Converting tropical forests to agriculture increases fire risk by fourfold

Abstract

Deforestation exacerbates climate change through greenhouse gas emissions, but other climatic alterations linked to the local biophysical changes remain poorly understood. Here, we assess the impact of tropical deforestation on fire weather risk – that is the climate conditions conducive to wildfires – using high-resolution convection-permitting climate simulations. We consider two land cover scenarios for the island of Borneo: land cover in 1980 (forest scenario) and land cover in 2050 (deforestation scenario) to force a convection-permitting climate model, using ERA-Interim reanalysis for the 2002-2016 period. Our findings revealed significant alterations in post-deforestation fire precursors such as increased temperature, wind speed and potential evapotranspiration and decreased humidity, cloud cover and precipitation. As a result, fire weather events that would occur once a year, are likely to occur four times a year following deforestation. Likewise, extreme conditions, such as those occurring on longer time-horizons greater than 20 years, the magnitude of extreme fire weather is likely to double following deforestation. These increases in extreme fire weather conditions demonstrate the key role of tropical forests in regulating regional climate processes, including reduced fire weather risk.

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Environ. Res. Lett. 17 (2022) 104019 https://doi.org/10.1088/1748-9326/ac8f5c
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LETTER
Converting tropical forests to agriculture increases re risk by
fourfold
Ralph Trancoso1,∗, Jozef Syktus1, Alvaro Salazar2, Marcus Thatcher3, Nathan Toombs4,
Kenneth Koon-Ho Wong5, Erik Meijaard1,6, Douglas Sheil7and Clive A McAlpine8
1The University of Queensland, School of Biological Sciences, St Lucia, QLD 4072, Australia
2Departamento de Biología, Facultad de Ciencias, Universidad de La Serena, Casilla 554, La Serena, Chile
3CSIRO Marine and Atmospheric Research, Aspendale, VIC 3195, Australia
4Department of Environment and Science, Brisbane, Queensland Government, Australia
5Queensland University of Technology, Faculty of Science, Brisbane, QLD 4000, Australia
6Borneo Futures, Bandar Seri Begawan, Brunei
7Forest Ecology and Forest Management Group, Wageningen University & Research, PO Box 47, 6700 AA Wageningen, The Netherlands
8The University of Queensland, School of Earth and Environmental Sciences, St Lucia, QLD 4072, Australia
∗Author to whom any correspondence should be addressed.
E-mail: r.trancoso@uq.edu.au
Keywords: convection-permitting, fire risk, climate modelling, deforestation, climate extremes, fire weather
Supplementary material for this article is available online
Abstract
Deforestation exacerbates climate change through greenhouse gas emissions, but other climatic
alterations linked to the local biophysical changes from deforestation remain poorly understood.
Here, we assess the impact of tropical deforestation on fire weather risk—defined as the climate
conditions conducive to wildfires—using high-resolution convection-permitting climate
simulations. We consider two land cover scenarios for the island of Borneo: land cover in 1980
(forest scenario) and land cover in 2050 (deforestation scenario) to force a convection-permitting
climate model, using boundary conditions from ERA-Interim reanalysis for the 2002–2016 period.
Our findings revealed significant alterations in post-deforestation fire precursors such as increased
temperature, wind speed and potential evapotranspiration and decreased humidity, cloud cover
and precipitation. As a result, fire weather events that would occur once a year in the forested
scenario, are likely to occur four times a year following deforestation. Likewise, for extreme
conditions, such as those occurring on longer time-horizons than 20 years, the magnitude of
extreme fire weather is likely to double following deforestation. These increases in extreme fire
weather conditions demonstrate the key role of tropical forests in regulating regional climate
processes, including reduced fire weather risk.
1. Introduction
Tropical deforestation and its environmental con-
sequences remain a major global concern (Song et al
2018, Hansen et al 2020, Trancoso 2021). Vast areas
of tropical forests were converted to agriculture in
the Amazon, Congo, and Southeast Asia (SE Asia)
over the past decade (FAO 2020) and large areas of
remaining tropical forests are at risk of conversion
(Meijaard et al 2020). Deforestation increased signi-
ficantly across SE Asia in the 2000s (Hansen et al 2013,
Lewis et al 2015), but decreased over the past decade
in some areas (Gaveau et al 2019, FAO 2020), such
as Borneo, a hotspot of deforestation in the region.
Conversion to oil palm and other plantation crops
have contributed to nearly 50% of the recent defor-
estation (Gaveau et al 2019) and with the continuing
demand for vegetable oil, the drivers of deforestation
are expected to continue (Meijaard et al 2020).
Forest fires have become more frequent and more
intense in SE Asia in recent decades, causing major
economic and environmental issues, including air
pollution and human health impacts, increased GHG
emissions, and a loss in forest ecosystem services
(Cochrane 2003, Bowman et al 2017). Deforesta-
tion and forest fragmentation, in turn, contribute
to increased fire risk, which is further exacerbated
by climatic variability frequently associated with
© 2022 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
El Niño conditions (the dry phase of ENSO) and
anthropogenic climate change. These factors are
impacting ‘fire weather conditions’—these being the
atmospheric component of wildfire risk, by increas-
ing temperature extremes and water vapour deficit
(Findell et al 2017, Abatzoglou et al 2019, Park et al
2021, Zhong et al 2021). The interplay between defor-
estation and fire is evident in Borneo, which experi-
enced extensive deforestation between 1973 and 2015
(Miettinen et al 2016), with many associated vegeta-
tion fires (Gaveau et al 2019). Droughts, timber har-
vesting and forest fragmentation increase the likeli-
hood of forest die-back, flammability and repeated
fires (Siegert et al 2001, van Nieuwstadt and Sheil
2005, Corlett 2016, Brando et al 2019). Selective tree
removal, as commonly practiced in Bornean forests,
makes remaining forests more vulnerable to fires due
to the opening of the canopy and increased drying
within the forest understorey (Langner et al 2007,
Staal et al 2015). In southern and eastern Borneo,
the climate has become hotter and drier with more
frequent extremes because of historical deforestation
(McAlpine et al 2018).
The assimilation of land cover maps into cli-
mate models in one way to assess the impact of
land cover changes on regional climate and thus can
be used to understand how continuing deforestation
could affect the regional climate. However, the spa-
tial resolution of conventional global climate mod-
els (100–200 km) or regional models (10–20 km) and
the nuances of regional land cover change (<1 km)
are poorly matched. Also, the convection paramet-
rization used in these models limit their ability to
resolve fine scale climate processes (such as con-
vection) associated with the changes in energy and
moisture exchanges between vegetation and atmo-
sphere. Nevertheless, climate models can also be
run at very-high spatial resolution (1–4 km), such
as those used for weather forecasting, which are
called convection-permitting models (CPMs) (Prein
et al 2015, Guichard and Couvreux 2017). CPMs
represent land-surface characteristics and explicitly
resolve small-scale processes in the atmosphere such
as the movement of liquid or gas driven by differ-
ences in temperature, which is expected to reduce
projection uncertainty associated with the convec-
tion parameterizations of coarser resolution climate
models (RCMs) (Kendon et al 2021). Most stud-
ies on the added value of CPMs have focused on
extreme climate, especially convective rainfall (Lucas-
Picher et al 2021). However, by explicitly represent-
ing the landscape, CPMs also offer a great opportun-
ity to tackle how changes in land cover may affect
regional climate. Previous assessments of CPM sim-
ulations against observations, such as satellite data
and flux towers across different land covers have
shown CPMs perform better than RCMs distinguish-
ing differences in regional climate over different
land covers (Vanden Broucke and Van Lipzig 2017,
Ge et al 2021). Yet, to the best of our knowledge, the
impact of tropical deforestation on regional climate—
that is the changes in the vegetation-atmosphere feed-
