Chappell et al. (2015). The global significance of omitting soil erosion from soil organic carbon cycling schemes

Soils are estimated to store up to 80% of the organic carbon

in the terrestrial biosphere (2,376–2,450 Pg C to a depth of 2 m)
and contain more than three times the organic carbon in the
atmosphere

6

. The amount of carbon dioxide (CO

2

) captured

and converted to SOC annually through terrestrial net primary
production or released as CO

2

by soil microbial respiration is

about an order of magnitude greater than the annual increase in
atmospheric CO

2

(ref. 7). Soil therefore represents a substantial

component within the global carbon cycle and small changes in
the SOC stock may result in large changes of atmospheric CO

2

particularly over tens to hundreds of years

8

. Similarly, global climate

change could influence fixation and respiration with implications
for terrestrial ecosystems and feedbacks to global biogeochemistry
and radiative forcing

9

.

Soil erosion is a global issue

10

that occurs more intensively on

cultivated land than on rangeland

11

. Since agricultural expansion,

many global regions have subsequently introduced conservation
agriculture to reduce soil erosion and consequently there is consid-
erable spatial and temporal variation of soil erosion associated with
the history of land use and management (for example, conservation
agriculture in Australia

12–14

). Soil erosion has removed consider-

able quantities of topsoil

10

, which may continue and perhaps be

exacerbated by projected extremes, for example, Australian climate
change

15

. There is renewed awareness of the global significance of

soil erosion probably due in part to the debate about whether soil

erosion is a sink or source

16

of CO

2

and its impact on soil nutrient

redistribution and global biogeochemistry

17

.

Carbon cycling schemes, for example, RothC (ref. 2), Century

18

and crop models such as APSIM (ref. 19) are used to predict C
change for different environmental and management conditions.
These types of model underpin regional assessments of the terres-
trial carbon budget that consider the processes of net primary pro-
duction and respiration. One of the most widely used SOC turnover
models is RothC. For example, SOC cycling in RothC underpins the
Australian Government’s spatial modelling framework Full Carbon
Accounting Model (FullCAM) and the modified Carnegie–Ames–
Stanford approach (CASA) within the LSM CABLE

20

.

Here we demonstrate that SOC flux should also include losses

(and gains) due to soil erosion (and deposition). We modified
the C cycling scheme RothC (version 26.3) to include an erosion
component (RothCE; Supplementary Section 1.1). For several long-
term experimental plots in Australia (Supplementary Section 1.3)
we measured

137

Cs and estimated net (1973–1993) soil erosion

using three approaches (Table 1 and Supplementary Sections
1.4 and 1.5). These results demonstrated that plots had been
exposed to soil erosion involving the loss of SOC (SOC erosion).
The different estimates of soil erosion were used with RothCE,
established decomposition rates for Australia

21

and the measured

SOC fractions, to estimate SOC loss with and without erosion
(Fig. 1). Depending on the magnitude of erosion there may be a
considerable difference between the model predictions with and
without erosion. The net C flux of optimized RothCE was compared
with that of the model without erosion and showed a consistent
under-estimate of net C flux in the presence of soil erosion that we
interpreted as SOC erosion (Table 1 and Fig. 1).

We adopted the RothCE optimized approach (Supplementary

Section 1.4) for estimating soil erosion at each of the other 98
long-term plots across Australia where no

137

Cs measurements were

available (Supplementary Section 2 and Supplementary Table 1).
The RothCE model was fitted to SOC fraction data with and without
soil erosion to estimate net SOC flux with and without erosion.
Approximately half of the model optimizations implicated erosion
(mean

= −

5.55 and s.d.

=

9.69 t soil ha

−

1

yr

−

1

) and the remainder

estimated soil erosion at 0. We summarized the results (Fig. 2) by
plotting soil erosion (

<

0) against the net C flux difference with and

without erosion (SOC erosion). The predicted magnitude of SOC
erosion was strongly dependent on the intensity of soil erosion. A
power function fitted best the scatter of these data (

R

2

=

0.84,

p

value

<

0.01) and the relation demonstrated that as soil erosion increased

so too did SOC erosion but at a much reduced rate. We validated
the plot scale relation by plotting (Fig. 2; red symbols) global soil
erosion and SOC erosion estimates from the catchment scale

. That

relation seemed robust to the issue of scale because those regional

1

CSIRO, Land and Water, GPO Box 1666, Canberra, Australian Capital Territory 2601, Australia.

2

CSIRO, Agriculture, Urrbrae, South Australia 5064,

Australia.

3

Woods Hole Research Center, 149 Woods Hole Road, Falmouth, Massachusetts 02540, USA.

*

e-mail:

adrian.chappell@csiro.au

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Table 1 |

Estimates of soil erosion for selected long-term Waite rotation trials.

