Directory UMM :Data Elmu:jurnal:A:Agricultural & Forest Meterology:Vol102Issue4May2000:

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Carbon dioxide and methane fluxes from an

intermittently flooded paddy field

Akira Miyata

a,∗

_{, Ray Leuning}

_{, Owen Thomas Denmead}

_{, Joon Kim}

_{, Yoshinobu Harazono}

a a_{National Institute of Agro-Environmental Sciences, Tsukuba 305-8604, Japan}

b_{CSIRO Land and Water, F.C. Pye Laboratory, Canberra, GPO Box 1666, ACT 2601, Australia}

c_{National Research Laboratory for Atmospheric Modeling Studies, Department of Atmospheric Sciences, Yonsei University,} Seoul 120-749, South Korea

Received 5 January 1999; received in revised form 16 December 1999; accepted 27 December 1999

Abstract

To assess the role of floodwater in controlling the exchanges of CO2and CH4from soil, floodwater and the canopy in

intermittently flooded rice paddies, an intensive field campaign (IREX96) was conducted in Japan during August 1996. Eddy covariance was employed to measure fluxes of heat, water vapor and CO2. The flux-gradient method was used to

determine CH4 fluxes from measured profiles of CH4 concentrations, with the required eddy diffusivity estimated using

a modified aerodynamic approach or CO2 as a reference scalar. When the paddy was drained, net CO2 uptake from the

atmosphere during daytime was 23% less, and nighttime CO2emissions were almost twice as great, than when the paddy

was flooded. The mean daily CO2uptake on the drained days was 14.5 g m−2,<50% of the mean for the flooded days. These

differences in the CO2budget were mainly due to increased CO2emissions from the soil surface under drained conditions

resulting from the removal of the diffusion barrier caused by the floodwater. Small changes in canopy photosynthesis observed between flooded and drained paddies had little influence on the CO2budget and could be explained by sensitivity of stomata

to humidity saturation deficit. The CH4flux for the drained paddy showed distinct diurnal variation with a maximum of

∼1.3mg CH4m−2s−1in the afternoon, but after reflooding the peak flux decreased to<0.9mg CH4m−2s−1. Mean daily

CH4emissions were 28% larger for the drained paddy than when it was flooded. As with the CO2flux, the larger CH4flux

on the drained days can be attributed to reduced resistance of CH4transfer from the soil to air by removal of the floodwater.

Keywords: Carbon dioxide flux; Methane flux; Rice; Eddy covariance; Gradient

1. Introduction

Rice paddies in monsoonal Asia have an important role in the global budget of greenhouse gases such as CO2and CH4(IPCC, 1995), but there is still consid-erable uncertainty in the magnitude of the net fluxes from these ecosystems. Many of the factors control-ling gas exchange between rice paddies and the

atmo-∗_{Corresponding author.}

sphere are different from those in dryland agriculture and other ecosystems because rice is flooded during most of its cultivation period. Field studies designed to measure net fluxes and to improve our understanding of the factors controlling the fluxes are thus needed.

CO2 exchange as well as energy balances of rice paddies have been studied intensively in the 1950s and 1960s using conventional micrometeorological techniques such as the aerodynamic and Bowen ratio methods (Uchijima, 1976). Since the 1980s, the 0168-1923/00/$ – see front matter © 2000 Elsevier Science B.V. All rights reserved.

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development of fast response CO2 analyzers enabled us to measure CO2 fluxes over a rice canopy by the eddy covariance method (Ohtaki and Matsui, 1982; Ohtaki, 1984), which gave us more reliable flux esti-mates than before. However, the mechanism of CO2 exchange between rice paddies and the atmosphere is not fully understood. For example, using eddy covari-ance measurements, Tsukamoto (1993) found a sig-nificantly smaller net CO2 flux from the atmosphere to a rice canopy when the field was drained com-pared to when it was flooded, but the reason for the difference was not clear. The existence of floodwater, anaerobic soil or changes in the micrometeorological environment with flooding will influence root activity, photosynthesis and respiration of rice plants. Activity of aquatic plants such as algae in the floodwater may also affect CO2 exchange between rice paddies and the atmosphere. Many of the data obtained so far are not sufficiently detailed to examine the influence of these factors on the CO2exchange in rice paddies.

Paddy fields are also one of largest sources in the global budget of CH4. Based on incubation experi-ments in a laboratory, Koyama (1963) first estimated the CH4 production rate by world rice production to be 190 Tg per year. Since the 1980s there have been numerous field measurements of CH4fluxes in various rice paddies over the world (e.g. Cicerone and Shet-ter, 1981; Holzapfel-Pschorn and Seiler, 1986; Schütz et al., 1989; Sass et al., 1990; Yagi and Minami, 1990; Khalil et al., 1998), leading to revised estimates of the global CH4 emission from rice paddies of 60 Tg per year, but with uncertainty ranging from 20 to 100 Tg per year (IPCC, 1995).

Most estimates of CH4fluxes have used chambers placed over plants, soil and paddy water, but chambers disturb the environment during measurement. Several pioneering studies on net CH4 fluxes over rice pad-dies using non-disturbing micrometeorological meth-ods have now been made (Denmead, 1991; Simpson et al., 1995; Harazono et al., 1996). The flux-gradient approach was used in these studies rather than the eddy covariance technique because, unlike for CO2, fast-response gas analyzers for CH4 have not been available until very recently. Eddy covariance mea-surements using tunable diode laser absorption spec-troscopy are now becoming available (Verma et al., 1992; Shurpali et al., 1993; Edwards et al., 1994; Kim et al., 1998a, b).

Net exchanges of CO2and CH4between rice pad-dies and the atmosphere are controlled by several bi-ological and physical processes. During the daytime plant photosynthesis leads to uptake of CO2 from both the atmosphere and from respired CO2 emit-ted by the soil and floodwater. Respiration at night leads to an efflux of CO2 to the atmosphere. CH4 is released to the atmosphere by ebullition, diffusion across the water-air interface and by transport through aerenchyma, well-developed intracellular air spaces which supply atmospheric oxygen from pores in the leaves, through the plant stems, and to the roots of the rice plants (Nouchi, 1994). Up to 90% of CH4 emis-sion occurs through the aerenchyma in undisturbed paddy fields (Minami and Neue, 1994).

To improve understanding of the process control-ling CO2 and CH4 exchanges in rice paddies, an intensive field experiment called IREX96 (the 1996 International Rice Experiment) was conducted in Japan during August 1996. In this paper, we present measurements of CO2 and CH4 fluxes over a rice canopy obtained using micrometeorological tech-niques; eddy covariance for CO2 and flux-gradient methods for CH4. The measurements were used to assess the role of floodwater in controlling the ex-changes of CO2and CH4from the soil, the floodwater and the plant canopy. Factors controlling exchange processes are examined further in a companion pa-per (Leuning et al., 2000), where we estimate source strength distributions for CO2 and CH4 within the rice using an analysis of turbulent dispersion and measured concentration profiles.

2. CH444flux measurement using flux-gradient theory

Methane fluxes over the rice canopy were measured using two methods based on flux-gradient theory; an aerodynamic method and a gradient technique which uses the eddy covariance flux of CO2, a reference scalar (tracer). These methods have been used conven-tionally for the measurement of fluxes of gases as well as sensible heat and latent heat (e.g. Inoue et al., 1958, 1969). In this study we used a modified aerodynamic method (Harazono and Miyata, 1997), and there-fore it is instructive to describe here the methods in detail.

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We assume horizontal uniformity in surface fluxes, statistically stationary turbulence, and that the pro-duction and destruction of the gas within the surface layer can be neglected. Following the custom, an over-bar represents time-averaged quantities, and a prime does deviation from the time-averaged value. From Monin–Obukhov similarity theory, the vertical flux of the gas F is related to the mean vertical gradient of the gas mass mixing ratio s as follows (e.g. Fowler and Duyzer, 1989; Denmead, 1994):

F = −ρ_aKg(z)

∂s ∂z = −ρa

κu∗(z−d)

φg(ζ )

∂s

∂z (1)

where Kg(z) is the eddy diffusivity at a height z, d is the zero-plane displacement, u∗is the friction velocity,

ρa is the density of dry air and κ is von Karman’s constant (0.4). The termφg(ζ) in Eq. (1) provides the correction to the eddy diffusivity as a function of the Monin–Obukhov stability parameterζ, defined as

ζ =z−d

L = −

κg(z−d)w′_θ′ v

u3 ∗θv′

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where, L is the Monin–Obukhov length, w′_θ′ v is the covariance between the vertical wind component w and the virtual potential temperatureθv. This is given by,θv=θ (1+0.61q)≈θ+0.61θ q, whereθ is the potential temperature and q is the specific humidity. By definition,w′_θ′

v = H / ρCp+0.61 θ /ρE, where

H is the sensible heat flux, E is the water vapour flux, ρis the density of moist air and Cpis the specific heat capacity of air at constant pressure.

