Methane emissions from rice paddies natural wetlands, and lakes in China: synthesis and new estimate

Abstract

Sources of methane (CH₄) become highly variable for countries undergoing a heightened period of development due to both human activity and climate change. An urgent need therefore exists to budget key sources of CH₄, such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), which are sensitive to these changes. For this study, references in relation to CH₄ emissions from rice paddies, natural wetlands, and lakes in China were first reviewed and then reestimated based on the review itself. Total emissions from the three CH₄ sources were 11.25 Tg CH₄ yr⁻¹ (ranging from 7.98 to 15.16 Tg CH₄ yr⁻¹). Among the emissions, 8.11 Tg CH₄ yr⁻¹ (ranging from 5.20 to 11.36 Tg CH₄ yr⁻¹) derived from rice paddies, 2.69 Tg CH₄ yr⁻¹ (ranging from 2.46 to 3.20 Tg CH₄ yr⁻¹) from natural wetlands, and 0.46 Tg CH₄ yr⁻¹ (ranging from 0.33 to 0.59 Tg CH₄ yr⁻¹) from lakes (including reservoirs and ponds). Plentiful water and warm conditions, as well as its large rice paddy area make rice paddies in southeastern China the greatest overall source of CH₄, accounting for approximately 55% of total paddy emissions. Natural wetland estimates were slightly higher than the other estimates owing to the higher CH₄ emissions recorded within Qinghai-Tibetan Plateau peatlands. Total CH₄ emissions from lakes were estimated for the first time by this study, with three quarters from the littoral zone and one quarter from lake surfaces. Rice paddies, natural wetlands, and lakes are not constant sources of CH₄, but decreasing ones influenced by anthropogenic activity and climate change. A new progress-based model used in conjunction with more observations through model-data fusion approach could help obtain better estimates and insights with regard to CH₄ emissions deriving from wetlands and lakes in China.

Introduction

Methane (CH₄) is an important greenhouse gas (GHG) that possesses power beyond carbon dioxide (CO₂) to influence warming within the atmosphere by an approximate magnitude of 21 on a per mole basis (Van Ham et al., 2000). Moreover, CH₄ exerts strong influence over the chemistry of the troposphere and the stratosphere (Cicerone & Oremland, 1988). A study has recently reported that gas–aerosol interactions substantially alter the relative importance of various GHGs emissions. This is especially true for CH₄ emissions that have larger overall impacts than current carbon-trading schemes, which modified its radiative forcing from +0.48 W m⁻² to +0.90 W m⁻² (Forster et al., 2007; Shindell et al., 2009). CH₄, therefore, has a considerable impact on the earth's climate system, second anthropogenic GHG only to CO₂. Atmospheric CH₄ is primarily emitted from biological sources and this accounts for more than 70% of the global total (Denman et al., 2007). CH₄ is consumed primarily through oxidation by way of OH within the troposphere (Le Mer & Roger, 2001; Denman et al., 2007). Since the preindustrial era, its atmospheric concentration has increased from 700 ppb to almost 1800 ppb (Dlugokencky et al., 2009). Moreover, a renewed growth in CH₄ atmospheric concentration occurred around the beginning of 2007 (Rigby et al., 2008; Dlugokencky et al., 2009) following a near zero-growth decade. The existing state of the global CH₄ budget must therefore be addressed without delay (Heimann, 2010).

CH₄ emissions that occur in wetlands (natural and constructed) and aquatic ecosystems are combined results of CH₄ production, oxidation, and transportation. Anaerobic conditions can produce CH₄ as an end product of organic matter degradation by way of acetoclastic and hydrogenotrophic methanogenic archaea (Conrad, 1996). CH₄ produced under these conditions is then partly oxidized by methanotrophic bacteria within oxic zones (King, 1992; Segers, 1998; Bastviken et al., 2003). Three major mechanisms exist that drive CH₄ transportation: molecular diffusion (Barber et al., 1988), bubble ebullition (Joyce & Jewell, 2003; Baird et al., 2004), and plant-mediated transportation (Dacey & Klug, 1979; Joabsson et al., 1999). The primary factors that influence CH₄ emissions include temperature, the quantity and quality of the methanogens substrate, the water regime, soil redox potential, pH, salinity, sulfate concentration, and etc. (Wang et al., 1996; Segers, 1998; Le Mer & Roger, 2001). Although CH₄ emissions and its regulation are well understood, expansive studies in remote regions and more details concerning its processes are needed to upscale and enrich the overall knowledgebase.

Wetlands (natural and constructed) are the single largest source of atmospheric CH₄ emissions, accounting for approximately 39–112 Tg CH₄ yr⁻¹ from rice paddies and 100–231 Tg CH₄ yr⁻¹ from natural wetlands (Chen & Prinn, 2006; Denman et al., 2007). These ecosystems contribute around one-third of the total global CH₄ emissions to the atmosphere (Singh et al., 2000). Large CH₄ emissions coming from lakes have also caused increasing interest in the scientific community, for their contribution of 8–48 Tg CH₄ yr⁻¹ (Bastviken et al., 2004). Moreover, several studies have designated northern thaw lakes as recognized CH₄ emission ‘hotspots’ with an estimated source strength of approximately 24.20 ± 10.50 Tg CH₄ yr⁻¹ (Zimov et al., 1997; Walter et al., 2006, 2007a,b, 2008). They further indicated that inland waters offset about 25% of the estimated land carbon sink (Bastviken et al., 2011). Wetlands and lakes remain important CH₄ sources within the global CH₄ budget, but considerable uncertainties still exist, mainly arising, from the large spatiotemporal variation that occurs at different scales and the limited range of observational conditions (Middelburg et al., 2002; Denman et al., 2007). Thus, it would be very helpful to estimate CH₄ emissions from wetlands and lakes on national, regional, as well as global scales (Bastviken et al., 2004; Walter et al., 2007a; Saarnio et al., 2009). Increased knowledge concerning CH₄ emissions from wetlands and lakes in China is important to understand the CH₄ budget of China as well as the CH₄ budget of the world at large.

