Projections of changes in grassland soil organic ... - Wiley Online Library

European Journal of Soil Science, April 2013, 64, 229–238

doi: 10.1111/ejss.12014

Projections of changes in grassland soil organic carbon under climate change are relatively insensitive to methods of model initialization N . S e n a p a t i a , P . S m i t h b , B . W i l s o n a,c , J . B . Y e l u r i p a t i b , H . D a n i e l a , P . L o c k w o o d a & S . G h o s h a,d a

School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia, b Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, Aberdeen AB24 3UU, UK, c NSW Office of Environment and Heritage, PO Box U221, Armidale, NSW 2351, Australia, and d Centre for Urban Greenery and Ecology, National Parks Board, Singapore 259569, Singapore

Summary Model initialization in soil organic carbon (SOC) turnover models has often been described as a crucial step in making future projections. Model initialization by the spin-up of pools of SOC (model equilibrium run) has been questioned, because equilibrium has to be assumed. Measured SOC pools are independent of model assumptions and are thought to reflect better real site conditions. It has been suggested that model initialization with measured SOC fractions could provide an advantage over model spin-up of SOC pools. In this study we tested this suggestion in relatively undisturbed native grasslands in Australia. We tested the Rothamsted SOC turnover model (RothC) under climate change at 12 sites with three different initialization methods, viz. model initialization with (i) spin-up of model pools with inert organic matter (IOM) pool size calculated from a regression equation, (ii) spin-up of model pools with measured IOM and (iii) all pools estimated from measured fractions. Averaged over the sites and initialization methods, maximum absolute variations (absolute differences in projected SOC stocks expressed as a percentage of initial 2008 SOC stocks) as well as averaged absolute variations throughout the projection period were very small (2.2 and 1.6%, respectively). Averaged across the sites, there were no significant differences in projected grassland SOC stocks under climate change after 93 years of simulation with model initialization by different methods and averaged absolute variation was only 1.6% across initialization methods. These findings suggest that in a relatively undisturbed land-use system such as native grassland, projections of SOC under climate change are relatively insensitive to the model initialization method.

Introduction Grasslands are one of the most widespread terrestrial ecosystems, covering approximately 40% of the global land surface, and containing the largest share (39%) of terrestrial soil carbon (C) stocks (about 580 Gt C) (White et al., 2000). Any change in storage in this large grassland soil C reserve would have a substantial and long-lived effect on global C cycles (Parton et al., 1995). Climate change will be a key driver of change in soil C over the 21st century, and estimates of changes in grassland soil C stocks over the next century are of critical importance (Smith et al., 2005). In order to predict climate change impacts on soil organic carbon Correspondence: N. Senapati. E-mail: [email protected] Received 1 April 2012; revised version accepted 5 November 2012 © 2013 The Authors Journal compilation © 2013 British Society of Soil Science

(SOC), many turnover models have been developed. Despite their diversity, most of the models have the same basic structure in that they divide SOC into multiple conceptual pools with varying inherent turnover rates governed by first-order rate constants modified by climatic and edaphic reduction factors (Falloon & Smith, 2000; Smith et al., 2002). The Rothamsted carbon turnover model (RothC) (Coleman & Jenkinson, 1996) is one of the more notable among these and is being widely used for arable, forest and grassland soils (Smith et al., 2005, 2006). In this model, SOC is divided into four active pools of decomposable plant material (DPM), resistant plant material (RPM), microbial biomass (BIO) and humified organic matter (HUM) and one inert organic matter (IOM) pool. All these pools, except IOM, decompose by firstorder decay constants (year−1 ) of 10 for DPM, 0.3 for RPM, 0.66 for BIO and 0.02 for HUM (Coleman & Jenkinson, 1996).

