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WATER RESOURCES RESEARCH, VOL. 44, W02433, doi:10.1029/2006WR005779, 2008
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Analysis of terrestrial water storage changes from GRACE
and GLDAS
Tajdarul H. Syed,1 James S. Famiglietti,1 Matthew Rodell,2 Jianli Chen,3
and Clark R. Wilson4
Received 28 November 2006; revised 9 August 2007; accepted 8 November 2007; published 22 February 2008.
[1] Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) has
provided first estimates of land water storage variations by monitoring the time-variable
component of Earth’s gravity field. Here we characterize spatial-temporal variations in
terrestrial water storage changes (TWSC) from GRACE and compare them to those
simulated with the Global Land Data Assimilation System (GLDAS). Additionally, we use
GLDAS simulations to infer how TWSC is partitioned into snow, canopy water and soil
water components, and to understand how variations in the hydrologic fluxes act to
enhance or dissipate the stores. Results quantify the range of GRACE-derived storage
changes during the studied period and place them in the context of seasonal variations in
global climate and hydrologic extremes including drought and flood, by impacting land
memory processes. The role of the largest continental river basins as major locations for
freshwater redistribution is highlighted. GRACE-based storage changes are in good
agreement with those obtained from GLDAS simulations. Analysis of GLDAS-simulated
TWSC illustrates several key characteristics of spatial and temporal land water storage
variations. Global averages of TWSC were partitioned nearly equally between soil
moisture and snow water equivalent, while zonal averages of TWSC revealed the
importance of soil moisture storage at low latitudes and snow storage at high latitudes.
Evapotranspiration plays a key role in dissipating globally averaged terrestrial water
storage. Latitudinal averages showed how precipitation dominates TWSC variations in the
tropics, evapotranspiration is most effective in the midlatitudes, and snowmelt runoff is a
key dissipating flux at high latitudes. Results have implications for monitoring water
storage response to climate variability and change, and for constraining land model
hydrology simulations.
Citation: Syed, T. H., J. S. Famiglietti, M. Rodell, J. Chen, and C. R. Wilson (2008), Analysis of terrestrial water storage changes
from GRACE and GLDAS, Water Resour. Res., 44, W02433, doi:10.1029/2006WR005779.
1. Introduction
[2] Terrestrial water storage (TWS) is defined as all forms
of water stored above and underneath the surface of the
Earth. TWS is a key component of the terrestrial and global
hydrological cycles, exerting important control over the
water, energy and biogeochemical fluxes, thereby playing
a major role in Earth’s climate system [Famiglietti, 2004].
For example, soil water storage affects the partitioning of
water and energy fluxes at the land surface, with implications for precipitation recycling, hydrologic extremes
including drought and flood and by impacting land memory
processes [Shukla and Mintz, 1982; Eltahir and Bras,
1
Department of Earth System Science, University of California, Irvine,
California, USA.
2
Hydrological Sciences Branch, NASA Goddard Space Flight Center,
Greenbelt, Maryland, USA.
3
Center for Space Research, University of Texas at Austin, Austin,
Texas, USA.
4
Department of Geological Sciences, University of Texas at Austin,
Austin, Texas, USA.
Copyright 2008 by the American Geophysical Union.
0043-1397/08/2006WR005779$09.00
1996]. Surface water storage impacts rates of freshwater,
sediment and nutrient transport, and plays an important role
in greenhouse gas emissions to the atmosphere [Richey et
al., 2002]. TWS is a key unknown in the calculation of
current rates of global mean sea level rise [Church et al.,
2001]; and it impacts Earth rotation variations such as
length of day [Chao and O’Connor, 1988]. As an integrated
measure of surface and groundwater availability, TWS has
significant implications for water resources management.
[3] In spite of its manifold importance, until recently,
TWS has not been adequately measured at the continental
scale [Lettenmaier and Famiglietti, 2006]. This is primarily
due to the lack of a comprehensive global network for
routine TWS monitoring. While ground and satellite based
techniques can measure some individual components such
as soil moisture [Njoku et al., 2003] and surface water
[Alsdorf and Lettenmaier, 2003], there has been no integrated measurement of TWS.
[4] The dearth of direct observations of large scale TWS
estimates was resolved by the launch of Gravity Recovery
and Climate Experiment (GRACE) twin satellite mission in
March, 2002. Although primarily aimed at accurately mapping time variations in Earth’s gravity field at 30 day
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intervals, GRACE has shown remarkable prospects for
inferring water mass changes over the globe [Tapley et
al., 2004a; Wahr et al., 2004].
[5] Most GRACE hydrology studies to date, including
those described above and in section 2 below, have dealt
with either comparison of derived terrestrial water storage
anomalies (TWSA, i.e., TWS deviations from the mean
rather than month to month changes) to models and to
limited observations; methods of data processing and error
analyses; and to new applications for monitoring TWS
components and fluxes. While the capability for GRACE
to monitor continental scale anomalies and changes in
monthly water storage is now well documented, little if
any work has addressed fundamental issues such as the
characterization of its space-time variability and its role in
terrestrial hydroclimatology, namely how observed TWSC
is distributed among the terrestrial subsurface and surface
stores, and how the fluxes of precipitation, evapotranspiration and runoff act to enhance or dissipate the storages.
