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Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia
e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
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Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia Victor Brovkin a,∗ , Alexander Cherkinsky b , Sergey Goryachkin c a b c
Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany Center for Applied Isotope Studies, University of Georgia, 120 Riverbend Road, Athens, GA 30602, USA Institute of Geography, Russian Academy of Sciences, Staromonetny 29, Moscow 109017, Russia
a r t i c l e
i n f o
a b s t r a c t
Article history:
Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km2 area of European
Published on line 22 April 2008
Russia were estimated based on
14
which strongly affects the topsoil Keywords:
C data. The rates are corrected for bomb radiocarbon
14
C balance. The approach is applied for carbon stored
in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates
Carbon cycle
of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1 ), inter-
Climate change
mediate for tundra soils (0.25% year−1 ), and low for grassland soils (0.02–0.08% year−1 ) with
Soil carbon
the exception of southern Chernozems (0.32% year−1 ). In the soil layer of 20–100 cm depth,
Radiocarbon
the turnover rates were much lower for all soil types (0.01–0.06% year−1 ) except for peat bog
European Russia
soils of the southern taiga (0.14% year−1 ). Combined with a map of soil type distribution
Model
and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from
14
C measurements that most soil
organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond. © 2008 Elsevier B.V. All rights reserved.
1.
Introduction
Soil carbon is the main component of the terrestrial carbon cycle. Estimates of storages of soil organic matter (SOM) in the upper 1 m layer vary in the range of 1500–2000 GtC (Post et al., 1982, 1997; Batjes, 1996; Prentice et al., 2001) depending on the way used to account for organic carbon storage in wetlands.
Soil layers deeper than 1 m contain several hundred PgC in the form of peat in northern ecosystems (e.g. Gorham, 1991) and organic carbon in moist tropical forest soils (Trumbore et al., 1995). Additionally, about 950 PgC are stored in inorganic (carbonate) form, predominantly in drylands (Lal, 2004). These ample storages of soil carbon outweigh by a factor of three to six the plant biomass estimated in the range of 470–660 PgC
∗ Corresponding author. Present address: Max Planck Institute for Meteorology, Bundesstrasse 55, 20146 Hamburg, Germany. Tel.: +49 40 41173 339; fax: +49 40 41173 350. E-mail address: [email protected] (V. Brovkin). 0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2008.03.018
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(Prentice et al., 2001). In the case of continued greenhouse gas emissions, global mean air temperature is projected to increase by up to 6.4 ◦ C during the 21st century (IPCC-2007, SPM). Consequent drastic changes in plant productivity and in the soil thermal and hydrological balance will strongly affect terrestrial carbon storage and, through the land-atmosphere CO2 exchange, the atmospheric CO2 concentration. The feedback loop between CO2 and climate most likely has amplified climate change in the past (Scheffer et al., 2006; Torn and Harte, 2006) and could substantially increase global warming in the future (Cox et al., 2000; Kirschbaum, 2000). Global coupled climate–carbon cycle models are the best tools currently available for the assessment of changes in global carbon balance in the future. Within the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), 11 coupled climate–carbon cycle simulations of different complexity performed simulations over the 21st century (Friedlingstein et al., 2006). All but one model simulated a reduction of SOM turnover time, and some models show a strong negative impact of climate change on turnover time (up to 1 year decrease per 1 ◦ C global warming). This assessment is very preliminary because the SOM balance is one of the most crudely represented processes in the global carbon models. One of the biggest uncertainties is the decomposition of the inert (stable or recalcitrant) organic carbon. It is likely that biological processes consume the recalcitrant SOM as well but little is currently known about these processes (Prentice et al., 2001). An effect of soil temperature change on SOM decomposition rate is doubtless, but its magnitude in long-term dynamics is currently a matter of debate (Knorr et al., 2005; Reichstein et al., 2005; Fang et al., 2006). Here, we focus on time scales of the SOM decomposition based on radiocarbon measurements using the soil formation model formulated by Cherkinsky and Brovkin (1993). A similar type of model has been applied by Trumbore et al. (1995), Perruchoud (1996) and Gaudinski et al. (2000) for evaluation of soil carbon cycling in tropical and temperate forests. Hahn and Buchmann (2004) accounted for two pools (active and passive) based on prescribing a threshold in 14 C activity for active carbon in the soil. This method is more advanced than the bulk carbon models mentioned before, but it does require a specification of organic input by above-ground and below-ground litter. Since these data were not available for us, we applied hereafter the model by Cherkinsky and Brovkin (1993) of unfractionated SOM and combined it with database on soil carbon storage in different soil types of European Russia.
of organic matter remaining after the organism’s death obeys the exponential decay: I(t) = A e−t ,
(1)
where is the decay-rate of 14 C (14 C half-time is 5730 years), A is the specific activity of atmospheric carbon and C12 (t), C14 (t) are the contents of carbon isotopes in organic matter (the content of stable 12 C isotope does not change with time). In accordance with Eq. (1) T=
−1 I(t) ln A
(2)
is 14 C age of analysed organic matter. Eq. (2) is widely used to estimate the period of time since the organism’s death. This equation implicitly assume a stable 14 C concentration in the atmosphere, which is approximately true at least for the last several thousand years. Eqs. (1) and (2) refer to a “closed carbon system”, which presumes no carbon exchange with the environment. In contrast, the soil carbon represents an open system. The 14 C age calculated from Eq. (2) can thus not be interpreted as the absolute age in the context of SOM (Sharpenseel, 1971) but has the meaning of a mean residence time of soil organic carbon. Its reciprocal is a turnover rate of soil carbon, m: m=
1 . T
(3)
Soil organic matter is a heterogeneous system and its turnover rate depends on the fraction of soil carbon, depth of soil sample, soil type and many other factors (Schimel et al., 1994).
2.2.
Model of soil organic profile formation
The model of a monogenetic soil organic profile formation under stable conditions of pedogenesis is based on the following assumptions: • the soil organic profile develops from the surface downward as a result of the increased involvement of rocks in humusforming processes; • the underlying layers are formed later than the overlying ones; • rates of organic detritus input and of humus turnover are constant.
2.
Methods
Under these assumptions, carbon accumulation in the soil can be represented by
2.1.
Radiocarbon (14 C) analysis
⎧ ⎨ dC12 (t) = p(1 − A) − mC12 (t) dt
The method is based on the fact that due to -decay the specific carbon activity
⎩ dC14 (t) = pA − mC (t) − C (t) 14 14
C14 (t) I(t) = C12
14 C,
,
(4)
dt
where p(1 − A) and pA define the amounts of input of 12 C and respectively, and C12 (t), C14 (t) are the contents of carbon isotopes in soil. Assuming as a first approximation constant
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inflow and turnover of carbon, we obtain from (4):
⎧ ⎨ C12 (t) = p(1 − A) (1 − e−mt ) m
⎩ C (t) = p A (1 − e−(m+)t ) 14
(5)
,
m+
resulting in a specific activity I(t) =
C14 (t) mA 1 − e−mt . = C12 (t) (m + )(1 − A) 1 − e−(m+)t
(6)
Fig. 1 – Change of specific carbon activity (% of NBS) in the atmosphere used in Eq. (9) (Bolin, 1986).
Since A ≈ 10−12 , we can replace 1 − A with 1 in Eqs. (5) and (6). For equilibrium conditions we find from (5): C∗12 =
p , m
C∗14 =
pA , m+
I∗ =
mA , m+
(7)
where I* is the specific carbon activity for equilibrium case, and A is the specific carbon activity for the input flux of plant detritus. From (7) we have: m=
I∗ , A − I∗
(8)
where I* is the specific humus activity, measured by radiocarbon analysis of soil samples.
2.3.
