Testing a distributed snowpack simulation model against spatial observations

Trophic interactions and dynamic herbivore responses to snowpack

“…The predictor variables we examined were: snow water equivalent ("swe") from the Langur Yellowstone snow model [37], "winter day", a continuous variable starting with 1 on the first day of winter (here taken to be 15 November), and proceeding to 30 April, "wolf", or the number of wolves photographed at each site on each day, and "sex", 0 for mature males and 1 for females and immature males; mature males were identified in photographs by their antlers throughout early and mid-winter, and prominent pedicels after antler shedding in March. Daily wolf detections serve as a simple and transparent proxy for predation risk that, on the one hand, ignores habitat influences that can modify wolf-elk interactions [cf.…”

Assessment of optimum snowmelt model complexity, Cariboo Highlands, British Columbia, Canada.

“…In BC, particularly in the interior of the province, runoff is predominantly snowmelt-driven (Moore and Wondzell 2005;Eaton and Moore 2007); snowmelt contributes more than 50% of the annual river discharge in the interior of BC (Stewart et al 2004). The pattern of snow accumulation and melt in mountainous catchments is complex as it is modified by atmospheric, biologic, and topographic variations and their interactions (Male and Granger 1981;Olyphant 1986a;Pliiss and Ohmura 1997;Watson et al 2006;Ellis and Pomeroy 2007). For nival systems, the annual low streamflow typically occurs during winter as the precipitation is stored in solid form, although low streamflows can also occur in late summer or early fall during dry years (Moore and Wondzell 2005;Eaton and Moore 2007).…”

“…equations in Dingman 2002, for example), but a number of assumptions and approximations need to be made. Watson et al (2006) opted for a daily time step in their energy balance study due to the difficulty of estimating meteorological variables at sub-daily time scales. Walter et al (2005) modelled snowmelt with a physically based energy budget using no more data than required for simple temperature-index modelsdaily maximum and minimum air temperatures and precipitation.…”

“…However, this model still required the use of 30 parameters, 11 of which had to be calibrated for the site. Specific problems related to melt timing were noted under deep snowpack situations, which were hypothesized to be related to excessive accumulation of heat deficit (Watson et al 2006). However, the timing of snowpack ripening was correctly simulated, which may have been due to the balancing of errors in heat flux simulations to and from the snowpack (Watson et al 2006).…”

“…Specific problems related to melt timing were noted under deep snowpack situations, which were hypothesized to be related to excessive accumulation of heat deficit (Watson et al 2006). However, the timing of snowpack ripening was correctly simulated, which may have been due to the balancing of errors in heat flux simulations to and from the snowpack (Watson et al 2006). The additional data required by energy balance models combined with uncertainties in input data plus the need for calibration in some models can cause greater uncertainties compared to simpler temperature-index models (Franz et al 2008).…”