The influence of catchment morphology li

Catena 126 (2015) 117–125

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Catena
journal homepage: www.elsevier.com/locate/catena

The influence of catchment morphology, lithology and land use on soil
organic carbon export in a Mediterranean mountain region
Elisabet Nadeu a,⁎, Juan M. Quiñonero-Rubio b, Joris de Vente a, Carolina Boix-Fayos a
a
b

Soil and Water Conservation Department, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), Campus Universitario de Espinardo, 30100 Murcia, Spain
Departamento de Geografía, Universidad de Murcia, Campus de la Merced, 30001 Murcia, Spain

a r t i c l e

i n f o

Article history:

Received 5 December 2013
Received in revised form 6 November 2014
Accepted 11 November 2014
Available online xxxx
Keywords:
Soil organic carbon
Soil erosion
Enrichment ratio
Land use
Geomorphology
Topography

a b s t r a c t
Soil erosion processes play an important role in the redistribution of soil organic carbon over the landscape. The
factors controlling this carbon (C) redistribution have been thoroughly studied at the plot scale, but knowledge at
the landscape level is still limited. In this study, we explore the role of different factors on C export at the catchment scale. We measured the C concentration in sediment deposited behind check-dams at the outlet of catchments ranging between 7 ha and 438 ha and combined it with specific sediment yield rates (SSY, Mg ha−1 y−1)
to estimate catchment specific C yields (SCY, g m−2 y−1). Correlation analysis between C concentration, SCY and
morphological, lithological and land use data derived from GIS analysis and interpretation of ortophotoimages
was conducted. The results showed a close relationship between SCY rates and catchment morphometric properties such as catchment area, slope gradient and lithology, while C concentration in sediments was correlated to
the percentage of forest cover in a catchment. In addition, it is suggested that morphological properties such as

average slope gradient, drainage area and drainage density could have implications for the fate and stability of
C stored at depositional sites through changes in the in-depth variability of C concentration in sediments and
in the concentration of two measured C fractions. Further research is needed in this direction.
© 2014 Elsevier B.V. All rights reserved.

1. Introduction
Soil organic carbon redistribution by soil erosion processes and its
transport into the aquatic system can play a significant role in the biogeochemical cycling of carbon (C) (Battin et al., 2009; Berhe et al.,
2007; Quinton et al., 2010; Stallard, 1998). Recent estimates of organic
C loads transported and buried with sediments in large rivers have
stressed the importance of catchment-derived C fluxes in total C budgets (Ran et al., 2014; Aufdenkampe et al., 2011). However, since only
a very small percentage of eroded C is exported from catchments
(Chaplot et al., 2005; Smith et al., 2005), the re-mobilization (continuous detachment and deposition) of eroded C (Rumpel et al., 2014) and
the environmental conditions (e.g. temperature, humidity) during
transport and deposition within catchment boundaries will have a
strong impact on its fate (Gregorich et al., 1998).
Understanding changes in the fate of eroded soil organic carbon during the erosion process demands for increased knowledge on the factors
involved along its different phases of detachment, transport and deposition. Over the last decades, several studies have shown how C export
from soils and its concentration in sediments depend on a number of
factors, including: rainfall characteristics, soil texture, slope gradient

⁎ Corresponding author at: Earth and Life Institute — TECLIM, Université catholique de
Louvain, 1348 Louvain-la-Neuve, Belgium. Tel.: +32 10472455.
E-mail address: elisabet.nadeu@uclouvain.be (E. Nadeu).

http://dx.doi.org/10.1016/j.catena.2014.11.006
0341-8162/© 2014 Elsevier B.V. All rights reserved.

and length, geomorphological factors or land use and management
(e.g. Owens et al., 2002; Polyakov and Lal, 2004; Schiettecatte et al.,
2008). Nevertheless, most of these studies have been plot-based, considering the controlling factors separately and focused mainly on interill
erosion, thus limiting their applicability at the catchment scale where a
large number of factors and erosion processes interact.
More recently, and fulfilling the need of broader scale studies and
quantification of catchment scale C budgets, a shift from plot to full
slope or catchment scale approaches has been observed. These new
studies identified topography, and consequently geomorphic processes (e.g. Chaplot et al., 2010; Guo et al., 2010; Hoffmann et al.,
2014; Yoo et al., 2006) as well as differences in the stability of SOC
between landform positions (Berhe et al., 2008; Chaplot and
Poesen, 2012; Doetterl et al., 2012; Hancock et al., 2010) as important variables to explain soil organic carbon (SOC) spatial distribution. In addition, it has also been reported how changes in land
cover can modify the amount of C exported by erosion by changing

SOC stocks at source sites by changing potential runoff rates (Chaplot
et al., 2009; Molina et al., 2007; Sitaula et al., 2004; Thothong et al.,
2011). Despite an increase in the number of landscape scale studies,
many aspects of how transport and selectivity during the redistribution
of SOC may affect carbon dynamics are unknown (Kirkels et al., 2014)
and it is still difficult to find studies that integrate and assess the effect
of multiple factors potentially controlling C redistribution at the landscape level.

