Understanding the thermal regime of road embankments in cold climates during winter is essential for efficient road design and accurate estimation of maintenance frequencies to reduce freeze-induced damage. In response to the challenging climate conditions in northern Sweden, an experimental field setup was designed to assess the thermal impact of culverts and accumulated snow in ditches on the thermal regime of road embankments during a winter season. This study provides detailed information on the experimental setup, highlights potential challenges from installation phase to data acquisition, and addresses measurement errors. Methods to ensure accuracy and obtain reliable data are also presented. Additionally, some of the obtained measurement results are included in this paper. The results show that snow impacts the thermal regime of the embankment from the onset of accumulation in the ditch, when the snow cover is still thin, until it reaches a depth of 65 cm. Beyond this depth, the soil beneath the snow remains almost unfrozen throughout the winter season. Additionally, the temperature distribution measurements within the embankment indicate that freezing progresses faster near the culvert compared to the rest of the embankment. However, once the culvert ends are insulated by snow cover, the frost depth in the soil near the culvert does not increase significantly, while the rest of the road continues to freeze gradually to greater depths throughout the winter season. The measurement results presented in this study provide researchers with a reliable dataset for validating numerical models in related research areas simulating cold-climate conditions. Additionally, these results enhance the understanding of the thermal regime of road embankments in typical cold climates and offer valuable insights for planning road maintenance and construction in such regions. Furthermore, this study provides essential information for researchers aiming to design and optimize experimental measurement setups in similar investigations.

Cold climate viticulture is challenged by climatic variability, including increased frost risk, shorter growing seasons, and unpredictable weather events that impact vine productivity and grape quality. Global warming is altering traditional viticulture zones, prompting the exploration of new regions for grape cultivation, the selection of climate-resilient cultivars, and the implementation of adaptive practices. This review synthesizes recent advances in adaptive viticulture practices and plant growth regulator applications, highlighting novel molecular and physiological insights on cold stress resilience and berry quality. Key strategies include delayed winter pruning to mitigate frost damage, osmoprotectant application to improve freeze tolerance, and canopy management techniques (cluster thinning and defoliation) to enhance berry ripening and wine composition. Their effectiveness depends on vineyard microclimate, soil properties and variety-specific physiological response. Cover cropping is examined for its role in vine vigor regulation, improving soil microbial diversity, and water retention, though its effectiveness depends on soil type, participation patterns, and vineyard management practices. Recent transcriptomic and metabolomic studies have provided new regulatory mechanisms in cold stress adaptation, highlighting the regulatory roles of abscisic acid, brassinosteroids, ethylene, and salicylic acid in dormancy induction, oxidative stress response, and osmotic regulation. Reflective mulch technologies are currently examined for their ability to enhance light interception, modulating secondary metabolite accumulation, improving technological maturity (soluble solids, pH, and titratable acidity) and enhancing phenolic compounds content. The effectiveness of these strategies remains highly site-specific, influenced by variety selection and pruning methods particularly due to their differences on sugar accumulation and berry weight. Future research should prioritize long-term vineyard trials to refine these adaptive strategies, integrate genetic and transcriptomic insights into breeding programs to improve cold hardiness, and develop precision viticulture tools tailored to cold climate vineyard management.

Seasonally ice-bound ground are subjected to cyclic freeze-thaw processes, which can significantly degrade their mechanical properties, including static strength (Ss). To accurately characterize and predict the Ss of seasonally frozen soils, this research employs advanced machine learning techniques. Specifically, the study utilizes the Least Square Support Vector Regression (LSSVR) method, which is known for its robust performance in nonlinear regression tasks. A critical aspect of the LSSVR model is the appropriate selection of its hyperparameters, namely the penalty agent (c) and the breadth of the kernel function (g). To determine these parameters with high precision, the research integrates the LSSVR model with two novel optimization techniques: the Flow Direction Algorithm (FDA) and the Artificial Rabbit Optimization (ARO). The resulting hybrid models, denoted as LS(ARO) and LS(FDA), are designed to outperform the previously published Artificial Neural Network (ANN) approach in predicting the Ss of seasonally frozen soils. The Implementation of the proposed hybrid approaches is assessed by a comprehensive database of 120 soil samples collected from relevant published studies. The input parameters used in the frameworks include water content, negative temperature, confining stress, freeze-thaw processes, thawing time, and compaction ratio. The results demonstrate the superiority of the hybrid models, with the LS(ARO) network achieving remarkable R2 amounts of 0.9924 and 0.9976 during the train and test steps, respectively. Moreover, the LS(ARO) model outperformed the LS(FDA) and the previously reported ANN model in terms of other performance metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results of the present research expand the recognizing and predictive capabilities of Ss in seasonally frozen soils, which is crucial for infrastructure design and construction in cold regions. The integration of the LSSVR technique with the novel FDA and ARO optimization algorithms represent a significant advancement in the field of hybrid regression analysis for geotechnical engineering applications.

Layers of volcanic ash and Andosol soils derived from the ash may play an important role in preserving snow and ice as well as in the development of permafrost conditions in (a) the immediate vicinity of volcanoes at high elevations or at high latitudes and (b) land areas that are often distant from volcanic activity and are either prone to permafrost or covered by snow and ice, but have been affected by subaerial ash deposition. The special properties of volcanic ash are critically reviewed, particularly in relation to recent research in Kamchatka in the Far East of Russia. Of special importance are the thermal properties, the unfrozen water contents of ash layers, and the rate of volcanic glass weathering. Weathering of volcanic glass results in the development of amorphous clay minerals (e.g. allophane, opal, palagonite), but occurs at a much slower rate under cold compared to warm climate conditions. Existing data reveal (1) a strong correlation between the thermal conductivity, the water/ice content, and the mineralogy of the weathered part of the volcanic ash, (2) that an increase in the amounts of amorphous clay minerals (allophane, palagonite) increases the proportion of unfrozen water and decreases the thermal conductivity, and (3) that amorphous silica does not alter to halloysite or other clay minerals, even in the Early Pleistocene age (Kamchatka) volcanic ashes or in the Miocene and Pliocene deposits of Antarctica due to the cold temperatures. The significance of these findings are discussed in relation to past climate reconstruction and the influence of volcanic ash on permafrost aggradation and degradation, snow and ice ablation, and the development of glaciers.