229

230 N.Senapati et al. A major limitation of these models is that these various SOC pools are based on qualitative concepts rather than measureable entities and often do not correspond to measureable fractions (Smith et al., 2002). Thus, the division of SOC into different pools introduces an initialization problem (Falloon & Smith, 2000). Model initialization has been described as the most crucial part of simulation study as it can influence whether model-predicted soil C will increase or decrease (Falloon & Smith, 2000; Carvalhais et al., 2008; Hashimoto et al., 2011a). Incorrect or flawed initialization of soil C pools potentially leads to incorrect assessment of inter-annual variability, and can also produce fallacious trends in output as the state variables drift back towards the model ideal (Yeluripati et al., 2009). The usual approach to solve the initialization problem is to achieve the initial SOC pool distribution by a ‘spin-up’ run of the model, that is a run of the model over several hundreds to thousands of years to find equilibrium/steady state SOC, assuming initial SOC and its distribution among the model pools are at equilibrium/steady state with the current land-use and climatic conditions (Smith et al., 2005; Coleman & Jenkinson, 2008). The observed soils, however, may not be at equilibrium because of disturbances such as fire, erosion, land-use and management changes (Wutzler & Reichstein, 2007). The equilibrium/steady state assumption for the ecosystem C cycle has

been challenged (Cannell & Thornley, 2003; Ludwig et al., 2010) and its limitation in modelling approaches emphasized (Wutzler & Reichstein, 2007). Another significant uncertainty in spin-up runs is the initial estimate of slowly decomposable organic C or inert organic matter (IOM) (Falloon & Smith, 2000). The use of measured SOC has the advantage that the processes that are ignored in the model, but which influence SOC, are taken into account in SOC partitioning. Thus measured fractions better reflect the real site-specific conditions under which SOC is accumulated (Zimmermann et al., 2007; Xu et al., 2011). Moreover, measured fractions are determined independently of the model. Once the SOC pools are measured by a reliable fractionation method, model initialization issues can be solved and model performance can be improved (Leifeld et al., 2009). One such SOC fractionation method, which potentially relates measured SOC fractions to RothC pools, was proposed by Zimmermann et al. (2007). To test whether model projections of SOC were sensitive to the model initialization method, we used data from 12 native grassland sites to run the SOC turnover model RothC to project changes in SOC under climate change. Soils were fractionated by using the methods of Zimmermann et al. (2007) and three different methods of model initialization were used, viz. model initialization with (i) spin-up of model pools with inert organic matter (IOM) pool size calculated from a regression

Table 1 Site characteristics of 12 grassland sites across the northern slopes and plains of NSW, Australia Native grassland site

Site No

Sub-region

Latitude ◦

Longitude ◦

Time under the regime (year)

Soil type

Clay content / % Management

1

Cryon Plains

30 1’16”S

148 28’15”E

> 100

Vertosols

64.0

2

Cryon Plains

29◦ 57’34”S

148◦ 21’24”E

> 100

Vertosols

58.3

3 4

Cryon Plains Doreen Plains

30◦ 0’21”S 30.6◦ 45”S

148◦ 20’50”E 149◦ 13’44”E

> 150 > 100

Vertosols Vertosols

51.3 59.4

5

Doreen Plains

30◦ 10’76”S

149◦ 12’6”E

> 100

Vertosols

50.2

6

Doreen Plains

30◦ 6’34”S

149◦ 12’52”E

> 150

Vertosols

60.6

7

29◦ 35’24”S

149◦ 9’38”E

> 100

Vertosols

53.7

29◦ 35’20”S

149◦ 9’38”E

> 150

Vertosols

51.3

29◦ 29’36”S

149◦ 14’33”E

> 150

Vertosols

54.6

Never grazed, fertilized or disturbed

28◦ 40’26”S

150◦ 20’24”E

> 100

Vertosols

52.3

11

Lower Gwydir Flood Plains Lower Gwydir Flood Plains Lower Gwydir Flood Plains Boomi Whalan Flood plains Krui Plains

Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Never grazed, fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Never grazed, fertilized or disturbed

29◦ 2’2”S

150◦ 10’16”E

> 100

Vertosols

40.0

12

Krui Plains

29◦ 0’52”S

150◦ 9’55”E

> 100

Vertosols

40.6

Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed Lightly and periodically grazed, never fertilized or disturbed

8 9 10

© 2013 The Authors Journal compilation © 2013 British Society of Soil Science, European Journal of Soil Science, 64, 229–238

Model initialization effects on SOC projection

231

Table 2 The proportion of carbon assumed to be added to the soil as plant carbon input in each month under native grassland across the sites (note that the study region is in the southern hemisphere) Month

1

2

3

4

5

6

7

8

9

10

11

12

Grassland

0.15

0.15

0.10

0.10

0.10

0.05

0.05

0.05

0.05

0.05

0.05

0.10

equation (Falloon et al., 1998), (ii) spin-up of model pools with measured IOM and (iii) all pools estimated from measured fractions.

