Snowpack dynamics play a key role in controlling hydrological and ecological processes at various scales, but snow monitoring remains problematic. 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. Thus, 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) with 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 in average) and spatial patterns. Both data assimilation configurations, using geographical distances or a space of topographical dimensions, managed to improve 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.

Juan Ignacio López

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Marginal snowpacks are a critical part of the hydrology and ecology of temperate mountain regions. However, their sensitivity to temperature increase is uncertain due to their phenology which sits between seasonal and ephemeral snowpacks. In this paper, we investigate the sensitivity and the potential response of marginal snowpacks to a warmer climate by leveraging simulated snow series under various combinations of mean winter temperature (T) and precipitation (P), and varied meteorological conditions over 21 years, at 24 mountain sites globally. Marginal snowpacks are found to be more sensitive to temperature increases compared to both seasonal, and ephemeral snowpacks. According to all considered T and P combinations, 64% of marginal snowpacks would transition to ephemeral or disappear, compared to only 21% and 24% for seasonal and ephemeral snowpacks, respectively. The study reveals new insight on the impacts of a warming climate on marginal snowpacks, including: the advance of melt-out dates (-21 days) and a reduction in snowmelt contribution to runoff (-11%). Further, we estimate the global distribution of marginal snowpack based on T and P from ERA5-Land reanalysis. The total area covered by marginal snowpack would decrease by 2.7% under +1º C, with a more significant decreases under +2 ºC (5.7%) and +3 ºC (10.5%) warming. This study places marginal snowpacks at the forefront of where climate change will pose the greatest threats to mountain and montane water resources and environmental systems globally, with profound shifts even under optimistic future climate scenarios (+1ºC).