Quantifying the relative importance of greenhouse gas emissions from current and future savanna land use change across northern Australia

Abstract

The clearing and burning of tropical savanna leads to globally significant emissions of greenhouse gases (GHGs); however there is large uncertainty relating to the magnitude of this flux. Australia's tropical savannas occupy the northern quarter of the continent, a region of increasing interest for further exploitation of land and water resources. Land use decisions across this vast biome have the potential to influence the national greenhouse gas budget. To better quantify emissions from savanna deforestation and investigate the impact of deforestation on national GHG emissions, we undertook a paired site measurement campaign where emissions were quantified from two tropical savanna woodland sites; one that was deforested and prepared for agricultural land use and a second analogue site that remained uncleared for the duration of a 22-month campaign. At both sites, net ecosystem exchange of CO₂ was measured using the eddy covariance method. Observations at the deforested site were continuous before, during and after the clearing event, providing high-resolution data that tracked CO₂ emissions through nine phases of land use change. At the deforested site, post-clearing debris was allowed to cure for 6 months and was subsequently burnt, followed by extensive soil preparation for cropping. During the debris burning, fluxes of CO₂ as measured by the eddy covariance tower were excluded. For this phase, emissions were estimated by quantifying on-site biomass prior to deforestation and applying savanna-specific emission factors to estimate a fire-derived GHG emission that included both CO₂ and non-CO₂ gases. The total fuel mass that was consumed during the debris burning was 40.9MgCha^-1 and included above- and below-ground woody biomass, course woody debris, twigs, leaf litter and C₄ grass fuels. Emissions from the burning were added to the net CO₂ fluxes as measured by the eddy covariance tower for other post-deforestation phases to provide a total GHG emission from this land use change. The total emission from this savanna woodland was 148.3MgCO₂-eha^-1 with the debris burning responsible for 121.9MgCO₂-eha^-1 or 82% of the total emission. The remaining emission was attributed to CO₂ efflux from soil disturbance during site preparation for agriculture (10% of the total emission) and decay of debris during the curing period prior to burning (8%). Over the same period, fluxes at the uncleared savanna woodland site were measured using a second flux tower and over the 22-month observation period, cumulative net ecosystem exchange (NEE) was a net carbon sink of -2.1MgCha^-1, or -7.7MgCO₂-eha^-1. Estimated emissions for this savanna type were then extrapolated to a regional-scale to (1) provide estimates of the magnitude of GHG emissions from any future deforestation and (2) compare them with GHG emissions from prescribed savanna burning that occurs across the northern Australian savanna every year. Emissions from current rate of annual savanna deforestation across northern Australia was double that of reported (non-CO₂ only) savanna burning. However, if the total GHG emission, CO₂ plus non-CO₂ emissions, is accounted for, burning emissions are an order of magnitude larger than that arising from savanna deforestation. We examined a scenario of expanded land use that required additional deforestation of savanna woodlands over and above current rates. This analysis suggested that significant expansion of deforestation area across the northern savanna woodlands could add an additional 3% to Australia's national GHG account for the duration of the land use change. This bottom-up study provides data that can reduce uncertainty associated with land use change for this extensive tropical ecosystem and provide an assessment of the relative magnitude of GHG emissions from savanna burning and deforestation. Such knowledge can contribute to informing land use decision making processes associated with land and water resource development.

Biogeosciences, 13, 62856303, 2016 www.biogeosciences.net/13/6285/2016/ doi:10.5194/bg-13-6285-2016 Author(s) 2016. CC Attribution 3.0 License.

Mila Bristow1,2, Lindsay B. Hutley1, Jason Beringer3, Stephen J. Livesley4, Andrew C. Edwards1, and Stefan K. Arndt4

1School of Environment, Research Institute for the Environment and Livelihoods, Charles Darwin University, NT, 0909, Australia

2Department of Primary Industry and Fisheries, Berrimah, NT, 0828, Australia

3School of Earth and Environment, The University of Western Australia, Crawley, WA, 6009, Australia

4School of Ecosystem and Forest Sciences, The University of Melbourne, Burnley, Victoria, 3121, Australia Correspondence to: Lindsay B. Hutley ([email protected])

Received: 15 May 2016 Published in Biogeosciences Discuss.: 19 May 2016Revised: 22 September 2016 Accepted: 22 September 2016 Published: 23 November 2016

Abstract. The clearing and burning of tropical savanna leads to globally signicant emissions of greenhouse gases (GHGs); however there is large uncertainty relating to the magnitude of this ux. Australias tropical savannas occupy the northern quarter of the continent, a region of increasing interest for further exploitation of land and water resources. Land use decisions across this vast biome have the potential to inuence the national greenhouse gas budget. To better quantify emissions from savanna deforestation and investigate the impact of deforestation on national GHG emissions, we undertook a paired site measurement campaign where emissions were quantied from two tropical savanna woodland sites; one that was deforested and prepared for agricultural land use and a second analogue site that remained uncleared for the duration of a 22-month campaign. At both sites, net ecosystem exchange of CO2 was measured using the eddy covariance method. Observations at the deforested site were continuous before, during and after the clearing event, providing high-resolution data that tracked CO2 emissions through nine phases of land use change. At the deforested site, post-clearing debris was allowed to cure for 6 months and was subsequently burnt, followed by extensive soil preparation for cropping.

