Seasonal Variations of Total Terrestrial Water Storages in Major River Basins
Taikan Oki1, Pat Yeh1, Kei Yoshimura1, Hyungjun Kim1, Yanjun Shen2, Thanh Ngo-Duc1,Shinta Seto1, and Shinjiro Kanae1
12Institute of Industrial Science, University of Tokyo, Tokyo, Japan
Center for Agricultural Resources Research, Chinese Academy of Sciences, Bejing, China
Correspondence: Taikan OKI (taikan@iis.u-tokyo.ac.jp)

ABSTRACTThe total terrestrial water storage (TWS) in six major river basins of the world for the period 1986-1995 wereestimated from (1) the Phase-2 of the Global Soil Wetness Project (GSWP2) Multi-Model Analysis (MMA),(2) the combined land-atmospheric water balance method using the ERA-40 and JRA-25 global reanalysis
data, and (3) the satellite observation by the Gravity Recovery And Climate Experiment (GRACE). The
seasonal variations of the TWS estimated from these three methods were compared, and the results indicate
remarkable agreement among each other. It was found that in Amazon River basin, the river channel storage
plays a more significant role than soil moisture in the seasonal variations of TWS. This result suggests that it
is important to include the contribution from the river channel water in the estimation of global TWS
distribution and global water budgets.
INTRODUCTION
Global water cycle directly affects the global circulation of both atmosphere and ocean and hence isinstrumental in shaping weather and climate of the Earth. However, our quantitative knowledge of the global
water cycle is quite poor, large-scale measurements of the states and fluxes for various global reservoirs ontime scales appropriate to their dynamics are deficient. Terrestrial water storage (TWS), as a fundamental
component of global water cycle that include groundwater, soil moisture, snow water equivalent, and water inriver, lakes, ponds, reservoirs and wetlands, is of great importance for water resources, climate, agriculture
and ecosystem. TWS controls the partitioning of precipitation into evaporation and runoff, and the partitioningof net radiation into the sensible and latent heat fluxes. On the other hand, terrestrial water storage change is abasic quantity in closing the terrestrial water balance [Ngo-Duc et al., 2005; Hirabayashi et al. 2005; Güntneret al., 2007; Yeh and Famiglietti, 2008]
Despite its importance, there are no extensive networks currently existent for monitoring large-scale variations ofTWS and its individual components. Reliable datasets of large-scale TWS are extremely scarce. Historically,
global hydrological cycles have been assessed by a synthesis of in-situ observational data, such as precipitation
measured by rain gauges and streamflow at gauging stations. On the other hand, global dataset of atmosphericconditions estimated by the four-dimensional data assimilation (4DDA) technique has enabled global waterbalance estimation by the atmospheric or combined land-atmosphere water balance computations [Rasmusson,1968; Yeh et al., 1998; Seneviratne et al., 2004; Hirsch et al., 2006, 2007; Yeh and Famiglietti, 2008]. The
column integrated water vapor convergence provides a global distribution of precipitation minus
evapotranspiration, if the temporal variation of precipitable water is considered to be zero. Among various1

components of global water cycle, the TWS is one of the most difficult to estimate. The combined
land-atmosphere water balance computation utilizing the atmospheric reanalysis data and river discharge can beused to estimate the temporal change of area-averaged TWS over river basins.
In this study, the seasonal variations of TWS in the six selected major river basins (Table 1) from 1986 to1995 were estimated by using reanalysis data of ERA-40 and JRA-25, and they were compared with theestimates from both the GRACE (Gravity Recovery And Climate Experiment, see Tapley et al. 2004) satellite
observation and the Phase-2 of the Global Soil Wetness Project (GSWP2) [Dirmeyer et al. 2006]. Under theGlobal Land Atmosphere Study (GLASS), the GSWP2 produced the first global 1x1 degree Multi-ModelAnalysis (MMA) of land surface hydrological variables and fluxes for the 10-year period of 1986-1995 at thedaily time scale [Gao and Dirmeyer, 2006, Guo and Dirmeyer, 2006a, b]. Thirteen land surface models weredriven by the best available forcing data of the atmospheric conditions (precipitation, downward radiation, airhumidity, air temperature, wind speed, and air pressure) with temporal resolution of 3-hourly or higher. In thisstudy, water balance components in major continental river basins were post-processed and the seasonalchanges in soil moisture, snow water equivalent, and the water in river channel from the global runoff routing
model Total Runoff Integrating Pathways (TRIP) [Oki et al., 1999] were analyzed.

METHODS
The seasonal variations of the TWS in six selected major river basins were estimated by the land-atmosphericwater balance method. The water vapor flux convergence was estimated using the ECMWF ERA-40 and JMA
JRA-25 global reanalysis data for the period 1986-1995. The 10-year mean value of the atmospheric watervapor convergence was adjusted to match with the climatological mean of the corresponding river discharge
from GRDC (Global Runoff Data Center). The seasonal variation of TWS was estimated from the combined
land-atmosphere water balance method. The components in the TWS change were investigated using themulti-model products from the GSWP2-MMA. The seasonal changes of the TWS in six selected river basins
estimated by both methods were compared to the estimate from the GRACE satellite observation, andbasically these three estimates correspond fairly well among each other as shown in the next section.

No. RiverStationGRDC-ID12
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AmazonMississippi
Ob
Yenisei
Mackenzie

3629000
4127800
2912600
29091504208025

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MissouriObidosVicksburg
Salekhard
IgarkaArcticred river
HermannLon
Lat
(°N)
(°E)
-55.5
-2.5
-91.5
32.5
66.5
66.5
86.5
67.5
-133.567.54122900-91.4

Drainagearea- (km2)
4,640,300
2,964,255
2,949,998
2,440,000
1,660,000

38.7 1,357,678

Period1968-1996
1931-1998
1954-1999
1955-1999
1972-1996
1927-2000

Table 1. Six world major river basins selected for analysis in this study. The locations of the most downstream streamflow
measurement station, their station ID defined by GRDC, the drainage area (given by GRDC and by the TRIP river network),
and the localizations (longitude, Lon, and latitude, Lat) and the discharge observation period are shown in this table. All 6stations have GRDC daily discharge observations during the GSWP2-period (1986-1995).

