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| Titel |
Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez catchment in Southern France |
| VerfasserIn |
E. Harader, V. Borrell-Estupina, S. Ricci, M. Coustau, O. Thual, A. Piacentini, C. Bouvier |
| 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 ; 16, no. 11 ; Nr. 16, no. 11 (2012-11-16), S.4247-4264 |
| Datensatznummer |
250013572
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| Publikation (Nr.) |
 copernicus.org/hess-16-4247-2012.pdf |
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| Zusammenfassung |
| The present study explores the application of a data assimilation (DA)
procedure to correct the radar rainfall inputs of an event-based,
distributed, parsimonious hydrological model. An extended Kalman filter
algorithm was built on top of a rainfall-runoff model in order to assimilate
discharge observations at the catchment outlet. This work focuses primarily
on the uncertainty in the rainfall data and considers this as the principal
source of error in the simulated discharges, neglecting simplifications in
the hydrological model structure and poor knowledge of catchment physics. The
study site is the 114 km2 Lez catchment near Montpellier, France. This
catchment is subject to heavy orographic rainfall and characterised by a
karstic geology, leading to flash flooding events. The hydrological model
uses a derived version of the SCS method, combined with a Lag and Route
transfer function. Because the radar rainfall input to the model depends on
geographical features and cloud structures, it is particularly uncertain and
results in significant errors in the simulated discharges. This study seeks
to demonstrate that a simple DA algorithm is capable of rendering radar
rainfall suitable for hydrological forecasting. To test this hypothesis, the
DA analysis was applied to estimate a constant hyetograph correction to each
of 19 flood events. The analysis was carried in two different modes: by
assimilating observations at all available time steps, referred to here as
reanalysis mode, and by using only observations up to 3 h before the flood
peak to mimic an operational environment, referred to as pseudo-forecast
mode. In reanalysis mode, the resulting correction of the radar rainfall data
was then compared to the mean field bias (MFB), a corrective coefficient
determined using rain gauge measurements. It was shown that the radar
rainfall corrected using DA leads to improved discharge simulations and
Nash-Sutcliffe efficiency criteria compared to the MFB correction. In
pseudo-forecast mode, the reduction of the uncertainty in the rainfall data
leads to a reduction of the error in the simulated discharge, but uncertainty
from the model parameterisation diminishes data assimilation efficiency.
While the DA algorithm used is this study is effective in correcting
uncertain radar rainfall, model uncertainty remains an important challenge
for flood forecasting within the Lez catchment. |
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