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methane emission from rice paddies natural wetlands,lakes in China synthesis new estimate


Global Change Biology (2013) 19, 19–32, doi: 10.1111/gcb.12034

REVIEW

Methane emissions from rice paddies natural wetlands, lakes in China: synthesis new estimate
HUAI CHEN*?, QIU’AN ZHU*, CHANGHUI PENG?*, NING WU?§, YANFEN WANG?, X I U Q I N F A N G ? , H O N G J I A N G k, W E N H U A X I A N G * * , J I E C H A N G ? ? , X I A N G W E N D E N G * * and G U I R U I Y U ? ? *Laboratory for Ecological Forecasting and Global Change, Northwest A&F University, Yangling 712100, China, ?Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China, ?Department of Biology Sciences, Institute of Environment Sciences, University of Quebec, Montreal, QC, Canada, H3C 3P8, §International Centre for Integrated Mountain Development, GPO Box 3226, Khumaltar, Kathmandu, Nepal, ?College of Life Sciences, Graduate University of Chinese Academy of Sciences, Beijing 100049, China, kDepartment of Geographical Science, Nanjing University, Nanjing 210093, China, **Ecology Research Section, Central-South University of Forestry and Technology, Changsha, Hunan 410004, China, ??College of Life Sciences, Zhejiang University, Hangzhou 310058, China, ??Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

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 ?rst 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 ?rst 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 in?uenced 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.
Keywords: agriculture, anthropogenic activity, CH4 budget, climate change, Qinghai-Tibetan Plateau Received 2 May 2012; revised version received 13 July 2012 and accepted 11 August 2012

Introduction
Methane (CH4) is an important greenhouse gas (GHG) that possesses power beyond carbon dioxide (CO2) to in?uence warming within the atmosphere by an approximate magnitude of 21 on a per mole basis (Van Ham et al., 2000). Moreover, CH4 exerts strong in?uence over the chemistry of the troposphere and the stratosphere (Cicerone & Oremland, 1988). A study has
Correspondence: Qiu’an Zhu, tel. + 86 29 870 80609, fax + 86 29 870 81044, e-mail: qiuan.zhu@gmail.com; Changhui Peng, e-mail: peng.changhui@uqam.ca; Huai Chen, e-mail: chenhuai81@gmail.com

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 modi?ed 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
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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 in?uence 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 scienti?c 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 ?ve 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
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Fig. 1 Seasonal mean CH4 emissions from ?ve different rice cultivation regions of China from 1987 to 2007.

?ve 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). 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 ?ooding and the large input of organic fertilizer in many rice paddies (Wei et al., 2000; Jiang et al., 2006).
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Fig. 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 signi?cant 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 in?uenced 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 in?uencing 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 suf?ciently 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 ?ooding 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 ?ve 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.,

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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 signi?cant factor that in?uences 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 ?ooded 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 signi?cantly (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 ?eld 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 in?uence 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 ?eld 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 ?eld 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 in?uence 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). Speci?c 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 ?ux 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 ?ooding (Bartlett et al., 1987) (Van der Nat & Middelburg, 2000; Chang & Yan, 2003), coastal
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in CH4 emissions in an open fen (Chen et al., 2009a). However, a strong negative partial correlation between CH4 emission ?uxes 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 ?ooding shifts to seasonal ?ooding 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 in?uencing 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 signi?cant 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 in?uence diurnal emission variation in CH4 emissions (Ding et al., 2004a; Chen et al., 2010a,b).

Fig. 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.

salt marshes showed small CH4 emission ?uxes 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). For the same lack of signi?cant 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 in?uencing factors (Van der Nat & Middelburg, 2000). Water regime itself is a dominant in?uential 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 ?ooding is always higher than those of seasonal ?ooding 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 signi?cant 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). Standingwater depth on the Qinghai-Tibetan Plateau was regarded as the key factor in?uencing spatial variations
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CH4 emission from lakes (including ponds and reservoirs) in China
The ?rst 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

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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 signi?cant 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 QinghaiTibetan 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 con?rmed 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 signi?cantly in?uenced 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 signi?cant positive correlation between DOC and CH4 emission rates, indicating that emergent plants provide the primary substrate for CH4 production. Signi?cant 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 in?uencing 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., 2002a,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 speci?c calculations and estimates (Appendix S2), this study estimated total CH4 emissions
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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
Table 1 Total CH4 emissions (Tg CH4 yr?1) during different rice seasons for different agriculture ecosystem zones (AEZ) of China (Appendix S1) Region AEZ 7 AEZ 6A AEZ 6B AEZ 8A AEZ 5 Total Early rice 1.39 0.078 0.020 0 0 1.488 Late rice 2.05 0.098 0.007 0 0 2.155 Single rice 0.984 0.878 2.09 0.271 0.239 4.462 Total 4.424 1.054 2.117 0.271 0.239 8.105

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).

