Methane emissions from newly created marshes in the drawdown area of the Three Gorges Reservoir


  • Huai Chen,

    1. College of Resources and Environment, Chongqing University, Chongqing, China
    2. Key Laboratory for the Exploitation of South-West Resources and Environmental Disaster Control Engineering, Ministry of Education, Chongqing University, Chongqing, China
    3. Gradute School, Chinese Academy of Sciences, Beijing, China
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  • Yuyuan Wu,

    1. College of Resources and Environment, Chongqing University, Chongqing, China
    2. Key Laboratory for the Exploitation of South-West Resources and Environmental Disaster Control Engineering, Ministry of Education, Chongqing University, Chongqing, China
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  • Xingzhong Yuan,

    1. College of Resources and Environment, Chongqing University, Chongqing, China
    2. Key Laboratory for the Exploitation of South-West Resources and Environmental Disaster Control Engineering, Ministry of Education, Chongqing University, Chongqing, China
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  • Yongheng Gao,

    1. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China
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  • Ning Wu,

    1. Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
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  • Dan Zhu

    1. Gradute School, Chinese Academy of Sciences, Beijing, China
    2. Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
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[1] The study aimed to understand the methane (CH4) emission and its controlling factors in the Three Gorges Reservoir Region and to explore its implication for large dams. We measured CH4 emissions from four vegetation stands in newly created marshes in the drawdown area of the Three Gorges Reservoir, China, in the summer of 2008. The results showed highly spatial variations of methane emissions among the four stands, with the smallest emission (0.25 ± 0.65 mg CH4 m−2 h−1) in the Juncus amuricus stand, and the greatest (14.9 ± 10.9 mg CH4 m−2 h−1) in the Scirpus triqueter stand. We found that the spatial variations of CH4 emissions are caused by difference in standing water depth and dissolved organic carbon (DOC). Results also showed a special seasonal variation of CH4 emissions in this area, i.e., maximal emissions in early July followed by a low and steady value before the winter flooding. The seasonality of CH4 emissions was found closely related to temperature and standing water depth. Because of the large area of the drawdown zones for global dam reservoirs and a large CH4 emission rate, such newly created marshes should not be neglected when estimating CH4 emissions from reservoirs.

1. Introduction

[2] Large dams have always played an indispensible role in human development all over the world. They are usually used to provide drinking water, control floods, irrigate crops, facilitate navigation, and generate electricity. People used to think large dams represented progress in hydraulic engineering. However, they gradually recognized the harm of such dams to environment in the past several decades [Milliman, 1997; Wu et al., 2004]. Dams have resulted in not only large-scale habitat fragmentation [Wu et al., 2003], but also emission of greenhouse gases to the atmosphere [Abril et al., 2005; Rudd et al., 1993], especially methane (CH4). This is because anoxic conditions prevailing at the bottom of the reservoir favor production of CH4 and its possible emission into the atmosphere [Galy-Lacaux et al., 1997]. Moreover, the seasonally exposed bottom of the reservoir may play a more important role in CH4 emission [Fearnside, 2002]. In fact, during the last 2000 years, ancient large-scale water-management projects might have altered atmospheric CH4 in China and India [Ruddiman, 2003]. Therefore the clean, green image of dams may have been overstated [Giles, 2006].

