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Keywords:

  • Keeling plot;
  • carbon dioxide;
  • eddy covariance;
  • C4 photosynthesis;
  • drought;
  • biosphere-atmosphere exchange

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] We made weekly measurements of carbon (δ13C) and oxygen (δ18O) isotopes of atmospheric CO2 in a C3/C4 tallgrass prairie during the growing season for 3 years with contrasting soil moisture conditions. Air samples above and within canopies were collected using 100-ml flasks at night to characterize isotopic composition of ecosystem respiration. We used a two-source mixing line (Keeling plot) approach to estimate isotope ratios of ecosystem respired CO2 for both carbon (δ13CR) and oxygen (δ18OR). Measured net ecosystem CO2 exchange (NEE) showed the largest net carbon uptake in 2004, followed by 2003 and 2002. This interannual difference in NEE strongly depends on the amount and distribution of precipitation received by this tallgrass prairie. Precipitation also affects the timing of the seasonal transition from C3 dominance in spring to C4 dominance in summer. Variations of δ13CR showed that C4 plants dominated ecosystem respiration in 2003 and 2004, except in early spring when C3 plants were more active. In contrast, contributions of C3 plants were relatively higher for an extended period in the summer of 2002, when a severe drought occurred. Typically, C3 forbs extract water and nutrients from soil layers below that of the C4 grasses and remain photosynthetically active in periods when C4 grasses have water stress that limits photosynthesis. Drought-reduced C4 grass photosynthesis was lower than temperature-limited C3 forb growth during this period. We used an integrated isotope land surface model (ISOLSM) to simulate (and compare to measurements) net CO2 fluxes, δ18O values of leaf and soil water, and δ18O values of aboveground and soil respiration. The Keeling plot analysis becomes less reliable for estimating δ18OR values when the surface soil is dry. We suspect this is due to low CO2 production in the soil when water is limiting, in which case the invasion (abiotic) effect is more significant. ISOLSM reasonably captured seasonal variations of measured δ18OR in all 3 years, indicating the model's consistency of predicting δ18OR in different soil water conditions. Model simulations also showed that nighttime δ18O values of aboveground respiration were variable, often becoming very positive in water-stressed conditions primarily because of the low relative humidity and resultant elevated δ18O values of leaf water.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Measurements of the stable C and O isotope ratios of atmospheric CO2 have been used to estimate gross carbon exchanges in terrestrial ecosystems, although problems with the methods have been identified [Ogée et al., 2004; Riley, 2005]. This opportunity of using stable isotopes of atmospheric CO2 as tracers is particularly useful in grassland ecosystems where both C3 and C4 photosynthesis coexist. C3 and C4 plants have distinct carbon isotope ratios (δ13C), reflecting differences in physiology and in the fractionation expressed for major carboxylation enzymes [Farquhar et al., 1989]. A number of studies have demonstrated the utility of using measured δ13C values in CO2 to partition ecosystem production into relative contributions of C3 and C4 photosynthesis over a growing season [e.g., Still et al., 2003; Lai et al., 2003].

[3] Friedli et al. [1987] concluded that 18O exchange with leaf and soil water was responsible for the observed seasonal variation of 18O in atmospheric CO2 over Switzerland. Francey and Tans [1987] showed a latitudinal pattern of δ18O in atmospheric CO2, and noted the potential of using the 18O content of CO2 for partitioning net ecosystem exchange (NEE) fluxes into photosynthetic and respiratory components. Farquhar et al. [1993] first laid out a mechanistic framework for global estimates of terrestrial discrimination against 18O of CO2, and for global-scale terrestrial NEE partitioning using the 18O content of CO2. Other global studies applying these concepts have followed [Ciais et al., 1997; Peylin et al., 1999; Cuntz et al., 2003a, 2003b].

[4] These studies showed that a large portion of atmospheric CO2 entering leaf intercellular air space exchanges oxygen molecules with leaf water before returning to the atmosphere. Consequently, CO2 molecules diffusing out of leaf stomata are typically labeled with leaf water δ18O signatures [Francey and Tans, 1987]. A similar 18O equilibration process also occurs between atmospheric CO2 and soil water [Hesterberg and Siegenthaler, 1991; Tans, 1998; Amundson et al., 1998; Miller et al., 1999; Stern et al., 1999, 2001]. Leaf water usually has a higher content of 18O relative to soil water because of evaporative enrichment [Dongmann et al., 1974]. This distinction in the 18O contents of leaf and soil water establishes the basis for using C18OO as a tracer in terrestrial carbon studies. For example, differences in the oxygen isotope ratio of net CO2 fluxes emitted from canopy and soil allow for partitioning nighttime respiration into aboveground and belowground compartments [Mortazavi and Chanton, 2002; Bowling et al., 2003a]. Other studies have investigated diurnal and vertical fluctuations in δ18O value of CO2 within forest ecosystems [Flanagan et al., 1997, 1999; Buchmann et al., 1997; Sternberg et al., 1998; Harwood et al., 1999; Bowling et al., 2003b]. Recently, Ogée et al. [2004] showed that uncertainties in the measurement and interpretation of atmospheric δ18O values might limit our ability to use the isotopic approach for partitioning NEE.

[5] Previous studies have also investigated variations in the δ18O value of CO2 in grassland or agricultural systems. Yakir and Wang [1996] used measured δ18O values of CO2 to partition NEE fluxes into photosynthesis and respiration in different crop fields. Riley et al. [2002, 2003] used simulations from a mechanistic model to interpret temporal fluctuations of δ18O in leaf water, water vapor, and canopy CO2 fluxes observed in a tallgrass prairie in Oklahoma, USA. However, no continuous isotope measurements were made previously to examine seasonal and interannual variability in the δ13C and δ18O of canopy CO2 in grassland systems.

[6] Temperature is the most important environmental variable determining the seasonal transition of the abundance of C3 grasses in spring to C4 grasses in summer in shortgrass prairies [Kemp and Williams, 1980] and in upland mixed grass prairies [Ode et al., 1980]. The general distribution of C4 grasses is more closely related to temperature than to any other factor [Teeri, 1988]. In tallgrass prairies, water availability also strongly influences carbon fluxes and ecophysiological processes [Knapp, 1984, 1985; Kim and Verma, 1991; Steward and Verma, 1992; Axmann and Knapp, 1993; Knapp and Medina, 1999]. The climate in the Flint Hills tallgrass prairie region possesses large interannual variation in water availability [Borchert, 1950]. That characteristic provides the motivation for investigating how seasonal dynamics of C3 and C4 plants impact 13C and 18O contents in ecosystem CO2 fluxes.

