Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace‐Athabasca Delta, Canada
Bibliographic record
Abstract
The transport of fine sediment, carried in suspension by water, is central to the hydrology, geomorphology, and ecological functioning of river floodplains and deltas. An extensive new field data set for the Peace‐Athabasca Delta (PAD), Canada quantifies robust positive relationships between in situ suspended sediment concentration (SSC) and remotely sensed visible/near‐infrared reflectance. These relationships are exploited using SPOT and ASTER satellite images to map suspended sediment concentrations across the PAD for four days in 2006 and 2007, revealing strong variations in water sources and flow patterns, including flow reversals in major distributaries. Near‐daily monitoring with 276 MODIS satellite images tracks hydrologic recharge of floodplain lakes, as revealed by episodic infusions of sediment‐rich water from the Athabasca River. The timing and magnitude of lake recharge are linked to springtime water level on the Athabasca River, suggesting a system sensitive to changes in river flow regime. Moreover, recharge timing differentiates lakes that are frequently and extensively recharged from those recharged more rarely. Finally, we present a first estimation of river flow velocity based on remotely sensed SSC, though saturation may occur at velocities >0.6 m/s. Viewed collectively, the different remote sensing methodologies presented here suggest strong value for visible/near‐infrared remote sensing of suspended sediment to assess hydrologic and sediment transport processes in complex flow environments. Field observations including nephelometric turbidity, specific conductivity, water temperature, Secchi disk depth, suspended sediment concentration, and water level are archived at the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (available at http://daac.ornl.gov//HYDROCLIMATOLOGY/guides/PAD.html ).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".