During the debris burning, uxes of CO2 as measured by the eddy covariance tower were excluded. For this phase, emissions were estimated by quantifying on-site biomass prior to deforestation and applying savanna-specic emission

Quantifying the relative importance of greenhouse gas emissions from current and future savanna land use changeacross northern Australia

factors to estimate a re-derived GHG emission that included both CO2 and non-CO2 gases. The total fuel mass that was consumed during the debris burning was 40.9 Mg C ha1 and included above- and below-ground woody biomass, course woody debris, twigs, leaf litter and C4 grass fuels. Emissions from the burning were added to the net CO2 uxes as measured by the eddy covariance tower for other post-deforestation phases to provide a total GHG emission from this land use change.

The total emission from this savanna woodland was 148.3 Mg CO2-e ha1 with the debris burning responsible for 121.9 Mg CO2-e ha1 or 82 % of the total emission. The remaining emission was attributed to CO2 efux from soil disturbance during site preparation for agriculture (10 % of the total emission) and decay of debris during the curing period prior to burning (8 %). Over the same period, uxes at the uncleared savanna woodland site were measured using a second ux tower and over the 22-month observation period, cumulative net ecosystem exchange (NEE) was a net carbon sink of 2.1 Mg C ha1, or 7.7 Mg CO2-e ha1.

Estimated emissions for this savanna type were then extrapolated to a regional-scale to (1) provide estimates of the magnitude of GHG emissions from any future deforestation and (2) compare them with GHG emissions from prescribed savanna burning that occurs across the northern Australian savanna every year. Emissions from current rate of annual savanna deforestation across northern Australia was double

Published by Copernicus Publications on behalf of the European Geosciences Union.

6286 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

that of reported (non-CO2 only) savanna burning. However, if the total GHG emission, CO2 plus non-CO2 emissions, is accounted for, burning emissions are an order of magnitude larger than that arising from savanna deforestation. We examined a scenario of expanded land use that required additional deforestation of savanna woodlands over and above current rates. This analysis suggested that signicant expansion of deforestation area across the northern savanna woodlands could add an additional 3 % to Australias national GHG account for the duration of the land use change. This bottom-up study provides data that can reduce uncertainty associated with land use change for this extensive tropical ecosystem and provide an assessment of the relative magnitude of GHG emissions from savanna burning and deforestation. Such knowledge can contribute to informing land use decision making processes associated with land and water resource development.

1 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 [notdef] 0.5 Pg C yr1 to the

atmosphere, which is approximately 10 % of anthropogenic carbon emissions (Le Qur 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 re 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 yr1, 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 (Fearn-side 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 re from these biomes are the largest contributors to global LUC emissions (Le Qur 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 worlds 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 of9.2 million ha, and LUC and associated economic development in northern Australia is a government imperative and this is likely to involve expansion and intensication of grazing, irrigated cropping, horticulture and forestry (Committee on Northern Australia, 2014). Drivers of this potential expansion in food and bre 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 scientic 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 signicant 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 uxes from contrasting land uses (Lambin et al., 2013). These studies typically compare microclimate and uxes of GHGs from pastures and/or crops with adjacent for-

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fail to regenerate cover via natural regrowth or restoration planting (Commonwealth of Australia, 2015a).

2.1 Study sites

Both savanna woodland sites were located within the DouglasDaly river catchment approximately 300 km south of Darwin, Northern Territory (Fig. 1). Both sites are OzFlux sites (http://www.ozflux.org.au

Web End =www.ozux.org.au ), with ux 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 ux tower network that aims to provide continental-scale monitoring of CO2 uxes and surface energy balance to assess trends and improve predictions of Australias 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 (Ta

ble 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.

Both sites were classied 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) ([notdef]SD) at the UC site was

estimated at 1180 [notdef] 225 mm (19702012, Australian Water

Availability Project (AWAP), http://www.csiro.au/awap

Web End =www.csiro.au/awap ), similar to the CS site at 1107 [notdef] 342 mm (19852013, Bureau of Mete

orology 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, http://www.firenorth.org.au

Web End =www.renorth.org.au ) and indicated that res had occurred within the ux footprint of the UC ux tower in 5 out of the last 13 years (20002013), whereas no res had occurred within the footprint of the CS site. The average re return time for the entire Australian savanna biome is 3.1 years (Beringer et al., 2015).

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est 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 ux 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 Australias 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 uxes 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 re, a signi-cant source of GHG emissions across northern Australia.

