70 8 IMPLICATIONS OF THE FINDINGS ........................................................ 77 8.1 Overall implications .................................................................................... 77 8.2 Conclusions ................................................................................................ 78 8.3 Possible future work ................................................................................... 78 9 REFERENCES ....................................................................................... 80 iv
TABLES
Table 2.1 UK Soil Carbon Data Sets used in this project .......................................... 7 Table 2.2 Land use conversion protocols used in the data tool .............................. 12 Table 2.3 Carbon content % (0-15cm) for the main land uses across the data sets 18 Table 2.4 Carbon stocks (kg/km2 to 15 cm) using the CS pedo-transfer function to
estimate bulk density ............................................................................................... 19 Table 3.1 The annual average DOC export from catchments with the extreme values
as defined by dominant soil or land use characteristics used in this project ................. 24 Table 3.2 The export coefficient for each significant land use and soil type and the
predicted DOC flux from that soil type or land use when considered across the entire UK.
The upper and lower estimates are based upon the standard errors in the export
coefficients given in equation (iv). ............................................................................ 28 Table 3.3 Loadings on the first four principal components with eigenvalues >1 and
the first with an eigenvalue <1. ................................................................................ 29 Table 3.4 Comparison of export values of nitrogen and carbon species for major
Western European rivers and the river with the largest DOC export in the world as
reported by Alexander et al. (1998) with values derived for the UK from this study...... 31 Table 4.1 Extent of organic-mineral soils in England, Wales and Scotland (% of total
land area for each country) ...................................................................................... 36 Table 4.2 Representative series profiles for mineral, organic-mineral and organic soils
taken from the National Soil Inventory for England and Wales. Profiles are for
permanent grasslands. ............................................................................................. 37 Table 4.3 soils
Estimates for UK stocks of soil carbon, with a focus on organic-mineral
.......................................................................................................... 39 Table 4.4 stocks
Relative contribution of organic-mineral soils to the UK’s total soil carbon
.......................................................................................................... 40 Table 4.5 Comparison of measured bulk density (g cm-3) with estimated values and
ranges from Countryside Survey 2007 and values from other bulk density equations. .. 43 Table 4.6 Extent of salt marsh sites in the United Kingdom based on data from Burd
(1995) and JNCC (2005). ......................................................................................... 47 Table 4.7 Trends in salt marsh sites in the United Kingdom from the JNCC 2005
National Trend Assessment. ..................................................................................... 48 Table 4.8 Extent of saltmarsh sites (ha) in the United Kingdom obtained from this
study compared with previous estimates. .................................................................. 51 Table 4.9 Soils associated with salt marshes in England, Wales and Scotland.
Classification according to Avery (1980). ................................................................... 52 Table 4.10 Soil carbon stocks (Tg) in saltmarshes by land use and region. C stock A =
using Howard et al. (1995) bulk density equation and C stock B = using modified Smith
et al. (2007) bulk density equation. .......................................................................... 53 v
Table 4.11 Influence of soil type on soil carbon stocks (Tg) in salt marshes. Estimated
C stock assuming all soils are alluvial gleys and difference (%) to C stocks assuming a
diversity of soil types (from Table 4.10). ................................................................... 53 Table 4.12 Estimated carbon stocks of salt marsh A = using Howard et al. (1995) bulk
density equation and B = using modified Smith et al. (2007) bulk density equation.
Northern Ireland values are estimated from available data sources. ............................ 54 Table 5.1 Topsoil (0-15 cm) carbon stocks for tillage, temporary and permanent
grassland in Britain. ................................................................................................. 58 Table 5.2 Land management characteristics of the regions selected for this study. . 59 Table 5.3 Total area of managed agricultural land and woodland (million hectares)
for each scenario in England, Wales and Scotland. ..................................................... 61 Table 5.4 Total topsoil (0-15 cm) C stocks (million tonnes) in managed agricultural
land for each scenario in England, Wales and Scotland. ............................................. 62 Table 5.5 GHG emissions form UK upland peat soils with all values expressed as
Mtonnes CO2-eq yr-1................................................................................................. 63 Table 6.1 Estimates of change in SOC stocks and nitrous oxide emissions resulting
from land use change on mineral and organic-mineral soils. All estimates expressed in t
CO2-eq. ha-1 yr-1 as per Smith et al. (2008). For derivation of estimates, see text......... 65 Table 6.2 Estimates of change in SOC stocks and methane and nitrous oxide
emissions resulting from land use change on organic soils. All estimates expressed in t
CO2-eq. ha-1 yr-1 as per Smith et al. (2008). For derivation of estimates, see text. ....... 68 vi
FIGURES
Figure 1.1 Schematic of the project approach .......................................................... 3 Figure 1.2 Soil texture classifications used in the project .......................................... 4 Figure 2.1 Soil carbon and land use (data derived from the NSI 1978 data set) ....... 16 Figure 2.2 Variation of soil carbon content across a temperature gradient ............... 17 Figure 3.1 Location of monitoring points for which a DOC export could be calculated
for the period 2001-2007 ......................................................................................... 25 Figure 3.2 The annual average DOC export for each 1 km2 across Great Britain....... 28 Figure 3.3 text.
Comparison of PC2 and PC3. For the meaning of the letters refer to the
.......................................................................................................... 30 Figure 4.1 Influence of bulk density equations on C stocks of representative soil
profiles. Meas=measured CS2007; Ecosse=Smith et al. 2009; Howard=Howard et al.
1995; CS=Emmett et al. 2010; Shiel=Shiel and Rimmer 1984. ................................... 41 Figure 4.2 Influence of bulk density equations on the contribution of organic-mineral
soils to simulated soil C stocks, using NSI_EW representative soil profiles, CS2007 soils
data and area of soils from Bradley et al., 2005. Meas=measured from CS2007;
Ecosse=Smith et al. 2007b; Howard=Howard et al. 1995; CS=Emmett et al. 2010;
Shiel=Shiel and Rimmer 1984................................................................................... 42 Figure 4.3 Relationship between measured bulk density and soil carbon values from
Countryside Survey 2007. Data from NERC. .............................................................. 42 Figure 4.4 Sensitivity of carbon stocks (t ha-1) in representative soil profiles to intrinsic
variation in bulk density. CS Eqn = stocks calculated using the CS bulk density equation;
Lower and Upper = CS bulk density equation +/- ~95% intervals. ............................. 44 Figure 4.5 Sensitivity of total soil carbon stocks (% Tg) to the intrinsic variation in
bulk density. CS Eqn = stocks calculated using the CS bulk density equation; Lower and
Upper ranges = CS bulk density equation +/- 95% intervals. ..................................... 44 Figure 7.1 The total change in GHG emissions (kt CO2-eq. ha-1 over 20 years) for
England, Scotland and Wales under each land use change scenario. (a) Historical Best
Case scenario, (b) Recent Best Case scenario, (c) Worst Case 5% scenario, (d) Worst
Case 10% scenario and (e) Worst Case 20% scenario. .............................................. 71 Figure 7.2 The total change in GHG emissions (Mt CO2-eq. ha-1 over 20 years –
estimates using the mean mitigation factor shown) for each county under each land use
change scenario. (a) Historical Best Case scenario, (b) Recent Best Case scenario, (c)
Worst Case 5% scenario, (d) Worst Case 10% scenario and (e) Worst Case 20%
scenario.
.......................................................................................................... 73 The user interface of the tool as seen on-screen ........... Error! Bookmark not defined. vii
ACKNOWLEDGMENTS
The Project Team would like to thank Alex Higgins of AFBI Northern Ireland, Allan Lilly
and Anne Marsden of the Macauley Institute, Pat Bellamy and Guy Kirk of Cranfield
University, Claire Wood and Bridget Emmet of the Centre for Ecology & Hydrology, and
John Archer and Zoe Frogbrook of the Environment Agency of England and Wales.
Finally, we gratefully acknowledge the help of the Centre for Ecology & Hydrology for
access to Countryside Survey data, which is owned by NERC.
ix
1
INTRODUCTION
This project builds on the outputs and findings of a previous Defra Project - SP0562 that established an expert group to assess the availability and quality of data on soil
carbon stocks and fluxes in the UK. The project concluded that data on stocks and fluxes
of soil organic carbon in the UK are available in sufficient quantity to be able to draw
conclusions on the status of UK soil carbon without recourse to significant additional
research. However, while the data are available they are highly variable in terms of
content, coverage, date range, and ownership. As a result, they are neither directly
comparable nor are they well integrated; consequently, what could be a valuable and
extensive data set on soil organic carbon is fragmented and unusable. In addition,
available information on soil carbon fluxes in UK soils was unclear, with different
conclusions drawn by different researchers. A third issue is that coverage of soil carbon
data is focused on lowland agricultural soils and limited information is available on
upland soils. As a result, the prediction of soil carbon behaviour and fate has been
subject to a range of uncertainties. The outcome of SP0562 was a strategy and work
programme to integrate and combine the available data on soil carbon stocks and fluxes
in the UK, which formed the basis for this current project.
1.1
Scientific aims and objectives
This project aimed to determine the availability and reliability of existing data on UK soil
carbon stocks and fluxes, to integrate these data into a usable decision tool, to quantify
the influence of land use change on soil carbon stocks and fluxes in Great Britain, and to
assess the effectiveness of potential methods of reducing these emissions. This work is
in line with wider concerns over climate change and carbon emissions at a national
scale. The overall aims were to:
•
Deliver a methodology by which available soil carbon data may be integrated and
used to provide evidence on the behaviour and fate of soil organic carbon in the UK;
and
•
Reduce the uncertainties associated with predictions of soil organic carbon behaviour
and fate.
These aims were achieved through the following objectives:
•
Integrating and re-interpreting UK data to produce a simple decision tool through
which different soil C fluxes can be quantified (Work Packages 1 & 2).
•
Identifying gaps in existing data and prioritising areas for new research which can be
co-supported by other soil carbon flux calculators (Work package 3).
1
•
Improving understanding of the effects of land use and management on processes
driving the spatial and temporal properties of carbon in soils (Work Packages 4 & 5).
•
Using the decision tool to define the ”rules/inputs” in running soil C flux models to
deliver UK-wide estimates of soil C flux for several established land use and
management scenarios, within specified levels of confidence (Tier 2).
•
Providing robust evidence to policymakers through which an understanding of the
impact of policies on soil organic carbon (SOC) losses can be gained.
1.2
Project Methodology
In order to meet the objectives, the project was organised into integrated work
packages split into two tiers. This structure is illustrated in Figure 1.1. Tier 1 focussed on
developing and subsequently populating a decision tool. Tier 2 used the decision tool to
generate UK-wide predictions of soil C stocks and fluxes in the context of land
management. The individual work packages in Tier 1 ran in parallel and delivered inputs
into the decision tool which were used to achieve the objectives in Tier 2.
Tier 1 was divided into five separate work packages; the work leader(s) for each task is
shown in brackets:
•
Work package 1: integration and coordination of UK soil carbon data from a range of
sources (the data tool) (Declan Barraclough, Environment Agency) (Section 2).
•
Work package 2: assessment of the scale of soil carbon fluxes as a result of the
transfer of dissolved organic carbon (DOC) from soil to the fluvial system (Fred
Worral, University of Durham) (Section 3).
•
Work package 3: identification and quantification of the depth and extent of organic
and organic-mineral soils (Heleina Black and Alan Lilly, Macaulay Institute) (Section
4).
•
Work package 4: determination and definition of key land use scenarios and changes
in relation to soil carbon (Fred Worral, University of Durham; Anne Bhogal, ADAS)
(Section 5).
•
Work package 5: development of a decision tool (Pete Smith, University of
Aberdeen) (Section 6).
Work Packages 1 - 4 were used as an input into the decision tool developed in Work
package 5 (shown in Annex 1), which was then used in Tier 2 (Section 7) of the project
to generate countrywide predictions of soil C stocks and fluxes in the context of land
management. Tier 2 of the project was led by Pete Smith of the University of Aberdeen.
2
Details of specific methods used in each of the work packages will be discussed where
relevant in the following sections on the results of the work.
Expert Group Meeting
Project plan
Identified work
packages
Tier 1 work packages
Individual work packages
Running of C stocks
and fluxes models with inputs/rules
defined by the decision tool for
A number of scenarios at the national
scale
Tier 2 work package
Review phase, options appraisal and
Research needs identified
Figure 1.1
Schematic of the project approach
The project as a whole examined the flux of carbon and consequent changes in carbon
stocks on a range of soil types as a result of a range of land use changes. These land
uses and soil types are referred to repeatedly through the text and are defined below.
Soils were defined by their texture using a simplified version of the method used by
Hodgson (1997), which is illustrated in Figure 1.2. Four soil types are defined and used
within this project:
•
Peat: >29% organic carbon.
•
Organic soils: 14% - 29% organic carbon content when clay content >50%; when
clay content is <50% the boundary between organic and organic mineral soils is
defined by the function: Carbon (%) = 0.05 clay (%) + 12.
•
Organic-mineral soils: 6% - 14% organic carbon content when clay content >50%;
when clay content is <50% the boundary between organic mineral soils and mineral
soils is defined by the function: Carbon (%) = 0.05 clay (%) + 3.5.
3
•
Mineral soils: <6% organic carbon content when clay content >50%; when clay
content is <50% the boundary between organic mineral soils and mineral soils is
defined by the function: Carbon (%) = 0.05 clay (%) + 3.5.
Scot peat>35% OC
NI peat >20% OC
Horizon types
35
Peat
Limit determined by LoI =50%
30
Organic Carbon
25
Organic (loamy <50% sand;
or sandy peat>50% sand)
20
Organic (peaty loam <50% sand;
or peaty sand >50% sand)
15
Organic mineral
Also termed Humose
y = 0.05x + 12
10
5
Mineral
y = 0.05x + 3.5
0
0
Figure 1.2
20
40
clay
60
80
100
Soil texture classifications used in the project
Land use types were divided into lowland and upland areas, defined as the area above
or below the intake wall (i.e. the boundary wall marking the upper limit of cultivated
land or improved pasture). Land use types were defined in a range of ways:
•
•
Lowland; including:
o
Arable; Temporary grassland (<5 years); Permanent grassland (>5 years)
(Work package 4, Tier 2).
o
Arable; Grass; Urban (Work package 2).
o
A best and worst case scenario were developed based on the quantity of land
under permanent grassland.
Upland; including
o
Peat soils, with areas of burnt vegetation, forested land, bare soil and
drained land measured or estimated (Work package 4, Section 5).
4
o
A series of scenarios were developed based on the presence, absence and
combination of these characteristics (Work package 4, Section 5).
Further details of these definitions are included in the description of the relevant work
packages, as necessary. The methods used in each work package of the project and the
results obtained are described in the following section, categorised by the five work
packages of Tier 1 and Tier 2.
5
2
WORK PACKAGE 1. RE-EXAMINATION AND
INTERPRETATION
OF
EXISTING
SOILS
DATABASES AND DATA SOURCES
This work package quantified the uncertainty in baseline soil carbon data and identified
the areas of inconsistency between existing UK data sets. The output from this work was
a dataset of UK soil carbon data that was derived from the available data and
rationalised to be as consistent as possible. The data were fed directly into the decision
tool being developed in Work Package 5 in the form of improved estimates of soil C
stocks and explicit statements as to their reliability. The decision tool required metadata
or summary data, but to ensure these data were consistent and as accurate as possible
this work package was aimed at using raw data, mostly under licence, to provide
consistency and to highlight mismatches.
The United Kingdom has a number of data sets reporting soil carbon concentrations (as
%C) and some reporting soil carbon stocks, reported as either t C ha-1 or kg C m-2 to a
given depth. In total, 17 data sets on soil C were identified, of which 14 were eventually
obtained for use in the project (the others could not be obtained). Two of the datasets
obtained for use in this project are those reporting carbon inventories (collated data)
and comprise derived data, so were not used for the estimates of soil carbon content
and carbon stocks derived from the original raw measurements. The datasets used and
the two collated datasets are shown in Table 2.1.
In many cases access to the data was only possible by purchasing licenses for the use of
the data, which slowed down the process of data procurement and had subsequent
effects on the timing of the rest of the work. The collation of all these data sets was the
first time a serious attempt has been made to combine and create an overarching soil C
dataset for UK soils. The resulting “data tool” is a key output of the overall project.
