The importance of incorporating diurnally fluctuating stream discharge in stream temperature energy balance models
Bibliographic record
Abstract
Abstract Although stream temperature energy balance models are useful to predict temperature through time and space, a major unresolved question is whether fluctuations in stream discharge reduce model accuracy when not exactly represented. However, high‐frequency (e.g., subdaily) discharge observations are often unavailable for such simulations, and therefore, diurnal streamflow fluctuations are not typically represented in energy balance models. These fluctuations are common due to evapotranspiration, snow pack or glacial melt, tidal influences within estuaries, and regulated river flows. In this work, we show when to account for diurnally fluctuating streamflow. To investigate how diurnal streamflow fluctuations affect predicted stream temperatures, we used a deterministic stream temperature model to simulate stream temperature along a reach in the Quilcayhuanca Valley, Peru, where discharge varies diurnally due to glacial melt. Diurnally fluctuating streamflow was varied alongside groundwater contributions via a series of computational experiments to assess how uncertainty in reach hydrology may impact simulated stream temperature. Results indicated that stream temperatures were more sensitive to the rate of groundwater inflow to the reach compared with the timing and amplitude of diurnal fluctuations in streamflow. Although incorporating observed diurnal fluctuations in discharge resulted in a small improvement in model RMSE, we also assessed other diurnal discharge signals and found that high amplitude signals were more influential on modelled stream temperatures when the discharge peaked at specific times. Results also showed that regardless of the diurnal discharge signal, the estimated groundwater flux to the reach only varied from 1.7% to 11.7% of the upstream discharge. However, diurnal discharge fluctuations likely have a stronger influence over longer reaches and in streams where the daily range in discharge is larger, indicating that diurnal fluctuations in stream discharge should be considered in certain settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".