For Review OnlySpatial variability of soil carbon and water storage across
loess deposit catena in China’s Loess Plateau region
Journal: Canadian Journal of Soil Science
Manuscript ID CJSS-2019-0144.R2
Manuscript Type: Article
Date Submitted by the Author: 10-Apr-2020
Complete List of Authors: Wang, Yi; Institute of Geographic Sciences and Natural Resources ResearchMao, Na; Northwest A & F UniversityWang, Jiao; Institute of Geographic Sciences and Natural Resources Research CASHuang, Laiming; Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesJia, Xiaoxu; , Institute of Geographic Sciences and Natural Resources ResearchShao, Mingan; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences
Keywords: soil carbon, soil moisture, land use, vegetation restoration, Loess Plateau
Is the invited manuscript for consideration in a Special
Issue?:Not applicable (regular submission)
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Spatial variability of soil carbon and water storage across loess
deposit catena in China’s Loess Plateau region
Yi Wang 1, 3, Na Mao 2, 3 †, Jiao Wang1, 2, 3 *, Laiming Huang 1, 2, 3 *, Xiaoxu Jia 1, 2, 3,
Ming′an Shao 1, 2, 3
1 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic
Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101,
China
2 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute
of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China
3 College of Resources and Environment, University of Chinese Academy of Sciences,
Beijing 100049, China
† Co-first author: Yi Wang and Na Mao
Corresponding author: [email protected]; [email protected]
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ABSTRACT
The impact of hillslope vegetation restoration on the distribution and variability of carbon
and water storage was studied across two catenary sequences of soils in Liudaogou watershed
of China’s Loess Plateau. Soil organic carbon storage (SOCS) under different land uses in the
two catenas decreased significantly in the upper soil layers (< 50 cm), but was relatively
stable in the deeper soil layers (> 50 cm). However, soil inorganic carbon storage (SICS) in
the two catenas fluctuated (two maxima) with increasing soil depth. There was no significant
difference of SOCS within 200 cm soil profile between forestlands (FO) and grasslands (GR)
at catenary scale (p > 0.05). However, SICS in the 0–200 cm soil profile differed markedly
between FO and GR (p < 0.05) in both catenas due to different degrees of root-facilitated
CaCO3 redistribution. Based on the coefficient of variance (CV), soil water storage (SWS)
was divided into three layers — active layer (0–100 cm, CV = 20–30%), sub-active layer
(100–200 cm, CV = 10–20%) and stable layer (200–500 cm, CV < 10%). SWS0-500 cm under
GR was slightly higher than those under FO on the two slopes due to higher water
consumption under tree plantation than native grasses. SOCS, SICS and SWS can be
predicted by multiple regression equations using different soil properties. The study
demonstrated differential responses of SOCS, SICS and SWS to vegetation restoration at
catenary scale, which was critical for improving ecosystem model predictions of soil carbon
and water fluxes in sloping lands.
Keywords: soil carbon; soil moisture; land use; vegetation restoration; Loess Plateau
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INTRODUCTION
Soil carbon and water reserves have direct impact on crop yield (Lal, 2004; McColl et al.
2017), vegetation growth (Boonjung and Fukai, 1996; Palacio et al. 2014) and nutrient
cycling (D’Odorico et al 2003), all of which in turn affect the productivity and functions of
terrestrial ecosystems (Schmidt et al. 2011). The arid and semi-arid regions of China’s Loess
Plateau (CLP) are recognized as important players in global carbon and water cycles because
of the huge carbon and water stocks in the deep and large loess deposits. The estimated
organic carbon storage in the CLP varies from 859 to 5044 Mg ha–1 depending on the loess
thickness (Jia et al., 2020). Zhu et al. (2019) have found that vadose zone water is
approximately 3.1 × 1012 m3 (±27.5%) in the CLP. However, CLP is also considered as one
of the most vulnerable areas affected by natural (e.g., precipitation variability) and
anthropogenic (e.g., vegetation restoration) disturbances (Fu et al. 2017). Thus,
“Grain-for-Green Project” and “Natural Forest Protection” were implemented for improving
ecosystem service and functions in this region (Chen et al. 2007; Cao et al. 2011).
Consequently, land uses changed significantly over the past several decades with large areas
of sloping farmlands (FA) converting to forestlands (FO) and grasslands (GR). This has
resulted in alterations in vegetation cover and evapotranspiration (Li et al. 2012), changes in
soil hydrological and nutrient cycles (Jia and Shao, 2014; Deng and Shangguan, 2017) and
improvements of ecosystem services and functions (Su and Fu, 2013). Therefore,
understanding the variations and controls of soil carbon and water storage in arid/semi-arid
regions of CLP is critical for the prediction of how carbon and water reserves respond to
vegetation restoration.
There are extensive studies on soil carbon storage and its distribution at different spatial
and temporal scales in CLP. For instance, it is reported that soil inorganic carbon storage
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(SICS) in the 0–1 m soil layer is 2.1 fold greater than soil organic carbon storage (SOCS) in
CLP (Tan et al. 2014). The distribution, stock and stability of both organic and inorganic
carbon in CLP vary with soil depth (Mao et al. 2018), vegetation cover (Fu et al. 2010), land
use pattern (Wei et al. 2009) and time span of management practice (Wang et al. 2012a; Liu
et al. 2014). More recently, Jia et al. (2017a) evaluated the relative contributions of different
environmental factors to the variations in SOCS at different layers of the 0–5 m soil profile (n
= 86) along a south-north transect of CLP. The results showed that the degree to which
climate, soil properties and land use contributed to the variations in SOCS varied
significantly with soil depth at regional scale. Liu et al. (2017) studied soil carbon dynamics
across a chronosequence of GR restoration and noted that soil carbon accumulation was due
to the increase of SOCS, because SICS decreased at the depth of 0–1 m in the restored GR at
decadal time scale.
In addition to spatio-temporal variations in soil carbon, horizontal and vertical
distributions of soil water storage (SWS) in CLP have also been extensively studied (Huang
and Shao, 2019). For instance, SWS has been investigated in CLP at slope scale (e.g., Jia et al.
