High methane emissions largely attributed to ebullitive fluxes from a

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Environmental Processes

High methane emissions largely attributed to ebullitive fluxes from a subtropical river draining a rice paddy watershed in China Shuang Wu, Shuqing Li, Ziheng Zou, Tao Hu, Zhiqiang Hu, Shuwei Liu, and Jianwen Zou Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05286 • Publication Date (Web): 13 Mar 2019 Downloaded from http://pubs.acs.org on March 16, 2019

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Environmental Science & Technology

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Research Article

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Number of tables: 1

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Number of figures: 4

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Number of text pages: 30

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Number of references: 51

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High methane emissions largely attributed to ebullitive fluxes from a

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subtropical river draining a rice paddy watershed in China

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Author list:

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Shuang Wu1,a, Shuqing Li1,b, Ziheng Zouc, Tao Hua, Zhiqiang Hud, Shuwei Liua,b* and Jianwen Zoua,b

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Affiliations:

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a

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Environmental Sciences, Nanjing Agricultural University, Nanjing, China; bJiangsu Key Lab and

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Engineering Center for Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center for

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Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing, China; cCollege

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of Overseas Education, Nanjing Tech University, Nanjing, China; dTaizhou University, Taizhou, China.

Jiangsu Key Laboratory of Low Carbon Agriculture and GHGs Mitigation, College of Resources and

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1

These authors contributed equally to this work

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Keywords: Agricultural impacted watershed; Methane; Diffusive flux; Ebullitive flux;

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Greenhous gas; Subtropical river

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To be submitted to Environmental Science & Technology

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(Revised version, manuscript ID: es-2018-052869) 1

ACS Paragon Plus Environment


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ABSTRACT

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Rivers are of increasing concern as sources of atmospheric methane (CH4), while

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estimates of global CH4 emissions from rivers are poorly constrained due to a lack of

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representative measurements in tropical and subtropical latitudes. Measurements of

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complete CH4 flux components from subtropical rivers draining agricultural watersheds

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are particularly important since these rivers are subject to large organic and nutrient loads.

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Two-year measurements of CH4 fluxes were taken to assess the magnitude of CH4

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emissions from the Lixiahe River (a tributary of the Grand Canal) draining a subtropical

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rice paddy watershed in China. Over the two-year period, annual CH4 emissions averaged

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29.52 mmol m-2 d-1, amounting to 10.78 mol m-2 yr-1, making the river a strong source of

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atmospheric CH4. The CH4 emissions from rivers during the rice-growing season (June–

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October) accounted for approximately 70% of the annual total, with flux rates at one to

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two orders of magnitude greater than those for rice paddies in this area. Ebullition

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contributed to 44–56% of the overall CH4 emissions from the rivers and dominated the

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emission pathways during the summer months. Our data highlight that rivers draining

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agricultural watersheds may constitute a larger component of anthropogenic CH4

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emissions than is currently documented in China.

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TOC (Table of Contents)

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 INTRODUCTION

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Methane (CH4) is a potent greenhouse gas (GHG) that exhibits a relative flux-sustained

52

global warming potential (SGWP) of 45 times that of carbon dioxide (CO2) on a 100-year

53

time horizon.1 The attribution of observed rising biogenic CH4 emissions to anthropogenic

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and natural sources is difficult since the current network is insufficient to characterize

55

emission rates by region and source process.1 While natural wetlands and rice paddies

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have been well documented as major sources of atmospheric CH4, freshwaters impacted

57

by agriculture as an anthropogenic source of CH4 are poorly represented in global CH4

58

budgets.2–6

59

While several studies have dedicated to quantify global flux strength of CH4 from

60

freshwaters, the current estimates of global CH4 emissions from freshwater bodies are still

61

poorly constrained, primarily due to the small amount of data and limited geographic

62

distribution of measurements. Global CH4 emissions from freshwater are estimated to be 3



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40-231 Tg yr-1, showing high uncertainties across studies.3,4,7 The current estimate of CH4

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emissions from rivers even spans more than one order of magnitude (1.5-28 Tg yr-1).3,5,8,9

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Global CH4 emissions from rivers were estimated to be 1.5 Tg yr-1 in an earlier study but

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were only based on 13 measurements from nine studies, dramatically underestimated for

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the tropics.3,9 By summarizing a larger dataset, Stanley et al.5 recently updated the

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estimate rate to be 26.8-28.0 Tg yr-1. As the authors cautioned, however, the geographic

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distribution of studies is still clustered, with heavy representation from Europe and North

70

America, while it is extremely limited in vast continental areas, such as in Asia, the

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Middle East and Australia.3,5,8

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Given that CH4 emissions from waters are closely related to organic material and

