Borrelli et al. (2020). Land use and climate change impacts on global soil erosion by water (2015-2070)

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both in terms of land use and climate projection. Recent ad-
vancements in remote sensing, wider availability of earth ob-
servation data, and increased processing of big datasets have
enabled the development of new global vegetation indices and
land cover products with both higher spatial resolution and ac-
curacy (15, 16) (

, Global Land Use/Cover and Future

Change). The same goes for the recent release of climate data-
sets, including bias-corrected climate projections of multiple
bioclimatic variables (17), which through robust spatial interpolation
methods allow computation of global estimates of rainfall erosivity,
more closely related to rainfall intensity than rainfall volume (18, 19).
In this work, three alternative scenarios (2.6, 4.5, and 8.5) are tested
using the Shared Socioeconomic Pathway and Representative Con-
centration Pathway (SSP-RCP), greenhouse gas trajectories, and
multiple General Climate Models (GCMs), at a resolution (sub km)
that updates the output, making it more suitable for decision-making.

Soil erosion can be mitigated using sustainable land manage-

ment techniques and suitable policy incentives. Arable land,
subject to conservation agriculture (CA) worldwide, is estimated
to cover 11 to 14% (current study) or

∼

1.42 billion hectares

globally (20). Compared to a baseline scenario without any soil
conservation practices, estimates indicate an overall global soil
erosion reduction of about 7.1% under conservation practices
(12). The footprint that human activities have left on the world

’

s

soils are tangible (3). Soil erosion also affects nutrient and soil
carbon cycling (21). The associated losses of nutrients, such as
nitrogen and phosphorus, and organic carbon (22) have shown
compromising long-term effects on the local ability of certain soils
to meet agricultural production and ecosystem service demands
(9). Global consumption of the three main fertilizer nutrients (N,
P, and K) used to maintain, or improve, soil fertility is currently
growing on a yearly average by 1.5, 2.2, and 2.4%, respectively
(23). This comes along with high onsite economic costs for the
land users to contain the production losses (12) and important
cascading offsite environmental impacts (24). The pressure on
fertile soils (9) through an exacerbation of soil erosion and its
environmental degradation effects (25) is further strained by 1)
the growing population with an estimated peak of 9.4 billion in
2070 (26), 2) the trend toward meat-intensive diets, and 3) a global
climate that tends toward a more vigorous hydrological cycle (27,
28) as discussed above. These are all issues policy teams need to
deal with.

Today, policy makers emphasize the need for an evidence-

based approach. This modeling effort forms part of a wider
program of work coordinated through the United Nations to
inform national policy makers. Soil erosion is closely linked to
gross domestic product (GDP) (12), with poorer countries often
experiencing the greatest impacts. Hence this effort is designed to
provide a globally consistent assessment to help target policy effort.
It will inform countries, especially those that are often least able to
identify risk, to focus resources to mitigate degradation by erosion
most efficiently and effectively. Moreover, the UN endorsed the
World Overview of Conservation Approaches and Technologies
(WOCAT) database (29), which contains freely available informa-
tion on sustainable land management (SLM) practices. By com-
bining the model outputs with WOCAT SLMs, the two resources
give decision makers a powerful set of national scale tools to
identify water erosion hotspots at appropriate scales and mitigate
them locally through intervention planning. While past studies have
provided insights into the possible future trends of global soil ero-
sion by water with reference to land use and climate changes (13),
they have not offered the resolution for guiding policy intervention.
Unique to our study is the attempt to project future changes in soil
erosion at the global level using the state-of-the-knowledge har-
monized set of land use and climate scenarios developed according
to the new SSP-RCP adopted by the UN Intergovernmental Panel
on Climate Change (IPCC). This article is a synthesis of UN col-
laborative effort to tackle this important socioeconomic and

environmental challenge; contributing evidence in support of the
upcoming UN Decade on Ecosystem Restoration (2021 to 2030).
Our hope is that this contribution, along with others, will inform and
empower decision makers globally to tackle soil threats and develop
concerted global and national strategies for soil monitoring,
conservation, and restoration.

