From Unmodelable to Understandable: A Model Agnostic Approach in Prairie Pothole Hydrology
Bibliographic record
Abstract
Modelling the streamflow of flat, and pothole-dominated prairie or arctic regions presents challenges due to the influence of variable Non-Contributing Areas (NCAs) on converting local runoff to streamflow. Various models have been developed to represent these NCAs and their impact on streamflow prediction. However, these models may not adequately capture NCAs dynamics, rely heavily on calibration, are not applicable to large-scale basins, or are not model agnostic. In response, we introduce an open-source and model-agnostic version of a revised Hysteretic Depressional Storage (HDS) model. This model incorporates an improved numerical solution that accurately captures the hysteretic relationships of prairie potholes and NCAs, and their effect on streamflow generation. The revised HDS model is implemented and tested in three hydrological models (HYPE, MESH, and SUMMA) on a prairie pothole basin in Canada. Results demonstrate enhanced simulations of streamflow responses in the tested basins. Notably, the modified models successfully replicate the known hysteretic relationships between depressional storage and contributing areas in the region. The open-source HDS implementation approach facilitates integration into hydrologic or land surface modelling systems, enabling improvements in simulating complex hydrology and streamflow patterns globally.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".