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Soil organic carbon content and storage of raised field wetlands in different functional zones of a typical shallow freshwater lake, China.


Some researchers have reported that wetlands, particularly peatlands, play important roles in global carbon (C) cycling because they are the best ecosystems for sequestering C (Mitra et al. 2005; Melanie 2009). Gorham (1995) estimated that soil organic carbon (SOC) of wetlands accounts for 20-25% of the earth's total, and a significant portion of the stored C has been released to the atmosphere due to the disturbance and exploitation of wetlands (Wylynko 1999). Chaplot et al. (2010) also stated that SOC stock was closely linked to potential emission of greenhouse gases (i.e. C[O.sub.2] and C[H.sub.4]), whose fluxes can be increased due to SOC decomposition or reduced by C[O.sub.2] assimilation and fixation. Therefore, it is essential for scientists and regulators worldwide to better understand and assess spatial distribution of SOC stock in wetlands to manage the C balance of the biosphere and alleviate global warming (Blazejewski et al. 2009; Martin et al. 2011).

Storage of SOC relies on the balance between C input from primary production and C output derived from C decomposition (Trumbore and Harden 1997). Spatial distribution patterns of SOC vary widely in different types of wetlands due to different landscape positions, hydrological cycles, vegetation types, and soil properties (Trumbore and Harden 1997; Blazejewski et al. 2009; Martin et al. 2011). Stocks of SOC are usually lower in coastal areas than in other landscapes (Martin et al. 2011). Li et al. (2004) suggested that unsaturated inundation would result in a loss of SOC storage in wetlands due to C[O.sub.2] emission. Bai et al. (2010) reported that SOC was positively correlated with soil bulk density and negatively correlated with soil moisture. Additionally, Ju and Chen (2005) found that land-use change could greatly influence SOC content and storage. It is difficult to study C sequestration potential and other related processes because of the complexity of decomposition processes and the lability of the balance between methanogenic microorganisms and associated fluxes (Mitra et al. 2005). Numerous studies have focussed on how to qualify spatial and temporal variations in C storage and its fluxes (Chmura et al. 2003; Law et al. 2004). Ju and Chen (2005) demonstrated spatial distribution of C storage in forests and wetlands in Canada using the ecological model TRIPLEX1.0 (Zhang et al. 2008), geographic information system (GIS), and the ecosystem C-budget model InTEC V3.0 (Chen et al. 2000); however, there existed large uncertainty in the estimation of C. Also, vertical changes in SOC along soil profiles and high levels of heterogeneity among landscape positions make it difficult to accurately detect the C storage per unit area at a large scale (Adhikari et al. 2009; Franzluebbers and Stuedemann 2009).

Baiyangdian Lake is the largest freshwater lake and a typical shallow inland lake in North China. Local residents have created many raised-field wetlands in the lake for planting reeds. Also, a provincial nature reserve has been established, and several functional zones were classified for different open-water uses. Although much research has focussed on SOC stocks in wetlands, little information is available on SOC stocks in raised-field wetlands of a shallow freshwater lake with different functional zones at a large scale (Morvan et al. 2008). Therefore, the primary objectives of this work were: (1) to investigate the horizontal and vertical distributions of SOC content and storage in raised-field wetlands of Baiyangdian Lake, and (2) to identify the relationship between SOC and other soil properties.

Materials and methods

Site description

Baiyangdian Lake is in Anxin County of Hebei Province (38[degrees]43'-39[degrees]02'N, 115[degrees]38' 116[degrees]0UE) (Fig. 1). It covers 366 [km.sup.2] and consists of 143 shallow lakes and 800ha of Phragmites australis marshes, which are hydrologically linked together by >3700 ditches and fiver courses (Xu 2004). Baiyangdian Lake has a warm temperate and semi-humid climate with annual mean temperature of 7.3-12.7[degrees]C. The annual mean evaporation is 1369 mm, and the annual mean precipitation is 563.9 mm, 80% of which falls in summer (June August) (Jiang et al. 2009; Gao et al. 2012).

Baiyangdian Lake is classified into five typical functional zones (Fig. 1) according to their landscape positions, environmental functions, water quality, and human activities. The first functional zone (zone 1) is the inlet zone of Baiyangdian Lake, which is the water source of the lake; zone 2 is a static water reserve zone with severe swampiness due to well protection from human interferences; zone 3 is the outlet zone of the lake, where water runs out of the lake, and it is far away from pollution sources; zone 4 is the aquaculture zone, where local residents farm a large number of ducks, fishes, shrimps, and crabs; and zone 5 is the polluted region, which is greatly influenced by domestic and industrial sewage discharge (Shen and Liu 2008).

