Methane Emissions from Unique Wetlands in China: Case Studies, Meta Analyses and Modelling 9783110341041, 9783110300215

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Huai Chen, Ning Wu, Changhui Peng, Yanfen Wang Methane Emissions from Unique Wetlands in China

Huai Chen, Ning Wu, Changhui Peng, Yanfen Wang

Methane Emissions from Unique Wetlands in China Case Studies, Meta Analyses and Modelling

This work is co-published by Higher Education Press and Walter de Gruyter GmbH.

ISBN 978-3-11-030021-5 e-ISBN (PDF) 978-3-11-034104-1 e-ISBN (EPUB) 978-3-11-038561-8 Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2015 Higher Education Press and Walter de Gruyter GmbH, Berlin/Boston Cover image: OldCatPhoto/iStock/Thinkstock Printing and binding: CPI books GmbH, Leck ♾Printed on acid-free paper Printed in Germany www.degruyter.com

List of Contributors Chen, Huai Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China [email protected] Wu, Ning Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China [email protected] Peng, Changhui Northwest Agriculture and Forest University, Yangling, China Institut des Sciences de l’Environnement, Universit´e du Qu´ebec `a Montr´eal (UQAM), Montr´eal, Canada [email protected] Wang, Yanfen University of Chinese Academy of Sciences, Beijing, China [email protected] Tian, Jianqing Insititute of Microbiology, Chinese Academy of Sciences, Beijing, China [email protected] Zhu, Qiu’an Northwest Agriculture and Forest University, Yangling, China [email protected] Yuan, Xingzhong Chongqing University, Chongqing, China [email protected] Zhu, Dan Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China zhudan [email protected]

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List of Contributors

Gao, Yongheng Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China [email protected] He, Yixin Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China [email protected] Yang, Gang Southwest University of Science and Technology, Mianyang, China [email protected]

Preface The world is undergoing rapid change in many factors, especially climate, especially global warming, that control the structure, function and services of ecosystems. Increasing atmospheric concentration of greenhouse gases is proven to be responsible for global warming. Due to its powerful warming potential, methane (CH4 ), has a considerable impact on the earth s climate system second anthropogenic greenhouse gas only to CO2 . Sources of CH4 become highly variable for countries undergoing a heightened period of development (e.g. China) due to both human activity and climate change. An urgent need therefore exists to understand key sources of CH4 , such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), especially those unique ones in specific countries, which are sensitive to these changes. This book was written to provide a systematic basis for understanding CH4 fluxes from unique wetlands of China and their sensitivity to environmental and biotic factors. This book is intended to introduce CH4 fluxes from wetlands to climate managers, policy makers, practicing scientists, modellists, advanced undergraduate students, beginning graduate students from a wide array of disciplines, such as ecology, climatology, geography, forestry, microbiology, etc. We also provide access to the rapidly expanding literature in CH4 fluxes of wetlands in China that contribute to fully understanding of the budget of CH4 fluxes of wetlands in China and their trends. The first chapter of the book (by Huai Chen, Ning Wu, Yanfen Wang, Changhui Peng) provides the context for understanding CH4 fluxes from wetlands. We introduce the importance of CH4 as a greenhouse gas and wetlands as the important source of CH4 , then briefly review the studies about CH4 fluxes from wetlands in China. We show why we chose Zoige alpine wetlands and Three Gorges Reservoir as the case studies in the book and list our objectives. The second chapter of the book (by Huai Chen, Ning Wu, Yanfen Wang, Dan Zhu, Yongheng Gao), we fully describe spatial (from habitats, ecosystem to landscape) and temporal variations (from diurnal, seasonal to inter-annual) of CH4 emissions from Zoige wetlands at different scales. The third chapter of the book (by Huai Chen, Xingzhong Yuan, Yixin He), we put our pioneer results about CH4 emissions from littoral wetlands and the surface of the Three Gorges Reservoir. The fourth chapter of the book (by Jianqing Tian, Huai Chen, Yanfen Wang),

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we discuss about methanogens and methanogensis in Zoige wetlands, as well as their changes responsive to vegetation types and climate change. The fifth chapter of the book (by Huai Chen, Changhui Peng, Qiu’an Zhu, Ning Wu, Yanfen Wang, Gang Yang), we review references in relation to CH4 emissions from rice paddies, natural wetlands, and lakes in China and then re-estimate the total amount based upon the review itself. In the last chapter of the book (by Qiu’an Zhu, Changhui Peng, Huai Chen), we try to model methane emissions from wetlands and a case study in China. Primary funding for this book was came from the National Basic Research Program of China (2013CB956602), the International S&T Cooperation Program of China (S2013GI0408), 100 Talents Program of the Chinese Academy of Sciences, Program for New Century Excellent Talents in University (NCET-120477), and the National Natural Science Foundation of China (No. 31100348). Many individuals have contributed to the development of this book. We would like to show our gratitude to Ms. Wan Xiong, Dr. Chuan Zhao, Ms. Wei Li, Mr. Lile Zeng, Mr. Wei Zhan, Mr. Erxiong Zhu, Mr. Xinwei Liu, Ms. Dan Xue, Ms. Lifan Xiao, Mr. Xinhua Jiang, Mr. Ben Miller and Ms. Xinya Huang for their great help in editing. We particularly thank our family and their patience makes this book possible. We also thank the great many friends and colleagues who helped us by commenting on our book draft. Comments and suggestions for improvement are welcome and will be gratefully appreciated. Huai Chen Chengdu, China

Contents

Chapter 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7

Chapter 2 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5

Methane is an Important Greenhouse Gas

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Methane as an Important Greenhouse Gas 1 Methane Emission Processes in Wetlands 2 3 Wetlands as an Important Source of Methane Briefly Advances in Studies about Methane Emissions from Wetlands in China 3 5 Zoige Alpine Wetlands and Methane Emissions Three Gorges Reservoir and Methane Emissions 5 6 Objectives References 7

Methane Emissions from Zoige Alpine Wetlands

Diurnal Variation of Methane Emissions from an Alpine Wetland Introduction 13 14 Materials and Methods Results 16 18 Discussion Conclusions 21 Determinants Influencing Seasonal Variations of Methane Emissions from Alpine Wetlands in Zoige Plateau and Their Implications 22 Introduction Materials and Methods 23 24 Results Discussion 31 Conclusions 34 Spatial Variations on Methane Emissions from Zoige Alpine Wetlands 35 35 Introduction Materials and Methods 36 37 Results Discussion 42 44 Conclusions

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x 2.4 2.4.1 2.4.2 2.4.3 2.4.4 2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.5.5 2.6 2.6.1 2.6.2 2.6.3 2.6.4

Contents

Methane Effluxes from Littoral Zone of a Lake on Qinghai–Tibetan 44 Plateau Introduction 45 46 Materials and Methods Results 48 53 Discussion Methane Fluxes from Alpine Wetlands of Zoige Plateau in Relation to Water Regime and Vegetation under Two Scales 55 Introduction 57 58 Materials and Methods Results 61 68 Discussion Conclusions 71 Inter-annual Variations of Methane Emission from an Open Fen on Qinghai–Tibetan Plateau: a Three-year Study 71 Introduction 72 73 Materials and Methods Results 76 80 Discussion References 83

Chapter 3 3.1 3.1.1 3.1.2 3.1.3 3.1.4 3.2 3.2.1 3.2.2 3.2.3

Methane Emissions from Newly Created Marshes in Drawdown Area 93 of Three Gorges Reservoir Introduction 94 95 Materials and Methods Results 98 101 Discussion Methane Emissions from Surface of Three Gorge Dam Reservoir 105 Introduction Materials and Methods 105 106 Results and Discussions References 109

Chapter 4 4.1 4.1.1 4.1.2 4.2 4.2.1 4.2.2

Methane Emissions from Three Gorges 93 Reservoir

Methanogens and Methanogensis in Zoige 115 Wetlands

Methanogenic Communities in Zoige Wetlands 115 115 Methanogenic Communities Composition in Zoige Wetlands New Methanogenic Species in Zoige Wetlands 116 Influencing Factors of Methanogenic Community Structure in Zoige Wetlands 120 120 Vegetation Type Temperature 130

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4.2.3

Declining Precipitation 146 References

Chapter 5 5.1 5.1.1 5.1.2 5.2 5.2.1 5.2.2 5.3 5.3.1 5.3.2 5.4 5.4.1 5.4.2 5.4.3 5.4.4 5.5

Methane Emissions from Rice Paddies, Natural 155 Wetlands and Lakes in China

CH4 Emission Rates from Rice Paddies in China 156 Rice Cultivation in China and Overview of Its CH4 Emission Estimates 156 159 CH4 Emissions from Rice Paddies in China CH4 Emission Rates from Natural Wetlands in China 164 Natural Wetlands in China and Overview of Their CH4 Emission Estimates 164 CH4 Emissions from Natural Wetlands in China 166 169 CH4 Emission Rates from Lakes and Reservoirs in China Lakes and Reservoirs in China and Overview of Their CH4 Emission 169 Estimates CH4 Emission Rates from Lakes and Reservoirs in China 170 172 CH4 Emission Rate Estimation CH4 Emission Rate Estimation from Rice Paddies in China 172 175 CH4 Emission Estimation from Natural Wetlands in China CH4 Emission Estimates from Lakes and Reservoirs in China 178 Total CH4 Emissions from Rice Paddies, Wetlands and Lakes in China 181 Limitations, Uncertainties and Future Directions 183 184 References

Chapter 6 6.1 6.2 6.2.1 6.2.2 6.3 6.3.1 6.3.2 6.3.3

Index

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Modelling Methane Emissions of Wetlands in 195 China

Overview of Methane Emission Modelling 195 197 Wetland Methane Emission Model Construction Integrated Biosphere Simulator and Water Table Modelling 199 Methane Module Wetland Methane Emission Model Validation and Sensitivity Analysis 203 Sensitivity Index for Initial Sensitivity Analysis 203 Initial Sensitivity Analysis 204 205 Model Performance in China References 208

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Chapter 1

Methane is an Important Greenhouse Gas Huai Chen, Ning Wu, Yanfen Wang, Changhui Peng

1.1

Methane as an Important Greenhouse Gas

A distinguished Victorian scientist, John Tyndall FRS (1820–1893), was one of the first to appreciate that trace gas constituents within the atmosphere act as so-called “greenhouse gases” (GHG). Methane (CH4 ) is an important 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 [1]. Moreover, CH4 exerts a strong influence on chemistry of the troposphere, stratosphere and many other greenhouse gases including ozone (O3 ), hydroxyl radicals (—OH), and carbon monoxide (CO) by the way of photochemical reactions [2]. A study has recently reported that gas-aerosol interactions substantially alter the relative importance of various GHG emissions. This is especially true for CH4 emissions that have larger overall impacts than current carbon-trading schemes or those found within the Kyoto Protocol, which modified its radiative forcing from +0.48 W m−2 to +0.90 W m−2 [3,4]. CH4 , therefore, has a considerable impact on the earth’s climate system second only to CO2 . Atmospheric CH4 is primarily emitted from biological sources and, this accounts for more than 70% of the global total [5]. CH4 is consumed primarily through oxidation by way of ·OH within the troposphere [5,6]. Since the preindustrial era (1750), its atmospheric concentration has increased from 700 ppb to almost 1,800 ppb [7]. Moreover, a renewed growth in CH4 atmospheric concentration occurred around the beginning of 2007 [7,8] following a near zero-growth decade. The existing state of the global CH4 budget must therefore be addressed without delay.

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Methane Emission Processes in Wetlands

CH4 emissions that occur in wetlands (natural or constructed) and aquatic ecosystems are the result of interactions between several biological, chemical and physical processes that primarily include CH4 production, oxidation and transportation (see Fig.1.1 for a summary of these processes). Certain studies have indicated that CH4 is also aerobically produced and emitted by plants [9-11]. Anaerobic conditions can also produce CH4 as an end product of organic matter degradation by the way of acetoclastic and hydrogenotrophic methanogenic archaea [12]. CH4 produced under these conditions is then partly oxidized by methanotrophic bacteria within oxidized zones (the rhizosphere, the lower part of culms, the soil-water interface, and submersion water) [13-15]. For understanding CH4 emissions from wetlands or lakes, it is essential to understand CH4 transportation. There are three major mechanisms existed that drive CH4 transportation: molecular diffusion, bubble ebullition [16,17], and plantmediated transportation [18,19]. The CH4 transportation mechanism is quite variable within various wetlands and lake zones. Due to this, CH4 emissions from wetlands and lakes are an inherently complex biogeochemical process in which the physical factors involved in the three processes mentioned above can influence emissions. The primary factors that influence CH4 emissions include temperature, the quantity and quality of the methanogen substrate, the water regime, the soil redox potential, pH, salinity, sulfate concentration, etc.[6,15,20]. Although the knowledge concerning CH4 emissions and their regulations is un-

Fig. 1.1 A simplified illustration of CH4 emissions from rice paddies, natural wetlands and lakes.

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derstood, expansive studies in remote regions and more details concerning its processes (especially microbial processes) are needed to upscale and increase the overall knowledge base.

1.3 Wetlands as an Important Source of Methane Wetlands are the single largest source of atmospheric CH4 emissions due to the prevalence of waterlogged and anoxic conditions, accounting for approximately 148 Tg CH4 yr−1 (1 Tg = 1012 g) compared to natural wetlands and 112 Tg CH4 yr−1 compared to rice paddies [5,21]. These ecosystems contribute more than 40% of the total global CH4 emissions to the atmosphere [22]. Large CH4 emissions coming from lakes have also caused the increasing interest of lates in the scientific community. This source has recently been estimated to contribute from 8 to 48 Tg CH4 yr−1 [23]. Moreover, several studies have designated northern thaw lakes as recognized CH4 emission “hotspots” with an estimated source strength of approximately 24.2 ± 10.5 Tg CH4 yr−1 [24-28]. Wetlands and lakes remain important CH4 sources within the global CH4 budget, but considerable uncertainties in relation to their emission output still exist. Such uncertainty primarily arises from the large spatiotemporal variation that occurs for different scales and the limited range of observational conditions [5,29]. It, therefore, would be highly desirable to procure estimates of CH4 emissions from wetlands and lakes on national, regional as well as global scales — a topic that has been highlighted in many other studies [23,27,30]. The 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. Estimating CH4 emissions from Chinese wetlands and lakes, however, is extremely difficult to accomplish owing to the great expansive area of wetlands and lakes, its complex distributional patterns, the many different types of wetlands and lakes that exist in the country, and the complex dynamics that takes place in wetlands and lakes as a result of land use changes that occur in countries undergoing heightened periods of development.

1.4

Briefly Advances in Studies about Methane Emissions from Wetlands in China

Multiple studies on rice paddies CH4 emissions in China have already been carried out [31-39], and some have even made efforts to estimate the total emission

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rate for the country [34,40-43]. Recent studies on natural wetland CH4 emissions in China have been published [44-50] that offer preliminary national estimates [51]. Although CH4 emission data from lakes and reservoirs are important to the national CH4 budget [23], especially with regard to lakes undergoing thawing or experiencing eutrophication [27,52], only a few studies have related directly to China [53-55]. The above-mentioned studies were primarily carried out in northeastern, southeastern and southwestern China (Fig.1.2).

Fig.1.2 Main CH4 emission study sites from rice paddies, wetlands and lakes in China. Outline download website: https://219.238.166.215/mcp/index.asp (GS (2008) 1464). (see also colour figure)

No synthesis study investigating CH4 emissions that have commented on a comprehensive CH4 budget for both cultivated wetland areas (rice paddies) and non-cultivated wetlands and lakes in China exists thus far to the best knowledge of the authors of this study. Therefore, (i) studying methane emissions from unique wetlands, furthermore, and (ii) carrying out 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 from sources such as these. It is of great importance to the international scientific community to obtain a reasonable and comprehensive picture of CH4 emissions from unique wetlands in China and how this contributes to the global CH4 budget.