Table 2.1
Data Set
Name
UK Soil Carbon Data Sets used in this project
Date
Owner
and or
contact
Cranfield
(Pat
Bellamy)
Coverage
(E, S, W,
NI)
E, W
National
Inventory
Soil
1978-1982
National
Inventory
Soil
2003
Cranfield
(Pat
Bellamy)
E, W
1978-1982
Dr A Lilly
S
NSIS
(Relevant)
Parameters
Depth(s)
C analysis
method
No. of
samples
SOC, soil texture
land use, bulk
density
derived
using
pdf
of
Howard et al.
(1995).
SOC, soil texture,
bulk
density
estimated
as
above.
0-15cm
<20%C
dichromate
oxidation;
>20%C loss
on ignition
5686
0-15cm
2361
SOC, soil texture,
bulk density not
measured.
By horizon
<20%C
dichromate
oxidation;
>20%C loss
on ignition
LOI
7
3076
Data Set
Name
Date
NI Soil
1995
Data
1995
Owner
and or
contact
A Higgins
NI Soil
2005
Data
2004/5
A Higgins
NI
Woodland
survey 1971
1971
Natural
England
E, W and S
Woodland
survey 2001
2001
Natural
England
E, W and S
SOC, soil texture,
grid reference.
0-15cm
LOI
1648
Representative
Soil
Survey
Scheme (RSSS)
1969-2002
J Archer
E, W
SOC, soil texture,
land use.
0-15cm
dichromate
oxidation
>22000
Countryside
Survey
1978
NERC
CEH
E, S, W, NI
0-15cm
LOI (@375oC
for 12 hr)
1248
Countryside
Survey
1998
NERC
CEH
E, S, W, NI
0-15cm
LOI (@ 550oC
for 2 hr)
1141
ESA Database
1995
ADAS
E
SOC, soil texture,
bulk density done
in
2007
from
which pdf derived
relating bd to
%C. This was
used to estimate
stocks in 1978
and 1998.
SOC, soil texture,
bulk density done
in
2007
from
which pdf derived
relating bd to
%C. This was
used to estimate
stocks in 1978
and 1998.
SOC, texture.
0-7.5cm
642
ESA Database
1995/1996
ADAS
W
SOC, texture.
0-7.5cm
LOI
(converted to
SOC
using
Ball
1964
relationship)
LOI
(converted to
SOC
using
Ball
1964
relationship)
na
I.
Bradley
NSRI
E, W,
and NI
SOC, bulk density
dominant
soil
series, land use.
Not stated
Not stated
1240
na
A Lilly
S
SOC, soil texture,
bd estimated by
pdf, 4 land uses.
Horizons
LOI
1344
Collated Data
sets
Soil C inventory
data collated by
I Bradley for
Defra
project
CC02421
ECOSSE2 data
set
Coverage
(E, S, W,
NI)
NI
S
(Relevant)
Parameters
Depth(s)
C analysis
method
No. of
samples
SOC, soil texture,
land use, bulk
density done on
selected horizons.
SOC, soil texture,
land use, bulk
density estimates
on
0-5
cm
horizon only.
SOC, soil texture,
grid reference.
0-7.5 cm
and
A
horizon
LOI
1338
0-7.5 cm
and
A
horizon
LOI
583
0-15cm
LOI
1648
198
na: not applicable. E = England, S = Scotland, W = Wales, NI = Northern Ireland. LOI = Loss on
ignition. bd = bulk density. pdf = pedo-transfer function
8
2.1
Data processing
The data gathered from the various sources were processed into a single data set via
four stages:
Stage 1: Removal of records missing either soil carbon content or land use descriptors
Estimating changes in soil carbon stocks resulting from land use changes requires, as a
minimum, data on soil carbon content, sampling depth, land use and bulk density. Bulk
density is dealt with later; the initial pre-processing removed all those records missing
either soil carbon contents or land use descriptors, or both.
Stage 2: Derivation of a simplified soil texture classification based on carbon and clay
content
The project reports carbon stocks using a simplified soil classification based on carbon
and clay content as shown in Figure 1.2. For mineral and organic mineral soils the clay
content is a determinant when it is less than 50%. Where no data on clay content were
available, a simplified classifier was used based only on organic carbon with mineral soils
defined as <5% organic carbon and organic mineral soils as those with <13% organic
carbon.
Where grid references but no soil texture information were available, GIS methodology
was used to map the sample location onto the NSRI soil vector map for England and
Wales to retrieve the soil series and its soil texture information.
Stage 3: “Normalization” of land use descriptors
Inconsistencies in land use classifications inevitably introduce some error when
comparing across data sets. Grassland descriptions are particularly problematic:
definitions of permanent and temporary grassland are inconsistent and only two data
sets, those from the Countryside Survey, employ the terms neutral, calcareous and acid
grassland.
Land use descriptions across the data sets were “normalized” using the protocols set out
in Table 2.2.
Stage 4: Derivation of soil bulk density using pedo-transfer functions
Few of the data sets include coincident bulk density measurements. In developing the
data tool (see below) it was decided to incorporate two possible pedo-transfer functions
to estimate bulk density from other parameters.
The first is:
9
bD = 1.3-(0.275 *ln(Corg/10))
(1)
where bD = soil bulk density (g cm-3)
Corg = soil organic matter content (g kg-1)
This pedo-transfer function is used by Bellamy et al. (2005) but was derived by Howard
et al. (1995).
The second is:
bD = 1.29*e-(0.0206)*Corg+2.51*e-(0.0003)*Corg-2.057
(2)
(B. Emmet pers. comm.)
Stage 5: Reformatting data for incorporation in a data tool
Data sets were reformatted for inclusion in a simple Excel-based data tool allowing the
user to interrogate the data either within a single data set, or across sets. The field
structure is set out below:
Grid reference
10 or 12 character reference (depending on the data set) in the
format AA XXXX XXXX or AA XXXXX XXXXX
Land use
as set out in Table 2.2
Horizons
number of (depth) horizons for which data are available
Date
sample date
Lower depth 1
depth in cm of first sample (assumes upper depth=0)
%OC
organic carbon content for first depth
Bulk density
bD (if available) for first depth
%clay
clay content (if available) for first depth
%silt
silt content (if available) for first depth
%sand
sand content for first depth
Simple texture
simplified soil texture (mineral, organic mineral, organic, peat) for
first depth
Upper depth 2
top of next sample depth in cm
Lower depth 2
bottom of next sample depth
10
Then the fields “Date” to “Simple texture” repeated for each sample depth.
11
Table 2.2
Land use conversion protocols used in the data tool
Corresponding Land use in the original data set
Land Use in the
Tool
Arable
NSI
Inventory
NSI 1984
NSI 2001
CS 1978
CS1998
RSS
EW
woodland
1971
EW
woodland
2001
England
ESA
Wales
ESA
Ecosse
Arable
Arable
Arable
Arable & hort
Arable & hort
Arable
nd
nd
nd
nd
Arable
woodland
woodland
Woodland
NSIS
Arable
Deciduous
Deciduous
Deciduous
broad-leafed
woodland
broad-leafed
woodland
Deciduous
Coniferous
Coniferous
Coniferous
Coniferous
Coniferous
woodland
Coniferous
woodland
Coniferous
Horticultural
crops
Horticultural
crops
Horticultural
crops
Horticultural
crops
Ley grassland
Ley
grassland
Ley
grassland
Ley
grassland
Ley
grassland
Permanent
grass
permanent
grass
permanent
grass
permanent
grass
permanent
grass
Upland grass
Deciduous
woodland/
deciduous
forest/
deciduous
scrub
conifer
forest/conifer
forest/agroforestry
Horticulture
improved grass
upland grass
Arable
Ley
grassland
permanent
grass
improved grass
permanent
grass
permanent
grass
grassland
upland grass
upland grass
Neutral grass
neutral grass
neutral grass
Acid grassland
acid grassland
acid grassland
12
NI 2005
Arable
forestry
Deciduous
Grassland
NI 1995
pasture/
pasture
poor/setaside
Deciduous
woodland/deciduous
forest/deciduous scrub
Horticulture
Corresponding Land use in the original data set
Land Use in the
Tool
NSI
Inventory
NSI 1984
NSI 2001
Calcareous
grassland
CS 1978
CS1998
calcareous
grassland
calcareous grassland
EW
woodland
1971
RSS
EW
woodland
2001
England
ESA
Wales
ESA
Ecosse
NSIS
NI 1995
NI 2005
golf
course/urban
amenity
Recreation
Rough grazing
rough
grazing
rough
grazing
Saltmarsh
salt marsh
salt marsh
Scrub
scrub
scrub
Bog
bog
bog
Upland heath
upland heath
upland heath
Lowland heath
lowland
heath
lowland
heath
seminatural/rough
grazing
salt marsh
fen/marsh/swamp/
bog
fen/marsh/swamp/
bog
bog
upland heath
dwarf shrub heath
dwarf shrub heath
nd = no data
hort = horticulture
13
Semi-natural/rough
grazing
2.2
The Data Tool
To aid routine interrogations of the data, a simple data tool was developed in Excel. This
allows the user to perform the more obvious interrogations routinely (Annex 1). A
description of how to use the tool is provided in Annex 1, but due to the strict licensing
agreements on UK soils data this tool is not available for use outside this project.
2.3
Using the Data Tool
Only summary results are presented below, to illustrate the possibilities presented by the
data sets. Work package 5 and Tier 2 will present the land use change scenario results
in detail. Tables 2.3 and 2.4 show the carbon contents and stocks to 15 cm for the main
land uses in the 15 data sets. Results with an asterisk are derived from five or fewer
records.
Carbon contents of arable soils range from 2.4 – 3.1% for those data sets focused on
England and Wales. The Countryside Survey data from 1978 and 1998, although they
include Scottish sites, are also in the range 2.7 - 3%. Results from Scotland and
Northern Ireland alone are higher, ranging from 3.5 - 5.2%.
The data tool derives both descriptive (i.e. means, standard deviations and skew) and
comparative statistics. In general, soil carbon data are not normally distributed and
comparative statistics are performed on log-transformed data. The large number of
records in some of the data sets means that despite considerable variance, statistical
power is high. Thus, the mean carbon content results for arable soils from the NSI 1978
data (1884 records) and those from Countryside Survey in 1978 (251 records) are
significantly different (p=0.05) at 3.1 and 3%.
The variation in soil carbon content (0 - 15 cm) across a range of land uses is shown in
Figure 2.1 (data derived from the NSI 1978 data set).
15
30
25
20
Figure 2.1
upland
grass
coniferous
woodland
deciduous
woodland
permanent
grass
ley grass
15
10
5
0
arable
%C (0-15 cm)
Soil carbon and land use
Soil carbon and land use (data derived from the NSI 1978 data
set)
Comparisons across data sets for grassland are problematic because many definitions
are specific to particular data sets (e.g. only the Countryside Survey used “acid grass”).
Acid and upland grass ranges from 3.6 – 18.8% C (0 - 15 cm); rough grazing in England
and Wales and Northern Ireland is 10.3 - 12.6%; in Northern Ireland rough grazing
ranges from 37.7 - 41.4. The methods of data determination are likely to be less marked
than those differences of land use. Why?
The tool also selects data by geographical area based on the National Grid 100 x 100 km
grid squares. Figure 2.2 shows the carbon content of arable mineral soils (red points)
and grassland organic-mineral soils across a range of grid squares with similar rainfall
but differing mean annual temperatures.
16
%C (0-15 cm)
soil carbon vs temperature (medium
rainfall)
9.5
7.5
5.5
3.5
1.5
8
9
10
11
o
Mean temperature ( C 1941-1970)
Figure 2.2
Variation of soil carbon content across a temperature gradient
Initial analysis of these data suggests no discernable temperature signal, but more work
remains to be done.
17
Table 2.3
Carbon content % (0-15cm) for the main land uses across the data sets
NSI
Inventory
NSI
1984
NSI
2001
CS
1978
CS
1998
CS
2007
RSS
EW
woodland
1971
EW
woodland
2001
England
ESA
Wales
ESA
Ecosse
NSIS
NI
1995
NI2005
arable
3.0
3.2
2.6
3.0
2.8
-
2.4
-
-
-
-
5.2
3.5
5.2
3.9
woodland
10.7
9.5
10.0
Data set
Land use
30.0
8.2
deciduous
6.2
5.6
6.8
10.0
16.0*
2.86*
coniferous
10.0
8.7
20.5
21.9
30.7
39.0
orchard
3.4
hort. crops
4.4
4.6
5.1
4.2
4.5
ley grass
5.9
3.8
3.3
2.8
12.3
10.3
grassland
upland grass
5.2
6.6
18.8
31.6
neutral grass
5.3
5.8
calc. grass
9.50*
12.56*
acid grass
24.2
25.8
34.0
37.8
montane
10.6*
bog
40.0
salt marsh
6.2
4.94*
scrub
6.4
5.1
upland heath
30.2
18.9
lowland heath
*Five or fewer records
7.5
8.7
17.1*
51.0
9.84*
41.4
29.8
6.5
6.7
37.7
41.4
5.7
10.6
5.3
26.7
44.8
4.01*
perm. grass
rough grazing
7.6
26.6
39.9
18
Carbon stocks (kg/km2 to 15 cm) using the CS pedo-transfer function to estimate bulk density
Table 2.4
NSI
Inventory
NSI
1984
NSI
2001
CS
1978
CS
1998
arable
6.9
7.4
6.3
7.4
6.8
woodland
20.0
Data set
CS
2007
RSS
EW
woodland
1971
EW
woodland
2001
England
ESA
Wales
ESA
Ecosse
NSIS
NI
1995
NI2005
11.8
8.6
11.7
9.5
Land use
5.8
20.9
21.8
45.1
18.5
deciduous
13.7
13.0
14.2
19.7
27.5*
7.1*
16.4
coniferous
19.0
17.7
33.8
36.0
42.2
56.8
59.3
orchard
8.1
hort. crops
9.4
9.7*
perm. grass
10.3
11.7
10.1
10.8
ley grass
12.6
9.2
8.1
7.0
22.7
21.0
rough grazing
grassland
12.0
12.2
neutral grass
12.3
13.2
calc. grass
21.1*
26.9*
acid grass
39.4
41.8
upland grass
41.6
23.5*
34.5*
bog
55.5
47.7
salt marsh
13.9
11.9*
scrub
13.7
12.1
upland heath
13.0
14.8
32.8
montane
lowland heath
*Five or fewer records
14.3
45.8
33.3
17.1
19.2
48.5
58.7
50.3
54.3
65.5
22.1*
57.1
45.4
42.5
55.9
19
15.0
2.4
Summary
This work package has delivered a methodology to enable the integration and direct
comparison of data sets of soil carbon stocks in the UK. These data sets have been
quality assured and redefined so that they can be used together; this is the first time
this has been undertaken. A data tool has been developed in Microsoft Excel to conduct
basic assessments and interrogation of the available data, and the input data for the tool
have been derived from multiple raw data sets. The metadata outputs from the data tool
feed directly into the decision tool described in Work Package 5 and shown in Annex 2.
20
3
WORK PACKAGE 2. INVESTIGATION OF THE
LINKAGE BETWEEN DISSOLVED ORGANIC
CARBON FLUX VIA FLUVIAL PATHWAYS AND
SOIL CARBON LOSS
This task quantified fluxes of carbon from soils via fluvial transfer as dissolved organic
carbon (DOC). Other than soil surveys, DOC data are the only other spatially widespread
set of soil carbon data available in the UK, and the only one related to carbon flux.
Currently the link between fluvial transport of DOC and soil C is poorly understood. To
address this issue, existing data were re-evaluated to assess the impact of refining C
flux estimates through a relatively simple assessment of DOC transfer and soil C in a
limited number of well-characterised and delineated catchments.
The aims of this task were:
•
To understand the land use and soil controls on DOC loss from the terrestrial
biosphere, especially the role of mineral soil.
•
To use any significant relationships to calculate the flux of DOC from the UK using
extrapolation rather than interpolation.
•
To estimate the loss of DOC in-stream and quantify the impact of DOC losses from
soil on levels of atmospheric CO2 and the loss of DOC at source.
•
If relationships were found to be significant, to include DOC flux in the decision tool
in Work Package 5.
This work package compared the flux of DOC for a given catchment to the physical
characteristics of the catchment. By comparing DOC flux to catchment properties it is
possible to compare across catchments, and by allowing for differences in land use and
soil type, it becomes possible to compare flux from different size catchments. Any
relationships found can then be interpreted and extrapolated. The comparison of DOC
flux from different size catchments means that it is possible to measure the amount of
DOC lost in streams and to estimate the flux of DOC at source.