Soil sampling and analysis

Soil samples with 5 7 replicates were taken from a depth of 0-30cm at 10-cm intervals using a metal auger at each functional zone. All soil samples were placed in polyethylene bags and immediately brought to the laboratory, where they were air-dried at room temperature for >3 weeks. All air-dried soils were sieved through a 2-mm nylon sieve to remove coarse debris and stones. Some of the soil samples were used for soil particle analysis, pH, electrical conductivity, and salinity. All other air-dried soils were ground and sieved until all particles passed a 0.149-mm nylon sieve, for the determination of soil organic matter and other soil properties. Three bulk density (BD) cores (4.8 cm diameter) from each sampling site were collected along the soil profile from a depth of 0-30 cm with 10-cm intervals, oven-dried at 105[degrees]C for 24 h, and weighed for the determination for BD and moisture content (WC) (Ferraro and Ghersa 2007).

Soil pH, salinity, and electrical conductivity were determined using a pH meter and a conductivity meter (soil: water, 1 : 5). Soil particle size was measured using an automatic granularity laser (Microtrac $3500, Microtrac Inc., Montgomeryville, PA, USA). Total nitrogen (TN) was determined by a modified micro-Kjeldahl digestion procedure (Ferraro and Ghersa 2007). Total C (TC) was measured using a Carlo Erba NA1500 Series II elemental analyser (CE Elantech, Inc., Lakewood, N J, USA). Soil organic matter (SOM) was determined using the Walkley and Black (1934) method and was converted to SOC by the Bemmelen index (0.58) with determination by conventional wet combustion with [Cr.sub.2][O.sub.7.sup.-] (Wen 1984). Available phosphorous (AP) was extracted by 0.5 mol/L of NaHC[O.sub.3] in alkaline conditions (pH 8.5).

The SOC content was estimated in Eqns 1 and 2 by SOC density (SOCd, kg/[m.sup.3]) and SOC storage (SOCs, t/[km.sup.2]):

SOCd = [SOC.sub.i] x [BD.sub.i] (1)

SOCs = [n.summation over (i=1)] [SOC.sub.1] x [BDi.sub.] x [H.sub.i] (2)

where [H.sub.i] is the soil thickness of layer i (cm), [BD.sub.i] is bulk density of layer i (g/[cm.sup.3]), and [SOC.sub.i] is SOC content (g/kg) at layer i (Bai et al. 2010; Martin et al. 2011).

To evaluate the ability of SOC storage, a carbon pool index (CPI) was developed (Blair et al. 1997; Chivenge et al. 2005), and the formula is given as:

CPI = sample SOCs/reference SOCs (3)

where sample SOCs is SOC storage in different functional zones, while reference SOCs is the highest SOC storage among the functional zones.

Geostatistical analysis and correlation analysis

Geostatistical analysis method aims to quantify the uncertainty in estimating unsampled values of regionalised variables. In order to carry out spatial analysis using GIS technique, the longitude and latitude of each sampling site were recorded and then put into Excel tables (Microsoft Corporation) in the decimal form; longitudes were placed in the first column marked by 'X', and latitudes in the second column marked by 'Y' (Cesaroni et al. 1997). The SOC data were correspondingly entered into Excel tables following the spatial location and then extracted from the Excel tables to ArcGis9.3 software by the command 'add XY data' (Huang et al. 2012). After that the SOC data were transferred to a normal distribution to obtain a better spatial distribution of the interpolated value. The ordinary kriging interpolation method and spherical model were applied to produce contour maps, and then clipped to the shape of the study area; finally, contour maps of SOC distribution within the defined boundary were made (Hangen et al. 2010; Chowdhury et al. 2010).

A correlation analysis was conducted using the SPSS 16.0 statistical package (Blumenthal 2010) to examine the most probable relationship between SOC and other soil properties (i.e. TN, AP, pH, BD, WC, and salinity).

Results and discussion

Average SOC content and SOC density

The mean SOC content was 13.14 [+ or -] 1.72 x [10.sup.3] mg/kg, and the mean SOC density was 14.56 [+ or -] 2.16 kg/[m.sup.3] in the raised-field wetlands of Baiyangdian Lake (Table 1). With the exception of the inlet and outlet zones, the values of SOC content and SOC density followed the order 0-10 cm > 10-20 cm >20-30 cm. This was probably explained by the fact that the aboveground litter and belowground root detritus could lead to higher C accumulation in the surface soils (Bai et al. 2005). Some researchers have also reported that SOC content decreases with depth in other ecosystems, and they ascribed this to high clay contents in surface soils due to erosion (Arriaga and Lowery 2005; Franzluebbers and Stuedemann 2005; Hiederer 2009).

