Evaluation of the performance of frequency and chronological pairing techniques in synthesising long-term streamflow
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».