Analysing the temporal dynamics of model performance for hydrological models

  • 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 physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of claThe 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 physicsbased 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.show moreshow less

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Metadaten
Author:Dominik Reusser, Theresa Blume, Bettina Schaefli, Erwin Zehe
URN:urn:nbn:de:kobv:517-opus-45114
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (paper 140)
Document Type:Postprint
Language:English
Date of Publication (online):2010/07/19
Year of Completion:2009
Publishing Institution:Universität Potsdam
Release Date:2010/07/19
Tag:Catchment; Improved calibration; Process identification; Rainfall-runoff response; Soil-moisture
Source:Hydrology and earth system sciences 13 (2009), 7, S. 999 - 1018
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geoökologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (English):License LogoCreative Commons - Attribution 3.0 unported
Notes extern:
The article was originally published by Copernicus Publications:
Hydrology and Earth System Sciences. - 13 (2009), 7, S. 999-1018
ISSN 1027-5606