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Keywords:

  • terrestrial ecosystem biogeochemistry model;
  • soil organic matter dynamics;
  • radiocarbon;
  • carbon;
  • nitrogen;
  • soil chronosequence

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

[1] We examine soil organic matter (SOM) turnover and transport using C and N isotopes in soil profiles sampled circa 1949, 1978, and 1998 (a period spanning pulse thermonuclear 14C enrichment of the atmosphere) along a 3-million-year annual grassland soil chronosequence. Temporal differences in soil Δ14C profiles indicate that inputs of recently living organic matter (OM) occur primarily in the upper 20–30 cm but suggest that OM inputs can occur below the primary rooting zone. A three-pool SOM model with downward transport captures most observed variation in Δ14C, percentages of C and N, δ13C, and δ15N, supporting the commonly accepted concept of three distinct SOM pools. The model suggests that the importance of the decadal SOM pool in N dynamics is greatest in young and old soils. Altered hydrology and possibly low pH and/or P dynamics in highly developed old soils cause changes in soil C and N turnover and transport of importance for soil biogeochemistry models.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

[2] Following years of successful research defining the role of interregional variation in climate, vegetation type, and land used in soil organic carbon (SOC) storage and turnover [Burke et al., 1989; Houghton, 1995; Houghton et al., 1999; Jobbagy and Jackson, 2000; Post et al., 1982; Raich and Schlesinger, 1992; Schimel et al., 1994; Trumbore et al., 1996; Trumbore, 1993], a logical step is to investigate the role of other factors that control the variation in C dynamics [Schimel et al., 1994], particularly within geographical regions [Anderson, 1995]. Many of Earth's most important agricultural regions are situated on the floodplains of ancient and modern river systems. The alluvium that composes these landscapes varies in age and source, imprinting these regions with a mosaic of different soil types. Within similar alluvium, soil properties such as mineralogy, texture, and P availability vary predictably as a function of soil age [Harden, 1987; White et al., 1996; White, 1995] and are known to control aspects of SOC dynamics [Burke et al., 1989; Christensen, 1996; Oades, 1995; Oades and Waters, 1991; VanVeen and Kuikman, 1990; Torn et al., 1997].

[3] To investigate the role of soil age in SOC storage and turnover, we utilize four sites along an annual grassland chronosequence near Merced, CA, USA [Harden, 1987] representing a model ecosystem for studying soil organic matter (SOM) dynamics. The soil parent material is granitic alluvium and is therefore representative of the dominant mineralogy present on continents. Changes in soil texture and mineralogy have been thoroughly quantified [Harden, 1987; White et al., 1996]. For all but the oldest site, this material is also uniform and well-drained, thereby facilitating models for soil-borne transport mechanisms. Additionally, to augment contemporary sampling for the use of Δ14C as a tracer of SOC turnover, the field area was sampled systematically as a function of soil age circa 1949 [Arkley, 1962, 1964] and 1978 [Harden, 1987]. We therefore quantify rates of soil C and N turnover and transport and investigate the processes controlling SOM dynamics by fitting a model to concentrations of C, 13C, 14C, N, and 15N obtained as a function of soil depth in soils ranging from <3 kyr to 3 Myr in age.

2. Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

2.1. Chronosequence Sites

[4] Archived and contemporary samples were obtained from uneroded or slightly eroded sites on alluvial terraces and fans formed by the Merced and Tuolumne Rivers, in the Eastern San Joaquin Valley, CA, USA (Figure 1) [Arkley, 1962; Harden, 1987]. The soil parent material at all sites consisted of arkosic alluvium derived primarily from granodioritic rocks of the Sierra Nevada Batholith. All sites are in close proximity (∼43 km; see Table 1) and therefore have similar Mediterranean climates (hot, dry summers and cool, wet winters), with mean annual temperatures of 16°C and mean annual precipitation of 300 mm [Arkley, 1962; Brenner et al., 2001; Harden, 1987]. The Mediterranean climate supports a flora dominated by annual grasses and forbs but with a secondary component of deep-rooted oaks that access deep water sources at the <3 and 600 kyr sites (see Table 1). Species and functional group composition of annual plants varies substantially from year to year in California annual grasslands, with a large biomass of N-fixing leguminous species in some years [Jones and Woodmansee, 1979]. Site data are given in Table 1 and include references to descriptions of archived soils if available.

image

Figure 1. (a) A map depicting the intensively studied area (white outline), within a larger region for which the study area is representative (black area). (b) Landform ages within Merced County, California, where most study sites were located. The 600 ky site is located just north of the region shown. All study sites are located within the white line in Figure 1a.

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Table 1. Information Describing the Sites Sampled
Soil Age/Geologic UnitYears Sampled1997–1998 Location (Lat./Long)VegetationSoil SeriesSurface Texture/Parent Material TextureaParent Material SourceDistance from 1997–1998 SamplingReference (Srchived Samples)
  • a

    As inferred by Harden [1987], and similarly estimated for 1 Myr soil. Texture is for A horizon. Also see White et al. [1996] for detailed texture and mineralogy.

  • b

    Post-Modesto alluvium may contain a significant contribution from low-elevation watersheds composed of metasedimentary and volcanic rocks [Harden, 1987].

  • c

    200 and 600 kyr glacial outwash events are believed to punctuate episodes of alpine glaciation in high elevations of the Sierra Nevada [Harden, 1987]. Glaciated catchments at high elevation are dominated by the Sierra Nevada Batholith, which have a granodioritic composition.

  • d

    Plio-Pliestocene deposits derived from uplift of the Sierra Nevada and believed to include greater contributions from metasedimentary and volcanic rocks of the foothills, including older terraces [Marchand and Allwardt, 1981]. These differences in mineralogy lead to exclusion of the 1 Myr sites from the ideal chronosequence [e.g., Harden, 1987; J. Harden, personal communication, 2001].

  • e

    Pliocene deposits derived from uplift of Sierra Nevada. The <2 mm soil parent material is believed to be similar in texture and mineralogy to 200 and 600 kyr parent material [Harden, 1986; White et al., 1996].

<3 ky/Post-Modesto194937.51384°Nannual grassland/oak savannaGrangevillesandy loam/sandy loamrecent floodplain alluvium derived from Sierra Nevadab1 kmArkley [1962]
 1978120.46292°W    10 kmHarden [1987]
 1997     
200 ky/Riverbank (2)194937.52°Nannual grasslandSnellingsandy loam/sandy loamglacial outwash alluvium derived from Sierra Nevadab∼10mArkley [1962]
 1978120.59°W    ∼5mHarden [1987]
 1997     
600 ky/Turlock Lake195237.62577°Nannual grassland/scattered oaksCometa or Montpellierloamy sand/loamy sandglacial outwash alluvium derived from Sierra Nevadac100 mArkley [1964]
 1978120.59085°W    15 mHarden [1987]
 1997      
1 My/North Merced Gravel194937.40°Nannual grasslandCorning or Reddinggravelly loam/gravelly sandy loambraided channel alluvium derived from Sierra Nevada and Foothillsd∼10 kmArkley [1962]
 1949120.47°W    ∼11 kmArkley [1962]
3 My/China Hat197837.464440°Nannual grasslandCorning or Reddinggravelly loam/gravelly sandy loambraided channel alluvium derived from Sierra Nevadae∼200 mHarden [1987]
 1998120.36906°W    

[5] The presence of archived samples matched to undisturbed contemporary sites constrained the selection of terraces to soil ages of <3, 200, and 600 kyr and 1 to 3 Myr. During site selection, we chose the undisturbed locations closest to the sites where archived samples were collected, optimizing the site similarity while avoiding evidence of disturbance. Matches were nearly ideal for the 200 and 600 kyr sites, but the 3 Myr terrace was not sampled circa 1949 (see Table 1). Archived 1949 samples from a 1 Myr terrace provide a suitable substitute, but the 1 Myr soils were not resampled in 1997–1998 due to erosion or heavy grazing at these locations. Differences between the 1 and 3 Myr soils include soil parent material that may have differed slightly and greater depths to the duripan in the 3 Myr soil than in the 1 Myr soil. The 1 to 3 Myr soils are assumed to be equivalent for this study.

[6] A survey was conducted of all available published data for soil profiles developed on similar granitic alluvium parent material within the black area in Figure 1 [Arkley, 1962, 1964; Arkley and California Agricultural Experiment Station, 1954; Harden, 1987; Huntington, 1971; Weir and California Agricultural Experiment Station, 1952; Weir et al., 1956]. Data for all profiles having C data and bulk density and percentage of gravel data (or for which bulk density could be estimated) were tabulated to create Figure 2b.

image

Figure 2. (a) Total <2 μm clay in ESJV soil profiles to a depth of 1 m. Profiles obtained from soil surveys and Harden [1987] within black area in Figure 1a. (b) Total SOC storage to a depth of 1 m in ESJV soil profiles. Bars at bottom indicate age ranges which were binned. The number of samples in each age bin is indicated. Profiles obtained from soil surveys and Harden [1987] within black area in Figure 1a. Outer error bars represent standard deviations; inner error bars represent standard error. (c) Total measured C and N inputs from aboveground and belowground biomass harvests at peak biomass in spring 1998 and 1999. Belowground inputs for 3 My are based on 1999 only. These data can be viewed as measures of plant productivity.

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2.2. Soil Sampling and Preparation

[7] Archived and contemporary samples were collected from horizons identified in soil profiles. Contemporary soil pits were described and sampled according to standard methods [Soil Survey Staff, 1993], and similar procedures were followed for archived soils [Arkley, 1962, 1964; Harden, 1987]. Profile descriptions for contemporary soil profiles appear in Appendix A. The A1 and A2 horizons identified in contemporary profiles are directly comparable across sites, and appear to be comparable to similar depth increments in archived soils. Deeper horizons may not be directly comparable between sites.

[8] A1 (∼0–2 cm) samples were obtained by quantitatively sampling a 15-cm square area to the lower boundary of the horizon, yielding a direct measurement of mass per unit area within the sample. Other samples were collected to integrate across the face of the soil pit. Below the A1 horizon, bulk density was obtained by coring and/or clod methods and reports the average of all available measurements. Adjusted bulk densities (BDadj) consider the reduction in <2 mm material per unit volume as a result of >2 mm gravel, cobbles, and stones. We estimated the volumetric contribution of gravel following sieving and estimated the percentage of cobbles and stones in each horizon using standardized charts. Bulk densities for many prebomb soil samples were estimated based on data for contemporary profiles and Harden [1987].

[9] The 1997–1998 soils were dried in an enclosed dust-free oven (<50°C). Archived soil samples were stored in an air-dried state in glass jars, except for the 1978 samples, which were stored in paper cans. Samples were passed though a soil splitter to obtain representative ∼100-g samples for sieving. The ∼100-g subsamples were sieved to remove >2-mm material including coarse organic matter (OM) debris and gravel. Samples were then passed through a smaller soil splitter to obtain samples of 2–8 g for further analysis. Bulk soil samples (2–5 g) were ground in a corundum mortar and pestle to pass a 70-mesh sieve for C, N, and isotope analysis. Some data reported for archived <3 kyr soils were obtained previously by J. Harden (USGS, Menlo Park) using similar methods, except that soils were sieved using a 2-mm-mesh sieve, were ground to 100 mesh, and may not have been split using a soil splitter following sieving.

