Evaluation of the SWAT model's snowmelt hydrology in a northwestern Minnesota watershed (original) (raw)

2005, Transactions of the Asabe

Snowmelt hydrology is a very important component for applying SWAT (Soil and Water Assessment Tool) in watersheds where the stream flows in spring are predominantly generated from melting snow. However, there is a lack of information about the performance of this component because most published studies were conducted in rainfall-runoff dominant watersheds. The objective of this study was to evaluate the performance of the SWAT model's snowmelt hydrology by simulating stream flows for the Wild Rice River watershed, located in northwestern Minnesota. Along with the three snowmelt-related parameters determined to be sensitive for the simulation (snowmelt temperature, maximum snowmelt factor, and snowpack temperature lag factor), eight additional parameters (surface runoff lag coefficient, Muskingum translation coefficients for normal and low flows, SCS curve number, threshold depth of water in the shallow aquifer required for return flow to occur, groundwater "revap" coefficient, threshold depth of water in the shallow aquifer for "revap" or percolation to the deep aquifer to occur, and soil evaporation compensation factor) were adjusted using the PEST (Parameter ESTimation) software. Subsequently, the PEST-determined values for these parameters were manually adjusted to further refine the model. In addition to two commonly used statistics (Nash-Sutcliffe coefficient, and coefficient of determination), a measure designated "performance virtue" was developed and used to evaluate the model. This evaluation indicated that for the study watershed, the SWAT model had a good performance on simulating the monthly, seasonal, and annual mean discharges and a satisfactory performance on predicting the daily discharges. When analyzed alone, the daily stream flows in spring, which were predominantly generated from melting snow, could be predicted with an acceptable accuracy, and the corresponding monthly and seasonal mean discharges could be simulated very well. Further, the model had an overall better performance for evaluation years with a larger snowpack than for those with a smaller snowpack, and tended to perform relatively better for one of the stations tested than for the other.