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| Titel |
Towards a more representative parametrisation of hydrologic models via synthesizing the strengths of Particle Swarm Optimisation and Robust Parameter Estimation |
| VerfasserIn |
T. Krauße, J. Cullmann |
| 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 ; 16, no. 2 ; Nr. 16, no. 2 (2012-02-27), S.603-629 |
| Datensatznummer |
250013186
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| Publikation (Nr.) |
 copernicus.org/hess-16-603-2012.pdf |
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| Zusammenfassung |
| The development of methods for estimating the parameters of hydrologic models
considering uncertainties has been of high interest in hydrologic research
over the last years. In particular methods which understand the estimation of
hydrologic model parameters as a geometric search of a set of robust
performing parameter vectors by application of the concept of data depth
found growing research interest. Bárdossy and Singh (2008) presented a first
Robust Parameter Estimation Method (ROPE) and applied it for the
calibration of a conceptual rainfall-runoff model with daily time step. The
basic idea of this algorithm is to identify a set of model parameter vectors
with high model performance called good parameters and subsequently generate
a set of parameter vectors with high data depth with respect to the first
set. Both steps are repeated iteratively until a stopping criterion is met.
The results estimated in this case study show the high potential of the
principle of data depth to be used for the estimation of hydrologic model
parameters. In this paper we present some further developments that address
the most important shortcomings of the original ROPE approach. We developed a
stratified depth based sampling approach that improves the sampling from
non-elliptic and multi-modal distributions. It provides a higher efficiency
for the sampling of deep points in parameter spaces with higher
dimensionality. Another modification addresses the problem of a too strong
shrinking of the estimated set of robust parameter vectors that might lead to
overfitting for model calibration with a small amount of calibration data.
This contradicts the principle of robustness. Therefore, we suggest to split
the available calibration data into two sets and use one set to control the
overfitting. All modifications were implemented into a further developed ROPE
approach that is called Advanced Robust Parameter Estimation (AROPE).
However, in this approach the estimation of the good parameters is still
based on an ineffective Monte Carlo approach. Therefore we developed another
approach called ROPE with Particle Swarm Optimisation (ROPE-PSO) that
substitutes the Monte Carlo approach with a more effective and efficient
approach based on Particle Swarm Optimisation. Two case studies demonstrate
the improvements of the developed algorithms when compared with the first
ROPE approach and two other classical optimisation approaches calibrating
a process oriented hydrologic model with hourly time step. The focus of both
case studies is on modelling flood events in a small catchment characterised
by extreme process dynamics. The calibration problem was repeated with higher
dimensionality considering the uncertainty in the soil hydraulic parameters
and another conceptual parameter of the soil module. We discuss the estimated
results and propose further possibilities in order to apply ROPE as a
well-founded parameter estimation and uncertainty analysis tool. |
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