Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests

Abstract. Snowpack dynamics play a key role in controlling hydrological and ecological processes at various scales, but snow monitoring remains challenging. Data assimilation techniques are emerging as promising tools to improve uncertain snowpack simulations by fusing state-of-the-art numerical models with information rich, but noisy observations. However, the occlusion of the ground below the forest canopy limits the retrieval of snowpack information from remote sensing tools. Remote sensing observations in these environments are spatially incomplete, impeding the implementation of fully distributed data assimilation techniques. Here we propose different experiments to propagate the information obtained in forest clearings, where it is possible to retrieve observations, towards the sub-canopy, where the point of view of remote sensors is occluded. The experiments were conducted in forests within Sagehen Creek watershed (California, USA), by updating simulations conducted with the Flexible Snow Model (FSM2) using airborne lidar snow data using the Multiple Snow data Assimilation system (MuSA). The successful experiments improved the reference simulations significantly both in terms of validation metrics (correlation coefficient from R=0.1 to R ~0.8 on average) and spatial patterns. Data assimilation configurations using geographical distances and space of topographical dimensions, improved the reference run. However, those creating a space of synthetic coordinates by combining the spatiotemporal data assimilation with a principal components analysis did not show any improvement, even degrading some validation metrics. Future data assimilation initiatives would benefit from building specific localization functions that are able to model the spatial snowpack relationships at different resolutions.