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Article

Impacts of Climate and Land Cover on Soil Organic Carbon in the Eastern Qilian Mountains, China

by
Junju Zhou
1,2,
Dongxiang Xue
1,*,
Li Lei
3,*,
Lanying Wang
4,
Guoshuang Zhong
5,
Chunfang Liu
1,2,
Juan Xiang
1,
Meihua Huang
1,
Wei Feng
1,
Qiaoqiao Li
1,
Yaru Zhao
1 and
Guofeng Zhu
1,2
1
College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
2
Gansu Engineering Research Center of Land Use and Comprehension Consolidation, Lanzhou 730070, China
3
Management Bureau of Shiyang River Basin, Water Resources Bureau of Gansu Province, Wuwei 733000, China
4
The Administrative center for China’s Agenda 21, Beijing 1000381, China
5
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco–Environmental Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5790; https://doi.org/10.3390/su11205790
Submission received: 26 August 2019 / Revised: 15 October 2019 / Accepted: 17 October 2019 / Published: 18 October 2019
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Abstract

:
Soil, as the largest organic carbon pool of terrestrial ecosystem, plays a significant role in regulating the global carbon cycle, atmospheric carbon dioxide (CO2) levels, and global climate change. It is of great significance to scientifically understand the change rule and influence mechanism of soil organic carbon (SOC) to further understand the "source–sink" transformation of SOC and its influence on climate change. In this paper, the spatiotemporal distribution characteristics and influencing mechanism of SOC were analyzed by means of field investigation and laboratory analysis and the measured data in the Eastern Qilian Mountains. The results showed that the average SOC content of 0–50 cm was 35.74 ± 4.15 g/kg and the range of coefficients of variation (CV) between 48.84% and 75.84%, which suggested that the SOC content exhibited moderate heterogeneity at each soil layer of the Eastern Qilian Mountains. In four land cover types, the SOC content of forestland was the highest, followed by alpine meadow, grassland, and wilderness, which presented surface enrichment, and there was a decreasing trend with the soil depth. From the perspective of seasonal dynamics, there was a uniform pattern of SOC content in different land cover types, shown to be the highest in winter, followed by autumn, spring, and summer, and with the biggest difference between winter and summer appearing in the surface layer. At the same time, our study suggested that the SOC content of different land cover types was closely related to aboveground biomass and negatively related to both the mean monthly temperature and the mean monthly precipitation. Therefore, the distribution and variation of SOC was the result of a combination of climate, vegetation, and other factors.