Compared with other soil layers, the surface soil was more severely eroded due to the hydrological process of water impulsion and human activities in these zones, and the lowest SOC content and SOC density in subsurface soils of the inlet zone might be due to the rushing water and diverse runoff of other original sources (Chaplot et al. 2006). Chivenge et al. (2005) also stated that soil runoff and erosion were pathways through which SOC could be lost. The lowest SOC content and SOC density appeared in the inlet zone, and much higher SOC content (2.08 x [10.sup.4] mg/kg) and SOC density (2.17 x [10.sup.4] kg/[m.sup.3]) were observed above 30 cm in both the water reserve and outlet zones, which were more than twice as high as those in the other zones (Table 1). Lower SOC content and SOC density occurred in the industrial zone due to the cooling water discharged from factories, which increased the water temperature nearby (annual mean temperature is 20.0[degrees]C in this zone, while <17[degrees]C in other zones) and improved SOC decomposition. Chmura et al. (2003) also demonstrated that SOC density in mangrove swamps and Spartina patens marshes declined with increasing annual mean temperature.

Spatial distribution pattern of soil organic carbon storage As shown in Fig. 2, SOC storage differed among the five zones. de Bello et al. (2006) proposed that plant functional diversity might be a strong predictor of ecosystem processes, which have an effect on SOC storage through decomposition and nutrient cycling. Yoo et al. (2006) also emphasised that spatial variations in decomposition rates were constrained by soil texture and other micro environmental factors, which may influence the changes in SOC contents.

There were two zones with lower SOC storage (lighter colour area) distributed in the south-east of the water reserve zone and the east of the inlet zone, and two zones with higher SOC storage (deeper colour area) in the north-west of the water reserve zone and the east of the whole Baiyangdian Lake (Fig. 2). Generally, SOC storage and spatial heterogeneity were higher in the upper two soil layers, in the top 30 cm, the total amount of SOC stored was approximately equal to 19 676.68 t/[km.sup.2] in the water reserve zone, which accounted for 34.01% of the total SOC storage of the raised-field wetlands in Baiyangdian Lake and was the highest value among the five zones. The lowest value was observed in the inlet zone, with an estimated 4838.97 t/[km.sup.2], accounting for 8.36% of the total SOC storage (Table 2). The lower SOC storage in the industrial zone might be associated with cooling water discharged from the industrial cooling chambers in this zone, with water temperature of 20[degrees]C. Decomposition rate of SOC is doubled if temperature is increased by 10[degrees]C (Frolking et al. 2002; Chmura et al. 2003). The SOC storage was lower in the aquaculture zone than in the water reserve zone. One potential explanation is that the anaerobic environment was strongly disturbed by human activities, which could lead to a higher decomposition rate of the large amount of SOC stored there (Adhikari et al. 2009). The highest SOC storage occurred in the water reserve zone, which is attributed to less disturbance, prolonged waterlogging, and a stable biochemical environment, where long anaerobic periods would contribute to the high storage of SOC (Fissore et al. 2009; Prusty et al. 2009). Wang et al. (2004) reported that the influence of human activities on C storage and fluxes had exceeded the effects from the natural variability. Marks et al. (2009) suggested that management could modify SOC accumulation. Therefore, the raised fields of Baiyangdian Lake could play a significant role in the storage of SOC, and steps towards reducing human activities in this region should be taken to control SOC emitted to the atmosphere as C[O.sub.2].

The vertical distribution of total SOC stored in the raised-field wetlands of Baiyangdian Lake is shown in Fig. 3. Except for the inlet and outlet zones, other functional zones showed a decreasing trend along soil profiles. There was a peak value of SOC storage at 1 10-20 cm soil depth in the outlet zone, while the lowest value appeared at the same soil depth in the inlet zone (Fig. 3). Shi et al. (2007) also found a peak value of SOC storage at 15cm soil depth in Carex lasiocarpa and Phragmites communis marshes in Sanjiang Plain. Generally, the total SOC storage in this region decreased with depth, and the proportions at each soil layer followed the order 0-10cm (38.49%)>10-20cm (31.70%)>20-30cm (29.81%) (Fig. 3, Table 2). This is consistent with the results reported by Bai et al. (2007) that SOC storage decreased with depth in natural alkaline wetlands. Aboveground litter and belowground root detritus might be the key factors controlling the pattern of SOC distribution, which could contribute to a higher C accumulation in surface soils (Bai et al. 2005).

