ECOHYDROLOGICAL MODELING OF BEAVER DAMS
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Notice bibliographique
Résumé
Beavers (Castor canadensis and C. fiber) are expanding in their native range in North America and Eurasia and are expanding their range into urban environments and the Arctic tundra. Outside their natural range, they are also in Southern Patagonia because of historic releases in the fur industry. Given the broad geographical span of this expansion, it is critical to understand and predict the hydrology of beaver-dominated landscapes. Beavers build dams that modify the water balance and modulate streamflow through different flow states, which might result in drought and flood mitigation. To date, four published hydrological models have been developed to predict these impacts; however, these models were unable to represent dam variability and dynamics. In this study, a model specific to beaver dams was developed to predict the impacts of beaver dams on hydrology by including the flow state dynamics and the heterogeneity of dams and ponds. First, through the instrumentation of the montane peatland of Sibbald Fen in the Canadian Rocky Mountains, I determined that flow state changes of beaver dams are dynamic on a much shorter scale than previously documented. The shifts from one flow state to another happen regularly, have limited synchronicity within dam sequences, and can be predicted. In Sibbald, 66% to 80% of the flow state changes coincided with rainfall-runoff triggers and no changes were associated with biota using the dams. Following this flow state dynamic, I then developed an open-source model called BeaverPy in Python to simulate key features of dams and their impact on hydrology. Five single flow states and mixed combinations were included to identify their dynamics using a vector-based modeling approach, which accounted for changes in dam structures. Simulating individual and in-sequence dams from Sibbald Fen demonstrated that BeaverPy successfully models streamflow modulation by beaver dams, water storage in ponds, and flow state changes. Metrics for simulated vs. measured behavior for streamflow showed a good agreement in root mean squared error (g in beaver-dominated environments, thereby enhancing the understanding of how to incorporate beaver dams into flood mitigation and stream restoration projects and climate change initiatives.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 | 0,001 |
| 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 découle