FACTORS INFLUENCING THE VARIABILITY OF MIDWINTER SNOW-DEPTH DECREASE IN THE NORTHERN GREAT PLAINS OF THE UNITED STATES

Abstract

Meltwater from snow provides an important source of fresh water for agriculture, domestic use, and hydroelectric power in many regions of the world. In the winter and early spring, however, rapid ablation of the snowpack can lead to environmental hazards such as flooding. Most snowmelt research has relied on measured data, which have limited the spatial and temporal resolution of the studies. Generally, these studies have been conducted at single locations over periods of a few days to a few weeks. This project investigates snowdepth changes on a more expansive scale by using a one-dimensional mass- and energy-balance model (SNTHERM) to compute the convective and radiative fluxes associated with large snow-depth change events. Midwinter snow-depth changes and the variability in snow-depth changes are evaluated for a 25-year time period over the northern Great Plains region of the United States during February. Statistical analyses indicate that variations in sensible heat flux have the greatest influence on midwinter variability of snow-depth changes. This is shown by the consistently strong relationship between modeled sensible heat-flux values and changes in snow depth over the entire study period and at nearly every station. Mean daily temperature followed by mean daily dew-point temperature show the next strongest statistical relationships with snow-cover ablation. Radiative fluxes and net solar radiation in particular, have low correlations with changes in snow depth and are therefore found to have little impact on snow-depth change variability during the month of February. Although net solar radiation is not highly correlated with the variability of decreases in depth, its energy input is nevertheless an important source of energy for ablation processes. [Key words: snow melt, modeling, Great Plains.]