Multiple studies on rice paddies CH₄ emissions in China have already been carried out (Wang & Shangguan, 1996; Cai et al., 2001; Wang & Li, 2002; Liu et al., 2003; Huang et al., 2004; Zou et al., 2005; Zheng et al., 2006; Khalil et al., 2008; Ma et al., 2008; Yang et al., 2010; Feng et al., 2012), and some have even made efforts to estimate the total emission for the country (Cao et al., 1995; Kern et al., 1997; Huang et al., 1998, 2006; Khalil et al., 1998; Wang & Li, 2002). Recent studies on natural wetland CH₄ emissions in China have been published (Jin et al., 1999; Ding et al., 2002; Hirota et al., 2004; Wang & Han, 2005; Chen et al., 2008; Song et al., 2009; Wang et al., 2009; Sun et al., 2011) that offer preliminary national estimates (Ding et al., 2004b). Although CH₄ emission data from lakes and reservoirs are important to the national CH₄ budget (Bastviken et al., 2004; Yang et al., 2011), only few studies are located in China (Duan et al., 2005; Chen et al., 2009b, 2011; Zheng et al., 2011; Yang et al., 2012). The abovementioned studies were primarily carried out in northeastern, southeastern, and southwestern China (Fig. S1). So far to the best of the authors’ knowledge, there is no synthesis study investigating CH₄ emissions on a comprehensive CH₄ budget for either cultivated wetland areas (rice paddies) and noncultivated wetlands or lakes in China. Therefore, systematic analyses on studies concerning CH₄ emissions from rice paddies, wetlands, and lakes in China are urgently needed to arrive at a total CH₄ emission estimate.

In light of such a rationale, this study has two primary objectives: (1) to review and analyze existing studies on CH₄ emissions from rice paddies, natural wetlands, and lakes in China; and (2) to provide new estimates of the total CH₄ emissions from these sources.

CH₄ emission from rice paddies in China

On the basis of the analyses of data taken from 49 articles, we obtained 412 sets of mean seasonal CH₄ emission rates under different water regimes and fertilizer treatments in all five rice cultivation regions in China (Appendix S1 and Table S1), and calculated a mean emission rate (±SD) of 11.35 ± 12.41 mg CH₄ m⁻² h⁻¹. This is lower than earlier estimates (Khalil et al., 1991; Wang et al., 1993). In fact, the mean CH₄ emission rate during the last 20 years showed a noted decline in all five major rice cultivation regions in China (Fig. 1). This was partly the result of changes in irrigation, organic manure input, and rotational patterns led by managerial decision-making practices (Cai, 1997; Kern et al., 1997; Li et al., 2002a,b; Li et al., 2005; Yang et al., 2010). Moreover, the northward moving of rice cultivation trend in China (Hijmans, 2007) should also contribute to a decline in CH₄ emissions, as rice paddies discharge considerably less CH₄ in northern regions than in southern regions (Fig. 1). However, certain increasing CH₄ emission trends were emerging: (1) from the application of straw that has been highly encouraged by the Chinese government to retard increasing agricultural soil degradation (Wegener et al., 2008; Ma et al., 2009; Wang et al., 2012); (2) from domestic sewage water used to irrigate rice paddies (Zou et al., 2009) due to a gradual decline in freshwater availability (Zai et al., 2006); (3) from Azolla that has dominated rice paddies surface water due to water pollution caused by irrigation practices (Chen et al., 1997; Ying et al., 2000); and (4) from increased atmospheric carbon dioxide concentration due to global change (Zheng et al., 2006).

Figure 1. Seasonal mean CH₄ emissions from five different rice cultivation regions of China from 1987 to 2007.

More than half of all published measurements were taken during the preceding 20 years in southeastern China (Table S1), a region that applied the typical practice of double rice crop plantation. Rice paddies in this region were recognized as a dominant source of CH₄ in China due its expansive area (13.5 million ha, almost half the total of China) and the relatively high emission rates detected (Yan et al., 2003; Huang et al., 2006). The highest emission rate (18.23 ± 1.18 mg CH₄ m⁻² h⁻¹, Fig. 2) was reported in southwestern China (AEZ 6B), partly due to the year-round flooding and the large input of organic fertilizer in many rice paddies (Wei et al., 2000; Jiang et al., 2006).

Figure 2. CH₄ emission rates from rice paddies located within different Agricultural Ecosystem Zones in China. Different letters (a, b, and c) above the boxes indicate significant differences in CH₄ emissions between zones. Dashes mean the maximums and minimums; crosses mean the 99th and 1st percentiles; the range of each column is from 25th percentile to 75th percentile; the small box in each column indicates the mean; the dash in each column is the median.