[10] Some <3 kyr horizons contained small quantities of carbonate in the subsoil, which was removed by treatment with 0.1-N HCl. Care is required during the acid dissolution of carbonates because HCl has been used as an extractant to separate SOC into pools with different Δ14C signatures [Trumbore and Zheng, 1996]. We selected 0.1-N HCl based on experiments, which showed no observable dissolution of OM, while stronger acids (1 N) dissolved SOM, resulting in discoloration of the solution and measurable loss of SOM from test samples that did not contain carbonate. Only the <3 kyr soils were treated with acid.

[11] A density fractionation method similar to that reported by Baisden et al. [2002] was applied to samples from deep (71–119 cm) argillic horizons in the 200 and 600 kyr and 3 Myr soils. Samples (5–7 g) were sonicated in 2.22 g cm−3 sodium polytungstate (SPT) solution in an ice bath for 6 min at 50% pulse and 66% power using a Branson 350 W Sonifier with a probe tip. Following centrifugation sufficient to settle particles >0.2 μm and >2.22 g cm−3, we removed the light fraction (LF; <2.22 g cm−3) by aspiration. The dense fraction (DF; >2.22 g cm−3) was washed with deionized (DI) water four times, repeating the centrifugation after each washing step. The DF was freeze-dried and ground to 70 mesh using a corundum mortar and pestle. This procedure differs from that used by Baisden et al. [2002] in that fractions were not separated first at densities <2.22 g cm−3 and that the LF was not recovered. Instead, the C and N concentrations in the LF were obtained by difference and therefore include particulate organic matter (POM) and aggregates <2.22 g cm−3 as well as dissolved organic matter (DOM) lost in the SPT and subsequent DI water rinses. DOM was not insignificant as coloration of the SPT and DI rinses was observed [for details see also the work of Baisden et al., 2002].

2.3. Normalized Soil Depth

[12] Throughout, we normalize soil depth to a BDadj of 1.65 g cm−3 by multiplying horizon thickness by the ratio of the 1.65 g cm−3 to the measured bulk density. We normalize bulk density for several reasons. First, concentrations can be presented in familiar units (%) while allowing graphical comparison of C and N inventory. Second, normalizing to a standard BDadj corrects for cross-site variation in processes that alter soil bulk density, particularly in near-surface horizons. At the sites, the variation in these processes includes the relative activity of burrowing organisms, the presence or absence of livestock, the relative abundance of plant roots, and the presence of gravels and cobbles. Third, normalized depth simplifies the model presented in section 4. Throughout this work, normalized soil depth will be referred to in units of cm*, while soil depth measured in cm (without the asterisk) is not normalized.

2.4. Plant Sampling and Treatment

[13] Aboveground biomass was harvested at all four sites at the end of the growing season in both 1998 (late April) and 1999 (early May). All upright stems were included in the aboveground biomass while litter on the ground surface was not. We harvested four 0.33 m × 0.33 m quadrats in 1998 and five 0.25 m × 0.25 m quadrats in 1999. We freeze-dried 1998 samples and ground each sample individually on a roller mill in ∼2- to 4-L reinforced porcelain jars with corundum grinding pellets. The 1999 samples were oven-dried (60°C), bulked, and homogenized using a 3 Wiley mill. Small aliquots were then pulverized using a SPEX 6800 cryogenic grinding mill. Stable isotope data are reported for 1998 samples only, as 1999 samples dried poorly and were therefore analyzed for total C and N only.

[14] Belowground biomass was harvested in 5.2-cm-diameter soil cores to 15-cm depth at <3, 200, and 600 kyr sites in 1998 and at all four sites in 1999 at the same time aboveground biomass was harvested. Cores were driven downward following the collection of aboveground biomass, near the center of the quadrat. Thus, surface litter is included in the cores. This sampling protocol emphasizes the collection of all OM in the ecosystem, assuring that a reproducible operational distinction is drawn between above- and belowground OM inputs.

[15] In 1998, four cores were sampled per site, and the sampling rate increased to 10 cores per site in 1999. The high gravel content at the 3 Myr site prevented successful coring in 1998. Cores were kept frozen until analysis. The samples were washed through a sieve to capture intact roots and other coarse OM on a 2-mm round hole sieve. When possible the A1 and A2 horizons were separated and washed individually, although data have been combined for presentation here. The >2-mm OM was freeze-dried and pulverized using either a jar mill with steel grinding rods (1998 samples) or a SPEX 6800 cryogenic grinding mill (1999 samples). We operationally defined belowground biomass as any material collected on a 2-mm round hole sieve during root washing.

2.5. Total C, N, and Isotope Analysis

[16] The δ13C, δ15N, C content, and N content of plant samples were determined on a Europa 20/20 elemental analyzer-continuous flow-isotope ratio mass spectrometer (EA-CF-IRMS) in the Center for Stable Isotope Biogeochemistry at the University of California, Berkeley. Soil δ15N and percentage of N were determined using an EA-CF-IRMS system at the USGS (Menlo Park, CA), consisting of a Micromass Optima and a Carlo Erba 1500. Soil subsamples for the analysis of Δ14C, δ13C, and percentage of C were sealed in evacuated vycor tubes with 0.5 g Cu, 1.0 g CuO, and a strip of Ag foil. These samples were combusted for 4 hours or more at 850°C, cooled at 1°C min−1, and held for 2 hours at 650°C before further cooling [Boutton, 1996; Minagawa et al., 1984]. The CO2 was cryogenically purified and measured manometrically to allow determination of percentage of C. For most samples, the CO2 was split into subsamples for the separate analysis of δ13C and Δ14C. Determination of δ13C was performed on a VG Prism III dual inlet mass spectrometer at Lawrence Berkeley National Laboratory. CO2 was converted to graphite for 14C analysis at the Center for Accelerator Mass Spectrometry (CAMS), Lawrence Livermore National Laboratory (LLNL). We report δ13C and δ15N in ‰ (per mil) relative to PDB and atmospheric N2, respectively. Radiocarbon data are presented in Δ14C notation, also in ‰, where the standard (95% of the activity of NBS oxalic acid) has approximately the same Δ14C value as the preindustrial atmosphere. The Δ14C values are corrected for stable isotope fractionation based on δ13C.

[17] Stable isotope data from the Europa EA-CF-IRMS are corrected for linearity as described by Baisden et al. [2002], while δ15N data from the Optima EA-CF-IRMS are corrected as described elsewhere [Brenner et al., 2001]. Internal laboratory standards were used to demonstrate that data obtained from different instruments are generally comparable within reported uncertainty. At least 10% of stable isotope analyses were duplicated, with increasing rates of duplication for deep soil samples with low OM concentrations. For comparison with other published data, we calibrated the internal standards to international isotope standards (NIST 8547 IAEA N-1, NIST 8540 PEF-1, and NIST 8542 ANU Sucrose). Additionally, data from the Europa EA-CF-IRMS were corrected to NIST 1547 Peach Leaves, assuming δ13C = −25.9‰ and δ15N = 2.1‰. Typical analytical uncertainty for δ13C (0.1‰) and Δ14C (5‰) are less than expected natural variability. Typical analytical uncertainty in δ15N (0.3‰ for plants and surface soils, and up to ∼0.8‰ for soils with low percentage of N) approaches the variation likely to be obtained from replicate sampling, particularly in deep soil. Error bars are not included (unless noted) where only analytical uncertainty was available. When reported, uncertainties indicate standard errors of replicate samples taken from the field, unless otherwise noted. All ratios of C:N are reported on a mass basis.

3. Analytical Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

3.1. C and N Storage and Plant Inputs

[18] How do soil clay content, soil C storage, and plant productivity vary as a function of soil age? The clay content of soil profiles observed in soil profiles from the eastern San Joaquin Valley (black area in Figure 1a) to 1 m in depth displays a roughly power law increase in clay content versus soil age (Figure 2a). In contrast, the highest SOC storage is observed in the youngest soils (<3 kyr), while soils older than ∼100 kyr show a remarkably consistent level of SOC storage (Figure 2b). Based on measured inputs of plant biomass to the soil (Figure 2c), the high SOC storage in young soils may be associated with higher plant inputs. But SOC storage is not strongly controlled by plant C inputs in older soils since C inputs decrease markedly in the oldest soils, yet storage is relatively constant or perhaps marginally higher. Thus based on a relatively constant SOC pool size in 100 kyr to 3 Myr soils, and decreases in the flux into the SOC pool, SOC turnover rates appear to slow as soils age, a result consistent with the increasing abundance of SOM sequestering secondary minerals, acidic pH, and altered hydrology of very old soils [Harden, 1987; White et al., 1996; White, 1995].

3.2. C and N Inventory Versus Soil Depth

[19] Figure 3 displays the soil C and N concentrations and C:N ratio, as well as the δ13C and δ15N values of SOM from the four 1997–1998 profiles as a function of soil depth. Figure 4 displays ∼1949 and 1997–1998 SOM Δ14C values of SOM as a function of soil depth. Note that in Figures 3 and 4, we normalize soil depth to a BDadj of 1.65 g cm−3. Throughout this work, normalized soil depth is in units of cm*, while soil depth measured in cm (without the asterisk) is not normalized (see section 2.3). After normalizing for bulk density, the 200 and 600 kyr profiles retain approximately their original “thickness” although their upper horizons are effectively compressed and their deeper horizons slightly extended (see Table 2 for BD values). The <3 kyr and 3 Myr profiles appear to be reduced in “thickness” by approximately one-third, owing to the low BD of the <3 kyr profile and the high >2 mm content of the 3 Myr profile.

image

Figure 3. Selected bulk-soil data for 1997–1998 soil profiles. (a) %C. (b) %N. (c) C/N ratio. (d) δ13C. (e) δ15N.

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image

Figure 4. Δ14C values for 1949–52, ∼1978 and 1997–1998 bulk soil samples as a function of soil depth normalized to an adjusted bulk density of 1.65 g cm−3. For each soil age, archived and contemporary Δ14C profiles can be compared in greater detail in Figures 710.

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[20] C and N concentration profiles (Figures 3a and 3b) display a similar pattern at all four sites. For both elements, a large proportion of total profile C and N is stored near the soil surface in the zone corresponding to the shallow rooting depth of annual grasses (note high root densities in upper ∼10 cm in Table 2). A much lower, but significant, storage of C and particularly N exists to considerable depths, particularly in the <3 kyr profile, which may contain alluvial OM. These observations are consistent with other evaluations of the relationship between root inputs and SOM in grasslands [Gill et al., 1999; Jobbagy et al., 1999].

