Estimating the carbon stock of a blanket peat region using a peat ...

CATENA-01607; No of Pages 11 Catena xxx (2011) xxx–xxx

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Estimating the carbon stock of a blanket peat region using a peat depth inference model Nicholas M. Holden, John Connolly ⁎ UCD Bioresources Research Centre (Biosystems Engineering), UCD School of Agriculture, Food Science and Veterinary Medicine, Agriculture and Food Science Centre, University College Dublin, Belfield, Dublin 4, Ireland

a r t i c l e

i n f o

Article history: Received 21 September 2010 Received in revised form 9 February 2011 Accepted 10 February 2011 Available online xxxx Keywords: Peat depth model Soil organic carbon Soil organic carbon stock Blanket bog

a b s t r a c t At the global scale peatlands are an important soil organic carbon (SOC) pool. They sequester, store and emit carbon dioxide and methane and have a large carbon content per unit area. In Ireland, peatlands cover between 17% and 20% of the land area and contain a significant, but poorly quantified amount of SOC. Peatlands may function as a persistent sink for atmospheric CO2. In Ireland the detailed information that is required to calculate the peatland SOC pool, such as peat depth, area and carbon density, is inconsistent in quality and coverage. The objective of this research was to develop an improved method for estimating the depth of blanket peat from elevation, slope and disturbance data to allow more accurate estimations of the SOC pool for blanket peatlands. The model was formulated to predict peat depth at a resolution of 100 ha (1 km2). The model correctly captured the trend and accounted for 58 to 63% of the observed variation in peat depth in the Wicklow Mountains on the east coast of Ireland. Given that the surface of a blanket peatland masks unknown undulations at the mineral/peat interface this was a successful outcome. Using the peat depth model, it was estimated that blanket peatland in the Wicklow Mountains contained 2.30 Mt of carbon. This compares to the previously published values ranging from 0.45 Mt C to 2.18 Mt C. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Peatlands are an important, global soil organic carbon (SOC) pool (Gorham, 1991). Northern peatlands contain ~33% of SOC (Gorham, 1991; Turunen et al., 2002), and make significant contributions to carbon dioxide (CO2) fluxes between the land and atmosphere (Clymo et al., 1998; Tolonen and Turunen, 1996). Peatlands can function as a persistent sink for atmospheric CO2, (Clymo et al., 1998; Tolonen and Turunen, 1996; Waddington and Warner, 2001). However, due to their spatial and temporal variability, specific contributions to national carbon balances are difficult to quantify (Arneth et al., 2002; Bubier et al., 1998; Griffis et al., 2000; Kuhry and Vitt, 1996). As peatlands cover between 17% and 20% of the land area in Ireland, it is necessary to quantify the national peat resource for Ireland. Recent efforts focused on deriving an accurate peatland map (Connolly and Holden, 2009; Connolly et al., 2007). To date, field survey (Hammond, 1979), aerial photography (Cruickshank and Tomlinson, 1990; Vitt et al., 2000), remote sensing (Brossard et al., 2000; Milton et al., 2005; O'Sullivan, 1994) and GIS-techniques (Connolly and Holden, 2009; Connolly et al., 2007; Talkkari and Nevalainen, 2003) have been employed to estimate peatland extent. The modelling of peat depth is a necessary task because depth data are sparse. Turunen et al. (2002) used mean peat depth data calculated from

⁎ Corresponding author. Tel.: +353 1 716 7331; fax: +353 1 716 7415. E-mail address: [email protected] (J. Connolly).

