Runoff simulation in the Canagagigue Creek watershed using the MIKE SHE model
Notice bibliographique
Résumé
The Canagagigue Creek watershed, located in the Grand River Basin, is one of the fastest developing areas in Ontario. The watershed hydrology has changed considerably due to the increased anthropogenic activities, producing frequent floods and droughts as well as water quality problems. MIKE SHE, a watershed-scale model, was used to simulate surface runoff from the Canagagigue Creek watershed. Various management scenarios affecting the surface hydrology were also evaluated. The model was calibrated for four years (1994-95 to 1997-98) and validated for another four years (1990-91 to 19931-94). For the calibration period, the correlation between the observed and simulated daily runoff was satisfactory, as shown by the coefficient of determination value of 0.59. The coefficient of determination was 0.44 for the validation period. The Nash-Sutcliffe coefficients obtained were 59% and 40% for the calibration and validation period, respectively. Use of daily input data together with a simplistic snowmelt routine, was found to affect model performance during the winter/spring period. Henceforth, model performance can be greatly improved by adopting a more comprehensive method for simulating snowmelt and incorporating the frozen soil conditions. Overall, the model was able to simulate surface runoff reasonably well on annual, seasonal, monthly, and daily intervals, representing all the hydrological components adequately. With the various management scenarios simulated, it was found that the deforestation scenario considerably increased the total flow (11%). On the other hand, the high runoff peaks were decreased and low flows were increased considerably in the application of the tile drainage scenario. It was also observed that surface flow increased in wet years and decreased in normal and dry years in the climate change scenario. Though impacts of certain scenarios were almost negligible, their effects were significant when associated with the percentage area under transformation. Hence, it was concluded that the model can be used to simulate various management scenarios to solve hydrologic problems in the Southern Ontario climatic condition.
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 | 0,002 | 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 ».