[3] CH4 is an important greenhouse gas, about 21 times more powerful at warming the atmosphere than carbon dioxide (CO2) on a per mole basis [Van Ham et al., 2000]. Anthropogenic CH4 sources dominate present-day CH4 budgets, accounting for more than 60% of the global budget [Denman et al., 2007]. The main anthropogenic sources include rice agriculture, waste, livestock and biomass burning. Recently, several studies have shown that large dams may also represent a fraction of the anthropogenic CH4 [Abril et al., 2005; Duchemin et al., 2006; Lima et al., 2008], with a roughly estimated emission of 69.3 Tg CH4 from global large dams [Saint Louis et al., 2000]. However, in recent studies about dam CH4, researchers often paid much attention to CH4 emission from reservoir surface, turbines and spillways [Abril et al., 2005; Guérin et al., 2006; Saint Louis et al., 2000], but ignored that from drawdown areas (littoral zones, especially wetlands) of dam lakes when the water recedes. Littoral zones is in fact one of the major sources of atmospheric CH4 [Bergstrom et al., 2007; Chen et al., 2009; Dan et al., 2004; Fearnside, 2002; Juutinen et al., 2001, 2003; Kankaala et al., 2004; Wand et al., 2006]. Therefore data from drawdown areas of large dam lakes will help us to re-estimate the global dam CH4 emission.

[4] Among the large dams in the world, the 2335 m long and 185 m high Three Gorges Dam (TGD) on the Yangtze River of China is the biggest and thus a good example. It has a great drawdown area of about 350 km2, approximately one third of the dam lake when fully operating. The Three Gorges Reservoir Region (TGRR) is about 660 km long and 58,000 km2 in watershed area, greater than Switzerland [Stone, 2008; Wu et al., 2003]. When operating at full capacity, the total inundated area in the TGRR is estimated to be about 1080 km2 [Wu et al., 2004]. After the winter flooding in late 2008 and early 2009, the water table became 145 m again, receding from 172 m, and more than 100 km2 of marshes were formed in the lowland of the drawdown area of the Three Gorges Reservoir (TGR) in about a month. These new marshes on the border of the TGR may emit a substantial amount of CH4 in summer. To take measures on CH4 mitigation, and even to use it as a renewable energy source [Ramos et al., 2009], we need more research to quantify the CH4 emission from the marshes in the drawdown area of the TGR.

[5] In light of such understanding, we set for the study two main objectives: (1) to understand the CH4 emission and its controlling factors in the drawdown area and (2) to explore its implication for the TGR and other large dam reservoirs.

2. Materials and Methods

2.1. Study Sites

[6] The study was conducted in Pengxi River Wetland Reserve (31°5′37.74″-31°12′30.26″N, 108°27′45.05″-108°27′0.05″E), which covers a total area of 36.86 km2, with 1920 ha littoral wetlands along the dam lake (Figure 1). The Pengxi River is one of the secondary branches of the Yangtze River in the TGRR. The region is characterized by north subtropical humid monsoonal climatic conditions with average annual precipitation 1200 mm and temperature 18.2°C. Usually a summer drought occurred from mid-July to mid-August. After the damming of the TGR, many freshwater marshes had been created in the region, especially in the 30-m vertical drawdown zone along the dam-lake.

Figure 1.

Location of the study area, a new created marsh of TGR.

[7] A typical gully marsh was chosen for this study. After the flooding of 2007, cascading terraced marshes were well developed [Mitsch et al., 2008] and four different kinds of wetland plant stands formed in the gully, including Typha angustifolia stand, Juncus amuricus stand, Scirpus triqueter stand and Paspalum distichum stand. The greatest root density was found in the 0- to 20-cm soil depth in all sites. Detailed information about the study area was listed in Table 1.

Table 1. Total Biomass, Plant Height (With Range) and Water Depth (With Range) in Four Vegetation Stands in New Created Marshes of the Three Gorges Reservoir Region
StandsPlant Biomassa (g DW m−2)Plant Heightb (cm)Water Depthb (cm)Total Carbon (g kg−1)
  • a

    Mean value during the measurement period, from July to September 2008.

  • b

    The range of water depth and plant height indicates minimum and maximum values at the same points during the measurement period.