the third National Forest Inventory of Finland to calculate an average longterm apparent rate of carbon accumulation (LORCA). In the Western Siberian Lowlands (WSL), Kremenetski et al. (2003) and Sheng et al. (2004) both used unpublished peat depth data to calculate the SOC pools. Kremenetski et al. (2003) found that SOC pool estimates varied with different datasets. Tarnocai et al. (2009) used soils up to 300 cm deep to estimate the SOC pool in the northern circumpolar permafrost region. They had 66 to 80% confidence in their North American data. In Eurasia, however, this dropped to 33 to 66% due to data scarcity. In Northern Ireland, Scotland and Wales, disparate sources of data were used to calculated peat SOC pools (Cruickshank et al., 1998; Scottish Executive, 2007). This paper combines the Derived Irish Peatland Map Version 2 (DIPMV2) (Connolly and Holden, 2009) (the best available estimate of peatland extent in Ireland) with a peat depth inference model and a modified method of estimating peat carbon density from peat depth, to create a more accurate estimate of a blanket peat SOC pool. Peat SOC pools can be estimated using peatland extent and carbon density (Tomlinson, 2005), where carbon density (Dc, t C ha− 1) is Dc = d⋅Db ⋅C⋅10000

ð1Þ

(d = depth (m); Db = bulk density (t m− 3); C = proportion of carbon (t t− 1). The required data include: spatial extent of peat, peat depth, carbon content (CC) and bulk density (Db). In Ireland, such data are inconsistent in quality, coverage and availability (Tomlinson, 2005). Attempts to quantitatively estimate peat SOC pools for Ireland and Northern Ireland have been made. Cruickshank et al. (1998) used

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Please cite this article as: Holden, N.M., Connolly, J., Estimating the carbon stock of a blanket peat region using a peat depth inference model, Catena (2011), doi:10.1016/j.catena.2011.02.002

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N.M. Holden, J. Connolly / Catena xxx (2011) xxx–xxx

Milne and Brown's (1997), CC to peat depth ratio for UK peat for deeper peat in Northern Ireland. Eaton et al. (2008), subsequently used it, along with peatland spatial extent from CORINE 2000, and average peat depth values from Hammond (1979) to estimate Ireland's peat SOC pool. Tomlinson (2005) calculated peatland spatial extent largely from the General Soil Map (GSM) (Gardiner and Radford, 1980), disparate data sources and expert opinion to derive CC, Db and average peat depth. In recent years improvements have been made in peatland mapping in Ireland (Connolly and Holden, 2009; Connolly et al., 2007). The objective of this paper is to build on these improvements by presenting a peat depth inference model to estimate the SOC pool of the Wicklow Mountains in eastern Ireland.

summer of 2007. A GIS was used to select transects in the Wicklow Mountains. 621 peat depth sample points were designated at 100 m intervals along the transects and their coordinates uploaded to a GPS. Depth was measured at each sample point, using a 6 m threaded bar. The calibration and test datasets consisted of 241 and 380 randomly selected samples. 2.2. The peat depth inference model The model was formulated to estimate a generalised peat depth at a resolution of 100 ha (1 km2). Three conceptual relationships are defined that are used to infer peat depth, and the model is implemented as a four-step process.

2. Materials and methods 2.1. Field sampling Field sampling was conducted on upland blanket bogs in the Wicklow Mountains, Ireland 53°9′0″N 6°24′0″W (Fig. 1) during the

2.2.1. Step 1. Elevation threshold At a 100 ha scale, it was assumed that a maximum possible peat depth can develop through soil formation (dmax). Based on observations, it is known that the surface of blanket peat is relatively smooth and continuous, in contrast, the basal mineral interface is undulating

Fig. 1. The upland blanket bog resource in Wicklow. After Connolly and Holden, 2009.


N.M. Holden, J. Connolly / Catena xxx (2011) xxx–xxx Table 1 Initial and optimum estimates of model parameter values. Et is the elevation threshold, da is the maximum depth of peat above Et and db is the maximum depth of peat below Et, aa and ab are the fitting parameters in Eq. 3 for above and below Et.

