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Response of African humid tropical forests
to recent rainfall anomalies
Salvi Asefi-Najafabady1 and Sassan Saatchi1,2
1
2
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Research
Cite this article: Asefi-Najafabady S, Saatchi
S. 2013 Response of African humid tropical
forests to recent rainfall anomalies. Phil
Trans R Soc B 368: 20120306.
http://dx.doi.org/10.1098/rstb.2012.0306
One contribution of 18 to a Theme Issue
‘Change in African rainforests: past, present
and future’.
Subject Areas:
environmental science
Keywords:
African rainforest, drought, climate change,
resilience
Author for correspondence:
Salvi Asefi-Najafabady
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rstb.2012.0306 or
via http://rstb.royalsocietypublishing.org.
Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
During the last decade, strong negative rainfall anomalies resulting from
increased sea surface temperature in the tropical Atlantic have caused extensive droughts in rainforests of western Amazonia, exerting persistent effects
on the forest canopy. In contrast, there have been no significant impacts on
rainforests of West and Central Africa during the same period, despite
large-scale droughts and rainfall anomalies during the same period. Using a
combination of rainfall observations from meteorological stations from the Climate Research Unit (CRU; 1950–2009) and satellite observations of the
Tropical Rainfall Measuring Mission (TRMM; 1998–2010), we show that
West and Central Africa experienced strong negative water deficit (WD)
anomalies over the last decade, particularly in 2005, 2006 and 2007. These
anomalies were a continuation of an increasing drying trend in the region
that started in the 1970s. We monitored the response of forests to extreme rainfall anomalies of the past decade by analysing the microwave scatterometer
data from QuickSCAT (1999–2009) sensitive to variations in canopy water
content and structure. Unlike in Amazonia, we found no significant impacts
of extreme WD events on forests of Central Africa, suggesting potential adaptability of these forests to short-term severe droughts. Only forests near the
savanna boundary in West Africa and in fragmented landscapes of the northern Congo Basin responded to extreme droughts with widespread canopy
disturbance that lasted only during the period of WD. Time-series analyses
of CRU and TRMM data show most regions in Central and West Africa
experience seasonal or decadal extreme WDs (less than 2600 mm). We
hypothesize that the long-term historical extreme WDs with gradual drying
trends in the 1970s have increased the adaptability of humid tropical forests
in Africa to droughts.
1. Introduction
In the second half of the twentieth century, a drying trend has been observed by
the instrumental records in West Africa and the northern edge of the tropical
African rainforest zone [1]. This drying trend has been primarily associated
with changing of a natural low-frequency mode (65 –80 years) of the sea surface
temperature, known as the Atlantic Multidecadal Oscillation [2]. Recent studies
also show that the West African drought is part of historical phases of alternating wet and dry periods of the West African monsoon that has been a natural
pattern over the past 3000 years [3,4]. Other studies focusing on palaeoclimate
data have broadly indicated that forests in the Atlantic Equatorial region and
south and central Congo Basin have experienced dramatic changes (forest
loss) over the last 4000 years related to variations in rainfall patterns caused
by sea surface temperature anomalies and human impacts [5,6]. Given this history, a key unresolved question is related to the resilience of African rainforest
and its response to future changes of climate [7].
Extensive research in Amazonia has shown a relatively rapid response of forests to extreme climate anomaly [8–10]. In the past decade, Amazonian forests
experienced two major droughts within the span of only 5 years, both associated
with the warming of tropical Atlantic Ocean [11]. The droughts occurred during
the 2005 and 2010 dry seasons, causing the water level of the Amazon River drop
& 2013 The Author(s) Published by the Royal Society. All rights reserved.
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data over the past century to understand the severity of
recent rainfall anomalies with respect to long-term patterns.
