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| Titel |
Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation |
| VerfasserIn |
Y. Tang, P. Reed, T. Wagener, K. Werkhoven |
| 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 ; 11, no. 2 ; Nr. 11, no. 2 (2007-02-05), S.793-817 |
| Datensatznummer |
250009217
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| Publikation (Nr.) |
 copernicus.org/hess-11-793-2007.pdf |
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| Zusammenfassung |
| This study seeks to identify sensitivity tools that will advance our
understanding of lumped hydrologic models for the purposes of model
improvement, calibration efficiency and improved measurement
schemes. Four sensitivity analysis methods were tested: (1) local
analysis using parameter estimation software (PEST), (2) regional
sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and
(4) Sobol's method. The methods' relative efficiencies and
effectiveness have been analyzed and compared. These four
sensitivity methods were applied to the lumped Sacramento soil
moisture accounting model (SAC-SMA) coupled with SNOW-17. Results
from this study characterize model sensitivities for two medium
sized watersheds within the Juniata River Basin in Pennsylvania,
USA. Comparative results for the 4 sensitivity methods are presented
for a 3-year time series with 1 h, 6 h, and 24 h time
intervals. The results of this study show that model parameter
sensitivities are heavily impacted by the choice of analysis method
as well as the model time interval. Differences between the two
adjacent watersheds also suggest strong influences of local physical
characteristics on the sensitivity methods' results. This study also
contributes a comprehensive assessment of the repeatability,
robustness, efficiency, and ease-of-implementation of the four
sensitivity methods. Overall ANOVA and Sobol's method were shown to
be superior to RSA and PEST. Relative to one another, ANOVA has
reduced computational requirements and Sobol's method yielded more
robust sensitivity rankings. |
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