2 Methods

In this study we used a paired site approach, where concurrent uxes 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 uxes 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 dened under Australias National Greenhouse Accounts as the loss of forest/woodland cover due to direct human-induced actions that

6288 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

Figure 1. Location of the uncleared site (UC) and the cleared savanna (CS) sites south of Darwin, Northern Territory. The inset gure shows the distribution of the savanna biome across northern Australia as dened by Fox et al. (2001).

Table 1. Site characteristics for the uncleared savanna (UC) and cleared (CS) sites. Site soil orders are given as in Isbell (2002) with savanna vegetation classied using Fox et al. (2001). Fire frequency was estimated from re mapping taken from the North Australian Fire Information system (NAFI, http://www.firenorth.org.au

Web End =www.renorth.org.au ) for 20002012. The re frequency estimate for the CS site excluded the debris res 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[prime]33.12[prime][prime] S, 131 23[prime]17.16[prime][prime] E 14 33[prime]48.71[prime][prime]S, 132 28[prime]39.47[prime][prime] E Soils Red Kandosol Red Kandosol

Vegetation type Savanna woodland with mixed grasses Map unit D4. E. tetrodonta, C. latifolia, Terminalia grandiora, Sorghum spp., Heteropogon triticeus

Savanna woodland with mixed grasses

Map unit D4. E. tetrodonta, Erythrophleum chlorostachys, Corymbia. bleeseri, Sorghum spp., H. triticeusMap unit area (km2) 59 986 59 986Fire frequency (yr1) 0.23 0.07

Basal area (m2 ha1) 8.3 6.8

Canopy height (m) 16.4 14.2

Above-ground biomass (Mg C ha1) 30.6 [notdef] 9.2 26.2 [notdef] 7.0

Stem density (ha1) 330 [notdef] 58 643 [notdef] 102

Overstorey LAI (wet/dry) NA/0.8 0.9/0.5

MODIS LAI (wet/dry) 1.5/0.9 1.6/1.0MAP (mm) 1372a/1180b 1107cMax 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, 20072012. b Gridded precipitation (AWAP, 19702012). c Tindal BoM station (14.52 S, 132.38 E, data from 1985 to 2013). NA stands for not available.

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using standard plots of Fh +Fe vs. Fn Fg using 30 min ux

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 lling of uxes was undertaken using DINGO, which uses an articial neural network (ANN) model following Beringer et al. (2007). Model training uses gradient information in a truncated Newton algorithm. NEE and uxes of sensible, latent and ground heat uxes were modelled using the ANN with incoming solar radiation, VPD (vapour pressure decit), 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 nighttime 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.

2.3 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 t. Only MODIS values with a quality ag 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

Web End =http://lpdaac.usgs.gov/sites/default/les/public/modis/ http://lpdaac.usgs.gov/sites/default/files/public/modis/docs/MODIS-LAI-FPAR-User-Guide.pdf

Web End =docs/MODIS-LAI-FPAR-User-Guide.pdf http://lpdaac.usgs.gov/sites/default/files/public/modis/docs/MODIS-LAI-FPAR-User-Guide.pdf

Web End = ).

2.4 Land use conversion

The specic 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

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2.2 Flux measurements and data processing

Eddy covariance systems at both sites consisted of CSAT3 3-D ultrasonic anemometers (Campbell Scientic 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 ux (Fg, W m2) and volumetric soil moisture ( v, m3 m3) from surface to 2.5 m depths were measured at both sites. The radiation balance was measured using a CNR4 net radiometer (Fn, W m2) (Kipp and Zonen,

Zurich).

Thirty minute covariances were stored using data loggers (CR3000, Campbell Scientic, Logan), and data postprocessing 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-lled 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 uxes. 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 ux to sensible heat ux (Fh, W m2) and application of the WPL correction to the latent heat (Fe, W m2 and CO2 uxes (Fc) (Isaac et al., 2016). Level 3 data also include the correction of the ground heat ux for storage in the layer above the heat ux plates (Mayocchi and Bristow, 1995).

Gap lling of meteorology and uxes along with ux 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 lling and partitioning for Ozux (DINGO) system as described by Beringer et al. (2016b). In summary, DINGO gap lls meteorological variables (air temperature, specic humidity, wind speed and barometric pressure) using nearby Bureau of Meteorology (BoM, http://www.bom.gov.au

Web End =www.bom.gov.au ) automatic weather stations that were correlated with tower observations. All radiation streams were gap-lled 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-lled using either nearby BoM stations or AWAP data. Soil temperature and moisture were lled 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

6290 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

Table 2. 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 ux 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 SepOct 2011 Intact savanna 16 21.5 Wet season pre-clearing Oct 2011Feb 2012 Intact savanna 16 21.5 Wet season clearing MarMay 2012 Savanna deforested using bulldozers, followed by debris decomposition, understory grass germination

3 7

Dry season pre-burn MayAug 2012 Vegetation debris curing, understorey grass growth 2 7 Debris burning Aug 2012 Debris and grasses burnt, soil ripped to 60 cm to removeroots, roots and remaining debris stockpiled, reburnt

2 7

Dry season post-burn AugNov 2012 Grass and shrubs germination and resprouting 1 7 Early wet season Nov 2012Jan 2013 Removal remaining below-ground biomass. Wet season rains stimulates grass growth, shrub resprouting andgrowth

1 7

0 3

Dry season AprJul 2013 Soil cultivation in stages 0 3

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 0.10.2 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.

