Incorporating The Variable Contributing Area Concept in The HYPE Modelling Framework
Bibliographic record
Abstract
Modelling the hydrology of the North American prairie region is complicated by the dominance of cold region processes and by the flat topography, which contains millions of depressions. The depressions contribute to variable contributing areas in prairie basins, due to their varying water storage. The relationships between the depressional storage, and the contributing fraction are hysteretic and strongly influence the basin responses. Most hydrological models do not represent these complex hysteretic relationships, and therefore struggle in simulating the hydrology of the region. In this study, we propose a novel Hysteretic Depressional Storage (HDS) algorithm that is based on the known hysteretic properties of prairie depressions. HDS is implemented into the HYPE modelling framework to improve the simulations of prairie streamflow by accounting for the variable contributing area. The modified HYPE, and the original program are tested on two depression-dominated basins in Saskatchewan, Canada. The modified HYPE model show improved simulation of streamflows compared to the original HYPE model. The HDS algorithm can contribute to improving the streamflow simulation of not only the North American prairie region, but also in the arctic and Siberian regions, which are dominated by the same complex depressional storages. The modified HYPE model should also improve the estimates of surface water storage and the resulting evaporative fluxes in these regions, increasing model fidelity and improving water budget estimates in these complex terrains, especially under changing climates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".