The ongoing permafrost degradation in the Three-River Source Region (TRSR) poses serious threats to ecosystems, water resources, and infrastructure projects. As the China Water Tower and a vital barrier for the high-altitude ecological security of China, the TRSR is particularly vulnerable to such changes. The extent and severity of permafrost degradation are primarily governed by heat transfer dynamics, with soil thermal conductivity (STC) playing a crucial role in regulating thermal equilibrium. However, research on STC is hindered by insufficient in-situ measurements. To address this gap, we conducted in-situ measurements of STC at soil depths of 0-40 cm across 58 plots at 12 sites in the TRSR (244 records) during July and August 2023. The driving mechanisms influencing STC variations were further analyzed through laboratory experiments in September and October 2023. Spatially, STC increases from west to east and vertically with soil depth. Control experiments revealed that STC at negative temperatures is markedly higher than that at positive temperatures and increases with volumetric moisture content, particularly in inorganic soils, sand and loamy sand. This effect is more pronounced at subzero temperatures. Meanwhile, our results show that an artificial neural network model (R-2 = 0.78, p < 0.0001) incorporating ten measured soil physical parameters, outperforms traditional theoretical and empirical models in predicting STC. These findings contribute to a deeper understanding of permafrost formation, evolution, and its responses to climate change in the TRSR.

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.