Abstract Machine learning (ML) has emerged as an effective tool for estimating snow depth and snow water equivalent at unsampled times and locations. Airborne lidar surveys are particularly useful for ML applications: the high‐resolution, high‐precision snow depth data allow for algorithm training and testing to an extent and spatial resolution not previously possible. Here, we train a random forest model to estimate snow depth relative to a nearby Snotel site using static physiographic data and dynamic (i.e., time‐dependent) snowpack data as predictor variables and lidar for the target variable. The model output is daily, 50 m resolution snow depth for basins that have both lidar and Snotel data in Colorado. We evaluated multiple approaches for random forest training: using historic lidar data in a basin (temporal transfer), using lidar data from other basins in a region (spatial transfer), and both together. The three approaches yield RMSE values ranging from 0.37 to 0.44 m at 50 m resolution, achieving lower errors compared to ML studies at higher resolutions. Model error decreases when outputs are upscaled, with RMSE values of 0.17 and 0.10 m for the 4 km and basin scales, respectively. The model scenario which includes both temporally and spatially transferred lidar data is the most robust to the number and timing of lidar surveys used in model training. This framework extends the spatial footprint of Snotel and the temporal coverage of lidar by leveraging the strengths of the two data sets, with applications for water resource management and validation of gridded snow products.
Herbert et al. (Fri,) studied this question.