Greenhouse Gas Emissions from Boreal Reservoirs in Manitoba and

Environ. Sci. Technol. 2009 43, 8908–8915

Greenhouse Gas Emissions from Boreal Reservoirs in Manitoba and Que´ bec, Canada, Measured with Automated Systems M A U D D E M A R T Y , † J U L I E B A S T I E N , * ,† ALAIN TREMBLAY,‡ RAYMOND H. HESSLEIN,§ AND ROBERT GILL| Environnement Illimite´ Inc., 1453 Saint-Timotheé, Montreál, Que´bec, Canada, Hydro-Que´bec, 75 Rene´-Le´vesque West, Montreál, Que´bec, Canada, Fisheries and Oceans Canada, Freshwater Institute, 501 University Crescent, Winnipeg, Manitoba, Canada, and Manitoba Hydro, 360 Portage Avenue (16), Winnipeg, Manitoba, Canada

Received December 18, 2008. Revised manuscript received October 5, 2009. Accepted October 8, 2009.

Growing concern over the contribution of freshwater reservoirs to increases in atmospheric greenhouse gas (GHG) concentrations and the relevance of long-term continuous measurements has led Fisheries and Oceans Canada, in conjunction with Manitoba Hydro, to develop continuous GHG monitors. Continuous water pCO2, pCH4, and pO2 measurements were gathered to estimate gas fluxes in one temperate reservoir (Rivie`re-desPrairies) and two boreal reservoirs (Eastmain-1 and RobertBourassa) in Que´bec, and in four boreal reservoirs (Grand Rapids, Jenpeg, Kettle, and McArthur Falls) in Manitoba, Canada. Mean daily CO2 fluxes ranged between 7 and 14 mmolCO2 · m-2 · d-1 in Manitoba and between 15 and 55 mmolCO2 · m-2 · d-1 in Que´bec. Summertime episodes of water undersaturation in CO2 were observed at Jenpeg, Kettle, and McArthur, suggesting higher productivities of these systems compared to the other systems studied. Mean daily CH4 fluxes ranged between 0 and 69 µmolCH4 · m-2 · d-1 in Manitoba and between 9 and 48 µmolCH4 · m-2 · d-1 in Que´bec. Comparisons of results obtained in the Eastmain-1 area using automated monitors, floating chambers or dissolved gas analyses over multiple-station field campaigns demonstrated that a continuous GHG monitor at a single sampling station provided representative and robust results.

Introduction Terrestrial soils typically provide very active loci for the decomposition of organic matter. With the flooding of terrestrial ecosystems through reservoir creation, the chemistry and biology of the flooded areas are modified: labile carbon and nutrients are released in the water column, enhancing bacterial productivity and stimulating the overall production of the reservoir ecosystem, including plankton and fish communities (1-3). Bacterial degradation of organic matter in the flooded soils and in the water column produces * Corresponding author e-mail: [email protected]. † Environnement Illimite´ Inc. ‡ Hydro-Que´bec. § Fisheries and Oceans Canada. | Manitoba Hydro. 8908

