Evaluation of Filtering Methods for Hydrograph Separation in Small Agricultural Watersheds in Québec, Canada
Notice bibliographique
Résumé
Highlights Agricultural hydrology is complex due to the management of surface and subsurface flow to increase productivity. This study provides an interpretation of hydrological functioning, using a geochemical tracer (electrical conductivity) as a reference method, for hydrograph separation and evaluation of filtering methods. Filtering method efficiency must be interpreted according to season, year, watershed relief, and management practices. Routine application of basic filtering concepts is not sufficient to address the heterogeneity of hydrological processes in agricultural watersheds. Abstract. Streamflow hydrographs summarize the behavior of watersheds. Their separation into quick and slow components requires hydrological knowledge of the specific drainage area. To better understand the hydrological response of 14 small agricultural watersheds in Québec, Canada, covering different physiographic attributes ranging from lowlands to hilly and steep landscapes, streamflow electrical conductivity was used as a geochemical tracer. These agricultural watersheds have undergone significant management practices, including artificial drainage. The objective of this research was to evaluate the performance of existing automated filter methods for hydrograph separation (BFLOW, UKIH, PART, FIXED, SLIDE, LOCMIN, and Eckhardt). The geochemical method was used as a reference for comparison with the filter methods. Comparison of the slow flow estimates from non-calibrated filters, using a MANOVA model, showed that the filter performance increased under conditions with high contributions of quick runoff to the stream, such as during snowmelt (spring season), during heavy precipitation, and in subwatersheds with landscape conditions more prone to quick runoff. However, filter performance decreased as hydrological processes predisposed more flow to slower pathways, typically in summer and fall, as well as in lowland landscapes generally associated with high rates of tile drainage rather than in hilly and steep relief. Underlying the filter assumptions is the classic concept of a rainfall event with quick runoff as the main source of the drainage area response. Thus, slow flow is associated with a low threshold response. Eckhardt filter simulations were in good agreement with the geochemical method after calibration, based on model statistical measures (R, NSE, and PBIAS). However, larger errors were associated with higher flow values. The slow flow overestimations were more pronounced during periods of extreme events, i.e., spring runoff and heavy precipitation. The linear concept of the Eckhardt filter yields no information on slow flow response behavior that could be useful in capturing its temporal variability. Because the routing of water has been managed to improve agricultural productivity, these hydrological modifications resulted in a more complex slow flow response. The performance of filtering methods is thus affected. Therefore, simplifications of filter assumptions are less likely to provide more effective estimates of slow flow. Furthermore, given the heterogeneity of hydrological processes due to seasonal climatic characteristics, the routine application of basic filter concepts is not sufficient to address the variable nature of the hydrological response. The variability scale of geochemical separation, from regional (agro-climatic) to local (adjacent watersheds), proved that it is always relevant to have adequate separation. However, the validation of filters without a tracer is limited and almost unsuitable for these agricultural watersheds. Keywords: Agricultural watershed, Artificial drainage, Electrical conductivity, Filtering method, Geochemical method, Hydrograph separation, MANOVA, Quick flow, Slow flow, Tile drainage.
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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) | 0,000 | 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 ».