Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods

In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model. Key Points Evaluating methods for assimilating snow observations into distributed models Assimilation can improve model skill also at locations without observations Assimilation of fluxes appears more successful than assimilation of states (Less)

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