Juncus amuricus28851 (30–72)2.2 (0–7)15.4
Typha angustifolia323128 (86–147)3.5 (0.5–8)13.3
Scirpus triqueter38788 (46–120)8.7 (0–21)19.7
Paspalum distichum51548 (29–63)3.8 (0–19)24.4

2.2. Soil Physical-Chemical Characteristics

[8] Redox potentials and temperatures were taken with a portable digital meter (EcoScan pH6, Eutech Instruments Pte Ltd., Singapore). Water temperatures, ground surface temperatures and soil temperatures at the depth of 5 and 10 cm were manually recorded. Standing water depths in the growing season were recorded with a ruler. Soil samples were collected at 10-cm soil depth in July, August and September. Soil samples intended for the total carbon content (TC), total nitrogen content (TN), total phosphorus content (TP) analyses were passed through a 2 mm screen to remove plant crowns, visible roots and root fragments. Samples were air dried and analyzed for TC by the potassium-dichromate oxidation procedure, TN by the semimicro Kjeldahl procedure, and TP by UV-1601 spectrophotometer, after H2SO4-HClO4 digestion [Saarnio and Silvola, 1999]. After removing roots, fresh soils were centrifuged at 12 880 g for 10 min. The supernatant aliquot was used to measure dissolved organic carbon (DOC) concentration. DOC concentration was detected by a total organic carbon (TOC) analyzer (multiN/C 2100, Analytik Jena AG, Jena, Germany).

2.3. Growth of Vegetation

[9] Plant heights in each stand were recorded with a ruler once every ten days from July to September. Aboveground biomass of each stand was measured by harvesting plants from the abovementioned area of 1 m × 1 m in mid-August. Three replicates were used to measure biomass of each stand. The weight of dry biomass was measured after drying at 60°C.

2.4. Establishment of Sampling Plots and CH4 Emission Measurement

[10] To minimize disturbance to the marsh, boardwalk was installed in the sampling area. For each plant stand, six plots were established along such boardwalk, where CH4 emission was measured at 10-day intervals from July to September.

[11] The CH4 emission is measured with vented static chambers [Hutchinson and Mosier, 1981]. The chambers (30 cm in diameter, 50 cm in height) were made of cylindrical polyvinyl chloride (PVC) pipe. Details about the chambers were described by Chen et al. [2008].

[12] Four air samples from each chamber were taken at 10-min intervals over a 30-min period after enclosure, stored in 50 mL air-tight vacuumed vials. The CH4 concentration was determined by a gas chromatography (PE Clarus 500, PerkinElmer, Inc., USA), equipped with a FID (flame ionization detector) operating at 350°C and a 2 m Porapak 80–100 Q Column. The column oven temperature was 35°C and the carrier gas was N2 with a flow rate of 20 cm3 min−1. The minimum detectable concentration was 1 × 10−3μL L−1 (ppb). Certified CH4 standard in 4.9 μL L−1 (China CH4 National Research Center for Certified Reference Materials, Beijing) was used for calibration.

[13] The flux J of methane was calculated as

equation image

Where dC/dt is the rate of concentration change; M is the molar mass of CH4; P is the atmosphere pressure of the sampling site; T is the absolute temperature of the sampling time; V0, P0, T0 is the molar volume, atmosphere pressure, and absolute temperature, respectively, under the standard condition; H is the chamber height over the water surface.

2.5. Statistical Analysis

[14] Mean CH4 emissions, surface and soil temperature, Eh, standing water depth and aboveground biomass for each vegetation type were calculated by averaging the six replicates for each sampling day. Nonparametrical Mann-Whitney U was used to compare the fluxes between different sites. The CH4 emissions were related to environmental variables by Pearson correlation analysis. Linear regression analyses were carried out with CH4 emissions as dependent variable, soil and vegetation characteristics as independent variables. Mutual relationships among variables were then investigated by principal component analysis (PCA). Stepwise multiple linear regression analysis was used to correlate CH4 emissions with the principal components. The effect of a certain variable was considered statistically significant for p < 0.05. The above analyses were performed with the SPSS 11.5 statistical package.

