Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model's estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.

The surface energy budget is closely related to freeze-thaw processes and is also a key issue for land surface process research in permafrost regions. In this study, in situ data collected from 2005 to 2015 at the Tanggula site were used to analyze surface energy regimes, the interaction between surface energy budget and freeze-thaw processes. The results confirmed that surface energy flux in the permafrost region of the Qinghai-Tibetan Plateau exhibited obvious seasonal variations. Annual average net radiation (R-n) for 2010 was 86.5 W m(-2), with the largest being in July and smallest in November. Surface soil heat flux (G(0)) was positive during warm seasons but negative in cold seasons with annual average value of 2.7 W m(-2). Variations in R-n and G(0) were closely related to freeze-thaw processes. Sensible heat flux (H) was the main energy budget component during cold seasons, whereas latent heat flux (LE) dominated surface energy distribution in warm seasons. Freeze-thaw processes, snow cover, precipitation, and surface conditions were important influence factors for surface energy flux. Albedo was strongly dependent on soil moisture content and ground surface state, increasing significantly when land surface was covered with deep snow, and exhibited negative correlation with surface soil moisture content. Energy variation was significantly related to active layer thaw depth. Soil heat balance coefficient K was > 1 during the investigation time period, indicating the permafrost in the Tanggula area tended to degrade.

Global warming is likely to transform Siberian environments. Recent eco-hydrological evidence indicates that water and carbon cycles have been changing rapidly, with potentially serious effects on the Siberian flora and fauna. We have comprehensively analysed dendrochronological, hydrological, and meteorological data and satellite remote sensing data to track changes in vegetation and the water and carbon cycles in the Lena River Basin, eastern Siberia. The basin is largely covered with larch forest and receives little precipitation. However, from 2005 to 2008 the central part of the basin experienced an extraordinarily high level of precipitation in late summer and winter. This resulted in the degradation of permafrost, forest, and hydrological elements in the region. Dendrochronological data implied that this event was the only incidence of such conditions in the previous 150 years. Based on data collected before and after the event, we developed a permafrost-ecosystem model, including surface soil freeze-thawing processes, to better represent the heat, water, and carbon fluxes in the region. We focused on the surface soil layer, in which an increased thawing depth is now apparent, surface soil moisture, and net primary production. An analysis of observed and model-simulated data indicated that the annual maximum thawing depth (AMTD) had increased gradually on a decadal scale and deepened abruptly after 2005. Climatological analyses of atmospheric water circulation over the region indicated that the recent increases in precipitation over the central Lena River Basin were partly related to cyclone activity. Consequently, the increased precipitation from late-summer to winter resulted in increases in soil moisture, soil temperature, and AMTD in the region.