CH₄ emissions from rice paddies are not only influenced by natural factors but also by intensive managerial practices. Some studies have shown that the management of rice paddies plays even a more important role in controlling CH₄ emissions than natural factors do (Cai, 1997). It is well understood that water regimes and fertilizers are the most influencing factors for CH₄ emissions from rice paddies (Cai, 1997; Yan et al., 2003; Li et al., 2005; Wang et al., 2012). Although more than 90% of rice paddies are sufficiently irrigated in China, irrigation management of rice paddies differ throughout the country and have changed greatly in the last 20 years (Li et al., 2002a,b). CH₄ emissions from rice paddies have been recorded as high as 44.61 ± 11.98 mg CH₄ m⁻² h⁻¹ in Chongqing and Sichuan provinces (Khalil et al., 1991; Cai et al., 2000; Xu et al., 2000), 42.93 ± 11.36 mg CH₄ m⁻² h⁻¹ in Zhejiang Province (Wassmann et al., 1993), 33.32 ± 17.01 mg CH₄ m⁻² h⁻¹ in Beijing (Chen et al., 1993; Wang et al., 2000), and 20.04 ± 5.93 mg CH₄ m⁻² h⁻¹ in Jiangsu Province (Xu et al., 2000), due to continuous flooding and fertilizer application. Because of a gradual decline in freshwater availability (Zai et al., 2006), plus ambitions to increase yields, a mid-season drainage method has been adopted throughout China during the last 20 years (Shen et al., 1998). This, coupled with other drainage treatments greatly reduced CH₄ emissions from rice paddies in all five major rice cultivation regions (Chen et al., 1993; Cai et al., 1994, 2000; Lu et al., 2000; Ren et al., 2002; Yue et al., 2005; Zou et al., 2005). Water saving treatments in northern China even led to an almost complete cessation of CH₄ emissions from rice paddies (Kreye et al., 2007). Besides water regime practices during the rice growing season, water irrigation during the nonrice crop season was also a significant factor that influences CH₄ emissions during the rice growing season (Cai et al., 2000; Han et al., 2005). In southwestern China, CH₄ emission rate from paddies with a following drying crop or fallow accounted for 11–65% of that measured from year-round flooded paddies (Wei et al., 2000; Han et al., 2005). Moreover, due to freshwater shortages, domestic sewage water was used to irrigate rice paddies in China in combination with water saving irrigation techniques (Zai et al., 2006). Zou et al. (2009) reported that in comparison with freshwater irrigation, sewage irrigation treatments increased CH₄ emissions for paddy plots with (27%) or without (33%) addition of chemical N due to the richness of organic matter within the water. Such results proposed more attention to sewage irrigation when estimating CH₄ emission from rice paddies, especially in the developing countries.

To increase yields while conserving soil fertility, organic materials (animal manure, green manure, fermented residues, and etc.) have been applied to paddies prior to the rice growing season (Yan et al., 2003). The effect of organic input stimuli on CH₄ emissions from rice paddies has been well documented in China (Chen et al., 1993; Wassmann et al., 1993; Cai, 1997; Ren et al., 2002). Based on the data attained for this study, CH₄ emissions from rice paddies free of organic inputs were only 47.3% that of rice paddies with organic inputs, which agrees with Yan et al. (2003). The quality and quantity of manure also affected CH₄ emissions from rice paddies to a large degree (Cai et al., 1994; Lu et al., 2000; Yan et al., 2003). For example, unfermented animal manure as well as green manure increased CH₄ emissions significantly (Khalil et al., 1991; Chen et al., 1993; Wassmann et al., 1993; Zou et al., 2005; Liu et al., 2008), whereas decomposed biogas residue increased CH₄ emissions either slightly or not at all (Chen et al., 1993; Lu et al., 2000). CH₄ emissions from rice paddies increase with an increase in organic inputs (Cai, 1997; Yang & Chang, 1997). When a large amount (25.5 t ha⁻¹) of rice straw and animal manure was applied to a field in Hunan Province, for example, the CH₄ emission rate was up to 56.2 mg CH₄ m⁻² h⁻¹ (Wassmann et al., 1993). Organic manure application methods also have a considerable influence on CH₄ emissions (Lu et al., 2000; Wegener et al., 2008; Ma et al., 2009). Compared with the uniform amalgamation method where fertilizer is incorporated evenly within the topsoil layer, ditch mulching method and strip mulching method decreased CH₄ emissions by 23–32% and by 32%, respectively (Ma et al., 2009). Some researchers have even argued that the application time itself was an important factor too (Lu et al., 2000).

The management of crop rotation during the nonrice growing season has been also shown to have a great effect on CH₄ emissions during the subsequent rice growing season (Lu et al., 1999, 2000; Wei et al., 2000; Han et al., 2005). Cai et al. (2000) observed a very low CH₄ emission value (0.14 mg CH₄ m⁻² h⁻¹) in a single late rice paddy field in Guangzhou (AEZ 7). Lu et al. (1999) also observed that after a year of planting vegetables, CH₄ emissions from a single early growth rice paddy field in Guangzhou was as low as 0.21 mg CH₄ m⁻² h⁻¹. A traditional duck-rice complex ecosystem located in southeastern China was found to have lower CH₄ emissions due to higher dissolved oxygen content and the elimination of aquatic weeds (Huang et al., 2005; Fu et al., 2006; Zhan et al., 2008). Other factors may also influence CH₄ emissions, including N-fertilizers (Chen et al., 1993; Wassmann et al., 1993; Yao & Chen, 1994; Lu et al., 1998), sulfates (Yao & Chen, 1994; Cai et al., 2000), and rice cultivars (Yao & Chen, 1994; Xu et al., 1999; Cai et al., 2000; Jia et al., 2002).

