Probabilistic dynamic rating curves using auxiliary information
Bibliographic record
Abstract
Rating curves play a vital part in hydrology for producing streamflow time-series. The derived streamflow is an integral component to any hydrological study and therefore requires proper quantification of not only a discharge point value, but also an uncertainty measure. Using multivariate Gaussian distributions as kernels, a probabilistic rating curve was developed from the conditional distribution as an alternative model for the standard deterministic rating curve. Auxiliary information from a run-of-river hydroelectric project, as well as the temporal variability from the gauging measurements, were used to study the possible reduction in the uncertainty of the developed rating curve. The temporal information was modeled using an exponential function that updated upon receiving new gaugings and the sluicing model was a continuously updated kernel distribution that assigned more weight to gaugings taken after a sluicing event. Four models of varying complexity were created and their performance was evaluated using information theory measures such as surprise and the Kullback-Leibler divergence measure. The results indicate that probabilistic rating curves are useful tools for modeling and evaluating the dynamic uncertainty of the curves. The uncertainty was shown to be reduced by up to 19% by including the temporal information of the gaugings and sluicing information. Auxiliary information can be beneficial to rating curve development and an argument is made for why probabilistic rating curves should become a norm in the hydrology field.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".