backs following forest replacement, and the effect
on regional atmospheric processes—has not been
studied through CPMs. Deforestation is known to
increase fire weather risk, but studies quantifying
and attributing them are still limited (Findell et al
2017, Zhong et al 2021). Specifically, how the elimin-
ation of forest-atmosphere feedbacks and water and
energy exchanges following deforestation may affect
the precursors of fire weather (e.g. rainfall, evapo-
transpiration, wind, temperature, humidity) is largely
unknown. The use of CPMs, however, offers the
opportunity to track both landscape and climate pro-
cesses simultaneously with a great potential to tease
out how deforestation impacts the precursors of fire
weather risk.
In this paper, we investigate the impact of con-
tinuing the conversion of Borneo’s forest to agricul-
ture on its regional climate using CPMs. The specific
objectives are to: (a) assess the key climate drivers
of fire weather following deforestation; (b) examine
changes in selected climate extreme indices impacting
Borneo’s climate; and (c) quantify the changes in fire
weather risk induced by deforestation. We developed
two land cover scenarios: the forest scenario and the
deforestation scenario to force a CPM, using ERA-
Interim reanalysis for the 2002–2016 period to assess
the impact of deforestation on fire risk.
2. Data and methods
2.1. Study area
Borneo is the world’s second largest tropical island
(743 000 km2) after New Guinea and is governed
by three countries: Malaysia (Sarawak and Sabah),
Indonesia (West, Central, South, East, and North
Kalimantan), and the sovereign state of Brunei ‘Neg-
ara Brunei Darussalam’. It has an equatorial climate
with relatively constant temperatures (25 ◦C–35 ◦C)
in lowland areas and variable precipitation (Hendon
2003). Precipitation patterns are dominated by mon-
soonal circulation (Tangang et al 2017) associated
with the southeast ‘dry’ monsoon (May–October)
and a northwest ‘wet’ monsoon (November–April).
Borneo’s highest rainfall is in the West, north-western
and central mountain areas, with drier and more sea-
sonal conditions in the eastern and especially south-
eastern part of the island. The island’s main natural
vegetation is the tall species-rich evergreen rain forest,
dominated by canopy trees.
2.2. Land cover scenarios
In order to assess the likely impact of future defor-
estation on Borneo’s climate, we developed two land
cover datasets to use in high-resolution (4 km grid
cell size) climate modelling experiments. The two
scenarios are representative of land cover in 1980
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Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
(forest scenario) and the projected land cover by 2050
(deforestation scenario; figure 1). The forest scenario
(figure 2(a)) was derived from the Advanced Very
High-Resolution Radiometer satellite data at 1 km
spatial resolution (Hansen et al 2000), using a hier-
archy of pair-wise class trees where a logic based on
vegetation form was applied to find the vegetation
classes. The overall agreement per pixel is 65% and the
agreement of forest ranged from 81%–92% (Hansen
et al 2000). To construct the deforestation scenario,
we first derived a 2015 land cover based on MODIS
and Sentinel satellites. This product consists of level-
1 Ground Range Detected Sentinel-1 images along
with MODIS data for palm plantation detection on
humid SE Asia which were mosaicked and resampled
to 250 m of spatial resolution (Miettinen et al 2016).
The reported accuracy is 91.6% for forest, 85.9% for
plantations and 76.5% for vegetation mosaic. The
large-scale closed canopy oil palm plantation had an
accuracy of 93.7%.
The land cover data were reclassified to IGBP
classes (Chapman et al 2020), where forests were
represented by ‘evergreen broadleaf forest’ (which
includes mangroves, peat swamp forest, lowland ever-
green forest, lower montane evergreen forest and
upper montane evergreen forest). Regrowth vegeta-
tion and timber plantations were classified as ‘veget-
ation mosaic’ and oil palm was included as an addi-
tional class as IGBP classification does not account for
perennial woody crops. A cellular automata approach
was applied to derive progressive deforestation from
2015 to 2050, using historical deforestation rates from
1980 to 2015 over lowland areas (⩽200 mASL). The
approach gradually allocates non-forested gridcells,
allowing deforestation to expand over non-deforested
cells, driven by connectivity and precluded by topo-
graphic and legal territorial constraints. We used the
observed forest loss between 1980 and 2015 to predict
forest loss to the year 2050. Intact forests that were not
protected (Forest Management Zones and Conserva-
tion Areas) were replaced by contiguous areas of three
oil palm classes (1–5 years, 6–12 years, >12 years)
and vegetation mosaic (lands with a mosaic of crop-
lands, regrowth forests, shrublands and timber plant-
ations where no one component occupies more than
60% of the landscape). The resulting land cover for
2050 is shown in figure 2(b). Both mapping products
were resampled to 4 km to match the Conformal
Cubic Atmospheric Model (CCAM) resolution and
the vegetation classes were simplified to represent the
impacts of the conversion of forest to oil palm planta-
tions and vegetation mosaics. In our experiment, the
forest scenario and the climate simulations driven by
it are the baseline to assess the impacts of deforesta-
tion on climate. The extent of deforestation by 2050
is consistent with projected changes used for the Land
Use Model Intercomparison Project—LUMIP (Hurtt
et al 2020). Between 1980 and 2050, 422 168 km2of
Borneo’s forest was projected to be cleared, which is
equivalent to 57.7% of Borneo’s land surface area. It
is important to note that the deforestation scenario
was designed as a plausible future land cover, consist-
ent with recent past trends, in order to investigate the
impact on climate of continuing deforestation. It is
not intended to be the most likely scenario.
2.3. Experimental design
We used the CABLE Land Model (Community Atmo-
sphere Biosphere Land Exchange version 2.0 CABLE)
and the CCAM variable RCMs, both developed
by Commonwealth Scientific Research Organisa-
tion (CSIRO), to assess the impacts of deforesta-
tion on the climate of Borneo (figure 1). CCAM is
a global atmospheric model that simulates regional
climate over a selected area using a variable resolu-
tion grid (MacGregor and Dix 2008, Thatcher and
MacGregor 2009). It has been used for regional cli-
mate impact assessments (Trancoso et al 2020, Eccles
et al 2021). The biosphere atmosphere exchange
was described using CABLE (see Kowalczyk et al
2006 for details). CABLE models radiation, heat,
water vapour and momentum fluxes across the
landscape-atmosphere interface. It captures the inter-
action among the microclimate, plant physiology and
hydrology, enabling vegetation-soil full aerodynamic
and radiative interactions (Kowalczyk et al 2006).
CABLE’s land surface flux sub-model estimates the
coupled transpiration, stomatal conductance, pho-
tosynthesis and partitioning of net available energy
between latent and sensible heat of sunlit and shaded
leaves (Wang and Leuning 1998). In CABLE, func-
tional vegetation characteristics such as leaf area index
(lai), surface roughness, and albedo are represented
by a mosaic approach with up to six dominant veget-
ation types within grid cells. The method estimates a
mixed signal of varying functional vegetation charac-
teristics by simulating climate fluxes individually for
the six dominant vegetation classes and then linearly
combining weights of fractional vegetation at the grid
cell basis. In CABLE, IGBP categories are assimil-
ated as plant functional types (PFT), which have spe-
cific parametrizations for LAI, roughness and albedo.
Three additional PFTs were created for palm oil age
classes (1–5 years, 6–12 years, >12 years), which were
parametrised as 0.5, 0.7 and 0.9 for fractional cover
and 4, 10 and 12 m of height respectively to represent
the changes in canopy properties with age (Meijide
et al 2017), with roughness set by the canopy height
and albedo calculated by radiation balance.
The climate modelling experiments for the forest
and deforestation scenarios were run using the stretch
grid mode of CCAM with a convection-permitting
spatial resolution of 4 km over Borneo. The high-
resolution experiments were run using the spectral
nudging approach through the scaling selective fil-
ter as described by Thatcher and MacGregor (2009).
Instead of nudging laterally like RCMs, CCAM uses
spectral nudging, which is a large-scale nudging
3

