Comparison of threshold hydrologic response across northern catchments
Bibliographic record
Abstract
Abstract Nine mid‐latitude to high‐latitude headwater catchments – part of the Northern Watershed Ecosystem Response to Climate Change (North‐Watch) programme – were used to analyze threshold response to rainfall and snowmelt‐driven events and link the different responses to the catchment characteristics of the nine sites. The North‐Watch data include daily time‐series of various lengths of multiple variables such as air temperature, precipitation and discharge. Rainfall and meltwater inputs were differentiated using a degree‐day snowmelt approach. Distinct hydrological events were identified, and precipitation‐runoff response curves were visually assessed. Results showed that eight of nine catchments showed runoff initiation thresholds and effective precipitation input thresholds. For rainfall‐triggered events, catchment hydroclimatic and physical characteristics (e.g. mean annual air temperature, median flow path distance to the stream, median sub‐catchment area) were strong predictors of threshold strength. For snowmelt‐driven events, however, thresholds and the factors controlling precipitation‐runoff response were difficult to identify. The variability in catchments responses to snowmelt was not fully explained by runoff initiation thresholds and input magnitude thresholds. The quantification of input intensity thresholds (e.g. snow melting and permafrost thawing rates) is likely required for an adequate characterization of nonlinear spring runoff generation in such northern environments. Copyright © 2014 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 | 0.000 | 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 | 0.000 | 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".