Page 1
E N V I R O N M E N T A L S T U D I E S
Copyright © 2018
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
NonCommercial
License 4.0 (CC BY-NC).
Soil erosion is unlikely to drive a future carbon
sink in Europe
Emanuele Lugato
1
*, Pete Smith
2
, Pasquale Borrelli
3
, Panos Panagos
1
, Cristiano Ballabio
1
,
Alberto Orgiazzi
1
, Oihane Fernandez-Ugalde
1
, Luca Montanarella
1
, Arwyn Jones
1
Understanding of the processes governing soil organic carbon turnover is confounded by the fact that C feedbacks driven
by soil erosion have not yet been fully explored at large scale. However, in a changing climate, variation in rainfall erosivity
(and hence soil erosion) may change the amount of C displacement, hence inducing feedbacks onto the land C cycle. Using
a consistent biogeochemistry-erosion model framework to quantify the impact of future climate on the C cycle, we show
that C input increases were offset by higher heterotrophic respiration under climate change. Taking into account all the
additional feedbacks and C fluxes due to displacement by erosion, we estimated a net source of 0.92 to 10.1 Tg C year
−
1
from agricultural soils in the European Union to the atmosphere over the period 2016
–
2100. These ranges represented a
weaker and stronger C source compared to a simulation without erosion (1.8 Tg C year
−
1
), respectively, and were
dependent on the erosion-driven C loss parameterization, which is still very uncertain. However, when setting a baseline
with current erosion rates, the accelerated erosion scenario resulted in 35% more eroded C, but its feedback on the C cycle
was marginal. Our results challenge the idea that higher erosion driven by climate will lead to a C sink in the near future.
INTRODUCTION
The latest projections of soil organic carbon (SOC) changes from dif-
ferent Earth system models (ESMs) show a very wide range of values,
adding uncertainty on land-atmospheric carbon (C) feedback under cli-
mate change (
1
). Because the soil is the largest terrestrial C pool, differ-
ent representations of SOC turnover may amplify the magnitude and
even the sign of the C balance (
2
). Recent studies suggest that the general
soil C sink simulated by 2100 is too optimistic, as ESMs underestimate
the mean age of SOC (
3
). Moreover, the level of confidence of current
projections and the possibility of improving process descriptions of SOC
dynamics may be further limited by the exclusion of relevant processes,
such as soil erosion (
4
). This erosional process not only redistributes
SOC across the landscape but also generates different feedbacks (
5
,
6
).
In eroding landscape positions, the C that is laterally displaced can be
partially replaced with new photosynthates stabilized by the less C-
saturated subsoil exposed by erosion, generating a continuous C sink
(that is, dynamic replacement) (
7
). The displaced SOC can be mineral-
ized during transport and preserved in depositional sites with different
degrees of efficiency (
8
). Depending on the strength of these processes,
which remain the topic of scientific debate, erosion has been claimed to
induce both a net source and net sink of C (
9
–
11
). A recent study, for
instance, estimates that erosion has offset one-third of C emissions from
land cover change since the onset of agriculture (
12
). This global-scale
analysis highlights how agricultural perturbation of more stable land use
(grassland and forest) could generate a C sink over millennia (
13
). In
these approaches, the net effect of soil erosion on soil-atmosphere C ex-
change is inferred from the measurement of SOC stock change against a
reference soil profile, which may integrate the effect of other drivers in
addition to the induced erosion effect on the C cycle (the so-called black
box model). For instance, primary productivity and soil respiration can
be affected by anthropogenic interventions in eroding/depositional
areas, leading to a SOC change not entirely driven by the
“
erosion effect.
”
Therefore, inventory-based approaches are really useful for tracking past
changes but are unlikely to be useful for predicting future change in
which the impacts of projected climate on both vertical C fluxes (pho-
tosynthesis and respiration) and, indirectly, C redistribution by rainfall
erosivity variation are important drivers. As erosion is a widespread phe-
nomenon, especially on current agricultural land use (
14
), any approach
oriented toward projecting future changes requires a framework that can
dynamically assess variation of drivers at the same time, integrating cur-
rent erosion rates as a baseline condition.
The erosion-induced C feedbacks have been studied at the scale of
small hillslopes and watersheds (
6
), but the lack of large-scale data, in
conjunction with gaps in process understanding, has prevented the cou-
pling of erosion/distribution processes in large-scale modeling. For ex-
plaining past changes, several large-scale data-driven approaches (
15
) or
integrated small-scale modeling (
16
,
17
) studies have been published,
but no study to date has attempted to project future changes, which is
the subject of this paper.
Biogeochemistry-erosion model framework
Here, we use a consistent biogeochemistry-erosion model framework to
quantify the impact of future climate on the C cycle, taking into account
accelerated water erosion through variation in rainfall erosivity (
18
). The
model couples the process-based biogeochemistry model CENTURY
(
19
,
20
) and the RUSLE2015 erosion model (
21
), the latter based on
the Revised Universal Soil Loss Equation (RUSLE). The strength of this
approach is that (i) any SOC change related to land use or management
activity, including the effect of climate on plant productivity and soil
respiration, propagates as a different amount (and type) of displaced
SOC, potentially inducing a dynamic feedback, and (ii) land use and
management directly affect the soil erosion rates, giving a bidirectional
feedback on C balance.