Results and Discussion

Achieving the UN Sustainable Development Goals (SDGs) and
the UN strategies for soil conservation (30) requires understand-
ing of the location and magnitude of erosion at global scales, now
and in the future. Here, we address this challenge, in support of
decision-making, by forecasting global changes in soil erosion by
water, driven by land use and climate change, until 2070. We
analyze mitigation opportunities through current standards of
CAand reflect on possible socioenvironmental impacts related to
future changes in water erosion. We also discuss the limits of our
approach, quantifying the uncertainty of our estimates.

To estimate future soil erosion rates, we feed the high-

resolution (250

×

250 m) RUSLE-based (12) modeling plat-

form Global Soil Erosion Modeling (GloSEM) (

,

Soil Erosion Modeling) with future projections of land use
obtained from the integrated assessment model (IAM) (30) (

, Global Land Use/Cover and Future Change) and cli-

mate erosivity computed following the approach used in previous
continental and global scale studies (18, 31) (

, Climate

and Future Change). The 250 m pixel resolution reflects land cover
at the field scale, capturing the emergent mosaic of land use that
facilitates or mitigates erosion processes. Multiple land use and cli-
mate scenarios, following three Representative Concentration
Pathways (IMAGE SSP1-RCP2.6, MESSAGE-GLOBIOM SSP2-
RCP4.5, and REMIND-MAGPIE SSP5-RCP8.5, with the numbers
referring to the increase in radiative forcing in W

·

m

−

2

by 2100 (

, Representative Concentration Pathway) greenhouse gas

concentration trajectories adopted by the IPCC for its Fifth As-
sessment Report (AR5) in 2014, are modeled. Despite some limits
and uncertainties addressed in the course of the manuscript, the
modeling approach allows for a first estimation of soil displacement
by water erosion due to sheet and rill erosion processes (referred to
as

“

soil erosion

”

in the following technical definition in

,

Definition of Soil Erosion). These are the dominant processes on
upper hill slopes because they are responsible for the largest part of
soil displacement on agricultural fields and intensively used grassland
(12) and found to be a fair proxy of a wider set of soil erosion
processes by water (32).

What is the global pattern of soil erosion? The worldwide

spatial pattern of estimated soil erosion in 2015 is illustrated in Fig.
1

A

. Our modeling results suggest that water erosion is a common

phenomenon under all climatic conditions across all observed
continents. However, the distribution of the spatial soil erosion
patterns suggests that soil erosion seems to threaten areas of large-
scale reclamation such as major agricultural sectors, especially if it
occurs in conjunction with concentrated intense rainfall events
(Southern Brazil, Argentina, India, East China, Midwestern United
States, Ethiopia, and Mediterranean Europe). Locally, steep slopes
and high-relief topography also experience high erosion rates (e.g.,
Andes, Himalayas, Verkhoyansk Range, and Alaska Mountain
Range) together with regions with generally sparse vegetation cover
across the year.

In the 2015 scenario, we estimate global soil erosion equal to

43

+

9.2

−

7

Pg yr

−

1

. The confidence intervals account for the uncer-

tainty of the spatial predictions estimated using a Markov Chain
Monte Carlo (MCMC) approach, the uncertainty of estimating
the area under CA the uncertainty related to the effectiveness of
the CA practices, and the uncertainty in regional rainfall
intensity-kinetic energy relationships. The increase of about ca. 8
Pg yr

−

1

compared to previous figures reported in Borrelli et al.

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(12) is a consequence of the increase in the global modeling area
(from

∼

84.1 to

∼

95.5% of the Earth

’

s land surface) and to a

lesser extent to the land use differences derived from adopting
the land use data of Hurtt et al. (30) in the current study. This
estimate is lower than the figures previously presented in the
scientific literature adopting similar modeling approaches, e.g.,
Yang et al. (13) (ca. 132 Pg yr

−

1

) and Ito (14) (ca. 172 Pg yr

−

1

). It

supports the findings reported in the latest reference document
(6) of the UN on the status of global soil resources, which in-
dicates a value below 50 Pg yr

−

1

as a more realistic quantitative

figure of global soil erosion by the processes considered (6).
Insights in support of the plausibility of our modeling estimates
and GloSEM limitations are reported below. We provide further
information on Model Performance Evaluation and Limitations
of GloSEM below and in the

.