2013), watershed scale (e.g., Hu et al. 2017) and regional scale (e.g., Jia et al. 2017b). There
are also studies investigating the controls on the spatio-temporal patterns of SWS in the CLP
region (e.g., Yang et al. 2014; Qiao et al. 2018). The dramatic increase in vegetation cover
has led to severe soil moisture depletion and widespread distribution of dried soil layers
(Wang et al. 2010; Wang et al. 2012b) and extensive negative effects on eco-hydrological
processes in CLP because of the significant reduction in stream runoff (Wang et al., 2011a).
However, Wang et al. (2018) recently noted that it is somewhat subjective to state that land
surface drying is the result of revegetation. Nevertheless, revegetation in CLP is approaching
the sustainable water resource limit, which endangers the health and services of the restored
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ecosystems (Feng et al. 2016).
Soil carbon and water cycles are closely related because i) stomatal response to changes
in soil moisture simultaneously controls transpiration and CO2 uptake, and ii) microbial
decomposition of organic matter is strongly constrained by soil moisture condition
(Diaz-Pereira et al. 2019). Studies show that the effect of soil organic matter (SOM) on soil
water retention and dynamics is driven by the affinity of SOM to water and the effect of
SOM on soil structure and bulk density (Rawls et al. 2003; Manns and Berg, 2014). On the
other hand, water stored in different soil layers affects soil carbon distribution, fraction and
stability (Zhang and Shangguan, 2016a; Mao et al. 2018). However, the relationships
between soil water storage and soil carbon storage vary with environmental conditions and
human activity.
While studies have been performed to characterize the vertical distribution of soil carbon
and water storage at catchment or regional scale, less is reported at catena scale in CLP,
where the variability in topography and hillslope vegetation restoration substantively affect a
wide number of soil properties. Topography influences the quantity and distribution of SOC
through the dynamic processes of soil erosion and deposition (Fissore et al., 2017; Shi et al.,
2019). It also controls the distribution and stock of soil water by influencing the vertical and
lateral water flow and changing the depth of groundwater in the mountainous areas (Xiang et
al., 2017). A comprehensive analysis of previous studies on soil water dynamics at different
spatial scales demonstrated that topographic controls started at slope scale, reached maximum
at catchment scale, and then decreased at regional scale (Huang and Shao, 2019). This
suggests that the controlling factors and their contributions to the spatial variability of soil
water and possible soil carbon are scale-dependent because of the tightly coupled
hydrological and nutrient cycling (Huang and Shao, 2019). Although there is a huge
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repository of studies on SOCS, SICS or SWS, very few studies focused on the combined
interactions of SOCS, SICS and SWS in CLP. This, however, is useful for sustainable
management of soil carbon and water in the process of vegetation restoration and ecosystem
reconstruction. Water flow in the soil determines the movements of solutes and colloids that
may cause translocation of soil carbon in the slope land (Huang and Shao, 2019). In addition,
soil water status controls the microbial activities, which are critical to the accumulation or
release of soil organic carbon (Diaz-Pereira et al. 2019). A better understanding of the
complex interplay of soil carbon and water at the catena scale is thus crucial in developing
ecosystem models for more accurate predictions of soil water/carbon fluxes in the slope
lands.
The objectives of this study were to investigate: i) the vertical distribution of soil carbon
and water storage across two catenas in Liudaogou watershed in CLP; ii) the interactive
relations between soil carbon and water storage at catenary scale; and iii) the factors that
control hillslope distribution of soil carbon and water storage.
MATERIALS AND METHODS
Study area
Two catenary sequences on the west- and northeast-facing slopes (W-SP and NE-SP) in
Liudaogou watershed in CLP (38°46′–38°51′N, 110°21′–110°23′E) (Fig.1) were selected due
to the lack of different land uses in the south- or north-facing slope. FO and GR alternately
occur in patches from the head to the foot of the selected west- and northeast-facing slopes
(Fig.1), providing a natural experiment for evaluating effects of hillslope vegetation
restoration on soil carbon and water distribution. The study area belongs to a semi-arid
climate and has a mean annual temperature of 8.4 °C and a mean annual rainfall of 437 mm.
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Over 70% of the precipitation occurs during the summer months from June to September.
The lowest and highest temperatures generally occur in January and July, respectively. The
studied watershed covers an area of 6.89 km2 and the altitude ranges from 1000 to 1300 m.
Soil is formed from the loess deposits with low-fertility and loose-structure. The main soil
types are Hapli-Ustic Cambosols and Calci-Orthic Aridosols according to Chinese Soil
Taxonomy (Cooperative Research Group on Chinese Soil Taxonomy, 2001); or Halustepts
and Haplocalcids according to Keys to Soil Taxonomy (Soil Survey Staff, 2010). As the main
land feature, slope land, which is characterized as severe soil erosion and degradation,
occupies 76.5% of the total area in the investigated watershed (Mao et al. 2018).
In order to control soil erosion and improve ecosystem services in CLP, a series of
restoration measures (including “Grain for Green Project” and “Natural Forest Protection”)
have been initiated by the Chinese government since the 1990s. Typical plants used for
vegetation restoration include korshinsk peashrub (Caragana korshinskii kom), purple alfalfa
(Medicago sativa) and apricot trees (Prunus armeniaca). Abandoned croplands are generally
recovered by natural vegetation such as bunge needlegrass (Stipa bungeana Trin) and
dahurica bush clover (Lespedeza dahurica). Further details about the study area have been
reported elsewhere (e.g., Jia et al 2013; Mao et al. 2018).
The average slopes of NE-SP and W-SP are 14° and 21° (Fig. 1c), respectively. The
NE-SP and W-SP are separated by a deep gully with a width of about 2 km. The restored GR
and FO alternately occur in patches from the head to the foot of the slopes (Fig. 1c). The
dominant vegetation type and its coverage in GR and FO on the two slopes are shown in Fig.
1c. Briefly, the dominant vegetation types on the NE-SP are bunge needlegrass (Stipa
bungeana Trin), crested wheatgrasses (Agropyron cristatum), korshinsk peashrub (Caragana
korshinskii kom) and apricot trees (Prunus armeniaca). The typical vegetation types on the
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W-SP include herba melict (Melilotus suaveolens Ledeb.), bunge needlegrass (Stipa
bungeana Trin) and apricot trees (Prunus armeniaca) (Fig. 1c).
