Introduction
An increase in greenhouse gas (GHG) emissions through human-related
activities is leading to rapid change in the climate system (IPCC, 2013). It
is, therefore, crucial to obtain data describing the net GHG balance at
regional to global scales to better characterise anthropogenic forcing of the
atmosphere (Tubiello et al., 2015). Emissions from land use change (LUC) are
the integral of ecosystem transformations that can include emissions from
deforestation and conversion to agriculture, logging and harvest activity,
shifting cultivation, as well as regrowth sinks following harvest and/or
abandonment of previously cleared agriculture lands (Houghton al., 2012). At
present, LUC emits 0.9 ± 0.5 Pg C yr-1 to the atmosphere,
which is approximately 10 % of anthropogenic carbon emissions (Le
Quéré et al., 2014). Data sources and methods used to estimate LUC
emissions are diverse. These include census-based historical land use
reconstructions and land use statistics, satellite estimates of biomass
change through time (Baccini et al., 2012), satellite-monitored fire activity
and burn area estimates associated with deforestation (van der Werf et al.,
2010). In addition, there is increasing use of ecosystem models coupled with
remote sensing to estimate emissions from LUC (Galford et al., 2011).
Emissions associated with the LUC sector have the highest degree of
uncertainty given the complexity of processes involving net emissions and
Houghton et al. (2012) assessed this uncertainty at
∼ 0.5 Pg C yr-1, which is of the same order of magnitude as the
emissions themselves. Uncertainties in estimating GHG emissions arising from
savanna clearing, associated debris burning and conversion to agriculture are
greater than those for tropical forests (Fearnside et al., 2009). It is
important to quantify the emissions and their uncertainties in savannas,
particularly because tropical savanna woodland and grasslands occupy a large
area globally (27.6 million km2), greater than tropical forest (17.5 million km2, Grace et al., 2006). Deforestation and associated fire from
these biomes are the largest contributors to global LUC emissions (Le
Quéré et al., 2014). Much of these GHG emissions are from the
Brazilian Amazonia, an agricultural area that has been expanding since the
1990s. However, over the last decade, the rate of tropical forest
deforestation in this region has decreased from 16 000 km2 in the early
2000s to ∼ 6500 km2 by 2010 (Lapola et al., 2014), but
at the expense of the Brazilian Cerrado, a vast savanna biome of some
2.04 million km2, where clearing rates have been maintained (Ferreira
et al., 2013, 2016; Galford et al., 2013). Given the suitability of the
Cerrado topography and soils for mechanised agriculture, the Cerrado may
become the principal region of LUC in Brazil (Lapola et al., 2014).
Northern Australia is one of the world's major tropical savanna regions,
extending some 1.93 million km2 across north-western Western Australia,
the northern half of the Northern Territory and Queensland (Fisher and
Edwards, 2015). This biome occupies approximately one quarter of the
Australian continent and since European arrival, 5 % has been cleared for
improved pasture, horticulture and cropping (Landsberg et al. 2011), making
it one of least disturbed savanna regions in the world (Woinarski et al.,
2007). However, this small percentage equates to a substantial area of
9.2 million ha, and LUC and associated economic development in northern
Australia is a government imperative and this is likely to involve expansion
and intensification of grazing, irrigated cropping, horticulture and forestry
(Committee on Northern Australia, 2014). Drivers of this potential expansion
in food and fibre production include the exploitation of the growing markets of
Asia as well as domestic factors such as the perception that land and water
resources of northern Australia can provide a future agricultural resource base
to offset the expected declines in agricultural productivity in southern
Australia due to adverse impacts of climate change (Steffan and Hughes,
2013).
Historically, intensive agricultural developments in northern Australia have
been implemented based on limited scientific knowledge with dysfunctional
policy and market settings, and as a result there has been limited success
(Cook, 2009). Future expansion needs to be underpinned by sound understanding
of the consequences of regional-scale land transformation on carbon and water
budgets and GHG emissions. Any significant expansion in northern agricultural
production would require clearance of native savanna vegetation, with unknown
increases in GHG emissions. Most LUC studies occur at catchment, regional or
biome scales (Houghton et al., 2012) and are not underpinned by good
understanding of underlying processes. However, there are an increasing
number of plot-scale studies using eddy covariance and chamber methods to
provide direct measures of net GHG fluxes from contrasting land uses (Lambin
et al., 2013). These studies typically compare microclimate and fluxes of
GHGs from pastures and/or crops with adjacent forest ecosystems under a range
of management conditions (e.g. Anthoni et al., 2004; Zona et al., 2013) or
natural grasslands and different cropping types (e.g. Zenone et al., 2011).
In tropical regions, there is a focus on transitions from forest to pasture
and from forest to crops for food or bioenergy production (Galford et al.,
2011; Wolf et al., 2011; Sakai et al., 2004).
There are few studies that directly measure GHG emissions and sinks prior to,
during and after LUC at a single site. Land use change can involve rapid
changes in net GHG emissions over varying temporal scales (minutes, hours
and seasonal cycles) and continuous flux measurements are essential to
capture the magnitude of these events (Hutley et al., 2005). However, there
are no direct observations of emissions from savanna clearing in northern
Australia, contributing to the uncertainty associated with the LUC sector in
Australia's national GHG accounts (Commonwealth of Australia, 2015a).
Our objective is to provide a comprehensive assessment of GHG emissions
associated with savanna clearing. Our aims are to (1) quantify the typical
rates of CO2 exchange of intact tropical savanna and make comparative
measurements from an analogue site that was to be cleared, (2) quantify
CO2 fluxes before, during and after a clearing event, (3) estimate both
CO2 and non-CO2 (CH4 and N2O) GHG emissions arising from
burning of cleared debris and (4) quantify ecosystem-scale GHG balance for
this land use conversion and compare it with emissions from savanna fire, a
significant source of GHG emissions across northern Australia.
Methods
In this study we used a paired site approach, where concurrent fluxes of
CO2, water vapour and energy were measured using eddy covariance towers
from an uncleared savanna woodland site and a similar savanna woodland site
on the same soil type that was to be cleared, burnt and prepared for
agricultural production. Fluxes of CO2 were monitored for 161 days prior
to clearing at both sites with observations continuing during the clearing
event (deforestation) and for another 507 days through phases of woody debris
and grass curing, burning and soil preparation through raking and ploughing.
The entire observation period was 668 days. Flux observations of net CO2
exchange were combined with on-site biomass measurements and regionally
calibrated pyrogenic emissions factors to estimate emissions of CO2,
CH4 and N2O (Meyer et al., 2012; Commonwealth of Australia, 2015b)
from burning of the cleared debris that was a key component of the land
conversion. Fire-derived emissions were combined with net CO2 fluxes
from the land conversion phases to provide a total net emission in units of
CO2-e for this LUC. In this paper, we use the term “deforestation” to
describe savanna clearing. Deforestation is defined under Australia's
National Greenhouse Accounts as the loss of forest/woodland cover due to
direct human-induced actions that fail to regenerate cover via natural
regrowth or restoration planting (Commonwealth of Australia, 2015a).
Study sites
Both savanna woodland sites were located within the Douglas–Daly river
catchment approximately 300 km south of Darwin, Northern Territory (Fig. 1).
Both sites are OzFlux sites (www.ozflux.org.au), with flux observations
ongoing at the uncleared savanna (UC) site since 2007 (Beringer et al., 2011,
2016a; Hutley et al., 2011). OzFlux is the regional
Australian and New Zealand flux tower network that aims to provide
continental-scale monitoring of CO2 fluxes and surface energy balance to
assess trends and improve predictions of Australia's terrestrial biosphere
and climate (Beringer et al., 2016a). The UC site is broadly
representative of Australian tropical savanna woodland found on deep, well
drained sandy loam soils at sites with ∼ 1000 mm MAP (Table 1). The
cleared savanna site (CS) was carefully selected to ensure the vegetation and
soils were as similar to the UC site as possible and with topography suitable
for eddy covariance measurements.
