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| Titel |
The bias in GRACE estimates of continental water storage variations |
| VerfasserIn |
R. Klees, E. A. Zapreeva, H. C. Winsemius, H. H. G. Savenije |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 11, no. 4 ; Nr. 11, no. 4 (2007-05-03), S.1227-1241 |
| Datensatznummer |
250009381
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| Publikation (Nr.) |
 copernicus.org/hess-11-1227-2007.pdf |
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| Zusammenfassung |
The estimation of terrestrial water storage variations at river
basin scale is among the best documented applications of the GRACE
(Gravity and Climate Experiment) satellite gravity mission. In
particular, it is expected that GRACE closes the water balance at
river basin scale and allows the verification, improvement and
modeling of the related hydrological processes by combining GRACE
amplitude estimates with hydrological models'
output and in-situ data.
When computing monthly mean storage variations from GRACE gravity
field models, spatial filtering is mandatory to reduce GRACE errors,
but at the same time yields biased amplitude estimates.
The objective of this paper is three-fold. Firstly, we want to
compute and analyze amplitude and time behaviour of the bias in
GRACE estimates of monthly mean water storage variations for several
target areas in Southern Africa. In particular, we want to know the
relation between bias and the choice of the filter correlation
length, the size of the target area, and the amplitude of mass
variations inside and outside the target area. Secondly, we want to
know to what extent the bias can be corrected for using a priori
information about mass variations. Thirdly, we want to quantify
errors in the estimated bias due to uncertainties in the a priori
information about mass variations that are used to compute the bias.
The target areas are located in Southern Africa around the Zambezi
river basin. The latest release of monthly GRACE gravity field
models have been used for the period from January 2003 until March
2006. An accurate and properly calibrated regional hydrological
model has been developed for this area and its surroundings and
provides the necessary a priori information about mass variations
inside and outside the target areas.
The main conclusion of the study is that spatial smoothing
significantly biases GRACE estimates of the amplitude of annual and
monthly mean water storage variations and that bias correction using
existing hydrological models significantly improves the quality of
GRACE estimates. For most of the practical applications, the bias
will be positive, which implies that GRACE underestimates the
amplitudes. The bias is mainly determined by the filter correlation
length; in the case of 1000 km smoothing, which is shown to be an
appropriate choice for the target areas, the annual bias attains
values up to 50% of the annual storage; the monthly bias is even
larger with a maximum value of 75% of the monthly storage. A
priori information about mass variations can provide reasonably
accurate estimates of the bias, which significantly improves the
quality of GRACE water storage amplitudes. For the target areas in
Southern Africa, we show that after bias correction, GRACE annual
amplitudes differ between 0 and 30 mm from the output of a
regional hydrological model, which is between 0% and 25% of
the storage. Annual phase shifts are small, not exceeding 0.25
months, i.e. 7.5 deg. It is shown that after bias correction, the
fit between GRACE and a hydrological model is overoptimistic, if the
same hydrological model is used to estimate the bias and to compare
with GRACE. If another hydrological model is used to compute the
bias, the fit is less, although the improvement is still very
significant compared with uncorrected GRACE estimates of water
storage variations. Therefore, the proposed approach for bias
correction works for the target areas subject to this study. It may
also be an option for other target areas provided that some
reasonable a priori information about water storage variations are
available. |
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