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below (db) the elevation threshold was also defined as calibratable parameters. Thus If E100 b Et then dmax = db else dmax = da :

ð2Þ

Optimum estimate Parameter

Initial estimate

Google Earth

CORINE 2000

Et (m) da (cm) db (cm) aa ab CORINE disturbed CORINE undisturbed

527 229 500 0.20 0.10 0.75 0.00

553 245 712 0.22 0.21 – –

527 185 609 0.20 0.13 0.41 0.00

(peat soil development masks the original surface features). It was assumed that there is a maximum possible depth of peat soil that could have accumulated since peat formation began (Table 1). The most widespread time period for peat initiation was between 5100 and 3100 years BP (Tallis, 1998). Thorp and Glanville (2003) dated charcoal in the basal layer of blanket bog in the Liffey catchment to 6800–6630 years BP. Based on field observations (Fig. 2) it was noted that there was a cut-off in the maximum peat depth with elevation, this elevation threshold (Et) was defined as a calibratable parameter. The lower temperatures that occur above the Et limit vegetation growth and limit peat depth. Grid cells of 100 ha were defined that corresponded to the Irish National Grid and the mean elevation (E100) of each grid cell was calculated from a digital terrain model (DTM) (Mc Ginnity et al., 2005). Maximum depth of peat above (da) and

2.2.2. Step 2. Disturbance It was assumed that disturbance would reduce the depth of peat at any given location. Disturbance includes physical extraction (Chapman et al., 2003; Charman, 2002; Mc Ginnity et al., 2005), shrinkage caused by drainage and subsequent oxidation of peat (Charman, 2002) and erosion (Bradshaw and McGee, 1988). Initially the spatial extent of disturbance was taken from the CORINE 2000 landcover dataset (EPA, 2003). For areas classified as disturbed in CORINE 2000 it was initially assumed that relative proportion of disturbance (D) was 0.75 and for undisturbed areas was 0.00. After field sampling it was noted that the CORINE 2000 dataset for the Wicklow Mountains was quite poor at capturing peatland disturbance. It also only allowed for a binary classification; disturbed and not disturbed. For this reason, disturbance was estimated using high resolution Quickbird imagery (2.4 to 2.8 m) data from Google Earth (GE) (53° N 6° W). A grid of 1800 25 ha cells was created for the Wicklow Mountains in ArcGIS and exported to GE. A single operator then estimated the D value in each cell using standard proportion charts, which give an indication of the percent of area that is disturbed (e.g. Hodgson, 1978), and predefined criteria; proportion of area subject to peat cutting, erosion (i.e. lacking vegetation) and evidence of anthropogenic drainage systems. The operator followed a series of guidelines to ensure that each grid cell was examined as objectively as possible. The resulting disturbance database was exported back to ArcGIS as a raster layer. Examples of three 25 ha areas with 0.1, 0.5 and 0.9 disturbances are presented in Fig. 3. The intact proportion (I) was defined as ð3Þ

I = 1−D:

2.2.3. Step 3. Slope regulation of peat soil depth In general peat soil does not develop on steep slopes N25° (Bellamy, 1986). An examination of the calibration dataset (Fig. 4) indicated that an exponential curve would best capture the relationship between slope and peat soil depth. Slope at a 1 ha resolution was derived from a DTM (Mc Ginnity et al., 2005). Depth at a 1 ha resolution (d1) was estimated from slope (s) using an exponential relationship with a negative fitting parameter (ax), maximum peat depth (dmax) and intact proportion (I): −ax s

di = e

⋅I⋅d max :

ð4Þ

The model was formulated to allow a different value for the fitting parameter for sites above Et (aa) and below (ab). 2.2.4. Step 4. Model output Estimated peat depth was reported as the average peat depth at a resolution of 100 ha (d100), calculated as the average predicted depth in a National Irish Grid cell, based on the number of available samples (n): d100 =

Fig. 2. The relationship between observed peat depth (cm) and 100 ha average elevation (m) in (A) the calibration dataset and (B) the independent test dataset. Vertical dashed line is the critical elevation threshold (Et) and horizontal dashed lines are the maximum peat depths above (da) and below (db) the critical elevation threshold. Line positions reflect the initial parameter values.