This work package drew extensively on the data available from the Harmonised
Monitoring Scheme (HMS; Bellamy and Wilkinson 2001). There are 56 HMS sites in
Scotland and 214 sites in England and Wales. Data available from the HMS were
augmented by data from regular water quality monitoring undertaken by the
Environment Agency of England and Wales (EA) and the Scottish Environment Protection
Agency (SEPA). This study only considered sites where monitoring was coincident with
flow monitoring, otherwise a flux calculation would have been impossible; data were
21
also rejected from any year at any site where there were fewer than 12 samples in that
year. Additional data for water colour were converted to DOC concentration by
calibrating water colour against DOC concentration for the individual sites using
techniques developed by Worrall and Burt (2007).
A wide range of methods have been proposed for calculating river fluxes from
concentration and flow data (e.g. De Vries and Klavers 1994). For water quality
parameters with a strong seasonal component such as DOC or water colour, Littlewood
et al. (1998) recommend the use of “method 5” where data are relatively sparse.
However, HMS sampling is generally aperiodic and “method 5” assumes regular
sampling. Therefore, an alternative has been proposed here that accounts for differing
sampling frequencies:
N
Fy = K ∑ nC i Qi
(i)
1
ny =
Ay
Ny
(ii)
Where: F = the annual flux at the site; Ci = the measured concentration at the site at
time i; Qi= the river discharge at time i; K = a conversion factor which takes into
account the units used; Ny = the number of samples at the site in that year; and Ay =
the number of days in that year, i.e. this can vary with a leap year.
Catchment properties assessed included soil, land-use, and hydrological characteristics.
The dominant soil of each 1 km2 grid square in Great Britain was classified into mineral,
organic-mineral, and organic soils as previously defined. The land use for each grid
square was classified into arable, grass, and urban, based on the June Agricultural
Census for 2004. In addition, the numbers of cattle and sheep in each 1 km2 were
counted using the census data. The catchment area to each monitoring point for which
DOC flux information was available was calculated from the CEH Wallingford digital
terrain model which has a 50 m grid interval and a 0.1 m altitude interval. Soil and land
use characteristics based on 1 km2 were summed across the catchment areas to the
monitoring points for which DOC flux information was available. It was also possible to
give a range of hydrological characteristics for each catchment. The hydrological
measures used were: the base flow index (BFI; Gustard et al. 1992), the average actual
evaporation, and the standard average annual rainfall for each catchment for which DOC
flux data were available.
3.1
Statistical treatment of the data
The DOC data were compared to catchment characteristics in a number of ways. First,
the DOC data were considered both as the average annual flux for the catchment for the
22
period 2001 to 2007 and as the average annual export (flux/area). Multiple linear
regression was used to compare both the average annual flux and the average annual
export to catchment characteristics. The regression was used to assess the relationship
between average flux, or average export, and the size of the catchment on the basis
that if there are significant in-stream losses this should be discernible from the
relationship between total flux, or average export. In order to judge the relationship
between flux or export, and area, the best-fit significant model was calculated. If the
best-fit model included catchment area then the model was recalculated excluding
catchment area and the residuals of that model were compared to the catchment area.
In using regression to filter the DOC data for effects other than that of catchment area,
care was taken to consider information that was a proxy or collinear with catchment
area, e.g. area of arable land in a catchment is a collinear with catchment area, but this
is less true for percentage arable area within the catchment. For any statistically
significant model derived from the multiple linear regression, an analysis of residuals
was performed where a standardised residual (residual divided by its standard deviation)
greater than 2 was considered an outlier and worthy of further investigation.
Second, the average annual flux, or average export, was compared only with those soil
and land use characteristics that are mappable across Great Britain, i.e. any significant
relationship found can be applied across Great Britain and then summed across the
country in order to estimate the total UK flux. It should be noted that DOC export data
were only available for Great Britain and not for Northern Ireland, so it is only possible
to map DOC export across Great Britain. However, land use and soil summaries are
available for the whole of the UK and so an estimated total DOC flux from the country
can be made.
Finally, both average annual flux and export were compared to catchment characteristics
using principal component analysis (PCA) in order to assess whether groups or clusters
of catchments existed in the data or whether multiple linear relationships exist within the
dataset. The PCA was carried out using percentage land use and soil characteristics so
that the influence of, and collinearity with, catchment area was minimised. Only the
principal components with an eigenvalue >1 and the first with an eigenvalue <1 were
considered.
The study updates the papers of Worrall and Burt (2007) and Worrall et al. (2009) who
both used HMS data in order to estimate the DOC flux from Great Britain, the former to
2003 and the latter to 2005. Data from the HMS is now available to the end of 2007, so
this study first used the same technique as the previous papers in order to update the
DOC flux record for the UK and provide a comparison for flux calculations based upon
extrapolation from linear models based on catchment characteristics.
23
3.2
Results
Between the years 2000 and 2007 it was possible to calculate a flux for 169 catchments
for which complete land use and soil characteristics are available (Figure 3.1). The range
of DOC export values in the 169 study catchments varied between 0.1 and 11.8 tonnes
C km-2 yr-1 (Table 3.1). A qualitative survey of the data shows that if the extremes are
considered then mineral soils have the lowest DOC export, but that the organic-mineral
soils appear to have a higher DOC export than the catchments with 100% organic soils
(Table 3.1). For comparison of land uses there is no individual catchment that has a
single land use as defined by this study. Despite this, by considering the catchments
with the maximum of a type it would appear that arable land use has distinctly lower
DOC export than either grass or urban land use. However, it will only be possible to
understand significant end-members and export coefficients for DOC export after
multivariate analysis.
Table 3.1
The annual average DOC export from catchments with the
extreme values as defined by dominant soil or land use
characteristics used in this project
Catchment characteristic
100% Mineral soil
100% Organic-Mineral soil
100% Organic soil
71% Arable, 13% Grass
78% Grass, 4% Arable
36% Urban, 30% Arable
Max. Export
Min. Export
Rivers
Upper Hull, Lee
Nant-y-Fendrod
Mawddach, Ogwen
Lower Hull
Taf
Tame
Wyre
Stour
24
DOC export (tonnes C km-2 yr-1)
0.5 – 1.2
6.4
4.0 – 4.3
1.3
4.6
4.8
11.8
0.1
Figure 3.1
Location of monitoring points for which a DOC export could be
calculated for the period 2001-2007
Using multiple regression the best-fit model for the average DOC flux was developed
(Equation i).
Only variables that were found to be significant at least at the 95% level are included in
equation (i) and the numbers in the brackets are the standard errors of each coefficient.
Note that equation (i) implies that arable land is an active sink of DOC and, furthermore,
that there is no loss of DOC with increased catchment area as there is no significant
effect due to catchment area. In terms of the annual average DOC export, the best-fit
equation is Equation ii.
7.4
4.4
6.9
(3.7)
3.5
2.6
2.5
3898
(0.7)
(1.1)
25
(0.3)
(0.4)
(0.4)
(1900)
(Eq. i)
r2 = 0.87, n = 169
Where: DOCflux = the average annual DOC flux (tonnes C yr-1); Evapact = actual annual
evaporation (mm yr-1); Arable = the area of arable land in the catchment (km2); Urban
= the area of urban development in the catchment (km2); Mineral = area of mineral
soils in the catchment (km2); OrgMin = the area of organic-mineral soils in the
catchment (km2); and Organic = the area of organic soils in the catchment (km2).
:
2
0.03%
(1.0)
0.05%
(0.01)
0.03%
(0.02)
0.024%
(0.01)
(0.008) (Eq. ii)
r2 = 0.38, n = 169
Where: %X = the percentage of a given land use (Arable, Urban or Grass) or soil type
(Organic), where these terms have the same meaning as for equation (i). Again the
percentage of arable land appears to be a sink of DOC and there is no significant role for
the catchment area.
Neither equation (i) nor (ii) could be directly extrapolated or mapped across Great
Britain. Therefore as an alternative approach, only mappable variables were included in
the multiple regression analysis, in which case the best-fit equation was:
3.8
4.8
(1.0)
2.7
(0.7)
2.7
(0.3)
6.7
(0.4)
(0.4) (Eq. iii)
r2 = 0.87, n = 169
Equation (iii) could be interpreted as an export coefficient type model. However, as
above, the catchment area is not a significant variable. It is possible that other land use
descriptors are collinear with catchment area, i.e. the extent of arable land increases
with increasing catchment area. Therefore, the negative coefficient for area of arable
land (Arable) may not reflect an adsorption of DOC from that land use; rather, it is a
proxy for DOC loss with increasing scale. Therefore, as an alternative approach, a model
for DOCflux was calculated that definitely included catchment area and then other land
use and soil characters were added if they make a significant improvement. On this
basis the best-fit equation is:
6.7
2.4
2.6
3.4
26
9.2
2.7
(1.1)
(0.6)
(0.5)
(0.5)
(0.6)
(0.6)
(Eq. iv)
r2 = 0.86, n = 169
Where: Area = the catchment area (km2).
Although this equation has a marginally worse fit than equations (i) and (iii) it has a
suite of coefficients and significant variables that are more physically interpretable and
mappable. Equation (iv) can now be physically interpreted, first as an export coefficient
model and, second, it demonstrates a significant role for in-stream losses. Equation (iv)
no longer predicts that arable land is a significant sink of DOC export and indeed does
not predict that it has a significant effect at all. The coefficients of the equation can be
directly interpreted as export coefficients (Table 3.2), e.g. the equation predicts that the
DOC export from 1 km2 of organic soils would be 9.2 ± 0.6 tonnes C km-2 yr-1 where the
quoted error is the standard error in the coefficient. Equation (iv) shows, as would be
expected a priori, that mineral soils are a smaller source of DOC than organic-mineral
soils which are in turn a smaller source than organic soils (Table 3.2). Furthermore, and
as would be expected, the organic soils are a source of DOC more than twice as strong
as the organo-mineral soils. Likewise, equation (iv) predicts that both urban and grazed
land are sources of DOC, with urban land area being a substantially larger source. The
significant role and source size for urban land as indicated by equation (iv) justifies the
approach taken by this study in not correcting for waste effluent sources of DOC, as the
regression analysis has in effect accounted for it. Of course, grazed land also has a soil
type and so grazed land on a mineral soil would be predicted to be releasing DOC at 5.0
tonnes C km-2 yr-1.
As catchment area increases the DOC export would decrease (equation iv), which is
direct evidence for in-stream losses of DOC. Equation (iv) suggests that the in-stream
loss of DOC is linear with catchment scale. However, the approach taken in the
derivation of the equation only allows for a linear response. Therefore, equation (iv) was
recalculated using all variables except Area and then the residuals of this new equation
were plotted against the catchment area in order to assess the nature of the
relationship. This process showed that a linear fit was reasonable and no other
relationship was suggested.
Equation (iv) can be used both to map DOCflux on a 1 km2 grid square and to give an
estimate of the total DOC flux from the UK (Figure 3.2; Table 3.2). The map shows that,
as expected, the areas of high organic soils in the west or north of Great Britain are the
important sources of DOC, but urban centres and the lowland peat soils of Eastern
England also show up as discrete hot spots of DOC export. Using the export coefficients
it is possible to assess the contribution of each significant soil and land use type to the
27
overall flux of DOC from the UK. In this case it can be seen that organic soils represent
by far the largest source of DOC, but the loss of DOC in-stream is larger still.
Figure 3.2
The annual average DOC export for each 1 km2 across Great
Britain.
Table 3.2
The export coefficient for each significant land use and soil
type and the predicted DOC flux from that soil type or land use
when considered across the entire UK. The upper and lower
estimates are based upon the standard errors in the export
coefficients given in equation (iv).
Soil type or
land use
Mineral soils
Export
coefficient
(tonnes C km2
yr-1
2.6
DOC flux (ktonnes
C yr-1)
253.6
28
Upper
estimate
(ktonnes C
yr-1)
302.4
Lower
estimate
(ktonnes C
yr-1)
204.9
Soil type or
land use
Organic-mineral
soils
Organic soils
Urban
Grazing
Area
Total UK flux
Export
coefficient
(tonnes C km2
yr-1
3.4
9.2
6.7
2.4
-2.7
222.5
Upper
estimate
(ktonnes C
yr-1)
261.8
Lower
estimate
(ktonnes C
yr-1)
183.2
588.6
234.5
268.6
-658.8
909.0
627.0
273
335.8
-536.8
1263.1
550.2
196
201.5
-780.8
554.9
DOC flux (ktonnes
C yr-1)
The residual analysis of equation (iv) and application of a critical absolute magnitude to
the standardised residual value suggests that there are five catchments that are underpredicted (Rivers Ribble, Weaver, Tyne, North Tyne and Tame) and three catchments
that are over-predicted (Rivers Dee, Avon and Severn). It is difficult to discern common
features between those catchments that are under-predicted but those that are overpredicted are catchments that are some of the largest in the dataset.
The results including average annual export were inconclusive and only those involving
the average annual DOC flux are analysed here (Table 3.3). An examination of the
loadings on the principal components shows that it is principal components 2 and 3 that
have high magnitude loadings for the average DOC flux while for PC1 and PC4 the
magnitude of the loading for the average DOC flux was less than 0.04. For PC2 the
pattern of loadings suggests that average DOC flux is correlated with catchment area,
i.e. this component represents the increase of DOC with catchment area. The pattern of
loadings on PC3 correlates DOC flux with %Urban and %Organic but is negatively
correlated with %Grass, Sheepeq km-2 and %Mineral. A plot of PC2 and PC3 showed that
all the sites fall within a clear area bounded by two trends (OA and OB – Figure 3.3).
The catchments plotting at point O are the Rivers Yeo and Taf, i.e. small catchments
with a large proportion of grazing (Table 3.1). Point A is the River Severn, i.e. a large
river with a large DOC export, and point B is the River Tame, a catchment with the
largest urban proportion (Table 3.1). Therefore, it could be that the PCA is not revealing
details of the DOC export but represents contrasts in catchment characteristics in terms
of size, land use and soil types. The use of PCA has therefore illustrated that there are
no groupings or clusters of catchments with respect to DOC export, and thus a linear
model, albeit a multivariate linear regression, is appropriate.
Table 3.3
Loadings on the first four principal components
eigenvalues >1 and the first with an eigenvalue <1.
Variable
Average DOC flux
%Arable
%Urban
%Grass
Sheepeq km-2
PC1
0.04
-0.43
-0.23
0.29
0.40
PC2
-0.65
0.01
0.11
-0.22
-0.21
29
PC3
-0.30
0.18
-0.36
0.55
0.40
PC4
-0.02
0.39
-0.78
-0.34
-0.13
with
Variable
%Mineral
%Organic
Runoff (mm)
Area (km2)
Cumulative variance
explained (%)
PC1
-0.39
0.40
0.44
-0.10
44
PC2
-0.1
0.07
0.15
-0.66
64
PC3
0.34
-0.34
-0.15
-0.19
80
PC4
0.04
0.29
0.10
0.10
90
PC3
4
O
3
2
1
PC1
‐10
‐8
‐6
0
‐4
‐2
0
2
4
‐1
A
‐2
‐3
B
‐4
Figure 3.3
3.3
Comparison of PC2 and PC3. For the meaning of the letters
refer to the text.
Discussion
The flux of DOC from the UK as predicted by this study accords with that calculated by
other studies using less recent monitoring data. Worrall et al. (2009) calculated the
DOC flux for the UK for 1974 to 2007 using the method and updating the earlier study of
Worrall et al. (2007), and the average DOC flux of 827 ± 256 ktonnes C yr-1. In
comparison to other major European drainage basins (Table 3.4) it can be seen that the
UK is a “hotspot” of DOC export. However, if values were available for countries with
more extensive peat areas, e.g. Sweden, then the picture might be different.
30
Table 3.4
Comparison of export values of nitrogen and carbon species
for major Western European rivers and the river with the
largest DOC export in the world as reported by Alexander et
al. (1998) with values derived for the UK from this study.
River basin
Size (km2)
DOC (kg C km-2 yr-1)
Danube
817000
1.21
Elbe
148000
0.81
Po
70000
3.01
Rhine
164500
1.41
Seine
7390
0.91
Nushagak
25000
6.91
UK
244000
2.5 – 8.22
UK (This Project)
244000
2.3 – 5.2
Source of the comparative data, 1Alexander et al. (1998), 2Worrall et al. (2009).