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RESULTS
Figure 1 compares the 10-year (1986-95) monthly streamflow for 6 major basins from two cases ofGSWP2-MMA simulations, one with and another without TRIP routing, with the observed GRDC data. ForAmazon, Mackenzie, Mississippi and Missouri basins, the simulated TRIP-routed runoff generally agrees wellwith the GRDC data in terms of the seasonal and interannual variability, whereas for the Yenesei and Obbasins the GWSWP-2 was failed to capture the magnitudes of observed streamflow peaks. On the other hand,
the GSWP2 runoff without routing by TRIP exhibits an approximately 2-month earlier arrival of peaks thanobservations, indicating the significance of global runoff routing scheme for continental river basinstreamflow simulations.

Figure 1 Comparson of 10-year (1986-95) monthly streamflow from the GSWP2-MMA simulations(with and without TRIP routing) with the observations from GRDC.

Figure 2 presents the comparison of TWS estimates from three independent methods: GRACE, Land-atmospherewater balance method using ERA-40 and JRA-25 respectively, and GSWP2-MMA. The relative storages plottedin this figure are the TWS estimate subtracting its minimum storage of a year. The lower panel of the figure shows
the components of TWS: soil moisture, snow water equivalent, and river channel storage for each basin. The riverstorage is the channel water storage term output from TRIP. As shown, for most of the six basins the relative
storage estimate from land surface models (GWSP2-MMA) agrees reasonably well with GRACE estimates. Thisagreement is rather surprising for the Amazon and Mississippi basins. Moreover, the relative storages estimate
from the combined land-atmosphere water balance using reanalysis data and streamflow observations also
correspond fairly well with GRACE and GSWP2-MMA (except for the Amazon basin where the estimates byreanalysis data provide smaller seasonal amplitude). With regard to the storage components, the river storage
plays a more significant role in the seasonal variations of TWS than soil moisture for the Amazon basin. ForMississippi basin, however, the river storage is equally important to soil moisture storage, but this importancedecreases for the sub-basin Missouri River where soil moisture dominates the TWS. For three northern basins (Ob,Yenesei, and Mackenzie), as expected snow water equivalent dominates seasonal water storage variations fromwinter to the end of spring, while river storage is the only active storage component in summer months followingsnow melting.

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Figure 2 Seasonal change of total water storage estimated by satellite data (GRACE), land surface
models (GSWP2), and the land-atmosphere combined water balance method (ERA40 and JRA25).

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SUMMARYIn this study, the combined land-atmosphere water balance method, the output from the GSWP2 Multi-ModelAnalysis, and the remote sensing data by GRACE satellite were used to estimate the seasonal terrestrial waterstorage (TWS) variations over selected six major river basins of the world. The main objectives of this study
are to inter-compare these approaches and to explore what are the dominant components of TWS variation
over these river basins.
First the atmospheric water vapor convergence was estimated using the ECMWF ERA-40 and JMA JRA-25
reanalysis data for the period from 1986 to 1995. The seasonal change of TWS was then estimated from thecombined land-atmosphere water balance method, and compared with the land surface model-simulated TWS
from the multi-model product from the GSWP2. Both estimated were also compared with the TWS estimatesfrom the GRACE satellite. Even through the bias correction of atmospheric vapor convergence was required
to meet the water balance with the observed discharges, the estimates by the combined land-atmosphere waterbalance correspond fairly well with that by the GSWP2-MMA. Moreover, both of them in general agree wellwith the GRACE estimates. For the Amazon basin, the river channel storage plays the most significant role inthe seasonal variations of TWS. For Mississippi basin, the significance of river channel storage is equivalent
to soil moisture storage, while this significance decreases for the Missouri sub-basin where the soil moisturestorage dominates TWS. In northern basins (Mackenzie, Ob, and Yenesei), the contribution of snow waterequivalent is large in winter, whereas water stored in river channel contributes significantly to the seasonalchange of TWS during summer. From these results, it can be concluded that the reanalysis data can provide
useful information to validate continental hydrological model simulations for global hydrological cycle
studies.
The advantage of using the combined water balance computation is that it is based on the relatively abundant
atmospheric data that have been routinely measured for decades. The accuracy of this estimates lie to a large
extent on the accuracy of computed convergence. Most large-scale studies of atmospheric water cycle rely on thereanalysis data from 4DDA (four-dimensional data assimilation) system. However, these data contain systematic
errors and need corrections, but currently there is no any convincing way for this purpose [Yeh and Famiglietti,2008].
Given the constraints of the sptio-temporal resolution of the GRACE and the reanalysis data, and the lack ofglobal availability of the long-term river discharge data, continental hydrological models remain the only suitable
tool to estimate TWS variability before extensive network (either from ground or from space) of monitoringterrestrial water storage is established. However, most continental hydrological models do not account for all
water storage components. Therefore, research effort should be directed towards the enhancement ofparameterizations in global hydrological model [e.g. Oki et al. 1999; Yeh and Eltahir, 2005] such that theimportances of river channel storage, as shown for major river basins in this study, as well as of groundwater
storage can be faithfully represented in global simulations. Finally, since GRACE data can be an important
additional constraint on the output of hydrological models as they represent the total vertically integrated effect ofwater mass changes, the assimilation of GRACE data into land surface model simulation is essential.

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