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E. Nadeu et al. / Catena 126 (2015) 117–125

From a hydrological and geomorphological perspective, the transport of particulate (non-dissolved) C particles has been described to
closely follow sediment dynamics (Starr et al., 2000). Therefore, like
sediment, C redistribution can be expected to be influenced by lithology
(Bellin et al., 2011; Romero-Diaz et al., 2007), land use type and pattern
(Fiener et al., 2011; Van Rompaey et al., 2002), topography (RomeroDiaz et al., 2007), and sediment connectivity (Sougnez et al., 2011). In
addition, the immediate fate of transported C, whether deposited close
to the source soil or exported to the aquatic system, will depend on
the size and stability of the transported aggregates (Starr et al., 2000).

Notwithstanding, organic particles may also behave differently than
mineral particles due to their inherent characteristics (Walling, 1983),
being affected by additional processes that can alter the C concentration
and characteristics in sediments. For instance, the loss of C through mineralization during transport or after deposition (Lal, 2003), the proportion of fractions of C with different turnover rates and its transformation
along the landscape (Berhe and Kleber, 2013; Rumpel et al., 2014),
changes physical and chemical protection mechanisms of eroded particles (Berhe et al., 2008; Doetterl et al., 2012; Wang et al., 2014) and also
the accumulation of C in deposited sediments through the input of autochthonous organic matter (Stallard, 1998).
The transfer of C from soils to streams and catchment boundaries is,
therefore, complex and the few studies that have investigated the relationships between C export and different factors at the catchment scale
have mainly focused on dissolved or suspended C in agricultural catchments and their relationship with hydrological catchment properties
(Oeurng et al., 2011). In the Mediterranean region, where high soil erosion rates reduce the fertility of soils with already low SOC concentrations (Cantón et al., 2012), very little emphasis has been placed on the
study and quantification of the catchment scale transport and export
of eroded C. Increased knowledge on its controlling factors could help
in the development of more precise numerical models and improve
catchment management to preserve soil fertility and avoid large C exports into the aquatic system. Against this background, the objective
of this study is to explore the relationships between a range of catchment properties (morphometric, soils, lithology and land use) and the
C concentration in sediments and catchment scale C export by soil erosion processes.
2. Materials and methods
2.1. Site description
The study was conducted in a 50 km2 watershed located within the

province of Murcia, SE Spain (Fig. 1). The average altitude of the study
area is 1403 m and it is characterized by a dry–subhumid Mediterranean climate, belonging to the supramediterranean bioclimatic zone.
Mean annual precipitation and temperature are 583 mm and 13.3 °C

respectively (Boix-Fayos et al., 2007). Lithology is spatially heterogeneous and consists mainly of limestone, marls and sandstone from the
Mesozoic and Cenozoic (IGME, 1978). Dolomites and limestones occupy
the highest elevations in the catchment, often visible in the form of
small cliffs, while more marls and sandstones are found in the highly
dissected valley floors. While dolomites and limestones are considered
hard erosion resistant lithologies, marls and sandstones are unconsolidated erodible material (Romero-Díaz, 2003). Soils are mainly classified
as Calcaric Regosols and Calcaric Lithosols, although Cambisols and
Mollisols can also be found in certain slopes under forest cover (Alías
et al., 1991). Vegetation consists of a mixture of conifer forests, shrubland, pastures and dryland agriculture (Fig. 2). Rills and gullies are common features on both agricultural and naturally vegetated hillslopes,
while bank and channel erosion are active along the whole drainage
network. To address the problems arising from on-site and off-site
effects of soil erosion, the regional Government promoted hydrological
correction works involving afforestation and the construction of checkdams. The impacts of afforestation, construction of check-dams and
land use changes on geomorphological processes and sediment yield
in the catchment have been explored in detail in previous works
(Boix-Fayos et al., 2007, 2008; Nadeu et al., 2012; Quiñonero-Rubio

et al., 2014). These check-dams have divided the catchment into smaller
subunits (catchments), 18 of which were used in this study (Fig. 1 and
Table 1) with average altitudes ranging between 1146 m and 1714 m.
2.2. Field sampling
Sediment samples were taken from the sediment wedges created
behind each check-dam. Sediment deposition at these sites is often
event-based (Boix-Fayos et al., 2008) and size dependent; with a large
fraction of fine particles reaching the front of the wedge and coarser
particles mainly settling at the back (Nadeu et al., 2012). Deposition in
the sediment wedges can be regarded as a mixture of suspended
sediment (mostly reaching the front of the wedge) and bedload
transported sediment (preferably deposited in the back) (Nadeu et al.,
2012). For this study, sediments were sampled at the front of the
wedge, assuming them to be representative of potentially exported
sediment out of the catchment during the study period. The sediment
wedges were dry at the time of sampling, and only become saturated
and temporarily covered by a shallow water later after rainfall events
lasting over several days, a situation which occurs very seldom
throughout the year during autumn and spring. An auger was used to
sample in depth in 5 cm increments until bedrock was reached with