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 (1020 %, BoM, Tindal station, NT). Prior to ignition, 100 m re 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 ne

and coarse fuels (grasses, litter and twigs, dened below) and woody debris (heavy fuels). Heavy fuels that were not completely consumed following the initial burn were then stock-piled in rows 12 m in height and reignited until the

fuel was consumed (Plate 1c). Inspection of debris post- re suggested 5 % of ne 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).

Wet season JanMar 2013 All regenerated vegetation removed, soil bed preparation

2.5 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-specic emission factors for re 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 re 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 re practices with an emissions accounting framework, the Emissions Abatement through Savanna Fire Management (Commonwealth of Australia, 2015b, http://www.comlaw.gov.au/Series/F2013L01165

Web End =www.comlaw.gov.au/Series/ http://www.comlaw.gov.au/Series/F2013L01165

Web End =F2013L01165 ). This methodology is a legislative instrument that establishes procedures for abatement projects for prescribed savanna burning and denes emission factors for four fuel classes; ne (grass and litter < 6 mm diameter fragments), coarse (6 mm5 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 efciency (BEF) for each fuel type, dened as the mass of fuel exposed to re 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 (1000600 mm MAP) and for both early and late dry season res, which are res occur-ring after 1 August which typically have higher intensity and

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(a) (b)

(c) (d)

Plate 1. 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-re site preparation with all biomass consumed, Phase 9.

combustion efciencies than early dry season res (Russell-Smith et al., 2013).

We used these denitions of vegetation fuel type (woodland savanna with mixed grass) and associated EFs, carbon contents and N : C ratio values as dened in the methodology to estimate GHG emissions from the debris re using the following equation:

E =

Xi (FLj [notdef] BEFj [notdef] CCj [notdef] N : CN2O [notdef] EFi,j [notdef] GWPi), (1)

where E is the sum of emissions in Mg CO2-e ha1 for each GHG i (CO2, CH4 and N2O), FLj is the fuel load for fuel type j (ne, coarse, heavy) in Mg C ha1, BEFj is the burning efciency factor, CCj is the fractional carbon content, N : CN2O is the fuel nitrogen to carbon ratio for N2O emissions, EFi,j is 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 re differed from a typical savanna re in that there was a signicantly higher heavy fuel load present and it was of high intensity which consumed the vast majority of fuel (Plate 1c,d), reected in the assumed BEFs we used. The re-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.

2.6 Quantifying fuel loads

To accurately quantify emissions from the debris re, ne, coarse and heavy fuels were estimated using plots and tran-sects established across the 295 ha deforestation area. For ne fuels, six 100 m transects were randomly located and at 20 m intervals along each transect, all ne (grass, woody litter) and coarse (twigs, sticks) fuels were harvested from 1 m2 quadrats, dried and weighed to give a mean ne 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 re 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 cm3). Five rot classes (RCs)

were dened and assigned to each CWD fragment to cap-

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6292 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

Table 3. Cumulative precipitation and mean NEE, Re and GPP (Mg C ha1 month1) for each of the LUC phases at the CS site as measured by the ux tower. These uxes 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 signicantly different means labelled with an asterisk. On the days of ignition during the debris burning phase, ux data at the CS site were excluded. Integrated uxes are given for the post-clearing period (507 days) and the entire observation period (668 days) for both sites in Mg C ha1.

CS UC

Phase Period Rainfall NEE Re GPP Rainfall NEE Re GPP LULUC phases number (d) (mm) (Mg C ha1 month1) (mm) (Mg C ha1 month1)

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

Wetdry 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 nal bed prep. and cultivation 9 90 0 0.29 0.32 0.02 10.8 0.06 0.91 0.85(Mg C ha1) (Mg C ha1)

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 signicantly different mean NEE at the 5 % level, signicant at 1 %.

ture the decay gradient of fragments. These were dened 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 identiable) 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 cm3 (RC1) to 0.56 g cm3 (RC5).

Above-ground biomass was quantied by surveying all woody plants > 1.5 m in height or > 2 cm DBH across eight 50 [notdef] 50 m plots. All woody individuals were identied to

species and stem diameter at 1.3 m height (DBH) and tree height were measured. Region-specic 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).