∑di : n

ð5Þ

In summary, peat depth was inferred by (1) estimating maximum depth of peat formation at a 100 ha resolution; (2) estimating intact proportion at a 25 ha resolution or alternatively from the CORINE 2000 dataset; (3) estimating depth at a 1 ha resolution using an exponential relationship with slope; and (4) calculating a 100 ha average peat depth. The final step was undertaken because it was clear from the outset that it would not be appropriate to model depth at a very fine resolution, thus the output from the model had to be at a resolution appropriate to the model formulation, and consistent with its assumptions and limitations.


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2.3. Model implementation in GIS The model was implemented in ArcGIS using Eq. 4. Each variable was represented by a raster layer in the GIS. Digital data included disturbance data extracted from CORINE 2000 and GE and elevation and slope data extracted from the DTM using Hawth's Tools (Beyer, 2004). A map of peat depth was produced using a raster calculation. A comparison between the model using CORINE 2000 and GE disturbance data was undertaken because the GE method is not practical for larger areas. 2.4. Model testing Each 1 ha field sample was allocated to a 25 ha disturbance cell and a 100 ha Irish National Grid cell. Each 1 ha sample was characterised by:

Fig. 3. Examples of Google Earth images of 25 ha areas of the Wicklow Mountains judged to have (A) 0.1 disturbance, (B) 0.5 disturbance and (C) 0.9 disturbance.

Fig. 4. The relationship between slope (degrees) and peat depth (cm) at the 1 ha scale in (A) the calibration dataset (where aa = 0.2 and ab = 0.1), (B) the test dataset (where aa = 0.22 and ab = 0.21 — the optimum values using Google Earth) and (C) the test dataset (where aa = 0.20 and ab = 0.13 — the optimum values using CORINE 2000). Short dashed line represents the exponential relationship (Eq. 3) for sites above Et and long dashed line represents sites below Et.


N.M. Holden, J. Connolly / Catena xxx (2011) xxx–xxx Table 2 Carbon densities at various peat depths and total C stock in the Wicklow Mountains calculated using the spatial extent of the PDM. Peat depth (cm)

C densitya (t C ha− 1)

# of 100 ha grid cells

C stock (t C)

100 150 200 250 300 350 400 450 500 550 600 650 Total

530 830 1130 1470 1860 2250 2360 3303 3303 3303 3303 3303

139 24 16 13 10 4 5 1 1 2 1 1 217

7367000 1992000 1808000 1911000 1860000 900000 1180000 330300 330300 660600 330300 330300 18999800

a This column of C densities was calculated using several sources of Db data. For peat depth ranging from 0 to 400 cm the densities are extracted from Milne and Brown (1997) and Cruickshank et al. (1998) For peat that is deeper than 400 cm a set value of 0.10 g C cm3 was used from Bennett et al. (1990) Huang (2002) and Clymo (2004).

measured depth (cm); local slope (degrees, s in Eq. 4) estimated from the DTM; local elevation (m) estimated from the DTM; 100 ha average elevation (E100, in Eq. 2) and a disturbance proportion (no units, D in Eq. 3). For initial calibration and testing, the model was implemented as a spreadsheet. It was initially parameterised with best estimates of Et, da, db (Eq. 2), aa and ab (Eq. 4). A mean value of Et was established by an analysis of data in Fig. 2 and was set to 527 m, da was set to 230 m, db to 500 m (which equate to the 95th percentile value for sites above and below Et), aa to 0.2 and ab to 0.2, based on fitting curves by eye (Fig. 4). The value of D for CORINE 2000 disturbed and undisturbed sites was also treated as a calibratable parameter. A series of calibrations was then

Fig. 5. Frequency distribution of proportion of disturbance at 1 ha observation sites (based on 25 ha analysis of Google Earth images) for (A) calibration and (B) test datasets.