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carbon dioxide (CO2) and, in anoxic sediments, methane (CH4) (3, 4). The release of nutrients and enhanced organic matter decomposition occur over a short period of time after impoundment and generally return to natural values within 10 years (5). The water residence time, reservoir shape and volume, and amount and type of vegetation flooded are parameters affecting the duration of emissions until conditions similar to those prior to flooding are re-established (5, 6). Greenhouse gases (GHGs) are emitted not only from natural ecosystems (e.g., lakes, rivers, estuaries, beaver ponds) but also from reservoirs (5, 7, 8). There is consequently growing worldwide concern to determine the contribution of freshwater reservoirs to increasing GHG concentrations in the atmosphere (9-11). Part of this concern is related to understanding uncertainties in measuring representative GHG fluxes, since measurement techniques are not consistent throughout the world and often not properly documented. Moreover, evaluation of net GHG emissions from reservoirs is becoming more and more crucial to ensure accurate comparisons of energy production methods, evaluation of CO2 credits, and determination of national GHG inventories (5, 12). From hydroelectric reservoirs, GHGs can be emitted by three pathways: diffusion at the reservoir surface, (CO2, CH4), ebullition in the reservoir (mainly CH4), and degassing in the turbulent waters downstream of the reservoir (mainly CH4) (13-16). Generally, degassing and ebullition emissions are not reported for boreal regions because diffusive emissions are considered the major pathway, comprising over 95% of total emissions (6, 16, 17). Only diffusive emissions are presented here. To date, most of the information on CO2 and CH4 emissions was obtained from discrete summertime and, to a lesser extent, wintertime field campaigns based on floating chambers, gas partial pressure, and thin boundary layer (TBL) techniques (e.g., refs 5, 6, 13, 14, 18). The field campaigns have served mostly to document spatial variability. However, to adequately estimate mean annual GHG emissions from a water body, the temporal variability of GHG concentrations or emissions is crucial, and yet is usually poorly documented (e.g., refs 5, 13). Fisheries and Oceans Canada, in conjunction with Manitoba Hydro, developed automated systems for GHG measurements in order to document the temporal variability of CO2 and CH4 emissions throughout the year, increase the frequency of measurements, and significantly reduce the uncertainties around a representative mean flux from natural systems as well as from reservoirs. The systems were subsequently adapted to Hydro-Que´bec’s needs. In this paper, we report on the use of such an automated system that measured CO2, CH4 and O2 concentrations to obtain representative mean diffusive GHG fluxes from large water bodies. Data from two different morphological and geological regions are presented to illustrate the diversity of the studied systems and, to a lesser extent, to provide a first ecosystem comparison. It is beyond the scope of this article to provide an in-depth analysis of the physical characteristics of the reservoirs or of the biological and chemical characteristics of the watersheds to explain the differences in GHG fluxes among the reservoirs studied.

Site Description and Methods Sampling Plan and Locations. Automated GHG systems (monitors) have been in place at McArthur Falls (since 2003), Kettle (since 2004), Grand Rapids (since 2006) and Jenpeg 10.1021/es8035658 CCC: $40.75

 2009 American Chemical Society

Published on Web 10/23/2009