The friction velocity in Eq. (2) is measured by the eddy covariance method using a three-dimensional sonic anemometer. The covariance between w and the temperature from the sonic signal closely approxi-matesw′_θ′

vin Eq. (2) because of the humidity depen-dence of the sound speed (Kaimal and Gaynor, 1991; Hignett, 1992). By using u∗ andw′θ_v′ obtained from the sonic signal, the stability parameterζand therefore

φg(ζ) can be determined. Onceφgis known, we can calculate F from the mean vertical gradient of the gas mixing ratio using Eq. (1). In this paper, we call this the aerodynamic (AD) method, and use ‘AD–FCH4’ to represent the CH4flux determined by this method. An advantage of this method over the conventional aerodynamic approach (e.g. Monteith and Unsworth, 1990) is that u∗ and ζ determined from the eddy

covariance method are more reliable than those from the profiles of windspeed and temperature.

From a practical point of view, we must approxi-mate the vertical gradient of the mixing ratio using measurements made at two heights above the canopy,

z1and z2(z1<z2). By integrating Eq. (1) from z1to z2 and assuming that F and u∗ are constant between the two heights, we obtain

F = −Dρ_a1s= −κu∗ Z ζ2

ζ1

φg(ζ )

ζ dζ

−1

ρ_a1s (3) where subscripts 1 and 2 represent the values at z1and

z2, respectively, and1 denotes the difference of the quantity at z2from that at z1. D is referred to as the diffusion velocity or conductance. Eq. (3) was used to calculate the CH4flux from the mean difference of the gas mixing ratio between two heights above the canopy.

Based on the results of previous field studies over short vegetation, we assume that φg is equal to the dimensionless gradient of the potential temperatureφh (Denmead, 1994). Following Dyer and Hicks (1970) and Webb (1970) we used:

φh=(1−16ζ )−1/2 (ζ ≤0) (4a)

φh=1+5ζ (ζ ≥0) (4b)

The zero-plane displacement d, which is required for the determination ofζ and Kg(z), was assumed to be 0.7 times the mean plant height.

A second method which uses CO2 as a tracer to calculate CH4fluxes was also used in this study. This method assumes thatφgfor CO2and CH4 in Eq. (1) are equal and hence no stability corrections to the flux estimates are required. With these assumptions

FCH4=

1sCH4

1sCO2

FCO2=

MCH4

MCO2

1cCH4

1cCO2

FCO2 (5)

where c is the volume mixing ratio, M is the molecular mass, and subscript CH4 and CO2 represent CH4and CO2, respectively. FCH4 is determined using Eq. (5) from FCO2 measured by the eddy covariance method and the ratio of the mean vertical gradient of methane mixing ratio to that of CO2. In this paper, we call this method the ‘KCO2 method’, and use ‘KCO2–FCH4’ to represent the CH4 flux determined by this method. Potential temperature or water vapor can also be

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used as a reference scalar instead of CO2. These tracer methods are advisable particularly when the nighttime fluxes are being investigated because calm periods may invalidate the aerodynamic approach (Denmead, 1994). In this study, we choose CO2 as a tracer because the nighttime vertical gradients of CO2 mixing ratio can be measured more accurately than those of potential temperature or water vapor.

3. Experimental

3.1. Site description

IREX96 was conducted at the Hachihama experi-mental farm of Okayama University, Japan (34◦32′N, 133◦56′E, 2 m above sea level). The farm, approx-imately 300 m×300 m, is situated in a paddy area within reclaimed land facing Kojima Bay in the south-ern part of Okayama Prefecture. The soil is mainly clay (>60%; Kobashi et al., 1968). Rice cultivation on the farm has continued in a similar way every year since 1960. In 1996 rice (Oryza sativa L.; cultivar Akebono) was seeded to the dry paddy on 13 May with density of 60 kg ha−1 and a row spacing of 27 cm. Irrigation started on 19 June and the field was flooded continu-ously until 12 July. This was followed by an intermit-tent drainage practice with 4 days of flooding and 3 days drainage which continued until harvest on 30 Oc-tober. This intermittent drainage is aimed at removing salt from the paddy fields. Neither compost nor rice straw were applied to the paddy, but slow-release-type mineral fertilizers (N, P, K=77, 77, 77 kg ha−1) were applied at the time of seeding. The dry matter yield of 1996 was 5930 kg ha−1, which was 25% greater than the average yield from 1989 to 1995 (4740 kg ha−1).

The measurement of CO2and CH4fluxes over the canopy was conducted from 6 to 13 of August, about a month before heading of the rice plants on 5 Septem-ber. The paddy was drained from the afternoon of 6 August to the morning of 9 August, and was flooded to a depth of 8–10 cm for the remaining observation period. The plant height was about 0.72 m above the water surface, and the leaf area index (LAI) measured with a canopy analyzer (LAI-2000, LICOR Inc., Lin-coln, NE, USA) was 3.08±0.28 (the mean±standard deviation) with a spatial variability from 1.6 to 3.9. Yamamoto et al. (1995) showed LAIs of rice plants

measured with the canopy analyzer agreed well with those by destructive measurement (standard error was 0.28).

Micrometeorological sensors and air inlets were mounted on the masts at the center of the experi-mental farm. The fetch in the prevailing SE direction exceeded 300 m, and footprint analysis following Schuepp et al. (1990) indicated that >90% of the measured flux at a height of 2.2 m was expected to come from within the nearest 300 m of upwind area (Harazono et al., 1998).

3.2. Eddy covariance measurements

Friction velocity u∗, and the fluxes of sensible heat

H, water vapor E, and CO2FCO2, over the rice canopy were measured by the eddy covariance method. The eddy covariance method has been widely used for CO2 flux measurements above plant canopies and a useful summary of the technique can be found in Leuning and Judd (1996). A three-dimensional sonic anemometer (Solent, Model 1012R, Gill Instruments Ltd., Lymington, UK) with path length of 15 cm was installed at a height of 2.2 m above the water to mea-sure the fluctuations of three components of wind velocity. Fluctuations in virtual temperature were obtained from the vertical axis signal of the sonic anemometer (Kaimal and Gaynor, 1991; Hignett, 1992). To measure fluctuations in the CO2 and wa-ter vapor concentrations, a fast response infrared gas analyzer with a 20 cm span open-path (E009, Ad-vanet Inc., Okayama, Japan) was installed at the same height as the sonic anemometer with a horizontal sep-aration of 17 cm. The sensitivity of the gas analyzer to CO2 was calibrated before and after the experi-ment using three levels of standard gases (between 300 and 400 ppmv CO2 in N2, Takachiho Chemical Industrial Co. Ltd., Tokyo, Japan). The sensitivity of the analyzer to water vapor was factory-calibrated in a thermostatic chamber before the experiment. The data from the sonic anemometer and the gas ana-lyzer were sampled at 10 Hz using a 16-bit digital data recorder (DR-M2a, TEAC Co., Ltd., Tokyo, Japan).

The fluxes u∗, H, E and FCO2 were calculated on a 30 min basis from the covariances between the vertical wind velocity and corresponding quantities. A correction for path length averaging of the sonic

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anemometer and the gas analyzer, and that for sepa-ration of both sensors were applied following Moore (1986) and Leuning and Moncrieff (1990). The in-fluence of these corrections on each flux varies with atmospheric stability, but the average magnitudes of the corrections are as follows. The correction for path length averaging increased u∗and H by 0.5 and 2.9%, respectively, while corrections for path length averag-ing plus sensor separation increased E and FCO2 by 11.8 and 12.6%, respectively. The influence of density fluctuations arising from H and E (Webb et al., 1980) increased E by 4.9% and reduced FCO2 by 10.1%. Because we calibrated the CO2 sensor using CO2 in nitrogen (i.e. dry conditions), we were unable to de-termine the cross-sensitivity of the CO2 gas analyzer to water vapor (Leuning and Moncrieff, 1990). Had we applied the correction with the cross-sensitivity found for their E009 instrument (β/α=1×10−3in Eq. (8) of Leuning and Moncrieff (1990)), the magnitude of FCO2 would increase by 8.8% on average.

3.3. Measurement of gas concentration profiles

CH4 concentrations in sampled air were mea-sured using a non-dispersive infrared CH4 analyzer (GA-360E, Horiba Co. Ltd., Kyoto, Japan) equipped with a specially designed pre-conditioner to minimize the interference of non-methane hydrocarbons and water vapor (Harazono et al., 1995). The time con-stant of the analyzer was 8.5 s. The CH4analyzer was calibrated twice a day, around 0900 and 1700 hours using two reference cylinders with high grade air con-taining 1.7 ppmv CH4 (Takachiho; certified accuracy is ±2%). CO2 concentrations were measured using a non-dispersive infrared CO2 analyzer (LI-6251, LICOR), which was operated with a time constant of 1 s. The CO2 analyzer was calibrated at the same time as the CH4 analyzer using two cylinders with 350 and 400 ppmv CO2in N2(Takachiho).