CH₄ emission from natural wetlands in China

Chinese scientists have measured CH₄ emissions from almost all primary wetland types in China during the last 20 years except for inland salt marshes (Table S3). Wetlands from the Qinghai-Tibetan Plateau and the Sanjiang Plain have been designated as the two largest natural emitters of CH₄ due to their expansive area (Jin et al., 1999; Ding et al., 2004b). Specific wetlands such as tidal marshes, mangroves, and forested swamps also constitute an integral part of the CH₄ budget of natural wetlands (Chang & Yan, 2003; Mu et al., 2009; Tong et al., 2009; Wang et al., 2009). Winter CH₄ emissions from wetlands were also studied in a few researches (Wang & Han, 2005; Zhang et al., 2005; Chen et al., 2008).

No noticeable geographic regional differences were observed in CH₄ flux seasonal means in China, but great differences between wetland types were ascertained (Appendix S1, Table S3 and Fig. 3). The highest CH₄ emissions were recorded in freshwater marshes (up to 9.71 ± 5.53 mg CH₄ m⁻² h⁻¹) due to high standing-water depths during the growing season and much plant litter inundated (Ding et al., 2002). Cyperaceous plants dominating freshwater marshes also contribute to high CH₄ emissions with their high CH₄ transport capacity (Ding et al., 2005). Relatively high CH₄ emissions were also found in peatlands (6.46 ± 6.60 mg CH₄ m⁻² h⁻¹). However, due to high salinity and frequent tidal flooding (Bartlett et al., 1987) (Van der Nat & Middelburg, 2000; Chang & Yan, 2003), coastal salt marshes showed small CH₄ emission fluxes in China (2.89 ± 3.97 mg CH₄ m⁻² h⁻¹) (Chang & Yan, 2003; Tong et al., 2009; Wang et al., 2009; Ding et al., 2010; Zhang & Ding, 2011). Forested swamps showed very low CH₄ emissions, like swamps in the Xiaoxing'an mountain chain (0.61 ± 1.10 mg CH₄ m⁻² h⁻¹) (Sun et al., 2009) and mangroves (0.06 ± 0.07 mg CH₄ m⁻² h⁻¹) located in coastal provinces of China (Chang et al., 1999; Ye et al., 2000; Chen et al., 2010a,b).

Figure 3. CH₄ emission rates from different wetland types in China. Dashes mean the maximums and minimums; crosses mean the 99th and 1st percentiles; the range of each column is from 25th percentile to 75th percentile; the small box in each column indicates the mean; the dash in each column is the median.

For the same lack of significant geographical variation in CH₄ emissions from rice paddies in China (Cai, 1997), the mean seasonal CH₄ emissions from natural wetlands are not controlled by geographical factors, but other factors closely related to CH₄ production. CH₄ oxidation and transportation are less important influencing factors (Van der Nat & Middelburg, 2000). Water regime itself is a dominant influential determinant on CH₄ emissions from all natural wetlands types in China (Ding et al., 2002; Hirota et al., 2004; Song et al., 2009; Sun et al., 2009; Yu et al., 2009). The CH₄ emission from wetlands of continuous flooding is always higher than those of seasonal flooding in China (Table S2). The same was true in freshwater marshes in northeastern China (Song et al., 2009). Moreover, differences in standing-water depth also results in significant variations in CH₄ emissions. In northeastern China, for example, scientists measured relatively high CH₄ emission means in a Carex lasiocarpa marsh with deep standing water, and low CH₄ means in a Deyeuxia angustifolia marsh with shallow one (Ding et al., 2002). Standing-water depth on the Qinghai-Tibetan Plateau was regarded as the key factor influencing spatial variations in CH₄ emissions in an open fen (Chen et al., 2009a). However, a strong negative partial correlation between CH₄ emission fluxes and water depth was also found on the Qinghai-Tibetan Plateau during the growing season in a wetland. This indicates that water depth in itself is presumably more effective in controlling the aerial components of plants in CH₄ emission than in CH₄ production and oxidation (Hirota et al., 2004). In addition, wetland degradation due to water regime variability where continuous flooding shifts to seasonal flooding and where deep standing water shifts to shallow standing water may result in a reduction in CH₄ emissions from wetlands. The degradation that resulted in the great loss of wetlands in China, therefore, was an important CH₄ sink in itself (An et al., 2007).

Besides the water regime, plants themselves are another important influencing factor on CH₄ emissions from wetlands as plants not only provide a conduit for CH₄ emissions by way of aerenchyma but also provide substrates for CH₄ production by means of root decay and exudation (Joabsson et al., 1999). The positive relationship between plant biomass and CH₄ emissions have been observed in almost all types of natural wetlands in China (Ding et al., 2005; Chen et al., 2009a). Other plant-based predicators of wetland CH₄ emissions in China are species type (Ding et al., 2005), stem density (Hirota et al., 2004), and height (Chen et al., 2008). Moreover, wetland degradation results in plant succession from cyperaceous to gramineous plants that, in itself, leads to the reduction in the capacity of plants to transport CH₄ from wetlands to the atmosphere, further reducing CH₄ emissions from wetlands (Hirota et al., 2004; Chen et al., 2010a,b).

Due to sensitivity of CH₄ production to temperature (Segers, 1998), diurnal or seasonal variations in temperature are also an important factor for diurnal or seasonal variation in CH₄ emissions (Ding et al., 2004a; Hirota et al., 2004; Wang & Han, 2005; Chen et al., 2008; Mu et al., 2009; Wang et al., 2009; Zhang & Ding, 2011). However, no significant relationship exists at the diurnal scale between air temperature or mean pore water temperature and CH₄ emissions from wetlands in northeastern China as well as on the Qinghai-Tibetan Plateau, indicating that temperature either does not or only weakly influence diurnal emission variation in CH₄ emissions (Ding et al., 2004a; Chen et al., 2010a,b).

