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| Titel |
Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model |
| VerfasserIn |
T. Berezowski, J. Nossent, J. Chormański, O. Batelaan |
| 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 ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-21), S.1887-1904 |
| Datensatznummer |
250120689
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| Publikation (Nr.) |
 copernicus.org/hess-19-1887-2015.pdf |
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| Zusammenfassung |
| As the availability of spatially distributed data sets for distributed
rainfall-runoff modelling is strongly increasing, more attention should be paid
to the influence of the quality of the data on the calibration. While a lot
of progress has been made on using distributed data in simulations of
hydrological models, sensitivity of spatial data with respect to model
results is not well understood. In this paper we develop a spatial
sensitivity analysis method for spatial input data (snow cover fraction –
SCF) for a distributed rainfall-runoff model to investigate when the model is
differently subjected to SCF uncertainty in different zones of the model. The
analysis was focussed on the relation between the SCF sensitivity and the
physical and spatial parameters and processes of a distributed rainfall-runoff
model. The methodology is tested for the Biebrza River catchment, Poland, for
which a distributed WetSpa model is set up to simulate 2 years of daily
runoff. The sensitivity analysis uses the Latin-Hypercube
One-factor-At-a-Time (LH-OAT) algorithm, which employs different response
functions for each spatial parameter representing a 4 × 4 km snow
zone. The results show that the spatial patterns of sensitivity can be easily
interpreted by co-occurrence of different environmental factors such as
geomorphology, soil texture, land use, precipitation and temperature.
Moreover, the spatial pattern of sensitivity under different response
functions is related to different spatial parameters and physical processes.
The results clearly show that the LH-OAT algorithm is suitable for our
spatial sensitivity analysis approach and that the SCF is spatially sensitive
in the WetSpa model. The developed method can be easily applied to other
models and other spatial data. |
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