Air inlets for sample air were mounted at eight heights, 0.12, 0.24, 0.36, 0.48, 0.60, 0.72, 1.10 and 2.40 m above the water, and another inlet for refer-ence air was mounted at 2.50 m. The referrefer-ence air was required because the gas analyzers were operated in the differential mode which detected the differ-ences in infrared absorption between the sample air and the reference air. The gas concentrations at 1.10 and 2.40 m were used for the gas flux calculation by

use of the gradient method, while the whole profiles were used to infer sources and sinks of the gases in the canopy using an inverse Lagrangian analysis as described by Leuning et al. (2000). Teflon diaphragm pumps (MAA-P108-HB, Gas Manufacturing Corp., Benton harbor, MI., USA) and nylon tubing (10 mm ID) were used for air sampling. Air sampled at each inlet was drawn through an ice-trap to reduce mois-ture content, into a cylindrical PVC buffer (70 dm3in volume; time constant is about 15 min), then pumped to a T-junction, one arm of which was connected to a tube placed in a 60 cm deep water bubbler to control the pressure and the flow rate in the sampling line. The third arm of each T-junction was connected to a solenoid valve to permit selection of each air line in turn for gas analysis. The solenoid valves were controlled by a data logger and a personal computer. The air in the selected line passed through flow me-ters, dried further to a dew point temperature of 2◦C using a Peltier-cooled condenser (DH-209, Komatsu Electronics Inc., Tokyo, Japan), and then analyzed. The solenoid valve was switched every 2 min, and the sampling sequence was as follows: Line 1 (2.4 m), 2, 3, 4, 5, 6, 7, 8 (0.12 m), 7, 6, 5, 4, 3, 2, 1. . .. The full sampling sequence was thus completed in 30 min. This ‘staircase’ sampling technique eliminates linear trends in measurement of gas concentration differences.

Gas concentration data were sampled and recorded every 5 s using an A/D converter (Green Kit-88, Electric Systems Development, Tokyo, Japan) and a personal computer. The mean of the data sampled between 30 and 110 s after line switching was used to estimate the concentration at each height. After 1730 hours of 11 August, however, the CH4 concen-tration was averaged between 80 and 110 s after line switching because the CH4 analyzer was operated in the slow mode with a time constant of 26 s. The influence of insufficient response of the CH4analyzer in the slow mode on the average was estimated to be less than 3%, and was neglected. The standard error of the fluctuation of the analyzer’s output dur-ing the averagdur-ing period was 2.5 ppbv for the CH4 analyzer in the fast mode, 1.0 ppbv in the slow mode and 0.2 ppmv for the CO2 analyzer. These standard errors were used to estimate uncertainties in calcu-lated gas fluxes. The mean vertical difference of the gas concentration was calculated from 30 min means

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(the average of consecutive two cycles of the profile measurement) at 1.10 and 2.40 m.

3.4. Chamber measurement of CH4flux

CH4 fluxes were also measured using a closed chamber for comparison with the flux-gradient method. A bottom-less chamber, 0.36 m2in area, 1 m in height, made of acrylic resin, with an electric fan for circulation was employed for the measurement. Details of the chamber and air sampling method are described in Yagi and Minami (1993). The measure-ment was conducted on 13 August at two sites in the measurement plot, approximately 20 m to the west (western site) and 30 m to the east of the masts (east-ern site). At each site, two chambers were placed 4 m apart to examine the spatial variation of the flux. Air temperature inside the chamber Tc and soil tem-perature below it were monitored using thermistor thermometers. Air was sampled four times at 10 min intervals by pumping air into a Tedlar bag (GL Sci-ence, Tokyo, Japan). The chamber was placed 5 min before the first air sampling, and was removed imme-diately after the last (forth) sampling. Volume mixing ratios of CH4 in the bags were analyzed using a gas chromatograph with a flame-ionization detec-tor (GC-9A, Shimadzu, Kyoto, Japan) located in an air-conditioned laboratory. The volume mixing ratio was converted to density using Tc and the partial pressure of dry air in the chamber pa. The CH4 flux was deduced from the rate of change of CH4 den-sity with time as determined using linear regression. Leakage into the chamber caused by air sampling had an insignificant effect on the flux measurement be-cause sampling removed∼1% (4 dm3) of the chamber volume.

3.5. Supplementary measurements

Incident and reflected solar radiation, net radiation and photosynthetically active radiation were measured respectively with an Epply-type pyranometer (MR-22, Eko, Tokyo, Japan), a net radiometer (Q*6, Radiation Energy Balance Systems Inc., Seattle, WA, USA) and a quantum sensor (ML-020P, Eko). Soil heat flux was measured by three heat flux plates (MF-81, Eko), and the influence of the difference of thermal conductiv-ity between the plate (0.21 W m−1K−1) and the soil

(ca. 1.0 W m−1K−1) was corrected following Philip (1961). Water and soil temperatures (at 2 and 5 cm depth) were measured with T-type thermocouples. Changes in heat storage in floodwater was estimated from the change of water temperature. Water depth was measured with a float-type water gauge. The ver-tical profiles of air temperature and relative humidity were measured using ventilated Platinum resistance temperature sensors and capacitive humidity sen-sors (HUMITTER® 50Y, Vaisala, Helsinki, Finland) mounted at the same heights as the air inlets.

3.6. Floodwater depth and meteorological conditions

Water depth started decreasing in the morning of 6 August by drainage, and standing water disappeared at 1400 hours (Fig. 1a). Irrigation started at 0900 hours of 9 August, and water depth reached 10 cm around midday. The water level was maintained until the afternoon of 11 August, and afterwards gradually decreased with cessation of irrigation.

Clear days continued during the experiment, but meteorological conditions were a little different from day to day (Fig. 1). Wind direction (not shown in the figure) was constant, southeast, and windspeed showed clear diurnal variation: 2–3 m s−1in the daytime (ex-cept on 12 and 13 August), and declined to less than 0.5 m s−1 at night (Fig. 1b). On 12 and 13 August, windspeed was higher than on the other days as the re-sult of an approaching typhoon. The daily maximum air temperature at 2.4 m was 30–32◦C, and the daily minimum was 23–26◦C (Fig. 1c). Saturation deficit of air increased gradually in the morning, and reached a maximum of 9–15 g kg−1 in the late afternoon. On 10–11 August, higher air temperature and larger sat-uration deficit prevailed.

As shown in Fig. 1d, most of the net radiation in the daytime was partitioned into latent heat fluxλE (λis latent heat of vaporization of water), whereas H was less than 50 W m−2except on windy 13 August when it exceeded 80 W m−2. H changed sign from positive (upward transport) in the morning to negative in the af-ternoon. The Bowen ratio (H/λE) on drained days (7–8

August ) was 0.08 on average for 0900–1500 hours, while on flooded days (10–12 August) it ranged from −0.03 to 0.04. Further details of the energy bal-ance during IREX96 are given by Harazono et al. (1998).

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Fig. 1. Floodwater depth, surface energy balance and selected meteorological conditions during IREX96. (a) Floodwater depth (dw), (b) windspeed at a height of 2.2 m (U) measured with a sonic anemometer and friction velocity (u∗), (c) air temperature (Ta) and saturation deficit of the air at a height of 2.4 m in mixing ratio (Da), (d) net radiation (Rn), sensible heat flux (H) and latent heat flux (λE).

4. Results and discussion

4.1. Vertical profiles of CO2and CH4concentrations Typical profiles of CO2and CH4concentrations in the daytime and at night under drained and flooded conditions are shown in Fig. 2. Each profile is a 2 h mean, and noted time represents the beginning of the averaging period. The CO2profile in the daytime showed a minimum at the middle layer of the canopy owing to CO2 absorption by rice plants, while at night, the CO2 profile showed a monotonic decrease with height reflecting respiration by plants and the

soil. The CH4profiles showed a monotonic decrease with height both in the daytime and at night, but the vertical gradients of the concentrations at night un-der low wind conditions were 3–4 times as large as those in the daytime. A notable feature in Fig. 2 is that the vertical gradients of both gases were larger under drained conditions than under flooded condi-tions, although windspeeds were similar. The CO2 concentrations near the ground were also larger under drained than flooded conditions.

The vertical gradient of concentration above the canopy (between 1.10 and 2.40 m) decreased with

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Fig. 2. Examples of vertical profiles of CO2 (upper figures) and CH4 concentrations (lower figures) at a rice paddy in the daytime and at night. Each profile shows 2 h means of the difference from the concentration at 2.2 m. Closed circles indicate profiles under flooded condition with a depth of 10 cm, and open circles indicate profiles under drained condition. hcindicates canopy height. Beginning time of the averaging period and the mean horizontal windspeed are shown at the top of the figure.

15 ppbv m−1 at u∗ of 0.2 m s−1, and 10 ppbv m−1 at 0.3 m s−1. The gradient increased markedly up to 220 ppbv m−1 under stable atmospheric conditions with u∗<0.1 m s−1.

