Identification of critical source areas which contribute nutrients to snowmelt runoff
Notice bibliographique
Résumé
The presence of nutrients in snowmelt runoff from agricultural watersheds has been reported by previous studies. However, no study has answered the most important question “what areas of the watershed contribute nutrients to snowmelt runoff?” or addressed the factors that control snowmelt runoff water quality. This study was designed to (1) find the areas that contribute nutrient to snowmelt runoff (termed as critical source areas, CSA), and (2) understand the source and transport factors that control the snowmelt runoff water quality in the Canadian prairies. The findings of this study will provide vital information to understand snowmelt runoff water quality and for sustainable management of soil nutrients and snowmelt runoff water quality in the Canadian prairies. Source and transport factors and snowmelt runoff water quality were studied for two years on shoulder, backslope and footslope landform segments. The distribution of fall soil nutrients in the top 5 cm soil layer (available soil P [ASP], nitrate [NO3-] and ammonium [NH4+]), snow depth, snow water equivalent (SWE), snowmelt runoff and snowmelt runoff water quality (total P [TP], total dissolved P [TDP], NO3-N and sediment) were studied using closed and open plots placed on each landform segment. The influence of source and transport factors was evaluated in relation to snowmelt runoff water quality. The ASP had a distribution pattern of backslope < shoulder < footslope in 2003 before manure application (bma) and shoulder = backslope = footslope in 2004. The NO3- distributed as shoulder = backslope = footslope in 2003 (bma) and shoulder < backslope < footslope in 2004. However, NH4+ had a stable distribution of shoulder = backslope < footslope in 2003 bma and in 2004. The pre-melt SWE increased in the down slope direction having the lowest in the shoulder and backslope and the highest in the footslope in 2005. The average daily snowmelt runoff from 1 m2 plots did not vary between the shoulder and the backslope. Infiltration was dominant in 2004 while runoff was dominant in 2005. Of the three landform segments, the shoulder was the greatest contributor of runoff to the depression. The backslope contributed the least. Hog manure injection did not seem to influence snowmelt runoff water quality. Most nutrients and sediments were from the land surface. Analysis revealed that fall soil nutrient concentrations were not a dominant factor controlling the nutrients in the snowmelt runoff in this site. However, snowmelt runoff volume controlled snowmelt runoff water quality. Snowmelt runoff water quality did not vary between the landform segments. However, as a result of the dominance of shoulder in this landscape, the total transport of nutrients and sediment was the highest from shoulder. Where landform characteristics are similar to the study watershed, it may be argued that all landform segments are CSA. Runoff volume is the most influential factor in determining the importance of CSA and controlling the snowmelt runoff water quality.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».