an average of 84 ± 7 cm and a maximum of 150 cm in one of the
catchments. In addition to the depth profile, two sample replicates
were taken in 15 cm increments. Data from total sediment volume for
each sediment wedge was taken from a previous study (Boix-Fayos
et al., 2008). Soil sampling was carried out on a land-use based strategy,
taking samples from at least 2 locations per LULC class (see Section 2.4)
and catchment based on the 2008 land use distribution. The topsoil, 0–
10 cm, was sampled to characterize the material most likely to be mobilized by soil erosion processes. The sampling of soils and sediments was
done during several field campaigns over three years: 2004, 2009 and
2010.
2.3. Soil and sediment analyses

Fig. 1. Map of the study site. Numbers refer to catchment ID's.

Soil and sediment samples were analyzed for bulk density, particle
size distribution (PSD) and total organic carbon content (C). The sediment cores used for bulk density determination were later used for
PSD and organic carbon analyses. Therefore, sediment samples were
first oven-dried at 60 °C instead of 105 °C to avoid the loss of volatile
C fractions (Bates, 1993), weighted, and later sieved at 2 mm to proceed
with the rest of analyses. Soil samples were air-dried and sieved at

2 mm while those for bulk density determination were dried at 105 °C
and weighted. Particle size distribution was determined through a

E. Nadeu et al. / Catena 126 (2015) 117–125

119

Fig. 2. Fragmented landscape of Rogativa catchment (fallow land in front and different densities of forest cover, cereal fields and shrubland in the back).

combined wet sieving and laser diffractometry technique. Fine earth,
b2 mm, of soil and sediment samples was dry-sieved at 63 μm obtaining
two fractions. The two fractions, fine and coarse, were chemically dispersed using a mixture of sodium hexametaphosphate and sodium carbonate anhydrous for 18–24 h. Particle size was then measured on the
dispersed samples through wet sieving for the sand fraction (N 63 μm)
and through a laser diffractometry technique for the silt and clay fractions (b 63 μm) using a Coulter LS200 (Miami, USA). Two different techniques were applied to analyze organic carbon content: for samples
collected until 2009 the wet oxidation method was used, for which samples were preheated using a mixture of potassium dichromate and concentrated sulphuric acid to 170 °C (30 min) to ensure a complete
combustion (Yeomans and Bremner, 1988). For those samples taken

from 2010 onwards, C content was determined by dry combustion in
an elemental analyzer (FLASH EA 1112 Series Thermo). Because these
two techniques may lead to slightly different results, the difference between the data obtained through the two methods was tested on some

of the samples taken in 2010 (n = 17). The linear regression obtained
(r2 = 0.94) showed no significant differences between average values
of sample replicates (paired t-test, p N 0.05). Thus, no correction factor
between methods was applied.
In addition, for 12 of the catchments, organic carbon content was
measured on two size fractions which were obtained through a physical
separation by wet sieving conducted after shaking for 18 h 10 g of fine
earth with 50 ml of sodium hexametaphosphate (Cambardella and
Elliot, 1992). The two obtained fractions were defined as particulate

Table 1
Main characteristics of the study catchments.
Catchment ID

Dominant soils

Dominant lithology

Area (ha)

Dominant LULC (1981)a

SY (Mg y−1)b

Average slope (±sd) (%)

1
3
5
7
8
14
18
19
21
22
23
24
29
34
49
51
53
57

Calcaric regosols
Lithosols
Lithosols
Calcaric regosols
Lithosols, calcaric regosols
Calcaric regosols
Calcaric regosols
Calcaric regosols
Calcaric regosols
Calcaric regosols
Lithosols, CR
Calcaric regosols
Calcaric regosols
Calcaric regosols
Lithosols, calcaric regosols
Calcaric regosols
Calcaric regosols
Calcaric regosols

Marls
Quaternary, limestone
Limestone
Marls
Quaternary, marls, limestone
Marls
Marls
Marls, Limestone
Marls
Limestone
Limestone
Marls
Marls
Marls
Limestone, marls
Marls
Marls
Marls

17.7
118.0
241.1
11.1
253.7
12.3
41.0
20.6
13.4
66.8
437.7
17.8
7.4
15.4
37.2
48.4
9.7
10.7

Agr
Pas
Pas, LF
LF
Shr, LF
LF
Shr
LF, S
Shr
LF
LF
Shr, LF
LF
LF
Shr, LF
Pas
LF
LF

32.3
77.1
79.4
8.8
95.9
43.3
510.6
51.7
46.2
32.3
190.8
38.5
132.6
94.9
121.8
46.8
71.7
78.5

16
40
47
15
34
21
18
24
26
26
34
30
23
20
32
25
19
24

a
b

LULC classes: low density forest (LF), shrubland (Shr), pasture (Pas) and agriculture (Agr).
Sediment yield (Boix-Fayos et al., 2008).