How does land use impact our estimates? Comparing soil

erosion rates according to land use types, we find a decline in the
estimates from croplands to forests and other forms of vegeta-
tion. Soil erosion rates are in line with field measurements (7, 33).
Annual crops covered about 16% of land in 2015 and are estimated
to be responsible for 41% of the total predicted soil erosion. Overall,
the main agricultural lands (annual crops, permanent crops, and
managed pasture) are responsible for 54% (equal to 23.4

+

5.3

−

4.1

Pg yr

−

1

)

of the total soil erosion. Future scenarios suggest that the effects of
land use change may either decrease [SSP1-RCP2.6 (Fig. 1

B

)] or

increase [SSP2-RCP4.5 (Fig. 1

C

), SSP5-RCP8.5 (Fig. 1

D

)] soil ero-

sion by 2070. The divergent trends are the result of the possible dif-
ferent human development and societal choices described in the three
considered SSP-RCP. The IMAGE SSP1-RCP2.6 scenario, which

represents a pathway aiming at limiting the increase of global mean
temperature to a maximum of 2 °C by 2100, suggests a possible
contraction of the main agricultural land by 2070. The simulation of
this land use scenario in GloSEM yields a potential decrease in soil
erosion by water of

–

10% (global soil erosion equal to 38.5

+

8.6

−

6.2

). The

decrease in the simulated global soil erosion (

∼

4.5 Pg yr

−

1

) is the

result of a global reorganization of future lands, which according to
the IMAGE SSP1-RCP2.6, will tend toward an overall decrease of
agricultural areas in favor of an increase in forest and seminatural
vegetation areas (

, Fig. S1

A

). The annual crops and

managed pasture are estimated to decrease globally by ca. 0.9 and 2.7
million km

2

(equal to

−

2.9 and

−

2.4 Pg yr

−

1

of soil erosion, re-

spectively) under the SSP1-RCP2.6 scenario, whereas the permanent
crops follow a different trend with a potential increase estimated at
1.3 million km

2

(+0.2 Pg yr

−

1

). This scenario indicates a possible

reduction of the share of soil erosion in agricultural land from

∼

54%

in 2015 to

∼

48% in 2070. By contrast, the share of soil erosion in

agricultural land increases to 56% and 59% under the MESSAGE-
GLOBIOM SSP2-RCP4.5 and REMIND-MAGPIE SSP5-RCP8.5
scenarios, respectively. The MESSAGE-GLOBIOM SSP2-RCP4.5
is a low stabilization scenario that stabilized radiative forcing at
4.5 W/m

2

(

∼

650 ppm CO

2

-equivalent) before 2100. Under the SSP2-

RCP4.5 scenario, soil erosion by water would experience a slightly
increased estimate at +2% (global soil erosion equal to 43.9

+

9.1

−

6.8

),

mostly driven by the expansion of annual crops estimated at 2.1
million km

2

(+2.6 Pg yr

−

1

). This increase is partially compensated

for by the resulting contraction of the managed pasture (

−

1.2 Pg

yr

−

1

) and nonagricultural lands (

−

0.5 Pg yr

−

1

). Geographically, the

SSP2-RCP4.5 shows some mixed trends (

, Fig. S1

B

).

A

B

C

D

Fig. 1.

Soil erosion estimates predicted through the GloSEM. (

A

) illustrates the soil erosion rates divided into seven classes according to the European Soil

Bureau classification. (

B

–

D

) illustrate changes of the annual average soil erosion between 2015 and 2070 for three distinct RCP greenhouse gas trajectories.

The changes exclusively refer to effects of land use change. For these simulations, the climate of the year 2015 have been employed. (

B

–

D

) share the

same legend.

