Improving process representation in conceptual hydrological model calibration using climate simulations
Bibliographic record
Abstract
Abstract Different sets of calibrated model parameters can yield divergent hydrological simulations which in turn can lead to different operational decisions or scientific conclusions. In order to obtain reliable hydrological results, proper calibration is therefore fundamental. This article proposes a new calibration approach for conceptual hydrological models based on the paradigm that hydrological process representation, along with the reproduction of observed streamflows, need to be taken into account when assessing the performance of a hydrological model. Several studies have shown that complementary data can be used to improve hydrological process representation and make hydrological modeling more robust. In the current study, the process of interest is actual evapotranspiration (AET). In order to obtain a more realistic representation of AET, meteorological variables and the AET mean annual cycle simulated by a regional climate model (RCM) driven by reanalysis are used to impose constraints during the optimization procedure. This calibration strategy is compared to a second strategy which relies on AET derived from reference data and to the classical approach based solely on the reproduction of observed discharges. The different methodologies are applied to calibrate the lumped conceptual model HSAMI, used operationally at Hydro‐Québec, for six Canadian snow‐dominated basins with various hydrometeorological and physiographical characteristics.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".