Influence of turbidity and aeration on the albedo of mountain streams
Bibliographic record
Abstract
Abstract Stream surface albedo plays a key role in the energy balance of rivers and streams that are exposed to direct solar radiation. Most physically based analyses and models have incorporated a constant stream albedo between 0.03 and 0.10, based primarily on measurements from low‐gradient streams with low suspended sediment concentrations. However, albedo should vary with solar elevation angle, suspended sediment concentration, aeration, and fraction of direct versus diffuse radiation. The objective of this study was to quantify the dependence of albedo of mountain streams on the controlling factors and to develop a predictive model for use in physically based analysis and modelling of stream temperature, especially for future climate and land‐use scenarios. Stream surface albedo was measured at nine sites with a variety of gradients and suspended sediment characteristics in the southern Coast Mountains of British Columbia, Canada. As expected, albedo of low‐gradient, non‐white water (flatwater) streams increased with solar elevation angle, suspended sediment concentration, and proportion of diffuse to direct solar radiation, ranging between 0.025 during cloudy periods over clear water to 0.25 for turbid water at elevation angles of less than 20°. Albedo was enhanced in steep reaches or at channel steps and cascades where flow was visibly aerated, with a range of 0.09 to 0.33. In clear weather, albedo exhibited notable diurnal variability at flatwater sampling sites. For example, during late summer, surface albedo typically fluctuated between 0.08 and 0.15 on a daily basis at a flatwater site on the highly turbid, glacier‐fed Lillooet River. Multiple regression models explained approximately 60% and 40% of the variance under cross validation for flatwater and white water data subsets, respectively, with corresponding root mean square errors of approximately 0.02 and 0.06.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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 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".