Location of the uncleared site (UC) and the cleared savanna (CS)
sites south of Darwin, Northern Territory. The inset figure shows the
distribution of the savanna biome across northern Australia as defined by Fox
et al. (2001).
Site characteristics for the uncleared savanna (UC) and cleared (CS)
sites. Site soil orders are given as in Isbell (2002) with savanna
vegetation classified using Fox et al. (2001). Fire frequency was estimated
from fire mapping taken from the North Australian Fire Information system
(NAFI, www.firenorth.org.au) for 2000–2012. The fire frequency
estimate for the CS site excluded the debris fires in August 2012. Basal area
and stem density is provided for all woody stems > 2 cm DBH at
both sites. Mean site LAI for the UC is taken from Hutley et al. (2011) and
for the CS site, was estimated from canopy hemispherical photos, see text for
details.
Site
UC
CS
Location
14∘09′33.12′′ S, 131∘23′17.16′′ E
14∘33′48.71′′S, 132∘28′39.47′′ E
Soils
Red Kandosol
Red Kandosol
Vegetation type
Savanna woodland with mixed grasses Map unit D4. E. tetrodonta, C. latifolia, Terminalia grandiflora, Sorghum spp., Heteropogon triticeus
Savanna woodland with mixed grasses Map unit D4. E. tetrodonta, Erythrophleum chlorostachys, Corymbia. bleeseri, Sorghum spp., H. triticeus
Map unit area (km2)
59 986
59 986
Fire frequency (yr-1)
0.23
0.07
Basal area (m2 ha-1)
8.3
6.8
Canopy height (m)
16.4
14.2
Above-ground biomass (Mg C ha-1)
30.6 ± 9.2
26.2 ± 7.0
Stem density (ha-1)
330 ± 58
643 ± 102
Overstorey LAI (wet/dry)
NA/0.8
0.9/0.5
MODIS LAI (wet/dry)
1.5/0.9
1.6/1.0
MAP (mm)
1372a/1180b
1107c
Max Tair (∘C)
37.5 (Oct)/31.2 (Jun)
37.5 (Oct)/29.7 (Jun)
Min Tair (∘C)
23.8 (Jan)/12.6 (Jul)
25.0 (Nov)/13.7 (Jul)
a On-site observations, 2007–2012. b Gridded precipitation (AWAP,
1970–2012). c Tindal BoM station (14.52∘S, 132.38∘ E, data from
1985 to 2013). NA stands for not available.
Both sites were classified as savanna woodland (type 4B2, Aldrick and
Robinson 1972, 1 : 50 000 mapping) with an overstorey cover of 30 %,
equivalent to the Eucalypt woodland Major Vegetation Group (MVG) of the
National Vegetation Information System (NVIS, Commonwealth of Australia,
2003). The sites were dominated by an overstorey of Eucalyptus tetrodonta (F. Muell.), Corymbia latifolia (F. Muell.). Soils at
both the UC and CS sites were red kandosols of the haplic mesotrophic great
group (Isbell, 2002), characterised as deep, sandy loams (Table 1). The
long-term mean annual precipitation (MAP) (±SD) at the UC site was
estimated at 1180 ± 225 mm (1970–2012, Australian Water Availability
Project (AWAP), www.csiro.au/awap), similar to the CS site at 1107 ± 342 mm
(1985–2013, Bureau of Meteorology station, Tindal, NT). Slopes at both
sites were < 2 % with a fetch of ∼ 1.5 km at the UC site
and ∼ 1 km at the CS site. At both sites, 23 m guyed masts were
installed to support eddy covariance instruments at 21.5 m above-ground. The
tower at the CS site was moved 3 times to ensure adequate fetch was
maintained according to seasonal wind direction during clearing and phases of
the land use conversion. Instrument height was also adjusted given the height
of the surface post-clearing and during the soil tillage phase (Table 2).
Satellite-derived burnt area mapping is available across northern Australia at
250 m resolution (North Australian Fire Information system, NAFI,
www.firenorth.org.au) and indicated that fires had occurred within the flux
footprint of the UC flux tower in 5 out of the last 13 years (2000–2013),
whereas no fires had occurred within the footprint of the CS site. The
average fire return time for the entire Australian savanna biome is 3.1 years
(Beringer et al., 2015).
Flux measurements and data processing
Eddy covariance systems at both sites consisted of CSAT3 3-D ultrasonic
anemometers (Campbell Scientific Inc., Logan, USA) and a LI-7500 open-path
CO2/H2O analyser (Licor Inc., Lincoln, USA). Flux variables
were sampled at 10 Hz and covariances were stored every 30 min. The LI-7500
gas analysers were calibrated at approximately 6-month interval for the
duration of the data collection period and were highly stable. Mean daily
rainfall, air temperature, relatively humidity, soil heat flux (Fg,
W m-2) and volumetric soil moisture (θv, m3 m-3)
from surface to 2.5 m depths were measured at both sites. The radiation
balance was measured using a CNR4 net radiometer (Fn, W m-2) (Kipp
and Zonen, Zurich).
Characteristics of land conversion phases during the 668-day
observation period at the savanna clearing site (CS). Also given are the
canopy heights following LUC phases and flux instrument heights that were
adjusted following clearing, burning and then soil preparation phases.
Season
Period
LULUC
Canopy
Instrument
phases
height (m)
height (m)
Late dry season
Sep–Oct 2011
Intact savanna
16
21.5
Wet season pre-clearing
Oct 2011–Feb 2012
Intact savanna
16
21.5
Wet season clearing
Mar–May 2012
Savanna deforested using bulldozers, followed by debris decomposition, understory grass germination
3
7
Dry season pre-burn
May–Aug 2012
Vegetation debris curing, understorey grass growth
2
7
Debris burning
Aug 2012
Debris and grasses burnt, soil ripped to 60 cm to remove roots, roots and remaining debris stockpiled, reburnt
2
7
Dry season post-burn
Aug–Nov 2012
Grass and shrubs germination and resprouting
1
7
Early wet season
Nov 2012–Jan 2013
Removal remaining below-ground biomass. Wet season rains stimulates grass growth, shrub resprouting and growth
1
7
Wet season
Jan–Mar 2013
All regenerated vegetation removed, soil bed preparation
0
3
Dry season
Apr–Jul 2013
Soil cultivation in stages
0
3
Thirty minute covariances were stored using data loggers (CR3000, Campbell
Scientific, Logan), and data post-processing and quality control was
undertaken using the OzFluxQC system as described by Isaac et al. (2016).
In this system, data are processed through three
levels: Level 1 is the raw data as collected by the data logger, Level 2 are
quality-controlled data and Level 3 are post-processed and corrected but not
gap-filled data. Quality control measures at Level 2 include checks for
plausible value ranges, spike detection and removal, manual exclusion of
date and time ranges and diagnostic checks for all quantities involved in
the calculations to correct the fluxes. Quality checks make use of the
diagnostic information provided by the sonic anemometer and the infrared
gas analyser. Level 3 post-processing includes 2-dimensional coordinate
rotation, low- and high-pass frequency correction, conversion of virtual
heat flux to sensible heat flux (Fh, W m-2) and application of the
WPL correction to the latent heat (Fe, W m-2 and CO2 fluxes
(Fc) (Isaac et al., 2016). Level 3 data also include
the correction of the ground heat flux for storage in the layer above the
heat flux plates (Mayocchi and Bristow, 1995).