[7] Lai et al. [2003] showed considerable intraseasonal variations of the carbon isotope ratio of ecosystem-respired CO213CR) in the Flint Hill tallgrass prairie. Their δ13CR measurements suggested a generally reduced impact from C4 grass during a drought period in the early growing season of 2002. However, weekly values of δ13CR showed erratic fluctuations between signatures characterizing C4 and C3-like photosynthesis.

[8] It is not clear whether the intraseasonal variation shown by Lai et al. [2003] is a consequence of the extreme drought conditions, during which time interpretation of isotope measurements requires caution.

[9] In this study we report measurements of carbon and oxygen isotopes of ecosystem-respired CO2 in a tallgrass prairie, made continuously at weekly intervals for 3 years with contrasting precipitation input. Seasonal and interannual patterns of NEE fluxes (measured with eddy covariance) were also compared for the 3 years. We estimated carbon (δ13CR) and oxygen (δ18OR) isotope ratios of nocturnal ecosystem respiration using the Keeling plot approach. To interpret seasonal variations in the measured δ18OR, we employed a mechanistic model that incorporates oxygen isotopes in a land surface model (ISOLSM [Riley et al., 2002, 2003]). Factors that influence our interpretation of the seasonal variation in δ18OR are discussed.

2. Materials and Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Study Site

[10] This study was conducted in the Rannells Flint Hills Prairie near Manhattan, Kansas, USA (39°12′N, 96°35′W, 324 m above sea level). The site has a mixture of C3/C4 photosynthesis and is burned during the last 10 days of April every year. The vegetation was dominated by C4 grass species, primarily Andropogon gerardii, Sorghastrum nutans, and Andropogon scoparius. The C3 species included Carex, a sedge, and numerous forb species including Vernonia baldwinii, Artemesia ludoviciana, Ambrosia psilostachya, and Psoralea tenuiflora var. floribunda. The 15-year average annual precipitation is 878 mm, with 74% occurring between April and September. The average canopy height, defined as the height of the tallest vegetation structure, was about 0.6 m at peak growth in 2002 and 2003, but extended to about 1.2 m in 2004 as a result of favorable growing conditions (i.e., more precipitation).

[11] From 2002 to 2004, NEE and, carbon and oxygen isotope values of ecosystem respiration were measured at the ungrazed site in the Rannells prairie. Measured NEE fluxes were averaged to a half-hourly basis, while values of δ13CR and δ18OR were estimated on weekly intervals. To demonstrate the seasonal and interannual variations of measured NEE fluxes, we calculated average daytime and nighttime NEE fluxes on a weekly basis. We assume that each weekly measurement of δ13CR and δ18OR was representative of the carbon and oxygen isotope ratios of respired CO2 fluxes for that particular week.

2.2. Flux and Meteorological Measurements

[12] An open-path eddy covariance system consisting of a triaxial sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, Utah, USA) and a CO2/H2O gas analyzer (LI-7500, LI-Cor Inc., Lincoln, Nebraska, USA) was used to measure fluxes of momentum, CO2, sensible, and latent heat above the canopy [Ham and Heilman, 2003]. A CR23X data logger (Campbell Scientific) recorded 10 Hz signals to compute 30-min average fluxes. Lai et al. [2003] described measurements of meteorological variables, including net radiation, air temperature, relative humidity, precipitation and soil temperature, in this tallgrass ecosystem. No correction was applied to the nighttime flux data since this site is generally windy (with calm night representing 0.1% of all sampling time). More details about eddy covariance measurements are given by Ham and Heilman [2003].

2.3. Oxygen Isotopes of Water Samples

[13] In May and July of 2002, two field experiments were conducted to collect samples for measuring δ18O values of ecosystem water pools. Each experiment lasted for 3 days, and samples of water vapor, soil water, crown-root water, and bulk leaf water were collected 1–5 times a day to characterize diurnal patterns of δ18O in each water pool. We collected foliage samples by clipping the whole grass blade and storing them in glass vials immediately after collection. Three replicates of grass blades from three dominant C4 species were collected each time. We reported the average of 9 samples (±1 S.D.) as the δ18O value of canopy leaf water. Root water was sampled by collecting crown roots (the top portion of the rooting system where all fine roots converge). The δ18O value of crown-root water represents δ18O signatures of source water for this prairie. Profiles of soil samples were collected in general from the top 30 cm, with an increment of 10 cm from 5 soil pits. All water samples were stored in screw-cap glass vials carefully wrapped with Parafilm® to prevent evaporation and kept refrigerated or frozen until subsequent stable isotope ratio analyses.

[14] Atmospheric water vapor was cryogenically captured and analyzed for oxygen isotope ratios using the sampling protocol described by Helliker et al. [2002]. Air from three heights (0.5, 1, and 3 m above ground) was passed through sampling tubes placed in a dewar of crushed dry ice, allowing water vapor to condense on the inner walls of the glass tubing. The airflow rate was set at 5 cc s−1 with a sampling time of ∼ 20 min. Water vapor tubes were sealed with a rubber stopper, and wrapped with Parafilm® on the outside. Samples of water vapor, crown-root water, and bulk leaf water were collected concurrently every 3–4 hours between 0800 and 2000 local standard time (LST) during the two field experiments.

[15] Water samples were extracted in the laboratory using a cryogenic vacuum distillation apparatus [Ehleringer et al., 2000]. Each water sample equilibrated with dilute CO2 (CO2:N2 = 1:9) for 48 hours at 25°C. Batches of 9 samples were calibrated against 3 working water standards during each analysis run using an EA-CF-IRMS method described by Fessenden et al. [2002]. Precision of the δ18O analyses is ±0.2‰.