Soil erosion estimates

WF

WOF

2W4Pa

WW

Pa

Australian empirical model (t soil ha

−

1

yr

−

1

)

−

1.17

−

1.77

−

1.54

−

1.27

−

2.80

Mass-balance model (t soil ha

−

1

yr

−

1

)

−

8.90

−

11.40

−

12.50

−

8.10

−

16.80

RothC optimization (t soil ha

−

1

yr

−

1

)

−

3.58

−

7.18

−

4.54

0

−

3.46

Net C flux without erosion (t C ha

−

1

yr

−

1

)

0.88

1.16

1.99

0.92

3.26

Net C flux with erosion (t C ha

−

1

yr

−

1

)

0.98

1.42

2.17

0.92

3.44

Net C flux difference with and without erosion (t C ha

−

1

yr

−

1

)

0.10

0.26

0.18

0.00

0.18

Selected rotations: WF, wheat-fallow; WOF, wheat-oat-fallow; 2W4Pa, 2 years wheat, 4 years pasture; WW, wheat; Pa, pasture.

80

a

60

70

WF

WOF

50

40

C (t ha

−1

)

Year

30

20

10

0

1930

1950

1940

1960

1970

1980

1990

80

b

60

70

50

40

C (t ha

−1

)

Year

30

20

10

0

1930

1950

1940

1960

1970

1980

1990

80

c

60

70

50

40

C (t ha

−1

)

Year

30

20

10

0

1965

1975

1970

1980

1985

1990

1965

1975

1970

1980

1985

1990

80

d

60

70

50

40

C (t ha

−1

)

Year

30

20

10

0

1930

1950

1940

1960

1970

1980

1990

80

e

60

70

50

40

C (t ha

−1

)

Year

30

20

10

0

TOC

HUM

POC

IOM

TOC

HUM

POC

IOM

TOC

HUM

POC

IOM

TOC

HUM

POC

IOM

TOC

HUM

POC

IOM

2W4Pa

Pa

WW

Figure 1

| Temporal variation in measured (symbols) and modelled (lines) soil organic carbon for the Waite rotation trial plots.

a

–

e

, RothCE model

predictions used established decomposition rates for Australia (RPM

=

0.15 yr

−

1

) without soil erosion (black line), with soil erosion estimated using the

Australian empirical model (blue line) and the mass-balance model (green line) and optimized erosion estimates using the RothC model (red line). TOC,
total organic carbon; HUM, humic pool; POC, particulate organic carbon; IOM, inert organic matter; RPM, resistant plant material. The rotation acronym
(as defined in Table 1) is indicated in the top left corner of each plot.

188

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LETTERS

y

= 0.0225

x

0.9503

R

2

= 0.8414

0.01

0.1

1

0.1

1

10

100

SOC er

osion (t C ha

−1

yr

−1

)

Soil erosion (t C ha

−1

yr

−1

)

Australian calibration

Global validation

Figure 2

| Relation between soil erosion and the difference in net C flux.

The model RothC was calibrated with and without soil erosion using
Australian experimental (plot scale) trial data (blue diamonds). Validation
data (not used to produce the model; red squares) at the catchment scale
was provided from global data

. Both axes are in log scale.

estimates provided a relation with SOC erosion that was very similar
to the relation established for the Australian plot-based estimates.
We assumed that soil erosion was omitted in the models for SOC
accounting, SOC sequestration and LSMs (Supplementary Section
3.1). These findings and other considerations (Supplementary Sec-
tion 3.2) give confidence that the relation can be used to make
estimates across Australia and other global regions.

We estimated the total SOC erosion for agricultural and range-

land Australia and found that the combined estimate omitted
from the Australian terrestrial SOC budget was similar in magni-
tude to other included components (Supplementary Section 3.3).
The Australian National Greenhouse Gas Inventory

23

estimated

4.2 Tg C yr

−

1

for cropland using FullCAM based on RothC. That C

cycling model omits soil erosion and has been calibrated against data
used in this study for which we have estimated approximately half
of the trial plots have eroded. At those sites the national account
is overestimating the SOC loss as CO

2

due to soil respiration. If

we adopt the catchment-scale median net (1950s–1990) erosion for
agricultural Australia (

−

1.5 t ha

−

1

yr

−

1

; ref. 12) and assume it was

applicable to Australian cropland (approx. 26.2

×

10

6

ha) and esti-

mated (using Fig. 2) SOC erosion at 0.033 t C ha

−

1

yr

−

1

, it provides

an estimated 0.87 Tg C yr

−

1

. We reduced the estimate by assum-

ing that 20% of the eroded SOC is mineralized and reached the
atmosphere (Supplementary Section 1.5). Consequently, we esti-
mate 0.70 Tg C yr

−

1

for total SOC erosion that has been incorrectly

attributed to respiration in the Australian national account. Using
estimates of catchment-scale

137

Cs-derived net (1950s–1990) ero-

sion for cropland up to

−

4 t ha

−

1

yr

−

1

(25th percentile) suggested

that estimates of total SOC erosion for Australian cropland may be
up to 2.2 Tg C yr

−

1

(1.7 Tg C yr

−

1

after 20% mineralization). On this

basis the Australian national inventory has overestimated the net C
flux to the atmosphere as CO

2

by up to 40%.