Gap filling of meteorology and fluxes along with flux partitioning of net
ecosystem exchange (NEE) into gross primary productivity (GPP) and ecosystem
respiration (Re) was performed on the Level 3 data using the Dynamic
INtegrated Gap filling and partitioning for Ozflux (DINGO) system as
described by Beringer et al. (2016b). In summary,
DINGO gap fills meteorological variables (air temperature, specific
humidity, wind speed and barometric pressure) using nearby Bureau of
Meteorology (BoM, www.bom.gov.au) automatic weather stations that were
correlated with tower observations. All radiation streams were gap-filled
using a combination of MODIS albedo products (MOD09A1) and BoM gridded
global solar radiation and gridded daily meteorology from the Australian
Water Availability Project (AWAP) data set (Jones et al., 2009). Precipitation
was gap-filled using either nearby BoM stations or AWAP data. Soil
temperature and moisture were filled using the BIOS2 land surface
model (Haverd et al., 2013) run for each site and forced with BoM or AWAP
data. Energy balance closure was examined using standard plots of
Fh+Fe vs. Fn-Fg using 30 min flux data from both
sites (data not shown). For the CS site, closure was examined using data
grouped according to the nine LUC phases as given in Table 2. For the UC
site, all 30 min data from 2007 to 2015 were used.
Gap filling of fluxes was undertaken using DINGO, which uses an artificial
neural network (ANN) model following Beringer et al. (2007). Model training
uses gradient information in a truncated Newton algorithm. NEE and fluxes of
sensible, latent and ground heat fluxes were modelled using the ANN with
incoming solar radiation, VPD (vapour pressure deficit), soil moisture content, soil temperature, wind
speed and MODIS EVI as inputs. The ustar threshold for each site was
determined following Reichstein et al. (2005) and night-time
observations below the ustar threshold were replaced with ANN modelled values
of Re using soil moisture content, soil temperature, air temperature and
MODIS EVI as inputs. The ANN Re model was then applied to daylight
periods to estimate daytime respiration and GPP was calculated as the
difference between NEE and Re. For data collected at the CS site, a
unique ANN model was developed for each LUC phase given the differing canopy
and microclimatology of each phase. At each site, daily NEE, Re and GPP
were calculated for each day of each phase.
Leaf area index
Canopy leaf area index (LAI) at the CS site in the surrounding intact
savanna was measured using a 180∘ hemispherical lens (Nikon 10.5 mm,
f/2.8) after Macfarlane et al. (2007). Three savanna
transects were photographed seasonally on nine occasions over 2.1 years from
the pre-clearing phase (October 2011) to December 2013. Along each 100 m
transect, 11 hemispherical pictures were taken at 10 m intervals (33 photos
for each measure occasion). At both sites the LAI was also estimated using
MODIS Collection 5 LAI (MOD15A2) for a 1 km pixel around each tower. The
8-day product was interpolated to daily time series using a spline fit. Only
MODIS values with a quality flag of 0 for FparLai_QC were
used in the estimate, indicating the main algorithm that was used
(http://lpdaac.usgs.gov/sites/default/files/public/modis/docs/MODIS-LAI-FPAR-User-Guide.pdf).
Land use conversion
The specific sequence and timing of clearing, burning and land preparation
phases is given in Table 2. Conversion of woodland to agricultural land in
northern Australia is typically achieved by pulling trees over using large
chains held under tension between two bulldozers. Clearing occurs at the end
of the wet season when soil moisture is still high and soil strength low as
under these conditions trees are easily pulled over, with a large fraction
of the tree root mass extracted when pulled. At the CS site, 295 ha of
savanna were deforested between 2 and 6 March 2012 using this technique. A
permit for this land conversion had been issued by the regional land
management agency following an impact assessment and erosion control
planning. Chains were under tension and intercepted tree boles at 0.1–0.2 m
height above the ground, which assisted in pulling the trees and limited
damage to the soil surface. As a result, grasses, woody resprouts and
shrubs of the understorey remained largely intact following deforestation
(Plate 1a). Mechanised ripping of soil to 60 cm depth was also undertaken to
remove remaining coarse root material.
Key LUC phases associated with: (a) the clearing event, Phase 3;
(b) debris burning of the cured grass, litter and woody fuels following the 5-month curing period, Phase 5; (c) stockpiling and ignition of remaining
unburnt debris and (d) post-fire site preparation with all biomass consumed,
Phase 9.
A cost-effective method of removing cleared vegetation is curing (drying) and
subsequent burning and the land managers at the CS site left debris on site to
for 5 months through the dry season (March to August, 2011). Burning of
debris occurred over a 22-day period in the late dry season, August 2012
(Plate 1b), a period of consistent southerly trade winds of low relative
humidity (10–20 %, BoM, Tindal station, NT). Prior to ignition, 100 m
fire breaks were installed around the entire 295 ha block and then lit in
blocks of ∼ 80 ha in size. There was an initial ignition of the fine
and coarse fuels (grasses, litter and twigs, defined below) and woody debris
(heavy fuels). Heavy fuels that were not completely consumed following the
initial burn were then stock-piled in rows ∼ 1–2 m in height and
reignited until the fuel was consumed (Plate 1c). Inspection of debris post-
fire suggested ∼ 5 % of fine fuels remained as ash and
∼ 10 % of the heavy fuels remained as charcoal, and these were
subsequently incorporated into the top soil during soil bed preparation
(Plate 1d).
GHG emissions from debris burning
Emissions of CO2, CH4 and N2O from the debris burning were
estimated following the approach as outlined in the IPCC Good Practice
Guidelines (IPCC, 2003), which uses country or region-specific emission
factors for fire activity (indicated by burnt area) and the mass of fuel
pyrolysed to estimate the emission of each trace gas. This approach is well
developed for the fire regime of the northern Australian savanna and is described by
Russell-Smith et al. (2013) and Murphy et al. (2015a). These authors describe
a novel GHG emissions abatement methodology for savanna burning that
combines indigenous fire practices with an emissions accounting framework,
the Emissions Abatement through Savanna Fire Management (Commonwealth of
Australia, 2015b, www.comlaw.gov.au/Series/F2013L01165). This methodology is a
legislative instrument that establishes procedures for abatement projects for
prescribed savanna burning and defines emission factors for four fuel
classes; fine (grass and litter < 6 mm diameter fragments), coarse (6 mm–5 cm),
heavy (> 5 cm diameter) and shrubs fuels (Russell-Smith
et al., 2013). Emissions of GHGs are estimated based on vegetation type, fuel
mass per area for each fuel type, burn area, the burning efficiency (BEF) for
each fuel type, defined as the mass of fuel exposed to fire that is
pyrolysed, the fuel carbon content (%), elemental C : N ratios and
emission factors (EFs) for each GHG (CO2, CH4 and N2O) and
global warming potentials for each gas. Across the northern Australian savanna,
values for BEFs and EFs have been determined for both high (> 1000 mm
MAP) and low precipitation zones (1000–600 mm MAP) and for both early and
late dry season fires, which are fires occurring after 1 August which
typically have higher intensity and combustion efficiencies than early dry
season fires (Russell-Smith et al., 2013).
We used these definitions of vegetation fuel type (woodland savanna with
mixed grass) and associated EFs, carbon contents and N : C ratio values as defined
in the methodology to estimate GHG emissions from the debris fire using the
following equation:
E=∑i(FLj×BEFj×CCj×N:CN2O×EFi,j×GWPi),
where E is the sum of emissions in Mg CO2-e ha-1 for each GHG
i (CO2, CH4 and N2O), FLj is the fuel load for fuel
type j (fine, coarse, heavy) in Mg C ha-1, BEFj is the burning
efficiency factor, CCj is the fractional carbon content,
N : CN2O is the fuel nitrogen to carbon ratio for N2O
emissions, EFi,jis the emission factor for GHG i and fuel type j,
and GWPi is the global warming potential for each GHG i (after
Commonwealth of Australia, 2015b). The debris fire differed from a typical
savanna fire in that there was a significantly higher heavy fuel load present
and it was of high intensity which consumed the vast majority of fuel
(Plate 1c, d), reflected in the assumed BEFs we used. The fire-derived
emissions were combined with tower-derived NEE data from the post-clearing
phases (Table 3) to give a total emission in CO2-e for this LUC.
Cumulative precipitation and mean NEE, Re and GPP (Mg C ha-1 month-1)
for each of the LUC phases at the CS site as measured by the
flux tower. These fluxes are given for the UC site for these same periods.