Case of recent soils
The above model (Eqs. (4)–(8)) is appropriate in the case of constant concentration of 14 C in the atmosphere. However, the concentration of atmospheric radiocarbon in the past has been subject to fluctuations caused by the explosion of supernova stars, variations in solar activity, and oscillations of the geomagnetic field of Earth. These fluctuations are known for the last 8 thousand years with quite good precision and during the last decade the calibration curve was extended back to about 40 thousand radiocarbon years before present (Reimer et al., 2004; van der Plicht et al., 2004). The concentration of radiocarbon has been also significantly influenced by human activities. Since the industrial period is characterized by the ever-increasing use of fossil fuel with no 14 C, there has been a slight diminishing of its concentration in the atmosphere by about 1–2% (Suess effect), superimposed by a rapid increase due to nuclear tests in the atmosphere starting in the early 1950s. A maximum in test intensity in 1962–1964 resulted in a nearly twofold concentration of 14 C in the atmosphere as compared to the pre-nuclear period. Nuclear power plants also contribute significantly to the increase in the atmospheric radiocarbon concentration. Fig. 1 displays the change of the specific activity of carbon in the atmosphere after 1955 (Bolin, 1986; Levin and Kromer, 1997). This factor resulted in a significantly higher specific radioactivity in the plant organic tissues over the last five decades in comparison with the NBS standard that provides a standard reference for radiocarbon dating (NBS SRM 4990). This surplus is little dependent on the type of vegetation and ranges from 107% to 110% at present. With a certain time lag the increase of radioactivity takes place in the newly formed humus, too. This effect is mostly pro-
nounced in humus of soils with fast turnover, especially topsoils. The fluctuations of 14 C concentration in the atmosphere make it impossible to calculate the radiocarbon age and rates of humus turnover particularly in soils with rapid turnover using Eqs. (2) and (8). The parameter A in these equations ceases to be constant in the time interval 1956–1982, and instead, two stationary levels of specific activity are found: A —“pre-nuclear” activity (100%), and A —atmospheric activity after 1982 (130–135%). If the measured level of specific activity in a soil is, for example, 110%, how can this be interpreted? Calculation of a radiocarbon date with Eq. (2) if A equals A is senseless, because it yields a negative estimate of the mean residence time. With A = A the assessment gives a date of about 1.5 thousand years in contradiction with other radiocarbon dates for the soil. Besides, the specific activity of soil is not constant, but gradually increases with time in disagreement to Eq. (8). To overcome these difficulties with dating recent soils, Cherkinsky and Brovkin (1993) suggested a model for calculating the radiocarbon age of soils and the turnover rates of humus that takes into account the whole curve of change of specific activity of carbon in the atmosphere after 1956. An equilibration of the processes of humification and mineralization is assumed, i.e. input/output fluxes of 12 C do not change. In this case, the dynamics of I(t) is determined only by changes in A(t): dI(t) 1 dC14 (t) m = ∗ = [pA(t) − (m + )C14 (t)] dt C12 dt p = mA(t) − (m + )I(t).
(9)
Let us assume that by the time t0 (e.g. 1955 year B.P.) the amount of 14 C in soil has stabilized, then I(t0 ) = I* in accordance with Eq. (7). Thus, if the measured specific activity of the sample at time t1 ≥ t0 is I1 , it is possible to choose the m that agrees the condition: I(t1 ) = I1 . It may be shown that if for the atmospheric activity the inequality A(t) > A(t0 ) ∀ t > t0 holds, then Eq. (9) has a single solution: each I(t0 ) corresponds to a single value of m. Let us give several examples illustrating this technique. If the specific activity of soil with fast turnover of the organic material at a rate of 2.2% per year were 99.8% before 1950, then I(t) would increase to more than 100% within several years,
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prohibiting further calculation with Eq. (2). In a soil with a low rate of humus turnover (m = 0.1% per year), the specific activity changes much more slowly. In this case, I(t) slightly increases from 89.0% to 90.2% between 1950 and 1985, and reaches a value of 90.7% in the year 2000. Because of the slow change of I(t), Eq. (2) can still be used for some decades, though it will produce too high a rate. Thus, the error in estimating m grows from 15% in 1985 to 20% in 2000. This stresses the need for the time-specific adjustment of Eqs. (2) and (8).
3.
European Russia case study
3.1.
Methods and materials
A soil map by Rozov and Rudneva (1986) at scale 1:16,000,000 provided the spatial database of our study. In order to make this map useful for our objectives we had to refine it and to apply several changes to the map legend: • podbur soils (Entic Podzols) at the Kola Peninsula and Polar Ural were separated; • areas of podzolic soils (Albeluvisols) were subdivided geographically into northern and southern ones due to essential differences in the storage and turnover rate of soil carbon; • northern Histosols in tundra and northern taiga zones were considered separately from those of the more southern areas. Some regions were excluded from our analysis: there were no available radiocarbon data for the northern islands (e.g. Novaya Zemlya), the Kaliningrad region, the Azov wetland or rock regions of Northern Caucasus with their high heterogeneity of soil types. The soil map was digitized with the ARC/INFO software, areas of more than two hundred polygons were calculated. The total area of analysed soil cover is 3.7 ml km2 . For convenience in data processing and because of significant differences in carbon storage and turnover rates, these characteristics were calculated separately for horizons in detritus (litter, peat) and mineral soil (humus). We used published data (Afanasieva, 1966; Fridland and Lebedeva, 1974; Ignatenko, 1979; Nogina and Rode, 1977–1981; Sklyarov and Sharova, 1970) as well as our experimental data for estimating the spatial distribution of carbon storage, resulting in a database which includes the volume percentage of carbon for about 600 soil profiles. For evaluation of the carbon storage we used the regression equation suggested by Post et al. (1982): Bd = b0 + b1 d + b2 ln Cf , where Bd is the carbon storage within one layer (gC m−2 ), Cf is the volume percentage of carbon (%) in the considered soil layer, d is its depth (m), and b0 , b1 , b2 are the regression factors depending on the type of soil and the depth of the layer within the profile. The information in the database relates to different layers of the soil profiles. For the estimation of the average carbon content within a soil profile the approximated average value of carbon storage for every 10-cm layer was calculated. This procedure was carried out for all types of soils, and carbon
Fig. 2 – Profiles of soil carbon storages of mineral layers for podzolic, sod-podzolic (loam), and typical chernozem soils. The storage is shown in kgC/m2 per 10 cm layer centred at the layer depth.
content was determined for both the mineral and detritus horizons. The carbon profiles for three different types of mineral soil horizons are shown in Fig. 2. One can see there the sharp decrease of humus storage with depth in podzolic soil (Albeluvisols) (a), and smooth decrease in Chernozems (c). The most fertile type of soil, Chernozem, stores much more humus than the other soils. Carbon in litter of tundra and forest soils is accumulated in the upper 10 cm. In the bog-podzolic soils (Gleyic Albeluvisols), accumulation of litter and peat is usually limited to 30 cm depth. For Histosols we accounted fro a 1 m-deep layer of peat as owing to the radiocarbon data that is the lower limit for biological activity of carbon in this soil type. In the soils of the forest-steppe and steppe regions, carbon storage in litter was neglected because of its small magnitude and because a considerable part of the arable lands in this zone have no litter at all. The comparison of carbon storage for the different soil types is presented in Fig. 3. For an evaluation of the annual carbon exchange in the upper soil horizons of ETR we used the model (4)–(8) and corrected for radiocarbon activity in layers of recent soils in accordance with Eq. (9). The results of the 14 C-dating, which had been performed at the Radiocarbon Laboratory of the Institute of Geography of Russian Academy of Sciences (Margolina et al., 1988; Chichagova and Cherkinsky, 1993; Cherkinsky and Goryachkin, 1993), were used as a data basis for our analysis. Since the soil samples were not uniformly thick, we interpolated the data onto 10-cm vertical layers. The dependence of the turnover rate m on the soil depth h was approximated by one of the following types of function: • exponential, m(h) = ea+bh , • power, m(h) = ahb , where parameters a and b were validated on the basis of the experimental data. The function with the highest level of
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Fig. 3 – Soil carbon storage in kgC/m2 for 1 m soil layer shown separately for organic and mineral layers. Soil type number is in accordance with Table 1. The type number roughly corresponds to a north–south transect of the European territory of Russia.
authenticity was chosen for every soil type. For example, the more adequate function for sod-podzolic soils (Eutric Albeluvisols) was a power function (Fig. 4), which is more suitable as a description of the sharp decrease of turnover rate with depth. On the other hand, we used an exponential model for Chernozems because of the smoother decline of turnover rate with depth (Fig. 4).