4.2. CO2fluxes under drained and flooded conditions The time course of CO2 fluxes above the canopy measured by the eddy covariance method (FCO2) is shown in Fig. 3. Also shown are the time courses of air temperature within the canopy and soil temper-atures at 2 and 5 cm depth. Missing data for FCO2

are mainly due to the open-path infrared gas analyzer being out of range at night. The differences in noc-turnal FCO2 between drained and flooded conditions are clearly shown in Fig. 3a. The nocturnal FCO2 under drained conditions (from 7 to 8 August) was 0.41±0.07 mg CO2m−2s−1 (the mean±the standard deviation; 1900–0500 hours), whereas under flooded conditions (from 9 to 13 August) it was 0.19±0.06 mg CO2m−2s−1. The nocturnal FCO2 under flooded conditions are within the range of aboveground rice respiration rate measured by chambers about a month before heading (0.1–0.3 mg CO2m−2s−1; Yamaguchi

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Fig. 3. Time courses of (a) CO2 flux over the canopy by the eddy covariance method (FCO2), (b) air temperature at a height of 0.48 m, soil temperature at depths of 2 and 5 cm below the soil surface, (c) saturation deficit of the canopy (Ds) and the canopy conductance (Gc).

et al., 1975; Hirota and Takeda, 1978; Baker et al., 1992; Saitoh et al., 1998). This agreement confirms the nocturnal FCO2 measurement by the eddy co-variance method. Since mean nocturnal air temper-atures between drained and flooded conditions were very similar (differences <0.3◦C; see Fig. 3b), we expect plant respiration also to have been similar every night, and thus not responsible for observed differences in the nocturnal FCO2 between the two treatment periods. Possible explanations are discussed further.

During IREX96, CO2 emission rates from bare soil and from floodwater, FCO2,Swere measured near the masts by using a dynamic, dark-chamber method. Fluxes from the soil were measured on 7 and 8 August under drained conditions, whereas those from flood-water were measured in the morning of 6 August. The fluxes from the soil ranged from 0.20 to 0.43 mg CO2m−2s−1when soil temperature at 5 cm varied be-tween 26.0 to 28.5◦C (the nocturnal range of soil tem-perature shown in Fig. 3b), whereas the fluxes from the floodwater were two orders of magnitude smaller,

at severalmg CO2m−2s−1. These results suggest that the higher nocturnal CO2 fluxes measured above the canopy under drained conditions can be attributed to increased CO2 emission from the bare soil compared to that from floodwater. The strong response of soil microbial and root respiration to temperature cannot be responsible for the observed change in net noctur-nal CO2 flux, because under drained soil, nocturnal (1900–0500 hours) mean soil temperatures at 2 and 5 cm were lower by 2.3 and 1.1◦C, respectively, than under flooded conditions (Fig. 3b). This should lead to lower fluxes. Instead, higher fluxes under drained conditions result from the elimination of the resistance caused by the floodwater to the transport of respired CO2 from the soil to the atmosphere. Leuning et al. (2000) came to similar conclusions as a result of their analysis of source/sink distributions within the canopy.

The increase of soil respiration FCO2,S under drained conditions must affect not only the noctur-nal FCO2 over the canopy but also FCO2 during the day. We can use our measurements of soil respiration

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Fig. 4. Relationship between net photosynthesis rate of rice plants (Pn), canopy conductance (Gc) and photosynthetically active radiation flux density (Rp) on drained days (7–8 August; closed circles) and flooded days (10–12 August; open circles). In (c) and (d), Pn and Gc are normalized to the constraint function of saturation deficit fD(Ds)/fD(10), where fD(10) is the value of fDwhen Ds=10 g kg−1.

obtained at night to estimate the net photosynthesis rate of rice plants Pn using Pn=−FCO2+FCO2,S. As a first approximation we assumed that FCO2,S=0 mg CO2m−2s−1 for flooded soil and FCO2,S=0.21 mg CO2m−2s−1 under drained conditions, the excess amount of the nocturnal mean FCO2 compared to flooded conditions. Fig. 4a shows the relationship between Pn and photosynthetically active radiation flux density Rp when the paddy was drained (7–8 August) and flooded (10–12 August, the data on transitional 9 August were excluded). From the hy-perbolic curves fitted to the data, Pnwas larger when the paddy was drained than when it was flooded by 18% at Rpof 1500mmol m−2s−1, and by 22% at Rp of 2000mmol m−2s−1.

We next examine whether the changes in canopy photosynthesis can be explained by the higher air tem-peratures and saturation deficits observed during the flooded days compared to the drained days (Fig. 1c). To explore this, Pnwas analyzed in relation to canopy conductance in the so-called ‘big-leaf model’. The

canopy conductance Gcis defined as

Gc=

ρDs

(6) where Ecis the evapotranspiration rate from rice plants,

Dsis saturation deficit at the big leaf surface expressed as specific humidity (note that we are discussing the conductance and saturation deficit of the canopy only, excluding the soil/water surface). From the definition of the Penman–Monteith equation and Eq. (6), Ds is given as follows (Kelliher et al., 1993):

Ds=Da−

ε+1

Gaρ

Ec−

ε ε+1

Rn,c

(7) where Da is saturation deficit at a reference height (2.4 m), Gais the aerodynamic conductance, and Rn,c is the net radiation absorbed by the canopy. The ther-modynamic coefficient ε is expressed as ε=(λ/Cp) (dqsat/dT), where dqsat/dT is the slope of the saturation specific humidity–temperature curve. In this study, Ga was calculated from u∗and the mean horizontal

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wind-speed U at a height of 2.2 m such as Ga=u∗2/U. Rn,c was calculated from the extinction coefficient of net radiation by the rice canopy (0.66; Uchijima, 1961) and LAI. Ecwas calculated as E–Es, where Es is the evaporation rate from the soil/water surface which was estimated from the available energy at the soil/water surface (Leuning et al., 2000). The estimated Ec was 84%, on average, of the total water vapor flux mea-sured above the canopy E, both for drained and flooded conditions, and the ratio is in good agreement with a previous study (86% at LAI=3.08; Uchijima, 1961).

Daytime Ga ranged from 10 to 60 mm s−1, except on 12 and 13 August when it was windy and Ga in-creased up to 70 mm s−1. As shown in Fig. 3c, values of Ds in the daytime were generally<10 g kg−1, but in the afternoon of 10 and 11 August, Ds exceeded 13 g kg−1. As a result, Gc around noon of these two days was 12–13 mm s−1, compared to peak values of 14–18 mm s−1on other days. On the windy day of 13 August, Gc at midday exceeded 21 mm s−1when Ds was small.

The relationship between Gc and Rp (Fig. 4b) shows that canopy conductances on flooded days were smaller than drained days at the same Rp level. To examine the influence of saturation deficit on Gc quantitatively, we utilize constraint functions follow-ing Jarvis (1976) and Schulze et al. (1995):

Gc=Gc,maxf_R R_Pf_D(Ds) f_T(T1) (8) where Gc,max is the value of Gc without constraints, and the functions fR, fDand fT(between 0 and 1) ac-count for the constraints on Gc,max imposed by, irra-diance, Dsand leaf temperature Tl, respectively. As a first approximation we assumed that fT=1 (no con-straint) and that fDhas the hyperbolic form

f_D(Ds)=

1 1+Ds/Ds,1/2

(9) where Ds,1/2is the value of Dsat which Gc=Gc,max/2. We assumed a typical value of Ds,1/2=10 g kg−1 (Le-uning, 1995), and normalized both Pn and Gc to a standard humidity deficit of 10 g kg−1 through mul-tiplying by the factor f_D (10)/f_D (Ds). It is accept-able to normalize Pnby this factor as well because it is relatively insensitive to changes in Ds (see Leun-ing, 1995). Relationships between the normalized Pn and Gc as a function of Rp (Fig. 4c and d, respec-tively) now show little difference between the flooded

Fig. 5. Daily CO2 budget at the paddy. Daytime amounts are the sum of the CO2flux over the canopy from 0500 to 1900 hours, and nighttime amounts are from 1900 to 0500 hours on the following day. Bars indicate an uncertainty range of the estimated amount corresponding to the corrections to the CO2 flux by the eddy covariance method for path-length averaging and sensor separation.

and drained conditions. This suggests the difference in

Pnbetween drained and flooded days was principally due to the response of Gcto an increase in saturation deficit. The larger saturation deficit on flooded days than on drained days was caused by the differences in synoptic-scale meteorological conditions rather than the state of flooding of the paddy.

Daily CO2 budgets for the rice paddy are shown in Fig. 5. In the summation of FCO2, missing data were estimated from the Pn–Rprelation shown in Fig. 4a, while FCO2,S was assumed zero under flooded conditions and a constant (0.21 mg CO2m−2s−1) under drained conditions. The summation period for the daytime was from 0500 to 1900 hours, and for the nocturnal period from 1900 to 0500 hours. Bars show the uncertainty range of FCO2 which was esti-mated to be the same magnitude as the corrections for path length averaging and sensor separation. Since we applied these corrections to recover missing cospectrum in high frequency range, the magnitude of the corrections shows the degree of uncertainty in FCO2. The daily sum of Rp was similar on most days, 51–52 mol m−2d−1, except for slightly larger values on 10 August (57 mol m−2d−1) and 12 August (59 mol m−2d−1). As shown in Fig. 5, the average daytime CO2 uptake was 29.2 g CO2m−2 when the field was drained (7 and 8 August), 23% smaller than when it was flooded (10–12 August). The nighttime

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CO2emission on the drained days, on the other hand, was 14.7 mg CO2m−2, which was almost twice as much as on the flooded days. As a result, the average net daily (24 h) CO2uptake on the drained days was 14.5 g CO2m−2, while it was 29.8 g CO2m−2on the flooded days. As described earlier, these differences in the CO2budget between the two treatment periods are mainly due to differences in the rate of CO2 re-lease from the soil surface, and to a lesser extent the reduction of plant photosynthesis due to the larger saturation deficit on flooded days.

