Modelling the hydrology of an agricultural watershed in Quebec using slurp
Bibliographic record
Abstract
Romero, D., Madramootoo, C.A. and Enright, P. 2002. Modelling the hydrology of an agricultural watershed in Quebec using SLURP. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 44:1.11-1.20. Five years (1994-1998) of climatic and hydrologic data recorded on the small Ruisseau Saint Esprit agricultural watershed, located 50 km north of Montreal, Quebec, served to calibrate and validate the SLURP hydrological model. A Geographical Information System was used to store, analyze, and export the watershed information to the model. SLURP was calibrated with data recorded from 1994 to 1996 using an automatic calibration technique. The Nash/Sutcliffe coefficient of performance obtained through the automatic calibration feature of SLURP was 0.522 for daily runoff. Model validation was carried out by comparing predicted and measured daily, monthly, seasonal, and annual runoff using data from 1997 and 1998. Model validation using daily data yielded Nash/Sutcliffe coefficients of 0.659 (acceptable) and 0.483 (low) for 1997 and 1998, respectively. Hydrologic outputs predicted by the model on an annual basis (evapotranspiration, snowmelt and runoff) were acceptable, but runoff was over-predicted. On a seasonal basis, the model predicted runoff well during the non-growing season, but poorly over the growing season. SLURP-predicted actual ET compared well with the ETcorn predicted by the Baier and Robertson model. Timing of peak snowmelt-runoff was in most cases simulated within one or two days of the observed peak runoff, but runoff from snowmelt was greatly underpredicted by SLURP. This may be in part related to the lack of on-watershed spatially distributed measures of snow pack depth and snow-water equivalence, but also may be related to the inflexibility of the model to parameter tweaking after the autocalibration. Overall, the present study suggests that, with additional data, SLURP could be used for long-term estimates of the hydrology of the Saint Esprit watershed. Cinq ans (1994-1998) de donnees climatiques et hydrologiques enregistrees sur le petit bassin versant du Ruisseau Saint Esprit, 50 km au nord de Montreal, Quebec, servirent a calibrer et valider le modele hydrologique SLURP. L'information decrivant le bassin versant fut stockee, analysee et exportee au modele en utilisant un SIG. Le modele fut calibre par une technique d'optimisation automatique, utilisant trois ans de donnees (1994-96). Le coefficient de performance Nash/Sutcliffe (R) d'apres calibration fut de 0.522. Le modele fut valide en comparant l'ecoulement observe et l'ecoulement simule pour les annees 1997 et 1998, sur une base annuelle, saisonniere, mensuelle et quotidienne. Pour 1997 et 1998, sur une base quotidienne, des coefficients (R) de 0.659 (acceptable) et de 0.483 (bas), respectivement, furent obtenus. Les variables hydrologiques etudiees furent: l'ET, la fonte des neiges et l'ecoulement. L'ET simule par SLURP et l' ETmais simule par le modele Baier et Robertson ne furent pas sensiblement differents. Pour l'ET et la fonte les predictions furent acceptables, mais l'ecoulement fut largement surestime. Sur une base saisonniere, le modele performa bien hors-saison, mais mal durant la saison de culture. La synchronisation des niveaux maximums de fonte des neiges et d'ecoulement furent simules, dans la plupart des cas a un ou deux jours pres de l'ecoulement maximal observe. Cependant l'ecoulement provenant de la fonte fut fortement sous-estime. Cette erreur pourrait etre reliee en partie au manque d'une distribution spatiale de donnees d'epaisseur du manteau nival et d'equivalence neige-eau sur le bassin versant. Cette erreur pourrait aussi etre reliee a l'inflexibilite du modele aux ajustements des parametres apres l'autocalibration. Nos resultats suggerent qu'avec des donnees additionnelles, SLURP pourrait servir a l’estimation a long terme de l'hydrologie du petit basin versant de Saint-Esprit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".