[6] Here we present a detailed analysis of continental
scale water storage changes using GRACE and output from
a high quality global land hydrological modeling system.
Unlike the other studies described here, the emphasis of this
work is toward understanding the spatial-temporal variability in the role of different hydrologic fluxes and storages
influencing the magnitude and distribution of TWSC over
the globe. Note that the lack of global-scale observations of
TWSC necessitates a model-based approach to the analyses.
In the first part of our work we focus on the characterization
of spatial-temporal variability in observed storage changes
over land. Total water storage changes are quantified over
the different continents and in some of its largest river
basins. In the second part we compare estimates of TWSC
from GRACE and the Global Land Data Assimilation System
(GLDAS; Rodell et al., 2004b). Having demonstrated good
agreement between the two, in the third part of the analysis,
we discuss the zonal and global patterns of variability in
TWSC and how these patterns are controlled by the various
hydrologic and climatologic factors, using GLDAS-based
states and fluxes.
2. Background
[7] GRACE is a joint venture between NASA and the
DLR launched in March 2002. The mission objective is to
accurately measure the mean and time varying component
of Earth’s gravity field at monthly timescales for a period of
at least 5 years. The mission consists of twin satellites
spaced 220 km apart in a near circular polar orbit at an
altitude of 500 km. Spatial-temporal variations in Earth’s
gravity field affect the distance between the two satellites: a
continuous and accurate measurement of changes of this
distance (inter-satellite range) by the onboard K-Band
microwave ranging system [Tapley et al., 2004a], combined
with other ancillary data, enables precise maps of Earth’s
time-variable gravity field to be produced. Over land, time
variations of these global gravity fields are primarily due to
water mass variations [Wahr et al., 1998; Tapley et al.,
2004b]. This has allowed for the first time, observations of
variations in TWS at large river basin [Swenson et al., 2003;
Chen et al., 2005; Seo et al., 2006; Winsemius et al., 2006]
to continental scales [Wahr et al., 2004; Ramillien et al.,
2005; Klees et al., 2007]. Extraction of these hydrologic
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signals over land by the removal of effects from other timevarying geophysical factors is one of the prime motivations
behind the GRACE satellite mission and is an active area of
research.
[8] Rodell and Famiglietti [1999, 2001] showed promising results in pre-launch assessment of some of the key
aspects of GRACE, such as the potential detectability and
accuracy of measuring TWSC, using estimated GRACE
errors and modeled and observed water storage data. Since
the mission began, several studies have described GRACE’s
ability to detect water storage changes at varied spatial
scales over different parts of the globe [Wahr et al., 2004;
Ramillien et al., 2004], to monitor the mass balance of the
ice sheets [Velicogna and Wahr, 2006a, 2006b), to quantify
fluxes [Rodell et al., 2004a; Syed et al., 2005; Ramillien et
al., 2006; Swenson and Wahr, 2006a] and storages [Rodell
et al., 2007; Yeh et al., 2006; Frappart et al., 2006a;
Schmidt et al., 2006] in land surface hydrology and for
the validation and improvement of the terrestrial water
balance in global land surface models [Niu and Yang,
2006; Swenson and Milly, 2006]. In addition there are
numerous studies addressing different filtering techniques
to retrieve water storage change signals and its associated
error structures [Seo and Wilson, 2005; Swenson and Wahr,
2002, 2006b; Chen et al., 2005; Ramillien et al., 2005].
[9] Herein we present a study, complementary to earlier
studies, but primarily aimed at characterization and understanding of the role of TWSC in terrestrial hydroclimatology. Our overall emphasis is on the analysis of process
controls and partitioning of continental water storage
changes at varied spatial and temporal scales using state
of the art assimilated hydrological model data. This will
help us in trying to understand how the terrestrial storage is
partitioned at different spatial and temporal scales and how
these estimates are affected by the hydrologic fluxes at
similar scales.
3. Methods
[10] In order to investigate the water storage changes,
corrected GRACE Stokes coefficients (Level 2 Gravity Field
Product User Handbook, Bettadpur, S., 2003) provided by
the Center for Space Research (CSR) at the University of
Texas at Austin were expanded to degree and order 60 and
smoothed with a 1000 km half-width Gaussian averaging
kernel to produce the time varying gravity estimates. The
coefficients of the lowest degree zonal harmonics, the
degree two and order zero term was not taken into consideration, mainly due to large unquantifiable errors associated
with this term. Subsequently these smoothed spherical
harmonic coefficients were transformed into 1 1 degree
gridded data that reflect vertically integrated water mass
changes averaged over a few hundred kilometers with an
accuracy of 1.5 cm of equivalent water thickness [Wahr et
al., 2004]. Since we average gridded land water storage
changes over much larger spatial domains for this analysis,
the error at these spatial scales is smaller than 0.1 cm/month
[Ramillien et al., 2006]. Errors in GRACE data estimated
by the above mentioned studies represent a combination of
measurement and processing errors, see Wahr et al. [2006]
for additional details. The reader is referred to Wahr et al.