A previous study on the same site and at a similar rice growth stage (Tsukamoto, 1993, 1994) showed that the net downward CO2 flux between 0500 and 1900 hours was 33% smaller when the field was drained than when it was flooded with 10 cm of wa-ter. The IREX96 results are similar to this study, and it is now clear that the decrease in the net daytime downward CO2 flux under drained conditions was caused by an increase of CO2 emission from the soil surface. In the short term, intermittent drainage thus reduces net CO2 uptake from the atmosphere by rice paddies compared to continuously flooded paddies.

4.3. CH4fluxes under drained and flooded conditions The time courses of CH4 fluxes over the canopy as determined by the KCO2 method (KCO2–FCH4) are shown in Fig. 6. Most of the missing flux data in Fig. 6 are due to condensation of water in the sampling line which caused the malfunction of the gas analyz-ers. Bars on KCO2–FCH4, which are shown only every hour for clarity, represent an uncertainty range of the flux corresponding to the standard error of the vertical difference of CH4and CO2concentrations and the un-certainty of CO2flux by the eddy covariance method. In Fig. 6, the large uncertainty in the daytime flux is due to small vertical differences in CH4concentration. As shown in Fig. 6, KCO2–FCH4 on a 30 min basis showed large fluctuations ranging from 0.2 to 2.1mg CH4m−2s−1, mainly due to the small vertical differ-ence of CH4concentration over the canopy. Even so, a 7-term running mean of KCO2–FCH4 showed a dis-tinct diurnal variation under drained conditions, with larger fluxes (1.2–1.3mg CH4m−2s−1) in the after-noon than at night (0.3–0.4mg CH4m−2s−1). The CH4flux showed a significant decrease after

reflood-ing on 9 August, and diurnal variation of the flux de-clined on and after 10 August. The CH4fluxes on 13 August were uncertain because of large fluctuations and missing data, but it seems that the CH4 fluxes were recovering.

Detailed diurnal variations of KCO2–FCH4 and the CH4 flux by the aerodynamic method (AD–FCH4) from midday of 8 August to the evening of 9 August are shown in Fig. 7. Also shown are the diurnal vari-ations of the soil temperature at a depth of 2 cm and the windspeed at a height of 2.2 m. Bars on AD–FCH4 represent a range corresponding to the standard error of the vertical CH4 concentration difference and the uncertainty of conductance D (Eq. (3)) which origi-nates mainly fromζ. Under low windspeed conditions found at night, u∗ and H determined by the eddy covariance method have considerable uncertainties. Since ζ is proportional to u∗−3 (Eq. (2)), any mea-surement errors in u∗ are greatly amplified inζ. The measurement errors in H add further uncertainty toζ. As a result,ζ and D under low windspeed conditions were not reliable. In fact, the estimated uncertainty in AD–FCH4 under such conditions was one order of magnitude greater than that in KCO2–FCH4. For these reasons, we excluded AD–FCH4 when u∗ was

<0.1 m s−1(this threshold was equivalent to 0.7 m s−1 in horizontal windspeed at 2.2 m) from the figure and the following analysis. As shown in Fig. 7a, the day-time AD–FCH4 (the 7-term running mean) is a little smaller than KCO2–FCH4, but the fluxes by the two methods are generally in agreement with each other within the measurement error. Fig. 7 demonstrates that the pattern in the diurnal variation of the CH4 fluxes is quite similar to those of soil temperature and windspeed, which decrease gradually from the afternoon through the night and increase from the morning to the afternoon.

The daily budget of CH4 flux from the paddy is shown in Fig. 8. Missing flux data from 7 to 9 August were interpolated using relationships between the flux and soil temperature at a depth of 2 cm. The relation-ships were determined each day by fitting a polyno-mial function of second degree using the least squares method (the fitting passed thex2-test on a significant level of 5%). For other days, missing data were in-terpolated using the average of each 5 data points be-fore and after the missing period. As shown again in Fig. 8, the fluxes by the AD method are systematically

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Fig. 6. Time course of CH4 flux over the rice canopy. Pluses represent the fluxes by the KCO2 method (KCO2–FCH4), and bars, which are shown only every hour, represent a range of the flux corresponding to the standard error of the vertical differences of CH4 and CO2 concentrations and the uncertainty of the CO2 flux measured by the eddy covariance method. Solid lines denote the 7-term running mean of individual KCO2–FCH4. Closed squares and open squares denote the flux by the chamber measurement on 13 August at the western site and the eastern site, respectively.

Fig. 7. Diurnal variation of (a) CH4flux, (b) soil temperature at a depth of 2 cm (solid line), and windspeed (dotted line) from midday of 8 August to the evening of 9 August. Closed circles and open circles in Fig. 7(a) denote the CH4fluxes by the KCO2method (KCO2–FCH4) and by the AD method (AD–FCH4), respectively, and solid line and dotted line denote their 7-term running mean. Bars on KCO2–FCH4are the same as those in Fig. 6, while those on AD–FCH4represent a range corresponding to the standard error of the vertical difference of CH4concentration and an uncertainty of the conductance in Eq. (3).

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Fig. 8. Accumulated amount of methane emission from the paddy in the daytime (from 0500 to 1900 hours) and at night (from 1900 to 0500 hours on the following day) obtained by the KCO2 method and the AD method. The AD method was not available at nights from 7 to 10 August because of calm conditions. Bars represent uncertainty ranges of the accumulated amount which were estimated from the uncertainties of individual 30 min fluxes. smaller than those by the KCO2method, but the differ-ences between the two methods are within measure-ment error indicated by bars. The agreemeasure-ment between the two different micrometeorological methods gives us confidence in the calculated fluxes, although we have much fewer nighttime fluxes by the AD method than the KCO2 method.

Day-to-day change of the daily CH4 flux is well demonstrated in Fig. 8. The daytime fluxes (from 0500 to 1900 hours) by the KCO2method on 7 and 8 August were 52 and 43 mg CH4m−2, respectively, which de-creased to 30–33 mg CH4m−2for the flooded paddy on 10–12 August. As shown by bars in Fig. 8, the dif-ferences in the daytime CH4 flux are statistically in-significant except on 7 August, but a decreasing trend in the daytime CH4 flux from 7 to 12 August is ap-parent. The nighttime flux (from 1900 to 0500 hours), ranging between 15 and 28 mg CH4m−2, did not show a monotonic trend. As a result, the daily amount of CH4 emission from the paddy on drained days (in-cluding a transitional 9 August) ranged from 58 to 80 mg CH4m−2, which decreased to less than 60 mg CH4m−2 on following flooded days (the average for 10–12 August was 53 mg CH4m−2).

Among possible factors which affect the CH4flux, soil temperature can be influenced by drainage and flooding. In IREX96, however, daily maximum soil temperatures at a depth of 2 cm stayed at >32◦C on

and until 10 August, then dropped to <31◦C (Fig. 3b), whereas diurnal variation of CH4 flux declined on 10 August (Fig. 6). This indicates soil tempera-ture had a minor influence on the difference of CH4 fluxes between the two treatment periods. Instead, as with the CO2 flux, the increased CH4 flux resulted from the absence of floodwater which reduced the resistance to gas diffusion from the soil to the air under drained conditions. The change is not as dra-matic as for CO2 (Fig. 3a) because much of the CH4 is released to the air through plant-mediated trans-port (Minami and Neue, 1994) and the rest by dif-fusion from the soil to the air. After the reflooding on 9 August, the CH4 flux decreased because the diffusion from the soil to air was prevented by the water layer, but CH4 was still released through the plant-mediated process. Although our measurements were aborted on 13 August by a typhoon, we expect the CH4flux would recover gradually with the progress of soil reduction under continuously flooded (anaerobic) conditions.

Previous studies using chambers showed intensive CH4emission lasting some tens of hours following a drainage event (Neue and Sass, 1994). In a Japanese rice paddy, Yagi (1997) observed an increased CH4 emission after drainage which continued about two days and showed a diurnal variation with a maxi-mum in the daytime. From an analysis of the rela-tionship between the drainage duration and changes in CH4flux before and after a drainage, Yagi (1997) found drainage lasting more than 2 days reduced the CH4 flux after reflooding for a few weeks to a lower level than before drainage. The trend in the day-to-day change of the daily CH4 flux during IREX96 (Fig. 8) was consistent with the findings of these previ-ous studies. In IREX96, the daily CH4 flux on the drained days was larger than on the flooded days by 28% on average, while previous studies found much larger increases after drainage events (Neue and Sass, 1994; Yagi, 1997). The amount and duration of the temporary increase of CH4 emission after drainage depend on the accumulated amount of CH4 in the soil before drainage. The intermittent drainage at the present study site prevented the recovery of the pro-duction and the accumulation of CH4in the soil, and this resulted in a smaller increase in the daily CH4 flux after drainage than those observed in the previous studies.