Soil sampling and moisture monitoring
Based on different landscape positions and land uses, we selected six typical soil profiles
(P1–P6) on each slope; including three sites under GR and three sites under FO (Fig. 1c).
Each soil profile was excavated to the depth of 200 cm. Soil profiles were described in the
field according to the pedogenic horizons. Detailed descriptions of the pedogenic horizons in
each soil profile, including soil depth, boundary, color, texture, consistence, and root and
calcium nodules are given by Mao et al. (2018). Soils were classified as Haplocalcids
according to Keys to Soil Taxonomy (Soil Survey Staff, 2010). Both the undisturbed and
disturbed soil samples were collected at 10 cm intervals above 100 cm soil depth and at 20
cm intervals below 100 cm. The undisturbed soil samples collected by stainless steel cutting
rings (5 cm in both diameter and height) were used for measuring soil bulk density (BD) and
soil porosity. Composite disturbed soil samples were air-dried, ground and passed through
0.15–2 mm nylon sieves for measuring pH, particle-size distribution (PSD), soil organic
carbon (SOC) and soil inorganic carbon (SIC) contents.
The depth of precipitation infiltration and plant water acquisition could exceed 2 m,
which may influence deep soil moisture dynamics. Thus, aluminum neutron-probe access
tubes with a depth of 5.2 m were installed along each slope (Fig. 1c) for monitoring soil
water content (SWC). Volumetric SWC was determined monthly by the neutron probe device
(CNC503DR, China) at an interval of 10 cm and 20 cm respectively in the 0–1 m and 1–5 m
soil layers during the period of May to October 2016 and again April to October 2017. The
following calibrated equation was used for calculating volumetric SWC:
{ 𝑑 ≤ 10,𝜃 = 73.30𝐶𝑅 + 3.9565 (𝑛 = 7,𝑅2 = 0.8996,𝑝 < 0.001)𝑑 > 10,𝜃 = 60.09𝐶𝑅 + 1.8995 (𝑛 = 55,𝑅2 = 0.7578,𝑝 < 0.001) (1)
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where θ is volumetric SWC [%] and CR is slow-neutron counting rate at a given soil depth d
[cm]. The slow-neutron counting rate at a given soil depth is calculated as ratios of the
slow-neutron count to the standard count of the probe in its shield (which was 660 in this
study).
Laboratory analysis
BD was determined using the core approach (Grossman and Reinsch, 2002). The pH
was determined using a pH meter at soil/water ratio of 1:2.5 (Mclean, 1982). PSD was
analyzed by laser diffraction technique using Mastersizer 2000 Particle Size Analyzer
(Malvern Instrument, Malvern, England). Soil texture was classified based on USDA
textural classification. SOC was measured using the K2Cr2O7 oxidation method (Nelson et al.,
1982). CaCO3 was measured by dissolving soil samples in 1 M HCl and determining the
subsequent released CO2 (Dreimanis, 1962). SIC was calculated by multiplying CaCO3
content with a coefficient of 0.12 (Dreimanis, 1962). By assuming soil particle density is 2.65
g cm-3, soil total porosity (TP) was determined using Eq. (2):
Pt = (1 - BD/DS) × 100 (2)
where Pt is soil total porosity [%]; BD is bulk density [g cm-3]; and DS is soil particle density
[g cm-3], which is 2.65 g cm-3 in this study.
Soil porosity is very complex, which governs biological processes that supports life and
physicochemical processes that determine environment quality. In terms of their size, pores
of equivalent cylindrical diameter (ECD) < 30 μm are defined as capillary pores (Marshall et
al., 1996). Soil capillary porosity (CP) was determined by placing dried undisturbed soil
samples on a moist filter paper (1–3 μm) to allow the soil to absorb water through capillary
forces after 12 h. The mass increase was recorded and was used for determining capillary
water capacity of the soil sample. More details about the analysis of soil porosity have been
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given elsewhere (Liu et al. 1996). CP was calculated using Eq. (3), driven by the measured
BD and soil capillary water capacity data.
)×100Pc = (∆W/V (3)
where is soil capillary porosity [%]; is from the mass increase by water absorption Pc ∆W
through capillary forces after 12 h (g, i.e., cm3); and V is soil core volume [cm3], 100 is the
unit conversion coefficient.
Consequently, soil non-capillary porosity (NP) was calculated using Eq. (4):
P = Pt - Pc (4)
where P, Pt, and Pc are soil non-capillary porosity, total porosity and capillary porosity,
respectively.
SOCS (Mg ha-1) was calculated by Eq. (5):
SOCS = BD × SOC × D/10 (5)
where BD is soil bulk density [g cm-3]; SOC is soil organic carbon content [g kg-1]; and D is
soil thickness [cm], 10 is the unit conversion coefficient.
Accordingly, SICS (Mg ha-1) was calculated by Eq. (6):
SICS = BD × SIC × D/10 (6)
where BD is soil bulk density [g cm-3]; SIC is soil inorganic carbon content [g kg-1]; and D is
soil thickness [cm], 10 is the unit conversion coefficient.
The time-averaged SWC (θv) for GR and FO was calculated using Eq. (7):
θv =1ij∑
j
j = 1∑i
i = 1θi (7)
where i is the number of measurement times (i = 13); j is the number of experimental sites of
of GR and FO on each slope (j = 3); and θi is the measured SWC [%].
SWS [mm] was calculated using Eq. (8) as:
SWS = θv × D × 10 (8)
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where θv is soil water content [%], D is soil depth increment [cm], 10 is the unit conversion
coefficient.
Statistical analysis
All the individual datasets were grouped by land use and slope aspect to four groups
including NE-GR, NE-FO, W-GR and W-FO, which were then used for statistical analysis
including calculations of extremities (minimum and maximum), means, standard deviations
(SD) and coefficients of variation (CV). The differences within/among SOCS, SICS and
SWS in the same soil layer under different land uses and in different soil layers under the
same land use were determined by one-way ANOVA. In all cases, the distributions of the
data are normal as confirmed by the normal probability plot. Levene's test was used to assess
variance homogeneity before ANOVA. The correlations among SOC, SICS and SWS were
determined by the Pearson’s test. Multiple regression analyses were employed to obtain the
best fitted model that quantitatively described SOCS/SICS/SWS using different soil attributes.
