|dc.description.abstract||Snowmelt contribution to streamflow in snow-dominated watersheds has largely been limited to using the Center of Volume method, which looks at the day at which a certain amount of flow has passed, typically 20%, 50%, and 80%, referred to as tQ20, tQ50, and tQ80, respectively. We developed a new method to measure streamflow timing in the Southern Rocky Mountains of Colorado for 39 gauging stations from 1976 to 2015. We first manually extracted start and end days from the annual hydrograph of a small, medium, and large watershed to use as "truth." We then looked at the cumulative annual hydrograph and then found average spring and late fall baseflow. Using these average baseflows, we plotted the cumulative baseflow against the cumulative hydrograph and determined that the start and end of snowmelt contribution, tstart and tend, occurred when the cumulative hydrograph departed from the cumulative baseflow by a given baseflow factor. Using NSE and RMSE values, we determined that 10x and 17.5 baseflow were able to best represent the manually extracted values. NSE values ranged from 0.59 to 0.6 and 0.53 to 0.69 for tstart and tend, respectively; RMSE values ranged from 5.42 to 7.7 and 6.32 to 8.00, for tstart and tend, respectively. In comparison, NSE values ranged from -4.73 to -25.35 and -5.87 to -13.25 for tQ20 and tQ80, respectively; RMSE values ranged from 29.33 to 43.19 and 33.01 to 34.94 for tQ20 and tQ80, respectively. This new automated method was able to better predict values of start and end than what has been commonly used in the literature. We identified other variables related to snowmelt timing to streamflow, including the percent of flow and volume at the estimated tstart and tend, as well as the total duration of contribution. We used the correlation coefficient to help explain the variance in the observed trends of the different snowmelt timing variables, using different physiographic characteristics (mean slope, mean elevation, mean solar radiation, latitude, and longitude) as well as trends in winter precipitation and summer NDVI. Most of these trends were not statistically significant, but mean slope was best able to explain the variance in trends for tend, Q100, Qend, Qduration, %Qtend, and tQ80 (p < 0.05).