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As mentioned earlier, the diurnal variation in the CH4flux under drained conditions showed a distinct positive correlation with soil temperature and wind-speed (Fig. 7). This is because temperatures up to 30–35◦C accelerate CH4production by methanogenic bacteria in the soil, and the amount of CH4dissolved in the soil solution decreases with increasing temper-ature (Minami and Neue, 1994). Plant-mediated CH4 transport also shows an increase with increasing tem-perature (Nouchi et al., 1994; Hosono and Nouchi, 1996), although the relationships found by these au-thors were between the seasonal variation of CH4flux and temperature rather than the diurnal variation ob-served here. As well as these temperature effects, the distinct diurnal variation of the CH4 flux observed during IREX96 was influenced by windspeed which affects the diffusion resistance from the soil to the air under drained conditions. Drainage and flooding thus affect the CH4 flux by changing the diffusion resistance to CH4 transport from the soil to air as well as by changing the CH4production rates in the soil.

Methane fluxes were also measured by the closed chamber method on 13 August. The measurements were made in the morning (0930–1100 hours) and in the early afternoon (1215–1345 hours) at two adja-cent locations at each site, and the average of each pair of measurements are shown in Fig. 6. The fluxes at the eastern site were 1.2 and 1.3mg CH4m−2s−1, while those at the western site were 2.2 and 2.7mg CH4m−2s−1. Measurements using the chamber at the eastern site were comparable with the running mean of KCO2–FCH4, but fluxes from the western site were double the peak observed from the mi-crometeorological measurements. It is likely that spatial inhomogeneity caused by variation in den-sity of rice plants, nutrient status of the soil and water depth are partly responsible for difference in fluxes measured by the chambers at the two sites. In one of the few other comparisons published, Kane-masu et al. (1995) found that CH4 fluxes from rice paddies estimated using closed chambers exceeded micrometeorological measurements by a factor of 2. Further detailed studies are required to clarify whether spatial heterogeneity is sufficient to explain such discrepancies or whether there are systematic methodological differences between the measurement techniques.

5. Conclusion

During IREX96, net CO2 and CH4 fluxes were measured over an intermittently drained and flooded Japanese paddy field using micrometeorological methods. From the comparison of the gas fluxes un-der drained and flooded conditions we conclude: (1) The daily net uptake of CO2 from the atmosphere by the paddy field was 50% lower when the paddy was drained than when it was flooded. (2) The daily CH4 emission on drained days was 28% larger than on flooded days. (3) Enhanced fluxes of CO2 and CH4 from the drained soil were due to removal of the barrier to gas transport from the soil surface to the air caused by the floodwater. The present study made it clear how flooding and drainage affect the exchanges of CO2 and CH4 at rice paddies in the short term. We need further measurements throughout a rice cultivation period to assess the long term effect of an intermittent drainage practice on the exchanges of these gases at rice paddies.

Acknowledgements

We express special thanks to the following persons. E. Ohtaki, Okayama University, for sincere support during IREX96; Taejin Choi, Yonsei University, and C. Drury, CSIRO, for technical support in the field; T. Miura, Okayama University, for providing us with unpublished data at IREX96; M. Tada, the manager of the Hachihama experimental farm, for use of the facilities of the farm; E. Ishibashi, Okayama Prefecture Agricultural Experiment Station, for use of GC-FID; H. Sakai, NIAES, for providing us with rice respiration data.

This study was supported by ‘The Bilateral Inter-national Joint Research by Special Coordination Fund Promoting for Science and Technology (FY 1996)’ and ‘Japanese Study on the Behavior of Greenhouse Gases and Aerosols (FY 1990–1999)’ by Research and Development Bureau, Japan Science and Technology Agency. The travel of Australian authors to Japan was supported by the Australian Department of Science Multifunction Polis Program, and that of Korean au-thor was supported by Eco-Frontier Fellowship Pro-gram (FY 1996) by the Japan Environment Agency. The Korean author acknowledges support from the

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Ministry of Agriculture and Fisheries of Korea through the Special Project (295133-4) for the Agricultural Technology Development.

References

Baker, J.T., Laugel, F., Boote, K.J., Allen Jr, L.H., 1992. Effects of daytime carbon dioxide concentration on dark respiration in rice. Plant, Cell Environ. 15, 231–239.

Cicerone, R.J., Shetter, J.D., 1981. Sources of atmospheric methane: measurements in rice paddies and a discussion. J. Geophys. Res. 86, 7203–7209.

Denmead, O.T., 1991. Sources and sinks of greenhouse gases in the soil–plant environment. Vegetatio 91, 73–86.

Denmead, O.T., 1994. Measuring fluxes of greenhouse gases between rice fields and the atmosphere. In: Peng, S., et al. (Eds.), Climate Change and Rice. Springer, Berlin, pp. 15–29. Dyer, A.J., Hicks, B.B., 1970. Flux-gradient relationships in the

constant flux layer. Q. J. R. Meteorol. Soc. 96, 715–721. Edwards, G.C., Neumann, H.H., den Hartog, G., Thurtell, G.W.,

Kidd, G., 1994. Eddy correlation measurements of methane fluxes using a tunable diode laser at the Kinosheo Lake tower site during the Northern Wetlands Study (NOWES). J. Geophys. Res. 99, 1511–1517.

Fowler, D., Duyzer, J.H., 1989. Micrometeorological techniques for the measurement of trace gas exchange. In: Andreae, M.O., Schimel, D.S. (Eds.), Exchange of Trace Gases between Terrestrial Ecosystems and the Atmosphere. Wiley, Chichester, pp. 189–207.

Harazono, Y., Miyata, A., 1997. Evaluation of greenhouse gas fluxes over agricultural and natural ecosystems by means of micrometeorological methods. J. Agric. Meteorol. 52, 477–480. Harazono, Y., Miyata, A., Yoshimoto, M., Mikasa, H., Oku, T., 1995. Development of a movable NDIR-methane analyzer and its application for micrometeorological measurements of methane flux over grasslands. J. Agric. Meteorol. 51, 27–35 (in Japanese with English abstract and captions).

Harazono, Y., Monji, N., Miyata, A., Kita, K., Hamotani, K., Uchida, Y., Yoshimoto, M., Sano, T., Fujiwara, M., Isobe, S., Ogawa, T., 1996. Development of measurement methods for trace gas fluxes in the surface boundary layer and a basic examination of the flux evaluation. Bull. Natl. Inst. Agro-Environ. Sci., Tsukuba, Japan 13, 166–226 (in Japanese with English summary and captions).

Harazono, Y., Kim, J., Miyata, A., Choi, T., Yun, J.-I., Kim, J.-W., 1998. Measurement of energy budget components during the International Rice Experiment (IREX) in Japan. Hydrol. Process 12, 2081–2092.

Hignett, P., 1992. Corrections to temperature measurements with a sonic anemometer. Boundary-Layer Meteorol. 61, 175–187. Hirota, O., Takeda, T., 1978. Studies on utilization of solar

radiation by crop stands III. Relationships between conversion efficiency of solar radiation energy and respiration of construction and maintenance in rice and soybean plant populations. Jap. J. Crop Sci. 47, 336–343.

Holzapfel-Pschorn, A., Seiler, W., 1986. Methane emission during a vegetation period from an Italian rice paddy. J. Geophys. Res. 91, 11803–11814.

Hosono, T., Nouchi, I., 1996. Seasonal changes of methane flux and methane concentration in soil water in rice paddies. J. Agric. Meteorol. 52, 107–115 (in Japanese with English abstract and captions).

Inoue, E., Tani, N., Imai, K., Isobe, S., 1958. The aerodynamic measurement of photosynthesis over the wheat field. J. Agric. Meteorol., 13, 121–125 (in Japanese with English abstract). Inoue, E., Uchijima, Z., Saito, T., Isobe, S., Uemura, K., 1969. The

“Assimitron”, a newly devised instrument for measuring CO2 flux in the surface air layer. J. Agric. Meteorol. 25, 165–171. IPCC, 1995. Climate Change 1995: The Science of Climate

Change. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., Maskell, K. (Eds.), Cambridge Univ. Press, Cambridge.

Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Phil. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 273, 593–610. Kaimal, J.C., Gaynor, J.E., 1991. Another look at sonic

thermometry. Boundary-Layer Meteorol. 56, 401–410. Kanemasu, E.T., Flitcroft, I.D., Shah, T.D.H., Nie, D., Thurtell,

G.W., Kidd, G., Simpson, I., Lin, M., Neue, H.-U., Bronson, K., 1995. In: Peng, S., et al. (Eds.), Climate Change and Rice. Springer, Berlin, pp. 91–101.

