The Numerical Formulation of Simple Hysteretic Models to Simulate the Large‐Scale Hydrological Impacts of Prairie Depressions
Bibliographic record
Abstract
Abstract Topographic depressions have large impacts on the hydrology, ecology, and biogeochemistry in prairie environments. Topographic depressions control the hydrological flow regime through their role in storing water from precipitation and snow melt, and the storage dynamics of topographic depressions affect the retention, transformations, and transport of sediment, carbon, and nutrients. The water stored in topographic depressions supports a diversity of animal and plant species. More generally, depressional wetlands are an integral component of global water, energy, and biogeochemical cycles. In this technical note, we present simple models to simulate hysteretic relationships between storage and contributing area. We consider both spatially distributed and spatially integrated models. The spatially distributed models (the ensemble‐depression model) explicitly simulate storage dynamics for a distribution of discrete depressions. In these spatially distributed models, the large‐scale storage‐contributing fraction relationships are an emergent property of the modeled system. The spatially integrated models (the meta‐depression model) parameterize the large‐scale storage‐discharge relationships. The spatially integrated models are formulated in a way to represent hysteretic behavior. We present a suite of numerical experiments and demonstrate that the ensemble‐depression and meta‐depression models have similar large‐scale behavior. The simple hysteretic models defined here are formulated in a way so that they can easily be incorporated in conventional hydrological models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".