Monitoring seasonal and interannual water level variability using sentinel-3 radar altimetry data: Application to Lake Buyo from 2016 to 2020.
Bibliographic record
Abstract
In this work, we evaluate the contribution of radar altimetry in the analysis of water level variations in Lake Buyo, located in the southwest of Côte d'Ivoire. Lake Buyo is a major hydroelectric dam and plays an important role in economic, social and environmental terms. It is characterised by periods of high and low water which affect certain economic activities such as electricity production and fishing. Water level fluctuations in Lake Buyo also influence ecological processes and represent a marker of climate change in the region. The altimetry data considered in this study come from the Sentinel-3A satellite, more precisely from tracks n° 016 and n° 743 from orbits 8 and 372 respectively, which cover the area of interest. These level 2 data are available on the CTOH platform. They have been corrected from atmospheric and geophysical effects to make them operational. The calculation of the water level is based on the range measured by the altimeter and the sum of these corrections. The results indicate that the Buyo lake is intensively recharged from June to September. The time between December and May represents the drying period of the lake. Furthermore, the analysis of inter-annual variations shows that 2016 has the highest peak in the study period. From 2016 to 2020, the maximum water level heights show a decreasing trend with estimated values of 200.98 m; 200.55 m; 200.53 m; 200.05 m; 198.36 m. The trend in the water level of the lake is therefore constantly decreasing. Although these results have not yet been validated in the field, they constitute a very important preliminary database for monitoring Lake Buyo. Indeed, recent studies have evaluated the performance of several radar altimetry missions, including Sentinel-3A and Sentinel-3B for continental water level surveys. Keywords: Radar altimetry, Sentinel-3A, CTOH, Lake Buyo, Water level, Hydroelectric dam, Ivory Coast
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".