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 1. Schematic showing the experimental design for assessing how the conversion of Borneo’s forest impacts fire weather risk
using convection-permitting climate simulations. The landscape simulation component is shown on top with the derivation of
two scenarios—forest scenario (1980) and deforestation scenario (2050). ERA-Interim reanalysis for the 2002–2016 period was
nudged to CCAM (middle part), which is forced with the same boundary conditions but distinct land use scenarios as nested
simulations to understand the climate impacts of deforestation (bottom part).
following the GCMs, while allowing regional scales
to evolve independently. This is specifically designed
to allow the regional climate to evolve independ-
ently. The boundary conditions for higher resolution
downscaling was based on nudging data at six hourly
intervals derived from CCAM run at a 20 km spa-
tial resolution over the SE Asian region (see Chapman
et al 2020 for details), with boundary conditions from
ERA Interim analysis (Dee et al 2011) The variables
used were air temperature, wind, and surface pres-
sure. Humidity was not used to allow the hydro-
logical cycle to evolve independently in our CPM.
The two parallel climate simulations were performed
using CCAM at 4 km spatial resolution for the period
2002–2016 to assess the impact of land cover change
on Borneo’s climate. The period 2002–2016 was selec-
ted because it is representative of recent El Niño
events and has high-quality reanalysis data available.
The key differences between the forest and
deforestation scenarios as prescribed in the CCAM-
CABLE climate model experiments are shown in
figures 2(c) and (d). The oil palm plantations were
classified into three age-based classes—juveniles,
established and mature—where lai, vegetation frac-
tion and surface roughness (zolnd) increased with
age.
It is important to note that this research does not
aim to assess the impact of climate change. The exper-
iment was specifically designed to isolate the influ-
ence of land cover on regional climate—that is separ-
ate from climate change. By comparing two identical
climate simulations nudged to 15 year of historical
reanalysis with the same climate change signal in it,
we can attribute any emerging difference in regional
climate to the land cover change.
2.4. Data analysis
The analysis focussed on the drivers of fire weather
conditions resulting from the replacement of trop-
ical forest with oil palm plantations and vegetation
mosaic of regrowth and timber plantations. We used
data from the CCAM experiments to derive seasonal
averages over the length of experiments (2002–2016)
for annual and dry season (May–October). We also
analysed changes in climate extreme indices during
the 2015 El Niño event, where the Oceanic Niño
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Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Table 1. Summary of key variables derived from the climate modelling experiments used in this study.
Variable name Definition Time-scale
Mean climate
Surface temperature (tsu) Surface temperature is the temperature at or
near a surface.
Seasonal
Relative humidity (rhscrn) The amount of water vapour present in air
expressed as a percentage of the amount
needed for saturation at the same
temperature.
Seasonal
10 m wind speed (u10) Wind speed ten meters above the vegetation. Seasonal
Low cloud cover (lcc) Portion of sky covered by clouds whose base
heights are below 2 km altitude.
Seasonal
Climate extreme indices
Warm spell duration (wsd) Annual count of days with at least four
consecutive days when daily maximum
temperature >90th percentile
Annual
Wet days (wd) Annual count of days with daily
precipitation ⩾1 mm
Annual
Heavy precipitation days (hpd) Annual count of days with daily
precipitation ⩾10 mm
Annual
Water Balance
Precipitation (P) The water released from clouds in the form
of rain, freezing rain, sleet, snow, or hail.
Annual
Potential evapotranspiration (PET) The amount of evaporation that would
occur if a sufficient water source were
available.
Annual
Climatic water balance (WB) Difference between atmospheric supply (P)
and demand (PET), related to water deficit.
Monthly
Aridity index (AI) Measures the degree of aridity by the ratio
of atmospheric demand (PET) to
atmospheric supply (P)
Annual
Fire weather
McArthur Forest Fire Danger Index (FFDI) Measures the degree of danger of fire
associated to atmospheric conditions
6-hourly and daily
Index—a 3 month running mean of sea surface
temperature (SSTs) anomalies in the Niño 3.4 region
(5◦N–5◦S, 120◦–170◦W)—had the strongest positive
anomalies during the length of the experiment.
First, we focused on the changes in near sur-
face weather conditions associated with increasing
fire risk, including screen level temperature and rel-
ative humidity, 10 m wind speed and low cloud
cover (lcc). In addition, daily data was used to derive
selected extreme climate indices (Zhang et al 2011)
for warm spell duration, wet days and heavy pre-
cipitation days (table 1). We also assessed the water
balance-related components to constrain changes in
drought conditions, such as precipitation (P), poten-
tial evapotranspiration (PET), climatic water balance
(P—PET) and aridity index (PET/P) (Trancoso et al
2016). Two fire weather indices used globally were
selected as potentially relevant for this study—the
McArthur Forest Fire Danger Index (FFDI) (Dowdy
et al 2019) and the Fire Weather Index (FWI) (Van
Wagner 1974).
To select one of these indices we assessed daily
time-series from both the FFDI and FWI calculated
from ERA-5 reanalysis and made available by Coper-
nicus climate services. We extracted daily time-series
for the study area—Kalimantan Tengah in Indone-
sian Borneo for the 2002–2016 period. Figure S1
shows that the indices are rather similar and have
a linear relationship and correlation coefficient of
0.91. A more comprehensive assessment of the indices
(Dowdy et al 2010) indicated that: (a) FWI is more
sensitive to wind speed and rainfall and less sensit-
ive to temperature and relative humidity than FFDI;
(b) FWI and FFDI are similar regarding sensitivity to
wind speed, relative humidity and temperature; and
(c) the formulation of the FWI includes many com-
plexities such as conditional discontinuities and its
mathematical implementation is not as easy as for the
FFDI. As the increase in extreme fire weather risk has
also been linked to changing atmospheric humidity
and temperature (Jain et al 2022), we concluded that
the FFDI is more suitable for this application.
5