3.3. Indicators of Decomposition: C:N, δ13C, δ15N

[21] In general, C:N decreases while δ13C and δ15N values increase as a function of soil depth in the 1997–1998 profiles (Figure 3c). The only major exception to this pattern is the youngest site, where an excursion in δ13C and C:N at 55–98 cm* most likely results from recent alluvial deposition or deep root inputs. Soil C:N does decrease below the values generally expected for SOM in some clay-rich horizons. These values are best explained by ammonium fixation in clay minerals (no data available), which could also account for some of the variability in deep soil δ15N.

[22] Both δ13C and δ15N values vary significantly within soil profiles (Figures 3d and 3e), with δ15N displaying a more rapid increase with soil depth near the soil surface than δ13C. The difference between the response of C and N stable isotopes as a function of depth may result from the ∼2‰ change in the δ13C of atmospheric CO2 during the last few hundred years [Bird et al., 1996; Francey et al., 1999], or differences in the fractionating processes occurring at different depths or stages of decomposition [Nadelhoffer and Fry, 1988].

[23] The magnitude of the δ13C and δ15N shift as a function of soil depth is similar to the magnitude of variation in these tracers across a suite of five soil density fractions ranging from <1.6 g cm−3 free particulate matter (FPOM) to strongly mineral-associated >2.22 g cm−3 material [Baisden et al., 2002]. Therefore much of the variation in stable isotope ratios as a function of soil depth be associated with changes in the proportion of FPOM (low δ13C and δ15N) versus mineral-associated SOM (higher δ13C and δ15N), with the least mineral-associated SOM near the surface and the most mineral-associated SOM located deeper. These issues will be explored in greater detail after calibrating an SOM model to percentages of C and N, and Δ14C so that information on the rates of various processes can be considered in the discussion of the stable isotope data.

3.4. Radiocarbon

[24] Prebomb (1949–1952) bulk soil Δ14C values (open symbols in Figure 4) decrease linearly with normalized depth from the soil surface to horizons where water encounters barriers to downward movement. Within horizons, which limit water movement, abrupt Δ14C excursions toward older SOC occur. The most impenetrable layers encountered in the soils studied are duripans (silica cemented hardpans); duripans occur in the 1 to 3 Myr profiles and the 1997 600 kyr profile, as marked in Figure 4. In older soils, argillic (clay-rich) horizons also present a lesser barrier to water movement. In the 1-Myr profiles, the linear trend of Δ14C versus depth extends only to 35 cm*, but in the <3, 200, and 600 kyr prebomb profiles, which do not contain water-limiting horizons, the trend extends throughout the soil profile. Additionally, while the well-drained <3, 200, and 600 kyr profiles plot along a similar trajectory, both 1 Myr profiles display a different slope. These older 1 Myr soils display an excursion to very old Δ14C values in the vicinity of argillic and duric (silica-cemented) horizons. The linear Δ14C trends in zones of relatively uniform texture and more complex trends in horizons that limit water movement suggest that SOM transport mechanisms play an important role in the linear trend of Δ14C values versus depth and the difference between younger and older soils.

3.4.1. Rooting zone

[25] In the upper 30 cm* of soil profiles, the bulk Δ14C values of all contemporary soils (filled symbols in Figure 4) display higher Δ14C values than prebomb soils, and greater C and N contents (Figure 2), a result consistent with substantial recent OM inputs in this zone. Observed OM inputs include surface litter as well as root biomass (Table 4a in section 4.2 and root depth distributions in Table 2).

Table 2. Complete Soil Profile Characteristicsa
Soil SeriesAge, kyDepth, cmHorizonColor (ped)Color (crushed)TextureClay %StructureConsistenceClay Films>2 mm, %RootsPoresCarbonateBoundaryBDadj g/cm3pH 1:1 CaCl
MoistDryMoistDry
  • a

    Asterisk: texture could not be determined, as too much of the fine material was silicified.

Post-Modesto Formation
Grangeville<30–2.5A1  10YR 2/210YR 4/2sl∼122f gr, 2m grss, ps--3vf--aw0.866.0
  2.5–9A2  10YR 2/210YR 4/3sl∼102c sbk, 2f grss, po--3vf2f, 2m, 2c-aw1.136.1
  9–22A3  10YR 3/210YR 4/3sl∼102vc sbk, 2 f & m sbk, f & m grss, po--2vf, 2f, 1m1vf, 1f, 1m, 1c-cw1.326.6
  22–45A4  10YR 4/210YR 4/3sl∼121vc sbk, 1c sbkss, sp--1vf, 2f, 1m2vf, 1f, 2m, 1cegs1.357.0
  45–71A5  10YR 4/310YR 4/3sl∼10m/1m sbkss, po--2vf, 2f, 2m, 1c2vf, 1f, 2m, 1cedw1.267.6
  71–120Bw1  10YR 4/310YR 5/4sl∼10–12mss, sp--1vf, 1f, 2m, 1c2vf, 2f, 2medw1.448.0
  120–150Bw2  10YR 4/310YR 5/4sl∼10–12mss, sp--1vf, 1f, 1m, 1c1f, 1vf- 1.357.6
 
Riverbank Formation
Snelling2000–2A110YR 3/110YR 5/2 10YR 5/3sl8–101m gr to mso, po--3vf, 2f, 2m1vf-aw1.055.5
  2–10A210YR 4/210YR 6/3 10YR 5/3sl8–103m,co sbkss, po--2vf1vf-aw1.275.2
  10–29A310YR 4/310YR 6/3 10YR 6/3cosl8–102, co, vco sbkso to ss, po--1vf1vf, 1f, 1m-cs1.635.6
  29–59A410YR 4/310YR 6/3  cosl8–102, vc, co sbk to mso, po--1vf1vf, 2f, 2m, 1co-gs1.676.0
  59–75Bt17.5YR 4/47.5YR 6/4  cosl∼102, co, vco abkso to ss, pobr-1vf1f, 2m-cw(s)1.625.8
  75–104Bt27.5YR 4/310YR 6/4  cosl14–151, m, co, vco abkss, po to spbr, 1k pf, 2mk pf-1vf1m-gs1.846.0
  104–147Bt37.5YR 4/410YR 6/4  cosl12–141 co, vco abkso, pobr, 2t pf, 1mk pf, 1k po-1vf---gw(s)2.005.4
  147+Bt47.5YR 4/410YR 6/4  cosl10–12h.d.ss, po-<5----1.635.6
 
Turlock Lake Formation
 6000–2A1  10YR 4/2 cosl81vc grss, po-03vf--as1.085.8
  2–12A2  10YR 4/2 lcos51c grss, po-02+vf1f, 1m-cs1.465.1
  12–24A3  10YR 4/3 lcos51m sbk, 1c sbkss, po-02vf1f, 1m-cs1.585.4
  24–36A4  10YR 4/310YR 6/3lcos51c sbkss, po-<51vf2vf, 1f, 2m-gs1.565.8
  36–62AB  7.5YR 4/410YR 6/4lcos81vc sbkss, po-<51vf2vf, 1f, 2m-cw1.715.7
  62–76Bt1  5YR 4/45YR 5/4scl28–301c sbkvs, sp-<51vf–--db1.585.6
  76–94Bt2  5YR 4/6 sc38–401c sbkvs, p1mk pf<5---db1.535.3
  94–110Bt3  5YR 4/6 sc38–40m/f vc abkvs, p1n pf<5---ab1.595.0
  110+Bqm (duripan)  5YR 4/4 scl20ms, sp-0---b1.625.1
 
China Hat Formation
Redding3,0000–2A1 10YR 5/3 10YR 5/3l10–151vf sbkso, po-83vf1vf t/v, 1f t/v aw0.42N/A
  2–11A2 10YR 7/4 10YR 6/4l15–202m/c abk, 1f sbkss, ps-152vf2vf t cw1.414.5
 Combined11–21AB 10YR 6/4 10YR 6/4cl20–302c sbk, f sbks, pv1 po/pf82vf2vf t, 1f t cs1.343.7
  21–40Bt1 10YR 6/6 10YR 6/4gcl352c/vc sbks, p1mk pf/po202vf2vft, 2vf v, 1f t/I cw1.343.8
 Combined40–51Bt2 10YR 5/6 10YR 5/6cl352m/c abkss, ps4k pf/po/co172vf, pf + po2ft, 2vf t/v gs1.153.6
  51–71Bt3 10YR 5/6 10YR 5/6gcl352c/vc sbkss, p3k pf/po/co341vf, pf + po3f t/v, 2vf t/v as1.153.6
  71–92Bt4 10YR 5/6 10YR 5/6gcl351m sbks, ps3k pf (but peds rare)56-2f v/t, 2vf v/t as0.703.6
  92–108Btgqm1(duripan) 10YR 7/6 10YR 7/4**m*-90-1vf t gs0.473.6
  108–143Btgqm2 (duripan) 7.5YR 7/6, 10YR 8/3 7.5YR 7/6**m*-53-1vf t cs0.963.8
  143–1502Btgq 2.5YR 6/6, 7.5YR 8/4 7.5YR 6/6**m*-36-- -1.233.9
3.4.2. Hydrologic Effects Below Rooting Zone

[26] We measured the Δ14C values of prebomb and contemporary SOM well below the rooting zone to investigate the dynamics of deep SOM (Figure 4; see also Figures 710 in section 5.1). The <3 kyr soils exhibit no significant differences between prebomb and contemporary profiles below approximately 25 cm. In both the 200 and 600 kyr profiles, prebomb, and contemporary Δ14C values are similar in the 25- to 40-cm depth range, but differ by 50–100‰ at greater soil depths. The 1998 3 Myr profile displays Δ14C values that are 100–250‰ greater than prebomb values above the subsurface water-limiting horizons (duripans or claypans). The 1998 3 Myr soil displays extremely old SOC (−890‰ or conventional 14C age of ∼18 kyr) within the duripan (65 cm*) and decreases in Δ14C below the duripan. The patterns of deep soil bomb 14C enrichment appear to be related to soil hydrologic transport. The <3 kyr soil exhibits no clear movement of bomb 14C to the deep soil, and is also the only soil that does not have net leaching on an annual basis, upward water movement from the water table deposits carbonate within this profile. In the 200 and 600 kyr soils, 14C enrichment at depth corresponds to net leaching during the rainy season. In the 1 to 3 Myr soils, more complicated patterns are observed in both prebomb and contemporary Δ14C in relation to subsurface soil horizons that cause water to pond within the soil profile during the winter.

[27] Possible mechanisms for hydrologic transport of bomb 14C within soil profiles include colloidal transport and DOM transport. To examine the potential rates of dissolved and colloidal transport, we analyzed the percentage of C and Δ14C of a ∼1-cm-thick clay-rich layer deposited directly on top of the duripan in the 3 Myr soil. This layer may act as a “filter”, collecting DOM and colloidal OM passing downward through the soil profile. With 0.10% C, this layer has a 40% lower SOC concentration than the overlying horizon, but its Δ14C (−230‰,) is 110‰ higher than the bulk SOC Δ14C in the overlying horizon. This result is consistent with previous 14C research indicating that SOM associated with fine clay is younger than SOM in coarse clay and silt [Anderson and Paul, 1984].

