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| Titel |
Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment |
| VerfasserIn |
Z. Liu, M. L. V. Martina, E. Todini |
| 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. 4 ; Nr. 9, no. 4 (2005-10-07), S.347-364 |
| Datensatznummer |
250006968
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| Publikation (Nr.) |
 copernicus.org/hess-9-347-2005.pdf |
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| Zusammenfassung |
| TOPKAPI is a physically-based, fully distributed hydrological model with a
simple and parsimonious parameterisation. The original TOPKAPI is structured
around five modules that represent evapotranspiration, snowmelt, soil water,
surface water and channel water, respectively. Percolation to deep soil
layers was ignored in the old version of the TOPKAPI model since it was not
important in the basins to which the model was originally applied. Based on
published literature, this study developed a new version of the TOPKAPI
model, in which the new modules of interception, infiltration, percolation,
groundwater flow and lake/reservoir routing are included. This paper presents
an application study that makes a first attempt to derive information from
public domains through the internet on the topography, soil and land use
types for a case study Chinese catchment - the Upper Xixian catchment in
Huaihe River with an area of about 10000 km2, and apply a new version of
TOPKAPI to the catchment for flood simulation. A model parameter value
adjustment was performed using six months of the 1998 dataset. Calibration
did not use a curve fitting process, but was chiefly based upon moderate
variations of parameter values from those estimated on physical grounds, as
is common in traditional calibration. The hydrometeorological dataset of 2002
was then used to validate the model, both against the outlet discharge as
well as at an internal gauging station. Finally, to complete the model
performance analysis, parameter uncertainty and its effects on predictive
uncertainty were also assessed by estimating a posterior parameter
probability density via Bayesian inference. |
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