Evaluating SWOT inland water surface elevation with gage information in the United States and Canada
Bibliographic record
Abstract
The Surface Water and Ocean Topography (SWOT) mission enables transformative approaches to water resources management by offering high-resolution water surface elevation data and extents from novel satellite radar technology. This study presents the first large-scale assessment of SWOT water surface elevation data across the United States and Canada, evaluating elevation accuracy against in situ gage data. We compare SWOT Level 2 Hydrology data products (Version C) to in situ timeseries of water elevation using 421,677 paired observations across 2,143 river and 659 lake and reservoir gages. Data accuracy and temporal coverage of SWOT observations are explored as functions of various data quality filters. Paired measurements (median-normalized) of SWOT and in situ observations resulted in 68th percentile absolute elevation differences of 18.32 cm, 17.08 cm, and 7.79 cm for river reach, river node, and lake data, respectively, with an overall combined difference of 16.97 cm for filtered observations (79,909; 19% of total potential data volume). Here we emphasize the importance of applying quality filters (both self-reported and exogenous) to SWOT Version C data, with 68th percentile absolute elevation differences ranging from 61.23 cm to 75.93 cm for the full dataset. Filtering also improves the correlation statistics between SWOT and in situ water surface elevation data, increasing R2 values from 0.04 to 0.83. Overall, we find that SWOT river and lake elevations meet SWOT data accuracy requirements (elevation accuracies within 10 and 25 cm) with adherence to SWOT quality flags.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".