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Snow sublimation and seasonal snowpack variability, 2016

Sexstone, Graham A.

Colorado State University, Fort Collins, CO
NWT Accession Number: NWT1893


In the western United States, seasonal melt from snow in mountainous regions serves as an essential water resource for ecological and anthropological needs, and improving our abilities to quantify the amount of water stored in the seasonal snowpack and provide short-term forecasts of snowmelt inputs into river systems is a critical science endeavor. Two important uncertainties in characterizing the seasonal evolution of snow in mountainous environments are related to the inherent spatial variability of snow in complex terrain and the magnitude and variability of snow sublimation fluxes between snow and the atmosphere; these uncertainties motivate this collection of research which includes three studies conducted in the north-central Colorado Rocky Mountains. The first study uses fine resolution airborne lidar snow depth datasets to evaluate the spatial variability of snow within areas comparable to coarse scale model grids (i.e. subgrid variability at 500 m resolution). Snow depth coefficient of variation, which was used as a metric for evaluating subgrid snow variability, exhibited substantial variability in mountainous terrain and was well correlated with mean snow depth, land cover type, as well as canopy and topography characteristics. Results highlight that simple statistical models for predicting subgrid snow depth coefficient of variation in alpine and subalpine areas can provide useful parameterizations of subgrid snow distributions. Given that snow sublimation fluxes are expected to exert important influences on snow distributions, the second and third studies focus on measuring and modeling the variability and importance of snow sublimation. To evaluate the relative merits and measurement uncertainty of methods for quantifying snow sublimation in mountainous environments, a comparison was made between the eddy covariance, Bowen ratio- energy balance, bulk aerodynamic flux, and aerodynamic profile methods within two forested openings. Biases between methods are evaluated over a range of environmental conditions, which highlight limitations and uncertainties of each method as well as the challenges related to measuring surface sublimation in snow-covered regions. Results provide guidance for future investigations seeking to quantify snow sublimation through station measurements and suggest that the eddy covariance and/or bulk aerodynamic flux methods are superior for estimating surface sublimation in snow-covered forested openings. To evaluate the spatial variability and importance of snow sublimation, a process-based snow model is applied across a 3600 km domain over five water years. In-situ eddy covariance observations of snow sublimation compare well with modeled snow sublimation at sites dominated by surface and canopy sublimation, but highlight challenges with model evaluation at sites where blowing sublimation is prominent. Modeled snow sublimation shows considerable spatial variability at the hillslope scale that is evident across elevation gradients and between land cover types. Snow sublimation from forested areas (canopy plus surface sublimation) accounted for the majority of modeled sublimation losses across the study domain and highlights the importance of sublimation from snow stored in the forest canopy in this region. Model simulations suggest that snow sublimation is a significant component of the winter water balance, accounting for losses equivalent to 43 percent of total snowfall, and strongly influences snow distributions in this region. Results from this study have important implications for future water management and decision making.


Niwot Ridge LTER, NWT, snow sublimation, snowpack variability, lidar snow depth, eddy covariance, bulk aerodynamic flux

Related Field Location(s):

Outside study areaNiwot Ridge


Sexstone, Graham A., (2016) Snow sublimation and seasonal snowpack variability. Colorado State University, Fort Collins, CO

CU Boulder

This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-1637686. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necesarily reflect the views of the National Science Foundation.

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