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Impact of Tropical Deforestation and Forest Degradation on
Precipitation over Borneo Island
ATSUHIRO TAKAHASHI, TOMO’OMI KUMAGAI,a HIRONARI KANAMORI,HATSUKI FUJINAMI, AND TETSUYA HIYAMA
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan

MASAYUKI HARA
Center for Environmental Science in Saitama, Kazo, Saitama, Japan(Manuscript received 8 January 2017, in final form 10 July 2017)
ABSTRACTSoutheast Asian tropical rain forests in the Maritime Continent are among the most important biomes interms of global and regional water cycling. How land use and land cover change (LULCC) relating to deforestation and forest degradation alter the local hydroclimate over the island of Borneo is examined using theWeather Research and Forecasting (WRF) Model with an appropriate land surface model for describing theinfluence of changes in the vegetation status on the atmosphere. The model was validated against precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite 3B42 measurements. A mainnovelty in this analysis is that the diurnal cycle of precipitation over the island, which is a dominant climaticcharacteristic of the Maritime Continent, was successfully reproduced. To clarify the impact of the LULCC on
the precipitation regimes over the island, numerical experiments were performed with the model that
demonstrated the following. Deforestation that generates high albedo areas, such as bare lands, would
induce a reduction in precipitation because of reductions in evapotranspiration, convection, and horizontal
atmospheric moisture inflow. On the other hand, a decrease in evapotranspiration efficiency without changing
the surface albedo could increase precipitation due to an increase in convection and horizontal atmosphericmoisture inflow in compensation for the decrease in evapotranspiration. In detail, on the Maritime Continent,
through changes in the land surface heating process and land–sea breeze circulation, the LULCC would
impact the amplitude of the diurnal precipitation cycle in each region as defined according to the distancefrom the coast, resulting in changes in the precipitation regimes over the island.

1. Introduction
Tropical forests are a major source of global hydrologic
fluxes, profoundly influencing both global and regional
climates (see Kumagai et al. 2016). Humans have been
modifying tropical forest land cover for food and energy
production and for the development of tropical countries. Consequently, such modifications, that is, land use
and land cover change (LULCC), are being combinedwith climate change and are anticipated to impact theclimate on local, regional, and even global scales via
aCurrent affiliation: Graduate School of Agricultural and Life
Sciences, University of Tokyo, Tokyo, Japan.

Corresponding author: Masayuki Hara, hara.masayuki@pref.
saitama.lg.jp

teleconnections (Werth and Avissar 2002; Medvigy et al.
2013; Lawrence and Vandecar 2014; Mahmood et al.
2016). Among the LULCC-driven climate variabilities,
the alteration of precipitation regimes in the tropics has
been examined in many previous studies: in Amazonia
and Africa, convective activity in the dry season weakened after deforestation, leading to a strong intensification of drought (Voldoire and Royer 2004), andin Amazonia, above-normal (below normal) annual precipitation can enhance (suppress) the vegetation’s productivity, resulting in an increase (decrease) in thesubsequent year’s precipitation (Wang et al. 2011).
Lawrence and Vandecar (2014) summarized difficulties
in projecting the precipitation change induced by tropicaldeforestation on continental and local scales; for example, precipitation could be reduced or enhanced in accordance with the size and state of deforestation (a single

