Field study on drainage densities and rescaled width functions in a high‐altitude alpine catchment
Bibliographic record
Abstract
Abstract This paper addresses the effect of accurately mapping spatially heterogeneous drainage densities in high‐altitude alpine basins on Rescaled Width Functions (RWFs), used in some applications as a minimalist model of the hydrologic response. The channel network and 373 of its channel heads were mapped in the field in a high mountain catchment in the Swiss Alps. The mapped channel network is characterized by highly uneven drainage density, here described by the distribution of the length to the first channelized site computed along steepest descent from any unchannelled site. Various channel networks were extracted from a 1 m lidar‐derived digital terrain model and compared with the field‐mapped channel network using geomorphologic parameters, hillslope‐to‐channel distance and RWFs. Our results show that the channel network derived by statistical analysis of surface morphology is consistent with the field‐mapped network. Larger discrepancies were observed when the channel network was obtained with classical threshold‐based approaches relying on cumulative drainage area and local slope. The actual arrangement of the drainage densities has a significant impact on the RWFs. The discrepancy was largest between RWFs derived from classical extraction methods and RWFs derived with the field‐mapped network, indicating an inappropriate extraction of the channelled portion of the high‐altitude catchment that is a reflection of the variety of channel initiation processes. Our results suggest that spatial heterogeneity of the drainage density might play an important role in modelling streamflow generation. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".