The stepwise regression procedure was used to select the independent variables that would
result in the best possible model, while at the same time ensuring statistical significance of
the results. In this method, the best predictor variables, according to the statistical criterion,
are entered into the prediction equation, one after the other in successive steps, until no other
predictor variable meets the criterion. The predictors entering the regression model were
selected to be the ones with the largest partial correlation with the dependent variable. In
addition, the partial regression coefficient of a predictor must be significant at the 0.05 level,
and at least 0.01% of its variance has to be independent of the other predictor variables
(tolerance value). The performance of multiple regression models was evaluated by the
coefficient of determination (R2) and the p-value (p < 0.05). All the statistical analyses were
conducted using SPSS 24.0 (Statistical Package for Social Sciences). The package Origin Pro
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9.0 was used to plot the results of the statistical analyses.
RESULTS AND DISCUSSION
Soil properties variation
Fig. 2 shows the variations in basic soil properties under both GR and FO on the two
investigated slopes in the study area. The range of soil pH was 8.4–9.2, with higher mean
values for W-SP than the NE-SP (Fig. 2). Soil particles in the study area were mainly silt
(24.95%–70.32%), followed by sand (4.54%–70.70%) and then clay (4.35%–42.52%). There
was no significant difference between GR and FO on the same slope in terms of soil
properties. However, slope aspects had significant (p < 0.05) impacts on the distribution of
pH, clay, silt and sand in the soil (Fig. 2). This suggested that soil properties responded
differentially to changes in slope aspect, which is in consistent with the findings in Danangou
catchment in CLP (Qiu et al. 2001). The means of silt and clay contents on W-SP were
greater than those in NE-SP, while the sand content was higher in NE-SP (Fig. 2). The soil
BD ranged from 1.2 to 1.6 g cm-3, with a mean value close to 1.5 g cm-3 under both FO and
GR on the two slopes (Fig. 2). Similarly, the mean soil porosity changed relatively little
under both FO and GR on the two slopes. However, both BD and soil porosity varied
significantly from the soil profiles, which were evident in the large ranges shown in the box
plots in Fig. 2.
Soil organic carbon content and storage
The SOC contents and SOCS as a function of soil depth under different land uses and
slope aspects are plotted in Fig. 3. While SOC content significantly decreased in the 0–50 cm
soil layer, it was relatively stable in the soil profile below the 50 cm soil layer on both the
NE-SP and W-SP in the study area (Fig. 3a). The ranges of SOC contents were 0.62–4.42,
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0.75–4.41, 0.82–5.12 and 0.72–4.40 g kg-1 respectively for the NE-GR, NE-FO, W-GR and
W-FO. One-way ANOVA analysis showed that there were no significant differences in SOC
content either under the same or different land uses on the same or different slopes (p > 0.05).
For SOCS, it rapidly decreased within 0–50 cm soil layer and then relatively stabilized
below 50 cm depth under both GR and FO on the two slopes (Fig. 3b). The small increase in
SOCS in the soil profile below the 100 cm soil layer was attributed to the increase in BD,
because SOC contents remained relatively constant below 100 cm (Fig. 3a). In the soil layer
of 0–200 cm, SOCS was 31.60, 32.82, 38.47 and 36.94 Mg ha-1 respectively for NE-GR,
NE-FO, W-GR and W-FO; much lower than that in the humid region of southern China
(Chen et al. 2019). The range of SOCS in forest ecosystems in Hunan province was reported
to be 125.6–132.1 Mg ha-1 (Chen et al. 2019). Fu et al. (2010) ascribed the relatively low
SOCS in arid regions of CLP to the widespread soil erosion, low litterfall input and low soil
water-holding capacity compared to the clayey soils in humid tropical regions of China
(Huang et al., 2016). SOCS within 0–50 cm soil layer under different land uses represented
66% ~ 72% of SOCS in the top 100 cm and 41% ~ 46% of SOCS in the top 200 cm soil layer,
which suggested that there was more organic carbon sequestration in the upper (< 50 cm)
soils than in the deeper (> 50 cm) soils. Higher SOCS in shallow soils (< 50 cm) relative to
the deeper soils (> 50 cm) was due to the higher inputs of organic matter by root exudates,
dead roots and microbial biomass (Zhang et al. 2015). Our results were in agreement with the
accumulations of SOC in surface soils reported by Zhao et al. (2016), who investigated the
vertical distribution of SOCS under different land uses in Zhifanggou watershed in CLP.
There were no significant differences in SOCS in the same soil layer under either different
land uses or slope aspects. This disagreed with the findings by Shi et al. (2019) at watershed
or regional scale where significant differences were noted in SOCS under different land uses.
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This suggested that the response of SOCS to vegetation restoration varied with spatial scale.
Soil inorganic carbon content and storage
Fig. 4 plots the vertical distribution of SIC content and SICS under different land uses
and slope aspects in the study area. Generally, SIC content (4.11–18.69 g kg-1) and SICS
(6.16–29.00 Mg ha-1) under different land uses and slope aspects were much higher than SOC
content (0.62–5.12 g kg-1) and SOCS (0.94–7.37 Mg ha-1) (Fig. 3; Fig. 4). Higher SIC content
and SICS were related to alkaline soils in the study area, generally promoting the formation
of calcium carbonate (Zhao et al. 2016). SIC contents increased initially with soil depth
(showing two maxima) and then decreased downwards under NE-GR, NE-FO, and W-FO,
while the opposite trend of SIC distribution was observed for W-GR (Fig. 4a). The ranges of
SIC were 5.34–14.59, 5.91–11.18, 4.11–10.93 and 9.98–18.69 g kg-1 respectively for the
NE-GR, NE-FO, W-GR and W-FO in the study area. The statistical analysis showed that
discrepancies existed in SIC contents across different land uses on the same slope (e.g.,
W-FO vs. W-GR, p < 0.05) and the same land use on different slopes (e.g., W-FO vs. NE-FO,
p < 0.05). Different vegetation covers and slope aspects influence the availability of sunlight
and water, which in turn affect the amount and vertical distribution of SIC content (Yang et al.