DOI: 10.1175/JHM-D-17-0008.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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large patch is similar to distributed patches). Here, wemust note that, in turn, such LULCC-driven hydroclimate change could lead to significant negative impacts
on tropical forest ecological patterns and processes (seeMalhi et al. 2009; Kumagai and Porporato 2012) andlimitations to agricultural productivity in some regions(Lawrence and Vandecar 2014).
Southeast Asian tropical forests have the highest relative deforestation rate in the world (Canadell et al.
2007). Also, on the island of Borneo, LULCC, that is,deforestation and forest degradation, has been among
the most critical issues over the past several decades(Bryan et al. 2013; Gaveau et al. 2013, 2014), and might
have induced the observed decadal decline of annual
precipitation (see Kumagai et al. 2013). As the Southeast Asian tropics consist of the Maritime Continent,
whose geographical characteristics play a significant rolein the formation of large-scale circulations (Mabuchi
et al. 2005), the hydroclimate in this region may respondto the LULCC differently than do large continents suchas South America and Africa. Zhang et al. (2001)
showed that deforestation would induce a decrease inprecipitation over the Southeast Asian tropics. On thecontrary, Gotangco Castillo and Gurney (2013) suggested that deforestation could enhance precipitation
over the Southeast Asian tropics, possibly because water
vapor for precipitation can be supplied from the oceanthat surrounds islands in the Maritime Continent.
Smaller tropical islands, in which orographic precipitation hardly occurs, tend to be dry in a future warmer
climate (Karnauskas et al. 2016). On the other hand, onlarger islands, orographic precipitation should be dominant, and water vapor for forming the precipitation is
likely supplied from the ocean that surrounds the islands(Sobel et al. 2011). Previous studies (e.g., Ichikawa andYasunari 2006; Kanamori et al. 2013) have claimed thaton Borneo, there is a pronounced diurnal cycle of convection and precipitation strongly influenced by geographical traits, such as the thermal contrast between theislands, ocean, and mountain–valley circulations, andmodulated by large-scale intraseasonal oscillations in thetropical atmosphere [e.g., the Madden–Julian oscillation(MJO)]. Thus, a computation grid size of 20 km, which iscurrently among the finest scales for climate simulation
with general circulation models (GCMs), can be assumed
to be sufficient for simulating only thermally induced
local circulations, but, in the case of simulations of the
precipitation regimes in Borneo, is insufficient for reproducing the diurnal cycle of the convective system,
which is coupled with the land–sea, mountain–valley, andlarge-scale circulations (see Hara et al. 2009). Hara et al.
(2009) simulated the diurnal cycle of precipitation overBorneo using a 3-km horizontal grid with a regional

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climate model. The simulated precipitation shows a detailed cloud and precipitation process that cannot be
simulated by coarse-grid simulations. To our knowledge,
despite many numerical studies on the impact of the
LULCC on the local and global climate using GCMs in
the past several decades, no models with sufficiently fineresolution have been applied to numerical experiments tosimulate such complicated precipitation processes on the
Maritime Continent, except for a few GCMs with hugesupercomputers, such as the nonhydrostatic icosahedral
atmospheric model (NICAM) model (Satoh et al. 2014;
Kodama et al. 2015; Miyamoto et al. 2016).
Our goal is to investigate how the local climate, especially the precipitation regime, on Borneo will respond to LULCC. Toward this goal, we use a regional
climate model with a fine horizontal resolution sufficient
to simulate cloud and precipitation processes, which are
driven by geographical traits unique to Borneo, andconduct numerical experiments with scenarios of varying degrees of deforestation and degradation. Prior toassessing the impact of the LULCC on the hydroclimate,
the model is first validated with satellite rainfall measurements over the Maritime Continent.

2. Methodology
a. Site description
Borneo (18000 N, 1148000 E), our simulation object, isthe third-largest island in the world (743 330 km2) withhigh mountains (maximum altitude 4095 m; see Fig. 1).
Borneo was famous for its majestic rain forests. In the
1950s, more than 80% of its total land area was covered
with pristine forest; however, the high deforestation rate
(1.7% yr21), which is almost double that of the alreadyintense deforestation rate of the entire Southeast Asian
region, has resulted in an estimation of ;50% forest
cover (Langner et al. 2007). The dominant tree species is
dipterocarp, which is among the most valuable timber
species globally. This has led to intense logging pressure
on Borneo. The land cover areas categorized as ‘‘degraded forest and regrowth,’’ ‘‘cultivation forest mosaic,’’ and ‘‘dry/wet bare soil; grasslands; agriculture’’
reached up to 33 million ha, ;45% of the total land areaof Borneo (Langner et al. 2007).
The long-term daily grid precipitation datasets(APHRODITE’s Water Resources, available via http://
www.chikyu.ac.jp/precip/; Yatagai et al. 2012) showed alarge amount of precipitation over Borneo, that is, thespatiotemporal average (in 2000–07 and over the island)
is ;2000 mm yr21. Considering this drastic deforestationand forest degradation, it is logical to anticipate its impact on the regional hydroclimate; in reality, APHRODITE suggests a significant decline in precipitation over

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FIG. 1. (a) Domains for the simulations, (b) topography, (c) regions for analysis: inland (light gray), coastal
land (gray), and coastal sea (dark gray), and the land cover types for (d) Control and 0.2Gcmax and (e) Barren-1.
The black area in (e) denotes the lowland area below an altitude of 100 m MSL. Land cover type in the black areain Barren-1 is modified into bare land. Land cover type in Barren-2 assumes all areas over Borneo are bare land.
The land cover types depicted in colors in (d) and (e) are shown in the legend by colors and numbers: 5, cropland/grassland mosaic; 6, cropland/woodland mosaic; 7, grassland; 8, shrubland; 9, mixed shrubland/grassland;
10, savanna; 11, deciduous broadleaf forest; 13, evergreen broadleaf forest; 15, mixed forest; and 16, water bodies.

