The hysteretic and gatekeeping depressions model − A new model for variable connected fractions of prairie basins
Bibliographic record
Abstract
• Many basins in the prairie pothole region have varying contributing fractions. • The Hysteretic and Gatekeeping Depressions Model (HGDM) models these basins simply. • HGDM can work in the absence of detailed digital elevation model (DEM) data. The Prairie Pothole Region of western North America has unusual hydrology and hydrography. Its level, post-glacial topography means that many drainage basins are dominated by internally drained depressions, rather than having conventional dendritic drainage networks of stream channels. Modelling the hydrology of these regions is difficult because the relationship between depressional storage and the connected fraction of a basin is hysteretic. Existing models are either computationally intensive and require high-resolution Digital Elevation Model (DEM) data which may not exist or require calibration and cannot reproduce the hysteresis between the basin connected fraction and depressional storage. The Hysteretic and Gatekeeping Depressions Model (HGDM) has been developed to simplify modelling of prairie basins with variable connected/contributing fractions. The model uses “meta” depressions to model the hysteretic responses of small depressions and a discrete model of large depressions, which cause “gatekeeping”, meaning that they prevent upstream flows from reaching the outlet until the depressions are filled. The HGDM was added to the Cold Regions Hydrological Modelling (CRHM) platform which is one of the few models that has successfully simulated land surface hydrology in the Canadian Prairies. CRHM + HGDM is tested by modelling streamflows at Smith Creek, a basin in southeastern Saskatchewan, Canada. It is demonstrated that CRHM + HGDM can reproduce the relationship between the connected/contributing fractions of sub-basins and their depressional storage at least as well as existing models. Importantly, it appears that HGDM can be used with coarse-resolution DEMs, which may permit its use in the many locations where higher-resolution data is unavailable. The simplicity and limited parameterization needs of HGDM may allow for broader representation of depressions and variable contributing area in prairie hydrology.
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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 | 0.000 |
| 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".