Evaluation of the performance of frequency and chronological pairing techniques in synthesising long-term streamflow
Bibliographic record
Abstract
The mountainous regions of British Columbia, Canada, are unique landscapes that are providing opportunities for the development of small, run-of-river hydropower projects. To design these projects effectively, long-term streamflow records are required. Ideally, such designs would utilize long-term records directly from a Project site and hydrologic models used to generate site streamflow estimates would not be necessary. However, these projects are rarely located in streams with long-term records, and hydrologic models are invariably required. This research evaluates the performance of two pairing techniques that are used hydrotechnical consultants to derive long-term streamflow; Chronological Pairing (CP) and Empirical Frequency Pairing (EFP). Three hydro-climatic regimes, common in British Columbia, were investigated, including Pluvial, Glacial and Nival regimes. The evaluations discussed in this research were based on comparisons of derived long-term streamflow estimates through pairing studies, to actual measured streamflow records. The research found that EFP, which is a relatively new approach to synthetically generating long-term streamflow records, consistently out-performed CP techniques. EFP accurately and precisely modelled extremely high and low runoff percentiles (1st and 99th percentiles) as well as the mean annual discharge and Flood Frequency Analysis parameter inputs, such as the mean and standard deviation of the annual flood maxima. A key finding of this research was that between three and four years of high quality project streamflow data were necessary to provide a representative sample to derive a synthetic series that will not significantly improve with the incorporation of additional data. The results of this research will be useful when selecting hydrologic models for derivation of long-term streamflow estimates from short-term records in mountainous watersheds. The research also found several cautions in the use of both methods, but particularly in the CP method. The binding CP relationship and the influence of outliers, resulted in poor performance of CP in modelling long-term streamflow. Extrapolation above maximum measured values within the concurrent period was a technique that has a significant bearing on the accuracy and precision of the very highest percentiles, but especially on defining the variability of annual floods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".