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nutrients inputs,6,10,11 field measurements of CH4 fluxes from rivers draining agricultural

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watersheds would therefore be highly needed due to high nutrient and sediment loading

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rates through cropland drainage, leaching, runoff and soil erosion.7 High nutrient and

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sediment loads can stimulate CH4 production by providing microbial communities with

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organic substrates, and algal blooms from excessive nutrient loads can further add to this

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with more enriched labile C. These factors may together result in higher CH4 fluxes from

79

agricultural waters relative to other types of freshwater or freshwaters in more natural

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landscape areas.12–14 However, low rates of methanogenesis and the coldest environment

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unfavorable for methanogenesis during non-cropping months may balance high CH4

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emission rates during cropping duration, complicating efforts to predict the annual

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magnitude of CH4 emissions from rivers draining agricultural watersheds. Unfortunately,

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few measurements of CH4 fluxes from rivers have been taken on an annual basis, which 4


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would limit our ability to assess the magnitude of CH4 emissions from waters impacted by

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agriculture.6,13,15–17

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Methane produced in sediments can be transported to the water surface via gas bubble

88

ebullition, molecular diffusion, or advection through plant stems.13,16,18,19 Diffusive fluxes

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are determined from the water-air CH4 concentration gradient and piston velocity, which

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have been most frequently employed in previous studies. Although eddy-covariance

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method allows to capture ebullition of CH4 with fine resolution, ebullition fluxes have

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been poorly represented for being episodic and not representatively captured by the usual

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short-term chamber method.3,5,20 The previous efforts in tropical and subtropical rivers21-24

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have highlighted the potential importance of rivers as a source of atmospheric CH4, and

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recent studies have emphasized the importance of ebullition in shallow flowing

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waters,8,13,25 while few flux measurements have included ebullitive fluxes in the recently

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updated global database.5,26 Therefore, more studies on CH4 emissions from rivers are

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highly expected to include the evaluation of both ebullition and diffusive components.

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China is now the largest producer of rice and the second largest irrigator in the

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world.27 Frequent irrigation and drainage episodes are currently practiced in rice paddies,

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which have been threatening the rivers draining them in China with increased sediment

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loading and nutrient enrichment.28,29 While midseason drainage and moist irrigation can

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reduce seasonal CH4 emissions by as much as 50% in rice paddies,28 this reduction may

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be offset by increased CH4 emissions from rivers draining rice paddy watersheds in China.

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However, little is known about CH4 fluxes from rivers that receive high C and N loads

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through cropland drainage, leaching, runoff and soil erosion.14 A model study estimated 5



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total GHG emissions from agricultural irrigation to be 36.72-54.16 Tg yr-1 in China,30

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while this model estimate needs to be further examined by flux measurements.

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Here, we present the results from a two-year measurement of CH4 fluxes from rivers

110

draining a rice paddy watershed in southeast China. The main objectives of this study

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were first to gain insights into the magnitude of annual CH4 fluxes separated into diffusive

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and ebullitive components from rivers draining rice paddy watersheds. We also aimed to

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examine the dependence of seasonal CH4 fluxes on water and soil/sediment parameters.

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Overall, we attempted to shed light on the role of rivers draining agricultural watersheds

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in the national anthropogenic CH4 budget in China.

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 MATERIALS AND METHODS

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Study Site. Lixiahe River, a tributary of the Grand Canal, represents a typical river

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impacted by lowland agriculture in southeast China. It is located in Xinghua, Jiangsu

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Province, China (32°52’N, 119°50’E), where it has been dominated by paddy

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rice-cropping systems over hundreds of years. This rice paddy watershed is characterized

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by a subtropical monsoon climate, with an annual mean temperature of 17.8 °C and

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precipitation of 1090 mm. High organic material and N fertilizer inputs (typically applied

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at a seasonal rate of ~300 kg N ha-1) as well as different irrigation regimes (e.g.,

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waterlogging, midseason drainage and moist irrigation episodes) are currently practiced in

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rice paddies.29 The sampling area is 5 km downstream of the headwater stream of the river.

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The main river of the water body is 8 m wide, with an average water depth of 2.5 m. The

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average velocity of the water was approximately 0.5 m s-1 over the sampling days. Fall 6


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turnover occurs during the month of October to November, when thermal stratification

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begins to degrade and isothermal conditions are achieved. The details of water and

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sediment physicochemical properties are summarized in Table S1. Over the two-year

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experimental period, the chemical oxygen demand (COD) concentration in surface water

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(0-20 cm depth) averaged 55.62 mg L-1, ranging from 15.9 to 132.6 mg L-1.

