Ganga‐Brahmaputra river discharge from Jason‐2 radar altimetry: An update to the long‐term satellite‐derived estimates of continental freshwater forcing flux into the Bay of Bengal
Bibliographic record
Abstract
This paper discusses the use of Jason‐2 radar altimeter measurements to estimate the Ganga‐Brahmaputra surface freshwater flux into the Bay of Bengal for the period mid‐2008 to December 2011. A previous estimate was generated for 1993–2008 using TOPEX‐Poseidon, ERS‐2 and ENVISAT, and is now extended using Jason‐2. To take full advantages of the new availability of in situ rating curves, the processing scheme is adapted and the adjustments of the methodology are discussed here. First, using a large sample of in situ river height measurements, we estimate the standard error of Jason‐2–derived water levels over the Ganga and the Brahmaputra to be respectively of 0.28 m and 0.19 m, or less than ∼4% of the annual peak‐to‐peak variations of these two rivers. Using the in situ rating curves between water levels and river discharges, we show that Jason‐2 accurately infers Ganga and Brahmaputra instantaneous discharges for 2008–2011 with mean errors ranging from ∼2180 m 3 /s (6.5%) over the Brahmaputra to ∼1458 m 3 /s (13%) over the Ganga. The combined Ganga‐Brahmaputra monthly discharges meet the requirements of acceptable accuracy (15–20%) with a mean error of ∼16% for 2009–2011 and ∼17% for 1993–2011. The Ganga‐Brahmaputra monthly discharge at the river mouths is then presented, showing a marked interannual variability with a standard deviation of ∼12500 m 3 /s, much larger than the data set uncertainty. Finally, using in situ sea surface salinity observations, we illustrate the possible impact of extreme continental freshwater discharge event on the northern Bay of Bengal as observed in 2008.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".