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| Titel |
Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods |
| VerfasserIn |
W. Castaings, D. Dartus, F.-X. Le Dimet, G.-M. Saulnier |
| 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 ; 13, no. 4 ; Nr. 13, no. 4 (2009-04-24), S.503-517 |
| Datensatznummer |
250011832
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| Publikation (Nr.) |
 copernicus.org/hess-13-503-2009.pdf |
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| Zusammenfassung |
Variational methods are widely used for the analysis
and control of computationally intensive spatially distributed
systems. In particular, the adjoint state method enables
a very efficient calculation of the derivatives of an objective
function (response function to be analysed or cost
function to be optimised) with respect to model inputs.
In this contribution, it is shown that the potential of variational
methods for distributed catchment scale hydrology
should be considered. A distributed flash flood model, coupling
kinematic wave overland flow and Green Ampt infiltration,
is applied to a small catchment of the Thoré basin
and used as a relatively simple (synthetic observations) but
didactic application case.
It is shown that forward and adjoint sensitivity analysis
provide a local but extensive insight on the relation between
the assigned model parameters and the simulated hydrological
response. Spatially distributed parameter sensitivities can
be obtained for a very modest calculation effort (~6 times the
computing time of a single model run) and the singular value
decomposition (SVD) of the Jacobian matrix provides an interesting
perspective for the analysis of the rainfall-runoff relation.
For the estimation of model parameters, adjoint-based
derivatives were found exceedingly efficient in driving a
bound-constrained quasi-Newton algorithm. The reference
parameter set is retrieved independently from the optimization
initial condition when the very common dimension reduction
strategy (i.e. scalar multipliers) is adopted.
Furthermore, the sensitivity analysis results suggest that
most of the variability in this high-dimensional parameter
space can be captured with a few orthogonal directions.
A parametrization based on the SVD leading singular vectors
was found very promising but should be combined with
another regularization strategy in order to prevent overfitting. |
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