2.4. Flask Sampling and Isotope Analyses

[16] Air samples from three heights (0.1, 0.4, and 3 m) were collected using an automated sampling system, capable of filling 15 flasks on the basis of the specification of a data logger [Schauer et al., 2003]. Two flasks were collected 5-min apart in the midafternoon (usually between 1430 and 1530 LST) from the top intake. This flask pair was averaged for CO2 concentration and δ13C to estimate daytime canopy air. Beginning in March 2003, an extra pair of daytime flasks was collected on a separate day every week. Nighttime air samples were collected to attain a gradient of CO2 concentration ≥50 ppm over the course of a night using 100 mL flasks (Kontes Glass Co., Vineland, New Jersey). Flasks were sealed with vacuum-tight Teflon stopcocks. The specified CO2 range was typically achieved during the growing season. Nighttime sampling started an hour after sunset to avoid effects of photosynthesis, and air was drawn from 2 heights: 0.1 m and 0.4 m above ground. Flasks were filled at 5-min intervals, cycling between the bottom and middle inlets. A “panic” mode was initiated one hour before sunrise which filled all the remaining empty flasks before any photosynthetic uptake. If the specified CO2 gradient was not met, the sampler resets and repeats the same procedure the following day. In general, there are 11 flask samples for each Keeling plot. Air was dried by flowing through a magnesium perchlorate trap before collection to minimize storage effect on the δ18O of CO2 [White et al., 2002]. The majority of air samples were typically collected within the first 2 hours (∼2000–2200 LST) after the sampling started. A field person then checked on the data logger and collected flasks the next day if they were successfully filled the night before.

[17] Flasks were collected for isotope analyses on weekly intervals between May and November and on a monthly basis for the rest of the year [Lai et al., 2003, 2004, 2005]. Carbon and oxygen isotope ratios of CO2 were analyzed on a continuous flow isotope ratio mass spectrometer (Finnigan MAT 252, San Jose, California), while CO2 concentration was measured to a precision of 0.3 ppm using a bellow/IRGA system in 2002 [Lai et al., 2003]. Beginning in 2003, a GC-IRMS system was deployed to analyze a flask for δ13C, δ18O and concentration of atmospheric CO2. Measurement precision was determined to be 0.06‰ for δ13C, 0.11‰ for δ18O and 0.48 ppm for CO2 concentration [Schauer et al., 2005]. Precision of the GC-IRMS system significantly improved isotope ratio analyses (by ∼0.05‰) but slightly degraded the precision for CO2 concentration measurements (by ∼0.2 ppm). This analytical modification improves the overall accuracy of the Keeling plot analysis because of the relative greater improvements in isotope precision as compared to a smaller decrease in the precision of concentration measurements.

[18] In this study, we report carbon isotope ratios on the VPDB scale; oxygen isotope ratios in water and CO2 are both reported relative to the VSMOW scale [Coplen, 1996].

2.5. Isotope Ratios of Ecosystem Respiration

[19] A two-source mixing line approach, first developed by Keeling [Keeling, 1958, 1961], can be used to estimate the isotopic composition of ecosystem respiration (δR):

  • equation image

where C represents mixing ratios of CO2. Subscripts m and b represent measurements collected within the nocturnal boundary layer and the background atmosphere, respectively. In theory, equation (1) can be applied for both carbon (δ13CR) and oxygen (δ18OR) isotopes; indeed, many ecosystem studies have adopted the mixing line approach to estimate δ13CR [Flanagan et al., 1999; Buchmann et al., 1997; Buchmann and Ehleringer, 1998; Bowling et al., 2002; Ometto et al., 2002; Pataki et al., 2003] and δ18OR [Flanagan et al., 1997, 1999; Buchmann and Ehleringer, 1998; Harwood et al., 1999; Bowling et al., 2003a, 2003b]. In this study, if the standard error of an estimated δ13CR value was greater than 2‰ (3‰ for δ18OR), we excluded it from our analyses. We excluded 10 and 22% of the measured δ13CR and δ18OR values on this basis.

2.6. Brief Descriptions of ISOLSM

[20] ISOLSM [Riley et al., 2002] is an updated version of the NCAR Land Surface Model (LSM1.0 [Bonan, 1994; Bonan et al., 1997]) designed to simulate terrestrial ecosystem oxygen and carbon isotope exchanges in CO2 and H2O. We have successfully tested ISOLSM's CO2 flux predictions in several of the dominant vegetation types using measurements performed in the Atmospheric Radiation Measurement Climate Research Facility (ACRF) as part of the AmeriFlux program [Riley et al., 2003; Suyker and Verma, 2001] and against 3 years of surface measurements made during the FIFE campaign [Betts and Ball, 1998; Cooley et al., 2005].

[21] The isotope submodels in ISOLSM simulate the dominant processes impacting the δ18O value of the soil and leaf H2O and CO2 fluxes: advection and evaporation of H218O in soil water, CO2 and C18OO soil-gas transport, leaf water enrichment, interactions between soil and leaf H218O and CO2, and the δ18O value of canopy air space vapor. We have applied ISOLSM to examine (1) impacts of the atmospheric δ18O value of H2O and CO2 on ecosystem discrimination against C18OO [Riley et al., 2003], (2) impact of the enzyme carbonic anhydrase in soils [Riley et al., 2002], (3) impacts of gradients in the δ18O value of near-surface soil water on the δ18O value of the soil-surface CO2 flux [Riley, 2005; Riley et al., 2003], (4) impacts of land use change on regional surface CO2 and energy fluxes and near-surface climate [Cooley et al., 2005], and (5) uncertainties associated with the use of 18O in CO2 measurements to estimate gross CO2 fluxes from net ecosystem exchange measurements and atmospheric C18OO measurements [Riley and Still, 2003].

[22] ISOLSM is forced with measurements of air temperature, pressure, and vapor content, wind speed, CO2 concentration, downward shortwave and longwave radiation, precipitation amount and its isotopic ratio, and the δ18O value of above-canopy vapor (δ18Ov) and CO2. We estimated downward longwave radiation using measured air temperature, shortwave radiation (SW) from measurements of photosynthetically active radiation (PAR), and a conversion factor (CF) of 0.46 with the relationship: SW = PAR/CF. Using satellite data, Pinker and Laszlo [1992] derived relationships between PAR and SW for the globe. They showed that, in most cases, CF is between 0.44 and 0.50, with the mean and median values being 0.46. For comparison, meteorological measurements from the ARM Central Facility (ww.arm.gov) between May and October of 2003 indicate a midday mean (standard deviation) of 0.43 (0.07), indicating that our choice is within the range of values expected for this area. In the absence of continuous measurements of δ18Ov we assumed a value 11‰ less than the predicted isotopic composition of source water. This assumption was based on averages of δ18Ov and source water δ18O measured during the two experiments in May and July 2002. The averaged δ18Ov was −12.3‰ (±1.2; n = 11) and −16.6‰ (±1.2; n = 10), while the averages of source water δ18O were −2.0‰ (±1.3; n = 99) and −5.1‰ (±1.8; n = 90) in May and July, respectively. Many factors other than evapotranspiration (e.g., horizontal and vertical atmospheric advection) impact δ18Ov, which can have diurnal variations of up to 4‰ in this area [Helliker et al., 2002]. Our measurements in this grassland also showed diurnal variations about 4‰ in the two experimental periods in 2002. We assumed constant δ18Ov values relative to the source water δ18O in the ISOLSM simulations.