One-way ANOVA was used to test for differences between mean daily NEE for
each LUC phase with significantly different means labelled with an asterisk.
On the days of ignition during the debris burning phase, flux data at the CS
site were excluded. Integrated fluxes are given for the post-clearing period
(507 days) and the entire observation period (668 days) for both sites
in Mg C ha-1.
CS
UC
Phase
Period
Rainfall
NEE
Re
GPP
Rainfall
NEE
Re
GPP
LULUC phases
number
(d)
(mm)
(Mg C ha-1 month-1)
(mm)
(Mg C ha-1 month-1)
Intact canopy cover
1
161
736.6
-0.23
1.57
-1.79
1076.8
-0.25
1.45
-1.70
Clearing event
2
4
59.4
0.23*
1.95
-1.73
59.8
0.38*
1.80
-1.50
Wet–dry debris curing, decomposition
3
59
143.2
0.98**
1.39
-0.41
412.0
0.32**
1.53
-1.22
Dry season pre-burn
4
94
0
0.34**
0.57
-0.23
2.4
0.15**
0.94
-0.79
Fire emissions late dry
5
22
0
0.90**
0.76
0.0
0.0
-0.01**
0.71
-0.72
Dry season post-burn
6
67
2.2
0.31∗∗
0.37
-0.06
64.4
-0.28
0.64
-0.91
Early wet regrowth
7
80
361.0
0.03**
0.99
-0.96
345.8
-0.32
1.80
-2.12
Wet season site prep.
8
91
701.7
0.62**
0.99
-0.37
914.4
-0.20**
1.67
-1.88
Dry season final bed prep. and cultivation
9
90
0
0.29**
0.32
-0.02
10.8
0.06**
0.91
-0.85
(Mg C ha-1)
(Mg C ha-1)
Total post-clearing
507
1267.5
7.2**
12.8
-5.6
1809.6
-0.78**
20.7
-21.5
Total all phases
668
2004.1
6.0**
21.2
-15.2
2886.4
-2.1**
28.5
-30.6
* Denotes significantly different mean NEE at the 5 %
level, ** significant at 1 %.
Quantifying fuel loads
To accurately quantify emissions from the debris fire, fine, coarse and
heavy fuels were estimated using plots and transects established across the
295 ha deforestation area. For fine fuels, six 100 m transects were randomly
located and at 20 m intervals along each transect, all fine (grass, woody
litter) and coarse (twigs, sticks) fuels were harvested from 1 m2
quadrats, dried and weighed to give a mean fine and coarse fuel mass for the
site. We assigned on-site coarse woody debris (CWD), above-ground and
below-ground biomass estimates to the heavy fuel class (> 5 cm
diameter fragments). To quantify CWD, an additional six 100 m transects were
randomly located across the deforestation area and along each transect the
length and diameter of all intersected CWD fragments were recorded to
estimate fragment volume. In these savannas, large fragments (> 10 cm
diameter) are frequently hollowed from the action of termites and fire
and the diameter and length of the annulus of such fragments were measured
to estimate this missing volume. In addition, large fragments that were
tapered were treated as a frustum of a cone and a second diameter was taken
at the fragment end to improve volume estimation. Fragment volumes were
calculated and converted to mass using rot classes (RCs) and associated wood
densities (g cm-3). Five rot classes (RCs) were defined and assigned to
each CWD fragment to capture the decay gradient of fragments. These were
defined as recently fallen, solid wood (RC1), solid wood with or without
branches present but with signs of aging (RC2), obvious signs of weathering,
still solid wood, bark may or may not be present (RC3), signs of decay with
the wood sloughed and friable (RC4) and severely decayed fragments with
little structural integrity remaining (RC5). A wood density was assigned to
each RC and species (where identifiable) after Rose (2006) and
Brown (1997) to provide an accurate estimate of CWD mass that included decay
and hollowing. For the dominant Eucalyptus and Corymbia species
wood densities ranged from 0.7 g cm-3 (RC1) to 0.56 g cm-3 (RC5).
Above-ground biomass was quantified by surveying all woody plants > 1.5 m
in height or > 2 cm DBH across eight 50 × 50 m
plots. All woody individuals were identified to species and stem diameter at
1.3 m height (DBH) and tree height were measured. Region-specific allometric
equations are available for tree species found at the CS site (Williams et
al., 2005) and these were used to estimate above-ground biomass for each
individual tree and shrub based on DBH and height. Below-ground biomass was
calculated using the root / shoot ratio estimate of Eamus et al. (2002) for
these savanna stands, which was 0.38. These trees have large lateral roots in
the top 30 cm of soil, with no tap root and 90 % of root biomass is found
in the top 50 cm of soil (Eamus et al., 2002). As such, we assumed that
chaining and bulldozer clearing of all above-ground biomass followed by soil
ripping (ploughing) to 60 cm soil depth, plus mechanised removal of root
biomass associated with tree boles and subsequent burning, resulted in a
near-complete removal of both above- and below-ground woody biomass pools
(Plate 1d).
Deforestation and savanna burning emissions at catchment to regional
scales
The potential impact of any expanded deforestation across the northern Australian
savanna landscapes was assessed relative to historic deforestation rates and
resultant GHG emissions and arising from prescribed savanna burning. This
land management activity contributes ∼ 3 % to Australia's national
GHG emissions (Whitehead et al., 2014) and is 25 % of the Northern
Territory's annual emissions (Commonwealth of Australia, 2015a). Annual
emissions from these activities (historic and future savanna deforestation
and prescribed burning) were estimated at three spatial scales: (1) catchment,
(2) state/territory and (3) regional. Emissions estimates from deforestation
and savanna burning were compiled for (1) the Douglas–Daly river catchment
where the UC and CS sites are located (area 57 571 km2), a catchment
with less than 5 % of the native vegetation deforested to date (Lawes et
al., 2015) but earmarked for future development; (2) the savanna area of
Northern Territory (856 000 km2) and (3) the savanna region of
northern Australia as defined by Fox et al. (2001) with MAP > 600 mm,
an area of 1.93 million km2 (Fig. 1, insert).
Emissions of GHGs from historic deforestation from the Douglas–Daly
catchment were estimated using our estimates for savanna land conversion
combined with satellite-derived annual deforestation area (1990–2013) as
reported by Lawes et al. (2015) for this catchment to give a catchment-scale
mean annual estimate of emissions from deforestation in
Gg CO2-e yr-1. Annual deforestation emissions data for the
Northern Territory and the northern Australian savanna region were taken from
the National Greenhouse Gas Inventory (NGGI) for the same period 1990–2013.
The Department of Environment is responsible for reporting sources of
greenhouse gas emissions and removals by sinks in accordance with UNFCCC
Reporting Guidelines on Annual Inventories and the supplementary reporting
requirements under the Kyoto Protocol. State and Territory GHG Inventories
are reported for 1990 to 2013 (Commonwealth of Australia, 2015a) and we used
data for the Land Use, Land-Use Change and Forestry sector, Activity A.2
Deforestation. These emissions are reported for each state, but are not biome
based and for our regional savanna estimate, emissions data for Western
Australia, the Northern Territory and Queensland were used but were
calculated using the area within each state that was defined as savanna by
Fox et al. (2001, Fig. 1). Mean annual deforestation emissions from the
savanna area of each state and territory (1990–2013) were summed to
calculate a mean (±SD) annual deforestation rate for the northern
Australian savanna area (1.92 million km2) in
Gg CO2-e yr-1.