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Experimental Design. Three typical measurement sites, including main-river (MP),

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intersection (IP) and side-river points (SP), were located to simultaneously collect both

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the gas and surface water samples. Gas flux measurements and water samples were taken

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4–6 times a month, and sediment samples were collected twice a month. The samples

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were taken mainly between 9:00 and 11:00 h to minimize potential discrepancies due to

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diurnal variations.30 All the measurements at each site were taken with three replicates.

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CH4 Flux Measurements. The fluxes of CH4 were measured using the floating

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chamber method from September 15, 2014 to September 15, 2016. The chamber

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deployment, sampling procedures and configuration of the GC for measuring CH4 fluxes

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are extensively included in the Supporting Information (see Supporting Information, SI,

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Materials and Methods-Extensive), as detailed in our previous studies.28,31-34

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A water-air gas transfer equation was used to calculated the diffusive CH4 fluxes from

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surface water to the atmosphere following previously documented methods, whereby a

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gas-transfer coefficient of velocity across the water-air interface was adopted in addition

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to the water dissolved CH4 concentrations in equilibrium with the atmosphere.3,8,20

149 150

=

(

−

) × 1000

(1)

Where F is the flux of CH4 (mmol m-2 d-1), k is the piston velocity of CH4 across the 7



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water-air interface (m d-1), Cm is the concentration (mol m-3) of CH4 measured in the

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surface water, and Ce is the concentration (mol m-3) of CH4 in the surface water at

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equilibrium with the CH4 partial pressure in the floating chamber (typically calculated

154

from Henrys law).

155

As Sawakuchi et al.8 noted, however, the flux is partially driven by the change in

156

concentration in Eqn. (1), which shows that the flux will decrease with time as the

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concentration increases inside the chambers. Therefore, this simple calculation will

158

underestimate the instantaneous flux rate. To reduce this error, we solve for k by using the

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common gas law (PV = nRT) and make the equation continuous (dP/dt instead of Pt – P0).

160

Hence, the floating chamber-based total CH4 fluxes were determined by the Eqn. (2)

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instead of the Eqn. (1)8,34:

162

×

=

× 1000

(2)

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Where dP/dt is the slope of CH4 accumulation in the chamber (Pa d-1) over the

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sampling period, V is the chamber volume (m3), A is the chamber enclosed surface area

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(m2), R is the universal gas constant (8.314 Pa K-1 mol-1), and T is the temperature (K).

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Diffusive and Ebullition Components. To determine which chambers captured

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ebullition, we used the distributions and variance of the apparent k values as described in

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Bastviken et al.20,36 and Sawakuchi et al.8. According to Henrys Law (C = KhP), the CH4

169

concentration numbers are also expressed as corresponding gas pressure. Thus, Eqn. (1)

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could be rewritten as:

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=

(

−

) × 1000

(3)

where Kh is the Henry’s Law constant for CH4 (mol m-3 Pa-1), Pm is the partial 8


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pressure of CH4 in the chamber at equilibrium with Cm (Pa), and P0 is the partial pressure

174

of CH4 in the chamber at time 0 (approximately the same as atmosphere). Combining

175

Eqns (2) and (3), thus, k is calculated by the equation:

176

(

×

=

)

(4)

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Further, the calculated apparent k values for each chamber were normalized to

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Schmidt numbers of 600 (k600), allowing k values to be compared for any gas and

179

temperature.

180

=

(

)

.

(5)

181

where Sc is the Schmidt number of CH4 at a given temperature.35

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First, we normalized the calculated apparent k values for each chamber to Schmidt

183

numbers of 600 (k600), allowing k values to be compared for any gas and temperature.

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Assuming that the CH4 emission consists of ebullition and diffusive fluxes, ebullition

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makes the calculated apparent k600 values substantially greater than those receiving CH4

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only by diffusive flux, allowing the separation of the two flux components. Given that the

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diffusive flux is assumed to have a constant rate at a given sampling site and time, the

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chambers receiving only diffusive flux would have lower and similar flux rates and could

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be distinguished from the chambers receiving ebullition. However, once all chambers at

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one sampling site showed a large discrepancy in flux rates, suggesting that all chambers

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could have received ebullition. In this case, the lowest value similar with the diffusion

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measured in nearby sites was assumed as diffusion. In each measurement, the minimum

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k600 of CH4 (minimum k600-CH4) found in the same set of chambers was attributed solely to

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diffusive flux and we calculated the ratio of apparent k600 of CH4 (k600-CH4) and minimum 9



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k600 of CH4 (k600-CH4/minimun k600-CH4) for each chamber.

