High resolution modelling of temporal and spatial dynamics in soil conditions in subarctic Finland using HydroBlocks model

Abstract. The changing Arctic climate alters the dynamics of melting and freezing in the ground. A methodology has been developed to assess small magnitude, ice-induced earthquakes, called 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. Developing this methodology further on a larger scale, over the whole country, is challenging due to a lack of in-situ measurements of those 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. However, another challenge with large scale LSMs is that they often use coarse global soil datasets, and their simplified soil presentations can introduce errors in the modelling results. For that reason, using local soil data helps to achieve a more accurate representation of soil and improves the model performance. Hence, in this paper, the HydroBlocks model was run first by using coarser SoilGrids soil data and second, by using local soil data provided by the Geological Survey of Finland (GTK). 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. The snow model (snow water equivalent) and the modeled soil temperatures and soil water contents were compared with observational data to evaluate the model performance. For six observational snow water equivalent stations, with GTK's soil data the average RMSE and KGE were 29 mm and 0.34, respectively. The worst KGE was −0.30, and the best was 0.90. For the three observational soil stations, the soil temperature had an average RMSE and KGE of 1.3 °C and 0.7, respectively. The worst KGE was 0.22, and the best was 0.86. For the soil water content, using local soil data provided by the GTK significantly improved the modelling. The average RMSE and KGE were first, 0.15 vol/vol and −3.5, and after calibration, they were reduced to 0.03 vol/vol and increased to 0.09, respectively. For the calibrated model, the worst KGE was −0.8, and the best was 0.49. 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. In addition, modelled soil ice content in Talvikangas was observed. Furthermore, the obtained soil temperature and soil ice content show potential to 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.

Duke Scholars

Author Nathaniel Chaney Civil and Environmental Engineering