Evaluating Fish Stranding Downstream of E.B. Campbell Hydroelectric Station on the Saskatchewan River, SK
Notice bibliographique
Résumé
The number of hydroelectric stations is increasing globally to generate renewable energy resources. To provide energy when it is needed, stations can be operated under a hydropeaking regime, which results in a risk of fish stranding when water levels fall rapidly downstream. With the lack of regulation on hydropeaking and a limited understanding of fish stranding on a large riverscale, there is a need for methods to identify where and when fish stranding occurs due to hydropeaking in relation to ambient environmental conditions, as well as a way to quantify the number of fish impacted on a large riverscale that can be applied to other hydropeaking operations. Here, I studied fish stranding in three reaches downstream of E. B. Campbell Generating Station on the Saskatchewan River, Saskatchewan, Canada along a 16 km riverscape. Using an innovative remote photography approach with 45 trail cameras and traditional transect monitoring, conducting 323 transects, I observed fish stranding between June and October associated with hydropeaking. Juvenile sport and commercial fish species stranded in higher-than-expected proportions compared with small-bodied fish species. The remote photography approach provided more precisely identifies fish stranding timing and allowed me to associate the environmental and physical conditions with a given stranding event. The comparison of the two approaches resulted in similar stranded fish densities, but remote photography allowed for continuous observations whereas transect monitoring was limited by observer availability in the field. Remote photography allowed for additional information on the scavenging of stranded fish, with scavenging occurring on average within 240 min of stranding. The probability of fish stranding increased significantly with increasing water temperature and decreasing substrate particle size. \nBy applying a combination of physical habitat assessments, hydrodynamic modeling, and observations on fish stranding, I estimated the number of fish stranding over five-month sampling periods for three consecutive years. I modelled how many fish stranded during 2019, 2020, and 2021 along the 16 km study reach, to predict the number of stranded fish during an average, low and high flow year, respectively. I determined a change in wetted area of 90.05 km2, 53.02 km2, and 80.74 km2 for 2019, 2020, and 2021, respectively during June through October each year. The fish stranding density obtained from the field observations was applied to the change in wetted area determined from a hydrodynamic model. The highest number of stranded fish was estimated for 2021, the low flow year, where estimates ranged from 170,700 to 1,638,000 stranded fish, based on remote photography and transect monitoring densities, respectively. \nThese findings have important implications for hydroelectric flow management by introducing an innovative, standardized methodology to study the effects of hydropeaking events on fish stranding that can be applied to determine impacts on fish communities. Coupling in-field observations with hydrodynamic modeling to estimate fish stranding on a large scale allows us to gain a greater understanding of the timing and magnitude of effects of hydropeaking on downstream ecosystems, and can be transferred to other riverscapes threatened by hydropeaking.
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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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,168 | 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 ».