Hillslope grain size variation across evolving landscapes linked to climate, tectonics and lithology
Bibliographic record
Abstract
Abstract The grain size of bedload sediment regulates rates of river incision into bedrock and thus influences topographic response to temporal and spatial variations in climate, tectonics and lithology. Grain size in river networks, in turn, depends on the size distributions of rock particles produced by weathering on hillslopes, which vary with local climate, erosion rate and rock properties. Hence, understanding the evolution of erosional landscapes requires consideration of the role of grain size as both a driver and a response to topographic change. However, conventional landscape evolution models do not explicitly account for the role of grain size, in part because algorithms for predicting hillslope grain size have been lacking. Here, we couple a recently proposed model for grain size production on hillslopes with a conventional landscape evolution model, to explore the controls on grain size at the landscape scale. We conducted a series of numerical experiments, varying rock uplift rate, temperature, precipitation and rock properties, to evolve a suite of steady‐state and transient landscapes. Model simulations suggest that rock uplift rate, through its effect on erosion rate and hillslope residence time, is more influential than climate in controlling the variation in hillslope grain size distributions in tectonically active landscapes. Overall, coarser size distributions result from faster rates of uplift, as well as from colder and drier conditions, and lithologies with lower erodibility and weathering susceptibility. These results are broadly consistent with patterns of hillslope grain size variation reported in field studies but likely underpredict the potential magnitude of variation because of the limitations of the model linking grain size and hillslope weathering. This work is a first step toward incorporating grain‐sized explicit algorithms for bedrock incision into landscape evolution models to capture the potential for feedback among grain size, climate, tectonics and lithology in evolving landscapes.
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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".