Beyond Regime: A Stochastic Model of Floods, Bank Erosion, and Channel Migration
Bibliographic record
Abstract
Abstract Equilibrium or regime models based on a single formative (channel‐forming) discharge have been instrumental in developing a quantitative understanding of river channel dynamics. However, alternative paradigms can be used to ask fundamentally different questions about river channel behavior. In this paper, we present the Stochastic Channel Simulator (STOCHASIM), a simple biogeomorphic model that models the interplay between erosion and vegetation encroachment through changes in channel geometry. Results for a range of flood distributions are compared to predictions from a similar model based upon a traditional regime approach. Flood variability strongly influences the mean channel geometry and channel stability; the regime model and the stochastic model predict the same channel width when flow variability is low but diverge as flow variability increases. The return period of the formative flow required to match the geometry generated by a traditional regime approach increases systematically, from about 2 years for flow regimes in humid regions to nearly 8 years for more variable flow regimes, like those typical of arid regions. While the traditional regime approach provides a reasonable simplification for streams with little variability in the flood distribution, stochastic modeling may provide more realistic estimates of channel size as flood variability increases (e.g., in arid streams or small watersheds). The success of STOCHASIM in replicating realistic patterns of erosion, as well as the historical contingency often observed in natural streams, suggests that adopting a stochastic dynamics paradigm could advance geomorphology, just as it has done in hydrology, ecology, and other natural sciences.
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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 | 0.002 |
| 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 | 0.001 |
| 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".