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| Titel |
Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation |
| VerfasserIn |
Y. Tramblay, C. Bouvier, P.-A. Ayral, A. Marchandise |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 1 ; Nr. 11, no. 1 (2011-01-18), S.157-170 |
| Datensatznummer |
250009045
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| Publikation (Nr.) |
 copernicus.org/nhess-11-157-2011.pdf |
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| Zusammenfassung |
| A good knowledge of rainfall is essential for hydrological operational
purposes such as flood forecasting. The objective of this paper was to
analyze, on a relatively large sample of flood events, how rainfall-runoff
modeling using an event-based model can be sensitive to the use of spatial
rainfall compared to mean areal rainfall over the watershed. This comparison
was based not only on the model's efficiency in reproducing the flood events
but also through the estimation of the initial conditions by the model, using
different rainfall inputs. The initial conditions of soil moisture are indeed
a key factor for flood modeling in the Mediterranean region. In order to
provide a soil moisture index that could be related to the initial condition
of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model
developed by Météo-France was used. This study was done in the Gardon
catchment (545 km2) in South France, using uniform or spatial rainfall
data derived from rain gauge and radar for 16 flood events. The event-based
model considered combines the SCS runoff production model and the Lag and
Route routing model. Results show that spatial rainfall increases the
efficiency of the model. The advantage of using spatial rainfall is marked
for some of the largest flood events. In addition, the relationship between
the model's initial condition and the external predictor of soil moisture
provided by the SIM model is better when using spatial rainfall, in
particular when using spatial radar data with R2 values increasing from
0.61 to 0.72. |
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