(since 2006) generating stations (GS) in Manitoba, Canada (see Map S1, Supporting Information (SI)). Monitors have been in place since 2006 at Eastmain-1 (EM-1), RobertBourassa (LG-2), and Rivie`re-des-Prairies (RDP) generating stations in Que´bec, Canada. The catchment areas of the Eastmain and Robert-Bourassa regions are dominated by coniferous forest, shallow podzolic and peat soils, and igneous bedrock. They comprise oligotrophic systems with overall low primary production. The Rivie`re-des-Prairies catchment is dominated by deciduous forest, organic soils, and igneous bedrock. The catchment of the reservoirs in Manitoba is wide, complex, and influenced by agricultural lands as well as deciduous and boreal forests. The Rivie`re-des-Prairies and Manitoba reservoirs consist of eutrophic systems with overall medium to high primary production. More characteristics of the generating stations and information about the reservoirs are listed in SI Table S1. The GHG monitors at the Manitoba generating stations are connected to distribution pipes that draw raw water from upstream of the dams. The GHG monitors at the Que´bec generating stations EM-1 and LG-2 are connected to distribution pipes collecting water from the scroll cases, while that at Rivie`re-des-Prairies draws water from directly upstream of the dam. Automated Systems. The continuous gas monitor is modeled after Carignan’s design (7) and built of commercial components. CO2, CH4, and O2 are measured by three different types of sensors on a gas stream that has been equilibrated with the source water (see SI Figure S1). In brief, water is pumped from the source via a reversible peristaltic pump through a contactor comprised of a bundle of porous polypropylene tubes that acts as a water/air exchanger. Solenoid valves control the gas flow and switch it from the contactor to the air as required. After the measurements in water, and at the same time as the valves are repositioned for air sampling, the peristaltic pump is reversed to clear the lines of water. The sensors, which must remain dry, are housed in a “Drybox,” while the pump, valves, and tubing are installed in the “Wet Box.” While in monitoring mode, the device typically activates once every three hours. The monitoring operating cycle requires 22 min: one cycle of 20 min in water with two measurements (at 10 and 20 min), followed by one cycle of two minutes in air with one measurement. In monitoring mode, the device is typically operated once every three hours. This time step was an arbitrary choice and can be changed. This technique generates a large data set of continuous measurements for a single sampling location. Water temperature, instrument temperature, and gas loop pressure are also recorded. Control of all electrical devices, and collection and storage of data are performed by a Campbell Scientific data logger. CO2 partial pressure was measured with a LICOR LI-820 non-dispersive infrared sensor with an accuracy of 2-4%, depending on the cell length used. CH4 partial pressure was measured with a Neodim Panterra metal oxide semiconductor (MOS) probe sensor with an accuracy of 1%. O2 partial pressure was measured with a Qubits S101 probe diffusion-based O2 sensor with an accuracy of 0.01 mV (0-100%: 0-75 mV). Quality assurance, quality control methods, and equations used for flux calculations from partial pressure are described in detail in Bastien et al. (19) and in the SI. Briefly, to calculate diffusive fluxes using gas partial pressure, the thin boundary layer equation (eq 1), Cole and Caraco’s (20) gas transfer coefficient, k, and a series of secondary equations were used (details can be found in Bastien et al. (19) and in the SI). Four inputs are required for the diffusive flux calculation: the air and water gas concentrations, the wind speed 10 m above the water surface and the gas transfer velocity coefficient.

