Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model
Bibliographic record
Abstract
Abstract The advent of hydrological modeling frameworks that support multiple model structures using the same software enables both model structure and model parameters to be calibrated and assessed. To date, the identification of optimal model structure has typically been performed manually. Here, a continuous (rather than discrete) treatment of model structure is used, which enables simultaneous automatic calibration of model structure and parameters using a conventional real‐valued decision variable optimization algorithm (the dynamically dimensioned search algorithm, DDS). The method, referred to herein as blended model structure calibration (BMSC), relies upon the calculation of each hydrologic flux (e.g., for infiltration) as a weighted average of fluxes generated from multiple process algorithm options. This method is applied to 12 lumped MOPEX catchment models and compared to the calibration of 108 fixed model structures, representing all possible permutations of fixed model structures with the given process options in this study. The BMSC method consistently identified near‐optimal model structure (as evaluated using average model rank performance) at significantly lower computational cost than calibrating the collective of fixed structure models. The BMSC method also provides a useful tool in identifying dominant processes and model structures in catchments.
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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 | 0.000 | 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".