CH₄ emission from lakes (including ponds and reservoirs) in China

The first study concerning CH₄ emissions from lakes in China took place in Taiwan Province (Wang et al., 1998). Relatively low CH₄ emission rates were reported such as 0.07 mg CH₄ m⁻² h⁻¹ from alpine lake pelagic zones and 0.11 mg CH₄ m⁻² h⁻¹ from the pelagic zones of 26 lakes in the plains region of Taiwan. In the eastern plain region of mainland China, higher CH₄ emission rate means from the pelagic zones of Lake Donghu (0.97 ± 0.78 mg CH₄ m⁻² h⁻¹) and Lake Taihu (0.50 ± 1.90 mg CH₄ m⁻² h⁻¹) were recorded (Xing et al., 2005; Wang et al., 2006). Emission rates as high as 0.82 ± 0.22 mg CH₄ m⁻² h⁻¹ were observed in winter in the Lake Boyang pelagic zone (Chen et al., 2007); however, CH₄ emission rates were as low as 0.12 ± 0.06 mg CH₄ m⁻² h⁻¹ from the surface of hydroelectric reservoirs (Zheng et al., 2011), with significant variation among different land uses in the drawdown area during different water tables (Yang et al., 2012). Only sporadic CH₄ emission measurements were taken in Lake Fuxian, Lake Erhai, and Lake Dianchi on the Yunnan-Guizhou Plateau during winter months (Chen et al., 2007). No data concerning CH₄ emission rates from the pelagic zone of lakes exist for the Qinghai-Tibetan Plateau, the Mongolia-Xinjiang Plateau, and the Northeast China Plain. Littoral zones of lakes, however, especially those dominated by both submerged and emergent plants, were confirmed to be ‘hotspots’ in relation to CH₄ emissions from lakes located within China (Duan et al., 2005; Wang et al., 2006; Chen et al., 2009b), similar with studies carried out in other countries (Juutinen et al., 2003; Bergstrom et al., 2007). Not only CH₄ emissions but also emission pathways differ between the pelagic and littoral zones of lakes. For example, in pelagic zones CH₄ ebullition and diffusion are dominant emission pathways (Keller & Stallard, 1994; Kankaala et al., 2004), whereas in vegetated littoral zones plant-meditated emissions are the primary pathway (Kankaala et al., 2004).

The spatiotemporal variation in CH₄ emissions is controlled by complex factors in lakes (Juutinen et al., 2001; Joyce & Jewell, 2003; Bastviken et al., 2004; Bergstrom et al., 2007; Zheng et al., 2011). Researchers observed in China typical diurnal and seasonal patterns in relation to CH₄ emissions from pelagic and littoral zones that were significantly influenced by water and sediment temperatures (Wang & Shangguan, 1996; Wang et al., 1998, 2006; Xing et al., 2004, 2005; Duan et al., 2005; Chen et al., 2009b). CH₄ emissions were also positively correlated with net primary production but not with dissolved organic carbon (DOC) in eutrophic lakes in East China (Xing et al., 2005; Wang et al., 2006), indicating that phytoplankton rather than allochthonous organic matter regulated CH₄ emission from the surface of shallow eutrophic lakes. However, in the littoral wetlands of the Three Gorges Reservoir Region, Chen et al. (2009b) observed a significant positive correlation between DOC and CH₄ emission rates, indicating that emergent plants provide the primary substrate for CH₄ production.

Significant spatial variations in CH₄ emission were also observed from lakes in China. Compared with pelagic zones, littoral zones of lakes are a higher CH₄ emitter (Duan et al., 2005; Wang et al., 2006; Chen et al., 2009b). This is partly due to a greater organic carbon substrate for methanogens and additional pathways for CH₄ emissions. Moreover, differences in plant cover resulted in differences in CH₄ emissions in the littoral zone of lakes (Duan et al., 2005; Wang et al., 2006; Chen et al., 2009b). In addition to variation in plant cover, water depth was also a primary influencing factor on spatial variation in CH₄ emissions in the littoral zones of lakes (Duan et al., 2005; Chen et al., 2009b).

CH₄ emission estimation

Estimated CH₄ emission from rice paddies in China

Winchester et al. (1988) expressed concern about CH₄ emissions from rice paddies in China due to the expansive area of cultivation in the country. An annual CH₄ estimate from Chinese rice paddies was 30 Tg (Khalil et al., 1991), making it a world concern. This led to attempts of estimating total CH₄ emissions from Chinese rice paddies by extrapolating spatial data and modeling (Table S3). In the early 1990s, limited in situ data acquired in southern and southwestern China prompted relatively high estimates greater than 12 Tg CH₄ yr⁻¹ in average (Khalil et al., 1991; Wang et al., 1993, 1996; Wassmann et al., 1993; Cao et al., 1995). Some researchers even predicted that this number would grow in the future with ever increasing rice demands of an expanding population in China (Wang et al., 1993). However, more studies in the decade thereafter showed decreased rather than increased total emissions, due to the availability of more representative data as well as to the decrease in rice cultivation (Appendix S1). Considering organic manure input and water management, the estimates ranged roughly from 8 to 10 Tg CH₄ yr⁻¹ (Cai, 1997; Kern et al., 1997; Huang et al., 1998; Li et al.,,b), except for one study by land-use change modeling estimating total emissions as high as 20.4 Tg CH₄ for 1991 and 18.4 Tg CH₄ for 2010 (Verburg & Gon, 2001). In recent years, estimates fell to approximately 8 Tg CH₄ yr⁻¹ or even lower with more details about water and fertility management (Yan et al., 2003; Kang et al., 2004; Jiang et al., 2006; Li et al., 2006; Wang et al., 2008). It can therefore be concluded that total CH₄ emissions from rice paddies in China has fallen considerably in the last 20 years (Khalil et al., 2008), and this trend may continue into the future (Verburg & Gon, 2001; Li et al., 2006).

