Observations and modeling of hillslope throughflow temperatures in a coastal forested catchment
Bibliographic record
Abstract
Abstract A growing body of research on stream thermal regimes has highlighted the importance of heat advection associated with surface water and groundwater interactions, such as hyporheic exchange, groundwater discharge, and hillslope throughflow inputs. Existing catchment models that predict stream temperature use a variety of approaches to estimate throughflow temperatures, but none has been evaluated against field measurements of throughflow temperature. In this study, throughflow temperatures were monitored over two winters at 50 locations adjacent to a headwater stream (11 ha catchment area) located in the rain‐on‐snow zone of the Pacific Northwest. Existing approaches to estimate throughflow temperature under or overpredicted throughflow temperatures by up to 5°C, or were unable to represent the influence of transient snow cover. Therefore, a conceptual‐parametric model that is computationally efficient was developed that simulates hillslope hydrology and throughflow temperatures. The model structure includes an upslope reservoir that drains into a downslope reservoir that, in turn, drains into the stream. Vertical and lateral energy and water fluxes are simulated using simplified process representations. The model successfully predicts throughflow temperatures and highlights the dominant role of throughflow advection and the influence of snow cover on stream thermal regimes during high flow periods and rain‐on‐snow events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".