[28] To further investigate the nature of deep SOC, we performed a density fractionation (at 2.22 g cm−3 following ultrasonication) on contemporary samples from argillic horizons from the 200, 600 kyr, and 3 Myr profiles (Figure 5). The density fractionation recovers a dense fraction (DF) in which all OM is tightly bound to mineral particles. We define the “light fraction” (LF) as the material not recovered in the DF, and calculate its C percentage and Δ14C values by difference from the bulk measurements for the horizon. The LF therefore consists of POM and soil aggregates <2.22 g cm−3 as well as DOM. The LF represented 24, 35, and 14% of the total SOC (LF + DF in Figure 5) in the 200 kyr Bt2, 600 kyr Bt3, and 3 Myr Bt4 horizons, respectively. The LF displays a modern signature in the 200 kyr soil but has a Δ14C value consistent with a mixture of modern and ancient SOC in the 600 kyr and 3 Myr sites. In the 200 and 600 kyr soils, the DFs display Δ14C values similar to those found in prebomb soils at similar depths and these depths cannot be directly compared in the 1 to 3 Myr soils due to the different depths at which water-limiting horizons occur.

image

Figure 5. The Δ14C values of light and dense fractions separated in 2.22 g cm−3 sodium polytungstate (SPT) for the 1997–1998 200 ky 75–104 cm horizon, 600 ky 94–110 cm horizon and 3 My 71–92 cm horizon. The light fraction represents the difference between bulk soil and the dense fraction (obtained by mass balance) and therefore contains organic matter which dissolved in SPT or DI-water rinses.

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[29] Based on these results, 24% represents an upper estimate for the highly uncertain potential contribution of bomb-derived C to deep SOC at depths of 50–150 cm. This is consistent with similar calculations suggesting an ∼17% contribution of recent C to SOM at nearly 1 m in depth 10 years after tracer 14C labeling in a short grass steppe ecosystem [Gill et al., 1999]. A pulse of dissolved SOM moving through deep soils with a retardation time on the order of decades is also in accordance with measurements of the Δ14C value of riverine dissolved organic carbon (DOC) [Raymond and Bauer, 2001]. Further, inferences about the transport mechanisms responsible for deep soil bomb 14C enrichment will be made following the development of a model that appears to account for most of the other variation in SOM dynamics as a function of soil depth.

4. Multiisotope Model

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

4.1. Model Rationale

[30] A major source of uncertainty in soil C budgets results from limited understanding of SOM dynamics as a function of soil depth. At present, many widely used ecosystem biogeochemistry models do not explicitly represent soil depth and consider primarily surface horizons. Models that attempt to consider soil depth do not simulate SOC storage in long-term experiments more effectively than models that do not consider soil depth [Smith et al., 1997]. Furthermore, knowledge of SOC located between 20 cm and 1 m in depth relies mainly on inventories alone. Even inventories, have generally not characterized SOC below 1 m in depth. Yet recent work shows substantial SOC below the upper meter in soil, with evidence that deep SOC has been transported below the rooting zone [Jobbagy et al., 1999], although much deep SOC may also be the result of deep roots in tropical forests [Trumbore et al., 1995]. Depending on the rapidity of colloidal or dissolved phase transport or the magnitude of root inputs, these mechanisms could represent a relevant flux in global C budgets, with highly uncertain potential effects resulting from land use change.

[31] In the face of this uncertainty, several published models that include transport mechanisms suggest progress in the depth-dependent understanding SOM dynamics [Elzein and Balesdent, 1995; Feng et al., 1999; O'Brien and Stout, 1978]. In these models, 14C provides the best tracer of SOM transport and turnover. The radioactive decay of 14C (half-life ∼5730 years) permits measurement of passive (millennial) SOC turnover. In addition, the inadvertent 14C enrichment of the atmosphere during the period of atmospheric nuclear weapons testing provides an ideal tracer for SOC turnover on decadal timescales. Furthermore, the ongoing decline in the Δ14C of atmospheric CO2 can in some circumstances, trace SOC turnover on timescales as short as a few years [Gaudinski et al., 2000; Trumbore, 1993, 2000]. Below surface horizons, a lag time may need to be considered between the time SOM was created and the time it arrives in the horizon or soil fraction sampled [Gaudinski et al., 2000]. This problem can be overcome by considering SOM transport as a continuous function of soil depth [Elzein and Balesdent, 1995; Feng et al., 1999; O'Brien and Stout, 1978] in addition to multiple pools of SOM [Baisden et al., 2002; Elzein and Balesdent, 1995; Gaudinski et al., 2000].

[32] In contrast to the temporal record provided by Δ14C, if one can assume no changes in the δ13C or δ15N of OM inputs to the soil, then C and N stable isotopes trace decomposition processes within a soil profile [Nadelhoffer and Fry, 1988]. Generally, in well-drained soils, the δ15N of deep SOM is 3–10‰ higher than plant tissue and smaller enrichments are observed in δ13C [Brenner et al., 2001; Handley et al., 1999; Hogberg, 1997; Martinelli et al., 1999]. Particularly for N isotopes, this enrichment appears to represent the cumulative fractionation incurred during decomposition and humification processes [Evans and Ehleringer, 1993; Nadelhoffer and Fry, 1988]. SOM δ13C values present a more problematic tracer of decomposition for two reasons: the δ13C value of atmospheric CO2 has decreased by almost 2‰ since preindustrial times [Francey et al., 1999], and the δ13C values of plant inputs can change by ∼14‰ during C3/C4 vegetation transitions [Boutton, 1996]. If vegetation transitions have not occurred and changes in atmospheric δ13C are accounted for, δ13C mimics δ15N as an integrator of humification processes [Nadelhoffer and Fry, 1988; O'Brien and Stout, 1978], and appears to have particular value in controlled long-term experiments [Feng et al., 1999]. Nevertheless, stable isotope fractionation observed in soil profiles provides only a qualitative measure of soil processes unless transport mechanisms are resolved [Amundson and Baisden, 2000].

[33] Given the successful SOM transport models of Feng et al. [1999] for δ13C and Elzein and Balesdent [1995] for Δ14C, the next logical step is to combine the temporal information provided by Δ14C with the process-based information provided by δ13C and δ15N. The inclusion of N dynamics in the model represents an important step because soil N turnover provides the basis for many ecosystem biogeochemistry models. Soil N dynamics constrain SOC turnover and storage because: (1) the N released from SOM decomposition often limits the production of new plant material, and (2) stabilized and passive SOM pools appear to have relatively constant C:N ratios close to that of microbial biomass [Paul and Clark, 1996]. By including mass balance for N as well as C, we develop a transport model that empirically estimates the dynamics of soil C and N in a manner comparable to ecosystem biogeochemistry models.

4.2. Model Framework and Implementation

[34] We present a model of SOM turnover and transport in which zero-order surface and root litter inputs feed three pools of SOM with first-order decomposition rates and advective transport rates (Figure 6). The structure of the model is consistent with the active, stabilized, and passive SOM pools discussed by Baisden et al. [2002], and differs from the work of Elzein and Balesdent [1995] by not considering diffusive transport in addition to advection. The model structure is similar to that of three-pool ecosystem biogeochemistry models [e.g., Parton et al., 1996, 1987] and is applied to both C and N.

image

Figure 6. A schematic showing the flow of material between reservoirs in the transport model.

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[35] The mass balance for three pools of SOC is:

  • equation image
  • equation image
  • equation image

C1, C2, and C3 represent the three pools of SOM with roughly annual, decadal, and millennial turnover times, respectively. Depth is normalized for BDadj(1.65 g cm−3) and represented by z; z has positive values in the soil and is zero at the soil surface. The variables v1, v2, and v3 represent the respective downward transport rates for C1, C2, and C3, while k1, k2, and k3 represent the respective first-order decomposition constants. The variables t2 and t3 represent the transfer coefficients, which describe the transformation of C1 material into pools C2 and C3. FB represents the total flux of belowground plant-derived litter and is distributed into the soil in an exponentially decreasing form with the e-folding depth L (as depicted in Figure 6). The variable p allows for the translocation of small amounts of C3 to the soil surface to match the Δ14C data observed in archived prebomb uppermost horizons, in a manner consistent with the action of many burrowing organisms. Without this flux, C3 = 0 at z = 0. In this sense, the translocation of C3 replaces the diffusive transport employed by Elzein and Balesdent [1995] and may be more descriptive of processes ongoing in the soils studied. Equations (1a)(1c) can be solved at steady state given the following boundary conditions at the soil surface:

  • equation image
  • equation image
  • equation image

FA describes the aboveground litter flux to the soil surface, and P represents the flux of C3 translocated to the soil surface by burrowing activity. Substitution for C3 is based on equation (A7) in Appendix A.

[36] Given the system of differential equations in (1) and (2), we obtain steady state depth-dependent analytical solutions and calculate steady state C pool sizes (Appendix B). To evaluate the model for 14C, 13C, N, and 15N, we modify the parameters FA, FB, k1, k2, k3, t2, and t3 as indicated in Tables 3a and 3b, rather than specifically rewrite the equations for each element or isotope [as in the work by Amundson and Baisden, 2000]. The modification of FA, FB, k1, k2, k3, t2, and t3 requires additional parameters including the isotope ratios of above- and belowground inputs, fractionation factors, and C:N ratios, as described in Tables 3a and 3b. Some parameters (v1, v2, v3, and L) do not change for the different species modeled and therefore do not appear in Tables 3a or 3b. We calculate isotope ratios from the solutions to mass balance equations (given in Appendix B and modified as indicated in Tables 3a and 3b) for each isotope and convert these ratios to δ notation [Amundson and Baisden, 2000].

Table 3a. Model Parameter Substitutionsa
CFAFBt2t3k1k2k3
  • a

    The parameters given in the body of table can be substituted into equations (1)–(5) for the parameters shown in the top row for C, to give the mass balance equations written for 14C, 13C, N, and 15N as shown in the leftmost column. The subscripts “A” and “B” denote above- and belowground, respectively.

13C13RAFA13RBFB13αt2t213αt3t3k1k213α3k3
14C14RAFA14RBFBt2t3k1 + λk2 + λk3 + λ
NFA/C:N1FB/C:N1(C:N1/C:N2)t2(C:N1/C:N3)t3k1k2k3
15N15RAC:N1FA15RBC:N1FB15αt2(C:N1/C:N2)t215αt3(C:N1/C:N3)t3k1k2k3
Table 3b. Model Parameter Substitution Definitions
ParameterDefinition
  • a

    Isotope ratios (R) are obtained by the equation R = (δ/1000 − 1000)Rstd where δ is the δ13C, δ15N, or Δ14C value of the sample expressed in ‰, and Rstd is the approximate isotope ratio of the international standard for which δ13C, δ15N, or Δ14C is equal to zero.

