Modelling of temporal and spatial trends in soil conditions in Finland using HydroBlocks model

Abstract. The changing Arctic climate alters the dynamics of melting and freezing in the ground. An increasing number of frost quakes have been reported in boreal regions such as Finland and Canada, which can cause damage to infrastructure by fracturing roads and built structures. A methodology has been developed to assess frost quakes by estimating thermal stresses in the soil in Oulu, Finland. Information on temporally and spatially varying soil properties, such as soil temperature and soil ice content, is required to calculate thermal stress. Further developing this methodology on a larger scale, over Finland, is challenging due to a lack of in-situ measurements of these parameters with high spatial and temporal coverage. However, they can be simulated using land surface models, one of which is HydroBlocks. Previously, HydroBlocks has been applied in the contiguous United States. The goal of this paper was to configure the model in subarctic and arctic Finland. HydroBlocks' ability to produce accurate snow accumulation and melt approximations, as well as estimate soil temperature and soil water content at different depths in Finland, has not been evaluated before. In addition, maps and time series of soil ice content in Finland at different depths were produced. The snow model (snow water equivalent) and the modeled soil temperatures and soil water contents were compared with the observational data to evaluate the model performance. From the calibrated model, for six observational SWE stations, the average RMSE and KGE were 43 mm and 0.07, respectively. The worst KGE was -0.88, and the best was 0.78. From the calibrated model, for the three observational soil stations, the soil temperature had an average RMSE and KGE of 2.2 °C and 0.66, respectively. The worst KGE was 0.41, and the best was 0.89. For the soil water content, the average RMSE and KGE were first, 0.15 volvol and -4.88, and after calibration, they were reduced to 0.07 volvol and -0.75, respectively. For the calibrated model, the worst KGE was -2.2, and the best was 0.08. The modelling results emphasize the importance of calibrating the model with local soil hydraulic parameters. The modeling results indicate that outputs from HydroBlocks can generally predict soil conditions in Finland. Furthermore, the obtained soil temperature and soil ice content can be used to calculate thermal stresses in soils and identify frost quake-prone areas regionally across Finland over recent decades, ultimately estimating the risk caused by frost quakes.

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