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Methane is an Important Greenhouse Gas

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Zoige Alpine Wetlands and Methane Emissions

Recently, there are increasing number of studies measuring the methane emission rates from various wetlands all over the world [56-60]. But our knowledge of methane emissions from alpine or sub-alpine wetlands is far from sufficient to scrutinize the source strength of alpine wetlands, despite sporadic studies reported which were mainly located in the Rocky Mountains in American continent [46,61-64]. Hereupon detailed and informative data from other alpine regions all over the world, e.g. Zoige Plateau, will lead to a precise understanding of the global alpine budget of methane. Zoige Plateau (av. 3,400 m a.s.l.), the major methane emission hotspot in the eastern edge of Qinghai–Tibetan Plateau (av. 4,000 m a.s.l.) [47], is a complete and orbicular plateau surrounded by a series of alpine mountains (av. 4,000 m a.s.l.). The landscape of Zoige Plateau is special and dominated by numerous hills (the average relative height is 70–150 m) and the alluvial plateau flat, on which flow two branches of the Yellow River, named the Black River and the White River [65]. The plateau covers an area of 2.8 × 104 km2 . Numerous alpine wetlands and lakes have developed on the plateau, accounting for 17.8% of the plateau coverage [66]. And these ubiquitous alpine wetlands on the plateau were formed during the Early Holocene (9355±115BP) [67]. However, the research on the CH4 emission in Zoige alpine wetlands has lagged behind, so has systematic investigation on them. To scrutinize the CH4 efflux in Zoige wetlands, more research is urgently needed.

1.6

Three Gorges Reservoir and Methane Emissions

Large dams have always played an indispensible role in human development all over the world. They are usually used to provide drinking water, control floods, irrigate crops, facilitate navigation, and generate electricity. People used to think large dams represented progress in hydraulic engineering. But they gradually recognized the harm of such dams to environment in the past several decades [68,69]. Dams have resulted in not only large-scale habitat fragmentation [70], but also emission of greenhouse gases to the atmosphere [71,72], especially methane (CH4 ). This is because anoxic conditions prevailing at the bottom of the reservoir favor production of CH4 and its possible emission into the atmosphere [73]. Moreover, the seasonally exposed bottom of the reservoir may play a more

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important role in CH4 emission [74]. In fact, during the last 2,000 years, ancient large-scale water-management projects might have altered atmospheric CH4 in China and India [75]. Therefore, the clean and green image of dams may have been overstated [76]. Among the large dams in the world, the Three Gorges Dam (TGD) with 2,335 m long and 185 m high on the Yangtze River of China is the biggest and thus a good example. It has a great drawdown area of about 350 km2 , approximately one third of the dam lake when fully operating. The Three Gorges Reservoir Region (TGRR) is about 660 km long and 58,000 km2 in watershed area, greater than Switzerland [70,77]. When operating at full capacity, the total inundated area in the TGRR is estimated to be about 1,080 km2 [69].

1.7

Objectives

In light of the rationale explained above, the overall goal of this research is to advance the knowledge in methane emissions from unique wetlands (Three Gorges Reservoir and Zoige alpine wetlands) in China and preliminarily estimate total methane emissions from wetlands in China. The spatiotemporal variation of methane emissions from Zoige alpine wetlands is based on measurements from 2004 to 2009. The methane emission from the Three Gorges Reservoir was firstly measured since 2008. Finally, a meta-analysis is introduced to review and analyse the methane emission from wetlands and its budget in China. The objectives in detail are to: i. Understand the temporal variation of methane emissions in Zoige alpine wetlands and find out the key factors influencing the temporal variations of methane emissions. ii. Understand the spatial variation of methane emissions in Zoige alpine wetlands and find out the key factors influencing the spatial variations of methane emissions. iii. Understand the CH4 emission and its controlling factors in the drawdown area of the Three Gorges Reservoir(TGR). iv. Probe the CH4 emission and its controlling factors from the surface of the TGR. v. Explore implications of methane emissions for the TGR and other large dam reservoirs. vi. Review and analyze existing studies on CH4 emissions from rice paddies, natural wetlands, and lakes in China. vii. Provide new estimates and maps of the total CH4 emissions from wetlands and lakes in China based upon above-mentioned review and analyses.

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[33] Wang MX, Shangguan XJ. CH4 emission from various rice fields in P.R. China. ThApC 1996; 55:129–138. [34] Wang M, Li J. CH4 emission and oxidation in Chinese rice paddies. Nutr. Cycl. Agroecosys 2002; 64:43–55. [35] Ma J, Xu H, Yagi K, Cai Z. Methane emission from paddy soils as affected by wheat straw returning mode. Plant Soil 2008;313:167–174. [36] Liu J, Zhou X, Yu Q. Numerical analysis of the source-sink alternation of composite global warming potential of the paddy ecosystem in the Yangtze Delta. Sci. China Ser D 2003; 46:385–396. [37] Khalil MAK, Shearer MJ, Rasmussen RA, Duan C, Ren L. Production, oxidation, and emissiond of methane from rice fields in China. J. Geophys. Res. 2008; 113, G00A04:doi:10.1029/2007JG000461. [38] Huang Y, Zhang W, Zheng X, Li J, Yu Y. Modeling methane emission from rice paddies with various agricultural practices. J. Geophys. Res. 2004; 109, D08113:doi:10.1029/2003JD004401. [39] Cai Z, HaruoTsuruta, Rong X, Xu H, Yuan Z. CH4 emissions from rice paddies managed according to farmer’s practice in Hunan, China. Biogeochemistry 2001; 56:75–91. [40] Khalil MAK, Rasmussen RA, Shearer MJ, Chen ZL, Yao H, Yang J. Emissions of methane, nitrous oxide, and other trace gases from rice fields in China. J. Geophys. Res. 1998; 103 (D19), 25:241–250. [41] Kern JS, Gong Z, Zhang G, Zhou H, Luo G. Spatial analysis from methane emission from paddy soils in China and the potential for emissions reduction. Nutr. Cycl. Agroecosys 1997; 49:181–195. [42] Huang Y, Sass R, Fisher FM. Model estimates of methane emission from irrigated rice cultivation of China. Global Change Biol. 1998; 8:809–821. [43] Cao M, Dent JB, Heal OW. Methane emissions from China’s paddyland. Agr. Ecosyst. Environ. 1995; 55:129–137. [44] Chen H, Yao SP, Wu N, et al. Determinants influencing seasonal variations of methane emissions from alpine wetlands in Zoige Plateau and their implications. J. Geophys. Res. 2008; 113, D12303:10.1029/2006JD008072. [45] Ding WX, Cai ZC, Tsuruta H, Li XP. Effect of standing water depth on methane emissions from freshwater marshes in northeast China. Atmos. Environ. 2002; 36:5149–5157. [46] Hirota M, Tang Y, Hu Q, et al. Methane emissions from different vegetation zones in a Qinghai–Tibetan Plateau wetland. Soil Biol. Biochem. 2004; 36:737–748. [47] Jin HJ, Wu J, Cheng GD, Tomoko N, Sun GY. Methane emissions from wetlands on the Qinghai–Tibet Plateau. Chinese Sci. Bull. 1999; 44:2282–2286. [48] Song C, Xu X, Tian H, Wang Y. Ecosystem–atmosphere exchange of CH4 and N2 O and ecosystem respiration in wetlands in the Sanjiang Plain, Northeastern China. Global Change Biol. 2009; 15:692–705. [49] Wang D, Chen Z, Xu S. Methane emission from Yangtze estuarine wetland, China. J. Geophys. Res. 2009; 114:doi:10.1029/2008JG000857. [50] Wang ZP, Han XG. Diurnal variation in methane emissions in relation to plants and environmental variables in the Inner Mongolia marshes. Atmos. Environ. 2005; 39: 6295–6305.

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[51] Ding W, Cai Z, Wang D. Preliminary budget of methane emissions from natural wetlands in China. Atmos. Environ. 2004;38: 751–759. [52] Juutinen S, Alm J, Larmola T, et al. Major implication of the littoral zone for methane release from boreal lakes. Global Biogeochem. Cy. 2003; 17:413–424. [53] Chen H, Wu N, Yao SP, et al. High methane emissions from a littoral zone on the Qinghai-Tibetan Plateau. Atmos. Environ. 2009; 43:4995–5000. [54] Chen H, Wu Y, Yuan X, Gao Y, Wu N, Zhu D. Methane emissions from newly created marshes in the drawdown area of the Three Gorges Reservoir. J. Geophys. Res. 2009; 114, D18301:doi:10.1029/2009JD012410. [55] Duan XN, Wang XK, Mu YJ, Ouyang ZY. Seasonal and diurnal variations in methane emissions from Wuliangsu Lake in arid regions of China. Atmos. Environ. 2005; 39:4479–4487. [56] Purvaja R, Ramesh R, Frenzel P. Plant-mediated methane emission from an Indian mangrove. Global Change Biol. 2004;10:1825–1834. [57] Smith LK, Lewis WM, Chanton JP, Cronin G, Hamilton SK. Methane emissions from the Orinoco River floodplain, Venezuela. Biogeochemistry 2000; 51:113–140. [58] Verma A, Subramanian V, Ramesh R. Methane emissions from a coastal lagoon: Vembanad Lake, West Coast, India. Chemosphere 2002; 47:883–889. [59] Wang ZP, Han XG, Li LH. Methane emission patches in riparian marshes of the Inner Mongolia. Atmos. Environ. 2006; 40:5528–5532. [60] Bubier J, Moore T, Savage K, Crill P. A comparison of methane flux in a boreal landscape between a dry and a wet year. Global Biogeochem. Cy. 2005; 19:B1023– B. [61] Chimner RA, Cooper DJ. Carbon dynamics of pristine and hydrologically modified fens in the southern Rocky Mountains. Candian Journal of Botany 2003; 81:477–491. [62] West AE, Brooks PD, Fisk MC, et al. Landscape patterns of CH4 fluxes in an alpine tundra ecosystem. Biogeochemistry 1999; 45:243–264. [63] Wickland KP, Striegl RG, Mast MA, Clow DW. Carbon gas exchange at a southern Rocky Mountain wetland, 1996–1998. Global Biogeochem. Cy. 2001; 15:321– 335. [64] Wickland KP, Striegl RG, Schmidt SK, Mast MA. Methane flux in subalpine wetland and unsaturated soils in the southern Rocky Mountains. Global Biogeochem. Cy. 1999; 13:101–113. [65] Cai Y, Lang HQ, Wang XY. Mires of Zoige Plateau. Beijing,(in Chinese only): Science Press, 1965. [66] Zhou HM, Zen LX, Yu GN, Liu DC. Analysisutilization and protection of wetland resources in the Northwest Plateau of Sichuan Province Southwest China. Journal of Agricultural Sciences 1999; 12:69-70, (in Chinese only). [67] Wang MH. Pollen composition, paleovegetation and paleoclimate of peatlands in Zoige Plateau. Scientia Geographica Sinica 1987; 18:145–155, (in Chinese only). [68] Milliman J. Blessed dams or damned dams? Nature 1997; 386:325–327. [69] Wu JG, Huang JH, Han XG, et al. The Three Gorges Dam: an ecological perspective. Front. Ecol. Environ. 2004; 2:241–248. [70] Wu JG, Huang JH, Han XG, Xie ZQ, Gao XM. Three-Gorges Dam—Experiment in habitat fragmentation? Science 2003; 300:1239–1240.

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[71] Rudd JWM, Harris R, Kelly CA, Hecky RE. Are hydroelectric reservoirs significant sources of greenhouse gases? Ambio 1993; 22:246–248. [72] Abril G, Gu´erin F, Richard S, et al. Carbon dioxide and methane emissions and the carbon budget of a 10-year old tropical reservoir (Petit Saut, French Guiana). Global Biogeochem. Cy. 2005; 19, GB4007. [73] Galy-Lacaux C, Delmas R, Jambert C, et al. Gaseous emissions and oxygen consumption in hydroelectric dams: a case study in French Guyana. Global Biogeochem. Cy. 1997;11:471–483. [74] Fearnside PM. Greenhouse gas emissions from a hydroelectric reservoir (Brazil’s Tucuru´ı Dam) and the energy policy implications. Water, Air, Soil Pollut. 2002; 133:69–96. [75] Ruddiman W. The anthropogenic era began thousands of years ago. Climatic Change 2003; 61:261–293. [76] Giles J. Methane quashes green credentials of hydropower. Nature 2006; 444:254– 255. [77] Stone R. Three Gorges Dam: into the unknown. Science 2008; 321:628–632.

Chapter 2

Methane Emissions from Zoige Alpine Wetlands Huai Chen, Ning Wu, Yanfen Wang, Dan Zhu, Yongheng Gao

2.1

Diurnal Variation of Methane Emissions from an Alpine Wetland

Alpine wetland is a source for CH4 , but little is known about the methane emission from such wetland, especially about its diurnal pattern. We tried to probe the diurnal variation in the methane emission from the alpine wetland vegetation. The average methane emission rate was 9.6 ± 3.4 mg CH4 m−2 h−1 . There was an apparent diurnal variation pattern in the methane emission with a minor peak at 06 : 00 and a major one at 15 : 00. The sunrise peak was consistent with a two-way transport mechanism for plants (convective at daytime and diffusive at night-time). The CH4 emission was found significantly correlated with redox potentials. The afternoon peak could not be explained by the diurnal variation in soil temperature, but could be attributable to changes in the CH4 oxidation and production driven by the plant gas transport mechanism. The results have important implications for sampling and scaling strategies for estimating the methane emission from alpine wetlands.

2.1.1

Introduction

Methane is an important greenhouse gas. Due to its strong infrared absorption band characteristics and the recent worldwide atmospheric increases of it, methane contributed to the radiative forcing about 22% that by all long-lived greenhouse gases in the recent 50 years [1-3]. And it is also involved in a number

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of chemical reactions which can influence tropospheric and stratospheric ozone concentrations [4,5]. Because of the prevalence of waterlogged and anoxic conditions, natural wetlands become the single largest CH4 source [6]. The knowledge about the diurnal variation in CH4 emissions is not only of importance to assess the methane budget, but also imminent necessary to design the sampling time and strategy for estimating the amount of the CH4 emission study [7,8]. Therefore, studies are abundant in the CH4 efflux and its diurnal variation patterns from wetlands all over the world [7,9-14]. However, there is comparatively little knowledge about the CH4 emission efflux, even less about its diurnal variation patterns, from alpine wetlands [9,15-18]. Jin et al. reported that Zoige alpine wetlands were one of major methane emission centers of the Qinghai–Tibetan Plateau [16]. To scrutinize the CH4 efflux and its diurnal variation in Zoige wetlands, more research is urgently needed. The present chapter aims to: (i) understand the diurnal variation of methane emissions from the alpine wetland vegetation in Zoige Plateau; and (ii) find out the key factors influencing the diurnal variations of methane emissions.

2.1.2 2.1.2.1

Materials and Methods Study Site Description

The investigations were carried out in alpine wetlands of the Wetland National Nature Reserve of Zoige, located on the northeast edge of the Qinghai–Tibetan Plateau (33◦ 56′ N, 102◦ 52′ E, 3,430 m a.s.l.) from June to September in 2005. The Zoige Plateau has an average altitude of 3,500 m, with well-developed alpine lakes and peatlands. The alpine wetlands of this region are covering an area of 6,180 km2 , which is 31.5% of the whole plateau of Zoige. The region is characterized by cold Qinghai–Tibetan climatic conditions with average annual temperature 1.7◦ C and rainfall 650 mm. A typical close organic flat wetland was chosen for this study, which covered 28% of the whole Zoige wetlands, according to the Zoige wetlands classification of Mires of the Zoige Plateau [19]. Scattered in the hollow area were two predominant emergent plants: Carex muliensis and Eleocharis valleculosa. All sampling plots were set up in the vegetated hollow. The highest root density was found in the 0 to 20 cm soil depth. Beneath this layer, there was a peat layer about 50 to 80 cm depth. A 75 m × 75 m sampling square has been fenced from May, 2005.

