Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Response of African Humid Tropical Forests to Recent
Rainfall Anomalies
Salvi Asefi-Najafabady1 and Sassan Saatchi1,2
1
Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
Submitted for publication to
Philosophical Transactions of The Royal Society B
12/12/12
Corresponding Author:
Salvi Asefi-Najafabady
School of Life Sciences
Arizona State University
427 East Tyler Mall
Tempe, AZ, 85287
Email: [email protected]
Phone: (256) 426-7743
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S1
Figure S1: Annual averages of the numbers of CRU rainfall stations located in the
forested areas in 1997, 1989, 1999, 2001, 2005 and 2009.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S2
Figure S2: QSCAT seasonal backscatter normalized anomalies (12 panels on the left)
versus TRMM rainfall seasonal normalized anomalies (12 panels on the right) for 2005,
2006 and 2007. Seasons are defined as JFM (Jan, Feb, Mar), AMJ (Apr, May, Jun), JAS
(Jul, Aug, Sep) and OND (Oct, Nov, Dec).
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S3
Figure S3: Spatial distribution of the driest month in African forests (see methods
section below).
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S4
Figure S4: TRMM driest quarter total rainfall (mm) in 2005, 2006 and 2007 (upper row)
and TRMM wettest quarter total rainfall (mm) in 2005, 2006 and 2007 (bottom row). In
2005 African forests (particularly west and central regions) experienced severe drought
both in the driest and the wettest quarter. Drought persisted in 2006. In 2007 drought was
still persistent in northern and, southwest, south and western areas.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S5
Figure S5: Relation between QSCAT and Rainfall water deficit monthly anomaly over
region 1 showing significant correlation. The monthly anomaly was calculated by
averaging pixel level anomaly values over the entire region by masking non-forest and
savanna vegetation classes using the most recent MODIS land cover product.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S6a
Figure S6b
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S6: Comparison of QSCAT and TRMM data over regions undergoing anomaly
during 2005, 2006, 2007, 2008, 2009 and 2010 and over three forested regions: (a)
distribution of TRMM and QSCAT pixels over three regions, (b) annual distribution of
TRMM and QSCAT anomaly over three regions.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S7
Figure S7: Spatial distribution of QSCAT data range represented by the averaged annual
minimum and maximum backscatter values over African forests. The legend provides
backscatter difference in terms of power (m2/m2). Areas with significantly large
difference (>0.03 in power or about 0.5 dB) are associated with forest canopies impacted
by the climate variations over the past decade (2000-2009). The seasonal variations of
canopy water content or phenology are not extracted from the overall variations. Areas in
the forest savanna boundary zones in West Africa or northern Central Africa have natural
seasonal variations much smaller than 0.1 in backscatter power, indicating a larger impact
of climate anomalies in these regions.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Figure S8: TRMM monthly rainfall (mm) and QSCAT monthly backscatter values in
terms of power (m2 m-2) plotted for January 2000 to December 2009 averaged over entire
Africa, Region 1, Region 2 and Region 3. Boundaries for Regions 1, 2 and 3 are defined
in the figure on the upper right corner. Notice Region 1 (west African forests)
experienced one long dry season with the minimum rainfall values of less than 10 mm
causing more severe impact on the forest. While Regions 2 and Region 3 often
experienced 2 short dry seasons (known as double-dip) with minimum rainfall of about
50 mm and therefore causing less impact on the forest. Similar pattern of seasonality is
evident in QSCAT responding to rainfall.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Methods
Rainfall anomalies:
Monthly time series of TRMM precipitation (P) data at 0.25 degree spatial resolution was
used to calculate the monthly and seasonal precipitation anomalies 1998-2010. The
anomaly on a pixel-by-pixel basis (i, j) for each year (y) was computed as a departure
from the 1998-2010 mean (P1998-2010), excluding the measurement from year (y) and
normalized by the standard deviation STD:
Pyanomalu (i, j) =
Py (i, j)− < P1998−2010 (i, j) >
STD(P1998−2010 (i, j))
(1)
We calculated maximum water deficit (MWD) as a complementary measure of drought
severity. MWD is the maximum value of yearly-accumulated water deficit (WD) for
each pixel. TRMM monthly rainfall time series (1998-2010) was used to calculate
monthly WDs based on the assumption that the moist canopy of the tropical forests
transpire ~100 mm month-1 (Da Rocha et al., 2004; Shuttleworth, 1989) If the monthly
rainfall value falls below 100 mm, the forest will experience water deficit. WD for each
pixel and each month (n) (Arago et al., 2007):
If WDn −1 (i, j) − E(i, j) + Pn (i, j) < 0;
then WDn (i, j) = WDn −1 (i, j) − E(i, j) + Pn (i, j);
else WDn (i, j) = 0
(2)
where E(i,j) is the evapotranspiration and Pn (i,j) is the monthly precipitation at each
Pixel.
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
Rainfall drought indices:
A map was created (Fig. S4) to show the spatial distribution of the driest month defined
as the corresponding month (e.g. January, February, etc.) when WD is the highest in a
year. For each pixel we recorded the month that MWD occurred most frequently within
13 years of the TRMM rainfall record. For example, if 10 out of 13 years, the forest
experienced its MWD in July, then July was selected as the month that generally is the
driest in that pixel.
QSCAT anomalies:
We used QSCAT backscatter data (σ0) at H polarization in ascending mode from
morning passes (6:00 LST). Monthly, seasonal and driest quarter anomalies were
calculated for the entire time series using the equation:
0
σ y0 (i, j)− < σ 2000−2009
(i, j) >
σ anomaly(i, j) =
0
STD(σ 1999−2009
(i, j))
0
(3)
QSCAT, backscatter power values in linear scale (m2/m2) is used and not in dB
(decibels).
QSCAT driest quarter was created as the averaged backscatter values of three
consecutive months with lowest monthly backscatter values
We also created the map of the 10 years averaged difference between maximum and
minimum backscatter values over forested areas (Fig. S8). For each year we first
computed the average minimum and maximum backscatter values for each pixel. Then
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Phil Trans Roy Soc B 368 doi: 10.1098/rstb.2012.0306
the 10 years averages of minimum and maximum values were created and the difference
was found.
MODIS land cover product:
For all of the analysis, we used MODIS level-3 global 0.05° land cover type product
(MOD12C1) to identify the forested areas. The rest of the MODIS land cover types were
masked out from the analysis. MODIS land cover types used to identify forested regions
include: evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf
forest, deciduous broadleaf forest and mixed forests.
References:
Long D. G., M Drinkwater, B Holt, S Saatchi, Bertoia, C. (2001) Global ice and land
climate studies
using scatterometer image data.
EOS, Transaction of American
Geophysical Union, 82 (43).
Kimball J. S., K. C. McDonald, A. R. Keyser, S. Frolking and S. W. Running (2001),
Application of the NASA scatterometer (NSCAT) for determining the daily frozen and
nonfrozen landscape of Alaska. Remote Sensing of Environment, 75:113–126.
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