1. Introduction

As the largest carbon pool in terrestrial ecosystems [1,2], soil plays a significant role in global carbon dynamics [3,4]. It is estimated that about 1502 Pg (1 Pg = 1015 g) of organic carbon is stored in the soil at the 0–100 cm depth globally, accounting for 75% of total terrestrial ecosystems, which is almost three times that of terrestrial vegetation and twice that of the atmosphere [5]. Consequently, a slightly change in the SOC pool could significantly affect the atmospheric CO2 concentration, which would further exacerbate global climate change. At the same time, SOC content is intimately associated with soil fertility, structure and hydraulic properties, and biological processes [6,7]. Therefore, it is very necessary to estimate SOC stocks to understand the mechanisms of the carbon cycle and soil resource sustainable usage.
The study of SOC spatiotemporal distribution is the first step in the strategy for soil protection [8] and is also the basis for accurately calculating SOC stocks [9]. Many studies have estimated SOC distribution and reserves on global [10,11], national [12,13] and regional [14] scales. For example, Post et al. estimated global SOC storage to be 1395 × 1015 g based on 2696 soil profiles worldwide [15]. Wang et al. analyzed the spatial distribution characteristics of SOC in China based on China’s second soil census data and indicated that soil organic carbon content increases with latitude in the eastern and northern regions, but it was opposite in the western region [16]. Ghosh et al. studied the distribution of SOC in roadsides of Singapore’s urban areas using the measured data and found that there was a negative correlation between SOC content and urbanization level [17]. However, due to the high spatial heterogeneity of soil, the content of SOC varies greatly in different regions and different land cover types [18,19] and even at the same soil profile because of the high spatial variability of soil [12,20,21]. Therefore, it is of great significance for the accurate estimation of local carbon stocks to study the detailed distribution of SOC in specific areas.
The SOC content depends on the dynamic balance of input and output of SOC, the input mainly coming from the litter and root exudates of vegetation, while the output being mainly the mineralization of organic matter, root, and microbial respiration [22]. These processes are closely related to environmental factors such as climate, vegetation, and topography and time [23]. However, the influencing factors of SOC in different ecosystems are unique [24]. It has been shown that the distribution of SOC varies greatly in complex mountainous areas, affected by climate and topography [25]. To date, the spatiotemporal distribution of SOC in mountainous areas and its relationship with influencing factors such as temperature, precipitation, and vegetation are still weak links in soil carbon cycle research.
The Qilian Mountains are located in the northeast margin of the Qinghai-Tibet Plateau, which is a sensitive area for global climate change affected by the East Asian Monsoon and the Westerlies. In recent decades, global warming has been greatly influenced by the dynamics of soil carbon sequestration. With the intensification of human activities and the impact of global climate change, great changes have taken place in the alpine ecological fragile area of the Qilian Mountains. The soil environment is one of the main manifestations, which is also the self-response to the overall ecosystem change. In the past decades, scholars have conducted some relevant studies on SOC in the Qilian Mountains but mainly concentrated in the middle part [26,27,28,29], while the related research of the eastern section is less. As a consequence, four sampling sites with different land cover types based on the variation of vegetation vertical zone spectrum were selected in the Xiying River Basin of the Eastern Qilian Mountains to study the spatiotemporal distribution of SOC and the internal relationship with climate and vegetation types. The specific objectives were to: (1) explore the vertical distribution of SOC at 0–50 cm depth in different land cover types; (2) study the seasonal variation characteristics of SOC in different soil layers with different land cover types; and (3) analyze the relationship between SOC and climate and vegetation types.

2. Materials and Methods

2.1. Description of the Study Area

The study was carried out in the Xiying River Basin situated in Gansu Province, on the north slope of the Qilian Mountains at the northeast edge of the Tibetan Plateau, in Northwestern China, with coordinates of about 37°25’ N to 38°02’ N latitude and 101°40’ E to 102°23’ E longitude (Figure 1). It covered approximately 1727.5 km2, with elevation ranging from 1873 to 4854 m above sea level. The region is characterized by a semiarid and temperate continental alpine climate with a strong solar radiation and a large daily temperature difference, and the temperature decreases but precipitation increases with elevation. The mean annual precipitation varies from 300 to 600 mm, which falls mainly between June and September, and the mean annual potential evaporation was 705 mm from 1961 to 2015 [30]. With the elevation gradually increasing from northeast to southwest, the land cover types in the basin are wilderness, grassland, forestland, alpine meadow, and permanent glacier, successively, and the area is 211, 629.6, 397.8, 221.5, and 8.07 km2, respectively. Vegetation and soil patterns are strongly shaped by the topographic factors. As the elevation increases, the elevation-dependent climate controls the vegetation zones and causes them to transform from mountain wilderness steppe (1700–2300 m), mountain steppe (2300–2500 m), mountain forest steppe (2500–3300 m), and mountain shrub-meadow (3300–3800 m) to alpine meadow (≥3800 m). The main soil types are defined as Aridosols, Isohumosols, Argosols, and Cambosols according to the Chinese Soil Taxonomy, which are equivalent to the Calcisols, Kastanozems, Luvisols, and Cambisols of the FAO (Food and Agriculture Organization of the United Nations) soil taxonomy classification (1998).