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Thus, the McArthur FFDI was selected to meas-
ure the atmospheric risk of forest fire (Dowdy et al
2019). The risk of wildfire on the ground has two
levels of controls operating at the land surface and
atmospheric levels. The FFDI does not account for the
landscape controls such as vegetation characteristics,
fuel availability and potential sources of fire ignition.
Rather, it aims to assess solely the atmospheric com-
ponent of wildfire risk—that is the fire weather con-
ditions.
When comparing the differences between our
nested simulations, variables like tsu, rhscrn, P and
PET are more likely to reflect local impacts of defor-
estation on water and energy exchanges, whereas u10
and lcc are associated with changes in atmospheric
circulation processes as a result of deforestation.
The McArthur FFDI was calculated following
Dowdy et al (2019) as shown in equation (1).
FFDI =2e(0.0338 T+0.0234 W−0.0345 RH +0.987 ln(DF)−0.45)
(1)
The daily maximum temperature at a 2 m height
(T), mid-afternoon screen level relative humidity
(RH) and mid-afternoon 10 m wind speed (W) were
derived from the CCAM simulations. In addition, the
drought factor (DF) representing fuel availability was
based on a soil moisture deficit. The soil moisture
deficit is represented by the Keetch Byram Drought
Index calculated from daily rainfall and screen level
maximum temperature at a height of 2 m. For a
detailed description of the drought factor as well as
assessment of the performance of different drought
factors (see Holgate et al (2017)).
We assessed the changes in the return period of
FFDI focussing on values exceeding the 99.7th per-
centile (i.e. 1/365) of the daily gridded time-series,
which is equivalent to 1:1 year return period. The
daily data from the forest scenario for the full length
of the experiment (2002–2016) was used to determ-
ine the 1:1 year FFDI thresholds at grid cell basis. The
thresholds obtained for the forest scenario were used
to calculate the equivalent FFDI return period for the
deforestation scenario.
To assess the impact of deforestation on the fre-
quency of extreme events, the data was fitted to the
generalized extreme value (GEV) distribution (Coles
2001, van Oldenborgh et al 2021). To this end, we
sampled the annual maxima using focal spatial statist-
ics and estimated the extreme FFDI for both scenarios
at 1:5, 1:10, 1:20, 1:50 and 1:100 years return periods.
The GEV distribution is shown in equation (2).
P(x) = exp[−(1+ξx−µ
σ)−1/ξ](2)
where xis the FFDI; and µ,σand ξare the GEV fitting
parameters for location, scale and shape respectively.
To assess the continuous impacts of deforestation
on fire weather conditions at a regional scale, we con-
strained the analysis to the lowland regions of the
Indonesian province of Kalimantan Tengah (figure 2).
We extracted a timeseries of daily FFDI for grid cells
with a reduction in lai > 2 by 2050 to ensure they had
a substantial change in vegetation properties from the
forest to deforestation scenarios. We also assessed the
impact on higher recurrence intervals of FFDI spe-
cifically in tropical forest, oil palm and vegetation
mosaic. This approach ensured a clearer deforestation
signal with reduced noise from mixing Borneo’s cli-
mate regions and land covers.
The statistical significance of changes in climate
variables between the forest and deforestation scen-
arios was evaluated across the entire island of Borneo
as well as in deforested areas using the two-tailed t-test
modified to account for serial correlation in climate
data (Zwiers and Storch 1995).
3. Results
3.1. Impacts of deforestation on fire weather
precursors
In the forest scenario, representative of 1980’s land
cover, Borneo’s tropical forest covered 636 773 km2
(86.8% of the island). This is an estimate limited by
the coarse resolution satellite imagery, which does
not capture small-scale deforestation (e.g. <100 ha),
logging and forest degradation. However, it holds
great value for high-resolution climate modelling
(figure 2(a)). By 2050, we project an estimated
remaining forest cover of 239707 km2, meaning only
32.7% of Borneo’s surface is projected to sustain
tropical forests by 2050 (figure 2(b)), should sim-
ilar historical deforestation trends be maintained. The
deforested areas were converted to oil palm planta-
tions and vegetation mosaics, occupying 126 296 km2
(17.2%) and 367 930 km2(50.1%) of Borneo respect-
ively. Between 1980 to 2015, 37.1% of Borneo’s
primary forest cover had already been cleared, the
deforestation scenario projects a further 20.6% decline
in primary forest cover by 2050. The deforestation
rate projected for 2016–2050 was 46% lower than the
observed deforestation rate for the period 1980–2015.
This was driven by a reduction in the availability
of suitable areas for agriculture. The replacement of
the rough, variable height forest canopy with blocks
of uniform-aged oil palm and timber plantations
decreased the lai (figure 2(c)) and surface roughness
(zolnd; figure 2(d)).
The conversion of forest to oil palm plantations
and vegetation mosaic resulted in warmer and drier
near surface-atmospheric conditions, shifting the dis-
tributions of atmospheric precursors of wildfires. We
selected four climate metrics associated with con-
vection processes and fire risk to show how they
shift following the conversion of forest to palm oil
plantations. Figure 3assesses these changes over
6

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 2. Land cover scenarios used to assess the impacts of tropical deforestation on Borneo’s climate: (A) land use in
1980—forest scenario, (B) land use in 2050—deforestation scenario, change in (C) leaf area index (lai) and (D) surface roughness
(zolnd).
highly deforested areas across Kalimantan Tengah
(see figure 2(b)) using joint distributions—i.e. the
combined probability of two atmospheric precursors
of wildfire at the same location and time. The joint
distribution of seasonal anomalies in temperature
(tsu) and cloud cover (lcc) shows substantial changes
from the forest to the deforestation scenario. In the
simulation with the forest scenario, both tsu and
lcc had narrow distributions centred around the
long-term climatological averages. In the simulations
for the deforestation scenario, the anomalies show
a broader distribution with a reduction in lcc and
increases in seasonal tsu of up to 2 ◦C. In the deforesta-
tion scenario, more than 80% of the joint distribution
is outside of the forest scenario range for the periods
June–August (jja) and September–November (son)
(figures 3(a) and (b)). It is important to note that for
all the climate metrics assessed the mean estimated
changes following deforestation is greater than one
standard deviation of the forest scenario, which is
the baseline (refer to annotations in figure 3). This
suggests that the estimated changes are greater than
the uncertainty. In addition, the differences between
forest and deforestation scenarios are statistically sig-
nificant following the paired t-test for both tsu and
lcc in jja (tsu: t=−219.1, df =31 394, p< 0.001;
lcc: t=328.6, df =31 394, p< 0.001) and son (tsu:
t=−200.1, df =31 394, p< 0.001; lcc: t=340.7,
df =31 394, p< 0.001). The values reported with
the figures here and henceforth indicate the paired
t-test (t), degrees of freedom or number of grid cells
(df ) and statistical significance (p). Near surface wind
speed (u10) and relative humidity (rh) are amongst
the key meteorological drivers impacting fire weather.
Figures 3(C) and (d) show the joint distribution of
u10 and rh anomalies for jja and son seasons for both
7

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 3. Changes in the long-term dry season anomalies of key climate variables related to fire weather risk following
deforestation: (A) surface temperature and low cloud cover for June–August (jja), (B) surface temperature and low cloud cover
for September–November (son), (C) relative humidity and 10 m wind speed for jja and (D) relative humidity and 10 m wind
speed for son. The analysis is for the Kalimantan Tengah region which had a reduction in lai > 2 during the dry season (refer to
figure 1(c) for changes in lai). Forest and deforestation joint distributions were binned at 10% intervals.
scenarios. There is a separation of the joint distri-
butions with little overlap for jja and total separa-
tion for son. The differences between forest and defor-
estation scenarios are statistically significant follow-
ing the paired t-test for both u10 and rh in jja (u10:
t=−392.9, df =31 394, p< 0.001; rh: t=303.6,
df =31 394, p< 0.001) and son (u10: t=−392.9,
df =31 394, p< 0.001; rh: t=360.5, df =31 394,
p< 0.001). This represents a significant shift in the
near surface climate regime towards hotter and drier
conditions with increasing u10 as a consequence of
deforestation. Such changes were more pronounced
over the deforested regions in the deforestation scen-
ario, with reduced lcc by up to 10% and increased u10
by up to 0.6 m s−1(figures S1 and S2).
3.2. Impacts of deforestation on water balance and
aridity
Fire weather conditions conducive to wildfires rely
on the exchanges of water and energy between veget-
ation and the atmosphere. We next assess how the
water balance components as well as the aridity
index changed between the forest and deforestation
scenarios.
The water balance analysis during the 2002–
2016 simulations shows a simultaneous decrease
in precipitation (P) (figure 4(a); P: t=−22.9,
df =74 447, p< 0.001) with local reductions
of up to 0.37 mm d−1and increased potential
evaporation (PET) (figure 4(b); PET: t=107.7,
df =74 447, p< 0.001) with regional increases of up
8