[1998] and Tapley et al. [2004a] for a more detailed
description of the processing of GRACE data. Note that
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Table 1. GLDAS Variables Used in This Study
Parameters
Spatial Resolution
Temporal Resolution
Time Span
Spatial Extent
Precipitation (Includes both
solid and liquid rainfall) (P)
Total soil moisture (4 layers
from 0 – 200 cm depth) (TSM)
Evapotranspiration (E)
1° 1°
monthly sum
Jan’02 – Dec’04
1° 1°
monthly average
Jan’02 – Dec’04
1° 1°
monthly sum
Jan’02 – Dec’04
180°W – 180°E
90°N – 60°S
180°W – 180°E
90°N – 60°S
180°W – 180°E
90°N – 60°S
180°W – 180°E
90°N – 60°S
180°W – 180°E
90°N – 60°S
180°W – 180°E
90°N – 60°S
Runoff (Includes both surface
and subsurface flow)(R)
Canopy water storage (CWS)
1° 1°
monthly sum
Jan’02 – Dec’04
1° 1°
monthly average
Jan’02 – Dec’04
Snow Water Equivalent (SWE)
1° 1°
monthly average
Jan’02 – Dec’04
techniques for processing GRACE data continue to evolve
and improve [Han et al., 2005; Seo and Wilson, 2005;
Swenson and Wahr, 2006a]. The GRACE data set used in
this study is CSR RL01 which spans from April 2002 to
July 2004 excluding some months in 2002 (May, June and
July) and June in 2003. Longer time periods of GRACE
data are becoming available and will allow for studies of
interannual variations. The impact of the length of the
smoothing radius has also been addressed [Chen et al.,
2006]. However, for the purposes of this work, the abovedescribed data set sufficiently captures the key features of
terrestrial hydroclimatology.
[11] The primary land surface flux and storage component data were obtained from NASA’s Global Land Data
respectively, and t is time. TWS considered here constitutes
total column soil moisture (TSM), Snow Water Equivalent
(SWE) and Canopy Water Storage (CWS). Neither surface
water storage in inland water bodies nor groundwater storage
is represented in the model simulations. Both can be important
components of TWS in certain regions of the globe [Rodell
and Famiglietti, 2001; Frappart et al., 2006b]. Our analysis of
storage partitioning is therefore limited to TSM, SWE and
CWS and cannot give a complete description of the lateral and
vertical distribution of water storage until surface and
groundwater components are added to land model used here.
Such work is ongoing in our research team. Hence following
equation (1), estimates of TWSC from GLDAS that closely
approximate GRACE were calculated as follows
S soilð15;N Þ þ S snowð15;N Þ þ Scanopyð15;N Þ Ssoilð15N Þ þ Ssnowð15N Þ þ Scanopyð15N Þ
DS
¼
Dt N
Dt
Assimilation System (GLDAS) [Rodell et al., 2004b].
GLDAS parameterizes, forces, and constrains multiple land
surface models with ground and satellite observation based
data sets, toward the goal of accurate simulation of water
and energy cycle states and fluxes. For this study we used
1-degree, 3-hourly output from a 1979-present run of the
Noah land surface model [Ek et al., 2003] driven by
GLDAS. Because of the model’s inability to represent ice
sheet flow and mass balance, Antarctic was not simulated
and output from Greenland was excluded from the analysis.
For this investigation we extracted the relevant hydrological
fluxes and storages from January, 2002 to December, 2004,
and aggregated them to monthly averages or accumulations
as appropriate (Table 1).
[12] GRACE-derived TWSC estimates were obtained by
differencing the monthly TWS anomalies, which themselves were obtained by removing the mean gravity field
from each of the monthly GRACE solutions. These estimates of TWSC can be interpreted as average changes in
TWS from one month to the other.
[13] A comparable replication of GRACE observations
from GLDAS land surface output is based on the following
equation
DS
Dt
N
¼
S i;N Si;N 1
Dt
ð1Þ
where S represents the average TWS for the indexed day (i),
the subscripts i and N represent day of month and month
ð2Þ
[14] The terms on the right hand side are 15th day
averages of each calendar month of the year. We assume
that an averaged estimate of the 15th day can be considered
representative of the 30 day average. The method showed
promising results in an earlier study [Syed et al., 2005] and
also compared well with other published methods of aggregation of monthly fluxes [Swenson and Wahr, 2006a; Rodell
et al., 2004a]. Additionally, TWSC can be computed using a
monthly basin-scale terrestrial water balance which can be
approximated as follows
N
N
N
X
X
X
DS
¼
P
E
R
Dt N N 1
N 1
N 1
ð3Þ
where P is precipitation, R is runoff and E is evapotranspiration.
4. Results and Discussion
4.1. Water Storage Changes from GRACE
[15] In this section we characterize the spatial-temporal
variability in the observed water storage change signals
from GRACE. The underlying causes of these variations are
discussed in more detail in subsequent sections.
[16] Figure 1a shows that the time series of globally
averaged TWSC peaks during NH Winter (DJF) with an
amplitude of roughly 0.6 centimeters/month. Figure 1b
shows the latitudinal distribution of seasonally averaged
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Figure 1. (a) Monthly variations of globally averaged
GRACE-derived TWSC estimates (black dots) are shown
along with the fitted seasonal cycle (black solid line).