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2.1.2.2

Methane Emissions from Zoige Alpine Wetlands

15

Establishment of Sampling Plots and Methane Flux Measurement

Six plots in the study site were established to probe the diurnal variation in the CH4 emission along the installed boardwalk for minimizing the disturbance to the peatland during sampling. The methane flux was measured with vented closed chambers [20]. The chambers (30 cm in diameter, 50 cm in height) were made of the cylindrical polyvinyl chloride (PVC) pipe. Through the top surface of the chamber, there was a pipe (0.5 mm in diameter) to connect with the ambient atmosphere, with a spiral part inside the chamber. The chamber anchors (20 cm in height) were driven 8–15 cm (depending on the stability of soils) into the soil 48 hours prior to the flux measurement to maintain balance of the system. In order to minimize heating, the aluminum foil was employed to cover the whole chamber, except for the driven-in-soil part. When the measurements began, we bound chamber tops and anchors with a tight rubber belt (2.7 mm in thickness) to make sure that the whole chamber was airtight. On 15th and 16th of the four consecutive months from June to September, 2005, the methane flux were measured at 09:00, 12 : 00, 15 : 00, 18 : 00, 21 : 00, 00 : 00, 03 : 00 and 06 : 00 in Beijing standard time (GMT+8). For each plot, four samples of the chamber air were manually pulled into 50 mL syringes at 10 min intervals over a 30 min period after enclosure. Samples were injected into gas collecting bags (made in Dalian, China) and delivered to the Inner Mongolia Grassland Ecosystem Research Station, Chinese Academy of Sciences, for analysis. The CH4 concentration was determined by a gas chromatography, Hewlett 5890 Packard Series II equipped with an injection loop, a FID (flame-ionization detector) operating at 200◦ C and a 2 m stainless-steel column packed with 13 XMS (60/80 mesh). The column oven temperature was 55◦ C and the carrier gas was N2 with a flow rate of 30 mL min−1 . The certified CH4 standard in 9.39 mL L−1 (China CH4 National Research Center for Certified Reference Materials, Beijing) was used for calibration. The rate of CH4 increase in the chamber air was calculated from a linear regression of concentration measured versus time with an average air temperature.

2.1.2.3

Environmental Factors

A digital meter was introduced (EcoScan, pH6) for measurements of redox potentials and temperatures in the vertical profile. Redox potentials were measured at 5 cm intervals from 5 cm to 15 cm of the soil profile. The signal was consi-

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dered to be constant when the drift of Eh was within 1 mV min−1 . A reference electrode (200 mV) was employed to certify the meter before measuring. The water temperature in emergent plant sites and the ground surface temperature in dry hummock sites were recorded. Soil temperatures were measured at 5 cm and 10 cm of the soil profile. Soil samples were collected at a soil depth of 30 cm. The total carbon content, total nitrogen and phosphorus content were measured in the laboratory of Chengdu Institute of Biology, Chinese Academy of Sciences. The standing water depth and the hummock height over the standing water were recorded after the air sampling. The community height (the average height of vascular plants) of each plot was also recorded. In the non-growing season, the thaw depths and ice thickness were recorded at the same time.

2.1.2.4

Clipping Trials

Among the twelve plots, six were established for clipping trials, to determine the fraction of the methane mission via plant. The clipping trials were conducted at 7 : 00 to 8 : 30 am on 15 August. Before clipping, all the methane fluxes were measured at 7 : 00 am. Then the plants within each plot were clipped to about 3 cm below the water surface. After one hour for system balancing, the measurements of methane fluxes for these plant-removal plots were taken at 8 : 00 am.

2.1.2.5

Statistical Analysis

SPSS for windows 2000 was used for the statistical analysis. And we considered the factors and relationships as statistically significant, when P was less than 0.05.

2.1.3 2.1.3.1

Results Methane Emission and Its Diurnal Pattern

The average of the methane emission from the alpine wetland vegetation was 9.6 ± 3.4 mg CH4 m−2 h−1 (mean ± SD, n = 6) in the sampling season. Figure 2.1 recorded the diurnal pattern of the methane emission, which had two peaks: the minor peak at 06 : 00 and a major one at 15 : 00.

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Methane Emissions from Zoige Alpine Wetlands

17

Fig.2.1 The diurnal variation in methane emissions from the alpine wetland vegetation in the Zoige Plateau. The error bars illustrate the standard deviation with six replicates in all months.

2.1.3.2

Environmental Factors

In Table 2.1, the averages of environmental factors were recorded. The water temperature showed a unique-peak diurnal pattern, with the peak at 15 : 00 and the lowest value at 03 : 00. Soil temperatures at 5 cm and 10 cm soil depth also showed unique-peak diurnal patterns, with the peak at 18 : 00 and the lowest value at 09 : 00. Soil redox potentials at different depths showed similar diurnal patterns, with the peak at 06 : 00 and the lowest value at 15 : 00.

2.1.3.3

Key Factors Influencing Diurnal Pattern of Methane Emission

The correlation analysis showed that the CH4 emission was not significantly related to water temperature (R = 0.556; P > 0.05) and soil temperature at 5 cm and 10 cm depth (R = 0.213, 0.197; P > 0.05). Within a diurnal cycle, only 5 cm and 10 cm soil redox potentials were found significantly correlated with the methane flux (R = −0.618, −0.712, P < 0.05, Table 2.1).

2.1.3.4

Clipping Trials

Before clipping, methane fluxes are 8.44 ± 3.53 mg CH4 m−2 h−1 (mean ± SD, n = 6) and after clipping, methane fluxes are 3.88 ± 0.80 mg CH4 m−2 h−1 (mean ± SD, n = 6), decreased by 54.0%.

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Table 2.1 The average value of influencing factors and their correlative coefficient of the methane emission with a diurnal cycle in the alpine wetland. Influencing factors

Average value

Water temperature (◦ C)

Correlative coefficient of methane emission

13.7 ± 5.2

0.556

13.1 ± 2.1

0.213

10 cm soil temperature ( C)

13.0 ± 1.2

0.197

5 cm soil Eh (mV)

−129.2 ± 32.0

−0.618∗

10 cm soil Eh (mV)

−139.0 ± 32.4

−0.712∗

15 cm soil Eh (mV)

−143.1 ± 29.8

−0.510

◦

5 cm soil temperature ( C) ◦

−1

)

12.4 ± 3.0

ND

−1

)

226.3 ± 104.1

ND

P-total (g kg−1 )

0.65 ± 0.13

ND

Standing water depth (cm)

5.0 ± 3.4

ND

Plant height (cm)

25.3 ± 7.8

ND

N-total (g kg C-total (g kg

∗ Correlation is significant at the 0.05 level. ND means no data.

2.1.4 2.1.4.1

Discussion Methane Emission and Its Diurnal Pattern

In our study, the methane emission rate from alpine wetland vegetation in the Zoige Plateau of Southwest China, 9.6 ± 3.4 mg CH4 m−2 h−1 (mean ± SD, n = 6), was little higher than that from other alpine wetlands [15-18,21,22]. This was attributable to the fact that the soil of alpine wetlands on Qinghai–Tibetan Plateau is extremely rich in organic matters [23]. Due to its well-developed alpine wetlands and a high methane emission rate, the Zoige Plateau was considered to play an important role as a global CH4 source. However, because of a relatively short monitoring period and limited experimental conditions, such results should be confirmed in a further large experimental work with incorporation of the CH4 emission modeling. In this study, a clear diurnal pattern of the methane emission from alpine wetland vegetation in the Zoige Plateau was observed. One minor peak was just 1 hour after sunrise, and the major peak was 3 hours before sunset. The observed pattern is consistent with those from some vegetated wetlands [13,24-26], but different from some others, either vegetated or non-vegetated [7,13,14,25,27], which reported the unique peak of the methane emission in daytime.

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2.1.4.2

Methane Emissions from Zoige Alpine Wetlands

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Key Influencing Factors on Diurnal Pattern of Methane Emission

The correlation analysis showed that there did not exist any significant relationship between CH4 emissions and water temperature (R = 0.556; P > 0.05) or 5 cm and 10 cm soil temperatures (R = 0.213, 0.197; P > 0.05), indicating that temperature did not, or, at best, only weakly affected the diurnal emission variation. Van der Nat et al. found that the contribution of temperature to the diel variation was no more than 35% at the vegetated sites [13]. In other regions of China, consistently, no significant relationship between temperatures and the CH4 emission was found in a diurnal cycle [7,10,14]. Chanton et al. and Kaki et al. observed that the CH4 emission was associated with irradiance, thus the factors related to irradiance rather than temperature may control CH4 emissions [25,27]. However, due to limited data in our research, more measurements during more time are needed to clarify if and how the temperature influences the CH4 diurnal variations from alpine wetlands. In this study, when plants were clipped below the water surface, CH4 emissions dropped by 54% within one hour. Hence, the CH4 transport through vascular plants was a favourable mechanism in the alpine wetland. Two types of the gas transport exist in the plants: internal convective flow and diffusive gas exchange. Van der Nat et al. and Kaki et al. explained relatively the large diurnal variation by employing the diffusive transport in the dark and additional convective transport in light conditions [13,27]. When we counted the ratio of the highest flux to the lowest flux, it was 4.3, close to the values observed in mires, where plants transport gases using the convective mechanism. Based on the ratio, we suggest that the plants here employed the diffusive transport in the dark and additional convective transport under light condition. The sunrise emission peak observed here is similar to the sunrise peaks observed for other emergent plants exploiting diffusive transport during periods of darkness and additional (pressurised) convective transport during periods of sunshine [13,25,26]. The emission peaks following sunrise are likely caused by the initial ventilation of enhanced lacunal methane concentrations that have accumulated during darker periods when diffusion dominated gas transport. Ding et al. explained the diurnal pattern by a change in the CH4 oxidation in the rhizome and rhizosphere driven by plant photosynthesis through releasing oxygen [28]. The oxygen status is indirectly reflected by Eh. In this study, we also found that the diurnal variation in the CH4 emission was significantly related with 5 cm and 10 cm redox potentials (R = −0.618, −0.712, P < 0.05, Table 2.1). King suggested that plants not only transported CH4 from belowground to the atmosphere through lacunae, but also transported O2 from the atmosphere to the roots for the root respiration and CH4 oxidation [29]. The

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sunrise peak of Eh could be explained by plant transporting O2 through the convective mechanism [30]. The sunrise peak of Eh did not result in a significantly decrease in the methane emission, but the gradual change in the methane oxidation and production instead. After 3 h, the maximal CH4 oxidation was likely to occur, and resulted in a significant decrease in CH4 methane. With the temperature increasing (Fig.2.2), the CH4 production was stimulated. Meanwhile with the gradual oxygen exhaustion by soil and root respiration (redox potentials, Fig.2.3), the CH4 oxidation decreased gradually. And 3 h before sunset, the maximal CH4 production and the minimal CH4 oxidation were likely to occur. And this resulted in the major peak of CH4 emission 3 h before sunset.

Fig. 2.2 Diurnal variations in water temperature and soil temperatures at different soil depths in the alpine wetland. The error bars illustrate standard deviation with six replicates in all months.

Fig. 2.3 Diurnal variations in the redox potentials at different soil depths in the alpine wetland. The error bars illustrate standard deviation with six replicates in all months.

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2.1.5

Methane Emissions from Zoige Alpine Wetlands

21

Conclusions

An apparent diurnal variation pattern in the methane emission was observed with one minor peak at 06 : 00 and a major one at 15 : 00 from the alpine wetland vegetation in the Zoige Plateau in Southwest China. The sunrise peak was consistent with a two-way transport mechanism for the alpine wetland plants (convective at daytime and diffusive at night-time). And the CH4 emission was significantly correlated to redox potentials. The afternoon peak could not be explained by the diurnal variation in soil temperature, but could be attributable to changes in the CH4 oxidation and production driven by plant gas transport mechanism. And the diurnal variation of the methane emission from wetlands is important, especially when present plants are capable of exploiting more than one transport mechanism. Accordingly, sampling strategies for estimating the amount of methane emitted from wetlands have to be carefully designed in order to include this variation.

2.2

Determinants Influencing Seasonal Variations of Methane Emissions from Alpine Wetlands in Zoige Plateau and Their Implications

To understand the seasonality of the methane flux from alpine wetlands in the Zoige Plateau, 30 plots were set to measure the methane emissions in growing and non-growing seasons in three environmental types: dry hummock (DH), Carex muliensis (CM) and Eleocharis valleculosa (EV) sites. There were clearly seasonal patterns of the methane flux in different environmental types in growing and non-growing seasons. The mean methane emission rate was 14.45 mg CH4 m−2 h−1 (0.17 to 86.78 mg CH4 m−2 h−1 ) in the growing season, and 0.556 mg CH4 m−2 h−1 (0.002 to 6.722 mg CH4 m−2 h−1 ) in the non-growing season. In the growing season, the main maximum values of the methane flux were found in July and August, except for a peak value in September in CM sites. In the non-growing season, the similar seasonal variation pattern was shared among all the three sites, in which the methane emissions increased from February to April. In the growing season, the determining factors were surface temperatures (R2 = 0.55, P < 0.05), standing water depths (R2 = 0.32, P < 0.01) and plant community heights (R2 = 0.61, P < 0.01); while in the non-growing season, ice thickness (R2 = 0.27, P < 0.05; in CM and EV sites) was found most related

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to the flux. In our understanding, the seasonality of methane emissions in our study areas was temperature and plant growth dependent, and the water table position was also very important to shape the temperature-and-plant-growthdependent seasonal variation of the flux with its vigorous variations in alpine wetland ecosystems.

2.2.1

Introduction

Methane is a biogenic trace gas that plays a crucial role in the chemistry of Earth’s atmosphere. Its atmospheric concentration is one of the important factors controlling the earth’s climate [31,32]. The atmospheric CH4 concentration has been increasing at the rate of 0.5%–0.8% annually since the industrial revolution, and noteworthily, at a rate of 4.9 ppb yr−1 over the period 1992–1998 [33]. In the past 150 years, the CH4 contribution to the radiative forcing has been 35% by CO2 and about 22% by all long-lived greenhouse gases [3]. There are many sources for both anthropogenic and natural methanes. Due to the prevalence of waterlogged and anoxic conditions, natural wetlands have become an important source for the CH4 emission, contributing an estimated 24.8% of the global budget [33]. Methane emission rates reported in the literature vary widely, partly due to large diurnal and seasonal variations [34]. The methane emission from wetlands results from the interaction of several biological and physical processes in the soil [35]. The methane production is a microbiological process, which is predominantly controlled by the absence of oxygen and the amount of easily degradable material [36]. The vertical distribution of the methane production is related to the seasonal average standing water level [37], usually reaching a maximum just beneath the standing water level. Furthermore, the methane production is strongly regulated by the amount and quality of the available substrate, pH and temperature [38,39]. The wetland plant also plays a very important role in the main three aspects of methane emissions, including providing the conduit for the methane transportation, supplying substrates for methanogens through the root exudation and delivering O2 to oxidize methane through roots to rhizosphere [40,41]. Therefore, possible causes for seasonal variations of methane emissions are variations in temperature, substrates, plant and methanogen biomass [41-43]. Methane emissions from high-altitude wetlands are also of great importance because of the prevalence of waterlogged and anoxic conditions in seasonally thawed layers. However, our knowledge of CH4 emissions in alpine or subalpine wetlands is mainly confined to wetlands on the American continent [17,22], besides some sporadic reports from the Qinghai–Tibetan Plateau [15,16,28]. The Zoige alpine wetlands, located in eastern edge of the Qinghai–Tibetan Plateau, a

2

Methane Emissions from Zoige Alpine Wetlands

23

very important and sensitive area for climatic changes [44,45], are typical alpine wetlands which are of great importance as hotspots for biodiversity in the world [46]. Zoige alpine wetlands were one of the three major methane emission centers of the Qinghai–Tibetan Plateau [16]. However, the research on the CH4 emission in the Zoige alpine wetlands has lagged behind, so has systematic investigation on them. To scrutinize the CH4 efflux in Zoige wetlands, more research is urgently needed. The present chapter aims to: (i) understand the seasonal variation of methane emissions in growing and non-growing seasons in an alpine wetland in the Zoige Plateau; and (ii) find out the key factors influencing the seasonal variations of methane emissions.