Equation 1 shows the calculation of diffusive flux from automated system data: Flux ) kx(Cwater - Cair)(thin boundary layer model) (1) where Cwater is the concentration of gas in the water, Cair is the concentration of gas in the air, Flux is expressed in mmol of CO2 or µmol CH4 m-2 d-1 with the following: -x

Sc ( 600 )

kx ) k600

where kx is the gas exchange coefficient expressed in cm h-1, and Sc is the Schmidt number for CO2 or CH4, which is dependent on temperature (t): Sc(CO2) ) 1911.1 - 118.11t + 3.4527t2 - 0.04132t3 Sc(CH4) ) 1897.8 - 114.28t + 3.2902t2 - 0.039061t3 and 1.7 k600 ) 2.07 + (0.215 × U10 ) (Cole and Caraco’s (20) Equation)

where k600 is the gas exchange coefficient expressed in cm h-1 normalized for CO2 at 20 °C in fresh water with Schmidt number ) 600 and U10 ) 1.22 × U1 where U1 is the wind speed at the water surface and U10 is the frictionless wind speed at 10 m expressed in m s-1. Field Campaigns-Gas Partial Pressure (pCO2 - pCH4). To evaluate CO2 and CH4 spatial variability, more than 50 stations were spread over natural lakes and rivers, and Eastmain-1 reservoir. These were visited by either hydroplane or boat. Sampling was carried out mainly during the ice-free season between June and October. Sampling also took place, to a lesser extent, in winter between December and March to observe increases in GHG partial pressure under the ice and calculate the annual diffusive GHG flux (21). The water pCO2 was measured with a PP Systems EGM-4 nondispersive infrared (NDIR) device coupled to a Membrana Celgard gas exchange system composed of one Plexiglas cylinder traversed by a large number of polypropylene micrometric tubes. Samples from the upper 50 cm of surface water were collected by a peristaltic pump circulating continuously inside the Plexiglas cylinder. Air was pumped through the micrometric tubes in the opposite direction, allowing the air CO2 concentration to balance with the water CO2 concentration. The resulting CO2-enriched air was then analyzed with the EGM-4. The pCO2 data were recorded every minute over a 10-min period. The gas measurement accuracy was 0.1%. The pCH4 was measured with a gas chromatograph (GC). The surface water was collected with a peristaltic pump and kept in 60 mL syringes. In the laboratory, 30 mL of nitrogen was added to the syringe to extract dissolved gases by the headspace technique (22). The gas mixture was then analyzed with the GC within 24 h. These techniques generate a large data set from single stations sampled at different times. In regard to the automated systems, the gas solubility in water, Henry’s law, the thin boundary layer equation, and Cole and Caraco’s (20) gas transfer coefficient, as well as a series of secondary equations were used to calculate diffusive fluxes using gas partial pressure. Details of these techniques can be found in Lambert and Frećhette (23), Bastien et al. (18), and Tremblay and Bastien (6). VOL. 43, NO. 23, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. CO2 concentrations measured with automated systems at Grand Rapids (A), Jenpeg (B), Kettle (C), and McArthur (D) generating stations in Manitoba. Under-Ice GHG Accumulation and Total Diffusive Emissions Estimation. The formation of ice cover in winter prevents gas exchange between water and atmosphere. Therefore, it is not appropriate to calculate diffusive fluxes based on gas concentrations in water when reservoir and other water body surfaces are frozen. Gas concentration increases under ice, leading to an accumulation of CO2 in the water by the end of the ice period (21, 17). This increase in CO2 is not related to primary productivity, since such a process would imply an increase in organism respiration, as observed during the summer. Metabolic rates are known to decrease with lower temperature (24), suggesting that increased respiration rates are not responsible for the observed increases in CO2 concentrations. However, in springtime, when the ice melts, gases escape to the atmosphere within a few weeks (25-27). This flux includes the CO2 accumulated in the water during the ice period. The intensity of the resulting outgoing flux will depend on the magnitude of the gas sequestration in wintertime and on the water mixing intensity. Mixing intensity depends on the wind and temperature gradient between air and water (28). Since the diffusive flux is null during the ice period, only fluxes occurring during the ice-free period are considered in estimating a cumulative annual GHG flux (Table 2). The icefree period includes the maximum flux calculated in spring, corresponding to the onset of gas release as the ice melts, and the following summer and fall’s diffusive fluxes (Figure 1 (21)). 8910

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Since the dates of ice formation and melting were not precisely known, they were estimated using the CO2 concentration trends: onset of freezing was associated with increases in CO2 concentrations in late fall and melting was associated with decreases in CO2 concentrations in spring. The following periods therefore represent times of ice cover; the corresponding data were eliminated from the annual cumulative diffusive flux calculations (Figures 1 and 2): Grand Rapids GS, Manitoba: November 1, 2006 to March 1, 2007; Kettle GS, Manitoba: February 24, 2004 to March 20, 2004, October 27, 2004 to March 1, 2005, November 1, 2005 to March 20, 2006, and November 1, 2006 to March 1, 2007; McArthur GS, Manitoba: November 1, 2004 to March 1, 2005, November 1, 2005 to March 1, 2006, and November 1, 2006 to March 20, 2007; Eastmain-1 GS, Que´bec: January 1, 2007 to May 1, 2007; Robert-Bourassa GS, Que´bec: December 31, 2006 to May 1, 2007. Annual cumulative diffusive fluxes were not calculated for RDP GS, because of the short data record. From the annual CO2 and CH4 cumulative fluxes, annual GHG fluxes in CO2 equivalent (CO2eq) were calculated using eq 2. The CH4 flux in CO2eq was calculated using its global warming potential of 23 (12). GHG Flux ) CO2 flux + (23 × CH4 flux)

(2)

Results and Discussion Temporal Variations in Dissolved CO2 Concentrations. Manitoba Generating Stations. From both multiyear (McArthur

