Snow plays a crucial role in regulating water availability in eco-hydrological systems. Its spatial distribution is key for understanding melting dynamics, particularly in forests where snow amounts vary on small spatial scales (<3 m) compared to open areas. Uncrewed Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) measurements can quantify the snow distribution in forests at sufficiently high spatial resolution. Such datasets showed that snow distribution can be aggregated into spatial patterns featuring similar snow dynamics. However, there is no suitable dataset available to investigate whether these patterns persist throughout different seasons and how they differ between sites. This study introduces a new dataset comprising UAV-based LiDAR surveys, a dense automatic snow depth sensor network, and additional ground measurements, covering three seasons at two forested sites. The identification of snow distribution patterns from LiDAR data in one season is achieved using a clustering workflow, first presented by Geissler et al. (2023a). Identified patterns are subsequently used for spatially extrapolating observed time series of snow depth and snow water equivalent from a few locations. The results show that snow patterns are influenced by site-specific factors such as wind or radiation but are persistent over time. A comparison with physics-based snow model simulations underlines the added value of the presented observation-based snow products allowing a spatiotemporally continuous analysis of discrepancies. This study therefore advances our understanding of using forest snow patterns to create high-resolution, spatiotemporally continuous snow products with reduced measurement or computational effort.

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.