(b) Zonally averaged TWSC estimates from GRACE for
each season, JJA, SON, DJF and MAM. (c) Amplitudes of
seasonal cycles fitted to the zonally averaged absolute value
of TWSC estimates from GRACE.
TWSC. A clear dominance of the strongest water storage
change signals in a Southern Hemisphere (SH) 0° to 30° S
latitudinal band is apparent for all the seasons, with lesser
peaks in the NH subtropics and at 60°N. In the tropics,
Summer (Winter) is dominated by increases (decreases) in
TWSC, due to increases (decreases) in precipitation in
response to seasonal migration of the ITCZ. In contrast,
midlatitudes during JJA (DJF) are dominated by decreases
(increases) in TWSC, due to increases (decreases) in evapotranspiration. The polar regions are similar to the tropics,
but with slight JJA (DJF) increases (decreases) in TWSC,
particularly in the NH. The amplitude of the seasonal cycle
in the zonally averaged absolute value of TWSC (Figure 1c)
has associated peaks in the corresponding regions.
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[17] The TWSC variations in the tropics shown in
Figure 1b can be readily explained by the migration and
strength of the Inter Tropical Convergence Zone (ITCZ), with
maxima associated with enhanced precipitation. The hemispheric differences in the amplitudes of TWSC (±4 –5 cm/
month in SH; ±2 cm/month in NH) are manifestations of
greater land precipitation in the SH in comparison to the NH,
especially in equatorial South East Asia, South America and
Africa [Adler et al., 2003]. Minima correspond to shifts in the
subtropical depressions where evapotranspiration increases.
In addition to the large fluctuations in the tropics, there is a
NH midlatitude zone of much lower yet prominent variability
in the range of ±1centimeter/month. Positive storage changes
in DJF result from midlatitude polar frontal precipitation and
snow storage. Snowmelt and evapotranspiration account for
the decreasing (MAM) and negative (JJA, SON) peaks in this
zone. Note that the TWSC variations during SON and MAM
can be viewed as intermediate stages of the stronger endmembers prevalent during JJA and DJF.
[18] The amplitude of seasonal cycle in zonally averaged
value of TWSC (Figure 1c) provides perspective on the
magnitude of the storage changes, both positive and negative, across the continents. The greatest variation in storage
changes occur in the SH Tropics with an amplitude greater
than 7 cm/month, followed by the NH Tropics (3.2 cm/
month), the NH midlatitudes (2.4 cm/month) and the SH
midlatitudes (almost 2 cm/month). Figure 1c further highlights where the principal zones for mass exchange between
the land and the atmosphere or ocean occur, and that they
are consistent with the major features of the atmospheric
general circulation and global patterns of precipitation and
evaporation [Hartmann, 1994; Peixoto and Oort, 1992].
This also includes the desert regions with zero or low
TWSC (near 30° N and S).
[19] Figures 1b and 1c have two important implications
for terrestrial hydroclimatology. The first is that global scale
measurements of TWSC, available for the first time with
GRACE, have identified significant regions of dynamic
change, and that they are consistent with global patterns
of weather and climate. The second, more subtle implication
is that the GRACE mission has shown that terrestrial
water storage responds in predictable ways to precipitation and evaporation processes, hence providing important
‘‘memory’’ of past atmospheric phenomena.
[20] Table 2 lists the annual means, amplitudes of fitted
annual cycles and seasonal means of GRACE-based TWSC,
averaged for each continent and the river basins shown in
Figure 2. Although insignificant compared to the amplitude
of the cycles, annual mean values over Europe, South
America and Asia show a net accumulation of water mass
with values of 0.32 cm/month, 0.30 cm/month and 0.08 cm/
month respectively for the period of the GRACE data used
here. On the other hand, even lesser depletion of total water
storage is noted in Australia (0.13 cm/month), North America (0.06 cm/month) and Africa (0.02 cm/month). The
seasonal means again point to the influence of ITCZ migration
on the distribution of land water storage, similar to what we
have noted in Figure 1. While tropical basins in the NH gain
water (e.g., Yangtze (2.44 cm/month), Ganges/Brahmaputra
(4.65 cm/month), Orinoco (2.80 cm/month) and Niger
(2.03 cm/month)) during JJA from enhanced precipitation,
basins in the SH tropics and those in NH mid-to-high latitudes
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Table 2. Estimates of Annual Mean, Amplitude of Fitted Annual
Cycle and Seasonal Mean for the Continents and the Largest River
Basins
Region
North America
Mississippi
Mackenzie
South America
Amazon
Parana
Orinoco
Asia
Yangtze
Ganges/Brahmaputra
Amur
Yenisei
Ob
Lena
Africa
Congo
Nile
Niger
Zambezi
Europe
Volga
Danube
Dniepr
Don
Australia
Murray
Annual
Seasonal Mean, cm/month
Mean, Amplitude,
cm/month cm/month DJF MAM JJA SON
0.06
0.30
0.23
0.30
0.55
0.11
0.66
0.08
0.20
0.13
0.26
0.11
0.06
0.07
0.02
