Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub-Alpine Forest

Snow interacts with its environment in many ways and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially in forested environments. To increase the understanding of the spatio-temporal variability of snow and to validate snow models, reliable observation data at similar spatial and temporal scales is needed. For these purposes, airborne LiDAR surveys or time series derived from ground-based snow sensors are commonly used. However, these are limited either to one point in space or in time. A new, extensive data set of daily snow variability in a sub-alpine forest in the Alptal valley, Switzerland is presented. The core data set consists of a dense sensor network, repeated high-resolution LiDAR data acquired using a fixed-wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, four distinct spatial clusters of similar snow depth dynamics are determined. By combining these spatial clusters with the observed snow depth time series, daily high-resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to our knowledge that provide spatio-temporally continuous snow depth and SWE based almost exclusively on field data. The presented workflow is transferrable to different regions, climates and scales. Moreover, the results allow future field campaigns to find representative sensor locations and target their LiDAR surveys to derive similar continuous products with less involved effort.

Automated summary

Snow variability in forested environments is challenging to model due to its interactions with the environment. To better understand and validate snow models, reliable observation data at similar scales is required. This study presents a new extensive dataset of daily snow variability in a sub-alpine forest, Switzerland, obtained through a dense sensor network, high-resolution LiDAR data, and manual measurements. Machine learning algorithms are used to identify four spatial clusters with similar snow depth dynamics. By combining these clusters with observed snow depth time series, daily high-resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to provide continuous spatio-temporal snow depth and SWE based on field data.