Identifiability of model parameters across processes, models and performance criteria

In selecting a suitable set of performance criteria for model calibration, modellers commonly rely on statistical performance metrics. However, most performance criteria cannot identify all types of parameters. This paper tests 19 statistical performance metrics and hydrological signatures that capture the full spectrum of hydrological conditions regarding their suitability to identify model parameters. Four hydrological models (Raven-GR4J, HBV, SWAT+, and mHM) are applied to six German catchments. For better model comparison, their parameters were classified into five process groups. The newly developed “identifiability quote index” then assesses parameter identifiability for each combination of parameter and performance criterion.The results show that a combination of contrasting statistical performance metrics and hydrological signatures focusing on high, mid, and low flows is needed. Hydrological signatures provide insights that statistical metrics cannot. With average ident_quote values above 0.5, evapotranspiration parameters are better identifiable than subsurface parameters, and capacity parameters better than flux parameters.

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Publication Details

Journal
Hydrological Sciences Journal
Published
2026-10-28
DOI
https://doi.org/10.1080/02626667.2026.2753660
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Identifiability of model parameters across processes, models and performance criteria

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Hydrological Sciences Journal
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Identifiability of model parameters across processes, models and performance criteria

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article en

Abstract

In selecting a suitable set of performance criteria for model calibration, modellers commonly rely on statistical performance metrics. However, most performance criteria cannot identify all types of parameters. This paper tests 19 statistical performance metrics and hydrological signatures that capture the full spectrum of hydrological conditions regarding their suitability to identify model parameters. Four hydrological models (Raven-GR4J, HBV, SWAT+, and mHM) are applied to six German catchments. For better model comparison, their parameters were classified into five process groups. The newly developed “identifiability quote index” then assesses parameter identifiability for each combination of parameter and performance criterion.The results show that a combination of contrasting statistical performance metrics and hydrological signatures focusing on high, mid, and low flows is needed. Hydrological signatures provide insights that statistical metrics cannot. With average ident_quote values above 0.5, evapotranspiration parameters are better identifiable than subsurface parameters, and capacity parameters better than flux parameters.

Hydrological Sciences Journal
Karlsruhe Institute of Technology (DE), University of Calgary (CA), University of Potsdam (DE), Justus-Liebig-Universität Gießen (DE), University of Zurich (CH), Leibniz Institute of Freshwater Ecology and Inland Fisheries (DE), University of Rostock (DE), Swiss Federal Institute of Aquatic Science and Technology (CH), Technische Universität Dresden (DE)
Deutsche Forschungsgemeinschaft
Clean water and sanitation
Openalex Percentile: Top 25%
Hydrology and Watershed Management Studies
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