13RA13C/12C ratioa of aboveground plant inputs
13RB13C/12C ratio of belowground plant inputs
14RA14C/12C ratio of aboveground plant inputs
14RB14C/12C ratio of belowground plant inputs
15RA15N/14N ratio of aboveground plant inputs
15RB15N/14N ratio of belowground plant inputs
C:N1C:N ratio of SOM1
C:N2C:N ratio of SOM2
C:N3C:N ratio of SOM3
λdecay constant for 14C (1/8300 years)
13αt2preference for 13C relative to 12C during conversion of SOM1 to SOM2. 13C/12C of SOM2 divided by 13C/12C of SOM1. Reported as 13εt2 = (13αt2 − 1)/1000
13αt3preference for 13C relative to 12C during conversion of SOM1 to SOM3. 13C/12C of SOM3 divided by 13C/12C of SOM1. Reported as 13εt3 = (13αt3 − 1)/1000
15αt2preference for 15N relative to 14N during conversion of SOM1 to SOM2. 15N/14N of SOM2 divided by 15N/14N of SOM1. Reported as 15εt2 = (15αt2 − 1)/1000
15αt3preference for 15N relative to 14N during conversion of SOM1 to SOM3. 15N/14N of SOM3 divided by 15N/14N of SOM1. Reported as 15εt3 = (15αt3 − 1)/1000
13α3preference for 13C relative to 12C during decomposition of SOM3. 13C/12C of resultant CO2 divided by 13C/12C of SOM1. Reported as 15ε3 = (13α3 − 1)/1000

[37] The modeling of stable isotopes to understand soil processes requires the assignment of fractionation factors to appropriate mechanisms. The results obtained for these sites by density fractionation indicate that δ13C and δ15N vary systematically as function of mineral-organic association [Baisden et al., 2002]. Therefore we assumed that the most important isotope fractionations occur during the transfers (t2 and t3) of SOM1 to the more recalcitrant pools, SOM2, and SOM3 with the greatest fractionation occurring during the production of SOM3. If all fractionation occurs during transfers (which is true in the model for N, but not C) then the δ value of each pool is constant except for variation resulting from different δ values for above- and belowground inputs. Therefore the δ values obtained from the model for total SOM as a function of depth primarily reflect changes in the proportional contributions of each pool to total SOM.

[38] Tables 4a and 4b present measured values for the parameters describing aboveground and belowground input (FA and FB in the original equations). Before we can use the values in Tables 4a and 4b to begin fitting analytical solutions for C, the C:N ratio of above- or belowground inputs must be corrected to match that of SOM1. The model attributes changes in C:N from plant inputs to SOM1 to the loss of C, while N is conserved [Paul and Clark, 1996]. This correction is approximately equivalent to the representation of metabolic and structural litter pools with intraannual turnover rates in the CENTURY model [Parton et al., 1996], which control the partitioning of litter inputs between SOM pools. Similarly, Elzein and Balesdent [1995] note that the representation of rapid turnover metabolic and structural litter pools is unnecessary for modeling Δ14C data over multiyear timescales provided that the partitioning of OM between SOM pools can be determined empirically. We therefore select a C:N ratio (C:N1) for SOM1 based on data for FPOM fractions by Baisden et al. [2002]. Selected C:N1 values often matched those for belowground biomass (C:NB) but were less than those of aboveground biomass (C:NA). The C fluxes, FA, and FB, are corrected by multiplying by C:N1/C:NA and C:N1/C:NB, respectively.

Table 4a. Measured Plant Input Parameters Based on 1998 and 1999 Harvests
 AboveGrd C FABelowGrd C FBAboveGrd N FAC:NABelowGrd N FBC:NBC:N ratiosAbvGrd δ15Na δ15NABelowGr δ15N δ15NBAbvGrd δ13Ca δ13CABelowGr δ13C δ13CB
gC m−2 yr−1gC m−2 yr−1gN m−2 yr−1gN m−2 yr−1C:NAC:NB
  • a

    Stable isotope ratios for aboveground biomass are from 1998 samples only.

  • b

    All belowground biomass values reported for the 3 Myr site are from 1999 only.

<3 ky269 ± 40452 ± 12810.0 ± 1.422.2 ± 5.827200.6 ± 0.21.2 ± 0.4−29.1 ± 0.1−28.7 ± 0.2
200 ky282 ± 47279 ± 546.7 ± 1.111.0 ± 2.84225−2.5 ± 0.1−1.2 ± 0.3−28.1 ± 0.1−28.8 ± 0.2
600 ky178 ± 45357 ± 395.7 ± 1.920.3 ± 2.43118−1.8 ± 0.4−1.3 ± 0.3−28.2 ± 0.1−28.9 ± 0.1
3 Myb161 ± 20170 ± 335.7 ± 0.98.5 ± 1.628200.7 ± 0.91.6 ± 0.3−28.6 ± 0.3−29.4 ± 0.1
Table 4b. Measured Δ14C Values of Plant Inputs
Sample DescriptionΔ14C, ‰
1998 <3 ky aboveground biomass (4 quadrats, bulked)103.8 ± 7.8
1997 <3 ky 0–2 cm belowground biomass (>2 mm OM)123.5 ± 4.0
1997 200 ky 0–2 cm belowground biomass (>2 mm OM)103.1 ± 4.8
1997 600 ky 0–2 cm belowground biomass (>2 mm OM)142.9 ± 5.0
1998 3 My 0–2 cm belowground biomass (>2 mm OM)97.3 ± 4.1
1998 3 My aboveground biomass (4 quadrats, bulked)86.3 ± 4.8

[39] We report the results of analytical solutions for percentages of C and N, and δ15N using parameter values from Tables 4a and 4b. To incorporate changes in the δ13C and Δ14C of atmospheric CO2, we started with analytical solutions in 1909 and then integrated the model (in MATLAB) using finite difference equations equivalent to equations (1a)(1c). We averaged winter growing season (November–May) air sample Δ14C values from the most appropriate Northern Hemisphere sites [Berger, 1987; Levin and Kromer, 1997; Levin et al., 1994; Nydal and Lovseth, 1997] and obtain a Δ14C record for the period before atmospheric air sampling from vintage wines [Schonhofer, 1992]. A smoother record suffices for δ13C, so Southern Hemisphere data were used after applying a spline fit [Francey et al., 1995, 1999].

[40] To solve the model, parameter values were modified manually until a “best fit” for Δ14C, and percentages of C and N data were obtained. Parameters controlling δ13C and δ15N were then adjusted manually to fit these tracers. We attempted to use an iterative Leavenberg-Marquardt algorithm to obtain a least squares solutions for parameter values [after the works by Elzein and Balesdent, 1995] but found several sources of difficulty. First, the algorithm tended to find a local minima in the squared sum of errors that did not represent a true “best fit”. Second, weightings for the different types data were required but found to be as subjective as manual fitting. Difficulties included weighting the importance of the rare isotope relative to the total element, N relative to C, Δ14C relative to δ13C and δ15N, as well as surface soil relative to deeper soil. In manual fitting, we emphasized the percentage of C and Δ14C fits in contemporary A1, A2, and A3 horizons. The percentage of N data were given secondary weighting because Baisden et al. [2002] suggests that C:N ratios vary significantly within fractions that appear to be composed of similar proportions of OM pools with similar residence times. To reduce model run-times while appropriately simulating boundary conditions, we used higher depth resolution near the soil surface [after the works by Elzein and Balesdent, 1995]. In all cases, we summed over the depth increments for each horizon to obtain model estimates comparable to the measured sample for each horizon.

5. Model Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

5.1. Goodness of Model Fit

[41] Figures 710 display model results for the four chronosequence soils. At all sites, the model shows that SOM1 and SOM2 account for the high percentages of C and N in the upper ∼30 cm, as well as the deviation in contemporary Δ14C from prebomb values in this same depth range. The model also captures the general trend of the data for deep soil Δ14C values. Generally, the model matches the rapid transition in δ13C and δ15N from plant values near the soil surface to relatively stable deep soil values, which are 3–9‰ higher. The goodness of fit in these respects allows the model parameters (Tables 5a and 5b) to be compared across sites.

image

Figure 7. Measurements (crosses or asterisks) and model results (circles) for each horizon in <3 ky bulk soils. Q and R represent aboveground quadrats and belowground root cores, respectively. (a) 1997 % organic C. (b) 1997 %N. (c) 1949, 1978 and 1997 Δ14C. (d) 1949 and 1997 δ13C. (e) 1997 δ15N.

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image

Figure 8. Measurements (crosses or asterisks) and model results (circles) for each horizon in 200 ky bulk soils. Q and R represent aboveground quadrats and belowground root cores, respectively. (a) 1997 % organic C. (b) 1997 %N. (c) 1949, 1978 and 1997 δ13C. (d) 1949 and 1997 δ13C. (e) 1997 δ15N.

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image

Figure 9. Measurements (crosses or asterisks) and model results (circles) for each horizon in 600 ky bulk soils. Q and R represent aboveground quadrats and belowground root cores, respectively. (a) 1997 % organic C. (b) 1997 %N. (c) 1952, 1978 and 1997 Δ14C. (d) 1952 and 1997 δ13C. (e) 1997 δ15N.

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image

Figure 10. Measurements (crosses or asterisks) and model results (circles) for each horizon in 1–3 My bulk soils. Q and R represent aboveground quadrats and belowground root cores, respectively. (a) 1998 % organic C. (b) 1998 %N. (c) 1949, 1978 and 1998 Δ14C. (d) 1949 and 1998 δ13C. (e) 1998 δ15N. Upper surfaces of hydrologically limiting duripans are marked in Figure 10c.

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Table 5a. Model-Derived SOM Parameters
 Decomposition RatesTransfer coefficentsAdvective Transport RatesSOM C:N ratiosPassive RelocationRoot e-fold Depth
1/k1 yr1/k2 yr1/k3 yrt2t3v1 mm yr−1v2 mm yr−1v3 mm yr−1C:N1C:N2C:N3P gC m−2 yr−1L cm
<3 ky1.02333330.3600.00634.00.470.4015.010.010.00.303.5
200 ky1.03750000.0960.00144.00.490.3918.011.06.50.5010
600 ky1.01950000.0850.00123.20.280.4317.610.06.80.283.3
3 My1.61550000.1930.00310.50.260.1019.912.05.70.062.7
Table 5b. Model-Derived Stable Isotope Parameters
 15N Enrichment During13C Enrichment During
N1 [RIGHTWARDS ARROW] N215εt2N1 [RIGHTWARDS ARROW] N315εt3C1 [RIGHTWARDS ARROW] C213εt3C1 [RIGHTWARDS ARROW] C313εt3C3 Decomp 13εt3
<3 ky1.33.30.73.0−1.0
200 ky3.67.40.01.9−0.5
600 ky3.07.00.02.4−2.0
3 My0.05.50.03.5−0.5

[42] The <3 kyr soil displays poorer model fits than many other soils, with a deep soil Δ14C discrepancy of ∼10–50‰ and problems with fits for percentages of C and N near the surface (Figure 7). The <3 kyr model fits δ15N well, and δ13C well except for one very low δ13C at depth. Most problems in the model fits at the <3 kyr site and can be attributed to deep root inputs or the uncertain depositional history of this floodplain site.