2.2. Soil Sampling and Analysis

In this study, four sampling sites were selected to evaluate the spatiotemporal distribution differences and explore the influence of climate on SOC under different land covers according to typical vegetation cover types, including wilderness, grassland, forestland, and alpine meadow, respectively. Geographic coordinates and the elevations of the four sites were obtained using a portable global positioning system (GPS) receiver. Detailed information of the sampling sites is presented in Table 1. In each sampling site, we selected four sampling points according to the S-shaped sampling method to collect soil samples using a soil drilling sampler (5 cm inner diameter) from February to December 2017. The soil samples were collected at depths of 0–10, 10–20, 20–30, 30–40, and 40–50 cm in each sampling point. To reduce the effect of fine-scale soil heterogeneity, soil samples from each soil layer in four sampling points were mixed as the representative soil sample of that site. Thus, a total of 220 soil samples (4 sample sites × 5 depths × 11 months = 220 samples) were collected. The collected soil samples were sealed in polyethylene bags marked with sampling location, time, and soil depth and transported to the laboratory.
In the laboratory, soil samples were air dried at room temperature for two weeks. Prior to the soil analysis, the plant residues and gravel in the soil samples were removed. Air-dried soil samples were ground and passed through a 0.25 mm mesh sieve to remove coarse fragments and debris for SOC determination. Soil carbonates were removed by adding 1 M HCl. The content of SOC was finally determined by the dry combustion method using a C-N analyzer (Multi N/C 2100, Jena, Germany).
During the sampling period, meteorological factors such as temperature and precipitation at each sampling site were monitored by the automatic weather station (Spectrum WatchDog 2000, Elize, USA), and finally, the daily temperature and precipitation data of the four sampling sites were obtained.

2.3. Topsoil Concentration Factors (TCFs)

In this study, TCFs [20] were used to evaluate the effects of the SOC in four land cover types. If the value of TCFs (0–10 cm/0–50 cm) was more than 0.1, the SOC enrichment of the surface (0–10 cm) could be attributed to the plant cycle:
T C F s = S O C ( 0 − 10 c m ) / S O C ( 0 − 50 c m )
where SOC(0–10 cm) is the SOC content in the surface soil (0–10 cm) and SOC (0–50 cm) is the SOC content in 0–50 cm soil.

2.4. Statistical Analysis

All statistical analyses were carried out using the SPSS 24.0 statistical package (SPSS Inc., Chicago, IL, USA). The Origin 9.0 software (Origin Lab Inc., Washington, USA) was used to draw figures. The descriptive statistics was used to analyze the overall distribution characteristics of SOC content, and some basically statistical parameters were calculated including the mean, maximum, minimum, coefficients of variation (CV), kurtosis, and skewness. In particular, the CV was used to analyze the degree of SOC variation if the CV value was less than 0.1 for the weak variability, greater than 1.0 for the strong variability, and others for medium variability [31]. The Repeated measurements analysis of variance (ANOVA) was used to analyze the significant difference of soil organic carbon content among different cover types, soil layers, and seasons. The single least significant difference method (LSD) was used for multiple comparative analysis (P < 0.05). Linear regression was conducted to assess the relationship between SOC content and the annual temperature and the annual precipitation in four land cover types. Statistical significance was determined at the 95% confidence level.

3. Results

3.1. Statistical Description of SOC

The description statistics of the SOC of all samples (n = 220) in the study area are shown in Table 2. The general average content was 35.74 ± 4.15 g/kg, ranging from 0.39 to 127.00 g/kg. The surface content of SOC ranged from 9.81 to 127.00 g/kg, with an arithmetic mean of 45.12 g/kg, while the value of surface (0–10 cm) was the highest and a decreasing trend was observed along the soil profile. For every 10 cm increase in soil depth, SOC content decreased by 14.41%, 9.84%, 8.91%, and 10.09%, respectively, which indicated that the variation of SOC content from the 0–10 to the 10–20 cm soil layer was the largest, followed by from 30–40 to 40–50 cm. As an index of overall variation, CV ranged from 48.84% to 75.84%, which suggested that the SOC content exhibited moderate heterogeneity (10% < CV < 100%) at all depth intervals in four land cover types of the Eastern Qilian Mountains, according to the Nielsen and Bouma grading standards [31].