FIGURE 2. CO2 concentrations measured with automated systems at Eastmain-1 (A; EM-1), Rivie`re-des-Prairies (B; RDP) and Robert-Bourassa (C; LG-2) generating stations in Que´bec. and Kettle GSs) and single-year (Grand Rapids and Jenpeg GSs) monitoring, a clear general pattern emerged, as shown in Figure 1: (1) small summertime daily variations in CO2 water concentrations are very likely due to changes in photosynthetic and respiration rates; (2) increases in CO2 water concentrations from late autumn to early spring are due to gas sequestration under ice cover; this increase demonstrates that organic matter degradation continues throughout the winter, albeit at a lower rate, thus producing CO2; and (3) a rapid decrease in GHG concentrations during late spring demonstrates that gas escapes from water to atmosphere as the ice melts. The studied systems were supersaturated in CO2 throughout most of the monitored years, with water concentrations exceeding those measured in the reference atmosphere inside the GSs or the reference value of the Intergovernmental Panel on Climate Change (IPCC; (12)). Episodic undersaturation of CO2 was observed during the summer at all four generating stations. This likely reflects higher photosynthetic rates and resultant higher CO2 absorption. However, the lack of chlorophyll a measurements prevents further biological explanation. Maximum dissolved CO2 water concentrations were observed at Grand Rapids during mid-March of 2007, reaching 160 µmol · L-1. Que´bec Generating Stations. Results from EM-1 GS, as illustrated in Figure 2, clearly showed the same seasonal pattern as those described above for Manitoba. This trend was also observed at RDP, although the one-year record did not include the springtime melting period. At LG-2, dissolved CO2 concentrations seemed to decrease in June 2007 when

the record for this system began; this could correspond to an end-of-spring decrease. However, the corresponding autumn-winter increase in CO2 water concentrations described for the other study sites was not observed here. With the exception of a few values recorded at RDP in late October 2007, undersaturation of dissolved CO2 concentrations was not observed in the Que´bec systems, suggesting that these systems may be less productive than the four Manitoba systems. Maximum dissolved CO2 concentrations were observed at EM-1 in early May 2007, reaching 337 µmol · L-1, which is roughly double the maximum value observed in the Manitoba systems at Grand Rapids. This high value is not unexpected, since Eastmain-1 reservoir, flooded in 2005, is a young reservoir. It is well established that reservoirs impounded less than 10 years previously exhibit higher GHG emissions than older ones (5, 6, 16, 18). Temporal Variations in Dissolved CH4 and O2 Concentrations (All Stations). Dissolved CH4 concentrations reached a maximum of 1.0 µmol · L-1 at Grand Rapids, for Manitoba systems, and 0.8 µmol · L-1 at EM-1, for Que´bec systems (Table 1). Dissolved CH4 concentrations tended to vary less in winter than in summer, and unlike dissolved CO2 concentrations, dissolved CH4 concentrations showed no increases in winter. This could be explained by cold water temperatures of about 4 °C that reduce bacterial productivity, coupled with welloxygenated waters that oxidize CH4 (produced under ice) into CO2 (29, 30). Dissolved O2 concentrations remained in equilibrium with O2 air concentrations, apart from occasional undersaturation events. A clear seasonal pattern in O2 concentrations, both VOL. 43, NO. 23, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Mean, Median, Standard Deviation, Minimum and Maximum CO2, CH4 and O2 Fluxes CO2 flux -2