[23] We do not have δ18O measurements of precipitation (δ18Op) at this site, so we relied on two data sources for estimates of δ18Op values. Welker [2000] reported arithmetic averages of δ18Op from 3 years (1989–1991) at sites representative of the Gulf of Mexico storm track from Gulf coast of Texas, western Oklahoma, western Nebraska, and into southeastern Montana. We expect δ18Op values in Rannells Prairie to have similar seasonal characteristics and magnitude as those monitoring stations because it is in the pathway of this storm track. The averaged δ18Op values along this storm track ranged between −2 and −10‰ from Texas to Montana. The two stations closest to our site (western Oklahoma and western Nebraska) had average δ18Op values of −5 and −8‰. Although δ18Op values showed considerable variations between summer and winter rains, summer precipitation was confined to a smaller range (less than 5‰). The second data source was based on a model output [Bowen et al., 2005], which interpolates a global precipitation data set for water isotope analyses developed by IAEA. An online calculator for oxygen and hydrogen isotopes of precipitation at any locations is available at http://www.waterisotopes.org. On the basis of this model, δ18Op values varied between −4.4 and −5.5‰ between the month of May and August at our site (39.12°N, 96.35°W, elevation = 324 m), with an average of −5‰. Hence we assumed a constant δ18Op of 5‰ in our model simulation throughout the growing season for all 3 years. The short-term variability of δ18Op is not considered in the model, which contributes to the uncertainty in the modeled soil and leaf water δ18O, and consequently, the modeled δ18O of net CO2 fluxes.

[24] The ISOLSM simulations predict δ18O values of leaf water on the basis of predicted δ18O values of source water and canopy water vapor using the Craig–Gordon model [Craig and Gordon, 1965] with modifications for leaves as described by Flanagan et al. [1991]. Gillon and Yakir [2000a, 2000b, 2001] showed that the presence of carbonic anhydrase is lower in C4 relative to C3 plants. Consequently, there is a lower degree of 18O exchange between CO2 and leaf water in C4 grasses. We have used ISOLSM to evaluate the impact of incomplete equilibration between leaf water and CO2 on ecosystem discrimination in a tallgrass prairie [Riley et al., 2003]. For the work presented here we assume complete equilibration between CO2 and leaf water; this assumption will not impact our results since our focus is on nighttime respiration.

[25] CO2 in the soil profile approaches equilibrium with soil water with a characteristic time on the order of an hour [Riley, 2005]. It is important to note that the δ18O of CO2 in canopy air is influenced by the δ18O value of the net soil-surface CO2 flux, which, in turn, is impacted by the δ18O value of CO2 in the soil profile. The δ18O value of soil-respired CO218Os) will reflect (1) the δ18O value of CO2 in complete equilibration with soil water at depth (δ18Ose); (2) the δ18O value of CO2 in partial equilibrium with near-surface soil water; and (3) a theoretical diffusional fractionation, ɛD, of 8.7‰. Consequently, δ18Os will equal δ18Ose depleted in 18O by some kinetic fractionation (ɛDf) between −8.7 and 0‰ [Amundson et al., 1998], i.e.,

  • equation image

Miller et al. [1999] determined an effective kinetic fractionation of CO2 diffusing out of the soil to be 7.2‰ (with respect to water at about 10 cm depth) on the basis of a dynamic chamber experiment.

[26] Riley [2005] recently used a model simulation in a tallgrass prairie to show that gradients in the δ18O value of near-surface soil water have significant impacts on the δ18O value of the soil-surface CO2 flux. For the work presented here, we use the relationship developed in that study with near-surface water-filled pore space (W (%)) to estimate ɛDf:

  • equation image

3. Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Monthly Mean Concentrations, δ13C Values, and δ18O Values of Canopy CO2

[27] Measured concentrations, δ13C values, and δ18O values of CO2 in the canopy air were averaged and presented as monthly means in Table 1. Midafternoon flasks were collected from 3 m above ground, while nighttime flasks were collected from 2 heights between inlets at 0.1 and 0.4 m. Midafternoon air showed consistently lower CO2 concentrations, more positive δ13C, and usually higher δ18O compared to nighttime values. Photosynthetic uptake decreases CO2 concentration while enriches 13C content in the atmosphere. The δ18O discrimination during photosynthesis, via CO2 equilibration with 18O-enriched leaf water, possibly contributes to the more positive δ18O values of canopy CO2 in the afternoon. At night, respiration releases 13C-depleted CO2 into the atmosphere. The contrasting effect of photosynthesis and respiration on the carbon and oxygen isotopes was largely responsible for the difference between daytime and nighttime values of δ13C and δ18O. The seasonal minimum in the CO2 concentration and the maximum in the δ13C value were in July for 2003 and 2004, but 2002 showed a different pattern. Whether this difference was related to changes in photosynthesis or respiration can be further investigated using meteorological and flux measurements.

Table 1. Monthly Mean Concentrations, Carbon Isotope and Oxygen Isotope Ratios of CO2 in Canopy Air Measured in the Rannells Prairie, Kansasa
MonthCO2, ppmδ13C, ‰δ18O, ‰
0.1 mb0.4 mb3 mc0.1 mb0.4 mb3 mc0.1 mb0.4 mb3 mc
  • a

    Values in parenthesis are 1 S.D.

  • b

    Nighttime (usually between 2000 and 2200 LST) samples.

  • c

    Midafternoon (1430–1530 LST) samples.