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Major drivers of the increase can be found in Sub-Saharan Africa,
Eastern Europe, some parts of Eastern Asia, and South America.
The last scenario considers very high greenhouse gases (GHG)
emissions (REMIND-MAGPIE SSP5-RCP8.5) and yields a pos-
sible notable increase in future soil erosion by water of +10%
(global soil erosion equal to 47.3

+

9.5

−

7.3

). The overall increase of soil

erosion by water is estimated to be

∼

4.3 Pg yr

−

1

, primarily asso-

ciated with substantial increases in agricultural areas in Sub-
Saharan Africa, Brazil, India, Myanmar, and some districts of
China. The land use conditions reported by the SSP5-RCP8.5
scenario (

, Fig. S1

C

) for sizable agricultural districts in

North America, Europe, and Russia would result in a lack of
erosion reduction that characterizes the scenario SSP1-RCP2.6,
and to a lesser extent in the SSP2-RCP4.5 scenario.

What is the combined effect of future land use and climate

projections? The modeling results (Fig. 2

A

and

B

) suggest that

climate change is the major driver of the change in soil erosion.
The combined land use and climate simulations show a potential
substantial increase in average soil erosion totaling +30%
(SSP1-RCP2.6), +51% (SSP2-RCP4.5), and +66% (SSP5-
RCP8.5). Quantitatively, 56.1

+

20.6

−

16.4

, 64.8

+

28.5

−

21.4

, and 71.6

+

32.5

−

24.7

Pg yr

−

1

are predicted for the SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-
RCP8.5 scenarios. The wider confidence intervals are related to
the error propagation accounting for the variability of future
climate projections of the 14 GCMs used to assess future rainfall
erosivity (Fig. 3) and the set of uncertainties already considered
in the 2015 scenario. These large confidence intervals associated
with future climate projections reflect the higher uncertainty of
these estimates. The multiscenario comparison suggests that al-
though future land use changes can notably affect global soil
erosion processes through the expansion or contraction of
croplands, a global climate potentially moving toward more
vigorous hydrological cycles would be acting as a major driver of
future increases in soil erosion (Fig. 4). All SSP-RCP climate
scenarios processed in the Gaussian process regression (GPR)
model (

, Climate and Future Change) indicate pos-

sible substantial increases in future rainfall erosivity following
similar spatial patterns but different intensities (Fig. 2 and

, Fig. S2

). In absolute terms, the greatest increases oc-

cur in areas with tropical climates. Countries in temperate lati-
tudes and with subtropical climates, however, would not be
spared by increases of climate erosivity and experience increase
peaks up to 50%. In these climatic zones, substantial increases
might interest sectors of Eastern North America, Central and
Northern Europe, Middle East, and North and East Asia. This
provides important insight for policy makers identifying hotspots
and trying to mitigate land degradation around the globe.

What are the key findings for policy makers? Article 1 of the

UN Convention to Combat Desertification (UNCCD), ratified
in March 2020 by 197 countries, identifies soil erosion as a pri-
mary cause of land degradation, which, in turn, contributes to
poverty and inequality through its negative effects on agriculture,
food security, and ecosystems (34). Scientific evidence suggests
that global warming has already increased global economic in-
equality (35) and influenced immigration waves (36). The remark-
able increases predicted in the combined future land use and
climate change scenarios could have a wide range of negative ef-
fects at global, regional, and country levels for which this strategic
analysis is intended, and not the field or catchment scale. The first
and most obvious consideration emerging from this analysis is the
possible exacerbation of the climatic conditions, which could sub-
stantially increase global soil erosion (+30%) already in the low
GHG emission scenario (SSP1-RCP2.6). A climate-induced in-
crease in soil erosion, as indicated by our findings, with associated
land degradation and loss of key ecosystem services (9), would pose
a serious threat to the achievement of a large set of targets defined
by the UN strategy with the SDGs. This is particularly true for
achieving 1) a land degradation neutral world by 2030 (Goal 15;
Target 15.3); 2) maintaining soil quality for achieving food security
(Goal 2); 3) ensuring availability and sustainable management of
water resources (Goal 6); as well as 4) ending poverty (SDG 1); 5)
reducing inequality (SDG 10); and 6) taking action to combat
global warming (SDG 13).

