Wavelet Analysis of Tide-affected Low Streamflows Series
Bibliographic record
Abstract
In certain rivers that drain very flat terrains in coastal areas, the streamflow series observed at a flow-gauging station may come under the direct influence of the backwater effects of tides.The phenomena may be negligible under conditions of high flows but can be critical under some extreme low-flow conditions.The errors in low flow estimation are large if a proper de-noising is not implemented to remove the effects of the tidal effects.Scrutinizing the hydrologic time series using a standard time-frequency domain based Fourier transform methodology cannot resolve conclusively the sources of the noise.However, a new perspective can be obtained by using a wavelet transformation to analyze the time series in the time-scale domain.By using this approach, a case study involving a streamflow series observed at Kapit, Sarawak, Malaysia yielded conclusive evidence of the influence of tides at the flow-gauging site during the low flow period.Upon confirmation that the noise is indeed of tidal origin, the observed water level series was subjected to an appropriate wavelet-based de-noising procedure to derive a smoothed series.Then, together with an established rating curve, a de-noised discharge series could also be approximated.Low-flow quantiles were subsequently derived by fitting a suitable frequency distribution to the annual minimum series abstracted from the de-noised discharge series.The methodology presented illustrates the potential of using wavelet analysis methods in solving other similar problems.
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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) | 0.000 | 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".