Data_Sheet_1_Pursuit of Optimal Design for Winter-Balance Surveys of Valley-Glacier Ablation Areas.PDF

• Main Authors: Alexandra Pulwicki, Gwenn E. Flowers, Derek Bingham
• Format: Dataset
• Language: unknown
• Published: 2019
• Subjects: Solid Earth Sciences; Climate Science; Atmospheric Sciences not elsewhere classified; Exploration Geochemistry; Inorganic Geochemistry; Isotope Geochemistry; Organic Geochemistry; Geochemistry not elsewhere classified; Igneous and Metamorphic Petrology; Ore Deposit Petrology; Palaeontology (incl. Palynology); Structural Geology; Tectonics; Volcanology; Geology not elsewhere classified; Seismology and Seismic Exploration; Glaciology; Hydrogeology; Natural Hazards; Quaternary Environments; Earth Sciences not elsewhere classified; Evolutionary Impacts of Climate Change; glacier mass balance; winter balance; experimental design; snow survey; mountain glaciers; St. Elias Mountains; Yukon (Canada); Canada; Yukon; glacier*
• Online Access: https://doi.org/10.3389/feart.2019.00199.s001; https://figshare.com/articles/Data_Sheet_1_Pursuit_of_Optimal_Design_for_Winter-Balance_Surveys_of_Valley-Glacier_Ablation_Areas_PDF/9254189

Efficient collection of snow depth and density data is important in field surveys used to estimate the winter surface mass balance of glaciers. Simultaneously extensive, high resolution, and accurate snow-depth measurements can be difficult to obtain, so optimisation of measurement configuration and spacing is valuable in any survey design. Using in-situ data from the ablation areas of three glaciers in the St. Elias Mountains of Yukon, Canada, we consider six possible survey designs for snow-depth sampling and N = 6–200+ sampling locations per glacier. For each design and number of sampling locations, we use a linear regression on topographic parameters to estimate winter balance at unsampled locations and compare these estimates with known values. Average errors decrease sharply with increasing sample size up to N ≈ 10–15, but reliable error reduction for any given sampling scheme requires significantly higher N. Lower errors are often, but not always, associated with sampling schemes that employ quasi-regular spacing. With both real- and synthetic data, the common centreline survey produces the poorest results overall. The optimal design often requires sampling near the glacier margin, even at low N. The unconventional “hourglass” design performed best of all designs tested when evaluated against known values of winter balance.