Refining understanding of hydrological connectivity in a boreal catchment
Bibliographic record
Abstract
Abstract Literature has long documented how streamflow response in boreal hillslopes and catchments is influenced by storage capacities, thresholds, and landscape and runoff pathway heterogeneity. More recently, the influence these traits have on streamflow has been interpreted through the concept of hydrological connectivity. However, the nature of hydrological connectivity in boreal catchments has only begun to be described. The focus of hydrological connectivity studies at the catchment scale has been on discerning from which areas does runoff originate and when, but not necessarily discovering the source waters of this streamflow. This has been the realm of studies investigating residence time and runoff pathways. This article summarizes an investigation in a 155‐km 2 catchment in Canada's Northwest Territories that applied both hydrometric and geochemical methods to measure streamflow response, storage state, connectivity and source waters. The goal of this research was to determine if a catchment scale metric of connectivity could be sensitive to changes in source waters and runoff pathways in a boreal landscape, so as to improve the predictive role of connectivity metrics. Nine runoff events from three water years were evaluated. There were distinct patterns of hydrological connectivity, which included, among others, a non‐linear relationship with the runoff ratio and hysteresis with streamflow. The results indicate that one metric of connectivity alone could not encapsulate connectivity extent and quality, identify water sources and be used successfully to predict runoff response. Multiple metrics were needed that further encapsulated the role of precipitation and hydrological processes on both structural and dynamic connectivity. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".