This integrated model has already been used to quantify the current
impact of erosion in the agricultural soil of the European Union (EU) at
a resolution of 1 km
2
(fig. S1) (
22
), showing a high level of accuracy in
simulating dynamic replacement in comparison with previous studies
and the overall SOC budget (section S2: figs. S2 to S5). Some of the pre-
vious assumptions on sediment distribution that are required for running
the model at continental scale were further tested using a delivery/sedi-
mentary model, as detailed in section S3 (figs. S6 and S7 and table S1).
1
European Commission, Joint Research Centre, Sustainable Resources Directorate, Via
E. Fermi 2749, I-21027 Ispra (VA), Italy.
2
Institute of Biological and Environmental
Sciences, University of Aberdeen, 23 St. Machar Drive, Aberdeen AB24 3UU, UK.
3
Environmental Geosciences, University of Basel, Basel, Switzerland.
*Corresponding author. Email: emanuele.lugato@ec.europa.eu
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
1 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 2
The model was run for the Representative Concentration Pathway
(RCP) 4.5 climate scenario as simulated by the HadGEM2 climate
model, with current erosion (CE) and accelerated erosion (AE), the lat-
ter induced by the rainfall erosivity change as calculated recently by
Panagos
et al
. (
18
). When the model incorporates erosion, the C balance
accounts for C displacement components (see Materials and Methods)
and does not generate CO
2
fluxes until the C is mineralized. As we were
interested in the net C exchange between soil and atmosphere, the net
soil C flux was calculated
C
net
¼
C
I
C
H
C
Tm
C
Bm
ð
1
Þ
where C
I
and C
H
are the so-called vertical fluxes representing the atmo-
spheric CO
2
fixed in litter inputs and released by soil heterotrophic res-
piration, respectively, while C
Tm
and C
Bm
are the C mineralized during
transport and in the burial deposits. There is limited consensus in the
literature about C mineralization during transport and its preservation
at depositional sites (
23
). To provide a range of model uncertainty, we
ran the model with two configurations according to previous model sen-
sitivity analysis (
22
): the first enhancing the erosion-induced C sink ef-
fect (e) and the second minimizing it (r) (see Materials and Methods).
Last, we calculated the contribution of accelerated erosion (
D
C
AE
) on the
net C exchange as the difference between the scenario with accelerated
and current erosion (both under e and r uncertainty configurations)
D
C
netAE
¼
C
netAE
C
netCE
ð
2
Þ
RESULTS AND DISCUSSION
The effect of climate change on the terrestrial C cycle is predominantly
explained by productivity-driven changes in ESMs (
24
), with potential
A
<–150
–100
–50
–25
0
25
50
100
>150
B
C
<–12
–8
–4
–2
0
2
4
8
>12
D
Fig. 1. Response of C fluxes (Mg C km
−
2
year
−
1
) under the RCP 4.5 (implemented in HadGEM2) climate scenario for the accelerated soil erosion simulation (AE_e).
(
A
to
C
) Average difference in C input, soil heterotrophic respiration, and eroded C, respectively, between the 2090
–
2100 and 2000
–
2010 time frames. (
D
) Cumulative change
in net soil C flux over the period 2016
–
2100 (Mg C km
−
2
) in EU agricultural soils. In (D), negative values represent a C source to the atmosphere, while positive values are
a C sink.
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
2 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 3
significant biases when omitting the land management effect (
25
). Our
model framework, which includes a detailed description of land use and
agricultural management, also showed an increase in C input by 2100
(Fig. 1A), driven by the higher productivity under the HadGEM2
projection observable at mid-high latitude. This increase was almost
offset by enhanced soil respiration, which showed a similar continental
response to C input fluxes (Fig. 1B). By the end of the century, the model
also predicted an overall increase of C displaced by erosion, with an op-
posite trend in western countries (Fig. 1C) and some large areas such as
Andalusia and central France. This pattern partially overlapped with the
change in rainfall erosivity (fig. S8), as eroded C is dependent not only
on soil erosion but also on the evolution of SOC stock due to trends in
productivity and soil respiration. These complex feedbacks emerged in
the net soil flux figure (Fig. 1D), where no clear continental patterns of
land C sink and source were predicted over the period 2016
–
2100.
Looking at the continental C budget in the AE_e scenario (Fig. 2), we
estimated a small C sink from agricultural land equal to 0.09 Tg C year
−
1
(C
I
−
C
H
). Despite the fact that eroded C was more than one order of
magnitude lower than the vertical fluxes, its contribution was impor-
tant; the additional losses generated by C displacement (C
Tm
and
C
Bm
) offset the vertical C sink, leading to a C source from land equal
to 0.92 Tg C year
−
1
over the period 2016
–
2100. When we ran the model
without erosion, the C source from land was even higher and equal to
1.8 Tg C year
−
1
(table S2). This result suggests that climate change was
the overarching driver on net CO
2
losses, which were partially offset by
soil erosion through dynamic replacement (fig. S5), under this conserv-
ative model configuration.
In the AE_r scenario, the halving of the enrichment factor caused a
direct reduction of eroded C, which amounted to 63% than the AE_e
(Fig. 2). Despite the lower SOC loss from the soil profile by erosion, the
concomitant lower dynamic replacement led to a positive feedback on
the vertical fluxes (C
I
< C
H
with a value of
−
0.56 Tg C year
−
1
). In this
model configuration, we estimated a net soil C flux of
−
10.1 Tg C year
−
1
,
largely driven by the lower burial efficiency of buried SOC in the model
setup (that is, 0.2). Although this value represents a quite extreme
configuration of our model framework, a recent study indicated burial
efficiencies of 0.22 and 0.14 in colluvium and alluvium stores of tempe-
rate zones, respectively (
12
). In this scenario, the C feedbacks generated
by erosion resulted in a net soil C flux five times higher than that driven
by climate change alone (
−
10.1 versus
−
1.8 Tg C year
−
1
).