±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±

8
16
16
7
18
11
8
10
9
10
16
12
7
7
16
12
8
10

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E. Nadeu et al. / Catena 126 (2015) 117–125

organic carbon (N53 μm) (POC), and mineral associated organic carbon
(MOC) (b 53 μm) and their C content analyzed using the techniques
mentioned above. For these samples, total organic carbon was then assumed to be the sum of POC and MOC sample content.

limestones and dolomites while shrublands and agricultural fields
were found mainly on marls (Table 3).

2.4. Land use and land cover (LULC) and digital elevation model

Specific carbon yield is defined as the amount of eroded C per unit
area (g m−2 y−1) measured at the catchment's outlet (Eq. (2)). It was
estimated by combining the average C concentration in sediment samples (Csed) (%) and the specific sediment yield (SSY; g m−2 y−1) calculated by Boix-Fayos et al. (2008) from the volume of sediments
deposited behind check-dams corrected for the check-dam sediment
trapping efficiency. Due to a lack of data on C concentration for exported
sediment at our study site, we considered the C concentration of
exported sediments to be equal to that measured for sediments retained
behind the check-dams, since we were interested in the relative differences between catchments and not absolute exported values. However,
it must be taken into account that sediment trespassing the check-dams
may have higher C concentration than those retained behind them
(Fiener et al., 2005) and thus, that the values of exported C reported in
this study could be generally underestimated.

Catchment morphometric properties were derived from a digital elevation model obtained from airborne LiDAR data (from the Natmur08
Project of the General Directorate of Natural Heritage and Biodiversity,
Region of Murcia). A 20 m resolution resampled digital elevation
model was used to calculate the catchment hydrological properties
(Table 2) using the Spatial Analyst tools in ArcMap 10 (ESRI).
Ortophotoimages from two years: 1981 (General Directorate of
Landscape Planning, Region of Murcia, spatial resolution 1 m) and
2008 (Natmur-08 Project of the General Directorate of Natural Heritage
and Biodiversity, Region of Murcia, spatial resolution 0.45 m) were used
as a basis to digitize land use and land cover (LULC) in the catchments.
The 1981 image captured the LULC during the period of check-dam construction and afforestation, while the image of 2008 represents the
present LULC patterns. Five LULC classes were defined: high density forest (HF), low density forest (LF), shrubland (Shr), pastures (Pas) and
agriculture (Agr). Agriculture included fruit tree plantations (walnut
and almonds), irrigated vineyards as well as rainfed crops. HF and LF derived from both afforestations and natural vegetation succession. Land
use change analysis was done by comparing the LULC maps between
2008 and 1981. From these analyses, land use change ratios were calculated as follows (Eq. (1)):
A0881 ¼ Agricultural area in 2008=Agricultural area in 1981:

ð1Þ

The distribution of LULC classes was uneven among lithological
types. A higher percentage of forests and pastures was located on

Variables

Source
[1]
[1]
[1]

Sediment variables
SSY
Sand, silt, clay
SR

Specific sediment yield (Mg ha−1 yr−1)
Sand, silt and clay content in sediments (%)
Sedimentation rate (Mg ha−1 yr−1)

[2]
[3]
[2]

Drainage density
Bifurcation ratio between 1st and 2nd order
streams (Strahler definition used)

[1]
[1]

Carbon variables
Csed
SCY
CV Csed
POC
MOC
ERC
Stock81

Mean C concentration in sediments (%)
Specific carbon yield (g m−2 yr−1)
In-depth coefficient of variation for Csed (%)
Mean POC concentration in sediments (%)
Mean MOC concentration in sediments (%)
C enrichment ratio in sediments (−)
C stock in soils in 1981

[3]
[2] & [3]
[3]
[3]
[3]
[3]

Lithological variables
%marls
%dolomites
%limestone

Percentage of marls
Percentage of dolomites
Percentage of limestones

[4]
[4]
[4]

Percentage of total area occupied by a certain
land use (AA) for a specific year (XX) (%)

[5]

Land use variables
AAXX

ð2Þ

Carbon enrichment ratios are typically used to assess erosion selectivity by comparing the C concentration found in sediments with that
of their source soils. Ratios above one indicate higher C concentration
in sediments and vice versa. In order to determine a C enrichment
ratio (ERC) for deposited sediments in each catchment, the average
soil organic carbon (SOC) concentration in the topsoil of the hillslopes
was first calculated based on the weighted SOC stocks by land use distribution in each catchment from the 1981 LULC map (Eq. (3)). Average
SOC concentrations per land use type were: 3.2% for HF, 2.8% for LF,
1.7% for Shr, 1.5% for Pas and 1.0% for Agr (Nadeu, 2013).