2. Data and methods
Phil Trans R Soc B 368: 20120306
We used precipitation data from the Tropical Rainfall
Measuring Mission (TRMM; 3B42-v6; monthly, 0.258 resolution) for the period of 1998 –2010 to investigate the spatiotemporal distribution of rainfall and its seasonality for the
past decade. The monthly, seasonal and annual rainfall
anomalies on a pixel-by-pixel basis for each year were computed as a departure from the 1998 –2010 mean, excluding
the measurement from that year and normalized by the standard deviation (see methods in the electronic supplementary
material). We calculated maximum water deficit (MCWD) as
a complementary measure of drought severity using TRMM
monthly rainfall data. MCWD is the maximum value of
yearly-accumulated water deficit (WD) for each pixel based
on the assumption that the tropical forests transpire approximately 100 mm month21 based on ground measurements of
evapotranspiration in Amazonia [22–24], and using the
method provided in Aragão et al. [25] (see the electronic supplementary material). The assumption suggests that when
the monthly rainfall value falls below 100 mm, the forest
will experience WD. The maximum WD (MCWD) provides
the magnitude of WD signifying the intensity of drought
and its spatial footprint over the landscape.
We included several other indices to examine the spatiotemporal patterns of droughts, such as (i) the duration of
dry months (DMD) with the dry month being the month
when the rainfall is less than 100 mm, (ii) driest quarter
(DQ) rainfall as the rainfall of three consecutive driest
month in each year, (iii) total annual rainfall (TAR) in each
year, and (iv) wettest quarter (WQ) rainfall as total rainfall
of three consecutive wettest months in each year.
We performed a similar analysis using long-term rainfall
data from the Climate Research Unit (CRU TS 3.0: 1901–
2009) ground-based gridded observation of precipitation. We
used the CRU data over the period of 1950–1997 when there
were a reasonably large number of ground stations in most of
the African continent, and replaced the later years with
TRMM 1998–2010 data. We performed the long-term analysis
with the combined CRU and TRMM data over three large distinct regions in West Africa (region 1), west of the Congo Basin
(region 2) and east of the Congo Basin (region 3) to avoid potential spatial artefacts caused by periodic loss of station data, in
CRU data. The combined data were used to capture longterm WD values averaged over the three regions. The combined
data were not used in studying long-term anomaly due to
potential effect of the mismatch in the spatial rainfall pattern
between CRU and TRMM, particularly over the Congo Basin.
All anomaly calculations were performed separately with
CRU and TRMM data.
SeaWinds scatterometer data onboard QSCAT (2000–2009)
was used to examine the impact of the precipitation anomaly
and WD on the forest. QSCAT is an active radar sensor operating in microwave frequency (13.4 GHz) and provides daily
(6.00 and 18.00) measurements of the backscatter signal from
the top layer of the forest canopy. QSCAT observations are
not impacted by cloud cover, atmospheric aerosol and radiation
condition, and are sensitive to the structure and water content
of forest canopy, providing a reliable remote sensing technique
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to its lowest in the last century [11–13]. Ground observations
in Amazonia showed large-scale tree mortality and leaf fall of
canopy trees after the 2005 drought [10]. The response of the
Amazon forest to these droughts was also examined by
time-series satellite observations. Data from conventional optical sensors suggested inconclusive results. One study showed
the greening of the forest (increase in vegetation index) due to
higher active radiation (less clouds) during the early stages of
drought [14], while others proposed either browning of the
forest from water stress [15], or greening in later stages of
drought from opening of the canopy from potential tree mortality [16]. Satellite optical observations over tropical forests are
impacted by clouds and atmospheric aerosol, causing noise in
surface reflectance data [15]. The first cloud and smoke-free
observation of the impacts of recent droughts on the forests
of Amazonia was captured by time-series measurements of
the satellite microwave scatterometer (QuickSCAT, QSCAT)
[17]. With the advantage of penetrating through clouds and
aerosals, the radar backscatter data from QSCAT showed
large-scale changes of canopy structure and moisture in western Amazonia from the 2005 drought. Remarkably, despite
the gradual recovery of total rainfall in subsequent years, the
decrease in canopy backscatter persisted until the next major
drought, in 2010. The decline in backscatter is attributed to
changes in structure and water content associated with the
forest upper canopy, most probably associated with large
tree mortalities [18].
In Central Africa, unlike Amazonia, the lowland rainforests
exist under significantly drier conditions, with an average precipitation of 1500–2000 mm yr21 in the central Congo Basin.