-1

CH4 flux -2

-1

-2

-1

O2 flux -2

-1

-2

mmol · m · day-1

-2

· day-1

stations

statistics

mmol · m · day

mg · m · day

Grand Rapids

N mean median std deviation minimum maximum

2985 14 6 23 8 165

2985 624 249 1007 -347 7260

2612 36 6 73 -4 1728

2612 0.58 0.09 1.17 -0.07 27.72

2984 -8 -2 15 -116 13

2984 -254 -62 487 -3712 427

Jenpeg


1346 7 7 8 -21 48

1346 316 308 347 -926 2126

1346 69 50 69 -4 674

1346 1.11 0.80 1.11 -0.07 10.81

1344 -14 -11 11 -84 29

1344 -454 -356 367 -2679 918

Kettle


16217 12 9 12 -16 128

16217 514 378 548 -723 5646

3129 0 -1 5 -12 60

3129 -0.01 -0.02 0.07 -0.19 0.97

14721 -3 -2 6 -111 25

14721 -94 -56 197 -3542 810

McArthur


10585 8 7 9 -24 81

10585 367 299 388 -1057 3556

4432 2 0 12 -2 446

4432 0.04 -0.01 0.19 -0.03 7.15

10580 -2 -1 8 -86 65

10580 -71 -38 264 -2744 2092

EM-1


3304 55 39 52 0 447

3304 2426 1723 2287 -4 19676

3301 48 24 62 0 510

3301 0.77 0.38 0.99 -0.04 8.18

3287 -14 -15 71 -316 1899

3287 -442 -493 2286 -10113 60754

RDP

N Mean median std deviation minimum maximum

1324 15 10 18 -5 213

1324 665 446 794 -234 9380

1344 31 22 29 0 400

1344 0.49 0.36 0.47 -0.05 6.38

1347 -8 -7 9 -84 33

1347 -245 -211 275 -2694 1062

LG-2


1759 15 10 16 1 148

1759 661 438 708 45 6509

993 9 4 17 0 160

993 0.14 0.06 0.27 -0.09 2.56

1757 -9 -8 13 -94 34

1757 -285 -248 406 -3017 1093

dissolved and in air (just above the thin boundary layer), was generally observed. In fact, dissolved O2 concentrations reached 425 µmol · L-1 in winter, and decreased from April to July to reach values around 250 µmol · L-1. No additional physical or biological data were gathered to explain these variations. CO2, CH4 and O2 Daily Fluxes. Fluxes calculated from CO2, CH4 and O2 concentrations are presented in Table 1. The minimum calculated CO2 fluxes were negative for all stations, excluding LG-2, suggesting that there were periods of CO2 absorption by these water bodies. Maximum calculated CO2 fluxes corresponded to maximum concentrations measured in late spring at the onset of the ice-free period. The highest value was obtained at EM-1, with a CO2 flux of 447 mmol · CO2 · m-2 · d-1. The maximum mean daily CO2 flux of 55 mmol · CO2 · m-2 · d-1 was observed at EM-1, and was between 5 and 8 times those observed at other stations. The minimum mean daily CO2 flux was observed at Jenpeg (7 mmol · CO2 · m-2 · d-1). The flux measured at McArthur was nearly equal (8 mmol · CO2 · m-2 · d-1), while the four other 8912

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µmol · m · day

mg · m · day

mg · m

stations exhibited similar mean daily CO2 fluxes (12, 14, 15, and 15 mmol · CO2 · m-2 · d-1 for Kettle, Grand Rapids, LG-2 and RDP, respectively). Elevated fluxes measured at EM-1 are the result of the young age of the reservoir, which was only 2 years old in 2007. CO2 and CH4 emissions are known to increase rapidly after flooding, generally to about five times the values measured in natural aquatic ecosystems. Within a period of 10 years for CO2 and five years for CH4, the emission rates decrease rapidly to values typical of natural aquatic ecosystems (5, 6, 16). Because flooded trees decompose very slowly, emitted CO2 and CH4 are due primarily to the decomposition of the fraction of the labile organic matter in flooded soils. After the transition period of 10 years or less, CO2 emissions are comparable to those in natural water bodies, and are chiefly due to carbon entering the reservoir through runoff from the watershed. This transition period was shorter than 10 years for EM-1 reservoir: 3 years for CO2 and 2 years for CH4 (6). As shown in Table 1, the CH4 median values ranged from -4 to 1728