3.2.
Results
The spatial distribution of soil carbon storage is presented in Fig. 5a. All values were accounted for the upper 1 m of soil surface. In some bogs the peat storage extends to a depth of 12 m. Nevertheless, we did not take into account the carbon lying deeper than 1 m, as it is practically not involved in the decomposition process. The hydromorphic soils – Histosols, Gleyic
Albeluvisols – store significant amounts of peat in the organic horizon. The highest storage of about 60 kgC m−2 corresponds to Histosols; more than 90% of this carbon is in a form of peat. In terms of a geographical comparison from north to south, the storage is about 20 kgC m−2 in the northern areas of podbur (Entic Podzols) and tundra-gley (Turbic Cryosol) soils. It decreases to 10 kgC m−2 for podzolic (Dystric Albeluvisols) and sod-podzolic forest soils (Eutric Albeluvisols). Grassland fertile soils, Chernozems, are characterized by much higher humus storages from 20 to 35 kgC m−2 (Table 1). 50–60% of the area of Chernozems is used as an arable land, and 10–15% of Chernozems are used as meadows and pastures. Agricultural lands have a lower carbon content in comparison with soils left undisturbed in protected natural forest and grassland areas. The storage of carbon in natural chernozem soils in the upper 1 m layer could be up to 40–50 kg m−2 (Afanasieva, 1966). Chestnut and semi-desert soils (Kastanozems and Calcisols) in the south-eastern region of Russia have a relatively low soil carbon content of 3–10 kgC m−2 . In total, the analysed territory of about 3.7 million km2 contains 55 Pg of carbon in the upper 1 m of soil. Turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1 ), intermediate for tundra soils (0.25% year−1 ), and low for grassland soils (0.02–0.08% year−1 ) with exception of southern Chernozems (0.32% year−1 ), see Fig. 5b. In the soil layer at 20–100 cm depth the turnover rates were much lower for all soil types (0.01–0.06% year−1 ) except for peat bog soils of the southern taiga (0.14% year−1 ), see Table 1 and Fig. 5c. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)), see Fig. 5d. Calculated for the whole area, the annual soil carbon decomposition flux is 77 TgC/year. Let us caution that our approach does not distinguish the active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer and low fluxes of carbon from the soil.
4.
Fig. 4 – Profiles of turnover rates of soil carbon (10−3 year−1 ) for mineral layers of podzolic, sod-podzolic (loam), and typical chernozem soils.
Discussion
Our approach is based on direct measurements of 14 C intensity of soil samples. To interpret the results of these measurements, a model for the processes of 14 C accumulation and decomposition in soil (Cherkinsky and Brovkin, 1993) has been applied. The main model assumption is that the 12 C and 14 C in the soil is in an equilibrium state, or in other words, soils are in the final stage of their evolution. An assumption of a steady-state carbon cycle before the pre-industrial era is routinely used in models of the global carbon cycle (Cramer et al., 2001). It is important to compare different methods for estimation of the carbon turnover rate. For example, Basilevich and Shmakova (1986) considered the carbon balance in grass ecosystems. According to their data, the annual net primary production in the steppe zone of Central Russia is 600–900 gC m−2 year−1 in agroecosystems and 1200 gC m−2 year−1 in natural grasslands. In the former type of ecosystem, the biomass is allocated into agricul-
Table 1 – Carbon storages and carbon turnover rates for upper 1 m soil in European Russia N
Total
Area (×103 km2 )
Russian classification
WRB (FAO, 1998)
Podbur Tundra gley Peat bog, northern taiga Gley-podzolic Podzol, northern taiga Bog-podzolic Podzolic Podzol, middle taiga Peat bog southern taiga Sod-podzolic loam Sod-podzolic sand Grey Forest Leached Chernozems Typical Chernozems Ordinary Chernozems Meadows Chernozems Southern Chernozems Chestnut Calcareous Chernozem Semidesert
Entic Podzols Turbic Cryosol Histosols Gleyic Albeluvisols Haplic Podzols Gleyic Albeluvisols Dystric Albeluvisols Haplic Podzols Histosols Eutric Albeluvisols Ferric Podzols Phaeozems Luvic Chernozems Chernic Chernozems Calcic Chernozems Gleyic Chernozems Calcic Chernozems Haplic Kastanozems Calcic Chernozems Calcisols
Carbon storage (kg/m2 )
Turnover rate (×10−3 year−1 ; depth (cm) 0–20
60 110 104 187 199 199 315 178 57 616 245 253 226 86 118 37 122 208 122 269 3711
20.7 17.4 37.0 10.7 10.0 18.5 9.1 10.5 63.8 7.4 5.9 15.8 30.3 31.9 26.0 33.4 19.3 10.3 23.9 2.9
4.4 2.5 1.6 2.0 2.8 4.5 2.1 2.2 8.0 4.3 7.8 1.6 0.7 0.7 0.8 0.4 3.2 1.0 0.7 0.2
Annual C fluxes (g/(m2 year) Tg/year)
20–100 0.29 0.17 0.25 0.08 0.15 0.32 0.15 0.53 1.39 0.20 0.49 0.06 0.10 0.25 0.40 0.13 0.42 0.32 0.23 0.12
59.5 32.4 39.1 14.8 22.4 51.0 12.9 15.6 147.3 18.5 29.9 11.8 10.0 13.0 13.6 7.5 26.0 5.3 8.8 0.6
3.6 3.6 4.1 2.8 4.5 10.2 4.1 2.8 8.4 11.4 7.3 3.0 2.3 1.1 1.6 0.3 3.2 1.1 1.1 0.2
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Soil type
76.7
183
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tural production (400–500 gC m−2 year−1 ) and plant residues (400 gC m−2 year−1 ), while in grassland 400 gC m−2 year−1 are allocated to above-ground litter and 800 gC m−2 year−1 —to the below-ground root mortmass. Average time of decomposition of above-ground litter is 1 year. About 20% of residues forming the fast pools of litter and mortmass have a lifetime of 2–10 years. A small part of them is transformed into humus: in accordance with Gilmanov (1978), the humification rate of plant residues is 2–6% or 20–70 gC m−2 year−1 . The annual exchange rate of humus carbon calculated by radiocarbon data for Chernic Chernozems soils in this area is only 13 gC m−2 year−1 , or 2–4 times lower. There could be two reasons for this discrepancy:
• carbon humification and mineralization rates are not measured directly. Therefore, the expert guess in gC m−2 year−1 could exceed the real humification rate; • in our study, the mainly stable organic carbon part with a very low turnover rate was analysed.
