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Climate controls on the variability of fires in the tropics and subtropics
van der Werf, G.R.; Randerson, J.T; Giglio, L.; Gobron, N.; Dolman, A.J.
published in
Global Biogeochemical Cycles
2008
DOI (link to publisher)
10.1029/2007GB003122
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Link to publication in VU Research Portal
citation for published version (APA)
van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N., & Dolman, A. J. (2008). Climate controls on the
variability of fires in the tropics and subtropics. Global Biogeochemical Cycles, 22(GB3028).
https://doi.org/10.1029/2007GB003122
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Climate controls on the variability of fires in the tropics and subtropics
Guido R. van der Werf,
1
James T. Randerson,
2
Louis Giglio,
3
Nadine Gobron,
4
and A. J. Dolman
1
Received 12 October 2007; revised 15 May 2008; accepted 28 May 2008; published 5 September 2008.
[1] In the tropics and subtropics, most fires are set by humans for a wide range
of purposes. The total amount of burned area and fire emissions reflects a complex
interaction between climate, human activities, and ecosystem processes. Here we
used satellite-derived data sets of active fire detections, burned area, precipitation, and
the fraction of absorbed photosynthetically active radiation (fAPAR) during 1998–2006
to investigate this interaction. The total number of active fire detections and burned
area was highest in areas that had intermediate levels of both net primary production
(NPP; 500–1000 g C m
2
year
1
) and precipitation (1000–2000 mm year
1
),
with limits imposed by the length of the fire season in wetter ecosystems and by
fuel availability in drier ecosystems. For wet tropical forest ecosystems we developed a
metric called the fire-driven deforestation potential (FDP) that integrated information
about the length and intensity of the dry season. FDP partly explained the spatial
and interannual pattern of fire-driven deforestation across tropical forest regions.
This climate-fire link in combination with higher precipitation rates in the interior
of the Amazon suggests that a negative feedback on fire-driven deforestation
may exist as the deforestation front moves inward. In Africa, compared to the Amazon, a
smaller fraction of the tropical forest area had FDP values sufficiently low to prevent
fire use. Tropical forests in mainland Asia were highly vulnerable to fire, whereas forest
areas in equatorial Asia had, on average, the lowest FDP values. FDP and active fire
detections substantially increased in forests of equatorial Asia, however, during El Nin˜o
periods. In contrast to these wet ecosystems we found a positive relationship between
precipitation, fAPAR, NPP, and active fire detections in arid ecosystems. This relationship
was strongest in northern Australia and arid regions in Africa. Highest levels of fire
activity were observed in savanna ecosystems that were limited neither by fuel nor by the
length of the fire season. However, relations between annual precipitation or drought
extent and active fire detections were often poor here, hinting at the important role
of other factors, including land managers, in controlling spatial and temporal
variability of fire.
Citation: van der Werf, G. R., J. T. Randerson, L. Giglio, N. Gobron, and A. J. Dolman (2008), Climate controls on the variability
of fires in the tropics and subtropics, Global Biogeochem. Cycles, 22, GB3028, doi:10.1029/2007GB003122.
1. Introduction
[2] In the tropics and subtropics, fire is used for several
purposes including the clearing of forest for pasture or
agriculture [Goldammer, 1990; Cochrane, 2003], for nutri-
ent cycling, pest control, and grassland maintenance in
savanna ecosystems [Scholes and Archer, 1997], and for
the removal of agricultural waste [Yevich and Logan, 2003].
The only areas without fires are deserts where fuels are not
available and in equatorial tropical forests where precipita-
tion is high year-round. Savanna ecosystems with their
alternating wet and dry seasons when fuels respectively
build-up and dry out provide ideal fire conditions and
observations of fire from space have shown t hat these
ecosystems have the highest fire frequencies [Cahoon et
al., 1992; Barbosa et al., 1999; Stroppiana et al., 2000].
[
3] Fire dynamics in deforestation areas of tropical forests
have received considerable attention because of, including
the large impact of fire on regional biodiversity [Phillips,
1997; Curran et al., 2004] and because emissions from
these fires are an important driver of climate change
GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 22, GB3028, doi:10.1029/2007GB003122, 2008
Click
Here
for
Full
A
rticl
e
1
Department of Hydrology and Geo-Environmental Sciences, Faculty
of Earth and Life Sciences, VU University Amsterdam, Amsterdam,
Netherlands.
2
Department of Earth System Science, University of California, Irvine,
California, USA.
3
Science Systems and Applications, Inc., NASA Goddard Space Flight
Center, Greenbelt, Maryland, USA.
4
Global Environmental Monitoring Unit, Institute for Environment and
Sustainability, European Commission Joint Research Center, Ispra, Italy.
Copyright 2008 by the American Geophysical Union.
