A method to detect and characterize sub‐daily flow fluctuations
Bibliographic record
Abstract
Abstract Hydro peaking causes an important environmental impact on running water ecosystems. Many affected rivers have a poor ecological status. In rivers affected by hydro peaking, the flow conditions are highly complex and difficult to grasp. To develop a general framework for detecting and characterizing sub‐daily flow fluctuations, we analysed more than 500 Austrian hydrographs, covering the whole range from unimpacted to heavily impacted rivers. Different fluctuation types could be identified according to the potential source: e.g. sub‐daily flow fluctuations caused by hydro peaking, rainfall or snow and glacier melt. Additionally, the term ‘hydro fibrillation’ was established, to indicate frequently occurring artificial fluctuations with comparably low intensities. An automatic procedure was used to detect frequency and intensity of each flow fluctuation. Using variables based on duration curves of flow fluctuation rates (ramping rates), amplitudes, flow ratios, durations and daily numbers of fluctuations, a predictive model (linear discriminant analysis) was fitted to classify hydrographs into predominant fluctuation regimes. This is the basis for a detailed investigation of present sub‐daily flow regimes and to analyse the differences between the regimes. Based on the results, we finally propose a general framework that enables a standardized assessment of flow fluctuations regarding event intensities and/or event timing. The proposed framework offers a standardized selection of particular flow fluctuations referring to increase and decrease events separately. The selection of specific flow fluctuations can be defined with respect to several research questions (e.g. ecologically relevant fluctuations), which offers a wide range of applications. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".