Process‐Level Evaluation of a Hyper‐Resolution Forest Snow Model Using Distributed Multisensor Observations

Mazzotti, Giulia, Essery, Richard, Webster, Clare, Malle, Johanna and Jonas, Tobias (2020) Process‐Level Evaluation of a Hyper‐Resolution Forest Snow Model Using Distributed Multisensor Observations. Water Resources Research, 56 (9). e2020WR027572. ISSN 0043-1397

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Abstract

The complex dynamics of snow accumulation and melt processes under forest canopies entail major observational and modeling challenges, as they vary strongly in space and time. In this study, we present novel data sets acquired with mobile multisensor platforms in subalpine and boreal forest stands. These data sets include spatially and temporally resolved measurements of shortwave and longwave irradiance, air and snow surface temperatures, wind speed, and snow depth, all coregistered to canopy structure information. We then apply the energy balance snow model FSM2 to obtain concurrent, distributed simulations of the forest snowpack at very high (“hyper”) resolution (2 m). Our data sets allow us to assess the performance of alternative canopy representation strategies within FSM2 at the level of individual snow energy balance components and in a spatially explicit manner. We demonstrate the benefit of accounting for detailed spatial patterns of shortwave and longwave radiation transfer through the canopy and show the importance of describing wind attenuation by the canopy using stand-scale metrics. With the proposed canopy representation, snowmelt dynamics in discontinuous forest stands were successfully reproduced. Hyper-resolution simulations resolving these effects provide an optimal basis for assessing the snow-hydrological impacts of forest disturbances and for validating and improving the representation of forest snow processes in land surface models intended for coarser-scale applications.

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