This example suggests that only a minor part of the annual carbon input flux to grass ecosystems is involved in the slow exchange processes in soils. The major part of the carbon rotates in fast active pools. Their fraction in SOM storage in a 1-m layer could be as low as 10–20%. Moreover, most areas of Chernozem soils are used for agricultural purposes and a negative balance of soil organic matter is typical for such regions. In forest ecosystems, carbon storage in living phytomass and mortmass has a decadal lifetime. Harkness et al. (1986) distinguished the soil carbon into “young” and “old” fractions with the age less than 20 years and more than 300 years, respectively. The “young” cycling component comprises about 50% of humus in the top 10 cm of the soil, and less then 5% in deeper horizons. Hahn and Buchmann (2004) found a ratio of active to passive humus of 43–62% in the top layer (up to 20 cm) of different European forest soils. They assumed that all carbon below that is passive. High-latitude ecosystems contain about 25% of the total world soil carbon pool in the permafrost and the seasonally thawed soil layer (Prentice et al., 2001). A recent report
Fig. 5 – Maps of soil carbon traits for analysed territory of European Russia. Both mineral and organic soil layers are accounted for. (A) Soil carbon storage (kgC/m2 ), 0–100 cm. (B) Turnover rate (10−3 year−1 ), 0–20 cm layer. (C) Turnover rate (10−3 year−1 ), 20–100 cm layer. (D) Soil turnover flux, (gC m−2 year−1 ), 0–100 cm.
e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
185
Fig. 5 – (Continued ).
of Intergovernmental Panel on Climate Change suggests that “migration of boreal forest northward into tundra would initially lead to an increase in carbon storage in the ecosystem due to the larger biomass of trees than of herbs and shrubs, but over a longer time (e.g., centuries), changes in soil carbon would need to be considered to determine the net effect” (Denman et al., 2007). Results of our study indicate that SOM has faster turnover times in forests than in tundra ecosystems. This could be explained not only by generally higher temperatures in forest ecosystems, but also by changes in soil type. This is in line with conclusions by Goryackin et al. (2000) that an essential enhancement of the humus decomposition rate in high latitudes in Eurasia is possible in the case of a noticeable northward shift of the treeline. For the southern forest boundary our results for soil carbon cycling indicate a possibility of behaviour opposite to that on the taiga-tundra boundary. Projections of precipitation changes over land in 21st century are equivocal, but in general they suggest an increase in continental aridity. In the case of drier climate and a reduction of forest cover, forest soils could be replaced with grassland soils with a slower turnover.
This conclusion, in principle, is supported by geological evidence from the past. Alexandrovsky and Chichagova (1998a,b) reported a study of buried Chernozem soils in European Russia which were formed during the mid-Holocene climatic optimum and replaced later by forest soils because the climate became cooler and wetter. A similar Holocene soil dynamics was reported by Bork et al. (1998) for a site in Germany. However, a direct analogue with the Holocene period is inappropriate because anthropogenically-induced, decadal-scale climate change is much faster than the slow, millennial-scale climate dynamics in the Holocene. Besides, vegetation cover in this region is fully controlled by agricultural practice, and future formation of fertile natural grassland is very unlikely. However, with changes in vegetation cover, the soil type – and carbon cycling – will be changed as well. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, the effect of soil type changes, as well the clear evidence from 14 C measurements that most of soil organic carbon has a millennial time scale, have basically been neglected in
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the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond.
Acknowledgements This study, performed during the early 1990s when all the authors were working in scientific institutions in Moscow, was strongly influenced and encouraged by Prof. Yuri Svirezhev, a leader of the international school in global biospheric modelling. We will always remember him as an outstanding scientist with an extraordinary gift to see simplicity in very complex processes and a tremendous ability to synthesise knowledge from very different scientific disciplines.
references
Afanasieva, E.A., 1966. Chernozems of the Mid-Russian Upland. Nauka, Moscow, 224 pp (in Russian). Alexandrovsky, A.L., Chichagova, O.A., 1998a. The 14 C age of humic substances in paleosols. Radiocarbon 40 (2), 991–997. Alexandrovsky, A.L., Chichagova, O.A., 1998b. Radiocarbon age of Holocene paleosols of the East European forest-steppe zone. Catena 34, 197–207. Basilevich, N.I., Shmakova, E.I., 1986. Change of the meadow’s steppe geosystems under agricultural use. In: Grin, A.M., Mukhina, L.I. (Eds.), The Structure and Functioning of Geosystems. Proceedings of the All-Union Conference on Geosystem Monitoring. Institute of Geography, Moscow, pp. 123–143 (in Russian). Batjes, N.H., 1996. Total carbon and nitrogen in the soils of the world. European Journal of Soil Science 47, 151–163. Bolin, B., 1986. How much CO2 will remain in the atmosphere? The carbon cycle and projections for the future. In: Bolin, B., Doos, B.R., Jager, J., Warrick, R.A. (Eds.), The Greenhouse Effect, Climate Change and Ecosystems. SCOPE Report 29, John Wiley, New York, pp. 157–203. Bork, H.-R., Dalchow, C., Faust, B., Piorr, H.-P., Schatz, Th., 1998. Landschaftsentwicklung in Mitteleuropa. Gotha und Stuttgart (Klett-Perthes), 328 pp. Cherkinsky, A.E., Brovkin, V.A., 1993. Dynamics of radiocarbon in soils. Radiocarbon 35, 363–367. Cherkinsky, A.E., Goryachkin, S.V., 1993. Distribution and renovation time of soil carbon in boreal and subarctic ecosystems of European Russia. In: Vilson, T.S., Kolchugina, T.P. (Eds.), Carbon Cycling in Boreal Forests and Sub-Arctic Ecosystems, Proc. Int. Workshop, September 1991, EPA/600/R-93/084. Corvallis, Oregon, pp. 65–70. Chichagova, O.A., Cherkinsky, A.E., 1993. Problems in radiocarbon dating of soils. Radiocarbon 35, 351–362. Cox, P.M., Betts, R.A., Jones, C.D., Spall, S.A., Totterdell, I.J., 2000. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 750–1750. Cramer, W., Bondeau, A., Woodward, F.I., Prentice, I.C., Betts, R.A., Brovkin, V., Cox, P.M., Fisher, V., Foley, J.A., Friend, A.D., Kucharik, C., Lomas, M.R., Ramankutty, N., Sitch, S., Smith, B., White, A., Young-Molling, C., 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology 7, 357–373. Denman, K.L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P.M., Dickinson, R.E., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D., Lohmann, U., Ramachandran, S., da Silva Dias, P.L., Wofsy, S.C., Zhang, X., 2007. Couplings between changes in the
climate system and biogeochemistry. In: Solomon, S., et al. (Eds.), Climate Change 2007: The Physical Science Basis. Cambridge University Press, Cambridge. Fang, C., Smith, P., Smith, J.U., 2006. Is resistant soil organic matter more sensitive to temperature than the labile organic matter? Biogeosciences 3, 65–68. FAO/ISRIC/ISSS, 1998. World reference base for soil resources. FAO Soils Bulletin No. 84. FAO, Rome, 88 pp. Fridland, V.M., Lebedeva, I.I. (Eds.), 1974. Chernozems of the USSR. Kolos, Moscow, 558 pp (in Russian). Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Von Bloh, W., Brovkin, V., Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C., Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H.D., Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K.G., Schnur, R., Strassmann, K., Weaver, A.J., Yoshikawa, C., Zeng, N., 2006. Climate-carbon cycle feedback analysis: results from the C4MIP-model intercomparison. Journal of Climate 19, 3337–3353. Gaudinski, J.B., Trumbore, S.E., Davidson, E.A., Zheng, S.H., 2000. Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes. Biogeochemistry 51, 33–69. Gilmanov, T.G., 1978. Mathematical Modelling of Biochemical Cycles in Grasslands. Moscow State University, Moscow, 169 pp (in Russian). Gorham, E., 1991. Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecological Applications 1, 182–195. Goryackin, S.V., Cherkinsky, A.E., Chichagova, O.A., 2000. The soil organic carbon dynamics in high latitudes of Eurasia using 14 C data and the impact of potential climate change. In: Lal, R., Kimble, J.M., Stewart, B.A. (Eds.), Global Climate Change and Cold Regions Ecosystems. Lewis Publishers, Boca Raton, FL, pp. 145–162. Hahn, V., Buchmann, N., 2004. A new model for soil organic carbon turnover using bomb carbon. Global Biogeochemical Cycles 18, GB1019, doi:10.1029/2003GB002115. Harkness, D.D., Harrison, A.F., Bacon, P.J., 1986. The temporal distribution of ‘bomb’ 14 C in a forest soil. Radiocarbon 28, 328–337. Ignatenko, I.V., 1979. Soils of East European Tundra and Forest-tundra. Nauka, Moscow, 280 pp (in Russian). Kirschbaum, M.U.F., 2000. Will changes in soil organic matter act as a positive or negative feedback on global warming? Biogeochemistry 48, 21–51. Knorr, W., Prentice, I.C., House, J.I., Holland, E.A., 2005. Longterm sensitivity of soil carbon turnover to warming. Nature 433, 298–301. Lal, R., 2004. Soil carbon sequestration impacts on global climate change and food security. Science 304 (11), 1623–1627. Levin, I., Kromer, B., 1997. Twenty years of atmospheric 14 CO2 observations at Schauinsland Station, Germany. Radiocarbon 39, 205–218. Margolina, N.Y., Alexandrovsky, A.L., Ilichev, B.A., Cherkinsky, A.E., Chichagova, O.A., 1988. Age and Evolution of Chernozems. Nauka, Moscow, 144 pp (in Russian). Nogina, N.A., Rode, A.A. (Eds.), 1977–1981. Podzolic Soils of the European Part of the USSR, vols. 1–4. Nauka, Moscow, 1043 pp (in Russian). Perruchoud, D., 1996. Modeling the dynamics of nonliving organic carbon in a changing climate: a case study for temperate forests. Diss. ETH No. 11900. Swiss Federal Institute of ¨ Technology, Zurich, Switzerland, 213 pp. Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G., 1982. Soil carbon pools and world life zones. Nature 298, 156–159. Post, W.M., King, A.W., Wullschleger, S.D., 1997. Historical variations in terrestrial biospheric carbon storage. Global Biogeochemical Cycles 11, 99–109.