Kelliher, F.M., Leuning, R., Schulze, E.-D., 1993. Evaporation and canopy characteristics of coniferous forests and grasslands. Oecologia 95, 153–163.

Khalil, M.A., Rasmussen, R.A., Shearer, M.J., Dalluge, R.W., Ren, L.X., Duan, C.-L., 1998. Measurements of methane emission from rice fields in China. J. Geophys. Res. 103, 25181–25210. Kim, J., Verma, S.B., Billesbach, D.P., 1998a. Seasonal variation in methane emission from a temperate Phragmites-dominated marsh: effect of growth stage and plant-mediated transport. Global Change Biol. 5, 433–440.

Kim, J., Verma, S.B., Billesbach, D.P., Clement, R.J., 1998b. Diel variation in methane emission from a midlatitude prairie wetland: significance of convective throughflow in Phragmites australis. J. Geophys. Res. 103, 28029–28039.

Kobashi, H., Nagahori, K., Tanemura, C., Ogino, Y., 1968. Investigation of the physical and mechanical characteristics of poldered paddy fields in Kojima Bay. Scientific Reports on the Faculty of Agriculture, Okayama Univ. 31, 29–44 (in Japanese). Koyama, T., 1963. Gaseous metabolism in lake sediments and paddy soils and the production of atmospheric methane and hydrogen. J. Geophys. Res. 68, 3971–3973.

Leuning, R., 1995. A critical appraisal of combined stomatal-photosynthesis model for C3 plants. Plant, Cell Environ. 18, 339–357.

Leuning, R., Judd, M.J., 1996. The relative merits of open- and closed-path analysers for measurements of eddy fluxes. Global Change Biol. 2, 241–253.

Leuning, R., Moncrieff, J., 1990. Eddy-covariance CO2 flux measurements using open- and closed-path CO2 analyzers: corrections for analyzer water vapor sensitivity and damping of fluctuations in air sampling tubes. Boundary-Layer Meteorol. 53, 63–76.

(17)

Leuning, R., Denmead, O.T., Miyata, A., Kim, J., 2000. Source/sink distributions of heat, water vapor, carbon dioxide and methane in rice canopies estimated using Lagrangian dispersion analysis, Agric. For. Meteorol., submitted. Minami, K., Neue, H.-U., 1994. Rice paddies as a methane source.

Climate Change 27, 13–26.

Monteith, J.L., Unsworth, M.H., 1990. Crop Micrometeorology. In: Principles of Environmental Physics 2nd Edition. Arnold, London, pp. 231–244.

Moore, C.J., 1986. Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol. 37, 17– 35.

Neue, H.-U., Sass, R.L., 1994. Trace gas emissions from rice fields. In: Prinn, R.G. (Ed.), Global Atmospheric–Biospheric Chemistry. Plenum Press, New York, pp. 119–147.

Nouchi, I., 1994. Mechanisms of methane transport through rice plants. In: Minami, K., Mosier, A., Sass, R. (Eds.), CH4 and N2O. Global Emissions and Controls from Rice Fields and Other Agricultural and Industrial Sources. Yokendo, Tokyo, Japan, pp. 87–104.

Nouchi, I., Hosono, T., Aoki, K., Minami, K., 1994. Seasonal variation in methane flux from rice paddies associated with methane concentration in soil water, biomass and temperature, and its modeling. Plant and Soil 161, 195–208.

Ohtaki, E., 1984. Application of an infrared carbon dioxide and humidity instrument to studies of turbulent transport. Boundary-Layer Meteorol. 29, 85–107.

Ohtaki, E., Matsui, T., 1982. Infrared device for simultaneous measurement of atmospheric carbon dioxide and water vapor. Boundary-Layer Meteorol. 24, 109–119.

Philip, J.R., 1961. The theory of heat flux meters. J. Geophys. Res. 66, 571–579.

Saitoh, K., Sugimoto, M., Shimoda, H., 1998. Effects of dark respiration on dry matter production of field grown rice stand. Comparison of growth efficiencies in 1991 and 1992. Plant Prod. Sci. 1, 106–112.

Sass, R.L., Fisher, F.M., Harcombe, P.A., Turner, F.T., 1990. Methane production and emission in a Texas rice field. Global Biogeochem. Cycles 4, 47–68.

Schuepp, H., Leclerc, M.Y., Macpherson, J.I., Desjardins, R.L., 1990. Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Boundary-Layer Meteorol. 50, 355–373.

Schulze, E.-D., Leuning, R., Kelliher, F.M., 1995. Environmental regulation of surface conductance for evaporation from vegetation. Vegetatio 121, 79–87.

Schütz, H., Holzapfel-Pschorn, A., Conrad, R., Rennenberg, H., Seiler, W., 1989. A 3-year continuous record on the influence of daytime, season, and fertilizer treatment on methane emission

rates from an Italian rice paddy. J. Geophys. Res. 94, 16405– 16416.

Shurpali, N.J., Verma, S.B., Clement, R.J., Billesbach, D.P., 1993. Seasonal distribution of methane flux in a Minnesota peatland measured by eddy correlation. J. Geophys. Res. 98, 20649– 20655.

Simpson, I.J., Thurtell, G.W., Kidd, G.E., Lin, M., Demetriades-Shah, T.H., Flitcroft, I.D., Kanemasu, E.T., Nie, D., Bronson, K.F., Neue, H.U., 1995. Tunable diode laser measurements of methane fluxes from an irrigated rice paddy field in Philippines. J. Geophys. Res. 100, 7283–7290. Tsukamoto, O., 1993. Turbulent fluxes over paddy field under

various ponding depth. J. Agric. Meteorol. 49, 19–25 (in Japanese with English abstract and captions).

Tsukamoto, O., 1994. Reply to ‘Discussion on Turbulent fluxes over paddy field under various ponding depth’ by Harazono, Y. J. Agric. Meteorol. 49, 307–308 (in Japanese).

Uchijima, Z., 1961. On characteristics of heat balance of water layer under paddy plant cover. Bull. Natl. Inst. Agric. Sci., Tokyo, Japan A8, 243–265.

Uchijima, Z., 1976. Maize and rice. In: Monteith, J.L. (Ed.), Vegetation and the Atmosphere Vol. 2. Academic Press, London, pp. 33–64.

Verma, S.B., Ullman, F.G., Billesbach, D., Clement, R.J., Kim, J., Verry, E.S., 1992. Eddy correlation measurements of methane flux in a northern peatland ecosystem. Boundary-Layer Meteorol. 58, 289–304.

Webb, E.K., 1970. Profile relationships: the log-linear range, and extension to strong stability. Q. J. R. Meteorol. Soc. 106, 85– 100.

Webb, E.K., Pearman, G.I., Leuning, R., 1980. Correction of flux measurements for density effects due to heat and water vapor transfer. Q. J. R. Meteorol. Soc. 106, 85–100.

Yagi, K., 1997. Methane emission from paddy fields. Bull. Natl. Inst. Agro-Environ. Sci., Tsukuba, Japan 14, 96–210. Yagi, K., Minami, K., 1990. Effect of organic matter application

on methane emission from some Japanese paddy fields. Soil Sci. Plant Nutr. 36, 599–610.

Yagi, K., Minami, K., 1993. Spatial and temporal variations of methane flux from a rice paddy field. In: Oremland, R.S. (Ed.), Biogeochemistry of Global Change: Radiatively Active Trace Gases. Chapman & Hall, New York, pp. 353–368.

Yamaguchi, J., Watanabe, K., Tanaka, A., 1975. Studies on the growth efficiency of crop plant (Part 4). Respiratory rate and the growth efficiency of various organs of rice and maize. J. Sci. Soil Manure, Japan 46, 113–119.

Yamamoto, H., Suzuki, Y., Hayakawa, S., 1995. Estimation of leaf area index in crop canopies using plant canopy analyzer. Jap. J. Crop Sci. 64, 333–335.

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was 14.7 mg CO2m−2, which was almost twice as

much as on the flooded days. As a result, the average net daily (24 h) CO2uptake on the drained days was

14.5 g CO2m−2, while it was 29.8 g CO2m−2on the

flooded days. As described earlier, these differences in the CO2budget between the two treatment periods

are mainly due to differences in the rate of CO2

re-lease from the soil surface, and to a lesser extent the reduction of plant photosynthesis due to the larger saturation deficit on flooded days.

A previous study on the same site and at a similar rice growth stage (Tsukamoto, 1993, 1994) showed that the net downward CO2 flux between 0500 and

1900 hours was 33% smaller when the field was drained than when it was flooded with 10 cm of wa-ter. The IREX96 results are similar to this study, and it is now clear that the decrease in the net daytime downward CO2 flux under drained conditions was

caused by an increase of CO2 emission from the soil

surface. In the short term, intermittent drainage thus reduces net CO2 uptake from the atmosphere by rice

paddies compared to continuously flooded paddies.

4.3. CH4fluxes under drained and flooded conditions

The time courses of CH4 fluxes over the canopy

as determined by the KCO2 method (KCO2–FCH4) are

shown in Fig. 6. Most of the missing flux data in Fig. 6 are due to condensation of water in the sampling line which caused the malfunction of the gas analyz-ers. Bars on KCO2–FCH4, which are shown only every

hour for clarity, represent an uncertainty range of the flux corresponding to the standard error of the vertical difference of CH4and CO2concentrations and the

un-certainty of CO2flux by the eddy covariance method.