3. Results

3.1. CH4 Emission From Newly Created Marshes in the TGR

[15] Significant variations of CH4 emissions were found among the four different plant stands in the littoral marsh of the TGR (Figure 2). The greatest CH4 emissions (14.9 ± 10.9 mg CH4 m−2 h−1) were from S.triqueter stand, with the greatest water depth (Table 1). J.amuricus stand recorded the lowest CH4 emissions (0.25 ± 0.65 mg CH4 m−2 h−1). The intermediate CH4 emissions from T.angustifolia stand and P.distichum stand were 0.64 ± 1.1 mg CH4 m−2 h−1 and 6.8 ± 5.0 mg CH4 m−2 h−1, respectively.

Figure 2.

Methane emissions in different plant stands from July to September 2008. The different letters (a, b, c, d) above the box indicate significant differences in CH4 emissions among stands (nonparametric test, followed by Mann-Whitney U test, p < 0.05, n = 54).

[16] CH4 emissions in the newly created marsh displayed a strong seasonal trend in the sampling period (Figure 3). The emissions were highest in early summer, then decreased and maintained relatively steady with some slight fluctuation. In addition, CH4 influx was observed in mid-July in J.amuricus stand and T.angustifolia stand without the standing water because of the summer drought.

Figure 3.

Temporal variation of CH4 fluxes from July to September 2008. The error bars illustrate standard deviation with six replicates.

3.2. Key Factors Influencing CH4 Emissions From Marshes of the TGR

[17] The CH4 emission values from four kinds of littoral plant stands of the TGR were plotted against environmental factors (Figure 4). Significant relationships existed between CH4 emissions and factors including water temperature, standing water depth and DOC. Because of mutual correlation among these physical variables, principal components (PCs) were introduced to analyze the relationships among these variables. The principal component analysis on physical variables resulted in four components with eigenvalues larger than 1. The components all together explained 75% of total variance. The first component explained about 33% of the observed variance. Several variables (standing water depth, TC, TN and DOC) were significantly correlated with this first component. The second component explained about 25% of observed variance. Air temperature, soil temperature, standing water depth and DOC were found to be correlated with the second component. The second component could be seen as influenced by standing water table, soil biochemical processes and seasonal variation. Only 5- and 10-cm soil temperatures were found to be correlated with the third component (Table 2).

Figure 4.

Relations between methane emission and key influencing factors in the littoral marches of the TGR.

Table 2. Results of Principal Component Analysis (PCA) for Physical Variables in the Littoral Marches of the TGR
Physical VariablesComponent 1Component 2Component 3
  • a

    Correlation significant at p < 0.001.

Air temperature0.1370.303a−0.190
Water temperature0.0340.325a−0.047
5-cm soil temperature−0.1460.1730.429a
10-cm soil temperature−0.1620.0550.473a
Standing water depth0.183a0.245a0.048
Plant height−0.1370.030−0.165
Variance explained (%)33%25%17%

[18] A stepwise multiple linear regression analysis between PCA components and CH4 emissions showed that the second component had significant influence (r = 0.446, p < 0.01) and could be depicted as CH4 Emission = 8.89 + 7.63 × Component 2.