Only along the Atlantic coastal region of Central and West
Africa can rainfall reach 2500–3000 mm yr21, comparable
with central Amazonia. In the central Congo Basin, the duration of the dry season varies with the distance to the
equator in both hemispheres and also along an east-to-west
gradient [19]. Mean monthly temperature has almost no variations through the season, and the average annual
temperature generally stays below that of Amazonia owing
to higher elevation of the Congo Basin [1].
African and Amazon forests also have been exposed to
different drought patterns. The Amazon forests experienced
two short-term, intense droughts in the past decade, while
the African forests have been exposed to long-term reduction
in precipitation over the past three decades, with occasional
impacts by stronger drought events. The effects of long-term
drought are more complex than the short-term droughts as
forest composition and structure changes over time in order
to adapt and respond to long-term reduction in rainfall. The
recent study by Fauset et al. [20] showed that over the period
of 20 years of exposure to a drying trend, the composition
and structure of the Ghanaian forest species shifted from
wetter forest affiliated vegetation to favour deciduous, drier
forest canopy species with a need for intermediate light. Quantifying similar long-term effects over larger spatial scales
requires a combination of field measurements and dedicated
satellite observations sensitive to vegetation composition
[18,21]. However, there has been no report of the response of
the African rainforests over large spatial scales to drying
trends and rainfall anomalies of the past decade. Here, we
use satellite data to first quantify the extent and severity of
rainfall anomalies and droughts in African rainforests in the
past decade, and then to investigate the impacts of droughts
and water stress on the forest. We will also analyse rainfall
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Figure 1. (a) CRU DQ rainfall averaged over 1998– 2009, (b) TRMM DQ rainfall averaged over 1998 – 2009, (c) CRU DMD averaged over 1998 – 2009, (d ) TRMM DMD
averaged over 1998– 2009, (e) CRU total annual rainfall averaged over 1998– 2009, (f ) TRMM total annual rainfall averaged over 1998– 2009.
to monitor impact of climate on tropical forests [17,18,26].
Given the high frequency (2.2 cm) and the steep incidence
angle (468–548) of the QSCAT radar, the backscatter measurement over dense tropical forests is sensitive only to the top
canopy structure and moisture and has relatively no information about the underlying soil moisture [27–31] (see the
electronic supplementary material). We used 4-day composited
QSCAT backscatter data (s0) with enhanced resolutions of
4.45 km at H polarization in ascending mode from morning
passes (6.00 LST) to create backscatter anomalies of monthly,
seasonal and DQ (averaged backscatter values of three consecutive months with lowest monthly backscatter values) for the
entire time series. We limited the pixel extraction from
QSCAT and TRMM rainfall metrics to include only the tropical
forest regions by using a mask developed from the latest
MODIS land cover map (see the electronic supplementary
material for methods). The global wall-to-wall acquisition of
QSCAT data stopped in November 2009, limiting our analysis
of the impact of droughts on forest canopy up to 2009.
3. Results and discussion
(a) Rainfall patterns
Over Sub-Sahelian Africa, we found significant differences of
spatial patterns of rainfall measurements from CRU and
TRMM. We compared climatology of rainfall metrics derived
from 1998 to 2009 by CRU and TRMM, including DMD, DQ
and TAR. The spatial comparison of the metrics suggests that
the satellite observation of rainfall from TRMM has been able
to capture patterns of rainfall magnitude and seasonality undetected by CRU due to lack of operating ground stations,
particularly over the past decade [1]. The largest difference
between the two datasets exists in an area in the central
Congo Basin and eastern Democratic Republic of the Congo
(DRC) along the southern reaches of the Congo River. TRMM
data show a spatial pattern along the Congo River with low
TAR (TRMM: 1200 mm versus CRU: 1800 mm), and with distinct DQ and DMD different from the rest of the Central
Africa (figure 1). The changes in rainfall patterns reverse
along eastern mountains of DRC, around the borders with Burundi and Rwanda, and Lake Tanganyika, showing higher
rainfall (TRMM: 2200 mm versus CRU: 1200 mm) and shorter
dry season. Differences also exist in coastal regions of West
Africa, particularly in Ivory Coast, Liberia and Sierra Leone
where CRU interpolated rainfall data shows higher annual rainfall (TRMM: 1200 mm versus CRU: 2200 mm), shorter dry
months and lower rainfall during dry months (figure 1).