[43] The model produces excellent fits for Δ14C, and percentages of C and N in the 200 and 600 kyr sites (Figures 8 and 9), presumably because these sites meet the model's assumption of uniform transport as a function of normalized soil depth. The 200 kyr soil, and particularly the 600 kyr soil, display an increase in the percentages of C and N at depth in argillic horizons, which is not accounted for by the model. The model also fails to represent the deep Δ14C increase from the prebomb to the contemporary soil profiles shown in Figures 8 and 9, indicating that a process moving bomb-14C below the rooting zone is not accounted for. The model fits for δ15N are generally within expected variability for the data, while fits for δ13C are also good but indicate that the model could be improved for this tracer.

[44] In the 1 to 3 Myr soils (Figure 10), the model fits the data for percentages of C and N above the duripan, while the failure of the model to account for lower C and N concentrations below the duripan suggests that the modeled transport mechanisms do not effectively penetrate the duripan. The Δ14C data fits the model acceptably at the surface for the contemporary profiles and to ∼30 cm for the prebomb profiles. Since the fit is adequate in the horizons with elevated SOM contents, estimates of parameters for SOM1 and SOM2 are adequate, but confidence is lower in parameters describing SOM3, particularly v3. Given that measured δ15N values are highly uncertain due to low percentage of N below the duripan, the model achieves an excellent fit for δ15N, but fails to describe δ13C as well. Generally, most deviations from the model in the oldest soils can best be explained by the failure of the model to represent the complicated hydrology at this site. Nevertheless, the model sufficiently describes processes controlling the observed C, 14C, 13C, N, and 15N profiles to an extent that the cases in which the model fails to match empirical data provide an opportunity to examine processes not represented in the model. Some processes are examined below. Other processes, which might improve future models, include variation in decomposition rates (k1, k2, and k3) and transport rates (v1, v2, and v3) or transport mechanisms (e.g., diffusion versus advection) with depth.

5.2. Interpretation: Δ14C and Percentages of C and N

[45] We discuss Δ14C first because this tracer provides the most powerful description of SOM turnover and transport. In particular, the fine depth resolution of sampling in the two uppermost 1997–1998 horizons provides Δ14C data that are extremely sensitive to SOM1 and SOM2 turnover and transport parameters. The approximately 50‰ variation in contemporary A1 horizon Δ14C and >100‰ variation in contemporary A2 horizon Δ14C led to observed formation (t2) and turnover (k2) rates for SOM2 that differ by over a factor of 2 among the soils examined. Other model-derived parameters (t3, v1, v2, v3, L, C:N3, and P) also display large and systematic variation across the soils studied (see Tables 5a and 5b), but several sources of uncertainty must be considered when interpreting model results.

[46] In Figures 710, the percentages of C and N show that the contribution of passive SOM3 to total SOM is substantial throughout the soil profile. Since SOM3 ≅ total SOM below ∼30 cm*, the amount and depth distribution of deep SOM determine the size of SOM3 as well as its turnover rate (k3), formation rate (t3), and transport rate (v3). Therefore, extrapolation of the modeled passive SOM3 profile in the zone with negligible SOM1 and SOM2 plays an important role in obtaining estimates for the dynamics of SOM1 and SOM2 in the surface soil. The linear function of observed Δ14C versus depth below the rooting zone in the 1949–1952 200 and 600 kyr profiles allows the downward transport rate v3 to be set precisely, the 14C age of deep SOM is determined by the time since it was formed near the soil surface (after taking the small contribution of passive relocation into account). Values of t3 control the amount of OM transferred to the passive pool for transport below the rooting zone, while k3 determines the rate at which the observed percentages of C and N decline below the rooting zone.

[47] Recalling that passive SOM3 is negligible at the soil surface without passive SOM3 relocation, the model's consideration of passive SOM3 relocation improves model fits in all soils (Figures 7c10c). Without this model component, modeled prebomb Δ14C values for uppermost horizons deviate strongly from the observed data: the model cannot include enough old SOM to match the measured surface Δ14C values. Nevertheless, the uppermost horizons still have lower Δ14C than the model predicts. The nature of passive SOM near the soil surface therefore remains an important source of uncertainty in the model. The discrepancy could be explained if the upper horizons of archived soils (ranging from 9–28 cm thick) do not include material equivalent to the contemporary A1 (0–2 cm) horizon. These samples would then contain less young FPOM than the model predicts. Indeed, this often appears to be the case for thick upper horizons of 1978 soils. For example, in the 600 kyr soil the model overestimates the Δ14C of the thick upper horizon. The measured sample matches the model curve (line) but does not match the model average (circle) for this horizon, which includes considerable high-Δ14C SOC near the soil surface. We assume that this problem accounts for the observed differences between the model and the archived samples for the upper horizons plotted in Figures 710.

[48] The model adequately represents the variation in percentage of C in the upper 50 cm of soil in all soils except the <3 kyr profile. The youngest profile (<3 kyr) shows considerably greater storage at all depths below the uppermost horizon. In this case, deeper inputs from the roots of oak trees may account for the difference in the pattern of SOM storage, although a second possible cause is recent burial of SOM-rich surface soil by alluvial sediments. Indeed, the dramatic increase in C:N ratio in the fourth horizon (A4) sampled from this profile could be indicative of greater root inputs or a buried surface horizon.

[49] Much of the failure to obtain perfect percentage of C fits can be attributed to inappropriateness of the assumption of exponentially decreasing root inputs with depth. This problem is particularly evident in the <3 kyr soil since roots extend much deeper than the e-folding depth, L, suggests. Depositional history could account for the variation in percentages of C and N at the <3 kyr site, but more realistic root input functions would improve the model at all sites. The most appropriate treatment might be two separate exponential root input functions; one representing roots associated with nutrient uptake concentrated near the surface, and a second extending to greater depths associated with water uptake. The single exponential root input function preserves simplicity and consistency in the model given limited data to constrain the partitioning of belowground litter in this study.

[50] The percentages of C and N at depths below 50 cm show variation related to soil clay content and hydrologically limiting layers, which the model cannot account for. The increase in percentage of C in the 200 and 600 kyr soils appears to result from increased affinity for SOM in the clay-rich layer. The larger increase in percentage of N observed in these two soils most likely results from both increased affinity for SOM, and ammonia fixation within the clay minerals. The assumption that transport continues through these soil layers appears good based on the agreement between the modeled and observed percentages of C and N, and Δ14C observations below the argillic horizon in the 200 kyr soil.

[51] In the 3 Myr profile, the model fit for percentages of N and C does not deviate from the data within the upper portions of the very thick argillic horizon (15–66 cm*). But observed percentages of C and N data do decrease by over 50% at the depth (66 cm*), where the duripan limits downward transport. The model does not account for this sharp decline in SOM concentrations; the drop results from the fact that SOM transport mechanisms do not effectively penetrate the duripan. Indeed, the extremely old Δ14C value observed in the upper duripan of the 1998 3 Myr profile (−890‰) indicates a radiocarbon age of nearly 18,000 years. Interestingly, the increasing Δ14C with depths below the hydrologically limiting layer in both the 1949 1 Myr profiles and 1998 3 Myr profile appear to indicate that some SOM is transported through macropores in the duripan and disperses in the deeper soil.

[52] Since transport does not continue directly through the duripan as the model suggests, the fate of the SOM not moved below the duripan must be considered. In the absence of leaching, much of the water that ponds on the duripan during the rainy season most likely leaves the profile at the beginning of the dry season as evapotranspiration. The resulting upward transport of soil solution likely carries with it dissolved and colloidal OM. The combination of downward transport early in the rainy season, and upward transport at the beginning of the dry season, would have a net result resembling diffusion. Indeed, the linear alignment of contemporary Δ14C in this 3 Myr profile supports the concept of diffusive mixing of SOM as modeled by Elzein and Balesdent [1995].

[53] While the model describes many aspects of the soil profile well, uncertainty in the Δ14C value of plant inputs (Table 4b) must be considered when quantitatively interpreting model parameters. The model assumes that plant inputs possess the Δ14C value of the winter atmosphere derived from clean air sites. This assumption is not problematic for aboveground inputs except at the <3 kyr site where inputs from oaks may have higher Δ14C values because the trees access the water table, allowing them to remain photosynthetically active during the dry summer period when atmospheric Δ14C is 10–20‰ higher. This difference is not accounted for in the model. Moreover, belowground >2-mm material measured at the study sites differs from inferred atmospheric values by up to ∼50‰. This difference would be consistent with a lag time for belowground inputs, or a fraction of 10- to 30-year-old coarse POM in upper soil horizons. In addition, contributions of 14C-enriched CO2 during the midwinter months cannot be ruled out as extensive burning of prunings from nearby orchards occurs concurrently with atmospheric inversions.

[54] Tests of model sensitivity to Δ14C lag times of up to 5 years between atmospheric CO2 and active SOM do not improve model fits (not shown) and suggest that k2, k3, t1, and t2 do not react significantly to lag times. However, the same tests suggest that k1 must be considered highly uncertain. The low sensitivity of most parameters to the isotope ratio of plant inputs results from the presence of the passive C relocation term (P), which allows fitting of model Δ14C estimates to measured Δ14C data for the upper ∼10 cm*. Following adjustment of P, values of k2, k3, t1, and t2 react sensitively to the Δ14C difference between the A1 and A2 horizons, but not the absolute Δ14C value of these horizons. Generally, the A1/A2 Δ14C difference reflects the high proportion of FPOM in the A1 horizon and mineral-associated SOM fractions in the A2 and deeper horizons [Baisden et al., 2002].

[55] In summary, the model appears to successfully fit percentages of C and N, and Δ14C data in the 200 and 600 kyr profiles. The uniform soil texture above the argillic horizons in these profiles appears to fit the model assumptions of uniform transport rates quite well, allowing the model to be fit to observations with a high degree of precision. The 3 Myr and <3 kyr soils provide more difficult cases for model fitting, and suggest cases in which soil hydrology, depositional history, and differences in plant input functions may alter SOM turnover and transport. Given uncertainty in the transport mechanism and depth distribution of belowground plant inputs, some parameters such as transport velocities (v1, v2, and v3) and the e-folding depth of root inputs (L) may not be quantitatively comparable between all sites. Nevertheless, these <3 kyr and 3 Myr profiles are interpretable due to the power of the Δ14C as tracer, good sampling resolution, and the opportunity for comparison to results obtained from the more uniform 200 and 600 kyr profiles.