How can soil erosion be mitigated? According to present

knowledge, a climate-induced global increase in water erosion of
such a range of magnitude could be difficult to mitigate through
CA. Further runs of the GloSEM model to analyze mitigation
opportunities through CA suggest that, in order to offset the
most severe projected increase of global soil erosion enhanced by
global land use change by 2070 (SSP5-RCP8.5, equal to 5.5

+

1.1

−

0.8

Pg yr

−

1

), the crops under CA would need to increase globally to

ca. 60%. Current estimations indicate CA covering 11 to 14% of
the global arable land area. Keeping in mind the difficulty in de-
fining an optimal coverage of CA due to the different worldwide
climate, cropping systems, and actual erosion mitigation needs and
the current trends of CA, the targeted levels of CA could be
missed by several European, Asian, and African countries fol-
lowing the current soil erosion conservation path [FAO AQUA-
STAT (37)]. This is considering that CA is not always exempt from
shortcomings and environmental impacts itself (12). In addition,
many South American and Oceanian countries already reached
high percentages of cropland under CA in 2015, which limits their
possible contribution to offset the projected global increases. Ac-
cordingly, if an increase of soil erosion of +10%, driven only by land
use change in the SSP5-RCP8.5 scenario seems already difficult to

Fig. 2.

Soil erosion change between 2015 and 2070. The delta between the two observed periods [(RCP) greenhouse gas trajectories SSP1-RCP2.6 and SSP5-

RCP8.5] depends on the effects of land use and land cover change and climate change.

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mitigate, and would call for vigorous new conservation policies and
investments, then the combined effect of future land use and climate
projections modeled in the SSP5-RCP8.5 scenario may exacerbate
future soil erosion processes beyond the mitigation potential of
current CA standards.

How do the impacts of soil erosion propagate through the

ecosystem? Maintaining soil in good health is a primary concern
to farmers but the effects of soil erosion go beyond the loss of
fertile land. Recognizing the link between erosion and trends of
biodiversity and ecosystem service supply, the UN Intergovern-
mental science-policy Platform on Biodiversity and Ecosystem
Services (IPBES) is calling for contributions to understand the
service of sediment retention provided by natural landscapes at a
global scale. Although GloSEM lacks the ability to account for
deposition, on the basis of the findings provided by Grill et al.
(32) we argue that increased amounts of soil eroded away from
hill slopes result in increased sediment transport, which, in turn,
cause ecological disturbance in the river network and reservoirs.
The accumulation of these sediments in reservoirs could lead to
a reduction of storage capacity and drinking water quality (SDG
Goal 6: Clean Water and Sanitation). This is a challenge that
already affects several low-income tropical countries that are
susceptible to high levels of soil erosion and where unsafe water
sources account for 6% of deaths (38). Where rivers flow into the
sea, an increased deposit of sediments can exacerbate the cover
of coral reefs, further compromising the existence of this fragile
ecosystem (39) (SDG Goal 14: Life below water). In this regard,
the spatially explicit pixel structure of GloSEM makes it a
powerful tool for both mapping possible global status of soil
erosion by water as well as being a course proxy indicator for
sediment retention across the landscape. We observed that the

present GloSEM estimates are able to explain 65% of conti-
nental and 64% of global variance in observed sediment load
(32). Values reach up to more than 83% in three continents
i.e., North America, Europe, and Asia. More details can be
found in

, Model Performance Evaluation.