The observed differences are probably due to insufficient
working ground stations or potential problems in reporting
to international centres from different regions of Central and
West Africa (see the electronic supplementary material,
figure S1). The recent processing of TRMM data (3B43
product) includes ground observations to improve the magnitude of TRMM retrieval of rainfall, and the products are
calibrated and scaled to match the global monthly rain data.
Although there may still be errors in TRMM data, the most
recent products have relatively low bias in magnitude and
errors in capturing rainfall spatial patterns [32–34]. Contrary
to TRMM data, CRU gridded data are based on available
station data and spatial modelling, interpolating rainfall
gauge measurements to large distances (up to 1000 km) and
potentially missing legitimate rainfall patterns in areas with
paucity of station data [35]. In areas of large differences
between the two datasets, no station data were reported after
1990 to provide points in model interpolation such as in
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Figure 2. (a,b,c) TRMM, MCWD for 2005, 2006 and 2007. (d,e,f ) TRMM MCWD anomalies for 2005, 2006 and 2007. (g,h,i) DQ QSCAT anomalies for 2005, 2006 and 2007.
central DRC because of regional political conflicts (see the electronic supplementary material, figure S1). Similarly, in West
Africa, the number of stations dropped significantly over the
last decade (see the electronic supplementary material, figure
S1). The differences in magnitude and seasonality of rainfall
observed by TRMM and missing in recent CRU data may
have significant implications for the science community developing models to understand and predict climate interactions
with the land surface in the region.
(b) Short-term rainfall anomalies
Climate variations of the past decade were detected from three
rainfall metrics, DQ, WQ and MCWD, using 12 years of the
TRMM dataset. Together, these measures are strong predictors
of drought intensity and provide complementary spatial information on the extent of droughts [25,36], which correlates
with tree mortality [10]. In Africa, major drought signatures
appeared in three consecutive years, 2005, 2006 and 2007
(figure 2). In 2005, the low rainfall of the dry season resulted
in high MCWD values (greater than 700 mm) in these regions,
which persisted in 2006 with slightly less severity in central
regions and followed in 2007 along the forest–savanna regions
in northern DRC, western Africa and southern Gabon (figure 2).
In 2005, widespread rainfall anomalies of less than minus
1.5 s.d. (,21.5s) were observed during April, May, June
and July, August, September in Central and West African forests (see the electronic supplementary material, figure S2),
covering countries such as Gabon, Cameroon, north and central DRC as well as Liberia in West Africa. In 2006, rainfall
anomalies (,21.5s) appeared in DRC, Cameroon and
southern Gabon. Anomalies persisted in the northern edge
of the African forests through October, November, December
and January, February, March of the following year of 2007
(dry season in northern African forests; see the electronic supplementary material, figure S3). Strong negative rainfall
anomalies during the dry season of 2005, 2006 and 2007
resulted in widespread MCWD (less than 2700 mm) and
strong anomalies of ,21.5s over these regions (figure 2).
The rainfall of DQ did not increase above 150 mm over
almost the entire African forests (see the electronic supplementary material, figure S4). The lowest values of rainfall in the
DQ were observed in central DRC, the southern edge of the
Central African rainforest, southern and western Gabon, and
in the West African forests. In the wet season, rainfall remained
low (less than 400 mm) in the central DRC and northwestern
regions (less than 300 mm); however, other regions including
Gabon experienced higher rainfall (greater than 1000 mm)
during the wet season (see the electronic supplementary
material, figure S4).
(c) Long-term rainfall patterns
The short-term analysis of rainfall patterns and interannual
variability may be impacted by an overestimation of the
strength of the anomalies due to the short length of the analysis (approx. a decade). To examine this effect and understand
the recent magnitude and distribution of anomalies with
respect to long-term patterns, we developed a longer time
series, using the CRU monthly rainfall dataset from 1950 to
Phil Trans R Soc B 368: 20120306
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Figure 3. Monthly WD anomalies using a combination of CRU (1950 – 1997)
and TRMM (1998– 2010) data. African forests are divided into three regions
and WD values shown here are monthly averages over forest areas of (a) the
entire Africa, (b) West Africa, region 1, (c) Central Africa west, region 2, and
(d) Central Africa east, region 3.
