Evaluation of SWOT’s performance for river water level retrieval in the Yangtze River Basin
Bibliographic record
Abstract
Remotely sensed monitoring of river levels plays a crucial role in flood risk management and the optimal allocation of water resources. This study employs the Surface Water and Ocean Topography (SWOT) to extract water levels from the Yangtze and its 10 first-order tributaries in the Yangtze River Basin (YRB), China, evaluating its performance across varying river widths and terrain conditions. SWOT effectively captures river water level variations over thousands of kilometres, with a coverage of 79.1% in the upper reaches of the basin and 93.6% in the lower reaches, resulting in an average coverage of 85.7%, and enabling the derivation of river slopes at approximately 200 m resolution. After extracting water level time series at the node scale, topographic correction was applied to reduce the impact of river slope on the error budget, and the results were compared with those from Sentinel-3 and Jason-3. SWOT achieves RMSEs of less than 0.35 m at 18 stations out of the 23 gauging stations, below 0.2 m at 14 stations, and under 0.1 m at 2 stations, with an average RMSE of 0.29 m, outperforming both Sentinel-3 (1.29 m) and Jason-3 (3.13 m). Notably, even in the hydrologically challenging upstream regions, SWOT delivers stable and high-accuracy water level observations, with RMSEs ranging from 0.12 to 0.25 m. The analysis further reveals that river width, river slope and land cover types have a weak impact on SWOT performance. However, the presence of sandbanks, tributaries, and the occurrence of topographic layover effects are the primary causes of relatively large errors at some stations.
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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 | 0.002 |
| 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 | 0.001 |
| 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".