Study region: The Tibetan Plateau (TP), China, contains the world's largest permafrost area outside the Polar Regions. Study focus: This study investigates the precipitation-induced advective heat flux (E-Pre), which represents the energy transfer resulting from the temperature difference between rainfall and soil. Observational data from three permafrost monitoring sites (Qumalai, Xidatan, and Tanggula) were combined with simulations from the Community Land Model version 5.0 (CLM5.0) to quantify E-Pre precipitation infiltration depth, and the probability of infiltration reaching the frozen soil layer. The analysis further examines how precipitation amount, soil texture, soil moisture, and freeze-thaw state jointly control infiltration processes and influence the soil thermal regime. New hydrological insights for the region: Infiltration depth varies with initial soil moisture and precipitation duration, from shallow retention to deep percolation. E-Pre is generally negative, with maximum cooling of-84.14 W m(-2) at QML,-73.24 W m(-2) at XDT, and -56.63 W m(-2) at TGL, but becomes positive during prolonged summer rainfall, reaching 45.43 W m(-2) at QML. Diurnal soil temperature variations shift E-Pre from cooling by day to reduced cooling or warming at night. Across the TP, mean infiltration depth is similar to 5 cm, higher in southeastern Tibet, with a regional mean E-Pre of-0.08 W m(-2). Warming effects are concentrated in the southeastern and central TP, while cooling dominates the arid west and high-elevation north.

The representation of snow is a crucial aspect of land-surface modelling, as it has a strong influence on energy and water balances. Snow schemes with multiple layers have been shown to better describe the snowpack evolution and bring improvements to soil freezing and some hydrological processes. In this paper, the wider hydrological impact of the multi-layer snow scheme, implemented in the ECLand model, was analyzed globally on hundreds of catchments. ERA5-forced reanalysis simulations of ECLand were coupled to CaMa-Flood, as the hydrodynamic model to produce river discharge. Different sensitivity experiments were conducted to evaluate the impact of the ECLand snow and soil freezing scheme changes on the terrestrial hydrological processes, with particular focus on permafrost. It was found that the default multi-layer snow scheme can generally improve the river discharge simulation, with the exception of permafrost catchments, where snowmelt-driven floods are largely underestimated, due to the lack of surface runoff. It was also found that appropriate changes in the snow vertical discretization, destructive metamorphism, snow-soil thermal conductivity and soil freeze temperature could lead to large river discharge improvements in permafrost by adjusting the evolution of soil temperature, infiltration and the partitioning between surface and subsurface runoff.

Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901-2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at high spatial resolutions in Canada. Met1km includes eight climate variables (daily minimum, maximum, and mean air temperatures, precipitation, vapor pressure, wind speed, solar radiation, and downward longwave radiation) and is suitable to drive process-based permafrost and other land-surface models. Met1km was developed based on four coarser gridded meteorological datasets for the historical period. Future values were developed using the output of a new Canadian regional climate model under medium-low and high emission scenarios. These datasets were downscaled to 1-km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly anomaly time-series dataset. The accuracy of Met1km is similar to or better than the four coarser gridded datasets. The errors in long-term averages and average seasonal patterns are small. The error occurs mainly in day-to-day fluctuations, thus the error decreases significantly when averaged over 5 to 10 days. Met1km, as a data generating system, is relatively small in data volume, flexible to use, and easy to update when new or improved source datasets are available. The method can also be used to generate similar datasets for other regions, even for the entire global landmass.

Wind erosion along the Qinghai-Tibet Railway causes sand hazard and poses threats to the safety of trains and passengers. A coupled land-surface erosion model (Noah-MPWE) was developed to simulate the wind erosion along the railway. Comparison with the data from the Cs-137 isotope analysis shows that this coupled model could simulate the mean erosion amount reasonably. The coupled model was then applied to eight sites along the railway to investigate the wind-erosion distribution and variations from 1979 to 2012. Factors affecting wind erosion spatially and temporally were assessed as well. Majority wind erosion occurs in the non-monsoon season from December to April of the next year except for the site located in desert. The region between Wudaoliang and Tanggula has higher wind erosion occurrences and soil lose amount because of higher frequency of strong wind and relatively lower soil moisture than other sites. Inter-annually, all sites present a significant decreasing trend of annual soil loss with an average rate of - 0.18 kg m(-2) a(-1) in 1979-2012. Decreased frequency of strong wind, increased precipitation and soil moisture contribute to the reduction of wind erosion in 1979-2012. Snow cover duration and vegetation coverage also have great impact on the occurrence of wind erosion.