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In this study, the frequency distribution of this ratio (k600-CH4/minimun k600-CH4) for all

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chambers clearly showed two distinct groups of ratios, one between 1.0 and 2.5 and

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another > 2.5 (Figure 1). Thereby, a ratio of 2.5 was accepted as threshold for significant

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contribution of ebullition to total CH4 flux. For the chambers that received ebullition flux,

200

we first calculated the diffusive flux by Eqn. (1) using the averaged k600 from the other

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chambers that received diffusive flux only, and the remaining CH4 flux into the chambers

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was attributed to ebullition. Meanwhile, we also calculated k600 values of CO2 (k600-CO2)

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based on CO2 data in comparison with CH4 for each chamber. If the CH4 emissions were

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purely diffusive, then the values of k600-CH4 and k600-CO2 should tend to be identical. The

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ratio of k600-CH4 to k600-CO2 (k600-CH4/k600-CO2) was used as an indicator of ebullition when

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values of k600-CH4 far exceed those of k600-CO2 for the chambers capturing CH4 rich bubbles.

Measurement numbers

80

60

40

20

0

Calculated k 600-CH4/Minimum k 600-CH4

207 208

Figure 1. Frequency distribution of the calculated k600-CH4/minimum k600-CH4 ratio for CH4

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measurements using the floating chamber method, used for distinction between fluxes

210

consisting of both ebullition and diffusion and those of only diffusion. 10


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While gas sampling, surface water samples (0–30 cm depth) were collected to

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measure the dissolved CH4 concentrations. Water samples were collected using PTE vials

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(100 mL) and the PTE vials were injected by Helium (He, 99.999%) gas to stop biological

215

alteration of water samples. After water sampling, the vials were immediately stored at

216

4 °C during transportation prior to analysis within two hours. The wind speed at

217

approximately 1 m above the water surface was automatically measured with a previously

218

installed weather information recorder during gas and water sampling. Gas and water

219

samples were simultaneously taken with wind speed ranging from 0.41 to 6.4 m s-1. The

220

headspace equilibrium technique was used to measure dissolved CH4 concentrations.36,37

221

Other data measurements are included in the Supporting Information.

222

Statistical Analyses. Differences in CH4 emissions determined by the floating

223

chamber method among the three sampling sites over the two-year period were examined

224

by a two-way analysis of variance (ANOVA). Linear regressions were used to identify the

225

key factors influencing CH4 fluxes from agricultural irrigation waters. All statistical

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analyses were performed using SPSS version 19.0 (SPSS Inc., USA), and statistical

227

significance was determined at the 0.05 probability level.

11



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Figure 2. Seasonal dynamics of dissolved CH4 concentrations (a), floating chamber-based

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CH4 fluxes (b) and the water parameters (c-e) in the river draining a rice paddy watershed.

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Bars represent the mean ± SE (n = 3). The sampling sites of MP, IP and SP refer to

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main-river, intersection and side-river points, respectively.

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 RESULTS

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Dissolved CH4 Concentrations, CH4 Emissions and Water Parameters. Seasonal

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dissolved CH4 concentrations and CH4 emissions showed similar variation patterns

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(Figure 2a and 2b). These patterns did not differ among sampling sites or between the two

238

experimental years (Figure 2a and 2b). In general, both dissolved CH4 concentrations and

239

CH4 fluxes ascended steadily during the spring (March-May) until they attained their

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highest values in August over the summer months (June-August). Thereafter, they

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decreased dramatically and remained relatively lower during the autumn until they

242

reached their lowest point in the winter (December-February). Over the two-year period,

243

the mean and median of dissolved CH4 concentrations of surface water were 0.42 and

244

0.24 µmol L-1, respectively, ranging from a seasonal mean of 0.22 µmol L-1 in the winter

245

to 0.81 µmol L-1 in the summer. Among sampling sites, mean annual dissolved CH4

246

concentrations were higher at the MP site (0.46 µmol L-1) relative to the IP and SP sites

247

(0.39 µmol L-1).

248

The seasonal pattern of CH4 fluxes was generally similar to those of water

249

temperature and water dissolved organic carbon (DOC), while it was contrary to that of

250

water dissolved oxygen (DO). Over the two experimental years, single CH4 measurements 13



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greatly differed with time and site (ANOVA, p < 0.001), ranging from 0.07 to 297.86

252

mmol m-2 d-1 at the MP site, 0.05 to 155.19 mmol m-2 d-1 at the IP site, and 0.04 to 171.60

253

mmol m-2 d-1 at the SP site. The standard error of a single measurement ranged from 0.01

254

to 83.10 mmol m-2 d-1 at the MP site, 0.01 to 35.74 mmol m-2 d-1 at the IP site, and 0.01 to

255

36.59 mmol m-2 d-1 at the SP site (Figure 2b). Similar to CH4 fluxes, the water

256

temperature was lowest in December to January and highest in July to August. When

257

averaged across sites, the mean annual water temperature was 18.98 °C and 19.85 °C in

258

the first and second years, respectively (Figure 2c). Over the two-year period, the mean

259

(or median) water DO content of 258 measurements was 13.12 mg L-1 over the three

260

sampling sites, with a variation from 0.34 to 30.67 mg L-1 (Figure 2d). Across sampling

261

sites, water DOC concentrations averaged 78.1 mg L-1 and 92.6 mg L−1, ranging from

262

25.2 to 261.3 mg L-1 and from 17.2 to 283.6 mg L-1 in the first and second years,

263

respectively (Figure 2e).