2002
5445.7 (46.3)407.7 (15.8)371.4 (6.9)−9.3 (0.4)−9.0 (0.2)−8.3 (0.1)40.8 (1.3)40.6 (0.8)40.3 (0.6)
6413.0 (14.7)392.5 (3.8)367.5 (0.5)−8.7 (0.1)−8.3 (0.1)−8.1 (0.1)38.8 (0.3)39.3 (0.3)39.5 (0.1)
7431.1 (45.7)404.2 (19.5)373.4 (3.8)−9.1 (0.6)−8.8 (0.3)−8.2 (0.3)39.1 (1.0)39.6 (0.6)40.4 (0.6)
8509.2 (73.8)439.3 (56.3)363.9 (6.0)−9.6 (0.7)−9.1 (0.5)−8.1 (0.3)38.4 (1.3)39.3 (1.2)40.1 (0.4)
9503.1 (49.4)455.1 (73.8)366.8 (1.4)−9.4 (0.4)−9.0 (0.7)−8.0 (0.1)37.2 (1.2)38.1 (1.2)37.7 (1.7)
10429.2 (19.3)407.2 (16.5)375.7 (2.3)−9.1 (0.3)−9.0 (0.4)−8.5 (0.1)38.2 (0.4)38.7 (0.4)38.6 (0.4)
 
2003
5420.4 (21.9)402.1 (12.2)379.1 (2.6)−9.1 (0.3)−8.8 (0.2)−8.3 (0.1)39.8 (0.6)40.1 (0.6)40.4 (0.3)
6523.0 (73.4)456.5 (76.2)366.4 (5.2)−9.9 (0.7)−9.4 (0.7)−8.2 (0.4)37.9 (1.5)38.4 (1.4)40.2 (0.6)
7554.3 (72.8)457.1 (48.6)361.7 (3.6)−9.8 (0.4)−9.1 (0.5)−7.9 (0.2)38.5 (0.6)39.7 (1.0)40.9 (0.3)
8510.1 (31.8)451.7 (29.7)372.8 (9.1)−9.8 (0.5)−9.3 (0.6)−8.2 (0.3)38.1 (0.8)39.2 (0.3)40.6 (0.8)
9509.3 (83.2)406.8 (28.9)370.3 (4.4)−9.5 (0.6)−8.6 (0.4)−8.0 (0.2)37.0 (1.5)39.1 (1.6)39.8 (0.9)
10433.4 (23.8)399.2 (16.9)378.8 (5.8)−9.2 (0.3)−8.7 (0.4)−8.4 (0.3)39.4 (0.7)39.8 (0.6)39.4 (1.1)
 
2004
5405.3 (15.3)399.2 (9.5)376.7 (6.8)−9.1 (0.3)−9.0 (0.3)−8.5 (0.2)40.8 (1.0)40.9 (1.0)41.1 (1.0)
6488.5 (76.2)415.3 (20.1)367.6 (7.1)−9.8 (0.8)−9.3 (0.6)−8.2 (0.3)38.4 (1.2)40.0 (0.6)40.7 (0.6)
7663.1 (181.3)434.6 (26.2)358.2 (13.7)−10.1 (0.6)−9.2 (0.5)−7.9 (0.4)37.5 (0.6)38.9 (0.6)40.7 (0.6)
8488.7 (81.3)408.5 (35.7)361.8 (8.6)−9.4 (0.8)−8.8 (0.6)−8.0 (0.4)37.8 (1.6)39.3 (0.8)40.6 (0.9)
9484.9 (44.0)434.1 (40.7)368.5 (7.0)−9.4 (0.4)−8.9 (0.5)−8.1 (0.2)38.8 (0.8)39.5 (0.7)41.0 (0.5)
10427.7 (17.4)398.6 (7.3)386.1 (6.9)−9.6 (0.3)−9.3 (0.3)−9.0 (0.4)39.4 (0.3)39.7 (0.2)39.7 (0.7)

3.2. Precipitation and Air Temperature

[28] Figure 1 shows monthly precipitation and averaged air temperature measured in the Rannells prairie during the growing season in 2002, 2003, and 2004. Monthly mean air temperature increased from about 13°C in April to a peak of 25°C in July, and then decreased again in the fall. Monthly averaged air temperature showed little interannual variation between 2002 and 2003, but was about 3°C cooler during July and August of 2004. The amount of precipitation varied tremendously among the 3 years. The amount of precipitation between April and September was 494, 653, 746 mm in 2002, 2003, and 2004, respectively, compared to the long-term average of 649 mm. Particularly, in the month of June, a record low precipitation input (∼10 mm) was received in 2002. This was only 8% of the rainfall amount received for the same month averaged over 15 years. By contrast, twice the average rainfall was received in June of 2004. Hence we considered 2002 a drought year, 2003 a normal year, and 2004 a wet year in this ecosystem. These differences in precipitation have significant implications for the ecosystem carbon balance because the primary growth period for tallgrass prairie is during the first half of the season, and growth is fueled by both stored soil water and precipitation. Less than normal precipitation during the early growing season has a greater effect on biomass production than it would in the mid or late season.

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Figure 1. Monthly precipitation and averaged air temperature measured at the Rannells prairie site between 2002 and 2004.

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3.3. Seasonal Patterns of NEE Fluxes

[29] Figure 2 shows seasonal and interannual variability of weekly averaged daytime and nighttime NEE fluxes. Substantial differences in weekly NEE fluxes were observed between years. Here negative fluxes represent carbon uptake by the prairie. The period shown here (May to mid-October) corresponds to the time when our study site appears to have the capacity to be a carbon sink (roughly defined as the growing season for this ecosystem).

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Figure 2. Net ecosystem exchange (NEE) of CO2 fluxes measured by the eddy covariance system at the ungrazed site in the Rannells prairie between 2002 and 2004. Data shown are weekly averages of daytime fluxes (open) and nighttime fluxes (shaded). Negative fluxes represent carbon uptake by the prairie, occurring from May to mid-October as shown here between weeks 17 and 41.

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[30] A prescribed control burn takes place yearly at the end of April. The burn removed accumulations of the current year litter layer and the mass of dead grasses, allowing soil to warm more quickly [Hulbert, 1988]. Uptake of CO2 can be detected shortly after the burn in all 3 years. However, the capacity of carbon uptake varied significantly from year to year. In 2002, large negative NEE fluxes were observed in the early spring, but quickly diminished midway into June when a severe drought occurred at the site (Figure 1). This pattern likely resulted from decreased photosynthetic capacity under water stress conditions [Lai et al., 2003]. Suyker and Verma [2001] showed that the apparent quantum yield of a tallgrass prairie in northern Oklahoma, which has similar species composition and environmental conditions as our site, was significantly lower when soil moisture was limiting. Nighttime fluxes were also decreased during the drought, likely because of a combined effect of reduced autotrophic respiration and microbial activities. The drought caused daily sums of NEE flux to be nearly neutral [Lai et al., 2003].