3.2. Vertical Distribution of SOC

As shown in Figure 2, forestland has the highest SOC content, followed by alpine meadow, grassland, and wilderness with values of 78.22 ± 2.11, 30.39 ± 1.12, 21.57 ± 0.56, and 12.79 ± 0.38 g/kg, respectively. Above all, there was a decreasing trend for SOC with the depth in four different land cover types. The relationship between SOC content and soil depth can be modeled using a regression function, and there was a negative correlation. The SOC content in forestland had the largest decline rate on vertical gradient per ten centimeters, which was –7.98 g/kg, followed by alpine meadow, grassland, and wilderness, with values of −5.21, −2.18, and −1.13 g/kg, respectively. Except for the wilderness, the R2 of other types were greater than 0.90, suggesting that soil depth had a significant influence on SOC content. In other words, the vertical decline rate increased with the increasing of SOC content.

3.3. Seasonal Dynamics of SOC

The result of Repeated Measurements ANOVA shown in Table 3 indicate that the land cover types, soil layer, season, and the interaction between two factors had significant influence on SOC (P < 0.05), but the interaction among the three factors was not significant (P > 0.05). With the change of seasons, the SOC content in different land cover types showed a similar distribution trend, which was that the value of autumn and winter was higher than that of spring and summer and the highest was in winter and the lowest in summer and there was a significant variation. However, the SOC content had great variety in different land cover types. In different land cover types, the SOC content was the highest in the topsoil and decreased with the increase of soil depth. In the 0–10 cm soil layer, the difference between autumn and winter of alpine meadow and spring was significant, and the difference between summer and other seasons was significant in forestland. In the 10–20 cm soil layer, the summer and other seasons significantly different in grassland. In the 20–30 cm soil layer, the difference between summer and other seasons in forestland was significant (P < 0.05), while the difference in other soil layers in different seasons was not significant (P > 0.05) (Figure 3).As can be seen from Table 4, the variation of SOC in different soil layers between winter and summer was significantly higher in forestland than that in other cover types (P < 0.05), with the mean of variation 34.10 g/kg, which is equivalent to 4.16 times that of alpine meadow, 4.51 times that of the grassland, and 5.38 times that of the desert, and indicating that the higher the SOC content was, the greater the fluctuation between winter and summer. As a result, land cover types have significant effects on soil organic carbon content but have little effect on seasonal rhythm.
The content of SOC in different land cover types was represented through mean ± SE. The histogram represents the SOC content in different soil layers, and the black line is the standard error bar. Capital letters indicate significant differences among the different seasons in the same soil layer, while lowercase letters indicate significant differences among different soil layers in the same season (P < 0.05).

4. Discussion

4.1. Comparison of SOC with Other Mountainous Regions

SOC in mountainous areas were not only affected by climate, soil parent material, and hydrological conditions, but also by vegetation, altitude, and other factors [32,33]. Our study indicated that the SOC average content was 35.74 ± 4.15 g/kg in 0–50 cm for the Eastern Qilian Mountains, which was close to the result of the middle of the Qilian Mountain in Gansu Province [34] and the Karkonosze Mountain of Southwestern Poland [35]. The values of SOC content in this study were higher than the karst mountain area of the Guizhou province [36] and were lower than those reported by Désiré et al. [37] from Mount Bambouto of Central Africa and by Xu et al. [38] from the middle of Tianshan in the Xinjiang province (Table 5). Although the geomorphology type was uniform, and the climate conditions, soil types, parent materials, and land use types could be different, one of these factors had the potential to cause differences of SOC distribution. The Banbuto Mountains in Central Africa belonged to a tropical rainforest climate, with abundant rainfall and high net productivity of vegetation, which was beneficial to the input of organic matter. The middle of Tianshan Mountains belonged to the temperate continental mountain climate, and the vertical zonality was obvious. With the increase of altitude, the climate gradually changed to cold and wet. The surface vegetation was mainly low vegetation but rich, and the decomposition rate of organic matter was extremely slow. The humus layer was well developed, which was conducive to the accumulation of organic matter. Although the karst mountain areas in the center of the Guizhou province were located in a subtropical monsoon climate zone, the phenomenon rocky desertification was serious, which led to a low input of SOC, and a relatively high temperature accelerated the decomposition of SOC, resulting in lower SOC content than the temperate continental climate zone. Therefore, the content of SOC and its dynamics mainly depended on the balance of SOC input and degradation, which was determined by various factors such as climate, vegetation, and soil properties.