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Prentice, I.C., Farquhar, G.D., Fasham, M.J.R., et al., 2001. The carbon cycle and atmospheric carbon dioxide. In: Houghton, J.T., et al. (Eds.), Climate Change 2001: The Scientific Basis. Cambridge University Press, New York. ¨ ´ O., Ciais, P., Schulze, E.-D., Reichstein, M., Katterer, T., Andren, Cramer, W., Papale, D., Valentini, R., 2005. Temperature sensitivity of decomposition in relation to soil organic matter pools: critique and outlook. Biogeosciences 2, 317–321. Reimer, P.J., Baillie, M.G.L., Bard, E., Bayliss, A., Beck, J.W., Bertrand, C., Blackwell, P.G., Buck, C.E., Burr, G., Cutler, K.B., Damon, P.E., Edwards, R.L., Fairbanks, R.G., Friedrich, M., Guilderson, T.P., Hughen, K.A., Kromer, B., McCormac, F.G., Manning, S., Bronk Ramsey, C., Reimer, R.W., Remmele, S., Southon, J.R., Stuiver, M., Talamo, S., Taylor, T.W., van der Plicht, J., Weyhenmeyer, C.E., 2004. IntCal04 terrestrial radiocarbon age calibration, 0–26 cal kyr BP. Radiocarbon 46, 1029–1058. Rozov, N.N., Rudneva, E.N., 1986. Soil map of the USSR. In: Atlas of the USSR. GUGK, Moscow. Scheffer, M., Brovkin, V., Cox, P.M., 2006. Positive feedback between global warming and atmospheric CO2 concentration inferred from past climate change. Geophysical Research Letters 33, L10702, doi:10.1029/2005GL025044. Schimel, D.S., Braswell, B.H., Holland, E.A., McKeown, R., Ojima, D.S., Painter, T.H., Parton, W.J., Townsend, A.R., 1994. Climatic,
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edaphic and biotic controls over storage and turnover of carbon in soils. Global Biogeochemical Cycles 8, 279–293. Sharpenseel, H.W., 1971. Radiocarbon dating of soils-problems, troubles, hopes. In: Paleopedology: Origin, Nature and Dating of Paleosoils. ISSS and Israel Universities Press, Jerusalem, pp. 77–88. Sklyarov, G.A., Sharova, A.S., 1970. Soils of European North Forests. Nauka, Moscow, 271 pp (in Russian). Torn, M.S., Harte, J., 2006. Missing feedbacks, asymmetric uncertainties, and the underestimation of future warming. Geophysical Research Letters 33, L10703, doi:10.1029/2005GL025540. Trumbore, S.E., Davidson, E.A., Decamargo, P.B., Nepstad, D.C., Martinelli, L.A., 1995. Belowground cycling of carbon in forests and pastures of eastern Amazonia. Global Biogeochemical Cycles 9, 515–528. van der Plicht, J., Beck, J.W., Bard, E., Baillie, M.G.L., Blacjwell, P.G., Buck, C.E., Friedrich, M., Guilderson, T.P., Hughen, K.A., Kromer, B., McCormac, F.G., Bronk Ramsey, C., Reimer, P.J., Reimer, R.W., Remmele, S., Richards, D.A., Southon, J.R., Stuiver, M., Weyhemmeyer, 2004. NotCal04—comparison/calibration 14 C records 26–50 cal kyr BP. Radiocarbon 46, 1225–1238.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/ecolmodel
Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia Victor Brovkin a,∗ , Alexander Cherkinsky b , Sergey Goryachkin c a b c
Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany Center for Applied Isotope Studies, University of Georgia, 120 Riverbend Road, Athens, GA 30602, USA Institute of Geography, Russian Academy of Sciences, Staromonetny 29, Moscow 109017, Russia
a r t i c l e
i n f o
a b s t r a c t
Article history:
Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km2 area of European
Published on line 22 April 2008
Russia were estimated based on
14
which strongly affects the topsoil Keywords:
C data. The rates are corrected for bomb radiocarbon
14
C balance. The approach is applied for carbon stored
in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates
Carbon cycle
of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1 ), inter-
Climate change
mediate for tundra soils (0.25% year−1 ), and low for grassland soils (0.02–0.08% year−1 ) with
Soil carbon
the exception of southern Chernozems (0.32% year−1 ). In the soil layer of 20–100 cm depth,
Radiocarbon
the turnover rates were much lower for all soil types (0.01–0.06% year−1 ) except for peat bog
European Russia
soils of the southern taiga (0.14% year−1 ). Combined with a map of soil type distribution
Model
and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from
14
C measurements that most soil
organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond. © 2008 Elsevier B.V. All rights reserved.
1.
Introduction
Soil carbon is the main component of the terrestrial carbon cycle. Estimates of storages of soil organic matter (SOM) in the upper 1 m layer vary in the range of 1500–2000 GtC (Post et al., 1982, 1997; Batjes, 1996; Prentice et al., 2001) depending on the way used to account for organic carbon storage in wetlands.