2.2.2 2.2.2.1

Materials and Methods Site Description

The similar description was made in Section 2.1.2.1.

2.2.2.2

Sampling Plots Establishment and Methane Flux Measurement

Thirty plots in the study site were established along the installed boardwalk to minimize the disturbance to the peatland during sampling. They included three above-mentioned environmental types: Carex muliensis (CM), Eleocharis valleculosa (EV) and dry hummock (DH). Among the thirty plots, 10 were for dry hummock sites, 9 for Carex muliensis sites, and 11 for Eleocharis valleculosa sites. The distance between any two plots is more than 5 m and less than 10 m, depending on the distribution of the Carex muliensis Eleocharis valleculosa and dry hummock patches in the sampling area. On the 16th of the four consecutive months from June to September, 2005 and the same date from February to April, 2006, the methane flux was measured at 09:00 in Beijing standard time (GMT+8). Additional measurements were performed on 28th of August, 2005 and March, 2006, in light of the consideration that August was the peak growing stage of plants and March was the month when the soil was unfrozen and the ice was thawing quickly. For more synchronized results, when measurements were taken, each person was in charge of 5 plots. In 2006, not all of the thirty plots were measured due to the feasibility and possibility in that frigid environment. 11 plots were chosen to measure, including

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5 for dry hummock sites, 3 for Carex muliensis (CM) sites,and 3 for Eleocharis valleculosa (EV) sites. In the non-growing season, both Carex muliensis and Eleocharis valleculosa sites were covered with ice. The detailed information about the vented closed chamber and gas sampling, and analyses about the methane concentration were described in Section 2.1.2.2.

2.2.2.3

Environmental Factors

The similar description was made in Section 2.1.2.3.

2.2.2.4

Statistical Analysis

Mean methane fluxes, surface and soil temperature, Eh, standing water depth, plant community height and aboveground biomass for each vegetation type were calculated by averaging the nine to eleven replicates for each sampling day. A full general linear model in which season was treated as an independent variable was used to compare the differences of environmental factors and methane fluxes in the growing season, and to assess the significance of impacts of the environmental type, season, and the combined effect of the two on methane fluxes and environmental factors. Simple linear regression analyses were carried out with CH4 emissions as a dependent variable, and soil and vegetation characteristics as independent variables. The effect of a certain variable was considered significant when P < 0.05 and highly significant when P < 0.01.

2.2.3 2.2.3.1

Results Seasonal Variation of Physical Factors

On the whole year scale, the physical factors varied greatly between growing and non-growing seasons (Table 2.2). Temperatures in the profile of sediments and redox potentials were much higher in growing season than those in the nongrowing season.

ND ND ND

−18.36±12.68 7.12±3.93

5 cm sediment Eh (mV)

10 cm sediment Eh (mV)

15 cm sediment Eh (mV)

The standing water table (cm)

ND means no data.

14.90±2.14

10 cm sediment temperature (◦ C) 12.67±1.33

13.82±2.57

18.05±5.12

14.10±5.10

EV

1.52±0.22

3.58±0.67

6.79±1.97

ND

−160.36±41.85 −181.20±74.51 ND

−153.34±39.70 −182.40±55.50 ND

10.66±5.67

DH

CM

3.10±0.53

6.80±0.66

5.75±7.36

ND

ND

ND

−114.33±6.66

−109.33±9.01 −116.00±4.58

−105.67±12.70 −118.00±8.54

3.03±0.80

6.77±0.89

4.66±5.80

EV 0.50±0.73

Non-growing season

0.090±0.061 1.47±2.40

−146.08±38.97 −156.84±70.87 ND

13.71±1.68

15.09±2.83

16.10±3.00

5 cm sediment temperature (◦ C)

22.85±12.33 17.97±4.44

6.79±2.17

CM

Water temperature or surface temperature (◦ C) 15.56±4.89

CH4 emission (mg CH4 m−2 h−1 )

DH

Growing season

Table 2.2 Averages of variables determined at dry hummock (DH) sites, Carex muliensis (CM) sites, and Eleocharis valleculosa (EV) sites in growing season and non-growing season.

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In the growing season, physical factors showed obvious seasonal variation patterns (Table 2.3). The air temperature, surface temperature and temperatures in the profile of sediments showed similar seasonal variation patterns in which the highest temperature was recorded in August in all three sites (Fig.2.4 just listing the seasonal variation of the surface temperature). The standing water table showed two peaks in June and September respectively in all three sites (Fig.2.5). The plant community height reached its greatest values in July in all three sites (Fig.2.5). The redox potentials at different depths of the vertical profile showed the similar seasonal variation in the growing season. The lowest values recorded were in August (Fig.2.6). Table 2.3 Significance of impacts of environmental types, season, and their combined effect on CH4 emission and environmental factors in growing season. Combined effect of Environmental type Season environmental type and season CH4 emission (mg CH4 m−2 h−1 )

∗∗

∗∗

∗

Water temperature or surface temperature (◦ C)

ns

∗∗

ns

5 cm temperature (◦ C)

∗

∗∗

ns

10 cm soil temperature (◦ C)

∗∗

∗∗

ns

5 cm soil Eh (mV)

ns

∗

ns

10 cm soil Eh (mV)

∗∗

ns

ns

15 cm soil Eh (mV)

ns

ns

ns

Standing water table (cm)

∗∗

∗∗

ns

Community height (cm)

ns

∗∗

ns

∗ Significant impact P < 0.05, ∗∗ highly significant impact, P < 0.01. ns, no significant impact.

In the non-growing season, the seasonal variation of physical factors was also apparent. The surface temperature and temperatures in the profile of sediments reached their highest values from February to April (Fig.2.4 just listing the seasonal variation of the surface temperature). The ice thickness recorded its

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Fig. 2.4 Seasonal variations of ground or water temperature and methane emissions at dry hummock (DH) sites, Carex muliensis (CM) sites, and Eleocharis valleculosa (EV) sites in growing season and non-growing season.

Fig. 2.5 Seasonal variations of plant community height and standing water table at dry hummock (DH) sites, Carex muliensis (CM) sites, and Eleocharis valleculosa (EV) sites in a growing season.

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highest value in February and then decreased to zero in April both in Carex muliensis (CM) sites and Eleocharis valleculosa (EV) sites, while the thaw depth reached its highest value from February to March. In April the sediment almost thawed in the dry hummock sites (Fig.2.7).

2.2.3.2 Seasonal Variation of Methane Emissions In the growing season, seasonal variation patterns of methane emissions were different among all three sites (Table 2.3, Fig.2.4). In the Carex muliensis (CM) sites, the maximum of methane emissions was recorded in September, while the secondary peak was recorded in July. In Eleocharis valleculosa (EV) sites, the maximal methane emission was recorded in July and then methane emission decreased gradually. In the dry hummock (DH) sites, however, the greatest value for the growing season was in August. And the methane emissions were higher in Carex muliensis (CM) and Eleocharis valleculosa (EV) sites than those in the dry hummock sites. In the non-growing season, the similar seasonal variation pattern shared among all the three sites, in which the methane emissions increased from February to April (Table 2.2, Fig.2.4).

Fig. 2.6 Seasonal variations of redox potentials at the vertical profile of sediments in both Carex muliensis (CM) sites, and Eleocharis valleculosa (EV) sites in the growing season.

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Fig. 2.7 Seasonal variation of ice thickness in both Carex muliensis (CM) sites, and Eleocharis valleculosa (EV) sites and seasonal variation of thaw depth in dry hummock (DH) sites in the non-growing season.

2.2.3.3

Key Factors of Seasonal Variations in Methane Emissions

In the growing season, surface temperatures were significantly related to methane emissions (R2 = 0.55, P < 0.05). The standing water depth and plant community height were found most correlated to methane emissions in three sites (R2 = 0.32, 0.61, P < 0.01). In Eleocharis valleculosa (EV) sites, only the plant community height was significantly related to methane emissions (R2 = 0.99, P < 0.05). In the dry hummock sites, 5 cm, 10 cm sediment temperatures and plant community height were found most related to methane emissions (R2 = 0.47, 0.39, 0.51, P < 0.01) (Table 2.4). In the non-growing season, surface temperatures were significantly correlated to methane emissions in all main plots, however, the coefficient was relatively low (R2 = 0.12, P < 0.05). In Eleocharis valleculosa (EV) sites, surface temperatures were significantly correlated to methane emissions (R2 = 0.31, P < 0.05). In both Carex muliensis (CM) sites and Eleocharis valleculosa (EV) sites, ice thicknesses were significantly correlated to methane emissions (R2 = 0.27, P < 0.05). In the dry hummock sites, no measured factors were found significantly correlated to methane emissions (Table 2.4).

Growing season F = −2.620 + 1.011T (R2 = 0.55, P < 0.05: in three sites)

ND means no data.

F = −3.459 + 0.639T (R2 = 0.47, P < 0.01: Dry hummock sites) ◦ 10cm sediment temperature ( C) F = −4.232 + 0.742T (R2 = 0.39, P < 0.01: Dry hummock sites) The standing water depth (cm) F = 15.10 + 0.293D (R2 = 0.32, P < 0.01: in three sites) The plant community height (cm) F = −3.287 + 0.792D (R2 = 0.61, P < 0.01: in three sites) F = −14.314 + 0.971D (R2 = 0.99, P < 0.05: Eleocharis valleculosa sites) F = 2.372 + 0.375D (R2 = 0.51, P < 0.05: Dry hummock sites) Ice thickness (cm) ND

5cm sediment temperature (◦ C)

Surface temperature (◦ C)

Factors Non-growing season

F = 0.322 − 0.053T (R2 = 0.27, P < 0.05: two emergent plants sites)

ND

ND

ND

F = −0.06 + 0.102T (R2 = 0.12, P < 0.05: in three sites) F = 0.44 + 0.075T (R2 = 0.31, P < 0.05: Eleocharis valleculosa sites) ND

Equation

Table 2.4 Liner regressive equations between methane emissions and key factors in two emergent plants sites (Carex muliensis sites and Eleocharis valleculosa sites) and dry hummock sites in growing and non-growing seasons.

30 Huai Chen et al.

2

2.2.4 2.2.4.1

Methane Emissions from Zoige Alpine Wetlands

31

Discussion Seasonal Variation of Methane Emissions

A mean methane emission rate was 14.45 mg CH4 m−2 h−1 (0.17 to 86.78 mg CH4 m−2 h−1 ) in the growing season, and 0.556 mg CH4 m−2 h−1 (0.002 to 6.722 mg CH4 m−2 h−1 ) in the non-growing season. Methane emissions from the Zoige Plateau wetlands was higher than that from other alpine wetlands [1518,21,22,47]. This was attributable to the fact that the soil of alpine wetlands on the Qinghai–Tibetan Plateau is extremely rich in organic matters [23] and that Zoige peatlands were one center of the CH4 release on the Qinghai–Tibetan Plateau [16]. Due to its well-developed alpine wetlands [19,48] and a high emission rate, the Zoige Plateau was considered to be playing an important role as a global CH4 source. However, because of the Zoige alpine wetlands’ sensitivity and frangibility to climatic changes, even on the same plateau, another research reported a mean methane emission rate as 4.51 mg CH4 m−2 h−1 [47], which was much lower than the value in the present research. Therefore, a long-term multisite methane flux monitoring research is urgently needed to make more rational estimate of the methane flux from alpine wetlands on the Zoige Plateau. A pronounced seasonal variation of methane emissions, i.e., maximal emissions in July and August and low but obvious emissions in winter, was found in the alpine wetland of the Zoige Plateau, which was also observed in boreal peatlands [49,50], littoral zones of boreal lakes [51], tundra wetlands [52], temperate wetlands [53] and lakes [10]. The seasonal variations of methane emissions were ascribed to ecological determinants, e.g. vegetation, climate and water regime. Using the eddy covariance technique (which was employed with a tunable diode laser spectrometer to quantify the methane flux) to monitor methane emissions in a prairie marsh, Kim et al. found that the maximum methane emission was recorded in the late summer (August) and the overall seasonal variation of methane emissions was significantly correlated to the sediment temperature [53]. After two-summer measurements of the methane flux in tundra wetlands, Nakano et al. reported that temporal variation in the methane flux at waterlogged sites of permafrost areas was controlled by the thermal regime of a seasonal thaw layer; for a summer-season variation, the methane flux was significantly correlated to centimeter-degrees, the product of temperature and thaw depth [52]. Based on three-year measurements in a boreal lake, Kankaala et al. reported that the seasonal variation in methane emissions was significantly related to sediment temperature, but more weakly related to plant biomass [51]. In our study, for the growing season, the seasonal variation of the methane flux was found significantly correlated to surface temperatures, standing water depths and plant community heights; for the non-growing season, the ice thick-

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ness was found best related to the flux (Table 2.4). In our understanding, the seasonality of methane emissions in the study area was temperature-and-plantgrowth-dependent.

2.2.4.2

Temperatures

The temperature accounted for the seasonal variation of methane emissions due to its effect on methanogenesis, methane transportation around roots [54], plant growth, freezing and thawing, etc. In temperate wetlands, the sediment temperature was significantly correlated to the methane flux [53]. About high-latitude wetlands (> 60◦ N), several researches illustrated the relations between temperatures and the methane flux. A correlation between flux and temperature was found only for wetter sites, while drier sites showed no such correlation [55,56]. Bartlett and Harriss reported that seasonal changes in flux were closely correlated to ground temperature for both wet and dry sites [57]. The best correlations were found between flux and centimeter-degree, the product of temperature and thaw depth [52,58]. These findings were generally accorded with ours. However, in the wet sites (emergent plant sites), the result of the present research showed that it’s not sediment temperature, but surface temperature (ground or water temperature) that was found significantly correlated to the methane emission in both growing season and non-growing season (Table 2.4). In our understanding, the special alpine climate, especially the changeable thermal condition, should be considered as a rational explanation. In that case, more detailed research is needed to prove it. For the dry hummock sites (drier sites), 5 cm and 10 cm sediment temperatures were best predictors for the seasonal variation of methane emissions in the growing season (Table 2.4) due to their deeper anaerobic layers (which is sensitive to temperature variation) without standing water. In the non-growing season, the surface temperatures were significantly correlated to methane emissions in all main plots. However, the correlation coefficient (R2 = 0.12) was low and therefore hard to explain the methane flux in the non-growing season. In tundra wetlands, Nakano et al. reported that temporal variation in the methane flux at waterlogged sites of permafrost areas was controlled by the thermal regime of a seasonal thaw layer [52]. We tried to do some measurements on ice temperature and thaw layer depth, but due to the limited data collection and the sensitivity and fragility of alpine ecosystems, the rational relation between the thaw layer depth and methane emission was not found in this study. But it remains an interesting issue in alpine wetlands. In the high-frigid wetlands, such as boreal wetlands, tundra and alpine wetlands, temperature was a key ecological factor that limited the activity of methanogens (Tian et al., unpublished) and then

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greatly influenced the methane emission. Therefore, the seasonal thermal variation would result in the relatively seasonal variation pattern of the methane emission. In another word, the seasonal variation of the methane flux in the alpine wetland of the Zoige Plateau was temperature-dependent.

