Geostatistical methods for estimating snowmelt contribution to the seasonal water balance in an alpine watershed

2006 Fall.Includes bibliographical references.The performance of nine spatial interpolation models was evaluated to estimate snowmelt contributions to streamflow in the West Glacier Lake watershed (0.61 km2), in the Snowy Range Mountains of Wyoming. Streamflow from the West Glacier Lake watershed has been previously estimated at 40% to 130% greater than measured precipitation inputs. Additional input into the watershed had been attributed to a permanent snowfield in the upper portion of the watershed covering approximately 2.4% of the watershed area. However, the excess output may be a result of inaccurate estimation of water quantities using current precipitation and stream gauging methods. In April 2005, near peak accumulation snow depth measurements and snow density measurements were collected within West Glacier Lake watershed. The distribution of snow water equivalent (SWE) was calculated as the product of snow depth, snow density, and snow-covered-area (SCA). Snow depths were spatially distributed throughout the watershed through nine spatial interpolation models. Snow densities were spatially distributed through a multiple linear regression. The nine spatial snow depth models explained 18% to 94% of the observed variance in the measured snow depths. Co-kriging with solar radiation produced the best results explaining 94% of the observed variance in snow depth measurements. The annual water balance, expressed as equivalent water depths for water year 2005, was total precipitation (1,481 mm), snowpack sublimation (251 mm), and streamflow (1,000 mm), resulting in an evapotranspiration estimate of 230 mm. Estimated SWE from the field survey data was 67% greater than precipitation gauge estimates and accounted for 85% of the annual streamflow. Summer precipitation was not a significant contributor to the annual hydrograph and was also less than snowpack sublimation. Precipitation gauge values were unrepresentative of actual precipitation depths, and several spatially distributed snow depth models provided better estimates of precipitation inputs

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