4.2. Impact of Τemperature on SOC

A negative correlation was observed between the mean monthly temperature and SOC content in forestland, alpine meadow, grassland, and wilderness, but the latter two are not significant in the east of the Qilian Mountains, which was basically consistent with many previous studies [39,40]. However, there were differences in the degree of SOC content affected by the mean monthly temperature change in different land cover types. The slopes of the four land cover types are –1.33, –0.55, –0.21, and –0.19 in descending order, suggesting that the SOC content of forestland decreased the fastest, followed by alpine meadow, grassland, and wilderness with the increase of temperature, which indicated that the higher the SOC content is, the faster it will decline when the temperature increases (Figure 4).
Generally speaking, the temperature mainly affected the accumulation and decomposition of SOC through the adjustment of soil temperature, which determined the dynamic change of SOC content. The decomposition of SOC was mainly controlled by microbial activities, and temperature was the main factor affecting microbial activities and processes [41,42,43]. The increase of temperature led to the enhancement of microbial activity in the soil, promoted the process of soil mineralization, accelerated the decomposition of SOC, and led to more carbon being released from the soil into the atmosphere, which were not conducive to the accumulation of SOC [44]. By contrast, the process of soil mineralization was restrained when the temperature decreased, which was beneficial to the fixation of more carbon into the soil. Existing studies [45] had found that when the temperature increased by 2 °C and 4 °C, the decline rate of SOC was 13.6% and 18.9%, respectively. In summary, with the increase of temperature and original SOC content, the decline rate of SOC content increased.

4.3. Impact of Precipitation on SOC

It was found that there was a negative correlation between the mean monthly precipitation and SOC content in four land cover types, and other land cover types were significant except for wilderness; this was similar to the results of Berthrong et al. in the La Plata Basin and Fan et al. in the Liaodong Mountain [46,47]. Here, our study showed that the effects of precipitation and temperature changes on soil organic carbon are slightly different. The slopes of soil organic carbon content with the change of precipitation are –0.35, –0.095, –0.07, and –0.06 in forestland, alpine meadow, wilderness, and grassland, respectively (Figure 5), indicating that precipitation has the greatest impact on forestland, followed by alpine meadow, wilderness, and grassland.
In general, precipitation mainly affected the dynamic balance of SOC content input and output by regulating soil moisture. In the arid Qilian Mountains area of Northwest China, the increase of precipitation would cause the soil moisture to increase, leading to the dispersion and cracking of soil aggregates, and the originally fixed SOC would be dissolved and released, which would provide more nutrients for soil microbial activities, thereby improving the soil respiration rate and promoting SOC mineralization [33], which was not conducive to the accumulation of SOC. Furthermore, the combined effects of cold temperature and low precipitation in winter hindered the decomposition of SOC, so that soil stored the most carbon in winter, while the opposite was true in summer. Therefore, temperature and precipitation jointly affected the distribution of SOC, especially in seasonal variation.

