Evaluation the suitability of contrasting performance metrics and signature measures with the identifiability quote index
Bibliographic record
Abstract
Good representation of the hydrological system in models is required to provide reliable predictions. The selection of a suitable set of performance criteria is a core decision in identifying the optimal parameter set(s) during model calibration. As each performance criterion focuses on different parts of the hydrograph, their selection often determines which parameter values are selected as optimal for representing the rainfall-runoff behaviour in a catchment. Knowning which performance criteria are most suitable for which purpose, model or catchment is difficult to determine.We therefore selected a set of 16 classical performance metrics and signature measures which together cover all phases of the hydrograph to test their suitability for identifying different types of parameters. We used four hydrological models (HBV, SWAT+, mHM and RAVEN-GR4J) in six catchments belonging to diverse landscapes in Germany. All model parameters were grouped into five process groups (snow, evapotranspiration, soil, surface and subsurface processes) to make the parameters comparable between the models. We then developed a metric called “identifiability quote index” which shows the degree of identifiability for each combination of parameter and performance criterion.Our results show that the classical performance criteria (e.g. NSE, KGE) are not sufficient to identify suitable values for all parameters. Signature measures (e.g. flashiness index, baseflow index) often have a higher “identifiability quote index” for specific cases and are suitable for either capacity or flux parameters. The degree of identifiability tends to vary between processes and models, but evapotranspiration parameters are generally highly identifiable with water-balance related metrics. The more complex a model is (e.g. mHM, SWAT+), the more difficult it is to determine parameter identifiabilities.In conclusion our study shows that a set of contrasting performance metrics and signature measures are needed to represent the whole hydrological system and to accurately identify the parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".