Which economies will be impacted the most? At country level,

as inferable from Fig. 2, the distribution of predicted future soil
erosion patterns suggests that high-income countries, generally
in temperate latitudes, may have less increase in erosion; while
low- and middle-income tropical and subtropical countries may
be the most susceptible to high increases of erosion. These in-
sights are corroborated by the results presented in Fig. 5 corre-
lating, at country level the GDP per capita, the consumption of
fertilizers and the predicted increase of soil erosion between
2015 and 2070 (SSP5-RCP8.5 scenario). Except for Asia, which
shows a heterogeneous situation, the other continents form visible
clusters. African countries with lower GDP per capita and con-
sumption of fertilizers in 2070 may experience the higher increase
of soil erosion, with the magnitude expressed by the diameter of
the circles. A similar situation can be observed for some Asian
countries and to a lesser extent for some South American coun-
tries. By contrast, wealthy countries in Europe, North America,
and Oceania, where levels of consumption of fertilizers are higher,
show considerably lower projected future increases of erosion.
Tropical countries such as Peru, Brazil, several countries in
Western Africa, Cameroon, Ethiopia, Somalia, Kenya, Yemen,
Southern Pakistan, India, Myanmar, Southeast China, Philippines,
and Indonesia may be substantially affected by increased soil
erosion. Today, roughly 2.5 billion people live in these countries.
Some of sub-Saharan Africa indicates signs of considerable de-
mographic expansion (40) with net migration flow. Future

A

B

C

D

Fig. 3.

Rainfall erosivity estimates. (

A

) illustrates the global erosivity map at 30 arc-seconds (

∼

1 km at the equator) based on a GPR proposed by Panagos et al.

(18). (

B

–

D

) illustrate changes of the annual rainfall erosivity between 2015 and the 2070 SSP1-RCP2.6 (

B

), 2070 SSP2-RCP4.5 and 2070 SSP1-RCP2.6 (

C

), and

2070 SSP5-RCP8.5 and 2070 SSP2-RCP4.5 (

D

). For the rainfall erosivity scenarios of 2070, average values of the 14 GCMs of the WorldClim database version 1.4

have been considered.

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exacerbation of lands already degraded losing their ecosystem
functioning and experiencing decreased agricultural productivity
may aggravate the processes that displace human beings. Most of
the erosion increase will occur in low-income countries that suffer
from poverty and that will generally tend to be more negatively
affected by climate change. To avoid the worst, it is pivotal for
countries, especially in the Global South, to support the diffusion
of sustainable farming practices.

It is of general interest to all countries around the world to

avoid a decrease in agricultural productivity (41). According to
our preliminary estimates given here, the effect of climate
change will likely be so pronounced that it will overwhelm the
mitigation potential of adopting soil-conserving agricultural
practices. Still, without a change in agricultural practices, the
effect would be multiple times worse. Countries have powerful
options to positively influence their rates of soil erosion (42).

Soil loss

21.78

Pg yr

-1

Overall

R-factor

48.9

[%]

Soil loss

20.9

Pg yr

-1

Climate

Forest/ grass

-1.9

Area [%]

Soil loss

-0.51

Pg yr

-1

Soil loss

1.39

Pg yr

-1

Agriculture

7.4

Area [%]

Land use

Soil loss

28.58

Pg yr

-1

Overall

R-factor

56.4

[%]

Soil loss

24.26

Pg yr

-1

Climate

Forest/ grass

-2.1

Area [%]

Soil loss

-0.41

Pg yr

-1

Soil loss

4.72

Pg yr

-1

Agriculture

8.4

Area [%]

Land use

Soil loss

13.08

Pg yr

-1

Overall

R-factor

40.9

[%]

Soil loss

17.6

Pg yr

-1

Climate

Forest/ grass

1.5

Area [%]

Soil loss

0.32

Pg yr

-1

Soil loss

-4.85

Pg yr

-1

Agriculture

-9.9

Area [%]

Land use

A

B

C

Fig. 4.

Flow diagram of the projected land use and climate changes. (

Insets

,

A

–

C

) demonstrates the projected net change of the land surface (million km

2

),

climate, and soil erosion (Pg yr

−

1

) for the SSP1-RCP2.6 (insert

A

), SSP2-RCP4.5 (insert

B

), and SSP5-RCP8.5 (insert

C

).