þ 1:0  RAgr

Topographical variables
Area
Drainage area (ha)
Alt
Altitude (m)
Slp
Catchment average slope (%)

Stream morphology
DD
BR

SCY ¼ SSY  Csed :

SOC ¼ ð3:2
  RH F Þ þð2:8  RL F Þ þ ð1:7  Rshr Þ þ ð1:5  RPas Þ

Table 2
Catchment properties used in the analyses.
Abbreviation

2.5. Estimation of specific C yield (SCY) and C enrichment ratios

Source: [1] Calculated from DEM; [2] Data from Boix-Fayos et al., 2008; [3] soil/sediment
samples from this study; [4] (IGME, 1978); [5] Digital ortophotoimages (see Section 2.4).

ð3Þ

where RHF is a ratio that represents the area occupied by HF relative to
the total area (thus, the sum of all LULC equals 1). Then, the C enrichment ratio (ERC) for each catchment was calculated by dividing the
mean carbon concentration in the sediment profile (Csed) by the SOCLULC
of the 1981 LULC map. The 1981 map was used to calculate SOC stocks
and enrichment ratios because it was considered to be the most representative for the period between the construction of the check-dams
and the sampling campaigns.
2.6. Topography and river morphology
Topography in the catchments was represented by the average slope
gradient calculated for each based on the 20 m digital elevation model.
Stream order was calculated based on the Strahler classification, which
was used to derive a catchment drainage density (DD; km km−2) value
for first order streams and a bifurcation ratio (BR) between the first and
second order streams for each catchment. All these variables were calculated using the spatial analyst tools in ArcMap 10 (ESRI).

Table 3
Percentage of each LULC class in 1981 under the main lithological classes.
%

Limestone

Marls

Quaternary

Other

HF
LF
Shr
Pas
Agr

43
54
16
50
6

26
33
54
33
80

27
7
14
13
11

4
5
15
4
2

E. Nadeu et al. / Catena 126 (2015) 117–125

121

2.7. Data analyses

3.3. Correlation analysis and scatter plots

A set of explanatory variables was selected (Table 2) based on those
found to be relevant for soil erosion and C redistribution in a preliminary
literature review (Nadeu, 2013). Correlation analysis was performed
between C related variables and catchment variables describing topography, river morphology, lithology, LULC and LULC change of each catchment. An initial screening was done to remove independent variables
that were not correlated to any of the dependent study variables as
well as topographical variables that showed high correlation between
them (r N 0.90, p b 0.01). Further, a principal component analysis
(PCA) was performed with the R open source software (R, 2011) on
the selected variables to group them. All reported means are accompanied by their associated standard error unless otherwise specified.

3.3.1. SCY, SSY
In general, SCY was lower in larger catchments and in those with
higher slope gradients. SCY values were higher for those catchments
with a higher percentage of marls and lower in catchments dominated
by lithologies of limestones and dolomites (Fig. 4a). DD's were higher
on marl dominated catchments than on those where limestones and dolomites were prevalent (Mann–Whitney test, p b 0.01), while a positive
correlation between percentage of dolomites and average slope was
found (r = 0.84, p b 0.01). Despite the association of certain LULC classes with lithology, and the correlations found between lithological types
and SSY, no significant correlations were found between LULC and SCY
or SSY. Additionally, SSY and SCY were significantly correlated to DD
(Table 6).

3. Results
3.1. LULC and its change (1981–2008)
Important LULC changes were experienced in the area due to agriculture abandonment and afforestations between 1956 and 1981, reducing the agricultural surface and increasing vegetation cover in 60%
of the catchment area (Boix-Fayos et al., 2007). After the construction
of check-dams and the main afforestations (1977–1978), we observed
that LULC changes consisted basically in a greening where almost one
half of the low density forest present in 1981 was reclassified as high
density forest; more than half of the shrubland sites decreased in
favor of both forest classes while one third of the pastures were converted to low density forests. Agricultural area retained most of its spatial
extent from 1981 to 2008 (Table 4). Over 90% of the high density forests
and pastures were located in catchments with average altitudes above
1300 m.

3.2. Specific carbon yield (SCY; g m−2 y−1) and C concentration
Average specific carbon yield (SCY) for all 18 catchments, estimated
by combining specific sediment yield (SSY) values and C concentration
in sediments (Csed), was 4.0 ± 1.1 g m−2 y−1. Yet, with a median value
of 2.1 g m−2 y−1 the distribution was skewed to the lower values
(Fig. 3). Csed averaged 1.1 ± 0.3% for the 18 catchments, although a
high variability was found between catchments and even within sediment profiles in depth, with coefficients of variation ranging between
10 and 78% (Table 5). This indicates that a high variability of Csed values
could be found within a single sediment wedge, but that the profile average was not necessarily different between catchments. In the 13
catchments where it was measured, MOC represented 63 ± 7% of the
C, while the rest was found in the form of POC (Table 5). Csed in marl
dominated catchments was 1.08 ± 0.16% while in limestone or dolomite dominated catchments it was 1.26 ± 0.26%, although differences
between both were not significant. ER values were in all cases lower
than unity with an average of 0.5 ± 0.1.

