EVALUATION OF THE SWAT MODEL’S SNOWMELT HYDROLOGY IN A NORTHWESTERN MINNESOTA WATERSHED

Abstract

Snowmelt hydrology is a very important component for applying SWAT (Soil and Water Assessment Tool) inwatersheds where the stream flows in spring are predominantly generated from melting snow. However, there is a lack ofinformation about the performance of this component because most published studies were conducted in rainfall-runoffdominant watersheds. The objective of this study was to evaluate the performance of the SWAT models snowmelt hydrologyby simulating stream flows for the Wild Rice River watershed, located in northwestern Minnesota. Along with the threesnowmelt-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 translationcoefficients for normal and low flows, SCS curve number, threshold depth of water in the shallow aquifer required for returnflow to occur, groundwater revap coefficient, threshold depth of water in the shallow aquifer for revap or percolationto the deep aquifer to occur, and soil evaporation compensation factor) were adjusted using the PEST (ParameterESTimation) software. Subsequently, the PEST-determined values for these parameters were manually adjusted to furtherrefine 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 thatfor the study watershed, the SWAT model had a good performance on simulating the monthly, seasonal, and annual meandischarges and a satisfactory performance on predicting the daily discharges. When analyzed alone, the daily stream flowsin spring, which were predominantly generated from melting snow, could be predicted with an acceptable accuracy, and thecorresponding monthly and seasonal mean discharges could be simulated very well. Further, the model had an overall betterperformance for evaluation years with a larger snowpack than for those with a smaller snowpack, and tended to performrelatively better for one of the stations tested than for the other.