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conducted using “Solver” with automatic scaling (the optimisation tool in Microsoft Excel, which uses Generalized Reduced Gradient nonlinear code, (Lasdon and Allan, 2002) with the following target: (i) Pearson's r = 1 with 1 ha data; (ii) gradient= 1 with 1 ha data; (iii) Pearson's r = 1 followed by gradient = 1 with 1 ha data; and (iv) gradient= 1 followed by Pearson's r = 1 with 1 ha data. Pearson's r was calculated as the linear correlation coefficient between the observed peat depth at 1 ha and the predicted peat depth at 1 ha. Gradient was calculated as the slope term for the linear regression between the observed and predicted peat depth at 1 ha. A type I linear regression was used because the error rate in the observed peat depth was assumed to be a small fraction of the predicted error (McArdle, 1988), and this assumption permitted automation of the optimisation procedure. As r approaches 1, the residuals of the best fit line between the observed and predicted peat depth are minimised and as slope approaches 1, the bias is minimised. Other optimisation approaches were considered (e.g. working at 100 ha scale and using statistical sampling of parameters), but these yielded poorer results than the method selected. To evaluate the parameters used gradient and Pearson's r at 1 ha and 100 ha were examined for the calibration dataset. When the best parameter values were found, they were applied to the independent test dataset and the model skill could then be evaluated. 2.5. Application of the peat depth inference model in Wicklow to calculate peat SOC pool The peat depth inference model (PDM) was used to estimate peat depth and SOC content for the Wicklow Mountains. Depth data, modelled by the PDM, ranged from 0 to 611 cm. Peatland spatial extent was obtained from the DIPMV2 (Connolly and Holden, 2009). C density was estimated from the literature. The Cruickshank et al. (1998) extrapolation of Milne and Brown's (1997) C density to depth ratio was

Fig. 6. Observed vs. predicted peat soil depth at the 1 ha scale for (A) the Google Earth test dataset and (B) the CORINE 2000 test dataset.


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it adequately represented the difference in the maximum observed peat depth by altitude seen in the datasets (Fig. 2, Table 1). The maximum peat depth above Et (da) was set to 245 cm and below (db) to 712 cm (Table 1). As the maximum probe depth was 600 cm and in some cases the probe did not reach the mineral interface, the value of 712 cm is consistent with expectation and observation. The value of 245 cm is slightly less than the maximum observation (Fig. 2a). Using CORINE 2000 disturbance data, Et remained unchanged after calibration while da was set to 185 cm and db to 609 cm (Table 1). These differences reflect the binary nature of the CORINE 2000 disturbance values. 3.2. Disturbance relationship (step 2) The estimated GE disturbance ranged from 0.0 to 1.0 with most sites being judged to have 0.0 to 0.2 of disturbance (Fig. 5). This meant that the effect of I (Eq. 3) was quite small and most sites were predicted to be of a depth similar to undisturbed based on the exponential component of Eq. 3. The calibrated value of D using CORINE data was 0.00 for sites classified as undisturbed and 0.41 for sites classified as disturbed (Table 1). There was a very little difference in the performance of the model when using the data from two different data sources (Table 1). This suggested that for national scale mapping it will be appropriate to use CORINE 2000 data to estimate the effect of disturbance on peat depth. 3.3. Depth/slope relationship (step 3) There was a significant difference (p b 0.0001) for the calibration dataset in peat soil depth between samples above (shallower) Et and samples below (deeper) using both GE and CORINE 2000 disturbance data. This difference in depth was captured in the parameterisation of the model by initialising with different values for aa, ab, da and db, and the relationship between depth and slope was captured by the exponential function (Fig. 4). Following calibration using GE disturbance data there was a little difference in the parameter values (aa = 0.22 and ab = 0.21) but da = 245 m and db = 712 m were clearly separated (Table 1). In the independent test dataset samples below Et were deeper than those above, but the relationship was not as significant (p = 0.06) for GE derived data, using CORINE 2000 data the significant difference was retained because Et did not change after calibration. The final curve fit for the independent test dataset (Fig. 2) is not a best-fit curve as it is a fit based on its overall role in the model which is optimised globally across Eqs. 1, 2 and 3. Tests using step-bystep optimisation yielded poor results from the model. Calibration using CORINE 2000 disturbance data resulted in aa and ab values remaining similar to the initial estimates.

