ИСТИНА

Seasonal freezing is one of the most widespread cryogenic processes is the world. Seasonal freezing layer forms during cold season and underlain by thawed deposits (Kudryavtsev et al., 1978). When soil freezes, it often experiences frost heaving, which causes uneven movements of soil surface and thus, deformations of low-loaded facilities and asphalt cracking. Current methods of seasonal freeze accounting at construction are listed in state documents (construction codes and requirements, or SP), but they provide approximate estimations of seasonally frozen layer thickness and do not consider factor of changing natural conditions. This study is attempt of numerical calculation of seasonal freezing layer thickness in different landscapes. Moscow river valley near Zvenigorod Biological Station was considered as key site for in-situ observations during cold season of 2017/18. Climate data was retrieved from Novy Ierusalim weather station (https://rp5.ru/) and data loggers installed within study area. Representative points (e.g. floodplain, river terrace slope and surface, ravines, etc.) were chosen for acquisition field data sets: landscape conditions, soil type and moisture, snow thickness and soil freezing depth. In order to trace the dynamics of snow accumulation and freezing layer increment, filed studies were divided into 2 stages: the beginning (early December) and the end (mid-February) of cold season. Field dataset was used for calculations of seasonal freezing depths with simple Stefan model and regression equations. The winter of 2018/19 was relatively warm and snowy: cold sum (accumulated air daily average temperature) was -420..-430°C; average snow cover depth was 26 cm. Increased snow cover hindered intensive cooling of grounds, thus cold sum on soil surface was only about -8°C. As a result, majority of representative points displayed less than 10 cm of freezing by the middle of February. Calculated seasonal freezing depths closely match the observed values after validation. Modeled estimations of seasonal freezing layer thickness in combination with prognostic climate data allow to predict impact of frost heaving on engineering facilities and infrastructure and to optimize construction expenses. The study was supported by RFBR grant 18-08-60080.