4. Discussions

4.1. Spatial and Seasonal Variations in CH4 Emissions From the Newly Created Marshes

[19] Mean (SD) CH4 emission rate from the newly created marshes of the TGR was 6.7 ± 13.3 mg CH4 m−2 h−1 (ranging from −0.69 to 104.3 mg CH4 m−2 h−1) in the growing season of 2008. This was much higher than that from boreal littoral marshes, higher than that from rice paddies (ranging from 0.03 to12.7 mg CH4 m−2 h−1) in the same region [Han et al., 2005], and close to temperate marshes and tropical floodplains (Table 3). We did not measure CH4 emissions from the lake surface, though. Through comparing to surfaces of three tropical reservoirs (5.1 ± 5.8 mg CH4 m−2 h−1 for Petit Saut, 1.4 ± 2.0 mg CH4 m−2 h−1 for Balbina, and 3.3 ± 3.9 mg CH4 m−2 h−1 for Samuel) and a temperate lake (−2.5 to 5.7 mg CH4 m−2 h−1), which are all lower than that of the present study, therefore, we understood that the CH4 emission rate of the drawdown area should be higher than that of the reservoir surface of the TGR [Guérin et al., 2006; Wang et al., 2006]. On the basis of the average CH4 emission from Petit Saut, Balbina, and Samuel, we assumed the CH4 emission from the water surface of the TGR as 3.3 mg CH4 m−2 h−1. Calculating with the surface area (1080 km2), we further estimated the total CH4 emission from the surface as 3.6 Mg CH4 h−1. With the mean CH4 emission value of 6.7 ± 13.3 mg CH4 m−2 h−1 in the marsh area of TGR (100 km2 in area), the total CH4 emission from the marsh area of TGR could be estimated as about 0.67 Mg CH4 h−1, 19% of that from the surface. Considering the fact that the area of newly created marshes is only about 10% of the surface area, the newly created marshes in the drawdown area could be a “hotspot” of CH4 emission.

Table 3. CH4 Emissions From Littoral Marshes in Comparison With Similar Wetlands in Different Climates
LocationWetland TypeCH4 Flux (mg m−2 h−1)References
Boreal Zone
Finland, Lake Mekrijärvi and HeposelkäLittoral marshes−0.2–14.2Juutinen et al. [2001]
Finland, Lake EkojärviLittoral marshes1.1–27.2Kankaala et al. [2003]
Finland, Lake Alinen RautjärviLittoral marshes0–3.8Kankaala et al. [2005]
Temperate Zone
China, Meiliang Bay in Taihu LakeLittoral marshes−1.7–131Wang et al. [2006]
China, TGRRLittoral marshes−0.69–104.3This study
Tropical Zone
Brazil, in PantanalFloodplains (flooded seasonally)0.04–91.1Marani and Alvala [2007]

[20] Furthermore, Whiting and Chanton [1993] showed that CH4 emissions from wetlands are controlled by primary production (PP), in other terms, CH4 emission is correlated to a net CO2 uptake. Evidences have also showed that approximately 3% of carbon fixed by photosynthesis at peak growing season was emitted as methane from wet sedge tundra [King and Reeburgh, 2002; King et al., 2002]. In our studying area, after impounding, the dominant ecosystems in the drawdown area transmitted from subtropical forests (known as a CH4 sink) to seasonally flooded freshwater marshes. Because of the limited exposure time (June to October) of the drawdown area, PP in the drawdown area might decrease after impounding. If we do not consider CH4 produced by plants under aerobic conditions [Keppler et al., 2006], though PP may be lower after impounding, because of the seasonal waterlogging and warm subtropical climate, the drawdown area may have been transitioned from a net CH4 sink to a net CH4 source.

[21] In the littoral marshes, CH4 emissions differ considerably within even a short (1–50 m) distance [Juutinen et al., 2001; Kaki et al., 2001]. Significant spatial variations of CH4 emissions were also found in the littoral marsh of the TGR (Figure 2). The high short-scale spatial heterogeneity suggests the need of fine-scale investigation. Like many other studies [Bubier et al., 1993; Ding et al., 2002], the present study showed a positive correlation between CH4 emission and standing water depths (Figure 4). Moreover, among the four plant stands of this study, the sequence of CH4 emission was accorded with the sequence of standing water depths (water table) (Table 1 and Figure 2). The correlation between CH4 emissions and vegetation characteristics (aerenchymal shoots; below-ground biomass; above-ground biomass) has also been found in many studies [Bubier et al., 1993; Greenup et al., 2000; Saarnio and Silvola, 1999; Van den Pol-Van Dasselaar et al., 1999]. In our study, plant biomass was not found to be significantly correlated with CH4 emission (p = 0.159). However, we found that CH4 emissions were significantly correlated with DOC content (Figure 3), which was partly derived from plants. The correlation observed here may be because of the fact that both pore water DOC and CH4 are produced by intense microbial activity in the sediments.