Soil layers deeper than 1 m contain several hundred PgC in the form of peat in northern ecosystems (e.g. Gorham, 1991) and organic carbon in moist tropical forest soils (Trumbore et al., 1995). Additionally, about 950 PgC are stored in inorganic (carbonate) form, predominantly in drylands (Lal, 2004). These ample storages of soil carbon outweigh by a factor of three to six the plant biomass estimated in the range of 470–660 PgC
∗ Corresponding author. Present address: Max Planck Institute for Meteorology, Bundesstrasse 55, 20146 Hamburg, Germany. Tel.: +49 40 41173 339; fax: +49 40 41173 350. E-mail address: [email protected] (V. Brovkin). 0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2008.03.018
179
e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
(Prentice et al., 2001). In the case of continued greenhouse gas emissions, global mean air temperature is projected to increase by up to 6.4 ◦ C during the 21st century (IPCC-2007, SPM). Consequent drastic changes in plant productivity and in the soil thermal and hydrological balance will strongly affect terrestrial carbon storage and, through the land-atmosphere CO2 exchange, the atmospheric CO2 concentration. The feedback loop between CO2 and climate most likely has amplified climate change in the past (Scheffer et al., 2006; Torn and Harte, 2006) and could substantially increase global warming in the future (Cox et al., 2000; Kirschbaum, 2000). Global coupled climate–carbon cycle models are the best tools currently available for the assessment of changes in global carbon balance in the future. Within the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), 11 coupled climate–carbon cycle simulations of different complexity performed simulations over the 21st century (Friedlingstein et al., 2006). All but one model simulated a reduction of SOM turnover time, and some models show a strong negative impact of climate change on turnover time (up to 1 year decrease per 1 ◦ C global warming). This assessment is very preliminary because the SOM balance is one of the most crudely represented processes in the global carbon models. One of the biggest uncertainties is the decomposition of the inert (stable or recalcitrant) organic carbon. It is likely that biological processes consume the recalcitrant SOM as well but little is currently known about these processes (Prentice et al., 2001). An effect of soil temperature change on SOM decomposition rate is doubtless, but its magnitude in long-term dynamics is currently a matter of debate (Knorr et al., 2005; Reichstein et al., 2005; Fang et al., 2006). Here, we focus on time scales of the SOM decomposition based on radiocarbon measurements using the soil formation model formulated by Cherkinsky and Brovkin (1993). A similar type of model has been applied by Trumbore et al. (1995), Perruchoud (1996) and Gaudinski et al. (2000) for evaluation of soil carbon cycling in tropical and temperate forests. Hahn and Buchmann (2004) accounted for two pools (active and passive) based on prescribing a threshold in 14 C activity for active carbon in the soil. This method is more advanced than the bulk carbon models mentioned before, but it does require a specification of organic input by above-ground and below-ground litter. Since these data were not available for us, we applied hereafter the model by Cherkinsky and Brovkin (1993) of unfractionated SOM and combined it with database on soil carbon storage in different soil types of European Russia.
of organic matter remaining after the organism’s death obeys the exponential decay: I(t) = A e−t ,
(1)
where is the decay-rate of 14 C (14 C half-time is 5730 years), A is the specific activity of atmospheric carbon and C12 (t), C14 (t) are the contents of carbon isotopes in organic matter (the content of stable 12 C isotope does not change with time). In accordance with Eq. (1) T=
−1 I(t) ln A
(2)
is 14 C age of analysed organic matter. Eq. (2) is widely used to estimate the period of time since the organism’s death. This equation implicitly assume a stable 14 C concentration in the atmosphere, which is approximately true at least for the last several thousand years. Eqs. (1) and (2) refer to a “closed carbon system”, which presumes no carbon exchange with the environment. In contrast, the soil carbon represents an open system. The 14 C age calculated from Eq. (2) can thus not be interpreted as the absolute age in the context of SOM (Sharpenseel, 1971) but has the meaning of a mean residence time of soil organic carbon. Its reciprocal is a turnover rate of soil carbon, m: m=
1 . T
(3)
Soil organic matter is a heterogeneous system and its turnover rate depends on the fraction of soil carbon, depth of soil sample, soil type and many other factors (Schimel et al., 1994).
2.2.
Model of soil organic profile formation
The model of a monogenetic soil organic profile formation under stable conditions of pedogenesis is based on the following assumptions: • the soil organic profile develops from the surface downward as a result of the increased involvement of rocks in humusforming processes; • the underlying layers are formed later than the overlying ones; • rates of organic detritus input and of humus turnover are constant.
2.
Methods
Under these assumptions, carbon accumulation in the soil can be represented by
2.1.
Radiocarbon (14 C) analysis
⎧ ⎨ dC12 (t) = p(1 − A) − mC12 (t) dt
The method is based on the fact that due to -decay the specific carbon activity
⎩ dC14 (t) = pA − mC (t) − C (t) 14 14
C14 (t) I(t) = C12
14 C,
,
(4)
dt
where p(1 − A) and pA define the amounts of input of 12 C and respectively, and C12 (t), C14 (t) are the contents of carbon isotopes in soil. Assuming as a first approximation constant
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e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
inflow and turnover of carbon, we obtain from (4):
⎧ ⎨ C12 (t) = p(1 − A) (1 − e−mt ) m
⎩ C (t) = p A (1 − e−(m+)t ) 14
(5)
,
m+
resulting in a specific activity I(t) =
C14 (t) mA 1 − e−mt . = C12 (t) (m + )(1 − A) 1 − e−(m+)t
(6)
Fig. 1 – Change of specific carbon activity (% of NBS) in the atmosphere used in Eq. (9) (Bolin, 1986).
Since A ≈ 10−12 , we can replace 1 − A with 1 in Eqs. (5) and (6). For equilibrium conditions we find from (5): C∗12 =
p , m
C∗14 =
pA , m+
I∗ =
mA , m+
(7)
where I* is the specific carbon activity for equilibrium case, and A is the specific carbon activity for the input flux of plant detritus. From (7) we have: m=
I∗ , A − I∗
(8)
where I* is the specific humus activity, measured by radiocarbon analysis of soil samples.
2.3.
Case of recent soils
The above model (Eqs. (4)–(8)) is appropriate in the case of constant concentration of 14 C in the atmosphere. However, the concentration of atmospheric radiocarbon in the past has been subject to fluctuations caused by the explosion of supernova stars, variations in solar activity, and oscillations of the geomagnetic field of Earth. These fluctuations are known for the last 8 thousand years with quite good precision and during the last decade the calibration curve was extended back to about 40 thousand radiocarbon years before present (Reimer et al., 2004; van der Plicht et al., 2004). The concentration of radiocarbon has been also significantly influenced by human activities. Since the industrial period is characterized by the ever-increasing use of fossil fuel with no 14 C, there has been a slight diminishing of its concentration in the atmosphere by about 1–2% (Suess effect), superimposed by a rapid increase due to nuclear tests in the atmosphere starting in the early 1950s. A maximum in test intensity in 1962–1964 resulted in a nearly twofold concentration of 14 C in the atmosphere as compared to the pre-nuclear period. Nuclear power plants also contribute significantly to the increase in the atmospheric radiocarbon concentration. Fig. 1 displays the change of the specific activity of carbon in the atmosphere after 1955 (Bolin, 1986; Levin and Kromer, 1997). This factor resulted in a significantly higher specific radioactivity in the plant organic tissues over the last five decades in comparison with the NBS standard that provides a standard reference for radiocarbon dating (NBS SRM 4990). This surplus is little dependent on the type of vegetation and ranges from 107% to 110% at present. With a certain time lag the increase of radioactivity takes place in the newly formed humus, too. This effect is mostly pro-
nounced in humus of soils with fast turnover, especially topsoils. The fluctuations of 14 C concentration in the atmosphere make it impossible to calculate the radiocarbon age and rates of humus turnover particularly in soils with rapid turnover using Eqs. (2) and (8). The parameter A in these equations ceases to be constant in the time interval 1956–1982, and instead, two stationary levels of specific activity are found: A —“pre-nuclear” activity (100%), and A —atmospheric activity after 1982 (130–135%). If the measured level of specific activity in a soil is, for example, 110%, how can this be interpreted? Calculation of a radiocarbon date with Eq. (2) if A equals A is senseless, because it yields a negative estimate of the mean residence time. With A = A the assessment gives a date of about 1.5 thousand years in contradiction with other radiocarbon dates for the soil. Besides, the specific activity of soil is not constant, but gradually increases with time in disagreement to Eq. (8). To overcome these difficulties with dating recent soils, Cherkinsky and Brovkin (1993) suggested a model for calculating the radiocarbon age of soils and the turnover rates of humus that takes into account the whole curve of change of specific activity of carbon in the atmosphere after 1956. An equilibration of the processes of humification and mineralization is assumed, i.e. input/output fluxes of 12 C do not change. In this case, the dynamics of I(t) is determined only by changes in A(t): dI(t) 1 dC14 (t) m = ∗ = [pA(t) − (m + )C14 (t)] dt C12 dt p = mA(t) − (m + )I(t).