In Fig. 6, the large uncertainty in the daytime flux is due to small vertical differences in CH4concentration.

As shown in Fig. 6, KCO2–FCH4 on a 30 min basis

showed large fluctuations ranging from 0.2 to 2.1mg

CH4m−2s−1, mainly due to the small vertical

differ-ence of CH4concentration over the canopy. Even so,

a 7-term running mean of KCO2–FCH4 showed a

dis-tinct diurnal variation under drained conditions, with larger fluxes (1.2–1.3mg CH4m−2s−1) in the

after-noon than at night (0.3–0.4mg CH4m−2s−1). The

CH4flux showed a significant decrease after

reflood-clined on and after 10 August. The CH4fluxes on 13

August were uncertain because of large fluctuations and missing data, but it seems that the CH4 fluxes

were recovering.

Detailed diurnal variations of KCO2–FCH4 and the

CH4 flux by the aerodynamic method (AD–FCH4)

from midday of 8 August to the evening of 9 August are shown in Fig. 7. Also shown are the diurnal vari-ations of the soil temperature at a depth of 2 cm and the windspeed at a height of 2.2 m. Bars on AD–FCH4

represent a range corresponding to the standard error of the vertical CH4 concentration difference and the

uncertainty of conductance D (Eq. (3)) which origi-nates mainly fromζ. Under low windspeed conditions found at night, u∗ and H determined by the eddy

covariance method have considerable uncertainties. Since ζ is proportional to u∗−3 (Eq. (2)), any

mea-surement errors in u∗ are greatly amplified inζ. The

measurement errors in H add further uncertainty toζ. As a result,ζ and D under low windspeed conditions were not reliable. In fact, the estimated uncertainty in AD–FCH4 under such conditions was one order

of magnitude greater than that in KCO2–FCH4. For

these reasons, we excluded AD–FCH4 when u∗ was

<0.1 m s−1_{(this threshold was equivalent to 0.7 m s}−1

in horizontal windspeed at 2.2 m) from the figure and the following analysis. As shown in Fig. 7a, the day-time AD–FCH4 (the 7-term running mean) is a little

smaller than KCO2–FCH4, but the fluxes by the two

methods are generally in agreement with each other within the measurement error. Fig. 7 demonstrates that the pattern in the diurnal variation of the CH4

fluxes is quite similar to those of soil temperature and windspeed, which decrease gradually from the afternoon through the night and increase from the morning to the afternoon.

The daily budget of CH4 flux from the paddy is

shown in Fig. 8. Missing flux data from 7 to 9 August were interpolated using relationships between the flux and soil temperature at a depth of 2 cm. The relation-ships were determined each day by fitting a polyno-mial function of second degree using the least squares method (the fitting passed thex2-test on a significant

level of 5%). For other days, missing data were in-terpolated using the average of each 5 data points be-fore and after the missing period. As shown again in Fig. 8, the fluxes by the AD method are systematically

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smaller than those by the KCO2method, but the

differ-ences between the two methods are within measure-ment error indicated by bars. The agreemeasure-ment between the two different micrometeorological methods gives us confidence in the calculated fluxes, although we have much fewer nighttime fluxes by the AD method than the KCO2 method.

Day-to-day change of the daily CH4 flux is well

demonstrated in Fig. 8. The daytime fluxes (from 0500 to 1900 hours) by the KCO2method on 7 and 8 August

were 52 and 43 mg CH4m−2, respectively, which

de-creased to 30–33 mg CH4m−2for the flooded paddy

on 10–12 August. As shown by bars in Fig. 8, the dif-ferences in the daytime CH4 flux are statistically

in-significant except on 7 August, but a decreasing trend in the daytime CH4 flux from 7 to 12 August is

ap-parent. The nighttime flux (from 1900 to 0500 hours), ranging between 15 and 28 mg CH4m−2, did not show

a monotonic trend. As a result, the daily amount of CH4 emission from the paddy on drained days

(in-cluding a transitional 9 August) ranged from 58 to 80 mg CH4m−2, which decreased to less than 60 mg

CH4m−2 on following flooded days (the average for

10–12 August was 53 mg CH4m−2).

Among possible factors which affect the CH4flux,

soil temperature can be influenced by drainage and flooding. In IREX96, however, daily maximum soil temperatures at a depth of 2 cm stayed at >32◦C on

3b), whereas diurnal variation of CH4 flux declined

on 10 August (Fig. 6). This indicates soil tempera-ture had a minor influence on the difference of CH4

fluxes between the two treatment periods. Instead, as with the CO2 flux, the increased CH4 flux resulted

from the absence of floodwater which reduced the resistance to gas diffusion from the soil to the air under drained conditions. The change is not as dra-matic as for CO2 (Fig. 3a) because much of the CH4

is released to the air through plant-mediated trans-port (Minami and Neue, 1994) and the rest by dif-fusion from the soil to the air. After the reflooding on 9 August, the CH4 flux decreased because the

diffusion from the soil to air was prevented by the water layer, but CH4 was still released through the

plant-mediated process. Although our measurements were aborted on 13 August by a typhoon, we expect the CH4flux would recover gradually with the progress of

soil reduction under continuously flooded (anaerobic) conditions.

Previous studies using chambers showed intensive CH4emission lasting some tens of hours following a

drainage event (Neue and Sass, 1994). In a Japanese rice paddy, Yagi (1997) observed an increased CH4

emission after drainage which continued about two days and showed a diurnal variation with a maxi-mum in the daytime. From an analysis of the rela-tionship between the drainage duration and changes in CH4flux before and after a drainage, Yagi (1997)

found drainage lasting more than 2 days reduced the CH4 flux after reflooding for a few weeks to a lower

level than before drainage. The trend in the day-to-day change of the daily CH4 flux during IREX96 (Fig.

8) was consistent with the findings of these previ-ous studies. In IREX96, the daily CH4 flux on the

drained days was larger than on the flooded days by 28% on average, while previous studies found much larger increases after drainage events (Neue and Sass, 1994; Yagi, 1997). The amount and duration of the temporary increase of CH4 emission after drainage

depend on the accumulated amount of CH4 in the

soil before drainage. The intermittent drainage at the present study site prevented the recovery of the pro-duction and the accumulation of CH4in the soil, and

this resulted in a smaller increase in the daily CH4

flux after drainage than those observed in the previous studies.

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As mentioned earlier, the diurnal variation in the CH4flux under drained conditions showed a distinct

positive correlation with soil temperature and wind-speed (Fig. 7). This is because temperatures up to 30–35◦C accelerate CH4production by methanogenic

bacteria in the soil, and the amount of CH4dissolved

in the soil solution decreases with increasing temper-ature (Minami and Neue, 1994). Plant-mediated CH4

transport also shows an increase with increasing tem-perature (Nouchi et al., 1994; Hosono and Nouchi, 1996), although the relationships found by these au-thors were between the seasonal variation of CH4flux

and temperature rather than the diurnal variation ob-served here. As well as these temperature effects, the distinct diurnal variation of the CH4 flux observed

during IREX96 was influenced by windspeed which affects the diffusion resistance from the soil to the air under drained conditions. Drainage and flooding thus affect the CH4 flux by changing the diffusion

resistance to CH4 transport from the soil to air as

well as by changing the CH4production rates in the

soil.

while those at the western site were 2.2 and 2.7mg

CH4m−2s−1. Measurements using the chamber at

the eastern site were comparable with the running mean of KCO2–FCH4, but fluxes from the western

site were double the peak observed from the mi-crometeorological measurements. It is likely that spatial inhomogeneity caused by variation in den-sity of rice plants, nutrient status of the soil and water depth are partly responsible for difference in fluxes measured by the chambers at the two sites. In one of the few other comparisons published, Kane-masu et al. (1995) found that CH4 fluxes from rice

paddies estimated using closed chambers exceeded micrometeorological measurements by a factor of 2. Further detailed studies are required to clarify whether spatial heterogeneity is sufficient to explain such discrepancies or whether there are systematic methodological differences between the measurement techniques.

5. Conclusion

During IREX96, net CO2 and CH4 fluxes were

measured over an intermittently drained and flooded Japanese paddy field using micrometeorological methods. From the comparison of the gas fluxes un-der drained and flooded conditions we conclude: (1) The daily net uptake of CO2 from the atmosphere

by the paddy field was 50% lower when the paddy was drained than when it was flooded. (2) The daily CH4 emission on drained days was 28% larger than

on flooded days. (3) Enhanced fluxes of CO2 and

CH4 from the drained soil were due to removal of

the barrier to gas transport from the soil surface to the air caused by the floodwater. The present study made it clear how flooding and drainage affect the exchanges of CO2 and CH4 at rice paddies in the

short term. We need further measurements throughout a rice cultivation period to assess the long term effect of an intermittent drainage practice on the exchanges of these gases at rice paddies.