0886-6236/08/2007GB003122$12.00
GB3028 1of13
[Forster et al.,2007].Deforestationisahuman-driven
process, and deforestation rates are partly dependent on
political and economic incentives [Murdiyarso et al., 2004;
Morton et al., 2006]. Climate, however, may provide
additional constraints because the use of fire in the land
clearing process is more effective when fuels dry out for
longer periods. Emissions in deforestation areas are there-
fore usually higher in drought years [Siegert et al., 2001;
Page et al., 2002; Nepstad et al., 2004; van der Werf et al.,
2004; Randerson et al., 2005] and the use of fire may
increase in the future as a result of regional and global
climate change [Hoffmann et al., 2003]. Once burned,
fragmentation and partial loss of canopy cover allow for a
more rapid drying of fuels. This initiates a positive feedback
loop that may increase fire activity in tropical forests
[Cochrane et al., 1999; Nepstad et al., 1999].
[
4] In most savanna ecosystems, the length of the dry
season is not a limiting f actor for fire. The amount of fuel is
much lower than in forested regions. Fires here primarily
consume herbaceous vegetation and thus fuel loads depend
on the productivity of the preceding wet season. In principle,
higher precipitation rates a llow for higher rates of net
primary production (NPP) and biomass at the onset of the
dry season [Griffin et al., 1983]. In Kruger National Park,
van Wilgen et al. [2000] observed a strong positive corre-
lation between precipitation rates during the wet season and
fire activity during the following dry season. Spessa et al.
[2005] and Randerson et al. [2005] found the same positive
precipitation - fire activity relationship in northern Australia
using different satellite data sets.
[
5] Besides precipitation, grazing and land use also in-
fluence fuel loads so the precipitation –fire relation may not
be uniform. Grazing may lower the amount of fuel and the
intensity of fires, allowing for woody encroachment which
would not occur with more intense fires [van Langevelde et
al., 2003]. These interactions may influence the relationship
between climate and fire activity. In the absence of fire,
most current savanna regions would have a vastly different
composition with substantial increases in tree cover [Bond
et al., 2005].
[
6] Regional studies like the ones mentioned above have
convincingly highlighted the important role of climate in
shaping spatial and interannual variability in fire activity. A
global analysis of the tropics and subtropics that systemat-
ically examines the sensitivity of fire activity across mois-
ture and productivity gradients is now feasible with almost
10 years of satellite-derived fire activity and precipitation
from the Tropical Rainfall Measuring Mission (TRMM)
satellite.
[
7] Here we investigate relations between climate, NPP,
and fire activity in the global tropics and subtropics. We
used observations of fires derived from TRMM Visible and
Infrared Scanner (TRMM-VIRS) [Giglio et al., 2003] and
burned area derived from the Moderate Resolution Imaging
Spectroradiometer (MODIS) [Giglio et al., 2006]. We also
used TRMM satellite retrieved precipitation rates, and Sea-
viewing Wide Field-of-view Sensor (SeaWiFS) fraction of
absorbed photosynthetically active radiation (fAPAR) as
input to the Carnegie-Ames-Stanford-Approach (CASA)
biogeochemical model to estimate NPP. We show fire
activity was highest in ecosystems with intermediate levels
of productivity and that fires limited by fuel availability in
arid regions and by the length of the dry season in moist
regions. We also show how climate partly regulated the
amount of burning in tropical forests with important impli-
cations for future deforestation rates. Our quantitative
assessment of the role of climate in shaping spatial and
temporal variability in fire activity may be beneficial for
further improving and testing fire modules in dynamic
global vegetation models (DGVMs) aiming to predict
future fire patterns.
2. Data Sets and Methods
[8] For our analysis we used several data streams from
sensors on-board the TRMM satellite, which has an orbit
inclined at 35° and a spatial coverage between 38°N and
38°S[Kummerow et al., 1998]. The orbital properties of
TRMM were designed to allow for a progressing overpass
time, spanning over one complete diurnal cycle within a
month. This allows for a compreh ensive asse ssment of
rainfall and fire activity, both of which show pronounced
diurnal cycles [Prins and Menzel, 1992; Negri et al., 2002;
Giglio, 2007]. The platform carries several instruments,
including the Precipitation Radar (PR) and TRMM Micro-
wave Imager (TMI) that were primarily designed to study
rainfall [Kummerow et al., 1998], and the Visible and
Infrared Scanner (VIRS) that is used to observe fires [Giglio
et al., 2003] in addition to its use for other purposes. We used
fAPAR from SeaWiFS on the SeaStar satellite to estimate
NPP. TRMM was launched in November 1997 and SeaWiFS
in August 1997, and both satellites are still in operation. We
used data from January 1998 –November 2007, but focused
on the 1998–2006 period for those analyses where annual
data was needed (e.g., trends in fire activity). Analysis of
burned area was confined to the 2001–2006 period because
of the availability of MODIS observations. Tropical forest
extent was based on the IGBP land cover classif ication
scheme using the MODIS MOD12Q1 land cover type data
set for 2001 [Friedl et al., 2002].