[31] Ecosystem net C uptake increased toward the end of July, when water finally became available from precipitation. However, carbon uptake in 2002 was substantially reduced because of the drought during the otherwise peak growth period (see NEE in 2003 and 2004). On the contrary, we observed large uptake of atmospheric CO2 by this prairie throughout the growing season in 2004, a wet year with no water limitation. The amount of carbon sequestered during the growing season was greatest in 2004, intermediate in 2003, and smallest in 2002. The rate of transition from carbon sink to carbon source is controlled by the gradual canopy senescence in the fall [Ham and Knapp, 1998]. Therefore the decrease of the NEE flux toward the end of August in 2003 (∼week 34 in Figure 2) indicates a canopy response to a mild drought (discussed later).

[32] The pattern observed in 2003 was perhaps the most representative of this prairie ecosystem on the basis of average precipitation. Consistent with results from other studies [Knapp, 1984, 1985; Kim and Verma, 1991; Verma et al., 1992; Briggs and Knapp, 1995; Ham et al., 1995], Figures 1 and 2 suggest that the capacity of carbon sequestration of a tallgrass prairie is very sensitive to the amount and distribution of precipitation. Rainfall in May and June has a large impact on the interannual variability of CO2 exchange in grassland ecosystems [Ham et al., 1995; Kim and Verma, 1991; Suyker and Verma, 2001]. In C3/C4 mixed grasslands, water availability could also affect interactions between C3 and C4 grasses. We investigate seasonal dynamics of C3 and C4 photosynthesis using carbon isotope measurements next.

3.4. Seasonal Patterns of δ13CR Measurements

[33] The seasonal transition of C3 abundance in spring to C4 dominance in summer has been shown in shortgrass and tallgrass ecosystems in North America Great Plains [Kemp and Williams, 1980; Ode et al., 1980; Barnes et al., 1983; Monson et al., 1983]. Using atmospheric δ13C measurements, Still et al. [2003] demonstrated that the apparent contribution of C4-derived carbon to ecosystem respiration increased from ∼40% in spring to over 80% in fall 1999. Given the divergent pattern of water input, we expect significant differences in the carbon isotope ratio of ecosystem fluxes between the 3 years in this prairie. Figure 3 shows weekly measurements of δ13CR. Values of δ13CR were more scattered during the summer of 2002, likely because of the impact of the drought on C4 photosynthesis. Despite warmer temperature favoring C4 photosynthesis in the summer [Ehleringer et al., 1997], water stress reduced photosynthetic uptake of C4 grasses [Lai et al., 2003]. Contributions of C3 plants were relatively higher for an extended period in 2002. Lai et al. [2003] showed that the more C3-like δ13C signals were related to wind speeds and directions. Still et al. [2003] also noted the effect of wind on the δ13CR measurements in a tallgrass prairie in northern Oklahoma. An alternative explanation is that the more negative δ13C signals observed in 2002 summer were due to the forb populations maintaining photosynthesis using deeper soil water. By contrast, values of δ13CR showed that C4 plants dominated ecosystem respiration in 2003 and 2004, except in early spring when C3 plants were more active because the cooler temperature favored C3 grasses and forbs [Teeri, 1988; Ehleringer et al., 1997]. In 2003 and 2004 C4 photosynthesis quickly became the major contributor as temperature increased. However, the timing of this shift from C3 to C4 photosynthesis depends on the timing of precipitation between years.

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Figure 3. Weekly measurements of carbon isotope ratios of ecosystem respiration (δ13CR, ± S.E.). Shaded blocks indicate nongrowing season.

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[34] We aggregated weekly δ13CR values and summarized this seasonal pattern on a monthly basis in Figure 4. We calculated the fraction of C4 contribution (f) using a two-source mixing model [Still et al., 2003], i.e., δ13CR = fδ13C4 + (1 − f) δ13C3, where δ13C3 = −27.9‰ (±0.54 S.E.) and δ13C4 = −12.3‰ (±0.19 S.E.) are measured carbon isotope ratios of leaf organic matter for C3 and C4 species, respectively [Lai et al., 2003]. In general, the fraction of C4 contribution changed from about 50% in spring to greater than 85% in summer. A dip in this fraction occurred in May (June in 2002), partially reflecting the effect of the prescribed control burn that cleaned up the canopy floor where considerable amount of C4 biomass from the year before would otherwise decompose. In contrast with the seasonal pattern in 2003 and 2004, the fraction of C4 contribution continued to decrease in June 2002. Concurrently, this site had very low photosynthetic uptake (Figure 2). It is likely that drought-reduced photosynthesis of C4 grasses surpassed temperature-limited growth of C3 forbs during this period. In this system, δ13CR values seem to reflect the influence of microclimate on aboveground canopy on weekly timescales.

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Figure 4. Comparison of monthly averages of δ13CR (top) and the fraction of C4 contribution (bottom) for the 3 years. The arrow indicates the timing of a prescribed control burn conducted once a year at the Rannells prairie. The shaded lines indicate measured δ13C boundaries based on leaf organic matter from dominant C3 (−27.9‰ ± 0.54 S.E.) and C4 (−12.3‰ ± 0.19 S.E.) species.

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3.5. ISOLSM 18O Predictions

[35] We used ISOLSM to simulate oxygen isotope ratios of ecosystem water pools and fluxes and gross and net CO2 fluxes. Our goal in applying the model was to interpret the interannual differences in nighttime δ18OR values resulting from the complex interactions between H218O pools and CO2 fluxes. To estimate δ18OR values using the Keeling plot approach is sometimes difficult because the assumption of a two-source system is violated more often than for δ13C. The approach requires relative contributions of canopy and soil efflux to remain unchanged over the time when air samples were collected [Pataki et al., 2003]. This assumption is particularly problematic when invasion (abiotic) fluxes are strong [Tans, 1998]. Here the invasion (abiotic) effect refers to the diffusion of atmospheric CO2 into the soil, where it equilibrates isotopically with soil water before diffusing back out [Tans, 1998; Amundson et al., 1998; Miller et al., 1999; Stern et al., 1999, 2001]. Even when the above assumption was met, measuring δ18OR remains a challenge because the δ18O value of leaf (and to a lesser extent, soil) water can change over the course of a night, especially in grass species when progressive 18O enrichment occurs [Helliker and Ehleringer, 2000]. Hence continuous measurements of δ18OR are rare and often difficult to interpret. To demonstrate that ISOLSM is a reliable tool for predicting ecosystem fluxes and isotope ratios, we compare model predictions with (1) measured NEE, latent heat (LE), and sensible heat (H) fluxes and (2) measured δ18O values of leaf and source water. We then compare predictions to measured δ18OR values under conditions we believe met the assumptions describe above.

