Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Abstract

Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially when additional components such as vegetation add complexity. To increase our understanding of the spatio-temporal variability of snow and to validate snow models, we need reliable observation data at similar spatial and temporal scales. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are limited either to one point in space or in time. We present a new, extensive dataset of snow variability in a sub-alpine forest in the Alptal, Switzerland. The core dataset 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, we determine four distinct spatial clusters of similar snow depth dynamics. These clusters are characterized and further used to derive daily snow depth and snow water equivalent (SWE) maps. The results underline the complex relation of topography and canopy cover towards snow accumulation and ablation. The derived products are the first to our knowledge that provide daily, high-resolution snow depth and SWE based almost exclusively on field data. They are therefore ideally suited for the validation of distributed snow models. Our approach can be applied to other project areas and improve our understanding of the spatio-temporal variability of snow in forested environments.

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

“…The raster data (LiDAR‐derived HS maps, CHM, Digital Terrain Model, Clusters, stacks of daily HS and SWE) used within this study are available at the FreiDok repository from https://doi.org/10.6094/UNIFR/232647 with Creative Commons CC BY‐NC‐SA license (Geissler et al., 2023). In the same repository, SnoMoS HS time series as well as the snow survey data is made available.…”

Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter

“…In the last decades, unpiloted aerial vehicles (UAV) and airplanes have been widely used to map the spatial distributions of snow depth on a catchment to smaller scales. Photogrammetry (Eberhard et al, 2021), and light detection and ranging (lidar; Geissler et al, 2023;Harder et al, 2019) have been extensively used for this purpose. The most notable effort is the airborne snow observatory (ASO;Painter et al, 2016)), where snow depth has been extensively mapped over a large number of basins over the western part of the North American continent.…”