(9)
Let us assume that by the time t0 (e.g. 1955 year B.P.) the amount of 14 C in soil has stabilized, then I(t0 ) = I* in accordance with Eq. (7). Thus, if the measured specific activity of the sample at time t1 ≥ t0 is I1 , it is possible to choose the m that agrees the condition: I(t1 ) = I1 . It may be shown that if for the atmospheric activity the inequality A(t) > A(t0 ) ∀ t > t0 holds, then Eq. (9) has a single solution: each I(t0 ) corresponds to a single value of m. Let us give several examples illustrating this technique. If the specific activity of soil with fast turnover of the organic material at a rate of 2.2% per year were 99.8% before 1950, then I(t) would increase to more than 100% within several years,
e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
181
prohibiting further calculation with Eq. (2). In a soil with a low rate of humus turnover (m = 0.1% per year), the specific activity changes much more slowly. In this case, I(t) slightly increases from 89.0% to 90.2% between 1950 and 1985, and reaches a value of 90.7% in the year 2000. Because of the slow change of I(t), Eq. (2) can still be used for some decades, though it will produce too high a rate. Thus, the error in estimating m grows from 15% in 1985 to 20% in 2000. This stresses the need for the time-specific adjustment of Eqs. (2) and (8).
3.
European Russia case study
3.1.
Methods and materials
A soil map by Rozov and Rudneva (1986) at scale 1:16,000,000 provided the spatial database of our study. In order to make this map useful for our objectives we had to refine it and to apply several changes to the map legend: • podbur soils (Entic Podzols) at the Kola Peninsula and Polar Ural were separated; • areas of podzolic soils (Albeluvisols) were subdivided geographically into northern and southern ones due to essential differences in the storage and turnover rate of soil carbon; • northern Histosols in tundra and northern taiga zones were considered separately from those of the more southern areas. Some regions were excluded from our analysis: there were no available radiocarbon data for the northern islands (e.g. Novaya Zemlya), the Kaliningrad region, the Azov wetland or rock regions of Northern Caucasus with their high heterogeneity of soil types. The soil map was digitized with the ARC/INFO software, areas of more than two hundred polygons were calculated. The total area of analysed soil cover is 3.7 ml km2 . For convenience in data processing and because of significant differences in carbon storage and turnover rates, these characteristics were calculated separately for horizons in detritus (litter, peat) and mineral soil (humus). We used published data (Afanasieva, 1966; Fridland and Lebedeva, 1974; Ignatenko, 1979; Nogina and Rode, 1977–1981; Sklyarov and Sharova, 1970) as well as our experimental data for estimating the spatial distribution of carbon storage, resulting in a database which includes the volume percentage of carbon for about 600 soil profiles. For evaluation of the carbon storage we used the regression equation suggested by Post et al. (1982): Bd = b0 + b1 d + b2 ln Cf , where Bd is the carbon storage within one layer (gC m−2 ), Cf is the volume percentage of carbon (%) in the considered soil layer, d is its depth (m), and b0 , b1 , b2 are the regression factors depending on the type of soil and the depth of the layer within the profile. The information in the database relates to different layers of the soil profiles. For the estimation of the average carbon content within a soil profile the approximated average value of carbon storage for every 10-cm layer was calculated. This procedure was carried out for all types of soils, and carbon
Fig. 2 – Profiles of soil carbon storages of mineral layers for podzolic, sod-podzolic (loam), and typical chernozem soils. The storage is shown in kgC/m2 per 10 cm layer centred at the layer depth.
content was determined for both the mineral and detritus horizons. The carbon profiles for three different types of mineral soil horizons are shown in Fig. 2. One can see there the sharp decrease of humus storage with depth in podzolic soil (Albeluvisols) (a), and smooth decrease in Chernozems (c). The most fertile type of soil, Chernozem, stores much more humus than the other soils. Carbon in litter of tundra and forest soils is accumulated in the upper 10 cm. In the bog-podzolic soils (Gleyic Albeluvisols), accumulation of litter and peat is usually limited to 30 cm depth. For Histosols we accounted fro a 1 m-deep layer of peat as owing to the radiocarbon data that is the lower limit for biological activity of carbon in this soil type. In the soils of the forest-steppe and steppe regions, carbon storage in litter was neglected because of its small magnitude and because a considerable part of the arable lands in this zone have no litter at all. The comparison of carbon storage for the different soil types is presented in Fig. 3. For an evaluation of the annual carbon exchange in the upper soil horizons of ETR we used the model (4)–(8) and corrected for radiocarbon activity in layers of recent soils in accordance with Eq. (9). The results of the 14 C-dating, which had been performed at the Radiocarbon Laboratory of the Institute of Geography of Russian Academy of Sciences (Margolina et al., 1988; Chichagova and Cherkinsky, 1993; Cherkinsky and Goryachkin, 1993), were used as a data basis for our analysis. Since the soil samples were not uniformly thick, we interpolated the data onto 10-cm vertical layers. The dependence of the turnover rate m on the soil depth h was approximated by one of the following types of function: • exponential, m(h) = ea+bh , • power, m(h) = ahb , where parameters a and b were validated on the basis of the experimental data. The function with the highest level of
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e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
Fig. 3 – Soil carbon storage in kgC/m2 for 1 m soil layer shown separately for organic and mineral layers. Soil type number is in accordance with Table 1. The type number roughly corresponds to a north–south transect of the European territory of Russia.
authenticity was chosen for every soil type. For example, the more adequate function for sod-podzolic soils (Eutric Albeluvisols) was a power function (Fig. 4), which is more suitable as a description of the sharp decrease of turnover rate with depth. On the other hand, we used an exponential model for Chernozems because of the smoother decline of turnover rate with depth (Fig. 4).
3.2.
Results
The spatial distribution of soil carbon storage is presented in Fig. 5a. All values were accounted for the upper 1 m of soil surface. In some bogs the peat storage extends to a depth of 12 m. Nevertheless, we did not take into account the carbon lying deeper than 1 m, as it is practically not involved in the decomposition process. The hydromorphic soils – Histosols, Gleyic
Albeluvisols – store significant amounts of peat in the organic horizon. The highest storage of about 60 kgC m−2 corresponds to Histosols; more than 90% of this carbon is in a form of peat. In terms of a geographical comparison from north to south, the storage is about 20 kgC m−2 in the northern areas of podbur (Entic Podzols) and tundra-gley (Turbic Cryosol) soils. It decreases to 10 kgC m−2 for podzolic (Dystric Albeluvisols) and sod-podzolic forest soils (Eutric Albeluvisols). Grassland fertile soils, Chernozems, are characterized by much higher humus storages from 20 to 35 kgC m−2 (Table 1). 50–60% of the area of Chernozems is used as an arable land, and 10–15% of Chernozems are used as meadows and pastures. Agricultural lands have a lower carbon content in comparison with soils left undisturbed in protected natural forest and grassland areas. The storage of carbon in natural chernozem soils in the upper 1 m layer could be up to 40–50 kg m−2 (Afanasieva, 1966). Chestnut and semi-desert soils (Kastanozems and Calcisols) in the south-eastern region of Russia have a relatively low soil carbon content of 3–10 kgC m−2 . In total, the analysed territory of about 3.7 million km2 contains 55 Pg of carbon in the upper 1 m of soil. Turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1 ), intermediate for tundra soils (0.25% year−1 ), and low for grassland soils (0.02–0.08% year−1 ) with exception of southern Chernozems (0.32% year−1 ), see Fig. 5b. In the soil layer at 20–100 cm depth the turnover rates were much lower for all soil types (0.01–0.06% year−1 ) except for peat bog soils of the southern taiga (0.14% year−1 ), see Table 1 and Fig. 5c. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)), see Fig. 5d. Calculated for the whole area, the annual soil carbon decomposition flux is 77 TgC/year. Let us caution that our approach does not distinguish the active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer and low fluxes of carbon from the soil.
4.
Fig. 4 – Profiles of turnover rates of soil carbon (10−3 year−1 ) for mineral layers of podzolic, sod-podzolic (loam), and typical chernozem soils.