The omission of SOC erosion in crop production models has

implications for potential SOC sequestration in Australia and else-
where. For example, simulations with APSIM (ref. 24) and extrap-
olations based on C measurements

25

have suggested that Australian

cropland could potentially increase C stocks (0–30 cm) by about
1 Pg C (over 100 years) and would be a net sink of atmospheric
carbon dioxide under conservation agricultural practices. Assuming
our first approximations to the total catchment-scale C erosion for
Australian cropland (up to 1.7 Tg C yr

−

1

) are applicable, over the 100

year time period up to 0.17 Pg C would be removed by soil erosion
and therefore up to 17% of the potential C sink is overestimated.

We estimated the SOC erosion (Fig. 2) for all lands within the

main global regions and compared the SOC erosion with estimates
of terrestrial C flux made by LSMs. We assumed that the LSMs
had used a SOC cycling model that had been calibrated against
long-term sites that had been exposed to soil erosion. We partitioned
the estimates between water erosion (Table 2) and wind erosion/dust
emission (Supplementary Section 3.3 and Supplementary Table 2).
The range of SOC erosion was then compared with the range of LSM
estimates of terrestrial C flux (2000–2010) taken from the Global
Carbon Atlas (Supplementary Section 4). The difference (%; Table 2)
was calculated to provide a range of the impact of SOC erosion
between the minimum SOC erosion (adjusted for mineralization;
Supplementary Section 1.5) and the minimum model estimate of the
terrestrial C flux and also the maximum SOC erosion (adjusted for
mineralization) and the maximum model estimate of the terrestrial
C flux. The differences in Table 2 were interpreted as uncertainty in
net C flux.

We interpreted the global regions’ net SOC loss due to water

erosion and mineralization as uncertainty in the terrestrial C flux
by 14–403 Tg C yr

−

1

. These regional estimates are between

+

35

and

−

82% of the long-term (2000–2010) model terrestrial C flux

for these regions. The global uncertainty due to the omission
of SOC erosion is 0.3–1.0 Pg C yr

−

1

, which is between

−

18 and

−

27% of the global long-term terrestrial C flux (Table 2). Up

to around one-quarter of the uncertainty in global C flux could
be attributed to the omission of SOC erosion with implications
for recent interpretations that excluded SOC erosion about model
uncertainty

3,4

and trends and variability in global ecosystems

5

. For

the conterminous USA about 11

±

1 Tg C yr

−

1

are lost through rivers

to the coastal ocean

26

. It is the same order of magnitude as our

estimates for North America. Our minima estimates used regional

Table 2 |

Soil organic carbon (SOC) erosion and model estimates of terrestrial SOC flux for major regions of the Earth.

Min–max

∗

water erosion
(t ha

−

1

yr

−

1

)

SOC erosion
(t C ha

−

1

yr

−

1

)

Water eroded
area (ha

×

10

6

)

†

Total SOC erosion
(Tg C yr

−

1

)

SOC erosion with
20% oxidation
(Tg C yr

−

1

)

Terrestrial C flux
(Pg C yr

−

1

)

‡

Difference
(%)

Africa

3.7–12.9

0.08–0.26

1,000

78.0–255.6

62–204

−

0.1,

−

1.9

62.4–10.8

Asia

4.3–16.6

0.09–0.32

1,550

138.2–503.5

111–403

−

0.21,

−

0.49

52.6–82.2

S. America

5.7–22.1

0.12–0.43

510

59.9–217.4

48–174

−

0.18,

−

1.82

26.6–9.6

N. America

4.4–12.3

0.09–0.24

430

39.7–105.0

32–84

−

0.22,

−

0.85

14.4–9.9

Europe

6.7–13.4

0.14–0.27

300

41.0–79.5

33–64

−

0.05,

−

0.45

65.5–14.1

Oceania

1.7–9.5

0.04–0.19

460

17.5–87.9

14–70

+

0.04,

−

0.46

+

35.0–15.3

Global

4.1–15.2

0.09–0.30

4,250

369.0–1,269.6

295–1,016

−

1.67,

−

3.82

17.7–26.6

∗

Minima and maxima water erosion estimates for these regions

and sediment yield estimates

6

†

Estimates of land area affected by water and tillage erosion

‡

The terrestrial flux (negative away

from land) provided is the range of long-term mean (2000–2010) of the land surface models (LSMs) estimates available from the Global Carbon Atlas. The difference is the mineralized proportion of
the eroded SOC relative to the minimum and maximum model estimate of the terrestrial C flux expressed as a percentage of the LSMs estimates.