Emissions from savanna burning were calculated using the online Savanna
Burning Abatement Tool (SAVBat2, www.savbat2.net.au) using the
predefined vegetation fuel types (VFTs) mapping for the northern Australian
savanna (Fisher and Edwards, 2015; Thackway, 2014), both components of the
Emissions Abatement through Savanna Fire Management methodology. SAVBat2
combines satellite-derived burnt area mapping (www.firenorth.org.au)
with fuel load estimates from VFT mapping, GHG emission factors and burn
efficiencies to estimate annual emissions from burn areas. In accordance with
IPCC accounting rules, only non-CO2 emissions are reported for savanna
burning as it is assumed that CO2 emissions from dry season burning is
offset by regrowth of vegetation (mostly C4 grasses) in subsequent wet
season(s) (IPCC, 1997). However, for comparisons with deforestation
emissions, we calculated emissions of CO2 as well as non-CO2
emissions. SAVBat2 estimates were compiled for the same areas as savanna
deforestation estimates; the Douglas–Daly river catchment, savanna of the NT
and the northern Australian savanna. Mean annual burning emissions for
1990–2013 were calculated and are reported as non-CO2 (CH4,
N2O) and total emissions (CO2, CH4 and N2O) in
Gg CO2-e yr-1.
Emissions from expanded deforestation across northern Australia
Emissions from expanded deforestation across northern Australia was estimated
by upscaling our estimate of deforestation emissions per hectare from
catchment areas identified as having future clearing potential. These areas
were based on the land use assessment of northern Australian catchments by
Petheram et al. (2014) and identified catchments with development potential
based upon surface water storage and proximity of land resources suitable for
irrigation development for agriculture, horticulture or improved pastures.
Using these criteria, suitable catchments were identified in Western
Australia (Fitzroy River, Ord Stage 3; 75 000 ha potential area), the
Northern Territory (Victoria, Roper Rivers, Ord Stage 3, Darwin-Wildman River
area; 114 500 ha) and Queensland (Archer, Wenlock, Normanby, Mitchel
Rivers; 120 000 ha). This gives a potential savanna deforestation area of
311 000 ha, equivalent to an additional 16 % of cleared land over and
above the 1 886 512 ha that has been cleared across the savanna biome
since 1990 (Commonwealth of Australia, 2015a). Projected emissions included
mean annual emissions from historic deforestation rates plus emissions from
this expanded deforestation scenario. Expanded deforestation areas were
calculated assuming any such clearing would occur over a 5-year period and
are reported as non-CO2 (CH4, N2O) and total emissions
(CO2, CH4 and N2O) in Gg CO2-e yr-1.
Results
Pre-clearing site comparisons
Pre-clearing meteorology, flux observations and energy balance closure for UC
and CS sites were compared (Fig. 2). Mean monthly NEE, Re and GPP for
each LUC phase for both sites are given in Table 3. Flux measurements prior
to clearing were made for 161 days, a period spanning the late dry to early
wet season transition (September–December) through to the middle of the wet season
(January–February, Table 2). Flux data at the CS site were validated by
assessing energy balance closure, with a regression between energy balance
components suggesting closure was high with a slope of 0.91 and an R2 of
0.95 (n= 4778). Site differences for each phase were tested using one-way
ANOVA using daily mean NEE with days as replicates. For Phase 1, mean daily
NEE was not significantly different between the two sites during
(P < 0.64, df= 321). Seasonal patterns of Tair,
VPD (Fig. 2b), LAI (Fig. 2c)
and C fluxes (NEE, GPP, Re, Fig. 2d) were similar when both sites were
intact, although precipitation was 340 mm higher at the UC site (Table 3).
Comparative meteorology and fluxes for the uncleared (UC) and
cleared savanna CS sites prior to the clearing event. Data spans the late
dry season (September 2011) through to the middle of the wet season prior to the
clearing event of 2–6 March 2012. Plots include (a) daily precipitation
(black bars UC site, grey bars CS site), mean daily Tair (black lines
UC, grey CS), (b) mean daily VPD (dashed lines; black UC, grey CS),
(c) interpolated 8-day MODIS LAI (black UC, grey CS), (d) NEE (black UC, grey CS)
partitioned into Re (red UC, pink CS) and GPP (dark green UC, pale
green CS).
At both sites, NEE shifted from being a weak sink of less than
-1 µmol CO2 m-2 s-1 during the late dry season to a net source of
CO2 during the early wet season (Fig. 2d). During this period, Re
increased rapidly from +2 to +5 µmol m-2 s-1
in early October with the onset of wet season rain, but
then remained relatively constant for the remainder of the wet season. As the
wet season progressed, temporal patterns of GPP were similar at both sites,
then steadily increased to -6 to -7 µmol m-2 s-1 and remained
at this rate until they cleared (March 2012). Re was relatively stable during this
period and NEE increased to -2 µmol m-2 s-1 through the wet
season (December to February). Despite the higher precipitation received at
the UC site, mean monthly NEE, GPP and Re differed by < 10 %
(Table 3, intact canopy phase). Normalising fluxes by MODIS LAI for each site
further reduced differences to 2 % (data not shown), suggesting site
differences were small and the UC site provides a suitable control for the CS
site.
Fluxes following clearing
Clearing of the 295 ha block commenced on 2 March 2012 and the bulldozers
reached the footprint of the flux tower at ∼ 09:00 h local time on 6 March
(Fig. 3). As for Phase 1, energy balance closure of flux tower data for
LUC Phases 2 to 4 (post-clearing phases) was high, with a slope > 0.9 and R2 > 0.92. Over all phases at the CS site, closure
was lower, with a slope of 0.81 (R2= 0.95, n= 26 395), similar to
that of the UC site at 0.87 (R2= 0.93, n= 99 998).
The 4-day clearing event occurred during relatively high soil moisture
conditions, with surface (5 cm depth) θv ranging from 0.08 to 0.10 m3 m-3
and subsoil θv (50 cm depth) ranging from 0.12 to
0.14 m3 m-3. As a result, pre-clearing fluxes were high and NEE
reached -15 µmol CO2 m-2 s-1 during the middle of
the day (Fig. 3). Mean daily NEE for the week prior to clearing was a net
CO2 sink of -0.60 ± 0.63 µmol m-2 s-1 and was
not significantly different to mean daily NEE at the UC site of -0.80 ± 0.93 µmol m-2 s-1
(ANOVA, P < 0.03). For the 3
weeks following clearing, the CS site rapidly became a net source of CO2
with a mean daily NEE of +4.38 ± 0.24 µmol m-2 s-1,
with a much reduced diurnal amplitude and no response to precipitation events
(Fig. 3a, b). High closure (slope > 0.9) was observed during Phases 2
to 4, although this was reduced (slope = 0.75) for the post-fire and soil
preparation, Phases 6–9.
Table 3 provides values of precipitation and monthly NEE, Re and GPP for the
seven LUC phases following clearing, namely debris decomposition and curing
(153 days), burning (22 days), wet season regrowth (80 days), followed by
soil tillage and preparation of irrigated raised soil beds (181 days). For
each phase, the comparable flux estimate from the UC site is estimated for
all post-clearing phases and for the entire observation period. Following
clearing, GPP at the CS site was reduced by a factor of 3.5 when compared to
the UC for the same period (March 2012–January 2013, Table 3). While
greatly reduced, GPP still occurred at the CS site during this 13.7-month
period (-0.38 Mg C ha-1 month-1) via resprouting of felled
overstorey and subdominant trees and shrubs, as well as grass germination
and growth stimulated by early wet season precipitation (November
2012–January 2013, 361 mm, Table 3). Ecosystem respiration during this period
was higher at the UC site (+1.12 Mg C ha-1 month-1) when
compared to the CS site (+0.82 Mg C ha-1 month-1) and, given
the large decline in GPP, the CS site was a small net C source at
+0.51 Mg C ha-1 month-1, compared to the UC site which was
a weak sink of -0.03 Mg C ha-1 month-1.
(a) Daily precipitation and (b) diurnal patterns of NEE at the CS site
for the week prior to the clearing event of 2–6 March 2012 (vertical bar)
and 3 weeks post-clearing.
