 |
| Titel |
Analysing the temporal dynamics of model performance for hydrological models |
| VerfasserIn |
D. E. Reusser, T. Blume, B. Schaefli, E. Zehe |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 13, no. 7 ; Nr. 13, no. 7 (2009-07-07), S.999-1018 |
| Datensatznummer |
250011926
|
| Publikation (Nr.) |
 copernicus.org/hess-13-999-2009.pdf |
|
|
|
|
|
| Zusammenfassung |
| The temporal dynamics of hydrological model performance gives insights into
errors that cannot be obtained from global performance measures assigning a
single number to the fit of a simulated time series to an observed reference
series. These errors can include errors in data, model parameters, or model
structure. Dealing with a set of performance measures evaluated at a high
temporal resolution implies analyzing and interpreting a high dimensional
data set. This paper presents a method for such a hydrological model
performance assessment with a high temporal resolution and illustrates its
application for two very different rainfall-runoff modeling case studies. The
first is the Wilde Weisseritz case study, a headwater catchment in the
eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The
second is the Malalcahuello case study, a headwater catchment in the Chilean
Andes, simulated with the physics-based model Catflow. The proposed
time-resolved performance assessment starts with the computation of a large
set of classically used performance measures for a moving window. The key of
the developed approach is a data-reduction method based on self-organizing
maps (SOMs) and cluster analysis to classify the high-dimensional performance
matrix. Synthetic peak errors are used to interpret the resulting error
classes. The final outcome of the proposed method is a time series of the
occurrence of dominant error types. For the two case studies analyzed here, 6
such error types have been identified. They show clear temporal patterns,
which can lead to the identification of model structural errors. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|