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Borneo in the period from 1951 to 2007 to be from
;2600 to 1900 mm yr21 (Kumagai et al. 2013).

b. The model
The model used here is the Advanced Research versionof the Weather Research and Forecasting (WRF) Model(ARW, version 3.5.1; Skamarock et al. 2008), including the
Noah land surface model (Mitchell 2005). Parameterization schemes, the area of model domains, and the simulation period in this study were similar to those in Haraet al. (2009), except for the boundary conditions and theversion of the WRF Model. Two-way nesting was employed: the outer domain (D01) has 142 3 142 grid points
with 17.5-km grid spacing, and the inner domain (D02) has
511 3 541 grid points with 3.5-km grid spacing (Fig. 1a).
Both domains have 35 vertical layers from the groundsurface to the pressure level of 50 hPa. As for the cloud
microphysics scheme, we employed the WRF singlemoment 6-class scheme. Cumulus parameterization wasused in D01 (the Kain–Fritsch convective scheme) but not
in D02. The Dudhia scheme was used for longwave radiation and shortwave radiation. The Mellor–Yamada–Janjić scheme was employed for the scheme of turbulent
kinetic energy in the planetary boundary layer.

c. Simulations
Computations were performed during the period from1200 local time (LT) 25 March to 1800 LT 30 April 2004
(35 days). As described in Hara et al. (2009), no tropical
cyclones or active MJO convection areas (i.e., in the MJO
inactive season) were observed over Borneo throughout
the simulation period. The anomaly of the Niño-3.4 index
for the period is 0.06, which means no significant El Niño
and La Niña events, according to the National Oceanicand Atmospheric Administration/Climate Prediction
Center. In general, precipitation amount in the MJO inactive season is less than in the MJO active season, so thesimulated impact of land use change is expected to beclearer than in the MJO active season. On the Maritime
Continent, year-to-year variability of precipitation
amount is controlled by seasonal changes brought by
MJO and/or El Niño. Of the 35 days, the first 5.5 days in
March 2004 were used for spinup, and the following
30 days in April 2004 were an analysis object. The initialand boundary conditions for the simulation are found in
the ERA-Interim reanalysis dataset (Dee et al. 2011).
Types of topography (Fig. 1b) and land cover (Fig. 1d)were provided as defaults in the ARW version 3.5.1. We
note that the dominant species in the types of land coveron the island is broadleaf evergreen (Figs. 1d,e). The
precipitation simulations were validated using the
3-hourly data of the Tropical Rainfall Measuring
Mission (TRMM) satellite 3B42v7 datasets (NASAVOLUME 18

Goddard Earth Sciences Data and Information Services
Center, available at https://disc.gsfc.nasa.gov/).
Figure 1c shows the three separated regions for analysis:
inland, coastal land, and coastal sea. The inland and thecoastal land areas are almost the same, and the coastal
sea area is about 1.5 times that of the other two land areas.
We constructed four types of numerical experiments. Both
1) Control and 2) 0.2Gcmax assume actual land cover types(Fig. 1d), but the maximum stomatal conductance (Gcmax)
in 0.2Gcmax is 0.2 times that in Control. In 3) Barren-1 and
4) Barren-2, land cover types in the lowland area below
100 m above sea level (Fig. 1e) and in all areas of the island
were changed to bare land, respectively. Here, note that
all the numerical experiments were performed in conditions of ample soil moisture, that is, no soil moisture limitation on stomatal conductance, and that the multiplier 0.2
in 0.2Gcmax was determined so that evapotranspiration
over the island in 0.2Gcmax and Barren-1 can be almost
the same. For simulation purposes, it should be constructive
to compare Control to the other three experiments andevaluate the mechanisms causing hydroclimate shifts between them. The simulation setup and consecutive changeof land use parameter are summarized in Table 1. In
Barren-1 and Barren-2, albedos over the island increase,but surface emissivity and roughness decrease becauseof decreasing forest area. In 0.2Gcmax, only maximum
stomatal conductance has changed from Control.