Cumulative NEE over all the post-clearing LUC phases was
+7.2 Mg C ha-1 at the CS site compared to a net sink of
-0.78 Mg C ha-1 at the UC site (Table 3). The temporal dynamics of
cumulative NEE across all LUC phases (note differences in phase duration) is
summarised in Fig. 4, which compares fluxes from both sites for the complete
observation period. Three significant periods of C emission are evident in
Fig. 4. Firstly, the clearing event and the subsequent switch from a C sink
to a net source of 1.9 Mg C ha-1 due to soil disturbance and the
decomposition of biomass. Secondly, this was followed by a reduction in
source strength over the dry season of 2012, attributable to a reduction in
Re during the dry season (2012 dry season pre-burn phase, Table 3).
Thirdly, there were other major emissions attributed to soil tillage and bed
preparation in the wet and dry seasons of 2013, a cumulative net emission of
+2.75 Mg C ha-1 that occurred over the final 6 months (Fig. 4) in
preparation for cropping. Over this phase, the UC site was a net sink of
-0.62 Mg C ha-1.
Cumulative NEE from the CS (red line) and UC sites (black line) for
each land use phase (see Table 2 for details) over the entire observational
period, September 2011 to July 2013. The UC site is a long-term savanna site
of the Australian flux network (OzFlux, see Beringer et al., 2016a) and
using the sites' 8-year flux record (2007–2013), the long-term cumulative
mean NEE is plotted for each land use phase of (grey line; ±95 %
CI). The dashed line indicates zero net CO2 flux.
Emissions from debris burning
Table 4 gives fuels loads, BEFs, EFs, carbon content and N : C ratios for
each fuel type used to estimate the GHG emission from the debris burning.
Fuel load was dominated by heavy fuels with a mean (±SD) above-ground
biomass of 26.9 ± 7.0 Mg C ha-1 and a range of 14.4 to
39.3 Mg C ha-1 across the eight biomass plots. The mean below-ground
biomass was estimated at 9.0 ± 2.4 Mg C ha-1 and CWD was
1.4 ± 0.6 Mg C ha-1. Fine and coarse fuels were 1.4 ± 0.7
and 0.5 ± 1.0 Mg C ha-1 respectively, giving a total fuel mass
of 38.2 Mg C ha-1. Using these fuel loads with savanna EFs and the
BEFs estimated for the site gave emissions of CO2, CH4 and N2O
for each fuel type and the emission from debris burning totalled
121.9 Mg CO2-e ha-1, with 9.5 % of this total comprising
non-CO2 emissions (Table 4).
Measured fuel loads, assumed burning efficiencies (BEFs), carbon
contents, N : C ratio and emissions factors (EFs) used to estimate GHG emissions
from the burning of the post-deforestation fine, coarse and heavy fuel
debris. Emission factors, carbon content and C : N ratio were assumed for the
vegetation fuel type woodland savanna with mixed grass (code hWMi) as given
in the Emissions Abatement through the Savanna Fire Management methodology
(Commonwealth of Australia, 2015b), available at
www.legislation.gov.au/Details/F2015L00344 and Meyers et al. (2012).
Fuel type
Fuel load
BEF
Carbon
N : C
EF CO2
EF CH4
EF N2O
Emissions (Mg CO2-e ha-1)
(Mg C ha-1)
content
ratio
CO2
CH4
N2O
Total
Fine
1.1 ± 0.70
0.95
0.46
0.0096
0.97
0.0031
0.0075
3.9
0.1
0.04
4.0
Coarse
0.5 ± 1.0
0.9
0.46
0.0081
0.92
0.0031
0.0075
1.5
0.0
0.01
1.6
Heavy – AGB
26.2 ± 7.0
0.9
0.46
0.0081
0.87
0.01
0.0036
75.2
7.9
0.32
83.4
Heavy – CWD
1.4 ± 0.6
0.9
0.46
0.0081
0.87
0.01
0.0036
4.0
2.7
0.11
28.5
Heavy – BGB
9.0 ± 2.4
0.9
0.46
0.0081
0.87
0.01
0.0036
25.7
0.0
0.02
4.4
Total
110.2
11.1
0.50
121.9
Total GHG emission
Emissions derived from debris burning need to be combined with the
post-clearing NEE as measured by the EC system to provide a total GHG
emissions estimate from this LUC in units of CO2-e. The LUC phases
following clearing spanned a 502-day period (Table 3), and NEE was
+7.2 Mg C ha-1 or +26.4 Mg CO2-e ha-1. In
comparison, NEE from the UC site over the same period was
-0.78 Mg C ha-1 or -2.9 CO2-e ha-1.
Adding NEE from post-clearing phases (Phases 2–9, Table 3) to
emissions from debris burning (Table 4) gave a total emission of
+148.3 Mg CO2-e ha-1 for the CS site. The CO2-only
emission from debris burning plus post-clearing NEE was
+136.7 Mg CO2 ha-1, which was a flux 45 times larger than the
observed savanna CO2 sink at the UC site over the post-clearing period.
Upscaled and projected emissions from deforestation and savanna
burning
Table 5 provides mean (±SD) GHG emissions estimates for savanna burning
and deforestation for 1990–2013. At all spatial scales, annual mean burnt
area dwarfed the mean annual land area deforested. For the Douglas–Daly
catchment area, reported non-CO2 emissions from savanna burning were
577 ± 124 Gg CO2-e yr-1, almost 4 times larger than
emissions from the mean annual savanna deforestation rate of
163 ± 162 Gg CO2-e yr-1. For the Northern Territory
savanna, mean annual burning emissions were an order of magnitude larger than
mean annual deforestation emissions (Table 4) and 2 orders of magnitude
larger if CO2 emissions were included. At a regional scale, the mean
annual deforestation rate across the northern Australian savanna was
16 161 ± 5601 Gg CO2-e yr-1, with emissions from Queensland
savanna area dominating this amount at
15 762 ± 5566 Gg CO2 yr-1. This is double that of the
reported (non-CO2 only) emission from prescribed burning at
6740± 1740 Gg CO2-e yr-1 (Table 5).
Greenhouse gas emissions for 1990–2013 from prescribed savanna
burning and savanna deforestation at catchment (Douglas–Daly rivers),
state/territory (Northern Territory savanna area) and regional scales
(northern Australian savanna area, Fig. 1). For savanna burning, burnt area
and associated mean annual emissions (±SD) are given for both reported
non-CO2 (CH4, N2O) and total emissions (CO2, CH4 and
N2O). For the identical areas as used for savanna burning, mean annual
GHG emissions from deforestation (±SD) are given. For the Douglas–Daly
river catchment, deforestation area was taken from Lawes et al. (2015) and
combined with deforestation emissions from the CS site. Deforestation
emissions (1990–2013) for the NT and the northern Australian savanna area
are taken from the State and Territory Greenhouse Gas Inventories
(Commonwealth of Australia, 2015a). In bold text are the emissions associated
with the current deforestation rate plus expanded deforestation areas as
identified by Petheram et al. (2014), which are combined with emissions from
the CS site to give an upscaled estimate of potential emissions with
agricultural development at the three spatial scales.
Savanna region
Savanna burning
Savanna deforestation
Burnt areaa
Emissions non-CO2a
Emissions totala
Deforestation area
Emissions total
Expanded deforestation
Expanded emissions
(ha yr-1)
(Gg CO2-e yr-1)
(Gg CO2-e yr-1)
(ha yr-1)
(Gg CO2-e yr-1)
aread (ha)
totald (Gg CO2-e yr-1)
Douglas–Daly river catchment
2 482 100 ± 490 400
577 ± 124
14 270 ± 3064
1275 ± 454b
163 ± 162b
20 000
756
Northern Territory
13 419 410 ± 487 300
3490 ± 922
86 255 ± 22 880
1717 ± 611c
398 ± 128c
114 500
3413
Northern Australian
32 249 254 ± 11 176 004
6740 ± 1729
166 586 ± 42 725
78 605 ± 34 976c
16 161 ± 5601c
311 000
24 393
a Burnt area and emissions data
estimated using the on-line Savanna Burning Abatement Tool (SAVBat2),
1990–2013. These emissions are CH4 and N2O only.
b Deforestation area data taken from Lawes et al. (2015), upscaled
using the emissions from the CS site from this study, 1990–2013.
c Deforestation area and emissions data taken from the State and
Territory Greenhouse Gas Inventories (Commonwealth of Australia, 2015a),
1990–2013. d Expanded deforestation area data taken from
catchments as identified by Petheram et al. (2014), upscaled using the GHG
emissions from the CS site from this study and added to historic emissions.