However, this project has gone one step further than these previous studies. All studies
cited above give values of the DOC flux at the tidal limit, and not at the source as the
DOC leaves the terrestrial biosphere. In the case of this study, it is possible to assess the
scale of the source in which the DOC flux at source is 1568 ±232 ktonnes C yr-1, or 6.4
tonnes C km-2 yr-1. Worrall et al. (2007) attempted to estimate the loss of DOC at source
for England and Wales only by assuming that the BOD (Biological Oxygen Demand) flux
at the tidal limit represents the capacity of the fluvial network to remove DOC; in this
case the DOC export at source is equivalent to 5.6 tonnes C km-2 yr-1. However, this
latter approach assumes a fluvial residence time in the UK river network of 5 days, i.e.
the length of the standard BOD measurement incubation. Furthermore, it is not very
surprising that this study gives a larger value of DOC export source than the previous
study, as this present study was able to include Scotland, which has a greater proportion
of organic soils.
However, it is possible that the DOC flux estimated at source is still an underestimate as
the catchments considered here are never smaller than 40 km2, and so this study makes
an extrapolation to smaller catchments based on DOC losses for catchments between 40
and 9898 km2. There is field evidence to suggest that in-stream losses of DOC are
concentrated in the zero and first-order streams. Worrall et al. (2006) have shown that
40% of DOC is lost across an 818 km2 catchment, but that 32% of that loss is in the first
11.5 km2. In this study the percentage loss varies with the soil and land use type and it
has been possible to identify an export coefficient for significant land use types.
The question for this work package in the context of the overall project is, could DOC
export ever give an indication of the magnitude or status of the carbon budget of the
terrestrial environment? Firstly, there is now doubt that DOC export is a vital component
of the terrestrial carbon budget. However, this study suggests that there is still some
way to go in using the present routine monitoring of DOC as an indicator of terrestrial
carbon budgets. The reason for this is that this study can only go to 40 km2 resolution
31
and we have demonstrated that a large proportion of the DOC may already have been
lost from the system before that scale. However, there is another possibility: a number
of studies of the net ecosystem exchange of CO2 (NEE) are now well advanced
especially for peat soils. In general, for any soil to be a net sink of carbon the following
must be true:
(Eq. v)
.
Where: NEE = net ecosystem exchange of CO2 (tonnes C km-2 yr-1) = the flux due to
DOC, POC, CH4 and diss.CO2 (tonnes C km-2 yr-1).
If it were ever found that the DOC > NEE then that catchment, soil or ecosystem could
not be a net sink of carbon regardless of values of POC, CH4 or diss.CO2 flux. Given our
improving knowledge of NEE across the UK and for different management and land use
settings, it could soon be possible to know what magnitude of DOC export is critical in
defining whether an ecosystem is a net sink or net source.
3.4
Summary
The work package has considered records of DOC export from rivers in Great Britain
and, by characterising the catchments in which these records were made, has been able
to identify the controls on DOC export and provide significant models for explaining this.
This work package has:
•
Developed models that explained 87% of the variation in DOC flux across 169
catchments;
•
Found significant export coefficients for urban and grazed land, as well as for
mineral, organo-mineral and organic soils;
•
Found a significant decline in DOC flux with increased catchment area, and that this
decline is linear across catchment areas from 40 to 9848 km2;
•
Mapped DOC export across Great Britain; and
•
Estimated that the average annual DOC flux from the UK was 909 ± 354 ktonnes C
yr-1, although the loss of DOC at source was 1568 ± 232 ktonnes C yr-1.
It is still probable that the fluxes calculated here are underestimates of the actual DOC
flux and, although this study can propose a method for connecting DOC flux to the
status of the terrestrial carbon budget, it was not possible to go that far within current
datasets.
This work package has identified data gaps in relation to the flux of DOC and has
developed a methodology for their measurement, resulting in an estimate of DOC flux
32
for the whole of Great Britain extrapolated from catchment data distributed across the
country. However, these data have not at this stage been incorporated into the decision
tool as there are too many uncertainties associated with the outputs.
33
4
WORK PACKAGE 3. ACCOUNTING FOR ALL
THE CARBON
A number of key areas have been identified that are poorly accounted for in terms of
national assessments of soil C stocks and fluxes. These areas do not regularly fall within
the remit and modelling estimates of research, where the focus is on either lowland,
predominantly mineral soils, or upland peats. The two main areas of potential
uncertainty are organic-mineral soils and, particularly, salt marshes, although there are
others, such as woodland soils, which also merit consideration.
The importance of these areas for soil C stocks and fluxes has not been clearly
understood to date. The aim of this work package was to make a quantitative
assessment of the importance of these land use areas and, if appropriate, to include
them within the decision tool as a potential additional category to lowlands and uplands.
This work package is aimed at ensuring that all the key soil type/land uses in terms of C
stocks and fluxes are included in the decision tool in Work Package 5.
4.1
Organic-mineral soils
Organic-mineral soils are extensive throughout the UK (~30% of total soil cover) and
make a significant contribution to UK soil carbon stocks and fluxes, in particular through
N2O emissions and DOC releases. The soils are typified by shallow upper soil horizons
overlying mineral or rock and are particularly prevalent in Scotland and Wales, as shown
in Table 4.1. Various regional definitions are used for classifying organic-mineral soils
across the UK (e.g. Smith et al. 2007a, Lilly et al. 2009, Holden et al. 2007). All are
based on the characteristics of soil profiles and identify organic-mineral soils as those
with significant organic matter content in relatively shallow upper soil horizons. In
England and Wales organic-mineral soils are those with organic surface horizons <40 cm
thick, overlying mineral horizons or rock; the major soil sub-groups associated with such
soils in England and Wales are stagnohumic gleys, humic gleys, humic sandy gleys, and
humic alluvial gley soils. In Scotland, organic mineral soils have organic surface horizons
<50 cm thick overlying mineral horizons or rock, with the organic surface horizon often
anaerobic and under waterlogged conditions. Many humus-iron podzols are now
cultivated and no longer have an organic surface horizon due to incorporation with the
underlying mineral horizons. These soils are mostly humus-iron podzol (uncultivated),
peaty podzol, subalpine podzol, alpine podzol, peaty gley, humic gley, peaty ranker
(including podzolic ranker), peaty lithosol, and peaty alluvium.
Table 4.1 highlights that organic-mineral soils are fairly extensive throughout the UK,
particularly Scotland. The determination of baseline soil carbon stocks and fluxes in
these soils would provide an indication of their importance and the need for their
inclusion as a separate category in the tool.
35
Table 4.1
Extent of organic-mineral soils in England, Wales and Scotland
(% of total land area for each country)
England1
Wales1
Stagnopodzol
1.8
7.3
Stagnohumic gley
Humic alluvial gley
Humic sandy gley
Humic gley + humic rankers
Podzol
Humus iron podzol
Peaty podzol
Subalpine podzol
Alpine podzol
Peaty gley
Humic gley
Peaty ranker
Lithosol
Peat alluvium
All organic-mineral soils
3.5
1.0
0.1
0.2
7.3
Soil type
Scotland2
1.4
1.3
10.8
15.5
4.9
0.7
21.8
0.1
0.9
<0.1.
6.6
17.3
54.7
1. Source: Holden et al., 2007. Derived from Landis, England and Wales.
2. Source: Scottish soil survey data, Macaulay Land Use Research Institute.
Several recent reports have assessed the significance of organic-mineral soils with
respect to soil carbon stocks and greenhouse gas fluxes (Dawson and Smith 2007,
Holden et al. 2007, Lilly et al. 2009a, Lilly et al. 2009b, Ostle et al. 2009, Smith et al.
2007a, Smith et al. 2009, Tomlinson and Milne 2006). The primary aims of this study
were as follows:
•
Evaluate and revise, if appropriate, the published soil carbon stocks for organicmineral soils used for inventory assessments (Bradley et al. 2005) using recently
published information on soil C stocks of organic-mineral and organic soils.
•
Examine the influence of soil parameters on the relative importance of carbon stocks
in organic-mineral soils. Several recent studies have reviewed variations in soil
carbon stocks. These include soil depth, bulk density, spatial heterogeneity and
analytical methods.
4.2
Methods for organic-mineral soils
The study utilised soils information generated in Work Package 1, which provided profile
and topsoil data via the soil C and digital soil maps for each country. Data sources
included several new or updated nationwide soils datasets, such as the National Soil
Inventory for England and Wales (NSIEW); National Soil Inventory for Scotland (NSIS);
36
AFBI Soil Survey for Northern Ireland (AFBI); Representative Soil Sampling Scheme;
Resurvey of British Woodlands; and the Countryside Survey of Great Britain. All sampling
schemes contain data which can be used to assess the size and significance of soil
carbon stocks in organic-mineral soils. Profile data from the NSIEW, NSIS and AFBI have
been used previously to generate UK soil C stocks (Bradley et al. 2005).
Characteristic soil profile descriptions for soil series in each country, along with typical
values for soil properties, their ranges, and derived functional values were obtained from
various surveys and sampling schemes across UK. This was used by Bradley et al.
(2005) to calculate soil C stocks across the UK and was subsequently incorporated into
the Defra Soil Carbon database. Representative profiles were made available for
England/Wales and Scotland from Task 1. These are not entirely consistent with the
Defra Soil Carbon database and were not used to generate equivalent UK soil C stocks.
Bulk density (g cm-3), soil carbon value (%) and depth (cm) are the primary components
of soil carbon stock calculations, along with spatial extent (km2). To explore the
significance of variation in bulk density and soil carbon values, three representative soil
series profiles were selected from the National Soil Inventory Profile Database (Table
4.2). The profiles reflect the typical characteristics of a mineral, organic-mineral and
organic soil. These profiles were used to explore how variation in soil parameters could
alter the contribution of organic-mineral soils to the total UK soil C stock. Spatial extent
was assumed to be 73.3%, 19.8% and 6.9% of the UK for mineral, organic-mineral and
organic soils respectively (c.f. Bradley et al., 2005).
Table 4.2
Soil
type
Representative series profiles for mineral, organic-mineral and
organic soils taken from the National Soil Inventory for
England and Wales. Profiles are for permanent grasslands.
Soil series
Soil
subgroup
Mineral
CREDITON
5.41
Organicmineral
WENALLT
7.21
Organic
MENDHAM
10.24
Horizon*
Depth
(cm)
LOI
(%)
C
(%)
Clay
(%)
Silt
(%)
1
2
3
4
1
2
3
4
5
6
1
2
3
20
35
30
65
20
15
15
20
20
60
30
30
90
6.4
2.2
0.8
0.2
44.6
4.6
1.8
0.8
0.6
0.4
50
96
82
3.2
1.1
0.4
0.1
22.3
2.3
0.9
0.4
0.3
0.2
25
48
41
18
17
14
9
26
25
17
18
18
13
0
0
0
43
41
31
22
36
58
58
53
55
66
0
0
0
*Horizons characterised according to respective soil surveys for England/ Wales and Scotland.
37
Bulk density (dry weight) was estimated by commonly used “pedo-transfer” equations
which use available data on other soil properties. Four approaches were used within this
study to estimate bulk density:
(A) bD =1.3-(0.275*LN(SOC%)). This equation has been widely used to calculate
soil C stocks across Britain (Howard et al. 1995, Bradley et al. 2005, Bellamy et
al. 2005).
(B) The approach adopted by Smith et al. (2009) to improve estimates of carbon
stocks in organic and organic-mineral soils where;
1) For horizons where soil organic carbon values <28%:
bD = 1.5202 - (0.04716*SOC%)+(0.01251*clay%)-(0.00456*silt%)[eq A]
2) For horizons with soil organic carbon value >20-28%:
bD= 1.77*LN(SOC%)
[eq B]
(C) Equation derived from Countryside Survey 2007 (Emmett et al. 2010), adapted
to SOC%:
bD = (1.29*EXP(-0.0206*(SOC%*10)))+(2.51*EXP(-0.0003*(SOC%*10))-2.057)
[eq C]
(D) An equation derived from the long-term Cockle Park experiment (Shiel and
Rimmer 1984):
bD = 1.62-(0.82*LOG10(SOC%))
[eq D]
Where:
bD = bulk density
LN = natural log
LOG10 = log to the base 10
EXP = exponential
The only source of consistent measurements of bulk density across Great Britain comes
from Countryside Survey 2007. These data were obtained from the top 15 cm of the soil
profile and are directly comparable to Countryside Survey 2007 values for soil carbon.
Soil carbon stocks (t C ha-1) were calculated for each horizon of the three soil profiles
using representative mean percentage data for soil carbon, sand, silt, and clay and all
four equations for bulk density estimation. Measured bulk density values from
Countryside Survey 2007 were also included for corresponding soil carbon contents. The
horizon data were summed to generate the total density of carbon in each
38
representative soil profile (t C ha-1) using each bulk density method. Profile data were
then multiplied by area to generate illustrative soil C stocks for mineral, organic-mineral
and organic soils in Great Britain using the five bulk density methods. This comparison
assessed the influence of bulk density and soil carbon data on data for soil C stocks
rather than an actual calculation of soil C stocks for the country. This would require use
of the original Defra soil C database to recalculate stocks for all representative soil series
using the relative proportions of soil series, along with land cover, in 1 km2. This is an
intensive task beyond the scope of the current study.
4.3
The significance of carbon stocks in organic-mineral
soils
The primary reference for UK soil C stock estimates is Bradley et al. (2005), who
estimated the total UK soil C stock as 1019 Tg based on a unified soil depth of 1 m with
the equation for soil bulk density based on Howard et al. (1995). Smith et al. (2007) reestimated the soil C stocks for organic and organic-mineral soils in Scotland and Wales
by incorporating further information on the depth of organic soils and applying an
alternative soil bulk density equation. Table 4.3 compares the outcome of these two
studies and shows that there is approximately a 1240 Tg difference in mean UK soil C
stock estimates, which is primarily accounted for in the revised depth estimates for
peats of >1 m depth in Scotland and Wales. Smith et al. (2007) also estimated the
stocks for organic-mineral soils to be approximately 20% higher (228 Tg) than Bradley
et al. (1995), although there is little variation in the percentage contribution of organicmineral soils to the UK’s total soil carbon stock (21.5% compared to 22.3%). The main
change is a reduction in the relative contribution of mineral soils to the UK’s total soil C
stock, with the greater stock values for organic soils (Table 4.4).
Table 4.3
Estimates for UK stocks of soil carbon, with a focus on
organic-mineral soils
Data sources
From Bradley et al. (2005). Stocks
estimated to 1 m depth.
Revised UK estimates: Bradley et
al. (2005) for England; revised
Scotland & Wales data from Smith
et al. (2007); Cruickshank et al.
(1998) for Northern Ireland.
Country
England
Wales
Scotland
Northern Ireland
UK
England
Wales
Scotland
Northern Ireland
UK
39
Total soil C
stock
(Mean Tg)
1740
340
2187
296
4562
1740
410
3263
386
5799
Organicmineral soils
(Mean Tg)
167
59
754
39
1019
167
74.5
957
48
1247
Table 4.4
Data
sources
Relative contribution of organic-mineral soils to the UK’s total
soil carbon stocks
Country
Soil C stock as % of country
stocks
OrganicOrganic
Mineral
mineral
9.6
17.0
65.9
17.4
19.7
53.8
34.5
41.3
22.8
13.2
30.4
55.7
England
Wales
Bradley et
Scotland
al. 2005.
NI
UK
England
9.6
17.0
Revised
Wales
18.2
29.6
UK
Scotland
29.3
54.5
estimates.
NI
12.5
42.0
1
UK
1. Bradley et al. (2005) for England; revised
Cruickshank et al. (1998) for Northern Ireland.
Soil C stocks as % of UK
stocks
Organic
Organic
Mineral
-mineral
3.7
6.5
25.1
1.3
1.5
4.0
16.5
19.8
10.9
0.9
2.0
3.6
22.3
29.7
43.7
65.9
2.9
5.1
19.8
44.7
1.3
2.1
3.2
15.3
16.5
30.7
8.6
45.2
0.8
2.8
3.0
21.5
40.7
34.4
Scotland & Wales data from Smith et al. (2007);
4.4
Influence of soil parameters on the carbon stocks of
organic mineral soils
4.4.1
Bulk density
Figure 4.1 illustrates the influence of the four different estimates of bulk density as well
as measured bulk density on the estimates of soil carbon stocks in representative
profiles. This illustrates that the soil carbon stock estimates for the representative profile
for an organic-mineral soil are not greatly influenced by the application of different bulk
density calculations and that far greater variability occurs when they are applied to
organic soils (Figure 4.1). This variability is subsequently transferred to the calculations
of UK soil carbon stocks (Figure 4.2).