5.3. Interpretation: δ13C and δ15N

[56] The SOM turnover and transport model, once calibrated to percentages of C and N, and Δ14C, offers an opportunity to use observed δ13C and δ15N of SOM to interpret decomposition processes within soil profiles. This is particularly true in the 200 and 600 kyr soils where the model fits for δ13C and δ15N are excellent, providing a semiindependent indication that the model represents SOM dynamics with fidelity.

[57] The modeled soil δ13C and δ15N values result from the proportions of the three SOM pools present at a given depth, and the fractionation factors that determine the difference between the δ values of each SOM pool. In this respect, the model presented here differs from the single SON pool model [Brenner et al., 2001] equation image or equation image, define the difference in δ values between plant inputs and deep soil SOM3. Secondly, the values for equation image or equation image describe the difference between plant input values and SOM2, and therefore determine δ values of SOM near the soil surface. In some cases, equation image or equation image were zero, indicating no fractionation during the transfer from SOM1 to SOM2.

[58] In a few cases for δ13C, a small but highly uncertain fractionation factor, 13ε3, suggests fractionating losses from the passive SOM3 pool. A similar fractionation factor, 15ε3, was not included for N losses from SOM3. It is plausible that either (1) the variability in nitrogen isotope data prevents detection of an N isotope fractionation (15ε3) associated with marginally significant observed C-isotope fractionation (13ε3); or (2) there is no significant fractionation in the loss pathway of N (and possibly C) from SOM3. No observed fractionation associated with loss from SOM3 implies either a nonfractionating pathway of loss (such as DOM) or a pathway of loss in which the rate-limiting mechanism is downstream of SOM3 by one or more short-lived reservoirs. Fractionation during decomposition from SOM3 could occur since losses from this pool are most likely highly incremental and the “reactant” remains preserved in soil. Nevertheless, fractionation during decomposition of passive SOM appears to be small and uncertain.

[59] As Figures 710 show, the δ13C and δ15N model fits were highly successful at the 200 and 600 kyr sites, and also successful for δ15N at the <3 kyr and 3 Myr sites. The model appears to be appropriate at the <3 kyr site for δ13C, except for one data point, which appears to result from root inputs or a buried surface horizon. At the 1 to 3 Myr site, δ13C displays a linear trend with the increase in δ13C extending much deeper than the model predicts. This difference between the model and observations at the 1 to 3 Myr site could be accounted for by the same diffusive mixing of dissolved and colloidal OM, which appears to cause deviations from the models predicted behavior for Δ14C.

[60] The success of the model for stable isotopes supports the conceptual model presented by Baisden et al. [2002] that 13C and 15N enrichment occurs during the preservation of C and N in mineral-associated (stabilized and passive) SOM. The model-derived fractionation factors (Table 5b) suggest that the greatest fractionation occurs during passive SOM formation while lesser and variable fractionation factors apply to stabilized SOM formation. Presuming that passive SOM is more intimately associated with mineral surfaces than stabilized SOM, the relative size of these two fractionation factors agrees with the conclusion of Baisden et al. [2002] that 13C and 15N enrichment is positively correlated with the degree of mineral-association.

[61] Most importantly, the good fits obtained for δ13C and δ15N based on a well-constrained multipool Δ14C-transport model allow some modifications and clarification of the concepts presented in previous models for stable isotopes in SOM [Brenner et al., 2001; Evans and Ehleringer, 1993; Feng et al., 1999; Martinelli et al., 1999; Nadelhoffer and Fry, 1988]. Generally, these models assume a single homogenous pool of SOM and cannot constrain the transport of OM within the soil profile. Most models invoke Rayleigh distillation, in which a “batch” of SOM (the reactant) undergoes 13C and 15N enrichment due to loss of a 13C and 15N depleted product. As a result, no distinction exists for SOM preserved in stabilized or passive SOM pools. The more detailed model presented here is no longer consistent with a “batch” distillation; all SOM pools have simultaneous inputs and losses, and SOM1 has several loss pathways. This distinction may be minor from the point of view of many modeling exercises, but from a mechanistic modeling standpoint the distinction can be important. If fractionation occurs during the transfers to preserved pools, then neither a single fractionation factor nor source δ15N values can be inferred based on a simple Rayleigh fractionation relationship, commonly plotted as the logarithm of concentration versus δ values [e.g., Evans and Ehleringer, 1993]. Here, the observed δ15N and δ13C values for the soils studied are approximately linear with respect to the logarithm of concentration, but the model structure suggests that this relationship is merely a coincidence. Nevertheless, one pool SOM models may still represent simplified ecosystems appropriately when a principal purpose of the model is to describe the plant-soil difference in δ values [Brenner et al., 2001], a difference dominated by the 15N and 13C enrichment of SOM2 and SOM3, which are large relative to SOM1 in most soils.

[62] It is also important to note that deviation of real transport processes (i.e., advective versus diffusive) from those modeled can cause incorrect estimation of fractionation factors [Amundson and Baisden, 2000]. Additionally, interannual variation in plant input δ13C or δ15N and depth-dependence of δ13C or δ15N in root inputs could cause errors in estimated fractionation factors. Given plausible differences in transport processes between sites, and limited observations of up to ∼2‰ interannual and depth-dependent variation at the sites, we do not compare the fractionation factors (Table 5b) across sites.

[63] Nevertheless, the success of the model for stable isotopes suggests that the information captured in stable isotopes can be represented successfully in three-pool models of SOM dynamics, perhaps allowing constraint of the depth-dependence of SOM pools. There is one caveat however: the model suggests that most fractionation occurs during the creation of the large passive SOM3 pool, while much smaller differences appear in δ13C and δ15N between the two faster cycling pools, which govern most ecosystem processes. Nevertheless, the passive SOM3 pool is large and poorly understood. Given the large differences inferred in δ13C and δ15N between passive and faster cycling SOM pools, stable isotopes could be useful for investigating the potential for changes in this poorly understood SOM pool.

5.4. Implications for SOM Dynamics as a Function of Soil Age

[64] Application of an SOM turnover and transport model to a soil chronosequence presents the opportunity to examine the relationship between the model-derived parameters and soil age, as well as results obtained from a semiindependent method. Figure 11a confirms that the transport model (presented here) and the semiindependent method presented in the companion paper [Baisden et al., 2002] yield similar turnover rates and pools sizes for three pools of SOC. Using Δ14C from archived and contemporary samples permits particularly good constraint of decadal stabilized (C2) SOM turnover rates and pool sizes based on incorporation of the bomb 14C-spike. Pool sizes for active and passive SOM pools appear reliable, but turnover rates for these pools must be considered approximate.

image

Figure 11. (a) Comparison of pool sizes and stabilized pool residence times derived from the transport model (TM) and density fractionation (DF) methods for surface soil horizons. (b) Total soil N in active, stabilized and passive pools from the transport model to infinite depth. (c) Total soil organic C in active, stabilized and passive pools from the transport model to infinite depth. For Figures 11a–11c, all pool sizes are based on 1997–1998 profiles only.

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[65] The estimated stabilized (C2) SOM turnover rates agree nearly perfectly in the 200 kyr soil, and display a similar pattern across the 600 kyr and 3 Myr soils but differ between methods by up to 8 years. Stabilized SOM resides longest in the 200 kyr soil, with increasing rates of stabilized SOM turnover in the 600 kyr and 3 Myr soils. Comparison of values cannot be made for the <3 kyr soil since the density fractionation procedure was not performed for Δ14C. Based on the transport model only, the youngest soil (<3 kyr) displays intermediate stabilized SOM residence times, and a different partitioning of SOM, with much more stabilized SOM in the surface horizons. Within the older three soils, the size of the stabilized C2 pool appears to be roughly constant within the ∼0- to 10-cm depth increment (Figure 11a), but clearly decreases with soil age on a whole soil basis (Figure 11c).

[66] Inspection of Figure 11c suggests that observed changes in the whole soil C turnover rate (flux/pool size; based on Figures 2b and 2c) appear to result from changes in the partitioning of SOM between pools with different residence times. Within the 200 kyr to 3 Myr age range, older soils tend to have a smaller proportion of active and stabilized SOM relative to passive SOM. Despite this, at least one reservoir, the stabilized SOM pool displays decreasing residence times as soil age increases. Therefore, examination of the sizes and turnover rates of the three SOM pools can be more appropriate than discussion of the apparent decreases in whole soil C turnover rates.

[67] The results presented above and in Tables 5a and 5b suggest systematic variation in a number of model-derived parameters as a function of soil age, particularly within the 200 kyr to 3 Myr age range. While some model-derived parameters (ε's, P, and L) are too uncertain given the poorer 1 to 3 Myr model fits to permit substantial comparison across soil age, certain parameters do vary as expected given increases in clay content and development of water-limiting horizons with soil age. In particular, systematic decreases in transport rates (v1, v2, and v3), and increases in stabilized and passive SOM formation rates (t2 and t3) may be linked to increases in clay content (Table 1 and Appendix A).

[68] Conversely, increases in the modeled turnover rates of stabilized SOM (k2) (Table 5a) and decreases in stabilized SOM pool size (Figures 11b and 11c) with increasing soil clay content appears to conflict with the accepted understanding of the role of clay in SOM storage and turnover [Burke et al., 1989; Oades, 1995; Parton et al., 1987]. The 200 kyr site, which seems ideal for plant growth in many respects, has a large pool of SOM2 that turns over twice as slowly as the SOM2 pool at the oldest site (k2 values in Tables 5a and 5b). Yet at the oldest site, where soil pH dips below 4 (Appendix A), soil texture and hydrology seem more prone to create a large SOM2 pool with a very slow turnover rate, but do just the opposite. Could the increased turnover and decreased pool size of stabilized SOM2 result from increased demand for nutrients in “impoverished” older soils, as suggested by the conceptual model for stabilized SOM turnover in the work of Baisden et al. [2002]?

[69] Estimating the turnover of a limiting nutrient is one of the most critical controls in many ecosystem biogeochemistry models, so the use of the Δ14C model to obtain independent empirically derived estimates of N turnover has considerable value. SOM turnover implies the mineralization of organic N into plant available forms. In turn, plant available N often constrains plant productivity and therefore the magnitude of inputs to the SOM pool. We calculate the N turnover rates presented in Figure 12 as the result of multiplying the pool sizes (calculated from equations (A4)(A6) in Appendix A; with modifications for N as per Tables 3a and 3b) by their turnover rates, k1, k2, and k3. As required by mass balance, the total N turnover is equal to the values measured as inputs from quadrats (Table 4a and Figure 2c). More importantly, the youngest and oldest sites show higher proportional contributions to N availability from the slower cycling SOM2, than do middle aged sites. Given the hypothesis that stabilized SOM can be decomposed to release nutrients following the disruption of the soil structure stabilizing this SOM pool by growing plant roots or filamentous microorganisms [Baisden et al., 2002], the model-derived results indicating faster SOM2 in older sites could plausibly be explained if greater demand for N exists in older sites.

image

Figure 12. Nitrogen turnover (mineralization) calculated as the size of each N pool multiplied by its turnover rate. Turnover from N3 is negligible (1% of total or less). Pool sizes are based on 1997–1998 profiles.