Emissions estimates that include future deforestation rates would be
equivalent to savanna burning, at least for the duration of the additional
deforestation. For the Douglas–Daly catchment, this future emission is
estimated at 756 Gg CO2-e yr-1 and across the Northern Territory
savanna area, this would be 3413 Gg CO2-e yr-1, rates of
emission that are equivalent to burning emissions catchment (Douglas–Daly,
577 ± 124) and state scales (Northern Territory savanna,
3490 ± 922 Gg CO2-e yr-1).
Emissions that include future deforestation rates for
the northern Australian savanna region were estimated at
24 393 Gg CO2-e yr-1
and would be 3 times the reported savanna-burning annual
emissions (Table 5).
Discussion
Australia has lost approximately 40 % of its native forest and woodland
since colonisation (Bradshaw, 2012), with most of this clearing for primary
production in the eastern and south-eastern coastal region. Attention has now
turned to the productivity potential of the largely intact northern savanna
landscapes, which will involve trade-offs between management of land and
water resources for primary production and biodiversity conservation (Adams
and Pressey, 2014; Grundy et al., 2016). Globally and in Australia, savanna
fire ecology and fire-derived GHG emissions have been reasonably well
researched (Beringer et al., 1995; Cook and Meyer, 2009; Livesley et al.,
2011; Meyer et al., 2012; Walsh et al., 2014; van der Werf et al., 2010) and
the impacts of fire on the functional ecology of the Australian savanna has been
recently reviewed by Beringer et al. (2015). In this study, we focussed on
savanna deforestation and land preparation for agricultural use. These phases
result in a series of events that may lead to pulsed GHG emissions that would
otherwise be missed or greatly underestimated by episodic measurements taken
at a weekly or monthly frequency after an initial tree-felling event (Neill
et al., 2006; Weitz et al., 1998).
We used the eddy covariance methodology as it provides a direct and
non-destructive measurement of the net exchange of CO2 and other GHG
gases at high temporal resolution, ranging from 30 min intervals to
daily, monthly, seasonal and annual estimates. The method is useful as a
full carbon accounting tool as all exchanges of CO2 from autotrophic and
heterotrophic components of the ecosystem undergoing change are quantified
(Hutley et al., 2005). This approach provides essential data for bottom-up
GHG and carbon accounting studies as micrometeorological conditions and
associated fluxes can be tracked through time for the duration of a land use
conversion.
At the CS site, burning of post-clearing debris comprised 82 % of the
total emission of 148.4 Mg CO2-e ha-1, with the remainder
attributed to NEE as measured by the flux tower. This flux comprised
significant CO2 losses via respiration of debris, enhanced soil CO2
efflux from soil disturbance and tillage, which was partially offset by net
uptake of CO2 from woody resprouting post-clearing and periods of grass
growth following wet season rainfall (Fig. 4). Soil disturbance via ripping,
tillage and preparation was responsible for 10 % of the CO2 emission
from the conversion. The EC flux tower was in operation during the clearing
event, demonstrating the utility of this method as the switch of the
ecosystem from being a net CO2 sink to being a net source. This occurred
over a number of hours as the clearing event was completed (Fig. 3). During
the LUC phase changes, there was little evidence of major pulses of CO2
flux, instead there was a rapid transition to a new diurnal pattern following
the clearing (Fig. 3) or the commencement of soil preparation (data not
shown). This is in contrast to non-CO2 flux emissions, in particular
N2O, with short-term emissions often following disturbances (Grover et
al., 2012; Zona et al., 2013) and can account for a significant fraction of
annual emissions.
The net CO2 source measured by the flux tower represents an emission
that would be missed if vegetation biomass density alone was used to estimate
LUC emissions, which is the approach currently used in remote sensing LUC
studies at regional and continental scales. The total GHG emission we report
in this study is more accurately described as a land conversion, as it
includes the oxidation of biomass plus emissions associated with soil
disturbance and tillage required for a conversion to a cropping or grazing
system.
The emission estimate from this study does not include non-CO2
soil-derived fluxes of CH4 and N2O, which can be significant for
LUC events in certain ecosystems (Tian et al., 2015). Grover et al. (2012)
compared soil CO2 and non-CO2 fluxes from native savanna with young
pasture and old pastures (5–7 and 25–30 years old) in the Douglas–Daly
river catchment. Soil emissions of CO2-e were 30 % greater on the
pasture sites compared with native savanna sites, with this change being
dominated by increases in CO2 emission and soil CH4 exchange
shifting from a small net sink to a small net source at the pasture sites.
Non-CO2 soil fluxes were generally small, especially N2O emissions,
although these measurements were made many years after the LUC event and
there is uncertainty as to their relevance for a recently deforested and
converted savanna site. An additional pathway for CH4 and N2O
emissions in these savannas is via termite activity (Jamali et al., 2011a,
b). In our study, termite mounds were abundant across the CS site but were
largely destroyed by clearing and soil preparation, potentially reducing the
net non-CO2 emission following conversion. Further work is required to
quantify these non-CO2 fluxes not associated with debris burning to
refine our total emission estimate for savanna deforestation.
This land conversion represents the loss of decades of carbon accumulation in
these mesic savanna (> 1000 mm MAP), ecosystems which are currently
thought to be a weak carbon sink (Beringer et al., 2015). The 8-year ensemble
mean NEE for the UC site was -0.11 ± 0.16 Mg C ha-1 yr-1
and is representative of a savanna site at a near-equilibrium state in terms
of carbon balance given the low fire frequency (3 in 13 years, Table 1) with
high severity fires uncommon (1 in 8 years of flux measurements). The annual
increase in tree biomass at this UC site is 0.6 t C ha-1 yr-1
(Rudge, Hutley, Beringer, unpublished data) and, given an above-ground
standing biomass of 28 t C ha-1, suggests a regeneration period of
approximately four to five decades after stand replacement disturbance event
such as deforestation for this savanna type.
Even after the large pool of carbon is lost following oxidation of biomass,
carbon loss may continue on cleared land via continued soil carbon
mineralisation, leading to a slow decline in soil carbon storage that is
frequently reported for forest to cropping LUC (Jarecki and Lal, 2003; Lal
and Follett, 2009). Conversion of forest or woodland to improved pasture
grazing may result in either increases or decreases in soil carbon
(Sanderman et al., 2010). Alternatively, it is possible that carbon
sequestration may occur post-clearing via woody regrowth if a cleared site
is abandoned and not further prepared for cultivation. This has actually
been a relatively common transition and a significant sequestration pathway
that needs to be included in savanna LUC assessments (Henry et al., 2015).
Admittedly, if savanna-cleared land does fully transition to a cropping
system, some fraction of the lost carbon could also be replaced or
sequestered by new horticultural or forestry land uses.
There are few detailed, plot-scale studies of GHG emissions from savanna
clearing in northern Australia. Several studies (Law and Garnett, 2009, 2011)
used the Full Carbon Accounting Model (FullCAM Ver 3.0, Commonwealth of
Australia, 2015a; Richards and Evans, 2004) to generate spatial maps of
above- and below-ground biomass and soil organic carbon pools across the NT.