40
1800
Total C t ha for entire soil profile
1600
1400
1200
1000
800
600
400
200
organic
0
organo-mineral
meas
ECOSSE
Howard
mineral
CS
bulk density estimation method
Figure 4.1
soil type
Shiel
Influence of bulk density equations on C stocks of
representative soil profiles. Meas=measured CS2007;
Ecosse=Smith et al. 2009; Howard=Howard et al. 1995;
CS=Emmett et al. 2010; Shiel=Shiel and Rimmer 1984.
7000
organic
organo-mineral
mineral
6000
simulated soil C stock (Tg)
5000
4000
3000
2000
1000
0
measured
ECOSSE
Howard
bulk density equations
41
CS
Shiel
Figure 4.2
Influence of bulk density equations on the contribution of
organic-mineral soils to simulated soil C stocks, using NSI_EW
representative soil profiles, CS2007 soils data and area of soils
from Bradley et al., 2005. Meas=measured from CS2007;
Ecosse=Smith et al. 2007b; Howard=Howard et al. 1995;
CS=Emmett et al. 2010; Shiel=Shiel and Rimmer 1984.
Most of the estimates of soil bulk density used in this study assume that bulk density is
constant for a given soil organic carbon content. However, Figure 4.3 compares
measured bulk density values and soil organic carbon content from the Countryside
Survey (2007). This shows that there is a wider variation in bulk density that is not
necessarily accounted for in estimated bulk density calculations. One exception is the
Ecosse equation (Equation A; Smith et al. 2009), which accounts for variations in silt and
clay content when assessing bulk density.
2.0
1.8
1.6
bulk density (g cm3)
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0
10
20
30
40
50
60
soil carbon content (%)
Figure 4.3
Relationship between measured bulk density and soil carbon
values from Countryside Survey 2007. Data from NERC.
The variation between measured and estimated bulk densities from Countryside Survey
2007 were also used to investigate the sensitivity of soil C stocks to bulk density. Table
4.5 illustrates ~95% upper and lower confidence intervals associated with estimated
bulk density data included within Countryside Survey 2007. These intervals fall within
the ranges of values generated by the different bulk density equations at lower SOC
contents, but are lower than the estimates from other equations at mid-range SOC
42
contents (~28% and ~45%). At this range, there is the greatest variation in estimating
bulk density using the four equations (max. 0.44 g cm-3 and min. 0.25 g cm-3). However,
the measured data for these soil organic carbon contents are in line with the
Countryside Survey data.
Table 4.5
Comparison of measured bulk density (g cm-3) with estimated
values and ranges from Countryside Survey 2007 and values
from other bulk density equations.
SOC
(%)
Measured
(g cm-3)
CS eqn
Upper
range
Lower
range
Ecosse
Howard
Shiel
2.53
27.50
45.1
1.31
0.21
0.18
1.20
0.26
0.14
1.00
0.18
0.06
1.40
0.34
0.22
1.45
0.40
0.19
1.05
0.39
0.25
1.29
0.44
0.26
Figure 4.4 illustrates the effect of this range in estimates of bulk density on calculations
of soil carbon stocks by profile, while Figure 4.5 shows their influence when applied to
national calculations of soil carbon stocks. This variability has a significant effect on
organic and, to a lesser extent, on the organic-mineral soils. These intervals have an
obvious influence on the C stock of the representative profile for the organic soils and,
to a lesser extent, the organic-mineral soils. These results clearly demonstrate that
variation in bulk density will alter the size of the UK soil stock and, to some degree, the
contribution of organic-mineral soils to this stock, although this relative contribution is
affected primarily by changes in the stocks of organic and mineral soils rather than
changes to the stock of organic-mineral soils. The greatest variability in the results
would appear to be generated through the relatively poor capacity of all equations to
predictive the bulk density of soils with carbon content >~15%. The development of a
more robust equation is hindered, in part, by the lack of measured bulk density and
associated soils data within the range 15 to 45% SOC. However, the intrinsic variation in
bulk density would appear to have a far greater influence than differences between
individual estimation methods; at high SOC values this may reflect variations in sampling
and analytical efficiencies along with properties such as stoniness and moisture content,
rather than clay and silt content.
43
2000
organic
organo-mineral
mineral
soil carbon (t ha)
1500
1000
500
0
Lower
Figure 4.4
CS eqn
Upper
Sensitivity of carbon stocks (t ha-1) in representative soil
profiles to intrinsic variation in bulk density. CS Eqn = stocks
calculated using the CS bulk density equation; Lower and
Upper = CS bulk density equation +/- ~95% intervals.
proportion (%)
60
50
40
30
20
10
mineral
organo-mineral
0
organic
U
Eqn
L
Figure 4.5
soil type
Sensitivity of total soil carbon stocks (% Tg) to the intrinsic
variation in bulk density. CS Eqn = stocks calculated using the
CS bulk density equation; Lower and Upper ranges = CS bulk
density equation +/- 95% intervals.
44
4.4.2
Soil carbon content
Variation in soil carbon contents between nationwide soil sampling schemes may be due
to analytical methods as well as conversion factors. The variation in estimates of
percentage soil carbon content has remarkably little influence on the carbon stocks of
the representative soil profiles, including that of the organic-mineral soil. This, in turn,
has little effect on the total soil carbon stocks or the relative contribution of organicmineral soils to these stocks. Variations in the estimates of soil carbon content account
for ~10% of the variation in total soil carbon stocks from the representative profiles for
mineral, organic-mineral, and organic soils. This variation is likely to increase if
representative mean values for soil series are used to calculate soil carbon stocks,
particularly where higher estimates of soil organic carbon are used.
There are distinct distributions for soil carbon values between the different sampling
schemes, reflecting regional differences in the soils being sampled. For example,
Countryside Survey and NSIS contain a greater proportion of soils with SOC >40%,
reflecting the predominance of organic soils in Scotland. However, the ranges of soil
carbon values are similar across all sampling schemes, with the exception of the second
sampling of the NSIEW which, unlike the first sample, does not have any soil carbon
values above 50%. It is impossible at present to separate out the causes of the
discrepancies in soil carbon values between the different sampling schemes or the
differences in soil carbon distributions within the NSIEW survey. There has been no
widespread use of a common Standard Reference Material (SRM) for the analysis of soil
carbon content or loss-on-ignition. This also makes it impossible to produce a
comprehensive soil carbon dataset for the UK using all available data sources. This could
be remedied by the analysis of a sub-set of samples from all schemes using the same
analytical method with an appropriate SRM. A focus would be on resolving the variation
in higher soil carbon values, in particular those between 20% and 40% SOC where there
are relatively few data points in the UK. Resolution of the causes of variation in soil
carbon values across the different sampling schemes would establish a means of directly
integrating all available UK soil carbon data and using the information to improve soil
carbon stock and change assessments for less well sampled soils, habitats and land
uses.
4.5
Summary of findings on organic mineral soils
Organic-mineral soils are responsible for ~21.5% of the UK soil carbon stock based on a
revision of country-level stocks using recent data from Scotland and Wales. This is a
small and relatively insignificant decline (-0.8%) on IPCC inventory estimates. Increases
in the estimates for organic soils are far more significant. If the current carbon stocks for
organic soils in England are underestimated due to depth and bulk density, then the
relative contribution of organic-mineral soils to UK soil C stocks will be less than 21.5%.
45
Further evaluations of the contribution of organic-mineral soils to UK soil stocks will
depend on revisions of stock estimates for organic soils, primarily in England.
Variation in bulk density can be introduced by using different estimation equations or by
different methods of field assessment. The variation in estimated bulk density will alter
the size of the UK soil carbon stock and the contribution of organic-mineral soils to this
stock. This relative contribution is affected primarily by changes in the stocks of organic
and mineral soils rather than changes to the stock of organic-mineral soils themselves. A
comparison of the estimation equations shows that the greatest differences appear to be
a result of the relatively poor capacity of all equations to predict the bulk density of soils
with carbon content >15%.
Intrinsic variation in bulk density has a greater influence than differences between
individual estimation methods. The Ecosse approach (Smith et al. 2007b) incorporates
other soil properties into the equation for soils with SOC <28% to characterise this
variation and could be extended to soils with >28% SOC. The development of a more
robust equation with relevant confidence intervals is hindered, in part, by the lack of
measured bulk density and associated soils data, particularly in the range 20% to 40%
SOC.
Actual and simulated variations in soil carbon values, explored using data from the
different sampling schemes, had little influence on the carbon stocks of individual
representative profiles for organic-mineral, organic, and mineral soils. However, using
soil carbon values from the different data sources would produce different UK soil C
stock estimates if using the comprehensive Defra Soil Carbon database approach
(Bradley et al., 2005). In particular, NSIEW and AFBI would produce lower stock
estimates for organic and organic-mineral soils than NSIS, CS and RSBW. These
differences would reflect lower estimates of soil carbon at >20%, i.e. ~ 40% soil organic
matter content.
Given the range and quantity of soil carbon data that are now available across the UK,
consideration should be given to resolving the current discrepancies between and within
sampling schemes. The ability to use and interchange all available data would go some
way to addressing data gaps in both bulk density and soil carbon values where soil
organic matter is greater than 30%, in particular between 32% and 80% SOM. All
schemes currently under-sample soils with these levels of SOM, which is reflected in the
accuracy of current bulk density estimation methods and the efficiencies of soil organic
matter conversions to soil carbon values. UK-wide compatible data would help to
improve estimation of stocks and changes in all soil types, including organic-mineral
soils.
46
Future evaluation of soil carbon stocks of organic-mineral soils should be carried out via
a detailed analysis of all representative soil series profiles for organic, mineral, and
organic-mineral soils using the comprehensive Defra Soil Carbon database.
Inventory-related values of fluxes from organic-mineral soils, in particular N2O and DOC,
would benefit from updating and consideration of regional versus national potential to
reduce fluxes based on intrinsic soil characteristics. This requires a comprehensive
assessment of information generated by recent experimental and modelling advances,
and application of new modelling approaches across the UK (e.g. Smith et al. 2007b,
Holden et al. 2007, Lilly et al. 2009).
4.6
Salt marshes and soil organic carbon
Salt marshes are located around the UK coastline and their extent is approximately
45,800 ha (JNCC 2005). Table 4.6 illustrates the distribution of salt marsh around the
UK; the majority is located in England, while Scotland and Wales have a similar area of
salt marsh and Northern Ireland has a very limited area (around 0.5% of the UK total).
Note that in Table 4.6 the UK total is greater than the sum of the totals from the
individual countries. This is because the data come from different surveys conducted at
different times. The UK survey dates from before 1990 and, although the data and
coverage of the survey were good, the information is now out of date due to habitat
loss. No work has been carried out to provide an updated estimate of total UK salt
marsh. The compatibility of the surveys is unknown so the validity of summing the data
from individual countries is unknown.
Hazelden and Boorman (1991) observe that salt marsh was once more extensive around
the coast of the UK than it is now, particularly in north Kent and East Anglia where much
has been reclaimed over the centuries to create farmland. The location and boundaries
of salt marshes are also naturally dynamic and represent a balance between landscape
morphology and water flows which influence sedimentation and erosion rates (Defra and
Environment Agency 2005). These influences on salt marsh extent will introduce a
degree of variability in the estimates of soil C stock in salt marshes.
Table 4.6
Country
England
Northern
Ireland
Scotland
Extent of salt marsh sites in the United Kingdom based on
data from Burd (1995) and JNCC (2005).
Area
(ha)
32500
Area
(%)
71.3
Date
Pre-1995
250
0.52
2005
6000
14.8
2003
Extent
Sample or
full survey
Partial
survey
Sample or
full survey
47
Adequate
data
No
Data source /
comments
Burd (1989)
Yes
NI HAP published
in March 2005.
Survey
incomplete and
most area-based
data and
No
Country
Area
(ha)
Area
(%)
Date
Extent
Adequate
data
Wales
5800
13.36
1997
Sample or
full survey
No
UK
45820
100
Pre-1995
Sample or
full survey
No
Data source /
comments
vegetation data
from 1980s.
CCW's Lowland
Habitat Survey of
Wales, 1987-1997
Pre-1990 survey
data and
coverage good,
but now out of
date. Habitat loss
known especially
in SE England.
Table 4.7 summarises current knowledge on trends in salt marshes across the UK.
Overall, the area of salt marsh has been declining across the country, with the exception
of Northern Ireland. However, it should be noted that in all countries, except Northern
Ireland, the available data on both trends and extent are considered inadequate. Field
surveys, in combination with aerial or satellite interpretation, are required to update
changes to salt marsh extent and status and to improve estimates of current salt marsh
soil C stocks.
Table 4.7
Trends in salt marsh sites in the United Kingdom from the
JNCC 2005 National Trend Assessment.
Country
Trend
Date
Extent
England
Declining
(continuing/
accelerating)
2004
Partial
survey
Northern
Ireland
Scotland
Stable
2005
Yes
No clear
trend
2005
Partial
survey
Partial
survey
Wales
Declining
(slowing)
Declining
(continuing/
accelerating)
2004
Partial
survey
Best guess
No
UK
2005
Adequate
data
No
No
48
No
Data source /
comments
Report to English
Nature by Posford
Haskoning in 2004
which assessed extent
of salt marsh loss in a
number of sites, based
on interpretation of air
photo and other survey
data - no new survey
was undertaken.
Information recent only
for SSSIs. Survey base
poorer than other UK
countries.
CCW's Rapid
Assessment Survey
No comprehensive UKwide assessment of
trends. Based on
country assessments, it
Country
4.7
Trend
Date
Extent
Adequate
data
Data source /
comments
is likely that the
declining trend will
continue. Decline is
unlikely to be offset by
any increases
elsewhere in the UK.
Methods of calculation for salt marshes
The work undertaken within this work package followed the basic principles laid out in
Bradley et al. (2005) where soil carbon stocks are estimated from the extent of land use,
the soil types within land uses, and the carbon stocks of these soils types. The methods
used to achieve this are described in the following paragraphs.
Land use was defined by the Land Cover Map 2000 (LCM2000) sub-level 2 which
corresponds to the JNCC Broad Habitat classification and identifies salt marshes as a
distinct category. The grid location and area (in m2) for each salt marsh polygon in
LCM2000 were identified and calculated and the information used to generate a total
area of salt marsh for England and Wales (combined) and Scotland. Available literature
was also reviewed to compare the results with previously published data. A spatial
overlay of LCM2000 and digital soil maps was used to obtain information on soil types
within salt marsh areas for England, Wales and Scotland; appropriate data were not
available for Northern Ireland. A significant proportion of salt marsh areas were
classified as sea or other non-soil units, due partly (but not completely) to disparities
between the two map overlays. Resolution of this issue would improve the accuracy of
the information derived from this source and give an improved estimate of soil carbon
stocks and fluxes. All soils were converted to the Avery classification of soil groups to
estimate the proportion of different soils in salt marshes across Britain (Avery 1980).
Following a similar procedure to Bradley et al. (1995), soil carbon stocks for salt marsh
soils to a depth of 1 metre were estimated using representative values and derived
functional values for soil series and associations. Estimates for soil C stock in Northern
Ireland were obtained by applying representative soil values for Avery soil groups
proportionally to the JNCC estimate of salt marsh area in Northern Ireland (250 ha). Soil
carbon density (t C ha-1) was calculated for each soil sampling interval in each soil series
or association (up to a maximum of seven horizons) using the representative data for
organic carbon, sand, silt, and clay (all %) and bulk density. These data were allocated
to the dominant land use types in salt marsh areas (i.e. grassland or semi natural land).
This information was used to derive total soil carbon densities for representative soil
series and associations within the dominant land uses and then used to estimate soil
carbon stocks for salt marshes by dominant land use. The term “density” (t C ha-1) is
49
used to distinguish the amount of carbon in a soil profile as opposed to soil carbon stock
(Tg), where extent is taken into consideration.
