Permafrost over the Qinghai–Tibet Plateau (QTP) has received increasing attention due to its high sensitivity to climate change. Numerous spatial modeling studies have been conducted on the QTP to assess the status of permafrost, project future changes in permafrost, and diagnose contributors to permafrost degradation. Due to the scarcity of ground stations on the QTP, these modeling studies are often hampered by the lack of validation references, calibration targets, and model constraints; however, a high-quality permafrost distribution map would be a good option as a benchmark for spatial simulations. Existing permafrost distribution maps for the QTP can poorly serve this purpose. An ideal benchmark map for spatial modeling should be methodologically sound, of sufficient accuracy, and based on observations from mapping years rather than all historical data spanning several decades. Therefore, in this study, we created a new permafrost distribution map for the QTP in 2010 using a novel permafrost mapping approach with satellite-derived ground surface thawing and freezing indices as inputs and survey-based subregion permafrost maps as constraints. This approach accounted for the effects of local factors by incorporating (into the model) an empirical soil parameter whose values were optimally estimated through spatial clustering and parameter optimization constrained by survey-based subregion permafrost maps, and the approach was also improved to reduce parametric equifinality. This new map showed a total permafrost area of about 1.086×10⁶ km² (41.2 % of the QTP area) and seasonally frozen ground of about 1.447×10⁶ km² (54.9 %) in 2010, excluding glaciers and lakes. Validations using survey-based subregion permafrost maps (κ=0.74) and borehole records (overall accuracy =0.85 and κ=0.43) showed a higher accuracy of this map compared with two other recent maps. Inspection of regions with obvious distinctions between the maps affirms that the permafrost distribution on this map is more realistic than that on the Zou et al. (2017) map. Given the demonstrated excellent accuracy, this map can serve as a benchmark map for constraining/validating land surface simulations on the QTP and as a historical reference for projecting future permafrost changes on the QTP in the context of global warming. The dataset is available from the repository hosted on Figshare (Cao et al., 2022): https://doi.org/10.6084/m9.figshare.19642362.

Microwave remote sensing techniques provide a direct measurement of surface soil moisture (SM), with advantages for all-weather observations and solid physics. However, most satellite microwave soil moisture products fail to meet the requirements of land surface studies for high-resolution surface soil moisture data due to their coarse spatial resolutions. Although many approaches have been proposed to downscale the spatial resolution of satellite soil moisture products, most of them have been tested in flat areas where the surface is relatively homogeneous. Thus, those established approaches are often inapplicable for downscaling in cold alpine areas with complex terrain where multiple factors control the variations in surface soil moisture. In this work, we re-inferred and verified the mathematical assumption behind a semi-physical approach for downscaling satellite soil moisture data and extended this approach for cold alpine areas. Instead of directly deriving SM from proxy variables, this approach relies on a relationship between two standardized variables of SM and apparent thermal inertia (ATI), in which the sub grid standard deviation for SM is estimated by a physical hydraulic model taking soil texture data as input. The approach was applied to downscale the soil moisture active passive (SMAP) daily data in a typical cold alpine basin, i.e., the Babao River basin located in the Qilian Mountains of Northwest China. We observed good linearity between the computed ATI and SM observations on most wireless sensor network sites installed in the study basin, which justifies the underlying assumption. The sub grid standard deviations for the SMAP grid estimated through the Mualem-van Genuchten model can broadly represent the real characteristics. The downscaled 1-km resolution results correlated well with the in-situ SM observations, with an average correlation coefficient of 0.74 and a small root mean square error (0.096 cm³/cm³). The downscaled results show more and consistent textural details than the original SMAP data. After removal of biases in the original SMAP data even higher agreements with the observations can be achieved. These results demonstrate the adequacy of the proposed semi-physical approach for downscaling satellite soil moisture data in cold alpine areas, and the resultant fine-resolution data can serve as useful databases for land surface and hydrological studies in those areas.

Permafrost, a critical global cryospheric component, supports subarctic boreal forests but is frequently disturbed by wildfires, an important driver of permafrost degradation. Wildfires reduce vegetation, organic layers, and surface albedo, leading to active layer thickening and ground subsidence. Recent studies using interferometric synthetic aperture radar (InSAR) have confirmed the rapid and extensive post‐fire permafrost degradation, and have largely focused on short‐term impacts. However, the longer‐term post‐fire permafrost deformation, potentially persisting for decades, remains poorly understood due to limited data. Here, we applied InSAR in North Yukon to detect deformation signals across multiple fire scars in the past five decades. Using a chronosequence (space‐for‐time substitution) approach, we summarize a continuous trajectory of post‐fire permafrost evolution: (a) an initial degradation stage, characterized by abrupt subsidence up to 50 mm/year and gradually slowing over the first decade, with cumulative subsidence exceeding 100 mm locally; (b) an aggradation stage from approximately 15 to 30 years after fire, marked by ground uplift reaching 25 mm/year before gradually declining, compensating for the earlier subsidence; and (c) a stabilization stage beyond three to four decades, where permafrost nearly recovers to pre‐fire conditions with indistinguishable deformation between burned and unburned areas. Notably, the rarely‐reported uplift phase appears closely related to vegetation regeneration and fire‐greening feedback that provide thermal protection, suggesting a critical mechanism of permafrost recovery. These findings provide new insights into the resilience of boreal‐permafrost systems to wildfires and also underscore the importance of long‐term InSAR monitoring in understanding permafrost responses to wildfires under climate change.

In the context of global climate change, the Qinghai-Tibetan plateau (QTP) has experienced unprecedented changes in its local climate. While general circulation models (GCM) are able to forecast global-scale future climate change trends, further work needs to be done to develop techniques to apply GCM-predicted trends at site scale to facilitate local ecohydrological response studies. Given the QTP’s unique altitude-controlled climate pattern, the applicability of the quantile–quantile (Q-Q) adjustment approach for this purpose remains largely unknown and warrants investigation. In this study, this approach was evaluated at 36 sites to ensure the results are representative of different climatic and surface conditions on the QTP. Considering the practical needs of QTP studies, the study aims to assess its capability for downscaling monthly GCM simulations of major variables onto the site scale, including precipitation, air temperature, wind speed, relative humidity, and air pressure, based on two GCMs. The calibrated projections at the sites were verified against the observations and compared with those from two commonly used adjustment methods—the quantile-mapping method and the delta method. The results show that the general trends of most variables considered are well adjusted at all sites, with a quantile pair of 25–75% for all the variables except precipitation where 10–90% is used. The calibrated results are generally close to the observed values, with the best performance in air pressure, followed by air temperature and relative humidity. The performance is relatively limited in adjusting wind speed and precipitation. The accuracies decline as the adjustment extends into the future; a wider adjustment window may help increase the performance for the variables subject to climate changes. It is found that the performance of the adjustment is generally independent of the locations and seasons, but is strongly determined by the quality of GCM simulations. The Q-Q adjustment works better for the meteorological variables with fewer fluctuations and daily extremes. Variables with more similarities in probability density functions between the observations and GCM simulations tend to perform better in adjustment. Generally, this approach outperforms the two peer methods with broader applicability and higher accuracies for most major variables.