A regionalized river water quality model calibration method based on watershed physical characteristics: application to the Cau River in Vietnam
Bibliographic record
Abstract
A methodology is proposed for the calibration of river water quality models on large watersheds, in the absence of intensive measurements for water quality and quantity. This methodology is based on: 1) the use of the results from a hydrological model to provide the required hydrological variables to the water quality model; 2) five assumptions for the definition of initial and boundary conditions; 3) a three-step regionalized calibration method, in which the specific characteristics of the different subwatersheds are taken into account and 4) the adjustment of some parameters in order to reproduce processes that are not explicitly represented in the model. The regionalized calibration method relies on a comprehensive study of the land use and characteristics on each subwatershed and the definition of different sets of parameters values in distinct regions. Application to the Cau River, in Vietnam, with QUAL-GIBSI, an adaptation of the QUAL2E model, showed that: i ) calibration and validation results were significantly improved by applying regionalized calibration as compared to an initial calibration for which a single set of parameters values was used for the whole simulated river stretch and ii ) use of a hydrological model to provide discharge at various points in the watershed allowed to overcome the lack of detailed measurements of discharge at locations other than the watershed outlet.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".