The following equation was applied to estimate soil carbon density (t C ha-1) for
individual soil sampling intervals:
Cdensityi = CSOCi * BDi * Depthi * 100
Where:
CStocki = soil organic carbon density per depth interval (i); t C ha-1
CSOCi = soil organic carbon content per depth interval (i); % or g C per 100 g
bD = bulk density of soil fine fractions (<2 mm) at depth interval (i); g cm-3
Depthi = depth of sampling interval; cm
100 = assumes that there are no fractions >2mm. This can be corrected where the
proportion of >2mm fraction is known.
Two approaches were adopted to estimate bulk density in this study: these are shown in
the section on organic-mineral soils (equations A and B). Soil carbon density (t C ha-1)
for entire soil profiles, to a maximum of 1 metre depth, were then calculated for
representative soil series and soil associations by the following:
Profile SOCdensityi = Cdensity1 + Cdensity2 + … + Cdensityn
Where:
Profile SOCdensity1-n = total soil carbon density to 1 metre depth for measured profiles of
representative soil series or soil associations; t C ha-1.
Cdensity1 + Cdensity2 + … + Cdensityn = sum of soil carbon in each soil sampling
interval/horizon to a maximum of 1 metre depth (maximum seven horizons in this
instance); t C ha-1.
This method differs from Bradley et al. (2005) as it only calculates the total soil carbon
density using available data for sampling depths. There has been no estimation of Cdensity
below actual sampling depths, e.g. where the soil profile was less than 1 metre deep.
Bradley et al. (2005) also calculated Cdensity at 0 to 30 cm and then 30 to 100 cm. For
England and Wales, soil profile carbon density by soil series information was used to
calculate mean soil carbon density (+ 1 standard deviation) by major soil subgroup. Soil
carbon stocks (Tg) for salt marshes were then calculated by the following:
50
SOCstocka-n= (Profile SOCdensitya * Areaa) + (Profile SOCdensityb* Areab) + … + (Profile
SOCdensityn* Arean) * 10-6
Where:
SOCstocka-n = total soil carbon stock of salt marshes to 1 metre depth for the two
dominant land uses in each region (Tg);
Profile SOCdensitya = representative soil carbon density of major soil subgroup or soil
association (t C ha-1);
Areaa = Area of major soil subgroup or soil association within salt marshes (km2);
For areas of unknown soil type, ProfileSOCdensity was estimated as an average of the
representative soil types recorded in salt marshes.
4.7.1
Area of Salt Marsh
The total area of salt marsh in Great Britain (England, Wales and Scotland) was
estimated at 45,771 ha. This compares favourably with previously published information,
as illustrated in Table 4.3; the discrepancies are likely to be a reflection of the methods
of estimation used and uncertainties over data. For example, the JNCC estimates were
primarily derived from pre-1995 field surveys and are considered incomplete. Cannell et
al. (1995) estimated a far lower salt marsh extent in Scotland and a greater extent in
England, which may reflect the attribution of salt marshes at national boundaries. The
low area of salt marsh reported for Northern Ireland (76 ha) has been superseded by
more recent and relatively good data which identified 250 ha of salt marsh in this region
(Table 4.8).
Table 4.8
Extent of saltmarsh sites (ha) in the United Kingdom obtained
from this study compared with previous estimates.
Regions
England
Wales
Scotland
GB
Northern Ireland
UK
This study
(derived from
LCM2000)
33,261
5,981
6,469
45,711
*
JNCC 2005
Boorman
2003
Cannell et
al. 1995
32,462
5,800
6,000
44,262
250
44,512
32,500
6,039
6,748
45,287
239
45,526
35,200
6,160
2,640
44,000
76
44,076
*see earlier text
Soils of salt marshes: The results from overlaying national soil maps with LCM2000 are
presented in Table 4.9. These highlight that salt marsh soils are dominated by alluvial
gley soil types (~62%). However, a significant proportion (~48%) may consist of a wide
51
diversity of other soils (>30 soil groups). These may reflect conversion of salt marshes
to agricultural use along with natural soil development in salt marsh/coastal areas. None
of these soils contribute >7% of the total area of soils in salt marshes. It is difficult to
establish just how diverse soil types are within salt marshes. There have been few field
surveys in this habitat type, while there is a lack of habitat information associated with
historical national scale soil surveys. However, this diversity has some significance for
calculating soil C stocks (see below) and for estimating GHG emissions.
Table 4.9
Soils associated with salt marshes in England, Wales and
Scotland. Classification according to Avery (1980).
Soil groups from
Avery
2.2
% salt
marsh
area
43.24
8.1
5.4
Unclassified
7.1
19.02
6.77
5.59
4.45
3.6
4.41
5.7
2.28
8.5
8.4
6.1
Remaining groups with
small coverage
1.90
1.87
1.27
4.7.2
9.20
Description
unripened alluvial gley soils
alluvial gley soils
brown earth soils, loamy and non-alluvial
stagnogley soils, seasonally waterlogged
sand pararendzina; unconsolidated calcareous sandy
deposits other than alluvium
argillic brown earths, loamy soils with signs of clay
enrichment
humic-alluvial gley soils, with enriched organic matter
topsoil
argillic gley soils, waterlogged with clay enrichment
brown podzolic soils
ca. 24 soil groups
Regional soil carbon stocks in salt marshes
The results for regional soil carbon stocks for two land use types (permanent grassland
or other semi-natural land use) and using two soil bulk density equations are presented
in Table 4.10. The range of total C stock for salt marshes in England and Wales is 9.841
to 13.696 Tg C, and 1.115 to 2.301 Tg in Scotland. Although there is a significant range
in these soil C stock estimates, the overall contribution of salt marshes to regional C
inventories is relatively small.
The results imply that land use has a significant influence on the carbon stocks in salt
marshes, with greater stocks in soils with the least intensive agricultural management.
However, the results also demonstrate that the estimates of bulk density introduce
significant variation in stock values. Using equation A, the average bulk density was
0.726 g cm-3 and using equation B it was 1.266 g cm-3. Equation A is closer to the bulk
density estimates used by Cannell et al. (1995). However equation B considers the
presence of clay and silt in soils with SOC <20%, while equation A does not. In addition,
52
this study only estimated soil C stock to 1 m depth, following the procedures of Bradley
et al. (2005). This limitation is likely to underestimate the total C stock in salt marsh
soils significantly since the profiles of these soils can reach several metres in depth
(Chmura et al. 2003).
Table 4.10
Soil carbon stocks (Tg) in saltmarshes by land use and region.
C stock A = using Howard et al. (1995) bulk density equation
and C stock B = using modified Smith et al. (2007) bulk
density equation.
Regions
Land use
England and Wales
Permanent grassland
Other semi-natural
Grassland
Other semi-natural
Scotland
4.8
Soil C A stock to 1
m depth (Tg); Eqn
A
Soil C B stock
to 1 m depth
(Tg); Eqn B
9.841
10.005
1.115
1.974
12.706
13.696
1.624
2.301
Influence of soil type on soil carbon stocks
There is a degree of uncertainty over the proportions of different soil types in salt
marshes due to a lack of field survey information. To investigate the potential influence
of this heterogeneity, soil C stocks were recalculated assuming that all soils would be
alluvial soil types (Equation B). The results (Table 4.11) indicate that soil C stocks would
be significantly higher in most instances, reflecting differences in both bulk density and
carbon content in the upper soil horizons. Estimates of salt marsh soil C stocks would
benefit from more accurate mapping of soil types within this habitat.
Table 4.11
Regions
England
and Wales
Influence of soil type on soil carbon stocks (Tg) in salt
marshes. Estimated C stock assuming all soils are alluvial
gleys and difference (%) to C stocks assuming a diversity of
soil types (from Table 4.10).
Land use
Permanent
grassland
Other
Grassland
Scotland
Semi-natural
Soil type and %
difference against
range of soil types
Alluvial
% difference
Alluvial
% difference
Alluvial
% difference
Alluvial
% difference
53
C stock A
(Tg)
14.077
143
17.912
179
1.374
123
2.318
C stock B
(Tg)
18.083
142
23.899
174
1.585
98
2.815
208
173
4.8.1
Estimates for soil carbon stocks in UK salt marshes
Table 4.12 shows estimated soil carbon stocks in UK salt marshes by country, which is
between 10.982 to 26.823 Tg. This represents 0.24 to 0.59% of the UK’s total soil C
stock (4562 Tg), as estimated by Bradley et al. (2005). In England and Wales salt
marshes account for <1.15% of soil C stock and 0.26% of regional land cover. In
Scotland the equivalent figures are <0.12% from 0.08% of land cover. These
differences imply that the soils in salt marshes of England and Wales are relatively high
in carbon content compared to other land uses, but this is not the case in Scotland
where soils of relatively high organic matter predominate in all land uses.
Table 4.12
Soil type
100%
alluvial
Range of
soil types
4.8.2
Estimated carbon stocks of salt marsh A = using Howard et al.
(1995) bulk density equation and B = using modified Smith et
al. (2007) bulk density equation. Northern Ireland values are
estimated from available data sources.
Regions
England and
Wales
Scotland
Northern
Ireland
UK
England and
Wales
Scotland
Northern
Ireland
UK
C stock A (Tg)
C stock B (Tg)
Grassland
(permanent)
14.077
Other/Seminatural
17.912
Grassland
(permanent)
18.083
Other/Seminatural
23.899
1.374
0.066
2.318
0.09
1.585
0.076
2.815
0.109
15.517
9.841
20.32
10.005
19.744
12.706
26.823
13.696
1.115
1.974
0.030
1.624
0.035
2.301
0.043
12.009
14.365
16.040
0.026
10.982
Sequestration of carbon in salt marsh soils
Cannell et al. (1995) estimated that the sequestration of carbon on salt marshes (i.e.
accretion from sedimentary and fluvial processes) was <0.1 Mt C yr−1 + 20%. Based on
average topsoil C contents of salt marsh soils in England, Wales and Scotland, this would
equate to an annual increase of 0.3 mm in topsoil depth. Brew and Pye (2002) suggest
that vertical accumulation of sediment in certain salt marsh areas could reach 70–100
cm over the next century. Therefore, taking a maximum potential accumulation rate of 1
cm per year, the rate of C sequestration in salt marsh soils would be 0.25 Mt C yr−1 in
England and Wales, and 0.08 Mt C yr−1 in Scotland, and total C sequestration across the
UK would be 0.33 Mt C yr−1. It should be borne in mind that estimates from this study
are based on generic and historical statistics for accretion rates in UK salt marshes. They
54
do not account for any substantial losses in salt marsh area, which could significantly
reduce this sequestration rate. Contemporary and preferably site-level data would be
required for validation, taking into account potential C losses through DOC (e.g.
Boorman et al. 1994, Tappin et al. 2003).
Although CH4 fluxes are negligible, salt marshes can be sources of N2O, especially in
estuaries where there is nutrient enrichment. Although there have been no extensive
field assessments of GHG emissions across the range of UK salt marshes, site surveys
and modelling exercises indicate that the global warming potential of salt marsh soils
through N2O emissions is relatively low and contributes <1% to UK inventory
calculations (Sozanska et al. 2002, Kenny et al. 2004).
4.9
Summary of findings on salt marshes
This is the first assessment of soil profile carbon stocks in UK salt marsh soils and it has
established that salt marshes contribute <0.59% to the UK’s total soil carbon stock.
However, these soils could be sequestering C at relatively fast rates compared to other
soil types, and at higher rates than previously estimated. With respect to GWP, salt
marsh soils appear to remain relatively small sources of N2O, which could be further
reduced through reductions in nutrient enrichment. There remain various sources of
uncertainty in estimating soil carbon stocks and fluxes in UK salt marshes, which could
be reduced through:
•
Improved estimates for the extent of UK salt marshes and the heterogeneity of soil
types within salt marshes;
•
Improved information on the land use and management of UK salt marshes and how
these relate to soil types;
•
Improved data on the characteristics of salt marsh soils to include depth of UK salt
marsh soils, carbon content to depth, and measured bulk densities; and
•
Integrated field assessments that could assess the global warming potential of UK
salt marshes by accounting for both C accumulation, through processes such as
accretion and root inputs, and GHG fluxes.
4.10 Summary
This work package has improved understanding of the influence of organic-mineral soils
on soil carbon stocks in a typical soil profile and extrapolated this information to provide
an estimate of UK soil carbon stocks for organic-mineral soils in relation to organic and
mineral soils.
55
This work package has also quantified the extent of salt marshes around the UK
established the potential carbon stocks and fluxes associated with this land area
were previously unaccounted for. Although there is scope for refinement of
estimates provided, the indication is that salt marshes account for a relatively
percentage of the UK soil carbon stock and flux.
56
and
that
the
low
5
WORK PACKAGE 4. THE DEVELOPMENT OF
LAND USE AND MANAGEMENT SCENARIOS
The spatial extent of land use change in ”change in C per hectare” must be defined in
order to interpret the outputs of the decision tool (Work Package 5). A range of
scenarios was delineated, representing the reasonable extremes of land management in
the UK, and these are presented below. This work package provided areas of land use
change at a national level with reference to soil C stocks and fluxes. These situations
were used directly in Tier 2 to extrapolate the outputs of the decision tool to a county
and national scale.
5.1
Lowland scenarios
Three scenarios were considered for managed agricultural land in lowland Britain:
1. Business as usual – recent day (2004 baseline year) land use & management.
2. Historical “best case” – interwar period (i.e. 1930): maximum area of grassland (and
associated soil C storage).
3. Potential improvement scenario – arable reversion to permanent grassland in
groundwater protection zones.
Agricultural census statistics (dating back to 1890) were used to determine the area of
the following land uses at a county level (using current county boundaries) in lowland
Britain for each scenario (Anon 1968; Comber et al. 2008):
•
Permanent grassland (>5 years)
•
Temporary grassland (<5 years)
•
Tillage land
The current area of woodland was also determined for the ”business as usual” scenario,
using the Forestry Commission’s National Inventory of Woodland and Trees. Each area
was broadly divided according to soil type (mineral <5% SOC; organic-mineral 5 - 12%
SOC; organic >12% SOC) using National Soils Inventory data for England and Wales
(McGrath and Loveland 1992) and Scotland (Lilly et al. 2009b).
The “potential improvement scenario” was developed using 2004 as a baseline, with all
tillage land within groundwater protection zones reverted to permanent grassland. In
this case, the 1998 Nitrate Vulnerable Zones were considered (SI 1998), which were
largely within England (334 ha) with a small area of Wales (0.33 ha).
57
Outputs were in the form of an area in millions of ha for each land use and each
scenario, to be fed directly into the Tier 2 modelling exercise. In addition, estimates of
the total topsoil (0 - 15 cm) C stocks for managed agricultural land (tillage, temporary
and permanent grassland) within each of the scenarios were calculated using average
soil C contents for each land use obtained from the NSI and NSIS databases, assuming a
standard bulk density of 1.3 g cm-3 (Table 5.1).
Table 5.1
Topsoil (0-15 cm) carbon stocks for tillage, temporary and
permanent grassland in Britain.
England and Walesa
Scotlandb
c
%C
t C/ha
%C
Arable (tillage)
3.13
61.0
3.54
Temporary grassland
3.84
74.9
4.95
Permanent grassland
5.05
98.5
7.24
a
NSI 1983 (Cranfield University)
b
NSIS 1985 (Macaulay Institute)
c
Assumes ”standard” bulk density of 1.3 g cm-3 and soil depth of 15 cm (i.e.
Land use
t C/hac
69.0
96.5
141.2
1950 t ha-1 topsoil)
Estimates of the amount of C stored under arable-reversion grassland (”potential
improvement scenario”) were determined using the C stock under tillage (arable land)
and applying a C accumulation factor of 0.44 t C ha-1 yr-1 (Smith et al. 2007c) for a
period of 20 years.
