Tracer‐based assessment of flow paths, storage and runoff generation in northern catchments: a review
Bibliographic record
Abstract
Abstract We examine how tracer studies have enhanced our understanding of flow paths, residence times and sources of stream flow in northern catchments. We define northern catchments as non‐glacial sites in the temperate conifer/boreal/permafrost zone, focussing our review mainly on sites in North America and Europe. Improved empirical and theoretical understanding of hydrological functioning has advanced the analytical tools available for tracer‐based hydrograph separations, derivation of transit time distributions and tracer‐aided rainfall‐runoff models that are better able to link hydrological response to storage changes. However, the lack of comprehensive tracer data sets still hinders development of a generalized understanding of how northern catchments will respond to change. This paucity of empirical data leads to many outstanding research needs, particularly in rapidly changing areas that are already responding to climatic warming and economic development. To continually improve our understanding of hydrological processes in these regions our knowledge needs to be advanced using a range of techniques and approaches. Recent technological developments for improved monitoring, distributed hydrological sensor systems, more economic analysis of large sample numbers in conjunction with novel, tracer‐aided modelling approaches and the use of remote sensing have the potential to help the understanding of the northern hydrological systems as well as inform policy at a time of rapid environmental change. 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".