Table 4
Transition matrix between LULC classes for the period 1981–2008 (percentage of change).
Rows indicate the original LULC class in 1981 and columns the LULC in 2008. LULC classes:
high density forest (HF), low density forest (LF), shrubland (Shr), pasture (Pas) and agriculture (Agr).
From/to

HF

LF

Shr

Pas

Agr

HF
LF
Shr
Pas
Agr

98
46
20
6
4

1
53
37
33
0

0
1
41
8
8

0
0
1
53
9

0
0
2
0
80

3.3.2. Csed, enrichment ratio and C fractions
Concentration of C in sediments was negatively correlated to SSY
and SCY, although not significant at a p b 0.05 (Table 6). In fact, we
did not find significant (p b 0.05) correlations between Csed and catchment or channel morphological variables, although several trends
could be observed for its coefficient of variation. For example, the
variability of the Csed within the sediment profiles (CV of Csed) was
higher in catchments with low average slopes (Fig. 4b) and high DD
(Table 6). Moreover, Csed was positively correlated to the percentage
of high density forest cover for the two studied years and to that of pasture in 1981. The enrichment ratio was negatively correlated to the C
stock in soils present in 1981, positively to the percentage of pasture
cover in 1981, and it also had the highest positive correlation coefficient
with sediment clay content (Table 6).
When looking at the C concentration by size fractions in sediment
samples, significant correlations were found with the catchment and
channel morphological properties (Table 6), especially in the case of
MOC. Significant positive correlations between MOC concentration
and drainage area (Fig. 4c), between MOC concentration and average
slope gradient, and a negative correlation between MOC concentration
and SSY and SCY were observed. POC concentration was negatively correlated to SSY and DD and positively to average slope. However, MOC
and POC concentrations were not significantly correlated to the percentage of LULC extent for the three studied periods, or to their ratio
of change.
3.4. Classification of catchment-controlling factors for SCY
The results of the principal component analysis showed that the
studied variables could be grouped into three major components
explaining 74% of the variance or overall data variability, (43% by the
first component and 60% the two first components), although correlation coefficients between components and variables were generally
low (r b 0.5). The first component (Fig. 5) explained lithological and topographical properties of the catchments. The second component
(Fig. 5) was negatively correlated to DD, clay content in sediments, carbon stock in soils (in 1981) and the percentage of HF in 1981. The third
component was badly correlated to catchment properties and mainly
grouped sedimentological variables and carbon variables (SSY, SCY,
Csed, ER and MOC).
Although the catchments in the PCA were located scattered around
the biplot of the 1st and 2nd components, several of them appear to
be differentiated from others. This is the case of catchments with large
catchment areas and dominated by limestone lithology (ID's 3, 5, 8,
23), a group of catchments characterized by marl lithology, high DD,
high HF cover and high C stocks in soils, (ID's 7, 14, 53), and a third
group, more numerous and disperse, with marl lithology, large SSY
values, and low C concentration in soils and sediments (ID's 1, 18, 19,
21, 29, 34, 49) (Fig. 5).

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E. Nadeu et al. / Catena 126 (2015) 117–125

Fig. 3. Histogram of SCY values.

4. Discussion
4.1. Factors controlling SCY and Csed
Among all studied variables, catchment area, average slope gradient
and DD were significantly correlated to SCY. The association of lower
SCY values with larger catchments can be partly due to the increased
probability of deposition within larger catchments as is often described
for SSY (de Vente and Poesen, 2005; van Noordwijk et al., 1997;
Walling, 1983). Yet, deposition of C and its fate during transport and deposition phases is not only area dependent but, among other factors,
also depends on the size and stability of the transported aggregates,
with coarser particles depositing first (Slattery and Burt, 1997; Starr
et al., 2000; Walling, 1983). MOC at the study site is associated with
250–20 μm sized microaggregates (Nadeu et al., 2011) which may explain that higher MOC concentrations were found in sediments of larger
catchments with longer transport distances and potentially a relatively
Table 5
Average Csed, POC and MOC concentrations (%) and clay percentage in the sediments of the
catchments. Standard deviation reported.
Catchment ID

Csed

1
3
5
7
8
14
18
19
21
22
23
24
29
34
49
51
53
57
Average

1.2
1.4
1.5
1.0
1.6
1.1
0.9
0.9
1.0
1.2
1.4
1.4
1.0
1.0
0.6
1.1
1.5
1.1
1.1

±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±

0.3
0.1
0.2
0.7
0.4
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.4
0.3
0.2
0.1
0.7
0.3
0.3