2.7 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 Australias na

tional GHG emissions (Whitehead et al., 2014) and is 25 % of the Northern Territorys 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 DouglasDaly 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 dened 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 DouglasDaly catchment were estimated using our estimates for savanna land conversion combined with satellite-derived annual deforestation area (19902013) 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 yr1. Annual deforestation emissions data for the

Northern Territory and the northern Australian savanna re-

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M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions 6293

gion were taken from the National Greenhouse Gas Inventory (NGGI) for the same period 19902013. 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 dened as savanna by Fox et al. (2001, Fig. 1). Mean annual deforestation emissions from the savanna area of each state and territory (19902013) were summed to calculate a mean ([notdef]SD) annual deforestation rate for the northern Australian

savanna area (1.92 million km2) in Gg CO2-e yr1.

Emissions from savanna burning were calculated using the online Savanna Burning Abatement Tool (SAVBat2, http://www.savbat2.net.au

Web End =www. http://www.savbat2.net.au

Web End =savbat2.net.au ) using the predened 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 (http://www.firenorth.org.au

Web End =www.renorth.org.au ) with fuel load estimates from VFT mapping, GHG emission factors and burn efciencies 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 re-growth 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 DouglasDaly river catchment, savanna of the NT and the northern Australian savanna. Mean annual burning emissions for 19902013 were calculated and are reported as non-CO2 (CH4, N2O) and total emissions (CO2, CH4 and N2O)

in Gg CO2-e yr1.

2.8 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 identied as having future clearing potential. These areas were based on the land use assessment of northern Australian catchments by Petheram et al. (2014) and identied 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 identied 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 yr1.

3 Results

3.1 Pre-clearing site comparisons

Pre-clearing meteorology, ux 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 (SeptemberDecember) through to the middle of the wet season (JanuaryFebruary, 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 signicantly different between the two sites during (P < 0.64, df = 321). Seasonal patterns of Tair,

VPD (Fig. 2b), LAI (Fig. 2c) and C uxes (NEE, GPP, Re, Fig. 2d) were similar when both sites were intact, although precipitation was 340 mm higher at the UC site (Table 3).

At both sites, NEE shifted from being a weak sink of less than 1 mol CO2 m2 s1 during the late dry sea

son to a net source of CO2 during the early wet season (Fig. 2d). During this period, Re increased rapidly from +2 to +5 mol m2 s1 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 mol m2 s1 and remained at this

rate until they cleared (March 2012). Re was relatively stable during this period and NEE increased to 2 mol m2 s1

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 uxes by MODIS LAI for each site fur-

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6294 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

(a)

(a)

a)

0

100

30

T air (oC) Rainfall (mm)

(mm)

Rainfall (mm)

20

50

10

0

4

30

0

3

25

20

(b)

2

VPD (kPa)

20

15

NEE (mol CO 2m-2s-1)

15

1

10

10

5

2

0

(c)

-5

LAI

1

-10

-15

0

8

6

Figure 3. (a) Daily precipitation and (b) diurnal patterns of NEE at the CS site for the week prior to the clearing event of 26 March 2012 (vertical bar) and 3 weeks post-clearing.

15 mol CO2 m2 s1 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 [notdef] 0.63 mol m2 s1 and was

not signicantly different to mean daily NEE at the UC site of 0.80 [notdef] 0.93 mol m2 s1 (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 [notdef] 0.24 mol m2 s1, 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-

re and soil preparation, Phases 69.

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 ux 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 2012January 2013, Table 3). While greatly reduced, GPP still occurred at the CS site during this 13.7-month period (0.38 Mg C ha1 month1) via resprouting of felled over-

storey and subdominant trees and shrubs, as well as grass germination and growth stimulated by early wet season precipitation (November 2012January 2013, 361 mm, Table 3).Ecosystem respiration during this period was higher at the UC site (+1.12 Mg C ha1 month1) when compared to the

CS site (+0.82 Mg C ha1 month1) and, given the large

decline in GPP, the CS site was a small net C source at

+0.51 Mg C ha1 month1, compared to the UC site which was a weak sink of 0.03 Mg C ha1 month1.

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4

CO 2 flux (mol m-2 s-1)

2

0

-2

-4

-6

-8

-10

23-Sep-11 23-Oct-11 22-Nov-11 22-Dec-11 21-Jan-12 20-Feb-12

Figure 2. Comparative meteorology and uxes 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 26 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).

ther reduced differences to 2 % (data not shown), suggesting site differences were small and the UC site provides a suitable control for the CS site.

3.2 Fluxes following clearing

Clearing of the 295 ha block commenced on 2 March 2012 and the bulldozers reached the footprint of the ux tower at

09:00 h local time on 6 March (Fig. 3). As for Phase 1, energy balance closure of ux 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) v ranging from 0.08 to 0.10 m3 m3 and subsoil v (50 cm depth) ranging from 0.12 to 0.14 m3 m3. As a result, pre-clearing uxes were high and NEE reached

-20

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-1.5

emission from debris burning totalled 121.9 Mg CO2-e ha1, with 9.5 % of this total comprising non-CO2 emissions (Table 4).