Materials and methods Soil data Soil samples from 12 native grassland sites across the northern slopes and plains of New South Wales (NSW), Australia (latitude 148◦ E to 151◦ E and longitude 29◦ S to 31◦ S), were derived from the NSW Land and Soil Condition Monitoring Program (NSW LSCMP) (Chapman et al., 2009a). The region has a summer dominant rainfall with an annual average of between 500 and 700 mm. Average minimum temperatures range from 0 to 9◦ C in the winter and 12 to 20◦ C in summer, while average maximum temperatures range from 12 to 20◦ C and 27 to 33◦ C, respectively. Among the different native grasses, wallaby grass (Austrodanthonia setacea (R.Br.) H.P.Linder), kangaroo grass/red grass (Themeda australis (R.Br.) Stapf ), spear grass (Heteropogon sp.) and needle grass (Triraphis mollis R.Br.) are common (ANBG, 2012). The dominant soil type in the region is Vertosol, with a range of other soil types such as Chromosols, Sodosols, Kandosols and Tenosols that are less common (Isbell et al., 1997). These soils are equivalent to Vertisols, and Alfisols or Ultisols or Aridisols, respectively (Soil Survey Staff, 1999). The details of the 12 grassland sites are summarized in Table 1. At each site, 10 soil cores were collected from within a 25 × 25 m sample plot, using a random ‘latin square’ sampling design, which ensured complete coverage of the sample area while retaining a random element to the sampling (Chapman et al., 2009b). Soils were sampled in early 2008 with a manual coring device of 50 mm diameter, driven into the soil to a depth of 30 cm. To reduce field variability and replication, and to get enough soil for analysis for each site, nine cores were selected from the available 10 and were bulked in groups of three to provide three replicate samples for each site. The mean of three replications within each site was used as site estimate.

Soil samples were stored in cool (< 5◦ C) and dark conditions until they could be processed. Samples were dried at 40◦ C for 48 hours (after Wilson et al., 2009) and then crushed to pass a < 2 mm sieve. Each sample was then analysed for SOC fractions by using a modified physical and chemical procedure as proposed by Zimmermann et al. (2007). In brief, 30 g of soil (< 2 mm) was added to 150 ml water and dispersed with a calibrated ultrasonic probe-type (Branson, Sonifer 250, USA) with an output energy of 22 J ml−1 . This dispersed suspension was then wet sieved over a 63 μm aperture sieve until the rinsing water was clear. The fraction > 63 μm, containing the sand fraction and stable aggregates (S + A) together with particulate organic matter (POM), was dried at 40◦ C and weighed. The suspension < 63 μm was filtered through a 0.45-μm aperture nylon mesh and the material > 0.45 μm (silt and clay fraction, s + c), was dried at 40◦ C and weighed. The amount of dissolved SOC (DOC) was measured from an aliquot of the filtrate < 0.45 μm. Instead of sodium polytungstate, sodium iodide (NaI) was used to separate POM by stirring the fraction > 63 μm with NaI at a density of 1.8 g cm−3 . The mixture was centrifuged at 1000 g for 15 minutes and the light fraction was decanted. Both fractions (POM and S + A) were washed several times with deionized water to remove all NaI, dried at 40◦ C and weighed. A chemically resistant C fraction (rSOC) was extracted from the s + c fraction by sodium hypochlorite (NaOCl) oxidation with 6% (60 g l−1 ) NaOCl at room temperature. One gram of s + c fraction was oxidized for 18 hours at 25◦ C with 50 ml of 6% NaOCl adjusted to pH 8 with concentrated HCl. The oxidation residue was centrifuged at 1000 g for 15 minutes, decanted, washed with deionized water and centrifuged again. This oxidation step was repeated twice. The C contents of solid fractions were measured by dry combustion with a LECO carbon analyser (TruSpecR CN Analyser, LECO Corporation, St Joseph, Michigan, USA) and of liquid samples (DOC) by thermal oxidation with a liquid analyser (Sievers InnovOx Laboratory Total Organic Carbon Analyser, GE Analytical Instruments, Boulder, Colorado, USA). Soil C stocks