The C displacement between eroding and depositional areas also in-
duced different C feedbacks mediated by the associated nitrogen (N)
(that is, in the organic matter), in turn affecting crop productivity at
different landscape positions (see fig. S9 and related discussion) (
26
).
At the EU level, we calculated a C
I
loss of 0.05 Tg C year
−
1
per Tg of
C eroded, which suggests relatively small reductions in productivity
over the period 2016
–
2100, as supported by other studies (
27
). Con-
versely, the effect may be much higher locally and the magnitude of
our estimate may be conservative, if the increase in rainfall erosivity
will be associated with a higher frequency of extreme events (
28
). In
CENTURY, the unique feedback on plant productivity due to erosion is
related to the displacement of N (associated with SOC) and its turnover,
while the model does not simulate mechanistically physicochemical soil
changes (for example, in structure, texture, and loss of all nutrients) due
to rain-driven intensive erosion events or the direct impact on the veg-
etation (
29
). Direct and indirect effects of erosion on plant productivity
may then be very important (
30
). Despite the fact that the direct plant
response to erosion is only modulated by N in our model, this is the first
attempt to dynamically include lateral C fluxes in a process-based model
framework at continental scale.
C
netAE
[–0.01; –2.34]
C
I
[604.40; 604.98]
C
E
[18.61; 11.91]
C
S
[9.02; 9.02]
DOC
[8.86; 8.88]
C
D
[16.23; 9.54]
C
B
[13.48; 10.64]
C
Bm
[0.67; 8.51]
C
Tm
[0.33; 1.06]
C
H
[604.31; 605.54]
C
net
[–0.92; –10.13]
C
R
[2.05; 1.31]
Fluxes [e; r]
C
I
[604.46; 604.99]
C
E
[13.76; 8.79]
C
S
[6.65; 6.65]
DOC
[8.86; 8.88]
C
D
[12.0; 7.04]
C
B
[9.93; 7.86]
C
Bm
[0.5; 6.28]
C
Tm
[0.24; 0.78]
C
H
[604.63; 605.72]
C
net
[–0.91; –7.79]
C
R
[1.51; 0.97]
Fluxes [e; r]
Fig. 2. Cumulative C budget (Tg C year
−
1
) over the period 2016
–
2100 at the EU level in the accelerated (AE) and current (CE) soil erosion scenarios.
The
numbers in brackets [e; r] are the outcomes of the two model configurations: enhanced erosion-induced C sink (e), with the mineralization during transport set to 2%, the burial
efficiency to 95%, and the enrichment factor to 2, and reduced erosion-induced C sink (r), where the same parameters were set to 10%, 20%, and 1, respectively. Dark arrows
represent C displacements, while blue arrows represent C fluxes as C exchanges with the atmosphere (CO
2
-C). For the net soil flux, negative values represent a C source to the
atmosphere, while positive values represent a C sink (see Materials and Methods for the full C balance component details). The agricultural area simulated (arable crops, grassland,
and permanent crops) covers 1.88 Mkm
2
.
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
3 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 4
Soil erosion is a widespread process that currently affects EU agri-
cultural soils (
21
); therefore, the evaluation of future C feedbacks driven
by its change requires a baseline reflecting the current conditions. To
quantify the potential C feedback of accelerated erosion in the future,
we ran the model, keeping erosion rates constant at current levels (cur-
rent erosion: CE scenarios). With this run, we estimated that the future
variation in rainfall erosivity led to 35% more C displaced by erosion in
both configurations (e and r; Fig. 2). Overall, the climate-induced ero-
sion feedback (
D
C
netAE
) was marginal in the e scenario but was more
pronounced in r, with a loss of 2.34 Tg C year
−
1
(Fig. 2 and table S2).
Considering the contrasting ranges of parameterization in the two con-
figurations, it is likely that we are catching the magnitude of this feed-
back, although there are no data against which to compare the model.
Despite the fact that accelerated erosion induced a negligible C
source continentally under the AE_e scenario (0.01 Tg C year
−
1
), we
observed a marked regional variability (Fig. 3A). In 35% of the cells (that
is, 66 Mha), we estimated an induced C sink by the accelerated erosion,
completely offset by sources for the remaining agricultural areas. In the
AE_r scenario, the erosion-induced C sink was present in only 13% of
the agricultural soils (Fig. 3B), while lower values than those in AE_e
were estimated all over the continent.
Projections of land C fluxes in current Earth system simulations un-
der climate change are particularly affected by C turnover in soils (
31
)
and are still missing the erosion feedbacks over decadal scales (
32
). This
study may be a preliminary step toward coupling landscape dynamics in
the biogeochemistry component of land schemes. Our results highlight
how the interaction among soil variability, local management, climate,
and geophysical processes leads to a variable C response, both in space
and in sign of emissions. Upscaling of inventory-based approaches to
large areas should therefore be done with care so that processes that
have inherent variability are not oversimplified. Furthermore, our
results challenge the suggestion that erosion will lead to a C sink in
the near future, but they also claim for new researches investigating
poorly known landscape C processes.