3. Results and discussion
a. Comparison with the observation
Characteristics of the diurnal cycle of precipitation in
the Southeast Asia region have been investigated previously, and it was found that there are distinct peaks ofthe diurnal cycle at specific times of the day for each location in the Southeast Asian tropics (Oki and Mushiake
1994; Ohsawa et al. 2001; Ichikawa and Yasunari 2006;
Kumagai and Kume 2012; Hara et al. 2009; Kanamori
et al. 2013; Bhatt et al. 2016; Fujita et al. 2010; Takahashi
et al. 2010; Kodama et al. 2015). TRMM measurements
revealed climatological diurnal cycles around the Maritime Continent (Nesbitt and Zipser 2003; Kikuchi andWang 2008; Biasutti et al. 2012). Overall, the diurnal cycleof precipitation over the ocean features a low amplitude
and an early morning peak, with a high amplitude and anafternoon peak over the land.
Figure 2 shows monthly averaged diurnal variations inobserved precipitation Phr-obs, constructed using TRMM
3B42 data, and simulated precipitation in the numericalexperiment Control Phr-cal in the three analyzed regions:inland, coastal land, and coastal sea. The Phr-obs wasconsistent with the climatological diurnal cycles aroundNOVEMBER 2017

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TABLE 1. Summarized table of simulation design and consecutive parameter change in the land. The magnitude of the change of albedo,
surface emissivity, and roughness due to the land use change are indicated. The 1 sign indicates positive, and 2 indicates negative. The
number of signs indicates the magnitude of the change.

Land use

AlbedoSurfaceemissivity
Roughness

Control

Barren-1

Barren-20.2Gcmax

USGS land use (default
land use data of WRF
v3.5.1)

Land use lower than
100 m MSL changed
into bare land
1
2

Land use all over theisland changed intobare land
11
22

Maximum stomatal conductance is
0.2 times of the original value allover the island
No change
No change

2

22

the Maritime Continent. Also, our results were consistent
with previous studies on Borneo (Hara et al. 2009;
Kanamori et al. 2013), in which remarkable phase propagation toward inland areas occurs from noon to evening
in correspondence with the distance of the observationsites from the coast. The analyses of Hara et al. (2009)
implied that sea breeze fronts that form in the coastal areain the afternoon migrate to the inland area in the evening,remain even after sunset, and converge in the mid-inlandarea before midnight, resulting in a precipitation peakfrom the evening to midnight in the inland area.
It was found that the Phr-cal captured the canonicalform of the Phr-obs in the three analyzed regions (Fig. 2).
Kanamori et al. (2013) suggested that in western Borneo, the precipitation amount from TRMM 3B42 tends
to be overestimated (underestimated) in coastal (interior) areas as compared with in situ data. Thus,
given TRMM’s tendency, the discrepancies between
the Phr-cal and the Phr-obs in inland (overestimation at
midnight) and coastal land and coastal sea areas (underestimations in the afternoon and the early morning,respectively) could be neglected. Here, it should be
noted that the model successfully reproduced the midnight, afternoon, and early morning peaks of precipitation in the inland, coastal land, and coastal sea
areas, respectively. Also note that those peaks are generated by the offshore–inland propagation of convection[cf. discussions on offshore convection in Houze et al.
(1981)], which the higher-resolution and nonhydrostatic
model without cumulus parameterization allowed us toreproduce (see Hara et al. 2009; Bhatt et al. 2016).
Outputs from the model were independently validated
using TRMM datasets; therefore, we confirmed that themodel could be used to explore the detailed physicalmechanisms and feedback regarding how the LULCC
over Borneo impacts the local climate, as described next.

No change

an atmospheric boundary layer (ABL; see Bonan 2008).
How available energy on the land surface is partitioned
into latent and sensible heat fluxes is critical to changes
in the ABL height and the moistening of the lower atmosphere (see Siqueira et al. 2009). In comparison withthe latent heat flux (LHF) in the Control, LHFs werereduced over the island in 0.2Gcmax and Barren-2 andonly in the lowland areas in Barren-1 (Fig. 3). We foundthat the LHF in the deforested areas in Barren-1 andBarren-2 decreased to ;40% of the LHF in the Control.
Total evapotranspiration over the island in the simulated month was estimated to be 115, 85, 88, and 44 mm

b. Impact of the LULCC: Land surface processes
The LULCC must lead to an alteration of the energybalance on the land surface, which affects the generationof clouds and precipitation through the development of

FIG. 2. Monthly averaged diurnal variation in precipitationfrom TRMM 3B42v7 datasets (solid circles) and simulated in the
numerical experiment Control (lines) in the analyzed regions:inland, coastal land, and coastal sea (see Fig. 1c).