FIGURE 3. Mean CO2 flux (A) and CH4 flux (B) (dotted lines), median fluxes (solid lines), 25th and 75th percentiles (boxes), and 10th and 90th percentiles (error bars) measured during field campaigns (dark boxes) representing single stations sampled at different times (EGM-4 partial pressure (CO2), GC partial pressure technique (CH4), and floating chamber techniques (CO2 and CH4)) and with the automated systems (white boxes). µmol · CH4 · m-2 · d-1 and are comparable to the median value of 538 µmol · CH4 · m-2 · d-1 reported by IPCC (12) for boreal flooded lands as well as the values reported by Tremblay (5) and Bastien (6). Maximum and minimum mean daily CH4 fluxes were observed at Jenpeg (69 µmol · m-2 · d-1) and Kettle, respectively (-0.4 µmol · m-2 · d-1), the latter value showing periods of CH4 absorption at Kettle. Comparison with Data from Other Methods of Diffusive Flux Measurements. Gross CO2 and CH4 emissions were measured using floating chambers or the gas partial pressure and TBL techniques in Eastmain-1 reservoir during the summers of 2007 and 2008. The measured values ranged from 508 to 2000 mmol · CO2 · m-2 · d-1 and from 11 to 259 µmol · CH4 · m-2 · d-1, respectively. These fluxes are not significantly different (t test, p < 0.05) than those measured with the automated systems installed on floating rafts in Eastmain-1 reservoir over the same periods of time (Figure 3, Table 1). Similar values, ranging from 5 to 184 mmol · CO2 · m-2 · d-1 and from 113 to 3375 µmol · CH4 · m-2 · d-1, were observed in boreal reservoirs in Finland (30), Newfoundland and Labrador (18), Manitoba, and British Colombia (5, 16, 31), and Que´bec (5, 6). Comparable CO2 and CH4 flux values, ranging from 7 to 29 mmol · CO2 · m-2 · d-1 and from 50 to 5100 µmol · CH4 · m-2 · d-1, were previously reported elsewhere for natural boreal lakes (16, 30, 32). (See SI Table S2, for a review of GHG fluxes measured in various ecosystems). Therefore, mean CO2 and CH4 daily fluxes calculated from the automated systems’ data are comparable to fluxes directly measured at the water-air interface using more traditional floating chamber and gas partial pressure techniques. The use of automated systems is thus suitable for the measurement of both spatial and temporal variations in CO2 and CH4 fluxes from water bodies. Annual Cumulative Fluxes. The use of one automated system inside generating stations is based on the fact that

the total water volume of the reservoir passes through the generating station at least once a year (short residence times). Mean daily values integrated over the year therefore give a representative annual flux of the water body. The annual cumulative fluxes are the sum of the daily fluxes calculated between spring, when values are highest due to the maximum gas concentration under the ice cover, and late autumn, when fluxes are at their lowest (Table 2). For EM-1 and LG-2 these calculations were possible for both CO2 and CH4 fluxes, whereas for the Nelson, Saskatchewan, and Winnipeg Rivers systems gaps in the time series data prevented us to calculate CH4 annual cumulative fluxes. The maximum annual cumulative CO2 fluxes were observed at EM-1 (13 230 mmol · CO2. · m-2 · yr-1), which exhibited a total GHG emissions of 13.6 mol · CO2eq · m-2 · yr-1, attributable to the recent reservoir’s impoundment in 2005 (5, 6, 16). With the exception of the young EM-1 reservoir, all the reservoirs studied have approximately the same range of total CO2 emissions, indicating similarities in the processes causing GHG emissions from boreal reservoirs (5, 16). Advantages and Drawbacks of Using Automated Systems. The chief advantage of automated systems is their independence, allowing frequent measurement of one or many parameters and providing a better understanding of ecosystem dynamics. For example, research presented in this paper demonstrates the magnitude of CO2 accumulation under ice cover, and thus stresses the importance of springtime sampling for future projects based on discrete CO2 and CH4 sampling. The automated systems in this study were installed inside the generating stations and, as all the water contained in the reservoir eventually passes through the turbines, mean daily values integrated over the year gave a representative flux of the water body. To determine whether the results from these units were representative