Annual CH4 emissions (mol m-2)

30 Ebullition 25

Site (S): p < 0.01 Year (Y): p < 0.01

Diffusion

S×Y: non-significant

20

15

10

5

0 SP

IP

MP

First-year

SP

IP

MP

Second-year

264 265

Figure 3. Annual total of CH4 emissions and diffusive and ebullitive components at the 14


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three sampling sites in agricultural irrigation waters over the two experimental years. Bars

267

represent the mean ± SE (n = 3).

268 269

Annual total of CH4 fluxes greatly differed with the sampling site and experimental

270

year, while they were not significantly affected by their interaction (Figure 3). At an

271

identical sampling site, average annual CH4 fluxes were significantly greater in the second

272

experimental year (September 2015–September 2016) than in the first year (Figure 3).

273

Among the three sampling sites, CH4 fluxes were the greatest at the MP site, with an

274

average of 29.33 and 64.80 mmol m-2 d-1 in the first and second experimental years,

275

respectively. Annual CH4 fluxes at the SP site averaged 13.13 mmol m-2 d-1, amounting to

276

4.79 mol m-2 in the first year, which was 44% lower than the average fluxes in the second

277

year (Figure 3). Over the two experimental years, annual CH4 emissions at the IP site

278

totaled 6.52–10.33 mol m-2, with an annual average flux of 17.85–28.31 mmol m-2 d-1,

279

which was 16–27% greater than that at the SP site (Figure 3). In contrast, annual CH4

280

fluxes averaged 20.10 and 38.95 mmol m-2 d-1, amounting to 7.34 and 14.22 mol m-2 in

281

the first and second years, respectively. Across the two experimental years, annual CH4

282

emissions totaled 10.78 mol m-2, with an average flux of 29.52 mmol m-2 d-1. Substantial

283

CH4 emissions occurred during the rice-growing season (June–October), accounting for

284

68-73% of the annual total.

15



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Table 1. Seasonal variations in CH4 fluxes (Mean ± SE, mmol m-2 d-1), diffusive and ebullitive components, and k600-CH4/k600-CO2 for the river

286

draining rice paddy watersheds. First year

Second year

Autumn

Winter

Total fluxes

5.40 ± 1.53

0.62 ± 0.02

1.05 ± 0.24

68.66 ± 10.28

12.62 ± 4.08

3.71 ± 0.69

38.94 ± 6.89

96.05 ± 13.26

Diffusive fluxes

2.88 ± 0.35

0.62 ± 0.02

0.80 ± 0.08

8.72 ± 0.71

8.64 ± 1.76

2.54 ± 0.56

8.03 ± 1.01

9.72 ± 0.74

Ebullition

2.52 ± 1.52

0

0.26 ± 0.12

59.94 ± 9.87

3.98 ± 0.96

1.17 ± 0.11

30.92 ± 6.75

86.33 ± 12.87

47

0

24

87

32

31

79

89

0.94 ± 0.09

4.59 ± 1.38

33.52 ± 5.16

13.30 ± 3.88

5.27 ± 1.48

28.50 ± 4.16

58.80 ± 11.70

Ebullition (%) k600-CH4/k600-CO2

18.95 ± 6.32

Spring

Summer

Autumn

16


Winter

Spring

Summer

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120

80

k600-CH4/k600-CO2

(a)-Spring

SP

IP

(b)-Summer

MP

60

90

40

60

20

30

0

0 Mar

Apr

May

Jun

120

Jul

Aug

Jan

Feb

20 (c)-Autumn

(d)-Winter

100

16

80 12 60 8 40 4

20

0

0 Sept

Oct

Nov

Dec Month

287 288

Figure 4. Ratios of k600-CH4 to k600-CO2 in four seasons from September 2014 to September

289

2016 in agricultural irrigation waters (error bars SE, n =3).

