Quantifying complementary measures of climate-driven sediment dynamics on alluvial fans
Bibliographic record
Abstract
The effects of environmental change on eroding landscapes and their sedimentary products remains poorly understood. While sediment routing systems at the Earth’s surface can record changes in past environmental boundary conditions, the extent to which landscapes can buffer signals of climate change—of varying magnitude and timescale— is contentious. Mountain catchments and their alluvial fans offer one way to address this question, as they form accessible sediment routing systems in which source and sink are closely coupled and sediment budgets can be closed. Here we consider the extent to which sediment granulometry in stream-flow-dominated alluvial fans records signals of past environmental change. We focus on well-constrained field examples in Death Valley, California, such as the Hanaupah Canyon Fan, which have experienced climate forcing associated with late Pleistocene glacial-interglacial cycles. Using field-derived measures of grain size, we compare three complementary methods that can be used to reconstruct sediment dynamics on alluvial fans. First, we use a self-similarity analysis of sediment calibre to reconstruct sediment mobility on fans over time. Second, we use a downstream-fining model to evaluate the extent to which different depositional units on the fans may record changing sediment fluxes from source catchments. Third, we adopt a palaeohydrological approach to reconstruct unit discharges, bed shear stresses and instantaneous sediment transport capacities for fans, based on field measures of hydraulic geometry and grain size. We evaluate the extent to which these three methods provide consistent results, and we quantify how grain mobility, water and sediment discharge scale with documented variations in the regional climate. Our work demonstrates the potential for using alluvial-fan sedimentology as an archive of information about palaeo-environmental changes, including quantitative measures of past hydroclimate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".