0.28
0.12
0.05
0.01
0.32
0.50
0.31
0.58
0.46
0.14
0.11
0.50
1.33
1.32
4.10
7.60
3.41
3.27
0.60
2.69
5.80
0.46
3.06
4.21
1.87
0.60
1.95
1.90
4.04
5.18
3.67
4.93
4.34
5.28
4.84
2.50
1.90
0.73
1.33
1.87
2.44
3.85
2.75
2.10
0.16
1.44
1.85
0.28
1.31
1.45
0.66
0.05
0.36
0.32
1.92
3.20
1.81
2.27
2.87
3.17
2.90
1.67
1.01
0.18
0.57
0.46
0.87
1.86
1.03
3.23
0.31
0.34
1.30
0.39
0.62
1.41
0.86
0.21
0.72
1.03
0.18
0.42
0.17
0.11
0.37
0.76
0.32
0.13
0.14
0.34
0.06
1.17
1.45
2.78
0.26
2.80
0.16
2.44
4.65
0.05
2.50
2.81
1.93
0.33
2.57
0.99
2.04
3.40
1.20
1.42
2.01
1.16
1.36
1.89
1.34
0.18
0.28
0.89
1.43
2.69
0.83
2.73
0.10
1.45
1.02
0.07
0.42
0.04
0.75
0.22
0.96
0.26
0.64
0.49
0.59
0.69
0.79
0.64
0.56
0.41
0.26
tend to lose water (e.g., Zambezi (3.40 cm/month),
Amazon (2.80 cm/month), Congo (2.74 cm/month), Ob
(2.80 cm/month) and Lena (1.93 cm/month)) due to lack
of precipitation and increased evapotranspiration. On the
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contrary, basins in the SH tropics tends to gain water during
DJF while those in the NH tropics experience a net loss in
storage, underscoring the dominant role of climate in defining
the spatiotemporal heterogeneity of observed storage
change. Furthermore, the amplitude of the annual cycles in
South America (4.10 cm/month) stands out from those for
the rest of the continents, including the Amazon basin
(7.60 cm/month) which has the largest amplitude among the
river basins. Amplitudes of variability secondary to those in the
Amazon are found in Ganges/Brahmaputra (5.80 cm/month),
Dniepr (5.28 cm/month) and Zambezi (5.18 cm/month) river
basins.
[21] It is important to note here that, while it is necessary
to smooth the Stokes coefficients from GRACE to reduce
the noise in derived mass change fields, the process also
suppresses the variability of the storage change signal. The
length scale used for smoothing further affects the derived
storage change estimates. While a large averaging radius
can decrease the strength in the storage change signal [Chen
et al., 2006], a smaller radius can produce spurious northsouth stripes [Swenson and Wahr, 2006b]. Hence our
estimates of mean (annual and seasonal) and amplitude of
seasonal cycles based on the use of 1000 km half-width
Gaussian averaging kernel are conservative characterizations
of basin-to-continental storage changes observed by
GRACE.
[22] To understand the relative contributions from the
large river basins in Figure 2 toward the TWSC for an entire
continent, ratios of the sum of absolute value of TWSC in a
basin to that of the continent were computed for North
America, South America, Africa and Asia. Figure 3 illustrates the relative contributions of some of the largest river
basins toward the total storage change in North America,
South America, Africa and Asia. Also shown in the figure
Figure 2. River basins referred to in this study: (1) Mackenzie; (2) Mississippi; (3) Magdalena;
(4) Orinoco; (5) Amazon; (6) Parana; (7) Volta; (8) Niger; (9) Congo; (10) Zambezi; (11) Nile; (12) Danube;
(13) Dniepr; (14) Don; (15) Volga; (16) Ob; (17) Yenisei; (18) Lena; (19) Amur; (20) Ganges/Brahmaputra;
(21) Yangtze; (22) Mekong; (23) Murray.
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Figure 3. Ratio of GRACE-derived TWSC in the listed river basin to that of the entire continent. Total
represents the sum of ratios for the river basins considered in each continent. Also shown are (above each
bar) the percentage of continental area occupied by each of the river basins.
is the percentage of continental area residing within each
of the river basins. The results show that just a few of
these river basins can account for a notable portion of the
total storage change over the entire continent in which the
basins are located. This is particularly noteworthy in
continental South America, where the change in the
Amazon basin is on average of about 45% of the continental storage change, and the aggregate (Amazon, Parana
and Orinoco) contributes about 70% while the contributing
area is 44% of the area of South America. To a lesser
degree, similar results are also seen in Africa and Asia,
where aggregated storage changes in the basins shown
account for 50%, 35% of the continental water storage
changes respectively.
4.2. GRACE – GLDAS Comparisons
[23] In this section, we compare seasonal estimates of
TWSC from GRACE to those from GLDAS [Rodell et al.,
2004b]. For consistency with the GRACE data, TWSC
from GLDAS was computed using equations (1) – (2).
Although not a perfect reproduction of observations,
global model output such as that from GLDAS captures
the magnitude and variability of terrestrial hydrology sufficiently enough, so that in the absence of any similar, global
observational data sets, it provides a reasonable opportunity
for evaluation and understanding of the GRACE hydrology
signal [Syed et al., 2004]. For comparison with GRACE,
GLDAS-based TWSC estimates were converted into spherical harmonic coefficients, smoothed with a 1000 km half-
width Gaussian averaging kernel and transformed into 1 1
degree gridded data.