5.2
Upland scenarios
Due to the paucity of data for some upland scenarios, it was necessary to use the
Durham Carbon Model in order to assess the equivalent CO2 budget of upland peat soils
and to calculate the equivalent CO2 budget of selected regions of peat soils under a
range of management and land use scenarios (Worrall et al., 2009). The modelling
considered five regions of upland peat soils (Table 5.2). The regions were selected to
give an adequate sample of UK upland peat soils from which national estimates could be
extrapolated. For each region an area of at least 100 km2 was selected where, for each
1 km2 grid square (grid squares set to coincide with the Ordnance Survey National Grid),
peat soil covered at least 10% of that 1 km2. The classification of soils for each 1 km2
grid square was on the basis of the HOST classification (Boorman et al. 1995). Once an
area of at least 100 km2 had been defined then a recent, geo-referenced aerial
photograph of each 1 km2 within that area was examined and the land management on
the peat soils classified as to the percentage of each grid square that showed evidence
of burn management, drainage, bare soil, and forestation. The presence of burning as
identified from aerial photographs does not give an indication of the frequency of
burning in that area. Therefore, it was assumed that burn frequencies could reasonably
be between 10 and 20 years and the exact frequency of burning was randomly
estimated as an integer value from a uniform distribution between these two values.
58
Furthermore, the year of burning was randomly assigned within the model run period.
Wherever drains were identified in the aerial photographs the drain spacing was
measured; otherwise it was randomly assigned a drain spacing of between 10 and 20 m,
based on previous experience in these regions (Worral et al. 2006). For each region the
Durham Carbon Model was run for the following scenarios:
•
Business as usual – the land use is set as described above with no intervention.
•
Cessation of managed burning – as if there was no managed burning at any time
during the period from which data were reviewed or before.
•
Cessation of grazing – there is no grazing during the study period.
•
Blocking of all drains and gullies – the presence of all drains and gullies is removed
as if they had never existed at the start of the study period.
•
Re-vegetation – the percentage of bare soil is decreased to a small default value of
1%.
•
All possible interventions.
Each scenario was run for the 10 year period 1997 - 2006 and the average CO2 budget
for the 10-year period used as the budget estimates. A 10-year study period was taken
so that the result averages across flood and drought years. In total, 10308 model runs
were considered covering 1718 km2.
Table 5.2
Region
Peak District
Nidderdale
(Yorkshire Dales)
Galloway (South
West Scotland)
Migneint (North
West Wales)
Dartmoor
Total
Land management characteristics of the regions selected for
this study.
Area
surveyed
(km2)
550
471
Drained/gullied
(km2)
Grazed
(km2)
Burnt
(km2)
Afforested
(km2)
134
52
Bare
soil
(km2)
20
13
527
460
186
111
23
11
352
0
4
154
0
198
235
28
6
214
6
21
110
1718
3
217 (12.6%)
1
44
(2.5%)
109
1464
(85.2%)
0
303
(17.6%)
1
254
(14.8%)
In order to make the assessment the data were sorted by the management types that
can be considered by the Durham Carbon Model (presence/absence of: burning, grazing,
drainage, bare soil, or forest plantation), and the predicted budgets were then assessed
using analysis of variance (ANOVA) in order to assess which management or land use
interventions had a significant effect upon the equivalent CO2 budget. On the basis of
59
the significant differences found, land use factors could be derived and confidence limits
calculated on these.
The emission factor for CH4 is expressed separately from all other carbon uptake and
release pathways and is expressed as tonnes CH4/ha/yr. All other carbon release
pathways are expressed in terms of their CO2 equivalence (expressed as tonnes
CO2/ha/yr) and, in order to do this, atmospherically-active equivalence of the fluvial loss
components has to be judged. In order to account for the atmospherically-active
component of fluvial flux this study makes the following assumption:
CO2 equi = CO2 resp + 0.4CO2 DOC + CO2 diss .CO 2 − CO2 PP
(Eq. vi)
Where: CO2equi = total equivalent CO2 budget of the area (tonnes equivalent CO2 ha-2 yr1
); CO2x = annual equivalent CO2 budget (tonnes equivalent CO2 km-2 yr-1) where x is:
resp = net ecosystem respiration; DOC = fluvial flux of DOC; diss.CO2 = annual
dissolved CO2 flux; and PP = annual uptake from primary productivity.
This approach to understanding fluvial flux is based upon a conservative approach from
field observations (Worrall et al. 2006). It should be noted that this approach assumes
that carbon lost as POC is not atmospherically-active and is not based on the value of
DOC loss in-stream calculated elsewhere in this project.
By convention, the vector of each emission factor expressed as a positive is in fact a loss
from the soil and a gain to the atmosphere.
The land use and management observations from Table 5.2 are combined with emission
factors for CO2 and CH4 over the following scenarios:
1. Business as usual – the present distribution of land use is used as outlined in Table
5.3.
2. Realistic worst-case – the realistic worst case can be viewed in several ways as it is
not possible for peat soils to be both afforested and to be managed as grouse moor
at the same time. Therefore, the worst case scenario is a choice of the combination
of how much land to ascribe to afforestation and grouse moor management. This
study considers that afforestation doubles and that the remaining land is then
grazed, drained and comes under burn management.
3. Best case – in this case all peat soil areas are restored to pristine condition, i.e. all
drains are blocked, all burning ceases, grazing is removed, bare soil is revegetated,
and forestry is restored to blanket bog.
60
5.3
Results for lowlands
Table 5.3 gives the areas associated with each land use and scenario for use in the Tier
2 modelling exercise.
Table 5.3
Total area of managed agricultural land and woodland (million
hectares) for each scenario in England, Wales and Scotland.
Scenario &
Tillage
country
Business as usual (2004)
England
4.41
Wales
0.07
Scotland
0.74
GB total
5.22
Historical “best” (1930)
England
2.56
Wales
0.15
Scotland
0.64
GB total
3.35
Potential improvement
England
4.08
Wales
0.07
Scotland
0.74
GB total
4.89
a
Temporary
grassland
Permanent
grassland
Total
managed
Woodlanda
0.66
0.10
0.27
1.03
3.05
0.98
0.75
4.78
8.12
1.15
1.76
11.0
9.18
1.42
3.1
13.7
0.80
0.12
0.61
1.53
5.30
0.87
0.63
6.80
8.66
1.14
1.88
11.7
nd
nd
nd
nd
0.66
0.10
0.27
1.03
3.38b
0.98b
0.75
5.11
8.12
1.15
1.76
11.0
nd
nd
nd
nd
Taken from NIWT database (Forestry Commission) for the current “business as usual” scenario only (2004).
0.33 ha of arable reversion grassland in England, 340ha in Wales.
nd = no data.
b
For each scenario the total area of managed agricultural land was very similar at
approximately 11 million hectares, with a slightly greater area (0.7 m ha) of managed
agricultural land in 1930. There was also a considerably greater area of permanent
grassland (2 million ha) in 1930 compared to the present day. All of this additional
grassland was in England, with a proportionally lower area of tillage land. Conversion of
tillage land to permanent grassland within the 1998 NVZ regions of England (SI 1998)
only resulted in a very small increase in the total grassland area, compared with a 2004
baseline.
Table 5.4 shows the changes in C stocks in managed agricultural land for each scenario.
Total topsoil (0 - 15cm) C stocks for managed agricultural land ranged from 910 mT in
2004 to 1032 mT in 1930, and were of a similar magnitude to those reported by Bradley
et al. (2005) for arable and pasture topsoils (0 - 30 cm) in Britain. There was an
estimated 120 mT (12%) reduction in topsoil C stocks in managed agricultural land
between 1930 and 2004, largely due to the 30% reduction in permanent grassland area
(Table 5.4), although there was also a small (c. 6%) decrease in the total managed
agricultural land area during this period.
61
Table 5.4
Total topsoil (0-15 cm) C stocks (million tonnes) in managed
agricultural land for each scenario in England, Wales and
Scotland.
Scenario & country
Tillage
Business as usual (2004)
England
269
Wales
4
Scotland
51
GB total
324
Historical “best” (1930)
England
156
Wales
9
Scotland
44
GB total
209
Potential improvement
England
249
Wales
4
Scotland
51
GB total
304
a
Temporary
grassland
Permanent
grassland
Total managed
50
8
26
84
300
96
106
502
619
108
183
910
60
9
59
128
522
85
88
695
738
103
191
1032
50
8
26
84
323a
96
106
525
622
108
183
913
300mT in permanent grassland and 23 mT in arable reversion grassland, assuming a C accumulation rate
of 0.44 t ha-1 yr-1 over 20 years (Smith et al. 2007b).
Arable reversion to grassland in ground water protection zones only had a very small
impact on estimated C stocks (0.3% increase or 3 mT). Smith et al. (2007c) suggested C
accumulation following land use change from arable to grassland would be in the range
-0.02 to 0.9 t ha-1 yr-1, with a mean of 0.44 t ha-1 yr-1 (used in this scenario). Even using
the higher estimate of C accumulation, the total impact on C stocks is small (5 mT or
0.6% increase).
5.4
Results for uplands
Given the estimates of the area of UK peat the results for the scenarios outlined are
given in Table 5.5 and suggest that UK peat soils are a considerable sink of GHG and
would remain so even with major land use intervention. However, these estimates have
several important limitations:
1. Validation - the analysis is based on results from the Durham Carbon Model and not
from field observations. The Durham Carbon Model has been calibrated against field
observations, but it has never been validated for any site.
2. Altitude - the model predicts that altitude is a significant covariate controlling the
GHG flux from a peat soil. The distribution of peat soils with altitude was not
available to this study but it should be noted that the model predicts that the largest
sinks of GHG will be at sea level.
62
3. Afforestation – this study only considers the soil component of afforesting peat soils
and does not take into account the carbon stored in the forest biomass as it grows,
which would offset the losses from the soil over the time of forest growth. However,
afforestation acts to transfer carbon stocks from below ground to above ground.
Table 5.5
GHG emissions form UK upland peat soils with all values
expressed as Mtonnes CO2-eq yr-1.
Business as
usual
Worst case
Best case
5.5
Summary
5.5.1
Lowlands
Average
-8.1
UK peat area
High
-10.9
Low
-5.3
-3.2
-11.5
-4.3
-15.5
-2.1
-7.5
The three scenarios tested (1930, 2004 and potential improvement scenario) had similar
total areas of managed agricultural land for lowland Britain (at c. 11 million hectares),
but different proportions of permanent grassland (predominantly associated with
managed land in England).
Differences in the area of managed grassland had an impact on the total topsoil (0 - 15
cm) C stocks, with a c. 30% decline in permanent grassland between 1930 and 2004
having the greatest impact (c. 12% reduction in C stocks).
Arable reversion to permanent grassland in groundwater protection zones (1998 NVZ
areas) had only a small impact on C stocks (c. 0.3 - 0.5% increase).
5.5.2
Uplands
Under all of the scenarios tested, upland peat areas (business as usual, worst case and
pristine) proved to be a carbon sink.
Limitations in the modelling techniques related to validation of data, lowland peat areas,
and above ground biomass under afforestation are unlikely to affect the net effect of the
site as a carbon sink.
This work package has helped to improve understanding of the influence of land use
management on soil carbon stocks and fluxes across Great Britain. It has also quantified
the scale of the impact of these land use changes in terms of soil carbon and provides a
clear input to Work Package 5.
63
6
WORK PACKAGE 5. THE DEVELOPMENT OF
SOIL CARBON STOCKS AND FLUXES DECISION
TOOL
The objective of this work package was to develop a decision tool capable of providing a
quantitative estimate of the ”per hectare” soil C stock change and flux for several land
use change scenarios. Land use change and land management change have significant
greenhouse gas mitigation potential globally. Smith et al. (2007c, 2008) estimated the
yearly global mitigation potential in agriculture to be 4200, 2600 and 1600 Mt CO2-eq.
yr-1 at 100, 50 and 20 United States Dollars (USD) per tonne CO2-eq-1, respectively. The
mitigation potential is cost-competitive with other potential management options in
other sectors (Barker et al. 2007), showing that land use has a significant role to play in
addressing climate change. Change in land use may lead to changes in soil carbon
content (Guo and Gifford 2002; Smith et al. 2000a), with different tillage practises
reducing the SOC stock (Guo and Gifford 2002; Johnston 1973). Similarly, when
croplands are converted to grasslands or woodland, SOC stocks tend to increase (Guo
and Gifford 2002; Johnston 1973; Jenkinson 1990). Almost 90% of the global total
agricultural mitigation potential arises either from soil carbon sequestration in mineral
soils, or through carbon emission reduction in cultivated organic soils. Table 6.1 shows
the per area mitigation potentials for the land use change scenarios used in this
analysis. For mineral soils there were insufficient data to estimate changes in baseline
methane emission/oxidation under land use change, but for two land use changes there
was sufficient information to estimate changes in baseline nitrous oxide after land use
changes (the others are assumed to be zero). Details of how the SOC-nitrous oxide flux
changes were estimated for different land use changes are described in more detail
below.
64
Table 6.1
Estimates of change in SOC stocks and nitrous oxide emissions resulting from land use change on
mineral and organic-mineral soils. All estimates expressed in t CO2-eq. ha-1 yr-1 as per Smith et al.
(2008). For derivation of estimates, see text.
Land Use Change
CO2 (t CO2 ha-1 yr-1)
Permanent grass to arable
Permanent grass to temporary grass
Permanent grass to forestry
Arable to permanent grass
Arable to temporary grass
Arable to forestry
Temporary grass to permanent grass
Temporary grass to arable
Temporary grass to forestry
Forestry to permanent grass
Forestry to arable
Forestry to temporary grass
Mean
-9.3
-7.9
-2.25
1.61
0.16
1.59
7.9
-1.54
1.59
1.54
-6.16
-6.16
Low
-8.4
-7.4
-1.5
-0.07
-0.01
1.59
7.4
-0.62
1.59
1.54
-4.62
-4.62
65
High
-10.6
-8.3
-3.0
3.3
0.33
1.59
8.3
-2.46
1.59
1.54
-7.7
-7.7
N2O (t
yr-1)
Mean
0.0
0.0
0.0
2.3
0.23
0.0
0.0
0.0
0.0
0.0
0.0
0.0
CO2-eq ha-1
Low
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
High
0.0
0.0
0.0
4.6
0.46
0.0
0.0
0.0
0.0
0.0
0.0
0.0
All GHG (t CO2-eq ha1
yr-1)
Mean Low
High
-9.3
-8.4
-10.6
-7.9
-7.4
-8.3
-2.25 -1.5
-3
3.91
-0.07
7.9
0.39
-0.01
0.79
1.59
1.59
1.59
7.9
7.4
8.3
-1.54 -0.62
-2.46
1.59
1.59
1.59
1.54
1.54
1.54
-6.16 -4.62
-7.7
-6.16 -4.62
-7.7
For permanent grass to cropland, the SOC figures were derived from the Rothamsted,
Highfield and Fosters, and the Woburn ley-arable field trials (Johnston 1973). At the
Highfield and Fosters sites, reseeded grass (i.e. cropland converted to permanent
pasture) was shown to be 2.9 - 3.5% greater in SOC stock each year (annualised) than
crop fields. We assume that conversion causes loss of 2.9 - 3.5% of SOC per year, on
the basis of no contrary evidence. Using a mean cropland SOC stock of 84 t C ha-1 to 30
cm depth (Smith et al. 2000b), this is equivalent to a decrease in SOC of 2.4 - 2.9 t C
ha-1 yr-1 = 8.4 - 10.6 t CO2-eq. yr-1. The mean is taken as the middle of this range. For
the reverse transition, cropland to permanent grassland, the global figures for
conversion of temperate cropland to permanent moist grassland set-aside from Smith et
al. (2008) were used. This also has a nitrous oxide component (see Smith et al. (2008)
for full details).
For permanent grass to temporary grass, in the absence of better data, figures were
derived from Johnston (1973). In these trials, reseeded grass was shown to be 2.4 2.7% lower in SOC stock each year (annualised) when converted from permanent grass
to ley-arable rotation, so conversion is assumed to cause a loss of 2.4 - 2.7% of SOC per
year. Using a mean cropland SOC stock of 84 t C ha-1 to 30 cm depth (Smith et al.
2000b) this is equivalent to a decrease in SOC of 2.0 - 2.3 t C ha-1 yr-1 = 7.4 - 8.3 t CO2eq. yr-1. A mean of 7.85 t CO2-eq. ha-1 yr-1 is used. For the reverse transition, temporary
grassland to permanent grassland, the same values are used for the increase in SOC.