CV Csed

MOC%

Clay

26
10
11
78
22
22
23
19
14
12
23
19
38
26
28
11
49
16
25 ± 16

61
–
54
62
73
–
72
69
61
58
58
59
–
66
–
72
–
51
63 ± 7

16
26
16
13
18
15
15
16
15
21
11
12
18
16
15
13
15
12
16

±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±

2
2
1
3
1
7
6
2
2
11
3
1
3
2
2
1
6
2
3

good protection of MOC against mineralization within those aggregates.
In fact, Boix-Fayos et al. (2014), in a study at the same site, showed that
sediments passing the check-dams and transported in runoff had a
larger proportion of clay and fine silt particles relative to those deposited behind the check-dams. Furthermore, given that Boix-Fayos
et al. (2014) also showed that the distribution of microaggregates
(b 250 μm) in the sediment wedges was not different from that in soils,
it can be assumed that either these sediments traveled short distances
or re-aggregation occurred after deposition. On the other side, the negative correlation between SCY, SSY and mean slope gradients, although
previously reported in catchment scale studies (Bellin et al., 2011; Lü
et al., 2012; Romero-Diaz et al., 2007), has not yet been satisfactorily explained. At our study site this could be explained by high average slope
gradients in catchments dominated by hard lithologies such as dolomites
and limestones, leading to low SSY rates. Thus, as has been described in
other studies (Molina et al., 2008; Romero-Diaz et al., 2007), lithology is
an important factor determining sediment and carbon export.
Despite the fact that channel erosion processes often increase in
importance with increasing catchment drainage areas (de Vente and
Poesen, 2005), the relationship between catchment DD and catchment
C export has received very little attention up to date. Sediment and C deliveries measured in river systems have been found higher than those of
hillslopes (Chaplot and Poesen, 2012) indicating the importance of considering these processes when establishing regional sediment and C
budgets. The high DD attributed to marl dominated catchments may
indicate higher erodibility and the presence of concentrated erosion
processes together with channel erosion. This would explain the lower
Csed in marl dominated catchments and the negative correlation between SSY and Csed, which has already been observed in areas where
deep soil or the channel bed are important sources of sediment
(Romero-Díaz et al., 2012). Therefore, the low ER values found for all
catchments may indicate the importance of massive erosion processes
transporting large sediment volumes with low C concentration
(Nadeu et al., 2011). This is in opposition to results obtained from catchments where interill erosion dominates and a selective removal of C is
observed leading to high ER values in sediments (Wang et al., 2010).
In addition to the lithological control, 80% of the total agricultural
area (which is more prone to erosion) was located on marls, increasing
sediment and C export values, while catchments dominated by

E. Nadeu et al. / Catena 126 (2015) 117–125

123

Table 6
Pearson correlation coefficients for selected variables.
lnSSY

lnSCY

lnCsed

lnCVCsed

ERC

POC

lnMOC

N

(18)

(18)

(18)

(18)

(18)

(13)

(13)

lnsSSY
lnSCY
lnArea
Slope
DDa
BRa
lnClay
Stock81
HF08a
HF81a
Pas81a

1
0.98⁎⁎ −0.45
0.36
0.98⁎⁎
1
−0.28
0.33
−0.77⁎⁎ −0.72⁎⁎
0.42 −0.47⁎
−0.60⁎⁎ −0.55⁎
0.35 −0.60⁎⁎
0.50⁎
0.51⁎ −0.33
0.49⁎
−0.46
−0.39
0.48⁎ −0.28
−0.13
−0.10
0.15 −0.28
−0.14
−0.10
0.27
0.26
−0.37
−0.30
0.57⁎ −0.18
−0.38
−0.33
0.53⁎
0.12
−0.29
−0.30
0.47⁎ −0.46

−0.20
−0.08
0.43
0.35
−0.23
0.67⁎⁎
0.43
−0.50⁎
−0.01
−0.20
0.48⁎

−0.56⁎ −0.68⁎
−0.49
−0.60⁎
0.50
0.82⁎⁎
0.71⁎⁎
0.62⁎
−0.57⁎ −0.52
0.06
0.64⁎⁎
−0.14
0.23
0.30
0.38
0.43

0.28
−0.05
0.48
0.35
0.42

Significance levels marked as:
⁎ p b 0.5.
⁎⁎ p b 0.01.
a
Indicates that spearman rank correlation coefficient was used because of a non-normal distribution of the variable.

indicate that efficient evacuation of water and sediments can result in
a decreased opportunity for C mineralization during transport and a
higher proportion of the eroded C reaching the sediment wedges. However, since detailed hydrological factors and their impact on SOC export
were not included in this study due to insufficient available data, this remains a hypothesis that needs to be further evaluated.
4.2. Implications for the fate of redistributed carbon
Resolving the uncertainties on the impact of soil erosion on the C
cycle implies understanding how C dynamics are modified by soil redistribution at eroding and depositional sites and during transport (Van
Oost et al., 2007). While aggregate breakdown and preferential mineralization of the more labile fraction of C have been associated with increased C release to the atmosphere (Lal, 2003), several studies have
suggested that deposition can actually lead to increased C sequestration
rates in terrestrial ecosystems by reducing C mineralization rates (Berhe
et al., 2008; Wang et al., 2013). However, the sequestration potential of
depositional sites could be lower than previously thought due to ongoing mineralization at depositional sites (Van Oost et al., 2012) and