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FIG. 3. Monthly mean LHF for the (a) Control, (b) 0.2Gcmax, (d) Barren-1, and (f) Barren-2 and the difference in LHF (DLHF) between
each experiment [(c) 0.2Gcmax, (e) Barren-1, and (g) Barren-2] and the Control.

in the Control, 0.2Gcmax, Barren-1, and Barren-2 experiments, respectively.
In 0.2Gcmax, the reduction of the LHF significantly
enhanced the sensible heat flux (SHF) over the island
because with the unchanged surface albedo in the‘‘green’’ areas, the available energy on the land surface

was little changed (Figs. 4b,c). Meanwhile, despite decreases in the LHF, the SHF in both Barren-1 andBarren-2 appreciably decreased in the coastal areas, due
to the higher albedo and the lower surface roughness z0 in
the deforested areas (Figs. 4d–g). In the higher-elevation
areas (.100 m MSL) of Barren-1, in which forested areas

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FIG. 4. As in Fig. 3, but for SHF.

with unchanged albedo and z0 still remain and the LHF isunchanged (see Fig. 3e), the SHF was not altered(Fig. 4e). Notably, it is found that the SHFs in both
Barren-1 and Barren-2 increased in the deforested inland
areas (Fig. 4e,g), probably because the reduction in thepartition of the available energy to the LHF and the lower
z0 increased the surface temperature.

The monthly mean horizontal water vapor flux Qf
integrated from the ground surface to a pressure levelof 100 hPa was calculated at 1200, 1800, 0000, and 0600
LT (Fig. 5). Divergence Qdiv of the mean Qf for theControl showed the following. At 1200 LT (Fig. 5a),the control simulation shows apparent land breeze
anomalies and the occurrence of resultant positive and2914

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FIG. 5. Monthly averaged time variations at (a)–(d) 1200, (e)–(h)1800, (i)–(l) 0000, and (m)–(p) 0600 LT in the divergence Qdiv of thehorizontal water vapor flux Qf integrated vertically from the ground surface to 100 hPa for the (first column) Control, and the difference in
the Qdiv (DQdiv) between each experiment [(second column) 0.2Gcmax, (third column) Barren-1, and (fourth column) Barren-2 ] and theControl. To highlight the contrast, Qdiv and Qf vectors in the Control are represented as differences between each LT calculation and thedaily mean of the Control. Arrows denote differences in the Qf between each experiment and the Control.

negative Qdiv anomalies in the coastal sea and thecoastal land regions, respectively. This might cause
little precipitation around 1200 LT over the island(Fig. 2). At 1800 LT (Fig. 5e), prominent sea breeze
anomalies and the strong convergence anomalies of Qf
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the coastal land and the inland regions (Fig. 2). At 0000 LT(Fig. 5i), there was likely a sea breeze, resulting in theconvergence of Qf and precipitation (see Fig. 2) in the inland region. At 0600 LT (Fig. 5m), there were land breeze
anomalies over the coastal zone of the island, leading topositive Qdiv and little precipitation over the island.
Figure 5 also shows the differences in Qdiv between
each experiment and the Control: at 1200 LT (Figs. 5b–d),
in Barren-1 and Barren-2, Qdiv was strengthened in the
coastal land region. At 1800 LT (Figs. 5f–h), in all thecases, Qdiv tended to be stronger over the island.
In 0.2Gcmax (Fig. 5f), there was some convergenceof Qf in the inland region because of the furtherstrengthened sea breeze. In Barren-2 (Fig. 5h), we
found very large land breeze anomalies and the resultant strong Qdiv over the island. At 0000 LT(Figs. 5j–l) in 0.2Gcmax (Fig. 5j), there was a strong
sea breeze and, apparently, strengthened convergenceof Qf in the inland region, while stronger Qdiv was
found in Barren-1 and, especially, Barren-2 (Figs. 5k,l).
At 0600 LT (Figs. 5n–p), we could detect the enhanced
sea breeze and convergence-like Qf over the island in
Barren-2 (Fig. 5p).
The monthly averaged Qdiv values for the Control,
0.2Gcmax, Barren-1, and Barren-2 simulations are
summarized in Fig. 6. The enhanced convergence,
mainly due to the drastic increase in the Qf into the inland areas at midnight (cf. Fig. 5j), appeared over theinland region in 0.2Gcmax (Fig. 6c). By contrast, Qdiv in
Barren-1 was somewhat neutral (Fig. 6e), and Qdiv in
Barren-2 became positive over a large area of the island(Fig. 6g), mainly because of the enhanced Qdiv in theafternoon (cf. Fig. 5h).
Local convection conditions can be represented using
the convective available potential energy (CAPE), whichis calculated by accumulating buoyant energy from thelevel of free convection to the limit of convection (see Yin
et al. 2014). The monthly mean CAPE values at 1200, 1800,
0000, and 0600 LT were simulated, considering the adiabatic lifting of an air parcel from a pressure height of
850 hPa (Fig. 7). Unlike CAPE in the simulation of Swannet al. (2015) (;2000–3500 J kg21), the CAPE in this study
was at most ;1000 J kg21, comparable to Yin et al.’s(2014) observation. This suggests that changes in the
CAPE induced by land surface process changes certainly
affect the convective state and precipitation regime(Swann et al. 2015). The simulation results were as follows:in the Control, there were areas with large CAPE values in
the inland region in the afternoon and evening (Figs. 7a,e),and, as expected, values of the CAPE were small over theisland from the nighttime to the morning (Figs. 7i,m). In
0.2Gcmax (Figs. 7b,f,j,n), the CAPE increased over theisland, especially at 1800 LT, except for the mountainous