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 ?ux from speci?c 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

Fig. 4 Total CH4 emissions from rice paddies located within different provinces of China in 2008. ? 2012 Blackwell Publishing Ltd, Global Change Biology, 19, 19–32

26 H . C H E N et al.
Table 2 Estimates of CH4 emissions (Gg CH4 yr?1) from wetlands for different regions in China Wetland type PD Region QW NC FM QW NC SC SM QH NC SC NC SC Water regime CF SF CF SF CF SF CF SF CF SF CF SF CF SF TF SF TF Growing season 318.0 700.0 39.2 91.6 76.6 168.6 169.0 180.1 104.1 125.4 47.0 55.0 26.4 30.9 23.2 4.1 0.4 2158.7 Nongrowing season 26.0 60.7 9.9 8.1 6.3 14.6 12.5 10.3 1.2 2.8 5.5 12.9 3.1 7.2 2.1 0.5 0.1 188.9 Annual mean 344 760.7 49.1 99.7 82.9 183.2 181.5 190.4 105.3 128.2 52.5 67.9 29.5 38.1 25.3 4.6 0.5 2347.6

S Total

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 ?ooding; SF, seasonal ?ooding; TF, tidal ?ooding.

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 ?ooded 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).

Fig. 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.

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
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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) 9 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 con?rmed 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 arti?cial 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).

Fig. 6 Total CH4 emissions from natural and arti?cial lakes located within different provinces of China in 2008 (Taiwan is not represented).

Total CH4 emissions from rice paddies, natural wetlands, and lakes in China
Through abovementioned review and estimates, we preliminarily estimated the total methane emission
Table 3 Estimates of CH4 emissions (Gg CH4 yr?1) from lakes, ponds, and reservoirs located within different regions of China Pond and reservoir Littoral 114.3 15.5 27.1 89.8 2.6 249.3 Surface 0.2 2.0 1.4 32.3 3.1 39 Littoral 1.1 13.7 9.9 47.3 3.3 75.3 Total 132.7 33.4 44 235.8 11.6 457.5

Lake Region QTP NE MX EP YG Total Surface 17.1 2.2 5.6 66.4 2.6 93.9

QTP, Qinghai-Tibetan Plateau; NE, northeastern China; MX, Mongolia-Xinjiang Plateau; EP, plains of eastern China; YG, Yunan-Guizhou Plateau.
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from rice paddies, natural wetlands, and lakes (natural and arti?cial) 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 fertil-

28 H . C H E N et al.

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

ization) 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 ?ooded 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 ?ooding area varies greatly in response to climate change.

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 ?eld observation and area in different regions. At last, reasonable estimates should therefore include more new results widely accepted by the scienti?c 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 ?eld 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)
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development of a decision support system to manage CH4 emissions and to provide scienti?cally 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.
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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.

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Supporting Information
Additional Supporting Information may be found in the online version of this article:
Figure S1. Main CH4 emission study sites from rice paddies, wetlands, and lakes in China. Some of these sites are very close to each other, especially for the rice paddy sites, thus not easily differentiable in this ?gure. Appendix S1. Rice cultivation, wetlands, and lakes in China. Appendix S2. Methods for CH4 emission estimation from rice paddies, wetlands and lakes. Table S1. Average CH4 emission rates (mg CH4 m?2 h?1 ± SD) from rice paddies under different periods of development. Table S2. CH4 emission rates (Tg CH4 yr?1) from different wetlands located within different regions of China. Table S3. Estimates of total CH4 emission rates from rice paddies in China. Table S4. CH4 emission rates from lakes and reservoirs in China. Table S5. Estimates in relation to CH4 emissions from rice paddies, wetlands, and lakes from other nations or regions around the world.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

? 2012 Blackwell Publishing Ltd, Global Change Biology, 19, 19–32


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