2018). Under different land uses on the two slopes, SICS fluctuated with soil depth, showing
a double-peak curve at varying soil depths (Fig. 4b). This was similar to SICS trend reported
by Chang et al. (2012), where it was noted that SICS was higher in deep soil layers due to the
dissolution and leaching of calcium carbonate from the topsoil. SICS within 0–200 cm soil
layer was 272.13, 278.07, 238.67 and 400.33 Mg ha-1 respectively for NE-GR, NE-FO,
W-GR and W-FO. SICS accounted for over 89% of total carbon pool (i.e., the sum of SOCS
and SICS) and was also 6.2–10.8 fold greater than SOCS in the study area. Higher SICS
relative to SOCS has also been reported in other studies in arid/semi-arid regions of CLP
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(Tan et al. 2014). In the 0–200 cm soil profile, SICS in FO was significantly different (p <
0.05) from that in GR on the west-facing slope, which might be due to the extreme high SICS
of W-FO. Different root distributions under FO and WR may also affect the formation,
eluviation and illuviation of CaCO3 (Mao et al. 2018). Significant differences of SICS were
also observed for the lower three soil layers under the same land use type and slope aspect as
compared with those in the upper soil layers (Fig. 4b).
Soil water content and storage
The vertical distributions and temporal variations of mean SWC under different land
uses on the two slopes are plotted in Fig. 5. For the period of study, SWC increased initially
with soil depth to a maximum and then fluctuated with soil depth under both GR and FO on
the two slopes. However, the depth of maximum SWC varied both with time and land use
(Fig. 5). This was primarily driven by variations in rainfall amount and plant soil water
uptake, affecting the rate and depth of precipitation infiltration (Wang et al. 2013). In
addition, the presence of calcium carbonate concretions (Zhou et al. 2009) and furrows dug
by ants (Li et al. 2019) affects water infiltration depth by altering soil structure and porosity.
The highest SWC under the different land uses and slope aspects occurred on 15th of July
2016, which agreed with the observed rain storm on the 7th of July (136.6 mm) and the 10th
of July (70.6 mm) of the same year. The time-averaged mean SWC for the 0–500 cm soil
profile was 19.2% and 17.9% respectively for W-GR and NE-GR, which were higher than
those for W-FO (18.7%) and NE-FO (16.8%) (p < 0.05). Higher SWC has also been observed
in GR than in FO in other studies in Liudaogou watershed, and ascribed to lower
evapotranspiration in GR than in FO (Jia and Shao, 2014).
Based on coefficient of variance (CV), SWS in the 0–500 cm soil profile under different
land uses on the two slopes was divided into three layers (Table 1) — active soil layer (0–100
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cm, CV = 20–30%), sub-active soil layer (100–200 cm, CV = 10–20%) and relatively stable
soil layer (200–500 cm, CV < 10%). As also noted by Gao and Shao (2012) and Wang et al.
(2015a), variability in SWS in deep soil layer was also smaller than that in shallow soil layers.
The capacity of shallow soil layers to exhibit high SWS variation was attributed to the rapid
and frequent exchange of water and energy via solar radiation, precipitation and
evapotranspiration (Zhang and Shangguan, 2016b). SWS in the 0–500 cm soil profile was
slightly higher in GR (1056 mm in NE-SP and 941 mm on W-SP) than in FO (1054 mm in
NE-SP and 917 mm on W-SP) on the two slopes. This indicated that as compared with GR,
there was more water consumption by forest tree plants. Jia et al. (2017b) also showed that
artificial forests consumed more water than grasslands and croplands because of the higher
transpiration. Nevertheless, there might be other factors (e.g., solar radiation, wind speed)
that mask the influence of transpiration on SWS of FO and WR on the NE-SP. Excessive
afforestation can result in the formation of dry soil layers in CLP (e.g., Wang et al. 2010,
2011b, 2012b, 2015b, 2018). This not only degrades soil and vegetation by hindering water
flow from the soil to plants (Shangguan, 2007), but also exerts negative effect on ecosystem
functions and services by weakening soil water storage capability (Jia et al. 2017b).
The relationship between soil carbon and water storage
The relationships between SOCS, SICS and SWS at different soil depths (0–50 cm,
0–100 cm, 0–150 cm and 0–200 cm) were analyzed, but were only significant for the 0–50
cm and the 0–100 cm soil layers (Fig. 6). SOCS was negatively correlated to SWS in all
scenarios (NE-FO, NE-GR, W-FO and W-GR) at the depth of 0–50 cm and 0–100 cm (p <
0.05). This was attributed to the concurrence of SOM accumulation and soil water
consumption during vegetation restoration (Zhang and Shangguan, 2016a). The increase of
SOM provides the essential nutrients for vegetation growth and thus increases the vegetation
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cover. This leads to an increase in both interception and transpiration, which in turn decreases
SWS (Zhang and Shangguan, 2016a). On the other hand, SOM accumulation concomitantly
affects soil physical attributes with a magnitude depending on the amount and constituent of
accumulated SOM (Kay, 1998). For instance, increase in SOM improves soil structure and
macroporosity, which indirectly promotes vegetation growth and thus enhances soil water
consumption by increased transpiration (Deng et al. 2013; Zhao et al., 2010).
The correlation between SICS and SWS at the depth of 0–50 cm and 0–100 cm are
shown in Fig. 6. For the 0–50 cm soil under GR, the SICS was positively correlated to SWS
on NE-SP (p < 0.05), while the SICS was negatively correlated to SWS on W-SP (p < 0.05).
No significant correlation was observed between SICS and SWS in the 0–100 cm soil layer
for GR on the two slopes. For FO, however, SICS in the 0–50 cm and the 0–100 cm soil
layers was positively correlated with SWS on both NE-SP and W-SP (p < 0.05). The positive
correlations between SICS and SWS under NE-FO (0–50 cm and 0–100 cm), W-FO (0–50
cm and 0–100 cm) and NE-GR (0–50 cm) (Fig. 6) may be due to the coincidence of higher
contents of SIC and SWC in the soil profile (Fig. 4a and Fig. 5). In contrast, SIC and SWC in
the 0–50 cm soil layer under W-GR respectively decreased and increased with soil depth
(Fig. 4a and Fig. 5), which caused the negative correlation between SICS and SWS (Fig. 6).