CONCLUSIONS
Our modeling framework has some limitations. Soil erosion by water is
a selective process that redistributes different size particles across the
landscape, in turn potentially changing the environmental conditions
affecting plant growth and SOC decomposition (
33
). In addition, the
displaced C is associated to different mineral/particle fractions (for ex-
ample, C bound to mineral particles, C in aggregates, and particulate C)
(
34
), and the turnover of these different fractions is not yet implemented
into the large-scale models, which are still currently based on the com-
partment approach of
“
conceptual
”
C pools (
35
). We estimate that the
development of full sediment delivery models coupled with new genera-
tions of biogeochemical models based on measurable C fractions will
take another decade. To do this, we will need more information and
increased process understanding to transfer microscale processes, such
as aggregate breakdown, to millions of hectares. Meanwhile, we could
account for lateral C fluxes with feasible approaches, as we have de-
scribed here. Future land C projections rely on complex Earth system
and biogeochemical models that do not incorporate the erosion process,
lowering the level of confidence of our predictions. We have attempted
to include the main lateral and vertical C fluxes as driven by a climate
change projection to increase the confidence of future projections of soil
C erosion on the C cycle.
MATERIALS AND METHODS
Model framework and configuration
The model framework was based on coupling the process-based biogeo-
chemistry model CENTURY to the RUSLE2015 erosion model. This
A
<–2.0
–0.8
–0.4
–0.2
0
0.2
0.4
0.8
>2
B
Fig. 3. Climate-induced erosion feedback on net soil C flux (Mg C km
−
2
year
−
1
).
The values represent the difference in net soil C flux (
D
C
netAE
) between the accelerated
erosion (AE) and current erosion (CE) scenarios (Eq. 2) over the period 2016
–
2100. (
A
and
B
) The e and r model configurations. Negative values indicate an erosion-induced source
of C to the atmosphere driven by a variation in rainfall erosivity, while positive values show an erosion-induced C sink.
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
4 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 5
model integration has been presented in previous studies, and a sum-
mary flowchart is given as fig. S1. We summarized the model setup and
main assumptions as follows.
1) The CENTURY model was ran at a resolution of 1 km
2
for the
agricultural soil of the EU, using the soil erosion from RUSLE2015
model as input for CENTURY. Starting from 1900, the erosion process
was implemented, keeping the climate, soil, and topographic factors
(R, K, and LS, respectively) constant. While we considered K and LS
factors quite invariable on a centennial scale, the C factor associated
with the crop type was dynamically varied with crop rotations and land
use changes. The simulated land use was based on the CORINE Land
Cover 1990, 2000, and 2006, supplemented with Eurostat (Statistical
Office of the European Communities)/FAO (Food and Agriculture
Organization of the United Nations) statistics to build up crop rota-
tions and implement consistent agronomic inputs (fertilization, irriga-
tion, etc.). Before 1990, we assumed the same land use but with
different agricultural techniques characterized by lower productivity
crops, lower rates of mineral N, and different rotation schemes.
2) Originally, we assumed that each 1-km
2
grid cell is composed of
an eroding area and a depositional area, the latter retaining a proportion
of eroded C. The partition was based on the study of Van Oost
et al
.
(
11
), who found that 53 to 95% of eroded SOC were retained in the
catchment and redeposited in a limited area (14 to 35%) within the
same catchment. Taking the central values, we assumed that 25% of
our grid cells were depositional areas, which received 70% of eroded soil.
The remaining 30% was accounted for as leaving the grid cell as
potential sediments and C discharged to riverine systems. These as-
sumptions on sediment distributions, necessary to work at continental
scale, were further tested using a delivery/sedimentary model in regional
simulations, as detailed in section S3. After the intercomparison be-
tween the original and the sedimentary model
–
driven configuration,
we set a new sediment delivery ratio of 0.11, as most of the sediments
were predicted to be retained in land under the delivery/sedimentary
model runs.
3) The replacement of eroded soil in the fixed profile comes by
“
re-
cruitment
”
from the subsoil layers (SSLs), characterized by a SOC
composition defined quantitatively and qualitatively as a partition
among the three CENTURY SOC pools (active, slow, and passive).
These pools are functionally defined on the basis of mean residence
time, as opposed to measured fractions, and thus cannot be constrained
by measurements applicable at the EU scale. The most rational ap-
proach was to adopt the calibration of Harden
et al
. (
7
), which implicitly
related the subsoil composition to the topsoil composition; accordingly,
the subsoil SOC pools at time
t
= 0 (that is, 1900) were assumed to be
composed as a fraction of the top soil pools, as follows
SSL
ð
t
0
Þ ¼ ð
0
:
2
Active surface
Þ þ ð
0
:
4
Slow surface
Þþ
ð
0
:
8
Passive surface
Þ
The lower horizon pools were decremented at each time step using
the following relationships for each of the three SOC pools (i)
SSL
i
ð
t
Þ ¼
SSL
i
ð
t
1
Þ
SSL
i
ð
t
1
Þ
FLOST
ð
t
Þ
where
t
is the time and FLOST is the fraction of topsoil lost to erosion
[for the sake of clarity, SSL
i
(
t
−
1) × FLOST(
t
) is the C moving up from
subsoil annually].
Although CENTURY does not explicitly simulate depositional
processes, they can be mimicked while ensuring conservation of mass
(soil and C) within the system boundaries. First, we calculated the
amount of soil transported from the eroding to the depositional frac-
tion of each grid cell. Second, we simulated the burial effect of the dep-
osition event as a
“
negative
”
erosion event, which moves a SOC
fraction below the fixed soil profile (C burial flux), proportional to
the amount of soil deposited on the surface. Last, the associated
amount of deposited C was controlled by setting the incoming SOC
pool in the model. This procedure required an iterative process,
running the model year by year, first in the eroding area and then
in the depositional area of each pixel. A check of the C balance closure
was done at the end of simulations.
