Southeastern Tibetan Plateau growth revealed by inverse analysis of landscape evolution model
Bibliographic record
Abstract
The Cenozoic history of the Tibetan Plateau topography is critical for understanding the evolution of the Indian-Eurasian collision, climate, and biodiversity. However, the long-term growth and landscape evolution of the Tibetan Plateau remains ambiguous, it remains unclear if plateau uplift occurred soon after the India-Asia collision in the Paleogene or later in the Neogene. As the landscape evolution is controlled mainly by mountain uplift and surface processes, the present-day river profiles and the drainage basin geometries preserve important information that can be extracted to infer the long-term history of mountain uplift with numerical models. Here we focus on the southeastern (SE) Tibetan Plateau where three of the world’s largest rivers draining the Tibetan Plateau (the Yangtze, Mekong, and Salween Rivers, i.e., Three Rivers) have incised deep valleys with distinctive geomorphic signatures. We reproduce the uplift history of the SE Tibetan Plateau using a 2D landscape evolution model, which simultaneously solves fluvial erosion and sediment transport processes in the drainage basins of the Three Rivers region. Our model was optimized through a formal inverse analysis with a large number of forward simulations, which aims to reconcile the transient states of the present-day river profiles. The modeling results were ultimately compared to existing thermochronologic and paleoelevation datasets to help decipher between competing tectonic models that predict contrasting topographic evolutions. Our results suggest initially low elevations during the Paleogene, followed by a gradual southeastward propagation of topographic uplift of the plateau margin until present day. The modeling thus does not support Paleogene formation of the SE Tibetan Plateau with a major subsequent degradation via upstream fluvial erosion. The scenario with pre-existing high-elevation plateau or plateau degradation will result in much wider river channels with knickpoints that propagated upstream much further away from the plateau margin compared to observed river profiles. The quantitative constraints on landscape evolution achieved based on drainage patterns in SE Tibet indicate a powerful tool potentially applicable to other regions to infer important implications for the evolution of Indian-Eurasian collision, Asian monsoons, and biodiversity, as well as the geodynamic forces involved in collisional orogens.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".