2009 and used aggregated rainfall values for three large forest
regions. The general areas of these regions were determined
by dividing the entire African forests into three major zones
that experienced sever water stress anomalies during the
last decade (derived from TRMM data). The aggregated dataset eliminated the problems associated with the missing
station data by ensuring enough station points in selected
regions to provide stable average rainfall measurements.
Using the aggregated dataset, we computed monthly WD
for the period of 1950–2010 (figure 3). We assumed CRU
rainfall data prior to the year 1998 are reliable as more
stations were available in the region. Remarkably, it
appears that the drought events of the past decade observed by TRMM have been a continuation of an increasing
drying trend that started in the 1970s resulting in frequent
extreme WDs.
Over the entire West and Central African forests, the WD
has large interannual variability, but the last decades (starting
Despite the strong and prolonged (more than six months)
drought in 2005 and 2006, areas that experienced significant
rainfall and WD anomalies showed no significant negative
backscatter anomalies, which would have indicated water
stress on forests altering canopy properties (structure and
water content; figure 2). Negative anomalies of ,21.5s only
appeared in scattered patches in DRC, Gabon and Cameroon
from April, May, June 2005 to October, November, December
2006 (see the electronic supplementary material, figure S2).
This result is in contrast to what was observed in the
Amazon forests during and after the 2005 drought, in which
severe QSCAT backscatter anomalies resulted from the
impact of strong WDs in southwestern Amazonia [17,18].
Strong canopy backscatter anomalies of less than 22.0s were
observed following the rainfall anomalies of October, November, December 2006 and January, February, March 2007,
in western African forest, across northern DRC and over
the forest–savanna boundary regions (see the electronic
supplementary material, figure S2).
Over the three regions in Central and West Africa, we
extracted the monthly QSCAT backscatter and TRMM WD
anomalies over the forested pixels (figure 4) by eliminating
deforested pixels captured by the MODIS land cover map
and MODIS continuous field percent tree cover data [38].
Over the combined Central and West African forest regions,
QSCAT and WD anomaly stayed decoupled and showed no
significant correlation ( p , 0.1), suggesting the rainfall
anomalies caused no significant impact on forest canopy.
Region 1 in West Africa, covering tropical forests of Ghana,
Ivory Coast, Liberia and Sierra Leone experienced a severe
WD anomaly from 2005 to 2008 with the largest deficit in
mid-2007 (greater than 23.0s; electronic supplementary
material, figure S2). During this period, QSCAT backscatter
anomaly gradually became negative with a moderately
strong response in January, February, March 2007 (greater
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in the 1970s) show the strongest deviation from long-term
mean. Particularly in forests in West Africa (region 1) and
the Congo Basin east (region 3), recent low WDs appear to
be the strongest of the past 108 years. On average, region 3
experienced approximately threefold deviation from the
long-term mean (less than 50 mm deficit). Relatively, region
1 has the largest mean WD (approx. 200 mm) compared
with the other regions, indicating the forests are in a strong
seasonal environment. Since the 1970s, the WD in region 1
has increased gradually. However, during the last decade,
low rainfall years causing about four times larger WDs
have impacted region 1 in West Africa for several years.
We performed a time-series analysis using the Breaks in
Additive Season and Trend (BFAST) algorithm [37], based
on the iterative decomposition of time-series data, and
found no major significant break in the time-series data
suggesting a major shift in the rainfall patterns. However,
the trend analysis showed significant negative trend in WD
and monthly rainfall in West Africa since the early 1970s
(R 2 ¼ 0.16, p , 0.05). The other regions had no significant
long-term trends but showed more frequent extreme WD
events during the last two decades in region 3 and comparatively moderate WDs in region 2. In general, region 2 had the
lowest WD, but highly fluctuating, with large WDs occurring
on an approximately decadal cycle.