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areas, where the CAPE was intrinsically high. In Barren-1(Figs. 7c,g,k,o) and, more markedly, in Barren-2 (Figs. 7d,h,l,p), the CAPE decreased overall. The reduction was
drastic in the coastal areas and from the evening to thenighttime for Barren-1 (Figs. 7g,k) and from the afternoon
to the nighttime for Barren-2 (Figs. 7d,h,l). It is interesting
to note that in the three numerical experiments, the increase/decrease in the CAPE corresponded to those in
specific humidity at a height of 850 hPa and in cloud wateraround a height of 800 hPa (data not shown here),
implying a decrease/increase in the lifting condensationlevel (LCL). The impact of the LULCC on precipitation
regimes can be explained by a combination of the evolution of the ABL induced by the available radiative energy
partitioning, the CAPE change, and the LCL change (seeSiqueira et al. 2009), as discussed next.

c. Impact of the LULCC: Precipitation
Figure 8 compares the diurnal cycles of the precipitation simulated in the numerical experiments.
Again, in the Control, the diurnal cycles of precipitation
were well reproduced over the island (see Fig. 2). In
0.2Gcmax, the precipitation increased mainly in the inland region from the night (2000 LT) to the morning(0800 LT; Fig. 8), which can be partly attributed to someconvergence of Qf (Figs. 5f,j,n) and enhanced clouddevelopment caused by the increase in the CAPE
(Fig. 7f, j) around the time. It is interesting to note that
deforestation in the lowland area (Barren-1 in Fig. 8),
which is almost equivalent to that in the coastal landarea, appreciably reduced the precipitation in not only
the coastal land region (from the afternoon through theevening) but also in the inland region (from the evening
through the early morning), echoing the conclusions ofLawton et al. (2001), who demonstrated that changes inclouds over mountainous areas result from deforestationin nearby lowland areas. This decrease in precipitationin the inland region in Barren-1 might be caused by thedepressed CAPE in the inland region around midnight(Fig. 7k). In Barren-2 (Fig. 8), the precipitation was
further reduced in the inland area (from the afternoon
through midnight) and in the coastal land area (mainlyin the afternoon), due to the drastic divergence of Qf(Figs. 5h,l) and the depressed CAPE (Figs. 7d,h) aroundthat time. A reduction in the precipitation in the coastal
sea area can be seen only in the morning in Barren-2(Fig. 8). Note that the LULCC over Borneo causes anincrease (in 0.2Gcmax) and/or a decrease (in Barren-1
and Barren-2) in the amplitude of the precipitation diurnal cycle. This suggests that the changes in the land
surface heating process (see Dai 2001) from the LULCC
would reinforce or weaken the land–sea breeze circulation in the studied area, that is, the night precipitation

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FIG. 6. As in Fig. 3, but for Qdiv. Arrows in (c), (e), and (g) denote differences in Qf between each experiment and the Control.

peak in the inland region, the afternoon peak in the
coastal land area, and the morning peak in the coastal
sea area (see Ichikawa and Yasunari 2006; Teo et al.
2011; Kanamori et al. 2013).
The spatial distribution of the monthly accumulated
precipitation Pmon in each numerical experiment andthe differences in Pmon between each numerical experiment and the Control are shown in Fig. 9. In the simulation that assumed a change in the gas exchange traitsof the forest canopy, that is, 0.2Gcmax (Figs. 9b,c), Pmon
increased as compared to the Control, mainly because of
an increase in the precipitation in the inland region andNOVEMBER 2017