Based on our specific calculations and estimates (Appendix S2), this study estimated total CH₄ emissions from rice paddies in China at 8.11 Tg CH₄ yr⁻¹ (ranging from 5.20 to 11.36 Tg CH₄ yr⁻¹), which falls in the range of previous estimates (Table S3). For lack of raw material about Taiwan, we adopted directly the estimate for Taiwanese rice paddies (0.034 Tg CH₄ yr⁻¹) by Taiwanese scientists (Yang & Chang, 2001). Due to plentiful water and warm conditions, rice paddies in AEZ 7 were the greatest source of CH₄ emissions, approximately 55% of the total emissions (Table 1). Rice paddies in AEZ 6B where the highest emission rates were measured emitted approximately 2.12 Tg CH₄ yr⁻¹. This measurement was larger than the combined emissions from the other three AEZ systems (AEZ 6A, AEZ 5, and AEZ 8A). Moreover, rice paddies in Hunan and Jiangxi (AEZ 7) are the two provinces that emitted the highest amounts of CH₄ in China, approximately 1.30 Tg CH₄ yr⁻¹ and 1.06 Tg CH₄ yr⁻¹, respectively (Fig. 4), greater than those of AEZ 6A, AEZ 5, and AEZ 8A (Table 1). Sichuan ranked the third highest provincial CH₄ emitter in China (approximately 0.92 Tg CH₄ yr⁻¹). For emissions during different rice seasons, the greatest source of CH₄ was the single rice paddy (4.46 Tg CH₄ yr⁻¹), the second one the late rice paddy (2.16 Tg CH₄ yr⁻¹), and the lowest source the early rice paddy (1.49 Tg CH₄ yr⁻¹) (Table 1).

Figure 4. Total CH₄ emissions from rice paddies located within different provinces of China in 2008.

Table 1. Total CH₄ emissions (Tg CH₄ yr⁻¹) during different rice seasons for different agriculture ecosystem zones (AEZ) of China (Appendix S1)

Region	Early rice	Late rice	Single rice	Total
AEZ 7	1.39	2.05	0.984	4.424
AEZ 6A	0.078	0.098	0.878	1.054
AEZ 6B	0.020	0.007	2.09	2.117
AEZ 8A	0	0	0.271	0.271
AEZ 5	0	0	0.239	0.239
Total	1.488	2.155	4.462	8.105

Estimated CH₄ emission from natural wetlands

Early estimation of CH₄ emission from natural wetlands in china was 1.7 Tg CH₄ yr⁻¹ (Khalil et al., 1993) and 2.2 Tg CH₄ yr⁻¹ (Wang et al., 1993). Due to a lack of data in terms of area as well as CH₄ flux from specific wetlands, estimates of CH₄ emissions from natural wetlands in China are still considered preliminary (Jin et al., 1999; Ding et al., 2004b). With the limited data acquired from the Qinghai-Tibetan Plateau, Jin et al. (1999) estimated CH₄ emissions from natural wetlands in China at 2.0 Tg CH₄ yr⁻¹. Ding et al. (2004b) arrived at a more reasonable estimate of 1.76 Tg CH₄ yr⁻¹ with more detailed information. Most recently, using a process model, some researchers reported that the annual CH₄ emission from wetlands substantially varied from 1.73 to 3.20 Tg CH₄ yr⁻¹ (Xu & Tian, 2012). However, these estimates did not include salt marshes (especially tidal marshes), which are in fact an important source of CH₄ (Lipschultz, 1981; Wang et al., 2009) and should be taken into consideration for a truer estimate of CH₄ emission from natural wetlands in China.

Wetland dynamics are an important factor controlling the overall CH₄ budget of natural wetlands in China. Wetland degradation, for example, can decrease the water table and make soil more aerobic, resulting in a reduction in CH₄ emissions. Moreover, CH₄ consumption was found to occur in dried peatlands during the growing season in northeastern China (Yu et al., 2009). Wetland reclamation for purposes of agriculture also resulted in an overall methane reduction (Jiang et al., 2009; Huang et al., 2010). For example, marshland conversion into cropland in northeastern China resulted in a cumulative reduction of 28 Tg over a 50-year period from 1950 to 2000 (Huang et al., 2010). On the other hand, wetland restoration and creation increased the water table and soil anaerobic conditions of wetlands, making degraded wetlands strong sources of CH₄ once again. Only few studies have noted an increase in CH₄ emissions due to wetland restoration and creation in China (Chen et al., 2009b). Thus, attention must be paid to wetland dynamics when discussing CH₄ emissions of natural wetlands in China.