For permanent grass to forestry the 10 - 20% percentage loss of SOC figures from Guo
and Gifford (2002) were used as calculated through the review of 74 publications and 83
individual observations. These data are scaled to UK soils, on the basis of precipitation,
as 8.4 -16.8 t C ha-1 over 20 years. The 20 year transition period is assumed (IPCC
1997, 2006) giving a loss over 20 years (as per IPCC 2006) of 0.42 - 0.84 t C ha-1 yr-1 =
1.5 - 3.0 t CO2-eq. ha-1 yr-1. A mean of 2.25 t CO2-eq. ha-1 yr-1 is used. For the reverse
transition, forestry to permanent grass, the ~10% increase figure of Guo and Gifford
(2002) is used. Using the same conversion, this gives an estimated gain of 1.54 t CO2eq. ha-1 yr-1 with the same figure used for mean, minimum and maximum in the absence
of better data.
For cropland to temporary grass, reseeded permanent grassland soils were shown to be
2.9 - 3.5% higher in SOC stock each year (annualised) than cropland in the Rothamsted
Highfield and Fosters, and Woburn ley-arable trials (Johnston 1973). At the same sites,
ley-arable rotations were shown to have a 0.3 - 0.8% higher SOC stock each year
(annualised) than croplands, hence ley-arable grass is up to 10 times less effective than
permanent grass in sequestering carbon. For this reason the temperate-moist “cropland
to permanent grass” figures from Smith et al. (2008) (including a nitrous oxide
component) were divided by 10. For the reverse transition, temporary grass to cropland,
the temporary grassland increases of 0.2 - 0.87% SOC per year (annualised) relative to
cropland in Rothamsted, Highfield and Fosters, and Woburn ley-arable trials (Johnston
66
1973), were used. Temporary grassland to cropland conversion was therefore assumed
to cause a loss of 0.2 - 0.9% of SOC per year. Using a mean cropland SOC stock of 84 t
C ha-1 to 30 cm depth (Smith et al. 2000b), this is equivalent to a decrease in SOC of
0.17 - 0.67 t C ha-1 yr-1 = 0.62 - 2.46 t CO2-eq. yr-1. We have used a mean of 1.54 t
CO2-eq. ha-1 yr-1.
For cropland to forest conversion, the previous value used by Smith et al. (2000c),
based on Rothamsted cropland to natural woodland reversion experiments was used.
Natural woodland was considered to be close enough to forest to be useable in the
context of this exercise. The value of 1.59 t CO2-eq. ha-1 yr-1 was used for mean,
minimum and maximum in the absence of better data. For the reverse transition, forest
to cropland, the Guo and Gifford (2002) figures of 30 - 50% loss on converting forest to
cropland were used, which scales (based on precipitation) to 25.2 - 42 t C over 20 years
for UK soils. If the change is assumed to occur over 20 years (as per IPCC 1997, 2006)
this is 1.26 - 2.1 t C ha-1 yr-1, equal to a range of 4.62 - 7.70 t CO2-eq. ha-1 yr-1. A mean
of this range 6.16 t CO2-eq. ha-1 yr-1 is used.
For temporary grassland to forestry there are no reliable data, so the same value is used
as for cropland to forestry. Similarly, for forestry to temporary grassland, the same value
as for forestry to cropland is used. These changes are likely to be relatively small when
compared to the other changes listed and so this perhaps represents an assumption.
For organic soils, cultivation and drainage (assumed to occur for land use transitions:
semi-natural vegetation/permanent grassland to cropland or temporary grassland) were
assumed to follow the pattern used by Smith et al. (2008) and result in large carbon
loss, a small decrease in methane emissions, and a slight decrease in nitrous oxide
emissions (Table 6.2). Reversion of croplands or temporary grassland to semi-natural
vegetation or permanent grassland was assumed to result in equivalent effects in the
opposite direction (Table 6.2; Smith et al. 2008).
6.1
Summary
Screen shots of the decision tool and how these data have been built in are shown in
Annex 1. The results that are presented above and those below in Tier 2 have been
generated using the decision tool that has, in turn, been fed by the outputs from Work
Packages 1 - 4.
This work package has synthesised the data provided by Work Packages 1, 2, 3 and 4 to
quantify the potential soil carbon gain or loss as a result of a range of land use change
scenarios. This has helped to improve understanding of the effect of land use change on
soil carbon stocks and fluxes.
67
Table 6.2
Estimates of change in SOC stocks and methane and nitrous oxide emissions resulting from land use
change on organic soils. All estimates expressed in t CO2-eq. ha-1 yr-1 as per Smith et al. (2008). For
derivation of estimates, see text.
Land Use Change
CO2
(t CO2 ha-1 yr-1)
Mean Low
high
N2O
(t CO2-eq ha-1 yr-1)
Mean Low
high
All GHG
(t CO2-eq ha-1 yr-1)
Mean Low
high
-69.67
CH4
(t CO2-eq ha-1 yr-1)
Mea Low
high
n
3.32
0.05
15.3
Semi-natural vegetation/permanent
-36.67
-3.67
-0.16
-0.05
-0.28
-33.51
-3.67
-54.65
36.67
3.67
69.67
-3.32
0.16
0.05
0.28
33.51
3.67
54.65
grassland to cropland or temporary
grassland
Cropland or temporary grassland to
-0.05
semi-natural vegetation
68
-15.3
7
TIER 2: UK-WIDE PREDICTIONS OF SOIL
CARBON STOCKS AND FLUXES IN THE
CONTEXT OF LAND USE
Using the data made available through the Tier 1 work packages, a series of carbon
fluxes were derived for a range of future land use scenarios and compared against the
baseline year of 2004. The land use change scenarios considered within Tier 2 were for
managed agricultural land in lowland Britain:
•
Historical Best Case - interwar period (i.e. 1930): maximum area of grassland (and
associated soil C storage).
•
Recent Best Case - Potential improvement scenario – arable reversion to permanent
grassland in groundwater protection zones (as classified for England and Wales in
1998).
•
Worst case 5% - plough out of 5% of permanent grassland to cropland, applied
evenly to all British counties.
•
Worst case 10% - plough out of 10% permanent grassland to cropland, applied
evenly to all British counties.
•
Worst case 20% - plough out of 20% permanent grassland to cropland, applied
evenly to all British counties.
Worst case scenarios greater than 20% plough out of grassland were not examined, as
cropland distribution is limited by factors such as climate, soil type, and aspect, so much
of the UK is not suitable for crop production. Agricultural census statistics (dating back
to 1890) were used to determine the area of the following land uses (at a county level,
using 2004 county boundaries) in lowland Great Britain for 1930 (Historical Best Case),
and for the Baseline in 2004:
•
Permanent grassland
•
Temporary grassland
•
Arable land (sometimes referred to as “tillage land”).
The current area of woodland was determined for the Baseline scenario, using the
National Inventory of Woodland and Trees (NIWT, Forestry Commission).
The total change in green house gas (GHG) emissions over 20 years, expressed as kt
CO2-eq. ha-1 is illustrated as a series of boxplots in Figure 7.1 (a - e) for England,
Scotland and Wales under each land use change scenario. The Historical Best Case
69
scenario (Figure 7.1a) shows a GHG benefit and SOC increase in England, but SOC
losses and increased GHG emissions in Scotland and Wales relative to the 2004 baseline.
This largely arises from increased grassland, especially permanent grass, in most of
England, but more cropland (at the expense of grassland) in Scotland and Wales. The
effect in Wales is slight, but in Scotland the most notable loss of grassland area (and
thereby SOC loss) occurs on the west coast, in particular in Argyll & Bute, Dumfries &
Galloway, Highland, and the Western Isles, but also in Orkney (Figure 7.2a).
The Recent Best Case scenario (Figure 7.1b) affects only England, with no land use
change in Scotland and only very small area changes in Wales. Even for England, the
change in GHG emission reduction and SOC increase is an order of magnitude lower
than for the Historical Best Case scenario.
The Worst Case grassland plough outs to cropland scenarios are all applied evenly to all
counties in Great Britain, so the effect is proportional to the grassland area in each
county. A change of 5% permanent grassland to cropland in Great Britain under the
Worst Case 5% scenario would result in increased GHG emissions and decreased SOC
stocks, giving a net impact of -70 Mt CO2-eq. over 20 years, comprised of -37, -17 and 16 Mt CO2-eq. over 20 years for England, Scotland and Wales, respectively (Figure 7.1c).
Equivalent figures for the Worst Case 10% and Worst Case 20% are as follows: Worst
Case 10% = -75, -35 and -32 Mt CO2-eq. over 20 years for England, Scotland and
Wales, respectively, and Worst Case 20% = -148, -70, -63 Mt CO2-eq. over 20 years for
England, Scotland and Wales, respectively (Figures 7.2a – 7.2e for Worst Case 10% and
Worst Case 20%, respectively).
The total changes in GHG emissions over 20 years (Mt CO2-eq. ha-1) for each county
under each land use change scenario are mapped in Figure 7.2.
7.1
Summary
This work package has used the inputs made available through the Tier 1 work
packages to define the rules governing data quality and the nature of the inputs
required to produce a workable decision tool of the fluxes and stocks of soil organic
carbon across Great Britain. The decision tool has been successfully applied to a range
of land use scenarios, including best and worst case predictions for upland and lowland
areas to produce maps of the various soil fluxes under different scenarios.
70
Figure 7.1
The total change in GHG emissions (kt CO2-eq. ha-1 over 20
years) for England, Scotland and Wales under each land
use change scenario. (a) Historical Best Case scenario, (b)
Recent Best Case scenario, (c) Worst Case 5% scenario, (d)
Worst Case 10% scenario and (e) Worst Case 20%
scenario.
350000.00
a)
300000.00
Change in GHG (kt CO2-eq.)
250000.00
200000.00
150000.00
100000.00
50000.00
0.00
England
Scotland
Wales
-50000.00
-100000.00
Scotland
-150000.00
Country
b)
60000.00
350000.00
Change in GHG (kt CO2-eq.)
Change in GHG (kt CO2-eq.)
50000.00
300000.00
250000.00
40000.00
200000.00
30000.00
150000.00
100000.00
20000.00
50000.00
10000.00
0.00
England
Scotland
England
Scotland
Wales
0.00
-50000.00
-100000.00
-10000.00
-150000.00
Wales
Country
Country
71
0.00
England
Scotland
Wales
Change in GHG (kt CO2-eq.)
-50000.00
-100000.00
c)
-150000.00
-200000.00
-250000.00
Country
0.00
England
Scotland
Wales
Change in GHG (kt CO2-eq.)
-20000.00
-40000.00
-60000.00
-80000.00
d)
-100000.00
-120000.00
Country
0.00
England
Scotland
Wales
Change in GHG (kt CO2-eq.)
-10000.00
-20000.00
-30000.00
-40000.00
e)
-50000.00
-60000.00
Country
72
Figure 7.2
a)
The total change in GHG emissions (Mt CO2-eq. ha-1 over 20 years – estimates using the mean mitigation
factor shown) for each county under each land use change scenario. (a) Historical Best Case scenario, (b)
Recent Best Case scenario, (c) Worst Case 5% scenario, (d) Worst Case 10% scenario and (e) Worst Case
20% scenario.
b)
73
c)
d)
74
e)
75
8
IMPLICATIONS OF THE FINDINGS
8.1
Overall implications
Land use change within the agricultural sector may not be a feasible large scale option
for climate mitigation, with land use largely being determined by agricultural market
conditions. Indeed, much of the potential global mitigation within this sector will be by
changing management on land that remains in agricultural use (Smith et al. 2007c,
2008). We are therefore not proposing large scale land use change as a mitigation
option, but instead have examined the consequences in terms of GHGs should land use
change occur through other pressures.
The Historical Best Case scenario presents land use as it has been in the past (1930),
but agricultural land use is not considered likely to return to conditions similar to these
in the near future. We included this scenario as it presents a real case of land use for
Great Britain, to show what SOC stocks/GHG emissions would have been under such
circumstances. However, the historical best case for Great Britain as a whole is clearly
not a “best case” for Scotland in terms of GHG emissions/SOC storage, with SOC storage
higher in the baseline. The Recent Best Case scenario is probably closer to what could
feasibly be achieved in the near term, but it only has a significant impact in England,
with no significant impact in Wales or Scotland. Even for England, the impact is an order
of magnitude lower than that of the historical best case scenario. Conversion of cropland
to grassland is not a feasible mitigation option if a) there is no market incentive to
convert (i.e. demand for livestock products derived from the grassland), or b) cropland
area increases elsewhere to meet the demand for cropland products. In such a case,
GHG emissions are simply displaced with no net GHG benefit (see Berry et al. 2008;
Carlton et al. 2009, 2010). In any case, livestock emissions (from the new grassland if
used to raise more livestock) would increase, thus negating at least some, if not all, of
the GHG benefit.
The Worst Case scenarios, with 5, 10 and 20% plough out of permanent grassland,
represent potential futures should cropland products (largely cereals in the UK) increase
in market value to favour more crop production at the expense of grasslands, leading to
a trade-off with grazed livestock production.
The Historical Best Case scenario delivers a Great Britain emission reduction of ~220 Mt
CO2-eq. over 20 years, or an annual GHG emission reduction of ~11 Mt CO2-eq. yr-1
relative to the 2004 baseline. The Worst Case 20% grassland plough out scenario
increases Great Britain emissions by a similar magnitude (-280 Mt CO2-eq. over 20
years; -14 Mt CO2-eq. yr-1). In the context of overall UK GHG emissions (635 Mt CO2-eq.
yr-1 in 2007; Committee on Climate Change 2010) the yearly reductions/increases
examined here are small, accounting for <2% of yearly annual GHG emissions, even for
the most extreme scenario (Worst Case 20%). However, in the context of current UK
77
Land Use, Land-Use Change and Forestry (LULUCF) emissions, the changes in GHG
emissions examined here are considerable. The Recent Best Case scenario would deliver
further emission reductions (-1.4 Mt CO2-eq. yr-1), whereas even limited grassland
plough out would result in an increase of emissions of around 3.5 Mt CO2-eq. yr-1.
8.2
Conclusions
Changes between agricultural land uses (transitions between permanent and temporary
grassland and cropland) in Great Britain are likely to be a limited option for GHG
mitigation, as land use is largely determined by market factors for agricultural products,
and the suitability of the land. However, the impact of even relatively small agricultural
land use change in Great Britain could have a significant impact in comparison with the
current estimates of GHG emissions from land. In comparison with total UK GHG
emissions, however, even the most extreme feasible land use change scenarios account
for <2% of current emissions.
An assessment of soil profile carbon stocks in organic-mineral soils and salt marsh soils
has established that salt marshes contribute <0.59% to the UK’s total soil carbon stock,
although these soils could be sequestering C at relatively fast rates compared to other
soil types, and at higher rates than previously estimated. With respect to GWP, salt
marsh soils appear to remain relatively small sources of N2O which could be further
reduced through reductions in nutrient enrichment.
8.3
Possible future work
This project has found that there is the need to improve access to soil datasets in the
UK. Without this access there will remain areas of controversy, contention,
disagreement, and obfuscation.
Refinement of the existing model could also be achieved through accounting for
additional DOC fluxes by resolving issues of data quality, suitability, coverage, format
and gaps in availability, as discussed particularly in the sections on Tasks 2 and 3. The
Environment Agency and SEPA are increasing the amount of monitoring of dissolved
organic carbon in freshwater in line with drivers from the Water Framework Directive.
These data could resolve some of the outstanding issues cited above.
There is likely to be greater benefit in terms of the predictive capacity of any decision
tool, from the examination of the effects of changes in land management as opposed to
land use change. In particular, this may relate to fertilizer use or stocking density and
cultivation practice.
There is a need for more measurement of changes in C stocks (to depth) following
arable reversion to permanent grassland and also in ley-arable rotations. At present the
lack of UK data means that it is very unclear how much of the C stored by rotational
78
grass is “lost” when that grassland is ploughed out. Informed assumptions on this have
been made in line with the restrictions of the current datasets, but uncertainties are
significant.
Subsoil C is also an issue. Most datasets look at the topsoil only and therefore do not
account for the carbon in the subsoil. Many studies claim a benefit of reduced tillage,
but only measure the topsoil. When the subsoil is included the benefit often disappears.
Target studies to examine the behaviour of subsoil carbon under a range of tillage
regimes would improve estimates of changes in C stocks. This would provide a sound
evidence-base for policy makers on which to recommend practices for C loss mitigation.
This project has clearly shown a need for improved estimates of the extent of UK salt
marshes and the heterogeneity of soil types within salt marshes. This could be
particularly focussed upon C sequestration. In addition, improved data on the
characteristics of salt marsh soils to include depth of UK salt marsh soils, carbon content
to depth, and measured bulk densities would greatly improve estimates of C stocks.
79
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