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soil erosion and affected land area

. With the exception of the

region of South America our estimates of net SOC flux match
closely those of previous estimates

including our estimate of global

SOC erosion by water at 369 Tg C yr

−

1

. Consequently, we believe

our estimates of the SOC erosion are reasonable and our approach
robust for making estimates in global regions.

The estimates of wind erosion/dust emission are an order

of magnitude smaller than those for water erosion despite C
erosion being similar per hectare (Supplementary Section 3.3 and
Supplementary Table 2). Between the regions soil erosion by wind is
slightly smaller than that of water erosion, but land area affected by
water and tillage erosion is here quantified as much larger than that
affected by wind erosion. Consequently, even small SOC erosion by
water produces substantial totals that amount to large proportions
of the current C flux.

The inclusion of soil erosion in LSMs could create a realistic

feedback between soil erosion and dynamic soil characteristics
(for example, clay content, soil depth) but which are held static
at present. Soil erosion removes preferentially the fine, C- and
nutrient-rich fractions

27

, which may change over time the soil

albedo, soil temperature, soil moisture holding capacity and soil
hydraulic properties. Surface lowering will most likely change the
soil micro-environment, the decomposition rate and rooting depth.
For example, the flux of soil could be made equivalent to a depth
of soil removed. Catchment-scale global soil erosion (from Table 2;
for example, 10 t ha

−

1

yr

−

1

=

0.1 g cm

−

2

yr

−

1

) over the past 100 years

would amount to 7 cm surface lowering (soil bulk density of
1.3 g cm

−

3

). This surface lowering would then require the LSMs to

replace the soil surface (0–5 cm) characteristics with those from
deeper soil layers with implications for changed hydrologic and
energy budgets. To our knowledge, there are no water erosion
schemes coupled to LSMs and dust emission schemes are coupled
only for atmospheric radiative purposes. Implementing a realistic
and dynamic feedback between soil redistribution (erosion and
deposition), C cycling and dynamic soil characteristics would
improve the fidelity of LSMs and global climate models with
little cost to parsimony. Until soil erosion schemes are properly
(dynamically) coupled to the LSMs, they could include

137

Cs-derived

net (1950s–1990) soil redistribution maps that account for the fate
of eroded soil

28

. Much work remains necessary to understand the

fate of the eroded C and the role of dynamic replacement

29,30

. In

the absence of global maps of net soil redistribution, those of gross
soil erosion

http://ageis.climatechange.gov.au

could be used as a first approximation and an external

variable in the LSMs. This boundary condition soil erosion layer,
perhaps initially static over time, would be the only additional
information necessary to drive the revised C cycle (for example,
RothCE) in the LSM. Subsequently, the boundary condition soil
erosion data could be considered dynamic as influenced by land use
change and global soil conservation (conservation agriculture)

11,13

.

Ultimately, soil erosion would be estimated intrinsically using a
physical basis from available data in much the same way as wind
erosion and dust emission are estimated and partially coupled to
LSMs. Notably, including soil erosion in the LSMs would enable
wind erosion and dust emission to be coupled fully and provide
the realistic land surface feedback that would, for example, lower
soil surfaces and regulate the provision of erodible sources and
avoid static prescriptions. In any case, there is sufficient evidence
of the global significance of SOC erosion and data sets available to
encourage the development of LSMs to include SOC erosion.

Received 4 August 2014; accepted 18 August 2015;
published online 26 October 2015

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LETTERS

Acknowledgements

Financial support for this research was provided by CSIRO Agriculture. The authors are
grateful to Y. Li for providing the

137

Cs measurements and to P. Canadell, Z. Luo and

R. Farquharson for comments on early versions of the document. Any errors or
omissions in the manuscript remain the responsibility of the authors.

Author contributions

A.C. conceived of the project in consultation with J.B. and J.S. J.S. organized the soil
samples and measurement of

137

Cs and A.C. calculated the estimates of soil

redistribution. A.C. wrote the code in Matlab to analyse the data, performed the analyses

and interpreted the results in consultation with J.B. and J.S. A.C. led the manuscript
writing with contributions from J.B. and J.S.

Additional information

Supplementary information is available in the

. Reprints and

permissions information is available online at

www.nature.com/reprints

Correspondence and requests for materials should be addressed to A.C.

Competing financial interests

The authors declare no competing financial interests.

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