Future rainfall erosivity and erosion
The methodology for the estimation of the future rainfall erosivity was
presented in a very recent study (
18
). Summarizing, this made use of the
REDES (Rainfall Erosivity Database at European Scale) and a statistical
approach (Gaussian process regression), used to spatially interpolate
rainfall erosivity data with climatic scenarios. Using the HadGEM2 gen-
eral circulation model climate projections (for RCP 4.5), available at the
WorldClim repository, the rainfall erosivity was calculated for two time
periods, namely, 2050 and 2070. Therefore, the soil erosion was recal-
culated, keeping the current factors of RUSLE, except the rainfall ero-
sivity. The ratio between the future and the current soil erosion by water
is reported in fig. S8.
Climate change scenarios with and without
accelerated erosion
The model was run with the current climate up to 2015 and with the
same HadGEM2-ES_rcp45 scenario for the 2016
–
2100 time frame for
consistency. Monthly precipitation and maximum and minimum air
temperature were downloaded from the World Climate Research
Programme
–
Coordinated Regional Downscaling Experiment (WCR-
CORDEX) portal (https://esgf-node.ipsl.upmc.fr/search/cordex-ipsl/).
The present land use and agricultural management remained the same
to isolate the climate effect on both productivity/turnover and erosion C
feedbacks.
In the accelerated erosion (AE) scenario, we linearly interpolated the
soil erosion rates since 2016, considering the two soil erosion projec-
tions at 2050 and 2070. The name
“
accelerated erosion
”
refers to the
average effect of rainfall erosivity change on increasing soil erosion.
Higher and lower rainfall erosivity patterns are present at the EU level,
as depicted in fig. S8. In the current erosion (CE) scenario, we kept the
actual soil erosion rates.
Given the fact that there is limited consensus on C preservation/
decomposition upon its displacement, we ran the model with two con-
figurations, enhancing (e model setup) and minimizing (r model setup)
the erosion as an induced C sink (or source). In particular, in the e
configuration, the enrichment factor (that is, the C concentration in
eroded sediments with respect to the bulk soils) was set to 2, the C min-
eralization during sediment transport to 2% yearly, and the burial effi-
ciency (that is, the amount of C preserved when moved downward into
the soil profile) to 95%. In the r configuration, the three parameters were
set to 1, 10, and 20%, respectively.
According to previous sensitivity analyses, a higher enrichment
factor promotes a higher dynamic replacement of C (inducing a C sink
in eroding position), while lower mineralization and higher burial effi-
ciency preserve the displaced C. Considering the ranges of parameters
from different studies, our configuration should reflect the outer bound-
ary of the erosion effect in inducing a sink/source of C.
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
5 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 6
C budget
The CENTURY model was parameterized to simulate the soil C and N
turnover in a fixed profile of 0 to 30 cm. When the model incorporates
soil erosion (
22
), an upward C flux from a subsoil pool follows the ero-
sion event (as described in point 3 of the
“
Model framework and
configuration
”
section). Moreover, the C balance accounts for C
displacement components, some of them representing C moving
outside the land domain (such as C to rivers, lakes, and ocean). For
the purpose of the study, we set our boundary conditions to the land
and, despite estimating a lump term accounting for C delivered to the
riverine system, we did not calculate its decomposition (to CO
2
) related
to the turnover outside the land.
For the eroding area, the C balance is
D
SOC
¼
C
I
þ
C
S
C
H
C
E
DOC
ð
3
Þ
where
D
SOC is the SOC stock change in the fixed profile, C
I
is the C
input through the remaining net primary productivity (NPP; after C
exportation by harvest but including roots and manure), C
H
is the het-
erotrophic respiration, C
S
is the incoming SOC from a deeper layer, C
E
is the lateral C flux by sediment transport, and DOC is the C exported as
dissolved organic C.
For the depositional area, the balance is
D
SOC
¼
C
I
þ
C
D
C
H
C
B
DOC
ð
4
Þ
where C
D
is the C deposited coming from eroding areas and C
B
is
the C that is moved out (that is, buried) of the simulated profile as
a consequence of soil deposition.
However, the net changes in SOC storage do not directly equate to
the net CO
2
exchange from soil to the atmosphere due to the combina-
tion of vertical C fluxes (C
I
−
C
H
) of eroding and depositional areas and
the mineralized fraction of SOC upon displacement. Therefore, the net
soil C flux (as CO
2
) in the grid cell is given by
C
net
¼ ½
C
I
C
H
C
Tm
C
Bm
ð
5
Þ
where [C
I
−
C
H
] is the net vertical C flux, C
Tm
represents the fraction of
eroded SOC mineralized during the transport, and C
Bm
is the fraction of
buried C that is mineralized in depositional area. Because the uncertain-
ty in simulating C turnover in deeper layers increases with depth (due to
cumulative sediment deposition), the cumulative C
B
fluxes, over the pe-
riod 2016
–
2100, were assumed to be preserved from decomposition
with two contrasting burial efficiency rates over a 100-year horizon
(0.95 and 0.2). The corresponding CO
2
flux to the atmosphere was then
calculated as C
B
× (1
−
burial efficiency).
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/4/11/eaau3523/DC1
Section S1. Model framework
Section S2. Model validation
Section S3. Simplified versus delivery sediment soil budget
Fig. S1. Modeling framework of the integration between the CENTURY biogeochemistry model
application and the RUSLE2015 erosion model.