Table 3 Equilibrium soil carbon inputs in 2008 when measured inert organic matter was used in the model spin-up run in 12 grassland sites across the northern slopes and plains of NSW, Australia Grassland site

Mean SE

Site 1

Site 2

Site 3

Site 4

Site 5

Site 6

Site 7

Site 8

0.6779 0.02324

0.8801 0.06949

0.7006 0.01269

Equilibrium soil carbon input / t C ha−1 year−1 1.0190 1.5638 0.9508 1.0465 0.9616 0.03763 0.02014 0.06188 0.01020 0.03717

Site 9

Site 10

Site 11

Site 12

0.7783 0.01616

1.2901 0.03574

1.3595 0.06668

0.9953 0.06939

Mean, mean over site replication; SE, standard error from site replication. © 2013 The Authors Journal compilation © 2013 British Society of Soil Science, European Journal of Soil Science, 64, 229–238

232 N.Senapati et al.

(b) 3

90 80

80

1.5 40 1

30 20

0.5

2

60 50

1.5 40 1

30 20

70

Fraction of total SOC / %

50

2.5 70


60


2


2.5 70

2

50 40 1

DPM

BIO

RPM

HUM

30 20

0.5

10 0

60

1.5

0.5

0

90

80

2.5 Fraction of total SOC / %

(c) 3

90


(a) 3

10 0

10

0

IOM

DPM

BIO

RPM

HUM

0

IOM

0 DPM

BIO

RPM HUM

IOM

Figure 1 Box plots for the RothC model pools (DPM, BIO, RPM, HUM and IOM) as a fraction (%) of total SOC at model initialization under three different methods of model initialization, viz. (a) method 1 (spin-up of model pools using IOM calculated from the Falloon et al., 1998, equation), (b) method 2 (spin-up of model pools using measured IOM) and (c) method 3 (all pools estimated from measured fractions). Note the different scales. Table 4 Initial soil organic carbon pools (2008) under three different methods of model initialization averaged across 12 grassland sites in the northern slopes and plains of NSW, Australia Model initialization method

Method 1 Method 2 Method 3 Sites Initialization methods

Soil organic carbon pool / t C ha−1 DPM Mean

SE

RPM Mean

SE

BIO Mean

0.12 0.12 0.13

0.010 0.009 0.104

6.16 6.06 5.43

0.521 0.471 0.564

0.69 0.68 0.70

4.54 × 10−6 0.3480

1.08 × 10−6 0.0951

SE 0.058 0.052 0.055 P value

HUM Mean

SE

IOM Mean

SE

26.55 26.13 26.73

2.224 2.003 2.109

2.96 3.48 3.48

0.288 0.641 0.641

< 2.0 × 10−16 0.2939

< 2.0 × 10−16 0.2920

6.99 × 10−9 0.8208

Mean, mean over sites (number of sites = 12); SE, standard error from sites (number of sites = 12). Method 1: spin-up of model pools using inert organic matter (IOM) calculated from the Falloon et al. (1998) equation. Method 2: spin-up of model pools using measured IOM. Method 3: all pools estimated from measured fractions.

