Abstract

The presence of dust on the snowpack accelerates snowmelt. This has been observed through snowpack and hydrometeorological measurements at a small study watershed in southwestern Colorado. For a 13-year period, we quantified the annual dust-enhanced energy absorption (DEAE) and used this information to model the snowpack melt-out under observed (with dust present) and clean conditions (no dust). We determine the difference in snow cover duration between actual (dust present) and simulated ideal (clean) snowpack (ΔSAG) to characterize the shifts in melt timing for each year. We compute the center of mass of runoff (tQ50) as a characteristic of snowmelt. DEAE, ΔSAG and tQ50 vary from year to year, and are dictated by the quantity of snow accumulation, and to a lesser extent the number of dust events, the annual dust loading, and springtime snowfall.

Highlights

  • Shortwave radiation provides most of the energy for melt in continental mountain snowpacks [1,2]

  • While WY2015 had less than the 13-year average peak SWE, peak SWE occurred the latest due to snow accumulation in May (Figures 3a and 4b)

  • At a site in southern Colorado, the nature of the snowpack varies due to inter-annual fluctuations, and the snowmelt characteristics are a function of the amount and timing of dust events together with spring precipitation patterns

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Summary

Introduction

Shortwave radiation provides most of the energy for melt in continental mountain snowpacks [1,2]. Deposition of light absorbing particulates (LAPs), such as dust, ash, black carbon, needles and tree litter, onto the snowpack reduces its albedo (reflectance as measured by the ratio of incoming vs reflected shortwave radiation) and alters the snowpack energy balance [2–12]. Airborne dust from deserts deposited on a Colorado snowpack [13] have been shown to reduce the surface albedo [14], especially in the visible portion of the shortwave radiation spectrum (Figure 1). This reduction in albedo accelerated the timing of snowmelt by 18 to 51 days [2,15]. Streamflow forecasting in the UCRB benefits from incorporating albedo reduction resulting from aeolian dust deposition

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