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FIG. 7. As in Fig. 5, but for the CAPE calculated considering the adiabatic lifting of an air parcel from 850 hPa.

from the night to the morning (see Fig. 8). In the simulations assuming two deforestation patterns, that is,Barren-1 and Barren-2 (Figs. 9d–g), Pmon apparently
decreased over the island, despite small areas with increases in the Pmon. This decrease could be caused by the
reduction of precipitation in the inland and coastal

inland regions and from the afternoon to the earlymorning (see Fig. 8).
Finally, the cause and effects of the precipitation
changes induced by the LULCC are summarized inFig. 10, where all of the variables (i.e., evapotranspiration E, precipitation P, CAPE, horizontal water vapor

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reduced. As the E was also reduced, SHF changed little in
Barren-1 and Barren-2. The larger SHF should develop ahigher ABL in 0.2Gcmax than in Barren-1. Despite thesame reduction in E for both experiments, there was an
inflow of Qdiv over the island, the atmosphere below the
ABL was more moistened, and the height of the LCL
decreased in 0.2Gcmax. Thus, 0.2Gcmax would produce
more favorable conditions for a convective precipitation
trigger (cf. Siqueira et al. 2009).
Furthermore, the larger increase in SHF led to anincrease in CAPE in 0.2Gcmax, and in Barren-1 andBarren-2, the smaller increase in SHF could not compensate for the effect of the reduction of atmosphericmoisture, leading to a decrease in CAPE (Fig. 10). In
general, an increase in CAPE results in an increase in P
(e.g., Swann et al. 2015). In addition to this, the enhanced CAPE in an inland area of the island should
reinforce Qdiv from the sea to the inland region (see Fu
et al. 1999), resulting in further increments of P.

4. Conclusions and implications
FIG. 8. As in Fig. 2, but for the precipitation simulated in the numericalexperiments of the Control, 0.2Gcmax, Barren-1, and Barren-2.

flux Qdiv, horizontal wind velocity U, albedo, and SHF)
were integrated over Borneo in the studied month. Parameters Qdiv and U were calculated as radius components of the polar coordinates whose center is located at
the center of Borneo, and their positive values represent
quantities directed from the center to the outside of theisland. Again, as compared to the Control, P increasedby 8.5% in 0.2Gcmax, while P decreased by 16% and
43% in Barren-1 and Barren-2, respectively (Fig. 10).
As imposed preconditions, the reduction of E was thesame in 0.2Gcmax and Barren-1, and E in Barren-2 wasmore than twofold the E of 0.2Gcmax and Barren-1(Fig. 10a). On the other hand, convergence of the air
mass flow and resulting convergence of Qdiv (negativevalues of U and Qdiv) were found in 0.2Gcmax. In
Barren-1 and Barren-2, outflow of air mass and Qdiv
from the island occurred. Here, it is important to notethat in all of the numerical experiments, the quantities of
P change cannot be explained by only the quantities ofthe local E changes, and the Qdiv in the objective area
must play a key role when considering how much P increases or decreases there (cf. Spracklen et al. 2012).
In 0.2Gcmax, no change in the albedo and the decreasein E (latent heat) lead to the least change in land surface
available energy and a significant increase in the SHF(Figs. 10a,b). In Barren-1 and Barren-2, the albedo increased appreciably, and thus, the available energy wasPrevious analysis explored the vulnerability of the local
hydroclimate to deforestation and forest degradation on
Borneo (Kumagai et al. 2013). To quantify how the
LULCC affects the land surface processes, the surfaceinduced mesoscale circulation, the atmospheric interaction between land and sea, and the resulting regional
precipitation over the island, we used the WRF Modelwith an appropriate land surface model to describe theinfluence of changes in vegetation status on the atmosphere (Noah LSM; Mitchell 2005). Since the diurnal
cycle of precipitation is a dominant feature of the atmospheric water cycle over the Maritime Continent(Ichikawa and Yasunari 2006; Kanamori et al. 2013), themodel’s ability to reproduce such a precipitation regime is
crucial to the present study’s investigation (Hara et al.
2009; Bhatt et al. 2016). In this study, employing higherresolution grid spacing, an appropriate convective
parameterization scheme, and a cloud microphysicalprocess scheme in the model enabled us to successfully
reproduce the observed precipitation characteristics overthe island of Borneo. After validating the model, we
proceeded to numerical experiments to clarify the impactof the LULCC on the precipitation regimens over theisland, demonstrating the following:1) Deforestation that generates bare land or areas withhigh albedo would induce a reduction in precipitation. Deforestation directly decreases forest
evapotranspiration, and the albedo increment accompanied by deforestation restricts convection
and horizontal atmospheric moisture inflow throughNOVEMBER 2017

TAKAHASHI ET AL.