1

10

100

1,000

10,000

1000

100,000

Fe

r

Ɵ

lizer

cons

um

p

Ɵ

o

n

(kh/

ha o

f arable lan

d

)

10,000

GDP per capita in US dollars

Africa

Asia

Europe

North America

Oceania

South America

Fig. 5.

Fertilizer consumption versus GDP. Fertilizer consumption per hectare (year 2015) versus GDP) per capita (year 2011, expressed in US dollars) and the

increase of soil erosion (%) between 2015 and 2070 for the SSP5-RCP 8.5. The magnitude of the increase of soil erosion is indicated by the size of the circles.
The color of the circles refers to the continents.

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Countries thus have both an incentive and a toolbox of potential
measures to mitigate their soil erosion rates. Given the growing
challenge of soil erosion, it is important to act now, act fast, and
act comprehensively. Only by doing this through agricultural
policy, interventions, and better soil governance will we ensure
management to protect the health of our food production and
riverine ecosystems.

Model Performance Evaluation.

With regard to the validation of

the estimates, as described in previous studies (12, 43), the val-
idation sensu strictu of regional or larger scale applications of a
model such as GloSEM is challenging due to the lack of long-
term field-scale measurements; even for the present scenario
simulations. The GloSEM estimates are presented with an un-
precedented number of procedures to evaluate the performance
of a global scale soil erosion model. This provides some highly
relevant insights in support of the plausibility of the estimates of
a global soil erosion model (

, Model Performance

Evaluation).The procedures include: 1) the assessment of the
uncertainty of the spatial predictions estimated as a probability
distribution through the use of Bayesian modeling; 2) the eval-
uation of the ability of the model to estimate soil erosion in
cropland to a level close to these high-resolution regional as-
sessments; 3) the verification that our estimates for different
land use/cover units fall in the ranges of empirical measurements
(metadata analysis); 4) the comparison of GloSEM soil erosion
estimates with sediment transport recorded at 398 globally dis-
tributed gauging stations; 5) the evaluation of the level of
agreement with expert-based and remote sensing-based UN as-
sessments; 6) the attempt to evaluate GloSEM performance by
comparing its estimates against field soil erosion measurements
at plot scale; 7) the evaluation of the performance of the GPR
for the present-day climate conditions; 8) the evaluation of the
rainfall erosivity forecasting capacity; and 9) the assessment of
the effect of every RUSLE input factor on model prediction
using a sensitivity analysis.

We tested the GPR model to evaluate if it retains its predic-

tion capability over different time frames. This is particularly
important to verify our overall assumption that the GPR ap-
proach is able to perform in the temporal domain as well as in
the spatial domain that we assessed previously (22). We split our
data into two time sets (fitting pre-2000; validating post-2000).
The results indicate a very good GPR prediction capacity for the
pre-2000 training set (0.85 R

2

) and a good prediction for the

post-2000 validation set (0.6 R

2

). More details are provided in

the

, Model Performance Evaluation.

Despite the positive insights gained through the model per-

formance evaluation, we recognize that GloSEM, as a prediction
model, founded on data-driven assumptions and a semiempirical
structure, has limitations that might not allow it to capture reality
fully, as is to be expected. This is particularly true for future
projections which further depend on climate and land use/cover
projections with higher uncertainty. The positive results high-
lighted by the cross-check analysis, however, suggest that the
evidence reported here does represent a valuable source of
preliminary information to support decision makers developing
national and international strategies for soil conservation.

Limitations of GloSEM.

As recognized by Borrelli et al. (12), and

deepened in review studies (44, 45), large-scale RUSLE-based
models like GloSEM that rely on data-driven assumptions have a
large number of limitations. Despite the employment of
state-of-the-art statistical methods, interpolation techniques, and
remote sensing, GloSEM has its roots in an empirical equation
like RUSLE, which heavily depends on a database that fits to the
actual conditions to which it is applied. In addition, it lacks the
ability to predict processes other than sheet and rill erosion.
Besides the intrinsic limitations related to the RUSLE scheme,

GloSEM suffers from other data-driven limitations due to the
global scale at which it operates. A detailed description of
GloSEM limitations are provided in

, Limitations of

GloSEM. This having been said, there is no alternative approach
yet developed, and despite limitations the modeling provides
pioneering assessment to determine potential erosion at
global scales.

