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| Titel |
Significance of spatial variability in precipitation for process-oriented modelling: results from two nested catchments using radar and ground station data |
| VerfasserIn |
D. Tetzlaff, S. Uhlenbrook |
| 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 ; 9, no. 1/2 ; Nr. 9, no. 1/2 (2005-06-09), S.29-41 |
| Datensatznummer |
250006587
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| Publikation (Nr.) |
 copernicus.org/hess-9-29-2005.pdf |
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| Zusammenfassung |
| The importance of considering the spatial distribution of rainfall for
process-oriented hydrological modelling is well-known. However, the
application of rainfall radar data to provide such detailed spatial
resolution is still under debate. In this study the process-oriented
TACD (Tracer Aided Catchment model, Distributed) model had been used to investigate the
effects of different spatially distributed rainfall input on simulated
discharge and runoff components on an event base. TACD is fully
distributed (50x50m2 raster cells) and was applied on an hourly
base. As model input rainfall data from up to 7 ground stations and high
resolution rainfall radar data from operational C-band radar were used. For
seven rainfall events the discharge simulations were investigated in further
detail for the mountainous Brugga catchment (40km2) and the St.
Wilhelmer Talbach (15.2km2) sub-basin, which are located in the
Southern Black Forest Mountains, south-west Germany. The significance of
spatial variable precipitation data was clearly demonstrated. Dependent on
event characteristics, localized rain cells were occasionally poorly
captured even by a dense ground station network, and this resulted in
inadequate model results. For such events, radar data can provide better
input data. However, an extensive data adjustment using ground station data
is required. For this purpose a method was developed that considers the
temporal variability in rainfall intensity in high temporal resolution in
combination with the total rainfall amount of both data sets. The use of the
distributed catchment model allowed further insights into spatially variable
impacts of different rainfall estimates. Impacts for discharge predictions
are the largest in areas that are dominated by the production of fast runoff
components. The improvements for distributed runoff simulation using high
resolution rainfall radar input data are strongly dependent on the
investigated scale, the event characteristics and the existing monitoring
network. |
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