Streamflow regime of a lake‐stream system based on long‐term data from a high‐density hydrometric network
Bibliographic record
Abstract
Abstract Northern landscapes are dominated by a mosaic of lakes and streams, yet only a limited number of studies have explored how these lake‐stream networks influence streamflow regimes. In order to gain further insight into the hydrologic behaviour of lake‐stream systems, we conducted a study using long‐term streamflow data to investigate the annual‐, seasonal‐ and event‐scale streamflow regimes of a lake‐stream network at the Turkey Lakes Watershed (TLW) in central Ontario, Canada. Streamflow metrics were compared for seven lake and 12 no‐lake catchments within the TLW, in addition to 14 no‐lake catchments from other forested landscapes. It was difficult to attribute patterns in annual streamflow regimes to the influence of lakes due to the confounding influence of catchment size; however, streamflow regimes appeared to be less flashy at locations with more lake influence. In addition, lake catchments showed high similarity in streamflow regimes across seasons, whereas no‐lake catchments showed more similarity to lake catchments during wet seasons but less similarity during dry seasons. Event‐scale streamflow regimes further downstream from lake outlets were associated with greater increases in peakflow response and hydrograph rise rate following rain events than locations closer to lake outlets. Antecedent conditions were also important for both the peakflow response and rise rate, but less so than the amount of rainfall during the event. Variability in streamflow across lake‐stream networks appears to be driven by interactions between delayed contributions from lakes and relatively rapid runoff contributions from hillslopes and tributaries without lakes. In addition, streamflow regimes are influenced by temporal changes in lake storage deficits, which are a function of lake and catchment properties, as well as hydrometeorological conditions. Our results highlight that a network‐scale perspective that incorporates lakes and streams is needed to understand how these landscapes will hydrologically respond to environmental change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".