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 4. Projected changes in water availability due to deforestation in Borneo. Long-term average (2002–2016) change in
annual: (A) precipitation (P), (B) potential evapotranspiration (PET), (C) climatic water balance (P-PET) and (D) aridity index
(PET/P). Time-series of (E) climatic water balance and (F) cumulative climatic water balance. Dashed blue and red lines on
(E) show long-term averages for 1980 (P-PET =23.6 mm) and 2050 (P-PET =15.40 mm) respectively.
to 0.38 mm d−1in highly deforested areas. The com-
bined effect of these P and PET is expressed through
the climatic water balance WB, which increased
up to 0.5 mm d−1(figure 4(c); WB: t=−83.1,
df =74 447, p< 0.001). There was a widespread
reduction in WB within deforested areas, espe-
cially in the lowland regions of southern Borneo
where most deforestation is projected. Similarly, an
increase in the Aridity Index by up to 0.15 occur-
ring during the 2002–2016 period also indicates dry-
ing (figure 4(d); AI: t=98.4, df =74 447, p< 0.001).
The regions within Kalimantan Tengah (figure 2(c))
with changes in lai > 2 m2m−2experienced a
decrease in water availability with an average reduc-
tion of 8.2 mm/month (figure 4(e)). When accu-
mulated for the period 2002–2016, there was a
9

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
substatntial reduction in the water budget of more
than 1200 mm between the forest and deforestation
scenarios (figure 4(f)).
3.3. Impacts of deforestation and El Niño on
climate extreme indices
The shift in near surface climate regime towards hot-
ter and drier conditions also affects climate extremes.
We next test if the conversion of forest to deforesta-
tion affects a selection of climate extreme indices and
if the magnitude of the impact is increased during the
2015 El Niño event, which had the strongest posit-
ive SSTs anomalies in the Niño 3.4 region during the
length of the experiment.
Our selected climate extreme indices show an
increase in the warm spell duration during the dry
season of up to 7 d on average for the duration of
the simulations (2002–2016) (figure 5(a); t=200.8,
df =1116 719, p< 0.001) and up to 16 d dur-
ing the 2015 El Niño event (figure 5(b); t=69.24,
df =74 447, p< 0.001). Likewise, the average number
of wet days during the dry season declined by as much
as 7 d in highly deforested locations over the simula-
tion period (figure 5(c); t=−115.8, df =1116 719,
p< 0.001) and by 14 d in the 2015 El Niño event
(figure 5(d); t=−69.56, df =74 447, p< 0.001). The
heavy precipitation days are reduced during the dry
season with up to 8 d yr−1for the full length of the
simulations (figure 5(e); t=−74.0, df =1116 719,
p< 0.001) and up to 13 d yr−1during the 2015
El Niño event (figure 5(f); t=64.86, df =74 447,
p< 0.001). Hence, our results show that the con-
version of forest to deforestation has increased the
incidence of extreme heat and heavy precipitation
events throughout the experiment, and this impact
was intensified during the 2015 El Niño. It is worth
noting that during the 2009–2010 El Niño event, the
magnitude of the changes of these events was still
greater than for the 2015 El Niño event, despite the
weaker signal on sea tsu anomalies (figure S4). There-
fore, ENSO is undoubtedly a major driver of differ-
ences in climate extreme indices, but not the only one.
There are other factors playing out, such as the Indian
Ocean SSTs and surface heat flux variations (Hendon
2003) that we do not examine.
3.4. Impacts of deforestation on fire weather risk
To assess the cumulative impact of deforestation on
fire weather risk, we calculated the FFDI using data
from forest and deforestation scenarios. The FFDI
measures the atmospheric risk of fire, combining
variables such as rainfall, evaporation, wind speed,
temperature and humidity. Figure 6(a) shows the
temporal variability of FFDI from 2002 to 2016 for the
forest and deforestation simulations. The data shows
the FFDI area average for the Kalimantan Tengah
region, for gridcells with a reduction in lai > 2.
The key difference between the two scenarios is the
increasing frequency of high FFDI values for the
deforestation scenario compared to the forest scenario.
For instance, the most prominent fire weather con-
ditions estimated for the forest scenario throughout
the 15 years period, would be equalled or exceeded
four times in the deforestation scenario over the same
period. This means a change in the return period of
the FFDI obtained for the forest scenario from 1:15
to 1:3.75 years after converting forest to oil palm
plantations and vegetation mosaics. These changes in
FFDI values are most prominent during the dry sea-
son with an average increase in daily values of 45%
for jja and 40% for son for those regions experien-
cing a reduction in lai > 2 in Kalimantan Tengah
(figure 6(b)). To illustrate spatial changes in the fre-
quency distribution of FFDI values between the two
scenarios, we computed the equivalent FFDI return
period for the deforestation scenario based on the
1:1 year threshold obtained from the forest scenario.
Figure 6(c) shows the spatial pattern of FFDI return
period for the deforestation scenario equivalent to
1:1 year return period in the forest scenario. The fre-
quency of fire weather risk increased by more than
fourfold across 18.6% of Borneo, especially over low-
lands converted to oil palm plantations and vegeta-
tion mosaics, where the most prominent yearly fire
weather conditions occur at least every three months.
This means that the atmospheric conditions condu-
cive to wildfires that occur once a year in the forest
scenario, become more frequently after the replace-
ment of forest to plantations and vegetation mosaics,
occurring four times a year across highly deforested
regions, such as Kalimantan Tengah. When explor-
ing higher return periods by fitting a GEV statistical
distribution, the increasing fire weather risk is main-
tained for the areas converted to oil palm and veget-
ation mosaic (figure 6(d)). While areas with tropical
forest had little to no changes in the extreme FFDI (i.e.
median change of 3.2%–3.6% for return periods of
5–100 years), the areas converted to vegetation mosa-
ics experienced substantial changes in extreme FFDI
(i.e. median change of 79.6%–109.5% for return peri-
ods of 5–100 years). Interestingly, oil palm planta-
tions had a more progressive increase in the changes
in extreme FFDI when compared to the other land
uses, with median changes of 33.9%–89.1% for return
periods of 5–100 years.
4. Discussion
We used a high-resolution convection-permitting cli-
mate model (CCAM) forced by ERA-Interim reana-
lysis to quantify the impact of converting tropical
forests to agriculture on fire weather risk across
Borneo’s ecosystems. Our results show that the key
drivers of extreme fire weather—including precipita-
tion, potential evapotranspiration, temperature, and
relative humidity are altered at the landscape scale by
deforestation, which in turn affects regional atmo-
spheric processes such as wind speed and cloud cover.
10

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 5. Change in climate extreme indices following deforestation (deforestation scenario—forest scenario) over Borneo for the
climatological average (2002–2016) and the 2015 El Niño: (A) climatology of warm spell duration over the dry season (May to
October—mjjaso); (B) warm spell duration on El Niño over the dry season; (C) climatology of wet days over the dry season;
(D) wet days on El Niño over the dry season; (E) annual climatology of heavy precipitation days; and (F) annual heavy
precipitation days on El Niño.
Hence, once in a year fire weather conditions in
largely intact forest cover, are projected to occur every
three months following deforestation. Borneo has
been a hotspot of deforestation associated with the
conversion to oil palm plantations and has seen an
increase in fire frequency in recent decades (Gaveau
11