290 291

Diffusive and Ebullitive Fluxes. Similar to the total CH4 fluxes, both diffusive and

292

ebullitive fluxes showed parallel seasonal variations, with the lowest rates in the winter

293

and the highest in the summer (Table 1). The seasonal average of diffusive fluxes ranged

294

from 0.62 to 8.72 mmol m-2 d-1 in the first year, which was lower than that with a range of

295

2.54–9.72 mmol m-2 d-1 in the second year (Table 1). Diffusion was the main component

296

of CH4 emissions when total CH4 fluxes were under 12.50 mmol m-2 d-1, particularly in

297

the winter and late autumn. With the exception that ebullition was not pronounced in the

298

winter of the first year, seasonal ebullitive fluxes showed large variation over the seasons, 17



299

with the lowest rate of 0.26 mmol m-2 d-1 in the spring of the first year and the highest rate

300

of 86.33 mmol m-2 d-1 in the summer of the second year (Table 1). On average, diffusion

301

contributed approximately 56% and 42% to the total CH4 emissions, while almost 44%

302

and 58% of total CH4 emissions could be attributed to ebullition in the first and second

303

years, respectively (Figure 3). Among the three sampling sites, ebullitive fluxes were the

304

highest at the MP site, while they were lowest at the SP site (Figure 3).

305

Piston Velocities for CO2 and CH4. We estimated the median and mean of gas piston

306

velocities (k600) to be 0.27 and 0.28 m d-1 for CO2 and 2.32 and 6.74 m d-1 for CH4,

307

respectively. The ratio of k600-CH4 to k600-CO2, referring to the difference between the CH4

308

and CO2 k600 values, was much higher in the summer and early autumn, while it was

309

relatively lower in the winter (Figure 4). Over the two experimental years, the mean ratio

310

of k600-CH4 to k600-CO2 was estimated to be 2.9 in the winter, 16.5–21.8 in the spring and

311

autumn, and 45.1 in the summer. Overall, greater k600 values for CH4 relative to CO2

312

suggested that floating chambers intercepted CH4-rich bubbles rising to the water surface,

313

particularly in the summer and early autumn. On the other hand, the difference between

314

the k600-CH4 and k600-CO2 values was most extreme at the MP site, except for a relatively

315

higher ratio at the IP site in September (Figure 4).

316

Dependence of CH4 Fluxes on Water/Sediment Parameters. By pooling

317

measurements over the two years, the CH4 fluxes were positively related to water

318

temperature, DOC and NO3--N, while they were negatively related to water DO

319

concentrations (Figure S1). Relative to water parameters, CH4 fluxes were positively

320

related to sediment pH, electrical conductivity, organic matter and DOC (Figure S2).

321 322 323

 DISCUSSION CH4 Emissions in Comparison with Previous Estimates. The CH4 fluxes in his 18


Page 18 of 31

Page 19 of 31


324

study were generally within the range previously reported in tropical and subtropical lakes

325

and rivers, but had relatively higher release levels.12,38-41 Several explanations may be

326

given for higher CH4 emissions in this study. First, we believe that this may be associated

327

with our high frequency of sampling measurements, which was typically once a week,

328

allowing us to capture more flux peaks (Figure 2). Second, the use of floating chambers at

329

different sampling sites enabled us to capture a larger number of events with ebullitive

330

fluxes.8 Annual total CH4 emissions, including both diffusive and ebullitive fluxes from

331

the rivers, averaged 29.52 mmol m-2 d-1 across the two years, amounting to 10.78 mol m-2

332

yr-1 in this study. This rate is generally greater than in previous studies in rivers impacted

333

by agriculture in this subtropical watershed using only dissolved CH4 concentration data

334

to calculate passive diffusive fluxes of CH4.22,23 In this watershed, previous studies

335

estimated annual diffusive CH4 emissions to be 0.53 mol m-2 in hypereutrophic Donghu

336

Lake and 0.04–6.50 mol m-2 in Taihu Lake,22,23 which is almost comparable to our

337

diffusive CH4 emission rates (2.88–7.25 mol m-2). Third, high nutrient loading,

338

sedimentation and algae blooms in the summer months can stimulate methanogenesis in

339

rivers draining rice paddies, resulting in CH4 emissions that are higher than previously

340

reported in rivers and lakes not impacted by agriculture8 but that are fairly comparable to

341

the estimates of studies on subtropical Mexican eutrophic rivers and streams draining

342

human-dominated landscapes in Wisconsin, USA.12,14 In addition, as a consequence of

343

cropland irrigation and drainage episodes, outgassing to the atmosphere of dissolved CH4

344

delivered by drainage flows may also contribute to CH4 emissions from rivers draining

345

rice paddies, as similarly documented in reservoirs.12

346

Spatiotemporal Variation in CH4 Fluxes. Our results showed that CH4 fluxes were

347

the greatest at the MP site among sampling sites. The greatest CH4 fluxes at the MP site

348

could be primarily ascribed to the nutrient and sediment loads through drainage water, 19