Fig. S2. Distribution of LUCAS sampling location for all land uses.
Fig. S3. Comparison of cumulative SOC stock distribution between simulated and LUCAS
values grouped at the national level.
Fig. S4. Comparison of cumulative SOC stock distribution between simulated and LUCAS
values grouped for environmental factors.
Fig. S5. Comparison of modeled erosion-induced vertical C exchange against inventory-based
data.
Fig. S6. Selected regions where the model configuration intercomparison was run.
Fig. S7. Simulated C balance components of CENTURY driven by the WATEM model in
comparison with the standard original and corrected sedimentary parameterization.
Fig. S8. Change in soil erosion by 2050 and 2070 due to projected changes in rainfall erosivity.
Fig. S9. Distribution of change in NPP between depositional and eroding areas for the different
scenarios simulated.
Table S1. Comparison of C budget between different couplings of CENTURY with erosion/
delivery sediment parameterizations (Mg C km
−
1
year
−
1
) in the selected regions.
Table S2. Cumulative C budget (Tg C year
−
1
) over the period 2016
–
2100 at the EU level for
scenarios with accelerated (AE) and current (CE) soil erosion.
References (
36
–
44
)
REFERENCES AND NOTES
1. Y. Luo, A. Ahlström, S. D. Allison, N. H. Batjes, V. Brovkin, N. Carvalhais, A. Chappell, P. Ciais,
E. A. Davidson, A. Finzi, K. Georgiou, B. Guenet, O. Hararuk, J. Harden, Y. He, F. Hopkins, L. Jiang,
C. Koven, R. B. Jackson, C. D. Jones, M. Lara, J. Liang, A. David McGuire, W. Parton, C. Peng,
J. Randerson, A. Salazar, C. A. Sierra, M. J. Smith, H. Tian, K. E. O. Todd-Brown, M. S. Torn,
K. J. van Groenigen, Y. Wang, T. O. West, Y. Wei, W. R. Wieder, J. Xia, X. Xu, X. Xu, T. Zhou, Toward
more realistic projections of soil carbon dynamics by Earth system models.
Global Biogeochem.
Cycles
30
, 40
–
56 (2016).
2. M. A. Bradford, W. R. Wieder, G. B. Bonan, N. Fierer, P. A. Raymond, T. W. Crowther,
Managing uncertainty in soil carbon feedbacks to climate change.
Nat. Clim.
Chang.
6
, 751
–
758 (2016).
3. Y. He, S. E. Trumbore, M. S. Torn, J. W. Harden, L. J. S. Vaughn, S. D. Allison, J. T. Randerson,
Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century.
Science
353
, 1419
–
1424 (2016).
4. A. A. Berhe, J. Harte, J. W. Harden, M. S. Torn, The significance of the erosion-induced
terrestrial carbon sink.
Bioscience
57
, 337
–
346 (2007).
5. F. M. S. A. Kirkels, L. H. Cammeraat, N. J. Kuhn, The fate of soil organic carbon upon
erosion, transport and deposition in agricultural landscapes
—
A review of different
concepts.
Geomorphology
226
, 94
–
105 (2014).
6. S. Doetterl, A. A. Berhe, E. Nadeu, Z. Wang, M. Sommer, P. Fiener, Erosion, deposition and
soil carbon: A review of process-level controls, experimental tools and models to
address C cycling in dynamic landscapes.
Earth Sci. Rev.
154
, 102
–
122 (2015).
7. J. W. Harden, J. M. Sharpe, W. J. Parton, D. S. Ojima, T. L. Fries, T. G. Huntington,
S. M. Dabney, Dynamic replacement and loss of soil carbon on eroding cropland.
Global Biogeochem. Cycles
13
, 885
–
901 (1999).
8. Z. Wang, K. Van Oost, A. Lang, T. Quine, W. Clymans, R. Merckx, B. Notebaert, G. Govers,
The fate of buried organic carbon in colluvial soils: A long-term perspective.
Biogeosciences
11
, 873
–
883 (2014).
9. R. Lal, Soil erosion and the global carbon budget.
Environ. Int.
29
, 437
–
450 (2003).
10. R. Lal, Accelerated soil erosion as a source of atmospheric CO
2
.
Soil Tillage Res.
(2018);
https://doi.org/10.1016/j.still.2018.02.001.
11. K. Van Oost, T. A. Quine, G. Govers, S. De Gryze, J. Six, J. W. Harden, J. C. Ritchie,
G. W. McCarty, G. Heckrath, C. Kosmas, J. V. Giraldez, J. R. Marques da Silva, R. Merckx, The
impact of agricultural soil erosion on the global carbon cycle.
Science
318
, 626
–
629
(2007).
12. Z. Wang, T. Hoffmann, J. Six, J. O. Kaplan, G. Govers, S. Doetterl, K. Van Oost, Human-
induced erosion has offset one-third of carbon emissions from land cover change.
Nat. Clim. Chang.
7
, 345
–
349 (2017).
13. J. Sanderman, A. A. Berhe, Biogeochemistry: The soil carbon erosion paradox.
Nat. Clim.
Chang.
7
, 317
–
319 (2017).
14. P. Borrelli, D. A. Robinson, L. R. Fleischer, E. Lugato, C. Ballabio, C. Alewell, K. Meusburger,
S. Modugno, B. Schütt, V. Ferro, V. Bagarello, K. Van Oost, L. Montanarella, P. Panagos,
An assessment of the global impact of 21st century land use change on soil erosion.