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 6. Change in Forest Fire Danger Index (FFDI) resulting from deforestation: (A) daily FFDI time-series for the forest and
deforestation scenarios for the Kalimantan Tengah region with reduction in lai > 2; (B) relationship between the daily FFDI for the
two scenarios for the Kalimantan Tengah region with reduction in lai > 2. Dashed black line is the 1:1 line; (C) FFDI return period
for the deforestation scenario equivalent to 1:1 year return period in the forest scenario; (D) changes in the higher FFDI return
periods obtained with GEV fitting after deforestation per land cover categories. Outliers are not shown to enhance visualization.
et al 2019) with these fires associated with land
conversion and El Niño droughts (Sloan et al 2017).
This is consistent with our main findings in which
deforestation increases fire weather risk, especially
during El Niño conditions. We provide further evid-
ence on the detrimental impact of deforestation
on fire weather risk by quantifying the changes in
fire weather precursors and its recurrence, assuming
deforestation trends are maintained from 1980 to
2050.
Tropical rainforests play a critical role in regulat-
ing regional climate by maintaining the optimal range
of temperature and moisture. Extensive areas of intact
forest ensure the optimal range is maintained by the
evaporative cooling in the canopy (Bonan 2008). The
clearing of tropical forest cover disrupts the climate
regulation function of forests permitting hotter and
more extreme temperatures (Thirumalai et al 2017).
This effect is especially pronounced during hot and
dry conditions such as those occurring in Borneo dur-
ing El Niño events (Taufik et al 2017). Our results are
consistent with these findings. We show increases in
the duration of longer warm spells, and decreased fre-
quency of wet days and heavy precipitation days after
deforestation. Fewer rain days matters as reduced
rainfall increases water deficit and fuel flammability,
thus increasing fire risk (Field et al 2016). Sustained
declines in precipitation over longer periods reduce
water availability, and hence increase fire risk.
Deforestation and forest fragmentation result in
remnant forest being more vulnerable to fires due to
the opening of the canopy, increased extent of edge
habitats, and hotter and drier conditions (Cochrane
and Laurance 2002, Langner et al 2007, Staal et al
2015). The higher humidity and surface roughness of
forested environments also contribute to the main-
tenance of key regional atmospheric processes (Baker
and Spracklen 2019). In our experiment, deforest-
ation reduced the cloud cover by up to 10% and
increased surface wind speed by up to 0.6 m s−1. The
effect of clearing the forest was amplified in the dry
season and during El Niño events, where the dura-
tion of warm spells increased by up to 7 d/year and the
number of wet days decreased by up to 7 d/year over
southern Borneo on average. These changes are amp-
lified 2-3-fold during El Niño events. Likewise, the
atmospheric water balance was substantially altered
over deforested areas with decreased precipitation
and increased potential evapotranspiration. In sum-
mary, the results suggest that deforestation alters the
12

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Figure 7. Schematic representation of main findings of the impacts of deforestation (conversion of forest to agriculture) on fire
weather conditions at local and regional scales with amplification of impacts via atmospheric teleconnections associated with El
Niño.
atmospheric conditions conducive with wildfires at
both the local and regional scales (figure 7). This
is consistent with what Zhong et al (2021) found
for the United States, where fire weather conditions
and fire risk were intensified following replacement
of native vegetation with anthropogenic land cover.
Similarly, recent assessments in the Amazon indicate
that the alterations in forest microclimate resulting
from deforestation—mostly exchanges in moisture
and energy (Baker and Spracklen 2019), contribute to
increasing probability of fire occurrence (Fonseca et al
2019).
The impacts of deforestation on fire weather are
exacerbated by droughts (Rogers et al 2020). Also,
the edge effects associated with oil palm plantations
extends over 300 m into the forests (Nunes et al
2021), increasing fire risk across the forest’s edges.
Consequently, the compounding effects of deforest-
ation, droughts and fires can bring tropical forests
closer to a tipping point beyond which run-away
cycles of deforestation-induced fires result in further
deforestation (Zemp et al 2017, Lovejoy and Nobre
2018, Lenton et al 2019, Staal et al 2020). Our mod-
els, despite their innovative scale and ability to bet-
ter resolve key atmospheric processes, remain sim-
plifications and uncertainties around the simulation
of rainfall and moist convection remain a recognised
challenge for climate models (Stevens and Bony 2013,
Marotzke et al 2017). CPMs such as applied here,
however, emerged as enhanced alternatives to tackle
these challenges across specific regions and offer a
new avenue to unravel the climate impacts of chan-
ging landscapes.
5. Conclusion
We assessed the impact of deforestation on Borneo’s
fire weather conditions using a landscape-climate
modelling approach with a convection-permitting
climate model. Deforestation increased fire weather
risk, especially in southern and eastern Borneo (e.g.
Indonesian Province of Kalimantan Tengah), where
most of the forest clearing occurred. These impacts
arose at both the local and regional scales by alter-
ing the water and energy exchanges and modifying
the local climate (i.e. reduced precipitation, increased
potential evapotranspiration, increased temperature,
and reduced relative humidity), which in turn, affects
regional climatic circulation processes resulting in
reduced cloud cover and increased wind speed. We
show that the higher FFDI following the El Niño con-
ditions exacerbated these changes, further increasing
fire risk. Viewed in their entirety, our findings indic-
ate that fire weather conditions that would otherwise
occur once a year, may occur every three months
following deforestation. This represents a fourfold
increase in fire weather risk attributed to deforest-
ation. In addition, under more extreme conditions,
such as those occurring on longer time-horizons (e.g.
return periods ⩾20 years), the event magnitude
may become twice as great following deforestation.
The results clearly demonstrate the important role
13