[24] Global plots of seasonal storage change estimates
obtained from GRACE and GLDAS are shown in Figure 4.
GLDAS results used here are for the same period as the
GRACE measurements. There is very good overall agreement between the two estimates with Root Mean Square
Errors (RMSE) ranging between 1 cm/month in JJA and
0.7 cm/month in DJF. Some of biggest storage change
signals, consistent with Figure 3, are occurring in the
Amazon, Ganges/Brahmaputra, Congo river basins and over
large regions of Northern Europe and Western North America. While there are some small differences in magnitude of
the TWSC estimates, GLDAS performs reasonably in
capturing the global spatial patterns of observed storage
changes at seasonal timescales.
[25] Time series of TWSC from GRACE and GLDAS for
four of the major river basins in continental North and South
America are shown in Figure 5. Also included in the plots for
Mississippi and Amazon basins are independent estimates of
TWSC from a combined land-atmosphere water balance
(LAWB) [Syed et al., 2005]. GLDAS estimates agree very
well with GRACE, with RMSE values of 1.5 cm/month in
Mississippi and Mackenzie and 2.5 cm/month in Amazon
and Parana river basins. Estimates of storage change from
GLDAS and LAWB also track each other fairly well in both
the Amazon (RMSE = 4.5 cm/month) and Mississippi
(RMSE = 1.6 cm/month) basins except for the periods of
September-October in 2002 and late JJA 2003. Discrepan-
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Figure 4. Spatial patterns of seasonally averaged TWSC (cm/month) from GRACE and GLDAS. On
the basis of the seasonal averages computed for the period of April 2002 till July 2004.
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Figure 5. TWSC estimates from GRACE (GRC) and GLDAS (GLD) in 4 of the largest river basins in
continental North and South America. Also included for Mississippi and Amazon basins are TWSC from
a Land-Atmosphere Water Balance (LAWB).
cies between TWSC estimates from GLDAS and LAWB are
attributed to errors in the horizontal divergence of water
vapor (DivQ) and are discussed in detail by Syed et al.
[2005]. Furthermore, model estimates of storage change are
less variable than GRACE-derived storage changes, primarily due to the absence of contributions from surface and
groundwater in the simulations.
[26] Overall, Figures 4 and 5 show good agreement in
the spatial-temporal variability of TWSC estimates from
GRACE and GLDAS. The differences in magnitude between the two estimates can either be due to model
deficiencies, such as inadequate snow or missing surface
or groundwater components in the models, or due to
uncertainties in the GRACE data (e.g., due to processing,
aliasing, instrument error, etc.). One consequence of the
GRACE errors is that true water storage change signals
may be enhanced or dampened in both regional and global
scales [Swenson and Wahr, 2006b; Chen et al., 2006a; Seo
and Wilson, 2005]. Nevertheless, we believe that the
agreement between GRACE and GLDAS is sufficient, so
that GLDAS output fields can be studied to better under-
stand the processes contributing to terrestrial water storage
variations.
4.3. Analysis of Process Controls in TWSC
4.3.1. Time Series Analysis
[27] Globally averaged TWSC, along with its storage
(TSM, SWE and CWS, following equation (2)) and flux
(P, E and R) components, obtained from GLDAS, are
shown in Figure 6. The annual cycles (solid line) of TWSC
and its storage components (Figure 6a) show distinctive
seasonal variations with the lowest values during the
months of June– July and highs around December of each
year. The insignificant role played by CWS in TWSC
variations is also clear from the figure so that it is excluded
from further discussion.
[28] Changes in TSM and SWE contribute nearly equally
toward temporal variability in globally averaged TWSC
estimates in terms of both amplitude and phase. SWE
estimates, while limited in geographic extent, contribute
significantly toward globally averaged TWSC estimates by
the virtue of the larger magnitude of snow water storage. On
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Figure 6. (a) Time series of globally averaged TWSC and changes in its storage components: total soil
moisture (TSM), snow water equivalent (SWE) and canopy water storage (CWS). (b) Time series of
globally averaged TWSC and the hydrologic fluxes, precipitation (P), runoff (R) and evapotranspiration
(E). All the variables shown here are based on GLDAS outputs. Shown are the monthly estimates
(symbols) and fitted seasonal cycles (solid lines) of each variable in their respective colors.
the other hand TSM estimates have a contrasting feature,
i.e., wider spatial extent but smaller magnitudes. Furthermore, peak TSM storage lags that of SWE, indicative of the
contribution of snowmelt to soil moisture recharge.
[29] Variations in SWE are driven by NH snow storage,
which peaks in DJF. Total soil moisture peaks in DJF and
reaches a minimum in JJA. The wetting sequence starts in
SON and ends in the following MAM, and is an integrated
effect of the monsoons in both the hemispheres and snowmelt episodes. Global soil moisture begins to dry considerably during late-MAM and reaches its lowest in mid-JJA,
when evaporation depletes the water stored from the previous season’s rain and snowmelt events.
[30] Time series of TWSC flux components (Figure 6b)
also show distinct annual periodicity and significantly
greater amplitudes in the variability of E and TWSC in
comparison to P and R. The role of E as a major influence
on the variability of globally averaged TWSC estimates
becomes quite apparent from the similarity in amplitude and
cross-varying nature of the estimates (TWSC and E are
almost mirror images of each other).