[22] In this study, we found a special seasonal variation of CH4 emissions, i.e., maximal emissions in early July, immediately after the exposure of the drawdown area, followed with a comparatively low and steady value in the rest of sampling period. Such seasonal pattern of CH4 emissions was different from those of natural marshes, including boreal peatlands, littoral zones of boreal lakes, alpine wetlands, in which the beginning of growing season shows a comparatively low emission, while the peak emission is recorded in the peak growing season (mid-July to August) [Alm et al., 1999; Chen et al., 2008; Kankaala et al., 2004]. The reason may be that the annual summer drought (from mid-July to mid-August in this area) limits CH4 emissions in July and August. In the drier period, CH4 emission rates would be at a low level because of low standing water depths.

[23] In our study, water temperatures were found to greatly influence the seasonal variations of CH4 emissions (Figure 4). The seasonal variations of CH4 emissions are ascribed to the seasonal balance of CH4 production, oxidation and transportation. The main factors which may influence the CH4 production rates over the growing season are changes in temperature [Yavitt et al., 1987], the amount of available organic substrate [Saarnio et al., 1997] and the size of the active anaerobic microbial biomass [Bergman et al., 2000]. Seasonal variations of CH4 oxidation and transportation have been reported to be correlated with temperature, light intensity, water level changes [Cicerone and Oremland, 1988] and seasonality in the functioning or physiology of the plants [van der Nat and Middelburg, 1998].

4.2. Implications for Large Dam Reservoirs

[24] In the recent ten years, scientists have gradually realized the role of large dams in emitting a substantial amount of GHGs, especially as “virtual CH4 factories” [Abril et al., 2005; Bambace et al., 2007; Fearnside, 2002; Fearnside, 2004; Giles, 2006; Guérin et al., 2006; Saint Louis et al., 2000]. A little disappointingly, the importance of dam-generated CH4 has largely been overlooked [Cullenward and Victor, 2006].

[25] Traditionally, dam CH4 budget is consisted of CH4 released at dam reservoir surface, turbines (of hydropower projects), spillways, and downstream [Abril et al., 2005]. However, created marshes in the drawdown area of the dam reservoir have been overlooked as a CH4 emission source of large dams [Abril et al., 2005; Fearnside, 2005; Guérin et al., 2006; Lima et al., 2008; Saint Louis et al., 2000]. In the present study, we preliminarily estimated that the CH4 emission of littoral marshes (covering only 10% of the surface area of TGR) was about 19% of the total CH4 emission from the surface of TGR. Therefore such newly created marshes should never be a negligible source of dam-generated CH4, considering 1) a higher CH4 emission rate comparing with that from the reservoir surface and 2) the great area of the drawdown zones of dam lakes which cover about 26,000 km2, estimated with an assumed low cover rate 10% of the drawdown zone of the dam reservoir and the total area of 0.26 million km2 for the global surface area of all reservoirs [Downing et al., 2006]. Moreover, a reasonable upscaling estimate for such ignored CH4 source could be attained on the basis of more data about CH4 emission from created marshes in the drawdown zones of reservoirs and detailed information about the drawdown area and drawdown duration of different dam reservoirs.


[26] This study was financially supported by the National Natural Foundation of China (50749045), Chinese Academy of Sciences (KZCX2-YW-418), and Natural Science Foundation Project of CQ CSTC (2009BB7182). Pengxihe wetland research station was thanked for allowing and assisting us to do our research there. We thank Wan Xiong for her analytical editing of our manuscript, Wang Jianxiu for his suggestions and logistic arrangement on our field measurements, Meng Wang for his help with gas sample analysis, and Fei Yao for his assistance in gas sampling.