Evaluating Observational Methods to Quantify Snow Duration under Diverse Forest Canopies

Abstract:

Forests cover over 40% of the seasonally snow-covered regions in North America. However, operational snow networks are located primarily in forest clearings, and optical remote sensing cannot see through tree canopies to detect forest snowpack. Due to the complex influence of the forest on snowpack duration, ground observations in forests are essential. We therefore consider the effectiveness of different strategies to observe snow covered area under forests. At our study location in the Pacific Northwest, we simultaneously deployed fiber-optic cable, stand-alone ground temperature sensors, and time-lapse digital cameras in three different forest treatments: control second-growth forest, thinned forest, and forest gaps (one tree height in diameter). We derived fractional snow covered area and snow duration metrics from the co-located instruments to assess optimal spatial resolution and sampling configuration. The fiber-optic cable and the camera detected a significant difference of 8 days in mean snow duration between the gap and control plots. Monte Carlo experiments based on our results suggest that 10 m spacing of self-recording ground temperature sensors across a 40 m forest plot will capture mean snow duration to ± 5 days whereas 6 m spacing reduces the 95% confidence interval to ± 3 days. We further tested the representativeness of sampling one plot per treatment group by observing snow duration across replicated forest plots at the same elevation, and at a set of forest plots 250 m higher. Relative relationships between snow duration in the forest treatments are consistent between replicated plots, elevation, and two winters of data.