Effects of Land Use and Hydrophysical Drivers on Temporal and Spatial Variability of Phosphorus and Nitrate Export in an Agricultural Subwatershed in Southern Ontario, Canada
Notice bibliographique
Résumé
The eutrophication of streams and lakes has been a long recognized problem in North America, particularly in Lake Erie where harmful and nuisance algal blooms have had many deleterious effects on aquatic ecosystems. Non-point source (NPS) pollution from agriculture has been identified as a key contributor of excess nutrients, namely phosphorus (P) and nitrogen (N), in the Great Lakes basin. There remains a need for increased understanding of the processes and drivers of nutrient losses from agricultural watersheds in order to better limit the negative influence of excess nutrients on receiving water bodies. Much of the existing research on agricultural nutrient export has focused on the growing season and there is a need to better characterize the seasonality of nutrient processes, as well as understand the important nutrient transport pathways. The objectives of this research were to identify key source areas (‘hot spots’) and peak periods (‘hot moments’) of nutrient export in an agricultural watershed and to draw inferences between the observed nutrient export and sub-catchment land use and practices. This research also characterizes the role of antecedent moisture conditions (AMC), event size, discharge, and flowpath contributions as potential drivers of the spatial and temporal variability in nutrient loads and concentrations. Streamflow and water chemistry were monitored over a 16-month period at four sites with differing land uses, in the Hopewell Creek watershed in Southern Ontario. The western lobe of the watershed was observed to be the ‘hot spot’ for P loads during all seasons, while temporally, the early spring snowmelt period was identified as the ‘hot moment’ throughout the watershed. The area of the watershed with the highest proportion of tile-drained land did not correspond to the P ‘hot spot’, and was instead an area with high peak flows and livestock operations. Flowpath contributions were shown to be an important driver of total phosphorus (TP) concentrations and nitrate (NO3-) loads through stepwise multiple linear regressions. This research emphasizes the importance of year-round event based monitoring programs for estimating nutrient export and further, that subwatershed scale studies can be used to identify nutrient hot spots in an agriculturally dominated catchment with spatially variable land use practices. Flowpath contributions were found to be important drivers of nutrient dynamics and this suggests that understanding flowpath contributions in agricultural subwaterheds can increase the predictive power for nutrient export models.
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 | 0,000 | 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».