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Abstract
Calibration of large scale hydrological models have traditionally been performed using
point observations, which are often sparsely distributed. The Gravity Recovery
And Climate Experiment (GRACE) mission provides global remote sensing information
about mass fluxes with unprecedented accuracy, which can be used for calibration
of such models.
Mass concentration (mascon) parameters used at the Goddard Space Flight Center
are spatial and temporal step functions of equivalent water height in predefined regions,
estimated directly from the level1B Kband RangeRate (KBRR) data from
GRACE. The mascon parameters are recovered through least squares inversion of
a normal equation system, which is based on partial derivatives of the KBRR data
residuals with respect to the mascon parameters. Spatial and temporal constraints
are added for stability reasons, and the recovered mascon parameters represent mass
redistributions on/near the surface of the Earth. A grid of 1.251.5 and 1.51.5
blocks1 (latitudelongitude) is used.
A simple water balance model of the Okavango River Basin covering parts of Angola,
Namibia, and Botswana, is build using a modified Budyko type framework on
each of seven subcatchments, derived for the river basin from a digital elevation
model. The hydrological model is initially calibrated to discharge and mass variations
in a 1.251.5 grid every ten days from five years of GRACE mascon only
solutions, using a joint sequential calibration function.
Coupling of the mascon method with the hydrological model is done by chaining of
partial derivatives, so that the normal equation system is solved for model parameters
instead of mascon parameters. The mass variations from GRACE are relative,
meaning that the origin is arbitrary, while the terrestrial water storage variations
from model, are absolute. Thus, a bias exists between the model output and the
GRACE derived mass variations, which must be accounted for by the use of bias
parameters. One bias parameter is introduced for every mascon block, in order to
account for the difference in level between the GRACE derived mass variations and
the hydrological model, and spatial constraints on the bias parameters are used.
The coupling method is tested with different correlation distances on the bias constraint
equations, and different scaling of the bias parameter constraints as well as
the mascon parameter constraint equations. The results are evaluated by comparing
the observed and simulated data with respect to the KBRR data, the discharge data,
and the terrestrial water storage from the GRACE mascon only solutions used for the initial calibration of the model. The discharge and terrestrial water storage data
were also used for the initial joint model calibration.
In the coupled inversion, the adjustment of the hydrology parameter in the model
is in general very small, since the model was already precalibrated. The terrestrial
water storage output from the model, using the adjusted parameter value, shows a
higher annual amplitude (14.79 cm) than the mascon only solution (12.09 cm), the
10day spherical harmonic solutions from CNES/GRGS (8.85 cm), and the terrestrial
water storage from GLDAS/Noah (11.39 cm), for the same area. The annual signal
peaks around March to April. The timing of signal peaks for the model output is
earlier than for the mascon only solution, but later than the GLDAS/Noah TWS and
the CNES/GRGS SH solutions. The deviations are 10–20 days.
From this point of view, the tuning of hydrological models with KBRR data is certainly
feasible, though highly time consuming and complicated at the moment. The
method definitely has potential and should be tested with more model parameters
and for larger models.
Original language  English 

Place of Publication  Kgs. Lyngby, Denmark 

Publisher  Technical University of Denmark 
Publication status  Published  Jun 2011 
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 1 Finished

High resolution timelapse gravity field from GRACE for hydrological modelling
Krogh, P. E., Andersen, O. B., BauerGottwein, P., Knudsen, P., Lemoine, F. G. & Tscherning, C. C.
01/07/2007 → 22/06/2011
Project: PhD