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| Titel |
From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models |
| VerfasserIn |
J. D. Herman, J. B. Kollat, P. M. Reed, T. Wagener |
| 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 ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-17), S.5109-5125 |
| Datensatznummer |
250086033
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| Publikation (Nr.) |
 copernicus.org/hess-17-5109-2013.pdf |
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| Zusammenfassung |
| Distributed watershed models are now widely used in practice to simulate
runoff responses at high spatial and temporal resolutions. Counter to this
purpose, diagnostic analyses of distributed models currently aggregate
performance measures in space and/or time and are thus disconnected from the
models' operational and scientific goals. To address this disconnect, this
study contributes a novel approach for computing and visualizing time-varying
global sensitivity indices for spatially distributed model parameters. The
high-resolution model diagnostics employ the method of Morris to identify
evolving patterns in dominant model processes at sub-daily timescales over a
six-month period. The method is demonstrated on the United States National
Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model
(HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events
are selected from within the six-month period to investigate the patterns in
spatiotemporal sensitivities that emerge as a function of forcing patterns as
well as wet-to-dry transitions. Events with similar magnitudes and durations
exhibit significantly different performance controls in space and time,
indicating that the diagnostic inferences drawn from representative events
will be heavily biased by the a priori selection of those events. By
contrast, this study demonstrates high-resolution time-varying sensitivity
analysis, requiring no assumptions regarding representative events and
allowing modelers to identify transitions between sets of dominant parameters
or processes a posteriori. The proposed approach details the dynamics
of parameter sensitivity in nearly continuous time, providing critical
diagnostic insights into the underlying model processes driving predictions.
Furthermore, the approach offers the potential to identify transition points
between dominant parameters and processes in the absence of observations,
such as under nonstationarity. |
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