2.2.4.3

Plant Community Height

The plant growth has often been shown to control seasonal variations of methane emissions in vegetated wetlands [59,60]. In wetlands, this is related both to the effectively pressurized ventilation and the oxidation of methane in rhizosphere of actively growing plants and to emissions of substrates from rhizomes easily available for methanogens [51,53,61]. Kim et al. found the peak methane emission occurred 2–3 weeks after the peak shoot biomass [53]; however, Brix et al. observed the maximal methane emission before the peak plant biomass, due to high water table and high availability of labile organic compounds for methanogens [61]. Kankaala et al. found a more weakly relation between seasonal dynamics of methane emissions and plant growth [51]. In contrast to the above-mentioned references, the present research chose the plant community height, which can directly indicate shoot biomass, as the predictor of the plant growth [62]. The seasonality of the methane flux was best correlated to seasonal dynamics of the plant community height (P Polygonum amphibium > wet meadow. In the open fen, methane emission rates of Eleocharis valleculosa and Carex muliensis were higher than that of dry hummock. In the steep riparian zone, weak methane emissions were noted in Equisetum fluviatile and meadow, while naked shoals were identified as the weak consumer of methane. In the meadow on hills, both communities were weak consumers of methane, with the consumption of methane in the slope meadow little stronger than naked sites (Fig.2.15). In addition, the seasonality showed great effect on methane emissions in each wet ecosystem type, while no significant seasonal differences were observed in each dry ecosystem type (Figs.2.15, 2.16 and Table 2.13).

2.5.3.4

Key Factors Influencing Methane Flux on Both Scales

On the landscape scale, aboveground biomass and standing water depth were found significantly correlated with the methane flux (R2 = 0.36, P < 0.1; R2 = 0.57, P < 0.01, Fig.2.17).

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Fig. 2.16 Monthly and seasonal mean methane fluxes from different micro-sites in a certain ecosystem in the growing season. In the steep littoral wetland, GM: Glyceria maxima, CM: Carex muliensis, HV: Hippuris vulgaris; in the Smooth littoral wetland, NS: naked shoals, PA: Polygonum amphibium, WM: wet meadow; in the Meadows on hills, NS: naked sites, SM: slope meadow ; in the steep riparian zone, M: meadow, EF: Equisetum fluviatile, NS: naked shoals; in the open fen, CM: Carex muliensis, EV: Eleocharis valleculosa, DH: Dry hummock.

Fig. 2.17 The relation between the methane emission and standing water depth in the growing season among different ecosystems on the Zoige Plateau. F : mean CH4 emission, mg CH4 m−2 h−1 ; D: standing water depth, cm.

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On the ecosystem scale, different factors influenced methane fluxes from different micro-sites in each ecosystem (Table 2.15). In wet ecosystems (which were covered with the standing water), the reduction of the standing water depth by 10 cm reduced the growing season CH4 emissions by a factor of 5.5 to 18 (the mean values of CH4 emissions were 11.95, 2.12 and 0.65 mg CH4 m−2 h−1 for SL, OP and L, respectively, in Table 2.14). In the open fen, the standing water depth and aboveground biomass were found significantly correlated with the methane flux (R2 = 0.98, 0.95, P < 0.05). In the smooth littoral wetland, only standing water depth was significantly correlated with the methane flux (R2 = 0.61, P < 0.05). In the steep littoral wetland, aboveground biomass was significantly correlated with the methane flux (R2 = 0.54, P < 0.05). In dry ecosystem types, no significant relations were found between environmental factors and methane fluxes.

Table 2.15 Squares of relative coefficients between environmental factors and methane emissions among different micro-sites within each wet ecosystem. Standing water depth

Plant Biomass

Plant density

Plant height

Open fen

0.98∗∗

0.95∗∗

ns

0.91∗∗

Smooth littoral wetland

0.61∗∗

ns

ns

ns

Steep littoral wetland

ns

0.54∗∗

ns

ns

∗∗ highly significant relationship, P < 0.05; ns: no significant impact.

2.5.4 2.5.4.1

Discussion Comparison with Other Researches

Few regional methane flux estimates have been made for complex landscapes. In the BOREAS (BOReal Ecosystem-Atmosphere Study) Northern Study Area (NSA), an average summer consumption of 0.4 mg CH4 m−2 d−1 was estimated from upland soils [114]. In the Kuparuk River basin in Alaska, an overall annual CH4 flux of 0.8 g m−2 yr−1 was estimated, based on measurement during the frost-free season and spatial extrapolations of tundra, wetlands, and open water system [115]. In our study, mean methane emission from the Zoige Plateau, 2.45

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mg CH4 m−2 h−1 ,fell into the range of methane emission rate reported by a number of studies in other alpine wetlands [15-18,21,22,45].

2.5.4.2

Ecosystem Types and Methane Flux on Landscape Scale

Ecosystem types showed significant impacts on methane fluxes in the Zoige Plateau. This is consistent with similar studies in other wetland ecosystems [22,116-118]. In this study, the water regime manipulated the landscape pattern of methane fluxes in the Zoige Plateau over the investigation period. Many studies suggested a positive correlation between the emission of CH4 and water regime [67,113]. In this one, the methane flux dependency on the standing water depth was great, in contrast with the low coefficient of correlation pointed out by Whalen and Reeburgh [58]. According to Sommer and Fiedler [117], the reduction of groundwater table by 7 cm reduced the growing season CH4 emissions by a factor of 6.5 to 12 in Southwest-Germany wetlands, which is similar to our results in alpine wet ecosystems. In dry ecosystems, the methane flux was especially more dependent on landscape topography. Waddington and Roulet [116] found that there was a consistent pattern of the methane emission depending on landscape position (margin of peatland vs. central plateau) in the dry landscape. Ambus and Christensen [118] also reported that there was the topography-dependent CH4 flux in Danish wetlands. These geographical characteristics (such as topographical or positional characteristics) were apparently related with the water table. The correlation between emissions and plant biomass has been found in many studies [67,74]. However, on different scales, this situation is more complicated. In our study, on the landscape scale, the difference of plant biomass cannot sufficiently explain the landscape pattern of methane fluxes in the Zoige Plateau, probably due to the pronounced water regime differences among dry and wet ecosystems.

2.5.4.3

Vegetation Types and Methane Flux on Ecosystem Scale

There are many references based on vegetation types to estimate the methane flux in different ecosystems all over the world [15,93,119-123]. Some studies also attempted to estimate the methane flux based on soil types [117]. In our study, we observed that the vegetation characteristics had significant impacts on the methane flux within each wet ecosystem, but no significant impact on it within any dry ecosystem.

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Vegetation is currently considered to control methane emissions from wetlands, because aquatic plants affect the production, consumption and transport of methane [60,83,124]. In wet ecosystems, the spatial variation of methane fluxes is characterized by the dominant plants that varied in life form, density and biomass of shoot, gas-transporting mechanism, and root-rhime architecture [15]. Significant differences of plant biomass were found among different vegetation types within each wet ecosystem. In the open fen and steep littoral wetland, plant biomass was bound to explain the spatial variation of methane emissions among the three micro-sites. However, in the smooth littoral wetland, the naked shoal employed diffusion and ebullition to transport methane to atmosphere; methane fluxes were probably controlled by diffusion and ebullition from plants and soil surface in Polygonum amphibium stands (the submerged plants zone) [125]. In other words, the difference in stem density and biomass did not result in distinction in methane emissions, probably due to the difference in gas-transport mechanism or the fact that most methane was emitted by diffusion and ebullition. In the Zoige Plateau, even on the whole Qinghai–Tibetan Plateau, the atmospheric methane concentration was 15% to 20% higher and more variable than most of other places, due to the fragile ecosystems sensitive to climatic changes [44,126]. Therefore, the relationship between methane fluxes and vegetation characteristics were not so clear as in other researches [74,113]. Not completely in accordance with Bubier et al.[85], we suggested that vegetation may have predicative value for methane emissions, including the consideration of the regional climatic and ecological characteristics about the area studied. Moreover, the water regime was not mentioned as a predictive factor of methane emissions on such scale in other studies, probably due to lack of significant difference in it among different micro-sites [74,113]. In our study, the standing water depth was significantly different among micro-sites within each wet ecosystem, and significant relations between the standing water depth and methane emissions were observed among different micro-sites in the open fen and smooth littoral wetland. Therefore, the standing water depth should be judged as a considerable predictive factor in each wet ecosystem. In typical grasslands, which share similar ecological characteristics with alpine meadows, the spatial and temporal patterns of methane fluxes were significantly related to the soil moisture content, temperatures and soil diffusivity. These factors are deemed to have controlling the effect on the methane diffusion and methanotrophic activity [127-131]. In our observation, no significant spatial and seasonal differences of methane fluxes were found among different micro-sites in each dry ecosystem. These results suggested that factors other than micro-sites exerted a large impact on methane fluxes, or that there were not enough samples for methane flux measurements for the high spatial and temporal variability. Therefore, in dry ecosystems, vegetation has no predicative values for methane

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fluxes, while factors such as soil type, highly relevant to the soil moisture content and soil diffusivity, may explain the spatial pattern of methane fluxes under such scale [117,130].

2.5.5

Conclusions

In our study, the methane flux from the Zoige Plateau fell into the range of methane emission rates reported by a number of studies in other alpine wetlands. Ecosystem types had significant impact on methane fluxes. On the landscape scale, the methane flux was significantly related with the water regime, which was the major factor to explain the landscape variation of methane fluxes; while vegetation characteristics, such as aboveground biomass, cannot sufficiently explain the landscape pattern of methane fluxes. Under the ecosystem scale, micro-sites have the significant impact on methane fluxes within each wet ecosystem due to difference in vegetation. However, because of alpine wetlands’ sensibility and frangibility to climatic changes, vegetation may have predicative value for methane emissions based on a comprehensive understanding including the regional climatic and ecological characteristics. In our observations, no significant spatial differences of methane fluxes were found among different micro-sites in each dry ecosystem. These results suggested that vegetation had no predicative values for methane fluxes in dry ecosystems, while factors such as soil type, highly related to soil moisture content and soil diffusivity, may explain the spatial pattern of methane fluxes under such scale.

2.6

Inter-annual Variations of Methane Emission from an Open Fen on Qinghai–Tibetan Plateau: a Three-year Study

The study aimed to understand the inter-annual variations of methane (CH4 ) emissions from an open fen on the Qinghai–Tibetan Plateau (QTP) from 2005 to 2007. The weighted mean CH4 emission rate was 8.37 ± 11.32 mg CH4 m−2 h−1 during the summers from 2005 to 2007, falling in the range of CH4 fluxes reported by other studies, with significant inter-annual and spatial variations. The CH4 emissions of the year of 2006 (2.11 ± 3.48 mg CH4 m−2 h−1 ) were 82% lower than the mean value of the years 2005 and 2007 (13.91±17.80 mg CH4 m−2 h−1 and 9.44 ± 14.32 mg CH4 m−2 h−1 , respectively), responding to the interannual changes of standing water depths during the growing season of the three

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years. Significant drawdown of standing water depths is believed to cause such significant reduction in CH4 emissions from wetlands in the year 2006, probably through changing the methanogen composition and decreasing its community size as well as activating methanotrophs to enhance the CH4 oxidation. Our results are helpful to understand the inter-annual variations of CH4 emissions and provide a more reasonable regional budget of CH4 emissions from wetlands on the QTP and even for world-wide natural wetlands under climate change.

2.6.1

Introduction

Methane (CH4 ) is an important greenhouse gas, about 25 times more powerful in warming the atmosphere than carbon dioxide (CO2 ) for the time horizon of 100 years [132]. In particular, CH4 emissions have a larger impact on the climate than what was claimed in current carbon-trading schemes or in the Kyoto Protocol, which modified its radiative forcing as +0.48 W m−2 [88]. Given its atmospheric concentration, CH4 is regarded as an important greenhouse gas only second to CO2 . Due to the prevalence of waterlogged and anoxic conditions, wetlands are the largest natural source for atmospheric CH4 emissions, about 148 Tg CH4 yr−1 (1 Tg = 1012 g) from natural wetlands [132,133], contributing over 25% of the global CH4 emission to the atmosphere [134]. Moreover, wetlands represent not only one of the most important sources for methane emissions, but also the most uncertain one. Such uncertainty arises primarily from the large spatiotemporal variation that occurs in different scales and the limited data of specific wetlands [109,132]. Therefore, we need to fill into place the jigsaw pieces of data on specific wetlands from different regions, if we want to get the whole picture of CH4 emissions from wetlands. The Qinghai–Tibetan Plateau (QTP) is the largest and highest plateau in the world with an area of 2.5 million square kilometers. There are many lakes and wetlands on the plateau, with about 50% of wetlands and 51% of lakes of China unevenly distributed here [28]. On the eastern edge of QTP, there is the largest highland wetland in the world, the Zoige alpine wetlands [135], which is, for its high altitude, a very important and sensitive area for climatic changes [44], as well as hotspots for biodiversity in the world [46]. Though there are several studies about CH4 emissions from wetlands on the plateau [15,16,47,97,136138], these studies only discussed short-term variations of CH4 emissions, not including the inter-annual variation of CH4 emissions and their determinants. Wetlands on QTP are sensitive to climate changes and the plateau has experienced abrupt climate changes [139]. In the past decades, trends of precipitation showed an overall slight increase with high inter-annual variations at the whole-

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plateau scale [140,141]. This is also true for our study area. During the past 50 years, we observed a slight increase trend in the annual precipitation with high inter-annual variations. During our growing season measurements from 2005 to 2007, we encountered a dry year (2006) compared with the annual precipitation average during the period from 1957 to 2007. Moreover, in our study, the chosen open fen was usually seasonally flooded, thus having obvious seasonal and interannual dynamics of standing water depths. This made an opportunity for us to test if CH4 emissions were significantly variable annually and if standing water depths were the dominant factor on inter-annual variations of CH4 emissions.

2.6.2 2.6.2.1

Materials and Methods Ethics Statement

Our field studies were approved by the Bureau of National Nature Reserve of Zoige Wetland. The study was observational, involving no cruelty to animals, no damage to habitats and no harm to endangered plants, and thus no review from the ethnic committee was required in China. All the work was carried out under the Wildlife Protection Law of the People’s Republic of China.

2.6.2.2

Site Description

The investigations were carried out in an alpine wetland of National Nature Reserve of Zoige Wetland (33◦ 56′ N, 102◦ 52′ E, 3,430 m a.s.l.), located on the northeast edge of QTP. The Zoige wetlands is on the Ramsar List of Wetlands of International Importance (2008), with ubiquitous alpine wetlands on the plateau formed during the Early Holocene (9, 355 ± 115 BP) [112]. The region is characterized by cold Qinghai–Tibetan climatic conditions with average annual precipitation 645 ± 92 mm and temperature 1.21 ± 0.75◦ C from 1957 to 2007 (Fig.2.18a). A typical open fen was chosen in this study, which is about 28% of Zoige wetlands, covering an area of 7.08×105 hm2 [135]. The fen is consisted of three stands, including the Kobresia tibetica on the hummock (covering about 40% of the whole site), which is almost never flooded, emergent Carex muliensis and Eleocharis valleculosa stands in the hollow (covering about 25% and 35%, respectively), which are usually flooded with some sporadically drainages. Due to warming and hydrological dynamics, this fen is usually confronted with water table drawdowns in the mid-summer, especially for dry years.

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Fig. 2.18 Weather conditions of Zoige. (a) Annual air temperature and precipitation from 1957 to 2007 of Zoige County; (b) Daily air temperature and precipitation from 2005 to 2007 in the study area. (see also colour figure)

2.6.2.3

Weather and Soil Physical Characteristics

The local weather data were obtained from China Meteorological Data Sharing Service System (http://www.cma.gov.cn/2011qxfw/2011qsjgx/index.htm) from 1957 to 2007. During the monthly measurement of methane fluxes, air temperatures were also recorded. Redox potentials and temperatures (soil and water) were taken with a portable digital meter (EcoScan pH6, Eutech Instruments Pte Ltd, Singapore). Water temperatures, ground surface temperatures and soil temperatures at the depth of 5 cm and 10 cm were manually recorded for each

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of the 18 plots. Standing water depths in the growing season were recorded with a ruler.