The SOC storage capacity in these raised-field wetlands of Baiyangdian Lake was calculated by the CPI (Table 3). The water reserve zone was selected as the reference zone due to its better ecological protection and the highest SOC storage. The results showed that the values of CPI increased with soil depth in the outlet and aquaculture zones, whereas the opposite changes appeared in the industrial zone. The CPI value was in the order water reserve zone > aquaculture zone > outlet zone > industrial zone> inlet zone, which may be attributed to the effects of human activities and environmental factors such as runoff (Adhikari et al. 2009), temperature (Chmura et al. 2003), and organism detritus (Mufioz et al. 2007), since these factors can increase or decrease SOC storage. Some researchers have demonstrated that higher temperature and lower SOC contents can lead to lower SOC storage with lower CPI values (Bai et al. 2007; Chmura et al. 2003; Chivenge et al. 2005). This indicates that the inlet zone had the highest potential capacity for SOC storage, while the aquaculture zone had the lowest potential (theoretical) capacity. Blair et al. (1997) and Chivenge et al. (2005) believed that it was difficult for zones with lower CPIs to be rehabilitated, especially those zones with higher initial SOC storage. Therefore, there may be a higher potential to restore SOC storage in the aquaculture zone than the inlet zone.

Key factors influencing the distribution of soil organic carbon

Table 4 shows the relationships between SOC and other soil properties. There was a strong positive correlation between SOC and WC (P<0.01) and a weak negative correlation between SOC and pH (P>0.05), which was in agreement with the findings of Bai et al. (2010). Adhikari et al. (2009) showed that soil moisture and soil pH values would affect SOC storage in wetlands. Martin et al. (2011) demonstrated that acid forest soils exhibited higher SOC storages. Moreover, Trumbore and Harden (1997) and Meersmans et al. (2008) stated that soil moisture was the key controller of SOC storage, by regulating the C assimilated and decomposed. Therefore, any activities that decrease soil water content, such as draining arable fields, would increase the risk of transforming the stored SOC in soil to C[O.sub.2] in the atmosphere (Chivenge et al. 2007).

The BD showed negative loadings on SOC (P<0.01, Table 4). Avnimelech et al. (2001) and Wu et al. (2003) also reported a negative correlation between BD and SOC by a logarithmic equation. No significant relationships were observed between SOC and TN or AP (P>0.05). However, significant correlations between N or P and SOM have been reported in peat soils and marsh soils (Bai et al. 2005; Tong et al. 2005; Gao 2006).


In Baiyangdian Lake, SOC decreased with depth along soil profiles. Among the five functional zones, the inlet zone showed the lowest SOC content and storage, whereas the outlet and water reserve zones had the highest value of SOC. The water reserve and outlet zones showed higher spatial variations in SOC content and storage for all soil layers. The surface soil layer had more SOC and higher heterogeneity compared with other soil layers. However, lower SOC storage appeared in the south-east of the water reserve zone and the east of the inlet zone, whereas higher SOC storage was observed in the north-west of the water reserve zone and the east of the whole Baiyangdian Lake. SOC storage had higher spatial heterogeneity in upper soil profiles, and the highest SOC storage in the top 30 cm occurred in the water reserve zone, while the lowest value was for the inlet zone of the lake. The aquaculture zone had a high CPl, followed by the outlet zone and industrial zone, and the inlet zone showed the lowest value. Bulk density and water content were the key drivers for controlling SOC storage. Meanwhile, it is necessary to control human activities to elevate SOC storage in raised-field wetlands in the lake. 10.1071/SR12236

Received 26 May 2012, accepted 21 November 2012, published online 18 January 2013


This work was financially supported by China National Funds for Distinguished Young Scientists (No. 51125035), National Basic Research Program (No. 2010CB951102), National Science Foundation for Innovative Research Group (No. 51121003), Program for New Century Excellent Talents in University (NECT-10-0235), and the Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (No. 132009).


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Laibin Huang (A), Junhong Bai (A,B), Haifeng Gao (A), Rong Xiao (A), Peipei Liu (A), and Bin Chen (A)

(A) State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, P.R. China.

