A least-squares collocation approach to densifying river level from multi-mission satellite altimetry; Case study Mackenzie River basin
Bibliographic record
Abstract
Measuring river water level is essential for the global freshwater system monitoring, water resource management, hydrological model development, and climate change assessment. Despite its importance, the number of in-situ gauges has decreased over the recent decades. Moreover, many of the river systems are monitored either sparsely or not long enough to investigate their long-term evolution. Satellite altimetry is a unique technique that has enabled quantifying river levels for more than 25 years. Single mission altimetric water level time series can be obtained at the intersection of the satellite ground tracks and the river. For operational hydrology, however, single mission satellite altimetry is limited in its spatial and temporal sampling governed by the orbit configuration. This study proposes a framework to estimate the long-term sub-monthly river water level over the entire river using Least-Squares Collocation (LSC) by benefiting from multi-mission altimetric water levels (both interleaved and repeat orbit missions). The proposed method allows us to obtain dense water level observations both in time and space. We present the results over the Mackenzie River basin, located in Canada, and validate against in-situ data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".