2.6.2.4

Sampling Plots Establishment and CH4 Flux Measurement

Eighteen plots in the study site were established for three consecutive growing seasons (July to September) from 2005 to 2007. Among the 18 plots, six were for K. tibetica stand, six for C. muliensis stand and six for E. valleculosa stand. In the three years, we took monthly measurements from July to September. The CH4 emission was measured with vented static chambers [20,98]. The chambers (30 cm in diameter, 50 cm in height) were made of cylindrical polyvinyl chloride (PVC) pipes. Details about the chambers were described in reference [97]. Four air samples from each chamber were taken at 10-minute intervals over a 30 minute period after enclosure, stored in 5 mL airtight vacuumed vials. The CH4 concentration was determined by a gas chromatography (PE Clarus 500, PerkinElmer, Inc., USA), equipped with a flame ionization detector (FID), operating at 350 ◦ C and a 2-m Porapak 80–100 Q Column. The column oven temperature was 35 ◦ C and the carrier gas was N2 with a flow rate of 30 cm3 min−1 . The CH4 flux J was calculated as: J=

dc M P T0 · ·H · · dt V0 P0 T

Where dc/dt is the rate of concentration change; M is the molar mass of CH4 ; P is the atmosphere pressure of the sampling site; T is the absolute temperature of the sampling time; V0 , P0 , and T0 are the molar volume (22.4 L mol−1 ), atmosphere pressure (101.325 kPa) and absolute temperature (273.15 K), respectively, under the standard condition; H is the chamber height over the water surface.

2.6.2.5

Calculation and Statistical Analysis

Mean CH4 emissions, surface and soil temperatures, Eh, and standing water depths for each stand type were calculated by averaging the replicates for each sampling date. A full general linear model in which stand and year were treated as fixed factors was used to compare the differences of environmental factors and CH4 emissions in the three summers, and to assess the significance of impacts on stand and year, and the combined effect of both on CH4 emissions and environmental factors. Multiple analysis of variance (MANOVA) was used to compare

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averages of CH4 emissions for each stand of each sampling date and averages of CH4 emissions for all stands in each year. The CH4 emissions were related to environmental variables by Pearson correlation analysis in each year. The effect of a certain variable was considered statistically significant for P < 0.05. The above analyses were performed with the SPSS 11.5 for Windows.

2.6.3 2.6.3.1

Results Variation in Air Temperature, Precipitation and Standing Water Depths

From the year 1957 to 2007, our study area showed a very obvious warming trend and a slight drying trend with significant inter-annual variations (P < 0.01, Fig.2.18a). During the past five decades, the average annual precipitation was 645 ± 92 mm and the annual mean daily temperature was 1.06 ± 0.6◦ C. For the experiment years (2005–2007), the annual mean precipitation and air temperature were 599 mm and 2.2◦ C (Fig.2.18b). For each of the three years, the warmest month was July and the coldest month January. Also in all three years more than 65% precipitation was distributed in the growing season (from June to September), about 431.1 mm in 2005, 354.7 mm in 2006, and 407.2 mm in 2007, with significantly less rainfall in 2006 than that in 2005 and 2007 (P < 0.05). However, the mean temperature in the growing season was not significantly different among the three years (10.2◦ C in 2005, 10.9◦ C in 2006 and 9.9 ◦ C in 2007). During the three years, the lowest annual precipitation (526.3 mm) and the warmest mean daily air temperature (2.6 ◦ C) were recorded in 2006, a significantly drier and warmer year based on the annual averages of 1957 through 2007 (P < 0.01). During the summers of 2005 to 2007, standing water depths of the hollow stands (C. muliensis and E. valleculosa) varied markedly (Fig.2.19). In the never-flooded hummock (K. tibetica stand), since the water table was the height of hummocks from the surface of the standing water, it also varied greatly due to the dynamics of standing water depths. Among the three stands, there were significant variations of standing water depths during the three summers (Table 2.16). However, standing water depths showed no significant difference between C. muliensis (6.8 ± 3.7 cm) and E. valleculos stands (7.1 ± 5.1 cm) except for that in July 2007. Moreover, the standing water depths of 2005 (10.6 ± 4.5 cm) were significantly higher than that of 2006 (4.3 ± 3.4 cm) and 2007 (6.0 ± 2.7 cm), while there was no significant difference between the latter two.

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Fig. 2.19 Standing water depths of the hollow stands during the growing seasons from 2005 to 2007. Different letters indicate significant difference (P < 0.05).

Table 2.16 Significance of impacts of year, stand types and their combined effect on CH4 emissions and environmental factors in the growing season.

CH4 emission

Year

Stand types

Combined effect of year and stand

∗∗

∗∗

∗

Surface temperature

∗∗

ns

ns

5 cm temperature

∗∗

ns

ns

10 cm soil temperature

∗∗

ns

ns

Standing water depths

∗

∗∗

∗∗

∗ Significant impact P < 0.05; ∗∗ highly significant impact, P < 0.01; ns: no significant impact.

2.6.3.2

CH4 Fluxes from Three Stands

We found that different stands had different CH4 emissions during the study period (Fig. 2.20). The CH4 emission (mean ± SD) from the open fen was about 8.68 ± 14.33 mg CH4 m−2 h−1 . The C. muliensis stand emitted CH4 at the highest rate, about 12.97 ± 14.50 mg CH4 m−2 h−1 . The K. tibetica stand emitted CH4 at the lowest rate, about 2.65 ± 3.74 mg CH4 m−2 h−1 , and the E. valleculosa stand emitted CH4 at an intermediate emission rate about 11.09 ± 19.04 mg CH4 m−2 h−1 . Comparing the three-year means of each stand, we also found that CH4 emissions from C. muliensis and E. valleculosa stands

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was significantly higher than that from K. tibetica stand, with no significant difference between the former two (Fig.2.20a). However, this trend was not the same for each year. For example, CH4 emissions from C. muliensis and E. valleculosa stands was significantly higher than that from K. tibetica stand in the years 2005 and 2007, while there was no significant difference among the three stands in 2006 (Fig.2.21).

Fig. 2.20 Spatiotemporal variations of CH4 fluxes. (a) Mean CH4 fluxes in different stands during the growing seasons; (b) Inter-annual variation of CH4 emission from the open fen of 2005 to 2007. Different letters indicate significant difference for each panel (P acetate > H2 /CO2 (Fig.4.7). Both methanol and TMA can be used to produce CH4 at the same time. H2 /CO2 was hardly used as substrate by hydrogenotrophic methanogens in this area (Fig.4.7).

Fig. 4.7 CH4 production rates from four different substrates at 30◦ C (TMA= Trimethyaine, CK= without adding substrate). The rates were calculated by linear regression analysis.

When samples were pre-incubated at 6◦ C, methanogens used all supplemental substrates except H2 /CO2 to form CH4 very quickly from the second day of the incubation at 30◦ C, the seventh day of the incubation at 15◦ C with a short-lag phase. The final CH4 concentration with methanol supplemented as substrate at 15◦ C was higher than that at 30◦ C (Fig.4.8).

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Fig. 4.8 The CH4 final concentration produced from methanol at 30◦ C and 15◦ C after pre-incubation at 6◦ C. The circle and square symbol represented 15◦ C and 30◦ C, respectively.

4.2.2.3

Discussion

(1) Temperature effects Supposing a linearity between CH4 production in situ and in laboratory, CH4 production in laboratory can be used to reflect production in situ [89]. Hence, under the laboratory incubation conditions, it is not only possible but also convenient to understand the relationship between temperature and CH4 production when other regulating factors are controlled. In this study, CH4 production can be detected at all temperatures with different rates. The average rates at all depths showed that CH4 production rates were approximately 2–3 times and 7–10 times greater at 30◦ C than that at 15◦ C and 6◦ C, respectively. Hence, CH4 production rates strongly depended on temperature. Besides methane, the acetate was accumulated in fresh soil of the zoige wetland during the first month at 15◦ C and 6◦ C. During the subsequent months, acetate was transformed into CH4 at 15◦ C; while the acetate kept untransformed at 6◦ C. The explanation was that 15–20◦C was a threshold for end products formation in low-temperature environments like tundra permafrost soil, lake sediments and paddy soil [90-92]. (2) Substrates influenced CH4 production Theoretically, about two thirds of CH4 is produced from acetogenic methanogenesis [93]. Our incubation experiments with soil samples from alpine ecosystem provided the evidence that CH4 produced from acetate and methanol is much

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more than that from H2 /CO2 . Jiang et al. reported that the methanol-derived methane could account for a large portion in the Eleocharis valleculosa soil in same peatlands [55]. In situ, methanol is emitted from flowering plants [94-96] and plant decay [97]. Pectin of the dead plant material alone can produce 800 Tg yr−1 methanol, and the large part is going to dissolve in soil water and be utilized as a microorganism substrate [95]. 90% to 95% of the Zoige Plateau was covered with plant. Because of long-term waterlogged conditions, such wetlands are able to release much methanol, which may supply substrates for methanogens. Furthermore, the phylogenetic tree revealed that most of sequences were affiliated with methylotrophic and acetoclastic methanogens [15], which indirectly support most of CH4 origination from acetate or/and methanol. (3) Soil depth influenced CH4 production In the anoxic environment with constant thermal conditions, the potential CH4 production depends on substrate availability [44]. In the Zoige wetlands, the maximum CH4 production rate took place within the upper 10 cm and the rate then decreased with soil depth at all incubation temperatures (Fig.4.7). This is consistent with previous studies [64,71,98]. The decreased potential CH4 production with depths may be associated with different substrate availabilities at varied depths. As the fresh organic matter is a dominant factor for the spatial variation in methane production [85], the shallow layer is younger and contains more labile and easily mineralized carbon sources from the newly deposited organic matter [84], which will lead to a higher methanogens activity and the overcoming of methanogens in such layer. Additionally, deeper peat deposits in natural wetlands showed limited production rates due to unfavorable component accumulations for the microbiological activity, such as ligins, and phenolic or humic substrates. Another explanation is possible, i.e. other micro-organisms in deeper layers out-competed methanogens and resulted in low potential CH4 production and high acetate accumulation. These results, acetate accumulation increased with soil depths, suggested that the capability of methanogens competing with other acetogenic bacteria at deeper layers was weaker than at the upper layers at low temperature.

4.2.2.4

Conclusion

The activity of methanogens is strongly depended on temperature, substrate and soil depth which indicated they are important influence factors for CH4 produc-

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tion. Since methanogensis occurred at a widely temperature range even at a low temperature (6◦ C), it is consistent with its cold environment. Notably, methanol could serve as the more important methanogenic precursors than expected in the Zoige wetlands. In future, the contribution of methanol-derived methane should be paid more attention. To some extent, global warming might result in more CH4 emissions from wetlands on QTP due to the rising temperature and more substrates from the plants.

4.2.3

Declining Precipitation

The climate change has significantly affected natural ecosystems around the world, and has especially affected seasonally flooded wetlands; the sensitivity of these wetlands to the global climate change has accelerated their degradation [99,100]. Climate-induced changes in wetland ecosystems, including longer dry periods and more intense rainfall events, could affect the water regime and thereby influence the balance between aerobic and anaerobic soil processes [51]. Because wetland microbial communities are key drivers of biogeochemical cycling [101], their response to changes in precipitation could be important for predicting changes in ecosystem processes [102]. In wetlands, methanogenic archaea, a group of obligately anaerobic microorganisms, are exclusively responsible for CH4 production. The relationship between the methanogenic community structure and environmental factors has been intensively studied in wetlands, and differences in the methanogenic community structure among wetlands were found to be significantly related to vegetation type, temperature, pH, and substrate [48,50,83,103-105]. Although CH4 production in wetland soils is known to be affected by drying, wetting, or fluctuations in water table levels [106-108], limited information is available on whether changing precipitation regimes affect the archaeal community structure. In the rice paddy soil, the methanogenic community grew insufficiently during the dry period and maintained a relatively constant population size which was inactive [107,109]. In the riparian soil, however, methanogenic sequences were abundant only in soils that were flooded permanently or frequently [110]. In Arctic soils, methanogenic archaea were detected only in wet soil during the spring melt [51]. In an Italian rice field, drought reduced the activity of the methanogenic community and reduced CH4 production [52]. Methanogens in the aerated soils were able to be activated by incubating the soils as slurry under anoxic conditions with rapid methane production [111,112]. Based on these findings, extreme drought should greatly affect the size and structure of archaea communities in natural wetlands.

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The wetland region on the Zoige Plateau (3,400 m a.s.l.) is the major CH4 emission hotspot on the eastern edge of the Qinghai–Tibetan Plateau. This region is located in the cold Qinghai–Tibetan climatic zone and is sensitive to climate change [13]. Climate change at this site is characterized by continuously rising temperatures and slightly declining precipitation, which may decrease soil moisture and consequently cause vegetation change and wetland degradation [113]. In the last decade, several studies have used molecular methods to study methanogenic communities in the Zoige wetlands; according to these studies, the Zoige wetlands contain diverse archaea communities [15] whose structure and dynamics are affected by vegetation type, plant height, and soil organic carbon [105,114]. However, the response of these archaea communities to climate change, and especially to drought, has not been studied in this region. The objective of this study was to understand the effect of drought on the structure and size of archaeal communities in the Zoige wetlands. In this study, 16S rRNA-based molecular methods were used to construct clone libraries and to examine the composition and size of archaeal communities in two years. The soil samples were collected from the Zoige wetlands in two consecutive years (the year 2006 was a drought year and the year 2007 was normal one).

4.2.3.1

Materials and Methods

(1) Sample site description and sample collection Soil samples were collected from an open fen at the Wetland National Nature Reserve of Zoige (33◦ 56′ N, 102◦ 52′ E, 3,430 m a.s.l.). This region is located in the cold Qinghai–Tibetan climatic zone. Two predominant plant populations, Carex muliensis (C) and Eleocharis valleculosa (E), cover about 95% of the entire site. The mean annual precipitation is approximately 650 mm (1996– 2005), and the mean annual temperature is 1.7◦ C [57]. The maximum and minimum temperatures are 9.1◦ C to 11.4◦C in July and −8.2◦ C to −10.9◦C in January [54]. The pH values range from 6.6 to 7.0. Soil samples to a depth of 10–15 cm were collected in early of August 2006 and August 2007. Three replicates were collected for each of the two plant populations (two sites in total, one per population) in each of the two years, giving 12 samples in total (the same two sites were sampled in both years). A detailed description of the soil sampling is provided by Zhang et al.[15]. The samples were placed in an ice box and transported to the laboratory for immediate processing. (2) Acetate concentration measurement The acetate concentration in the pore water was measured by a gas chromatography GC-14B (Shimadzu) [115,116], equipped with C18 column. The C18 column

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(1 m by 2 mm) was packed with GDX401, a medium with polar copolymer of nitrogen heterocyclic ring monomer and divinylbenzene. N2 acted as the carrier gas. The temperatures for column, injector and detector were 220◦ C, 250◦C and 280◦ C, respectively. (3) DNA extraction, PCR amplification and phylogenetic analysis Total DNA was extracted with a FastDNA SPIN kit for soil according to the manufacturer’s instructions (Qbiogene, Carlsbad, Calif.). Guanidine thiocyanate (5.5 mmol L−1 ; Sigma) was used to remove PCR-inhibiting compounds (mainly humic acids) from the extract. The archaeal 16S rRNA gene was amplified with the primer pair A109f/A915r or A915r labeled at the 5’ end with 6carboxyfluorescein [58]. PCR was included a primary denaturation step of 3 min at 94◦ C; followed by 32 cycles of 30 s at 94◦ C, 45 s at 53◦ C, and 90 s at 72◦ C; and a final DNA synthesis for 5 min at 72◦ C. PCR products were purified with a QIA quick PCR purification kit (QIAGEN, Hilden, Germany). The purified PCR products were inserted into the pUCm-T vector and sequenced by SinoGenoMax Co., Ltd (Beijing, China). Chimera sequences of 16S rRNA genes were identified by Chimera Check of the Ribosomal Database Project (release 10). The 16S rRNA sequences were submitted to GenBank, and a search for similar sequences was conducted using the BLAST algorithm. The best matching sequences were retrieved from the database and aligned, and similarity analysis was performed by CLUSTAL X [31]. The phylogenetic tree was constructed using the neighbor-joining method implemented in MEGA 4.0 [32]. The topologies of the resultant tree were evaluated by bootstrap analysis [33] based on 1,000 re-samplings. (4) Terminal restriction fragment length polymorphism (T-RFLP) analysis T-RFLP analysis was carried out as described previously [60]. Briefly, labeled and purified PCR products were subjected to T-RFLP analysis with the restriction enzyme TaqI (Promega, USA). The PCR products were also analyzed with an ABI PRISM 373 DNA sequencer (Applied Biosystems, USA). The electropherograms were analyzed with GeneScan version 2.1 (Applied Biosystems, USA). Relative amplicon frequencies were determined based on relative signal intensities of terminal restriction fragments (T-RFs) as indicated by peak heights [61]. Signals with peak heights less than 100 relative fluorescence units were regarded as background noise and were excluded from the analysis. The percentage of fluorescence intensity represented by each T-RF was calculated relative to the total fluorescence intensity of all T-RFs. T-RFs were assigned to phylogenetic groups by comparing in silico terminal fragments of clone sequences and by T-RFLP analysis of clones.