2.1. Active Fire Detections
[
9] Active fire detections are pixels where a fire was
observed during the satellite overpass. Most algorithms to
detect fires are based on the strong radiance from fires in
the mid-infrared [Dozier, 1981]. Active fire products have
been developed for several sensors including the Advanced
Very High-Resolution Radiometer (AVHRR), Along Track
Scanning Radiometer (ATSR), MODIS, and VIRS. Here we
used the TRMM-VIRS product that includes corrections for
missing observations due to cloud cover [Giglio et al.,
2003]. TRMM-VIRS active fire detections are available on
a monthly time step with a 0.5° 0.5° spatial resolution
from http://daac.gsfc.nasa.gov/precipitation/trmmVirsFire.
shtml and shown in Figure 1a.
2.2. Burned Area
[
10] Active fire detections indicate the presence of fire at
the time of overpass, but these detections give no direct
information about fire size. Although active fires are useful
GB3028 VAN DER WERF ET AL.: CLIMATE CONTROLS ON FIRES
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GB3028
to detect spa tial and temporal vari ability in fire activity,
information on burned area is necessary to estimate the
spatial extent. Two global burned area data sets exist for
the year 2000 (GBA2000 described by Gre´goire et al. [2003]
and GLOBSCAR described by Tansey et al. [2004]). Multi-
year products based on MODIS [Roy et al., 2005] and SPOT-
VGT [Tansey et al., 2008] recently have become available.
Here we used data from the global burned area product
developed by Giglio et al. [2006]. This product relates Terra
MODIS fire hot spots to 500 meter Terra MODIS burned area
for selected regions, using ancillary data on vegetation
continuous fields and the ‘‘cluster’’ size of the hot spots. A
region-specific ‘‘burned area per active fire detected’’ scalar
is derived as a function of these ancillary data, and extrap-
olated in time and space to estimate burned area during the
MODIS era (starting in 2001) [Giglio et al., 2006]. For
clarity, we will use the term burned fraction, which is the
fraction of the total area of a grid cell that burned during a
given time interval.
2.3. Precipitation
[
11] We used the 3B43 time series from TRMM that is
based on accumulations of the direct TRMM measurements
from both PR and TMI sensors in combination with global
gridded rain gauge data. The time series has a monthly time
step and a 0.25° 0.25° spatial resolution [Huffman et al.,
1995]. A map with mean annual precipitation (MAP) is
shown in Figure 1b.
2.4. fAPAR and NPP
[
12] To estimate the fraction of photosynthetically active
radiation that is absorbed by plant canopies (fAPAR), we
used the SeaWiFS-derived product developed by Gobron et
al. [2002]. This product uses information from the blue
spectral band, which is sensitive to the aerosol loading in
the atmosphere, to account for atmospheric effects. The
algorithm follows two steps: (1) the spectral bidirectional
reflectance factors measured in the red and near-infrared
are first rectified for atmospheric contamination using the
blue band and adjusted to account for angular effects, and
(2) the rectified red and near-infrared bands are then
combined to derive fAPAR [Gobron et al., 2006].
[
13] For NPP we used a submodule from the CASA
biogeochemical model [Potter et al., 1993]. NPP was
calculated for each grid cell and month as the product of
photosynthetically active radiation (PAR), fAPAR, and a
light use efficiency (LUE) that depended locally on temper-
ature and moisture [Field et al., 1998]. PAR was derived
from Bishop and Rossow [1991] and we used GISTEMP
temperature anomalies [Hansen et al., 1999] in combination
with the CRU 1961– 1990 temperature climatology [New et
al., 1999] and TRMM prec ipitation as data sources to
calculate the m oisture and temperature controls on the
LUE. In our analysis we used a maximum unstressed LUE
of 0.5 g C/MJ PAR that was derived from a comparison of
modeled and observed NPP [van der Werf et al., 2006]. In
Figure 1c a map of mean annual NPP is shown. Mean annual
Figure 1. (a) TRMM-VIRS active fire detections (year
1
, color scale capped at 500 detections year
1
),
(b) TRMM-derived precipitation rates (mm year
1
, capped at 3000 mm year
1
), and (c) net primary
production (g C m
2
year
1
) based on SeaWiFS fAPAR. Fire data were summed, and precipitation and
NPP were averaged to a 1° 1° spatial resolution. All panels show the mean over 1998–2006.