to a depth of 30 cm were calculated as a product of measured C concentration (%), bulk density (g cm−3 ) and soil depth (cm), and expressed in units of t C ha−1 . The SOC pools were then derived from the measured SOC fractions (after Zimmermann et al., 2007). The sum of measured C in POM and DOC fractions corresponds to the sum of DPM and RPM pools of RothC; and the sum of measured C in the S + A fraction and s + c fraction without the rSOC fraction (s + c – rSOC) corresponds to the sum of BIO and HUM pools. The measured C in the rSOC fraction is directly associated with the IOM pool. The sum of C in POM and DOC fractions was split into DPM and RPM pools using the ratio of DPM:RPM obtained by RothC under equilibrium conditions for each individual site. The same procedure was also used to separate the sum of C in S + A and s + c – rSOC fractions into BIO and HUM pools. All the pools converted from the measured C fractions are hereafter referred to as ‘measured SOC pools’.

Climatic data Future climate change scenario for study sites for the period 2012–2100 were created by using OzClim developed by CSIRO Atmospheric Research and the International Global Change Institute (http://www.csiro.au/ozclim) (Ricketts & Page, 2007). This down-scaling web-based tool predicts climate change for the whole 21st century across Australia in 5-year increments, using the patterns of change extracted separately from the outputs of each of 23 individual general circulation models (GCMs). These GCMs are those used for preparation of the IPCC 4th Assessment Report. Patterns of change consist of grids of slope of a given variable. At each grid point, a time series of the value of a climate variable at a particular month and year has been regressed against estimated global warming, computed for a given GCM model and year. One of the best performing (across Australia) GCMs (CSIRO Mk3.5, CSIRO & Bureau of Meteorology, 2007) was selected for the present study with its medium climate



233

(b) Air temperature 40

Rainfall 100

2008 Temperature

2008 Rainfall

2100 Temperature

2100 Rainfall

80 Rainfal / mm

Temperature / °C

30

20

60

40

10 20

0

0 1

2

3

4

5

6

7

8

9

10

11

12

1

Month

2

3

4

5

6

7

8

9

10

11

12

Month

Figure 2 (a) Average monthly air temperature and (b) monthly rainfall in 2008 and corresponding projected values in 2100 as predicted by CSIRO Mk3.5 when forced with A2 emission scenario.

sensitivity forced with a central emission scenario A2 (provincial enterprise), as reported in the IPCC Special Report on Emissions Scenarios (SRES) (Nakićenović et al., 2000). Monthly climate change scenarios for the study sites were downloaded as relative change from the baseline for the whole 21st century in 5-year increments. Absolute monthly climate scenarios for the period 2012–2100 were developed by adding the monthly climate change to the baseline. Baseline is the average climate over 30 years (1975–2004). Baseline climate data for the study sites were obtained from the Australian Bureau of Meteorology, SILO patch point dataset (Bureau of Meteorology, 2011). Open panevaporation for different climate change scenarios was calculated by using a ‘synthetic pan evaporation regression model’ after its calibration with the baseline climate, as proposed by Rayner (2005) for the Australian environment.

Model initialization To project changes in SOC stocks in response to climate change for the period 2008–2100, RothC was initialized by three different methods separately as follows.

Method 1: spin-up of model pools using IOM calculated from the Falloon et al. equation. The amount of IOM in this initialization method was calculated from total SOC data with the Falloon et al. (1998) equation and hereafter is referred as Falloon-IOM. Then the model was run to equilibrium under constant environmental conditions by providing the amount of IOM following the method described in Smith et al. (2005). The constant climatic conditions were taken to be the average of climatic data from 1975 to 2004. RothC is known to be relatively insensitive to the distribution of C inputs through the year (Smith et al., 2005); the proportions of plant material added

to the soil in each month were set to describe the pattern of inputs for a typical Australian native grassland (note that the study region is in the southern hemisphere), as given in Table 2. Initially, the total plant input was obtained by summing these proportions to provide the first point of initialization. Plant cover was assumed to occur all year round in grassland. After the first equilibrium run (10 000 years) the annual plant addition was adjusted and redistributed through the months as in Table 2. Thus the equilibrium run was repeated to match the simulated total SOC with the measured data. At the end of the spin-up run, the model partitioned total SOC into pools (DPM, RPM, BIO and HUM, except IOM, which was provided), which are hereafter referred to as ‘model spin-up SOC pools’. RothC was then initialized with Falloon-IOM and the model spin-up SOC pools.

