Metrics that Matter: Objective Functions and Their Impact on Signature Representation in Conceptual Hydrological Models
Bibliographic record
Abstract
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), and their respective logarithmic variants, as well as four more recently proposed metrics. We evaluate the representation of 15 hydrological signatures that capture a relevant selection of streamflow characteristics to determine generalizable strengths and weaknesses of individual OFs across different models and catchments. Results show that the choice of OF can significantly affect a model's capability to simulate different hydrological signatures such as runoff ratios, extreme flow percentiles, and certain baseflow characteristics. While certain signatures, particularly those related to flow variability, are relatively insensitive to OF choice, others exhibit large performance shifts across different OFs. Generally, no single OF simultaneously achieved high performance across all tested signatures, highlighting that a single-objective calibration is unlikely to lead to an all-purpose model. Our results reinforce calls to choose objective functions deliberately and in line with the objectives of a study. They also provide initial guidance on which metrics highlight particular facets of streamflow behaviour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".