2919

FIG. 9. As in Fig. 3, but for Pmon.

the effects of depressed available radiative energy
and sensible heat flux. This results in a decreased
water supply to the local atmosphere, and, thus,
reduced precipitation on the island.
2) On the other hand, a decrease in evapotranspiration
efficiency, that is, canopy conductance, possibly
caused by a change in the vegetation type, withoutchanging the surface albedo could increase precipitation. An increase in sensible heat flux due to theminimal change in available energy and a decrease in
the latent heat flux would produce active convection
and draw the horizontal atmospheric moisture flow,compensating for the decreased moisture supply tothe atmosphere by evapotranspiration. This possibly

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FIG. 10. Differences in evapotranspiration dE, precipitation dP, sensible heat flux dSHF, horizontal wind velocity
dU, horizontal water vapor flux dQdiv, and convective available potential energy dCAPE between each experiment(0.2Gcmax, Barren-1, and Barren-2) and Control. The dCAPE, dQdiv, and dU were simulated at 850 hPa. (a) Direct
effect due to the simulation setup, that is, dE, dP, dSHF. (b) Effect of feedback processes, that is dU, dQdiv, anddCAPE. The error bars indicate standard errors calculated from the day-to-day variability.

leads to an increase in precipitation on the island,
despite the decrease in evapotranspiration.
3) In the Maritime Continent, through changes in theland surface heating process and land–sea breeze
circulation, the LULCC would impact the amplitudeof the diurnal precipitation cycle in each region on
the island, defined according to the distance from thecoast. Also, the changes should be propagated in the
precipitation regimes from the coastal to the inland
regions: deforestation only in the lowland and coastal
areas would affect the precipitation regimes in themountainous and inland areas.
In practice, these findings might imply that the impact of
the LULCC on the local hydroclimate on Borneo is highly
uncertain. In particular, in the case of Southeast Asian
tropical forests, deforestation does not necessarily mean
fully bare lands, and the land surface properties of thesecondary vegetation, such as evapotranspiration and surface albedo, quickly resemble those of the original forest
again (Giambelluca et al. 1999; Giambelluca 2002). Also,
the evapotranspiration of the plantation developed after
deforestation cannot be declared appreciably changed
from that of the original tropical forest (e.g., Giambelluca
et al. 2016). Therefore, the next step for the present study
should be simulations utilizing more realistic future deforestation scenarios for Borneo (cf. Gaveau et al. 2014).
With global implications, sea surface temperature (SST)
forcing should be taken into account because of the SST’s
pervasive influence on the mean simulated climate (seeVoldoire and Royer 2004). Impacts of the LULCC on thelocal hydroclimate could pose a considerable risk to agricultural productivity and forest dynamics downstream of the
LULCC (see Spracklen et al. 2012; Lawrence and Vandecar2014). On a smaller scale, in this study, a significant decreasein precipitation was found in the forested mountainous areaadjacent to the deforested lowland area. This could lead tochanges in forest dynamics, for example, high rate of mortality, in such a transitional zone, in turn, causing feedback
to the atmosphere (see Delire et al. 2004; Wang et al. 2011).
As Mahmood et al. (2016) pointed out, despite the recognition of both LULCC-driven biogeochemical (e.g., enhanced carbon emissions) and biogeophysical (e.g., changed
water cycle and surface energy fluxes) impacts on the climate system by the scientific community, international
protocols on the mitigation of LULCC-driven climatechange do little to address the biogeophysical impact: mostprotocols only focus on the reduction of carbon emissions.
Thus, to bring the biogeophysical impact to the attention ofpolicy-makers and the general public, we need to provide
information derived from more mechanistic and quantitative assessments. Although there still remains a lack of realistic understanding of the LULCC-driven impact on
Borneo’s local climate, we believe that the outcomes
and findings of the present analysis could be the firststep and could be attractive starting points for more
extensive and technically difficult experiments.
Acknowledgments. This work was supported by Grantsin-Aid for Scientific Research (15H02645 and 25281005)
and the granted project ‘‘Program for Risk Information on
Climate Change’’ from the Ministry of Education, Culture,
Sports, Science and Technology, Japan.
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