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
of tropical forests in regulating microclimate and
regional climate processes, including fire weather.
Therefore, further deforestation and land conversion
for agriculture in Borneo are likely to increase wild-
fires, in conjunction with climate change.
Data availability statement
Data will be made available at the Terrestrial Ecosys-
tem Research Network portal.
The data that support the findings of this study are
available upon reasonable request from the authors.
Acknowledgments
This research was funded by the Australian Research
Council Discovery Project Grant No. DP160102107.
It was supported by computational resources
provided by the Australian Government through
National Computational Infrastructure under the
National Computational Merit Allocation Scheme.
We thank Sarah Chapman for providing comments to
improve readability. We acknowledge UQ-Research
Computing Centre and QCIF for accessing the
clusters and QRISCloud data storage infrastructure
as well as the Queensland Government to perform
the analysis using high performance computation.
Author contributions
R T, J S and C A M conceived the original idea and
drafted the paper. All other authors (A S, M T, N T,
K K W, E M and D S) have provided input to the paper
and participated in various ways in the data collection
and processing.
Conflict of interest
The authors declare no competing interest.
Computer code
Computer code is available upon request.
ORCID iDs
Ralph Trancoso https://orcid.org/0000-0002-
9697-7005
Jozef Syktus https://orcid.org/0000-0003-1782-
3073
Clive A McAlpine https://orcid.org/0000-0003-
0457-8144
References
Abatzoglou J T, Williams A P and Barbero R 2019 Global
emergence of anthropogenic climate change in fire weather
indices Geophys. Res. Lett. 46 326–36
Atwood E C, Englhart S, Lorenz E, Halle W, Wiedemann W and
Siegert F 2016 Detection and characterization of low
temperature peat fires during the 2015 fire catastrophe in
Indonesia using a new high-sensitivity fire monitoring
satellite sensor (firebird) PLoS One 11 e0159410
Baker J C and Spracklen D V 2019 Climate benefits of intact
Amazon forests and the biophysical consequences of
disturbance Front. For. Glob. Change 247
Bonan G B 2008 Forests and climate change: forcings, feedbacks,
and the climate benefits of forests Science 320 1444–9
Bowman D M, Williamson G J, Abatzoglou J T, Kolden C A,
Cochrane M A and Smith A M 2017 Human exposure and
sensitivity to globally extreme wildfire events Nat. Ecol. Evol.
11–6
Brando P M, Paolucci L, Ummenhofer C C, Ordway E M,
Hartmann H, Cattau M E, Rattis L, Medjibe V, Coe M T and
Balch J 2019 Droughts, wildfires, and forest carbon cycling: a
pantropical synthesis Annu. Rev. Earth Planet. Sci. 47 555–81
Chapman S, Syktus J, Trancoso R, Salazar A, Thatcher M,
Watson J E, Meijaard E, Sheil D, Dargusch P and
McAlpine C A 2020 Compounding impact of deforestation
on Borneo’s climate during El Niño events Environ. Res. Lett.
15 084006
Chen C C, Lin H W, Yu J Y and Lo M H 2016 The 2015 Borneo
fires: what have we learned from the 1997 and 2006 El
Niños? Environ. Res. Lett. 11 104003
Cochrane M A 2003 Fire science for rainforests Nature 421 913–9
Cochrane M A and Laurance W F 2002 Fire as a large-scale edge
effect in Amazonian forests J. Tropical Ecol. 18 311–25
Coles S 2001 An Introduction to Statistical Modeling of Extreme
Values (Springer Series in Statistics vol 208) (London:
Springer) p 208
Corlett R T 2016 The impacts of droughts in tropical forests
Trends Plant Sci. 21 584–93
Dee D P et al 2011 The ERA-Interim reanalysis: Configuration
and performance of the data assimilation system Q. J. R.
Meteorol. Soc. 137 553–97
Dowdy A J, Mills G A, Finkele K and de Groot W 2010 Index
sensitivity analysis applied to the Canadian forest fire
weather index and the McArthur forest fire danger index
Meteorol. Appl. 17 298–312
Dowdy A J, Ye H, Pepler A, Thatcher M, Osbrough S L, Evans J P
and McCarthy N 2019 Future changes in extreme weather
and pyroconvection risk factors for Australian wildfires Sci.
Rep. 91–11
Eccles R, Zhang H, Hamilton D, Trancoso R and Syktus J 2021
Impacts of climate change on streamflow and floodplain
inundation in a coastal subtropical catchment Adv. Water
Resour. 147 103825
FAO 2020 Global Forest Resources Assessment 2020 (Rome: Food
and Agriculture Organization)
Field R D et al 2016 Indonesian fire activity and smoke pollution
in 2015 show persistent nonlinear sensitivity to El
Niño-induced drought Proc. Natl Acad. Sci. 113 9204–9
Findell K L, Berg A, Gentine P, Krasting J P, Lintner B R,
Malyshev S, Santanello J A and Shevliakova E 2017 The
impact of anthropogenic land use and land cover change on
regional climate extremes Nat. Commun. 81–10
Folwell S S, Taylor C M and Stratton R A 2022 Contrasting
contributions of surface hydrological pathways in
convection permitting and parameterised climate
simulations over Africa and their feedbacks on the
atmosphere Clim. Dyn. 59 633–48
Fonseca M G, Alves L M, Aguiar A P D, Arai E, Anderson L O,
Rosan T M, Shimabukuro Y E and de Arag˜
aoLEOEC
2019 Effects of climate and land-use change scenarios on fire
probability during the 21st century in the Brazilian Amazon
Glob. Change Biol. 25 2931–46
Gaveau D L A, Locatelli B, Salim M A, Yaen H, Pacheco P and
Sheil D 2019 Rise and fall of forest loss and industrial
plantations in Borneo (2000–2017) Conserv. Lett. 12 e12622
Ge J, Qiu B, Chu B, Li D, Jiang L, Zhou W and Guo W 2021
Evaluation of coupled regional climate models in
representing the local biophysical effects of afforestation
over continental China J. Clim. 34 9879–98
14