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result was a clear indication that despite the occurrence of
severe WD anomalies, forest canopies in this region had no
indication of canopy disturbance (loss of canopy water content, change or damage of the canopy structure or large tree
mortality). The forests in this region receive more rainfall
than any other regions in the Congo Basin (2000–
3000 mm yr21) and have frequent low cloud cover, impeding
the radiation and the potential evapotranspiration [1]. The
forest response in region 3, in the centre of DRC and along
the extent of the Congo River, was also similar to region 2,
with the distinction that the WD anomaly was less frequent.
The anomaly in WD persisted over the northern DRC
with values greater than 21.5s over more than 30% of
the region. However, on average, no significant QSCAT
anomalies were observed in response to negative WD
anomalies (figure 4). On the contrary, in region 1, the correlation between ensemble pixels undergoing WD anomaly
and pixels with QSCAT anomaly was significant, suggesting
short-term effect of droughts on the forest canopy.
4. Discussion
TRMM WD anomaly
QSCAT backscatter anomaly
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Figure 4. Variations of monthly TRMM WD and QSCAT backscatter anomalies
averaged over forest pixels in four regions: (a) entire Africa, (b) region 1,
(c) region 2 and (d) region 3.
than 21.5s) and with significant positive correlation with
WD anomaly ( p , 0.01) (see the electronic supplementary
material, figure S5). QSCAT response suggested an impact
of the short-term drought on the forest canopy (e.g. canopy
disturbance, tree mortality or leaf fall) that persisted and
slowly recovered before the end of 2008 when WD gradually
recovered. As the analysis excluded the deforested, fire and
savanna pixels by consulting the high-resolution (500 m)
MODIS land cover maps and products, we expect the
strong QSCAT response is mainly due to the effect of
droughts on the forest canopy.
For regions 2 and 3, in the eastern and western Congo
Basin, respectively, the QSCAT response to strong WD was
relatively moderate (figures 2 and 4). The western region of
Central Africa (region 2), covering forests of DRC, Gabon,
Equatorial Guinea and southern Cameroon, experienced several strong droughts in the last decade with WD anomaly
increasing greater than 24.0s. However, QSCAT backscatter
showed no significant anomaly during this period. The
The absence of any evidence of strong or persistent impact of
severe droughts on the rainforests of Central Africa and West
Africa suggests that these forests may be more adapted to tolerate high WD short-time events in comparison with the
forests in other regions of moist tropics. QSCAT backscatter
data have the capability of detecting any widespread changes
of forest canopy properties such as damages or mortality of
emergent trees as the first response to droughts [9,39]. These
changes were readily observed in moist tropical forests of
Amazonia almost immediately after an intense rainfall
anomaly [17,18]. Our results focused over the past decade
also show the QSCAT response to negative rainfall or WD
anomaly on average (see the electronic supplementary
material, figure S6a), and spatially (see the electronic supplementary material, figure 6Sb). However, unlike Amazonia
the QSCAT response to severe drought events was not persistent over a longer period and only showed strong correlation
with WD anomaly for the same time or with about one
month time lag (see the electronic supplementary material,
figure S5). These short-term changes in QSCAT backscatter
data represent minor canopy disturbance or a decline of
canopy water content during the extreme WD event. A comparison with 2005 and 2010 droughts in Western Amazonia
[18] suggest that the MCWD in Central and West Africa
during drought years (figure 2) was significantly larger than
Amazonia by more than 200 mm. However, unlike in Amazonia, the QSCAT response was relatively weak and not
persistent over most of the African humid forest, providing a
strong indication that these extreme events did not create
major disturbance.
The longer time-series analyses of rainfall suggest that the
lack of persistent QSCAT anomaly over the last decade is
probably due to the long-term climate condition with frequent WDs and potential adaptability of vegetation
characteristics of the region. A 60 year time-series analysis
of the combined CRU and TRMM dataset (figure 3)
showed the observed negative anomalies of the past decade
are a continuation of a gradual drying and more frequent
rainfall anomalies in most regions of the African forests, indicating the long-term exposure of the African forests to
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Acknowledgements. The enhanced resolution QSCAT data were produced by Prof. David Long at the Brigham Young University as
part of the NASA Sponsored Scatterometer Climate Record
Pathfinder.
Funding statement. The research was partially supported by NASA
grants at the Jet Propulsion Laboratory.
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