Sensitivity of simulated flow fields and bathymetries in meandering channels to the choice of a morphodynamic model
Bibliographic record
Abstract
Abstract Morphodynamic models are used by river practitioners and scientists to simulate geomorphic change in natural and artificial river channels. It has long been recognized that these models are sensitive to the choice of parameter values, and proper calibration is now common practice. This paper investigates the less recognized impact of the choice of the model itself. All morphodynamic models purport to simulate the same flow and sediment dynamics, often relying on the same governing equations. Yet in solving these equations, the models have different underlying assumptions, for example regarding spatial discretization, turbulence, sediment inflow, lateral friction, and bed load transport. These differences are not always considered by the average model user, who might expect similar predictions from calibrated models. Here, a series of numerical simulations in meandering channels was undertaken to test whether six morphodynamic codes (BASEMENT, CCHE‐2D, NAYS, SSIIM‐1, TELEMAC‐2D and TELEMAC‐3D) would yield significantly different equilibrium bathymetries if subjected to identical, initial flow conditions. We found that, despite producing moderately similar velocity patterns on a fixed‐flat bed (regression coefficient r of 0.77 ± 0.20), the codes disagree substantially with respect to simulated bathymetries ( r = 0.49 ± 0.31). We relate these discrepancies to differences in the codes' assumptions. Results were configuration specific, i.e. codes that perform well for a given channel configuration do not necessarily perform well with higher or lower sinuosity configurations. Finally, limited correlation is found between accuracy and code complexity; the inclusion of algorithms that explicitly account for the effects of local bed slope and channel curvature effects on transport magnitude and direction does not guarantee accuracy. The range of solutions obtained from the evaluated codes emphasizes the need for carefully considering the choice of code. We recommend the creation of a central repository providing universal validation cases and documentation of recognized fluvial codes in commonly studied fluvial settings. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".