Based on the detailed calculation (Appendix S2), this study estimated the total CH₄ emission from natural wetlands in mainland China at 2.35 Tg CH₄ yr⁻¹ (ranging from 2.12 to 2.86 Tg CH₄ yr⁻¹), with 2.16 Tg CH₄ emitted during the growing season and 0.19 Tg CH₄ during the nongrowing season (Table 2). For lack of raw material about Taiwan, we adopted directly the estimate for Taiwanese natural wetlands (0.34 Tg CH₄ yr⁻¹) by Taiwanese scientists (Chang & Yan, 2003). As a result, this study estimated the total CH₄ emission rate in China at approximately 2.69 Tg CH₄ yr⁻¹, ranging from 2.46 to 3.20 Tg CH₄ yr⁻¹. This estimate is slightly higher than other estimates (Jin et al., 1999; Ding et al., 2004b; Xu & Tian, 2012), probably for two reasons: (1) higher CH₄ emissions were recorded in peatlands on the Qinghai-Tibetan Plateau (Hirota et al., 2004; Chen et al., 2008), making peatlands the largest overall source of CH₄ in China (1.25 Tg CH₄ yr⁻¹, Table 2); and (2) salt marsh measurements became available and proved that salt marshes in themselves are important sources of CH₄ in China (0.21 Tg CH₄ yr⁻¹, Table 2). This study also noted that seasonally flooded wetlands were a major source of CH₄, amounting to 63.4% of the total CH₄ emissions in China (Fig. 5). The freshwater marsh estimate of this study was just 0.87 Tg CH₄ yr⁻¹, only 76.2% that of the estimate established by Ding et al. (2004b) that did not take into consideration the effects of changing water regimes due to wetland degradation. Combining new CH₄ emission data from different wetlands and taking into account changing water regimes due to wetland degradation, we realized that it was natural wetlands on the Qinghai-Tibetan Plateau and in western China, not wetlands in northeastern China (Jin et al., 1999; Ding et al., 2004b), emitted the most CH₄ in China, about 1.49 Tg CH₄ yr⁻¹ or approximately 63.5% of the total emissions from natural wetlands (Fig. 5).

Figure 5. CH₄ emission rates from natural wetlands under various water regimes located within different regions of China. QW, Qinghai-Tibetan Plateau and the western region; NE, northeastern China; OTHER, other regions of China.

Table 2. Estimates of CH₄ emissions (Gg CH₄ yr⁻¹) from wetlands for different regions in China

Wetland type	Region	Water regime	Growing season	Nongrowing season	Annual mean
PD, peatland; FM, freshwater marsh; SM, salt marsh; S, swamp; QW, Qinghai-Tibetan Plateau and western China; NC, North China; SC, South China; CF, continuous flooding; SF, seasonal flooding; TF, tidal flooding.
PD	QW	CF	318.0	26.0	344
		SF	700.0	60.7	760.7
	NC	CF	39.2	9.9	49.1
		SF	91.6	8.1	99.7
FM	QW	CF	76.6	6.3	82.9
		SF	168.6	14.6	183.2
	NC	CF	169.0	12.5	181.5
		SF	180.1	10.3	190.4
	SC	CF	104.1	1.2	105.3
		SF	125.4	2.8	128.2
SM	QH	CF	47.0	5.5	52.5
		SF	55.0	12.9	67.9
	NC	CF	26.4	3.1	29.5
		SF	30.9	7.2	38.1
	SC	TF	23.2	2.1	25.3
S	NC	SF	4.1	0.5	4.6
	SC	TF	0.4	0.1	0.5
Total			2158.7	188.9	2347.6

CH₄ emission estimates from lakes (including ponds and reservoirs) in China

Although inland water systems are regarded as a source of CH₄ (Bastviken et al., 2004, 2011), only limited CH₄ emission data from lakes and reservoirs exist, and most of the existing data relate to boreal lakes (Juutinen et al., 2003; Kankaala et al., 2004; Walter et al., 2007b) and tropical reservoirs (Galy-Lacaux et al., 1997; Fearnside, 2002). In China, related studies are sparse at best (Table S4), and no reasonable CH₄ emission estimate has been proposed to date. Chen et al. (2007) have, however, estimated that all lakes in China combined emitted (3.22 ± 2.75) × 10⁻² Tg CH₄ yr⁻¹ in the winter based on limited measurements available. However, the other preliminary estimate indicated that Chinese lakes emitted CH₄ about 3.0 Tg CH₄ yr⁻¹ (Li et al., 2011).

Based on the calculations in Appendix S2, our study estimated the total CH₄ emission from lakes of mainland China at 0.46 Tg CH₄ yr⁻¹ (ranging from 0.32 to 0.59 Tg CH₄ yr⁻¹) including 0.33 Tg CH₄ yr⁻¹ (ranging from 0.19 to 0.46 Tg CH₄ yr⁻¹) from littoral zones and only 0.13 Tg CH₄ yr⁻¹ from the lake surface (Table 3). The preliminary estimate of the total CH₄ emission rate in China (0.46 Tg CH₄ yr⁻¹, ranging from 0.33 to 0.59 Tg CH₄ yr⁻¹) by this study was established after adding CH₄ emissions from lakes located within Taiwan (0.001 Tg CH₄ yr⁻¹) (Wang et al., 1998). The results also confirmed that the littoral zone is a ‘hotspot’ for CH₄ emissions on both regional and national scales (Table S4) compared with the lake surface (Juutinen et al., 2003; Chen et al., 2009b). Natural and artificial lakes in the eastern plains of China were the largest source of CH₄ (Table S4), contributing approximately 49.4% of national CH₄ from lakes in China. Due to the large coverage of lakes on the Qinghai-Tibetan Plateau, CH₄ emissions from lakes were also high (Table S4), approximately 30.5% of all CH₄ from lakes in China. Moreover, Tibet and Qinghai situated within the Qinghai-Tibetan Plateau are the two largest CH₄-emitting provinces in China (Fig. 6).

Figure 6. Total CH₄ emissions from natural and artificial lakes located within different provinces of China in 2008 (Taiwan is not represented).