There were studies also showing that soil water was a critical element for the formation of
soil carbonate in the study area (Liu et al. 2014; Zhao et al. 2016). However, our study
suggested that the effects of soil moisture on carbonate also depended on land use and slope
aspect. Futrue investigations of root distributions under different land uses and slope aspects
are important for better understanding the complex interactions between SICS and SWS.
The relationship between soil properties, soil carbon and water storage
Table 2 illustrates the correlations between SOCS, SICS, SWS and other soil properties
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(weighted-mean values) for the 0–200 cm soil layer. SOCS under different land uses on the
two slopes was negatively related to pH, BD and SWC, but positively correlated with CP;
except for W-GR (Table 2). The observed negative relationships between SOCS and pH/BD
agreed with the findings by Liu et al. (2017). pH affects plant growth and soil microbial
biomass, which are strongly related with SOC accumulation and decomposition. As near
neutral conditions are considered to be best for plant growth and microbial activity
(McCauley et al. 2009; Rousk et al. 2010), high soil pH (8.4–9.2) in our study would restrict
both plant and microbial biomass. This in turn results in low SOC input. As shown in Table 2,
BD had negative correlation with SOCS, indicating that surface soils with higher SOC
generally have lower BD. This is because the low-density organic matter accumulation in the
surface soils could reduce the bulk density of soil (Johnson et al. 2015). While SWC
increased with increasing soil depth, SOC content was highest in the top soil; which could
cause negative correlation between SOCS and SWC. This is because surface soils with higher
root density and root biomass would consume more water than the deeper soils (Chen et al.,
2010), while returning more organic matter by root exudates, dead roots and microbial
biomass (Zhang et al., 2015). In addition, SP can influence profile distribution of SOC
through leaching and precipitation because of its effects on soil water transportation (Zhang
and Shangguan, 2016a).
Under different land uses, SICS was positively correlated with BD and clay content
(Table 2), indicating that SICS increased with increasing BD and clay content under all
studied land uses and slope aspects. The observed positive correlation between SICS and BD
was similar to that by Wang et al. (2016), where fine-texture soils with higher BD were noted
to have higher SICS. The formation of CaCO3 and clay is often gradual during long-term
pedogenesis; simultaneously transfered to deeper soils through leaching in the relatively high
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porosity loess soils (Gong et al. 2007). This resulted in the positive correlation between SICS
and clay content. Soil pH was considered as one of the vital variables that explain changes of
SICS as it affects the formation of carbonate (Liu et al. 2014; Civeira, 2016; Mu et al. 2016).
However, our study only showed significant positive correlation between SICS and pH for
GR on the W-SP. Previous studies have shown that soil carbonates could be lost through
three potential pathways: (i) emission into the atmosphere as CO2; (ii) dissolution and
leaching into the subsoil or groundwater; and (iii) translocation to nearby regions with
surface runoff (Liu et al., 2014; Yang et al., 2018). These processes may mask the
relationship between SICS and pH in NE-FO, NE-GR and W-FO, which requires further
investigation. SWS was positively correlated to BD under different vegetation covers (Table
2), consistant with the reported results by Zhang et al. (2019). SWS had positive relationships
with clay and silt content and negative correlations with sand content (Table 2). This was
because soils with larger amount of clay and silt had greater soil water capacity (Zhang et al.
2019). Soil texture influences soil water conservation and root distribution, which in turn
affects water storage and consumption in soils (Gui et al. 2010; Li et al. 2016).
Our results suggested that variations in SOCS, SICS and SWS were simutaneously
driven by different soil properties. Thus, an accurate estimation of SOCS, SICS and SWS
requires consideration of potential interactions of these soil parameters. Multiple regression
(MLR) models are widely applied to evaluate and quantify the effect of two or more
independent variables on a dependent one. In this study, we used multiple regression analysis
to build models that quantitatively described SOCS, SICS and SWS as a combined product of
different soil properties for each land use on the two slopes (Table 3). The simple linear
regression models generally had R2 lower than 0.385 (p < 0.05). In contrast, MLR models
(Table 3) provided an improved prediction of SOCS, SICS and SWS than the simple linear
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regression models for the analysis at catenary scale of soil properties in CLP. However, the
MLR models used to predict SOCS, SICS and SWS in Table 3 may be location and land
cover dependent, which needs to be addressed in the future.
CONCLUSION
The distribution and variability of soil carbon and water storage under different land
uses and slope aspects were investigated on two catenary sequences of loess soils deposit in
Liudaogou watershed in CLP. In the study area, SOCS under different land uses in both
catenas decreased significantly in the upper soil layer (< 50 cm) (p < 0.01), but remained
relatively stable in the deeper soil layer (> 50 cm) (p > 0.01). In contrast, SICS under
different land uses in both catenas fluctuated with increasing soil depth and showed two
maxima at varying soil depths. There was no significant difference in SOCS in the 0–200 cm
soil profile between FO and GR at the catenary scale. However, SICS in the 0–200 cm soil
profile differed markedly between FO and GR (p < 0.05) in both catenas. This variation was
attributed to the differences in root distribution, which affected the formation, eluviation and
illuviation of CaCO3. Based on the coefficient of variance (CV), SWS in the 0–500 cm soil
profile under different land uses in both catenas was divided into three layers — active soil
layer (0–100 cm, CV = 20–30%), sub-active soil layer (100–200 cm, CV = 10–20%) and
relatively stable soil layer (200–500 cm, CV < 10%). SWS in the 0–500 cm soil profile under
GR was slightly higher than those under FO on the two slopes due to higher water
consumption under tree plantation than native grasses. The different soil properties which
affected SOCS, SICS and SWS were quantitatively predicted by multiple regression
equations. Our study demonstrated that there was differential response of SOCS, SICS and
SWS to vegetation restoration at catenary scale. These differences can be incorporated into
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ecosystem models to improve predictions of soil carbon and water flux on the vast slope
lands across the arid and semi-arid regions of CLP.
ACKNOWLEDGEMENTS
This study was supported by projects from the Ministry of Science and Technology of
China (Grant No. 2016YFC0501605), National Natural Science Foundation of China (Grant
No. 41601221), The Second Tibetan Plateau Scientific Expedition and Research (STEP)
program (Grant No. 2019QZKK0306), Chinese Academy of Sciences (XDA23070202), the
Youth Innovation Promotion Association of Chinese Academy of Sciences (2019052), State
Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil
and Water Conservation, CAS & MWR (A314021402-2010) and Bingwei Outstanding
Young Talent Project from the Institute of Geographical Sciences and Natural Resources
Research (Grant No. 2017RC203). We would like to extend our utmost appreciation to the
anonymous reviewers who provided detailed and constructive comments.
