Automating drainage direction and physiographic inputs to the CEQUEAU hydrological model: sensitivity testing on the lower Saint John River watershed, Canada
Bibliographic record
Abstract
CEQUEAU is a process-based hydrological model capable of simulating river flows and temperatures. Despite an active user base, no facility yet exists for the automatic assembly and input of watershed data required for flow simulations. CEQUEAU can therefore be time-consuming to implement, particularly on large (≥104 km2) watersheds. We detail a new MATLAB toolbox designed to remove this key limitation by automatically computing CEQUEAU's key drainage direction and physiographic inputs from geographic information system (GIS) data. With the toolbox, model implementation can now be achieved extremely quickly (<1.5 hr) given suitable inputs. This time saving enabled us to assess CEQUEAU's sensitivity to changes in grid size by implementing the model on a large (14,990 km2) watershed at successively decreasing resolution (2.5 km to 112 km), using a fixed calibration parameter set. Results of this analysis showed that despite some model strength fluctuations linked to variability in computed basin size/land-use, only a minor decrease in model strength (mean Nash–Sutcliffe efficiency (NSE) reduction = 0.03) was observed at relatively fine resolutions (2.5 km to 20 km). Although results might change if the model was recalibrated at each resolution step, findings indicate that CEQUEAU is able to provide realistic flow simulations at a wide range of resolutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".