3.4 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 ha1

or +26.4 Mg CO2-e ha1. In comparison, NEE from the

UC site over the same period was 0.78 Mg C ha1 or

2.9 CO2-e ha1. Adding NEE from post-clearing phases (Phases 29, Table 3) to emissions from debris burning (Table 4) gave a total emission of +148.3 Mg CO2-e ha1 for the

CS site. The CO2-only emission from debris burning plus post-clearing NEE was +136.7 Mg CO2 ha1, which was a

ux 45 times larger than the observed savanna CO2 sink at the UC site over the post-clearing period.

3.5 Upscaled and projected emissions from deforestation and savanna burning

Table 5 provides mean ([notdef]SD) GHG emissions estimates for

savanna burning and deforestation for 19902013. At all spatial scales, annual mean burnt area dwarfed the mean annual land area deforested. For the DouglasDaly catchment area, reported non-CO2 emissions from savanna burning were 577 [notdef] 124 Gg CO2-e yr1, almost 4 times larger

than emissions from the mean annual savanna deforestation rate of 163 [notdef] 162 Gg CO2-e yr1. For the Northern Terri

tory 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 [notdef] 5601 Gg CO2-e yr1, with emissions

from Queensland savanna area dominating this amount at 15 762 [notdef] 5566 Gg CO2 yr1. This is double that of the re

ported (non-CO2 only) emission from prescribed burning at 6740[notdef] 1740 Gg CO2-e yr1 (Table 5).

Emissions estimates that include future deforestation rates would be equivalent to savanna burning, at least for the duration of the additional deforestation. For the DouglasDaly catchment, this future emission is estimated at 756 Gg CO2-e yr1 and across the Northern Territory savanna area, this would be 3413 Gg CO2-e yr1, rates of emission that are equivalent to burning emissions catchment (DouglasDaly, 577 [notdef] 124) and state scales (Northern Territory savanna,

3490 [notdef] 922 Gg CO2-e yr1). Emissions that include future

deforestation rates for the northern Australian savanna region were estimated at 24 393 Gg CO2-e yr1 and would be 3 times the reported savanna-burning annual emissions (Table 5).

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Intact savanna

Dry season curing, rotting

Debris burning

Dry season post-burn

Early wetregrowth Wet season

soil prep

Dry season soil preparation, bed cultivation

2.5

Aug-11 Dec-11 Mar-12 Jun-12 Sep-12 Jan-13 Apr-13 Jul-13

Clearing event

Wet-dry debris curing, rotting

2.0

Cumulative NEE (Mg C haphase)

1.5

CS

UC

UC long term

1.0

0.5

0.0

-0.5

-1.0

Figure 4. 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 ux network (OzFlux, see Beringer et al., 2016a) and using the sites 8-year ux record (20072013), the long-term cumulative mean NEE is plotted for each land use phase of (grey line; [notdef]95 % CI). The dashed line

indicates zero net CO2 ux.

Cumulative NEE over all the post-clearing LUC phases was +7.2 Mg C ha1 at the CS site compared to a net sink

of 0.78 Mg C ha1 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 uxes from both sites for the complete observation period. Three signicant 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 ha1 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 ha1

that occurred over the nal 6 months (Fig. 4) in preparation for cropping. Over this phase, the UC site was a net sink of

0.62 Mg C ha1.

3.3 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 ([notdef]SD) above-ground

biomass of 26.9 [notdef] 7.0 Mg C ha1 and a range of 14.4 to

39.3 Mg C ha1 across the eight biomass plots. The mean below-ground biomass was estimated at 9.0 [notdef] 2.4 Mg C ha1

and CWD was 1.4 [notdef] 0.6 Mg C ha1. Fine and coarse fuels

were 1.4 [notdef] 0.7 and 0.5 [notdef] 1.0 Mg C ha1 respectively, giving

a total fuel mass of 38.2 Mg C ha1. 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

debris, enhanced soil CO2 efux 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 ux 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 ux, 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 ux emissions, in particular

N2O, with short-term emissions often following disturbances (Grover et al., 2012; Zona et al., 2013) and can account for a signicant fraction of annual emissions.