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Table 1. Statistical descriptive analysis of 20 cm intervals of soil water storage in the 0–500 cm soil profile
under different land uses on the two investigated slopes in China’s Loess Plateau
Land use
Descriptive statistics
Active soil layer(0–100 cm)
Sub-active soil layer(100–200 cm)
Relatively stable soil layer (200–500 cm)
NE-GR Min (mm) 8.91 22.37 9.08Max (mm) 74.99 71.88 67.66Mean (mm) 35.45 41.23 44.64
SD 11.14 5.18 3.60CV (%) 31.42 12.56 8.07
NE-FO Min (mm) 10.28 20.55 22.09Max (mm) 75.64 59.22 68.33Mean (mm) 38.64 41.34 42.19
SD 10.38 4.57 3.80CV (%) 26.86 11.05 9.01
W-GR Min (mm) 12.69 19.46 13.81Max (mm) 81.10 67.22 60.18Mean (mm) 39.21 39.33 36.60
SD 11.98 4.28 3.58CV (%) 30.55 10.88 9.78
W-FO Min (mm) 14.09 9.38 7.81Max (mm) 83.99 63.22 58.89Mean (mm) 42.11 41.68 32.65
SD 12.54 4.86 3.01CV (%) 29.78 11.66 9.22
Note: NE, northeast-facing; W, west-facing; GR, grassland; FO, forestland; Min, minimum value; Max, maximum value; SD, standard deviation; CV, coefficient of variation.
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Table 2. Pearson’s correlation between paired combinations of SOCS, SICS, SWS and other soil properties (weighted mean vaules) for the 0–200 cm soil profile in northeast-facing and
west-facing slopes in China’s Loess Plateau.
Land uses Variables pH Clay Silt Sand BD NP CP TP SWC SOC SICNE-GR SOCS -0.834*** -0.617 -0.740** 0.798** -0.801*** 0.515* 0.656** 0.421 -0.613* - -
SICS 0.296 0.675* 0.315 -0.476 0.265 0.462 -0.201 -0.124 0.734* - -SWS 0.244 0.320 0.639* -0.568 0.224 0.567* 0.202 0.265 - -0.409 -0.388
NE-FO SOCS -0.881*** -0.410 -0.113 0.189 -0.556* -0.285 0.462 0.352 -0.935*** - -SICS 0.176 0.647* 0.639* -0.643** 0.538* 0.137 -0.673** -0.661** 0.038 - -SWS 0.215 0.542* 0.752** -0.750** 0.412 0.292 -0.475 -0.409 - -0.473 0.107
W-GR SOCS -0.526* -0.235 -0.640* 0.539* -0.592* -0.183 -0.391 -0.334 -0.886*** - -SICS 0.546* 0.609* 0.184 -0.445 0.557* 0.699** 0.027 0.100 -0.556 - -SWS 0.658** 0.639* 0.165 -0.449 0.568* 0.829*** 0.301 0.380 - -0.495 0.706**
W-FO SOCS -0.563* -0.541* -0.299 0.592* -0.812*** -0.248 0.699** 0.550* -0.959*** - -SICS -0.262 0.627* 0.590* -0.707** 0.760* 0.654* -0.773** -0.442 0.494 - -SWS -0.411 0.184 0.502 -0.386 0.521* 0.457 -0.447 -0.168 - -0.625* 0.069
Note: NE, northeast-facing; W, west-facing; GR, grassland; FO, forestland; BD, bulk density; NP, non-capillary porosity; CP, capillary porosity; TP, total porosity; SWC, soil water content; SOC, soil organic carbon content; SIC, soil inorganic carbon content; *** significant at p < 0.001; ** = significant at p < 0.01; * significant at p < 0.05.
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Table 3. Multiple regression models of SOCS, SICS, SWS and other soil properties of the 0–200 cm soil layer
under different vegetation types in Liudaogou watershed in China’s Loess Plateau
Land use
Model R2 Sig
NE-GR SOCS=395.913-185.510ln(pH)+0.242CP 0.844 0.000SICS=-21.303+1.031Clay+1.286SWCSWS=159.869-44.511ln(Silt)+7.694NP
0.5200.473
0.0370.041
NE-FO SOCS=50.471-1.726ln(pH)+3.848ln(BD)-11.347ln(SWC) 0.913 0.000SICS=1060.292+0.859(Clay+Silt)-7.133eBD+3.690CP-317.811ln(TP) 0.591 0.045SWS=96.840-1.370(Clay+Sand) 0.580 0.001
W-GR SOCS=-119.413+82.370ln(pH)-12.744ln(BD)-16.164ln(SWC)-0.052(Clay+Silt)
0.904 0.000
SICS=-26.569+0.018eNP+2.031Clay 0.440 0.031SWS=-375.808+47.019pH+0.883Clay+5.374NP-36.291BD 0.744 0.017
W-FO SOCS=233.158-13.822ln(BD)-102.953ln(pH) 0.882 0.000SICS=126.066-2.463eCP
SWS=93.515-22.965ln(SOC)-13.663eBD
0.7740.596
0.0000.004
Note: NE, northeast-facing; W, west-facing; GR, grassland; FO, forestland; SOCS, soil organic carbon storage; SICS, soil inorganic carbon storage; SWS, soil water storage; SWC, soil water content; BD, bulk density; NP, non-capillary porosity; CP, capillary porosity.
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Fig. 1. Location of the study area in China (a) and in Shaanxi Province (b) depicting the
northeast-facing slope (NE-SP) and west-facing slope (W-SP) selected for collection of soil
profile data (P1–P6) and installation of neutron access tubes (GR1–GR6, FO1–FO6) (c). The
major species and its coverage in grassland (GR) and forestland (FO) on each slope were
shown in the box (c). Numbers in the box represent altitudes of soil sampling and moisture
monitoring sites. Figure (a) and figure (b) were created using ArcMap version 10.5.0.
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For Review OnlyFig. 2. Box plot diagrams showing the distribution of basic soil properties under different
land uses on two investigated slopes in Liudaogou watershed in China’s Loess Plateau.