Method 2: spin-up of model pools using measured IOM. In this method, the amount of IOM was directly measured by chemical oxidation of the s + c fraction (NaOCl oxidation, after Zimmermann et al., 2007) as discussed in the ‘Soil data’ section. The model spin-up run was made by using the same method above but with measured IOM instead of Falloon-IOM. RothC was then initialized with measured IOM and model spin-up SOC pools. Method 3: all pools estimated from measured fractions. The model was initialized with all pools estimated from the fractionation method of Zimmermann et al. (2007) as discussed in the ‘Soil data’ section.

Model run Equilibrium annual plant input C, as obtained by the spin-up run, was used to run the model for the future projection, thus plant inputs were kept the same as in the present equilibrium. Leifeld


234 N.Senapati et al. et al. (2009) indicated that the sizing of the IOM pool by means of chemical fractionation (NaOCl oxidation, for example) is better than using the Falloon equation. Therefore, the equilibrium C input of method 2, where measured IOM was used in the spin-up run, was selected (Table 3), and the same annual plant addition was used to run the model for all three initialization methods, to estimate the initialization effect. After initializing RothC in 2008 using three methods separately, the simulations were continued from 2008 to 2100 using the same plant addition, but under a projected climate change scenario as described in the ‘Climate data’ section.

Statistical analysis A general linear model (GLM) was fitted to test for significant differences in initial SOC pools between the three different methods of model initialization (fixed effects), assuming 12 grassland sites as replicate blocks (thus, SOC pools at different sites represent sites + initialization methods; number of total observation = 12 sites × three initialization methods = 36). A similar model was used to check for any significant differences in future projected SOC stocks at the end of projection period 2008–2100 between different model initialization methods across the sites. All the statistical analysis was conducted with statistical package R-version 2.11.1 (R Development Core Team, 2010). Data for initial SOC pools and projected SOC stocks were tested for normality and homogeneity of variance within treatments across blocks (sites) using the Shapiro–Wilk normality test and Bartlett’s test, respectively, with a critical level of P < 0.05. Data (IOM) were log-transformed (log10 ) when there were deviations from the assumptions of normality and equal variances.

Results SOC pools during model initialization using three different initialization methods Figure 1 shows the distribution of SOC pools under the three different methods of model initialization. Averaged over the three initialization methods and sites, the maximum portion of total SOC was in the active pools of HUM (72.9%) and RPM (16.3%) whereas the other two active pools of DPM (0.3%) and BIO (1.9%) together accounted for a smaller share (2.2%). The inactive IOM contained 8.6% of the total SOC. RPM and HUM pools constituted the maximum share (approximately 98%) of the active SOC pools. Table 4 shows the absolute quantities of initial (2008) SOC pools under the three different model initialization methods averaged across the 12 grassland sites. There were strong site effects (P < 0.001) on initial SOC pools (Table 4). This is inevitable because of wide variability in, for example, climate, soils (clay content), annual soil C inputs and total SOC stocks across the 12 grassland sites (Tables 1, 3). However, when averaged across the sites, there was no significant difference in any SOC pool between the three different initialization methods (P > 0.05) (Table 4).

