Downstream versus upstream propagation of fluvial erosion in orogenic plateaus: Example of the eastern Tibetan Plateau
Bibliographic record
Abstract
Fluvial erosion of small mountain belts is widely represented as a wave of upstream migration of knickpoints, starting from a stationary boundary of a high topography created by increased rock uplift rates. However, mountain belts such as the Tibetan Plateau and the central Andes are large, and fluvial erosion remains poorly constrained when orogens expand in width with their boundaries continuously advancing towards the foreland. Here we propose a simple analytical solution for a laterally expanding orogen dominated by fluvial erosion, and apply it to the propagation of Eastern Tibet where the plateau margin is characterized by widespread low-relief surfaces incised by steep river valleys. Our analytical solution is based on the assumption that the topography of Eastern Tibet was built by high uplift rates located in a belt along the plateau margins migrating outwards during plateau growth, as well as carved by erosion of large rivers originating from the interior of the plateau. We validate our analytical solution by comparing it to numerical models and various types of data from five large rivers in Eastern Tibet (Salween, Mekong, Yangtze, Yalong, and Dadu Rivers). The results show that the models with optimized parameters are generally consistent with the observed river-profile morphologies, exhumation magnitudes, and low-temperature thermochronometric ages. We also tested whether the observations on topography and exhumation could also be explained by a period of headward erosion and plateau retreat, the consequence of an early formation of the Tibetan Plateau. By testing various fluvial erodibilities and model durations, we could not reproduce the observed topographies, river profiles, and exhumation magnitudes. The tested model also predicts an increase in thermochronometric ages from the center to the margin of the plateau, opposite to the observed trend of ages. Our results thus show that the long-term fluvial erosion in Eastern Tibet features mainly a downstream migration of high erosion rates, which is fundamentally different from the headward erosion of most of small mountain rivers and a major plateau margin retreat. The characteristics described by our simple analytical solution may represent a common pattern of outward growing mountains and plateaus in tectonically active regions on Earth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".