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[70] Ultimately, greater plant demand for N in older soils must result from increases in N losses or decreases in rates of N supply from the atmosphere. Figure 13 presents several lines of evidence for changes in N supply and loss as a function of soil age in study area. Evidence for increased N losses is limited, but straightforward. Figure 13b presents lysimeter data collected near the study sites [White, 1995], which shows increasing soil solution NO3 concentrations with soil age. The large pools of NO3 in the older soils are subject to loss via leaching or denitrification [Paul and Clark, 1996]. In support of this, loss parameters calculated for each soil in Baisden and Amundson [2002] (based on model parameters in Table 5a) suggest the highest rate of loss in the oldest soil.

image

Figure 13. Relationships between N, P and soil age. (a) Counts of mineral apatite (the primary P mineral) and pH versus soil age from Harden [1987]. Low pH may increase P occlusion in Fe oxide minerals. (b) Lysimeters in the vicinity of the field sites display decreasing phosphate and increasing nitrate soil solution concentrations. (c) Greenhouse fertilization experiments conducted using similar soils from Madera County (∼20 km south of Merced) display limitation by N alone in young soils, but increasing levels of N&P colimitation in older soils. Experimental data from Ripperdan, San Joaquin and Redding soils by J. Vlamis reported by Weir [1956]. Fertilization equivalent to 220, 330 and 110 kg/ha of N, P and K, respectively.

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[71] Examination of changes in N input rates requires knowledge of P dynamics, which exists for chronosequences of comparable length [Chadwick et al., 1999; Vitousek et al., 1997; Vitousek and Farrington, 1997; Walker and Syers, 1976]. Figure 13 presents available data summarizing P dynamics in the study area, while Figure 14 presents hypothetical relationships among P, SOM dynamics, and soil age based on Figure 13 and the model of Walker and Syers [1976] modified to include atmospheric deposition [Newman, 1995; Vitousek and Farrington, 1997; Walker and Syers, 1976].

image

Figure 14. Hypothesized long-term control of SOM turnover and storage by P dynamics. Modified from Walker and Syers [1976] to include atmospheric deposition of P, and consider observed reduction in P availability in very old soils due increased occlusion at low pH. See text for discussion.

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[72] Researchers have hypothesized that long-term limitation by rock-derived nutrients such as P may limit symbiotic N-fixation, particularly in older soils [Schlesinger, 1997; Walker and Adams, 1958]. Symbiotic N-fixers are present in California annual grassland ecosystems and respond vigorously to experimental P and S additions [Arkley, 1962; Jones and Woodmansee, 1979]. Figure 13a shows that stocks of weatherable P (apatite) in the chronosequence soils decrease markedly during the first ∼100 kyr of soil development. Much of the apatite remaining in >200 kyr soils may be contained within more slowly weathering minerals such as feldspars [Syers and Walker, 1969; White, 1995]. Additionally, low pH in very old soils may permit consumption of available P by occlusion within iron oxides, which are abundant in older soils [Harden, 1987; McBride, 1994; White et al., 1996].

[73] Given decreased rates of P input from weathering (Figure 14a) and increased rates of P occlusion, decreased P availability can be expected in older soils (Figure 14c) provided that P inputs from atmospheric sources is small or constant (Figure 14a) [Newman, 1995]. In support, data from fertility experiments (Figure 13c) performed on chronosequence soils collected just south of the intensively studied area suggests that N availability alone limits plant production early in soil development, but N and P availability colimit production in older soils [Weir, 1956]. Recent conceptual models based on investigations of fixer/nonfixer competition [Vitousek and Field, 1999] and plant-mycorrhizal interactions [Grogan and Chapin, 2000] elucidate mechanisms that likely lead to limitation of symbiotic N-fixation, and therefore N availability, by P availability in older soils.

[74] If N-fixation in the oldest soils is limited by decreasing P availability, and N losses are equal to or greater than those in other sites, the hypothesis of increasing N-demand fueling increasing rates of stabilized SOM turnover in older sites appears reasonable. Despite changes in N demand, and stabilized SOM turnover, total SOM storage may remain relatively constant or even increase considering that C:P ratios in SOM and losses from the SOM pool are not thought to remain relatively constant as is the case for C:N ratios [Neff et al., 2000; Schlesinger, 1997; Tiessen et al., 1982]. Overall, while the interpretation of P dynamics across this chronosequence is based primarily on published results from other chronosequences, and constrained by limited data from the field area, P availability appears to represent an important control on ecosystem C and N dynamics, which merits further examination.

5.5. Implications for General Understanding of SOM

[75] The transport model points toward two areas in which improved understanding of SOM dynamics are needed. First, uncertainty remains in the estimates of passive SOM3 near the soil surface; the model does not exactly match measured Δ14C values in the uppermost horizons of 1949–1952 profiles. The difference between the prebomb Δ14C data and the model estimate could be accounted if nonuniform sampling did not include the OM-rich surface soil (∼0–2 cm) completely. Alternatively, the difference between modeled and measured 1949–1952 surface horizon Δ14C could be accounted for by charcoal in these soil layers. Owing to the importance of partitioning SOM between pools with different residence times, better understanding of the sources of passive SOM near the soil surface is needed.

[76] Second, the apparent contribution of bomb-derived C to depths well below 50 cm suggests that DOC transport represents an important process in annual grassland ecosystems. Many researchers have sensibly dismissed the importance of DOC transport in nonhumid systems [e.g., Feng et al., 1999]. Yet mechanisms for DOC production exist that may actually be specific to seasonally dry systems: in response to osmotic shock during rapid wetting events, bacteria may either burst or release intracellular proteins, sugars and amino acids into soil solution to reduce intracellular water potential [Halverson et al., 2000; Kieft et al., 1987].

[77] Considering the large reservoir of deep SOC [Jobbagy et al., 1999] and the fact that DOM represents an important fate for nutrients in undisturbed systems [Hedin et al., 1995], these estimates support suggestions that decadally cycling SOM could represent significant fluxes in long-term ecosystem C and N budgets [Neff and Asner, 2001]. Raymond et al. [2001] support these suggestions with evidence that riverine DOC has a decadal residence time in terrestrial ecosystems. If as suggested by results for the 200 kyr soil derived from Figure 5, ∼24% of the C in the 50- to 150-cm depth interval turns over on timescales as fast as 20 years, this would represent a flux of ∼23 g m−2 yr−1. Extrapolating to the global area of temperate grasslands (8 × 108 ha) [Schlesinger, 1997], this becomes a flux of 0.2 Pg C yr−1. Further extrapolating across all terrestrial ecosystems, the natural cycling of deep SOM could approach the magnitude of uncertainty and imbalance in the global C budget (1–3 Pg C yr−1). Given the extent of land-use change and alteration of the global N cycle, both of which could alter rates of DOC production [Aitkenhead and McDowell, 2000; Currie et al., 1996; Goodale et al., 2000], the resulting disequilibrium in deep soil C could warrant consideration in global and regional C budgets. Clearly, this result merits further consideration and research.

[78] Despite these remaining uncertainties, the turnover and transport model we developed for multiple isotope systems in SOM was highly successful, particularly in soils with uniform soil texture and straightforward hydrology. The model allows the empirical calculation of residence times and transport rates for three pools of SOM in a manner that is consistent with most ecosystem biogeochemistry simulation models, and may be useful for including soil depth a working model component. The model agrees with estimates of SOM turnover times obtained from soil density fractions isolated from the same soils, and appears to be consistent with the finding that SOM residence times in the slow pool are less variable than C:N ratio or stable isotope ratios [Baisden et al., 2002]. Finally, the model suggests that three pools of SOM permit a realistic treatment of soil δ13C and δ15N, and that observed SOM 13C and 15N enrichment results primarily during the transfers of SOM from the fast cycling SOM1 to passive SOM3, and to a lesser and more variable extent the slow cycling SOM2. Thus stable isotope fractionation appears to occur primarily during preservation of stabilized and passive SOM. Overall, uniform treatment of complimentary information obtained from all available C and N isotopes within a single multipool transport model permits improved understanding of each isotopic system, and SOM turnover as whole.

Appendix A.: Analytical Solutions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

[79] The mass balance described by equations (1a), (1b), and (1c) as well as (2a), (2b), and (2c) can be solved analytically. In this appendix, we present solutions derived in Matlab v5.2 using the symbolic toolbox.

[80] Before solving the mass balance equations for the transport model, it is useful to remove transport and derive analytical solutions for the steady-state pool sizes. With transport and depth removed, equations (1a), (1b), and (1c) become

  • equation image
  • equation image
  • equation image

The equations above yield steady-state solutions for C1, C2, and C3 equivalent to the solution for equations (1) and (2) provided that the latter equations are evaluated to a sufficiently large depth that the solution approximates that obtained to infinite depth. These solutions to equations (A1), (A2), and (A3) are

  • equation image
  • equation image
  • equation image

[81] Note that equation (A6) allows the passive relocation flux, P, to be evaluated

  • equation image

and utilized in the upper boundary condition for C3 (equation (2c)).

[82] Returning to the transport model (equations (1) and (2)), we present separate solutions for aboveground and belowground plant inputs (FA and FB, respectively). The separation of input sources simplifies the solutions and potentially allows tracking the OM derived from each source within the soil. For aboveground inputs only (FB = 0),

  • equation image
  • equation image
  • equation image

Similarly, for belowground inputs only (FA = 0),

  • equation image
  • equation image
  • equation image

Lastly, the relocation of passive SOM must also be considered (see equation (2c)):

  • equation image

in which the final solution results from substituting for P based on equation (A7).

[83] The final solutions to the transport model presented in equations (1a), (1b), and (1c) and (2a), (2b), and (2c) are then

  • equation image
  • equation image
  • equation image

To evaluate the model for 14C, 13C, N, and 15N, the parameters FA, FB, k1, k2, k3, t2, and t3 may be modified as indicated in Table 3.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

[84] We thank Birgit Claus, Brian Frantz, Paula Zermeno, Steve Silva, J. Bartolome, and P. VanHorn for sample preparation assistance. We thank L. Sonder and M. Hendricks for useful discussions regarding the applicability of diffusive and advective transport mechanisms. We thank J. Southon and J. Randerson for assistance with atmospheric Δ14C and δ13C data, respectively. W. Riley and K. Yoo provided useful comments on model structure. J. Neff provided suggestions for interpreting the potential role of DOM transport. J. Neff, A. Parshotam, and two anonymous reviewers have provided helpful reviews of earlier drafts. Funding for this work was provided by a NASA ESS Fellowship to WTB, the Kearney Foundation for Soil Science, an LLNL-UC collaborative research program, and the University of California DANR.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Analytical Results
  6. 4. Multiisotope Model
  7. 5. Model Results and Discussion
  8. Appendix A.: Analytical Solutions
  9. Acknowledgments
  10. References
  11. Supporting Information

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