349

leaching, runoff and soil erosion directly into the main-river location, rather than the

350

intersection and side-river locations. Over the two-year period, the mean and median of

351

sediment DOC contents at the MP site were 27–48% and 24–41% greater than those at the

352

IP and SP sites, respectively. In the river water, mean NH4+-N and NO3 --N contents at the

353

MP site were also 18–27% and 10–14% greater than those at the IP and SP sites,

354

respectively. These higher sediment organic C and nutrient loads would have contributed

355

to greater CH4 emissions at the MP site relative to the IP and SP sites,41,42 as supported by

356

the evidence that CH4 emissions were highly related to sediment C and water nutrient

357

loads in this study (Supporting Information, Figures S1 and S2). In addition, greater CH4

358

fluxes could also be associated with higher water-dissolved CH4 concentrations and a

359

larger ebullition contribution at the MP site relative to at the other two sites. Among

360

sampling sites, mean annual dissolved CH4 concentrations and the contribution of

361

ebullitive CH4 fluxes to the overall total CH4 fluxes at the MP site were 14–16% and 20–

362

36% greater than those at IP and SP sites, respectively (Table 1, Figure 3). Therefore, both

363

diffusive and ebullitive fluxes contributed to greater CH4 fluxes at the MP site than at the

364

other two sites.

365

Seasonal dynamics of CH4 fluxes from the rivers were typically characterized by a

366

pattern in which CH4 fluxes were the lowest in the winter and the highest in the summer,

367

and small peaks frequently happened during the late spring and early autumn (Figure 2).

368

This seasonal pattern was in line with those reported in other waters impacted by

369

agriculture.10,18 The seasonal pattern of CH4 fluxes was generally in agreement with those

370

of water temperature and DOC but in contrast to that of water DO. On average, mean

371

monthly fluxes of CH4 showed two orders of magnitude differences between the winter

372

and summer seasons (Table 1). Low CH4 fluxes in the winter were primarily ascribed to

373

low C and nutrient loads, low water temperature and high O2 availability in bottom water 20


Page 20 of 31

Page 21 of 31


374

and sediments, which reduces the substrate availability and metabolic activity of

375

methanogens.43 The higher dissolved CH4 during the 2016 autumn season was not

376

accompanied by a higher CH4 flux, which could be associated with high surface water DO

377

(Figure 2) and strong thermal stratification that prevented the advective transport of CH4

378

from the hypolimnion to the epilimnion11 and then reduced ebullitive CH4 emissions

379

(Table 1). In contrast, surface CH4 emissions from the waters reached a maximum in the

380

summer months. Intensive nutrient and sediments loads, high water temperature and low

381

O2 availability in bottom waters and sediments would facilitate sediment CH4 production

382

and diffusive CH4 fluxes in the summer. Greater CH4 emissions could also be ascribed to

383

higher ebullitive CH4 fluxes in the summer (Table 1), as supported by higher ratios of

384

k600-CH4 to k600-CO2 in the summer than in other seasons (Figure 4). In particular, frequent

385

water withdrawal and recharge courses due to frequent irrigation and drainage in rice

386

paddies would create the environments where algal blooms from excessive nutrient loads

387

could further enrich water and sediment labile C and deplete water O2. Therefore, our

388

results suggested that the seasonal variation in CH4 emissions could be larger in rivers

389

draining agricultural watersheds than in watersheds not impacted by agriculture.

390

Moreover, CH4 emissions also showed large interannual variations in this study. On

391

average, annual CH4 emissions were significantly greater in the second year than in the

392

first year (Figure 2), which is attributed to a combination of water and sediment variables,

393

including water temperature (mean annual: 18.98 ºC in the first year vs. 19.85 ºC in the

394

second year), DOC (78.1 vs. 92.6 mg L-1), DO (15.76 vs. 10.58 mg L-1), and sediment

395

SOC (77.18 vs. 90.89 mg kg-1). All these factors together would have consistently

396

contributed to greater CH4 emissions in the second year than in the first year. In addition

397

to higher mean annual dissolved CH4 concentrations in water (0.34 vs. 0.49 µmol L-1),

398

mean seasonal ebullitive CH4 fluxes were much greater in the second year than in the first 21



399

year (Table 1). Therefore, both diffusive and ebullitive fluxes were responsible for

400

interannual variations of CH4 emissions between the two experimental years.