(B) Corresponding author. Email:

Table 1. Average soil organic carbon content and density in the
Baiyangdian lake

Zone            Soil          Average               Average SOC
                depth       SOC content               density
                (cm)     ([10.sup.3] mg/kg)       (kg/[m.sup.3])

Inlet           0-10     7.61 [+ or -] 1.88     10.16 [+ or -] 4.16
                10-20    3.31 [+ or -] 0.95      4.64 [+ or -] 0.91
                20-30    6.28 [+ or -] 3.08      9.40 [+ or -] 6.10
                0-30     5.73 [+ or -] 2.20      8.07 [+ or -] 2.99
Water reserve   0-10    24.55 [+ or -] 22.61    26.13 [+ or -] 23.64
                10-20   20.93 [+ or -] 18.68    21.17 [+ or -] 19.89
                20-30   16.96 [+ or -] 10.24    18.29 [+ or -] 9.91
                0-30    20.81 [+ or -] 3.80     21.86 [+ or -] 3.97
Outlet          0-10    21.11 [+ or -] 15.99    21.94 [+ or -] 18.65
                10-20   21.03 [+ or -] 22.87    22.65 [+ or -] 24.39
                20-30   22.87 [+ or -] 24.79    19.86 [+ or -] 19.71
                0-30    21.67 [+ or -] 1.04     21.48 [+ or -] 0.43
Aquaculture     0-10    11.73 [+ or -] 9.22     13.19 [+ or -] 7.74
                10-20   10.62 [+ or -] 1.18     12.62 [+ or -] 2.76
                20-30   10.59 [+ or -] 4.22     12.34 [+ or -] 6.53
                0-30    10.98 [+ or -] 0.65     12.72 [+ or -] 1.45
Industrial      0-10    10.61 [+ or -] 9.37     13.66 [+ or -] 11.25
                10-20    5.02 [+ or -] 1.02     7.424 [+ or -] 1.05
                20-30    3.89 [+ or -] 2.41      4.89 [+ or -] 2.06
                0-30     6.51 [+ or -] 3.60      8.66 [+ or -] 4.51
Whole area      0-10    15.12 [+ or -] 7.30    17.016 [+ or -] 6.72
                10-20   12.18 [+ or -] 8.48     13.70 [+ or -] 8.04
                20-30   12.12 [+ or -] 7.80     12.96 [+ or -] 6.21
                0-30    13.14 [+ or -] 1.72     14.56 [+ or -] 2.16

Table 2. Estimated value of soil organic carbon storage in different
functional cones in the Baiyangdian Lake (t/[km.sup.2])

Soil depth    Inlet     Industrial    Outlet     Aquaculture

0-10 cm      2030.99    2731.98       4387.62     5276.16
10-20 cm      927.94    1483.05       4529.99     5047.49
20-30 cm     1880.03     978.42       3971.62     4935.86
Sum          4838.97    5193.45      12889.24    15259.51
Proportion      8.36%      8.98%        22.28%      26.37%

Soil depth   Water reserve      Sum      Proportion

0-10 cm       7840.33        22267.08     38.49%
10-20 cm      6350.26        18338.73     31.70%
20-30 cm      5486.09        17252.03     29.82%
Sum          19676.68        57857.84    100.00%
Proportion      34.01%         100.00%

Table 3. Carbon pool index in different functional zones of the
Baiyangdian Lake

Soil depth   Inlet   Industrial   Outlet   Aquaculture   Water reserve

0-10 cm      0.26       0.35       0.56       0.67           1.00
10-20 cm     0.15       0.23       0.71       0.79           1.00
20-30 cm     0.34       0.18       0.72       0.90           1.00
0-30 cm      0.25       0.26       0.66       0.78           1.00

Soil depth   Whole lake   Potential ability

0-10 cm         0.71            0.29
10-20 cm        0.72            0.28
20-30 cm        0.79            0.21
0-30 cm         0.74            0.26

Table 4. Correlation matrix between soil organic carbon (SOC) and
other soil properties in the Baiyangdian Lake
TN, Total nitrogen; AP, available phosphorus; BD, bulk density;
WC, water content. * P<0.05; ** P<0.01

              SOC         TN          AP

TN         -0.046          1
AP         -0.047       0.785 **       1
pH         -0.194      -0.099       0.062
BD         -0.544 **   -0.078      -0.032
WC          0.608 **   -0.004       0.029
Salinity    0.149       0.565 **    0.598 **

              pH         BD          WC

pH            1
BD          0.264 *       1
WC         -0.292 *    0.412 **      1
Salinity    0.105     -0.072      0.396 **
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Article Details
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Author:Huang, Laibin; Bai, Junhong; Gao, Haifeng; Xiao, Rong; Liu, Peipei; Chen, Bin
Publication:Soil Research
Article Type:Report
Geographic Code:9CHIN
Date:Nov 1, 2012
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