Fig. 7. Observed vs. predicted peat soil depth at the 100 ha scale for (A) the Google Earth test dataset and (B) the CORINE 2000 test dataset (horizontal dashed.

explored further to determine if it was useful for this study (Table 2). At the maximum modelled peat depth in Wicklow, C density was 4500 t C ha −1 and Db was 0.136 g cm3 (assuming a C content of 51% (Hammond, 1979; Tomlinson, 2005)). This extrapolated Db value was thought to be too high. Several authors have studied deep peat (450 cm to 700 cm) and Db in Scotland and the West of Ireland (Bennett et al., 1990; Clymo, 2004; Huang, 2002) and have found values ranging from 0.09 to 0.114 g cm3. Therefore a more conservative value of 0.10 g cm3 was assumed for all peat deeper than 450 cm, leading to a C density value of 3303 t C ha− 1 at 611 cm (Table 2). These data were combined using GIS to calculate the peat SOC pool for peat soils in the Wicklow Mountains. 3. Results and discussion

3.4. Calibration and test results 3.1. Depth relationship (step 1) The optimum model parameters (Table 1) were all of similar magnitude to the initial estimates. For GE derived data the increase in Et and da reflected the algorithm used to find the optimum value and

The value of Et was set to 553 m after calibration using GE disturbance data. Visual assessment of the relationship indicated that

Table 3 The linear regression relationships at 1 ha and 100 ha resolutions for the calibration and test datasets using Google Earth and CORINE 2000 disturbance data. (For analysis of the difference of intercept from 0 the hypothesised value for calculating t was 0 and for the difference of slope from 1 the hypothesised value for calculating t was 1.). Dataset

Resolution Linear regression 2

Disturbance from Google Earth

Intercept Slope

R Intercept different from 0? Slope different from 1? a

Disturbance from CORINE

Calibration

Calibration

Test

Test

Calibration

Calibration

Test

Test

1 ha 11.7 0.9 0.45 No Yesa

100 ha − 34.1 1.3 0.58 No No

1 ha 27.3 0.8 0.47 Yes Yes

100 ha 0.7 0.9 0.55 No No

1 ha 25.5 0.75 0.48 Yes No

100 ha 1.56 0.92 0.62 No No

1 ha 39.9 0.67 0.44 Yes No

100 ha 22.9 0.66 0.63 No No

Marginal.



the structure of the calibration dataset (Fig. 2a). In terms of model skill, the difference between the initial and optimum estimates had little impact on the quality of predictions attained. The increase in db made a little difference to the goodness-of-fit, but exerted control over the slope term in the linear regression of the relationship between the observed and the predicted. The larger value of db made the fit line closely correspond to the 1:1 line and thus removed bias from the model fit. The exponential parameter values (ax) fine-tuned the fit of the data to achieve the maximum value of Pearson's r. Using CORINE 2000 data less adjustment of initial parameters occurred during calibration. The relationship between the observed and the predicted peat soil depth at 1 ha (Fig. 6) and 100 ha (Fig. 7) scales was reasonably well approximated by a simple linear regression model (Table 3). The linear regression coefficients were evaluated using the t statistic for, in the case of intercept, difference from zero, and in the case of slope,