Conclusions

With some degree of uncertainty, GloSEM allows prediction of
both state and change of soil erosion, identifying hotspots thanks
to its high resolution (250

×

250 m) and predicting future vari-

ation based on projections of change in land use, soil conserva-
tion practices, and climate change. Its estimates provide a useful
knowledge base to support decision makers in considering the
development of more resilient agricultural systems, such as ag-
roforestry, regenerative agriculture, or other emerging tech-
niques able to go beyond current CA strategies. These need to
take into consideration that we may need to deal with important
changes to climate now and in the coming decades. Both SSP1-
RCP2.6 and SSP5-RCP8.5 scenarios provide clues on possible fu-
ture local conditions requiring adaptation to drastic climate change
conditions. These appear to be mainly located within highly pop-
ulated tropical countries. The ability of GloSEM to identify hot-
spots and areas of concern at the global scale provides the basis for
a more strategic approach in directing local monitoring/modeling.
In addition, the dynamic nature of the model makes it suitable for
both ex-ante and ex-post policy evaluation. Scientifically, the way
forward for GloSEM is to produce free and easily accessible
knowledge on global soil erosion dynamics to be shared with ad-
joining disciplines. The modeling framework presented in this study
adopts standardized data in an adequate format to communicate
with adjacent disciplines and moves us toward robust, reproducible,
and open data science. It aims at facilitating the consideration of
soil erosion processes and deriving land degradation impacts in the
next assessment reports of the IPCC.

Materials and Methods

The RUSLE-based Global Soil Erosion Modeling platform (GloSEM) (12) (

, Modeling Soil Erosion by Water) was updated to establish a more

comprehensive modeling framework to estimate future global soil erosion
scenarios that dynamically integrate climate and land use change scenarios
developed according to the new SSP-RCP adopted by the Intergovernmental
Panel on Climate Change (IPCC). Here, GloSEM is combined with multiple
alternative scenarios of future (2070) land use developments of the Land Use
Harmonization (LUH2) project (30) (

, Chapter Global Land Use/

Cover and Future Change) as well as climate developments advanced in
collaboration with the Joint Research Centre (JRC) of the European Com-
mission (6, 7) (

, Climate and Future Change). Standardized input

data are used to allow for exchange of knowledge or possible future inte-
gration of this study

’

s output with those of adjacent disciplines.

Study Area.

The study area comprises the land surface of 202 countries for

which the Food and Agriculture Organization Corporate Statistical Database
(FAOSTAT) (

www.fao.org/faostat/en/

) currently offers statistics. The model-

ing area totals about 143 million km

2

(

∼

95.5% of the Earth

’

s land surface),

currently providing living space for a global population of ca. 7.5 billion
people and estimated to rise to about 9 billion around 2070.

Uncertainty Analysis and Error Propagation.

A summary of error propagation

in present and future predictions is calculated considering the uncertainty of
the spatial predictions estimated using a MCMC, the uncertainty of esti-
mating the area under CA (considered only for the scenario of 2015), the
uncertainty related to the effectiveness of the CA practices using a Monte
Carlo method approach, and the uncertainty in regional rainfall intensity-
kinetic energy relationships (

, Uncertainty Analysis). Further

uncertainties related to the variation found in the 14 different GCMs is also
considered. The error propagation is the square root of the sum of squares
of the different uncertainties.

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Borrelli et al.

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Model Performance Evaluation.

A validation sensu strictu of a (R)USLE-type

model applied at large scale is not limited by the absence of long-term field-
scale measurements (12, 43). Considering that the ability of the GloSEM
model to accurately measure the amount of displaced soil cannot be
assessed, a cross-comparison of the modeling results to gain insights on the
plausibility of the modeling predictions was performed. This was done
comparing our estimate with the ones provided by other global and re-
gional soil erosion assessments and empirical observations.

Data Availability.

All data supporting the findings of this study are available

within the article text and