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(5) Quantitative PCR Quantitative PCR (qPCR) was performed with an ABI Prism 7,000 sequence detection system (Applied Biosystems, USA). Three pure culture of methanogens were used as the quantitative standards of total Archaea. DNA were extracted from culture and quantified. Equal amounts of DNA from three pure culture were mixed and quantified again. TDNA were then 10-fold serially diluted to 10-1010 16S rRNA molecules per microlitre and used to generate standard curves of 16S rRNA copies for Archaea. Reaction signals were generated by binding SYBR green to double-stranded DNA. The 16S rDNA copy number was calculated [117,118] by assuming an average molecular weight of 660 Da for a base pair in double-stranded DNA [119]. The primers Arc787 (5′ –ATTAGATACCCSBGTAGTCC–3′ ) and Arc1059 (5′ – GCCATGCACCWCCTCT–3′ ) were used [120]. PCR was performed in 25 µL volumes using special PCR tubes (Axygen, USA). The reaction solutions contained 12.5 µL of SYBR Green 2× Master Mix (Applied Biosystems, USA), 1 µL of DNA template prepared as described earlier, 100 nmol L−1 of each primer, and sufficient ddH2 O to increase the final volume to 25 µL. PCR was initiated at 50◦ C for 2 min to optimize the AmpErase uracil-N-glycosylase activity; this was followed by denaturation at 95◦ C for 10 min and then 40 cycles of amplification that included DNA denaturation at 95◦ C for 30 s, primer annealing at 57◦ C for 40 s, and elongation at 72◦ C for 40 s. Fluorescence data were collected during the elongation step. The reactions were performed in three replicates. Additional details are provided in Zhang et al. [15]. (6) Statistic analysis Factorial and one-way ANOVA analyses of effects for year and plant type were done using the SPSS 16.0. To visualize the differences in the archaeal community between samples and years, we performed correspondence analysis (CA) with CANOCO version 4.5 for Windows (Microcomputer Power, Ithaca NY, USA). The significance of effects for drought and plant type on the community structure was tested on the sum of eigenvalues by the permutation test (9,999 replicate runs) available in the program. (7) Sequence accession numbers The 16S rRNA gene sequences obtained in this study have been deposited in the EMBL, GenBank, and DDBJ nucleotide sequences databases under accession numbers JN851705 to JN851726, and FJ479769 to FJ479778.

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Results

According to the meteorological record, the average precipitation of the Zoige Plateau was 650 mm between 1996 and 2005 [54]. Precipitation was 526 mm in 2006 and 610 mm in 2007, i.e., both years were drier than usual but 2006 was especially dry. As noted earlier, 2006 was considered to be a drought year and 2007 to be a non-drought year. Meanwhile, the precipitation in July of 2006 and 2007 were 101 and 139 mm, the soil water content was 70% and 87% in sampling time of 2006 and 2007, respectively. The data from E. valleculosa (E) stands in samples collected in 2006 and 2007 are referred to as 6E and 7E, respectively. Likewise, 6C and 7C refer to the data from C. muliensis (C) stands sampled in August of 2006 and 2007, respectively. One clone library of archaeal 16S rRNA genes containing 30 clones was constructed from 6C, one containing 30 clones was constructed from 7C, and another containing 30 clones was constructed from 6E. The sequencing analysis showed that the archaeal community consists of Methanosaeta, Methanomicrobiales, Methanobacteriales, uncultured Rice Cluster II (RC-II), and uncultured Crenarchaeota (Fig.4.9). Although the predominant groups were Methanomicrobiales, Methanobacteriales and Methanosaeta at both sites and in both years (Fig.4.9), RC-II was dominant in 6C, 6E and 7E with 20%–25% relative abundance, and a 394 bp Methanomicrobiales was not detected in the 6E samples (Fig.4.10). The relative abundances of archaeal community members were estimated using T-RFLP analyses. Based on in silico analyses of clone sequences, different terminal restriction fragments (T-RFs) could be assigned to different archaeal lineages as follows: 78 bp to Methanomicrobiales, 87 bp to Methanobacteriales, 282 bp to Methanosaeta, 394 bp to Methanomicrobiales, 486 bp to RC-II, and 735 bp to uncultured Crenarchaeota. T-RFLP patterns were similar among the soil samples regardless of sampling dates and vegetation types but the relative abundance of T-RFs varied between years and vegetation types (Fig.4.10). With respect to changes from 2006 to 2007, there was a decrease in Methanobacteriales and RC-II groups, an increase in Methanomicrobiales and Methanosaeta, and no obvious change in Crenarchaeota. The difference in the archaeal community structure between drought and nondrought was tested by CA (Fig.4.11). The first CA axis, which accounted for 53.9% of the variation, separated the archaeal communities of drought samples (6E and 6C) from those of non-drought samples (7E and 7C), indicating that archaeal community composition in drought samples was significantly different from that in non-drought. The second CA axis, which accounted for 31.8% of the variation, differentiated the E. valleculosa samples (7E and 6E) from the C. muliensis samples (7C and 6C), indicating that the archaeal commu-

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Fig.4.9 Phylogenetic relationships of representative archaeal 16S rRNA gene sequences obtained from 6E soil (soil collected beneath Eleocharis valleculosa in 2006) and 6C and 7C soil (soil collected beneath Carex muliensis in 2006 and 2007). The sequences obtained from this study are indicated by bold characters. The tree, which is based on a consensus length of 780 bp of 16S rRNA gene sequences, was constructed by the neighbor-joining method and was rooted with Aquifex pyrohilus. The topology of the tree was estimated by bootstraps based on 1,000 replications. Numbers at branch points are percentages supported by bootstrap evaluation. The GenBank accession number of each reference sequence is shown in parentheses after each strain. The bar represents 2% sequence divergence.

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Fig.4.10 The relative abundance of individual T-RFs from T-RFLP analysis targeting archaeal 16S rRNA genes amplified from DNA extracted from soil beneath Eleocharis valleculosa (E) or Carex muliensis (C) in 2006 and 2007.

Fig. 4.11 The ordination plot of correspondence analysis (CA) of T-RFLP fingerprints of the archaeal community detected in soil beneath Carex muliensis (C) and Eleocharis valleculosa (E). The numbers preceding C and E indicate the year of sampling (6 =2006, 7 = 2007). The black dot: T-RF, and the number indicating the length of T-RF fragment.

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nity composition was significantly different beneath the two plant populations. The difference in T-RFLP data between vegetation types was smaller than the difference between drought. Permutation tests showed that archaeal T-RFLP patterns were significantly affected by drought (trace=0.072, P < 0.001) , water content (trace=0.072, P < 0.01) and plant type (trace=0.03; P < 0.05). qPCR was carried out to explore the quantitative differences in the archaeal community between 2006 and 2007. The 16S rDNA copy number was 3.01 ± 0.54 × 109 in 7C, 2.34 ± 0.67 × 109 in 7E, 3.35 ± 0.59 × 108 in 6C, and 2.96 ± 0.39 × 108 in 6E. The differences among all four kinds of samples were significant (P 0 if W T  Zθs,min if W T > Zθs,min

where, Vtot is total water content in the soil profile, Zacro is the maximum water table depth (taken as 30 cm, adopted from Granberg et al. [29]), φ is the soil porosity, θs,min is the minimum volumetric water content at the soil surface (taken as 0.25, adopted from Granberg et al. [29]), Zθs,min is the maximum depth where evaporation influences soil moisture (taken as 10 cm, adopted from Granberg et al. [29]). Az is the gradient in the linearly decreasing interval, calculated as: Az = (φ − θs,min )/Zθs,min A negative value of the water table indicates that the water table is below the soil surface, while a positive value of the water table indicates the water table is above the soil surface.

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6.2.2

Modelling Methane Emissions of Wetlands in China

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Methane Module

The methane emission module was adapted and integrated from several previous studies and models [5,6,8,15-17]. Three major processes including methane production, methane transport (ebullition, diffusion, and plant mediated transport) and methane oxidation were coupled with IBIS as described below. Methane will produce in each soil layers while the soil condition is favourable. The change of CH4 for each time step in each soil layer is decided the magnitude of methane production (P roCH4 ), oxidation (OxiCH4 ), and three-ways transportation (EbuCH4 , DifCH4 , P M TCH4 ). For each soil layer, the change of methane content is the deficit between production (P roCH4 ) and consumption/emission (sum of OxiCH4 , EbuCH4 , DifCH4 , P M TCH4 ). The total methane flux to the atmosphere is the sum of EbuCH4 , DifCH4 and P M TCH4 .

6.2.2.1

Methane Production

CH4 production is considered as the final stage of organic matter mineralization in anaerobic ecosystems such as wetlands [3]. CH4 production is usually expressed as the relationship with plant primary production, decomposable soil organic carbon, CO2 exchange rates, or soil heterotrophic respiration rates [3,8,11,33,34]. With the supply of carbon substrate from the plant primary production, always regarding as the ultimate source, CH4 production also depends on the soil environmental condition such as water table, soil temperature, and hydrological regime [3,8,35]. Since methane is considered as an end production of biological reduction of CO2 or organic carbon under anaerobic conditions [5,36], here we followed the assumption that CH4 production is directly related to heterotrophic respiration and there are no delays between fermentation and CH4 production [17]. The CH4 production was calculated as a proportion of heterotrophic respiration (CO2 –C) with the modification factors of soil temperature, Eh and pH. P roCH4 = RH × r × fST × fpH × fEh where RH is the soil heterotrophic respiration rate, which is calculated from biogeochemical module of IBIS as the size change of soil carbon pool in each time step. fST , fpH and fEh represent the methane production factors of soil temperature, pH and redox potential, respectively. r is the release ratio of methane to carbon dioxide. We consider there is no methane production at sub-zero temperature since studies show that the methane production below zero is small, or is significantly lower than the growing season [37,38]. The studies also suggested that the

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emitted CH4 in winter season was produced in the summer season already and only stored in the soil profile [39,40]. The methane is produced between zero and the extreme high temperature. The relationship between soil temperature and methane production was adapted from Zhang et al. as described below:  if Tsoil < 0  0  fST = 0 if Tsoil > Tmax    xt vt × exp (xt × (1 − vt)) if 0  Tsoil  Tmax where,

vt =

�

Tmax − Tsoil Tmax − Topt

�

xt = (log(Q10 ) × (Tmax − Topt ))2 × �2 � 1/2 /400.0 1.0 + (1.0 + 40.0/(log(Q10 ) × (Tmax − Topt ))) and, Tmax and Topt are the highest temperature and optimum temperature for methane production with values of 45◦ C and 25◦ C, respectively [6]. Tsoil is the soil temperature. The effect of temperature, usually represented by Q10 value, has high degree of uncertainty with a broad range [41,42]. Cao et al. used a Q10 value as 2.0 [3] while Walter and Heimann [8] and Walter et al. [11] used a Q10 value as 6.0. The methanogenesis has a Q10 value ranged from 1.2 to 3.5 in laboratory studies [3] and some studies showed the range of observed Q10 values ranging from 1.7 to 16 [42,43]. Zhuang et al. used an ecosystem-specific Q10 coefficient to evaluate soil temperature effects on methane production at the northern high latitudes area [9]. We used a base Q10 value of 3.0 [6] for the simulation at different validation sites, then tried to get an optimal value for each individual site in the parameter calibration. The soil pH affects methanogenesis with a tolerance range from 5.5 to 9.0, while the optimal value ranges from 6.4 to 7.8 with peak values in the range from 6.9 to 7.1 [10,44]. Walter and Heimann included the effects of pH on CH4 production in the tuning parameter [8]. We adapted the approach in Cao et al. to express the relationship between soil pH and CH4 production as:

fpH

 0 if pHsoil > pHhigh or pHsoil < pHlow    � � � pHhigh −pHopt = � pHhigh − pHsoil pHopt −pHlow  pHsoil − pHlow   × pHopt − pHlow pHhigh − pHopt

where pHlow and pHhigh represent the low and up limitation of the pH effects interval with values 4.0 and 9.0, respectively [10]. The optimal value was set as 7.0.

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201

Inundation will cause the low redox potential and anaerobic soil environment which will stimulate methanogenesis. Cao et al. assumed that the CH4 production process was switched on or off when the redox potential was below or above −200mV [10]. A linear relationship between the soil water table position and CH4 production was used to represent the effects of the redox potential in another study [3]. Li assume that CH4 will be produced as soon as the soil Eh level reaches 150mV or lower [5]. We used the relationship between the redox potential and methane production generalized by Zhang et al. [6] based on the studies of Fiedler and Sommer [45], and Reinoud Segers [46]. When the redox potential is between the ranges from −200mV to −100mV, the effect factor on methanogenesis is diminishes linearly from 1 to 0. Otherwise, the factor is equal to 1.0 and 0.0 when the redox potential is less than −200mV and greater than −100mV, respectively. Eh is related to the continuing inundating days and the soil layer is considered as inundation when the water-filled pore space (WFPS) is greater than 0.95. The Eh calculation is also based on the root distribution and the position of the water table [6,9,47]. A constant value to represent the proportion of the decomposed organic carbon transferred to CH4 in Cao et al.’s studies [3,10]. In Walter et al.’s studies [8,11], a tuning parameter was used to adjust the amplitude of simulated methane emissions [8,11]. The parameter was calculated using a simple multiple linear regression of soil organic carbon and mean annual temperature. Zhuang et al. used an ecosystem-specific potential rate for CH4 production [9]. We adapted the assumption used in CLM4Me [7,17] and LPJ-WHyMe [15,16] that CH4 production in the anaerobic portion of soil column is related to the soil heterotrophic respiration and the soil substrate for methanogenesis is considered as a fraction of soil heterotrophic respiration. The release ratio of methane to carbon dioxide in the methane production equation is an adjustable parameter and determined through parameter fitting as described by Wania et al. [15].