GB3028 VAN DER WERF ET AL.: CLIMATE CONTROLS ON FIRES
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GB3028
NPP was 40 Pg C year
1
for our study region (between
38°N and 38°S).
2.5. Fire-Climate Metrics
[
14] Our main objective was t o determine the role of
climatic controls on spatial and interannual variability in fire
activity. For this, fire activity was quantified as the total
number of active fire detections or the burned fraction during
each fire season. For each grid cell, we defined the fire season
as the period starting 3 months before and ending 4 months
after the average peak fire month (Figure 2). In most areas
fires were confined to a seasonal interval that was consider-
ably shorter than the 8 month fire season we defined here.
Thus, over 98% of all TRMM active fires were included in
our analysis. The remaining active fire detections were
associated with volcanoes, gas flares, and fires burning
outside the regular fire season. In Australia, where interan-
nual variability in the peak fire month is relatively large, our
approach still captured 94% of the fire detections. We defined
the peak fire month as the month with the maximum number
of active fire detections over 9 annual fire cycles. We defined
a fuel accumulation period as the 13 month period starting 12
months before the average peak fire month. We chose this
period to include most of the precipitation available for the
growth of annual (herbaceous) plant functional types during
the preceding wet season (Figure 2).
[
15] We defined a fire-driven deforestation potential
(FDP) scalar to investigate the role of drought on fire
activity in tropical forests. The FDP scalar combines infor-
mation about both the length and the intensity of the dry
season:
FDP
x;y;t
¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
#DM
x;y;t
=8
1 PPT
DM
x;y;t
=100
q
ð1Þ
where #DM is the number of dry months within the 8 month
fire season. Dry months were defined as a month with
precipitation (PPT) below 100 mm month
1
[Phillips et al.,
1994; Saleska et al., 2003]. PPT
DM
represents the mean
precipitation during these dry months. This scalar was
calculated for each grid cell (x,y) and fire season (t) and
yielded a value that was 1 for grid cells with 8 months with
zero precipitation, and 0 when precipitation never dropped
below 100 mm month
1
during the fire season. For the
tropical forest grid cells that did not have any active fire
observations to define a peak fire month, we extrapolated
the peak fire month from neighboring grid cells, taking into
account shifts in PPT expected near the equator.
3. Results
[16] In deforestation areas within Southeast Asia and
the Amazon, fire activity increased during dry years
(Figure 3a, areas in red with a positive correlation between
FDP and active fire detections), whereas in arid ecosystems,
including the Sahel, the Kalahari Desert in southern Africa,
and northern Australia, fire activity increased during wet
years (Figure 3b, areas in red with a positive correlation
between pr ecipi tation rates during the fuel accumu lation
season and active fire detections). In Figure 3c these two
different responses are summarized for different p-levels,
areas in red are grid cells where FDP and fire activity were
positively correlated, while areas in blue are grid cells where
precipitation during the growing season and fire activity were
positively correlated. Areas having a negative or positive
relation between fire and both FDP and PPT during the
growing season were assigned the limiting factor that resulted
in the lowest p-value.
[
17] The difference in response to drought is shown in more
detail for a wet (southern Borneo) and an arid (northwest
Australia) ecosystem in Figure 4. Fire activity in wet eco-
systems was limited by the length of the dry season, while fire
activity in arid ecosystems was limited by the amount of
precipitation during the wet season, which partly governed
fAPAR and the amount of fuel available to burn (Figure 4c).
[
18] Areas receiving about 1000 mm year
1
MAP were
neither limited by fuel nor by the length of the dry season,
but there was no clear MAP threshold separating the two
limiting factors. In wet ecosystems where interannual
variability in FDP explained more than 50% of the
variance in interannual fire activity the 10th percentile,
median, and 90th perce ntile MAP values were 881, 1564,
and 2717 mm year
1
. In arid ecosystems where IAV in
growing season precipitation was a better predictor these
MAP values were 408, 658, and 1377 mm year
1
, respec-
tively. Maximum fire activity occurred at intermediate levels
of precipitation and NPP (Figures 5 and 6). Below we further
describe results for deforestation regions, ecosystems with
intermediate product ivity, and arid ecosystems.
3.1. Tropical Forest Ecosystems
[
19] In southern Borneo, we found a strong relationship
between fire and the length and intensity of the dry season
as represented by FDP (Figures 3a and 4a). FDP and fire
activity were also positively correlated in most of the arc of
deforestation in the Amazon (the south-eastern edge of the
Figure 2. Mean monthly precipitation rates and active fire
detections for all grid cells where fire activity was observed.
The fire season is defined here as the 8-month period starting
3 months before and ending 4 months after the peak fire
month (PFM). The 13-month period preceding and including
the peak fire month was used to estimate precipitation
levels during the period when herbaceous fuels typically
accumulate.
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