[31] The dominance of NH climatology is reflected in the
high and low E values during JJA and DJF, primarily driven
by high and low NH insolation respectively. P estimates,
although higher in magnitude, have the smallest amplitude
among the other fluxes, with highs and lows in late JJA and
early MAM respectively. This is also in part due to the
greater percentage of land in the Northern Hemisphere. The
amplitude of the annual cycle of globally averaged R is also
small relative to E, and closely follows the annual cycles of
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Figure 7. Latitudinal profile of zonally averaged TWSC and changes in its storage components (TSM,
SWE and CWS) obtained from GLDAS for the four seasons, DJF, MAM, JJA and SON.
P and E. Hence we observe that as P, the sole source of
water into the system, increases, so too do E and R, so that
globally averaged TWSC and P are actually out-of-phase.
The larger amplitude of E dominates annual variations in
TWSC.
4.3.2. Spatial Analysis
[32] Zonal Variability. In this section we explore how
latitudinal variations in GLDAS estimates of TSM and
SWE contribute to TWSC, and we characterize how P, E,
and R fluxes enhance or dissipate TWSC. Here we focus on
seasonal timescales instead of monthly.
[33] Throughout the seasons, changes in TSM contribute
the most toward TWSC in the tropics, while SWE is critical
in the NH high latitudes (Figure 7). The highest and the
lowest values of TWSC and TSM are mainly centered on
15° north and south of the equator. In addition, the plots
also reveal seasonal variations in the latitudinal extent of
snow dominance in the estimates of storage change. During
DJF, SWE begins contributing to TWSC near 40°N latitude,
while in JJA the two estimates become closely related
nearer to 60°N. As a result we see increased TWSC in the
midlatitudes (40°N – 60°N) during DJF from snow storage,
and a subsequent drop in TWSC values during MAM due to
snowpack melting. The role of SWE in high latitudes
becomes more evident in MAM, when the values of TWSC
near 60°N drop along with SWE, even with the coinciding
increase in total column soil moisture estimates due to soil
water recharge by snowmelt.
[34] During SON and MAM, the overall variability of
TWSC and its storage components is intermediary in nature
when compared to JJA and DJF. In section 4.1 we discussed
similar behavior.
[35] The latitudinal variability of TWSC and its component fluxes is shown in Figure 8 for each season. High
amplitudes of variability in TWSC estimates along with P, R
and E are noted in the ITCZ. As in Figure 1, these high
values shift across the equator toward the south during DJF
and toward the north during JJA, following the natural
variability of the ITCZ.
[36] The principal role of P in controlling the zonal
averages of TWSC in the tropics is evident from the zonal
profiles of all the seasons. While there is little difference in
the estimates of E in the tropics through the seasons,
significant seasonal variations are noted in the storage
changes of the region, mostly related to P. Storage changes
in the midlatitudes are more closely controlled by the E
since P decreases away from the tropics.
[37] In addition to the maximum TWSC values found in
the tropics, a secondary maxima is located around 60°N and
60°S. Secondary maxima in P in this region result from
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Figure 8. Latitudinal profile of zonally averaged TWSC and terrestrial hydrologic fluxes (P, R and E)
obtained from GLDAS for the four seasons, DJF, MAM, JJA and SON.
polar frontal convergence. In DJF, low values of E and R
contribute to increased TWSC in NH while a significantly
increased E in the SH leads to a decrease in TWSC. In
MAM and JJA, increases in snowmelt-derived R lead to
decreases in TWSC. In fact the MAM and JJA TWSC at the
NH mid-to-high latitudes is nearly a mirror image of R due
to snow. In summary, we find that P is a dominant control
on TWSC variations in the tropics, E plays a critical role in
the midlatitudes, while snow accumulation and snowmeltdriven R is significant in at high latitudes.
[38] Global Variability. The global distribution of seasonally averaged TWSC and its component fluxes are shown in
Figures 9 and 10 for DJF and JJA respectively. Note that
different scales are used to portray the spatial heterogeneity
over the globe.
[39] Figures 9 and 10 support the ideas outlined above
regarding how the various fluxes act to increase or
decrease TWSC. However, as discussed in section 4.1, a
distinctive feature discernible in all the spatial plots is that
the greatest storage changes occur in major river basins
over the globe. Some of the key changes in the SH tropics
are associated with the Amazon, Parana and Congo river
basins and those in the NH with Ganges/Brahmaputra
basins in India/Bangladesh along with Mekong in south
East Asia and some major African river basins such as the
Niger and Volta.
[40] In response to the shifting ITCZ, during DJF, South
American river basins north of the equator (Orinoco and
Magdalena) are seen to lose water, whereas those located
south of the equator (Amazon) gain. Similarly, the river
basins above the equator in Africa and South East Asia
(Niger, Volta and Ganges/Brahmaputra) tend to lose water
during this season and the basins below the equator (Congo
and Zambezi) gain water.