Fig. 4. Scatter plots representing: a) %marls versus SCY, b) average catchment slope versus
CVCsed, c) logarithm of catchment area versus MOC concentration.

dolomites and limestones were mostly covered by shrubland or forest
and had lower C export values. The significant land use changes that
took place in the catchments during the last 50 years reduced sediment
export and changed channel morphology (Boix-Fayos et al., 2007).
Although, we did not find significant correlations between land use
changes and SCY, the positive correlations between Csed and the percentage of area occupied by pastures (for 1981) and by forest (HF)
(for both 1981 and 2008) (Table 6) indicates that the increase in vegetation cover increased SOC stocks and lead to an increase in Csed. However, given the negative correlation coefficient between HF percentage
and SSY, reported as well in other studies (Lü et al., 2012), an increase
in vegetation cover also reduces soil erodibility and the volume of
exported sediment, and, consequently, SCY does not necessarily increase. The positive correlation found between Csed and BR could

Fig. 5. Top: Biplot showing components 1 and 2 on the X and Y axes, respectively. Variables
are represented with arrows and the location of each catchment in relation to the variables
in bold numbers corresponding to their ID.

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E. Nadeu et al. / Catena 126 (2015) 117–125

has a strong dependence on the proportion between labile and stable C
types in deposited sediments (Rumpel et al., 2014) and on the environmental conditions at depositional sites (e.g. soil humidity, temperature,
burial depth) (Gregorich et al., 1998). The results from our study suggest that in the studied mountain catchment, land use and morphometric properties such as slope, drainage area and drainage density could
exert not only an influence on total redistributed C (through sediment
redistribution) but also on C dynamics at depositional sites by controlling the concentration of different C fractions and the variability of
total C concentration in sediment profiles. Given the significance of hillslope and floodplain C storage for the C balance (Hoffmann et al., 2013a,
2013b; Ran et al., 2014), further research is needed combining data on
the characteristics of redistributed C with information on mechanisms
of C stability at depositional sites and the role of local environmental
conditions. This would contribute towards constraining the value of
the potential C mineralization at depositional settings, which remains
a key uncertainty in the biogeochemical carbon cycling.

4.3. Considerations regarding the estimation of C export from catchments
The fact that no significant correlation between SCY and Csed was
found can indicate that Csed could have a smaller influence on C export
than the total sediment yield at the scale of this study. To further test
this observation, and illustrate the risk of extrapolating regional obtained values of C concentration or soil erosion rates, we compared the sum
of our estimated total C export from the 18 catchments (25 Mg y−1)
with values derived by using two different approaches: (i) a combination of SSY and soil organic carbon (SOC) concentrations from the
hillslopes, as used by Dymond (2010), and (ii) taking a general SSY
rate for catchments in SE Spain from Cantón et al. (2012) and our measured values of Csed for each catchment. From the first calculation we
obtained a total export of 59 Mg C y−1, while using the second approach
we obtained a total export of 162 Mg C y−1, more than five times the
originally estimated value. These results indicate, on one hand, that
the use of SOC concentrations instead of C concentration in sediments
to derive mobilized C can overestimate C export to the river system
when the ER is below unity, which is in agreement with observations
by Dymond (2010), while for ER values above unity C export would
be underestimated. On the other, the second result exemplifies how
the use of average regional soil erosion to calculate C export from catchments is not recommended.

5. Conclusions
Understanding the redistribution of soil organic carbon by soil erosion processes beyond plot level remains challenging. In this study, we
evaluated the role of catchment morphology, lithology and land use
characteristics, and found that large SCY rates were associated with
low average slope gradients and marl lithologies. On the contrary, SCY
was relatively low in catchments dominated by resistant lithologies
and where the area covered by forest increased during the studied period, although a higher extent of high density forest in the drainage areas
was associated to higher C concentration in sediments. C concentration
in sediments was in all cases lower than the average C concentration in
the catchment's soils, mainly due to the presence of massive erosion
processes, like channel and gully erosion, that transport large sediment
volumes with low C concentrations. Overall the results suggest that in
mountain catchments, morphometric properties such as slope, drainage
area and drainage density could exert not only an influence on sediment
transport but also on C dynamics at depositional sites by controlling the
concentration of different C fractions and the variability of total C concentration in sediment profiles. Further empirical research is needed
to test this and the role of other variables related to catchment hydrology and local environmental characteristics.

Acknowledgments
This research was supported with funds from the former Spanish
Ministry of Science and Innovation through the ERCO project (CGL2007-62590/BTE) and with funds from the Regional Séneca Foundation
through the ESUMA project (11859/P/09). E. Nadeu and J. de Vente acknowledge funding from the former Spanish Ministry of Science and Innovation through an FPI predoctoral fellowship (BES-2008-002379)
and a ‘Juan de la Cierva’ research grant (JCI-2011-08941), respectively.
We thank the members of the Soil Erosion and Conservation Group at
the CEBAS-CSIC for their valuable help in the field and laboratory
work and two anonymous reviewers for their contribution to the final
version of the manuscript.
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