6.2.2.2

CH4 Oxidation

Methane is oxidized by aerobic methanotrophs in the soil and occurs in the unsaturated zone above the water table [3,5,9]. Cao et al. calculate the rate of methane oxidation based on a linear relationship with the gross primary production [3]. Li calculated the methane oxidation rate as a function of the soil CH4 concentration and Eh [5]. Since methane oxidation is primary controlled by the CH4 concentration, redox potential, and soil temperature [46], we used an equation based on these factors to calculate methane oxidation rate. OxiCH4 = CCH4 × fCH4 × fST × fEh

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The factor of the CH4 concentration (fCH4 ) was represented with the MichaelisMenten kinetics relationship: CCH4 /(KCH4 + CCH4 ), where CCH4 is the methane concentration, KCH4 is the half saturation coefficient with respect to the CH4 concentration [8,17]. A Q10 value was used to quantity effects of the soil temperature on the methane oxidation (fST ). According to the previous studies, the base value of Q10 was chosen as 2 [6,8,46]. The effect redox potential on the methane oxidation (fEh ) was estimated based on the general relationship between the redox potential and methane oxidation illustrated in the study by Zhang et al. [6] which was generated from Fiedler and Sommer [45] and Reinoud Segers [46]. The factor of the redox potential is set to zero and 1.0 when the values of Eh are below −200mV and above 200mV, respectively. Two simple linear functions represent the redox potential effects in the ranges of −200mV to −100mV and −100mV to 200mV and varied from 0 to 0.75 and 0.75 to 1, respectively.

6.2.2.3

Methane Emission Process

In early studies, there is no specific methane emission process in CH4 modelling [3,10]. Gradually, the major methane emission processes including diffusion, ebullition and plant mediated transportation are considered much detailed as well as complex in the model [5,8,15,17]. For examples, Li used a highly simplified scheme to model the methane diffusion between soil layers, assumed that the ebullition occurs at the surface layer and the plant mediated transportation is a function of the methane concentration and plant aerenchyma [5], while in the study by Walter and Heimann, the methane diffusion was calculated using a function based on Fick’s first law. The methane ebullition occurred when the methane concentration in a specific soil layer exceeded a certain threshold, and the plant mediated transported methane was quantified by factors of the plant density, plant type, roots distribution, plant growing state and rhizospheric oxidation [8]. We estimated the methane diffusion between soil layers using Fick’s law based on the concentration gradient of methane in the soil profile [8,9]. The diffusion coefficient for each soil layer was modeled as: Di = Da × fcoarse × ftort × SoilP oro × (1 − W F P Si ) + Dw × W F P Si where Da and Dw are the molecular diffusion coefficients of methane in the air with value of 0.2 cm2 s−1 and in the water with value of 0.00002 cm2 s−1 respectively [8]. They reflect the difference in the rate of molecular diffusion of methane through unsaturated versus saturated soil layers. fcoarse is the relative volume of the coarse pores depending on the soil texture [9]. ftort is the tortuousity

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coefficient with value of 0.66 [8]. WFPS is the water filled pore space. Bubbles will be formed as soon as the methane concentration in the soil profile is over a certain threshold which is a relative rapid channel for methane emission [8]. Walter and Heimann used the vegetation cover fraction to estimate the methane threshold [8]. A constant threshold value of 750 µmol L−1 is used in our study for the methane ebullition emission process including factors of the methane concentration and soil temperature. The vascular plants provide an effective pathway for methane to be transported to the atmosphere [8,48]. We adapted the assumption provided by Walter and Heimann that the plant-mediated flux is proportional to the methane concentration in the soil related to the concentration gradient between soil and the atmosphere [8]. A simple equation used to simulate the plant-mediated emission based on the plant aerenchyma factor: P M TCH4 = frhi × faer × CH4gra where frhi is the factor of rhizospheric oxidation, suggesting that methane will be oxidised at a relative large proportion in the highly oxic rhizospheric zone before entering the plant tissue [15]. The rhizospheric oxidation fraction is the plant type dependent and can range from 20% to 100% [15,49]. We use a constant value of 0.5 for this factor [6]. faer is the factor of plant aerenchyma, which is estimated as a function of the root length density [6]. CH4gra is the methane concentration deficit factor between the soil profile and atmosphere.

6.3

Wetland Methane Emission Model Validation and Sensitivity Analysis

6.3.1

Sensitivity Index for Initial Sensitivity Analysis

Sensitivity is generally expressed as the ratio between a relative change of model output and a relative change of a parameter. The sensitivity index described in Lenhart et al. [50] was used to quantify sensitivity in this study. The sensitivity index (I) is expressed as a finite difference in the approximation of a partial derivative, which indicates the dependence of a variable (y) from a parameter (x). I=

(y2 − y1 )/y0 2∆x/x0

where y0 is the model output with an initial parameter of x0 . The initial parameter value varied by ±∆x (x1 = x0 − ∆x and x2 = x0 + ∆x) between

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the corresponding yielding values y1 and y2 . The sensitivity index symbol (I) indicates the direction of the model reaction to parameter change. According Lenhart et al., calculated sensitivity indices are ranked into four classes [50]. The model sensitivity for a specific parameter is low to negligible when the absolute value of the sensitivity index is less than 0.05 but very high when the absolute value of the sensitivity index is greater than or equal to 1.0.

6.3.2

Initial Sensitivity Analysis

Table 6.1 lists the major parameters in the CH4 module described in Section 6.1. Some of the parameters adopted values that have been fully discussed and supported in previous studies. Based on analyses carried out in previous studies, Table 6.1 List of major parameters in CH4 production, oxidation and transportation. Process

Methane production

Methane oxidation

Parameters Values

Unit

Tmax

45

◦

Topt

25

◦

pHhigh

9

−

pHlow

4

−

pHopt

7

−

r

0.1–0.4 −

Q10

1.7–16

−

KCH4

5

µmol

Q10

1.4–2.4 −

ftort

0.66

−

Da

0.2

cm2 s−1

Dw

0.00002 cm2 s−1

frhi

0.5

Methane transportation

C C

−

Description highest temperature for methane production optimum temperature for methane production highest pH for methane production lowest pH for methane production optimum pH for methane production ratio of CH4 and CO2 Q10 for methane production Michaelis-Menten coefficients Q10 for methane oxidation tortuousity coefficient molecular diffusion coefficients of methane in the air molecular diffusion coefficients of methane in the water factor of rhizospheric oxidation

References This study This study [3,6,9] [3,6,9] [3,6,9] [6,15] [8,42] [6,8] [6-8,46] [8] [8]

[8] [6,15]

6

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only three parameters (the release ratio of CH4 to CO2 , Q10 for CH4 production, and Q10 for CH4 oxidation) were selected for sensitivity experiments in order to make sensitivity analysis processes simple and efficient. Changing scenarios for values of selected parameters in the sensitivity test are listed in Table 6.2. The base value (e.g., initial value) and range for each parameter were adopted from previous studies. Two sites were selected for sensitivity analysis testing. Table 6.2 Sensitivity test scenarios for selected parameters. Parameter

Base value Changing (A) Changing (B) Changing (C)

r (CH4 /CO2 )

0.2

±0.5

±1.0

±1.5

Q10 (methane production)

2

±0.5

±1.0

±1.5

Q10 (methane oxidation)

2

±0.5

±1.0

–

For the model, the sensitivity analysis results indicate that the release ratio of CH4 to CO2 (Fig.6.1 a1,a2) and Q10 for CH4 production (Fig.6.1 c1,d1,c2,d2) were very sensitive. However, the sensitivity index of Q10 for CH4 oxidation was less than 0.05 during most months for the two test sites (Fig.6.1 b1,b2). This implies a very low model sensitivity of the Q10 parameter for the CH4 oxidation process. Seasonal patterns of sensitivity indices show that the model was more sensitive to parameters during winter in most situations. The sensitivity level for Q10 in the methane production process was increasing with the changing magnitude of the Q10 value (Fig.6.1 d1,d2). Although the sensitivity index of Q10 for CH4 oxidation was higher than 1.0 during a handful of months at site BOREAS SSA, it was much lower compared to other two parameters. To simplify the parameter fitting and make processes efficient as well as to assess the model performance while reducing fluctuating parameters to as few as possible, Q10 for CH4 oxidation was set as a constant value (2.0) and only two adjustable parameters were chosen (the release ratio of CH4 to CO2 and Q10 for CH4 production) during the parameter fitting and model calibration discussed below.

6.3.3

Model Performance in China

Two sites were selected to test the model, one in northeast of China and one in the Qinghai–Tibetan Plateau. This was because natural wetlands and floodplains in China are primarily located in these two regions. Figure 6.2 a and b show comparisons between field measurements and model simulations in the Sanjiang Plain in northeast of China. Figure 6.2 a shows mean CH4 emissions from wetlands of different plant types in a study by Huang et al.[51] compared

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Fig.6.1 Sensitivity analysis of three parameters in the CH4 module at two selected sites. The ratio of CH4 to CO2 (a1, a2); Q10 for CH4 oxidation (b1,b2); and the Q10 for CH4 production (c1,d1; c2,d2). A, B, and C denote changing scenarios of three parameters (see Table 6.2). (see also colour figure)

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to CH4 emission rates of inundated marshes in a study by Song et al.[52]. CH4 emission rates reported by Huang et al.[51] were relatively low. Simulated CH4 emissions for 2002–2003 were much higher than observed. For 2003–2004, CH4 emission rates were higher based on Song et al.[52] by an approximate magnitude of 2.5 compared to Huang et al.[51]. Simulated results were within the range of these two independent studies. Observed data (provided in Fig.6.2 b) were digitized from each study. The wetland plant type used in the studies was Carex lasiocarpa [53-57].The peak value (in the growing season) of observed data in a study by Ding et al.[53] was slightly higher than modelled results for 2001–2002, while observed data in a study by Hao et al.[56] was lower than those reported in a study by Ding et al. [53] as well as this study’s simulation for 2002. Modelled monthly CH4 emission rates stood in good agreement with studies by Cui [55] and Yang et al. [54] for 1995 and 2003, respectively.

Fig. 6.2 Comparison of modeled and observed CH4 emissions for the two selected sites in China. (see also colour figure)

For those studies carried out in the Qinghai–Tibetan Plateau, modelled peak values were slightly higher than observed data collected from studies with the exception of a comparison for 2005 reported in a study by Chen et al.[58]. It was observed that reported CH4 emission rates by Chen et al.[58] for 2005 were much higher than their reported emission rates for 2006 and also higher than

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emission rates observed by Wang et al. [59] and Ding et al. [53] even though site locations (provided in Fig.6.2 c, d) were situated very close to each other. Figure 6.2 also shows that natural wetland CH4 emission rates in northeast of China were higher by several orders of magnitude compared to the Qinghai–Tibetan Plateau. Figure 6.3 shows the simulated geographic distribution of annual CH4 emissions rate for 2008 from natural wetlands in China.

Fig. 6.3 Simulated CH4 emission rates from natural wetlands of China. Outline download website:https://219.238.166.215/mcp/index.asp (GS (2008) 1464). (see also colour figure)

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Index

Q10 , 205 A acetoclastic methanogenesis, 115 agriculture, 156 anaerobic metabolism, 33 anoxic conditions, 3 anthropogenic activity, 170 atmosphere, 1 average, 16 B biogenic trace gas, 22 biogeochemical process, 2 Biosphere Simulator, 197 bubble ebullition, 2 C C cycling, 197 Canonical correspondence analysis (CCA), 128 carbon dioxide, 1 Carex muliensis, 29 CH4 budget, 165 CH4 concentration, 15 CH4 production, 2 CH4 sources, 3 chamber, 15 climate change, 196 climate system, 1 climatic conditions, 14 clipping trials, 16 coarse pores, 202 coastal wetlands, 168 combined effect, 75 communities composition, 115 correlative coefficient, 18 D deficit factor, 203 DGVM, 196, 197

ditch mulching, 163 diurnal cycle, 17 diurnal variation, 13 DNA extraction, 123 DNA-DNA relatedness, 119 drainage, 173 drawdown area, 95 dry hummock sites, 29 drying crops, 163 Dynamic Global Vegetation Model, 196 E ebullition, 199 ecological determinants, 31 ecosystem types, 69 eddy covariance technique, 31 electron micrographs, 118 emission rates, 156 environmental factors, 15 environmental variables, 123 essential factor, 198 estimation, 172 F fallow, 163 field measurements, 205 G gas chromatography, 15 gas transport, 19 global methane, 115 Gram negative, 117 greenhouse gas, 1 growing season, 24, 173 growth factor, 120 H Huahu lake, 46 hybridization, 120 hydrogenotrophic methanogens, 115 hydrological component, 197

214

Index

I IBIS, 197 ice thicknesses, 29 implications, 6 in situ, 135 incubation, 131 index symbol, 204 Integrated Biosphere Simulator, 197 inter-annual variations, 71 irradiance, 19 irregular coccoid, 117 K key factors, 6 L labile organic compounds, 33 lakes, 6 Lakes and Reservoirs, 169 landscape scale, 61 Large dams, 5 linear regression analyses, 24 littoral zone, 44 M managerial decision, 159 measurements, 19 mechanism, 19 methane, 1 methane emission, 2 methane source, 115 Methanoculleus, 119 methanogen biomass, 22 methanogen structure, 115 methanogenesis, 32 methanogenic archaea, 115 methanotrophs, 36 micro-sites, 68 microbiological process, 22 mid-season drainage, 163 modelling, 195 moist cultivation, 163 molecular diffusion, 2, 202 multiple studies, 3 multivariate analysis, 37 N natural factors, 162 nature wetlands, 115 newly created marshes, 98

non-growing season, 24 not mobile, 117 O open fen, 71 organic fertilizer, 162 organic manure input, 159 overview, 195 oxidation, 2 oxygen, 22 P pathways, 42 PCR, 123 peatland, 122 phenomenon, 34 phenotypic and phylogenetic characteristics, 120 phenotypic features, 120 phosphorus content, 16 phylogenetic analysis, 119 physical factors, 24 plant aerenchyma, 203 plant community height, 33 plant functional types, 197 plant growth, 122 plant-mediated transportations, 2 porewater, 42 precipitation, 76 Q Qinghai–Tibetan Plateau, 116, 164 R radiative forcing, 45 ratio, 19 reclamation, 165 redox potential variation, 34 relationship, 43 resisted the disruption, 117 respiration, 19 rhizosphere, 22 rhizospheric oxidation, 203 rice paddies, 6 rotational patterns, 159 S salinity, 2 salinity range, 118 sampling plots, 23

Index

sampling strategies, 21 scales, 3 seasonal dynamics, 33 seasonal variations, 21 sediments, 26 sensitive area, 122 sensitivity analysis, 203 sewage irrigation, 163 shoot biomass, 33 simulation, 197 soil organic carbon, 53 soil temperature, 17 soil texture, 202 soil-water interface, 2 solely substrate, 120 spatial heterogeneity, 36 spatial variations, 35 spatiotemporal dynamics, 122 sporosyntropha, 116 spring thaw, 35 standing water depth, 44 statistical analysis, 16 strictly anaerobic, 117 sub-alpine wetlands, 5 substrate, 2 substrate utilization tests, 118 sulfate concentration, 2 sunset, 20 synthesis study, 4 T temperature, 130 temperature profile, 118 terminal restriction fragment length polymorphism (T-RFLP), 123

the correlation analysis, 17 the substrate spectrum, 118 Three Gorges Reservoir, 5 tidal flooding, 168 topography, 58 total carbon content, 16 total nitrogen, 16 transportation, 2 U uncertainty, 200 V vascular plants, 19 vegetation, 55 vegetation type, 120 vertical distribution, 22 W warming, 1 water diversion, 165 water filled pore space, 203 water irrigation, 159 water regime, 55 water surface, 19 water tables, 196 water temperature, 17 waterlogging, 103 wetland dynamics, 165 wetlands, 2, 3 Z Zoige Alpine Wetlands, 5 Zoige Wetlands, 115

215