4.4. Correlation Analysis
[41] Figure 11 shows the global and latitudinal distribution of the correlation coefficients between monthly
GLDAS-based TWSC and the hydrologic fluxes for the
entire length of the simulation. P acts as a positive flux in
terrestrial water balance; hence areas with positive correlations are interpreted as areas where the values of TWSC are
largely impacted by P. On the contrary, evapotranspiration
and runoff are variables that deplete water storage; hence
negative correlations are indicative of the regions where
these processes are most effective in controlling magnitude
and variability of the continental water storage changes.
[42] A comparison of the three global correlation plots
suggests that positive correlations between precipitation
and water storage changes (Figure 11a, first column) have
the maximum spatial coverage over the globe followed
by the negative correlations between E and R (Figures 11b
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Figure 9. DJF average of TWSC and fluxes from GLDAS in cm/month: (a) TWSC; (b) precipitation;
(c) runoff; and (d) evapotranspiration.
and 11c, first column). The latitudinal dependence of the
controlling processes discussed in the previous section (Figures 9 and 10) is also evident here. The tropics are consistently dominated by the high positive correlations between P
and TWSC while the TWSC estimates in the NH midlatitudes
are correlated more with E than with P. Figure 11 shows a
significant increase in correlation between E and TWSC
(Figure 11b, second column) in the region between 30° –
70° N/S and the concomitant decrease in correlation between
P and TWSC (Figure 11a, second column). In addition, the
dominance of snowmelt-derived runoff in the NH high
latitudes is also distinctly discernible from the considerably
higher absolute values of correlation between R and TWSC
(Figure 11c, second column).
5. Summary and Conclusions
[43] In this study we characterize TWSC variations using
GRACE and GLDAS. The results discussed here illustrate
spatial-temporal variability of water storage changes over
land, with implications for a better understanding of terrestrial water balance and its role in the global hydrologic cycle.
[44] Global, zonal and basin-scale estimates of GRACEbased storage changes showed a wide range in variability
and magnitude, emphasizing the space-time heterogeneity
in TWSC response. Manifestations of hemispheric differences in precipitation were noted in seasonal TWSC. In the
SH tropics seasonally averaged TWSC had higher ampli-
tudes (±4 – 5 cm/month) of latitudinal variability in comparison to those in the NH (±2 cm/month). Zonally averaged
TWSC was found to have the greatest amplitude in the SH
tropics (7 cm/month), and the spatial distribution showed
major TWSC signals coincident with some of the largest
river basins. Comparisons between GLDAS and GRACEbased estimates of TWSC at river basin scales compared
well with RMSE of 1.5 cm/month in Mississippi and
Mackenzie and 2.5 cm/month in Amazon and Parana.
[45] Analysis of the hydrologic components in the terrestrial water balance from GLDAS revealed the partitioning and process controls of TWSC, both globally and
varying with latitude. The Noah land model used in the
GLDAS simulations did not include surface and groundwater stores, so that we were unable to quantify their
potentially considerable contributions to storage changes
in some regions. Global averages of TWSC were found to
be partitioned nearly equally between TSM and SWE.
Analysis of zonally averaged TWSC showed how storage
varies by latitude, with changes in soil moisture accounting
for most of the storage change at low and midlatitudes,
whereas at high latitudes, TWSC was more closely associated with changes in SWE. Globally averaged estimates of
fluxes showed that E plays a key role in dissipating Pdriven storage anomalies. Zonal analysis highlighted variations in the role of these fluxes with respect to TWSC. The
P flux dominates TWSC variations in the tropics, and E
plays a critical role in the midlatitudes. In addition, MAM-
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Figure 10. JJA average of TWSC and fluxes from GLDAS in cm/month: (a) TWSC; (b) precipitation;
(c) runoff; and (d) evapotranspiration.
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Figure 11. Spatial and latitudinal distribution of the correlation coefficients between GLDAS-based
TWSC and (a) P; (b) E; and (c) R.
snowmelt-runoff played a particularly important role in
space-time variability of TWSC in the NH high latitudes.
The results were further reconfirmed by the correlation
analysis, which showed P as the leading flux over major
portions of the globe, followed by E in the midlatitudes and
R in the NH high latitudes.
[46] Comparison of GRACE-based TWSC with GLDAS
model simulations also underscores the potential for validating and improving global land surface models [Swenson
and Milly, 2006; Niu and Yang, 2006; Lettenmaier and
Famiglietti, 2006] using GRACE data. Some of the noted
differences between GRACE-based TWSC and GLDAS
can in part be attributed to the missing surface and groundwater components, or snow parameterization deficiencies.
For example, Niu and Yang [2006] and Niu et al. [2007]
showed better agreement with GRACE storage anomalies
after including a groundwater component in their land
surface parameterization. Future work will be directed
toward analysis and comparison of GRACE observations
with multiple hydrological models with and without explicit
representation of surface and groundwater components in
order to fully characterize their role in TWS variations.
Results also suggest that with longer time series, GRACE
will contribute to improved understanding of how terrestrial
water storage responds to climate change and variability.
[47] Acknowledgments. This research was sponsored by NASA
grants NNG04GE99G, NNG04GF22G and a NASA Earth System Science
Fellowship to the first author, with additional support from the NASA
Terrestrial Hydrology and Solid Earth and Natural Hazards Programs.
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