The net CO2 source measured by the ux 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 uxes of CH4 and N2O, which can be signicant for LUC events in certain ecosystems (Tian et al., 2015). Grover et al. (2012) compared soil CO2 and non-CO2 uxes from native savanna with young pasture and old pastures (57 and 2530 years old) in the DouglasDaly 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 uxes were generally small, especially N2O emissions, although these mea-

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6296 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

Table 4. Measured fuel loads, assumed burning efciencies (BEFs), carbon contents, N : C ratio and emissions factors (EFs) used to estimate GHG emissions from the burning of the post-deforestation ne, 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 http://www.legislation.gov.au/Details/F2015L00344

Web End =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 ha1) (Mg C ha1) content ratio CO2 CH4 N2O Total

Fine 1.1 [notdef] 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 [notdef] 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 [notdef] 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 [notdef] 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 [notdef] 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

4 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 re ecology and re-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 re 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 quantied (Hutley et al., 2005). This approach provides essential data for bottom-up GHG and carbon accounting studies as micrometeorological conditions and associated uxes 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 ha1, with the remainder attributed to NEE as measured by the ux tower.This ux comprised signicant CO2 losses via respiration of

M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions 6297

Table5.Greenhousegasemissionsfor19902013fromprescribedsavannaburningandsavannadeforestationatcatchment(DouglasDalyrivers),state/territory(NorthernTerritory

savannaarea)andregionalscales(northernAustraliansavannaarea,Fig.1).Forsavannaburning,burntareaandassociatedmeanannualemissions([notdef]SD)aregivenforbothreported

surements 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 uxes not associated with debris burning to rene 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 [notdef] 0.16 Mg C ha1 yr1 and

is representative of a savanna site at a near-equilibrium state in terms of carbon balance given the low re frequency (3 in 13 years, Table 1) with high severity res uncommon (1 in 8 years of ux measurements). The annual increase in tree biomass at this UC site is 0.6 t C ha1 yr1 (Rudge, Hutley, Beringer, unpublished data) and, given an above-ground standing biomass of 28 t C ha1, suggests a regeneration period of approximately four to ve 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 re-growth if a cleared site is abandoned and not further prepared for cultivation. This has actually been a relatively common transition and a signicant 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 Gar-

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non-CO 2(CH 4,N 2O)andtotalemissions(CO 2,CH 4andN 2O).Fortheidenticalareasasusedforsavannaburning,meanannualGHGemissionsfromdeforestation([notdef]SD)aregiven.

FortheDouglasDalyrivercatchment,deforestationareawastakenfromLawesetal.(2015)andcombinedwithdeforestationemissionsfromtheCSsite.Deforestationemissions

(19902013)fortheNTandthenorthernAustraliansavannaareaaretakenfromtheStateandTerritoryGreenhouseGasInventories(CommonwealthofAustralia,2015a).Inboldtext

aretheemissionsassociatedwiththecurrentdeforestationrateplusexpandeddeforestationareasasidentiedbyPetherametal.(2014),whicharecombinedwithemissionsfromthe

CSsitetogiveanupscaledestimateofpotentialemissionswithagriculturaldevelopmentatthethreespatialscales.

SavannaregionSavannaburningSavannadeforestation

Burntareaa Emissions non-COa 2 Emissions totala Deforestation area Emissions total Expanded deforestation Expanded emissions

(hayr1 ) (Gg CO 2 -e yr1 ) (Gg CO 2 -e yr1 ) (ha yr1 ) (Gg CO 2 -e yr1 ) aread (ha) totald (Gg CO 2 -e yr1 )

a Burnt area and emissions data estimated using the on-line Savanna Burning Abatement Tool (SAVBat2), 19902013. These emissions are CH 4 and N 2 O only.b Deforestation area data taken from Lawes et al. (2015), upscaled using the emissions from the CS

sitefromthisstudy,19902013.c Deforestation area and emissions data taken from the State and Territory Greenhouse Gas Inventories (Commonwealth of Australia, 2015a), 19902013.d Expanded deforestation area data taken from catchments as identied

byPetherametal.(2014),upscaledusingtheGHGemissionsfromtheCSsitefromthisstudyandaddedtohistoricemissions.

b 20 000 756

NorthernTerritory13419410[notdef]4873003490[notdef]92286255[notdef]228801717[notdef]611

c 114 500 3413

NorthernAustralian32249254[notdef]111760046740[notdef]1729166586[notdef]4272578605[notdef]34976

c 311 000 24 393

b 163 [notdef] 162

c 398 [notdef] 128

c 16 161 [notdef] 5601

DouglasDalyrivercatchment2482100[notdef]490400577[notdef]12414270[notdef]30641275[notdef]454

6298 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

nett (2009) examined deforestation emissions from the Eucalypt woodland NVIS vegetation class, as per UC and CS site classication. Modelled emissions were 136 [notdef] 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. Australias major beef-producing areas across central and southern Queensland and northern central New South Wales were classied into 11 bioregions, with the northernmost bioregion, the northern Brigalow Belt, falling within the savanna biome.Vegetation biomass from this bioregion was estimated at84.7 [notdef] 7.1 Mg ha1 or 41.4 Mg C ha1, 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 re emissions are included. As such, the IPCC assumption that CO2 emissions from the previous years burning are recovered by the following years 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 re 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 yr1, Table 5) could be well in excess of current annual burning emissions (6740 Gg CO2-e yr1, Table 5), at least for the period of enhanced clearing, which in this study we assumed to be 5 years.

