Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models.
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| Title: | Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models. |
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| Authors: | Wagener, Peter1,2 (AUTHOR) peter.wagener@ucalgary.ca, Knoben, Wouter J. M.1 (AUTHOR), Schütze, Niels2 (AUTHOR), Spieler, Diana1,2 (AUTHOR) |
| Source: | Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 15, p4867-4888. 22p. |
| Subject Terms: | *Cost functions, *Streamflow, *Random forest algorithms, *Parameter estimation, *Runoff analysis, *Hydrologic models, *Watersheds |
| Abstract: | Although objective functions (OFs) are widely discussed in the literature, many modelling studies still default to a few common metrics, without much consideration of their relative strengths and weaknesses. This paper systematically investigates the impact of OF choice on the representation of various streamflow characteristics across 47 conceptual models and 10 hydro-climatically diverse catchments selected from the CARAVAN dataset. We use eight different OFs for calibration, including the Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), a variant of each that uses a streamflow transformation, and four more recently proposed metrics. We further account for parameter uncertainty by retaining the top 500 parameter sets for each combination of model, basin and OF. We evaluate the representation of 15 hydrological signatures that capture a relevant selection of streamflow characteristics to determine generalisable strengths and weaknesses of individual OFs across different models and hydroclimatic conditions. A random forest importance analysis showed that OF choice is often more influential than model structure choice in determining signature representation, with catchment characteristics remaining the dominant control. While certain signatures, particularly those related to flow variability, are relatively insensitive to OF choice, other signatures exhibit large performance shifts across different OFs. As other studies have already noted, no single OF performs best across all signatures. Our consistent multi-model and multi-catchment experiment now provides generalisable guidance on which OF best reproduces which aspect of streamflow behaviour. This helps to better select objective functions most appropriate for a specific modelling purpose and provides insights into which metric combinations can cover a broader range of flow characteristics. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Although objective functions (OFs) are widely discussed in the literature, many modelling studies still default to a few common metrics, without much consideration of their relative strengths and weaknesses. This paper systematically investigates the impact of OF choice on the representation of various streamflow characteristics across 47 conceptual models and 10 hydro-climatically diverse catchments selected from the CARAVAN dataset. We use eight different OFs for calibration, including the Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), a variant of each that uses a streamflow transformation, and four more recently proposed metrics. We further account for parameter uncertainty by retaining the top 500 parameter sets for each combination of model, basin and OF. We evaluate the representation of 15 hydrological signatures that capture a relevant selection of streamflow characteristics to determine generalisable strengths and weaknesses of individual OFs across different models and hydroclimatic conditions. A random forest importance analysis showed that OF choice is often more influential than model structure choice in determining signature representation, with catchment characteristics remaining the dominant control. While certain signatures, particularly those related to flow variability, are relatively insensitive to OF choice, other signatures exhibit large performance shifts across different OFs. As other studies have already noted, no single OF performs best across all signatures. Our consistent multi-model and multi-catchment experiment now provides generalisable guidance on which OF best reproduces which aspect of streamflow behaviour. This helps to better select objective functions most appropriate for a specific modelling purpose and provides insights into which metric combinations can cover a broader range of flow characteristics. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10275606 |
| DOI: | 10.5194/hess-30-4867-2026 |