4.4. Impact of Vegetation on SOC

Previous studies have shown that land cover types have a greater impact on SOC distribution, especially on topsoil [48]. Topsoil concentration factors (TCFs) were usually used to evaluate the effects of plant cycle on biogeochemistry [49]. It can be seen from the box plots that the mean TCFs of the four land cover types all exceeded 0.1, indicating that the surface SOC enrichment was caused by the plant cycle (Figure 6). The TCFs of alpine meadow were the largest, followed by grassland and forestland, and wilderness was the lowest.
Consistent with many previous studies, the SOC content of 0–50 cm soil under different land cover types showed a gradually decreasing trend with the deepening of the soil depth [24,49,50]. It was mainly related to the distribution of vegetation litters and roots [20,51]. The amount of vegetation litter and the depth of root distribution were different in different land cover types. Through the process of humification and leaching, the organic matter in the surface litters migrated from the surface of the soil to the deep layer. In addition, the surface was the most important distribution area of the plant root system, and the distribution of plant roots decreased with the increase of soil depth. Concretely, the distribution of roots was the deepest in forestland, followed by grassland, and the shallowest in alpine meadow, where most were concentrated in the surface layer [52,53]. Except for a small amount of drought-tolerant vegetation in the surface layer of wilderness, the SOC content in other soil layers was closely related to soil properties. Therefore, vegetation litter distribution, root distribution, and maximum root depth of plants play an important role in the SOC distribution pattern.
Many studies have shown that plant production, such as litter aboveground parts of the vegetation and secretions from underground roots and debris from fine root turnover, was the main input source of SOC in arid and semiarid regions [54,55]. The distribution pattern of photosynthetic products varied greatly in different vegetation types, so the content of SOC differed greatly in different land cover types. It was found that the aboveground biomass of forestland in the Eastern Qilian Mountains was the highest (125.00 ± 3.00 g ·m–2), followed by alpine meadow (78.10± 17.80 g ·m–2), grassland (48.30 ± 6.20 g ·m–2), and the lowest in wilderness (11.00 ± 8.00 g ·m–2) (Table 6). Correspondingly, SOC content was also shown to be higher in forestland and alpine meadow, while there was a lower content of SOC in grassland and wilderness. This was mainly because the dominant species of forestland was Picea crassifolia Kom., and its surface was covered with a large amount of litter for the year, which was beneficial to the accumulation of SOC. The dominant species of alpine meadow were Kobresia humilis Serg. and Elymus nutans Griseb., whose productivity was relatively high. Grassland and wilderness vegetation were mainly drought-tolerant vegetation such as Stipa krylovii Roshev., Agropyron cristatum (L.) Gaertn., and Stipa breviflora Griseb. with low vegetation productivity. Thus, only a small amount of organic carbon enters the soil. As a result, vegetation largely determined the input of SOC, which ultimately affected the level of SOC content.

4.5. Comprehensive Impact of Various Factors on SOC

The distribution pattern of precipitation, temperature, and vegetation gradually changed with the increase of elevation, thus affecting the spatial pattern of SOC [24]. Mountain ecosystems often have obvious differences in landscape on the vertical gradient. The temperature decreased and precipitation increased with elevation from low to high. The decreasing rate of temperature is 0.58 °C/100 m, and the increasing rate of precipitation is 24.0 mm/100 m [59]. The results showed that the SOC was the highest at the middle altitude forestland, followed by the alpine meadow at the highest altitude, grassland at the lower elevation, and wilderness, which had the lowest SOC content (Figure 7). This was mainly because of the large input of forestland plant residues; temperature and humidity were suitable, which was conducive to the accumulation of SOC. Although there was not too much exogenous organic carbon in alpine meadow, the precipitation was relatively high and the temperature was relatively low, so the microbial activity was not high, and the decomposition of SOC was slow. However, there was little organic matter input in grassland and wilderness and the climate was relatively arid, which was not beneficial for the accumulation of organic matter. Therefore, the spatiotemporal distribution of SOC was essentially the result of the comprehensive action of climate factors, soil parent materials, and vegetation distribution.

5. Conclusions

The spatiotemporal distribution of SOC was the result of the comprehensiveness of various factors, such as soil parent material, climate, land cover type, and other factors. The mean SOC content was 35.74 g/kg in 0–50 cm, ranging from 3.94 to 127.00 g/kg in the Eastern Qilian Mountains. The SOC content of forestland was significantly higher than for other land cover types, followed by alpine meadow, grassland, and wilderness. The surface SOC content was higher than other layers, and there was a decreasing trend for SOC with the depth in four different land cover types. The SOC content was significantly correlated with the aboveground biomass of different land cover types. From the perspective of seasonal dynamics, there was a uniform seasonal distribution pattern of SOC content in different land cover types, which was shown to be the highest in winter, followed by autumn, spring, and summer, which was mainly influenced by temperature and precipitation. However, the spatiotemporal distribution of SOC was the result of the comprehensive action of various factors. The influence of these factors on SOC was a relatively complex problem, which required us conducting much deeper and more comprehensive quantitative research to interpret the mechanism.
This study preliminarily explored the effects of climate change indifferent land cover types on the spatiotemporal variation of SOC content in the Qilian Mountains and revealed the internal relationship between SOC and various influencing factors, and there was a trend where both temperature and precipitation were negatively correlated with SOC content. In particular, in the context of global warming, the rise of temperature will inevitably accelerate the release of CO2 from the soil into the atmosphere if proper precautions are not taken, and the carbon sink function might be weakened, which could even turn some regions from a “carbon sink” to a “carbon source”, further accelerating global warming trend and forming a vicious circle. Therefore, all countries and regions, as well as individuals should take some effective measures to reduce CO2 emissions and the decomposition and release of SOC, to slow down global warming. Meanwhile, we should adjust the structure of land use through measures such as afforestation, returning farmland to forest and grassland to enhance the carbon storage function of soil, further promoting the virtuous cycle of the ecological system.