In 2013, Australias 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 [notdef] 50.8 Mg CO2-e ha1 us

ing 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 ha1 (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 yr1. For the duration of the expanded deforestation, this is a 2.2 % increase to Australias nation emission over and above the historic savanna LUC emissions (16 161 Gg CO2-e yr1), which are 2.9 % of national emissions. Using our nding from ux 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 yr1 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 ux, which typically have an error of 1020 % (Aubinet et al., 2012). In this study, energy balance closure suggested uxes were underestimated by up to 13 % across the entire observation period. Energy balance closure ranged from <10 % ux loss during the intact canopy phase to > 20 % error during the nal three LUC phases when the ux instruments were at 3 m height measuring net soil CO2 emissions from the smoothed, vegetation-free ploughed soil surface during preparation.Secondly, it is difcult to predict the nature of future deforestation (rate, area, specic 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 identied 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. DouglasDaly catchment, Adams and Pressey, 2014) or over 1 million ha across northern Australia (Petheram et al., 2014), areas that may be unlikely

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M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions 6299

given capital investment requirements as well as conservation and cultural considerations. Our comparison with burning emissions is also inuenced 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 [notdef] 100 Gg CO2-

e yr1 (95 % CI), an error of 3035 % of the mean. Uncertainty was ascribed to errors in remotely sensed burn area mapping, fuel load estimation, spatial variation of re 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 classication, and we used site-specic fuel load estimations used in our calculations, all of which would reduce the error associated with our re emissions estimate. Russell-Smith et al. (2009b) also reported low coefcients of variability (CV %) of for BEFs across ne, course and heavy fuel types for high severity res, ranging from 0.3 to 11 % and 2 % CV for EFs for CH4 and N2O. Site-specic 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 res(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 char

coal at the CS site. This assumed BEF is also consistent with FullCAM (4.00.3) BEF of 0.98 for forest re 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 res. However, given the detailed on-site measurements of fuel load, error in our re-derived emissions would be of the order of 20 % or less.

5 Conclusions

While GHG emissions from savanna deforestation are dominated by debris burning, emissions from soil tillage and soil bed preparation are likely to be 20 % of the total emission, suggesting that satellite-based emissions based on oxidation of cleared vegetation alone do not capture all phases of LUC

prior to cultivation. Savanna burning, using the area as dened in this study, was 1.5 % of Australias national GHG emissions and is of similar magnitude to emissions associated with historic savanna deforestation. However, the deforestation scenario could increase Australias GHG emissions by at least 3 % per annum for the duration of the expansion, depending on the area and deforestation rate. These are indicative estimates only, but suggest that the impacts of northern agricultural development will have an impact on the national GHG budget and will need to be considered in northern land use decision making processes. These considerations are also particularly relevant given the emission reduction targets set by Australia following the 21st Conference of Parties to the UN Framework Convention on Climate Change (COP21/CMP11) to reduce GHG emissions by 26 to 28 % from 2005 to 2030.

6 Data availability

Flux data from the UC and CS tower sites is available from Australias Terrestrial Ecosystem Research Network (TERN, http://tern.org.au/

Web End =http://tern.org.au/ ) OzFlux Data Portal. Data can be accessed from http://data.ozflux.org.au/portal/site/datainfo.jspx

Web End =http://data.ozux.org.au/portal/site/datainfo.jspx under a user licence as described at http://data.ozflux.org.au/portal/site/licenceinfo.jspx

Web End =http://data.ozux.org.au/portal/ http://data.ozflux.org.au/portal/site/licenceinfo.jspx

Web End =site/licenceinfo.jspx . Fire-derived GHG emissions data can be estimated via the SavBat2 online calculation tool as described in the text using emissions factors and burning efcacies as given in Table 4. Deforestation data are available via the technical reports as cited, with savanna-specic data available on request to the corresponding author.

Acknowledgements. Financial support for this study was provided by the Australian Research Councils Linkage Project LP100100073 and Discovery Project DP0772981. Jason Beringer is funded under the Australian Research Councils Future Fellowship program (FT1110602). Support for ux data collection and archiving was provided by Peter Isaac of the Australian ux network, OzFlux (http://www.ozflux.org.au

Web End =www.ozux.org.au ), which is funded by the Australian Terrestrial Ecosystem Research Network (TERN, http://www.tern.org.au

Web End =www.tern.org.au ). Chris and Bridget Schulz provided access to the property and eld assistance throughout all phases of the land use change we monitored. We are grateful for the technical expertise and eld assistance of Matthew Northwood and Michael Brand who maintained the eddy covariance tower. Yan-Shih Lin, Amanda Lilleyman and Allison OKeefe provided eld support during the intensive eld campaigns. Thanks also to the Department of Environment for provision of savanna-specic deforestation GHG emissions data, 19902013 and the two reviewers who provided constructive comments on the original manuscript.

Edited by: B. AmiroReviewed by: B. Amiro and one anonymous referee

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6300 M. Bristow et al.: Quantifying the relative importance of greenhouse gas emissions

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