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
Guichard F and Couvreux F 2017 A short review of numerical
cloud-resolving models Tellus A69 1373578
Hansen M C et al 2013 High-resolution global maps of
21st-century forest cover change Science 342 850–3
Hansen M C, DeFries R S, Townshend J R G and Sohlberg R 2000
Global land cover classification at 1 km spatial resolution
using a classification tree approach Int. J. Remote Sens.
21 1331–64
Hansen M C, Wang L, Song X P, Tyukavina A, Turubanova S,
Potapov P V and Stehman S V 2020 The fate of tropical
forest fragments Sci. Adv. 6eaax8574
Hendon H H 2003 Indonesian rainfall variability: impacts of
ENSO and local air–sea interaction J. Clim. 16 1775–90
Hijioka Y, Lin E, Pereira J J, Corlett R T, Cui X, Insarov G E,
Lasco R D, Lindgren E and Surjan A 2014 Asia Climate
Change 2014: Impacts, Adaptation, and Vulnerability. Part B:
Regional Aspects. Contribution of Working Group II to the
Fifth Assessment Report of the Intergovernmental Panel on
Climate Change ed V R Barros et al (Cambridge: Cambridge
University Press) pp 1327–70
Holgate C M, van Dijk A I, Cary G J and Yebra M 2017 Using
alternative soil moisture estimates in the McArthur forest
fire danger index Int. J. Wildland Fire 26 806–19
Hurtt G C, Chini L, Sahajpal R, Frolking S, Bodirsky B L, Calvin K
and Hasegawa T 2020 Harmonization of global land use
change and management for the period 850–2100 (LUH2)
for CMIP6 Geosci. Model Dev. 13 5425–64
Jain P, Castellanos-Acuna D, Coogan S C, Abatzoglou J T and
Flannigan M D 2022 Observed increases in extreme fire
weather driven by atmospheric humidity and temperature
Nat. Clim. Change 12 63–70
Jucker T, Hardwick S R, Both S, Elias D M O, Ewers R M,
Milodowski D T, Swinfield T and Coomes D A 2018 Canopy
structure and topography jointly constrain the microclimate
of human-modified tropical landscapes Glob. Change Biol.
24 5243–58
Kendon E J, Prein A F, Senior C A and Stirling A 2021 Challenges
and outlook for convection-permitting climate modelling
Phil. Trans. R. Soc. A379 20190547
Kowalczyk E, Wang Y P, Law R M, Davies H L, Mcgregor J L and
Abramowitz G 2006 The CSIRO Atmosphere Biosphere Land
Exchange (CABLE) Model for Use in Climate Models and as
an Offline Model (Aspendale, VIC: CSIRO)
Langner A, Miettinen J and Siegert F 2007 Land cover change
2002–2005 in Borneo and the role of fire derived from
MODIS imagery Glob. Change Biol. 13 2329–40
Lenton T M, Rockström J, Gaffney O, Rahmstorf S, Richardson K,
Steffen W and Schellnhuber H J 2019 Climate tipping
points—too risky to bet against Nature 575 592–5
Lewis S L, Edwards D P and Galbraith D 2015 Increasing human
dominance of tropical forests Science 349 827–32
Lovejoy T E and Nobre C 2018 Amazon tipping point Sci. Adv.
4eaat2340
Lucas-Picher P, Argüeso D, Brisson E, Tramblay Y, Berg P,
Lemonsu A and Caillaud C 2021 Convection-permitting
modeling with regional climate models: latest developments
and next steps Wiley Interdiscip. Rev. Clim. Change 12 e731
Marotzke J, Jakob C, Bony S, Dirmeyer P A, O’Gorman P A,
Hawkins E, Perkins-Kirkpatrick S, Le Quere C, Nowicki S
and Paulavets K 2017 Climate research must sharpen its
view Nat. Clim. Change 789–91
McAlpine C A, Johnson A, Salazar A, Syktus J, Wilson K,
Meijaard E, Seabrook L, Dargusch P, Nordin H and Sheil D
2018 Forest loss and Borneo’s climate Environ. Res. Lett.
13 044009
Mcgregor J L and Dix M R 2008 An Updated Description of the
Conformal-Cubic Atmospheric Model High Resolution
Numerical Modelling of the Atmosphere and Ocean
ed K Hamilton and O Wataru (New York: Springer) pp
51–75
Meijaard E et al 2020 The environmental impacts of palm oil in
context Nat. Plants 61418–26
Meijide A, Röll A, Fan Y, Herbst M, Niu F, Tiedemann F, June T,
Rauf A, Hölscher D and Knohl A 2017 Controls of water and
energy fluxes in oil palm plantations: environmental
variables and oil palm age Agric. For. Meteorol. 239 71–85
Miettinen J, Shi C and Liew S C 2016 2015 Land cover map of
Southeast Asia at 250 m spatial resolution Remote Sens. Lett.
7701–10
Nunes M H, Jucker T, Riutta T, Svátek M, Kvasnica J, Rejžek M
and Coomes D A 2021 Recovery of logged forest fragments
in a human-modified tropical landscape during the 2015–16
El Niño Nat. Commun. 12 1–11
Park C Y, Takahashi K, Takakura J, Li F, Fujimori S, Hasegawa T,
Ito A and Lee D K 2021 How will deforestation and
vegetation degradation affect global fire activity? Earth’s
Future 9e2020EF001786
Prein A F et al 2015 A review on regional convection-permitting
climate modeling: demonstrations, prospects, and
challenges Rev. Geophys. 53 323–61
Rogers B M, Balch J K, Goetz S J, Lehmann C E and Turetsky M
2020 Focus on changing fire regimes: interactions with
climate, ecosystems, and society Environ. Res. Lett.
15 030201
Sheil D 2018 Forests, atmospheric water and an uncertain future:
the new biology of the global water cycle For. Ecosyst. 51–22
Siegert F, Ruecker G, Hinrich A and Hoffmann A 2001 Increased
damage from fires in logged forests during droughts caused
by El Niño Nature 414 437–40
Singh J, Ashfaq M, Skinner C B, Anderson W B and Singh D 2021
Amplified risk of spatially compounding droughts during
co-occurrences of modes of natural ocean variability npj
Clim. Atmos. Sci. 41–14
Sloan S, Locatelli B, Wooster M J and Gaveau D L 2017 Fire
activity in Borneo driven by industrial land conversion and
drought during El Niño periods, 1982–2010 Glob. Environ.
Change 47 95–109
Song X P, Hansen M C, Stehman S V, Potapov P V, Tyukavina A,
Vermote E F and Townshend J R 2018 Global land change
from 1982 to 2016 Nature 560 639–43
Staal A, Dekker S C, Hirota M and van Nes E H 2015 Synergistic
effects of drought and deforestation on the resilience of the
south-eastern Amazon rainforest Ecol. Complexity 22 65–75
Staal A, Fetzer I, Wang-Erlandsson L, Bosmans J H, Dekker S C,
van Nes E H, Rockström J and Tuinenburg O A 2020
Hysteresis of tropical forests in the 21st century Nat.
Commun. 11 1–8
Stevens B and Bony S 2013 What are climate models missing?
Science 340 1053–4
Tangang S, Salimun F, Aldrian E, Sopaheluwakan E A and
Juneng L 2017 ENSO modulation of seasonal rainfall and
extremes in Indonesia Clim. Dyn. 51 2559–80
Taufik M, Torfs P J J, Uijlenhoet R, Jones P, Murdiyarso D and Van
Lanen H A 2017 Amplification of wildfire area burnt by
hydrological drought in the humid tropics Nat. Clim.
Change 7428–31
Thatcher M and Mcgregor J L 2009 A technique for dynamically
downscaling daily-averaged GCM datasets using the
conformal cubic atmospheric model Mon. Weather Rev.
139 79–95
Thatcher M and Mcgregor J L 2009 Using a scale-selective filter
for dynamical downscaling with the conformal cubic
atmospheric model Mon. Weather Rev. 137 1742–52
Thirumalai K, DiNezio P N, Okumura Y and Deser C 2017
Extreme temperatures in Southeast Asia caused by El Niño
and worsened by global warming Nat. Commun. 815531
Trancoso R 2021 Changing Amazon deforestation patterns:
urgent need to restore command and control policies and
market interventions Environ. Res. Lett. 16 041004
Trancoso R, Larsen J R, McAlpine C, McVicar T R and Phinn S
2016 Linking the Budyko framework and the Dunne
diagram J. Hydrol. 535 581–97
Trancoso R, Syktus J, Toombs N, Ahrens D, Wong K K H and
Dalla Pozza R 2020 Heatwaves intensification in Australia: a
15

Environ. Res. Lett. 17 (2022) 104019 R Trancoso et al
consistent trajectory across past, present and future Sci.
Total Environ. 742 140521
van Nieuwstadt M G L and Sheil D 2005 Drought, fire and tree
survival in a Borneo rain forest, East Kalimantan, Indonesia
J. Ecol. 93 191–201
van Oldenborgh G J et al 2021 Attribution of the Australian
bushfire risk to anthropogenic climate change Nat. Hazards
Earth Syst. Sci. 21 941–60
Van Wagner C E 1974 Structure of the Canadian Forest Fire
Weather Index (Ottawa, ON: Canadian Forestry Service)
Publication No. 1333
Vanden Broucke S and Van Lipzig N 2017 Do
convection-permitting models improve the representation
of the impact of LUC? Clim. Dyn. 49 2749–63
Wang B, Luo X, Yang Y-M, Sun W, Cane M A, Cai W, Yeh S-W
and Liu J 2019 Historical change of El Niño properties sheds
light on future changes of extreme El Niño Proc. Natl Acad.
Sci. 116 22512–7
Wang Y P and Leuning R 1998 A two-leaf model for canopy
conductance, photosynthesis and partitioning of available
energy I model description and comparison with a
multi-layered model Agric. For. Meteorol. 91 89–111
Zemp D C, Schleussner C F, Barbosa H M, Hirota M, Montade V,
Sampaio G, Staal A, Wang-Erlandsson L and Rammig A
2017 Self-amplified Amazon forest loss due to
vegetation-atmosphere feedbacks Nat. Commun. 81–10
Zhang X, Alexander L, Hegerl G C, Jones P, Tank A K,
Peterson T C, Trewin B and Zwiers F W 2011 Indices for
monitoring changes in extremes based on daily temperature
and precipitation data Wiley Interdiscip. Rev. Clim. Change
2851–70
Zhong S et al 2021 Will land use land cover change drive
atmospheric conditions to become more conducive to
wildfires in the United States? Int. J. Climatol. 41 3578–97
Zwiers F W and Storch H V 1995 Taking serial correlation into
account in tests of the mean J. Clim. 8336–51
16