Table 3. Estimates of CH₄ emissions (Gg CH₄ yr⁻¹) from lakes, ponds, and reservoirs located within different regions of China

Region	Lake		Pondand reservoir		Total
	Surface	Littoral	Surface	Littoral
QTP, Qinghai-Tibetan Plateau; NE, northeastern China; MX, Mongolia-Xinjiang Plateau; EP, plains of eastern China; YG, Yunan-Guizhou Plateau.
QTP	17.1	114.3	0.2	1.1	132.7
NE	2.2	15.5	2.0	13.7	33.4
MX	5.6	27.1	1.4	9.9	44
EP	66.4	89.8	32.3	47.3	235.8
YG	2.6	2.6	3.1	3.3	11.6
Total	93.9	249.3	39	75.3	457.5

Total CH₄ emissions from rice paddies, natural wetlands, and lakes in China

Through abovementioned review and estimates, we preliminarily estimated the total methane emission from rice paddies, natural wetlands, and lakes (natural and artificial) as 11.25 Tg CH₄ yr⁻¹ (ranging from 7.98 to 15.16 Tg CH₄ yr⁻¹), with a certain level of uncertainty in water regime and spatial variation. This is approximately only 2.0% of global sources of CH₄ (Denman et al., 2007) and 32.8% of the national total estimate of China (anthropogenic emission excluding natural wetland, lake, reservoirs, and ponds: 34.29 Tg CH₄ yr⁻¹) presented by the People's Republic of China Initial National Communication on Climate Change (2004), which was submitted to the UNFCCC (http://unfccc.int/resource/docs/natc/chnnc1exsum.pdf). Rice paddies are the largest source of CH₄ among the three sources investigated in this study on a national scale and in provinces in AEZ 5 to 7 (Appendix S1 and Fig. 7). Only in provinces situated within AEZ 8 did CH₄ emissions from wetlands or lakes measure higher than those from rice paddies. This is probably due to the scarcity of rice paddies in the region if any. Moreover, CH₄ emissions from rice paddies in China contributed a great deal to the global rice paddy emission budget when recent estimates were tallied up (Table S5) (Yan et al., 2009). Total CH₄ emission from natural wetlands in China is lower than that in the United States of America, Europe, and other regions around the world (Cao et al., 1998; Juutinen et al., 2003; Potter et al., 2006; Saarnio et al., 2009; Nahkil & Mitch, 2011). Moreover, CH₄ emissions from lakes and reservoirs in China are just a small proportion of the global budget (Saint Louis et al., 2000; Walter et al., 2007b; Nevison et al., 2008). In China, rice paddies, natural wetlands, and lakes are not stable sources of CH₄, but may even decrease owing to anthropogenic activity and climate change. For rice paddies, CH₄ emissions are sensitive to farming practices (irrigation and fertilization) that can change in response to economic and political pressures (Khalil & Rasumussen, 1993). Drainage methods used during the growing season, less organic fertilizer input, and rotational pattern shifts, for example, have all led to a considerable decrease in CH₄ emissions from rice paddies in the last 20 years (Li et al., 2005; Khalil et al., 2008). In addition, reducing rice cultivation area within southern China would lead to decreased CH₄ emissions (Wang et al., 2008). Lake and wetland degradation or reclamation for agriculture purposes can also decrease the overall CH₄ emissions (Huang et al., 2010; Xu & Tian, 2012). Climate change, especially the rising temperature, may greatly enhance CH₄ emissions given unchanged water status of wetlands (Aselmann & Crutzen, 1989). However, increased temperatures may also reduce CH₄ emissions through reduction in soil moisture content (Cao et al., 1998). For example, seasonally flooded wetlands are the primary source of CH₄, contributing to 63.4% of all emissions from natural wetlands in China (Fig. 5). This source is highly variable because the flooding area varies greatly in response to climate change.

Figure 7. Annual CH₄ emissions from rice paddies, wetlands, and lakes located within the different provinces of China.

Conclusions

Similar with other estimation methods, the bottom-up approach left the estimates in this study with the following inevitable limitations and uncertainties. First, limited point measurements were assumed to represent national conditions, which caused overestimation or underestimation of emissions from the three sources of this study. Second, understanding the extent of temporal and spatial variations in CH₄ emissions is important in reducing estimate uncertainty, especially for wetlands and rice paddies. Third, another great cause of uncertainty is wetland area or extent dynamics, which was recently proven to be closely related to interannual variations in CH₄ emissions from natural wetlands (Ringeval et al., 2010). Fourth, some uncertainty also exists between the different methods or systems used to carry out measurements, and even in the mismatch between intensity of field observation and area in different regions. At last, reasonable estimates should therefore include more new results widely accepted by the scientific community.

New research directions may need to be proposed in the future upon discussion of these key issues in relation to the (1) collection of a greater amount of observational field data at various temporal and spatial scales; (2) development of improved process-based CH₄ models in consideration of the three primary sources of CH₄ emissions discussed earlier on the three primary sources of CH₄ emissions; (3) development of a new model-data fusion framework (Peng et al., 2011) to calibrate and validate CH₄ models to an updated CH₄ observational database and to forecast the national CH₄ budget under changing environmental conditions; (4) introduction of inverse modeling to integrate satellite measurements of column CH₄ and isotopes of carbon and hydrogen to scrutinize CH₄ sources in China; (5) development of a decision support system to manage CH₄ emissions and to provide scientifically sound information for policy makers in China as well to contribute to China's national goal of a 40–45% carbon intensity reduction by 2020.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 31100348), Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant, and the China QianRen program. The authors give special thanks to Ms. Wan Xiong for her editing and valuable comments on the manuscript. We also thank the subject editor and anonymous reviewers for their detailed evaluation and constructive suggestions on our manuscript.