REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Abstract

The simple, empirical degree‐day approach for calculating snowmelt and runoff from mountain basins has been in use for more than 60 years. It is frequently suggested that the degree‐day method be replaced by the more physically‐based energy balance approach. The degree‐day approach, however, maintains its popularity, applicability, and effectiveness. It is shown that the degree‐day method is reliable for computing total snowmelt depths for periods of a week to the entire snowmelt season. It can also be used for daily snowmelt depths when utilized in connection with an adequate snowmelt runoff model for computing the basin runoff. The degree‐day ratio is shown to vary seasonally as opposed to being constant as is often assumed. Additionally, in order to evaluate the degree‐day ratio correctly, the changing snow cover extent in a basin during the snowmelt season must be taken into account. It is also possible to combine the degree‐day approach with a radiation component so that short time interval (<24 hours) computations of snowmelt depth can be made. When snowmelt input is transformed to basin output (runoff) by a snowmelt runoff model, there is little difference between the degree‐day approach and a radiation‐based approach. This is fortuitous because the physically‐based energy balance models will not soon displace the degree‐day methods because of their excessive data requirements.

Estimating degree-day factors from MODIS for snowmelt runoff modeling

“…The value of snow density can be affected by the duration of the snow cover. In high elevation subcatchments, temperatures tend to be lower which leads to more snowfall and more opportunity for compaction and settling which, in turn, tends to result in higher snow densities (Rango and Martinec, 1995). The spatial pattern of DDF S can be attributed to the interaction of climate and basin topography as well as vegetation: At higher elevations, soils tend to be thin and air temperatures tend to be low, which are unfavorable conditions for the growth of vegetation.…”

“…DDF S are expected to increase with increasing elevation and increasing snow density (Li and Williams, 2008). Forest regions often have lower values of DDF S than open regions (Rango and Martinec, 1995). The identification of DDF S has been an important yet complex issue for the application of the temperature-index model for snowmelt runoff modeling.…”

“…Daly et al (2000) merged interpolated point-measured SWE with snow covered area derived from satellite data to obtain spatial snow water equivalent and estimated spatially distributed DDF S by calibration to spatial snow water equivalent. Bormann et al (2013Bormann et al ( , 2014 coupled the method developed by Sturm et al (2010) to estimate snow density as the ratio between point-measured SWE and snow depth data with the empirical relationship between DDF S and snow density of Rango and Martinec (1995) to estimate daily variable DDF S . In these methods, detailed observations of snow water equivalent in the basin are needed.…”

Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations

“…The constant degree-day method, the simplest temperatureindex snowmelt method, is based on the assumpti on that melt rates depend solely on air temperature [53]. According to this formulation , the rate of snowmel t, at location x and at time t; M (x, t) (mm h À1 ) is given by:…”

Simulating the water budget of a Prairie Potholes complex from LiDAR and hydrological models in North Dakota, USA

“…Snowfall accumulates on the ground as snowpack in terms of snow water equivalent (SWE). Snowpack melting was modelled with the degree-day method as M t = 0.274T t , where M t is the snowmelt depth (m) on day t, T t is the mean daily temperature ( • C) and 0.274 is the approximate degree-day ratio (cm • C −1 d −1 ) (Rango and Martinec 1995). When T t < 0 • C, M t = 0 (i.e.…”