TABLE 2. 2007 Cumulative CO2 and CH4 Fluxes (mmol CO2eq m-2 yr-1 g) and Total CO2 Equivalent Emissions (CO2 Plus CH4) (mol CO2eq m-2 yr-1 g and CO2eq m-2 yr-1) for Grand Rapids, Kettle, Jenpeg, McArthur, EM-1, and LG-2 Generating Stationsa system

generating station

Saskatchewan R. Grand Rapids Nelson R. Kettle Jenpeg Winnipeg R. McArthur La Grande R. EM-1 LG-2 a

days without reservoir CO2 flux CH4 flux total emissions total emissions ice area (km2) (mmol CO2 m-2 yr-1) (mmol CH 4 m-2 yr-1) (mol CO2eq m-2 yr-1) (g CO2eq m-2 yr-1) 256 275 250 255 214 216

3277 319 NA 100 603 2815

3321 4292 550 1766 13 230 4544

ND ND ND ND 17.0 42.2

ND ND ND ND 13.6 5.5

ND ND ND ND 588.4 215.5

ND: Not determined.

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of the entire reservoir, the results presented here were compared to those obtained during an exhaustive study aimed at measuring spatial variations of CO2 and CH4 daytime partial pressures and corresponding calculated daily fluxes at EM-1 (6, 21). The latter results were collected during two consecutive summers (July 2007, July and September 2008) and winters (March 2007, January and March 2008). By extrapolating over the missing fluxes between discrete field campaigns, we estimated the springtime CO2 concentration peaks and releases. By assuming no winter fluxes, we were able to estimate the annual cumulative CO2 and CH4 fluxes, and total GHG emissions at EM-1 (21). The resulting total average annual emissions of 12.2 molCO2eq · m-2 · yr-1 are comparable to the values calculated in this study from automated system values (13.6 molCO2eq · m-2 · yr-1; Table 2). Based on this comparison, using single automated systems located inside generating stations is representative of CO2 and CH4 emissions from the reservoir as a whole. However, some limitations of automated system use and fabrication should be noted. These systems are experimental in nature and require regular maintenance, roughly every two to three months. They must be handled carefully, because certain components must remain dry. Each CH4 sensor is calibrated individually to unique set points obtained from the manufacturer, rendering standard calibration between different automated systems impossible. Furthermore, the CH4 sensor’s detection limit is not sufficiently sensitive. For ecosystem dynamic studies, correlated chemical and biological variables such as pH, dissolved organic carbon and chlorophyll a should be measured to better understand the processes leading to CO2 and CH4 emission. At the moment, the automated systems are not designed to accommodate these measurements. The next generation of sensors and automated GHG systems is currently under development and promises to improve on many of the above shortcomings.

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(6) (7) (8)

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(10) (11) (12) (13)

Acknowledgments We acknowledge Sarah Wakelin and Marcus Smith, from Manitoba Hydro, and Ste´phane Lorrain, from Environnement Illimite´, who were involved in this project. We also thank Morris Holoka, from Fisheries and Oceans Canada, Freshwater Institute, and Jean-Louis Frećhette from Environnement Illimite´ for their contribution to the development of these automated systems. This study was funded by Manitoba Hydro, Hydro-Que´bec, Fisheries and Oceans Canada, and the Canadian government’s Panel on Research and Development (PERD).

Supporting Information Available Locations (Map S1) and characteristics (Table S1) of the sampled generating stations, a schematic representation of the automated system used (Figure S1), a review of CO2 and CH4 fluxes measured in different ecosystems with three different methods (Table S2), and details about quality assurance and flux calculations. Figure 1. CO2 concentrations measured with automated systems at Grand Rapids (A), Jenpeg (B), Kettle (C), and McArthur (D) generating stations in Manitoba. This material is available free of charge via the Internet at http://pubs.acs.org.

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Literature Cited (1) Marty, J.; Planas, D.; Pinel-Alloul, B.; Me´thot. G. Planktonic community dynamics over time in a large reservoir and their influence on carbon budgets. In Greenhouse Gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments; Tremblay, A., Varfalvy, L., Roehm, C., Garneau, M.; Eds.; Springer-Verlag: New York, 2005. (2) Planas, D.; Paquet, S.; Saint Pierre, A. Production-consumption of CO2 in reservoirs and lakes in relation to plankton metabolism. 8914

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