The Utility of Infrequent Snow Depth Images for Deriving Continuous Space-Time Estimates of Seasonal Snow Water Equivalent

Snow water equivalent (SWE), particularly in mountains regions, has been an elusive hydrologic measurement. We examine the utility of a data assimilation approach to generate space-time continuous estimates of SWE from more readily available snow depth (SD) measurements. A multitemporal lidar data set provides a unique opportunity to assimilate single SD images and verify posterior estimates against SD images at nonassimilation times. Application over three water years shows significant improvement in the posterior estimates with an average correlation between estimated and measured SD fields of 0.88 compared to 0.52 for prior estimates that do not benefit from the assimilated SD data. We also show that posterior estimates are consistent with independent in situ SWE and streamflow measurements. This work demonstrates that using high-resolution/high-accuracy, but infrequent, SD measurements combined with a data assimilation framework could make significant inroads toward the goal of spatially distributed SWE and snowmelt estimates at the global scale. Plain Language Summery The mass of water stored in seasonal snowpacks (snow water equivalent) is a key parameter for water supply management. Yet, despite its importance and decades of effort, snow water equivalent has been an elusive variable to characterize over the mountainous areas of the globe, with no existing satellite capable of providing routine estimates of these important water stores. We propose a new approach using so-called data assimilation techniques to transform infrequently available measurements of snow depth to continuous estimates of snow water equivalent and snowmelt rates. We show that even a single snow depth measurement near the time of peak snow accumulation can result in accurate estimation of the evolution of snow water equivalent and snowmelt rates across the water year. Our results suggest a potential new pathway for the hydrologic community in its long-pursued goal of global-scale estimates (from satellite remote sensing) of snow water equivalent and its space-time variability. If applied at large scales with satellite-derived snow depth measurements, such methods could have significant implications for improved water resource and hazard management.