The FullCAM model uses spatial and temporal soil, climate, precipitation data
with NVIS major vegetation classes to simulate carbon losses (as GHG
emissions) and uptake between the terrestrial biological system and the
atmosphere. Land use change scenarios can be run within the model and Law and
Garnett (2009) examined deforestation emissions from the Eucalypt woodland
NVIS vegetation class, as per UC and CS site classification. Modelled
emissions were 136 ± 42 Mg CO2-e, comparable to our
deforestation estimate of 121.4 Mg CO2-e. Henry et al. (2015) used a
life cycle assessment approach to quantify GHG emissions from LUC associated
with beef production in eastern Australia. Australia's major beef-producing
areas across central and southern Queensland and northern central New South
Wales were classified into 11 bioregions, with the northernmost bioregion,
the northern Brigalow Belt, falling within the savanna biome. Vegetation
biomass from this bioregion was estimated at 84.7 ± 7.1 Mg ha-1
or ∼ 41.4 Mg C ha-1, with an emission estimated at
129 Mg CO2-e (Henry et al., 2015), similar to the woodland biomass
density and resultant emission with deforestation from the CS site of this
study.
Our emissions estimate is robust for this vegetation class and can be
upscaled and compared with other land sector activities such as prescribed
savanna burning. At a regional scale, current levels of savanna burning
dominate emissions compared to land clearing rates (Table 5). The cumulative
deforestation area across the savanna region since 1990 (1 886 512 ha) is 17 times
smaller than the mean annual savanna burn area (32 Mha, Table 5), as
approximately 30 to 70 % of the savanna area is burnt annually
(Russell-Smith et al., 2009a). Modelling NEP for savanna biome for 1990–2010
(Beringer et al., 2015; Haverd et al., 2013) suggests the northern Australian
savanna is near carbon neutrality or is a weak source of CO2 to the
atmosphere once regional-scale fire emissions are included. As such, the IPCC
assumption that CO2 emissions from the previous year's burning are
recovered by the following year's wet season growth may have some validity
for regional-scale GHG accounting. This assumption at plot-to-catchment
scales may not be valid, as localised interannual variability in rainfall,
site history and fire management can result in either net accumulation or
loss of carbon (Hutley and Beringer, 2011; Murphy et al., 2014, 2015b).
Assuming year-to-year CO2 emitted from burning is resequestered,
assessment of the non-CO2 only emissions from savanna burning with
deforestation is useful. This comparison suggests projected deforestation
emissions (24 393 Gg CO2-e yr-1, Table 5) could be well in
excess of current annual burning emissions (6740 Gg CO2-e yr-1,
Table 5), at least for the period of enhanced clearing, which in this study
we assumed to be 5 years.
In 2013, Australia's total reported GHG emission was 548 440 Gg CO2-e
and the impact of expanded savanna deforestation on the national emission can
be estimated using data in Table 5, which provide estimates of mean annual
emissions from the deforestation area, giving a mean annual deforestation
emission per ha averaged for the entire savanna area, which is
221 ± 50.8 Mg CO2-e ha-1 using 1990 to 2013 data
(Commonwealth of Australia, 2015a). This value represents a spatially
averaged emission as it is derived from the full range of savanna vegetation
types and above-ground biomass, which across the Northern Territory savanna
area ranges from 10 to 70 Mg C ha-1 (Law and Garnett, 2011). Assuming
this emission per ha, an additional 311 000 ha of savanna deforestation,
cleared over a 5-year period, adds 12 099 Gg CO2-e yr-1. For
the duration of the expanded deforestation, this is a 2.2 % increase to
Australia's nation emission over and above the historic savanna LUC emissions
(16 161 Gg CO2-e yr-1),
which are 2.9 % of national emissions. Using our finding from
flux tower measurements that a land conversion (deforestation followed by
site tillage and preparation for cultivation) adds an additional 18 % of
GHG emissions to a deforestation event, expansion of northern land
development could add an additional 3 % or
33 350 Gg CO2-e yr-1 to the reportable national GHG emissions
for the duration of the expanded deforestation period.
This assessment is subject to a number of uncertainties. Firstly, a component
of our emissions estimate is based on eddy covariance measurements of
CO2 flux, which typically have an error of 10–20 % (Aubinet et al.,
2012). In this study, energy balance closure suggested fluxes were
underestimated by up to 13 % across the entire observation period. Energy
balance closure ranged from <10 % flux loss during the intact
canopy phase to > 20 % error during the final three LUC phases
when the flux instruments were at 3 m height measuring net soil CO2
emissions from the smoothed, vegetation-free ploughed soil surface during
preparation. Secondly, it is difficult to predict the nature of future
deforestation (rate, area, specific location) and the emission comparisons
presented here are indicative only. Catchments selected by Petheram et al. (2014)
regarded as suitable or with potential for future development were
based on biophysical properties only, were unconstrained by the regulatory
environment and did not account for conservation and cultural values placed
on identified land and water resources. In addition, challenges to
agricultural expansion in northern Australia include uncertain land and water
tenure, high development costs and lack of existing water infrastructure,
logistics and technical constraints, lack of human capital and distance to
markets, all factors that may restrict land clearing. It is well understood
that the availability and cost of water for irrigated, or irrigation-assisted
agriculture is critical for viable agriculture in northern Australia
(Petheram et al., 2008, 2009). Australian governmental policies currently
support small-scale, precinct or project-scale approaches, based on
well-understood water and soil resources, where water allocation is capped.
The current policy and market instruments are likely to ensure that
development remains measured and restricted, unlike development of previous
decades in other regions of eastern and southern Australia.
As a result we used a conservative estimate of potential land suitability
area (311 000 ha over a 5-year clearing period), as estimates of assumed
clearable area ranging up to 700 000 ha (e.g.
Douglas–Daly catchment, Adams and Pressey, 2014) or over 1 million ha across
northern Australia (Petheram et al., 2014), areas that may be unlikely given
capital investment requirements as well as conservation and cultural
considerations. Our comparison with burning emissions is also influenced by
the deforestation period we assume. This was based on patterns of historic
rates of clearing as there are periods when deforestation rates have easily
exceeded 311 000 ha over 5-year periods, particularly in Queensland
(Commonwealth of Australia, 2015a) and a longer duration of deforestation
reduces the impact on annual national GHG accounting.
There is also uncertainty arising from our emissions from debris burning.
Russell-Smith et al. (2009b) estimated errors associated with emissions
estimates from the Western Arnhem Land Fire Abatement (WALFA) project, a
savanna burning based GHG abatement scheme operating in the Northern
Territory. This is a project area of the 23 893 km2 consisting of a
wide range of vegetation types including open-forest and woodland savanna and
sandstone heaths in escarpment areas. Russell-Smith et al. (2009b) estimated
the accountable emissions from savanna burning at
272 ± 100 Gg CO2-e yr-1
(95 % CI), an error of 30–35 % of the mean. Uncertainty was
ascribed to errors in remotely sensed burn area mapping, fuel load
estimation, spatial variation of fire severity, errors in BEF for each fuel
class and EFs. At the spatial scale of our study area, there were no
uncertainties with the burnt area, vegetation structure or fuel type
classification, and we used site-specific fuel load estimations used in our
calculations, all of which would reduce the error associated with our fire
emissions estimate. Russell-Smith et al. (2009b) also reported low
coefficients of variability (CV %) of for BEFs across fine, course and
heavy fuel types for high severity fires, ranging from 0.3 to 11 % and
2 % CV for EFs for CH4 and N2O. Site-specific sources of error
include high spatial variability of on-site fuel loads which had a CV %
of ∼ 70 % (Table 4) and uncertainty associated with the BEF we
assumed for coarse and heavy fuel loads (0.9), which is higher than that
derived for late dry season savanna fires (0.36, 0.31 respectively,
Russell-Smith et al., 2009b). This value was assumed as repeat burning of
coarse and heavy fuels ensured ∼ 10 % of biomass remained as ash
and charcoal at the CS site. This assumed BEF is also consistent with FullCAM
(4.00.3) BEF of 0.98 for forest fire with 100 % of trees killed, although
this is setting is based on Surawski et al. (2012) who found little empirical
evidence for BEF for stand replacement fires. However, given the detailed
on-site measurements of fuel load, error in our fire-derived emissions would
be of the order of 20 % or less.