Discussion
Our approach is based on direct measurements of 14 C intensity of soil samples. To interpret the results of these measurements, a model for the processes of 14 C accumulation and decomposition in soil (Cherkinsky and Brovkin, 1993) has been applied. The main model assumption is that the 12 C and 14 C in the soil is in an equilibrium state, or in other words, soils are in the final stage of their evolution. An assumption of a steady-state carbon cycle before the pre-industrial era is routinely used in models of the global carbon cycle (Cramer et al., 2001). It is important to compare different methods for estimation of the carbon turnover rate. For example, Basilevich and Shmakova (1986) considered the carbon balance in grass ecosystems. According to their data, the annual net primary production in the steppe zone of Central Russia is 600–900 gC m−2 year−1 in agroecosystems and 1200 gC m−2 year−1 in natural grasslands. In the former type of ecosystem, the biomass is allocated into agricul-
Table 1 – Carbon storages and carbon turnover rates for upper 1 m soil in European Russia N
Total
Area (×103 km2 )
Russian classification
WRB (FAO, 1998)
Podbur Tundra gley Peat bog, northern taiga Gley-podzolic Podzol, northern taiga Bog-podzolic Podzolic Podzol, middle taiga Peat bog southern taiga Sod-podzolic loam Sod-podzolic sand Grey Forest Leached Chernozems Typical Chernozems Ordinary Chernozems Meadows Chernozems Southern Chernozems Chestnut Calcareous Chernozem Semidesert
Entic Podzols Turbic Cryosol Histosols Gleyic Albeluvisols Haplic Podzols Gleyic Albeluvisols Dystric Albeluvisols Haplic Podzols Histosols Eutric Albeluvisols Ferric Podzols Phaeozems Luvic Chernozems Chernic Chernozems Calcic Chernozems Gleyic Chernozems Calcic Chernozems Haplic Kastanozems Calcic Chernozems Calcisols
Carbon storage (kg/m2 )
Turnover rate (×10−3 year−1 ; depth (cm) 0–20
60 110 104 187 199 199 315 178 57 616 245 253 226 86 118 37 122 208 122 269 3711
20.7 17.4 37.0 10.7 10.0 18.5 9.1 10.5 63.8 7.4 5.9 15.8 30.3 31.9 26.0 33.4 19.3 10.3 23.9 2.9
4.4 2.5 1.6 2.0 2.8 4.5 2.1 2.2 8.0 4.3 7.8 1.6 0.7 0.7 0.8 0.4 3.2 1.0 0.7 0.2
Annual C fluxes (g/(m2 year) Tg/year)
20–100 0.29 0.17 0.25 0.08 0.15 0.32 0.15 0.53 1.39 0.20 0.49 0.06 0.10 0.25 0.40 0.13 0.42 0.32 0.23 0.12
59.5 32.4 39.1 14.8 22.4 51.0 12.9 15.6 147.3 18.5 29.9 11.8 10.0 13.0 13.6 7.5 26.0 5.3 8.8 0.6
3.6 3.6 4.1 2.8 4.5 10.2 4.1 2.8 8.4 11.4 7.3 3.0 2.3 1.1 1.6 0.3 3.2 1.1 1.1 0.2
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Soil type
76.7
183
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tural production (400–500 gC m−2 year−1 ) and plant residues (400 gC m−2 year−1 ), while in grassland 400 gC m−2 year−1 are allocated to above-ground litter and 800 gC m−2 year−1 —to the below-ground root mortmass. Average time of decomposition of above-ground litter is 1 year. About 20% of residues forming the fast pools of litter and mortmass have a lifetime of 2–10 years. A small part of them is transformed into humus: in accordance with Gilmanov (1978), the humification rate of plant residues is 2–6% or 20–70 gC m−2 year−1 . The annual exchange rate of humus carbon calculated by radiocarbon data for Chernic Chernozems soils in this area is only 13 gC m−2 year−1 , or 2–4 times lower. There could be two reasons for this discrepancy:
• carbon humification and mineralization rates are not measured directly. Therefore, the expert guess in gC m−2 year−1 could exceed the real humification rate; • in our study, the mainly stable organic carbon part with a very low turnover rate was analysed.
This example suggests that only a minor part of the annual carbon input flux to grass ecosystems is involved in the slow exchange processes in soils. The major part of the carbon rotates in fast active pools. Their fraction in SOM storage in a 1-m layer could be as low as 10–20%. Moreover, most areas of Chernozem soils are used for agricultural purposes and a negative balance of soil organic matter is typical for such regions. In forest ecosystems, carbon storage in living phytomass and mortmass has a decadal lifetime. Harkness et al. (1986) distinguished the soil carbon into “young” and “old” fractions with the age less than 20 years and more than 300 years, respectively. The “young” cycling component comprises about 50% of humus in the top 10 cm of the soil, and less then 5% in deeper horizons. Hahn and Buchmann (2004) found a ratio of active to passive humus of 43–62% in the top layer (up to 20 cm) of different European forest soils. They assumed that all carbon below that is passive. High-latitude ecosystems contain about 25% of the total world soil carbon pool in the permafrost and the seasonally thawed soil layer (Prentice et al., 2001). A recent report
Fig. 5 – Maps of soil carbon traits for analysed territory of European Russia. Both mineral and organic soil layers are accounted for. (A) Soil carbon storage (kgC/m2 ), 0–100 cm. (B) Turnover rate (10−3 year−1 ), 0–20 cm layer. (C) Turnover rate (10−3 year−1 ), 20–100 cm layer. (D) Soil turnover flux, (gC m−2 year−1 ), 0–100 cm.
e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 178–187
185
Fig. 5 – (Continued ).
of Intergovernmental Panel on Climate Change suggests that “migration of boreal forest northward into tundra would initially lead to an increase in carbon storage in the ecosystem due to the larger biomass of trees than of herbs and shrubs, but over a longer time (e.g., centuries), changes in soil carbon would need to be considered to determine the net effect” (Denman et al., 2007). Results of our study indicate that SOM has faster turnover times in forests than in tundra ecosystems. This could be explained not only by generally higher temperatures in forest ecosystems, but also by changes in soil type. This is in line with conclusions by Goryackin et al. (2000) that an essential enhancement of the humus decomposition rate in high latitudes in Eurasia is possible in the case of a noticeable northward shift of the treeline. For the southern forest boundary our results for soil carbon cycling indicate a possibility of behaviour opposite to that on the taiga-tundra boundary. Projections of precipitation changes over land in 21st century are equivocal, but in general they suggest an increase in continental aridity. In the case of drier climate and a reduction of forest cover, forest soils could be replaced with grassland soils with a slower turnover.
This conclusion, in principle, is supported by geological evidence from the past. Alexandrovsky and Chichagova (1998a,b) reported a study of buried Chernozem soils in European Russia which were formed during the mid-Holocene climatic optimum and replaced later by forest soils because the climate became cooler and wetter. A similar Holocene soil dynamics was reported by Bork et al. (1998) for a site in Germany. However, a direct analogue with the Holocene period is inappropriate because anthropogenically-induced, decadal-scale climate change is much faster than the slow, millennial-scale climate dynamics in the Holocene. Besides, vegetation cover in this region is fully controlled by agricultural practice, and future formation of fertile natural grassland is very unlikely. However, with changes in vegetation cover, the soil type – and carbon cycling – will be changed as well. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, the effect of soil type changes, as well the clear evidence from 14 C measurements that most of soil organic carbon has a millennial time scale, have basically been neglected in
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the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond.
Acknowledgements This study, performed during the early 1990s when all the authors were working in scientific institutions in Moscow, was strongly influenced and encouraged by Prof. Yuri Svirezhev, a leader of the international school in global biospheric modelling. We will always remember him as an outstanding scientist with an extraordinary gift to see simplicity in very complex processes and a tremendous ability to synthesise knowledge from very different scientific disciplines.
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