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difference from one. A model with slope = 1 and intercept = 0 would be unbiased. The goodness-of-fit was evaluated using R2. In the case of the GE calibration dataset, the model accounted for 58% of the observed depth variation at the 100 ha scale with an unbiased model. For the independent test dataset derived from GE data, the model accounted for 55% of the observed depth at the 100 ha scale and was unbiased. Using CORINE 2000, the model accounted for 63% of the observed depth variation at 100 ha (unbiased model). Given that the surface of a blanket peatland masks unknown undulations at the mineral/peat interface, a simple inference model that accounts for 58 to 63% of observed variation in peat depth, at 100 ha, was regarded as successful. Despite the visible scatter in the predictions (Figs. 6 and 7) the trend is captured correctly by the model and it was concluded that it will provide an improvement over previous peat depth estimates for the region. The overall performance of the model in statistical terms was improved by using CORINE 2000 disturbance data, but the quality

Fig. 8. Peat depth map for the Wicklow Mountains based on Google Earth analysis of peat disturbance.


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Fig. 9. Peat depth map for the Wicklow Mountains based on CORINE 2000 analysis of peat disturbance.

of the trend line at 100 ha (Fig. 7b) was not as good. This indicates that CORINE 2000 data will be suitable for applying the model on a national scale. 3.5. Peat depth map for Wicklow The model was implemented using Google Earth and CORINE 2000 derived data (Figs. 8 and 9). Both maps are very similar, and based on independent testing captured around 60% of the spatial variation in peat depth for the region. The PDM method improves on the work of Eaton et al. (2008) and Tomlinson (2005), as both used fixed “average” depth values to estimate peat SOC pools. Homogeneity models that assume peatlands have spatially uniform physical characteristics such as depth can lead to erroneous estimations of the carbon pool (Sheng et al., 2004). The heterogeneity of peat depth is observed throughout the globe, depths vary across both local and

regional scales (Sheng et al., 2004; Beilman et al., 2008; Jaenicke et al., 2008; Chapman et al., 2009; Weishampel et al., 2009). In the ECOSSE project, the effect of the homogeneity model was minimised using weighted averages (Scottish Executive, 2007). The PDM methodology is advantageous because it correctly captures the trend of observed peat depth distribution reducing both reliance on a homogeneity model and cost of survey. 3.6. Peat SOC pool map for the Wicklow Mountains The peat SOC pool for the Wicklow Mountains, calculated by multiplying spatial extent, peat depth and C density, was estimated to be 2.30 Mt C. This compares to the values of 0.45 Mt C (Tomlinson, 2005) and 2.18 Mt C (Eaton et al., 2008) (Table 4). The differences in these amounts may be due to the use of different peat depths and spatial extent (Chapman et al., 2009). Tomlinson (2005) appears to have significantly



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Table 4 The SOC stock of peat in Wicklow using different sources for peat soil spatial extent, depth and C density. Study

Spatial extent

Wicklow area (ha)

Average depth (m)

Average C density t C ha− 1

Mass of SOC Mt C

Tomlinson (2005) Eaton et al. (2008) This Study(interp) This Study — dist This Study — obs Eaton et al. (2008) Tomlinson (2005)

GSM CORINE2000 DIPMV2 PDM_actual PDM_obs PDM PDM

10848a 34277d 28080g 21700j 21700 21700 21700

1.2/0.6b 1.2e Distributionh Distributionh 1.22 1.2 1.2

420c 636f 819i 875k 625 636 420.2

0.46 2.18 2.30 1.89 1.36 1.38 0.9

a b c d e f g h i k j

The spatial extent of Montane Blanket peat in Wicklow according to General Soil Map. Tomlinson (2005), used a depth 1.2 m for intact and 0.6 m for man-modified peatlands. The average C density for upland blanket bogs by Tomlinson (2005), Eaton et al. (2008). The spatial extent of Montane Blanket peat in Wicklow according to CORINE 2000. Eaton et al. (2008) use Hammond's average depth for Montane blanket bogs. The average C density for upland blanket bogs by Tomlinson (2005), Eaton et al. (2008). The spatial extent of Montane Blanket peat in Wicklow according to DIPMV2. Distribution of depths from the peat depth inference model. The average C density for blanket bogs in Wicklow calculated from the interpolated data (Fig. 11) when a distribution of depth values is used. The average C density for blanket bogs in Wicklow calculated from the peat depth inference model data (Fig. 3). The spatial extent of Montane Blanket peat in Wicklow according to PDM.