401

Ebullitive CH4 Fluxes. The current estimates of freshwater CH4 emissions are still

402

constrained with a poor assessment of ebullitive fluxes.3,5 In the present study, the floating

403

chambers captured both diffusive and ebullitive CH4 fluxes from the rivers draining rice

404

paddy watersheds. The ebullitive CH4 fluxes were responsible for more than 50% of the

405

total emissions over the two years. This value fell well within the range of 10–80%, as

406

reported on the contribution of ebullition to the total CH4 emissions from streams and

407

rivers.8,25,44,45 For example, Sawakuchi et al.8 found that ebullition contributed to more

408

than 50% of the total CH4 emissions for some rivers in the Amazon Basin. In particular,

409

ebullitive fluxes showed a large seasonal variation, and summer ebullitive fluxes

410

contributed to almost 90% of the total CH4 emissions. High ebullitive CH4 fluxes are

411

primarily ascribed to large organic material loads fueling CH4-containing bubble

412

formation in sediments and high water temperature facilitating bubble transportation to

413

the atmosphere in shallow freshwaters.46,47 This may be intensified in watersheds

414

dominated by rice paddies and wetlands subject to high soil erosion rates and sediment

415

loading.

416

The k600 values for CH4 relative to CO2 also provided insight into the relative

417

importance of diffusive and ebullitive pathways. The k600-CO2 values suggested that

418

diffusion was the dominant CO2 emission pathway, and the high value of k600-CH4 may

419

imply that the CH4 emission pathway attributed more to ebullition rather than diffusion.48

420

The mean and median of k600-CO2 values were comparable to values reported for diffusive

421

emissions, suggesting that the floating chambers did not artificially disturb the gas

422

exchange between the air and water interface.13,18,49 The mean k600-CH4 values were in good

423

agreement with those reported in a large river and a reservoir by Beaulieu et al.13,49, but 22


Page 22 of 31

Page 23 of 31


424

greater than the values reported in boreal lakes.18 Greater piston velocities calculated from

425

CH4 relative to CO2 were found for the majority of measurements in this study, suggesting

426

the presence of surfacing CH4-rich bubbles in addition to gas diffusive fluxes.50,51 In

427

particular, the k600-CH4 values and ratios of k600-CH4 to k600-CO2 were greatest and extremely

428

high in the summer months and at the MP site (Figure 4), suggesting that wind speed and

429

water velocity at the main-river location relative to the intersection and side-river location

430

may favor greater sediment formation of CH4-containing bubbles and their transportation,

431

especially in summer months.

432

In the present study, the rivers draining rice paddy watersheds showed a consistent

433

source of atmospheric CH4 throughout the years. On average, annual CH4 emissions from

434

the rivers draining rice paddy watersheds in this study were one to 2 orders of magnitude

435

greater than previously reported for rice paddies in this watershed.28,31,33,39 Given field

436

drainage has been fairly approved to be an effective practice to reduce CH4 emissions

437

from rice paddies, this reduction could be offset by high CH4 emissions as a consequence

438

of nutrient enrichment and sediment loading in rivers draining rice paddy watersheds.

439

While rice paddies have been well recognized as one of major sources of CH4 emissions,

440

this study highlights the importance of including rivers draining agricultural watersheds in

441

national anthropogenic CH4 budget in China given that China is the largest paddy rice

442

production and the second agricultural irrigation countries in the world.

443 444

 AUTHOR INFORMATION

445

Corresponding author

446

*Phone: +86 25 8439 6286; Fax: +86 25 8439 5210; Email: [email protected]

447

Author Contributions

448

1

S.W. and S.L. contributed equally to this work 23



449

Notes

450

The authors declare no competing financial interest.

451 452

 ACKNOWLEDGMENTS

453

This work was supported by the Natural Science Foundation of China (41301244,

454

41771268), the National Key Research and Development Program of China

455

(2016YFD0201200), the Fundamental Research Funds for the Central Universities

456

(KYZ201621, KJQN201516) and PADA.

457 458

 SUPPORTING INFORMATION

459

Supplementary Texts

460

The details on CH4 flux measurements and the other data measurements are integral parts

461

of the ‘Materials and Methods’ and the extensive “Discussion” on the dependence of CH4

462

emissions on water and sediment parameters and mitigation strategies.

463

Supplementary Tables

464

Table S1. Physicochemical properties of water and sediment (0–20 cm depth) in the river.

465

Supplementary Figures

466

Figure S1. Dependence of CH4 fluxes on water parameters in agricultural irrigation water.

467

(a) water temperature; (b) water dissolved oxygen (DO); (c) water dissolved organic

468

carbon (DOC); (d) water nitrate concentrations (NO3--N).

469

Figure S2. Dependence of CH4 fluxes on sediment parameters in rivers that drain rice

470

paddy watersheds. (a) sediment pH; (b) sediment electrical conductivity; (c) sediment

471

organic matters; (d) sediment dissolved organic carbon (DOC).

472 473



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High methane emissions largely attributed to ebullitive fluxes from a

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