Journal
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
Volume 53, Issue 5, Pages 961-976Publisher
IAHS PRESS, INST HYDROLOGY
DOI: 10.1623/hysj.53.5.961
Keywords
calibration; seasonal effects; performance; snowmelt modelling; southeastern Canada; SWAT; water quality
Categories
Funding
- Le Fonds Quebecois de la Recherche sur la Nature et les Technologies (FQRNT)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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The degradation of the river water quality in Canadian rural catchments is of concerti. In these catchments, the Soil Water Assessment Tool (SWAT) model can help better understand the problems related to diffuse pollution. The numerous documented applications of SWAT have been dominated by areas uniquely driven by rainfall. Given that Canadian hydroclimatic conditions differ due to the presence of a seasonal snowpack of long duration, evaluation of the hydrological performance needs to be performed prior to attempting any water quality simulations. The objective of the present work is to evaluate the hydrological behaviour of the SWAT model under snowmelt and rainfall for two small watersheds located in southeastern Canada. Different calibration schemes are evaluated including seasonal effects. One-year calibration gave satisfactory daily performances measured with Nash-Sutcliffe efficiency (NS) ranging between 61 and 83% and deviations of volume (D-nu) between -10 and 1%, while in validation, NS was 40-73% and D, between -20 and -3%. The SWAT model has difficulties in reconciling both seasons. When winter and summer data are used separately to calibrate the model, the model performance is still much better for the winter season than for the summer one. However, the latter is considerably improved when only summer observations are provided for calibration. Conversely, calibration based strictly on the winter observations provides no real advantage over that based oil all available data. A two-step composite calibration, which optimizes the SWAT snow accumulation and melt-related parameters oil the winter data, after all other model parameters have been optimized oil the summer data, provides a compromise.
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