Nat. Commun.
8
, 2013 (2017).
15. Y. Yue, J. Ni, P. Ciais, S. Piao, T. Wang, M. Huang, A. G. L. Borthwick, T. Li, Y. Wang,
A. Chappell, K. Van Oost, Lateral transport of soil carbon and land
−
atmosphere
CO
2
flux induced by water erosion in China.
Proc. Natl. Acad. Sci. U.S.A.
113
,
6617
–
6622 (2016).
16. E. Nadeu, A. Gobin, P. Fiener, B. van Wesemael, K. van Oost, Modelling the impact of
agricultural management on soil carbon stocks at the regional scale: The role of lateral
fluxes.
Glob. Chang. Biol.
21
, 3181
–
3192 (2015).
17. M. Lacoste, V. Viaud, D. Michot, C. Walter, Landscape-scale modelling of erosion
processes and soil carbon dynamics under land-use and climate change in
agroecosystems.
Eur. J. Soil Sci.
66
, 780
–
791 (2015).
18. P. Panagos, C. Ballabio, K. Meusburger, J. Spinoni, C. Alewell, P. Borrelli, Towards estimates
of future rainfall erosivity in Europe based on REDES and WorldClim datasets.
J. Hydrol.
548
, 251
–
262 (2017).
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
6 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 7
19. E. Lugato, P. Panagos, F. Bampa, A. Jones, L. Montanarella, A new baseline of organic
carbon stock in European agricultural soils using a modelling approach.
Glob. Chang.
Biol.
20
, 313
–
326 (2014).
20. E. Lugato, F. Bampa, P. Panagos, L. Montanarella, A. Jones, Potential carbon sequestration
of European arable soils estimated by modelling a comprehensive set of management
practices.
Glob. Chang. Biol.
20
, 3557
–
3567 (2014).
21. P. Panagos, P. Borrelli, J. Poesen, C. Ballabio, E. Lugato, K. Meusburger, L. Montanarella,
C. Alewell, The new assessment of soil loss by water erosion in Europe.
Environ. Sci.
Policy
54
, 438
–
447 (2015).
22. E. Lugato, K. Paustian, P. Panagos, A. Jones, P. Borrelli, Quantifying the erosion effect on
current carbon budget of European agricultural soils at high spatial resolution.
Glob. Chang. Biol.
22
, 1976
–
1984 (2016).
23. Y. Hu, A. A. Berhe, M. L. Fogel, G. J. Heckrath, N. J. Kuhn, Transport-distance specific
SOC distribution: Does it skew erosion induced C fluxes?
Biogeochemistry
128
, 339
–
351
(2016).
24. C. D. Koven, J. Q. Chambers, K. Georgiou, R. Knox, R. Negron-Juarez, W. J. Riley, V. K. Arora,
V. Brovkin, P. Friedlingstein, C. D. Jones, Controls on terrestrial carbon feedbacks by
productivity versus turnover in the CMIP5 Earth System Models.
Biogeosciences
12
,
5211
–
5228 (2015).
25. S. S. Nyawira, J. E. M. S. Nabel, V. Brovkin, J. Pongratz, Input-driven versus turnover-driven
controls of simulated changes in soil carbon due to land-use change.
Environ. Res. Lett.
12
, 084015 (2017).
26. A. A. Berhe, R. T. Barnes, J. Six, E. Marín-Spiotta, Role of soil erosion in biogeochemical
cycling of essential elements: Carbon, nitrogen, and phosphorus.
Annu. Rev. Earth
Planet. Sci.
46
, 521
–
548 (2018).
27. M. M. Bakker, G. Govers, R. A. Jones, M. D. A. Rounsevell, The effect of soil erosion on
Europe
’
s crop yields.
Ecosystems
10
, 1209
–
1219 (2007).
28. E. M. Fischer, U. Beyerle, R. Knutti, Robust spatially aggregated projections of climate
extremes.
Nat. Clim. Chang.
3
, 1033
–
1038 (2013).
29. M. Reichstein, M. Bahn, P. Ciais, D. Frank, M. D. Mahecha, S. I. Seneviratne, J. Zscheischler,
C. Beer, N. Buchmann, D. C. Frank, D. Papale, A. Rammig, P. Smith, K. Thonicke,
M. van der Velde, S. Vicca, A. Walz, M. Wattenbach, Climate extremes and the carbon
cycle.
Nature
500
, 287
–
295 (2013).
30. R. Lal, W. C. Moldenhauer, Effects of soil erosion on crop productivity.
CRC Crit. Rev.
Plant Sci.
5
, 303
–
367 (1987).
31. K. E. O. Todd-Brown, J. T. Randerson, F. Hopkins, V. Arora, T. Hajima, C. Jones,
E. Shevliakova, J. Tjiputra, E. Volodin, T. Wu, Q. Zhang, S. D. Allison, Changes in soil
organic carbon storage predicted by Earth system models during the 21st century.
Biogeosciences
11
, 2341
–
2356 (2014).
32. A. Chappell, J. Baldock, J. Sanderman, The global significance of omitting soil erosion
from soil organic carbon cycling schemes.
Nat. Clim. Chang.
6
, 187
–
191 (2016).
33. H. Xiao, Z. Li, X. Chang, B. Huang, X. Nie, C. Liu, L. Liu, D. Wang, J. Jiang, The mineralization
and sequestration of organic carbon in relation to agricultural soil erosion.
Geoderma
329
, 73
–
81 (2018).
