Geophysical Research Letters
Supporting Information for
Observation of the disruption of hydro-ecological equilibrium in southwest
Amazonia mediated by drought
Eduardo Eiji Maeda1*, Hyungjun Kim2, Luiz E. O. C. Aragão3, 4, James S. Famiglietti5,6, Taikan
Oki2
1University
of Helsinki, Department of Geosciences and Geography, P.O. Box 68, FI-00014, Helsinki, Finland
2University
3National
of Tokyo, Institute of Industrial Science, 153-8505, Tokyo, Japan
Institute for Space Research (INPE), Avenida dos Astronautas 1758, São Jose dos Campos-SP, Brazil
4College
of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, UK.
5University
6NASA
of California, Irvine, Department of Earth System Science, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Contents of this file
Figures S1 to S6
Introduction
The auxiliary material provides additional information and figures, which are not critical for the
main manuscript, but gives a more complete overview of the study area, data and methods.
Section 1 provides details on the data and methods used in our study, including a more detailed
explanation of the BFAST methodology.
Section 2 consists in five figures (S2, S3, S4, S5 and S6), which provide additional information to
support arguments described in the main manuscript.
1. Material and methods
1.1. River discharge data
Monthly river discharge data for the four watersheds presented in Figure 1 were obtained from
the ORE HYBAM project (Environmental Research Observatory - Geodynamical, hydrological
and biogeochemical control of erosion/alteration and material transport in the Amazon basin).
Monthly observations from January 2002 to August 2013 were used to assess the Cumulative
Water Deficit (CWD) in each watershed (Equation 3). Eventual gaps in the dataset were filled
using simple linear interpolation.
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1.2. Rainfall data
Rainfall data was obtained from the Tropical Rainfall Measuring Mission (TRMM) 3B43
product, version 7. The 3B43 product consists of monthly precipitation rate, at 0.25ox0.25o spatial
resolution, which combines the estimates generated by sensors on board of the TRMM,
geostationary satellites and ground data. The ground data is obtained from NOAA’s Climate
Anomaly Monitoring System (CAMS), and the global rain gauge product produced by the Global
Precipitation Climatology Center (GPCC).
1.3. Terrestrial water storage
Here we define terrestrial water storage as the sum of soil moisture, groundwater, biomass water,
and surface waters in lakes, reservoirs, wetlands and river channels. Changes in terrestrial water
storage were accessed using gravity field measurements from the Gravity Recovery and Climate
Experiment (GRACE).
Three monthly GRACE solutions, from different processing centers, were used to compile
monthly terrestrial water storage anomalies (TWSA) over the Amazon basin [Landerer and
Swenson, 2012]. The solutions used are the GFZ (GeoforschungsZentrum Potsdam), CSR (Center
for Space Research at University of Texas, Austin), and JPL (Jet Propulsion Laboratory). The
three solutions were combined by simple arithmetic mean of the gravity fields, which according
to recent studies is the most effective approach for reducing the noise in the gravity field solutions
[Sakumura et al., 2014]. The spatial resolution of all solutions is 1o x 1o.
The GRACE data were corrected for attenuations on surface mass variations at small spatial
scales by multiplying the solution grids by a scale factor grid [Landerer and Swenson, 2012]. The
scaling grid is composed by a set of coefficients, which are provided to restore the energy
removed during the sampling and post-processing of GRACE observations.
1.4. QSCAT
In this study we used SeaWinds-QSCAT Enhanced Resolution Image Products (version 2),
provided by NASA’s Scatterometer Climate Record Pathfinder project. The product used in this
study combines ascending (morning) and descending (evening) orbital passes, and is based on
SeaWinds "egg" backscatter measurements. The nominal spatial resolution for egg images is 4.45
km. We chose to use only H polarization, given that previous studies indicate that results using V
polarization show no significant differences [Saatchi et al., 2013].
1.5. Evergreen areas mask
QSCAT backscatter values were collected only in evergreen forests inside the Amazon basin. The
evergreen areas were identified using long-term (2001-2012) MODIS EVI averages for the subequatorial dry season (i.e. July, August and September). Then, an empirical threshold was defined
to separate evergreen areas from the other targets. The result of this procedure is demonstrated in
Figure S1. A comparison with land cover maps compiled using high-resolution data [Hansen et
al., 2013] indicate that this threshold could separate deforested areas and water bodies with an
accuracy of approximately 95%.
Monthly EVI imagery for the dry months were compiled using 1 km x1 km 16-day composites
provided in the MODIS MOD13A2 product. A 16-day EVI composite image contains the bestquality EVI values within a 16-day period. The monthly records were obtained by combining the
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two 16-day composites within a month, with the exception of September, when only one 16-day
composite image was used. The MODIS vegetation index products are accompanied with explicit
quality flags for clouds and aerosols, which were used to remove low quality pixels.
Figure S1. Masking of agricultural areas and cerrado ecosystem vegetation. (a) Average EVI
image from the sub-equatorial dry season (July-September) before masking; (b) EVI image with
agricultural areas and cerrado vegetation masked.
1.6. BFAST analysis
The BFAST analysis combines iterative time series decomposition methods with algorithms for
detecting breakpoints within the time series. Here, the method was applied to identify trend
breaks within the TWSA, backscatter and rainfall time series. A detailed description of the
method is presented in Verbesselt et al [2010].
The parameters used for the decomposition model and the trend detection algorithm were the
same for all variables. The harmonic model was chosen to fit the seasonal component and detect
potential seasonal breaks [Verbesselt et al., 2010]. The maximum number of breaks within the
time series was set to 2, and a significance level of 0.01 was used for detecting seasonal and trend
breaks. In Figures 4 and S4, Wt refers to the seasonal component and Vt refers to the
deseasonalized data. The values are obtained using the following equations:
Wt= Yt – Tt
Vt= Yt – St
where Yt is the data used as input, Tt is the fitted trend component, and St it the fitted seasonal
component (see Verbesselt et al. 2010 for details on the calculations of Tt).
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2. Supporting results
Figure S2. Water deficit and annual rainfall anomalies in the Amazon basin, considering
the drainage area upstream from Obidos river discharge station.
Figure S3. (a) River discharge [mm month-1] and (b) river discharge standardized anomalies in
the Solimões river, recorded at the Manacapuru station (3.31° S; 60.63° W); (c) monthly changes
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in terrestrial water storage (dS/dT) at the Solimões river watershed and (d) standardized
anomalies of dS/dT.
Figure S4. (a) Significance of the relationship between seasonally adjusted QSCAT backscatter
and GRACE TWSA. The dashed line squares show the geographical location of grids A and B,
representing areas of equatorial forest and savanna transition, respectively, which are used for the
BFAST analysis in Figure S5. (b) Relationship (R2) between rainfall and forest backscatter.
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Figure S5. Seasonal decomposition and trend break analysis (BFAST) carried out in the QSCAT
backscatter, GRACE total water storage anomalies (TWSA) and TRMM rainfall datasets, over
grids A and B (Figure S4a). Trend breaks were detected using a confidence level of 99%.
In order to assure that the low correlation between TWSA and QSCAT backscatter was not
caused by a lagged response of vegetation, we performed cross-correlation analysis with lags
between -3 and 3 months (Figure S5). We found that, even after considering a lag of up to three
months, there of no evidences of a positive relationship between TWSA and backscatter in
equatorial regions (Grid A) and savanna transition zones (Grid B).
Figure S6. Cross-correlation analysis between QSCAT backscatter and GRACE TWSA
carried out over grid A (equatorial area) and grid B (savanna transition area).
References
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