Horizontal lines inside boxes denote the median. Different letters indicate significant
difference between the averages of soil properties under different land uses on the two slopes
at p < 0.05. Note: NE, northeast-facing; W, west-facing; GR, grassland; FO, forestland.
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0 2 4 6 8
190
170
150
130
110
95
85
75
65
55
45
35
25
15
5
0 2 4 6 8 10 12
AbcAcdAbd
AbcAcAb
Abc
Acef
AbcAcAbc
Abc
Ac AcdAbAbc
AbcAcAbAcef
AcAdAcAd
AcAdAcAdf
AcAdAcAdAcAdAcAdeAcAdAcdAdAbcAdAcAcd
AbcAdAcAcd
AbcAcdAcd AbcAbAbAb Ab
AaAaAa
(b)
Soil organic carbon storage (Mg ha-1)
NE-GR NE-FO W-GR
W-FO
Aa
(a)
NE-GR NE-FO W-GR W-FO
Soil organic carbon content (g kg-1)
Soil
dept
h (c
m)
Fig. 3. Vertical distribution of soil organic carbon content (a) and soil organic carbon storage
(b) under grassland and forestland on the northeast-facing and west-facing slopes in the study
area. The error bars indicate standard errors of three GR or FO sites on the same slope.
Lowercase letters in Fig. 3b indicate significant difference among different soil layers under
the same land use and slope aspect (i.e., bars with the same color in different soil layers),
while uppercase letters indicate significant difference between different land uses in the same
soil layer (i.e., bars with different color in the same soil layer) (p < 0.05). Note: NE,
northeast-facing; W, west-facing; GR, grassland; FO, forestland.
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0 10 20 30
190
170
150
130
110
95
85
75
65
55
45
35
25
15
5
0 10 20 30 40 50 60 70
AbcAbAbAa
AaAabAabAa
ABabABabBaAab
AabAaAabAa
AbcAbAb
AaAbcAb AbAa
AbcAb AabAa
ABbAa
ABabBa
AabAaAa
Aa
AbcAb Ab Aa
AbcAb Ab
Aa
AaAbcAb AbAaAbcAb Ab
AaAbcAbAb Aa
AcAbAb
(b)
Soil inorganic carbon storage (Mg ha-1)
NE-GR NE-FO W-GR W-FO
Aa
(a)
Soil
dept
h (c
m)
Soil inorganic carbon content (g kg-1)
NE-GR NE-FO W-GR W-FO
Fig. 4. Vertical distribution of soil inorganic carbon content (a) and soil inorganic carbon
storage (b) under different land uses in China’s Loess Plateau. The error bars indicate
standard errors of three GR or FO sites on the same slope. Lowercase letters in Fig. 4b
indicate significant difference between different soil layers under the same land use (i.e., bars
with the same color in different soil layers), while uppercase letters indicate significant
difference between different land uses in the same soil layer (i.e., bars with different color in
the same soil layer) (p < 0.05). Note: NE, northeast-facing; W, west-facing; GR, grassland;
FO, forestland.
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For Review Only500
400
300
200
100
W-FOW-GR
NE-FONE-GR
5.00
10.0
15.0
20.0
25.0
30.0
35.0
500
400
300
200
100
Soil
dept
h (c
m)
2017
-10
2017
-09
2017
-08
2017
-07
2017
-06
2017
-05
2017
-04
2016
-10
2016
-09
2016
-08
2016
-07
2016
-06
Soil
dept
h (c
m)
Date (Year-Month)
2016
-05
2017
-10
2017
-09
2017
-08
2017
-07
2017
-06
2017
-05
2017
-04
2016
-10
2016
-09
2016
-08
2016
-07
2016
-06
Date (Year-Month)
5.00
10.0
15.0
20.0
25.0
30.0
35.0
2016
-05
Fig. 5. Temporal characteristics of mean soil water content under different land uses on two
investigated slopes on China’s Loess Plateau. Note: NE, northeast-facing; W, west-facing;
GR, grassland; FO, forestland.
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0
5
10
15
0
5
10
15
0
5
10
15
10 20 30 400
5
10
15
10 20 30 40
0-50 cm layer
NE-GR
y = 102.23e-0.17x, R2 = 0.98, p < 0.01
0-50 cm layer
SOCS
y = 26.51lnx-65.91, R2 = 0.78, p < 0.05SICS
Soil
inor
gani
c ca
rbon
stor
age
(Mg
ha-1
)
Soil
orga
nic
carb
on st
orag
e (M
g ha
-1)
NE-FO
SOCSy = 2.55x-4.23, R2=0.93, p < 0.05
SICSy = 17.63lnx-45.71, R2 = 0.97, p < 0.01
0-100 cm layer
NE-GR
y = 71.03e-0.15x, R2 = 0.97, p < 0.01
0-100 cm layerSOCS
Soil water storage (mm)
0
10
20
30
40
50
SICS
NE-FO
SOCSy = 4.85×105x-3.70, R2 = 0.91, p < 0.01
0
10
20
30
40
50
SICSy = 0.11x1.42, R2 = 0.84, p < 0.01
W-GR
y = 2.34x-4.85, R2 = 0.96, p < 0.01SOCS
y = 6326.31x-2.06, R2=0.85, p < 0.05SICS
W-FO
SOCSy = -6.26lnx+23.78, R2 = 0.95, p < 0.01
SICSy = 2.19x0.61, R2 = 0.87, p < 0.05
W-GR
SOCSy = 7.46x-4.48, R2 = 0.95, p < 0.01
0
10
20
30
40
50
SICS
W-FO
SOCSy = -6.30lnx+23.78, R2 = 0.88, p < 0.01
0
10
20
30
40
50
SICSy = 4.70e0.05x, R2 = 0.70, p < 0.01
Fig. 6. Regressions between soil carbon and soil water storage at the 0–50 cm and 0–100 cm
soil depths in the study area. There is no statistical significance of the regression model
between SICS and SWS in 0-100 cm soil layer under NE-GR and W-GR (p > 0.05). Note:
SOCS, soil organic carbon storage; SICS, soil inorganic carbon storage. NE, northeast-facing;
W, west-facing; GR, grassland; FO, forestland.
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