Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2017-28
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
01 Feb 2017
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Identifying the connective strength between model parameters and performance criteria
Björn Guse1, Matthias Pfannerstill1, Abror Gafurov2, Jens Kiesel3,1, Christian Lehr4,5, and Nicola Fohrer1 1Christian-Albrechts-University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
2GFZ German Research Centre for Geosciences, Potsdam, Germany
3Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
4Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Landscape Hydrology, Muencheberg, Germany
5University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
Abstract. In hydrological models, parameters are used to adapt the model to the conditions of the catchments. Hereby, the parameters need to be identified based on their role in controlling the hydrological behaviour in the model. For parameter identification, multiple and complementary performance criteria are used, which have to capture the different aspects of hydrological response of catchments. A reliable parameter identification depends on how distinctly a model parameter can be assigned to one of the performance criteria.

We introduce an analysis that reveals the connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the inter-relationship between model parameters and performance criteria. In our analysis of connective strength, model simulations are carried out based on a Latin Hypercube sampling. Ten performance criteria in cluding the NSE, the KGE and its three components (alpha, beta and r) as well as the RSR for different segments of the flow duration curve (FDC) are calculated.

With a joint analysis of two regression trees (RT), it is derived how a model parameter is connected to the different performance criteria. At first, RTs are constructed using each performance criteria as target variable to detect the most relevant model parameters for each performance criteria. A second RT approach using each parameter as target variable detects which performance criterion is impacted by changes in parameter values. Based on this, appropriate performance criteria are identified for each model parameter.

A high bijective connective strength is calculated for low and mid flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of the KGE and of the FDC segments. It is emphasised under which conditions these performance criteria provide insights into a precise parameter identification. Separate performance criteria are required to identify dominant parameters on low and mid flow conditions, whilst the number of required performance criteria for high flows increases with the process complexity in the catchment. Overall, the analysis of the connective strength using RTs contribute towards a better handling of parameters and performance criteria in hydrological modelling.


Citation: Guse, B., Pfannerstill, M., Gafurov, A., Kiesel, J., Lehr, C., and Fohrer, N.: Identifying the connective strength between model parameters and performance criteria, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2017-28, in review, 2017.
Björn Guse et al.
Björn Guse et al.
Björn Guse et al.

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Short summary
Performance measures are used to evaluate the representation of hydrological processes in the parameters of hydrological models. In this study, it is investigated how strongly model parameters and performance measures are connected. It was found that relationships are different for varying flow conditions, indicating that precise parameter identification requires multiple performance measures. The suggested approach contributes to a better handling of parameters in hydrological modelling.
Performance measures are used to evaluate the representation of hydrological processes in the...
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