Joel Homan’s research attempts to estimate snowpack distribution patterns at a watershed scale in the Arctic. Hear more about his approach at this week’s seminar on Friday, October 26th.
Friday Seminar Series
- What: Improved Sampling Strategy for Arctic Snow Distribution
- Who: Joel Homan
- When: 3:30-4:30 p.m. Friday, Oct. 26
- Where: 531 Duckering
Watershed scale hydrologic models require good estimates of the spatially distributed snow water equivalent (SWE) at winter’s end. Snow on the ground in treeless Arctic environments is susceptible to significant wind redistribution, which results in very heterogeneous snowpacks, with greater quantities of snow collection in depressions, valley bottoms and leeward sides of ridges. In the Arctic, precipitation and snow gauges are very poor indicators of the actual spatial snowpack distribution, particularly at winter’s end when ablation occurs. Snow distribution patterns are similar from year to year because they are largely controlled by the interaction of topography, vegetation, and consistent weather patterns. From one year to the next, none of these controls radically change. Consequently, shallow and deep areas of snow tend to be spatially predetermined, resulting in depth (or SWE) differences that may vary as a whole, but not relative to each other, from year to year. This work attempts to identify snowpack distribution patterns at a watershed scale in the Arctic. Snow patterns are intended to be established by numerous field survey points from past end-of-winter field campaigns. All measured SWE values represent a certain percentage of a given watershed. Some may represent small-scale anomalies (local scale), while others might represent a large-scale area (regional scale). Since we are interested in identifying snowpack distribution patterns at a watershed scale, we aim to develop an improved point-source sampling strategy that only surveys regional representative areas. This will only be possible if the extreme high and low SWE measurements that represent local-scale snow conditions are removed in the sampled data set. The integration of these pattern identification methods will produce a hybrid approach to identifying snowpack distribution patterns. Improvement in our estimates of the snowpack distribution will aid in the forecasting of snowmelt runoff events, which are the most significant hydrologic event of the year for larger Arctic watersheds.