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| Titel |
Robust multi-objective calibration strategies – possibilities for improving flood forecasting |
| VerfasserIn |
T. Krauße, J. Cullmann, P. Saile, G. H. Schmitz |
| 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. 10 ; Nr. 16, no. 10 (2012-10-15), S.3579-3606 |
| Datensatznummer |
250013515
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| Publikation (Nr.) |
 copernicus.org/hess-16-3579-2012.pdf |
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| Zusammenfassung |
| Process-oriented rainfall-runoff models are designed to approximate the
complex hydrologic processes within a specific catchment and in particular to
simulate the discharge at the catchment outlet. Most of these models exhibit
a high degree of complexity and require the determination of various
parameters by calibration. Recently, automatic calibration methods became
popular in order to identify parameter vectors with high corresponding model
performance. The model performance is often assessed by a purpose-oriented
objective function. Practical experience suggests that in many situations one
single objective function cannot adequately describe the model's ability to
represent any aspect of the catchment's behaviour. This is regardless of whether
the objective is aggregated of several criteria that measure different
(possibly opposite) aspects of the system behaviour. One strategy to
circumvent this problem is to define multiple objective functions and to
apply a multi-objective optimisation algorithm to identify the set of Pareto
optimal or non-dominated solutions. Nonetheless, there is a major
disadvantage of automatic calibration procedures that understand the problem
of model calibration just as the solution of an optimisation problem: due to
the complex-shaped response surface, the estimated solution of the
optimisation problem can result in different near-optimum parameter vectors
that can lead to a very different performance on the validation data.
Bárdossy and Singh (2008) studied this problem for single-objective calibration
problems using the example of hydrological models and proposed a geometrical
sampling approach called Robust Parameter Estimation (ROPE). This approach applies
the concept of data depth in order to overcome the shortcomings of automatic
calibration procedures and find a set of robust parameter vectors.
Recent studies confirmed the effectivity of this method. However, all ROPE
approaches published so far just identify robust model parameter vectors with
respect to one single objective. The consideration of multiple objectives is
just possible by aggregation. In this paper, we present an approach that
combines the principles of multi-objective optimisation and depth-based
sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE). It
applies a multi-objective optimisation algorithm in order to identify
non-dominated robust model parameter vectors. Subsequently, it samples
parameter vectors with high data depth using a further developed sampling
algorithm presented in Krauße and Cullmann (2012a). We study the effectivity of the
proposed method using synthetical test functions and for the calibration of a
distributed hydrologic model with focus on flood events in a small,
pre-alpine, and fast responding catchment
in Switzerland. |
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