A New Concept to Calibrate and Evaluate a Hydrological Model Based on Functional Data Analysis
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Bibliographic record
Abstract
Performance measures are widely used in hydrological modeling to provide objective evaluation of the match between simulated and observed system output (i.e. discharge). Each performance measure emphasises a particular aspect of a hydrograph, and the use of a particular performance measure on a specific metric typically means discounting one aspect at the expense of another (e.g. high flows vs low flows). This is mainly because most performance measures reflect the adequacy of simulations using one calculated value based on residuals between daily or hourly series of simulated and observed streamflows. However, it would be more practical to conserve the temporal flow variability of the entire annual hydrograph than to focus merely on flood peaks, for instance. Functional data analysis is a mathematical tool that allows the comparison of such data. In this paper, a methodology for model calibration and evaluation that considers an annual hydrograph as a single observation instead of 365 daily observations, based on functional data analysis, is proposed. The model is evaluated on its ability to reproduce the same shape and variability as the observed hydrographs. The functional statistics, defined for each time step, are used to construct the objective function for model calibration as well as for further model evaluation. A case study is presented to evaluate the hydrological CEQUEAU model on the Lac St-Jean drainage basin. The concept that we describe is general and can be used with any calibration scheme or model evaluation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it