Fig. 10. The carbon density distribution (t C ha− 1) in the Wicklow Mountains.


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underestimated peatland extent in the Wicklow Mountains (10 848 ha) by relying on the General Soil Map. The DIPMV2 estimates a spatial extent of 28 080 ha (Connolly & Holden, 2009). Eaton et al. (2008) achieved a reasonable estimate of peatland extent (34 277 ha) using CORINE 2000. On a national scale CORINE 2000, a landcover map, tends to underestimate total peatland area (Connolly et al., 2007). However, in the Wicklow Mountains, its accuracy is better. Tomlinson (2005) used an average depth of 120 cm for undisturbed peat and 60 cm for disturbed peat. Eaton et al. (2008) used 120 cm for all peat. Based on the PDM field observations (Fig. 1), the assumptions used by previous authors capture the positive skew of the depth distribution, but ignore the significant tail of deeper peat which is associated with greater carbon densities (N=621; mean = 122.462, standard deviation = 125.951, variance = 15863.6, skewness=1.82594, and kurtosis=3.41639). Tomlinson's (2005), estimation of the SOC pool is reduced by using smaller peatland areas and assuming disturbed areas are 50% shallower. The main reason for the PDM's estimate of a larger peat SOC pool is that it captures the deep peat depths. Finally, the estimation of C density by profile depth (Table 2) also had an influence on the calculated total SOC pool. According to Cruickshank et al. (1998) deep peat has a C density of 4500 t C ha− 1, this results in an extra 312,800 t C in comparison to the PDM method. The assumption used here is that C density does not continue to increase with depth thus the total estimated C pool is reduced by about 1.5%. While the impact is relatively small in the Wicklow Mountains, (the assumption influences just 2.8% of the peatland area), it may be more important elsewhere. The peat SOC stock in the Wicklow Mountains is unevenly distributed (Fig. 10). The largest amount of stored C is found in the north and east of the region, in areas that are relatively flat and smaller stocks are found in areas with high elevation and steep slopes; this is borne out by field observation. With this information on the spatial distribution of peat depth and the SOC pool, it should be possible to improve land use management and ensure proper protection of this critical national resource. 4. Conclusions The production of a peat depth map for Wicklow demonstrates the potential of this methodology for national and regional scale mapping of peat SOC stocks. Previous studies of the SOC pool used average peat depths which may be less suitable because deeper peat contains more C. In this study we improved on homogeneity models employed by Eaton et al. (2008) and Tomlinson (2005) by developing a model that estimates peat depth at the 100 ha scale. The results from this research show that peat depth for these areas can be modelled and mapped using readily quantifiable parameters including; elevation, disturbance and slope. These modelled values can be integrated with a peat SOC pool model to create more accurate estimates of the amount of C in the ecosystem. Where data are available ground penetrating radar surveys could be used to further validate the model. The model is an improvement on using an average depth value and captures the trend of peat depth in an unbiased manner. This methodology has the potential to be applicable to wider areas where blanket peat occurs, provided reliable automated methods of estimating disturbance are available. Acknowledgements The authors wish to thank the Environmental Protection Agency, Ireland for funding this project under Environmental Research Technological Development and Innovation (ERTDI) Programme which is part of the Productive Sector Operational Programme 2000–2006, financed by the Irish Government under the National Development Plan 2000–2006. Thanks are also extended to Dr. Chaosheng Zhang (Geostatistician) for his critical review of the paper and to Dr. David Wilson for his constructive criticism and valuable suggestions.

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