3.6. Comparisons Between Measured and Modeled Fluxes

[36] Figure 5 shows comparisons between measured and modeled NEE, LE, and H fluxes over the same 10-day period in 3 different years. The selected period addressed contrasting soil moisture conditions that were representative of a dry (2002), a moderate (2003), and a wet year (2004). Despite a relatively larger (∼20%) underestimation of LE and H in 2004, ISOLSM simulated diurnal patterns of NEE, LE, and H fluxes with robust agreements in contrasting soil moisture conditions. This agreement was typical for the entire season in all 3 years except during periods of intense precipitation. The greater model discrepancy during rain events is likely due to larger uncertainties in the measured or derived input variables (e.g., shortwave radiation) and fluxes. Nevertheless, Figure 5 provides confidence that ISOLSM correctly describes canopy conductances, soil fluxes, and the energy balance in variable soil moisture conditions for this site.

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Figure 5. Comparisons between measured and modeled NEE, latent (LE), and sensible (H) heat fluxes for the same 10-day period in the 3 years. Open circles represent measurements, and solid lines are model results.

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[37] Comparisons in Figure 5 also highlight the dynamics of NEE flux and the energy partitioning between LE and H in this tallgrass prairie. Canopy leaf area index (LAI) from clipped biomass collection was 1.8, 4.0, and 3.4 m2 m−2 in this period for the 3 years, respectively. Midday NEE in drought periods was about 25% of that when water was not limiting (Figure 5). Given that nighttime respiration also decreased during drought (Figures 2 and 5), the reduction of midday NEE is likely not due solely to an increase in respiration. It appears that factors other than a smaller LAI also affect gross photosynthetic uptake in water stress conditions, likely because of a reduced quantum yield capacity [Suyker and Verma, 2001] or maximum carboxylation rate of Rubisco [Collello et al., 1998].

[38] Contrasting LE fluxes suggest remarkably different water use strategies between C3 forbs and C4 grasses under different soil water conditions. The highest LE in 2004 was partially due to consistently higher soil evaporation based on model simulations. LE fluxes in the drought period were lower than those in the wettest year, but surprisingly higher than those in 2003 (Figure 5), a period with moderate water availability. Comparing NEE and LE patterns between years, it was interesting that the water use efficiency (WUE, roughly defined as NEE/LE here) at midday was the highest in 2003. C4 plants are known to have higher WUE than C3 plants [Downes, 1969; Long, 1985, 1999; Ehleringer and Monson, 1993]. Given the severe drought that occurred in the same period, we would have expected a more conservative water use in 2002. Lai et al. [2003] modeled NEE fluxes in this prairie and showed that C3 forbs contributed relatively more to the NEE flux during drought because of their ability to access deep soil water [Weaver, 1958], which explained the higher LE and lower WUE when compared to 2003.

3.7. Comparisons Between Measured and Modeled δ18O Values of Leaf and Source Water

[39] Figure 6 shows comparisons between measured and modeled δ18O of leaf and source waters for two field experiments in 2002. Note that leaf and source water measurements were not made in 2003 or 2004. Modeled source water δ18O values were relatively constant throughout each of our experiments, consistent with measurements. Modeled leaf water δ18O values compared reasonably well with measurements in early morning and late afternoon, but the model predicted heavier than measured midday leaf water δ18O values. The Craig-Gordon model has been shown to overpredict the δ18O value of bulk leaf water in several ecosystem types [Dongmann et al., 1974; Leaney et al., 1985; Bariac et al., 1989; Flanagan and Ehleringer, 1991; Yakir, 1992; Roden and Ehleringer, 1999]. This discrepancy was explained by a retrodiffusive flux that mixes fractionated water from the evaporation sites with the advected flux of nonfractionated water (Péclet effect [Farquhar and Lloyd, 1993]). In our sensitivity test, the discrepancy between modeled and measured leaf water δ18O at midday was improved when we included the Péclet effect (not shown). However, for the current study, modeled leaf water δ18O at night is a more critical parameter and relatively accurately predicted.

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Figure 6. Comparisons between measured and modeled δ18O values of leaf and source water for two 3-day periods in 2002.

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[40] Modeled δ18O values of leaf water showed notable differences at night between the two periods (Figure 6). They remained enriched relative to the source water δ18O throughout the night in May, but were closer to the δ18O value of source water in July. Relative humidity was about 90% for both periods at night. Leaf water δ18O estimates using the Craig-Gordon model are more sensitive to δ18O values of above-canopy water vapor (δ18Ov) when relative humidity is high. Differences between δ18Ov values in the two periods (about 4‰) can explain the difference between nighttime δ18O values of leaf water in the model. We do not have direct measurements of nighttime leaf water δ18O. Nevertheless, given the closer agreement between modeled and measured leaf water δ18O in early morning and later afternoon, we believe ISOLSM adequately described leaf water δ18O values at night for our estimates of nighttime δ18OR values. On this basis, we modeled δ18O values of leaf water in the growing season for all 3 years using meteorological data from the flux tower, because we did not have temperature and relative humidity measurements at the canopy height in 2003 and 2004. We recognized the importance of a vertical gradient in water vapor concentration between the height of the canopy (0.5 m) and the sensor (3 m). A correction was applied to the tower-based relative humidity data using a second-order polynomial regression, developed on the basis of measurements from a vertical profile conducted in the summer of 2002 (data not shown).

3.8. Seasonal Patterns of Measured and Modeled δ18OR

[41] Figure 7 shows comparisons between measured and modeled nighttime δ18OR and modeled soil moisture contents in the top 10 cm for the three growing seasons. Measured δ18OR showed considerable interseasonal and intraseasonal variations. ISOLSM δ18OR predicted these variations quite well, especially for the 2003 summer where the model successfully described a decreasing trend of δ18OR between DOY 150–190 and an increasing trend between DOY 190–240. We do not know the cause of the relatively less positive δ18OR value observed on DOY 244 (22.7 ± 0.5‰, SMOW). This measurement was made immediately after a substantial rain event. It was likely that one precipitation had a relatively depleted δ18O value, which was not described in the model. The model predicted the relatively smaller variation in δ18OR observed throughout 2004, as compared to 2003. Modeled δ18OR also showed close agreements with measurements in 2002 and 2004, indicating ISOLSM's consistency of predicting δ18OR in very different soil moisture conditions. This relatively good model versus measurement agreement gave us confidence in using ISOLSM to investigate factors affecting seasonal variations of δ18OR.