Author Contributions

All authors contributed to the paper. Concretely, Conceptualization, L.W., G.Z. (Guoshuang Zhong), C.L.; Data curation, D.X., Q.L., Y.Z.; Funding acquisition, J.Z. and G.Z. (Guofeng Zhu); Investigation, D.X.; Methodology, D.X.; Resources, L.L.; Software, D.X., J.X., M.H., W.F.; Writing – original draft, D.X.; Writing – review & editing, J.Z.

Funding

This research was supported by the National Natural Science Foundation of China (41761047, 41661005, and 41661084).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area and soil sampling locations in the Eastern Qilian Mountains.
Figure 1. Study area and soil sampling locations in the Eastern Qilian Mountains.
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Figure 2. Vertical distribution characteristics of SOC content.
Figure 2. Vertical distribution characteristics of SOC content.
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Figure 3. Seasonal variation of SOC content in different land cover types.
Figure 3. Seasonal variation of SOC content in different land cover types.
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Figure 4. Relationships between the mean monthly temperature and SOC content in different land cover types (the gray shaded areas show the mean 95% confidence intervals).
Figure 4. Relationships between the mean monthly temperature and SOC content in different land cover types (the gray shaded areas show the mean 95% confidence intervals).
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Figure 5. Relationships between the mean monthly precipitation and SOC content in different land cover types (the gray shaded areas show the mean 95% confidence intervals).
Figure 5. Relationships between the mean monthly precipitation and SOC content in different land cover types (the gray shaded areas show the mean 95% confidence intervals).
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Figure 6. Box plot of topsoil concentration factors of different land cover types. The boxes indicate the range of the 99% confidence interval and the line inside represents the median. The top and lower bars are the maximum and minimum value, respectively.
Figure 6. Box plot of topsoil concentration factors of different land cover types. The boxes indicate the range of the 99% confidence interval and the line inside represents the median. The top and lower bars are the maximum and minimum value, respectively.
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Figure 7. Spatial distribution of SOC in soil layers of 0–50 cm.
Figure 7. Spatial distribution of SOC in soil layers of 0–50 cm.
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Table
Table 1. The description of four sampling plots in the Eastern Qilian Mountains.
Table 1. The description of four sampling plots in the Eastern Qilian Mountains.
ItemsBDZHLZHJXXYWG
Lon-E(degree)101.85101.89102.01102.18
Lat-N(degree)37.5537.7037.8237.89
Alt(m)3590272123902097
Samples55555555
MAT(°C)−0.193.346.67.86
AP(mm)595.1431.9363.5262.5
The MAT (mean annual temperature) and AP (annual precipitation) data were obtained by the automatic weather station (Spectrum WatchDog 2000, Elize, USA).
Table
Table 2. Summary statistics of soil organic carbon (SOC) content at depth intervals (n = 220).
Table 2. Summary statistics of soil organic carbon (SOC) content at depth intervals (n = 220).
