Journal Article

https://doi.org/10.1029/2021WR030271

Deschamps-Berger, C., Cluzet, B., Dumont, M., Lafaysse, M., Berthier, E., Fanise, P., & Gascoin, S. (2022). Improving the spatial distribution of snow cover simulations by assimilation of satellite stereoscopic imagery. Water Resources Research, e2021WR030271, https://doi.org/10.1029/2021WR030271

Moutain snow cover is highly variable both spatially and temporally and has a tremendous impact on ecosystems and human activities. Numerical models provide continuous estimates of the variability of snow cover properties in time and space. However, they suffer from large uncertainties, for instance originating from errors in the meteorological inputs. Here, we show that the snow depth variability at 250 m spatial resolution can be well simulated by assimilating snow depth maps from satellite photogrammetry in a detailed snowpack model. The assimilation of a single snow depth map per snow season using a particle filter is sufficient to improve the simulated snow depth and its spatial variability, originally poorly represented due to missing physical processes and errors in the precipitation inputs. Assimilation of snow depth only is nevertheless not sufficient for both compensating for strong bias in precipitation and for selecting the most appropriate representation of the physical processes in the snow model. Regarding this limitation, combined assimilation of snow depths maps and other snow observations, such as snow cover area, surface temperature or reflectance, is a promising avenue for accurate simulations of mountain snow cover.

Key Points
-Assimilation of one satellite snow depth map corrects bias in precipitation and improves the modeled spatial variability of the snow depth
-In case of a large bias in precipitation, assimilation of a snow depth map can modify incidentally how the snowpack bulk density is modeled
-This approach, which combines satellite data for assimilation and validation, could be transferred to unmonitored basins

Snow in mountains is critical as it controls water availability for ecosystems and human societies, when it is most needed. In the mountains the snow depth is both hard to map due to its spatial variability and crucial to estimate water resources. Nowadays, the best estimations of the snow depth distribution combine models and spatially distributed snow depth measurements. In this work, we build upon this approach by combining a recently developed snow depth mapping method with a state-of-the-art model through a technique called assimilation. The assimilation of snow depth maps derived from satellite photogrammetry corrects bias in the precipitation and improves the spatial variability of the simulated snow depth. The workflow presented can be transferred to any mountain range, showing a promising way to study water resources in remote areas.

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INARCH

https://doi.org/10.1029/2021WR030271