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Figure 7. Comparisons between measured and modeled δ18OR values and modeled average soil moisture contents in the top 10 cm for the three growing seasons. Solid circles represent measurements, and solid lines are model results.

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[42] We could not obtain reliable δ18OR measurements from the Keeling plot analysis between DOY 160–207 in 2002. Air samples collected during this period had little or no correlation between δ18O and 1/CO2 on a Keeling plot. We suspect this was due to low CO2 production in the soil when water was limiting. Under these conditions, the amount of CO2 exchanged between the atmosphere and the soil was likely much higher than the net CO2 added from the soil. Consequently, the invasion (abiotic) effect was amplified. Tans [1998] used a model to demonstrate that strong invasion creates large errors when estimating δ18OR with a Keeling plot. Our results indicate that the decoupling between δ18O values and CO2 concentrations prevents reliable Keeling plot analysis when the soil surface is dry. As described by Miller et al. [1999], the Keeling plot approach appears to be more robust for estimating δ18OR in moister soil conditions.

[43] The high δ18OR values occurring in 2002 just before day 210 contrast sharply with those that occur after day 210. The period before day 210 is very dry, with low soil moisture (Figure 7) and relative humidity (shown as 10-day averages before and after the precipitation event in Figure 8a). A substantial precipitation event on day 210 replenished soil moisture and increased relative humidity throughout the day. The near-surface soil water was heavier by about 4‰ preceding the precipitation event (Figure 8b) because of the period of sustained evaporative enrichment. This enrichment, and the impact of lower relative humidity over the course of the day, caused leaf water to become relatively enriched (Figure 8b) compared to the period after the precipitation event. Thus both components of nighttime respiration (above and belowground) were enriched, leading to the substantial enrichment of the total CO2 respiration flux. If these modeled values were correct, a Keeling plot analysis would be ineffective because of the strong invasion effect.

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Figure 8. (a) Comparisons between measured relative humidity and (b) comparisons between modeled leaf and soil water δ18O values in the top 10 cm between two periods with contrasting soil moisture conditions. Values reported are 10-day averages representing a dry (DOY 199–208) and a wet (DOY 211–221) period.

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[44] Measured and modeled δ18OR showed less seasonal variation in 2004, a wet year without water limitation in the growing season. When soil water was abundant, δ18OR values appeared to be affected by the relative contribution of soil to total respiration (data not shown). This relationship is especially evident in the summer of 2003. δ18OR values were more positive when the contribution of soil respiration became greater, suggesting soil-respired CO2 had higher δ18O values than aboveground respiration. This observation is supported by model predictions, which showed more positive δ18O in surface soil water than in nighttime leaf water. This modeling result contradicts the common assumption that leaf water δ18O values are more enriched than soil water, which is often true during the day. At night, leaf water δ18O could stay enriched relative to source water, particularly when the leaf water turnover rate is low [Cernusak et al., 2002; Farquhar and Cernusak, 2005; Lai et al., 2006]. Our measurements showed that δ18O values of soil water in the top 10 cm could be enriched by nearly 6‰ from surface evaporation (data not shown). During these periods, the soil C18OO flux is more 18O enriched relative to aboveground respiration at night. The δ18O signatures of soil-respired CO2 and aboveground respiration are usually close to each other in this situation (within a few ‰).

3.9. Potential Sources of Errors in the Model

[45] Riley et al. [2002, 2003] modeled δ18O values of soil water between 0 and 20 cm below the surface over an extended period in a tallgrass prairie in northern Oklahoma. They showed that δ18O values of surface soil water (0–2.5 cm) were very responsive to evaporation and precipitation. Below 2.5 cm, diurnal fluctuations in the δ18O value of soil water were much smaller. Previous studies showed that surface enrichment does not appear to affect δ18O values of soil efflux because the equilibration process mostly occurs at depth [Amundson et al., 1998; Miller et al., 1999; Stern et al., 1999, 2001]. In contrast, Riley [2005] used a model simulation to show that gradients in the δ18O value of near-surface soil water have significant impacts on the δ18O value of the soil-surface CO2 flux. Further work needs to be done to characterize the impacts on δ18OR of competition between soil-gas diffusion and isotopic equilibration with near-surface soil water.

[46] Short-term variation in the δ18O value of soil-respired CO2 would likely be affected by the δ18O value of individual rain events, which we did not specify in our simulations. Using predicted δ18O values of source water to estimate above-canopy vapor also likely contributes to error, since vapor 18O content is also impacted by horizontal advection, mixing with the free troposphere, and surface evaporation. Further, as mentioned earlier, diurnal variations in δ18Ov can be large. In previous work we examined the impact of these variations on daily averaged leaf and near-surface soil water δ18O values and found them to be smaller than impacts associated with errors in daily averaged values of above-canopy vapor δ18O values [Riley et al., 2003]. We do not expect that the assumption of a constant value for the δ18O value of above-canopy CO2 will substantially impact nighttime δ18OR [Riley et al., 2003].

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[47] In this study we showed carbon and oxygen isotopes of respired CO2 fluxes measured in a tallgrass prairie. These measurements were made continuously on a weekly basis during the growing season for 3 years with contrasting precipitation inputs. Measurements of NEE flux showed considerable seasonal and interannual variations. Consistent with previous studies, we found the timing and distribution of precipitation have large impacts on carbon exchange in this grassland. We found ISOLSM capable of predicting oxygen isotope ratios of ecosystem respiration (δ18OR) in contrasting soil moisture conditions. Using Keeling plot analysis for estimating δ18OR is less reliable when surface soil is dry. ISOLSM modeled very positive δ18OR values under water stressed conditions. The combination of low relative humidity and high δ18O enrichment in surface soil water during drought contributed to the very positive predicted δ18OR values.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[48] This research was supported through the Terrestrial Carbon Processes (TCP) program by the office of Science (BER), U.S. Department of Energy, under grant DE-FG03-00ER63012 and partially through the Great Plains Regional Center of the National Institute for Global Environmental Change (NIGEC) under Cooperative Agreement DE-FC03-90ER61010 and grant FG03-99ER62863/A002. Financial support does not constitute an endorsement by DOE of the views expressed in this article. The authors would like to thank Lisa Auen, Eric Stange, and Timothy Jackson for field assistance. We are grateful to Craig Cook, Mike Lott, Shela Patrickson, C. F. Kitty, S. Bush, and M. Moody for stable isotope analyses in the laboratory.

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  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and Methods
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jgrd12488-sup-0001-t01.txtplain text document2KTab-delimited Table 1.

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