Downscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting
Notice bibliographique
Résumé
Abstract This study investigates the use of large-scale ensemble weather predictions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System [GFS; formerly known as Medium-Range Forecast (MRF)] for improving week-2 hydrologic forecasting. The ensemble weather predictor variables are used to downscale daily precipitation and temperature series at two meteorological stations in the Saguenay watershed in northeastern Canada. Three data-driven methods—namely, the statistical downscaling model (SDSM), the time-lagged feed-forward neural network (TLFN), and evolutionary polynomial regression (EPR)—are used as comparative downscaling models. The downscaled results of the best models are used as additional inputs in two hydrological models, namely Hydrologiska Byråns Vattenbalansavdelning (HBV2005) and a Bayesian neural network (BNN)-based hydrologic model, for up to 14-day-ahead reservoir inflow and river flow forecasting. The performance of the two hydrologic models is compared, the ultimate objective being to improve week-2 (7–14-day ahead) forecasts. To identify a suitable approach for using the ensemble weather data in the downscaling experiments, six scenarios are evaluated. It is found that the best approach to downscaling the ensemble weather predictions is to use the means of the predictor members derived from the two grid points closest to the local meteorological station of interest. The downscaling results show that all three models have a relatively good performance in downscaling daily temperature series, but the results are in general less accurate for daily precipitation. The TLFN and EPR models have quite close performance in most cases, and they both perform better than SDSM. The hydrologic forecasting results show that for both reservoir inflow and river flow, the HBV model has better performance when downscaled meteorological predictions are included, while there is no significant improvement for the BNN model. For the week-2 forecast, an improvement of about 18% on average is obtained for both streamflow and reservoir inflow forecasts. However, for the spring season where accurate peak flow forecast is of main concern, an improvement of about 26% on average is achieved. It is also shown that using only downscaled temperature in spring reservoir inflow forecasting, the improvements for week 2 range from 16% to 24%. Overall, the forecast results show that large-scale ensemble weather predictions can be effectively exploited through statistical downscaling tools for improved week-2 hydrologic forecasts. The forecast results also indicate that even imperfect medium-range (week 2) weather predictions can be very useful for producing significantly improved week-2 hydrologic forecasts.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».