DepthMean ± SE (g/kg)Min (g/kg)Max (g/kg)SkewnessKurtosisCV (%)
0–10 cm45.12 ± 5.089.81127.001.180.1548.84
10–20 cm38.65 ± 4.457.35102.001.190.0956.42
20–30 cm34.80 ± 4.165.2096.501.290.5162.39
30–40 cm31.66 ± 3.804.4393.801.240.1668.35
40–50 cm28.48 ± 3.243.94771.220.1175.84
0–50 cm35.74 ± 4.153.941271.220.262.37
CV: coefficient of variation, SE: standard error.
Table
Table 3. Repeated Measurements ANOVA of SOC in different soil layers of different land cover types.
Table 3. Repeated Measurements ANOVA of SOC in different soil layers of different land cover types.
dfFSig.
Land cover type3203.2450.000
Soil layer470.7190.000
Season35.1780.006
Land cover type × Soil layer1211.2170.000
Land cover type × Season93.4880.005
Soil layer × Season122.4940.032
Land cover type × Soil layer × Season361.2170.218
Residuals28
df: degree of freedom; F: the statistics of F test; Sig.: significance of difference.
Table
Table 4. The variation of soil organic carbon between winter and summer in different soil layers (g/kg).
Table 4. The variation of soil organic carbon between winter and summer in different soil layers (g/kg).
Soil DepthAlpine MeadowForestlandGrasslandWilderness
0–10 cm14.22 ± 1.5945.86 ± 8.778.63 ± 1.329.33 ± 0.86
10–20 cm8.80 ± 1.3532.13 ± 5.318.10 ± 1.485.68 ± 0.85
20–30 cm5.13 ± 1.7135.56 ± 5.797.16 ± 1.104.88 ± 0.13
30–40 cm8.42 ± 1.8929.70 ± 4.556.15 ± 1.415.56 ± 0.67
40–50 cm4.38 ± 0.327.33 ± 3.557.77 ± 1.266.20 ± 0.33
Table
Table 5. Comparison of SOC with other mountain areas.
Table 5. Comparison of SOC with other mountain areas.
RegionSOC Content Range (g/kg)Mean (g/kg)Reference
Central Africa (Mount Bambouto)8.02–154.1156.23[37]
SW Poland (Karkonosze Mountain)1.70–190.0033.43[35]
Guizhou (Karst mountains area)0.23–128.7415.72[36]
Xinjiang (Milddle Tianshan)3.13–280.3748.28[38]
Gansu (Middle Qilian mountain)1.10–190.0036.00[34]
Table
Table 6. Biomass of main vegetation in different cover types (g ·m–2).
Table 6. Biomass of main vegetation in different cover types (g ·m–2).
Land Cover TypesDominant SpeciesBiomassData Source
Alpine meadowKobresia humilis Serg.、Elymus nutans Griseb.78.10 ± 17.80[56]
ForestlandPicea crassifolia Kom.125.0 0± 3.00[57]
GrasslandStipa krylovii Roshev.、Agropyron cristatum (L.) Gaertn.48.30 ± 6.20[58]
WildernessStipa breviflora Griseb.11.00 ± 8.00[56]

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Zhou, J.; Xue, D.; Lei, L.; Wang, L.; Zhong, G.; Liu, C.; Xiang, J.; Huang, M.; Feng, W.; Li, Q.; et al. Impacts of Climate and Land Cover on Soil Organic Carbon in the Eastern Qilian Mountains, China. Sustainability 2019, 11, 5790. https://doi.org/10.3390/su11205790

AMA Style

Zhou J, Xue D, Lei L, Wang L, Zhong G, Liu C, Xiang J, Huang M, Feng W, Li Q, et al. Impacts of Climate and Land Cover on Soil Organic Carbon in the Eastern Qilian Mountains, China. Sustainability. 2019; 11(20):5790. https://doi.org/10.3390/su11205790

Chicago/Turabian Style

Zhou, Junju, Dongxiang Xue, Li Lei, Lanying Wang, Guoshuang Zhong, Chunfang Liu, Juan Xiang, Meihua Huang, Wei Feng, Qiaoqiao Li, and et al. 2019. "Impacts of Climate and Land Cover on Soil Organic Carbon in the Eastern Qilian Mountains, China" Sustainability 11, no. 20: 5790. https://doi.org/10.3390/su11205790

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