Influence of methods of initializing RothC on projected grassland soil carbon Across the sites, CSIRO Mk3.5 predicted an increase in average monthly air temperature of 5.6◦ C, and a decrease in annual rainfall of 39% under the A2 emission scenario by 2100 when compared with 2008 (Figure 2). Figure 3 shows initial and projected total SOC stocks throughout the simulation period (2008–2100) at all the sites. Averaged over the sites, Table 5 shows initial (2008) measured total SOC stock and projected SOC stocks for 2100 when RothC was initialized by the three different methods separately. Grassland total SOC stocks in 2008 across the sites ranged from 23.8 to 58.9 t C ha−1 (Figure 3). When direct climate impacts on SOC were considered alone, averaged over the methods of model initialization, RothC predicted an average fall of 10–11% from 2008 SOC stocks across the sites, resulting from climate change, by 2100 (Table 5 and Figure 3). Averaged over the methods of model initialization, there were significant (P < 0.001) site effects on projected SOC stocks at the end of the projection period in 2100 after 93 years of projection (Table 5). This site effect is obvious because of the large variability in the initial (2008) SOC stocks and SOC pools, climate, soils (clay content), annual soil C inputs, etc., across the grassland sites (Tables 1 and 3, Figure 3). However, averaged across all sites, there were no significant differences in projected SOC stocks between the different methods of model initialization at the end of the projection period 2008–2100 (P > 0.05) (Table 5). Averaged over the initialization methods and sites, maximum and average absolute variations (absolute differences in projected SOC stocks expressed as a percentage of initial 2008 SOC stocks) throughout the simulation period (2008–2100) resulting from the different initialization methods in the projection of SOC stocks under climate change were small (2.2 and 1.6%, respectively) (Figure 3). At the end of the projection period, after 93 years, the averaged absolute variation in the projection of grassland SOC resulting from the different methods of model initialization was also small (1.6%) (Figure 3).

Discussion Projected increases in mean air temperature are expected to accelerate SOC decomposition and loss of SOC in the future if soil moisture is not a limiting factor (Smith et al., 2005, 2006). The direct impact of climate on grassland SOC stocks of the present study (10–11% reduction over 93 years) was comparable with other similar studies reported by Smith et al. (2005) and Xu et al. (2011), who projected a loss of 6–10% of the European grassland SOC stocks over 90 years (1990–2080) depending on the emission scenarios, and a loss of 2–6% of grassland SOC stocks in Ireland across different emission scenarios over 40 years (2021–2060). It was suggested by Skjemstad et al. (2004) from their simulation study with RothC that model performance could be improved by reducing the default decomposition rate constant of RPM (kRPM ) from 0.3 to 0.15 year−1 under Australian



235

Table 5 Measured SOC stock in 2008 and projected SOC stocks under climate change in 2100 averaged across 12 grassland sites in the northern slopes and plains of NSW, Australia, under three different methods of model initialization Initial grassland SOC stock / t C ha−1

Measured SOC stock in 2008

Predicted grassland SOC stock in 2100 / t C ha−1 Mean

SE

Model initialization method

Mean

SE

36.5

3.10

Method 1 Method 2 Method 3

32.4 32.7 33.0

2.65 2.82 2.86

Sites

Initialization methods

< 2.0 × 10−16

0.1287

P value

Mean, mean over sites (number of sites = 12); SE, standard error from sites (number of sites = 12). Method 1: spin-up of model pools using inert organic matter (IOM) calculated from the Falloon et al. (1998) equation. Method 2: spin-up of model pools using measured IOM. Method 3: all pools estimated from measured fractions.

conditions. This suggestion was based on the empirical model fitting of their experimental datasets in RothC under cropped land. However, in another study from Australian cropped land, Liu et al. (2009) did not find any overall model improvement by reducing the default value of kRPM from 0.3 to 0.15 year−1 . Therefore, a default kRPM value of 0.3 year−1 was used in the present projections. For the projection of SOC under climate change, distribution of black/charcoal-C is important (Lehmann et al., 2008). Although at the continental scale, black C comprises 0–82% of total SOC in Australia, its existence is localized, and depends on natural vegetation fire and regular burning of above-ground biomass (Lehmann et al., 2008). Vegetation fire and regular burning of above-ground biomass is uncommon in the native grasslands in the study region, so black C is not expected to dominate and in any case would form part of the measured IOM fraction determined by chemical oxidation of soil with NaOCl. Our average measured IOM was 8.6%, which included the contribution from black C. This is less than the average proportion of black C (20.4%; n = 452) reported from Australian soils by Lehmann et al. (2008). In order to apply a model for projection studies, model initialization is required (Hashimoto et al., 2011a). The most common method for initialization is model spin-up. Initial carbon cycle steady state is the main assumption behind model spinup runs. However, many authors have challenged this common assumption in modelling (Cannell & Thornley, 2003; Carvalhais et al., 2008). In the real world, periodic disturbances usually prevent ecosystems reaching a truly steady state. The steady state of natural ecosystems can be disturbed by human influences such as change in land-use or management or by natural events or extreme climate such as forest fire or drought. Once disturbed, it may take many years to attain steady state again. Many researchers have developed different techniques for model initialization, particularly for ecosystems that have various disturbances and management changes histories, and are far from real steady state (Wutzler & Reichstein, 2007; Yeluripati et al., 2009; Hashimoto et al., 2011a). However, a carbon-cycle steady state will most

