For Review Only · For Review Only 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 †,

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)

https://mc.manuscriptcentral.com/cjss-pubs

Canadian Journal of Soil Science

For Review Only

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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mailto:[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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Zhao, W., Zhang, R., Huang, C.Q., Wang, B.Q., Cao, H., Koopal, L.K., and Tan, W.F. 2016.

Effect of different vegetation cover on the vertical distribution of soil organic and

inorganic carbon in the Zhifanggou Watershed on the loess plateau. Catena, 139(3):

191–198.

Zhou, B.B., Shao, M.A., Shao, H.B. 2009. Effects of rock fragments on water movement and

solute transport in a Loess Plateau soil.Comptes Rendus Geoscience, 341(6): 462–472.

Zhu, Y.J., Jia, X.X., Qiao, J.B., Binley, A., Horton, R., Hu, W., Wang, Y.Q., Shao, M.A.

2019. Capacity and distribution of water stored in the vadose zone of the Chinese Loess

Plateau. Vadose Zone Journal, 18: 180203. doi:10.2136/vzj2018.11.0203

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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.

Page 33 of 39



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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.

Page 34 of 39



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.

Page 35 of 39



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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.

Page 36 of 39



For Review Only

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)


Aa

(a)

Soil

dept

h (c

m)

Soil inorganic carbon content (g kg-1)


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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For Review Only

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.

Page 39 of 39



Documents

For Review Only · For Review Only 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 †,