Inconsistencias temporales en los patrones espaciales del equivalente de agua en nieve: regresiones entre telemetría de nieve y topografía en la cuenca del río Colorado

The relation between snow water equivalent (SWE) and 28 variables (27 topographically-based topographic variables and canopy density) for the Colorado River Basin, USA was explored through a multi-variate regression. These variables include location, slope and aspect at different scales, derived variables to indicate the distance to sources of moisture and proximity to and characteristics of obstacles between these moisture sources and areas of snow accumulation, and canopy density. A weekly time step of snow telemetry (SNOTEL) SWE data from 1990 through 1999 was used. The most important variables were elevation and regional scale (81 km²) slope. Since the seasonal and inter-annual variability is high, a regression relationship should be formulated for each time step. The inter-annual variation in the relation between SWE and topographic variables partially corresponded with the amount of snow accumulated over the season and the El Niño Southern Oscillation cycle.Se analiza la relación entre el equivalente de agua en la nieve (SWE) y 28 variables (27 variables topográficas y otra basada en la densidad del dosel) para la Cuenca del Río Colorado, EE.UU. mediante regresión multivariante. Estas variables incluyen la localización, pendiente y orientación a diferentes escalas, además de variables derivadas para indicar la distancia a las fuentes de humedad y la proximidad a las barreras topográficas, además de las características de las barreras topográficas entre las fuentes de humedad, las áreas de acumulación de nieve y la densidad del dosel. Se utilizaron telemetrías semanales de nieve (SNOTEL) desde 1990 hasta 1999. Las variables más importantes fueron la elevación y la pendiente a escala regional (81 km²). Dada la alta variabilidad estacional e interanual, fue necesario establecer regresiones específicas para cada intervalo disponible de datos. La variación interanual en la relación entre variables topográficas y el SWE se corresponde con la cantidad de nieve acumulada a lo largo de la temporada y el ciclo de El Niño-Oscilación del Pacífico Sur

Mapping snow cover and snow depth across the Lake Limnopolar watershed on Byers Peninsula, Livingston Island, Maritime Antarctica

Few parts of Antarctica are not permanently covered in ice. The retreat of the ice sheet from Byers Peninsula on western Livingston Island, Maritime Antarctica, has provided a new area of seasonal snow cover. Snow surveys were conducted in late November 2008 at the time of peak accumulation across the 1 km2 Lake Limnopolar watershed. Topographic variables were derived from a digital elevation model to determine the variables controlling the presence or absence of snow and the distribution of snow depth. Classification with binary regression trees showed that wind related variables dominated the presence and depth of snow. The product of the sine of aspect and the sine of slope was the first variable in both regression trees. Density profiles were also measured and illustrated a relatively homogeneous snowpack over space at peak snow accumulation. Copyright © Antarctic Science Ltd 2013.Travel to and accommodation in South America was provided by the International Programs at Colorado State University. Travel to Antarctica and logistical support for fieldwork was provided by the Spanish Polar Programme, grant POL2006-06635/CGL from the Education and Culture Ministry (Spain), and from the Las Palmas crew (Spanish Navy). Carlos Calvo of the UTM (Maritime Technology Unit, CSIC) and Brendan Keely of the University of York assisted with data collection. Adam Winstral of the Agricultural Research Service in Boise ID provided the code to compute the maximum upwind slope. We would also like to thank two anonymous reviewers for their important insight and their assistance in improving this paper. Thanks are due to Javier Zabalza of the Instituto Pirenaico de Ecologı´a for his assistance with the GIS work.Peer Reviewe

GLEES (Glacier Lakes Ecosystem Experiments Site) Snow Depth Data Measured Annually at Peak Accumulation from 2005 to 2014

Point measurements of snow depth data were collected at approximately peak snow accumulation each winter for a 10-year period from 2005 to 2014 (2005-04-20; 2006-05-02; 2007-04-17; 2008-04-24; 2009-04-30; 2010-05-06; 2011-04-28; 2012-04-10; 2013-05-02; 2014-05-01) around the West Glacier Lake Watershed at GLEES (Glacier Lakes Ecosystem Experiments Site) (41.37255627, -106.2676067; 41.38350614, -106.2505978). Data were collected as part of the research by Dr. Douglas M. Hultstrand (https://mountainscholar.org/handle/10217/233658; https://mountainscholar.org/handle/10217/232572) and others. Snow depth was measured with an anodized aluminum depth probe and the location was measured with a hand-held Garmin Global Positioning System (GPS) unit. The data were collected by the Colorado State University (CSU) in conjunction with the United States Department of Agriculture U.S. Forest Service Rocky Mountain Research Station

Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, USA, to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data

Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, USA, to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data