probably be achieved where ecosystem disturbances are at a minimum, as in native grassland. In the present study, spin-up of initial SOC pools and model independent measurements were very similar. This result supports our assumption of a carbon cycle steady state in the model spin-up in native grasslands. In the present study, soil C inputs were derived from an inverse fitting of RothC to initial SOC levels. Net primary productivity (NPP) in native grassland sites was not measured independently. Although a part of NPP is lost before incorporation into the soil as C inputs, a number of studies have shown that estimated C inputs from inverse modelling with RothC and independent NPP estimates (for example from MODIS) match well (Hashimoto et al., 2011b). In our study, changes in NPP from a change in future climate were not included, as we wanted to focus on SOC impacts rather than total ecosystem carbon impacts, and we are focussing on SOC model initialization issues. Increased future temperature, CO2 fertilization and N deposition might increase NPP of grassland, whereas extreme climatic events and decreased rainfall could reduce grassland NPP (Holden & Brereton, 2002). Again, future improvement in technology such as the introduction of legumes to improve native grassland, and management practices including the application of fertilizer, could also influence future NPP under grassland. These changes are very uncertain and also difficult to incorporate in the calculation of NPP. It is also uncertain whether changes in NPP will be proportional to the change in C input in soil, and thus the influence of NPP should therefore be regarded as the maximum possible impact, with zero increase in soil C inputs as a minimum effect (Smith et al., 2005). The study showed that, averaged across the sites, there were no significant differences in projected grassland SOC stocks under climate change after 93 years with model initialization using different methods. The very similar model spin-up pools and measured SOC pools in these relatively undisturbed native grassland systems would seem to support the use of model spin-up SOC pools for model initialization. Xu et al. (2011) found some diffences in projection of SOC stocks with model initialization by spin-up pools compared with measured SOC


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Figure 3 Projected grassland SOC stocks (mean over site replication) under climate change throughout the projection period 2008–2100 with three different methods of model initialization in 12 grassland sites across the northern slopes and plains of NSW, Australia. Note the different scales.

pools in a similar simulation study of 40 years with RothC in Irish grasslands. However, grasslands in the Xu et al. (2011) study had relatively disturbed histories and the question remained unanswered whether the differences in their results were significant.

In another simulation study of 18 years across arable land and managed grassland with the CENTURY model in England and Wales, Foereid et al. (2012) reported insensitivity of the model to the methods of model initialization in projection of SOC under climate change as long as the initial carbon content is correct.



They suggested that model initialization with steady state pool structure might be adequate for projection under climate change. Averaged over the sites and initialization methods, maximum absolute variations (absolute differences in projected SOC stocks expressed as a percentage of initial 2008 SOC stocks), as well as averaged absolute variations throughout the projection period (93 years), were negligible in our study (Figure 3). Averaged over the sites, we found that absolute variation caused by the initialization methods after 93 years was also very small. Our study implies that for studies of the impact of climate on SOC stocks in relatively undisturbed land-use systems such as native grassland, model projections are relatively insensitive to the methods of model initialization, and simple model spin-up of SOC pools is as reliable for model initialization as more labour intensive and expensive soil fractionation methods.

Acknowledgements We would like to thank New South Wales Office of Environment and Heritage for providing soil samples from the state-wide Soil Monitoring Program. We would like to thank the Universities of New England (Australia) and Aberdeen (Great Britain) for project support. We also would like to thank Terry Koen, New South Wales Office of Environment and Heritage, for his valuable advice on statistics.

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