|Rango, Albert - Al|
Submitted to: Western Snow Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/20/2002
Publication Date: 5/20/2002
Citation: DEWALLE, D.R., HENDERSON, Z., RANGO, A. SPATIAL AND TEMPORAL VARIATIONS IN SNOWMELT DEGREE-DAY FACTORS COMPUTED FROM SNOTEL DATA IN THE UPPER RIO GRANDE BASIN. PROCEEDINGS OF THE WESTERN SNOW CONFERENCE. 2002. P. 73-81. Interpretive Summary: The Degree-Day Approach to estimate snowmelt in mountain basins is widely used because it employs the daily average temperature which is usually available in these types of basins. To use this approach, however, the degree-day factor or coefficient must be calculated or assumed. Remotely telemetered SNOTEL data include the necessary information to calculate the degree-day coefficient. It was found that SNOTEL data can help improve runoff forecasting from mountain basins and can show that degree-day coefficients increase throughout the snowmelt season. Corrections need to be applied to the degree-day coefficient determination for varying forest cover between sites and for cloudy weather and days with precipitation. Because the Degree-Day Approach for estimating snowmelt is widely used, the SNOTEL data should be especially useful to both Natural Resources Conservation Service scientists and scientists of other federal and state agencies charged with snowmelt runoff forecasting.
Technical Abstract: The spatial and temporal variation of degree-day melt factors (DDF¿s) computed from SNOTEL data were evaluated for the Upper Rio Grande Basin to improve modeling and forecasting of snowmelt runoff. Data from seven SNOTEL sites in the Upper Rio Grande Basin were analyzed for the 1996-2000 melt seasons. Average degree-day factors varied among sites with varying density of surrounding forest cover. DDF¿s also varied among years due to incidence of cloudy weather and timing of melt during the season. Degree-day factors for each year at each site generally increased linearly with Julian date during the melt season, but the rate of change increased with site exposure. Best predictions of daily melt using SNOTEL data were obtained using daily degree days, Julian date, and an indicator variable for presence/absence of precipitation. Overall, SNOTEL data produced estimates of daily DDF¿s that were in good agreement with data from previous studies.