34. F. Wilken, P. Fiener, K. Van Oost, Modelling a century of soil redistribution processes and
carbon delivery from small watersheds using a multi-class sediment transport model.
Earth Surf. Dyn.
5
, 113
–
124 (2017).
35.
R. Abramoff, X. Xu, M. Hartman, S. O
’
Brien, W. Feng, E. Davidson, A. Finzi, D. Moorhead,
J. Schimel, M. Torn, M. A. Mayes, The Millennial model: In search of measurable pools
and transformations for modeling soil carbon in the new century.
Biogeochemistry
137
,
51
–
71 (2018).
36. T. P. Robinson, G. R. Winter, G. Conchedda, T. P. Van Boeckel, V. Ercoli, E. Palamara,
G. Cinardi, L. D
’
Aietti, S. I. Hay, M. Gilbert, Mapping the global distribution of livestock.
PLOS ONE
9
, e96084 (2014).
37. S. Siebert, P. Döll, J. Hoogeveen, J.-M. Faures, K. Frenken, S. Feick, Development and validation
of the global map of irrigation areas.
Hydrol. Earth Syst. Sci.
2
, 1299
–
1327 (2005).
38. G. Tóth, A. Jones, L. Montanarella, The LUCAS topsoil database and derived information
on the regional variability of cropland topsoil properties in the European Union.
Environ. Monit. Assess.
185
, 7409
–
7425 (2013).
39. E. Goidts, B. Van Wesemael, M. Crucifix, Magnitude and sources of uncertainties in soil
organic carbon (SOC) stock assessments at various scales.
Eur. J. Soil Sci.
60
, 723
–
739
(2009).
40. K. Van Oost, G. Govers, P. Desmet, Evaluating the effects of changes in landscape
structure on soil erosion by water and tillage.
Landsc. Ecol.
15
, 577
–
589 (2000).
41. A. J. J. Van Rompaey, G. Verstraeten, K. Van Oost, G. Govers, J. Poesen, Modelling mean
annual sediment yield using a distributed approach.
Earth Surf. Process. Landf.
26
,
1221
–
1236 (2001).
42. P. J. J. Desmet, G. Govers, A GIS procedure for automatically calculating the USLE LS factor on
topographically complex landscape units.
J. Soil Water Conserv.
51
, 427
–
433 (1996).
43. P. Borrelli, K. Van Oost, K. Meusburger, C. Alewell, E. Lugato, P. Panagos, A step towards a
holistic assessment of soil degradation in Europe: Coupling on-site erosion with sediment
transfer and carbon fluxes.
Environ. Res.
161
, 291
–
298 (2018).
44. K. Beven, A. Binley, GLUE: 20 years on.
Hydrol. Process.
28
, 5897
–
5918 (2014).
Acknowledgments:
We thank I. Biavetti for graphical support in designing Fig. 2.
Funding:
The work was carried out as part of the JRC
’
s Institutional Work Programme
under the Natural Capital Soil Project (Project 702), Work Package 5037
“
Soil for Climate
Change.
”
Author contributions:
E.L. developed the research concepts and conducted the
modeling and analyses. E.L., P.S., and P.B. performed the data interpretation. E.L. took a lead on
writing the paper with contributions from P.S. All authors assisted in the revision of the
manuscript.
Competing interests:
The authors declare that they have no competing interests.
Data and materials availability:
All data needed to evaluate the conclusions in the paper
are available at the European Soil Data Centre (ESDAC) of the European Commission Joint
Research Centre (http://esdac.jrc.ec.europa.eu/). Additional data related to this paper may be
requested from the authors.
Submitted 1 June 2018
Accepted 18 October 2018
Published 14 November 2018
10.1126/sciadv.aau3523
Citation:
E. Lugato, P. Smith, P. Borrelli, P. Panagos, C. Ballabio, A. Orgiazzi, O. Fernandez-Ugalde,
L. Montanarella, A. Jones, Soil erosion is unlikely to drive a future carbon sink in Europe.
Sci. Adv.
4
,
eaau3523 (2018).
S C I E N C E A D V A N C E S |
R E S E A R C H A R T I C L E
Lugato
et al
.
Sci. Adv.
2018;
4
: eaau3523
14 November 2018
7 of 7
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from
Page 8
Soil erosion is unlikely to drive a future carbon sink in Europe
Fernandez-Ugalde, Luca Montanarella and Arwyn Jones
Emanuele Lugato, Pete Smith, Pasquale Borrelli, Panos Panagos, Cristiano Ballabio, Alberto Orgiazzi, Oihane
DOI: 10.1126/sciadv.aau3523
(11), eaau3523.
4
Sci Adv
ARTICLE TOOLS
http://advances.sciencemag.org/content/4/11/eaau3523
MATERIALS
SUPPLEMENTARY
http://advances.sciencemag.org/content/suppl/2018/11/09/4.11.eaau3523.DC1
REFERENCES
http://advances.sciencemag.org/content/4/11/eaau3523#BIBL
This article cites 43 articles, 3 of which you can access for free
PERMISSIONS
http://www.sciencemag.org/help/reprints-and-permissions
Terms of Service
Use of this article is subject to the
registered trademark of AAAS.
is a
Science Advances
Association for the Advancement of Science. No claim to original U.S. Government Works. The title
York Avenue NW, Washington, DC 20005. 2017 © The Authors, some rights reserved; exclusive licensee American
(ISSN 2375-2548) is published by the American Association for the Advancement of Science, 1200 New
Science Advances
on November 19, 2018
http://advances.sciencemag.org/
Downloaded from